Perfluorooctanoic acid (PFOA) and related chemicals among the per- and polyfluoroalkyl substances are widely distributed in the environment. Adverse health effects may occur even at low exposure levels. A large-scale contamination of drinking water resources, especially the rivers Möhne and Ruhr, was detected in North Rhine-Westphalia, Germany, in summer 2006. As a result, concentration data are available from the water supply stations along these rivers and partly from the water network of areas supplied by them. Measurements started after the contamination’s discovery. In addition, there are sparse data from stations in other regions. Further information on the supply structure (river system, station-to-area relations) and expert statements on contamination risks are available. Within the first state-wide environmental-epidemiological study on the general population, these data are temporally and spatially modelled to assign estimated exposure values to the resident population. A generalized linear model with an inverse link offers consistent temporal approaches to model each station’s PFOA data along the river Ruhr and copes with a steeply decreasing temporal data pattern at mainly affected locations. The river’s segments between the main junctions are the most important factor to explain the spatial structure, besides local effects. Deductions from supply stations to areas and, therefore, to the residents’ risk are possible via estimated supply proportions. The resulting potential correlation structure of the supply areas is dominated by the common water supply from the Ruhr. Other areas are often isolated and, therefore, need to be modelled separately. The contamination is homogeneous within most of the areas.

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In this article, we analyze perinatal data with birth weight (BW) as primarily interesting response variable. Gestational age (GA) is usually an important covariate and included in polynomial form. However, in opposition to this univariate regression, bivariate modeling of BW and GA is recommended to distinguish effects on each, on both, and between them. Rather than a parametric bivariate distribution, we apply conditional copula regression, where marginal distributions of BW and GA (not necessarily of the same form) can be estimated independently, and where the dependence structure is modeled conditional on the covariates separately from these marginals. In the resulting distributional regression models, all parameters of the two marginals and the copula parameter are observation-specific. Besides biometric and obstetric information, data on drinking water contamination and maternal smoking are included as environmental covariates. While the Gaussian distribution is suitable for BW, the skewed GA data are better modeled by the three-parametric Dagum distribution. The Clayton copula performs better than the Gumbel and the symmetric Gaussian copula, indicating lower tail dependence (stronger dependence when both variables are low), although this nonlinear dependence between BW and GA is surprisingly weak and only influenced by Cesarean section. A non-linear trend of BW on GA is detected by a classical univariate model that is polynomial with respect to the effect of GA. Linear effects on BW mean are similar in both models, while our distributional copula regression also reveals covariates' effects on all other parameters.