National Inventory Research Articles

Construction of land-use change matrix and estimation of greenhouse gas inventory focusing on settlements in South Korea

BackgroundFive ministries are involved in estimating the greenhouse gas (GHG) inventory in the South Korean land use, land-use change, and forestry (LULUCF) sectors. However, these ministries have not established a consistent land classification standard between land-use categories. Therefore, the GHG inventory is estimated at the approach 1 level with no spatial clarity between land-use categories. Moreover, the settlements category is not estimated because activity data and the spatial scope are lacking. This study proposed a methodology for constructing a land-use change (LUC) matrix in the LULUCF sector for improving approach level and estimating the GHG inventory in the settlements.ResultWe examined 10 sets of spatiotemporal data in South Korea to construct a LUC matrix. To maintain consistency in the spatial land classification, we constructed a LUC matrix using cadastral maps, which provide useful data for consistent land-use classification in South Korea. The LUC matrix was divided into remaining and land-converted settlements between 2005 and 2019 with estimated areas of 878,393.17 and 203,260.42 ha, respectively. CO2 emissions, according to Intergovernmental Panel Climate Change’s Guideline Tier 1, were estimated at 18.94 MtCO2 for 15 years, with an annual CO2 emission of 1.26 MtCO2 yr−1. CO2 emission by land conversion type was found to be the largest at 16.93 MtCO2 in the case of forest converted to settlements. In addition, the area with the largest CO2 emission density was Sejong-si at 7.59 tCO2/ha.ConclusionBased on reviewing available spatial data in South Korea, it is possible to improve Approach 3, which is more advanced than previous Approach 1 in the settlement category. In addition, the national GHG inventory also can be estimated by our constructed LUC matrix and activity data in this study. Under the many discussions about developing the Approach system, this study can provide in-detail information on developing LUC in South Korea in the settlement category as well as suggesting a methodology for constructing the LUC matrix for countries with similar problems to South Korea.

Decision Tree-Based Ensemble Model for Predicting National Greenhouse Gas Emissions in Saudi Arabia

Greenhouse gas (GHG) emissions must be precisely estimated in order to predict climate change and achieve environmental sustainability in a country. GHG emissions are estimated using empirical models, but this is difficult since it requires a wide variety of data and specific national or regional parameters. In contrast, artificial intelligence (AI)-based methods for estimating GHG emissions are gaining popularity. While progress is evident in this field abroad, the application of an AI model to predict greenhouse gas emissions in Saudi Arabia is in its early stages. This study applied decision trees (DT) and their ensembles to model national GHG emissions. Three AI models, namely bagged decision tree, boosted decision tree, and gradient boosted decision tree, were investigated. Results of the DT models were compared with the feed forward neural network model. In this study, population, energy consumption, gross domestic product (GDP), urbanization, per capita income (PCI), foreign direct investment (FDI), and GHG emission information from 1970 to 2021 were used to construct a suitable dataset to train and validate the model. The developed model was used to predict Saudi Arabia’s national GHG emissions up to the year 2040. The results indicated that the bagged decision tree has the highest coefficient of determination (R2) performance on the testing dataset, with a value of 0.90. The same method also has the lowest root mean square error (0.84 GtCO2e) and mean absolute percentage error (0.29 GtCO2e), suggesting that it exhibited the best performance. The model predicted that GHG emissions in 2040 will range between 852 and 867 million tons of CO2 equivalent. In addition, Shapley analysis showed that the importance of input parameters can be ranked as urbanization rate, GDP, PCI, energy consumption, population, and FDI. The findings of this study will aid decision makers in understanding the complex relationships between the numerous drivers and the significance of diverse socioeconomic factors in defining national GHG inventories. The findings will enhance the tracking of national GHG emissions and facilitate the concentration of appropriate activities to mitigate climate change.

