scholarly journals Investigation of Water Quality Parameters Discharged from Textile Dyeing Industries

2015 ◽  
Vol 7 (1) ◽  
pp. 257-263 ◽  
Author(s):  
A Munnaf ◽  
MS Islam ◽  
TR Tusher ◽  
MH Kabir ◽  
MAH Molla
Keyword(s):  
Water Quality ◽  
Textile Industry ◽  
Rapid Development ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Human Consumption ◽  
Wet Season ◽  
Treatment Processes ◽  
Textile Dyeing

Rapid development of textile industry and direct deposition of the effluents into sewage networks produced disturbances in treatment processes and exert pollution loads on water bodies. The study was conducted to investigate the water quality parameters discharged from seven textile dyeing industries at Konabari in Gazipur region of Bangladesh during March to December, 2011, and also to evaluate the harmful effects of effluents on the surrounding environment. Emphasis was given on the investigation of important water quality parameters, which include temperature, pH, total suspended solids (TSS), total dissolved solids (TDS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD), along with the management techniques of effluents discharged from textile dyeing industries. The study depicted that the DO values were nil or below the standard values in all industries which was very alarming for environment. The concentrations of BOD, COD, TDS and TSS were very high which indicate the presence of water pollutants. The study was focused on the pollution implications of effluents from textile industries around the study area, because of the risk of human exposure and environmental degradation by these massive discharged effluents. The water quality deteriorated in dry season than the wet season and the surface water around the studied area was highly polluted due to the industrial activities and should totally avoid for human consumption without proper treatment. It is therefore recommended that the careless discharge of the effluents should be discouraged and appropriate management system should be taken immediately to reduce the water pollution for saving the environment.DOI: http://dx.doi.org/10.3329/jesnr.v7i1.22180 J. Environ. Sci. & Natural Resources, 7(1): 257-263 2014

scholarly journals Recognition of spatial framework for water quality and its relation with land use/cover types from a new perspective: A case study of Jinghe Oasis in Xinjiang, China

2017 ◽  
Author(s):  
Fei Zhang ◽  
Juan Wang ◽  
Xiaoping Wang
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Wet Season ◽  
Spatial Framework ◽  
Cover Type ◽  
Type Change ◽  
Land Water

Abstract. Quality evaluation for surface water is an important issue in water resource management and protection. To understand the relation between the spatial framework of water quality in Jinghe Oasis and the change in land use/cover type, we first divide 47 water sampling sites measured in May and October 2015 into 6 cluster layers using the self-organizing map (SOM) method based on non-hierarchical k-means classification, and then determine the distribution characteristics of water quality from the time sequence. Water quality indices include chemical oxygen demand (COD), biological oxygen demand (BOD), suspended solids (SS), total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH3−N), chromaticity (SD), and turbidity (NUT). On the basis of the results, we collect data regarding changes in farmland land, forest-grass land, water body, salinized land, and other land types during the wet and dry seasons and combine these data with the classification results of the GF-1 remote sensing satellite obtained in May and October 2015. We then discuss the influences of land use/cover type on water quality at different layers and seasons. The results indicate that Clusters 1 to 3 provide monitoring samples for the wet season (May 2015), whereas Clusters 4 to 6 provide monitoring samples for the dry season (October 2015). In general, the COD, SS, NUT, TN, and NH3−N contents are high in Clusters 1 and 2. The SD values for Clusters 1, 4, and 6 are high. Moreover, high BOD and TP values are mainly concentrated in Clusters 4 and 6. Through the discussion on the relation between different layers of water quality and land use/cover type change, we determine that the influences of farmland land, forest-grassland, and salinized land are significant on the water quality parameters in Jinghe Oasis. In Clusters 1, 2, and 6, the size of the water area also influences the change in water quality parameters to a certain extent. In addition, the influences of various land use/cover types on the water quality parameters in the research zone during different seasons exhibit the following order: farmland land → forest-grass land → salinized land → water body → others. Moreover, influence is less during the wet season than during the dry season. In conclusion, developing research on the relation between the spatial framework of water quality in Jinghe Oasis and land use/cover type change will be significant for the time sequence distribution of water quality in arid regions from both theoretical and practical perspectives.

