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

Assessing the effectiveness of PM2.5 pollution control from the perspective of interprovincial transport and PM2.5 mitigation costs across China

Due to the transboundary nature of air pollutants, a province's efforts to improve air quality can reduce PM2.5 concentration in the surrounding area. The inter-provincial PM2.5 pollution transport could bring great challenges to related environmental management work, such as financial fund allocation and subsidy policy formulation. Herein, we examined the transport characteristics of PM2.5 pollution across provinces in 2013 and 2020 via chemical transport modeling and then monetized inter-provincial contributions of PM2.5 improvement based on pollutant emission control costs. We found that approximately 60% of the PM2.5 pollution was from local sources, while the remaining 40% originated from outside provinces. Furthermore, about 1011 billion RMB of provincial air pollutant abatement costs contributed to the PM2.5 concentration decline in other provinces during 2013-2020, accounting for 41.2% of the total abatement costs. Provinces with lower unit improvement costs for PM2.5, such as Jiangsu, Hebei, and Shandong, were major contributors, while Guangdong, Guangxi, and Fujian, bearing higher unit costs, were among the main beneficiaries. Our study identifies provinces that contribute to air quality improvement in other provinces, have high economic efficiency, and provide a quantitative framework for determining inter-provincial compensations. This study also reveals the uneven distribution of pollution abatement costs (PM2.5 improvement/abatement costs) due to transboundary PM2.5 transport, calling for adopting inter-provincial economic compensation policies. Such mechanisms ensure equitable cost-sharing and effective regional air quality management.

Unveiling the health impacts of air pollution transport in China

The transport of atmospheric pollutants plays a pivotal role in regional air pollution, highlighting critical concerns over the unequal health outcomes that arise from such transport. While previous researches predominantly focused on key areas in the battle against air pollution, the intensification of control measures necessitates a national perspective to comprehend the health impacts due to pollution transport. Our study establishes an integrated assessment framework that combine an emission-concentration response surface model with a health impact evaluation model to analyse the nationwide health impacts of PM2.5 and O3 pollution transport across China's 31 provinces. We found that, interprovincial transport of PM2.5 and O3 contributed to 747,000 and 110,000 deaths respectively in 2017, which amounts to 38% and 48% of deaths caused by total anthropogenic emissions. North, East, and Central China together contribute 82% and 69% to the health impacts caused by regional PM2.5 and O3 transport respectively, and the transport among these three regions is also significant. The analysis of interprovincial health impact transport shows that, for PM2.5, the top contributors are Hebei, Shandong, Henan, Anhui, and Jiangsu, with the most affected being Henan, Shandong, Jiangsu, Hebei, and Guangdong. Regarding O3, Shandong, Hebei, Henan, Jiangsu, and Anhui contribute the most, while Henan, Shandong, Hebei, Jiangsu, and Anhui are the most affected. This study can shed lights on regional control strategies by prioritizing control areas based on the health impact of air pollution transport in China.

Substantial contribution of transported emissions to organic aerosol in Beijing

Haze in Beijing is linked to atmospherically formed secondary organic aerosol, which has been shown to be particularly harmful to human health. However, the sources and formation pathways of these secondary aerosols remain largely unknown, hindering effective pollution mitigation. Here we have quantified the sources of organic aerosol via direct near-molecular observations in central Beijing. In winter, organic aerosol pollution arises mainly from fresh solid-fuel emissions and secondary organic aerosols originating from both solid-fuel combustion and aqueous processes, probably involving multiphase chemistry with aromatic compounds. The most severe haze is linked to secondary organic aerosols originating from solid-fuel combustion, transported from the Beijing-Tianjing-Hebei Plain and rural mountainous areas west of Beijing. In summer, the increased fraction of secondary organic aerosol is dominated by aromatic emissions from the Xi'an-Shanghai-Beijing region, while the contribution of biogenic emissions remains relatively small. Overall, we identify the main sources of secondary organic aerosol affecting Beijing, which clearly extend beyond the local emissions in Beijing. Our results suggest that targeting key organic precursor emission sectors regionally may be needed to effectively mitigate organic aerosol pollution.

Health-Oriented Emission Control Strategy of Energy Utilization and Its Co-CO2 Benefits: A Case Study of the Yangtze River Delta, China

Reducing air pollutants and CO2 emissions from energy utilization is crucial for achieving the dual objectives of clean air and carbon neutrality in China. Thus, an optimized health-oriented strategy is urgently needed. Herein, by coupling a CO2 and air pollutants emission inventory with response surface models for PM2.5-associated mortality, we shed light on the effectiveness of protecting human health and co-CO2 benefit from reducing fuel-related emissions and generate a health-oriented strategy for the Yangtze River Delta (YRD). Results reveal that oil consumption is the primary contributor to fuel-related PM2.5 pollution and premature deaths in the YRD. Significantly, curtailing fuel consumption in transportation is the most effective measure to alleviate the fuel-related PM2.5 health impact, which also has the greatest cobenefits for CO2 emission reduction on a regional scale. Reducing fuel consumption will achieve substantial health improvements especially in eastern YRD, with nonroad vehicle emission reductions being particularly impactful for health protection, while on-road vehicles present the greatest potential for CO2 reductions. Scenario analysis confirms the importance of mitigating oil consumption in the transportation sector in addressing PM2.5 pollution and climate change.

Spatially and Temporally Differentiated NOx and VOCs Emission Abatement Could Effectively Gain O3-Related Health Benefits

The increasing level of O3 pollution in China significantly exacerbates the long-term O3 health damage, and an optimized health-oriented strategy for NOx and VOCs emission abatement is needed. Here, we developed an integrated evaluation and optimization system for the O3 control strategy by merging a response surface model for the O3-related mortality and an optimization module. Applying this system to the Yangtze River Delta (YRD), we evaluated driving factors for mortality changes from 2013 to 2017, quantified spatial and temporal O3-related mortality responses to precursor emission abatement, and optimized a health-oriented control strategy. Results indicate that insufficient NOx emission abatement combined with deficient VOCs control from 2013 to 2017 aggravated O3-related mortality, particularly during spring and autumn. Northern YRD should promote VOCs control due to higher VOC-limited characteristics, whereas fastening NOx emission abatement is more favorable in southern YRD. Moreover, promotion of NOx mitigation in late spring and summer and facilitating VOCs control in spring and autumn could further reduce O3-related mortality by nearly 10% compared to the control strategy without seasonal differences. These findings highlight that a spatially and temporally differentiated NOx and VOCs emission control strategy could gain more O3-related health benefits, offering valuable insights to regions with severe ozone pollution all over the world.

Mismatched Social Welfare Allocation and PM2.5-Related Health Damage along Value Chains within China

Value chains have played a critical part in the growth. However, the fairness of the social welfare allocation along the value chain is largely underinvestigated, especially when considering the harmful environmental and health effects associated with the production processes. We used fine-scale profiling to analyze the social welfare allocation along China's domestic value chain within the context of environmental and health effects and investigated the underlying mechanisms. Our results suggested that the top 10% regions in the value chain obtained 2.9 times more social income and 2.1 times more job opportunities than the average, with much lower health damage. Further inspection showed a significant contribution of the "siphon effect"─major resource providers suffer the most in terms of localized health damage along with insufficient social welfare for compensation. We found that inter-region atmosphere transport results in redistribution for 53% health damages, which decreases the welfare-damage mismatch at "suffering" regions but also causes serious health damage to more than half of regions and populations in total. Specifically, around 10% of regions have lower social welfare and also experienced a significant increase in health damage caused by atmospheric transport. These results highlighted the necessity of a value chain-oriented, quantitative compensation-driven policy.

72-hour real-time forecasting of ambient PM2.5 by hybrid graph deep neural network with aggregated neighborhood spatiotemporal information

The observation-based air pollution forecasting method has high computational efficiency over traditional numerical models, but a poor ability in long-term (after 6 h) forecasting due to a lack of detailed representation of atmospheric processes associated with the pollution transport. To address such limitation, here we propose a novel real-time air pollution forecasting model that applies a hybrid graph deep neural network (GNN_LSTM) to dynamically capture the spatiotemporal correlations among neighborhood monitoring sites to better represent the physical mechanism of pollutant transport across the space with the graph structure which is established with features (angle, wind speed, and wind direction) of neighborhood sites to quantify their interactions. Such design substantially improves the model performance in 72-hour PM_2.5 forecasting over the whole Beijing-Tianjin-Hebei region (overall R² increases from 0.6 to 0.79), particularly for polluted episodes (PM_2.5 concentration > 55 µg/m³) with pronounced regional transport to be captured by GNN_LSTM model. The inclusion of the AOD feature further enhances the model performance in predicting PM_2.5 over the sites where the AOD can inform additional aloft PM_2.5 pollution features related to regional transport. The importance of neighborhood site (particularly for those in the upwind flow pathway of the target area) features for long-term PM_2.5 forecast is demonstrated by the increased performance in predicting PM_2.5 in the target city (Beijing) with the inclusion of additional 128 neighborhood sites. Moreover, the newly developed GNN_LSTM model also implies the "source"-receptor relationship, as impacts from distanced sites associated with regional transport grow along with the forecasting time (from 0% to 38% in 72 h) following the wind flow. Such results suggest the great potential of GNN_LSTM in long-term air quality forecasting and air pollution prevention.

Differences between VOCs and NOx transport contributions, their impacts on O3, and implications for O3 pollution mitigation based on CMAQ simulation over the Yangtze River Delta, China

The relationship between O₃ and its precursors during urban polluted episodes remains unclear. In this study, the simultaneous source apportionment of VOCs, NO_x, and O₃ over the Yangtze River Delta (YRD) region during the O₃ polluted episode on July 24-30, 2018, was performed based on the Integrated Source Apportionment Method (ISAM) embedded in the Community Multiscale Air Quality Modeling System (CMAQ). The results of the ISAM were compared with those of the Brute Force Method (BFM) and Positive Matrix Factorization (PMF). Furthermore, the differences between the transport contributions of VOCs and NO_x, and their impacts on O₃ were analyzed. The results indicate that observations of VOCs species can be well captured by simulated VOCs, and the ISAM has a significant advantage in the source apportionment of VOCs, especially for sources emitting highly reactive species. In the clean and polluted periods, the local contribution percentages of VOCs in urban sites ranged from 60 % to 77 %, much higher than those of NO_x (31 %-43 %) and O₃ (16 %-33 %). NO_x and O₃ have strong transport abilities with high and close contribution percentages, which are highly correlated, mainly because oxygen atoms produced by the photolysis of NO₂ in the aged air mass combined rapidly with O₂ to form O₃ during transport. The VOCs chemical loss caused by the oxidation of OH radicals during transport makes the ability of VOCs for long-distance transport much weaker than that of NO_x. Furthermore, owing to the sufficient aging of VOCs, those contributed by long-distance transport have little effect on O₃. To a certain extent, controlling one's NO_x emissions can help other cities more, while controlling one's VOCs emissions can help itself more. Therefore, it is recommended to attach enough importance to joint prevention and control of NO_x among cities and even long-distance areas to alleviate regional O₃ pollution.

Source Contributions to PM2.5-Related Mortality and Costs: Evidence for Emission Allocation and Compensation Strategies in China

The emissions from various pollution sources were not proportional to their contributions to ambient PM_2.5 concentrations and associated health burdens. That means even with the same total abatement targets, different abatement allocation strategies across emission sources can have distinct health benefits. Insufficient knowledge of various sources' contributions to health burdens in China, the country suffering substantial PM_2.5-related deaths, hindered the government from seeking optimized abatement allocation strategies. In this context, we separated the contributions of 155 emission sources (31 provinces × 5 sectors) to PM_2.5-related mortality across China in 2017 by coupling the Comprehensive Air Quality Model with Extensions (CAMx), Weather Research and Forecasting model (WRF), and health impact assessment model. We further identified the priority-control emission sources and quantified interprovincial ecological compensation volumes to alleviate inequality induced by emission allocation strategies. Results showed that PM_2.5 pollution caused 899,443 excess deaths and around 127 billion USD costs in 2017. Approximately half of the deaths and costs were attributable to emissions from sources outside the boundary of the regions where the deaths occurred. Twenty-five out of 155 emission sources that contributed to the top 60% mortality burdens and had high marginal abatement efficiencies in China shall be the priority-control emission sources. A 1 μg/m³ decrease of PM_2.5 concentration in regions where these key emission sources occur shall be compensated by 76-153 million USD in their receptor regions. Our study sheds light on the sources' contributions to mortality burdens and costs and provides scientific evidence for optimizing the emission allocation and compensation strategies in China. It also has wide implications for other countries suffering similar problems.

Extreme Emission Reduction Requirements for China to Achieve World Health Organization Global Air Quality Guidelines

A big gap exists between current air quality in China and the World Health Organization (WHO) global air quality guidelines (AQG) released in 2021. Previous studies on air pollution control have focused on emission reduction demand in China but ignored the influence of transboundary pollution, which has been proven to have a significant impact on air quality in China. Here, we develop an emission-concentration response surface model coupled with transboundary pollution to quantify the emission reduction demand for China to achieve WHO AQG. China cannot achieve WHO AQG by its own emission reduction for high transboundary pollution of both PM_2.5 and O₃. Reducing transboundary pollution will loosen the reduction demand for NH₃ and VOCs emissions in China. However, to meet 10 μg·m^-3 for PM_2.5 and 60 μg·m^-3 for peak season O₃, China still needs to reduce its emissions of SO₂, NO_x, NH₃, VOCs, and primary PM_2.5 by more than 95, 95, 76, 62, and 96% respectively, on the basis of 2015. We highlight that both extreme emission reduction in China and great efforts in addressing transboundary air pollution are crucial to reach WHO AQG.

An acid rain-friendly NH3 control strategy to maximize benefits toward human health and nitrogen deposition

Ammonia (NH₃) abatement remains controversial in China owing to its effectiveness in reducing PM_2.5 pollution and nitrogen deposition but with the potential risk of promoting acid rain formation, necessitating scientific guidance. Here, we propose a novel method for designing an NH₃ control strategy to mitigate both air pollution and nitrogen deposition without significantly exacerbating acid rain. This method involves extending the response surface model (RSM) to deposition using a delicately developed polynomial response function of deposition (i.e., dep-RSM). The Yangtze River Delta (YRD) dep-RSM application reveals that 16 out of 41 cities have NH₃ control potentials from 15 % to 71 %. Excellent NH₃ control potentials have been noted between April and June (78 %-92 %). From 2013 to 2017, the effective SO₂ and NO_x control significantly reduced wet sulfur and oxidized nitrogen deposition, providing considerable NH₃ abatement potentials (15 %-24 %) to further reduce PM_2.5 and nitrogen deposition by up to 2 % and 9 %, respectively, without acid rain exacerbation (the wet neutralization factor was maintained). Additionally, 57 % and 73 % NH₃ emission reduction potentials were obtained under acid rain constraints with 75 % and 86 % reductions in the other precursors to reduce the average PM_2.5 concentration below 25 and 15 μg/m³, and an additional 8408 and 14,459 premature deaths could only be avoided at an extra cost of 8.7 and 19.7 billion CNY, respectively. Meanwhile, the N deposition considerably reduced by 10 and 13 kgN/ha·yr. However, the YRD region could still simultaneously obtain substantial amounts of PM_2.5 and N deposition mitigation using the strategy proposed herein. The expanded optimization system can be directly adopted by policymakers to implement coordinated control in regions or countries facing the same NH₃ control conundrum.

Accurate prediction of air quality response to emissions for effective control policy design

Designing effective control policy requires accurate quantification of the relationship between the ambient concentrations of O₃ and PM_2.5 and the emissions of their precursors. However, the challenge is that precursor reduction does not necessarily lead to decreases in the concentrations of O₃ and PM_2.5, which are formed by multiple precursors under complex physical and chemical processes; this calls for the development of advanced model technologies to provide accurate predictions of the nonlinear responses of air quality to emissions. Different from the traditional sensitivity analysis and source apportionment methods, the reduced form models (RFMs) based on chemical transport models (CTMs) are able to quantify air quality responses to emissions more accurately and efficiently with lower computational cost. Here we review recent approaches used in RFMs and compare their structures, advantages and disadvantages, performance and applications. In general, RFMs are classified into three types including (1) sensitivity-based models, (2) models with simplified chemistry and physical processes, and (3) statistical models, with considerable differences in principles, characteristics and application ranges. The prediction of nonlinear responses by RFMs enables more in-depth analysis, not only in terms of real-time prediction of concentrations and quantification of human exposure, health impacts and economic damage, but also in optimizing control policies. Notably, data assimilation and emission inventory inversion based on the nonlinear response of concentrations to emissions can also be greatly beneficial to air pollution control management. In future studies, improvement in the performance of CTMs is exceedingly crucial to obtain a more reliable baseline for the prediction of air quality responses. Development of models to determine the air quality response to emissions under varying meteorological conditions is also necessary in the context of future climate changes, which pose great challenges to the quantification of response relationships. Additionally, with rising requirements for fine-scale air quality management, improving the performance of urban-scale simulations is worth considering. In short, accurate predictions of the response of air quality to emissions, though challenging, holds great promise for the present as well as for future scenarios.

Synergetic PM2.5 and O3 control strategy for the Yangtze River Delta, China

PM_2.5 concentrations have dramatically reduced in key regions of China during the period 2013-2017, while O₃ has increased. Hence there is an urgent demand to develop a synergetic regional PM_2.5 and O₃ control strategy. This study develops an emission-to-concentration response surface model and proposes a synergetic pathway for PM_2.5 and O₃ control in the Yangtze River Delta (YRD) based on the framework of the Air Benefit and Cost and Attainment Assessment System (ABaCAS). Results suggest that the regional emissions of NOx, SO₂, NH₃, VOCs (volatile organic compounds) and primary PM_2.5 should be reduced by 18%, 23%, 14%, 17% and 33% compared with 2017 to achieve 25% and 5% decreases of PM_2.5 and O₃ in 2025, and that the emission reduction ratios will need to be 50%, 26%, 28%, 28% and 55% to attain the National Ambient Air Quality Standard. To effectively reduce the O₃ pollution in the central and eastern YRD, VOCs controls need to be strengthened to reduce O₃ by 5%, and then NOx reduction should be accelerated for air quality attainment. Meanwhile, control of primary PM_2.5 emissions shall be prioritized to address the severe PM_2.5 pollution in the northern YRD. For most cities in the YRD, the VOCs emission reduction ratio should be higher than that for NOx in Spring and Autumn. NOx control should be increased in summer rather than winter when a strong VOC-limited regime occurs. Besides, regarding the emission control of industrial processes, on-road vehicle and residential sources shall be prioritized and the joint control area should be enlarged to include Shandong, Jiangxi and Hubei Province for effective O₃ control.

