A review of municipal solid waste data for Harare, Zimbabwe

Municipal solid waste (MSW) data sources in Harare metropolitan province show significantly varying data with regards to generation and composition. The sources of variations include data lumping; exclusion of MSW managed outside the formal system and remain uncollected, lack of a clear definition of what constitutes MSW within the Zimbabwean context as well as temporal variations. It is therefore important for waste generation and characterisation studies to be undertaken building upon the already existing datasets to ensure the accuracy and reliability needed for data credibility for use in MSW management planning. ensure reliability and accuracy for its use as baseline data for sustainable MSW management planning. 2 MATERIALS AND METHODS 2.1 Description of the study area Harare metropolitan province comprises of Harare, the Capital City of Zimbabwe and its 2 dormitory towns of Chitungwiza and Epworth with a total population of just over 2 million (Zimstat, 2013). The uniqueness of Harare metropolitan province is its location upstream in the catchment of its potable water sources. The mismanagement of MSW generated in Harare metropolitan province is contributing to the eutrophic status of Lake Chivero. At present, slightly over 400 thousand tons of municipal solid waste is generated in Harare metropolitan province (Makarichi et al., 2019) with reported collection falling from 52% in 2011 to 48.7% in 2016 (EMA, 2016) indicating that almost half of the MSW generated remaining uncollected. Solid waste generated in Harare metropolitan province is being indiscriminately collected and dumped at the three official poorly managed dumpsites which are unprotected without leachate infiltration into groundwater prevention mechanisms namely Pomona for Harare, Chitungwiza for Chitungwiza and Golden Quarry for Epworth. Pomona covers an area of 100 hectares and has been operational since 1985 (Chijarira, 2013). The City of Harare Management records of 2010 indicate that the disposal capacity of Pomona dumpsite is expected to be exhausted by 2020. This calls for the need to redesign and define future integrated and sustainable municipal solid waste management strategies. Such future management strategies can only be feasible if reliable and accurate MSW data on generation, composition, characteristics and properties is available. Hence need to assess the accuracy and reliability of the available data which is the purpose of this study. 2.2 Review of few selected MSW generation and characterisation methodologies MSW constitutes household waste generally reported to constitute between 55 to 80% with markets and or commercials areas constituting between 10 to 30% and varying contributions from institutions, streets and industries (Nabegu, 2010, Okot-Okumu, 2012). Therefore, MSW data from these sources need to be accounted for in any MSW data to ensure its reliability and accuracy. Estimating MSW data should involve the collection of MSW from where it is generated (households, restaurants, streets, supermarkets, offices) according to the criteria established by Tchobanoglous and Kreith (2002) as well as ensuring that MSW managed outside the official management system is also incorporated as argued by Abel (2007). Temporal variations on a seasonal, monthly and week day scale (Tchobanoglous et al., 1993, Vesilind et al., 2002, Hanc et al., 2011, Gómez et al., 2009, Denafas et al., 2014) and geospatial variations (Miezah et al., 2015) exist in the quantity and composition of MSW generated depending on the prevailing socio economic situation. Estimation of MSW generation and characterisation data therefore need to consider all the MSW streams, temporal and spatial variations and the socio economic or demographic profiling (low density or high income, high density or low income and medium density or medium income of households). Palanivel and Sulaiman (2014) randomly collected three 20kgs samples of MSW being disposed at a landfill per fortnight in winter and summer thereby considering seasonal variations and assumed 100% MSW collection efficiency which is rarely the case as there is also MSW that remains uncollected and managed outside the official systems. Suthar and Singh (2015) selected a sample of 144 households from 11 systematically identified blocks of varying socio economic status in Dehradun city of India. MSW generated from restaurants, supermarkets, hotels, schools, offices and streets was considered with no seasonal variations bringing some limitations regarding accuracy and reliability of the MSW data. Dali et al (2011) used three-stage stratified cluster sampling technique to analyse solid waste generated from 336 households that represented four socio-economic strata of Kathmandu Metropolitan City in Nepal considering MSW generated from restaurants, hotels, schools and streets as well and assuming the negligibility of temporal scale variations. Miezah et al (2015) considered three socio economic classes where households were determined using stratified, purposive and direct sampling technique in all the Capital Cities of the ten regions in Ghana without considering alternative MSW streams and temporal variations. 