Improving the accuracy of NO2 concentrations derived from remote sensing using localized factors based on random forest algorithm

FU Miao

doi:10.3969/j.issn.1673-6141.2023.03.007

[1] M Li, Q Zhang, J Kurokawa et al. MIX: A mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects. Atmospheric Chemistry & Physics, 15, 34813-34869(2015).

[2] M J Bechle, D B Millet, J D Marshall. National spatiotemporal exposure surface for NO2: Monthly scaling of a satellite-derived land-use regression, 2000―2010. Environmental Science & Technology, 49, 12297-12305(2015).

[3] K L Chan, E Khorsandi, S Liu et al. Estimation of surface NO2 concentrations over Germany from TROPOMI satellite observations using a machine learning method. Remote Sensing, 13, 969(2021).

[4] M J Cooper, R V Martin, C A McLinden et al. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument. Environmental Research Letters, 15, 104013(2020).

[5] L D Knibbs, MG Hewson, M J Bechle et al. A national satellite-based land-use regression model for air pollution exposure assessment in Australia. Environmental Research, 135, 204-211(2014).

[6] E V Novotny, M J Bechle, D B Millet et al. National satellite-based land-use regression: NO2 in the United States. Environmental Science & Technology, 45, 4407-4414(2011).

[7] A Larkin, J A Geddes, R V Martin et al. Global land use regression model for nitrogen dioxide air pollution. Environmental Science & Technology, 51, 6957-6964(2017).

[8] A Harper,, P N Baker,, Y Xia, et al. Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study. Atmospheric Pollution Research, 12, 101096(2021).

[9] Y Y Shi, R D Li, J Qiu et al. Spatial distribution simulation and underlying surface factors analysis of NO2 concentration based on land use regression. Journal of Geo-Information Science, 19, 10-19(2017).

[10] J S Anand, P S Monks. Estimating daily surface NO2 concentrations from satellite data―A case study over Hong Kong using land use regression models. Atmospheric Chemistry and Physics, 17, 8211-8230(2017).

[11] Q He, Q Kai, J B Cohen et al. Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements. Environmental Research Letters, 15, 125011(2020).

[12] J Chen, K de Hoogh, J Gulliver et al. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide. Environment International, 130, 104934(2019).

[13] Z Wang, I Uno, K Yumimoto et al. Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in situ observations, satellite retrievals and model simulations. Atmospheric Environment, 244, 117972(2021).

[14] L N Lamsal, R V Martin, A van Donkelaar et al. Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument. Journal of Geophysical Research: Atmospheres, 113, D16308(2008).

[15] K L Zheng, Y Huang, X Y Yao et al. Correlation between PM2.5, NO2 and tourism activities, weather factors in Zhangjiajie City. Journal of Atmospheric and Environmental Optics, 15, 347-356(2020).

[16] L Breiman. Random forests. Machine Learning, 45, 5-32(2001).

[17] L Zhai, S Li, B Zou et al. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas. Atmospheric Environment, 181, 145-154(2018).

[18] X Meng, L Chen, J Cai et al. A land use regression model for estimating the NO2 concentration in Shanghai, China. Environmental Research, 137, 308-315(2015).

[19] F Gilliland, E Avol, P Kinney et al. Air pollution exposure assessment for epidemiologic studies of pregnant women and children: Lessons learned from the Centers for Children's Environmental Health and Disease Prevention Research. Environmental Health Perspectives, 113, 1447-1454(2005).

[21] W B Yang. An Extension of Geographically Weighted Regression with Flexible Bandwidths(2014).

[22] R Zhao, C X Zhang, Y Wu et al. Analysis of spatio-temporal variations of tropospheric nitrogen dioxide in the North China plain based on EMI. Journal of Atmospheric and Environmental Optics, 16, 186-196(2021).

[23] X H Wang, Y Z Xu, C X Zhang et al. Spatial-temporal variation of tropospheric NO2 concentration in Pearl River Delta based on EMI observations. Journal of Atmospheric and Environmental Optics, 16, 197-206(2021).

[24] H J Lee, P Koutrakis. Daily ambient NO2 concentration predictions using satellite ozone monitoring instrument NO2 data and land use regression. Environmental Science & Technology, 48, 2305-2311(2014).

[25] J B Gu, L F Chen, C Yu et al. Ground-level NO2 concentrations over China inferred from the satellite OMI and CMAQ model simulations. Remote Sensing, 9, 519(2017).