Mimicking atmospheric photochemical modelling with a deep neural network.

Fast and accurate prediction of ambient ozone (O3) formed from atmospheric photochemical processes is crucial for designing effective O3 pollution control strategies in the context of climate change. The chemical transport model (CTM) is the fundamental tool for O3 prediction and policy design, however, existing CTM-based approaches are computationally expensive, and resource burdens limit their usage and effectiveness in air quality management. Here we proposed a novel method (noted as DeepCTM) that using deep learning to mimic CTM simulations to improve the computational efficiency of photochemical modeling. The well-trained DeepCTM successfully reproduces CTM-simulated O3 concentration using input features of precursor emissions, meteorological factors, and initial conditions. The advantage of the DeepCTM is its high efficiency in identifying the dominant contributors to O3 formation and quantifying the O3 response to variations in emissions and meteorology. The emission-meteorology-concentration linkages implied by the DeepCTM are consistent with known mechanisms of atmospheric chemistry, indicating that the DeepCTM is also scientifically reasonable. The DeepCTM application in China suggests that O3 concentrations are strongly influenced by the initialized O3 concentration, as well as emission and meteorological factors during daytime when O3 is formed photochemically. The variation of meteorological factors such as short-wave radiation can also significantly modulate the O3 chemistry. The DeepCTM developed in this study exhibits great potential for efficiently representing the complex atmospheric system and can provide policymakers with urgently needed information for designing effective control strategies to mitigate O3 pollution.

Examining PM2.5 concentrations and exposure using multiple models.

Epidemiologic studies have found associations between fine particulate matter (PM2.5) exposure and adverse health effects using exposure models that incorporate monitoring data and other relevant information. Here, we use nine PM2.5 concentration models (i.e., exposure models) that span a wide range of methods to investigate i) PM2.5 concentrations in 2011, ii) potential changes in PM2.5 concentrations between 2011 and 2028 due to on-the-books regulations, and iii) PM2.5 exposure for the U.S. population and four racial/ethnic groups. The exposure models included two geophysical chemical transport models (CTMs), two interpolation methods, a satellite-derived aerosol optical depth-based method, a Bayesian statistical regression model, and three data-rich machine learning methods. We focused on annual predictions that were regridded to 12-km resolution over the conterminous U.S., but also considered 1-km predictions in sensitivity analyses. The exposure models predicted broadly consistent PM2.5 concentrations, with relatively high concentrations on average over the eastern U.S. and greater variability in the western U.S. However, differences in national concentration distributions (median standard deviation: 1.00 µg m-3) and spatial distributions over urban areas were evident. Further exploration of these differences and their implications for specific applications would be valuable. PM2.5 concentrations were estimated to decrease by about 1 µg m-3 on average due to modeled emission changes between 2011 and 2028, with decreases of more than 3 µg m-3 in areas with relatively high 2011 concentrations that were projected to experience relatively large emission reductions. Agreement among models was closer for population-weighted than uniformly weighted averages across the domain. About 50% of the population was estimated to experience PM2.5 concentrations less than 10 µg m-3 in 2011 and PM2.5 improvements of about 2 µg m-3 due to modeled emission changes between 2011 and 2028. Two inequality metrics were used to characterize differences in exposure among the four racial/ethnic groups. The metrics generally yielded consistent information and suggest that the modeled emission reductions between 2011 and 2028 would reduce absolute exposure inequality on average.

Deep Learning for Prediction of the Air Quality Response to Emission Changes.

Efficient prediction of the air quality response to emission changes is a prerequisite for an integrated assessment system in developing effective control policies. Yet, representing the nonlinear response of air quality to emission controls with accuracy remains a major barrier in air quality-related decision making. Here, we demonstrate a novel method that combines deep learning approaches with chemical indicators of pollutant formation to quickly estimate the coefficients of air quality response functions using ambient concentrations of 18 chemical indicators simulated with a comprehensive atmospheric chemical transport model (CTM). By requiring only two CTM simulations for model application, the new method significantly enhances the computational efficiency compared to existing methods that achieve lower accuracy despite requiring 20+ CTM simulations (the benchmark statistical model). Our results demonstrate the utility of deep learning approaches for capturing the nonlinearity of atmospheric chemistry and physics and the prospects of the new method to support effective policymaking in other environment systems.

Estimation of abatement potentials and costs of air pollution emissions in China.

Understanding the air pollution emission abatement potential and associated control cost is a prerequisite to design cost efficient control policies. In this study, a linear programming algorithm model, International Control Cost Estimate Tool, was updated with cost data for applications of 56 types of end-of-pipe technologies and five types of renewable energy in 10 major sectors namely power generation, industry combustion, cement production, iron and steel production, other industry processes, domestic combustion, transportation, solvent use, livestock rearing, and fertilizer use. The updated model was implemented to estimate the abatement potential and marginal cost of multiple pollutants in China. The total maximum abatement potentials of sulfur dioxide (SO2), nitrogen oxides (NOx), primary particulate matter (PM2.5), non-volatile organic compounds (NMVOCs), and ammonia (NH3) in China were estimated to be 19.2, 20.8, 9.1, 17.2 and 8.6 Mt, respectively, which accounted for 89.7%, 89.9%, 94.6%, 74.0%, and 80.2% of their total emissions in 2014, respectively. The associated control cost of such reductions was estimated as 92.5, 469.7, 75.7, 449.0, and 361.8 billion CNY in SO2, NOx, primary PM2.5, NMVOCs and NH3, respectively. Shandong, Jiangsu, Henan, Zhejiang, and Guangdong provinces exhibited large abatement potentials for all pollutants. Provincial disparity analysis shows that high GDP regions tend to have higher reduction potential and total abatement costs. End-of-pipe technologies tended be a cost-efficient way to control pollution in industries processes (i.e., cement plants, iron and steel plants, lime production, building ceramic production, glass and brick production), whereas such technologies were less cost-effective in fossil fuel-related sectors (i.e., power plants, industry combustion, domestic combustion, and transportation) compared with renewable energy. The abatement potentials and marginal abatement cost curves developed in this study can further be used as a crucial component in an integrated model to design optimized cost-efficient control policies.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

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.

Least-cost control strategy optimization for air quality attainment of Beijing-Tianjin-Hebei region in China.

Control strategies can be optimized to attain air quality standards at minimal cost through selecting optimal combinations of controls on various pollutants and regional sources. In this study, we developed a module for least-cost control strategy optimization based on a real-time prediction system of the responses of pollution concentrations to emissions changes and marginal cost curves of pollutant controls. Different from other method, in this study the relationship between pollution concentrations to and precursor emissions was derived from multiple air quality simulations in which the nonlinear interactions among different precursor emissions can be well addressed. Hypothetical control pathways were designed to attain certain air quality goals for particulate matter (PM2.5) and ozone (O3) in the Beijing-Tianjin-Hebei region under the 2014 baseline emission level. Results suggest that reducing local primary PM emissions was the most cost-efficient method to attain the ambient PM2.5 standard, whereas for O3 attainment, reducing regional emission sources of gaseous pollutants (i.e., SO2, NOx, and volatile organic compounds (VOCs)) exhibited greater effectiveness. NH3 controls may be cost-efficient in achieving strengthened PM2.5 targets; however, they might not help in reducing O3. To achieve both PM2.5 (<35 µg m-3) and O3 (daily 1-h maxima concentration < 100 ppb) targets in Beijing, the reduced rates in BTH regions of NOx, SO2, NH3, VOCs and primary PM are 75%, 75%, 5%, 55%, and 85%, respectively from the emission levels in the year of 2014. Local reduction is the most effective method of attaining moderate PM2.5 and O3 targets; however, to achieve more aggressive air quality goals, the same level of reductions must be conducted across the whole Beijing-Tianjin-Hebei region.

Effects of air pollution control measures on air quality improvement in Guangzhou, China.

