Domestic and Irrigation Water Quality on the Southern Slopes of Mount Kilimanjaro

Domestic and Irrigation Water Quality on the Southern Slopes of Mount Kilimanjaro | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Domestic and Irrigation Water Quality on the Southern Slopes of Mount Kilimanjaro Fabia Codalli, Frank Shagega, Lutz Breuer, Subira Munishi, Suzanne Jacobs This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4628568/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study assessed the quality of water for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering 8 different water types were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international requirements for drinking and irrigation water quality. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for E. coli and turbidity were within the reference standards. A generalized problem of faecal contamination was found in the study area, including in domestic water, which highlights the need to identify sources of contamination and remediate before distribution. The drinking water quality index (DWQI) classified 77% of the samples as unsuitable, 4% as poor or very poor and 19% as good or excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. All samples were classified as safe for irrigation according to the irrigation water quality index (IWQI). However, five other irrigation indices (Kelley’s Index, Soluble Sodium Percentage, Permeability Index, Residual Sodium Bicarbonate and Magnesium Ratio) showed potential problems with excess of sodium and magnesium. A combination of indices is recommended for assessing water quality for irrigation use. Hydrology Environmental Chemistry water quality irrigation drinking water quality index Kilimanjaro Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction “Maji ni uhai”, Swahili for “water is life”, is the motto that appears along the highway when approaching Moshi town. Moshi is located in the foothills of Mt. Kilimanjaro, Tanzania, and its strong relationship with water is not surprising. Acting as a water tower, Mt. Kilimanjaro generates and supplies water resources along its slopes and the adjacent lowlands, as well as to the Pangani River Basin (PRB) (IUCN, 2009 ; Wamucii et al., 2021 ). According to Komakech et al. ( 2011 ), approximately 80% of the population in the PRB depends on agriculture for both livelihood and export, and 80% of the water coming from the Kilimanjaro region is used for irrigation. On the fertile and densely populated southern slopes of Africa’s highest mountain, the Chagga people have lived and shaped the upper part of the territory for more than 400 years with their small-scale “homegarden” farming and locally managed canal (furrow) system, while the lower part is characterised by intensive agriculture and urbanized areas, particularly in Moshi and surrounding areas (Hemp, 2002 ; IUCN, 2009 ). Global dynamics, including the growing human population (Said et al., 2019 ; URT, 2022 ), evidence of climate change (Appelhans et al., 2016 ; Otte et al., 2017 ) and land use and land cover changes on the slopes (Hemp, 2005 a, 2006b; Mbonile et al., 2003 ; Misana, 2012 ; Peters et al., 2019 ; Said et al., 2019 ) have had an impact on the people, their livelihoods and the environment, with water resources being highly affected. Concerns regarding water quality in the Mt. Kilimanjaro and PRB region are related to elevated nutrient concentrations in groundwater (Mckenzie et al., 2010 ), rivers and sediments with increasing concentrations (Hellar-Kihampa et al., 2013 ; PBWB/IUCN, 2009 ), most likely due to intensive agricultural, horticultural and livestock activities. Pesticide residues from agricultural applications have been found in a river in the lowlands (Hellar-Kihampa, 2011 ) and metal compounds have been found in surface waters in the lower parts of the PRB, probably due to urbanization and agricultural activities (Hellar-Kihampa, et al., 2013 ; Selemani et al., 2022). In the nearby Meru District Council, widespread faecal contamination was found in groundwater and rivers (Elisante & Muzuka, 2016 ; Kitalika et al., 2017 ), while some cases of anthropogenic pollution occurred in the form of high concentrations of nutrients and chlorine (Kitalika et al., 2018 ; Makoba & Muzuka, 2019 ). Multivariate factor analysis, water quality indices (WQI) and the innovative use of fuzzy logic are different approaches used to assess water quality (Kachroud et al., 2019 ; Kitalika et al., 2018 ; Tyagi et al., 2020 ). The WQI is one of the most commonly used and accepted methods for effectively summarising, interpreting and communicating a large datasets of water quality data to the general public and decision makers in a simple and informative way (Brown et al., 1970 ; Tyagi et al., 2020 ; Uddin et al., 2021 ). The steps to calculate the WQI include: parameter selection, calculation of a dimensionless index for each parameter, weight assignment, the aggregation of the calculated subindices in a single-value WQI, and, finally, the ranking of the WQI in qualitative classes. Since the first WQI was developed in the 1960s, several approaches have been implemented to aggregate quantitative variables into qualitative values using different mathematical methods, while eliminating subjectivity and expert bias. Several reviews have been published with the aim of describing, classifying and evaluating the advantages and disadvantages of the most popular WQIs (Chidiac et al., 2023 ; Fortes et al., 2023 ; Lukhabi et al., 2023 ; Tyagi et al., 2020 ; Uddin et al., 2021 ; Al Yousif & Chabuk, 2023 ). However, a universal WQI cannot be defined, because the selection and the weighting of parameters should be adapted to each application and context (Fortes et al., 2023 ; Kachroud et al., 2019 ; Tyagi et al., 2020 ). Parameter weighting is a crucial step to obtain the accuracy of a result and to determine the relative importance of the settings. The modified Weighted Water Quality Index, for example, is based on reference water quality requirements for a targeted use, reducing the risk of subjectivity (Paun et al., 2016 ; Tyagi et al., 2020 ; Al Yousif & Chabuk, 2023 ). Given the importance of water for drinking and agricultural use on the southern slopes of Mt. Kilimanjaro, this study aims to assess the water quality of different water types (i.e., water from streams, taps, springs, rainfall, groundwater, a lake and irrigation canals) for drinking and irrigation purposes using the modified Weighted Water Quality Index. 2 Materials and Methods 2.1 Study area This research was conducted on the southern slopes of Mt. Kilimanjaro in Tanzania. The spatial extent of the study area ranges from elevations of 700 m a.s.l. to 1,900 m a.s.l. and from the Kikafu River in the west to Lake Chala in the east (Fig. 1 ). It represents the densely populated area below the Kilimanjaro National Park boundary, where human settlements and agricultural activities are located around Moshi town. According to Hemp ( 2002 ), this part of the southern slopes can be divided into two main ecological zones. The lower zone, below approximately 1,000 m a.s.l., is characterised by colline savannah, and the upper zone, above 1,000 m a.s.l. and up to the national park boundary, is dominated by agricultural and horticultural activities. The colline savannah zone is hot and dry and is characterised by intensive crop production (especially maize, beans and sunflowers) and grazing. Patches of former savannah vegetation can also be found around Lake Chala in the eastern zone. Traditional agroforestry systems (Chagga homegardens), as well as banana and coffee plantations characterise the upper zone. Here, the deepest valleys and gorges can still contain patches of former submontane forest. Annual rainfall is distributed across two distinct rainy seasons, i.e., a long one from March to May, and a short one around November. The dry seasons are from January to February and from June to September. The mean annual rainfall increases rather linearly from the lowlands, from approximately 900 mm at 800 m a.s.l., to 2,700 mm at 2,200 m a.s.l. (Appelhans et al., 2016 ; Hemp, 2006a ; Røhr & Killingtveit, 2003 ). Mt. Kilimanjaro is located in the southern part of the East African Rift system. The lava that flowed down the southern slopes of Mt. Kilimanjaro formed olivine and alkali basalts, phonolites, trachytes, nephelinites and pyroclastic rocks (Mckenzie et al., 2010 ; Schlüter, 2005 ; Scoon, 2016 ). The soils developed are highly fertile alkaline types such as andosols (Kuehnel, 2014 ; Little & Aeolus Lee, 2006 ). The combination of fertile soils and favourable climatic conditions has made the southern slopes area known as the “breadbasket” of Tanzania (IUCN, 2003 ). Mt. Kilimanjaro, together with Mt. Meru, is part of the upper PRB, the primary headwaters of the basin. The river network formed in this area flows to the Nyumba ya Mungu dam, an essential source of hydroelectric power and livelihoods such as fishing and water for irrigation. Permanent rivers, small seasonal streams and numerous springs originating on the southern slopes of Mt. Kilimanjaro are the primary sources of water for domestic and agricultural use and are distributed through an extensive network of traditional furrows, or mfongo in the Chagga dialect, which overcomes the steep topography of the area (Kimaro et al., 2019 ; Lein, 2004 ; Mckenzie et al., 2010 ; Røhr, 2003 ; Soini, 2005 ; Tagseth, 2010 ). Glacial meltwater does not appear to contribute significantly to the recharge of water resources today (Hemp, 2005 b; Mckenzie et al., 2010 ; Røhr & Killingtveit, 2003 ; Selemani et al., 2017 ). 2.2 Sampling design and parameters To assess water quality for drinking and irrigation purposes, water samples from 51 sampling points were collected on the southern slopes of Mt. Kilimanjaro (Fig. 1 , Appendix 1) during a 10-day snaphot sampling campaign (Breuer et al., 2015 ; Grayson et al., 1997 ) in February 2023 during the dry season under low flow conditions. Samples were collected from streams (STR, n = 19), irrigation canals (CAN, n = 7), domestic water (DOM, n = 8), springs (SPR, n = 9), lake (LAK, n = 1), groundwater (GWA, n = 4) and rainfall (RAI, n = 3). Five stream samples were collected close to the Kilimanjaro National Park boundary and were considered to be streams in undisturbed or natural conditions (STN) with minimal anthropogenic impact. Streams, streams in natural conditions, irrigation canals, domestic water, springs and a lake were used to assess the drinking water quality, while streams, streams in natural conditions, irrigation canals, springs, groundwater, a lake and rainfall were used to assess the irrigation water quality. The sampling sites for surface water (streams, canals and springs) were distributed along the altitudinal gradient and from the eastern to the western sides of the southern slopes of Mt. Kilimanjaro to represent the water quality of the main rivers in the area, Kikafu, Weru Weru, Karanga, Rau and Himo, as well as 2 additional springs between the Rau and Himo River catchments, i.e., Miwaleni and Mkongo springs. Domestic water samples were collected from private and public taps distributed along the altitudinal gradient and east-west extension of the study area. Groundwater is not an important water source for domestic or irrigation purposes on the southern slopes of Mt. Kilimanjaro. Nevertheless, 4 groundwater samples were collected from boreholes within the study area, one from a coffee plantation and 3 from monitoring boreholes located in Moshi town. The three rainwater samples were collected at two different locations prior to the two-week sampling campaign. One sample was taken from Lake Chala on the eastern edge of the study area. The sample was taken at a lodge that pumped from approximately 4 m below the lake surface to a tank. Several parameters, such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity and water temperature, were measured in situ using a portable multiparameter meter (EC/temperature sensor WTW TetraCon 925-3 and pH/temperature sensor WTW Sentix 940 attached to a multimeter WTW MultiLine Multi 3630 IDS, Xylem, Germany; TSS/turbidity TSS Portable, HACH, USA). All the other parameters were analysed in laboratories. For this purpose, water samples were collected in 1 l PE plastic bottles for the analysis of total hardness (TH) and total alkalinity (ALK) through titrimetric methods. Separate water samples were collected in 500 ml sterilized glass bottles for the analysis of Escherichia coli ( E. coli ) through membrane filtration. These four parameters were analysed within 24 h from sampling by the Ngurdoto Research Campus Water Laboratory based in Usa River, Arusha, Tanzania. A third water sample was collected, filtered (KX Syringe Filter, PP, 30 mm diameter, 0.45 µm, Kinesis Ltd., St. Neods, UK) and stored in 150 ml PE plastic bottles for the analysis of calcium (Ca 2+ ), magnesium (Mg 2+ ), potassium (K + ) and sodium (Na + ) through inductively coupled plasma optical emission spectroscopy (Varian 720-ES ICP-OES, Varian (now Agilent), CA, USA), chloride (Cl − ), fluoride (F − ), nitrate (NO 3 − ) and sulphate (SO 4 2− ) through ion chromatography (DX-120, Dionex Corporation, CA, USA). These parameters were analysed at the Institute for Landscape, Ecology and Resource Management and the Department of Soil Science and Soil Conservation, Justus Liebig University of Giessen (Germany). All the samples were refrigerated in a cooler box with ice during fieldwork and frozen until laboratory analysis. 2.3 Data quality control Data quality control to identify potential anomalies in sampling or laboratory testing included the analysis of one field duplicate sample and the reanalysis of 17 samples for Ca 2+ , Mg 2+ , K + , Na + , Cl − , SO 4 2− and F − by a laboratory in Germany (Chemisches und Mikrobiologisches Institut UEG GmbH). The field duplicate sample was obtained by collecting two samples from the same location, at the same time and under the same conditions. The relative percentage difference (RPD) for each parameter was calculated using equation Eq. 1: \(RPD= \frac{\left|Sample1-Sample2\right|}{\left(\frac{Sample1+Sample2}{2}\right)}*100\) Eq 1 There is no fixed “acceptable limit” established for the RPD, as it depends on several factors such as the matrix and the analytical method. Generally, for water samples, the RPD starts to be significant when it exceeds 20–30% (DES, 2019 ; US EPA, 2015 ). 