机动车禁行区街区尺度污染物时空分布——基于自行车车载走航

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (2316 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要以武汉第七届世界军人运动会期间为研究时段,以机动车辆管控的东湖风景区为研究区域,开展基于自行车车载走航形式的大气污染物浓度实时观测,填补该区域空气质量监测数据空白,为管控措施的效果评估和街区尺度健康风险评估提供精细数据.结果表明,走航观测所测得的ρ(CO)、ρ(NO₂)、ρ(PM_2.5)和ρ(O₃)平均值均高于监测站点,平均超过2.1倍.管控期机动车禁行区ρ(CO)、ρ(BC)、ρ(NO₂)、ρ(PM_2.5)和ρ(O₃)的平均值分别为0.97mg/m³, 5.6μg/m³, 57.8μg/m³, 76.3μg/m³和208.3μg/m³,非禁行区分别为1.1mg/m³, 4.7μg/m³, 60.9μg/m³, 72.2μg/m³和197.7μg/m³.车群行驶缓慢的地方如路口和信号灯处容易出现高值,ρ(BC)、ρ(CO)、ρ(O₃)和ρ(PM_2.5)可分别高达20.1μg/m³, 1.8mg/m³, 282.9μg/m³和106.3μg/m³.对应时间省控站点所测得的ρ(CO)、ρ(O₃)和ρ(PM_2.5)分别为0.73mg/m³, 128.0μg/m³和31.0μg/m³,明显低估街区尺度上人群污染物暴露浓度.基于TPRS法计算背景浓度,发现管控期CO、NO₂、PM₁、PM_2.5、PM₁₀、O₃和BC的背景浓度占比分别为75.9%, 63.2%, 77.6%, 77.5%, 78.8%, 80.7%和37.4%,在管控后均有所上升.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刁伟源
	孔少飞
	郑煌
	徐家平
	郑淑睿
	牛真真
	程溢
	胡尧
	祁士华

关键词 ：大气污染物, 武汉军运会, 自行车车载走航, 街区尺度, 时空分布

Abstract：In order to fill the gap of air quality datasets and provide detailed information for assessing effects of pollution control measures and human health risks at a block scale, real-time observations with a vehicle-mounted bicycle navigation were conducted to monitor variations in the atmospheric pollutants during the 7th World Military Games Period at the East Lake Scenic Area of Wuhan where motor vehicle was prohibited. The results indicate that the measured values of ρ(CO), ρ(NO₂), ρ(PM_2.5) and ρ(O₃) were averaged up to 2.1higher in the study area than in a nearby monitoring station. During the control period, the average concentrations of ρ(CO), ρ(BC), ρ(NO₂), ρ(PM_2.5) and ρ(O₃) were 970, 5.6, 57.8, 76.3 and 208.3μg/m³, respectively, in the vehicle prohibited zone, and 1100, 4.7, 60.9, 72.2 and 197.7μg/m³, respectively, in the non-prohibited. The hotspots located at places of intersections and traffic lights, where the ρ(BC),ρ(CO), ρ(O₃) and ρ(PM_2.5) amounted up to 20.1, 1800, 282.9 and 106.3μg/m³, respectively; while the values of ρ(CO), ρ(O₃) and ρ(PM_2.5) measured at a nearby monitoring station were 730, 128.0 and 31.0μg/m³, respectively, which obviously were lower than (or underestimated) the concentrations of these pollutants actually exposed to people at the block scale. During the control period, the proportions of the background concentrations for CO, NO₂, PM₁, PM_2.5, PM₁₀, O₃ and BC were 75.9%, 63.2%, 77.6%, 77.5%, 78.8%, 80.7% and 37.4%, respectively, which all increased after the control.

Key words： air pollutant Wuhan Military Games vehicle-mounted bicycles block scale spatiotemporal distribution

收稿日期: 2022-10-20

PACS:

X831

基金资助:湖北省科技厅技术创新专项重大项目(2017ACA089);国家自然科学基金资助项目(41830965)

通讯作者: 孔少飞,教授,kongshaofei@cug.edu.cn E-mail: kongshaofei@cug.edu.cn

作者简介: 刁伟源(1999-),男,河南郑州人,中国地质大学(武汉)硕士研究生,主要研究方向为基于走航观测的区域污染物时空分布.927181669@qq.com.

