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 stroke; effect modification by sociodemographic and environmental factors. A cohort study from Denmark.

OBJECTIVES: Air pollution increases the risk of stroke, but the literature on identifying susceptible subgroups of populations is scarce and inconsistent. The aim of this study was to investigate if the association between air pollution and risk of stroke differed by sociodemographic factors, financial stress, comorbid conditions, and residential road traffic noise, population density and green space. METHODS: We assessed long-term exposure to air pollution with ultrafine particles, PM2.5, elemental carbon and NO2 for a cohort of 1,971,246 Danes aged 50-85 years. During follow-up from 2005 to 2017, we identified 83,211 incident stroke cases. We used Cox proportional hazards model (relative risk) and Aalen additive hazards models (absolute risk) to estimate associations and confidence intervals (CI) between 5-year running means of air pollution at the residence and risk of stroke in population strata. RESULTS: All four pollutants were associated with higher risk of stroke. The association between air pollution and stroke was strongest among individuals with comorbidities, with shorter education, lower income and being retired. The results also indicated stronger associations among individuals living in less populated areas, and with low noise levels and more green space around the residence. Estimates of absolute risk seemed better suited to detect such interactions than estimates of relative risk. For example for PM2.5 the hazard ratio for stroke was 1.28 (95%CI: 1.22-1.34) and 1.26 (95%CI: 1.16-1.37) among those with mandatory and medium/long education respectively. The corresponding rate difference estimates per 100,000 person years were 568 (95%CI: 543-594) and 423(95%CI: 390-456) CONCLUSION: The associations between air pollution and risk of stroke was stronger among individuals of lower socioeconomic status or with pre-existing comorbid conditions. Absolute risk estimates were better suited to identify such effect modification.

Long-term exposure to residential transportation noise and mortality: A nationwide cohort study.

Studies have indicated that transportation noise is associated with higher cardiovascular mortality, whereas evidence of noise as a risk factor for respiratory and cancer mortality is scarce and inconclusive. Also, knowledge on effects of low-level noise on mortality is very limited. We aimed to investigate associations between road and railway noise and natural-cause and cause-specific mortality in the Danish population. We estimated address-specific road and railway noise at the most (LdenMax) and least (LdenMin) exposed façades for all residential addresses in Denmark from 1990 to 2017 using high-quality exposure models. Using these data, we calculated 10-year time-weighted mean noise exposure for 2.6 million Danes aged >50 years, of whom 600,492 died from natural causes during a mean follow-up of 11.7 years. We analyzed data using Cox proportional hazards models with adjustment for individual and area-level sociodemographic variables and air pollution (PM2.5 and NO2). We found that a 10-year mean exposure to road LdenMax and road LdenMin per 10 dB were associated with hazard ratios (95% confidence intervals) of, respectively, 1.09 (1.09; 1.10) and 1.10 (1.10; 1.11) for natural-cause mortality, 1.09 (1.08; 1.10) and 1.09 (1.08; 1.10) for cardiovascular mortality, 1.13 (1.12; 1.14) and 1.17 (1.16; 1.19) for respiratory mortality and 1.03 (1.02; 1.03) and 1.06 (1.05; 1.07) for cancer mortality. For LdenMax, the associations followed linear exposure-response relationships from 35 dB to 60-<65 dB, after which the function levelled off. For LdenMin, exposure-response relationships were linear from 35 dB and up, with some levelling off at high noise levels for natural-cause and cardiovascular mortality. Railway noise did not seem associated with higher mortality in an exposure-response dependent manner. In conclusion, road traffic noise was associated with higher mortality and the increase in risk started well below the current World Health Organization guideline limit for road traffic noise of 53 dB.

Transportation Noise and Risk of Tinnitus: A Nationwide Cohort Study from Denmark.

BACKGROUND: There is a growing body of evidence linking residential exposure to transportation noise with several nonauditory health outcomes. However, auditory outcomes, such as tinnitus, are virtually unexplored. OBJECTIVES: We aimed to investigate the association between residential transportation noise and risk of incident tinnitus. METHODS: We conducted a nationwide cohort study including all residents in Denmark age ≥30y, of whom 40,692 were diagnosed with tinnitus. We modeled road traffic and railway noise at the most (Ldenmax) and least (Ldenmin) exposed façades of all Danish addresses from 1990 until 2017. For all participants, we calculated 1-, 5-, and 10-y time-weighted mean noise exposure and retrieved detailed information on individual- and area-level socioeconomic covariates. We conducted analyses using Cox proportional hazards models. RESULTS: We found positive associations between exposure to road traffic noise and risk of tinnitus, with hazard ratios of 1.06 [95% confidence interval (CI): 1.04, 1.08] and 1.02 (95% CI: 1.01, 1.03) per 10-dB increase in 10-y Ldenmin and Ldenmax, respectively. Highest risk estimates were found for women, people without a hearing loss, people with high education and income, and people who had never been in a blue-collar job. The association with road Ldenmin followed a positive, monotonic exposure-response relationship. We found no association between railway noise and tinnitus. DISCUSSION: To our knowledge, this is the first study to show that residential exposure to road traffic noise may increase risk of tinnitus, suggesting noise may negatively affect the auditory system. If confirmed, this finding adds to the growing evidence of road traffic noise as a harmful pollutant with a substantial health burden. https://doi.org/10.1289/EHP11248.

Estimation of the effect on air quality of retrofitting SCRT on urban buses in Copenhagen.

This study estimates the effect on air quality of retrofitting SCRT on about 300 urban buses in Copenhagen from September 2015 to March 2016. The retrofitted buses were of Euro III, Euro IV and EEV emission standards. The specific SCRT technology applies ammonia as injected into the exhaust as a gas as opposed to normally as a liquid (urea). This technology is more efficient in reducing NOx emissions, especially under urban driving conditions with relatively low exhaust temperatures. The estimation of the effect is based on air quality model calculations for 98 selected busy streets in Copenhagen for 2015 based on, among others, information from the Zealand public transport agency about buses with and without retrofitted SCRT. More detailed analyses were conducted for two of the streets where fixed air quality measuring stations are located in Copenhagen. Furthermore, a before-after analysis of the development of measured concentrations at fixed measuring stations was carried out to isolate the effect of the retrofitted SCRT. The model calculations showed substantial reductions in emissions of NOx and exhaust particles from each bus (90%) but low reductions in concentrations of NO2, PM2.5 and PM10, respectively 3% for 98 streets on average for NO2, and 0.1%[0.2%] for PM2.5 and 0.07%[0.1%] for PM10 for H.C. Andersen Boulevard and [Jagtvej]. Based on the analysis of trends in the measurements it was not possible to isolate an effect of SCRT on urban buses in Copenhagen probably due to the large variations in meteorology affecting the variations in concentrations.

Hyperlocal variation of nitrogen dioxide, black carbon, and ultrafine particles measured with Google Street View cars in Amsterdam and Copenhagen.

Hyperlocal air quality maps are becoming increasingly common, as they provide useful insights into the spatial variation and sources of air pollutants. In this study, we produced several high-resolution concentration maps to assess the spatial differences of three traffic-related pollutants, Nitrogen dioxide (NO2), Black Carbon (BC) and Ultrafine Particles (UFP), in Amsterdam, the Netherlands, and Copenhagen, Denmark. All maps were based on a mixed-effect model approach by using state-of-the-art mobile measurements conducted by Google Street View (GSV) cars, during October 2018 - March 2020, and Land-use Regression (LUR) models based on several land-use and traffic predictor variables. We then explored the concentration ratio between the different normalised pollutants to understand possible contributing sources to the observed hyperlocal variations. The maps developed in this work reflect, (i) expected elevated pollution concentrations along busy roads, and (ii) similar concentration patterns on specific road types, e.g., motorways, for both cities. In the ratio maps, we observed a clear pattern of elevated concentrations of UFP near the airport in both cities, compared to BC and NO2. This is the first study to produce hyperlocal maps for BC and UFP using high-quality mobile measurements. These maps are important for policymakers and health-effect studies, trying to disentangle individual effects of key air pollutants of interest (e.g., UFP).

