Effectiveness of Automatic Monitoring Sensors for Predicting Stormwater Quality in a Low Impact Development System

One of the major limitations of monitoring LID facilities is the large manpower requirements leading to additional costs. In order to address this concern, this study was conducted to evaluate the effectiveness of real-time monitoring sensors for predicting the pollutant loads in stormwater. Specifically, the measured data from the sensors were correlated to long-term monitored data to determine the potential of sensors as alternative to on-site monitoring activities. An infiltration trench located at the Kongju National University in South Korea was monitored to collect pertinent stormwater quality data. On-site monitoring was performed from 2013 to 2017 to determine the chemical oxygen demand (COD), total suspended solids (TSS), total phosphorus (TP), and total nitrogen (TN) loads in stormwater. Sensors that measure the moisture content, temperature, and conductivity of the soil and media layers were also installed in the infiltration trench to derive a correlation among the soil and water quality parameters. TSS and COD in stormwater were found to be highly correlated (r = 0.66). Moreover, it was noted that TSS have significant correlations with COD, TN, and TP (p < 0.05). During periods of dry days, environmental factors such as fine dust concentration, antecedent dry days, temperature, and humidity affect the deposition of non-point pollutants on impermeable surfaces. Using the data collected from the sensors, it can be deduced that soil and water quality parameters formed a linear relationship. This indicated that the data obtained soil sensors can be used to predict stormwater quality in LID systems.