Abstract
Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.
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References
Bluett, J., Dey, K., & Fisher, G. (2008). Assessing vehicle air pollution emissions. Client Report No: CHC2008-001, National Institute of Water and Atmospheric Research, Christchurch,New Zealand. Prepared for the Department of the Environment, Water, Heritage and the Arts.
Brauer, M., Hoek, G., van Vliet, P., Melifste, K., Fischer, P., Wijga, A., Koopman, L., Neijens, H., Gerritsen, H., Kerkhof, M., Heinrich, J., Bellander, T., & Brunekreef, B. (2002). Air pollution from traffic and the development of respiratory infections and asthmatic and allergic symptoms in children. American Journal of Respiratory and Critical Care Medicine, 166, 1092–1098.
Brunekreef, B., & Holgate, S. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.
Bureau of Transport and Regional Economics. (2005). Health impacts of transport emissions in Australia—Economic costs, Vol. 63. ACT: Department of Transport and Regional Services, Canberra.
Department of the Environment, Water, Heritage and the Arts (2009). Second National In-Service Emissions Study (NISE2) - light duty petrol vehicle emissions testing. Final report, Australian Government.
Dyson, C., & Zito, R. (2005). NISE2—Contract 1 sample design and data analysis. Tech. rep., Transport Systems Centre, Department of the Environment and Heritage.
Gehring, U., Wijga, A., Fischer, P, de Jongste, J., Kerkhof, M., Koppelman, G., Smit, H., & Brunekreef, B. (2011). Traffic-related air pollution, preterm birth and term birth weight in the PIAMA birth cohort study. Environmental Research, 111, 125–135.
Kanji, G. (1999). 100 statistical tests.SAGE Publications Ltd, London.
Loader, C. (1999). Local likelihood and regression. New York: Springer.
Loader, C. (2010). Local regression, likelihood and density estimation. Version 1.5-6.
Miller, K., Siscovick, D., Sheppard, L., Shepherd, K., Sullivan, J., Anderson, G., & Kaufman, J. (2007). Long-term exposure to air pollution and incidence of cardiovascular events in women. The New England Journal of Medicine, 356(5), 447–458.
Power, M., Weisskopf, M., Alexeef, S., Coull, B., Sipro, A., & Schwartz, J. (2011). Traffic-related air pollution and cognitive function in a cohort of older men. Environmental Health Perspectives, 119(5), 682–687.
Raaschou-Nielsen, O., Sørensen, M., Ketzel, M., Hertel, O., Loft, S., Tjønneland, A., Overvad, K., & Andersen, Z. (2013). Long-term exposure to traffic-related air pollution and diabetes-associated mortality: a cohort study. Diabetologia, 56, 36–46.
Scoggins, A., Kjellstrom, T., Fisher, G., Connor, J., & Gimson, N. (2003). Spatial analysis of annual air pollution exposure and mortality. Science of the Total Environment, 321, 71–85.
Smit, R., Poelman, M., & Schrijver, J. (2008). Improved road traffic emission inventories by adding mean speed distributions. Atmospheric Environment, 42(5), 916–926.
Stephenson, P., & Giandomenico, E. (2010). A summary of the EPA University of South Australia motor vehicle emissions inventory model. Tech. report, Air & Noise Group, Science &, Assessment Division, South Australian Enivironment Protection Authority.
Health Panel on the Health Effects of Traffic-Related Air Pollution (2010). Traffic-related air pollution: A critical review of the literature on emissions. HEI Special Report 17, Health Effects Institute, Boston, MA.
Van Mierlo, J., Maggetto, G., van de Burgwal, E., & Gense, R. (2004). Driving style and traffic measures—Influence on vehicle emissions and fuel consumption. Proceedings of the Institution of Mechanical Enginers, 218(1), 43–50.
Wand, M., & Jones, M. (1995). Kernel Smoothing. Chapman & Hall Ltd, London, UK.
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This research would not have been possible without the continual support of the South Australian Environment Protection Authority to whom we would like to extend our sincere thanks and appreciation.
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Schultz, L., Shah, P., Giandomenico, E. et al. Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions. Environ Model Assess 21, 169–179 (2016). https://doi.org/10.1007/s10666-015-9463-5
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DOI: https://doi.org/10.1007/s10666-015-9463-5