Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4051-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4051-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022
Hongfei Hao
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, College Urban and Environmental Sciences, Peking University, Beijing 100871, China
Guocan Wu
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jianbao Liu
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, College Urban and Environmental Sciences, Peking University, Beijing 100871, China
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
Related authors
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data, 16, 3233–3260, https://doi.org/10.5194/essd-16-3233-2024, https://doi.org/10.5194/essd-16-3233-2024, 2024
Short summary
Short summary
In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
Yueming Dong, Jing Li, Zhenyu Zhang, Chongzhao Zhang, and Qiurui Li
Earth Syst. Sci. Data, 17, 3873–3892, https://doi.org/10.5194/essd-17-3873-2025, https://doi.org/10.5194/essd-17-3873-2025, 2025
Short summary
Short summary
This study develops two merged global land aerosol single-scattering albedo (SSA) datasets by combining AERONET ground observations and two satellite datasets using an ensemble Kalman filter data synergy method. The merged datasets exhibit significantly improved accuracy compared to the original satellite data. These results can provide more reliable estimates of aerosol scattering and absorption properties, essential for improving climate modeling and assessing aerosol climate effects.
Chong Li, Oleg Dubovik, Jing Li, David Fuertes, Anton Lopatin, Pavel Litvinov, Tatsiana Lapyonok, Lukas Bindreiter, Christian Matar, Yiqi Chu, and Wangshu Tan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2694, https://doi.org/10.5194/egusphere-2025-2694, 2025
Short summary
Short summary
Using observational data from Japan’s geostationary satellite – Himawari-8 , this study improved how we track air pollution (aerosols) across East Asia and the Western Pacific. By applying an advanced aerosol retrieval algorithm called GRASP, we were able to more accurately observe both atmospheric and ground conditions and their dynamics over time. The results closely matched ground-based measurements and showed potential for even better monitoring when combined with ground-based lidar data.
Guanyu Liu, Jing Li, Sheng Yue, Lulu Zhang, and Chongzhao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1871, https://doi.org/10.5194/egusphere-2025-1871, 2025
Short summary
Short summary
This study introduces a novel method to retrieve aerosol optical depth (AOD) at night using ground-based microwave radiometers, overcoming the limitation of traditional shortwave-based techniques that cannot operate in darkness. This result enables continuous aerosol monitoring and highlighting microwave radiometry's underutilized potential in atmospheric research.
Zhenyu Zhang, Jing Li, Huizheng Che, Yueming Dong, Oleg Dubovik, Thomas Eck, Pawan Gupta, Brent Holben, Jhoon Kim, Elena Lind, Trailokya Saud, Sachchida Nand Tripathi, and Tong Ying
Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025, https://doi.org/10.5194/acp-25-4617-2025, 2025
Short summary
Short summary
We used ground-based remote sensing data from the Aerosol Robotic Network to examine long-term trends in aerosol characteristics. We found aerosol loadings generally decreased globally, and aerosols became more scattering. These changes are closely related to variations in aerosol compositions, such as decreased anthropogenic emissions over East Asia, Europe, and North America; increased anthropogenic sources over northern India; and increased dust activity over the Arabian Peninsula.
Yanyi He, Kaicun Wang, Kun Yang, Chunlüe Zhou, Changkun Shao, and Changjian Yin
Earth Syst. Sci. Data, 17, 1595–1611, https://doi.org/10.5194/essd-17-1595-2025, https://doi.org/10.5194/essd-17-1595-2025, 2025
Short summary
Short summary
To address key gaps in data availability and homogeneity with regard to sunshine duration, we compiled raw data and made a homogenization protocol to produce a homogenized daily observational dataset of sunshine duration from 1961 to 2022 in China. The dataset avoids a sharp drop in zero-value frequency after 2019 as caused by the instrument upgrade but is also more continuous for various periods. This dataset is crucial for accurately assessing dimming and brightening and for supporting other applications.
Shouye Xue and Guocan Wu
EGUsphere, https://doi.org/10.5194/egusphere-2025-762, https://doi.org/10.5194/egusphere-2025-762, 2025
Short summary
Short summary
Soil moisture is influenced by both precipitation and evapotranspiration, with spatial heterogeneities and seasonal variations across different ecological zones. In this study, the joint distributions of precipitation and soil moisture were analyzed at monthly and annual scales. The negative dependences between soil moisture and precipitation were found, due to soil property changes induced by land–surface interactions. The results enhance our understandings in drought and hydrometeorology.