2.4 Water quality standards and guidelines 2.4.1 Drinking water quality Drinking water quality was assessed using EC, pH, TDS, turbidity, TH, E. coli , chloride, fluoride, nitrate and sulphate and compared with Tanzanian and international standards. The parameters were selected according to the guidelines of the Ministry of Water and Irrigation ( 2018 , Table 3.1b) to be routinely monitored at the drinking water sources or intakes. Colour was only qualitatively assessed, while temperature and alkalinity were excluded due to the lack of standard reference values. Instead, fluoride, chloride and sulphate were included in the list because they are considered to be possible local specific water quality issues in the study area by the Ministry of Water and Irrigation ( 2018 ). Table 1 shows the standards set by the Tanzania Bureau of Standards (TBS) (TZS 789:2016, TBS 2016 ) and those set by the World Health Organization (WHO, 2022 ), as well as the potential effects on human health if these standards are exceeded. Given that the standards set by the TBS are equal to or more restrictive than those set by the WHO, the Tanzanian standards were chosen as the reference. These standards distinguish between treated and natural drinking water. In this study, the former standards were used only for domestic water, while the latter were used for all other water sources. Table 1 Standards for drinking water according to the Tanzania Bureau of Standards (TBS) and the World Health Organization (WHO) and their potential health effects if the standards are exceeded Parameter Unit TBS. Potable water (TBS, 2016 ) WHO guidelines for drinking-water quality (WHO, 2022 ) Potential effects on human health Treated potable water Natural potable water EC µS/cm 1500 2500 - Indicator of pollution events (further studies are necessary to understand the causes) pH - 6.5–8.5 5.5–9.5 Not of health concern at levels found in drinking-water Bitter taste of water, effects on mucous membrane, dry, itchy and irritated skin, possible mobilization of harmful chemical constituents ( e.g. metals and nutrients) TDS mg/l 700 1500 Not of health concern at levels found in drinking-water Unpalatability of water, scale deposition in the water treatment, storage and distribution system Turbidity NTU 5 25 Not defined Unpalatability of water, indicator of potential pollution ( e.g. metals and bacteria) TH mg CaCO 3 /l 300 600 Not of health concern at levels found in drinking-water Unpalatability of water, skin irritation, scale deposition in the water treatment, storage and distribution system E. coli CFU/100 ml 0 0 0 Meningitis, bacteraemia, urinary tract and intestinal infections F − mg/l 1.5 1.5 1.5 Risk of dental and skeletal fluorosis Cl − mg/l 250 250 No health-based guideline value is proposed Unpalatability of water, corrosion of metals in the distribution system, increase the concentration of metals in the supply NO 3 − mg/l 45 45 50 Methaemoglobinaemia and thyroid effects in the most sensitive population SO 4 2− mg/l 400 400 Not of health concern at levels found in drinking-water Possible laxative and gastrointestinal effects 2.4.2 Irrigation water quality Irrigation water quality was assessed using EC, pH, TDS, calcium, magnesium, sodium, potassium, chloride, nitrate-nitrogen (NO 3 -N), sulphate and sodium adsorption ratio (SAR) and was compared with Tanzanian and international standards. The SAR is a commonly used parameter to evaluate water for irrigation (Berhe, 2020 ; Kumar & Maurya, 2023 ), which expresses the relative ratio of sodium to the sum of calcium and magnesium concentrations. It is calculated using the equation (Eq. 2) (Richards, 1954 ): \(SAR=\frac{{Na}^{+}}{\sqrt{\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}\) Eq 2 with ion concentrations in meq/l. SAR is also known as the "sodicity hazard" because sodium is likely to replace calcium and magnesium in the soil, which can lead to a general degradation of the soil structure through compaction, a reduction in saturated hydraulic conductivity and aeration, and thus affecting crop production (Kumar & Maurya, 2023 ; Shil et al., 2019 ). Table 2 shows the guidelines for restricting irrigation water use set by the TBS (TZS 2067:2017, TBS 2017 ) and by the Food and Agriculture Organization (FAO, Ayers & Westcot, 1985 ). The guidelines consist of three levels of severity, which should not be taken as absolute values, as the guidelines were designed to cover a wide range of conditions (Ayers & Westcot, 1985 ). In this study, we used the strictest guideline requirements for each parameter. In addition, Table 2 provides a brief description of the impacts on crops if the guidelines are exceeded (Ayers & Westcot, 1985 ; Ingram, 2014 ). Table 2 Tanzania Bureau of Standards (TBS) and Food and Agriculture Organization (FAO) guidelines for restricting irrigation water use and the potential impact on crops if the guidelines are exceeded Parameter Unit TBS. Water for irrigation (TBS, 2017 ) FAO water quality for agriculture (Ayers & Westcot, 1985 ) Possible impacts on crops Degree of restriction of use Restriction on use No problem Increasing problem Severe problem No Slight to Moderate Severe EC µS/cm 3,000 3,000 Increase of soil salinity causing physiological drought, reduction of plant growth and crop yield pH - 8.4 8.4 Effects on plant growth and irrigation equipment TDS mg/l 2,000 2,000 Increase of soil salinity causing physiological drought, reduction of plant growth and crop yield Ca 2+ mg/l > 400.8 Increase of pH, decreases nutrient availability for plants Mg 2+ mg/l > 60.8 Increase of pH, decreases nutrient availability for plants Na + mg/l > 920 Leaf burn, scorch and dead tissue along the outside edges of leaves, loss of soil structure, reduction of infiltration capacity and aeration K + mg/l > 78.2 Not a concern for plant growth. Indicator of contamination from fertilisers Cl − mg/l 355 355 Inhibition of plant growth, reduction of phosphorus availability to plants, leaf burn and drying of leaf tip NO 3 -N mg/l 30 30 Overstimulation of growth, delayed maturity and poor crop quality SO 4 2− mg/l > 960.6 Reduction in phosphorus availability to plants SAR - 9 9 Loss of soil structure, reduction of infiltration capacity and aeration 2.5 Water quality indices We estimated WQIs for drinking and irrigation water quality based on the parameters described. The WQI used here is an adapted version of the Weighted Arithmetic Water Quality Index developed by Brown et al. ( 1970 ). This index provides flexibility by choosing the number and type of parameters and by selecting different types of water sources (Paun et al., 2016 ; Tyagi et al., 2020 ; Al Yousif & Chabuk, 2023 ). In addition, the index calculation is simple, as it involves a single basic mathematical equation and is replicable, as each parameter weight is based on standards and guidelines of reference. However, this approach also has some drawbacks. For example, it overemphasises the values of a parameter that exceeds the standards and, depending on the choice of parameters, it may not carry enough information about the real quality situation of the water (Paun et al., 2016 ; Tyagi et al., 2020 ; Al Yousif & Chabuk, 2023 ). Therefore, the adapted version of the Weighted Arithmetic Water Quality Index avoids giving undue importance to those parameters that exceed the standards and guidelines by adjusting the calculation of each parameter weight. Several previous studies have adopted this version (Krishna kumar et al., 2015 ; Kumar & Maurya, 2023 ; Sutradhar & Mondal, 2021 ) and it was also suggested by Lukhabi et al. ( 2023 ) in their review of water quality indices for water quality monitoring in Africa. The procedures for calculating the WQI for drinking water (DWQI) and irrigation (IWQI) are the same. The parameters and their respective standards and guidelines selected for the calculation of the DWQI and the IWQI are presented in Table 1 and Table 2 . Four steps are involved in the calculation of the indices. First, a weight ( w i ) is assigned to each parameter ( i ): w i ranges from 1 to 5 and it is assigned based on the percentage of samples within the reference standards or guidelines. The higher the percentage of samples within the reference standards or guidelines for a selected parameter, the lower the relative importance of this parameter, i.e., the lower the weight assigned. Specifically, for a percentage of samples within the reference standards or guidelines between 0–20, 21–40, 41–60, 61–80 and 81–100%, the weights applied are 5, 4, 3, 2 and 1, respectively. Second, a relative weight ( Rw i ) for each parameter ( i ) is calculated using the equation Eq. 3 by dividing its weight ( w i ) by the sum of the weights of all parameters: \({Rw}_{i}=\frac{{w}_{i}}{\sum _{i=1}^{n}{w}_{i}}\) Eq 3 Third, a quality rating scale ( q i ) for each parameter ( i ) is calculated using equation Eq. 4 by dividing the concentration ( C i ) of parameter i in the water sample by the standard (for drinking water) or the guideline of use restriction (for irrigation water) of parameter i ( S i ): \({q}_{i}=\frac{{C}_{i}}{{S}_{i}}*100\) Eq 4 In the fourth and final step, the WQI for each sample is calculated according to equation Eq. 5 by summing the multiplication of the relative weights ( Rw i ) and the quality rating scales ( q i ) of the n parameters as follows: \(WQI= \sum _{i=1}^{n}\left({Rw}_{i}*{q}_{i}\right)\) Eq 5 The calculated DWQI and IWQI are numbers that indicate the overall quality for drinking and irrigation purposes, respectively. The indices are finally grouped into categories, 5 for the DWQI and 4 for the IWQI, as shown in Table 3 . Table 3 Classification of the DWQI (a)) and IWQI (b)) a) b) DWQI value Categories for DWQI IWQI value Categories for IWQI < 50 Excellent 451 Severe restriction > 300 Unsuitable (Raychaudhuri et al., 2014 ; Sutradhar & Mondal, 2021 ) (Raychaudhuri et al., 2014 ; Sahu & Sikdar, 2008 ) In addition, 5 further indices commonly used for assessing irrigation water quality were considered to classify the suitability of water for irrigation use, i.e., Kelley’s Index (KI), Soluble Sodium Percentage (SSP) also called sodium hazard (Na%), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR) (Kumar & Maurya, 2023 ; Makoba & Muzuka, 2019 ; Sutradhar & Mondal, 2021 ). Table 4 describes the indicators, their classification and the irrigation suitability for each class. Table 4 Description and classification of irrigation suitability indicators. The ion concentrations are expressed in meq/l. The abbreviations for the indicators are: Kelley’s Index (KI), Soluble Sodium Percentage (SSP), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR) Indicator Equation Classification Irrigation suitability Description KI (-) \(\frac{{Na}^{+}}{{Mg}^{2+}+{Ca}^{2+}}\) 1 Suitable Unsuitable Excess sodium in water, soil permeability reduction SSP (%) \(\frac{{Na}^{+}+{K}^{+}}{{Mg}^{2+}+{Ca}^{2+}+{Na}^{+}+{K}^{+}}*100\) 80 Excellent Good Permissible Doubtful Unsuitable Reduction of water transport capacity resulting in hard and dry soil PI (%) \(\frac{{Na}^{+}+\sqrt{{HCO}_{3}^{-}}}{{Mg}^{2+}+{Ca}^{2+}+{Na}^{+}}*100\) > 75 25–75 < 25 Good Marginal Unsuitable Reduction of soil permeability RSBC (meq/l) \({HCO}_{3}^{-}-{Ca}^{2+}\) 10 Safe Marginal Unsuitable Loss of soil structure, reduction of soil permeability MR (%) \(\frac{{Mg}^{2+}}{{Mg}^{2+}+{Ca}^{2+}}*100\) 50 Suitable Unsuitable Increase salinity, decrease phosphorus binding capacity of the soil, less friability of the soil 3 Results and discussion 3.1 Data quality control The calculated RPD for the field duplicate and the seventeen reanalysed samples was less than 30% for all the parameters except for fluoride, sulphate and chloride. The concentrations of fluoride and sulphate in the samples were less than 10 times the detection limits. In this case, it is generally accepted that the RPD could exceed the reference value. However, the case of chloride should be considered when interpreting the results. After the quality control of the analytical results described above, we have decided to proceed with the analysis of the data; however, we took into account the potential sources of error. 3.2 Drinking water quality Forty-four samples from streams (n = 19, including 5 streams in natural conditions), springs (n = 9), irrigation canals (n = 7), one lake and domestic water (n = 8) were analysed to assess drinking water quality. All the selected types are used in the study area as drinking water and for other domestic uses. All the selected parameters fell within the drinking water standards (Table 1 ) except for E. coli and turbidity (Fig. 2 , Appendix 2). E. coli is a bacterium that lives in the intestines of warm-blooded animals (i.e., mammals and birds) and is commonly found in human and animal faeces. Its presence is therefore an indicator of recent faecal contamination of water, as it generally does not survive long time outside its host. Although some strains can cause serious diseases, such as meningitis, bacteraemia (presence of bacteria in the blood), urinary tract and intestinal infections (nausea, vomiting and diarrhoea), the majority of E. coli strains are harmless. However, the detection of E. coli in water also indicates the possible presence of other disease-causing bacterial pathogens, such as Salmonella spp. and Shigella spp. (WHO, 2022 ). A recent study reported that Shigella and enteroinvasive E. coli were among the most common diarrhoea-associated pathogens detected in children under five years of age admitted with diarrhoea to healthcare facilities in the town of Moshi (Hugho et al., 2023 ). Infection with these pathogens occurs through contact with animals and humans that host the bacteria and, more commonly, through consumption of contaminated food or water. E. coli can reach surface waters in a variety of ways, including leakage from sewage or septic systems; improper disposal of human waste; runoff from agricultural, grazing and manure storage areas; effluent from wastewater treatment plants; and direct access to surface waters by livestock and wildlife. Previous studies carried out in the nearby Meru District Council, showed that a significant number of water samples from streams, springs and boreholes exceeded the drinking water quality standards for microbial contamination. Contamination was found to be greater during the wet season due to runoff, and at lower elevations due to increasing population and livelihood activities (Elisante & Muzuka, 2016 ; Kitalika et al., 2017 ). According to the TBS and the WHO standards, water used for domestic purposes should be free of E. coli . One or more colonies were found in 86% of the water samples. The bacterial counts ranged from 0 to 1,600 CFU/100 ml, with the highest average found in springs (393 CFU/100 ml), followed by streams (315 CFU/100 ml) and domestic water (275 CFU/100 ml). The widespread occurrence of E. coli in our surface water samples is therefore not surprising. Kitalika et al. ( 2017 ) and Elisante & Muzuka ( 2016 ) also reported counts of faecal coliform colonies of > 0 CFU/100 ml during the dry season in rivers and groundwater in Meru District, Tanzania, close to the study area. This can be explained by the fact that during the dry season, animals gather along the river as a major source of drinking water (Kitalika et al., 2017 ) or by the proximity to pit latrines, farms and animal sheds (Elisante & Muzuka, 2016 ). The domestic water supply system in the study area sources water