water quality assessment dissertation

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• Published: 16 December 2020

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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• Environmental sciences

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.

Martin, V. M. & Joel, A. T. History of the Urban Environment (University of Pittsburgh Press, Pittsburgh, 2012).

Google Scholar  

Wang, J., Liu, X. D. & Lu, J. Urban river pollution control and remediation. Procedia Environ. Sci. 13 , 1856–1862 (2012).

Article   CAS   Google Scholar  

Zhang, X., Wu, Y. & Gu, B. Urban rivers as hotspots of regional nitrogen pollution. Environ. Pollut. 205 , 139–144 (2015).

Article   CAS   PubMed   Google Scholar  

Harding, L. W. et al. Long-term trends, current status, and transitions of water quality in Chesapeake Bay. Sci. Rep. 9 , 1–19 (2019).

John, V., Jain, P., Rahate, M. & Labhasetwar, P. Assessment of deterioration in water quality from source to household storage in semi-urban settings of developing countries. Environ. Monit. Assess. 186 , 725–734 (2014).

Mishra, B. K. et al. Assessment of Bagmati river pollution in Kathmandu Valley: scenario-based modeling and analysis for sustainable urban development. Sustain. Water Qual. Ecol. https://doi.org/10.1016/j.swaqe.2017.06.001 (2017).

Article   Google Scholar  

Xu, Z. et al. Urban river pollution control in developing countries. Nat. Sustain. 2 , 158–160 (2019).

UN-Water. Sustainable Development Goal 6 Synthesis Report on Water and Sanitation 2018. Un (2018). https://doi.org/10.1126/science.278.5339.827 .

UNEP. A Snapshot of the World’s Water Quality: Towards a global assessment . (United Nations Environment Programme, 2016).

Wada, Y. et al. Modeling global water use for the 21st century: the Water Futures and Solutions (WFaS) initiative and its approaches. Geosci. Model Dev. https://doi.org/10.5194/gmd-9-175-2016 (2016).

WWAP. The United Nations World Water Development Report 2019: Leaving No One Behind (2019).

Fan, M. & Shibata, H. Simulation of watershed hydrology and stream water quality under land use and climate change scenarios in Teshio River watershed, northern Japan. Ecol. Indic. https://doi.org/10.1016/j.ecolind.2014.11.003 (2015).

Putro, B., Kjeldsen, T. R., Hutchins, M. G. & Miller, J. An empirical investigation of climate and land-use effects on water quantity and quality in two urbanising catchments in the southern United Kingdom. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2015.12.132 (2016).

Article   PubMed   Google Scholar  

Li, B., Rodell, M., Sheffield, J., Wood, E. & Sutanudjaja, E. Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models. Sci. Rep. 9 , 10746 (2019).

Article   ADS   PubMed   PubMed Central   CAS   Google Scholar  

Jaeger, W. K. et al. Scope and limitations of drought management within complex human–natural systems. Nat. Sustain. https://doi.org/10.1038/s41893-019-0326-y (2019).

Pastor, A. V. et al. The global nexus of food–trade–water sustaining environmental flows by 2050. Nat. Sustain. https://doi.org/10.1038/s41893-019-0287-1 (2019).

Melo, D. C. D. et al. The big picture of field hydrology studies in Brazil. Hydrol. Sci. J. 65 , 1262–1280 (2020).

Dixon, W. & Chiswell, B. Review of aquatic monitoring program design. Water Res. 30 , 1935–1948 (1996).

Wang, Y., Xiang, C., Zhao, P., Mao, G. & Du, H. A bibliometric analysis for the research on river water quality assessment and simulation during 2000–2014. Scientometrics 108 , 1333–1346 (2016).

Ji, X., Dahlgren, R. A. & Zhang, M. Comparison of seven water quality assessment methods for the characterization and management of highly impaired river systems. Environ. Monit. Assess. 188 , 15 (2016).

Article   PubMed   CAS   Google Scholar  

Deng, W. & Wang, G. A novel water quality data analysis framework based on time-series data mining. J. Environ. Manag. 196 , 365–375 (2017).

Singh, S. et al. Development of indices for surface and ground water quality assessment and characterization for Indian conditions. Environ. Monit. Assess. 191 , 182 (2019).

Mladenović-Ranisavljević, I. I., Takić, L. & Nikolić, D. Water quality assessment based on combined multi-criteria decision-making method with index method. Water Resour. Manag. 32 , 2261–2276 (2018).

Chen, S. K., Jang, C. S. & Chou, C. Y. Assessment of spatiotemporal variations in river water quality for sustainable environmental and recreational management in the highly urbanized Danshui River basin. Environ. Monit. Assess. 191 , 100 (2019).

Rakotondrabe, F. et al. Water quality assessment in the Bétaré-Oya gold mining area (East-Cameroon): multivariate statistical analysis approach. Sci. Total Environ. 610–611 , 831–844 (2018).

Article   ADS   PubMed   CAS   Google Scholar  

Horton, R. K. An Index Number System for Rating Water Quality. J. Water Pollut. Control Fed. (1965).

Brown, R. M., McClelland, N. I., Deininger, R. A. & Tozer, R. G. A water quality index—do we dare?. Water Sew. Work 117 , 339–343 (1970).

Wu, Z. et al. Water quality assessment based on the water quality index method in Lake Poyang: the largest freshwater lake in China. Sci. Rep. 7 , 1–10 (2017).

Article   ADS   CAS   Google Scholar  

Singh, K. P., Malik, A., Mohan, D. & Sinha, S. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study. Water Res. 38 , 3980–3992 (2004).

Dutta, S., Dwivedi, A. & Suresh Kumar, M. Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river. Environ. Monit. Assess. 190 , 718 (2018).

Malsy, M., Flörke, M. & Borchardt, D. What drives the water quality changes in the Selenga Basin: climate change or socio-economic development?. Reg. Environ. Change 17 , 1977–1989 (2017).

Pacheco, F. S. et al. Water quality longitudinal profile of the Paraíba do Sul River, Brazil during an extreme drought event. Limnol. Oceanogr. 62 , S131–S146 (2017).

Brazilian National Congress. Brazilian National Water Resources Policy. Federal Law n. 9433. (1997).

ANA. Brazilian Water Resources Report—2017 (National Water Agency (Brazil), 2018).

Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403 , 853–858 (2000).

Article   ADS   CAS   PubMed   Google Scholar  

Russo, G. Biodiversity: biodiversity’s bright spot. Nature 462 , 266–269 (2009).

Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. https://doi.org/10.1016/j.biocon.2009.02.021 (2009).

Tabarelli, M., Aguiar, A. V., Ribeiro, M. C., Metzger, J. P. & Peres, C. A. Prospects for biodiversity conservation in the Atlantic Forest: lessons from aging human-modified landscapes. Biol. Conserv. 143 , 2328–2340 (2010).

Bogoni, J. A., Pires, J. S. R., Graipel, M. E., Peroni, N. & Peres, C. A. Wish you were here: how defaunated is the Atlantic Forest biome of its medium- to large-bodied mammal fauna?. PLoS ONE 13 , e0204515 (2018).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Rezende, C. L. et al. From hotspot to hopespot: an opportunity for the Brazilian Atlantic Forest. Perspect. Ecol. Conserv. 16 , 208–214 (2018).

CEIVAP & PROFILL. Plano de Bacia: Consolidação do diagnóstico . http://54.94.199.16:8080/publicacoesArquivos/ceivap/arq_pubMidia_Processo_030-2018_AGVP_PS_PIRH-Atualizacao_TOMO_I_R03_2.pdf (2018).

Villas-Boas, M. D., Olivera, F. & de Azevedo, J. P. S. Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks. Environ. Monit. Assess. https://doi.org/10.1007/s10661-017-6134-9 (2017).

Morais, A., Villas-Boas, M., Bastos, A., Monteiro, A. & Araújo, L. Estudos para um diagnóstico quali-quantitativo em bacias experimentais – Estudo de Caso: Bacia do rio Piabanha. In II Seminário de Recursos Hídricos da Bacia Hidrográfica do Paraíba do Sul: Recuperação de Áreas Degradadas, Serviços Ambientais e Sustentabilidade 173–180 (2009). https://doi.org/10.4136/serhidro.23 .

Azevedo, J. P. S. de. Relatório final do projeto HIDROECO/Piabanha: Metodologia para Determinação de Vazões Ambientais na Região Serrana do RJ Integrando Aspectos Hidrometeorológicos, Ecológicos e Socioeconômico. Volume 1: Informações Quali-quantitativas (2017).

de Mello, F. V. et al. Current state of contamination by persistent organic pollutants and trace elements on Piabanha River Basin—Rio de Janeiro, Brazil. Orbital Electron. J. Chem. 10 , 327–336 (2018).

Chiappori, D., Hora, M. & Azevedo, J. Interface between hydropower generation and other water uses in the Piabanha River Basin in Brazil. Br. J. Appl. Sci. Technol. https://doi.org/10.9734/bjast/2016/23935 (2016).

da Silva, P. V. R. M., Pecly, J. O. G. & de Azevedo, J. P. S. Uso de traçadores fluorescentes para determinar características de transporte e dispersão no Rio Piabanha (RJ) para a modelagem quali-quantitativa pelo HEC-RAS. Eng. Sanit. Ambient. https://doi.org/10.1590/s1413-41522017150187 (2017).

de Costa, D. A., dos Assumpção, R. S. F. V., de Azevedo, J. P. S. & dos Santos, M. A. On water resources management instruments—Framing—as a tool for river rehabilitation. Saúde em Debate 43 , 35–50 (2019).

Abdul-Aziz, O. I., Wilson, B. N. & Gulliver, J. S. An extended stochastic harmonic analysis algorithm: application for dissolved oxygen. Water Resour. Res. 43 , W08417 (2007).

