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Hyperlocal Air Pollution Mapping: A Scalable Transfer Learning LUR Approach for Mobile Monitoring.
Yuan, Zhendong; Kerckhoffs, Jules; Li, Hao; Khan, Jibran; Hoek, Gerard; Vermeulen, Roel.
Afiliación
  • Yuan Z; Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands.
  • Kerckhoffs J; Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands.
  • Li H; Professorship of Big Geospatial Data Management, Technical University of Munich, 85521 Ottobrunn, Germany.
  • Khan J; Department of Environmental Science, Aarhus University, DK-4000 Roskilde, Denmark.
  • Hoek G; Danish Big Data Centre for Environment and Health (BERTHA), Aarhus University, DK-4000 Roskilde, Denmark.
  • Vermeulen R; Institute for Risk Assessment Sciences, Utrecht University, 3584 CM Utrecht, Netherlands.
Environ Sci Technol ; 58(32): 14372-14383, 2024 Aug 13.
Article en En | MEDLINE | ID: mdl-39082120
ABSTRACT
Addressing the challenge of mapping hyperlocal air pollution in areas without local monitoring, we evaluated unsupervised transfer learning-based land-use regression (LUR) models developed using mobile monitoring data from other cities CORrelation ALignment (Coral) and its inverse distance-weighted modification (IDW_Coral). These models mitigated domain shifts and transferred patterns learned from mobile air quality monitoring campaigns in Copenhagen and Rotterdam to estimate annual average air pollution levels in Amsterdam (50m road segments) without involving any Amsterdam measurements in model development. For nitrogen dioxide (NO2), IDW_Coral outperformed Copenhagen and Rotterdam LUR models directly applied to Amsterdam, achieving MAE (4.47 µg/m3) and RMSE (5.36 µg/m3) comparable to a locally fitted LUR model (AMS_SLR) developed using Amsterdam mobile measurements collected for 160 days. IDW_Coral yielded an R2 of 0.35, similar to that of the AMS_SLR based on 20 collection days, suggesting a minimum requirement of 20-day mobile monitoring to capture city-specific insights. For ultrafine particles (UFP), IDW_Coral's citywide predictions strongly correlated with previously published mixed-effect models fitted with 160-day Amsterdam measurements (Pearson correlation of 0.71 for UFP and 0.72 for NO2). IDW_Coral demands no direct measurements in the target area, showcasing its potential for large-scale applications and offering significant economic efficiencies in executing mobile monitoring campaigns.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Monitoreo del Ambiente / Contaminantes Atmosféricos / Contaminación del Aire Idioma: En Revista: Environ Sci Technol Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Estados Unidos