Surface Monitoring of Fire Pollution
2023; American Geophysical Union; Linguagem: Inglês
10.1002/9781119757030.ch6
ISSN2328-8779
Autores Tópico(s)Atmospheric chemistry and aerosols
ResumoChapter 6 Surface Monitoring of Fire Pollution Allison E. Bredder, Allison E. Bredder Department of Geographical Sciences, University of Maryland, College Park, Maryland, USASearch for more papers by this author Allison E. Bredder, Allison E. Bredder Department of Geographical Sciences, University of Maryland, College Park, Maryland, USASearch for more papers by this author Book Editor(s):Tatiana V. Loboda, Tatiana V. LobodaSearch for more papers by this authorNancy H. F. French, Nancy H. F. FrenchSearch for more papers by this authorRobin C. Puett, Robin C. PuettSearch for more papers by this author First published: 20 October 2023 https://doi.org/10.1002/9781119757030.ch6Book Series:Geophysical Monograph Series AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary This chapter discusses efforts to measure surface observations of air pollution at the country scale. The countries with the most comprehensive regulatory systems to monitor air pollution are the older industrial nations such as countries in the United Kingdom and the United States. Recent proliferation of low-cost air quality sensors (LCAQS) are making near-real-time air pollution monitoring more prevalent across the globe. While unique challenges exist between regulatory and LCAQS data access and usability, there are common challenges in using these data for decision support and research applications. This chapter discusses common statistical methods for estimating air pollution including spatial interpolation methods, statistical regression methods, machine learning, and chemical transport modeling. REFERENCES Affenzeller , M. , Burlacu , B. , Dorfer , V. , Dorl , S. , Halmerbauer , G. , Königswieser , T. , et al. ( 2020 ). White box vs. black box modeling: On the performance of deep learning, random forests, and symbolic regression in solving regression problems . In R. 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