Climate change exacerbates the environmental impacts of agriculture.

Agriculture's global environmental impacts are widely expected to continue expanding, driven by population and economic growth and dietary changes. This Review highlights climate change as an additional amplifier of agriculture's environmental impacts, by reducing agricultural productivity, reducing the efficacy of agrochemicals, increasing soil erosion, accelerating the growth and expanding the range of crop diseases and pests, and increasing land clearing. We identify multiple pathways through which climate change intensifies agricultural greenhouse gas emissions, creating a potentially powerful climate change-reinforcing feedback loop. The challenges raised by climate change underscore the urgent need to transition to sustainable, climate-resilient agricultural systems. This requires investments that both accelerate adoption of proven solutions that provide multiple benefits, and that discover and scale new beneficial processes and food products.

Sources of PM2.5 exposure misclassification in three Finnish register-based study populations and the impact on attenuation of health effect estimates.

Air pollution is a leading environmental health risk factor. The risk estimates, primarily based on air pollution epidemiology, are sensitive to exposure misclassification, which can result in underestimation. To address some of these challenges, our aim is to investigate how the length of the period over which the exposure is averaged, trends in long-term PM2.5 concentrations, and the seasonal variability are associated with each other. Furthermore, we assess the impact of residential relocation on exposure levels and quantify random exposure misclassification due to modelling and its impact on the attenuation of effects with respect to averaging time. We used nested air quality modelling across Finland, gridded population, and address histories from three study populations: the MATEX pregnancy and preschool children cohorts, as well as the FINPARK study's individuals diagnosed with Parkinson's disease and their controls, to estimate PM2.5 exposures. The prediction error was estimated by comparing modelled concentrations to observations and by using previous estimates for random monitoring instrument error. Due to the decreasing trend in PM2.5 concentrations, exposure levels rose progressively with longer averaging times, increasing by up to 28 % over a 16-year period. The shorter the exposure period, the more pronounced the seasonal effects: pregnant mothers' trimester-specific exposures were 13-22 % higher for trimesters ending in spring and 10-16 % lower for those ending in autumn compared to the average for the entire pregnancy. Residential relocation had a relatively minor impact on the exposure levels of the preschool children and adult FINPARK study population, but this effect was possibly partly masked by the decreasing trend. The results indicated that using predicted concentrations led to random exposure misclassification and potentially attenuated health effects. This effect became more notable when increasing the length of the exposure period from 3 months to 5 years, doubling the underestimation ratio from 1.5 to 3.1.

Pregnancy outcomes as related to in utero exposure to air pollution and greenness: The Life-GAP Project.

Background: Lower birth weight and preterm birth may increase the risk of adverse health outcomes later in life. We examined whether maternal exposure to air pollution and greenness during pregnancy is associated with offspring birth weight and preterm birth. Methods: We analyzed data on 4286 singleton births from 2358 mothers from Respiratory Health in Northern Europe, a prospective questionnaire-based cohort study (1990-2010). Mixed-effects regression models with random intercepts for mothers and centers were used to estimate the association of exposures to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), black carbon (BC), and greenness (Normalized Difference Vegetation Index in 300m-buffers [NDVI300m]) with birth outcomes, adjusting for potential confounders. Results: Median (interquartile range [IQR]) exposures to PM2.5, PM10, NO2, O3, BC, and NDVI300m during pregnancy were 8.4(5.0) µg/m3, 14.4(8.3) µg/m3, 14.0(11.0) µg/m3, 54.7(10.2) µg/m3, 0.47(0.41) µg/m3, and 0.31(0.20), respectively. IQR increases in air pollution exposures during pregnancy were associated with decreased birth weight and the strongest association was seen for PM2.5 (-49g; 95% confidence interval [CI] = -83, -16). However, O3 showed an opposite association. IQR increase in NDVI300m was associated with an increase in birth weight of 25 g (95% CI = 7, 44). Preterm birth was not associated with the exposures. Conclusion: Increased greenness and decreased air pollution may contribute to healthier pregnancies and improve overall health in the next generation. This emphasizes the need to adopt policies that target the reduction of air pollution emissions and exposure of the population.

Associations of long-term exposure to air pollution and greenness with incidence of chronic obstructive pulmonary disease in Northern Europe: The Life-GAP project.

