中国环境科学
 
 
中国环境科学  2019, Vol. 39 Issue (12): 4950-4958    DOI:
大气污染与控制 最新目录| 下期目录| 过刊浏览| 高级检索 |
四川盆地PM2.5与PM10高分辨率时空分布及关联分析
汤宇磊1, 杨复沫1,2, 詹宇1,2
1. 四川大学建筑与环境学院, 四川 成都 610065;
2. 国家烟气脱硫工程技术研究中心, 四川 成都 610065
High resolution spatiotemporal distributionand correlation analysis of PM2.5 and PM10 concentrations in the Sichuan Basin
TANG Yu-lei1, YANG Fu-mo1,2, ZHAN Yu1,2
1. College of Architecture and Environment, Sichuan University, Chengdu 610065, China;
2. National Engineering Research Center for Flus Gas Desulfurization, Chengdu 610065, China

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摘要 

为深入了解四川盆地PM2.5与PM10污染情况,通过机器学习的方法,基于卫星遥感气溶胶产品(MAIAC)与国家环境空气质量监测网数据以及气象、地理、社会经济变量等,构建2个随机森林机器学习模型(R2均为0.86),反演四川盆地2013~2017年间1km网格逐日PM2.5与PM10浓度时空分布,并分析两者的时空关联性.结果表明:2013~2017年四川盆地地面PM2.5与PM10平均浓度分别为47.8,75.2μg/m3.PM2.5与PM10浓度空间上均整体呈现"倒月牙"状分布,西部与南部区域浓度值较高.5a间,区域颗粒物浓度逐年递减,总降幅均达到27%,季节上则均具有"冬高夏低"的特点;PM2.5与PM10浓度空间相关性显著(相关系数0.96),呈现"内强外弱"的格局,春夏季相关系数(0.91、0.90)低于秋冬季(0.96、0.96).盆地西南部PM2.5与PM10比值较高,比值高低的季节性排序为冬季 > 秋季 > 夏季 > 春季.

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汤宇磊
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关键词 颗粒物气溶胶光学厚度机器学习卫星遥感四川盆地时空分布    
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 PM2.5 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 PM2.5 and PM10 (R2=0.86 for both). The multiyear regional average PM2.5 and PM10 concentrations were 47.8 and 75.2μg/m3, respectively. The PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 to PM10 was higher in the southwestern basin and showed adescending order of winter > fall > summer > spring.

Key wordsparticulate matter    aerosol optical depth    machine learning    satellite remote sensing    Sichuan basin    spatiotemporal distribution   
收稿日期: 2019-05-13     
PACS: X513  
基金资助:

国家自然科学基金项目(41875162);四川省科技计划资助(2018SZDZX0023,2018SZ0316)

通讯作者: 詹宇,副研究员,yzhan@scu.edu.cn     E-mail: yzhan@scu.edu.cn
作者简介: 汤宇磊(1990-),男,安徽合肥人,四川大学硕士研究生,主要从事环境遥感与建模研究.发表论文1篇.
引用本文:   
汤宇磊, 杨复沫, 詹宇. 四川盆地PM2.5与PM10高分辨率时空分布及关联分析[J]. 中国环境科学, 2019, 39(12): 4950-4958. TANG Yu-lei, YANG Fu-mo, ZHAN Yu. High resolution spatiotemporal distributionand correlation analysis of PM2.5 and PM10 concentrations in the Sichuan Basin. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(12): 4950-4958.
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