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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 µgNm-3) and mean absolute error (MAE: 0.81 µgNm-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 µgNm-3 , and MAE=0.55 µgNm-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 µgNm-3); followed by the Yangtze River Delta (YRD) (2.56±1.12 µgNm-3), Pearl River Delta (PRD) (1.68±0.81 µgNm-3), and Sichuan Basin (1.53±0.63 µgNm-3), and the lowest one in the Tibetan Plateau (0.42±0.25 µgNm-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<0.05), while it decreased sharply from 2014 to 2015 at a rate of -0.40 µgNm-3yr-1 (p<0.05). The ambient NO3- levels in Beijing–Tianjin–Hebei (BTH), YRD, and PRD displayed gradual increases at a rate of 0.20, 0.11, and 0.05 µgNm-3yr-1 (p<0.05) during 2005–2013, respectively. The gradual increases in NO3- concentrations in these regions from 2005 to 2013 were due to the fact that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase in energy consumption. Afterwards, the government further strengthened these emission reduction measures and thus caused the dramatic decreases in NO3- concentrations in these regions from 2013 to 2015 (p<0.05). The long-term NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005–2015 are open access at 10.5281/zenodo.3988307 (Li et al., 2020c). [ABSTRACT FROM AUTHOR]

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 < 0.05), while it decreased sharply from 2014 to 2015 at a speed of -0.40 μg/m3/year (p < 0.05). The ambient NO3- levels in Beijing-Tianjin-Hebei (BTH), YRD, and PRD displayed gradual increases at the speed of 0.13, 0.08, and 0.03 μg/m3/year (p < 0.05) during 2005-2014, respectively. The gradual increases of NO3- concentrations in these regions from 2005 to 2014 were due to that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase of energy consumption. Afterwards, the government further strengthened these emission reduction measures, and thus caused the dramatic decreases of NO3- concentrations in these regions from 2014 to 2015 (p < 0.05). The long-term NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005-2015 are open access in https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c). [ABSTRACT FROM AUTHOR]