from streams and springs located inside the national park. In the upper land, this supply can be either local, without treatment, or managed by the Moshi Urban Water Supply and Sanitation Authority or by Community-based Water Supply Organizations, which treat the water source with simple chlorination at the source and before distribution. However, the occurrence of E. coli in all sampled domestic waters is a cause for concern, as most of these waters are collected within the national park boundaries, where livestock and human impacts are very low. One possible explanation is the failure of the water distribution infrastructure in the area. Fingerprint analyses for source attribution are recommended to further identify sources of faecal contamination (Peed et al., 2011 ; Ragot et al., 2023 ; Tillett et al., 2018 ), and develop WASH (water, sanitation and hygiene) strategies in the area. Community education on safe water, potential threats and correct domestic water treatments is needed to safeguard public health in the region. Turbidity is an optical property of water and it describes its cloudiness due to suspended matter such as (organic) particles, chemical precipitates and organisms. Turbidity is not a direct indicator of health risk, but particles in the water can provide food and shelter for pathogens and protect them from the effects of disinfection. The particles can also provide a surface for other contaminants, such as metals, to adhere to, increasing the effort and relative cost of water treatment. Previous research indicated a strong positive correlation between E. coli and turbidity (Chatanga et al., 2019 ; Hamilton & Luffman, 2009 ; Travis et al., 2023 ). However, this is not reflected in the results of our campaign. In addition, high levels of turbidity can make water unattractive for drinking for aesthetic reasons (Opiyo et al., 2022 ; WHO, 2022 ). The measured turbidity values ranged from 0.4 to 63.2 nephelometric turbidity units (NTU). The stream turbidity results are in the same range as those found in rivers in Meru District (Jeihanipour et al., 2018 ; Kitalika et al., 2018 ). Only 4 out of the 44 samples exceeded the standards of 25 and 5 NTU for natural and treated potable water, respectively (TBS, 2016 ). These samples were collected from three irrigation canals and one domestic water (DOM_5) taken from a public tap in the village of Mnini (6.4 NTU). While the water from the irrigation canals is not intended for domestic use, and the population could be advised not to use it, for the public taps, regular testing and frequent visual checks are useful for detecting an increase in turbidity and therefore addressing possible failures in the distribution system. 3.2.1 Drinking water quality index (DWQI) To describe the overall quality of the drinking water, a WQI was calculated for each drinking water sample (DWQI). A weight w i and a relative weight Rw i were assigned to each parameter based on the percentage of samples within the quality standards (Table 5 ). The calculated DWQI ranged from 3 to 57,148 (Appendix 3). According to the classification shown in Table 3 a, 14 % of the samples were classified as excellent quality, 5% as good, 2% as poor, 2% as very poor and 77% as unsuitable for drinking. As shown in Fig. 3 , all the samples taken from domestic taps, streams in natural conditions and Lake Chala were found to be unsuitable for drinking, as were the majority of the streams and springs. Surprisingly, the best water quality was found in the irrigation canals. The number of E. coli colonies found in the water samples strongly influenced the calculation of the DWQI. This is because the higher the number of colonies, the higher the quality rating scale for E. coli ( q Ecoli ), calculated as the percentage ratio of the colonies in the sample ( C Ecoli ) with respect to the drinking water standards ( S Ecoli ) (Eq. 4). Note that for the calculation of the quality rating scale ( q i ) for E. coli , the quality standard S i in Eq. 4 was set to 1 CFU/100 ml even if it should be “absent” (TBS, 2016 ; WHO, 2022 ), as otherwise, using 0 CFU/100 ml, it would result in a division by zero. A high q Ecoli leads to a higher WQI; in fact, if E. coli were not considered as one of the parameters, all the samples would be of excellent quality. A similar effect was found by Kitalika et al. ( 2017 ), where the WQI changed the trend of the water quality of the rivers after fluoride was included in the WQI calculation. This study demonstrates the use of the DWQI as a valuable tool for decision making. However, critical analysis of the results and a good knowledge of the water system are fundamental for the correct interpretation of the DWQI classifications. Table 5 Standard limits, weights and relative weights of the selected parameters used to calculate the DWQI Parameter Unit Treated potable water* Natural potable water % samples within the standard limit Weight ( w i ) Relative weight ( Rw i ) EC µS/cm 1,500 2,500 100 1 0.07 pH - 6.5–8.5 5.5–9.5 100 1 0.07 TDS mg/l 700 1,500 100 1 0.07 Turbidity NTU 5 25 91 1 0.07 TH mg CaCO 3 /l 300 600 100 1 0.07 E. coli CFU/100 ml 0 0 14 5 0.36 F − mg/l 250 250 100 1 0.07 Cl − mg/l 1.5 1.5 100 1 0.07 NO 3 − mg/l 45 45 100 1 0.07 SO 4 2− mg/l 400 400 100 1 0.07 * Used only for domestic water 3.3 Irrigation water quality Forty-three samples from streams (n = 19, including 5 streams in natural conditions), springs (n = 9), irrigation canals (n = 7), groundwater (n = 4), rainfall (n = 3) and one lake were analysed to assess the irrigation water quality. All the selected parameters were within the guidelines of restriction of use for irrigation (Table 2 ) with the exception of pH (Fig. 4 , Appendix 2). pH values outside the range are rarely problematic (Ayers & Westcot, 1985 ; Pescod, 1992 ), but they could be a warning for abnormalities. A high pH is often associated with high levels of bicarbonate and carbonate, which can lead to the precipitation of calcium and magnesium as unsoluble minerals, leaving sodium as the dominant ion in the solution. An increase in the sodicity of water can lead to a decrease in the water infiltration rate and soil gas exchange by degrading the soil structure through swelling and dispersion of clays (Bauder et al., 2014 ; Pescod, 1992 ). The precipitation of Ca and Mg minerals could also cause problems with the irrigation equipment. It is important to consider both pH and alkalinity. Alkalinity is a measure of the ability of water to neutralise acidity (APHA, 2017 ). The higher the alkalinity, the greater the resistance to pH change. Therefore, when high pH and high alkalinity occur together, the pH of the water is difficult to change, so the pH of the soil will also increase, leading to mineral and nutrient deficiencies (Bauder et al., 2014 ; Fernandez, 2018 ; Ingram, 2014 ). Acidic water can mobilize trace elements, such as heavy metals, contribute to soil acidification and damage metal pipes and tanks through corrosion (Pescod, 1992 ). Seven percent of the samples exceeded the pH guidelines set by the Tanzanian authorities (TBS, 2017 ) and FAO (Ayers & Westcot, 1985 ), which range from 6.5 to 8.4. The lowest values were found in three springs (5.7, 5.7 and 6.2) and the highest values in a stream (Kikafu) and Lake Chala (8.5 and 8.7, respectively). Only the water sample from Lake Chala had a high alkalinity (202 mg CaCO 3 /l); thus, the remaining samples, which had low alkalinity, should not be a cause for concern. Several water treatment techniques can be used in agriculture to correct either the irrigation water or the substrate pH if highly alkalinity water has to be used for irrigation. Proper fertiliser selection or acid injection are two common techniques, although they can be expensive and not environmentally friendly. Finding an alternative water source for irrigation may be the best solution in some cases (Ingram, 2014 ). 3.3.1 Irrigation water quality index (IWQI) and other irrigation indices A WQI was also calculated for each sample for irrigation water quality (IWQI). The weight w i and the relative weight Rw i were assigned to each parameter based on the percentage of samples within the water use restriction guidelines (Table 6 ). The calculated IWQI ranged from 2 to 27 (Appendix 3). According to the classification shown in Table 3 b), all water samples can be used for irrigation without restrictions (Fig. 5 ). The results highlight the generally good quality of water for irrigation use on the southern slopes of Mt. Kilimanjaro compared to water quality in nearby areas. In the Mwanga District, a district to the southeast of our study area, high concentrations of SO 4 2− have been found in irrigation water for paddy rice, associated with intensive use of synthetic fertilisers (Mpanda et al., 2021 ). In the Kikafu River, further south of the study area and downstream of Moshi town, high levels of EC, TDS and NO 3 -N were found, exceeding the guidelines for irrigation use. These levels are likely to be the result of domestic waste and agrochemical run-off (Hellar-Kihampa et al., 2013 ). Table 6 Guidelines of restriction of water use for irrigation purposes, weights and relative weights of the selected parameters used to calculate the IWQI Parameter Unit Guideline of restriction % samples within the standard limit Weight (w i ) Relative weight (Rw i ) EC µS/cm 750 100 1 0.09 pH - 6.5–8.4 88 1 0.09 TDS mg/l 450 100 1 0.09 NO 3 -N mg/l 5 100 1 0.09 Cl − mg/l 142 100 1 0.09 SO 4 2− mg/l 960.6 100 1 0.09 Ca 2+ mg/l 400.8 100 1 0.09 K + mg/l 78.2 100 1 0.09 Mg 2+ mg/l 60.8 100 1 0.09 Na + mg/l 920 100 1 0.09 SAR - 3 100 1 0.09 In addition to the IWQI, other indices were calculated to investigate the suitability of water for irrigation purposes. The number and percentage of water samples falling into each irrigation suitability indicator class are shown in Table 7 . The classification of each water sample is shown in Appendix 3, and the boxplots per water type are available in Appendix 4. Natural waters with a Kelley’s Index (KI) greater than 1 have an excess of sodium and are therefore considered unsuitable for irrigation. The KI values of the samples ranged from 0.12 to 4.66, of which 67.4% were suitable for irrigation. All the samples classified as unsuitable were found in the central section of the study area (Karanga and Rau catchments), with the highest value found in an irrigation canal in the upper part (CAN_07). This sample was also the only sample classified as unsuitable (> 80%) for irrigation based on the Soluble Sodium Percentage (SSP), an indicator of the amount of sodium in the water that can cause an increase in soil salinity and therefore affect plant growth. The SSP values for the other samples in the study area ranged from 12.3–75.2%. With a similar distribution as for the KI, samples classified as doubtful for the SSP (23.3%) belonged to the central section of the study area (Karanga and Rau catchments). The majority of the samples (approximately 74%) were classified as of permissible, good or excellent quality (SSP < 60%) for irrigation use. In contrast, most of the samples classified as unsuitable based on the Magnesium Ratio (MR) were found in the eastern part of the study area (Himo catchment, Miwaleni and Lake Chala). An imbalance between magnesium and calcium concentrations toward a higher level of magnesium tends to deteriorate the soil structure by increasing soil alkalinity. Although the concentrations of magnesium and calcium in the samples were within the guidelines of water use restriction for irrigation (Ayers and Westcot 1985 ), the MR showed that 21% of the samples had an excess of magnesium relative to calcium (MR > 50%). The range of MR was from 12.8–75.8%, with the highest value found in Lake Chala. Long-term irrigation with mineral-rich water can affect soil permeability, which is influenced by sodium, calcium, magnesium and bicarbonate. The Permeability Index (PI) is an indicator used to study these characteristics. Within the water samples, the PI ranged from 64.2–498%, with the lowest value occurring at Miwaleni Spring (SPR_07), the only sample classified as marginal (25–75%) for irrigation use. Similar to the IWQI, all the water samples were safe for irrigation use based on the Residual Sodium Bicarbonate (RSBC): this result indicates that the amount of bicarbonate compared to calcium was not high enough to trigger the formation of sodium bicarbonate, which can cause the dissolution of organic matter in the soil, and thus degrades soil structure. These results show that the calculated IWQI alone is not sufficient for characterising the suitability of water samples for irrigation use. It should also be noted that, in addition to water quality, other factors such as, soil type, structure and composition, crop type and pattern, meteorological variables and irrigation type, are important in determining the suitability of water for irrigation purposes (Ayers & Westcot, 1985 ; Makoba & Muzuka, 2019 ; Mulwa, 2020 ). Table 7 Number and percentage of water samples classified into each irrigation suitability indicator class. The abbreviations for the indicators are as follows: Kelley’s Index (KI), Soluble Sodium Percentage (SSP), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR) Indicator Classification Irrigation suitability Nº samples % samples KI (-) > 1 Unsuitable 14 32.6 80 Unsuitable 1 2.3 60–80 Doubtful 10 23.3 40–60 Permissible 12 27.9 20–40 Good 13 30.2 75 Good 42 97.7 25–75 Marginal 1 2.3 10 Unsuitable 0 0 5–10 Marginal 0 0 50 Unsuitable 9 20.9 < 50 Suitable 34 79.1 4 Conclusions The results of this study provide a snapshot of the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro. Drinking water quality was generally good, except for the presence of faecal contamination, which was found in most of the water samples. This is reflected in the DWQI classification of the water samples, 77% of which were classified as unsuitable for drinking, 4% as poor or very poor and 19% as good or excellent. All the samples were classified as suitable for irrigation use after the calculation of the IWQI. However, other irrigation water quality indices revealed potential problems with irrigation water that the IWQI could not identify. In particular, a possible excess of sodium could be problematic for crops in the central part of the study area, while a possible excess of magnesium could be problematic in the eastern part. However, further research is needed to understand the sources of faecal contamination and to ensure safe drinking water for the community. Likewise, domestic water should be regularly monitored and treated for pathogenic bacteria before distribution. Public awareness campaigns on the safety, threats and possible treatment of contamination of water resources are also needed. Furthermore, studying the relationship between soil structure, crop yield and irrigation water quality will also be useful for understanding the best crop selection and appropriate remediation methods. Finally, we acknowledge the limitations of this study, which only includes spot measurements during the dry season. Conducting regular water sampling campaigns throughout the year would be useful for gaining additional insight into the temporal variation of water quality. Declarations Aknowledgements We would like to thank the Tanzania Commission for Science and Technology (COSTECH) and the Tanzania Wildlife Research Institute (TAWIRI) for granting us the research permits; all the people who kindly allowed us to collect water samples from their properties; Flora Auma Wambyakale, Lightness Deus and all the technicians at the Ngurdoto Research Campus Water Laboratory for their patience in waiting for our samples every day for two weeks and for their meticulous analysis; Ebeni Maro and Mgeta Kaswamila for their unconditional support in the field; and all the staff at Nkweseko Research Station for their daily support. This research was funded by the German Research Foundation (DFG) in the framework of the DFG Research Unit “The role of nature for human well-being in the Kilimanjaro Social-Ecological System (Kili-SES)” (FOR 5064), subproject “SP 1: Biodiversity and the supply of regulating NCP”, grant number BR2238/35-1. 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Environmental Science and Pollution Research , 24 (33), 26092–26110. https://doi.org/10.1007/s11356-017-0221-x Shil, S., Singh, U. K., & Mehta, P. (2019). Water quality assessment of a tropical river using water quality index (WQI), multivariate statistical techniques and GIS. Applied Water Science , 9 (7), 168. https://doi.org/10.1007/s13201-019-1045-2 Soini, E. (2005). Changing livelihoods on the slopes of Mt. Kilimanjaro, Tanzania: Challenges and opportunities in the Chagga homegarden system. Agroforestry Systems , 64 (2), 157–167. https://doi.org/10.1007/s10457-004-1023-y Sutradhar, S., & Mondal, P. (2021). Groundwater suitability assessment based on water quality index and hydrochemical characterization of Suri Sadar Sub-division, West Bengal. Ecological Informatics , 64 , 101335. https://doi.org/10.1016/j.ecoinf.2021.101335 Tagseth, M. (2010). Studies of the Waterscape of Kilimanjaro, Tanzania: Water Management in Hill Furrow Irrigation (Doctoral Thesis) . Norwegian University of Science and Technology. Retrieved from http://hdl.handle.net/11250/265336 TBS. (2016). TZS 789:2016 Potable Water. TBS. (2017). TZS 2067:2017 Water for irrigation. Tillett, B. J., Sharley, D., Almeida, M. I. G. S., Valenzuela, I., Hoffmann, A. A., & Pettigrove, V. (2018). A short work-flow to effectively source faecal pollution in recreational waters – A case study. Science of the Total Environment , 644 , 1503–1510. https://doi.org/10.1016/j.scitotenv.2018.07.005 Travis, R. E., Wilkins, K. L., & Kephart, C. M. (2023). Assessing Escherichia coli and Microbial Source Tracking Markers in the Rio Grande in the South Valley, Albuquerque, New Mexico, 2020–21. USGS Scientific Investigations Report , 2023 . https://doi.org/10.3133/sir20235019 Tyagi, S., Sharma, B., Singh, P., & Dobhal, R. (2020). Water Quality Assessment in Terms of Water Quality Index. American Journal of Water Resources , 1 (3), 34–38. https://doi.org/10.12691/ajwr-1-3-3 Uddin, M. G., Nash, S., & Olbert, A. I. (2021). A review of water quality index models and their use for assessing surface water quality. Ecological Indicators , 122 , 107218. https://doi.org/10.1016/j.ecolind.2020.107218 URT. (2022). The 2022 Population and Housing Census: Administrative Units Population Distribution Report . National Population and House Census of Tanzania. National Bureau of Statistics, Dar es Salaam, Tanzania (Vol. 1A). US EPA. (2015). Guidance on preparing a QA project plan. In Quality Assurance Project Plan Development Tool (p. 30). US EPA. Retrieved from https://www.epa.gov/sites/default/files/2015-06/documents/module1.pdf Wamucii, C. N., van Oel, P. R., Ligtenberg, A., Gathenya, J. M., & Teuling, A. J. (2021). Land use and climate change effects on water yield from East African forested water towers. Hydrology and Earth System Sciences , 25 (11), 5641–5665. https://doi.org/10.5194/hess-25-5641-2021 WHO. (2022). Guidelines for drinking-water quality: fourth edition incorporating the first and second addenda . Geneva. Retrieved from https://www.who.int/publications/i/item/9789240045064 Al Yousif, M., & Chabuk, A. (2023). Assessment Water Quality Indices of Surface Water for Drinking and Irrigation Applications – A Comparison Review. Journal of Ecological Engineering , 24 (5), 40–55. https://doi.org/10.12911/22998993/161194 Additional Declarations The authors declare no competing interests. Supplementary Files Appendices.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4628568","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318884946,"identity":"cb2d9a59-a20c-4916-b22b-12772a059998","order_by":0,"name":"Fabia Codalli","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0003-5226-2352","institution":"Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich Buff Ring 26 (iFZ), 35392 Giessen, Germany.","correspondingAuthor":true,"prefix":"","firstName":"Fabia","middleName":"","lastName":"Codalli","suffix":""},{"id":318884947,"identity":"28395de8-a34b-40b6-85fa-9ba1ed681678","order_by":1,"name":"Frank Shagega","email":"","orcid":"https://orcid.org/0009-0005-7174-2778","institution":"Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich Buff Ring 26 (iFZ), 35392 Giessen, Germany. Water Resources Engineering Department, University of Dar es Salaam, Tanzania","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Shagega","suffix":""},{"id":318884948,"identity":"256c719e-69b4-45a9-87b1-c8c6a47ddbf3","order_by":2,"name":"Lutz Breuer","email":"","orcid":"https://orcid.org/0000-0001-9720-1076","institution":"Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich Buff Ring 26 (iFZ), 35392 Giessen, Germany. Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Germany.","correspondingAuthor":false,"prefix":"","firstName":"Lutz","middleName":"","lastName":"Breuer","suffix":""},{"id":318884949,"identity":"e9a6dcc1-ac32-4911-9363-8c2a40eab704","order_by":3,"name":"Subira Munishi","email":"","orcid":"https://orcid.org/0000-0002-4976-0305","institution":"Water Resources Engineering Department, University of Dar es Salaam, Tanzania.","correspondingAuthor":false,"prefix":"","firstName":"Subira","middleName":"","lastName":"Munishi","suffix":""},{"id":318884950,"identity":"24884335-e41c-4ff9-b8d7-b6e164340457","order_by":4,"name":"Suzanne Jacobs","email":"","orcid":"https://orcid.org/0000-0003-2223-6973","institution":"Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (iFZ), Justus Liebig University Giessen, Heinrich Buff Ring 26 (iFZ), 35392 Giessen, Germany. Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Germany","correspondingAuthor":false,"prefix":"","firstName":"Suzanne","middleName":"","lastName":"Jacobs","suffix":""}],"badges":[],"createdAt":"2024-06-24 08:20:22","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4628568/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4628568/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59110753,"identity":"bd688aa4-b52c-4060-8008-927a122a13ce","added_by":"auto","created_at":"2024-06-26 13:04:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1737524,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area and sampling points on the southern slopes of Mt. Kilimanjaro. The catchments of the main rivers were delineated upstream from the Pangani Basin Water Board (PBWB) gauging stations, using the 30 m resolution Digital Elevation Model (DEM) of Hemp et al. (2005). The abbreviations for the sampling points are STN=stream in natural conditions, STR=stream, CAN=canal, DOM=domestic water, SPR=spring, LAK=lake, GWA=groundwater and RAI=rainfall\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/7912c260e14175b94d54d9d3.png"},{"id":59110144,"identity":"9dd09926-1166-4cf7-a642-865648a66a50","added_by":"auto","created_at":"2024-06-26 12:56:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":239362,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots per water type showing the analytical results of the 10 parameters analysed to assess drinking water quality. The black dashed line represents the standard for natural water and the grey one for treated water (TBS, 2016). If only the black dashed line is shown, it means that the two standards coincide. The water types are coded as follows: STN=stream in natural conditions, STR=stream, SPR=spring, CAN=irrigation canal, LAK=lake and DOM=domestic water\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/a7086067f203ad5c41e43756.png"},{"id":59110140,"identity":"c9789fa8-cf58-45ff-a1bb-8a95027e5d67","added_by":"auto","created_at":"2024-06-26 12:56:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96724,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots per water type showing the DWQI calculated for each sample (black dot) and the index classification into categories. The water types are coded as follows: STN=stream in natural conditions, STR=stream, SPR=spring, CAN=irrigation canal, LAK=lake and DOM=domestic water\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/d7d3d76ed48118e72f9343a9.png"},{"id":59110142,"identity":"a78ec70c-6e2d-4f41-b102-c34985ee4177","added_by":"auto","created_at":"2024-06-26 12:56:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232372,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots per water type representing the analytical results of the 11 parameters analysed to assess irrigation water quality. The black dashed line represents the guideline of restriction of water use for irrigation (Ayers \u0026amp; Westcot, 1985; TBS, 2017). The water types are coded as follows: STN=stream in natural conditions, STR=stream, SPR=spring, CAN=irrigation canal, GWA=groundwater, RAI=rainfall and LAK=lake\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/1ddf97b92fa5849493608746.png"},{"id":59110141,"identity":"a7e40da5-37c3-40a9-bfea-2d90757c1ee5","added_by":"auto","created_at":"2024-06-26 12:56:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85548,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots per water type showing the IWQI calculated for each sample (black dot) and the index classification into categories. The water types are coded as follows: STN=stream in natural conditions, STR=stream, SPR=spring, CAN=irrigation canal, GWA=groundwater, RAI=rainfall and LAK=lake\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/5930331b99c82145f8f1b81f.png"},{"id":59111696,"identity":"400f5eba-9455-4b41-bc8c-705a9b4deb23","added_by":"auto","created_at":"2024-06-26 13:12:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4132997,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/4520989f-559f-4265-aa4a-be99c75ff7b6.pdf"},{"id":59110752,"identity":"e709fef9-e292-49ef-8bf0-0045af51b558","added_by":"auto","created_at":"2024-06-26 13:04:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278882,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-4628568/v1/eb00da5250301832fedcb300.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDomestic and Irrigation Water Quality on the Southern Slopes of Mount Kilimanjaro\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003e\u0026ldquo;Maji ni uhai\u0026rdquo;, Swahili for \u0026ldquo;water is life\u0026rdquo;, is the motto that appears along the highway when approaching Moshi town. Moshi is located in the foothills of Mt. Kilimanjaro, Tanzania, and its strong relationship with water is not surprising. Acting as a water tower, Mt. Kilimanjaro generates and supplies water resources along its slopes and the adjacent lowlands, as well as to the Pangani River Basin (PRB) (IUCN, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wamucii et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to Komakech et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), approximately 80% of the population in the PRB depends on agriculture for both livelihood and export, and 80% of the water coming from the Kilimanjaro region is used for irrigation. On the fertile and densely populated southern slopes of Africa\u0026rsquo;s highest mountain, the Chagga people have lived and shaped the upper part of the territory for more than 400 years with their small-scale \u0026ldquo;homegarden\u0026rdquo; farming and locally managed canal (furrow) system, while the lower part is characterised by intensive agriculture and urbanized areas, particularly in Moshi and surrounding areas (Hemp, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; IUCN, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Global dynamics, including the growing human population (Said et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; URT, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), evidence of climate change (Appelhans et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Otte et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and land use and land cover changes on the slopes (Hemp, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003ea, 2006b; Mbonile et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Misana, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peters et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Said et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) have had an impact on the people, their livelihoods and the environment, with water resources being highly affected.\u003c/p\u003e \u003cp\u003eConcerns regarding water quality in the Mt. Kilimanjaro and PRB region are related to elevated nutrient concentrations in groundwater (Mckenzie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), rivers and sediments with increasing concentrations (Hellar-Kihampa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; PBWB/IUCN, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), most likely due to intensive agricultural, horticultural and livestock activities. Pesticide residues from agricultural applications have been found in a river in the lowlands (Hellar-Kihampa, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and metal compounds have been found in surface waters in the lower parts of the PRB, probably due to urbanization and agricultural activities (Hellar-Kihampa, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Selemani et al., 2022). In the nearby Meru District Council, widespread faecal contamination was found in groundwater and rivers (Elisante \u0026amp; Muzuka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kitalika et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while some cases of anthropogenic pollution occurred in the form of high concentrations of nutrients and chlorine (Kitalika et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Makoba \u0026amp; Muzuka, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMultivariate factor analysis, water quality indices (WQI) and the innovative use of fuzzy logic are different approaches used to assess water quality (Kachroud et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kitalika et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The WQI is one of the most commonly used and accepted methods for effectively summarising, interpreting and communicating a large datasets of water quality data to the general public and decision makers in a simple and informative way (Brown et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Uddin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The steps to calculate the WQI include: parameter selection, calculation of a dimensionless index for each parameter, weight assignment, the aggregation of the calculated subindices in a single-value WQI, and, finally, the ranking of the WQI in qualitative classes. Since the first WQI was developed in the 1960s, several approaches have been implemented to aggregate quantitative variables into qualitative values using different mathematical methods, while eliminating subjectivity and expert bias. Several reviews have been published with the aim of describing, classifying and evaluating the advantages and disadvantages of the most popular WQIs (Chidiac et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fortes et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lukhabi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Uddin et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Al Yousif \u0026amp; Chabuk, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, a universal WQI cannot be defined, because the selection and the weighting of parameters should be adapted to each application and context (Fortes et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kachroud et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Parameter weighting is a crucial step to obtain the accuracy of a result and to determine the relative importance of the settings. The modified Weighted Water Quality Index, for example, is based on reference water quality requirements for a targeted use, reducing the risk of subjectivity (Paun et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al Yousif \u0026amp; Chabuk, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the importance of water for drinking and agricultural use on the southern slopes of Mt. Kilimanjaro, this study aims to assess the water quality of different water types (i.e., water from streams, taps, springs, rainfall, groundwater, a lake and irrigation canals) for drinking and irrigation purposes using the modified Weighted Water Quality Index.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThis research was conducted on the southern slopes of Mt. Kilimanjaro in Tanzania. The spatial extent of the study area ranges from elevations of 700 m a.s.l. to 1,900 m a.s.l. and from the Kikafu River in the west to Lake Chala in the east (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It represents the densely populated area below the Kilimanjaro National Park boundary, where human settlements and agricultural activities are located around Moshi town.\u003c/p\u003e \u003cp\u003eAccording to Hemp (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), this part of the southern slopes can be divided into two main ecological zones. The lower zone, below approximately 1,000 m a.s.l., is characterised by colline savannah, and the upper zone, above 1,000 m a.s.l. and up to the national park boundary, is dominated by agricultural and horticultural activities. The colline savannah zone is hot and dry and is characterised by intensive crop production (especially maize, beans and sunflowers) and grazing. Patches of former savannah vegetation can also be found around Lake Chala in the eastern zone. Traditional agroforestry systems (Chagga homegardens), as well as banana and coffee plantations characterise the upper zone. Here, the deepest valleys and gorges can still contain patches of former submontane forest.\u003c/p\u003e \u003cp\u003eAnnual rainfall is distributed across two distinct rainy seasons, i.e., a long one from March to May, and a short one around November. The dry seasons are from January to February and from June to September. The mean annual rainfall increases rather linearly from the lowlands, from approximately 900 mm at 800 m a.s.l., to 2,700 mm at 2,200 m a.s.l. (Appelhans et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hemp, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006a\u003c/span\u003e; R\u0026oslash;hr \u0026amp; Killingtveit, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMt. Kilimanjaro is located in the southern part of the East African Rift system. The lava that flowed down the southern slopes of Mt. Kilimanjaro formed olivine and alkali basalts, phonolites, trachytes, nephelinites and pyroclastic rocks (Mckenzie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Schl\u0026uuml;ter, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Scoon, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The soils developed are highly fertile alkaline types such as andosols (Kuehnel, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Little \u0026amp; Aeolus Lee, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The combination of fertile soils and favourable climatic conditions has made the southern slopes area known as the \u0026ldquo;breadbasket\u0026rdquo; of Tanzania (IUCN, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMt. Kilimanjaro, together with Mt. Meru, is part of the upper PRB, the primary headwaters of the basin. The river network formed in this area flows to the Nyumba ya Mungu dam, an essential source of hydroelectric power and livelihoods such as fishing and water for irrigation. Permanent rivers, small seasonal streams and numerous springs originating on the southern slopes of Mt. Kilimanjaro are the primary sources of water for domestic and agricultural use and are distributed through an extensive network of traditional furrows, or \u003cem\u003emfongo\u003c/em\u003e in the Chagga dialect, which overcomes the steep topography of the area (Kimaro et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lein, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Mckenzie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; R\u0026oslash;hr, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Soini, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tagseth, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Glacial meltwater does not appear to contribute significantly to the recharge of water resources today (Hemp, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2005\u003c/span\u003eb; Mckenzie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; R\u0026oslash;hr \u0026amp; Killingtveit, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Selemani et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling design and parameters\u003c/h2\u003e \u003cp\u003eTo assess water quality for drinking and irrigation purposes, water samples from 51 sampling points were collected on the southern slopes of Mt. Kilimanjaro (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Appendix 1) during a 10-day snaphot sampling campaign (Breuer et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Grayson et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) in February 2023 during the dry season under low flow conditions. Samples were collected from streams (STR, n\u0026thinsp;=\u0026thinsp;19), irrigation canals (CAN, n\u0026thinsp;=\u0026thinsp;7), domestic water (DOM, n\u0026thinsp;=\u0026thinsp;8), springs (SPR, n\u0026thinsp;=\u0026thinsp;9), lake (LAK, n\u0026thinsp;=\u0026thinsp;1), groundwater (GWA, n\u0026thinsp;=\u0026thinsp;4) and rainfall (RAI, n\u0026thinsp;=\u0026thinsp;3). Five stream samples were collected close to the Kilimanjaro National Park boundary and were considered to be streams in undisturbed or natural conditions (STN) with minimal anthropogenic impact. Streams, streams in natural conditions, irrigation canals, domestic water, springs and a lake were used to assess the drinking water quality, while streams, streams in natural conditions, irrigation canals, springs, groundwater, a lake and rainfall were used to assess the irrigation water quality.\u003c/p\u003e \u003cp\u003eThe sampling sites for surface water (streams, canals and springs) were distributed along the altitudinal gradient and from the eastern to the western sides of the southern slopes of Mt. Kilimanjaro to represent the water quality of the main rivers in the area, Kikafu, Weru Weru, Karanga, Rau and Himo, as well as 2 additional springs between the Rau and Himo River catchments, i.e., Miwaleni and Mkongo springs. Domestic water samples were collected from private and public taps distributed along the altitudinal gradient and east-west extension of the study area. Groundwater is not an important water source for domestic or irrigation purposes on the southern slopes of Mt. Kilimanjaro. Nevertheless, 4 groundwater samples were collected from boreholes within the study area, one from a coffee plantation and 3 from monitoring boreholes located in Moshi town. The three rainwater samples were collected at two different locations prior to the two-week sampling campaign. One sample was taken from Lake Chala on the eastern edge of the study area. The sample was taken at a lodge that pumped from approximately 4 m below the lake surface to a tank.\u003c/p\u003e \u003cp\u003eSeveral parameters, such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity and water temperature, were measured \u003cem\u003ein situ\u003c/em\u003e using a portable multiparameter meter (EC/temperature sensor WTW TetraCon 925-3 and pH/temperature sensor WTW Sentix 940 attached to a multimeter WTW MultiLine Multi 3630 IDS, Xylem, Germany; TSS/turbidity TSS Portable, HACH, USA).\u003c/p\u003e \u003cp\u003eAll the other parameters were analysed in laboratories. For this purpose, water samples were collected in 1 l PE plastic bottles for the analysis of total hardness (TH) and total alkalinity (ALK) through titrimetric methods. Separate water samples were collected in 500 ml sterilized glass bottles for the analysis of \u003cem\u003eEscherichia coli\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e) through membrane filtration. These four parameters were analysed within 24 h from sampling by the Ngurdoto Research Campus Water Laboratory based in Usa River, Arusha, Tanzania. A third water sample was collected, filtered (KX Syringe Filter, PP, 30 mm diameter, 0.45 \u0026micro;m, Kinesis Ltd., St. Neods, UK) and stored in 150 ml PE plastic bottles for the analysis of calcium (Ca\u003csup\u003e2+\u003c/sup\u003e), magnesium (Mg\u003csup\u003e2+\u003c/sup\u003e), potassium (K\u003csup\u003e+\u003c/sup\u003e) and sodium (Na\u003csup\u003e+\u003c/sup\u003e) through inductively coupled plasma optical emission spectroscopy (Varian 720-ES ICP-OES, Varian (now Agilent), CA, USA), chloride (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e), fluoride (F\u003csup\u003e\u0026minus;\u003c/sup\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and sulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e) through ion chromatography (DX-120, Dionex Corporation, CA, USA). These parameters were analysed at the Institute for Landscape, Ecology and Resource Management and the Department of Soil Science and Soil Conservation, Justus Liebig University of Giessen (Germany). All the samples were refrigerated in a cooler box with ice during fieldwork and frozen until laboratory analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data quality control\u003c/h2\u003e \u003cp\u003eData quality control to identify potential anomalies in sampling or laboratory testing included the analysis of one field duplicate sample and the reanalysis of 17 samples for Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and F\u003csup\u003e\u0026minus;\u003c/sup\u003e by a laboratory in Germany (Chemisches und Mikrobiologisches Institut UEG GmbH). The field duplicate sample was obtained by collecting two samples from the same location, at the same time and under the same conditions. The relative percentage difference (RPD) for each parameter was calculated using equation Eq.\u0026nbsp;1:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(RPD= \\frac{\\left|Sample1-Sample2\\right|}{\\left(\\frac{Sample1+Sample2}{2}\\right)}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere is no fixed \u0026ldquo;acceptable limit\u0026rdquo; established for the RPD, as it depends on several factors such as the matrix and the analytical method. Generally, for water samples, the RPD starts to be significant when it exceeds 20\u0026ndash;30% (DES, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; US EPA, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Water quality standards and guidelines\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Drinking water quality\u003c/h2\u003e \u003cp\u003eDrinking water quality was assessed using EC, pH, TDS, turbidity, TH, \u003cem\u003eE. coli\u003c/em\u003e, chloride, fluoride, nitrate and sulphate and compared with Tanzanian and international standards. The parameters were selected according to the guidelines of the Ministry of Water and Irrigation (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Table\u0026nbsp;3.1b) to be routinely monitored at the drinking water sources or intakes. Colour was only qualitatively assessed, while temperature and alkalinity were excluded due to the lack of standard reference values. Instead, fluoride, chloride and sulphate were included in the list because they are considered to be possible local specific water quality issues in the study area by the Ministry of Water and Irrigation (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the standards set by the Tanzania Bureau of Standards (TBS) (TZS 789:2016, TBS \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and those set by the World Health Organization (WHO, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as well as the potential effects on human health if these standards are exceeded. Given that the standards set by the TBS are equal to or more restrictive than those set by the WHO, the Tanzanian standards were chosen as the reference. These standards distinguish between treated and natural drinking water. In this study, the former standards were used only for domestic water, while the latter were used for all other water sources.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandards for drinking water according to the Tanzania Bureau of Standards (TBS) and the World Health Organization (WHO) and their potential health effects if the standards are exceeded\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTBS. Potable water (TBS, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWHO guidelines for drinking-water quality\u003c/p\u003e \u003cp\u003e(WHO, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePotential effects on human health\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreated potable water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural potable water\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndicator of pollution events (further studies are necessary to understand the causes)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026ndash;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot of health concern at levels found in drinking-water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBitter taste of water, effects on mucous membrane, dry, itchy and irritated skin, possible mobilization of harmful chemical constituents (\u003cem\u003ee.g.