Multiscale Air Quality Modeling System Research Articles

Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO<sub>2</sub> measurements

Abstract. The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO2) observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO2 are overestimated over the contiguous US (CONUS), with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO2. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO2 in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO2 biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO2. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO2, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted by around 100%.

Role of sectoral and multi-pollutant emission control strategies in improving atmospheric visibility in the Yangtze River Delta, China

The Community Multi-scale Air Quality modeling system is used to investigate the response of atmospheric visibility to the emission reduction from different sectors (i.e. industries, traffic and power plants) in the Yangtze River Delta, China. Visibility improvement from exclusive reduction of NOx or VOC emission was most inefficient. Sulfate and organic aerosol would rebound if NOx emission was exclusively reduced from any emission sector. The most efficient way to improve the atmospheric visibility was proven to be the multi-pollutant control strategies. Simultaneous emission reductions (20–50%) on NOx, VOC and PM from the industrial and mobile sectors could result in 0.3–1.0 km visibility improvement. And the emission controls on both NOx (85%) and SO2 (90%) from power plants gained the largest visibility improvement of up to 4.0 km among all the scenarios. The seasonal visibility improvement subject to emission controls was higher in summer while lower in the other seasons.

A comparison of atmospheric composition using the Carbon Bond and Regional Atmospheric Chemistry Mechanisms

Abstract. We incorporate the recently developed Regional Atmospheric Chemistry Mechanism (version 2, RACM2) into the Community Multiscale Air Quality modeling system for comparison with the existing 2005 Carbon Bond mechanism with updated toluene chemistry (CB05TU). Compared to CB05TU, RACM2 enhances the domain-wide monthly mean hydroxyl radical concentrations by 46% and nitric acid by 26%. However, it reduces hydrogen peroxide by 2%, peroxyacetic acid by 94%, methyl hydrogen peroxide by 19%, peroxyacetyl nitrate by 40%, and organic nitrate by 41%. RACM2 enhances ozone compared to CB05TU at all ambient levels. Although it exhibited greater overestimates at lower observed concentrations, it displayed an improved performance at higher observed concentrations. The RACM2 ozone predictions are also supported by increased ozone production efficiency that agrees better with observations. Compared to CB05TU, RACM2 enhances the domain-wide monthly mean sulfate by 10%, nitrate by 6%, ammonium by 10%, anthropogenic secondary organic aerosols by 42%, biogenic secondary organic aerosols by 5%, and in-cloud secondary organic aerosols by 7%. Increased inorganic and organic aerosols with RACM2 agree better with observed data. Any air pollution control strategies developed using the two mechanisms do not differ appreciably.

Premature mortality in Japan due to ozone

In Japan, all 47 prefectures conduct routine air quality monitoring at 1145 stations throughout the country to assess environmental effects. This study aims to provide a better understanding of possible estimations of premature mortality in Japan caused by exposure to monitored and modeled concentrations of tropospheric ozone during the period from January to December, 2005. The spatial distribution and temporal variation of ozone concentrations were modeled using the Models-3 Community Multiscale Air Quality modeling system coupled with the Regional Emission Inventory in Asia (CMAQ/REAS). Premature mortality caused by exposure to ozone was calculated assuming a relative risk (RR) value of 1.003 [95% Confidence Interval (CI): 1.001–1.004] for concentrations above 35 ppb according to the SOMO35 index (annual Sum of daily maximum 8-h Ozone Means Over 35 ppb) recommended by WHO (2008). Based on CMAQ/REAS simulations, the estimated all-cause premature mortality in 2005 is about 13,000 (95% CI: 4320–17,300) cases. This value is 2.5 times greater than the estimated premature mortality based on monitored ozone concentrations, which is 5220 (95% CI: 1740–6960) cases.

