[1] Miyazaki K, Eskes H J, Sudo K. Global NOx emission estimates derived from an assimilation of OMI tropospheric NO2 columns[J]. Atmospheric Chemistry and Physics, 12, 2263-2288(2012).

[2] Ryerson T B. Effect of petrochemical industrial emissions of reactive alkenes and NOx on tropospheric ozone formation in Houston, Texas[J]. Journal of Geophysical Research, 108, 4249(2003).

[3] Shi Y, Xia Y F, Lu B H et al. Emission inventory and trends of NOx for China, 2000-2020[J]. Journal of Zhejiang University SCIENCE A, 15, 454-464(2014).

[4] Hoseinzadeh E, Taha P, Wei C A et al. The impact of air pollutants, UV exposure and geographic location on vitamin D deficiency[J]. Food and Chemical Toxicology, 113, 241-254(2018).

[5] Shin H H, Stieb D, Burnett R et al. Tracking national and regional spatial-temporal mortality risk associated with NO2 concentrations in Canada: a Bayesian hierarchical two-level model[J]. Risk Analysis, 32, 513-530(2012).

[6] Smith B J, Nitschke M, Pilotto L S et al. Health effects of daily indoor nitrogen dioxide exposure in people with asthma[J]. The European Respiratory Journal, 16, 879-885(2000).

[7] Thompson A M. The oxidizing capacity of the earth′s atmosphere: probable past and future changes[J]. Science, 256, 1157-1165(1992).

[8] Shi Q, Zheng S, Zhu W Z et al. The short-term effects of NO2 on blood pressure and pulse pressure in patients with hypertension[J]. China Environmental Science, 40, 3627-3635(2020).

[9] Richter A, Burrows J P, Nüß H et al. Increase in tropospheric nitrogen dioxide over China observed from space[J]. Nature, 437, 129-132(2005).

[10] van der A R J, Eskes H J, Boersma K F et al. Trends, seasonal variability and dominant NOx source derived from a ten year record of NO2 measured from space[J]. Journal of Geophysical Research, 113, D04302(2008).

[11] Dong J D, Chen X L, Cai X B et al. Analysis of the temporal and spatial variation of atmospheric quality from 2015 to 2019 based on China atmospheric environment monitoring station[J]. Journal of Geo-Information Science, 22, 1983-1995(2020).

[12] Zhou M, Chang J H, Chen S C et al. Aerosol type recognition model based on naive Bayesian classifier[J]. Acta Optica Sinica, 42, 1801006(2022).

[13] Wu S C, Wang X H, Ye H H et al. Atmospheric CO2 cooperative inversion algorithm applied to GF-5 satellite[J]. Acta Optica Sinica, 41, 1501002(2021).

[14] Zhan Y, Luo Y Z, Deng X F et al. Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model[J]. Environmental Science & Technology, 52, 4180-4189(2018).

[15] You J W, Zou B, Zhao X G et al. Estimating ground-level NO2 concentrations across China using random forests regression modeling[J]. China Environmental Science, 39, 969-979(2019).

[16] Li L F, Wu J J. Spatiotemporal estimation of satellite-borne and ground-level NO2 using full residual deep networks[J]. Remote Sensing of Environment, 254, 112257(2021).

[17] He Q, Qin K, 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).

[18] Wang C J, Wang T, Wang P C et al. Comparison and validation of TROPOMI and OMI NO2 observations over China[J]. Atmosphere, 11, 636(2020).

[19] Tang F Y, Zhou H J, Wang W H et al. Absorbing aerosol index inversion algorithm of TROPOMI and its application[J]. Acta Optica Sinica, 41, 1601001(2021).

[20] 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).

[21] Chi Y L, Fan M, Zhao C F et al. Machine learning-based estimation of ground-level NO2 concentrations over China[J]. Science of the Total Environment, 807, 150721(2022).

[22] Wei J, Liu S, Li Z Q et al. Ground-level NO2 surveillance from space across China for high resolution using interpretable spatiotemporally weighted artificial intelligence[J]. Environmental Science & Technology, 56, 9988-9998(2022).

[23] Müller I, Erbertseder T, Taubenböck H. Tropospheric NO2: Explorative analyses of spatial variability and impact factors[J]. Remote Sensing of Environment, 270, 112839(2022).

[24] Sekiya T, Miyazaki K, Eskes H et al. A comparison of the impact of TROPOMI and OMI tropospheric NO2 on global chemical data assimilation[J]. Atmospheric Measurement Techniques, 15, 1703-1728(2022).

[25] Shtein A, Kloog I, Schwartz J et al. Estimating daily PM2.5 and PM10 over Italy using an ensemble model[J]. Environmental Science & Technology, 54, 120-128(2019).

[26] Li L F. Geographically weighted machine learning and downscaling for high-resolution spatiotemporal estimations of wind speed[J]. Remote Sensing, 11, 1378(2019).

[27] Meng Z L, Zhang Z T, Yang Y et al. Improved XGBoost stray current prediction and explanatory model[J]. Laser & Optoelectronics Progress, 59, 1215011(2022).