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data, 16, 3233–3260, https://doi.org/10.5194/essd-16-3233-2024, https://doi.org/10.5194/essd-16-3233-2024, 2024
Short summary
Short summary
In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
Liang Chang, Jing Li, Jingjing Ren, Changrui Xiong, and Lu Zhang
Atmos. Meas. Tech., 17, 2637–2648, https://doi.org/10.5194/amt-17-2637-2024, https://doi.org/10.5194/amt-17-2637-2024, 2024
Short summary
Short summary
We described a modified lidar inversion algorithm to retrieve aerosol extinction and size distribution simultaneously from two-wavelength elastic lidar measurements. Its major advantage is that the lidar ratio of each layer is determined iteratively by a lidar ratio–Ångström exponent lookup table. The algorithm was applied to the Raman lidar and CALIOP measurements. The retrieved results by our method are in good agreement with those achieved by Raman method.
Wenying He, Hongbin Chen, Hongyong Yu, Jun Li, Jidong Pan, Shuqing Ma, Xuefen Zhang, Rang Guo, Bingke Zhao, Xi Chen, Xiangao Xia, and Kaicun Wang
Atmos. Meas. Tech., 17, 135–144, https://doi.org/10.5194/amt-17-135-2024, https://doi.org/10.5194/amt-17-135-2024, 2024
Short summary
Short summary
The Marine Weather Observer (MWO) system completed a long-term observation, actively approaching the center of Typhoon Sinlaku on 24 July–2 August 2020, over the South China Sea. The in situ observations were evaluated through comparison with buoy observations during the evolution of Typhoon Sinlaku. As a mobile observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO.
Guanyu Liu, Jing Li, and Tong Ying
Atmos. Chem. Phys., 23, 9217–9228, https://doi.org/10.5194/acp-23-9217-2023, https://doi.org/10.5194/acp-23-9217-2023, 2023
Short summary
Short summary
Fires in Australia are positively correlated with the El Niño–Southern Oscillation (ENSO). However, the correlation between ENSO and the Australian Fire Weather Index (FWI) increases from 0.17 to 0.70 when the Atlantic Multidecadal Oscillation (AMO) shifts from a negative to positive phase. This is explained by the teleconnection effect through which the warmer AMO generates Rossby wave trains and results in high pressures and a weather condition conducive to wildfires.
Zhongjing Jiang and Jing Li
Atmos. Chem. Phys., 22, 7273–7285, https://doi.org/10.5194/acp-22-7273-2022, https://doi.org/10.5194/acp-22-7273-2022, 2022
Short summary
Short summary
This study investigates the changes of tropospheric ozone in China associated with EP and CP El Niño, using satellite observations and the GEOS-Chem model. We found that El Niño generally leads to lower tropospheric ozone (LTO) decrease over most parts of China; La Niña acts the opposite. The difference between LTO changes during EP and CP El Niño primarily lies in southern China. Regional transport and chemical processes play the leading and secondary roles in driving the LTO changes.
Qian Ma, Kaicun Wang, Yanyi He, Liangyuan Su, Qizhong Wu, Han Liu, and Youren Zhang
Earth Syst. Sci. Data, 14, 463–477, https://doi.org/10.5194/essd-14-463-2022, https://doi.org/10.5194/essd-14-463-2022, 2022
Short summary
Short summary
Surface incident solar radiation plays a key role in atmospheric circulation, the water cycle, and ecological equilibrium on Earth. A homogenized century-long surface incident solar radiation dataset was obtained over Japan.
Liangying Zeng, Yang Yang, Hailong Wang, Jing Wang, Jing Li, Lili Ren, Huimin Li, Yang Zhou, Pinya Wang, and Hong Liao
Atmos. Chem. Phys., 21, 10745–10761, https://doi.org/10.5194/acp-21-10745-2021, https://doi.org/10.5194/acp-21-10745-2021, 2021
Short summary
Short summary
Using an aerosol–climate model, the impacts of El Niño with different durations on aerosols in China are examined. The modulation on aerosol concentrations and haze days by short-duration El Niño events is 2–3 times more than that by long-duration El Niño events in China. The frequency of short-duration El Niño has been increasing significantly in recent decades, suggesting that El Niño events have exerted increasingly intense modulation on aerosol pollution in China over the past few decades.
Fei Feng and Kaicun Wang
Earth Syst. Sci. Data, 13, 907–922, https://doi.org/10.5194/essd-13-907-2021, https://doi.org/10.5194/essd-13-907-2021, 2021
Zhongjing Jiang, Jing Li, Xiao Lu, Cheng Gong, Lin Zhang, and Hong Liao
Atmos. Chem. Phys., 21, 2601–2613, https://doi.org/10.5194/acp-21-2601-2021, https://doi.org/10.5194/acp-21-2601-2021, 2021
Short summary
Short summary
This study demonstrates that the intensity of the western Pacific subtropical high (WPSH), a major synoptic pattern in the northern Pacific during summer, can induce a dipole change in surface ozone pollution over eastern China. Ozone concentration increases in the north and decreases in the south during the strong WPSH phase, and vice versa. The change in chemical processes associated with the WPSH change plays a decisive role, whereas the natural emission of ozone precursors accounts for ~ 30 %.
