Abstract

Abstract. The Beijing–Tianjin–Hebei (BTH) region has been suffering from the most severe fine-particle (PM2. 5) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM2. 5 concentrations has been a challenging task because of the complicated nonlinear relationships between PM2. 5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the extended response surface modeling (ERSM) technique to investigate the nonlinear response of PM2. 5 concentrations to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM2. 5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that, among all air pollutants, primary inorganic PM2. 5 makes the largest contribution (24–36 %) to PM2. 5 concentrations. The contribution of primary inorganic PM2. 5 emissions is especially high in heavily polluted winter and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM2. 5 control strategies. The total contributions of all precursors (nitrogen oxides, NOx; sulfur dioxides, SO2; ammonia, NH3; non-methane volatile organic compounds, NMVOCs; intermediate-volatility organic compounds, IVOCs; primary organic aerosol, POA) to PM2. 5 concentrations range between 31 and 48 %. Among these precursors, PM2. 5 concentrations are primarily sensitive to the emissions of NH3, NMVOC + IVOC, and POA. The sensitivities increase substantially for NH3 and NOx and decrease slightly for POA and NMVOC + IVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM2. 5 concentrations. The contributions of primary inorganic PM2. 5 emissions to PM2. 5 concentrations are dominated by local emission sources, which account for over 75 % of the total primary inorganic PM2. 5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.

Highlights

  • IntroductionChina is one of the regions with the highest concentration of PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 μm) in the world (van Donkelaar et al, 2015)

  • China is one of the regions with the highest concentration of PM2.5 in the world (van Donkelaar et al, 2015)

  • Following Zhao et al (2015b), we assess the performance of the extended response surface modeling (ERSM) prediction system using the “out-of-sample” and 2D-isopleth validation methods, which focus on the accuracy and stability of the prediction system, respectively

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Summary

Introduction

China is one of the regions with the highest concentration of PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 μm) in the world (van Donkelaar et al, 2015). The problem is especially serious over the Beijing–Tianjin–Hebei (BTH) region, one of the most populous and developed regions in China. Annual average PM2.5 concentrations in this region reached 85–110 μg m−3 during 2013-2015, which approximately triple the standard threshold (35 μg m−3) and far exceed those in other metropolitan regions (Wang et al, 2017). D. Wang et al, 2016), and the monetized loss over the BTH region is as high as 134 billion Chinese Yuan, representing 2.2 % of regional gross domestic product (GDP) (Lv and Li, 2016). PM2.5 substantially affects global and regional climate by absorbing and scattering solar radiation and by altering cloud properties (IPCC, 2013; Seinfeld et al, 2016; Zhao et al, 2017a), which in turn exert an impact on regional air quality D. Wang et al, 2014; Zhao et al, 2017b)

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