Air Pollution Inequality in Malawi: A Comparative Regional Analysis Using Sentinel-5P and Cloud-Based Geospatial Tools

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Understanding the spatiotemporal dynamics of major atmospheric pollutants is essential for effective mitigation strategies. Objective To analyse the spatiotemporal trends and seasonal variations of nitrogen dioxide (NO₂), sulphur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) across Malawi’s Central, Northern, and Southern regions from 2019 to 2024. Method To extract pollutant concentrations, satellite remote sensing data from Sentinel-5P were processed in Google Earth Engine (GEE). Spatial and temporal analyses in a GIS environment quantified regional variability and seasonal patterns. Results The Central Region recorded consistent NO₂ increases during the dry season, with June levels rising by 48% (R² = 0.92), largely from urban and industrial emissions. Northern and Southern regions showed highly variable NO₂ and SO₂ trends, including a + 7579% NO₂ spike in the Southern Region linked to biomass burning and new industrial activities. Seasonal CO peaks shifted, with Southern October values up 53.5%. O₃ concentrations rose notably in dry months, especially in the Northern Region (+ 6.45% in October). Conclusion Malawi's distinct regional and seasonal pollution profiles are shaped by anthropogenic and climatic drivers, requiring targeted interventions. Unique Contribution: This is the first high-resolution, multi-year spatial analysis of four key pollutants across Malawi’s three regions using Sentinel-5P, providing critical evidence for air quality policy and climate-resilient planning. Key Recommendation: Establish a nationwide air quality monitoring network, strengthen emission control enforcement, and integrate air quality management with climate adaptation strategies. Environmental Engineering Air pollution Spatiotemporal analysis Remote sensing Seasonal variability Air quality management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1.0 Introduction Air pollution has emerged as a critical environmental and public health challenge globally, with disproportionate impacts in low- and middle-income countries, particularly across Sub-Saharan Africa (Jarraud and Steiner, 2012; UNEP, 2021). Malawi, a predominantly agrarian and energy-insecure nation in Southeastern Africa, is experiencing progressive deterioration in air quality, especially in urban and peri-urban centres. Contributing factors include rapid urbanisation, population growth, heavy reliance on biomass fuels, unregulated industrial activities, and increased use of diesel-powered generators (Sicard et al., 2023 ; UN-Habitat, 2023). Despite these drivers, the country lacks an integrated air quality monitoring network, and comprehensive baseline data on spatiotemporal pollution dynamics remain scarce. Across Malawi’s three regions, Northern, Central, and Southern, major urban centres such as Mzuzu, Lilongwe, Blantyre, and Zomba face mounting environmental pressures due to economic concentration, informal industrialisation, and insufficient pollution control infrastructure. Key pollutants, including carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ozone (O₃), and aerosols, originate from household cooking, vehicular emissions, industrial combustion, and biomass burning (Kambewa and Chiwaula, 2010 ; Ngo et al., 2019 ; Szopa and V. Naik, 2023 ). These have been linked to cardiovascular and respiratory illnesses, ecosystem degradation, and climate forcing (Jarraud and Steiner, 2012a ; Fowler et al., 2020 ). Sub-Saharan Africa is increasingly facing a silent but deadly air quality crisis, with ambient air pollution now recognised as a major contributor to premature mortality and public health burden in the region (Ahmed et al., 2023 ; Rentschler and Leonova, 2023 ). In Malawi, the situation is particularly acute due to the overwhelming dependence on biomass fuels, used by approximately 97% of households for cooking (NSO, 2019). The effects of both indoor and outdoor air pollution are thus intimately linked, creating a complex environmental health challenge. The health impacts are well documented, ranging from acute lower respiratory infections, especially in children, to chronic obstructive pulmonary disease (COPD), cardiovascular complications, and ischemic heart disease in adults (Miles, 2021 ; Rentschler and Leonova, 2023 ). Despite the severity of this environmental threat, systematic air quality monitoring remains grossly inadequate in most African countries, including Malawi. Existing research is typically episodic, localised, and reliant on proxy measurements such as fuel type or self-reported exposure, rather than direct, sustained pollutant monitoring (Rushingabigwi et al., 2020 ; Munthali et al., 2025 ). This lack of reliable empirical data significantly hinders evidence-based policy development and the deployment of early warning systems to mitigate health crises induced by air pollution. The primary pollutants of concern in Malawi’s urban and peri-urban areas include carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), Ozone (O 3 ), and aerosols. CO, emitted largely through the incomplete combustion of biomass and fossil fuels, inhibits oxygen transport in the bloodstream and can be fatal at high concentrations (Rentschler and Leonova, 2023 ). NO₂, a product of vehicular traffic and industrial combustion, exacerbates respiratory illnesses and contributes to smog and acid rain formation (Fowler et al., 2020 ). SO₂, commonly associated with the combustion of diesel and industrial flaring, causes acidification of ecosystems and can lead to agricultural losses and respiratory distress (Rushingabigwi et al., 2020 ; UNEP, 2021). Ozone, although not directly toxic, is a potent greenhouse gas, intensifying climate change and indirectly contributing to health hazards through extreme heat, flooding, and altered disease dynamics (Fowler et al., 2020 ). Aerosols, originating from dust, biomass burning, and industrial emissions, are among the most dangerous pollutants due to their fine particulate nature, allowing deep penetration into lung tissues and resulting in both cardiovascular and respiratory morbidity (Szopa et al , 2023). In response to the limitations of conventional monitoring infrastructure, satellite-based remote sensing technologies have emerged as critical tools for air quality assessment in low-resource settings. The European Space Agency’s Sentinel-5 Precursor (Sentinel-5P) satellite, equipped with the TROPOspheric Monitoring Instrument (TROPOMI), provides high-resolution, near-real-time global data on trace gases and aerosols, including CO, NO₂, SO₂, O 3 , and aerosol indices (Verhoelst et al., 2021 ; Zhang et al., 2022 ). These datasets are freely accessible and highly compatible with cloud-based geospatial platforms such as Google Earth Engine (GEE), enabling researchers to perform large-scale, long-term spatiotemporal analyses with minimal infrastructure requirements (Rushingabigwi et al., 2020 ; Reshi, Pichuka and Tripathi, 2024 ). The application of Sentinel-5P data in African contexts has gained momentum, with studies in cities like Lagos, Nairobi, and Johannesburg revealing trends in pollutant concentrations, seasonal dynamics, and impacts of policy interventions or shocks such as COVID-19 lockdowns (Ahmed et al., 2023 ; Rentschler and Leonova, 2023 ). However, in Malawi, such approaches remain underexplored. This study addresses a critical gap by utilising Sentinel-5P and GEE to analyse the spatial and temporal variability of atmospheric pollutants in Southern Malawi between 2019 and 2024. In doing so, it provides foundational insights into regional pollution patterns and lays the groundwork for data-informed environmental health policies and adaptive governance. Given the absence of extensive ground-based monitoring, satellite remote sensing provides a cost-effective, scalable solution for large, data-scarce areas (Ngo, Kokoyo and Klopp, 2017 ; Szopa and V. Naik, 2023 ). This study employs Sentinel-5 Precursor (Sentinel-5P) data from the TROPOspheric Monitoring Instrument (TROPOMI) to investigate regional disparities in atmospheric pollution across Malawi between 2019 and 2024. Specifically, it (1) quantifies spatial and temporal variations in pollutant concentrations and (2) examines their relationship with socio-environmental factors such as population density, energy poverty, land use change, and industrial activity. 