Spatio-Temporal Analysis of Greenhouse Gases Concentration in Nigeria from 2019 to 2023 Using Sentinel-5P

Spatio-Temporal Analysis of Greenhouse Gases Concentration in Nigeria from 2019 to 2023 Using Sentinel-5P | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatio-Temporal Analysis of Greenhouse Gases Concentration in Nigeria from 2019 to 2023 Using Sentinel-5P Akinlabi Akintuyi, Emmanuel Wunude, Progress Akpan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7847659/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Greenhouse gases (GHGs) are critical components of Earth’s atmosphere, playing an essential role in maintaining the planet’s energy. This study analysed the spatial and temporal dynamics of GHGs - carbon monoxide (CO), methane (CH₄), nitrogen dioxide (NO₂), ozone (O₃), and sulphur dioxide (SO₂) emissions in Nigeria from 2019 to 2023, utilizing satellite data from the Sentinel-5 Precursor processed through Google Earth Engine (GEE) and results were obtained through JavaScript coding. Findings reveal distinct emission patterns: CO levels fluctuated moderately, peaking at 0.043 mol/m² in 2020 before declining to 0.041 mol/m² by 2023, suggesting incremental air quality improvements. Conversely, CH₄ concentrations rose steadily from 1,878.17 ppb to 1,924.82 ppb, linked to escalating fossil fuel extraction and agricultural practices. NO₂ exhibited a consistent upward trajectory, reaching 0.0000240 mol/m² in 2023, driven by industrial expansion and vehicular emissions. O₃ levels remained stable near 0.120 ppm, while SO₂ emissions, predominantly from industrial sources, increased marginally to 0.0000063 mol/m² by 2023. The study underscores Nigeria’s evolving GHGs profile, emphasizing the urgent need for stringent emission controls, particularly targeting industrial pollutants and methane sources. Policy recommendations include transitioning to renewable energy, enforcing stricter industrial regulations, and promoting sustainable agricultural practices to curb rising emissions. Although CO reductions indicate partial progress, the persistent rise in CH₄, NO₂, and SO₂ highlights gaps in current mitigation strategies. This study advocates for integrated waste management systems and enhanced monitoring frameworks to address Nigeria’s growing climate challenges and align with global sustainability goals. Geographic Information Systems Climate Analysis and Modeling Atmospheric concentration Google Earth Engine Greenhouse gases (GHGs) Remote Sensing Sentinel-5P Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 INTRODUCTION Greenhouse gases (GHGs) are critical components of Earth’s atmosphere, playing an essential role in maintaining the planet’s energy balance by trapping heat through a process known as the greenhouse effect. This process is essential for sustaining temperatures that allow life to thrive. However, anthropogenic activities, particularly fossil fuel combustion, deforestation, and industrialization, have drastically altered GHG levels, leading to increased global temperatures, a phenomenon commonly referred to as global warming. The accumulation of GHGs such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) since the industrial revolution has been unparalleled in recent history, with CO₂ levels now exceeding pre-industrial averages by over 130 parts per million (ppm). This escalation has profound implications for global climate stability, with rising GHG concentrations altering weather patterns, disrupting ecosystems, and posing risks to human health and infrastructure (Intergovernmental Panel on Climate Change (IPCC), 2021). Methane (CH₄) is of particular concern in the global warming context due to its high global warming potential, which is over 20 times that of CO₂ on a per-molecule basis. CH₄ levels have increased significantly due to a variety of factors, including fossil fuel extraction, agricultural activities (particularly rice paddies and livestock), and waste management practices (International Energy Agency (IEA), 2024). The role of methane in exacerbating climate change has attracted considerable research attention, as its sources are often localized and concentrated in specific industrial and agricultural regions. In Nigeria, for instance, fossil fuel extraction, notably in the oil-rich Niger Delta, is a major contributor to CH₄ emissions through practices like gas flaring and leakage during oil drilling. These practices contribute to localized increases in atmospheric CH₄ posing both regional and global environmental risks (Faraday & Oluwabunmi, 2024). Additionally, N₂O is another potent GHG that, despite its lower concentration compared to CO₂ and CH₄, has a significant impact on the climate due to its high global warming potential and its longevity in the atmosphere. N₂O emissions primarily arise from agricultural activities, including fertilizer use, and to a lesser extent, from industrial processes and fossil fuel combustion. Furthermore, the widespread use of synthetic fertilizers and the expansion of intensive agricultural practices have contributed to the steady increase in N₂O emissions. This trend is concerning as N₂O contributes to both climate change and the depletion of the stratospheric ozone layer, which protects life on Earth from harmful ultraviolet (UV) radiation (Tian et al. , 2023; Omokpariola et al. , 2024). Understanding the spatial and temporal distribution of these GHGs is essential for developing effective climate policies. Globally, there is significant spatial variability in GHG concentrations, often reflecting differences in economic activity, industrialization levels, and regional climate conditions (WHO, 2023). In Nigeria, GHG emissions are closely tied to the country’s socio-economic dynamics, with key contributions from the energy, transportation, and agricultural sectors (Kevin-Alerechi, 2022). The rapid urbanization and industrialization in Nigerian cities, particularly in Lagos and Abuja, have led to increased emissions from vehicles and industry. Conversely, rural areas experience significant emissions due to deforestation, land-use changes, and agricultural activities, which are further intensified by population growth and demand for resources (Fasona et al. , 2019; Akintuyi et al. , 2021). While direct measurement of GHGs through ground-based monitoring is limited in Nigeria, advancements in remote sensing technology have provided invaluable data for tracking and analysing atmospheric GHG levels. Satellite missions such as NASA’s Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse Gas Observing Satellite (GOSAT) developed by the Japan Aerospace Exploration Agency, and the Sentinel-5 Precursor (S5P) by the European Space Agency have revolutionized GHG monitoring by offering large-scale, continuous data on atmospheric CO₂, CH₄, and other pollutants (Reuter et al. , 2022; IPCC, 2023; Nassar et al. , 2023). These remote sensing techniques are particularly valuable for