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Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) deposition on urban green enables the collection of particulate pollution from a diversity of contexts, and insight into the physico-chemical profiles of PM is key for identifying main polluting sources. This study reports on the morphological and elemental characterization of PM2–10 deposited on ivy leaves from five different environments (forest, rural, roadside, train, industry) in the region of Antwerp, Belgium. Ca. 40,000 leaf-deposited particles were thoroughly investigated by particle-based analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX) and their physico-chemical characteristics were explored for PM source apportionment purposes. The size distribution of all deposited particles was biased towards small-sized PM, with 32% of the particles smaller than 2.5 μm (PM2.5) and median diameters of 2.80–3.09 μm. The source type influenced both the particles' size and morphology (aspect ratio and shape), with roadside particles being overall the smallest in size and the most spherical. While forest and rural elemental profiles were associated with natural PM, the industry particles revealed the highest anthropogenic metal input. PM2–10 profiles for roadside and train sites were rather comparable and only distinguishable when evaluating the fine (2–2.5 μm) and coarse (2.5–10 μm) PM fractions separately, which enabled the identification of a larger contribution of combustion-derived particles (small, circular, Fe-enriched) at the roadside compared to the train. Random forest prediction model classified the source type correctly for 61–85% of the leaf-deposited PM. The still modest classification accuracy denotes the influence of regional background PM and demands for additional fingerprinting techniques to facilitate source apportionment. Nonetheless, the obtained results demonstrate the utility of leaf particle-based analysis to fingerprint and pinpoint source-specific PM, particularly when considering both the composition and size of leaf-deposited particles. [ABSTRACT FROM AUTHOR]

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]

Atmospheric dust deposition on plants enables the collection of site-specific particulate matter (PM). Knowing the morphology and composition of PM aids in disclosing their emitting sources as well as the associated human health risk. Therefore, this study aimed for a leaf-level holistic analysis of dust accumulation on plant leaves. Plant species (ivy and strawberry) with distinct leaf macro- and micro-morphology were exposed during 3 months at a moderate road traffic site in Antwerp, Belgium. Leaves collected every three weeks were analyzed for their magnetic signature, morphology and elemental content, by a combination of techniques (biomagnetic analyses, ED-XRF, HR-ICP-MS, SEM). Dust accumulation on the leaves was observed both visually (SEM) and magnetically, while the metal enrichment was limited (only evident for Cr) and more variable over time. Temporal dynamics during the second half of the exposure period, due to precipitation events and reduction of atmospheric pollution input, were evidenced in our results (elements/magnetically/SEM). Ivy accumulated more dust than strawberry leaves and seemed less susceptible to wash-off, even though strawberry leaves contain trichomes and a rugged micromorphology, leaf traits considered to be important for capturing PM. The magnetic enrichment (in small-grained, SD/PSD magnetite particles), on the other hand, was not species-specific, indicating a common contributing source. Variations in pollution contributions, meteorological phenomena, leaf traits, particle deposition (and encapsulation) versus micronutrients depletion, are discussed in light of the conducted monitoring campaign. Although not completely elucidative, the complex, multifactorial process of leaf dust accumulation can better be understood through a combination of techniques. • Ivy and strawberry leaves followed up every three weeks for a three months period. • Dust accumulation observed visually and magnetically, yet limited in metal built-up. • Ivy accumulated more than strawberry, with the latter more susceptible to wash-off. • Site-source and precipitation dynamics over time were detected by leaf biomonitoring. • Combination of techniques assists in understanding the complex leaf-dust interaction. [ABSTRACT FROM AUTHOR]

Humans are generally exposed to per- and polyfluoroalkyl substances (PFAS) through their diet. Whilst plenty of data are available on commercial food products, little information exists on the contribution of self-cultivated food, such as home-produced eggs (HPE), to the dietary PFAS intake in humans. The prevalence of 17 legacy and emerging PFAS in HPE (N = 70) from free-ranging laying hens was examined at 35 private gardens, situated within a 10 km radius from a fluorochemical plant in Antwerp (Belgium). Potential influences from housing conditions (feed type and number of individuals) and age of the chickens on the egg concentrations was examined, and possible human health risks were evaluated. Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) were detected in all samples. PFOS was the dominant compound and concentrations (range: 0.13–241 ng/g wet weight) steeply decreased with distance from the fluorochemical plant, while there was no clear distance trend for other PFAS. Laying hens receiving an obligate diet of kitchen leftovers, exhibited higher PFOS and PFOA concentrations in their eggs than hens feeding only on commercial food, suggesting that garden produce may be a relevant exposure pathway to both chickens and humans. The age of laying hens affected egg PFAS concentrations, with younger hens exhibiting significantly higher egg PFOA concentrations. Based on a modest human consumption scenario of two eggs per week, the European health guideline was exceeded in ≥67% of the locations for all age classes, both nearby and further away (till 10 km) from the plant site. These results indicate that PFAS exposure via HPE causes potential human health risks. Extensive analysis in other self-cultivated food items on a larger spatial scale is highly recommended, taking into account potential factors that may affect PFAS bioavailability to garden produce. [Display omitted] • Eight PFAS were detected in homegrown eggs of free-ranging laying hens. • PFOS was the dominant compound and concentrations decreased from the fluorochemical plant. • Diet and age of laying hens were related to PFOS and PFOA egg concentrations. • Homegrown eggs can be an important exposure pathway of PFAS to humans. • Based on exposure estimation via egg intake, health guidelines were often exceeded. [ABSTRACT FROM AUTHOR]