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Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012
Abstract
Regulatory and scientific applications of photochemical models are typically evaluated by comparing model estimates to measured values. It is important to compare quantitative model performance metrics to a benchmark or other studies to provide confidence in the modeling results. Since strict model performance guidelines may not be appropriate for many applications, model evaluations presented in recent literature have been compiled to provide a general assessment of model performance over a broad range of modeling systems, modeling periods, intended use, and spatial scales. Operational model performance is compiled for ozone, total PM2.5, speciated PM2.5, and wet deposition of sulfate, nitrate, ammonium, and mercury. The common features of the model performance compiled from literature are photochemical models that have been applied over the United States or Canada and use modeling platforms intended to generally support research, regulatory or forecasting applications. A total of 69 peer-reviewed articles which include operational model evaluations and were published between 2006 and March 2012 are compiled to summarize typical model performance. The range of reported performance is presented in graphical and tabular form to provide context for operational performance evaluation of future photochemical model applications. In addition, recommendations are provided regarding which performance metrics are most useful for comparing model applications and the best approaches to match model estimates and observations in time and space for the purposes of metric aggregations.
... 72−75 Total PM 2.5 performance statistics are shown in Figure 2. Models A and B simulated higher concentrations than models C and D which sometimes resulted in positive biases, though all the models agreed in directionality and order of magnitude of performance statistics. Using ranges of performance statistics from literature, 72 we found that models met or exceeded literature IQR (r > 0.4) for Pearson's r in all cases, but only met or exceeded 4 (8) cases for NMB (MB). 72,75 Denver met or exceeded performance goals more than other study sites, being the only site with NMB within literature values (IQR of −21.1 to 10.4% and goal of < ± 10%) and one of 2 sites with MB within literature IQR (−0.9 to 1.8 μg m −3 ). ...
... Using ranges of performance statistics from literature, 72 we found that models met or exceeded literature IQR (r > 0.4) for Pearson's r in all cases, but only met or exceeded 4 (8) cases for NMB (MB). 72,75 Denver met or exceeded performance goals more than other study sites, being the only site with NMB within literature values (IQR of −21.1 to 10.4% and goal of < ± 10%) and one of 2 sites with MB within literature IQR (−0.9 to 1.8 μg m −3 ). All models met or outperformed literature RMSE (IQR of 5.9 to 10.4 μg m −3 ) at the same sites but did not show significant differences within the same site. ...
... All models met or outperformed literature RMSE (IQR of 5.9 to 10.4 μg m −3 ) at the same sites but did not show significant differences within the same site. 72 When comparing models B through D to the default A on an hourly basis (the finest temporal resolution in the model outputs), we can get a larger sample for comparison of the mechanisms. Using a one-way analysis of variance test (ANOVA) on hourly modeled PM 2.5 at all sites, we found that these differences were likely to be the result of the choice of mechanism (F = 6.37, p ≪ 0.05) rather than stochastic variability within the model. ...
Chemical transport models are used for federal compliance demonstrations when areas are out of attainment, but there is no guidance for choosing a chemical mechanism. With the 2024 change of the annual PM2.5 standard and the prevalence of multiday wintertime inversion episodes in the western U.S., understanding the wintertime performance of chemical transport models is important. This study explores the impact of chemical mechanism choice on the Community Multiscale Air Quality (CMAQ) model performance for PM2.5 and implications for attainment demonstration in inversion-prone areas in the western United States. Total and speciated PM2.5 observations were used to evaluate wintertime CMAQ simulations using four chemical mechanisms. The study evaluated intermechanism differences in total and secondary PM2.5 and the impact of meteorology at sites with observed multiday temperature inversions. Model performance for total PM2.5 was similar across chemical mechanisms, but intermechanism differences for total and secondary PM2.5 were exacerbated during inversion periods, suggesting that modeled chemistry contributes to the model bias. Results suggest that nitrate, ammonium, and organic carbon are secondary species for which model results do not agree or perform to standard evaluation metrics in scientific literature. These findings show a need for mechanistic investigations of the causes of these differences.
... For wind direction, GE and MB were used. The expressions of these metrics are described in the EEA (2011) [39] and in the study of Simon et al. (2012) [40]. ...
... For wind direction, GE and MB were used. The expressions of these metrics are described in the EEA (2011) [39] and in the study of Simon et al. (2012) [40]. ...
... For short-term air quality modeling performance, we evaluated the CO (maximum mean in 1 h and 8 h, per day), PM 2.5 (mean in 24 h), and O 3 (maximum mean in 8 h, per day) concentrations during periods consistent with the Ecuadorian air quality legislation and the WHO guidelines [17,41]. For these variables, we used MB, RMSE, fractional bias (FB), the mean normalized bias (MNB), and the correlation coefficient (r) [40]. ...
... For wind direction, GE and MB were used. The expressions of these statistics can be found in the technical guide by the European Environment Agency (2011) [30] and in Simon et al. (2012) [31]. The performance for rainfall modeling was assessed through the Equation (1) metric. ...
... For wind direction, GE and MB were used. The expressions of these statistics can be found in the technical guide by the European Environment Agency (2011) [30] and in Simon et al. (2012) [31]. The performance for rainfall modeling was assessed through the Equation (1) metric. ...
... For short-term (daily) air quality, we used the records from the MUN station ( Figure 1c,d) to assess the performance for modeling the CO, PM2.5, and O3 (Table 4) concentration, during periods established by the national regulation and the WHO guidelines [16,31,34], using the MB, RMSE, the fractional bias (FB), the mean normalized bias (MNB), and the correlation coefficient (r) [31]. ...
Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognosis of cloud formation, rainfall, energy levels reaching the surface, and air quality. Working with a spatial resolution of 1 km, we studied the influence of cumulus parameterization schemes coded in the Weather Research and Forecasting with Chemistry Version 3.2 (WRF-Chem 3.2) for modeling in an Andean city in Southern Ecuador (Cuenca, 2500 masl), during September 2014. To assess performance, we used meteorological records from the urban area and stations located mainly over the Cordillera, with heights above 3000 masl, and air quality records from the urban area. Firstly, we did not use any cumulus parameterization (0 No Cumulus). Then, we considered four schemes: 1 Kain–Fritsch, 2 Betts–Miller–Janjic, 3 Grell–Devenyi, and 4 Grell-3 Ensemble. On average, the 0 No Cumulus option was better for modeling meteorological variables over the urban area, capturing 66.5% of records and being the best for precipitation (77.8%). However, 1 Kain–Fritsch was better for temperature (78.7%), and 3 Grell–Devenyi was better for wind speed (77.0%) and wind direction (37.9%). All the options provided acceptable and comparable performances for modeling short-term and long-term air quality variables. The results suggested that using no cumulus scheme could be beneficial for holistically modeling meteorological and air quality variables in the urban area. However, all the options, including deactivating the cumulus scheme, overestimated the total amount of precipitation over the Cordillera, implying that its modeling needs to be improved, particularly for studies on water supply and hydrological management. All the options also overestimated the solar radiation levels at the surface. New WRF-Chem versions and microphysics parameterization, the other component directly related to cloud and rainfall processes, must be assessed. In the future, a more refined inner domain, or an inner domain that combines a higher resolution (less than 1 km) over the Cordillera, with 1 km cells over the urban area, can be assessed.
... GE and MB were used to assess wind 6 direction. The expressions of these metrics are described in the EEA (2011) [33] and in Simon et al. (2012) [34]. The performance for rainfall modeling was assessed through the Equation (1) metric. ...
... GE and MB were used to assess wind 6 direction. The expressions of these metrics are described in the EEA (2011) [33] and in Simon et al. (2012) [34]. The performance for rainfall modeling was assessed through the Equation (1) metric. ...
... For short-term air quality, we used the records from the MUN station (Figure 1c,d) to assess the performance for modeling the CO (maximum mean in 1-h and 8-h per day), PM2.5 (mean in 24-h), and O3 (maximum mean in 8-h daily) concentrations during periods consistent with the national regulation and the WHO guidelines [20,34,37], using the MB, RMSE, the fractional bias (FB), the mean normalized bias (MNB), and the correlation coefficient (r) [34]. ...
Cumulus parameterization schemes model the subgrid-scale effects of moist convection, affecting the prognostic of cloud formation, rainfall, energy levels reaching the surface, and air quality. Working with a spatial resolution of 1 km, we studied the influence of cumulus parameterization schemes coded in the Weather Research & Forecasting with Chemistry Version 3.2 (WRF-Chem 3.2) for atmospheric modeling in Cuenca, an Andean city of Southern Ecuador (2500 masl), during September 2014. For assessing the performance, we used meteorological records from the urban area and stations located mainly over the Cordillera, with heights above 3000 masl, and air quality records from the urban area. Firstly, we did not use any cumulus parameterization (0 No Cumulus). Then we considered four schemes: 1 Kain-Fritsch, 2 Betts-Miller-Janjic, 3 Grell-Devenyi, and 4 Grell-3 Ensemble. On average, the 0 No Cumulus option was better for modeling meteorological variables over the urban area, capturing 66.5% of records and being the best for precipitation (77.8%). However, 1 Kaint-Fritsch was better for temperature (78.7%) and 3 Grell-Devenyi for wind speed (77.0%) and wind direction (37.9%). All the options provided acceptable and comparable performances for modeling short-term and long-term air quality variables. The results suggested that using no cumulus scheme could be beneficial for holistically modeling meteorological and air quality variables in the urban area. However, all the options, including deactivating the cumulus scheme, overestimated the total amount of precipitation over the Cordillera, implying that its modeling needs to be improved, particularly for studies on water supply and hydrological management. New WRF-Chem versions and microphysics parameterization, the other component directly related to cloud and rainfall processes, must be assessed.
... For wind direction, GE and MB were used. The expressions of these metrics are described in the EEA (2011) [39] and in the study of Simon et al. (2012) [40]. ...
... For wind direction, GE and MB were used. The expressions of these metrics are described in the EEA (2011) [39] and in the study of Simon et al. (2012) [40]. ...
... For short-term air quality modeling performance, we evaluated the CO (maximum mean in 1 h and 8 h, per day), PM 2.5 (mean in 24 h), and O 3 (maximum mean in 8 h, per day) concentrations during periods consistent with the Ecuadorian air quality legislation and the WHO guidelines [17,41]. For these variables, we used MB, RMSE, fractional bias (FB), the mean normalized bias (MNB), and the correlation coefficient (r) [40]. ...
Surface interactions occur near the land–atmosphere interface, thus affecting the temperature, convection, boundary layer, and stability of the atmosphere. A proper representation of surface interactions is a crucial component for numerical atmospheric and air quality modeling. We assessed four land surface schemes—1. 5-layer thermal diffusion scheme (1 5-Layer); 2. unified Noah land surface model (2 Noah); 3. rapid update cycle (3 RUC) land surface model; and 4. Pleim–Xiu land surface model (4 Pleim–Xiu)—from the Weather Research and Forecasting with Chemistry (WRF-Chem V3.2) model for the purposes of atmospheric modeling in Cuenca, which is a region with a complex topography and land use configuration and which is located in the Southern Andean region, in Ecuador. For this purpose, we modeled the meteorological and air quality variables during September 2014. It was found that the meteorological and short-term air quality variables were better modeled through the 2 Noah scheme. Long-term (mean monthly) air quality variables were better modeled by the 1 5-Layer and 3 RUC options. On average, the 2 Noah scheme was better at modeling meteorology and air quality. In addition, we assessed the 2 Noah scheme combined with the urban canopy model (UCM) (5 Noah UCM), which was developed as an option to represent the urban effects at a subgrid-scale. Results indicated that the performance of the 5 Noah UCM scheme was not better at modeling than the 2 Noah scheme alone. Moreover, the 5 Noah UCM scheme notably decreased the modeling performance for carbon monoxide and fine particulate matter. These results complement previous assessments of other schemes, allowing us to recommend a basic configuration of parameters for atmospheric modeling in the Andean region of Ecuador.
... To verify the accuracy of the simulation results, the WRF simulations were validated using the mean fractional bias (MFB), mean fractional error (MFE), normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R). During the WRF simulations, all model performance criteria were considered satisfied when NMB ≤ ±30%, NME ≤ 50%, and R ≥ 0.40 [39,40]. For the CMAQ simulations, based on the reference criteria proposed by Boylan et al. [41], the model simulation was excellent when MFB ≤ ±30% and MFE ≤ ±50% and the model simulation was acceptable when MFB ≤ ±60% and MFE ≤ ±75%. ...
... Table S2 statistically compares the O 3 concentration simulation results and monitoring values. The comparison showed that the simulation effect of pollutants was basically within the acceptable range [39][40][41], and the MFB and MFE of all months satisfied −60% ≤ MFB ≤ 60% and MFE ≤ 75%. Figure S3 exhibits the peak changes in pollutants simulated using the CMAQ model. The trends of the observed and simulated values were basically identical, and the overall simulated values for each site were low. ...
The increasingly severe nocturnal ozone enhancement (NOE) event pollution is widely concerning. Therefore, based on the observed hourly O3 concentrations from 2015 to 2023, this study analyzes the characteristics of NOE events over Putian City. The analysis results show that the frequency of NOE events over Putian City is high, at about 127 days annually, with a high frequency in April and a low frequency in July and August. Most NOE events corresponded to a nocturnal O3 peak concentration (NOP) of <120 μg/m³. Moreover, they mainly occurred between 1:00–3:00 and 7:00. The physicochemical processes over Putian City in April, October, and November 2020 were simulated using the Weather Research and Forecasting (WRF, version 4.3.3) model coupled with the Community Multiscale Air Quality (CMAQ, version 5.4) model. The results suggest that O3 transport, especially horizontal transport from the eastern sea and Zhejiang Province and vertical transport from the upper atmosphere, could be the major cause of NOE events over Putian City. Furthermore, the nocturnal movement of the pollution zone, along with the aggregation of O3 due to weakened dry deposition and the influence of a stable boundary layer obstructed by mountain terrain, significantly influenced the overall O3 concentration. Thus, NOE events over Putian City stem from the interaction among these physicochemical processes. The study results emphasize the importance of O3 control in Putian City and suggest the implementation of strict joint regional control measures for to improve air quality.
... Model performance can be evaluated by comparing model estimates to measured pollutant concentrations. For this analysis we compared our ozone results against the range of bias and Table 5 April 11-September 29, 2023, EMBER MDA8 ozone model performance statistics calculated as described in Simon et al. [ 12 ]. error values reported in the literature for recent state-of-the science model simulations [ 12 , 13 ]. ...
... Table 5 provides model performance statistics for MDA8 ozone concentrations against ozone monitors in the nine NOAA climate regions. These performance statistics are in the range of those reported for modeling studies by Simon et al. [ 12 ] and Emery et al. [ 13 ]. In addition, for the purpose of evaluating the ability of EMBER for capturing ozone impacts from fires, spatial and temporal patterns of known fire plumes were compared to model predictions of both ozone and PM 2. 5 . ...
The Expedited Modeling of Burn Events Results (EMBER) dataset consists of 36-km grid-spacing Community Multiscale Air Quality (CMAQ) photochemical modeling for the summer of 2023. For emissions, these simulations utilized representative monthly and day-of-week anthropogenic emissions from a recent year and preliminary day-specific 2023 fire emissions derived using BlueSky pipeline. The base model run simulated ozone concentrations across the contiguous US during Apr 11-Sep 29, 2023. Two zero-out model runs simulated ozone levels that would have occurred in the US (1) in the absence of fire emissions (“Zero Fires”) and (2) in the absence of only Canadian wildfire emissions (“Zero Canadian Fires”). Fire impacts on ozone were then estimated as the difference between ozone simulated in the base EMBER run compared to the ozone simulated in each of the zero out model runs. EMBER is presented as a screening level dataset due to the emissions limitations and the 36-km grid-spacing used in these simulations.
... Statistical comparisons involved mean bias (MB) and error (ME), normalized bias (NMB) and error (NME), and the Pearson correlation coefficient (r). These particular metrics align with those listed by Simon et al. [109] and Emery et al. [110], the latter of which established benchmarks for well-performing models based on a 10 year history of previous US photochemical modeling applications. The mathematical representation of these metrics is listed below: Statistical comparisons involved mean bias (MB) and error (ME), normalized bias (NMB) and error (NME), and the Pearson correlation coefficient (r). ...
