Enhanced roadside drainage system for environmentally sensitive areas

Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations

Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers. Identifying the factors influencing energy consumption is crucial for enhancing the energy efficiency of WWTPs. To address this, the unit energy consumption (UEC) of WWTPs was predicted using machine learning models. In order to accurately evaluate WWTPs' energy utilization efficiency, a comprehensive energy evaluation indicator, UEC (kWh/kg TODremoved) was utilized in this study. Among the prediction models, the eXtreme Gradient Boosting (XGBoost) achieves the highest prediction accuracy. SHapley Additive exPlanations (SHAP) was adopted as the model explanation system, and the results revealed that UEC was negatively affected by TN concentration, which was the most influential factor. The stoichiometry-based model calculation result indicates that the nitrification consumes average 77 % of the overall oxygen demand. SHAP analysis illustrated that the UEC of main technologies decreases with increasing influential factors. Partial dependence plot (PDP) compared average UEC of these technologies and SBR consumed the least amount of energy. The research also indicated that low influent TN concentration is the main problem in China. Consequently, it is imperative to exert efforts in ensuring the influent TN concentration while simultaneously making appropriate adjustments to the treatment process. This study provides valuable implications and methods for retrofitting and upgrading WWTPs.

Environmental impact of Hsuehshan Tunnel on water quality at Feitsui Reservoir and its tributaries

To investigate the possible impact of the traffic flow of mountainous roads and the construction and operation of a long tunnel on the water quality of a reservoir, this study conducts statistical analysis on water quality, meteorological, and traffic data of the Feitsui Reservoir and its upstream tributaries over the last three decades. Results from statistical regression analysis indicate that in the upstream area where the traffic flow is low, water quality varies insignificantly with rainfall and traffic flow, providing as a background reference of natural environment. Water quality near a conventional highway on which many vehicles travel through the catchment area is significantly affected by both rainfall and traffic flow since the drainage system of a conventional highway sends its gathered water into adjacent rivers. Not only does traffic flow generate contaminants, but also the construction of the Hsuehshan Tunnel of the No. 5 Expressway, Taiwan, in the catchment area of the Feitsui Reservoir generates pollution. Drainage, silt settling and retarding basin, and wastewater treatment facilities near the construction site mitigate the impact of tunnel construction and traffic flow on the environment. The No. 5 Expressway makes good use of viaduct and tunnel structures, collecting water from pavements within the catchment area into sewage facilities, filtering it, and then emitting it outside the catchment area, forming a closed system over the Feitsui Reservoir. The Expressway now shortens travel time from two hours to 40 min and accommodates 7-13 times previous traffic flows, insignificant influencing water quality in the upstream tributaries of the reservoir, demonstrating the effectiveness of its environmental protection measures.

Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM). For each of the studied parameters, 144 different (144 × 8 models) linear models (ARIMA) are presented, with the superior model of each parameter being selected based on statistical indices. Moreover, 48 nonlinear models (ORELM) and 48 hybrid models (ARIMA-ORELM) were considered. The use of linear and nonlinear approaches to model the linear and nonlinear terms (respectively) of each time series in the hybrid model increased the efficiency and accuracy of the predictions for all of the time series. The influent wastewater nonlinear TSS model and the effluent COD and BOD models attained the best performance with a high correlation coefficient of 0.95. The use of hybrid models improved the prediction capability of all quality parameters with the best performance being achieved for the effluent BOD model (R² = 0.99).

Characterization of Nutrient and Metal Leaching in Roadside Ditches Maintained with Cool and Warm Season Grasses

Roadside ditches play an important role in the quantity and quality of receiving waters. Very little, however, is known about the fate and transport of nutrients and trace metals in roadside ditches, especially their leaching to shallow groundwater. This study sought to document selected water quality constituent levels in infiltrated water (i.e., leachate) in roadside ditches maintained with permanent vegetation. Leachate sampling wells were installed in four roadside ditches, and water samples were collected from the wells following major rainfall events during the years 2016 and 2017. The samples were analyzed for nutrient and metal concentrations. Results indicated that nutrient concentrations in the water samples range from 0.00600 to 0.0107 mg/L for orthophosphate (PO4–P), 0.00500 to 6.80 mg/L for nitrate (NO3–N), 0 to 0.007 mg/L for nitrite (NO2–N), and 0.0100 to 314 mg/L for chloride (Cl−). Concentrations of the metals examined varied between 0.0100 and 104 mg/L in water samples. While there was no specific pattern in both nutrient and metal concentrations when roadside ditches maintained with cool season grass were compared to those of warm season grass ditches, results suggest that grass types will likely affect differently uptake of nutrients and metals in the ditches.

Controls of Chloride Loading and Impairment at the River Network Scale in New England

Chloride contamination of rivers due to nonpoint sources is increasing throughout developed temperate regions due to road salt application in winter. We developed a river-network model of chloride loading to watersheds to estimate road salt application rates and investigated the meteorological factors that control riverine impairment by chloride at concentrations above thresholds protective of aquatic organisms. Chloride loading from road salt was simulated in the Merrimack River watershed in New Hampshire, which has gradients in development density. After calibration to a regional network of stream chloride data, the model captured the distribution of regional discharge and chloride observations with efficiencies of 93 and 75%, respectively. The estimate of road salt application is within uncertainties of inventoried estimates of road salt loading and is 122 to 214% greater than recommended targets. Model predictions of chloride showed seasonal variation in chloride concentrations despite a large groundwater storage pool. Interannual variation of mean summer chloride concentration near the outlet varied up to 18%, and the total river length exceeding impairment thresholds varied 12%. Annual snowfall, which drives road salt loading, correlated with chloride impairment only in headwater streams, whereas concentration variability at the outlet was driven primarily by dilution from clean runoff-draining undeveloped forested areas of the watershed. The role of summer meteorology complicates the protection of freshwater systems from chloride contamination.