Selenium concentration in herring from the Baltic Sea tracks decadal and spatial trends in external sources

Selenium (Se) has a narrow range between nutritionally optimal and toxic concentrations for many organisms, including fish and humans. However, the degree to which humans are affecting Se concentrations in coastal food webs with diffuse Se sources is not well described. Here we examine large-scale drivers of spatio-temporal variability in Se concentration in herring from the Baltic Sea (coastal sea) to explore the anthropogenic impact on a species from the pelagic food web. We analyze data from three herring muscle time series covering three decades (1979–2010) and herring liver time series from 20 stations across the Baltic Sea covering a fourth decade (2009–2019). We find a 0.7–2.0% per annum (n = 26–30) Se decline in herring muscle samples from 0.34 ± 0.02 μg g−1 ww in 1979–1981 to 0.18 ± 0.03 μg g−1 ww in 2008–2010. This decrease continues in the liver samples during the fourth decade (6 of 20 stations show significant decrease). We also find increasing North-South and East-West gradients in herring Se concentrations. Using our observations, modelled Se deposition (spatio-temporal information) and estimated Se river discharge (spatial information), we show that the spatial variability in herring Se tracks the variability in external source loads. Further, between 1979 and 2010 we report a ∼5% per annum decline in direct Se deposition and a more gradual, 0.7–2.0% per annum, decline in herring Se concentrations. The slower rate of decrease for herring can be explained by stable or only slowly decreasing riverine inputs of Se to the Baltic Sea as well as recycling of Se within the coastal system. Both processes can reduce the effect of the trend predicted from direct Se deposition. We show that changing atmospheric emissions of Se may influence Se concentrations of a pelagic fish species in a coastal area through direct deposition and riverine inputs from the terrestrial landscape

Constraining Atmospheric Selenium Emissions Using Observations, Global Modeling, and Bayesian Inference

Selenium (Se) is an essential dietary element for humans and animals, and the atmosphere is an important source of Se to soils. However, estimates of global atmospheric Se fluxes are highly uncertain. To constrain these uncertainties, we use a global model of atmospheric Se cycling and a database of more than 600 sites where Se in aerosol has been measured. Applying Bayesian inference techniques, we determine the probability distributions of global Se emissions from the four major sources: anthropogenic activities, volcanoes, marine biosphere, and terrestrial biosphere. Between 29 and 36 Gg of Se are emitted to the atmosphere every year, doubling previous estimates of emissions. Using emission parameters optimized by aerosol network measurements, our model shows good agreement with the aerosol Se observations (R2 = 0.66), as well as with independent aerosol (0.59) and wet deposition measurements (0.57). Both model and measurements show a decline in Se over North America in the last two decades because of changes in technology and energy policy. Our results highlight the role of the ocean as a net atmospheric Se sink, with around 7 Gg yr–1 of Se transferred from land through the atmosphere. The constrained Se emissions represent a substantial step forward in understanding the global Se cycle.ISSN:0013-936XISSN:1520-585

Isotopic source signatures: Impact of regional variability on the δ13CH4 trend and spatial distribution

ISSN:1352-2310ISSN:0004-6981ISSN:1352-326

Evaluating atmospheric mercury (Hg) uptake by vegetation in a chemistry-transport model

We study the uptake of atmospheric mercury by vegetation in a chemical transport model and available observations. Due to the importance of this sink in the global mercury cycle, perturbations to forested areas can elevate mercury risks.</jats:p

Deforestation as an Anthropogenic Driver of Mercury Pollution

Deforestation reduces the capacity of the terrestrial biosphere to take up toxic pollutant mercury (Hg) and enhances the release of secondary Hg from soils. The consequences of deforestation for Hg cycling are not currently considered by anthropogenic emission inventories or specifically addressed under the global Minamata Convention on Mercury. Using global Hg modeling constrained by field observations, we estimate that net Hg fluxes to the atmosphere due to deforestation are 217 Mg year-1 (95% confidence interval (CI): 134-1650 Mg year-1) for 2015, approximately 10% of global primary anthropogenic emissions. If deforestation of the Amazon rainforest continues at business-as-usual rates, net Hg emissions from the region will increase by 153 Mg year-1 by 2050 (CI: 97-418 Mg year-1), enhancing the transport and subsequent deposition of Hg to aquatic ecosystems. Substantial Hg emissions reductions are found for two potential cases of land use policies: conservation of the Amazon rainforest (92 Mg year-1, 95% CI: 59-234 Mg year-1) and global reforestation (98 Mg year-1, 95% CI: 64-449 Mg year-1). We conclude that deforestation-related emissions should be incorporated as an anthropogenic source in Hg inventories and that land use policy could be leveraged to address global Hg pollution.</p

Data and code for the publication: "Deforestation as an anthropogenic driver of mercury pollution"

A. Feinberg, Sep 2023 [email protected] Essential data and code for the publication: Feinberg et al. : Deforestation as an anthropogenic driver of mercury pollution The directories include: 1) analysis_scripts/ - all analysis scripts used to produce input data and figures for paper 2) Erosion_data/ - Erosion model (GloSEM) output 3) GC_code/ - Archived GEOS-Chem code used to simulate the runs in this paper 4) GC_data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs: HIST - run0311 BAU - run0312 GOV- run0313 SAV - run0315 RFR - run0314 Deforesting different regions for EF calculations: DFR_Afrotropic - run0321 DFR_Indomalayan - run0322 DFR_China - run0323 DFR_Neotropic - run0324 DFR_Palearctic - run0325 DFR_Australasia - run0326 DFR_Nearctic - run0327 DFR_Amazon = SAV - run0315 5) input_data/ - input data used to run GEOS-Chem Please refer to other README.md files within sub-directories and contact me for any questionsFunding from: Swiss National Science Foundation: Early Postdoc.Mobility grant to A.F. (P2EZP2_195424) and Ambizione grant to M.J. (PZ00P2_174101) US National Science Foundation: grant (#1924148) to N.E.S. Academic Transition Grant from Eawag to J.