| |
1. |
Alam, M.S., McNabola, A. (2015) Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis. Journal of the Air & Waste Management Association, 65(5), 628-640.
|
2. |
Alimissis, A., Philippopoulos, K., Tzanis, C.G., Deligiorgi, D. (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmospheric Environment, 191, 205-213.
|
3. |
Asuero, A.G., Sayago, A., González, A.G. (2006) The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36(1), 41-59.
|
4. |
Bartier, P.M., Keller, C.P. (1996) Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW). Computers and Geosciences, 22(7), 795-799.
|
5. |
Boaz, R.M., Lawson, A.B., Pearce, J.L. (2019) Multivariate air pollution prediction modeling with partial missingness. Environmetrics, 30(7), e2592.
|
6. |
Chen, S., Oliva, P., Zhang, P. (2018) Air Pollution and Mental Health: Evidence from China.
|
7. |
Crouse, D.L., Goldberg, M.S., Ross, N.A. (2009) A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada. Atmospheric Environment, 43(32), 5075-5084.
|
8. |
Curtis, L., Rea, W., Smith-Willis, P., Fenyves, E., Pan, Y. (2006) Adverse health effects of outdoor air pollutants. Environment International, 32(6), 815-830.
|
9. |
Cusworth, D.H., Mickley, L.J., Sulprizio, M.P., Liu, T., Marlier, M.E., Defries, R.S., Guttikunda, S.K., Gupta, P. (2018) Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India. Environmental Research Letters, 13(4), 044018.
|
10. |
Deligiorgi, D., Philippopoulos, K. (2011) Spatial interpolation methodologies in urban air pollution modeling: application for the greater area of metropolitan Athens, Greece. Advanced Air Pollution, 17, 341-362.
|
11. |
Dominick, D., Juahir, H., Latif, M.T., Zain, S.M., Aris, A.Z. (2012) Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmospheric Environment, 60, 172-181.
|
12. |
Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., Lin, S. (2017) A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W2), 15-22.
|
13. |
Guo, H., Sahu, S.K., Kota, S.H., Zhang, H. (2019) Characterization and health risks of criteria air pollutants in Delhi, 2017. Chemosphere, 225, 27-34.
|
14. |
Kerckhoffs, J., Hoek, G., Gehring, U., Vermeulen, R. (2021) Modelling nationwide spatial variation of ultrafine particles based on mobile monitoring. Environment International, 154(2), 106569.
|
15. |
Kumar, N., Middey, A., Rao, P.S. (2017) Prediction and examination of seasonal variation of Ozone with meteorological parameter through artificial neural network at NEERI, Nagpur, India. Urban Climate, 20, 148-167.
|
16. |
Li, J., Heap, A.D. (2011) A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors. Ecological Informatics, 6(3), 228-241.
|
17. |
Li, J., Heap, A.D. (2014) Spatial interpolation methods applied in the environmental sciences: A review. Environmental Modelling & Software, 53, 173-189.
|
18. |
Manan, D.N.A., Aizuddin, A.N., Hod, R. (2018) Effect of Air Pollution and Hospital Admission: A Systematic Review. Annals of Global Health, 84(4), 670.
|
19. |
Merklinger-Gruchala, A., Jasienska, G., Kapiszewska, M. (2017) Effect of Air Pollution on Menstrual Cycle Length - A Prognostic Factor of Women’s Reproductive Health. International Journal of Environmental Research and Public Health, 14(7), 816.
|
20. |
Mishra, D., Goyal, P. (2015) Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra. Atmospheric Pollution Research, 6(1), 99-106.
|
21. |
Mortimer, K.M., Neas, L.M., Dockery, D.W., Redline, S., Tager, I.B. (2002) The effect of air pollution on inner-city children with asthma. European Respiratory Journal, 19(4), 699-705.
|
22. |
Nagendra, S.M.S., Khare, M. (2005) Modelling urban air quality using artificial neural network. Clean Technologies and Environmental Policy, 7(2), 116-126.
|
23. |
Nagendra, S.M.S., Khare, M. (2006) Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecological Modelling, 190(1-2), 99-115.
|
24. |
Osseiron, N., Lindmeier, C. (2018) 9 out of 10 people worldwide breathe polluted air. https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action |
25. |
Papaleonidas, A., Iliadis, L. (2013) Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data. Evolving Systems, 4(4), 221-233.
|
26. |
Qi, Y., Li, Q., Karimian, H., Liu, D. (2019) A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1-10.
|
27. |
Rigol, J.P., Jarvis, C.H., Stuart, N. (2001) Artificial neural networks as a tool for spatial interpolation. International Journal of Geographical Information Science, 15(4), 323-343.
|
28. |
Roy, M.P. (2021) Air pollution and Covid-19: experience from India. European Review for Medical and Pharmacological Sciences, 25(8), 3375–3376.
|
29. |
Russo, A., Soares, A.O. (2014) Hybrid Model for Urban Air Pollution Forecasting: A Stochastic Spatio-Temporal Approach. Mathematical Geosciences, 46(1), 75-93.
|
30. |
Singh, K.P., Gupta, S., Kumar, A., Shukla, S.P. (2012) Linear and nonlinear modeling approaches for urban air quality prediction. Science of the Total Environment, 426, 244-255.
|
31. |
Singh, V., Singh, S., Biswal, A. (2021) Exceedances and trends of particulate matter (PM2.5) in five Indian megacities. Science of the Total Environment, 750, 141461.
|
32. |
Vicente-Serrano, S.M., Saz-Sánchez, M.A., Cuadrat, J.M. (2003) Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): Application to annual precipitation and temperature. Climate Research, 24(2), 161-180.
|
33. |
Wang, J., Song, G. (2018) A Deep Spatial-Temporal Ensemble Model for Air Quality Prediction. Neurocomputing, 314, 198-206.
|
34. |
Wen, C., Liu, S., Yao, X., Peng, L., Li, X., Hu, Y., Chi, T. (2019) A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. Science of The Total Environment, 654, 1091-1099.
|
35. |
Wong, C.M., Ma, S., Hedley, A.J., Lam, T.H. (2001) Effect of air pollution on daily mortality in Hong Kong. Environmental Health Perspectives, 109(4), 335-340.
|
36. |
WHO (World Health Organization) (2018) Ambient (outdoor) air pollution [Fact sheet]. https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health |
37. |
Yeganeh, B., Hewson, M.G., Clifford, S., Tavassoli, A., Knibbs, L.D., Morawska, L. (2018) Estimating the spatiotemporal variation of NO2 concentration using an adaptive neuro-fuzzy inference system. Environmental Modelling & Software, 100, 222-235.
|
38. |
Zou, B., Wang, M., Wan, N., Wilson, J.G., Fang, X., Tang, Y. (2015) Spatial modeling of PM2.5 concentrations with a multifactoral radial basis function neural network. Environmental Science and Pollution Research, 22(14), 10395-10404.
|