regions like Nigeria, where ground-based monitoring infrastructure is limited. By measuring the absorption of sunlight in specific wavelengths, satellites like OCO-2 and S5P can provide high-resolution data on GHG concentrations, enabling scientists to identify emission hotspots, track seasonal trends, and estimate emissions with unprecedented accuracy. Furthermore, remote sensing data can reveal how specific activities, such as industrial emissions or agricultural practices, contribute to regional climate change dynamics. In Nigeria, the deployment of satellite remote sensing technologies has proven critical for detecting and mapping high-emission zones. These include the Niger Delta region, where industrial fossil fuel extraction and gas flaring dominate emissions, and northern agricultural areas, which exhibit elevated methane (CH₄) concentrations due to enteric fermentation in livestock and anaerobic conditions in irrigated rice paddies (Adagunodo et al. , 2022). Addressing Nigeria’s GHG emissions is critical for advancing global climate mitigation efforts and achieving sustainable development priorities. As a signatory to the Paris Agreement, Nigeria has outlined ambitious targets in its updated Nationally Determined Contributions (NDCs), including the elimination of routine gas flaring by 2030, scaling renewable energy infrastructure to supply 30% of national electricity by 2030, and adopting climate-smart agricultural practices to curb methane (CH₄) emissions from livestock and rice production (Federal Government of Nigeria, 2021; World Bank, 2022; UNEP, 2023). However, realizing these commitments requires robust, spatially explicit emissions data to inform evidence-based policymaking. Emerging remote sensing technologies, such as the European Space Agency’s Sentinel-5P TROPOMI and NASA’s OCO-3, now enable high-resolution monitoring of CO₂ and CH₄ hotspots across Nigeria’s oil-producing Niger Delta and northern agricultural zones. These datasets, when integrated with localized ground-truthing campaigns, can address historical gaps in emission inventories and enhance accountability in mitigation strategies (Adagunodo et al. , 2022; Parker et al. , 2023; Okoduwa & Amaechi, 2023). This study aims to contribute to these efforts by conducting a temporal and spatial analysis of GHG concentrations in Nigeria from 2019 to 2023, utilizing satellite data from the Sentinel-5P mission focusing on CO, CH₄, NO₂, O₃, and SO₂. MATERIALS AND METHODS Study Area The study area lies approximately between longitudes 3°E and 15°E and latitudes 4°N and 14°N, spanning a land area of approximately 923,768 km 2 . The study area is located on the west of Africa bordering the Atlantic Ocean in the south, Benin Republic in the west, Niger Republic in the north and Cameroun in the east (Fig. 1). The geographical location spans a broad range of ecosystems and climates, making it an essential area for understanding greenhouse gas (GHG) dynamics. Nigeria is Africa’s most populous country with an estimated population of 218.5 million (Akinyemi & Isiugo-Abanihe, 2024) and one of the continent’s major contributors to GHG emissions due to its reliance on fossil fuels, expanding urbanization, and agricultural activities. Nigeria experiences a predominantly warm climate throughout the year, characterized by distinct wet and dry seasons. The wet season typically occurs from April to October, while the dry season spans November to March. The study area experiences an average annual rainfall of approximately 1,750 millimetres. Coastal regions typically receive higher amounts due to their proximity to the Atlantic Ocean. Annually, the dry season occurs from November to March and is characterized by the influence of the Harmattan winds originating from the Sahara Desert. These winds contribute to December and January being the driest months of the year (Onafeso, 2023). The study area plays a critical role in global and regional climate change discussions. As an oil-dependent economy, the country’s industrial activities especially in the Niger Delta are a significant source of CH₄ and CO emissions. Gas flaring, deforestation, and urbanization exacerbate environmental issues, contributing to increased GHG concentrations. Additionally, Nigeria’s diverse land use, which includes agriculture, industrial development, and growing urban populations, makes it a prime location for examining the effects of anthropogenic activities on GHG concentrations. Data Source and Characteristics The study used the data derived from the Sentinel-5P satellite which is the heart of the data framework for monitoring greenhouse gases (GHGs) and assessing air quality. Equipped with the TROPOMI (Tropospheric Monitoring Instrument) sensor to gather detailed information about a range of atmospheric gases such as carbon monoxide (CO), methane (CH₄), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and ozone (O₃). As illustrated in Table 1 , the dataset provides nationwide coverage of Nigeria for the years 2019 to 2023. Sentinel-5P satellite data, distinguished by its spatial resolution of 7 km x 3.5 km, was selected to capture detailed spatiotemporal variations in CO, CH₄, NO₂, SO₂, and O₃ concentrations across Nigeria. Table 1 Sources and characteristics of data Data Type Description Resolution / Scale Temporal Coverage Sources Carbon Monoxide (CO) Satellite data on atmospheric pollutants (CO₂), Nigeria Spatial & spectral resolutions – 3.5 x 5.5 km (UV: 270–320 nm) (VIS: 310–500 nm) 7 x 7 km (NIR: 675–775 nm). (SWIR: 2305–2385 nm) January 2019 – December 2023 (Daily coverage) Copernicus Data Space Ecosystem https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO Methane (CH₄) Satellite data on atmospheric pollutants (CH₄)Nigeria Spatial & spectral resolutions – 3.5 x 5.5 km (UV: 270–320 nm) (VIS: 310–500 nm) 7 x 7 km (NIR: 675–775 nm). (SWIR: 2305–2385 nm) January 2019 – December 2023 (Daily coverage) Copernicus Data Space Ecosystem https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4 Nitrogen Dioxide (NO₂) Satellite data on atmospheric pollutants (NO₂), Nigeria Spatial & spectral resolutions – 3.5 x 5.5 km (UV: 270–320 nm) (VIS: 310–500 nm) 7 x 7 km (NIR: 675–775 nm). (SWIR: 2305–2385 nm) January 2019 – December 2023 (Daily coverage) Copernicus Data Space Ecosystem https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 Sulphur Dioxide (SO₂) Satellite data on atmospheric pollutants (SO₂), Nigeria Spatial & spectral resolutions – 3.5 x 5.5 km (UV: 270–320 nm) (VIS: 310–500 nm) 7 x 7 km (NIR: 675–775 nm). (SWIR: 2305–2385 nm) January 2019 – December 2023 (Daily coverage) Copernicus Data Space Ecosystem https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2 Ozone (O₃) Satellite data on atmospheric pollutants (O 3 ), Nigeria Spatial & spectral resolutions – 3.5 x 5.5 km (UV: 270–320 nm) (VIS: 310–500 nm) 7 x 7 km (NIR: 675–775 nm). (SWIR: 2305–2385 nm) January 2019 – December 2023 (Daily coverage) Copernicus Data Space Ecosystem https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3 Administrative Map Vector 1:250,000 NA LABCARS, Dept. of Geography, University of Lagos, Lagos This concentration data, acquired from the Copernicus Sentinel Data Hub, comprised atmospheric column measurements. These records were systematically analysed to identify temporal patterns and spatial distributions over the study period. In order to account for seasonal variations in influencing factors, pollutant concentrations were analysed separately for the rainy (April–October) and dry (November–March) seasons. During the wet season, reduced pollutant levels are typically observed, attributed to enhanced atmospheric scavenging by precipitation. Conversely, the dry season is characterized by elevated concentrations, predominantly driven by increased emissions from industrial and vehicular sources. Data Processing and Analysis This study employed a systematic methodology for processing and analysing Sentinel-5P satellite data to assess air quality within Nigeria. Geospatial filtering was applied utilizing the Country’s administrative boundary shapefile, ensuring analysis was confined to the defined geographic extent. Daily gridded pollutant concentration data underwent extraction and subsequent temporal aggregation to derive monthly and annual averages, facilitating the discernment of long-term temporal patterns. Concurrently, rigorous quality control was implemented through the application of pre-existing Google Earth Engine (GEE) cloud masks to identify and exclude cloud-contaminated pixels, thereby mitigating atmospheric interference and enhancing data fidelity. To evaluate distinct seasonal variations, monthly averages were further aggregated into seasonal means CO, CH₄, NO₂, SO₂, and O₃, specifically comparing the dry season (November–March) and the rainy season (April–October). Additionally, a year-on-year analytical framework was employed to quantify temporal variations in air quality, explicitly capturing the impact of discrete events such as the 2020 COVID-19 pandemic lockdown. This temporal analysis leveraged Google Earth Engine's (GEE) integrated statistical reducers, including mean and standard deviation computations. Concurrently, GEE's JavaScript-based application programming interface (API) facilitated interactive spatiotemporal data visualization and time-series exploration. To elucidate underlying pollutant trends, linear regression models were applied, yielding insights into both spatial and temporal patterns across Nigeria. Furthermore, GEE enabled the systematic generation of geospatial distributions depicting concentrations of CO, CH₄, NO₂, SO₂, and O₃. These distributions, visualized via colour-coded thematic maps, served to identify significant pollution hotspots, with pronounced intensity correlating spatially with areas of dense anthropogenic emission sources (Fig. 2 ). Collectively, this integrative methodological approach provides a robust characterization of air quality dynamics and pollution distribution throughout the study period. RESULTS AND DISCUSSION Spatial Concentration of CO, 2019 - 2023 The study revealed that CO concentrations across Nigeria ranged from 0.031 and 0.059 mol/m 2 during the study period, exhibiting slight interannual fluctuations (Figure 3). A slight reduction was observed in 2023, with maximum concentrations declining to 0.053 mol/m 2 . Spatial Concentration of CH 4 , 2019 - 2023 The spatial CH 4 concentration increased substantially nationwide, rising 17.9% from 1,758.01 mol/m 2 in 2019 to 2,073.31 mol/m 2 as presented in Figure 4. Spatial Concentration of NO 2 , 2019 - 2023 The study further revealed as presented in Figure 5 that NO 2 concentration ranged from 0.000012 to 0.000134 mol/m 2, peaking in 2021 across the study area. Spatial Concentration of O 3 , 2019 - 2023 The O 3 concentration remained relatively stable nationally (0.117 - 0.123 mol/m 2 ), with a subtle peak in 2022 (0.1227 mol/m 2 ) as presented in Figure 6. Spatial Concentration of SO 2 , 2019 - 2023 The SO 2 concentrations remained low throughout the study period (-0.000132 to 0.000146 mol/m 2 ), with negative values indicating retrieval uncertainties as shown in Figure 7. Temporal Trends The mean concentration of CO for the five-year period exhibited a slight overall downward trend as presented in Figure 8. The average was 0.042 in 2019, rose slightly to 0.043 in 2020, and subsequently decline to 0.041 by 2023. The pattern indicates some initial variability, followed by a sustained downward trajectory. The diverging trends observed highlight the complex interplay of various anthropogenic sectors and policy effectiveness. The marginal decrease in CO concentrations post-2020 is a positive indicator, potentially resulting from improved vehicular emissions standards and a gradual shift towards cleaner technologies (Shi et al., 2021). The anomalous 2020 increase may be attributed to pandemic-related shifts, where reduced transportation emissions were potentially offset by increased residential energy use and sustained operations in certain industrial sectors (Venter et al., 2020). CH 4 concentrations demonstrated a clear and persistent upward trajectory for the period of study. The mean concentration increased steadily from 1878.17 ppb in 2019 to 1924.82 ppb in 2023 (Figure 9), making a net increase of 46.65 units over the study period, indicating a significant rise in emissions. This increase is strongly linked to the expansion of fossil fuel infrastructure, which is prone to systematic leaks (Alvarez et al., 2018), and agricultural sources, particularly enteric fermentation from growing livestock populations and methane generation from landfills due to inadequate waste management practices (Saunois et al., 2020). The steady year-on-year growth suggests that current measures are profoundly insufficient to curb emissions from these sectors. The mean concentration of NO₂ showed a consistent increase. Starting from a low of 0.0000204 in 2020, concentrations rose steadily, peaking at 0.00002405 in 2023 as presented in Figure 10. The rise in NO₂ concentrations aligns with increased vehicular traffic, industrial activity, and fossil-fuel-based power generation, particularly following the economic recovery post-2020 (Dutheil et al., 2021). As a key precursor to particulate matter and ozone, increasing NO₂ levels pose a direct threat to respiratory health. The stability of ozone concentrations, despite rising NO₂ levels, is likely due to the non-linear photochemistry of ozone formation, which can be titrated in high-NOₓ environments, and the competing effects of emission control regulations on its precursors (Monks et al., 2015). The marginal upward trend may be driven by the overall increase in precursor emissions. Tropospheric ozone levels remained relatively stable. The mean concentration ranged from 0.119 in 2019 to a peak of 0.122 in 2022. The overall trend suggests a very slight increase, but the changes are minimal as shown in Figure 11. The average concentration of SO₂ showed a progressive increase from 2019 to 2023, beginning at -0.0000152 and reaching 0.0000063 as shown in Figure 12. This positive trend, while numerically small, is environmentally significant. The increase in SO₂, a primary marker of fossil fuel combustion (particularly coal and heavy oils) in industry and power plants, points to a continued and growing reliance on these polluting fuels (Klimont et al., 2013). In the Nigerian context, this is intrinsically linked to the oil and gas industry and insufficient flaring practices (Otu et al., 2021). The persistence of SO₂ emissions indicates a critical gap in regulatory enforcement and a lag in the transition to cleaner energy sources, exacerbating risks of acid rain and respiratory illnesses. CONCLUSION AND RECOMMENDATIONS The analysis from 2019 to 2023 reveals a concerning trajectory for air quality. The decline in CO is a modest success, but it is overwhelmingly counteracted by the significant and steady rise in CH₄, NO₂, and SO₂. The stability of O₃ is precarious and susceptible to increases with rising precursor emissions. To counter these trends, a comprehensive and enforced policy framework is essential in Nigeria. There should be enhanced monitoring and enforcement, whereby the regulatory bodies to monitor emissions and enforce existing air quality standards, particularly for the oil/gas sectors. Future research should employ source apportionment modelling to precisely quantify contributions from different sectors and assess the health burden attributable to these pollutant trends. Declarations Funding This research received no external funding. All expenses related to data acquisition, processing, and analysis were covered by the authors. 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12","display":"","copyAsset":false,"role":"figure","size":53767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Concentration of SO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e, 2019-2023\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7847659/v1/e1f500d943e17c58fc0213a6.png"},{"id":93640305,"identity":"e739b802-52b1-4821-bd6a-380c238252f2","added_by":"auto","created_at":"2025-10-16 02:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3979002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847659/v1/3c1c1272-5613-49a4-834b-67673b265063.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSpatio-Temporal Analysis of Greenhouse Gases Concentration in Nigeria from 2019 to 2023 Using Sentinel-5P\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGreenhouse gases (GHGs) are critical components of Earth\u0026rsquo;s atmosphere, playing an essential role in maintaining the planet\u0026rsquo;s energy balance by trapping heat through a process known as the greenhouse effect. This process is essential for sustaining temperatures that allow life to thrive. However, anthropogenic activities, particularly fossil fuel combustion, deforestation, and industrialization, have drastically altered GHG levels, leading to increased global temperatures, a phenomenon commonly referred to as global warming. The accumulation of GHGs such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) since the industrial revolution has been unparalleled in recent history, with CO₂ levels now exceeding pre-industrial averages by over 130 parts per million (ppm). This escalation has profound implications for global climate stability, with rising GHG concentrations altering weather patterns, disrupting ecosystems, and posing risks to human health and infrastructure (Intergovernmental Panel on Climate Change (IPCC), 2021). Methane (CH₄) is of particular concern in the global warming context due to its high global warming potential, which is over 20 times that of CO₂ on a per-molecule basis. CH₄ levels have increased significantly due to a variety of factors, including fossil fuel extraction, agricultural activities (particularly rice paddies and livestock), and waste management practices (International Energy Agency (IEA), 2024). The role of methane in exacerbating climate change has attracted considerable research attention, as its sources are often localized and concentrated in specific industrial and agricultural regions. In Nigeria, for instance, fossil fuel extraction, notably in the oil-rich Niger Delta, is a major contributor to CH₄ emissions through practices like gas flaring and leakage during oil drilling. These practices contribute to localized increases in atmospheric CH₄ posing both regional and global environmental risks (Faraday \u0026amp; Oluwabunmi, 2024). Additionally, N₂O is another potent GHG that, despite its lower concentration compared to CO₂ and CH₄, has a significant impact on the climate due to its high global warming potential and its longevity in the atmosphere. N₂O emissions primarily arise from agricultural activities, including fertilizer use, and to a lesser extent, from industrial processes and fossil fuel combustion. Furthermore, the widespread use of synthetic fertilizers and the expansion of intensive agricultural practices have contributed to the steady increase in N₂O emissions. This trend is concerning as N₂O contributes to both climate change and the depletion of the stratospheric ozone layer, which protects life on Earth from harmful ultraviolet (UV) radiation (Tian \u003cem\u003eet al.\u003c/em\u003e, 2023; Omokpariola \u003cem\u003eet al.\u003c/em\u003e, 2024).\u003c/p\u003e\u003cp\u003eUnderstanding the spatial and temporal distribution of these GHGs is essential for developing effective climate policies. Globally, there is significant spatial variability in GHG concentrations, often reflecting differences in economic activity, industrialization levels, and regional climate conditions (WHO, 2023). In Nigeria, GHG emissions are closely tied to the country\u0026rsquo;s socio-economic dynamics, with key contributions from the energy, transportation, and agricultural sectors (Kevin-Alerechi, 2022). The rapid urbanization and industrialization in Nigerian cities, particularly in Lagos and Abuja, have led to increased emissions from vehicles and industry. Conversely, rural areas experience significant emissions due to deforestation, land-use changes, and agricultural activities, which are further intensified by population growth and demand for resources (Fasona \u003cem\u003eet al.\u003c/em\u003e, 2019; Akintuyi \u003cem\u003eet al.\u003c/em\u003e, 2021). While direct measurement of GHGs through ground-based monitoring is limited in Nigeria, advancements in remote sensing technology have provided invaluable data for tracking and analysing atmospheric GHG levels. Satellite missions such as NASA\u0026rsquo;s Orbiting Carbon Observatory-2 (OCO-2), the Greenhouse Gas Observing Satellite (GOSAT) developed by the Japan Aerospace Exploration Agency, and the Sentinel-5 Precursor (S5P) by the European Space Agency have revolutionized GHG monitoring by offering large-scale, continuous data on atmospheric CO₂, CH₄, and other pollutants (Reuter \u003cem\u003eet al.