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The rapid response to the COVID-19 pandemic led to unprecedented decreases in economic activities, thereby reducing the pollutant emissions. A random forest (RF) model was applied to determine the respective contributions of meteorology and anthropogenic emissions to the changes in air quality. The result suggested that the strict lockdown measures significantly decreased primary components such as Cr (−67 %) and Fe (−61 %) in PM2.5 (p), whereas the higher relative humidity (RH) and NH3 level and the lower air temperature (T) remarkably enhanced the production of secondary aerosol, including SO42- (29 %), NO3- (29 %), and NH4+ (21 %) (p). The positive matrix factorization (PMF) result suggested that the contribution ratios of secondary formation (SF), industrial process (IP), biomass burning (BB), coal combustion (CC), and road dust (RD) changed from 36 %, 27 %, 21 %, 12 %, and 4 % before the COVID-19 outbreak to 44 %, 20 %, 20 %, 9 %, and 7 %, respectively. The rapid increase in the contribution ratio derived from SF to PM2.5 implied that the intermittent haze events during the COVID-19 period were characterized by secondary aerosol pollution, which was mainly contributed by the unfavorable meteorological conditions and high NH3 level.

High loadings of nitrate (NO3-) in the aerosol over China significantly exacerbate the air quality and pose a great threat to ecosystem safety through dry–wet deposition. Unfortunately, limited ground-level observation data make it challenging to fully reflect the spatial pattern of NO3- levels across China. Until now, long-term monthly particulate NO3- datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3- dataset at 0.25∘ resolution over China during 2005–2015 was developed by assimilating surface observations, satellite products, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient-boosting decision tree (GBDT), and extreme gradient-boosting (XGBoost) methods. The new developed product featured an excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 µg N m−3) and mean absolute error (MAE: 0.81 µg N m−3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scales. Moreover, the dataset displayed good agreement with (R2=0.85, RMSE=0.74 µg N m−3, and MAE=0.55 µg N m−3) some unlearned data collected from previous studies. The spatiotemporal variations in the developed product were also shown. The estimated NO3- concentration showed the highest value in the North China Plain (NCP) (3.55±1.25 µg N m−3); followed by the Yangtze River Delta (YRD) (2.56±1.12 µg N m−3), Pearl River Delta (PRD) (1.68±0.81 µg N m−3), and Sichuan Basin (1.53±0.63 µg N m−3), and the lowest one in the Tibetan Plateau (0.42±0.25 µg N m−3). The higher ambient NO3- concentrations in the NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3- pollution in the Sichuan Basin. The lowest ambient NO3- concentration in the Tibetan Plateau was contributed by the scarce anthropogenic emission and favourable meteorological factors (e.g. high wind speed). In addition, the ambient NO3- concentration showed a marked increasing tendency of 0.10 µgNm-3yr-1 during 2005–2014 (p

We developed a two-stage model called the random-forest–generalised additive model (RF–GAM), based on satellite data, meteorological factors, and other geographical covariates, to predict the surface 8 h O3 concentrations across the remote Tibetan Plateau. The 10-fold cross-validation result suggested that RF–GAM showed excellent performance, with the highest R2 value (0.76) and lowest root-mean-square error (RMSE) (14.41 µ g m −3 ), compared with other seven machine-learning models. The predictive performance of RF–GAM showed significant seasonal discrepancy, with the highest R2 value observed in summer (0.74), followed by winter (0.69) and autumn (0.67), and the lowest one in spring (0.64). Additionally, the unlearning ground-observed O3 data collected from open-access websites were applied to test the transferring ability of the novel model and confirmed that the model was robust in predicting the surface 8 h O3 concentration during other periods ( R2=0.67 , RMSE = 25.68 µ g m −3 ). RF–GAM was then used to predict the daily 8 h O3 level over the Tibetan Plateau during 2005–2018 for the first time. It was found that the estimated O3 concentration displayed a slow increase, from 64.74±8.30 µ g m −3 to 66.45±8.67 µ g m −3 from 2005 to 2015, whereas it decreased from the peak to 65.87±8.52 µ g m −3 during 2015–2018. Besides this, the estimated 8 h O3 concentrations exhibited notable spatial variation, with the highest values in some cities of the northern Tibetan Plateau, such as Huangnan ( 73.48±4.53 µ g m −3 ) and Hainan ( 72.24±5.34 µ g m −3 ), followed by the cities in the central region, including Lhasa ( 65.99±7.24 µ g m −3 ) and Shigatse ( 65.15±6.14 µ g m −3 ), and the lowest O3 concentration occurred in a city of the southeastern Tibetan Plateau called Aba ( 55.17±12.77 µ g m −3 ). Based on the 8 h O3 critical value (100 µ g m −3 ) provided by the World Health Organization (WHO), we further estimated the annual mean nonattainment days over the Tibetan Plateau. It should be noted that most of the cities on the Tibetan Plateau had excellent air quality, while several cities (e.g. Huangnan, Haidong, and Guoluo) still suffered from more than 40 nonattainment days each year, which should be given more attention in order to alleviate local O3 pollution. The results shown herein confirm that the novel hybrid model improves the prediction accuracy and can be applied to assess the potential health risk, particularly in remote regions with few monitoring sites.

