Pre-monsoon Research Articles

Seasonal Surges in Bacterial Diversity along the Coastal Waters of the Eastern Arabian Sea

The upwelling phenomenon plays a vital role within marine ecosystems, transporting essential nutrients from the bottom to the surface and boosting biological productivity. However, the bacterial community structure in upwelling zones along the western coast of India (WCI) is understudied. This research systematically examines bacterial diversity across three seasons—pre-monsoon (PR), monsoon (MN), and post-monsoon (PM)—using next-generation sequencing. Our findings show distinct spatial patterns of bacterial communities in the Arabian Sea and demonstrate that ecological variations influence bacterial distribution in this dynamic environment. During MN, the bacterial community exhibited greater species diversity but lower overall abundance compared to PR and PM. Non-Metric MDS cluster analysis revealed a 78% similarity (at order level) between PR and PM, indicating that MN supports unique bacterial diversity. KEGG analysis showed significant seasonal variations in metabolic functions, with increased functional potential during MN. Additionally, Carbohydrate-Active enZymes (CAZymes) analysis revealed distinct seasonal profiles, among which the GH13 enzymes were the most prevalent glycoside hydrolases during MN, predominantly being sucrose phosphorylase and glucosidase, known for breaking down glucan deposits derived from phytoplankton. The CAZymes profiles supported taxonomic and KEGG pathway findings, reinforcing that microbial communities are seasonally distinct and functionally adapted to changing availability of nutrients.

Hybrid Naïve Bayes Gaussian mixture models and SAR polarimetry based automatic flooded vegetation studies using PALSAR-2 data

Flood mapping using Synthetic Aperture Radar (SAR) data impose limitations in fully distinguishing flood under vegetation due to false double bounce returns from inundated tree trunks along with seasonal heterogeneities devised from changing land cover settings. In addition, rapid mapping of flooded vegetation is challenging during near real time applications. In this paper a fully automatic novel supervised classification approach called polarimetric Naïve Bayes is proposed that combines polarimetric information with series of Gaussian mixture models in Naïve Bayes framework to detect various flooded vegetation classes. It also allows the user to choose class configuration and eliminates creation of Region of Interest (ROI) for supervised training. The proposed approach uses scattering information from pre monsoon PolSAR dataset in training step to create ROIs for buildings and other features. In the next step series of Gaussian Mixtures are used for density estimation for different features in Bayesian multiclass problem. The newly developed classifier applied on 2016 Assam flood event resulted in precise mapping of at least three different vegetation classes under flood such as submerged vegetation, wetlands and floating vegetation. Under optimal class configuration, the approach showed better performance compared to other supervised techniques applied on the same data set such as MLE, Mahalanobis, Minimum Euclidean distance, and SVM classifications in delineating flood, submerged vegetation, wetlands and floating vegetation with Producer’s Accuracy of 98.6%, 81.1%, 94% and 51.5% respectively and combined Overall accuracy of 95.5% for flooded vegetation class. This method also detected multiple vegetation classes with better accuracy compared to similar methods.

Environmental drivers of mesozooplankton dynamics in the Munroe Island, adjacent to Ashtamudi Estuary, India.

