Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Predicting Particulate Matter (PM 10) Levels in Morocco: A 5-Day Forecast Using the Analog Ensemble Method

Accurate prediction of Particulate Matter (PM 10) levels, an indicator of natural pollutants such as those resulting from dust storms, is crucial for public health and environmental planning. This study aims to provide accurate forecasts of PM 10 over Morocco for five days. The Analog Ensemble (AnEn) and the Bias Correction (AnEnBc) techniques were employed to post-process PM 10 forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) global atmospheric composition forecasts, using CAMS reanalysis data as a reference. The results show substantial prediction improvements: the Root Mean Square Error (RMSE) decreased from 63.83 μg/m 3 in the original forecasts to 44.73 μg/m 3 with AnEn and AnEnBc, while the Mean Absolute Error (MAE) reduced from 36.70 μg/m 3 to 24.30 μg/m 3. Additionally, the coefficient of determination (R 2) increased more than twofold from 29.11% to 65.18%, and the Pearson correlation coefficient increased from 0.61 to 0.82. This is the first use of this approach for Morocco and the Middle East and North Africa and has the potential for translation into early and more accurate warnings of PM 10 pollution events. The application of such approaches in environmental policies and public health decision making can minimize air pollution health impacts.

Generating a long-term (2003-2020) hourly 0.25° global PM2.5 dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS)

Generating a long-term high-spatiotemporal resolution global PM_2.5 dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). Hence, this paper developed a deep learning-based framework (DeepCAMS) to downscale CAMS PM_2.5 product on the spatiotemporal dimension for resolution enhancement. The nonlinear statistical downscaling from low-resolution (LR) to high-resolution (HR) data can be learned from the high quality (0.25°, hourly) but short-term (2018-2020) Goddard Earth Observing System composition forecast (GEOS-CF) system PM_2.5 product. Compared to the conventional spatiotemporal interpolation methods, simulation validations on GEOS-CF demonstrate that DeepCAMS is capable of producing accurate temporal variations with an improvement of Root-Mean-Squared Error (RMSE) of 0.84 (4.46 to 5.30) ug/m³ and spatial details with an improvement of Mean Absolute Error (MAE) of 0.16 (0.34 to 0.50) ug/m³. The real validations on CAMS reflect convincing spatial consistency and temporal continuity at both regional and global scales. Furthermore, the proposed dataset is validated with OpenAQ air quality data from 2017 to 2019, and the in-situ validations illustrate that the DeepCAMS maintains the consistent precision (R: 0.597) as the original CAMS (R: 0.593) while tripling the spatiotemporal resolution. The proposed dataset will be available at https://doi.org/10.5281/zenodo.6381600.

Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy.