No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
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.
... 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.
... 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 O3 Vd 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 O3 Vd 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.
... The analysis of these sensitivity simulations indicates that the choice of lateral boundary conditions was the largest driver of differences in mean model concentrations and 350 biases compared to the corresponding CMAQv5.3.1 simulations analyzed in Appel et al. (2021). Overall, 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 overall sensitivity to M3Dry vs. STAGE is smaller than the sensitivity to model input data sets and boundary conditions that represent the largescale chemical environment. ...
... The model evaluation results presented in Section 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 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 U.S. while the reverse is the case over eastern Canada and along the West Coast. In contrast, during winter STAGE has higher O3 Vd 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 U.S. and southern Canada. Analysis of the diagnostic variables defined for the AQMEII4 project, i.e. grid-scale and land-use (LU) 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 O3 Vd during daytime hours in summer for both schemes. Employing LU-specific diagnostics, results show that daytime Vd varies by a factor of 2 between 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/year) and STAGE (76.2 Tg/yr), but pathway-specific fluxes to individual LU types can vary more substantially on both annual and seasonal scales which would affect estimates of O3 damages 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 largest during summer and in areas characterized by the largest differences in the fractional coverages of the forest, planted/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 both by differences in the fractional coverages and spatial distributions of different LU categories as well as the characterization of these categories through variables like surface roughness and vegetation fraction in look-up 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.
... Favourable meteorological conditions for photochemical episodes have been extensively studied (Camalier et al. 2007;Simon et al. 2012;McNider & Pour-Biazar 2020). For example, sunny weather and low wind velocity lead to pollution accumulation and O 3 production (Xue et al. 2014;Carter et al. 2017). ...
... The seasonal variation of ground-level O 3 was noted in many regions of the world (Simon et al. 2012;Tang et al. 2013;Maji et al. 2019) in MDR, the change in ground-level O 3 concentration is quite complicated according to the season; in the dry season, the O 3 concentration is generally higher than that in the wet season with O 3 values. In the month with highest concentration of the dry season, February, the 1-h average concentration ranged from 17.2 to 102.1 µg/ m 3 , which was still significantly lower than the allowable limit of NAAQS (QCVN 05:2013/BTNMT-hourly average with 200 μg/m 3 ), but different effects on crops should be considered. ...
The Mekong Delta region (MDR), also known as Vietnam’s rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country’s total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, the concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is therefore the subject of research. This study aims to simulate the spatiotemporal distribution of ground-level ozone in the area and evaluate the impact of precursor emissions and meteorological factors on the spatiotemporal distributions of ozone concentrations. The study area was divided into seven zones, including six agro-ecological zones (AEZs) and one low-mountainous area, mainly to clarify the role of emissions in each AEZ. The simulation results showed that ground-level O3 in the MDR ranged from 40.39 to 52.13 µg/m³. In six agro-ecological zones, the average annual ground-level O3 concentration was relatively high and was the highest in zone 6 (CPZ) and zone 3 (LXZ) with values of 96.18 µg/m³ (exceeding 1.60 times the WHO Guidelines 2021) and 94.86 µg/m³ (exceeding 1.58 times the WHO Guidelines 2021), respectively. In each zone, the annual average O3 concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NOx and NMVOCs, are the main contributors to O3 pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NOx/year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, temperature (T), relative humidity (RH), and surface pressure (P) were the three main factors that contributed to the increase in ground-level ozone. The spatio-temporal distribution of ground-level O3 in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policy-makers formulate plans for agro-industrial development in the entire region.
Graphical Abstract
... 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. ...
... From 2016 to 2021, the MUN station measured 8.1 to 37.8 µg m −3 . For comparison purposes, from 2016 to 2020, the maximum 24 h mean PM 2.5 concentrations on 1 January measured in Quito, the capital of Ecuador, ranged between 13.4 and 121.5 µg m −3[54], indicating that air pollution due to New Year's emissions was of a higher magnitude there than in Cuenca. ...
Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m−3
(maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m−3, the current World Health Organization guideline. We estimated the corresponding PM2.5 emissions
and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%,
or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM2.5 emissions on 31 December.
... 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.
... El análisis de sensibilidad es un ajuste de adaptación espacial en el que características propias de una superficie pueden subestimar los datos reales de la atmósfera; a través de juicios expertos para un área en particular, se determina una configuración optima de los parámetros de entrada del modelo, obteniendo mayor validez las predicciones realizadas. Normalmente, el estado de la exactitud del modelo se evalúa por un procedimiento que implica la comparación de las estimaciones de concentraciones de calidad del aire medidas y con pruebas estadísticas o medidas de rendimiento, tales como el sesgo, error, correlación, entre otros (Fox, 1981;Simon et al., 2012). La confiabilidad de los resultados del modelo evidencia las fuentes, el receptor y las consecuencias directas a nivel local o regional, siendo el garante de la toma de decisiones en relación con planes de acción, evaluación de técnicas y procedimientos, nuevas estrategias, planificación territorial y responsabilidades legislativas. ...
... In our analysis, layers above this definition of PBL are considered to be in the FT. Comparisons of q-inferred PBL height from the models and HSRL-2's cloud-top height (CTH) during ORACLES 2016 are presented in Figures S 2 to S 8 using the mean absolute error (MAE) and the mean bias error (MBE) (Simon et al., 2012): 245 ...
The southeast 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 activity that occurs 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 (AOD) 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° and 15°W–15°E (excluding land) among the ESMs. 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 and their similarities in the mean AODs are the result of cancellation of high and low AOD biases. GEOS-FP, MERRA-2, and ALADIN produce 24 %–36 % less AOD and tend to misplace more aerosols in the PBL compared to aircraft-based observations. 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.
