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.     

A Mathematical Model for the Effects of Fertilization on Nitrogen Concentrations in Unsaturated Soil on Blueberry Farms in Southern New Jersey

Nitrogen is commonly known as a food source for crops. However, the nitrogen compounds used in crop fertilizers, most commonly nitrate (NO₃) and ammonium (NH₄), are not widely understood. Blueberry plants do not take up these compounds as efficiently as organic nitrogen so varying amounts of leaching into the soil and groundwater will occur. A biogeochemical model consisting of ordinary and partial differential equations is implemented to computationally predict the concentrations of nitrate and ammonium in unsaturated soil of blueberry plants, specifically in the southern region of New Jersey. The model takes into account the type of soil of the region, the nitrate uptake of the plant, the water content in the roots region, the pressure heads in the soil pores, and the application rates of fertilizers containing nitrate, ammonium, and organic nitrogen. Computational simulations demonstrate that the model accounts for natural processes and, in addition, show that commonly used fertilizer application rates cause unnecessarily high concentrations of both nitrate and ammonium in the unsaturated soil level. Further, simulations show that decreasing nitrate fertilizer applications by 85.7% in annual and 91.8% in bi-annual schedules provides an optimal system for safe reapplication.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Recycled sand in lime-based mortars

The increasing awareness of the society about safe guarding heritage buildings and at the same time protecting the environment promotes strategies of combining principles of restoration with environmentally friendly materials and techniques. Along these lines, an experimental program was carried out in order to investigate the possibility of producing repair, lime-based mortars used in historic buildings incorporating secondary materials. The alternative material tested was recycled fine aggregates originating from mixed construction and demolition waste. Extensive tests on the raw materials have been performed and mortar mixtures were produced using different binding systems with natural, standard and recycled sand in order to compare their mechanical, physical and microstructure properties. The study reveals the improved behavior of lime mortars, even at early ages, due to the reaction of lime with the Al and Si constituents of the fine recycled sand. The role of the recycled sand was more beneficial in lime mortars rather than the lime–pozzolan or lime–pozzolan–cement mortars as a decrease in their performance was recorded in the latter cases due to the mortars’ structure.     

Seaweeds as bioindicators of heavy metals off a hot spot area on the Egyptian Mediterranean Coast during 2008–2010

Concentrations of Cu, Zn, Cd, Pb, Ni, Co, Fe, Mn, and Hg were measured successively in water, sediments, and six macroalgal species belonging to three algal classes during 3 years (2008–2010) from Abu Qir Bay, Alexandria, Egypt: Chlorophyceae (Enteromorpha compressa, Ulva fasciata), Phaeophyceae (Padina boryana), and Rhodophyceae (Jania rubens, Hypnea musciformis, Pterocladia capillacea). The study aimed to assess the bioaccumulation potential of the seaweeds, as well as to evaluate the extent of heavy metal contamination in the selected study site. Metals were analyzed using atomic absorption spectrophotometry coupled with MH-10 hydride system. The obtained data showed that the highest mean concentrations of Cu, Zn, Fe, and Mn were recorded in E. compressa; Cd, Ni, and Hg exhibited their highest mean concentrations in P. boryana, while Pb and Co were found in J. rubens. Abundance of the heavy metals in the algal species was as follow: Fe?>?Mn?>?Zn?>?Pb?>?Ni?>?Co?>?Cu?>?Cd?>?Hg. E. compressa showed the maximum metal pollution index (MPI) which was 11.55. Bioconcentration factor (BCF) for the metals in algae was relatively high with a maximum value for Mn. The Tomlinson pollution load index (PLI) values for the recorded algal species were low, which ranged between 1.00 in P. boryana and 2.72 in E. compressa. Enrichment factors for sediments were low fluctuating between 0.43 for Hg to 2.33 for Mn. Accordingly, the green alga E. compressa, brown alga P. boryana, and red alga J. rubens can be nominated as bioindicators. Based on MPI and PLI indices, Abu Qir Bay in the present study is considered as low-contaminated area.     

