Spatiotemporal distributions of roadside PM2.5 and CO concentrations based on mobile observations
WANG Zhan-yong1, CAI Ming1, PENG Zhong-ren2, GAO Ya2
1. Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Engineering, Sun Yat-sen University, Guangzhou 510006, China; 2. Center for Intelligent Transportation System & Unmanned Aerial Vehicle Applications Research, Shanghai Jiaotong University, Shanghai 200240, China
Abstract：This study proposed a data preprocessing method for mobile traffic pollution observation based on previous work. The model was validated using 7days' (26 runs) observations of PM2.5 and CO concentrations collected in Shanghai. The data revealed the spatial distributions and temporal variations of PM2.5 and CO concentrations. Results showed that, an objective and comparable air pollutant distribution was characterized by the methods selected to remove the abnormal samples of high values, to correct the pollution background, and to determine the spatio-temporal scale. The high pollutant concentrations along the busy road intersections and their adjacent road sections were attributed to factors including large traffic flows, high proportion of diesel vehicles, frequent congestion and poor air flow. At these locations, PM2.5 and CO concentrations were 1.7~2.8 and 12~20times larger than observations on the clean campus, respectively. The living or production area showed about 3-fold higher PM2.5 concentrations when compared with the campus, while this for CO in the living area was not prominent. The averaged PM2.5 concentration of the whole area had a descending order in early morning, morning, afternoon and noon during a day. The averaged CO concentration was close in early morning and morning, which was greater than noon and afternoon. High humidity and low wind speed were unfavourable to air pollutant diffusion, and led to an accumulation of high pollutant concentrations along arterial roads in early morning.
王占永, 蔡铭, 彭仲仁, 高雅. 基于移动观测的路边PM2.5和CO浓度的时空分布[J]. 中国环境科学, 2017, 37(12): 4428-4434.
WANG Zhan-yong, CAI Ming, PENG Zhong-ren, GAO Ya. Spatiotemporal distributions of roadside PM2.5 and CO concentrations based on mobile observations. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(12): 4428-4434.
HEI. 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.
[2]
Gozzi F, Ventura G D, Marcelli A. Mobile monitoring of particulate matter:State of art and perspectives[J]. Atmospheric Pollution Research, 2016,7:228-234.
[3]
Pattinson W, Longley I, Kingham S. Using mobile monitoring to visualise diurnal variation of traffic pollutants across two near-highway neighbourhoods[J]. Atmospheric Environment, 2014,94:782-792.
[4]
Poppel M V, 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.
[5]
Bossche J V D, 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.
[6]
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, 7:2169-2183.
[7]
Yu Q, Lu Y, Xiao S, et al. Commuters' exposure to PM1.0 by common travel modes in Shanghai[J]. Atmospheric Environment, 2012,59:39-46.
[8]
Li B, Lei X N, Xiu G L, et al. Personal exposure to black carbon during commuting in peak and off-peak hours in Shanghai[J]. Science of the Total Environment, 2015,524-525:237-245.
[9]
Wang Z, Lu F, He H D, et al. Fine-scale estimation of carbon monoxide and fine particulate matter concentrations in proximity to a road intersection by using wavelet neural network with genetic algorithm[J]. Atmospheric Environment, 2015,104:264-272.
[10]
Wang Z, He H D, Lu F, et al. Hybrid model for prediction of carbon monoxide and fine particulate matter concentrations near road intersection[J]. Transportation Research Record:Journal of the Transportation Research Board, 2015,2503:29-38.
[11]
Wang Z, Lu Q C, He H D, et al. Investigation of the spatiotemporal variation and influencing factors on fine particulate matter and carbon monoxide concentrations near a road intersection[J]. Frontiers of Earth Science, 2017,11(1):63-75.
Levy I, Mihele C, Lu G, et al. Elucidating multipollutant exposure across a complex metropolitan area by systematic deployment of a mobile laboratory[J]. Atmospheric Chemistry and Physics, 2014,14:7173-7193.
[15]
Hagler G S W, Thoma E D, Baldauf R W. High-resolution mobile monitoring of carbon monoxide and ultrafine particle concentrations in a near-road environment[J]. Journal of the Air & Waste Management Association, 2010,60(3):328-336.
[16]
Kimbrough S, Baldauf R W, Hagler G S W, et al. Long-term continuous measurement of near-road air pollution in Las Vegas:seasonal variability in traffic emissions impact on local air quality[J]. Air Quality Atmosphere & Health, 2013,6(1):295-305.
[17]
Peters J, Theunis J, Poppel M V, et al. Monitoring PM10 and ultrafine particles in urban environments using mobile measurements[J]. Aerosol and Air quality Research, 2013,13:509-522.
[18]
Zhu Y, Kuhn T, Mayo P, et al. Comparison of daytime and nighttime concentration profiles and size distributions of ultrafine particles near a major highway[J]. Environmental Science & Technology, 2006,40(8):2531-2536.
[19]
Brachtl M V, Durant J L, Paez C, et al. Spatial and temporal variations and mobile source emissions of polycyclic aromatic hydrocarbons in Quito, Ecuador[J]. Environmental Pollution, 2009,157:528-536.
[20]
Jian L, Zhao Y, Zhu Y P, et al. An application of ARIMA model to predict submicron particle concentrations from meteorolnical factors at a busy roadside in Hangzhou, China[J]. Science of the Total Environment, 2012,426(2):336-345.