The ambient air quality of Guangzhou in 2016 has significantly improved since Guangzhou and its surrounding cities implemented a series of air pollution control measures from 2014 to 2016. This study not only estimated the effects of meteorology and emission control measures on air quality improvement in Guangzhou but also assessed the contributions of emissions reduction from various sources through the combination of observation data and simulation results from Weather Research and Forecasting - Community Multiscale Air Quality (WRF-CMAQ) modeling system. Results showed that the favorable meteorological conditions in 2016 alleviated the air pollution. Compared to change in meteorology, implementing emission control measures in Guangzhou and surrounding cities was more beneficial for air quality improvement, and it could reduce the concentrations of SO2, NO2, PM2.5, PM10, and O3 by 9.7 µg m-3 (48.4%), 9.2 µg m-3 (17.7%), 7.7 µg m-3 (14.6%), 9.7 µg m-3 (13.4%), and 12.0 µg m-3 (7.7%), respectively. Furthermore, emission control measures that implemented in Guangzhou contributed most to the concentration reduction of SO2, NO2, PM2.5, and PM10 (46.0% for SO2, 15.2% for NO2, 9.4% for PM2.5, and 9.1% for PM10), and it increased O3 concentration by 2.4%. With respect to the individual contributions of source emissions reduction, power sector emissions reduction showed the greatest contribution in reducing the concentrations of SO2, NO2, PM2.5, and PM10 due to the implementation of Ultra-Clean control technology. As for O3 mitigation, VOCs product-related source emissions reduction was most effective, and followed by transportation source emissions reduction, while the reductions of power sector, industrial boiler, and industrial process source might not be as effective. Our findings provide scientific advice for the Guangzhou government to formulate air pollution prevention and control policies in the future.

Health benefit assessment of PM2.5 reduction in Pearl River Delta region of China using a model-monitor data fusion approach.

The Pearl River Delta (PRD), one of the most polluted and populous regions of China, experienced a 28% reduction in fine particulate matter (PM2.5) concentration between 2013 (47 µg/m3) and 2015 (34 µg/m3) under a stringent national policy known as the Air Pollution Prevention and Control Action Plan (hereafter Action Plan). In this study, the health and economic benefits associated with PM2.5 reductions in PRD during 2013-2015 were estimated using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) software. To create reliable gridded PM2.5 surfaces for BenMAP-CE calculations, a data fusion tool which incorporates the accuracy of monitoring data and the spatial coverage of predictions from the Community Multiscale Air Quality (CMAQ) model has been developed. The population-weighted average PM2.5 concentration over PRD was predicted to decline by 24%. PM2.5-related mortality was estimated to decrease by more than 3800 due to decreases in stroke (48%), ischemic heart disease (IHD) (35%), chronic obstructive pulmonary disease (COPD) (10%), and lung cancer (LC) (7%). A 13% reduction in PM2.5-related premature deaths from these four causes yielded a large economic benefit of about 1300 million US dollars. Our research suggests that the Action Plan played a major role in reducing emissions and additional measures should be implemented to further reduce PM2.5 pollution and protect public health in the future.

The Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE): A tool to estimate the health and economic benefits of reducing air pollution.

A number of software tools exist to estimate the health and economic impacts associated with air quality changes. Over the past 15 years, the U.S. Environmental Protection Agency and its partners invested substantial time and resources in developing the Environmental Benefits Mapping and Analysis Program - Community Edition (BenMAP-CE). BenMAP-CE is a publicly available, PC-based open source software program that can be configured to conduct health impact assessments to inform air quality policies anywhere in the world. The developers coded the platform in C# and made the source code available in GitHub, with the goal of building a collaborative relationship with programmers with expertise in other environmental modeling programs. The team recently improved the BenMAP-CE user experience and incorporated new features, while also building a cadre of analysts and BenMAP-CE training instructors in Latin America and Southeast Asia.

Quantifying Nonlinear Multiregional Contributions to Ozone and Fine Particles Using an Updated Response Surface Modeling Technique.

Tropospheric ozone (O3) and fine particles (PM2.5) come from both local and regional emissions sources. Due to the nonlinearity in the response of O3 and PM2.5 to their precursors, contributions from multiregional sources are challenging to quantify. Here we developed an updated extended response surface modeling technique (ERSMv2.0) to address this challenge. Multiregional contributions were estimated as the sum of three components: (1) the impacts of local chemistry on the formation of the pollutant associated with the change in its precursor levels at the receptor region; (2) regional transport of the pollutant from the source region to the receptor region; and (3) interregional effects among multiple regions, representing the impacts on the contribution from one source region by other source regions. Three components were quantified individually in the case study of Beijing-Tianjin-Hebei using the ERSMv2.0 model. For PM2.5 in most cases, the contribution from local chemistry (i.e., component 1) is greater than the contribution from regional transport (i.e., component 2). However, regional transport is more important for O3. For both O3 and PM2.5, the contribution from regional sources increases during high-pollution episodes, suggesting the importance of joint controls on regional sources for reducing the heavy air pollution.