A review of the technology and applications of methods for evaluating the transport of air pollutants

A variety of methods based on air quality models, including tracer methods, the brute-force method (BFM), decoupled direct method (DDM), high-order decoupled direct method (HDDM), response surface models (RSMs) and so on forth, have been widely used to study the transport of air pollutants. These methods have good applicability for the transport of air pollutants with simple formation mechanisms. However, differences in research conclusions on secondary pollutants with obvious nonlinear characteristics have been reported. For example, the tracer method is suitable for the study of simplified scenarios, while HDDM and RSMs are more suitable for the study for nonlinear pollutants. Multiple observation techniques, including conventional air pollutant observation, lidar observation, air sounding balloons, vehicle-mounted and ship-borne technology, aerial surveys, and remote sensing observations, have been utilized to investigate air pollutant transport characteristics with time resolution as high as 1 sec. In addition, based on a multi-regional input-output model combined with emission inventories, the transfer of air pollutant emissions can be evaluated and applied to study the air pollutant transport characteristics. Observational technologies have advantages in temporal resolution and accuracy, while modeling technologies are more flexible in spatial resolution and research plan setting. In order to accurately quantify the transport characteristics of pollutants, it is necessary to develop a research method for interactive verification of observation and simulation. Quantitative evaluation of the transport of air pollutants from different angles can provide a scientific basis for regional joint prevention and control.

Developing China's roadmap for air quality improvement: A review on technology development and future prospects

Air pollution control policies in China have been experiencing profound changes, highlighting a strategic transformation from total pollutant emission control to air quality improvement, along with the shifting targets starting from acid rain and NO_x emissions to PM_2.5 pollution, and then the emerging O₃ challenges. The marvelous achievements have been made with the dramatic decrease of SO₂ emission and fundamental improvement of PM_2.5 concentration. Despite these achievements, China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality, which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas (GHG) emissions. Against this background, an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China's long-term air quality improvement strategy. This paper systematically reviews the technical system for developing the air quality improvement roadmap, which was integrated from the research output of China's National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies (hereafter Special NKP), covering mid- and long-term air quality target setting techniques, quantitative analysis techniques for emission reduction targets corresponding to air quality targets, and pathway optimization techniques for realizing reduction targets. The experience and lessons derived from the reviews have implications for the reformation of China's air quality improvement roadmap in facing challenges of synergistic mitigation of PM_2.5 and O₃, and the coupling with climate change mitigation.

Responses of nitrogen and sulfur deposition to NH3 emission control in the Yangtze River Delta, China

NH₃ emission control has proven to be of great importance in reducing PM_2.5 concentrations in China, while how it affects nitrogen/sulfur (N/S) deposition is still unclear. This study expanded the response surface model method to quantify the responses of N/S deposition to the emission control of precursors (NO_x, SO₂, NH₃, VOCs and primary PM_2.5) in the Yangtze River Delta, China. NH₃ control was found to have higher efficiency in reducing N/S deposition than NO_x and SO₂ alone. The reduced N deposition response to NH₃ emission control was higher in the northern part of the YRD region, whereas oxidized N deposition decreased sharply in the region with a low N critical load. Synergetic effect was found in reducing N deposition when we controlled the NH₃ and NO_x emissions simultaneously. Compared with the sum effect of individual NH₃ and NO_x emission control, the extra benefits from the synergy controls accounted for 4.4% (1.23 kg N·ha^-1·yr^-1) of the total N deposition, of which 81% came from the oxidized N deposition. The YRD region could receive the largest synergetic effect with a 1:1 ratio of NO_x:NH₃ emission reduction. The NH₃ emission control increases the dry deposition of acid substances and worsens acid rain though it reduces the wet S/oxidized N deposition. These findings highlight the effectiveness of NH₃ emission control and suggest a multi-pollutant control strategy for reducing N/S deposition. The response surface model method for deposition also provides a reference for other regions in China and other countries.

Analysis of the Characteristics of Ozone Pollution in the North China Plain from 2016 to 2020

As a major gaseous pollutant, ozone (O3) adversely affects human health and ecosystems. In recent years, ozone pollution in China has gradually become a prominent issue, especially in the North China Plain (NCP). To study the long-term spatio-temporal variation patterns of O3 in the NCP, this study selected 230 monitoring stations in the NCP from 2016 to 2020 as research objects, used the Kriging interpolation method and global Moran’s index to discuss the spatial-temporal distribution of O3, combining meteorological and social statistical data to analyze the causes underlying regional differences. The temporal analysis demonstrated that the O3-8h average concentrations increased annually from 2016 to 2018 and decreased from 2019 to 2020. The O3 concentrations were higher in spring and summer (117.89–154.20 μg/m3) and lower in autumn and winter (53.81–92.95 μg/m3). The spatial analysis revealed that O3 concentrations were low in the north and south of the NCP but high in the central area. The spatial distribution of O3 exhibited considerable cross-seasonal variability. Both meteorological conditions of high temperature and low pressure increased O3 concentrations. The spatial distribution of O3 varied depending on the period. However, the central and western regions of the Shandong Province were constantly characterized by high O3 concentrations. This pattern has been likely formed by heavy industry in the Shandong Province, as large-scale industrial production and frequent traffic flows produce a large amount of precursors, thereby exacerbating regional O3 pollution. These characteristics were attributed to emission reduction policies, meteorological conditions, the emission intensity of anthropogenic sources, and regional transport in the NCP. Overall, for cities with heavy industrial facilities in the central NCP, a timely adjustment of the energy and industrial structure, effectively controlling the emission of precursors, promoting new clean energy, and strengthening regional joint prevention and control are effective ways to alleviate O3 pollution.

Source contribution analysis of PM2.5 using Response Surface Model and Particulate Source Apportionment Technology over the PRD region, China

Identifying the emission source contributions to PM_2.5 is essential for a sound PM_2.5 pollution control policy. In this study, we conduct a comparative analysis of PM_2.5 source contributions over the Pearl River Delta (PRD) region of China using two advanced source contribution modeling techniques: Response Surface Model (RSM) and Particulate Source Apportionment Technology (PSAT). Our comparative analyses show that RSM and PSAT can both reasonably predict the contribution of primary PM_2.5 emission sources to PM_2.5 formation due to its linear nature. For the secondary PM_2.5 formed by the nonlinear reactions among PM_2.5 precursors, however, our study shows that PSAT appears to have limitations in quantifying the nonlinear contribution of PM_2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM_2.5 precursors (e.g., PM_2.5 disbenefits due to local NO_x emission reductions in major cities with high NO_x emissions). The pilot study case results show that for the ambient PM_2.5 in the central cities (Guangzhou, Shenzhen, Foshan, Dongguan, and Zhongshan) of the PRD, the regional source emissions contribute the most by 42-66%; the dust emissions are the top contribution sources (29-34% by RSM and 27-31% by PSAT), and the mobile sources are listed as the secondary contributors accounting for 16-25% by RSM and 19-30% by PSAT among the anthropogenic emission sources. The city-scale cooperation on emission reductions and the enhancement of dust and mobile emission control are recommended to effectively reduce the ambient PM_2.5 concentration in the PRD.

