The current-use flame retardants (FRs) tri(2-chloroethyl) phosphate (TCEP), tri(chloroiso-propyl) phosphate (TCIPP), triphenyl phosphate (TPHP), 2-ethylhexyl-2,3,4,5-tetrabromobenzoate (EH-TBB), bis(2-ethylhexyl) tetrabromophthalate (BEH-TEBP) and hexabromobenzene (HBBz) can be transported to the Arctic over long distances, but might also be emitted locally from the use and disposal of FR-containing products. The aim of this study was to assess the importance of local sources and long-range transport for these FRs, based on predicted environmental concentrations at Nuuk, Greenland, derived from the modelling of long-range atmospheric transport and local FR emissions and fate, respectively. Following an FR mass flow of geographically stratified production, use and waste, emissions of the selected FRs to air, soil and water were estimated for the northern hemisphere and at the local level. The Danish Eulerian Hemispheric Model (DEHM) was adjusted to model the atmospheric transport to Nuuk, and a local model was established for Nuuk to calculate local concentrations in air, soil and seawater. Comparisons with measurements in Arctic air were limited by data availability. High emission scenarios showed better agreement with measured values than low emission scenarios, indicating a potential underestimation of real concentrations by the model. Generally, the locally determined concentrations exceeded the long-range transported ones for all media (air, soil, seawater), with the exception of the high emission scenario for HBBz where the two components were more similar. A preliminary screening of environmental and human health risks resulted in risk characterisation ratios ≪ 1.

The atmospheric concentration of 7 carcinogenic polycyclic aromatic hydrocarbons (PAHs) is modelled by the Danish Eulerian Hemispheric Model (DEHM) for 42 years from 1979 to 2020. As common by-products of combustion processes, PAHs have been widely measured by different international monitoring systems. Our purpose is to compare modelled data and measured records from world-wide long-term monitoring sites to evaluate the accuracy of DEHM in simulating atmospheric concentrations of the 7 carcinogenic PAHs and lay the foundation for studying PAHs’ large-scale environmental fate. The results show that DEHM can achieve satisfactory accuracy in simulating atmospheric concentrations of these PAHs in the Northern Hemisphere.

Polycyclic aromatic hydrocarbons (PAHs) are common by-products from combustion processes with adverse health effects. The Emissions Database for Global Atmospheric Research (EDGAR) and Peking University (PKU) both provide emission data for four PAHs: benzo[a]pyrene (BaP), benzo[b]fluoranthene (BbF), benz[k]fluoranthene (BkF), indeno[1,2,3-cd]pyrene (IcdP). There are differences in emission sources and calculation methods between the two datasets, resulting in differences in predicted values. Here, the Danish Eulerian Hemispheric Model (DEHM) is applied to compare the spatiotemporal difference and performance of these two datasets.

This study estimated PM2.5 (atmospheric fine particulate matter with aerodynamic diameter ≤2.5 µg) concentrations and the health burden in mainland China from 2010 to 2049 under two scenarios: Current Legistaions and Maximum Technical Feasible Reductions. We assess premature deaths from PM2.5 exposure, examining sources like coal combustion, biomass burning, industry, and tailpipe emission from on-road transport. Results show that central and eastern China account for 75% of PM2.5-related deaths, with biomass burning (40%) and industry (34%) as primary contributors. Under the Current Legistaions and Maximum Technical Feasible Reductions scenarios, PM2.5-related premature deaths are projected to decrease by 43% and 80% (linear EVA) and by 28% increase and 40% decrease (nonlinear EVA) from 2010 to 2049. Assuming a linear relationship, the Maximum Technical Feasible Reductions scenario estimates that reduced PM2.5 exposure could avoid 1.55 million premature deaths annually by 2049 compared to 2010, primarily from coal combustion for heating, biomass burning, industry, and tailpipe emission from on-road transport.

The Copernicus Atmosphere Monitoring Service (CAMS) delivers a wide range of free and open products in relation to atmospheric composition at global and regional scales. The CAMS Regional Service produces daily forecasts, analyses, and reanalyses of air quality in Europe. This service relies on a distributed modelling production by 11 teams in 10 European countries: CHIMERE (France), DEHM (Denmark), EMEP (Norway), EURAD-IM (Germany), GEM-AQ (Poland), LOTOS-EUROS (the Netherlands), MATCH (Sweden), MINNI (Italy), MOCAGE (France), MONARCH (Spain), and SILAM (Finland). The project management and coordination of the service is conducted by a Centralised Regional Production Unit. Every day, each model produces 24 h analyses for the previous day and 97 h forecasts for 19 chemical species over a spatial domain at 0.1 x 0.1 degrees resolution (approximately 10 km x 10 km), with 420 points in latitude and 700 in longitude and 10 vertical levels. Six pollen species are also delivered for the surface forecasts. The 11 individual models are then combined into an ENSEMBLE median. In total, more than 82 billion data points are made available for public use on a daily basis.The design of the system follows clear technical requirements in terms of consistency in the model setup and forcing fields (meteorology, surface anthropogenic emission fluxes, and chemical boundary conditions). But it also benefits from a diversity in the description of atmospheric processes through the design of the 11 European chemistry-transport models (CTMs) involved.The present article aims to provide a comprehensive technical documentation, both for the setup and for the diversity of CTMs involved in the service. We also include an overview of the main output products, their public dissemination, and the related evaluation and quality control strategy.

