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Abstract:
A WRF-CAMx-PSAT model was applied to analyze the impact of the electric power industry on urban agglomerations along the middle reaches of the Yangtze River. A high-resolution emission inventory based on a bottom-up approach was developed for air quality simulation. A typical month with heavy air pollution in this region (i.e., January) was chosen for simulation, and two mitigation scenarios were set for assessing lower capacity power units' impact on regional air quality. One scenario was for shutting down the lower capacity power units, and the other was for replacing lower capacity power units with higher capacity power units. Results showed that lower capacity power units contributed bigger pollutant concentrations to the regional contribution of the electric power industry. The concentration contributions of SO2, NOx, and PM2.5 in the two mitigation scenarios were reduced by 36.2%-39.8%, 30.5%-33.5%, and 25.9%-30.7%, respectively, than those under the current situation. Meanwhile, the decreases in pollutant concentration contribution for different regions were very similar for the two mitigation scenarios. In addition, lower capacity power units in four regions (i.e., northwest of Hubei province, west of Hunan province, Xiang-Jing-Yi economic belt region, and Hefei-centered urban agglomerations) contributed obviously to the regional pollutant concentration contributions of the electric power industry. Regional pollutant concentration contributions in the two mitigation scenarios were reduced by 40%-70%. Therefore, lower capacity power units make a bigger impact on the air quality of urban agglomerations along the middle reaches of the Yangtze River and should be paid special attention to achieve better regional air quality. © 2018, Science Press. All right reserved.
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Environmental Science
ISSN: 0250-3301
Year: 2018
Issue: 8
Volume: 39
Page: 3476-3484
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count: 3
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 7
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