Seasonality in the distribution of dinoflagellates with special reference to harmful algal species in tropical coastal environment,Bay of Bengal

A study was carried out in the coastal waters of Kalpakkam, southeast coast of India, to find out the seasonal variation in dinoflagellate community structure. Samples were collected for a period of 4 years during 2006–2010. During the study 69 species of dinoflagellates were encountered among which Ceratium furca and Prorocentrum micans were most common during all the seasons. Genus Ceratium was found to be the most diverse one with 23 species which was followed by genus Protoperidinium with 16 species. Of 69 species, 27 species were considered as dominant based on their abundance during pre-monsoon (PRM), monsoon (MON) and post-monsoon (POM) periods. Relatively high density and diversity of dinoflagellates were encountered during the PRM period as compared to the MON and POM periods. Abundance pattern of dinoflagellates for three seasons showed the following trend: PRM?>?POM?>?MON. Salinity showed a positive correlation with dinoflagellate community showing its importance in dinoflagellate growth and sustenance. Ammonia and phosphate developed negative correlation with dinoflagellate density indicating the utilization of these nutrients by the dinoflagellate community. The presence of three dinoflagellate associations, broadly representing the three seasons experienced at this location, was evident from the cluster analysis. The study revealed presence of 19 relatively abundant toxic/red tide forming dinoflagellate species in the coastal waters of Kalpakkam.     

Rainwater toxicity and contamination study from São Paulo Metropolitan Region, Brazil

Wet deposition is an important process that removes pollutants from the atmosphere and transfers them to waters and soil. The goal of this study was to assess the biological effects of the atmospheric contamination of rainwater in the metropolitan area of São Paulo (MASP) using Daphnia similis, Ceriodaphnia dubia, and Vibrio fischeri. Experimental assays were carried out according to standard toxicity methodology. Twenty-three rainwater samples were collected from October 2007 to December 2008, at the Nuclear Research Institute (IPEN), in MASP. Major ions were determined by ionic chromatography, which showed NH₄ ⁺ and NO₃ ^? as prevalent ions. Ecotoxicological results confirmed toxic potential of rainwater, as all samples were toxic to D. similis and C. dubia. The V. fischeri luminescence reduction confirmed those negative effects of rainwater and percentage inhibition of relative luminescence ranged from 0.2 to 0.9 for 16 samples. Worse conditions were observed during the rainy season, suggesting convective rains are more effective in transferring contaminants and toxicity from atmosphere to surface.     

Persistence and dissipation of flubendiamide and its risk assessment on gherkin (Cucumis anguria L.)

A supervised open field trial was conducted to evaluate the dissipation pattern and risk assessment of flubendiamide in gherkin fruits following foliar application of Fame 480 SC at 60 and 120 g a.i.?ha^?1. Samples of gherkin fruits were drawn at different time intervals and quantified by HPLC-DAD. The maximum initial deposits of flubendiamide on gherkin were found to be 0.79 and 1.52 mg kg^?1, respectively, at recommended and double the recommended doses. The dissipation pattern of flubendiamide followed a first-order kinetics with half-lives of 1.87 to 2.16 days at 60 and 120 g a.i.?ha^?1, respectively. The limit of quantification of flubendiamide and desiodo flubendiamide was observed to be 0.01 mg kg^?1 for gherkin fruit and soil substrates. Theoretical maximum residue contribution (TMRC) for flubendiamide was calculated and found to be well below the maximum permissible intake (MPI) on gherkin fruits. Thus, the application of flubendiamide at the recommended dose on gherkin fruits presents no human health risks and safe to consumers.     

Nanocatalyzed biodiesel synthesis from the oily contents of Marine brown alga Dictyota dichotoma

This research article demonstrates biodiesel synthesis through the methanolysis of the oily contents (4.02 ± 0.27% w/w on dried basis) of Dictyota dichotoma collected from the coast of Hawksbay, Pakistan. The metal oxides (CaO, MgO, ZnO, and TiO₂) used as nanocatalysts were refluxed (5% K₂SO₄), calcinated (850 °C) and characterized by Atomic Force Microscopy (AFM) which produced 93.2% w/w FAME (biodiesel) at relatively mild condition (5% catalyst, 65 °C, 3 h, 18:1 molar ratio) using CaO. Whereas, MgO, ZnO, and TiO₂ produced 92.4%, 72.5%, and 31.8% w/w FAME, respectively at elevated condition (225 °C). Thus, CaO was considered to be the best catalyst among the others. This tri-phase reaction require continuous fast mixing and the yield depends on the reaction parameters like catalyst amount, temperature, reaction time and molar ratio (methanol: oil). The reusability of these heterogeneous catalysts simplified the purification step, reduced the waste generation and make the final product technically and economically viable.