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

Long-term exposure to road traffic noise and all-cause and cause-specific mortality: a Danish Nurse Cohort study.

BACKGROUND: Long-term road traffic noise exposure is linked to cardio-metabolic disease morbidity, whereas evidence on mortality remains limited. OBJECTIVES: We investigated association of long-term exposure to road traffic noise with all-cause and cause-specific mortality. METHODS: We linked 22,858 females from the Danish Nurse Cohort (DNC), recruited into the Danish Register of Causes of Death up to 2014. Road traffic noise levels since 1970 were modelled by Nord2000 as the annual mean of a weighted 24 h average (Lden). Cox regression models examined the associations between Lden (5-year and 23-year means) and all-cause and cause-specific mortalities, adjusting for lifestyle and exposure to PM2.5 (particulate matter with diameter < 2.5 µm) and NO2 (nitrogen dioxide). RESULTS: During follow-up (mean 17.4 years), 3902 nurses died: 1622 from cancer, 922 from CVDs (289 from stroke), 338 from respiratory diseases (186 from chronic obstructive pulmonary disease, 114 from lower respiratory tract infections [ALRIs]), 234 from dementia, 95 from psychiatric disorders, and 79 from diabetes. Hazard ratios (95% confidence intervals) for all-cause mortality from fully-adjusted models were 1.06 (1.01, 1.11) and 1.09 (1.03, 1.15) per 10 dB of 5-year and 23-year mean Lden, respectively, which attenuated slightly in our main model (fully-adjusted plus PM2.5: 1.04 [1.00, 1.10]; 1.08 [1.02, 1.13]). Main model estimates suggested the strongest associations between 5-year mean Lden and diabetes (1.14: 0.81, 1.61), ALRIs (1.13: 0.84, 1.54), dementia (1.12: 0.90, 1.38), and stroke (1.10: 0.91, 1.31), whereas associations with 23-year mean Lden were suggested for respiratory diseases (1.15: 0.95, 1.39), psychiatric disorders (1.11: 0.78, 1.59), and all cancers (1.08: 0.99, 1.17). DISCUSSION: Among the female nurses from the DNC, we observed that long-term exposure to road traffic noise led to premature mortality, independently of air pollution, and its adverse effects may extend well beyond those on the cardio-metabolic system to include respiratory diseases, cancer, neurodegenerative and psychiatric disorders.

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.

Long-Term Exposure to Air Pollution, Road Traffic Noise, and Heart Failure Incidence: The Danish Nurse Cohort.

Background We examined the association of long-term exposure to air pollution and road traffic noise with incident heart failure (HF). Methods And Results Using data on female nurses from the Danish Nurse Cohort (aged >44 years), we investigated associations between 3-year mean exposures to air pollution and road traffic noise and incident HF using Cox regression models, adjusting for relevant confounders. Incidence of HF was defined as the first hospital contact (inpatient, outpatient, or emergency) between cohort baseline (1993 or 1999) and December 31, 2014, based on the Danish National Patient Register. Annual mean levels of particulate matter with a diameter <2.5 µm since 1990 and NO2 and road traffic noise since 1970 were estimated at participants' residences. Of the 22 189 nurses, 484 developed HF. We detected associations with all 3 pollutants, with hazard ratios (HRs) of 1.17 (95% CI, 1.01-1.36), 1.10 (95% CI, 0.99-1.22), and 1.12 (95% CI, 0.99-1.26) per increase of 5.1 µg/m3 in particulate matter with a diameter <2.5 µm, 8.6 µg/m3 in NO2, and 9.3 dB in road traffic noise, respectively. We observed an enhanced risk of HF incidence for those exposed to high levels of the 3 pollutants; however, the effect modification of coexposure was not statistically significant. Former smokers and nurses with hypertension showed the strongest associations with particulate matter with a diameter <2.5 µm (Peffect modification<0.05). Conclusions We found that long-term exposures to air pollution and road traffic noise were independently associated with HF.

Transportation noise and risk for colorectal cancer: a nationwide study covering Denmark.

PURPOSE: Few studies have suggested that traffic noise is a risk factor for cancer, but evidence is inconclusive. We aimed to investigate whether road traffic and railway noise are associated with risk of colorectal cancer. METHODS: We obtained address history for all 3.5 million people above 40 years of age and living in Denmark for the period 1990-2017 and estimated road traffic and railway noise (Lden) at the most and least exposed facades of all addresses as well as air pollution (PM2.5). During follow-up (2000-2017), 35,881 persons developed colon cancer and 19,755 developed rectal cancer. Information on individual and area-level demographic and socioeconomic variables was collected from Danish registries. We analyzed data using Cox proportional hazards models, including traffic noise as time-varying 10-year average exposure. RESULTS: Exposure to road traffic noise at the most exposed façade was associated with an incidence rate ratio and 95% confidence interval for proximal colon cancer of 1.018 (0.999-1.038) per 10 dB higher noise. We observed no associations for road traffic noise at the least exposed façade or for railway noise in relation to proximal colon cancer. Also, we found no association between road traffic or railway noise and risk for distal colon cancer or rectal cancer. CONCLUSION: Traffic noise did not seem associated with higher risk for colorectal cancer, although the suggestion of a slightly higher risk of proximal colon cancer following exposure to road traffic noise warrants further research.

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 exposure to road traffic noise and incident myocardial infarction: A Danish Nurse Cohort study.

BACKGROUND: Evidence of nonauditory health effects of road traffic noise exposure is growing. This prospective cohort study aimed to estimate the association between long-term exposure to road traffic noise above a threshold and incident myocardial infarction (MI) in Denmark. METHODS: In the Danish Nurse Cohort study, we used data of 22,378 women, at recruitment in 1993 and 1999, who reported information on MI risk factors. The participants' first hospital contact or out-of-hospital death due to MI were followed-up until 2014. We investigated a relationship between residential exposures to road traffic noise levels (Lden) up to 23 years and incident MI (overall, nonfatal, and fatal) using time-varying Cox regression models adjusting for potential confounders and air pollutants. We estimated thresholds of road traffic noise (53, 56, and 58 dB) associated with incident MI in a piece-wise linear regression model. RESULTS: Of the 22,378 participants, 633 developed MI, 502 of which were nonfatal. We observed a non-linear relationship between the 23-year running mean of Lden and incident MI with a threshold level of 56 dB, above which hazard ratios (95% confidence intervals) were 1.30 (0.97, 1.75) for overall and 1.46 (1.05, 2.03) for nonfatal MI per 10 dB. The association with nonfatal MI attenuated slightly to 1.34 (0.95, 1.90) after adjustment for fine particles. CONCLUSIONS: We found that long-term exposure to road traffic noise above 56 dB may increase the risk of MI. The study findings suggest that road traffic noise above 56 dB may need regulation in addition to the regulation of ambient pollutants.

Road and railway noise and risk for breast cancer: A nationwide study covering Denmark.

