Application of Fuzzy Synthetic Evaluation for the Air Quality Assessment in the Selected Cities of China
International Journal of Environmental Protection and Policy
Volume 6, Issue 2, March 2018, Pages: 50-55
Received: Jun. 20, 2018;
Published: Jun. 21, 2018
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Linan Yu, Airborne Survey and Remote Sensing Center of Nuclear Industry, Shi Jiazhuang, China
Haiyang He, Airborne Survey and Remote Sensing Center of Nuclear Industry, Shi Jiazhuang, China
Guoming Zhang, Airborne Survey and Remote Sensing Center of Nuclear Industry, Shi Jiazhuang, China
In this research, the air quality of six selected cities in China are evaluated according to the air monitoring data. Air pollutants including SO2, NO2, PM10, PM2.5, CO and O3 are chose as air quality indicators and compared with the ambient air quality standards of China (GB3095-2012). Using the fuzzy theory, the fuzzy synthetic evaluation model are constructed, and the air quality of the six selected cities are evaluated. Results show that the air quality of Beijing, Tianjing, Taiyuan, Dalian, Wuhan and Kunming in the year of 2014 belong to the second level in the ambient air quality standards of China (GB3095-2012). The air quality of the cities also obey the order: Kunming > Dalian > Taiyuan > Beijing > Wuhan > Tianjing. It seems that PM2.5 and PM10 are the main pollutants in the atmosphere of the six selected cities. These results can help the environmental regulators to make the right policy in environmental management.
Application of Fuzzy Synthetic Evaluation for the Air Quality Assessment in the Selected Cities of China, International Journal of Environmental Protection and Policy.
Vol. 6, No. 2,
2018, pp. 50-55.
Yuan, X., Mu, R., Zuo, J., Wang, Q., 2015, Economic Development, Energy Consumption, and Air Pollution: A Critical Assessment in China, Human and Ecological Risk Assessment: An International Journal [J], 21:3, 781-798.
EPI (Environmental Performance Index), 2016. Global Metrics for the Environment-The Environmental Performance Index Ranks Countries Performance on High-priority Environmental Issues [R]. (http://environment.yale.edu/news/article/2016-yaleenvironmental-performance-index-released/).
Chinese Ministry of Environmental Protection (CMEP). Report on the State of Environment in China in 2013 [R], CMEP: Beijing, China, 2014. (In Chinese).
Zhou X, Xu Y, Yuan S, et al. Performance and potential of solar updraft tower used as an effective measure to alleviate Chinese urban haze problem [J]. Renewable and Sustainable Energy Reviews, 2015, 51: 1499-1508.
Wu Y X, Liu X. Application of Fuzzy Mathematical Comprehensive Evaluation for the Air Quality Evaluation in Dazhou [J]. Sichuan Environment, 2011, 30 (5): 63-66.
Assessment and Analysis of Air Quality of Weinan from 2008 to 2014 [J]. Henan Science, 2015, 33 (10): 1838-1842.
Fu H, Chen T. Application of fuzzy mathematical comprehensive evaluation of the atmosphere quality evaluation in Wuhan [J]. JOURNAL-HUBEI UNIVERSITY NATURAL SCIENCE EDITION, 2007, 29 (3): 298-301.
Onkal-Engin G, Demir I, Hiz H. Assessment of urban air quality in Istanbul using fuzzy synthetic evaluation [J]. Atmospheric Environment, 2004, 38 (23): 3809-3815.
USEPA United States Environmental Protection Agency, 2009. Technical Assistance Document for the Reporting of Daily Air Quality—The Air Quality Index (AQI) [R].
PAHO Pan American Health Organization, 2015. Available at: http://www.paho.org (accessed August 2015) [Z].
MEP (Ministry of Environmental Protection), 2015. Enhancing Capability to Support Decision – A Summary of Environmental Monitoring in the First Half of 2015[Z]. 〈http://www.mep.gov.cn/xxgk/hjyw/201508/t20150804_307682.shtml〉.
