{"title":"Research Progress, Challenges and Prospects of PM2.5 Concentration Estimation using Satellite Data","authors":"Shoutao Zhu, Jiayi Tang, Xiaolu Zhou, Peng Li, Zelin Liu, Cicheng Zhang, Ziying Zou, Tong Li, C. Peng","doi":"10.1139/er-2022-0125","DOIUrl":null,"url":null,"abstract":"Satellite data are vital for understanding the large-scale spatial distribution of PM2.5 due to their low cost, wide coverage, and all-weather capability. Estimation of particulate matter (PM2.5) using satellite aerosol optical depth (AOD) product is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning and combinatorial analysis) and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is less than others. We found significant differences in modeling accuracy between different seasons (p<0.01) and different spatial resolutions (p<0.01). We explained these phenomena. Finally, we summarized the research process, present challenges and future directions in this field. We opined that low-cost mobile devices combined with transfer learning or hybrid modeling offered research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation for developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.","PeriodicalId":50514,"journal":{"name":"Environmental Reviews","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Reviews","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1139/er-2022-0125","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Satellite data are vital for understanding the large-scale spatial distribution of PM2.5 due to their low cost, wide coverage, and all-weather capability. Estimation of particulate matter (PM2.5) using satellite aerosol optical depth (AOD) product is a popular method. In this paper, we review the PM2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning and combinatorial analysis) and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is less than others. We found significant differences in modeling accuracy between different seasons (p<0.01) and different spatial resolutions (p<0.01). We explained these phenomena. Finally, we summarized the research process, present challenges and future directions in this field. We opined that low-cost mobile devices combined with transfer learning or hybrid modeling offered research opportunities in areas with limited PM2.5 monitoring stations and historical PM2.5 estimation. These methods can be a good choice for air pollution estimation for developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.
期刊介绍:
Published since 1993, Environmental Reviews is a quarterly journal that presents authoritative literature reviews on a wide range of environmental science and associated environmental studies topics, with emphasis on the effects on and response of both natural and manmade ecosystems to anthropogenic stress. The authorship and scope are international, with critical literature reviews submitted and invited on such topics as sustainability, water supply management, climate change, harvesting impacts, acid rain, pesticide use, lake acidification, air and marine pollution, oil and gas development, biological control, food chain biomagnification, rehabilitation of polluted aquatic systems, erosion, forestry, bio-indicators of environmental stress, conservation of biodiversity, and many other environmental issues.