Spatial Variations In Water Quality Research Articles

Water Quality Assessment Using the Water Quality Index, and Geographic Information Systems in Nador Canal, Morocco

In recent decades, water pollution has emerged as a significant concern, posing a threat to both humans and natural ecosystems. This study aimed to assess the spatial variations in water quality in the Nador Canal, Morocco using the water quality index combined with multivariate statistical techniques and geographic information systems (GIS). The parameters examined were then compared to the maximum permissible limit values recommended by Moroccan surface water standards and the World Health Organization. The results indicated that the WQI of Nador Canal is generally suitable for irrigation and unsuitable for drinking. It indicated that the quality of surface water in the Nador Canal is affected by organic pollutants, and this is evidenced by low levels of dissolved oxygen, the levels of elements were high, especially chlorine and sodium, in addition to a high concentration of ammonia, nitrate, and phosphate in some of the stations studied. Principal component analysis (PCA) results revealed that PC1–PC3 collectively accounted for 73% of the variation in surface water quality within the study area. The cluster analysis also proves that the water quality is relatively polluted. Potential contributing factors to surface water pollution include changes in the hydrological regime, household waste discharges, and agricultural activities. These findings furnish essential insights into water quality, particularly its suitability for irrigation, and contribute to enhancing the water quality management system of the Nador Canal in the Gharb region of Morocco.

Water Quality Prediction of Small-Micro Water Body Based on the Intelligent-Algorithm-Optimized Support Vector Machine Regression Method and Unmanned Aerial Vehicles Multispectral Data

Accurate prediction of spatial variation in water quality in small microwaters remains a challenging task due to the complexity and inherent limitations of the optical properties of small microwaters. In this paper, based on unmanned aerial vehicles (UAV) multispectral images and a small amount of measured water quality data, the performance of seven intelligent algorithm-optimized SVR models in predicting the concentration of chlorophyll (Chla), total phosphorus (TP), ammonia nitrogen (NH3-N), and turbidity (TUB) in small and micro water bodies were compared and analyzed. The results show that the Gray Wolf optimized SVR model (GWO-SVR) has the highest comprehensive performance, with R2 of 0.915, 0.827, 0.838, and 0.800, respectively. In addition, even when dealing with limited training samples and different data in different periods, the GWO-SVR model also shows remarkable stability and portability. Finally, according to the forecast results, the influencing factors of water pollution were discussed. This method has practical significance in improving the intelligence level of small and micro water body monitoring.

Scales of Connectivity within Stream Temperature Networks of the Clackamas River Basin, Oregon

Water quality varies along the stream network; thus, considering the directional, dendritic nature of stream networks with surrounding landscape variables is essential in explaining spatial variations of water quality. Using a spatially extensive stream temperature monitoring effort in the Clackamas River Basin in the United States, we first compare spatial scales of analysis of atmospheric, landscape, and in-stream explanatory variables through their correlation with summer stream temperatures. We then derive a predictive stream temperature model with factors representing the spatial variation of local climate, recent wildfire effects, and discharge. Finally, we compare nonspatial multiple linear regression to a spatial stream network (SSN) model to assess the combined importance of the spatial scale of analysis and flow-connected stream distance in explaining total variation in stream temperatures. Most explanatory variables show the most highly significant relationships to stream temperature when derived as a percentage of the total upstream area above observation sites. Elevation and vegetation cover, however, were most significantly correlated to stream temperature at the riparian buffer area scale and the local reach contributing area scale, respectively. Multiple regression analysis using total upstream burned area, total upstream area with underlying High Cascades geology, and the elevation within the 100-m-wide riparian area explained 81 percent of variation in stream temperature. SSN outperformed this nonspatial statistical model, however, in explaining the total variation in stream temperature. These comparisons of scaled data sets demonstrate both the local and cumulative upstream effects on stream temperature, providing a spatial network-informed framework to those prioritizing watershed restoration and wildfire recovery activities.

Water quality assessment of the Educandos basin in Manaus, Brazil: use of the water quality index and multivariate statistical techniques.

