The thesis examines the spatial and temporal distribution of iron and manganese in groundwater in Chandrapur District, Maharashtra. The study aims to understand the distribution of iron and manganese with respect to source type, depth, season and other factors. It also aims to identify plausible sources of iron and manganese in groundwater. The methodology involves literature review, sampling groundwater from identified locations, analyzing iron and manganese concentrations, and interpreting the data. Key findings show seasonal and spatial variations in iron and manganese levels, with some sources exceeding Indian drinking water standards. Depth, rainfall, and altitude also influence concentration levels. The study helps characterize groundwater quality in the district and identify sources of contamination.
•Initial (reconnaissance) assessment….
♦Basic knowledge of geological and environmental conditions
♦Review of previous investigation results
♦Measurements of water levels
♦Sampling & preliminary assessment of water quality
This document provides an overview of various groundwater exploration methods, including surface and subsurface techniques. Surface methods involve minimal facilities and include geomorphological analysis of landforms, geological and structural mapping, soil and vegetation analysis, remote sensing, and surface geophysical methods like electrical resistivity and seismic surveys. Subsurface methods like borehole logging and test drilling provide direct observations but are more expensive. Together, a multi-method approach can be used to explore groundwater resources and locate potential zones for development.
identification of ground water potential zones using gis and remote sensingtp jayamohan
This document summarizes a study that mapped groundwater potential zones in the Muvattupuzha block of Kerala, India using GIS and remote sensing. Key factors like geology, geomorphology, lineaments, drainage density, rainfall, land use, slope and soils were analyzed as layers in GIS. Weighted overlay analysis was used to delineate excellent, moderate and poor groundwater potential zones. Validation with field data found good correlation. The study aims to aid groundwater development and management to address water scarcity in the region.
Application of RS and GIS in Groundwater Prospects ZonationVishwanath Awati
This document discusses using remote sensing and GIS techniques to map groundwater prospects zones. It presents a case study of applying these methods in Bata Valley, Himachal Pradesh, India. The methodology involves developing thematic maps of factors like geology, land use, and water levels. These maps are then overlaid and analyzed in GIS to identify zones of good, moderate, or poor groundwater potential. The study concludes these techniques can effectively map groundwater prospects and inform management plans.
This document provides an introduction and overview of groundwater modeling. It discusses why groundwater modeling is needed for effective groundwater management. It outlines the modeling process, including developing a conceptual model, selecting governing equations, model design, calibration, validation, and using the model for prediction. It describes different types of mathematical models, including analytical, finite difference, and finite element models. It emphasizes that a modeling protocol should establish the modeling purpose and ensure the conceptual model adequately represents the system behavior. The document stresses the importance of calibration, verification, and sensitivity analysis to evaluate a model's ability to reproduce measured conditions and the effects of uncertainty.
APPLICATIONS OF REMOTE SENSING AND GIS IN WATERSHED MANAGEMENTSriram Chakravarthy
This document discusses watershed management and the role of remote sensing and GIS applications. It begins with defining a watershed and the watershed approach. It then discusses watershed characterization, prioritization, development activities, and monitoring. Remote sensing provides synoptic data to map natural resources within watersheds. GIS is used to integrate spatial data for watershed delineation and analysis. The goal of watershed management is sustainable development through activities like water conservation, afforestation, and improving livelihoods.
Spatial analysis of groundwater quality using GIS systemPavan Grandhi
To analyze systematically for physio-chemical parameters such as pH, Total Hardness, Electrical Conductivity and Chemical Oxygen Demand (COD).
Generate Ground Water Quality Map based in Jnanabharathi ward no.129, Bangalore, Karnataka state, India
•Initial (reconnaissance) assessment….
♦Basic knowledge of geological and environmental conditions
♦Review of previous investigation results
♦Measurements of water levels
♦Sampling & preliminary assessment of water quality
This document provides an overview of various groundwater exploration methods, including surface and subsurface techniques. Surface methods involve minimal facilities and include geomorphological analysis of landforms, geological and structural mapping, soil and vegetation analysis, remote sensing, and surface geophysical methods like electrical resistivity and seismic surveys. Subsurface methods like borehole logging and test drilling provide direct observations but are more expensive. Together, a multi-method approach can be used to explore groundwater resources and locate potential zones for development.
identification of ground water potential zones using gis and remote sensingtp jayamohan
This document summarizes a study that mapped groundwater potential zones in the Muvattupuzha block of Kerala, India using GIS and remote sensing. Key factors like geology, geomorphology, lineaments, drainage density, rainfall, land use, slope and soils were analyzed as layers in GIS. Weighted overlay analysis was used to delineate excellent, moderate and poor groundwater potential zones. Validation with field data found good correlation. The study aims to aid groundwater development and management to address water scarcity in the region.
Application of RS and GIS in Groundwater Prospects ZonationVishwanath Awati
This document discusses using remote sensing and GIS techniques to map groundwater prospects zones. It presents a case study of applying these methods in Bata Valley, Himachal Pradesh, India. The methodology involves developing thematic maps of factors like geology, land use, and water levels. These maps are then overlaid and analyzed in GIS to identify zones of good, moderate, or poor groundwater potential. The study concludes these techniques can effectively map groundwater prospects and inform management plans.
This document provides an introduction and overview of groundwater modeling. It discusses why groundwater modeling is needed for effective groundwater management. It outlines the modeling process, including developing a conceptual model, selecting governing equations, model design, calibration, validation, and using the model for prediction. It describes different types of mathematical models, including analytical, finite difference, and finite element models. It emphasizes that a modeling protocol should establish the modeling purpose and ensure the conceptual model adequately represents the system behavior. The document stresses the importance of calibration, verification, and sensitivity analysis to evaluate a model's ability to reproduce measured conditions and the effects of uncertainty.
APPLICATIONS OF REMOTE SENSING AND GIS IN WATERSHED MANAGEMENTSriram Chakravarthy
This document discusses watershed management and the role of remote sensing and GIS applications. It begins with defining a watershed and the watershed approach. It then discusses watershed characterization, prioritization, development activities, and monitoring. Remote sensing provides synoptic data to map natural resources within watersheds. GIS is used to integrate spatial data for watershed delineation and analysis. The goal of watershed management is sustainable development through activities like water conservation, afforestation, and improving livelihoods.
Spatial analysis of groundwater quality using GIS systemPavan Grandhi
To analyze systematically for physio-chemical parameters such as pH, Total Hardness, Electrical Conductivity and Chemical Oxygen Demand (COD).
Generate Ground Water Quality Map based in Jnanabharathi ward no.129, Bangalore, Karnataka state, India
Ground water Arsenic Contamination in IndiaDr Sayan Das
Extent, related research and remedication meassures
Chemistry of arsenic, Use of arsenic, reference value , Oxidation method, Ion exchange method, Membrane method
This document outlines groundwater management strategies for municipal officials. It notes that while the region receives abundant precipitation, local overuse and water quality problems are still possible if left unmanaged. It then describes a model groundwater protection ordinance that has been adopted by several Dutchess County towns. The ordinance establishes development standards and best practices to safeguard both groundwater quantity and quality. These include regulating certain land uses, prohibiting new underground fuel tanks, guidance for cluster subdivisions, and more rigorous pumping test requirements. The model aims to preserve aquifer and stream flows while also addressing issues like pharmaceutical contamination and climate change impacts. Towns can adopt this law or planning boards can apply its guidance under the State Environmental Quality Review Act.
This presentation deals with the recent advancement in the field of ground water sampling and analysis technique and water born survey as well as Indian scenario to interpret.
Remote sensing application in monitoring and management of soil, water and ai...Jayvir Solanki
Remote sensing uses satellite or aircraft sensors to monitor the environment without direct contact. It can monitor soil, water, and air pollution over large areas in a timely manner. Satellite imagery is used to monitor air quality by detecting pollutants and aerosols. Water quality is monitored by measuring changes in the spectral signature of surface water caused by substances like sediments, algae, and thermal releases. Remote sensing provides synoptic views of large areas but has limitations like spectral interference and inability to distinguish low concentrations of pollutants. It is a useful tool for environmental monitoring when used in conjunction with field data.
IDENTIFICATION OF GROUNDWATER POTENTIAL ZONES USING REMOTE SENSING AND GEOGRA...IAEME Publication
The document describes a study that used remote sensing and GIS techniques to identify groundwater potential zones in the Konakaluva sub-basin of India. Various thematic maps were generated from satellite imagery and other data sources. These maps were overlaid and assigned weights based on their influence on groundwater occurrence. Soil data was given the highest weight of 40%, while land use/cover and drainage density were also significant at 25% and 10%, respectively. An integrated groundwater potential zones map was produced that classified areas as very good, good, fair, moderate or poor potential zones based on the overlay analysis. The results can help with better planning and management of groundwater resources in the study area.
