The need for establishing the emerging pollutants for various rural households has witnessed a paradigm-shift towards health-related issues. Low-income groups have inadvertently contributed to environmental degradation due to improper hygienic conditions. In this paper, statistical analysis on gathered data from regional households in Karnataka has been evaluated for the health status of the individuals. A selected area based on village patterns was presented with a sample questionnaire in order to gather the patterns of household conditions prevailing in the area. A broad categorization based on the Fuzzy C clustering approach has classifiers in the form of demographic and socio-economic responses, water supply factors, sanitation, indoor air pollution, and solid waste management approaches. For each of the categories, the emerging pollutant has been identified which collectively points to ‘Poverty’ being the primary cause of deteriorating household conditions among people in such rural areas. The results of the questionnaire indicated a strong correlation between the prevalence of pollution and ill health in the low-income category of people. The authors opine that this investigation would contribute to the Government initiative of the Swachh Bharat Mission.

  • First-ever statistical investigation into low-income groups towards environmental degradation.

  • Preliminary research using machine learning techniques to identify emerging pollutants in a selected rural area.

  • Addressing ‘poverty’ as not only a financial aspect but also as a primary cause for health deterioration among low-income groups.

  • Strong correlation analysis backed by sufficient statistical evidence link ill-health and low-income of people in selected areas.

  • This research provides evidence to facilitate Swachh Bharat Mission of India.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The environment is everything that surrounds or environs us. A synergistic approach towards the environment allows us to impact it – in a good way or the other – which can be beneficial or agonistic. There is a growing awareness and common concern globally about the increasing degradation of the global climate. Increasing concerns for the environment can be addressed from a household level which sometimes goes unnoticed by people. Conditions in low-income groups from villages – especially in developing countries – inadvertently contribute to environmental degradation due to their declining household conditions. People should be made aware of the household environment which could be an inception point for the immediate influence on their lives, health, and well-being, especially in developing countries (Barnes et al. 2009; Shivashankara 2011).

The rural population in India was reported at 65.07% in 2020, according to the World Bank collection of development indicators, compiled from officially recognised sources. The quality of their lives depends on having a clean, decent, safe home to live in, and raising a family. When the quality of the drinking water at home is questionable, the health effects are likely to be far more severe than those arising from river pollution. Much of the same applies when kitchens are smoky, indoor air pollution, sanitary facilities are poor solid waste remains uncontrolled (Karambelas et al. 2018). Due to uncontrolled urbanization in India, environmental degradation has been occurring very rapidly, leading to an acute shortage of housing, poor water quality, excessive air pollution, inadequate sanitation, and associated problems of solid and hazardous wastes (Cairncross 2003; Fewtrell et al. 2005; Rathnamala et al. 2020, 2021). To ensure a good quality of life in village areas, the environmental conditions of the dwellers living there are of utmost importance (Behera et al. 2012). Environment and health are inextricably interlinked which needs to be monitored in real-time through computationally cheap machine learning approaches (Bhowmik 2018; Mucchielli et al. 2020). The physical environment such as drinking water, sanitation, housing, and air, has a considerable effect on the health status and well-being of people, contributes to communicable diseases, and prolongs epidemiological transition (Seager et al. 1999; Rufener et al. 2010; Wu et al. 2020). The socio-cultural environment such as changing lifestyles, modernization, occupational differentiation, and aspirations to improve the quality of life, not only results in new health problems but also places new demands on health systems.

Although disparity arises between the various categories of low-income households, the findings from Surjadi (1995) suggest that there is a large-felt need for improved water supplies, which was corroborated by physical indicators. Albeit the need for developing and extending the piped water system, households with access to piped water were the ones requiring the greatest levels of improvement (Kumar et al. 2017). Elevated concentrations of dust fungi, repeated mould, and water damage was found to be evident with increasing indoor pollution as reported by Dales (Dales et al. 1997). Additionally, the detailed report found that even though the relative risk is weak in areas with low levels of pollution, ambient air pollution, and especially particulate matter and SO2, it requires more attention considering the number of people exposed.

In a more recent set-up, Gowda & Shivashankara (2008) considered urban slums in Bangalore Agglomerations. The Silicon Valley of India had a population of 6.52 million in 2001 with 20% population in slums – reporting the social, economic, and physical aspects of the environment. This work was quickly followed up by an exploration of the migration process in India by the same group of authors (Gowda & Shivashankara 2008; Gowda 2010). Logistic Regression analysis shows that migration is influenced by both ‘push’ and ‘pull’ factors, such as employment problems, extreme poverty, natural disasters, and wage rates and higher income probability, better facilities, joining relatives/families. These studies established the relation between income, household environment, and health which indicated that the slum dwellers are deprived on account of every environmental and social parameter.

