Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10 |
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| Affiliation: | 1. Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China;2. College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350108, China;3. International Center for Adaptation Planning and Design (iAdapt), School of Landscape Architecture and Planning, College of Design, Construction, and Planning, University of Florida, P.O. Box 115706, Gainesville, FL, 32611-5706, USA;4. State Environmental Protection Key Laboratory of the Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China;1. School of Engineering and Built Environment, Griffith University, QLD, Australia;2. Cities Research Institute, Griffith University, QLD, Australia;3. Griffith Climate Change Response Program, Griffith University, QLD, Australia;4. Griffith Centre for Coastal Management, Griffith University, QLD, Australia;1. Center for Urban Transport Emission Research (CUTER), College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China;2. Tianjin Eco-Environmental Monitoring Center, Tianjin 300191, China;3. Tianjin-Tianbinruicheng Environmental Technology and Engineering Co., Ltd., Tianjin 300190, China;1. Innovation and Policy Center for Population Health and Sustainable Environment, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan;2. Institute of Environmental and Occupational Health Science, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan;3. Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, No. 17, Xuzhou Rd, Taipei, 10055, Taiwan;4. Institute of Statistical Science, Academia Sinica, No. 128, Academia Rd., Sec. 2, Nankang, Taipei, 11529, Taiwan;5. Department of Environmental Engineering, Innovation and Development Center of Sustainable Agriculture (IDCSA), National Chung-Hsing University, Taiwan. No. 145, Xingda Rd., Taichung, 40227, Taiwan;6. Greenpeace East Asia, No.109, Sec. 1, Chongqing S. Rd., Taipei, 10045, Taiwan |
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| Abstract: | The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM10) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM10 concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM10 at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM10 concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM10, gaseous pollutants and meteorological variables respectively. |
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| Keywords: | Road traffic contribution Bivariate polar plot Artificial neural network Particulate matter |
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