Introduction

Waterborne diseases are communicable diseases caused by pathogenic microbes that are mostly transmitted by polluted fresh water, either in bathing, cleaning, drinking, or in the cooking of food. Although, microbes or pollutants, either directly or indirectly, are responsible for spread of these diseases, water is the crucial medium for transmission of these diseases, and henceforth, they are labelled as waterborne diseases. The factors influencing waterborne disease are innumerable. Occurrence of waterborne disease is attributed to inadequate access to water, irregularity in water availability, consumption of contaminated water, inadequate sanitation and hygiene and these are currently affecting more than 30% of world population (Schwarzenbach et al. 2006; Devipriya and Kalaivani 2012) The other contributors of waterborne disease are illiteracy and ignorance, socio-economic status, personal and family hygiene activities, under nutrition and malnutrition, social determinants, land use land cover, population and locational factors and increasing built-up surfaces (Mukherjee 1990; Wijk-Sijbesma 1998; Zodpey et al. 1998; Trivedi et al. 2001; Nanan et al. 2003; Thapar and Sanderson 2004; Marale et al. 2012; Tetteh-Quarcoo et al. 2013; Wakode et al. 2016; Bidhuri and Jain 2019). According to a WHO estimate, approximately 2 million avoidable deaths and 123 million avoidable disability-adjusted life years annually are caused by insufficient water, sanitation and hygiene (WHO 2019).

Urban environment especially in underdeveloped countries is providing conditions that give rise to waterborne diseases by affecting the behaviour and characteristics of groundwater (Alirol et al. 2011). Humans have added many impurities to both surface and groundwater like industrial and commercial solvents, metals and acids, salts, sediments, pesticides and faecal matter. With over utilization of water, groundwater table is decreasing and contamination rate is increasing, posing a major threat to public health. Data has revealed that the between the year 2007 and 2017, the level of groundwater in India has dropped by 61% (Verma 2019). Contaminated water can spread diseases like diarrhoea, dysentery, cholera, typhoid and polio. Consumption of polluted drinking water is estimated to cause over five lakh diarrhoeal deaths every year (WHO 2018).

Use of GISfor public health is a substantial method to identify spatial patterns and associations. GIShas been emerged as a successful tool to help spatial decision-making in public health through application and evolution of analytical techniques to solve the healthcare planning problems (Khashoggi and Murad 2020). Regarding distribution of waterborne diseases it is rare for a single factor to work individually and consideration of only few factors is not enough to understand the emergence and prevalence of diseases (Saravanan et al. 2016) Therefore, in the present study multi criteria decision making has been used for consideration of determining factors and making decision towards vulnerability of areas to waterborne diseases. Multi criteria decision analysis is an effective method to solve such problems and it raises decision maker’s confidence by uniting the information (Saaty 2000). Multi-criteria decision making method is adopted due to its ability of defining and evaluating objective and subjective measures. These techniques could make the decision making more rational, unambiguous and efficient. Multi-criteria decision making, in spatial analysis, combine the layers of spatial data signifying different criteria and also specify ways by which layers were merged. The most popular method of multi-criteria decision making is Analytical Hierarchy Process, which determines the weights of criteria chosen. The criteria could be both quantitative and qualitative (Kahraman 2008).

Several researches have been conducted to find out the determining factors of waterborne diseases (McKee et al. 2000; Musa et al. 2013; Siddiqui et al. 2012; Olowe et al. 2016). Rodrigues et al. (2015) examined the impact of socio-economic factors on the spread of drinking water diseases. The results showed that increasing investment in water provision in the area, improved sanitation facilities and availability of affordable water purification services can improve the health of the population. Cesa et al. (2016) examined the relationship between waterborne diseases and socio-environmental factors in southern Brazil. The most important factors found associated with waterborne illnesses were source and quality of drinking water, inadequate disposal of sewage, recurrent flooding and inadequate cleaning of water reservoirs. Study has emphasized the role of sanitation facilities in maintaining public and environmental health. Mohsin et al. (2013) analysed the impact of drinking water quality on residents’ health and found that concentration of electrical conductivity, total dissolved solids, and total hardness were above the permissible limit in the water lead to waterborne illnesses like diarrhoea, typhoid, cholera etc.

According to a United Nations report, in India, annually over one lakhs deaths occur due to water related diseases. It is believed that nearly 70% of drinking water supply in India is contaminated with sewage discharges. According to UN report, out of 122 countries, India ranks 120 in respect of quality of water available to its residents which represents its poor quality of water (Chabba 2013). It is maintained that the high occurrence of waterborne diseases such as cholera and diarrhoea are the result of poor quality of drinking water in India as about two-thirds of the households in the country do not treat their water before consumption. According to an NSO report, the condition is particularly distressing in Bihar, Uttar Pradesh and West Bengal states, where the large mainstream, both in urban and rural areas, consume untreated water (Edwin 2019).

Azamgarh a medium sized city in Uttar Pradesh has faced several outbreaks of cholera, jaundice and diarrhoea in the past several years. Local government is taking various measures to solve the problem through extension of water supply lines, chlorination of drinking water, proper cleaning of water tanks, building of toilets under clean India Mission, however not much difference has been seen on the ground level. Therefore the best approach could be the identification of the zones that are most vulnerable to water borne disease by analysing all the possible contributory factors in the city. This zonation will not only help the health officials to target suffering population but also planners to implement planning schemes and policies at local levels.