Studies in the previous decades have shown the tremendous importance of having a clean and uninterrupted water supply in households. Esrey et al. (1985) – in their study – concluded that the reduction of enteric pathogen ingestion even by a small amount would subject a greater impact on severe diarrhoea as compared to its mild counterpart. From an Indian perspective, the financial capital Mumbai is infamous for its slum dwellings. The survey conducted byKumar Karn & Harada (2002),considered a sample of 1,070 households which showed a very low water consumption pattern of 30 l/c.d. In addition to the impact of neighbourhood water pollution and sanitation, such diseases were also positively correlated with low water consumption and poverty-related factor as poor housing and family income (Reddy & Behera 2006,). Roberts et al. (2001) evaluated the health impact of water that stayed covered or used with a spout. The water from the source wells had little to no contamination where the analysis revealed a 69% reduction in the mean faecal coli levels using the bucket system. Water filters and boiling water used as potable water also led to the reduction of diarrhoea in children. In particular, a coastal city of India – Vishakhapatnam – employs integrated low-cost sanitation (ILCS) system in most of the housing units. Based on the study conducted by Murali & Rani (2005), increasing difficulties in the prevention of groundwater contamination was primarily attributed to the high concentration of nitrates in the area. The spread of methemoglobinemia and cancer has been associated with the use of water containing more than 45 mg/L of nitrate.

In their seminal work on the topic, Sharma et al. (1998) conducted case studies in two urban slums in Delhi – namely Kusumpur Pahari and Kathputly Colony – and concluded that the use of wood fuel and kerosene in slum people led to acute lower respiratory infection and bronchitis. Additionally, it was found that indoor air pollution has the potential to induce health hazards, eventually leading to increased medical costs and loss of production. This is particularly evident in poorer communities with the continuing dependency on biofuel, that sometimes leads to incomplete combustion, coupled with deteriorated housing conditions. Women and children are by far the worst sufferers of indoor air pollution that leads to chronic obstructive pulmonary diseases and acute respiratory infections in children from a very early age. Air pollution from the use of solid household fuels is now recognised to be a major health risk in developing countries.Goldemberg et al. (2018),postulated that the provision of clean energy services lies at the intersection of climate, health, and energy access, which precisely forms the basis of the present research.Smith et al. (2000),focussed their findings on the use of biomass fuel cycles with an emissions database of the most pertinent primary air pollutants in India. With 3 billion people worldwide reliant on solid fuels for cooking, India is found to lead the charts in the emission of PM2.5 and ozone pollutants – evidenced byRooney et al. (2019) – that lead to inadvertent disruptions in climatic patterns.

Solid waste management (SWM) encompasses planning, engineering, organization, administration, and financial and legal aspects of activities associated with the generation, storage, collection, transfer and transport, processing, and disposal of municipal solid wastes. The annual rate of growth of the urban population is 3.09%. SWM is an obligatory function of urban local bodies (ULBs) in India. However, this service is poorly performed, resulting in problems of health, sanitation, and environmental degradation. Yongsi et al. (2008) in their study explored the environmental sanitation and health risks in tropical urban settings. The extensive investigation revealed a diarrhoeic prevalence of 14.4% (437 cases of diarrhoea in the 31,034 children examined). Also, among risk factors studied, household refuse management methods used by city dwellers were statistically to these diarrhoeas.

India's flagship sanitation intervention – Swachh Bharat Abhiyan – was set out to end open defecation by October 2019 (Jain et al. 2020). The use of socio-economic approaches to explore perspectives on ecology determined the structural constraints of the proposed approach. Additionally, Dandabathula et al. (2019) found strong evidence to attribute the occurrence of diarrhoeal disease outbreaks to poor living conditions in rural areas. Environmental variables have been found to play a big role in determining a SWOT framework for the Swachh Bharat Abhiyan that was broadly articulated by Jangra et al. (2016). The mission aims to cover more than 1 crore households in rural areas in terms of waste management, emerging pollutants identification, and cleanliness programmes (Singh et al. 2018). Towards this, the present research promises to evaluate the possible emerging pollutants in selected rural areas that could aid in the framework implementation of the Swachh Bharat Abhiyan.

