Abstract

Abstract. The Beijing–Tianjin–Hebei (BTH) region is a metropolitan area with the most severe fine particle (PM2.5) pollution in China. An accurate emission inventory plays an important role in air pollution control policy making. In this study, we develop a unit-based emission inventory for industrial sectors in the BTH region, including power plants, industrial boilers, steel, non-ferrous metal smelting, coking plants, cement, glass, brick, lime, ceramics, refineries, and chemical industries, based on detailed information for each enterprise, such as location, annual production, production technology/processes, and air pollution control facilities. In the BTH region, the emissions of sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter with diameter less than 10 µm (PM10), PM2.5, black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) from industrial sectors were 869, 1164, 910, 622, 71, 63, and 1390 kt in 2014, respectively, accounting for a respective 61 %, 55 %, 62 %, 56 %, 58 %, 22 %, and 36 % of the total emissions. Compared with the traditional proxy-based emission inventory, much less emissions in the high-resolution unit-based inventory are allocated to the urban centers due to the accurate positioning of industrial enterprises. We apply the Community Multi-scale Air Quality (CMAQ; version 5.0.2) model simulation to evaluate the unit-based inventory. The simulation results show that the unit-based emission inventory shows better performance with respect to both PM2.5 and gaseous pollutants than the proxy-based emission inventory. The normalized mean biases (NMBs) are 81 %, 21 %, 1 %, and −7 % for the concentrations of SO2, NO2, ozone (O3), and PM2.5, respectively, with the unit-based inventory, in contrast to 124 %, 39 %, −8 %, and 9 % with the proxy-based inventory; furthermore, the concentration gradients of PM2.5, which are defined as the ratio of the urban concentration to the suburban concentration, are 1.6, 2.1, and 1.5 in January and 1.3, 1.5, and 1.3 in July, for simulations with the unit-based inventory, simulations with the proxy-based inventory, and observations, respectively, in Beijing. For O3, the corresponding gradients are 0.7, 0.5, and 0.9 in January and 0.9, 0.8, and 1.1 in July, implying that the unit-based emission inventory better reproduces the distributions of pollutant emissions between the urban and suburban areas.

Highlights

  • The Beijing–Tianjin–Hebei (BTH) region is the political, economic, and cultural center of China

  • The pollutant emissions from each industrial enterprise are calculated from activity level, the emission factor, and the removal efficiency of control technology, as shown in the following equation: Ei,j = Aj × EFi,j × 1 − ηi,j where Ei,j is emissions of pollutant i from industrial enterprise j, Aj is the activity level of industrial enterprise j, EFi,j is the uncontrolled emission factor of pollutant i from industrial enterprise j, and ηi,j is the removal efficiency of pollutant i by control technology in enterprise j . ηi,j is determined by the production process and control technology of the industrial enterprise

  • Power plants account for 13 %, 16 %, and 4 % of the total SO2, nitrogen oxide (NOx), and PM2.5 emissions, respectively, whereas the contributions to non-methane volatile organic compounds (NMVOCs) and NH3 emissions are negwww.atmos-chem-phys.net/19/3447/2019/

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Summary

Introduction

The Beijing–Tianjin–Hebei (BTH) region is the political, economic, and cultural center of China. H. Zheng et al.: Development of a unit-based industrial emission inventory in the BTH region an adequate understanding of the sources and the formation mechanism of serious air pollution in this area. Qi et al (2017) established an emission inventory in the BTH region in which power and major industrial sources were treated as point sources. These studies usually focused on one or several major industries, and did not cover all industrial sectors in the BTH region These previous studies seldom validated the unit-based emission inventory or evaluated the improvement it brings to air quality simulation. We developed a unit-based emission inventory of industrial sectors for the BTH region. In order to study the influence of the point sources, we compared the simulation results of this emission inventory with those of a traditional proxy-based emission inventory

High-resolution emission inventory for the BTH region
Air quality model configuration
Air pollutant emissions in the BTH region
Evaluation of the unit-based emission inventory
Conclusion

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