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Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods.
Jin, Meng-Yi; Gallagher, John; Li, Xiao-Bing; Lu, Kai-Fa; Peng, Zhong-Ren; He, Hong-Di.
Afiliación
  • Jin MY; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications Research, School of Naval Architecture, Ocean and Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Gallagher J; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, The University of Dublin, Dublin, D02 PN40, Ireland.
  • Li XB; Department of Civil, Structural & Environmental Engineering, Trinity College Dublin, The University of Dublin, Dublin, D02 PN40, Ireland.
  • Lu KF; Institute for Environmental and Climate Research, Jinan University, Guangzhou, 510632, China.
  • Peng ZR; iAdapt: International Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, Gainesville, FL, 32611-5706, USA.
  • He HD; iAdapt: International Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, Gainesville, FL, 32611-5706, USA. zpeng@ufl.edu.
Environ Monit Assess ; 196(8): 767, 2024 Jul 29.
Article en En | MEDLINE | ID: mdl-39073498
ABSTRACT
In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Emisiones de Vehículos / Monitoreo del Ambiente / Contaminantes Atmosféricos / Material Particulado País/Región como asunto: Asia Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Emisiones de Vehículos / Monitoreo del Ambiente / Contaminantes Atmosféricos / Material Particulado País/Región como asunto: Asia Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos