Estimation of particulate matter concentrations in Turkiye using a random forest model based on satellite AOD retrievals


TUNA TUYGUN G., ELBİR T.

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, no.9, pp.3469-3491, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2023
  • Doi Number: 10.1007/s00477-023-02459-4
  • Journal Name: STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, CAB Abstracts, Compendex, Environment Index, Geobase, Index Islamicus, Pollution Abstracts, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3469-3491
  • Keywords: Bias analysis, MODIS AOD, PM10 estimation, Random forest, Türkiye
  • Dokuz Eylül University Affiliated: Yes

Abstract

This study estimates intra-daily PM10 concentrations at 213 inland and coastal monitoring sites in Turkiye from 2008 to 2019 using satellite-based aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS). An estimation model based on the random forest (RF) approach was developed using the AOD data from the Terra satellite, the meteorological data, and aerosol diagnostics from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and other auxiliary variables. First, the correlation between the matched PM10 concentrations and MODIS AOD was investigated simply with the quadrant regression (QR) approach. Next, the feature selection procedure was applied to obtain the most significant predictive variables for the estimation model. Then, the spatial and temporal performances of the developed RF model were intensely discussed. Finally, a bias analysis based on the most influential input parameters was also performed to examine the potential errors in the estimated PM10 concentrations. As a result, the RF model showed moderately good performance, with a correlation coefficient (R) of 0.72 and low root mean square error (RMSE) over the entire country, which was better than the results of previous studies in the region. Moreover, the model better estimated PM10 concentrations at individual monitoring sites (with R up to 0.90), particularly in coastal regions. However, overfitting occurred in areas with low populations and few monitoring stations. Additionally, the RF model's performance varied slightly across different seasons, such as autumn (R approximately equal to 0.69), spring (R approximately equal to 0.65), winter (R approximately equal to 0.64), and summer (R approximately equal to 0.60), and it did not adequately estimate intra-daily PM10 concentrations at the seasonal scale. Furthermore, the bias analyses indicated that higher PM10 and dust mass concentrations, u and v wind components were significant parameters that caused bias in the estimations. Finally, this study provides valuable information for further applications in PM10 patterns and represents the first step toward constructing a high-resolution satellite-based air quality monitoring network in Turkiye.