Comparison of Exposure Estimation Methods for Air Pollutants

Background and Aims: Ambient monitors are often used to estimate air pollution exposure for epidemiological studies. This approach is efficient and economical but has limitations, including restricted coverage and resolution. Although three-dimensional air quality modeling has its own limitations, this emerging exposure approach has the potential to address some challenges of monitoring networks. We evaluated differences in exposure estimates obtained using monitoring data and simulation results based on the Community Multi-scale Air Quality (CMAQ) modeling system. Methods: Spatially-aggregated (county-level) exposure estimates for PM2.5 were calculated for the eastern two-thirds of the U.S. for years 2002-2006 from three sources: (1) 24hr ambient monitoring data; (2) 12 x 12 km gridded CMAQ simulation results; and (3) concentration estimates from a Bayesian space-time downscaler model “fusing” PM2.5 monitoring data with 12-km gridded CMAQ output (i.e., downscaler-derived exposures). Results: Exposure estimates generated from CMAQ and the downscaler provide greater spatial and temporal coverage compared to estimates from monitor data. The CMAQ and CMAQ-downscaler exposure estimates included 2818 counties and the monitoring approach produced estimates for 190 counties, which contain ~51% of the population within the CMAQ domain. Exposure estimates derived from CMAQ results underestimated PM2.5 levels (normalized mean bias [NMB]=-4.0%), while downscaler-derived exposures slightly overestimated observed PM2.5 (NMB=1.0%). Across time, correlation between monitor-derived exposure estimates and CMAQ- and downscaler-derived exposure estimates were 0.62 and 0.97, respectively. Conclusions: The downscaler approach had superior performance based on several metrics. Epidemiology may benefit from use of regional air quality models, with improved spatial and temporal coverage and ability to study populations far from monitors that may differ from those near monitors.

Major Factors Influencing the Health Impacts from Controlling Air Pollutants with Nonlinear Chemistry: An Application to China

Predicting the human-health effects of reducing atmospheric emissions of nitrogen oxide (NOx) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM2.5) and ozone concentrations that result from control of NOx emissions alone and in conjunction with other pollutants in and outside the mega-city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration-response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NOx emission reductions, valued at $300-$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NOx emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.

Analysis of NO, NO2, and O3 Between Model Simulations and Ground-Based, Aircraft, and Satellite Observations

Regional air quality model simulations with Community Multiscale Air Quality Modeling System (CMAQ) were evaluated using multiple platforms for a 2-month summer period. In this context, inter-comparisons of the model with available satellite-based observations of NO2, ground- and aircraft-based observations of NO, NO2, NO x , NO y , and O3 were performed. NO2 comparisons found low biases in CMAQ results when using both ground- and satellite-based observations. Aircraft-based observations, on the other hand, indicated a higher positive bias and error, but the overall NO2 vertical profile was captured well by the model. The highest correlation was observed with satellite-based NO2 observations indicating that the model and satellite found similar spatial gradients. NO concentrations were underestimated in comparison with both ground- and aircraft-based observations, especially near the surface, indicating the limitations of the model to simulate primary pollutant concentrations at point observations when there are sources nearby. NO y comparisons found positive biases in the model when using both ground- and aircraft-based observations. The main reason for this overestimation was the consistent overestimation of peroxyacyl nitrates (PANs) in CMAQ results. Modeled O3 concentrations compared well with lowest biases and errors when compared to aircraft- and ground-based observations. The O3 vertical profile indicated a small positive bias in the model results near the surface similar to the comparison with ground-based observations. However, a negative bias in the model was observed above 2 km. Comparison with aircraft-based observations revealed significant overestimations in PAN and OH concentrations. Overestimation of the modeled OH concentrations is particularly important considering the effect of OH in atmospheric reactions.

Impact of a future H<sub>2</sub>-based road transportation sector on the composition and chemistry of the atmosphere – Part 1: Tropospheric composition and air quality

