Placeholder image for staff profiles

Professor Matthias Ketzel


Visiting Professor (Senior Scientist, Aarhus University)

My publications

Publications

Valencia Victor H., Hertel Ole, Ketzel Matthias, Levin Gregor (2019) Modeling urban background air pollution in Quito, Ecuador, Atmospheric Pollution Research 11 (4) pp. 646-666 Elsevier
This study estimates air pollution at urban background level for Quito, Ecuador, using the Urban Background Model (UBM) developed at Aarhus University, Denmark. Hourly concentrations of CO, NO2, NOx, O3, PM2.5 and SO2 were calculated for the year 2009. UBM performance is evaluated at six monitoring locations. The air pollution emission inventory was scaled, using calibration factors, until modeled concentrations were in line with observations. Predicted values were graphically and statistically evaluated by comparison to measurements. The statistical assessment is conducted for: Fraction of predictions within a factor of two of the observations (FAC2), Fractional mean bias (FB), Normalized mean-square error (NMSE) and Normalized absolute difference (NAD). Results show that the UBM model successfully predicts concentrations of CO, NO2, NOx, O3 and PM2.5 while the predicted SO2 concentrations are unsatisfactory. PM2.5 modeling meets the criteria of acceptance, but their results depend largely on the regional levels, so the quality of this information is extremely relevant. The UBM model was applied for the years 2008 and 2010 using meteorological data retrieved from the modeling sites with emissions and calibration factors derived for the year 2009, showing a performance similar to that of 2009. The findings confirm the applicability of UBM to predict air pollution at the urban background level in Quito. Satisfactory results are obtained by applying meteorological data derived from any of the available monitoring stations. The unsatisfactory results for SO2 suggest that emission data should be reviewed and that this cannot be obtained simply by scaling.
Hvidtfeldt Ulla Arthur, Sørensen Mette, Geels Camilla, Ketzel Matthias, Khan Jibran, Tjønneland Anne, Overvad Kim, Brandt Jørgen, Raaschou-Nielsen Ole (2019) Long-term residential exposure to PM2.5, PM10, black carbon, NO2, and ozone and mortality in a Danish cohort, Environment International 123 pp. 265-272 Elsevier
Air pollutants such as NO2 and PM2.5 have consistently been linked to mortality, but only few previous studies
have addressed associations with long-term exposure to black carbon (BC) and ozone (O3).
We investigated the association between PM2.5, PM10, BC, NO2, and O3 and mortality in a Danish cohort of
49,564 individuals who were followed up from enrollment in 1993?1997 through 2015. Residential address
history from 1979 onwards was combined with air pollution exposure obtained by the state-of-the-art, validated,
THOR/AirGIS air pollution modelling system, and information on residential traffic noise exposure, lifestyle and
socio-demography.
We observed higher risks of all-cause as well as cardiovascular disease (CVD) mortality with higher long-term
exposure to PM2.5, PM10, BC, and NO2. For PM2.5 and CVD mortality, a hazard ratio (HR) of 1.29 (95% CI:
1.13?1.47) per 5 ¼g/m3 was observed, and correspondingly HRs of 1.16 (95% CI: 1.05?1.27) and 1.11 (95% CI:
1.04?1.17) were observed for BC (per 1 ¼g/m3) and NO2 (per 10 ¼g/m3), respectively. Adjustment for noise gave
slightly lower estimates for the air pollutants and CVD mortality. Inverse relationships were observed for O3.
None of the investigated air pollutants were related to risk of respiratory mortality. Stratified analyses suggested
that the elevated risks of CVD and all-cause mortality in relation to long-term PM, NO2 and BC exposure were
restricted to males.
This study supports a role of PM, BC, and NO2 in all-cause and CVD mortality independent of road traffic
noise exposure.
Chen Jie, de Hoogh Kees, Gulliver John, Hoffmann Barbara, Hertel Ole, Ketzel Matthias, Bauwelinck Mariska, van Donkelaar Aaron, Hvidtfeldt Ulla A., Katsouyanni Klea, Janssen Nicole A.H., Martin Randall V., Samoli Evangelia, Schwartz Per E., Stafoggia Massimo, Bellander Tom, Strak Maciek, Wolf Kathrin, Vienneau Danielle, Vermeulen Roel, Brunekreef Bert, Hoek Gerard (2019) A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide, Environment International 130 104934 Elsevier

Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.

This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.

For PM2.5, the models performed similarly across algorithms with a mean CV R² of 0.59 and a mean EV R² of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R²~0.63; EV R² 0.58?0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R² 0.48?0.57; EV R² 0.39?0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R²/>/0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R²s ranging from 0.57 to 0.62, and EV R²s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R²/>/0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.

Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

Poulsen Aslak Harbo, Raaschou-Nielsen Ole, Peña Alfredo, Hahmann Andrea N., Nordsborg Rikke Baastrup, Ketzel Matthias, Brandt Jørgen, Sørensen Mette (2019) Impact of Long-Term Exposure to Wind Turbine Noise on Redemption of Sleep Medication and Antidepressants: A Nationwide Cohort Study, Environmental Health Perspectives 127 (3) 037005 pp. 037005-1 - 037005-9 National Institute of Environmental Health Sciences (NIEHS)

Background:
Noise from wind turbines (WTs) is associated with annoyance and, potentially, sleep disturbances.

Objectives:
Our objective was to investigate whether long-term WT noise (WTN) exposure is associated with the redemption of prescriptions for sleep medication and antidepressants.

Methods:
For all Danish dwellings within a radius of 20-WT heights and for 25% of randomly selected dwellings within a radius of 20-to 40-WT heights, we estimated nighttime outdoor and low-frequency (LF) indoor WTN, using information on WT type and simulated hourly wind. During follow-up from 1996 to 2013, 68,696 adults redeemed sleep medication and 82,373 redeemed antidepressants, from eligible populations of 583,968 and 584,891, respectively. We used Poisson regression with adjustment for individual and area-level covariates.

Results:
Five-year mean outdoor nighttime WTN of e42 dB was associated with a hazard ratio (HR) = 1.14 [95% confidence interval (CI]: 0.98, 1.33) for sleep medication and HR = 1.17 (95% CI: 1.01, 1.35) for antidepressants (compared with exposure to WTN of Â24 dB). We found no overall association with indoor nighttime LF WTN. In age-stratified analyses, the association with outdoor nighttime WTN was strongest among persons e65y of age, with HRs (95% CIs) for the highest exposure group (e42 dB) of 1.68 (1.27, 2.21) for sleep medication and 1.23 (0.90, 1.69) for antidepressants. For indoor nighttime LF WTN, the HRs (95% CIs) among persons e65y of age exposed to e15 dB were 1.37 (0.81, 2.31) for sleep medication and 1.34 (0.80, 2.22) for antidepressants.

Conclusions:
We observed high levels of outdoor WTN to be associated with redemption of sleep medication and antidepressants among the elderly, suggesting that WTN may potentially be associated with sleep and mental health.

Poulsen Aslak Harbo, Raaschou-Nielsen Ole, Peña Alfredo, Hahmann Andrea N., Nordsborg Rikke Baastrup, Ketzel Matthias, Brandt Jørgen, Sørensen Mette (2019) Long-Term Exposure to Wind Turbine Noise and Risk for Myocardial Infarction and Stroke: A Nationwide Cohort Study, Environmental Health Perspectives 127 (3) 037004 pp. 037004-1 National Institute of Environmental Health Sciences (NIEHS)

Background:
Noise from wind turbines (WTs) is reported as more annoying than traffic noise at similar levels, raising concerns as to whether WT noise (WTN) increases risk for cardiovascular disease, as observed for traffic noise.

Objectives:
We aimed to investigate whether long-term exposure to WTN increases risk of myocardial infarction (MI) and stroke.

Methods:
We identified all Danish dwellings within a radius 20 times the height of the closest WT and 25% of the dwellings within 20?40 times the height of the closest WT. Using data on WT type and simulated hourly wind at each WT, we estimated hourly outdoor and low frequency (LF) indoor WTN for each dwelling and derived 1-y and 5-y running nighttime averages. We used hospital and mortality registries to identify all incident cases of MI (n=19,145) and stroke (n=18,064) among all adults age 25?85 y (n=717,453), who lived in one of these dwellings for eone year over the period 1982?2013. We used Poisson regression to estimate incidence rate ratios (IRRs) adjusted for individual- and area-level covariates.

Results:
IRRs for MI in association with 5-y nighttime outdoor WTN Ã42 (vs. Â24) dB(A) and indoor LF WTN Ã15 (vs. Â5) dB(A) were 1.21 [95% confidence interval (CI): 0.91, 1.62; 47 exposed cases] and 1.29 (95% CI: 0.73, 2.28; 12 exposed cases), respectively. IRRs for intermediate categories of outdoor WTN [24?30, 30?36, and 36?42 dB(A) vs. Â24 dB(A)] were slightly above the null and of similar size: 1.08 (95% CI: 1.04, 1.12), 1.07 (95% CI: 1.00, 1.12), and 1.06 (95% CI: 0.93, 1.22), respectively. For stroke, IRRs for the second and third outdoor exposure groups were similar to those for MI, but near or below the null for higher exposures.

Conclusions:
We did not find convincing evidence of associations between WTN and MI or stroke.

Cramer Johannah, Therming Jørgensen Jeanette, Sørensen Mette, Backalarz Claus, Laursen Jens Elgaard, Ketzel Matthias, Hertel Ole, Jensen Steen Solvang, Simonsen Mette Kildevæld, Bräuner Elvira Vaclavik, Andersen Zorana Jovanovic (2019) Road traffic noise and markers of adiposity in the Danish Nurse Cohort: A cross-sectional study, Environmental Research 172 pp. 502-510 Elsevier

Background

Studies have suggested that traffic noise is associated with markers of obesity. We investigated the association of exposure to road traffic noise with body mass index (BMI) and waist circumference in the Danish Nurse Cohort.

Methods

We used data on 15,501 female nurses (aged >44 years) from the nationwide Danish Nurse Cohort who, in 1999, reported information on self-measured height, weight, and waist circumference, together with information on socioeconomic status, lifestyle, work and health. Road traffic noise at the most exposed façade of the residence was estimated using Nord2000 as the annual mean of a weighted 24-h average (Lden). We used multiple linear regression models to examine associations of road traffic noise levels in 1999 (1-year mean) with BMI and waist circumference, adjusting for potential confounders, and evaluated effect modification by degree of urbanization, air pollution levels, night shift work, job strain, sedative use, sleep aid use, and family history of obesity.

Results

We did not observe associations between road traffic noise (per 10/dB increase in the 1-year mean Lden) and BMI (kg/m2) (²: 0.00; 95% confidence interval (CI): ?0.07, 0.07) or waist circumference (cm) (²: ?0.09; 95% CI: ?0.31, 0.31) in the fully adjusted model. We found significant effect modification of job strain and degree of urbanization on the associations between Lden and both BMI and waist circumference. Job strained nurses were associated with a 0.41 BMI-point increase, (95% CI: 0.06, 0.76) and a 1.00/cm increase in waist circumference (95% CI: 0.00, 2.00). Nurses living in urban areas had a statistically significant positive association of Lden with BMI (²: 0.26; 95% CI: 0.11, 0.42), whilst no association was found for nurses living in suburban and rural areas.

Conclusion

Our results suggest that road traffic noise exposure in nurses with particular susceptibilities, such as those with job strain, or living in urban areas, may lead to increased BMI, a marker of adiposity.

