Survey of Lead in Drinking Water from Schools and Child Care Centers Operating as Public Water Suppliers in North Carolina, USA: Implications for Future Legislation.
Jake A CarterRobert J ErhardtBradley T JonesGeorge L DonatiPublished in: Environmental science & technology (2020)
Few schools and child care facilities test for Pb in their drinking water. Reviewing the United States Environmental Protection Agency Lead and Copper rule data can contribute to guiding future legislation on Pb testing. This work aims to (i) identify variations in Pb levels in North Carolina school and child care drinking water by building age, (ii) evaluate the effect of corrosion control measures on reducing these levels, and (iii) evaluate the adequacy of Pb reporting limits according to modern instrumentation. To achieve these objectives, information on 26,608 water samples collected in 206 North Carolina child centers between 1991 and 2019 has been analyzed. Lead concentrations were above a recently proposed 5 μg/L trigger level in 12.3%, 10.4%, 7.5%, and 0.9% of samples from pre-1987, 1987-1990, 1991-2013, and post-2013 buildings, respectively. Thus, recently proposed legislation requiring testing only for pre-1987 (or pre-1991) buildings will fail to identify all centers at risk. The odds that a greater than 5 μg/L Pb level is detected has been decreasing over the years, with a faster decreasing rate for buildings reporting corrosion control. Over 15% of samples report a method detection limit of 5 μg/L. For accurate results, future legislation should require sub-μg/L detection limits, which are easily achievable with commonly available instrumentation.
Keyphrases
- drinking water
- heavy metals
- mental health
- healthcare
- health risk assessment
- health risk
- palliative care
- current status
- aqueous solution
- quality improvement
- tertiary care
- adverse drug
- physical activity
- pain management
- affordable care act
- label free
- machine learning
- cross sectional
- mass spectrometry
- health information
- social media
- climate change
- drug induced
- artificial intelligence
- sensitive detection