Improvements of response surface modeling with self-adaptive machine learning method for PM2.5 and O3 predictions

Quickly quantifying the PM_2.5 or O₃ response to their precursor emission changes is a key point for developing effective control policies. The polynomial function-based response surface model (pf-RSM) can rapidly predict the nonlinear response of PM_2.5 and O₃ to precursors, but has drawbacks of overload computation and marginal effects (relatively larger prediction errors under strict control scenarios). To improve the performance of pf-RSM, a novel self-adaptive RSM (SA-RSM) was proposed by integrating the machine learning-based stepwise regression for establishing robust models to increase the computational efficiency and the collinearity diagnosis for reducing marginal effects caused by overfitting. The pilot study case demonstrated that compared with pf-RSM, SA-RSM can effectively reduce the training number by 70% and 40% and the fitting time by 40% and 52%, and decrease the prediction error by 49% and 74% for PM_2.5 and O₃ predictions respectively; moreover, the isopleths of PM_2.5 or O₃ as a function of their precursors generated by SA-RSM were more similar to those derived by chemical transport model (CTM), after successfully addressing the marginal effect issue. With the improved computation efficiency and prediction performance, SA-RSM is expected as a better scientific tool for decision-makers to make sound PM_2.5 and O₃ control policies.

Optimization of a NOx and VOC Cooperative Control Strategy Based on Clean Air Benefits

Serious ambient PM_2.5 and O₃ pollution is one of the most important environmental challenges of China, necessitating an urgent cost-effective cocontrol strategy. Herein, we introduced a novel integrated assessment system to optimize a NO_x and volatile organic compound (VOC) control strategy for the synergistic reduction of ambient PM_2.5 and O₃ pollution. Focusing on the Beijing-Tianjin-Hebei cities and their surrounding regions, which are experiencing the most serious PM_2.5 and O₃ pollution in China, we found that NO_x emission reduction (64-81%) is essential to attain the air quality standard no matter how much VOC emission is reduced. However, the synergistic VOC control is strongly recommended considering its substantially human health and crop production benefits, which are estimated up to 163 (PM_2.5-related) and 101 (O₃-related) billion CHY during the reduction of considerable emissions. Notably, such benefits will be greatly reduced if the synergistic VOC reduction is delayed. This study also highlights the necessity of simultaneous VOC and NO_x emission control in winter while enhancing the NO_x control in the summer, which is contrary to the current control strategy adopted in China. These findings point out the right pathways for future policy making on comitigating PM_2.5 and O₃ pollution in China and other countries.

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.

Novel Method for Ozone Isopleth Construction and Diagnosis for the Ozone Control Strategy of Chinese Cities

Ozone (O₃) isopleths describe the nonlinear responses of O₃ concentrations to changes in nitrogen oxides (NO_X) and volatile organic compounds (VOCs) and thus are pivotal to the determination of O₃ control requirements. In this study, we innovatively use the Community Multiscale Air Quality model with the high-order decoupled direct method (CMAQ-HDDM) to simulate O₃ pollution of China in 2017 and derive O₃ isopleths for individual cities. Our simulation covering the entire China Mainland suggests severe O₃ pollution as 97% of the residents experienced at least 1 day, in 2017, in excess of Chinese Level-II Ambient Air Quality Standards for O₃ as 160 μg·m^-3 (81.5 ppbV equally). The O₃ responses to emissions of precursors vary widely across individual cities. Densely populated metropolitan areas such as Jing-Jin-Ji, Yangtze River Delta, and Pearl River Delta are following NO_X-saturated regimes, where a small amount of NO_X reduction increases O₃. Ambient O₃ pollution in the eastern region generally is limited by VOCs, while in the west by NO_X. The city-specific O₃ isopleths generated in this study are instrumental in forming hybrid and differentiated strategies for O₃ abatement in China.

Source impact and contribution analysis of ambient ozone using multi-modeling approaches over the Pearl River Delta region, China

Quantification of source impacts and contributions is a key element for the design of effective air pollution control policies. In this study, O₃ source impacts and contributions were comprehensively assessed over the Pearl River Delta (PRD) region of China using brute-force method (BFM), response surface modeling with BFM (RSM-BFM) and differential method (RSM-DM) respectively, high-order decoupled direct method (HDDM), and ozone source apportionment technology (OSAT). The multi-modeling comparison results indicated that under typical nonlinear atmospheric conditions during the O₃ formation, BFM, RSM-BFM, and HDDM seemed to be appropriate for assessing the impact of single source emissions; however, the results of HDDM could deviate from those of BFM when the emission reduction ratio was higher than 50 %. Under multi-source control scenarios, the results of source contribution analyses obtained from RSM-DM and OSAT were reasonably well, but the performance of OSAT was limited by its capability in representing the nonlinearity of O₃ response to emission reductions of its precursors, particularly NO_x. The results of this pilot study in the PRD showed that the RSM-DM appeared to replicate the nonlinearity of O₃ chemistry reasonably well (e.g., O₃ disbenefits due to local NO_x emission reductions in Guangzhou city). Based on the source contribution results, on-road mobile (including both NO_x and VOC emissions) and industrial process (mainly VOC emissions) sources were identified as two major contribution sectors by both RSM-DM and OSAT, contributing an average of 31.5 % and 11.4 % (estimated by RSM-DM) and 29.2 % and 13.0 % (estimated by OSAT) respectively to O₃ formation in 9 cities of the PRD. Therefore, the reinforced emission reductions on NO_x and VOC from on-road mobile and industrial process sources in the central cities (i.e., Guangzhou, Foshan, Dongguan, Shenzhen, and Zhongshan) were suggested to effectively mitigate the ambient O₃ levels in the PRD.

Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM_2.5 pollution in recent years. However, few modeling studies have been performed on the sources of PM_2.5, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM_2.5 and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM_2.5 pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM_2.5 in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM_2.5 in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m³, whereas a 50%-60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m³. Moreover, a reduction in traffic emissions resulted in an increase in the O₃ concentration. The results of the response surface method showed that PM_2.5 in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.

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.

Emission source-based ozone isopleth and isosurface diagrams and their significance in ozone pollution control strategies

In the past decade, ozone (O₃) pollution has been continuously worsening in most developing countries. The accurate identification of the nonlinear relationship between O₃ and its precursors is a prerequisite for formulating effective O₃ control measures. At present, precursor-based O₃ isopleth diagrams are widely used to infer O₃ control strategy at a particular location. However, there is frequently a large gap between the O₃-precursor nonlinearity delineated by the O₃ isopleths and the emission source control measures to reduce O₃ levels. Consequently, we developed an emission source-based O₃ isopleth diagram that directly illustrates the O₃ level changes in response to synergistic control on two types of emission sources using a validated numerical modeling system and the latest regional emission inventory. Isopleths can be further upgraded to isosurfaces when co-control on three types of emission sources is investigated. Using Guangzhou and Foshan as examples, we demonstrate that similar precursor-based O₃ isopleths can be associated with significantly different emission source co-control strategies. In Guangzhou, controlling solvent use emissions was the most effective approach to reduce peak O₃ levels. In Foshan, co-control of on-road mobile, solvent use, and fixed combustion sources with a ratio of 3:1:2 or 3:1:3 was best to effectively reduce the peak O₃ levels below 145 ppbv. This study underscores the importance of using emission source-based O₃ isopleths and isosurface diagrams to guide a precursor emission control strategy that can effectively reduce the peak O₃ levels in a particular area.

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2

Air quality modeling for research and regulatory applications often involves executing many emissions sensitivity cases to quantify impacts of hypothetical scenarios, estimate source contributions, or quantify uncertainties. Despite the prevalence of this task, conventional approaches for perturbing emissions in chemical transport models like the Community Multiscale Air Quality (CMAQ) model require extensive offline creation and finalization of alternative emissions input files. This workflow is often time-consuming, error-prone, inconsistent among model users, difficult to document, and dependent on increased hard disk resources. The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module, a component of CMAQv5.3 and beyond, addresses these limitations by performing these modifications online during the air quality simulation. Further, the model contains an Emission Control Interface which allows users to prescribe both simple and highly complex emissions scaling operations with control over individual or multiple chemical species, emissions sources, and spatial areas of interest. DESID further enhances the transparency of its operations with extensive error-checking and optional gridded output of processed emission fields. These new features are of high value to many air quality applications including routine perturbation studies, atmospheric chemistry research, and coupling with external models (e.g., energy system models, reduced-form models).

The Detailed Emissions Scaling, Isolation, and Diagnostic (DESID) module in the Community Multiscale Air Quality (CMAQ) modeling system version 5.3.2

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).

Data assimilation of ambient concentrations of multiple air pollutants using an emission-concentration response modeling framework

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure and related health impacts, especially in China that experiences both PM_2.5 and O₃ pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants, and thus cannot support simultaneous assimilation for both PM_2.5 and O₃. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM_2.5 and O₃ in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NO_x, SO₂, NH₃, VOC and primary PM_2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O₃ and PM_2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO₂ in January, April and October, and SO₂ in April, but overestimate NH₃ in April and VOC in January and October. Primary PM_2.5 emissions are also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH₃ observations to improve the RSM-assimilation performance and emission inventories.

Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China

The pandemic of coronavirus disease 2019 (COVID-19) resulted in a stringent lockdown in China to reduce the infection rate. We adopted a machine learning technique to analyze the air quality impacts of the COVID-19 lockdown from January to April 2020 for six megacities with different lockdown durations. Compared with the scenario without lockdowns, we estimated that the lockdown reduced ambient NO2 concentrations by 36-53% during the most restrictive periods, which involved Level-1 public health emergency response control actions. Several cities lifted the Level-1 control actions during February and March, and the avoided NO2 concentrations subsequently dropped below 10% in late April. Traffic analysis during the same periods in Beijing and Chengdu confirmed that traffic emission changes were a major factor in the substantial NO2 reduction, but they were also associated with increased O3 concentrations. The lockdown also reduced PM2.5 concentrations, although heavy pollution episodes occurred on certain days due to the enhanced formation of secondary aerosols in association with the increased atmospheric oxidizing capacity. We also observed that the changes in air pollution levels decreased as the lockdown was gradually eased in various cities.

Regional transport in Beijing-Tianjin-Hebei region and its changes during 2014-2017: The impacts of meteorology and emission reduction

Emissions of air pollutants have been dramatically reduced in the Beijing-Tianjin-Hebei (BTH) region of China during 2014-2017. However, impacts of emission reduction on regional air quality are not well quantified. This study evaluates the impacts of emission reduction and inter-annual meteorological conditions on regional air pollution transport in BTH region by employing Community Multiscale Air Quality model embedded with the Integrated Source Apportionment Model (CMAQ-ISAM). Results suggest that the regional transport contributed 32.5%-68.4% of total PM_2.5 mass concentrations and 52.4%-83.2% of sulfate, nitrate and ammonium in 2017. During 2014-2017, the annual averaged PM_2.5 concentrations in BTH region decreased by 33%, of which the decrease of local emissions, inter-regional transport and transport from outside the BTH region contributed for 47%, 25%, and 28%, respectively. Emission reductions (91%) mitigate not only the impacts of local sources, but also influence the regional transport with similar magnitude, demonstrating the effectiveness of multiple regional joint controls. The variation of meteorology contributes only 9% to the decrease of PM_2.5 in BTH, with higher contributions from the change of regional transport compared to local sources since the regional transport is more sensitive to the meteorology variation. The impacts of meteorological variations are considerable, with over 20% on the relative changes of local and regional contributions, and up to 40% on regional transport in spring and winter. Therefore, more strengthened regional joint air pollution control is suggested in winter and spring for this region.

