Using Conditional Probability to Predict Solar-Powered Pump-and-Treat Performance

The purpose of remediation projects is to protect human health and the environment by removing harmful substances from the environment. The carbon footprint of an environmental remediation site can be reduced by powering the project with green or sustainable energy. This paper describes a sustainable remediation project that involves the use of a single-axis passive tracking photovoltaic array to power a pump-and-treat system at a Missouri Drycleaning Environmental Response Trust Fund site. The tetrachloroethene-contaminated groundwater is pumped by way of a positive displacement piston pump and treated using granular activated carbon. Previous works studying the performance of solar-powered pumping systems were performed using known or constant water heads that are typically unknown at active remediation sites. A stochastic analysis was performed using the two inherently random variables solar radiation and pumping flow rates. Two models were developed for the estimation of the amount of water that would be pumped from a solar-powered system given (1) the amount of solar radiation observed at the site, or (2) the amount of energy consumed by the pump, both of which can be determined by using data from the free online resource PVWatts. The results showed that even given substantial effects from bioremediation activities, a long-form model was able to accurately predict data within the central range of probabilities for five of the seven months studie