引用本文:

刁伟源, 孔少飞, 郑煌, 徐家平, 郑淑睿, 牛真真, 程溢, 胡尧, 祁士华. 机动车禁行区街区尺度污染物时空分布——基于自行车车载走航[J]. 中国环境科学, 2023, 43(5): 2095-2105. DIAO Wei-yuan, KONG Shao-fei, ZHENG Huang, XU Jia-ping, ZHENG Shu-rui, NIU Zhen-zhen, CHENG Yi, HU Yao, QI Shi-hua. Spatiotemporal distribution of block scale air pollutants in motor-vehicle prohibited zone based on mobile observations of vehicle-mounted bicycles. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(5): 2095-2105.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I5/2095

[1]	Merritt A S, Georgellis A, Andersson N, et al. Personal exposure to black carbon in Stockholm, using different intra-urban transport modes[J]. Science of the Total Environment, 2019,674:279-287.
[2]	Lin C, Hu D, Jia X, et al. The relationship between personal exposure and ambient PM2.5 and black carbon in Beijing[J]. Science of the Total Environment, 2020,737:139801.
[3]	Ljungman P L S, Li W, Rice M B, et al. Long- and short-term air pollution exposure and measures of arterial stiffness in the Framingham Heart Study[J]. Environment International, 2018,121:139-147.
[4]	Clark S N, Alli A S, Brauer M, et al. High-resolution spatiotemporal measurement of air and environmental noise pollution in Sub-Saharan African cities:Pathways to Equitable Health Cities Study protocol for Accra, Ghana[J]. BMJ Open, 2020,10:5798.
[5]	Bigazzi A Y, Rouleau M. Can traffic management strategies improve urban air quality?A review of the evidence[J]. Journal of Transport & Health, 2017,7:111-124.
[6]	Rodriguez-Rey D, Guevara M, Linares M P, et al. To what extent the traffic restriction policies applied in Barcelona city can improve its air quality?[J]. Science of the Total Environment, 2022,807:150743.
[7]	Chen Y, Gu P, Schulte N, et al. A new mobile monitoring approach to characterize community-scale air pollution patterns and identify local high pollution zones[J]. Atmospheric Environment, 2022,272:118936.
[8]	Pierangeli I, Nieuwenhuijsen M J, Cirach M, et al. Health equity and burden of childhood asthma-related to air pollution in Barcelona[J]. Environmental Research, 2020,186:109067.
[9]	Zhang Y, Ye X, Wang S, et al. Large-eddy simulation of traffic- related air pollution at a very high resolution in a mega-city:evaluation against mobile sensors and insights for influencing factors[J]. Atmospheric Chemistry and Physics, 2021,21(4):2917-2929.
[10]	Belkacem I, Khardi S, Helali A, et al. The influence of urban road traffic on nanoparticles:Roadside measurements[J]. Atmospheric Environment, 2020,242:117786.
[11]	Song R, Presto A A, Saha P, et al. Spatial variations in urban air pollution:impacts of diesel bus traffic and restaurant cooking at small scales[J]. Air Quality, Atmosphere & Health, 2021,14(12):2059-2072.
[12]	Liu X, Zhang X, Schnelle-Kreis J, et al. Spatiotemporal characteristics and driving factors of black carbon in Augsburg, Germany:Combination of mobile monitoring and street view images[J]. Environmental Science & Technology, 2021,55(1):160-168.
[13]	Weuve J, Bennett E E, Ranker L, et al. Exposure to air pollution in relation to risk of dementia and related outcomes:An updated systematic review of the epidemiological literature[J]. Environmental Health Perspectives, 2021,129:096001.
[14]	Delgado-Saborit J M, Guercio V, Gowers A M, et al. A critical review of the epidemiological evidence of effects of air pollution on dementia, cognitive function and cognitive decline in adult population[J]. Science of the Total Environment, 2021,757,doi:10.1016/j.scitotenv. 2020.143734.
[15]	Calderon-Garciduenas L, Ayala A. Air pollution, ultrafine particles, and your brain:Are combustion nanoparticle emissions and engineered nanoparticles causing preventable fatal neurodegenerative diseases and common neuropsychiatric outcomes?[J]. Environmental Science & Technology, 2022,56(11):6847-6856.
[16]	Samad A, Vogt U. Mobile air quality measurements using bicycle to obtain spatial distribution and high temporal resolution in and around the city center of Stuttgart[J]. Atmospheric Environment, 2021,244:117915.
[17]	Minet L, Liu R, Valois M F, et al. Development and comparison of air pollution exposure surfaces derived from on-road mobile monitoring and short-term stationary sidewalk measurements[J]. Environmental Science & Technology, 2018,52(6):3512-3519.