Groundwater pollution occurs when pollutants make their way into groundwater and contaminate it. A pollutant plume spreads through an aquifer, intersecting with groundwater wells or daylighting into surface water. Pollution can come from septic systems, landfills, wastewater treatment plants, petrol stations, agriculture, and naturally occurring contaminants. Protecting groundwater requires preventing pollution through monitoring aquifers and landfills, replacing old fuel tanks, and strictly regulating toxic waste disposal.
The document discusses hydraulic conductivity, which measures the ability of a material like soil or rock to transmit fluids through pores and fractures under an applied hydraulic gradient. It describes hydraulic conductivity as being important for calculating groundwater movement rates and outlines experimental and empirical methods for determining it in the field or laboratory, such as constant head tests, falling head tests, or correlations with soil properties. Hydraulic conductivity is the constant in Darcy's Law and is defined as the volume of water that will move through a porous medium per unit time under a unit hydraulic gradient through a unit area measured perpendicular to flow.
Remote sensing and GIS techniques can contribute significantly to groundwater modeling efforts. Remote sensing provides spatial data on land cover, vegetation, rainfall, and terrain that are important model inputs. GIS allows integration of diverse data layers, conceptualization of recharge/discharge areas, and output visualization. However, remote sensing has limitations, such as an inability to directly measure groundwater levels or recharge. Overall, combining remote sensing, GIS, and field data can improve conceptual models and produce more accurate modeling results for groundwater management.
The document provides an outline for a presentation on the SWAT (Soil and Water Assessment Tool) hydrological model. It begins with an introduction to hydrological modeling and the development and utilities of the SWAT model. It describes the data requirements, model framework, and step-by-step procedure to run the model. A case study applying the SWAT model to the Simly Dam watershed in Pakistan is summarized. The limitations and future developments of the SWAT model are briefly discussed, followed by references.
This document discusses the importance of groundwater regime monitoring for groundwater management. It defines groundwater regime monitoring as collecting water level measurements from observation wells at regular time intervals to provide quantitative and qualitative information about groundwater. The key points are:
1) Groundwater monitoring is required to understand changes in groundwater quantity and quality over time due to slow hydrologic processes, and to design effective groundwater management programs.
2) A monitoring network is established with representative wells measured for water levels and quality quarterly.
3) Water level and quality data are analyzed statistically and spatially to identify trends and relationships helping groundwater management.
4) Challenges include data loss, high costs, and difficulty
The document discusses how topographic maps in India are organized and identified using a hierarchical system of map sheets with different scales. Topographic maps are divided into million sheet sections at 1:1,000,000 scale, degree sheets at 1:250,000 scale, toposheets at 1:50,000 scale, and further subdivided toposheets at 1:25,000 scale that are identified by letters and numbers corresponding to their location and scale. This system allows precise identification of location for any place shown on topographic maps in India.
Heavy metal pollution in soil and its mitigation aspect by Dr. Tarik MitranDr. Tarik Mitran
Heavy metal pollution in soil is a serious problem. Some key points:
- Heavy metals like lead, cadmium, arsenic, chromium, and mercury are toxic even in small amounts and can accumulate in the food chain.
- Sources of heavy metal pollution include industrial, agricultural, and mining activities which release these metals into the environment.
- Heavy metals can be taken up by plants and crops irrigated with contaminated water, accumulating in plant tissues and eventually entering the food chain. This poses risks to human and animal health.
- Remediating contaminated soils requires understanding the chemical processes by which heavy metals move and change form in the soil-water-air system over time. Mitigation strategies aim to reduce
The document is a project report presentation on assessing groundwater quality in Raichur Taluk, Karnataka, India. It includes sections on introduction, literature review, study area and data collection, water quality assessment parameters, and conclusions. 21 sampling locations in and around Raichur Taluk were tested for various water quality parameters including pH, turbidity, electrical conductivity, total dissolved solids, total hardness, calcium hardness, magnesium hardness, alkalinity, fluoride, sulphate, chloride, and acidity. The results found that most parameters were within acceptable limits according to drinking water standards, however some locations had higher levels of parameters like fluoride, sulphate and chloride.
The document discusses soil sampling procedures and methods. It describes different types of soil sampling including disturbed sampling, undisturbed sampling, random sampling, grid sampling, zone sampling, and topographic/geographic unit sampling. It provides details on sampling depths and tools for different field types such as vegetables, field crops, and orchards. Finally, it lists common soil sampling tools including shovels, augers, split-spoon samplers, and shelby tube samplers.
Water quality mapping for solapur district using gisadsulprashantr
The document outlines a GIS project to map water quality in Solapur District, India. It discusses collecting groundwater quality data from 2007-2009, calculating a Water Quality Index (WQI) based on parameters like pH, hardness, nitrates, and mapping the results in a GIS. The methodology includes georeferencing maps, creating shapefiles, entering attribute data, performing spatial queries to analyze tehsils with high or low nitrates, and generating output maps and charts of WQI, nitrates, and water quality classifications. The results found one tehsil had excellent water quality based on WQI and that agricultural runoff is a major contributor to nitrate contamination.
This document discusses the use of geographic information systems (GIS) in water resource management and assessment. It provides examples of GIS applications in watershed management, groundwater assessment, flood management, and water quality studies. It then describes a case study that developed a GIS-based decision support system to assess watershed runoff in the Kk3 Macro Watershed in India. Key steps included delineating sub-watersheds, creating soil and land use maps, determining hydrologic response units, computing runoff, and generating thematic runoff maps. The system allows users to update rainfall data and evaluate variations in spatial runoff distribution over time.
Surface Water modelling using Remote SensingArk Arjun
1) The document discusses remote sensing and runoff estimation using the SCS curve number method. Remote sensing involves obtaining information about objects through non-contact sensors.
2) Runoff estimation is the first step in water management. The SCS-CN method estimates runoff as a function of land use, soil type, and rainfall.
3) The study area's topographic maps, rainfall data, land use maps, and soil data were collected and used to classify land cover, model rainfall-runoff, and estimate runoff volume using the SCS-CN method.
This document summarizes a study assessing the surface water quality of Perumbakkam Lake in Kanchipuram district, South India using Geographic Information Systems (GIS). 15 water samples were collected from locations around the lake and analyzed for physicochemical parameters like pH, alkalinity, hardness, chlorides, TDS, fluoride, iron, and ammonia. The results were mapped spatially using GIS to analyze the spatial variation in water quality. Most parameters were within permissible limits except for high TDS levels at some locations, possibly due to industrial and domestic waste discharge. The study aims to help monitor water quality and identify sources of pollution impacting the lake.
Impact of Climate Change on Groundwater ResourcesC. P. Kumar
This document summarizes the impact of climate change on groundwater resources. It discusses how climate change can affect factors like precipitation, temperature, and evapotranspiration, which then impact groundwater recharge and levels. Higher temperatures and variability in rainfall from climate change could mean more fluctuations in groundwater levels and potential saline intrusion in coastal aquifers. Quantifying the full impact on groundwater requires downscaling climate models and coupling them with hydrological models to estimate changes in groundwater recharge over time. Key concerns are potential decreases in groundwater supplies and quality issues, as groundwater serves as a major global source of potable water.
Impact of Iron and Steel Industry on Ground Water Quality of Tungabhadra Rive...IJARIIT
Bellary district has 25 % of India's Iron ore reserves and is well known for its rich iron and manganese ore
reserves. Iron ore deposits in Bellary district are widespread and have been a backbone to industrial development in the region.
The environmental impact of large scale mining activities includes soil erosion, formation of sinkholes, loss of biodiversity,
and contamination of soil, groundwater and surface water by chemicals from mining processes.In this paper, efforts have been
made to assess the quality of Tunga - Bhadra river water extensive survey and laboratory analysis which would give the
information about ‘Impacts on reservoir water quality’ due to the Iron and steel industry. Also an attempt has made for
controlling the groundwater pollution, which would serve as a basis to evolve suitable management strategy for the District.
Therefore there is a significant changes in values of different parameters of ground water sources indicate the influence of
industrial wastes on ground water.
This study examined water quality in a small residential wetland in Spokane, Washington. Water samples were taken from sites along the drainage gradient leading to the wetland and within the wetland. Concentrations of ions like magnesium and sodium increased from upstream to within the wetland, likely due to evapoconcentration. While concentrations of major ions accumulated in the wetland, concentrations of potential heavy metals like lead and zinc remained below EPA aquatic life standards. The results indicate the wetland shows no evidence of geochemical hazards from surrounding anthropogenic activities like vehicle traffic or land use.
Ground water Arsenic Contamination in IndiaDr Sayan Das
Extent, related research and remedication meassures
Chemistry of arsenic, Use of arsenic, reference value , Oxidation method, Ion exchange method, Membrane method
This document outlines groundwater management strategies for municipal officials. It notes that while the region receives abundant precipitation, local overuse and water quality problems are still possible if left unmanaged. It then describes a model groundwater protection ordinance that has been adopted by several Dutchess County towns. The ordinance establishes development standards and best practices to safeguard both groundwater quantity and quality. These include regulating certain land uses, prohibiting new underground fuel tanks, guidance for cluster subdivisions, and more rigorous pumping test requirements. The model aims to preserve aquifer and stream flows while also addressing issues like pharmaceutical contamination and climate change impacts. Towns can adopt this law or planning boards can apply its guidance under the State Environmental Quality Review Act.
This presentation deals with the recent advancement in the field of ground water sampling and analysis technique and water born survey as well as Indian scenario to interpret.
Remote sensing application in monitoring and management of soil, water and ai...Jayvir Solanki
Remote sensing uses satellite or aircraft sensors to monitor the environment without direct contact. It can monitor soil, water, and air pollution over large areas in a timely manner. Satellite imagery is used to monitor air quality by detecting pollutants and aerosols. Water quality is monitored by measuring changes in the spectral signature of surface water caused by substances like sediments, algae, and thermal releases. Remote sensing provides synoptic views of large areas but has limitations like spectral interference and inability to distinguish low concentrations of pollutants. It is a useful tool for environmental monitoring when used in conjunction with field data.
IDENTIFICATION OF GROUNDWATER POTENTIAL ZONES USING REMOTE SENSING AND GEOGRA...IAEME Publication
The document describes a study that used remote sensing and GIS techniques to identify groundwater potential zones in the Konakaluva sub-basin of India. Various thematic maps were generated from satellite imagery and other data sources. These maps were overlaid and assigned weights based on their influence on groundwater occurrence. Soil data was given the highest weight of 40%, while land use/cover and drainage density were also significant at 25% and 10%, respectively. An integrated groundwater potential zones map was produced that classified areas as very good, good, fair, moderate or poor potential zones based on the overlay analysis. The results can help with better planning and management of groundwater resources in the study area.
Groundwater pollution occurs when pollutants make their way into groundwater and contaminate it. A pollutant plume spreads through an aquifer, intersecting with groundwater wells or daylighting into surface water. Pollution can come from septic systems, landfills, wastewater treatment plants, petrol stations, agriculture, and naturally occurring contaminants. Protecting groundwater requires preventing pollution through monitoring aquifers and landfills, replacing old fuel tanks, and strictly regulating toxic waste disposal.
The document discusses hydraulic conductivity, which measures the ability of a material like soil or rock to transmit fluids through pores and fractures under an applied hydraulic gradient. It describes hydraulic conductivity as being important for calculating groundwater movement rates and outlines experimental and empirical methods for determining it in the field or laboratory, such as constant head tests, falling head tests, or correlations with soil properties. Hydraulic conductivity is the constant in Darcy's Law and is defined as the volume of water that will move through a porous medium per unit time under a unit hydraulic gradient through a unit area measured perpendicular to flow.
Remote sensing and GIS techniques can contribute significantly to groundwater modeling efforts. Remote sensing provides spatial data on land cover, vegetation, rainfall, and terrain that are important model inputs. GIS allows integration of diverse data layers, conceptualization of recharge/discharge areas, and output visualization. However, remote sensing has limitations, such as an inability to directly measure groundwater levels or recharge. Overall, combining remote sensing, GIS, and field data can improve conceptual models and produce more accurate modeling results for groundwater management.
The document provides an outline for a presentation on the SWAT (Soil and Water Assessment Tool) hydrological model. It begins with an introduction to hydrological modeling and the development and utilities of the SWAT model. It describes the data requirements, model framework, and step-by-step procedure to run the model. A case study applying the SWAT model to the Simly Dam watershed in Pakistan is summarized. The limitations and future developments of the SWAT model are briefly discussed, followed by references.
This document discusses the importance of groundwater regime monitoring for groundwater management. It defines groundwater regime monitoring as collecting water level measurements from observation wells at regular time intervals to provide quantitative and qualitative information about groundwater. The key points are:
1) Groundwater monitoring is required to understand changes in groundwater quantity and quality over time due to slow hydrologic processes, and to design effective groundwater management programs.
2) A monitoring network is established with representative wells measured for water levels and quality quarterly.
3) Water level and quality data are analyzed statistically and spatially to identify trends and relationships helping groundwater management.
4) Challenges include data loss, high costs, and difficulty
The document discusses how topographic maps in India are organized and identified using a hierarchical system of map sheets with different scales. Topographic maps are divided into million sheet sections at 1:1,000,000 scale, degree sheets at 1:250,000 scale, toposheets at 1:50,000 scale, and further subdivided toposheets at 1:25,000 scale that are identified by letters and numbers corresponding to their location and scale. This system allows precise identification of location for any place shown on topographic maps in India.
Heavy metal pollution in soil and its mitigation aspect by Dr. Tarik MitranDr. Tarik Mitran
Heavy metal pollution in soil is a serious problem. Some key points:
- Heavy metals like lead, cadmium, arsenic, chromium, and mercury are toxic even in small amounts and can accumulate in the food chain.
- Sources of heavy metal pollution include industrial, agricultural, and mining activities which release these metals into the environment.
- Heavy metals can be taken up by plants and crops irrigated with contaminated water, accumulating in plant tissues and eventually entering the food chain. This poses risks to human and animal health.
- Remediating contaminated soils requires understanding the chemical processes by which heavy metals move and change form in the soil-water-air system over time. Mitigation strategies aim to reduce
The document is a project report presentation on assessing groundwater quality in Raichur Taluk, Karnataka, India. It includes sections on introduction, literature review, study area and data collection, water quality assessment parameters, and conclusions. 21 sampling locations in and around Raichur Taluk were tested for various water quality parameters including pH, turbidity, electrical conductivity, total dissolved solids, total hardness, calcium hardness, magnesium hardness, alkalinity, fluoride, sulphate, chloride, and acidity. The results found that most parameters were within acceptable limits according to drinking water standards, however some locations had higher levels of parameters like fluoride, sulphate and chloride.
The document discusses soil sampling procedures and methods. It describes different types of soil sampling including disturbed sampling, undisturbed sampling, random sampling, grid sampling, zone sampling, and topographic/geographic unit sampling. It provides details on sampling depths and tools for different field types such as vegetables, field crops, and orchards. Finally, it lists common soil sampling tools including shovels, augers, split-spoon samplers, and shelby tube samplers.
Water quality mapping for solapur district using gisadsulprashantr
The document outlines a GIS project to map water quality in Solapur District, India. It discusses collecting groundwater quality data from 2007-2009, calculating a Water Quality Index (WQI) based on parameters like pH, hardness, nitrates, and mapping the results in a GIS. The methodology includes georeferencing maps, creating shapefiles, entering attribute data, performing spatial queries to analyze tehsils with high or low nitrates, and generating output maps and charts of WQI, nitrates, and water quality classifications. The results found one tehsil had excellent water quality based on WQI and that agricultural runoff is a major contributor to nitrate contamination.
This document discusses the use of geographic information systems (GIS) in water resource management and assessment. It provides examples of GIS applications in watershed management, groundwater assessment, flood management, and water quality studies. It then describes a case study that developed a GIS-based decision support system to assess watershed runoff in the Kk3 Macro Watershed in India. Key steps included delineating sub-watersheds, creating soil and land use maps, determining hydrologic response units, computing runoff, and generating thematic runoff maps. The system allows users to update rainfall data and evaluate variations in spatial runoff distribution over time.
Surface Water modelling using Remote SensingArk Arjun
1) The document discusses remote sensing and runoff estimation using the SCS curve number method. Remote sensing involves obtaining information about objects through non-contact sensors.
2) Runoff estimation is the first step in water management. The SCS-CN method estimates runoff as a function of land use, soil type, and rainfall.
3) The study area's topographic maps, rainfall data, land use maps, and soil data were collected and used to classify land cover, model rainfall-runoff, and estimate runoff volume using the SCS-CN method.
This document summarizes a study assessing the surface water quality of Perumbakkam Lake in Kanchipuram district, South India using Geographic Information Systems (GIS). 15 water samples were collected from locations around the lake and analyzed for physicochemical parameters like pH, alkalinity, hardness, chlorides, TDS, fluoride, iron, and ammonia. The results were mapped spatially using GIS to analyze the spatial variation in water quality. Most parameters were within permissible limits except for high TDS levels at some locations, possibly due to industrial and domestic waste discharge. The study aims to help monitor water quality and identify sources of pollution impacting the lake.