... The mathematical representation of these metrics is listed below: Statistical comparisons involved mean bias (MB) and error (ME), normalized bias (NMB) and error (NME), and the Pearson correlation coefficient (r). These particular metrics align with those listed by Simon et al. [109] and Emery et al. [110], the latter of which established benchmarks for well-performing models based on a 10 year history of previous US photochemical modeling applications. The mathematical representation of these metrics is listed below: ...
The Comprehensive Air Quality Model with extensions (CAMx) is an open-source, state-of-the-science photochemical grid model that addresses tropospheric air pollution (ozone, particulates, air toxics) over spatial scales ranging from neighborhoods to continents. CAMx has been in continuous development for over 25 years and has been used by numerous entities ranging from government to industry to academia to support regulatory actions and scientific research addressing a variety of air quality issues. Here, we describe the technical formulation of CAMx v7.20, publicly released in May 2022. To illustrate an example of regional and seasonal model performance for predicted ozone and fine particulate matter (PM2.5), we summarize a model evaluation from a recent 2016 national-scale CAMx application over nine climate zones contained within the conterminous US. We show that statistical performance for warm season maximum 8 h ozone is consistently within benchmark statistical criteria for bias, gross error, and correlation over all climate zones, and often near statistical goals. Statistical performance for 24 h PM2.5 and constituents fluctuate around statistical criteria with more seasonal and regional variability that can be attributed to different sources of uncertainty among PM2.5 species (e.g., weather influences, chemical treatments and interactions, emissions uncertainty, and ammonia treatments). We close with a mention of new features and capabilities that are planned for the next public releases of the model in 2024 and beyond.
... We considered that the model captured the observed records if the difference was less than 50%. [54], indicating that air pollution due to New Year's emissions was of a higher magnitude there than in Cuenca. Figure 5 compares the hourly PM 2.5 records with the corresponding modeling levels at the six stations. ...
... Differences between records and modeled values ranged between 0.3% and 13.9%. The linear fit reached a value of 0.89 for the coefficient of determination, highlighting the strength of the relationship between records and modeled values [54]. These metrics indicated that the simulation of PM 2.5 dispersion was properly performed. ...
... Both models have been shown to appropriately replicate the amount and relative proportions of O 3 and chemically speciated PM 2.5 when compared to ambient measurements. 10,11 Further, these tools have been used to assess O 3 and PM 2.5 impacts from single sources [12][13][14][15] and complex emission control programs including power plant trading programs 16 and motor-vehicle technology implementation. 17 Since photochemical transport models have a detailed representation of chemistry and physical processes related to formation and transport of O 3 and particles, their application requires computing resources and technical expertise which may not be readily available to some who are interested in understanding how ambient O 3 and PM 2.5 may be inuenced by a change in emissions. ...
... Table 3 provides mean change in air quality concentrations averaged across all 12 km land-based contiguous US grid cells in CAMx and SABAQS. In addition, mean bias (MB), normalized mean bias (NMB), and spatial correlation (r) are calculated as described in ref. 11. Here a negative NMB indicates that SABAQS predicts a larger impact from CPPP than CAMx and a positive NMB indicates that SABAQS predicts a smaller impact from CPP than CAMx. ...
Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by...
... The results for 2010 are similar to those for 2016, again with negative biases for all variables. A comparison between 2010 and 2016 shows that the model captured the sign of the observed changes for precipitation and wet deposition fluxes, with wetter conditions in 2016 but mostly The results presented in this section demonstrate that the AQMEII4 CMAQ simulations perform similarly to other comparable regional-scale modeling studies (Emery et al., 2017;Kelly et al., 2019;Simon et al., 2012;Appel et al., 2021). The results presented in the Supplement show that the choice of the CMAQ dry deposition scheme (M3Dry vs. STAGE) has a smaller impact on aggregated model performance metrics than the sensitivity of CMAQ results to model input datasets and boundary conditions that represent the large-scale chemical environment. ...
... The model evaluation results presented in Sect. 3.1 demonstrate that the AQMEII4 CMAQ simulations perform similarly to other comparable regional-scale modeling studies (Emery et al., 2017;Kelly et al., 2019;Simon et al., 2012;Appel et al., 2021). The analysis of several sensitivity simulations presented in the Supplement indicates that the choice of lateral boundary conditions was the largest driver of differences in mean model concentrations and biases compared to the corresponding CMAQv5.3.1 simulations analyzed in Appel et al. (2021). ...
The fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4) is conducting a diagnostic intercomparison and evaluation of deposition simulated by regional-scale air quality models over North America and Europe. In this study, we analyze annual AQMEII4 simulations performed with the Community Multiscale Air Quality Model (CMAQ) version 5.3.1 over North America. These simulations were configured with both the M3Dry and Surface Tiled Aerosol and Gas Exchange (STAGE) dry deposition schemes available in CMAQ. A comparison of observed and modeled concentrations and wet deposition fluxes shows that the AQMEII4 CMAQ simulations perform similarly to other contemporary regional-scale modeling studies. During summer, M3Dry has higher ozone (O3) deposition velocities (Vd) and lower mixing ratios than STAGE for much of the eastern US, while the reverse is the case over eastern Canada and along the US West Coast. In contrast, during winter STAGE has higher O3Vd and lower mixing ratios than M3Dry over most of the southern half of the modeling domain, while the reverse is the case for much of the northern US and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e., grid-scale and land-use-specific effective conductances and deposition fluxes for the major dry deposition pathways, reveals generally higher summertime stomatal and wintertime cuticular grid-scale effective conductances for M3Dry and generally higher soil grid-scale effective conductances (for both vegetated and bare soil) for STAGE in both summer and winter. On a domain-wide basis, the stomatal grid-scale effective conductances account for about half of the total O3Vd during daytime hours in summer for both schemes. Employing land-use-specific diagnostics, results show that daytime Vd varies by a factor of 2 between land use (LU) categories. Furthermore, M3Dry vs. STAGE differences are most pronounced for the stomatal and vegetated soil pathway for the forest LU categories, with M3Dry estimating larger effective conductances for the stomatal pathway and STAGE estimating larger effective conductances for the vegetated soil pathway for these LU categories. Annual domain total O3 deposition fluxes differ only slightly between M3Dry (74.4 Tg yr-1) and STAGE (76.2 Tg yr-1), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales, which would affect estimates of O3 damage to sensitive vegetation. A comparison of two simulations differing only in their LU classification scheme shows that the differences in LU cause seasonal mean O3 mixing ratio differences on the order of 1 ppb across large portions of the domain, with the differences generally being largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted and cultivated, and grassland LU categories. These differences are generally smaller than the M3Dry vs. STAGE differences outside the summer season but have a similar magnitude during summer. Results indicate that the deposition impacts of LU differences are caused by differences in the fractional coverages and spatial distributions of different LU categories and the characterization of these categories through variables like surface roughness and vegetation fraction in lookup tables used in the land surface model and deposition schemes. Overall, the analyses and results presented in this study illustrate how the diagnostic grid-scale and LU-specific dry deposition variables adopted for AQMEII4 can provide insights into similarities and differences between the CMAQ M3Dry and STAGE dry deposition schemes that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.
... It is made by free radical polymerizing vinyl acetate to create polymerization, then hydrolyzing in PVA chains (Baker, 2012) With relation to biomaterials, specifically, biodegradation is a crucial factor. PVA is a common vinyl polymer and is thought to be non-biodegradable since its main chain is made up primarily of carboncarbon bonds (Alves et al., 2016;Simon et al., 2012). Hence included in various formulations a large main chain scission following for functional administration. ...
PLA-PVA films have desirable unique, unparalleled properties such as good oxygen and moisture barrier, excellent mechanical strength and low toxicity. The biodegradability of PLA and PVA offers a sustainable alternative to traditional plastic packaging. The PLA-PVA film stacking technology and its potential benefits for sustainable packaging. The XRD pattern of PVA exhibited sharp diffraction peaks at 20.0 ° and 41.0 °, reflecting its semi-crystalline nature, with the peak at 20.0 ° where a main diffraction of PLA was found around 16.5 °. PVA generally crystallizes in the monoclinic unit cell with dimensions of a= 7.81 Å, b= 2.52 Å, c= 5.51 Å, and α=γ= 90.0 °, and β= 91.70 °. PLA depicted semi-crystalline synthetic polymer with α form which most stable polymorph with shape an orthorhombic unit cell. Taking two typical spacing showed in 0.40 ± 0.02 nm and 0.26 ± 0.03 nm acquired from HR-TEM interface accord with (200) and (002) Miller indices of PVA. The HR-TEM images also computed the d-spacing of PLA 0.340 nm with respective fringe. SEM is used to obtain the morphology such a nanostructure PVA-PLA composite materials. The size, shape, purity, surface morphology and distribution of PLA-PVA composite were explored by SEM. The research explores the structure and properties of biodegradable PVA-PLA nanocomposite films using XRD, SEM and HR-TEM. The findings reveal how these materials can be made stronger and more sustainable, making them ideal for eco-friendly packaging.
... Statistical comparisons involved mean bias (MB) and error (ME), normalized bias (NMB) and error (NME), and correlation coefficient (r). These particular metrics align with those listed by Simon et al. (2012) and Emery et al. (2017), the latter of which established benchmarks for well-performing models based on a 10 year history of previous US photochemical modeling applications. The mathematical representation of these metrics is listed below: 540 where P and O are predicted and observed (measured) concentrations, respectively, at each time and site, overbars represent mean quantities over time and site, and summations are over all N times and sites. ...
The Comprehensive Air quality Model with extensions (CAMx) is an open-source, state-of-the-science photochemical grid model that addresses tropospheric air pollution (ozone, particulates, air toxics) over spatial scales ranging from neighborhoods to continents. CAMx has been in continuous development for over 25 years and used by numerous entities ranging from government to industry to academia to support regulatory actions and scientific research addressing a variety of air quality issues. Here we describe the technical formulation of CAMx v7.20, the current publicly available model version. To illustrate an example of regional and seasonal model performance for predicted ozone and fine particulate matter (PM2.5), we summarize a model evaluation from a recent 2016 national-scale CAMx application over nine climate zones contained within the conterminous US. From that evaluation, we find that statistical performance for warm season maximum 8-hour ozone is consistently within benchmark statistical criteria for bias, gross error, and correlation over all climate zones, and often near statistical goals. Statistical performance for 24-hour PM2.5 and constituents fluctuate around statistical criteria with more seasonal and regional variability that can be attributed to different sources of uncertainty among PM2.5 species (e.g., weather influences, chemical treatments and interactions, emissions uncertainty, and ammonia treatments). We close with a mention of new features and capabilities that will be included in upcoming public releases of the model.
... After training the model, we evaluated the correctness of our learned schemes in emulating the reference solver. Following common practice for the evaluation of CTMs (Simon et al. 2012), we evaluated the performance of the learned scheme using three different statistics: mean absolute error, root mean square error, and R 2 , all averaged throughout the entire simulation rather than for a single timestep. We tested our model's ability to replicate the full 10-day simulation it was trained on-which is longer than the 10-timestep segments of that simulation which were used as training data-as well as additional tests of model generality as described below. ...
We developed and applied a machine-learned discretization for one-dimensional (1D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accurate, three-stencil numerical solver but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger time step—and trained our neural-network-based scheme to match the subsampled integration data. In this way, we created an operator that has low resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1D scheme—when combined with a splitting technique—can be used to predict 2D advection and is in some cases more stable and accurate than the low-resolution reference solver in 2D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution.
Significance Statement
Chemical transport modeling (CTM) is an essential tool for studying air pollution. CTM simulations take a long computing time. Modeling pollutant transport (advection) is the second most computationally intensive part of the model. Decreasing the resolution not only reduces the advection computing time but also decreases accuracy. We employed machine learning to reduce the resolution of advection while keeping the accuracy. We verified the robustness of our solver with several generalization testing scenarios. In our 2D simulation, our solver showed up to 100 times faster simulation with fair accuracy. Integrating our approach to existing CTMs will allow broadened participation in the study of air pollution and related solutions.
... For comparison with box model results of γ(N 2 O 5 ) and Φ(ClNO 2 ) and flight measurements of N 2 O 5 and ClNO 2 concentration, we used the standard EPA performance metrics of normalized mean bias (NMB), normalized mean error (NME), root mean square error (RMSE), and Pearson's correlation coefficient (r) (Dennis et al., 2010;Simon et al., 2012). Equations for each metric are listed in Text S3 in Supporting Information S1. ...
Nitrogen oxides (NOx) have adverse human health impacts and play a central role in the production of ozone and PM2.5. Nighttime heterogeneous chemistry regulates the nocturnal reservoirs and sinks of NOx such as N2O5 removal and ClNO2 production. However, existing parameterizations of nocturnal NOx heterogeneous chemistry in air quality models do not capture the variability in observations. Here, we implemented for the first time in the Community Multiscale Air Quality (CMAQ) model the Gaston N2O5 uptake (γ(N2O5)) mechanism that accounts for the role of particulate organic matter in regulating N2O5 uptake and the Staudt ClNO2 yield (Φ(ClNO2)) mechanism that includes the role of reactive solutes in suppressing ClNO2 production. With the Gaston and Staudt parameterizations, the coarse mode contributed modestly to N2O5 loss (17.2%) but significantly to ClNO2 production (60.3%), highlighting the impact of coarse mode chemistry. The Gaston γ(N2O5) parameterization in the fine mode increased agreement between modeled N2O5 concentration and observations (RMSEnew = 0.37ppb) compared to the model default (RMSEdefault = 0.43ppb). The Gaston γ(N2O5) parameterization was overall biased low due to underestimates in modeled particle oxygen to carbon ratio (O:C). The Staudt Φ(ClNO2) parameterization resulted in further underestimation (NMBnew = −73.7%) compared to the model default (NMBdefault = −37.9%) because of underestimation of fine mode particle chloride concentration. We expect that the updated parameterizations may more accurately capture the mean state and variability in γ(N2O5) and Φ(ClNO2) under conditions where model particulate O:C and chloride are better represented.
... The correlation coefficient (R) gives information about the coincidence of temporal evolution. Mean bias (MB) and mean error (ME) were widely used to indicate the overall difference between observations and simulations (Emery et al., 2017;Gleckler et al., 2008;Simon et al., 2012). The former also implies the information of underestimation or overestimation. ...
Vertical exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) is a key link in coupling the earth's surface and upper atmosphere. This process is usually quantified by numerical simulations, while its reliability is not well assessed until now. Using space‐time intensified ABL observations, we evaluate the ABL‐FT air mass exchange flux derived from the Weather Research and Forecast (WRF) model. A six‐site sounding experiment is conducted in the North China Plain during the wintertime of 2019. The measured data is processed to provide enough information to derive the vertical exchange flux corresponding to the model‐based result, so that a systematic comparison is conducted. Three physical processes involved in ABL‐FT vertical exchange are quantitatively evaluated, that is, temporal variation of ABL height, advection across the inclined ABL top, and vertical motion at the ABL‐FT interface. Results show that the model‐based and observation‐based fluxes are generally agreed in temporal evolution (R = 0.67, p < 0.01), both characterized by 4–6 days periodicity and diurnal cycle. Their relative mean error was about 45% during the whole study period, mainly stemming from the vertical motion term and the advection crossing term. The model inaccuracy in representing these relevant processes at the ABL top is largely responsible for the discrepancy. Besides, the difference may also be attributed to the observational uncertainty (∼22%) that is caused by the measurement's difficulties in determining ABL spatial variation and acquiring vertical velocity. Through this study, the credibility and limitation of the WRF model in deriving ABL‐FT exchange flux are quantified.
