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Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012

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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.

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... Initial conditions and lateral boundary inflow of chemical species was based on a global GEOS-Chem simulation of 2011 . Aggregated maximum daily 8-h average (MDA8) O 3 mean bias (1.6 ppb) and normalized bias (3.7%) were within the range reported by other regional modeling studies (Simon et al., 2012), while mean error (6.8 ppb) and normalized mean error (15.5%) were below the median value of these metrics reported by other studies (Simon et al., 2012). Aggregated annual PM 2.5 mean bias (− 0.2 μg/m 3 ), normalized mean bias (− 4.3%), mean error (2.4 μg/m 3 ), and normalized mean error (50.8%) were all within the range of reported metrics for other regional modeling studies (Simon et al., 2012). ...
... Initial conditions and lateral boundary inflow of chemical species was based on a global GEOS-Chem simulation of 2011 . Aggregated maximum daily 8-h average (MDA8) O 3 mean bias (1.6 ppb) and normalized bias (3.7%) were within the range reported by other regional modeling studies (Simon et al., 2012), while mean error (6.8 ppb) and normalized mean error (15.5%) were below the median value of these metrics reported by other studies (Simon et al., 2012). Aggregated annual PM 2.5 mean bias (− 0.2 μg/m 3 ), normalized mean bias (− 4.3%), mean error (2.4 μg/m 3 ), and normalized mean error (50.8%) were all within the range of reported metrics for other regional modeling studies (Simon et al., 2012). ...
... Aggregated maximum daily 8-h average (MDA8) O 3 mean bias (1.6 ppb) and normalized bias (3.7%) were within the range reported by other regional modeling studies (Simon et al., 2012), while mean error (6.8 ppb) and normalized mean error (15.5%) were below the median value of these metrics reported by other studies (Simon et al., 2012). Aggregated annual PM 2.5 mean bias (− 0.2 μg/m 3 ), normalized mean bias (− 4.3%), mean error (2.4 μg/m 3 ), and normalized mean error (50.8%) were all within the range of reported metrics for other regional modeling studies (Simon et al., 2012). Additional evaluation of the model performance can be found in Online Resource 1. ...
Article
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Emissions from energy production, conversion, and use are associated with adverse effects on human health and climate. We use the Community Multiscale Air Quality (CMAQ) model and the Benefits Mapping and Analysis Program (BenMAP) to quantify effects of three potential emission abatement policies in the USA. The policies impose emission fees designed to internalize externalities associated with ozone and particulate matter (PM) pollution and greenhouse gas emissions. A business as usual case is compared to policies in which fees are applied to energy sector emissions of health impacting pollutants: NOx, SO2, PM10, PM2.5, and volatile organic compounds (VOCs), and/or greenhouse gases: CO2 and CH4. Net policy benefits are calculated by summing the health and climate benefits and subtracting the increased energy system cost. For comparison with the detailed model results, benefits are also estimated by the simplified approach of multiplying emission changes by fixed estimates of health damages per ton of emissions. Annual net benefits in 2045 are $173 billion with health-related fees and $116 billion with climate-based fees. A combined policy, with fees on emissions of both greenhouse gases (GHG) and health impacting pollutants, has annual net benefits of $189 billion in 2045. Co-benefits are unevenly distributed. Health benefits of GHG fees are only 40% as large as health benefits from air quality fees. Climate benefits of health fees are 87% as large as those from climate-based fees. Thus, each policy has comparable climate benefits, but air quality and corresponding health improvements are smaller when not specifically targeted.
... Although these uncertainties are large, multiple studies have shown that modeled emissions capture trends sufficiently as to allow air quality models to provide adequate estimates of air quality and changes over time (Foley et al. 2015a(Foley et al. , 2015bGégo et al. 2008;Gilliland et al. 2008;McDonald et al. 2013McDonald et al. , 2012Simon et al. 2012). For statistical modeling, capturing trends in emissions changes is more important than the absolute emissions amount. ...
... CMAQ tends to overestimate PM 2.5 through October to March and underestimate from April to September ( Figure A4-2). This is consistent with what was observed in the previous studies (Simon et al., 2012). EC, OC, nitrate, and ammonium contribute to the cold month overestimation, and OC, sulfate, and ammonium contribute to the warm month underestimation ( Figures A4-3 through A4-7). ...
... This means that, on average, concentrations simulated by CMAQ are biased high compared to observations on low ambient concentration days, and biased low on high concentration days. This issue-a damped response to various factors, such as emissions or meteorology-has been observed previously (Bencala and Seinfeld 1979;Koo et al. 2015;Simon et al. 2012). ...
Introduction: The United States and Western Europe have seen great improvements in air quality, presumably in response to various regulations curtailing emissions from the broad range of sources that have contributed to local, regional, and global pollution. Such regulations, and the ensuing controls, however, have not come without costs, which are estimated at tens of billions of dollars per year. These costs motivate accountability-related questions such as, to what extent do regulations lead to emissions changes? More important, to what degree have the regulations provided the expected human health benefits? Here, the impacts of specific regulations on both electricity generating unit (EGU) and on-road mobile sources are examined through the classical accountability process laid out in the 2003 Health Effects Institute report linking regulations to emissions to air quality to health effects, with a focus on the 1999-2013 period. This analysis centers on regulatory actions in the southeastern United States and their effects on health outcomes in the 5-county Atlanta metropolitan area. The regulations examined are largely driven by the 1990 Clean Air Act Amendments (C). This work investigates regulatory actions and controls promulgated on EGUs: the Acid Rain Program (ARP), the NOx Budget Trading Program (NBP), and the Clean Air Interstate Rule (CAIR) - and mobile sources: Tier 2 Gasoline Vehicle Standards and the 2007 Heavy Duty Diesel Rule. Methods: Each step in the classic accountability process was addressed using one or more methods. Linking regulations to emissions was accomplished by identifying major federal regulations and the associated state regulations, along with analysis of individual facility emissions and control technologies and emissions modeling (e.g., using the U.S. Environmental Protection Agency's [U.S. EPA's] MOtor Vehicle Emissions Simulator [MOVES] mobile-source model). Regulators, including those from state environmental and transportation agencies, along with the public service commissions, play an important role in implementing federal rules and were involved along with other regional stakeholders in the study. We used trend analysis, air quality modeling, satellite data, and a ratio-of-ratios technique to investigate a critical current issue, a potential large bias in mobile-source oxides of nitrogen (NOx) emissions estimates. The second link, emissions-air quality relationships, was addressed using both empirical analyses as well as chemical transport modeling employing the Community Multiscale Air Quality (CMAQ) model. Kolmogorov-Zurbenko filtering accounting for day of the year was used to separate the air quality signal into long-term, seasonal, weekday-holiday, and short-term meteorological signals. Regression modeling was then used to link emissions and meteorology to ambient concentrations for each of the species examined (ozone [O3], particulate matter ≤ 2.5 μm in aerodynamic diameter [PM2.5], nitrogen dioxide [NO2], sulfur dioxide [SO2], carbon monoxide [CO], sulfate [SO4-2], nitrate [NO3-], ammonium [NH4+], organic carbon [OC], and elemental carbon [EC]). CMAQ modeling was likewise used to link emissions changes to air quality changes, as well as to further establish the relative roles of meteorology versus emissions change impacts on air quality trends. CMAQ and empirical modeling were used to investigate aerosol acidity trends, employing the ISORROPIA II thermodynamic equilibrium model to calculate pH based on aerosol composition. The relationships between emissions and meteorology were then used to construct estimated counterfactual air quality time series of daily pollutant concentrations that would have occurred in the absence of the regulations. Uncertainties in counterfactual air quality were captured by the construction of 5,000 pollutant time series using a Monte Carlo sampling technique, accounting for uncertainties in emissions and model parameters. Health impacts of the regulatory actions were assessed using data on cardiorespiratory emergency department (ED) visits, using patient-level data in the Atlanta area for the 1999-2013 period. Four outcome groups were chosen based on previous studies identifying associations with ambient air pollution: a combined respiratory disease (RD) category; the subgroup of RD presenting with asthma; a combined cardiovascular disease (CVD) category; and the subgroup of CVD presenting with congestive heart failure (CHF). Models were fit to estimate the joint effects of multiple pollutants on ED visits in a time-series framework, using Poisson generalized linear models accounting for overdispersion, with a priori model formulations for temporal and meteorological covariates and lag structures. Several parameterizations were considered for the joint-effects models, including different sets of pollutants and models with nonlinear pollutant terms and first-order interactions among pollutants. Use of different periods for parameter estimates was assessed, as associations between pollutant levels and ED visits varied over the study period. A 7-pollutant, nonlinear model with pollutant interaction terms was chosen as the baseline model and fitted using pollutant and outcome data from 1999-2005 before regulations might have substantially changed the toxicity of pollutant mixtures. In separate analyses, these models were fitted using pollutant and outcome data from the entire 1999-2013 study period. Daily counterfactual time series of pollutant concentrations were then input into the health models, and the differences between the observed and counterfactual concentrations were used to estimate the impacts of the regulations on daily counts of ED visits. To account for the uncertainty in both the estimation of the counterfactual time series of ambient pollutant levels and the estimation of the health model parameters, we simulated 5,000 sets of parameter estimates using a multivariate normal distribution based on the observed variance-covariance matrix, allowing for uncertainty at each step of the chain of accountability. Sensitivity tests were conducted to assess the robustness of the results. Results: EGU NOx and SO2 emissions in the Southeast decreased by 82% and 83%, respectively, between 1999 and 2013, while mobile-source emissions controls led to estimated decreases in Atlanta-area pollutant emissions of between 61% and 93%, depending on pollutant. While EGU emissions were measured, mobile-source emissions were modeled. Our results are supportive of a potential high bias in mobile-source NOx and CO emissions estimates. Air quality benefits from regulatory actions have increased as programs have been fully implemented and have had varying impacts over different seasons. In a scenario that accounted for all emissions reductions across the period, observed Atlanta central monitoring site maximum daily 8-hour (MDA8h) O3 was estimated to have been reduced by controls in the summertime and increased in the wintertime, with a change in mean annual MDA8h O3 from 39.7 ppb (counterfactual) to 38.4 ppb (observed). PM2.5 reductions were observed year-round, with average 2013 values at 8.9 μg/m3 (observed) versus 19.1 μg/m3 (counterfactual). Empirical and CMAQ analyses found that long-term meteorological trends across the Southeast over the period examined played little role in the distribution of species concentrations, while emissions changes explained the decreases observed. Aerosol pH, which plays a key role in aerosol formation and dynamics and may have health implications, was typically very low (on the order of 1-2, but sometimes much lower), with little trend over time despite the stringent SO2 controls and SO42- reductions. Using health models fit from 1999-2005, emissions reductions from all selected pollution-control policies led to an estimated 55,794 cardiorespiratory disease ED visits prevented (i.e., fewer observed ED visits than would have been expected under counterfactual scenarios) - 52,717 RD visits, of which 38,038 were for asthma, and 3,057 CVD visits, of which 2,104 were for CHF - among the residents of the 5-county area over the 1999-2013 period, an area with approximately 3.5 million people in 2013. During the final two years of the study (2012-2013), when pollution-control policies were most fully implemented and the associated benefits realized, these policies were estimated to prevent 5.9% of the RD ED visits that would have occurred in the absence of the policies (95% interval estimate: -0.4% to 12.3%); 16.5% of the asthma ED visits (95% interval estimate: 7.5% to 25.1%); 2.3% of the CVD ED visits (95% interval estimate: -1.8% to 6.2%); and -.6% of the CHF ED visits (95% interval estimate: 26.3% to 10.4%). Estimates of ED visits prevented were generally lower when using health models fit for the entire 1999-2013 study period. Sensitivity analyses were conducted to show the impact of the choice of parameterization of the health models and to assess alternative definitions of the study area. When impacts were assessed for separate policy interventions, policies affecting emissions from EGUs, especially the ARP and the NBP, appeared to have had the greatest effect on prevention of RD and asthma ED visits. Conclusions: This study demonstrates the effectiveness of regulations on improving air quality and health in the southeastern United States. It also demonstrates the complexities of accountability assessments as uncertainties are introduced in each step of the classic accountability process. While accounting for uncertainties in emissions, air quality-emissions relationships, and health models does lead to relatively large uncertainties in the estimated outcomes due to specific regulations, overall the benefits of regulations have been substantial.
... The 10-km model is analogous to the operational model used by the Meteorological Service of Canada for AQHI forecasting. The metrics are typical of those found in the literature to assess AQ model performance [66][67][68]; namely, normalized mean bias (NMB), root mean square error (RMSE) and the correlation coefficient (R). In looking at the NOx statistics, the model NMB was +5.4% and correlation coefficient, R = 0.64. ...
... Correlation coefficient scores were slightly worse with the higher spatial resolution model for NO2, NOx and Ox and slightly better for O3. Simon et al. (2012) reported RMSE values in the range, 15-20 ppbv, for hourly O3 comparisons in other model validation studies [68], which are higher than the RMSE values here. The SI section also includes a section evaluating the 2.5-km GEM-MACH-TEB model against the 10-km GEM-MACH model for all the hourly data at the 12 GTHA sites. ...