\u003c/em\u003e metals and nutrients)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot of health concern at levels found in drinking-water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnpalatability of water, scale deposition in the water treatment, storage and distribution system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNTU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot defined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnpalatability of water, indicator of potential pollution (\u003cem\u003ee.g.\u003c/em\u003e metals and bacteria)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg CaCO\u003csub\u003e3\u003c/sub\u003e/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot of health concern at levels found in drinking-water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnpalatability of water, skin irritation, scale deposition in the water treatment, storage and distribution system\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMeningitis, bacteraemia, urinary tract and intestinal infections\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRisk of dental and skeletal fluorosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo health-based guideline value is proposed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnpalatability of water, corrosion of metals in the distribution system, increase the concentration of metals in the supply\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMethaemoglobinaemia and thyroid effects in the most sensitive population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot of health concern at levels found in drinking-water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePossible laxative and gastrointestinal effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Irrigation water quality\u003c/h2\u003e \u003cp\u003eIrrigation water quality was assessed using EC, pH, TDS, calcium, magnesium, sodium, potassium, chloride, nitrate-nitrogen (NO\u003csub\u003e3\u003c/sub\u003e-N), sulphate and sodium adsorption ratio (SAR) and was compared with Tanzanian and international standards. The SAR is a commonly used parameter to evaluate water for irrigation (Berhe, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kumar \u0026amp; Maurya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which expresses the relative ratio of sodium to the sum of calcium and magnesium concentrations. It is calculated using the equation (Eq.\u0026nbsp;2) (Richards, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1954\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(SAR=\\frac{{Na}^{+}}{\\sqrt{\\frac{{Ca}^{2+}+{Mg}^{2+}}{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewith ion concentrations in meq/l. SAR is also known as the \"sodicity hazard\" because sodium is likely to replace calcium and magnesium in the soil, which can lead to a general degradation of the soil structure through compaction, a reduction in saturated hydraulic conductivity and aeration, and thus affecting crop production (Kumar \u0026amp; Maurya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shil et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the guidelines for restricting irrigation water use set by the TBS (TZS 2067:2017, TBS \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and by the Food and Agriculture Organization (FAO, Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). The guidelines consist of three levels of severity, which should not be taken as absolute values, as the guidelines were designed to cover a wide range of conditions (Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). In this study, we used the strictest guideline requirements for each parameter. In addition, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a brief description of the impacts on crops if the guidelines are exceeded (Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Ingram, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTanzania Bureau of Standards (TBS) and Food and Agriculture Organization (FAO) guidelines for restricting irrigation water use and the potential impact on crops if the guidelines are exceeded\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eTBS. Water for irrigation\u003c/p\u003e \u003cp\u003e(TBS, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eFAO water quality for agriculture\u003c/p\u003e \u003cp\u003e(Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePossible impacts on crops\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eDegree of restriction of use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eRestriction on use\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncreasing problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere problem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlight to Moderate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e750-3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e700-3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease of soil salinity causing physiological drought, reduction of plant growth and crop yield\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.5\u0026ndash;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.5\u0026ndash;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEffects on plant growth and irrigation equipment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e450-2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e450-2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease of soil salinity causing physiological drought, reduction of plant growth and crop yield\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease of pH, decreases nutrient availability for plants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eIncrease of pH, decreases nutrient availability for plants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLeaf burn, scorch and dead tissue along the outside edges of leaves, loss of soil structure, reduction of infiltration capacity and aeration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;78.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNot a concern for plant growth. Indicator of contamination from fertilisers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142\u0026ndash;355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e142\u0026ndash;355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eInhibition of plant growth, reduction of phosphorus availability to plants, leaf burn and drying of leaf tip\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOverstimulation of growth, delayed maturity and poor crop quality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;960.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReduction in phosphorus availability to plants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLoss of soil structure, reduction of infiltration capacity and aeration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Water quality indices\u003c/h2\u003e \u003cp\u003eWe estimated WQIs for drinking and irrigation water quality based on the parameters described. The WQI used here is an adapted version of the Weighted Arithmetic Water Quality Index developed by Brown et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). This index provides flexibility by choosing the number and type of parameters and by selecting different types of water sources (Paun et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al Yousif \u0026amp; Chabuk, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, the index calculation is simple, as it involves a single basic mathematical equation and is replicable, as each parameter weight is based on standards and guidelines of reference. However, this approach also has some drawbacks. For example, it overemphasises the values of a parameter that exceeds the standards and, depending on the choice of parameters, it may not carry enough information about the real quality situation of the water (Paun et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tyagi et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Al Yousif \u0026amp; Chabuk, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the adapted version of the Weighted Arithmetic Water Quality Index avoids giving undue importance to those parameters that exceed the standards and guidelines by adjusting the calculation of each parameter weight. Several previous studies have adopted this version (Krishna kumar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar \u0026amp; Maurya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sutradhar \u0026amp; Mondal, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and it was also suggested by Lukhabi et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in their review of water quality indices for water quality monitoring in Africa.\u003c/p\u003e \u003cp\u003eThe procedures for calculating the WQI for drinking water (DWQI) and irrigation (IWQI) are the same. The parameters and their respective standards and guidelines selected for the calculation of the DWQI and the IWQI are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Four steps are involved in the calculation of the indices.\u003c/p\u003e \u003cp\u003eFirst, a weight (\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) is assigned to each parameter (\u003cem\u003ei\u003c/em\u003e): \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e ranges from 1 to 5 and it is assigned based on the percentage of samples within the reference standards or guidelines. The higher the percentage of samples within the reference standards or guidelines for a selected parameter, the lower the relative importance of this parameter, i.e., the lower the weight assigned. Specifically, for a percentage of samples within the reference standards or guidelines between 0\u0026ndash;20, 21\u0026ndash;40, 41\u0026ndash;60, 61\u0026ndash;80 and 81\u0026ndash;100%, the weights applied are 5, 4, 3, 2 and 1, respectively.\u003c/p\u003e \u003cp\u003eSecond, a relative weight (\u003cem\u003eRw\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) for each parameter (\u003cem\u003ei\u003c/em\u003e) is calculated using the equation Eq.\u0026nbsp;3 by dividing its weight (\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) by the sum of the weights of all parameters:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Rw}_{i}=\\frac{{w}_{i}}{\\sum _{i=1}^{n}{w}_{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThird, a quality rating scale (\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) for each parameter (\u003cem\u003ei\u003c/em\u003e) is calculated using equation Eq.\u0026nbsp;4 by dividing the concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) of parameter \u003cem\u003ei\u003c/em\u003e in the water sample by the standard (for drinking water) or the guideline of use restriction (for irrigation water) of parameter \u003cem\u003ei\u003c/em\u003e (\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e):\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({q}_{i}=\\frac{{C}_{i}}{{S}_{i}}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq 4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the fourth and final step, the WQI for each sample is calculated according to equation Eq.\u0026nbsp;5 by summing the multiplication of the relative weights (\u003cem\u003eRw\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) and the quality rating scales (\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) of the \u003cem\u003en\u003c/em\u003e parameters as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(WQI= \\sum _{i=1}^{n}\\left({Rw}_{i}*{q}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEq 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe calculated DWQI and IWQI are numbers that indicate the overall quality for drinking and irrigation purposes, respectively. The indices are finally grouped into categories, 5 for the DWQI and 4 for the IWQI, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification of the DWQI (a)) and IWQI (b))\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ea)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDWQI value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCategories for DWQI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eIWQI value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCategories for IWQI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo restriction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSlight restriction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e101\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301\u0026ndash;450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate restriction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e201\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere restriction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e(Raychaudhuri et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sutradhar \u0026amp; Mondal, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Raychaudhuri et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sahu \u0026amp; Sikdar, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn addition, 5 further indices commonly used for assessing irrigation water quality were considered to classify the suitability of water for irrigation use, i.e., Kelley\u0026rsquo;s Index (KI), Soluble Sodium Percentage (SSP) also called sodium hazard (Na%), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR) (Kumar \u0026amp; Maurya, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Makoba \u0026amp; Muzuka, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sutradhar \u0026amp; Mondal, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the indicators, their classification and the irrigation suitability for each class.