In the recent years, the presence of micropollutants in drinking water has become an issue of growing global concern. Great attention is paid to persistent toxic micropollutants that belong to several families (e.g. pesticides, perfluorinated compounds, pharmaceuticals, endocrine disrupting compounds) and are present at trace concentrations (ranging from ng/l to μg/l) in aquatic environments [1]. Due to their low concentration, monitoring databases are usually rich in censored data (e.g. samples with concentrations reported below the limit of quantification (LOQ)) that are typically eliminated or replaced with a value between 0 and LOQ [2]. These traditional methods present some limitations and can lead to erroneous conclusions on the presence of persistent micropollutants in the source water, treatment efficiencies, quality of the produced water and associated human health risk. Alternative methods, based on the principles of survival analysis, allow to estimate the statistical distribution of the whole dataset, combining the values above the LOQ with the information contained in the proportion of censored data [3]. The methods applied in this work are Maximum Likelihood Estimation or non-parametric techniques (e.g. Kaplan-Meier). Monitoring data of 5,362 groundwater (GW) and 12,344 drinking water samples collected from 2012 to 2017 in the city of Milan, Italy were analysed. Several persistent micropollutants, including pesticides and perfluorinated compounds, were selected for this study. This study demonstrated the benefits of the innovative methods in the assessment of data statistical distribution, highlighting the more accurate estimation of the distribution median, 95° and 98° quantiles, especially for high percentages of censored data. The resulting statistical distributions were used for several applications: time trend evaluation in GW micropollutant concentrations, optimization of well management, treatment efficiency evaluation. Moreover, they have been applied to assess the residual health risk associated with low concentration micropollutants and the risk reduction resulting by treatment and/or management intervention in the drinking water treatment plants. This study highlighted high discrepancy in the results obtained with traditional and innovative techniques related to the evaluation of the presence, fate and health risk associated to persistent and toxic micropollutants.

A statistical assessment of persistent micropollutants occurrence, fate and health risk using censored water quality data

B. Cantoni;R. Delli Compagni;A. Turolla;M. Antonelli
2018-01-01

Abstract

In the recent years, the presence of micropollutants in drinking water has become an issue of growing global concern. Great attention is paid to persistent toxic micropollutants that belong to several families (e.g. pesticides, perfluorinated compounds, pharmaceuticals, endocrine disrupting compounds) and are present at trace concentrations (ranging from ng/l to μg/l) in aquatic environments [1]. Due to their low concentration, monitoring databases are usually rich in censored data (e.g. samples with concentrations reported below the limit of quantification (LOQ)) that are typically eliminated or replaced with a value between 0 and LOQ [2]. These traditional methods present some limitations and can lead to erroneous conclusions on the presence of persistent micropollutants in the source water, treatment efficiencies, quality of the produced water and associated human health risk. Alternative methods, based on the principles of survival analysis, allow to estimate the statistical distribution of the whole dataset, combining the values above the LOQ with the information contained in the proportion of censored data [3]. The methods applied in this work are Maximum Likelihood Estimation or non-parametric techniques (e.g. Kaplan-Meier). Monitoring data of 5,362 groundwater (GW) and 12,344 drinking water samples collected from 2012 to 2017 in the city of Milan, Italy were analysed. Several persistent micropollutants, including pesticides and perfluorinated compounds, were selected for this study. This study demonstrated the benefits of the innovative methods in the assessment of data statistical distribution, highlighting the more accurate estimation of the distribution median, 95° and 98° quantiles, especially for high percentages of censored data. The resulting statistical distributions were used for several applications: time trend evaluation in GW micropollutant concentrations, optimization of well management, treatment efficiency evaluation. Moreover, they have been applied to assess the residual health risk associated with low concentration micropollutants and the risk reduction resulting by treatment and/or management intervention in the drinking water treatment plants. This study highlighted high discrepancy in the results obtained with traditional and innovative techniques related to the evaluation of the presence, fate and health risk associated to persistent and toxic micropollutants.
2018
File in questo prodotto:
File Dimensione Formato  
Cantoni et al., 2018.pdf

Accesso riservato

Descrizione: Abstract
: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 197.33 kB
Formato Adobe PDF
197.33 kB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1079770
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact