The impact of global change on economic values of water for Public Irrigation Schemes at the São Francisco River Basin in Brazil

Economic values of water for the main Public Irrigation Schemes in the sub-middle region of the São Francisco River Basin, in northeastern Brazil, are determined in this study using an integration of a global agro-economic land and water use (MAgPIE) with a local economic model (Positive Mathematical Programming). As in the latter, the water values depend on the crops grown, and as Brazilian agriculture is strongly influenced by the global market, we used a regionalized version of the global model adapted to the region in order to simulate the crop land use, which is in turn determined by changes in global demand, trade barriers, and climate. The allocation of sugarcane and fruit crops projected with climate change by the global model, showed an impact on the average yields and on the water costs in the main schemes resulting in changes in the water values locally. The economic values for all schemes in the baseline year were higher than the water prices established for agricultural use in the basin. In the future, these water values will be higher in all the schemes. The highest water values currently and in the future were identified in municipalities with a significant proportion of area growing irrigated sugarcane. Being aware of current water values of each user in a baseline year and in a projected future under global climate and socioeconomic changes, decision makers should improve water allocation policies at local scale, in order to avoid conflicts and unsustainable development in the future.     

The humanitarian common logistic operating picture: a solution to the inter‐agency coordination challenge

Although significant progress has been made in developing the practice of humanitarian logistics, further improvements in efficiency and effectiveness have the potential to save lives and reduce suffering. This paper explores how the military/emergency services’ concept of a common operating picture (COP) can be adapted to the humanitarian logistics context, and analyses a practical and proven approach to addressing the key challenge of inter‐agency coordination and decision‐making. Successful adaptation could provide the mechanism through which predicted and actual demands, together with the location and status of material in transit, are captured, evaluated, and presented in real time as the basis for enhanced decision‐making between actors in the humanitarian supply network. Through the introduction of a humanitarian logistics COP and its linkages to national disaster management systems, local communities and countries affected by disasters and emergencies will be better placed to oversee and manage their response activities.     

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.     

Potential impacts of climate change on agricultural land use suitability of the Hungarian counties

Central and Eastern European countries are a hotspot area when analyzing the impacts of climate change on agricultural and environmental sectors. This paper conducts a socio-economic evaluation of climate risks on crop production in Hungary, using panel data models. The region has a special location in the Carpathian basin, where the spatial distribution of precipitation varies highly from humid conditions in the western part to semiarid conditions in eastern Hungary. Under current conditions, crop systems are mainly rainfed, and water licences are massively underexploited. However, water stress projected by climate change scenarios could completely change this situation. In the near future (2021–2050), most of the crops examined could have better climatic conditions, while at the end of the century (2071–2100), lower yields are expected. Adaptation strategies must be based on an integrated evaluation which links economic and climatic aspects, and since the results show important differences in the case of individual systems, it is clear that the response has to be crop and region specific.