M. Camara; N.R.B. Jamil; F.B. Abdullah
Abstract
Rapid development and population growth have resulted in an ever-increasing level of water pollution in Malaysia. Therefore, this study was conducted to assess water quality of Selangor River in Malaysia. The data collected under the river water quality monitoring program by the Department of environment ...
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Rapid development and population growth have resulted in an ever-increasing level of water pollution in Malaysia. Therefore, this study was conducted to assess water quality of Selangor River in Malaysia. The data collected under the river water quality monitoring program by the Department of environment from 2005 to 2015 were used for statistical analyses. The local water quality indices were computed and a trend detection technique and cluster analysis were applied, respectively, to detect changes and spatial disparity in water quality trends. The results showed that the river water is of good quality at all stations, with the exception of 1SR01 and 1SR09 located upstream, which recorded moderate water quality indices of 68 and 71, respectively. The results of trend analysis showed downward trends in dissolved oxygen, biochemical oxygen demand and ammonia nitrogen, for most water quality stations, as well as increasing trends in chemical oxygen, suspended solids, pH and temperature for most stations. In addition, the results of cluster and time series analyses showed that the trend variation in dissolved oxygen, pH, and temperature between the station clusters is relatively low as compared to chemical oxygen demand, biochemical oxygen demand, suspended solids, and ammonia nitrogen. With the peak concentration of 13 mg/L for dissolved oxygen observed in cluster 2 in 2014, and the highest decrease in suspended solids (8 mg/L) observed in cluster 1 for 2015. This finding demonstrates that these combined statistical analyses can be a useful approach for assessing water quality for adequate management of water resources.
G. Elkiran; V. Nourani; S.I. Abba; J. Abdullahi
Abstract
ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura ...
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ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygen at the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multi-linear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models.
F. Esfandi; A.H. Mahvi; M. Mosaferi; F. Armanfar; M. Hejazi; S. Maleki
Abstract
Eutrophication is considered as a serious problem in water reservoirs. Awareness about the eutrophic status of each reservoir could help in providing a better understanding of the problem in a global scale. The present study was conducted to assess temporal and spatial eutrophication index in a water ...
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Eutrophication is considered as a serious problem in water reservoirs. Awareness about the eutrophic status of each reservoir could help in providing a better understanding of the problem in a global scale. The present study was conducted to assess temporal and spatial eutrophication index in a water reservoir (Sahand dam) in the northwest of Iran. Physico-chemical parametres that are effective on eutrphic condition occurrence were analyzed, and trophic state index was calculated on a scale of 0-100 by measuring Secchi disk depth, chlorophyll a, total phosphorus, total nitrogen, total suspended solids, and phosphorus P/N ratio. Moreover, using the overlapping, the reservoir was mapped based on the mentioned index. Seasonal variation of dissolved solids in the reservoir was recorded due to precipitation and subsequent dilution and evaporation. Thermal stratification was observed during the summer months. The total trophic state index value was calculated as 55.5- 58.07, with minimum value belonging to P/N and maximum value belonging to suspended solids for individual parameters. There were some spatial and temporal differences for trophic state index in the reservoir. It was found that the whole area of the reservoir was in almost moderately upper-mesotrophic condition and in some target stations it was very close to eutrophic condition. The worst condition was observed in Qaranqu River as the main input to the reservoir. Due to the significant impact of suspended particles resulting from erosion of the surrounding lands on TSI value, there is an urgent need for mitigation measures to intercept eutrophication.
K. A. Ullah; J. Jiang; P. Wang
Abstract
Surface waters are the most important economic resource for humans which provide water for agricultural, industrial and anthropogenic activities. Surface water quality plays vital role in protecting aquatic ecosystems. Unplanned urbanization, intense agricultural activities and deforestation are positively ...
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Surface waters are the most important economic resource for humans which provide water for agricultural, industrial and anthropogenic activities. Surface water quality plays vital role in protecting aquatic ecosystems. Unplanned urbanization, intense agricultural activities and deforestation are positively associated with carbon, nitrogen and phosphorous related water quality parameters. Multiple buffers give robust land use land cover and water quality model and highlight the impacts of land use land cover characteristics on water quality parameters at various scales which will guide watershed managers for particular application of best management practices to enhance stream health. Traditionally, water quality data collections are based on discrete sampling and were analyzed through statistical techniques which were designed for spatially isolated measurements. Traditional multivariate statistical approaches uncover hidden information in water quality data but they are unable to expose spatial relationship. The complexity of information in water quality data needs new statistical approaches which uncover spatiotemporal variability. This review briefly discusses influences of land use land cover characteristics on surface water quality, effects of spatial scale on land use land cover- water quality relationship, and water quality modeling using various statistical approaches. Every statistical method has unique purpose, application and solves different problems. This review article pinpoints that how statistical approaches in combination with spatial scale can be applied to develop statistically significant land use land cover- water quality relationship for better water quality evaluation.
Environmental Management
U.G. Abhijna
Abstract
Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared ...
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Multivariate statistical techniques such as cluster analysis, multidimensional scaling and principal component analysis were applied to evaluate the temporal and spatial variations in water quality data set generated for two years (2008-2010) from six monitoring stations of Veli-Akkulam Lake and compared with a regional reference lake Vellayani of south India. Seasonal variations of 14 different physicochemical parameters analyzed were as follows: pH (6.42-7.48), water temperature (26.0-31.28°C), salinity (0.50-26.81 ppt), electrical conductivity (47-20656.31 µs/cm), dissolved oxygen (0.078-7.65 mg/L), free carbon-dioxide (3.8-51.8 mg/L), total hardness (27.20-2166.6 mg/L), total dissolved solids (84.66-4195 mg/L), biochemical oxygen demand (1.57-25.78 mg/L), chemical oxygen demand (5.35-71.14 mg/L), nitrate (0.012-0.321 µg/ml), nitrite (0.24-0.79 µg/ml), phosphate (0.04-5.88 mg/L), and sulfate (0.27-27.8 mg/L). Cluster analysis showed four clusters based on the similarity of water quality characteristics among sampling stations during three different seasons (pre-monsoon, monsoon and post-monsoon). Multidimensional scaling in conjunction with cluster analysis identified four distinct groups of sites with varied water quality conditions such as upstream, transitional and downstream conditions in Veli-Akkulam Lake and a reference condition at Vellayani Lake. Principal Component Analysis showed that Veli-Akkulam Lake was seriously deteriorated in water quality while acceptable water quality conditions were observed at reference lake Vellayani. Thus the present study could estimate the effectiveness of multivariate statistical approaches for assessing water quality conditions in lakes.