Keeping all these aspects in the mind, in the present study an attempt has been made to develop a model to identify vulnerable areas to waterborne diseases. Its first step involves identification of influential factors to waterborne diseases while second step involves mapping of vulnerable zones to waterborne diseases using multi-criteria decision making Fuzzy AHP model. Although, modelling of waterborne diseases prone areas and influential factors have been attempted in the previous studies (Olajuyigbe 2012; Rodrigues et al. 2015; Olowe et al. 2016; Bidhuri and Jain 2019), however none of the studies have used Fuzzy AHP for the same. Use of GIS for spatial analysis was carried out by Olajuyigbe et al. (2012) and Bidhuri and Jain (2019) and the later have also used analytic hierarchy process in the model. Although AHP is one of the most extensively used method of multi-criteria decision making, but in AHP there could be inconsistency at the stage of pair wise comparison matrix (Cheng 1997; Kahraman et al. 2003). The AHP method was modified by Van Laarhoven and Pedrycz (1983) to develop fuzzy AHP. They used fuzzy set theory which involves a different systematic method to give weights to the criteria (Tan et al. 2014). Studies have maintained that fuzzy numbers are more accurate to weight different criteria (Chang 1996; Kahraman et al. 2003; Aryafar et al. 2013; Şener et al. 2018; Das and Pal 2019; Golabi and Radmanesh 2019). In our model, application of Fuzzy AHP as multi criteria decision making tool along with GIS is analysed for zonation of disease vulnerable areas. The mapping of vulnerable zones will help administrative authorities in planning preventing measures and executing control programs and providing health services on the basis of spatial information of disease prone areas. The model applied in the study may also be adopted in other studies to assess vulnerable areas to different diseases.

Material and methods

Study area

Azamgarh city is a medium sized city situated in the fertile land of middle Ganga plain in north India, in the eastern part of the Indian state of Uttar Pradesh (Fig. 1). Azamgarh city enjoys a humid subtropical climate (Köppens’ climate classification, Cwa) with big difference in the temperatures of summer and winter season. Summers last from early April to October with prevailing monsoon seasons and are also tremendously hot. Temperature in summers varies between 22 and 46 °C. Winters are cold with very large diurnal ranges. Temperature in December and January drops down to below 5 °C. Fog is another common feature of the winters. Azamgarh city is the administrative headquarters of Azamgarh district. It is situated on the road leading from Allahabad through Jaunpur to Gorakhpur. Total population of the City is 110,983 (Census of India 2011) and as per the Projection, the population of the City is expected to reach 160,000 by 2031 (Master Plan 2011). The city covers an area of 12.71 km2. About half is used for housing and rest for commercial, industrial, transport, administrative, recreational parks and green spaces. The city is subdivided into 25 wards and has 16,294 households (District Census Handbook 2011). Average population density of Azamgarh is 8731 persons/km2 and household density is 1281 houses/km2. Out of the total population of 110,983, in the city, 57,878 are males while 53,105 are females (Census of India 2011). Azamgarh city has a service economy base and its main workforce is engaged in various administrative, educational as well as health services. City has a workforce of 21.93% out of which 2.80% are engaged in primary activities, 6.39% in secondary activities while 90.81% workforce is involved in tertiary sector of the economy. Proportion of workforce engaged in secondary sector is low due to absence of any industries in the city. Azamgarh city, being headquarters of Azamgarh division has administration as the main occupation, however apart from people engaged in white collar jobs, a considerable share of population is also engaged in lowly jobs.

Fig. 1
figure 1

Location map of study area

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Study design

Multi-criteria decision making using fuzzy analytical hierarchy process (FAHP) has been employed in the study to identify vulnerable zones to waterborne diseases. The study is divided into two stages. First stage involves analysis of three decision factors to waterborne diseases involving socio-economic factors, quality of drinking water and environmental factors. These decision factors include eight decision criteria for example socio-economic factor involves population density, household density, literacy rate, proper supply of water in the houses, sanitation facilities in the houses. The decision factor quality of drinking water involves one criteria i.e. Water quality index (WQI) incorporating ten physico-chemical parameters i.e. pH, TDS, TSS, EC, alkalinity, total hardness, Calcium, Magnesium, Chloride and Chlorine. The third decision factor involves two decision criteria i.e. land use land cover and built-up index. In the second stage, attempt is made is identify vulnerable zones to waterborne diseases where multi-criteria decision making-FAHP using extent analysis method of Chang (1996) is adopted assign weights to all the decision criteria. Weighted overlay analysis is performed to obtain final vulnerability map to waterborne diseases. Validation of the result has also been performed using ground data of prevalence of waterborne diseases. The overall methodology adopted in the present study has been shown in the flowchart (Fig. 2).

