Quantitative assessment of household waste composition and segregation pattern in urban settings of Ujjain, Madhya Pradesh: a pick analysis/waste composition analysis study

Introduction: Effective management of municipal solid waste (MSW)is a persistent challenge in India, where rapid urbanization and limited infrastructure contribute to unscientific disposal practices. Household (HH) waste constitutes the largest share of MSW, yet reliable data on its generation and composition remain scarce. This study presents a baseline assessment conducted in Ujjain, Madhya Pradesh, as part of the I-MISS intervention on HH waste segregation.

Methodology: A cross-sectional survey was undertaken among 215 HHs across slum and non-slum areas. Waste was collected daily for seven consecutive days, sorted into 23 predefined fractions, and analyzed for per capita and per HH-level generation, composition, and mis-sorting.

Results: Findings reveal an average daily per capita generation of 141.0 g and 146.7 g,and per HH generation of 651.4 g. Organic waste formed the largest fraction (69.1%), followed by plastics, paper, and textiles. Nearly half (48.1%) of the dry waste stream was mis-sorted, mainly due to organic and sanitary fractions being placed incorrectly. Mis-sorting was more pronounced in slum HHs, particularly for sanitary and hazardous sharp waste. Statistical analysis highlighted that family size, house type, and socio-economic status (SES) significantly influenced waste generation patterns, with smaller HHs and middle-income groups producing more waste.

Discussion: The study underscores the urgent need to strengthen HH-level segregation through targeted behavioral interventions and improved infrastructure. Emphasizing composting, recycling, and the safe disposal of sanitary and hazardous waste can enhance resource recovery and reduce environmental risks. These findings provide crucial evidence to inform urban waste management policies and design locally appropriate, sustainable strategies for Indian cities.

Highlights

• Seven days of continuous door-to-door HHwaste collection and segregation were conducted in Ujjain city.

• Average daily waste generation was 141.0 g per capita and 651.4 g per HH, with organic waste (69.1%) as the major fraction.

• Nearly 48% of dry waste was mis-sorted, mainly due to organic and sanitary waste.

• HHsize, type of house, and SES significantly influenced waste generation patterns.

Introduction

Effective municipal solid waste (MSW) management remains a pressing challenge in low- and middle-income countries (LMICs) (Vinti and Vaccari, 2022). India’s rapid urbanization and population growth exacerbated the waste management issue due to the limited infrastructure and resources (Chikowore, 2021; Goswami et al., 2024). According to the Central Pollution Control Board (CPCB) annual report 2020–21, India generates 62 million tonnes (Mt) per year of solid waste, of which only 50% is treated and 32% remains unaccounted (CPCB, 2022). This trend in waste generation is projected to continue rising, with India expected to surpass countries like Germany, which is currently having 4^th rank in total waste generation (Gour and Singh, 2023). Household (HH) waste makes up a substantial portion of MSW in India (Singh et al., 2024), with more than half comprised of biodegradable organic fraction (CPCB, 2022). Despite the implementation of Solid Waste Management (SWM) Rules 2016 (Ministry of Environment, Forest and Climate change, 2016), most of the generated waste is unscientifically managed mainly via open dumping and unscientific landfilling in most of the regions in India (Farooq et al., 2024; Meena et al., 2023; Patel et al., 2023). Waste management is even more challenging with the diverse composition of the waste stream, consisting of food waste, plastics, papers, metals, electronics, etc. (Radhakrishnan et al., 2024). The generation, composition, and management vary with communities and regions within the country (Goswami et al., 2024).

Thus, understanding the waste generation rate and the composition of the generated waste is of utmost importance in developing solutions for waste management issues (Awasthi et al., 2023). Precise and reliable data at the regional level is vital in planning and assessing the environmental impact of waste management (Saidan et al., 2017; Suthar and Singh, 2015). Currently, comprehensive data on waste composition is lacking in India, which is one of the factors for inadequate waste management (Radhakrishnan et al., 2024).