BACKGROUND: Prolonged exposure to air pollution has been linked to adverse respiratory health, yet the evidence concerning its association with chronic obstructive pulmonary disease (COPD) is inconsistent. The evidence of a greenness effect on chronic respiratory diseases is limited. OBJECTIVE: This study aimed to investigate the association between long-term exposure to particulate matter (PM2.5 and PM10), black carbon (BC), nitrogen dioxide (NO2), ozone (O3) and greenness (as measured by the normalized difference vegetation index - NDVI) and incidence of self-reported chronic bronchitis or COPD (CB/COPD). METHODS: We analyzed data from 5355 adults from 7 centers participating in the Respiratory Health in Northern Europe (RHINE) study. Mean exposures to air pollution and greenness were assessed at available residential addresses in 1990, 2000 and 2010 using air dispersion models and satellite data, respectively. Poisson regression with log person-time as an offset was employed to analyze the association between air pollution, greenness, and CB/COPD incidence, adjusting for confounders. RESULTS: Overall, there were 328 incident cases of CB/COPD during 2010-2023. Despite wide statistical uncertainty, we found a trend for a positive association between NO2 exposure and CB/COPD incidence, with incidence rate ratios (IRRs) per 10 µg/m³ difference ranging between 1.13 (95% CI: 0.90-1.41) in 1990 and 1.18 (95% CI: 0.96-1.45) in 2000. O3 showed a tendency for inverse association with CB/COPD incidence (IRR from 0.84 (95% CI: 0.66-1.07) in 2000 to 0.88 (95% CI: 0.69-1.14) in 2010. No consistent association was found between PM, BC and greenness with CB/COPD incidence across different exposure time windows. CONCLUSION: Consistent with prior research, our study suggests that individuals exposed to higher concentrations of NO2 may face an elevated risk of developing COPD, although evidence remains inconclusive. Greenness was not associated with CB/COPD incidence, while O3 showed a tendency for an inverse association with the outcome.

Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China.

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 (0.8 nmolmin-1m-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.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019.

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Exposure to air pollution and risk of respiratory tract infections in the adult Danish population-a nationwide study.

OBJECTIVES: The association between air pollution and risk of respiratory tract infection (RTI) in adults needs to be clarified in settings with low to moderate levels of air pollution. We investigated this in the Danish population between 2004 and 2016. METHODS: We included 3 653 490 persons aged 18-64 years in a nested case-control study. Exposure was defined as the average daily concentration at the individual's residential address of CO, NOX, NO2, O3, SO2, NH3, PPM2.5, black carbon, organic carbon, mineral dust, sea salt, secondary inorganic aerosols, SO42-, NO3-, NH4+, secondary organic aerosols, PM2.5, and PM10 during a 3-month exposure window. RTIs were defined by hospitalization for RTIs. Incidence rate ratios (IRRs) and 95% CIs were estimated comparing highest with lowest decile of exposure using conditional logistic regression models. RESULTS: In total, 188 439 incident cases of RTI were identified. Exposure to most air pollutants was positively associated with risk of RTI. For example, NO2 showed an IRR of 1.52 (CI: 1.48-1.55), and PM2.5 showed an IRR of 1.45 (CI: 1.40-1.50). In contrast, exposure to sea salt, PM10, NH3, and O3 was negatively associated with a risk of RTIs. DISCUSSION: In this nationwide study comprising adults, exposure to air pollution was associated with risk of RTIs and subgroups hereof. Sea salt, PM10, NH3, and O3 may be proxies for rural areas, as the levels of these species in Denmark are higher near the western coastlines and/or in rural areas with fewer combustion sources.

Long-term exposure to low-level air pollution and greenness and mortality in Northern Europe. The Life-GAP project.

BACKGROUND: Air pollution has been linked to mortality, but there are few studies examining the association with different exposure time windows spanning across several decades. The evidence for the effects of green space and mortality is contradictory. OBJECTIVE: We investigated all-cause mortality in relation to exposure to particulate matter (PM2.5 and PM10), black carbon (BC), nitrogen dioxide (NO2), ozone (O3) and greenness (normalized difference vegetation index - NDVI) across different exposure time windows. METHODS: The exposure assessment was based on a combination of the Danish Eulerian Hemispheric Model and the Urban Background Model for the years 1990, 2000 and 2010. The analysis included a complete case dataset with 9,135 participants from the third Respiratory Health in Northern Europe study (RHINE III), aged 40-65 years in 2010, with mortality follow-up to 2021. We performed Cox proportional hazard models, adjusting for potential confounders. RESULTS: Altogether, 327 (3.6 %) persons died in the period 2010-2021. Increased exposures in 1990 of PM2.5, PM10, BC and NO2 were associated with increased all-cause mortality hazard ratios of 1.40 (95 % CI1.04-1.87 per 5 µg/m3), 1.33 (95 % CI: 1.02-1.74 per 10 µg/m3), 1.16 (95 % CI: 0.98-1.38 per 0.4 µg/m3) and 1.17 (95 % CI: 0.92-1.50 per 10 µg/m3), respectively. No statistically significant associations were observed between air pollution and mortality in other time windows. O3 showed an inverse association with mortality, while no association was observed between greenness and mortality. Adjusting for NDVI increased the hazard ratios for PM2.5, PM10, BC and NO2 exposures in 1990. We did not find significant interactions between greenness and air pollution metrics. CONCLUSION: Long term exposure to even low levels of air pollution is associated with mortality. Opening up for a long latency period, our findings indicate that air pollution exposures over time may be even more harmful than anticipated.

Medium term moderate to low-level air pollution exposure is associated with higher C-reactive protein among healthy Danish blood donors.

Air pollution is a significant contributor to the global burden of disease with a plethora of associated health effects such as pulmonary and systemic inflammation. C-reactive protein (CRP) is associated with a wide range of diseases and is associated with several exposures. Studies on the effect of air pollution exposure on CRP levels in low to moderate pollution settings have shown inconsistent results. In this cross-sectional study high sensitivity CRP measurements on 18,463 Danish blood donors were linked to modelled air pollution data for NOx, NO2, O3, CO, SO2, NH3, mineral dust, black carbon, organic carbon, sea salt, secondary inorganic aerosols and its components, primary PM2.5, secondary organic aerosols, total PM2.5, and total PM10 at their residential address over the previous month. Associations were analysed using ordered logistic regression with CRP quartile as individuals outcome and air pollution exposure as scaled deciles. Analyses were adjusted for health related and socioeconomic covariates using health questionnaires and Danish register data. Exposure to different air pollution components was generally associated with higher CRP (odds ratio estimates ranging from 1.11 to 1.67), while exposure to a few air pollution components was associated with lower CRP. For example, exposure to NO2 increased the odds of high CRP 1.32-fold (95%CI 1.16-1.49), while exposure to NH3 decreased the odds of high CRP 0.81-fold (95%CI 0.73-0.89). This large study among healthy individuals found air pollution exposure to be associated with increased levels of CRP even in a setting with low to moderate air pollution levels.

Perspectives on environment and health research in Denmark.

AIMS: We provide an overview of nationwide environmental data available for Denmark and its linkage potentials to individual-level records with the aim of promoting research on the potential impact of the local surrounding environment on human health. BACKGROUND: Researchers in Denmark have unique opportunities for conducting large population-based studies treating the entire Danish population as one big, open and dynamic cohort based on nationally complete population and health registries. So far, most research in this area has utilised individual- and family-level information to study the clustering of disease in families, comorbidities, risk of, and prognosis after, disease onset, and social gradients in disease risk. Linking environmental data in time and space to individuals enables novel possibilities for studying the health effects of the social, built and physical environment. METHODS: We describe the possible linkage between individuals and their local surrounding environment to establish the exposome - that is, the total environmental exposure of an individual over their life course. CONCLUSIONS: The currently available nationwide longitudinal environmental data in Denmark constitutes a valuable and globally rare asset that can help explore the impact of the exposome on human health.

Long-term exposure to low-level particulate air pollution and Parkinson's disease diagnosis - A Finnish register-based study.