... The real-time hourly measured total PM 2.5 mass was taken from the National Observation Network of Atmospheric Pollutants established by CNEMC. To provide a comprehensive picture of the model's ability to capture the magnitude of and variation in pollutant concentrations, several statistical metrics including the root mean square error (RMSE), correlation coefficient (R), index of agreement (IOA), normalized mean bias (NMB), normalized mean error (NME), mean fractional bias (MFB), and mean fractional error (MFE) were considered (Simon et al., 2012). A detailed description can be found in the supplementary information (S1). ...
... And how different emission 72 scenarios or meteorological conditions will affect the concentrations? (Simon et al., 2012). 73 74 ...
Numerous studies have used air quality models to estimate pollutant concentrations in the Metropolitan Area of São Paulo (MASP) by using different inputs and assumptions. Our objectives are to summarize these studies, compare their performance, configurations, and inputs, and recommend areas of further research. We examined 29 air quality modeling studies that focused on ozone (O3) and fine particulate matter (PM2.5) performed over the MASP, published from 2001 to 2023. The California Institute of Technology airshed model (CIT) was the most used offline model, while the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) was the most used online model. Because the main source of air pollution in the MASP is the vehicular fleet, it is commonly used as the only anthropogenic input emissions. Simulation periods were typically the end of winter and during spring, seasons with higher O3 and PM2.5 concentrations. Model performance for hourly ozone is good with half of the studies with Pearson correlation above 0.6 and root mean square error (RMSE) ranging from 7.7 to 27.1 ppb. Fewer studies modeled PM2.5 and their performance is not as good as ozone estimates. Lack of information on emission sources, pollutant measurements, and urban meteorology parameters is the main limitation to perform air quality modeling. Nevertheless, researchers have used measurement campaign data to update emission factors, estimate temporal emission profiles, and estimate volatile organic compounds (VOCs) and aerosol speciation. They also tested different emission spatial disaggregation approaches and transitioned to global meteorological reanalysis with a higher spatial resolution. Areas of research to explore are further evaluation of models’ physics and chemical configurations, the impact of climate change on air quality, the use of satellite data, data assimilation techniques, and using model results in health impact studies. This work provides an overview of advancements in air quality modeling within the MASP and offers practical approaches for modeling air quality in other South American cities with limited data, particularly those heavily impacted by vehicle emissions.
... The Community Multiscale Air Quality (CMAQ) is a chemical transport AQM (CTM) developed within the U.S. Environmental Protection Agency and is commonly used to develop emissions control strategies for criteria air pollutants (6)(7)(8) and quantify the impact of distinct air pollution sources (9,10). The model is one of the most widely used in air quality modeling systems in recent years (11). Generally, advancements in chemical mechanisms and transport have improved the accuracy of AQMs and the ability to reproduce atmospheric air pollution concentrations at monitored locations (12)(13)(14)(15)(16). ...
The Earth’s atmosphere is extremely complex due to the presence of several dynamic processes, such as dispersion, diffusion, deposition, and chemical reactions. There is a pressing need to improve the predictability of air quality models by integrating more of these scientific processes with an increasing number of chemical species into the mechanisms. These enhancements degrade the computational efficiency of the most comprehensive modeling applications, leading to a significant increase in simulation time. Offline chemical transport models (CTM) spend considerable time simulating large atmospheric domains, primarily on solving for the gas-phase chemistry. To reduce the simulation time while maintaining the integrity of the models, we utilized graphics processing units (GPUs) to replace the central processing units (CPU) for computing the most expensive science processes by successfully migrating the gas-phase chemistry solver onto a GPU to reduce computational time. The actual kernel computing time for the solver is twice as fast as the CPU with the BLKSIZE of 8,000; however, the GPU solver incurs communication time costs due to the of moving data back and forth between the CPU host system memory to the GPU memory. In this paper, we focus on compiling of the Community Multiscale Air Quality (CMAQ) model with CUDA kernels, migrating the gas-phase CTM solver onto the GPU, and optimizing the solver to improve GPU computational efficiency. Our positive results from the migrated solver show significant promise for intensive parallel computing applications on GPU devices reduce simulation time and accelerating air quality research.
... A hierarchy of models of the underlying science is required to provide detailed advice for the science-into-policy process. There are excellent reviews and intercomparisons of complex ozone models on all scales from urban to regional to global, addressing issues of atmospheric chemistry, boundary layer processes and atmospheric transport (Simon et al., 2012;Turnock et al., 2020;Young et al., 2013). However, there is also a requirement for conceptual models that aim to advance our understanding of tropospheric ozone by simplifying and capturing the essence of the most salient physical and chemical processes that control observed ozone abundances. ...
Elevated tropospheric ozone concentrations driven by anthropogenic precursor emissions are an environmental hazard scientifically similar to the depletion of the stratospheric ozone layer and global climate change; however, the tropospheric ozone issue lacks the generally accepted, international assessment efforts that have greatly informed our understanding of the other two. Here, we briefly review those successful science-into-policy approaches and outline the elements required to conduct a similar process for tropospheric ozone. Particular emphasis is placed on the need to establish a conceptual model to fully understand the underpinning science, useful policy metrics, and motivating international policy forums for regulating anthropogenic ozone production over the hemispheric and global scales, thereby expanding beyond the traditional regional, air basin approach that has dominated air quality regulatory philosophy to date.
... Complementary to field studies, Chemical Transport Models (CTMs) and data-based machine learning methods [11,12] have been widely used to enhance understanding of ozone formation and transport processes. Despite significant advancements in models, the prediction of ozone, especially during high ozone episodes, remains a challenging problem, particularly in complex coastal regions where local emissions, transport processes, and meteorological conditions interact in complex ways [13][14][15][16]. ...
This study investigates the influence of meteorology initialization on surface ozone prediction in the Great Lakes region using Canada’s operational air quality model (GEM-MACH) at a 2.5 km horizontal resolution. Two different initialization techniques are compared, and it is found that the four-dimensional incremental analysis updating (IAU) method yields improved model performance for surface ozone prediction. The IAU run shows better ozone regression line statistics (y = 0.7x + 14.9, R² = 0.2) compared to the non-IAU run (y = 0.6x + 23.1, R² = 0.1), with improved MB and NMB values (3.9 ppb and 8.9%, respectively) compared to the non-IAU run (4.1 ppb and 9.3%). Furthermore, analyzing ozone prediction sensitivity to model initialization time reveals that the 18z initialization leads to enhanced performance, particularly during high ozone exceedance days, with an improved regression slope of 0.9 compared to 0.7 for the 00z and 12z runs. The MB also improves to −0.2 ppb in the 18z run compared to −2.8 ppb and −3.9 ppb for the 00z and 12z runs, respectively. The analysis of meteorological fields reveals that the improved ozone predictions at 18z are linked to a more accurate representation of afternoon wind speed. This improvement enhances the transport of ozone, contributing to the overall improvement in ozone predictions.
... The correlation slope is 1.04, the model intercept is −1.4 ppbv, and the correlation coefficient is 0.79. The model performance is similar to the results from prior studies [10] and peer air quality model systems [41][42][43][44][45]. ...
While transportation emissions have declined over the past several decades, volatile organic compound (VOC) emissions from solvent use applications have increased as urban areas expand. In this work, the Canadian air quality model (GEM-MACH-TEB) is used to assess the importance of solvent emissions during the Michigan Ontario Ozone Source Experiment (MOOSE). Model predictions are compared to ozone and total mono-substituted aromatics (TOLU) observations collected in Windsor, Ontario. For summer 2018, model estimates of TOLU from solvent emissions are smaller (30% for an 8 h daytime average) in Windsor than estimates from positive matrix factorization (44% for a 24 h average). The use of updated U.S. solvent emissions from the EPA’s VCPy (Volatile Chemical Product framework) for summer 2021 simulations increases the solvent use source contribution over Detroit/Windsor (30–50% for an 8 h daytime average). This also provides a more uniform spatial distribution across the U.S./Canada border (30–50% for an 8 h daytime average). Long-chain alkanes are the dominant speciation in the model’s air pollutant emission inventory and in the observation-derived solvent use factor. Summertime 8 h daytime ozone decreased by 0.4% over Windsor for a 10% solvent use VOC emission reduction scenario. A 10% mobile NOx emission reduction scenario resulted in a 0.6% O3 decrease over Windsor and more widespread changes over the study region.
... These statistical descriptors are root mean square error (RMSE), normalise mean bias (NMB), normalise mean error (NME), fractional bias (FB), and mean absolute error (MAE), tabulated in Table 4. These are some of the most frequently used predictive performance metrics for the assessment of model performance (Chambliss et al., 2020;Simon et al., 2012). These parameters measured bias and error statistics metrics between two data sets, which measure the EPS's tendency to over-or under-measure as well as calculate the magnitude of the difference between values observed with the reference monitor and EPS. ...
An effective micro-level air quality management plan requires high-resolution monitoring of pollutants. India has already developed a vast network of air quality monitoring stations, both manual and real time, located primarily in urban areas, including megacities. The air quality monitoring network consists of conventional manual stations and real time Continuous Ambient Air Quality Monitoring Stations (CAAQMS) which comprise state-of-the-art analysers and instruments. India is currently in the early stages of developing and adopting economical portable sensor (EPS) in air quality monitoring systems. Protocols need to be established for field calibration and testing. The present research work is an attempt to develop a performance-based assessment framework for the selection of EPS for air quality monitoring. The two-stage selection protocol includes a review of the factory calibration data and a comparison of EPS data with a reference monitor, i.e. a portable calibrated monitor and a CAAQMS. Methods deployed include calculation of central tendency, dispersion around a central value, calculation of statistical parameters for data comparison, and plotting pollution rose and diurnal profile (peak and non-peak pollution measurement). Four commercially available EPS were tested blind, out of which, data from EPS 2 (S2) and EPS 3 (S3) were closer to reference stations at both locations. The selection was made by evaluating monitoring results, physical features, measurement range, and frequency along with examining capital cost. This proposed approach can be used to increase the usability of EPS in the development of micro-level air quality management strategies, other than regulatory compliance. For regulatory compliance, additional research is needed, including field calibration and evaluating EPS performance through additional variables. This proposed framework may be used as starting point, for such experiments, in order to develop confidence in the use of EPS.
... InMAP predictions and applied scaling methodologies were evaluated using normalized mean bias (NMB), normalized mean error (NME), mean bias (MB), mean error (ME), and squared Pearson correlation coefficient (R 2 ) values where P i is predicted InMAP speciated concentrations and O i is observed (i.e., satellite-derived speciated concentrations or ground-level monitor data). These statistic metrics are the most commonly used to evaluate model predictions of PM 2.5 (Simon et al., 2012). ...
Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra‐urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city‐scale decision‐making. To reduce InMAP's biases and increase its relevancy for urban‐scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite‐derived speciated PM2.5 from Washington University and ground‐level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground‐monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: −48%, pNO3: 8%, pNH4: 69%), but with city‐specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city‐scaling approach (15%–27%). The city‐specific scaling method also improves the R² value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36–0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non‐EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide −6%).
... The last rows reflect conditions when observed hourly ozone was above 50 ppb. (Simon et al., 2012) and found that two thirds of modelling studies reported hourly and MDA8 NMB < 15%, NME < 25% and r > 0.50. ...
Chemical mechanisms describe how emissions of gases and particles evolve in the atmosphere and are used within chemical transport models to evaluate past, current, and future air quality. Thus, a chemical mechanism must provide robust and accurate predictions of air pollutants if it is to be considered for use by regulatory bodies. In this work, we provide an initial evaluation of the Community Regional Atmospheric Chemical Multiphase Mechanism (CRACMMv1.0) by assessing CRACMMv1.0 predictions of surface ozone (O3) across the Northeast U.S. during the summer of 2018 within the Community Multiscale Air Quality (CMAQ) modeling system. CRACMMv1.0 O3 predictions of hourly and maximum daily 8-hour average (MDA8) ozone were lower than those estimated by the Regional Atmospheric Chemical Mechanism (RACM2_ae6), which better matched surface network observations in the Northeast US (RACM2_ae6 mean bias of +4.2 ppb for all hours and +4.3 ppb for MDA8; CRACMMv1.0 mean bias of +2.1 ppb for all hours and +2.7 ppb for MDA8). Box model calculations combined with results from CMAQ emission reduction simulations indicated high sensitivity of O3 to compounds with biogenic sources. In addition, these calculations indicated the differences between CRACMMv1.0 and RACM2_ae6 O3 predictions were largely explained by updates to the inorganic rate constants (reflecting the latest assessment values) and by updates to the representation of monoterpene chemistry. Updates to other reactive organic carbon systems between RACM2_ae6 and CRACMMv1.0 also affected ozone predictions and their sensitivity to emissions. Specifically, CRACMMv1.0 benzene, toluene, and xylene chemistry led to efficient NOx cycling such that CRACMMv1.0 predicted controlling aromatics reduces ozone without rural O3 disbenefits. In contrast, semivolatile to intermediate volatility alkanes introduced in CRACMMv1.0 acted to suppress O3 formation across the regional background through the sequestration of nitrogen oxides (NOx) in organic nitrates. Overall, these analyses showed that the CRACMMv1.0 mechanism within the CMAQ model was able to reasonably simulate ozone concentrations in the Northeast US during the summer of 2018 with similar magnitude and diurnal variation as the current operational Carbon Bond (CB6r3_ae7) and good model performance compared to recent modelling studies in the literature.
... However, only mean fractional error for Oakdale complies the benchmark of mean fractional error of 35% (highlighted with green). Simon et al. (2012) reported the root mean square errors are in the range of 15-20 ppb for hourly ozone concentrations in most of model validation studies, and the root mean square error in our studies are far under that. Figure 28 is the diurnal variations for predicted and observed hourly ozone concentration in each season at Chullora (Sydney east), Richmond (Sydney north-west), Bringelly (Sydney south-west), Wollongong (Illawarra) and Newcastle (Newcastle). ...
https://www.environment.nsw.gov.au/topics/air/research/current-research/sydney-air-quality-study
... We select studies that use the same statistical metrics as in Table S1 in Supporting Information S1, simulate time periods after the year 2000, and integrate a similar CTM (WRF-CMAQ or WRF-Chem). We do not focus on other benchmark studies that use coarser and/or older versions of CTMs or emissions models (Emery et al., 2017;Simon et al., 2012). Model performance relative to observations of 5 pollutants over 5 different time-averaging periods: hourly (red), daily (yellow), daily maximum (blue), and monthly (black). ...
The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two‐way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF‐CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF‐CMAQ's performance using data from the National Climatic Data Center and Environmental Protection Agency Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation's meteorological and chemical performance while also resolving key details in areas of high exposure and impact, that is, urban environments. At 1.3 km, we find that most air quality‐relevant meteorological components of WRF‐CMAQ perform at or above community benchmarks. WRF‐CMAQ's chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (>30%). Simulated spatial pollutant patterns show distinct urban‐rural footprints, with urban NO2 and PM2.5 20%–60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high‐pollution areas within individual urban neighborhoods and characterize seasonal changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high‐resolution simulations, particularly over urban settings.
Ground-level ozone (O3) has emerged as a significant air pollutant in China, attracting increasing attention from both the scientific community and policymakers. Chemical transport models (CTMs) serve as crucial tools in addressing O3 pollution, with frequent applications in predicting O3 concentrations, identifying source contributions, and formulating effective control strategies. The accuracy and reliability of the simulated O3 concentrations are typically assessed through model performance evaluation (MPE). However, the wide array of CTMs available, variations in input data, model setups, and other factors result in a broad range of differences between simulated and observed O3 concentrations, highlighting the necessity of standardized benchmarks in O3 evaluation.