Several factors, such as HH type, HH size, education level, and occupation of the family members are affecting waste generation and waste composition, which are documented by various studies (Awasthi et al., 2023; Deshpande et al., 2024; Goswami et al., 2024; T. Singh et al., 2024). Waste generators have a significant responsibility in managing the waste from the source, which was emphasized in the SWM rule 2016 (Ministry of Environment, Forest and Climate change, 2016), as their actions directly influence the quality and quantity of waste generated. Understanding the importance of waste composition analysis is essential, as it provides valuable insights into the types of waste being produced, which can inform targeted waste reduction strategies and infrastructure development. By equipping residents with knowledge about the environmental impact of improper waste disposal and the benefits of recycling, communities can foster a culture of sustainability.

The present study represents the baseline assessment of an intervention study entitled “Effects of improved information and volunteer support on segregation of solid waste at the HHlevel in urban settings in India (I-MISS) - a cluster Randomized Control Trial (cRCT) (Kalyanasundaram et al., 2021).

Waste composition or pick analysis is a method used to characterize and categorize the different types of materials present in a waste stream (Dahlén and Lagerkvist, 2008). This process is significant for understanding the waste composition, identifying opportunities for recycling and waste minimization, and developing effective strategies for waste management (Dahlén and Lagerkvist, 2008). Understanding the waste composition helps develop an effective waste management system (Bisinella et al., 2017). It allows municipalities and organizations to modify their strategies based on the types and quantities of waste generated. Identifying the components of waste that can be recycled or reused enables better resource recovery planning (Bisinella et al., 2017). This can contribute to developing recycling programs and various other waste management programs. Understanding waste composition provides insights into waste management behaviors and the factors influencing the waste generation and disposal practices of community members, who are the key stakeholders in SWM (Suthar and Singh, 2015). This information helps policymakers develop effective waste management strategies based on local needs (Suthar and Singh, 2015).

The insights from the waste composition analysis, help to understand the segregation practices of the community members. Furthermore, the findings of this study can serve as a model for other communities facing similar waste management challenges, demonstrating the importance of understanding local waste generation characteristics as a foundation for effective and sustainable waste management strategies. Comprehensive waste composition analysis, therefore, serves as a cornerstone for achieving sustainable waste management goals, especially in regions where waste management systems are still developing.

The current study is the first in India to involve continuous door-to-door waste collection and segregation over a one-week period to identify the waste composition and obtain region-specific data. The present study was conducted to estimate the per capita solid waste generation among selected slum and non-slum HHs, to understand the composition of waste from HHs in Ujjain city, to determine the proportion of misclassified or missorted waste, and to identify the socio-economic factors influencing waste generation at the HH level.

Methodology

Study design

A cross-sectional survey that collects socio-demographic details of the HH members and seven days of continuous waste collection from the selected HHs.

Study setting

The study was conducted in Ujjain city, which has a total population of approximately half a million (Population Census, 2011). Ujjain comprises152,488 HHs, of which 120,141 people reside in slum areas. Ujjain is ranked 10^thin the country under Swachh Survekshan rankings, among cities with a population ranging from 100,000 to one million, and holds the 2nd rank in the state. It was awarded the best city in “Maximum citizen participation,” and it has a 3-star rating for the Garbage Free Cities (GFC) ranking (Swachh Survekshan, 2022).

Eight wards from 54 administrative wards under Ujjain Municipal Corporation (UMC) were selected for the I-MISS project using simple random sampling. Eight wards, comprising 880 HHs, that includes slum and non-slum HHs, were selected for the cRCT. The study setting and sampling details are provided in the protocol (Kalyanasundaram et al., 2021). The geographical map of the study setting is illustrated in Figure 1.

Figure 1

Figure 1. Geographical map of Ujjain city within the state of Madhya Pradesh, India.

