Assessment of NO2 and PM2.5 exposure with air quality sensors
- 1University of Stuttgart, Department of Flue Gas Cleaning and Air Quality Control, 70569 Stuttgart, Germany
- 2University of Hohenheim, Institute of Physics and Meteorology, Stuttgart, 70599, Germany
- 3Department of Pneumology, Evangelische Lungenklinik Berlin Buch, 13125 Berlin, Germany
- 4Universitätsmedizin Berlin, Institute of Physiology, Charité, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- 5Ambulante Pneumologie mit Allergie Zentrum, 70178 Stuttgart, Germany
According to the World Health Organization, poor air quality contributes heavily to the Global Burden of Disease, causing more than 6.7 million deaths each year due to both ambient air pollution and household air pollution. With advances in air pollution monitoring technology, evidence on the adverse health effects of air pollution has been increasing. Still, the understanding of personal exposure is limited by the low spatial resolution of fixed outdoor monitoring stations. Low-cost sensors have the potential to enhance personal exposure prediction at scales required for population-based research.
In this study, we carried out a pilot project to evaluate the feasibility of using low-cost sensors at fixed-locations for epidemiological investigations. Stationary sensor systems for NO2 and PM2.5 were custom-built and deployed both in- and outside the homes of individuals diagnosed with asthma or chronic obstructive pulmonary disease (COPD). Measurements were taken for approximately 30 days at each participant’s home. The study was designed to evaluate the performance of the air quality sensors over a longer timeframe, which so far has not been thoroughly studied (Sesé et al. 2023). Participants self-reported symptom data to study the relationship between indoor air quality and health. Participants recorded their daily activities as well, as part of examining the exposure estimates and indoor pollutant sources. To evaluate the exposure misclassification, the potential dose was calculated using the data of an outdoor monitoring station and the indoor sensors, as well as the generic and the activity-specific inhalation rate. Steps completed prior to this analysis include a study on a low-cost dryer for the PM sensor to prevent the overestimation of the mass concentration due to the hygroscopic growth of particles (Chacón-Mateos et al. 2022), and processing of the NO2 data using machine learning to evaluate the uncertainty, reproducibility, reliability, and sensitivity of the sensors. The results of this work highlight the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. With the appropriate methodology and a robust calibration, air quality sensors can provide us with useful information and show promise for epidemiological investigations.
References:
Sesé, L.; Gille, T.; Pau, G.; Dessimond, B.; Uzunhan, Y.; Bouvry, D. et al. (2023): Low-cost air quality portable sensors and their potential use in respiratory health. In Int. J. Tuberc. Lung Dis. 27 (11), pp. 803–809. DOI: 10.5588/ijtld.23.0197.
Chacón-Mateos, Miriam; Laquai, Bernd; Vogt, Ulrich; Stubenrauch, Cosima (2022): Evaluation of a low-cost dryer for a low-cost optical particle counter. In Atmos. Meas. Tech. 15 (24), pp. 7395–7410. DOI: 10.5194/amt-15-7395-2022
How to cite: Chacon-Mateos, M., Remy, E., Liebers, U., Witt, C., Heimann, F., and Vogt, U.: Assessment of NO2 and PM2.5 exposure with air quality sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6478, https://doi.org/10.5194/egusphere-egu24-6478, 2024.