An introspective study into the literature revealed a survey of sources persistent to the environmental effects in India – and in extension, the world. The key objectives of this study are based on the gap areas identified through this extensive literature survey, considering the key inputs from epidemiological and environmental studies. Based on this premise, the gap areas in the literature can be identified as follows:

  • 1.

    Requirement of an extensive survey in the backdrop of national and international context to evaluate demographic and socio-economic variables in rural areas and to connect statistical models with inferences observed from disparate income groups.

  • 2.

    Albeit extensive work addressed till now, it is necessary to scrutinize drinking water characteristics and storage quality of household water based on the study area, namely, Kolar, Chickballapur, and Bengaluru rural areas. Simultaneous characteristics of wastewater flowing through the built drainage area will also be investigated.

  • 3.

    Based on a survey questionnaire, the demographic and socio-economic variables need to be analysed for a detailed assessment of solid waste. In the context of rural area investigation, this has not been previously carried out. A subsequent air pollution inquisition pivoted around the type of fuel used for cooking promises to be a primary step undertaken to correlate with SWM.

  • 4.

    A rigorous statistical analysis – incorporating facets from machine learning-based algorithms – call for an immediate assessment of the relationship between household environmental variables, income profile, and the health disease situation prevailing in rural areas.

  • 5.

    Based on the above-mentioned analysis – considering the paucity of literature in this area – emerging pollutants commonly prevailing in rural areas need to be identified. The fundamental approach of investigation-inquisition-inference requires to be adopted within a rural household framework. This work promises to upgrade and properly implement the Government of India's initiative of the Swachh Bharat Mission.

The key gap areas pave the way for chalking down the objectives of this study. It is to the best knowledge of the authors that the incorporation of a rigorous statistical framework consistent with mathematical bases has never been previously explored. It is only through a statistical framework that the variables can be aligned according to their importance, for a supervised machine learning process in clustering, training, validation, and finally, prediction. Albeit the prevalence of scientific investigations in this area focussing mostly on the questionnaire-survey approach, the present work extends this thought process a step further and integrates a mathematical consistency to arrive at guidelines. These identified gap areas promise to make a preliminary attempt at improving the health conditions of rural households who are at times constrained by income.

Selection of research area

Karnataka is the eighth largest state in India, accounting for 5.13% of the country's total population. Karnataka witnesses' variable rainfall, prides itself on diverse soil types, and rotating cropping patterns (Gowda 2010). The state has been divided into ten agro-climatic zones. The study area of the research work includes three districts, namely Kolar, Chickballapur, and Bengaluru Rural area.

Chikkaballapur District is situated between 77 °40′–77 °45′ Eastern longitude and 13 °20′–13 °30′ Northern latitude. This district has been identified as a chronically drought-prone area. The meteorological history reveals that out of 11 consecutive years, a staggering 8–9 years presented harsh drought conditions. The Chickballapur District consists of six Taluks Gowribidanur, Gudibande, Bagepalli, Chikkaballapur, Sidlaghatta, and Chintamani, as shown in Figure 1. Bangalore Rural – a part of the Southern Karnataka Plateau – is located in the southeastern corner of Karnataka State. The district lies between the northern latitudes of 12 °51′ and 13 °30′ and east longitudes of 77 °10′ and 77 °58′. The detailed statistics of Kolar, Chickballapur, and Bengaluru Districts are shown in Table 1. To study household environment variables, villages were randomly selected in Kolar, Chickballapur, and Bengaluru rural districts. In the study area, 65 small-sized villages, 26 medium size villages, and 13 large villages were selected for study purposes. The size of the villages is declared by the district statistical department, village panchayat, and taluk offices. Figure 2 shows the sampling villages in all the districts.

Table 1

Estimation of the surveyed households from the selected area

SNName of districtNumber of habited villagesNumber of villages sampledTotal number of householdsSurveyed households (10%)
Kolar 1,980 40 9,070 970 
Chikaballapur 1,842 40 7,600 760 
Bengaluru Rural 1,196 24 6,400 640 
 Total 5,018 104 23,070 2,370 
SNName of districtNumber of habited villagesNumber of villages sampledTotal number of householdsSurveyed households (10%)
Kolar 1,980 40 9,070 970 
Chikaballapur 1,842 40 7,600 760 
Bengaluru Rural 1,196 24 6,400 640 
 Total 5,018 104 23,070 2,370 
Figure 1

Location of the study area.

Figure 1

Location of the study area.

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Figure 2

Sampling villages in all the districts.

Figure 2

Sampling villages in all the districts.

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Out of the 23,070 households, 2,370 (10%) were sampled for this study, as indicated in bold in Table 1. The villages are located and distributed all over the districts. Hence, to incorporate the location characteristics in the selection process of villages, a simple random sampling technique has been done keeping in mind that the households selected represented persons belonging to different income groups.

Primary and secondary data collection

The present study considers the responses obtained from an extensive questionnaire. Empirically categorizing the data into a primary and secondary nature provides a preliminary classification of the data. The primary survey mainly comprises observation and interviews through the questionnaire method. With 35 questions, five crucial parts are considered – ranging from the demographic and socio-economic of different variables in rural households, water supply, sanitation, air pollutant, and health factors. As it is challenging to interview all the households and conduct a socio-economic survey to collect information from villages, for the purpose of this study, the villages have been spatially stratified into three zones (namely higher income, medium income, and low income) based on the monthly income of the people. The rationale behind adopting household income as a descriptor of social background lies in the fact that this variable is a tangible reality in people's lives which provides an idea regarding the economic status of households. The categorization essentially followed a normal distribution with the following classes:

  • 1.

    Low-income group, members who earn between Rs 1,000 and 6,000 per month.

  • 2.

    Medium-income group, members who earn between Rs 6,001 and 10,000 per month.

  • 3.

    High-income group, members who earn above Rs 10,001 per month.

The questionnaire used in the interview was developed with the help of a questionnaire (Supplementary Material, Appendix-I) adopting the styles from studies conducted by the Stockholm Environment Institute, Organization for Economic Co-operation and Development (OECD), and Gowda & Shivshankara (2007). Data from secondary sources has been collected from government sources like Census Handbook 2011 Karnataka, Taluk office Kolar, Chickballapur, and Bengaluru rural districts, Karnataka State Pollution Control Board, Drought Monitoring Cell, District Statistical report, and various other forms of records maintained by Government departments on the subject matter. Secondary data are helpful to study the objectives of the present study.

Household water supply and sanitation

The study is based on primary data. Data on housing conditions, bathroom and sanitation facilities, household water supply, sullage and drainage of water, disposal of garbage and solid waste, household pests, and the effect of all these factors on the health of the residents were collected with the help of a questionnaire (Supplementary Material, Appendix-I) from a comprehensive household survey of villages (Gwimbi et al. 2019). Furthermore, the same concept has been adopted here too. The survey was conducted in February 2021 with a sampling of about 2,370 households from 102 villages of Kolar, Chickballapur, and Bengaluru rural districts. There was variation in the number of households sampled from different villages due to differences in the size and number of families in different villages.

Indoor air pollution and respiration data collection

The study will be conducted in villages of low, middle, and large village sizes in Kolar, Chickballapur, and Bengaluru rural districts. They were virtually indistinguishable from each other in terms of socio-economic status, economy, diet, home construction, and access to health care. They matched well with critical characteristics and will ensure proper representation of the characteristics. While evaluating indoor air pollution in the sampled households, the factors which were considered are the place of cooking food (in the kitchen or the veranda or the multipurpose room or the open air), fuel used for cooking (wood/sawdust/dung cake/dry leaves or kerosene/electricity/LPG), smoking of cigarettes/beedi inside the house (Yes or No), several cigarettes/beedi smoked per 24 h, outdoor smoke entering inside the house, and lastly exit of the indoor smoke (through ventilation or remains inside the house). In addition to pollution from cooking fires and cigarettes, people are also exposed to indoor air pollution through mosquito coils (Supplementary Material, Appendix-I).

Study on solid waste in rural areas

To assess public awareness of the negative consequences of solid waste accumulation, interviews were conducted among village dwellers, and feedback was obtained using questionnaires (Supplementary Material, Appendix-I). The questionnaires also included other relevant demographic details, occupational structures, socio-economic structures, etc. Data were collected through field observations, interviews, and questionnaire surveys during the study period (Figure 3).

Figure 3

Sampling programme in the study area.

Figure 3

Sampling programme in the study area.

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Statistical techniques of data analysis

The survey data were analysed using both descriptive and inferential statistics. While descriptive statistics is mainly used to classify and summarise the numerical data, inferential statistics were used to make inferences by using the corresponding characteristics of the sample households. A series of statistical analyses were performed to produce descriptive and inferential measures.