The World Health Organization (WHO) updated its Global Air Quality Guidelines in 2021 due to growing evidence on adverse health impacts of air pollution even at low concentrations. We used a suite of regional atmospheric chemistry models to simulate fine particulate matter (PM2.5) and ozone (O3) levels over Europe in 2015–2050 and assessed the compliance of European countries with the new guidelines under different emission scenarios. The results show that 65% of the EU countries will comply with the PM2.5 target value (5 µg m−3) by 2050 under ambitious emission reductions (SSP1-2.6). Under less ambitious mitigation scenarios (SSP2-4.5 and SSP3-7.0), the compliance level is only 10%. In addition, none of the EU countries will comply with the O3 target value (60 µg m−3), while interim values are achieved in most of the EU countries, partly under SSP2-4.5, and to a large extent under SSP1-2.6. These results highlight that reaching the new WHO limit values will be challenging for Europe, partly due to natural contribution to PM2.5 reaching up to 50% in some regions. Our findings imply the necessity of more drastic emission reductions to meet the targets.

High-resolution air quality data are critical for exposure assessment, regulatory compliance, and urban planning. In this study, we present modelled annual mean concentrations of NO2, PM2.5, PM10, Black Carbon (BC), and particle number concentration (PNC) for all ~2.5 million Danish addresses in 2019 using the Air Quality at Your Street 2.0 system. The modelling framework combines coupled chemistry–transport models (DEHM/UBM/OSPM) with input from the Green Mobility Model and GPS-based vehicle speed data. Model outputs were evaluated against observations from the Danish Air Quality Monitoring Programme, showing strong agreement for NO2, PM2.5, PM10, and BC, but notable overestimation of PNC background levels and underestimation of street contributions. Indicative exceedances of NO2 EU limit values decreased markedly from 2012 to 2019, while exceedances of updated EU and WHO guidelines persist, especially for particulate matter. This work identifies key sources of model uncertainty and supports high-resolution national-scale assessment and citizen access via an interactive map.

The objective of this study is to estimate the welfare economic costs of premature cardiopulmonary disease (CPD) mortality in Europe and Asia Minor under a middle-of-the-road scenario for global warming. It projects future heat-related CPD fatalities in urban areas over the next 25 years for 317 regions of 39 countries by applying regionalized exposure-response functions for heat- and air pollution-related premature mortality. These functions are derived from datasets of daily counts of CPD deaths from 1994 to 2018 for over 30 million people, capturing the different sensitivities to heat across climate gradients. As using simple average summer temperatures can mask important variations, methodologically we operationalize heat spell intensity based on the Eurostat metric of cooling degree-days. We find that heat-related CPD mortality could triple by midcentury from its pre-1990 level. Based on Organisation for Economic Co-operation and Development (OECD) methodology for the economic valuation of premature mortality, this amounts to an estimated EUR 90 billion in annual welfare economic costs. For 10 countries in southeastern Europe, costs may well exceed 1% of their annual GDP, reaching up to 4% in a heat wave year. A further important outcome of the study stems from its exploration of the interactive effects of air pollution and heat spells for premature mortality. We find that deep reductions in air pollution, beyond requirements in the EU's recently revised Ambient Air Quality Directive, could prevent up to 190000 heat-related deaths over the next 25 years, positioning air quality improvements as a critical adaptation strategy. Our findings underscore the urgency of better-integrated climate and public health policies.

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by −9.7 % for CH4, −21 % for CO, and −18 % for O3. Results for CH4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons.

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 jig m- 3 (0.8 nmol min -1 m- 3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen -Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass -burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

China has long-term high PM2.5 levels, and its oxidative potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP refers to the ability of PM2.5 to induce oxidative stress (OS). OP and PM2.5 are influenced by meteorological factors, anthropogenic emission sources, and atmospheric aging. Although their impact on PM2.5 has been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emission profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had annual average PM2.5 concentrations exceeding the Chinese concentration limit. For OP, 36 % of the regions have OP below 1 nmolmin-1m-3, 41 % have OP between 1 and 2 nmolmin-1m-3, and 23 % have OP above 2 nmolmin-1m-3, which are in line with previous measurement studies. Analysis of the simulations indicates that meteorological conditions contributed 46 % and 65 % to PM2.5 concentrations and OP variability, respectively, while anthropogenic emissions contributed 54 % and 35 % to PM2.5 concentrations and OP variability, respectively. The emission sensitivity analysis also highlighted the fact that PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning.

[This corrects the article DOI: 10.1016/j.lanepe.2024.101091.].