The city of Manaus has undergone an industrialization process that has negatively influenced the wastewater that makes up the urban part of the city. Monitoring water quality parameters and seasonal changes are essential for decision-making for the recovery of the basin's waters. The study evaluated, through the Water Quality Index (WQI) and multivariate statistical techniques, the seasonal and spatial variation in water quality in the Educandos basin, Manaus. The 14 sampling points were selected in the years 2020, 2021 and 2022. The analyzes followed the recommendations of the Stantard Methods for the Examination of Water and Wastewater. The IQA showed that seasonality has not been enough to cause an improvement in water quality. Cluster analysis ranked similar sample points in relation to water quality. The main component showed that turbidity, total solids and biological oxygen demand are the most relevant parameters for PC1, while for PC2 they were Total Coliforms, Total Nitrogen and pH. The multivariate analysis proved to be efficient for monitoring the basin, considering the process of environmental deterioration, resulting from the receipt of waste of mixed domestic and industrial origin.

Application of Multivariable Statistical and Geo-Spatial Techniques for Evaluation of Water Quality of Rudrasagar Wetland, the Ramsar Site of India

The water quality of Rudrasagar Lake, the second-largest natural reservoir of Tripura is of great ecological and economic importance as it serves a diverse range of purposes, including fishing, irrigation, aquaculture, domestic use, and recreation activities. This study investigates the water quality of the study area, an esteemed Ramsar site in North Eastern India, using a combined application of multivariable statistical and geospatial techniques. In this study, 24 water samples were designed based on their use and collected along the periphery and the inner areas of the lake employing the Latin Square Matrix. This research also examines the spatial variations of water quality involving quartile-based water quality categorization of parameters, with Pearson’s Correlation analysis, Principal Component Analysis (PCA), and Hierarchy Cluster Analysis (HCA) applied for dimension reduction. The analysis involved quartile-based water quality categorization of parameters, with PCA and HCA applied for dimension reduction. Meanwhile, the Inverse distance weighted (IDW) approach was used to interpolate the spatial distribution of the quartile score using the ArcGIS platform. The Bureau of Indian Standards (BIS) was followed for water quality assessment. The results revealed significant spatial variation, providing valuable insights for future water management strategies. PCA indicates 57.26% of the variance in the dataset, whereas samples were classified into three subgroups and two groups in a dendrogram representing the result of the HCA. This study demonstrates the utility of PCA, HCA, and IDW interpolation in water quality assessment, highlighting the effect of human-induced activities in the lake’s vicinity.

Combined Multivariate Statistical Techniques and Water Quality Index (WQI) to Evaluate Spatial Variation in Water Quality.

In present study, Water Quality Index (WQI) has been assessed of the Rawal Lake which is a major source of drinking water for people in the Federal Capital, Islamabad, and its adjacent city Rawalpindi in Pakistan. For this, the principal component analysis (PCA) and WQI were applied as an integrated approach to quantitatively explore difference based on spatial variation in 11 water quality parameters of the five major feeding tributaries of the Rawal Lake, Pakistan. The results of temperature in water, total dissolved solids, pH, electrical conductivity, chlorides and sulfates were well within the allowable World Health Organisation's (WHO) limits. However, the heavy metals like cadmium and lead were above permissible limits by the WHO in tributaries of Bari Imam and Rumli. Moreover, this has been proven by the Pearson correlation which suggested strong positive correlation (0.910*) between lead and cadmium. The results of present study were subjected to statistical analysis, i.e., PCA which gave three major factors contributing 96.5% of the total variance. For factor 1, pH, TDS, alkalinity, chlorides, sulfates and zinc have highest factor loading values (>0.60) and presented that these parameters were among the most significant parameters of first factor. As per the WQI results, the water was categorised in two major classes indicating that water of Bari Imam and Rumli is highly contaminated with heavy metals and totally unsuitable for drinking purposes. Based on the results of the present study, it is suggested to make heavy metals consideration as an integrated component in future planning for maintaining water quality of the Rawal Lake and its tributaries.