\u003c/em\u003e, 2022; IPCC, 2023; Nassar \u003cem\u003eet al.\u003c/em\u003e, 2023). These remote sensing techniques are particularly valuable for regions like Nigeria, where ground-based monitoring infrastructure is limited. By measuring the absorption of sunlight in specific wavelengths, satellites like OCO-2 and S5P can provide high-resolution data on GHG concentrations, enabling scientists to identify emission hotspots, track seasonal trends, and estimate emissions with unprecedented accuracy. Furthermore, remote sensing data can reveal how specific activities, such as industrial emissions or agricultural practices, contribute to regional climate change dynamics.\u003c/p\u003e\u003cp\u003eIn Nigeria, the deployment of satellite remote sensing technologies has proven critical for detecting and mapping high-emission zones. These include the Niger Delta region, where industrial fossil fuel extraction and gas flaring dominate emissions, and northern agricultural areas, which exhibit elevated methane (CH₄) concentrations due to enteric fermentation in livestock and anaerobic conditions in irrigated rice paddies (Adagunodo \u003cem\u003eet al.\u003c/em\u003e, 2022). Addressing Nigeria\u0026rsquo;s GHG emissions is critical for advancing global climate mitigation efforts and achieving sustainable development priorities. As a signatory to the Paris Agreement, Nigeria has outlined ambitious targets in its updated Nationally Determined Contributions (NDCs), including the elimination of routine gas flaring by 2030, scaling renewable energy infrastructure to supply 30% of national electricity by 2030, and adopting climate-smart agricultural practices to curb methane (CH₄) emissions from livestock and rice production (Federal Government of Nigeria, 2021; World Bank, 2022; UNEP, 2023). However, realizing these commitments requires robust, spatially explicit emissions data to inform evidence-based policymaking. Emerging remote sensing technologies, such as the European Space Agency\u0026rsquo;s Sentinel-5P TROPOMI and NASA\u0026rsquo;s OCO-3, now enable high-resolution monitoring of CO₂ and CH₄ hotspots across Nigeria\u0026rsquo;s oil-producing Niger Delta and northern agricultural zones. These datasets, when integrated with localized ground-truthing campaigns, can address historical gaps in emission inventories and enhance accountability in mitigation strategies (Adagunodo \u003cem\u003eet al.\u003c/em\u003e, 2022; Parker \u003cem\u003eet al.\u003c/em\u003e, 2023; Okoduwa \u0026amp; Amaechi, 2023).\u003c/p\u003e\u003cp\u003eThis study aims to contribute to these efforts by conducting a temporal and spatial analysis of GHG concentrations in Nigeria from 2019 to 2023, utilizing satellite data from the Sentinel-5P mission focusing on CO, CH₄, NO₂, O₃, and SO₂.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Area\u003c/h2\u003e\n \u003cp\u003eThe study area lies approximately between longitudes 3\u0026deg;E and 15\u0026deg;E and latitudes 4\u0026deg;N and 14\u0026deg;N, spanning a land area of approximately 923,768 km\u003csup\u003e2\u003c/sup\u003e. The study area is located on the west of Africa bordering the Atlantic Ocean in the south, Benin Republic in the west, Niger Republic in the north and Cameroun in the east (Fig. 1). The geographical location spans a broad range of ecosystems and climates, making it an essential area for understanding greenhouse gas (GHG) dynamics. Nigeria is Africa\u0026rsquo;s most populous country with an estimated population of 218.5 million (Akinyemi \u0026amp; Isiugo-Abanihe, 2024) and one of the continent\u0026rsquo;s major contributors to GHG emissions due to its reliance on fossil fuels, expanding urbanization, and agricultural activities. Nigeria experiences a predominantly warm climate throughout the year, characterized by distinct wet and dry seasons. The wet season typically occurs from April to October, while the dry season spans November to March. The study area experiences an average annual rainfall of approximately 1,750 millimetres. Coastal regions typically receive higher amounts due to their proximity to the Atlantic Ocean. Annually, the dry season occurs from November to March and is characterized by the influence of the Harmattan winds originating from the Sahara Desert. These winds contribute to December and January being the driest months of the year (Onafeso, 2023). The study area plays a critical role in global and regional climate change discussions. As an oil-dependent economy, the country\u0026rsquo;s industrial activities especially in the Niger Delta are a significant source of CH₄ and CO emissions. Gas flaring, deforestation, and urbanization exacerbate environmental issues, contributing to increased GHG concentrations. Additionally, Nigeria\u0026rsquo;s diverse land use, which includes agriculture, industrial development, and growing urban populations, makes it a prime location for examining the effects of anthropogenic activities on GHG concentrations.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData Source and Characteristics\u003c/h3\u003e\n\u003cp\u003eThe study used the data derived from the Sentinel-5P satellite which is the heart of the data framework for monitoring greenhouse gases (GHGs) and assessing air quality. Equipped with the TROPOMI (Tropospheric Monitoring Instrument) sensor to gather detailed information about a range of atmospheric gases such as carbon monoxide (CO), methane (CH₄), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), and ozone (O₃). As illustrated in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the dataset provides nationwide coverage of Nigeria for the years 2019 to 2023. Sentinel-5P satellite data, distinguished by its spatial resolution of 7 km x 3.5 km, was selected to capture detailed spatiotemporal variations in CO, CH₄, NO₂, SO₂, and O₃ concentrations across Nigeria.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSources and characteristics of data\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eData Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResolution / Scale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTemporal Coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSources\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbon Monoxide (CO)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite data on atmospheric pollutants (CO₂), Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial \u0026amp; spectral resolutions \u0026ndash;\u003c/p\u003e\n \u003cp\u003e3.5 x 5.5 km\u003c/p\u003e\n \u003cp\u003e(UV: 270\u0026ndash;320 nm)\u003c/p\u003e\n \u003cp\u003e(VIS: 310\u0026ndash;500 nm)\u003c/p\u003e\n \u003cp\u003e7 x 7 km\u003c/p\u003e\n \u003cp\u003e(NIR: 675\u0026ndash;775 nm).