Bo Dan, Xiaogu Zheng, Guocan Wu, and Tao Li
Hydrol. Earth Syst. Sci., 24, 5187–5201, https://doi.org/10.5194/hess-24-5187-2020, https://doi.org/10.5194/hess-24-5187-2020, 2020
Short summary
Short summary
Data assimilation is a procedure to generate an optimal combination of the state variable in geoscience, based on the model outputs and observations. The ensemble Kalman filter (EnKF) scheme is a widely used assimilation method in soil moisture estimation. This study proposed several modifications of EnKF for improving this assimilation. The study shows that the quality of the assimilation result is improved, while the degree of water budget imbalance is reduced.
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Ali, M. A., Bilal, M., Wang, Y., Nichol, J. E., Mhawish, A., Qiu, Z., de Leeuw, G., Zhang, Y., Zhan, Y., Liao, K., Almazroui, M., Dambul, R., Shahid, S., and Islam, M. N.: Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China, Atmos. Environ., 288, 119297, https://doi.org/10.1016/j.atmosenv.2022.119297, 2022.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2000), Zenodo [data set], https://doi.org/10.5281/zenodo.8307595, 2023a.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001), Zenodo [data set], https://doi.org/10.5281/zenodo.8307597, 2023b.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2002), Zenodo [data set], https://doi.org/10.5281/zenodo.8307599, 2023c.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2003), Zenodo [data set], https://doi.org/10.5281/zenodo.8307601, 2023d.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2004), Zenodo [data set], https://doi.org/10.5281/zenodo.8307605, 2023e.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2005), Zenodo [data set], https://doi.org/10.5281/zenodo.8307607, 2023f.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2006), Zenodo [data set], https://doi.org/10.5281/zenodo.8308225, 2023g.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2007), Zenodo [data set], https://doi.org/10.5281/zenodo.8308227, 2023h.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2008), Zenodo [data set], https://doi.org/10.5281/zenodo.8308231, 2023i.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2009), Zenodo [data set], https://doi.org/10.5281/zenodo.8308233, 2023j.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2010), Zenodo [data set], https://doi.org/10.5281/zenodo.8308237, 2023k.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2011), Zenodo [data set], https://doi.org/10.5281/zenodo.8310586, 2023l.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2012), Zenodo [data set], https://doi.org/10.5281/zenodo.8310590, 2023m.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013), Zenodo [data set], https://doi.org/10.5281/zenodo.8310702, 2023n.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2014), Zenodo [data set], https://doi.org/10.5281/zenodo.8310704, 2023o.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015), Zenodo [data set], https://doi.org/10.5281/zenodo.8310706, 2023p.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2016), Zenodo [data set], https://doi.org/10.5281/zenodo.8310708, 2023q.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2017), Zenodo [data set], https://doi.org/10.5281/zenodo.8310711, 2023r.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2018), Zenodo [data set], https://doi.org/10.5281/zenodo.8313603, 2023s.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2019), Zenodo [data set], https://doi.org/10.5281/zenodo.8313611, 2023t.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2020), Zenodo [data set], https://doi.org/10.5281/zenodo.8313613, 2023u.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2021), Zenodo [data set], https://doi.org/10.5281/zenodo.8313615, 2023v.
Bai, K., Li, K., Shao, L., Li, X., Liu, C., Li, Z., Ma, M., Han, D., Sun, Y., Zheng, Z., Li, R., Chang, N.-B., and Guo, J.: LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics, Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, 2024.
Beckerman, B. S., Jerrett, M., Serre, M., Martin, R. V., Lee, S.-J., Van Donkelaar, A., Ross, Z., Su, J., and Burnett, R. T.: A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States, Environ. Sci. Technol., 47, 7233–7241, https://doi.org/10.1021/es400039u, 2013.
Bergstrom, R. W., Pilewskie, P., Russell, P. B., Redemann, J., Bond, T. C., Quinn, P. K., and Sierau, B.: Spectral absorption properties of atmospheric aerosols, Atmos. Chem. Phys., 7, 5937–5943, https://doi.org/10.5194/acp-7-5937-2007, 2007.
Boers, R., van Weele, M., van Meijgaard, E., Savenije, M., Siebesma, A. P., Bosveld, F., and Stammes, P.: Observations and projections of visibility and aerosol optical thickness (1956–2100) in the Netherlands: impacts of time-varying aerosol composition and hygroscopicity, Environ. Res. Lett., 10, 015003, https://doi.org/10.1088/1748-9326/10/1/015003, 2015.
Boys, B., Martin, R., Van Donkelaar, A., MacDonell, R., Hsu, N., Cooper, M., Yantosca, R., Lu, Z., Streets, D., and Zhang, Q.: Fifteen-year global time series of satellite-derived fine particulate matter, Environ. Sci. Technol., 48, 11109–11118, https://doi.org/10.1021/es502113p, 2014.
Browne, M. W.: Cross-validation methods, J. Math. Psychol., 44, 108–132, https://doi.org/10.1006/jmps.1999.1279, 2000.
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, 2015.
Buchard, V., da Silva, A. M., Randles, C. A., Colarco, P., Ferrare, R., Hair, J., Hostetler, C., Tackett, J., and Winker, D.: Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States, Atmos. Environ., 125, 100–111, https://doi.org/10.1016/j.atmosenv.2015.11.004, 2016.
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Chafe, Z. A., Brauer, M., Klimont, Z., Van Dingenen, R., Mehta, S., Rao, S., Riahi, K., Dentener, F., and Smith, K. R.: Household Cooking with Solid Fuels Contributes to Ambient PM2.5 Air Pollution and the Burden of Disease, Environ. Health Persp., 122, 1314–1320, https://doi.org/10.1289/ehp.1206340, 2014.
Chang, K.-L., Petropavlovskikh, I., Cooper, O. R., Schultz, M. G., and Wang, T.: Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia, Elementa: Science of the Anthropocene, 5, 50, https://doi.org/10.1525/elementa.243, 2017.
Che, H., Xia, X., Zhu, J., Hong, W., and Shi, G.: Aerosol optical properties under the condition of heavy haze over an urban site of Beijing, China, Environ. Sci. Pollut. R., 22, 1043–1053, https://doi.org/10.1007/s11356-014-3415-5, 2014.
Chen, A., Zhao, C., and Fan, T.: Spatio-temporal distribution of aerosol direct radiative forcing over mid-latitude regions in north hemisphere estimated from satellite observations, Atmos. Res., 266, 105938, https://doi.org/10.1016/j.atmosres.2021.105938, 2022.
Chen, Z., Chen, D., Zhao, C., Kwan, M.-p., Cai, J., Zhuang, Y., Zhao, B., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.: Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism, Environ. Int., 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Chow, J. C., Doraiswamy, P., Watson, J. G., Chen, L. W. A., Ho, S. S. H., and Sodeman, D. A.: Advances in Integrated and Continuous Measurements for Particle Mass and Chemical Composition, Japca J. Air Waste Ma., 58, 141–163, https://doi.org/10.3155/1047-3289.58.2.141, 2008.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C. A., III, Shin, H., Straif, K., Shaddick, G., Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L., and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918, https://doi.org/10.1016/s0140-6736(17)30505-6, 2017.
Dabek-Zlotorzynska, E., Dann, T. F., Martinelango, P. K., Celo, V., Brook, J. R., Mathieu, D., Ding, L., and Austin, C. C.: Canadian National Air Pollution Surveillance (NAPS) PM2.5 speciation program: Methodology and PM2.5 chemical composition for the years 2003–2008, Atmos. Environ., 45, 673-686, https://doi.org/10.1016/j.atmosenv.2010.10.024, 2011.
Davies, J.: CEPA – The Canadian. Environmental Protection Act, JAPCA, 38, 1111–1113, https://doi.org/10.1080/08940630.1988.10466452, 1988.
Demerjian, K. L.: A review of national monitoring networks in North America, Atmos. Environ., 34, 1861–1884, https://doi.org/10.1016/S1352-2310(99)00452-5, 2000.
Fan, H., Zhao, C., Yang, Y., and Yang, X.: Spatio-Temporal Variations of the Ratios and Its Application to Air Pollution Type Classification in China, Front. Environ. Sci., 9, 692440, https://doi.org/10.3389/fenvs.2021.692440, 2021.
Friedman, J. H.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Ganguly, T., Selvaraj, K. L., and Guttikunda, S. K.: National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans, Atmospheric Environment X, 8, 100096, https://doi.org/10.1016/j.aeaoa.2020.100096, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Goff, J. A.: Saturation pressure of water on the new Kelvin temperature scale, Transactions of the American Society of Heating and Ventilating Engineers, 63, 347–354, 1957.
Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., Denier van der Gon, H., Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., and Klimont, Z.: Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period, Climatic Change, 109, 163–190, https://doi.org/10.1007/s10584-011-0154-1, 2011.
Green, D. and Fuller, G. W.: The implications of tapered element oscillating microbalance (TEOM) software configuration on particulate matter measurements in the UK and Europe, Atmos. Environ., 40, 5608–5616, https://doi.org/10.1016/j.atmosenv.2006.04.052, 2006.
Gui, K., Che, H., Zeng, Z., Wang, Y., Zhai, S., Wang, Z., Luo, M., Zhang, L., Liao, T., and Zhao, H.: Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model, Environ. Int., 141, 105801, https://doi.org/10.1016/j.envint.2020.105801, 2020.
Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M., Zeng, L., Molina, M. J., and Zhang, R.: Elucidating severe urban haze formation in China, P. Natl. Acad. Sci. USA, 111, 17373–17378, https://doi.org/10.1073/pnas.1419604111, 2014.
Hall, E. and Gilliam, J.: Reference and Equivalent Methods Used to Measure National Ambient Air Quality Standards (NAAQS) Criteria Air Pollutants – Volume I, https://doi.org/10.13140/RG.2.1.3471.8329, 2016.
Hammer, M. S., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A. M., Hsu, N. C., Levy, R. C., Garay, M. J., Kalashnikova, O. V., and Kahn, R. A.: Global estimates and long-term trends of fine particulate matter concentrations (1998–2018), Environ. Sci. Technol., 54, 7879–7890, https://doi.org/10.1021/acs.est.0c01764, 2020.
Hao, H., Wang, K., Wu, G., Liu, J., and Li, J.: PM2.5 concentrations based on near-surface visibility at 4011 sites in the Northern Hemisphere from 1959 to 2022, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.301127, 2024.
Hastie, T. and Tibshirani, R.: Generalized Additive Models: Some Applications, J. Am. Stat. Assoc., 82, 371–386, https://doi.org/10.1080/01621459.1987.10478440, 1987.
He, Q., Gao, K., Zhang, L., Song, Y., and Zhang, M.: Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018, Environ. Int., 156, 106726, https://doi.org/10.1016/j.envint.2021.106726, 2021.
Hsu, N., Lee, J., Sayer, A., Carletta, N., Chen, S. H., Tucker, C., Holben, B., and Tsay, S. C.: Retrieving near-global aerosol loading over land and ocean from AVHRR, J. Geophys. Res-Atmos., 122, 9968–9989, https://doi.org/10.1002/2017JD026932, 2017.
Huang, W., Tan, J., Kan, H., Zhao, N., Song, W., Song, G., Chen, G., Jiang, L., Jiang, C., and Chen, R.: Visibility, air quality and daily mortality in Shanghai, China, Sci. Total Environ., 407, 3295–3300, https://doi.org/10.1016/j.scitotenv.2009.02.019, 2009.
Husar, R. B., Husar, J. D., and Martin, L.: Distribution of continental surface aerosol extinction based on visual range data, Atmos. Environ., 34, 5067–5078, https://doi.org/10.1016/s1352-2310(00)00324-1, 2000.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Jin, C., Wang, Y., Li, T., and Yuan, Q.: Global validation and hybrid calibration of CAMS and MERRA-2 PM2.5 reanalysis products based on OpenAQ platform, Atmos. Environ., 274, 118972, https://doi.org/10.1016/j.atmosenv.2022.118972, 2022.
Kammann, E. E. and Wand, M. P.: Geoadditive Models, J. R. Stat. Soc. C-Appl., 52, 1–18, https://doi.org/10.1111/1467-9876.00385, 2003.
Kendall, M. G.: Rank correlation methods, Griffin, https://psycnet.apa.org/record/1948-15040-000 (last access: 30 August 2024), 1948.
Kim, K.-H., Kabir, E., and Kabir, S.: A review on the human health impact of airborne particulate matter, Environ. Int., 74, 136–143, https://doi.org/10.1016/j.envint.2014.10.005, 2015.
Kuklinska, K., Wolska, L., and Namiesnik, J.: Air quality policy in the US and the EU – a review, Atmos. Pollut. Res., 6, 129–137, https://doi.org/10.5094/APR.2015.015, 2015.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, https://doi.org/10.1038/nature15371, 2015.
Li, C., Martin, R. V., Boys, B. L., van Donkelaar, A., and Ruzzante, S.: Evaluation and application of multi-decadal visibility data for trend analysis of atmospheric haze, Atmos. Chem. Phys., 16, 2435–2457, https://doi.org/10.5194/acp-16-2435-2016, 2016.
Li, C., Martin, R. V., van Donkelaar, A., Boys, B. L., Hammer, M. S., Xu, J.-W., Marais, E. A., Reff, A., Strum, M., and Ridley, D. A.: Trends in chemical composition of global and regional population-weighted fine particulate matter estimated for 25 years, Environ. Sci. Technol., 51, 11185–11195, https://doi.org/10.1021/acs.est.7b02530, 2017.
Li, J., Han, X., Jin, M., Zhang, X., and Wang, S.: Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population's PM2.5 exposure from 1998 to 2016, Environ. Int., 128, 46–62, https://doi.org/10.1016/j.envint.2019.04.026, 2019.
Li, J., Garshick, E., Hart, J. E., Li, L., Shi, L., Al-Hemoud, A., Huang, S., and Koutrakis, P.: Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing, Environ. Int., 151, 106445, https://doi.org/10.1016/j.envint.2021.106445, 2021.
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nat. Rev. Earth. Environ., 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022.