[18]	Simon M C, Patton A P, Naumova E N, et al. Combining measurements from mobile monitoring and a reference site to develop models of ambient ultrafine particle number concentration at residences[J]. Environmental Science & Technology, 2018,52(12):6985-6995.
[19]	Blanco M N, Gassett A, Gould T, et al. Characterization of annual average traffic-related air pollution concentrations in the greater seattle area from a year-long mobile monitoring campaign[J]. Environmental Science & Technology, 2022,56(16):11460-11472.
[20]	Yu Y T, Xiang S, Li R, et al. Characterizing spatial variations of city-wide elevated PM10 and PM2.5 concentrations using taxi-based mobile monitoring[J]. Science of the Total Environment, 2022,829:154478.
[21]	Krecl P, Cipoli Y A, Targino A C, et al. Cyclists'exposure to air pollution under different traffic management strategies[J]. Science of the Total Environment, 2020,723:138043.
[22]	Messier K P, Chambliss S E, Gani S, et al. Mapping air pollution with google street view cars:efficient approaches with mobile monitoring and land use regression[J]. Environmental Science & Technology, 2018,52(21):12563-12572.
[23]	Hou Q, An X Q, Wang Y, et al. An evaluation of resident exposure to respirable particulate matter and health economic loss in Beijing during Beijing 2008 Olympic Games[J]. Science of the Total Environment, 2010,408(19):4026-4032.
[24]	程念亮,李云婷,张大伟,等.2014年APEC期间北京市空气质量改善分析[J].环境科学, 2016,37(1):66-73. Cheng N L, Li Y T, Zhang D W, et al. Improvement of air quality during APEC in Beijing in 2014[J]. Environmental Science, 2016, 37(1):66-73.
[25]	Wang Y, Liao H. Effect of emission control measures on ozone concentrations in Hangzhou during G20 meeting in 2016[J]. Chemosphere, 2020,261:127729.
[26]	Hofman J, Samson R, Joosen S, et al. Cyclist exposure to black carbon, ultrafine particles and heavy metals:An experimental study along two commuting routes near Antwerp, Belgium[J]. Environmental Research, 2018,164:530-538.
[27]	Baron R, Saffel J. Amperometric gas sensors as a low cost emerging technology platform for air quality monitoring applications:A review[J]. Sensors, 2017,2:1553-1566.
[28]	Cross E S, Williams L R, Lewis D K, et al. Use of electrochemical sensors for measurement of air pollution:correcting interference response and validating measurements[J]. Atmospheric Measurement Techniques, 2017,10(9):3575-3588.
[29]	Hagler G S, Lin M Y, Khlystov A, et al. Field investigation of roadside vegetative and structural barrier impact on near-road ultrafine particle concentrations under a variety of wind conditions[J]. Science of the Total Environment, 2012,419:7-15.
[30]	Wang S, Ma Y, Wang Z, et al. Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors:impacts of COVID- 19pandemic lockdown[J]. Atmospheric Chemistry and Physics, 2021,21(9):7199-7215.
[31]	Van den Bossche J, Peters J, Verwaeren J, et al. Mobile monitoring for mapping spatial variation in urban air quality:Development and validation of a methodology based on an extensive dataset[J]. Atmospheric Environment, 2015,105:148-161.
[32]	Van Poppel M, Peters J, Bleux N. Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments[J]. Environmental Pollution, 2013,183:224-233.
[33]	Brantley H L, Hagler G S W, Kimbrough E S, et al. Mobile air monitoring data-processing strategies and effects on spatial air pollution trends[J]. Atmospheric Measurement Techniques, 2014,7(7):2169-2183.
[34]	Vardoulakis S, Gonzalez-Flesca N, Fisher E B, et al. Spatial variability of air pollution in the vicinity of a permanent monbitoring station in central Paris[J]. Atmospheric Environment, 2005,39(15):2725-2736.
[35]	Zhu Y, Chen J, Bi X, et al. Spatial and temporal representativeness of point measurements for nitrogen dioxide pollution levels in cities[J]. Atmospheric Chemistry and Physics, 2020,20:13241-13251.
[36]	Padro-Martinez L T, Patton A P, Trull J B, et al. Mobile monitoring of particle number concentration and other traffic-related air pollutants in a near-highway neighborhood over the course of a year[J]. Atmospheric Environment, 2012,61:253-264.
[37]	GB3095-2012环境空气质量标准[S]. GB3095-2012 Ambient air quality standards[S].
[38]	Hu H, Chen Q, Qian Q, et al. Impacts of traffic and street characteristics on the exposure of cycling commuters to PM2.5 and PM10 in urban street environments[J]. Building and Environment, 2021,188:107476.
[39]	Kumar P, Patton A P, Durant J L, et al. A review of factors impacting exposure to PM2.5, ultrafine particles and black carbon in Asian transport microenvironments[J]. Atmospheric Environment, 2018,187:301-316.