\u003c/p\u003e\n \u003cp\u003e(SWIR: 2305\u0026ndash;2385 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2019 \u0026ndash; December 2023 (Daily coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopernicus Data Space Ecosystem\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CO\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethane (CH₄)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite data on atmospheric pollutants (CH₄)Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial \u0026amp; spectral resolutions \u0026ndash;\u003c/p\u003e\n \u003cp\u003e3.5 x 5.5 km\u003c/p\u003e\n \u003cp\u003e(UV: 270\u0026ndash;320 nm)\u003c/p\u003e\n \u003cp\u003e(VIS: 310\u0026ndash;500 nm)\u003c/p\u003e\n \u003cp\u003e7 x 7 km\u003c/p\u003e\n \u003cp\u003e(NIR: 675\u0026ndash;775 nm).\u003c/p\u003e\n \u003cp\u003e(SWIR: 2305\u0026ndash;2385 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2019 \u0026ndash; December 2023 (Daily coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopernicus Data Space Ecosystem\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_CH4\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrogen Dioxide (NO₂)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite data on atmospheric pollutants (NO₂), Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial \u0026amp; spectral resolutions \u0026ndash;\u003c/p\u003e\n \u003cp\u003e3.5 x 5.5 km\u003c/p\u003e\n \u003cp\u003e(UV: 270\u0026ndash;320 nm)\u003c/p\u003e\n \u003cp\u003e(VIS: 310\u0026ndash;500 nm)\u003c/p\u003e\n \u003cp\u003e7 x 7 km\u003c/p\u003e\n \u003cp\u003e(NIR: 675\u0026ndash;775 nm).\u003c/p\u003e\n \u003cp\u003e(SWIR: 2305\u0026ndash;2385 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2019 \u0026ndash; December 2023 (Daily coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopernicus Data Space Ecosystem\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulphur Dioxide (SO₂)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite data on atmospheric pollutants (SO₂), Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial \u0026amp; spectral resolutions \u0026ndash;\u003c/p\u003e\n \u003cp\u003e3.5 x 5.5 km\u003c/p\u003e\n \u003cp\u003e(UV: 270\u0026ndash;320 nm)\u003c/p\u003e\n \u003cp\u003e(VIS: 310\u0026ndash;500 nm)\u003c/p\u003e\n \u003cp\u003e7 x 7 km\u003c/p\u003e\n \u003cp\u003e(NIR: 675\u0026ndash;775 nm).\u003c/p\u003e\n \u003cp\u003e(SWIR: 2305\u0026ndash;2385 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2019 \u0026ndash; December 2023 (Daily coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopernicus Data Space Ecosystem\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOzone (O₃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite data on atmospheric pollutants (O\u003csub\u003e3\u003c/sub\u003e), Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpatial \u0026amp; spectral resolutions \u0026ndash;\u003c/p\u003e\n \u003cp\u003e3.5 x 5.5 km\u003c/p\u003e\n \u003cp\u003e(UV: 270\u0026ndash;320 nm)\u003c/p\u003e\n \u003cp\u003e(VIS: 310\u0026ndash;500 nm)\u003c/p\u003e\n \u003cp\u003e7 x 7 km\u003c/p\u003e\n \u003cp\u003e(NIR: 675\u0026ndash;775 nm).\u003c/p\u003e\n \u003cp\u003e(SWIR: 2305\u0026ndash;2385 nm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanuary 2019 \u0026ndash; December 2023 (Daily coverage)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopernicus Data Space Ecosystem\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdministrative Map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1:250,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLABCARS, Dept. of Geography, University of Lagos, Lagos\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThis concentration data, acquired from the Copernicus Sentinel Data Hub, comprised atmospheric column measurements. These records were systematically analysed to identify temporal patterns and spatial distributions over the study period. In order to account for seasonal variations in influencing factors, pollutant concentrations were analysed separately for the rainy (April\u0026ndash;October) and dry (November\u0026ndash;March) seasons. During the wet season, reduced pollutant levels are typically observed, attributed to enhanced atmospheric scavenging by precipitation. Conversely, the dry season is characterized by elevated concentrations, predominantly driven by increased emissions from industrial and vehicular sources.\u003c/p\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eThis study employed a systematic methodology for processing and analysing Sentinel-5P satellite data to assess air quality within Nigeria. Geospatial filtering was applied utilizing the Country\u0026rsquo;s administrative boundary shapefile, ensuring analysis was confined to the defined geographic extent. Daily gridded pollutant concentration data underwent extraction and subsequent temporal aggregation to derive monthly and annual averages, facilitating the discernment of long-term temporal patterns. Concurrently, rigorous quality control was implemented through the application of pre-existing Google Earth Engine (GEE) cloud masks to identify and exclude cloud-contaminated pixels, thereby mitigating atmospheric interference and enhancing data fidelity. To evaluate distinct seasonal variations, monthly averages were further aggregated into seasonal means CO, CH₄, NO₂, SO₂, and O₃, specifically comparing the dry season (November\u0026ndash;March) and the rainy season (April\u0026ndash;October). Additionally, a year-on-year analytical framework was employed to quantify temporal variations in air quality, explicitly capturing the impact of discrete events such as the 2020 COVID-19 pandemic lockdown. This temporal analysis leveraged Google Earth Engine\u0026apos;s (GEE) integrated statistical reducers, including mean and standard deviation computations. Concurrently, GEE\u0026apos;s JavaScript-based application programming interface (API) facilitated interactive spatiotemporal data visualization and time-series exploration. To elucidate underlying pollutant trends, linear regression models were applied, yielding insights into both spatial and temporal patterns across Nigeria. Furthermore, GEE enabled the systematic generation of geospatial distributions depicting concentrations of CO, CH₄, NO₂, SO₂, and O₃. These distributions, visualized via colour-coded thematic maps, served to identify significant pollution hotspots, with pronounced intensity correlating spatially with areas of dense anthropogenic emission sources (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Collectively, this integrative methodological approach provides a robust characterization of air quality dynamics and pollution distribution throughout the study period.