The response of mesozooplankton is critical in assessing the health of an estuarine ecosystem. Reports on the spatial and temporal dynamics of mesozooplankton community in estuarine and backwaters of the Southern parts of India are scanty. In this scenario, we appraised the community structure of mesozooplankton and their spatio-temporal dynamics based on various multivariate statistical assessments. A total of 31 taxa were identified and the abundance was principally dominated by Copepoda followed by Luciferidae during three sampling seasons. The most abundant species were: Paracalanus parvus parvus, Pseudodiaptomus aurivillii, Temora stylifera, and Pseudodiaptomus serricaudatus. Canonical correspondence analysis and Spearman's correlation coefficients underlined that salinity, turbidity, conductivity, temperature, dissolved oxygen, chlorophyll a (Chl-a), and nutrients were the principal environmental variables strongly linked with mesozooplankton dynamics in Munroe Island. The highest abundance of mesozooplankton was recorded in MoN (monsoon), followed by PrM (pre-monsoon), and PoM (post-monsoon). Hierarchical clustering confirmed that the grouping of sampling stations is based on the estuarine and freshwater influences on mesozooplankton abundance. During the entire investigation, various ecological indices were observed in good condition. Moreover, the optimum environmental conditions during the PoM season are marked with the highest indices values. Overall, multivariate investigations undoubtedly proved the suitability of mesozooplankton communities as potential bioindicators for spatial and seasonal ecological assessments. Our investigation emphasizes the high assemblages of mesozooplankton and their responses to various environmental variables and highlights the significance of long-term ecological monitoring in a threatened ecosystem like Munroe Island.

Land use & land cover dynamics and its relationship with nitrate pollution in groundwater around inactive mine areas using geospatial techniques, SW part of Cuddapah basin, Southern India

Geospatial maps can show how the ineffective operations of inactive mines affect water and aquifer quality. As such, the purpose of this study is to assess the impact of mining and irrigation on the aquifer ecosystem through the evaluation of LULC and slope maps through the application of Landsat 8 OLI/TIRS and DEM data. A total of 50 groundwater samples were prepared from villages in the close proximity to inactive mines during pre and post monsoon periods in 2021. The results of the analysis revealed alarming statistics, that 14% of groundwater samples exceeded the WHO nitrate limit in pre & post monsoon season, indicating a high-risk in the study area. According to guidelines (USEPA, 2014), 34% in pre-monsoon and 26% post-monsoon of samples exceeded the THI levels for adults and children respectively, indicating non-carcinogenic health risks. In addition, 80% of the samples in both seasons exhibited high NPI values, indicating nitrate contamination associated with blue baby syndrome. From the Geospatial analysis the findings from the LULC classification indicate that there has been a significant increase in cropland area from 2016 to 2021 due to changes in forest land, fallow land, and water resources. These problems have been exacerbated by the expansion of cultivated land, which has increased from 71.1 square kilometers in 2016 to 118 square kilometers in 2021, accounting for 13.1% of the total area. This expansion, coupled with elevated water body resource availability, has compounded the nitrate pollution including in intensely irrigated regions. The slope map analysis revealed that the inactive mines occur at low slope, high rainfall areas and these are compounded by runoff from other sources such as domestic and agricultural wastes. For these matters, sealing and remediating these inactive mines is essential so as to prevent further nitrate leakage.

Seasonal dynamics and hydrogeological assessment of Najafgarh drain and its irrigation channels for agricultural purposes in Southwest Delhi, India

ABSTRACT This research paper focuses on evaluating the suitability of drain water, whether untreated or treated, for irrigation purposes in a peri-urban region of southwest Delhi, India. A total of 11 drain water samples were collected during the pre-monsoon (PRM) and post-monsoon (POM) seasons. A comprehensive assessment of various water quality parameters, including pH, EC, major cations (Na+, Ca2+, Mg2+, K+), anions (Cl−, HCO3−, SO42−, NO3−, F−), heavy metals, and irrigation water quality (SAR, Na%, RSC, KR, MH, PI) were analyzed. The results revealed that the drain water was highly unsuitable and marginally suitable for irrigation in PRM and POM. The Wilcox diagram classified 91% of samples as doubtful to unsuitable during PRM but only 63% during POM due to the rainfall. Heavy metal concentrations often exceeded permissible limits, with Fe ranging from 0.98 to 8.65 ppm (PRM) and 0.004 to 10.32 ppm (POM). Other metals like Mn (0.12–0.98 ppm PRM), Zn (0.02–2.54 ppm POM), Cd (0–0.031 ppm POM), and Cu (0.07–0.345 ppm POM) also showed elevated levels. The chemical composition of the water was influenced by evaporation and rock–water interactions as indicated by Gibbs diagrams in both the seasons.

