thesis of water quality

A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

• Review paper
• Published: 11 March 2023
• Volume 22 , pages 349–395, ( 2023 )

Cite this article

• Sandra Chidiac   ORCID: orcid.org/0000-0002-1822-119X 1 ,
• Paula El Najjar 1 , 2 ,
• Naim Ouaini 1 ,
• Youssef El Rayess 1 &
• Desiree El Azzi 1 , 3  

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Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

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1 Introduction

Water is the vital natural resource with social and economic values for human beings (Kumar 2018 ). Without water, existence of man would be threatened (Zhang 2017 ). The most important drinking sources in the world are surface water and groundwater (Paun et al. 2016 ).

Currently, more than 1.1 billion people do not have access to clean drinking water and it is estimated that nearly two-thirds of all nations will experience water stress by the year 2025 (Kumar 2018 ).

With the extensive social and economic growth, such as human factors, climate and hydrology may lead to accumulation of pollutants in the surface water that may result in gradual change of the water source quality (Shan 2011 ).

The optimal quantity and acceptable quality of water is one of the essential needs to survive as mentioned earlier, but the maintenance of an acceptable quality of water is a challenge in the sector of water resources management (Mukate et al. 2019 ). Accordingly, the water quality of water bodies can be tested through changes in physical, chemical and biological characteristics related to anthropogenic or natural phenomena (Britto et al. 2018 ).

Therefore, water quality of any specific water body can be tested using physical, chemical and biological parameters also called variables, by collecting samples and obtaining data at specific locations (Britto et al. 2018 ; Tyagi et al. 2013 ).

To that end, the suitability of water sources for human consumption has been described in terms of Water Quality Index (WQI), which is one of the most effective ways to describe the quality of water, by reducing the bulk of information into a single value ranging between 0 and 100 (Tyagi et al. 2013 ).

Hence, the objective of the study is to review the WQI concept by listing some of the important water quality indices used worldwide for water quality assessment, listing the advantages and disadvantages of the selected indices and finally reviewing some water quality studies worldwide.

2 Water quality index

2.1 history of water quality concept.

In the last decade of the twentieth century, many organizations involved in water control, used the water quality indices for water quality assessment (Paun et al. 2016 ). In the 1960’s, the water quality indices was introduced to assess the water quality in rivers (Hamlat et al. 2017 ).

Horton ( 1965 ), initially developed a system for rating water quality through index numbers, offering a tool for water pollution abatement, since the terms “water quality” and “pollution” are related. The first step to develop an index is to select a list of 10 variables for the index’s construction, which are: sewage treatment, dissolved oxygen (DO), pH, coliforms, electroconductivity (EC), carbon chloroform extract (CCE), alkalinity, chloride, temperature and obvious pollution. The next step is to assign a scale value between zero and 100 for each variable depending on the quality or concentration. The last step, is to designate to each variable is a relative weighting factor to show their importance and influence on the quality index (the higher the assigned weight, the more impact it has on the water quality index, consequently it is more important) (Horton 1965 ).

Later on, Brown et al. ( 1970 ) established a new water quality index (WQI) with nine variables: DO, coliforms, pH, temperature, biochemical oxygen demand (BOD), total phosphate, nitrate concentrations, turbidity and solid content based on a basic arithmetic weighting using arithmetic mean to calculate the rating of each variable. These rates are then converted not temporary weights. Finally, each temporary weight is divided by the sum of all the temporary weights in order to get the final weight of each variable (Kachroud et al. 2019a ; Shah and Joshi 2017 ). In 1973, Brown et al., considered that a geometric aggregation (a way to aggregate variables, and being more sensitive when a variable exceeds the norm) is better than an arithmetic one. The National Sanitation Foundation (NSF) supported this effort (Kachroud et al. 2019a ; Shah and Joshi 2017 ).

Steinhart et al. ( 1982 ) developed a novel environmental quality index (EQI) for the Great Lakes ecosystem in North America. Nine variables were selected for this index: biological, physical, chemical and toxic. These variables were: specific conductance or electroconductivity, chloride, total phosphorus, fecal Coliforms, chlorophyll a , suspended solids, obvious pollution (aesthetic state), toxic inorganic contaminants, and toxic organic contaminants. Raw data were converted to subindex and each subindex was multiplied by a weighting factor (a value of 0.1 for chemical, physical and biological factors but 0.15 for toxic substances). The final score ranged between 0 (poor quality) and 100 (best quality) (Lumb et al. 2011a ; Tirkey et al. 2015 ).

Dinius ( 1987 ), developed a WQI based on multiplicative aggregation having a scale expressed with values as percentage, where 100% expressed a perfect water quality (Shah and Joshi 2017 ).

In the mid 90’s, a new WQI was introduced to Canada by the province of British Columbia, and used as an increasing index to evaluate water quality (Lumb et al. 2011b ; Shah and Joshi 2017 ). A while after, the Water Quality Guidelines Task Group of the Canadian Council of Ministers of the Environment (CCME) modified the original British Columbia Water Quality Index (BCWQI) and endorsed it as the CCME WQI in 2001(Bharti and Katyal 2011 ; Lumb et al. 2011b ).

In 1996, the Watershed Enhancement Program (WEPWQI) was established in Dayton Ohio, including water quality variables, flow measurements and water clarity or turbidity. Taking into consideration pesticide and Polycyclic Aromatic Hydrocarbon (PAH) contamination, is what distinguished this index from the NSFWQI (Kachroud et al. 2019a , b ).

Liou et al. (2003) established a WQI in Taiwan on the Keya River. The index employed thirteen variables: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH, toxicity, cadmium (Cd), lead (Pb), copper (Cu) and zinc (Zn). These variables were downsized to nine based on environmental and health significance: Fecal coliforms, DO, ammonia nitrogen, BOD, suspended solids, turbidity, temperature, pH and toxicity. Each variable was converted into an actual value ranging on a scale from 0 to 100 (worst to highest). This index is based on the geometric means (an aggregation function that could eliminate the ambiguous caused from smaller weightings) of the standardized values (Akhtar et al. 2021 ; Liou et al. 2004 ; Uddin et al. 2021 ).

Said et al. ( 2004 ) implemented a new WQI using the logarithmic aggregation applied in streams waterbodies in Florida (USA), based on only 5 variables: DO, total phosphate, turbidity, fecal coliforms and specific conductance. The main idea was to decrease the number of variables and change the aggregation method using the logarithmic aggregation (this function does not require any sub-indices and any standardization of the variables). This index ranged from 0 to 3, the latter being the ideal value (Akhtar et al. 2021 ; Kachroud et al. 2019a , b ; Said et al. 2004 ; Uddin et al. 2021 ).

The Malaysian WQI (MWQI) was carried out in 2007, including six variables: DO, BOD, Chemical Oxygen Demand (COD), Ammonia Nitrogen, suspended solids and pH. For each variable, a curve was established to transform the actual value of the variable into a non-dimensional sub-index value.

The next step is to determine the weighting of the variables by considering the experts panel opinions. The final score is determined using the additive aggregation formula (where sub-indices values and their weightings are summed), extending from 0 (polluted) to 100 (clean) (Uddin et al. 2021 ).

The Hanh and Almeida indices were established respectively in 2010 on surface water in Vietnam and 2012 on the Potrero de los Funes in Argentina, based on 8 (color, suspended solids, DO, BOD, COD, chloride, total coliforms and orthophosphate) and 10 (color, pH, COD, fecal coliforms, total coliforms, total phosphate, nitrates, detergent, enterococci and Escherichia coli .) water quality variables. Both indices were based on rating curve- based sum-indexing system (Uddin et al. 2021 ).

The most recent developed WQI model in the literature was carried out in 2017. This index tried to reduce uncertainty present in other water quality indices. The West Java Water Quality Index (WJWQI) applied in the Java Sea in Indonesia was based on thirteen crucial water quality variables: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol, chloride, Zn, Pb, mercury (Hg) and fecal coliforms. Using two screening steps (based on statistical assessment), parameter (variable) redundancy was determined to only 9: temperature, suspended solids, COD, DO, nitrite, total phosphate, detergent, phenol and chloride. Sub-indices were obtained for those nine variables and weights were allocated based on expert opinions, using the same multiplicative aggregation as the NSFWQI. The WJWQI suggested 5 quality classes ranging from poor (5–25) to excellent (90–100) (Uddin et al. 2021 ).

2.2 Phases of WQI development

Mainly, WQI concept is based on many factors as displayed in Fig.  1 and described in the following steps:

Phases of WQI development

Parameter selection for measurement of water quality (Shah and Joshi 2017 ):

The selection is carried out based on the management objectives and the environmental characteristics of the research area (Yan et al. 2015 ). Many variables are recommended, since they have a considerable impact on water quality and derive from 5 classes namely, oxygen level, eutrophication, health aspects, physical characteristics and dissolved substances (Tyagi et al. 2013 ).

Transformation of the raw data parameter into a common scale (Paun et al. 2016 ):

Different statistical approach can be used for transformation, all parameters are transformed from raw data that have different dimensions and units (ppm, saturation, percentage etc.) into a common scale, a non-dimensional scale and sub-indices are generated (Poonam et al. 2013 ; Tirkey et al. 2015 ).

Providing weights to the parameters (Tripathi and Singal 2019 ):

Weights are assigned to each parameter according to their importance and their impact on water quality, expert opinion is needed to assign weights (Tirkey et al. 2015 ). Weightage depends on the permissible limits assigned by International and National agencies in water drinking (Shah and Joshi 2017 ).

Aggregation of sub-index values to obtain the final WQI:

WQI is the sum of rating and weightage of all the parameters (Tripathi and Singal 2019 ).

It is important to note that in some indices, statistical approaches are commonly used such as factor analysis (FA), principal component analysis (PCA), discriminant analysis (DA) and cluster analysis (CA). Using these statistical approaches improves accuracy of the index and reduce subjective assumptions (Tirkey et al. 2015 ).

2.3 Evolution of WQI research

2.3.1 per year.

According to Scopus ( 2022 ), the yearly evolution of WQI's research is illustrated in Fig.  2 (from 1978 till 2022).

Evolution of WQI research per year (Scopus 2022 )

Overall, it is clear that the number of research has grown over time, especially in the most recent years. The number of studies remained shy between 1975 and 1988 (ranging from 1 to 13 research). In 1998, the number improved to 46 studies and increased gradually to 466 publications in 2011.The WQI's studies have grown significantly over the past decade, demonstrating that the WQI has become a significant research topic with the goal of reaching its maximum in 2022 (1316 studies) (Scopus, 2022 ).

2.3.2 Per country

In Fig.  3 , the development of WQI research is depicted visually per country from 1975 to 2022.

Evolution of WQI research per country (Scopus 2022 )

According to Scopus ( 2022 ), the top three countries were China, India and the United States, with 2356, 1678 and 1241 studies, respectively. Iran, Brazil, and Italy occupy the fourth, fifth, and sixth spots, respectively (409, 375 and 336 study). Malaysia and Spain have approximately the same number of studies, respectively 321 and 320 study. The studies in the remaining countries decrease gradually from 303 document in Spain to 210 documents in Turkey. This demonstrates that developing nations, like India, place a high value on the development of water quality protection even though they lack strong economic power, cutting-edge technology, and a top-notch scientific research team. This is because water quality is crucial to the long-term social and economic development of those nations (Zhang 2019 ).

2.4 Different methods for WQI determination

Water quality indices are tools to determine water quality. Those indices demand basic concepts and knowledge about water issues (Singh et al. 2013 ). There are many water quality indices such as the: National Sanitation Foundation Water Quality Index (NSFWQI), Canadian Council of Ministers of Environment Water Quality Index (CCMEWQI), Oregon Water Quality Index (OWQI), and Weight Arithmetic Water Quality Index (WAWQI) (Paun et al. 2016 ).

These water quality indices are applied in particular areas, based on many parameters compared to specific regional standards. Moreover, they are used to illustrate annual cycles, spatio-temporal variations and trends in water quality (Paun et al. 2016 ). That is to say that, these indices reflect the rank of water quality in lakes, streams, rivers, and reservoirs (Kizar 2018 ).

Accordingly, in this section a general review of available worldwide used indices is presented.

2.4.1 National sanitation foundation (NSFWQI)

The NSFWQI was developed in 1970 by the National Sanitation Foundation (NSF) of the United States (Hamlat et al. 2017 ; Samadi et al. 2015 ). This WQI has been widely field tested and is used to calculate and evaluate the WQI of many water bodies (Hamlat et al. 2017 ). However, this index belongs to the public indices group. It represents a general water quality and does not take into account the water’s use capacities, furthermore, it ignores all types of water consumption in the evaluation process (Bharti and Katyal 2011 ; Ewaid 2017 ).

The NSFWQI has been widely applied and accepted in Asian, African and European countries (Singh et al. 2013 ), and is based on the analysis of nine variables or parameters, such as, BOD, DO, Nitrate (NO 3 ), Total Phosphate (PO 4 ), Temperature, Turbidity, Total Solids(TS), pH, and Fecal Coliforms (FC).

Some of the index parameters have different importance, therefore, a weighted mean for each parameter is assigned, based on expert opinion which have grounded their opinions on the environmental significance, the recommended principles and uses of water body and the sum of these weights is equal to 1 (Table 1 ) (Ewaid 2017 ; Uddin et al. 2021 ).

Due to environmental issues, the NSFWQI has changed overtime. The TS parameter was substituted by the Total Dissolved Solids (TDS) or Total Suspended Solids (TSS), the Total Phosphate by orthophosphate, and the FC by E. coli (Oliveira et al. 2019 ).

The mathematical expression of the NSFWQI is given by the following Eq. ( 1 ) (Tyagi et al. 2013 ):

where, Qi is the sub-index for ith water quality parameter. Wi is the weight associated with ith water quality parameter. n is the number of water quality parameters.

This method ranges from 0 to 100, where 100 represents perfect water quality conditions, while zero indicates water that is not suitable for the use and needs further treatment (Samadi et al. 2015 ).

The ratings are defined in the following Table 2 .

In 1972, the Dinius index (DWQI) happened to be the second modified version of the NSF (USA). Expended in 1987 using the Delphi method, the DWQI included twelve parameters (with their assigned weights): Temperature (0.077), color (0.063), pH (0.077), DO (0.109), BOD (0.097), EC (0.079), alkalinity (0.063), chloride (0.074), coliform count (0.090), E. coli (0.116). total hardness (0.065) and nitrate (0.090). Without any conversion process, the DWQI used the measured variable concentrations directly as the sub-index values (Kachroud et al. 2019b ; Uddin et al. 2021 ).

Sukmawati and Rusni assessed in 2018 the water quality in Beratan lake (Bali), choosing five representative stations for water sampling representing each side of the lake, using the NSFWQI. NSFWQI’s nine parameters mentioned above were measured in each station. The findings indicated that the NSFWQI for the Beratan lake was seventy-eight suggesting a good water quality. Despite this, both pH and FC were below the required score (Sukmawati and Rusni 2019 ).

The NSFWQI indicated a good water quality while having an inadequate value for fecal coliforms and pH. For that reason, WQIs must be adapted and developed so that any minor change in the value of any parameter affects the total value of the water quality index.

A study conducted by Zhan et al. ( 2021 ) , concerning the monitoring of water quality and examining WQI trends of raw water in Macao (China) was established from 2002 to 2019 adopting the NSFWQI. NSFWQI's initial model included nine parameters (DO, FC, pH, BOD, temperature, total phosphates, and nitrates), each parameter was given a weight and the parameters used had a significant impact on the WQI calculation outcomes. Two sets of possible parameters were investigated in this study in order to determine the impact of various parameters. The first option was to keep the original 9-parameter model, however, in the second scenario, up to twenty-one parameters were chosen, selected by Principal Component Analysis (PCA).

