Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller d particulates (i.e., PM, PM, and PM), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM and PM) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM, PM, and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM, 53.47% lower in PM, and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments.
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http://dx.doi.org/10.1007/s10661-024-13104-0 | DOI Listing |
Landsc Ecol
December 2024
Institute of Environment Sciences, University of Quebec at Montreal, Montreal, QC H3C 3P8 Canada.
Context: Trees play a vital role in reducing street-level particulate matter (PM) pollution in metropolitan areas. However, the optimal tree growth type for maximizing the retention of various sizes of PM remains uncertain.
Objectives: This study assessed the PM reduction capabilities of evergreen and deciduous broadleaf street trees, focusing on how leaf phenology influences the dispersion of pollutants across particle sizes.
Sci Total Environ
December 2024
School of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China. Electronic address:
Gaps between buildings facing the streets can effectively accelerate the natural removal of pollutants in street canyons by enhancing ventilation and diffusion processes. This removal process is closely related to gap permeability (P, ratio of gap width to street length) and building density (λ) surrounding the streets. However, the integrated effects of these two parameters on pollutant dispersion remain unclear, because of the limited computational resources and the difficulty of traditional modeling methods to discrete the numerous buildings.
View Article and Find Full Text PDFSci Total Environ
December 2024
Atmospheric Composition Research, Finnish Meteorological Institute, 00101 Helsinki, Finland. Electronic address:
Sci Total Environ
December 2024
Institute of Mechanical, Process and Energy Engineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, Scotland, United Kingdom; Building Physics and Sustainable Design, Department of Civil Engineering, KU Leuven, Leuven, Belgium. Electronic address:
Idealized sources are commonly used to reproduce the traffic emission in street canyons in experimental and numerical investigations. However, it remains unclear whether idealized sources can accurately reproduce the pollutant dispersion compared to more realistic sources. The goal of this paper is to investigate the impact of idealized and realistic sources on traffic-induced pollutant concentration in a street canyon by numerical simulation with Computational Fluid Dynamics (CFD).
View Article and Find Full Text PDFJ Environ Manage
November 2024
Laboratory for the Science of Risks (LSR), IMT Mines Alès, 30100, Alès, France.
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