Imperial College London


Faculty of EngineeringDepartment of Civil and Environmental Engineering

Honorary Research Associate



+44 (0)20 7594 6120m.sule08 Website




304Skempton BuildingSouth Kensington Campus






BibTex format

author = {Kis, Z and Koppelaar, RHEM and Sule, MN and Mensah, FK and Wang, X and Triantafyllidis, C and Van, Dam KH and Shah, N},
doi = {10.3390/w10091278},
journal = {Water},
title = {Framework for WASH sector data improvements in data-poor environments, applied to Accra, Ghana},
url = {},
volume = {10},
year = {2018}

RIS format (EndNote, RefMan)

AB - Improvements in water, sanitation and hygiene (WASH) service provision are hampered by limited open data availability. This paper presents a data integration framework, collects the data and develops a material flow model, which aids data-based policy and infrastructure development for the WASH sector. This model provides a robust quantitative mapping of the complete anthropogenic WASH flow-cycle: from raw water intake to water use, wastewater and excreta generation, discharge and treatment. This approach integrates various available sources using a process-chain bottom-up engineering approach to improve the quality of WASH planning. The data integration framework and the modelling methodology are applied to the Greater Accra Metropolitan Area (GAMA), Ghana. The highest level of understanding of the GAMA WASH sector is achieved, promoting scenario testing for future WASH developments. The results show 96% of the population had access to improved safe water in 2010 if sachet and bottled water was included, but only 67% if excluded. Additionally, 66% of 338,000 m3 per day of generated wastewater is unsafely disposed locally, with 23% entering open drains, and 11% sewage pipes, indicating poor sanitation coverage. Total treated wastewater is <0.5% in 2014, with only 18% of 43,000 m3 per day treatment capacity operational. The combined data sets are made available to support research and sustainable development activities.
AU - Kis,Z
AU - Koppelaar,RHEM
AU - Sule,MN
AU - Mensah,FK
AU - Wang,X
AU - Triantafyllidis,C
AU - Van,Dam KH
AU - Shah,N
DO - 10.3390/w10091278
PY - 2018///
SN - 2073-4441
TI - Framework for WASH sector data improvements in data-poor environments, applied to Accra, Ghana
T2 - Water
UR -
UR -
VL - 10
ER -