Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor Deployment

Background: The increasing availability and usage of low-cost air quality sensors (LCS) presents both opportunities and challenges in terms of data accuracy, reliability, precision and interpretation. Various low cost sensors types differ in the degree of accuracy reliability and precision They can also be influenced by environmental conditions like temperatures and humidity. This study assesses three LCS, E-Samplers, Modulair^TM and AirQO, deployed alongside a reference-grade Beta Attenuation Monitor (BAM-1022) in Nairobi, Kenya, to upraise their performance under varying conditions and explore the strategies for calibration and integration into the monitoring networks.

Methods: The study used BAM-1022 data to validate and calibrate the LCS installed at the University of Nairobi’s Parklands Campus (27 February 2024 to 26 December 2024).  We analyzed sensor accuracy, precision and response to pollution across wet and dry seasons and varying temperature and humidity levels. We aligned the LCS data with BAM-1022 measurements using tailored correction factors and multiple linear regression (MLR) models. We used the coefficient of determination, represented by R-squared (R²), a statistical measure of how close the data from the LCS are from the data from the BAM and the Pearson correlation, r to show the strength of the linear relationship between the sensor measurements and reference measurements. Additionally, we conducted paired t-tests to determine whether statistically significant differences existed between the BAM-1022 and each LCS, and one-sample t-tests to find out if there was a statistically significant difference in the values recorded by low-cost sensors themselves. The study also explored the potential of LCS to improve spatial coverage and resolution while addressing challenges like sensor drift and environmental interference.

Results: The Modulair^TM sensor showed closer measurements in reference to BAM-1022 measurements (R²= 0.82, r =0.9458) followed by AirQO (R²=0.54, r =0.8933) and E-Sampler (R²=0.36, r =0.7166). During wet season, Modulair^TM maintained the closer measurements (R²=0.73, r =0.9123) with AirQO (R²=0.36, r =0.7219) and E-Sampler (R²=0.21, r =0.7812) showing lower alignment. Similar trend was observed in dry season with Modulair^TM (R²=0.8, r=0.8124) followed by AirQO (R²=0.51, r=0.7001) and E-Sampler (R²=0.28, r=0.6996). During high PM_2.5 concentration periods (July to December), Modulair^TM reported higher values than the BAM on certain days. AirQO generally recorded lower values except during these high concentration periods while the E-Samplers fluctuated between higher or lower values across the collocation period. Consequently, correction factors of -12.5, 31.55 and 29.65 were derived for Modulair^TM,AirQO and E-Samplers respectively. Statistical analysis revealed a significant difference between the BAM measurements and LCS (p-value < 0.001). However, no significant differences were observed between the measurements of each of the low-cost sensors.

Conclusion: The LCS can enhance air quality monitoring networks when collocated appropriately and, consistently and carefully calibrated. The readings should be corrected against reference sensor for accurate and reliable data.  Collocation with reference monitors or among the LCS units for regions with limited access to high-end monitoring infrastructure such as Nairobi is key before deployment. Air quality modeling can create a comprehensive monitoring networks hence improved spatial resolution and public health insights.