Extreme learning machine-based prediction of uptake of pharmaceuticals in reclaimed water-irrigated lettuces in the Region of Murcia, Spain

The availability of water resources is limited, and rising consumption has increased pressure on natural resources. Therefore, reclaimed water represents an alternative option for use in urban areas, industry and, in particular, agriculture. Recent research has shown that some pharmaceutical compounds are not fully removed by wastewater treatment plants (WWTPs) and may eventually be released into agricultural systems through the application of wastewater-based resources (sludge and effluent). The present study develops an intelligent expert system (based on a feed forward neural network trained via an extreme learning machine algorithm) for predicting the carbamazepine (CBZ) and diclofenac (DCF) content in lettuce tissues irrigated with reclaimed water from WWTPs. This reduces laboratory costs, mitigates the negative impacts on the environment and leads to more effective, safer decisions on the use of reclaimed water in agriculture. The results obtained, which were validated through statistical testing, demonstrate that the intelligent expert system is well calibrated and reliable. Finally, this system was used to predict maps of CBZ and DCF accumulation in lettuce crops if they were watered with effluent from 10 WWTPs located in the Region of Murcia (Spain). In conclusion, our system provides highly accurate predictions of the amount of CBZ and DCF contained in different lettuce tissues (roots and leaves) and the predicted concentrations do not present any health risk. (C) 2018 IAgrE. Published by Elsevier Ltd. All rights reserved.