Water quality assessment of river Beas, India, using multivariate and remote sensing techniques

A review on heavy metal-induced toxicity in fishes: Bioaccumulation, antioxidant defense system, histopathological manifestations, and transcriptional profiling of genes

AIM

This review provides information about heavy metal occurrence in the environment, destructive mechanisms, and lethal effects on fish.

SUMMARY

Heavy metals (HMs) are one of the major causes of environmental contamination globally. The advancement of industries has led to the emanation of toxic substances into the environment. HMs are stable, imperishable compounds and can accumulate in different fish organs when they reach the aquatic regimes. The most ubiquitous HMs are chromium, arsenic, mercury, cadmium, lead, copper, and nickel which can pollute the environment and affect the physiology of fishes. Accumulation of metals in the fish organs causes structural lesions and functional disturbances. Contamination of heavy metals induces oxidative stress, histopathological manifestations, and altered transcriptional gene regulation in the exposed fishes.

CONCLUSION

Heavy metal bioaccumulation leads to different anomalies in the non-target species. Metal toxicity may cause aquatic organisms to exhibit cellular dysfunction and disturb ecological equilibrium.

Anthropogenic impacts on urban blue space and its reciprocal effect on human and socio-ecological health

Quantifying anthropogenic impacts on blue space (BS) and its effect on human and socio-ecological health was least explored. The present study aimed to do this in reference to the urban BS transformation scenario of Eastern India. To measure BS transformation, Landsat image-based water indices were run from 1990 to 2021. Anthropogenic impact score (AIS) and 7 components scores of 78 selected BS on 70 parameters related data driven from the field. Total 345 respondents were taken for human and socio-ecological health assessment. For this, depression (DEP), anxiety (ANX), stress (STR), physical activities (PA), social capital (SC), therapeutic landscape (TL) and environment building (EB) parameters were taken. The result exhibited that BS was reduced. About 50% of urban core BS was reported highly impacted. Human and socio-ecological health was identified as good in proximity to BS, but it was observed better in the cases of larger peripheral BS. AIS on BS was found to be positively associated with mental health (0.47-0.63) and negatively associated with PA, SC, TL and EB (-0.50 to -0.90). Standard residual in ordinary least square was reported low (-1.5 to 1.5) in 95% BS. Therefore, BS health restoration and management is crucial for sustaining the living environment.

Brief status of contamination in surface water of rivers of India by heavy metals: a review with pollution indices and health risk assessment

Water is polluted via various means; among these, heavy metal (HM) contamination is of great concern because of the involvement of metal toxicity and its effect on aquatic environment. The significance and novelty of this study is that it focuses on assessment of HMs in the surface water of Indian rivers only from 1991 to 2021. For this, multivariate studies were used to find multiple sources of HMs. The average concentrations of Fe, Cr, Pb, Ni, Cd, Mn, Hg, Co, and As in surface water of rivers were found to far exceed the permitted limits established by both World Health Organisation and Bureau of Indian Standards. The HM indices like HM pollution, degree of contamination, evaluation index, water pollution, and toxicity load data all indicated that the rivers under investigation are heavily polluted by HMs. In this study, health risk assessment indicated non-carcinogenic effects of Fe, Cr, Cu, Pb, Cd, Mn, Hg, Co, and As in children and those of Fe, Cr, Pb, Cd, Hg, Co, and As in adults. Values investigated for Cancer index were higher for Cr, Pb, Ni, Cd, and As indicating a high risk of cancer development in adults and children via the ingestion pathway than the cutaneous pathway. Moreover, children are more prone to be exposed to both non-carcinogenic and carcinogenic effects of HMs than adults. To reduce human dangers, remediation approaches, such as environment-friendly, cost-effective adsorbents, phytoremediation and bio-remediation, as well as tools like bio-sensors, should be included in river management plans.

