Machine Learning (ML) algorithms provide an alternative for the prediction of pollutant concentration. We compared eight ML algorithms (Linear Regression (LR), uniform weighting k-Nearest Neighbor (UW-kNN), variable weighting k-Nearest Neighbor (VW-kNN), Support Vector Regression (SVR), Artificial Neural Network (ANN), Regression Tree (RT), Random Forest (RF), and Adaptive Boosting (AdB)) to evaluate the feasibility of ML approaches for estimation of Total Suspended Solids (TSS) using the national stormwater quality database. Six factors were used as features to train the algorithms with TSS concentration as the target parameter: Drainage area, land use, percent of imperviousness, rainfall depth, runoff volume, and antecedent dry days. Comparisons among the ML methods demonstrated a higher degree of variability in model performance, with the coefficient of determination (R²) and Nash–Sutcliffe (NSE) values ranging from 0.15 to 0.77. The Root Mean Square (RMSE) values ranged from 110 mg/L to 220 mg/L. The best fit was obtained using the AdB and RF models, with R² values of 0.77 and 0.74 in the training step and 0.67 and 0.64 in the prediction step. The NSE values were 0.76 and 0.72 in the training step and 0.67 and 0.62 in the prediction step. The predictions from AdB were sensitive to all six factors. However, the sensitivity level was variable.

A site-scale integrated decision support tool (i-DSTss) is developed for selection and sizing of stormwater Best Management Practices (BMPs). The tool has several component modules—hydrology, BMP selection, BMP sizing, and life-cycle cost analysis (LCCA)—integrated into a single platform. The hydrology module predicts runoff from small catchment on event and continuous basis using the Green-Ampt and Curve Number methods. The module predicted runoff from a small residential area and a parking lot with R² value of 0.77 and 0.74, respectively. The BMP selection module recommends a BMP type appropriate for a site based on economic, technical, social and environmental criteria using a multi-criteria optimization approach. The BMP sizing module includes sizing options for green roofs, infiltration-based BMPs, and storage-based BMPs. A mass balance approach is implemented for all types of BMPs. The tool predicted outflow rates from a permeable pavement with R² value of 0.89. A cost module is included where capital, operation and maintenance, and rehabilitation costs are estimated based on BMP size obtained from the sizing module. The i-DSTss is built on an accessible platform (Microsoft Excel VBA) and can be operated with a basic skillset. The i-DSTss is intended for designers, regulators, and municipalities for quick analysis of scenarios involving interaction among several factors.

This research investigated the removal of heavy metal ions (Cd, Cu, Pb, and Zn) and metalloid (As) common to stormwater runoff onto biochar-based media arranged in multiple configurations. Laboratory scale column experiments were conducted to quantify heavy metal removal efficiencies using sand, biochar, and nZVI-modified biochar (BC-nZVI) in four media configurations: a homogeneous mixture of sand and biochar (BCM); biochar layered in sand (BCL); BC-nZVI layered in sand (BCZ); and sand as a control. An inverse modeling approach was implemented to measured moisture and experimental data to determine media hydraulic parameters (θ_r, θ_s, α, n and K_s) and adsorption coefficients. The experiment was conducted using laboratory synthesized stormwater over 200 days at a rate of 5 cm/day. BCZ exhibited an excellent removal (99%) of As due to the high attachment to nZVI, via surface complexations. Biochar with abundant surface oxygen functional groups exhibited a great (99%) removal of Cd and Zn in both BCL and BCM columns. Water contents were observed 66.0, 44.3, 41.4, and 7.2% for BCL, BCM, BCZ, and sand, respectively. The attachment coefficients varied from 21.5 to 44.9, 16.1 to 19.3, 18.8 to 26.0, and 9.6 to 19.9 L/kg for BCL, BCM, BCZ, and sand, respectively. This study’s output provides useful information for stormwater management practices.