3.0 Data Collection and Processing 3.1 Data Sources and Study Area This study utilised satellite-based remote sensing datasets to evaluate the spatiotemporal dynamics of key atmospheric pollutants across Malawi, encompassing the Northern, Central, and Southern regions. Malawi, a landlocked country in south-eastern Africa, spans an area of approximately 118,484 km² and is characterised by diverse topography, including the Great Rift Valley, Lake Malawi, highland plateaus, and low-lying floodplains. The country’s climate is largely tropical, with distinct wet (November–April) and dry (May–October) seasons, which influence pollutant dispersion and atmospheric chemistry (Mapoma and Xie, 2013 ). Urbanisation is concentrated in major cities such as Lilongwe, Blantyre, Mzuzu, and Zomba, which have experienced rapid population growth and expansion of informal settlements. This growth, combined with an energy mix dominated by biomass fuels (over 85% of households depend on firewood or charcoal for cooking), has intensified anthropogenic emissions (Mcsweeney, New and Lizcano, 2010 ). In addition, vehicular traffic growth, unregulated industrial activities, and seasonal biomass burning contribute significantly to degraded air quality (Rybarczyk et al., 2025 ). Rural areas are also impacted by biomass combustion for domestic energy and agricultural residue burning, particularly during the dry season. Atmospheric pollutant data were obtained from the Sentinel-5 Precursor (Sentinel-5P) satellite mission, processed via the Google Earth Engine (GEE) platform. The analysis focused on two temporal benchmarks, 2019 and 2024, to quantify changes in pollutant levels over five years. Target pollutants included carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ozone (O₃), and aerosols all of which are critical indicators of air quality and proxies for anthropogenic environmental pressure (Goshua, Akdis and Nadeau, 2022 ). These pollutants were selected due to their documented links to respiratory and cardiovascular health risks, ecosystem degradation, and their roles as short-lived climate forcers in regional climate dynamics. 3.2 Sentinel-5P and the TROPOMI Instrument The Sentinel-5 Precursor satellite, launched by the European Space Agency (ESA) under the Copernicus Earth Observation Programme, is equipped with the TROPOspheric Monitoring Instrument (TROPOMI). This advanced spectrometer captures high-resolution data on various trace gases and aerosols with a spatial resolution of approximately 7 km by 3.5 km (Goldberg et al., 2021 ). TROPOMI measures vertical column densities of atmospheric constituents, with CO, NO₂, and SO₂ values reported in mol/m², Ozone in mole fraction, and aerosols as a unitless index representing optical thickness (Amegah and Agyei-Mensah, 2017 ; Fowler et al., 2020 ). Sentinel-5P’s high temporal frequency and spatial coverage make it particularly valuable in data-scarce environments like Malawi, where ground-based air quality monitoring stations are limited or non-existent (Cai et al., 2022 ). Although satellite-based observations may be influenced by local meteorological conditions and surface reflectance variability, they provide a robust and reliable approximation for regional-scale air pollution assessments (Miles, 2021 ; Rentschler and Leonova, 2023 ). 4.0 Results 4.1 Trends and Regional Disparities in NO₂ Pollution Across the Three Regions Between 2019 and 2024, nitrogen dioxide (NO₂) concentrations across Malawi’s Central, Northern, and Southern regions showed notable spatial and temporal variation in both level and fluctuation. In the Central Region, NO₂ levels demonstrated statistically significant (R = 0.004) and steady increases, especially during the dry season (August–October). This seasonal trend is likely due to reduced atmospheric dispersion and ongoing human activities, such as urban transport emissions and industrial operations (Jarraud and Steiner, 2012b ). October saw a 7.6% increase, while June experienced the highest rise at 48%. A high coefficient of determination (R² = 0.92) indicated strong consistency over time. The Northern Region exhibited greater variability between years, marked by episodic peaks, for example, a July maximum increase of + 354% and a September average rise of + 580% and sharp declines, including a May average decrease of − 514%. The marked rise in standard deviation suggests intensification of short-lived, high-emission episodes, potentially linked to unregulated industrial or biomass-burning activities (Mapoma et al., 2014 ). The Southern Region experienced the most extreme and erratic fluctuations, with notable surges in March (+ 1711%) and November (+ 7579%). The absence of a consistent temporal pattern was reflected in the very weak correlation between 2019 and 2024 concentrations (R² = 0.03). Such volatility may be associated with recent industrial expansions, shifts in agricultural residue burning, or climate-related influences on atmospheric chemistry and dispersion (IPCC, 2024). These regional patterns are consistent with previous studies in Sub-Saharan Africa reporting elevated NO₂ concentrations in urbanised and industrial hubs such as Lagos, Nigeria (Olusola et al., 2021 ) and Johannesburg, South Africa (Matandirotya and Burger, 2023 ), where anthropogenic activities and seasonal meteorology jointly influence pollutant dynamics. However, the magnitude of episodic spikes observed in Malawi’s Southern Region exceeds those reported in comparable contexts, suggesting unique local drivers, possibly tied to rapid, unregulated industrialisation and shifting climatic conditions. The results are shown in Fig. 3 below: 4.2 Trends and Regional Disparities in SO₂ Pollution Across the Three Regions A comparative spatiotemporal analysis of sulphur dioxide (SO₂) concentrations in Malawi’s Northern, Central, and Southern regions from 2019 to 2024 reveals substantial regional disparities and month-specific emission anomalies. The Northern region experienced heightened variability with pronounced SO₂ surges in July (+ 354%) and September (+ 580%) and a notable drop in May (− 514%), reflecting increased pollution episodes and potential anthropogenic triggers such as biomass burning or seasonal agricultural activities (Kosamu et al., 2013 ). The Central region displayed relatively erratic trends, characterised by weak correlation (R² ≈ 0.24) between 2019 and 2024, with high increases in July and September and sharp declines in May and November, suggesting influence from localised urban-industrial emissions (Utembe, 2015 ). Meanwhile, the Southern region exhibited a stronger temporal correlation (R² = 0.7492), indicating consistent monthly emission patterns, yet recorded extreme monthly increases in March (+ 1711%) and November (+ 7561%), pointing to episodic events likely linked to informal sector emissions or climatic variability (Matandirotya and Burger, 2023 ). These findings align with studies that emphasise the role of meteorological conditions and human activities in shaping regional SO₂ profiles across Sub-Saharan Africa (Borge, Lange and Kehew, 2023 ; Okello et al., 2023 ). The results are shown in Fig. 4 below; 4.3 Trends and Regional Disparities in CO Pollution Across the Three Regions Between 2019 and 2024, mean monthly carbon monoxide (CO) concentrations across Malawi’s Southern, Northern, and Central regions exhibited distinct spatial and temporal patterns, reflecting seasonally driven emission cycles and evolving source dynamics. In the Southern Region, peak CO concentrations in 2019 occurred in September (0.0561 mol m⁻²), whereas in 2024 the seasonal maximum shifted to October (0.0825 mol m⁻²). Substantial increases were recorded in June (+ 57.3%) and October (+ 53.5%), while notable declines occurred in February, September (− 36.5%), and November, suggesting a displacement in the timing of peak emissions. Despite these changes, the strong positive correlation between 2019 and 2024 (R² >0.70) indicates persistent seasonal cycles, albeit with varying intensities. These patterns are consistent with continental observations from MOPITT-derived datasets, which link CO seasonality to biomass burning regimes and anthropogenic activity patterns (Naus et al., 2022 ). The Northern Region in 2024 experienced widespread reductions in maximum (COₘₐₓ), mean (COₘₑₐₙ), minimum (COₘ i ₙ), and variability (COₛₜd) values, with the most pronounced declines in January and October. This may indicate improved air quality and stabilised emission patterns, potentially driven by reduced biomass burning, shifts in fuel usage, or localised mitigation measures (Orina et al., 2024 ). In the Central Region, a strong linear relationship between 2019 and 2024 monthly averages (R² ≈ 0.85) was observed. Increases occurred in June (+ 71.1%), October (+ 63.9%), and May (+ 7.4%), while February and September showed declines, suggesting both seasonal consistency and changing source contributions (Leguijt et al., 2023 ). The results are shown in Fig. 5 below: 4.4 Trends and Regional Disparities in O 3 Pollution Across the Three Regions Between 2019 and 2024, monthly ozone (O₃) concentrations across Malawi’s Central, Northern, and Southern regions exhibited distinct temporal and spatial patterns, shaped by both seasonal meteorological conditions and anthropogenic influences. In the Central Region, regression analysis indicated a moderate correlation between the two years (R² ≈ 0.51), with notable increases during the late dry-season