Assessing spatial fluctuations, temporal variability, and measurement error in estimated levels of disinfection by-products in tap water: implications for exposure assessment.

  • 1 January 2004
    • journal article
    • Vol. 61  (1) , 65-72
Abstract
To assess spatial fluctuations, temporal variability, and errors due to sampling and analysis in levels of disinfection by-products in routine monitoring tap water samples and in water samples collected in households within the same distribution system for an exposure assessment study. Mixed effects models were applied to quantify seasonal effects and the degree to which trihalomethane (THM) levels vary among households or locations relative to variation over time within seasons for any given location. In a separate analysis, the proportion of total variation due to measurement error arising from sampling and analysis was also quantified. THM levels were higher in the summer relative to other seasons. Differences in the relative magnitude of the intra- and inter-household components of variation were observed between the two sets of THM measurements, with a greater proportion of the variation due to differences within seasons for the routine monitoring data and a greater proportion of the variation due to differences across locations for the exposure assessment study data. Such differences likely arose due to differences in the strategies used to select sites for sampling and in the time periods over which the data were collected. With the exception of bromodichloromethane, measurement errors due to sampling and analysis contributed a small proportion of the total variation in THM levels. The utility of routine monitoring data in assigning exposure in epidemiological studies is limited because such data may not represent the magnitude of spatial variability in levels of disinfection by-products across the distribution system. Measurement error contributes a relatively small proportion to the total variation in THM levels, which suggests that gathering a greater number of samples over time with fewer replicates collected at each sampling location is more efficient and would likely yield improved estimates of household exposure.