OBJECTIVE: Previous studies have suggested that transportation noise may increase risk for breast cancer, but existing literature is scarce and inconclusive. We aimed to investigate associations between road traffic and railway noise and risk for breast cancer across the entire Danish female population. METHODS: For all 2.8 million residential addresses across Denmark, we modelled road and railway noise at the most and least exposed façades for the period 1990-2017. We calculated 10-year time-weighted mean noise exposure for 1.8 million women aged >35 years, of whom 66,006 developed breast cancer during follow-up from 2000 to 2017. We analysed data using Cox proportional hazards models with noise exposure included as 10-year running means and adjusted for a number of individual and area-level socioeconomic co-variates and air pollution with fine particles estimated for all addresses. RESULTS: For exposures at the least exposed façade, we found that a 10 dB increase in 10-year time-weighted noise was associated with incidence rate ratios (IRRs) and 95% confidence intervals (CI) for breast cancer of 1.032 (1.019-1.046) for road noise and 1.023 (0.993-1.053) for railway noise. For exposures at the most exposed façade, the IRRs (95% CIs) were 1.012 (1.002-1.022) for road noise and 1.020 (1.001-1.039) for railway noise. Associations were strongest among women with human epidermal growth factor receptor 2 negative breast cancer. CONCLUSIONS: Road traffic and railway noise were associated with higher risk for breast cancer, especially noise at the least exposed façade, which is a proxy for noise exposure during sleep.

The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions.

It is believed that weather conditions such as temperature and humidity have effects on COVID-19 transmission. However, these effects are not clear due to the limited observations and difficulties in separating impact of social distancing. COVID-19 data and social-economic features of 1236 regions in the world (1112 regions at the provincial level and 124 countries with the small land area) were collected. Large-scale satellite data was combined with these data with a regression analysis model to explore the effects of temperature and relative humidity on COVID-19 spreading, as well as the possible transmission risk by seasonal cycles. The result shows that temperature and relative humidity are negatively correlated with COVID-19 transmission throughout the world. Government intervention (e.g. lockdown policies) and lower population movement contributed to decrease the new daily case ratio. Weather conditions are not the decisive factor in COVID-19 transmission, in that government intervention as well as public awareness, could contribute to the mitigation of the spreading of the virus. So, it deserves a dynamic government policy to mitigate COVID-19 transmission in winter.

Comparison of Road Traffic Noise prediction models: CNOSSOS-EU, Nord2000 and TRANEX.

Road traffic noise is the most pervasive source of ambient outdoor noise pollution in Europe. Traffic noise prediction models vary in parameterisation and therefore may produce different estimates of noise levels depending on the geographical setting in terms of emissions sources and propagation field. This paper compares three such models: the European standard, Common Noise Assessment Methods for the EU Member States (hereafter, CNOSSOS), Nord2000 and Traffic Noise Exposure (TRANEX) model based on the UK methodology, in terms of their source and propagation characteristics. The tools are also compared by analysing estimated noise (LAeq) from CNOSSOS, Nord2000 (2006 version), and TRANEX for more than one hundred test cases (N = 111) covering a variety of source and receiver configurations (e.g. varying source to receiver distance). The main aim of this approach was to investigate the potential pattern in differences between models' performance for certain types of configurations. Discrepancies in performance may thus be linked to the differences in parameterisations of the CNOSSOS, Nord2000, and TRANEX (e.g. handling of diffraction, refraction). In most cases, both CNOSSOS and TRANEX reproduced LAeq levels of Nord2000 (2006 version) within three to five dBA (CNOSSOS: 87%, TRANEX: 94%). The differences in LAeq levels of CNOSSOS, compared to Nord2000, can be related to several shortcomings of the existing CNOSSOS algorithms (e.g. ground attenuation, multiple diffractions, and mean ground plane). The analyses show that more research is required in order to improve CNOSSOS for its implementation in the EU. In this context, amendments for CNOSSOS proposed by an EU Working Group hold significant potential. Overall, both CNOSSOS and TRANEX produced similar results, with TRANEX reproducing Nord2000 LAeq values slightly better than the CNOSSOS. The lack of measured noise data highlights one of the significant limitations of this study and needs to be addressed in future work.

Evaluation of OSPM against air quality measurements in Brazil - the case study of Fortaleza, Ceará.

