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310 Research products

  • SDSN - Greece
  • 2014-2023
  • US
  • Mémoires en Sciences de l'Information et de la Communication

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gustin, Mae Sexauer; Dunham-Cheatham, Sarrah; Allen, Natalie; Choma, Nicole; +7 Authors

    The Hg research community needs methods to more accurately measure atmospheric Hg concentrations and chemistry. The Reactive Mercury Active System (RMAS) uses cation exchange, nylon, and PTFE membranes to determine reactive mercury (RM), gaseous oxidized mercury, and particulate-bound mercury (PBM) concentrations and chemistry, respectively. New data for Atlanta, Georgia (NRGT) demonstrated that particulate-bound Hg was dominant and the chemistry was primarily N and S HgII compounds. At Great Salt Lake, Utah (GSL), RM was predominately PBM, with NS > organics > halogen > O HgII compounds. At Guadalupe Mountains National Park, Texas (GUMO), halogenated compound concentrations were lowest when air interacting with the site was primarily derived from the Midwest, and highest when the air was sourced from Mexico. At Amsterdam Island, Southern Indian Ocean, compounds were primarily halogenated with some N, S, and organic HgII compounds potentially associated with biological activity. The GEOS-Chem model was applied to see if it predicted measurements at five field sites. Model values were higher than observations at GSL, slightly lower at NRGT, and observations were an order of magnitude higher than modeled values for GUMO and Reno, Nevada. In general, data collected from 13 locations indicated that N, S, and organic RM compounds were associated with city and forest locations, halogenated compounds were sourced from the marine boundary layer, and O compounds were associated with long-range transport. Data being developed currently, and in the past, suggest there are multiple forms of RM that modelers must consider, and PBM is an important component of RM.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ArchiMer - Instituti...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    The Science of The Total Environment
    Article . 2023 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ArchiMer - Instituti...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      The Science of The Total Environment
      Article . 2023 . Peer-reviewed
      License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Eleftherios Ioannidis; Kathy S. Law; Jean-Christophe Raut; Louis Marelle; +9 Authors

    Anthropogenic and natural emissions contribute to enhanced concentrations of aerosols in the Arctic winter and early spring, with most attention being paid to anthropogenic aerosols that contribute to so-called Arctic haze. Less-well-studied wintertime sea-spray aerosols (SSAs) under Arctic haze conditions are the focus of this study, since they can make an important contribution to wintertime Arctic aerosol abundances. Analysis of field campaign data shows evidence for enhanced local sources of SSAs, including marine organics at Utqiaġvik (formerly known as Barrow) in northern Alaska, United States, during winter 2014. Models tend to underestimate sub-micron SSAs and overestimate super-micron SSAs in the Arctic during winter, including the base version of the Weather Research Forecast coupled with Chemistry (WRF-Chem) model used here, which includes a widely used SSA source function based on Gong et al. (1997). Quasi-hemispheric simulations for winter 2014 including updated wind speed and sea-surface temperature (SST) SSA emission dependencies and sources of marine sea-salt organics and sea-salt sulfate lead to significantly improved model performance compared to observations at remote Arctic sites, notably for coarse-mode sodium and chloride, which are reduced. The improved model also simulates more realistic contributions of SSAs to inorganic aerosols at different sites, ranging from 20 %–93 % in the observations. Two-thirds of the improved model performance is from the inclusion of the dependence on SSTs. The simulation of nitrate aerosols is also improved due to less heterogeneous uptake of nitric acid on SSAs in the coarse mode and related increases in fine-mode nitrate. This highlights the importance of interactions between natural SSAs and inorganic anthropogenic aerosols that contribute to Arctic haze. Simulation of organic aerosols and the fraction of sea-salt sulfate are also improved compared to observations. However, the model underestimates episodes with elevated observed concentrations of SSA components and sub-micron non-sea-salt sulfate at some Arctic sites, notably at Utqiaġvik. Possible reasons are explored in higher-resolution runs over northern Alaska for periods corresponding to the Utqiaġvik field campaign in January and February 2014. The addition of a local source of sea-salt marine organics, based on the campaign data, increases modelled organic aerosols over northern Alaska. However, comparison with previous available data suggests that local natural sources from open leads, as well as local anthropogenic sources, are underestimated in the model. Missing local anthropogenic sources may also explain the low modelled (sub-micron) non-sea-salt sulfate at Utqiaġvik. The introduction of a higher wind speed dependence for sub-micron SSA emissions, also based on Arctic data, reduces biases in modelled sub-micron SSAs, while sea-ice fractions, including open leads, are shown to be an important factor controlling modelled super-micron, rather than sub-micron, SSAs over the north coast of Alaska. The regional results presented here show that modelled SSAs are more sensitive to wind speed dependence but that realistic modelling of sea-ice distributions is needed for the simulation of local SSAs, including marine organics. This study supports findings from the Utqiaġvik field campaign that open leads are the primary source of fresh and aged SSAs, including marine organic aerosols, during wintertime at Utqiaġvik; these findings do not suggest an influence from blowing snow and frost flowers. To improve model simulations of Arctic wintertime aerosols, new field data on processes that influence wintertime SSA production, in particular for fine-mode aerosols, are needed as is improved understanding about possible local anthropogenic sources.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PURE Aarhus Universi...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    NARCIS
    Article . 2023
    Data sources: NARCIS
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Article . 2023
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ PURE Aarhus Universi...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2023
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mytilinaios, Michail; Basart, Sara; Ciamprone, Sergio; Cuesta, Juan; +9 Authors

    Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Chemistr...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Atmospheric Chemistr...arrow_drop_down
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Xavier Faïn; David M. Etheridge; Kévin Fourteau; Patricia Martinerie; +15 Authors

    Carbon monoxide (CO) is a naturally occurring atmospheric trace gas, a regulated pollutant, and one of the main components determining the oxidative capacity of the atmosphere. Evaluating climate–chemistry models under different conditions than today and constraining past CO sources requires a reliable record of atmospheric CO mixing ratios ([CO]) that includes data since preindustrial times. Here, we report the first continuous record of atmospheric [CO] for Southern Hemisphere (SH) high latitudes over the past 3 millennia. Our continuous record is a composite of three high-resolution Antarctic ice core gas records and firn air measurements from seven Antarctic locations. The ice core gas [CO] records were measured by continuous flow analysis (CFA), using an optical feedback cavity-enhanced absorption spectrometer (OF-CEAS), achieving excellent external precision (2.8–8.8 ppb; 2σ) and consistently low blanks (ranging from 4.1±1.2 to 7.4±1.4 ppb), thus enabling paleo-atmospheric interpretations. Six new firn air [CO] Antarctic datasets collected between 1993 and 2016 CE at the DE08-2, DSSW19K, DSSW20K, South Pole, Aurora Basin North (ABN), and Lock-In sites (and one previously published firn CO dataset at Berkner) were used to reconstruct the atmospheric history of CO from ∼1897 CE, using inverse modeling that incorporates the influence of gas transport in firn. Excellent consistency was observed between the youngest ice core gas [CO] and the [CO] from the base of the firn and between the recent firn [CO] and atmospheric [CO] measurements at Mawson station (eastern Antarctica), yielding a consistent and contiguous record of CO across these different archives. Our Antarctic [CO] record is relatively stable from −835 to 1500 CE, with mixing ratios within a 30–45 ppb range (2σ). There is a ∼5 ppb decrease in [CO] to a minimum at around 1700 CE during the Little Ice Age. CO mixing ratios then increase over time to reach a maximum of ∼54 ppb by ∼1985 CE. Most of the industrial period [CO] growth occurred between about 1940 to 1985 CE, after which there was an overall [CO] decrease, as observed in Greenland firn air and later at atmospheric monitoring sites and attributed partly to reduced CO emissions from combustion sources. Our Antarctic ice core gas CO observations differ from previously published records in two key aspects. First, our mixing ratios are significantly lower than reported previously, suggesting that previous studies underestimated blank contributions. Second, our new CO record does not show a maximum in the late 1800s. The absence of a [CO] peak around the turn of the century argues against there being a peak in Southern Hemisphere biomass burning at this time, which is in agreement with (i) other paleofire proxies such as ethane or acetylene and (ii) conclusions reached by paleofire modeling. The combined ice core and firn air [CO] history, spanning −835 to 1992 CE, extended to the present by the Mawson atmospheric record, provides a useful benchmark for future atmospheric chemistry modeling studies.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Climate of the Past ...arrow_drop_down
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    Climate of the Past (CP)
    Other literature type . 2023
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    Climate of the Past (CP)
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    https://doi.org/10.5194/cp-202...
    Preprint . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Climate of the Past (CP)
    Other literature type . 2023
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      Climate of the Past (CP)
      Other literature type . 2023
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      Climate of the Past (CP)
      Article . 2023 . Peer-reviewed
      License: CC BY
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      https://doi.org/10.5194/cp-202...
      Preprint . 2023 . Peer-reviewed
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      Climate of the Past (CP)
      Other literature type . 2023
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    Authors: William F. Swanson; Chris D. Holmes; William R. Simpson; Kaitlyn Confer; +8 Authors

