High spatial variability was defined using three methods. Methods 1 and 2 are based on the absolute differences between at least two sampling locations in the distribution system of ≥20 and ≥30 μg/L, respectively. If any PWS exceeds these differences for at least 1 sample date during the 10 year study period, then they are classified as having high spatial variability. Based on intra-system THM4 differences of ≥20 μg/L (Method 1), 108 PWSs (75%) had high spatial variability compared to 95 PWSs (66%) using Method 2. Over half of these PWSs had high spatial variability for at least 25% of their sampling dates, according to either method. The percentage of PWSs with high spatial variability for HAA5 was slightly lower with 79 PWSs (59%) and 60 PWSs (45%) according to Method 1 and 2, respectively. Similar to THM4, over half of the PWSs had high spatial variability for 25% or more of their sampling dates. We also examined spatial variability based on a ≥30 μg/L (Method 2) difference for THM4 based on 99 PWSs (69%) using chlorination and 14 PWSs (10%) using chloramination during the entire period. Among the chlorinated systems, 28% of the PWSs had high spatial variability compared to 21% of the chloraminated sample dates. Using a subset of the data (17 PWSs), we compared these spatial variation approaches (Methods 1 and 2) to another approach (Method 3) that classifies spatial variation using three exposure categories. We found comparable results for Methods 1 and 3, with 76% of PWSs experiencing high spatial variation by both methods. In conclusion, the spatial variability noted across several water systems could result inmisclassification bias if system averages are used to estimate individual-level trihalomethane and haloacetic acid exposures. Therefore, exposure assessment approaches should target drinking water distribution systems with minimal spatial variability for epidemiological application.", pages = "172-179", url = "http://www.ingentaconnect.com/content/wef/wefproc/2011/00002011/00000003/art00019", doi = "doi:10.2175/193864711802863724", keyword = "exposure assessment, exposure misclassification, spatial variability, Disinfection by-products, drinking water" }