Environmental Sustainability of Bricks in an Emerging Economy: Current Environmental Hotspots and Mitigation Potentials for the Future

Clay-fired bricks are widely used in emerging economies mainly because of the unavailability of higher-grade construction and building materials; however, they are associated with significant social and environmental damage. The environmental burdens associated with the fuels and materials required for brick production are huge, as they have both direct and indirect effects along supply chains. This study aimed to assess the environmental footprints of brick production along supply chains, focusing on relevant environmental issues in brick production: carbon dioxide emissions, water consumption, and land use. We demonstrate that fuelwood consumption constitutes a major share of the carbon and water footprints, whereas built-up land for brick drying dominates the major effect of land use. Our expansion of the ecological footprint method enables a comparison of the effects of three different environmental issues with the same land area dimension, which reveals the relative severity of carbon dioxide emissions that account for up to 20% of the national CO2 inventory. For the potential reduction in the environmental footprint of brick production, scenario analysis showed a substantial reduction by replacing clay-fired bricks with concrete bricks. The shift to alternative materials, together with mitigation measures for brick production, can effectively alleviate the environmental pressures of construction materials in future emerging economies.

Harmonising the land-use flux estimates of global models and national inventories for 2000–2020

Abstract. As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO2 yr−1) in global land-use CO2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO2 yr−1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO2 yr−1 in the same period. Most of the gap is found on forest land (3.5 GtCO2 yr−1), with differences also for deforestation (2.4 GtCO2 yr−1), for fluxes from other land uses (1.0 GtCO2 yr−1) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO2 yr−1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO2 yr−1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at https://doi.org/10.5281/zenodo.7650360.

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

The extent of windfarm infrastructures on recognised European blanket bogs

Peatland environments are the Earth’s largest terrestrial carbon store and have the potential to act as carbon sinks. However, the development of windfarms on peatlands is affecting their morphology, hydrology, ground-level climate conditions, carbon functions and vegetation, and long-term consequences still need to be assessed. Blanket bogs are a rare type of ombrotrophic peatland that are typical of oceanic areas with high precipitation and low temperatures. Their distribution has been mapped across Europe, where they are mainly located on hill summits where wind energy potential is higher, making them attractive sites for windfarm developments. The promotion of renewable energy is currently a priority given the environmental and economic drive to increase low-carbon energy production. Establishing windfarms on peatland in pursuit of greener energy, therefore, risks compromising and undermining the green-energy transition. Despite this, the extent of windfarm infrastructures on blanket bogs have not yet been reported at the European scale. This research reports the extent of windfarm infrastructures on recognised blanket bogs, with a geographical focus on Europe, where blanket bogs have been mapped systematically. Under the EU Habitats Directive (92/43/EEC), there are 36 European regions NUTS level 2 with recognised blanket bogs. Of these, 12 have windfarm developments, including 644 wind turbines, 253.4 km of vehicular access tracks and an affected area of 207.6 ha, mainly in Ireland and Scotland where the extent of blanket bogs is also higher. However, despite Spain having under 0.2% of Europe’s recognised blanket bogs area, this was the most affected country. In Scotland, a comparison of the recognised blanket bogs under the Habitats Directive (92/43/EEC) with blanket bogs recorded in national inventories indicates that the extent of windfarm developments was higher, with 1,063 wind turbines and 634.5 km of vehicular access tracks. Our results highlight the extent of windfarm developments on blanket bog habitat, both in areas where peatlands are broadly distributed across the landscape, and also in areas where this recognised habitat is particularly rare. There is a pressing need to assess the long-term impacts of windfarms on peatlands to ensure that efforts to meet energy targets result only in carbon sequestration, and do not jeopardise ecosystem services. Blanket bogs represent a particularly vulnerable habitat, the study of which should be prioritised updating national and international inventories to protect and restore this habitat.

Implementing IPCC 2019 Guidelines into a National Inventory: Impacts of Key Changes in Austrian Cattle and Pig Farming

This study examined enteric and excreta emissions from cattle and pigs with a focus on effects of changed feeding practices. We assessed the impact of a revision of the Austrian Greenhouse Gas and Air Pollutant Inventory (national method, NM), i.e., the implementation of the Tier2-method of the IPCC-2019 guidelines, to a more dynamic integration of past and present feeding practices. Cattle—in particular, dairy cows—had the highest contribution to enteric CH4 emissions and to nitrogen (Nex) and volatile-solid (VSex) excretion, independent of the assessment method (NM or IPCC-2019). These emissions as well as excreta quantities are directly associated with feeding. The most relevant changes from implementing IPCC-2019 were (i) reduced enteric CH4 over the entire time series and (ii) increased Nex and VSex, especially for the period from 1990 to 2005. Additionally, uncertainties in the emissions and excreta were analyzed and related to the quantities of protein consumed. From 1990 to 2020, favorable trends per unit of protein were shown due to increased performance and concomitantly reduced animal numbers. The changes were especially pronounced for CH4, Nex, and VSex from dairy cows (−40% to −46%) but also substantial for other cattle (−26% to −31%), breeding pigs (−12% to −28%), and partially growing-fattening pigs (−3% to −20%). Future mitigation potential may result from reduced dietary crude-protein content, especially in pigs, and the use of feed additives. Feed additives for ruminants with enteric CH4-mitigating effects showed a particularly high reduction potential for the total amount of greenhouse gases from the livestock sector.