BACKGROUND: There is mixed evidence for an association between particulate matter air pollution and Parkinson's disease despite biological plausibility. OBJECTIVES: We studied the association between particulate air pollution, its components and Parkinson's disease (PD) risk. METHODS: We conducted a nested case-control study within the population of Finland using national registers. A total of 22,189 incident PD cases diagnosed between 1996 and 2015 were matched by age, sex and region with up to seven controls (n = 148,009) per case. Time weighted average air pollution exposure to particulate matter and its components was modelled at the residential addresses, accounting for move history, for the 16 years preceding diagnosis. Conditional logistic regression analysis was used to evaluate the association between air pollution and PD. Different exposure periods (6-16 years, 11-16 years, 5-10 years, 0-5 years) before the index date (date of PD diagnosis) were applied. RESULTS: Time-weighted average exposures were relatively low at 12.1 ± 6.5 µg/m3 (mean ± SD) for PM10 and 7.7 ± 3.2 µg/m3 for PM2.5. No associations were found between PM2.5 or PM10 exposure 6-16 years before index date and PD (OR: 0.99; 95% CI: 0.96, 1.02; per IQR of 3.9 µg/m3 and OR: 0.99; 95% CI: 0.96, 1.01; per IQR of 7.8 µg/m3, respectively). However, inverse associations were observed for the same exposure period with black carbon (OR: 0.96; 95% CI: 0.93, 0.99; per IQR of 0.6 µg/m3), sulphate (OR: 0.79; 95% CI: 0.68, 0.92; per IQR of 1.2 µg/m3), secondary organic aerosols (OR: 0.86; 95% CI: 0.80, 0.93; per IQR of 0.1 µg/m3) and sea salt (OR: 0.92; 95% CI: 0.87, 0.98; per IQR of 0.1 µg/m3). DISCUSSION: Low-level particulate matter air pollution was not associated with increased risk of incident PD in this Finnish nationwide population. The observed weak inverse associations with specific particle components should be investigated further.

PM2.5 air pollution components and mortality in Denmark.

BACKGROUND: Ambient fine particulate matter (PM2.5) causes millions of deaths every year worldwide. Identification of the most harmful types of PM2.5 would facilitate efficient prevention strategies. OBJECTIVES: The aim of this study was to investigate associations between components of PM2.5 and mortality in a nation-wide Danish population. METHODS: Our study base was Danes born 1921-1985 and aged 30-85 years, who were followed up for mortality from 1991 to 2015. We included 678,465 natural cause mortality cases and selected five age, sex and calendar time matched controls to each case from the study base. We retrieved the address history of the study population from Danish registries and assessed five-year average concentrations of eight PM2.5 components using deterministic Chemistry-Transport Models air pollution models. We estimated mortality rate ratios (MRRs) by conditional logistic regression and adjusted for socio-demographical factors at individual and neighborhood level. RESULTS: Single pollutant models showed the strongest associations between natural cause mortality and an interquartile increase in sulfate particles (SO4--) (MRR: 1.123; 95 % CI: 1.100-1.147 per 1.5 µg/m3) and secondary organic aerosol (SOA) (MRR: 1.054; 95 % CI: 1.048-1.061 per 0.050 µg/m3). Two-pollutant models showed robust associations between SO4-- and SOA and natural cause mortality. Elemental carbon and mineral dust showed robust associations with higher respiratory and lung cancer mortality. CONCLUSION: This nation-wide study found robust associations between natural cause mortality and SO4-- particles and SOA, which is in line with the results of previous studies. Elemental carbon and mineral dust showed robust associations with higher respiratory and lung cancer mortality.

Air pollution with NO2, PM2.5, and elemental carbon in relation to risk of breast cancer- a nationwide case-control study from Denmark.

Air pollution with particulate matter is an established lung carcinogen. Studies have suggested an association with breast cancer, but the evidence is inconsistent. METHODS: From nationwide registers, we identified all breast cancer cases (n = 55 745) in Denmark between 2000 and 2014. We matched one control for each case on age and year of birth. We used a multi-scale dispersion model to estimate outdoor concentrations of particulate matter <2.5 µm (PM2.5), elemental carbon (EC) and nitrogen dioxide (NO2) as time-weighted average over all addresses up to 20 years prior to diagnosis. We calculated odds ratios (OR) and 95% confidence intervals (CI) by conditional logistic regression with adjustment for marital status, educational level, occupational status, personal income, region of origin, medication and area-level socio-economic indicators. RESULTS: A 10 µg/m3 higher PM2.5 was associated with an OR for breast cancer of 1.21 (95% CI: 1.11-1.33). The corresponding ORs for EC (per 1 µg/m3) and NO2 (per 10 µg/m3) were 1.03 (95% CI: 1.00-1.07) and 1.03 (95% CI: 1.01-1.06), respectively. In multi-pollutant models, the OR for PM2.5 changed only little, whereas ORs for EC or NO2 approached the null. In an analysis of persons below 55 years, PM2.5 was associated with an OR of 1.32 (95% CI: 1.09-1.60) per 10 µg/m3 increase. CONCLUSION: We found evidence of an association between the investigated air pollutants and breast cancer, especially PM2.5. There were indications that the association differed by age at diagnosis. We were not able to include all potential confounders and thus, results should be interpreted with caution.

Present-day and future PM2.5 and O3-related global and regional premature mortality in the EVAv6.0 health impact assessment model.

We used the EVAv6.0 system to estimate the present (2015) and future (2015-2050) global PM2.5 and O3-related premature mortalities, using simulated surface concentrations from the GISS-E2.1-G Earth system model. The PM2.5-related global premature mortality is estimated to be 4.3 and 4.4 million by the non-linear and linear models, respectively. Ischemic heart diseases are found to be the leading cause of PM2.5-related premature deaths, contributing by 35% globally. Both long-term and short-term O3-related premature deaths are estimated to be around 1 million, globally. Overall, PM2.5 and O3-related premature mortality leads to 5.3-5.4 million premature deaths, globally. The global burden of premature deaths is mainly driven by the Asian region, which in 2015 contributes by 75% of the total global premature deaths. An increase from 6.2% to 8% in the PM2.5 relative risk as recommended by the WHO leads to an increase of PM2.5-related premature mortality by 28%, to 5.7 million. Finally, bias correcting the simulated PM2.5 concentrations in 2015 leads to an increase of up to 73% in the global PM2.5-related premature mortality, leading to a total number of global premature deaths of up to 7.7 million, implying the necessity of bias correction to get more robust health burden estimates. PM2.5 and O3-related premature mortality in 2050 decreases by up to 57% and 18%, respectively, due to emission reductions alone. However, the projected increase and aging of the population leads to increases of premature mortality by up to a factor of 2, showing that the population exposed to air pollution is more important than the level of air pollutants, highlighting that the population dynamics should be considered when setting up health assessment systems.

Europe-wide air pollution modeling from 2000 to 2019 using geographically weighted regression.

Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.

Health impacts of PM2.5 originating from residential wood combustion in four nordic cities.

BACKGROUND: Residential wood combustion (RWC) is one of the largest sources of fine particles (PM2.5) in the Nordic cities. The current study aims to calculate the related health effects in four studied city areas in Sweden, Finland, Norway, and Denmark. METHODS: Health impact assessment (HIA) was employed as the methodology to quantify the health burden. Firstly, the RWC induced annual average PM2.5 concentrations from local sources were estimated with air pollution dispersion modelling. Secondly, the baseline mortality rates were retrieved from the national health registers. Thirdly, the concentration-response function from a previous epidemiological study was applied. For the health impact calculations, the WHO-developed tool AirQ + was used. RESULTS: Amongst the studied city areas, the local RWC induced PM2.5 concentration was lowest in the Helsinki Metropolitan Area (population-weighted annual average concentration 0.46 µg m- 3) and highest in Oslo (2.77 µg m- 3). Each year, particulate matter attributed to RWC caused around 19 premature deaths in Umeå (95% CI: 8-29), 85 in the Helsinki Metropolitan Area (95% CI: 35-129), 78 in Copenhagen (95% CI: 33-118), and 232 premature deaths in Oslo (95% CI: 97-346). The average loss of life years per premature death case was approximately ten years; however, in the whole population, this reflects on average a decrease in life expectancy by 0.25 (0.10-0.36) years. In terms of the relative contributions in cities, life expectancy will be decreased by 0.10 (95% CI: 0.05-0.16), 0.18 (95% CI: 0.07-0.28), 0.22 (95% CI: 0.09-0.33) and 0.63 (95% CI: 0.26-0.96) years in the Helsinki Metropolitan Area, Umeå, Copenhagen and Oslo respectively. The number of years of life lost was lowest in Umeå (172, 95% CI: 71-260) and highest in Oslo (2458, 95% CI: 1033-3669). CONCLUSIONS: All four Nordic city areas have a substantial amount of domestic heating, and RWC is one of the most significant sources of PM2.5. This implicates a substantial predicted impact on public health in terms of premature mortality. Thus, several public health measures are needed to reduce the RWC emissions.