Rajwa-Kuligiewicz, A., Bialik, R. J. & Rowiński, P. M. Dissolved oxygen and water temperature dynamics in lowland rivers over various timescales. J. Hydrol. Hydromechanics 63 , 353–363 (2015).

United States Environmental Protection Agency. Quality criteria for water . https://nepis.epa.gov/Exe/ZyPDF.cgi/00001MGA.PDF?Dockey=00001MGA.PDF (1986).

Imperador, Á. do. Our history. https://www.grupoaguasdobrasil.com.br/aguas-imperador/en/ (2020).

ANA. Atlas Esgotos—Despoluição de bacias hidrográficas . (Brazilian National Water Agency, 2017).

Karthe, D., Lin, P.-Y. & Westphal, K. Instream coliform gradients in the Holtemme, a small headwater stream in the Elbe River Basin, Northern Germany. Front. Earth Sci. 11 , 544–553 (2017).

Article   ADS   Google Scholar  

von Sperling, M. & von Sperling, E. Challenges for bathing in rivers in terms of compliance with coliform standards. Case study in a large urbanized basin (das Velhas River, Brazil). Water Sci. Technol. 67 , 2534–2542 (2013).

Bae, H. Changes of river’s water quality responded to rainfall events. Environ. Ecol. Res. 1 , 21–25 (2013).

Yu, S., Xu, Z., Wu, W. & Zuo, D. Effect of land use types on stream water quality under seasonal variation and topographic characteristics in the Wei River basin, China. Ecol. Indic. 60 , 202–212 (2016).

Lumb, A., Sharma, T. C. & Bibeault, J.-F. A review of genesis and evolution of Water Quality Index (WQI) and some future directions. Water Qual. Exposure Health 3 , 11–24 (2011).

Jouanneau, S. et al. Methods for assessing biochemical oxygen demand (BOD): a review. Water Res. 49 , 62–82 (2014).

Vigiak, O. et al. Predicting biochemical oxygen demand in European freshwater bodies. Sci. Total Environ. 666 , 1089–1105 (2019).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Ishii, S. & Sadowsky, M. J. Escherichia coli in the environment: implications for water quality and human health. Microbes Environ. 23 , 101–108 (2008).

Odonkor, S. T. & Ampofo, J. K. Escherichia coli as an indicator of bacteriological quality of water: an overview. Microbiol. Res. (Pavia) 4 , 2 (2013).

Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry. Biogeochemistry: an analysis of global change 3rd edn. (Elsevier, Amsterdam , 2013). https://doi.org/10.1016/C2010-0-66291-2 .

Book   Google Scholar  

Tipping, E. et al. Atmospheric deposition of phosphorus to land and freshwater. Environ. Sci. Process. Impacts https://doi.org/10.1039/c3em00641g (2014).

Withers, P. J. A. & Jarvie, H. P. Delivery and cycling of phosphorus in rivers: a review. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2008.08.002 (2008).

Sharpley, A. Agricultural phosphorus, water quality, and poultry production: are they compatible?. Poult. Sci. https://doi.org/10.1093/ps/78.5.660 (1999).

House, W. A. & Denison, F. H. Exchange of inorganic phosphate between river waters and bed-sediments. Environ. Sci. Technol. https://doi.org/10.1021/es020039z (2002).

Alvim, B. R. Dinâmica do nitrogênio e fósforo em águas fluviais de uma bacia hidrográfica com diferentes usos do solo no Sudeste do Brasil (Universidade Federal Fluminense, 2016).

Molinari, B. S. Modelagem espacial da qualidade de água na bacia do rio Piabanha/RJ (Universidade Federal do Rio de Janeiro, 2015).

Jaji, M. O., Bamgbose, O., Odukoya, O. O. & Arowolo, T. A. Water quality assessment of Ogun river, South West Nigeria. Environ. Monit. Assess. 133 , 473–482 (2007).

Mitra, S. et al. Water quality assessment of the ecologically stressed Hooghly River Estuary, India: a multivariate approach. Mar. Pollut. Bull. 126 , 592–599 (2018).

Guo, H. Y., Wang, X. R. & Zhu, J. G. Quantification and index of non-point source pollution in Taihu Lake region with GIS. Environ. Geochem. Health https://doi.org/10.1023/B:EGAH.0000039577.67508.76 (2004).

Khuhawar, M. Y., Zaman Brohi, R. O., Jahangir, T. M. & Lanjwani, M. F. Water quality assessment of Ramser site, Indus Delta, Sindh, Pakistan. Environ. Monit. Assess. 190 , 492 (2018).

Alves, R. I. S. et al. Water quality assessment of the Pardo River Basin, Brazil: a multivariate approach using limnological parameters, metal concentrations and indicator bacteria. Arch. Environ. Contam. Toxicol. 75 , 199–212 (2018).

Liang, B. et al. Distribution, sources, and water quality assessment of dissolved heavy metals in the Jiulongjiang River water, Southeast China. Int. J. Environ. Res. Public Health 15 , 2752 (2018).

Article   CAS   PubMed Central   Google Scholar  

Brazil. Brazilian National Environment Council ( CONAMA ) Resolution n. 357. Provides the classification of water bodies and environmental guidelines for their framework, as well as establishes the conditions and standards for effluents discharge 1–27 (2005).

Thomann, R. V. Time-series analyses of water-quality data. J. Sanit. Eng. Div. 93 , 1–24 (1967).

Araújo, L. M. N. de. Identification of precipitation and soil moisture hydrological patterns at Piabanha river basin. Ph.D. thesis. (Federal University of Rio de Janiero, 2016).

Brazil. Brazilian National Sanitation Information System (SNIS). http://www.snis.gov.br/ (2020).

CEIVAP/PROFILL. Integrated plan for water resources in the watershed of the Paraíba do Sul river . http://sigaceivap.org.br:8080/publicacoesArquivos/ceivap/arq_pubMidia_AGVP_PS_PIRH_PP-06_REV03_FINAL.pdf (2020).

American Public Health Association. Standard Method for Examination of Water and Wastewater . (American Public Health Association, 2012).

McClelland, N. I. Water Quality Index Application in the Kansas River Basin (1974).

Brown, R.M., McClelland, N.I., Deininger, R.A., Landwehr, J. M. Validating the WQI . National meeting of American Society of Civil Engineers on water resources engineering (1973).

Noori, R., Berndtsson, R., Hosseinzadeh, M., Adamowski, J. F. & Abyaneh, M. R. A critical review on the application of the National Sanitation Foundation Water Quality Index. Environ. Pollut. 244 , 575–587 (2019).

Kachroud, M., Trolard, F., Kefi, M., Jebari, S. & Bourrié, G. Water quality indices: challenges and application limits in the literature. Water (Switzerland) 11 , 1–26 (2019).

CETESB, Companhia Ambiental do Estado de São Paulo. Qualidade das Águas Interiores no Estado de São Paulo - Apêndice D—Índices de Qualidade das Águas . https://cetesb.sp.gov.br/aguas-interiores/publicacoes-e-relatorios/ (2019).

Hotelling, H. Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24 , 417–441 (1933).

Article   MATH   Google Scholar  

Helena, B. et al. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res. https://doi.org/10.1016/S0043-1354(99)00225-0 (2000).

Vega, M., Pardo, R., Barrado, E. & Debán, L. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 32 , 3581–3592 (1998).

Sergeant, C. J., Starkey, E. N., Bartz, K. K., Wilson, M. H. & Mueter, F. J. A practitioner’s guide for exploring water quality patterns using principal components analysis and procrustes. Environ. Monit. Assess. 188 , 249 (2016).

Cerny, B. A. & Kaiser, H. F. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behav. Res. https://doi.org/10.1207/s15327906mbr1201_3 (1977).

Kaiser, H. F. An index of factorial simplicity. Psychometrika https://doi.org/10.1007/BF02291575 (1974).

Arsham, H. & Lovric, M. Bartlett’s Test. In International encyclopedia of statistical science (ed. Lovric, M.) 87–88 (Springer, Berlin, 2011). https://doi.org/10.1007/978-3-642-04898-2_132 .

Chapter   MATH   Google Scholar  

Mann, H. B. Nonparametric tests against trend. Econometrica 13 , 245 (1945).

Article   MathSciNet   MATH   Google Scholar  

Kendall, M. G. Rank correlation methods (Oxford University Press, Oxford, 1975).

Hirsch, R. M., Slack, J. R. & Smith, R. A. Techniques of trend analysis for monthly water quality data. Water Resour. Res. 18 , 107–121 (1982).

Whitfield, P. H. Identification and characterization of transient water quality events by Fourier analysis. Environ. Int. 21 , 571–575 (1995).

Harris, J., Loftis, J. C. & Montgomery, R. H. Statistical methods for characterizing ground-water quality. Ground Water 25 , 185–193 (1987).

Hipel, K. W. & McLeod, A. I. Time series modelling of water resources and environmental systems. Time Ser. Model. Water Resour. Environ. Syst. https://doi.org/10.1016/0022-1694(95)90010-1 (1994).

