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Understanding the bioaccumulation mechanisms of per- and polyfluoroalkyl substances (PFASs) across different chain-lengths, isomers and functional groups represents a monumental scientific challenge with implications for chemical regulation. Here, we investigate how the differential tissue distribution and bioaccumulation behavior of 25 PFASs in crucian carp from two field sites impacted by point sources can provide information about the processes governing uptake, distribution and elimination of PFASs. Median tissue/blood ratios (TBRs) were consistently <1 for all PFASs and tissues except bile which displayed a distinct distribution pattern and enrichment of several perfluoroalkyl sulfonic acids. Transformation of concentration data into relative body burdens (RBBs) demonstrated that blood, gonads, and muscle together accounted for >90% of the amount of PFASs in the organism. Principal component analyses of TBRs and RBBs showed that the functional group was a relatively more important predictor of internal distribution than chain-length for PFASs. Whole body bioaccumulation factors (BAFs) for short-chain PFASs deviated from the positive relationship with hydrophobicity observed for longer-chain homologues. Overall, our results suggest that TBR, RBB, and BAF patterns were most consistent with protein binding mechanisms although partitioning to phospholipids may contribute to the accumulation of long-chain PFASs in specific tissues.
2018
2018
Comparison of dust-layer heights from active and passive satellite sensors
Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol-layer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP-derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).
2018
2018
Pergamon Press
2018
2018
2018
Although air pollution is one of the most significant environmental factors posing a threat to human health worldwide, air quality data are scarce or not easily accessible in most European countries. The current work aims to develop a centralized air quality data hub that enables citizens to contribute to air quality monitoring. In this work, data from official air quality monitoring stations are combined with air pollution estimates from sky-depicting photos and from low-cost sensing devices that citizens build on their own so that citizens receive improved information about the quality of the air they breathe. Additionally, a data fusion algorithm merges air quality information from various sources to provide information in areas where no air quality measurements exist.
MDPI
2018
An Infrastructural Analysis of a Crowdsourcing Tool for Environmental Research
In this paper, we adopt information infrastructure design principles and concepts from the theory of critical mass to analyze and evaluate the socio-technical conditions that hindered the successful bootstrapping processes of a crowdsourcing tool for environmental research. The crowdsourcing tool was designed to improve the estimation of emissions from burning wood for residential heating in urban areas in Norway by collecting geolocation data on wood consumption and stove types. Our analysis identifies three groups of users, namely scientists, wood consumers (end users), and key stakeholders, that the IT capability of the tool needs to support. At this stage, we determined that the tool was more useful to the scientists than the other two groups, which was attributed to its low uptake. We uncovered various underlying issues through further analysis of means by which the tool becomes useful to key stakeholders. One particular issue concerned the tension between existing data collection practices, which are based on statistical methods, and the nature of crowdsourcing, which is based on the principle of open call with no sampling techniques. From our analysis, we concluded that developing crowdsourcing tools for research requires increasing the tool’s benefits for key stakeholders by addressing these underlying issues. Inferring from the theory of critical mass for collective action, we recommend that developers of crowdsourcing tools include a function that allows users to view the contributions of other users.
2018
A combination of local (i.e. firefighting training facilities) and remote sources (i.e., long-range transport) are assumed to be responsible for the occurrence of per- and polyfluoroalkyl substances (PFASs) in Svalbard (Norwegian Arctic). However, no systematic elucidation of local PFASs sources have been conducted yet. Therefore, a survey was performed aiming at identifying local PFASs pollution sources on the island of Spitsbergen (Svalbard, Norway). Soil, fresh water (lake, draining rivers), sea water, melt-water run-off, surface snow and coastal sediment samples were collected from Longyearbyen (Norwegian mining town), Ny-Ålesund (research facility) and the Lake Linnévatnet area (background site) during several campaigns (2014-2016) and analysed for 14 individual target PFASs. For background site (Linnévatnet area, sampling during April to June 2015), ∑PFAS levels ranged from 0.4 – 4 ng/L in surface lake water (n = 20). PFAS in melt water from the contributing glaciers showed similar concentrations (~4 ng/L, n = 2). The short chain perfluorobutanoate (PFBA) was predominant in lake water (60-80% of the ∑PFASs), meltwater (20-30 %) and run-off water (40 %). Long range transport is assumed to be the major PFAS source. In Longyearbyen, 5 water samples (i.e. 2 seawater, 3 run-off) were collected near the local firefighting training site (FFTS) in November 2014 and June 2015, respectively. The highest PFAS levels were found in FFTS melt water run-off (118 ng/L). PFOS was the most abundant compound in the FFTS meltwater run-off (53 – 58 % PFASs). At the research station Ny-Ålesund, sea water (n = 6), soil (n = 9) and fresh water (n = 10) were collected in June 2016. Low ∑PFAS concentrations were determined for sea water (5 - 6 ng/L), whereas high ∑PFAS concentrations were found in run-off water (113 – 119 ng/L) and soil (211 – 800 ng/g dry weight (dw)) collected close to the local FFTS. In addition, high ∑PFAS levels (127 ng/L) were also found in fresh water from lake Solvatnet close to former sewage treatment facility. Overall, at both FFTS affected sites (soil, water), PFOS was the most abundant compound (60 – 69% of ∑PFASs). FFTS and landfill locations were identified as major PFASs sources for Svalbard settlements.
2018