Time-series regression models to study the short-term effects of environmental factors on health

Time series regression models are especially suitable in epidemiology for evaluating short-term effects of time-varying exposures on health. The problem is that potential for confounding in time series regression is very high. Thus, it is important that trend and seasonality are properly accounted for. Our paper reviews the statistical models commonly used in time-series regression methods, specially allowing for serial correlation, make them potentially useful for selected epidemiological purposes. In particular, we discuss the use of time-series regression for counts using a wide range Generalised Linear Models as well as Generalised Additive Models. In addition, recently critical points in using statistical software for GAM were stressed, and reanalyses of time series data on air pollution and health were performed in order to update already published. Applications are offered through an example on the relationship between asthma emergency admissions and photochemical air pollutants

The use of pulse amplitude modulated fluorescence techniques for metal toxicity assessment in fluvial biofilms

Metal pollution in rivers is in great concern with human activities in the fluvial watershed. This thesis aims to investigate the potential use of chl-a fluorescence parameters as biomarkers of metal toxicity, and to find cause-effect relationships between metal exposures, other environmental factor (i.e. light), and functional and structural biofilm responses. This thesis demonstrates that the use of chl-a fluorescence parameters allows detect early effects on biofilms caused by zinc toxicity, both in the laboratory as in polluted rivers. In microcosm experiments, the use of chl-a fluorescence parameters allows evaluates structural changes on photosynthetic apparatus and in algal groups’ composition of biofilms long-term exposed to zinc. In order to evaluate the effects of chronic metal pollution in rivers, it is recommended the use of biofilm translocation experiments and the use of a multi-biomarker approach.