四川盆地PM<sub>2.5</sub>与PM<sub>10</sub>高分辨率时空分布及关联分析

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Abstract

In order to advance the understandings of the regional air pollution in the Sichuan Basin, two machine learning based models, random forests (RF), were developed to estimate the daily PM_2.5 and PM₁₀ concentrations on the 1km grid from 2013 to 2017. The datasets used for the model training included the satellite-retrieved aerosol optical depth (AOD) from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) product, the ground-based observations from the state-managed air quality monitoring network, as well as the meteorological, geographical, and socioeconomic variables. The RF models showed superior performance in predicting PM_2.5 and PM₁₀ (R²=0.86 for both). The multiyear regional average PM_2.5 and PM₁₀ concentrations were 47.8 and 75.2μg/m³, respectively. The PM_2.5 and PM₁₀ levels were predicted to be higher in the western and southern basin than the other areas, exhibiting a shape of crescent. During these five years, the PM_2.5 and PM₁₀ concentrations both decreased by 27%. The particulate matter concentrations exhibited obvious seasonality with the highest in winter and the lowest in summer. The spatial distributions of PM_2.5 and PM₁₀ showed high correlation (r=0.96), with stronger correlation in the lowland areas and slightly weaker correlation in the surrounding mountainous area. The correlations in spring (r=0.91) and summer (r=0.90) were relatively lower than those in fall (r=0.96) and winter (r=0.96). Ratio of PM_2.5 to PM₁₀ was higher in the southwestern basin and showed adescending order of winter > fall > summer > spring.

Key words： particulate matter aerosol optical depth machine learning satellite remote sensing Sichuan basin spatiotemporal distribution

Received: 13 May 2019

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X513

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	TANG Yu-lei
	YANG Fu-mo
	ZHAN Yu

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TANG Yu-lei,YANG Fu-mo,ZHAN Yu. High resolution spatiotemporal distributionand correlation analysis of PM_2.5 and PM₁₀ concentrations in the Sichuan Basin[J]. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(12): 4950-4958.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2019/V39/I12/4950

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