High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

Abstract

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

Quantitative Study on American COVID-19 Epidemic Predictions and Scenario Simulations

“…In addition, geographic models have long been used to predict infectious diseases, such as the GWR model in Schistosoma haematobium [30], human leptospirosis [31], Dengue Fever [32], leptospirosis [33], GTWR in Porcine Reproductive and Respiratory Syndrome [34], MGWR in tropical parasitic diseases [35]. Another emerging geographical neural network weighted regression model (GNNWR model), geographically and temporally neural network weighted regression model is famous for their high-precision regression analysis and skilled handling of large data volumes, which have been used in the spatially non-stationary red tide [36], estimating the CO 2 emissions [37], land surface temperature downscaling [38], exploring fine-scale distributions of surface dissolved silicate in coastal seas [39], and predicted the ground NO 2 concentration [40]. The essence of the spread of the COVID-19 pandemic is the complex interplay of temporal and spatial variations, marked by significant spatiotemporal heterogeneity.…”