(1) Background: This work evaluated the usability of commercial “low-cost” air quality sensor systems to substantiate evidence-based policy making. (2) Methods: Two commercially available sensor systems (Airly, Kunak) were benchmarked at a regulatory air quality monitoring station (AQMS) and subsequently deployed in Kampenhout and Sint-Niklaas (Belgium) to address real-world policy concerns: (a) what is the pollution contribution from road traffic near a school and at a central city square and (b) do local traffic interventions result in quantifiable air quality impacts? (3) Results: The considered sensor systems performed well in terms of data capture, correlation and intra-sensor uncertainty. Their accuracy was improved via local re-calibration, up to data quality levels for indicative measurements as set in the Air Quality Directive (U_exp < 50% for PM and <25% for NO₂). A methodological setup was proposed using local background and source locations, allowing for quantification of the (3.1) maximum potential impact of local policy interventions and (3.2) air quality impacts from different traffic interventions with local contribution reductions of up to 89% for NO₂ and 60% for NO throughout the considered 3 month monitoring period; (4) Conclusions: Our results indicate that commercial air quality sensor systems are able to accurately quantify air quality impacts from (even short-lived) local traffic measures and contribute to evidence-based policy making under the condition of a proper methodological setup (background normalization) and data quality (recurrent calibration) procedure. The applied methodology and learnings were distilled in a blueprint for air quality sensor networks for replication actions in other cities.

This study presents a fit-for-purpose lab and field evaluation of commercially available portable sensor systems for PM, NO₂, and/or BC. The main aim of the study is to identify portable sensor systems that are capable of reliably quantifying dynamic exposure gradients in urban environments. After an initial literature and market study resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked under laboratory and real-word conditions. We evaluated the comparability to reference analyzers, sensor precision, and sensitivity towards environmental confounders (temperature, humidity, and O₃). Moreover, we evaluated if the sensor accuracy can be improved by applying a lab or field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM (R² = 0.68–0.9, Uexp = 16–66%, BSU = 0.1–0.7 µg/m³) and BC (R² = 0.82–0.83), but maturity of the tested NO₂ sensors is still low (R² = 0.38–0.55, Uexp = 111–614%) and additional efforts are needed in terms of signal noise and calibration, as proven by the performance after multilinear calibration (R² = 0.75–0.83, Uexp = 37–44%)). The horizontal accuracy of the built-in GPS was generally good, achieving <10 m accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and the resulting impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

This study presents a fit-for-purpose evaluation of commercially available portable sensor systems, measuring PM, NO2 and/or BC, through extensive lab- and field benchmarking. The main aim of the study is to identify sensor systems that can be used for mobile monitoring by citizens to assess dynamic exposure in ambient air. After an initial literature study and market search resulting in 39 sensor systems, 10 sensor systems were ultimately purchased and benchmarked in laboratory and real-word conditions. We evaluated comparability to reference analyzers, sensor precision and sensitivity to temperature, humidity and O3. Moreover, we evaluated if accuracy can be improved by the lab and field calibration. Because the targeted application of the sensor systems under evaluation is mobile monitoring, we conducted a mobile field test in an urban environment to evaluate the GPS accuracy and potential impacts from vibrations on the resulting sensor signals. Results of the considered sensor systems indicate that out-of-the-box performance is relatively good for PM and BC, but maturity of the tested NO2 sensors is still low and additional efforts are needed in terms of signal noise and calibration. The horizontal accuracy of the build-in GPS was generally good achieving a <10m horizontal accuracy for all sensor systems. More accurate and dynamic exposure assessments in contemporary urban environments are crucial to study real-world exposure of individuals and exposome impacts on potential health endpoints. A greater availability of mobile monitoring systems capable of quantifying urban pollutant gradients will further boost this line of research.

Air pollution exposure can lead to exacerbation of respiratory disorders in children. Using sensitive biomarkers helps to assess the impact of air pollution on children’s respiratory health and combining protein, genetic and epigenetic biomarkers gives insights on their interrelatedness. Most studies do not contain such an integrated approach and investigate these biomarkers individually in blood, although its collection in children is challenging. Our study aimed at assessing the feasibility of conducting future integrated larger-scale studies evaluating respiratory health risks of air pollution episodes in children, based on a qualitative analysis of the technical and logistic aspects of a small-scale field study involving 42 children. This included the preparation, collection and storage of non-invasive samples (urine, saliva), the measurement of general and respiratory health parameters and the measurement of specific biomarkers (genetic, protein, epigenetic) of respiratory health and air pollution exposure. Bottlenecks were identified and modifications were proposed to expand this integrated study to a higher number of children, time points and locations. This would allow for non-invasive assessment of the impact of air pollution exposure on the respiratory health of children in future larger-scale studies, which is critical for the development of policies or measures at the population level.

Exposure to the air pollutant particulate matter (PM) is associated with increased risks of respiratory diseases and enhancement of airway inflammation in children. In the context of large scale air pollution studies, it can be challenging to measure fractional exhaled nitric oxide (FeNO) as indicator of lung inflammation. Urinary CC16 (U-CC16) is a potential biomarker of increased lung permeability and toxicity, increasing following short-term PM_2.5 exposure. The single nucleotide polymorphism (SNP) CC16 G38A (rs3741240) affects CC16 levels and respiratory health. Our study aimed at assessing the use of U-CC16 (incl. CC16 G38A from saliva) as potential alternative for FeNO by investigating their mutual correlation in children exposed to PM. Samples from a small-scale study conducted in 42 children from urban (n = 19) and rural (n = 23) schools examined at two time points, were analysed. When considering recent (lag1) low level exposure to PM_2.5 as air pollution measurement, we found that U-CC16 was positively associated with FeNO (β = 0.23; 95% CI [-0.01; 0.47]; p = 0.06) in an adjusted analysis using a linear mixed effects model. Further, we observed a positive association between PM_2.5 and FeNO (β = 0.56; 95% CI [0.02; 1.09]; p = 0.04) and higher FeNO in urban school children as compared to rural school children (β = 0.72; 95% CI [0.12; 1.31]; p = 0.02). Although more investigations are needed, our results suggest that inflammatory responses evidenced by increased FeNO are accompanied by potential increased lung epithelium permeability and injury, evidenced by increased U-CC16. In future large scale studies, where FeNO measurement is less feasible, the integrated analysis of U-CC16 and CC16 G38A, using noninvasive samples, might be a suitable alternative to assess the impact of air pollution exposure on the respiratory health of children, which is critical for policy development at population level.