Comparative study on groundwater quality assessment of Chennai District, Tamil Nadu during 2019-2020

The groundwater usage for household, industrial and agricultural needs is largely depends upon the nature and composition of various dissolved components present in the water. A comparison of groundwater quality of Chennai district in 2019 and 2020 for its potability and its hydrogeochemical characteristics were studied using the physicochemical data of representative samples from 36 observation wells obtained during pre monsoon and post monsoon seasons. The chosen wells are spatially distributed in the study region. The present work was carried out using the water quality data consisting of various physicochemical parameters and major ions concentration pertaining to groundwater analysis of the study location obtained from State Groundwater and Surface Water Resources Data Centre (SGSRDC), Taramani, Chennai, India. Water quality parameters such as pH, total dissolved solids(TDS), total hardness(TH), total alkalinity(TA), Ca2+, Mg2+, Cl‒, SO42‒, and NO3‒,were used to calculate the water quality index (WQI) by weighted arithmetic method. The groundwater quality is ascertained from the calculated WQI values with reference to Bureau of Indian Standards (BIS). The results of WQI calculation reveals that in the pre monsoon period of both 2019 and 2020, around 30 % samples fall under good category and 55 % samples fall under poor quality. In the post monsoon period of 2019, 70 % samples are of good quality, whereas for 2020, 61 % samples are of poor quality. Correlation analysis of water quality parameters and bivariant plots of major ions were plotted to decipher the hydrogeochemical characteristics of groundwater. TDS, TH and electrical conductivity (EC) exhibits a strong positive correlations with Ca2+, Mg2+, Na+, Cl- and SO42- ions. The Piper diagrams of the ground water samples of the study area reveal its hydrochemical facies as Na-Cl type. The Gibbs diagram of the groundwater samples shows that water-rock interactions and evaporation are the predominant factors in controlling the ground water chemistry.

Groundwater quality assessment for drinking, irrigation purposes and fuzzy logic-based water quality index for industrial suitability in Walajapet taluk, Ranipet district, Tamil Nadu, India

ABSTRACT Ranipet district in Tamil Nadu is one of the major industrial zones in India. Groundwater has been extensively used in this region for drinking, agricultural, and industrial purposes. For finding the groundwater quality, representative samples have been collected in the premonsoon (PRM) and postmonsoon (POM) seasons of 2023 and analysed for major physicochemical parameters. The drinking suitability was analysed based on drinking water quality index, and it showed that 68.52 and 70.37% of samples from the PRM and POM seasons are classified as poor groundwater for drinking. The groundwater's suitability for irrigation was analysed based on various irrigation quality indices, and it highlighted that most of the groundwater was suitable for agriculture. Industrial suitability was determined by several industrial quality indices. Due to the simultaneous application of several indices, a sample is categorised into many water quality groups, introducing bias into handling and decision-making. The Mamdani fuzzy inference system creates a unique integrated industrial water quality index to address this issue. The index revealed that only 24.07 and 9.26% of samples from the PRM and POM seasons, respectively, were free of corrosion or scaling, while the remaining samples were inappropriate for industrial use.

Chromophoric dissolved organic matter traces seasonally changing coastal processes in a river-influenced region of the western Bay of Bengal.