The latter statistical method was used to learn more about the primary elements that contributed to water quality variations, and to calculate the impact of each attribute on the quality of raw water. Based on the PCA results, the 21-parameter model was chosen. The results showed that the quality of raw water in Macao has been relatively stable in the period of interest and appeared an upward trend overall. Furthermore, the outcome of environmental elements, such as natural events, the region's hydrology and meteorology, can have a significant impact on water quality. On the other hand, Macao's raw water quality met China's Class III water quality requirements and the raw water pollution was relatively low. Consequently, human activities didn’t have a significant impact on water quality due to effective treatment and protection measures (Zhan et al. 2021 ).

Tampo et al. ( 2022 ) undertook a recent study in Adjougba (Togo), in the valley of Zio River. Water samples were collected from the surface water (SW), ground water (GW) and treated wastewater (TWW), intending to compare the water quality of these resources for irrigation and domestic use.

Hence, WQIs, water suitability indicators for irrigation purposes (WSI-IPs) and raw water quality parameters were compared using statistical analysis (factor analysis and Spearman’s correlation).

Moreover, the results proposed that he water resources are suitable for irrigation and domestic use: TWW suitable for irrigation use, GW suitable for domestic use and SW suitable for irrigation use.

The NSFWQI and overall index of pollution (OPI) parameters were tested, and the results demonstrated that the sodium absorption ratio, EC, residual sodium carbonate, Chloride and FC are the most effective parameters for determining if water is suitable for irrigation.

On the other hand, EC, DO, pH, turbidity, COD, hardness, FC, nitrates, national sanitation foundation's water quality index (NSFWQI), and overall index of pollution (OPI) are the most reliable in the detection of water suitability for domestic use (Tampo et al. 2022 ).

Following these studies, it is worth examining the NSFWQI. This index can be used with other WQI models in studies on rivers, lakes etc., since one index can show different results than another index, in view of the fact that some indices might be affected by other variations such as seasonal variation.

Additionally, the NSFWQI should be developed and adapted to each river, so that any change in any value will affect the entire water quality. It is unhelpful to have a good water quality yet a low score of a parameter that can affect human health (case of FC).

2.4.2 Canadian council of ministers of the environment water quality index (CCMEWQI)

The Canadian Water Quality Index adopted the conceptual model of the British Colombia Water Quality Index (BCWQI), based on relative sub-indices (Kizar 2018 ).

The CCMEWQI provides a water quality assessment for the suitability of water bodies, to support aquatic life in specific monitoring sites in Canada (Paun et al. 2016 ). In addition, this index gives information about the water quality for both management and the public. It can furthermore be applied in many water agencies in various countries with slight modification (Tyagi et al. 2013 ).

The CCMEWQI method simplifies the complex and technical data. It tests the multi-variable water quality data and compares the data to benchmarks determined by the user (Tirkey et al. 2015 ). The sampling protocol requires at least four parameters sampled at least four times but does not indicate which ones should be used; the user must decide ( Uddin et al. 2021 ). Yet, the parameters may vary from one station to another (Tyagi et al. 2013 ).

After the water body, the objective and the period of time have been defined the three factors of the CWQI are calculated (Baghapour et al. 2013 ; Canadian Council of Ministers of the Environment 1999 ):

The scope (F1) represents the percentage of variables that failed to meet the objective (above or below the acceptable range of the selected parameter) at least once (failed variables), relative to the total number of variables.

The frequency (F2) represents the percentage of tests which do not meet the objectives (above or below the acceptable range of the selected parameter) (failed tests).

The amplitude represents the amount by which failed tests values did not meet their objectives (above or below the acceptable range of the selected parameter). It is calculated in three steps.

The excursion is termed each time the number of an individual parameter is further than (when the objective is a minimum, less than) the objective and is calculated by two Eqs. ( 4 , 5 ) referring to two cases. In case the test value must not exceed the objective:

For the cases in which the test value must not fall below the objective:

The normalized sum of excursions, or nse , is calculated by summing the excursions of individual tests from their objectives and diving by the total number of tests (both meetings and not meeting their objectives):

F3 is then calculated an asymptotic function that scales the normalized sum of the excursions from objectives (nse) to yield a range between 0 and 100:

Finally, the CMEWQI can be obtained from the following equation, where the index changes in direct proportion to changes in all three factors.

where 1.732 is a scaling factor and normalizes the resultant values to a range between 0 and 100, where 0 refers to the worst quality and one hundred represents the best water quality.

Once the CCME WQI value has been determined, water quality in ranked as shown in Table 3

Ramírez-Morales et al. ( 2021 ) investigated in their study the measuring of pesticides and water quality indices in three agriculturally impacted micro catchments in Costa Rica between 2012 and 2014. Surface water and sediment samples were obtained during the monitoring experiment.

The specifications of the water included: Pesticides, temperature, DO, oxygen saturation, BOD, TP, NO3, sulfate, ammonium, COD, conductivity, pH and TSS.

Sediment parameters included forty-two pesticides with different families including carbamate, triazine, organophosphate, phthalimide, pyrethroid, uracil, benzimidazole, substituted urea, organochlorine, imidazole, oxadiazole, diphenyl ether and bridged diphenyl.

WQIs are effective tools since they combine information from several variables into a broad picture of the water body's state. Two WQIs were calculated using the physicochemical parameters: The Canadian Council of Ministers of the Environment (CCME) WQI and the National Sanitation Foundation (NSF) WQI.

These were chosen since they are both extensively used and use different criteria to determine water quality: The NSF WQI has fixed parameters, weights, and threshold values, whereas the CCME has parameters and threshold values that are customizable.

The assessment of water quality using physico-chemical characteristics and the WQI revealed that the CCME WQI and the NSF WQI have distinct criteria. CCME WQI categorized sampling point as marginal/bad quality, while most sampling locations were categorized as good quality in the NSF WQI. Seemingly, the water quality classifications appeared to be affected by seasonal variations: during the wet season, the majority of the CCME WQI values deteriorated, implying that precipitation and runoff introduced debris into the riverbed. Thus, it’s crucial to compare WQIs because they use various factors, criteria, and threshold values, which might lead to different outcomes (Ramírez-Morales et al. 2021 ).

Yotova et al. ( 2021 ) directed an analysis on the Mesta River located between Greece and Bulgaria. The Bulgarian section of the Mesta River basin, which is under the supervision of the West-Aegean Region Basin Directorate, was being researched. The goal was to evaluate the surface water quality of ten points of the river using a novel approach that combines composite WQI developed by the CCME and Self organizing map (SOM) on the required monitoring data that include: DO, pH, EC, ammonium, nitrite, nitrate, total phosphate, BOD and TSS.

The use of WQI factors in SOM calculations allows for the identification of specific WQI profiles for various object groups and identifying groupings of river basin which have similar sampling conditions. The use of both could reveal and estimate the origin and magnitude of anthropogenic pressure. In addition, it might be determined that untreated residential wastewaters are to blame for deviations from high quality requirements in the Mesta River catchment.

Interestingly, this study reveals that WQI appear more accurate and specific when combined with a statistical test such as the SOM (Yotova et al. 2021 ).

2.4.3 Oregon water quality index (OWQI)

The Oregon Water Quality Index is a single number that creates a score to evaluate the water quality of Oregon’s stream and apply this method in other geographical region (Hamlat et al. 2017 ; Singh et al. 2013 ). The OWQI was widely accepted and applied in Oregon (USA) and Idaho (USA) (Sutadian et al. 2016 ).

Additionally, the OWQI is a variant of the NSFWQI, and is used to assess water quality for swimming and fishing, it is also used to manage major streams (Lumb et al. 2011b ). Since the introduction of the OWQI in 1970, the science of water quality has improved noticeably, and since 1978, index developers have benefited from increasing understanding of stream functionality (Bharti and Katyal 2011 ). The Oregon index belongs to the specific consumption indices group. It is a water classification based on the kind of consumption and application such as drinking, industrial, etc. (Shah and Joshi 2017 ).

The original OWQI dropped off in 1983, due to excessive resources required for calculating and reporting results. However, improvement in software and computer hardware availability, in addition to the desire for an accessible water quality information, renewed interest in the index (Cude 2001 ).

Simplicity, availability of required quality parameters, and the determination of sub-indexes by curve or analytical relations are some advantages of this approach (Darvishi et al. 2016a ). The process combines eight variables including temperature, dissolved oxygen (percent saturation and concentration), biochemical oxygen demand (BOD), pH, total solids, ammonia and nitrate nitrogen, total phosphorous and bacteria (Brown 2019 ). Equal weight parameters were used for this index and has the same effect on the final factor (Darvishi et al. 2016a ; Sutadian et al. 2016 ).

The Oregon index is calculated by the following Eq.  9 (Darvishi et al. 2016a ):

where,n is the number of parameters (n = 8) SI i is the value of parameter i.

Furthermore, the OWQI scores range from 10 for the worse case to 100 as the ideal water quality illustrated in the following Table 4 (Brown 2019 ).

Kareem et al. ( 2021 ) using three water quality indices, attempted to analyze the Euphrates River (Iraq) water quality for irrigation purposes in three different stations: WAWQI, CCMEWQI AND OWQI.

For fifteen parameters, the annual average value was calculated, which included: pH, BOD, Turbidity, orthophosphate, Total Hardness, Sulphate, Nitrate, Alkalinity, Potassium Sodium, Magnesium, Chloride, DO, Calcium and TDS.

The OWQI showed that the river is “very poor”, and since the sub-index of the OWQI does not rely on standard-parameter compliance, there are no differences between the two inclusion and exclusion scenarios, which is not the case in both WAWQI and CCMEWQI (Kareem et al. 2021 ).

Similarly, the OWQI showed a very bad quality category, and it is unfit for human consumption, compared to the NSFWQI and Wilcox indices who both showed a better quality of water in Darvishi et al., study conducted on the Talar River (Iran) (Darvishi et al. 2016b ).

2.4.4 Weighted arithmetic water quality index (WAWQI)

The weighted arithmetic index is used to calculate the treated water quality index, in other terms, this method classifies the water quality according to the degree of purity by using the most commonly measured water quality variables (Kizar 2018 ; Paun et al. 2016 ).This procedure has been widely used by scientists (Singh et al. 2013 ).

Three steps are essential in order to calculate the WAWQI:

Further quality rating or sub-index was calculated using the following equation (Jena et al. 2013 ):

Qn is the quality rating for the nth water quality parameter.

Vn is the observed value of the nth parameter at a given sampling station.

Vo is the ideal value of the nth parameter in a pure water.

Sn is the standard permissible value of the nth parameter.

The quality rating or sub index corresponding to nth parameter is a number reflecting the relative value of this parameter in polluted water with respect to its permissible standard value (Yogendra & Puttaiah 2008 ).

The unit weight was calculated by a value inversely proportional to the recommended standard values (Sn) of the corresponding parameters (Jena et al. 2013 ):

Wn is the unit weight for the nth parameter.

K is the constant of proportionality.

Sn is the standard value of the nth parameter.

The overall WQI is the aggregation of the quality rating (Qn) and the unit weight (Wn) linearly (Jena et al. 2013 ):

After calculating the WQI, the measurement scale classifies the water quality from “unsuitable water” to “excellent water quality” as given in the following Table 5 .

Sarwar et al. ( 2020 ) carried out a study in Chaugachcha and Manirampur Upazila of Jashore District (Bangladesh). The goal of this study was to determine the quality of groundwater and its appropriateness for drinking, using the WAWQI including nine parameters: turbidity, EC, pH, TDS, nitrate, ammonium, sodium, potassium and iron. Many samplings point was taken from Chaugachcha and Manirampur, and WQI differences were indicated (ranging from very poor to excellent). These variations in WQI were very certainly attributable to variances in geographical location. Another possibility could be variations in the parent materials from which the soil was created, which should be confirmed using experimental data. It is worth mentioning that every selected parameter was taken into consideration during calculation. Similarly, the water quality differed in Manirampur due to the elements contained in the water samples that had a big impact on the water quality (Sarwar et al. 2020 ).

In 2021, García-Ávila et al. undertook a comparative study between the CCMEWQI and WAWQI for the purpose of determining the water quality in the city of Azogues (Ecuador). Twelve parameters were analyzed: pH, turbidity, color, total dissolved solids, electrical conductivity, total hardness, alkalinity, nitrates, phosphates, sulfates, chlorides and residual chlorine over 6 months. The average WAWQI value was calculated suggesting that 16.67% of the distribution system was of 'excellent' quality and 83.33% was of 'good' quality, while the CCMEWQI indicated that 100% of the system was of ‘excellent’ quality.

This difference designated that the parameters having a low maximum allowable concentration have an impact on WAWQI and that WAWQI is a valuable tool to determine the quality of drinking water and have a better understanding of it (García-Ávila et al. 2022a , b ).

2.4.5 Additional water quality indices

The earliest WQI was based on a mathematical function that sums up all sub-indices, as detailed in the 2.1. History of water quality concept section (Aljanabi et al. 2021 ). The Dinius index (1972), the OWQI (1980), and the West Java index (2017) were later modified from the Horton index, which served as a paradigm for later WQI development (Banda and Kumarasamy 2020 ).

Based on eleven physical, chemical, organic, and microbiological factors, the Scottish Research Development Department (SRDDWQI) created in 1976 was based on the NSFWQI and Delphi methods used in Iran, Romania, and Portugal. Modified into the Bascaron index (1979) in Spain, which was based on 26 parameters that were unevenly weighted with a subjective representation that allowed an overestimation of the contamination level. The House index (1989) in the UK valued the parameters directly as sub-indices. The altered version was adopted as Croatia's Dalmatian index in 1999.

The Ross WQI (1977) was created in the USA using only 4 parameters and did not develop into any further indices.

In 1982, the Dalmatian and House WQI were used to create the Environmental Quality Index, which is detailed in Sect.  2.1 . This index continues to be difficult to understand and less powerful than other indices (Lumb et al. 2011a ; Uddin et al. 2021 ).

The Smith index (1990), is based on 7 factors and the Delphi technique in New Zealand, attempts to eliminate eclipsing difficulties and does not apply any weighting, raising concerns about the index's accuracy (Aljanabi et al. 2021 ; Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

The Dojildo index (1994) was based on 26 flexible, unweighted parameters and does not represent the water's total quality.

With the absence of essential parameters, the eclipse problem is a type of fixed-parameter selection. The Liou index (2004) was established in Taiwan to evaluate the Keya River based on 6 water characteristics that were immediately used into sub-index values. Additionally, because of the aggregation function, uncertainty is unrelated to the lowest sub-index ranking (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Said index (2004) assessed water quality using only 4 parameters, which is thought to be a deficient number for accuracy and a comprehensive picture of the water quality. Furthermore, a fixed parameter system prevents the addition of any new parameters.

Later, the Hanh index (2010), which used hybrid aggregation methods and gave an ambiguous final result, was developed from the Said index.

In addition to eliminating hazardous and biological indicators, the Malaysia River WQI (MRWQI developed in the 2.1 section) (2007) was an unfair and closed system that was relied on an expert's judgment, which is seen as being subjective and may produce ambiguous findings (Banda and Kumarasamy 2020 ; Uddin et al. 2021 ).

Table illustrated the main data of the studies published during 2020–2022 on water quality assessments and their major findings:

2.5 Advantages and disadvantages of the selected water quality indices

A comparison of the selected indices is done by listing the advantages and disadvantages of every index listed in the Table 7 below.

2.6 New attempts of WQI studies

Many studies were conducted to test the water quality of rivers, dams, groundwater, etc. using multiple water quality indices throughout the years. Various studies have been portrayed here in.