scholarly journals Investigation of Some Water Quality Parameters of the Buriganga River

2013 ◽  
Vol 5 (2) ◽  
pp. 47-52 ◽  
Author(s):  
ASM Saifullah ◽  
MH Kabir ◽  
A Khatun ◽  
S Roy ◽  
MS Sheikh
Keyword(s):  
Water Quality ◽  
Winter Season ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Dry Season ◽  
Water Quality Parameters ◽  
Wet Season ◽  
Mean Values ◽  
Different Depths ◽  
Buriganga River

This study deals with the investigation of water quality of the Buriganga river, Dhaka. For this purpose, samples were collected from five locations of the Buriganga river of Bangladesh during wet (monsoon) and dry (winter) season in 2011 to determine the spatial distribution and temporal variation of various water quality parameters. Water samples were collected from three different depths of river. The color was light brown in wet season and slightly black to black color in dry season. The water was found slightly acidic to slightly alkaline (6.6-7.5). Water temperature ranged from 18.2°C (dry) to 27.04°C (wet). The river was found to be highly turbid both in dry and wet season. Biochemical Oxygen Demand (BOD), Electric Conductivity (EC) and Total Dissolved Solids (TDS) were found higher in the dry season compared to that of wet season, while Dissolved Oxygen (DO) was found higher in wet season. The mean values of parameters were EC: wet- 1685 ?s/cm, dry-2250 ?s/cm; DO: wet- 4.9 mg/L, dry-3.7 mg/L; BOD: wet- 26.4 mg/L, dry- 33.4 mg/L; TDS: wet-238 mg/L, dry- 579 mg/L; transparency: wet- 24.6 cm, dry- 22.8 cm.DOI: http://dx.doi.org/10.3329/jesnr.v5i2.14600 J. Environ. Sci. & Natural Resources, 5(2): 47-52 2012

The Chao Phraya River Basin: water quality and anthropogenic influences

2018 ◽  
Vol 19 (5) ◽  
pp. 1287-1294 ◽  
Author(s):  
Nuanchan Singkran ◽  
Pitchaya Anantawong ◽  
Naree Intharawichian ◽  
Karika Kunta
Keyword(s):  
Water Quality ◽  
Land Use ◽  
River Basin ◽  
River Mouth ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Paddy Fields ◽  
Coliform Bacteria ◽  
Wet Season ◽  
Chao Phraya River

Abstract Land use influences and trends in water quality parameters were determined for the Chao Phraya River, Thailand. Dissolved oxygen (DO), biochemical oxygen demand (BOD), and nitrate-nitrogen (NO3-N) showed significant trends (R2 ≥ 0.5) across the year, while total phosphorus (TP) and faecal coliform bacteria (FCB) showed significant trends only in the wet season. DO increased, but BOD, NO3-N, and TP decreased, from the lower section (river kilometres (rkm) 7–58 from the river mouth) through the middle section (rkm 58–143) to the upper section (rkm 143–379) of the river. Lead and mercury showed weak/no trends (R2 < 0.5). Based on the river section, major land use groups were a combination of urban and built-up areas (43%) and aquaculture (21%) in the lower river basin, paddy fields (56%) and urban and built-up areas (21%) in the middle river basin, and paddy fields (44%) and other agricultural areas (34%) in the upper river basin. Most water quality and land use attributes had significantly positive or negative correlations (at P ≤ 0.05) among each other. The river was in crisis because of high FCB concentrations. Serious measures are suggested to manage FCB and relevant human activities in the river basin.

scholarly journals Cladoceran abundance in relation to water quality parameters of Chakki Talab, Bodhan, Telangana