    Abstract. Reactive halogens play a prominent role in the atmospheric chemistry of the Arctic during springtime. Field measurements and modeling studies suggest that halogens are emitted into the atmosphere from snowpack and reactions on wind-blown snow-sourced aerosols. The relative importance of snowpack and blowing snow sources is still debated, both at local scales and regionally throughout the Arctic. To understand the implications of these halogen sources on a pan-Arctic scale, we simulate Arctic reactive bromine chemistry in the atmospheric chemical transport model GEOS-Chem. Two mechanisms are included: (1) a blowing snow sea salt aerosol formation mechanism and (2) a snowpack mechanism assuming uniform molecular bromine production from all snow surfaces. We compare simulations including neither mechanism, each mechanism individually, and both mechanisms to examine conditions where one process may dominate or the mechanisms may interact. We compare the models using these mechanisms to observations of bromine monoxide (BrO) derived from multiple-axis differential optical absorption spectroscopy (MAX-DOAS) instruments on O-Buoy platforms on the sea ice and at a coastal site in Utqiaġvik, Alaska, during spring 2015. Model estimations of hourly and monthly average BrO are improved by assuming a constant yield of 0.1 % molecular bromine from all snowpack surfaces on ozone deposition. The blowing snow aerosol mechanism increases modeled BrO by providing more bromide-rich aerosol surface area for reactive bromine recycling. The snowpack mechanism led to increased model BrO across the Arctic Ocean with maximum production in coastal regions, whereas the blowing snow aerosol mechanism increases BrO in specific areas due to high surface wind speeds. Our uniform snowpack source has a greater impact on BrO mixing ratios than the blowing snow source. Model results best replicate several features of BrO observations during spring 2015 when using both mechanisms in conjunction, adding evidence that these mechanisms are both active during the Arctic spring. Extending our transport model throughout the entire year leads to predictions of enhanced fall BrO that are not supported by observations.

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    Atmospheric Chemistry and Physics (ACP)
    Article . 2022 . Peer-reviewed
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      Atmospheric Chemistry and Physics (ACP)
      Article . 2022 . Peer-reviewed
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    Authors: Raphael Seguin; David Mouillot; Joshua E. Cinner; Rick D. Stuart Smith; +5 Authors

    AbstractTropical reefs and the fish relying on them are under increasing pressure. Shallow-reef fish provide important ecological information in addition to sustaining fisheries, tourism and more. Although empirical metrics of fish biomass are widely used in fisheries management, metrics of biomass production—how much new biomass is produced over time—are rarely estimated even though such production informs potential fisheries yields. Here we estimate fish standing biomass (B), biomass production (P, the rate of biomass accumulation) and biomass turnover (P/B ratio, the rate of biomass replacement) for 1,979 tropical reef sites spanning 39 tropical countries. On the basis of fish standing biomass and biomass turnover, we propose a conceptual framework that splits reefs into three classes to visualize ecological and socio-economic risk and help guide spatial management interventions (for example, marine protected areas) to optimize returns on conservation efforts. At large scales, high turnover was associated with high human pressure and low primary productivity, whereas high biomass was associated with low human pressure and high primary productivity. Going beyond standing fish biomass to consider dynamic ecological processes can better guide regional coral reef conservation and sustainable fisheries management.

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    Nature Sustainability
    Article . 2022 . Peer-reviewed
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      Nature Sustainability
      Article . 2022 . Peer-reviewed
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    Authors: Nuria Benavent; Anoop S. Mahajan; Qinyi Li; Carlos A. Cuevas; +30 Authors

