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2022
This scoping study proposes a methodology to develop indicator(s) on the risk of chemicals on various types of ecosystems, habitats and species. This indicator is designed to answer the very different needs coming from environmental policy and strategies. Its aim is to reflect the consequences of human activities (e.g., farming) on ecosystems taking into account different protection goals that can cover specific or protected habitats (or the relevant communities or species in these habitats) or intensively used areas such as agricultural landscapes, which are very relevant in terms of representativity.
In order to develop such an indicator, the methodological approach retained to calculate maps showing a specific risk, illustrated as different degrees of exceedance of tolerable effect thresholds, is to combine different layers of information on which areas/habitats/ecosystems, contain which species, that are exposed to which amounts of chemicals. Information on the sensitivity of the species towards different toxicological effects, and how sensitive the species are to the applied chemicals, is used as ‘connectors’ between the different layers of spatial information.
The report highlights the data required for the development of such an indicator and their availability through a review of existing databases. A case study illustrates the applicability of the indicator and the need for further development.
ETC/HE
2022
Health Risk Assessment of Air Pollution and the Impact of the New WHO Guidelines
Air pollution is a major cause of premature death and disease and is the single largest environmental health risk in Europe. Heart disease and stroke are the most common reasons for premature deaths attributable to air pollution, followed by lung diseases and lung cancer.
The health risk assessment methodology assumptions have been recently adapted to follow the recommendations by the World Health Organisation (WHO), released in 2021. The new global air quality guidelines by WHO provide up-to-date health-based guideline levels for major health-damaging air pollutants and new recommendations for assessing the risk of exposure to air pollution.
This report estimates the health risk related to air pollution in 2020 based on the latest methodology. The estimates consider the number of premature deaths and years of life lost related to exposure to fine particulate matter, ozone and nitrogen dioxide, both for the 27 Member States of the European Union and for additional 14 European countries (Albania, Andorra, Bosnia and Herzegovina, Iceland, Kosovo, Liechtenstein, Monaco, Montenegro, North Macedonia, Norway, San Marino, Serbia, Switzerland, and Türkiye).
A sensitivity analysis to the changes in concentration-response functions and counterfactual concentrations is performed to understand the impact of such changes on the mortality outcome estimates. The sensitivity analysis included both old and new health risk methodology assumptions but also the recommendation from the ELAPSE study on the concentration response functions. The ELAPSE project includes some of the most recent studies on the health effects at low air pollution levels by examining associations between exposures to relatively low levels of air pollution across Europe, including levels below the current EU standards.
The results for 2020 show that the largest health risks are estimated for the countries with the largest populations. However, in relative terms, when considering e.g., years of life lost per 100 000 inhabitants, the largest relative risks are observed in central and eastern European countries for PM2.5, in central and southern European countries for NO2, and south and eastern European for O3. The lowest impact is found for the northern and north-western parts of Europe, where the concentrations are lowest. The number of premature deaths attributed to air pollution in 2020 compared to 2019, increased for PM2.5 and decreased for NO2 and O3. Apart from the changes in concentrations and demographics, the COVID-19 pandemics seems to also have an influence on these changes. For PM2.5, the reduction in concentrations were counteracted by the excess of deaths due to the pandemics. In the case of NO2, the reduction in concentrations was more pronounced as a result of the lockdown measures and the drastic reduction in traffic and its impact in reducing mortality was bigger than the increasing impact of excess of deaths due to COVID-19.
Changing assumptions on concentration-response functions and counterfactual concentrations have implications for estimating mortality health outcomes. The sensitivity analysis shows that it is not straightforward to assess which assumptions estimates the highest health impacts when both factors change. In this case, the final outcome will depend on the concentration at the grid-cell level. The latest assumptions are expected to reduce the health outcomes for PM2.5 and increase for NO2 and O3, when compared to the previous one. When aggregated to all countries, the health outcomes are reduced by over 40 % for PM2.5 and increased by 50 % and 30 % for NO2 and O3, respectively, in 2020. However, this change varies across countries depending on the concentration level the population in the individual countries is exposed to.
ETC/HE
2022
Epidemiological studies have increasingly shown that ambient air pollution is not only associated with mortality but also with the occurrence of a number of long and short-term diseases. Further, the Global Burden of Disease study clearly indicated, that e. g. particulate matter pollution is also associated with a considerable burden of disease related to morbidity effects.
