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Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data

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Abstract

The accurate and informative space-time mapping of air pollutants is a crucial component of many human exposure studies. In the present work, space-time maps of daily distributions of PM2.5 and NO2 concentrations were generated in the severely polluted northern China region using the Bayesian maximum entropy (BME) method. This method can incorporate hard PM2.5 and NO2 data (obtained at ground-level monitoring sites), and various kinds of soft (uncertain) data, including satellite data processed in terms of machine learning techniques, meteorological variables, and geographical predictors. The BME maps of space-time PM2.5 and NO2 concentrations over northern China generated during the winter season (when severe haze episodes occur frequently) were realistic and informative. As regards their numerical accuracy, for the space-time PM2.5 estimates, the tenfold cross-validation R 2 and the RMSE were, respectively, 0.86 and 14.37 μg/m3; for the space-time NO2 estimates, the R 2 and RMSE values were, respectively, 0.85 and 6.93 μg/m3. Lastly, it was shown that the BME method performed better than the mainstream spatiotemporal ordinary kriging technique in terms of the higher R 2 values of both the predicted PM2.5 and NO2 concentration maps.

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Acknowledgements

The authors acknowledge with appreciation the valuable comments made by the three referees. This research was supported by a grant from the National Science Foundation of China (Grant No. NSFC 41671399).

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Authors and Affiliations

  1. Institute of Islands and Coastal Ecosystems, Zhejiang University, Zhoushan, China

    Qutu Jiang & George Christakos

  2. Department of Geography, San Diego State University, San Diego, CA, USA

    George Christakos

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  1. Qutu Jiang
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  2. George Christakos
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Correspondence to George Christakos.

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Jiang, Q., Christakos, G. Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data. Air Qual Atmos Health 11, 23–33 (2018). https://doi.org/10.1007/s11869-017-0514-8

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  • DOI: https://doi.org/10.1007/s11869-017-0514-8

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