The contribution of vehicle emissions to air pollution is considered a large environmental and health problem in big Brazilian cities caused, among other factors, by slow renewal of the old vehicle fleet. Brazilian studies usually only consider traffic-related issues in transportation analysis, with minor assessments of emissions and close to non-existent assessment of air quality. On this background, this research aimed to calibrate and evaluate the Operational Street Pollution Model (OSPM®) to Brazilian conditions by implementing Brazilian emission factors. The urban background concentrations were modeled with the Urban Background Model (UBM) as part of the air quality system (THOR-AirPAS). In this case, we used meteorological data from a ground meteorological station outside Fortaleza processed by meteorological pre-processor and regional background concentrations from the Integrated Forecast System (IFS) as input to UBM. New air quality measurements were collected in busy streets of the city of Fortaleza during the year of 2017. The study collected samples of daily NO2 and PM10 concentrations to evaluate OSPM daily estimations. In addition, a transportation travel demand model (TRANUS) has been calibrated to the case study area with observed traffic data collected, in order to provide Annual Average Daily Traffic (AADT) as inputs to OSPM®. Two sets of emission factors were evaluated. Official Brazilian emission factors were applied to OSPM®, as well as adjusted emission factors derived in the project based on calibration that were higher than the official emission factors. Data showed that concentrations are significantly influenced by meteorological factors (such as temperature, wind speeds, wind directions), and especially precipitation for PM10 concentrations. OSPM® simulated results showed concentration levels and patterns close to air quality measurements with default emission factors and calibrated emission for UBM but large underestimations if official emissions were used for both UBM and OSPM. Implications: Busy urban streets in Brazilian cities with intense flow of diesel vehicles (such as buses and trucks) can significantly increase air pollution, especially for NO2 and PM10. With OSPM calibrated and evaluated to Brazilian conditions, the model system can be used by authorities to assess the impact of policy measures, such as vehicle access restrictions in Low Emission Zones, in order to consider not only traffic related issues, but also air pollution due to mobile sources with outdated emission technologies.

Long-term exposure to low levels of air pollution and mortality adjusting for road traffic noise: A Danish Nurse Cohort study.

BACKGROUND: The association between air pollution and mortality is well established, yet some uncertainties remain: there are few studies that account for road traffic noise exposure or that consider in detail the shape of the exposure-response function for cause-specific mortality outcomes, especially at low-levels of exposure. OBJECTIVES: We examined the association between long-term exposure to particulate matter [(PM) with a diameter of <2.5 µm (PM2.5), <10 µm (PM10)], and nitrogen dioxide (NO2) and total and cause-specific mortality, accounting for road traffic noise. METHODS: We used data on 24,541 females (age > 44 years) from the Danish Nurse Cohort, who were recruited in 1993 or 1999, and linked to the Danish Causes of Death Register for follow-up on date of death and its cause, until the end of 2013. Annual mean concentrations of PM2.5, PM10, and NO2 at the participants' residences since 1990 were estimated using the Danish DEHM/UBM/AirGIS dispersion model, and annual mean road traffic noise levels (Lden) were estimated using the Nord2000 model. We examined associations between the three-year running mean of PM2.5, PM10, and NO2 with total and cause-specific mortality by using time-varying Cox Regression models, adjusting for individual characteristics and residential road traffic noise. RESULTS: During the study period, 3,708 nurses died: 843 from cardiovascular disease (CVD), 310 from respiratory disease (RD), and 64 from diabetes. In the fully adjusted models, including road traffic noise, we detected associations of three-year running mean of PM2.5 with total (hazard ratio; 95% confidence interval: 1.06; 1.01-1.11), CVD (1.14; 1.03-1.26), and diabetes mortality (1.41; 1.05-1.90), per interquartile range of 4.39 µg/m3. In a subset of the cohort exposed to PM2.5 < 20 µg/m3, we found even stronger association with total (1.19; 1.11-1.27), CVD (1.27; 1.01-1.46), RD (1.27; 1.00-1.60), and diabetes mortality (1.44; 0.83-2.48). We found similar associations with PM10 and none with NO2. All associations were robust to adjustment for road traffic noise. DISCUSSION: Long-term exposure to low-levels of PM2.5 and PM10 is associated with total mortality, and mortality from CVD, RD, and diabetes. Associations were even stronger at the PM2.5 levels below EU limit values and were independent of road traffic noise.

Road Traffic Noise Exposure and Filled Prescriptions for Antihypertensive Medication: A Danish Cohort Study.

BACKGROUND: Epidemiological research on effects of transportation noise on incident hypertension is inconsistent. OBJECTIVES: We aimed to investigate whether residential road traffic noise increases the risk for hypertension. METHODS: In a population-based cohort of 57,053 individuals 50-64 years of age at enrollment, we identified 21,241 individuals who fulfilled our case definition of filling ≥2 prescriptions and ≥180 defined daily doses of antihypertensive drugs (AHTs) within a year, during a mean follow-up time of 14.0 y. Residential addresses from 1987 to 2016 were obtained from national registers, and road traffic noise at the most exposed façade as well as the least exposed façade was modeled for all addresses. Analyses were conducted using Cox proportional hazards models. RESULTS: We found no associations between the 10-y mean exposure to road traffic noise and filled prescriptions for AHTs, with incidence rate ratios (IRRs) of 0.999 [95% confidence intervals (CI): 0.980, 1.019)] per 10-dB increase in road traffic noise at the most exposed façade and of 1.001 (95% CI: 0.977, 1.026) at the least exposed façade. Interaction analyses suggested an association with road traffic noise at the least exposed façade among subpopulations of current smokers and obese individuals. CONCLUSION: The present study does not support an association between road traffic noise and filled prescriptions for AHTs. https://doi.org/10.1289/EHP6273.

The spatial relationship between traffic-related air pollution and noise in two Danish cities: Implications for health-related studies.

Air pollution and noise originating from urban road traffic have been linked to the adverse health effects e.g. cardiovascular disease (CVD), although their generation and propagation mechanisms vary. We aimed to (i) develop a tool to model exposures to air pollution and noise using harmonized inputs based on similar geographical structure (ii) explore the relationship (using Spearman's rank correlation) of both pollutions at residential exposure level (iii) investigate the influence of traffic speed and Annual Average Daily Traffic (AADT) on air-noise relationship. The annual average (2005) air pollution (NOx, NO2, PM10, PM2.5) and noise levels (Lday, Leve, Lnight, Lden, LAeq,24h) are modelled at address locations in Copenhagen and Roskilde (N = 11,000 and 1500). The new AirGIS system together with the Operational Street Pollution Model (OSPM®) is used to produce air pollution estimates. Whereas, noise is estimated using Common Noise Assessment Methods in the EU (CNOSSOS-EU, hereafter CNOSSOS) with relatively coarser inputs (100 m CORINE land cover, simplified vehicle composition). In addition, noise estimates (Lday, Leve, Lnight) from CNOSSOS are also compared with noise estimates from Road Traffic Noise 1996 (RTN-96, one of the Nordic noise prediction standards). The overall air-noise correlation structure varied significantly in the range |rS| = 0.01-0.42, which was mainly affected by the background concentrations of air pollution as well as non-traffic emission sources. Moreover, neither AADT nor traffic speed showed substantial influence on the air-noise relationship. The noise levels estimated by CNOSSOS were substantially lower, and showed much lower variation than levels obtained by RTN-96. CNOSSOS, therefore, needs to be further evaluated using more detailed inputs (e.g. 10 m land cover polygons) to assess its feasibility for epidemiological noise exposure studies in Denmark. Lower to moderate air-noise correlations point towards significant potential to determine the independent health effects of air pollution and noise.