Mercury isotope fractionation during the exchange of Hg between the atmosphere and land surfaces: implications for atmospheric Hg cycles

2020 ◽  
Author(s):  
Wei Zhu ◽  
Xuewu Fu ◽  
Hui Zhang ◽  
Chen Liu ◽  
Ben Yu ◽  
...  
Keyword(s):  
Land Surface ◽  
Isotope Fractionation ◽  
Agricultural Soils ◽  
Mining Area ◽  
Balance Model ◽  
Flux Chamber ◽  
Biogeochemical Models ◽  
Mass Dependent Fractionation ◽  
Hg Mining ◽  
Soil Hg

<p>Mercury (Hg) is a neurotoxic pollutant distributed globally via atmospheric transportation of elemental Hg (Hg(0)). Both anthropogenic and natural processes emit Hg to the atmosphere, where the later contributes up to approximately two thirds of the total emissions. Hg(II) in the Earth’s surface can be reduced chemically and biologically, resulted subsequent re-emission of Hg(0) back to the atmosphere. The Hg(0) exhibits bi-directional exchange (i.e., deposition and/or emission) between the land surface and atmosphere. Soil is the largest terrestrial Hg reservoir and its interaction with the atmosphere influences the atmospheric Hg cycling largely. Hg(0) emission from the terrestrial surfaces soil has been postulated to carry a negative MDF and positive MIF in the global Hg biogeochemical models. However, to date, no experimental evidence support that the complex terrestrial soil Hg(0) emission in accordance with this hypothetical simplification.</p><p>We coupled the <em>in-situ</em> Hg(0) dynamic flux chamber measurement and stable Hg isotope analysis to report a first dataset on the Hg isotope fractionation during the exchange of Hg(0) between the atmosphere and  8 soils and 1 cinnabar surfaces. The effect of air-soil/cinnabar exchange shifted Hg(0) concentrations in the flux chamber [i.e., (Hg(0)<sub>chamber</sub>-Hg(0)<sub>ambient</sub>)/Hg(0)<sub>chamber</sub>] by a factor of -0.29 – 0.90, corresponding to Hg(0) exchange fluxes ranging from -773 – 14457 ng m<sup>-2</sup> h<sup>-1</sup>. Our results showed that the exchange of Hg(0) between the atmosphere and soil/cinnabar could lead to an enrichment of both light and heavy isotopes (δ<sup>202</sup>Hg signatures) in Hg(0), as well as depletion or enrichment of odd isotopes (Δ<sup>199</sup>Hg signatures). This highlighted that multiple processes controlled the land-atmosphere exchange of Hg(0) and affected Hg isotope fractionation. Using a conservative isotope mass balance model, we found urban soils Hg(0) emission exhibited large variations in both δ<sup>202</sup>Hg (-3.04 to -0.34‰) and Δ<sup>199</sup>Hg (-0.60 to 0.38‰), which might be controlled by the Hg isotopic signatures in soils and environmental factors. The isotope signatures of Hg(0) emitted from agricultural background soils (δ<sup>202</sup>Hg = -1.31 ± 1.09‰, Δ<sup>199</sup>Hg = -0.26 ± 0.16‰, 1σ, n=15) and Hg-enriched agricultural soils in Hg mining area (δ<sup>202</sup>Hg = 0.51 ± 1.09‰, Δ<sup>199</sup>Hg = -0.10 ± 0.11‰, 1σ, n=12) exhibited contrasting mass dependent fractionation (MDF). Photo-reduction of soil Hg(II) coordinated to sulfurless ligands likely dominated the MIF of Hg isotope during the exchange of Hg between the atmosphere and  both urban and agricultural soils. While the positive shift of δ<sup>202</sup>Hg in mining area suggested that other processes including sorption and oxidation were also important in controlling MDF of Hg isotope during air/soil exchange. In a line with Hg-enriched agricultural soils, the forest soil emitted Hg(0) in Hg mining area enriched in heavy isotopes relative to the soil but depleted in odd isotopes. Hg(0) emission from cinnabar ore waste exhibited significant negative δ<sup>202</sup>Hg (-2.21 to -1.67‰) but positive Δ<sup>199</sup>Hg (0.17 to 0.38‰). Our results demonstrate complex Hg isotope fractionation during air-soil/cinnabar Hg(0) exchange resulted contrasting enrichment or depletion effects on the atmospheric Hg isotope compositions, thus have important implications for understanding the atmospheric Hg isotope signatures and modeling the global Hg cycling.</p>

scholarly journals Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system

2018 ◽  
Vol 11 (2) ◽  
pp. 541-560 ◽  
Author(s):  
Przemyslaw Zelazowski ◽  
Chris Huntingford ◽  
Lina M. Mercado ◽  
Nathalie Schaller
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Land Surface ◽  
Land Surface Model ◽  
Individual Performance ◽  
Balance Model ◽  
Surface Model ◽  
Near Surface ◽  
Global Circulation Models ◽  
Version 2.0

Abstract. Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25±5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44±4.37 and 14.98±4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

scholarly journals Plant thermal tolerance: a global synthesis for future research

2020 ◽  
Author(s):  
Sonya Geange ◽  
Pieter Arnold ◽  
Alexandra Catling ◽  
Onoriode Coast ◽  
Alicia Cook ◽  
...  
Keyword(s):  
Cold Tolerance ◽  
Land Surface ◽  
Thermal Tolerance ◽  
Extreme Temperature ◽  
Critical Time ◽  
Functional Trait ◽  
The Body ◽  
Future Research ◽  
Biogeochemical Models ◽  
Analytical Approaches