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription and classification of irrigation suitability indicators. The ion concentrations are expressed in meq/l. The abbreviations for the indicators are: Kelley\u0026rsquo;s Index (KI), Soluble Sodium Percentage (SSP), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIrrigation suitability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKI (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Na}^{+}}{{Mg}^{2+}+{Ca}^{2+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExcess sodium in water, soil permeability reduction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Na}^{+}+{K}^{+}}{{Mg}^{2+}+{Ca}^{2+}+{Na}^{+}+{K}^{+}}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003cp\u003e40\u0026ndash;60\u003c/p\u003e \u003cp\u003e60\u0026ndash;80\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003ePermissible\u003c/p\u003e \u003cp\u003eDoubtful\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduction of water transport capacity resulting in hard and dry soil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Na}^{+}+\\sqrt{{HCO}_{3}^{-}}}{{Mg}^{2+}+{Ca}^{2+}+{Na}^{+}}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e \u003cp\u003e25\u0026ndash;75\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReduction of soil permeability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRSBC (meq/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({HCO}_{3}^{-}-{Ca}^{2+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSafe\u003c/p\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLoss of soil structure, reduction of soil permeability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{Mg}^{2+}}{{Mg}^{2+}+{Ca}^{2+}}*100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIncrease salinity, decrease phosphorus binding capacity of the soil, less friability of the soil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data quality control\u003c/h2\u003e \u003cp\u003eThe calculated RPD for the field duplicate and the seventeen reanalysed samples was less than 30% for all the parameters except for fluoride, sulphate and chloride. The concentrations of fluoride and sulphate in the samples were less than 10 times the detection limits. In this case, it is generally accepted that the RPD could exceed the reference value. However, the case of chloride should be considered when interpreting the results.\u003c/p\u003e \u003cp\u003eAfter the quality control of the analytical results described above, we have decided to proceed with the analysis of the data; however, we took into account the potential sources of error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Drinking water quality\u003c/h2\u003e \u003cp\u003eForty-four samples from streams (n\u0026thinsp;=\u0026thinsp;19, including 5 streams in natural conditions), springs (n\u0026thinsp;=\u0026thinsp;9), irrigation canals (n\u0026thinsp;=\u0026thinsp;7), one lake and domestic water (n\u0026thinsp;=\u0026thinsp;8) were analysed to assess drinking water quality. All the selected types are used in the study area as drinking water and for other domestic uses. All the selected parameters fell within the drinking water standards (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) except for \u003cem\u003eE. coli\u003c/em\u003e and turbidity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Appendix 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eE. coli\u003c/em\u003e is a bacterium that lives in the intestines of warm-blooded animals (i.e., mammals and birds) and is commonly found in human and animal faeces. Its presence is therefore an indicator of recent faecal contamination of water, as it generally does not survive long time outside its host. Although some strains can cause serious diseases, such as meningitis, bacteraemia (presence of bacteria in the blood), urinary tract and intestinal infections (nausea, vomiting and diarrhoea), the majority of \u003cem\u003eE. coli\u003c/em\u003e strains are harmless. However, the detection of \u003cem\u003eE. coli\u003c/em\u003e in water also indicates the possible presence of other disease-causing bacterial pathogens, such as \u003cem\u003eSalmonella\u003c/em\u003e spp. and \u003cem\u003eShigella\u003c/em\u003e spp. (WHO, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A recent study reported that \u003cem\u003eShigella\u003c/em\u003e and enteroinvasive \u003cem\u003eE. coli\u003c/em\u003e were among the most common diarrhoea-associated pathogens detected in children under five years of age admitted with diarrhoea to healthcare facilities in the town of Moshi (Hugho et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Infection with these pathogens occurs through contact with animals and humans that host the bacteria and, more commonly, through consumption of contaminated food or water. \u003cem\u003eE. coli\u003c/em\u003e can reach surface waters in a variety of ways, including leakage from sewage or septic systems; improper disposal of human waste; runoff from agricultural, grazing and manure storage areas; effluent from wastewater treatment plants; and direct access to surface waters by livestock and wildlife. Previous studies carried out in the nearby Meru District Council, showed that a significant number of water samples from streams, springs and boreholes exceeded the drinking water quality standards for microbial contamination. Contamination was found to be greater during the wet season due to runoff, and at lower elevations due to increasing population and livelihood activities (Elisante \u0026amp; Muzuka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kitalika et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the TBS and the WHO standards, water used for domestic purposes should be free of \u003cem\u003eE. coli\u003c/em\u003e. One or more colonies were found in 86% of the water samples. The bacterial counts ranged from 0 to 1,600 CFU/100 ml, with the highest average found in springs (393 CFU/100 ml), followed by streams (315 CFU/100 ml) and domestic water (275 CFU/100 ml). The widespread occurrence of \u003cem\u003eE. coli\u003c/em\u003e in our surface water samples is therefore not surprising. Kitalika et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Elisante \u0026amp; Muzuka (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also reported counts of faecal coliform colonies of \u0026gt;\u0026thinsp;0 CFU/100 ml during the dry season in rivers and groundwater in Meru District, Tanzania, close to the study area. This can be explained by the fact that during the dry season, animals gather along the river as a major source of drinking water (Kitalika et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) or by the proximity to pit latrines, farms and animal sheds (Elisante \u0026amp; Muzuka, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The domestic water supply system in the study area sources water from streams and springs located inside the national park. In the upper land, this supply can be either local, without treatment, or managed by the Moshi Urban Water Supply and Sanitation Authority or by Community-based Water Supply Organizations, which treat the water source with simple chlorination at the source and before distribution. However, the occurrence of \u003cem\u003eE. coli\u003c/em\u003e in all sampled domestic waters is a cause for concern, as most of these waters are collected within the national park boundaries, where livestock and human impacts are very low. One possible explanation is the failure of the water distribution infrastructure in the area. Fingerprint analyses for source attribution are recommended to further identify sources of faecal contamination (Peed et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ragot et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tillett et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and develop WASH (water, sanitation and hygiene) strategies in the area. Community education on safe water, potential threats and correct domestic water treatments is needed to safeguard public health in the region.\u003c/p\u003e \u003cp\u003eTurbidity is an optical property of water and it describes its cloudiness due to suspended matter such as (organic) particles, chemical precipitates and organisms. Turbidity is not a direct indicator of health risk, but particles in the water can provide food and shelter for pathogens and protect them from the effects of disinfection. The particles can also provide a surface for other contaminants, such as metals, to adhere to, increasing the effort and relative cost of water treatment. Previous research indicated a strong positive correlation between \u003cem\u003eE. coli\u003c/em\u003e and turbidity (Chatanga et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hamilton \u0026amp; Luffman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Travis et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, this is not reflected in the results of our campaign. In addition, high levels of turbidity can make water unattractive for drinking for aesthetic reasons (Opiyo et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; WHO, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe measured turbidity values ranged from 0.4 to 63.2 nephelometric turbidity units (NTU). The stream turbidity results are in the same range as those found in rivers in Meru District (Jeihanipour et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kitalika et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Only 4 out of the 44 samples exceeded the standards of 25 and 5 NTU for natural and treated potable water, respectively (TBS, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These samples were collected from three irrigation canals and one domestic water (DOM_5) taken from a public tap in the village of Mnini (6.4 NTU). While the water from the irrigation canals is not intended for domestic use, and the population could be advised not to use it, for the public taps, regular testing and frequent visual checks are useful for detecting an increase in turbidity and therefore addressing possible failures in the distribution system.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Drinking water quality index (DWQI)\u003c/h2\u003e \u003cp\u003eTo describe the overall quality of the drinking water, a WQI was calculated for each drinking water sample (DWQI). A weight \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and a relative weight \u003cem\u003eRw\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e were assigned to each parameter based on the percentage of samples within the quality standards (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The calculated DWQI ranged from 3 to 57,148 (Appendix 3). According to the classification shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003e14\u003c/b\u003e% of the samples were classified as excellent quality, 5% as good, 2% as poor, 2% as very poor and 77% as unsuitable for drinking. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all the samples taken from domestic taps, streams in natural conditions and Lake Chala were found to be unsuitable for drinking, as were the majority of the streams and springs. Surprisingly, the best water quality was found in the irrigation canals. The number of \u003cem\u003eE. coli\u003c/em\u003e colonies found in the water samples strongly influenced the calculation of the DWQI. This is because the higher the number of colonies, the higher the quality rating scale for \u003cem\u003eE. coli\u003c/em\u003e (\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eEcoli\u003c/em\u003e\u003c/sub\u003e), calculated as the percentage ratio of the colonies in the sample (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e\u003cem\u003eEcoli\u003c/em\u003e\u003c/sub\u003e) with respect to the drinking water standards (\u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003eEcoli\u003c/em\u003e\u003c/sub\u003e) (Eq.\u0026nbsp;4). Note that for the calculation of the quality rating scale (\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) for \u003cem\u003eE. coli\u003c/em\u003e, the quality standard \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e in Eq.\u0026nbsp;4 was set to 1 CFU/100 ml even if it should be \u0026ldquo;absent\u0026rdquo; (TBS, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; WHO, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), as otherwise, using 0 CFU/100 ml, it would result in a division by zero. A high \u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003eEcoli\u003c/em\u003e\u003c/sub\u003e leads to a higher WQI; in fact, if \u003cem\u003eE. coli\u003c/em\u003e were not considered as one of the parameters, all the samples would be of excellent quality. A similar effect was found by Kitalika et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), where the WQI changed the trend of the water quality of the rivers after fluoride was included in the WQI calculation.\u003c/p\u003e \u003cp\u003eThis study demonstrates the use of the DWQI as a valuable tool for decision making. However, critical analysis of the results and a good knowledge of the water system are fundamental for the correct interpretation of the DWQI classifications.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandard limits, weights and relative weights of the selected parameters used to calculate the DWQI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTreated potable water*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNatural potable water\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% samples within the standard limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeight (\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRelative weight (\u003cem\u003eRw\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u0026ndash;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNTU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg CaCO\u003csub\u003e3\u003c/sub\u003e/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFU/100 ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Used only for domestic water\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Irrigation water quality\u003c/h2\u003e \u003cp\u003eForty-three samples from streams (n\u0026thinsp;=\u0026thinsp;19, including 5 streams in natural conditions), springs (n\u0026thinsp;=\u0026thinsp;9), irrigation canals (n\u0026thinsp;=\u0026thinsp;7), groundwater (n\u0026thinsp;=\u0026thinsp;4), rainfall (n\u0026thinsp;=\u0026thinsp;3) and one lake were analysed to assess the irrigation water quality. All the selected parameters were within the guidelines of restriction of use for irrigation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with the exception of pH (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Appendix 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003epH values outside the range are rarely problematic (Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Pescod, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), but they could be a warning for abnormalities. A high pH is often associated with high levels of bicarbonate and carbonate, which can lead to the precipitation of calcium and magnesium as unsoluble minerals, leaving sodium as the dominant ion in the solution. An increase in the sodicity of water can lead to a decrease in the water infiltration rate and soil gas exchange by degrading the soil structure through swelling and dispersion of clays (Bauder et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pescod, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). The precipitation of Ca and Mg minerals could also cause problems with the irrigation equipment. It is important