An interpretable deep forest model for estimating hourly PM₁₀ concentration in China using Himawari-8 data

Highlights

• An interpretable deep forest model was used to estimate the hourly PM₁₀ in China.

• A 10-fold cross validation showed that the R² of hourly PM₁₀ was 0.82–0.88.

• The R² values of monthly and seasonal average PM₁₀ were 0.91 and 0.94, respectively.

• AOD contributed highest (∼13.5%) to the importance of features in the model.

• The contribution of long-range transport dust to PM₁₀ in northern China was 38.1%.

Abstract

Rapid urbanization and industrialization in China had led to increased pollutants emission. PM₁₀ is one of the main components of air pollutants, which significantly impacts human health, environment, and regional or global climate. In this study, a new machine learning deep forest (DF) model was used to construct the aerosol optical depth (AOD) and near-ground PM₁₀ concentration (AOD-PM₁₀) model. The DF model combines the advantages of deep neural networks and tree models, which can provide model interpretability. Combined with the Himawari-8 AOD, meteorological, and auxiliary factors, the hourly PM₁₀ concentration in China (spatial resolution: 0.05 × 0.05°) was obtained. The results show that AOD has the highest contribution to the importance of features in the AOD-PM₁₀ model, accounting for approximately 13.5%, and the contributions of boundary layer height, temperature, and relative humidity to the importance of features were 11%, 8.6%, and 7%, respectively. A 10-fold cross-validation was used to evaluate the performance of the model. The hourly cross validation results from 09:00 to 16:00 (Beijing time) show that the R² range was 0.82–0.88, and the root mean square error and absolute mean error were 18.55–23.12 μg/m³ and 11.54–16.82 μg/m³, respectively. The R² values of daily, monthly, seasonal, and annual average PM₁₀ estimated by the model were 0.87, 0.91, 0.94, and 0.94, respectively. The areas with high PM₁₀ concentrations are mainly in northern China, especially in the North China Plain, and the peak value of daily average PM₁₀ can reach 91 μg/m³; the Intraday variation of PM₁₀ in southern China ranges from 67 μg/m³ to 72 μg/m³. A large-scale dust weather process was analyzed. Based on the AOD-PM₁₀ model, the contribution of long-range transport dust to PM₁₀ in China and Northern China were 25.6% and 38.1%, respectively. The PM₁₀ measured by the station and estimated by the DF model indicated good consistency.