Preprints
https://doi.org/10.5194/essd-2020-243
https://doi.org/10.5194/essd-2020-243

  26 Nov 2020

26 Nov 2020

Review status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

Long-term trends of ambient nitrate (NO3) concentrations across China based on ensemble machine-learning models

Rui Li1, Lulu Cui1, Yilong Zhao1, Wenhui Zhou1, and Hongbo Fu1,2,3 Rui Li et al.
  • 1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science & Engineering, Institute of Atmospheric Sciences, Fudan University, Shanghai, 200433, P.R. China
  • 2Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, P.R. China
  • 3Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, P.R. China

Abstract. 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).

Rui Li et al.

 
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Rui Li et al.

Data sets

Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models Rui Li, Lulu Cui, Yilong Zhao, Wenhui Zhou, and Hongbo Fu https://doi.org/10.5281/zenodo.3988307

Rui Li et al.

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Short summary
A unique monthly NO3 dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating multi-source variables. 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).The dataset also exhibited relatively robust performance at the spatial and temporal scale. The dataset over China could deeepen the knowledge of the status of N pollution in China.