||1. Yuan, P. and L. An, The Combined Analysis among PM2. 5, PM10 as Well as Other Air Pollutants, and the Meteorological Factor. 2016.|
2. Art?nano, B., et al., Influence of traffic on the PM10 and PM2. 5 urban aerosol fractions in Madrid (Spain). Science of the Total Environment, 2004. 334: p. 111-123.
3. Martuzevicius, D., et al., Traffic-related PM2. 5 aerosol in residential houses located near major highways: indoor versus outdoor concentrations. Atmospheric Environment, 2008. 42(27): p. 6575-6585.
4. Mehta, A.J., et al., Long-term exposure to ambient fine particulate matter and renal function in older men: the veterans administration normative aging study. Environmental health perspectives, 2016. 124(9): p. 1353.
5. Wang, Y., et al., Long-term exposure to PM2. 5 and mortality among older adults in the southeastern US. Epidemiology, 2017. 28(2): p. 207-214.
6. Hung, L.-J., et al., Traffic air pollution and risk of death from ovarian cancer in Taiwan: fine particulate matter (PM2. 5) as a proxy marker. Journal of Toxicology and Environmental Health, Part A, 2012. 75(3): p. 174-182.
7. Hwang, S.-L., et al., Association between atmospheric fine particulate matter and hospital admissions for chronic obstructive pulmonary disease in Southwestern Taiwan: a population-based study. International journal of environmental research and public health, 2016. 13(4): p. 366.
8. Joo, Y.-H., S.-S. Lee, and K.-H. Park, Association between chronic laryngitis and particulate matter based on the Korea National Health and Nutrition Examination Survey 2008–2012. PloS one, 2015. 10(7): p. e0133180.
9. Poulsen, A.H., et al., Air pollution from traffic and risk for brain tumors: a nationwide study in Denmark. Cancer Causes & Control, 2016. 27(4): p. 473-480.
10. Yu, H.-L. and L.-C. Chien, Short-term population-based non-linear concentration–response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis. Journal of Exposure Science and Environmental Epidemiology, 2016. 26(2): p. 197.
11. Asadollahfardi, G., H. Zangooei, and S.H. Aria, Predicting PM 2.5 concentrations using artificial neural networks and Markov chain, a case study Karaj City. Asian Journal of Atmospheric Environment, 2016. 10(2): p. 67-79.
12. Athanasiadis, I.N., et al. Applying machine learning techniques on air quality data for real-time decision support. in First international NAISO symposium on information technologies in environmental engineering (ITEE′2003), Gdansk, Poland. 2003.
13. Feng, X., et al., Artificial neural networks forecasting of PM2. 5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmospheric Environment, 2015. 107: p. 118-128.
14. Hu, X., et al., Estimating PM2. 5 Concentrations in the Conterminous United States Using the Random Forest Approach. Environmental Science & Technology, 2017. 51(12): p. 6936-6944.
15. Nieto, P.G., et al., A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study. Applied Mathematics and Computation, 2013. 219(17): p. 8923-8937.
16. Pan, B. Application of XGBoost algorithm in hourly PM2. 5 concentration prediction. in IOP Conference Series: Earth and Environmental Science. 2018. IOP Publishing.
17. Perez, P., A. Trier, and J. Reyes, Prediction of PM2. 5 concentrations several hours in advance using neural networks in Santiago, Chile. Atmospheric Environment, 2000. 34(8): p. 1189-1196.
18. Singh, K.P., S. Gupta, and P. Rai, Identifying pollution sources and predicting urban air quality using ensemble learning methods. Atmospheric Environment, 2013. 80: p. 426-437.
19. Xu, Y., W. Yang, and J. Wang, Air quality early-warning system for cities in China. Atmospheric Environment, 2017. 148: p. 239-257.
20. Zhan, Y., et al., Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environmental Pollution, 2018. 233: p. 464-473.
21. Zheng, Y., F. Liu, and H.-P. Hsieh. U-air: When urban air quality inference meets big data. in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013. ACM.
22. Varatharajan, R., et al., Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis. Multimedia Tools and Applications, 2017: p. 1-21.
23. Bartier, P.M. and C.P. Keller, Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Computers & Geosciences, 1996. 22(7): p. 795-799.
24. Lu, G.Y. and D.W. Wong, An adaptive inverse-distance weighting spatial interpolation technique. Computers & geosciences, 2008. 34(9): p. 1044-1055.
25. Baxter, L.K., et al., Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations. Journal of Exposure Science and Environmental Epidemiology, 2013. 23(6): p. 654.
26. Khademi, F., et al., Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Frontiers of Structural and Civil Engineering, 2017. 11(1): p. 90-99.
27. Pedregosa, F., et al., Scikit-learn: Machine learning in Python. Journal of machine learning research, 2011. 12(Oct): p. 2825-2830.
28. Coops, N.C., et al., Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process?based growth model and decision tree analysis. Applied Vegetation Science, 2011. 14(3): p. 402-414.
29. Thelwall, M., A web crawler design for data mining. Journal of Information Science, 2001. 27(5): p. 319-325.
30. Vargiu, E. and M. Urru, Exploiting web scraping in a collaborative filtering-based approach to web advertising. Artificial Intelligence Research, 2012. 2(1): p. 44.
31. Li, T., et al., Estimating Ground?Level PM2. 5 by Fusing Satellite and Station Observations: A Geo?Intelligent Deep Learning Approach. Geophysical Research Letters, 2017. 44(23).