months of October (+ 5.19%) and September (+ 4.94%), and marked declines in mid-year months such as July (− 7.28%) and August (− 6.80%). This pattern is consistent with enhanced photochemical O₃ formation under high solar radiation and low humidity in the dry season, and reduced production during the cooler, wetter mid-year months due to greater atmospheric dispersion and diminished precursor availability (Donnou et al., 2024 ; Wang et al., 2025 ). The Northern Region displayed a slightly different seasonal evolution. O₃ levels in early 2024 were generally lower than in 2019, followed by sharp increases from September to December. The strongest mean percentage increases occurred in October (+ 6.45%) and December (+ 6.14%), patterns that may be linked to biomass burning and intensified solar irradiance during the late dry season (Bourgeois et al., 2021 ). Standard deviation increases in months such as May and September suggest heightened variability in O₃ behaviour, possibly due to fluctuating meteorological drivers or episodic emission events. In the Southern Region, analysis revealed a modest overall increase in mean ozone (O₃) concentrations, particularly in the latter part of the year. The high coefficient of determination (R² = 0.85) combined with statistical significance (p < 0.05) indicates that temporal variations are well explained by linear trends, reflecting consistent seasonal dynamics. Early-year declines were consistent with the cooling effects and enhanced precipitation typical of Malawi’s wet season (Lee et al., 2021 ). Collectively, these findings confirm region-specific O₃ dynamics, with all three zones exhibiting dry-season enhancements (September–December) and mid-year reductions. The observed patterns are broadly consistent with studies across southern Africa that link seasonal O₃ variability to biomass burning cycles, solar radiation intensity, and regional meteorology (Bourgeois et al., 2021 ; Donnou et al., 2024 ). The results in Fig. 6 below; 5. Discussion The spatiotemporal analysis of atmospheric pollutants NO₂, SO₂, CO, and O₃ across Malawi’s Central, Northern, and Southern regions from 2019 to 2024 reveals significant regional disparities, temporal shifts, and pollutant-specific dynamics shaped by anthropogenic activities, meteorological variability, and land use changes. These findings broadly align with patterns documented in Sub-Saharan Africa but also provide novel insights into local emission sources and atmospheric behaviour within Malawi’s diverse ecological and socio-economic contexts. Consistent with observations in urban-industrial centres across Sub-Saharan Africa (Liu et al., 2016 ; Abulude et al., 2021 ), NO₂ concentrations increased significantly in Malawi’s Central Region, especially during the dry season. The high temporal correlation (R² = 0.92) and seasonal peaks observed suggest that urban traffic emissions, industrial activity, and stagnant atmospheric conditions are principal drivers. This corroborates findings from Lagos Utembe ( 2015 ) and Brauer et al. ( 2024 ) in Johannesburg, where similar anthropogenic sources combined with meteorological factors exacerbate NO₂ pollution. However, the extreme and erratic NO₂ spikes in the Northern and Southern regions, including an unprecedented 7579% increase in the South, diverge from more stable urban trends reported elsewhere. These episodic surges likely stem from unregulated biomass burning, emergent industrial activities, and localised meteorological anomalies, echoing patterns seen in rural and peri-urban zones affected by shifting land management and climate variability (Naus et al., 2022 ; Matandirotya and Burger, 2023 ). This volatility emphasises the critical role of non-urban emission sources in Malawi, extending current regional understandings, which often focus predominantly on urban pollution. SO₂ trends further highlight regional heterogeneity. The Southern Region experienced sharp March and November increases with a moderate correlation (R² = 0.75), indicative of episodic emissions possibly linked to informal combustion activities and seasonal agricultural practices. These observations align with studies from West Africa and parts of East Africa where SO₂ fluxes reflect biomass burning and small-scale industrial emissions (Donnou et al., 2024 ; Wang et al., 2025 ). Meanwhile, the Northern Region’s pronounced SO₂ fluctuations suggest temporally concentrated anthropogenic activities, perhaps associated with seasonal crop residue burning or biomass fuel use (Abulude et al., 2021 ; Lee et al., 2021 ). This spatially differentiated SO₂ behaviour underlines the importance of incorporating seasonal agricultural and domestic energy-use patterns into pollution mitigation strategies. Carbon monoxide (CO) exhibited strong seasonal correlations (R² >0.7) in the Central and Southern regions, with peak concentrations shifting from September to October between 2019 and 2024. This temporal shift corresponds with changes in biomass burning cycles, land clearing activities, and evolving regional atmospheric circulation patterns, consistent with MOPITT satellite analyses across southern Africa (Abulude et al., 2021 ; Issn, 2024). Contrastingly, the Northern Region displayed general declines in CO metrics, suggesting either effective emission regulation, reduced biomass burning, or differing meteorological influences. These divergent trends highlight the complex interaction of anthropogenic and climatic factors modulating CO emissions regionally. Ozone (O₃) concentrations increased consistently during the dry months (September–December) across all regions, with moderate to strong inter-annual correlations, particularly in the Central and Northern zones. This seasonal enhancement aligns with known photochemical formation processes driven by precursor pollutants and intense solar radiation, as described in earlier studies across southern Africa and other tropical regions (Liu et al., 2016 ; Wang et al., 2025 ). Notably, elevated variability in May and September indicates atmospheric sensitivity to both anthropogenic emissions and meteorological parameters such as temperature and humidity, consistent with IPCC (2024) findings. These results reinforce the critical influence of both emission sources and climatic conditions on ozone dynamics, underscoring the need for integrated air quality and climate policies. Collectively, the spatiotemporal variability of these pollutants in Malawi reflects broader regional trends influenced by rapid urbanisation, climate change, and often weak regulatory frameworks (Jarraud and Steiner, 2012a ; Utembe, 2015 ; Leguijt et al., 2023 ; Okello et al., 2023 ; IPCC, 2024). However, this study extends current knowledge by providing detailed, region-specific temporal analyses that reveal the significance of episodic and non-urban sources such as biomass burning and informal industries on air quality. 6. Conclusion and recommendations This study provides a comprehensive assessment of the spatiotemporal dynamics of key atmospheric pollutants NO₂, SO₂, CO, and O₃ across Malawi’s Central, Northern, and Southern regions between 2019 and 2024. The findings reveal significant regional disparities in both the magnitude and variability of pollutant concentrations, shaped by seasonal cycles, anthropogenic activities, and meteorological factors. The Central Region exhibited relatively stable yet consistently increasing NO₂ and CO levels, indicating persistent urban and industrial emissions. In contrast, the Northern and Southern regions experienced high pollutant variability, with episodic spikes in NO₂ and SO₂ concentrations, suggesting the influence of localised, unregulated sources such as biomass burning, informal industrial activity, and climate-induced changes in dispersion. The shifting seasonal peaks in CO and O₃ further reflect evolving emission patterns and atmospheric chemistry influenced by land use changes and photochemical activity. These findings underscore the urgent need for region-specific air quality interventions, especially in light of Malawi’s rapid urbanisation, weak regulatory enforcement, and vulnerability to climate change. To effectively address the observed disparities in air pollution across Malawi’s regions, several strategic recommendations are proposed. First, there is a need to establish a National Air Quality Monitoring Network by expanding ground-based infrastructure to complement satellite data and provide real-time, high-resolution measurements for key pollutants such as NO₂, SO₂, CO, and O₃. Developing region-specific emission inventories through detailed sectoral studies, particularly in transport, industry, agriculture, and domestic fuel use, will help identify dominant pollution sources. Promoting clean and efficient technologies, such as improved cooking stoves, sustainable farming practices, and low-emission transport, is essential to reduce both point and diffuse emissions. Seasonal emission control policies should be implemented during the dry season when pollution concentrations are typically highest due to poor atmospheric dispersion and increased combustion. Strengthening environmental governance through regulatory enforcement, incentives, and public engagement will ensure better compliance and accountability. Enhancing public awareness and education campaigns on air pollution sources, health risks, and personal protective measures is also critical, particularly in vulnerable communities. Moreover, integrating air quality management into national climate action plans can yield co-benefits for both environmental health and climate resilience. Supporting further research and capacity building will improve scientific understanding and inform data-driven policymaking, while leveraging remote sensing, GIS, and machine learning tools will enhance pollution tracking and hotspot identification. Finally, the development of early warning and response systems based on pollutant thresholds can provide timely advisories and protect public health during high-risk periods. Limitations : This study acknowledges several limitations. The spatial and temporal resolution of available pollutant data may limit the granularity of the findings. Additionally, the reliance on satellite-derived data and limited ground-based measurements may introduce uncertainties related to local atmospheric conditions. Use of Large Language Models (LLMs) : To enhance clarity and academic rigour, Large Language Models (LLMs) were utilised for paraphrasing and grammatical corrections throughout the manuscript. The author carefully reviewed and verified all edits to ensure that the original meaning and scientific accuracy were preserved. Declarations Conflict of Interest: The author declares no conflict of interest. All referenced works have been duly cited. Funding: This research was conducted without any external funding. Availability of Data and Materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Abulude, F.O., Damodharan, U., Acha, S., Adamu, A. and Arifalo, K.M. 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Orina, F., Amukoye, E., Bowyer, C., Chakaya, J., Das, D., Devereux, G., Dobson, R., Dragosits, U., Gray, C., Kiplimo, R., Lesosky, M., Loh, M., Meme, H., Mortimer, K., Ndombi, A., Pearson, C., Price, H., Twigg, M., West, S. and Semple, S. (2024) ‘Household carbon monoxide (CO) concentrations in a large African city: An unquantified public health burden?’, Environmental Pollution , 351(December 2023), p. 124054. Available at: https://doi.org/10.1016/j.envpol.2024.124054. Rentschler, J. and Leonova, N. (2023) ‘Global air pollution exposure and poverty’, Nature Communications , 14(1), pp. 1–11. Available at: https://doi.org/10.1038/s41467-023-39797-4. Reshi, A.R., Pichuka, S. and Tripathi, A. (2024) ‘Applications of Sentinel-5P TROPOMI Satellite Sensor: A Review’, IEEE Sensors Journal , 24(13), pp. 20312–20321. Available at: https://doi.org/10.1109/JSEN.2024.3355714. Rushingabigwi, G., Nsengiyumva, P., Sibomana, L., Twizere, C. and Kalisa, W. (2020) ‘Analysis of the atmospheric dust in Africa: The breathable dust’s fine particulate matter PM2.5 in correlation with carbon monoxide’, Atmospheric Environment , 224(August 2019), p. 117319. Available at: https://doi.org/10.1016/j.atmosenv.2020.117319. Rybarczyk, Y., Zalakeviciute, R., Ereminaite, M. and Costa-Stolz, I. (2025) ‘Causal effect of PM2.5 on the urban heat island’, Frontiers in Big Data , 8. Available at: https://doi.org/10.3389/fdata.2025.1546223. Sicard, P., Agathokleous, E., Anenberg, S.C., De Marco, A., Paoletti, E. and Calatayud, V. (2023) ‘Trends in urban air pollution over the last two decades: A global perspective’, Science of the Total Environment , 858(November 2022), p. 160064. Available at: https://doi.org/10.1016/j.scitotenv.2022.160064. Szopa, S.. and V. Naik (2023) Intergovernmental Panel on Climate Change: Short-lived Climate Forcers , Climate Change 2021 – The Physical Science Basis . Available at: https://doi.org/10.1017/9781009157896.008.817. 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Verhoelst, T., Compernolle, S., Pinardi, G., Lambert, J.C., Eskes, H.J., Eichmann, K.U., Fjæraa, A.M., Granville, J., Niemeijer, S., Cede, A., Tiefengraber, M., Hendrick, F., Pazmiño, A., Bais, A., Bazureau, A., Folkert Boersma, K., Bognar, K., Dehn, A., Donner, S., Elokhov, A., Gebetsberger, M., Goutail, F., Grutter De La Mora, M., Gruzdev, A., Gratsea, M., Hansen, G.H., Irie, H., Jepsen, N., Kanaya, Y., Karagkiozidis, D., Kivi, R., Kreher, K., Levelt, P.F., Liu, C., Müller, M., Navarro Comas, M., Piters, A.J.M., Pommereau, J.P., Portafaix, T., Prados-Roman, C., Puentedura, O., Querel, R., Remmers, J., Richter, A., Rimmer, J., Cárdenas, C.R., De Miguel, L.S., Sinyakov, V.P., Stremme, W., Strong, K., Van Roozendael, M., Pepijn Veefkind, J., Wagner, T., Wittrock, F., Yela González, M. and Zehner, C. (2021) ‘Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks’, Atmospheric Measurement Techniques , 14(1), pp. 481–510. Available at: https://doi.org/10.5194/amt-14-481-2021. Wang, Y., Li, K., Chen, X., Yang, Z., Tang, M., Campos, P.M.D., Yang, Y., Yue, X. and Liao, H. (2025) ‘Revisiting the high tropospheric ozone over southern Africa: role of biomass burning and anthropogenic emissions’, Atmospheric Chemistry and Physics , 25(8), pp. 4455–4475. Available at: https://doi.org/10.5194/acp-25-4455-2025. Zhang, H., Yi, M., Wang, Y., Zhang, Y., Xiao, K., Si, J., Shi, N., Sun, L., Miao, Z., Zhao, T., Sun, X., Liu, Z., Gao, J. and Li, J. (2022) ‘Air pollution and recurrence of cardiovascular events after ST-segment elevation myocardial infarction’, Atherosclerosis , 342(July 2021), pp. 1–8. Available at: https://doi.org/10.1016/j.atherosclerosis.2021.12.012. 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Malawi.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/9a3e55008fdb7a1eb2b45748.jpg"},{"id":88950030,"identity":"f9ddd03d-f7c7-4002-af16-6a856c4d51d5","added_by":"auto","created_at":"2025-08-13 05:42:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial-temporal trend of pollutant concentration\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/23ecf33177394cf01d85dc0c.jpg"},{"id":88948567,"identity":"8581292f-863d-4246-9152-01ebd272fe49","added_by":"auto","created_at":"2025-08-13 05:34:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":168141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e mean variation, trend and regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/8e446f35c49c021f3ce0fcec.jpg"},{"id":88948557,"identity":"38008273-c0c2-48df-a68b-baa0aa68798c","added_by":"auto","created_at":"2025-08-13 05:34:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e mean variation, trend and regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/7c3ffd62f9d58c63d8a3013f.jpg"},{"id":88948569,"identity":"bafe00a8-f9a2-4eea-b2a2-e261031e5815","added_by":"auto","created_at":"2025-08-13 05:34:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":172319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCO mean variation, trend and regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/bf2a47319a0fd311881711ea.jpg"},{"id":88948620,"identity":"3664386e-209c-409b-982b-c58a10fd7f75","added_by":"auto","created_at":"2025-08-13 05:34:48","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":160259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e3 \u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003emean variation, trend and regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/1838e07617a64d1b77f4068c.jpg"},{"id":88951007,"identity":"edb3a291-f4f8-4a5e-b844-effec9c1e92e","added_by":"auto","created_at":"2025-08-13 05:50:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1691891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7357386/v1/6cdc67b4-d615-4bc8-a56e-9cc3cc2989ca.