The high rate of urbanisation is one of the key causes of poor air quality, particularly in low- and middle-income countries where the monitoring systems remain inadequate amidst weak policy implementation. Nairobi City County, the largest city in East and Central Africa, is witnessing a continuously growing population, thus an increase in vehicle and motorbike volume, and industrialisation, which together lead to a high level of criteria air pollutants in its varied land use areas. This study examined the spatio-temporal Air Quality Index (AQI) across four land use patterns in Nairobi, including commercial, residential, industrial, and green park, using data from six administrative sub-counties: Starehe, Westlands, Kibra, Ruaraka, Makadara, and Embakasi East for the period 2020 to 2024. Carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) gas concentrations were acquired from the Sentinel-5 Precursor (Sentinel-5P) satellite, and processed using Google Earth Engine (GEE), while the fine particulate matter (PM2.5) concentrations were obtained using ground-based low-cost sensors deployed in collaboration with AirQo. One-way Analysis of Variance (ANOVA) was used to test the differences in pollutant concentrations across the four spatial land use zones, with repeated measures ANOVA applied to assess the temporal variation across the morning, afternoon, and evening measurement periods. The one-way ANOVA results revealed that the pollutant levels across the four land use patterns differed significantly (F = 5.41, p = 0.0001). On the other hand, the post-hoc Tukey HSD test found that the pairwise differences between industrial and commercial areas (p = 0.005) and between commercial and green park areas (p = 0.023) were significant. The repeated measures ANOVA of temporal analysis showed that there was a significant difference in the concentrations between the times of the day (F = 4.57, p = 0.0192), with a significant variation between evening and afternoon (p = 0.011) and a not statistically significant variation between morning and afternoon (p = 0.052). The weighted AQI calculations derived from the study data verified that commercial areas had the highest overall AQI of 115.452 among the four land use areas, which aligns with their heavy vehicular traffic and high density of business activities. These findings support the claim that the structure of land use and time of day significantly influence pollutant levels in Nairobi. Specific policy interventions on traffic management,
References
[1]
Bililign, S., Brown, S.S., Westervelt, D.M., Kumar, R., Tang, W., Flocke, F., et al. (2024) East African Megacity Air Quality: Rationale and Framework for a Measurement and Modeling Program. Bulletin of the American Meteorological Society, 105, E1584-E1602. https://doi.org/10.1175/bams-d-23-0098.1
[2]
Shelton, S., Liyanage, G., Jayasekara, S., Pushpawela, B., Rathnayake, U., Jayasundara, A., et al. (2022) Seasonal Variability of Air Pollutants and Their Relationships to Meteorological Parameters in an Urban Environment. Advances in Meteorology, 2022, 1-18. https://doi.org/10.1155/2022/5628911
[3]
Venter, Z.S., Hassani, A., Stange, E., Schneider, P. and Castell, N. (2024) Reassessing the Role of Urban Green Space in Air Pollution Control. Proceedings of the National Academy of Sciences, 121, e2306200121. https://doi.org/10.1073/pnas.2306200121
[4]
Liu, J., Qian, Y., Wang, S., Gu, W. and Guo, D. (2025) Characterizing Diurnal Variations and Driving Factors of Major Gaseous Pollutants across China. Atmospheric Environment, 367, Article 121759. https://doi.org/10.1016/j.atmosenv.2025.121759
[5]
Chhabra, P. (2024) The Effect of Different Meteorological Factors on the Concentrations of Air Pollutants. EarthArXiv (California Digital Library).
[6]
Pope, F.D., Gatari, M., Ng’ang’a, D., Poynter, A. and Blake, R. (2018) Airborne Particulate Matter Monitoring in Kenya Using Calibrated Low-Cost Sensors. Atmospheric Chemistry and Physics, 18, 15403-15418. https://doi.org/10.5194/acp-18-15403-2018
[7]
Silva, A.C.T., Branco, P.T.B.S. and Sousa, S.I.V. (2022) Impact of COVID-19 Pandemic on Air Quality: A Systematic Review. International Journal of Environmental Research and Public Health, 19, Article 1950. https://doi.org/10.3390/ijerph19041950
[8]
Oguge, O., Nyamondo, J., Adera, N., Okolla, L., Okoth, B., Anyango, S., et al. (2024) Fine Particulate Matter Air Pollution and Health Implications for Nairobi, Kenya. Environmental Epidemiology, 8, e307. https://doi.org/10.1097/ee9.0000000000000307
[9]
Kirago, L., Gatari, M.J., Gustafsson, ？. and Andersson, A. (2022) Black Carbon Emissions from Traffic Contribute Substantially to Air Pollution in Nairobi, Kenya. Communications Earth & Environment, 3, Article No. 74. https://doi.org/10.1038/s43247-022-00400-1
[10]
van Geffen, J., Eskes, H., Compernolle, S., Pinardi, G., Verhoelst, T., Lambert, J., et al. (2022) Sentinel-5P TROPOMI NO2 Retrieval: Impact of Version V2.2 Improvements and Comparisons with OMI and Ground-Based Data. Atmospheric Measurement Techniques, 15, 2037-2060. https://doi.org/10.5194/amt-15-2037-2022
[11]
Rahman, M.M., Kamruzzaman, M., Faridatul, M.I. and Szabó, G. (2025) Spatiotemporal Dynamics of Urban Air Pollution in Dhaka City (2020-2024) Using Time-Series Sentinel-5P Satellite Images and Google Earth Engine (GEE). Environments, 12, Article 274. https://doi.org/10.3390/environments12080274
[12]
Mwangi, P. (2024) Evaluating the Spatio-Temporal Distribution of Nitrogen Dioxide, Land Surface Temperature and NDVI in Nairobi City County. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 237-244. https://doi.org/10.5194/isprs-annals-x-3-2024-237-2024
[13]
Sameh, S., Zarzoura, F. and El-Mewafi, M. (2023) Spatio-Temporal Analysis Mapping of Air Quality Monitoring in Cairo Using Sentinel-5 Satellite Data and Google Earth Engine. Mansoura Engineering Journal, 49, Article 3. https://doi.org/10.58491/2735-4202.3122
[14]
Kaplan, G. and Avdan, Z.Y. (2020) Space-Borne Air Pollution Observation from Sentinel-5P TROPOMI: Relationship between Pollutants, Geographical and Demographic Data. International Journal of Engineering and Geosciences, 5, 130-137. https://doi.org/10.26833/ijeg.644089
[15]
Google Earth Engine (2024) A Planetary-Scale Platform for Earth Science Data & Analysis. https://earthengine.google.com/
[16]
Maurya, N.K., Pandey, P.C., Sarkar, S., Kumar, R. and Srivastava, P.K. (2022) Spatio-Temporal Monitoring of Atmospheric Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of Stubble Burning a Case Study. ISPRS International Journal of Geo-Information, 11, Article 301. https://doi.org/10.3390/ijgi11050301
[17]
Kenya National Bureau of Statistics (2019) Kenya Population & Housing Census 2019. https://www.knbs.or.ke/reports/kenya-census-2019/
[18]
Kanyangi, W., Atela, J., Mwaniki, G., Randa, T., Agevi, H. and Akinyi, E. (2025) Political Economy of the Air Quality Management of Nairobi City. Frontiers in Environmental Health, 4, Article 1534700. https://doi.org/10.3389/fenvh.2025.1534700
[19]
Kenya Meteorological Department (2024) Kenya Meteorological Department: Ministry of Environment, Climate Change and Forestry. Meteo.go.ke. https://meteo.go.ke/