4D-Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS-Chem Adjoint With Bi-Directional and Uni-Directional Flux Schemes.

We conduct the first 4D-Var inversion of NH3 accounting for NH3 bi-directional flux, using CrIS satellite NH3 observations over Europe in 2016. We find posterior NH3 emissions peak more in springtime than prior emissions at continental to national scales, and annually they are generally smaller than the prior emissions over central Europe, but larger over most of the rest of Europe. Annual posterior anthropogenic NH3 emissions for 25 European Union members (EU25) are 25% higher than the prior emissions and very close (<2% difference) to other inventories. Our posterior annual anthropogenic emissions for EU25, the UK, the Netherlands, and Switzerland are generally 10%-20% smaller than when treating NH3 fluxes as uni-directional emissions, while the monthly regional difference can be up to 34% (Switzerland in July). Compared to monthly mean in-situ observations, our posterior NH3 emissions from both schemes generally improve the magnitude and seasonality of simulated surface NH3 and bulk NH x wet deposition throughout most of Europe, whereas evaluation against hourly measurements at a background site shows the bi-directional scheme better captures observed diurnal variability of surface NH3. This contrast highlights the need for accurately simulating diurnal variability of NH3 in assimilation of sun-synchronous observations and also the potential value of future geostationary satellite observations. Overall, our top-down ammonia emissions can help to examine the effectiveness of air pollution control policies to facilitate future air pollution management, as well as helping us understand the uncertainty in top-down NH3 emissions estimates associated with treatment of NH3 surface exchange.

Analysis of mortality metrics associated with a comprehensive range of disorders in Denmark, 2000 to 2018: A population-based cohort study.

BACKGROUND: The provision of different types of mortality metrics (e.g., mortality rate ratios [MRRs] and life expectancy) allows the research community to access a more informative set of health metrics. The aim of this study was to provide a panel of mortality metrics associated with a comprehensive range of disorders and to design a web page to visualize all results. METHODS AND FINDINGS: In a population-based cohort of all 7,378,598 persons living in Denmark at some point between 2000 and 2018, we identified individuals diagnosed at hospitals with 1,803 specific categories of disorders through the International Classification of Diseases-10th Revision (ICD-10) in the National Patient Register. Information on date and cause of death was obtained from the Registry of Causes of Death. For each of the disorders, a panel of epidemiological and mortality metrics was estimated, including incidence rates, age-of-onset distributions, MRRs, and differences in life expectancy (estimated as life years lost [LYLs]). Additionally, we examined models that adjusted for measures of air pollution to explore potential associations with MRRs. We focus on 39 general medical conditions to simplify the presentation of results, which cover 10 broad categories: circulatory, endocrine, pulmonary, gastrointestinal, urogenital, musculoskeletal, hematologic, mental, and neurologic conditions and cancer. A total of 3,676,694 males and 3,701,904 females were followed up for 101.7 million person-years. During the 19-year follow-up period, 1,034,273 persons (14.0%) died. For 37 of the 39 selected medical conditions, mortality rates were larger and life expectancy shorter compared to the Danish general population. For these 37 disorders, MRRs ranged from 1.09 (95% confidence interval [CI]: 1.09 to 1.10) for vision problems to 7.85 (7.77 to 7.93) for chronic liver disease, while LYLs ranged from 0.31 (0.14 to 0.47) years (approximately 16 weeks) for allergy to 17.05 (16.95 to 17.15) years for chronic liver disease. Adjustment for air pollution had very little impact on the estimates; however, a limitation of the study is the possibility that the association between the different disorders and mortality could be explained by other underlying factors associated with both the disorder and mortality. CONCLUSIONS: In this study, we show estimates of incidence, age of onset, age of death, and mortality metrics (both MRRs and LYLs) for a comprehensive range of disorders. The interactive data visualization site (https://nbepi.com/atlas) allows more fine-grained analysis of the link between a range of disorders and key mortality estimates.