Fig. 2
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Flowchart

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Criteria selection- data collection and processing

Socio-economic factors

The total five criteria selected under socio-economic factors are population density, household density, literacy rate, availability of drinking water and availability of sanitation facilities in the houses. Out of them, the data for ward-wise population density, household density and literacy rate has been derived by Primary Census Abstract of the Census of India 2011 (Table 1). Field survey was carried out to collect data regarding water supply conditions in the houses as well as provision of sanitation facilities in various households. Choropleth maps of population density, household density, literacy rate, irregular water supply and improper sanitation in the houses have been generated with the help of Arc GIS 10.3.

Table 1 Data sources
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Quality of drinking water-WQI calculation

To evaluate the quality of drinking water, water samples were collected from nine houses from the different parts of the city and sent to environmental engineering lab for analysis. Ten parameters of water, including pH, conductivity, TDS, TSS, total hardness, alkalinity, calcium and magnesium hardness, chloride and chlorine were tested for all the samples and water quality index was calculated. The point data related to water quality were converted to spatial layer using IDW interpolation tool in Arc GIS.

Determination of Water Quality Index (WQI) of the collected water samples involved following steps.

First of all, each parameter was given weights (wi), on the basis of its relative significance in overall quality of water (1–5) (Table 2).

Table 2 Relative weights of different parameters (2019)
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Afterwards, relative weight (Wi) of the parameters was calculated using the subsequent equation

$$ W_{i} \, = \, \frac{{w_{i} }}{{\mathop \sum \nolimits_{i = 1}^{n} w_{i} }} $$
(1)

where Wi is the relative weight, wi is the weight of each parameter, and n is the number of parameters.

A quality rating scale for each parameter except pH is assigned by dividing its concentration in each water sample by its respective standard according to guidelines (BIS), and the result is multiplied by 100.

$$ q_{i} \, = \, \frac{{C_{i} }}{{S_{i} }}\, \times \,100 $$
(2)

where qi is the quality rating, Ci is concentration of each parameter in each sample expressed in mg/L, Si is Index drinking water standard for the each parameter in mg/L, For pH, quality rating scale is calculated as

$$ Q_{{{\text{ph}}}} \, = \,\left[ {\frac{{C_{i} - V_{i} }}{{S_{i} - V_{i} }}} \right]\, \times \,100 $$
(3)

where Ci and Si are the same as in Eq. (2), Vi is the ideal value for pH i.e. 7.0.

For computing WQI, the sub-index (SI) is first determined for each is determined for each parameter, as given below;

$$ {\text{SI}}_{i} \, = \,W_{i} \,\, \times \,\,q_{i} $$
(4)
$$ {\text{Water Quality}}\;{\text{Index }}\,({\text{WQI)}}\, = \, \mathop \sum \limits_{i = 1}^{n} Si $$
(5)

where SIi is the sub index of ith parameter; Wi is relative weight of ith parameter; Qi is the rating based on concentration of ith parameter, and n is the number of chemical parameters.

The WQI values are grouped into five categories, ranging from excellent water quality to water unsuitable for drinking (Table 3).

Table 3 Water quality index range
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Environmental factors

Land use land cover map generation

Geometrically and radiometrically corrected sentinel 2A (10 m) satellite image was used for LULC generation (Table 1). For preparation of land use land cover map, supervised maximum likelihood classification technique has been employed. 200 spectral signatures of matching pixels, for each class were selected for maximum likelihood method (Ganaie et al. 2020). Different land use land cover classes were generated by grouping spectrally alike signatures. The resultant land use land cover classes were built-up, agricultural lands, vegetation, water bodies and open green spaces.

Built-up Index calculation

Landsat 8 OLI (30 m) was employed to calculate built-up index (Table 1). Built-up areas in the city are demarcated with the help of various indices, most common among them is normalised difference built up index (NDBI). However use of NDBI for demarcation of built-up area has been criticized on the ground that it is unable to differentiate between built areas and barren lands (Zha et al. 2003). Therefore, another index developed by He et al. (2010) has been used in the present study. Built-up Index (BU) has been calculated by using the formula.

$$ {\text{BU}}\, = \,{\text{NDBI}}\, - \,{\text{NDVI}} $$
(6)
$$ {\text{where NDVI}}\, = \,\left( {{\text{NIR}}\, - \,R} \right)/\left( {{\text{NIR}}\, + \,R} \right) $$
(7)
$$ {\text{NDBI}}\, = \,\left( {{\text{SWIR}}\, - \,{\text{NIR}}} \right)/\left( {{\text{SWIR}}\, + \,{\text{NIR}}} \right) $$
(8)

The higher the value of a pixel in built-up index, the higher is the possibility of the pixel being a built-up area (He et al. 2010).

Lastly, all the criteria layers were processed using ArcGIS 10.3 to have similar projection (UTM) and cell size (30 m) for further investigations.

Multi-criteria decision making: FAHP

Analytic hierarchy process has traditionally been considered as a tool to make measurement and quantitative judgment in newer fields. However, several decision making and problem solving jobs are so complex that it becomes difficult to understand them quantitatively. Therefore people are using knowledge that is rather imprecise than precise. Here comes the FAHP. Fuzzy set theory takes after human decisions and makes use of estimated information and vagueness to generate decisions. The FAHP converts decisions from crisp to fuzzy, therefore consenting the decision maker to reject vague criteria parameters (Mikhailov and Tsvetinov 2004). Based on how one criterion is important to the other criterion, triangular fuzzy numbers were allocated to the criteria under consideration for vulnerability analysis. Table 4 shows the triangular fuzzy numbers representing the importance of criteria over each other.