In the first stage of analysis, data on characteristics and different factors in poor households have been analysed. Statistical analyses were carried out using to establish a relationship between income, environment, and health by a structured questionnaire with 36 items designed. The questionnaires are designed in the following dimensions of demographic and socio-economic variables, water and wastewater characteristics, indoor air pollution characteristics, SWM, and health disease profiles (Rashid & Pandit 2019). The factors in the dimension of water supply, drainage, and air pollutant are identified using the Chi-Square test, considering the p-value as 0.05 for statistical significance. From the identified factors, modelling the relationship between factors and the health profile of the households is done using Fuzzy C mean clustering. The model was formed based on Fuzzy C mean with the output variance explained by the input variables.

Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, and intensity. Different similarity measures may be chosen based on the data or the application. In general, the algorithm functions as follows:

  1. Choose a number of clusters.

  2. Assign coefficients randomly to each data point for being in the clusters.

  3. Repeat until the algorithm has converged (that is, the coefficients' change between two iterations is no more than the given sensitivity threshold).

  4. Compute the centroid for each cluster.

  5. For each data point, compute its coefficients of being in the clusters.

The algorithm first attempts to create a partition of a finite collection of n elements with data points into a collection of c fuzzy clusters with respect to some given criterion.

Given a finite set of data, the algorithm returns a list of c cluster centres and a partition matrix and where each element tells the degree to which element, belongs to the cluster . The aim is to minimise the objective function:
formula
where
formula

Demographic and socio-economic responses

From the survey, the responses for the demographic and socio-economic questionnaire items are distributed as shown in Figure 4.

Figure 4

Distribution of the sampled households according to demographic and socio-economic responses.

Figure 4

Distribution of the sampled households according to demographic and socio-economic responses.

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In an attempt to conceptualise the health effects rising due to various environmental factors, the Chi-Square test was conducted on the questionnaire data. Interestingly, the results of the analysis clearly corroborated with the ground truth – aligning with the raw questionnaire data – and adhering to the rural behavioural patterns of the people.

In the case of demographic and social variables, the various factors considered spanned from people living in a house, their occupation, to the type of dwelling. Rural India has always deviated from the concept of a nuclear family, and this is evident from the Chi-Squared value of 309.75 with a degree of freedom 3. There is a 1 in 10,000 chance that this hypothesis will be nullified, and this corroborates with the ground truth of excessive family members in a single household. The majority of the people have a basic primary degree (as mandated by the free Government of India initiatives) and this is produced by the Chi-Squared value of 671.66. As always, there is a 1 in 10,000 chance of this value exceeding the significance value <0.00001. The majority of the people are occupied as wage labourers as most people migrate to the urban areas for daily jobs. This is strongly suggested by the extremely high Chi-Squared value of 2,170.17 under the third category of the aforesaid classification. Finally, most of the dwellers live in Semi-Kutcha houses, evidenced by a significantly high Chi-Squared value of 1,852.31 – under the 3 degree of freedom constraint – and a 1 in 10,000 chance of exceeding this. Poverty is the emerging pollutant in the socio-economic setting – an option quite obvious – especially due to the lack of proper hygiene facilities in the rural household (Table 2).

Table 2

Demographic and socio-economic variables with Chi-Square and p-values

VariablesChi-Square valuep-value
Demographic and socio-economic 
 Persons living in a house 309.75 <0.00001 
 Education 671.66 <0.00001 
 Occupation 2,170.17 <0.00001 
 Type of house 1,852.31 <0.00001 
Health profile 
 Short-term health effects due to air pollution 354.22 <0.00001 
 Long-term health effects due to air pollution 80.92 <0.00001 
 Symptoms of frequent water-induced diseases 83.56 <0.00001 
 Dengue or malaria from wastewater 61.08 <0.00001 
 Excessive fluoride content 62.57 <0.00001 
VariablesChi-Square valuep-value
Demographic and socio-economic 
 Persons living in a house 309.75 <0.00001 
 Education 671.66 <0.00001 
 Occupation 2,170.17 <0.00001 
 Type of house 1,852.31 <0.00001 
Health profile 
 Short-term health effects due to air pollution 354.22 <0.00001 
 Long-term health effects due to air pollution 80.92 <0.00001 
 Symptoms of frequent water-induced diseases 83.56 <0.00001 
 Dengue or malaria from wastewater 61.08 <0.00001 
 Excessive fluoride content 62.57 <0.00001 