... In order to quantitatively evaluate the error between the experimental and simulated data, a set of statistical performance methods proposed by Simon et al. [28] was adopted to evaluate the WRF numerical simulation results. Statistical performance indicators include fractional mean error (ME), root-mean-square error (RMSE), normalized mean error (NME), mean normalized error (MNE), and bias (FB). ...
In this paper, the Weather Research and Forecasting (WRF) model is coupled with the computational fluid dynamics (CFD) model to study the diffusion model of the accidental leakage of hazardous gas under different atmospheric stability conditions. First, the field test at Nanjing University was used to validate the different turbulence models of CFD. The experimental data confirm that the realizable k-ε model can describe the behavior of hazardous gas diffusion. On this basis, the diffusion process of the accidental release of tracer gas under different atmospheric stability conditions is simulated. The results show that atmospheric stability has a significant effect on the flow field distribution and the area of plume of hazardous substances. The ambient wind deflects under unstable conditions and vertical turbulence is slightly larger than that under neutral and stable conditions. Under stable conditions, the dilution of harmful gases is suppressed due to weak turbulent mixing. In addition, stable atmospheric conditions can increase near-surface gas concentrations.
... The model-observation biases of pNO 3 due to uncertainties in model representation could be universal, which can also be found in other counties. The overestimation of pNO 3 has also been reported in the United States (Heald et al. 2012) and Europe (Colette et al. 2011), which is a common issue in many models (Holt et al. 2015;Simon et al. 2012 In Europe, several studies attributed the underestimation of pNO 3 to the simplified treatment of NH 3 /NH 4 + partitioning (Mezuman et al. 2016) and the missing coarse nitrate chemistry (Terrenoire et al. 2015). ...
Particulate nitrate (pNO3) is now becoming the principal component of PM2.5 during severe winter haze episodes in many cities of China. To gain a comprehensive understanding of the key factors controlling pNO3 formation and driving its trends, we reviewed the recent pNO3 modeling studies which mainly focused on the formation mechanism and recent trends of pNO3 as well as its responses to emission controls in China. The results indicate that although recent chemical transport models (CTMs) can reasonably capture the spatial-temporal variations of pNO3, model-observation biases still exist due to large uncertainties in the parameterization of dinitrogen pentoxide (N2O5) uptake and ammonia (NH3) emissions, insufficient heterogeneous reaction mechanism, and the predicted low sulfate concentrations in current CTMs. The heterogeneous hydrolysis of N2O5 dominates nocturnal pNO3 formation, however, the contribution to total pNO3 varies among studies, ranging from 21.0% to 51.6%. Moreover, the continuously increasing PM2.5 pNO3 fraction in recent years is mainly due to the decreased sulfur dioxide emissions, the enhanced atmospheric oxidation capacity (AOC), and the weakened nitrate deposition. Reducing NH3 emissions is found to be the most effective control strategy for mitigating pNO3 pollution in China. This review suggests that more field measurements are needed to constrain the parameterization of heterogeneous N2O5 and nitrogen dioxide (NO2) uptake. Future studies are also needed to quantify the relationships of pNO3 to AOC, O3, NOx, and volatile organic compounds (VOCs) in different regions of China under different meteorological conditions. Research on multiple-pollutant control strategies involving NH3, NOX, and VOCs is required to mitigate pNO3 pollution, especially during severe winter haze events.
... Favourable meteorological conditions for photochemical episodes have been extensively studied (Camalier et al., 2007;Simon et al., 2012;McNider et al., 2020). For example, sunny weather and low wind velocity lead to pollution accumulation and O 3 production (Xue et al., 2014;Carter et al., 2017). ...
The Mekong Delta region (MDR), also known as Vietnam's rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country's total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is, therefore, the subject of research. This study has a goals to simulate the spatio–temporal distribution of ground-level ozone in the area, also to evaluate the impact of precursor emissions and meteorological factors on spatio–temporal distributions of concentration. The study area was divided into six agro-ecological zones to clarify the role of emissions in each zone. The simulation results showed that the ground-level O 3 in the MDR ranged from 40.39 µg/m ³ to 52.13 µg/m ³ . In six agro-ecological zones, the average annual ground-level O 3 concentration was relatively high, and was the highest in areas six (CZ) and seven (CPZ), with 46.11 µg/m ³ and 46.41 µg/m ³ , respectively. In each zone, the annual average O 3 concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NO x and NMVOCs, are the main contributors to O 3 pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NO x /year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, relative humidity (RH) and surface pressure (P) were the two main factors that contributed to the increase in ground-level ozone. The spatio–temporal distribution of ground-level O 3 in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policymakers formulate plans for agro-industrial development in the entire region.
... CASTNET sites available for at least 13 of the 16 years simulated with 75% annual coverage each year are included in this analysis, resulting in 75 valid sites meeting the minimum requirements (Table 130 S2). The EQUATES simulations are evaluated by comparing with available monitoring data using several model performance statistics (Simon et al., 2012). ...