2.3 Available MSW data for Harare metropolitan province Three sources of MSW data in Harare metropolitan province were obtained and analysed (Zimstat, 2016, EMA, 2014, Makarichi et al., 2019). The Ministry of Environment, Water and Climate (MEWC) in 2011 contracted the Institute of Environmental Studies (IES) of the University of Zimbabwe to undertake a baseline assessment of waste generation and management systems that characterised Zimbabwe in 2011 whose outcome facilitated the development of the national integrated solid waste management plan. The national biennial urban waste data collected by Zimstat (2016) is used by the United Nations Statistics Division (UNSD) and United Nations Environment Programme in the development of the UNSD International Environment Statistics Database. Makarichi (2019) estimated waste composition and generation to assess the suitability of MSW generated in Harare metropolitan province for thermochemical waste to energy conversion. The accuracy and reliability of these MSW data sources together with the appropriateness of the methodology used for data collection and estimation is vital in that the national integrated solid waste management plan was developed based on the EMA data, and also the UNSD International Environment Statistics Database is a source of data used by various stakeholders for decision making, research , and as well as thermochemical waste to energy conversion options in Harare. 3 RESULTS AND DISCUSSIONS Tables 1 – 6 show the national, Harare metropolitan province and city specific MSW generation and composition for the three data sources. Table 1. MSW generation in Zimbabwean urban environments (Zimstat, 2016, EMA, 2014) Waste stream Zimstat, 2016 EMA, 2014***** 2014 2015 2011 1,000 tons Commercial activities 485,72 Academic activities 72,03 Medical activities 34,14 Industrial activities 442,84 Other economic activities 100.53* 126.16*** Residential areas or households 291.64** 293.18 **** 614.84 Total 392.16 419.34 1649.57 *Data refer to Bindura, Bulawayo, Chitungwiza, Epworth and Mvurwi only **Data refer to Bindura, Bulawayo, Chitungwiza, Epworth, Kariba, Kwekwe, Masvingo, Mutare, Mvurwi, Norton, Nyanga and Plumtree only ***Data refer to Beitbridge, Bindura, Bulawayo, Chitungwiza, Epworth and Mvurwi only ****Data refer to Beitbridge, Bindura, Bulawayo, Chitungwiza, Epworth, Kariba, Kwekwe, Masvingo, Mutare, Mvurwi, Norton, Nyanga and Plumtree only ***** Data refer to Harare, Bulawayo, Chitungwiza, Mutare, Gweru, Masvingo, Chinhoyi, Chegutu, Ruwa, Epworth, Domboshava and Murehwa Table 1 shows that the national MSW generation data possesses discrepancies possibly emanating from a number of factors. The Zimstat datasets only considers MSW collected and managed within the official systems of urban environments leading to underestimation. What constitutes MSW differs in both datasets with Zimstat datasets considering other sources apart from households waste namely waste generated from ISIC divisions 36, 37, 39 and 45 to 99 while excluding waste from ISIC 38 activities associated with waste collection, treatment and disposal and materials recovery. The EMA data includes all solid waste from households or residential areas including other solids that does not constitute MSW with annual solid waste figures from commercial, academic, medical institutions and industry also being lumped inclusive of MSW constituents as shown in Table 3. The lumping associated with the EMA dataset therefore brings along with challenges in extracting accurate and reliable MSW data. Both datasets in Table 1 are not for the same urban environments and do not cover all the national urban environments resulting in underestimation and distortions. Table 2. Harare metropolitan province MSW generation data (Zimstat, 2016) Category Unit 2014 2015 Total population of the Province 1,000 inhabitants 2,067.50 2,123.11 Average percentage population served by MW collection % 61.40* 67.45* Total amount of municipal waste generated