to consider both pH and alkalinity. Alkalinity is a measure of the ability of water to neutralise acidity (APHA, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The higher the alkalinity, the greater the resistance to pH change. Therefore, when high pH and high alkalinity occur together, the pH of the water is difficult to change, so the pH of the soil will also increase, leading to mineral and nutrient deficiencies (Bauder et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fernandez, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ingram, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Acidic water can mobilize trace elements, such as heavy metals, contribute to soil acidification and damage metal pipes and tanks through corrosion (Pescod, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeven percent of the samples exceeded the pH guidelines set by the Tanzanian authorities (TBS, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and FAO (Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), which range from 6.5 to 8.4. The lowest values were found in three springs (5.7, 5.7 and 6.2) and the highest values in a stream (Kikafu) and Lake Chala (8.5 and 8.7, respectively). Only the water sample from Lake Chala had a high alkalinity (202 mg CaCO\u003csub\u003e3\u003c/sub\u003e/l); thus, the remaining samples, which had low alkalinity, should not be a cause for concern. Several water treatment techniques can be used in agriculture to correct either the irrigation water or the substrate pH if highly alkalinity water has to be used for irrigation. Proper fertiliser selection or acid injection are two common techniques, although they can be expensive and not environmentally friendly. Finding an alternative water source for irrigation may be the best solution in some cases (Ingram, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Irrigation water quality index (IWQI) and other irrigation indices\u003c/h2\u003e \u003cp\u003eA WQI was also calculated for each sample for irrigation water quality (IWQI). The weight \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and the relative weight \u003cem\u003eRw\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e were assigned to each parameter based on the percentage of samples within the water use restriction guidelines (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The calculated IWQI ranged from 2 to 27 (Appendix 3). According to the classification shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), all water samples can be used for irrigation without restrictions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The results highlight the generally good quality of water for irrigation use on the southern slopes of Mt. Kilimanjaro compared to water quality in nearby areas. In the Mwanga District, a district to the southeast of our study area, high concentrations of SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e have been found in irrigation water for paddy rice, associated with intensive use of synthetic fertilisers (Mpanda et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Kikafu River, further south of the study area and downstream of Moshi town, high levels of EC, TDS and NO\u003csub\u003e3\u003c/sub\u003e-N were found, exceeding the guidelines for irrigation use. These levels are likely to be the result of domestic waste and agrochemical run-off (Hellar-Kihampa et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGuidelines of restriction of water use for irrigation purposes, weights and relative weights of the selected parameters used to calculate the IWQI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuideline of restriction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% samples within the standard limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeight (w\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelative weight (Rw\u003csub\u003ei\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u0026ndash;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e960.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e400.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the IWQI, other indices were calculated to investigate the suitability of water for irrigation purposes. The number and percentage of water samples falling into each irrigation suitability indicator class are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The classification of each water sample is shown in Appendix 3, and the boxplots per water type are available in Appendix 4.\u003c/p\u003e \u003cp\u003eNatural waters with a Kelley\u0026rsquo;s Index (KI) greater than 1 have an excess of sodium and are therefore considered unsuitable for irrigation. The KI values of the samples ranged from 0.12 to 4.66, of which 67.4% were suitable for irrigation. All the samples classified as unsuitable were found in the central section of the study area (Karanga and Rau catchments), with the highest value found in an irrigation canal in the upper part (CAN_07). This sample was also the only sample classified as unsuitable (\u0026gt;\u0026thinsp;80%) for irrigation based on the Soluble Sodium Percentage (SSP), an indicator of the amount of sodium in the water that can cause an increase in soil salinity and therefore affect plant growth. The SSP values for the other samples in the study area ranged from 12.3\u0026ndash;75.2%. With a similar distribution as for the KI, samples classified as doubtful for the SSP (23.3%) belonged to the central section of the study area (Karanga and Rau catchments). The majority of the samples (approximately 74%) were classified as of permissible, good or excellent quality (SSP\u0026thinsp;\u0026lt;\u0026thinsp;60%) for irrigation use. In contrast, most of the samples classified as unsuitable based on the Magnesium Ratio (MR) were found in the eastern part of the study area (Himo catchment, Miwaleni and Lake Chala). An imbalance between magnesium and calcium concentrations toward a higher level of magnesium tends to deteriorate the soil structure by increasing soil alkalinity. Although the concentrations of magnesium and calcium in the samples were within the guidelines of water use restriction for irrigation (Ayers and Westcot \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), the MR showed that 21% of the samples had an excess of magnesium relative to calcium (MR\u0026thinsp;\u0026gt;\u0026thinsp;50%). The range of MR was from 12.8\u0026ndash;75.8%, with the highest value found in Lake Chala.\u003c/p\u003e \u003cp\u003eLong-term irrigation with mineral-rich water can affect soil permeability, which is influenced by sodium, calcium, magnesium and bicarbonate. The Permeability Index (PI) is an indicator used to study these characteristics. Within the water samples, the PI ranged from 64.2\u0026ndash;498%, with the lowest value occurring at Miwaleni Spring (SPR_07), the only sample classified as marginal (25\u0026ndash;75%) for irrigation use.\u003c/p\u003e \u003cp\u003eSimilar to the IWQI, all the water samples were safe for irrigation use based on the Residual Sodium Bicarbonate (RSBC): this result indicates that the amount of bicarbonate compared to calcium was not high enough to trigger the formation of sodium bicarbonate, which can cause the dissolution of organic matter in the soil, and thus degrades soil structure.\u003c/p\u003e \u003cp\u003eThese results show that the calculated IWQI alone is not sufficient for characterising the suitability of water samples for irrigation use. It should also be noted that, in addition to water quality, other factors such as, soil type, structure and composition, crop type and pattern, meteorological variables and irrigation type, are important in determining the suitability of water for irrigation purposes (Ayers \u0026amp; Westcot, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Makoba \u0026amp; Muzuka, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mulwa, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber and percentage of water samples classified into each irrigation suitability indicator class. The abbreviations for the indicators are as follows: Kelley\u0026rsquo;s Index (KI), Soluble Sodium Percentage (SSP), Permeability Index (PI), Residual Sodium Bicarbonate (RSBC) and Magnesium Ratio (MR)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrigation suitability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026ordm; samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% samples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKI (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSSP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoubtful\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePermissible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExcellent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRSBC (meq/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarginal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSuitable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThe results of this study provide a snapshot of the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro. Drinking water quality was generally good, except for the presence of faecal contamination, which was found in most of the water samples. This is reflected in the DWQI classification of the water samples, 77% of which were classified as unsuitable for drinking, 4% as poor or very poor and 19% as good or excellent. All the samples were classified as suitable for irrigation use after the calculation of the IWQI. However, other irrigation water quality indices revealed potential problems with irrigation water that the IWQI could not identify. In particular, a possible excess of sodium could be problematic for crops in the central part of the study area, while a possible excess of magnesium could be problematic in the eastern part.\u003c/p\u003e \u003cp\u003eHowever, further research is needed to understand the sources of faecal contamination and to ensure safe drinking water for the community. Likewise, domestic water should be regularly monitored and treated for pathogenic bacteria before distribution. Public awareness campaigns on the safety, threats and possible treatment of contamination of water resources are also needed. Furthermore, studying the relationship between soil structure, crop yield and irrigation water quality will also be useful for understanding the best crop selection and appropriate remediation methods. Finally, we acknowledge the limitations of this study, which only includes spot measurements during the dry season. Conducting regular water sampling campaigns throughout the year would be useful for gaining additional insight into the temporal variation of water quality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Tanzania Commission for Science and Technology (COSTECH) and the Tanzania Wildlife Research Institute (TAWIRI) for granting us the research permits; all the people who kindly allowed us to collect water samples from their properties; Flora Auma Wambyakale, Lightness Deus and all the technicians at the Ngurdoto Research Campus Water Laboratory for their patience in waiting for our samples every day for two weeks and for their meticulous analysis; Ebeni Maro and Mgeta\u0026nbsp;Kaswamila for their unconditional support in the field; and all the staff at Nkweseko Research Station for their daily support. This research was funded by the German Research Foundation (DFG) in the framework of the DFG Research Unit \u0026ldquo;The role of nature for human well-being in the Kilimanjaro Social-Ecological System (Kili-SES)\u0026rdquo; (FOR 5064), subproject \u0026ldquo;SP 1: Biodiversity and the supply of regulating NCP\u0026rdquo;, grant number BR2238/35-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study\u0026apos;s conception and design. Data collection was performed by FC and FS. FC analysed the data and wrote the first manuscript draft. All authors contributed to revising and finalizing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThere are no competing interests in the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAPHA. (2017). Standard Methods for Examination of Water and Wastewater. \u003cem\u003eAmerican Public Health Association\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(23), 1545.\u003c/li\u003e\n \u003cli\u003eAppelhans, T., Mwangomo, E., Otte, I., Detsch, F., Nauss, T., \u0026amp; Hemp, A. (2016). 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Assessment Water Quality Indices of Surface Water for Drinking and Irrigation Applications \u0026ndash; A Comparison Review. \u003cem\u003eJournal of Ecological Engineering\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(5), 40\u0026ndash;55. https://doi.org/10.12911/22998993/161194\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"water quality, irrigation, drinking, water quality index, Kilimanjaro","lastPublishedDoi":"10.21203/rs.3.rs-4628568/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4628568/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed the quality of water for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering 8 different water types were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international requirements for drinking and irrigation water quality. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for \u003cem\u003eE. coli\u003c/em\u003e and turbidity were within the reference standards. A generalized problem of faecal contamination was found in the study area, including in domestic water, which highlights the need to identify sources of contamination and remediate before distribution. The drinking water quality index (DWQI) classified 77% of the samples as unsuitable, 4% as poor or very poor and 19% as good or excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. All samples were classified as safe for irrigation according to the irrigation water quality index (IWQI). However, five other irrigation indices (Kelley\u0026rsquo;s Index, Soluble Sodium Percentage, Permeability Index, Residual Sodium Bicarbonate and Magnesium Ratio) showed potential problems with excess of sodium and magnesium. A combination of indices is recommended for assessing water quality for irrigation use.\u003c/p\u003e","manuscriptTitle":"Domestic and Irrigation Water Quality on the Southern Slopes of Mount Kilimanjaro","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-26 12:56:20","doi":"10.21203/rs.3.rs-4628568/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca25b491-7aa5-4fd8-84d2-3170527356a9","owner":[],"postedDate":"June 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33720351,"name":"Hydrology"},{"id":33720352,"name":"Environmental Chemistry"}],"tags":[],"updatedAt":"2024-06-26T12:56:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-26 12:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4628568","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4628568","identity":"rs-4628568","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}