A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument
Methane is a potent greenhouse gas emitted into the atmosphere by anthropogenic (60%) and biological (40%) sources. Its growth is attributed to the industrial revolution, with sectors such as energy production, agriculture, and waste treatment taking the lead as emitters. South Africa is committed t...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2022
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| Online Access: | https://eprints.nottingham.ac.uk/71808/ |
| _version_ | 1848800691739426816 |
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| author | Maliehe, K.A. |
| author_facet | Maliehe, K.A. |
| author_sort | Maliehe, K.A. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Methane is a potent greenhouse gas emitted into the atmosphere by anthropogenic (60%) and biological (40%) sources. Its growth is attributed to the industrial revolution, with sectors such as energy production, agriculture, and waste treatment taking the lead as emitters. South Africa is committed to monitoring its growth to lessen the effects of climate change on the globe. The study identified methane (CH4) emission hotspots over South Africa from space-based solar backscatter measurements using observations of TROPOspheric Monitoring Instrument (TROPOMI) and compared observed concentrations to surface in-situ data and to an Emissions Database for Global Atmospheric Research (EDGAR) database. Even though no statistical correlation was found between the space-based observations and bottom-up inventory, correlations could be identified visually. Weak positive correlation exists between the space-based observations and surface observations. Monthly CH4 total-averaged dry air mole fraction (XCH4) were predicted for the year 2022 using seven statistical models based on a time series of three and half years. The predictions were compared to actual monthly XCH4 for 2022 and evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE) performance metrics. The Holt-Winters’s additive (HWA) model performed best with a RMSE of 4.95 and MAPE of 25% due to its capability to capture both the trend and seasonality components of the data well. The study demonstrated the capability of TROPOMI for the estimation of CH4 concentrations in the atmosphere and the identification of trend patterns along both spatial and temporal profiles. |
| first_indexed | 2025-11-14T20:55:35Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-71808 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:55:35Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-718082025-02-28T15:16:39Z https://eprints.nottingham.ac.uk/71808/ A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument Maliehe, K.A. Methane is a potent greenhouse gas emitted into the atmosphere by anthropogenic (60%) and biological (40%) sources. Its growth is attributed to the industrial revolution, with sectors such as energy production, agriculture, and waste treatment taking the lead as emitters. South Africa is committed to monitoring its growth to lessen the effects of climate change on the globe. The study identified methane (CH4) emission hotspots over South Africa from space-based solar backscatter measurements using observations of TROPOspheric Monitoring Instrument (TROPOMI) and compared observed concentrations to surface in-situ data and to an Emissions Database for Global Atmospheric Research (EDGAR) database. Even though no statistical correlation was found between the space-based observations and bottom-up inventory, correlations could be identified visually. Weak positive correlation exists between the space-based observations and surface observations. Monthly CH4 total-averaged dry air mole fraction (XCH4) were predicted for the year 2022 using seven statistical models based on a time series of three and half years. The predictions were compared to actual monthly XCH4 for 2022 and evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE) performance metrics. The Holt-Winters’s additive (HWA) model performed best with a RMSE of 4.95 and MAPE of 25% due to its capability to capture both the trend and seasonality components of the data well. The study demonstrated the capability of TROPOMI for the estimation of CH4 concentrations in the atmosphere and the identification of trend patterns along both spatial and temporal profiles. 2022-12-13 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/71808/1/MRes_Dissertation%20_MALIEHE_KA.pdf Maliehe, K.A. (2022) A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument. MRes thesis, University of Nottingham. Methane TROPOMI Time series Forecasting Greenhouse gases Emissions Sentinel-5P |
| spellingShingle | Methane TROPOMI Time series Forecasting Greenhouse gases Emissions Sentinel-5P Maliehe, K.A. A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title | A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title_full | A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title_fullStr | A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title_full_unstemmed | A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title_short | A spatiotemporal analysis of methane emissions in South Africa using observations of Sentinel-5P’s TROPOspheric Monitoring Instrument |
| title_sort | spatiotemporal analysis of methane emissions in south africa using observations of sentinel-5p’s tropospheric monitoring instrument |
| topic | Methane TROPOMI Time series Forecasting Greenhouse gases Emissions Sentinel-5P |
| url | https://eprints.nottingham.ac.uk/71808/ |