Table 4 Triangular fuzzy numbers and their importance
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To calculate the criterion weights, the triangular fuzzy numbers were given in a pairwise comparison matrix in the FAHP (Table 9).

Values of each criterion through extent analysis can be presented as follows:

$$ gi = X_{gi}^{1} , X_{gi}^{2} , X_{gi}^{3} ,X_{gi}^{4} , \ldots ..X_{gi}^{n} $$

where gi (i = 1, 2, 3, 4, …, n) is the goal set for each criterion. All values of \(X_{gi}^{j}\) are triangular fuzzy number (j = 1, 2, 3, 4, … m). Extent analysis method by Chang (1996) was used to measure synthetic extent of the values of the pairwise comparisons. The step-wise description of FAHP as given by Chang (1996) is given below:

Fuzzy synthetic extent value (S1) is expressed as:

$$ S_{i} = \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j} \, \otimes \,\left[ {\mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{g = 1}^{m} X_{gi}^{j} } \right]^{ - 1} $$
(9)

It involves calculation of

$$ \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j} $$
(10)

Through Fuzzy addition operation, X extent value of particular matrix is obtained as expressed in the following equation. The new set is obtained for further use i.e. (a, b, u).

$$ \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j} = \left( {\mathop \sum \limits_{j = 1}^{m} a_{j} \mathop \sum \limits_{j = 1}^{m} b_{j} \mathop \sum \limits_{j = 1}^{m} c_{j} } \right) $$
(11)

where a is lower limit value, b is middle limit value and c represents upper limit value. From the set of a, b and c, we obtain the following equation:

$$ \left[ {\mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j} } \right]^{ - 1} $$
(12)

Further operation on fuzzy value for \(X_{gi}^{j} = 1,2,3,4, \ldots .\;m\) is performed

$$ \left[ {\mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j} } \right]^{ - 1} \, = \,\left( {\mathop \sum \limits_{j = 1}^{m} a_{j} \mathop \sum \limits_{j = 1}^{m} b_{j} \mathop \sum \limits_{j = 1}^{m} c_{j} } \right) $$
(13)

The inverse vector of convex fuzzy number is calculated as:

$$ \mathop \sum \limits_{i = 1}^{n} \mathop \sum \limits_{j = 1}^{m} X_{gi}^{j - 1} = \left[ {{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {\mathop \sum \nolimits_{i = 1}^{n} b}}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${\mathop \sum \nolimits_{i = 1}^{n} b}$}},{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {\mathop \sum \nolimits_{i = 1}^{n} c_{j} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${\mathop \sum \nolimits_{i = 1}^{n} c_{j} }$}},{\raise0.7ex\hbox{$1$} \!\mathord{\left/ {\vphantom {1 {\mathop \sum \nolimits_{i = 1}^{n} a_{j} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${\mathop \sum \nolimits_{i = 1}^{n} a_{j} }$}}} \right] $$
(14)

The degree of possibility for \(X_{1} \ge X_{2}\) can be calculated as:

$$ V\left( {X_{1} \ge X_{2} } \right)\, = \,\begin{array}{*{20}c} {{\text{sup}}} \\ {x \ge y} \\ \end{array} \left[ {{\text{min}}\left( {\mu_{X1} \left( x \right),\mu_{X2} \left( y \right)} \right)} \right] $$
(15)

where x and y are the membership function value of each criterion. Since X1 and X2 are convex fuzzy numbers we have

$$ V\left( {X_{1} \ge X_{2} } \right) = 1 $$
$$ V\left( {X_{1} \ge X_{2} } \right) = hgt\left( {X_{1} \cap X_{2} } \right) = \mu b_{1} \left( d \right) $$
(16)

where d is the highest intersection point between \(\left( {\mu_{X1} } \right)\) and \(\left( {\mu_{X2} } \right)\).

When \(X_{1} = \left( {a_{1} , b_{1} ,c_{1} } \right)\) and \(X_{2} = \left( {a_{2} , b_{2} ,c_{2} } \right)\), the ordination of d is expressed as below:

$$ V\left( {X_{2} \ge X_{1} } \right) = hgt \left( {X_{1} \Uparrow X_{2} } \right) = \frac{{a_{1} - b_{1} }}{{\left( {b_{2} - c_{2} } \right) - \left( {b_{1} - a_{1} } \right)}} $$
(17)

The convex fuzzy number and its degree of possibility to be greater than k convex fuzzy number \(X_{1} \, = \,1,2,3,4,\; \ldots ,\;k\) can be expressed by:

$$ V\left( {X \ge X_{1} ,X_{2} , X_{3} ,\; \ldots \;X_{k} } \right)\; = \;V\left[ {\left( {X \ge X_{1} } \right) \;{\text{and}}\; \left( {X \ge X_{2} } \right) \ldots \;{\text{and}}\; \left( {X \ge X_{k} } \right)} \right] \, = \,\min V\left( {X \ge X_{i} } \right),\,i = 1,2,3, \ldots ,\;k $$
(18)

Equation (18) is supported by.