Study population

For the present waste composition study, two of the eight I-MISS project wards were then selected by simple random sampling, and all 110 trial HHs in each of these two wards were included, resulting in a study sample of 220 HHs. Thus, although data for this analysis come from only two wards, both the ward-level selection (from the eight I-MISS project wards) and the inclusion of all trial HHs within those wards follow probabilistic procedures embedded in the published trial framework. This approach helps to reduce selection bias and provides more robust estimates of HH waste generation and segregation for the I-MISS project wards. There are1,113 members in the HHs of the selected two wards, including both slum and non-slum HHs, to ensure that the sample representation is from all socio-economic groups.

Pilot study

Pilot study was conducted in April 2022 to assess the feasibility of pick analysis/ waste composition, to estimate the resources (manpower, materials, and cost) required, and to understand the operational difficulties of conducting the waste composition study (Kalyanasundaram et al., 2023).

Training

Twenty-three volunteers, seventeen for pick analysis and six for visual inspection (non-pick analysis), were recruited for the waste composition analysis study based on their willingness to participate. Four female and thirteen male volunteers were selected for pick analysis. They were further divided into four groups (two per ward), with one supervisor assigned to each group. All volunteers were given 6 days of training along with mock practice.

Data collection method (pick analysis)

The waste composition, analysis, was conducted from February to March 2023 for continuous 8 days, in which the first day was considered a “trial day,” during which the waste from desired HHs was collected and taken to the segregation site for disposal to ensure that the main pick analysis did not include waste from the previous days. The sampling period was guided by both feasibility (given the intensive manual collection and sorting) and current methodological guidance for municipal solid waste composition studies. International standards (ASTM D5231-92, 2016) recommend determining mean composition from manual sorting of samples collected over at least one week, and many HH waste composition studies similarly use single multi-day campaigns to balance representativeness and logistical constraints (Dahlén and Lagerkvist, 2008). The waste was collected in the morning, between 6 and 9 a.m., before the UMC waste collection vehicle reached the HHs. The schedule of UMC vehicles was documented before the study, and accordingly, the wards were visited for waste collection. We ensured that there were no special events or festivals during data collection to prevent the routine waste generation rate of the HHs from being affected. All 220HHs were visited a day before the waste collection, and the members were informed regarding the waste collection and its purpose. The visit also served to familiarize the volunteers with the HHs and the HH members. The weighing instruments used in the study were calibrated prior to its commencement. Permission from the UMC was obtained prior to the study, and one copy of the permission was provided to all the supervisors. Additionally, approval was granted for the request to utilize the UMC-designated segregation site and the designated helpers for the segregation process. The detailed step-by-step process of waste composition analysis is as follows:

1. Door-to-door waste collection: Four groups comprising a total of 17 volunteers and 4 supervisors were assigned for waste collection. For each ward, two groups were assigned that include 4–5 volunteers and a supervisor in each group. The two groups in each ward evenly divided the responsibility for collecting waste from five clusters, including in total 110 HHs. The volunteers visited the allocated HHs daily for 7 days and collected the waste generated from each HH in separate color-coded bags, with each color representing a different type of waste. Five color-coded polythene bags were used separately for dry, wet, sanitary, hazardous, and unsegregated waste. The weight of the empty polythene was recorded before waste collection and removed during the data analysis stage to ensure the accurate measurements. All the volunteers were provided with masks and gloves to ensure their safety. Special roles were assigned to each volunteer within each group. Upon visiting each house, the HH members were told to bring their HH waste, and one volunteer inspected the HH waste and asked members to tell the types of waste in their bins. Another volunteer documented the type of waste reported by the HH members, and an estimated percentage of mis-sorted waste in the bin was recorded in the field form. The third volunteer collected the waste into separate color-coded bags based on the type of waste told by the HH members. Once collected, the bags were sealed immediately, and HH IDs and cluster codes were marked on each bag. The sealed bags were then kept together in larger polythene bags according to waste types. There were three color codes for big polythene bags. One was for dry waste, which also includes sanitary and hazardous waste; the second was for organic waste, and the third was for unsegregated waste. Once the larger polythene bag was filled, it was sealed and labeled with the number of HHs and the cluster code over the bag. Each such larger polythene bag was referred to as a “lot.” Each lot comprises waste from the same cluster. The fourth volunteer shifted the sealed lots to the waste collection vehicle.