Response surface modeling-based source contribution analysis and VOC emission control policy assessment in a typical ozone-polluted urban Shunde, China.

To develop a sound ozone (O3) pollution control strategy, it is important to well understand and characterize the source contribution due to the complex chemical and physical formation processes of O3. Using the "Shunde" city as a pilot summer case study, we apply an innovative response surface modeling (RSM) methodology based on the Community Multi-Scale Air Quality (CMAQ) modeling simulations to identify the O3 regime and provide dynamic analysis of the precursor contributions to effectively assess the O3 impacts of volatile organic compound (VOC) control strategy. Our results show that Shunde is a typical VOC-limited urban O3 polluted city. The "Jiangmen" city, as the main upper wind area during July 2014, its VOCs and nitrogen oxides (NOx) emissions make up the largest contribution (9.06%). On the contrary, the contribution from local (Shunde) emission is lowest (6.35%) among the seven neighbor regions. The local VOCs industrial source emission has the largest contribution comparing to other precursor emission sectors in Shunde. The results of dynamic source contribution analysis further show that the local NOx control could slightly increase the ground O3 under low (10.00%) and medium (40.00%) reduction ratios, while it could start to turn positive to decrease ground O3 under the high NOx abatement ratio (75.00%). The real-time assessment of O3 impacts from VOCs control strategies in Pearl River Delta (PRD) shows that the joint regional VOCs emission control policy will effectively reduce the ground O3 concentration in Shunde.

Evaluation of health benefit using BenMAP-CE with an integrated scheme of model and monitor data during Guangzhou Asian Games.

Guangzhou is the capital and largest city (land area: 7287 km(2)) of Guangdong province in South China. The air quality in Guangzhou typically worsens in November due to unfavorable meteorological conditions for pollutant dispersion. During the Guangzhou Asian Games in November 2010, the Guangzhou government carried out a number of emission control measures that significantly improved the air quality. In this paper, we estimated the acute health outcome changes related to the air quality improvement during the 2010 Guangzhou Asian Games using a next-generation, fully-integrated assessment system for air quality and health benefits. This advanced system generates air quality data by fusing model and monitoring data instead of using monitoring data alone, which provides more reliable results. The air quality estimates retain the spatial distribution of model results while calibrating the value with observations. The results show that the mean PM2.5 concentration in November 2010 decreased by 3.5 µg/m(3) compared to that in 2009 due to the emission control measures. From the analysis, we estimate that the air quality improvement avoided 106 premature deaths, 1869 cases of hospital admission, and 20,026 cases of outpatient visits. The overall cost benefit of the improved air quality is estimated to be 165 million CNY, with the avoided premature death contributing 90% of this figure. The research demonstrates that BenMAP-CE is capable of assessing the health and cost benefits of air pollution control for sound policy making.

A case study of development and application of a streamlined control and response modeling system for PM2.5 attainment assessment in China.

This article describes the development and application of a streamlined air control and response modeling system with a novel response surface modeling-linear coupled fitting method and a new module to provide streamlined model data for PM2.5 attainment assessment in China. This method is capable of significantly reducing the dimensions required to establish a response surface model, as well as capturing more realistic response of PM2.5 to emission changes with a limited number of model simulations. The newly developed module establishes a data link between the system and the Software for Model Attainment Test-Community Edition (SMAT-CE), and has the ability to rapidly provide model responses to emission control scenarios for SMAT-CE using a simple interface. The performance of this streamlined system is demonstrated through a case study of the Yangtze River Delta (YRD) in China. Our results show that this system is capable of reproducing the Community Multi-Scale Air Quality (CMAQ) model simulation results with maximum mean normalized error<3.5%. It is also demonstrated that primary emissions make a major contribution to ambient levels of PM2.5 in January and August (e.g., more than 50% contributed by primary emissions in Shanghai), and Shanghai needs to have regional emission control both locally and in its neighboring provinces to meet China's annual PM2.5 National Ambient Air Quality Standard. The streamlined system provides a real-time control/response assessment to identify the contributions of major emission sources to ambient PM2.5 (and potentially O3 as well) and streamline air quality data for SMAT-CE to perform attainment assessments.