\(\mathop d\limits^{`} \left( {A_{i} } \right) = \min V (S_{i} \ge S_{k} ),\) (where k = 1,2,3, …. n; \(k \ne i\)).

The weight vectors are expressed as

$$ W_{i} = \left[ {\mathop d\limits^{`} \left( {A_{1} } \right),\mathop d\limits^{`} \left( {A_{2} } \right),\mathop d\limits^{`} \left( {A_{3} } \right), \ldots \ldots \mathop d\limits^{`} \left( {A_{n} } \right)} \right]^{T} $$
(19)

where \(A_{i} \, = \,1,2,3,\, \ldots ,\;n\)

The following equation expresses the process of normalisation of weight factors:

$$ W = \left[ {d\left( {A_{1} } \right),d\left( {A_{2} } \right),d\left( {A_{3} } \right),\; \ldots ,\;d\left( {A_{n} } \right)} \right]^{T} $$
(20)

Weighted overlay analysis

Vulnerability analysis in GIS environment includes analysis of numerous criteria layers. Weighted overlay analysis was executed after standardisation and calculation of the weight of each criterion through FAHP. For weighted overlay analysis each criteria layer was converted into raster format. Further, the different weighted layers were reclassified into a similar vulnerability scale ranging from 1 to 5. These reclassified layers were then overlaid where the rank of each sub-criteria layer and weight assigned to each criteria layer were calculated and a summation of the products was used to obtain the final vulnerability map.

$$ S = \mathop \sum \limits_{i = 1}^{n} W_{i} X_{i} $$
(21)

where S is the vulnerability index for each map pixel, wi is weights of the factor I, xi is the criterion score of the factor I, n is the number of vulnerability layers (Drobne and Lisec 2009).

Results

Factors influencing waterborne diseases

Socio-economic factors

The decision criteria included under socio-economic factors are population density, household density, literacy rate, irregular supply of water and improper sanitation. Population density is one of the major determinants of waterborne diseases. It represents pressure on existing services like water and sanitation resulting in decreasing per capita availability of these services. Kelly et al. (1996) have taken population density into consideration while examining prevalence and aetiology of persistent diarrhoea in urban Zambia. People living in densely populated areas are more likely to get sick from existing diseases as there are more people exposed to the problem. In densely populated areas, there is more chance for people to come into contact with faecal microbes through contact with their environment or with other people. Enteric infections are more prevalent in areas with high population density as compared to low population density areas and inadequate sanitation poses a bigger risk for enteric pathogen infection in densely populated areas (Cairncross and Feachem 1993). It has been found that water-associated infectious diseases are significantly interrelated with social and environmental factors and different areas are differently affected by various diseases (Yang et al. 2012). Population density in Azamgarh city ranges from 2992 to 33,426 persons/km2 (Fig. 3).

Fig. 3
figure 3

Population density

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Another important factor behind distribution of waterborne diseases is household density which is defined as number of households per unit area. Higher household density has been found associated with increased risk of infection with enteric pathogens like salmonella, Escherichia coli etc. (Bates et al. 2007). Another study revealed that despite better access to health care facilities in the region, chronic protozoan infection was more common in the more densely populated households, than that with more dispersed households (Halpenny et al. 2012). Studies have revealed that with rapid urbanisation and increasing household densities, distance between houses and community latrines has decreased which has resulted in closer proximity of latrines with water sources leading to contamination of tube-well water (Adane et al. 2017). Household density in Azamgarh city ranges from 467 to 4691 houses/km2 (Fig. 4).

Fig. 4
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Household density

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Another important factor of socio-economic status that could affect the distribution of diseases is level of literacy among the population. Literacy rate is associated with people’s awareness about disease transmission. Illiterate population may have less knowledge on transmission of waterborne diseases and is more prone to disease pathogens (Malik et al. 2012). Low level of mother’s education is persistently recognized as a major factor prompting high diarrhoea incidences in the houses (Agha 2000; Balk et al. 2003; Halvorson et al. 2011; Safari et al. 2016). Average literacy rate of Azamgarh city is 75.70%. Literacy rate varies from 65.97% in some wards of the city to 87.93% in other parts of the city (Fig. 5).

Fig. 5
figure 5

Literacy rate

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Safe potable water supply is vital to human development and health. It has been estimated that with improvement in water supply system and sanitation facilities, the global burden of diseases could be reduced to almost one-tenth (Björklund et al. 2009). Regular supply of water is vital to achieve proper sanitation and hygiene. Provision of improved water supply and sanitation can also reduce diarrhoeal disease by almost 90% (Water UN 2012). WHO has estimated that diarrhoeal morbidity was reduced by 21% with improved water supply (WHO 2004). Water supply data was collected during field survey in Azamgarh city. Groundwater was found as the primary source of water in the city. Public and private were the two sources of water supply. Sources of water supply in the city include private pumps, municipal piped connections, municipal taps and public hand pumps. Apart from households having private pumps inside their houses, rest did not get a regular water supply for 24 h. It has been found that of the total sampled households about 42.8% household were not getting regular supply of water and they were getting water for 1–6, 6–12 and 12–18 h only (Fig. 6).

Fig. 6
figure 6

Irregular water supply

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Studies have suggested that while fighting waterborne diseases, sanitation facilities are as important and urgent as availability of safe drinking water and hygiene. WHO has stated that a considerable proportion of diarrhoeal diseases can be stopped by providing safe drinking-water and adequate sanitation and hygiene (WHO 2017). A recent study has also found hygiene and sanitation conditions within houses as significant risk factors for diarrhoea (Oloruntoba et al. 2014). Data for sanitation conditions inside houses was collected during field survey in the city. Availability of proper toilet facility in the houses has been considered as proper sanitation facility in the houses. Inadequate access to toilet can lead to open defecation which can lead to soil and water contamination in the region. During field survey it was found that about 19.97% of the sampled households did not have toilet facility in the houses and they were practising open defecation, using community toilets or neighbour’s toilets (Fig. 7).

Fig. 7
figure 7

Improper sanitation

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Quality of the drinking water-water quality index (WQI)

With increasing population, urbanization and industrialization, the quality of water has deteriorated (Trivedi et al. 2001; Tyagi et al., 2013). Risk of groundwater contamination and water associated problems increases as utilization of groundwater exceeds the rate of groundwater recharge. Water quality of a particular area is measured using physical, chemical and biological parameters. The values of these parameters are detrimental to human health if they crossed the standard demarcated limits (EPA 2005; BIS 2012; WHO 2012; CPCB 2013). To examine the quality of drinking water in the city, a total of nine samples of the drinking water from in-house submersible pumps, shallow hand pumps and municipal taps were collected and sent to Environmental Engineering Lab for analysis. Ten parameters of water, including pH, conductivity, TDS, TSS, total hardness, alkalinity, calcium and magnesium hardness, chloride and chlorine were tested for all the samples. Concentration of parameters in different samples of the water along with standard value of parameters according to Bureau of Indian Standards (BIS) and WHO are presented in Table 5 and Fig. 8. In the present analysis, desirable limits provided by BIS are taken as standard values for water parameters. However, for the parameters for which BIS did not provide any standard like EC and TSS etc., WHO and other standards are used.

Table 5 Concentration of parameters in different water samples of Azamgarh City (2019)
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Fig. 8
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Concentration of different parameters

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To obtain an inclusive representation of the groundwater quality in the city, the water quality index (WQI) is calculated. The calculated WQI values and categories of water of the different samples are shown in Table 6. Inverse distance weighing (IDW) method in Arc GIS is used to create an interpolation map of WQI for the City (Fig. 9). The WQI of the water samples, range from 95 to 147 with an average value of 117, which indicates an average to poor quality of water. Out of the total nine samples, five fall under the category of poor water while remaining four secure their places in good category of water. None of the samples could reach the excellent category of water. From the Table 6 it is clear that water samples that fell under the head of good water were collected from submersible pumps while water samples collected from municipal taps and public hand pumps could not make into the good category of water. The most obvious reason of this phenomenon could be the depth of the water table from which water was fetched. While submersible pumps draw water from the deeper strata, water in hand pumps come from the upper strata of the groundwater. Chances of contamination are high in upper aquifers due to leaching of pollutants from the surface than the aquifers much deeper inside the ground. Although, water into the municipal taps also comes from very deep ground level, distribution of water involves pipes lying just below the ground, i.e. 4 to 5 feet or sometimes above it. These pipes are susceptible to breakage due to heavy vehicles stepping on it or other reasons. Therefore the probability of mixing of dirt, sand and sewage into the piped drinking water cannot be ignored. Therefore it can be safely said that water in upper aquifers that is fetched by hand pumps is contaminated and needs proper treatment before consumption. Another study made by Abdullah et al. (2012) has also concluded that water of Azamgarh city, particularly the upper strata ground water which is fetched from regular hand pumps and wells, is not appropriate for drinking. Majority of the parameters of tested water are above the acceptable limits recommended by WHO and ICMR (Abdullah et al. 2012). It has been observed during the field survey that a considerable proportion of population was fetching water from hand pumps and treatment of water before consumption was also rare. A study published in Amar Ujala (daily newspaper) in 2019 has also claimed that people are getting sick due to consumption of contaminated water (Fareed 2019).

Table 6 Water quality index for different samples in Azamgarh City (2019)
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Fig. 9
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Water quality index

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Environmental factors

Land use land cover has been considered indirectly responsible for waterborne diseases by their direct impact on water quality. Studies have found that built-up area has a positive correlation with water pollution while forest lands and grasslands have a negative correlation with water pollution (Huang et al. 2013; Jamal and Ahmad 2020). Asadi et al. (2007) have also tried to correlate land use land cover with water quality and found that water quality was poor in dense residential areas. With increasing urbanisation and change in the land use, chemical parameters of the ground water like pH, turbidity, TDS, DO, BOD, presence of E. coli, fecal coliforms are deeply affected resulting in waterborne diseases like cholera and schistomiasis (Marale et al. 2012; Tetteh-Quarcoo et al. 2013; Saravanan et al. 2016). Agricultural lands are also emerging as threat for ground water quality due to irrational use of fertilizers (Mishra et al. 2014) Land use land cover in Azamgarh city is divided into five classes, i.e. built-up area, vegetation, agricultural lands, open green spaces and water bodies (Fig. 10). Areas covered under built-up areas as well as agricultural lands are considered as more prone for waterborne diseases.

