We are conducting an epidemiological study on the association between disinfection by-product concentrations in drinking water and adverse birth outcomes in the UK, using trihalomethane (THM) concentrations over defined water zones as an exposure index. Here we construct statistical models using sparse routinely collected THMs measurements to obtain quarterly estimates of mean THM concentrations for each water zone. We modelled the THM measurements using a Bayesian hierarchical mixture model, taking into account heterogeneity in THM concentrations between water originating from different source types, quarterly variation in THM concentrations and uncertainty in the true value of undetected and rounded measurements. Quarterly estimates of mean THM concentrations plus estimates of the water source type (ground, lowland surface or upland surface) were obtained for each water zone. THM concentration estimates were typically highest from July to September (third quarter), and varied considerably between water sources. Our exposure estimates were categorized into 'low', 'medium' and 'high' THM classes. Our modelled quarterly exposure estimates were compared to a simple alternative: annual means of the raw data for each water zone. In all, 15-25% of exposure estimates were classified differently. The modelled THM estimates led to slightly stronger and more precise estimates of association with risk of still birth and low birth weight than did the raw annual means. We conclude that our modelling approach enabled us to provide robust quarterly estimates of ecological exposure to THMs in a situation where the raw data were too sparse to base exposure assessment on empirical summaries alone.