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAir Pollution Inequality in Malawi: A Comparative Regional Analysis Using Sentinel-5P and Cloud-Based Geospatial Tools\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eAir pollution has emerged as a critical environmental and public health challenge globally, with disproportionate impacts in low- and middle-income countries, particularly across Sub-Saharan Africa (Jarraud and Steiner, 2012; UNEP, 2021). Malawi, a predominantly agrarian and energy-insecure nation in Southeastern Africa, is experiencing progressive deterioration in air quality, especially in urban and peri-urban centres. Contributing factors include rapid urbanisation, population growth, heavy reliance on biomass fuels, unregulated industrial activities, and increased use of diesel-powered generators (Sicard et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UN-Habitat, 2023). Despite these drivers, the country lacks an integrated air quality monitoring network, and comprehensive baseline data on spatiotemporal pollution dynamics remain scarce. Across Malawi\u0026rsquo;s three regions, Northern, Central, and Southern, major urban centres such as Mzuzu, Lilongwe, Blantyre, and Zomba face mounting environmental pressures due to economic concentration, informal industrialisation, and insufficient pollution control infrastructure. Key pollutants, including carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ozone (O₃), and aerosols, originate from household cooking, vehicular emissions, industrial combustion, and biomass burning (Kambewa and Chiwaula, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ngo et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Szopa and V. Naik, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These have been linked to cardiovascular and respiratory illnesses, ecosystem degradation, and climate forcing (Jarraud and Steiner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; Fowler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSub-Saharan Africa is increasingly facing a silent but deadly air quality crisis, with ambient air pollution now recognised as a major contributor to premature mortality and public health burden in the region (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rentschler and Leonova, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Malawi, the situation is particularly acute due to the overwhelming dependence on biomass fuels, used by approximately 97% of households for cooking (NSO, 2019). The effects of both indoor and outdoor air pollution are thus intimately linked, creating a complex environmental health challenge. The health impacts are well documented, ranging from acute lower respiratory infections, especially in children, to chronic obstructive pulmonary disease (COPD), cardiovascular complications, and ischemic heart disease in adults (Miles, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rentschler and Leonova, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the severity of this environmental threat, systematic air quality monitoring remains grossly inadequate in most African countries, including Malawi. Existing research is typically episodic, localised, and reliant on proxy measurements such as fuel type or self-reported exposure, rather than direct, sustained pollutant monitoring (Rushingabigwi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Munthali et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This lack of reliable empirical data significantly hinders evidence-based policy development and the deployment of early warning systems to mitigate health crises induced by air pollution.\u003c/p\u003e\u003cp\u003eThe primary pollutants of concern in Malawi\u0026rsquo;s urban and peri-urban areas include carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), Ozone (O\u003csup\u003e3\u003c/sup\u003e), and aerosols. CO, emitted largely through the incomplete combustion of biomass and fossil fuels, inhibits oxygen transport in the bloodstream and can be fatal at high concentrations (Rentschler and Leonova, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). NO₂, a product of vehicular traffic and industrial combustion, exacerbates respiratory illnesses and contributes to smog and acid rain formation (Fowler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). SO₂, commonly associated with the combustion of diesel and industrial flaring, causes acidification of ecosystems and can lead to agricultural losses and respiratory distress (Rushingabigwi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; UNEP, 2021). Ozone, although not directly toxic, is a potent greenhouse gas, intensifying climate change and indirectly contributing to health hazards through extreme heat, flooding, and altered disease dynamics (Fowler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Aerosols, originating from dust, biomass burning, and industrial emissions, are among the most dangerous pollutants due to their fine particulate nature, allowing deep penetration into lung tissues and resulting in both cardiovascular and respiratory morbidity (Szopa \u003cem\u003eet al\u003c/em\u003e, 2023). In response to the limitations of conventional monitoring infrastructure, satellite-based remote sensing technologies have emerged as critical tools for air quality assessment in low-resource settings. The European Space Agency\u0026rsquo;s Sentinel-5 Precursor (Sentinel-5P) satellite, equipped with the TROPOspheric Monitoring Instrument (TROPOMI), provides high-resolution, near-real-time global data on trace gases and aerosols, including CO, NO₂, SO₂, O\u003csub\u003e3\u003c/sub\u003e, and aerosol indices (Verhoelst et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These datasets are freely accessible and highly compatible with cloud-based geospatial platforms such as Google Earth Engine (GEE), enabling researchers to perform large-scale, long-term spatiotemporal analyses with minimal infrastructure requirements (Rushingabigwi et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reshi, Pichuka and Tripathi, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe application of Sentinel-5P data in African contexts has gained momentum, with studies in cities like Lagos, Nairobi, and Johannesburg revealing trends in pollutant concentrations, seasonal dynamics, and impacts of policy interventions or shocks such as COVID-19 lockdowns (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rentschler and Leonova, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, in Malawi, such approaches remain underexplored. This study addresses a critical gap by utilising Sentinel-5P and GEE to analyse the spatial and temporal variability of atmospheric pollutants in Southern Malawi between 2019 and 2024. In doing so, it provides foundational insights into regional pollution patterns and lays the groundwork for data-informed environmental health policies and adaptive governance. Given the absence of extensive ground-based monitoring, satellite remote sensing provides a cost-effective, scalable solution for large, data-scarce areas (Ngo, Kokoyo and Klopp, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Szopa and V. Naik, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study employs Sentinel-5 Precursor (Sentinel-5P) data from the TROPOspheric Monitoring Instrument (TROPOMI) to investigate regional disparities in atmospheric pollution across Malawi between 2019 and 2024. Specifically, it (1) quantifies spatial and temporal variations in pollutant concentrations and (2) examines their relationship with socio-environmental factors such as population density, energy poverty, land use change, and industrial activity.\u003c/p\u003e"},{"header":"3.0 Data Collection and Processing","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data Sources and Study Area\u003c/h2\u003e\u003cp\u003eThis study utilised satellite-based remote sensing datasets to evaluate the spatiotemporal dynamics of key atmospheric pollutants across Malawi, encompassing the Northern, Central, and Southern regions. Malawi, a landlocked country in south-eastern Africa, spans an area of approximately 118,484 km\u0026sup2; and is characterised by diverse topography, including the Great Rift Valley, Lake Malawi, highland plateaus, and low-lying floodplains. The country\u0026rsquo;s climate is largely tropical, with distinct wet (November\u0026ndash;April) and dry (May\u0026ndash;October) seasons, which influence pollutant dispersion and atmospheric chemistry (Mapoma and Xie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Urbanisation is concentrated in major cities such as Lilongwe, Blantyre, Mzuzu, and Zomba, which have experienced rapid population growth and expansion of informal settlements. This growth, combined with an energy mix dominated by biomass fuels (over 85% of households depend on firewood or charcoal for cooking), has intensified anthropogenic emissions (Mcsweeney, New and Lizcano, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, vehicular traffic growth, unregulated industrial activities, and seasonal biomass burning contribute significantly to degraded air quality (Rybarczyk et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Rural areas are also impacted by biomass combustion for domestic energy and agricultural residue burning, particularly during the dry season.\u003c/p\u003e\u003cp\u003eAtmospheric pollutant data were obtained from the Sentinel-5 Precursor (Sentinel-5P) satellite mission, processed via the Google Earth Engine (GEE) platform. The analysis focused on two temporal benchmarks, 2019 and 2024, to quantify changes in pollutant levels over five years. Target pollutants included carbon monoxide (CO), nitrogen dioxide (NO₂), sulphur dioxide (SO₂), ozone (O₃), and aerosols all of which are critical indicators of air quality and proxies for anthropogenic environmental pressure (Goshua, Akdis and Nadeau, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These pollutants were selected due to their documented links to respiratory and cardiovascular health risks, ecosystem degradation, and their roles as short-lived climate forcers in regional climate dynamics.