Water supply factors

Access to clean water is a fundamental right of every human being (Roberts et al. 2001). Without clean drinking water, human beings cannot perform their intended bodily functions. In the survey, the rural areas under study showed a skewed dependency on public tap water. Now, this water is at most times polluted with impurities and is the primary cause of health effects in the people consuming it. The Chi-Square value for it at approximately 850 does justice to the actual truth in the rural areas. The water supply is fairly irregular in the selected areas of study and there is a strong tendency for the people to store water in plastic containers (as these are easily and cheaply available). It was observed from the analysis that the action of replacing the stored water exceeds 7 days at a stretch with most of the households and the high Chi-Squared value of 912.17 is strong evidence of that testament. The amount of water per person per day is moderately high (as compared to their urban counterparts) which is attributed to the excessive cleaning of merchandise and personal hygiene after a day of hard work. A Chi-Squared value of 82.73 with a 1 in 10,000 chance of exceeding provides a direct correspondence to the ground truth. Furthermore, the quality of water lies with a tail skew on the moderate side, although the emergence of poor quality of water is strict. Finally, it is found that the heavy dependency on tap water for drinking is the major emerging pollutant concerning the health of the individuals (Table 3 and Figure 5).

Table 3

Water supply variables with Chi-Square and p-values

Water supply factorsChi-Square valuep-value
Supply of ground water for drinking 850.83 <0.00001 
Regularity in water supply 39.90 <0.00001 
Storage of drinking water 350.34 <0.00001 
Replacement of drinking water 912.17 <0.00001 
Frequency in water storage cleaning 912.17 <0.00001 
Amount of domestic water LPCD 82.74 <0.00001 
Quality of water 169.15 <0.00001 
Water supply factorsChi-Square valuep-value
Supply of ground water for drinking 850.83 <0.00001 
Regularity in water supply 39.90 <0.00001 
Storage of drinking water 350.34 <0.00001 
Replacement of drinking water 912.17 <0.00001 
Frequency in water storage cleaning 912.17 <0.00001 
Amount of domestic water LPCD 82.74 <0.00001 
Quality of water 169.15 <0.00001 
Figure 5

Distribution of the sampled households according to water supply factors.

Figure 5

Distribution of the sampled households according to water supply factors.

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Sanitation

With increasing family members in a household, drainage and sanitation play a very important role in uplifting the quality of life, and subsequently, the health of the individuals. In this context, the Chi-Squared value of approximately 179 indicates a near-perfect correspondence to the good availability of drainage in the selected area for study (Table 4 and Figure 6). However, the availability of lined drainage still poses a concern for the individuals and the chances of it reversing against the other degree of freedom seem bleak. The drainage systems are partially closed which provides semblance to the ground reality. As evident from the Chi-Squared value for the availability of stagnant water, with proper awareness – especially the Swachh Bharat Mission – this practice can be abolished in the near future. A direct correspondence of mosquito breeding, leading to malaria and dengue, is evident from the analysis carried out. Additionally, the availability of toilets within the household premises is devoid – demonstrated by the moderately high Chi-Squared value – which forms one of the major health concerns among individuals. The emerging pollutant towards sanitation concerns is the tendency of storing water that gets stagnant and leads to health issues, especially, malaria and dengue.

Table 4

Sanitation variables with Chi-Square and p-values

SanitationChi-Square valuep-value
Sanitation availability 178.93 <0.00001 
Lining on drainage 371.91 <0.00001 
Type of drainage system 321.12 <0.00001 
Availability of stagnant water 39.81 <0.00001 
Breeding of mosquitoes 39.81 <0.00001 
Availability of toilet 1,569.16 <0.00001 
Dumping of human waste from the toilet 1,569.16  
Maintenance of drainage 912.17 <0.00001 
SanitationChi-Square valuep-value
Sanitation availability 178.93 <0.00001 
Lining on drainage 371.91 <0.00001 
Type of drainage system 321.12 <0.00001 
Availability of stagnant water 39.81 <0.00001 
Breeding of mosquitoes 39.81 <0.00001 
Availability of toilet 1,569.16 <0.00001 
Dumping of human waste from the toilet 1,569.16  
Maintenance of drainage 912.17 <0.00001 
Figure 6

Percentage distribution of the sampled households according to sanitation.

Figure 6

Percentage distribution of the sampled households according to sanitation.