2. Transportation: The collected lot of waste was transported to the UMC-designated segregation site. Two vehicles were hired for waste collection.

3. Segregation site: The segregation site was organized according to the types of waste. The site was divided into two sections for each ward: one for dry waste, including dry, sanitary, hazardous, and unsegregated waste, and another for wet waste, where it was segregated into different waste fractions. Similarly, two weighing sections were arranged for each ward.

4. Weighing: The waste transported to the segregation site was shifted by the volunteers to the respective weighing sections; wet waste in the wet waste weighing section and dry waste in the dry waste weighing section corresponded to the wards. Each color-coded bag from the respective lot was weighed by one volunteer and recorded in the study form by another volunteer. The weight of the polythene bag used for the waste collection was recorded before the waste collection. The weightedbags with waste were emptied into a 60 L dustbin and shifted to the respective segregation section (dry and wet section).

5. Segregation: After weighing, each waste was transferred to the segregation platform. Twenty-three waste fractions were developed by the study team, under the category of wet and dry waste. The dry waste category include paper wastes (cardboards, newspapers and news prints,tetrapacks, etc.), plastic waste (packaged plastic, PET bottles and other plastic wastes), glass wastes, metal waste, textile wastes, wood waste, and those classified as sanitary and hazardous waste. The wet waste category mainly includes food waste and garden waste. The detailed list of waste fraction used in this study is given in the supplementary file 1. The segregation platform was divided into two sections: dry waste and wet waste section. In the dry waste section, twenty-two dustbins, labeled with the names of different waste fractions, were arranged. Similarly, three labeled dustbins were organized in the wet waste section, where only wet wastes were segregated. Volunteers, supervisors, and UMC-assigned waste management workers were involved in the segregation process. Personal protective items like gloves, masks, aprons, and head and foot covers were distributed to everyone involved in the segregation process to avoid the risk of exposure to harmful substances, germs, or allergens. After segregation, the different waste fractions were deposited into the respective dustbins.

6. Weighing of segregated waste: The segregated waste fractions were weighed separately, and the weight was recorded in the study form. The weight of the polythene bags where wastes were deposited was recorded earlier.

At the end of the day, all the collected and segregated waste was handed over to the municipality for further waste management. Further, the respective supervisors reviewed all the forms to ensure no mistakes in the data. Subsequently, the data forms were stored in separate files based on different clusters.

Data entry and analysis

The data recorded on paper forms was entered into an Excel sheet. Data was analyzed using Excel and SPSS version 25. Per capita, per HH waste generation (total/dry/wet waste), and the proportion of each waste fraction were calculated. The proportion of the misclassified waste in dry and wet waste bins was calculated. The socio-demographic variables, such as type of residence, were categorized as slum/non-slum, based on the I-MISS-cRCT protocol (Kalyanasundaram et al., 2023) that followed the definition given by the Census of India (Ministry of Home Affairs, Government of India, 2011; Nolan, 2015). The socio-economic status (SES) was determined based on the modified Kuppuswamy scale 2023 (M. Radhakrishnan and Nagaraja, 2023). This scale incorporates parameters such as the education and occupation of the HH head and the monthly family income. Based on this composite score, HHs were initially categorized into five classes: upper, upper-middle, lower-middle, upper-lower, and lower. For easier presentation and interpretation, these five categories were later consolidated into three classes: lower, upper lower, and lower middle/upper middle. The type of house; pucca, kaccha and semipucca was determined based on the criteria of the census of India (Ministry of Home Affairs, Government of India, 2011). According to census of India, pucca house refers to the house in which the roof and wall made of permanent materials such as stones, bricks, concrete, cement etc. whereas kachha house made of temporary materials such as grass, mud, bamboo, plastic sheets etc. The semi pucca houses refers to the house in which either the roof or wall, (not both) made of permanent materials and the other with temporary materials. The total number of family members were recoded as families having up to three members, 4–6 members and greater than six members (as family divide in three group). The correlation among waste generation, total number of family members, and income was calculated. Kolmogorov–Smirnov and Shapiro–Wilk tests were done to determine the normality of the data. The p value <0.05 indicates the non-normal distribution of the data and expressed in Median and Inter Quartile Range (IQR). The Mann–Whitney U test was applied for two-group comparisons (e.g., area of residence& type of house), and the Kruskal-Wallis test was used for variables with more than two categories (e.g., family size &SES). A p-value of <0.05 was considered statistically significant.