Abstract. Vehicles burning fossil fuel emit a number of substances that change the composition and chemistry of the atmosphere, and contribute to global air and water pollution and climate change. For example, nitrogen oxides and volatile organic compounds (VOCs) emitted as byproducts of fossil fuel combustion are key precursors to ground-level ozone and aerosol formation. In addition, on-road vehicles are major CO2 emitters. In order to tackle these problems, molecular hydrogen (H2) has been proposed as an energy carrier to substitute for fossil fuels in the future. However, before implementing any such strategy it is crucial to evaluate its potential impacts on air quality and climate. Here, we evaluate the impact of a future (2050) H2-based road transportation sector on tropospheric chemistry and air quality for several possible growth and technology adoption scenarios. The growth scenarios are based on the high and low emissions Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios, A1FI and B1, respectively. The technological adoption scenarios include H2 fuel cell and H2 internal combustion engine options. The impacts are evaluated with the Community Atmospheric Model Chemistry global chemistry transport model (CAM-Chem). Higher resolution simulations focusing on the contiguous United States are also carried out with the Community Multiscale Air Quality Modeling System (CMAQ) regional chemistry transport model. For all scenarios future air quality improves with the adoption of a H2-based road transportation sector; however, the magnitude and type of improvement depend on the scenario. Model results show that the adoption of H2 fuel cells would decrease tropospheric burdens of ozone (7%), CO (14%), NOx (16%), soot (17%), sulfate aerosol (4%), and ammonium nitrate aerosol (12%) in the A1FI scenario, and would decrease those of ozone (5%), CO (4%), NOx (11%), soot (7%), sulfate aerosol (4%), and ammonium nitrate aerosol (9%) in the B1 scenario. The adoption of H2 internal combustion engines would decrease tropospheric burdens of ozone (1%), CO (18%), soot (17%), and sulfate aerosol (3%) in the A1FI scenario, and would decrease those of ozone (1%), CO (7%), soot (7%), and sulfate aerosol (3%) in the B1 scenario. In the future, people residing in the contiguous United States could expect to experience significantly fewer days of elevated levels of pollution if a H2 fuel cell road transportation sector were to be adopted. Health benefits of transitioning to a H2 economy for citizens in developing nations, like China and India, will be much more dramatic, particularly in megacities with severe, intensifying air-quality problems.

Simulating smoke transport from wildland fires with a regional‐scale air quality model: Sensitivity to uncertain wind fields

AbstractUncertainties associated with meteorological inputs which are propagated through atmospheric chemical transport models may constrain their ability to replicate the effects of wildland fires on air quality. Here, we investigate the sensitivity of predicted fine particulate matter (PM2.5) levels to uncertain wind fields by simulating the air quality impacts of two fires on an urban area with the Community Multiscale Air Quality modeling system (CMAQ). Brute‐force sensitivity analyses show that modeled concentrations at receptors downwind from the fires are highly sensitive to variations in wind speed and direction. Additionally, uncertainty in wind fields produced with the Weather Research and Forecasting model was assessed by evaluating meteorological predictions against surface and upper air observations. Significant differences between predicted and observed wind fields were identified. Simulated PM2.5 concentrations at urban sites displayed large sensitivities to wind perturbations within the error range of meteorological inputs. The analyses demonstrate that normalized errors in CMAQ predictions attempting to model the regional impacts of fires on PM2.5 levels could be as high as 100% due to inaccuracies in wind data. Meteorological drivers may largely account for the considerable discrepancies between monitoring site observations and predicted concentrations. The results of this study demonstrate that limitations in fire‐related air quality simulations cannot be overcome by solely improving emission rates.

Development of a response surface model of aviation's air quality impacts in the United States

The air quality impacts of aviation are becoming increasingly important given their impact on human health and the projected growth of aviation. In the United States, the government has set targets to manage and reduce the environmental impacts of aviation. In an environmental policy assessment context it is often necessary to rapidly evaluate many possible scenarios, and quantification of uncertainty is important. This makes direct application of comprehensive air quality models such as the Community Multiscale Air Quality (CMAQ) modeling system impractical due to computational cost. Here we develop a response surface model (RSM) – a form of rapid surrogate model – of the impact of aviation emissions on air quality in the United States. We develop an RSM design space and populate it with results from 46 CMAQ simulations, and perform cross-validation of the resultant RSM. The RSM models present-day as well as future impacts amid changing population and non-aviation emissions sources. This enables rapid estimates of the (particulate matter) air quality and human health impacts of aviation emissions scenarios. Cross-couplings between precursor gaseous emissions and PM2.5 species are found, consistent with competition for atmospheric ammonia. We apply the RSM to quantify the human health benefits of emissions reductions in 2018. Using the RSM we estimate that in 2005, aviation landing and takeoff emissions cause ∼195 [90% CI: 80–340] early deaths, while the same emissions cause ∼350 [90% CI: 145–610] mortalities in 2018. An emissions tradespace between aviation NOx and SOx emissions is constructed. It is found that with fleet-wide desulfurization of jet fuel, a 35% reduction in aviation NOx emissions would result in maintaining the same level of aviation-attributable early deaths in 2018 relative to 2005 levels, while an 80% reduction in NOx emissions would half aviation-attributable early deaths.

Performance evaluation of atmospheric particulate matter modeling for East Asia

This study used the Community Multi-scale Air Quality modeling system associated with emission and meteorological datasets to conduct a one-year Particulate Matter (PM) simulation in East Asia. The performance of the PM simulation results was evaluated against observed data using high-spatial resolution monitoring data from Taiwan (e.g., concentration of PM and its compositions and precursors), Air Pollution Index-derived data in 82 cities of China, and satellite-derived surface PM2.5 concentrations. Based on error analysis of two indicators (overall bias and gross error), the validity of model performance is demonstrated. Daily comparison results at the various stations (cities) in 2007 indicate that the simulations at Taiwan's monitoring stations were superior to those in cities in China. Overall, the PM10 and PM2.5 concentrations in Taiwan were underestimated by 11 and 10%, respectively, whereas the simulated PM10 concentrations in six regions of China showed significant differences, with the simulation overall bias ranging between −55 and 52%. There are two primary reasons for the large differences in model performance between Taiwan and China. First, the grid resolution for China was coarser than that for Taiwan because of the limitation of emission data resolution in the Regional Emission Inventory in Asia (REAS). Second, the spatial distribution and magnitude of emissions in the REAS might be incorrect in some regions, particularly in the domain boundary areas of China. In addition to the relatively good emission database for Taiwan, the reasonable emission estimates for the central areas of China may be partially responsible for better model performance of PM in Taiwan.

Environmental effects of the recent emission changes in China: implications for particulate matter pollution and soil acidification

Driven by a rapid increase of energy consumption and emerging pollution control policies, air pollutant emissions have changed dramatically in China during 2005–2010. This study developed a multi-pollutant emission inventory, and used the community multi-scale air quality (CMAQ) modeling system to evaluate the impact of the emission changes on particulate matter pollution and soil acidification. During 2005–2010, the emissions of SO2, PM10 and PM2.5 decreased by 14.9%, 15.1% and 11.7%, respectively. In contrast, the emissions of NOX, NMVOC and NH3 increased by 33.8%, 21.0% and 10.4%, respectively. The emission trends differed notably in different regions. Driven by emission changes, PM2.5 concentrations decreased by 2–17 μg m−3 in most of the North China Plain, the Yangtze River Delta and the Pearl River Delta, while increasing by 4.5–16 μg m−3 in most of the Sichuan Basin and Eastern Hubei. The changes of PM2.5 emissions led to the decline of primary PM2.5 concentrations in most of Eastern China. As an effect solely of emission changes, nitrate concentrations increased across most of China; sulfate concentrations decreased in most of Eastern China, with the largest reduction in the North China Plain, while they increased in the Sichuan Basin and parts of the Pearl River Delta and Eastern Hubei. The concentrations of secondary inorganic aerosols (SIA) and the extinction coefficient increased in most of China, especially in the Sichuan Basin and Eastern Hubei, implying that the NOX and NH3 emissions should be reduced simultaneously in China. Combining the acidification effects of S and N, the exceedance of critical loads decreased across the country, but increased in the Sichuan Basin, the Pearl River Delta and Eastern Hubei, where the soil acidification was the most serious. Different control policies need to be implemented in different regions.

Impact of national NOx and SO2 control policies on particulate matter pollution in China