In addition to the most recent EEA’s health risk assessments, this report estimates the morbidity related health burden associated with exposure to the same three key air pollutants: fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3). Years lived with disability (YLDs) or attributable hospitalisation cases are assessed for the year 2019 for numerous European countries, depending on the respective data availability. Besides, the methodological approach as well as reviews on evidence-based health outcomes, health data and concentration-response functions are provided.
For the ten considered risk-outcome pairs, the results showed the highest morbidity related burden of disease in Europe for PM2.5 associated with chronic obstructive pulmonary disease (COPD) with 51.6 YLDs per 100 000 inhabitants ≥25 years. For NO2 the highest morbidity burden resulted from diabetes mellitus (DM) with 54.6 YLDs per 100 000 inhabitants ≥35 years. For short-term O3 exposure hospital admissions due to respiratory diseases were estimated at 18 attributable cases per 100 000 inhabitants ≥65 years.
In addition to the estimates, the report contains suggestions for further sensitivity analyses. These would allow a better assessment of the effects resulting from different input data on the results.
The estimations presented in this report are the first of its kind that are carried out for a wide range of morbidity health outcomes associated with different outdoor air pollutants in Europe, using a consistent methodology and data from European health databases.
ETC/HE
2022
Inferring surface energy fluxes using drone data assimilation in large eddy simulations
Spatially representative estimates of surface energy exchange from field measurements are required for improving and validating Earth system models and satellite remote sensing algorithms. The scarcity of flux measurements can limit understanding of ecohydrological responses to climate warming, especially in remote regions with limited infrastructure. Direct field measurements often apply the eddy covariance method on stationary towers, but recently, drone-based measurements of temperature, humidity, and wind speed have been suggested as a viable alternative to quantify the turbulent fluxes of sensible (H) and latent heat (LE). A data assimilation framework to infer uncertainty-aware surface flux estimates from sparse and noisy drone-based observations is developed and tested using a turbulence-resolving large eddy simulation (LES) as a forward model to connect surface fluxes to drone observations. The proposed framework explicitly represents the sequential collection of drone data, accounts for sensor noise, includes uncertainty in boundary and initial conditions, and jointly estimates the posterior distribution of a multivariate parameter space. Assuming typical flight times and observational errors of light-weight, multi-rotor drone systems, we first evaluate the information gain and performance of different ensemble-based data assimilation schemes in experiments with synthetically generated observations. It is shown that an iterative ensemble smoother outperforms both the non-iterative ensemble smoother and the particle batch smoother in the given problem, yielding well-calibrated posterior uncertainty with continuous ranked probability scores of 12 W m−2 for both H and LE, with standard deviations of 37 W m−2 (H) and 46 W m−2 (LE) for a 12 min vertical step profile by a single drone. Increasing flight times, using observations from multiple drones, and further narrowing the prior distributions of the initial conditions are viable for reducing the posterior spread. Sampling strategies prioritizing space–time exploration without temporal averaging, instead of hovering at fixed locations while averaging, enhance the non-linearities in the forward model and can lead to biased flux results with ensemble-based assimilation schemes. In a set of 18 real-world field experiments at two wetland sites in Norway, drone data assimilation estimates agree with independent eddy covariance estimates, with root mean square error values of 37 W m−2 (H), 52 W m−2 (LE), and 58 W m−2 (H+LE) and correlation coefficients of 0.90 (H), 0.40 (LE), and 0.83 (H+LE). While this comparison uses the simplifying assumptions of flux homogeneity, stationarity, and flat terrain, it is emphasized that the drone data assimilation framework is not confined to these assumptions and can thus readily be extended to more complex cases and other scalar fluxes, such as for trace gases in future studies.