First Physico-Chemical and Traditional Characterisation of Lake Tseny, Northwest Madagascar

This study was designed to assess the biophysical structure, traditional practices and water quality of Lake Tseny in northwest Madagascar. The lake was divided into five zones: the central zone and the littoral zones (east, north, west and south). Transects and direct observation were used to estimate submerged tree and vegetation cover. Information on local traditions was collected through interviews with key people and visits to sacred sites. Water quality was assessed during the rainy and dry seasons, 2017–2018. Samples were taken twice a day, in the early morning and evening, at randomly selected points at intervals of 400m. The aquatic vegetation covered over 75% of the lakeshore. Most of the submerged tree trunks were recorded along the western area. Five sacred sites associated with eight forms of traditional taboos to reduce the potential source of water pollution were recorded in and around the lake. The lake is bowl shaped. The water was warm (T° 25.3 - 35.6 °C), slightly alkaline (pH 7.1 - 8.5), well oxygenated (DO 4.63 - 9.6 mg/l ) and less mineralised (EC 71.70 - 122.40 µS/cm). We found a seasonal difference in water quality between the rainy and dry seasons. Results showed spatial variation in water quality between zones. A comparison of the mean values with water quality standards showed that Lake Tseny has good water conditions for domestic purposes and suitable habitats for aquatic fauna throughout the year. These preliminary results provide reference data on the lake's characteristics. Due to the increasing human activities in the area, regular monitoring of water quality and physical characteristics should be carried out.

Effects of Hydrological Season on the Relationship between Land Use and Surface Water Quality

In recent decades, land use patterns have changed significantly in highly urbanized areas, which is usually linked with the spatial variation of surface water quality at the catchment scale, but little attention has been paid to how hydrological seasons affect this relationship. Taking Pudong New Area of Shanghai, China, as an example, this paper evaluated the influence of hydrological seasons on the relationship between land use and water quality under different hydrological buffers. It was shown that the contribution of land use to the spatial variation of water quality is approximately 30%. In addition, the explanatory ability was greatest in the average season while it was smaller in the dry and wet seasons. Land uses showed scale effects; at a smaller scale, urban areas, agricultural land and water areas were the most important land uses affected by water quality. As the buffers changed from 500 to 1500 m, the impact of urban areas decreased significantly, while that of agricultural land and water areas increased rapidly; however, when the buffer was greater than 1000 m, the explanatory ability of water areas did not increase further but remained stable. Green space is only significant at the 200 m and 500 m scales, which showed the effect of improving river quality. This study is expected to provide references for future decision making of urban construction, environmental planning and management.

A preliminary assessment of spatial variation of water quality of Ratuwa river.

This work helps to identify the source of pollution in water and characterize the water quality which is essential to water management for sustainable development. Therefore, the main objective of this work is to evaluate the spatial distribution of the water quality of Ratuwa river and its tributaries. The water samples were collected from six discrete sampling locations and fifteen parameters were tested using respective well-calibrated equipment and standard APHA methods. The physicochemical analysis, water quality index, and correlation matrix method were employed to evaluate the spatial variation of the water quality of Ratuwa river. Turbidity was the most polluting factor in river water. The results showed the spatial variation of the water quality index (WQI) from 39.3 to 70.5, which fell in the range of "good" to "poor" water quality status. None of the water samples was either "excellent" or "unsuitable for drinking." The water quality was "Poor" upstream and downstream of Ratuwa river due to the high value of turbidity. Chaju river was found to have unpolluted whereas Dipeni river was slightly polluted due to domestic and municipal wastes. Hence, the deterioration of water quality can be attributed to natural and anthropogenic sources.

Hydrochemical characterization of the Ramsar-listed Koshi Tappu Wetland, Nepal

Nepal abounds a vast array of freshwater bodies, from sub-tropical lowlands to glacier-fed highlands with varying water quality. This study evaluated the spatial variations in water quality at the Koshi Tappu Wetland, the first Ramsar site of Nepal, located in the eastern Tarai region within the Koshi Tappu Wildlife Reserve. Nineteen water quality parameters were chosen and analyzed from twenty-one different sampling points within the wetland. Parameters including pH, EC, TDS, turbidity, and DO were analyzed on-site, whereas HCO3-, Cl-, NH4+, NO3-, SO42- PO43-, K+, TH, Ca2+, Mg2+, CaH, MgH, Na+, and Fe3+ were analyzed in the laboratory. Multivariate methods such as hierarchical agglomerative cluster analysis (CA) and principal component analysis (PCA), and geochemical indices such as piper and mixing diagrams were applied to assess the spatial variation in water quality. Findings indicated Ca2+ as the principal cation and HCO3- as the principal anion regulating the hydrochemistry of the wetland. Based on CA, three spatial clusters were observed, which depicted variations in chemical composition with the PCA results highlighting the primary contamination sources and controlling factors of the sampling locations with 84.13% of the total variance. Findings from the PCA and ionic relationship analyses elucidated that the hydrochemistry of the Koshi Tappu wetland is mainly controlled by carbonate weathering processes with a minor contribution of silicate weathering and anthropogenic activities.