• Per capita waste generation was obtained through dividing the total waste generated by the total HH by number of family members, multiplied by the number of days [Total waste collected / (total number of family members *number of days)]. It helps understand the average waste generated individually within the study population during the study period. It offers insights into waste generation patterns across the population.

• Per HH waste generation was obtained by dividing total waste generated by the number of HHs, multiplied by the number of days [Total waste generated / (number of HHs * number of days)]. It will provide the average amount of waste generated by each HHduring the study period.

• The proportion of mis-sorted waste is calculated by determining the fraction of incorrectly sorted waste out of the total waste. It indicates the effectiveness of waste segregation practices by the HH members, in which lower values suggest better segregation.

• The proportion of each waste fraction is important for waste management planning and is calculated by dividing the total weight of a specific waste fraction by the total weight of the waste.

Results

In the present study, 220 HHs (176 non-slum and 44 slum HHs) from two wards under UMC were visited for waste collection, including however, waste collection was done only from 215 due to the relocation of few HHs or the members were not present in during the waste collection. The socio-demographic characteristics of the study HHs are shown in Table 1.

Table 1

Table 1. Socio-demographic characteristics of study households.

These study findings provide essential insights into the generation and composition of different types of waste produced from HHs. The study found that the average per capita waste generation was 141.0 g, whereas 651.4 g was generated per HH. The largest component of waste generated was wet waste, with an average per capita generation of 81.2 g, followed by dry waste, with 51.8 g/person/day. Similarly, at the HH level, 382.7 g of wet waste was generated, accounting for the largest portion of HH waste, and an average of237 g of dry waste was generated daily from HHs. The generation of unsegregated waste (22.8 g/HH/day) reflects the inconsistency in the segregation practices at HH. Table 2 details the average daily per capita (individual) and per HH generation of different types of waste from the study HHs.

Table 2

Table 2. Average daily waste generation (in grams) at household and per capita level.

Individual waste and HHwaste were strongly correlated (r = 0.756, p < 0.001). Family size was negatively related to individual waste (r = −0.229, p < 0.001) but positively related to HHwaste (r = 0 0.327, p < 0.001), indicating that larger families generate more total waste but slightly less waste per person. Income showed weak associations with waste, displaying no meaningful correlation with individual waste (r = −0.057) and only a small positive correlation with HHwaste (r = 0.113). Income was also weakly but significantly related to family size (r = 0.129, p < 0 0.001).

Waste generation may be influenced by various factors such as type of house, type of residence, family size, and SES. Our study highlights a significant association between waste generation and socio-demographic variables such as family size, type of house, and SES. Although the type of residence (slum/non-slum) was not statistically significant for waste generation, slum HHs had slightly higher median values than non-slum HHs. The findings from our study show that individuals residing in pucca houses generate a higher amount of waste, specifically wet waste (p = 0.047), while individuals from Semi-pucca/Kachha houses generate the lowest amount. There is no significant difference in dry waste generation and the type of house. Our study shows that family size has a significant influence on HH waste generation (wet waste-p = 0.014; dry waste-p = 0.005; total waste p = 0.001). Findings revealed an inverse relationship, in which higher per capita waste generation occurs in smaller HHs (up to 03 members), contrary to larger HHs (>6 members) which produce significantly less per capita waste. There was no statistical significance shown by SES waste generation. Although, individuals from middle economic groups produce a higher amount of waste, compared to other socioeconomic classes. The per capita daily generation of wet, dry, and total waste based on various socio-demographic variables is detailed in Table 3.

