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Introduction: Recently, epidemiological studies have found a link between air pollution (AP) and individual components of the metabolic syndrome (MetS), a condition predisposing to cardiometabolic diseases. However, very few studies have explored a possible association between air pollution and MetS. Objective: We analyzed the effects of long-term exposure to airborne particulate matter and NO2 on prevalence and incidence of MetS. Methods: We used data of the population-based prospective Heinz Nixdorf Recall study (baseline 2000–2003) to investigate the association(s) between AP exposure and MetS prevalence at baseline (n = 4457) and MetS incidence at first follow-up visit (n = 3074; average follow-up: 5.1 years). Mean annual exposure to size-fractioned particulate matter (PM10, PM2.5, PMcoarse, and PM2.5abs) and nitrogen dioxide (NO2) was assessed using a land use regression model. MetS was defined as central obesity plus two out of four additional risk factors (i.e., elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure or elevated plasma glucose). We estimated odds ratios (ORs) of MetS prevalence and incidence per interquartile range (IQR) of exposure, adjusting for demographic and lifestyle variables. Results: We observed a MetS prevalence of 20.7% (n = 922) and an incidence of 9.7% (n = 299). NO2 was positively associated with MetS prevalence, with an OR increase per IQR of 1.12 (95%-CI 1.02–1.24, IQR = 6.1 μg/m3). PM10 and PM2.5 were both borderline positively associated with MetS incidence, with ORs of 1.14 (95%-CI 0.99–1.32, IQR = 2.1 μg/m3) and 1.19 (95%-CI 0.98–1.44, IQR = 1.5 μg/m3) per IQR, respectively. Conclusion: In summary, we found a weak positive association between air pollution and MetS. The strongest and most consistent effects were observed between NO2 and prevalent MetS. Keywords: Air pollution, Particulate matter, NO2, Metabolic syndrome, Noise, Epidemiology

Investigations of adverse effects of air pollution (AP) and ambient noise on cognitive functions are apparently scarce, and findings seem to be inconsistent. The aim of this study was to examine the associations of long-term exposure to AP and traffic noise with cognitive performance. At the second examination of the population-based Heinz Nixdorf Recall study (2006–2008), cognitive performance was evaluated in 4086 participants. Long-term residential exposure to size-specific particulate matter (PM) and nitrogen oxides (NOx) with land use regression, to and traffic noise (weighted 24-h (LDEN) and nighttime (LNIGHT) means), was assessed according to the European Union (EU) Directive 2002/49/EC. Multiple regression models were calculated for the relationship of environmental exposures with a global cognitive score (GCS) and in five cognitive subtests, using single- and two-exposure models. In fully adjusted models, several AP metrics were negatively associated with four of five subtests and with GC...

Road traffic is one of the main causes of poor air quality in European cities. The air pollution burden due to road traffic in a street canyon consists of shares from local traffic and contributions of vehicles driving elsewhere in the city as well as elsewhere on a larger scale. Are electric vehicles a solution for air quality problems in cities? Do they reduce noise levels in street canyons significantly? The aim of this sensitivity study is to investigate the regional and local effects of electric vehicles on noise and air quality taking possible effects of additional electricity production into account. Focus of the present study lies for air quality on the regional scale in North Rhine-Westphalia and the overall effect in some selected street canyons, to be more precise the annual average PM10 and NO2 concentrations. A sensitivity study using the chemistry transport model EURAD and a screening model for street canyons was carried out. The influence on noise levels was analysed based on measurements of vehicles at different speeds. It turns out that road traffic has a significant impact on the regional air pollution levels. Furthermore it is shown that the reduction potential is bigger for NO2 than for PM10. With regard to EC limit value compliance a major share of electric vehicles could be a solution for the NO2 problems in moderately polluted street canyons. One of our findings is that if the additional electricity need causes additional emissions these counteract the possible reduction, especially for PM10. The noise reduction potential of electric vehicles is only significant for vehicles moving at low speeds.

Background/aim Prior studies have shown possible links between medium-term air pollution (AP) exposure and glucose metabolism markers. We investigated whether associations between accumulation mode particle number concentration (PN AM ) and glucose metabolism measures are robust to adjustment for other potential co-pollutants. Methods This analysis included observations from non-diabetic participants (n obs =7,108) of the population-based Heinz Nixdorf Recall study at baseline (2000–2003) and follow-up examination (2006–2008). Particulate matter (PM), accumulation mode particle number (PN AM ), and NO 2 exposures were estimated for each examination at every participant’s residence using the spatiotemporal EURopean Air pollution Dispersion (EURAD) chemistry transport model. Associations between a range of short- and medium-term PN AM exposures (1- to 182 day average prior to blood draw) and glucose metabolism measures (blood glucose, HbA1c) were assessed for robustness to co-adjustment for PM 2.5 , PM 10 , and NO 2 using a mixed effects linear regression model Results PN AM exposure showed low to medium correlation with PM 2.5 , PM 10 , and NO 2 (e.g., 0.33–0.53 for 28 day exposures). In the model with only PN AM and blood glucose, associations were seen for 14- to 91 day mean exposures windows with strongest associations observed for the 60 day window (1.05 mg/dL per 1580 n/mL (95% CI: 0.46 to 1.63)). Co-adjustment for PM 2.5 attenuated associations at all time points (e.g., 0.67 mg/dL per 1580 n/mL (95% CI: −0.22 to 1.55) for the 60 day window) whereas associations for PN AM remained largely unchanged upon adjustment for PM 10 and NO 2 . For HbA1c, strongest associations were apparent for the 105 day exposure window (0.096 p.p. per 1257 n/mL (95% CI: 0.075 to 0.116)). HbA1c model estimates were largely unchanged upon adjustment for co-pollutants. Conclusion The associations between exposure to PN AM and glucose metabolism measures were robust to adjustment for co-pollutants, possibly suggesting that exposure to small particles plays an independent role in influencing glucose regulation.

An ensemble of meteorological simulations with the WRF model at convection-allowing resolution (2 km) is analysed in a multi-variable evaluation framework over Europe. Besides temperature and precipitation, utilized variables are relative humidity, boundary layer height, shortwave radiation, wind speed, convective and large-scale precipitation in view of explaining some of the biases. Furthermore, the forecast skill of evapotranspiration and irrigation water need is ultimately assessed. It is found that the modelled temperature exhibits a small but significant negative bias during the cold period in the snow-covered northeast regions. Total precipitation exhibits positive bias during all seasons but autumn, peaking in the spring months. The varying physics configurations resulted in significant differences for the simulated minimum temperature, summer rainfall, relative humidity, solar radiation and planetary boundary layer height. The interaction of the temperature and moisture profiles with the different microphysics schemes, results in excess convective precipitation using MYJ/WSM6 compared to YSU/Thompson. With respect to evapotranspiration and irrigation need, the errors using the MYJ configuration were in opposite directions and eventually cancel out, producing overall smaller biases. WRF was able to dynamically downscale global forecast data into finer resolutions in space and time for hydro-meteorological applications such as the irrigation management. Its skill was sensitive to the geographical location and physical configuration, driven by the variable relative importance of evapotranspiration and rainfall.

Background: Despite the importance of understanding the connection between air pollution exposure and diabetes, studies investigating links between air pollution and glucose metabolism in nondiabetic adults are limited. Objective: We aimed to estimate the association of medium-term air pollution exposures with blood glucose and glycated hemoglobin A1c (HbA1c) among nondiabetics. Methods: This study included observations from nondiabetic participants (nobs=7,108) of the population-based Heinz Nixdorf Recall study at baseline (2000–2003) and follow-up examination (2006–2008). Daily fine particulate matter (aerodynamic diameter≤2.5 μm, PM2.5; aerodynamic diameter≤10 μm, PM10), accumulation mode particle number (PNAM), and nitrogen dioxide (NO2) exposures were estimated at participants’ residences using the spatiotemporal European Air Pollution Dispersion (EURAD) chemistry transport model. We evaluated the associations between medium-term air pollution exposures (28- and 91-d means) and glucose metabolism measures using mixed linear regression and adjusting for season, meteorology, and personal characteristics. A range of other exposure windows (1-, 2-, 3-, 7-, 14-, 45-, 60-, 75-, 105-, 120-, and 182-d means) were also evaluated to identify potentially relevant biological windows. Results: We observed positive associations between PM2.5 and PNAM exposures and blood glucose levels [e.g., 28-d PM2.5: 0.91 mg/dL (95% CI: 0.38, 1.44) per 5.7 μg/m3]. PM2.5, PM10, and PNAM exposures were positively associated with HbA1c [e.g., 91-d PM2.5: 0.07 p.p. (95% CI: 0.04, 0.10) per 4.0 μg/m3]. Mean exposures during longer exposure windows (75- to 105-d) were most strongly associated with HbA1c, whereas 7- to 45-d exposures were most strongly associated with blood glucose. NO2 exposure was not associated with blood glucose or with HbA1c. Conclusions: Medium-term PM and PNAM exposures were positively associated with glucose measures in nondiabetic adults. These findings indicate that reducing ambient air pollution levels may decrease the risk of diabetes. OA gold - CA extern