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eSpatial Concentration of CO, 2019 - 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study revealed that CO concentrations across Nigeria ranged from 0.031 and 0.059 mol/m\u003csup\u003e2\u003c/sup\u003e during the study period, exhibiting slight interannual fluctuations (Figure 3). A slight reduction was observed in 2023, with maximum concentrations declining to 0.053 mol/m\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Concentration of CH\u003csub\u003e4\u003c/sub\u003e, 2019 - 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial CH\u003csub\u003e4\u003c/sub\u003e concentration increased substantially nationwide, rising 17.9% from 1,758.01 mol/m\u003csup\u003e2\u003c/sup\u003e in 2019 to 2,073.31 mol/m\u003csup\u003e2\u003c/sup\u003e as presented in Figure 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Concentration of NO\u003csub\u003e2\u003c/sub\u003e, 2019 - 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study further revealed as presented in Figure 5 that NO\u003csub\u003e2\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/sub\u003econcentration ranged from 0.000012 to 0.000134 mol/m\u003csup\u003e2,\u0026nbsp;\u003c/sup\u003epeaking in 2021 across the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Concentration of O\u003csub\u003e3\u003c/sub\u003e, 2019 - 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe O\u003csub\u003e3\u003c/sub\u003e concentration remained relatively stable nationally (0.117 - 0.123 mol/m\u003csup\u003e2\u003c/sup\u003e), with a subtle peak in 2022 (0.1227 mol/m\u003csup\u003e2\u003c/sup\u003e) as presented in Figure 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Concentration of SO\u003csub\u003e2\u003c/sub\u003e, 2019 - 2023\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SO\u003csub\u003e2\u003c/sub\u003e concentrations remained low throughout the study period (-0.000132 to 0.000146 mol/m\u003csup\u003e2\u003c/sup\u003e), with negative values indicating retrieval uncertainties as shown in Figure 7.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Trends\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean concentration of CO for the five-year period exhibited a slight overall downward trend as presented in Figure 8. The average was 0.042 in 2019, rose slightly to 0.043 in 2020, and subsequently decline to 0.041 by 2023. The pattern indicates some initial variability, followed by a sustained downward trajectory. The diverging trends observed highlight the complex interplay of various anthropogenic sectors and policy effectiveness. The marginal decrease in CO concentrations post-2020 is a positive indicator, potentially resulting from improved vehicular emissions standards and a gradual shift towards cleaner technologies (Shi \u003cem\u003eet al.,\u003c/em\u003e 2021). The anomalous 2020 increase may be attributed to pandemic-related shifts, where reduced transportation emissions were potentially offset by increased residential energy use and sustained operations in certain industrial sectors (Venter \u003cem\u003eet al.,\u003c/em\u003e 2020).\u003c/p\u003e\n\u003cp\u003eCH\u003csub\u003e4\u003c/sub\u003e concentrations demonstrated a clear and persistent upward trajectory for the period of study. The mean concentration increased steadily from 1878.17 ppb in 2019 to 1924.82 ppb in 2023 (Figure 9), making a net increase of 46.65 units over the study period, indicating a significant rise in emissions. This increase is strongly linked to the expansion of fossil fuel infrastructure, which is prone to systematic leaks (Alvarez \u003cem\u003eet al.,\u003c/em\u003e 2018), and agricultural sources, particularly enteric fermentation from growing livestock populations and methane generation from landfills due to inadequate waste management practices (Saunois \u003cem\u003eet al.,\u003c/em\u003e 2020). The steady year-on-year growth suggests that current measures are profoundly insufficient to curb emissions from these sectors.\u003c/p\u003e\n\u003cp\u003eThe mean concentration of NO₂ showed a consistent increase. Starting from a low of 0.0000204 in 2020, concentrations rose steadily, peaking at 0.00002405 in 2023 as presented in Figure 10. The rise in NO₂ concentrations aligns with increased vehicular traffic, industrial activity, and fossil-fuel-based power generation, particularly following the economic recovery post-2020 (Dutheil \u003cem\u003eet al.,\u003c/em\u003e 2021). As a key precursor to particulate matter and ozone, increasing NO₂ levels pose a direct threat to respiratory health. The stability of ozone concentrations, despite rising NO₂ levels, is likely due to the non-linear photochemistry of ozone formation, which can be titrated in high-NOₓ environments, and the competing effects of emission control regulations on its precursors (Monks \u003cem\u003eet al.,\u003c/em\u003e 2015). The marginal upward trend may be driven by the overall increase in precursor emissions.\u003c/p\u003e\n\u003cp\u003eTropospheric ozone levels remained relatively stable. The mean concentration ranged from 0.119 in 2019 to a peak of 0.122 in 2022. The overall trend suggests a very slight increase, but the changes are minimal as shown in Figure 11.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average concentration of SO₂ showed a progressive increase from 2019 to 2023, beginning at -0.0000152 and reaching 0.0000063 as shown in Figure 12. This positive trend, while numerically small, is environmentally significant. The increase in SO₂, a primary marker of fossil fuel combustion (particularly coal and heavy oils) in industry and power plants, points to a continued and growing reliance on these polluting fuels (Klimont \u003cem\u003eet al.,\u003c/em\u003e 2013). In the Nigerian context, this is intrinsically linked to the oil and gas industry and insufficient flaring practices (Otu \u003cem\u003eet al.,\u003c/em\u003e 2021). The persistence of SO₂ emissions indicates a critical gap in regulatory enforcement and a lag in the transition to cleaner energy sources, exacerbating risks of acid rain and respiratory illnesses.\u003c/p\u003e"},{"header":"CONCLUSION AND RECOMMENDATIONS","content":"\u003cp\u003eThe analysis from 2019 to 2023 reveals a concerning trajectory for air quality. The decline in CO is a modest success, but it is overwhelmingly counteracted by the significant and steady rise in CH₄, NO₂, and SO₂. The stability of O₃ is precarious and susceptible to increases with rising precursor emissions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo counter these trends, a comprehensive and enforced policy framework is essential in Nigeria. There should be enhanced monitoring and enforcement, whereby the regulatory bodies to monitor emissions and enforce existing air quality standards, particularly for the oil/gas sectors. Future research should employ source apportionment modelling to precisely quantify contributions from different sectors and assess the health burden attributable to these pollutant trends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding. All expenses related to data acquisition, processing, and analysis were covered by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no