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

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.

Evaluating cost and benefit of air pollution control policies in China: A systematic review

China has put great efforts into air pollution control over the past years and recently committed to its most ambitious climate target. Cost and benefit analysis has been widely used to evaluate the control policies in terms of past performance, future reduction potential, and direct and indirect impacts. To understand the cost and benefit analysis for air pollution control in China, we conducted a bibliometric review of more than 100 studies published over the past two decades, including the current research progress, most commonly adopted methods, and core findings. The control target in cost and benefit analysis has shifted in three stages, from individual and primary pollution control, moving to joint prevention of multiple and secondary pollutants, and then towards synergistic control of air pollution and carbon. With the expansion of the research scope, the integrated assessment model has gradually demonstrated the necessity for long-term ex-anti policy simulation, especially for dealing with complex factors. To ensure long-term air quality, climate, public health, and sustainable economic development, substantial evidence from published studies has suggested that China needs to continue its efforts in the upstream adjustment of the energy system and industrial structure with multi-regional and -sector collaboration. This cost and benefit review paper provides decision-makers with the fundamental information and knowledge gaps in air pollution control strategies in China, and direction for facing future challenges.

Is the key-treatment-in-key-areas approach in air pollution control policy effective? Evidence from the action plan for air pollution prevention and control in China

The "Action Plan for Air Pollution Prevention and Control from 2013 to 2017" (APAPPC) establishes the key treatment for key areas in Beijing-Tianjin-Hebei and its surroundings (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in China. Is the key-treatment-in-key-areas approach effective? Except the targeted pollutants, does there exist the synergistic effects of other air pollutants? Are there differences between the north and the south about the effectiveness of this approach? To answer these new and key questions, this study uses the difference-in-difference (DID) model to evaluate whether it is more effective to implement key treatment policies for two targeted pollutants, PM_2.5 and PM_coarse (PM_2.5-PM₁₀), and verifies the synergistic emission-reduction effects of the policies on eight other emissions: SO₂, NO_X, VOC, CO, NH₃, BC, OC, and CO₂. At the same time, the policy effects are evaluated nationwide, in the north, and in the south using data from 2008 to 2017. The results show the following: (1) The APAPPC's establishment of the key treatment in key areas significantly reduces PM_2.5 and PM_coarse by 7.25 % and 10.49 %, respectively, compared with non-key areas nationwide. (2) The key-treatment-in-key-areas policy has synergistic effects on six other emissions: SO₂, NO_X, CO, BC, OC, and CO₂. (3) If viewing the north and the south separately, BTH beats other counterparts in the north by a large margin, while there is no significant difference among provinces and/or areas in the south. This margin is much larger than the one between key areas and other provinces nationwide. Therefore, this paper suggests that key treatment policies in key areas should continue to be implemented, especially for cities that have not dropped, but rather increased, their PM_2.5 concentrations. Additionally, air pollution goals should incorporate China's carbon peaking and neutrality goals to reduce air pollutants and greenhouse gas emissions.

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.

Characteristics of ambient volatile organic compounds during spring O3 pollution episode in Chengdu, China

Surface ozone (O₃) has become a critical pollutant impeding air quality improvement in many Chinese megacities. Chengdu is a megacity located in Sichuan Basin in southwest China, where O₃ pollution occurs frequently in both spring and summer. In order to understand the elevated O₃ during spring in Chengdu, we conducted sampling campaign at three sites during O₃ pollution episodes in April. Volatile organic compounds (VOCs) compositions at each site were similar, and oxygenated VOCs (OVOCs) concentrations accounted for the highest proportion (35%-45%), followed by alkanes, alkens (including acetylene), halohydrocarbons, and aromatics. The sensitivity of O₃ to its precursors was analyzed using an observation based box model. The relative incremental reactivity of OVOCs was larger than other precursors, suggesting that they also played the dominant role in O₃ formation. Furthermore, the positive matrix factorization model was used to identify the dominant emission sources and to evaluate their contribution to VOCs in the city. The main sources of VOCs in spring were from combustion (27.75%), industrial manufacturing (24.17%), vehicle exhaust (20.35%), and solvent utilization (18.35%). Discussions on VOCs and NO_x reduction schemes suggested that Chengdu was typical in the VOC-limited regime, and VOC emission reduction would help to prevent and control O₃. The analysis of emission reduction scenarios based on VOCs sources showed that the emission reduction ratio of VOCs to NO₂ needs to reach more than 3 in order to achieve O₃ prevention. Emission reduction from vehicular exhaust source and solvent utilization source may be more effective.

Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PM2.5 in China

Exposure to PM_2.5 can seriously endanger public health. Policies for controlling PM_2.5 need to consider health hazards under different circumstances. Unlike most studies on the concentration, distribution, and influencing factors of PM_2.5, the present study focuses on the impact of PM_2.5 on human health. We analysed the spatial-temporal evolution of health impact and economic loss caused by PM_2.5 exposure using the log-linear exposure-response function and benefit transfer method. The results indicate that the number of people affected by PM_2.5 pollution fluctuated and began to decline after reaching a peak in 2014, benefiting from the Air Pollution Prevention and Control Action Plan. Regarding the total economic loss, the temporal pattern continued to rise until 2014 and then declined, with an annual mean of 86,886.94 million USD, accounting for 1.71% of China’s GDP. For the spatial pattern, the health impact and economic loss show a strong spatial correlation and remarkable polarisation phenomena, with high values in East China, North China, Central China, and South China, but low values in Southwest China, Northwest China, and Northeast China. The spatial-temporal characterisation of PM_2.5 health hazards is visualised and analysed accordingly, which can provide a reference for more comprehensive and effective policy decisions.

An Integration Method for Regional PM2.5 Pollution Control Optimization Based on Meta-Analysis and Systematic Review

PM_2.5 pollution in China is becoming increasingly severe, threatening public health. The major goal of this study is to evaluate the mortality rate attributed to PM_2.5 pollution and design pollution mitigation schemes in a southern district of China through a two-objective optimization model. The mortality rate is estimated by health effect evaluation model. Subjected to limited data information, it is assumed that the meta-analysis method, through summarizing and combining the research results on the same subject, was suitable to estimate the percentage of deaths caused by PM_2.5 pollution. The critical parameters, such as the total number of deaths and the background concentration of PM_2.5, were obtained through on-site survey, data collection, literature search, policy analysis, and expert consultation. The equations for estimating the number of deaths caused by PM_2.5 pollution were established by incorporating the relationship coefficient of exposure to reaction, calculated residual PM_2.5 concentration of affected region, and statistical total base number of deaths into a general framework. To balance the cost from air quality improvement and human health risks, a two-objective optimization model was developed. The first objective is to minimize the mortality rate attributable to PM_2.5 pollution, and the second objective is to minimize the total system cost over three periods. The optimization results demonstrated that the combination of weights assigned to the two objectives significantly influenced the model output. For example, a high weight value assigned to minimizing the number of deaths results in the increased use of treatment techniques with higher efficiencies and a dramatic decrease in pollutant concentrations. In contrast, a model weighted more toward minimizing economic loss may lead to an increase in the death toll due to exposure to higher air pollution levels. The effective application of this model in the Nanshan District of Shenzhen City, China, is expected to serve as a basis for similar work in other parts of the world in the future.

A new approach for health-oriented ozone control strategy: Adjoint-based optimization of NOx emission reductions using metaheuristic algorithms

While levels of particulate matters in the Pearl River Delta Region (PRD) show a significant reduction, ozone (O₃) has an opposite increasing trend, becoming the critical air quality target in this decade. Emission control strategies are typically formulated sector by sector, spatial variability in emissions reductions and health impacts of air pollutants may not be taken into account, affecting the overall effectiveness of control strategies. This study proposes an adjoint-based optimization framework to facilitate health-oriented O₃ control over PRD. The location-specific adjoint sensitivity coefficients, which reflect the spatiotemporal influences from emissions of nitrogen dioxide (NO_x) on O₃ health impacts, are combined with metaheuristic algorithms to minimize the O₃-related premature mortalities over receptor regions. Using the proposed optimization methodology, the regional O₃ health benefits under current emission reduction policy can be increased by 16-27%. The results show that relatively larger NO_x emissions reductions occurred at highly developed and populated areas. Particularly, significant reductions in NO_x emissions are observed at Shenzhen and urban Guangzhou. Furthermore, implementing regional NO_x emissions abatement has advantages to achieve an overall O₃ health benefits for all cities. The interregional influences of NO_x emissions abatement between cities indicate a promising strategy of health-oriented O₃ control in PRD.

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.

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).

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM_2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM_2.5 concentrations for a given input configuration of emission changes. PM_2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM_2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM_2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m^-3, remaining above the World Health Organization annual guideline of 10 μg m^-3. The greatest reductions in PM_2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m^-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China

Air pollution exposure remains a leading public health problem in China. The use of chemical transport models to quantify the impacts of various emission changes on air quality is limited by their large computational demands. Machine learning models can emulate chemical transport models to provide computationally efficient predictions of outputs based on statistical associations with inputs. We developed novel emulators relating emission changes in five key anthropogenic sectors (residential, industry, land transport, agriculture, and power generation) to winter ambient fine particulate matter (PM2.5) concentrations across China. The emulators were optimized based on Gaussian process regressors with Matern kernels. The emulators predicted 99.9% of the variance in PM2.5 concentrations for a given input configuration of emission changes. PM2.5 concentrations are primarily sensitive to residential (51%-94% of first-order sensitivity index), industrial (7%-31%), and agricultural emissions (0%-24%). Sensitivities of PM2.5 concentrations to land transport and power generation emissions are all under 5%, except in South West China where land transport emissions contributed 13%. The largest reduction in winter PM2.5 exposure for changes in the five emission sectors is by 68%-81%, down to 15.3-25.9 μg m-3, remaining above the World Health Organization annual guideline of 10 μg m-3. The greatest reductions in PM2.5 exposure are driven by reducing residential and industrial emissions, emphasizing the importance of emission reductions in these key sectors. We show that the annual National Air Quality Target of 35 μg m-3 is unlikely to be achieved during winter without strong emission reductions from the residential and industrial sectors.

An Improved Gray Wolf Optimization Algorithm to Solve Engineering Problems

With the rapid development of the economy, the disparity between supply and demand of resources is becoming increasingly prominent in engineering design. In this paper, an improved gray wolf optimization algorithm is proposed (IGWO) to optimize engineering design problems. First, a tent map is used to generate the initial location of the gray wolf population, which evenly distributes the gray wolf population and lays the foundation for a diversified global search process. Second, Gaussian mutation perturbation is used to perform various operations on the current optimal solution to avoid the algorithm falling into local optima. Finally, a cosine control factor is introduced to balance the global and local exploration capabilities of the algorithm and to improve the convergence speed. The IGWO algorithm is applied to four engineering optimization problems with different typical complexity, including a pressure vessel design, a tension spring design, a welding beam design and a three-truss design. The experimental results show that the IGWO algorithm is superior to other comparison algorithms in terms of optimal performance, solution stability, applicability and effectiveness; and can better solve the problem of resource waste in engineering design. The IGWO also optimizes 23 different types of function problems and uses Wilcoxon rank-sum test and Friedman test to verify the 23 test problems. The results show that the IGWO algorithm has higher convergence speed, convergence precision and robustness compared with other algorithms.

CABOT-O3: An Optimization Model for Air Quality Benefit-Cost and Distributional Impacts Analysis

Macpherson et al. (2017) presented a mathematical programming model that identifies minimum-cost control strategies that reduce emissions regionally to meet ambient air quality targets. This project introduces the Cost And Benefit Optimization Tool for Ozone (CABOT-O₃), which extends the previous model by updating emissions and air quality relationships, adding a health impacts module, and quantifying distributional impacts. The tool draws upon source apportionment photochemical air quality modeling to characterize the contribution of emissions reductions to ambient ozone concentrations across the contiguous United States. The health impacts analysis module estimates the change in the number and economic value of premature deaths using modeled changes in ozone levels resulting from the application of emission control strategies. These extensions allow us to evaluate strategies to attain ozone air quality standards at minimum cost or to maximize net benefit, while assessing the change in the distribution of health impacts. In a case study applied to stationary pollution sources, we find that, when compared to minimizing costs to meet a uniform ozone standard, maximizing net benefits results in greater emissions and ozone concentration reductions in some parts of the country and fewer in others. Our results highlight potential equity-efficiency trade-offs in designing air quality policies.

Using Costs and Health Benefits to Estimate the Priority of Air Pollution Control Action Plan: A Case Study in Taiwan

A comparative analysis was conducted between the costs and health benefits of the Air Pollution Control Action Plan (APCAP), which can be implemented in any country to improve air quality and human health. In this study, air quality modeling was used to simulate several scenarios and implement the Kriging method to describe the PM2.5 reduction concentration instantly. Then, health benefits were estimated using the environmental benefit mapping and analysis program (BenMAP) with results from the air quality modeling and Kriging method. To estimate the priority of APCAP, 14 pollution control measures that cover point, mobile, and area sources of air pollution in Taiwan were analyzed. The results indicate that the health benefits of the Taiwan APCAP (TAPCAP) are generally greater than the technical costs. Thus, the implementation of this strategy may result in net benefits. In addition, the benefit-to-control cost ratio for health for the 14 pollution control measures was calculated. The results provide evidence to prioritize the implementation of air quality policies with a higher benefit-cost ratio.