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 studies have investigated the spatial and temporal trends of atmospheric deposition, few assess dry deposition, incorporate a measurement-model fusion approach to improve wet deposition estimates, or focus on changes within specific US climate regions. In this analysis, we evaluate wet, dry, and total N and S deposition from multiyear simulations across climatologically consistent regions within the contiguous US (CONUS). Community Multiscale Air Quality (CMAQ) model estimates from 2002 to 2017 from the EPA’s 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 and improvements to organic N chemistry. The model generally underestimates wet deposition of SO4, NO3, and NH4 compared to National Atmospheric Deposition Program observations. Measurement-model fusion 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. Comparisons to Clean Air Status and Trends network ambient concentrations show the model underestimates NH4 and SO4 and overestimates SO2 and TNO3. Model agreement 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. 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/yr in the Northeast and Southeast and by ─0.06 kg-N/ha/yr 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; 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 between 2002–2009 than 2010–2017, suggesting a slowdown of the rate of decline. The average total N deposition budget over the CONUS decreases from 7.8 kg-N/ha in 2002 to 6.3 kg-N/ha in 2017 due to declines in oxidized N deposition from NOx emission controls. Across the US 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. Future progress in decreasing the total N budget remains exceedingly difficult without new controls on ammonia emissions, including from agricultural sources as the demand for food grows and oxidized N emissions continue to decrease due to emission controls implemented to achieve the National Ambient Air Quality Standards.
... Then, we used data from the first 70% of years for model calibration and the remaining 30% for validation. Finally, Nash-Sutcliffe coefficient (NASH), root mean square errors (RMSE), mean absolute percentage error (MAE), correlation coefficient (r), and normalized mean bias (NMB) were used to assess the predictive performance of the models [26,36,37], which were defined as: ...
Vegetation is a key indicator of the health of most terrestrial ecosystems and different types of vegetation exhibit different sensitivity to climate change. The Yarlung Zangbo River Basin (YZRB) is one of the highest basins in the world and has a wide variety of vegetation types because of its complex topographic and climatic conditions. In this paper, the sensitivity to climate change for different vegetation types, as reflected by the Normalized Difference Vegetation Index (NDVI), was assessed in the YZRB. Three machine learning models, including multiple linear regression, support vector machine, and random forest, were adopted to simulate the response of each vegetation type to climatic variables. We selected random forest, which showed the highest performance in both the calibration and validation periods, to assess the sensitivity of the NDVI to temperature and precipitation changes on an annual and monthly scale using hypothetical climatic scenarios. The results indicated there were positive responses of the NDVI to temperature and precipitation changes, and the NDVI was more sensitive to temperature than to precipitation on an annual scale. The NDVI was predicted to increase by 1.60%–4.68% when the temperature increased by 1.5 °C, while it only changed by 0.06%–0.24% when the precipitation increased by 10% in the YZRB. Monthly, the vegetation was more sensitive to temperature changes in spring and summer. Spatially, the vegetation was more sensitive to temperature increases in the upper and middle reaches, where the existing temperatures were cooler. The time-lag effects of climate were also analyzed in detail. For both temperature and precipitation, Needleleaf Forest and Broadleaf Forest had longer time lags than those of other vegetation types. These findings are useful for understanding the eco-hydrological processes of the Tibetan Plateau.
... All three models in general underestimated surface measured PM 2.5 mass concentration (Figure 7), which has also been briefly discussed in Part I of this work . Underestimation of surface PM 2.5 mass concentration in atmospheric chemical models in summer time are common (e.g., McKeen et al., 2007;Simon et al., 2012) due to a variety of reasons such as inaccurate anthropogenic emission inventories, errors in boundary/ initial conditions used for regional models and simplified chemistry/physical scheme. Specifically in this work, for example, not including dust emissions in CMAQ or SOA in GEOS-Chem could contribute to the underestimation of surface PM 2.5 concentration, which could subsequently cause underestimation of AOD values. ...
This work serves as the second of a two‐part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multisatellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS‐Chem, WRF‐Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over nonrural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and nonrural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias‐corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two‐part study develops a multimodel and multi‐AOD bias‐correction technique that has the potential to improve PM2.5 forecasts in both rural and nonrural areas in near real time, and be readily implemented at state levels.
... Model performance for the base-case CTM simulation was evaluated by comparison with available monitoring data for PM 2.5 , PM 2.5 components, and MDA8 ozone (Supplementary Text S1, Supplementary Table S1) (Supplementary Materials, Tables S1-S4). The model performance statistics are generally within ranges reported in previous applications [40,41] and support the modeling here. However, overpredictions of PM 2.5 organic carbon concentrations were evident in January, possibly due to issues with emissions or meteorology as well as gas-particle partitioning of primary organic aerosol. ...
Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOx emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.
... Model performance from R 2 is comparable to that of other air quality studies. A review analysis that compared photochemical air quality models estimates of elemental carbon (a surrogate of BC) to observations in 19 studies showed median R 2 values of~0.20 (Simon et al., 2012). A more recent/relevant study developed an inverse dispersion model to obtain emissions inputs (as opposed to our study which adjusts them) from fixed sites stratified R 2 by month (Ahangar et al., 2019). ...
Isolating air pollution sources in a complex transportation environment to quantify their contribution is challenging, particularly with sparse stationary measurements. Mobile measurements can add finer spatial resolution to support source apportionment, but they exhibit limitations when characterizing long term concentrations. Dispersion models can help overcome these limitations. However, they are only as reliable as their input emissions inventories. Herein, we developed an innovative method to revise emissions through inverse modeling and improve dispersion modeling predictions using stationary/mobile measurements. One specific revision estimated an adjustment factor of ~306 for warehouse emissions, indicating a significant underestimation of our initial estimates. This revised emission rate scaled up nationally would correspond to ~3.5% of the total Black Carbon emissions in the U.S. Nevertheless, domain-specific revisions only contribute to a 4% increase of area source emissions while improving R² from monthly estimates at fixed sites by 38%. After revising emissions through inverse dispersion modeling, we combine this model with stationary/mobile measurements through Bayesian Maximum Entropy (I-DISP BME) to produce temporally coarse yet spatially fine data fusion. We compare this novel data fusion approach to BME using only measurements (Flat BME). A 10-fold conventional cross-validation (representative of months with mobile measurements) shows that all BME methods have R² values that range from 0.787 to 0.798. A 2-fold cross-validation (representative of months with no mobile measurements) shows that the R² for I-DISP BME increases by a factor 90 when compared to Flat BME. Furthermore, not only is our novel I-DISP BME method more accurate than the classic Flat BME method, but the area it detects as highly exposed can be up to 5 times larger than that detected by the less accurate Flat BME method.