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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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de Andrade Costa, D., Soares de Azevedo, J.P., dos Santos, M.A. et al. Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest. Sci Rep 10 , 22038 (2020). https://doi.org/10.1038/s41598-020-78563-0

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Groundwater quality assessment using water quality index (WQI) under GIS framework

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Groundwater is an important source for drinking water supply in hard rock terrain of Bundelkhand massif particularly in District Mahoba, Uttar Pradesh, India. An attempt has been made in this work to understand the suitability of groundwater for human consumption. The parameters like pH, electrical conductivity, total dissolved solids, alkalinity , total hardness, calcium, magnesium, sodium, potassium, bicarbonate, sulfate, chloride, fluoride, nitrate, copper, manganese, silver, zinc, iron and nickel were analysed to estimate the groundwater quality. The water quality index (WQI) has been applied to categorize the water quality viz: excellent, good, poor, etc. which is quite useful to infer the quality of water to the people and policy makers in the concerned area. The WQI in the study area ranges from 4.75 to 115.93. The overall WQI in the study area indicates that the groundwater is safe and potable except few localized pockets in Charkhari and Jaitpur Blocks. The Hill-Piper Trilinear diagram reveals that the groundwater of the study area falls under Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) types. The granite-gneiss contains orthoclase feldspar and biotite minerals which after weathering yields bicarbonate and chloride rich groundwater. The correlation matrix has been created and analysed to observe their significant impetus on the assessment of groundwater quality. The current study suggests that the groundwater of the area under deteriorated water quality needs treatment before consumption and also to be protected from the perils of geogenic/anthropogenic contamination.

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Introduction

In India, there has been a tremendous increase in the demand for groundwater due to rapid growth of population, accelerated pace of industrialization and urbanization (Yisa and Jimoh 2010 ). The availability and quality of groundwater are badly affected at an alarming rate due to anthropogenic activities viz. overexploitation and improper waste disposal (industrial, domestic and agricultural) to groundwater reservoirs (Panda and Sinha 1991; Kavitha et al. 2019a , 2019b ). Consequently, human health is seriously threatened by the prevailing agricultural practices particularly in relation to excessive application of fertilizers; unsanitary conditions and disposal of sewage into groundwater (Panigrahi et al. 2012 ). The groundwater quality also varies with depth of water, seasonal changes, leached dissolved salts and sub-surface environment (Gebrehiwot et al. 2011 ). According to the World Health Organization (WHO 2017 ), about 80% of all the diseases in human beings are water-borne. Once the groundwater is contaminated, it is difficult to ensure its restoration and proper quality by preventing the pollutants from the source. It, therefore, becomes imperative to monitor the quality of groundwater regularly, and to device ways and means to protect it from contamination. The quality of groundwater is deciphered using various physical, chemical and biological characteristics of water (Diersing and Nancy 2009 ; Panneerselvam et al. 2020a ). It is a measure of health and hygiene of groundwater concerning the need and purpose of human consumption (Johnson et al. 1997 ; Panneerselvam et al. 2020b ).

In recent years, the assessment and monitoring of groundwater quality on a regular basis is being carried out using Geographic Information System (GIS) technique added with the IDW interpolation method and has proved itself as a powerful tool for evaluating and analysing spatial information of water resources (Aravindan et al. 2010 ; Shankar et al. 2010 , 2011a , b ; Venkateswaran et al. 2012 ; Selvam et al. 2013b; Magesh and Elango 2019 ; Balamurugan et al. 2020b ; Soujanya Kamble et al. 2020 ). It is an economically feasible and time-efficient technique for transforming huge data sets to generate various spatial distribution maps and projections revealing trends, associations and sources of contaminants/pollutants. In this work, GIS technique has been used for spatial evaluation of various groundwater quality parameters.

In this study, the physicochemical properties of forty-three groundwater samples collected from wells and hand pumps were determined and compared with international standards of WHO for drinking and domestic uses based on Water Quality Index (WQI). The WQI was first developed by Horton ( 1965 ) based on weighted arithmetical calculation. A number of researchers (Brown et al. 1972 ; GEMS UNEP 2007; Kavitha and Elangovan 2010 ; Alobaidy et al. 2010 ; Shankar and Kawo 2019 ; Bawoke and Anteneh 2020 developed various WQI models based on weighing and rating of different water quality parameters which is derived by the weighted arithmetic method. The WQI is a dimensionless number with values ranking between 0 and 100. The WQI is a unique digital rating expression that expresses overall water quality status viz. excellent, good, poor, etc. at a certain space and time based on various water quality parameters. Thus, the WQI is being used as an important tool to compare the quality of groundwater and their management (Jagadeeswari and Ramesh 2012 ) in a particular region; and is helpful for selecting appropriate economically feasible treatment process to cope up with the concerned quality issues. It depicts the composite impact of different water quality parameters and communicates water quality information to the public and legislative policy-makers to shape strong policy and implement the water quality programs (Kalavathy et al. 2011 ) by the government.

Mineral intractions strongly influence groundwater hydrochemistry in aquifers and disintegration of minerals from various source rocks (Cerar and Urbanc 2013 ; Modibo Sidibé et al. 2019 ). Hydrochemistry of the analysed samples indicates that the mean abundance of major cations is present in order of Na ++  > Ca 2+  > Mg 2+  > K + while major anions in order of \({\text{HCO}}_{3}^{ - }\)  >  \({\text{NO}}_{3}^{ - }\)  > Cl −  >  \({\text{SO}}_{4}^{2 - }\)  > F − . The study shows that the sodium is dominant alkali while calcium and magnesium are the dominant alkaline earth metal leached in the aquafer due to rock water interaction affecting the quality of groundwater. Sodium in aquafer is derived from the weathering of halite and silicate minerals such as feldspar (Khan et al. 2014 ; Mostafa et al. 2017 ). The critical evaluation of Hill-Piper Trilinear diagram reflects Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − , Ca 2+ - \({\text{HCO}}_{3}^{ - }\) , mixed Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) , Na + - \({\text{HCO}}_{3}^{ - }\) and Ca 2+ -Cl − type hydro-chemical facies in decreasing order of dominance. The Hydro-chemical characterization of groundwater reveals that the nature of aquifer is controlled by type of water, source and level of contamination (Aghazadeh et al. 2017 ; Brhane 2018 ). Hence, in order to keep the health of any aquaculture system, particularly an aquifer system at an optimal level, certain water quality indicators or parameters must be regularly monitored and controlled. Therefore, the objective of the study is to calculate the WQI of groundwater in order to assess its suitability for human consumption using the GIS interpolation technique and statistical approach in the study area.

Mahoba district is the south-western district of Uttar Pradesh which is adjacent to the state of Madhya Pradesh in south and Hamirpur (UP) in the north. The study area falls under the survey of India (SOI) toposheets no. 54O and 63C lies between latitude N25°01′30″ to N25°39′40″ and longitude E79°15′00″ to E80°10′30″ and covers an area of approximately 2933.59 km 2 . River Dhasan separates the district Mahoba from Jhansi in the west. A certainpart of Jhansi and Banda district has been merged in newly constructed Mahoba district in 1995 (bifurcated from Hamirpur). Mahoba district consists of three tehsils Kulpahar, Charkhari, Mahoba and four blocks Panwari, Jaitpur, Charkhari, Kabrai (Fig.  1 a). Kabrai is the biggest block fromaerial coverage as well as population point of view. Jaitpur is the smallest block from aerial coverage and Charkhari from population point of view. The study area experiences a typical subtropical climate punctuated by long and intense summer, with distinct seasons. The area receives an average annual precipitation of 864 mm mainly from the south-west monsoon. The temperature of the coldest month (January) is 8.3°C while the temperature of the hottest month (May) shoots upto 47.5°C. The entire area under investigation is characterised by highly jointed/fractured Bundelkhand granite (Archean age) with thin soil cover. Physiographically , the area is characterised by Bundelkhand massif terrain and is marked by the occurrence of solitary or clustered hillocks and intervening low relief with undulating plains. Two major physiographic units are: (1) Southern part having high relief with hillocks- This is south of 20°25′ N latitude & maximum altitude is 340 mamsl, reserved forest. Granitoids and intervening pegmatitic veins and numbers of quartz veins are observed. (2) Northern part relatively low relief with lower hillocks- In between 25°25′N and 25°39′N latitude and maximum altitude is 310 mamsl. The area in and around Panwari is mainly covered with thick alluvium, and hard rock is encountered only below 35 mbgl, coverage with seasonal forest. Pedi plain, pediment inselberg and buried pediplains are present.

a Study area map depicting the sampling sites. b Geological map of study area

Geological and hydrogeological set-up

The granite, particularly leucogranite, older and younger alluvium consisting of clay, silt, sand and gravel mainly comprises the study area. The geological set-up of the study area indicates that the most dominant lithology is leucogranite covering mainly central and eastern part while recent alluvium covers the northern part (Fig.  1 b). At places, few patches of pink granite have also been recorded which appears enclosed in leucogranite or adjacent to its outcrop.The occurrence of groundwater is highly uncertain and unpredictable in this hilly and rugged terrain as it does not allow percolation and storages underground. The presence of porosity depends on the intensity of weathering and rock fracture which is responsible for groundwater occurrence, its quantity and flow mostly in permeable zones of weathered rock formations and under secondary porosity in the deep fractured zone. Groundwater recharge in the study area is triggered by the depth of overburden 7 m (Jaitpur-Kulpahar area) to 35 m (parts of Mahoba Tahsil and Charkhari block) as well as the intensity of weathering.