The optical characteristics of colored dissolved organic matter (CDOM) serve as a convenient tool for evaluating coastal processes, e.g., river runoff, anthropogenic inputs, primary production, and bacterial/photochemical processes. We conducted a study on the seasonal and spatial variability of absorbance and fluorescence characteristics of CDOM and nutrients in the coastal waters near the Gauthami estuary of River Godavari, the largest peninsular river of India, for a year. The surface aCDOM(350) showed a significant inverse relation with salinity in the coastal region, indicating a conservative mixing of marine and terrestrial end members. The aCDOM(350) was not conservative in the offshore (100m isobath) waters due to enrichment by secondary sources. Seasonal variability in optical properties indicated diverse sources for CDOM, as revealed by principal component analysis. The excitation-emission matrix (EEM) spectra followed by parallel factor analysis (EEM-PARAFAC) revealed four distinct fluorophores. The tyrosine (B) fluorophore showed a predominant increase in the post-monsoon season (October to January), while tryptophan (T) was relatively more enriched, coincident with nutrient enrichment and transparency increase during the early monsoon phase (July). The biological index (BIX), which reflects recent photosynthetic activity, also displayed relatively higher values during the early monsoon. The humic fluorophores A and M, and humification index (HIX) were relatively enriched during the later phase of monsoon (July-October). HIX was > 4 in a few samples of the offshore region (100-m isobath) and indicated a probable contamination from drill-mud (bentonite) used in hydrocarbon exploration. During the monsoon, the relationship between T and B with CDOM was not evident due to the masking of B fluorescence in intact protein. However, during the post-monsoon (POM) and pre-monsoon (PRM) periods, this masking effect was not observed, likely due to protein degradation via bacterial and photochemical processes, respectively. Temporal variability in nutrients indicated that high ammonium levels were produced during POM (OM bacterial degradation), and high nitrite levels were observed during PRM (due to primary production). This study provides foundational insights into the use of CDOM for understanding the impact of diverse environmental, river discharge, and anthropogenic factors on coastal ecosystems.

Assessment of spatio-temporal characteristics of groundwater quality at Khordha District, India

Groundwater is the major basis for drinking in various parts of our country. However, it gets contaminated by the toxic materials found in rocks, because of which it becomes unfit for various domestic and irrigation purposes. In present study, the groundwater quality and its availability for various purposes were examined by the parameters such as pH, DO, EC, TDS, temperature, salinity, Alkalinity, Total hardness, calcium hardness, magnesium hardness, chloride, fluoride, nitrate and phosphate during the post monsoonseason of 2022 and pre monsoon season of 2023 from 49 sampling locations. The above mentioned parameters were used for assessing appropriateness of groundwater for irrigation and domestic purposes by comparing them with the Bureau of Indian standards (BIS). The results revealed that groundwater shows wide variations among various parameters between two seasons during 2022–23. Obtained results show that water used for potable purposes by people in various parts of study area doesn’t meet necessary standards.In some of the sampling locations, it is found that the water exhibits very poor quality or unfit maybe for one season or both seasons. Analysis of parameters is done for calculating correlation coefficient of specified parameters. Strong linear relationships, both positive and negative, were found between several pairs of water quality measures according to their research.

Analyzing water level variability in Odisha: insights from multi-year data and spatial analysis

A comprehensive analysis of long-term water level trends is essential for freshwater sustainability. Given that Odisha heavily relies on agriculture, the monitoring and management of groundwater and its fluctuations are imperative for ensuring future sustainability in the state. Here, we analyzed the trend in Groundwater using water level data for a 30-year period (1990–2020) for the entire Odisha region. Moreover, to determine the long term variability, critical zones of future groundwater variability and controlling parameters of the water level change, we used spatio-temporal water level data of 746 locations. Water level rise of coastal districts during post-monsoon (POM), corresponds to the intensity of rainfall received, thus rising, however other districts of Odisha, showing decline in water level during the same season is due to shortage of rainfall, increase in population at a sudden, and over pumping due to industrial activities. Similarly, during pre-monsoon (PRM), water level shows an increasing trend in hard rock terrain of Odisha implying rabi crop irrigation, high density drainage network and lesser population density. Feature selection techniques were used in this study to know the parameters controlling most to this water level fluctuation in the entire Odisha state. Precipitation followed by landuse & landcover, lithology and population density are controlling the most for the long term water level change. Drainage, elevation, lithology and slope are positively related to the water level change while others are negatively related. It is also inferred that the districts like Mayurbhanj, Sundargarh, Keonjhar, Kandhamal, Boudh, Dhenkanal, Gajapati, Koraput and Kalahandi contain most of the high critical zone concerning future availability of groundwater while most of the coastal regions are safe.