Li, S., Chen, L., Huang, G., Lin, J., Yan, Y., Ni, R., Huo, Y., Wang, J., Liu, M., and Weng, H.: Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations, Atmos. Environ., 222, 117121, https://doi.org/10.1016/j.atmosenv.2019.117121, 2020.
Liao, H., Chang, W., and Yang, Y.: Climatic Effects of Air Pollutants over China: A Review, Adv. Atmos. Sci., 32, 115–139, https://doi.org/10.1007/s00376-014-0013-x, 2015.
Lim, C.-H., Ryu, J., Choi, Y., Jeon, S. W., and Lee, W.-K.: Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016), Environ. Int., 144, 106011, https://doi.org/10.1016/j.envint.2020.106011, 2020.
Liu, M., Bi, J., and Ma, Z.: Visibility-based PM2.5 concentrations in China: 1957–1964 and 1973–2014, Environ. Sci. Technol., 51, 13161–13169, https://doi.org/10.1021/acs.est.7b03468, 2017.
Liu, M., Huang, X., Song, Y., Tang, J., Cao, J., Zhang, X., Zhang, Q., Wang, S., Xu, T., Kang, L., Cai, X., Zhang, H., Yang, F., Wang, H., Yu, J. Z., Lau, A. K. H., He, L., Huang, X., Duan, L., Ding, A., Xue, L., Gao, J., Liu, B., and Zhu, T.: Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain, P. Natl. Acad. Sci. USA, 116, 7760–7765, https://doi.org/10.1073/pnas.1814880116, 2019.
Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., Tong, S., Bi, J., Huang, L., and Liu, Y.: Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013, Environ. Health Persp., 124, 184–192, https://doi.org/10.1289/ehp.1409481, 2016.
Mandal, S., Madhipatla, K. K., Guttikunda, S., Kloog, I., Prabhakaran, D., Schwartz, J. D., and Team, G. H. I.: Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016, Atmos. Environ., 224, 117309, https://doi.org/10.1016/j.atmosenv.2020.117309, 2020.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
Meng, X., Hand, J. L., Schichtel, B. A., and Liu, Y.: Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005–2015, Environ. Int., 121, 1137–1147, https://doi.org/10.1016/j.envint.2018.10.029, 2018.
Miao, Y. and Liu, S.: Linkages between aerosol pollution and planetary boundary layer structure in China, Sci. Total Environ., 650, 288–296, https://doi.org/10.1016/j.scitotenv.2018.09.032, 2019.
Molnár, A., Mészáros, E., Imre, K., and Rüll, A.: Trends in visibility over Hungary between 1996 and 2002, Atmos. Environ., 42, 2621–2629, https://doi.org/10.1016/j.atmosenv.2007.05.012, 2008.
Nagaraja Rao, C., Stowe, L., and McClain, E.: Remote sensing of aerosols over the oceans using AVHRR data Theory, practice and applications, Int. J. Remote Sens., 10, 743–749, https://doi.org/10.1080/01431168908903915, 1989.
Pant, P., Lal, R. M., Guttikunda, S. K., Russell, A. G., Nagpure, A. S., Ramaswami, A., and Peltier, R. E.: Monitoring particulate matter in India: recent trends and future outlook, Air Qual. Atmos. Hlth., 12, 45–58, https://doi.org/10.1007/s11869-018-0629-6, 2019.
Park, A., Guillas, S., and Petropavlovskikh, I.: Trends in stratospheric ozone profiles using functional mixed models, Atmos. Chem. Phys., 13, 11473–11501, https://doi.org/10.5194/acp-13-11473-2013, 2013.
Polansky, L. and Robbins, M. M.: Generalized additive mixed models for disentangling long-term trends, local anomalies, and seasonality in fruit tree phenology, Ecol. Evol., 3, 3141–3151, https://doi.org/10.1002/ece3.707, 2013.
Pui, D. Y. H., Chen, S.-C., and Zuo, Z.: PM2.5 in China: Measurements, sources, visibility and health effects, and mitigation, Particuology, 13, 1–26, https://doi.org/10.1016/j.partic.2013.11.001, 2014.
Qi, G., Wei, W., Wang, Z., Wang, Z., and Wei, L.: The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning, J. Environ. Manage., 325, 116671, https://doi.org/10.1016/j.jenvman.2022.116671, 2023.
Ramanathan, V., Crutzen, P. J., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001.
Ravindra, K., Rattan, P., Mor, S., and Aggarwal, A. N.: Generalized additive models: Building evidence of air pollution, climate change and human health, Environ. Int., 132, 104987, https://doi.org/10.1016/j.envint.2019.104987, 2019.
Ravindra, K., Vakacherla, S., Singh, T., Upadhya, A. R., Rattan, P., and Mor, S.: Long-term trend of PM2.5 over five Indian megacities using a new statistical approach, Stoch. Env. Res. Risk A., 38, 715–725, https://doi.org/10.1007/s00477-023-02595-x, 2024.
