Groundwater risk assessment at a heavily industrialised catchment and the associated impacts on a peri-urban wetland

Industrial and agricultural activities often impose significant pressures to the groundwater quality and consequently degrade wetland ecosystems that depend mostly on subsurface water flow. Groundwater vulnerability and risk mapping is a widely used approach to assess the natural protection of aquifers and the associated pollution potential from human activities. In the particular study, the relatively new Pan-European methodology (COP method) has been applied in a highly industrialized peri-urban wetland catchment, located close to Athens city, to map the intrinsic vulnerability of the aquifer and evaluate the risk potential originating from local land uses. Groundwater analysis results for various parameters, including Phenols, PCBs and nutrients, have been used to validate the vulnerability and risk estimations while a biological assessment occurred to associate the mapping results with the wetland's ecological status. The results indicated that even though the natural protection of the aquifer is relatively high due to the dominant hydrogeologic and geomorphologic conditions, the groundwater pollution risk is considerable, mainly because of the existing hazardous land uses. The water quality of the groundwater accredited these findings and the ecological status of this peri-urban wetland also indicated significant impacts from industrial effluents.     

Human health effects of air pollution

Hazardous chemicals escape to the environment by a number of natural and/or anthropogenic activities and may cause adverse effects on human health and the environment. Increased combustion of fossil fuels in the last century is responsible for the progressive change in the atmospheric composition. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. It ranges from minor upper respiratory irritation to chronic respiratory and heart disease, lung cancer, acute respiratory infections in children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or asthmatic attacks. In addition, short- and long-term exposures have also been linked with premature mortality and reduced life expectancy. These effects of air pollutants on human health and their mechanism of action are briefly discussed.     

Alpha-fetoprotein and acetylcholinesterase are not predictive of fetal junctional epidermolysis bullosa,Herlitz variant

Junctional epidermolysis bullosa, Herlitz variant (junctional EB-Herlitz) is a lethal autosomal recessive skin disorder currently amenable to prenatal diagnosis only by direct analysis of fetal skin. However, elevated levels of alpha-fetoprotein, as well as the presence of acetylcholinesterase in amniotic fluid, have been associated with other severe fetal genodermatoses. Fetal skin samplings were performed in ten pregnancies at risk for fetal junctional EB-Herlitz, with three fetuses affected on the basis of electron microscopic detection of blisters within the lamina lucida and abnormal hemidesmosomes. In neither affected nor unaffected pregnancies were maternal serum or amniotic fluid alpha-fetoprotein levels elevated. Moreover, alphafetoprotein levels in both maternal serum and amniotic fluid were not statistically different comparing affected and unaffected fetuses. Acetylcholinesterase was not present in the amniotic fluid samples of the three affected pregnancies. Unlike other severe fetal genodermatoses, neither alpha-fetoprotein nor acetylcholinesterase was predictive of junctional EB-Herlitz.     

Fertility of female mice fed coumestrol and diethylstilbestrol

Abstract

Coumestrol, a compound produced by various legumes which exerts estrogen‐like activity in animals, and diethylstilbestrol (DES) were studied as chemical agents for controlling reproduction in mice. Female mice were fed control diets or diets containing 100 ppm coumestrol for eight days. Female mice were exposed to males and reproductive tracts examined 14 days later. Litter size was not affected by 100 ppm dietary coumestrol but feed consumption was reduced 17%. Similar trials were conducted with mice fed 1 ppm DES. Vaginal plugs were present in 50% of the females fed 1 ppm DES, but no fetal pups were present. Feed intake was reduced 37% by the DES. Levels of 0, .1, .25, .50, .75 and 1.0 ppm DES were compared in two strains of mice, Swiss and ICR. Reproduction in both strains was totally inhibited by all DES treatments. The use of DES to control rodent populations warrants further investigation.     

Ultrasound-assisted emulsification-microextraction of fragrance allergens in water

A method based on ultrasound-assisted emulsification-microextraction (USAEME) and gas chromatography-mass spectrometry (GC-MS) has been developed for the analysis of regulated fragrance allergens in water. Extraction conditions such as the type of solvent, extraction temperature, irradiation time, and salting-out effect were optimized using a multivariate approach. Compounds were extracted during 2 min in an acoustically emulsified media formed by 100 μL chloroform and 10 mL sample. The USAEME process provided an efficient and exhaustive extraction (enrichment factor ～100) and, after centrifugation, the extract was ready for GC analysis. Validation was performed using spiked ultrapure water as well as other most complex matrices such as sewage water. Recoveries between 75% and 110% were generally obtained, and precision was characterized by RSD values <10% in most cases. The limits of detection (LODs) were at the sub-nanogram per millilitre level. The proposed procedure was applied to the determination of allergens in several real samples including tap water, baby bathwater, recreational place water, public washing place water, and sewage water. The presence of some of the target compounds was confirmed in all the samples excluding tap water, demonstrating the ubiquity of this group of cosmetic and personal care products ingredients.     

