Infiltrated Pits: Using Regional Groundwater Data to Estimate Methane Emissions from Pit Latrines

• Published in: Hydrology 2023-05, Vol.10 (5), p.114
• Main Authors: Reddy, Olivia, Rahman, Mostaquimur, Nijhawan, Anisha, Pregnolato, Maria, Howard, Guy
• Format: Article
• Language: English
• Subjects: Anaerobic conditions; Anoxic conditions; Climate change; Contamination; Depth perception; Distribution; Emissions; Environmental aspects; Floods; greenhouse gas emissions; Groundwater; Groundwater data; Groundwater levels; Groundwater table; Hydrologic data; Hydrology; Incentives; Information systems; Intergovernmental Panel on Climate Change; Interpolation; Latrines; Mathematical functions; Methane; onsite sanitation; pit latrine; Population; Remote sensing; Rural areas; Sanitation; Sanitation facilities; Sanitation systems; Seasonal variation; Seasonal variations; Toilets; Water table; Water, Underground
• ISSN: 2306-5338; 2306-5338
• DOI: 10.3390/hydrology10050114

On-site sanitation systems (OSS), such as pit latrines, are an important source of methane (CH4), with emissions increasing when they are wet, and this occurs when anaerobic conditions dominate. This paper presents the development of a model, which uses seasonal changes in groundwater to account for the fluctuating inundation of pit latrines, and, therefore, the associated CH4 emissions from varying degrees of anerobic conditions are examined. Given that observed timeseries of groundwater table depth at high enough spatial and temporal resolutions are often difficult to obtain in low- and middle-income countries (LMICs), inverse distance weighted (IDW) interpolation is used to generate values for a whole region, which is then used, alongside average pit latrine depth, to determine areas of pit latrine inundation. Outcomes are further informed with open-source contextual data, covering population, urban/rural split, and sanitation facility data, before using methodologies from the Intergovernmental Panel on Climate Change (IPCC) to generate CH4 emissions data. As a case study, we use data from Senegal to illustrate how this model works. Results show total CH4 emissions for the month of January to be ~1.69 kt CH4. We have also discussed the potential use of satellite remote sensing data in regions where access to historical groundwater data is limited. Understanding when the pit conditions are most likely to change could lead to incentives for better management strategies, as well as a reduction in CH4 production.