<p>Extreme temperature events are increasing in frequency and intensity across the globe. These extremes, rather than averages, drive species evolution and determine survival by profoundly changing the structure and fluidity of cell membranes, altering enzyme function, and denaturing proteins. Given not only our dependence on agricultural crops and natural vegetation, but also the role of photosynthetic processes within the carbon and hydrological cycles, it is imperative to assess the state of our understanding of the potential impacts of extreme events on plants. Scaling responses from the molecular and organ level to ecosystem function is not without challenge however. There is vast literature on plant thermal tolerance research, but the body of literature is so large, the approaches so disparate and often siloed among disciplines, that research in this field risks floundering at a critical time. We conducted a systematic review of more than 21,500 studies spanning over 100 years of research that yielded almost 1,700 included studies on the tolerance of cultivated and wild land plants to both heat and cold. Our review indicates that most studies on thermal tolerance focus on the cold tolerance of cultivated species (52%) and only a trivial percentage of studies have considered both heat and cold tolerance of any given species (~5%). Combined heat and cold tolerance are important in areas where plants are exposed to extremes of both or may be in the future. This review illustrates the global distribution and concentrations of thermal tolerance studies and the diversity of thermal tolerance methods, ranging from molecular to biochemical, physiological and physical examinations, from transgenic model plants to agricultural and horticultural crops, to natural forest trees, shrubs, and grassland herbs. Critically, it also demonstrates that methods and metrics for assessing thermal tolerance are far from standardised, such that our potential to achieve mechanistic insight and compare across species and biomes is compromised. Without reconciling these issues, the scope for incorporating this critical ecological information into vegetation elements of land surface models may be limited. To aid this, we identify priorities for achieving efficient, reliable, and repeatable research across the spectrum of plant thermal tolerance. These priorities, including meta-analytical approaches and comparative experimental work, will not only further fundamental plant science, but will prove essential next steps if we are to integrate such diverse data on a critical plant functional trait into a usable metric within biogeochemical models.</p>

scholarly journals Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles

2018 ◽  
Vol 22 (7) ◽  
pp. 3863-3882 ◽  
Author(s):  
Fuxing Wang ◽  
Jan Polcher ◽  
Philippe Peylin ◽  
Vladislav Bastrikov
Keyword(s):  
Iberian Peninsula ◽  
Land Surface ◽  
Measurement Errors ◽  
River Discharge ◽  
Water Footprint ◽  
Human Impacts ◽  
Land Surface Model ◽  
Systematic Errors ◽  
Balance Model ◽  
Surface Model

Abstract. River discharge plays an important role in earth's water cycle, but it is difficult to estimate due to un-gauged rivers, human activities and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for un-gauged rivers, but it only provides annual mean values which is insufficient for oceanic modelings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated with the models. We use data assimilation techniques by merging the modeled river discharges by the ORCHIDEE (without human processes currently) LSM and the observations from the Global Runoff Data Centre (GRDC) to obtain optimized discharges over the entire basin. The “model systematic errors” and “human impacts” (dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variable runoff and drainage over each sub-watershed. The method is illustrated over the Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHIDEE represents a realistic discharge over the north of the Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30–150 % over the south and northeast regions where the blue water footprint is large. The normalized bias has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The “correction” increases the interannual variability in river discharge because of the fluctuation of water usage. The E (P−E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias-corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model.

scholarly journals Air/Surface Exchange of Gaseous Elemental Mercury at Different Landscapes in Mississippi, USA

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 538 ◽  
Author(s):  
James Cizdziel ◽  
Yi Jiang ◽  
Divya Nallamothu ◽  
J. Brewer ◽  
Zhiqiang Gao
Keyword(s):  
Forest Floor ◽  
Agricultural Soils ◽  
Elemental Mercury ◽  
Ecological Impacts ◽  
Wetland Soil ◽  
Flux Chamber ◽  
Surface Exchange ◽  
Gaseous Elemental Mercury ◽  
Emission Fluxes ◽  
Mesocosm Experiments

Mercury (Hg) is a global pollutant with human health and ecological impacts. Gas exchange between terrestrial surfaces and the atmosphere is an important route for Hg to enter and exit ecosystems. Here, we used a dynamic flux chamber to measure gaseous elemental Hg (GEM) exchange over different landscapes in Mississippi, including in situ measurements for a wetland (soil and water), forest floor, pond, mowed field and grass-covered lawn, as well as mesocosm experiments for three different agricultural soils. Fluxes were measured during both the summer and winter. Mean ambient levels of GEM ranged between 0.93–1.57 ng m−3. GEM emission fluxes varied diurnally with higher daytime fluxes, driven primarily by solar radiation, and lower and more stable nighttime fluxes, dependent mostly on temperature. GEM fluxes (ng m−2 h−1) were seasonally dependent with net emission during the summer (mean 2.15, range 0.32 to 4.92) and net deposition during the winter (−0.12, range −0.32 to 0.12). Total Hg concentrations in the soil ranged from 17.1 ng g−1 to 127 ng g−1 but were not a good predictor of GEM emissions. GEM flux and soil temperature were correlated over the forest floor, and the corresponding activation energy for Hg emission was ~31 kcal mol−1 using the Arrhenius equation. There were significant differences in GEM fluxes between the habitats with emissions for grass > wetland soil > mowed field > pond > wetland water ≈ forest ≈ agriculture soils. Overall, we demonstrate that these diverse landscapes serve as both sources and sinks for airborne Hg depending on the season and meteorological factors.

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