Assessing coastal waters of American Samoa: territory-wide water quality data provide a critical “big-picture” view for this tropical archipelago

The coastal waters of American Samoa’s five high islands (Tutuila, Aunu’u, Ofu, Olosega, and Ta’u) were surveyed in 2004 using a probabilistic design. Water quality data were collected from the near-shore coastal habitat, defined as all near-shore coastal waters including embayments, extending out to 1/4 mile off-shore. Hydrography and water column samples were collected, and water quality data were compared to the Territorial water quality standards for pH, dissolved oxygen (DO), Enterococcus, chlorophyll a, water clarity, total nitrogen, and total phosphorus. All station measurements for pH, DO, and Enterococcus satisfied the local water quality standards, although some fraction of the Territory could not be assessed for either DO or Enterococcus. With respect to chlorophyll a, 66 ± 18% of Territory coastal waters complied with the standard, while 34 ± 18% failed to comply with the standard. For water clarity, 54 ± 18% of the Territorial waters complied with the standard while 42 ± 7% failed to comply. Territorial waters satisfied the standards for total nitrogen and phosphorus 72 ± 17% and 92 ± 10%, respectively. These data provide the first “big-picture” view of water quality in the near shore region around the high islands of American Samoa. While the picture is encouraging, these data suggest emerging water quality concerns.     

Diel cycles of arsenic speciation due to photooxidation in acid mine drainage from the Iberian Pyrite Belt (Sw Spain)

Twenty four hours diel cycles of arsenic speciation in Acid Mine Drainage (AMD) due to photooxidation have been reported for the first time. AMD samples were taken during 48 h (31st March and 1st April, 2005) at 6 h intervals from the effluent of a massive abandoned polymetallic sulphide mine of the Iberian Pyrite Belt (Sw Spain). Samples were preserved in situ using cationic exchange prior to analysis by coupled high performance liquid chromatography, hydride generation and atomic fluorescence spectrometry (HPLC-HG-AFS) for arsenic speciation. The results indicated the presence of inorganic arsenic species with daily means of 262mugl(-1) for As(V) and 107 microg l(-1) for As(III). No marked diel trend was observed for As(V). However, a marked diel trend was observed for As(III) in the two studied days, with maximum concentrations during nighttime (141-143 microg l(-1)) and minimum concentrations at daytime (72-77 microg l(-1)). This difference in concentration during daytime and nighttime is ca. 100%. A similar diel cycle was observed for iron. An explanation for the arsenic diel cycles observed is the light induced photooxidation of As(III) and the elimination of As(V) due to its adsorption onto Fe precipitates during the daytime. Furthermore, the diel changes in arsenic speciation emphasize the importance of designing suitable sampling strategies in AMD systems.     

Photocatalytic reduction of carbon dioxide over Cu/TiO<Subscript>2</Subscript> photocatalysts

The photocatalytic reduction of CO₂ with H₂O was investigated using Cu/TiO₂ photocatalysts in aqueous solution. For this purpose, Cu/TiO₂ photocatalysts (with 0.2, 0.9, 2, 4, and 6 wt.% of Cu) have been synthesized via sol-gel method. The photocatalysts were extensively characterized by means of inductively coupled plasma optical emission spectrometry (ICP-OES), N₂ physisorption (BET), XRD, UV-vis DRS, FT-IR, Raman spectroscopy, TEM-EDX, and photoelectrochemical measurements. The as-prepared photocatalysts contain anatase as a major crystalline phase with a crystallite size around 13 nm. By increasing the amount of Cu, specific surface area and band gap energy decreased in addition to the formation of large agglomeration of CuO. Results revealed that the photocatalytic reduction of CO₂ decreased in the presence of Cu/TiO₂ in comparison to pure TiO₂, which might be associated to the formation of CuO phase acting as a recombination center of generated electron-hole pair. Decreasing of photoactivity can also be connected with a very low position of conduction band of photocatalysts with high Cu content, which makes H₂ production necessary for CO₂ reduction more difficult.     

Elemental and isotopic determination of lead (Pb) in particulate matter in the Brazilian city of Goiânia (GO) using ICP-MS technique

Environmental Science and Pollution Research - The toxic metal lead (Pb) can be harmful to human health in various manners, but is also considered as a distinguished tracer of environmental...     