2022
Wetland emission and atmospheric sink changes explain methane growth in 2020
Atmospheric methane growth reached an exceptionally high rate of 15.1 ± 0.4 parts per billion per year in 2020 despite a probable decrease in anthropogenic methane emissions during COVID-19 lockdowns. Here we quantify changes in methane sources and in its atmospheric sink in 2020 compared with 2019. We find that, globally, total anthropogenic emissions decreased by 1.2 ± 0.1 teragrams of methane per year (Tg CH4 yr−1), fire emissions decreased by 6.5 ± 0.1 Tg CH4 yr−1 and wetland emissions increased by 6.0 ± 2.3 Tg CH4 yr−1. Tropospheric OH concentration decreased by 1.6 ± 0.2 per cent relative to 2019, mainly as a result of lower anthropogenic nitrogen oxide (NOx) emissions and associated lower free tropospheric ozone during pandemic lockdowns. From atmospheric inversions, we also infer that global net emissions increased by 6.9 ± 2.1 Tg CH4 yr−1 in 2020 relative to 2019, and global methane removal from reaction with OH decreased by 7.5 ± 0.8 Tg CH4 yr−1. Therefore, we attribute the methane growth rate anomaly in 2020 relative to 2019 to lower OH sink (53 ± 10 per cent) and higher natural emissions (47 ± 16 per cent), mostly from wetlands. In line with previous findings, our results imply that wetland methane emissions are sensitive to a warmer and wetter climate and could act as a positive feedback mechanism in the future. Our study also suggests that nitrogen oxide emission trends need to be taken into account when implementing the global anthropogenic methane emissions reduction pledge.
2022
2022
Impacts of snow assimilation on seasonal snow and meteorological forecasts for the Tibetan Plateau
The Tibetan Plateau (TP) contains the largest amount of snow outside the polar regions and is the source of many major rivers in Asia. An accurate long-range (i.e. seasonal) meteorological forecast is of great importance for this region. The fifth-generation seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (SEAS5) provides global long-range meteorological forecasts including over the TP. However, SEAS5 uses land initial conditions produced by assimilating Interactive Multisensor Snow and Ice Mapping System (IMS) snow data only below 1500 m altitude, which may affect the forecast skill of SEAS5 over mountainous regions like the TP. To investigate the impacts of snow assimilation on the forecasts of snow, temperature and precipitation, twin ensemble reforecasts are initialized with and without snow assimilation above 1500 m altitude over the TP for spring and summer 2018. Significant changes occur in the springtime. Without snow assimilation, the reforecasts overestimate snow cover and snow depth while underestimating daily temperature over the TP. Compared to satellite-based estimates, precipitation reforecasts perform better in the west TP (WTP) than in the east TP (ETP). With snow assimilation, the reforecasts of snow cover, snow depth and temperature are consistently improved in the TP in the spring. However, the positive bias between the precipitation reforecasts and satellite observations worsens in the ETP. Compared to the experiment with no snow assimilation, the snow assimilation experiment significantly increases temperature and precipitation for the ETP and around the longitude 95∘ E. The higher temperature after snow assimilation, in particular the cold bias reduction after initialization, can be attributed to the effects of a more realistic, decreased snowpack, providing favourable conditions for generating more precipitation. Overall, snow assimilation can improve seasonal forecasts through the interaction between land and atmosphere.
2022
On behalf of Aluminiumindustriens Miljøsekretariat (AMS), NILU – Norwegian Institute for Air Research reviewed existing data on ambient air quality around aluminium smelters from the period 1992 – 2020. Changes in production technologies and treatment technologies have been implemented in this time period. Emissions to air and ambient concentrations of most compounds typically measured (PAHs, fluorides, sulphur dioxide, particulate matter, heavy metals) have decreased since the beginning of the 1990s as a result of improvement of the production technology.
NILU
2022
Using the example of sulfur hexafluoride (SF6), we investigate the use of Lagrangian particle dispersion models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global-distribution-based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods can cause large systematic errors, which lead to inversion results that are sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that show a better agreement with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 d, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods might help to better constrain emissions in regions poorly covered by the global SF6 monitoring network. We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well-placed flask sampling sites would have great value for improving global a posteriori emission fields.
2022
High-Resolution Emissions from Wood Burning in Norway—The Effect of Cabin Emissions
Emissions from wood burning for heating in secondary homes or cabins is an important part in the development of high-resolution emissions in specific areas. Norway is used as case study as 20% of the national wood consumption for heating occurs in cabins. Our study first shows a method to estimate emissions from cabins based on traffic data to derive cabin occupancy, which combined with heating need allows for the spatial and temporal distribution of emissions. The combination of residential (RWC) and cabin wood combustion (CWC) emissions shows large spatial and temporal differences, and a temporally “cabin population” can in areas be orders of magnitude larger than the registered population. While RWC emissions have been steadily reduced, CWC have kept relatively constant or even increased, which results in an increase in the cabin share to total heating emissions up to 25–35%. When comparing with regional emission inventories, our study shows that the gradient between rural and urban areas is not well-represented in regional inventories, which resembles a population-based distribution and does not allocate emissions in cabin municipalities. CWC emissions may become an increasing environmental concern as higher densification trends in mountain areas are observed.
