Evaluation of Heavy Pollution Weather Characteristics and Emission Reduction Effect in Langfang.

Autor: WANG Ming, DU Guimin, LI Jin, LIU Daxi, LYU Zhe, NIE Teng, LI Guohao, CUI Jiansheng
Předmět:
Zdroj: Environmental Science & Technology (10036504); 2024, Vol. 47 Issue 3, p67-79, 13p
Abstrakt: The Ministry of Ecology and Environment of the People ' s Republic of China formulated the " Differentiated Response Requirement Based on Performance Grading" measures for heavy air pollution alerts in July 2019 to promote air quality improvement and economic development. Eleven alerts (four yellow, six orange, and one red alert) in Langfang were classified into 8 pollution processes. The results showed that the mean concentration of PM2.5 was 41.4~129.0 µg/m³. The pollution processes were classified according to the backward trajectory analysis surface meteorological field changes, including southwest air masses, southeast air masses, northwest air masses and local air masses. The back of high pressure, bottom of low pressure, and weak pressure system might lead to the transport from southern regions. Moreover, uniform and weak pressure fields were the major meteorological fields in the northwest direction and during local pollution episodes. The mean and peak concentrations of PM2.5 in the southwest and local pollution processes were higher than those in the southeast and northwest, indicating that when these two types of pollution occur, the air quality is even worse. In addition, based on the emission reduction ratios of pollutants at various warning levels, the WRF-CMAQ model was used to evaluate the effectiveness of the differentiated response requirements based on performance grading during the 11 warning periods. The warning period, the mean concentration of PM2.5 during the warning period decreased by 2.6% to 5.9%, and the peak concentration of PM2.5 decreased by 0.7% to 7.7%. Overall, implementing emergency measures effectively improved the air quality and played a role in "peak shaving". [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index