Table 3

Table 3. Daily generation of organic waste, dry waste and total waste based on different type of residence, type of house, family size and socio-economic status by individuals.

Further analysis of each section (dry and wet) of collected waste revealed that almost half (48.1%) of the waste in the dry waste section was mis-sorted, with a higher percentage of organic waste (35.6%), followed by sanitary waste (6.5%). Though a slight difference in the proportion of mis-sorted waste between slum and non-slum HHs, a notable percentage of sanitary waste (10.5%) and sharp waste (2.9%) was found in the dry bin from slum HHs, which is comparatively higher than non-slum HHs. However, mis-sorted organic waste was higher (37.4%) in non-slum HHs. Only 6% of mis-sorted waste was found in the wet waste section. The percentage distribution of mis-sorted waste in dry and wet waste bins slum and non-slumHHs is shown in Table 4.

Table 4

Table 4. Percentage distribution of mis-sorted waste in total dry waste bin and total wet waste bin during one week.

The detailed segregation of the collected waste showed that the largest proportion (69%) of the waste generated was organic food waste. The other significant waste fraction that makes up a notable portion of the waste was recyclable materials, including plastic packaging (3%), other plastics (3%), paper packaging (2.5%), other paper (2.3%), and other residual waste (4%). Textile/cloth waste (3.14%) and sanitary waste (2.85%) also comprise a notable share in the total waste composition. A minor share (below 3%) of waste was represented by e-waste, hazardous pharmaceuticals, sharp, chemical, and other wood waste. Non-slum HHs generate significantly more waste across most of the categories, however, slum HHs produce a relatively higher proportion of sanitary and residual waste. Figure 2 illustrates the distribution of HH waste fraction, while Table 5 compares the percentage composition of waste between slum and non-slum HHs.

Figure 2

Figure 2. Proportion of waste fraction generated from all HHs.

Table 5

Table 5. Percentage distribution of different waste fraction (s) by the type of residence.

Discussion

Understanding the waste generation patterns of HHs, along with analyzing the key factors influencing waste generation, is important for framing the best interventions to improve waste management. This study examines the waste generation and segregation patterns of 215 HHs, highlighting the key factors influencing HH waste generation and segregation, and understanding the key differences across residence type (slum/non-slum), family size, SES, and house type.

The average daily per capita waste generation was 141 g, which was less compared to the daily per capita waste generation of UMC of 440.5 g/person/day (Sahoo et al., 2024). Our findings are slightly higher than the national average of 123.45 g/person/day and the state average of <100 g/person/day (CPCB, 2021). It also aligns with the daily per capita waste generation of other Indian cities such as Nashik (190 g), Imphal (190 g), Rajkot (210 g), and Guwahati (200 g) (Meena et al., 2023). In our study, more than half of the total waste is organic waste (69%), followed by plastic and paper waste. Similar findings of higher organic waste generation were observed in studies by Ramachandra et al. (2018), Phuong et al. (2021), and Suthar and Singh (2015). The findings suggest that most of the waste generated can be effectively managed through proper segregation practices. The higher amount of organic waste creates an opportunity to promote composting and biogas techniques, which were successfully implemented in cities like Indore (Samuel et al., 2024), Bobbili, Mysuru, and Vengurla (Atin and Subhasish, 2021). Dry waste comprises the second major portion of the waste composition, which mainly includes recyclables such as plastic (6.3%), paper (6.3%), and textiles (3.8%). The plastic waste generation was similar to the study done in Dehradun (~7%) (Suthar and Singh, 2015) and Puducherry (10.4%) (Pattnaik and Reddy, 2010), whereas a higher percentage of plastic waste (22%) was observed in a Varanasi study (Srivastava et al., 2014). It opens the opportunity to encourage recycling, which not only benefits the environment but also creates employment opportunities (Asanousi Lamma, 2021).

