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ACCENT workshop on network harmonization and data intercomparability 28 - 30 January 2008, Las Vegas, USA. ACCENT Report, 2.08
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Accumulation of the polyfluorinated ether sulfonate F53-B in food items from two provinces in China.
2015
Accumulation patterns of polychlorinated alkanes in an Arctic marine food web
Polychlorinated alkanes (PCAs), otherwise known as chlorinated paraffins, are contaminants of emerging Arctic concern where our understanding of their occurrence and trophic transfer in Arctic food webs remains limited. To investigate biomagnification potential of PCAs, we analyzed short-chain PCAs: C10-C13 and medium-chain PCAs-C14-17 in three Arctic species: polar cod (Boreogadus saida), ringed seal (Pusa hispida), and polar bear (Ursus maritimus) and Subarctic capelin (Mallotus villosus) samples collected from the northern Barents Sea in 2017 and 2021. PCAs-C10-13 concentrations were low, but detectable in all species, while PCAs-C14-17 concentrations were mainly below detection limits in the mammals. PCAs did not biomagnify, as the lowest concentrations were found in polar bear (0.7 ng g−1 lw) and the highest in capelin (56.9 ng g−1 lw). The PCA homologue profiles were similar among Arctic species, with PCAs-C10-13 dominating in polar cod and marine mammals, which may suggest a contribution from long-range atmospheric transport.
In contrast, PCAs-C14-17 were most abundant in the Subarctic capelin, likely reflecting a different exposure. Despite differing PCAs-C14-17 concentrations among the two fish species, their PCAs-C14-17 homologue profile was similar, indicating uniform global production trends. Subarctic capelin is increasingly being preyed upon by Arctic predators and may facilitate the biological transport of PCAs-C14-17 into Arctic ecosystems.
These findings suggest that climate-driven shifts in species distribution may have the potential to alter contaminant exposure pathways in Arctic marine food webs.
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2023
Accurate Lightweight Calibration Methods for Mobile Low-Cost Particulate Matter Sensors
Monitoring air pollution is a critical step towards improving public health, particularly when it comes to identifying the primary air pollutants that can have an impact on human health. Among these pollutants, particulate matter (PM) with a diameter of up to 2.5 μm (or PM2.5) is of particular concern, making it important to continuously and accurately monitor pollution related to PM. The emergence of mobile low-cost PM sensors has made it possible to monitor PM levels continuously in a greater number of locations. However, the accuracy of mobile low-cost PM sensors is often questionable as it depends on geographical factors such as local atmospheric conditions. <p>This paper presents new calibration methods for mobile low-cost PM sensors that can correct inaccurate measurements from the sensors in real-time. Our new methods leverage Neural Architecture Search (NAS) to improve the accuracy and efficiency of calibration models for mobile low-cost PM sensors. The experimental evaluation shows that the new methods reduce accuracy error by more than 26% compared with the state-of-the-art methods. Moreover, the new methods are lightweight, taking less than 2.5 ms to correct each PM measurement on Intel Neural Compute Stick 2, an AI-accelerator for edge devices deployed in air pollution monitoring platforms.
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