Ecological risk assessment and phytomanagement of trace metals in the sediments of mangroves associated with the Ramsar sites of Kerala, southern India

The study investigated trace metal accumulation in the sediments of three major mangrove ecosystems associated with the Ramsar sites of Kerala state, the ecological risks they pose, and the absorption, accumulation, and translocation of metals in five dominant mangrove species, as these systems are heavily impacted by anthropogenic interventions. The trace metal concentrations (mg kg^-1) in the sediment of different mangrove habitats of Kerala ranged from 20 to 295 for Cu, 65 to 350 for Zn, 72 to 151 for Pb, 11 to 210 for Ni, 42 to 228 for Mn, 0 to 6 for Cd,124 to 565 for Cr, and 0 to 2.9 for Ag. An overall enrichment of metals was recorded in sediment, exceeding the prescribed effects range median (ERM) of consensus-based sediment quality guidelines (SQGs) for Cu and Cr concentrations at Munroe Island and Ni at Vypin, indicating a detrimental risk to biota in the sediments. Principal component analysis and a higher geoaccumulation index indicated the contribution of trace metals from industries, agricultural runoff, and urban waste disposal. The ecological risk index suggested that cadmium poses a very high risk to the mangrove ecosystem at Vypin. Furthermore, the bioconcentration factor for various trace metals in Avicennia marina and Lumnitzera racemosa in Ayiramthengu was > 2, suggesting that these species can accumulate trace metals, particularly Cr, Cd, and Pb. Further, our findings suggest that A. marina may be considered as an efficient metal trap for Cd in aerial parts, as indicated by the significant translocation factor (> 1) combined with the bioconcentration factor. Therefore, the study revealed that Munroe island and Vypin had a considerable level of contamination for toxic metals and Avicennia marina could be a promising candidate species for the phytoremediation of these trace metals in the coastal settings of Kerala state.

Impact of physiochemical properties, microbes and biochar on bioavailability of toxic elements in the soil: a review

The pollution of toxic elements (TEs) in the ecosystem exhibits detrimental effects on the human health. In this paper, we debated remediation approaches for TEs polluted soils via immobilization methods employing numerous amendments with reverence to type of soil and metals, and amendment, immobilization competence, fundamental processes and field applicability. We argued the influence of pH, soil organic matter, textural properties, microbes, speciation and biochar on the bioavailability of TEs. All these properties of soil, microbes and biochar are imperative for effective and safe application of these methods in remediation of TEs contamination in the ecosystem. Further, the application of physiochemical properties, microbes and biochar as amendments has significant synergistic impacts not only on absorption of elements but also on diminution of toxic elements.

A study on water quality parameters estimation for urban rivers based on ground hyperspectral remote sensing technology

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH₄-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN₄-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH₄-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH₄-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.

Analysis of the Spatio-Temporal Variation of the Thermal Pattern of River Ganges in Proximity to Varanasi, India

The temperature of a river is a fundamental aspect of its water quality and has a bearing on its ecosystem to a greater extent. Therefore, in systematic planning for optimal stream monitoring programs involving the determination of location at monitoring stations, understanding this crucial thermal parameter is much desired. This would help to give an integrated scenario regarding physical, chemical, and biological processes occurring in the river ecosystem. Water quality parameters of the river such as dissolved oxygen, pH, salinity get affected due to change in river thermal patterns. In this study, the Landsat-8 TIR sensor was used to study spatial and temporal variations of river temperature. Thermal bands of 23 cloud-free Landsat-8 images from June 2013 to November 2020 were processed to prepare thermal maps of a stretch of river Ganges at Varanasi, India. The work has been validated by in-situ temperature measurement with a portable thermal sensor having high accuracy (± 0.1 °C). A good correlation (R² = 0.927 and RMSE = 0.956) was observed between the sensor's estimated temperature and the in-situ temperature. The results exemplify that water surface temperature at confluence points was relatively higher due to the incoming effluents than the mid-river temperature. The ‘confluence point 3’ has the least relative temperature variation. The relative temperature variation has been more prominent for the month of February in comparison to June and November. Owing to the time series data availability and worldwide coverage of the Landsat-8 satellite, the present work provides a promising strategy for studying the thermal patterns in other rivers.

