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.

Regional Sources and Sinks of Atmospheric Particulate Selenium in the United States Based on Seasonality Profiles.

Selenium (Se) is an essential nutrient for humans and enters our food chain through bioavailable Se in soil. Atmospheric deposition is a major source of Se to soils, driving the need to investigate the sources and sinks of atmospheric Se. Here, we used Se concentrations from PM2.5 data at 82 sites from 1988 to 2010 from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network in the US to identify the sources and sinks of particulate Se. We identified 6 distinct seasonal profiles of atmospheric Se, grouped by geographical location: West, Southwest, Midwest, Southeast, Northeast, and North Northeast. Across most of the regions, coal combustion is the largest Se source, with a terrestrial source dominating in the West. We also found evidence for gas-to-particle partitioning in the wintertime in the Northeast. Wet deposition is an important sink of particulate Se, as determined by Se/PM2.5 ratios. The Se concentrations from the IMPROVE network compare well to modeled output from a global chemistry-climate model, SOCOL-AER, except in the Southeast US. Our analysis constrains the sources and sinks of atmospheric Se, thereby improving the predictions of Se distribution under climate change.

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.

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.

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

Mercury (Hg), a neurotoxic heavy metal, is transferred to marine and terrestrial ecosystems through atmospheric transport. Recent studies have highlighted the role of vegetation uptake as a sink for atmospheric elemental mercury (Hg0) and a source of Hg to soils. However, the global magnitude of the Hg0 vegetation uptake flux is highly uncertain, with estimates ranging 1000-4000 Mg per year. To constrain this sink, we compare simulations in the chemical transport model GEOS-Chem with a compiled database of litterfall, throughfall, and flux tower measurements from 93 forested sites. The prior version of GEOS-Chem predicts median Hg0 dry deposition velocities similar to litterfall measurements from Northern hemisphere temperate and boreal forests (∼0.03 cm s-1), yet it underestimates measurements from a flux tower study (0.04 cm s-1vs. 0.07 cm s-1) and Amazon litterfall (0.05 cm s-1vs. 0.17 cm s-1). After revising the Hg0 reactivity within the dry deposition parametrization to match flux tower and Amazon measurements, GEOS-Chem displays improved agreement with the seasonality of atmospheric Hg0 observations in the Northern midlatitudes. Additionally, the modelled bias in Hg0 concentrations in South America decreases from +0.21 ng m-3 to +0.05 ng m-3. We calculate a global flux of Hg0 dry deposition to land of 2276 Mg per year, approximately double previous model estimates. The Amazon rainforest contributes 29% of the total Hg0 land sink, yet continued deforestation and climate change threatens the rainforest's stability and thus its role as an important Hg sink. In an illustrative worst-case scenario where the Amazon is completely converted to savannah, GEOS-Chem predicts that an additional 283 Mg Hg per year would deposit to the ocean, where it can bioaccumulate in the marine food chain. Biosphere-atmosphere interactions thus play a crucial role in global Hg cycling and should be considered in assessments of future Hg pollution.

Anthropogenic emission is the main contributor to the rise of atmospheric methane during 1993-2017.

Atmospheric methane (CH4) concentrations have shown a puzzling resumption in growth since 2007 following a period of stabilization from 2000 to 2006. Multiple hypotheses have been proposed to explain the temporal variations in CH4 growth, and attribute the rise of atmospheric CH4 either to increases in emissions from fossil fuel activities, agriculture and natural wetlands, or to a decrease in the atmospheric chemical sink. Here, we use a comprehensive ensemble of CH4 source estimates and isotopic δ13C-CH4 source signature data to show that the resumption of CH4 growth is most likely due to increased anthropogenic emissions. Our emission scenarios that have the fewest biases with respect to isotopic composition suggest that the agriculture, landfill and waste sectors were responsible for 53 ± 13% of the renewed growth over the period 2007-2017 compared to 2000-2006; industrial fossil fuel sources explained an additional 34 ± 24%, and wetland sources contributed the least at 13 ± 9%. The hypothesis that a large increase in emissions from natural wetlands drove the decrease in atmospheric δ13C-CH4 values cannot be reconciled with current process-based wetland CH4 models. This finding suggests the need for increased wetland measurements to better understand the contemporary and future role of wetlands in the rise of atmospheric methane and climate feedback. Our findings highlight the predominant role of anthropogenic activities in driving the growth of atmospheric CH4 concentrations.

Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry-climate model.

Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40-50% in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~30% in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU - approximately 33% larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, "SOCOLv3.1", which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx), carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90% of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20% more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.