Recently, epidemiological studies have found a link between air pollution (AP) and individual components of the metabolic syndrome (MetS), a condition predisposing to cardiometabolic diseases. However, very few studies have explored a possible association between air pollution and MetS.We analyzed the effects of long-term exposure to airborne particulate matter and NOWe used data of the population-based prospective Heinz Nixdorf Recall study (baseline 2000-2003) to investigate the association(s) between AP exposure and MetS prevalence at baseline (n = 4457) and MetS incidence at first follow-up visit (n = 3074; average follow-up: 5.1 years). Mean annual exposure to size-fractioned particulate matter (PMWe observed a MetS prevalence of 20.7% (n = 922) and an incidence of 9.7% (n = 299). NOIn summary, we found a weak positive association between air pollution and MetS. The strongest and most consistent effects were observed between NO

Background: Long-term exposures to particulate matter air pollution (PM2.5 and PM10) and high traffic load have been associated with markers of systemic inflammation. Epidemiological investigations have focused primarily on total PM, which represents a mixture of pollutants originating from different sources. Objective: We investigated associations between source-specific PM and high-sensitive C-reactive protein (hs-CRP), an independent predictor of cardiovascular disease. Methods: We used data from the first (2000–2003) and second examination (2006–2008) of the Heinz Nixdorf Recall study, a prospective population-based German cohort of initially 4,814 participants (45–75 years of age). We estimated residential long-term exposure to local traffic- and industry-specific fine particulate matter (PM2.5) at participants’ residences using a chemistry transport model. We used a linear mixed model with a random participant intercept to estimate associations of source-specific PM and natural log-transformed hs-CRP, controlling for age, sex, education, body mass index, low- and high-density lipoprotein cholesterol, smoking variables, physical activity, season, humidity, and city (8,204 total observations). Results: A 1-μg/m3 increase in total PM2.5 was associated with a 4.53% increase in hs-CRP concentration (95% CI: 2.76, 6.33%). hs-CRP was 17.89% (95% CI: 7.66, 29.09%) and 7.96% (95% CI: 3.45, 12.67%) higher in association with 1-μg/m3 increases in traffic- and industry-specific PM2.5, respectively. Results for PM10 were similar. Conclusions: Long-term exposure to local traffic-specific PM (PM2.5, PM10) was more strongly associated with systemic inflammation than total PM. Associations of local industry-specific PM were slightly stronger but not significantly different from associations with total PM. Citation: Hennig F, Fuks K, Moebus S, Weinmayr G, Memmesheimer M, Jakobs H, Bröcker-Preuss M, Führer-Sakel D, Möhlenkamp S, Erbel R, Jöckel KH, Hoffmann B, Heinz Nixdorf Recall Study Investigative Group. 2014. Association between source-specific particulate matter air pollution and hs-CRP: local traffic and industrial emissions. Environ Health Perspect 122:703–710; http://dx.doi.org/10.1289/ehp.1307081

For the simultaneous analysis of short- and long-term effects of air pollution in the Heinz Nixdorf Recall Cohort a sophisticated exposure modelling was performed. The dispersion and chemistry transport model EURAD (European Air Pollution Dispersion) was used for the estimation of hourly concentrations of a number of pollutants for a horizontal grid with a cell size of 1 km² covering the whole study area (three large adjacent cities in a highly urbanized region in Western Germany) for the years 2000–2003 and 2006–2008. For each 1 km² cell we estimated the mean concentration by calculating daily means from the hourly concentrations modelled by the EURAD process. The modelled concentrations showed an overall tendency to decrease from 2001 to 2008 whereas the trend in the single grid cells and study period was inhomogeneous. Participant-related exposure slightly increased from 2001 to 2003 followed by a decrease from 2006 to 2008. The exposure modelling enables a very flexible exposure assessment compared to conventional modelling approaches which either use central monitoring or temporally static spatial contrasts. The modelling allows the calculation of an average exposure concentration for any place and time within the study region and study period with a high spatial and temporal resolution. This is important for the assessment of short-, medium and long-term effects of air pollution on human health in epidemiological studies.