Assessment of water quality condition and spatiotemporal patterns in selected wetlands of Punjab, India

Wetlands are one of the most productive aquatic ecosystems on earth, and their water quality is an indicative of their suitability for maintaining various ecosystem services. In this study, different statistical techniques and water quality index (WQI) were employed to access the status and spatiotemporal patterns in water quality of seven selected (two natural and five manmade) wetlands of Punjab. The results revealed that the status of water quality in the selected wetlands was between good and poor during studied seasons (summer, monsoon, and winter) of year 2019. The principal component analysis identified three groups of wetlands with distinct water quality characteristics with spatial patterns: Kahnuwan Chhamb and Keshopur Miani having nearly similar values of pH, total dissolve salts, electrical conductivity, chemical oxygen demand, total alkalinity, bicarbonate and ammonium content; Ropar, Kanjli, and Harike having higher value of nutrients than the other wetlands; and Ranjit Sagar and Nangal with low value of measured water quality characteristics. Further, analysis of variance revealed that all analyzed water quality parameters showed temporal patterns in water quality except water pH, electrical conductivity, dissolved oxygen, biological oxygen demand, and phosphate content. This comparative study enhanced our knowledge about the spatiotemporal patterns in water quality and in the future will be helpful to the policymakers and concerned authorities for developing better water quality management strategies for these wetlands.

Water Quality Index (WQI) as a Potential Proxy for Remote Sensing Evaluation of Water Quality in Arid Areas

Water Resource Sustainability Management plays a vitally important role in ensuring sustainable development, especially in water-stressed arid regions throughout the world. In order to achieve sustainable development, it is necessary to study and monitor the water quality in the arid region of Central Asia, an area that is increasingly affected by climate change. In recent decades, the rapid deterioration of water quality in the Ebinur Lake basin in Xinjiang (China) has severely threatened sustainable economic development. This study selected the Ebinur Lake basin as the study target, with the purpose of revealing the response between the water quality index and water body reflectivity, and to describe the relationship between the water quality index and water reflectivity. The methodology employed remote sensing techniques that establish a water quality index monitoring model to monitor water quality. The results of our study include: (1) the Water Quality Index (WQI) that was used to evaluate the water environment in Ebinur Lake indicates a lower water quality of Ebinur Lake, with a WQI value as high as 4000; (2) an introduction of the spectral derivative method that realizes the extraction of spectral information from a water body to better mine the information of spectral data through remote sensing, and the results also prove that the spectral derivative method can improve the relationship between the water body spectral and WQI, whereby R2 is 0.6 at the most sensitive wavelengths; (3) the correlation between the spectral sensitivity index and WQI was greater than 0.6 at the significance level of 0.01 when multi-source spectral data were integrated with the spectral index (DI, RI and NDI) and fluorescence baseline; and (4) the distribution map of WQI in Ebinur Lake was obtained by the optimal model, which was constructed based on the third derivative data of Sentinel 2 data. We concluded that the water quality in the northwest of Ebinur Lake was the lowest in the region. In conclusion, we found that remote sensing techniques were highly effective and laid a foundation for water quality detection in arid areas.

Integration of Water Quality Indices and Multivariate Modeling for Assessing Surface Water Quality in Qaroun Lake, Egypt

Water quality has deteriorated in recent years as a result of rising population and unplanned development, impacting ecosystem health. The water quality parameters of Qaroun Lake are contaminated to varying degrees, particularly for aquatic life consumption. For that, the objective of this work is to improve the assessments of surface water quality and to determine the different geo-environmental parameters affecting the lake environmental system in Qaroun Lake utilizing the weighted arithmetic water quality index (WAWQI) and four pollution indices (heavy metal pollution index (HPI), metal index (MI), contamination index (Cd), and pollution index (PI), that are enhanced by multivariate analyses as cluster analysis (CA), principal component analysis (PCA), and support vector machine regression (SVMR). Surface water samples were collected at 16 different locations from the lake during years 2018 and 2019. Thirteen physiochemical parameters were measured and used to calculate water quality indices (WQIs). The WQIs of Qaroun Lake such WAWQI, HPI, MI, Cd, PI revealed a different degree of contamination, with respect to aquatic life utilization. The WQIs result revealed that surface water in the lake is unsuitable, high polluted, and seriously affected by pollution for an aquatic environment. The PI findings revealed that surface water samples of Qaroun Lake were significantly impacted by Al, moderately affected by Cd and Cu, and while slightly affected by Zn due to uncontrolled releases of domestic and industrial wastewater. Furthermore, increasing salinity accelerates the deterioration of the lake aquatic environment. Therefore, sewage and drainage wastewater should be treated before discharging into the lake. The SVMR models based on physiochemical parameters presented the highest performance as an alternative method to predict the WQIs. For example, the calibration (Val.) and the validation (Val.) models performed best in assessing the WQIs with R2 (0.99) and with R2 (0.97–0.99), respectively. Finally, a combination of WQIs, CA, PCA, and SVMR approaches could be employed to assess surface water quality in Qaroun Lake.

Projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments using artificial neural networks

Microplastics pollution and their interaction with heavy metal ions have gained global concern. It is essential to develop models to predict the sorption capacity of heavy metal ions onto microplastics in global aquatic environments, and to connect the laboratory study results with the field measurement results. In this paper, the artificial neural networks (ANN) models were established based on literature data. for The results showed that the ANN model could predict the sorption capacity of heavy metal ions (including Cd, Pb, Cr, Cu, and Zn) onto microplastics in the global environments with high correlation coefficient (R) values (0.926∼0.994). The predicted sorption capacity was influenced by the initial concentration of heavy metal ions and the salinity in surrounding water. The predicted sorption capacity in rivers and lakes was higher than that in the ocean. Aged microplastics had higher affinity to heavy metal ions than virgin microplastics. The predicted sorption capacity of Cd, Pb, and Zn ions onto large microplastics (5 mm) was less than 0.12 μg/g. The predicted amount was in agreement with the field measurement results, suggesting that the laboratory studies can provide useful information for projecting the sorption capacity of heavy metal ions onto microplastics in global aquatic environments.

Impact assessment of metal contamination in surface water of Sutlej River (India) on human health risks

The Sutlej river in Indian Punjab is getting contaminated by industrial waste, sewage, and agricultural runoff. Most of the previous studies on metal contamination of water are mainly confined to the small stretch of river. Therefore, a systematic study was carried out to assess the spatial and temporal variability of metal contamination in water from the entry point of Sutlej River in Indian Punjab to its tail end when it leaves the country. The likelihood of cancer risk was also estimated though human health risk assessment. The water samples (between 76 and 91) were collected from Sutlej river during pre- (April) and post-monsoon (September-October) seasons of the years 2017 and 2018. There samples were analyzed for 10 metals (Zn, Cu, Fe, Mn, Ni, Cd, Pb, Co, Cr and As) using Atomic Absorption Spectrophotometer. Results showed that metal concentration in water was higher along the transboundary during both the seasons. The concentration of metals in water was higher in pre-monsoon season than post-monsoon season. Multivariate analysis in different seasons showed that biophysical variables and social drivers (including anthropogenic activities) have great influence on the concentration of metals. Spatial distribution of all the metals in water and heavy metal pollution index showed that metal contamination was higher along the transboundary followed by the area from the confluence of contaminated drain with river to transboundary along with localized spots in the river flowing in Indian Punjab. The likelihood of cancer risk due to ingestion of metals through water was in the order: Cd > Ni > Cr > As and the risk is higher in the areas along the transboundary. These results are useful for formulating the action plan to improve the water quality of Sutlej river and its environs including water-soil-plant continuum which affect human health.

Combining Water Quality Indices and Multivariate Modeling to Assess Surface Water Quality in the Northern Nile Delta, Egypt

Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (DWQI) and four pollution indices (PIs), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The DWQI results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The PI results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the DWQI and PIs, which were based on all major ions and heavy metals, provided the best estimations with R2 = 1 for the DWQI and PIs. In conclusion, integration between the DWQI and PIs is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the DWQI and PIs.

