Provningsjämförelse / Proficiency Test 2020-1, Suspenderat material och slam / Suspended solids and sludgeDownload
Fluorine Mass Balance and Suspect Screening in Marine Mammals from the Northern Hemisphere
There is increasing evidence that the ~20 routinely monitored per- and polyfluoroalkyl substances (PFASs) account for only a fraction of extractable organofluorine (EOF) occurring in the environment. To assess whether PFAS exposure is being underestimated in marine mammals from the Northern Hemisphere, we performed a fluorine mass balance on liver tissues from 11 different species using a combination of targeted PFAS analysis, EOF and total fluorine determination, and suspect screening. Samples were obtained from the east coast United States (US), west and east coast of Greenland, Iceland, and Sweden from 2000-2017. Of the 36 target PFASs, perfluorooctane sulfonate (PFOS) dominated in all but one Icelandic and three US samples, where the 7:3 fluorotelomer carboxylic acid (7:3 FTCA) was prevalent. This is the first report of 7:3 FTCA in polar bears (~1000 ng/g, ww) and cetaceans (<6-190 ng/g, ww). In 18 out of 25 samples, EOF was not significantly greater than fluorine concentrations derived from sum target PFASs. For the remaining 7 samples (mostly from the US east coast), 30-75% of the EOF was unidentified. Suspect screening revealed an additional 33 PFASs (not included in the targeted analysis) bringing the total to 59 detected PFASs from 12 different classes. Overall, these results highlight the importance of a multi-platform approach for accurately characterizing PFAS exposure in marine mammals.
The MILAN campaign: Studying diel light effects on the air-sea interfaceDownload
The sea surface microlayer (SML) at the air–sea interface is <1 mm thick, but it is physically, chemically, and biologically distinct from the underlying water and the atmosphere above. Wind-driven turbulence and solar radiation are important drivers of SML physical and biogeochemical properties. Given that the SML is involved in all air–sea exchanges of mass and energy, its response to solar radiation, especially in relation to how it regulates the air–sea exchange of climate-relevant gases and aerosols, is surprisingly poorly characterized. MILAN (Sea Surface Microlayer at Night) was an international, multidisciplinary campaign designed to specifically address this issue. In spring 2017, we deployed diverse sampling platforms (research vessels, radio-controlled catamaran, free-drifting buoy) to study full diel cycles in the coastal North Sea SML and in underlying water, and installed a land-based aerosol sampler. We also carried out concurrent ex situ experiments using several microsensors, a laboratory gas exchange tank, a solar simulator, and a sea spray simulation chamber. In this paper we outline the diversity of approaches employed and some initial results obtained during MILAN. Our observations of diel SML variability show, for example, an influence of (i) changing solar radiation on the quantity and quality of organic material and (ii) diel changes in wind intensity primarily forcing air–sea CO2 exchange. Thus, MILAN underlines the value and the need of multidiciplinary campaigns for integrating SML complexity into the context of air–sea interaction.
A Random Forest Approach to Estimate Daily Particulate Matter, Nitrogen Dioxide, and Ozone at Fine Spatial Resolution in Sweden
Air pollution is one of the leading causes of mortality worldwide. An accurate assessment
of its spatial and temporal distribution is mandatory to conduct epidemiological studies able to
estimate long-term (e.g., annual) and short-term (e.g., daily) health effects. While spatiotemporal
models for particulate matter (PM) have been developed in several countries, estimates of daily
nitrogen dioxide (NO2) and ozone (O3) concentrations at high spatial resolution are lacking, and no
such models have been developed in Sweden. We collected data on daily air pollutant
concentrations from routine monitoring networks over the period 2005–2016 and matched them
with satellite data, dispersion models, meteorological parameters, and land-use variables. We
developed a machine-learning approach, the random forest (RF), to estimate daily concentrations
of PM10 (PM<10 microns), PM2.5 (PM<2.5 microns), PM2.5–10 (PM between 2.5 and 10 microns), NO2,
and O3 for each squared kilometer of Sweden over the period 2005–2016. Our models were able to
describe between 64% (PM10) and 78% (O3) of air pollutant variability in held-out observations, and
between 37% (NO2) and 61% (O3) in held-out monitors, with no major differences across years and
seasons and better performance in larger cities such as Stockholm. These estimates will allow to
investigate air pollution effects across the whole of Sweden, including suburban and rural areas,
previously neglected by epidemiological investigations.