TITLE:
Analysis of Spatio-Temporal Distribution of Air Quality Index in Nairobi City County: Observations from Sentinel-5P TROPOMI
AUTHORS:
Maryvine Vena Nyanchoka, Ezekiel Ndunda, Esther Kitur, Kanyiva Muindi, Esther Judith
KEYWORDS:
Spatial Variation, Temporal Variation, Air Pollutants, Land Use Patterns, Air Quality Index, Sentinel-5P TROPOMI
JOURNAL NAME:
Open Journal of Air Pollution,
Vol.15 No.3,
September
24,
2026
ABSTRACT: 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, emissions, and strategic urban greening should be recommended to reduce exposure to pollution in all land use areas.