Materials and methods

The groundwater samples were collected during pre-monsoon (June 2016) period from the study area according to standard procedures of the American Public Health Association (APHA, 2017). The sampling locations were marked with the help of global positioning system (GPS) as shown in the Fig.  1 a. Samples were collected from the location through hand pump (depth: approx. 40 m) and dug wells (depth: 8–30 m ) as shown in Fig.  2 a–t. The collecting bottles (High-Density Polythene, HDPE) of one-litre capacity each were sterilized under the aseptic condition to avoid unpredictable contamination and subsequent changes in the characteristics of groundwater. Water samples were filtered using Whatman 42 filter paper (pore size 2.5 μm) prior to collection in the bottle. The sample was kept in the ice-box (portable) and brought to NABL accredited (ISO 17,025: 2017) laboratory of Central Ground Water Board (CGWB), Lucknow and Department of Soil Science & Agricultural Chemistry, Banaras Hindu University, Varanasi, UP, India. The samples were stored in a chemical laboratory at temperature 4–5 °C. The samples for metallic parameters were added 2 ml elemental grade nitric acid to obtain the pH 2–3 after acidification. The samples were pre-filtered in the laboratory to carry out the analysis. In the present study, a total of 20 groundwater quality parameters of forty-three samples were analysed as per test standard methods (APHA 2017) in the laboratory except for unstable parameters viz. hydrogen ion concentration (pH), electrical conductivity (EC) and total dissolved solids (TDS) which are determined by portable device (pH-meter, EC-meter and TDS-meter) in situ. Alkalinity (AK), Total hardness (TH), calcium (Ca 2+ ), magnesium (Mg 2+ ), bicarbonate ( \({\text{HCO}}_{3}^{ - }\) ) and chloride (Cl − ) were analysed using volumetric titrations; sodium (Na + ) and potassium (K + ) were analysed using systronics flame photometer model 129; nitrate ( \({\text{NO}}_{3}^{ - }\) ), fluoride (F − ), sulfate ( \({\text{SO}}_{4}^{2 - }\) ), were analysed using shimadzu 1800 spectrophotometer. Prior to analysis of the heavy metals viz. copper (Cu), manganese (Mn), silver (Ag), zinc (Zn), iron (Fe) and nickel (Ni); the groundwater samples were acidified with 1:1 nitric acid and concentrated ten times. The samples were subjected to analysis using Shimadzu 6701 Atomic Absorption Spectrophotometer (AAS) on flame mode with hollow cathode lamps of metal under analysis. The concentration of metal is displayed on the monitor. The standards of the metallic parameters were prepared from National Institute of Standards and Technology (NIST) certified (Certified Reference Materials) CRM as per NABL guidelines of 17,025:2017.

a – b Spatial distribution map of pH and EC. c – h Spatial distribution map of TDS, AK, TH, Ca 2+ , Mg 2+ and Na + . i – n : spatial distribution map of K + , \({\text{HCO}}_{3}^{ - }\) , \({\text{SO}}_{4}^{2 - }\) , Cl − , F − and \({\text{NO}}_{3}^{ - }\) . o – t Spatial distribution map of Cu, Mn, Ag, Zn, Fe and Ni

The quality assurance and quality control (QA/QC) procedure of the data has been considered during the study. Approximately half of the volume (500 ml) of samples were specially separated and checked in the laboratory to ensure QA/QC mechanisms. The accuracy of the chemical analysis has been validated by charge balance errors and samples < 5% error were considered.

The inverse distance weighted (IDW) interpolation technique used in this study is now-adays an effective tool for spatial interpolation of groundwater quality parameters leading to the generation of spatial distribution maps (Magesh et al. 2013 ; Kawo and Shankar 2018 ; Balamurugan et al. 2020b ; Sarfo and Shankar 2020 ). The weights were assigned to various parameters at each location based on distance and were calculated, taking into consideration the closest specified locations. The distribution of each groundwater quality parameter has been demarcated in different zones on spatial distribution map viz. acceptable/desirable and permissible limits according to BIS (2012, 2015) and WHO ( 2017 ) for drinking purpose. The statistical analysis and correlation matrix of the analysed groundwater quality parameters have been laid down as shown in Tables 1 and 2 , respectively.

The water quality index (WQI)

The WQI has been determined using the drinking water quality standard recommended by the World Health Organization (WHO 2017 ). The Water Quality Index has been calculated using the weighted arithmetic method, which was originally proposed by Horton ( 1965 ) and developed by Brown et al. ( 1972 ). The weighted arithmetic water quality index (WQI) is represented in the following way:

where n  = number of variables or parameters, W i  = unit weight for the i th parameter, Q i  = quality rating (sub-index) of the i th water quality parameter.

The unit weight ( W i ) of the various water quality parameters are inversely proportional to the recommended standards for the corresponding parameters.

where, W i  = unit weight for the i th parameter, S n  = standard value for i th parameters, K  = proportional constant,

The value of K has been considered ‘1′ here and is calculated using the mentioned equation below:

According to Brown et al. ( 1972 ), the value of quality rating or sub-index ( Q i ) is calculated using the equation as given below:

where V o = observed value of i th parameter at a given sampling site, V i = ideal value of i th parameter in pure water, S n = standard permissible value of i th parameter.

All the ideal values (V i ) are taken as zero for drinking water except pH and dissolved oxygen (Tripathy and Sahu 2005 ). In case of pH, the ideal value is 7.0 (for natural/pure water) while the permissible value is 8.5 (for polluted water). Similarly, for dissolved oxygen, the ideal value is 14.6 mg/L while the standard permissible value for drinking water is 5 mg/L. Therefore, the quality rating for pH and Dissolved Oxygen are calculated from the equations respectively as shown below:

where, V pH  = observed value of pH, V do  = observed value of dissolved oxygen.

If, Q i  = 0 implies complete absence of contaminants while 0 < Q i  < 100 implies that, the contaminants are within the prescribed standard. When Q i  > 100 implies that, the contaminants are above the standards.

The classification of water quality, based on its water quality index (WQI) after Brown et al. ( 1972 ); Chatterjee and Raziuddin ( 2002 ) and Shankar and Kawo ( 2019 ) have been considered here in this study for further reference which is mentioned in Table 3 .

Result and discussion

Groundwater quality parameters.

In this study based on the selected parameters as discussed above the groundwater quality maps have been prepared with the help of ArcGIS software 10.1 as shown in Fig.  2 a–t. In the following lines, the various parameters considered in the study are being discussed: The Bureau of Indian Standard (BIS 2012, 2015) and World Health Organization (WHO 2017 ) of drinking water standards have been considered as a reference in this study.

Hydrogen ion concentration (pH)

It is an important indicator for assessing the quality and pollution of any aquifer system as it is closely related to other chemical constituents of water. The presence of hydrogen ion concentration is measured in terms of pH range. Water, in its pure form shows a neutral pH which indicates hydrogen ion concentration. In the present study, the range of pH varies between 6.81 (minimum) to 8.32 (maximum) which is within the acceptable limit (6.5–8.5, avg: 7.81) indicating the alkaline nature of groundwater (ideal range of pH for human consumption: 6.5–8.5).

Electrical conductivity (EC)

In fact, it is a measure of the ability of any substance or solution to conduct electrical current through the water. EC is directly proportional to the dissolved material in a water sample. The desirable limit of EC for drinking purpose is 750 µS/cm. In this study, the electrical conductivity varies between 286 and 1162 µS/cm. High EC at some sites suggests the mixing of sewage in groundwater as these sites are near dense urbanization.

Total dissolved solids (TDS)

The weight of residue expresses it after a water sample is evaporated to dry state. It includes calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride and sulfate. In the present study, it ranges between 280 to 879 mg/l (< 500 mg/l TDS for potable water as per BIS.). The agricultural practices, residential runoff, leaching of soil causing contamination and point source water pollution discharge from industrial or sewage treatment plants are the primary sources for TDS (Boyd 2000 ).

Alkalinity (AK)

It is a measure of the carbonate, bicarbonate and hydroxide ions present in water. The desirable limit of alkalinity in potable water is 200 mg/l, above which the taste of water becomes unpleasant. In the study area, the alkalinity ranges between 50 to 452 mg/l, which is within the permissible limit (600 mg/l).

Total hardness (TH)

It is the amount of dissolved calcium and magnesium in the water. Water moving through soil and rock dissolves naturally occurring minerals and carries them into the groundwater as it is a great solvent for calcium and magnesium. In this study, hardness ranges between 70 to 592 mg/l, which is within the permissible limits (600 mg/l). The high concentration of TH in groundwater may cause heart disease and kidney stone in human beings.

Calcium (Ca 2+ )

It enters into the aquifer system from the leaching of calcium bearing minerals. In the study area, the calcium concentration ranges from 12 to 112 mg/l and is within the permissible limit (200 mg/l). The lesser concentration of Ca 2+ in the groundwater satisfies the chemical weathering and dissolution of fluorite, consequently resulting in an increase of fluoride concentration.

Magnesium (Mg 2+ )

It is an important parameter responsible for the hardness of the water. In the study area, the concentration ranges between 2.4 to 120 mg/l and is present in little excess of the permissible limit (100 mg/l).

Sodium (Na + )

It is a highly reactive alkali metal. It is present in most of the groundwater. Many rocks and soils contain sodium compounds, which easily dissolves to liberate sodium in groundwater. In the study area, it ranges from 48.71 to 244.4 mg/l. The high concentration of Na + indicates weathering of rock-forming minerals i.e., silicate minerals (alkali feldspars) and/or dissolution of soil salts present therein due to evaporation (Stallard and Edmond 1983 ). In the aquifers, the high Na + concentration in groundwater may be related to the mechanism of cation exchange (Kangjoo Kim and Seong-Taekyun 2005).

Potassium (K + )

It is present in many minerals and most of the rocks. Many of these rocks are relatively soluble and releases potassium, the concentration of which increases with time in groundwater. In this study, it varies between 0.87 to 2.7 mg/l.

Bicarbonate ( \({\text{HCO}}_{3}^{ - }\) )

It is produced by the reaction of carbon dioxide with water on carbonate rocks viz. limestone and dolomite. The carbon-dioxide present in the soil reacts with the rock-forming minerals is responsible for the presence of bicarbonate, producing an alkaline environment in the groundwater. In the study area it varies between 36.61 to 536.95 mg/l and is within the permissible limit of 600 mg/l.

