Title: Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Abstract

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then usedmore » to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.« less

Authors:

Lobo, G. P. ^[1]; Laraway, J. ^[1]; Gadgil, A. J. ^[1]

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+ Show Author Affiliations

1. Univ. of California, Berkeley, CA (United States)

Publication Date:

Fri Sep 03 00:00:00 EDT 2021

Research Org.:

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); National Science Foundation (NSF)

OSTI Identifier:

1821159

Grant/Contract Number:  

AC02-05CH11231; DGE-1633740

Resource Type:

Accepted Manuscript

Journal Name:

Science of the Total Environment

Additional Journal Information:

Journal Volume: 803; Journal ID: ISSN 0048-9697

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTANT EFFECTS ON LIVING ORGS. AND BIOL. MAT.; lead in school drinking water; lead leaching; machine learning; environmental justice; public data mining