Stojiljkovic An, Kauhaniemi Mari, Kukkonen Jaakko, Kupiainen Kaarle, Karppinen Ari, Denby Bruce Rolstad, Kousa Anu, Niemi Jarkko V., Ketzel Matthias (2019) The impact of measures to reduce ambient air PM10 concentrations originating from road dust, evaluated for a street canyon in Helsinki, Atmospheric Chemistry and Physics 19 (17) pp. 11199-11212 Copernicus Publications
We have numerically evaluated how effective selected potential measures would be for reducing the impact of road dust on ambient air particulate matter (PM10). The selected measures included a reduction of the use of studded tyres on light-duty vehicles and a reduction of the use of salt or sand for traction control. We have evaluated these measures for a street canyon located in central Helsinki
for four years (2007?2009 and 2014). Air quality measurements were conducted in the street canyon for two years, 2009 and 2014. Two road dust emission models, NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and FORE (Forecasting Of Road dust Emissions), were applied
in combination with the Operational Street Pollution Model (OSPM), a street canyon dispersion model, to compute the street increments of PM10 (i.e. the fraction of PM10 concentration originating from traffic emissions at the street level) within the street canyon. The predicted concentrations were compared with the air quality measurements. Both road dust emission models reproduced the seasonal variability of the PM10 concentrations fairly well but under-predicted the annual mean values. It was found that the largest reductions of concentrations could potentially be achieved by reducing the fraction of vehicles that use studded tyres. For instance, a 30% decrease in the number of vehicles using studded tyres would result in an average decrease in the non-exhaust street increment of PM10 from 10% to 22 %, depending on the model used and the year considered. Modelled contributions of traction sand and salt to the annual mean non-exhaust street increment of PM10 ranged from 4% to 20% for the traction sand and from 0.1% to 4% for the traction salt. The results presented here can be used to support the development of optimal strategies for reducing high springtime particulate matter concentrations originating from road dust.
Ottosen Thor-Bjørn, Ketzel Matthias, Skov Henrik, Hertel Ole, Brandt Jørgen, Kakosimos Konstantinos E. (2019) Micro-scale modelling of the urban wind speed for air pollution applications, Scientific Reports 9 (1) Nature Publishing Group
Modelling wind speeds in urban areas have many applications e.g. in relation to assessment of wind
energy, modelling air pollution, and building design and engineering. Models for extrapolating the
urban wind speed exist, but little attention has been paid to the influence of the upwind terrain and
the foundations for the extrapolation schemes. To analyse the influence of the upwind terrain and the
foundations for the extrapolation of the urban wind speed, measurements from six urban and nonurban
stations were explored, and a model for the urban wind speed with and without upwind influence
was developed and validated. The agreement between the wind directions at the stations is found to be
good, and the influence of atmospheric stability, horizontal temperature gradients, land-sea breeze,
temperature, global radiation and Monin-Obukhov Length is found to be small, although future work
should explore if this is valid for other urban areas. Moreover, the model is found to perform reasonably
well, but the upwind influence is overestimated. Areas of model improvement are thus identified.
The upwind terrain thus influences the modelling of the urban wind speed to a large extent, and the
fundamental assumptions for the extrapolation scheme are fulfilled for this specific case.
Pedersen Marie, Halldorsson Thorhallur I., Ketzel Matthias, Grandström Charlotta, Raaschou-Nielsen Ole, Jensen Steen S., Grunnet Louise G., Vaag Allan, Sørensen Mette, Olsen Sjurdur F. (2019) Associations between ambient air pollution and noise from road traffic with blood pressure and insulin resistance in children from Denmark, Environmental Epidemiology 3 (5) e069 pp. 1-7 Wolters Kluwer

Background: Road traffic is a major source of air pollution and noise. Both exposures may contribute to increased blood pressure and metabolic disease; however, few studies have examined these relationships in children.

Objectives: We aimed to investigate whether long-term exposures to air pollution and noise from road traffic were associated with increased blood pressure and insulin resistance in children.

Methods: Cardiometabolic outcomes were derived from a follow-up examination of 629 children (10?15 years old) enrolled in the Danish National Birth Cohort. We evaluated associations with prenatal and postnatal residential exposure to nitrogen dioxide (NO2) and noise from road traffic (Lden) using historical addresses and linear regression models.

Results: A 10-unit increase in postnatal exposure to NO2 and Lden was associated with a 0.31 (?0.87, 1.48) and 0.18 (?0.61, 0.96) mmHg changes in diastolic blood pressure, respectively. In contrast, both exposures were associated with decreased systolic blood pressure. After adjustment and mutual adjustment for NO2, exposure to Lden was associated with a statistical significant decrease in systolic blood pressure both during prenatal and postnatal life, but the majority of the associations evaluated did not reach statistical significance. Inverse associations were observed for plasma fasting glucose, insulin, and HOMA of insulin resistance for both exposures, exposure windows, before and after adjustment.

Conclusions: The findings do not support evidence of associations between long-term exposures to air pollution and road traffic noise, increased blood pressure, and a metabolic profile characteristic of increased risk for glucose intolerance or type 2 diabetes later in life.

Hvidtfeldt Ulla Arthur, Geels Camilla, Sørensen Mette, Ketzel Matthias, Khan Jibran, Tjønneland Anne, Christensen Jesper Heile, Brandt Jørgen, Raaschou-Nielsen Ole (2019) Long-term residential exposure to PM2.5 constituents and mortality in a Danish cohort, Environment International 133 105268 pp. 1-8 Elsevier

Studies on health effects of long-term exposure to specific PM2.5 constituents are few. Previous studies have reported an association between black carbon (BC) exposure and cardiovascular diseases (CVD) and a few studies have found an association between sulfate exposure and mortality. These studies, however, relied mainly on exposure data from centrally located air-monitoring stations, which is a crude approximation of personal exposure.

We focused on specific chemical constituents of PM2.5, i.e. elemental and primary organic carbonaceous particles (BC/OC), sea salt, secondary inorganic aerosols (SIA, i.e. NO3?, NH4+, and SO42-), and secondary organic aerosols (SOA), in relation to all-cause, CVD and respiratory disease mortality.

We followed a Danish cohort of 49,564 individuals from enrollment in 1993?1997 through 2015. We combined residential address history from 1979 onwards with mean annual air pollution concentrations obtained by the AirGIS air pollution modelling system, lifestyle information from baseline questionnaires and socio-demography obtained by register linkage.

During 895,897 person-years of follow-up, 10,193 deaths from all causes occurred ? of which 2319 were CVD-related and 870 were related to respiratory disease. The 15-year time-weighted average concentrations of PM2.5, BC/OC, sea salt, SIA and SOA were 13.8, 2.8, 3.4, 4.9, and 0.3/µg/m3, respectively. For all-cause mortality, a higher risk was observed with higher exposure to PM2.5, BC/OC and SOA with adjusted hazard ratios of 1.03 (95% confidence intervals: 1.01, 1.05), 1.06 (1.03, 1.09), and 1.08 (1.03, 1.13) per interquartile range, respectively. The associations for BC/OC and SOA remained after adjustment for PM2.5 in two-pollutant models. For CVD mortality, we observed elevated risks with higher exposure to PM2.5, BC/OC and SIA. The results showed no clear relationship between sea salt and mortality.

In this study, we observed a relationship between long-term exposure to PM2.5, BC/OC, and SOA and all-cause mortality and between PM2.5, BC/OC, and SIA and CVD mortality.

Taj Tahir, Poulsen Aslak Harbo, Ketzel Matthias, Geels Camilla, Brandt Jørgen, Christensen Jesper Heile, Puett Robin, Hvidtfeldt Ulla Arthur, Sørensen Mette, Raaschou?Nielsen Ole (2020) Long?Term Exposure to Air Pollution and Risk of Non?Hodgkin lymphoma in Denmark: A Population?based Case?Control Study, International Journal of Cancer Wiley
There is limited evidence regarding a possible association between exposure to ambient air pollutants and the risk of non?Hodgkin lymphoma (NHL). Previous epidemiological studies have relied on crude estimations for air pollution exposure and/or small numbers of NHL cases. The objective of our study was to analyze this association based on air pollution modeled at the address level and NHL cases identified from the nationwide Danish Cancer Registry. We identified 20,874 incident NHL cases diagnosed between 1989 and 2014 and randomly selected 41,749 controls matched on age and gender among the entire Danish population. We used conditional logistic regression to estimate odds ratios (ORs) and adjusted for individual and neighborhood level sociodemographic variables. There was no association between exposure to PM2.5, BC, O3, SO2 or NO2 and overall risk of NHL but several air pollutants were associated with higher risk of follicular lymphoma, but statistically insignificant, for example, PM2.5 (OR = 1.15 per 5 ¼g/m3; 95% CI: 0.98?1.34) and lower risk for diffuse large B?cell lymphoma (OR = 0.92 per 5 ¼g/m3; 95% CI: 0.82?1.03). In this population?based study, we did not observe any convincing evidence of a higher overall risk for NHL with higher exposure to ambient air pollutants.
Thacher Jesse D., Poulsen Aslak H., Roswall Nina, Hvidtfeldt Ulla, Raaschou-Nielsen Ole, Solvang Jensen Steen, Ketzel Matthias, Brandt Jorgen, Overvad Kim, Tjonneland Anne, Munzel Thomas, Sorensen Mette (2020) Road Traffic Noise Exposure and Filled Prescriptions for Antihypertensive Medication: A Danish Cohort Study, Environmental Health Perspectives 128 (5) National Institute of Environmental Health Sciences
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 e2
prescriptions and e180 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

Khan Jibran, Kakosimos Konstantinos, Jensen Steen Solvang, Hertel Ole, Sørensen Mette, Gulliver John, Ketzel Matthias (2020) The spatial relationship between traffic-related air pollution and noise in two Danish cities: Implications for health-related studies, Science of The Total Environment 726 138577 Elsevier
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.