Effect of ferrate on green algae removal

Green algae Cladophora aegagropila, present in cooling water of thermal power plants, causes many problems and complications, especially during summer. However, algae and its metabolites are rarely eliminated by common removal methods. In this work, the elimination efficiency of electrochemically prepared potassium ferrate(VI) on algae from cooling water was investigated. The influence of experimental parameters, such as Fe(VI) dosage, application time, pH of the system, temperature and hydrodynamics of the solution on removal efficiency, was optimized. This study demonstrates that algae C. aegagropila can be effectively removed from cooling water by ferrate. Application of ferrate(VI) at the optimized dosage and under the suitable conditions (temperature, pH) leads to 100% removal of green algae Cladophora from the system. Environmentally friendly reduction products (Fe(III)) and coagulation properties favour the application of ferrate for the treatment of water contaminated with studied microorganisms compared to other methods such as chlorination and use of permanganate, where harmful products are produced.

     

Composition and quantification of the anthropogenic and natural fractions of wastes collected from the stormwater drainage system for discussions about the waste management and people behavior

A study was made of the composition of wastes collected from the pipes of the stormwater drainage system of Sorocaba, SP, Brazil (600 thousand inhabitants). A total of 10 samples weighing at least 100 kg each were sorted into 19 items to determine the fraction that can be considered natural (earth/sand, stones, organic matter, and water, the latter determined after oven-drying the samples) and the anthropogenic fraction (the remaining 15 items, especially construction and demolition wastes and packaging). Soil/sand was found to be the main item collected (52.5 % dry weight), followed by the water soaked into the waste (24.3 %), which meant that all the other wastes were saturated in mud, whose contents varied from 6.4 % (glass) to 87.2 % (metalized plastics packaging). In general, 83 % of the collected wastes can be classified as “natural,” but the remaining 17 % represent 2,000 kg of the most varied types of wastes discarded improperly every day on the streets of the city. This is an alarming amount of wastes that may clog parts of the drainage systems, causing troubles for all the population (like flooding) and must be strongly considered in municipal solid wastes management and in environmental education programs.     

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.     

Looking beyond the average agricultural impacts in defining adaptation needs in Europe

The effects of climate change on agriculture are often characterised by changes in the average productivity of crops; however, these indicators provide limited information regarding the risks associated with fluctuations in productivity resulting from future changes in climate variability that may also affect agriculture. In this context, this study evaluates the combined effects of the risks associated with anomalies reflected by changes in the mean crop yield and the variability of productivity in European agro-climatic regions under future climate change scenarios. The objective of this study is to evaluate adaptation needs and to identify regional effects that should be addressed with greater urgency in the light of the risks and opportunities that are identified. The results show differential effects on regional agriculture and highlight the importance of considering both regional average impacts and the variability in crop productivity in setting priorities for the adaptation and maintenance of rural incomes and agricultural insurance programmes.     

Integrated hydrologic–economic decision support system for groundwater use confronting climate change uncertainties in the Tunuyán River basin,Argentina

This study presents an integrated hydrologic–economic model as decision support system for groundwater use and incorporates uncertainties of climate change. The model was developed with the Vensim software (Ventana Systems) for system dynamic simulations. The software permitted the integration of economic variables along with hydrologic variables, in a unified format with the aim of evaluating the economic impacts of climate change on arid environments. To test the model, we applied it in one of the upper Tunuyán River sub-basin, located in the Mendoza Province (Argentina), where irrigation comes from groundwater. The model defines the best mix of crops and the total land use required to maximize the total river sub-basin monetary income, considering as a limit the amount of water that does not exceed the natural annual aquifer recharge. To estimate the impacts of climatic changes, four scenarios were compared: the business as usual (with the number of existing wells) in a dry year with a temperature increase of 4 °C; the business as usual in a wet year with an increase in temperature of 1.1 °C; an efficient use of wells in a dry year and a temperature increase of 4 °C and an efficient use of wells in a wet year with a temperature increase of 1.1 °C. Outputs calculated by the model were: land use per crop, total sub-basin net benefit, total sub-basin water extraction, water extraction limit depending on river discharge and total number of wells required to irrigate the entire area. Preliminary results showed that the number of existing wells exceeded the optimized number of wells required to sustainably irrigate the entire river sub-basin. Results indicated that in an average river discharge year, if wells were efficiently used, further rural development would be possible, until the limit of 350 million m³ of water extraction per year was reached (650 million m³ for a wet year and 180 million m³ for a dry year). The unified format and the low cost of the software license make the model a useful tool for Water Resources Management Institutions, particularly in developing countries.