Design and demonstration of a next-generation air quality attainment assessment system for PM2.5 and O3.

Due to the increasingly stringent standards, it is important to assess whether the proposed emission reduction will result in ambient concentrations that meet the standards. The Software for Model Attainment Test-Community Edition (SMAT-CE) is developed for demonstrating attainment of air quality standards of O3 and PM2.5. SMAT-CE improves computational efficiency and provides a number of advanced visualization and analytical functionalities on an integrated GIS platform. SMAT-CE incorporates historical measurements of air quality parameters and simulated air pollutant concentrations under a number of emission inventory scenarios to project the level of compliance to air quality standards in a targeted future year. An application case study of the software based on the U.S. National Ambient Air Quality Standards (NAAQS) shows that SMAT-CE is capable of demonstrating the air quality attainment of annual PM2.5 and 8-hour O3 for a proposed emission control policy.

Development and case study of a science-based software platform to support policy making on air quality.

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling (RSM) methodology and serves as a visualization and analysis tool (VAT) for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S. demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias <2% and assisting in air quality policy making in near real time.

Effectiveness of national air pollution control policies on the air quality in metropolitan areas of China.

Understanding the effectiveness of national air pollution controls is important for control policy design to improve the future air quality in China. This study evaluated the effectiveness of major national control policies implemented recently in China through a modeling analysis. The sulfur dioxide (SO2) control policy during the 11th Five Year Plan period (2006-2010) had succeeded in reducing the national SO2 emission in 2010 by 14% from its 2005 level, which correspondingly reduced ambient SO2 and sulfate (SO4(2-)) concentrations by 13%-15% and 8%-10% respectively over east China. The nitrogen oxides (NO(x)) control policy during the 12th Five Year Plan period (2011-2015) targets the reduction of the national NO(x) emission in 2015 by 10% on the basis of 2010. The simulation results suggest that such a reduction in NO(x) emission will reduce the ambient nitrogen dioxide (NO2), nitrate (NO3(-)), 1-hr maxima ozone (O3) concentrations and total nitrogen deposition by 8%, 3%-14%, 2% and 2%-4%, respectively over east China. The application of new emission standards for power plants will further reduce the NO2, NO3(-), 1-hr maxima O(3 concentrations and total nitrogen deposition by 2%-4%, 1%-6%, 0-2% and 1%-2%, respectively. Sensitivity analysis was conducted to evaluate the inter-provincial impacts of emission reduction in Beijing-Tianjin-Hebei and the Yangtze River Delta, which indicated the need to implement joint regional air pollution control.

Evaluating reduced-form modeling tools for simulating ozone and PM2.5 monetized health impacts.

Reduced-form modeling approaches are an increasingly popular way to rapidly estimate air quality and human health impacts related to changes in air pollutant emissions. These approaches reduce computation time by making simplifying assumptions about pollutant source characteristics, transport and chemistry. Two reduced form tools used by the Environmental Protection Agency in recent assessments are source apportionment-based benefit per ton (SA BPT) and source apportionment-based air quality surfaces (SABAQS). In this work, we apply these two reduced form tools to predict changes in ambient summer-season ozone, ambient annual PM2.5 component species and monetized health benefits for multiple sector-specific emission control scenarios: on-road mobile, electricity generating units (EGUs), cement kilns, petroleum refineries, and pulp and paper facilities. We then compare results against photochemical grid and standard health model-based estimates. We additionally compare monetized PM2.5 health benefits to values derived from three reduced form tools available in the literature: the Intervention Model for Air Pollution (InMAP), Air Pollution Emission Experiments and Policy Analysis (APEEP) version 2 (AP2) and Estimating Air pollution Social Impact Using Regression (EASIUR). Ozone and PM2.5 changes derived from SABAQS for EGU scenarios were well-correlated with values obtained from photochemical modeling simulations with spatial correlation coefficients between 0.64 and 0.89 for ozone and between 0.75 and 0.94 for PM2.5. SABAQS ambient ozone and PM2.5 bias when compared to photochemical modeling predictions varied by emissions scenario: SABAQS PM2.5 changes were overpredicted by up to 46% in one scenario and underpredicted by up to 19% in another scenario; SABAQS seasonal ozone changes were overpredicted by 34% to 83%. All tools predicted total PM2.5 benefits within a factor of 2 of the full-form predictions consistent with intercomparisons of reduced form tools available in the literature. As reduced form tools evolve, it is important to continue periodic comparison with comprehensive models to identify systematic biases in estimating air pollution impacts and resulting monetized health benefits.