The substantial amount of unsegregated waste (23.4 g/HH/day) and almost half (48.1%) of the mis-sorted waste in the dry waste bin highlight the inadequacy in the HH segregation practices. The mis-sorted waste in our study was mainly comprised of a significant share of organic waste, followed by sanitary waste, which is less than the Swedish study (Rousta and Ekström, 2013), where approximately 53% of the waste was found to be mis-sorted. Specifically, our study shows a notable proportion of sanitary and hazardous sharp waste. In addition, these concerns are significantly higher in slum areas, necessitating interventions that focus more on slum HHs. The reason for the presence of mis-sorted waste in slum HHs may be the use of single dustbins, misguided waste disposal practices, lack of segregation practices, and lack of awareness on segregation and disposal practices (Farooq et al., 2024; Kihila et al., 2021; Meena et al., 2023; Yoada et al., 2014).

As identified by various studies (Awasthi et al., 2023; Deshpande et al., 2024; Goswami et al., 2024; Saran et al., 2024; T. Singh et al., 2024), socio-demographic conditions play a significant role in HHwaste generation. In our study, factors such as family size, type of house, and SEShave a notable influence on waste generation. The current study shows that per capita waste generation is higher in small HHs (<6 members), which is supported by Sujauddin et al. (2008), in which per capita waste generation decreases with increased family size. Various other studies have supported the negative relationship between waste generation and family size (Gharagozloo and Ghazizade, 2023; Miezah et al., 2015). In contrast, several studies have documented a positive correlation between waste generation and family size (Khan et al., 2016; Noufal et al., 2020; Saran et al., 2024; Suthar and Singh, 2015). Higher per capita in smaller HHs could be attributable to inefficient resource utilization, more wastage per person, etc. In comparison, in larger HHs, resource consumption is shared, and they tend to utilize resources better. Similarly, kachha houses comparatively generate more waste, particularly wet waste, than pucca or semipucca houses. Rakesh et al. (2024) revealed a lack of awareness on waste segregation and disposal practices among HHs in the kachha type. Vetter-Gindele et al. (2019) explained a contrast result from our study, which identified higher wet waste generation from larger buildings. Similar findings were observed in a study conducted in Greater Accra (Chapman-Wardy et al., 2021). Considering the association of SES with waste generation, middle-classHHs generate more waste, especially organic/wet waste. The findings were corroborated by the study reported by Ramachandra et al. (2018), which found that middle-income HHs tend to generate more waste compared to lower- and higher-income HHs. A survey-based study in Chennai highlighted contrasting findings in which lower economic groups produce more waste (Deshpande et al., 2024), whereas our study revealed lesser waste generation in lower economic groups compared to other groups. Our study identified that median total and wet waste generation was higher in slum HHs than in non-slum HHs, although statistically not significant to explain the difference. However, the presence of a higher amount of sanitary and sharp waste in slum HHs suggests giving more priority to these areas while designing the interventions.

Overall, our findings explicitly suggest the strong need for public awareness of different waste types and their disposal methods. The study recommends considering family size, SES, and type of house to ensure the effectiveness of the interventions, since they have a significant role in determining HH waste generation.

Methodological considerations

This study presents the waste composition analysis/pick analysis that quantitatively measures and categorizes HH solid waste generated from 215 HHs of Ujjain city. The physical sorting of the collected waste provides detailed information on the composition of waste, segregation efficiency, opportunities for recycling and composting, and developing effective strategies for waste management.

Strength

This is the first study in India that involves seven days of continuous door-to-door waste collection and segregation to understand the region-specific data of waste composition to develop effective strategies to improve waste management. It revealed the actual HH waste segregation behavior of the residents in the study setting. Moreover, the method uncovered the socio-economic disparities in waste generation and segregation, which indicates the relevance of socio-economic factors to be considered in waste management planning and policy decisions.

