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Estimation of high-resolution PM2.5 concentrations based on gap-filling aerosol optical depth using gradient boosting model

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Abstract

Air pollution caused by fine particulate matter (PM2.5) may affect people’s health. At present, aerosol optical depth (AOD) products have been used to estimate PM2.5 concentrations. However, AOD always has a low coverage rate. High-resolution products cannot provide a wide range of data. The purpose of this study was to deal with the missing data problem using the interpolation technique and to estimate high-resolution PM2.5 concentrations by fitting the spatial and temporal variations of AOD and PM2.5. Firstly, we developed a gradient boosting model (XGBoost) to fill in the missing values. Secondly, we corrected the altitude and humidity of meteorological variables. We then used the mixed-effects model with the gap-filling AOD data to estimate the PM2.5 concentrations in this study area. Finally, we obtained the complete PM2.5 concentrations picture. The AOD coverage rate reached 100% after model estimation. Meanwhile, the estimation results of PM2.5 concentrations were also validated and the model worked well. The results indicate that it is necessary to fill in the missing values of AOD data for PM2.5 estimations. This study can provide complete AOD data, which makes the results of PM2.5 estimations more accurate and has some research value.

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Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The MODIS data were obtained from NASA. We would like to thank the team of AERONET for the hard work. We acknowledged that China National Environmental Monitoring Center provided the PM2.5 data from ground monitoring stations. We are thankful to the European Center for Medium-term Weather Forecast for the meteorological data.

Funding

This research is supported by the National Key Research and Development Program of China under Grant 2016YFC0400903, and the Fundamental Research Funds for the Central Universities under Grant DUT20LAB114.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Min Han

  2. Professional Technology Innovation Center of Distributed Control for Industrial Equipment of Liaoning Province, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Min Han

  3. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Shuqin Jia

  4. Department of Computer Technology and Application, Qinghai University, **ning, 810016, People’s Republic of China

    Chengkun Zhang

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  1. Min Han
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  2. Shuqin Jia
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  3. Chengkun Zhang
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Contributions

Min Han: supervision, writing-review and editing. Shuqin Jia: writing-original draft, investigation, conceptualization. Chengkun Zhang: data curation, software, methodology.

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Correspondence to Min Han.

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Han, M., Jia, S. & Zhang, C. Estimation of high-resolution PM2.5 concentrations based on gap-filling aerosol optical depth using gradient boosting model. Air Qual Atmos Health 15, 619–631 (2022). https://doi.org/10.1007/s11869-021-01149-w

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  • DOI: https://doi.org/10.1007/s11869-021-01149-w

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