Quality monitoring of inland water bodies using Google Earth Engine

Abstract Regular quality monitoring of inland water bodies is vital for identifying the areas with deteriorating water quality. Satellite remote sensing has been used for obtaining long-term trends that require the processing of many images. The computational load of processing a large number of satellite imageries can be eased by utilizing the cloud computing capabilities of Google Earth Engine (GEE). The present study explores the possibility of using the GEE platform for mapping the Trophic State Index (TSI) of an inland water body. The bottom of atmosphere (BOA) reflectance retrieved by the SIAC algorithm (used in the GEE platform) is assessed for its accuracy. The algorithm could retrieve only BOA reflectance at bands B3 and B4 of Sentinel 2L1C (S2) with reasonable accuracy. The study has identified the Normalized Difference of B3 and B4 bands of S2 (i.e., ND34) as the tool for mapping TSI of a water body using GEE. TSI from six imageries of three lakes was estimated with a mean error <17%. The capability of GEE as a rapid water quality monitoring tool is demonstrated by displaying the temporal and spatial variations of water quality across Vembanad Lake for the period 2016–2021.

Using Canadian Water Quality Index method to evaluate the spatio-variation of water quality and the impacts of quality parameters: a case study of Amasya's surface water (Northern Turkey).

In this study, the spatial variation of water quality in Yeşilırmak River passing through Amasya was investigated using the Canadian Water Quality Index (CWQI). For this aim, the measured 15 parameters in 3-month periods between the years 2008 and 2015 were used at 11 sample points from the Yeşilırmak River and its tributaries. The calculated CWQI scores using parameters of pH, Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), ammonia, ammonium, nitrite, nitrate, phosphate, iron, copper, zinc, potassium, sulfate, sulfite and chlorine range from 33 to 64. These scores indicate that the surface waters in the studied area are poor to marginal in quality. The effect of each parameter on the CWQI scores by excluding each parameter, one by one, considering the water quality of the Yeşilırmak River was investigated using the Hierarchical Cluster Analysis (HCA) method. It was determined that the presence and/or absence of the parameters, which caused an increase or decrease in CWQI scores, were ammonia, phosphate, COD, sulfide, iron, ammonium, nitrite, DO. On the other hand, the parameters having positive effects on CWQI are nitrate, chlorine and potassium. The HCA statistical analysis method is suitable for interpreting complex water quality datasets and understanding time/location dependent changes in water quality. HCA can be used effectively to group parameters in river water quality monitoring programs.

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

Seasonal Variation in the Water Quality and Eutrophication of Lake Xingyun in Southwestern China

It is crucial to understand the spatial-temporal variation of water quality for the water safety and eutrophication migration in plateau lakes. To identify the variation property and the main causes of eutrophication and continuous water quality deterioration, the water quality, including the water temperature (WT), dissolved oxygen (DO), pH, Chl-a, turbidity, total nitrogen (TN) and total phosphorus (TP), of Lake Xingyun was monitored from 2016 to 2021, and their spatial and temporal distribution characteristics were analyzed. The results show that there is no obvious thermal stratification in the vertical direction; pH and DO decrease with depth, which is caused by both physical and biochemical processes, especially at the bottom of Lake Xingyun, which has an anaerobic environment. The chlorophyll content was higher during the high-flow periods and varied significantly in the vertical direction; the spatial variation of water quality in Lake Xingyun was more obvious in the low-flow period and alkaline throughout the year. The average content of total phosphorus (TP) ranged between 0.33 and 0.53 mg/L during the high-flow periods and between 0.22 and 0.51 mg/L during the low-flow periods, while the average content of total nitrogen (TN) ranged between 1.92 and 2.62 mg/L and 1.36 and 2.53 mg/L during the high- and low-flow periods, respectively. The analysis of the inflow samples shows that exogenous nitrogen and phosphorus is the main pollution source affecting the nitrogen and phosphorus content of Lake Xingyun. The trophic level index (TLI) shows that Lake Xingyun is in eutrophication all year round, and even in areas less affected by the exogenous nutrient, there are still conditions for cyanobacterial blooms. This study shed new light on the water quality, eutrophication status and changes in Lake Xingyun, providing suggestions for controlling lake pollution and eutrophication mitigation.

Assessment of seasonal and spatial water quality variation in a cascading lake system in Chennai, India

Water quality and scarcity are among the most severe problems humans have been facing in the last decades. India, as a fast-developing country, is not an exception. The surface water quality has deteriorated due to anthropogenic activities. Another factor which impacts the water quality is the heavy rainfall during monsoon season. To maintain the quality and the sustainability of water resources, there is the need to study how human activities impact water quality. We hypothesized that the water quality can be impacted by the spatial land use types and by the seasonality. In the present study, seasonal and spatial water quality regarding physical, chemical, and biological parameters from a lake cascading system was assessed monthly from July to December 2019. Land use/cover data was produced by Impact Observatory, Microsoft, and Esri based on the 10-m Sentinel-2 images. Redundancy analysis was applied to investigate the relationship between land use/cover data and water quality in the riparian of 500 and 1000 m to the lakes. Our results showed clear temporal and spatial variation of water quality in 2019, with better water quality in rainy season (Oct.-Dec.) and downstream lakes while relatively worse water quality was recorded in dry season (Jul.-Sep.) and upstream lakes. The water quality variation was explained 27.8 % and 42.7 % by the land use types within 500 m and 1000 m buffer widths, respectively. The outlet of the catchment showed exceptional results due to the impact of a dumpsite. Our findings indicate that the water quality is highly impacted by human-induced land use/cover. The land use/cover types, such as crops, woodland and urban area, show negative impacts and relate to the high level of nutrient concentrations. In opposite, grass land shows positive effects and leads to better water quality. Our study confirms that the lake water quality is distinguished in both spatial and seasonal aspects. Monsoon season improves the water quality.

Monitoring of Urban Black-Odor Water Using UAV Multispectral Data Based on Extreme Gradient Boosting

During accelerated urbanization, the lack of attention to environmental protection and governance led to the formation of black-odor water. The existence of urban black-odor water not only affects the cityscape, but also threatens human health and damages urban ecosystems. The black-odor water bodies are small and hidden, so they require large-scale and high-resolution monitoring which offers a temporal and spatial variation of water quality frequently, and the unmanned aerial vehicle (UAV) with a multispectral instrument is up to the monitoring task. In this paper, the Nemerow comprehensive pollution index (NCPI) was introduced to assess the pollution degree of black-odor water in order to avoid inaccurate identification based on a single water parameter. Based on the UAV-borne multispectral data and NCPI of sampling points, regression models for inverting the parameter indicative of water quality were established using three artificial intelligence algorithms, namely extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR). The result shows that NCPI is qualified to evaluate the pollution level of black-odor water. The XGBoost regression (XGBR) model has the highest fitting accuracy on the training dataset (R2 = 0.99) and test dataset (R2 = 0.94), and it achieved the best retrieval effect on image inversion in the shortest time, which made it the best-fit model compared with the RF regression (RFR) model and the SVR model. According to inversion results based on the XGBR model, there was only a small size of mild black-odor water in the study area, which showed the achievement of water pollution treatment in Guangzhou. The research provides a theoretical framework and technical feasibility for the application of the combination of algorithms and UAV-borne multispectral images in the field of water quality inversion.