Limitations

This study has certain limitations. This analysis is based on HHs from two randomly selected wards within the I-MISS project trial area, which may limit the generalizability of findings to other urban contexts with different socio-cultural and infrastructural conditions. Waste collection and segregation were carried out continuously for seven days in one season, without accounting for seasonal or event-related variations in waste generation. However, as a part of the cRCT, there will be a follow-up assessments at different time points (midline, endline and post intervention), which will provide additional data on changes in HH waste generation and segregation behavior over time. The resource- and labor-intensive nature of the methodology, along with reliance on manual sorting, may have introduced minor classification or weighing errors. The per capita waste generation was calculated using the baseline number of HH members, and we did not record day-to-day presence or temporary visitors during the 7-day collection period. This may have led to some inaccuracy in PCG estimates, with possible underestimation when HH members were away but still included in the denominator, and overestimation when visitors or relatives staying with the HH contributed to waste generation but were not accounted for in the HH count. HH awareness of being observed could also have influenced segregation behavior. Finally, while the study focused on quantitative weight-based composition and mis-sorting, it did not capture qualitative aspects such as recyclability quality or underlying behavioral reasons for incorrect segregation.

Recommendations

The present study delves into the HH waste generation and composition patterns based on different socio-demographic conditions in an urban setting in India, suggesting the need for strengthening segregation behavior at HHs. The study findings recommend that policymakers promote composting, biogas, and recycling techniques, especially in remote areas, which help create a clean environment and also offer wealth opportunities to the community. The higher proportion of sanitary and sharp waste from the slum areas projects a lack of awareness and the underlying scarcity of infrastructure for proper disposal in these areas. This indicates to reinforce the public awareness programs, behavioral change communication interventions to promote waste segregation behavior, and improve waste disposal infrastructure, especially in remote areas.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving humans were approved by the study was approved by the Institutional Ethical Committee of ICMR – National Institute for Research in Environmental Health, Bhopal (NIREH/BPL/IEC/2020–21/41 dated April 21, 2020) and Institutional ethics committee of R D Gardi Medical College, Ujjain (03/2020 dated March 12, 2020). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – original draft. KK: Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Project administration, Visualization. DR: Formal analysis, Methodology, Investigation, Software, Validation, Writing – review & editing. SS: Validation, Writing – review & editing, Conceptualization, Data curation, Methodology, Supervision, Visualization. VP: Data curation, Methodology, Supervision, Writing – review & editing, Project administration. DV: Data curation, Writing – review & editing, Formal analysis, Visualization. RS: Data curation, Writing – review & editing, Methodology, Project administration, Supervision, Validation. KS: Methodology, Supervision, Validation, Writing – review & editing, Conceptualization. YS: Conceptualization, Methodology, Supervision, Writing – review & editing, Project administration, Visualization. AP: Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing, Validation. MP: Methodology, Project administration, Supervision, Validation, Writing – review & editing. SA: Methodology, Supervision, Writing – review & editing, Conceptualization, Visualization. CS: Methodology, Supervision, Writing – review & editing, Project administration, Resources. KR: Methodology, Supervision, Writing – review & editing, Conceptualization, Investigation, Validation, Visualization. VD: Conceptualization, Investigation, Methodology, Supervision, Writing – review & editing, Data curation, Formal analysis, Funding acquisition, Project administration, Resources.

Funding

The author(s) declared that financial support was received for this work and/or its publication. The work was supported by the Swedish Research Council (FORMAS) for Environment, Agricultural Sciences, and Spatial Planning (grant no: 2019–00439).

Acknowledgments

We sincerely thank the residents of Ujjain city for their participation in this study. We also acknowledge the Ujjain Municipal Corporation for granting permission to conduct the study and for providing logistical support. We are grateful to the staff of ICMR - NIREH (Water & Soil Division) and RDGMC, Ujjain for their assistance in data collection and database management. We also thank all I-MISS project staff and data collectors for their support during the pick analysis. We are further thankful to Mr. Priyank Soni, Ms. Reena Parihar, and Mr. Giriraj Singh for their support in field supervision.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was not used in the creation of this manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2025.1707300/full#supplementary-material

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