Invasive Spartina alterniflora marshes in China: a blue carbon sink at the expense of other ecosystem services

Coastal (marine) wetlands are recognized as one of the world's most efficient sinks for organic carbon (“blue carbon”), and credits for their restoration and conservation can be obtained from carbon markets. We reviewed 50 studies to compare the climate mitigation potential of the invasive cordgrass Spartina alterniflora to that of native vegetation in marshes along the coast of China. The importance of S alterniflora marshes as a carbon sink varied geographically; however, at all sites soils associated with S alterniflora emitted substantially more methane than soils populated with native plants. Because the species was deliberately introduced, the carbon stored in S alterniflora marshes could qualify for inclusion within China's national inventory of greenhouse‐gas emissions. However, in locations where it significantly increases carbon stocks, its dominance results in the loss of other ecosystem services. Therefore, if included in a national inventory, S alterniflora marshes could conflict with China's action plan for meeting UN Sustainable Development Goals.

ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation

The United Nations Framework Convention on Climate Change (UNFCCC) has recently established the Reducing Emissions from Deforestation and forest Degradation (REDD+) program, which requires countries to report their carbon emissions and sink estimates through national greenhouse gas inventories (NGHGI). Thus, developing automatic systems capable of estimating the carbon absorbed by forests without in situ observation becomes essential. To support this critical need, in this work, we introduce ReUse, a simple but effective deep learning approach to estimate the carbon absorbed by forest areas based on remote sensing. The proposed method's novelty is in using the public above-ground biomass (AGB) data from the European Space Agency's Climate Change Initiative Biomass project as ground truth to estimate the carbon sequestration capacity of any portion of land on Earth using Sentinel-2 images and a pixel-wise regressive UNet. The approach has been compared with two literature proposals using a private dataset and human-engineered features. The results show a more remarkable generalization ability of the proposed approach, with a decrease in Mean Absolute Error and Root Mean Square Error over the runner-up of 16.9 and 14.3 in the area of Vietnam, 4.7 and 5.1 in the area of Myanmar, 8.0 and 1.4 in the area of Central Europe, respectively. As a case study, we also report an analysis made for the Astroni area, a World Wildlife Fund (WWF) natural reserve struck by a large fire, producing predictions consistent with values found by experts in the field after in situ investigations. These results further support the use of such an approach for the early detection of AGB variations in urban and rural areas.

Effect of the Interaction between Excreta Type and Nitrogen Fertilizer on Greenhouse Gas and Ammonia Emissions in Pastures

This study aimed to evaluate the emission factor of N2O, CH4, and the volatilization of NH3 for the combination of feces or urine with increasing doses of ammonium nitrate in tropical palisade grass pastures. The emission of greenhouse gases was assessed in eight treatments combining feces and urine with doses (75 and 150 kg of N ha−1) of ammonium nitrate, (32% N). The emission factor of N2O was 0.11, 0.19, and 0.17% for feces, urine, and 75 kg N ha−1 year−1 (as ammonium nitrate) and showed an additive linear effect when feces or urine were combined with increasing doses of N fertilizer. The emission factor of CH4 of feces (0.18 kg CH4 animal−1 year−1) was similar irrespective of combination with ammonium nitrate. The N loss by volatilized NH3 has a decreasing linear effect (p < 0.05) for the combination of feces or urine with ammonium nitrate. We concluded that N2O and CH4 emission factors of feces and urine in tropical climate conditions are lower than those reported by the IPCC. However, their N2O emission factors are sharply enhanced when combined with ammonium nitrate. These results may contribute to improvements in national and regional greenhouse gas inventories of livestock production.