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Indoor air pollution

The quality of the air we breathe is directly related to our health. This becomes particularly important because the effects of air pollution are chronic in nature and do not appear as immediate symptoms. Earthen roads in rural areas are a great producer of dust which settles in the lungs of the people using and around these infrastructures. The chances of a reversal from earthen to matured concrete or bitumen roads are quite slim, with a high Chi-Squared value. A strategic location for cooking household food is important as the fumes exposed over a longer duration can prove to be toxic. It is the exact reason that most of the women who are homemakers show signs of health deterioration. Excessive Chi-Square value shows inclination towards open air cooking which can at times be harmful. Availability of proper ventilation is a concern for the rural areas as a strong Chi-Square analysis indicates its dearth. This is reflected in the low ventilation in the kitchen area of the houses under study. The predominance of wood-based cooking fairly corresponds to the agricultural practice followed by most people and a moderate Chi-Squared value indicating the smoking of cigarettes/beedi corroborates the health deterioration of individuals. The emerging pollutant of this study arises from the improper ventilation facilities – both at the kitchen and house level – which indicates health effects among individuals (Table 5 and Figure 7).

Table 5

Sanitation variables with Chi-Square and p-values

Indoor air pollutionChi-Square valuep-value
Classification of roads 965.36 <0.00001 
Place of cooking food 2,136.66 <0.00001 
Availability of house ventilation 1,371.12 <0.00001 
Availability of kitchen ventilation 988.06 <0.00001 
Type of fuel used for cooking 169.18 <0.00001 
Smoking cigarette/beedi inside house premises 651.53 <0.00001 
Indoor air pollutionChi-Square valuep-value
Classification of roads 965.36 <0.00001 
Place of cooking food 2,136.66 <0.00001 
Availability of house ventilation 1,371.12 <0.00001 
Availability of kitchen ventilation 988.06 <0.00001 
Type of fuel used for cooking 169.18 <0.00001 
Smoking cigarette/beedi inside house premises 651.53 <0.00001 
Figure 7

Percentage distribution of the sampled households according to indoor air pollution.

Figure 7

Percentage distribution of the sampled households according to indoor air pollution.

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Solid waste management

The aspect of SWM is crucial in the context of rural Indian households. The effect of improper waste management is compounded in rural areas where people mostly tend to improperly dispose of the waste – especially through garbage piling – leading to increasing pathogenic contractions, which are at times, hazardous. It was noted that the Chi-Squared value for the questionnaire on garbage pile up returned lower numbers indicating a paradigm-shift towards the Government mandated Swachh Bharat Mission. However, cases of garbage strewn on the ground did not go amiss as the traditional routine of burning the garbage proved to be the major contributor. Livestock solid waste being dumped in an open pit revealed the corresponding Chi statistic and the investigated household pointed towards kitchen waste as its major contributor. Unsurprisingly, kitchen waste proved to be the emerging pollutant for this case-specific scenario (Table 6 and Figure 8).

Table 6

Solid waste variables with Chi-Square and p-values

Solid waste managementChi-Square valuep-value
Dumping of garbage 45.48 <0.00001 
Garbage lying on the ground 588.38 <0.00001 
Solid waste disposal 194.18 <0.00001 
Livestock solid waste 6.23 <0.00001 
Composition of solid waste 233.33 <0.00001 
Solid waste managementChi-Square valuep-value
Dumping of garbage 45.48 <0.00001 
Garbage lying on the ground 588.38 <0.00001 
Solid waste disposal 194.18 <0.00001 
Livestock solid waste 6.23 <0.00001 
Composition of solid waste 233.33 <0.00001 
Figure 8

Percentage distribution of the sampled households according to solid waste management.

Figure 8

Percentage distribution of the sampled households according to solid waste management.

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Results of correlation for various income groups

For low-income group

The low-income group provides the actual assessment of the rural households in the country. There is a strong chance of people acquiring malaria, dysentery, and even chronic diseases – both in the shorter and the longer run – with a propensity towards fluorosis. The fundamental reasons, in particular, lie in the attributes of the people who are at times devoid of any medical treatment due to their low income. More often than not, it is this group of people who contribute to the spread of certain communicable diseases, unknowingly, mainly due to their lifestyle habits and traditional beliefs. This is an aspect that needs to be changed and it provides credence to the notion of ‘poverty’ is the cause for environmental pollution (Table 7 and Figure 9).

Table 7

Fuzzy C modelling for the low-income group

Health variablesR2MSE (Mean square error)
Q31 0.582719 1.634777 
Q32 0.538338 4.405048 
Q33 0.597336 0.389720 
Q34 0.711078 0.838756 
Q35 1.576890 
Health variablesR2MSE (Mean square error)
Q31 0.582719 1.634777 
Q32 0.538338 4.405048 
Q33 0.597336 0.389720 
Q34 0.711078 0.838756 
Q35 1.576890 
Figure 9

Correlation graph of questionnaire entry with health variables for the low-income group.