Liu, K., Liang, H., Yeh, K., Chen, C., 2009. A qualitative decision support for environmental impact assessment using fuzzy logic [J]. Journal of Environmental Informatics. 13 (2), 93–103.
Upadhyaya, G., Dashore, N., 2010. Monitoring of air pollution by using fuzzy logic [J]. International Journal on Computer Science & Engineering. 02 (07), 2282–2286.
Upadhyaya, G., Dashore, N., 2011. Fuzzy logic based model for monitoring air quality index [J]. Indian Journal of Science & Technology. 4 (3), 15–218.
Wang, B., Chen, Z., 2015. A model-based fuzzy set-OWA approach for integrated air pollution risk assessment [J]. Stochastic Environmental Research and Risk Assessment. 29 (5), 1413–1426. Mishra, D., Goyal, P., 2016. Neuro-fuzzy approach to forecast NO2 pollutants addressed to air quality dispersion model over Delhi, India [J]. Aerosol & Air Quality Research. 16, 166–174.
Feng, Q., Wu, S., Du, Y., Xue, H., Xiao, F., Ban, X., Li, X., 2013. Improving neural network prediction accuracy for PM10 individual air quality index pollution levels [J]. Environmental Engineering Science. 30 (12), 725–732.
Mishra, D., Goyal, P., 2016. Neuro-fuzzy approach to forecast no2 pollutants addressed to air quality dispersion model over Delhi [J]. India Aerosol Air Quality Resource, 16, 166–174.
Ruiz, J., Mayora, A., Torres, J., Ruiz, G., 1995. Short-term ozone forecasting by artificial neural networks [J]. Advances in Engineering Software. 23 (3), 143–149.
Wang, W., Men, C., Lu, W., 2008. Online prediction model based on support vector machine [J]. Neurocomputing 71 (4–6), 550–558.
Bishoi, B., Prakash, A., Jain, V., 2009. A comparative study of air quality index based on factor analysis and USEPA methods for an urban environment [J]. Aerosol & Air Quality Research. 9 (1), 1–17.
Yong, L., Huaicheng, G., Guozhu, M., Pingjian, Y., 2008. A Bayesian hierarchical model for urban air quality prediction under uncertainty [J]. Atmospheric Environment, 42, 8464–8469.
Abdullah, L., Khalid, N. D., 2012. Classification of Air Quality Using Fuzzy Synthetic Multiplication [J]. Environmental Monitoring and Assessment, 184, 6957–6965.
US EPA, 2009. Air Quality Index: A Guide to Air Quality and Your Health [R].
Cheng, W., Chen, Y., Zhang, J., Lyons, T., Pai, J., Chang, S., 2007. Comparison of the revised air quality index with the PSI and AQI indices [J]. Sci. Total Environ. 382, 191–198.
Wang L, Zhang P, Tan S, et al. Assessment of urban air quality in China using air pollution indices (APIs)[J]. Journal of the Air & Waste Management Association, 2013, 63 (2):170.
Wang, L., Jang, C., Zhang, Y., et al. Assessment of air quality benefits from national air pollution control policies in China. Part II: evaluation of air quality predictions and air quality benefits assessment [J]. Atmospheric Environment, 44 (2011): 3449-3457.
Ministry of Environmental Protection of the People’s Republic of China. The ambient air quality standards (GB3095-2012) [S]. Beijing: China Environmental Science Press, 2012.
Olvera-García, M., Carbajal-Hernández, J., Sánchez-Fernández, L., et al. Air quality assessment using a weighted Fuzzy Inference System [J]. Ecological Informatics, 2016, 33:57-74.
National Bureau of Statistics of the People’s Republic of China. The Statistical Yearbook of China [M]. Beijing: China Environmental Science Press, 2014.
Suppan P, Schrader S, Shen R, et al. Source apportionment and air quality impact assessment studies in Beijing, China [C], EGU General Assembly Conference. EGU General Assembly Conference Abstracts, 2012:12833.
Liu, J., Diamond J., 2005. China’s environment in a globalizing world [J]. Nature 435:1179–86.