MDPI
2022
Total oxidizable precursors assay for PFAS in human serum
Per- and polyfluoroalkyl substances (PFAS) are a class of chemicals including over 4700 substances. As a limited number of PFAS is routinely analyzed in human serum, complementary analytical methods are required to characterize the overlooked fraction. A promising tool is the total oxidizable precursors (TOP) assay to look for precursors by oxidation to perfluoroalkyl acids (PFAA). The TOP assay was originally developed for large volumes of water and had to be adapted for 250 μL of human serum. Optimization of the method was performed on serum samples spiked with model precursors. Oxidative conditions similar to previous TOP assay methods were not sufficient for complete oxidation of model precursors. Prolonged heating time (24 h) and higher oxidant amount (95 mg of Na2S2O8 per 225 μL of serum) were needed for complete conversion of the model precursors and accomplishing PFAA yields of 35–100 %. As some precursors are not fully converted to PFAA, the TOP assay can only provide semi-quantitative estimates of oxidizable precursors in human serum. However, the TOP assay can be used to give indications about the identity of unknown precursors by evaluating the oxidation products, including perfluoroalkyl sulfonic acids (PFSA) and perfluoroalkyl ether carboxylic acids (PFECA). The optimized TOP assay for human serum opens the possibility for high-throughput screening of human serum for undetected PFAA precursors.
Elsevier
2022
Monitoring of greenhouse gases and aerosols at Svalbard and Birkenes in 2021. Annual report.
This annual report for 2021 summarizes the activities and results of the greenhouse gas monitoring at the Zeppelin Observatory, situated on Svalbard, during the period 2001-2021, and the greenhouse gas monitoring and aerosol observations from Birkenes for 2009-2021.
NILU
2022
With the current possible presence of thousands of PFAS compounds in industrial emissions, there is an increasing need to assess the impacts of PFAS regulation of conventional PFAS on one hand and the exposure to emerging and yet unknown PFAS on the other. Today’s analytical methodologies using targeted approaches are not sufficient to determine the complete suite of PFAS present. To evaluate the presence of unknown PFAS, we investigated in this study the occurrence of an extended range of target PFAS in various species from the marine and terrestrial Norwegian environment, in relation to the extractable organic fluorine (EOF), which yields the total amount of organic fluorine. The results showed a varying presence of extractable fluorinated organics, with glaucous gull eggs, otter liver and polar bear plasma showing the highest EOF and a high abundance of PFAS as well. The targeted PFAS measurements explained 1% of the organic fluorine for moose liver as the lowest and 94% for otter liver as the highest. PFCAs like trifluoro acetic acid (TFA, reported semi-quantitatively), played a major role in explaining the organic fluorine present. Emerging PFAS as the perfluoroethylcyclohexane sulfonate (PFECHS), was found in polar bear plasma in quantifiable amounts for the first time, confirming earlier detection in arctic species far removed from emission sources. To enable a complete organic fluorine mass balance in wildlife, new approaches are needed, to uncover the presence of new emerging PFAS as cyclic- or ether PFAS together with chlorinated PFAS as well as fluorinated organic pesticides and pharmaceuticals.
Elsevier
2022
A freely available “in vitro dosimetry” web application is presented enabling users to predict the concentration of nanomaterials reaching the cell surface, and therefore available for attachment and internalization, from initial dispersion concentrations. The web application is based on the distorted grid (DG) model for the dispersion of engineered nanoparticles (NPs) in culture medium used for in vitro cellular experiments, in accordance with previously published protocols for cellular dosimetry determination. A series of in vitro experiments for six different NPs, with Ag and Au cores, are performed to demonstrate the convenience of the web application for calculation of exposure concentrations of NPs. Our results show that the exposure concentrations at the cell surface can be more than 30 times higher compared to the nominal or dispersed concentrations, depending on the NPs’ properties and their behavior in the cell culture medium. Therefore, the importance of calculating the exposure concentration at the bottom of the cell culture wells used for in vitro arrays, i.e., the particle concentration at the cell surface, is clearly presented, and the tool introduced here allows users easy access to such calculations. Widespread application of this web tool will increase the reliability of subsequent toxicity data, allowing improved correlation of the real exposure concentration with the observed toxicity, enabling the hazard potentials of different NPs to be compared on a more robust basis.
MDPI
2022
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