The influences of weir construction on salt water intrusion and water quality in a tidal estuary—assessment with modeling study

A three-dimensional hydrodynamic and water quality model was developed and applied to predict the changes in physical and biochemical processes that would result from the proposed Dadu Weir construction in the Wu River estuary, located in central Taiwan. A high-resolution unstructured grid was constructed to represent the narrow channels in the tidal estuary. The model was calibrated and verified with available hydrographic and water quality data measured in 2011. The overall performance of the model is in reasonable agreement with the measured water level, salinity, and water quality state variables. The model was then used to investigate the changes in salt water intrusion and water quality as a result of weir construction under low-flow conditions. The model simulations indicate that more tidal energy will propagate into the estuary after weir construction because of decreased freshwater discharges. The limits of salt water intrusion before and after weir construction coincide at a distance of 11.6 km from the mouth of the Wu River. This salt water intrusion limit is the reason that the Dadu Weir will be constructed at a distance of 12 km from the mouth of the Wu River. The weir will become a barrier to salt water intrusion. The simulation results indicate that the concentrations of dissolved oxygen and nutrients will decrease slightly after weir construction, while the chlorophyll a concentration in the middle reach will increase after weir construction. However, construction of the weir is predicted to have little influence on water quality conditions downstream of the Wu River estuary. The results of this case study provide quantitative estimates of the physical and biochemical changes expected to occur in this nature system due to human action.     

Application of multivariate statistical techniques to evaluation of water quality in the Mała Wełna River (Western Poland)

The paper presents the results of determinations of physico-chemical parameters of the Ma?a We?na waters, a river situated in Wielkopolska voivodeship (Western Poland). Samples for the physico-chemical analysis were taken in eight gauging cross-sections once a month between May and November 2006. To assess the physico-chemical composition of surface water, use was made of multivariate statistical methods of data analysis, viz. cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), and discriminant analysis (DA). They made it possible to observe similarities and differences in the physico-chemical composition of water in the gauging cross-sections, to identify water quality indicators suitable for characterising its temporal and spatial variability, to uncover hidden factors accounting for the structure of the data, and to assess the impact of man-made sources of water pollution.     

Performance assessment of geospatial simulation models of land-use change—a landscape metric-based approach

Performance evaluation is a critical step when developing land-use and cover change (LUCC) models. The present study proposes a spatially explicit model performance evaluation method, adopting a landscape metric-based approach. To quantify GEOMOD model performance, a set of composition- and configuration-based landscape metrics including number of patches, edge density, mean Euclidean nearest neighbor distance, largest patch index, class area, landscape shape index, and splitting index were employed. The model takes advantage of three decision rules including neighborhood effect, persistence of change direction, and urbanization suitability values. According to the results, while class area, largest patch index, and splitting indices demonstrated insignificant differences between spatial pattern of ground truth and simulated layers, there was a considerable inconsistency between simulation results and real dataset in terms of the remaining metrics. Specifically, simulation outputs were simplistic and the model tended to underestimate number of developed patches by producing a more compact landscape. Landscape-metric-based performance evaluation produces more detailed information (compared to conventional indices such as the Kappa index and overall accuracy) on the model’s behavior in replicating spatial heterogeneity features of a landscape such as frequency, fragmentation, isolation, and density. Finally, as the main characteristic of the proposed method, landscape metrics employ the maximum potential of observed and simulated layers for a performance evaluation procedure, provide a basis for more robust interpretation of a calibration process, and also deepen modeler insight into the main strengths and pitfalls of a specific land-use change model when simulating a spatiotemporal phenomenon.     

Cumulative effects of developed road network on woodland—a landscape approach

Population growth, during the twentieth century, has increased demand for new farmlands. Accordingly, road networks have rapidly been developed to facilitate and accelerate human access to the essential resources resulted in extensive land use changes. The present study aims at assessing cumulative effects of developed road network on tree cover of Golestan Province in northern Iran. In order to detect changes over the study period of 1987–2002, the LULC map of the study area was initially prepared from the satellite images of Landsat TM (1987) and ETM+ (2002) using maximum likelihood supervised classification method. Afterwards, a total number of seven landscape matrices were selected to detect cumulative effects of the developed road network on woodland cover. The obtained results indicated that the fragile patches are mainly located at a distance of 171–342 m from the roadside. Furthermore, the majority of the patches affected by cumulative effects of development activities are situated at a distance of 342–684 m from the roadside, over an approximate area of 55 ha. The analysis of landscape metrics revealed that the developed road network has increased the landscape metrics of “the number of patches” and “patches perimeter-area ratio”. It has also followed by a decrease in metrics such as “patches area”, “Euclidean nearest neighbor distance”, “patches proximity”, “shape index”, “contiguity”, and “mean patches fractal dimension”. The road network has also increased the “number of patches” and decreased the “mean patches area” representing further fragmentation of the landscape. With identification of highly affected wooldland cover patches, it would be possible to apply adaptive environmental management strategies to preserve and rehabilitate high-priority patches.     

