Airborne microplastic concentrations and deposition across the Weser River catchment.

Microplastic (MP) appears to be omnipresent in the atmosphere, raising concerns about dispersion across environmental compartments, ecological consequences and human health risks by inhalation. To date, data on the sources of atmospheric MP and deposition to river catchment areas are still sparse. We, therefore, took aerosol and total atmospheric deposition samples in the catchment area of the large German river Weser to estimate microplastic deposition fluxes (DFs) at six specific sites and airborne MP concentrations. Sampling in rural, suburban, and urban environments and wastewater treatment plants (WWTPs) was performed, aiming at a variation in airborne MP pollution and elucidating potential MP source areas. Aerosol samples were taken twice in April and October while monthly total deposition samples were collected over a period from March to October. Microplastics were detected in all analysed aerosol samples by Raman spectroscopy down to 4 µm, and in all 32 total deposition samples by µFT-IR down to 11 µm. Average MP number concentrations of 91 ± 47 m-3 were found in aerosol samples. The measured total MP number DFs ranged between 10 and 367 N m-2 day-1 (99 ± 85 mean ± SD) corresponding to total deposition of 0.05 ± 0.1 kg ha-1 per year and to an estimated 232 metric tons of plastic being deposited in the Weser River catchment annually. MP number DFs were higher in urban than rural sites. An effect of WWTPs on the MP abundance in air was not observed. Polypropylene, polyethylene, polyethylene terephthalate, polyvinyl chloride, polystyrene, and silicone fragments were found as the predominant polymer types in total deposition samples, while polyethylene particles dominated in aerosol samples. The results suggest that proximity to sources, especially to cities, increase the numbers of MP found in the atmosphere. It further indicates that atmospheric MP considerably contributes to the contamination of both aquatic and terrestrial habitats.

Site-scale modeling of surface ozone in Northern Bavaria using machine learning algorithms, regional dynamic models, and a hybrid model.

Ozone (O3) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O3 concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O3 concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O3 observations on a site scale with much higher accuracy than existing O3 models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O3 reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O3 events and the 1- and 24-h forecasts of hourly O3 were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O3, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). With SHAP, we showed an increasing effect of temperature on O3 concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O3 concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O3 concentrations during cold and wet periods. While recently developed MLAs for the spatial estimation of hourly O3 concentrations had a station-based root-mean-square error (RMSE) above 27 µg/m3, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 µg/m3. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O3 concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 µg/m3, respectively).