Sulfate ( \({\text{SO}}_{4}^{2 - }\) )

It is dissolved and leached from rocks containing gypsum, iron sulfides, and other sulfur bearing compounds. In the present study, it ranges between the 2.23 to 75.17 mg/l, which is well within the acceptable limit of 200 mg/l.

Chloride (Cl − )

In the present study the Cl − ranges between 70.92 to 276.59 mg/l which exceed the permissible limit (250 mg/l). The higher value of chlorine in groundwater makes it hazardous to human health (Pius et al. 2012 ; Sadat-Noori et al. 2014 ).

Fluoride (F − )

In groundwater fluoride is geogenic in nature. It is the lightest halogen, and one of the most reactive elements (Kaminsky et al. 1990 ). It usually occurs either in trace amounts or as a major ion with high concentration (Gaciri and Davies 1993 ; Apambire et al. 1997 ; Fantong et al. 2010 ). The groundwater contains fluorides released from various fluoride-bearing minerals mainly as a result of groundwater-host rock interaction. The study area comprising granite, granitic gneiss etc. is commonly found to contain fluorite (CaF 2 ) as an accessory mineral (Ozsvath 2006 ; Saxena and Ahmed 2003 ) which plays a significant role in controlling the geochemistry of fluoride (Deshmukh et al. 1995 ). In addition to fluorite it is also abundant in other rock-forming minerals like apatite, micas, amphiboles, and clay minerals (Karro and Uppin 2013 ; Narsimha and Sudarshan 2013 ; Naseem et al. 2010 ; Jha et al. 2010 ; Rafique et al. 2009 ; Carrillo-Rivera et al. 2002 ). In the present study, the fluoride concentration ranges from 0.11 to 3.91 mg/l. The concentration of fluoride exceeds the permissible limit (1.5 mg/l) in about 25% of the groundwater samples.

Nitrate ( \({\text{NO}}_{3}^{ - }\) )

Nitrate is naturally occurring ions and is a significant component in the nitrogen cycle. However, nitrate ion in groundwater is undesirable as it causes Methaemoglobinaemia in infants less than 6 months of age (Egereonu and Nwachukwu 2005 ). In general, its higher concentration causes health hazards if present beyond the permissible limit, 45 mg/l (Kumar et al. 2012 , 2014 ). In the study area, its concentration ranges from 86.95 to 210.4 mg/l. It is in excess of the permissible limits throughout the study area. The higher values of nitrate in potable water increases the chances of gastric ulcer/cancer, and other health hazards to infants and pregnant women (Rao 2006 ) also birth malformations and hypertension (Majumdar and Gupta 2000 ). The area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through lightning storms, bacteria present in soil and root of plants. Further, animal wastes, plants and animals remain also undergo ammonification in the soil producing ammonia which undergoes nitrification/ammonia oxidation by Nitrosomonas and Nitrobacter bacteria to form nitrate (Rivett et al. 2008 ; Galloway et al. 2004). Granitic rocks contain nitrogen concentrations up to 250 mg Nkg −1 with ammonium partitioned into the orthoclase feldspar to a greater extent than muscovite or biotite (Boyd et al. 1993 ). Geologic nitrogen (nitrogen contained in bedrock) contribute to the ecosystem with nitrogen saturation (more nitrogen available than required by biota) leading to leaching of nitrogen and consequently elevating nitrate concentrations in groundwater (Dahlgren 1994 ; Holloway et al. 1998 ). Nitrogen released through weathering has a greater impact on soil and water quality. Also, denitrification is significant in modifying the level to which nitrogen released through weathering of bedrock influencing the supply of nitrate in groundwater (McCray et al. 2005 ).

Copper (Cu)

It is a naturally occurring metal in rock, soil, plants, animals, and groundwater in very less concentration. The concentration of Cu may get enriched into the groundwater through quarrying and mining activities, farming practices, manufacturing operations and municipal or industrial waste released. Cu gets into drinking water either by contaminating of well water or corrosion of copper pipes in case of water is acidic. In this study, it ranges between 0 and 0.0078 mg/l, which is within the permissible limit (0.05 mg/l).

Manganese (Mn)

It occurs naturally in groundwater, especially in an anaerobic environment. The concentrations of Mn in groundwater is dependent upon rainfall chemistry, aquifer lithology, geochemical environment, groundwater flow paths and residence time, etc. which may vary significantly in space and time. It may be released by the leaching of the overlying soils and minerals in underlying rocks as well as from the minerals of the aquifer itself in groundwater. In the present study, manganese ranges between 0.005 and 0.221 mg/l, which is within the permissible limit (0.3 mg/l).

It naturally occurs usually in the form of insoluble and immobile oxides, sulfides and some salts. It is rarely present in groundwater, surface water and drinking water at concentrations above 5 µg/litre (WHO 2017 ). In the present study, the silver ranges between 0.000 and 0.021 mg/l, which is within the permissible limit (0.1 mg/l).

Though it occurs in significant quantities in rocks, groundwater seldom contains zinc above 0.1 mg/l. In the present study, the groundwater shows the negligible concentration of Zn (0.0136 mg/l) which is well within the acceptable limit (5 mg/l).

The most common sources of iron in groundwater is weathering of iron-bearing minerals and rocks. The iron occurs naturally in the reduced Fe 2+ state in the aquifer, but its dissolution increases its concentration in groundwater. Iron in this state is soluble and generally does not create any health hazard. If Fe 2+ state is oxidised to Fe 3+ state in contact with atmospheric oxygen or by the action of iron-related bacteria which forms insoluble hydroxides in groundwater. So, the concentration of iron in groundwater is often higher than those measured in surface water. In the present study, the iron ranges between 0.0994 and 0.4018 mg/l, which is within of the permissible limit 1.0 mg/l (BIS 2015).

Nickel (Ni)

The primary source of nickel in groundwater is from the dissolution of nickel ore bearing rocks. The source of nickel in drinking water is leaching from metals in contact such as water supply pipes and fittings. Ni usually occurs in the divalent state, but oxidation states of  +  1,  + 3, or  + 4 may also exist in nature. In the study area, it ranges between 0 and 0.0408 mg/l, and it crosses the permissible limit (0.02 mg/l).

Statistical analysis, correlation matrix and relative weightage

The relative weightage, general statistical analysis and correlation matrix of groundwater quality parameters are tabulated in Tables 4 , 1 and 2 , respectively. The correlation matrix of various 20 groundwater quality parameters, including 6 heavy metals was created and has been analysed using MS Excel 2016 Table 2 . Out of these, eight parameters viz. TDS, EC, Na + , Alkalinity, TH, Ca 2+ , Mg 2+ , \({\text{HCO}}_{3}^{ - }\) are significantly correlated, reflecting more than 0.50 correlation value. Further, TDS vs EC, Na + vs Alkalinity, TH as CaCO 3 − vs Ca 2+ and Mg 2+ , \({\text{HCO}}_{3}^{ - }\) vs Alkalinity and Na + indicates most relevant correlation having a significant impetus on the overall assessment of the quality of groundwater than any other major radicals and physical parameters. However, the majority of quality parameters are positively correlated with each other. A critical analysis of the correlation matrix for the heavy metals indicates that Cu is positively correlated with EC, TDS, Na + , K + , Cl − and \({\text{NO}}_{3}^{ - }\) . Similarly, Mn is positively correlated with pH, EC, TDS and Cu. While, Ag is positively correlated with pH, Ca 2+ , Mg 2+ , K + , TH, Cl − , \({\text{NO}}_{3}^{ - }\) and Mn. Further, Fe is positively correlated with TDS, Mg 2+ , Na + , TH, \({\text{HCO}}_{3}^{ - }\) , \({\text{SO}}_{4}^{2 - }\) , \({\text{NO}}_{3}^{ - }\) , Cu and Ag. Similarly, Ni is positively correlated with pH, EC, TDS, Ca 2+ , K + , \({\text{NO}}_{3}^{ - }\) and Mn.

The higher concentration of Ni, Fe and Mn may trigger the presence of other heavy metals viz. Pb, Cd and Cr which are very sensitive and significant heavy metal and needs to be observed carefully in future for groundwater quality in the study area. The presence of Fe, \({\text{SO}}_{4}^{2 - }\) and \({\text{NO}}_{3}^{ - }\) may trigger the presence of Cd (Chaurasia et al. 2018 ).

Spatial distribution pattern

The spatial distribution pattern of the contour maps of the groundwater quality parameters have been generated as represented in Fig.  2 a–t. The spatial distribution pattern of the pH indicates that the central part along NW–SE across the district with some scattered small patches throughout indicating the presence of alkaline groundwater (Fig.  2 a). In acidic water, fluoride is adsorbed on a clay surface, while in alkaline water, fluoride is desorbed from solid phases; therefore, alkaline pH is more favourable for fluoride dissolution, (Keshavarzi et al. 2010 ; Rafique et al. 2009 ; Saxena and Ahmed 2003 ; Rao 2009 ; Ravindra and Garg 2007 ; Vikas et al. 2009 ). The southern portion of the district in Kabrai Block is having high TDS (> 750 mg/l) in groundwater (Fig.  2 c) due to poor fluxing and highly weathered rock formations. Similarly, EC is mainly highest (> 900 mg/l) in the southern part with small scattered patches in central and NE part of the district (Fig.  2 b). This is in consonance with the higher TDS (significant positive correlation with EC) as evidenced by the correlation matrix of the quality parameters (Table 2 ). The alkalinity map clearly and significantly indicates that it is highest in the central part surrounded by gradually decreasing alkalinity outwards (Fig.  2 d). The bicarbonates trigger the alkalinity in groundwater (Adams et al. 2001 ). The quality of groundwater in a major portion of the study area is alkaline in nature, indicating that the dissolved carbonates are predominantly in the form of bicarbonates. A positive correlation is observed between the alkalinity of groundwater and fluoride (Table 2 ), consequently releasing fluoride in the groundwater. The spatial distribution map of Ca 2+ suggests varying concentration within permissible limit throughout the study area (Fig.  2 f) due to the presence of alkali feldspar in granite. Similarly, Mg 2+ is also distributed unevenly but falls within permissible limit with an exception in NE part of the district (Fig.  2 g). The spatial distribution pattern of TH reflects that the study area is characterized by moderately hard groundwater.