... For PM 2.5 , we can see from the MB, NMB and RMSE indicators that the simulated value is lower than the observed value, but the difference between the simulated value and the observed value is not large, with the correlation coefficient being higher than 0.7. Simon et al. (2012) suggested that the reduction of nitrate, sulfate and OC concentrations is one of the reasons for the underestimation of PM 2.5 in summer. NO 2 and O 3 are significantly negatively correlated, and the NO 2 simulation effect is not as good as those for O 3 and PM 2.5 . ...
This study uses the WRF-Chem model combined with the empirical kinetic modeling method (EKMA curve) to study the compound pollution event in Beijing that happened in 13–23 May 2017. Sensitivity tests are conducted to analyze ozone sensitivity to its precursors, and to develop emission reduction measures. The results suggest that the model can accurately simulate the compound pollution process of photochemistry and haze. When VOCs and NOx were reduced by the same proportion, the effect of O3 reduction at peak time was more obvious, and the effect during daytime was more significant than at night. The degree of change in ozone was peak time > daytime average. When reducing or increasing the ratio of precursors by 25% at the same time, the effect of reducing 25% VOCs on the average ozone concentration reduction was most significant. The degree of change in ozone decreased with increasing altitude, the location of the ozone maximum change shifted westward, and its range narrowed. As the altitude increases, the VOCs-limited zone decreases, VOCs sensitivity decreases, NOx sensitivity increases. The controlled area changed from near-surface VOCs-limited to high-altitude NOx-limited. Upon examining the EKMA curve, we have found that suburban and urban are sensitive to VOCs. The sensitivity tests indicate that when VOCs in suburban are reduced about 60%, the O3-1h concentration could reach the standard, and when VOCs of the urban decreased by about 50%, the O3-1h concentration could reach the standard. Thus, these findings could provide references for the control of compound air pollution in Beijing.
... Monitors in EPA's near-road monitoring network were excluded from this analysis because 12-km resolution grid boxes are not expected to accurately capture near-road conditions. Mean bias (MB) and normalized mean bias (NMB) were calculated as described in Simon et al. (2012). Ambient NO X monitors have known artifacts and often pick up additional NO Y species in their measurements (Dunlea et al., 2007;Dickerson et al., 2019). ...
Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO
X
) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO
X
and total gas-phase-oxidized reactive nitrogen (NO
Y
) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO
X
performance. Summertime NO
X
is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO
X
bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO
X
tends to be unbiased or underpredicted. In winter, the evening NO
X
overpredictions remain, but NO
X
is unbiased or underpredicted overnight, in the morning, and during the day. NO
X
overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO
X
. Modeled NO
X
performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO
X
emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO
Y
but do not improve summertime NO
X
bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO
X
performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.
... The set of equations can be found in the SI(Equation 1-6)ofLoría-Salazar et al., (2016). These evaluation metrics are commonly used to evaluate AQ model performance(Appel et al., 2011;Chang & Hanna, 2004; EPA 454/R- 08-003, 2008;Loría-Salazar et al., 2016;Simon et al., 2012;Wilkins et al., 2018). ...
We analyze new aerosol products from NASA satellite retrievals over the western USA during August 2013, with special attention to locally generated wildfire smoke and downwind plume structures. Aerosol optical depth (AOD) at 550 nm from MODerate Resolution Imaging Spectroradiometer (MODIS) (Terra and Aqua Collections 6 and 6.1) and Visible Infrared Imaging Radiometer Suite (VIIRS) Deep Blue (DB) and MODIS (Terra and Aqua) Multi-Angle Implementation of Atmospheric Correction (MAIAC) retrievals are evaluated against ground-based AErosol RObotic NETwork (AERONET) observations. We find a significant improvement in correlation with AERONET and other metrics in the latest DB AOD (MODIS C6.1 r² = 0.75, VIIRS r² = 0.79) compared to MODIS C6 (r² = 0.62). In general, MAIAC (r² = 0.84) and DB (MODIS C6.1 and VIIRS) present similar statistical evaluation metrics for the western USA and are useful tools to characterize aerosol loading associated with wildfire smoke. We also evaluate three novel NASA MODIS plume injection height (PIH) products, one from MAIAC and two from the Aerosol Single scattering albedo and layer Height Estimation (ASHE) (MODIS and VIIRS) algorithm. Both Terra and Aqua MAIAC PIHs statistically agree with ground-based and satellite lidar observations near the fire source, as do ASHE, although the latter is sensitive to assumptions about aerosol absorption properties. We introduce a first-order approximation Smoke Height Boundary Layer Ratio (SHBLR) to qualitatively distinguish between aerosol pollution within the planetary boundary layer and the free troposphere. We summarize the scope, limitations, and suggestions for scientific applications of surface level aerosol concentrations specific to wildfire emissions and smoke plumes using these novel NASA MODIS and VIIRS aerosol products.