Background:Available evidence suggests a link between exposure to transportation noise and an increased risk of obesity. We aimed to assess exposure-response functions for long-term residential exposure to road traffic, railway and aircraft noise, and markers of obesity.Methods:Our cross-sectional study is based on pooled data from 11 Nordic cohorts, including up to 162,639 individuals with either measured (69.2%) or self-reported obesity data. Residential exposure to transportation noise was estimated as a time-weighted average Lden 5 years before recruitment. Adjusted linear and logistic regression models were fitted to assess beta coefficients and odds ratios (OR) with 95% confidence intervals (CI) for body mass index, overweight, and obesity, as well as for waist circumference and central obesity. Furthermore, natural splines were fitted to assess the shape of the exposure-response functions.Results:For road traffic noise, the OR for obesity was 1.06 (95% CI = 1.03, 1.08) and for central obesity 1.03 (95% CI = 1.01, 1.05) per 10 dB Lden. Thresholds were observed at around 50-55 and 55-60 dB Lden, respectively, above which there was an approximate 10% risk increase per 10 dB Lden increment for both outcomes. However, linear associations only occurred in participants with measured obesity markers and were strongly influenced by the largest cohort. Similar risk estimates as for road traffic noise were found for railway noise, with no clear thresholds. For aircraft noise, results were uncertain due to the low number of exposed participants.Conclusion:Our results support an association between road traffic and railway noise and obesity.

Background:Transportation noise has been linked with cardiometabolic outcomes, yet whether it is a risk factor for atrial fibrillation (AF) remains inconclusive. We aimed to assess whether transportation noise was associated with AF in a large, pooled Nordic cohort. Methods:We pooled data from 11 Nordic cohorts, totaling 161,115 participants. Based on address history from five years before baseline until end of follow-up, road, railway, and aircraft noise was estimated at a residential level. Incident AF was ascertained via linkage to nationwide patient registries. Cox proportional hazards models were utilized to estimate associations between running 5-year time-weighted mean transportation noise (Lden) and AF after adjusting for sociodemographics, lifestyle, and air pollution. Findings:We identified 18,939 incident AF cases over a median follow-up of 19.6 years. Road traffic noise was associated with AF, with a hazard ratio (HR) and 95% confidence interval (CI) of 1.02 (1.00-1.04) per 10-dB of 5-year mean time-weighted exposure, which changed to 1.03 (1.01-1.06) when implementing a 53-dB cut-off. In effect modification analyses, the association for road traffic noise and AF appeared strongest in women and overweight and obese participants. Compared to exposures ≤40 dB, aircraft noise of 40.1-50 and > 50 dB were associated with HRs of 1.04 (0.93-1.16) and 1.12 (0.98-1.27), respectively. Railway noise was not associated with AF. We found a HR of 1.19 (1.02-1.40) among people exposed to noise from road (≥45 dB), railway (>40 dB), and aircraft (>40 dB) combined. Interpretation:Road traffic noise, and possibly aircraft noise, may be associated with elevated risk of AF. Funding:NordForsk.

This paper describes the DAnish Lagrangian Model (DALM), which is a new high-resolution air pollution model based on the concept of Lagrangian particles. The new model is developed from the simpler Gaussian plume-in-grid Urban Background Model (UBM) and is to be integrated with the DEHM/UBM/AirGIS modeling system, developed at Aarhus University. In the first part of the paper, the theoretical foundation of the model is presented, and the implementation of a large set of physical and numerical parameterizations is discussed. The second part describes the validation of DALM against measurements, applying different combinations of the implemented parameterizations. This validation demonstrates that DALM can accurately reproduce spatiotemporal patterns in the measured data and that its performance is more sensitive to parameterizations of vertical compared to horizontal transport. Conclusively, the combination of parameterizations yielding the best model performance is determined based on a ranking system, and future improvements to DALM are outlined.

AIMS:The three correlated environmental exposures (air pollution, road traffic noise, and green space) have all been associated with the risk of myocardial infarction (MI). The present study aimed to analyse their independent and cumulative association with MI. METHODS AND RESULTS:In a cohort of all Danes aged 50 or older in the period 2005-17, 5-year time-weighted average exposure to fine particles (PM2.5), ultrafine particles, elemental carbon, nitrogen dioxide (NO2), and road traffic noise at the most and least exposed façades of residence was estimated. Green space around residences was estimated from land use maps. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence interval (CI), and cumulative risk indices (CRIs) were calculated. All expressed per interquartile range. Models were adjusted for both individual and neighbourhood-level socio-demographic covariates. The cohort included 1 964 702 persons. During follow-up, 71 285 developed MI. In single-exposure models, all exposures were associated with an increased risk of MI. In multi-pollutant analyses, an independent association with risk of MI was observed for PM2.5 (HR: 1.026; 95% CI: 1.002-1.050), noise at most exposed façade (HR: 1.024; 95% CI: 1.012-1.035), and lack of green space within 150 m of residence (HR: 1.018; 95% CI: 1.010-1.027). All three factors contributed significantly to the CRI (1.089; 95% CI: 1.076-1.101). CONCLUSION:In a nationwide cohort study, air pollution, noise, and lack of green space were all independently associated with an increased risk of MI. The air pollutant PM2.5 was closest associated with MI risk.