Global evaluation of heavy metal content in surface water bodies: A meta-analysis using heavy metal pollution indices and multivariate statistical analyses

Water is polluted by increasing activities of population and the necessity to provide them with goods and services that use water as a vital resource. The contamination of water due to heavy metals (HMs) is a big concern for humankind; however, global studies related to this topic are scarce. Thus, the current review assesses the content of HMs in surface water bodies throughout the world from 1994 to 2019. To achieve this goal, multivariate analyses were applied in order to determine the possible sources of HMs. Among the analyzed HMs in a total of 147 publications, the average content of Cr, Mn, Co, Ni, As and Cd exceeded the permissible limits suggested by WHO and USEPA. The results of the heavy metal pollution index, evaluation index, the degree of contamination, water pollution and toxicity load showed that the examined water bodies are highly polluted by HMs. The results of median lethal toxicity index showed maximum toxicity in As, Co, Cr and Ni in the surface water bodies. Results of ingestion and dermal pathways for adults and children in the current analyzed review showed that As is the major contaminant. Moreover, Cr, Ni, As and Cd showed values that could be considered as a high risk for cancer generation via the ingestion pathway as compared to the dermal route. It is recommended that remediation techniques such as the introduction of aquatic phytoremediation plant species and adsorbents should be included in land management plans in order to reduce human risks.

Water quantity and hydrochemical quality monitoring of Laspias River, North Greece

The water quantity and quality of Laspias River, located in Xanthi Prefecture, Thrace, North Greece, are studied. A data collection program was designed at five stations along the main river course and its main tributary. Sampling frequency was about once every 10 days. Temperature (T), electrical conductivity (EC), pH, and dissolved oxygen were measured in situ simultaneously with discharge measurements. Moreover, water samples were collected and analyzed for the determination of nitrites, nitrates, ammonium, total phosphorus, biochemical oxygen demand, chemical oxygen demand, total suspended solids and alkalinity, according to standard methods. Anions (i.e., SO₄^-2 and Cl^-) and cations (i.e., Li⁺, Na⁺, Mg⁺², and Ca⁺²) were determined using ion chromatography (Dionex ICS-3000 ion chromatograph). The results showed that there is an impact of point (e.g., wastewater treatment plant effluent and farms) and non-point (e.g., agricultural activities) sources of pollution on the EC values, and chloride, nitrogen and phosphorus concentrations in the river water. The trophic state of Laspias River, based on nutrient concentrations, was found eutrophic. Based on the Canadian Council of Ministers of Environment Water Quality Index, the river water quality ranged from "poor" to "marginal" category, indicating the need for management measures in order to improve it.

A Review of Wetland Remote Sensing

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

Pre-sowing Seed Treatment with 24-Epibrassinolide Ameliorates Pesticide Stress in Brassica juncea L. through the Modulation of Stress Markers

The present experiment was designed to assess the effects of seed soaking with 24-epibrassinolide (EBR) on the physiology of Brassica juncea L. seedlings grown under imidacloprid (IMI) toxicity. Application of EBR increased the length of seedlings, dry weight, and pigment contents, polyphenols, total phenols, and organic acids under IMI toxicity. The expression of genes coding key enzymes of pigment, phenols, polyphenols, and organic acid biosynthetic pathways was also studied including CHLASE (chlorophyllase), PSY (phytoene synthase), CHS (chalcone synthase) and PAL (phenylalanine ammonialyase), CS (citrate synthase), SUCLG1 (succinyl Co-A ligase,), SDH (succinate dehydrogenase), FH (fumarate hydratase), MS (malate synthase). Multiple linear regression (MLR) analysis revealed that IMI application regressed negatively on seedling length, dry weight and total chlorophyll content. However, EBR seed treatment regressed positively on all the parameters studied. Moreover, interaction between IMI and EBR showed positive regression for growth parameters, content of pigments, total polyphenol, total phenol and malate, and expression of PSY and PAL. Negative interactions were noticed for the contents of fumarate, succinate and citrate, and expression of CHS and all genes studied related to organic acid metabolism. In conclusion, EBR enhanced the growth and contents of all studied metabolites by regulating the gene expression of B. juncea seedlings under IMI stress.