High loadings of nitrate (NO3−) in the aerosol over China significantly exacerbates the air quality and poses a great threaten on ecosystem safety through dry/wet deposition. Unfortunately, limited ground-level observation data makes it challenging to fully reflect the spatial pattern of NO3− level across China. Up to date, the long-term monthly NO3− datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3− dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating surface observation, satellite product, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). The new developed product featured excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 μg/m3) and mean absolute error (MAE: 0.81 μg/m3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scale. Moreover, the dataset displayed good agreement with (R2 = 0.85, RMSE = 0.74 μg/m3, and MAE = 0.55 μg/m3) some unlearning data collected from previous studies. The spatiotemporal variations of the developed product were also shown. The estimated NO3− concentration showed the highest value in North China Plain (NCP) (3.55 ± 1.25 μg/m3), followed by Yangtze River Delta (YRD (2.56 ± 1.12 g/m3)), Pearl River Delta (PRD (1.68 ± 0.81 μg/m3)), Sichuan Basin (1.53 ± 0.63 μg/m3), and the lowest one in Tibetan Plateau (0.42 ± 0.25 μg/m3). The higher ambient NO3− concentrations in NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3− pollution in Sichuan Basin. The lowest ambient NO3− concentration in Tibetan Plateau was contributed by the scarce anthropogenic emission and favorable meteorological factors (e.g., high wind speed). In addition, the ambient NO3− concentration showed marked increasing tendency of 0.10 μg/m3/year during 2005–2014 (p

We developed a two-stage model named random forest-generalized additive model (RF-GAM) based on satellite data, meteorological factors, and other geographical covariates to predict the surface 8-h O3 concentration across the remote Tibetan Plateau. The 10-fold cross-validation result suggested that RF-GAM showed the excellent performance with the highest R2 value (0.76) and lowest root mean square error (RMSE) (14.41 μg/m3) compared with other seven machine learning models. The predictive performance of RF-GAM model showed significantly seasonal discrepency with the highest R2 value observed in summer (0.74), followed by winter (0.69) and autumn (0.67), and the lowest one in spring (0.64). Additionally, the unlearning ground-observed O3 data collected from open websites were applied to test the transferring ability of the novel model, and confirmed that the model was robust to predict the surface 8-h O3 concentration during other periods (R2 = 0.67, RMSE = 25.68 μg/m3). RF-GAM was then used to predict the daily 8-h O3 level over Tibetan Plateau during 2005–2018 for the first time. It was found that the estimated O3 concentration displayed a slow increase from 64.74 ± 8.30 μg/m3 to 66.45 ± 8.67 μg/m3 2005 through 2015, whereas it decreased from the peak to 65.87 ± 8.52 μg/m3 during 2015–2018. Besides, the estimated 8-h O3 concentrations exhibited notably spatial variation with the highest values in some cities of North Tibetan Plateau such as Huangnan (73.48 ± 4.53 μg/m3) and Hainan (72.24 ± 5.34 μg/m3), followed by the cities in the central region including Lhasa (65.99 ± 7.24 μg/m3) and Shigatse (65.15 ± 6.14 μg/m3), and the lowest one in some cities of Southeast Tibetan Plateau such as Aba (55.17 ± 12.77 μg/m3). Based on the 8-h O3 critical value (100 μg/m3) scheduled by World Health Organization (WHO), we further estimated the annually mean nonattainment days over Tibetan Plateau this period. It should be noted that most of the cities in Tibetan Plateau shared with the excellent air quality, while several cities (e.g., Huangnan, Haidong, and Guoluo) still suffered from more than 40 nonattainment days each year, which should be paid more attention to alleviate local O3 pollution. The result shown herein confirms the novel hybrid model improves the prediction accuracy and can be applied to assess the potential health risk, particularly in the remote regions with sparse monitoring sites.