Massoud ( 2012 ) observed during a 5-year monitoring period, in order to classify the spatial and temporal variability and classify the water quality along a recreational section of the Damour river using a weighted WQI from nine physicochemical parameters measured during dry season. The WWQI scale ranged between “very bad” if the WQI falls in the range 0–25, to “excellent” if it falls in the range 91–100. The results revealed that the water quality of the Damour river if generally affected by the activities taking place along the watershed. The best quality was found in the upper sites and the worst at the estuary, due to recreational activities. If the Damour river is to be utilized it will require treatment prior any utilization (Massoud 2012 ).

Rubio-Arias et al. ( 2012 ) conducted a study in the Luis L. Leon dam located in Mexico. Monthly samples were collected at 10 random points of the dam at different depths, a total of 220 samples were collected and analyzed. Eleven parameters were considered for the WQI calculation, and WQI was calculated using the Weighted WQI equation and could be classified according to the following ranges: < 2.3 poor; from 2.3 to 2.8 good; and > 2.8 excellent. Rubio-Arias et al., remarked that the water could be categorized as good during the entire year. Nonetheless, some water points could be classified as poor due to some anthropogenic activities such as intensive farming, agricultural practices, dynamic urban growth, etc. This study confirms that water quality declined after the rainy season (Rubio-Arias et al. 2012 ).

In the same way, Haydar et al. ( 2014 ) evaluated the physical, chemical and microbiological characteristics of water in the upper and lower Litani basin, as well as in the lake of Qaraaoun. The samples were collected during the seasons of 2011–2012 from the determined sites and analyzed by PCA and the statistical computations of the physico-chemical parameters to extract correlation between variables. Thus, the statistical computations of the physico-chemical parameters showed a correlation between some parameters such as TDS, EC, Ammonium, Nitrate, Potassium and Phosphate. Different seasons revealed the presence of either mineral or anthropogenic or both sources of pollution caused by human interference from municipal wastewater and agricultural purposes discharged into the river. In addition, temporal effects were associated with seasonal variations of river flow, which caused the dilution if pollutants and, hence, variations in water quality (Haydar et al. 2014 ).

Another study conducted by Chaurasia et al., ( 2018 ), proposed a groundwater quality assessment in India using the WAWQI. Twenty-two parameters were taken into consideration for this assessment, however, only eight important parameters were chosen to calculate the WQI. The rating of water quality shows that the ground water in 20% of the study area is not suitable for drinking purpose and pollution load is comparatively high during rainy and summer seasons. Additionally, the study suggests that priority should be given to water quality monitoring and its management to protect the groundwater resource from contamination as well as provide technology to make the groundwater fit for domestic and drinking (Chaurasia et al. 2018 ).

Daou et al. ( 2018 ) evaluated the water quality of four major Lebanese rivers located in the four corners of Lebanon: Damour, Ibrahim, Kadisha and Orontes during the four seasons of the year 2010–2011. The assessment was done through the monitoring of a wide range of physical, chemical and microbiological parameters, these parameters were screened using PCA. PCA was able to discriminate each of the four rivers according to a different trophic state. The Ibrahim River polluted by mineral discharge from marble industries in its surroundings, as well as anthropogenic pollutants, and the Kadisha river polluted by anthropogenic wastes seemed to have the worst water quality. This large-scale evaluation of these four Lebanese rivers can serve as a water mass reference model (Daou et al. 2018 ).

Moreover, some studies compared many WQI methods. Kizar ( 2018 ), carried out a study on Shatt Al-Kufa in Iraq, nine locations and twelve parameters were selected. The water quality was calculated using two methods, the WAWQI and CWQI. The results revealed the same ranking of the river for both methods, in both methods the index decreased in winter and improved in other seasons (Kizar 2018 ).

On the other hand, Zotou et al. ( 2018 ), undertook a research on the Polyphytos Reservoir in Greece, taking into consideration thirteen water parameters and applying 5 WQIs: Prati’s Index of Pollution (developed in 1971, based on thirteen parameter and mathematical functions to convert the pollution concentration into new units. The results of PI classified water quality into medium classes (Gupta and Gupta 2021 ). Bhargava’s WQI (established in 1983, the BWQI categorize the parameters according to their type: bacterial indicators, heavy metals and toxins, physical parameters and organic and inorganic substances. The BWQI tends to classify the water quality into higher quality classes, which is the case in the mentioned study (Gupta and Gupta 2021 ). Oregon WQI, Dinius second index, Weighted Arithmetic WQI, in addition to the NSF and CCMEWQI. The results showed that Bhargava and NSF indices tend to classify the reservoir into superior quality classes, Prati’s and Dinius indices fall mainly into the middle classes of the quality ranking, while CCME and Oregon could be considered as “stricter” since they give results which range steadily between the lower quality classes (Zotou et al. 2018 ).

In their study, Ugochukwu et al. ( 2019 ) investigated the effects of acid mine drainage, waste discharge into the Ekulu River in Nigeria and other anthropogenic activities on the water quality of the river. The study was performed between two seasons, the rainy and dry season. Samples were collected in both seasons, furthermore, the physic-chemistry parameters and the heavy metals were analyzed. WQI procedure was estimated by assigning weights and relative weights to the parameters, ranking from “excellent water” (< 50) to “unsuitable for drinking” (> 300). The results showed the presence of heavy metals such as lead and cadmium deriving from acid mine drainage. In addition, the water quality index for all the locations in both seasons showed that the water ranked from “very poor” to “unsuitable for drinking”, therefore the water should be treated before any consumption, and that enough information to guide new implementations for river protection and public health was provided (Ugochukwu et al. 2019 ).

The latest study in Lebanon related to WQI was carried out by El Najjar et al. ( 2019 ), the purpose of the study was to evaluate the water quality of the Ibrahim River, one of the main Lebanese rivers. The samples were collected during fifteen months, and a total of twenty-eight physico-chemical and microbiological parameters were tested. The parameters were reduced to nine using the Principal Component Analysis (PCA) and Pearson Correlation. The Ibrahim WQI (IWQI) was finally calculated using these nine parameters and ranged between 0 and 25 referring to a “very bad” water quality, and between 91 and 100 referring to an “excellent” water quality. The IWQI showed a seasonal variation, with a medium quality during low -water periods and a good one during high-water periods (El Najjar et al. 2019 ).

3 Conclusion

WQI is a simple tool that gives a single value to water quality taking into consideration a specific number of physical, chemical, and biological parameters also called variables in order to represent water quality in an easy and understandable way. Water quality indices are used to assess water quality of different water bodies, and different sources. Each index is used according to the purpose of the assessment. The study reviewed the most important indices used in water quality, their mathematical forms and composition along with their advantages and disadvantages. These indices utilize parameters and are carried out by experts and government agencies globally. Nevertheless, there is no index so far that can be universally applied by water agencies, users and administrators from different countries, despite the efforts of researchers around the world (Paun et al. 2016 ). The study also reviewed some attempts on different water bodies utilizing different water quality indices, and the main studies performed in Lebanon on Lebanese rivers in order to determine the quality of the rivers (Table 6 ).

As mentioned in the article (Table 7 ); WQIs may undergo some limitations. Some indices could be biased, others are not specific, and they may not get affected by the value of an important parameter. Therefore, there is no interaction between the parameters.

Moreover, many studies exhibited a combination between WQIs and statistical techniques and analysis (such as the PCA, Pearson’s correlation etc.). with a view to obtain the relation between the parameters and which parameter might affect the water quality.

In other research, authors compared many WQIs to check the difference of water quality according to each index. Each index can provide different values depending on the sensitivity of the parameter. For that reason, WQIs should be connected to scientific advancements to develop and elaborate the index in many ways (example: ecologically). Therefore, an advanced WQI should be developed including first statistical techniques, such as Pearson correlation and multivariate statistical approach mainly Principal Component Analysis (PCA) and Cluster Analysis (CA), in order to determine secondly the interactions and correlations between the parameters such as TDS and EC, TDS and total alkalinity, total alkalinity and chloride, temperature and bacteriological parameters, consequently, a single parameter could be selected as representative of others. Finally, scientific and technological advancement for future studies such as GIS techniques, fuzzy logic technology to assess and enhance the water quality indices and cellphone-based sensors for water quality monitoring should be used.

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Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

• David de Andrade Costa 1 , 2 ,
• José Paulo Soares de Azevedo 1 ,
• Marco Aurélio dos Santos 1 &
• Rafaela dos Santos Facchetti Vinhaes Assumpção 3  

Scientific Reports volume  10 , Article number:  22038 ( 2020 ) Cite this article

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Fifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQI NSF ) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

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Introduction

Aquatic systems have been significantly affected by human activities causing water quality deterioration, decreasing water availability and reducing the carrying capacity of aquatic life 1 , 2 , 3 , 4 . Water quality deterioration still persists in developed countries, while it is a major problem in developing countries in which a substantial amount of sewage is discharged directly into rivers 5 , 6 , 7 , 8 . Moreover, according to UNEP 9 , water pollution has worsened since the 1990s in the majority of rivers in Latin America. The global concern with water availability and its quality has been growing, and it is estimated that the demand for water will increase between 20 and 30% by 2050 10 , 11 . In addition, spatial and temporal variations in the hydrological cycle and their uncertainties related to climate change may worsen this scenario 12 , 13 , 14 , 15 , 16 .

Monitoring water quality in order to assess its spatial and temporal variations is essential for water management and pollution control 17 . On the other hand, monitoring programs generate large data sets that require interpretation techniques 18 . There are a number of methods for water quality assessment, including single-factor, multi-index, fuzzy mathematics, grey system evaluation, artificial neural network, multi-criteria analysis, geographical interpolation and multivariate statistical approach 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 . Among them, the most used are the Water Quality Indexes (WQI) that transform a complex set of data into a single value indicative of water quality 26 , 27 and reflect its suitability for different uses 28 . Multivariate statistics is another widely used approach 29 , 30 , mainly with Principal Components Analysis (PCA) and Cluster Analysis (CA), helping to achieve a better understanding of the spatial and temporal dynamics of water quality.

A comparison of seven methods for assessing water quality indicated WQI as one of the best 20 . The assessment of Poyang Lake 28 , China and the upper Selenga River 31 , Mongolia showed that WQIs are suitable for the assessment of both interannual trends and seasonal variations 28 . Multivariate statistical techniques associated with WQI have been used for numerous water bodies world-wide, including the Nag River 30 , India, the Paraíba do Sul River 32 , Brazil, and the before mentioned Selenga River 31 . CA grouped the monitoring stations according to their similarities, while the PCA highlighted components that were related to its pollution sources 30 , 31 , 32 .

In order to ensure water quantity and quality, the Brazilian National Water Resources Policy 33 has established a management tool called Framework, according to the main intended uses of water. It has also created participatory management committees, the so-called Basin Committees, which, together with its technical agency, are responsible for the Framework establishment. Unfortunately, even after two decades, Brazil has had very few successful experiences on the subject 34 .

Brazil has a gigantic and complex hydrographic network present in many different ecosystems 34 . The Brazilian Atlantic Forest is one of the most biodiverse biomes on the planet 35 , 36 , extending along the Brazilian coast and currently covering only 11.4% of its original territory 37 under constant threats 38 , 39 , 40 . The hydrographic basin of the Paraíba do Sul river is located in this environment, which is the integration axis of the most industrialized Brazilian states, São Paulo, Rio de Janeiro and Minas Gerais, and home to around 6.2 million people 41 . A water transfer system regularly supplies another 9 million people in the metropolitan region of Rio de Janeiro, through the Guandu system. Another water transfer system connects the Paraíba do Sul river to the Cantareira system, complementing with 5 m 3 /s the water supply to over 9 million people in the metropolitan region of São Paulo 41 . These systems went through an intense water scarcity between 2014 and 2016 with severe impacts on water quality and availability 32 .

Our study is focused on the Piabanha River watershed, a strategic sub-basin of the Paraíba do Sul river, combining urban, industrial, rural characteristics, and large preserved fragments of Atlantic Forest 36 , 42 . The Piabanha Basin has been monitored for over 10 years with the Studies in Experimental and Representative Watersheds (EIBEX) project, a partnership between universities and government agencies 42 , 43 , 44 . The State Environmental Agency of Rio de Janeiro (INEA) has been monitoring the basin since 1980. Other studies in the region include the analysis of contamination by pesticides 45 , energy generation 46 and dispersion of pollutants 47 . The Piabanha Basin received international attention in Nature's article on biodiversity 36 . But in addition to forest preservation, can the Piabanha River support biodiversity? How is its water quality today? In this way, the Piabanha Basin Committee defined the Framework as a priority in its management plan (2018–2020) and to accomplish this goal, established water monitoring as a strategic action 48 .

Our study covers 40 years of monitoring, including government data, our research projects and, currently, a monitoring program that is being conducted with funding from the Piabanha Basin Committee. The main objectives were: (1) to carry out an updated diagnosis of water quality using multivariate techniques and WQI; (2) to examine the parameters that most influence water quality, and (3) to identify river stretches with similar water quality. Our study provides an extensive understanding of the Piabanha River and supports its Steering Committee in the application of public policies. This is a pilot project that can be a reference for other Framework programs for improving water quality in Brazil.

We have requested and received from INEA two water user databases of the Piabanha Basin. The first set corresponds to raw data from the National Water Resources Register (CNARH), with all the registrations until December 2017 and with 1549 registered interferences (water abstraction or effluent discharge). The second one is the registration validated by INEA until August 2018 by the Águas do Rio project comprising a total of 669 validated interferences. With these data, it was possible to build a georeferenced base. By so doing, it was possible to list the main effluent discharges by type for each monitoring station.

In the validated database, from the 669 interferences, 84% are water abstractions and 16% are effluent discharges. Water abstraction account for 425 m 3  day −1 with 75% from wells and 25% from rivers. On the other hand, effluent discharges are 89 m 3  day −1 . The largest volume of effluents comes from the sanitation sector with 57% of the total, whereas industries account for 33%, aquaculture with 4% and mining for 3% of discharges.

When comparing the two databases, it is clear that the universe of registered users is much larger than the universe of validated users; in other words, those whose data were made up by the state environmental agency and, therefore, received a license. For example, the validated database has only six interferences related to agriculture, in contrast to 789 interferences awaiting validation. This is a serious obstacle for water resources management in the region, which threatens the sustainability of water resources.

Short time monitoring and water quality index

In order to assess and compare the water quality of the Piabanha River, we calculated the Water Quality Index from the National Sanitation Foundation (WQI NSF ) using two datasets, the first one from 2012 and the last one from 2019 (Tables 1 and Table 2 ). The 2012 results (Fig.  1 A) oscillated between the bad and medium categories, generally with medium quality (50.5 ± 10.3). In 2019 (Fig.  1 B), the results ranged between the medium and good categories, in general with medium quality (61.6 ± 10.8).

WQI NSF spatial variation over each station from July to December ( A ) 2012 and ( B ) 2019. WQI NSF seasonal variation over the entire length of the river ( C ) 2012 and ( D ) 2019. The entire dataset can be found online as Supplementary Table S1 and S2 , respectively for 2012 and 2019.

Data sets show significant seasonal behavior (p < 0.05) (Fig.  1 C,D) between the end of the dry period (Jul, Aug, Sep) and the beginning of the rainy period (Oct, Nov, Dec) for the parameters DO, WT, pH, nitrate, phosphate and turbidity, while no significant seasonal difference (p > 0.05) was found for the parameters E. coli , BOD and TDS. The parameters that have most impacted the WQI NSF were coliforms and BOD. Ammonia and total phosphorus do not account to WQI NSF , but their concentration has violated Brazilian legislation and their influence can be better understood by PCA.

Principal components and clusters analysis

The 2019 dataset (n = 48), comprising six monitoring campaigns at the eight monitoring stations along the Piabanha River with 15 parameters analysed, was grouped by the average value of each parameter at each station (n = 8). Pearson’s correlation matrix is presented in Table 3 , most parameters showing a strong correlation (r > 0.5) with a confidence interval greater than 95% (α = 0.05). The KMO measures of sampling adequacy (n = 8) were near to 0.5 and the significance level of test of sphericity was less than 0.001, indicating that the data was fit for PCA and the correlation matrix is not an identity matrix and so the variables are significantly related. The Shapiro test confirmed the data normality (p > 0.01) for all parameters, except for E. coli .

ACP was applied to identify groups of parameters that influence water quality. PC 1, PC 2 and PC3 account for 72% (eigenvalue 10.74), 14% (eigenvalue 13.94) and 5% (eigenvalue 0.8), respectively, of the data variance. Components with eigenvalues larger than the unit were selected. That is, the first two components together account for 86% of the total variance. The loadings that compose the first two components are presented in the Table 4 and the stations that most influence the results are represented in Fig.  2 A.

Multivariate techniques. ( A ) PCA plot with station scores and parameters loadings. ( B ) Hierarchical clustering by Ward linkage with Euclidean distance. The entire dataset can be found as Supplementary Table S2 online.

PC1 was substantially correlated with practically all parameters. Stations number 1 to 4 loaded positively (loadings > 0.7) to PC1 with the parameters TDS, Alkalinity, Ammonia, Total Nitrogen, Phosphate, Total Phosphorus, DBO, COD, E. coli , while stations number 5 to 8 loaded negatively (loadings < − 0.7) with Nitrate, Turbidity, SS, pH and WT. PC2 was most influenced by stations in the urban area, notably station 1, and showed a positive correlation (loadings > 0.5) with OD, COD, BOD and less by SS (loading = 0.33), being more influenced by station 1 in the urban area. On the other hand, it was negatively correlated with E. coli (loading = − 0.66) with a large influence of station 3.

The sampling stations were grouped into three statistically significant clusters with 75% of similarity by agglomerative hierarchical clusterization based on the ward linkage by Euclidean distance (Fig.  2 B): cluster 1 (Stations 2 and 3), cluster 2 (Stations 7 and 8) and cluster 3 (Stations 1, 4, 5 and 6).

Longtime monitoring assessment based on Mann–Kendall rank test and Fourier transform

In a complementary way, in order to evaluate a possible trend on water quality and to detect the seasonal behavior of the basin, we used a time series with 40 years of monitoring. Since dissolved oxygen can be used as a surrogate variable for the general health of aquatic ecosystems 49 , 50 , 51 , it was selected to perform the Mann–Kendall rank test of randomness for the station more upstream and further downstream of the Piabanha River, PB002 and PB011 respectively. The upstream station showed a statistically significant increasing trend (n = 166, S = 1507, Z = 2.10, p < 0.03), whereas the downstream station does not show a statistically significant trend (n = 198, S = 1179, Z = 1.27, p = 0.20). The entire dataset can be found as Supplementary Table S3 and S4 .

To detect the seasonal behavior, we have applied a Fourier transform algorithm to the time series from 1980 to 2019 to the station PB011 (Fig.  3 A, which does not display a tendency behavior and can be considered as representative of the entire basin because it is the most downstream station. The data were organized in quarterly averages for the DO parameter. The two most powerful signals correspond to the frequencies of 0.25 and 0.45, nearly (Fig.  3 B) It corresponds to periods of 12 and 6 months, respectively. Taking into account this seasonality, we confirmed that our 2019 field campaigns are representative of seasonality comprising the final half of the dry season and the initial half of the rainy season.

( A ) Temporal distribution of dissolved oxygen from 1980 to 2019 at station PB002 (n = 160). ( B ) Periodogram. The entire dataset can be found in Supplementary Table S5 .

Water quality assessment

The Piabanha River had a better water quality in 2019 than in 2012, according to WQI NSF results (Fig.  1 ). The improvement was substantial over the first 40 km, rated as “bad” in most campaigns in 2012, while rated as medium in most campaigns in 2019 due to sewage collection and treatment system expansion. Since 2012, Petrópolis has built 50 km of sewage collection network and 7 new sewage treatment units 52 . These plants produce secondary level effluents through biological treatment, the plants flow capacity reaches about 800 L s −1 . These stations use different technologies such as: submerged aerated biofilters, anaerobic upflow reactor, moving bed biofilm reactor and upflow anaerobic sludge blanket reactor. Beside this, in some plants are used biosystems 53 . Water quality improved in stretches after 40 km due to self-purification processes and the contribution of clean tributaries. This is in line with findings from other rivers worldwide 31 , 54 , 55 .

Dry seasons, in general, presented better water quality indexes than rainy seasons. Other studies 28 , 56 , 57 have shown similar seasonal behavior, where water quality worsens in the rainy season due to sediments and pollutants input carried by the rain. In addition, most of the sewage network is the same network that collects rainwater. Thus, during rainy events, sewage is no longer treated and is discharged directly into rivers.

Although the WQI NSF had a medium rating in 2019, BOD and Coliforms were substantially above the maximum allowed by Brazilian regulation. In addition, the index is limited to the parameters used in its calculation 58 . This is the case for the ammonium parameter, which presented concentrations up to three times higher than allowed in Brazilian regulation, reminding that only nitrate is used in the WQI NSF . The same occurs with total phosphorus: only phosphate is considered, although it does not have a maximum value established by the Brazilian federal regulation. In what follows, we analyse these parameters in more detail.

Biochemical Oxygen Demand (BOD) is one of the most widely used criteria for water quality assessment. It provides information on the ready biodegradable fraction of the organic load in water 59 . High BOD concentrations reduce oxygen availability, mainly correlated to microbiological activity 60 . Its concentration ranged from 2.00 to 45 mg L −1 (average 7.69 ± 7.52) over the entire data, with its concentrations most of the time substantially above the maximum allowed by Brazilian regulation (5 mg L −1 ). Escherichia coli is naturally present in the intestinal tracts of warm-blooded animals and it is widely used as an indicator of fecal contamination 61 , 62 . Villas-Boas 42 pointed to fecal coliforms as the most relevant water quality parameter in the urban area of Petrópolis, mainly related to pollution caused by untreated domestic sewage.

Phosphorus is an essential nutrient for all forms of life 63 . Its availability can be related to atmospheric deposition 64 , anthropic uses of products such as detergents 65 and due to agricultural activities 66 . Orthophosphates are the most relevant in the aquatic environment as they are the main form of phosphate assimilated by aquatic vegetables 67 . Previous studies 42 , 68 , 69 in the Piabanha Basin found phosphate values in perfect agreement with ours. Alvim 68 points out that the main source of phosphorus for the Piabanha River is the sewage discharge and the higher concentrations are found during the rainy season.

Nitrate is a very common element in surface water since it is the end product of the aerobic decomposition of the organic nitrogenous compound 70 , 71 . Its sources are related to landscape composition, being influenced by both agricultural and urban uses 72 . Villas-Boas 42 found high concentration of nitrate and ammonium in the urban region of Piabanha River in agreement with this study. Alvim 68 reports that domestic sewage discharged into Piabanha River waters account for 43% of the nitrogen load, the atmospheric contribution for 31% and the farming activity for 15%.

The major contributors to water quality and stretches of river with similar water quality

The first two components together account for 86% of the total variance, indicating method high explanatory power of the method. It was far better than other similar studies around the world 29 , 30 , 71 , 73 , 74 , 75 . PC1 predominantly accounts for urban sewage pollution. This is clearly demonstrated by the fact that stations from 1 to 4, located in the urban area of Petrópolis, positively loaded PC1 with organic matter (BOD and COD), TDS and nutrients such as phosphorus and nitrogenous constituents, especially ammonia, indicating recent pollution. Even clearer is the fact that stations from 5 to 8 have negatively loaded with nitrate, showing the nitrogen compounds degradation in the downstream stretches of the urban area. On the other hand, the increase in nitrate concentrations in association with the increase in turbidity in stations outside the urban area may also be associated with land use, especially in agriculture.

PC2 is dominated by the dissolved oxygen parameter and other parameters that indicate the health of the river, as organic load and coliforms. It is explained by water pollution by organic matter and biological activity and reinforces the result of CP1. In the study region, sanitation is still a challenge to be faced by the government, especially in the first urban stretch, after 40 km from the source of the Piabanha River, this region has 26% of untreated sewage 53 .

Cluster analysis was used to group sampling stations into similarity classes indicating the stretches of river with similar water quality. As pointed out by Singh 29 , it implies that only one site in each cluster may serve as good in spatial assessment of the water quality as the whole cluster. So, the number of sampling sites can be reduced; hence, cost without losing any significance of the outcome. On the other hand, this interpretation should be done with caution since trends in different stretches can be very different, making future changes significant. Therefore, great care must be taken to reduce monitoring stations.

It is important to notice that the first cluster (S1, S6 and S4, S5) groups station 1 with station 6, the first one corresponding to the urban area of Petrópolis whose pollution stems from sewage and industrial effluents. Likewise, station 6 is located after the confluence of the Preto-Paquequer River, which crosses Teresópolis, the second largest city in the hydrographic basin, also with the presence of economic and industrial activities. Sand mining is the predominant activity near stations 4 and 5, which together receive the impact of five mining companies. Similarly, station 6, after the Preto River, receives the impact of seven sand mines. In fact, this group brings together economic activities whose impact on water quality is similar. Station 5 could be removed from the network monitoring in order to reduce costs.

The second cluster (S2 and S3) refers to the most urbanized section of the basin. When individually checking the quality parameters between these stations, one can conclude that they differ only by the diluting effect caused by the contribution of the Araras River, on the left bank, and of the Poço do Ferreira River, on the right bank, which receives its waters from the Bonfim River after its source in the Serra dos Órgãos National Park, an important federal conservation unit. Station 3 was introduced precisely to detect this diluting effect, but since the cluster analysis showed that it was not significant it is recommended to remove this station.

The third cluster (S7 and S8) has a very similar behavior: station 8 is just before the Piabanha River mouth and station 7 is located less than 10 km upstream of the mouth. In addition, on this stretch there are only three interferences registered as discharges. Thus, it is recommended to remove station 7, considering the importance of maintaining a station close to the river mouth.

Trend analysis and seasonal variation

Although it still presents systematic violations to Brazilian standards 76 , the water quality, in general, has improved in the Piabanha River over the past 40 years (Fig.  3 A,B). This statement is supported by the Mann–Kendall rank test of randomness, indicating a significant (p = 0.03) tendency to increase the values of the dissolved oxygen parameter at station PB002, located in the urban area of Petrópolis, which is highly impacted by effluent discharges, despite the fact that this region has municipal sewage treatment. PB011 presents high levels of DO, since the beginning of the time series exhibiting an almost monotonic behavior over time, thus it has no tendency. The high DO levels are due to both the river's reoxygenation process and the contribution of clean waters from its tributaries, such as the Fagundes River.

A strong annual and semi-annual seasonality was indicated by the power spectral density, which can be seen in the periodogram (Fig.  3 B) resulting from the Fast Fourier Transform. The results are in accordance with the literature 77 indicating that more than 90% of the total variance of dissolved oxygen is accounted for by the annual periodicity and the next four higher harmonics (semi-annual; tri-annual, etc.). Seasonality follows the rainfall regime with a dry period from April to September, and a wet period from October to March, according to Araújo's 78 study carried out in the Piabanha River basin.

Water quality at point PB002 started to improve in 2000, when the first sewage treatment plant in the city of Petrópolis came into operation. Currently, 95% of the population has access to drinking water, and the coverage of treated urban sewage is 85%. The municipality has 26 sewage treatment units, responsible for the treatment of 56.2 million liters per day. In relation to the other municipalities in the basin, according to the National Sanitation Information System 79 (SNIS), the municipality of Três Rios treats 2.97% of its sewage, while the other municipalities, Teresópolis, Areal, São José do Vale do Rio Preto, Paty do Alferes and Paraíba do Sul did not report their data to SNIS, potentially indicating that they do not perform sewage treatment. In other words, about 50% of the population has no formal access to sewage treatment services.

The diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy. In addition to the diagnosis which was carried out a georeferenced database was built. There are few cases of Framework in Brazil and none in the studied watershed. This makes this study relevant to Brazilian water resources management. The considerable number of users awaiting regularization from the State Environmental Institute is a limitation to implement the Framework and requires a joint effort of the watershed committee.

Answering our initial question, Piabanha River water quality is medium according to the WQI NSF and certainly is not able to support high levels of biodiversity. Some river stretches have quality compatible with class 4 according to the Brazilian regulation for the coliforms, BOD and TP parameters; hence, they cannot be used for irrigation, human or animal consumption, not even after treatment. On the other hand, the Framework must be carried out according to intended uses. Therefore, we recommend that the Piabanha Committee, in partnership with the State Public Ministry, lead actions to reduce the concentrations of these parameters, mainly in the sanitation sector.

It is recommended that the monitoring program be continued and expanded to stretches where conflicts between water uses occur, in order to implement the Framework to enforce the improvement of water quality. It is also important to point out that this study was financed with public resources from the Piabanha water resources fund and that the present analysis made possible to recommend the exclusion of three of the eight existing stations, thereby enabling the expansion of the monitoring to other tributaries of the Piabanha River under the influence of large population with practically no sanitation, notably the Rio Preto/Paquequer sub-basin.

This work describes a methodological approach that can be useful for other researches in environmental science and management. We have applied an integrated approach using data from different sources combined with data analysis based on WQI, PCA, CA, frequency analysis and trend analysis, which were used in a complementary way to understand a research problem.

Materials and methods

The Piabanha Basin is located in southern Brazil, belonging to the mountainous region of the State of Rio de Janeiro with an area of 2050 km 2 (Fig.  4 ). The Piabanha River source is at 1150 m of altitude and runs down 80 km until it flows into the Paraíba do Sul River at an altitude of 260 m. The upper portion of the basin presents a humid tropical climate. With steep slopes, annual rainfall exceeds 2000 mm. The lower portion of the basin has a sub-humid climate and the average rainfall decreases to 1300 mm. The seasons are well defined throughout the basin and the rainfall regime has symmetry in its distribution between the periods from January to June and from July to December 78 . The territory is home to 535 thousand people in 2018 80 . The two largest cities in the region, Petrópolis and Teresópolis, are located in the headwaters of the basins and give rise to the Piabanha and Preto rivers, respectively. Additionally, because the sewage treatment is limited and the river flows are low, high constituent concentrations are observed (e.g., fecal coliform, nitrate, and BOD), especially in urban areas 42 .

Study area, sample stations and interference points (water abstraction or effluxent discharge). This map was generated in the open source software QGIS version 3.14.15 ( https://qgis.org/ ).

Three sets of monitoring data have been used in this researchh (Fig.  4 ). The first and main one was the result of a monitoring program that is being conducted by the Piabanha watershed Committee, in which data from July to December 2019 have been analysed and are described in more details in the next item. The second were from 6 campaigns carried out in 2012 by HIDROECO project 44 also with financial resources from the Piabanha Committee which is used as a baseline for comparison purposes. The third was comprised of two stations of the basic monitoring network of the Rio de Janeiro Environmental Institute, with data from 1980 to the present, except for periods of data gaps.

A georeferenced database was also built containing water management data. Brazilian National Water Agency (ANA) has developed the National Water Resources Users Register (CNARH) for any bulk water user that changes regime, quantity or quality of a water body. It is a federal platform, but it can be managed by each state. Registration is a prerequisite for the other stages of uses regularization.

