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.

Introduction: Microplastic (MP) pollution research has experienced significant global growth, with an exponential increase in publications since 2015. Despite this surge, research focused on the Gulf region remains limited. This gap is particularly concerning given the Gulf's dense industrial activities and substantial waste production. This study provides the first bibliometric analysis of MP research globally and within the Gulf region, examining the current state and emerging trends while identifying specific topics for future studies.Methods: A bibliometric analysis of peer-reviewed articles published between 2000 and 2024 was conducted for global and Gulf region research. Bibliometric analysis employed several tools to identify trends, keyword networks, and research gaps, with manual refinement of keywords to enhance accuracy. The study also analyzed leading countries and institutions contributing to MP research.Results and Discussion: The analysis highlights significant global contributions, with China emerging as a leader in MP research globally and Iran playing a prominent role within the Gulf region. While global publications have increased exponentially since 2015, the Gulf region's research output remains underrepresented, accounting for only about 1.1% of global publications. This underscores the need for enhanced regional research to better integrate the Gulf into the global discourse on MPs. Keyword analysis reveals that certain areas of MP research remain unexplored in the Gulf region. The trend analysis shows an evolving focus globally, shifting from basic interactions of MPs with marine environments to more complex issues such as environmental health and ecosystem impacts. This progression indicates a maturing field that increasingly addresses the broader implications of MP pollution.

Urban stormwater runoff is a major pathway for microplastic (MP) pollution to aquatic environments. In this study, MPs from five stormwater catchments (SWCs) in the City of Kitchener, Ontario, Canada, were analyzed in water and sediment samples collected at their receiving stormwater ponds (SWPs). We integrated MP characterization targeting particles between 20 and 500 µm using a Laser Direct Infrared (LDIR) system with hydrological modeling, measured sediment accumulation rates, and particle to mass conversion calculations to quantify MP concentrations and loads. The LDIR data included polymer type, size class, and particle shape. Inflow concentrations to the SWPs ranged from 4.9 × 10⁶ to 4 × 10⁷ particles m^-3 and from 5.8 × 10^-2 to 0.9 g m^-3, yielding annual loads of 9.7 × 10¹¹ to 3.5 × 10¹² particles yr^-1 and 1.1 × 10⁴ to 8 × 10⁴ g yr^-1. Per unit impervious surface area MP yields fell in the range 3.6 × 10¹⁰ to 2.1 × 10¹¹ particles ha^-1 yr^-1 and 4.2 × 10² to 4.9 × 10³ g ha^-1 yr^-1, while normalized to the total SWC area produced 1.6 × 10¹⁰-1.4 × 10¹¹ particles ha^-1 yr^-1 and 1.9 × 10²-3.2 × 10³ g ha^-1 yr^-1. Fragments were the dominant morphology (94-98% by count; 83-91% by mass). MPs smaller than 100 µm comprised 92.6-97.3% of particle counts but only 32.1-60.8% of mass, whereas MPs larger than 500 µm accounted for 0.2-0.8% of particle counts but 5.9-33.9% of mass. Yields showed no systematic differences across broad land-use categories and no correlation with total population. Instead, positive associations were observed with residential population density and with SWC imperviousness, including strong correlations with rubber and several polymer-specific yields. Annual average daily traffic was not related to total MP loads from SWCs but aligned with the relative abundances of specific polymers, particularly polystyrene and polycarbonate. Yields of rubber particles correlated with overall MP yields, reinforcing the role of road traffic as a source. Road and mixed-impervious cover considered independently were not significant predictors, but their ratio explained the relative yields of fragments, fibers, and selected polymers.

Freely available multispectral satellite imagery enables frequent monitoring of lake Chlorophyll-a (Chl-a), but retrieval accuracy depends strongly on both atmospheric correction (AC) and model choice. Using more than 600 pixel–in-situ matchups (2000–2023) from more than 240 Landsat-5/7/8 and Sentinel-2 imagery in western Lake Ontario (±4-day window; 3×3 pixel means), we benchmarked four radiometric products (Level-1, Level-2, DOS, ACOLITE) with four machine learning models (LASSO, MDN, SVR, XGBoost). ACOLITE paired with XGBoost was consistently best on the held-out test sets: RMSLE = 0.34 (L5), 0.43 (L7), 0.56 (L8), and 0.42 (S2), with slopes ~0.7, 0.6, 0.4, and 0.7, respectively. Minimally corrected products (Level-1, DOS1) performed poorly (test RMSLE ≳ 0.6; slopes ≲ 0.1). For Landsat-8, the Provisional Aquatic Reflectance generalized better than LaSRC (test slope ≈ 0.6 vs 0.1; RMSLE 0.59 vs 0.68), but ACOLITE remained strongest overall. Against semi-empirical indices, ML—especially XGBoost—reduced errors and produced fits closer to unity across sensors. We then mapped seasonal Chl-a in Hamilton Harbour (2000–2024) to showcase model application. Monthly means rose from late spring to midsummer (May 13.2 μg L⁻¹; June 11.5; July 19.2; August 20.2), with persistent nearshore hotspots near the Royal Botanical Gardens and Windermere Basin. Mann–Kendall trend analyses during bloom months indicated little monotonic change: mean Sen's slopes were near zero (|mean| ≤ 2×10⁻⁴ μg L⁻¹ day⁻¹) and <15% of pixels showed significant trends per month. Thus, interannual variability dominates over simple linear trends. Overall, harmonizing radiometry with ACOLITE and deploying XGBoost provides a practical, cross-sensor pathway for robust, long-term Chl-a mapping, demonstrated here for a meso-eutrophic embayment of Western Lake Ontario.

The rapid growth in plastic production and mismanagement of plastic waste streams have raised environmental concerns, with microplastics (MPs) emerging as pervasive pollutants. This study quantifies stormwater MP exports from urban areas by examining five stormwater management ponds (SWPs) and their representative catchments in Kitchener, Ontario, Canada, using field sampling, laboratory extraction, and modeling approaches. Using Laser Direct Infrared (LDIR) spectroscopy, MP concentrations were determined for different MP shapes and polymer compositions, enabling the calculation of both particle- and mass-based fluxes. A hydrology model coupled with a mass balance approach was employed to estimate MP emission factors (i.e. export coefficients) and retention efficiencies in both particle- and mass-based units. Land use impacts were examined by classifying stormwater catchments through machine learning-aided analysis of aerial imagery. Sediment emissions were also quantified through surveys and samplings to explore potential correlations with MP exports. Industrial catchments showed the highest MP emission factor at 8.7×1011 particles ha-1 year-1 (19.6 kg ha-1 year-1), whereas residential areas exhibited the lowest emissions at 1.7×1011 particles ha-1 year-1 (2.3 kg ha-1 year-1). Fibrous MPs accounted for 2–6% of particle-based emissions but 10–24% by mass, highlighting differences in composition across land use types. Parking lots and traffic were key contributors to MP pollution, consistent with polymer composition analysis. SWP retention efficiencies ranged from 73–97% for total loads but varied for specific polymers, from minimal to complete retention. Retention performance was influenced by SWP design features such as inlet and outlet configurations, catchment wash-off dynamics, and hydraulic residence time. These findings emphasize the critical role of land use and SWP design in urban stormwater MP mitigation, with industrial and high-traffic areas contributing significantly to pollution. Understanding these dynamics provides actionable insights for mitigating MP emissions and optimizing SWP retention performance to protect aquatic ecosystems.