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.

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

Quickly quantifying the PM2.5 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 PM2.5 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 PM2.5 and O3 predictions respectively; moreover, the isopleths of PM2.5 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 PM2.5 and O3 control policies.

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 PM2.5 is essential for a sound PM2.5 pollution control policy. In this study, we conduct a comparative analysis of PM2.5 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 PM2.5 emission sources to PM2.5 formation due to its linear nature. For the secondary PM2.5 formed by the nonlinear reactions among PM2.5 precursors, however, our study shows that PSAT appears to have limitations in quantifying the nonlinear contribution of PM2.5 precursors to emission reductions, while RSM seems to better address the nonlinear relationship among PM2.5 precursors (e.g., PM2.5 disbenefits due to local NOx emission reductions in major cities with high NOx emissions). The pilot study case results show that for the ambient PM2.5 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 PM2.5 concentration in the 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.

A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards.

PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.

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

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

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

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

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

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

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

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