Journal of Atmospheric and Environmental Optics, Volume. 18, Issue 3, 258(2023)
Improving the accuracy of NO2 concentrations derived from remote sensing using localized factors based on random forest algorithm
[1] Li M, Zhang Q, Kurokawa J et al. MIX: A mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects[J]. Atmospheric Chemistry & Physics, 15, 34813-34869(2015).
[2] Bechle M J, Millet D B, Marshall J D. National spatiotemporal exposure surface for NO2: Monthly scaling of a satellite-derived land-use regression, 2000―2010[J]. Environmental Science & Technology, 49, 12297-12305(2015).
[3] Chan K L, Khorsandi E, Liu S et al. Estimation of surface NO2 concentrations over Germany from TROPOMI satellite observations using a machine learning method[J]. Remote Sensing, 13, 969(2021).
[4] Cooper M J, Martin R V, McLinden C A et al. Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument[J]. Environmental Research Letters, 15, 104013(2020).
[5] Knibbs L D, Hewson MG, Bechle M J et al. A national satellite-based land-use regression model for air pollution exposure assessment in Australia[J]. Environmental Research, 135, 204-211(2014).
[6] Novotny E V, Bechle M J, Millet D B et al. National satellite-based land-use regression: NO2 in the United States[J]. Environmental Science & Technology, 45, 4407-4414(2011).
[7] Larkin A, Geddes J A, Martin R V et al. Global land use regression model for nitrogen dioxide air pollution[J]. Environmental Science & Technology, 51, 6957-6964(2017).
[8] Harper, A, Baker, P N, Xia, Y et al. Development of spatiotemporal land use regression models for PM2.5 and NO2 in Chongqing, China, and exposure assessment for the CLIMB study[J]. Atmospheric Pollution Research, 12, 101096(2021).
[9] Shi Y Y, Li R D, Qiu J et al. Spatial distribution simulation and underlying surface factors analysis of NO2 concentration based on land use regression[J]. Journal of Geo-Information Science, 19, 10-19(2017).
[10] Anand J S, Monks P S. Estimating daily surface NO2 concentrations from satellite data―A case study over Hong Kong using land use regression models[J]. Atmospheric Chemistry and Physics, 17, 8211-8230(2017).
[11] He Q, Kai Q, Cohen J B et al. Spatially and temporally coherent reconstruction of tropospheric NO2 over China combining OMI and GOME-2B measurements[J]. Environmental Research Letters, 15, 125011(2020).
[12] Chen J, de Hoogh K, Gulliver J et al. A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide[J]. Environment International, 130, 104934(2019).
[13] Wang Z, Uno I, Yumimoto K 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[J]. Atmospheric Environment, 244, 117972(2021).
[14] Lamsal L N, Martin R V, van Donkelaar A et al. Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument[J]. Journal of Geophysical Research: Atmospheres, 113, D16308(2008).
[15] Zheng K L, Huang Y, Yao X Y et al. Correlation between PM2.5, NO2 and tourism activities, weather factors in Zhangjiajie City[J]. Journal of Atmospheric and Environmental Optics, 15, 347-356(2020).
[16] Breiman L. Random forests[J]. Machine Learning, 45, 5-32(2001).
[17] Zhai L, Li S, Zou B et al. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas[J]. Atmospheric Environment, 181, 145-154(2018).
[18] Meng X, Chen L, Cai J et al. A land use regression model for estimating the NO2 concentration in Shanghai, China[J]. Environmental Research, 137, 308-315(2015).
[19] Gilliland F, Avol E, Kinney P 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[J]. Environmental Health Perspectives, 113, 1447-1454(2005).
[21] Yang W B. An Extension of Geographically Weighted Regression with Flexible Bandwidths[D](2014).
[22] Zhao R, Zhang C X, Wu Y et al. Analysis of spatio-temporal variations of tropospheric nitrogen dioxide in the North China plain based on EMI[J]. Journal of Atmospheric and Environmental Optics, 16, 186-196(2021).
[23] Wang X H, Xu Y Z, Zhang C X et al. Spatial-temporal variation of tropospheric NO2 concentration in Pearl River Delta based on EMI observations[J]. Journal of Atmospheric and Environmental Optics, 16, 197-206(2021).
[24] Lee H J, Koutrakis P. Daily ambient NO2 concentration predictions using satellite ozone monitoring instrument NO2 data and land use regression[J]. Environmental Science & Technology, 48, 2305-2311(2014).
[25] Gu J B, Chen L F, Yu C et al. Ground-level NO2 concentrations over China inferred from the satellite OMI and CMAQ model simulations[J]. Remote Sensing, 9, 519(2017).
Get Citation
Copy Citation Text
Miao FU. Improving the accuracy of NO2 concentrations derived from remote sensing using localized factors based on random forest algorithm[J]. Journal of Atmospheric and Environmental Optics, 2023, 18(3): 258
Category:
Received: Jan. 11, 2022
Accepted: --
Published Online: Jun. 29, 2023
The Author Email: