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National Water-Quality Assessment Project (NAWQA)

Visit the nawqa science homepage, water-quality data and science for the nation, surface-water quality and ecology, data and science on the nation's streams and rivers, groundwater quality, dive deep—data and science on groundwater, our invisible, vital resource.

The NAWQA Project is the largest component of the NWQP as a primary source of objective and nationally consistent water-quality data and information on the quality of the Nation’s streams and groundwater.

NAWQA Science, Just a Click Away

Learn about current conditions and trends in the quality of the Nation's rivers, streams, and groundwater and the health of aquatic ecosystems. Easy access to data, mappers, and publications!

Water-Quality Quicklinks

• WaterWatch: Real-Time USGS Water-Quality Data
• Water Quality Portal: Data From Hundreds of Agencies
• Sediment Data Portal
• Pesticide Use Maps and Data
• Health-Based Screening Levels for Drinking and Source Water
• BioData: Aquatic Bioassessment Data for the Nation
• National Water-Quality Project Sampling Methods

The National Water Quality Program (NWQP) of the Water Mission Area conducts water-quality monitoring, assessment, and research activities that:

Assess the current quality of the Nation’s freshwater resources and how it is changing over time,

Explain how human activities and natural factors (e.g., land use, water use and climate variability) are affecting the quality of surface water and groundwater resources,

Determine the relative effects of important sources of impairment to water resources including contaminants, excess nutrients and sediment, and altered streamflow on aquatic ecosystems, and

Predict the effects of human activities, climate change, and management strategies on future water-quality and ecosystem conditions.

In 1991, Congress established NAWQA within the USGS to address a fundamental question: “What is the status of the Nation’s water quality and is it getting better or worse?” Since then, the NAWQA Project has been a primary source of objective and nationally consistent water-quality data and information on the quality of the Nation’s streams and groundwater. NAWQA Project data and models provide answers to where, when, and why the Nation’s water quality is degraded, and what can be done to improve and protect it for human and ecosystem needs.

What is a World Water Quality Assessment? 

The mandate.

The United Nations Environment Assembly (UNEA) of the United Nations Environment Programme (UNEP), in its third session held in Nairobi in 2017, adopted UNEP/EA.3/Res.10 on “Addressing water pollution to protect and restore water-related ecosystems”. The resolution recognizes that water from terrestrial, coastal and marine sources is essential for human health, well-being and livelihoods, ecosystem functioning and services, and the survival of all living species. It established a number of vital premises which constitute the foundation for the creation of a pathway towards a World Water Quality Assessment. 

The importance of water

Without good water quality, the health and well-being of humans and ecosystems would disappear. No living species can survive without water from either terrestrial, coastal or marine sources. Therefore, the dangers that pollution on land and in the sea, climate change or severe pathogen contamination, present to both the quantity and quality of water, in turn exacerbated by human activities such as urbanisation, industrial and agricultural activity and a lack of basic sanitation in many regions, represent some of the principal trials which society must undergo in the coming decades. These are scientific, ecological and social issues. It is the poor, the vulnerable, the discriminated, indigenous peoples, the woman and the child who are at most risk to the effects of water quality degradation and scarcity and who bear the brunt of the consequences of extreme weather events and the mismanagement of water resources. 

From data into action

In order to overcome these concerns, UNEP/EA.3/Res.10 outlined an urgency to eradicate the gaps in society’s knowledge of the state of water quality resulting from a lack of data and regular monitoring. It invited member States to establish and improve water quality monitoring networks and to enhance public access to relevant information on water quality status. It stated the case for the promotion of the employment of safe and efficient water use whilst augmenting water quality data collection and subsequent data sharing in order to support the implementation of the water-related Sustainable Development Goals (SDGs). Encouraging all stakeholders to embrace the concept of the engagement, at all political levels, of the public, private, academic, cultural and civil sectors (known collectively as the Quintuple Helix) supported by a strong programme of capacity development, the resolution reiterates that the availability and accessibility of adequate, predictable and sustainable resource mobilisation from all sources, technology development, dissemination, diffusion and transfer, on mutually agreed terms, and capacity-building are important to the effective prevention, reduction and management of water pollution.  

Effective data communication

It is necessary to understand and communicate to all stakeholders, ranging from the hitherto uninformed layperson to the highly specialised expert, from the political decision maker to the individual affected by a specific water quality-related event, what both the key drivers and key pressures are at any given time, in a manner which is accessible and therefore comprehensible to all.  Perhaps the most effective means of presenting water quality indicators in order to effectively inform policy makers is what is known as the Driver-Pressure-State-Impact-Response (DPSIR) Framework. DPSIR is widely used to analyse and understand complex global environmental changes. It provides a systematic means of assessing the impact of human activities on the environment, as well as identifying ways to address such impacts. It takes into account Drivers (the social, economic, political, and technological forces that influence human behaviour and drive environmental change such as population growth, urbanisation, and changes in consumption patterns), Pressures (the environmental factors that are affected by human activities such as pollution, habitat destruction, and resource depletion), State (the current condition of the environment, including the quality of air and water, the health of ecosystems, and the availability of natural resources), Impacts (the ecological, social, and economic consequences of environmental changes and Response (the actions taken to address environmental problems such as policies, regulations, and technological innovations). 

The DPSIR framework constitutes a comprehensive modus operandi and as such has influenced the manner in which the digital product presented here has been planned, so that it is capable of supporting tangible efforts to address a wide array of serious water quality-based threats and issues which were identified in UNEP/EA.3/Res.10 by the United Nations Environment Assembly at its third session in 2017.  

Why is the assessment digital?

This digital product is a pathway to comprehensive World Water Quality Assessment. It responds to the request formulated by the United Nations Environment Assembly to work with relevant international organisations to develop a World Water Quality Assessment.   

Being a digital platform permits the Pathway to the World Water Quality Assessment to be constantly updated and expanded. Just as importantly, it invites all people, no matter their level of expertise, to learn about water quality and how this is one of the topics that touches on all aspects of the triple planetary crisis of nature, pollution and biodiversity. The information contained here is presented in a manner accessible to all audiences by keeping a simple design, permitting the reader to obtain, if required, more in-depth analyses at the click of a button. This user-centred platform is aimed at satisfying the demands of any individual or entity who require information regarding water quality. 

The objective and ambition behind this digital social platform are that within a relatively short period of time, the Pathway to a World Water Quality Assessment becomes a comprehensive assessment, which, by combining research articles, access to data hubs and the inclusion of input from diverse sources of water quality information from around the globe, constitutes the principal point of reference for all water quality stakeholders, a meeting place for all concerned actors and an inspiration to society as a whole to understand, and take action on water pollution, one of the principal risks facing modern society especially in a world where we are not on track to achieve the environmental dimension of the Sustainable Development Goals. 

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Assessing and Reporting Water Quality (Questions and Answers)

This fact sheet answers basic questions about how states assess and report on water quality conditions, as summarized in the Assessment and Total Maximum Daily Load Tracking and Implementation System (ATTAINS) .

• How do states and other jurisdictions assess water quality?
• What is an Integrated Report?
• Why did EPA issue guidance to states to integrate their water quality reports?
• What are the five Integrated Report categories?
• Why aren’t all states integrated?
• When will all the state reports be integrated?
• What is the difference between the "good," "threatened," and "impaired" categories of use support?
• What are "Causes of Impairment"?
• What are "Sources of Impairment"?
• What kinds of monitoring data are used to make water quality assessments?
• Who collects monitoring data?
• Is water quality getting better or worse?
• How do we determine trends in water quality?
• What are statewide statistical surveys?
• What is the difference between statewide statistical surveys and site-specific, targeted monitoring?
• What is the relationship between the findings of the statewide statistical survey and the state’s site-specific, targeted monitoring program?
• What is a Total Maximum Daily Load (TMDL)?
• What is the advantage of the Water Quality Assessment and TMDL Information/ ATTAINS website compared to previous ways of depicting state information?
• Why are some states missing from rollups for specific reporting cycles (e.g., 2004, 2006) in ATTAINS?
• Are data from Tribes and interstate commissions in ATTAINS?

1. How do states and other jurisdictions assess water quality?

Water quality assessment begins with water quality standards. States and other jurisdictions adopt water quality standards for their waters. EPA must then approve these standards before they become effective under the Clean Water Act.

Water quality standards have three elements: the designated uses assigned to waters (e.g., swimming, the protection and propagation of aquatic life, drinking), the criteria or thresholds that protect fish and humans from exposure to levels of pollution that may cause adverse effects, and the anti-degradation policy intended to prevent waters from deteriorating from their current condition.

After setting standards, states assess their waters to determine the degree to which these standards are being met. To do so, states may take biological, chemical, and physical measures of their waters; sample fish tissue and sediments; and evaluate land use data, predictive models, and surveys.

For more information on state water quality standards, visit the National Water Quality Standards site .

An Integrated Report is a biennial state submittal that includes the state’s findings on the status of all its assessed waters (as required under section 305(b) of the Clean Water Act), a listing of its impaired waters and the causes of impairment, and the status of actions being taken to restore impaired waters (as required under section 303(d)).

EPA first issued guidance to the states in 2001 encouraging them to integrate their water quality assessment information into one report. Before the issuance of this guidance, these were separate state 305(b) and 303(d) reports, and in many cases the findings and assessment data in them did not agree. EPA has issued additional guidance on Integrated Reporting in subsequent years.

The purpose of this guidance was to streamline and reduce the reporting burden to the states and improve the information needed to make water quality management decisions. For information on the guidance issued by EPA, see  Implementing Clean Water Act Section 303(d): Impaired Waters and Total Maximum Daily Loads (TMDLs) .

States are required to place their assessed waters in one of five categories, as follows:

A more detailed explanation of the five categories can be found in the 2006 Integrated Report Guidance .

States are as a whole moving toward improved integration of their 305(b) and 303(d) reports. However, EPA guidance on integration is relatively new, and states are not required to integrate their reports. Because 303(d) lists require public comment and EPA approval, this process may delay the development of the 305(b) report, so states may prefer to prepare separate 303(d) and 305(b) reports.

Since states are not REQUIRED to integrate their 305(b) and 303(d) reports, there may always be some states that do not prepare integrated reports. However, most states are working toward integration.

Waters rated by the states as "good" fully support all of their designated uses.

Waters rated by the states as "threatened" currently support all of their designated uses, but one or more of those uses may become impaired in the future (i.e., water quality may be exhibiting a deteriorating trend) if pollution control actions are not taken.

Waters rated as "impaired" by the states cannot support one or more of their designated uses.

Where possible, states, tribes and other jurisdictions identify the pollutants or stressors causing water quality impairment. These causes of impairment keep waters from meeting the criteria adopted by the states to protect designated uses. Causes of impairment include chemical contaminants (such as PCBs, metals, and oxygen-depleting substances), physical conditions (such as elevated temperature, excessive siltation, or alterations of habitat), and biological contaminants (such as bacteria and noxious aquatic weeds).

Where possible, states, tribes and other jurisdictions identify where pollutants or stressors (causes of impairment) are coming from. These sources of impairment are the activities, facilities, or conditions that generate the pollutants that keep waters from meeting the criteria adopted by the states to protect designated uses. Sources of impairment include, for example, municipal sewage treatment plants, factories, storm sewers, modification of hydrology, agricultural runoff, and runoff from city streets.

State water quality assessments are normally based upon five broad types of monitoring data: biological integrity, chemical, physical, habitat, and toxicity. Each type of data yields an assessment that must then be integrated with other data types for an overall assessment. Depending on the designated use, one data type may be more informative than others for making the assessment. For example:

Biological integrity data are objective measurements of aquatic biological communities (usually aquatic insects, fish, or algae) used to evaluate the condition of an aquatic ecosystem. Biological data are best used when deciding whether waters support aquatic life uses.

Chemical data include measurements of key chemical constituents in water, sediments, and fish tissue. Examples of these measurements include metals, oils, pesticides, and nutrients such as nitrogen and phosphorus. Monitoring for specific chemicals helps states identify the causes for impairment and helps trace the source of the impairment.

Physical data include characteristics of water such as temperature, flow, dissolved oxygen, and pH. Physical attributes are useful screening indicators of potential problems, often because they can have an impact on the effects of chemicals.

Habitat assessments include descriptions of sites and surrounding land uses; condition of streamside vegetation; and measurement of features such as stream width, depth, flow and substrate. They are used to supplement and interpret other kinds of data.

Toxicity testing is used to determine whether an aquatic life use is being attained. Toxicity data are generated by exposing selected organisms such as fathead minnows or daphnia ("water fleas") to known dilutions of water taken from the sampling location. These tests can help determine whether poor water quality results from toxins or degraded habitat.

Hundreds of organizations around the country conduct some type of water quality monitoring. They also include state, interstate, tribal and local water quality agencies; research organizations such as universities; industries and sewage and water treatment plants; and citizen volunteer programs. Federal agencies such as the EPA and the U.S. Geological Survey, National Park Service and Forest Service also conduct water quality monitoring. They may collect water quality data for their own purposes or to share with government decision makers. States evaluate and use much of these data when preparing their water quality reports.

EPA’s national STORET Data Warehouse is a repository of much of this water quality information. It serves as an archive to protect our investment in water quality monitoring, and provides the interested public and water resource managers access to the wide range of data collected by these many sources.

It is not appropriate to use the information in this database to make statements about national trends in water quality. The methods states use to monitor and assess their waters and report their findings vary from state to state and even over time. Many states target their limited monitoring resources to waters of interest, and therefore assess only a small percentage of their waters. These may not reflect conditions in state waters as a whole. States often monitor a different set of waters from cycle to cycle. Even weather conditions - such as prolonged drought - can have an impact on whether waters meet their standards from one year to the next.

The science of monitoring and assessment itself changes. We know, for example, that a number of states have increased the amount of fish tissue sampling they conduct and as a result are finding more problems and issuing more protective fish consumption advisories. Improved monitoring, in short, can affect the information in this database by increasing the identification of water quality problems. States may also, over time, change how they issue or report fish consumption advisories.

National water quality trends are best determined using scientifically-based studies designed to sample water quality conditions at randomly-selected sites that are statistically representative of the Nation's many distinct ecological regions. EPA and the states have embarked on such probability-based studies of coastal waters, lakes and reservoirs, rivers and streams, and wetlands. For more information, see the National Aquatic Resource Surveys site .

Statewide statistical surveys are water quality assessments designed to yield unbiased estimates of the condition of a whole resource (such as all lakes or streams in a state) based on monitoring a representative sample of those waters. They can be used to track trends in water condition at the state scale or sub-state scale. Statistical surveys use standardized methods to quantify, with documented confidence, the extent of water quality problems and the extent of key stressors. Statistical surveys complement more traditional targeted monitoring and assessment programs that generally target only waters of concern or interest.

EPA’s 2010 Integrated Reporting guidance includes a reporting template to help states report the results of their statewide statistical surveys using ATTAINS.

States use two main approaches to assess water quality: statistical surveys and t argeted monitoring . Much like opinion polls or indicators of economic health, statewide statistical surveys sample a representative yet randomly selected set of waters of a certain type (e.g., streams and rivers, lakes) and draw unbiased estimates of the condition of all waters of that same type in the state.

Site-specific targeted monitoring, on the other hand, is aimed only at those waters judged to be of concern or interest. Targeted monitoring is used to provide needed information to support management decisions at watershed and local scales (e.g., whether a water meets its water quality standards) for only those individual waters monitored and should not be extrapolated to the larger universe of all of a state’s rivers and streams, lakes, etc.

There are many differences between statewide statistical surveys and site-specific targeted monitoring, even though it is possible that their findings may appear similar. These differences affect how they are best used to inform water quality management.

The two approaches differ in scope and design . They are assessing two different populations: statistical surveys generate an unbiased estimate of the whole resource (such as all streams), while targeted monitoring addresses only the subset of waters determined by the state to be of concern or particular interest. The two approaches may also differ in method . Statistical surveys use a set of consistent sampling and analytical methods to ensure that results can be aggregated and compared over time. A state’s targeted monitoring program may rely on sampling methods that vary by waterbody or watershed, management need, or over time.

State statistical surveys provide a standardized measure for tracking changes over time and evaluating, at a broad scale, progress in investments to protect and restore water quality. Targeted monitoring provides information on the nature of water quality problems for the subset of those waters that were assessed, allows the state to identify individual waters that are not meeting water quality standards, and helps states set priorities for those waters.

A Total Maximum Daily Load, or TMDL, is a calculation of the maximum amount of a pollutant that can be present in a segment and still allow attainment of water quality standards, and an allocation of that amount to the pollutant’s sources. The TMDL calculation is TMDL = WLA + LA + MOS, where, WLA is the sum of wasteload allocations (point sources), LA is the sum of load allocations (nonpoint sources and background), and MOS is the margin of safety.

The MOS accounts for any lack of knowledge concerning the relationship between load and wasteload allocations and water quality. The TMDL analysis must take into account critical conditions such as high and low flows and seasonal variations in water quality. The waste load allocation in a TMDL is implemented through NPDES permits, but there is no federal regulatory requirement to implement the allocation to nonpoint sources.

ATTAINS provides one dynamic, continuously updated website where water quality managers and the public can go to view a wide range of state-reported water quality information. ATTAINS for the first time allows the user to view tables and charts that summarize state-reported data for the nation as a whole, for individual states, for individual waters, and for the ten EPA regions. It gives the “full story” showing which waters have been assessed, which are impaired, and which are being (or have been) restored. The user can select the most recent available information or sort by reporting cycle. By displaying Integrated Report data in one location, ATTAINS allows for a more informed summary of the quality of state waters and will provide decision makers with better information on the actions necessary to protect and restore the waters of the U.S.

Some states are missing from cycle-specific rollups in ATTAINS because they either did not submit electronic data for that cycle by a final date required by EPA, or submitted data in a format incompatible with EPA systems. You may be able to find water quality information on that state by visiting the state water quality assessment website.

No. Neither tribes nor interstate commissions are required to submit 305(b) or 303(d) reports to EPA. However, they may have similar information posted on their own websites.

• Integrated Water Analysis
• Ambient Water Quality
• Community Financing
• Drinking Water
• Water Restoration
• Water Quality Models

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• Published: 11 June 2024

Assessment of drinking water quality using Water Quality Index and synthetic pollution index in urban areas of mega city Lahore: a GIS-based approach

• Maria Latif 1   na1 ,
• Nimra Nasir 1 ,
• Rab Nawaz 1 , 2 ,
• Iqra Nasim 1   na1 ,
• Khawar Sultan 1 ,
• Muhammad Atif Irshad 1 ,
• Ali Irfan 3 ,
• Turki M. Dawoud 4 ,
• Youssouf Ali Younous 5 ,
• Zulkfil Ahmed 6 &
• Mohammed Bourhia 7  

Scientific Reports volume  14 , Article number:  13416 ( 2024 ) Cite this article

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Metrics details

• Environmental sciences

The aim of the present study was to assess the drinking water quality in the selected urban areas of Lahore and to comprehend the public health status by addressing the basic drinking water quality parameters. Total 50 tap water samples were collected from groundwater in the two selected areas of district Lahore i.e., Gulshan-e-Ravi (site 1) and Samanabad (site 2). Water samples were analyzed in the laboratory to elucidate physico-chemical parameters including pH, turbidity, temperature, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), total hardness, magnesium hardness, and calcium hardness. These physico-chemical parameters were used to examine the Water Quality Index (WQI) and Synthetic Pollution Index (SPI) in order to characterize the water quality. Results of th selected physico-chemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. A GIS-based approach was used for mapping water quality, WQI, and SPI. Results of the present study revealed that the average value of temperature, pH, and DO of both study sites were within the WHO guidelines of 23.5 °C, 7.7, and 6.9 mg/L, respectively. The TDS level of site 1 was 192.56 mg/L (within WHO guidelines) and whereas, in site 2 it was found 612.84 mg/L (higher than WHO guidelines), respectively. Calcium hardness of site 1 and site 2 was observed within the range from 25.04 to 65.732 mg/L but, magnesium hardness values were higher than WHO guidelines. The major reason for poor water quality is old, worn-out water supply pipelines and improper waste disposal in the selected areas. The average WQI was found as 59.66 for site 1 and 77.30 for site 2. Results showed that the quality of the water was classified as “poor” for site 1 and “very poor “ for site 2. There is a need to address the problem of poor water quality and also raise the public awareness about the quality of drinking water and its associated health impacts.

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Introduction.

One of the most important necessities for preserving human health is the availability of clean drinking water. Water is the most prevalent compound on the Earth surface and is a renewable resource that is necessary for life to exist. Unfortunately, the water will become increasingly scarcer as a result of population growth, urbanization, and climate change 1 . Industrial effluents are recognized as major contaminants in groundwater and sewer waters. Industries that release waste and effluents into water bodies without treatment have affected the environment, endangered human health, and disrupted aquatic ecosystems. Groundwater is contaminated with heavy metals and other pollutants due to the widespread dumping of industrial waste and effluents into water bodies without any kind of treatment or filtration. For the efficient treatment of water bodies, there are a variety of treatment technologies such as improved oxidation processes, phytoremediation, and nano-remediation 2 , 3 , 4 . Water resources in developing nations, such as Pakistan, are contaminated due to a number of industrial and human activities. People rely on heavily contaminated sources, like shallow wells and boreholes, for drinking water due to the insufficient water supply, which creates serious health hazards. In addition, these polluted water sources are unfit for residential use, which makes it even harder for communities to get access to clean and safe drinking water 5 .

Recent technological developments, such as chemical composition of pipelines, can occasionally result in the pollution of drinking water with biological, physical, and chemical pollutants. Improper or inadequate supply of drinking water poses a serious threat to public health. The water quality in most of the Pakistani cities is deteriorating rapidly 6 . Human health is greatly affected by the lack of access to sanitary facilities and clean water. Every year, the use of tainted water and inadequate sanitation systems cause around 2.2 million deaths among the population in poorer nations. The Sustainable Development Goals (SDGs) estimate that 1.2 billion people worldwide lack access to even the most basic services related to water. Remarkably, eight out of ten people live in rural regions without access to basic drinking water services, and nearly half of them live in Least Developed Countries (LDCs) 7 . Water-borne illnesses account for over 60% of infant mortality rates. In Pakistan's rural areas, around 90% of the population lacks access to clean drinking water 8 . The UNICEF research states that 12.6% of newborn deaths and 7% of fertility in Pakistan are connected to water-associated diseases, including as cholera, diarrhea, malaria, hepatitis, typhoid fever, dysentery, and giardiasis, and that between 20 and 40% of patients in the country’s hospitals suffer from these conditions. Every year in Pakistan, between 0.2 and 0.25 million children die from diarrhea. Every year, 10,000 people in Karachi die from kidney infections caused by contaminated drinking water 9 .

Industrial discharge can have a substantial impact on drinking water quality by bringing numerous pollutants into the environment, which in turn causes 82% of diseases like cholera, dysentery, and typhoid. Water bodies are primarily affected due to discharge of untreated or inadequately treated effluents that contain harmful pollutants such heavy metals, organic compounds, and excessive salt content. The safety and quality of drinking water resources are at stake because these pollutants have the ability to contaminate surface water and seep into groundwater sources. Inadequate management and elimination of industrial waste can result in enduring environmental deterioration and possible health risks for populations reliant on these water supplies 10 . An official survey conducted across 12 districts in Punjab indicated that around 79% of drinking water samples were contaminated, while 88% of drinking water in rural areas was contaminated due to sewage discharge, heavy metals, microorganisms, and industrial effluents 11 , 12 .

It is crucial to comprehend the spatial distribution of environmental features in order to evaluate the quality of drinking water. However, it can be expensive to monitor the water quality, particularly in big groundwater basins. Therefore, dependable and flexible gadgets will be required to solve such problems. Use of technologies like geographic information systems (GIS) facilitate spatial analysis, water quality monitoring, and support strategic planning and decision-making processes linked to water management. In the event of an emergency or outbreak of a waterborne disease, GIS can also help with real-time tracking, monitoring, and visualization of the impacted areas, the people at risk, and the resources that are available. GIS-generated interactive maps and visualizations can be used to make communities aware about water-related issues and to increase public understanding of potential hazards, sources, and quality of drinking water. Through the application of GIS technology, the drinking water quality of the entire region may be presented with fewer observations, which lowers costs and improves the overall effectiveness of water management and monitoring initiatives. Synthetic pollution index (SPI) and the water quality index (WQI) are among the most often used methods for classifying and reflecting the quality of the water and pollution risk in a given area. WQI and SPI have been utilized by researchers from several countries to evaluate the water quality in various places 13 , 14 .

The purpose of the present study was to evaluate the quality of the drinking water in the Gulshan-e-Ravi and Samanabad localities using the WQI and SPI. These areas are particularly ancient and are posing health risks including waterborne and microbial ailments like diarrhea and cholera, etc. Major reasons that lie behind the water pollution are industrial discharges, low groundwater levels, dumping, and old and worn-out metallic pipes. The study area i.e., Gulshan-e-Ravi, and some colonies of Samanabad zone has severe issue of water pollution. This can be observed in the form of common waterborn diseases rate. The key objectives of the present study are to identify the location points having high physico-chemical attributes by the GIS approach and to calculate the WQI and SPI of selected areas by determining the physico-chemical parameters in the water samples.

Materials and methods

The study was conducted in a densely populated city, Lahore, Punjab province, Pakistan. The longitude and latitude are 31.5204° N and 74.3587° E 15 . Study areas were Gulshan-e-Ravi and Samanabad town which are situated in the Samanabad zone of Lahore as shown in Fig.  1 . Lahore is located on the northeast side of the country having an international border (Wahga Border) with India. The northern part of the city is considered a walled city (old city). It is the provincial capital of Punjab. Pakistan’s second most populated city is Lahore with a population of more than 13 million. It is the 26th most populated city in the world 16 . The climate of Lahore is comprised of five seasons. Pakistan is fortunate to have these distinct seasons: summer, winter, autmn, spring, and monsoon. These unique weather patterns and seasons are important to Pakistan’s geographical circumstances. The hottest month of Lahore is June having a 38.2 °C average temperature, while the coldest month is January having a 12 °C average temperature. In monsoon months, maximum rainfall is observed 17 . According to the 2017 census, Lahore’s population is 11,126,285. During the past decades, the inhabitants of Lahore have grown extensively.

Map showing study areas with sampling sites.

Hydrogeological setting

Lahore aquifer, a 400-m-thick unconsolidated alluvial complex in Bari Doab, is a highly transmissive, 25–70 m/day hydraulic conductor in the south-flowing Indus River system. It is reported that there are two aquifers in the Lahore area, the shallow and deep, separated by an aquitard 18 . Despite the heterogeneous composition of the alluvial complex, groundwater occurs under water table conditions 19 . Another study found that the soil in the Lahore area is predominately composed of quartz, muscovite, and clinochlore as major minerals with small percentages of heavy minerals and can be classified as silty clay forming part of Pleistocene deposits 20 . The River Ravi is a major recharge source and controls the overall hydrological flow in the study area. The area is generally flat, sloping slightly to the south and southwest direction with a gradient of 0.3–0.4 m/km.

With respect to land cover/land use of study areas, Gulshan-e-Ravi, a predominantly residential area in Lahore, offers a diverse range of housing options, including multi-story apartment complexes and large-yard homes. Gulshan-e-Ravi, situated on the Ravi River’s eastern bank is characterized by alluvial soils and sedimentary deposits, making it ideal for farming. Gulshan-e-Ravi’s hydrogeological characteristics are impacted by the Ravi River’s proximity. Groundwater is a major source of water for residents, primarily accessible through boreholes and tube wells. The most dominant land use in the study area is residential with a densly populated housing setting that changed rapidly between the years 2000 to 2005 and 2010 to 2015 21 , 22 . The local geological characteristics and the depth of the groundwater table can affect both the quantity and quality of the water. However, Samanabad, an older and established residential area in Lahore, is predominantly urban with a mixture of modern constructions and narrow streets. It is located on alluvial plains with sedimentary layers, has rich, ideal soil for agriculture, despite decreased agricultural land due to urbanization, and its water source comes from groundwater.

Physico-chemical analysis

Drinking water samples were collected from 2 locations in Lahore i.e., Gulshan-e-Ravi and Samanabad, which are old urban areas of District Lahore, Pakistan. 25 water samples were collected from both areas. New plastic water bottles were used for sample collection to avoid any kind of contamination, along with proper care and labeling of bottles. Bottles were properly washed 2 to 3 times and dried before the sampling. Random sampling method was used to collect the water samples at various locations within the study areas during the months of August and September. Water quality parameters are significantly influenced by the monsoon season, characterized by heavy rainfall. This precipitation can lead to increased runoff from urban areas, potentially transporting pollutants into water bodies. Rising temperatures, agricultural practices, and the discharge of untreated wastewater further contribute to the complex interplay of factors affecting water quality during these months 23 .

A tap was run for 2–3 min before collecting a sample to help flush out stagnant water. The sample bottle was held below tap flow, filled to the specific line, and sealed. The sample bottle was then labled. Longitudes and latitudes were also recorded instantly. Numerous water quality parameters, including physical (temperature, turbidity, TDS) and chemical (pH, EC, DO, total hardness (TH), calcium (Ca +2 ) hardness, and magnesium (Mg +2 ) hardness) were determined in the laboratory. Temperature and pH were measured at the site of sample collection. All physico-chemical parameters were analyzed by following the standard methods of the American Public Health Organization (APHA) and the American Society for Testing and Materials (ASTM) as shown in Table 1 . These guidelines were followed throughout the examination process. This commitment to established standards not only ensures the accuracy and reliability of the data but also facilitates comparability with existing research, contributing to a more robust and credible assessment of groundwater quality. ArcGIS (10.8) software was used, employing the interpolation technique, to develop spatial maps for identifying the areas with polluted drinking water.

Total hardness (TH), Ca +2 hardness, and Mg +2 hardness were analyzed using the standard EDTA titration method. For total hardness, an ammonia buffer solution was prepared and added to a 50-water sample. A pinch of Erichrome Black-T was added and suddenly the color of the sample changed from transparent to wine red. Then it was titrated against EDTA present in the burette, the colour changed to dark blue. Total hardness was calculated by observing the initial and final burette readings by the following formula;

For calcium (Ca +2 ) hardness, 2 ml of 1 M NaOH (sodium hydroxide) solution was prepared and a few drops were added to the 50 ml of water sample with the help of a pipette. Then after stirring, a pinch of murexide (C 8 H 3 N 5 O 6 –2 ) was added to the water sample. After a little stirring, the sample watercolor was changed to a pink color. Then, it was titrated against the EDTA solution present in the burette, and the color was changed from pink to purple indicating the presence of calcium. Calcium hardness was calculated by following the formula;

Magnesium (Mg +2 ) hardness is calculated by the difference between total and calcium hardness by given following formula;

Water Quality Index (WQI)

The nine significant physico-chemical parameters were utilized for estimation of WQI from the study site to assess the quality of drinking water. WHO permissible values for drinking water were used to compare these parameters using the formula for calculating WQI 25 .

To analyze WQI, firstly relative weight ( W i ) was calculated using the given formula:

K was calculated using;

where, Wi is the unit weight factor, K is the proportional constant, Si is the standard permissible value of i th parameter.

The unit weight for all the chosen nine parameters with their standard values was calculated. A number that reflects the relative value of the given parameter in the contaminated water referring to its permissible standard value is the quality rating scale (Qi) and it was calculated using the formula;

where, Q i is the quality rating scale of i th parameter, V i is the estimated permissible value and S i is standard permissible value of i th parameter.

All the values of V o were taken as 0 for the drinking water, except for pH and DO i.e., 7.0 and 40 ppm. After finding w i and q i ,, both values were multiplied with each other by having w i q i and then it was divided with w i :

Then overall WQI was calculated;

WQI for water samples of both sites 1 and 2 was calculated using the Eq. ( 10 ). WQI generally ranges between good to poor category 26 . The water quality of the selected areas was classified into different categories using WQI, as given in Table 2 .

Calculation of synthetic pollution index (SPI) model

The derivation and calculation of SPI involves different steps given below 28 :

Step 1: Constant of proportionality ( Ki ):

Step 2: Weight coefficient ( Wi ):

Step 3: Synthetic pollution index (SPI):

where, Si is the threshold value for an i th physicochemical parameter as per WHO guidelines and n is the total number of water quality parameters considered for analysis. Based on SPI, the water quality is classified into five categories as shown in Table 3 ;

Results and discussion

Results from the present study revealed the significant variations in different physico-chemical parameters of sampling sites. Some of the water samples had paramters’ values below and some of them had above the WHO guidelines for drinking water.

Analysis based on physico-chemical parameters

The pH level of a solution indicates its alkalinity or acidity, determined by the concentration of hydrogen ions within the solution. Typically, the pH scale varies from 0 to 14. At 25 °C, the acidic aqueous solutions have a pH under 7. While basic or alkaline aqueous solutions have a pH above 7. Furthermore, a pH level of 7 at 25 °C is considered as “neutral”. As the H 3 O + ions concentration becomes equal to the OH – ions concentration in pure water. Strong bases may have a pH above 7 to 14, while strong acids have a pH of less than 7 to 0 29 . WHO guideline for pH of drinking water is 6.5 to 8.5. In this study, most of the water samples had pH between the range, as shown in Fig.  2 . Block 1 of site 1 (Gulshan-e-Ravi) average pH was slightly below the WHO guideline i.e., 5.87. While, Block 1 and 3 of site 2 (Samanabad) average pH was slightly above the guideline i.e., 8.54 and 8.66. Some of the areas in Blocks 1 and 3 had water contamination issues due to old and corrosive water pipelines. The blue color in the pH map indicates high pH values (alkaline) exceeding the WHO guidelines in the study sites figure. Most of the problem lies in acidic water compared to basic water which causes skin issues. Also, the human kidney system is considered to be the best filtration system to maintain the acid–base situation in the human body. And alkaline water has an advantage in improving gut health and lowering blood sugar levels 30 .

Concentration of physical parameters: Temperature ( a ), TDS ( b ), and Turbidity ( c ) in drinking water of selected areas of Lahore.

The temperature of the water is also important physical parameter for assessing water quality. Temperature can affect many other factors as well and it can alter chemical and physical properties of water 31 .

According to WHO, the standard temperature for drinking water should be between 20 and 25 °C. Both study sites had temperatures within range except for Block 3 of Site 1 and Block 1 of Site 2. Both values were slightly above the guideline and this might be mainly due to the sample collection season. The temperature map shows variations in the temperature of both study sites. The most significant temperature fluctuations are depicted in yellow (22–24 °C), followed by orange and then green. Areas exceeding WHO guidelines are presented in red color. High temperatures may increase the microbial activity and this can affect other parameters such as pH and electrical conductivity.

The TDS consists of inorganic salts including Ca +2 , Cl − , K+, Na+, Mg +2 , HCO 3 −1 , and SO 4 −2 and a few other small amounts of such organic contents, minerals or metals which are dissolved in a specific amount of water 32 . Higher levels of TDS affect the drinking water quality. According to a study 33 , TDS in drinking water shouldn’t be more than 500 mg/L or ppm. If it exceeds more than 600 or 1000 mg/L, it is not considered fit for drinking. TDS are mostly increased by industrial sewage, rocks, urban runoff, silt, and the use of fertilizers and pesticides. WHO guideline for TDS in drinking water is 600 mg/L. Site 1 samples had TDS within the WHO guideline with an average maximum value of 192.5 mg/L. While site 2 had serious issues regarding TDS in drinking water. Drinking water in 4 out of 5 blocks had TDS higher than the WHO guideline, as shown in Fig.  2 . The average highest value of TDS was 779 mg/L in Block 1 of site 2. While, 80% of site 2 water samples had TDS higher than guidelines, exceeding 1000 mg/L.

The management of groundwater for domestic and agricultural consumption requires a thorough qualitative assessment and a comprehensive understanding of spatial variation 34 , 35 , 36 . For this purpose, spatial distribution maps were also incorporated into this study as shown in Fig.  3 . The map of chemical parameters such as TDS indicates high TDS values exceeding WHO standards in Site 2 (yellow, orange, and red). The most of the water samples had TDS level between 472 and 672 mg/L in site 2 while, Site 1 had a TDS level within the permissible range of WHO standards which is indicated by blue color (137–305 mg/L), as shown in Tables 4 and 5 . High levels of TDS in drinking water and domestic use can lead to nausea, vomiting, dizziness, lung irritation, and rashes. While long term usage of such water can cause chronic health issues such as liver and kidney failures, cancer, weak immunity, nervous system disorders, and birth defects in newborn babies. Pakistan is facing health risks due to poor monitoring and maintenance, ranking as one of South Asia’s most water-polluted countries with urban areas contributing to increasing health and environmental issues 37 , 38 .

Water quality status map for Gulshan Ravi (Site 1) and Samanabad (Site 2): Temperature ( a ), Turbidity ( b ), and TDS ( c ) are visualized through a GIS-based map, illustrating the water quality conditions at both sites.

Turbidity is caused by suspended waterborne particles, including fine inorganic or organic substances, sediment, and microscopic organisms like algae, scattering of light, and cloudy or opaque appearance of water. These particles can consist of fine sediments like silt or clay, and various others. A low level of turbidity indicates high clarity of water, while a high level of turbidity indicates low clarity of water 39 . According to a study 40 , drinking water turbidity should be less than 5 NTU (Nephelometric Turbidity Unit). High levels of turbidity may not seem aesthetically clean and water is not fit for drinking purpose. Most of the samples were within the permissible range recommended by WHO that indicated that the water in these sites was clear. One case was detected in block 1 of site 2, which had a slight 1% high turbidity in water, still, it made the water cloudy. The highest turbidity issue was reported in both sites but especially in site 2 have a turbidity of more than 5 NTU. High turbidity can hinder disinfection issues in the water and it can lead to high growth of microorganisms such as parasites, bacteria, and viruses. Drinking water with high turbidity can cause nausea, diarrhea, cramps, and headaches especially in infants, as they are more prone to diseases. Other than those, the elderly and weak immunity people can also be affected by such problems. Also, it seems poor aesthetically 33 and people boil water before use in case of high turbidity of water.

A greater EC indicates that the groundwater is more enriched in the salts. For dissolved ionizable solid (Na, Ca, and Mg salts) concentrations and salinity, EC works as an indicator. Due to the effect of anthropogenic activities, more pollutants move into groundwater and hence, EC increases 41 , 42 . It is measured in micro-Siemens per centimeters (µS/cm) or milli-Siemens per centimeters (mS/cm). i.e., 1 mS = 1000 µS and 1 µS = 0.001 mS. WHO guideline for EC in drinking water rages from 200 to 800 µS/cm, while 800 µS/cm is the MPL for drinking water. Block 2 (849.8), 3 (814.4), and 5 (844.6) of site 1 had more EC than the standard. While in site 2, block 1 (877.6), 3 (897), and 4 (818) had high EC specifically block 3 had the highest one followed by block 2 of site 2 and then block 2 and 5 of site 1. The highest EC (> 800 µS/cm) was recorded in both sites which is indicated by the white and tea-pink color. High EC causes high corrosiveness in the water. EC has no direct health link but it can lead to other fluctuations in parameters like pH, total hardness, and TDS, which can cause minerals like the taste of water and health issues like skin problems and gut problems.

Dissolved oxygen (DO), necessary for aquatic life, can be negatively affected by the presence of organic material, agricultural runoff and leaching, industrial waste, and dissolved gases, with concentrations below 5.0 mg/L 32 . Sufficient DO is essential for water quality, higher levels of DO affect aquatic life and potentially corrode water pipes, while low levels indicate increased microbial activity. WHO guideline for drinking water DO is 6.5 to 8 mg/L or ppm. Almost every sample was within the range of standard. The permissible value of DO in water indicates that oxygen concentration is fine for drinking purposes. DO concentration of both sites was within the range of WHO guideline except for 2 to 3 sampling points of site 2. The reason might be some anthropogenic factors which increase temperature and hence microbial activity starts.

Total hardness indictaes the magnesium (Mg) and calcium (Ca) dissolved in the water, to measure the solubility of water for drinking purposes, local households, and some industrial applications credited to the occurrence of Ca +2 , Mg 2+ , Cl − , HCO 3 −1 , and SO 4 −2 . Specifically, alkaline earth metals Ca and Mg in dissolved form, play an important role in water hardness. It is measured in milligrams per liter (mg/L) of calcium carbonates by combining overall contents 32 .

Water having hardness below 75 mg/L is soft water, followed by 76 to 150 mg/L lies in moderately hard water, 151–300 mg/L is categorized as hard water and more than 300 mg/L is considered very hard water 43 . WHO guidelines for total hardness should be no more than 500 mg/L in drinking water. Block 5 of site 2 had the highest water hardness recorded in the study area. Overall, site 2 water samples were mostly in the very hard water category and in contrast with site 1, most of the samples were soft water and moderately hard. This indicates that site 1 samples were in the permissible range of WHO having good water quality while site 2 had hard water issue due to densely populated areas and old scaly waterpipes. According to a study 44 , effective management strategies are required to prevent groundwater contamination and pollution, primarily in monitoring wells, and ensure daily access to alternative water sources for the local population.

The majority of water hardness was observed in site 2 having light and dark pink colors. While site 1 had total hardness within range and was shown with light and dark blue color. Although water hardness is not a health concern, it can cause problems in the home while washing clothes, dishwashing, bathing, and making clothes stiff and rough. Sticky soap curd is formed when soap is utilized with hard water, this can cause hurdles while cleaning. Also, it causes psychological issues may happen when this type of situation happens. While bathing, when the soap curd sticks with the body it prevents the removal of bacteria or dirt from the body and this can cause irritation and allergic itching problems in humans. In addition, water hardness reduces water flow in pipelines and hence Ca +2 and Mg 2+ deposits in the pipelines ultimately require pipe replacement 45 .

The amount of dissolved calcium in the water is represented in mg/L or ppm (parts per million) of calcium carbonates. Limestone is the major source of Ca hardness in water. Also, calcium can react with Fe, Zn, P, and Mg while reducing the absorption of other minerals. WHO standard for Ca 2+ contents or hardness in drinking water is 60 to 120 mg/L. Whereas, 120 mg/L is the MPL for any drinking water. Ca 2+ contents in current study areas varied depending upon the location. Block 1 and 5 of site 2 had the highest Ca 2+ contents i.e., 123 and 118 mg/L, which exceeded the standard of WHO. Site 1 of the study area had Ca 2+ contents within the permissible range and the water was considered as soft as shown in Fig.  5 . Moderately high Ca hardness was observed in some areas of Site 2 (shown with dark blue color). While most of the water samples of both study sites were within range as shown in Fig.  4 . High Ca 2+ contents can weaken the bones; forms kidney stones and it also interfere with our brain and heart working. All of these problems can lead to hypercalcemia.

Concentration of selected chemical parameters: pH ( a ), EC ( b ), DO ( c ), Total Hardness ( d ), Calcium hardness ( e ), and Mg hardness ( f ) in drinking water of selected sites of Lahore.

The presence of high Mg 2+ in the form of SO 4 −2 and CO 3 −2 in drinking water is magnesium hardness. It is measured in mg/L or ppm. Dolomite is the major cause of magnesium hardness in water 46 . The WHO standard for Mg 2+ concentration in water is 50 mg/L. Most of the values in the current study site were in the permissible range, but a few like block 1 of site 1 and block 5 of site two had slightly higher Mg 2+ content in water as shown in Fig.  4 . This indicates that Mg 2+ deposits were present in the pipelines due to high sewage content. The map indicates a magnesium hardness trend in both areas. Some areas with dark purple values had high Mg hardness as compared to others with light colors. High Mg 2+ can cause hypermagnesemia, which causes renal failure resulting in the reduced ability to remove magnesium from the kidney. Bowel functions can also be disturbed by high Mg 2+ contents. The local hydrological setting controls water movement in the south and southwest directions towards the Ravi River. Soil–water contact enhanced the dissolution of minerals, enriching water with sodium and calcium concentration (Fig.  5 ), resulting in the rise of total dissolved salts. Mineral–water contact time brings salt concentration (~ 1000 mg/L) levels that render it unsuitable for some uses.

Water quality status map for Gulshan Ravi (Site 1) and Samanabad (Site 2). EC ( a ), pH ( b ) and TH ( c ), Calcium hardness ( d ), Mg hardness ( e ) and DO ( f ) are presented using a GIS map to illustrate the water quality conditions at both sites 1 and 2.

Analysis based on the WQI model

The Water Quality Index maps were developed using ArcGIS software (10.8) on the basis of selective physico-chemical parameters, classified as excellent, very good, good, poor, and very poor 47 as mentioned in Table 2 . The factors affecting the water quality include all the physico-chemical parameters that were used to examine the water quality. These factors play a key role in identifying the water quality of an area 48 . Basically, this study includes the determination of physiochemical parameters of drinking water in current study sites and their water quality parameters, as shown in Table 5 . Based on water quality factors, the WQI produces a single value that indicates the total water quality in a specific area. This is a composite indicator that combines the effects of many water quality parameters and suitability for drinking purposes 1 , 49 , 50 .

The WQI is a statistical tool that simplifies the analysis of complex groundwater data 51 . WQI of site 1 and site 2 was 59.66 and 77.3, respectively. Site 1 WQI lies in a “Poor” rating of water quality. Site 2 WQI lies in the “Very Poor” rating of WQI as shown in Fig.  6 which indicates that both areas either had some physiochemical parameters within range, but overall, the water quality rating is very poor and it poses a serious health threat to the residents of these areas. A comparative difference between both sites with the help of an interpolation map shows both areas were shown with dark colors which indicate poor and very poor water quality (Fig.  7 ). Findings of the current study regarding WQI are in line with a study conducted by 52 to check water quality in western Lahore which has poor WQI and is unfit for human consumption. The observed differences in water quality, ranging from poor to very poor, can be attributed to a myriad of factors. Conversely, areas with poorer water quality experience contamination from industrial discharges, low groundwater levels, dumping, and old and worn-out metallic pipes. To address these disparities and improve water quality in deteriorating areas like Samanabad and Gulshan Ravi in Lahore, comprehensive mitigation measures are essential like water monitoring, and community awareness on responsible water usage are potential interventions. Additionally, strategic urban planning and infrastructure development can play a pivotal role in preventing further degradation and fostering long-term improvements in water quality.

Comparison of average WQI for Gulshan Ravi (Site 1) and Samanabad (Site 2).

Spatial distribution map showing WQI of both site 1 Gulshan Ravi and site 2 Samanabad.

Analysis based on the SPI Model

The findings of the analysis of water samples for the purpose of determining the quality of drinking water and classifying it using SPI are compiled in Table 3 .

Based on the SPI model, water samples of both areas were identified as “very polluted” as the SPI value was more than 3 indicating that there is a high risk of contamination of drinking water in these areas.

Water quality issues prevailing in the study area are similar to those found in other big urban areas of Pakistan 53 . SPI of water samples collected from a selected areas of Karachi varied from 0.6 to 6.6 and no water sample was found to be suitable for drinking purposes.

The relationship between WQI and SPI models

The respective WQI and SPI model categories of water were correlated using regression analysis in order to determine a relationship between them. The relationship shows a strong correlation between both models showing the R 2 value is 1, as shown in Fig.  8 . A series of studies have demonstrated a strong regression analysis between water quality index (WQI) and synthetic pollution index (SPI) in drinking water quality. Other study found a significant positive correlation between WQI and SPI, indicating an increase in pollution load 54 . This was further supported by another research study 55 , that reported a fair correlation between the two indices in the lower stretch of river Ganga. The threat of heavy metal pollution in drinking water, with a significant impact from Pb contamination are explored 56 . A study further improved the prediction of WQI using machine learning regression models, with linear regression and ridge offering the best performance 57 . These studies collectively underscore the importance of monitoring and addressing synthetic pollution in drinking water.

Regression analysis of Water Quality Index and Synthetic Pollution Index.

The aim of the present study was to assess the quality of drinking water in two urban areas of Lahore using physico-chemical analysis, Water Quality Index (WQI), and the Specific Pollution Index (SPI). The findings revealed that in site 1 (Samanabad) had significant issues related to water quality, affecting primarily major residential colonies and blocks with elevated physico-chemical parameters suh as TDS, temperature pH, Ca +2 , Mg +2 , turbidity, etc. The major reasons for poor water quality are old water pipelines, rapid urbanization, toxic ingredients seepage, improper waste disposal, and low groundwater levels in these areas. Major parameters recorded that were above the WHO guidelines were EC, pH, TDS, and hardness. Although some parameters of both areas were within range as prescribed by WHO guidelines, WQI indicated that both areas had overall poor (59.66) and very poor (77.30) water quality ratings. WQI describes a greater number of variables using a single value that indicates the overall quality of water in a certain area. It is concluded that the water quality in both study areas was found unsuitable for drinking, emphasizing the need for prompt action by local authorities. Stricter management of industrial effluent, public education campaigns about water conservation, and the search for alternative water supplies should be the top priorities for remedial action. To reduce the risk of pollution, there is need of maintaining and modernizing sewage infrastructure, distribution networks, and provision of water treatment plants.

Data availability

All data generated or analyzed during this study are included in this published article.

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Acknowledgements

The authors extend their appreciation to the Researchers Supporting Project number (RSP2024R197), King Saud University, Riyadh, Saudi Arabia. The authors acknowledge the Department of Environmental Sciences, The University of Lahore for providing technical support. Some part of the paper is extracted from the final year project of the second author of the paper. Thanks are also extended to the team at the analytical lab for providing the necessary facilities during this work.

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Department of Environmental Sciences, The University of Lahore, Lahore, 54000, Pakistan

Maria Latif, Nimra Nasir, Rab Nawaz, Iqra Nasim, Khawar Sultan & Muhammad Atif Irshad

Faculty of Engineering and Quantity Surveying, INTI International University, 71800, Nilai, Negeri Sembilan, Malaysia

Department of Chemistry, Government College University Faisalabad, Faisalabad, 38000, Pakistan

Botany and Microbiology Department, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia

Turki M. Dawoud

Evangelical College, BP 1200, N’Djamena, Chad

Youssouf Ali Younous

College of Resource and Civic Engineering, Northeast University, Shenyang, China

Zulkfil Ahmed

Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, 80060, Agadir, Morocco

Mohammed Bourhia

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Conceptualization, M.L. and R.N.; methodology, N.N. and I.N.; software, M.L. and K.S.; validation, M.A.I. and K.S.; formal analysis, A.I. and N.N.; investigation, N.N.; resources, A.I and K.S.; data curation, A.I. and N.N.; writing—original draft preparation, M.L.; writing—review and editing, R.N. and A.I.; visualization, MB; supervision, D.T.; project administration, Y.A.Y.

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Latif, M., Nasir, N., Nawaz, R. et al. Assessment of drinking water quality using Water Quality Index and synthetic pollution index in urban areas of mega city Lahore: a GIS-based approach. Sci Rep 14 , 13416 (2024). https://doi.org/10.1038/s41598-024-63296-1

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Research methodology, results and discussion, assessment of cau river water quality assessment using a combination of water quality and pollution indices.

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Cao Truong Son , Nguyen Thị Huong Giang , Trieu Phuong Thao , Nguyen Hai Nui , Nguyen Thanh Lam , Vo Huu Cong; Assessment of Cau River water quality assessment using a combination of water quality and pollution indices. Journal of Water Supply: Research and Technology-Aqua 1 March 2020; 69 (2): 160–172. doi: https://doi.org/10.2166/aqua.2020.122

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This research aims at using a combined water quality index (WQI) and pollution index (PI) to assess and characterize river water quality of Cau River which is one of the longest rivers in the north of Vietnam. Five different water quality and water pollution indices were used including the Water Quality Index (WQI), Comprehensive Pollution Index (CPI), Organic Pollution Index (OPI), Eutrophication Index (EI), and Trace Metal Pollution Index (TPI). The combined water pollution indices show more serious pollution towards the river downstream. In particular, CPI and OPI reveal a high risk of eutrophication. Cluster analysis was applied to classify water monitoring points into different quality groups in order to provide a better understanding of the water status in the river. This study indicates that a combined water quality analysis could be an option for decision making water use purposes while its single index shows the current situation of water quality.

Water is a vital commodity, both to sustain life and for the global economy. However, the quality of global water has rapidly declined for decades due to the impact of both natural and anthropogenic factors ( Vadde et al. 2018 ). Assessing water quality for different water use purposes, such as domestic use, irrigation, conservation and industrial usage, are an important strategy for food safety and human health. Water quality evaluation aims to identify the sources of water pollution and develop a strategy for sustainable water source management, maintaining and promoting human health and other social and economic growth ( Carroll et al. 2006 ). Surface water quality indexes have been developed and introduced worldwide by researchers with various applications of the Nation Sanitation Foundation Water Quality Index (NSFWQI) ( Carroll et al. 2006 ), the Water Quality Index (WQI) ( Gupta et al. 2003 ; Chaturvedi & Bassin 2009 ; Rocha et al. 2015 ; Sener et al. 2017 ), the Comprehensive Pollution Index (CPI) ( Matta et al. 2017 ), the Organic Pollution Index (OPI) ( Mezbour et al. 2018 ), the Trace Metal Pollution Index (TPI) ( Reza & Singh 2010 ), the Eutrophication Index (EI) ( Liu et al. 2011 ) based on the the database of water monitoring parameters. In Vietnam, research on water quality assessment mostly focuses on comparing the concentration of pollutant to the national surface water quality standard ( MONRE 2015 ). The WQI has been used for 10 years ( VEA 2011 ), however, a combination of WQI with other water pollution indices was not applied widely for water quality assessment research.

Vietnam has abundant water resources with up to 2,360 rivers being over 10 km in length. There are 16 river basins in the whole country with an area larger than 2,500 km 2 . The average annual surface flow of Vietnam's river basin ranges from 830 to 840 cubic meters per year and the annual rainfall is around 1,940 mm. Despite the abandance of natural water sources, Vietnam is still considered as a water shortage country due to three main causes. About 72% of the river basins are distributed outside the country, which accounts for 63% of the total national water source (equal to 520–525 billion cubic meter) and the volume of water coming from inside the country area only is 37%, around 310–315 billion cubic meters ( MONRE 2006 ). Vietnam water resources are distributed unequally throughout geographical location and seasons. The Cuu Long River delta basin has the largest water proportion at 60%, the Hong and Thai Binh rivers have nearly a fifth of the national water volume at 16%, and less than a quarter of the total water volume belongs to other rivers. Vietnam also has seasonal precipitation in which the rainfall is concentrated mostly in the summer season and the other seasons have less than a third of the total rainfall. Therefore, many areas suffer drought for several months, especially in the winter ( MONRE 2012 ). The other reason is water pollution. The water quality in Vietnam recently has been declining significantly under the pressure of population and economic growth.

Currently, the average water consumption is 9,560 cubic meter per capita annually. It is slightly lower than the middle water consumption country according to the average level of the International Water Resource Agency (IWRA). However, Vietnam accounts for a half (ca. 4,000 cubic meters) while other sources come from neighboring countries ( MONRE 2012 ). The Vietnamese government has implemented various national water monitoring programs to protect the water environment. From 2008 to 2018, the general water monitoring programs of ten main river basins were established and implemented. In this study, we use a monitoring database in Cau River basin (from 2008 to 2018) to calculate the quality index and pollution index. The results of this study provide clear scientific evidence to regulate the situation of water quality of this river. The case of Cau River water evaluation could propose a solution for the Vietnamese government in applying water quality indices to managing and monitoring water sources in the future.

Cau River is the longest river branch of the Thai Binh River system at 288 km and its basin area covers 6,030 km 2 ( Figure 1 ). The Cau River flows through four provinces including Bac Kan, Thai Nguyen, Bac Ninh and Bac Giang before being discharged into the Thai Binh River to the sea. The river's width changes following the flood season and drought season from 100 to 50 m, respectively. The river's bed is larger and the slope reduces to 0.05%. The downstream river is located in two provinces, Bac Giang and Bac Ninh, with an average elevation of 10–20 m ( MONRE 2006 ).

Cau River watershed in Vietnam.

According to the Center for Environmental Monitoring of Vietnam (CEM) water monitoring report (2018), there are 63 pollution sources discharging into Cau River which are scattered along four provinces: Bac Kan, Thai Nguyen, Bac Giang and Bac Ninh. The upper part is located in Bac Kan Province, and the population density is low at 65.65 persons per square kilometer. This area has ten pollution sources mostly related to agriculture production and residential activities. The middle part of the river flows through Thai Nguyen province, and it has a high population density as well as heavy industrial activities. CEM's water monitoring program at Cau River Basin has identified 16 sources of pollution in this area. The downstream of the river crosses two provinces, Bac Giang and Bac Ninh. These are populated places with diverse economies: agriculture, industrial and craft villages. The total waste water discharge into the river is estimated at 138.192 cubic meters per year ( Table 1 ). The location of these pollution sources is the foundation for the establishment of water monitoring points.

Characteristics of pollution sources of Cau River

Water sampling

Monitoring locations.

A total of 22 monitoring points were selected for sampling of which six points are located in Bac Kan province, seven points in Thai Nguyen Province and the remainder belong to Bac Giang and Bac Ninh Provinces. A description of water sampling location is summarized in Table 2 .

Description of sampling location in Cau River, Vietnam

Monitoring parameters

Water monitoring statistics of 22 points in the period of 2008–2018 from CEM was collected. Each water sample analysis contains 16 parameters which are described in Table 3 . The pH, WT, turbidity and dissolved oxygen (DO) were analyzed by a portable pH meter, WT meter, turbidity meter and DO meter (D–50 Series, Horiba, Co. Ltd), respectively. Ammonium, nitrate, nitrite and phosphates ion concentrations were determined using a spectrophotometer (UV/VIS–EVOLUTION, Model EV0300PC). Heavy metal concentrations were determined using atomic absorption spectrophotometer (AAS) (EPA method). Coliform was analyzed by counting methods (ISO 10304-1:2007).

Surface water parameters in the study

ISO, International Standardization Organization; SMEWW, Standard Method for Examination of Water and Wastewater; EPA, Environmental Protection Agency of the United States.

Water quality and water pollution indices

Water quality index (wqi).

Based on the calculated score of WQI, water quality is classified into five categories:

Level 1: WQI score obtained from 0 to 25: Water is extremely polluted, emergency treatment is required before reuse.

Level 2: WQI score obtained from 26 to 50: water quality is suitable for transportation and equivalent purposes.

Level 3: WQI score obtained from 51 to 75: water quality is suitable for irrigation and equivalent purposes.

Level 4: WQI score obtained from 76 to 90: water quality is suitable for domestic usage.

Level 5: WQI score obtained from 91 to 100: water quality is suitable for domestic water supply.

Comprehensive pollution index (CPI)

CPI is classified into five categories:

Category 1: CPI from 0 to 0.20 (clean);

Category 2: CPI from 0.21 to 0.40 (sub clean);

Category 3: CPI from 0.41 to 1.00 (slightly polluted);

Category 4: CPI from 1.01–2.00 (medium polluted);

In this study, we calculate CPI by using 12 water parameters: COD, BOD, TSS, amonium, phosphates, nitrate, nitrite, coliform, Fe, Cu, Zn and Cd. These parameters were analyzed in the Cau River water monitoring program.

Organic pollution index (OPI)

Eutrophication index (ei).

DIN concentration is calculated by the total concentration volume of nitrate, nitrite, and ammonium, whereas the DIP is calculated by the concentration of phosphate in water. The EI is classified into two categories:

EI < 0: Zero eutrophication

EI > 0: Eutrophication

Trace metal pollution index (TPI)

The TPI value is categorized into two groups:

Group 2: TPI > 1 – pollution

In this study, TPI is calculated based on the average concentration of four trace heavy metals: Fe, Cu, Zn and Cd. These are four monitoring indicators in the general water monitoring program of Vietnam.

Data analysis

T-test analysis was used to evaluate the significant difference of water indices between the rainy season and the dry season in Cau River. ANOVA was used to identify the significant difference between upstream, midstream and downstream. Cluster analysis was applied to evaluate the water quality and pollution level between monitoring points. The points which have similar polluted levels were gathered into one group. The results of cluster analysis could assist managers to conduct better water monitoring and management plans.

Water quality in Cau River

The average values of ten consecutive years of water quality show that TSS and COD exceed the regulated level indicated in Vietnam National Standards QCVN08:2015/BTMT, column A1 – standard for domestic water usage ( Table 4 ) ( MONRE 2015 ). The TSS concentration was double the permission level, ranging from 49.46 to 54.52 mg/L compared to 20 mg/L of regulated value. The COD concentration was also higher ranging from 11.63 to 13.62 mg/L. Other parameters met the regulated levels. The table also shows the changes of water quality in the dry season and the rainy season. In comparison, parameters of TSS, DO and NH 4 + in the rainy season were higher than in the dry season, contrary to other parameters. This result was a consequence of the pollutants diluting due to the increase of river water volume in the monsoon season ( MONRE 2006 ).

Variation of water quality of Cau River between the rainy season and the dry season

QCVN08:A1 = Vietnamese national technical regulation for surface water quality.

Table 5 presents the correlated relationship between monitoring parameters. The analysis shows the strong relations between physical and chemical parameters, especially the chemical parameters such as BOD, COD, NH 4 + , NO 2 – and NO 3 – . The correlations were more significant in the dry season compared to the rainy reason. In a similar study, Jahin et al. (2020) employed multivariate analysis to develop an irrigation water quality index for suface water in Kafr El-Sheikh Governorate and found that the elements in waster have similar dynamics.

Correlation matrix of water parameters in Cau River between the dry season and the rainy season

The water quality index

For overall quality assessment, data from 22 sampling points was calculated ( Table 6 ). The average WQI scores were 67.52 and 69.67 in the dry and rainy season, respectively, indicating sufficient quality for irrigation supply. The WQI decreased from upstream to downstream indicating an accumulation of pollutants from discharge sources. Figure 2 shows that WQIs of Cau River were fairly stable in the rainy season and generally satisfied the quality for irrigation and similar purposes according to WQI's classification. However, the water quality in the dry season highly fluctuated due to geographical locations and the impacts of pollution sources. In the upper and lower part of the river, the WQI scores were mostly higher than the rainy season. Monitoring point number 5 nearly achieved the quality for domestic usage. This was a consequence of the steep river bed and the increase of turbidity in the monsoon period. Monitoring point number 10 was considered as extremely polluted. Water quality at other points in the river were categorized as suitable for transportation. The results show that the water quality index could be used effectively for water supply purposes. Similar research has been comprehensively conducted in many countries such as Egypt ( Jahin et al. 2020 ), Sarayduzu Dam Lake, Turkey ( Kükrer & Mutlu 2019 ), Amazonia Rivers, Brazil ( Medeiros et al. 2017 ), and the Ganga River, India ( Tripsthi & Singal 2019 ).

Water quality indexes of Cau River

SD (standard deviation); (*), (**), (***) indicates level of significance at P -value is 0.1; 0.05 and 0.01.

WQI scores of 22 water monitoring points in Cau River.

Water pollution indices

The results of water pollution indices calculation in both the dry and rainy season are summarized in Table 7 .

Water pollution indices between the dry season and rainy season of Cau River

CPI, Comprehensive Pollution Index; OPI, Organic Pollution Index; EI, Eutrophication Index; TPI, Trace Heavy Metal Index. (*), (**), (***) = significant difference at 0.1, 0.05 and 0.01 levels.

The CPI data show the value of the entire river with no significant difference between dry and rainy seasons. In the dry season, the CPI of Cau River ranged from 0.50 to 1.57 with an average value of 1.08. According to the CPI's classification, this river was slightly and medium polluted. In the rainy season, the CPI of Cau River reached 0.66–1.37 with an average score at 0.96 and its quality was classified as the same level as the dry season.

Although the water quality of each water monitoring point tended to be better during the monsoon period, this difference was not statistically significant. However, the CPIs were different among the monitoring points upstream, midstream and downstream. The midstream had lower CPIs during the monsoon period in comparison to the dry season. We used the ANOVA test to analyze the significant difference levels of CPI in three parts of the river and the t-test to analyze the significant difference between the two seasons of the year. The results showed that the difference of CPI in three parts of the river was not statistically significant.

Similar to the CPI, the OPI in the dry season was higher than the rainy season (1.55 compared to 1.18). This result was significant with α = 0.1 ( P = 0.0538). The OPIs of Cau River could be classified into two groups: Good (0 < OPI < 1) and Polluted (1 < OPI < 4). Regarding spatial location, the OPI of the upstream was 0.92 and 0.63 in the dry and rainy season respectively. These scores were in the good quality category (0 < OPI < 1). However, this difference was not statically significant. In the midstream of the river, the OPI was substantially higher in the rainy season (1.35 compared to 0.79 in the dry season) and this difference was statistically significant with α = 0.01 ( P = 0.007559). The average OPI of the downstream in the dry season was 2.12, which was equal to the extremely polluted level, much higher than the OPI in the rainy season (1.83). However, the difference of OPI between the two seasons was not significant. Similar to the results of the CIP analysis, the OPI of the upstream had the highest quality and the downstream had the worst quality ( Figure 3 ). However, the difference of OPI among three geographical areas was not significant.

Cluster analysis water monitoring points in Cau River.

The EI of the entire river was obtained in the range of about 100–400 for both dry and rainy seasons. This indicates that the river was at high risk of eutrophication. However, the EI in the dry season was much higher than the rainy season and this difference was statistically significant with α = 0.1 ( P = 0.07977). According to geographical location, the EI of all monitoring points was higher than zero and increased from upstream to downstream of the river. There were significantly different average values of EI in both the dry season and the rainy season with α = 0.01 ( P = 0.001376) and α = 0.001 ( P = 0.000101) respectively . Specifically, the EI of the upstream was 128.57 and 85.54; the midstream was 224.49 and 101.64; downstream was 394.79 and 322.84 in the dry season and rainy season respectively. The EI in the dry season was always higher compared to the rainy season. However, this difference was only significant at midstream with α = 0.05 ( P = 0.019287).

Trace heavy metal index (TPI)

In contrast to other pollution indices, the TPIs of Cau River were low which clarified that this river was not polluted by trace heavy metals according to the TPI's classification. The rainy season also had higher TPI compared to the dry season, although this difference was not significant.

The TPI also slightly differed according to geographical locations. It was highest at the downstream and reduced in the upper part of the river. The difference of TPI in the upstream and downstream between the dry and rainy season was significant at α = 0.01 ( P = 0.006916) and α = 0.05 ( P = 0.026554). However, the average values of TPI at upstream, midstream and downstream monitoring points were not significantly different. The results of water pollution indices calculation determined that the level of pollution increased from the upper part to lower part of the river, specifically the CPI, OPI and EI, but not the TPI. The pollution level also tended to be higher in the dry season compared to the rainy season. This environmental status could be explained by the low density of pollution sources in the upstream. The midstream and downstream received more pressure from agricultural and industrial activities and populous areas. In addition, the accumulation of pollutants due to water flow in the downstream also contributed to higher concentrations of pollutants in this part of the river. The better water quality in the rainy season was a consequence of the increase of water volume which led to a higher dilution capacity. All trends of water pollution indices were reflected and are similar to the results of WQI calculations.

Water quality indices and water pollution indices among monitoring points

Cluster analysis was used in order to classify the monitoring points which have similar water quality characteristics. The computation categorized 22 points into five groups as summarized in Table 8 .

Clustering water monitoring points between dry season and rainy season

WQI, Water quality index; CPI, Comprehensive Pollution Index; OPI, Organic Pollution Index; EI, Eutrophication Index; TPI, Trace Heavy Metal Index.

According to Table 8 , cluster analysis shows the order of water quality of which group 1 represents highest quality and group 5 the lowest. Representing the entire river, the polluted points (belonging to groups 4 and 5) accounted for around 13% (3 of 21 points in total). Nevertheless, in the dry season, the number of polluted points in groups 4 and 5 was double and the number in group 1 significantly reduced to six monitoring points. The results of cluster analysis stated that the water quality in monsoon time of Cau River was better compared to the dry season.

Grouping water monitoring points with similar characteristics could assist managers in making decisions related to water use planning for different purposes, such as domestic water supply, aquaculture cultivation, irrigation, or other purposes. Furthermore, the results of water quality clustering also support managers in designing water quality monitoring systems, especially addressing high attention to the serious pollution points. These benefits provide meaningful foundations for sustainable water source management.

This study has suggested a possible combination of quality and pollution indices based on monitoring environmental parameters for river quality assessment. At the current state, the water quality meets the requirements of Vietnam National Environmental Standard QCVN08-(A1 category), the standard for domestic water supply. TSS and COD concentrations are higher than the regulation for domestic water supply. The average concentration of pollutants was lower in the dry season, excluding TSS, DO and NH 4 + . The results of WQI analysis indicate that the water of Cau River achieved the standard for irrigation purposes in both the dry season and rainy season. However, further study on bearing capacity of the river will be needed for water supply purposes.

The water quality indices varied depending on location and monitoring time. In the dry season, the water quality of the upstream was better than other parts of the river, followed by the midstream and downstream respectively.

Pollution Indices calculation indicates that the Cau River is polluted in different geographical conditions. Many locations of the river are contaminated by organic pollution with OIP > 1. The river water was at high risk of eutrophication as EI was above zero. The TPI of Cau River is in the safety level. All pollution indices in the Cau River tend to increase from upstream to midstream, then downstream. Cluster analysis grouped the water monitoring points into five groups with the quality reducing gradually from the first group to the fifth group. The classification of clustering analysis provided meaningful support for water pollution monitoring and appropriate solutions for the treatment of water for water supply.

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Assessment of water quality of key dams in jordan for irrigation purposes with insights on parameter thresholds.

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Gharaibeh, M.A.; Albalasmeh, A.A.; Obeidat, M.M. Assessment of Water Quality of Key Dams in Jordan for Irrigation Purposes with Insights on Parameter Thresholds. Water 2024 , 16 , 1726. https://doi.org/10.3390/w16121726

Gharaibeh MA, Albalasmeh AA, Obeidat MM. Assessment of Water Quality of Key Dams in Jordan for Irrigation Purposes with Insights on Parameter Thresholds. Water . 2024; 16(12):1726. https://doi.org/10.3390/w16121726

Gharaibeh, Mamoun A., Ammar A. Albalasmeh, and Mohammad M. Obeidat. 2024. "Assessment of Water Quality of Key Dams in Jordan for Irrigation Purposes with Insights on Parameter Thresholds" Water 16, no. 12: 1726. https://doi.org/10.3390/w16121726

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The combined use of GIS and water quality indices for environmental assessment of Ouislane River watershed, Morocco

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The Ouislane River, located in the R’Dom watershed, is significantly polluted owing to urbanization, industry, and agriculture. Given its importance to the region and its evident degradation, understanding the extent and nature of this pollution is crucial. The reasons for the assessment of water quality in the selected river are strongly related to industrial and domestic wastewater discharges and agricultural activities, which are considered to be the main causes of the deterioration in its quality. The novelty of this work is to integrate a geographical information system (GIS) technique with water quality models and to provide a good picture of the surface water adequacy. The present study aims to assess the water quality of the Ouislane River on the basis of measured water variable data for irrigation uses, utilizing a combination of GIS and water quality indices. The specific objectives of this research project are: (i) to assess its water quality status by using the water quality index (WQI), synthetic pollution index (SPI), and water quality parameters such as the sodium adsorption ratio (SAR), sodium percent (%Na), and residual sodium carbonate (RSC); and (ii) to classify and determine the suitability of the natural river waters for irrigation purposes. Fifteen samples from the river were collected to determine their suitability for irrigation use. The results showed that the WQI values range from 38 to 198, while the SPI values range from 0.99 to 5.5. This result was confirmed and validated by a satisfactory correlation between the WQI and the SPI ( R 2  = 0.99). On the basis of the GIS interpolated map, the WQI and SPI models show that the river water was suitable for irrigation purposes. According to the SAR, % Na + , and RSC values, irrigation use of the water was acceptable and permissible. The results of this research will help water managers develop strategies to mitigate the potential adverse effects of surface water pollution on environment. They will also improve water quality assessment in the region and could provide an essential reference for quality studies and valuable guidance for watershed management strategies.

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The authors thank the Thematic Project 4, Integrated Water Resources Management of the institutional university cooperation, and VLIR-UOS for financial support, equipment, and mission in Belgium.

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Abdennabi Alitane, Ali Essahlaoui & Narjisse Essahlaoui

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A.A.: conceptualization, data curation, formal analysis, methodology, software, writing—original draft, review & editing. E.A.: conceptualization, formal analysis, writing—review & editing, funding acquisition, supervision. E.A.Y: conceptualization, formal analysis, methodology, review & editing. E.N.: conceptualization, methodology, review & editing. C.J.C.: methodology, review & editing. E.Y.Y.: conceptualization, methodology, review & editing. A.V.G.: conceptualization, formal analysis, writing—review & editing, funding acquisition, supervision.

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Alitane, A., Essahlaoui, A., Yimer, E.A. et al. The combined use of GIS and water quality indices for environmental assessment of Ouislane River watershed, Morocco. Euro-Mediterr J Environ Integr (2024). https://doi.org/10.1007/s41207-024-00550-y

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DOI : https://doi.org/10.1007/s41207-024-00550-y

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