Hydrodynamic characterization of Corpus Christi Bay through modeling and observation

Christi Bay is a relatively flat, shallow, wind-driven system with an average depth of 3–4 m and a mean tidal range of 0.3 m. It is completely mixed most of the time, and as a result, depth-averaged models have, historically, been applied for hydrodynamic characterization supporting regulatory decisions on Texas coastal management. The bay is highly stratified during transitory periods of the summer with low wind conditions. This has important implications on sediment transport, nutrient cycling, and water quality-related issues, including hypoxia which is a key water quality concern for the bay. Detailed hydrodynamic characterization of the bay during the summer months included analysis of simulation results of 2-D hydrodynamic model and high-frequency (HF) in situ observations. The HF radar system resolved surface currents, whereas an acoustic Doppler current profiler (ADCP) measured current at different depths of the water column. The developed model successfully captured water surface elevation variation at the mouth of the bay (i.e., onshore boundary of the Gulf of Mexico) and at times within the bay. However, large discrepancies exist between model-computed depth-averaged water currents and observed surface currents. These discrepancies suggested the presence of a vertical gradient in the current structure which was further substantiated by the observed bi-directional current movement within the water column. In addition, observed vertical density gradients proved that the water column was stratified. Under this condition, the bottom layer became hypoxic due to inadequate mixing with the aerated surface water. Understanding the disparities between observations and model predictions provides critical insights about hydrodynamics and physical processes controlling water quality.     

Limitations of habitats as biodiversity surrogates for conservation planning in estuaries

Increasing pressures on global biodiversity and lack of data on the number and abundance of species have motivated conservation planners and researchers to use more readily available information as proxies or surrogates for biodiversity. “Habitat” is one of the most frequently used surrogates but its assumed value in marine conservation planning is not often tested. The present study developed and tested three alternative habitat classification schemes of increasing complexity for a large estuary in south-east Australia and tested their effectiveness in predicting spatial variation in macroinvertebrate biodiversity and selecting estuarine protected areas to represent species. The three habitat classification schemes were: (1) broad-scale habitats (e.g., mangroves and seagrass), (2) subdivision of each broad-scale habitat by a suite of environmental variables that varied significantly throughout the estuary, and (3) subdivision of each broad-scale habitat by the subset of environmental variables that best explained spatial variation in macroinvertebrate biodiversity. Macroinvertebrate assemblages differed significantly among the habitats in each classification scheme. For each classification scheme, habitat richness was significantly correlated with species richness, total density of macroinvertebrates, assemblage dissimilarity, and summed irreplaceability. However, in a reserve selection process designed to represent examples of each habitat, no habitat classification scheme represented species significantly better than a random selection of sites. Habitat classification schemes may represent variation in estuarine biodiversity; however, the results of this study suggest they are inefficient in designing representative networks of estuarine protected areas.     

Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study

In this paper, a new methodology is developed for integrated allocation of water and waste-loads in river basins utilizing a fuzzy transformation method (FTM). The fuzzy transformation method is used to incorporate the existing uncertainties in model inputs. In the proposed methodology, the FTM, as a simulation model, is utilized in an optimization framework for constructing a fuzzy water and waste-loads allocation model. In addition, economic as well as environmental impacts of water allocation to different water users are considered. For equitable water and waste load allocation, all possible coalition of water users are considered and total benefit of each coalition, which is a fuzzy number, is reallocated to water users who are participating in the coalition. The fuzzy cost savings are reallocated using a fuzzy nucleolus cooperative game and the FTM. As a case study, the Dez River system in south-west of Iran is modeled and analyzed using the methodology developed here. The results show the effectiveness of the methodology in optimal water and waste-loads allocations under uncertainty.     

Heavy metal bioaccumulation and risk assessment for wild and farmed beluga sturgeon caviar

Because of over-exploitation of sturgeon for caviar production, they have been listed worldwide in annex II of the Convention on International Trade in Endangered Species regulations; so caviar production using aquaculture is increasingly seen as a feasible way to reduce overfishing. The accumulation of the nonessential metals As, Ba, Cd, Hg, Pb, and Sn was determined in the caviar of farmed and wild Beluga sturgeon (Huso huso). The levels of As in both and Cd in wild samples were less than 0.01 mg kg^?1 wet weight, and the comparison for all of the metals studied did not show large fluctuations in metal concentrations between farmed and wild caviar samples. The average for each toxic metal was below the permissible limits proposed by the UK’s Ministry of Agriculture, Fisheries and Foods (2000). The maximum allowable daily consumption rate of caviar was calculated; however, the health risks from caviar consumption are uncertain because the amount of caviar consumed by heavy users is not known.     

The effectiveness of digital soil mapping to predict soil properties over low-relief areas

This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.     

Controlling energy consumption in residential buildings using air infiltration in humid climates

Environment, Development and Sustainability - Given the ever-increasing people’s need for housing, affordable housing and environmental comfort are important, and the use of inactive design...     

Determination of appropriate service quality attributes for household toilets in rural settlements of India based on user perception

In many developing countries, including India, governments have implemented programs for improvement of environment and health in rural settlements through the provision of a subsidized household toilet with specifications selected and benchmarked from the experts’ perspective with only partial consideration of users’ perception. Provision of household toilets with specifications or service quality as perceived by users is a requirement for the sustainability and sustained use of toilet infrastructure. Users do not judge only the overall quality of a service but also base their judgment on a few attributes which are either perceived to be relatively more important or where there is a wider gap between expectation and perception . This paper presents a review of the service quality attributes of the household toilet as found in the relevant literature and provides a step-by-step approach for identifying appropriate attributes based on users’ perspective. While an initial list of attributes was developed based on literature review, initial selection and modifications of the attributes’ definition and units were carried out as per our judgment. This list was further modified through focus groups discussions and incorporating expert opinions. The final list of attributes is selected based on the stated importance level of attributes as perceived by users. The study finds that the attributes prioritized by the users are different from the experts. While government policies emphasize on the construction of toilet, maintenance of toilet is found an important issue for the users.