    This study received funding from the European Research Council Executive Agency under the European Union’s Horizon 2020 Research and Innovation Program (project ERC‐2016‐COG 726349 CLIMAHAL and ERC-2016-STG 714621 GASPARCON) and the European Commission via the EMME-CARE project and was supported by the Consejo Superior de Investigaciones Científicas of Spain. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement no. 856612 and the Academy of Finland (project no. 334514). The Indian Institute of Tropical Meteorology is funded by the Ministry of Earth Sciences, Government of India. Ozone, CO, CH4 and AMS measurements were funded by the Swiss National Science Foundation (grant 200021_188478), the Swiss Polar Institute and U.S. National Science Foundation grants 1914781 and 1807163. J.S. holds the Ingvar Kamprad chair for extreme environments research, sponsored by Ferring Pharmaceuticals. Data reported in this manuscript were produced as part of the international MOSAiC expedition with tag MOSAiC20192020, with activities supported by Polarstern expedition AWI-PS122_00. H.S. was funded by the European ERA-PLANET projects iGOSP and iCUPE (consortium agreement no. 689443 for both projects). We thank FORMAS and the Swedish Polar Research Secretariat for support. We gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (project no. 268020496 – TRR 172) within the Transregional Collaborative Research Center ‘ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC)3’ in subproject C03. We thank I. Bourgeois (NOAA/CIRES) for providing the ATom NOx data. Unlike bromine, the effect of iodine chemistry on the Arctic surface ozone budget is poorly constrained. We present ship-based measurements of halogen oxides in the high Arctic boundary layer from the sunlit period of March to October 2020 and show that iodine enhances springtime tropospheric ozone depletion. We find that chemical reactions between iodine and ozone are the second highest contributor to ozone loss over the study period, after ozone photolysis-initiated loss and ahead of bromine. 6 pags., 2 figs. Peer reviewed

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    Nature Geoscience
    Article . 2022 . Peer-reviewed
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    Authors: Jingjing Liang; Javier G. P. Gamarra; Nicolas Picard; Mo Zhou; +217 Authors

    The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

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    Other literature type . Article . 2022
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    Repositori Obert UdL
    Article . 2022
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    IIASA PURE
    Article . 2022 . Peer-reviewed
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    Authors: Duquennoi, Christian; Martinez, José;

    The European Economic Community (EEC) and later the European Union (EU) have issued policies on waste during the last 50 years. This paper aims at analyzing EEC and EU's policymaking on waste management and circularity in agroecosystems as compared with other sectors of waste management (e.g., municipal, industrial, construction waste, etc.). Even if founded on the same general principles, and especially the precautionary principle, policymaking on waste and by-product management in agroecosystems differs from waste management in other sectors. In particular, agricultural waste management has been excluded from the European Waste Framework Directive, from its start in 1975 to this day. The issue of waste and by-products in agroecosystems has been addressed in multiple Directives and Regulations, historically aiming at reducing the potential negative impacts of residual organic matter application in agriculture. In the last decade, the swiftly growing interest for circular economy has triggered a breakthrough in traditional waste management, potentially affecting all economic sectors and enforcing systemic perspectives rather than more conventional “silo” approaches. Circularity in agroecosystems should thus become a major subject of EU's policymaking, but may suffer from its lack of a general framework, contrary to waste in other sectors. Moreover, agricultural valorization of urban residual organic streams may face several roadblocks in between differing legislations for agroecosystems and for “non-agricultural” systems. A systemic approach of the question of residual matter in agroecosystems, backing a strong policymaking framework for the sector, would be necessary in this context. Science-policymaking interactions are necessary to tackle these issues and should take innovative forms to address their complexity. Policy briefs, Policy Labs and the new European Commission Scientific Advice Mechanism represent existing innovative tools to take the topic of policymaking for sustainable waste management and circularity in agroecosystems forward.

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    Frontiers in Sustainable Food Systems
    Article . 2022 . Peer-reviewed
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      Frontiers in Sustainable Food Systems
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    Authors: Magnan, Alexandre K.; Oppenheimer, Michael; Garschagen, Matthias; Buchanan, Maya K.; +13 Authors

    AbstractSea level rise (SLR) will increase adaptation needs along low-lying coasts worldwide. Despite centuries of experience with coastal risk, knowledge about the effectiveness and feasibility of societal adaptation on the scale required in a warmer world remains limited. This paper contrasts end-century SLR risks under two warming and two adaptation scenarios, for four coastal settlement archetypes (Urban Atoll Islands, Arctic Communities, Large Tropical Agricultural Deltas, Resource-Rich Cities). We show that adaptation will be substantially beneficial to the continued habitability of most low-lying settlements over this century, at least until the RCP8.5 median SLR level is reached. However, diverse locations worldwide will experience adaptation limits over the course of this century, indicating situations where even ambitious adaptation cannot sufficiently offset a failure to effectively mitigate greenhouse-gas emissions.

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    Article . 2022
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    Article . 2022
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    Scientific Reports
    Article . 2022
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    Scientific Reports
    Article . 2022 . Peer-reviewed
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      Article . 2022
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      Scientific Reports
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