Monitoring campaigns and analytical procedures

Physical–chemical parameters were measured in situ using a multiparameter probe (YSI model 556) and a portable turbidimeter (HANNA model HI 98703-0), both previously calibrated and later verified. The samples were placed in specific containers for each analysis, for the necessary parameters the samples were preserved with H 2 SO 4 and kept at a temperature below 4 °C. Laboratory analyses (Table 1 ) were performed according to Standard Methods for the Examination of Water and Wastewater (SMWW) 81 . The laboratory has an accreditation certificate issued by the State Environmental Agency (INEA CCL No. IN044710) and also complies to ISO/IEC 17025 (CRL 1035).

Water Quality Index

A Water Quality Index (WQI) is an empirical expression which integrates significant physical, chemical and microbiological parameters of water quality into a single number 82 . It can be a powerful communication tool to simplify a complex set of parameters, whose individual interpretation can be difficult, into a single index representing the general water quality. A water quality index was initially proposed by Horton 26 and further developed by Brown 27 , 83 resulting in the National (USA) Sanitation Foundation Water Quality Index (WQI NSF ).

The original version of the WQI NSF established an additive expression 27 ; on the other hand, field data analysis suggested that the additive WQI lacked sensitivity in adequately reflecting the effect of a single low value parameter on the overall water quality. As a result, a multiplicative form of WQI was proposed 82 , 83 :

q i is the quality class for the n th variable, a number between 0 and 100, obtained from the respective average quality variation curve 82 , depending on the concentration of each nth variable. W i is the relative weight for the n th variable, number between 0 and 1, assigned according to the importance of the variable for overall quality conformation. WQI NSF is the National Sanitation Foundation Water Quality Index, a number between 0 and 100, rated as "excellent" (100 > WQI ≥ 90), "good," (90 > WQI ≥ 70), "medium" (70 > WQI ≥ 50), "bad" (50 > WQI ≥ 25) or "very bad" (25 > WQI ≥ 0).

The WQI NSF and its many adaptations have been widely used 84 , 85 , however, its use is not uniform, replacing parameters without the necessary adaptation of the respective curve of the indicator. In Brazil, since 1975 the WQI NSF has been used by CETESB (Environmental Company of the State of São Paulo). In the following decades, other Brazilian states adopted, with minor adaptations, this index, which today is the most widely used in the country. In the present study, the weights (w i ) have been used according to the methodology established by INEA (Environmental Institute of the State of Rio de Janeiro): DO (0.17); Fecal coliforms (0.16); pH and BOD (0.11); Nitrates, Phosphate and Temperature (0.10); Turbidity (0.08) and TDS (0.07), rather than total solids.

The replacement of the total solids for dissolved solids parameter may cause an average variation of 0.2% in the final result of WQI NSF , based on our estimates (n = 48, data 2019). In relation to microbiology, E. coli have been used instead of fecal coliforms, applying a correction factor 86 of 1.25 on the result of E. coli .

Principal component analysis and cluster analysis

Principal component analysis (PCA), as defined by Hotelling 87 , is a multivariate technique of covariance modeling that reduces the dimensionality of an originally correlated dataset, with the lowest possible information loss. A new set of variables containing new orthogonal, uncorrelated variables, is formed from a dataset of correlated variables, which are weighed linear combinations of the original variables 30 .

PCA technique extracts the eigenvalues and eigenvectors from the covariance matrix of original variables. The PCs are obtained by multiplying the original correlated variables with the eigenvector, which is a list of coefficients, frequently called “loadings” 29 , 30 , 88 , 89 . A widely accepted and simple qualitative rule proposes that loadings greater than 0.30 or less than − 0.30 are significant; loadings greater than 0.40 or less than − 0.40 are more important, whereas loadings greater than 0.50 or less than − 0.50 are very significant 90 . The suitability of data for PCA was evaluated by Kaiser–Meyer–Olkin 91 , 92 (KMO) measuring of sampling adequacy and Bartlett tests of sphericity 93 . The Shapiro test was evaluated to verify the data normality (α = 0.01).

Cluster analysis reveals the latent behavior of a dataset to categorize the objects into groups or clusters on the basis of similarities 30 , 88 , 89 . Hierarchical agglomerative cluster analysis (CA) classifies objects by first putting each object in a separate cluster, and then joins the clusters together stepwise until a single cluster remains 29 .

Timeseries analysis and trend detection

Mann–Kendall trend test is a nonparametric test used to identify a trend in a series, first proposed by Mann 94 and further improved by Kendall 95 and Hirsch 96 . The null hypothesis (H 0 ) for these tests is that there is no trend in the series. The tests are based on the calculation of Kendall's tau measure of association between two samples, which is itself based on the ranks with the samples. The variables are ranked in pairs, and the difference of each variable to its antecessor is calculated. The total number of pairs that present negative differences is subtracted from the number of pairs with positive differences (S). A positive value of S indicates an upward trend, and a negative value of S a downward trend. For n > 10, a normal approximation is used to calculate Z statistic which is used to calculate p-value 96 .

Fourier decomposition is a technique which allows the separation of frequency components from a data series with seasonal behavior from a complex water quality dataset 97 . Spectral analysis performed using a Fast Fourier Transform (FFT) algorithm is widely used in environmental studies, because it reveals the dominant influences and their scales 50 . Power spectral density (PSD) obtained from FFT and represented by periodograms is a recommended procedure to detect seasonality 98 , 99 .

Brazilian legal regulation

Brazilian fresh waters are divided into four classes, depending on the intended use 76 . The Special Class is intended mainly for the preservation of the natural balance of aquatic communities in fully protected conservation areas. Class 1 is designed for human consumption supply, after simplified treatment, for the protection of aquatic communities and for primary contact recreation. Class 2 requires conventional treatment for human consumption. Class 3 requires conventional or advanced treatment for human consumption and can be used to feed animals and irrigate some crops. Class 4 is intended only for navigation and landscape harmony. It is important to note that the Framework refers to the required water quality target according to water uses. The river basin committees are responsible for implementing the Framework, in accordance with the Brazilian National Water Resources Policy 33 . As long as the Framework is not established by the basin committee, fresh waters will be considered class 2 (Art. 42 CONAMA 357/2005) 76 .

Data availability

All data generated or analysed during this study are included in this published article and its Supplementary Information files.

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Acknowledgements

We thank the Piabanha Committee for financially support our research. We also thank Juliana Pereira Dias for helping with statistical analysis, Renata Demori Costa and Jamie Sweeney for the english review.

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Home > Books > Advances in Environmental Monitoring and Assessment

Evaluation of Water Quality Indices: Use, Evolution and Future Perspectives

Submitted: 02 March 2018 Reviewed: 11 June 2018 Published: 05 November 2018

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The evaluation of the quality of water bodies is of fundamental importance to the study and use of water. Aiming to improve the understanding of the phenomena which occur in these environments, several indices have been proposed over the years, using several statistical, mathematical and computational techniques. For this, it is necessary to know the variables which influence different water bodies. However, not all places are able to make the most diverse analyses due to the financial and sanitary conditions, which can promote greater expenses in treatment as well as make the limits of tolerance of the water quality higher. Nowadays, there is a need to formulate indices which can address climate change in its variables, making it even closer to reality. In this context, seeking to reduce the number of variables used, collection costs, laboratory analyses and a greater representativeness of the indices, multivariate statistical techniques and artificial intelligence are being increasingly used and obtaining expressive results. These advances contribute to the improvement of water quality indices, thus seeking to obtain one which portrays the various phenomena which occur in water bodies in a more rapid and coherent way with the reality and social context of water resources.

• water quality index
• statistical techniques
• machine learning
• artificial intelligence
• environmental monitoring

Author Information

Carlos alexandre borges garcia *.

• Universidade Federal de Sergipe, Brazil

Igor Santos Silva

Maria caroline silva mendonça, helenice leite garcia.

• Chemical Engineering Department, Universidade Federal de Sergipe, Brazil

*Address all correspondence to: [email protected]

1. Introduction

Several quality indices are proposed for evaluation and definition of different uses of water. These proposals are interesting so that the selection criteria of the parameters are more effective and portray the true environmental state of the water body. In addition, other important factors in this selection are the availability of analysis of variables by laboratories (if they have equipment, reagents and staff for such analysis), the financial question of the region (poorer regions have more flexible tolerance limits, whereas that water treatment is not as efficient), collection logistics and representativeness for an audience which often does not have the ability to interpret the analysis variables, thus requiring the use of quality indices.

These indices ought to be elaborated quickly, simply and objectively. In this way, the use of water quality indices (WQI) is fundamental to represent a large number of parameters in a single number. However, for this number to portray the reality of a water body, the correct selection of environmental parameters is essential.

This selection of environmental parameters ought to be fundamental not only for the elaboration of indices, but also for the monitoring of water resources. This monitoring has been understood as the definition of strategies to mitigate environmental problems which can guarantee sustainable development. A water quality index inserted in the context of monitoring also makes it easier to reproduce the information for those involved in both management and those who make direct use of water.

In this context, the modeling and construction of a single index which frames the water bodies, whether these are lentic or lotic, involve several factors which combined make difficult the existence of this uniqueness. The morphoclimatic diversity of each region of the world, climatic changes and innumerable anthropogenic activities, as well as local social and economic development are preponderant factors for different classifications of water quality and, consequently, several indices. In order to face these adversities, multivariate statistical techniques and artificial intelligence play a fundamental role, facilitating the framing and understanding in the search for a globally accepted index.

In this sense, in order to reduce the number of environmental parameters to be measured, the costs involved in collecting the water samples and the laboratory analyzes, in addition to seeking greater representativity of the indexes, multivariate statistical techniques and artificial intelligence have been used and the results have been significant and promising. Such techniques allow greater agreement between the variables most suitable for the formulation of an index and ought to be part of an environmental monitoring program.

2.1. Method of selection of variables

The water quality index (WQI) was initially proposed by Horton [ 1 ] as a linear summation function. This index consisted of a weighted sum of subscripts, divided by the sum of the weights multiplied by two coefficients related to temperature and the pollution of a watercourse. Horton [ 1 ], in his work, used as criteria of choice the variables most used in the analysis of a water body in a total of 10, making the application of the index more practical. These variables should represent all the water bodies in the country and should reflect the availability of the data, in order to obtain the smallest deviation among them [ 1 , 2 , 3 , 4 , 5 ].

In order to construct an index, four steps are taken: parameter selection, obtaining subindices; establishment of weights; use of aggregation functions. A criteria was developed based on the existing indices, as presented in Table 1 . Although indices are not expected to meet all these criteria, different weights can be attributed to each one, depending on the use, region, climate and water body. Thus, with the definition of weights, the criteria can be used to elaborate the index as comprehensively as possible [ 4 , 6 , 7 ].

Criteria established for the elaboration of an index [ 4 , 6 ].

2.2. Main indices

Based on the index proposed by Horton [ 1 ], Brown et al. [ 8 ] proposed the best-known and most widely used index in the world, i.e. the National Sanitation Foundation’s Water Quality Index (WQI-NSF). This index can be used to define water quality for different uses such as irrigation, water supply and navigation, as well as for various water bodies (lakes, reservoirs and rivers). In this index, nine parameters were used according to the criteria presented in Table 1 : temperature, pH, turbidity, phosphate, nitrate, total solids, dissolved oxygen (OD), biochemical oxygen demand (BOD) and fecal coliforms [ 8 , 9 , 10 , 11 ]. The WQI-NSF is calculated based on weights assigned to each parameter, according to a statistical survey conducted using the DELPHI technique, elaborated by 142 experts. The weights of each parameter are shown in Table 2 .

Parameter weights as index [ 20 ].

In 1973, The Engineering Division of the Scottish Research Development Department began a research into the development of the Scottish Water Quality Index (WQI-SCO). Using the Delphi technique, and based on the WQI-NSF, 10 parameters were selected for the WQI-SCO with their respective weights: OD (0.18); BOD (0.15), free ammonia (0.12); pH (0.09); total oxidized nitrogen (0.08); phosphate (0.08); suspended solids (0.07); temperature (0.05); conductivity (0.06) and Escherichia coli (0.12). The sum of the weights at this index must be equal to 1. Two forms of calculation were then tested: the arithmetic form ( Eq. 1 ) and the geometric form ( Eq. 2 ), the second one being more efficient and less biased to high quality indices, and therefore more used. [ 9 , 10 , 11 , 12 ]

S i corresponds to the parameter and w i to its weight.

In Spain, one of the indices most accepted by the scientific community was proposed by Bascaron [ 13 ], represented in Eq. (3) .

whereupon: n – number of parameters; C i – value of the subscripts after normalization; P i – assigned weight to each parameter.

The index proposed by Bascaron [ 13 ] presents normalization of the parameters seeking to balance the influence of each one on the final value of the index. In addition, it is a very flexible index in addition of other parameters [ 9 , 13 , 14 , 15 ].

In order to represent the trophic status of a reservoir through WQI, Steinhart et al. [ 16 ] developed the Environmental Quality Index (EQI) for the Great Lakes of North America. This index was elaborated to collaborate with the multimillion-dollar cleaning projects of these lakes developed in the 1970s. The authors sought to evaluate nine physical-chemical, biological and toxicity variables. These variables were based on specific electrical conductivity, concentration of chlorine, pollutants with specific characteristics of odor and color, organic and inorganic toxic contaminants. The obtained data were converted into subscripts through mathematical functions which included national and international tolerance limits. In addition, these sub-indices were multiplied by weights assigned to each type of variable (0.1 for chemical (C), physical (P) and biological (B) parameters and 0.15 for toxic (T) substances). The quality assignment range varies from bad (0) to optimal (100). Each index, then, has its corresponding symbology indicating which parameter was more problematic, for example, a 70C 1 P 1 assessment means that a chemical parameter and a physicist are outside the limits stipulated by the legislation, even the quality attribution being considered good [ 9 , 6 ].

In 1970, in Oregon, Canada, the O-WQI index was developed. This index was modified by Dunnette [ 17 ] for the evaluation of water in fishing regions in icy waters, and therefore very sensitive to high temperatures. This index became more used after Cude [ 18 ] re-evaluated it and added total phosphorus as an indication of the risk of eutrophication of the water body in Oregon [ 17 , 18 ].

Cude [ 18 ], by proposing modifications, stated that the use of weights for the parameters which compose a WQI is only justifiable when used for evaluation of the water body for single use. Therefore, this author eliminated the weights of the variables and used a non-harmonic root equation. The parameters selected were DO, Fecal coliforms, pH, nitrate + ammonia, total solids, BOD, total phosphorus and temperature. The evaluation ranges defined for the index classified the water in excellent (90–100), good (85–89), acceptable (80–84), bad (60–79) and poor (10–59) [ 4 , 14 , 17 , 18 , 19 , 20 , 21 ].

The O-WQI index has been used in several parts of the world after the modifications proposed by Cude [ 18 ]. The calculation of the harmonic mean by the square root, Eq. (4) , although it is more sensitive to the variations of the parameters, sometimes it can present ambiguity, as for example, the individual variables have good quality values and the average ones do not corroborate for this result of quality [ 4 , 18 , 19 , 20 , 21 ].

whereupon S i – the value of the subscript corresponding to the variable under analysis; n – number of parameters.

Also in relation to Canada, another well-accepted and used index (WQI-CCME) is proposed by the Canadian Council of Ministers of the Environment [ 22 ]. The objective was to manage the quality of water by the water treatment and distribution agencies in the country. This is an index which allows a flexibility in the alteration of the variables, being suitable for the most diverse uses and morphoclimatic characteristics of the hydrographic basin under analysis. In addition, there is no need to evaluate subindices, nor weights for each variable. The aggregation model consists of a few steps. First, the variables must be standardized and three factors are determined (F1, F2, F3), where F1 refers to how many parameters that are not within the quality standards ( Eq. (5) ), F2 is the percentage of samples which has one or more non-standard parameters ( Eq. (6) ), F3 is calculated in three steps: the number of times the individual concentration of a parameter is outside the limit allowed by the law, called the excursion calculation ( Eqs. (7) and (8) ); after the calculation of the excursion, the sum of the excursion values is divided by the total number of tests ( Eq. (9) ); then F3 is calculated through Eq. (10) . It should be noted that Eqs. (7) and (8) for the determination of the excursion are for concentrations of the parameters above and below the limits, respectively. Thus, to calculate the value of the WQI-CCME, we use Eq. (11) .

Some authors criticize the WQI-CCME for the calculation of F1, as it would not be adequate for a few variables (requires at least four) or with covariance among them [ 14 , 20 , 23 ].

The WQI calculation can be developed by several types of mathematical and statistical techniques. The indices, when created, tend to meet the needs of the region, the use of water and the parameters which can be measured. These criteria may bring some deviations in the calculation and, consequently, in the interpretation of the results. Smith [ 24 ] states that using, for example, a multiplicative WQI calculation method can lead the index to a low score if one of the variables has its individual score low. Thus, Smith [ 24 ] used an index based on a minimum operator in the aggregation of the subscript, according to Eq. (12) .

where upon: I, the sub-index of the i th parameter.

However, this type of WQI calculation ends up poorly reflecting the average quality since only one index will be attributed to the WQI, occurring the eclipsing phenomenon of the other variables. This index was adopted in New Zealand as a way of legislating and disseminating information about water quality [ 9 , 14 , 19 , 21 , 24 ].

The evaluation of WQI and its various forms of aggregation, the most commonly used being the harmonic mean, geometric, arithmetic and minimum function method mentioned above, should also take into account the financial question. The financial reality of the country, therefore, affects the choice of WQI. This factor must be observed, as this may reflect the amount of resources which will be allocated so that certain parameters can be better monitored. Methods such as the minimum function and the arithmetic method can eclipse and bring ambiguity to the final result and the financial investments destined to the improvement of certain parameters end up not bringing the desired solution. Experts affirm that there is a need for a better evaluation regarding the choice of the aggregation method and the weight criteria of the environmental variables.

Biological, limnological and ecological studies have been increasing in the last 40 years, and a revision of the proposed indices is necessary for either new variables to be included or their weights changed. In addition, there is a lack of indices which can better portray the impacts of climate change on the water body, as reported by Alves et al. [ 25 ] and that the improvement of the water quality indices would be fundamental. Alves et al. [ 25 ] point to the need for the development of indices that best portray tropical conditions such as those located in Brazil, since most of them were developed for temperate regions [ 4 , 7 , 18 , 25 , 26 ].

2.3. WQI for reservoirs

In Brazil, in 1975, the Environmental Company of the State of São Paulo (CETESB) started using the geometric quality index proposed by NSF ( Eq. (2) ), as shown in Figure 1 . A number of studies developed in the country are based on this index [ 27 , 28 , 29 , 30 ]. In addition, a specific index for reservoirs was developed in Brazil and is widely accepted by the scientific community and the National Water Agency (ANA), which is the Water Quality Index for Reservoirs (IQAR), developed by the Environmental Institute of Paraná (IAP) [ 31 , 32 , 33 ].

Average water quality variation curves of the WQI [ 33 ].

The Environmental Institute of Paraná (IAP) seeking an evaluation of the water quality in reservoirs created this IQAR. The selected variables were dissolved oxygen deficit, total inorganic nitrogen, total phosphorus, chemical oxygen demand, transparency, chlorophyll a, weather, average depth and cyanobacteria. It should be noted that the phytoplankton community (diversity, algal bloom and amount of cyanobacteria) was included in the matrix through the concentrations of chlorophyll a and cyanobacteria, due to their ecological importance in lentic ecosystems, however, this parameter received a different treatment. The developed matrix presents six classes of water quality, which were established from the calculation of the percentiles of 10, 25, 50, 75 and 90% of each of the most relevant variables and the selected parameters had weights assigned, as shown on Tables 3 and 4 [ 34 ].

Selected variables and its respective weight [ 34 ].

Average water column.

Mean of depths I and II.

Water quality matrix [ 34 ].

The water quality class to which each reservoir belongs is calculated through the Water Quality Index of Reservoirs (IQAR), according to Eq. (13) .

whereupon IQAR—Water Quality Index of Reservoirs; q i —water quality class in relation to the variable “ i, ” which can range from 1 to 6; w i —calculated weights for the variables “ i. ”

A partial IQAR is calculated from the data collected for each monitoring campaign. Then, the arithmetic mean of two or more partial indices are calculated to obtain the final IQAR and to define the quality classification of each reservoir. A reservoir is said to be impacted to much impacted if the IQAR varies from 0 to 1.5; the reservoir is poorly degraded when IQARs are between 1.51 and 2.5; moderately degraded when the IQAR values are between 2.51 and 3.5; the reservoir is considered to be degraded to polluted between 3.51 and 4.5; the reservoir is in the most polluted condition when IQAR has values between 4.51 and 5.5; and finally the reservoir is said to be extremely polluted when the IQA is equal to or above 5.51 [ 34 ].

Reservoirs are lentic environments that suffer greatly from the variation of temperature and oxygen by depth, leading to the appearance of several problems such as the process of eutrophication and anoxia in the deep parts, especially if this reservoir has organic matter in high concentrations and metals in its sediments. In this context, Lee et al. [ 35 ] sought to develop a WQI for reservoirs and lakes in South Korea, selecting parameters which would contribute to the solution of these problems and to describe the geology, climate and morphology of more than 70 lakes and reservoirs evaluated. The Water Quality Index for Lakes (LQI) was developed after the identification of the interrelationship of the parameters through the principal component analysis (PCA), and then four environmental parameters were selected: total organic carbon, chlorophyll a, total phosphorus and turbidity. Through the logistic regression, using a sigmoidal function for each parameter, the average LQI of the water body is calculated ( Table 5 ). The LQI ranges from 0 to 100, in which 0 corresponds to a reservoir classified as poor quality and 100 as optimal quality [ 35 ].

Lake quality index.

Adapted by the author Lee et al. [ 35 ].

COT, total organic carbon; Cl-a, Chlorophyll-a; PT, total; Turb, turbidity.

Azevedo Lopes et al. [ 37 ], seeking to evaluate the quality of water for recreational purposes, proposed the Index of Conditions for Bath (ICB) to evaluate water bodies in Brazil, using the Delphi statistical technique to define the index composition variables: E. coli , density of cyanobacteria, visual clarity and pH. The authors used the minimal operator method, as originally proposed by Smith [ 24 ]. These authors affirm that the minimal operator method is effective for the definition of an index of quality and use of water bodies for recreational purposes, since it is enough that one of the variables present a low score in order to define the quality of the water body [ 24 , 36 , 37 ].

2.4. WQI through fuzzy logic and recent statistical techniques

Searching for improvement and accuracy in water quality indices, some computational techniques, such as fuzzy logic and neural networks, have been used. The fuzzy logic presented by Zadeh [ 38 ] is widely used, mainly, in the environmental area in the issue of water quality, because it can avoid the ambiguity and the eclipsing effect of the variables. Several authors apply the fuzzy understanding to present the WQI with the smallest deviation and compare with the WQIs already developed. The selection of the variables does not obey a rigid criterion, and these can be chosen by the knowledge of the water bodies and the monitoring programs. Once the variables have been defined, they will be the input to the fuzzy system. The fuzzy inference system (FIS) consists then in the process of transforming these quantitative values into qualitative values (fuzzification) applying pertinence functions and established rules for the interaction between the parameters. Then, the inverse process (defuzzification) transforms these qualitative values into numerical ones (output). The accuracy of the use of fuzzy logic is related to the rules of interaction and the correct selection of parameters, which is a crucial step for the success of the index development [ 14 , 20 , 32 , 38 , 39 , 40 , 41 , 42 ].

In Brazil, several pieces of research using the fuzzy logic for the development of water quality indices for reservoirs and rivers in the states of Rio de Janeiro, São Paulo, Sergipe and others have been developed over the years obtaining expressive results for WQI [ 32 , 40 , 41 , 42 ].

In addition to the use of fuzzy logic, several indices have been proposed applying linear, nonlinear, multilinear regressions, principal component analysis (PCA) to identify weights, establish aggregation models and define variables which interfere with water body quality. These techniques for the development of water quality indices (WQI) are very recent in the area, showing at the same time a delayed insertion of this type of analysis in the improvement of WQIs, since many of the known indices were constructed using the statistical technique Delphi. The use of more sophisticated statistical techniques to reduce the size of the variables and to obtain more specific results in each analysis may be a way in the search for a single WQI which is possibly used worldwide. Although some authors defend this path, others believe that the need for an accepted model worldwide should be much more careful, especially regarding the use of the water body and the financial conditions of the country [ 9 , 31 , 43 , 44 , 45 ].

2.5. Future perspectives

The creation of new WQIs has been repetitive in terms of the aggregation models and the choice of parameters and, thus, basically without development of an index that is more adequate regarding the definition of the use and general applications. The use of less rigid, more objective formulas with flexibility in the choice of variables would contribute to the search for an accepted and efficient index worldwide. The construction of a more precise index is influenced by several factors, not only in terms of environmental parameters, but also in relation to the various aspects of environmental management. Thus, a method which would provide a kind of algorithm for the user to select the most coherent environmental variables would be very important. This selection should also be linked to the probable types of contamination of the water body in question, the financial aspects of the process for measuring these variables and the definition of the technical staff. Based on these considerations would be identified the use of water according to each legislation as well as the search for the appropriate water treatment would also be more efficient. In addition, the transmission of information would be faster between managers and society [ 4 , 9 , 19 ].

An important observation is the use of the WQI for agriculture which is still at an early stage, although works such as Stoner [ 46 ] present the division of the calculation for specific use of irrigation and public supply. Meireles et al. [ 47 ] used factor analysis (FA) and PCA to identify the variables which would most interfere with the amount of sodium in the soil, salinity and toxicity in the plants, in the Acaraú basin, Ceará. Out of 13 parameters evaluated by the authors, the ones which showed greater weight in the analysis were electrical conductivity, sodium, bicarbonate and Sodium Adsorption Index (SARo). The WQI was calculated by the sum of the quality product of each index multiplied by its respective weight and classified into five classes ( Table 6 ) [ 9 , 46 , 47 , 48 ].

WQI for irrigation [ 47 ].

Zahedi [ 49 ] sought to compare the index proposed by Meireles et al. [ 47 ] with the one developed in his work, through several statistical techniques, and to observe possible conflicts between the use of water from the wells for human supply and irrigation. This analysis aimed to identify water quality for both uses through WQIs. Much still has to be done to analyze conflicts over water use, but the indices can be a great support solution to these conflicts [ 46 , 47 , 49 , 50 ].

Thus, the present work contextualized the importance of the introduction of indices related to traditional mathematical techniques alongside the most modern techniques such as fuzzy logic (FL), neural networks (NN) and machine learning (ML). Water management is an area of human knowledge which involves not only social and economic aspects, but also involves the visualization of using analytical technology to obtain environmental data and advanced computational technology, as presented on Figure 2 .

Monitoring and proposing water quality indices.

Environmental management models are composed of tools which evaluate natural phenomena and relate them to quantitative and qualitative values for decision making. Knowledge about the different parameters which characterize an environmentally degraded area is now one of the pillars of sustainable development and it is also a way of maintaining the sovereignty of a state in relation to the monitoring of water resources.

3. Conclusion

The evaluation of water quality by indices with several types of aggregation functions has been applied for many years. However, the use of these indices should always be linked to the area in which the water body is influenced, the climate of the region, the geology, the activities developed around it and its use by the population, so that it is possible to identify which physical, chemical and biological variables to be measured.

The type of the water body, lentic or lotic, will also influence this selection of variables for the construction of the index and another important factor which must be inserted in this one concerns the impacts of climate change. In this context, in order to meet these objectives, it is necessary to develop indices which are less influenced by eclipsing and ambiguity effects, requiring the use of better aggregation functions and statistics, as suggested by several authors.

In this sense, multivariate statistical techniques, regressions, machine learning and artificial intelligence, are now of relevance and importance for the creation of indices which encompass the most diverse variables for each specific use of water. In addition, this creation will facilitate decision making and communication between environmental managers and society. It is important to emphasize that such methods would allow a greater flexibility in the selection of variables, making water quality indexes more assertive, efficient, and easier to understand and manipulate.

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Determinants of water source use, quality of water, sanitation and hygiene perceptions among urban households in North-West Ethiopia: A cross-sectional study

Shewayiref geremew gebremichael.

1 Department of Statistics, Debre Tabor University, Debre Tabor, Ethiopia

Emebet Yismaw

Belete dejen tsegaw, adeladilew dires shibeshi.

2 Department of Mathematics, Debre Tabor University, Debre Tabor, Ethiopia

Associated Data

The data underlying this study is publicly available at the Dryad repository, https://doi.org/10.5061/dryad.mw6m905w8 .

Clean water is an essential part of human healthy life and wellbeing. More recently, rapid population growth, high illiteracy rate, lack of sustainable development, and climate change; faces a global challenge in developing countries. The discontinuity of drinking water supply forces households either to use unsafe water storage materials or to use water from unsafe sources. The present study aimed to identify the determinants of water source types, use, quality of water, and sanitation perception of physical parameters among urban households in North-West Ethiopia.

A community-based cross-sectional study was conducted among households from February to March 2019. An interview-based a pre-tested and structured questionnaire was used to collect the data. Data collection samples were selected randomly and proportional to each of the kebeles’ households. MS Excel and R Version 3.6.2 were used to enter and analyze the data; respectively. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariate and multivariate logistic regressions were used to assess the association between independent and response variables.

Four hundred eighteen (418) households have participated. Based on the study undertaken,78.95% of households used improved and 21.05% of households used unimproved drinking water sources. Households drinking water sources were significantly associated with the age of the participant (x 2 = 20.392, df = 3), educational status (x 2 = 19.358, df = 4), source of income (x 2 = 21.777, df = 3), monthly income (x 2 = 13.322, df = 3), availability of additional facilities (x 2 = 98.144, df = 7), cleanness status (x 2 = 42.979, df = 4), scarcity of water (x 2 = 5.1388, df = 1) and family size (x 2 = 9.934, df = 2). The logistic regression analysis also indicated that those factors are significantly determining the water source types used by the households. Factors such as availability of toilet facility, household member type, and sex of the head of the household were not significantly associated with drinking water sources.

The uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. Therefore; the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources in the area.

1. Background

Clean water is an essential element for human health, wellbeing, and prosperity [ 1 ]. Every human being has the right to access safe drinking water. Currently, about one billion people, who live in the developing world, don’t have access to safe and adequate drinking water [ 2 ]. Water can be found from either improved or unimproved water sources. Improved sources include piped supplies (such as households with tap water in their dwelling, yard, or plot; or public stand posts) and non-piped supplies (such as boreholes, protected wells and springs, rainwater, and packaged or delivered water) [ 3 , 4 ]. To the contrary, unimproved water sources provide water collected from unprotected dug wells, unprotected springs, and surface water. The opposite of improved water sources has been termed unimproved water sources, based on the Joint Monitoring Program (JMP) [ 5 ] definitions.

Globally, between 2000 and 2015, the population using piped supplies increased from 3.5billion to 4.7 billion, while the population using non-piped supplies increased from 1.7billion to 2.1 billion. Evidence shows that globally two out of five people in rural areas and four out of five people in urban areas use piped supplies [ 4 ]. About 748 million people, mostly the poor and marginalized, there is a scarcity of using an improved water source supply and of these, almost a quarter (173 million) rely on untreated surface water, and over 90% live in rural areas [ 6 ]. About 547 million people did not have an improved drinking water supply in 2015. In a study conducted by Water.org [ 7 ], 42% of the population has access to a clean water supply and only 11% of that number has access to adequate sanitation services globally.

In Africa, only 60% of the population has access to improved sanitation services, but the situation is worse in rural areas, in which below half (45%) of the rural population has access to improved sanitation services. According to the World Health Organization (WHO), 2011 report, individuals with no access to improved sanitation are forced to defecate in open fields, in rivers, or near areas where children play and food is prepared [ 8 ].

The Ethiopian Demographic and Health Survey (EDHS) (CSA and ICF, 2016) [ 9 ] reported that 97% of urban households in Ethiopia have access to an improved source of drinking water and in rural areas, only 57% have improved water accessibility. Nevertheless, no reliable information is available on the readability of drinking water quality reports for further illustration [ 10 ]. Based on previous reports, Ethiopia is the country with the worst of all water quality problems in the world. It has the lowest water supply (42%) and sanitation coverage (28%) in sub-Saharan countries [ 11 ]. Ethiopia is considered as having one of the poorest sanitation and drinking water infrastructures [ 12 ]. About 52.1% of the population has been using unimproved sanitation facilities while 36% of them practiced open defecation [ 13 ]. In Ethiopia the discontinuity of drinking water supply affects the distribution of water to the community in need [ 14 ].

Due to the lack of accessibility of water in many rural areas, females are put to work on collecting water each morning to help their families [ 15 , 16 ]. The burden of water collection does not fall equally on all household members; the gender breakdown is consistent for both urban and rural areas [ 17 ]. The responsibility of collecting water-primarily falls on women, sons, or daughters of the households. Globally; women (64%), men (24%), girls (8%), and boys (4%) share the burden of collecting water [ 18 ]. Due to the presence of a burden on children, only 45% of kids attend primary education in Ethiopia [ 15 ], and also, water and sanitation-related sicknesses put severe burdens on health services and keep children out of school [ 19 ]. Younger household members are more likely to collect water, but this differs by place of residence; while only 22 percent of those who collect water in urban areas are children (aged 7 to 14). In rural areas, nearly 37 percent of water collectors are children [ 20 ].

The discontinuity of drinking water supply forces households to use water storage material or to use water from unimproved sources. In our study area, irregularity of water supply was observed and the community is forced to use unprotected water storage materials. Water stored in unprotected materials (such as unsafe Pot, Rotto, Jerikan, other plastic materials) for longer periods of might get contaminated and cause water-borne diseases. The water from unimproved water sources might be contaminated with animals, floods, and specks of dust through wind and human wastes. This ultimately causes human sickness.

As a result of different reasons, there is a continuing dearth of information on the identification of determining factors of using improved sources of drinking water in the study area. We aimed to address this knowledge gap and explored in detail, access, usage, and practices of water sources in North-West Ethiopia. This also provides valuable insights into access to safe water and consequentsocioeconomic conditions such as income, distances, and family size that can become a barrier to water access.

2.1 Study design, area and period

A community-based cross-sectional study design was conducted from February to March2019. The study was conducted in Debre Tabor District, which is situated in North-West Ethiopia ( Fig 1 ). Based on the district official report, the population of the town is expected to be 87,627 (2019 projected population). From this population a number of 49,535 households members are users of tap water in 2019, but the remaining 38,092 household members are not tapped, water users. Drinking water of the town comes from large reservoirs located in its surroundings, Farta Woreda, which is one of the administrative Woredas in the South Gondar Zonal Administration. In the district, there are a lot of wells, which were extracted to supplement the domestic water requirement in town. Based on previous evidence, the households of Debre Tabor town get pipe water supply only once a week [ 21 ].

2.2 Sample size determination

The town has about 17,526 households. The studied part of the population is made up of families that reside in the town, which comprise of men, women, and children, all of which are in different age groups. The average family size of the town was computed asabout4.53 per household (after a pilot survey was conducted in January 2019).

For the household survey, samples were selected using the sample size determination equation of Cochran (1977) [ 22 ]. The study used a single proportion formula, 95% confidence interval, the marginal error of 5%, and the non-response rate of 10%;

Z = 95% confidence limit (z-value at α = 0.05 is 1.96); N = Number of households in Debre Tabor town = 17526; p = 0.5; 1-p = 0.5; D = Marginal error or degree of accuracy = 0.05; n = 380+38 = 418.

Total sample size = 418. However, a sample size of 418 was used to eliminate any errors. The town has 6 kebeles (wards). The samples were selected randomly and taken proportionally from each kebeles and sub-kebeles.

2.3 Sampling method and sampling procedure

All households living in the study area at the time of data collection were included as the study population. Data collection sites were demarcated into 6 kebeles. The samples were selected randomly using a proportional allocation from each kebeles (wards) and sub-kebeles (sub-wards).

2.4 Data collection tools and techniques

The primary source of data was employed. The primary data gathering was including, household survey questionnaires (on paper) and personal observation. The content of the questionnaire was checked by public health professionals, who have had a profound experience in the area. The method of data collection was by investigator administered questionnaire. The investigators/researchers (people who speak the Amharic language) administered a questionnaire to participants regardless of their educational level. Seven data collectors were selected for data collection and two supervisors were assigned.

The questionnaire consisted of five sections namely; section I: socio-demographic data, section II: Sources of income, section III: water source observation, section IV: household water use and section V: Water quality and sanitation perception. There were key informant interviews and verification of the facilities using a checklist.

A detailed questionnaire was prepared in the native language of the households (Amharic) and included over 50 questions. A multiple-choice format was used to answer the majority of the questions. House-hold characteristics, such as the number of family size, educational level, monthly income of the household, type of occupation, sources of water, sanitation and hygiene, and awareness about household drinking water were included.

2.5 Study variables

The variables included in this study were taken based on perceptions of households and not verifiable water quality measures.

2.5.1 Dependent variables

The dependent variable was the source of drinking water (improved, unimproved). Based on WHO guidelines improved water sources consisted of piped water into dwellings, yards/plots, and public taps/stand-pipes, tube-wells/bore-holes, protected wells, protected springs, and rainwater. Bottled water was included as an improved water source if the household used another improved water source for other purposes, such as hand-washing and cooking. Unprotected wells, unprotected springs, tankers, trucks, a cart with tank/drum, and surface water were considered as ‘unimproved water sources’.

2.5.2 Independent variables

The independent variables included in this study are demographic, socio-economic, sanitation, and hygiene perception characteristics.

• The perceptions of households using water from unimproved sources(income, distance from home to water source, the presence of alternative water source, quality of water perception, adequacy of water, waiting time to fetch water, personal interest, and other reasons).
• The presence of scarcity of water in the area (yes, no).
• The reason that households believe the presence of the scarcity of water has occurred (government weakness, a local people problem, and both local people and government problems).
• The perception of households of the water they consume has a safety status (not safe at all, somewhat safe, partially safe, safe, and highly safe).
• Households’ perception of the indicator of water quality (color, taste, odor, disease attack, and the presence of all the cases).
• Households’ perception of the taste, odor, and color of the water from the improved and unimproved sources was the same (yes, no).
• The causes of water quality problem households perceive (water-containing material, animal wastes, human wastes, flood, and all cases).
• Treatment measures households had undertaken during unsafe drinking water (no use at all, boiling, sedimentation, using wuha agar, other methods, and use all measures).
• The number of times household members had got sickness due to water-related disease and visited health centers for physician assistance within one year before the survey time (not at all, once, twice, three times, more than three times).
• The presence of health extension workers’ assistance (yes, no) and the number of times the family was visited by health extension workers within one year before the survey (not at all, once, twice, three times, more than three times).
• Previous participation of household members in educational and awareness activities about sanitation and hygiene in their locality (yes, no). The presence of a latrine facility in the household compound (yes, no) and who have used the latrine (wife, husband, children, and all families, except children).The place household members were defecating (public, neighbor, open place, own toilet), and the presence of the culture of households washing hands after defecation (yes, no).

2.6 Data quality assurance

A pilot survey (pre-test) was incorporated to recheck the questionnaire and for sample size determination. Before the main survey, a mini-survey (pilot survey) was done on 30 households outside the study area (Bahir Dar city) to avoid exclusion of households who were in the study area due to the pre-test. After the pre-test questionnaire was done; question order, alternative option, skip pattern and an overlapping option were amended. Supervisors and data collectors were trained by the principal investigator. During data collection, data collectors were supervised by supervisors in close up.

2.7 Operational definitions

• Awareness: Understanding the implication and becoming conscious of conditions and practices concerning many things including hygiene and health.
• Improved sanitation: A sanitation system that is connected to the public sewer, septic tank and a pours toilet/latrine. For a simple pit latrine, it implies the use of a slab and ventilated improved latrine.
• Safe water: A water system that is well protected from contamination sources, treated with chemicals, and used in ways that prevent contamination.
• Sanitation: Act of cleanliness and containment of waste products to make the living and working environment free from matters that affect health and wellbeing.

2.8 Data processing and analysis

The collected data were coded and entered into MS Excel, cleaned, stored, and exported into R version 3.6.2 for analysis. Any error that occurred during data entry was corrected by revising the originally completed questionnaire. Descriptive statistics using frequencies and percentages were used to explain the sample data concerning the predictor variable. Both bivariable and multivariable logistic regression estimates were used to assess the association between the independent and the response variables. During the bivariate analysis p-value of 0.2 was included in the multivariate analysis to control the association of confounding variables with that of the response variable. In the multivariable analysis of binary logistic regression, variables with a p-value of 0.05 and 95% confidence interval were considered as statistically significant.

2.9 Ethical considerations

Prior to the study an ethical permission was obtained from Debre Tabor University, Faculty of Natural and Computational Sciences institutional review board (IRB). Verbal consent was obtained from study participants. Since the study participants were aged 18 and above years, we have not obtained written informed consent from participants. The confidentiality and privacy of participants were actively protected. All participants were assigned a unique identification number. Every effort was made to emphasize the volunteers of this study and decisions to stop prior to the interview were made.

The summary statistics for different explanatory variables ( Table 1 ) and the cross- tabulations among the types of water sources used by the households and the demographic, economic, sanitation, and hygiene perception of households ( Table 2 ) are presented. The results of chi-square tests of association and logistic regression are presented ( Table 2 ).

(Source: Survey February 2019).

NB : 1: Reference Category, *Yates’continuity correction (for X2 values) Significant codes: 0‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘‘ 1.

3.1 Summary statistics of explanatory variables

The summary statistics of the different explanatory variables used in this study are presented in ( Table 1 ).

3.2 Unimproved water source using practice

Households use both improved and unimproved water sources for their daily water consumption. Based on the present survey, about 330(78.95%) and 88(21.05%) households used improved and unimproved sources of water; respectively. Even if an improved source of water is of good quality, it is not readily available. Sources of water (improved, unimproved) versus different explanatory variables were presented ( Table 2 ). The reason why households respond to using unimproved sources of water thanimproved sources of water were shown ( Table 1 ).

Households using unimproved sources of water are due to income 51(12.20%), distance 19(4.55%), presence of alternative sources 19(4.55%), quality 83(19.86%), adequacy 15(3.59%), waiting for time 7(1.67%), interest 20(4.78%), all cases 4(0.96%), and other (cases other than the listed) 200(47.84%) than the improved sources of water. The quality of the improved sources of water is indeed better than the unimproved sources of water. About 83(19.86%) households preferred unimproved sources of water to improved sources. This might be due to the accessibility of unimproved sources. Nearly half or 200(47.84%) households preferred unimproved sources other than the reasons of income, distance, presence of alternative source, quality, adequacy, waiting time, and interest. Future investigations to determine the factors (other than listed in this study) that households prefer unimproved sources than improved sources shall be undertaken.

Of 418 household respondents, about 381(91.15%) perceive that water is scarce in the area; while 35(8.37%) respondents perceive that there is no scarcity of water. Two (0.48%) respondents refused to answer about the presence or absence of the scarcity of water in the area. From this, we can summarize that the scarcity of water is a serious issue because above 90% of households perceive that they live under scarce conditions. For about 200(47.84%) households, the reason to use unimproved sources were "Others" (i.e., other than the listed). This might be due to the presence of scarcity of water from improved sources that households would like to use from unimproved sources.

If water was scarce, the respondents’ households asked about who would be responsible for it. Of 418 households 358(85.65%) due to government, 49(11.72%) due to local people, 9(2.15%) due to both government and local people perceive that water scarcity occurred. About 2(0.48%) households who refused to say about the presence or absence of the scarcity of water didn’t like to state the concerned body to be responsible for.

3.3 Water safety status and quality indicator perception

The water safety status was presented by the Likert scale from low safety status to high safety status. The summary statistics ( Table 1 ) show the water-consuming safety status of 418 respondent households. About 83(19.85%) responded that the water consumed is highly safe, 228(54.55%) safe, 82(19.62%) partially safe, 23(5.50%) somewhat safe, and 2(0.48%) not safe at all; they believed about the water they consumed. For the majority of about 311(74.40%) households, the water they consumed was safe and highly safe. Even if for the majority (three-fourths) of the respondents the water safety status was safe; the remaining households were targeted to improve their water safeness.

From 418 responding households about 402(96.17%) had different perceptions about the quality of water being consumed from different sources. About 129(30.86%) made complaints about the color, 75(17.94%) said it hadbad taste, 16(3.83%) assumed microbial contamination, 61(14.59%) complained about its odor, for 121(28.95%) all above cases were provided, while about 16(3.83%) can’t determine the water quality which they consumed daily.

To know the general knowledge about the quality of water from improved and unimproved water sources, a question was posed to the households: "Is taste, odor, and color of the water from the unimproved source the same as from the improved source?" Of 418 respondents 107(25.60%) responded with "yes", about 305(72.96%) answered "no", while about 6(1.44%) responded that they cannot determine. The majority of 305(72.96%) households could not differentiate the quality of water using taste, odor, and color, either from improved or unimproved water sources. Even microorganisms cannot be identified by taste, odor, and color easily; using those identifiers to differentiate the water quality as cheap and fast in-door activity.

There are different causes of water quality problems from the source and in-door in accordance with the household reports. In-door, the water storage material takes the higher cause of the water quality problem. From the source animal and human wastes and floods are the main causes. ( Table 1 ) provides the different causes of water quality problems for the 418 households:219(52.39%) claim due to water containing material, 71(16.99%) due to animal wastes, 47(11.24%) due to human wastes, and27(6.46%) due to flood, and 54(12.92%) due to all mentioned cases.

Of 418 households about 286(68.42%) used boiling, about 73(17.46%) used sedimentation, about 2(0.49%) used chemical reagent, about 8(1.91%) used other treatment measures, about 13(3.11%) used all treatment measures (boiling, sedimentation, chemical); alternatively, while about 36(8.61%) didn’t use any treatment measures for unsafe drinking water. The majority of 286(68.42%) households’ used boiling treatment measure is due to its undergone in-door, cheap, and easy. Modern treatment measures like using chemical reagents are not common and accessible for low-income households. There are individuals who don’t use any measure to treat unsafe drinking water.

From 418 households, about 353(84.45%) household families were not sick at all due to water-related diseases in the previous 1 year. About 6(1.44%) household families were getting sick once due to water-related diseases and they had visited health centers; about 51(12.20%) household families were sick twice due to water-related diseases and of those about 37(8.85%) had visited health centers; about 6(1.44%) household families were sick three times and all had visited health centers, about 2(0.48%) household families were sick four and more times and all had visited health centers. Households, who were sick twice per year, had visited health centers three times. At the time, when the disease had gone worse, individuals visited health centers repeatedly. It is unusual for families who aren’t sick due to water-related diseases to visit health centers (i.e., all 353(84.45%) household families’, which werenot sick due to water-related diseases hadnot visited health centers for water-related diseases).

The status of households, who were visited by health extension workers (HEWs) per year are shown in Table 1 .Of 418 households about 168(40.19%) were visited by HEWs, while 250(59.81%) households were visited by HEWs in the previous year. Of 168(40.19%) households, who had visited by HEWs, 103(61.31%) visited once, 51(30.36%) visited twice, 9(5.36%) visited three times and 5(2.97%) households had visited four and more times per year. More than half of the responded households were never visited by HEWs. It shows poor management of the town health bureau. In the places where the communities live densely, communities needed assistance for good health and quality of life to eradicate communicable diseases. Health extension workers (HEWs) played a great role in the development of community health and quality of life. HEWs participated in the quality of life and family planning in the previous 15 years in the country.

3.4 Sanitation and hygiene practice

The participation of household members in educational and awareness activities about sanitation and hygiene was playing a great role in a healthy community and a clean environment. Of 418 households included in the survey, about 77(18.42%) had participated; while about 341(81.58%) had not participated in educational and awareness activities concerning sanitation and hygiene in their locality. Community education and awareness activities about good health, quality of life (QoL), sanitation, and hygiene were provided by health extension workers (HEWs) in the area. But, in the town, only 168(40.19%) households were visited by HEWs.

Regarding the availability of latrines in the households, about 408(97.61%) households had a latrine in the compound and/or surrounding for defecation; while about 10(2.39%) households did not have a latrine. Of those who had a latrine 408(97.61%) households), only about 402(98.53%) household toilets were accessible to all families. However, about 6(1.47%) household toilets were accessible for families except for children. About 397(94.97%) used their own toilet, about 15(3.59%) used open-field, about 4(0.96%) used public toilet, and about 2(0.48%) used the neighbor’s toilet for defecation. Open field toilet defecation was found within dense trees and drainage areas, which could easily disturb the surrounding environment and be eroded by the flood. Households use neighbor’s toilets at a time when their home and/or safety tank were at a building stage.

The culture of household members washing their hands after defecation, of 418 households about 374(89.47%) households had washed their hand, while about 44(10.53%) households hadn’t adopted the culture of washing hands after defecation. Among 374(89.47%) households had washed their hand with water and additionally with soap, but were inconsistently using soap. The country also motivated hand washing after defecation by memorizing a day per year, nationally.

3.5 Predictors of access to improved sources of drinking water

The chi-square test of association and the binary logistic regression results about the predictors of access to improved sources of drinking water were shown ( Table 2 ). The results demonstrated that there was no significant association between the availability of toilet facilities and types of water sources used by residents of the town (x2 = 1.251, df = 1, p-value = 0.2634). The availability of toilet facilities with improved sources was 98.18% (OR = 0.053; 95%CI: 0.0021–1.1600).

There was no significant association between household member type (x2 = 1.258, df = 1, p- value = 0.262) and the sex of the household head (x2 = 0.807, df = 1, p-value = 0.3691) with the type of water sources used by residents of the town. Household member type being spouse was 93.94% (OR = 2.78; 95%CI: 1.1537–8.2490) to be accessible to improved sources of drinking water. The sex of the household head being male was 63.64% (OR = 0.073; 95%CI: 0.0324–1.6494) to be accessible to improved sources of drinking water. Based on the 95% CI for the odds ratio of both household member type and sex of the household head did not significantly determine the source of water used by households.

There was a significant association between the age of the household head and the type of water source used by residents of the town (x2 = 20.392, df = 3, p-value = 0.0001). The age of the household head (18–30 years) was 6.171 times higher than the age of being below 18 years; with 20:91% (95%CI: 1.639–9.313; p-value = 0.019). The accessibility of improved sources of drinking water was lower in the older age group (> 45 years) than the medium age group (18–30 and 31–45 years), but better than the younger (below 18 years).

The educational status of household heads was significantly determined by the presence of improved sources of drinking water within the households (x2 = 19.358, df = 4, p- value = 0.0007). Being able to read and write 0.121 times 13.34% (95%CI: 0.0171–0.0721), diploma complete 0.434 times 28.48% (95%CI: 0.1351–1.322); and degree and above complete 0.015 times 35.76% (95%CI: 0.0148–1.2372) were lower than illiterate household heads to be accessible to improved sources of drinking water. However, household heads with high school complete were 4.407 times 12.12% (95%CI: 1.0578–21.3510) higher than illiterate household heads.

There was a significant association between the main source of income of the households and the type of water sources used by the town residents (x2 = 21.777, df = 3, p-value = 0.001). The main source of income of households’ through self-employer 22.12% (OR = 1.0182; 95%CI: 1.0082–1.5287), merchant 17.58% (OR = 1.0531; 95%CI: 1.0529–5.1995), and government employer 58.48% (OR = 1.862; 95%CI: 1.0647–11.5460) significantly determined the source of water from improved sources compared to households’ whose source of income was through agriculture. The accessibility of improved sources of drinking water was 86.2% increased for government-employed compared to agriculture income households.

Additionally, there was a significant association between the monthly income of households and the source of water used by the town residents (x2 = 13.322, df = 3, p-value = 0.004). Monthly income (1501–3000)(birr) was 24.54%(OR = 2.228; 95%CI:0.4812–10.779);income (3001–5000)(birr) was 49.09%(OR = 1.990; 95%CI: 1.0850–2.545); and income above 5001 (birr) 11.52%(OR = 1.39; 95%CI: 1.0034–2.102) compared to lower than 1500 (birr) income households. Facilities such as:—washing dish 38.18% (OR = 0.032; 95%CI: 0.002–0.432); fences 11.21% (OR = 0.067; 95%CI: 0.005–0.908), both washing dish and fences 1.21% (OR = 0.201; 95%CI: 0.062–0.656), and all facilities (OR = 4.734; 95%CI: 2.383–8.033) compared to no facilities at all observed to determine the source of water from improved sources, but not cattle trough and showers (x2 = 98.144, df = 7,p- value = 0.001).

There was a significant association between the cleanness status of the surrounding and the type of water used by the town residents (x2 = 42.979, df = 4, p-value = 0.001). Somewhat clean 4.24% (OR = 3.494, 95%CI: 1.597–7.390) and to be clean 63.64% (OR = 3.92, 95%CI: 2.316–5.977) compared to no clean surrounding were observed to determine the source of water from improved sources, but not partially clean and very clean surrounding cleanness status categories.

About two-hundred ninety-six (89.70%) of households had a scarcity of water from improved sources. Nearly nine out of ten households were under a scarcity of water from improved sources. 6.178 times (odds) of water scarcity occurred from improved sources than water from non-improved sources (95%CI: 2.788 12:854) and (x2 = 5.1388, df = 1, p- value = 0.0234).

Households with large family sizes had access to water from improved sources. Family size (3–5) was73.64% (OR = 0.694; 95%CI: 0.592–0.814), and family size (> = 6) was 20.30% (OR = 3.421; 95%CI: 2.312–5.063) compared to lower number of family sizes (< = 2). From this, we can conclude that a medium number of family size (3–5) got lower odds (decreased by 69.4%) to improve sources but a higher number of family size (> = 6) had gained higher odds (3.42 times) to improved sources.

4. Discussion

Different studies showed that many factors affect the supply of quality drinking water in households such as the age of household members [ 23 – 25 ], the age of the household head [ 26 ], the gender of household members [ 23 , 27 – 35 ], occupation of the household head [ 36 ], improved and unimproved water sources in rural and urban areas [ 23 , 27 , 37 – 39 ], households standard of living (income) [ 40 – 46 ], education level of household members [ 34 , 47 ], household size and composition [ 27 , 48 – 51 ]. The present study shows the associated factors of drinking water sources in urban households.

A study was undertaken in the area also concludes that nine of ten persons were under the problem of water scarcity; the supply was inadequate, and the quality was low [ 52 ]. The current study found that about 78.95% of the town population was using water from improved sources, while about 21.05% were using water from unimproved sources. A study was undertaken in the surrounding rural areas of the Debre Tabor Town, Farta district showing that about 57.10% of the population had access to improved water sources and the remaining from unimproved sources [ 53 ].

Unimproved water source using practice

The population of the town used both improved and unimproved water sources for their daily consumption. Households use unimproved sources of water that were associated with several reasons such as- income, distance, presence of alternative sources, quality, adequacy, waiting for time, interest, and other cases. About 4.55% of the population due to distance and 19.86% of the population due to quality used unimproved sources. Improved water sources in urban areas were located in short distance [ 54 ], and the quality of water was better from improved sources.

About 91.15% of the town population was under the problem of drinking water scarcity. It indicates that the supply was below 10%. The figure was lower compared to 60% of the population that had access to improved water sources in Africa and 42% water supply in sub-Saharan African countries [ 55 ]. About one billion population in the world has no access to safe and adequate water sources [ 56 ].The country report shows the presence of poor sanitation and drinking water infrastructure [ 12 ]. The lower supply of drinking water and the unimproved sanitation of households are associated [ 13 ]. It also affects the distribution of water in the area [ 14 ], and it leads to health risks [ 57 , 58 ]. The EDHS report in 2016 [ 59 ] indicates that 97% of the urban population in Ethiopia had access to an improved source of drinking water, even if its quality was not clear [ 60 ]. However, in the current setting, the supply was below 10%. A report [ 15 ] reasons out that the problem was occurring due to drought and the Horn of Africa regional instability. An international report of [ 20 ] also suggested the push and pool factors of poor water and sanitation.

In the study area, 85.65% of the population perceived that the scarcity of water was associated with poor administration factors of the local, regional, and national government. This might coincide with the international report [ 20 ] of different factors, such as the absence of good drinking water infrastructure [ 12 ] and discontinuous supply of drinking water [ 14 ]. In most African countries water scarcity was more severe in rural localities than in urban settings [ 61 ].

Water safety, quality and sanitation perception

The presence of drinking water is vital for every human being. About 74.40% of the population in the study area consumes safe water, but about a quarter of the population consumes this below the standard of safety. There is no pure water in nature [ 62 ], and about a billion people in the world don’t have access to safe drinking water [ 56 ]. Since unsafe water leads to water-borne diseases [ 41 , 63 ], it is specially at higher risk in rural households [ 64 ].The local government shall take this mandate to balance the right and responsibilities of its people. Water-borne diseases are a major concern for households [ 36 ] and highly affect households from developing countries who live in extreme conditions of poverty [ 65 ]. The risk of lack of safe water is more than from any man-made destruction (such as war, terrorism, and toxic weapons) [ 66 ]. Globally, millions of people die as a result of water-related diseases following the WHO report [ 66 ].

Households use different perceptions to identify the quality of water they consume daily. They have used color, taste, possible contamination with infectious diseases, and odor. About 3.83% of the study population couldn’t determine the water quality they consumed. Households have got the water they consumed from improved and unimproved sources.”Are the water perceptions similar from those two sources?" was asked to the households. About 25.60% of the population responded with similar perceptions were observed, but about 1.44% of the population responded as they couldn’t determine. About 72.96% of the population couldn’t differentiate the quality of water using taste, odor, and color, either from improved or unimproved sources.

In the developing world, peoples commonly do not have access to safe water [ 56 ] and some defecate in an open field [ 8 ]. Water sources are contaminated with domestic and industrial wastes [ 12 , 66 ]. About half of the study population was subjected to poor water quality due to water containing material, poor in-door practices; 16.99% were due to animal waste, and 11.24% due to human waste. In a place where water shortage is available, water may get stored for a long time. Water stored from 1 to 9 days increased the contamination level by 67% [ 57 ]. In the current setting as investigators observed, households stored water for several days. A study also shows that the town population had got water once per week [ 21 ]. The presence of animal and human wastes resulted in poor sanitation and hygiene. This leads to water-related sickness and diseases [ 41 , 58 , 65 , 66 ].

When unsafe drinking water was observed, households used treatment measures such as boiling, sedimentation, and chemicals, and a combination of two or more. 68.42% of the study population used boiling as a means of treatment measure, and 17.46% were using sedimentation. This might be due to the lower cost. Unsafe drinking water is the cause of many water-borne diseases and leads to health disorder.

About 84.45% of the study population didn’t get sick due to water-related diseases. This might be due to the cold weather conditions of the area that causes restricted dispersal of disease transmitting microorganisms or vectors. The better experience observed in the area was that households had visited health centers during illness. Only 40.19% of the study population had visited health extension workers (HEWs), which is lower than the country’s health extension coverage. In addition, HEWs visited household workers infrequently. Reasons for this need further investigation.

Community participation in educational awareness activities in surrounding and/or the town sanitation and hygiene were very low with 18.42%. The quality of latrines was not observed, but 97.61% of the population had access to latrine facilities. The present investigator recommended further studies on the quality of latrines in the town.

A study undertaken in Ethiopia stated that about 36% of the population practiced open-defecation [ 13 ] whereas the present study indicated a lower percentage with3.59%. When the inaccessibility of water has occurred, people were forced to open defecation [ 8 ]. Globally, open defecation declined from time to time [ 4 ]. In middle-income countries, 35% didn’t have water for hand washing with water and soap [ 20 ]. The current study shows a lower percentage of 10.53%. Adequate water supply, good sanitation facilities, and proper hygiene practices improve the lives of the community [ 67 ]. The scarcity of water is associated with reduced sanitation facilities.

5. Conclusion

This study provides data on access, usage, and practice of water sources among urban households in northwest Ethiopia. It provides valuable insights into the access to safe water and consequent demographic, socioeconomic conditions such as income, distance, and family size, sanitation, and hygiene perceptions of households that can be associated with access to improved water sources.

The major findings suggest that 78.95% of households used improved and 21.05% of households used unimproved water sources. Based on the reported evidence, the study suggests that as in most developing countries in Ethiopia, specifically in the study area, the scarcity of water, especially from improved sources was very severe. Particularly the town population is under the problem of water scarcity. Increasing demand from a population for safe and quality water forces the local governments to increase their supply. However, due to the lower supply of pure water to households, people put in force for using water from unimproved sources, which have a possibility to contaminate with infectious microorganisms and cause water-borne diseases. Even if the presence of adequate drinking water is vital for humans, only 74.40% of the population consumes safe water and the rest is below the standard.

The cause of unsafe water quality for a population of 52.19% is due to the water-containing material, indoor practices that are due to water shortages. Animal and human wastes are the second cause of water quality deterioration. It is better to protect water sources from any contamination and use water treatment measures, when the water is stored for a longtime.

In conclusion, the uses of drinking water from improved sources were determined by different demographic, socio-economic, sanitation, and hygiene-related factors. This study suggests an association of the sources of drinking water with the age of the household head, the educational level of the household head, the source of income, monthly income, facilities observed, cleanness status of the surrounding, water scarcity, and family size. These factors significantly determine the sources of water, from improved or unimproved sources, while the availability of toilet facilities, household member type, and sex of the household head were not significant. Thus, older headed households were closely related to the availability of improved sources of drinking water. The educational status of the household head significantly determined the type of water source to be used. The type of source of income associated with the type of water source to be used in the households (i.e., 86.2%; 5.31% and 1.82%; for government employer, merchant, and self-employed), respectively. It is recommended that the local, regional, and national governments and other supporting organizations shall improve the accessibility and adequacy of drinking water from improved sources through short and long time plan for the well-being of the community in the area.

In the long-run Health Extension Workers (HEWs) shall be given attention for the improvement of the community sanitation and hygiene practices and give awareness. This might reduce the practice of open defecation.

6. Limitations

This study was conducted with the data collected from the households’ perceptions about the source of water use, water quality, and sanitation and hygiene practices in the area. Thus, the current study didn’t undertake verifiable water quality measures.

Acknowledgments

The authors would like to thank the study participants, and the data collector for making this investigation is possible.

Abbreviations

Funding statement.

No fund is found for this study.

Data Availability