Knowledge of the quantity and the type of residual household waste (RHW) generated by a population within a given territory is essential for developing affordable, effective, and sustainable management strategies for waste. This study aims to accurately describe the composition of residential residual materials collected directly from households over the course of a year. Household waste was collected from urban and rural sectors that were representative of the study territory. Samples were collected during the winter, summer, and fall of 2014. A total of 3039 kg of RHW was collected and sorted into 9 categories and 39 subcategories. Statistical analysis showed, except for organic matter, that the weight percentage of each category of material did not significantly differ among sampling periods or locations. Therefore, the results for a category were compiled to generate a single value to calculate the relative abundance of each type of residual material. Organic matter made up the majority fraction of the RHW (53% to 66%). This was followed by plastics (9%), bulky items and renovation/demolition debris (6%), textiles (5%), metals (4%), paper and cardboard fiber (4%), glass (2%), and household hazardous waste (2%). This approach has allowed us to improve the accuracy of the data used in MRM, contribute to the creation of a regional dataset for waste, and develop a methodology more applicable to local realities. Specific to the immediate needs of municipal MRM, we updated knowledge regarding the generation, recovery, and disposal of the contents of the residential sector, and tracked the evolution and the variation of contents over a given period. We believe our methodology is applicable to other regions having similar characteristics in terms of climate, socio-economic status, and other parameters that affect the composition of RHW.
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http://dx.doi.org/10.1016/j.wasman.2018.04.019 | DOI Listing |
Waste Manag
January 2025
Department of Mathematics, University of Padova, Via Trieste, 63, Padova, 35121, Italy; Augmented Intelligence Center, Fondazione Bruno Kessler (FBK), Via Santa Croce, 77, Trento, 38122, Italy; Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, 9, Povo, 38123, Italy.
We explore the application of machine learning (ML) techniques to forecast door-to-door waste collection, addressing the challenges in municipal solid waste (MSW) management. ML models offer a promising solution to optimize waste collection operations, especially amid growing urban populations and evolving waste generation rates. Leveraging comprehensive data from a northeastern Italian municipality, including various waste types, our study investigates ML algorithms' efficacy in predicting household waste collection requirements.
View Article and Find Full Text PDFCien Saude Colet
December 2024
Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz. Rio de Janeiro RJ Brasil.
Estimating average blood pressure levels and prevalence of arterial hypertension (AH) and associated factors is essential to monitoring health and planning actions to combat noncommunicable diseases (NCDs) in Indigenous peoples in Brazil. This is a cross-sectional study that investigated average blood pressure levels and prevalence of arterial hypertension in 4,680 Indigenous women (aged 18-49 years), using data from the 1st National Survey of Health and Nutrition of Indigenous Peoples (2008-2009) and associated factors, such as through gamma regression and multilevel logistics. The prevalence of hypertension was 10.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States of America.
Background: Randomized controlled trials (RCTs) that evaluate the efficacy of an intervention remain underutilized in community-based environmental health research. RCTs that use a pragmatic design emphasize the effectiveness of interventions in complex, real world settings. Pragmatic trials may be especially relevant when community-based interventions address social and environmental determinants that threaten health equity.
View Article and Find Full Text PDFEnviron Res Health
March 2025
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, United States of America.
North Carolina (NC) ranks third among US states in both hog production and hurricanes. NC's hogs are housed in concentrated animal feeding operations (CAFOs) in the eastern, hurricane-prone part of the state. Hurricanes can inundate hog waste lagoons, transporting fecal bacteria that may cause acute gastrointestinal illness (AGI).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Environmental Technology, Faculty of Environment and Resource Studies, Mahasarakham University, Mahasarakham, 44150, Thailand.
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