China's air pollution control policies during the 12th Five Year Plan (2011–2015) are characterized by the targets of 10% nitrogen oxides (NOx) reduction and 8% sulfur dioxide (SO2) reduction from the 2010 levels. In this study, the Community Multi-scale Air Quality (CMAQ) modeling system was used to evaluate the impact of only SO2, only NOx, and joint SO2/NOx control measures on particulate matter pollution, the greatest concern for urban air quality in China. Four emission scenarios were developed for 2015, including a business-as-usual scenario, a reference NOx control scenario based on the governmental plan, an accelerated NOx control technology scenario, and a scenario assuming joint controls of NOx and SO2 based on the governmental plan. Under the planned NOx control measures, the annual mean concentrations of particulate matter less than or equal to 2.5 μm (PM2.5) decline by 1.5–6 μg m−3, i.e. 1.6%–8.5%, in the majority of eastern China. The largest reduction occurs in the middle reach of the Yangtze River. Under accelerated NOx control measures, the annual average PM2.5 concentration reductions (compared with the business-as-usual scenario) in eastern China are 65% higher than the reductions under planned control measures. The unusual increase of PM2.5 concentrations in the North China Plain and the Yangtze River Delta during January after the reductions of NOx emissions was an integrated effect of excessive NOx, the ammonia-rich inorganic aerosol chemistry, and the non-methane volatile organic compounds (NMVOC) sensitive photochemical regime. Under the joint controls of NOx and SO2, the annual mean PM2.5 concentrations decline over 3 μg m−3, i.e. 3.2%–13%, in the majority of eastern China, and some areas in the middle reach of the Yangtze River have reductions as large as 6–8.3 μg m−3, i.e. 5.0%–13%. The average PM2.5 concentration reductions in eastern China are 1.20 μg m−3, 3.14 μg m−3, 3.57 μg m−3, 4.22 μg m−3 in January, May, August, and November, respectively. The corresponding declining rates are 2.3%, 12.2%, 14.3%, and 8.1%, respectively. More stringent policies should be implemented in winter to reduce the heavy pollution periods. The annual average PM2.5 concentration reductions in three major city clusters are comparable with the average reductions of eastern China. Stringent regional control policies are required for the significant improvement of particulate air quality in major city clusters.

Evaluation of the Effect of Surface Ozone on Main Crops in East Asia: 2000, 2005, and 2020

In this paper, the possible effect of surface ozone on soybean, wheat, rice, and maize crops in East Asia in 2000, 2005, and 2020 is estimated. Spatial distribution and temporal variation of surface ozone concentrations are simulated using the Models-3 Community Multiscale Air Quality Modeling System coupled with the Regional Emission Inventory in Asia (CMAQ/REAS). The effect of surface ozone on main crops in East Asia is evaluated based on accumulated exposure over a threshold of 40 ppb (AOT40 index) during a period of 3 months of the growing season. We demonstrate some of the implications for policy-making in air quality management for East Asia by highlighting the effect of elevated surface ozone concentrations on harvest losses and the corresponding value of the main crops. These concentrations are calculated based on three scenarios of emission reduction policies in 2020: policy success case (PSC), reference case (REF), and policy failure case (PFC). Assuming no future changes in land use or cropping patterns from 2000 to 2020, we find that the highest relative yield (RY) losses are in wheat and soybean in East Asia. The RY losses for wheat are estimated to range between 17 and 35 % in 2000, 21 and 49 % in 2005, 18 and 36 % in 2020 (PSC), 20 and 46 % in 2020 (REF), and 22 and 62 % in 2020 (PFC); the corresponding values for rice are 6 and 12 %, 6 and 17 %, 6 and 15 %, 6 and 17 %, and 7 and 20 %; for soybean, they are 12 and 16 %, 19 and 25 %, 18 and 33 %, 21 and 40 %, and 25 and 49 %; and for maize, they are 3 and 4 %, 5.7 and 6 %, 6 and 9 %, 9 and 11 %, and 12 and 14 %. Quantitatively, the estimated losses in production of wheat in East Asia in 2000, 2005, and 2020 (PSC, REF, and PFC scenarios) are 32.4, 44.3, 42.2, 54.0, and 72.3 t, respectively; for rice, 34.9, 39.4, 42.4, 46.5, and 54.6 mmt; for soybean, 1.9, 3.3, 3.6, 4.9, and 7.0 mmt; and for maize, 3.6, 8.1, 11.4, 15.4, and 21.5 mmt. The estimated values of crop losses in East Asia in 2000, 2005, and 2020 (PSC, REF, and PFC scenarios) are as follows: 13.8, 17.4, 18.2, 21.3, and 26.7 billion Int. $. Therefore, adaptation measures in the PSC scenario in contrast to the PFC scenario could save around 8.5 billion Int. $ across East Asian countries in 2020.