Impact of Climate Change on Groundwater ResourcesC. P. Kumar
This document summarizes the impact of climate change on groundwater resources. It discusses how climate change can affect factors like precipitation, temperature, and evapotranspiration, which then impact groundwater recharge and levels. Higher temperatures and variability in rainfall from climate change could mean more fluctuations in groundwater levels and potential saline intrusion in coastal aquifers. Quantifying the full impact on groundwater requires downscaling climate models and coupling them with hydrological models to estimate changes in groundwater recharge over time. Key concerns are potential decreases in groundwater supplies and quality issues, as groundwater serves as a major global source of potable water.
Impact of Iron and Steel Industry on Ground Water Quality of Tungabhadra Rive...IJARIIT
Bellary district has 25 % of India's Iron ore reserves and is well known for its rich iron and manganese ore
reserves. Iron ore deposits in Bellary district are widespread and have been a backbone to industrial development in the region.
The environmental impact of large scale mining activities includes soil erosion, formation of sinkholes, loss of biodiversity,
and contamination of soil, groundwater and surface water by chemicals from mining processes.In this paper, efforts have been
made to assess the quality of Tunga - Bhadra river water extensive survey and laboratory analysis which would give the
information about ‘Impacts on reservoir water quality’ due to the Iron and steel industry. Also an attempt has made for
controlling the groundwater pollution, which would serve as a basis to evolve suitable management strategy for the District.
Therefore there is a significant changes in values of different parameters of ground water sources indicate the influence of
industrial wastes on ground water.
This study examined water quality in a small residential wetland in Spokane, Washington. Water samples were taken from sites along the drainage gradient leading to the wetland and within the wetland. Concentrations of ions like magnesium and sodium increased from upstream to within the wetland, likely due to evapoconcentration. While concentrations of major ions accumulated in the wetland, concentrations of potential heavy metals like lead and zinc remained below EPA aquatic life standards. The results indicate the wetland shows no evidence of geochemical hazards from surrounding anthropogenic activities like vehicle traffic or land use.
Separation, characterization and leaching behaviors of heavy metals in contam...Alexander Decker
The document summarizes a study on the characterization and leaching behaviors of heavy metals in contaminated river sediments. Sequential extraction tests found that heavy metal concentrations exceeded sediment quality standards. Acidic washing and chelation extraction treatments were then tested to remove heavy metals. Acid washing with 2N HCl for 120 minutes achieved the highest removal rates of 70-90% for different heavy metals. Chelation extraction with 0.5M citric acid for 120 minutes removed 36.69% of copper, while 0.5M EDTA for 120 minutes removed 45.83% of lead and 0.5M malic acid for 120 minutes removed 62.1% of zinc. The study concludes that acid washing and chelation agent extraction
Separation, characterization and leaching behaviors of heavy metals in contam...Alexander Decker
This document discusses a study on the separation, characterization, and leaching behaviors of heavy metals in contaminated river sediments. The study involved analyzing sediment samples from rivers in central Taiwan to understand the concentration and chemical forms of heavy metals present, including copper, lead, zinc, nickel, and chromium. Sequential extraction tests showed most metals exceeded sediment quality standards. Acid washing experiments found heavy metal removal efficiencies were highest for nickel, zinc, lead, copper, and chromium when washed with HCl for 120 minutes. Results indicate some metals exist in residual forms in sediments but become more exchangeable after washing, increasing bioavailability.
Environmental Qualitative assessment of rivers sedimentsGJESM Publication
In this study, the concentrations of heavy metals (Ca, Zn, Cu, Fe, Mn, Ni) in thesediment of Shavoor River in Khuzestan Province in Iran has been investigated. After the library studies and field studies, six samples of water
and sediment were taken from the river in order to evaluate heavy metal pollution in sediments. To determine the
geochemical phases of metals in sediment samples the 5-step method was used for chemical separation. For quantitative assessment of the severity of contamination in the sediments, the geochemical indicators such as enriched factor (EF) and the accumulation index (Igeo) were used. Also, the statistical analyses including methods such as correlation analysis cluster analysis the (CA), were conducted.The results of the experiments showed that the organic matter deposited varies
with the average of 2.49 and ranges between 1.95% and 3.43%. Samples showed concentrations of metals such as calcium, iron, manganese, copper and nickel at all the sampling points were below the global average, whereas the concentration of copper was slightly higher than the global scale. Enriched factor (EF) was calculated for the elements revealed that heavy metals are classified as non-infected. The Geo-accumulation Index showed that the studied elements were uninfected peers. Based on the results of multivariate statistical analysis it was concluded that metals such as manganese, copper, iron, nickel and zinc are mainly natural and calcium metal is likely to have an organic origin.
Assessment of heavy metal pollution index for groundwater around Jharia coalf...Innspub Net
Assessment of the seasonal variations of the groundwater with respect to heavy metals contamination. For this purpose, 29 groundwater samples were collected and analyzed for heavy metals such as cadmium, copper, iron, manganese, lead and zinc of Jharia coalfield region. In majority of the samples, the analyzed heavy metals are well within the desirable limits and water is potable for drinking purposes. However, concentration of the Fe and Mn exceeding the desirable limits in many groundwater samples in both the seasons. The HPI of groundwater was found 9.94 in pre-monsoon season and 5.24 in post-monsoon season. The HPI values of the samples within study area are found below the critical pollution index (100) in both the seasons, which shows that the groundwater was not polluted with respect of heavy metals.
A study of Heavy Metal Pollution in Groundwater of Malwa Region of Punjab, In...IJERA Editor
Among the different types of pollution, heavy metal pollution has become one of the major environmental issues in India. A number of studies show that high level of heavy metal exposure is a frequent cause of permanent intellectual and developmental disabilities. In this present study, the AAS method is used to determine the various heavy metal concentrations for 240 samples of Groundwater distributed in eight districts in Malwa Region of Punjab. The concentration values were compared with Standard Values given by BIS. The results showed that the maximum percentage of groundwater samples of Malwa region is beyond the permissible limits and that’s why not fit for drinking purposes and other domestic activities due to the presence of various heavy metals . The overall groundwater quality of Punjab for Arsenic, lead, Iron, Cobalt, Chromium, zinc and Mercury can also be detected and compared with BIS standards. The aim of this particular study was to investigate the distribution of Heavy metals in groundwater of Malwa Region of Punjab and its greater risks to public health. The results were compared with the recommended standards for drinking water of BIS to know the existing status and trend. Overall, water quality was found as unsatisfactory for drinking purposes in all the samples.
1) The document analyzes the physico-chemical parameters of groundwater samples collected from 10 locations in the Sangrampur Tehsil region of Buldana District, Maharashtra, India.
2) Testing found that most parameters were within acceptable levels for drinking water according to Indian standards, though a few locations showed higher levels of turbidity, COD, calcium, and magnesium after the monsoon season.
3) Overall groundwater quality was deemed not harmful for human use, but some parameters exceeded limits at individual locations possibly due to nearby industrial, mining or sewage influences, indicating increased human impact on water quality.
This document summarizes a study that assessed heavy metal contamination in sediments of the River Ravi in Pakistan. Sediment samples were collected from 19 stations along the river and its tributaries. The study found:
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The quality of any body of surface or ground water
is a function of either both natural influences and human
influences. Without human influences water quality would be
determined by the weathering of bedrock minerals, by the
atmospheric processes of evaporation, transpiration and the
deposition of dust and salt by wind, by the natural leaching of
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1. Spatial and Temporal Distribution of Groundwater
Iron and Manganese in the Chandrapur District of
Maharashtra and their Plausible Sources
Open Viva-Voce Test of the Ph.D. Thesis
Student: Rahul Krishna Kamble
Supervisor: Dr M G Thakare
Gondwana University, Gadchiroli, Maharashtra
Tuesday the February 27, 2018
2. Overview
• The issue
• Problems
• Gaps in the previous studies
• Methodology
• Findings
• Conclusions
• Questions and Answers
2
3. Introduction
• Water is indispensible part of human life.
• Over one billion people lack access to clean
safe water worldwide (Bresline, 2007; NAS,
2009).
• In India 200 million people do not have access
to clean drinking water (Datta, 2008).
3
4. • Without safe drinking water near dwellings,
the health and livelihood of families can be
severely affected (MacDonald et al., 2005;
United Nations, 2000).
• Groundwater exploitation is generally
considered as the only realistic option for
meeting dispersed rural water demand
(MacDonald et al., 2005).
4
5. The issue
5
Chemical constituent No. of States No. of Districts (in parts)
Arsenic 10 86
Fluoride 20 276
Nitrate 21 387
Iron 24 297
(Source: http://wrmin.nic.in/writereddata/CGWB_GroundWaterDepletion.ppt, Accessed October 28, 2015)
Contamination of Groundwater in India
6. Problems
6
IS 10500:1983
Substance/characteristics Requirement
(Desirable limit)
Permissible limit in the absence
of alternative source
Remarks
Iron 0.3 1.0 --
IS 10500:1991
Substance/characteristics Requirement
(Desirable limit)
Permissible limit in the absence
of alternative source
Remarks
Iron 0.3 1.0 --
IS 10500:2004
Substance/characteristics Requirement
(Desirable limit)
Permissible limit in the absence
of alternative source
Remarks
Iron 0.3 1.0 --
IS 10500:2012
Substance/
characteristics
Requirement
(Desirable limit)
Permissible limit in the
absence of alternative source
Remarks
Iron 0.3 No relaxation Total concentration of manganese
(as Mn) and iron (as Fe) shall not
exceed 0.3 mg/L.
Source: IS 10500 (of various years).
8. Objectives
1. To study the distribution of groundwater iron and
manganese concentration in the Chandrapur district.
• RQ 1.1 How groundwater iron concentration is distributed in
the Chandrapur district?
• RQ 1.2 How groundwater manganese concentration is
distributed in the Chandrapur district?
• RQ 1.3 Is there any different between distribution of
groundwater iron and manganese with respect to source such
as dug well and hand pump?
• RQ 1.4 Does age of water source (Dug well and hand pumps)
has any influence on concentration of groundwater iron in
study area?
(RQ = Research Question)
8
9. 2. To study the correlation with depth and distribution of
groundwater iron and manganese.
• RQ 2.1 How concentration of iron is correlated with depth of
water table?
• RQ 2.2 How concentration of manganese is correlated with
depth of water table?
• RQ 2.3 Is there any seasonal influence on concentration of
these heavy metals with respect to depth of water source?
• RQ 2.4 Which water source had higher concentration of these
two heavy metals?
• RQ 2.5 Is the concentration of iron in groundwater is
dependent on depth of the aquifer?
• RQ 2.6 Is the concentration of manganese in groundwater is
dependent on depth of the aquifer?
9
10. 3. To assess impacts of seasonal variation on distribution of
groundwater iron and manganese.
• RQ 3.1 Does different seasons of a year (summer, post-
monsoon and winter) have influence on groundwater iron and
manganese concentration?
• RQ 3.2 In which season maximum and minimum
concentration of these two heavy metals was observed?
• RQ 3.3 Is there any relation between groundwater depth,
season and concentration of these two heavy metals?
• RQ 3.4 Are other water quality parameters had influence on
groundwater iron and manganese concentration?
• RQ 3.5 What is the observation on comparison of these two
heavy metals concentration with Indian Standards for drinking
water?
10
11. 4. To assign plausible sources of iron and manganese in
groundwater.
• RQ 4.1 What can be plausible source(s) for presence of iron
concentration in groundwater?
• RQ 4.2 What can be plausible source(s) for presence of
manganese concentration in groundwater?
11
13. Approach
– Literature survey.
– Identification of sampling locations.
– Water sampling from the identified locations.
– Determination of Fe and Mn conc. from
groundwater samples.
– Distribution of these metals w.r.t. various
aspects.
– Interpretation of data.
– Thesis writing.
13
14. Significance of study
• Seasonal distribution of groundwater Fe and Mn.
• Distribution of groundwater Fe w.r.t. IS 10500:2012.
• Distribution of groundwater Mn w.r.t. WHO standard.
• Below ground level distribution of these two heavy
metals.
• Health threat assessment by ingestion of this water.
• Plausible sources for presence of these heavy metals.
14
15. Literature review
• National and international studies.
• Water contaminants studies were carried out.
• Iron and manganese attracted attention in last
few years.
• Groundwater iron was studied with aesthetic
aspect.
• Selected studies with health aspects for Mn.
• Spatial and temporal distribution was lacking.
• Only some studies for plausible sources.
15
16. Literature review (Cont…)
International studies (Selected)
Joode et al., (2016); Kovacevik (2016); Li et al.,
(2016), Palmucci et al., (2016), Siegal et al., (2015),
Bacquart et al., (2015), Begum et al., (2015), Huang
et al., (2015); etc.
National studies (Selected)
Dwivedi and Vankar (2014); Harish Raju (2014);
Sankar et al., (2014); Savita Kumari et al., (2014);
etc. reported groundwater iron and manganese
concentrations from different study areas.
16
17. Literature review (Cont…)
Health based aspects
• Khan et al., (2013b) ingestion of high level of
iron causes heart diseases, diabetes, thyroid
etc.
• Huang (2003) stated hepatitis B or C infection,
malignant tumour, colorectal tumors, liver,
lung, stomach, kidney diseases etc.
• Hafeman et al., (2007) reported infants
exposed to water manganese (>0.4 mg/L) leads
to elevated mortality risk in first year of life.
17
18. Gaps in previous studies
•No study was carried out pertaining to groundwater
iron and manganese from the Chandrapur district.
•Spatial and temporal distribution was lacking.
•Studies with respect to IS 10500:2012 (Second
Revision) and WHO standard were not carried out.
•Plausible sources of these two heavy metals were
not reported.
18
22. Methodologies
22
Parameter Standard method APHA (2005),
Reference No.
Instrument particular
Colour Visual Comparison method B of 2120 NA
Temperature Mercury thermometer B of 2550 Gera, GTI, India
pH Electrometric method B of 4500-H+ Digital pH meter, Electronics India, Model 101
Conductivity Conductivity meter B of 2510 Digital conductivity meter, Electronics India, Model 601
Total dissolved solids Total dissolved solids dried at 180 oC C of 2540 Hot air oven, Navyug, India
Alkalinity Titration method B of 2320 NA
Total hardness EDTA titration method C of 2340 NA
Chloride Argentometric method B of 4500-Cl- NA
Fluoride SPANDS method D of 4500-F- Double beam UV/Visible spectrophotometer, Electronics
India, Model 1372
Sulphate Turbidimetric method E of 4500-SO4
2- Double beam UV/Visible spectrophotometer, Electronics
India, Model 1372
Phosphate Stannous Chloride method D of 4500-P Double beam UV/Visible spectrophotometer, Electronics
India, Model 1372
Iron Inductively Coupled Plasma-OES C of 3500-Fe ICP-OES, Perkin Elmer, Germany, Dv 7000
Manganese Inductively Coupled Plasma-OES C of 3500-Mn ICP-OES, Perkin Elmer, Germany, Dv 7000
NA = Not Applicable
41. Water source, season and iron, manganese conc.
41
BDL- Below Detection Limit. All values in mg/L.
Season
Groundwater source
Average concentration, mg/L
Iron Manganese
Winter
Dug Well (n = 2, 5.55%) BDL 0.002
Hand Pump (n = 34, 94.44%) 3.005 0.212
Summer
Dug Well (n = 2, 5.55%) 0.176 0.009
Hand Pump (n = 34, 94.44%) 0.763 0.062
Post-monsoon
Dug Well (n = 2, 5.55%) 0.068 0.005
Hand Pump (n = 34, 94.44%) 0.613 0.061
42. Iron and manganese concentration
42
Fig. Groundwater iron conc. Fig. Groundwater manganese conc.
43. Rainfall vs. iron and manganese conc.
43
Rainfall range 1395-1500 mm 1601-1700 mm 1701-1800 mm 1801-1917 mm
Heavy metal
Iron
Average concentration 0.356 3.142 0.790 0.464
Manganese
Average concentration 0.033 0.136 0.117 0.106
Average concentration in mg/L.
44. Altitude vs. iron and manganese conc.
44
Altitude
Heavy metal
Low altitude
(152-197 m asl)
Medium altitude
(198-242 m asl)
High altitude
(243-287 m asl)
Iron 1.730 0.720 4.241
Manganese 0.105 0.107 0.097
Heavy metals average concentrations are reported in mg/L.
Attitude is reported in m above sea level (asl).
45. Altitude and rainfall vs. iron and manganese conc.
45
Rainfall range: 1395-1500 mm as 1, 1501-1600 mm as 2, 1601-1700 mm as 3, 1701-1800 mm as 4 and 1801-1917 mm as 5.
Iron and manganese concentration are average values in that altitude class.