... This underestimation reveals underestimation in emissions. The overestimation of PM2.5 in cold season (also reported in previous studies) could be attributed to the overestimation of the Organic Carbon and nitrate contribution during the specific period (Simon et al., 2012). Time series comparison also shows that PM2.5 WRF-CAMx concentrations (Fig. 8) are closer to observations than PM10 (Fig. 7) in agreement with other studies (Pateraki et al., 2013). ...
Post-processing techniques can provide significant improvement in the forecast skill of air quality models. In this study, the implementation of an analog-based technique to Comprehensive Air Quality Model with Extensions (CAMx) coupled with Weather Research and Forecasting (WRF) model results is examined. WRF-CAMx runs with a 2-km horizontal grid increment over Greece for one month of every season of the year 2012 (i.e., January, April, July and October). The analog ensemble (AnEn) technique attempts to improve the accuracy of ozone and particulate matter forecasts by using a method that searches for analogs in past forecasts. An optimization process that minimizes Root Mean Square Error (RMSE) metric has been used to find the best AnEn configuration. The corrected forecasts are computed with two approaches, i.e., AnEn 'mean' and AnEn 'bias correction' (AnEn-bias) approach. The methods are tested with observations from 23 surface stations for ozone, 16 stations for PM10 and 3 stations for PM2.5 for an 11-day period for each month. The results which are very similar for both techniques show an improvement of the forecast skill of all pollutants. The corrected forecasts have smaller RMSE and higher Correlation Coefficient (R). A reduction of 40 and 70% for AnEn RMSE values is found for ozone and particulate matter, respectively. For AnEn R, an improvement of 11% for ozone, 46% for PM10 and 26% for PM2.5 is estimated. These techniques are also successful in drastically reducing the mean bias of raw forecasts to close to zero.
... There are plenty of indicators that can be used to conduct a model performance evaluation (see EEA (2011) for a comprehensive list). We adopted the method proposed by Emery et al. (2017) which, based on previous research about the performance of photochemical models (Simon et al., 2012), proposed to update established benchmarks for ambient ozone (Doll, 1991) and PM concentrations (Boylan and Russell, 2006) and recommended the evaluation of three well-established statistical metrics: normalized mean bias (NMB), normalized mean error (NME) and correlation coefficient (r). Formal definition of these metrics is given in Table 2. ...
... Observational data for the surface stations were obtained from the airport meteorological database (http://www.weatherunderground. com/). Several quantitative performance metrics were used to compare hourly surface observations and model estimates: mean bias (MB), mean error (ME), root mean square error (RMSE), mean fractional bias (MFB), index of agreement (IOA) and correlation coefficient (R) based on recommendations of Emery et al. (2017) and Simon et al. (2012). A summary of these statistics by station is shown in Table S1. ...
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.
Simulation outputs from chemical transport models (CTMs) are essential to plan effective air quality policies. A key strength of these models is their ability to separate out source-specific components which facilitate the simulation of the potential impact of policy on future air quality. However, configuring and running these models is complex and computationally intensive, making the evaluation of multiple scenarios less accessible to many researchers and policy experts. The aim of this work is to present how Gaussian process emulation can provide a top-down approach to interrogating and interpreting the outputs from CTMs at minimal computational cost. A case study is presented (based on fine particle sources in the southwest of Western Australia) to illustrate how an emulator can be constructed to simultaneously evaluate changes in emissions from on-road transport and electricity sectors. This study demonstrates how emulation provides a flexible way of exploring local impacts of electric vehicles and wider regional effects of emissions from electricity generation. The potential for emulators to be applied to other settings involving air quality research is discussed.
Abstract:
Simulation outputs from chemical transport models (CTMs) are essential to plan effective air quality policies. A key strength of these models is their ability to separate out source-specific components which facilitate the simulation of the potential impact of policy on future air quality. However, configuring and running these models is complex and computationally intensive, making the evaluation of multiple scenarios less accessible to many researchers and policy experts. The aim of this work is to present how Gaussian process emulation can provide a top-down approach to interrogating and interpreting the outputs from CTMs at minimal computational cost. A case study is presented (based on fine particle sources in the southwest of Western Australia) to illustrate how an emulator can be constructed to simultaneously evaluate changes in emissions from on-road transport and electricity sectors. This study demonstrates how emulation provides a flexible way of exploring local impacts of electric vehicles and wider regional effects of emissions from electricity generation. The potential for emulators to be applied to other settings involving air quality research is discussed.
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 fully couples the chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMM v1.0 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 noncancer health risk estimated for primary HAPs from anthropogenic and biomass burning sources in the U.S., with the coverage of risk higher (>80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for 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 carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in number of hydrogens per carbon. In total, eleven 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 design of control strategies. CRACMMv1.0 will be available in CMAQv5.4.
In addition to photochemical production and horizontal regional transport, surface O3 concentration can also likely be affected by vertical transport, which is not well known so far. The process analysis was conducted by using the Regional Atmospheric Modeling System Community Multiscale Air Quality (RAMS-CMAQ) model to investigate photochemical production and the vertical transport mechanism of boundary-layer O3 during a typical O3 pollution episode in the North China Plain (NCP), and further quantify the contribution of vertical transport to surface O3. The diurnal variations of vertical budgets of O3 and NO2 in the boundary layer at multiple sites showed that there were substantial differences in the vertical distribution of O3 production and transport between urban and suburban/rural areas. In urban areas, surface O3 is consumed by titration reaction to generate NO2, which is then transported to the upper boundary layer and produces O3 by photochemical reaction. With the development of the boundary layer, the upper-layer O3 stored in the residual layer at nighttime can be transported vertically to the surface as the turbulent diffusion intensifies the next morning. While in suburban and rural areas, the vertical transport is relatively weaker because the photochemical formation of O3 occurs in the whole boundary layer, although it decreases slightly with the altitude. Model simulation showed that 20.6–27.9% of urban surface O3 changes in the morning (09:00–10:00 LST) was attributable to the downward transport from the residual layer, while it is 15.0–22.1% at suburban site. The vertical transport from above the boundary layer contributed 24.0–63.6% to daytime urban surface O3 changes, which was weak in suburban areas. Differences and similarities in O3 formation and transport mechanism in urban and suburban regions revealed here highlight the importance of earlier control and regional collaboration.