2018 ◽  
Vol 8 (01) ◽  
Author(s):  
Vasudha Lingampally ◽  
V.R. Solanki ◽  
D. L. Anuradha ◽  
Sabita Raja
Keyword(s):  
Water Quality ◽  
Dissolved Oxygen ◽  
Oxygen Demand ◽  
Total Dissolved Solids ◽  
Quality Parameters ◽  
Biological Oxygen Demand ◽  
Water Quality Parameters ◽  
Total Alkalinity ◽  
Total Hardness ◽  
One Year

In the present study an attempt has been made to evaluate water quality and related density of Cladocerans for a period of one year, October 2015 to September 2016. Water quality parameters such as temperature, PH, total dissolved solids, dissolved oxygen, biological oxygen demand, total alkalinity, total hardness, chlorides, phosphates, and nitrates are presented here to relate with the abundance of Cladocerans. The Cladoceran abundance reflects the eutrophic nature of the Chakki talab.

Determination of Water Quality Parameters in Wet Season of Surface Water in Trabzon

Ekoloji ◽  
2012 ◽  
Vol 21 (82) ◽  
pp. 77-88 ◽  
Author(s):  
Fatma Gultekin ◽  
Arzu Firat Ersoy ◽  
Esra Hatipoglu ◽  
Secil Celep
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Wet Season

scholarly journals Implementation of Long-Short Term Memory Neural Network (LSTM) for Predicting The Water Quality Parameters in Sungai Selangor

2021 ◽  
Vol 6 (4) ◽  
pp. 40-49
Author(s):  
Nur Natasya Mohd Anuar ◽  
Nur Fatihah Fauzi ◽  
Huda Zuhrah Ab Halim ◽  
Nur Izzati Khairudin ◽  
Nurizatul Syarfinas Ahmad Bakhtiar ◽  
...  
Keyword(s):  
Water Quality ◽  
Short Term Memory ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Short Term ◽  
Term Memory ◽  
Quality Of Water ◽  
Long Short Term Memory

Predictions of future events must be factored into decision-making. Predictions of water quality are critical to assist authorities in making operational, management, and strategic decisions to keep the quality of water supply monitored under specific criteria. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, the purpose of this paper is to develop and train a Long-Short Term Memory (LSTM) Neural Network to predict water quality parameters in the Selangor River. The primary goal of this study is to predict five (5) water quality parameters in the Selangor River, namely Biochemical Oxygen Demand (BOD), Ammonia Nitrogen (NH3-N), Chemical Oxygen Demand (COD), pH, and Dissolved Oxygen (DO), using secondary data from different monitoring stations along the river basin. The accuracy of this method was then measured using RMSE as the forecast measure. The results show that by using the Power of Hydrogen (pH), the dataset yielded the lowest RMSE value, with a minimum of 0.2106 at station 004 and a maximum of 1.2587 at station 001. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and revealed the future developing trend of water quality parameters, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of water parameters.

scholarly journals Study on water and soil quality parameters of shrimp and prawn farming in the southwest region of Bangladesh

2016 ◽  
Vol 13 (1) ◽  
pp. 153-160 ◽  
Author(s):  
MA Zafar ◽  
MM Haque ◽  
MSB Aziz ◽  
MM Alam
Keyword(s):  
Water Quality ◽  
Organic Carbon ◽  
Soil Quality ◽  
Total Nitrogen ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Wet Season ◽  
Ammonia Content ◽  
Phosphorous Content ◽  
Shrimp Farms

