城市慢行道路中交通颗粒物的时空分布

doi:10.12066/j.issn.1007-2861.2351

Abstract

Abstract:

To capture the high temporal and spatial resolution characteristics of air pollution in urban non-motorized lanes, a micro-sensor-based cycling measurement platform was established to collect samples of submicron particulate matter (PM_1.0 ) and black carbon (BC) in non-motorized lanes alongside an expressway in Fuzhou, China. The temporal and spatial variations of these traffic particles were then visually analyzed, and explanations were provided. Results showed that the concentrations of PM_1.0 and BC were significantly greater along the community side than on the river side and were greater in the morning and evening peak periods than in the noon. In the morning, the BC concentration showed a stable accumulation, but PM_1.0 had a greater volatility; the opposite was the case in the evening. The drop of particulate matter concentration in non-motorized lanes was positively correlated with the distance to the main road and the abundance of vegetation around. The cold spots of particles were far from the main road and near high vegetation coverage, whereas hot spots were mostly distributed in traffic environments with construction and congestion. BC hotspots were synchronized with complex road conditions, but PM_1.0 hotspots were also closely related to the surrounding environment. Therefore, focusing on the key traffic pollutants is necessary in designing show-moving traffic while considering the topographical characteristics, spatial conditions, and surrounding environment. This can improve air quality in non-motorized lanes, thereby enhancing healthy commuting.

Key words: atmospheric particulate matter, temporal and spatial distribution, mobile measurement, show-moving traffic

CLC Number:

U491.92

LUO Binru, CAO Ruhui, CHEN Xin, HU Xisheng, WANG Zhanyong. Spatiotemporal distribution of traffic particulate matter in urban non-motorized lanes[J]. Journal of Shanghai University（Natural Science Edition）, 2022, 28(4): 582-593.

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[1]	Barros N, Fontes T, Silva M P, et al. How wide should be the adjacent area to an urban motorway to prevent potential health impacts from traffic emissions?[J]. Transportation Research Part A: Policy and Practice, 2013, 50: 113-128. doi: 10.1016/j.tra.2013.01.021
[2]	Jazcilevich A, De La Cruz Zavala J, Erazo Arcos A M, et al. Sidewalk pollution flows caused by vehicular traffic place children at a higher acute exposure risk[J]. J Expo Sci Environ Epidemiol, 2019, 29(4): 491-499. doi: 10.1038/s41370-018-0083-4
[3]	Janssen N A, Hoek G, Simic-Lawson M, et al. Black carbon as an additional indicator of the adverse health effects of airborne particles compared with $PM_{10}$ and $PM_{2.5}$[J]. Environmental Health Perspectives, 2011, 119 (12),1691-1699. doi: 10.1289/ehp.1003369 pmid: 21810552
[4]	Wang Z, Zhong S, He H D, et al. Fine-scale variations in $PM_{2.5}$ and black carbon concentrations and corresponding influential factors at an urban road intersection[J]. Build Environ, 2018, 141: 215-225. doi: 10.1016/j.buildenv.2018.04.042
[5]	He H D, Lu W Z, Xue Y. Prediction of $PM_{10}$ concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration[J]. Atmos Environ, 2009, 43(40): 6336-6342. doi: 10.1016/j.atmosenv.2009.09.027
[6]	王占永. 城市交叉口细颗粒物和黑碳的时空分布及预测研究[D]. 上海: 上海交通大学, 2016.
[7]	HEI Panel on the Health Effects of Traffic-Related Air Pollution. Traffic-related air pollution: a critical review of the literature on emissions, exposure, and health effects[R]. HEI Special Report 17, Health Effects Institute, Boston, MA, 2010.
[8]	Gozzi F, Ventura G D, Marcelli A. Mobile monitoring of particulate matter: state of art and perspectives[J]. Atmospheric Pollution Research, 2016, 7(2): 228-234. doi: 10.1016/j.apr.2015.09.007
[9]	Laulainen N S. Summary of conclusions and recommendations from a visibility science workshop: technical basis and issues for a national assessment for visibility impairment[R]. Prepared for US DOE, Pacific Northwest Laboratory, PNL-8606, 1993.
[10]	Hagler G S W, Yelverton T L B, Vedantham R, et al. Post-processing method to reduce noise while preserving high time resolution in aethalometer real-time black carbon data[J]. Aerosol and Air Quality Research, 2011, 11(5): 539-546. doi: 10.4209/aaqr.2011.05.0055
[11]	吴健生, 廖星, 彭建, 等. 重庆市$PM_{2.5}$浓度空间分异模拟及影响因子[J]. 环境科学, 2015, 36(3): 759-767.
[12]	潘竟虎, 张文, 李俊峰, 等. 中国大范围雾霾期间主要城市空气污染物分布特征[J]. 生态学杂志, 2014, 33(12): 3423-3431.
[13]	Deshmukh P, Isakov V, Venkatram A, et al. The effects of roadside vegetation characteristics on local, near-road air quality[J]. Air Quality, Atmosphere & Health, 2018, 12(3): 259-270.
[14]	Tian Y, Liu X, Huo R, et al. Organic compound source profiles of $PM_{2.5}$ from traffic emissions, coal combustion, industrial processes and dust[J]. Chemosphere, 2021, 278: 130429. doi: 10.1016/j.chemosphere.2021.130429
[15]	Brachtl M V, Durant J L, Perez C P, et al. Spatial and temporal variations and mobile source emissions of polycyclic aromatic hydrocarbons in Quito, Ecuador[J]. Environ Pollut, 2009, 157(2): 528-36. doi: 10.1016/j.envpol.2008.09.041 pmid: 19004535
[16]	王占永, 蔡铭, 彭仲仁, 等. 基于移动观测的路边$PM_{2.5}$和CO浓度的时空分布[J]. 中国环境科学, 2017, 37(12), 4428-4434.
[17]	Dallmann T R, Kirchstetter T W, Demartini S J, et al. Quantifying on-road emissions from gasoline-powered motor vehicles: accounting for the presence of medium- and heavy-duty diesel trucks[J]. Environ Sci Technol, 2013, 47(23): 13873-13881. doi: 10.1021/es402875u pmid: 24215572

Spatiotemporal distribution of traffic particulate matter in urban non-motorized lanes

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