Seasonal variation of pharmaceutically active compounds in surface (Tagus River) and tap water (Central Spain)

Numerous studies have shown the presence of pharmaceutically active compounds (PhACs) in different environmental compartments, for example, in surface water or wastewater ranging from nanograms per litre to micrograms per litre. Likewise, some recent studies have pointed to seasonal variability, thus indicating that PhAcs concentrations in the aquatic environment may depend on the time of year. This work intended to find out (1) whether Tagus fluvial and drinking water were polluted with different groups of PhACs and (2) if their concentrations differed between winter and summer seasons. From the 58 substances analysed, 41 were found belonging to the main therapeutic groups. Statistical differences were seen for antibacterials, antidepressants, anxiolytics, antiepileptics, and cardiovascular drugs, with higher concentrations being detected in winter than in summer. These results might indicate that the PhACs analysed in this study undergo lower environmental degradation in winter than in summer. In order to confirm these initial results, a continuous monitoring should be performed especially on those PhACs that either because of an elevated consumption or an intrinsic chemical persistence are poorly degraded during winter months due to low temperatures and solar irradiation. It is especially important to identify which of these specific PhACs are in order to recommend their substitution by equally effective and safe substances but also environmentally friendly.     

Adverse birth outcomes in the vicinity of industrial installations in Spain 2004–2008

Industrial activity is one of the main sources of ambient pollution in developed countries. However, research analyzing its effect on birth outcomes is inconclusive. We analyzed the association between proximity of mother’s municipality of residence to industries from 24 different activity groups and risk of very (VPTB) and moderate (MPTB) preterm birth, very (VLBW) and moderate (MLBW) low birth weight, and small for gestational age (SGA) in Spain, 2004–2008. An ecological study was defined, and a “near vs. far” analysis (3.5 km threshold) was carried out using Hierarchical Bayesian models implemented via Integrated Nested Laplace Approximation. VPTB risk was higher for mothers living near pharmaceutical companies. Proximity to galvanization and hazardous waste management industries increased the risk of MPTB. Risk of VLBW was higher for mothers residing near pharmaceutical and non-hazardous or animal waste management industries. For MLBW many associations were found, being notable the proximity to mining, biocides and animal waste management plants. The strongest association for SGA was found with proximity to management animal waste plants. These results highlight the importance of further research on the relationship between proximity to industrial sites and the occurrence of adverse birth outcomes especially for the case of pharmaceutical and animal waste management activities.     

Partitioning, sources and variability of regional and local oxidant (OX = O3 + NO2) in a coastal rural area in the southwest of Iberian Peninsula

The purpose of this work is to investigate the behaviour and variability of oxidant levels (OX?=?NO₂?+?O₃), for the first time, in a rural coastal area in the southwest of the Iberian Peninsula, affected by several air masses types. Detailed database (built-up over the years 2008 to 2011, and containing around 500,000 data) from the Atmospheric Sounding Station “El Arenosillo” was used. The observed daily cycles of NO _x and OX were influenced by air masses coming from industrial and urban area. It can be seen that the concentration of OX is made up of a NO _x -independent ‘regional’ contribution (i.e. the O₃ background), and a linearly NO _x -dependent ‘local’ contribution from primary emissions, such as traffic. The local emission is very low in this area. Also, the regional contribution is similar to unpolluted sites and presents seasonal variation, being higher in May. However, our measurements showed that the proportion of OX in the form of NO₂ increases with the increase in NO _x concentration during the day. The higher proportion of NO₂ observed at night must be due to the conversion of NO to NO₂ by the NO?+?O₃ reaction. With regards to the source of the local NO _x -dependent contribution, it may be attributed to industrial emission, or the termolecular reaction 2NO?+?O₂?=?2NO₂, at high-NO _x levels and stagnant air during several days. Finally, we estimated the photolysis rate of NO₂, J _NO2, an important key atmospheric reaction coupled with ozone. We also present surface plots of annual variation of the daily mean NO _x and OX levels, which indicate that oxidants come from transport processes instead of local emissions associated as local photochemistry.     

Climate change, drought risk and land capability for agriculture: implications for land use in Scotland

Land capability classification systems define and communicate biophysical limitations on land use, including climate, soils and topography. They can therefore provide an accessible format for both scientists and decision-makers to share knowledge on climate change impacts and adaptation. Underlying such classifications are complex interactions that require dynamic spatial analysis, particularly between soil and climate. These relationships are investigated using a case study on drought risk for agriculture in Scotland, which is currently considered less significant than wetness-related issues. The impact of drought risk is assessed using an established empirical system for land capability linking indicator crops with water availability. This procedure is facilitated by spatial interpolation of climate and soil profile data to provide soil moisture deficits and plant available water on a regular 1-km grid. To evaluate potential impacts of future climate change, land capability classes are estimated using both large-scale ensemble (multi-simulation) data from the HadRM3 regional climate model and local-scale weather generator data (UKCP09) derived from multiple climate models. Results for the case study suggest that drought risk is likely to have a much more significant influence on land use in the future. This could potentially act to restrict the range of crops grown and hence reduce land capability in some areas unless strategic-level adaptation measures are developed that also integrate land use systems and water resources with the wider environment.     

Sensitivity of Portuguese forest fires to climatic, human, and landscape variables: subnational differences between fire drivers in extreme fire years and decadal averages

Within the changing fire regimes of Portugal, the relative importance of humans and climatic variability for regional fire statistics remains poorly understood. This work investigates the statistical relationship between temporal dynamics of fire events in Portugal and a set of socioeconomic, landscape, and climatic variables for the time periods of 1980–1990, 1991–2000, and extreme fires years. For 10 of 15 districts, it was possible to observe moderate shifts in the significance of fire drivers for the first two decadal periods. For others, pronounced changes of the significance of fire drivers were found across time. Results point toward a dynamic (perhaps highly non-linear) behavior of socioeconomic and landscape fire drivers, especially during the occurrence of extreme fire years of 2003 and 2005. At country level, population density alone explained 42% of the inter-annual and inter-district deviance in number of fires. At the same temporal and spatial scale, the explanatory power of temperature anomalies proved to explain 43% of area burnt. We highlight the necessity of including a broad set of socioeconomic and landscape fire drivers in order to account for potential significance shifts. In addition, although climate does trigger broad favorable fire conditions across Portugal mainland, socioeconomic and landscape factors proved to determine much of the complex fire patterns at a subnational scale.     

A biology-based dynamic approach for the reconciliation of acute and chronic toxicity tests: application to Daphnia magna

There is the need to integrate existing toxicity data in a coherent framework for extending their domain of applicability as well as their extrapolation potential. This integration would also reduce time and cost-consuming aspects of these tests and reduce animal usage. In this work, based on data extracted from literature, we have assessed the advantages that a dynamic biology-toxicant fate coupled model for Daphnia magna could provide when assessing toxicity data, in particular, the possibility to obtain from short-term (acute) toxicity test long-term (chronic) toxicity values taking into account the inherent variability of D. magna populations and the multiple sources of data. The results show that this approach overcomes some of the limitations of existing toxicity tests and that the prediction errors are considerably reduced when compared with the factor from 2 to 5 obtained using acute-to-chronic ratios.     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.