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Sentinel-5P and the TROPOMI Instrument\u003c/h2\u003e\u003cp\u003eThe Sentinel-5 Precursor satellite, launched by the European Space Agency (ESA) under the Copernicus Earth Observation Programme, is equipped with the TROPOspheric Monitoring Instrument (TROPOMI). This advanced spectrometer captures high-resolution data on various trace gases and aerosols with a spatial resolution of approximately 7 km by 3.5 km (Goldberg et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). TROPOMI measures vertical column densities of atmospheric constituents, with CO, NO₂, and SO₂ values reported in mol/m\u0026sup2;, Ozone in mole fraction, and aerosols as a unitless index representing optical thickness (Amegah and Agyei-Mensah, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fowler et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Sentinel-5P\u0026rsquo;s high temporal frequency and spatial coverage make it particularly valuable in data-scarce environments like Malawi, where ground-based air quality monitoring stations are limited or non-existent (Cai et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although satellite-based observations may be influenced by local meteorological conditions and surface reflectance variability, they provide a robust and reliable approximation for regional-scale air pollution assessments (Miles, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rentschler and Leonova, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.0 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Trends and Regional Disparities in NO₂ Pollution Across the Three Regions\u003c/h2\u003e\n \u003cp\u003eBetween 2019 and 2024, nitrogen dioxide (NO₂) concentrations across Malawi\u0026rsquo;s Central, Northern, and Southern regions showed notable spatial and temporal variation in both level and fluctuation. In the Central Region, NO₂ levels demonstrated statistically significant (R\u0026thinsp;=\u0026thinsp;0.004) and steady increases, especially during the dry season (August\u0026ndash;October). This seasonal trend is likely due to reduced atmospheric dispersion and ongoing human activities, such as urban transport emissions and industrial operations (Jarraud and Steiner, \u003cspan class=\"CitationRef\"\u003e2012b\u003c/span\u003e). October saw a 7.6% increase, while June experienced the highest rise at 48%. A high coefficient of determination (R\u0026sup2; = 0.92) indicated strong consistency over time. The Northern Region exhibited greater variability between years, marked by episodic peaks, for example, a July maximum increase of +\u0026thinsp;354% and a September average rise of +\u0026thinsp;580% and sharp declines, including a May average decrease of \u0026minus;\u0026thinsp;514%. The marked rise in standard deviation suggests intensification of short-lived, high-emission episodes, potentially linked to unregulated industrial or biomass-burning activities (Mapoma et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Southern Region experienced the most extreme and erratic fluctuations, with notable surges in March (+\u0026thinsp;1711%) and November (+\u0026thinsp;7579%). The absence of a consistent temporal pattern was reflected in the very weak correlation between 2019 and 2024 concentrations (R\u0026sup2; = 0.03). Such volatility may be associated with recent industrial expansions, shifts in agricultural residue burning, or climate-related influences on atmospheric chemistry and dispersion (IPCC, 2024). These regional patterns are consistent with previous studies in Sub-Saharan Africa reporting elevated NO₂ concentrations in urbanised and industrial hubs such as Lagos, Nigeria (Olusola et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Johannesburg, South Africa (Matandirotya and Burger, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), where anthropogenic activities and seasonal meteorology jointly influence pollutant dynamics. However, the magnitude of episodic spikes observed in Malawi\u0026rsquo;s Southern Region exceeds those reported in comparable contexts, suggesting unique local drivers, possibly tied to rapid, unregulated industrialisation and shifting climatic conditions. The results are shown in Fig. 3 below:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Trends and Regional Disparities in SO₂ Pollution Across the Three Regions\u003c/h2\u003e\n \u003cp\u003eA comparative spatiotemporal analysis of sulphur dioxide (SO₂) concentrations in Malawi\u0026rsquo;s Northern, Central, and Southern regions from 2019 to 2024 reveals substantial regional disparities and month-specific emission anomalies. The Northern region experienced heightened variability with pronounced SO₂ surges in July (+\u0026thinsp;354%) and September (+\u0026thinsp;580%) and a notable drop in May (\u0026minus;\u0026thinsp;514%), reflecting increased pollution episodes and potential anthropogenic triggers such as biomass burning or seasonal agricultural activities (Kosamu et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Central region displayed relatively erratic trends, characterised by weak correlation (R\u0026sup2; \u0026asymp; 0.24) between 2019 and 2024, with high increases in July and September and sharp declines in May and November, suggesting influence from localised urban-industrial emissions (Utembe, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Meanwhile, the Southern region exhibited a stronger temporal correlation (R\u0026sup2; = 0.7492), indicating consistent monthly emission patterns, yet recorded extreme monthly increases in March (+\u0026thinsp;1711%) and November (+\u0026thinsp;7561%), pointing to episodic events likely linked to informal sector emissions or climatic variability (Matandirotya and Burger, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). These findings align with studies that emphasise the role of meteorological conditions and human activities in shaping regional SO₂ profiles across Sub-Saharan Africa (Borge, Lange and Kehew, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Okello et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results are shown in Fig. 4 below;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Trends and Regional Disparities in CO Pollution Across the Three Regions\u003c/h2\u003e\n \u003cp\u003eBetween 2019 and 2024, mean monthly carbon monoxide (CO) concentrations across Malawi\u0026rsquo;s Southern, Northern, and Central regions exhibited distinct spatial and temporal patterns, reflecting seasonally driven emission cycles and evolving source dynamics. In the Southern Region, peak CO concentrations in 2019 occurred in September (0.0561 mol m⁻\u0026sup2;), whereas in 2024 the seasonal maximum shifted to October (0.0825 mol m⁻\u0026sup2;). Substantial increases were recorded in June (+\u0026thinsp;57.3%) and October (+\u0026thinsp;53.5%), while notable declines occurred in February, September (\u0026minus;\u0026thinsp;36.5%), and November, suggesting a displacement in the timing of peak emissions. Despite these changes, the strong positive correlation between 2019 and 2024 (R\u0026sup2; \u0026gt;0.70) indicates persistent seasonal cycles, albeit with varying intensities. These patterns are consistent with continental observations from MOPITT-derived datasets, which link CO seasonality to biomass burning regimes and anthropogenic activity patterns (Naus et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe Northern Region in 2024 experienced widespread reductions in maximum (COₘₐₓ), mean (COₘₑₐₙ), minimum (COₘ\u003csub\u003ei\u003c/sub\u003eₙ), and variability (COₛₜd) values, with the most pronounced declines in January and October. This may indicate improved air quality and stabilised emission patterns, potentially driven by reduced biomass burning, shifts in fuel usage, or localised mitigation measures (Orina et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Central Region, a strong linear relationship between 2019 and 2024 monthly averages (R\u0026sup2; \u0026asymp; 0.85) was observed. Increases occurred in June (+\u0026thinsp;71.1%), October (+\u0026thinsp;63.9%), and May (+\u0026thinsp;7.4%), while February and September showed declines, suggesting both seasonal consistency and changing source contributions (Leguijt et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The results are shown in Fig. 5 below:\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Trends and Regional Disparities in O\u003csub\u003e3\u003c/sub\u003e Pollution Across the Three Regions\u003c/h2\u003e\n \u003cp\u003eBetween 2019 and 2024, monthly ozone (O₃) concentrations across Malawi\u0026rsquo;s Central, Northern, and Southern regions exhibited distinct temporal and spatial patterns, shaped by both seasonal meteorological conditions and anthropogenic influences. In the Central Region, regression analysis indicated a moderate correlation between the two years (R\u0026sup2; \u0026asymp; 0.51), with notable increases during the late dry-season months of October (+\u0026thinsp;5.19%) and September (+\u0026thinsp;4.94%), and marked declines in mid-year months such as July (\u0026minus;\u0026thinsp;7.28%) and August (\u0026minus;\u0026thinsp;6.80%). This pattern is consistent with enhanced photochemical O₃ formation under high solar radiation and low humidity in the dry season, and reduced production during the cooler, wetter mid-year months due to greater atmospheric dispersion and diminished precursor availability (Donnou et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Northern Region displayed a slightly different seasonal evolution. O₃ levels in early 2024 were generally lower than in 2019, followed by sharp increases from September to December. The strongest mean percentage increases occurred in October (+\u0026thinsp;6.45%) and December (+\u0026thinsp;6.14%), patterns that may be linked to biomass burning and intensified solar irradiance during the late dry season (Bourgeois et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Standard deviation increases in months such as May and September suggest heightened variability in O₃ behaviour, possibly due to fluctuating meteorological drivers or episodic emission events.