The synergistic effects of various air pollutants on meteorological fields have not yet been widely studied and fully understood, especially at the junction of the Loess Plateau, Mongolian Plateau and Tibetan Plateau. Two experiments were carried out with and without the incorporation of air pollutants to explore the synergistic effects using the Weather Research and Forecasting (WRF) model and WRF model coupled with chemistry (WRF-Chem) at the junction of the above three plateaus. The incorporation of air pollutants led to negative and positive variation patterns of meteorological fields and improved the simulation accuracy of meteorological factors to some extent. The negative variation pattern was caused by the direct absorption and scattering of solar radiation by air pollutants, and the positive variation pattern was driven by the evaporation of clouds due to the synergistic semidirect effects of air pollutants, which was more significant in this region. In the positive variation pattern, the solar radiation flux reaching the ground and the outgoing longwave radiation at the top of the atmosphere were increased by approximately 2–8% and 1–4%, respectively, the surface was warmed by 0.2–0.6 K, PBLH increased by 3–12%, and RH decreased by 1–4%. Moreover, the change in wind speed was approximately −0.3–0.3 m/s in the region. Furthermore, the correlation of conventional air pollutants with changes in meteorological factors indicated that there were not simple linear effects of atmospheric pollutants on meteorological fields and that considering a single pollutant cannot reflect the synergistic effects of various air pollutants on meteorological fields. This study quantifies the synergistic effects of various air pollutants on meteorological fields, which is a key issue that cannot be ignored in the modelling of meteorological fields and in the exploration of the interactions between air pollutants and synoptic weather.
Clouds play an important role in the Earth’s climate system since they can affect various physical and chemical processes within the atmosphere. Misplacement of clouds is a major source of error in the numerical weather prediction (NWP) models, and it also impacts the accuracy of air quality simulations since the meteorology and air quality are directly coupled. In this study, a cloud assimilation technique was utilized to improve cloud placement within the Weather Research and Forecasting (WRF) model by assimilating Geostationary Operational Environmental Satellite (GOES)-derived cloud products. Meteorological outputs from the WRF model were then used as inputs for the Community Multiscale Air Quality (CMAQ) model. The impact of cloud assimilation on air quality was tested over the June-September 2016 period. The results indicated that, by modifying model clouds, cloud assimilation corrected surface solar radiation and photochemical reaction rates, altered light sensitive biogenic emissions, adjusted horizontal transport and vertical mixing, and finally improved the prediction of surface ozone concentration. Cloud assimilation improved daytime surface ozone prediction over most of the U.S. domain, with exceptions in California. On average, cloud assimilation improved the prediction of daytime peak ozone and reduced bias by 47% (~1.5 ppb). The largest improvement was seen over the southeast U.S. region (~2.6 ppb reduction in daytime peak ozone), where convective clouds are more frequent and transient and biogenic volatile organic compound (VOC) emissions are more intense than elsewhere.
PM2.5 and its toxic chemical components are critical air pollutants that have been associated with respiratory diseases and human mortality. Therefore, the accurate prediction of PM2.5 and its chemical components is necessary to formulate emission reduction measures. Although chemical transport models can provide continuous temporal and spatial estimates of pollutants, the uncertainties in emission inventories, meteorological processes, and chemical mechanisms reduce the accuracy of modeling results. In particular, the discrepancy between simulated PM2.5 components and ground monitoring data remain large, with varying degrees of over and underestimation. To improve the accuracy of numerical simulation forecasts, we applied three typical machine learning algorithms to calibrate deviations. For this, we employed major meteorological parameters along with simulated and observed pollutant concentrations as inputs. The results showed that random forest and support vector regression (SVR) models presented much better predictive performance (R = 0.71–0.81 and 0.76–0.82, respectively) compared with multiple linear regression (MLR, R = 0.41–0.61). Moreover, their root mean square error and mean absolute error were 17–76% and 33–79% lower than those of MLR, respectively. The SVR model presented the most accurate prediction of PM2.5 components. The predicted proportions of PM2.5 components reflected the variations in pollution sources, which can be used to analyze the causes of pollution and thereby support the air quality management. More accurate prediction of PM2.5 components can promote exposure assessments and provide a basis for health studies.
