Method Established for Source Apportionment of Human Health Risk in Regional Atmospheric Environment
Volume 7, Issue 6, December 2018, Pages: 268-274
Received: Oct. 18, 2018;
Accepted: Nov. 6, 2018;
Published: Nov. 30, 2018
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Huanbo Wu, Inner Mongolia Meteorological Services Center, Hohhot, China
Xiao Liu, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Wenkai Guo, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Qiang Chen, Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
Previous studies of source apportionment were only focused on contribution rates of pollutants concentration, but have not evaluated contribution rates of influencing degree of pollutants on people's health. To assess the health risk of pollution source to human health in the atmospheric environment, a method of source apportionment of human health risk, which the health risk assessment method combined with the source apportionment receptor model, was established in this research. Based on each pollution source contribution to metallic elements in inhalation particle matter (PM10) at the sampling site of Lanzhou University, the health risks contribution rates to exposed group were estimated according to the established method, and compared with the results of source apportionment. The results were as follows: the concentration contribution rates calculated by chemical mass balance (CMB) model rank from high to low as vehicle exhaust dust (43.4%), urban fugitive dust (29.9%), coal fly ash (21.5%), construction cement dust (1.2%) and metal smelt dust (0.7%); the non-carcinogen hazard index (Rn) contribution rates rank from high to low as urban fugitive dust (87.7%), vehicle exhaust dust (5.9%), coal fly ash (3.0%), metal smelt dust (2.5%) and construction cement dust (0.9%); the cancer risk value of carcinogen (Rc) contribution rates rank from high to low as urban fugitive dust (97.1%), vehicle exhaust dust (1.7%), coal fly ash (0.5%), metal smelt dust (0.5%) and construction cement dust (0.2%). Apparently, the concentration contribution rates were very different from the hazard index of non-carcinogen (Rn) contribution rates and the cancer risk value (Rc) contribution rates. The source with the highest concentration contribution was not the major influence on human health. The influence of source with the contribution rate lowest concentration contribution on human health should not be ignored. This method could also be used in health risk assessment of other pollutants from other sources.
Method Established for Source Apportionment of Human Health Risk in Regional Atmospheric Environment, Earth Sciences.
Vol. 7, No. 6,
2018, pp. 268-274.
Heal, M. R., Kumar, P., Harrison, R. M. Particles, air quality, policy and health [J], Chemical Society Reviews, 2012, 41(19): 6606-6630.
Kelly, F. J., Fussell, J. C. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter [J], Atmospheric Environment, 2012(60), 504-526.
Megido, L., Suárez, B., Negral, L., et al. Relationship between physico-chemical characteristics and potential toxicity of PM10 [J], Chemosphere, 2016(162), 73-79.
International Agency for Research on Cancer, and World Health Organization. IARC: Outdoor air pollution a leading environmental cause of cancer deaths. No. 221 [J], World Health Organization, 2013.
Brook, R. D., Rajagopalan, S., Pope, C. A., et al. Particulate matter air pollution and cardiovascular disease [J], Circulation, 2010(121): 2331-2378.
Kim, K. H., Kabir, E., Kabir, S. A review on the human health impact of airborne particulate matter [J], Environment International, 2015(74): 136-143.
Wang, W., Wang, Q. Impact and mechanism of ambient particulate matter on cardiovascular diseases [J], Journal of Environmental Health, 2009(26): 834-837. (in Chinese).
Daniels, M. J., Dominici, F., Samet, J. M., et al. Estimating particulate matter-mortality dose-response curves and threshold levels: an analysis of daily time-series for the 20 largest US cities [J], American Journal of Epidemiology, 2000(152): 397-406.
Hoek, G., Krishnan, R. M., Beelen, R., et al. Long-term air pollution exposure and cardio-respiratory mortality: a review [J], Environmental Health, 2013, 12(1): 43.
Zhang, L. W., Chen, X., Xue, X. D., et al. Long-term exposure to high particulate matter pollution and cardiovascular mortality: a 12-year cohort study in four cities in northern China [J], Environment International, 2014(62): 41-47. (in Chinese).
Levy, J. I., Hammitt, J. K., and Spengler, J. D. Estimating the mortality impacts of particulate matter: what can be learned from between-study variability [J], Environmental health perspectives, 2000(108): 109.
Peters, A., Dockery, D. W., Muller, J. E., et al. Increased particulate air pollution and the triggering of myocardial infarction [J], Circulation, 2001(103): 2810-2815.
Guo, S., Hu, M., Guo, Q., et al. Quantitative evaluation of emission controls on primary and secondary organic aerosol sources during Beijing 2008 Olympics [J], Atmospheric Chemistry and Physics, 2013, 13(16), 8303-8314.
Hopke, P. K., Ito, K., Mar, T., et al. PM source apportionment and health effects: 1. Inter comparison of source apportionment results [J]. Journal of Exposure Science and Environmental Epidemiology, 2006, 16 (3): 275-286.
Kampa, M., Castanas, E. Human health effects of air pollution [J], Environmental pollution, 2008, 151(2), 362-367.
Laden, F., Neas, L. M., Dockery, D. W., et al. Association of fine particulate matter from different sources with daily mortality in six US cities [J]. Environmental health perspectives, 2000, 108(10), 941-947.
Brown, D. G. Development of a Raoult’s law-based screening-level risk assessment methodology for coal tar and its application to ten tars obtained from former manufactured gas plants in the Eastern United States [J], Journal of Environmental Health, 2013(4): 1-11.
Dong, T., Li, T. X., Zhao, X. G., et al. Source and health risk assessment of heavy metals in ambient air PM10 from one coking plant [J], Chinese Journal of Environmental Science, 2014(35): 1238-1244. (in Chinese).
Chabukdhara, M., Nema, A. K. Heavy metals assessment in urban soil around industrial clusters in Ghaziabad, India: probabilistic health risk approach [J], Ecotoxicology and Environmental Safety, 2013(87): 57-64.
Jing, Y. The source profiles replacement and receptor data expansion and their application in source apportionment of PM10 in Lanzhou [C], MSc thesis Lanzhou University. Lanzhou, China, 2014. (in Chinese).
Chen, Q., Wu, H. B.. Establishment of method for health risk assessment of pollutants from fixed sources [J], Chinese Journal of Environmental Science, 2016(37): 1646-1652. (in Chinese).
RAIS. "The risk assessment information system." http://rais.ornl.gov/, 2013.
Fryer, M., Collins, C. D., et al. Human exposure modeling for chemical risk assessment: a review of current approaches and research and policy implications [J], Environmental Science and Policy, 2006, 9(3), 261-274.
Karim, Z., Qureshi, B. A. Health risk assessment of heavy metals in urban soil of Karachi, Pakistan [J], Human and Ecological Risk Assessment: An International Journal, 2014, 20(3), 658-667.