Building upon our previous work, this study conducted a thorough literature review of CTM applications simulating O3 in China from 2006 to 2021. A total of 216 relevant articles out of a total of 667 reviewed were identified to extract quantitative MPE results and key model configurations. From our analysis, two sets of benchmark values for six commonly used MPE metrics are proposed for CTM applications in China, categorized into “goal” benchmarks representing optimal model performance and “criteria” benchmarks representing achievable model performance across a majority of studies. It is recommended that the normalized mean bias (NMB) for hourly O3 and daily 8 h maximum O3 concentrations should ideally fall within ±15 % and ±10 %, respectively, to meet the goal benchmark. If the criteria benchmarks are to be met, the NMB should be within ±30 % and ±20 %, respectively. Moreover, uncertainties in O3 predictions due to uncertainties in various model inputs were quantified using the decoupled direct method (DDM) in a commonly used CTM. For the simulation period of June 2021, the total uncertainty of simulated O3 ranged from 4 to 25 µg m⁻³, with anthropogenic volatile organic compound (AVOC) emissions contributing most to the uncertainty regarding O3 in coastal regions and with O3 boundary conditions playing a dominant role in the northwestern region. The proposed benchmarks for assessing simulated O3 concentrations, in conjunction with our previous studies on PM2.5 and other criteria air pollutants, represent a comprehensive and systematic effort to establish a model performance framework for CTM applications in China. These benchmarks aim to support the growing modeling community in China by offering a robust set of evaluation metrics and establishing a consistent evaluation methodology relative to the body of prior research, thereby helping to establish the credibility and reliability of CTM applications. These statistical benchmarks need to be periodically updated as models advance and as better inputs become available in the future.
The Community Multiscale Air Quality (CMAQ) model simulates atmospheric phenomena, including advection, diffusion, gas-phase chemistry, aerosol physics and chemistry, and cloud processes. Gas-phase chemistry is often a major computational bottleneck due to its representation as large systems of coupled nonlinear stiff differential equations. We leverage the parallel computational performance of graphics processing unit (GPU) hardware to accelerate the numerical integration of these systems in CMAQ’s CHEM module. Our implementation, dubbed CMAQ-CUDA, in reference to its use in the Compute Unified Device Architecture (CUDA) general purpose GPU (GPGPU) computing solution, migrates CMAQ’s Rosenbrock solver from Fortran to CUDA Fortran. CMAQ-CUDA accelerates the Rosenbrock solver such that simulations using the chemical mechanisms RACM2, CB6R5, and SAPRC07 require only 51%, 50%, or 35% as much time, respectively, as CMAQv5.4 to complete a chemistry time step. Our results demonstrate that CMAQ is amenable to GPU acceleration and highlight a novel Rosenbrock solver implementation for reducing the computational burden imposed by the CHEM module.
Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.5) air pollution but has sparse regulatory air monitoring. Challenges for low-cost PM2.5 sensors in India include high-aerosol mass concentrations and pronounced regional and seasonal gradients in aerosol composition. Here, we report on a detailed long-time performance evaluation of a popular sensor, the Purple Air PA-II, at multiple sites in India. We established three distinct sites in India across land use categories and population density extremes (in urban Delhi and rural Hamirpur in north India and urban Bengaluru in south India), where we collocated the PA-II model with reference beta attenuation monitors. We evaluated the performance of uncalibrated sensor data, and then developed, optimized, and evaluated calibration models using a comprehensive feature selection process with a view to reproducibility in the Indian context. We assessed the seasonal and spatial transferability of sensor calibration schemes, which is especially important in India because of the paucity of reference instrumentation. Without calibration, the PA-II was moderately correlated with the reference signal (R2= 0.55–0.74) but was inaccurate (NRMSE ≥ 40 %). Relative to uncalibrated data, parsimonious annual calibration models improved the PurpleAir (PA) model performance at all sites (cross-validated NRMSE 20 %–30 %; R2= 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed among our sites, suggesting that local calibrations are desirable when possible. Using a moving-window calibration, we found that using seasonally specific information improves performance relative to a static annual calibration model, while a short-term calibration model generally does not transfer reliably to other seasons. Overall, we find that the PA-II model can provide reliable PM2.5 data with better than ±25 % precision and accuracy when paired with a rigorous calibration scheme that accounts for seasonality and local aerosol composition.
Atmospheric deposition of nitrogen (N) and sulfur (S) compounds from human activity has greatly declined in the United States (US) over the past several decades in response to emission controls set by the Clean Air Act. While many observational studies have investigated spatial and temporal trends of atmospheric deposition, modeling assessments can provide useful information over areas with sparse measurements, although they usually have larger horizontal resolutions and are limited by input data availability. In this analysis, we evaluate wet, dry, and total N and S deposition from multiyear simulations within the contiguous US (CONUS). Community Multiscale Air Quality (CMAQ) model estimates from the EPA's (Environmental Protection Agency) Air QUAlity TimE Series (EQUATES) project contain important model updates to atmospheric deposition algorithms compared to previous model data, including the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model which contains land-use-specific resistance parameterization and land-use-specific deposition estimates needed to estimate the differential impacts of N deposition to different land use types. First, we evaluate model estimates of wet deposition and ambient concentrations, finding underestimates of SO4, NO3, and NH4 wet deposition compared to National Atmospheric Deposition Program observations and underestimates of NH4 and SO4 and overestimates of SO2 and TNO3 (HNO3+NO3) compared to the Clean Air Status and Trends Network (CASTNET) ambient concentrations. Second, a measurement–model fusion approach employing a precipitation and bias correction to wet-deposition estimates is found to reduce model bias and improve correlations compared to the unadjusted model values. Model agreement of wet deposition is poor over parts of the West and Northern Rockies, due to errors in precipitation estimates caused by complex terrain and uncertainty in emissions at the relatively coarse 12 km grid resolution used in this study. Next, we assess modeled N and S deposition trends across climatologically consistent regions in the CONUS. Total deposition of N and S in the eastern US is larger than the western US with a steeper decreasing trend from 2002–2017; i.e., total N declined at a rate of approximately -0.30 kg N ha-1 yr-1 in the Northeast and Southeast and by -0.02 kg N ha-1 yr-1 in the Northwest and Southwest. Widespread increases in reduced N deposition across the Upper Midwest, Northern Rockies, and West indicate evolving atmospheric composition due to increased precipitation amounts over some areas, growing agricultural emissions, and regional NOx/SOx emission reductions shifting gas–aerosol partitioning; these increases in reduced N deposition are generally masked by the larger decreasing oxidized N trend. We find larger average declining trends of total N and S deposition between 2002–2009 than 2010–2017, suggesting a slowdown of the rate of decline likely in response to smaller emission reductions. Finally, we document changes in the modeled total N and S deposition budgets. The average annual total N deposition budget over the CONUS decreases from 7.8 in 2002 to 6.3 kg N ha-1 yr-1 in 2017 due to declines in oxidized N deposition from NOx emission controls. Across the CONUS during the 2002–2017 time period, the average contribution of dry deposition to the total N deposition budget drops from 60 % to 52 %, whereas wet deposition dominates the S budget rising from 45 % to 68 %. Our analysis extends upon the literature documenting the growing contribution of reduced N to the total deposition budget, particularly in the Upper Midwest and Northern Rockies, and documents a slowdown of the declining oxidized N deposition trend, which may have consequences on vegetation diversity and productivity.
The Multi-Angle Imager for Aerosols (MAIA), supported by NASA and the Italian Space Agency, is planned for launch into space in 2025. As part of its mission goal, outputs from a chemical transport model, the Unified Inputs for Weather Research and Forecasting Model coupled with Chemistry (UI-WRF-Chem), will be used together with satellite data and surface observations for estimating surface PM2.5. Here, we develop a method to improve UI-WRF-Chem with surface observations at the U.S. embassy in Ethiopia, one of MAIA’s primary target areas in east Africa. The method inversely models the diurnal profile and amount of anthropogenic aerosol and trace gas emissions. Low-cost PurpleAir sensor data are used for validation after applying calibration functions obtained from the collocated data at the embassy. With the emission updates in UI-WRF-Chem, independent validation for February 2022 at several different PurpleAir sites shows an increase in the linear correlation coefficients from 0.1–0.7 to 0.6–0.9 between observations and simulations of the diurnal variation of surface PM2.5. Furthermore, even by using the emissions optimized for February 2021, the UI-WRF-Chem forecast for March 2022 is also improved. Annual update of monthly emissions via inverse modeling has the potential and is needed to improve MAIA’s estimate of surface PM2.5.
With the gradual deepening of the research and governance of air pollution, chemical transport models (CTMs), especially the third-generation CTMs based on the "1 atm" theory, have been recognized as important tools for atmospheric environment research and air quality management. In this review article, we screened 2396 peer-reviewed manuscripts on the application of four pre-selected regional CTMs in the past five years. CAMx, CMAQ, WRF-Chem and NAQPMS models are well used in the simulation of atmospheric pollutants. In the simulation study of secondary pollutants such as O3, secondary organic aerosol (SOA), sulfates, nitrates, and ammonium (SNA), the CMAQ model has been widely applied. Secondly, model evaluation indicators are diverse, and the establishment of evaluation criteria has gone through the long-term efforts of predecessors. However, the model performance evaluation system still needs further specification. Furthermore, temporal-spatial resolution, emission inventory, meteorological field and atmospheric chemical mechanism are the main sources of uncertainty, and have certain interference with the simulation results. Among them, the inventory and mechanism are particularly important, and are also the top priorities in future simulation research.
In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12–35 μg/m3 hence classified as “Moderate” AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of “Unhealthy for Sensitive People”. Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.
Single source contribution to ambient O3 and PM2.5 have been estimated with photochemical grid models to support policy demonstrations for National Ambient Air Quality Standards, regional haze, and permit related programs. Limited field data exists to evaluate model representation of the spatial extent and chemical composition of plumes emitted by specific facilities. New tropospheric column measurements of NO2 and in-plume chemical measurements downwind of specific facilities allows for photochemical model evaluation of downwind plume extent, grid resolution impacts on plume concentration gradients, and source attribution methods. Here, photochemical models were applied with source sensitivity and source apportionment approaches to differentiate single source impacts on NO2 and O3 and compared with field study measurements. Source sensitivity approaches (e.g., brute-force difference method and decoupled direct method (DDM)) captured the spatial extent of NO2 plumes downwind of three facilities and the transition of near-source O3 titration to downwind production. Source apportionment approaches showed variability in terms of attributing the spatial extent of NO2 plumes and downwind O3 production. Each of the Community Multiscale Air Quality (CMAQ) source apportionment options predicted large O3 contribution from the TVA facility in the flight transects nearest the facility when measurements and source sensitivity approaches suggest titration was outpacing production. In general, CMAQ DDM tends to attribute more O3 to boundary inflow and less to within-domain NOX and VOC sources compared to CMAQ source apportionment. The photochemical modeling system was able to capture single source plumes using 1 to 12 km grid resolution with best representation of plume extent and magnitude at the finer resolutions. When modeled at 1 to 12 km grid resolution, primary and secondary PM2.5 impacts were highest at the source location and decrease as distance increases downwind. The use of coarser grid resolution for single source attribution resulted in predicted impacts highest near the source but lower peak source specific concentrations compared to finer grid resolution simulations because impacts were spread out over a larger area.
Chemical mechanisms describe how emissions of gases and particles evolve in the atmosphere and are used within chemical transport models to evaluate past, current, and future air quality. Thus, a chemical mechanism must provide robust and accurate predictions of air pollutants if it is to be considered for use by regulatory bodies. In this work, we provide an initial evaluation of the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMMv1.0) by assessing CRACMMv1.0 predictions of surface ozone (O3) across the northeastern US during the summer of 2018 within the Community Multiscale Air Quality (CMAQ) modeling system. CRACMMv1.0 O3 predictions of hourly and maximum daily 8 h average (MDA8) ozone were lower than those estimated by the Regional Atmospheric Chemistry Mechanism with aerosol module 6 (RACM2_ae6), which better matched surface network observations in the northeastern US (RACM2_ae6 mean bias of +4.2 ppb for all hours and +4.3 ppb for MDA8; CRACMMv1.0 mean bias of +2.1 ppb for all hours and +2.7 ppb for MDA8). Box model calculations combined with results from CMAQ emission reduction simulations indicated a high sensitivity of O3 to compounds with biogenic sources. In addition, these calculations indicated the differences between CRACMMv1.0 and RACM2_ae6 O3 predictions were largely explained by updates to the inorganic rate constants (reflecting the latest assessment values) and by updates to the representation of monoterpene chemistry. Updates to other reactive organic carbon systems between RACM2_ae6 and CRACMMv1.0 also affected ozone predictions and their sensitivity to emissions. Specifically, CRACMMv1.0 benzene, toluene, and xylene chemistry led to efficient NOx cycling such that CRACMMv1.0 predicted controlling aromatics reduces ozone without rural O3 disbenefits. In contrast, semivolatile and intermediate-volatility alkanes introduced in CRACMMv1.0 acted to suppress O3 formation across the regional background through the sequestration of nitrogen oxides (NOx) in organic nitrates. Overall, these analyses showed that the CRACMMv1.0 mechanism within the CMAQ model was able to reasonably simulate ozone concentrations in the northeastern US during the summer of 2018 with similar magnitude and diurnal variation as the current operational Carbon Bond (CB6r3_ae7) mechanism and good model performance compared to recent modeling studies in the literature.
Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which demonstrates a fully coupled representation of chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMMv1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total non-cancer emission-weighted toxicity estimated for primary HAPs from anthropogenic and biomass burning sources in the US, with the coverage of toxicity higher (> 80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for the ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low- to intermediate-volatility organic compounds (9 species), low- to intermediate-volatility oxygenated species (16 species), intermediate-volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate- and lower-volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second-generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ∼ 30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of species carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in the number of hydrogens per carbon. In total, 11 new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations, resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and the design of control strategies. CRACMMv1.0 is available in CMAQv5.4.
The southeastern Atlantic is home to an expansive smoke aerosol plume overlying a large cloud deck for approximately a third of the year. The aerosol plume is mainly attributed to the extensive biomass burning activities that occur in southern Africa. Current Earth system models (ESMs) reveal significant differences in their estimates of regional aerosol radiative effects over this region. Such large differences partially stem from uncertainties in the vertical distribution of aerosols in the troposphere. These uncertainties translate into different aerosol optical depths (AODs) in the planetary boundary layer (PBL) and the free troposphere (FT). This study examines differences of AOD fraction in the FT and AOD differences among ESMs (WRF-CAM5, WRF-FINN, GEOS-Chem, EAM-E3SM, ALADIN, GEOS-FP, and MERRA-2) and aircraft-based measurements from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign. Models frequently define the PBL as the well-mixed surface-based layer, but this definition misses the upper parts of decoupled PBLs, in which most low-level clouds occur. To account for the presence of decoupled boundary layers in the models, the height of maximum vertical gradient of specific humidity profiles from each model is used to define PBL heights.
Results indicate that the monthly mean contribution of AOD in the FT to the total-column AOD ranges from 44 % to 74 % in September 2016 and from 54 % to 71 % in August 2017 within the region bounded by 25∘ S–0∘ N–S and 15∘ W–15∘ E (excluding land) among the ESMs. ALADIN and GEOS-Chem show similar aerosol plume patterns to a derived above-cloud aerosol product from the Moderate Resolution Imaging Spectroradiometer (MODIS) during September 2016, but none of the models show a similar above-cloud plume pattern to MODIS in August 2017. Using the second-generation High Spectral Resolution Lidar (HSRL-2) to derive an aircraft-based constraint on the AOD and the fractional AOD, we found that WRF-CAM5 produces 40 % less AOD than those from the HSRL-2 measurements, but it performs well at separating AOD fraction between the FT and the PBL. AOD fractions in the FT for GEOS-Chem and EAM-E3SM are, respectively, 10 % and 15 % lower than the AOD fractions from the HSRL-2. Their similar mean AODs reflect a cancellation of high and low AOD biases. Compared with aircraft-based observations, GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL. The models generally underestimate AODs for measured AODs that are above 0.8, indicating their limitations at reproducing high AODs. The differences in the absolute AOD, FT AOD, and the vertical apportioning of AOD in different models highlight the need to continue improving the accuracy of modeled AOD distributions. These differences affect the sign and magnitude of the net aerosol radiative forcing, especially when aerosols are in contact with clouds.