전과정평가법을 활용한 고구마 및 풋옥수수의 탄소배출량 산정

In this study, we calculated the carbon emissions of sweet potato and corn production systems using Life Cycle Assessment methodology with the national Life Cycle Inventory database and the latest Korean statistics for agricultural products. Previous calculations of carbon emissions for sweet potato and corn were not accurate because they did not reflect current statistics and cultivation management. Fertilizer and electricity consumption are the highest among the materials input to 1 kg for sweet potato production, with fertilizer amount of 2.94E − 01 kg/kg and electricity consumption of 1.32E − 01 kWh/kg. Also, fertilizer amount is the highest among the materials input to 1 kg for corn production, with fertilizer amount of 9.16E − 01 kg/kg. Direct emission of greenhouse gases showed a carbon footprint of 2.70E − 01 kg CO₂-equivalent (eq.)/kg for sweet potato and 1.98E − 01 kg CO₂-eq./kg for corn. Also, the result of Life Cycle Impact Assessment for sweet potato and corn production systems, Global Warming Potential for characterization value was 2.70E − 01 kg CO₂-eq./kg for sweet potato and 1.98E − 01 kg CO₂-eq./kg for corn. The results of this study will be able to contribute to improving accuracy and reliability of carbon footprint of agricultural products.

Evaluation of a Model (RUMINANT) for Prediction of DMI and CH4 from Tropical Beef Cattle.

Simulation models represent a low-cost approach to evaluating agricultural systems. In the current study, the precision and accuracy of the RUMINANT model to predict dry matter intake (DMI) and methane emissions from beef cattle fed tropical diets (characteristic of Colombia) was assessed. Feed intake (DMI) and methane emissions were measured in Brahman steers housed in polytunnels and fed six forage diets. In addition, DMI and methane emissions were predicted by the RUMINANT model. The model's predictive capability was measured on the basis of precision: coefficients of variation (CV%) and determination (R2, percentage of variance accounted for by the model), and model efficiency (ME) and accuracy: the simulated/observed ratio (S/O ratio) and slope and mean bias (MB%). In addition, combined measurements of accuracy and precision were carried out by means of mean square prediction error (MSPE) and correlation correspondence coefficient (CCC) and their components. The predictive capability of the RUMINANT model to simulate DMI resulted as valuable for mean S/O ratio (1.07), MB% (2.23%), CV% (17%), R2 (0.886), ME (0.809), CCC (0.869). However, for methane emission simulations, the model substantially underestimated methane emissions (mean S/O ratio = 0.697, MB% = -30.5%), and ME and CCC were -0.431 and 0.485, respectively. In addition, a subset of data corresponding to diets with Leucaena was not observed to have a linear relationship between the observed and simulated values. It is suggested that this may be related to anti-methanogenic factors characteristic of Leucaena, which were not accounted for by the model. This study contributes to improving national inventories of greenhouse gases from the livestock of tropical countries.

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

BackgroundTropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well.ResultsOur classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites.ConclusionsOur findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

A Machine Learning Approach for Identification of Low-Head Dams

Identifying low-head dams (LHDs) and creating an inventory is a priority, as fatalities continue to occur at these structures. Because obstruction inventories do not specifically identify LHDs and they are not assigned a hazard classification, there is no official inventory of LHDs; a multi-agency taskforce is creating one now by identifying LHDs using Google Earth Pro (GE Pro). The purpose of this paper is to assess whether a machine learning approach can accelerate the creation of the national inventory. We implemented a machine learning approach to use a high-resolution remote sensing data with a Convolutional Neural Network (CNN) architecture. The model achieved 76% accuracy in identifying LHDs (true positives) and 95% accuracy identifying Non-low-head-dams (true negatives) on the validation set. We deployed the trained model for the National Hydrologic Geospatial Fabric (Hydrofabric) flowlines in the Provo River watershed. The results showed a high number of false positives and low accuracy due to the mismatch between Hydrofabric flowlines and actual waterways. We recommend improving the accuracies of the Hydrofabric waterway tracing algorithms to increase the percentage of correctly classified LHDs.

Evaluation of the nitrogen oxide emission inventory with TROPOMI observations

To compensate for the emissions missed or underestimated in the national bottom-up emission inventories, we apply the high spatial resolution satellite data from TROPOspheric Monitoring Instrument (TROPOMI) to estimate the top-down nitrogen oxide (NOx) emissions in regional scales. The NOx chemical lifetime is derived based on ground-based measurements of ozone photolysis rate, ultra-violet (UV) index, and temperature. For five designated regions of western Taiwan, the derived lifetime is about 1–2 h in summer and 2–4 h in winter. The retrieved 2021 annual emissions for regions near two major pollution sources, Taichung thermal power plant and Mailiao Industrial Zone, are comparable with the emission from the Continuous Emission Monitoring System (CEMS), with a difference of 6% and −12%, respectively. After validating the data and methods, the NOx emissions for five regions of western Taiwan are derived and applied to evaluate the bottom-up inventories. For northern and southern Taiwan, the top-down emissions agree well with emission inventories. The top-down emissions are 12%, 23%, and 16% higher than emission inventories for north-central, central, and south-central Taiwan, respectively. This indicates that the bottom-up inventories are underestimated from north-central to south-central Taiwan, which may be associated with the uncertainties from traffic sources. Given the various complex pollution sources, deriving NOx emissions from space allows us to acquire a better understanding of emissions on urban scales and improve the bottom-up emission inventories.

National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence

While wetland ecosystem services are widely recognized, the lack of fine-scale national inventories prevents successful implementation of conservation policies. Wetlands are difficult to map due to their complex fine-grained spatial pattern and fuzzy boundaries. However, the increasing amount of open high-spatial-resolution remote sensing data and accurately georeferenced field data archives, as well as progress in artificial intelligence (AI), provide opportunities for fine-scale national wetland mapping. The objective of this study was to map wetlands over mainland France (ca. 550,000 km2) by applying AI to environmental variables derived from remote sensing and archive field data. A random forest model was calibrated using spatial cross-validation according to the precision-recall area under the curve (PR-AUC) index using ca. 135,000 soil or flora plots from archive databases, as well as 5 m topographical variables derived from an airborne DTM and a geological map. The model was validated using an experimentally designed sampling strategy with ca. 3000 plots collected during a ground survey in 2021 along non-wetland/wetland transects. Map accuracy was then compared to those of nine existing wetland maps with global, European, or national coverage. The model-derived suitability map (PR-AUC 0.76) highlights the gradual boundaries and fine-grained pattern of wetlands. The binary map is significantly more accurate (F1-score 0.75, overall accuracy 0.67) than existing wetland maps. The approach and end-results are of important value for spatial planning and environmental management since the high-resolution suitability and binary maps enable more targeted conservation measures to support biodiversity conservation, water resources maintenance, and carbon storage.

Uncaptured mercury lost to the environment from waste electrical and electronic equipment (WEEE) in scrap metal and municipal wastes

Mercury is a persistent toxic substance and once in the environment it can be transformed by bacteria into methylmercury which bioaccumulates in food chains. Prevention of mercury releases are key to protecting human health. Alternatives to mercury in products are now available and further phase-outs are underway as the Minamata Convention progresses toward its aims. In the European Union, mercury is a priority substance for safe removal from WEEE collected and treated. Despite mercury phase outs, WEEE items will continue to necessitate mercury depollution for many years to come. Capture of mercury-added product waste and their replacement products at source protects health, the environment and progresses the circular economy in terms of job creation and critical raw materials recovery. This paper presents an analysis of mercury-containing WEEE observed in scrap metal sites and kerbside waste collection in Ireland, these items did not undergo depollution and so the mercury contained within is lost to the environment. Mercury-added products found in metal scrap included desktop PCs, laptops, screens, LCD, LED and plasma screens and monitors, and assorted fluorescent and special lamps. It is estimated that a minimum of 17.89 kgs of mercury was released to the environment in 2018 due to inappropriate WEEE management from demolition, renovation, and home maintenance activities. Whilst the impact of the Restriction of Hazardous Substances Directive in reducing mercury content in products is undoubtably positive, mercury emissions due to historic WEEE, professional WEEE, and batteries are not fully understood. The key loss of mercury was due to inappropriate gas discharge lamp handling at scrap metal sites and in municipal wastes. Protecting local environments from exposure and ensuring the safe recovery of mercury should be prioritised. The findings of this research highlight an urgent need to develop indicators of mercury-added product stocks and flows to optimise hazardous waste management in support of the circular economy. Improved material flow analysis with respect to mercury-added products are necessary for more accurate estimates in national inventories, thereby aiding global efforts to eliminate anthropogenic mercury releases especially from secondary release sources.

