Air pollution causes damage and imposes risks on human health, especially in cities, where the pollutant load is a major
concern, although the extent of these effects is still largely unknown. Thus, taking the busiest road traffic area in Portugal as
a local case study (600 m × 600 m domain, 4 m2
spatial resolution), the objective of this work was to investigate two health
risk methodologies (linear and nonlinear), which were applied for estimating short-term health impacts related to daily
variations of high-resolution ambient nitrogen dioxide (
NO2) concentrations modelled for winter and summer periods. Both
approaches are based on the same general equation and health input metrics, differing only in the relative risk calculation.
Health outcomes, translated into the total number of cases and subsequent damage costs, were compared, and their associated
uncertainties and challenges for health impact modelling were addressed. Overall, for the winter and summer periods,
health outcomes considering the whole simulation domain were lower using the nonlinear methodology (less 27% and 28%,
respectively). Spatially, these differences are more noticeable in locations with higher NO2
and population values, where the
highest health estimates were obtained. When the daily NO2
exposure was less than 6 μg.m−3, a fact that occurred in 95% of
the domain cells and in both periods, relatively small differences between approaches were found. Analysing the seasonality
effect, total health impacts derived from the linear and nonlinear applications were greater in summer (around 18% in both
approaches). This happens due to the magnitude and spatial variability of NO2,
as the other health input metrics remained
constant. This exploratory research in local scale health impact assessment (HIA) demonstrated that the use of refined input
data could contribute to more accurate health estimates and that the nonlinear approach is probably the most suitable for
characterising air pollution episodes, thus providing important support in HIA.
The extraction of olive pomace oil is a significant aspect of the edible oil industry in Mediterranean regions where olives are widely cultivated. The resulting wastewater generated from this industry is known to harbor pollutants, including residual solvents, oils, and chemicals from the refining process, that can have adverse effects on the environment and public health. Peroxy-electrocoagulation (PEC) is a method that can be used to treat wastewater from the olive pomace oil extraction industry. The purpose of the work was to reduce the concentration of pollutants in the effluent through the use of PEC with aluminum electrodes as a method of treatment. The Box-Behnken Design was used to study the relationship between hydrogen peroxide dosage (10, 20, and 30 g L-1), electric current density (5, 20 and 35 mA cm-2), and the initial pH (2.5, 3.5, and 4.5), in the PEC process, and the removal of chemical oxygen demand (COD) and total phenolic compounds (TPh). The highest removal was obtained with hydrogen peroxide dosage of 30 g L-1, and 20 mA cm-2, and with 29% of TPh removal at pH 2.5, and with 84% COD removal at pH 4.5. The procedure removed an average of 22% COD and 82% TPh. The concentration of hydrogen peroxide was one of the most significant factors in the process. Pre-treatment with other techniques is necessary to reduce harmful elements in the effluent before undergoing biological treatment.