This paper examines the operational performance of the Community Multiscale Air Quality (CMAQ) model simulations for 2002–2006 using both 36-km and 12-km horizontal grid spacing with a primary focus on the performance of the CMAQ model in predicting wet deposition of sulfate (SO4=), ammonium (NH4+) and nitrate (NO3−). Performance of the wet deposition species is determined by comparing CMAQ predicted concentrations to concentrations measured by the National Acid Deposition Program (NADP), specifically the National Trends Network (NTN). For SO4= wet deposition, the CMAQ model estimates were generally comparable between the 36-km and 12-km simulations for the eastern US, with the 12-km simulation giving slightly higher estimates of SO4= wet deposition than the 36-km simulation on average. The normalized mean bias (NMB) was slightly higher for the 12-km simulation, however, both simulations had annual biases that were less than ±15% for each of the five years. The model estimated SO4= wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH4+ wet deposition over the eastern US using both the 36-km and 12-km horizontal grid spacing, with a slightly larger underestimation in the 36-km simulation. The largest underestimations occur during the winter and spring periods, while the summer and fall have slightly smaller underestimations of NH4+ wet deposition. Annually, the NMB generally ranges between −10% and −16% for the 12-km simulation and −12% to −18% for the 36-km simulation over the five-year period for the eastern US. The underestimation in NH4+ wet deposition is likely due, in part, to the poor temporal and spatial representation of ammonia (NH3) emissions, particularly those emissions associated with fertilizer applications and NH3 bi-directional exchange. The model performance for estimates of NO3− wet deposition are mixed throughout the year, with the model largely underestimating NO3− wet deposition in the spring and summer in the eastern US, while the model has a relatively small bias in the fall and winter. Model estimates of NO3− wet deposition tend to be slightly lower for the 36-km simulation as compared to the 12-km simulation, particularly in the spring. Annually for the eastern US, the NMB ranges from roughly −12% to −20% for the 12-km simulation and −18% to −26% for the 36-km simulation. The underestimation of NO3− wet deposition in the spring and summer is due, in part, to a lack of lightning generated NO emissions in the upper troposphere, which can be a large source of NO in the spring and summer when lightning activity is the high. CMAQ model simulations that include the production of NO from lightning show a significant improvement in the NO3− wet deposition estimates in the eastern US in the summer. Model performance for the western US was generally not as good as that for the eastern US for all three wet deposition species.
Ten different approaches for applying lateral and top climatological boundary conditions for ozone have been evaluated using the off-line regional air-quality model AURAMS. All ten approaches employ the same climatological ozone profiles, but differ in the manner in which they are applied, via the inclusion or exclusion of (i) a dynamic adjustment of the climatological ozone profile in response to the model-predicted tropopause height, (ii) a sponge zone for ozone on the model top, (iii) upward extrapolation of the climatological ozone profile, and (iv) different mass consistency corrections. The model performance for each approach was evaluated against North American surface ozone and ozonesonde observations from the BAQS-Met field study period in the summer of 2007. The original daily one-hour maximum surface ozone biases of about +15 ppbv were greatly reduced (halved) in some simulations using alternative methodologies. However, comparisons to ozonesonde observations showed that the reduction in surface ozone bias sometimes came at the cost of significant positive biases in ozone concentrations in the free troposphere and upper troposphere. The best overall performance throughout the troposphere was achieved using a methodology that included dynamic tropopause height adjustment, no sponge zone at the model top, extrapolation of ozone when required above the limit of the climatology, and no mass consistency corrections (global mass conservation was still enforced). The simulation using this model version had a one-hour daily maximum surface ozone bias of +8.6 ppbv, with small reductions in model correlation, and the best comparison to ozonesonde profiles. This recommended and original methodologies were compared for two further case studies: a high-resolution simulation of the BAQS-Met measurement intensive, and a study of the downwind region of the Canadian Rockies. Significant improvements were noted for the high resolution simulations during the BAQS-Met measurement intensive period, both in formal statistical comparisons and time series comparisons of events at surface stations. The tests for the downwind-Rockies region showed that the coupling between vertical transport associated with troposphere/stratosphere exchange, and that associated with boundary layer turbulent mixing, may contribute to ozone positive biases.
Regional-scale chemical transport model predictions of urban organic aerosol to date tend to be biased low relative to observations, a limitation with important implications for applying such models to human exposure health studies. We used a nested version of Environment Canada's AURAMS model (42-to-15-to-2.5 km nested grid spacing) to predict organic aerosol concentrations for a temporal and spatial domain corresponding to the Border Air Quality and Meteorology Study (BAQS-Met), an air-quality field study that took place in the southern Great Lakes region in the summer of 2007. The use of three different horizontal grid spacings allowed the influence of this parameter to be examined. A domain-wide average for the 2.5 km domain and a matching 15 km subdomain yielded very similar organic aerosol averages (4.8 vs. 4.3 μg m−3, respectively). On regional scales, secondary organic aerosol dominated the organic aerosol composition and was adequately resolved by the 15 km model simulation. However, the shape of the organic aerosol concentration histogram for the Windsor urban station improved for the 2.5 km simulation relative to those from the 42 and 15 km simulations. The model histograms for the Bear Creek and Harrow rural stations were also improved in the high concentration "tail" region. As well the highest-resolution model results captured the midday 4 July organic-aerosol plume at Bear Creek with very good temporal correlation. These results suggest that accurate simulation of urban and large industrial plumes in the Great Lakes region requires the use of a high-resolution model in order to represent urban primary organic aerosol emissions, urban VOC emissions, and the secondary organic aerosol production rates properly. The positive feedback between the secondary organic aerosol production rate and existing organic mass concentration is also represented more accurately with the highest-resolution model. Not being able to capture these finer-scale features may partly explain the consistent negative bias reported in the literature when urban-scale organic aerosol evaluations are made using coarser-scale chemical transport models.
This study presents the results from two sets of 18-year air quality simulations over the Northeastern US performed with a regional photochemical modeling system. These two simulations utilize different sets of lateral boundary conditions, one corresponding to a time-invariant climatological vertical profile and the other derived from monthly mean concentrations extracted from archived ECHAM5-MOZART global simulations. The objective is to provide illustrative examples of how model performance in several key aspects – trends, intra- and interannual variability of ground-level ozone, and ozone/precursor relationships – can be evaluated against available observations, and to identify key inputs and processes that need to be considered when performing and improving such long-term simulations. To this end, several methods for comparing observed and simulated trends and variability of ground level ozone concentrations, ozone precursors and ozone/precursor relationships are introduced. The application of these methods to the simulation using time-invariant boundary conditions reveals that the observed downward trend in the upper percentiles of summertime ozone concentrations is captured by the model in both directionality and magnitude. However, for lower percentiles there is a marked disagreement between observed and simulated trends. In terms of variability, the simulations using the time-invariant boundary conditions underestimate observed inter-annual variability by 30%–50% depending on the percentiles of the distribution. The use of boundary conditions from the ECHAM5-MOZART simulations improves the representation of interannual variability but has an adverse impact on the simulated ozone trends. Moreover, biases in the global simulations have the potential to significantly affect ozone simulations throughout the modeling domain, both at the surface and aloft. The comparison of both simulations highlights the significant impact lateral boundary conditions can have on a regional air quality model's ability to simulate long-term ozone variability and trends, especially for the lower percentiles of the ozone distribution.
This paper presents a comparison of the operational performances of two Community Multiscale Air Quality (CMAQ) model v4.7 simulations that utilize input data from the 5th-generation Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) meteorological models. Two sets of CMAQ model simulations were performed for January and August 2006. One set utilized MM5 meteorology (MM5-CMAQ) and the other utilized WRF meteorology (WRF-CMAQ), while all other model inputs and options were kept the same. For January, predicted ozone (O3) mixing ratios were higher in the Southeast and lower Mid-west regions in the WRF-CMAQ simulation, resulting in slightly higher bias and error as compared to the MM5-CMAQ simulations. The higher predicted O3 mixing ratios are attributed to less dry deposition of O3 in the WRF-CMAQ simulation due to differences in the calculation of the vegetation fraction between the MM5 and WRF models. The WRF-CMAQ results showed better performance for particulate sulfate (SO42−), similar performance for nitrate (NO3−), and slightly worse performance for nitric acid (HNO3), total carbon (TC) and total fine particulate (PM2.5) mass than the corresponding MM5-CMAQ results. For August, predictions of O3 were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO42− were lower in the region surrounding the Ohio Valley and higher along the Gulf of Mexico in the WRF-CMAQ simulation, contributing to poorer model performance. The primary causes of the differences in the MM5-CMAQ and WRF-CMAQ simulations appear to be due to differences in the calculation of wind speed, planetary boundary layer height, cloud cover and the friction velocity ( u ∗) in the MM5 and WRF model simulations, while differences in the calculation of vegetation fraction and several other parameters result in smaller differences in the predicted CMAQ model concentrations. The performance for SO42−, NO3− and NH4+ wet deposition was similar for both simulations for January and August.
Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses common pitfalls of multiple regression including measurement uncertainty, colinearity of covariates, and dataset selection. As expected, summertime OM/OC ratios are larger than wintertime values across the US with all regional median OM/OC values tightly confined between 1.8 and 1.95. Further, we find that OM/OC ratios during the winter are distinctly larger in the eastern US than in the West (regional medians are 1.58, 1.64, and 1.85 in the great lakes, southeast, and northeast regions, versus 1.29 and 1.32 in the western and central states). We find less spatial variability in long-term averaged OM/OC ratios across the US (90% of our multiyear regressions predicted OM/OC ratios between 1.37 and 1.94) than previous studies (90% of OM/OC estimates from a previous regression study fell between 1.30 and 2.10). We attribute this difference largely to the inclusion of EC as a covariate in previous regression studies. Due to the colinearity of EC and OC, we believe that up to one-quarter of the OM/OC estimates in a previous study are biased low. In addition to estimating OM/OC ratios, our technique reveals trends that may be contrasted with conventional assumptions regarding nitrate, sulfate, and soil across the IMPROVE network. For example, our regressions show pronounced seasonal and spatial variability in both nitrate volatilization and sulfate neutralization and hydration.
1] A previous intercomparison of atmospheric mercury models in North America has been extended to compare simulated and observed wet deposition of mercury. Three regional-scale atmospheric mercury models were tested: the Community Multiscale Air Quality (CMAQ) model, the Regional Modeling System for Aerosols and Deposition (REMSAD), and the Trace Element Analysis Model (TEAM). These models were each employed using three sets of lateral boundary conditions to test their sensitivity to intercontinental transport of mercury. The same meteorological and pollutant emission data were used in each simulation. Observations of wet deposition were obtained from the National Atmospheric Deposition Program's Mercury Deposition Network. The regional models can explain 50–70% of the site-to-site variance in annual mercury wet deposition. CMAQ was found to have slightly superior agreement with observations of annual mercury deposition flux in terms of the mean value for all monitoring sites, but REMSAD showed the best correlation when measured by the coefficient of determination (r 2). With the exception of one CMAQ simulation, all of the models tended to simulate more wet deposition of mercury than was observed. TEAM exceeded the observed average annual wet deposition by 50% or more in all three of its simulations. CMAQ and REMSAD were better able to reproduce the observed seasonal distribution of mercury wet deposition than was TEAM, but TEAM showed the highest correlation for weekly wet deposition samples. An analysis of model accuracy at each observation site showed no obvious geographic patterns for correlation, bias, or error. Adjusting simulated mercury deposition on the basis of the difference between observed and simulated precipitation data improved the correlation and error scores for all of the models.
Evaluation of concentrations predicted by air quality models is needed to ensure that model results are compatible with observations. In this study aerosol properties derived from the Community Multiscale Air Quality (CMAQ) model-simulated aerosol mass concentrations are compared with routine data from NASA satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the Sun-synchronous Terra satellite, NASA's ground-based Aerosol Robotic Network (AERONET), and the ground-based Interagency Monitoring of Protected Visual Environment (IMPROVE) network. The motivation for this analysis is to determine how best to use these parameters in evaluating model-predicted PM2.5 concentrations. CMAQ surface extinction estimates due to scattering at 550 nm wavelength are compared with the IMPROVE nephelometer data obtained from 25 sites within the United States. It is found that model-predicted surface extinctions bear high correlations with nephelometer measured data. Sulfate fractional aerosol optical depth (AOD) is found to dominate in the northeastern part of the United States; hence ground-based measurement of sulfate concentrations have been compared with time series of columnar AOD as observed by the MODIS instrument and also with the CMAQ-predicted tropospheric column values obtained during the June-August period of 2001. CMAQ surface extinctions are found to be relatively higher than the IMPROVE nephelometer observations; however, there is a good agreement between CMAQ AOD trends and AERONET and MODIS data, obtained at the seven AERONET sites located in the eastern United States. CMAQ is also found to capture the day-to-day variability in the spatial AOD patterns. Monthly average satellite AOD estimates are found to be higher than the AOD data obtained using the CMAQ-predicted aerosol concentrations. Seasonal variation of satellite-measured aerosol intensive property ``Angstrom exponent'' (a gross indicator of the aerosol size distribution) is presented for four selected sites: one each in the eastern and central parts, and two in the western part of the continental United States. Variability of Angstrom exponent at these four selected sites is analyzed in conjunction with the variation of summertime AOD (observed and modeled), mass concentration (observed and modeled) and modeled SO4 average concentrations during the summer (June-August) period of the year 2001. Annual time series of Angstrom exponent data at the four selected sites show a large east-west variation.
This study presents detailed evaluation of the seasonal and episodic performance of the Community Multiscale Air Quality (CMAQ) modeling system applied to simulate air quality at a fine grid spacing (4 km horizontal resolution) in central California, where ozone air pollution problems are severe. A rich aerometric database collected during the summer 2000 Central California Ozone Study (CCOS) is used to prepare model inputs and to evaluate meteorological simulations and chemical outputs. We examine both temporal and spatial behaviors of ozone predictions. We highlight synoptically driven high-ozone events (exemplified by the four intensive operating periods (IOPs)) for evaluating both meteorological inputs and chemical outputs (ozone and its precursors) and compare them to the summer average. For most of the summer days, cross-domain normalized gross errors are less than 25% for modeled hourly ozone, and normalized biases are between ±15% for both hourly and peak (1 h and 8 h) ozone. The domain-wide aggregated metrics indicate similar performance between the IOPs and the whole summer with respect to predicted ozone and its precursors. Episode-to-episode differences in ozone predictions are more pronounced at a subregional level. The model performs consistently better in the San Joaquin Valley than other air basins, and episodic ozone predictions there are similar to the summer average. Poorer model performance (normalized peak ozone biases 15%) is found in the Sacramento Valley and the Bay Area and is most noticeable in episodes that are subject to the largest uncertainties in meteorological fields (wind directions in the Sacramento Valley and timing and strength of onshore flow in the Bay Area) within the boundary layer.
As part 1 in a series of papers describing long-term simulations using the Community Multiscale Air Quality (CMAQ) modeling system and subsequent process analyses and sensitivity simulations, this paper presents a comprehensive model evaluation for the full year of 2001 over the continental U.S. using both ground-based and satellite measurements. CMAQ is assessed for its ability to reproduce concentrations and long-term trends of major criteria pollutants such as surface ozone (O3) and fine particulate matter (PM2.5) and related variables such as indicator species, wet deposition fluxes, and column mass abundances of carbon monoxide (CO), nitrogen oxides (NO2), tropospheric ozone residuals (TORs), and aerosol optical depths (AODs). The domain-wide and site-specific evaluation of surface predictions shows an overall satisfactory performance in terms of normalized mean biases for annual mean maximum 1 h and 8 h average O3 mixing ratios (−11.6 to 0.1% and −4.6 to 3.0%, respectively), 24 h average concentrations of PM2.5 (4.2–35.3%), sulfate (−13.0 to 43.5%), and organic carbon (OC) (−37.6 to 24.8%), and wet deposition fluxes (−13.3 to 31.6%). Larger biases, however, occur in the concentrations and wet deposition fluxes of ammonium and nitrate domain-wide and in the concentrations of PM2.5, sulfate, black carbon, and OC at some urban/suburban sites. The reasons for such model biases may be errors in emissions, chemistry, aerosol processes, or meteorology. The evaluation of column mass predictions shows a good model performance in capturing the seasonal variations and magnitudes of column CO and NO2, but relatively poor performance in reproducing observed spatial distributions and magnitudes of TORs for winter and spring and those of AODs in all seasons. Possible reasons for the poor column predictions include the underestimates of emissions, inaccurate upper layer boundary conditions, lack of model treatments of sea salt and dust, and limitations and uncertainties in satellite data.
