Ali Reza Shahvaran

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.

Urbanization significantly impacts phosphorus (P) cycling and transport from terrestrial to aquatic ecosystems, hence, contributing to the eutrophication of receiving water bodies. Global urban growth projections underscore the need for a better understanding of the P sources, pathways, and eutrophication potential of urban stormwater runoff, as well as the effectiveness of current stormwater best management practices (BMPs) in mitigating urban P export. Traditional and low-impact development BMPs, including stormwater ponds and bioretention cells, while primarily intended to decrease peak flows, have the potential to attenuate P loads across urban areas. Here, we consolidate current knowledge on urban stormwater P, in the literature and through analysis of the International Stormwater BMP Database (ISBD), focusing on P concentrations, yields, loads, and chemical speciation. Moreover, we assess the P reduction or enrichment effects of urban stormwater BMPs and identify key knowledge gaps and research priorities. In particular, we propose that future research should comprise more detailed studies on the diversion of P to the subsurface by BMPs, the P biogeochemical transformation processes within BMPs, the interactions of P with other urban pollutants, the current and future impacts of climate change on urban P speciation and export loads, and expanding data collection across geographies.

Identifying historical trends in surface shortwave (solar) radiation (SSR) is essential for understanding the energy balance in northern high latitudes and its effects on lake ecosystems undergoing climate-induced changes, such as those in the Northwest Territories (NWT), Canada. Using Daily Surface Meteorology and Hydrology Data (Daymet) SSR—validated against CERES measurements with an R² of 0.95—this study quantified SSR trends from 1980 to 2023, investigated the relationship between total cloud cover (TCC) and SSR, and assessed the sensitivity of lake surface water temperature (LSWT) to SSR changes across continental NWT. The Mann–Kendall trend analysis showed contrasting SSR trends before and after 2000 across NWT ecozones. Over the 44-yr period, western zones such as the Taiga Cordillera and Taiga Plain exhibited negative SSR trends of approximately −1.4 and −0.7 W m⁻² decade⁻¹, respectively, whereas the eastern Taiga Shield showed an increase of about +0.6 W m⁻² decade⁻¹ (p ≤ 0.05 for most areas). Seasonally, summer SSR trends were mainly negative in western regions and positive in eastern regions, underscoring complex SSR dynamics in NWT. Regarding the influence of TCC on SSR, Pearson’s correlation analyses using both modeled and in situ TCC observations revealed a moderate-to-high negative correlation, particularly during spring, mainly in central and western NWT, excluding areas near the high-elevation mountainous boundaries of the Taiga Cordillera and Taiga Plains ecozones. The relationship between LSWT and SSR was varied; small lakes showed a positive correlation, while medium and large lakes located in the north exhibited negative correlations. Our findings suggest that lake thermal responses to SSR could be modulated by lake size and location, offering novel insights into NWT lake energy dynamics essential for NWT communities dependent on lakes and their ecosystems.

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.

Inland waters, including small lakes and ponds, play a major role in the global carbon cycle. While they typically act as organic carbon sinks, they also emit the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4) to the atmosphere. Nonetheless, small inland waters, defined as those with a surface area (SA) of less than 5 hectares (further referred as “ponds”), are often excluded from large-scale CO2 and CH4 budgets. To help overcome this gap, we reviewed available global datasets on inland waters and selected G1WBM, GLCF GIW, GSW, and OSM, because these datasets provide sufficient information on ponds to assess their global distribution. We further compiled a dataset of CO2 and CH4 emissions plus water chemistry data from 950 ponds worldwide from existing literature and databases. Next, we applied a Monte Carlo analysis to the estimated surface areas and CO2 and CH4 emission ranges of ponds. The results suggest that ponds with SA < 1 ha emit 0.25–0.42 Pg C yr-1, and those of 1–5 ha emit 0.18–0.45 Pg C yr-1, accounting for up to 14 and 17%, respectively, of the total carbon gas emissions from all-sized lakes and ponds worldwide. Our estimates thus further highlight the potentially disproportionate, yet poorly constrained, importance of ponds in global GHG budgets. In addition to water chemistry data, we also extracted global gridded hydrometeorological and socio-economic data matched to each HydroBASIN-delineated basin of the 950 lakes. Using Random Forest regression (RFR) models, we found that pond water pH and watershed urbanization were the most important predictors of the CO2 emissions, while electrical conductivity (EC), SA, and pond depth were the most important variables for the CH4 emissions. The RFR modeling revealed that ponds in urban areas typically exhibit elevated pH levels, probably due to the ubiquitous use of cement-based construction materials. High pH levels, in turn, suppress CO2 emissions by retaining dissolved inorganic carbon under the form of aqueous bicarbonate (HCO3-). The role of non-sulfate-derived EC in modulating the CH4 emissions is attributed to the effect of salinity on the mixing intensity and the associated impact on water column oxygenation. Overall, our findings confirm the significant role of ponds in global carbon cycling. At the same time, they suggest that site-specific characteristics, including land use and water chemistry, can induce considerable variability in GHG emissions from ponds.