Source and sectoral contribution analysis of PM2.5 based on efficient response surface modeling technique over Pearl River Delta Region of China

Identifying and quantifying source contributions of pollutant emissions are crucial for an effective control strategy to break through the bottleneck in reducing ambient PM_2.5 levels over the Pearl River Delta (PRD) region of China. In this study, an innovative response surface modeling technique with differential method (RSM-DM) has been developed and applied to investigate the PM_2.5 contributions from multiple regions, sectors, and pollutants over the PRD region in 2015. The new differential method, with the ability to reproduce the nonlinear response surface of PM_2.5 to precursor emissions by dissecting the emission changes into a series of small intervals, has shown to overcome the issue of the traditional brute force method in overestimating the accumulative contribution of precursor emissions to PM_2.5. The results of this case study showed that PM_2.5 in the PRD region was generally dominated by local emission sources (39-64%). Among the contributions of PM_2.5 from various sectors and pollutants, the primary PM_2.5 emissions from fugitive dust source contributed most (25-42%) to PM_2.5 levels. The contributions of agriculture NH₃ emissions (6-13%) could also play a significant role compared to other sectoral precursor emissions. Among the NO_X sectors, the emissions control of stationary combustion source could be most effective in reducing PM_2.5 levels over the PRD region.

Quantification of the enhancement of PM2.5 concentration by the downward transport of ozone from the stratosphere

The downward transport of ozone (O3) stemming from the stratosphere-to-troposphere exchange (STE) can be a significant contributor to background O3. Such enhancement of background O3 may also influence ground-level PM2.5, particularly in polluted regions which have abundant precursor emissions. In this study, we quantified the STE impact on tropospheric O3 and its subsequent influence on surface PM2.5 across the northern hemisphere. The sensitivity analyses was conducted by using a comprehensive hemispheric atmospheric modeling system. Results suggest the surface PM2.5 concentration can be considerably enhanced by the STE in polluted regions including East China, East US, and Europe, mostly in winter and spring. In winter, the PM2.5 concentrations in East China, East US, and Europe are estimated to be enhanced by 1.3%, 3.5% and 5.5% due to the STE. The STE-enhanced PM2.5 concentrations are typically higher on high pollution days suggesting likely increasing contributions in regions with the growing pollution levels. During the heavy polluted days, the PM2.5 concentrations in East China can be enhanced by 2.289 μg/m3 in winter and 2.034 μg/m3 in spring due to the STE. The STE-enhanced PM2.5 also exhibits strong diurnal variations following a pattern similar to the total PM2.5 concentration, with high increasing ratio in the morning and low at afternoon, suggesting that the enhancement is most pronounced during peak pollution events. The STE-enhanced PM2.5 is exclusively contributed by the increase of nitrate, ammonium, and secondary organic aerosol which in-turn are strongly influenced by the atmospheric oxidation capacity.

Real-time source contribution analysis of ambient ozone using an enhanced meta-modeling approach over the Pearl River Delta Region of China

The nonlinear response of O₃ to nitrogen oxides (NO_x) and volatile organic compounds (VOC) is not conducive to accurately identify the various source contributions and O₃-NO_x-VOC relationships. An enhanced meta-modeling approach, polynomial functions based response surface modeling coupled with the sectoral linear fitting technique (pf-ERSM-SL), integrating a new differential method (DM), was proposed to break through the limitation. The pf-ERSM-SL with DM was applied for analysis of O₃ formation regime and real-time source contributions in July and October 2015 over the Pearl River Delta Region (PRD) of Mainland China. According to evaluations, the pf-ERSM-SL with DM was proven to be effective in source apportionment when the traditional sensitivity analysis was unsuitable for deriving the source contributions in the nonlinear system. After diagnosing the O₃-NO_x-VOC relationships, O₃ formation in most regions of the PRD was identified as a distinctive NO_x-limited regime in July; in October, the initial VOC-limited regime was found at small emission reductions (less than 22-44%), but it will transit to NO_x-limited when further reductions were implemented. Investigation of the source contributions suggested that NO_x emissions were the dominated contributor when turning-off the anthropogenic emissions, occupying 85.41-94.90% and 52.60-75.37% of the peak O₃ responses in July and October respectively in the receptor regions of the PRD; NO_x emissions from the on-road mobile source (NO_x_ORM) in Guangzhou (GZ), Dongguan&Shenzhen (DG&SZ) and Zhongshan (ZS) were identified as the main contributors. Consequently, the reinforced control of NO_x_ORM is highly recommended to lower the ambient O₃ in the PRD effectively.

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.

Large-scale optimization of multi-pollutant control strategies in the Pearl River Delta region of China using a genetic algorithm in machine learning

A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~1035). The method was demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM2.5) and ozone (O3) over the Pearl River Delta (PRD) region of China. The GA was found to be >99% more efficient than the commonly used grid searching method while providing the same combination of optimized multi-pollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM2.5 (< 35 μg m-3) and O3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NOx (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM2.5 goals, SO2 reductions (> 9%) are needed as well. The estimated benefit-to-cost ratio of the optimal control strategy reached 17.7 in our application, demonstrating the value of multi-pollutant control for cost-effective air quality management in the PRD region.

Spatial-temporal variability of PM2.5 air quality in Beijing, China during 2013-2018

This study investigates spatial-temporal variability and trends of ambient PM_2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013-2018, the city-wide annual PM_2.5 concentrations decreased significantly by 40% (84 μg/m³ in 2013 vs. 50 μg/m³ in 2018). The decreasing PM_2.5 trend is more pronounced in winter and during the heating season (November-March), in urban areas, and at the median and upper percentiles of PM_2.5 concentrations. The 95th percentile PM_2.5 concentrations had decreased by 20 μg/m³/yr in the heating season and 16 μg/m³/yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM_2.5 concentration < 35 μg/m³) and a significant decrease in heavy pollution days (PM_2.5 concentration > 150 μg/m³). PM_2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM_2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM_2.5 levels were higher during winter and the heating season, when PM_2.5 emission was high due to space heating and mixing layer heights were low. PM_2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM_2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM_2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale.

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.