Figure  2 e The Ca 2+ and Mg 2+ ions present in the groundwater are possibly derived from leaching of calcium and magnesium bearing rock-formations in the study area. The fluoride in groundwater shows a negative correlation with Ca 2+ , indicating the high value of fluoride in groundwater in association with low Ca 2+ content. The correlation matrix clearly marks a significant positive correlation among Na + , alkalinity and TDS, which is being reflected from their respective spatial distribution maps (Fig.  2 c, d and h). Na + is highest in the central part (with small patches in the eastern part and insignificantly in the western part) which is in conformity with the alkalinity and TDS spatial distribution patterns. Although, the presence of K + is insignificant and its lower concentration within the permissible limit is covering a major portion of the district due to poor weathering of orthoclase. Its distribution pattern indicates conformity more or less with the TDS and Na + (Fig.  2 c, h, and i). \({\text{HCO}}_{3}^{ - }\) is an important quality parameter showing significant positive correlation (> 0.50) with alkalinity and Na + (Table 2 ) which is also reflected in the spatial distribution pattern of these parameters (Fig.  2 d, h, j). Although sulphate ( \({\text{SO}}_{4}^{2 - }\) ) is an important quality parameter. It is present within the permissible limit in the study.

area (Fig.  2 k). Chloride is slightly in excess in a larger patch, particularly in SE-part of the study area which may cause a health hazard. It is revealed from the spatial distribution map of chloride (Fig.  2 l). This is due to poor fluxing and presence of halite mineral. Fluoride (F − ) is an important quality parameter, especially with respect to the study area where it is present noticeably in scattered patches throughout the district. It is observed that mainly in NE part, the central part and SE part of the district the concentration of fluoride is in excess (2.82 mg/l to 3.91 mg/l) of permissible limit 1.5 mg/l (Fig.  2 m). The higher concentration (> 3.0 mg/l) of fluoride may lead to skeletal fluorosis (Raju et al 2009 ). Several factors viz. temperature, pH, presence or absence of complexing or precipitating ions and colloids, the solubility of fluorine bearing minerals (biotite and apatite), anion exchange capacity of the aquifer (OH − with F − ), size and type of geological formations traversed by groundwater and the contact time during which water remains in contact with the formation are responsible for fluoride concentration in groundwater (Apambire et al. 1997 ). The lithology of fractured rock reveals that it contains more fluoride bearing minerals than massive rocks (Pandey et al. 2016 ). Nitrate (NO 3 − ) in groundwater is mainly anthropogenic in nature which could be due to leaching from waste disposal, sanitary landfills, over-application of inorganic nitrate fertilizer or improper manure management practice (Chapman 1996 ). In this study, it is observed that nitrate is in excess of the permissible limits with varying degree of concentration throughout the district, causing health hazard (Fig.  2 n). The area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through lightning storms, bacteria present in soil and plants roots. Further, animal wastes, plants and animals remain also undergo ammonification in the soil producing ammonia which undergoes nitrification. The high values of nitrate in groundwater samples in the area may be due to unlined septic tanks and unplanned sewerage system that contaminates to the phreatic aquifer (Hei et al. 2020 ). Proper monitoring and concerned regulated effort are consistently required to get the assessment of nitrate impact on human health.

As far as heavy metals concentration in groundwater is concerned, Cu does not mark its noticeable presence (Fig.  2 o). Another, naturally occurring quality parameter is Mn which shows its presence within the permissible limit (Fig.  2 p). Silver and Zinc do not show any remarkable presence in the study area (Fig.  2 q and r). The study reveals a higher concentration of iron in groundwater in the Eastern part of the district due to secondary porosity and where ferrous (Fe 2+ ) ion usually occurs below the water table. The Fe 2+ after converting into Ferric (Fe 3+ ) state, becomes harmful and precipitated. This condition can be avoided naturally by raising the water table through groundwater recharging the affected area (Fig.  2 s). Nickel shows its remarkable presence in smaller patches in different areas (Fig.  2 t) due to the presence of heavy minerals like rutile and apatite.

Water quality index

The water quality index (WQI) map has been prepared using ArcGIS 10.1 on the basis of the selectively chosen quality parameters to decipher the various quality classes viz. excellent, good, poor, very poor and unsuitable at each hydro-station for drinking purpose (Tables 3 and 5 ; Fig.  3 ). The WQI Map of the study area indicates that major portion is having excellent (0–25) quality of groundwater while very poor (75–100) to unsuitable (> 100) quality is prevailing in small pockets in SW part (Fig.  3 ). The map clearly indicates that the quality of groundwater in Panwari Block belongs to excellent to good categories as for as potability for human consumption is concerned.

Water quality index map of the study area, District Mahoba

There is gradual variation in groundwater quality from very poor to excellent at the central part and outwards in the Charkhari Block. There is no noticeable change in the quality of groundwater except in the SW part of the Kabari Block. In the Jaitpur block, there is a significant.

variation in the quality class and the SW part (Nanwara, Ajnar and Khama) is characterized by poor, very poor and unsuitable categories (Fig.  3 ). Remaining part of the block falls under good to excellent groundwater quality. Overall, the quality of groundwater belongs to the excellent category in a major portion of the study area and is suitable for drinking as well as domestic uses.

Hydro-chemical facies

The major ions analysed are unevenly distributed and have been plotted on a Hill-Piper Trilinear diagram (Fig.  4 ). This diagram is comprised of two triangles at the base and one diamond shape at the top to represent the major significant cations and anions responsible for the nature of groundwater (Balamurugan et al. 2020a ). The piper diagram is used to categorize groundwater into various six types such as Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type, Na + -Cl − type, mixed Ca 2+ -Mg 2+ -Cl − type, Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) type, Na + - \({\text{HCO}}_{3}^{ - }\) type and Ca 2+ -Cl − type. A critical evaluation of the diagram reflects that 32.56% of the samples fall under Na + -Cl − type, 30.23% of the samples under mixed Ca 2+ -Mg 2+ -Cl − type, 16.28% of the samples under Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type, 13.95% of the samples under mixed Ca 2+ -Na + - \({\text{HCO}}_{3}^{ - }\) type, 4.65% of the samples under Na + - \({\text{HCO}}_{3}^{ - }\) type and 2.33% of the samples under Ca 2+ -Cl − type. Further, the observation reveals that the samples are distributed mainly into Na + -Cl − type, mixed Ca 2+ -Mg 2+ -Cl − type and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type reflecting higher concentration of sodium and calcium bearing salt/mineral. Hydrochemistry of the analysed samples indicate that the major cations are present in order Na +  > Ca 2+  > Mg 2+  > K + of mean abundance while anions are present in the mean abundance order of \({\text{HCO}}_{3}^{ - }\)  >  \({\text{NO}}_{3}^{ - }\)  > Cl −  >  \({\text{SO}}_{4}^{2 - }\)  > F − (Table 1 ). This reveals that sodium, chloride and bicarbonate dominate the ionic concentration in the groundwater due to action of weathering of minerals like halite and dolomite as well as ion exchange process.

Types of groundwater

The outcome of the present research in the hard rock area of the Bundelkhand region of India reveals that the groundwater has been deteriorated due to both geogenic and anthropogenic activities.

The study area is comprised mainly of granite and alkali granite, specifically in extreme southern which is responsible for leaching of fluoride in groundwater.

The thickness of overburden (loose soil and weathered rock) in the northern part of the study area is negligible. Therefore, there is a poor fluxing of groundwater which in turn triggers the concentration of TDS, fluoride and bicarbonate in groundwater.

Anthropogenic activities like unlined septic tanks and unplanned sewerage system have triggered the nitrate concentration in groundwater, particularly in the central and northern part of the study area. The rest of the area is safe and has potable groundwater. In addition, the area under study is granite-gneiss terrain where the atmospheric nitrogen is fixed and added to the soil as ammonia through natural lightning, bacteria present in soil and plants roots. Further, ammonification of animal wastes, plants and animal remains produces ammonia which undergoes nitrification.

Hydro-chemical facies reveal that the nature of groundwater is Na + -Cl − , mixed Ca 2+ -Mg 2+ -Cl − and Ca 2+ - \({\text{HCO}}_{3}^{ - }\) type in the study area.

The high value of WQI has been found, which is due to the higher values of chloride, fluoride, nitrate, manganese, iron, and nickel in the groundwater, which warrants immediate attention.

On the basis of WOI, it is concluded that the groundwater is safe and potable in the study area except for localized pockets in Jaitpur and Charkhari Blocks.

Adams S, Titus R, Pietersen K, Tredoux G, Harris C (2001) Hydrochemical characteristics of aquifers near Sutherland in the Western Karoo, South Africa. J Hydrol 241:91–103

Article   Google Scholar  

Aghazadeh N, Chitsazan M, Golestan Y (2017) Hydrochemistry and quality assessment of groundwater in the Ardabil area. Iran Appl Water Sci 7:3599–3616

Alobaidy AM, Abid HS, Maulood BK (2010) Application of water quality index for assessment of Dokan lake ecosystem, Kurdistan region, Iraqi. J Water Res Prot 2:792–798

Apambire WB, Boyle DR, Michel FA (1997) Geochemistry, genesis, and health implications of fluoriferous groundwater in the upper regions of Ghana. Environ Geol 33:13–22

APHA, AWWA, WEF (2017) Standard Methods for the Examination of Water and Wastewater. 23th edition., APHA, Washington, DC. v.6.

