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(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 (Uexp < 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. [ABSTRACT FROM AUTHOR]

Understanding the accumulation behaviour of atmospheric particles inside tree leaves is of great importance for the interpretation of biomagnetic monitoring results. In this study, we evaluated the temporal variation of the saturation isothermal remanent magnetisation (SIRM) of leaves of a roadside urban Platanus x acerifolia Willd. tree in Antwerp, Belgium. We hereby examined the seasonal development of the total leaf SIRM signal as well as the leaf-encapsulated fraction of the deposited dust, by washing the leaves before biomagnetic analysis. On average 38% of the leaf SIRM signal was exhibited by the leaf-encapsulated particles. Significant correlations were found between the SIRM and the cumulative daily average atmospheric PM10 and PM2.5 measurements. Moreover, a steady increase of the SIRM throughout the in-leaf season was observed endorsing the applicability of biomagnetic monitoring as a proxy for the time-integrated PM exposure of urban tree leaves. Strongest correlations were obtained for the SIRM of the leaf-encapsulated particles which confirms the dynamic nature of the leaf surface-accumulated particles. [ABSTRACT FROM AUTHOR]

Abstract: Increasing the spatial resolution of air quality assessments in urban environments is designated as a priority area within current research. Biomagnetic monitoring and air quality modelling are both methodologies able to provide information about the spatial variation of particulate pollutant levels within urban environments. This study evaluates both methods by comparing results of a biomagnetic monitoring campaign at 110 locations throughout Antwerp, Belgium, with modelled pollutant concentrations of PM10 and NO2. Due to the relation of biomagnetic monitoring with railway traffic, analyses were conducted for both all locations (n = 110) and railway traffic excluded locations (n = 67). While the general spatial variation, land use comparison and the relation with traffic intensity were comparable between the two applied methodologies, an overall bad agreement is obtained when the methodologies are correlated to each other. While no correlation was found between SIRM and PM10 results (p = 0.75 for n = 110 and p = 0.68 for n = 67), a significant but low (r ≤ 0.33) correlation was found between SIRM and NO2 (p < 0.01 for n = 110 and p = 0.04 for n = 67). While biomagnetic monitoring and air quality modelling are both able to provide high spatial resolution information about urban pollutant levels, we need to take into account some considerations. While uncertainty in the biomagnetic monitoring approach might arise from the processes that determine leaf particulate deposition and the incorporation of multiple emission sources with diverging magnetic composition, air quality modelling remains an approximation of reality which implies its dependency on accurate emission factors, implication of atmospheric processes and representation of the urban morphology. Therefore, continuous evaluation of model performance against measured data is essential to produce reliable model results. Nevertheless, this study demonstrates that in addition to telemetric monitoring networks, the combination of both air quality modelling and biomagnetic monitoring is a valuable approach to provide insights into the variation of atmospheric pollutants in heterogeneous urban environments. [Copyright &y& Elsevier]

Recent advances in sensor and IoT technologies allow for denser and mobile air quality measurements. These measurements are still spatiotemporally sparse at city-level, but can be interpolated using data-driven techniques. This work presents validation results of two machine-learning models to infer air quality sensor data in both space and time. Temporal validation exercises are performed at available regulatory monitoring stations following the FAIRMODE protocol. Both models show scalable to different mobile datasets with comparable prediction performance for PM 2.5 (R2 = 0.68–0.75, MAE = 2.99–2.82 μg m−3) and NO 2 (R2 = 0.8–0.82, MAE = 8.81–9.83 μg m−3) in Utrecht and Antwerp. In Oakland (Atlanta), we observed a lower performance for NO 2 (R2 = 0.46–0.41, MAE = 4.06–5.07) and BC (R2 = 0.31–0.28, MAE = 0.48–0.27), likely caused by the less representative monitoring coverage. Although comparable in terms of prediction performance, the Geographical Random Forest (GRF) model seems to achieve slightly better accuracies, while the correlations are typically higher for the Air Variational Graph Autoencoder (AVGAE) model. This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of complementary sensor data. The observed performance metrics approach current state-of-the-art chemical transport models in terms of performance while needing much lower resources, computational power, infrastructure and processing time. • Machine learning techniques can interpolate spatiotemporally sparse regulatory- and sensor-derived air quality data. • We present model validation results on different mobile datasets from Antwerp (BE), Utrecht (NL) and Oakland (US). • Following the FAIRMODE protocol, both models show to perform on different mobile datasets. • This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of sensor data. • Ultimately, model performance still depends on the applied sensor performance and spatiotemporal monitoring coverage. [ABSTRACT FROM AUTHOR]

As real-life experimental data on natural ventilation of atmospheric pollution levels in urban street canyons is still scarce and has proven to be complex, this study, experimentally evaluated the impact of an urban street canyon opening on local atmospheric pollution levels, during a 2-week field campaign in a typical urban street canyon in Antwerp, Belgium. Besides following up on atmospheric particulate matter (PM), ultrafine particles (UFPs) and black carbon (BC) levels, the magneto-chemical PM 10 composition was quantified to identify contributions of specific elements in enclosed versus open street canyon sections. Results indicated no higher overall PM, UFP and BC concentrations at the enclosed site compared to the open site, but significant day-to-day variability between both monitoring locations, depending on the experienced wind conditions. On days with oblique wind regimes (4 out of 14), natural ventilation was observed at the open location while higher element contributions of Ca, Fe, Co, Ni, Cu, Zn and Sr were exhibited at the enclosed location. Magnetic properties correlated with the PM 10 filter loading, and elemental content of Fe, Cr, Mn and Ti. Magnetic bivariate ratios identified finel-grained magnetite carriers with grain sizes below 0.1 μm, indicating similar magnetic source contributions at both monitoring locations. Our holistic approach, combining atmospheric monitoring with magneto-chemical PM characterization has shown the complex impact of real-life wind flow regimes, different source contributions and local traffic dynamics on the resulting pollutant concentrations and contribute to a better understanding on the urban ventilation processes of atmospheric pollution. Unlabelled Image • Real-life experimental study on natural ventilation in a typical urban street canyon. • Atmospheric PM 10 concentration and analytical magneto-chemical composition. • Natural ventilation was suggested under oblique wind regimes, but net effect was negligible. • Higher contributions of Ca, Fe, Co, Ni, Cu, Zn and Sr at the enclosed location. • Holistic approach contributes to a better understanding on urban pollution ventilation. [ABSTRACT FROM AUTHOR]