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2017
2009
An optimized low volume sampler was developed to determine both gas- and particle bound concentrations of short and medium-chain chlorinated paraffins (S/MCCPs). Background contamination was limited by the sampler design, providing method quantification limits (MQLs) at least two orders of magnitude lower than other studies within the gas (MQL: 500 pg (ΣSCCPs), 1.86 ng (ΣMCCPs)) and particle (MQL: 500 pg (ΣSCCPs), 1.72 ng (ΣMCCPs) phases. Good repeatability was observed between parallel indoor measurements (RSD ≤ 9.3% (gas), RSD ≤ 14% (particle)) with no breakthrough/saturation observed after a week of continuous sampling. For indoor air sampling, SCCPs were dominant within the gas phase (17 ± 4.9 ng/m3) compared to MCCPs (2.7 ± 0.8 ng/m3) while the opposite was observed in the particle bound fraction (0.28 ± 0.11 ng/m3 (ΣSCCPs) vs. 2.7 ± 1.0 ng/m3 (ΣMCCPs)). Only SCCPs in the gas phase could be detected reliably during outdoor sampling and were considerably lower compared to indoor concentrations (0.27 ± 0.10 ng/m3). Separation of the gas and particle bound phase was found to be crucial in applying the appropriate response factors for quantification based on the deconvoluted S/MCCP sample profile, thus avoiding over- (gas phase) or underestimation (particle phase) of reported concentrations. Very short chain chlorinated paraffins (vSCCPs, C5-C9) were also detected at equal or higher abundance compared to SCCP congener groups (C10-C13) congener groups, indicating an additional human indoor inhalation risk.
Elsevier
2021
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2019
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2015
In order to measure progress towards the aims outlined by the United Nations (UN) 2030 Agenda, data are needed for the different indicators that are linked to each UN Sustainable Development Goal (SDG). Where statistical or scientific data are not sufficient or available, alternative data sources, such as data from citizen science (CS) activities, could be used.
Statistics Norway, together with the Norwegian Association of Local and Regional Authorities, have developed a taxonomy for classifying indicators that are intended to measure the SDGs. The purpose of this taxonomy is to sort, evaluate, and compare different SDG indicators and to assess their usefulness by identifying their central properties and characteristics. This is done by organizing central characteristics under the three dimensions of Goal, Perspective, and Quality. The taxonomy is designed in a way that can help users to find the right indicators across sectors to measure progress towards the SDGs depending on their own context and strategic priorities. The Norwegian taxonomy also offers new opportunities for the re-use of data collected through CS activities. This paper presents the taxonomy and demonstrates how it can be applied for an indicator based on a CS data set, and we also suggest further use of CS data.
Ubiquity Press
2023
2014
2014