The U.S. Environmental Protection Agency provides guidelines for demonstrating that future 8-hr ozone (O3) design values will be at or below the National Ambient Air Quality Standards on the basis of the application of photochemical modeling systems to simulate the effect of emission reductions. These guidelines also require assessment of the model simulation against observations. In this study, we examined the link between the simulated relative responses to emission reductions and model performance as measured by operational evaluation metrics, a part of the model evaluation required by the guidance, which often is the cornerstone of model evaluation in many practical applications. To this end, summertime O3 concentrations were simulated with two modeling systems for both 2002 and 2009 emission conditions. One of these two modeling systems was applied with two different parameterizations for vertical mixing. Comparison of the simulated base-case 8-hr daily maximum O3 concentrations showed marked model-to-model differences of up to 20 ppb, resulting in significant differences in operational model performance measures. In contrast, only relatively minor differences were detected in the relative response of O3 concentrations to emission reductions, resulting in differences of a few ppb or less in estimated future year design values. These findings imply that operational model evaluation metrics provide little insight into the reliability of the actual model application in the regulatory setting (i.e., the estimation of relative changes). In agreement with the guidance, it is argued that more emphasis should be placed on the diagnostic evaluation of O3-precursor relationships and on the development and application of dynamic and retrospective evaluation approaches in which the response of the model to changes in meteorology and emissions is compared with observed changes. As an example, simulated relative O3 changes between 1995 and 2007 are compared against observed changes. It is suggested that such retrospective studies can serve as the starting point for targeted diagnostic studies in which individual aspects of the modeling system are evaluated and refined to improve the characterization of observed changes.
The Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID) with three improved gas/particle mass transfer approaches (i.e., bulk equilibrium (EQUI), hybrid (HYBR), and kinetic (KINE)) has been incorporated into the Weather Research and Forecast/Chemistry Model (WRF/Chem) (referred to as WRF/Chem-MADRID) and evaluated with a 5-day episode from the 2000 Texas Air Quality Study (TexAQS2000). WRF/Chem-MADRID demonstrates an overall good skill in simulating surface/aloft meteorological parameters and chemical concentrations of O3 and PM2.5, tropospheric O3 residuals, and aerosol optical depths. The discrepancies can be attributed to inaccuracies in meteorological predictions (e.g., overprediction in mid-day boundary layer height), sensitivity to meteorological schemes used (e.g., boundary layer and land-surface schemes), inaccurate total emissions or their hourly variations (e.g., HCHO, olefins, other inorganic aerosols) or uncounted wildfire emissions, uncertainties in initial and boundary conditions for some species (e.g., other inorganic aerosols, CO, and O3) at surface and aloft, and some missing/inactivated model treatments for this application (e.g., chlorine chemistry and secondary organic aerosol formation). Major differences in the results among the three gas/particle mass transfer approaches occur over coastal areas, where EQUI predicts higher PM2.5 than HYBR and KINE due to improperly redistributing condensed nitrate from chloride depletion process to fine PM mode. The net direct, semi-direct, and indirect effects of PM2.5 decrease domainwide shortwave radiation by 11.2–14.4 W m−2 (or 4.1–5.6%) and near-surface temperature by 0.06–0.14°C (or 0.2–0.4%), lead to 125 to 796 cm−3 cloud condensation nuclei at a supersaturation of 0.1%, produce cloud droplet numbers as high as 2064 cm−3, and reduce domainwide mean precipitation by 0.22–0.59 mm day−1.
Organic carbon (OC) and elemental carbon (EC) are operationally defined by the analysis methods, and different methods give in different results. The IMPROVE Iinteragency Monitoring of protected Visual Environments) and NIOSH (National Institute of Occupational Safety and Wealth) thermal evolution protocols present different operational definitions. These protocols are applied to 60 ambient and sonrce samples from different environments using the same instrument to quantify differences in implemented protocols on the same instrument. The protocols are equivalent for total carbon sampled on quartz-fiber filters. NIOSH EC was typically less than half of IMPROVE EC. The primary difference is the allocation of carbon evolving at the NIOSH 850 degrees C temperature in a helium atmosphere to the OC rather than EC fraction. increasing light transmission and reflectance during this temperature step indicate that this fraction should he classified as EC. When this portion of NIOSH OC is added to NIOSH EC, the IMPROVE and NIOSH analyses are in good agreement. The most probable explanation is that mineral oxides in the complex particle mixture on the filter are supplying oxygen to neighboring carbon particles at this high temperature. This has been demonstrated by the principle of the thermal manganese oxidation method that is also commonly used to distinguish OC from EC, For both methods, the optical pyrolysis adjustment to the EC fractions was always higher for transmittance than for reflectance. This is a secondary cause of differences between the two methods, with transmittance resulting in a lower EC loading than reflectance. The difference was most pronounced for very black filters on which neither reflectance nor transmittance accurately detected further blackening due to pyrolysis.
[1] During July and August, 2004, balloon-borne ozonesondes were released daily at 12 sites in the eastern USA and Canada, producing the largest single set of free tropospheric ozone measurements ever compiled for this region. At the same time, a number of air quality forecast models were run daily as part of a larger field experiment. In this paper, we compare these ozonesonde profiles with predicted ozone profiles from several versions of two of these forecast models, the Environment Canada CHRONOS and AURAMS models. We find that the models show considerable skill at predicting ozone in the planetary boundary layer and immediately above. Individual station biases are variable, but often small. Standard deviations of observation-forecast differences are large, however. Ozone variability in the models is somewhat higher than observed. Most strikingly, none of the model versions is able to reproduce the typical tropospheric ozone profile of increasing mixing ratio with altitude. Results from a sensitivity test suggest that the form of the ozone lateral boundary condition used by all model versions contributes significantly to the large ozone underpredictions in the middle and upper troposphere. The discrepancy could be reduced further by adding a downward flux of ozone from the model lid and by accounting for in situ production of ozone from lightning-generated NOx.
1] In this paper the European Monitoring and Evaluation Programme (EMEP) MSC-W model is used to assess our understanding of the sources of carbonaceous aerosol in Europe (organic carbon (OC), elemental carbon (EC), or their sum, total carbon (TC)). The modeling work makes use of new data from two extensive measurement campaigns in Europe, those of the CARBOSOL project and of the EMEP EC/OC campaign. As well as EC and OC measurements, we are able to compare with levoglucosan, a tracer of wood-burning emissions, and with the source apportionment (SA) analysis of Gelencsér et al. (2007), which apportioned TC into primary versus secondary and fossil fuel versus biogenic origin. The model results suggest that emissions of primary EC and OC from fossil fuel sources are probably captured to better than a factor of two at most sites. Discrepancies for wintertime OC at some sites can likely be accounted for in terms of missing wood-burning contributions. Two schemes for secondary organic aerosol (SOA) contribution are included in the model, and we show that model results for TC are very sensitive to the choice of scheme. In northern Europe the model seems to capture TC levels rather well with either SOA scheme, but in southern Europe the model strongly underpredicts TC. Comparison against the SA results shows severe underprediction of the SOA components. This modeling work confirms the difficulties of modeling SOA in Europe, but shows that primary emissions constitute a significant fraction of ambient TC.
The Eta-Community Multiscale Air Quality (CMAQ) model's forecast performance for ozone (O3), its precursors, and meteorological parameters has been assessed over the eastern United States with the observations obtained by aircraft, ship, ozonesonde, and lidar and two surface networks (AIRNOW and AIRMAP) during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) study. The results at the AIRNOW sites show that the model was able to reproduce the day-to-day variations of observed daily maximum 8-hour O3 and captured the majority (73%) of observed daily maximum 8-hour O3 within a factor of 1.5 with normalized mean bias of 22%. The model in general reproduced O3 vertical distributions on most of the days at low altitudes, but consistent overestimations above ∼6 km are evident because of a combination of effects related to the specifications of lateral boundary conditions from the Global Forecast System (GFS) as well as the model's coarse vertical resolution in the upper free troposphere. The model captured the vertical variation patterns of the observed values for other parameters (HNO3, SO2, NO2, HCHO, and NOy_sum (NOy_sum = NO + NO2 + HNO3 + PAN)) with some exceptions, depending on the studied areas and air mass characteristics. The consistent underestimation of CO by ∼30% from surface to high altitudes is partly attributed to the inadequate representation of the transport of pollution associated with Alaska forest fires from outside the domain. The model exhibited good performance for marine or continental clear airflows from the east/north /northwest/south and southwest flows influenced only by Boston city plumes but overestimation for southeast flows influenced by the long-range transport of urban plumes from both New York City and Boston.
1] The Community Multiscale Air Quality model (CMAQ) is used to simulate aerosol mass and composition in the Great Lakes region of North America in an annual study for 2002. Model predictions are evaluated against daily and weekly average speciated fine particle (PM 2.5) and bulk (PM 2.5 and PM 10) mass concentration measurements taken throughout the region by the Interagency Monitoring of Protected Visual Environments (IMPROVE), Speciation Trends Network (STN), and Clean Air Status and Trends Network (CASTNet) monitoring networks, and number concentration is evaluated using hourly observations at a rural site. Through detailed evaluation of model-measurement agreement over urban and remote areas, major features of aerosol seasonality are examined. Whereas nitrate (winter maximum) and sulfate (summer maximum) seasonal patterns are driven by climatic influence on aerosol thermodynamics, seasonality of ammonium and organic mass (OM) is driven by emissions. Production of anthropogenic secondary organic aerosol (SOA) and summertime ozone formation both reach regional maxima over the southern Great Lakes, where they are also most strongly temporally correlated. Although primary OM is more prevalent, insufficient SOA formation leads to summertime OM underprediction of more than 50%. By comparing temporal patterns in aerosol species between model and observations, we find that elemental carbon, OM, and PM 2.5 are overly correlated in CMAQ, suggesting that the model misses chemical, transport, or emissions processes differentiating these constituents. In contrast, sulfate and PM 2.5 are not sufficiently correlated in CMAQ, although CMAQ simulates sulfate with a high level of skill. Performance relative to ad hoc regional modeling goals and previous studies is average to excellent for most species throughout the year, and seasonal patterns are captured.
Emission inventories of NOx
, CO, and individual volatile organic compounds (VOC), highly resolved in space and time, belong to the most important input parameters for chemistry and transport models (CTM) used for ozone prediction. Because of the decisive influence of the input quality on the outcome of CTM simulations, the quality of emission inventories has to be assessed. This paper presents an experimental evaluation of the highly resolved emission inventories for the city of Augsburg. The emissions of the city, determined in March and October 1998 using mass balance and tracer techniques, and derived from the measured receptor concentration ratios, were compared with emissions modeled from an emission inventory. The modeled CO emissions were in agreement with the measured ones within the combined experimental and model uncertainties. More detailed CO emission model simulations suggest that the tendency of calculated CO emissions being smaller than the measured ones may be due to higher trafficactivity in Augsburg. Modeled NOx
emissions were in agreement with the measured ones within the combined experimental and model uncertainties. Large deviations between modeled and measured values have been found for some individual NMHC compounds. The measured NMHC emission fingerprints were dominated by mobile sources. Substantial model predicted NMHC emissions from the solvent use could not be detected by measurements suggesting that they may not be correctly represented by the emission model.
A 3-year, six-site, direct comparison between NADP and CANSAP deposition monitoring programs reveals a positive bias in reported CANSAP values over those reported by NADP. The bias is significant at most locations for most measured analytes. Reasons given for the bias include an inadequate seal on the mechanical lid of the CANSAP collector. Poor siting of the sampling equipment is further shown to exacerbate the problem.
Material balance of fine particulate matter (PM2.5) measured with the Federal Reference Method (FRM) is developed for one rural and five urban locations in the eastern half of the United States using routine Speciation Trends Network (STN) and FRM chemical measurements and thermodynamic models. The Aerosol Inorganics Model is used to estimate retained particle bound water, and an ammonium nitrate evaporation model is used to estimate nitrate concentrations retained on the Teflon-membrane filter of the FRM. To address large uncertainties in carbonaceous mass calculated from STN carbon measurements, retained carbonaceous mass is derived by material balance between PM2.5 FRM mass and estimates of its non-carbon constituents. The resulting sulfate, adjusted nitrate, derived water, inferred carbonaceous material balance approach (SANDWICH) is compared with reconstructed fine mass (RCFM) using the Interagency Monitoring of Protected Visual Environments monitoring program equation. For this study, the SANDWICH method resulted in ∼21-27% higher sulfate mass and ∼24-85% lower nitrate mass. The combined mass associated with sulfates and nitrates, however, are well within ±10% of the proportion derived using the more traditional RCFM method. The discrepancies between SANDWICH and measurement-derived carbonaceous mass vary from -21% to +56% on an annual basis and are attributed in part to urban-rural source influences and uncertainties in estimating FRM-retained carbonaceous mass.
The Canadian Hemispheric and Regional Ozone and NO x System (CHRONOS) has been designed for the prediction of atmospheric oxidants on both regional and hemispheric scales. The model simulates dispersion and complex chemistry of nitrogen oxides and volatile organic compounds (VOCs) using inventories of actual anthropogenic emission. The meteorological input for the model is provided by the Mesoscale Community Model (MC2) and the Global Environmental Model (GEM), the latter being the operational model at the Canadian Meteorological Center. In this paper, the model formulation, as well as a description of the numerical methods employed, are presented. The theoretical section is followed by an illustration of the model application for air quality forecasts at the Canadian Meteorological Center and for the evaluation of emission control strategies in Canada.
Organic particles are abundant in the troposphere and important for air quality and climate, but what are their sources?
Methylmercury is a known neurotoxin with deleterious health effects on humans and wildlife. Atmospheric deposition is the largest source of mercury loading to most terrestrial and aquatic ecosystems. Regional scale air quality models are needed to quantify mercury deposition resulting from complex emissions sources and physical and chemical processes that govern the fate of mercury in the atmosphere. Total mercury wet deposition estimates from multiple regional photochemical transport models applied at 12 km grid resolution over the continental United States compare well with observations (CAMx fractional error = 45%, CMAQ fractional error = 33%) despite uncertainties in global mercury emissions inventories and certain chemical transformation pathways. In addition, both CMAQ and CAMx well represent observed diel and seasonal patterns of Hg(0) and tend to exaggerate the diel patter of Hg(II) at AMNet monitor locations. The observed fraction of particulate mercury to total oxidized mercury (sum of particulate mercury and Hg(II)) is generally greater in colder months and during overnight hours. The modeling systems tend to capture these patterns but have a systematically lower fraction of particulate mercury to total oxidized mercury, especially in winter months.