Gasification of coal and biomass as a net carbon-negative power source for environment-friendly electricity generation in China

Realizing the goal of the Paris Agreement to limit global warming to 2 °C by the end of this century will most likely require deployment of carbon-negative technologies. It is particularly important that China, as the world's top carbon emitter, avoids being locked into carbon-intensive, coal-fired power-generation technologies and undertakes a smooth transition from high- to negative-carbon electricity production. We focus here on deploying a combination of coal and biomass energy to produce electricity in China using an integrated gasification cycle system combined with carbon capture and storage (CBECCS). Such a system will also reduce air pollutant emissions, thus contributing to China's near-term goal of improving air quality. We evaluate the bus-bar electricity-generation prices for CBECCS with mixing ratios of crop residues varying from 0 to 100%, as well as associated costs for carbon mitigation and cobenefits for air quality. We find that CBECCS systems employing a crop residue ratio of 35% could produce electricity with net-zero life-cycle emissions of greenhouse gases, with a levelized cost of electricity of no more than 9.2 US cents per kilowatt hour. A carbon price of approximately $52.0 per ton would make CBECCS cost-competitive with pulverized coal power plants. Therefore, our results provide critical insights for designing a CBECCS strategy in China to harness near-term air-quality cobenefits while laying the foundation for achieving negative carbon emissions in the long run.

Nonlinear relationships between air pollutant emissions and PM2.5-related health impacts in the Beijing-Tianjin-Hebei region

A direct and quantitative linkage of air pollution-related health effects to emissions from different sources is critically important for decision-making. While a number of studies have attributed the PM_2.5-related health impacts to emission sources, they have seldom examined the complicated nonlinear relationships between them. Here we investigate the nonlinear relationships between PM_2.5-related premature mortality in the Beijing-Tianjin-Hebei (BTH) region, one of the most polluted regions in the world, and emissions of different pollutants from multiple sectors and regions, through a combination of chemical transport model (CTM), extended response surface model (ERSM), and concentration-response functions (CRFs). The mortalities due to both long-term and short-term exposures to PM_2.5 are most sensitive to the emission reductions of primary PM_2.5, followed by NH₃, nonmethane volatile organic compounds and intermediate volatility organic compounds (NMVOC+IVOC). The sensitivities of long-term mortality to emissions of primary organic aerosol (POA), NMVOC+IVOC and SO₂ do not change much with reduction ratio, whereas the sensitivities to primary inorganic PM_2.5 (defined as all chemical components of primary PM_2.5 other than POA), NH₃ and NO_x increase significantly with the increase of reduction ratio. The emissions of primary PM_2.5, especially those from the residential and commercial sectors, contribute a larger fraction of mortality in winter (57-70%) than in other seasons (28-42%). When emissions of multiple pollutants or those from both local and regional emissions are controlled simultaneously, the overall sensitivity of long-term mortality is much larger than the arithmetic sum of the sensitivities to emissions of individual pollutants or from individual regions. This implies that a multi-pollutant, multi-sector and regional joint control strategy should be implemented to maximize the marginal health benefits. For NO_x emissions, we suggest a nationwide control strategy which significantly enhances the effectiveness for reducing mortality by avoiding possible side effects when only the emissions within the BTH region are reduced.

Contributions of inter-city and regional transport to PM2.5 concentrations in the Beijing-Tianjin-Hebei region and its implications on regional joint air pollution control

Regional transport plays an important role in the serious PM_2.5 pollutions in the Beijing-Tianjin-Hebei (BTH) region, China. Practical regional joint emission control strategies require quantitative assessments of the transport contribution among cities and regions. The Community Multiscale Air Quality model equipped with the Integrated Source Apportionment Model is used to simulate the contributions from 5 major emission sectors in 13 cities of the BTH region, and 4 surrounding provinces outside BTH for the year 2014. Annual averaged local contribution ranges from 32% to 63% for the 13 cities in the BTH region, where secondary components contributing more than primary components. Regional contribution ratio becomes larger and the transport distance longer in July and October than in January and March. For Beijing, local contributions are 62% and 69% in January and March respectively, and the regional transports are mainly from nearby cities such as Zhangjiakou, Baoding and Langfang. In July and October, local contributions in Beijing are only 33% and 38% respectively, and a large range of regions in the south have substantial contributions, where Shandong Province and Henan Province contribute 3.6-5.3 μg/m³. Analysis of daily contributions suggests that regional transport is stronger under higher PM_2.5 concentrations. During heavy pollution, local emissions in Beijing contribute 61%, 49%, 23% and 25% in January, march, July and October respectively, while during the clean days, the ratios are 88%, 88%, 76% and 57% respectively. Southerly regional transport during the rising phase of "saw tooth" pattern might be enhanced by weak cold high pressure and its easterly, northerly moving path. Among the major emission sectors, in winter, local domestic combustion is the most important source for Beijing, Tianjin and Shijiazhuang. In summer, transportation and domestic combustion are two important local sources for Beijing, while joint control in other cities should focus on industry.

Development and application of observable response indicators for design of an effective ozone and fine particle pollution control strategy in China

Designing effective control policies requires efficient quantification of the nonlinear response of air pollution to emissions. However, neither the current observable indicators nor the current indicators based on response-surface modeling (RSM) can fulfill this requirement. Therefore, this study developed new observable RSM-based indicators and applied them to ambient fine particle (PM_2.5) and ozone (O₃) pollution control in China. The performance of these observable indicators in predicting O₃ and PM_2.5 chemistry was compared with that of the current RSM-based indicators. H₂O₂×HCHO/NO₂ and total ammonia ratio, which exhibited the best performance among indicators, were proposed as new observable O₃- and PM_2.5-chemistry indicators, respectively. Strong correlations between RSM-based and traditional observable indicators suggested that a combination of ambient concentrations of certain chemical species can serve as an indicator to approximately quantify the response of O₃ and PM_2.5 to changes in precursor emissions. The observable RSM-based indicator for O₃ (observable peak ratio) effectively captured the strong NO_x-saturated regime in January and the NO_x-limited regime in July, as well as the strong NO_x-saturated regime in northern and eastern China and their key regions, including the Yangtze River Delta and Pearl River Delta. The observable RSM-based indicator for PM_2.5 (observable flex ratio) also captured strong NH₃-poor condition in January and NH₃-rich condition in April and July, as well as NH₃-rich in northern and eastern China and the Sichuan Basin. Moreover, analysis of these newly developed observable response indicators suggested that the simultaneous control of NH₃ and NO_x emissions produces greater benefits in provinces with higher PM_2.5 exposure by up to 1.2 μg m^-3 PM_2.5 per 10 % NH₃ reduction compared with NO_x control only. Control of volatile organic compound (VOC) emissions by as much as 40 % of NO_x controls is necessary to obtain the cobenefits of reducing both O₃ and PM_2.5 exposure at the national level when controlling NO_x emissions. However, the VOC-to-NO_x ratio required to maintain benefits varies significantly from 0 to 1.2 in different provinces, suggesting that a more localized control strategy should be designed for each province.