[20]
Cifuentes, F., Eskes, H., Piters, A., Gomez, J., Douros, J., Pinardi, G., et al. (2026) Uncertainty Assessment of TROPOMI NO2 over Europe Using Ground-Based Remote Sensing Observations. Atmospheric Measurement Techniques, 19, 2437-2477. https://doi.org/10.5194/amt-19-2437-2026
[21]
Le, S., Checkley, W., Dudley, L., Ssuuna, J., Ndyanabo, A., Bainomugisha, E., et al. (2025) Calibration of Low-Cost Sensor Data for Ambient PM2.5 Monitoring across Urban and Rural Settings in South Central Uganda. Atmospheric Pollution Research, 16, Article 102580. https://doi.org/10.1016/j.apr.2025.102580
[22]
Goshua, A., Akdis, C.A. and Nadeau, K.C. (2022) World Health Organization Global Air Quality Guideline Recommendations: Executive Summary. Allergy, 77, 1955-1960. https://doi.org/10.1111/all.15224
[23]
U.S. Environmental Protection Agency (2024) Final Updates to the Air Quality Index (AQI) for Particulate Matter Fact Sheet and Common Questions. https://www.epa.gov/system/files/documents/2024-02/pm-naaqs-air-quality-index-fact-sheet.pdf
[24]
Emmanuel, E.T., Oghenekome, O.C. and Odiyirin, B.P. (2025) Spatio-Temporal Variations of Air Pollutants in Urban Zones of Port Harcourt: Influence of Climatic Conditions, and Human Activities. International Journal of Research and Innovation in Social Science, 11, 1615-1629. https://doi.org/10.47772/ijriss.2025.907000134
[25]
Shilenje, Z.W., Maloba, S. and Ongoma, V. (2022) A Review on Household Air Pollution and Biomass Use over Kenya. Frontiers in Environmental Science, 10, Article 996038. https://doi.org/10.3389/fenvs.2022.996038
[26]
Wang, Y., Duan, X., Liang, T., Wang, L. and Wang, L. (2022) Analysis of Spatio-Temporal Distribution Characteristics and Socioeconomic Drivers of Urban Air Quality in China. Chemosphere, 291, Article 132799. https://doi.org/10.1016/j.chemosphere.2021.132799
[27]
Zhao, G., Pang, X., Li, J., Xing, B., Sun, S., Chen, L., et al. (2023) Temporal Variations and Spatial Distribution of Air Pollutants in Shaoxing, a City in Yangtze Delta, China Based on Mobile Monitoring Using a Sensor Package. Atmosphere, 14, Article 1093. https://doi.org/10.3390/atmos14071093
[28]
Istiana, T., Kurniawan, B., Soekirno, S., Nahas, A., Wihono, A., Nuryanto, D.E., et al. (2023) Causality Analysis of Air Quality and Meteorological Parameters for PM2.5 Characteristics Determination: Evidence from Jakarta. Aerosol and Air Quality Research, 23, Article 230014. https://doi.org/10.4209/aaqr.230014
[29]
Xu, S., Sun, C. and Liu, N. (2024) Road Congestion and Air Pollution—Analysis of Spatial and Temporal Congestion Effects. Science of the Total Environment, 945, Article 173896. https://doi.org/10.1016/j.scitotenv.2024.173896
[30]
Soleimanpour, M., Alizadeh, O. and Sabetghadam, S. (2023) Analysis of Diurnal to Seasonal Variations and Trends in Air Pollution Potential in an Urban Area. Scientific Reports, 13, Article No. 21065. https://doi.org/10.1038/s41598-023-48420-x
[31]
Deevi, R. and Lu, M. (2024) In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California. Atmosphere, 15, Article 1130. https://doi.org/10.3390/atmos15091130
[32]
Lugon, L., Kemgne, C., Vot, V.L., Mauchard, N., Quang, B.V., Wang, C., et al. (2025) How Far Can We Improve Urban Air Quality and Population Exposure by Changing Mobility? An Analysis in Paris. Science of the Total Environment, 1000, Article 180266. https://doi.org/10.1016/j.scitotenv.2025.180266