Spatial variability of water quality and sedimentary organic matter during winter season in coastal aquaculture zone of Korea

We investigated the composition, source, and reactivity of sedimentary organic matter (OM) present in aquaculture systems along the Korean coast. A multi-isotopic approach was applied to 216 surface sediments from five sections: western (W)-1, W-2, southern (S)-1, S-2, and eastern (E)-1 sections. The 15N-enriched total nitrogen signatures in the surface sediments of the W-1 section may indicate that a substantial fraction of sedimentary OM has anthropogenic origins exported through the Han River. Simultaneously, the deposition of allochthonous OM is predominant in the S sections (Jinhae and Masan Bays). The 34S-depleted patterns at the S-2 section may indicate that sulfate reduction occurring at the sedimentary boundary contributes to active OM decomposition, depending on the increased sedimentation of the aquaculture-derived OM. Our results highlight that isotopic-based source tracking near aquaculture systems provide important information for identifying anthropogenic contamination in coastal marine sediments and for improving environmental management.

Effects of ecological restoration on water quality and benthic macroinvertebrates in rural rivers of cold regions: A case study of the Huaide River, Northeast China

Various human activities can damage the structure of rural rivers ecosystems and reduce their self-purification capacity. In Northeast China, the cold climate also poses serious challenges to river restoration. What types of restoration technology should be adopted and what impacts the restoration project will bring, in terms of river ecological function improvements, should be further discussed. To investigate the effects of ecological restoration projects and different restoration measures on water quality and benthic macroinvertebrates of rural rivers in cold regions in Northeast China, benthic macroinvertebrates and 10 indicators of water quality including ammonium (NH4+-N), nitrate (NO3–-N), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), dissolved oxygen (DO), water temperature (Tem), pH (pH), electrical conductivity (EC) and chlorophyll-a (Chla) were monitored. We analyzed the temporal and spatial changes of benthic macroinvertebrates and 6 typical water quality indicators including NH4+-N, NO3–-N, TN, TP, COD and DO, which were collected in 4 seasons at 7 typical river sections. The Water Quality Index (WQI) was used to evaluate the seasonal and spatial variation of water quality during monitoring period, and a new WQImin model consisting of six crucial parameters, i.e., pH, DO, NH4+-N, TN, TP and COD, was established by principal component analysis. The results showed that the ecological restoration project enhanced the self-purification capacity of the river, and increased diversity of benthic macroinvertebrates. The maximum removal rates of the main pollution indicators NH4+-N, NO3–-N, TN, TP and COD were 96.49 %, 43.92 %, 49.69 %, 8.3 % and 25.95 %, respectively. The maximum increase rates of DO and benthic macroinvertebrates diversity were 77.96 % and 310.9 %, respectively. The operation effect of the restoration project had an obvious seasonal effect, with the best effect during the spring and summer. The project still has good repair effect in winter. In terms of water purification, the effect of the overflow weir combined with aquatic organisms was better than that of the single overflow weir in the river channel. There was the best effect of the engineering measures in summer. The ecological restoration project can improve the river water quality and the benthic macroinvertebrates conditions. Compared to before the restoration, WQImin values increased 168.81 % and 44.21 % in winter and summer, respectively, and Shannon index increased 6.57 % and 41.67 % in summer and autumn, respectively.

Long Term Comprehensive Evaluation of Temporal and Spatial Variation of Water Quality in Chaohu Lake, China

Long Term Comprehensive Evaluation of Temporal and Spatial Variation of Water Quality in Chaohu Lake, China

Temporal and Spatial Variation of Water Quality of Mayur River, Khulna, Bangladesh

Mayur River, locating north western side of Khulna, has enormous significance from numerous points of views like water reservoir, navigation etc. Unfortunately latrge scale water quality degradation took place due to pollution as a result of human interruption, unplanned and untreated crude dumping of domestic, industrial and household waste into it. The aim of this study are to carry out the temporal and spatial water quality assessment of selected locations of Mayur River. The water quality was found “Very Bad” in March-2019, April-2019 and May-2019 from station 1 to station 8 except station 4 in June-2019. From July-2019 to February-2020 the water quality was found “Bad” with some exception like station 6 in February-2020. For all the stations (S1 to S8) the regression equations indicate upward regression line and the R2 values show the plotted data fits the regression model ranging 34.28% to 64.21%. Journal of Engineering Science 13(1), 2022, 89-96