... Correlation coefficient scores were slightly worse with the higher spatial resolution model for NO2, NOx and Ox and slightly better for O3. Simon et al. (2012) reported RMSE values in the range, 15-20 ppbv, for hourly O3 comparisons in other model validation studies [68], which are higher than the RMSE values here. The SI section also includes a section evaluating the 2.5-km GEM-MACH-TEB model against the 10-km GEM-MACH model for all the hourly data at the 12 GTHA sites. ...
Article
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Surface-level ozone (O3) continues to be a significant health risk in the Greater Toronto Hamilton Area (GTHA) of Canada even though precursor emissions in the area have decreased significantly over the past two decades. In July 2015, Environment and Climate Change Canada (ECCC) led an intensive field study coincident with Toronto hosting the 2015 Pan American Games. During the field study, the daily 1-h maximum O3 standard (80 ppbv) was exceeded twice at a measurement site in North Toronto, once on July 12 and again on July 28. In this study, ECCC’s 2.5-km configuration of the Global Environmental Multi-scale (GEM) meteorological model was combined with the Modelling Air-quality and CHemistry (MACH) on-line atmospheric chemistry model and the Town Energy Balance (TEB) urban surface parameterization to create a new urban air quality modelling system. In general, the model results showed that the nested 2.5-km grid-spaced urban air quality model performed better in statistical scores compared to the piloting 10-km grid-spaced GEM-MACH model without TEB. Model analyses were performed with GEM-MACH-TEB for the two exceedance periods. The local meteorology for both cases consisted of light winds with the highest O3 predictions situated along lake-breeze fronts. For the July 28 case, O3 production sensitivity analysis along the trajectory of the lake-breeze circulation showed that the region of most efficient O3 production occurred in the updraft region of the lake-breeze front, as the precursors to O3 formation underwent vertical mixing. In this updraft region, the ozone production switches from volatile organic compound (VOC)-sensitive to NOx-sensitive, and the local net O3 production rate reaches a maximum. This transition in the chemical regime is a previously unidentified factor for why O3 surface-level mixing ratios maximize along the lake-breeze front. For the July 12 case, differences between the model and observed Lake Ontario water temperature and the strength of lake-breeze opposing wind flow play a role in differences in the timing of the lake-breeze, which impacts the predicted location of the O3 maximum north of Toronto.
... Here Mi and Oi are the i th model-observation concentration pair (here coupled in time and space), and N is the number concentration pairs (Simon et al. 2012). ...
... It can be seen that these statistics have been calculated for monitoring stations in Tasmania, Victoria and NSW. The plots include guidance lines which show acceptable (outer lines; biasasymptoting to ± 50%; error asymptoting to 70%) and aspirational (inner lines; bias-asymptoting to ± 30%, error asymptoting to 50%) goals for model performance (Dennis et al. 2010;Simon et al. 2012). ...
Technical Report
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In Victoria, Australia, there is a strong mandate to reduce fuel loads through prescribed burning. One of the challenges of this approach is that the meteorological conditions most suitable for burn management are also conducive to smoke build-up and the potential for population exposure to elevated concentrations of PM2.5. Given the generally narrow window of opportunity available each year to undertake the burning, land managers require access to robust smoke forecasting tools in order to minimise the air pollution exposure while addressing their program of burning. In response to this driver we have recently completed the development of a vertically-integrated air quality forecasting system for use by the Victorian Department of Environment, Land, Water and Planning (DELWP). The Smoke Emissions and Transport report documents the following. 1/ the field work and analysis undertaken to better characterise the combustion characteristics of Victorian forest fuels; 2/ the development of a three-tiered, cascading time-scale framework which provides progressive layers of intelligence for up to a 10–day outlook; 3/ the outcomes from applying the system to recent smoke exposure events; 4/ considerations for the on-going development of the system. Note that this report is hosted at the following site. https://www.ffm.vic.gov.au/research-and-publications/fire-research-and-adaptive-management-publications
... Here Mi and Oi are the i th model-observation concentration pair (here coupled in time and space), and N is the number concentration pairs (Simon et al. 2012). ...
... It can be seen that these statistics have been calculated for monitoring stations in Tasmania, Victoria and NSW. The plots include guidance lines which show acceptable (outer lines; biasasymptoting to ± 50%; error asymptoting to 70%) and aspirational (inner lines; bias-asymptoting to ± 30%, error asymptoting to 50%) goals for model performance (Dennis et al. 2010;Simon et al. 2012). ...
Technical Report
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This report documents the outcomes of the Schedule 9+10 Smoke Emissions Modelling and Smoke Transport Modelling projects (henceforth called ‘the project’). The project commenced in November 2012 with the aim of “Improving the Department of Environment, Land, Water and Planning (DELWP’s) capacity to model the spread and accumulation or dissipation of smoke for planned and unplanned fire events through improved smoke trajectory and accumulation or dissipation modelling”.
... 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. ...
Article
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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.
... Eulerian photochemical models implement mathematical algorithms to forecast air pollutant concentrations and deposition on local to continental scales. This model calculates the effects of emissions, transport, chemistry, particle physics and deposition to estimate concentrations of air pollutants in space and time(Simon, Baker, & Phillips, 2012).The GEOS-chem, CMAQ, TEAM, CAMx, REMSAD, CMAQ, WRF-chem and Aerosol Robotic Network (AERONET) are photochemical models that are able to provide daily PM 10 and PM 2.5 product based on Eulerian grid modelling techniques(Bey et al., 2001;Grell et al., 2005;Simon et al., 2012;Wang et al., 2012). A summary of GEOS-chem and WRF models follows.GEOS-chem is a global 3-D chemical transport model (CTM) for atmospheric composition which is determined by meteorological input from the Goddard EarthObserving System (GEOS) of the NASA Global Modelling Assimilation Office (GMAO). ...
... Eulerian photochemical models implement mathematical algorithms to forecast air pollutant concentrations and deposition on local to continental scales. This model calculates the effects of emissions, transport, chemistry, particle physics and deposition to estimate concentrations of air pollutants in space and time(Simon, Baker, & Phillips, 2012).The GEOS-chem, CMAQ, TEAM, CAMx, REMSAD, CMAQ, WRF-chem and Aerosol Robotic Network (AERONET) are photochemical models that are able to provide daily PM 10 and PM 2.5 product based on Eulerian grid modelling techniques(Bey et al., 2001;Grell et al., 2005;Simon et al., 2012;Wang et al., 2012). A summary of GEOS-chem and WRF models follows.GEOS-chem is a global 3-D chemical transport model (CTM) for atmospheric composition which is determined by meteorological input from the Goddard EarthObserving System (GEOS) of the NASA Global Modelling Assimilation Office (GMAO). ...