\u003c/p\u003e\n \u003cp\u003eIn the Southern Region, analysis revealed a modest overall increase in mean ozone (O₃) concentrations, particularly in the latter part of the year. The high coefficient of determination (R\u0026sup2; = 0.85) combined with statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicates that temporal variations are well explained by linear trends, reflecting consistent seasonal dynamics. Early-year declines were consistent with the cooling effects and enhanced precipitation typical of Malawi\u0026rsquo;s wet season (Lee et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Collectively, these findings confirm region-specific O₃ dynamics, with all three zones exhibiting dry-season enhancements (September\u0026ndash;December) and mid-year reductions. The observed patterns are broadly consistent with studies across southern Africa that link seasonal O₃ variability to biomass burning cycles, solar radiation intensity, and regional meteorology (Bourgeois et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Donnou et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The results in Fig. 6 below;\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe spatiotemporal analysis of atmospheric pollutants NO₂, SO₂, CO, and O₃ across Malawi\u0026rsquo;s Central, Northern, and Southern regions from 2019 to 2024 reveals significant regional disparities, temporal shifts, and pollutant-specific dynamics shaped by anthropogenic activities, meteorological variability, and land use changes. These findings broadly align with patterns documented in Sub-Saharan Africa but also provide novel insights into local emission sources and atmospheric behaviour within Malawi\u0026rsquo;s diverse ecological and socio-economic contexts. Consistent with observations in urban-industrial centres across Sub-Saharan Africa (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Abulude et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), NO₂ concentrations increased significantly in Malawi\u0026rsquo;s Central Region, especially during the dry season. The high temporal correlation (R\u0026sup2; = 0.92) and seasonal peaks observed suggest that urban traffic emissions, industrial activity, and stagnant atmospheric conditions are principal drivers. This corroborates findings from Lagos Utembe (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Brauer et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in Johannesburg, where similar anthropogenic sources combined with meteorological factors exacerbate NO₂ pollution. However, the extreme and erratic NO₂ spikes in the Northern and Southern regions, including an unprecedented 7579% increase in the South, diverge from more stable urban trends reported elsewhere. These episodic surges likely stem from unregulated biomass burning, emergent industrial activities, and localised meteorological anomalies, echoing patterns seen in rural and peri-urban zones affected by shifting land management and climate variability (Naus et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Matandirotya and Burger, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This volatility emphasises the critical role of non-urban emission sources in Malawi, extending current regional understandings, which often focus predominantly on urban pollution.\u003c/p\u003e\u003cp\u003eSO₂ trends further highlight regional heterogeneity. The Southern Region experienced sharp March and November increases with a moderate correlation (R\u0026sup2; = 0.75), indicative of episodic emissions possibly linked to informal combustion activities and seasonal agricultural practices. These observations align with studies from West Africa and parts of East Africa where SO₂ fluxes reflect biomass burning and small-scale industrial emissions (Donnou et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Meanwhile, the Northern Region\u0026rsquo;s pronounced SO₂ fluctuations suggest temporally concentrated anthropogenic activities, perhaps associated with seasonal crop residue burning or biomass fuel use (Abulude et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This spatially differentiated SO₂ behaviour underlines the importance of incorporating seasonal agricultural and domestic energy-use patterns into pollution mitigation strategies. Carbon monoxide (CO) exhibited strong seasonal correlations (R\u0026sup2; \u0026gt;0.7) in the Central and Southern regions, with peak concentrations shifting from September to October between 2019 and 2024. This temporal shift corresponds with changes in biomass burning cycles, land clearing activities, and evolving regional atmospheric circulation patterns, consistent with MOPITT satellite analyses across southern Africa (Abulude et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Issn, 2024). Contrastingly, the Northern Region displayed general declines in CO metrics, suggesting either effective emission regulation, reduced biomass burning, or differing meteorological influences. These divergent trends highlight the complex interaction of anthropogenic and climatic factors modulating CO emissions regionally.\u003c/p\u003e\u003cp\u003eOzone (O₃) concentrations increased consistently during the dry months (September\u0026ndash;December) across all regions, with moderate to strong inter-annual correlations, particularly in the Central and Northern zones. This seasonal enhancement aligns with known photochemical formation processes driven by precursor pollutants and intense solar radiation, as described in earlier studies across southern Africa and other tropical regions (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, elevated variability in May and September indicates atmospheric sensitivity to both anthropogenic emissions and meteorological parameters such as temperature and humidity, consistent with IPCC (2024) findings. These results reinforce the critical influence of both emission sources and climatic conditions on ozone dynamics, underscoring the need for integrated air quality and climate policies. Collectively, the spatiotemporal variability of these pollutants in Malawi reflects broader regional trends influenced by rapid urbanisation, climate change, and often weak regulatory frameworks (Jarraud and Steiner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; Utembe, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Leguijt et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Okello et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; IPCC, 2024). However, this study extends current knowledge by providing detailed, region-specific temporal analyses that reveal the significance of episodic and non-urban sources such as biomass burning and informal industries on air quality.\u003c/p\u003e"},{"header":"6. Conclusion and recommendations","content":"\u003cp\u003eThis study provides a comprehensive assessment of the spatiotemporal dynamics of key atmospheric pollutants NO₂, SO₂, CO, and O₃ across Malawi\u0026rsquo;s Central, Northern, and Southern regions between 2019 and 2024. The findings reveal significant regional disparities in both the magnitude and variability of pollutant concentrations, shaped by seasonal cycles, anthropogenic activities, and meteorological factors. The Central Region exhibited relatively stable yet consistently increasing NO₂ and CO levels, indicating persistent urban and industrial emissions. In contrast, the Northern and Southern regions experienced high pollutant variability, with episodic spikes in NO₂ and SO₂ concentrations, suggesting the influence of localised, unregulated sources such as biomass burning, informal industrial activity, and climate-induced changes in dispersion. The shifting seasonal peaks in CO and O₃ further reflect evolving emission patterns and atmospheric chemistry influenced by land use changes and photochemical activity. These findings underscore the urgent need for region-specific air quality interventions, especially in light of Malawi\u0026rsquo;s rapid urbanisation, weak regulatory enforcement, and vulnerability to climate change.