ABSTRACT This study investigates the potential utility of the application of a photochemical,modeling,system,in providing,simultaneous,forecasts of ozone,(O3) and fine particulate matter (PM2.5) over New York State. To this end, daily simulations from the Community Multiscale Air Quality (CMAQ) model for three extended time periods during 2004 and 2005 have been performed, and predictions were compared with observations,of ozone,and total and speciated PM2.5. Model performance,for 8-h daily maximum,O3 was found to be similar to other forecasting systems and to be better than that for the 24-h-averaged total PM2.5. Both pollutants exhibited no seasonal differences in model,performance.,CMAQ simulations successfully captured,the urban–rural and seasonal differences evident in observed,total and speciated PM2.5 concen- trations. However, total PM2.5 mass was strongly overestimated in the New York City metropolitan area, and further analysis of speciated observations,and model,predictions showed,that most of this overpredic- tion stems from organic aerosols and crustal material. An analysis of hourly speciated data measured,in Bronx County, New York, suggests that a combination of uncertainties in vertical mixing, magnitude, and temporal,allocation of emissions,and deposition processes,are all possible contributors to this overpredic- tion in the complex,urban area. Categorical evaluation of CMAQ simulations in terms of exceeding,two different threshold levels of the air quality index (AQI) again indicates better performance,for ozone than PM2.5 and better performance for lower exceedance thresholds. In most regions of New York State, the routine air quality forecasts based on observed,concentrations,and expert judgment,show,slightly better agreement with the observed distributions of AQI categories than do CMAQ simulations. However, CMAQ shows skill similar to these routine forecasts in terms of capturing the AQI tendency, that is, in predicting changes in air quality conditions. Overall, the results presented in this study reveal that additional research,and development,is needed,to improve,CMAQ simulations of PM2.5 concentrations,over New York State, especially for the New York City metropolitan area. On the other hand, because CMAQ simulations capture urban–rural concentration,gradients and day-to-day fluctuations in observed,air quality despite systematic overpredictions in some areas, it would be useful to develop tools that combine CMAQ’s
A method is presented and applied for evaluating an air quality model’s changes in pollutant concentrations stemming from changes in emissions while explicitly accounting for the uncertainties in the base emission inventory. Specifically, the Community Multiscale Air Quality (CMAQ) model is evaluated for its ability to simulate the change in ozone (O3) levels in response to significant reductions in nitric oxide (NOx = NO + NO2) emissions from the NOx State Implementation Plan (SIP) Call and vehicle fleet turnover between the years of 2002 and 2005. The dynamic model evaluation (i.e., the evaluation of a model’s ability to predict changes in pollutant levels given changes in emissions) differs from previous approaches by explicitly accounting for known uncertainties in the NOx emissions inventories. Uncertainty in three sectors of NOx emissions is considered – area sources, mobile sources, and point sources – and is propagated using sensitivity coefficients calculated by the decoupled direct method in three dimensions (DDM-3D). The change in O3 levels between 2002 and 2005 is estimated based on differences in the empirical distributions of the modeled and observed data during the two years. Results indicate that the CMAQ model is able to reproduce the observed change in daily maximum 8-hr average O3 levels at more than two-thirds of Air Quality System (AQS) monitoring locations when a relatively moderate amount of uncertainty (50%) is assumed in area and mobile emissions of NOx together with a low amount of uncertainty (3%) in the utility sector (elevated point sources) emissions. The impact of other sources of uncertainty in the model is also briefly explored.
Regional air quality models such as the Community Multiscale Air Quality (CMAQ) model have been widely used to study and simulate multi-scale air quality issues. Although they are capable of providing high quality atmospheric chemistry profiles through the utilization of high resolution inputs relating meteorology and emissions with chemical reactions, they cannot simulate air quality accurately if other input data are not appropriate and reliable. There have been few studies on the importance of chemical initial conditions (ICs) as it is considered that the impact of concentration fields specified at the beginning of simulation wears off quickly. This paper demonstrates that the significant errors during the early part of the simulation can damage model predictions and conversely if the ICs are prescribed appropriately with available observations, they can compensate for the shortcomings of the air quality prediction system especially when the episode-based emissions inputs representing real-life emission variations such as forest fires as well as the effects of long-range transport events that are not reflected in the basic model inputs. The key hypothesis of the present study is that prediction of aerosols can be improved by the initialization of the aerosol fields with the satellite-derived Aerosol Optical Depth (AOD). We compare the effects of using fine mode and total AOD for the initialization in terms of regional bias characteristics. We found that the impacts of two-step initial conditions adjustments could be substantial in the case of aerosol events such as wildfires, which the present modeling system does not consider during simulation due to the deficiency in the emission inputs. The total AOD case helped to refine PM2.5 predictions over the northwestern area, where wildfire events occurred, for the fire event days improving the correlation coefficient significantly from 0.12 to 0.67. CMAQ predicted PM2.5 concentrations in the fine mode case decreased by 10–50 μg/m3 in large areas of the northwestern region, resulting in more realistic PM2.5 predictions with reduced unusual high peak cells. This study suggest that the use of initial conditions adjusted by total or fine mode AOD can help to improve PM2.5 simulations, even though further refinements of the vertical distribution of aerosols are critically needed.
Excess wet and dry deposition of nitrogen-containing compounds is a concern at a number of national parks. The Rocky Mountain Atmospheric Nitrogen and Sulfur Study (RoMANS) was conducted during the spring and summer of 2006 to identify the overall mix of ambient and deposited sulfur and nitrogen at Rocky Mountain National Park (RMNP), in north-central Colorado. The Comprehensive Air Quality Model with extensions (CAMx) was used to simulate the fate of gaseous and particulate species subjected to multiple chemical and physical processes during RoMANS. This study presents an operational evaluation with a special emphasis on the model performance of reduced nitrogen species. The evaluation showed large negative biases and errors at RMNP and the entire domain for ammonia; therefore the model was considered inadequate for future source apportionment applications. The CAMx Integrated Processes Rate (IPR) analysis tool was used to elucidate the potential causes behind the poor model performance. IPR served as a tool to diagnose the relative contributions of individual physical and chemical processes to the final concentrations of reduced nitrogen species. The IPR analysis revealed that dry deposition is the largest sink of ammonia in the model, with some cells losing almost 100% of the available mass. Closer examination of the ammonia dry deposition velocities in CAMx found that they were up to a factor of 10 larger than those reported in the literature. A series of sensitivity simulations were then performed by changing the original deposition velocities with a simple multiplicative scaling factor. These simulations showed that even when the dry deposition values were altered to reduce their influence, the model was still unable to replicate the observed time series; i.e., it fixed the average bias, but it did not improve the precision.
The Air Indicator Report for Public Access and Community Tracking (AIRPACT) real-time numerical air quality forecast system operates daily in the Pacific Northwest region to predict hourly ozone, PM2.5 and related precursor and pollutant species. In an update to the existing AIRPACT forecast system, the MM5/SMOKE/CMAQ Eulerian modeling system replaces the MM5/CALMET/CALGRID model framework. The new system, AIRPACT-3, has a larger domain that encompasses Washington, Oregon, Idaho, and bordering areas. The system includes a highly dynamic emission processing subsystem which incorporates anthropogenic and biogenic emissions, as well as real-time wildfire emission estimates, and a dynamic dairy ammonia emissions module. As an initial evaluation of the system, forecast results were compared against measurement data for the August–November 2004 period. Analyses showed that the system is skillful in predicting episodic ozone conditions (8-h daily maxima) above 50 ppbv, but systematically over-predicts levels less than 40 ppbv. For fine particulate matter, PM2.5, the system captures the concentration differences between urban and rural regions, and captures qualitatively the speciated distribution of fine PM2.5 component concentrations. A separate emission sensitivity study shows the system can adequately simulate the PM pollution impacts from fire events; however, the new dairy ammonia emission module has lesser impact on the overall PM2.5 forecast performance.
Many counties are required to submit an emissions control plan to the U.S. Environmental Protection Agency to reduce concentrations of particulate matter of less than 2.5 μm in diameter (PM2.5), which are dominated by ammonium sulfate and ammonium nitrate in the central United States. These control scenarios are simulated with photochemical models, which use emissions and meteorological variables to simulate PM2.5 formation, transport, and deposition. A monitor study was established in the central United States to measure simultaneously the PM2.5 sulfate ion, nitrate ion, ammonium ion, and chemical precursor species sulfur dioxide, nitric acid, and ammonia during 2004. These data, combined with nearby meteorological observations, provide an opportunity to assess whether meteorological variables or deposition processes may introduce systematic biases in PM2.5 ammonium sulfate and ammonium nitrate predictions. Skill in estimating total wet deposition is assessed by comparing model output with National Atmospheric Deposition Program monitors in the region. Meteorological variables that are important for mass transport (wind vector) and thermodynamic chemistry (temperature and relative humidity) compare well to observations. A model sensitivity, in which the temperatures in the inorganic chemistry module are adjusted to compensate for an underprediction bias, shows a minimal model response in predicted PM2.5 ammonium nitrate. The dry deposition of sulfur dioxide seems to have a systematic impact on ambient estimates of sulfur dioxide in the photochemical model. An attempt to correlate bias and error in meteorological variables to bias and error in PM2.5 species showed the most relationship between relative humidity and temperature and ammonium nitraite. Wet deposition of total sulfate, nitrate, and ammonium tend to be underpredicted in the winter months.
The National Air Quality Forecast Capacity (NAQFC) system, which links NOAA’s North American Mesoscale (NAM) meteorological model with EPA’s Community Multiscale Air Quality (CMAQ) model, provided operational ozone (O3) and experimental fine particular matter (PM2.5) forecasts over the continental United States (CONUS) during 2008. This paper describes the implementation of a real-time Kalman Filter (KF) bias-adjustment technique to improve the accuracy of O3 and PM2.5 forecasts at discrete monitoring locations. The operational surface-level O3 and PM2.5 forecasts from the NAQFC system were post-processed by the KF bias-adjusted technique using near real-time hourly O3 and PM2.5 observations obtained from EPA’s AIRNow measurement network. The KF bias-adjusted forecasts were created daily, providing 24-h hourly bias-adjusted forecasts for O3 and PM2.5 at all AIRNow monitoring sites within the CONUS domain. The bias-adjustment post-processing implemented in this study requires minimal computational cost; requiring less than 10 min of CPU on a single processor Linux machine to generate 24-h hourly bias-adjusted forecasts over the entire CONUS domain.The results show that the real-time KF bias-adjusted forecasts for both O3 and PM2.5 have performed as well as or even better than the previous studies when the same technique was applied to the historical O3 and PM2.5 time series from archived AQF in earlier years. Compared to the raw forecasts, the KF forecasts displayed significant improvement in the daily maximum 8-h O3 and daily mean PM2.5 forecasts in terms of both discrete (i.e., reduced errors, increased correlation coefficients, and index of agreement) and categorical (increased hit rate and decreased false alarm ratio) evaluation metrics at almost all locations during the study period in 2008.
Part II presents a comprehensive evaluation of CMAQ for August of 2002 on twenty-one sensitivity simulations (detailed in Part I) in MM5 to investigate the model performance for O3 SIPs (State Implementation Plans) in the complex terrain. CMAQ performance was quite consistent with the results of MM5, meaning that accurate meteorological fields predicted in MM5 as an input resulted in good model performance of CMAQ. In this study, PBL scheme plays a more important role than its land surface models (LSMs) for the model performance of CMAQ. Our results have shown that the outputs of CMAQ on eighteen sensitivity simulations using two different nudging coefficients for winds (2.5 and 4.5 × 10−4 s−1, respectively) tend to under predict daily maximum 8-h ozone concentrations at valley areas except the TKE PBL sensitivity simulations (ETA M-Y PBL scheme with Noah LSMs and 5-layer soil model and Gayno-Seaman PBL) using 6.0 × 10−4 s−1 with positive MB (Mean Bias). At mountain areas, none of the sensitivity simulations has presented over predictions for 8-h O3, due to relatively poor meteorological model performance. When comparing 12-km and 4-km grid resolutions for the PX simulation in CMAQ statistics analysis, the CMAQ results at 12-km grid resolution consistently show under predictions of 8-h O3 at both of valley and mountain areas and particularly, it shows relatively poor model performance with a 15.1% of NMB (Normalized Mean Bias). Based on our sensitivity simulations, the TKE PBL sensitivity simulations using a maximum value (6 × 10−4) among other sensitivity simulations yielded better model performance of CMAQ at all areas in the complex terrain. As a result, the sensitivity of RRFs to the PBL scheme may be considerably significant with about 1–3 ppb in difference in determining whether the attainment test is passed or failed. Furthermore, we found that the result of CMAQ model performance depending on meteorological variations is affected on estimating RRFs for attainment demonstration, indicating that it is necessary to improve model performance. Overall, G_c (Gayo-Seaman PBL scheme) using the coefficient for winds, 6 × 10−4 s−1, sensitivity simulation predicts daily maximum 8-h ozone concentration closer to observations during a typical summer period from May to September and provides generally low future design values (DVFs) at valley and mountain areas compared to other simulations.
A number of actions have been undertaken within the National Atmospheric Deposition Program (NADP) to implement a regional mercury deposition network. This paper describes a field test designed to evaluate a collector design and protocol for implementation within a new Hg network. The collector chosen for evaluation is a “dual-orifice” collector, designed to sample precipitation for mercury and other metals simultaneously. The method chosen for Hg analysis was cold vapour atomic fluorescence spectroscopy (CVAFS). The weekly precipitation Hg concentrations range between 4.29 and 17.88 ng ℓ−1, with a volume-weighted mean of 10 ng ℓ−1 comparable to those reported in other ongoing studies in North America and Europe. Calculated deposition flux ranges from 43 to 358 ng m−2 week−1, with a mean of 186 ng m−2 week−1.
In virtually all published literature wherein closure between gravimetric and chemical measurements is tested, the concentration of particulate organics is estimated by multiplying the measured concentration of organic carbon (micrograms carbon/cubic meter air) by a factor of 1.2-1.4. This factor, which is an estimate of the average molecular weight per carbon weight for the organic aerosol, stems from very limited theoretical and laboratory studies conducted during the 1970s. This investigation suggests that 1.4 is the lowest reasonable estimate for the organic molecular weight per carbon weight for an urban aerosol and that 1.4 does not accurately represent the average organic molecular weight per carbon weight for a nonurban aerosol. Based on the current evaluation, ratios of 1.6 ± 0.2 for urban aerosols and 2.1 ± 0.2 for nonurban aerosols appear to be more accurate. Measurements are recommended. Literature values also suggest that 1.2 g/cm3 is a reasonable estimate for the organic aerosol density. This quantity is needed to convert between geometric and aerodynamic size distributions (e.g., to predict aerosol optical properties and understand cloud nucleating properties).
CMAQ was run to simulate urban and regional tropospheric conditions in the southeastern US over 14 days in July 1999 at 32, 8 and 2km grid spacings. Runs were made with either of two older mechanisms, Carbon Bond IV (CB4) and the Regional Acid Deposition Model, version 2 (RADM2), and with the more recent and complete California Statewide Air Pollution Research Center, version 1999 mechanism (SAPRC99) in a sensitivity matrix with a full emissions base case and separate 50% control scenarios for emissions of nitrogen oxides (NOX) and volatile organic compounds (VOC). Results from the base case were compared to observations at the Southeastern Aerosol Research and Characterization Study (SEARCH) site at Jefferson Street in Atlanta, GA (JST) and the Southern Oxidant Study (SOS) Cornelia Fort Airpark (CFA) site downwind of Nashville, TN. In the base case, SAPRC99 predicted more ozone (O3) than CB4 or RADM2 almost every hour and especially for afternoon maxima at both JST and CFA. Performance of the 8km models at JST was better than that of the 32km ones for all chemistries, reducing the 1h peak bias by as much as 30 percentage points; at CFA only the RADM2 8km model improved. The 2km solutions did not show improved performance over the 8km ones at either site, with normalized 1h bias in the peak O3 ranging from 21% at CFA to 43% at JST. In the emissions control cases, SAPRC99 was generally more responsive than CB4 and RADM2 to NOX and VOC controls, excepting hours at JST with predicted increased O3 from NOX control. Differential sensitivity to chemical mechanism varied by more than ±10% for NOX control at JST and CFA, and in a similar range for VOC control at JST. VOC control at the more strongly NOX- limited urban CFA site produced a differential sensitivity response of
A new windblown dust emissions module was recently implemented into A
Unified Regional Air Quality Modeling System (AURAMS), a Canadian
regional air quality model, to investigate the relative impact of
windblown dust versus anthropogenic fugitive dust on air quality in
North America. In order to apply the windblown dust emissions module to
the entire North American continent, a soil grain size distribution map
was developed using the outputs of 4 monthly runs of AURAMS for 2002 and
available PM2.5 dust content observations. The simulation
results using the new soil grain size distribution map showed that
inclusion of windblown dust emissions is essential to predict the impact
of dust aerosols on air quality in North America, especially in the
western United States. The windblown dust emissions varied widely by
season, whereas the anthropogenic fugitive dust emissions did not change
significantly. In the spring (April), the continental monthly average
emissions rate of windblown dust (4.1 × 107 kg/d) was
much higher than that of anthropogenic fugitive dust (1.5 ×
107 kg/d). The total amount of windblown dust emissions in
North America predicted by the model for 2002 was comparable to that of
anthropogenic fugitive dust emissions. Even with the inclusion of
windblown dust emissions, however, the model still had difficulty
simulating dust concentrations. Further improvements are needed, in
terms of both limitations of the windblown dust emission module and
uncertainties in the anthropogenic fugitive dust emissions inventories,
for improved dust modeling.