Samset, B. H., Lund, M. T., Bollasina, M., Myhre, G., and Wilcox, L.: Emerging Asian aerosol patterns, Nat. Geosci., 12, 582–584, https://doi.org/10.1038/s41561-019-0424-5, 2019.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Shen, Z., Cao, J., Zhang, L., Zhang, Q., Huang, R.-J., Liu, S., Zhao, Z., Zhu, C., Lei, Y., and Xu, H.: Retrieving historical ambient PM2.5 concentrations using existing visibility measurements in Xi'an, Northwest China, Atmos. Environ., 126, 15–20, https://doi.org/10.1016/j.atmosenv.2015.11.040, 2016.
Shi, Y., Matsunaga, T., Yamaguchi, Y., Li, Z., Gu, X., and Chen, X.: Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014, Sci. Total Environ., 615, 177–186, https://doi.org/10.1016/j.scitotenv.2017.09.241, 2018.
Singh, A., Avis, W. R., and Pope, F. D.: Visibility as a proxy for air quality in East Africa, Environ. Res. Lett., 15, 084002, https://doi.org/10.1088/1748-9326/ab8b12, 2020.
Singh, V., Singh, S., and Biswal, A.: Exceedances and trends of particulate matter (PM2.5) in five Indian megacities, Sci. Total Environ., 750, 141461, https://doi.org/10.1016/j.scitotenv.2020.141461, 2021.
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent Developments and Partnerships, B. Am. Meteorol. Soc., 92, 704–708, https://doi.org/10.1175/2011BAMS3015.1, 2011.
Su, L., Gao, C., Ren, X., Zhang, F., Cao, S., Zhang, S., Chen, T., Liu, M., Ni, B., and Liu, M.: Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China, Geosci. Front., 13, 101370, https://doi.org/10.1016/j.gsf.2022.101370, 2022.
Sun, E., Xu, X., Che, H., Tang, Z., Gui, K., An, L., Lu, C., and Shi, G.: Variation in MERRA-2 aerosol optical depth and absorption aerosol optical depth over China from 1980 to 2017, J. Atmos. Sol.-Terr. Phy., 186, 8–19, https://doi.org/10.1016/j.jastp.2019.01.019, 2019.
Tan, S., Wang, Y., Yuan, Q., Zheng, L., Li, T., Shen, H., and Zhang, L.: Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM, Environ. Res. Lett., 17, 034014, https://doi.org/10.1088/1748-9326/ac52c9, 2022.
Teixeira, A.: Analyse discrimante par arbre de décision binaire (CART: Classification And Regression Tree), Rev. Mal. Respir., 21, 1174–1176, https://doi.org/10.1016/S0761-8425(04)71596-X, 2004.
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Henri Theil's Contributions to Economics and Econometrics: Econometric Theory and Methodology, edited by: Raj, B. and Koerts, J., Springer Netherlands, Dordrecht, 345–381, https://doi.org/10.1007/978-94-011-2546-8_20, 1992.
Van Donkelaar, A., Martin, R. V., and Park, R. J.: Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing, J. Geophys. Res., 111, D21201, https://doi.org/10.1029/2005JD006996, 2006.
Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., and Villeneuve, P. J.: Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application, Environ. Health Persp., 118, 847–855, https://doi.org/10.1289/ehp.0901623, 2010.
Van Donkelaar, A., Martin, R. V., Brauer, M., and Boys, B. L.: Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter, Environ. Health Persp., 123, 135–143, https://doi.org/10.1289/ehp.1408646, 2015.
Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A. M., and Winker, D. M.: Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors, Environ. Sci. Technol., 50, 3762–3772, https://doi.org/10.1021/acs.est.5b05833, 2016.
van Donkelaar, A., Hammer, M. S., Bindle, L., Brauer, M., Brook, J. R., Garay, M. J., Hsu, N. C., Kalashnikova, O. V., Kahn, R. A., Lee, C., Levy, R. C., Lyapustin, A., Sayer, A. M., and Martin, R. V.: Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty, Environ. Sci. Technol., 55, 15287–15300, https://doi.org/10.1021/acs.est.1c05309, 2021.
Verbeke, G. and Lesaffre, E.: A Linear Mixed-Effects Model with Heterogeneity in the Random-Effects Population, J. Am. Stat. Assoc., 91, 217–221, https://doi.org/10.1080/01621459.1996.10476679, 1996.
Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, A., Szidat, S., Prevot, A. S. H., Hueglin, C., Bloemen, H., Wahlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.: Source apportionment of particulate matter in Europe: A review of methods and results, J. Aerosol Sci., 39, 827–849, https://doi.org/10.1016/j.jaerosci.2008.05.007, 2008.
