References
- Brokamp, C., Jandarov, R., Hossain, M., & Ryan, P. (2018). Predicting daily urban fine particulate matter concentrations using a random forest model. Environmental Science & Technology, 52(7), 4173–4179.
- Cao, G., Zhang, X., Zheng, F., & Wang, Y. (2006). Estimating the quantity of crop residues burnt in open field in China. Resources Science, 28(1), 9–13.
- Chu, D. A., Kaufman, Y. J., Zibordi, G., Chern, J. D., Mao, J., Li, C. C., & Holben, B. N. (2003). Global monitoring of air pollution over land from the earth observing system-terra moderate resolution imaging spectroradiometer (MODIS). Journal of Geophysical Research: Atmospheres, 108(D21). doi:10.1029/2002jd003179
- de Leeuw, G., Sogacheva, L., Rodriguez, E., Kourtidis, K.,Georgoulias, A. K., Alexandri, G., Amiridis, V., Proestakis, E.,Marinou, E., Xue, Y., and van der A, R. (2018). Two decades ofsatellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scalepatterns. Atmospheric Chemistry & Physics, 18,1573–1592
- Engel-Cox, J. A., Hoff, R. M., Rogers, R., Dimmick, F., Rush, A. C., Szykman, J. J., … Zell, E. R. (2006). Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. Atmospheric Environment, 40(40), 8056–8067.
- Engel-Cox, J. A., Holloman, C. H., Coutant, B. W., & Hoff, R. M. (2004). Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment, 38(16), 2495–2509.
- Fang, X., Zou, B., Liu, X., Sternberg, T., & Zhai, L. (2016). Satellite-based ground PM2.5 estimation using timely structure adaptive modeling. Remote Sensing of Environment, 186, 152–163.
- Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., & Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30), 5880–5892.
- He, Q., & Huang, B. (2018). Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling. Remote Sensing of Environment, 206, 72–83.
- Hu, X., Belle, J. H., Meng, X., Wildani, A., Waller, L. A., Strickland, M., & Liu, Y. (2017). Estimating PM2.5 concentrations in the conterminous United States using the random forest approach. Environmental Science & Technology, 51(12), 6936–6944.
- Koelemeijer, R. B. A., Homan, C. D., & Matthijsen, J. (2006). Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmospheric Environment, 40(27), 5304–5315.
- Kokhanovsky, A. A., Prikhach, A. S., Katsev, I. L., & Zege, E. P. (2009). Determination of particulate matter vertical columns using satellite observations. Atmospheric Measurement Techniques, 2(2), 327–335.
- Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., & Eck, T. F. (2010). Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry & Physics, 10(21), 10399–10420.
- Li, Y., Lin, C. Q., Lau, A. K. H., Liao, C. H., Zhang, Y. B., Zeng, W. T., … Tse, T. K. T. (2015). Assessing long-term trend of particulate matter pollution in the pearl river delta region using satellite remote sensing. Environmental Science & Technology, 49(19), 11670–11678.
- Li, Z., Zhang, Y., Shao, J., Li, B., Hong, J., Liu, D., … Qie, L. (2016). Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground: Method validation using ground-based measurements. Remote Sensing of Environment, 173, 59–68.
- Lin, C. Q., Li, Y., Yuan, Z. B., Lau, A. K. H., Li, C. C., & Fung, J. C. H. (2015). Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5. Remote Sensing of Environment, 156, 117–128.
- Liu, Y., Paciorek, C. J., & Koutrakis, P. (2009). Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information. Environmental Health Perspectives, 117(6), 886–892.
- Ma, Z., Hu, X., Huang, L., Bi, J., & Liu, Y. (2014). Estimating ground-level PM2.5 in China using satellite remote sensing. Environmental Science & Technology, 48(13), 7436–7444.
- Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., … Liu, Y. (2016). Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013. Environmental Health Perspectives, 124(2), 184–192.
- Petters, M. D., & Kreidenweis, S. M. (2007). A single parameter representation of hygroscopic growth and cloud condensation nucleus activity. Atmospheric Chemistry & Physics, 7(8), 1961–1971.
- Raut, J. C., & Chazette, P. (2009). Assessment of vertically-resolved PM10 from mobile lidar observations. Atmospheric Chemistry & Physics, 124(2), 8617–8638.
- Raut, J. C., Chazette, R., & Fortain, A. (2009). New approach using lidar measurements to characterize spatiotemporal aerosol mass distribution in an underground railway station in Paris. Atmospheric Environment, 43(3), 575–583.
- Shen, H., Li, T., Yuan, Q., & Zhang, L. (2018). Estimating regional ground-level PM2.5 directly from satellite top-of-atmosphere reflectance using deep belief networks. Journal of Geophysical Research: Atmospheres, 123(24), 13, 875–13,886.
- Van Donkelaar, A., Martin, R. V., Brauer, M., & Boys, B. L. (2015). Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environmental Health Perspectives, 123(2), 135–143.
- Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., & Villeneuve, P. J. (2010). Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. Environmental Health Perspectives, 118(6), 847–855.
- Van Donkelaar, A., Martin, R. V., Li, C., & Burnett, R. T. (2019). Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environmental Science & Technology, 53(5), 2595–2611.
- Van Donkelaar, A., Martin, R. V., & Park, R. J. (2006). Estimating ground-level PM 2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research: Atmospheres, 111(D21), D21.
- van Donkelaar A., Martin R.V., Pasch A.N., Szykman J.J., ZhangL., Wang Y.X., and Chen D. (2012). Improving the accuracy of dailysatellite-derived ground-level fine aerosol concentration estimatesfor North America. Environmental Science & Technology, 46,11971–11978.
- Wang, J., & Christopher, S. A. (2003). Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophysical Research Letters, 30(21). doi:10.1029/2003gl018174
- Wang, J., Xu, X. G., Spurr, R., Wang, Y. X., & Drury, E. (2010b). Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China. Remote Sensing of Environment, 114(11), 2575–2583.
- Wei, J., Peng, Y., Peng, Y., Sun, L., Peng, Y., Sun, L., & Cribb, M. (2019). Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sensing of Environment, 231, 111221.
- Wei Y., Li Z., Zhang Y., Chen C., Xie Y., Lv Y. and Dubovik O.(2021). Derivation of PM10 mass concentration from advancedsatellite retrieval products based on a semi-empirical physicalapproach. Remote Sensing of Environment, 256, 112319.
- Zhang, Y., Li, Z., Chang, W., Zhang, Y., De Leeuw, G., & Schauer, J. J. (2020). Satellite observations of PM2.5 changes and driving factors based forecasting over China 2000–2025. Remote Sensing, 12(16), 2518.
- Zhang, Y., & Li, Z. Q. (2015). Remote sensing of atmospheric fine particulate matter (PM2.5) mass concentration near the ground from satellite observation. Remote Sensing of Environment, 160, 252–262.