Thesis
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A stable power supply spurs economic growth and investment into the country and as such electricity is a crucial element in the greater national socio-economic framework – more so in an emerging/developing economy, such as South Africa. Since the development of electricity, pollution has been the challenge that today has caused electrical utility billions of dollars due to electrical flashover and outages. Overhead powerlines insulators may flashover at working voltage when the insulator surface is polluted and wet, thereby potentially causing outage and loss of infrastructure. There are various elements that threaten continuation of power supply; one of them is pollution that has settled on insulators. To ensure that the pollution on insulators do not cause major problems such as unplanned outages and loss of infrastructure insulator site pollution severity measurement are required during insulator design phase and infrastructure maintenance phase. During the design phase insulator pollution severity measurement are required to be taken every month for consecutive twenty four months cycle, as outlined on IEC/TS 60815-1:2008 standard. However, due to lack of resources and economical reasons, such measurements are not done according to the IEC standard, leading to great consequences of power outage and loss of infrastructure. This paper explores the use of geospatial science and technology to measure site pollution severity to be used during design phase and maintenance phase to schedule cleaning of insulators. The site pollution severity insulator measurement is determined by measuring equivalent salt deposit density (ESDD) and none soluble deposit density (NSDD). The study was carried out using atmospheric remote sensing science aerosol applications and geographic information science. The quantitative methodology will be used to correlate the historical measured site pollution severity and PM2.5 measurement obtained from satellite images. The study reveals that PM2.5 cannot be used as a measure of ESDD/NSDD, however it can be considered on power line maintenance scheduling. These results reveal that a lot of work needs to be done since they have been limited studies that have been previously conducted in the area of insulator and geospatial science.
... For instance, urban zones in complex terrain pose a great challenge to current meteorological modeling tools [9], and the low-wind, stable meteorological conditions that drive air pollution episodes are difficult to be represented in current models [10]. In a review of photochemical and PM 2.5 dispersion modeling applications in the US and Canada [11], the percentage of observed variance explained by the dispersion model (r 2 ) varies between 0.2 and 0.6 for hourly ozone and between 0.1 and 0.6 for PM 2.5 and its major species. These are interquartile ranges and, in some cases, the model performance is better. ...
Article
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Air pollution regulation requires knowing major sources on any given zone, setting specific controls, and assessing how health risks evolve in response to those controls. Receptor models (RM) can identify major sources: transport, industry, residential, etc. However, RM results are typically available for short term periods, and there is a paucity of RM results for developing countries. We propose to combine a cluster analysis (CA) of air pollution and meteorological measurements with a short-term RM analysis to estimate a long-term, hourly source apportionment of ambient PM2.5 and PM10. We have developed a proof of the concept for this proposed methodology in three case studies: a large metropolitan zone, a city with dominant residential wood burning (RWB) emissions, and a city in the middle of a desert region. We have found it feasible to identify the major sources in the CA results and obtain hourly time series of their contributions, effectively extending short-term RM results to the whole ambient monitoring period. This methodology adds value to existing ambient data. The hourly time series results would allow researchers to apportion health benefits associated with specific air pollution regulations, estimate source-specific trends, improve emission inventories, and conduct environmental justice studies, among several potential applications.
... The model's performance in reproducing the PM 2.5 and O 3 concentrations has been evaluated in Ding et al.(5,35) in comparison with ground-observed concentrations. Both the mean fractional biases (MFB; −14.2% for PM 2.5 and −11.1% for O 3 ) and the mean fractional errors (MFE, 21.6% for PM 2.5 and 17.0% for O 3 ) meet the US EPA recommended benchmark (MFB within ±60% and MFE within 75%)(36).The data were analyzed based on the 90th percentile of daily 8-h maxima for O 3 and annual mean of 24-h average for PM 2.5 , to be consistent with the index used in air quality standard in China. Following the method reported in earlier studies(5,37), the simulated baseline concentrations in 2015 were ...
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China is challenged with the simultaneous goals of improving air quality and mitigating climate change. The "Beautiful China" strategy , launched by the Chinese government in 2020, requires that all cities in China attain 35 μg/m 3 or below for annual mean concentration of PM 2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) by 2035. Meanwhile, China adopts a portfolio of low-carbon policies to meet its Nationally Determined Contribution (NDC) pledged in the Paris Agreement. Previous studies demonstrated the cobenefits to air pollution reduction from implementing low-carbon energy policies. Pathways for China to achieve dual targets of both air quality and CO 2 mitigation, however , have not been comprehensively explored. Here, we couple an integrated assessment model and an air quality model to evaluate air quality in China through 2035 under the NDC scenario and an alternative scenario (Co-Benefit Energy [CBE]) with enhanced low-carbon policies. Results indicate that some Chinese cities cannot meet the PM 2.5 target under the NDC scenario by 2035, even with the strictest end-of-pipe controls. Achieving the air quality target would require further reduction in emissions of multiple air pollutants by 6 to 32%, driving additional 22% reduction in CO 2 emissions relative to the NDC scenario. Results show that the in-cremental health benefit from improved air quality of CBE exceeds 8 times the additional costs of CO 2 mitigation, attributed particularly to the cost-effective reduction in household PM 2.5 exposure. The additional low-carbon energy polices required for China's air quality targets would lay an important foundation for its deep decarbonization aligned with the 2°C global temperature target.
... Such modeled variables can also be used as confounding factors in epidemiological studies. For the validation of the air quality model, Simon et al. (2012) reviewed the main statistical indicators used in the literature, and Emery et al. (2017) bring benchmark values for particulate matter for the indicators that they considered to be the most appropriate: mean normalized bias, mean normalized error, and correlation coefficient. ...
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The study of fine particles (PM2.5) and its relationship with health has not been much explored in Brazil. Only with Resolution CONAMA 491/2018 that PM2.5 was nationally considered a pollutant of interest, while the World Health Organization brings guidelines for its environmental concentration since 2006. PM2.5 monitoring in Brazil is still restricted to few Southeast municipalities. From Brazilian time series epidemiological studies that studied PM2.5 and its relationship with health, mainly due to respiratory causes, air quality modeling was mostly employed. This paper aims to survey epidemiological studies already carried out for PM2.5 in Brazil, discussing the use of monitored and modeled data for this purpose. The use of relative risks to estimate excess mortality and morbidity is also evidenced as a direct measure to quantify the benefits associated with air quality improvement, and an estimate for Brazilian municipalities is performed. Finally, the importance of well-designed emission control strategies is emphasized so that the health benefits of improving air quality are indeed significant.
... Various metrics including MB, ME, MNB and MNE (Emery et al., 2017) were employed to evaluate the models' performance of simulating MDA8 O 3 at the AQS sites within the LADCO-12 domain during the ozone season (Table 4). A cutoff of 60 ppb was applied to prevent MNB/MNE from being dominantly skewed by low concentrations and focus on high O 3 days that are more relevant to the NAAQS (Simon et al., 2012). ...
Article
To meet the requirements of the Clean Air Act's “good neighbor” provision, it is necessary to quantify the interstate transport of air pollution and determine if upwind states significantly contribute to noncompliance with the National Ambient Air Quality Standards (NAAQS) at downwind states. This study aims to estimate the contributions of ten southeastern states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) to ozone (O3), and identify significant associations with nonattainment or maintenance at monitors across the eastern United States (US) in 2017. Two widely used air quality models, the Comprehensive Air quality Model with extensions (CAMx) and the Community Multiscale Air Quality model (CMAQ), using different versions of the Carbon Bond (CB) chemical mechanism, were applied to predict 2017 O3 design values (DVFs) from 2011 O3 design values (DVCs) at the US Environmental Protection Agency's (EPA) Air Quality System (AQS) sites throughout the eastern US. Since 2017 design values are already published, the accuracy of the EPA-recommended modeling methodology for projecting current design values into the future only by changing the emissions was evaluated. The assumption that good model performance in the base year would result in accurate future year ozone projections was tested. In general, CAMx/CB6r2 simulated higher DVFs than CMAQ/CB05, with an average difference of 0.5 ppb. As a result, this study identified 24 sites in the eastern US as nonattainment (DVF ≥76 ppb) with CAMx/CB6r2 compared to only 16 sites with CMAQ/CB05. Using monitor cell relative response factors (RRFs) instead of 3 × 3 cell matrix maximum RRFs (the approach used in EPA's proposed Cross-State Air Pollution Rule (CSAPR) modeling) (EPA, 2015a), led to higher DVFs at some monitors and lower DVFs at others. Source apportionment analysis with the CAMx Anthropogenic Precursor Culpability Assessment (APCA) probing tool revealed that NOx emissions dominated over VOC emissions in contributing to O3 in downwind states. Contributions of total anthropogenic NOx emissions and contributions of Electric Generating Unit (EGU) NOx emissions from the southeastern states to O3 at nonattainment and maintenance sites were quantified separately using CAMx/APCA as well as CMAQ/zero-out simulations. Contributions obtained from CAMx/APCA were typically larger than the contributions obtained from CMAQ/zero-out. Therefore, a “significant contribution” determined by CAMx/APCA may be insignificant according to CMAQ/zero-out. “Significant contribution” was defined as an impact larger than 1% of the 2008 O3 NAAQS (i.e., ≥0.76 ppb). Alabama, Kentucky, Mississippi, Tennessee, Virginia and West Virginia were linked to 2, 3, 1, 1, 6 and 5 nonattainment sites with CAMx/APCA, while only Kentucky, Virginia and West Virginia were linked to 2, 3 and 2 nonattainment sites with CMAQ/zero-out. Additionally, for significant linkages, EGU NOx contributions were 10–55% of the total anthropogenic NOx contributions. The information produced in this study can be used by southeastern states to help develop “good neighbor” State Implementation Plans (SIPs).
... 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. ...
... These models of air quality analysis employ mathematical techniques to simulate physical and chemical processes occurring at the atmosphere (Castro e Apsley, 1997; Challa et al., 2009). Simon et al. (2012) reviewed the scientific literature between 2006 and March 2012, compiling and reviewing performance of several photochemical models using a wide array of statistical tools. During that period, CMAQmodeling was the researchers' preference, especially on studies regarding nitrates, ozone, PM 2,5 and sulfate. ...
... (9)), and correlation coefficient (r) (Eq. (10)), according to Simon et al. (2012) and Emery et al. (2017). ...
Article
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Emission inventories are one of the most critical inputs for the successful modeling of air quality. The performance of the modeling results is directly affected by the quality of atmospheric emission inventories. Consequently, the development of representative inventories is always required. Due to the lack of regional inventories in Brazil, this study aimed to investigate the use of the particulate matter (PM) emission estimation from the Brazilian top-down vehicle emission inventory (VEI) of 2012 for air quality modeling. Here, we focus on road vehicles since they are usually responsible for significant emissions of PM in urban areas. The total Brazilian emission of PM (63,000 t year−1) from vehicular sources was distributed into the urban areas of 5557 municipalities, with 1-km2 grid spacing, considering two approaches: (i) population and (ii) fleet of each city. A comparison with some local inventories is discussed. The inventory was compiled in the PREP-CHEM-SRC processor tool. One-month modeling (August 2015) was performed with WRF-Chem for the four metropolitan areas of Brazilian Southeast: Belo Horizonte (MABH), Great Vitória (MAGV), Rio de Janeiro (MARJ), and São Paulo (MASP). In addition, modeling with the Emission Database for Global Atmospheric Research (EDGAR) inventory was carried out to compare the results. Overall, EDGAR inventory obtained higher PM emissions than the VEI segregated by population and fleet, which is expected owing to considerations of additional sources of emission (e.g., industrial and residential). This higher emission of EDGAR resulted in higher PM10 and PM2.5 concentrations, overestimating the observations in MASP, while the proposed inventory well represented the ambient concentrations, obtaining better statistics indices. For the other three metropolitan areas, both EDGAR and the VEI inventories obtained consistent results. Therefore, the present work endorses the fact that vehicles are responsible for the more substantial contribution to PM emissions in the studied urban areas. Furthermore, the use of VEI can be representative for modeling air quality in the future.
... To assess the degree of agreement between the NASA/UAH insolation product and pyranometer measurements, some commonly used paired in space/time evaluation metrics are used. These include mean bias error (MBE), root-mean-square error (RMSE), and coefficient of determination (R 2 ) as detailed in Boylan and Russell (2006), Simon et al. (2012), and Ali and Abustan (2014). If the satellite-based insolation is denoted by M and pyranometer-measured insolation by O, then ...
