Composition analysis (pick analysis) of waste generated from household: A pilot study in Ujjain city, India
Madhanraj Kalyanasundaram1, Kavya Krishnan2, Surya Singh2
1ICMR- National Institute of Epidemiology, Chennai, 600077, Tamil Nadu, India.
Abstract:
Waste segregation is an essential function in improving waste management. Waste segregation not only facilitates recycling and reduces waste going to landfills, rather it can benefit our environment and human in various ways. A pick analysis of waste composition is used to characterize the household waste stream and thus can analyze the segregation rate among the residents. In addition, it can measure the actual waste sorting behaviour at the household/community level. The objective of the study was to assess feasibility of a large-scale waste composition study, identify methodological and operational challenges, and estimate the resources needed to conduct the main waste composition study in order to obtain and get indicative figures about waste generation, composition, and miss-sorted proportions. The study team went door-to-door to collect waste in colour coded bags. We also collected the socio-demographic data of the households. The collected waste was weighed and segregated to analyze the waste composition. The analysis was done among 45 households, and it was found that the per capita waste generation per day is 0.25 kg (0.24 kg from slum and 0.27 kg from non-slum). Challenges identified in conducting waste composition study were lack of standard waste fraction classifications, difficulty in recruitment of personnel to conduct study due to social taboo around waste, challenge in co-coordinating with Ujjain Municipal Corporation waste collection vehicle for collection of waste. 53 household activities were completed in 5 and half hours with INR 24685 (USD 300.5). Pick analysis could be adopted by the Ujjain Municipal Corporation after cost effective analysis to generate precise estimate of waste generation, resource recovery, efficient resource allocation and will help in future interventions and informed policy decision making to improve segregation.
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