Understanding of the formation mechanisms of ozone and particulate matter at a fine scale over the southeastern U.S.: Process analyses and responses to future-year emissions

Ozone (O3) and fine particle (PM2.5) formation over the southeastern U.S. are of a major concern due to high emissions of precursors and special weather conditions that are conducive to their formation. In this study, the Community Multiscale Air Quality (CMAQ) modeling system is applied to simulate the formation of major air pollutants over an area in the southeastern U.S. at a 4-km horizontal grid resolution for January, April, July, and October in 2002 and 2018. Model performance evaluation shows an overall satisfactory performance for O3 in all months and for PM2.5 in January and October at rural sites and in January, April, and October at urban sites. Large underpredictions in PM2.5 concentrations occur in April and July at rural sites and in July at urban sites, because of biases in meteorological predictions and underestimation of emissions of precursors. The model performance at 4-km in terms of O3, PM2.5 and PM2.5 components show some improvements but overall are not always better than that at 12-km. O3 chemistry is VOC-limited in urban areas and NOx-limited over the west of the mountain regions and the southern Georgia throughout the year, and VOC-limited over the rest of areas in January but NOx-limited in other months. Among all photochemical indicators examined, PH2O2/PHNO3 and O3/NOy are the most robust indicators. The domain is NH3-rich or neutral in all months, indicating a high potential for NH4NO3 formation and the sensitivity of PM2.5 formation to the emissions of SO2, NOx, and NH3. Surface O3 is accumulated primarily through vertical transport in urban, rural and coastal areas and both horizontal and vertical transport in mountain regions and produced via gas-phase chemistry at non-urban sites during daytime. The loss of O3 is attributed to gas-phase chemistry via NO titration in urban areas, and dry deposition and transport processes in rural and mountain areas. PM2.5 is produced by primary emissions and PM processes and lost through vertical and horizontal transport in urban areas. The combined effects of transport, emissions, and PM processes influence PM concentrations in other areas. The 2018 simulations project a decrease in PM2.5 concentrations and an improvement in visibility over almost the entire domain, slight decreases in O3 mixing ratios in urban areas in July and most non-urban areas in April and October but large increases in the rest of areas in other months, and a decrease in total N deposition fluxes in most areas except for central and eastern North Carolina and northern Georgia. The development of integrated emission control strategies should consider region-specific seasonality and differences in the responses of O3, PM2.5, visibility, and nitrogen deposition.

Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM2.5 sulfate (SO42-), nitrate (NO3−) and ammonium, and gaseous nitric acid (HNO3) and ammonia (NH3). The model approximately reproduced PM2.5 SO42− concentration, but clearly overestimated PM2.5 NO3− concentration, which was attributed to overestimation of production of ammonium nitrate (NH4NO3). This study conducted sensitivity analyses of factors associated with the model performance for PM2.5 NO3− concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH3 emission, HNO3 and NH3 dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH3 emission directly affected NH3 concentration, and substantially affected NH4NO3 concentration. Higher dry deposition velocities of HNO3 and NH3 led to substantial reductions of concentrations of the gaseous species and NH4NO3. Because uncertainties in NH3 emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM2.5 NO3−.Implications: The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.

Public health impacts of secondary particulate formation from aromatic hydrocarbons in gasoline

BackgroundAromatic hydrocarbons emitted from gasoline-powered vehicles contribute to the formation of secondary organic aerosol (SOA), which increases the atmospheric mass concentration of fine particles (PM2.5). Here we estimate the public health burden associated with exposures to the subset of PM2.5 that originates from vehicle emissions of aromatics under business as usual conditions.MethodsThe PM2.5 contribution from gasoline aromatics is estimated using the Community Multiscale Air Quality (CMAQ) modeling system and the results are compared to ambient measurements from the literature. Marginal PM2.5 annualized concentration changes are used to calculate premature mortalities using concentration-response functions, with a value of mortality reduction approach used to monetize the social cost of mortality impacts. Morbidity impacts are qualitatively discussed.ResultsModeled aromatic SOA concentrations from CMAQ fall short of ambient measurements by approximately a factor of two nationwide, with strong regional differences. After accounting for this model bias, the estimated public health impacts from exposure to PM2.5 originating from aromatic hydrocarbons in gasoline lead to a central estimate of approximately 3800 predicted premature mortalities nationwide, with estimates ranging from 1800 to over 4700 depending on the specific concentration-response function used. These impacts are associated with total social costs of $28.2B, and range from $13.6B to $34.9B in 2006$.ConclusionsThese preliminary quantitative estimates indicate particulates from vehicular emissions of aromatic hydrocarbons demonstrate a nontrivial public health burden. The results provide a baseline from which to evaluate potential public health impacts of changes in gasoline composition.