Water and soil quality parameters play a vital role for sustainable shrimp and prawn production which together is the leading exportable seafood product in Bangladesh contributing to a significant amount of foreign currency earnings. However, this sector is often negatively criticized by the consumers of importing countries for farm (locally called gher in Bengali) environment. In this context, an investigation was carried out to assess water and soil quality parameters of shrimp and prawn farms in southwest Bangladesh. This study was conducted at Dumuria and Paickgacha Upazila of Khulna district during dry and wet season in 2012. The data were collected from 9 shrimp and prawn farms and they were categorized in three different groups (as treatments) including 3 prawn (T1), 3 shrimp & prawn (T2) and 3 shrimp farms (T3). Water temperature, dissolved oxygen, pH, ammonia, nitrate, nitrite, alkalinity, salinity, total phosphorous and total hardness were measured using portable advanced HACH water quality test kit in both dry and wet season. Farm soil (sediment) quality parameters including pH, organic carbon, total nitrogen and available phosphorus were measured in the laboratory in wet season. It was found that most of the water quality parameters were in suitable range in both seasons for prawn, shrimp & prawn and shrimp farming. However, the ammonia content was 0.009 to 0.45 ppm and 0.2 to 0.6 ppm in shrimp farm during dry and wet season, respectively which was higher than the other category of farms. The higher ammonia content in shrimp farm might be due to the decomposition of aquatic weeds, organic matter, uneaten feed etc. creating stress to shrimp. Different co-relationships found between the water quality parameters in all the farming systems in the both seasons. In terms of soil quality parameters such as pH, organic carbon and total nitrogen, there was no significant difference between the farm categories. However, available phosphorous content was significantly higher in shrimp & prawn farm. Phosphorous content was found negatively correlated with pH and organic carbon content of farm sediment (soil). From the present study, it could be argued that ammonia is the main problem for shrimp farms that may cause severe disease outbreak which need to be addressed from the view point of research and development towards sustainable seafood production in Bangladesh.J. Bangladesh Agril. Univ. 13(1): 153-160, June 2015

scholarly journals Wavelet and statistical analyses of river water quality parameters: a case study in the Lower Minnesota River

2018 ◽  
Vol 13 (4) ◽  
pp. 922-931 ◽  
Author(s):  
Ang Gao ◽  
Shiqiang Wu ◽  
Senlin Zhu ◽  
Zhun Xu
Keyword(s):  
Water Quality ◽  
River Water ◽  
Wavelet Decomposition ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
River Water Quality ◽  
Water Quality Parameters ◽  
Strong Negative Correlation ◽  
Minnesota River ◽  
One Year

Abstract Statistical and wavelet analyses are useful tools for analyzing river water quality parameters. In this study, they were employed to study parameters including biochemical oxygen demand (BOD), dissolved oxygen (DO), nitrate (NO3), ammonium (NH4), phosphate (PO4), total phosphorus (TP), total Kjeldahl nitrogen (TKN), chlorophyll a (CHLA), total suspended solids (TSS) and water temperature (TEMP) monitored at five hydrologic stations on the Lower Minnesota River, USA. Strong positive correlations were observed between CHLA-BOD, TP-TKN, TP-TSS and TKN-TSS, with strong negative correlation between DO-TEMP. Daubechies wavelet at level 5 has been calculated for some key water quality parameters as it gives the finer scale approximation and decomposition of each water parameter. The results show that TEMP and DO have relative quasi-periodicity of about one year, while the quasi-periodicity of NH4 and PO4 are weaker than for TEMP and DO. Correlations between some parameters based on wavelet decomposition results are consistent. The fluctuation range characteristics of some parameters were also analyzed through wavelet decomposition.

scholarly journals Mapping Water Quality Parameters in Urban Rivers from Hyperspectral Images Using a New Self-Adapting Selection of Multiple Artificial Neural Networks

2020 ◽  
Vol 12 (2) ◽  
pp. 336 ◽  
Author(s):  
Yishan Zhang ◽  
Lun Wu ◽  
Huazhong Ren ◽  
Yu Liu ◽  
Yongqian Zheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Water Quality ◽  
Artificial Neural Networks ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Hyperspectral Remote Sensing ◽  
Water Quality Parameters ◽  
Proposed Model ◽  
Selection Of

