Characterization and Assessment of Stormwater Runoff Quality from Automobile Workshops in Nigeria Using Multivariate Linear Regression

An investigation into the pollution of stormwater runoff from automobile workshops in Nigeria was performed. Also, multivariate regression was used to predict the pH, oil, and grease (O&G) as well as the electrical conductivity (EC) in relation to the characteristics of the solids and metals pollutants of the untreated automobile workshop stormwater. The results indicated that automobile workshops contributed notable amounts of pollutants to stormwater runoff. Results were compared with Nigerian and USEPA standards. It was found that most of the parameters had mean value ranges far greater than standard limits. The multivariate regression showed variations in the results obtained from different automobile workshops. These variations could be due to the influence of factors such as the volume of automobile servicing activities and the waste generated from these activities that flow in the stormwater runoff. However, the bulk of the EC and pH of the stormwater were associated with the concentrations of the total dissolved solids and copper while the bulk of the O&G concentration was associated with the concentrations of lead and cadmium. It is recommended to treat automobile workshop stormwater to prevent detrimental effects in aquatic systems. Future research is aimed at modeling such treatment using multivariate regression techniques is warranted.

Characterizing the Stormwater Runoff Quality and Evaluating the Performance of Curbside Infiltration Systems to Improve Stormwater Quality of an Urban Catchment

The conveyance of stormwater has become a major concern for urban planners, considering its harmful effects for receiving water bodies, potentially disturbing their ecosystem. Therefore, it is important to characterize the quality of catchment outflows. This information can assist in planning for appropriate mitigation measures to reduce stormwater runoff discharge from the catchment. To achieve this aim, the article reports the field data from a typical urban catchment in Australia. The pollutant concentration from laboratory testing is then compared against national and international reported values. In addition, a stochastic catchment model was prepared using MUSIC. The study in particular reported on the techniques to model distributed curbside leaky wells with appropriate level of aggregation. The model informed regarding the efficacy of distributed curbside leaky well systems to improve the stormwater quality. The results indicated that catchment generated pollutant load, which is typical of Australian residential catchments. The use of distributed storages only marginally improves the quality of catchment outflows. It is because ability of distributed leaky wells depended on the intercepted runoff volume which is dependent on the hydrological storage volume of each device. Therefore, limited storage volume of current systems resulted in higher contributing area to storage ratio. This manifested in marginal intercepted volume, thereby only minimum reduction in pollutant transport from the catchment to outlet. Considering strong correlation between contributing impervious area and runoff pollutant generation, the study raised the concern that in lieu of following the policy of infill development, there can be potential increase in pollutant concentration in runoff outflows from Australian residential catchments. It is recommended to monitor stormwater quality from more residential catchments in their present conditions. This will assist in informed decision-making regarding adopting mitigations measures before considering developments.

Stormwater Runoff Treatment Using Rain Garden: Performance Monitoring and Development of Deep Learning-Based Water Quality Prediction Models

Twenty-three rainfall events were monitored to determine the characteristics of the stormwater runoff entering a rain garden facility and evaluate its performance in terms of pollutant removal and volume reduction. Data gathered during the five-year monitoring period were utilized to develop a deep learning-based model that can predict the concentrations of Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP). Findings revealed that the rain garden was capable of effectively reducing solids, organics, nutrients, and heavy metals from stormwater runoff during the five-year period when hydrologic and climate conditions have changed. Volume reduction was also high but can decrease over time due to the accumulation of solids in the facility which reduced the infiltration capacity and increased ponding and overflows especially during heavy rainfalls. A preliminary development of a water quality prediction model based on long short-term memory (LSTM) architecture was also developed to be able to potentially reduce the labor and costs associated with on-site monitoring in the future. The LSTM model predicted pollutant concentrations that are close to the actual values with a mean square error of 0.36 during calibration and a less than 10% difference from the measured values during validation. The study showed the potential of using deep learning architecture for the prediction of stormwater quality parameters entering rain gardens. While this study is still in the preliminary stage, it can potentially be improved for use in performance monitoring, decision-making regarding maintenance, and design of similar technologies in the future.

Permeable Reactive Concrete Using Recycled Waste Materials for Nutrient Contamination Removal in Urban Surface Runoff

In recent years, stormwater control measures (SCMs) such as permeable concrete pavement have been experimentally investigated and used to manage hydrologic and water quality impacts of stormwater runoff. Research revealed the potential of permeable pavement in reducing and delaying peak flow rate, reducing runoff volume, and capturing heavy metals and other particulate-bound pollutants from stormwater runoff. However, few studies have evaluated the effects of permeable pavement on nutrients in stormwater runoff. This research aims to produce permeable reactive concrete (PRC) from waste fly ash, waste gypsum board and waste coco peat and to investigate its effectiveness in removing nutrient contamination present in stormwater or urban surface runoff. The raw materials underwent through granulation process to produce granulated filtering media (GFM). Cylindrical samples of PRC were then made and subjected to various physical and water quality tests. The use of GFM as partial coarse aggregates of PRC for urban surface runoff management and nutrient contamination removal has been tested and evaluated. After performing all the tests, the researchers concluded that GFM as partial coarse aggregates of PRC is effective due to the significant increase in infiltration rate of the entire sample compared to the traditional permeable concrete that has an average infiltration rate of 2-6 mm/s. The results in the water quality test revealed that PRC with GFM as partial coarse aggregates lessen the nitrate, phosphate, and ammonia that are present on urban surface runoff.