Machine-Learning Source Apportionment of Particulate Pollution Aids Urban Emission Regulations.

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Bibliographic Details
Title: Machine-Learning Source Apportionment of Particulate Pollution Aids Urban Emission Regulations.
Authors: Peng X; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China.; State Environmental Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Ecological Environmental Monitoring Center, Guangzhou 510308, China., Ma HN; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., He LY; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., Cao LM; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China.; Environmental Laboratory, PKU-HKUST Shenzhen-Hong Kong Institution, Shenzhen 518057, China., Tang MX; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., Wang Y; Department of Earth System Science, Stanford University, Stanford, California 94305, United States., Chen DH; State Environmental Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Ecological Environmental Monitoring Center, Guangzhou 510308, China., Zhou Y; State Environmental Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Secondary Air Pollution Research, Guangdong Ecological Environmental Monitoring Center, Guangzhou 510308, China., Tang KJ; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., He L; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., Feng N; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., Zeng LW; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China., Wang Y; Department of Earth System Science, Stanford University, Stanford, California 94305, United States., Huang XF; Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
Source: Environmental science & technology [Environ Sci Technol] 2026 Apr 21; Vol. 60 (15), pp. 11564-11576. Date of Electronic Publication: 2026 Apr 08.
Publication Type: Journal Article
Journal Info: Publisher: American Chemical Society Country of Publication: United States NLM ID: 0213155 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5851 (Electronic) Linking ISSN: 0013936X NLM ISO Abbreviation: Environ Sci Technol Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1520-5851
DOI:10.1021/acs.est.5c14501