GIS and multiple-criteria evaluation for the optimisation of tsetse fly eradication programmes

Tsetse flies are the vectors of trypanosomes, the causal agent of trypanosomiasis, a widespread disease of livestock and people in Africa. Control of tsetse may open vast areas of land to livestock-keeping, with the associated benefits of developing mixed crop-livestock production systems. However, as well as possible positive impacts there are also risks: bush clearing would accelerate and cattle numbers would rise, leading to a reduction of vegetation cover, and an increase in runoff and erosion; there may also be increased pressure on conserved areas and reductions in biodiversity. The objective of this study is to show how remotely sensed and other environmental data can be combined in a decision support system to help inform tsetse control programmes in a manner that could be used to limit possible detrimental effects of tsetse control. For Zambia, a methodology is developed that combines a tree-based decision-support approach with the use of Multiple-Criteria Evaluation (MCE), within a Geographical Information System (GIS), in order to target areas for tsetse control. The results show clear differentiation of priority areas under a series of hypothetical scenarios, and some areas (e.g. northwest of Petauke in the Eastern Province of Zambia) are consistently flagged as high priority for control. It is also demonstrated that priority areas do not comprise isolated tsetse populations, meaning that disease control using an integrated approach is likely to be more economically viable than local eradication.     

Comparison of Machine Learning Models Performance on Simulating Reservoir Outflow: A Case Study of Two Reservoirs in Illinois,U.S.A.

Reservoir outflow is an important variable for understanding hydrological processes and water resource management. Natural streamflow variation, in addition to the streamflow regulation provided by dams and reservoirs, can make streamflow difficult to understand and predict. This makes them a challenge to accurately simulate hydrologic processes at a daily scale. In this study, three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were examined and compared to model reservoir outflow. Past, current, and future hydrologic and meteorological data were used as model inputs, and the outflow of next day was used as prediction. Simulation results demonstrated that all three models can reasonably simulate reservoir outflow. For Carlyle Lake, the coefficient of determination and Nash–Sutcliffe efficiency were each close to one for the three models. The coefficient of determination, relative mean bias, and root mean square error indicated that the SVM performed better than the RF and ANN, but the SVM output displayed a larger relative mean bias than that from RF and ANN. For Lake Shelbyville, the ANN model performed better than RF and SVM when considering the coefficient of determination, Nash–Sutcliffe efficiency, relative mean bias, and root mean square error. The study results demonstrate that the three ML algorithms (RF, SVM, and ANN) are all promising tools for simulating reservoir outflow. Both the accuracy and efficacy of the three ML algorithms are considered to support practitioners in planning reservoir management.     

The suitability of Macadamia and Juglans for cultivation in Nepal: an assessment based on spatial probability modelling using climate scenarios and in situ data

Regional Environmental Change - Global climate models predict temperature rises and changes in precipitation regimes that will shift regional climate zones and influence the viability of...     

Simultaneous determination of chlorothalonil and its metabolite 4-hydroxychlorothalonil in greenhouse air: dissipation process of chlorothalonil

An analytical method was developed and tested for the simultaneous determination of chlorothalonil and its main metabolite 4-hydroxychlorothalonil, in airborne samples. High performance liquid chromatography equipped with Ultra-violet detector was used to separate and quantify the analytes. Glass microfibre filters for the collection of the analytes' particles were tested. Solid sorbents, such as Tenax, Florisil, XAD-2 and silica gel, were studied to find out the most suitable material for the collection of the analytes in the gas phase. The results have shown that only chlorothalonil was trapped in the vapor phase with highest results obtained when silica gel was the sorbent of choice. Linearity was demonstrated in a wide concentration range 0.01-10.00 mg L(-1). Recoveries from spiked glass microfibre filters and silica gel cartridges for chlorothalonil and 4-hydroxychlorothalonil were almost quantitative. The quantification limits were calculated to be 8.4 and 19.6 ng m(-3) in air for chlorothalonil and 4-hydroxychlorothalonil, respectively. The two analytes spiked on the GF/A filters and silica gel cartridges were proven to be stable for more than 15 days, at 4degrees C and ambient temperature. The applicability of the present method was demonstrated by the analysis of the chlorothalonil and its metabolite in greenhouse air.