Article
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Incident solar radiation at the Earth’s surface, also called surface insolation, plays an important role in the Earth system as it affects surface energy balance, weather, climate, water supply, biochemical emissions, photochemical reactions, etc. The University of Alabama in Huntsville (UAH) and the NASA Short-term Prediction Research and Transition Center (SPoRT) have been generating and archiving several products, including insolation, from the Geostationary Operational Environmental Satellite (GOES) Imager for over a decade. The NASA/UAH insolation product has been used in studies to improve air quality simulations, biogenic emission estimates, correcting surface energy balance, and for cloud assimilation, but has not been thoroughly evaluated. In this study, the NASA/UAH insolation product is compared to surface pyranometer measurements from the Surface Radiation Budget Network (SURFRAD) and the U.S. Climate Reference Network (USCRN) for a 12-month period from March 2013 to February 2014. The insolation product has normalized bias values within 6% of the mean observation, a root-mean square error between 6-16%, and correlation coefficients greater than 0.96 for hourly insolation estimates. It also shows better performance without the presence of clouds. However, erroneous estimates may be produced for persistent snow-covered surfaces. Further, this study attempts to demonstrate the use of such satellite-based product for model evaluation. The NASA/UAH insolation product is compared to the downward shortwave radiation from the Rapid Refresh version 1 (RAPv1), and successfully capture the overestimation tendency in surface energy input as mentioned in Benjamin et al. (2016). Finally, future plans for improving the retrieval algorithm and developing GOES-16 insolation product are discussed.
... The environmental chamber experiments should focus on improved studies of both simple and complex mixtures of VOCs so that the new studies will support both topdown and bottom approaches to mechanism development. Detailed evaluations should be performed after the mechanisms are implemented in models and several protocols have been published for this purpose (Astitha et al. 2017;Eder and Yu 2006;Emery et al. 2017;Simon, Baker, and Phillips 2012). ...
Article
An essential component of a three-dimensional air quality model is its gas-phase mechanism. We present an overview of the necessary atmospheric chemistry and a discussion of the types of mechanisms with some specific examples such as the Master Chemical Mechanism, the Carbon Bond, SAPRC and the Regional Atmospheric Chemistry Mechanism (RACM). The first versions of the Carbon Bond and SAPRC mechanisms were developed through a hierarchy of chemical species approach that relied heavily on chemical environmental chamber data. Now a new approach has been proposed where the first step is to develop a highly detailed explicit mechanism such as the Master Chemical Mechanism and the second step is to test the detailed explicit mechanism against laboratory and field data. Finally, the detailed mechanism is condensed for use in a three-dimensional air quality model. Here it is argued that the development of highly detailed explicit mechanisms is very valuable for research, but we suggest that combining the hierarchy of chemical species and the detailed explicit mechanism approaches would be better than either alone. Implication: Many gas-phase mechanisms are available for urban, regional and global air quality modeling. A “hierarchy of chemical species approach,” relying heavily on smog-chamber data was used for the development of the early series of mechanisms. Now the development of large, explicit master mechanisms that may be condensed is a significant, trend. However, a continuing problem with air quality mechanism development is due to the high complexity of atmospheric chemistry and the current availability of laboratory measurements. This problem requires a balance between completeness and speculation so that models maintain their utility for policymakers.
... We also employed correlation coefficients (R) and normalized mean bias (NMB) (Simon et al., 2012) to evaluate the performance of the model. They are defined as follows: ...
Article
Using 2025 as the target year, we quantitatively assessed the reduction potentials of emissions of primary pollutants (including CO, HC, NOx, PM2.5 and PM10) under different vehicle control policies and the impacts of vehicle emission control policies in the BTH region on the regional PM2.5 concentration in winter and the surface ozone (O3) concentration in summer. Comparing the different scenarios, we found that (1) vehicle control policies will bring significant reductions in the emissions of primary pollutants. Among the individual policies, upgrading new vehicle emission standards and fuel quality in Beijing, Tianjin, and Hebei will be the most effective policy, with emission reductions of primary pollutants of 26.3%-54.7%, 38.0%-70.3% and 46.0%-81.6% in 2025, respectively; (2) for PM2.5 in winter, the Combined Scenario (CS) will lead to a reduction of 0.5-3.9 μg m-3 (3.5%-11.6%) for the monthly average PM2.5 concentrations in most areas. The monthly nitrate and ammonium concentrations would reduce by 5.8% and 5.3%, respectively, in the whole BTH region, indicating that vehicle emission control policies may play an important role in the reduction of PM2.5 concentrations in winter, especially for nitrate aerosols; and (3) for O3 concentrations in summer, vehicle emission control policies will lead to significant decreases. Under the CS scenario, the maximum reduction of monthly average O3 concentrations in the summer is approximately 3.6 ppb (5.9%). Most areas in the BTH region have a decrease of 15 ppb (7.5%) in peak values compared to the base scenario. However, in some VOC-sensitive areas in the BTH region, such as the southern urban areas, significant reductions in NOx may lead to increases in ozone concentrations. Our results highlight that season- and location-specific vehicle emission control measures are needed to alleviate ambient PM2.5 and O3 pollution effectively in this region due to the complex meteorological conditions and atmospheric chemical reactions.
... Statistical metrics included mean bias (MB), root mean square error (RMSE), normalised mean bias (NMB), and index of agreement (IOA) (Tables S2 -S3 ). Their definitions are details in Simon et al. ( Simon et al., 2012 ). The simulated and observed values of meteorological parameters reached 0.55-0.99, ...
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Beijing-Tianjin-Hebei (BTH) region in China suffers frequent and heavy pollution from particulate matter with aerodynamic diameter ≤ 2.5 m (PM 2.5). Despite the extensive studies conducted in this region, there are insufficient data on the chemical composition, formation mechanism, potential sources, and regional transport contribution of PM 2.5 in Zhangjiakou, Hebei province. In this study, the chemical and spatiotemporal variation in PM 2.5 in Zhangjiakou were investigated at four urban sites in Zhangjiakou in 2019. The mean (± standard deviation, SD) observed PM 2.5 concentration was 43.0 ± 34.2 g/m 3. The concentrations of PM 2.5 and its major components were generally higher in the south than in the north, and in winter than in summer. The major chemical components were organic matter (OM, 31.8%), secondary inorganic ions (SIA, 25.7%), and mineral dust (14.7%). Higher NO 3 − /EC, SO 4 2 − /EC, sulphur oxidation ratios (SOR), and nitrogen oxidation ratios (NOR) were observed on pollution days, implying intense secondary production of nitrate during pollution periods. Heterogeneous reactions were important contributors to secondary sulphate formation. Secondary organic carbon (SOC) proportions (i.e., SOC/OC and SOC/EC ratios) decreased markedly from clean days to heavy pollution days in response to enhanced primary organic carbon (POC) emissions on pollution days. The source contributions varied among sites, seasons, and pollution levels according to positive matrix factorization (PMF) model; combustion and SIA sources were the key factors for air pollutant emission reduction. Small-scale and short-distance air mass transport from the northwestern and southern regions greatly impacted PM 2.5 pollution according to potential source analysis. Weather Research and Forecast model (WRF) was coupled with Comprehensive Air Quality Model with Extensions (CAM x), and found that regional transport contributed significantly to PM 2.5 pollution in Zhangjiakou (74.6%). The results of this study indicate that joint prevention and control of regional air pollution would meaningfully improve air quality in the BTH region.
