Water Quality Parameters Research Articles

Investigation of Water Pollution in the Baghlan River Caused by Dust and Wastewater from the Ghori Cement Factory: A Case Study

Rivers are one of the most crucial fluid ecosystems, primarily due to their freshwater content. Understanding the trends in water quality changes in seas enables the anticipation of future challenges and the implementation of plans to address them. Water quality management necessitates the collection and analysis of a vast amount of data and water quality parameters. To facilitate this, various tools have been developed for simpler assessment of data and water quality, among which water quality indices are highly utilized. The objective of this study is to assess the water quality of the Balkh Sea using the (IRWQISC) and (IRWQIST) indices, alongside examining the levels of heavy metals, cations, anions, and its physical characteristics. Sampling was conducted at two time points: initially in October 2022, with heavy metal analysis performed using Flame Atomic Absorption Spectroscopy (FAAS), and subsequently in April 2023, where samples were collected from various points, and the concentrations of cations, anions, pH, and electrical conductivity (EC) were measured. Data analysis was carried out using SPSS version 26, and ArcGIS 10.4.1 was employed for map production. The research findings indicate a trend of heavy metal changes as follows: Al>As>Pb>Cd>(Cr=Co=Ni)>Hg. Based on the calculation of the toxicity index (IRWQIST), the studied water falls into the poor category. Consequently, sea water, due to the presence of heavy metals such as lead, cadmium, arsenic, and mercury, is relatively to very toxic and unsuitable for drinking. Additionally, it may cause detrimental effects for other uses such as agriculture and livestock farming.

Evaluation of Himalayan nettle (Urtica ardens) extract meal in common carp (Cyprinus carpio) feed: Impact on growth performance, digestive enzyme activity and antioxidant capacity

This research aims to evaluate the impact of an extract derived from the leaves of Himalayan nettle (Urtica ardens) leaf extract on common carp, Cyprinus carpio, growth, digestive enzymes, fish body carcass composition, antioxidant system, and water quality parameters. For a duration of 90 days, experimental diets containing nettle extract were given to 150 fish (12.38 ± 0.53 g) in 15 tanks (10 fish per tank). The five groups in triplicate were: control or T0 (untreated), T1 (fed 0.3% extract), T2 (0.6% extract), T3 (0.9% extract), and T4 (1.2% extract). Results revealed that nettle extract in the diet increases common carp growth up to 0.9% concentration of extract. Its feed conversion ratio decreases, while weight gain and specific growth rate rise significantly compared to the control diet. No mortality was observed during the 90-day experiment. Dietary nettle extract did not affect whole-body fibre and crude ash percentages (p > 0.05). Nettle-fed and control groups showed significant differences in crude protein, body fat, and total body moisture (p < 0.05). After a 90-day feeding trial, 0.9% nettle extract-fed fish had higher protease, amylase, lipase, and cellulase levels than the control group (p < 0.05). When nettle content reached 1.2%, enzyme activity decreased. Orthophosphate production peaked at 4 and 8 hours after feeding, while ammonia excretion peaked at 2 and 6 hours. The control group excreted the most ammonia and orthophosphate, while the 0.9% group excreted the least. Liver and kidney SOD levels were higher in common carp fed a 0.6% nettle extract diet. Feeding with a 0.9% nettle extract diet improved hepatic lipid peroxidation, gut and liver glutathione. Gut and kidney SOD, catalase, LPO, and GSH increased dramatically on the 1.2% diet. Statistically, the 0.9% nettle extract (T3) group showed the overall best results compared to the control diet. These findings demonstrate the potential of nettle extract as a dietary supplement in aquaculture.

Water function zone: A method to improve the accuracy of remote sensing retrieval of water bodies

In water monitoring, it is always a hot issue to exploring a method to improve the accuracy of remote sensing inversion. To this end, based on the actual investigation of the surrounding environment and water quality characteristics of the Fuyang river, this study has established five distinct water functional zones: reservoir (RS), village river (VR), industrial river (IR), artificial lake (AL), and urban river (UR), which are considered as subdivided datasets. Meanwhile, considering that lakes and reservoirs typically contain relatively stationary or slow-moving water bodies, while river channels are characterized by moving water bodies, three multi-type datasets have been constructed for comparison. These are based on the morphological and flow characteristics of different regions within the Fuyang river and include river channel (R), lake (L), and whole basin (W). Based on multi-spectral images of unmanned aerial vehicle (UAV) and measured water quality data, ten types of inversion models were to invert six kinds of water quality parameters, and compare and analyze the performance of the optimal inversion model of the corresponding data sets. The results show that the multivariate regression model is superior to the univariate regression model, and the mean coefficient of determination (R2) of the subdivided data set is 0.936, and the root mean square error (RMSE) and mean absolute error (MAE) appear decrease in different degrees compared with the W with 3.573 and 2.662, respectively. The mean ratio performance to interquartile (RPIQ) of the corresponding model in UR and VR are 3.963 and 2.748, respectively. Extremely Randomized Trees (ERT) model is more suitable for the inversion of multi-type data sets lacking obvious water quality characteristics and with complex components, while Categorical Boosting (CatBoost) model is more suitable for the inversion of subdivided data sets with obvious water quality characteristics. The current method has practical guiding significance for improving the application level of water monitoring technology in ecological environment protection and urban water resources protection.

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

Fish Diversity and Hydrological Parameters of Indrasagar Reservoir (Khandwa District)

The state of Madhya Pradesh is the central state of India &amp; one of the important aquatic biodiversity hotspots of the country, having bestowed with a large number of water bodies both lotic and lentic, the state boasts of rich fish biodiversity. The aim of this study is collection and identification of finfish that found in Indira sagar reservoir and the water quality parameter of the reservoir in which the fishes thriving. Total 54 species of 9 order were found in the reservoir in Khandwa district including labeo rohita,Catla catla, Cirrhinus mrigala, Ctenopharyngodon idella Labeo calbasu, Puntius sophore, Pethia ticto,Puntius chola, Cyprinus carpio, Pangasianodon hypophthalmus, Oreochromis mossambicus, Glossogobius giuris, Channa marulius, Channa gachua , Channa striata and Clarias magur etc. The 03 species of transplanted Indian major carps Catla catla, Cirrhinus mrigala and Labeo rohita are dominating the fishermen catches The authors are of firm opinion that the exotic species are posing competition for food and space to the native species.. To certain extend even hybrid of catla and rohu are available in the fish catches The water quality parameters were significantly found suitable for freshwater cultivable species including the temperature, dissolved oxygen, Total hardness , dissolved oxygen and temperature are higher in summer season and lower in winter season. This may be due to higher photosynthetic rate in summer season. Whereas the pH value is higher in winter season and lower in summer season. It indicates reduction in photosynthetic activities in winter which reduces assimilation of carbon dioxide and bicarbonates.

Seasonal and interannual changes (2005-2021) of lake water quality and the implications for sustainable management in a rapidly growing metropolitan region, central China.

A series of restoration measures such as municipal wastewater treatment and aquaculture closures have been implemented in Wuhan City during recent years. In order to explore the impact of restoration measures and climate change on lake water quality, long-term (2005-2021) water quality data of 47 lakes were explored to reveal spatiotemporal changes in lake water quality. Percentages of polluted lakes were calculated according to six water-quality parameters, including total phosphorus (TP), ammonia nitrogen (NH3-N), chemical oxygen demand (COD), biological oxygen demand (BOD), chemical oxygen demand using potassium permanganate as oxidant (CODMn) and petroleum contamination (PET), at interannual and monthly timescales. At the interannual timescale, percentages of COD, BOD, CODMn and PET pollution decreased significantly, suggestive of water quality improvement during recent years. At the monthly timescale, low percentages of NH3-N and BOD pollution in March 2020 probably resulted from the sharp reduction in human activities during the COVID-19 lockdown. At the monthly timescale, temperature was positively correlated with percentage of CODMn pollution, but negatively correlated with percentage of NH3-N pollution; precipitation was negatively correlated with percentage of BOD pollution. The similarity of water-quality parameters generally decreased with an increase in geographical distance between each pair of lakes. However, the regression coefficients between the similarity of lake water quality and the geographical distance decreased with time, probably resulting from enhanced similarity of water quality parameters among all lakes with rapid urbanization. Our results highlight the importance of active restoration measures for sustainable management of lakes in Wuhan City, as well as in similar developing regions.

Water Quality and the First-Flush Effect in Roof-Based Rainwater Harvesting, Part II: First Flush

Rainwater runoff samples from a range of roofing materials were temporally collected from 19 small-scale roof structures and two commercial buildings through simulated and actual storm events, and the concentrations of polycyclic aromatic hydrocarbons (PAHs), phosphorus flame retardants (PFLs), and pyrethroid insecticides and other water quality parameters were analyzed. In Part I of this research, the concentrations of these contaminants in roof runoff and soils receiving runoff from a range of roofing materials were evaluated. In Part II, recommendations have been developed for a first-flush exclusion to improve the quality of water harvesting for nonpotable uses. Recommendations focus on a first-flush diversion based on mass removals of total suspended solids (TSS) and PAHs linked to conductivity measurements throughout a storm event. Additionally, an upper-confidence limit (UCL) was constructed to determine the minimum diversion required to obtain 50, 75, 90, and 95% mass removal of TSS and PAH contaminants. The majority of TSS were produced during the initial 1.2 mm of runoff. Likewise, the majority of PAHs were removed during the initial 1.2 mm of runoff, except for the asphalt shingle roofs, where high PAHs were observed after 6 mm of runoff. The Texas Water Development Board (TWDB)-recommended first-flush diversion of one gallon for every 100 square feet of rooftop was not always adequate for removing 50% of TSS and PAHs from the roofs. Rainwater runoff conductivity decreased drastically between 1.2 to 2.4 mm of rainwater runoff. Diverting the first flush based on conductivity has the potential to also divert the majority of TSS and PAHs in roof runoff.

Exploring Endogenous Processes in Water Supply Systems: Insights from Statistical Methods and δ18O Analysis

Water used for water supply undergoes numerous changes that affect its composition prior to entering the water supply system (WSS). Once it enters the WSS, it is subject to numerous influences altering its physical and chemical composition, redox potential, and microbial quality. Observations of water quality parameters at different locations within the WSS indicate that it is justified to assume that these processes take place from the source to the end user. In this study, we used the results of routine everyday analyses (EC, T, pH, ORP, chloride, nitrate, nitrite, ammonium, and bacteria) supplemented by experimental data from a one-year sampling campaign assessing the main cations and anions and stable isotopes δ2H and δ18O. Through these data, the statistical significance of the differences between the concentrations of the basic water quality parameters among different WSS locations was determined, together with the water retention time in the system. The results indicate minor changes in water chemical composition within the observed WSS, remaining below the prescribed Maximum Contaminant Level (MCL) for human consumption. However, factors such as water retention time, CaCO3 deposition, pH fluctuations, and bacterial growth may influence its suitability, which necessitates further investigation into potential risks affecting water quality.

Physicochemical Characteristics of Water in the Bontanga Irrigation Dam in the Guinea Savannah Agro-Ecological Zone of Ghana

Fresh water resource is one of the most significant natural assets of a country. Irrigation dams are usually used for irrigation purposes and also often serve as a source of water for domestic use. Due to this, there is a need for proper maintenance, assessment, and sustainable use of irrigation dams. This study assessed the physicochemical properties of the water in the reservoir of Bontanga irrigation dam situated in the Guinea Savannah Agro-Ecological Zone of Ghana. The study assessed four (4) physical and fourteen (14) chemical water quality parameters of the water for irrigation to know whether they are within the acceptable threshold set by the Food and Agriculture Organization (FAO) and the World Health Organization (WHO). Sampling techniques used in this study were stratified and purposive samplings. The reservoir of the dam was divided into nine (9) strata and in each stratum, 5 samples were taken making a total of 45 samples. Samples taken in each strata were composited making it a total of 9 samples. The water samples were collected into well-labelled polythene bottles and kept in an ice chest with ice cubes. The samples were transported to the Ecological Laboratory of the University of Ghana for analysis. The parameters analyzed were pH, electrical conductivity (EC), temperature, total dissolved solutes (TDS), chloride (Cl-), sulphate (SO42-), bicarbonate (HCO3-), carbonate, PO4-P, nitrites/nitrate (NO3-N), total dissolved solids (TDS), magnesium (Mg2+), calcium (Ca2+), sodium (Na+), boron, total alkalinity, ammonia (NH4-N) and potassium (K+). A greater percentage of the results obtained were within acceptable limits except potassium which was slightly higher. This could be the result of farming activities in the catchment of the dam. In general, the water in Bontanga irrigation dam can be considered suitable for irrigation purposes.

Sorption-Based Removal Techniques for Microplastic Contamination of Tap Water

This study investigates the presence of microplastics in tap drinking water and evaluates the efficacy of various sorbents for their removal in the context of Kazakhstan’s water treatment system. Water samples taken in the cities of Kokshetau and Krasny Yar (Akmola region) were analyzed. Microplastics were detected in all samples, with concentrations ranging from 2.0 × 10−2 to 6.0 × 10−2 particles/dm3, predominantly in fiber form (74.1%). Outdated technologies and non-compliance with treatment regimens contribute to poor water quality, including high turbidity (87% of samples), color deviations (40% of samples), and acidity issues (20% of samples). To address these challenges, the study examined the sorption efficiency of different sorbents, with results indicating high retention rates (82.7–97.8%) for microplastic particles. Notably, aliphatic structures like PE and PP exhibited higher retention than PET. Among the sorbents tested, the synthesized carbon sorption material (CSM) demonstrated the highest efficiency in both microplastic retention and improvement in water quality parameters, making it a promising option for water treatment facilities and household filters.

Trends in Water Quality in a Tropical Kenyan River-estuary System: Responses to Anthropogenic Activities

Aims: To determine the spatial variation in physico-chemical water quality attributes in estuarine ecosystems of South Coast Kenya to inform its management. Study Design: We employed diagnostic research design where such factors as anthropogenic activities, hydrology, and accessibility were considered in choosing 12 sampling stations. A mixed sampling design (probability and non-probability) was used to sample. Place and Duration of Study: Four discharging rivers into estuarine system with a reference point were identified. 12 sampling points were earmarked and sampled for 12 months. Methodology: Nine selected water quality parameters were collected in triplicates monthly for 12 months. Temperature, dissolved oxygen, pH, conductivity, salinity and TDS were collected in situ using YSI Multiparameter meter (Professional plus) while nutrients were analyzed in the laboratory calorimetrically. Data was tested for normality and homogeneity of variances using Levene's and Shapiro-Wilk tests. Two-way analysis of variance (ANOVA), and Principal Component Analysis (PCA) were done using the 64-bit R Software version 4.3.0 to test for significant differences and correlate the parameters among the sampling sites respectively. Observed differences were considered statistically significant at P &lt; 0.05. Results: The measured environmental variables differed significantly among the sampling stations (P &lt; 0.05). temperature and salinity exhibited moderate to strong effect on water quality, while pH and nutrients (phosphates, nitrates, and ammonia) exhibited mixed effects with both negative and positive loadings on the Principal Components. It is demonstrated that the south coast estuary, Kenya is affected by two gradients: mineralization from the sea and the organic matter gradient at the upstream stations dominated by the fluvial inflows from the respective rivers. Conclusion: The south coast of Kenya estuary is impacted by natural processes and anthropogenic activities. we recommend for continued regular water quality assessment and monitoring to acquire sufficient data to shape policy frameworks towards its sustainable management.

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 &gt; 0.9, a Spectral Angle &lt; 10° and water quality mapping errors &lt; 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

Microplastics pollution in the Surma River, Bangladesh: A rising hazard to upstream water quality and aquatic life

The ecological health of freshwater rivers is deteriorating globally due to careless human activities, for instance, the emission of plastic garbage into the river. The current research was the first assessment of microplastics (MPs) pollution in water, sediment, and representative organisms (fish, crustacean, and bivalve) from the Surma River. Water, sediment, and organisms were sampled from six river sites (Site 1: Charkhai; Site 2: Golapganj; Site 3: Alampur; Site 4: Kazir Bazar; Site 5: Kanishail and Site 6: Lamakazi), and major water quality parameters were recorded during sampling. Thereafter, MPs in water, sediment, and organism samples were extracted, and then microscopically examined to categorize selected MPs types. The abundance of MPs, as well as size, and color distribution, were estimated. Polymer types were analyzed by ATR-FTIR, the color loss of MPs was recorded, the Pollution Load Index (PLI) was calculated, and the relationship between MPs and water quality parameters was analyzed. Sites 4 and 5 had comparatively poorer water quality than other sites. Microplastic fibers, fragments, and microbeads were consistently observed in water, sediment, and organisms. A substantial range of MPs in water, sediment, and organisms (37.33–686.67 items/L, 0.89–15.12 items/g, and 0.66–48.93 items/g, respectively) was recorded. There was a diverse color range, and MPs of <200 μm were prevalent in sampling areas. Six polymer types were identified by ATR-FTIR, namely Polyethylene (PE), Polyamide (PA), Polypropylene (PP), Cellulose acetate (CA), Polyethylene terephthalate (PET), and Polystyrene (PS), where PE (41%) was recognized as highly abundant. The highest PLI was documented in Site 4 followed by Site 5 both in water and sediment. Likewise, Sites 4 and 5 were substantially different from other study areas according to PCA. Overall, the pervasiveness of MPs was evident in the Surma River, which requires further attention and prompt actions.

Mapping of Physicochemical Properties of Groundwater Using QGIS of Bhopal District of Madhya Pradesh, India

This study investigates the groundwater quality in the Bhopal district, located in the Madhya Pradesh state of India. The water quality parameters spatial distribution map is created using QGIS Desktop 3.22.16. The research aims to calculate the groundwater suitability for purposes of drinking by collecting 47 samples from various locations within the district. The analysis includes eleven parameters related to groundwater quality, which is pH, Turbidity, Alkalinity, Total Dissolved Solids, Electrical Conductivity, Calcium Hardness, Total Hardness, Magnesium Hardness, Chloride, Sulphate, and Iron. Water Quality Index (WQI) has been computed to classify the ground water quality of the study area. Finally, a correlation matrix has been generated to explore the linear relationships between various water quality parameters, using correlation coefficients (r) as indicators of association. The present study shows that almost 80% of the quality is suitable for drinking, and about 20 % of the quality are under poor quality that needs treatment before consumption and at two locations the quality is very poor that needs urgent treatment.

Seasonal changes of Chlorophyll-A concentration in Jiujiang city based on remote sensing

Abstract. The Chlorophyll-a (Chla) concentration is an important parameter characterizing the water quality of rivers and lakes. Satellite remote sensing provides new opportunities for the quantitative monitoring of Chla concentration in large-scale water bodies. In this study, Sentinel-2 satellite remote sensing data integrated on the Google Earth Engine (GEE) remote sensing big data platform are employed, along with hourly measured water quality site data, to establish a quantitative inversion model for Chla concentration water quality parameters in Jiujiang City within Jiangxi Province, China. The Chla concentration is estimated for each quarter from 2020 to 2022, and the spatial distribution is analyzed, revealing the changing trend of Chla concentration over the past three years. The key findings are as follows: (1) The quantitative inversion model for Chla concentration has been validated with measured data, achieving a model accuracy of 0.53; (2) The spatial inversion results of Chla concentration exhibit an increasing trend that is consistent with actual measurement site results; (3) Owing to the influence of human activities and the peak-low water level of rivers and lakes, Chla concentration shows a discernible seasonal variation pattern. This methodology offers a new perspective for analyzing the seasonal variation characteristics of Chla concentration in rivers and lakes, providing valuable insights for the sustainable management of river and lake water quality.

Enhancing Domestic Wastewater Treatment: A Study on the Efficacy of Tiered Filtration and a Combined Photocatalytic Ozonation Methods

This study aimed to construct an optimized reactor using photocatalytic ozonation, and its effectiveness was determined using PermenLHK No. 86/2016. The Tanjung Uma housing estate in Batam city was selected as the study sample site for domestic wastewater because of its role as a significant water body that receives liquid waste from a majority of activities across the city. The experiment employed a quantitative research approach and a true experimental approach, testing variables (catalyst weight, UV irradiation time, and ozonation duration) in a laboratory setting (25°C and 40-70% Humidity). The study found that the optimized parameter (a combination of 2 h of irradiation, with 2 g of TiO2 along with 5 min of ozonation) resulted in a significant difference in water quality parameters compared to before treatment with 92% effectivity. It can be concluded that this Reactor prototype was highly successful in reducing environmental impacts based on its effectiveness

Changes in the characteristics of water quality parameters under the influence of dam construction

Changes in the characteristics of water quality parameters under the influence of dam construction

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.

IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system

IoT and machine learning approach for the determination of optimal freshwater replenishment rate in aquaponics system

Assessment of ground water quality parameters during post monsoon season in three Taluka of Sindhudurg district of Maharashtra using water quality index

Assessment of ground water quality parameters during post monsoon season in three Taluka of Sindhudurg district of Maharashtra using water quality index