Protection of water environments is an important part of overall environmental protection; hence, many people devote their efforts to monitoring and improving water quality. In this study, a self-adapting selection method of multiple artificial neural networks (ANNs) using hyperspectral remote sensing and ground-measured water quality data is proposed to quantitatively predict water quality parameters, including phosphorus, nitrogen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), and chlorophyll a. Seventy-nine ground measured data samples are used as training data in the establishment of the proposed model, and 30 samples are used as testing data. The proposed method based on traditional ANNs of numerical prediction involves feature selection of bands, self-adapting selection based on multiple selection criteria, stepwise backtracking, and combined weighted correlation. Water quality parameters are estimated with coefficient of determination R 2 ranging from 0.93 (phosphorus) to 0.98 (nitrogen), which is higher than the value (0.7 to 0.8) obtained by traditional ANNs. MPAE (mean percent of absolute error) values ranging from 5% to 11% are used rather than root mean square error to evaluate the predicting precision of the proposed model because the magnitude of each water quality parameter considerably differs, thereby providing reasonable and interpretable results. Compared with other ANNs with backpropagation, this study proposes an auto-adapting method assisted by the above-mentioned methods to select the best model with all settings, such as the number of hidden layers, number of neurons in each hidden layer, choice of optimizer, and activation function. Different settings for ANNS with backpropagation are important to improve precision and compatibility for different data. Furthermore, the proposed method is applied to hyperspectral remote sensing images collected using an unmanned aerial vehicle for monitoring the water quality in the Shiqi River, Zhongshan City, Guangdong Province, China. Obtained results indicate the locations of pollution sources.

scholarly journals Spatiotemporal Analysis of Surface Water Quality in Dong Thap Province, Vietnam Using Water Quality Index and Statistical Approaches

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 336
Author(s):  
Nguyen Thanh Giao ◽  
Phan Kim Anh ◽  
Huynh Thi Hong Nhien
Keyword(s):  
Water Pollution ◽  
Water Quality ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
E Coli

The study was conducted to spatiotemporally analyze the quality, location and critical water variables influencing water quality using water monitoring data from the Department of Environment and Natural Resources, Dong Thap province in 2019. The water quality parameters including turbidity, pH, temperature, dissolved oxygen (DO), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD), nitrite (N-NO2−), nitrate (N-NO3−), ammonium (N-NH4+), total nitrogen (TN), orthophosphate (P-PO43−), chloride (Cl−), oil and grease, sulfate (SO42−), coliforms, and Escherichia coli (E. coli) were collected at 58 locations with the frequency of four times per year (February, May, August, and November). These parameters were compared with national technical regulation on surface water quality—QCVN 08-MT: 2015/BTNMT. Water quality index (WQI) was calculated and spatially presented by geographical information system (GIS) tool. Pearson correlation analysis, cluster analysis (CA), and principal component analysis (PCA) were used to evaluate the correlation among water quality parameters, group and reduce the sampling sites, and identify key parameters and potential water pollution sources. The results showed that TSS, BOD, COD, N-NH4+, P-PO43−, coliforms, and E. coli were the significant concerns impairing the water quality. Water quality was assessed from poor to medium levels by WQI analysis. CA suggested that the current monitoring locations could be reduced from 58 sites to 43 sites which can be saved the total monitoring budget up to 25.85%. PCA showed that temperature, pH, TSS, DO, BOD, COD, N-NH4+, N-NO2−, TN, P-PO43−, coliforms, and E. coli were the key water parameters influencing water quality in Dong Thap province’s canals and rivers; thus, these parameters should be monitored annually. The water pollution sources were possibly hydrological conditions, water runoff, riverbank erosion, domestic and urban activities, and industrial and agricultural discharges. Significantly, the municipal and agricultural wastes could be decisive factors to the change of surface water quality in the study area. Further studies need to focus on identifying sources of water pollution for implementing appropriate water management strategies.

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