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.

Concomitant exposure to air pollution, green space and noise, and risk of myocardial infarction: a cohort study from Denmark.

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.

The present study aimed to analyse their independent and cumulative association of the three correlated environmental exposures: air pollution, road traffic noise, and green space with MI. Air pollution, noise, and lack of green space were all independently associated with MI.Risk estimates for air pollution, noise, and lack of green space were similar, indicating that all may be equally relevant targets for regulatory measures.

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.

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.

Concomitant exposure to air pollution, green space, and noise and risk of stroke: a cohort study from Denmark.

Background: Air pollution, road traffic noise, and green space are correlated factors, associated with risk of stroke. We investigated their independent relationship with stroke in multi-exposure analyses and estimated their cumulative stroke burden. Methods: For all persons, ≥50 years of age and living in Denmark from 2005 to 2017, we established complete address histories and estimated running 5-year mean exposure to fine particles (PM2.5), ultrafine particles, elemental carbon, nitrogen dioxide (NO2), and road traffic noise at the most, and least exposed façade. For air pollutants, we estimated total, and non-traffic contributions. Green space around the residence was estimated from land use maps. Hazard ratios (HR) and 95% confidence limits (CL) were estimated with Cox proportional hazards models and used to calculate cumulative risk indices (CRI). We adjusted for the individual and sociodemographic covariates available in our dataset (which did not include information about individual life styles and medical conditions). Findings: The cohort accumulated 18,344,976 years of follow-up and 94,256 cases of stroke. All exposures were associated with risk of stroke in single pollutant models. In multi-pollutant analyses, only PM2.5 (HR: 1.058, 95% CI: 1.040-1.075) and noise at most exposed façade (HR: 1.033, 95% CI: 1.024-1.042) were independently associated with a higher risk of stroke. Both noise and air pollution contributed substantially to the CRI (1.103, 95% CI: 1.092-1.114) in the model with noise, green space, and total PM2.5 concentrations. Interpretation: Environmental exposure to air pollution and noise were both independently associated with risk of stroke. Funding: Health Effects Institute (HEI) (Assistance Award No. R-82811201).

Air pollution and myocardial infarction; effect modification by sociodemographic and environmental factors. A cohort study from Denmark.

Air pollution is associated with increased risk of myocardial infarction (MI), but it is unresolved to what extent the association is modified by factors such as socioeconomic status, comorbidities, financial stress, residential green space, or road traffic noise. We formed a cohort of all (n = 1,964,702) Danes, aged 50-85 years, with 65,311 cases of MI during the followed-up period 2005-2017. For all participants we established residential five-year running average exposure to particulate matter <2.5 µm (PM2.5), ultrafine particles (UFP, <0.1 µm), elemental carbon (EC) and nitrogen dioxide (NO2). We evaluated risk in population strata, using Aalen additive hazards models to estimate absolute risk and Cox proportional hazards models to estimate relative risk of MI with 95% confidence intervals (CI). PM2.5 and the other pollutant were associated with MI. Lower education and lower income were associated with higher absolute risks of MI from air pollution, whereas no clear effect modification was apparent for relative risk estimates. For example, 5 µg/m3 higher PM2.5 was associated with HR for MI of 1.16 (95% CI: 1.10-1.22) among those with only mandatory education and 1.13 (95% CI: 1.03-1.24) among those with long education. The corresponding rate differences per 100,000 person years were 243 (95% CI: 216-271) and 358 (95% CI: 338-379), respectively. Higher level of comorbidity was consistently across all four pollutants associated with both higher absolute and relative risk of MI. In conclusion, people with comorbid conditions or of lower SES appeared more vulnerable to long-term exposure to air pollution and more cases of MI may be prevented by focused interventions in these groups.

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.

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.

Air pollution, road traffic noise and lack of greenness and risk of type 2 diabetes: A multi-exposure prospective study covering Denmark.

OBJECTIVE: Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective. METHODS: We estimated 5-year residential mean exposure to fine particles (PM2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO2) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates. RESULTS: In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO2 remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO2, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149). CONCLUSIONS: Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.

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.

Exposure to source-specific air pollution and risk for type 2 diabetes: a nationwide study covering Denmark.

BACKGROUND: Only few epidemiological studies have investigated whether chronic exposure to air pollution from different sources have different impacts on risk of diabetes. We aimed to investigate associations between air pollution from traffic versus non-traffic sources and risk of type 2 diabetes in the Danish population. METHODS: We estimated long-term exposure to traffic and non-traffic contributions of particulate matter with a diameter <2.5 µg (PM2.5), elemental carbon (EC), ultrafine particles (UFP) and nitrogen dioxide (NO2) for all persons living in Denmark for the period 2005-17. In total, 2.6 million persons aged >35 years were included, of whom 148 020 developed type 2 diabetes during follow-up. We applied Cox proportional hazards models for analyses, using 5-year time-weighted running means of air pollution and adjustment for individual- and area-level demographic and socioeconomic covariates. RESULTS: We found that 5-year exposure to all particle measures (PM2.5, UFP and EC) and NO2 were associated with higher type 2 diabetes risk. We observed that for UFP, EC and potentially PM2.5, the pollution originating from traffic was associated with higher risks than the non-traffic contributions, whereas for NO2 similar hazard ratios (HR) were observed. For example, in two-source models, hazard ratios (HRs) per interquartile change in traffic UFP, EC and PM2.5 were 1.025, 1.045 and 1.036, respectively, whereas for non-traffic UFP, EC and PM2.5, the HRs were 1.013, 1.018 and 1.001, respectively. CONCLUSIONS: Our finding of stronger associations with particulate matter from traffic compared with non-traffic sources implies that prevention strategies should focus on limiting traffic-related particulate matter air pollution.

Air pollution at the residence of Danish adults, by socio-demographic characteristics, morbidity, and address level characteristics.

BACKGROUND: Exposure to outdoor air pollution is associated with adverse health effects. Previous studies have indicated higher levels of air pollution in socially deprived areas. AIM: To investigate associations between air pollution and socio-demographic variables, comorbidity, stress, and green space at the residence in Denmark. METHODS: We included 2,237,346 persons living in Denmark, aged 35 years or older in 2017. We used the high resolution, multi-scale DEHM/UBM/AirGIS air pollution modelling system to calculate mean concentrations of air pollution with PM2.5, elemental carbon, ultrafine particles and NO2 at residences held the preceding five years. We used nationwide registries to retrieve information about socio-demographic indicators at the individual and neighborhood levels. We used general linear regression models to analyze associations between socio-demographic indicators and air pollution at the residence. RESULTS: Individuals with high SES (income, higher white-collar worker and high educational level) and of non-Danish origin were exposed to higher levels of air pollution than individuals of low SES and of Danish origin, respectively. We found comparable levels of air pollution according to sex, stress events and morbidity. For neighborhood level SES indicators, we found high air pollution levels in neighborhoods with low SES measured as proportion of social housing, sole providers, low income and unemployment. In contrast, we found higher air pollution levels in neighborhoods with higher educational level and a low proportion of manual labor. People living in an apartment and/or with little green space had higher air pollution levels. CONCLUSION: In Denmark, high levels of residential air pollution were associated with higher individual SES and non-Danish origin. For neighborhood-level indicators of SES, no consistent pattern was observed. These results highlight the need for analyzing many different socio-demographic indicators to understand the complex associations between SES and exposure to air pollution.

The effect of adjustment to register-based and questionnaire-based covariates on the association between air pollution and cardiometabolic disease.

OBJECTIVE: Recent studies on air pollution and disease have been based on millions of participants within a region or country, relying entirely on register-based confounder adjustment. We aimed to investigate the effects of increasing adjustment for register- and questionnaire-based covariates on the association between air pollution and cardiometabolic diseases. METHODS: In a population-based cohort of 246,766 eligible participants randomly selected across Denmark in 2010 and 2013 and followed up until December 31, 2017, we identified 3,247 myocardial infarction (MI) cases, 4,166 stroke cases and 6,366 type 2 diabetes cases. Based on historical address-information, we calculated 5-year time-weighted exposure to PM2.5 and NO2 modelled using a validated air pollution model. We used Cox proportional hazards models to calculate hazard ratios (HR) with increasing adjustment for a number of individual- and area-level register-based covariates as well as lifestyle covariates assessed through questionnaires. RESULTS: We found that a 5 µg/m3 higher PM2.5 was associated with HRs (95% CI) for MI, stroke and diabetes, of respectively, 1.18 (0.91-1.52), 1.11 (0.88-1.40) and 1.24 (1.03-1.50) in the fully adjusted models. For all three diseases, adjustment for either individual-level, area-level or lifestyle covariates, or combinations of these resulted in higher HRs compared to HRs adjusted only for age, sex and calendar-year, most marked for MI and diabetes. Further adjustment for lifestyle in models with full register-based individual- and area-level adjustment resulted in only minor changes in HRs for all three diseases. CONCLUSIONS: Our findings suggest that in studies of air pollution and cardiometabolic disease, which use an adjustment strategy with a broad range of register-based socioeconomic variables, there is no effect on risk estimates from subsequent lifestyle adjustment.

Long-term exposure to road traffic noise and stroke incidence: a Danish Nurse Cohort study.

BACKGROUND: Road traffic noise has been linked to increased risk of ischemic heart disease, yet evidence on stroke shows mixed results. We examine the association between long-term exposure to road traffic noise and incidence of stroke, overall and by subtype (ischemic or hemorrhagic), after adjustment for air pollution. METHODS: Twenty-five thousand six hundred and sixty female nurses from the Danish Nurse Cohort recruited in 1993 or 1999 were followed for stroke-related first-ever hospital contact until December 31st, 2014. Full residential address histories since 1970 were obtained and annual means of road traffic noise (Lden [dB]) and air pollutants (particulate matter with diameter < 2.5 µm and < 10 µm [PM2.5 and PM10], nitrogen dioxide [NO2], nitrogen oxides [NOx]) were determined using validated models. Time-varying Cox regression models were used to estimate hazard ratios (HR) (95% confidence intervals [CI]) for the associations of one-, three-, and 23-year running means of Lden preceding stroke (all, ischemic or hemorrhagic), adjusting for stroke risk factors and air pollutants. The World Health Organization and the Danish government's maximum exposure recommendations of 53 and 58 dB, respectively, were explored as potential Lden thresholds. RESULTS: Of 25,660 nurses, 1237 developed their first stroke (1089 ischemic, 148 hemorrhagic) during 16 years mean follow-up. For associations between a 1-year mean of Lden and overall stroke incidence, the estimated HR (95% CI) in the fully adjusted model was 1.06 (0.98-1.14) per 10 dB, which attenuated to 1.01 (0.93-1.09) and 1.00 (0.91-1.09) in models further adjusted for PM2.5 or NO2, respectively. Associations for other exposure periods or separately for ischemic or hemorrhagic stroke were similar. There was no evidence of a threshold association between Lden and stroke. CONCLUSIONS: Long-term exposure to road traffic noise was suggestively positively associated with the risk of overall stroke, although not after adjusting for air pollution.

Exposure to ambient air pollution during childhood and subsequent risk of self-harm: A national cohort study.

A growing body of evidence indicates that exposure to air pollution not only impacts on physical health but is also linked with a deterioration in mental health. We conducted the first study to investigate exposure to ambient particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) and nitrogen dioxide (NO2) during childhood and subsequent self-harm risk. The study cohort included persons born in Denmark between January 1, 1979 and December 31, 2006 (N = 1,424,670), with information on daily exposures to PM2.5 and NO2 at residence from birth to 10th birthday. Follow-up began from 10th birthday until first hospital-presenting self-harm episode, death, or December 31, 2016, whichever came first. Incidence rate ratios estimated by Poisson regression models revealed a dose relationship between increasing PM2.5 exposure and rising self-harm risk. Exposure to 17-19 µg/m3 of PM2.5 on average per day from birth to 10th birthday was associated with a 1.45 fold (95% CI 1.37-1.53) subsequently elevated self-harm risk compared with a mean daily exposure of <13 µg/m3, whilst those exposed to 19 µg/m3 or above on average per day had a 1.59 times (1.45-1.75) elevated risk. Higher mean daily exposure to NO2 during childhood was also linked with increased self-harm risk, but the dose-response relationship observed was less evident than for PM2.5. Covariate adjustment attenuated the associations, but risk remained independently elevated. Although causality cannot be assumed, these novel findings indicate a potential etiological involvement of ambient air pollution in the development of mental ill health.

Long-Term Exposure to Road Traffic Noise and Air Pollution, and Incident Atrial Fibrillation in the Danish Nurse Cohort.

BACKGROUND: Associations between long-term exposure to air pollution and road traffic noise have been established for ischemic heart disease, but findings have been mixed for atrial fibrillation (AF). OBJECTIVES: The goal of the study was to examine associations of long-term exposure to road traffic noise and air pollution with AF. METHODS: Time-varying Cox regression models were used to estimate associations of 1-, 3-, and 23-y mean road traffic noise and air pollution exposures with AF incidence in 23,528 women enrolled in the Danish Nurse Cohort (age >44y at baseline in 1993 or 1999). AF diagnoses were ascertained via the Danish National Patient Register. Annual mean weighted 24-h average road traffic noise levels (Lden) at the nurses' residences, since 1970, were estimated using the Nord2000 model, and annual mean levels of particulate matter with a diameter <2.5µm (PM2.5) and nitrogen dioxide (NO2) were estimated using the DEHM/UBM/AirGIS model. RESULTS: Of 23,528 nurses with no prior AF diagnosis at the cohort baseline, 1,522 developed AF during follow-up. In a fully adjusted model (including PM2.5), the estimated risk of AF was 18% higher [hazard ratio (HR); 95% confidence interval (CI): 1.18; 1.02, 1.36] in nurses with residential 3-y mean Lden levels >58 dB vs. <48 dB, with similar findings for 1-y mean exposures. A 3.9-µg/m3 increase in 3-y mean PM2.5 was associated with incident AF before and after adjustment for concurrent exposure to road traffic noise (HR 1.09; 95% CI: 1.00, 1.20 and 1.08; 95% CI: 0.97, 1.19, respectively). Associations with 1-y mean PM2.5 exposures were positive but closer to the null and not significant. Associations with NO2 were null for all time periods before and after adjustment for road traffic noise and inverse when adjusted for concurrent PM2.5. CONCLUSION: Our analysis of prospective data from a cohort of Danish female nurses followed for up to 14 y provided suggestive evidence of independent associations between incident AF and 1- and 3-y exposures to road traffic noise and PM2.5. https://doi.org/10.1289/EHP8090.

Long-Term Residential Exposure to Particulate Matter and Its Components, Nitrogen Dioxide and Ozone-A Northern Sweden Cohort Study on Mortality.

This study aims to estimate the mortality risk associated with air pollution in a Swedish cohort with relatively low exposure. Air pollution models were used to estimate annual mean concentrations of particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5), primary emitted carbonaceous particles (BC/pOC), sea salt, chemically formed particles grouped as secondary inorganic and organic aerosols (SIA and SOA) as well as ozone (O3) and nitrogen dioxide (NO2). The exposure, as a moving average was calculated based on home address for the time windows 1 year (lag 1), 1-5 years (lag 1-5) and 1-10 years (lag 1-10) preceding the death. During the study period, 1151 cases of natural mortality, 253 cases of cardiovascular disease (CVD) mortality and 113 cases of respiratory and lung cancer mortality were observed during 369,394 person-years of follow-up. Increased natural mortality was observed in association with NO2 (3% [95% CI -8-14%] per IQR) and PM2.5 (2% [95% CI -5-9%] for an IQR increase) and its components, except for SOA where a decreased risk was observed. Higher risk increases were observed for CVD mortality (e.g., 18% [95% CI 1-39%] per IQR for NO2). These findings at low exposure levels are relevant for future decisions concerning air quality policies.

Early childhood exposure to ambient air pollution is associated with increased risk of paediatric asthma: An administrative cohort study from Stockholm, Sweden.

INTRODUCTION: Asthma is a complex, heterogeneous disease and one of the most common chronic diseases among children. Exposure to ambient air pollution in early life and childhood may influence asthma aetiology, but it is uncertain which specific components of air pollution and exposure windows are of importance. The role of socio-economic status (SES) is also unclear. The aims of the present study are, therefore, to investigate how various exposure windows of different pollutants affect risk-induced asthma in early life and to explore the possible effect SES has on that relationship. METHODS: The study population was constructed using register data on all singleton births in the greater Stockholm area between 2006 and 2013. Exposure to ambient black carbon (BC), fine particulate matter (PM2.5), primary organic carbon (pOC) secondary organic aerosols (SOA), secondary inorganic aerosols, and oxidative potential at the residential address was modelled as mean values for the entire pregnancy period, the first year of life and the first three years of life. Swedish national registers were used to define the outcome: asthma diagnosis assessed at hospital during the first six years of life. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were modelled with Cox proportional hazards model with age as the underlying time-scale, adjusting for relevant potential confounding variables. RESULTS: An increased risk for developing childhood asthma was observed in association with exposure to PM2.5, pOC and SOA during the first three years of life. With an interquartile range increase in exposure, the HRs were 1.06 (95% CI: 1.01-1.10), 1.05 (95% CI: 1.02-1.09) and 1.02 (95% CI: 1.00-1.04), for PM2.5, pOC and SOA, respectively, in the fully adjusted models. Exposure during foetal life or the first year of life was not associated with asthma risk, and the other pollutants were not statistically significantly associated with increased risk. Furthermore, the increase in risk associated with PM2.5 and the components BC, pOC and SOA were stronger in areas with lower SES. CONCLUSION: Our results suggest that exposure to air pollution during the first three years of life may increase the risk for asthma in early childhood. The findings further imply a possible increased vulnerability to air pollution-attributed asthma among low SES children.