known financial or personal conflicts of interest that could influence the results reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized freely available remote sensing data from the Sentinel-5P satellite provided by the European Space Agency (ESA). No human or animal subjects were involved; therefore, ethical approval was not required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdagunodo, T. A., Sunmonu, L. A., \u0026amp; Adabanija, M. A. (2022). Satellite-based assessment of methane plumes from oil and gas infrastructure in the Niger Delta. \u003cem\u003eEnvironmental Pollution, 308,\u003c/em\u003e 119652. https://doi.org/10.1016/j.envpol.2022.119652\u003c/li\u003e\n\u003cli\u003eAkintuyi, A. O., Fasona, M. J., Ayeni, A. O. and Soneye, A. S. O. (2021): Land use/land cover and climate change interaction in the derived savannah region of Nigeria. \u003cem\u003eEnviron Monit Assess\u003c/em\u003e (2021) 193:848. https://doi.org/10.1007/s10661-021-09642-6. SPRINGER \u003c/li\u003e\n\u003cli\u003eAkinyemi, A. \u0026amp; Isiugo-Abanihe U. (2024). Demographic dynamics and development in Nigeria. \u003cem\u003eEtude Popul Afr., 27\u003c/em\u003e, 239-248.\u003c/li\u003e\n\u003cli\u003eAlvarez, R. 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Atmospheric Chemistry and Physics, 15(15), 8889-8973. https://doi.org/10.5194/acp-15-8889-2015\u003c/li\u003e\n\u003cli\u003eNassar, R., et al. (2023). Advancing CO₂ monitoring in the tropics: Insights from OCO-3 and OCO-2. \u003cem\u003eAtmospheric Chemistry and Physics, 23(12)\u003c/em\u003e, 6781\u0026ndash;6801. https://doi.org/10.5194/acp-23-6781-2023\u003c/li\u003e\n\u003cli\u003eOkoduwa, K. \u0026amp; Ameach, C. (2023). Monitoring and Mapping of Atmospheric Concentration of Carbon Monoxide, Sulphur Dioxide, and Nitrogen Dioxide from 2019 - 2022 in Benin City, Southern Nigeria. \u003cem\u003eJournal of Applied Sciences and Environmental Management, 27(12), \u003c/em\u003e2715-2722.\u003c/li\u003e\n\u003cli\u003eOmokpariola, D., Nduka, J. \u0026amp; Omokpariola, P. (2024). Short-term trends of air quality and pollutant concentrations in Nigeria from 2018\u0026ndash;2022 using tropospheric sentinel-5P and 3A/B satellite data. \u003cem\u003eDiscov Appl Sci 6, \u003c/em\u003e182. https://doi.org/10.1007/s42452-024-05856-8\u003c/li\u003e\n\u003cli\u003eOnafeso, O. D. (2023). The climate of Nigeria and its role in landscape modification. In A. Faniran, L. K. Jeje, O. A. Fashae, \u0026amp; A. O. Olusola (Eds\u003cem\u003e.), Landscapes and landforms of Nigeria\u003c/em\u003e (pp. 15-30). Springer. https://doi.org/10.1007/978-3-031-17972-3_2\u003c/li\u003e\n\u003cli\u003eOtu, I. E., Otu, E. O., \u0026amp; Udo, E. S. (2021). Air quality and environmental health in Niger Delta, Nigeria: A review. \u003cem\u003eJournal of Applied Sciences and Environmental Management\u003c/em\u003e, 25(5), 815-822.\u003c/li\u003e\n\u003cli\u003eParker, R., Buchwitz, M., Schneising, O., et al. (2023). 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TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. \u003cem\u003eRemote Sensing of Environment, 120\u003c/em\u003e, 70\u0026ndash;83. https://doi.org/10.1016/j.rse.2011.09.027\u003c/li\u003e\n\u003cli\u003eVenter, Z. S., Aunan, K., Chowdhury, S., \u0026amp; Lelieveld, J. (2020). COVID-19 lockdowns cause global air pollution declines. Proceedings of the National Academy of Sciences, 117(32), 18984-18990. https://doi.org/10.1073/pnas.2006853117\u003c/li\u003e\n\u003cli\u003eWorld Bank (2022). \u003cem\u003eNigeria climate priorities: Pathways for low-carbon, resilient growth\u003c/em\u003e. https://documents.worldbank.org/en/publication/documents-reports/documentdetail/099735303212335887\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO) (2023). Air quality and Health. Types of pollutants. https://www.who.int/teams/environment-climate-change-and-health/air-quality-and-health/health-impacts/types-of-pollutants. 2023.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Lagos","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atmospheric concentration, Google Earth Engine, Greenhouse gases (GHGs), Remote Sensing, Sentinel-5P","lastPublishedDoi":"10.21203/rs.3.rs-7847659/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7847659/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGreenhouse gases (GHGs) are critical components of Earth’s atmosphere, playing an essential role in maintaining the planet’s energy. This study analysed the spatial and temporal dynamics of GHGs - carbon monoxide (CO), methane (CH₄), nitrogen dioxide (NO₂), ozone (O₃), and sulphur dioxide (SO₂) emissions in Nigeria from 2019 to 2023, utilizing satellite data from the Sentinel-5 Precursor processed through Google Earth Engine (GEE) and results were obtained through JavaScript coding. Findings reveal distinct emission patterns: CO levels fluctuated moderately, peaking at 0.043 mol/m² in 2020 before declining to 0.041 mol/m² by 2023, suggesting incremental air quality improvements. Conversely, CH₄ concentrations rose steadily from 1,878.17 ppb to 1,924.82 ppb, linked to escalating fossil fuel extraction and agricultural practices. NO₂ exhibited a consistent upward trajectory, reaching 0.0000240 mol/m² in 2023, driven by industrial expansion and vehicular emissions. O₃ levels remained stable near 0.120 ppm, while SO₂ emissions, predominantly from industrial sources, increased marginally to 0.0000063 mol/m² by 2023. The study underscores Nigeria’s evolving GHGs profile, emphasizing the urgent need for stringent emission controls, particularly targeting industrial pollutants and methane sources. Policy recommendations include transitioning to renewable energy, enforcing stricter industrial regulations, and promoting sustainable agricultural practices to curb rising emissions. Although CO reductions indicate partial progress, the persistent rise in CH₄, NO₂, and SO₂ highlights gaps in current mitigation strategies. This study advocates for integrated waste management systems and enhanced monitoring frameworks to address Nigeria’s growing climate challenges and align with global sustainability goals.\u003c/p\u003e","manuscriptTitle":"Spatio-Temporal Analysis of Greenhouse Gases Concentration in Nigeria from 2019 to 2023 Using Sentinel-5P","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 02:03:26","doi":"10.21203/rs.3.rs-7847659/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecd4a02c-7e44-451e-b10d-1ff4088983ae","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56262517,"name":"Geographic Information Systems"},{"id":56262518,"name":"Climate Analysis and Modeling"}],"tags":[],"updatedAt":"2025-10-20T14:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 02:03:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7847659","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7847659","identity":"rs-7847659","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}