Characterization of coarse particulate matter in the western United States: a comparison between observation and modeling

Abstract. We provide a regional characterization of coarse particulate matter (PM10–2.5) spanning the western United States based on the analysis of measurements from 50 sites reported in the US EPA Air Quality System (AQS) and two state agencies. We found that the observed PM10–2.5 concentrations show significant spatial variability and distinct spatial patterns, associated with the distributions of land use/land cover and soil moisture. The highest concentrations were observed in the southwestern US, where sparse vegetation, shrublands or barren lands dominate with lower soil moistures, whereas the lowest concentrations were observed in areas dominated by grasslands, forest, or croplands with higher surface soil moistures. The observed PM10–2.5 concentrations also show variable seasonal, weekly, and diurnal patterns, indicating a variety of sources and their relative importance at different locations. The observed results were compared to modeled PM10–2.5 concentrations from an annual simulation using the Community Multiscale Air Quality modeling system (CMAQ) that has been designed for regulatory or policy assessments of a variety of pollutants including PM10, which consists of PM10–2.5 and fine particulate matter (PM2.5). The model under-predicts PM10–2.5 observations at 49 of 50 sites, among which 14 sites have annual observation means that are at least five times greater than model means. Model results also fail to reproduce their spatial patterns. Important sources (e.g. pollen, bacteria, fungal spores, and geogenic dust) were not included in the emission inventory used and/or the applied emissions were greatly under-estimated. Unlike the observed patterns that are more complex, modeled PM10–2.5 concentrations show the similar seasonal, weekly, and diurnal pattern; the temporal allocations in the modeling system need improvement. CMAQ does not include organic materials in PM10–2.5; however, speciation measurements show that organics constitute a significant component. The results improve our understanding of sources and behavior of PM10–2.5 and suggest avenues for future improvements to models that simulate PM10–2.5 emissions, transport and fate.

The NASA Lightning Nitrogen Oxides Model (LNOM): Application to air quality modeling

Recent improvements to the NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) and its application to the Community Multiscale Air Quality (CMAQ) modeling system are discussed. The LNOM analyzes Lightning Mapping Array (LMA) and National Lightning Detection Network™ (NLDN) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning NOx (=NO+NO2) production. The latest LNOM estimates of mean lightning channel length, the lightning 10-m segment altitude distribution, and the vertical profile of lightning NOx production are obtained. The primary improvement to the LNOM is the inclusion of non-return stroke lightning NOx production due to: hot core stepped and dart leaders, stepped leader corona sheath, K-changes, continuing currents, and M-components. The impact of including LNOM-estimates of lightning NOx for an August 2006 run of CMAQ is discussed. An estimate of global annual lightning NOx production is also provided using the NASA satellite global lightning climatology.

The effect of ground level ozone on vegetation: the case of spatial variability of crops in the People's Republic of China

This paper aims to show how spatial variability of crops in China could reduce the effect of ozone on crops. The spatiotemporal variations of ozone are simulated using Models-3 Community Multiscale Air Quality modelling system coupled with the Regional Emission Inventory in Asia (CMAQ/REAS). The ozone effect on crops is evaluated based on the AOT40 index (accumulated exposure over threshold of 40 ppb). We find that wheat and soybeans are more sensitive to ozone compared to rice and maize. Assuming no change in field management practices we find that in 2000 there are losses of approximately 30.5%, 10.4%, 2.8%, and 19.0% of total yields for wheat, rice, maize, and soybeans, respectively. Similarly, in 2005 the losses are approximately 47.2%, 12.5%, 5.6%, and 27.1%.