Estimation of chemical emissions from down-the-drain consumer products using consumer survey data at a country and wastewater treatment plant level. (February 2018) - Douziech, Mélanie
Abstract: Deriving reliable estimates of chemical emissions to the environment is a key challenge for impact and risk assessment methods and typically the associated uncertainty is not characterised. We have developed an approach to spatially quantify annual chemical emission loads to the aquatic environment together with their associated uncertainty using consumer survey data and publicly accessible and non-confidential data sources. The approach is applicable for chemicals widely used across a product sector. Product usage data from consumer survey studies in France, the Netherlands, South Korea and the USA were combined with information on typical product formulations, wastewater removal rates, and the spatial distribution of populations and wastewater treatment plants (WWTPs) in the four countries. Results are presented for three chemicals common to three types of personal care products (shampoo, conditioner, and bodywash) at WWTP and national levels. Uncertainty in WWTP-specific emission estimates was characterised with a 95% confidence interval and ranged up to a factor of 4.8 around the mean, mainly due to uncertainty associated with removal efficiency. Estimates of whole country product usage were comparable to total market estimates derived from sectorial market sales data with differences ranging from a factor 0.8 (for the Netherlands) to 5 (for the USA). The proposed approach is suitable where measured data on chemical emissions is missing and is applicable for use in risk assessments and chemical footprinting methods when applied to specific product categories. Graphical abstract: Image 1 Highlights: Chemical emission loads from the use of personal care products were quantified. Consumer use surveys from four countries were used. Point source emissions differed up to two orders of magnitude between countries. Emission uncertainty (95% confidence interval) was up to 5 times the mean value. This approach does not rely on confidential or commercial tonnage data.