In recent years, the presence of micropollutants in drinking water has become an issue of growing global concern. Due to their low concentrations, monitoring databases are usually rich in censored data (e.g. samples with concentrations reported below the limit of quantification, LOQ) which are typically eliminated or replaced with a value arbitrarily chosen between 0 and LOQ. These conventional methods have some limitations and can lead to erroneous conclusions on: presence of micropollutants in the source water, treatment efficiencies, produced water quality and associated human health risk. In this work, an advanced approach, based on Maximum Likelihood Estimation method for left-censored data (MLELC), was applied on monitoring data of 19 contaminants (metals, volatile organic compounds, pesticides and perfluorinated compounds) in 5362 groundwater (GW) and 12,344 drinking water (DW) samples, collected from 2012 to 2017 in 28 drinking water treatment plants in an urbanized area. This study demonstrates the benefits of MLELC method, especially for high percentages of censored data. Data are used to build statistical distributions which can be effectively used for several applications, such as the time trend evaluation of GW micropollutant concentrations and the estimation of treatment removal efficiency, highlighting the adequacy or the need for an upgrade. Moreover, the MLELC method has been applied to assess the human health risk associated with micropollutants, indicating the high discrepancy in the estimations obtained with conventional methods, whose results do not follow precautionary or sustainability criteria.

A statistical assessment of micropollutants occurrence, time trend, fate and human health risk using left-censored water quality data

Cantoni B.;Delli Compagni R.;Turolla A.;Epifani I.;Antonelli M.
2020-01-01

Abstract

In recent years, the presence of micropollutants in drinking water has become an issue of growing global concern. Due to their low concentrations, monitoring databases are usually rich in censored data (e.g. samples with concentrations reported below the limit of quantification, LOQ) which are typically eliminated or replaced with a value arbitrarily chosen between 0 and LOQ. These conventional methods have some limitations and can lead to erroneous conclusions on: presence of micropollutants in the source water, treatment efficiencies, produced water quality and associated human health risk. In this work, an advanced approach, based on Maximum Likelihood Estimation method for left-censored data (MLELC), was applied on monitoring data of 19 contaminants (metals, volatile organic compounds, pesticides and perfluorinated compounds) in 5362 groundwater (GW) and 12,344 drinking water (DW) samples, collected from 2012 to 2017 in 28 drinking water treatment plants in an urbanized area. This study demonstrates the benefits of MLELC method, especially for high percentages of censored data. Data are used to build statistical distributions which can be effectively used for several applications, such as the time trend evaluation of GW micropollutant concentrations and the estimation of treatment removal efficiency, highlighting the adequacy or the need for an upgrade. Moreover, the MLELC method has been applied to assess the human health risk associated with micropollutants, indicating the high discrepancy in the estimations obtained with conventional methods, whose results do not follow precautionary or sustainability criteria.
2020
Pesticides
Volatile Organic Compounds
Water
Water Pollution, Chemical
Water Purification
Water Quality
Environmental Monitoring
Censored data monitoring
Drinking water supply
Probabilistic risk assessment
Stochastical methods
Drinking Water
Groundwater
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1144500
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