\u003c/p\u003e\u003cp\u003eTo effectively address the observed disparities in air pollution across Malawi\u0026rsquo;s regions, several strategic recommendations are proposed. First, there is a need to establish a National Air Quality Monitoring Network by expanding ground-based infrastructure to complement satellite data and provide real-time, high-resolution measurements for key pollutants such as NO₂, SO₂, CO, and O₃. Developing region-specific emission inventories through detailed sectoral studies, particularly in transport, industry, agriculture, and domestic fuel use, will help identify dominant pollution sources. Promoting clean and efficient technologies, such as improved cooking stoves, sustainable farming practices, and low-emission transport, is essential to reduce both point and diffuse emissions. Seasonal emission control policies should be implemented during the dry season when pollution concentrations are typically highest due to poor atmospheric dispersion and increased combustion. Strengthening environmental governance through regulatory enforcement, incentives, and public engagement will ensure better compliance and accountability. Enhancing public awareness and education campaigns on air pollution sources, health risks, and personal protective measures is also critical, particularly in vulnerable communities. Moreover, integrating air quality management into national climate action plans can yield co-benefits for both environmental health and climate resilience. Supporting further research and capacity building will improve scientific understanding and inform data-driven policymaking, while leveraging remote sensing, GIS, and machine learning tools will enhance pollution tracking and hotspot identification. Finally, the development of early warning and response systems based on pollutant thresholds can provide timely advisories and protect public health during high-risk periods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eThis study acknowledges several limitations. The spatial and temporal resolution of available pollutant data may limit the granularity of the findings. Additionally, the reliance on satellite-derived data and limited ground-based measurements may introduce uncertainties related to local atmospheric conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUse of Large Language Models (LLMs)\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eTo enhance clarity and academic rigour, Large Language Models (LLMs) were utilised for paraphrasing and grammatical corrections throughout the manuscript. The author carefully reviewed and verified all edits to ensure that the original meaning and scientific accuracy were preserved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\u003cp\u003eThe author declares no conflict of interest. All referenced works have been duly cited.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research was conducted without any external funding.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials:\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbulude, F.O., Damodharan, U., Acha, S., Adamu, A. and Arifalo, K.M. (2021) \u0026lsquo;Preliminary Assessment of Air Pollution Quality Levels of Lagos, Nigeria\u0026rsquo;, \u003cem\u003eAerosol Science and Engineering\u003c/em\u003e, 5(3), pp. 275\u0026ndash;284. Available at: https://doi.org/10.1007/s41810-021-00099-1.\u003c/li\u003e\n\u003cli\u003eAhmed, M., Huan, W., Ali, N., Shafi, A., Ehsan, M., Abdelrahman, K., Khan, A.A., Abbasi, S.S. and Fnais, M.S. (2023) \u0026lsquo;The Effect of Energy Consumption, Income, and Population Growth on CO2 Emissions: Evidence from NARDL and Machine Learning Models\u0026rsquo;, \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, 15(15). Available at: https://doi.org/10.3390/su151511956.\u003c/li\u003e\n\u003cli\u003eAmegah, A.K. and Agyei-Mensah, S. (2017) \u0026lsquo;Urban air pollution in Sub-Saharan Africa: Time for action\u0026rsquo;, \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 220, pp. 738\u0026ndash;743. Available at: https://doi.org/10.1016/j.envpol.2016.09.042.\u003c/li\u003e\n\u003cli\u003eBorge, R., Lange, S. and Kehew, R. (2023) \u0026lsquo;Analysis of air quality issues and air quality management status in five major African cities\u0026rsquo;, \u003cem\u003eClean Air Journal\u003c/em\u003e, 33(2), pp. 1\u0026ndash;22. 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White Rose Research Online URL for this paper : Version : Published Version Article : Orina , F ., Amukoye , E ., Bowyer , C . et al . ( 17 more authors ) ( 2024 ) Household carbon monoxide ( CO ) concentrations in a large African city : An unquantified public health burden ? Household carbon monoxide ( CO ) concentrations in a large African city : An unquantified public health burden ? ☆\u0026rsquo;.\u003c/li\u003e\n\u003cli\u003eJarraud, M. and Steiner, A. (2012a) \u003cem\u003eSummary for policymakers\u003c/em\u003e, \u003cem\u003eManaging the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change\u003c/em\u003e. Available at: https://doi.org/10.1017/CBO9781139177245.003.\u003c/li\u003e\n\u003cli\u003eJarraud, M. and Steiner, A. 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(2022) \u0026lsquo;Air pollution and recurrence of cardiovascular events after ST-segment elevation myocardial infarction\u0026rsquo;, \u003cem\u003eAtherosclerosis\u003c/em\u003e, 342(July 2021), pp. 1\u0026ndash;8. Available at: https://doi.org/10.1016/j.atherosclerosis.2021.12.012.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Air pollution, Spatiotemporal analysis, Remote sensing, Seasonal variability, Air quality management","lastPublishedDoi":"10.21203/rs.3.rs-7357386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7357386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eAir pollution is an escalating environmental and public health issue in Malawi, driven by rapid urbanisation, industrial growth, and biomass burning. Understanding the spatiotemporal dynamics of major atmospheric pollutants is essential for effective mitigation strategies.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo analyse the spatiotemporal trends and seasonal variations of nitrogen dioxide (NO₂), sulphur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) across Malawi\u0026rsquo;s Central, Northern, and Southern regions from 2019 to 2024.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eTo extract pollutant concentrations, satellite remote sensing data from Sentinel-5P were processed in Google Earth Engine (GEE). Spatial and temporal analyses in a GIS environment quantified regional variability and seasonal patterns.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe Central Region recorded consistent NO₂ increases during the dry season, with June levels rising by 48% (R\u0026sup2; = 0.92), largely from urban and industrial emissions. Northern and Southern regions showed highly variable NO₂ and SO₂ trends, including a\u0026thinsp;+\u0026thinsp;7579% NO₂ spike in the Southern Region linked to biomass burning and new industrial activities. Seasonal CO peaks shifted, with Southern October values up 53.5%. O₃ concentrations rose notably in dry months, especially in the Northern Region (+\u0026thinsp;6.45% in October).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eMalawi's distinct regional and seasonal pollution profiles are shaped by anthropogenic and climatic drivers, requiring targeted interventions.\u003c/p\u003e\u003ch2\u003eUnique Contribution:\u003c/h2\u003e\u003cp\u003eThis is the first high-resolution, multi-year spatial analysis of four key pollutants across Malawi\u0026rsquo;s three regions using Sentinel-5P, providing critical evidence for air quality policy and climate-resilient planning.\u003c/p\u003e\u003ch2\u003eKey Recommendation:\u003c/h2\u003e\u003cp\u003eEstablish a nationwide air quality monitoring network, strengthen emission control enforcement, and integrate air quality management with climate adaptation strategies.\u003c/p\u003e","manuscriptTitle":"Air Pollution Inequality in Malawi: A Comparative Regional Analysis Using Sentinel-5P and Cloud-Based Geospatial Tools","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:34:39","doi":"10.21203/rs.3.rs-7357386/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":"d0d56597-c64c-4bdf-ad38-cb8791de2462","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53058760,"name":"Environmental Engineering"}],"tags":[],"updatedAt":"2025-08-13T05:34:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-13 05:34:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7357386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7357386","identity":"rs-7357386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}