[28] Song Y, Li Z R, Yang T T et al. Does the expansion of the joint prevention and control area improve the air quality? —evidence from China′s Jing-Jin-Ji region and surrounding areas[J]. Science of the Total Environment, 706, 136034(2020).

[29] Wen X, Chen W W, Chen B et al. Does the prohibition on open burning of straw mitigate air pollution? An empirical study in Jilin Province of China in the post-harvest season[J]. Journal of Environmental Management, 264, 110451(2020).

[30] Zhang F Y, Shi Y, Fang D K et al. Monitoring history and change trends of ambient air quality in China during the past four decades[J]. Journal of Environmental Management, 260, 110031(2020).

[31] Zheng Z H, Wu Z F, Chen Y B et al. Analysis of temporal and spatial variation characteristics of NO2 pollutants in Guangdong-Hong Kong-Macao Greater Bay Area based on Sentinel-5P satellite data[J]. China Environmental Science, 41, 63-72(2021).

[32] Boersma K F, Eskes H J, Richter A et al. Improving algorithms and uncertainty estimates for satellite NO2 retrievals: results from the quality assurance for the essential climate variables (QA4ECV) project[J]. Atmospheric Measurement Techniques, 11, 6651-6678(2018).

[33] Compernolle S, Verhoelst T, Pinardi G et al. Validation of aura-OMI QA4ECV NO2; climate data records with ground-based DOAS networks: the role of measurement and comparison uncertainties[J]. Atmospheric Chemistry and Physics, 20, 8017-8045(2020).

[34] van Geffen J, Boersma K F, Eskes H et al. S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI[J]. Atmospheric Measurement Techniques, 13, 1315-1335(2020).

[35] Freddy Grajales J, Baquero-Bernal A. Inference of surface concentrations of nitrogen dioxide (NO2) in Colombia from tropospheric columns of the ozone measurement instrument (OMI)[J]. Atmósfera, 27, 193-214(2014).

[36] 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).

[37] 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, 113, D16308(2008).

[38] 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).

[39] Young M T, Bechle M J, Sampson P D et al. Satellite-based NO2 and model validation in a national prediction model based on universal kriging and land-use regression[J]. Environmental Science & Technology, 50, 3686-3694(2016).

[40] Zhao J N, Xu J H, Lu D B et al. The spatial distribution simulation of PM2.5 concentration based on RF-LUR model: a case study of Yangtze River Delta[J]. Geography and Geo-Information Science, 34, 18-23(2018).

[41] Araki S, Shima M, Yamamoto K. Spatiotemporal land use random forest model for estimating metropolitan NO2 exposure in Japan[J]. Science of the Total Environment, 634, 1269-1277(2018).

[42] Kamińska J A. A random forest partition model for predicting NO2 concentrations from traffic flow and meteorological conditions[J]. Science of the Total Environment, 651, 475-483(2019).

[43] Breiman L. Random forests[J]. Machine Language, 45, 5-32(2001).

[44] Friedman J H. Greedy function approximation: a gradient boosting machine[J]. The Annals of Statistics, 29, 1189-1232(2001).

[45] Li Y F, Qin K, Li D et al. Estimation of ground-level ozone concentration based on GBRT[J]. China Environmental Science, 40, 997-1007(2020).

[46] Chen T Q, Guestrin C. XGBoost: a scalable tree boosting system[EB/OL]. https://arxiv.org/abs/1603.02754

[47] Zamani Joharestani M, Cao C X, Ni X L et al. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data[J]. Atmosphere, 10, 373(2019).

[48] Just A C, de Carli M M, Shtein A et al. Correcting measurement error in satellite aerosol optical depth with machine learning for modeling PM2.5 in the Northeastern USA[J]. Remote Sensing, 10, 803(2018).

[49] Zamani J M, Cao C X, Ni X L et al. PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data[J]. Atmosphere, 10, 373(2019).

[50] Liu J J. Mapping high resolution national daily NO2 exposure across the mainland of China using an ensemble algorithm[J]. Environmental Pollution, 279, 116932(2021).

[51] Ballester P L, de A Cardoso T, Moreira F P et al. 5-year incidence of suicide-risk in youth: a gradient tree boosting and SHAP study[J]. Journal of Affective Disorders, 295, 1049-1056(2021).

[52] Jabeur S B, Mefteh-Wali S, Viviani J L. Forecasting gold price with the XGBoost algorithm and SHAP interaction values[J]. Annals of Operations Research, 1-21(2021).

[53] Broccardo S, Heue K P, Walter D et al. Intra-pixel variability in satellite tropospheric NO2 column densities derived from simultaneous space-borne and airborne observations over the South African Highveld[J]. Atmospheric Measurement Techniques, 11, 2797-2819(2018).

[54] Judd L M, Al-Saadi J A, Janz S J et al. Evaluating the impact of spatial resolution on tropospheric NO2 column comparisons within urban areas using high-resolution airborne data[J]. Atmospheric Measurement Techniques, 12, 6091-6111(2019).

[55] Lamsal L N, Janz S J, Krotkov N A et al. High-resolution NO2 observations from the Airborne Compact Atmospheric Mapper: retrieval and validation[J]. Journal of Geophysical Research: Atmospheres, 122, 1953-1970(2017).