Low, 152-197 m asl Medium, 198-242 m asl High, 243-287 m asl
Rainfall range Sampling location Sampling location Sampling location
1 Nil 6 (24%) 1 (20%)
3 2 (33.33%) 4 (16%) 3 (60%)
4 1 (16.66%) 13 (52%) 1 (20%)
5 3 (50%) 2 (8%) Nil
n=6 (16.66%) n=25 (69.44%) n=5 (13.88%)
Fe = 1.730 mg/L
Mn = 0.105 mg/L
Fe = 0.720 mg/L
Mn = 0.107 mg/L
Fe = 4.241 mg/L
Mn = 0.097 mg/L
53. Average iron and manganese conc. ratio
53
Average iron manganese conc.
ratio scale
Sampling location
(Percentage)
<10.00 13 (36.11%)
10.01 to 50.00 12 (33.33%)
50.01 to 100.00 5 (13.88%)
100.01 to 200.00 4 (11.11%)
200.01 to 400.00 1 (2.77%)
400.01 to 450.00 1 (2.77%)
54. Summary of average iron and manganese conc. ratio
54
Particular Detail
Minimum 0.86 (Chichpalli, HP)
Maximum 404.73 (Ballarpur, HP)
Average 48.58
Standard deviation (±) 78.34
Variance 6137.88
Skewness 3.15
Kurtosis 12.08
65. Distribution of well samples (Iron)
65
Iron conc. (mg/L) Shallow well (<99 ft bgl), n (%) Deep well (>100 ft bgl), n (%)
Winter Summer Post-monsoon Winter Summer Post-monsoon
<0.3 (Acceptable limit) 4 (66.66%) 3 (50%) 5 (83.33%) 12 (40%) 10 (33.33%) 18 (60%)
>0.3 (Above the
acceptable limit)
2 (33.33%) 3 (50%) 1 (16.66%) 18 (60%) 20 (66.66%) 12 (40%)
Iron conc. range above
the acceptable limit
(mg/L)
Total shallow
well samples
(n=6)
Total shallow
well samples
(n=6)
Total shallow
well samples
(n=6)
Total deep well
samples (n=30)
Total deep well
samples
(n=30)
Total deep well
samples (n=30)
0.3-1.0 Nil 2 (66.66%) 1 (100%) 8 (44.44%) 16 (80%) 7 (58.33%)
1.1-2.0 Nil Nil Nil 5 (27.77%) 2 (10%) 4 (33.33%)
2.1-3.0 Nil Nil Nil 2 (11.11%) Nil Nil
3.1-5.0 1 (50%) 1 (33.33%) Nil Nil 2 (10%) 1 (8.33%)
>5.1 1 (50%) Nil Nil 3 (16.66%) Nil Nil
Total shallow
well samples
(n=2)
Total shallow
well samples
(n=3)
Total shallow
well samples
(n=1)
Total deep well
samples (n=18)
Total deep well
samples
(n=20)
Total deep well
samples (n=12)
66. Distribution of well samples (Manganese)
66
Manganese conc.
(mg/L)
Shallow well (<99 ft bgl), n (%) Deep well (>100 ft bgl), n (%)
Winter Summer Post-
monsoon
Winter Summer Post-monsoon
<0.1 (Acceptable
limit)
4 (66.66%) 5 (83.33%) 5 (83.33%) 18 (60%) 24 (80%) 25 (83.33%)
>0.1 (Above the
acceptable limit)
2 (33.33%) 1 (16.66%) 1 (16.66%) 12 (40%) 6 (20%) 5 (16.66%)
Manganese conc.
range above the
acceptable limit
(mg/L)
Total shallow
well samples
(n = 6)
Total shallow
well samples
(n = 6)
Total shallow
well samples
(n = 6)
Total deep well
samples
(n = 30)
Total deep well
samples
(n = 30)
Total deep well
samples
(n = 30)
0.1-0.5 2 (100%) 1 (100%) 1 (100%) 8 (66.66%) 6 (100%) 4 (80%)
0.51-1.0 Nil Nil Nil 3 (25%) Nil 1 (20%)
1.1-2.0 Nil Nil Nil 1 (8.33%) Nil Nil
Total shallow
well samples
(n = 2)
Total shallow
well sample
(n = 1)
Total shallow
well sample
(n = 1)
Total deep well samples (n
= 12)
Total deep well
samples
(n = 6)
Total deep well
samples (n = 5)
67. 67
Water source depth vs. iron concentration
Water source
depth
(ft bgl)
Average
depth
(ft bgl)
Water sample,
n (%)
Iron conc. range (average) in mg/L
Winter Summer Post-monsoon Average
<50 feet 35 3 (8.33) BDL-0.24
(0.080)
0.164-0.439
(0.263)
0.055-0.089
(0.074)
0.139
51-100 92.66 15 (41.66) BDL-47.100
(5.398)
0.171-3.825
(0.922)
0.084-4.022
(0.841)
2.387
101-150 140 7 (19.44) BDL-2.97
(0.883)
0.200-1.134
(0.533)
0.146-1.276
(0.494)
0.636
151-200 190 8 (22.22) BDL-5.74
(1.506)
0.240-0.892
(0.471)
0.098-0.458
(0.183)
0.720
201-250 250 2 (5.55) 0.030-0.687
(0.358)
0.466-0.627
(0.546)
0.155-1.465
(0.810)
0.571
251-300 300 1 (2.77) 1.997
(1.997)
3.084
(3.084)
1.627
(1.627)
2.236
BDL = Below detection limit
71. Iron, manganese conc. distribution on BIS
71
Heavy
metal
Season
IS 10500:2012 Observed concentration (mg/L) Number of sampling location (%)
Acceptable
limit (mg/L)
Permissible
limit (mg/L)
Min. Mix. Average Within the
acceptable limit
Above the
permissible
limit
Iron
Winter 0.3 No
relaxation
BDL 47.100 3.522 16 (44.44%) 20 (55.55%)
Summer 0.164 3.825 0.730 13 (36.11%) 23 (63.88%)
Post-
monsoon
0.055 4.022 0.582 23 (63.88%) 13 (36.11%)
Manganese
Winter 0.1 0.3 BDL 1.853 0.257 22 (61.11%) 7 (19.44%)
Summer 0.003 0.474 0.058 29 (80.55%) 1 (2.77%)
Post-
monsoon
0.002 0.761 0.058 30 (83.33%) 2 (5.55%)
Min.- Minimum, Max. - Maximum, BDL - Below detection limit. BIS into consideration is IS 10500:2012.
72. Fe and Mn distribution on BIS combinations
72
Values of iron and manganese concentrations are reported in mg/L. BIS into consideration is IS 10500:2012.
0.3 mg/L is acceptable limit of iron and 0.1 mg/L is acceptable limit of manganese.
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6
Season Fe <0.3
Mn <0.1
Fe <0.3
Mn >0.1
Fe >0.3
Mn <0.1
Fe >0.3
Mn >0.1
Iron, manganese or both above the
respective acceptable limit
Number of sampling location (%)
Winter 12 (33.33%) 4 (11.11%) 10 (27.77%) 10 (27.77%) n=24, 66.65%
Summer 13 (36.11%) Nil 16 (44.44%) 7 (19.44%) n=23, 63.88%
Post-
monsoon
22 (61.11%) 1 (2.77%) 8 (22.22%) 5 (13.88%) n=14, 38.87%
73. Distribution of Fe and Mn with well structure (Winter)
73
Well structure Heavy metal
Observed concentration (mg/L) Acceptable
limit*
(mg/L)
Comparison with
IS 10500:2012 standard,
Fold increase
Min. Max. Average Max. Average
Shallow well
(<100 ft bgl)
(n = 18, 50%)
Fe BDL 47.100 4.511 0.3 157 15.03
Mn BDL 0.972 0.168 0.1 9.72 1.68
Deep well
(101-150 ft bgl)
(n = 7, 19.44%)
Fe BDL 2.927 0.883 0.3 9.75 2.94
Mn BDL 1.853 0.367 0.1 18.53 3.67
Very deep well
(151-300 ft bgl)
(n = 11, 30.55)
Fe BDL 5.715 1.342 0.3 19.05 4.47
Mn BDL 0.791 0.146 0.1 7.91 1.46
Min.- Minimum, Max. - Maximum, BDL- Below detection limit. *Acceptable limit of IS 10500 : 2012 for iron and manganese
respectively.
74. Distribution of Fe and Mn with well structure (Summer)
74
Min.- Minimum, Max. – Maximum. *Acceptable limit of IS 10500 : 2012 for iron and manganese respectively.
Well structure Heavy
metal
Observed concentration (mg/L) Acceptable
limit*
(mg/L)
Comparison with IS 10500:2012
standard, Fold increase
Min. Max. Average Min. Max. Average
Shallow well
(<100 ft bgl)
(n = 18, 50%)
Fe 0.164 3.825 0.812 0.3 0.54 12.75 2.70
Mn 0.003 0.248 0.048 0.1 0.03 2.48 0.48
Deep well
(101-150 ft
bgl)
(n = 7, 19.44%)
Fe 0.200 1.134 0.533 0.3 0.66 3.78 1.77
Mn 0.004 0.474 0.103 0.1 0.04 4.74 1.03
Very deep well
(151-300 ft
bgl) (n = 11,
30.55%)
Fe 0.240 3.084 0.722 0.3 0.8 10.28 2.40
Mn 0.005 0.201 0.048 0.1 0.05 2.01 0.48
75. Distribution of Fe and Mn with well structure (Post-monsoon)
75
Min.- Minimum, Max. – Maximum. *Acceptable limit of IS 10500 : 2012 for iron and manganese respectively.
Well structure Heavy metal
Observed concentration (mg/L) Acceptable
limit* (mg/L)
Comparison with IS 10500:2012
standard, Fold increase
Min. Max. Average Min. Max. Average
Shallow well
(<100 ft bgl)
(n = 18, 50%)
Fe 0.055 4.022 0.713 0.3 0.183 13.40 2.37
Mn 0.002 0.312 0.049 0.1 0.02 3.12 0.49
Deep well (101-
150 ft bgl)
(n = 7, 19.44%)
Fe 0.146 1.276 0.494 0.3 0.486 4.25 1.64
Mn 0.002 0.761 0.125 0.1 0.02 7.61 1.25
Very deep well
(151-300 ft bgl)
(n = 11, 30.55%)
Fe 0.098 1.627 0.424 0.3 0.32 5.42 1.41
Mn 0.002 0.133 0.029 0.1 0.02 1.33 0.29
80. Well characteristics and distribution of Fe and Mn conc.
80
Variable n Range Average Median
Year of installation 36 1-60 15.27 11
Water source depth (ft bgl) 36 20-300 133.19 110
Altitude (m asl) 36 152-287 211.77 214.5
Iron (mg/L)
Winter 36 BDL-47.100 3.522 0.687
Summer 36 0.164-3.825 0.730 0.400
Post-monsoon 36 0.055-4.022 0.582 0.193
Manganese (mg/L)
Winter 36 BDL-1.853 0.257 0.098
Summer 36 0.003-0.474 0.058 0.016
Post-monsoon 36 0.002-0.761 0.058 0.012
81. Iron conc. categorisation on WHO, JECFA, IOM
81
n - Number of sampling locations. Average values are reported in mg/L.
*WHO (2006) aesthetic cut-off and IS 10500: 2012, Acceptable limit for iron (0.3 mg/L).
†JECFA provisional maximum tolerable daily intake for iron in water (WHO 1984, 2004).
‡Per litre equivalent of the Institute of Medicine (IOM) recommended tolerable upper intake level of 45 mg iron/day for daily iron
intake for adults (excluding iron supplements) assuming 2 L/day water consumption (Otten et al., 2006; WHO, 2006).
Iron conc. category Winter Summer Post-monsoon
n (%) Average n (%) Average n (%) Average
Minimal
0.0-<0.3* mg/L
16
(44.44%)
0.100 13
(36.11%)
0.222 23
(63.88%)
0.154
Elevated
0.3-2.0† mg/L
13
(36.11%)
0.980 20
(55.55%)
0.647 11
(30.55%)
0.880
High
>2.0-22.5‡
mg/L
6
(16.66%)
6.860 3
(8.33%)
3.485 2
(5.55%)
3.868
Very high
>22.5 mg/L
1
(2.77%)
47.100 -- -- -- --
82. Chronic daily intake and Hazard quotient
• Chronic daily intake (CDI) = C x DI/BW
where, C = Concentration of iron
DI = Daily intake of water in L (2 L/day)
BW = Body weight (72 kg) (Chrostowski, 1994
and USEPA, 1992).
• Hazard quotient (HQ) = CDI/RfD
RfD = Reference dose (Gerba, 2001 and USEPA,
1994)
• Hazard Indexmean = HQFe + HQMn
82
83. Chronic daily intake and Hazard quotient
83
Heavy metal Statistics (in mg/L) CDI, mg/kg-day HQ RfD,
mg/kg-daya
Season
Iron
Winter Min BDL, Max 47.100, Average 3.52 BDL, 1.308, 0.098 BDL, 1.869, 0.14
0.7
Summer Min 0.164, Max 3.825, Average 0.73 0.005, 0.106, 0.020 0.007, 0.151, 0.028
Post-
monsoon
Min 0.055, Max 4.022, Average 0.58 0.002, 0.112, 0.016 0.003, 0.160, 0.022
Manganese
Winter Min BDL, Max 1.853, Average 0.25 BDL, 0.051, 0.007 BDL, 0.364, 0.05
0.14
Summer Min 0.003, Max 0.474, Average 0.058 0.0001, 0.0131, 0.0016 0.0007, 0.0936, 0.0114
Post-
monsoon
Min 0.002, Max 0.761, Average 0.058 0.0001, 0.0211, 0.0013 0.0007, 0.1500, 0.0092
CDI - Chronic Daily Intake, HQ - Hazard Quotient, RfD - Reference dose
Min - Minimum, Max – Maximum, BDL- Below detection limit.
aRfD (USEPA, 2005)
84. Chronic daily intake and Hazard quotient
• CDI was below reference dose (RfD) of
respective metal.
• Health risk of ingestion of groundwater may
be negligible.
• The order of heavy metal toxicity Fe>Mn.
• Hazard Index <1.00, groundwater ingestion
confirmed as being safe.
• However, synergism effect of other metals
needs to be considered.
84
88. Pearson’s correlation coefficient water source characteristics (Winter)
88
Altitude Age Depth Iron Manganese
Altitude 1
Age 0.17196 1
Depth 0.07183 -0.1707 1
Iron -0.0496 -0.2125** -0.2009** 1
Manganese -0.0712 -0.1438 0.03149 0.08414 1
*Significant at 0.01 level; ** 0.05 level.
89. Pearson’s correlation coefficient water source characteristics (Summer)
89
*Significant at 0.01 level; ** 0.05 level.
Altitude Age Depth Iron Manganese
Altitude 1
Age 0.17196 1
Depth 0.07183 -0.1707 1
Iron -0.1388 -0.2129** 0.08912 1
Manganese -0.0092 -0.118 0.05821 0.24266** 1
90. Pearson’s correlation coefficient water source characteristics (Post-
monsoon)
90
*Significant at 0.01 level; ** 0.05 level.
Altitude Age Depth Iron Manganese
Altitude 1
Age 0.17196 1
Depth 0.07183 -0.1707 1
Iron -0.373* -0.2392** -0.0129 1
Manganese 0.3173* -0.2686* -0.033 0.04001 1
91. Principle Component Analysis (Winter)
91
Compo
nent
Initial Eigen value Extraction sums of
squared loading
Rotation sums of
squared loadings
Groun
dwater
charac
teristic
Component
matrixa
Rotated
component
matrix
PC1 PC2 PC1 PC2
Total
%Varia
nce
Cumula
tive %
Total
%Vari
ance
Cumul
ative
%
Total
%Varia
nce
Cumul
ative
%
1
2.432 48.637 48.637 2.432 48.637 48.637 1.935 38.706 38.706
Fe -.248 .776 -.263 .771
2
1.264 25.271 73.908 1.264 25.271 73.908 1.760 35.202 73.908
Mn .437 .473 .428 .481
3 .683 13.662 87.570 pH -.108 -.831 -.091 -.833
4 .578 11.553 99.123 TDS .947 -.117 .949 -.098
5 .044 .877 100.000 Cl- .974 .007 .974 .026
Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser Normalisation. a Rotation converged in 3
iterations.
92. Principle Component Analysis (Summer)
92
Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser Normalisation. a Rotation converged in 3
iterations.
Com
pone
nt
Initial Eigen value Extraction sums of
squared loadings
Rotation sums of
squared loadings
Groundwa
ter
characteri
stics
Component
matrixa
Rotated
component
matrix
Total
%Varia
nce
Cumula
tive %
Total
%Varia
nce
Cumul
ative %
Total
%Varia
nce
Cumula
tive %
PC1 PC2 PC1 PC2
1
2.050 40.997 40.997 2.050 40.997 40.997 2.000 40.007 40.007
Fe .172 -.829 -.095 .841
2
1.537 30.749 71.746 1.537 30.749 71.746 1.587 31.740 71.746
Mn .411 -.393 .268 .502
3
.851 17.011 88.757
pH -.256 .746 -.011 -.788
4
.522 10.440 99.196
TDS .934 .297 .980 .009
5
.040 .804 100.000
Cl- .956 .227 .979 .082
93. Principle Component Analysis (Post-monsoon)
93
Extraction method: Principal Component Analysis. Rotation method: Varimax with Kaiser Normalisation.
a Rotation converged in 3 iterations.