Effect of water quality parameters on the distribution of Pleuromamma (Copepoda–Calanoida) species in the Indian Ocean: a statistical approach

The region between 10 degrees N and 10 degrees S latitude was known to be congenial for distribution of Pleuromamma species. Diel and ontogenetic migrations were observed for Pleuromamma xiphias. Multivariate analyses such as factor analysis on species' abundance and predictive step-up multiple regression models of water quality parameters: temperature, salinity, dissolved oxygen and their first order interaction effects on Pleuromamma species' abundance, were carried out in the regions, off 10 degrees N, 10 degrees N to 10 degrees S, 10 degrees S to 30 degrees S, along the Somali Coast (38 degrees S-40 degrees S) and a fifth region along the western boundary of the above four regions. Relation between Pleuromamma species' abundance and the water quality parameters showed a decreasing trend in the variability explained (VE) from region 1 (VE = 55.19%) to region 4 (VE = 31.15%) in the models, indicating that the influence of other ecological factors was of higher significance in the south than in the north, with a north south gradient. Indices of diversity (Shannon-Weaver) and evenness (Heips) were calculated and lognormal distributions fitted for these indices were found to be a good fit (p < 0.05). The five regions were compared, based on critical ratio of the diversity index. Shannon-Weaver diversity showed higher values during night collections than day collections, justifying the tendency for diurnal variations. Diversity and dominance were highly (r = 0.95) correlated. A multilinkage cluster analysis by group averaging method for the species, based on the standardised values of log(10) (X + 1) transformed species' abundance, showed that Pleuromamma indica, Pleuromamma gracilis and Pleuromamma abdominalis were not segregated in any of the five regions. Bray and Curtis (1957) coefficient of similarity for the species in the five regions combined together, showed 50% similarity for the cluster containing circum-global species, Pleuromamma piseki, P. gracilis, P. abdominalis and P. borealis. The species, P. indica and P. xiphias entered this cluster at 22% similarity level. Multivariate factor analysis by row normalisation (for species) and column normalisation (for stations), using varimax rotation to simple structure for unique grouping of species as well as stations, carried out in the five regions, showed that the maximum number of species' clusters were obtained in region 2, indicating a higher variability in the ecological conditions in this region than in the other four regions. The significance of the factor model and the differential factor groups of species were also determined in the studied regions. On a broader scale, based on the ecological aspect, the first four regions could be reduced to two regions, viz, region 1 including the north off 10 degrees N, Central Indian Ocean and the area of subtropical convergence, and region 2 consisting exclusively of the equatorial region between 10 degrees N and 10 degrees S based on distribution of Pleuromamma species. The plot of the species abundance with respect to latitude also showed such a demarcation.     

Temporal variability in water quality parameters—a case study of drinking water reservoir in Florida, USA

Our objective was to evaluate changes in water quality parameters during 1983–2007 in a subtropical drinking water reservoir (area: 7 km²) located in Lake Manatee Watershed (area: 338 km²) in Florida, USA. Most water quality parameters (color, turbidity, Secchi depth, pH, EC, dissolved oxygen, total alkalinity, cations, anions, and lead) were below the Florida potable water standards. Concentrations of copper exceeded the potable water standard of <30 μg?l^?1 in about half of the samples. About 75 % of total N in lake was organic N (0.93 mg?l^?1) with the remainder (25 %) as inorganic N (NH₃-N: 0.19, NO₃-N: 0.17 mg?l^?1), while 86 % of total P was orthophosphate. Mean total N/P was <6:1 indicating N limitation in the lake. Mean monthly concentration of chlorophyll-a was much lower than the EPA water quality threshold of 20 μg?l^?1. Concentrations of total N showed significant increase from 1983 to 1994 and a decrease from 1997 to 2007. Total P showed significant increase during 1983–2007. Mean concentrations of total N (n?=?215; 1.24 mg?l^?1) were lower, and total P (n?=?286; 0.26 mg?l^?1) was much higher than the EPA numeric criteria of 1.27 mg total N l^?1 and 0.05 mg total P l^?1 for Florida’s colored lakes, respectively. Seasonal trends were observed for many water quality parameters where concentrations were typically elevated during wet months (June–September). Results suggest that reducing transport of organic N may be one potential option to protect water quality in this drinking water reservoir.     