GROUNDWATER QUALITY ANALYSIS AND ITS IMPACT ON AGRICULTURAL PRODUCTIVITY: A CASE STUDY

Ground water quality of twenty villages selected were from Karad taluka in Satara district was assessed during pre and post monsoon in the year 2017. The ground water samples were collected from bore wells and open wells, and their quality parameters were assessed for following attributes such as pH values ranges from in pre-monsoon and in post-monsoon, EC values in pre-monsoon and in post-monsoon of the samples. Most of the samples were found to be magnesium dominating in the pre-monsoon and in post-monsoon Ca2+ exceeds the Mg content in most of the water samples. The observation for post monsoon can result in Mg2+ toxicity which will be exhibited due to the continuous use this of water to crops. Cations and anions concentration varies in post-monsoon is Ca-8.251meq/L, Mg2+-11.05 meq/L, CO3-5.275 meq/L, HCO3-10.85, K-21.781, Na2+-8.73 respectively while in pre-monsoon Ca2+-9.625 meq/L, Mg2+-10.248 meq/L, CO3- 2.672meq/L, HCO3-8.762meq/L, K-13.092meq/L, Na-14.382meq/L respectively. Some parameters are analyzed statistically such as Mg Hazard, RSC, SSP, KR, RSBC, PI etc.to determine better efficiency of observations. RSC values varied from 1.1-25.2 meq/L in pre-monsoon and 80 % samples are under unsafe while 5 % are in moderate range, 15 % are safe in post-monsoon 1.1- 28.2 meq/L and 85 % are in unsafe, 5 % in moderate,10 % in safe mode, 5 % moderate, 10 % in safe mode for irrigation. SSP values in samples varied from 6.222-79.452 % (post-monsoon) to 6.671-72.84 % (Pre-monsoon); 35 % samples were good while 65 % samples were found unsuitable in pre-monsoon samples and 50 % good while 50 % unsafe in post-monsoon samples respectively. KR values in pre-monsoon observed 0.0303-6.22 meq/L while in post-monsoon it was 0.143 to 72.631 meq/L; according to Kelley’s ratio 75 % in suitable, 5 % marginal and 20 % unsuitable in pre monsoon while 65 % suitable, 5 % marginal, 30 % unsuitable for agriculture. RSBC values are observed in pre-monsoon 1.0-8.65 while in post-monsoon 0.f3-8.65 Permeability Index (PI) also analyzed it was observed in pre-monsoon 14.208-95.661 % in post-monsoon 15.327-71.549 was observed samples are under the categories of 15 % suitable, 55 % good (class II), 30 % unsuitable (class III) in pre-monsoon and 75 % good (class II), 25 % unsuitable (class III). The most of the analyzed water samples are under alkaline category; alkaline water should be managed carefully to avoid negative impacts on crops as well as on soil.

GIS-based groundwater potential zonation and assessment of groundwater quality and suitability for drinking and irrigation purposes in the Shanmughanadhi river basin, south India

To develop a specific watershed scenario, the quantity and quality of groundwater resources must be assessed and monitored locally. In the present study, an attempt has been made to identify possible groundwater potential zones, determine the quality of groundwater resources in this region, and evaluate their suitability for drinking and irrigation purposes. This goal has been achieved with the combined use of the weighted index overlay analysis (WIOA), groundwater quality index (GWQI), Remote Sensing (RS), and Geographic Information System (GIS) techniques. The proposed study area, Shanmuganadhi, is marked with superior rainfall, oscillating temperature, and runoff with litho-units encompassing charnockite (CHK) and hornblende biotite gneiss (HBG). Over-abstraction of groundwater and intensive agricultural practices have resulted in declining and degrading water quantity and quality in this area. The resulting integrated thematic map has been classified into five groundwater potential zones, namely poor, low, moderate, high, and very high, covering 3.2, 45.49, 15.3, 27.9, and 8.1% area, respectively. Sixty groundwater samples were collected during the pre-monsoon (PRM) and analyzed for major Physico-chemical parameters. From the water quality index assessment, over 28.0% of the samples fall within the “excellent,” 8.0% “good” and 4.0% “poor” in CHK, followed by 5.71% “excellent,” 48.57% “good,” 37.14% “poor,” 5.71% “very poor” and 2.87% “unsuitable” for drinking purposes in HBG terrain of this region. Irrigation indices also demonstrated that most of the groundwater samples in the CHK and HBG exposed zones exhibited good classes for irrigation purposes, as evidenced by the SAR and %Na+. The outcome, which shows the research area's groundwater potential zones, is beneficial for improved groundwater resource planning and management. It is concluded that for larger-scale groundwater-quality investigation and assessment, the groundwater quality index-making approach (GWQI) is more practical and dependable. Water planners and decision-makers may find it helpful in effectively controlling and monitoring groundwater quality at the basin or watershed scale. Reducing urban land use and natural runoff from farms can help prevent water contamination in some regions. To ensure the safe use of groundwater, long-term monitoring of these groundwater was suggested.

Acid mine drainage from coal mines in the eastern Himalayan sub-region: Hydrogeochemical processes, seasonal variations and insights from hydrogen and oxygen stable isotopes

Uncontrolled coal mining using non-scientific methods has presented a major threat to the quality of environment, particularly the water resources in eastern himalayan sub-region of India. Water bodies in the vicinity of mining areas are contaminated by acid mine drainage (AMD) that is released into streams and rivers. This study attempted to assess the impact of AMD, deciphering hydrogeochemical processes, seasonal fluctuations, and stable isotope features of water bodies flowing through and around coal mining areas. Self-organizing maps (SOMs) used to separate and categorize AMD, AMD-impacted and non-AMD impacted water from the different study locations for two sampling seasons revealed four clusters (C), with C1 and C2 impacted by AMD, C3 and C4 showing negligible to no impact of AMD. AMD impacted water was SO42− - Mg2+- Ca2+ hydrochemical type with sulphide oxidation and evaporation dominating water chemistry, followed by silicate weathering during both the sampling seasons. Water with negligible-to-no AMD-impact was Mg2+- Ca2+- SO42− to Ca2+ - HCO3− to mixed hydrochemical type with rock weathering and dissolution, followed by ion exchange as major factors controlling water chemistry during both the sampling seasons. Most of physicochemical parameters of C1 and C2 exceeded the prescribed limits, whereas in C3 and C4 water samples, parameters were found within the prescribed limits. Stable isotopes of hydrogen (δ2H) and oxygen (δ18O) during post-monsoon (PoM) varied between -41.04 ‰ and -29.98 ‰, and −6.60 ‰ to −3.94 ‰; and during pre-monsoon (PrM) varied between −58.18 ‰ and - 33.76 ‰ and −8.60 ‰ to −5.46 ‰. Deuterium excess (d-excess) ranged between 1.57 ‰ and 12.47 ‰ during PoM and 5.70 ‰ to 15.17 ‰ during PrM season. The stable isotopes analysis revealed that evaporation, mineral dissolution and mixing with rainwater are the key factors in study area.

A prevalence study on gastrointestinal parasitic infections of pig (Sus scrofa) in and around Meerut, Western Uttar Pradesh, India.