Aravindan S, Shankar K, Ganesh BP, Rajan KD (2010) Hydrogeochemical mapping of in the hard rock area of Gadilam River basin, using GIS technique. Tamil Nadu Indian J Appl Geochem 12(2):209–216

Google Scholar  

Balamurugan P, Kumar PS, Shankar K (2020a) Dataset on the suitability of groundwater for drinking and irrigation purposes in the Sarabanga River region, Tamil Nadu, India. Data in Brief 29:105255

Balamurugan P, Kumar PS, Shankar K, Nagavinothini R, Vijayasurya K (2020b) Non-carcinogenic risk assessment of groundwater in Southern Part of Salem District in Tamilnadu, India. J Chil Chem Soc 65:4697–4707

Bawoke GT, Anteneh ZL (2020) Spatial assessment and appraisal of groundwater suitability for drinking consumption in Andasa watershed using water quality index (WQI) and GIS techniques: Blue Nile Basin. Northwestern Ethiopia Cogent Eng 7(1):1748950

BIS (Bureau of Indian Standard) (2012) Indian standard drinking water specification, second revision, pp.1–16.

BIS (Bureau of Indian Standard) (2015) Indian standard drinking water specification. second revision, pp.2–6.

Boyd CE (2000) Water Quality an Introduction. Kluwer Acadamic Publi-shers, Boston, USA, p 330

Boyd SR, Hall A, Pillinger CT (1993) The measurement of d15N in crustal rocks by static vacuum mass spectrometry: Application to the origin of the ammonium in the Cornubian batholith, southwest England. Geochim Cosmochim Acta 57:1339–1347

Brhane GK (2018) Characterization of hydro chemistry and groundwater quality evaluation for drinking purpose in Adigrat area, Tigray, northern Ethiopia. Water Sci 32:213–229

Brown RM, Mccleiland NJ, Deiniger RA, O’Connor MF (1972) Water quality index-crossing the physical barrier. Res Jerusalem 6:787–797

Carrillo-Rivera JJ, Cardona A, Edmunds WM (2002) Use of abstraction regime and knowledge of hydrogeological conditions to control high-fluoride concentration in abstracted groundwater: San Luis Potosı basin, Mexico. J Hydrol 261:24–47

Cerar S, Urbanc J (2013) Carbonate chemistry and isotope characteristics of groundwater of Ljubljansko polje and Ljubljansko Barje aquifers in Slovenia. Sci World J 2013:1–11

Chapman D (1996) Water Quality Assessment-A guide to use of biota, sediments and water environmental monitoring 2nd Edition EPFN Spon. London. p.626.

Chatterjee C, Raziuddin M (2002) Determination of Water Quality Index (WQI) of a degraded river in Asansol industrial area (West Bengal). Nat Environ Pollut Technol 1:181–189

Chaurasia AK, Pandey HK, Tiwari SK, Prakash R, Pandey P, Ram A (2018) Groundwater quality assessment using water quality index (WQI) in parts of Varanasi district, Uttar Pradesh, India. J Geol Soc India 92:76–82

Dahlgren RA (1994) Soil acidification and nitrogen saturation from weathering of ammonium-bearing rock. Nature 368:838–841

Deshmukh AN, Shah KC, Sriram A (1995) Coal Ash: a source of fluoride pollution, a case study of Koradi thermal power station, District Nagpur. Maharashtra Gondwana Geol Mag 9:21–29

Diersing N, Nancy F (2009) Water quality: Frequently asked questions. Florida Brooks National Marine Sanctuary, Key West, FL

Egereonu UU, Nwachukwu UL (2005) Evaluation of the surface and groundwater resources of Efuru River Catchment, Mbano, South Eastern. Nigeria J Assoc Adv Model Simulat Tech Enterpr 66:53–71

Fantong WY, Satake H, Ayonghe SN, Suh EC, Adelana SM, Fantong EBS, Zhang J (2010) Geochemical provenance and spatial distribution of fluoride in groundwater of Mayo Tsanaga River Basin, Far North Region, Cameroon: implications for incidence of fluorosis and optimal consumption dose. Environ Geochem Health 32:147–163

Gaciri SJ, Davies TC (1993) The occurrence and geochemistry of fluoride in some natural waters of Kenya. J Hydrol 143:395–412

Galloway JN, Dentener FJ, Capone DG, Boyer EW, Howarth RW, Seitzinger SP, Asner GP, Cleveland CC, Green PA, Holland EA, Karl DM (2004) Nitrogen cycles: past, present, and future. Biogeochemistry 70:153–226

Gebrehiwot AB, Tadesse N, Jigar E (2011) Application of water quality index to assess suitablity of groundwater quality for drinking purposes in Hantebet watershed, Tigray, Northern Ethiopia. ISABB J Food Agric Sci 1:22–30

GEMS U (2007) Global drinking water quality index development and sensitivity analysis report. United Nations Environment Programme Global Environment Monitoring System/Water Programme. p.58.

Hei L, Bouchaou L, Tadoumant S, Reichert B (2020) Index-based groundwater vulnerability and water quality assessment in the arid region of Tata city (Morocco). Groundw Sustain Dev:100344

Holloway JM, Dahlgren RA, Hansen B, Casey WH (1998) Contribution of bedrock nitrogen to high nitrate concentrations in stream water. Nature 395:785–788

Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Federation 373:303–306

Jagadeeswari PB, Ramesh K (2012) Deciphering fresh and saline groundwater interface in south Chennai coastal aquifer, Tamil Nadu, India. Int J Res Chem Environ 2:123–132

Jha SK, Nayak AK, Sharma YK (2010) Potential fluoride contamination in the drinking water of Marks Nagar, Unnao district, UP, India. Environ Geochem Health 32:217–226

Johnson DL, Ambrose SH, Bassett TJ, Bowen ML, Crummey DE, Isaacson JS, Winter-Nelson AE (1997) Meanings of environmental terms. J Environ Qual 26:581–589

Kalavathy S, Sharma TR, Suresh KP (2011) Water quality index of river Cauvery in Tiruchirappalli district, Tamilnadu. Arch Environ Sci 5:55–61

Kaminsky LS, Mahoney MC, Leach J, Melius J, Jo Miller M (1990) Fluoride: benefits and risks of exposure. Crit Rev Oral Biol Med 1:261–281

Kangjoo K, Yun ST (2005) Buffering of sodium concentration by cation exchange in the groundwater system of a sandy aquifer. Geochem J 39:273–284

Karro E, Uppin M (2013) The occurrence and hydrochemistry of fluoride and boron in carbonate aquifer system, central and western Estonia. Environ Monit Assess 185:3735–3748

Kavitha MT, Divahar R, Meenambal T, Shankar K, VijaySingh R, Haile TD, Gadafa C (2019a) Dataset on the assessment of water quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu. Data in Brief 22:878–884

Kavitha MT, Shankar K, Divahar R, Meenambal T, Saravanan R (2019b) Impact of industrial wastewater disposal on surface water bodies in Kalingarayan canal, Erode district, Tamil Nadu, India. Arch Agric Environ Sci 4(4):379–387

Kavitha R, Elangovan K (2010) Ground water quality characteristics at Erode district, Tamilnadu India. Int J Environ Sci 1:163–175

Kawo NS, Shankar K (2018) Groundwater quality assessment using water quality index and GIS technique in Modjo River Basin, Central Ethiopia. J Afr Earth Sci 147:300–311

Keshavarzi B, Moore F, Esmaeili A, Rastmanesh F (2010) The source of fluoride toxicity in Muteh area, Isfahan. Iran Environ Earth Sci 61:777–786

Khan D, Hagras MA, Iqbal N (2014) Groundwater quality evaluation in Thal Doab of Indus Basin of Pakistan. Int J Modern Eng Res 4:36–47

Kumar SK, Chandrasekar N, Seralathan P, Godson PS, Magesh NS (2012) Hydrogeochemical study of shallow carbonate aquifers, Rameswaram Island, India. Environ Monit Assess 184:4127–4138

Kumar SK, Logeshkumaran A, Magesh NS, Godson PS, Chandrasekar N (2014) Hydro-geochemistry and application of water quality index (WQI) for groundwater quality assessment, Anna Nagar, part of Chennai City, Tamil Nadu, India. Appl Water Sci 5:335–343

Magesh NS, Elango L (2019) Spatio-temporal variations of fluoride in the groundwater of Dindigul District, Tamil Nadu, India: a comparative assessment using two interpolation techniques. GIS Geostat Techn Groundwater Sci:283–296.

Magesh NS, Krishnakumar S, Chandrasekar N, Soundranayagam JP (2013) Groundwater quality assessment using WQI and GIS techniques, Dindigul district, Tamil Nadu, India. Arab J Geosci 6:4179–4189

Majumdar D, Gupta N (2000) Nitrate pollution of groundwater and associated human health disorders. Indian J Environ Health 42:28–39

McCray JE, Kirkland SL, Siegrist RL, Thyne GD (2005) Model parameters for simulating fate and transport of on-site wastewater nutrients. Ground Water 43:628–639

Modibo Sidibé A, Lin X, Koné S (2019) Assessing groundwater mineralization process, quality, and isotopic recharge origin in the Sahel Region in Africa. Water 11:789

Mostafa MG, Uddin SMH, Haque ABMH (2017) Assessment of hydrogeochemistry and groundwater quality of Rajshahi City in Bangladesh. Appl Water Sci. 7:4663–4671

Narsimha A, Sudarshan V (2013) Hydrogeochemistry of groundwater in Basara area, Adilabad District, Andhra Pradesh, India. J Appl Geochem 15:224–237

Naseem S, Rafique T, Bashir E, Bhanger MI, Laghari A, Usmani TH (2010) Lithological influences on occurrence of high-fluoride groundwater in Nagar Parkar area, Thar Desert, Pakistan. Chemosphere 78:1313–1321

Ozsvath DL (2006) Fluoride concentrations in a crystalline bedrock aquifer Marathon County, Wisconsin. Environ Geol 50:132–138

Panda RB, Sahu BK, Garnaik BK, Sinha BK, Nayak A (1991) Investigation of water quality of Brahmani River. Indian J Environ Health 33:45–50

Pandey HK, Duggal SK, Jamatia A (2016) Fluoride contamination of groundwater and it’s hydrogeological evolution in District Sonbhadra (UP) India. Proc Natl Acad Sci, India, Sect A 86:81–93

Panigrahi T, Das KK, Dey BS, Panda RB (2012) Assessment of Water Quality of river Sono, Balasore. Int J Environ Sci 3:49–56

Panneerselvam B, Paramasivam SK, Karuppannan S et al (2020a) A GIS-based evaluation of hydrochemical characterisation of groundwater in hard rock region, South Tamil Nadu. India Arab J Geosci 13(17):1–22

Panneerselvam, B., Karuppannan, S., & Muniraj, K. (2020b). Evaluation of drinking and irrigation suitability of groundwater with special emphasizing the health risk posed by nitrate contamination using nitrate pollution index (NPI) and human health risk assessment (HHRA). Human and Ecological Risk Assessment: An Int J, pp 1–25.

Pius A, Jerome C, Sharma N (2012) Evaluation of groundwater quality in and around Peenya industrial area of Bangalore, South India using GIS techniques. Environ Monit Assess 184:4067–4077

Rafique T, Naseem S, Usmani TH, Bashir E, Khan FA, Bhanger MI (2009) Geochemical factors controlling the occurrence of high fluoride groundwater in the Nagar Parkar area, Sindh, Pakistan. J Hazard Mater 171:424–430

Raju NJ, Dey S, Das K (2009) Fluoride contamination in groundwater of Sonbhadra district, Uttar Pradesh, India. Curr Sci 96:979–985

Rao NS (2006) Nitrate pollution and its distribution in the groundwater of Srikakulam district, Andhra Pradesh, India. Environ Geol 51:631–645

Rao NS (2009) Fluoride in groundwater, Varaha River Basin, Visakhapatnam District, Andhra Pradesh, India. Environ Monit Assess 152:47–60

Ravindra K, Garg VK (2007) Hydro-chemical survey of groundwater of Hisar city and assessment of defluoridation methods used in India. Environ Monit Assess 132:33–43

Rivett MO, Russ SR, Morgan P, Smith JW, Bemment N, CD, (2008) Nitrate attenuation in groundwater: a review of biogeochemical controlling processes. Water Res 42:4215–4232

Sadat-Noori SM, Ebrahimi K, Liaghat AM (2014) Groundwater quality assessment using the Water Quality Index and GIS in Saveh-Nobaran aquifer. Iran Environ Earth Sci 71:3827–3843

Sarfo AK, Shankar K (2020) Application of geospatial technologies in the COVID-19 fight of Ghana. Transact Indian Nat Acad Eng 5:193–204

Saxena V, Ahmed S (2003) Inferring the chemical parameters for the dissolution of fluoride in groundwater. Environ Geol 43:731–736

Selvam S, Manimaran G, Sivasubramanian P (2013) Hydrochemical characteristics and GIS-based assessment of groundwater quality in the coastal aquifers of Tuticorin Corporation, Tamilnadu, India. Appl Water Sci 3:145–159

Shankar K, Aravindan S, Rajendran S (2010) GIS based groundwater quality mapping in Paravanar River Sub-Basin, Tamil Nadu, India. Int J Geomat Geosci 1:282–296

Shankar K, Aravindan S, Rajendran S (2011) Hydrogeochemistry of the Paravanar river sub-basin, Cuddalore District, Tamilnadu, India. E-J Chem 8:835–845

Shankar K, Aravindan S, Rajendran S (2011) Spatial distribution of groundwater quality in Paravanar river sub basin, Cuddalore district, Tamil Nadu. Int J Geomat Geosci 1:914–931

Shankar K, Kawo NS (2019) Groundwater quality assessment using geospatial techniques and WQI in North East of Adama Town, Oromia Region. Ethiopia Hydrospatial Anal 3(1):22–36

Soujanya Kamble B, Saxena PR, Kurakalva RM, Shankar K (2020) Evaluation of seasonal and temporal variations of groundwater quality around Jawaharnagar municipal solid waste dumpsite of Hyderabad city. India SN Appl Sci 2:498

Stallard RF, Edmond JM (1983) Geochemistry of the Amazon: 2. The influence of geology and weathering environment on the dissolved load. J Geophys Res: Oceans 88:9671–9688

Tripathy JK, Sahu KC (2005) Seasonal hydrochemistry of groundwater in the Barrier Spit system of the Chilika Lagoon, India. J Environ Hydrol 13:1–9

Venkateswaran S, Karuppannan S, Shankar K (2012) Groundwater quality in Pambar sub-basin, Tamil Nadu, India using GIS. Int J Recent Sci Res 3:782–787

Vikas C, Kushwaha RK, Pandit MK (2009) Hydrochemical status of groundwater in district Ajmer (NW India) with reference to fluoride distribution. J Geol Soc India 73:773–784

WHO (2017) Guideline for drinking water quality, 4th edn. World Health Organization, Geneva

Yisa J, Jimoh T (2010) Analytical studies on water quality index of river Landzu. Am J Appl Sci. 7:453–458

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Acknowledgements

Our thanks go to Central Groundwater Board (CGWB), Lucknow, NR, Region and Department of Soil Science & Agricultural Chemistry, Banaras Hindu University, Varanasi for their valuable support during the chemical analysis of groundwater samples.

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Ram, A., Tiwari, S.K., Pandey, H.K. et al. Groundwater quality assessment using water quality index (WQI) under GIS framework. Appl Water Sci 11 , 46 (2021). https://doi.org/10.1007/s13201-021-01376-7

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Senator Wiener’s Legislation to Ensure Public Schools and State Agencies Have Safe Drinking Water Passes the Senate Governmental Organization Committee

SACRAMENTO - Senator Scott Wiener (D-San Francisco)’s Senate Bill 1144, the Safe and Efficient Water Act, passed the Senate Governmental Organization Committee by a vote of 10-1. It will now head to the Senate Education Committee. SB 1144 requires public schools and state agencies to complete a water quality and efficiency assessment on their facilities. This bill ensures our public schools and state agencies have safe, drinkable water. Far too many school children and other California residents currently lack access to clean water.

Under SB 1144, the water systems at public schools and state agencies must undergo testing for lead, radon, Legionella, and other contaminants. It will also require schools and agencies to test their water systems for water use efficiency. If any plumbing fixture is found to contain levels of contaminants beyond the legal limits, or is found to use more water than the current standards for water efficiency, the operating agency must replace the fixture at the earliest practical time, subject to funding.

California communities across the state, and particularly communities of color, are at high risk of exposure to contaminated water, due partly to extensive agricultural activity and heavy use of groundwater for drinking. Currently, roughly 85% of Californians receive drinking water at least partially from groundwater sources. Agricultural runoff can lead to chemicals entering these water sources at levels unsafe for human consumption. 

School drinking water is frequently contaminated at unacceptable levels. 53% of reporting school districts in California found lead in at least one of their drinking water fountains on a campus. As with many other environmental pollutants, children are particularly at risk of experiencing the adverse health effects of water contamination. Exposure to contaminated water can result in an array of health issues that vary depending on the contaminant, as well as the intensity and duration of the exposure. Common health complications can include Legionnaires’ disease, liver and kidney problems, developmental and behavioral issues, and in some cases, cancer. 

In addition to water quality and efficiency assessment, SB 1144 will require schools and state agencies to determine the feasibility of operating a graywater system on site (or an alternative to a graywater site that connects to existing water recycling systems). Graywater systems are critically important for the environment, particularly when it comes to addressing California’s drought and water shortage crisis. These systems recycle wastewater for irrigation, or send water to be treated and then recycled for other uses. Schools or agencies that find a graywater system is feasible onsite must then install it at the earliest practical time, subject to funding.

Finally, under SB 1144, agencies or schools with any covered buildings that use a cooling tower system must create a Legionella management program. This program will include routine bacteriological culture sampling and Legionella culture sampling, as well as remediation and disinfection plans. Legionella is bacteria that can grow in water, HVAC or cooling tower systems and can cause a serious type of pneumonia, known as Legionnaire’s disease. 

For environmental and health reasons, California must do better when it comes to water safety and efficiency. We cannot allow people across the state – including children and low income communities – to continue to be exposed to harmful contaminants. California’s water shortage is another serious problem that threatens the future of our state. Increasing water efficiency and employing graywater systems for water reuse are two important strategies for saving California’s water supply and protecting our environment.

SB 1144 is sponsored by the California Pipe Trades Council.

“Access to safe, clean drinking water is a basic human right,” said Senator Scott Wiener. “SB 1144 helps ensure that children in California's public schools and anyone working at California’s state agencies will have cleaner and safer water to drink. And it also promotes water efficiency, which is more important than ever as we face a serious drought. California can provide clean and water for everyone in an efficient way – SB 1144 will ensure we are doing just that.”