A comprehensive air quality modeling project was carried out to simulate size and composition resolved airborne particulate matter concentrations in northern and central California using the pollutant concentration and meteorological data collected during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS) from December 15, 2000 to January 7, 2001. Measured 24-h average PM2.5 concentrations during this time period exceeded 180 μg m−3 at Bakersfield, making it the most severe PM2.5 air quality episode ever recorded in the United States with a rigorous measurement database to support modeling. In this paper, the UCD/CIT source-oriented air quality model is used to predict the concentrations of O3, NO, NO2, CO, elemental carbon (EC), organic compounds (OC), nitrate and PM2.5 mass concentration over a 24-day period using a horizontal resolution of 4 km × 4 km to cover all of central California. This is the first extensive evaluation of an air quality model in central California using the fine spatial resolution appropriate for the mountain–valley topography of the region combined with the relatively long multi-week time scales associated with winter stagnation events.
Photochemical grid model performance evaluation studies have been undertaken over the last decade as an integral part of ozone air pollution model development and application efforts and in support of model sensitivity analysis, monitoring program design, and applied research. These studies, sponsored by federal and state agencies, universities, and consulting firms, represent a significant body of information. A comprehensive review of past Eulerian model evaluation studies indicates that the overall accuracy of hourly averaged ozone predictions, paired in time and space, is of the order of 35-40%. However, considering the model's ability to reproduce the maximum observed concentration, independent of time or space pairing, overall prediction accuracies of 10−20% are found. For single-day ozone simulations, the overall negative bias is −10%.
The performance of the Eta-Community Multiscale Air Quality (CMAQ) modeling system in forecasting PM2.5 and chemical species is assessed over the eastern United States with the observations obtained by aircraft (NOAA P-3 and NASA DC-8) and four surface monitoring networks (AIRNOW, IMPROVE, CASTNet and STN) during the 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) study. The results of the statistical analysis at the AIRNOW sites show that the model was able to reproduce the day-to-day and spatial variations of observed PM2.5 and captured a majority (73%) of PM2.5 observations within a factor of 2, with normalized mean bias of -21%. The consistent underestimations in regional PM2.5 forecast at other networks (IMPROVE and STN) were mainly due to the underestimation of total carbonaceous aerosols at both urban and rural sites. The significant underestimation of the ``other'' category, which predominantly is composed of primary emitted trace elements in the current model configuration, is also one of the reasons leading to the underestimation of PM2.5 at rural sites. The systematic overestimations of SO4 2- both at the surface sites and aloft, in part, suggest too much SO2 cloud oxidation due to the overestimation of SO2 and H2O2 in the model. The underestimation of NH4 + at the rural sites and aloft may be attributed to the exclusion of some sources of NH3 in the emission inventory. The systematic underestimations of NO3 - may result from the general overestimations of SO4 2-. Note that there are compensating errors among the underestimation of PM2.5 species (such as total carbonaceous aerosols) and overestimation of PM2.5 species (such as SO4 2-), leading to generally better performance of PM2.5 mass. The systematic underestimation of biogenic isoprene (by ~30%) and terpene (by a factor of 4) suggests that their biogenic emissions may have been biased low, whereas the consistent overestimations of toluene by the model under the different conditions suggest that its anthropogenic emissions might be too high. The contributions of various physical and chemical processes governing the distribution of PM2.5 during this period are investigated through detailed analysis of model process budgets using the integrated process rate (IPR) analysis along back trajectories at five selected locations in Pennsylvania and Georgia. The results show that the dominant processes for PM2.5 formation and removal vary from the site to site, indicating significant spatial variability.
It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simu- lations to create "dynamic analyses" that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteoro- logical characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.
An existing plume-in-grid model for ozone and particulate matter, which provides an explicit treatment of stack plumes embedded within a three-dimensional grid-based Eulerian air quality model, is extended to include a comprehensive treatment of mercury (Hg) processes. The model is applied to the continental United States to investigate the subgrid-scale effects associated with Hg emissions from large elevated point sources on atmospheric Hg concentrations and deposition. The top thirty Hg-emitting power plants in the U.S. were selected for explicit plume-in-grid treatment. Two new processes are included in the Hg chemical mechanism: the gas-phase adsorption of reactive gaseous mercury (RGM) on atmospheric particulate matter and the reduction of RGM to elemental Hg by sulfur dioxide. The plume-in-grid treatment results in improved performance for Hg wet deposition over a purely Eulerian grid-based model, partial correction of overpredictions of wet deposition downwind of coal-fired power plants in the northeastern U.S., and decreases of approximately 10% in simulated dry and wet deposition over large parts of the eastern U.S., with larger decreases near the plants selected for plume-in-grid treatment. On average, 23% of ambient RGM is modeled to adsorb on atmospheric particulate matter.
A multipollutant model, the Community Multiscale Air Quality model paired with the Model of Aerosol Dynamics, Reaction, Ionization, and Dissolution (CMAQ-MADRID), is extended to include a comprehensive treatment of mercury processes and is applied to the simulation of the atmospheric deposition of sulfate and mercury over the United States during 1996. Model performance is evaluated first by comparison with annual sulfate wet deposition data from the National Atmospheric Deposition Program's National Trends Network; the coefficient of determination r(2) is 0.77, and the model normalized error and bias are 53% and -8%, respectively. When actual precipitation data are used to scale the deposition fluxes, r(2) improves to 0.91 and the error and bias change to 42% and -41%, respectively. The scaled results underscore a tendency of the model to underestimate sulfate wet deposition. Model performance for mercury wet deposition is then evaluated by comparison with data from the Mercury Deposition Network. For annual mercury wet deposition, r(2) is 0.28 and the normalized error and bias are 81% and 73%, respectively, when the modeled precipitation data are used. Model performance improves when actual precipitation data are used to scale deposition fluxes: r(2) increases to 0.41 and the error and bias decrease to 40% and 29%, respectively. The model reproduces the spatial pattern of sulfate wet deposition adequately with an increasing gradient from the upper Midwest to the Northeast, that is, from upwind to downwind of large sulfur dioxide sources in the Ohio River Valley. However, the model tends to overestimate mercury wet deposition in the Northeast downwind of these sources that also emit significant amounts of mercury. This "Pennsylvania anomaly" may be due to a partial misrepresentation of the mercury reduction-oxidation cycle, uncertainties in the dry deposition of divalent gaseous mercury Hg-II, incorrect speciation of mercury emissions, and/or uncharacterized emissions in the upper Midwest.
Examination of model sensitivity to horizontal grid resolutions can help identify optimal compromise in accuracy and computational efficiency for regulatory and research-grade applications of 3-D atmospheric models. In this Part III paper, the performance and sensitivity of simulated precipitation and wet deposition amounts by the MM5/CMAQ model to three horizontal grid resolutions (4-, 12-, and 36-km) are evaluated over North Carolina (NC).In contrast with simulated O3, PM2.5, and some PM2.5 species such as NH4+, simulated precipitation and wet deposition amounts are quite sensitive to grid resolutions. Compared with results at coarser resolutions, simulated precipitation amounts are lower in both August and December at 4-km, with the largest sensitivities to grid resolutions occurring in mountain and coastal regions of NC. For wet deposition predictions, the model performs the best for NO3− at 4-km and for NH4+ and SO42− at 12-km in August, but the best for NH4+ and NO3− at 36-km and for SO42− at 4-km in December. Such sensitivities and lack of clear trends in model performance at various resolutions can be attributed to seasonalities in meteorology and differences in characteristics of land use, emissions and concentrations of PM precursors, as well as nonlinear responses of chemistry and meteorology to grid resolutions. The overall performance trends demonstrate a high sensitivity in precipitation and wet deposition predictions over complex terrain and the fact that higher grid resolution does not always lead to improved model performance.
A substantial fraction of both gas-phase and aerosol organic compounds have not been, or have very rarely been, directly measured in the atmosphere. Goldstein and Galbally review current knowledge about atmospheric organic constituents by asking the following questions: What atmospheric organic compounds do we know about and understand? What organic compounds could be present as gases and in aerosols? What evidence exists for additional organic compounds in the atmosphere? How well do we understand the transformations and fate of atmospheric organics? They conclude by suggesting opportunities for future research directions.
A 3-year, six-site, direct comparison between NADP and CANSAP deposition monitoring programs reveals a positive bias in reported CANSAP values over those reported by NADP. The bias is significant at most locations for most measured analytes. Reasons given for the bias include an inadequate seal on the mechanical lid of the CANSAP collector. Poor siting of the sampling equipment is further shown to exacerbate the problem.
This study explores the extent to which uncertainties in the formulation of vertical diffusion schemes and in associated model parameters affect predictions of ozone levels and their responsiveness to emission controls in the Community Multiscale Air Quality (CMAQ) model. CMAQ is applied with two vertical diffusion schemes, eddy viscosity (K-theory) and the asymmetric convective model, version 2 (ACM2), to simulate ozone during the Second Texas Air Quality Study (TexAQS II) in 2006, enabling comparisons with a rich array of both ground-level and aloft ozone measurements. The high-order decoupled direct method (HDDM) was implemented into ACM2, and was extended to quantify the influence of parametric uncertainties in dry deposition velocity and eddy diffusivity in both vertical diffusion schemes in CMAQ. Choices of vertical diffusion scheme and parameter values did not strongly influence model performance in simulating ozone concentrations or predicted ozone-precursor responsiveness over much of the domain. However, in some high ozone locations, results show the 8-h ozone sensitivity to NOx may vary by approximately 20% due to the choice of vertical mixing scheme and by 5–10% due to the uncertainty in dry deposition velocities.Highlights► High-order Decoupled Direct method was implemented into ACM2 scheme. ► Dry deposition velocity and eddy diffusivity parameters were added into HDDM. ► Different vertical diffusion schemes did not strongly influence model performance. ► Ozone sensitivity to NOx varies by 20% due to choice of vertical mixing scheme. ► Ozone sensitivity to NOx varies by 5–10% due to dry deposition velocity uncertainty.
Gas and aerosol predictions from CMAQ simulations with horizontal grid spacings of 8-and 32-km are evaluated against available observations from CASTNet, IMPROVE, AIRS-AQS, SOS99/SOS99NASH, SEARCH, and ARIES for the southeastern US for the period of 1–10 July 1999. The predictions evaluated in this work include mixing ratios of O 3 (hourly, maximum 1-h, and 8-h average), NO x , HNO 3 , NO y , and mass concentrations of PM 10 , PM 2.5 , and PM 2.5 components. Our evaluation has shown that CMAQ tends to underpredict maximum 1-h O 3 mixing ratios on high O 3 days at some sites. It overpredicts the maximum and minimum hourly O 3 mixing ratios for most low O 3 days, the daytime and nighttime hourly, and the maximum 8-h average O 3 mixing ratios on most days at all sites. The model performance for hourly O 3 mixing ratios generally meets EPA's criteria but deteriorates for maximum 1-and 8-h average O 3 mixing ratios. CMAQ underpredicts the mass concentrations of PM 10 , PM 2.5 , and PM 2.5 composition and fails to reproduce their temporal variations (except for sulfate). Largest underpredictions occur for organic matter (OM 2.5) and nitrate 2.5 among all PM components. These underpredictions and overpredictions may be caused by inaccurate meteorological predictions (e.g., the PBL height, wind speed/direction, vertical mixing, temperature, and relative humidity) and boundary conditions for chemical species (e.g., O 3), underestimation in emissions (e.g., NO x , NH 3 , and primary OM), as well as uncertainties in model assumptions and treatments in aerosol chemistry and microphysics.
This part II paper first evaluates the simulated concentrations and wet deposition amounts of NH4+, NO3−, and SO42− using observations from several networks, then examines their sensitivities to four explicit microphysics schemes: Reisner 1 (R1), Reisner 2 (R2), Dudhia simple ice (SI), and Hsie warm rain (WR). For baseline simulation with R1, the concentrations of NH4+, NO3−, and SO42− are underpredicted in August. Concentrations of SO42− are underpredicted and those of NH4+ and NO3− are overpredicted in December. The wet deposition amounts of NH4+ and SO42− are overpredicted but those of NO3− are underpredicted in August. The wet deposition amounts of NO3− and NH4+ are overpredicted but those of SO42− are underpredicted in December. The simulated wet deposition amounts are sensitive to various schemes, which are most evident in December, with the best results for NH4+ and NO3− by WR and the best for SO42− by SI. A correlation exists between wet deposition amounts and precipitation in both months, with stronger magnitudes in August. Conversely, in December, as the correlation with precipitation decreases, that with aqueous-phase concentrations increases. These results are consistent with meteorological conditions since the summer convective precipitation events having larger intensities and therefore the meteorological forcing is expected to dominate August correlations. As these intensities decrease in December, the chemical forcing becomes more influential.
A harmonized comparative performance evaluation of A Unified Regional Air-quality Modelling System (AURAMS) v1.3.1b and Community Multiscale Air Quality (CMAQ) v4.6 air-quality modelling systems was conducted on the same North American grid for July 2002 using the same emission inventories, emissions processor, and input meteorology.Comparison of AURAMS- and CMAQ-predicted O3 concentrations against hourly surface measurement data showed a lower normalized mean bias (NMB) of 20.7% for AURAMS versus 46.4% for CMAQ. However, AURAMS and CMAQ had more similar normalized mean errors (NMEs) of 46.9% and 54.2%, respectively. Both models did similarly well in predicting daily 1-h O3 maximums; however, AURAMS performed better in calculating daily minimums. CMAQ's poorer performance for O3 is partly due to its inability to correctly predict nighttime lows.Total PM2.5 hourly surface concentration was under-predicted by both AURAMS and CMAQ with NMBs of −10.4% and −65.2%, respectively. However, as with O3, both models had similar NMEs of 68.0% and 70.6%, respectively. In general, AURAMS performance was better than CMAQ for all major PM2.5 species except nitrate and elemental carbon. Both models significantly under-predicted total organic aerosols (TOAs), although the mean AURAMS concentration was over four times larger than CMAQ's. The under-prediction of TOA was partly due to the exclusion of forest-fire emissions. Sea-salt aerosol made up approximately 50.2% of the AURAMS total PM2.5 surface concentration versus only 6.2% in CMAQ when averaged over all grid cells. When averaged over land cells only, sea-salt still contributed 13.9% to the total PM2.5 mass in AURAMS versus 2.0% in CMAQ.
The representation of cloud chemistry and scavenging processes in a new multiple-pollutant (unified) regional air-quality modelling system, AURAMS, is described. Aqueous-phase chemistry and scavenging processes are coupled explicitly with microphysical fields from the meteorological driver model and the size- and chemical-composition-resolved aerosols in AURAMS. The impact of aqueous-phase oxidation on regional aerosols (primarily sulphate), both in terms of mass and size distribution, is examined based on model simulations of a 1-week period over eastern North America. It is shown that aqueous-phase oxidation contributes about 30% to 40% of the total atmospheric sulphate production in this case. Cloud chemistry is also shown to modify the aerosol size distribution, which in turn can either enhance or reduce aerosol scattering efficiency in different geographic regions depending on where on the aerosol size spectrum the mass is added. The study also indicates that precipitation evaporation can be an important process in terms of tracer redistribution in the vertical. Whether and how to treat tracer release from precipitation evaporation can have a significant impact on model predictions of near-surface ambient tracer concentrations and wet deposition fluxes. Comparison between observations and AURAMS predictions shows that modelling cloud processing of gas and aerosols depends critically on the meteorological driver model's ability to predict cloud microphysics fields. In this case, the model underpredicted precipitation amount for the study period, which contributed to the underestimation of wet deposition and in turn may also have impacted the modelled ambient tracer concentrations.