The present study was conducted to record the prevalence of gastrointestinal parasites of pigs by faecal sample examination. Faecal samples were collected in three different seasons viz; monsoon, post monsoon and pre monsoon, from in and around Meerut district of Western Uttar Pradesh. A total of 642 faecal samples were collected for a period of one year (2020-21). The overall prevalence rate recorded for gastrointestinal parasites in pigs was 64.4%.The findings of the present work showed that seasons have a significant role on the prevalence of gastrointestinal parasites infection in pigs, with highest prevalence in monsoon (78.34%) followed by pre-monsoon (60.19%) and lowest in post- monsoon seasons (54.67%). Thus, the present study will be helpful in designing the effective control strategy of parasitic infections for optimum production in porcine industry as well as preventive measures against zoonotic diseases spread by pigs.

Assessing groundwater quality in Nand Samand catchment, Rajasthan, India: a GIS-based multi indices approach for drinking and irrigation water suitability

Abstract Consistently monitoring groundwater quality (GWQ) is essential to reduce the risk of geochemical contaminants and ensure its suitability for agriculture and human consumption. The current investigation aims to assess groundwater (GW) acceptability in the Nand Samand catchment (NSC), utilizing the water quality index (WQI) and irrigation water quality indices (IWQIs) for domestic and irrigation purposes. To achieve this, GW samples were collected from 95 open wells which were located spatially in the catchment, during the pre-monsoon (PRM) and post-monsoon (POM) seasons of 2019 and 2020, and subsequently analysed for 11 physico-chemical parameters. Electrical conductivity (EC) varied from 1.25 to 6.61 dS/m and 0.58 to 7.42 dS/m during the PRM and POM seasons, respectively. Total dissolved solids of the study area ranged between 180 and 1,180 (27%) to 1,180 and 2,180 (62%) during PRM 1,180 (63%) to 1,180–2,180 (31%) during POM, respectively. The study also computed the ‘sodium adsorption ratio’ (SAR) and ‘residual sodium carbonate’ (RSC) to estimate GW's appropriateness for agriculture, finding it suitable in most locations due to its balanced composition. Based on WQI, varying percentages of samples were classified as ‘good’ and ‘poor’ for potable water quality in both seasons.

The monsoon-groundwater nexus, impacts and adaptation strategies in the northern part of Chennai region, South India

The variations in groundwater quality and their influence on hydrochemical and microbial properties in the metropolitan cities of North Chennai have been scientifically examined in an attempt to comprehend the effects of urbanization. For this ongoing investigation, 50 groundwater samples from surface bore wells were collected in and around north Chennai, South India, during the pre-monsoon (PRm) season in June 2022 and 50 samples during post-monsoon (POm) in January 2023. Southeast of the desired extent, where urban enclaves predominate, has elevated salinity (12,500 mg/L) and electrical conductivity (EC) of 21,200 μS/cm. The predominant types shown in the Piper and Chadha models were sodium-chloride (Na-Cl). Additionally, the Gibbs diagram revealed the observation of evaporation dominance. The interaction between freshwater and seawater has been shown using ionic ratio analysis. A majority of the sample locations indicated the levels of standard plate count (SPC) and total coliforms (TC) contamination in the groundwater were above allowable limits (WHO). There was a slight improvement in groundwater quality during the post-monsoon session suggesting to a possible effect from recently recharged groundwater. In general, the groundwater quality of the research area is impacted by lithological changes, land use, and urban development. Water quality study may be useful in identifying potential health concerns and notify decisions about water management and treatment policies.

A comprehensive multivariate investigation of the water quality of Kallada River in Kerala, India

The Kallada River is a remarkable water resource in Kerala and reviving several villages through its glorifying journey. An investigation was directed to evaluate the spatial and temporal status of water quality in Kallada River, Kerala using multivariate statistical tools from February 2019 to January 2021. The status of fifteen environmental variables was detected by using standard methodology. The increased levels of conductivity, total dissolved solids, biochemical oxygen demand, hardness, alkalinity, calcium, and magnesium were detected during the post-monsoon (PoM) season and those parameters were tend to increase towards the downstream stations. Some other variables such as turbidity, dissolved oxygen, NO3−, SiO2, and PO43− were higher during the monsoon (MoN) season. Temperature, pH, and chemical oxygen demand were higher during the pre-monsoon (PrM) season. Furthermore, multivariate statistical tools like principal component analysis (PCA), agglomerative cluster analysis (AHC), and pearsons correlation were used to assess spatio-temporal changes in water quality to reveal pollution. Contrarily, the midstream stations such as K6, K10, and the entire downstream area (K11 to K15) of the Kallada River is under the threats of many anthropogenic activities such as sewage effluents, sand mining and agricultural runoff when compared to the entire upstream stations (K1 to K5), and three midstream stations (K7 to K9). The presence of various organic and inorganic pollutants begins from K6 and drastically rises at K11. Hence, efforts are required to prevent the contamination in the Kallada River.

Groundwater quality characterization using an integrated water quality index and multivariate statistical techniques.

This study attempts to characterize and interpret the groundwater quality (GWQ) using a GIS environment and multivariate statistical approach (MSA) for the Jakham River Basin (JRB) in Southern Rajasthan. In this paper, analysis of various statistical indicators such as the Water Quality Index (WQI) and multivariate statistical methods, i.e., principal component analysis and correspondence analysis (PCA and CA), were implemented on the pre and post-monsoon water quality datasets. All these methods help identify the most critical factor in controlling GWQ for potable water. In pre-monsoon (PRM) and post-monsoon (POM) seasons, the computed value of WQI has ranged between 28.28 to 116.74 and from 29.49 to 111.98, respectively. As per the GIS-based WQI findings, 63.42 percent of the groundwater samples during the PRM season and 42.02 percent during the POM were classed as 'good' and could be consumed for drinking. The Principal component analysis (PCA) is a suitable tool for simplification of the evaluation process in water quality analysis. The PCA correlation matrix defines the relation among the water quality parameters, which helps to detect the natural or anthropogenic influence on sub-surface water. The finding of PCA's factor analysis shows the impact of geological and human intervention, as increased levels of EC, TDS, Na+, Cl-, HCO3-, F-, and SO42- on potable water. In this study, hierarchical cluster analysis (HCA) was used to categories the WQ parameters for PRM and POR seasons using the Ward technique. The research outcomes of this study can be used as baseline data for GWQ development activities and protect human health from water-borne diseases in the southern region of Rajasthan.

Study of Trace Metals in Different Sites of Neyyar River Basin, Kerala, India

Rivers are one of the most important waterbodies depended on by the whole environment for their daily lives. But now a days, water-related incidents are increasing much more because of the improper lifestyle of human beings. Trace metals are one of the main causes of the contamination of water. The present study deals with the analysis of five trace metals using Atomic Absorption Spectrophotometer at five selected sites in Neyyar River. The study period is from March 2022 to February 2023. According to rainfall the study period was portioned into three main seasons, monsoon (March 2022 to June 2022), post monsoon (July 2022 to October 2022) and pre monsoon (November 2022 to February 2023). Trace metals such as nickel, zinc, copper, iron, and manganese were selected for this study. Statistical analysis was done by the help of Two-way analysis of variance (ANOVA). It shows variation in some parameters. The water in the Neyyar River is in good condition.

Monitoring the Impacts of Artificial Recharge Structures on Water Table at Ambedkar Nagar, Uttar Pradesh, India

To study the groundwater recharge through rainfall and artificial recharge structures in selected dry well in different locations of UP was experimented. The data was recorded (1998-2017) to execute the artificial recharge structure at the appropriate locations with best geological condition to enhance the recharge rate at least cost for control of declining groundwater level. Study reveals that the stochastic auto regressive lime series model is an effective tool for management of ground water resource at pre and post monsoon. The variation of pre and post monsoon ground water level is maximum as the physical soil characteristics including hydraulic conductivity may enhance the recharge rate at least cost.