Chemical transport models (CTMs) are widely used in scientific studies and air quality management. A particular application is to assess how pollutant concentrations, particularly ozone and PM, respond to emission controls. As part of a dynamic evaluation, two widely used CTMs, CMAQ and CAMx, were evaluated for how well they captured trends in observed surface ozone, as well as the changes in associated concentrations, between 2001 to 2011 and 2011 to 2016. Those periods were chosen as additional effort was used to improve emissions estimates for all three years. Prior studies found that both emissions inventories and observations indicate reductions in both ozone and NO2 during these periods, but studies have found that NO2 observations have not agreed as well with estimated emissions. In general, given the efforts to harmonize model inputs, the two models performed very similarly and captured the ozone declines for both the 2001–2011 and 2011–2016 periods at the national level, though moderate biases were found in some locations. At the national scale, ozone trends for the 2011–2016 period were better captured by the models than those for the 2001–2011 period. Both models overestimated the observed decrease in ground level NO2 at the higher end of the concentration spectrum. This is linked to, and can cause, the detected tendency of the models to have larger than observed increases in ozone at the lower end over time. At the metropolitan statistical area (MSA) level, the performance of capturing the ozone trends varied. For some MSA's, the models estimated the wrong direction in the trends; e.g., in Denver, where maximum 8-hr average ozone levels increased from 2001 to 2011, the models predicted a decrease. The US EPA recommends location specific Relative Response Factors (RRFs) to adjust results when applying the models for air quality management purposes. At the regional level, the simulated ozone RRFs were up to 8% larger than the observed RRFs for the 2001–2011 period, suggesting that the models underestimated the ozone decrease. For the 2011–2016 period, with a couple of exceptions, the simulated RRFs were up to 9% smaller than observed RRFs, indicating a negative bias in the simulated trend. For individual MSAs, the ratio of the simulated to observed RRFs ranged between 0.86 (Knoxville for 2001–2011) and 1.40 (Los Angeles for 2001–2011). This is possibly linked to potential biases in the estimated emissions trends. Such high biases could lead to similar biases in the estimated emissions controls required for an area to attain the air quality standards. The results indicate that there are both spatial and temporal biases in how well the two models have captured the observed ozone and NO2 trends. Air quality planning agencies should aim to diagnose and reduce such biases before using model results for air quality management.
Air pollution harms human health and the environment. Several regulatory efforts and different actions have been taken in the last decades by authorities. Air quality trend analysis represents a valid tool in assessing the impact of these actions taken both at national and local levels. This paper presents for the first time the capability of the Italian national chemical transport model, AMS-MINNI, in capturing the observed concentration trends of three air pollutants – NO2, inhalable particles having diameter less than 10 µm (PM10), and O3 – in Italy over the period 2003–2010. We firstly analyse the model performance finding it in line with the state of the art of regional air quality modelling. The modelled trends result in a general significant downward trend for the three pollutants and, in comparison with observations, the values of the simulated trends were of a similar magnitude for NO2 (in the range −3.0 to −0.5 µg m−3 yr−1), while a smaller range of trends was found than those observed for PM10 (−1.5 to −0.5 µg m−3 yr−1) and O3 maximum daily 8 h average concentration (−2.0 to −0.5 µg m−3 yr−1). As a general result, we find good agreement between modelled and observed trends; moreover, the model provides a greater spatial coverage and statistical significance of pollutant concentration trends with respect to observations, in particular for NO2. We also conduct a qualitative attempt to correlate the temporal concentration trends to meteorological and emission variability. Since no clear tendency in yearly meteorological anomalies (temperature, precipitation, geopotential height) was observed for the period investigated, we focus the discussion of concentration trends on emission variations. We point out that, due to the complex links between precursor emissions and air pollutant concentrations, emission reductions do not always result in a corresponding decrease in atmospheric concentrations, especially for those pollutants that are formed in the atmosphere such as O3 and the major fraction of PM10. These complex phenomena are still uncertain and their understanding is of the utmost importance in planning future policies for reducing air pollution and its impacts on health and ecosystems.
Air quality modeling for research and regulatory
applications often involves executing many emissions sensitivity cases to
quantify impacts of hypothetical scenarios, estimate source contributions, or
quantify uncertainties. Despite the prevalence of this task, conventional
approaches for perturbing emissions in chemical transport models like the
Community Multiscale Air Quality (CMAQ) model require extensive offline
creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID)
module, a component of CMAQv5.3 and beyond, addresses these limitations by
performing these modifications online during the air quality simulation.
Further, the model contains an Emission Control Interface which allows users
to prescribe both simple and highly complex emissions scaling operations
with control over individual or multiple chemical species, emissions
sources, and spatial areas of interest. DESID further enhances the
transparency of its operations with extensive error-checking and optional
gridded output of processed emission fields. These new features are of high
value to many air quality applications including routine perturbation
studies, atmospheric chemistry research, and coupling with external models
(e.g., energy system models, reduced-form models).
The Metropolitan Region of Greater Vitória (RMGV) is the most industrialized and
urbanized area of the state of Espírito Santo. This whole process has led to changes
in the local atmospheric circulation, such as the formation and intensification of the
urban heat island (ICU). Therefore, the main objective of this research was to study
the atmospheric impact due to the alteration of land use and coverage in the Greater
Vitória Metropolitan Region. This research will be developed in two stages: the first
will be the ICU observational study and the second will be the use of atmospheric
numerical modelling, using the Weather Research and Forecasting (WRF) model
coupled with the Building Effect Parameterization (BEP) model. The analysis of
observational data shows that the maximum hourly intensity of ICU
at RMGV was 7.35 ° C at 14h in October 2017 and 7.53 ° C at 15h in January 2018.
Despite the values Often, the heat island in the RMGV is less intense than in high
and medium latitude areas. It was also observed that the heat island is more intense
during the day, in summer, and is associated with the time of higher magnitude of
thermal load available in the environment and not that stored by the urban fabric.
From the LANDSAT-8 satellite images, it was found that the surface intensities and
spatial extensions of the ICU in the RMGV reach extremes -3 ° C to + 20 ° C,
presenting an amplitude of + 17 ° C. Values between + 2 ° C to + 8 ° C comprise the
largest area, and in urban areas, values of intensity + 5 ° C are commonly recorded.