Declining, seasonal-varying emissions of sulfur hexafluoride from the United States

Abstract. Sulfur hexafluoride (SF6) is the most potent greenhouse gas (GHG), and its atmospheric abundance, albeit small, has been increasing rapidly. Although SF6 is used to assess atmospheric transport modeling and its emissions influence the climate for millennia, SF6 emission magnitudes and distributions have substantial uncertainties. In this study, we used NOAA's ground-based and airborne measurements of SF6 to estimate SF6 emissions from the United States between 2007 and 2018. Our results suggest a substantial decline of US SF6 emissions, a trend also reported in the US Environmental Protection Agency's (EPA) national inventory submitted under the United Nations Framework Convention on Climate Change (UNFCCC), implying that US mitigation efforts have had some success. However, the magnitudes of annual emissions derived from atmospheric observations are 40 %–250 % higher than the EPA's national inventory and substantially lower than the Emissions Database for Global Atmospheric Research (EDGAR) inventory. The regional discrepancies between the atmosphere-based estimate and EPA's inventory suggest that emissions from electric power transmission and distribution (ETD) facilities and an SF6 production plant that did not or does not report to the EPA may be underestimated in the national inventory. Furthermore, the atmosphere-based estimates show higher emissions of SF6 in winter than in summer. These enhanced wintertime emissions may result from increased maintenance of ETD equipment in southern states and increased leakage through aging brittle seals in ETD in northern states during winter. The results of this study demonstrate the success of past US SF6 emission mitigations and suggest that substantial additional emission reductions might be achieved through efforts to minimize emissions during servicing or through improving sealing materials in ETD.

Present Knowledge and Future Perspectives of Atmospheric Emission Inventories of Toxic Trace Elements: A Critical Review.

Toxic trace elements (TEs) can pose serious risks to ecosystems and human health. However, a comprehensive understanding of atmospheric emission inventories for several concerning TEs has not yet been developed. In this study, we systematically reviewed the status and progress of existing research in developing atmospheric emission inventories of TEs focusing on global, regional, and sectoral scales. Multiple studies have strengthened our understanding of the global emission of TEs, despite attention being mainly focused on Hg and source classification in different studies showing large discrepancies. In contrast to those of developed countries and regions, the officially published emission inventory is still lacking in developing countries, despite the fact that studies on evaluating the emissions of TEs on a national scale or one specific source category have been numerous in recent years. Additionally, emissions of TEs emitted from waste incineration and traffic-related sources have produced growing concern with worldwide rapid urbanization. Although several studies attempt to estimate the emissions of TEs based on PM emissions and its source-specific chemical profiles, the emission factor approach is still the universal method. We call for more extensive and in-depth studies to establish a precise localization national emission inventory of TEs based on adequate field measurements and comprehensive investigation to reduce uncertainty.

New Perspectives for Living Traditions: Intangible Cultural Heritage in North-East Hungary

AbstractThe UNESCO World Heritage Convention (1972) was originally focused on nature conservation and built heritage. The immaterial aspect of the worldwide heritage discourse arrived at a turning point in 2003, when the Convention for the Safeguarding of Intangible Cultural Heritage was adopted. The definition of the intangible cultural heritage provides essential frames for a wide range of interpretations. The UNESCO concept focuses on inclusive, representative and community-based traditions which are contemporary and living at the same time. In this sense, the intangible cultural heritage conception is based on the fundamental dichotomy of tradition and modernity. For the communities concerned, a new perspective for living traditions is the process from tradition to heritage. There are four essential features of this process: participation, consciousness, organization and valorization. They can make a difference between tradition and heritage. The Hungarian model for the implementation of the UNESCO Convention is based on a bottom-up system, where the heritage bearers themselves initiate the nomination process for the National Inventory. It is based on their strong commitment to their heritage and it relies on their involvement and participation. In this paper, three case studies from North-East Hungary (Borsod-Abaúj-Zemplén County) represent different ways of “creating a heritage.” The various patterns are closely related to the ideas of identity, community cohesion, tourism, local economy and the preservation of living traditions.