Analysis and prediction of air pollutants in the independent coking industry in Shandong Province based on the CALPUFF model

被引：0
|
作者：
Chen X. [1 ]
Li J. [1 ]
Chen S. [1 ]
You Q. [2 ]
Chen X. [1 ]
Bo X. [1 ]
机构：
[1] College of Chemical Engineering, Beijing University of Chemical Technology, Beijing
[2] School of Management and Engineering, Capital University of Economics and Business, Beijing
来源：
Gongcheng Kexue Xuebao/Chinese Journal of Engineering | 2024年 / 46卷 / 07期
关键词：
CALPUFF; CO[!sub]2[!/sub; coking; environmental impacts; ultralow emission;
D O I：
10.13374/j.issn2095-9389.2023.07.31.001
中图分类号：
学科分类号：
摘要：
Since 2018, Shandong Province has successively issued a series of pollution prevention and control programs for air pollutants from the coking industry, which plays a key role in coke production in China. However, a comprehensive assessment of the effectiveness of these measures is lacking. For a better understanding of this issue, based on the published air pollutant emission inventory for the independent coking industry and related policies formulated in Shandong in 2018, this study developed business-as-usual scenarios (BAU-2018, BAU-2025, and BAU-2035) and emission reduction optimization scenarios (ERO-2025 and ERO-2035). The emission reduction potential of PM10, SO2, NOx, PM2.5, and CO2 and their corresponding contributions to Shandong’s air quality with the air quality model (CALPUFF) were assessed under different scenarios. In terms of scenario settings, the gross domestic product of the secondary industry and the coke output of independent coking enterprises were used for linear regression to predict the coke output in 2025 and 2035. The mesoscale atmospheric data model, Weather Research and Forecasting, provided simulated three-dimensional meteorological field data for this study. Regional terrain data (90 m) were obtained from the United States Geological Survey. The resolution of the land use type data was 30 m, according to our previous research results. We adopted the MESOPUFF II chemical mechanism to simulate SO2, NOx, SO42−, NO3−, HNO3, PM10, and PM2.5 pollutants. To ensure the accuracy of the enterprise location information, we examined the enterprise latitude and longitude information one by one using Google Earth location recognition and manual visual inspection. The results showed that in the BAU-2018 scenario, the annual contribution ratios of SO2, NOx, PM10, and PM2.5 in Shandong Province were 0.06%–0.84%, 0.01%–0.63%, 0.04%–0.19%, and 0.07%–0.21%, respectively. Linyi has the highest contribution of SO2 and NOx concentrations to air quality, whereas Jining has the highest contribution of PM10 and PM2.5 concentrations to air quality. Compared with the current scenario (BAU-2018), the emission and contribution concentration of each pollutant showed an alarming decrease in the ERO-2025 scenario. Under the ERO-2035 scenario, the results showed that SO2, NOx, PM10, PM2.5, and CO2 emissions would be reduced by 60.12%, 78.24%, 75.07%, 74.20%, and 37.47%, respectively, compared with those under the BAU-2018 scenario. In the ERO-2035 scenario, the average contribution concentrations of SO2, NOx, PM10, and PM2.5 decreased by 60.74%, 78.56%, 75.00%, and 74.53%, respectively. Moreover, the results of this study showed that the comprehensive implementation of ultralow emission standards in Shandong Province had a considerable impact on air pollutant reduction in the independent coking industry, and the synergistic reduction of air pollutants and carbon dioxide showed huge potential. © 2024 Science Press. All rights reserved.
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页码：1311 / 1323
页数：12
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