Improved gray water footprint calculation method based on a mass-balance model and on fuzzy synthetic evaluation

Gray Water Footprint (GWF), defined as the volume of fresh water required to assimilate the load of pollutants discharged into water based on natural background concentrations and existing ambient water quality standards, has been widely used as indicator in water quality management. In the present form, GWF values are mainly influenced by concentration value of the highest concentrated pollutant. Thus, the co-presence of other critical compounds is almost disregarded. GWF values, assessed against a set of quality thresholds (i.e.: different water quality classes set by regulations), support the classification of water bodies in different quality categories. However, since norms refer to many pollutants, an intrinsic fuzziness is embedded in the correct classification of water bodies affected by wastewater discharges. To mitigate these concerns, in this study, an improved GWF calculation framework is proposed based on a 2-phases calibration model. In the first phase, we consider the dilution and auto-purification process of multi-pollutants in natural waters based on a mass-balance model. In the second phase, a comprehensive evaluation method: fuzzy synthetic evaluation is applied to reflect the vagueness exist in evaluation criteria and determine the threshold of GWF. The potential application of the improved GWF calculation model was tested with a case study in Beijing. The results show that when the water quality target is set to Grade V, 7.64 x 10(9)-8.52 x 10(9) m(3) GWF would require to assimilate the pollution to meet the water quality standard. The lower limit of GWF threshold based on the improved model decreased by 17.85% in contrast with that calculated by conventional approach. In conventional method, the GWF is totally determined by the critical pollutant-TN so there's no connection with non-critical pollutants. Nonetheless, for the improved model, all pollutants are taken into account with a weighting factor. This study provides an exploration on how the GWF assessment can be advanced from the point of methodology. (C) 2019 Elsevier Ltd. All rights reserved.