Microplastics (MPs) are ubiquitous environmental contaminants with urban landscapes as major source areas of MPs and stormwater runoff as an important transport pathway to receiving aquatic environments. To better delineate the drivers of urban stormwater MP loads, we created a global dataset of stormwater MP concentrations extracted from 107 stormwater catchments (SWCs). Using this dataset, we trained and tested three optimized gradient boosting Machine Learning (ML) models. Twenty hydrometeorological and socioeconomic variables, as well as the MP size definitions considered in the individual SWCs, were included as potential predictors of the observed MP concentrations. CatBoost emerged as the best-performing ML model. Shapley additive explanations revealed that features related to hydrometeorological conditions, watershed characteristics and human activity, and plastic waste management practices contributed 34, 25, and 4.8%, respectively, to the model’s predictive performance. The MP size definition, that is, the lower size limit and the width of the size range, accounted for the remaining 36% variability in the predicted MP concentrations. The lack of a consistent definition of the MP size range among studies therefore represents a major source of uncertainty in the comparative analysis of urban stormwater MP concentrations. The proposed ML modeling approach can generate first estimates of MP concentrations in urban stormwater when data are sparse and serve as a quantitative tool for benchmarking the added value of including further data layers and applying uniform definitions of size classes of environmental MPs.

Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll-a (Chl-a) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl-a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators-intensity, extent, and severity-were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl-a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4-26 °C and wind speed 2.5-5. m s^-1). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management.

Chlorophyll-a concentration (Chl-a) is commonly used as a proxy for phytoplankton abundance in surface waters of large lakes. Mapping spatial and temporal Chl-a distributions derived from multispectral satellite data is therefore increasingly popular for monitoring trends in trophic state of these important ecosystems. We evaluated products of eleven atmospheric correction processors (LEDAPS, LaSRC, Sen2Cor, ACOLITE, ATCOR, C2RCC, DOS 1, FLAASH, iCOR, Polymer, and QUAC) and 27 reflectance indexes (including band-ratio, three-band, and four-band algorithms) recommended for Chl-a concentration retrieval. These were applied to the western basin of Lake Ontario by pairing 236 satellite scenes from Landsat 5, 7, 8, and Sentinel-2 acquired between 2000 and 2022 to 600 near-synchronous and co-located in situ-measured Chl-a concentrations. The in situ data were categorized based on location, seasonality, and Carlson’s Trophic State Index (TSI). Linear regression Chl-a models were calibrated for each processing scheme plus data category. The models were compared using a range of performance metrics. Categorization of data based on trophic state yielded improved outcomes. Furthermore, Sentinel-2 and Landsat 8 data provided the best results, while Landsat 5 and 7 underperformed. A total of 28 Chl-a models were developed across the different data categorization schemes, with RMSEs ranging from 1.1 to 14.1 μg/L. ACOLITE-corrected images paired with the blue-to-green band ratio emerged as the generally best performing scheme. However, model performance was dependent on the data filtration practices and varied between satellites.