Source attribution for mercury deposition in the contiguous United States: regional difference and seasonal variation.

Quantifying the contribution of emission sources responsible for mercury deposition in specific receptor regions helps develop emission control strategies that alleviate the impact on ecosystem and human health. In light of the maximum available control technology (MACT) rules proposed by U.S. Environmental Protection Agency (EPA) and the ongoing intergovernmental negotiation coordinated by United Nations Environmental Programme (UNEP) for mercury, the Community Multiscale Air Quality Modeling System (CMAQ-Hg) was applied to estimate the source contribution in six subregions of the contiguous United States (CONUS). The considered source categories include electric generating units (EGU), iron and steel industry (IRST), other industrial point sources excluding EGU and IRST (OIPM), the remaining anthropogenic sources (RA), natural processes (NAT), and out-of-boundary transport (BC). It is found that, on an annual basis, dry deposition accounts for two-thirds of total annual deposition in CONUS (474 Mg yr(-1)), mainly contributed by reactive gaseous mercury (about 60% of total deposition). The contribution from large point sources can be as high as 75% near the emission sources (< 100 km), indicating that emission reduction may result in direct deposition decrease near the source locations. Out-of-boundary transport contributes from 68% (Northeast) to 91% (West Central) of total deposition. Excluding the contribution from out-of boundary transport, EGU contributes to about 50% of deposition in the Northeast, Southeast, and East Central regions, whereas emissions from natural processes are more important in the Pacific and West Central regions (contributing up to 40% of deposition). This suggests that the implementation of the new EPA MACT standards will significantly benefit only these three regions. Emission speciation is a key factor for local deposition. The source contribution exhibits strong seasonal variation. Deposition is greater in warm seasons due to stronger Hg0 oxidation. However, the contribution from anthropogenic sources is smaller in warm seasons because of larger emissions from natural processes and stronger vertical mixing that facilitates transport.

Impact assessment of ammonia emissions on inorganic aerosols in East China using response surface modeling technique.

Ammonia (NH(3)) is one important precursor of inorganic fine particles; however, knowledge of the impacts of NH(3) emissions on aerosol formation in China is very limited. In this study, we have developed China's NH(3) emission inventory for 2005 and applied the Response Surface Modeling (RSM) technique upon a widely used regional air quality model, the Community Multi-Scale Air Quality Model (CMAQ). The purpose was to analyze the impacts of NH(3) emissions on fine particles for January, April, July, and October over east China, especially those most developed regions including the North China Plain (NCP), Yangtze River delta (YRD), and the Pearl River delta (PRD). The results indicate that NH(3) emissions contribute to 8-11% of PM(2.5) concentrations in these three regions, comparable with the contributions of SO(2) (9-11%) and NO(x) (5-11%) emissions. However, NH(3), SO(2), and NO(x) emissions present significant nonlinear impacts; the PM(2.5) responses to their emissions increase when more control efforts are taken mainly because of the transition between NH(3)-rich and NH(3)-poor conditions. Nitrate aerosol (NO(3)(-)) concentration is more sensitive to NO(x) emissions in NCP and YRD because of the abundant NH(3) emissions in the two regions, but it is equally or even more sensitive to NH(3) emissions in the PRD. In high NO(3)(-) pollution areas such as NCP and YRD, NH(3) is sufficiently abundant to neutralize extra nitric acid produced by an additional 25% of NO(x) emissions. The 90% increase of NH(3) emissions during 1990-2005 resulted in about 50-60% increases of NO(3)(-) and SO(4)(2-) aerosol concentrations. If no control measures are taken for NH(3) emissions, NO(3)(-) will be further enhanced in the future. Control of NH(3) emissions in winter, spring, and fall will benefit PM(2.5) reduction for most regions. However, to improve regional air quality and avoid exacerbating the acidity of aerosols, a more effective pathway is to adopt a multipollutant strategy to control NH(3) emissions in parallel with current SO(2) and NO(x) controls in China.

Predicting the Nonlinear Response of PM2.5 and Ozone to Precursor Emission Changes with a Response Surface Model.

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.