Wang, K., Dickinson, R. E., and Liang, S.: Clear Sky Visibility Has Decreased over Land Globally from 1973 to 2007, Science, 323, 1468–1470, https://doi.org/10.1126/science.1167549, 2009.
Wang, K. C., Dickinson, R. E., Su, L., and Trenberth, K. E.: Contrasting trends of mass and optical properties of aerosols over the Northern Hemisphere from 1992 to 2011, Atmos. Chem. Phys., 12, 9387–9398, https://doi.org/10.5194/acp-12-9387-2012, 2012.
Wang, Q., Kwan, M.-P., Zhou, K., Fan, J., Wang, Y., and Zhan, D.: The impacts of urbanization on fine particulate matter (PM2.5) concentrations: Empirical evidence from 135 countries worldwide, Environ. Pollut., 247, 989–998, https://doi.org/10.1016/j.envpol.2019.01.086, 2019.
Wang, Z., Li, J., Wang, Z., Yang, W., Tang, X., Ge, B., Yan, P., Zhu, L., Chen, X., Chen, H., Wand, W., Li, J., Liu, B., Wang, X., Wand, W., Zhao, Y., Lu, N., and Su, D.: Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control, Sci. China Earth Sci., 57, 3–13, https://doi.org/10.1007/s11430-013-4793-0, 2014.
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison, Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019a.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M.: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019b.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y.: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees, Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, 2020.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021.
Wood, S. N., Pya, N., and Säfken, B.: Smoothing Parameter and Model Selection for General Smooth Models, J. Am. Stat. Assoc., 111, 1548–1563, https://doi.org/10.1080/01621459.2016.1180986, 2016.
Wu, J., Zheng, H., Zhe, F., Xie, W., and Song, J.: Study on the relationship between urbanization and fine particulate matter (PM2.5) concentration and its implication in China, J. Clean. Prod., 182, 872–882, https://doi.org/10.1016/j.jclepro.2018.02.060, 2018.
Wu, W. and Zhang, Y.: Effects of particulate matter (PM2.5) and associated acidity on ecosystem functioning: response of leaf litter breakdown, Environ. Sci. Pollut. R., 25, 30720–30727, https://doi.org/10.1007/s11356-018-2922-1, 2018.
Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., and Zhang, Q.: Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations, Environ. Int., 123, 345–357, https://doi.org/10.1016/j.envint.2018.11.075, 2019.
Yang, X., Zhao, C., Yang, Y., Yan, X., and Fan, H.: Statistical aerosol properties associated with fire events from 2002 to 2019 and a case analysis in 2019 over Australia, Atmos. Chem. Phys., 21, 3833–3853, https://doi.org/10.5194/acp-21-3833-2021, 2021.
Zeng, Z., Gui, K., Wang, Z., Luo, M., Geng, H., Ge, E., An, J., Song, X., Ning, G., and Zhai, S.: Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning, Atmos. Res., 254, 105516, https://doi.org/10.1016/j.atmosres.2021.105516, 2021.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu, F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu, Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. A. Sci. USA, 116, 24463–24469, https://doi.org/10.1073/pnas.1907956116, 2019.
Zhang, S., Wu, J., Fan, W., Yang, Q., and Zhao, D.: Review of aerosol optical depth retrieval using visibility data, Earth-Sci. Rev., 200, 102986, https://doi.org/10.1016/j.earscirev.2019.102986, 2020.
Zhang, Z., Wu, W., Wei, J., Song, Y., Yan, X., Zhu, L., and Wang, Q.: Aerosol optical depth retrieval from visibility in China during 1973–2014, Atmos. Environ., 171, 38–48, https://doi.org/10.1016/j.atmosenv.2017.09.004, 2017.
Zhao, B., Su, Y., He, S., Zhong, M., and Cui, G.: Evolution and comparative assessment of ambient air quality standards in China, J. Integr. Environ. Sci., 13, 85–102, https://doi.org/10.1080/1943815X.2016.1150301, 2016.
Zhao, S., Yu, Y., Yin, D., He, J., Liu, N., Qu, J., and Xiao, J.: Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center, Environ. Int., 86, 92–106, https://doi.org/10.1016/j.envint.2015.11.003, 2016.
Zhong, J., Zhang, X., Gui, K., Wang, Y., Che, H., Shen, X., Zhang, L., Zhang, Y., Sun, J., and Zhang, W.: Robust prediction of hourly PM2.5 from meteorological data using LightGBM, Natl. Sci. Rev., 8, nwaa307, https://doi.org/10.1093/nsr/nwaa307, 2021.
Zhong, J., Zhang, X., Gui, K., Liao, J., Fei, Y., Jiang, L., Guo, L., Liu, L., Che, H., Wang, Y., Wang, D., and Zhou, Z.: Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China, Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, 2022.
Short summary
In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at more than 5000 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR).
In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine...
Altmetrics
Final-revised paper
Preprint