Water pollution of Sabarmati River—a Harbinger to potential disaster

River Sabarmati is one of the biggest and major river of Gujarat that runs through two major cities of Gujarat, Gandhinagar and Ahmedabad and finally meets the Gulf of Khambhat (GoK) in the Arabian Sea. A study was conducted to evaluate the water quality of this river, as it could possibly be one of the major sources for filling up Kalpasar, the proposed man-made freshwater reservoir supposed to be the biggest one in the world. A total of nine sampling stations were established covering 163 km stretch of the river from upstream of Gandhinagar city to Vataman near Sabarmati estuary. Physicochemical (temprature, pH, salinity, chloride, total dissolved solids, turbidity, dissolved oxygen, biochemical oxygen demand, phenol, and petroleum hydrocarbons), biological (phytoplankton), and microbiological (total and selective bacterial count) analyses indicated that the river stretch from Ahmedabad-Vasana barriage to Vataman was highly polluted due to perennial waste discharges mainly from municipal drainage and industries. An implementation of sustainable management plan with proper treatment of both municipal and industrial effluents is essential to prevent further deterioration of the water quality of this river.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

Modeling of nitrate concentration in groundwater using artificial intelligence approach—a case study of Gaza coastal aquifer

Nitrate concentration in groundwater is influenced by complex and interrelated variables, leading to great difficulty during the modeling process. The objectives of this study are (1) to evaluate the performance of two artificial intelligence (AI) techniques, namely artificial neural networks and support vector machine, in modeling groundwater nitrate concentration using scant input data, as well as (2) to assess the effect of data clustering as a pre-modeling technique on the developed models' performance. The AI models were developed using data from 22 municipal wells of the Gaza coastal aquifer in Palestine from 2000 to 2010. Results indicated high simulation performance, with the correlation coefficient and the mean average percentage error of the best model reaching 0.996 and 7 %, respectively. The variables that strongly influenced groundwater nitrate concentration were previous nitrate concentration, groundwater recharge, and on-ground nitrogen load of each land use land cover category in the well's vicinity. The results also demonstrated the merit of performing clustering of input data prior to the application of AI models. With their high performance and simplicity, the developed AI models can be effectively utilized to assess the effects of future management scenarios on groundwater nitrate concentration, leading to more reasonable groundwater resources management and decision-making     

Environmental impacts of perchlorate with special reference to fireworks—a review

Perchlorate is an inorganic anion that is used in solid rocket propellants, fireworks, munitions, signal flares, etc. The use of fireworks is identified as one of the main contributors in the increasing environmental perchlorate contamination. Although fireworks are displayed for entertainment, its environmental costs are dire. Perchlorates are also emerging as potent thyroid disruptors, and they have an impact on the ecology too. Many studies have shown that perchlorate contaminates the groundwater and the surface water, especially in the vicinity of fireworks manufacturing sites and fireworks display sites. The health and ecological impacts of perchlorate released in fireworks are yet to be fully assessed. This paper reviews fireworks as a source of perchlorate contamination and its expected adverse impacts.     

Assessment of dietary intakes of nineteen pesticide residues among five socioeconomic sections of Hyderabad—a total diet study approach

Total diet study approach was used to assess the dietary intakes of pesticide residues among the select population in Hyderabad. When assessed by a food frequency questionnaire, it was found that the food intakes varied among five socioeconomic sections (SES). Therefore, we intended to compare the intakes of pesticide residues through these foods among the five SES. A total of 195 foods from different markets were collected and analyzed for 19 pesticides. The residues were analyzed with a gas chromatograph and were confirmed with mass spectrometry. About 51 % of the samples were detected with one or more residues. Thirteen out of the 19 residues were present in levels above detection limits in various concentrations. The median concentrations of the residues in all the samples tested, ranged from 0.00010 to 0.33 mg/kg. Highest median concentration was for β-HCH in water samples. Exposures to all the residues were below the respective ADIs at both mean and 95th percentile levels of food intakes with highest estimated dietary intakes (EDIs) of β-HCH in both the cases. The EDIs of β-HCH were the highest among all the residues at both the intake levels among all the SES. The EDIs of β-HCH were significantly higher in lower SES than higher SES possibly due to the consumption of rice cooked in water contaminated with β-HCH. EDIs for other residues did not differ significantly among the five SES.