Figure 9

Correlation graph of questionnaire entry with health variables for the low-income group.

Close modal

For middle-income group

For the middle-class income category, the encouraging results on the absence of fluorosis provide encouragement to the notion that the average Tom, Dick, and Harry are aware of their hygiene and what they put into their diet. Again, there is a lower tendency of contracting malaria or any short or even longer chronic air pollution effects (evidenced by the lower R2 value). However, there is a spike in the occurrence of dysentery which explains almost 72% of the variance from the acquired and the analysed results. This might result from infections that are sometimes untreated due to negligence and prolonged exposure (Table 8 and Figure 10).

Table 8

Fuzzy C modelling for the middle-income group

Health variablesR2MSE (Mean square error)
Q31 0.489608 1.77241 
Q32 0.593564 3.04312 
Q33 0.719255 0.708243 
Q34 0.40018 0.770905 
Q35 4.12890 
Health variablesR2MSE (Mean square error)
Q31 0.489608 1.77241 
Q32 0.593564 3.04312 
Q33 0.719255 0.708243 
Q34 0.40018 0.770905 
Q35 4.12890 
Figure 10

Correlation graph of questionnaire entry with health variables for the middle-income group.

Figure 10

Correlation graph of questionnaire entry with health variables for the middle-income group.

Close modal

For high-income group

Consider the high-income category finally. The above table shows an uncanny R2 characteristic between the income group and the family members who are suffering – or in fact, have suffered – from malaria. There is an absolute unity value for this question which indicates that the people from this income group are the least likely to suffer from malaria with the most negligible (rather intangible) mean square error. Therefore, the result so obtained corroborates our earlier notion of higher income category of people have a lower tendency of acquiring this disease. However, the anomaly in the contraction of long-term effects due to air pollution can be attributed to the fact that this income group usually ventures into longer working hours with possible travel for work. This leads to contracting air pollutants on the commute and possible chronic disease in the longer run. The clustering also clearly revealed a 5-class classification and the symptoms of frequent diarrhoea/dysentery, and cholera, are not so common (Table 9 and Figure 11).

Table 9

Fuzzy C modelling for the high-income group

Health variablesR2MSE
Q31 0.5765 2.441739 
Q32 0.641396 8.471181 
Q33 0.571337 0.519067 
Q34 0.587229 0.96992 
Q35 5.45567 
Health variablesR2MSE
Q31 0.5765 2.441739 
Q32 0.641396 8.471181 
Q33 0.571337 0.519067 
Q34 0.587229 0.96992 
Q35 5.45567 
Figure 11

Correlation graph of questionnaire entry with health variables for the high-income group.

Figure 11

Correlation graph of questionnaire entry with health variables for the high-income group.

Close modal

This extensive investigation into the rural households of Karnataka led to eye-opening insights into the health and pollution conditions in these areas. First, a questionnaire was developed considering the socio-economic and demographic variables with classifications for water supply factors, sanitation, SWM, and indoor air pollution. The results of the questionnaire indicated a strong correlation between the prevalence of pollution and ill health in the low-income category of people. While the questionnaire itself required field investigations, the findings of this research work were first obtained through Chi-Squared statistics (with a significance test for null hypothesis) and then validated by the supervised Fuzzy C clustering paradigm. It was demonstrated that the skewness of the results in the statistical significance was directly related to the low-income category. Moreover, R2 values obtained from each income group presented the ground truth of the living conditions of the rural people. This work is expected to improve the quality of the people's living conditions who are presently dwelling in rural households. Using the results of this research, it is possible to align with the Government mandated Swachh Bharat Mission and set in stone the notion – ‘poverty is a great pollutant and the reason for ill health’. The findings of this research lead credence to the belief that the United Nations Sustainable Development Goals (UNSDGs) that prevailing circumstances in rural Indian households need to eliminate the practice of indoor cooking using fuels that lead to pollution. By 2040, emissions of main pollutants are projected to drop by 6080% relative to today, and associated health impacts are quantified at two million avoided deaths from ambient and household air pollution combined. In comparison to costs needed for the decarbonization of the global economy, additional investments in air pollution control and access to clean fuels are very modest against major societal gains. However, holistic and systemic policy assessment is required to avoid potential trade-offs.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

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