Fig. 10
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Land use land cover

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Considering the impact of built-up area on ground water quality, built-up index was used to identify built-up areas in the city. Built-up index is obtained by subtracting NDVI to NDBI. The greater the value of a pixel in built-up index, the higher is the possibility of the pixel being a built-up area (He et al. 2010). Studies have proved that in highly urbanised areas with high population density and intensive land use, ground water quality is especially vulnerable (Trivedi et al. 2001; Wakode et al. 2016). Built-up index has been divided into five categories ranging from −0.59 to 0.02 (Fig. 11). With higher built-up index, higher will be the vulnerability of that area to waterborne diseases.

Fig. 11
figure 11

Bulit-up index

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Zonation of vulnerable areas to waterborne diseases

Multi-criteria decision making using FAHP is used to identify vulnerable zones to waterborne diseases. A total of eight decision criteria were chosen under three decision factors. These decision factors were socio-economic factors, quality of drinking water-water quality index (WQI) and environmental factors. Thus study involved three decision factors which included eight decision criteria and eight decision criteria incorporated forty sub-criteria in a hierarchical manner (Table 7). The eight decision criteria were population density, household density, literacy rate, irregular water supply, improper sanitation, water quality index, land use land cover and built-up. Following the procedure of multi-criteria decision making, all the forty sub-criteria were categorized according to their risk value towards waterborne diseases. The risk intensity varied from 1 to 5, here 1 means very low risk values towards waterborne diseases to 5 means very high risk value towards waterborne diseases (Table 7). Afterwards, in order to identify vulnerable zones of waterborne diseases, all the decision criteria maps were converted into raster format and all the raster layers were reclassified using spatial analyst tool in Arc GIS. Weights of the decision criteria were obtained using FAHP. In the present study fuzzy ratios are applied in spite of exact ratios, which are a kind of reciprocal matrix (Table 8). The decision numbers for pair-wise matrixes were chosen on the basis of literature survey and field knowledge. Extent analysis method by Chang (1996) was used to measure synthetic extent of the values of the pairwise comparisons. After obtaining fuzzy weights of all the criteria, weights were calculated which were later converted into normalised weights (Wc). The obtained normalised weights of all the criteria are presented in the Table 8.

Table 7 Intensity of importance and relative risk values of the all the decision criteria
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Table 8 Fuzzified pairwise comparison matrix and weights of all the decision criteria
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Based on the triangular fuzzy numbers and extent analysis, the result shows that water quality index (weight 0.247 or 24%) irregular water supply (weight 0.204 or 20%) and improper sanitation in the houses (weight 0.194 or 19%) are the most significant factors controlling the distribution of waterborne diseases in the city. In the same way, literacy rate, household density, population density, land use land cover and built-up index are also playing crucial role in the occurrence of waterborne diseases with weights of 0.137, 0.084, 0.06, 0.058 and 0.016 respectively (Table 8).

Finally weighted overlay analysis in Arc GIS was run to generate vulnerability map to waterborne diseases in Azamgarh city based on weights of each decision criteria (Fig. 12). The result shows that about 1.99% area in the city lies in very high vulnerability zone, 16.48% in high category, 37.11% in medium category, 37.30 in low category and 7.12% in very low category. It has been found that most of the area under high vulnerable zones is found near the city centre in the central congested part of the city. The final output map of vulnerable zones in Azamgarh city shows that areas lying in highly vulnerable zones are those areas where irregularity in water supply is more common, proper sanitation facilities are unavailable, water quality index of the drinking water is poor, population density is more than 23,000 persons per km2 and household density is more than 2700 houses/km2.

Fig. 12
figure 12

Vulnerable zones for waterborne diseases

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Discussion

Modelling of vulnerability zones for waterborne diseases in Azamgarh city has been performed using multi-criteria decision making involving eight decision criteria and weighted overlay analysis. Decision criteria associated with socio-economic factors were population density, household density, literacy rate, irregular water supply and improper sanitation in the houses. Another decision criteria was WQI, which was calculated with the help of ten parameters of drinking water i.e. pH, TDS, TSS, EC, alkalinity, total hardness, Calcium, Magnesium, Chloride, Chlorine. Decision criteria from environmental factors involved land use land cover and normalised difference built-up index.

The results of this study are in covenant to several previous studies. A geospatial analysis to find waterborne diseases prone areas has also reveals that water quality index and water and sanitation related practices are significantly associated with waterborne diseases (Bidhuri and Jain 2019). Other studies conducted in Azamgarh city have also revealed that waterborne diseases like diarrhoea, typhoid and jaundice erratic significantly associated with erratic water supply and inadequate sanitation facilities (Jamal and Ajmal 2020a, b). Spatial analysis of different determinants responsible for waterborne diseases suggested that environmental factors like poor environmental sanitation and topography were the main factors behind outbreak of waterborne diseases (Olajuyigbe et al. 2012). Open defecation in public places and improper upkeep of drinking water supply systems were found as determining factors behind outbreak of diarrhoea in a southern Indian village (Sarkar et al. 2007). Studies have also associated consumption of contaminated water with spread of infectious deceases as diarrhoea, cholera and skin diseases in the tropical zones (Lye 2002).