... 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). ...
Article
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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.
... 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. ...
... 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). ...
Article
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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.
... 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). ...
Article
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.
... 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). ...
Article
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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.
... 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 . ...
Article
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.
... 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: ...
Article
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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. ...
Article
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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. ...
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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.
Article
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.
Article
Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.
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Three-dimensional chemical transport models are useful for spatial and temporal analysis of outdoor air quality. However, the suitability of boundary-layer parameterizations for air pollution modeling over deep, coastal valleys has seldom been tested. An evaluation of the Community Multiscale Air Quality (CMAQ) model performance for five planetary boundary-layer schemes (PBL) with the Weather Research and Forecasting (WRF) meteorological driver was conducted at 1-km horizontal resolution for fine particulate matter (PM2.5), sulfur dioxide (SO2) and nitrogen dioxide (NO2) over the Terrace-Kitimat valley of northwestern British Columbia, Canada. The top-ranked schemes were Mellor-Yamada-Nakanishi-Niino Level 3 (MYNN3) for PM2.5 and Mellor-Yamada-Janjic for NO2. Both schemes ranked high for absolute SO2 levels, but the MYJ and Asymmetric Convective Model, version 2 (ACM2) schemes qualitatively emulated peak summertime diurnal concentrations in the near field of elevated point sources. Greater nighttime SO2 concentrations with MYNN3 and Yonsei University PBL schemes, in less agreement with station monitoring 8 km downwind of emissions from tall stacks, suggested sustained pollutant mixing and downward transport within the nocturnal boundary layer. Consequently, for these two schemes with representations of nonlocal mass flux transfers between model layers, inland penetrations of pollutant plumes were farther than those of ACM2, MYJ, and University of Washington schemes. For NO2 and PM2.5 that mainly discharged passively from fugitive, ground-level sources, hence are less accurately quantified than SO2 emissions, the fully local MYJ, and semi-local MYNN3 PBL schemes more reasonably reproduced peak season concentrations than other schemes. It is concluded that for air pollution modeling in rugged, remote areas, the mode of pollutant emissions is important for the choice of a PBL scheme. PM2.5 was consistently underestimated by the various PBL schemes, and aspects for improving CMAQ simulations for a complex environment are discussed.
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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.
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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.
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Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were “perfect”. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.
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The San Joaquin Valley (SJV) of California has one of the nation's most severe wintertime PM2.5 pollution problems. The DISCOVER-AQ (Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality) field campaign took place in the SJV from January 16 to February 6, 2013. It captured two PM2.5 pollution episodes with peak 24-h concentrations approaching 70 μg/m3. Using meteorological fields generated from WRFv3.6, CMAQv5.0.2 was applied to simulate PM2.5 formation in the SJV from January 10 through February 10, 2013. Overall, the model was able to capture the observed accumulation of PM2.5 within the simulation period. The model was able to produce increased concentrations of ammonium nitrate and organic carbon, which are two major components of wintertime PM2.5 in the SJV. Comparison to measurements made by aircraft showed that there was general agreement between observed and modeled daytime vertical distributions of selected gas and particulate species, reflecting the adequacy of modeled daytime mixing layer heights. Excess ammonia predicted by the model implied that ammonium nitrate formation was limited by the availability of nitric acid, consistent with observations. Evaluation of the ammonium nitrate diurnal profile revealed that the observed morning increase of ammonium nitrate was also evident from the model. This paper demonstrates that the CMAQ model is able to simulate elevated wintertime PM2.5 formation observed in the SJV during the DISCOVER-AQ 2013 period, which featured both climatic (i.e., 2011–2014 California Drought) and emissions differences compared to a previous large air quality field campaign in the SJV during 1999–2000. Keywords: CMAQ, DISCOVER-AQ, PM2.5 modeling, San joaquin valley
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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.
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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.
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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.
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Albuquerque/Bernalillo County, New Mexico, is currently in attainment of the 2015 National Ambient Air Quality Standard (NAAQS) for ozone (70 ppb), but its ozone design values have increased in recent years. Air quality and source apportionment modeling with the Comprehensive Air Quality Model with Extensions (CAMx) was conducted for Albuquerque/Bernalillo County to develop a refined understanding of ozone source apportionment in the region, estimate ozone concentrations in the year 2025 based on projected changes in anthropogenic emissions, and evaluate the sensitivity of future ozone concentrations to various changes in local and non-local emissions. The study focused on two ozone episodes during June and July 2017 when 8-hr average ozone concentrations were greater than 70 ppb. Based on the modeling results, ozone during the June 2017 episode was found to be driven largely by contributions from non-local and regional emissions, whereas ozone during the July 2017 episode was driven more strongly by local emissions from within Albuquerque/Bernalillo County. On high ozone days, anthropogenic emissions from within Albuquerque/Bernalillo County contributed between 8% and 19% (6-14 ppb) of total ozone. Half of this local ozone contribution was from on-road mobile sources. Fire emissions contributed as much as 2 ppb of ozone on a given day. Contributions from large power plants in New Mexico were as large as 1 ppb on a given day but less than 0.5 ppb on most days. Modeled ozone concentrations in Albuquerque/Bernalillo County were also sensitive to emissions from oil and gas emissions in New Mexico. If projected emission reductions by 2025 materialize, these reductions could reduce future peak 8-hr average ozone concentrations by as much as 3-4% compared to 2017 values.
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A photochemical model platform for Hawaii, Puerto Rico, and Virgin Islands predicting O3, PM2.5, and regional haze would be useful to support assessments relevant for the National Ambient Air Quality Standards (NAAQS), Regional Haze Rule, and the Prevention of Significant Deterioration (PSD) program. These areas have not traditionally been modeled with photochemical transport models, but a reasonable representation of meteorology, emissions (natural and anthropogenic), chemistry, and deposition could support air quality management decisions in these areas. Here, a prognostic meteorological model (Weather Research and Forecasting) and photochemical transport (Community Multiscale Air Quality) model were applied for the entire year of 2016 at 27, 9, and 3 km grid resolution for areas covering the Hawaiian Islands and Puerto Rico/Virgin Islands. Model predictions were compared against surface and upper air meteorological and chemical measurements available in both areas. The vertical gradient of temperature, humidity, and winds in the troposphere was well represented. Surface layer meteorological model performance was spatially variable, but temperature tended to be underestimated in Hawaii. Chemically speciated daily average PM2.5 was generally well characterized by the modeling system at urban and rural monitors in Hawaii and Puerto Rico/Virgin Islands. Model performance was notably impacted by the wildfire emission methodology. Model performance was mixed for hourly SO2, NO2, PM2.5, and CO and was often related to how well local emissions sources were characterized. SO2 predictions were much lower than measurements at monitors near active volcanos on Hawaii, which was expected since volcanic emissions were not included in these model simulations. Further research is needed to assess emission inventory representation of these areas and how microscale meteorology influenced by the complex land-water and terrain interfaces impacts higher time resolution performance.