Compo
nent
Initial Eigen value Extraction sums of
squared loadings
Rotation sums of
squared loadings
Ground
water
charact
eristics
Component
matrixa
Rotated
component
matrix
Total
%Varian
ce
Cumulat
ive %
Total
%Vari
ance
Cumul
ative
%
Total
%Vari
ance
Cumul
ative
%
PC1 PC2 PC1 PC2
1
2.103 42.057 42.057 2.103 42.057 42.057 1.952 39.036 39.036
Fe -.497 .709 -.102 -.860
2
1.429 28.588 70.645 1.429 28.588 70.645 1.580 31.610 70.645
Mn .117 .313 .251 -.221
3
.962 19.233 89.878
pH .444 -.751 .036 .872
4
.454 9.075 98.953
TDS .931 .308 .966 .169
5
.052 1.047 100.000
Cl- .882 .411 .972 .055
94. One way ANOVA (Iron)
94
df - Degree of freedom, F - F test, Sig. - Significant.
Heavy metal Source of variations Sum of squares df Mean square F Sig.
Iron Between groups 114.638 2 57.319 2.501 0.087
Within groups 2406.085 105 22.915
Total 2520.72 107
95. One way ANOVA (Manganese)
95
df - Degree of freedom, F - F test, Sig. - Significant.
Heavy metal Source of variation Sum of squares df Mean square F Sig.
Manganese Between groups 0.485 2 0.243 4.595 0.012
Within groups 5.547 105 0.053
Total 6.032 107
113. Conclusions
1. Spatial and temporal unsymmetrical
distribution of Fe and Mn conc. was observed.
2. Elevated iron conc. were observed at selected
sampling locations (e.g. Ballarpur, HP).
3. Average Fe and Mn conc. had influence of
water source type (dug well/hand pump).
4. Hand pump samples had more Fe and Mn
conc. as compared with dug well samples.
5. Fe conc. above IS std. was
summer>winter>post-monsoon.
113
114. Conclusions (Cont…)
6. Groundwater pH (acidic) influences Fe and
Mn conc. (maximum concentration).
7. Depth wise distribution of Fe and Mn conc.
was observed.
8. Well structure (Shallow, deep and very deep)
had influence on Fe and Mn conc.
distribution.
9. Fe and Mn conc. above the acceptable limits
of IS 10500:2012 was observed which too
had influence of seasons on it.
114
115. Conclusions (Cont…)
10.Age, altitude and depth (in general) of water
source had no correlation (r) with Fe and Mn
conc.
11.Rainfall contributes in distribution of Fe and
Mn conc.
12.Higher (243-287 m asl) and lower (152-197
m asl) altitude had elevated Fe conc.
13.Altitude and rainfall together contribute to
distribution of Fe; however, Mn does not get
affected by these.
115
116. Conclusions (Cont…)
14.In some sampling locations Mn conc. was
more than Fe conc. (e.g. Naleshwar, HP).
15.Mn conc. above WHO previous standard (0.4
mg/L) was observed in natural aquatic
environment.
16.At a given time in a well probability of both
the metals above the acceptable limit of
IS10500:2012 can be possible.
17.TDS and Cl- had seasonal influence on Fe and
Mn conc.
116
117. Conclusions (Cont…)
18.No correlation (r) was observed between Fe
and Mn conc. in aquatic environment.
19.Inhabitants were under no immediate or
remote health threat from ingestion of this
groundwater.
20.Fe and Mn conc. were from geogenic in
origin.
21.Major contributors for Fe conc. can be
sandstone>granitic gneisses>alluvium and for
Mn, granitic gneisses>sandstone=limestone.
117
118. Future studies
1. An extended investigation for other heavy
metals.
2. Health effects of groundwater Mn on school
children needs to be ascertain.
3. Does this elevated groundwater Fe had
contribution to blood iron level in women.
4. Attempt should be carried out for presence of
groundwater arsenic from the study area.
5. Groundwater Fe and Mn combined removal
technology needs to be developed.
118
119. Contributions in subject domain
1. Groundwater source depth had influence on
Fe and Mn conc.
2. At some sampling locations Mn>Fe.
3. In a well probability of both the metals above
the acceptable limit of IS 10500:2012 can be
possible.
4. Age of water source had no influence on Fe
and Mn conc.
119
120. Contributions in subject domain (Cont…)
5. Groundwater pH had influence on Fe and Mn
conc.
6. TDS and Cl- had seasonal influence on Fe and
Mn conc.
7. In natural aquatic environment Mn conc. of
>0.4 mg/L was observed (previous WHO
standard).
120
121. Contribution in subject domain (Cont…)
8. Shallow and deep well had reported variable
concentrations within and above the acceptable
limit of IS 10500:2012.
9. Shallow well in winter season had maximum Fe
conc.; whereas, deep well in winter season had
maximum Mn conc.
10.Combined concentration of Fe and Mn above the
new ‘remark’ standard of IS 10500:2012 was
observed from number of sampling locations.
121
122. Implications
1. For setting up of future policies w.r.t.
construction of dug wells and installation of
hand-pumps and bore-wells.
2. Findings can be useful for local and district
authorities for identifying ‘threat areas’ and
considerably ‘safe areas’.
3. To arrange an appropriate alternative source
of drinking water so as to reduce exposure of
school students to groundwater manganese.
122
123. Implications (Cont…)
4. Remedial technologies for removal of
groundwater iron and manganese together.
5. The water source (hand pump/dug well) can
be painted with red colour (or any other
suitable colour) as a mark of indication.
6. Future human settlement and development by
Town Planning Department.
7. Due importance to be given by Public Health
Department.
123
124. Recommendations
1. Inhabitants should be made aware of the
presence of these heavy metals.
2. Special indication mark in the form of colour
coding (colour painting) should be carried out
so as to identify these water sources.
3. Cost effective, environment friendly and easy
to adopt iron and manganese removal
technology (together) needs to be developed.
124
125. Recommendations (Cont…)
4. Future planning for groundwater extraction
source identification needs to be carried out
by taking into consideration water source
depth (ft bgl) and water source type (dug
well/hand pump).
5. Geology of the area needs to be given due
attention for future development of
settlements and extraction of groundwater.
125
126. Limitations
1. Only two heavy metals were studied.
2. Health impacts especially on children was not
assessed.
3. Combined removal technology for iron and
manganese was not attempted.
126
127. Publications
1. Kamble, R.K. and Thakare, M.G. Spatial distribution of
groundwater iron and manganese in Chandrapur district,
Central India. Gurukul International Multidisciplinary
Research Journal, 2016, IV (VI): 182-192 (Impact Factor
2.254).
2. Kamble, R.K. and Thakare, M.G. Spatio-Temporal
distribution of groundwater iron in Chandrapur district,
Central India. Gurukul International Multidisciplinary
Research Journal, 2016, III (VI): 253-266 (Impact Factor
2.254).
3. Kamble, R.K. and Thakare, M.G. Spatial distribution of
groundwater manganese in Chandrapur district, Central
India. SPM Students Research Magazine, 2016, 5 (1): 7-39.
127
128. Publications (Cont…)
4. Kamble, R.K. and Thakare, M.G. Spatial distribution of
iron in groundwater of Chandrapur district, Central India.
SPM Students Research Magazine, 2015, 4 (1): 51-66.
5. Kamble, R.K. and Thakare, M.G. Status and role of
manganese in the environment. International Journal of
Environment, 2014, 3 (3): 222-234.
6. Kamble, R.K., Thakare, M.G., Iron in the environment.
Indian Journal of Environmental Protection, 2013, 33 (11),
881-888.
128
129. References (From this presentation)
Bacquart, T., Frisbie, S., Mitchell, E., Grigg, L., Cole, C., Small, C., & Sarkar, B. (2015). Multiple inorganic
toxic substances containing the groundwater of Myingyan township, Myanmar: Arsenic, manganese,
fluoride, iron and uranium. Science of the Total Environment, 517, 232-245.
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131. Acknowledgements
• Hon’ble Shri Shantaramji Potdukhe Sir
• Hon’ble Dr N V Kalyankar Sir
• Hon’ble Prof G Ramakrishna Naidu Sir
• Other Respected Referees (Madam/Sir)
• Hon’ble Dr V S Ainchwar Sir
• Late Dr J A Sheikh Sir
• Dr R P Ingole Sir
• Dr M G Thakare Sir
• Staff members of Ph.D. cell, GU, Gadchiroli
• Staff members of Sardar Patel College, Chandrapur
131
132. Thank you
• Mr Bhupen Burman
• Ms Kavita S Raipurkar Madam
• Mr Prafulkumar P Vaidhya
• Dr Swapnil K Gudadhe
• Ms Naznin
• Ms Priyanka
• Ms Pooja
• Ms Farin
132
133. My Family
• Dr Krishna M Kamble
• Late Mrs Leela K Kamble
• Mrs Pradnya R Kamble
• Mr Amit K Kamble
• Ms Shweta K Kamble
133