The effects of urbanization in the simulations performed with the WRF-BEP model
show that the heat island provided an average 5 ° C increase in air temperature,
corroborated by the increase in sensible heat flow and reduction of latent heat, as
well as, favoured an increase of Bowen's ratio up to 10 times. The heat island also
favored an increase in the convergence of air at the edges of the RMGV, making the
breeze arrive until one hour earlier, on 10/30 at 9am. With the presence of the city,
the sea breeze was stagnant on the coast during the day.
Racial-ethnic minorities in the United States are exposed to disproportionately high levels of ambient fine particulate air pollution (PM 2.5 ), the largest environmental cause of human mortality. However, it is unknown which emission sources drive this disparity and whether differences exist by emission sector, geography, or demographics. Quantifying the PM 2.5 exposure caused by each emitter type, we show that nearly all major emission categories—consistently across states, urban and rural areas, income levels, and exposure levels—contribute to the systemic PM 2.5 exposure disparity experienced by people of color. We identify the most inequitable emission source types by state and city, thereby highlighting potential opportunities for addressing this persistent environmental inequity.
Numerical air quality models (AQMs) have been applied more frequently over
the past decade to address diverse scientific and regulatory issues
associated with deteriorated air quality in China. Thorough evaluation of a
model's ability to replicate monitored conditions (i.e., a model performance
evaluation or MPE) helps to illuminate the robustness and reliability of the
baseline modeling results and subsequent analyses. However, with numerous
input data requirements, diverse model configurations, and the scientific
evolution of the models themselves, no two AQM applications are the same and
their performance results should be expected to differ. MPE procedures have
been developed for Europe and North America, but there is currently no
uniform set of MPE procedures and associated benchmarks for China. Here we
present an extensive review of model performance for fine particulate matter
(PM2.5) AQM applications to China and, from this context, propose a set
of statistical benchmarks that can be used to objectively evaluate model
performance for PM2.5 AQM applications in China. We compiled MPE
results from 307 peer-reviewed articles published between 2006 and 2019,
which applied five of the most frequently used AQMs in China. We analyze
influences on the range of reported statistics from different model
configurations, including modeling regions and seasons, spatial resolution
of modeling grids, temporal resolution of the MPE, etc. Analysis using a
random forest method shows that the choices of emission inventory, grid
resolution, and aerosol- and gas-phase chemistry are the top three factors
affecting model performance for PM2.5. We propose benchmarks for six
frequently used evaluation metrics for AQM applications in China, including
two tiers – “goals” and “criteria” – where goals represent the
best model performance that a model is currently expected to achieve and
criteria represent the model performance that the majority of studies
can meet. Our results formed a benchmark framework for the modeling
performance of PM2.5 and its chemical species in China. For instance,
in order to meet the goal and criteria, the normalized mean bias (NMB) for
total PM2.5 should be within 10 % and 20 %, while the normalized
mean error (NME) should be within 35 % and 45 %, respectively. The goal
and criteria values of correlation coefficients for evaluating hourly and
daily PM2.5 are 0.70 and 0.60, respectively; corresponding values are
higher when the index of agreement (IOA) is used (0.80 for goal and 0.70 for
criteria). Results from this study will support the ever-growing modeling
community in China by providing a more objective assessment and context for
how well their results compare with previous studies and to better
demonstrate the credibility and robustness of their AQM applications prior
to subsequent regulatory assessments.
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 (SO<sub>4</sub><sup>=</sup>), ammonium (NH<sub>4</sub><sup>+</sup>) and nitrate (NO<sub>3</sub><sup>−</sup>). 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 SO<sub>4</sub><sup>=</sup> 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 SO<sub>4</sub><sup>=</sup> 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 SO<sub>4</sub><sup>=</sup> wet deposition values improved when they were adjusted to account for biases in the model estimated precipitation. The CMAQ model underestimates NH<sub>4</sub><sup>+</sup> 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 NH<sub>4</sub><sup>+</sup> 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 NH<sub>4</sub><sup>+</sup> wet deposition is likely due, in part, to the poor temporal and spatial representation of ammonia (NH<sub>3</sub>) emissions, particularly those emissions associated with fertilizer applications and NH<sub>3</sub> bi-directional exchange. The model performance for estimates of NO<sub>3</sub><sup>−</sup> wet deposition are mixed throughout the year, with the model largely underestimating NO<sub>3</sub><sup>−</sup> 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 NO<sub>3</sub><sup>−</sup> 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 NO<sub>3</sub><sup>−</sup> 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 NO<sub>3</sub><sup>−</sup> 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<sup>−3</sup>, 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 (O<sub>3</sub>) 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 O<sub>3</sub> mixing ratios are attributed to less dry deposition of O<sub>3</sub> 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 (SO<sub>4</sub><sup>2−</sup>), similar performance for nitrate (NO<sub>3</sub><sup>−</sup>), and slightly worse performance for nitric acid (HNO<sub>3</sub>), total carbon (TC) and total fine particulate (PM<sub>2.5</sub>) mass than the corresponding MM5-CMAQ results. For August, predictions of O<sub>3</sub> were notably higher in the WRF-CMAQ simulation, particularly in the southern United States, resulting in increased model bias. Concentrations of predicted particulate SO<sub>4</sub><sup>2−</sup> 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 <sub>∗</sub>) 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 SO<sub>4</sub><sup>2−</sup>, NO<sub>3</sub><sup>−</sup> and NH<sub>4</sub><sup>+</sup> 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<sub align="right"> x </sub> 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., th