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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.
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Air quality management involves investigating areas where pollutant concentrations are above guideline or standard values to minimize its effect on human health. Particulate matter (PM) is one of the most studied pollutants, and its relationship with health has been widely outlined. To guide the construction and improvement of air quality policies, the impact of PM on the four Brazilian southeast metropolitan areas was investigated. One-year long modeling of PM10 and PM2.5 was performed with the WRF-Chem model for 2015 to quantify daily and annual PM concentrations in 102 cities. Avoidable mortality due to diverse causes and morbidity due to respiratory and circular system diseases were estimated concerning WHO guidelines, which was adopted in Brazil as a final standard to be reached in the future; although there is no deadline set for its implementation yet. Results showed satisfactory representation of meteorology and ambient PM concentrations. An overestimation in PM concentrations for some monitoring stations was observed, mainly in São Paulo metropolitan area. Cities around capitals with high modelled annual PM2.5 concentrations do not monitor this pollutant. The total avoidable deaths estimated for the region, related to PM2.5, were 32,000 ± 5,300 due to all-cause mortality, between 16,000 ± 2,100 and 51,000 ± 3,000 due non-accidental causes, between 7,300 ± 1,300 and 16,700 ± 1,500 due to cardiovascular disease, between 4,750 ± 900 and 10,950 ± 870 due ischemic heart diseases and 1,220 ± 330 avoidable deaths due to lung cancer. Avoidable respiratory hospitalizations were greater for PM2.5 among ‘children’ age group than for PM10 (all age group) except in São Paulo metropolitan area. For circulatory system diseases, 9,840 ± 3,950 avoidable hospitalizations in the elderly related to a decrease in PM2.5 concentrations were estimated. This study endorses that more restrictive air quality standards, human exposure, and health effects are essential factors to consider in urban air quality management.
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This work is the first of a two-part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble-based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS-Chem, WRF-Chem, and WRF-CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20-50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least-square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite-based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output.
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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).
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The aerosol direct feedback effects (ADFEs) are neglected in traditional air quality modeling studies (where meteorology is used as input and not affected by the chemistry and aerosol microphysics) for esti-mating the impacts of aircraft emissions on air quality. In this study, aircraft landing and take-off (LTO) attributable change of O3 and PM2.5 concentrations through ADFEs for the year 2005 within the contigu-ous United States (CONUS) are quantified by a coupled meteorology-chemistry modeling system: Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model. We first quantified the effects of ADFEs of all aerosols within the CONUS (not the effects of aircraft LTO emissions) on surface meteorology and air quality and found that ADFEs changed on average the downward short-wave radiation (SWR), 2-m temperature (T2), planetary boundary layer (PBL) height, O3 and PM2.5 by −7.38 W/m², −0.47 K, −20.72 m, −0.41 ppb and +0.28 μg/m³ in 2005. We also found a seasonal influence where ADFE-influenced change (decrease) of SWR, T2, PBL, O3 and change (increase) of PM2.5 were higher in summer than in winter. We found that the aircraft LTO emissions’ contribution to domain average surface concen-tration of O3 and PM2.5 were +0.0065 ppb and +0.0022 μg/m³ respectively when ADFEs are accounted for. The ADFEs decrease aircraft LTO attributable surface O3 and PM2.5 change by 21% and 23% respectively comparing with that without ADFE in 2005. We also found that in both without-and-with ADFE cases, the aircraft LTO emissions increases domain average of O3 from April to October and decreases from November to March showing a strong seasonal pattern. Our modeling study revealed that use of a coupled model with ADFE shows localized changes in air quality by aircraft LTO emissions across the domain which were masked when looking at domain averages for both O3 and PM2.5, and which may be important for accurately quantifying health risk due to air pollution exposures in densely populated areas.
Thesis
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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.
Preprint
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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 micrometres (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 models applications. The modelled trends result in a general significant downward trend for the three pollutants and, in comparison with observations, the values of the simulated slopes show the same magnitude for NO2 (in the range −3.0 ÷ −0.5 ug m−3 yr−1), while a smaller variability is detected for PM10 (−1.5 ÷ −0.5 ug m−3 yr−1) and O3-maximum daily 8-hour average concentration (−2.0 ÷ −0.5 ug m−3 yr−1). As a general result, we find a good agreement between modelled and observed trends; moreover, the model allowed to extend both the spatial coverage and the statistical significance of pollutants' concentrations 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 concentrations trends on emissions variations. We point out that, due to the complex links between precursors emissions and air pollutants 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.
Article
On-road emissions sources degrade air quality, and these sources have been highly regulated. Epidemiological and environmental justice studies often use road proximity as a proxy for traffic-related air pollution (TRAP) exposure, and other studies employ air quality models or satellite observations. To assess these metrics' abilities to reproduce observed near-road concentration gradients and changes over time, we apply a hierarchical linear regression to ground-based observations, long-term air quality model simulations using Community Multiscale Air Quality (CMAQ), and satellite products. Across 1980-2019, observed TRAP concentrations decreased, and road proximity was positively correlated with TRAP. For all pollutants, concentrations decreased fastest at locations with higher road proximity, resulting in "flatter" concentration fields in recent years. This flattening unfolded at a relatively constant rate for NO x , whereas the flattening of CO concentration fields has slowed. CMAQ largely captures observed spatial-temporal NO2 trends across 2002-2010 but overstates the relationships between CO and elemental carbon fine particulate matter (EC) road proximity. Satellite NO x measures overstate concentration reductions near roads. We show how this perspective provides evidence that California's on-road vehicle regulations led to substantial decreases in NO2, NO x , and EC in California, with other states that adopted California's light-duty automobile standards showing mixed benefits over states that did not adopt these standards.
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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.
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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.
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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 tends to be time-consuming, error-prone, inconsistent among model users and difficult to document while consuming increased computer storage space. 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).
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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[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.
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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.
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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.
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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.
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