In a scientific analysis, one of the most crucial tasks is validation. In spatial analysis, it is very important to analyse the results with ground conditions. Azamgarh city has faced several cholera and jaundice outbreaks in the previous years. In the year 2016, several cases of cholera were recorded in the city due to improper cleaning of municipal water tank. A study published in Amar Ujala in 2019 has also claimed that people are getting sick owing to intake of contaminated water (Fareed 2019). Therefore, a comprehensive field investigation was conducted and information was collected regarding prevalent waterborne diseases in the city, using structured questionnaire. 10% households from each ward were selected for interview. The results revealed that most common waterborne diseases in the city are typhoid, diarrhoea, skin infections, cholera and jaundice out of which diarrhoea showed the highest proportion (Table 9). Waterborne diseases like cholera, gastroenteritis and diarrhoea break out each year in the summer and rainy times in the country due to poor quality drinking water and sanitation and Azamgarh is no exception. Typhoid is a bacterial infection caused by Salmonella typhi. Typhoid causing bacteria i.e. Salmonella typhi, spreads through faecal-oral route. Contaminated food and water as well as poor sanitation or direct contact with contaminated people is the main causes behind typhoid. Typhoid fever, if not treated, can be fatal in case of 30% people. Cholera is a critical diarrhoeal ailment generally spreading through contaminated water. It results in severe diarrhoea and dehydration. Cholera is caused by consumption of food or drink that is contaminated with bacterium called Vibreo Cholerae. Diarrhoea is the passing of loose and watery stool three or more times a day. Diarrhoea can be caused by a variety of viral, bacterial and parasitic beings. Acute diarrhoea is generally caused by salmonella bacteria, rotavirus or norovirus or giardia parasite. Infectious diarrhoea is generally caused by eating or drinking of contaminated food or water or from person to person due to poor hygiene.

Table 9 Ward wise distribution of prevalent diseases in Azamgarh city
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Spatial analysis of waterborne diseases was made using proportionate symbols in GIS environment (Fig. 13). It has been found that prevalence of waterborne diseases is the highest near the city centre, in the wards Paharpur, Jalandhari, Seetaram, Gurutola, Katra and Bazbahadur and in the southern end of the city in wards Harbanshpur and Sarfuddinpur. Prevalence of waterborne diseases was low in the wards like Civil lines, Raidopur, Mukeriganj, Heerapatti and Arazibagh. Finally layer of waterborne diseases in Azamgarh city was merged with vulnerable zones map of waterborne diseases in the city (Fig. 14). The result shows that high and low prevalence of diseases coincided with high and low vulnerable zones of waterborne diseases respectively; validating the role of socio-economic and environmental factors, water quality index and multi-criteria decision making for vulnerability analysis.

Fig.13
figure 13

Prevalent waterborne diseases in the city

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Fig. 14
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Prevalent diseases vis-a-vis vulnerable zones in the city

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Therefore, our model to identify vulnerable zones to waterborne disease is validated. The model has helped to find areas in the city that require instant attention to curb the incidence of waterborne diseases. Therefore it can be said that use of Fuzzy AHP in combination with GIS offers useful results in demarcation of vulnerable zones to various diseases. Multi-criteria decision making using Fuzzy AHP is becoming more powerful approach to take complex decisions for identifying vulnerable areas.

Conclusion

The study has been conducted into two stages. First stage involves spatial analysis of the decision factors responsible for waterborne diseases in Azamgarh city. Three decision factors i.e. socio-economic factors, quality of drinking water and environmental factors were selected where socio-economic factors involved five decision criteria i.e. population density, household density, literacy rate, irregular water supply, improper sanitation; quality of drinking water involved one criteria i.e. water quality index and environmental factors involved land use land cover and built-up index. Therefore total eight criteria were analysed. In the second stage, considering all the eight criteria, multi-criteria decision making using FAHP-extent analysis method was performed to obtain weights of all the criteria. The result shows that, the most influential factors for occurrence of waterborne diseases are quality of drinking water, irregular water supply, and improper sanitation with 0.247, 0.204 and 0.194 weights respectively. Moreover, weighted overlay analysis considering weights of all the decision criteria was used to generate waterborne diseases vulnerability map. The map shows that highly vulnerable zones to waterborne diseases were found near city centre in areas with high population and household density, where facilities of water and sanitation and sanitation are inadequate and drinking water is fetched from shallow hand pumps. The results were finally validated with the help of ground data of prevalence of waterborne diseases. The result shows that prevalence of waterborne diseases in the city is closely associated with the zones showing very high and high vulnerability to waterborne diseases, generated by spatial vulnerability analysis in the city. Therefore, our model to identify vulnerable zones to waterborne disease is validated. Study maintains that GIS in combination with multi criteria decision making Fuzzy AHP offers useful results in demarcation of vulnerable zones to various diseases. The vulnerable zones for waterborne diseases on the city map will help city planners for diagnostic planning, implementation and monitoring the programmes in particular vulnerable areas along with minimizing duplication of efforts and thus making implementation more targeted, effective and comprehensive. The study suggests implementation of fuzzy AHP and Geospatial techniques for modelling of vulnerable zones of various diseases.