[40]	Chen C, Zhang H, Li H, et al. Chemical characteristics and source apportionment of ambient PM1.0 and PM2.5 in a polluted city in North China plain[J]. Atmospheric Environment, 2020,242:117867.
[41]	Li Y, Henze D K, Jack D, et al. Assessing public health burden associated with exposure to ambient black carbon in the United States[J]. Science of the Total Environment, 2016,539:515-525.
[42]	Jiang N, Yin S, Guo Y, et al. Characteristics of mass concentration, chemical composition, source apportionment of PM2.5 and PM10 and health risk assessment in the emerging megacity in China[J]. Atmospheric Pollution Research, 2018,9(2):309-321.
[43]	Tan F, Guo Y, Zhang W, et al. Large-scale spraying of roads with water contributes to, rather than prevents, air Pollution[J]. Toxics, 2021,9(6):122.
[44]	Steve H, Julian M, Brimblecombe P. On-biycle exposure to particulate air pollution:Particle number, black carbon, PM2.5, and particle size[J]. Atmospheric Environment, 2015,122:65-73.
[45]	Tao M, Huang H, Chen N, et al. Contrasting effects of emission control on air pollution in Central China during the 2019 Military World Games based on satellite and ground observations[J]. Atmospheric Research, 2021,259:105657.
[46]	Apte J S, Messier K P, Gani S, et al. High-resolution air pollution mapping with google street view cars:Exploiting big data[J]. Environmental Science & Technology, 2017,51(12):6999-7008.
[47]	Raparthi N, Debbarma S, Phuleria H C. Development of real-world emission factors for on-road vehicles from motorway tunnel measurements[J]. Atmospheric Environment:X, 2021,10,doi:10.1016/j.aeaoa.2021.100113.
[48]	Machaczka O, Jirik V, Brezinova V, et al. Evaluation of fine and ultrafine particles proportion in airborne dust in an industrial area[J]. International Journal of Environmental Research Public Health, 2021,18,doi:10.3390/ijerph18178915.
[49]	Hussein T, Saleh S, dos Santos V, et al. Black carbon and particulate matter concentrations in eastern mediterranean urban conditions:An assessment based on integrated stationary and mobile observations[J]. Atmosphere, 2019,10(6):323-346.
[50]	Zhao B, Yu L, Wang C, et al. Urban air pollution mapping using fleet vehicles as mobile monitors and machine learning[J]. Environmental Science & Technology, 2021,55(8):5579-5588.
[51]	Liu Y, Zhao X, Wang J, et al. A comprehensive 2018-based vehicle emission inventory and its spatial-temporal characteristics in the central liaoning urban agglomeration, China[J]. International Journal of Environmental Research Public Health, 2022,19(4):2033-2052.
[52]	Zhang Q, Streets D, Carmichael G, et al. Asian emissions in 2006 for NASA INTEX-B mission[J]. Atmospheric Chemistry and Physics, 2009,9:5131-5153.
[53]	Hu S, Paulson S E, Fruin S, et al. Observation of elevated air pollutant concentrations in a residential neighborhood of los angeles california using a mobile platform[J]. Atmospheric Environment, 2012,51:311- 319.
[54]	Sullivan R C, Pryor S C. Quantifying spatiotemporal variability of fine particles in an urban environment using combined fixed and mobile measurements[J]. Atmospheric Environment, 2014,9:664-671.
[55]	Li L Y, Chen Y, Xie S D. Spatio-temporal variation of biogenic volatile organic compounds emissions in China[J]. Environmental Pollution, 2013,182:157-168.
[56]	Matyssek R, Baumgarten M, Hummel U, et al. Canopy-level stomatal narrowing in adult Fagus sylvatica under O3 stress-means of preventing enhanced O3 uptake under high O3 exposure?[J]. Environmental Pollution, 2015,196:518-526.
[57]	Huang Y, Lei C, Liu C H, et al. A review of strategies for mitigating roadside air pollution in urban street canyons[J]. Environmental Pollution, 2021,280:116971.
[58]	Wei P, Brimblecombe P, Yang F H, et al. Determination of local traffic emission and non- local background source contribution to on- road air pollution using fixed- route mobile air sensor network[J]. Environmental Pollution, 2021,290:118055.
[59]	Yu C H, Fan Z, Lioy P J, et al. A novel mobile monitoring approach to characterize spatial and temporal variation in traffic-related air pollutants in an urban community[J]. Atmospheric Environment, 2016,141:161-173.
[60]	Jiang L, Xia Y, Wang L, et al. Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system[J]. Atmospheric Chemistry and Physics, 2021,21(22):16985-17002.