This Week in Air Quality Research: Novel Insights and Sensor Advances (23-June-2025)
I have been keeping up with air quality research to understand emerging risks, technological advances, and policy implications. This week I’m spotlighting literature that highlights the increasing role of low-cost sensors, real-time exposure assessment, and high-resolution monitoring—from global cities to African urban centers. Below, I summarize some key papers, discuss their impact, and provide references for deeper exploration. African-focused research is separated for emphasis.
🌍 Global Research Highlights
Occupational Exposure & Sensor Innovation
- Real-Time PM Exposure in Mining
- Summary: Pradhan et al. used low-cost sensors to monitor particulate matter (PM) exposure among heavy earth moving machinery (HEMM) operators in opencast coal mines. Findings show that in-cabin exposures, while lower than outside, still exceed safety thresholds—highlighting the need for better occupational protections.
- Novelty: Real-time, occupation-specific exposure assessment using scalable sensor networks.
- Reference: Estimation of real-time PM exposure and associated health risk of HEMM operators using low-cost sensors in a highly mechanised opencast coal mine (Pradhan et al., 2025) [1]
- Correcting Low-Cost Sensor Data for Humidity
- Summary: Lekamge & Oswin developed practical approaches for correcting relative humidity (RH) biases in low-cost PM2.5 sensors—crucial for ensuring data reliability in diverse climates.
- Impact: Paves the way for regulatory-grade use of affordable sensor networks.
- Reference: Exploration of a practical approach to providing RH corrections to low cost sensor networks (Lekamge & Oswin, 2025) [2]
- Bus-Based Urban Air Quality Monitoring
- Summary: Matroca evaluated a sensor system installed on buses, demonstrating cost-effective, real-time urban air quality mapping. Challenges remain in calibration and data consistency.
- Potential: Scalable for city-wide, dynamic pollution tracking.
- Reference: A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities (Matroca, 2025) [3]
Urban Pollution Dynamics & Source Attribution
- Mobile PM10 Hotspot Mapping
- Summary: Walzelova et al. used mobile sensors for high-definition mapping of PM10 hotspots in urban areas, revealing sources often missed by stationary monitors.
- Reference: Simultaneous mobile PM10 monitoring provides high definition spatial and time localization of hotspots of poor air quality in an urban environment (PDF) (Walzelova et al., 2025) [4]
- Commuter Exposure to NO2 and PM2.5
- Summary: Mainka et al. assessed personal exposure during commutes, finding higher exposures in cars and during winter. Highlights the importance of microenvironments in health risk.
- Reference: Exposure to NO2 and PM2.5 While Commuting: Utility of Low-Cost Sensor (Mainka et al., 2025) [5]
- Aircraft Ultrafine Particle Emissions
- Summary: Van Loenen et al. differentiated ultrafine particle (UFP) emissions from aircraft in airport-adjacent communities, providing crucial data for local air quality management.
- Reference: Aircraft Arrival and Departure Contributions to Ultrafine Particle Size Distribution in a Near-Airport Community (van Loenen et al., 2025) [6]
Source-Specific & Modeling Advances
- Vehicle Emissions in Real Traffic
- Summary: Lu et al. measured real-world emissions (CO, NOx, CO2, PM2.5) from gasoline and diesel vehicles, showing diesel’s outsized PM2.5 contribution.
- Reference: Real-traffic emissions of CO, NOx, CO2, and PM2.5 from vehicles using a portable emission measurement system (Lu et al., 2025) [7]
- Oil Imports and PM2.5 in China
- Summary: Tosun et al. linked China’s oil imports to rising PM2.5 concentrations, emphasizing the global interconnectedness of air pollution.
- Reference: Analyzing the impact of oil imports on air pollution proxied by PM2.5 concentrations in China (Tosun et al., 2025) [8]
- Urban PM2.5 Simulation with Mobile Data
- Summary: Li et al. used mobile monitoring to improve spatiotemporal PM2.5 modeling in cities, boosting accuracy by 40% over traditional methods.
- Reference: Fine simulation and spatiotemporal analysis of urban PM2.5 using Mobile monitoring data (Li et al., 2025) [9]
🌍 Africa-Focused Research
Nairobi, Kenya: Long-Term PM2.5 Monitoring
- Summary: Waiguru et al. conducted the longest low-cost sensor study in Nairobi (2020–2022), mapping PM2.5 variability across multiple sites. Seasonal peaks were observed during dry periods, with industrial and traffic-heavy zones consistently exceeding safe limits.
- Novelty: Provides rare, high-resolution, long-term data for sub-Saharan Africa, informing targeted interventions and policy.
- Reference: Seasonal multisite low-cost sensor measurements to estimate spatial and temporal variability of particulate matter pollution in Nairobi, Kenya (Waiguru et al., 2025) [10]
📝 Conclusion
This week’s research advances our understanding of air quality through innovative sensor networks, improved data correction methods, and fine-grained exposure mapping. The Nairobi study is a standout for Africa, offering much-needed data in a region often overlooked in air quality research. These works collectively drive actionable insights for policymakers, urban planners, and public health advocates.
References
[1] D. S. Pradhan, A. K. Patra, A. Penchala, and S. Santra, “Estimation of real-time PM exposure and associated health risk of HEMM operators using low-cost sensors in a highly mechanised opencast coal mine,” Air Quality, Atmosphere & Health, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s11869-025-01769-6
[2] S. A. Lekamge and H. P. Oswin, “Exploration of a practical approach to providing RH corrections to low cost sensor networks,” npj Climate and Atmospheric Science, 2025. [Online]. Available: https://www.nature.com/articles/s41612-025-01115-8
[3] B. Matroca, “A Low-Cost Sensor System Installed in Buses to Monitor Air Quality in Cities,” International Journal of Environmental Research and Public Health, 2025. [Online]. Available: https://www.academia.edu/download/100408779/pdf.pdf
[4] K. Walzelova, S. Walzel, and J. Hovorka, “Simultaneous mobile PM10 monitoring provides high definition spatial and time localization of hotspots of poor air quality in an urban environment (PDF),” European Journal of Environmental Sciences, 2025. [Online]. Available: https://karolinum.cz/data/clanek/14905/EJES_15_1_0034.pdf
[5] A. Mainka, W. Nocoń, A. Malinowska, J. Pfajfer, et al., “Exposure to NO2 and PM2.5 While Commuting: Utility of Low-Cost Sensor,” Applied Sciences, vol. 15, no. 11, p. 5965, 2025. [Online]. Available: https://www.mdpi.com/2076-3417/15/11/5965
[6] B. D. van Loenen, F. Black-Ingersoll, J. L. Durant, J. I. Levy, et al., “Aircraft Arrival and Departure Contributions to Ultrafine Particle Size Distribution in a Near-Airport Community,” Environmental Science & Technology, 2025. [Online]. Available: https://pubs.acs.org/doi/abs/10.1021/acs.est.5c04799
[7] C. Lu, S. Dong, S. Huang, S. Gao, J. Fu, X. Tian, S. Lin, et al., “Real-traffic emissions of CO, NOx, CO2, and PM2.5 from vehicles using a portable emission measurement system,” Air Quality, Atmosphere & Health, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s11869-025-01765-w
[8] T. T. Tosun, M. Ojaghlou, and E. Ugurlu, “Analyzing the impact of oil imports on air pollution proxied by PM2.5 concentrations in China,” International Journal of Environmental Quality, 2025. [Online]. Available: https://www.emerald.com/insight/content/doi/10.1108/MEQ-12-2024-0592/full/html
[9] D. Li, X. Jin, F. Xu, J. Liang, and X. Wang, “Fine simulation and spatiotemporal analysis of urban PM2.5 using Mobile monitoring data,” Urban Climate, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2212095525002147
[10] E. N. Waiguru, M. R. Giordano, M. Beekmann, et al., “Seasonal multisite low-cost sensor measurements to estimate spatial and temporal variability of particulate matter pollution in Nairobi, Kenya,” Atmospheric Pollution Research, 2025. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1309104225002326