AIMS: Living close to high traffic has been linked to subclinical atherosclerosis, however it is not clear, whether fine particulate matter (PM) air pollution or noise, two important traffic-related exposures, are responsible for the association. We investigate the independent associations of long-term exposure to fine PM and road traffic noise with thoracic aortic calcification (TAC), a reliable measure of subclinical atherosclerosis. METHODS AND RESULTS: We used baseline data (2000-2003) from the German Heinz Nixdorf Recall Study, a population-based cohort of 4814 randomly selected participants. We assessed residential long-term exposure to PM with a chemistry transport model, and to road traffic noise using façade levels from noise models as weighted 24 h mean noise (Lden) and night-time noise (Lnight). Thoracic aortic calcification was quantified from non-contrast enhanced electron beam computed tomography. We used multiple linear regression to estimate associations of environmental exposures with ln(TAC+1), adjusting for each other, individual, and neighbourhood characteristics. In 4238 participants (mean age 60 years, 49.9% male), PM2.5 (aerodynamic diameter ≤2.5 µm) and Lnight are both associated with an increasing TAC-burden of 18.1% (95% CI: 6.6; 30.9%) per 2.4 µg/m(3) PM2.5 and 3.9% (95% CI 0.0; 8.0%) per 5dB(A) Lnight, respectively, in the full model and after mutual adjustment. We did not observe effect measure modification of the PM2.5 association by Lnight or vice versa. CONCLUSION: Long-term exposure to fine PM and night-time traffic noise are both independently associated with subclinical atherosclerosis and may both contribute to the association of traffic proximity with atherosclerosis.

Background: Exposure to air pollution activates the innate immune system and influences the adaptive immune system in experimental settings. We investigated the association of residential long-term exposure to particulate matter (PM) and NO2 air pollution with monoclonal gammopathy of undetermined significance (MGUS) as a marker of adaptive immune system activation. Methods: We used data from the baseline (2000−2003), 5-year (2006–2008) and 10-year (2011–2015) follow-up examinations of the German Heinz Nixdorf Recall cohort study of 4814 participants (45–75years). Residential exposure to PM size fractions and NO2 was estimated by land-use regression (ESCAPE-LUR, annual mean 2008/2009) and dispersion chemistry transport models (EURAD-CTM, 3-year mean at baseline). We used logistic regression to estimate the effects of air pollutants on incident MGUS, adjusting for age, sex, education, smoking status, physical activity, and BMI. As a non-linear approach, we looked at quartiles (2–4) of the air pollutants in comparison to quartile 1. Results: Of the 3949 participants with complete data, 100 developed MGUS during the 10-year follow-up. In the main model, only PMcoarse was associated with incident MGUS (OR per IQR (1.9μg/m3): 1.32, 95% CI 1.04–1.67). We further found positive associations between PM size fractions estimated by ESCAPE-LUR and incident MGUS by quartiles of exposure (OR Q4 vs Q1: PM2.5 2.03 (1.08–3.80); PM10 1.97 (1.05–3.67); PMcoarse 1.98 (1.09–3.60)). Conclusions: Our results indicate that an association between long-term exposure to PM and MGUS may exist. Further epidemiologic studies are needed to corroborate this possible link. Keywords: Air pollution, Particulate matter, Monoclonal gammopathy of undetermined significance, MGUS, Cohort study

Objectives: The aim of this study was to investigate the association of long-term residential exposure to fine particles with carotid intima-media thickness (CIMT).Background: Experimental and epid...

Transport of atmospheric trace gases during a blocking event in the troposphere and lower stratosphere (UTLS) in August 1997 is studied. Considering the frequency of such events it is obvious that they play a significant role for climate and air chemistry and thus the atmospheric environment. The event has been selected because a unique set of composition observations carried out by the CRISTA (Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere) instrument in the UTLS was available for the period of blocking. The regional European Atmospheric Dispersion (EURAD) model system was used for a detailed analysis focusing on ozone and CFC 11 (CFCl3, Freon) distributions and their temporal and spatial variability. The combination of the CRISTA data and a regional transport chemistry model enabled a unique analysis of transport behaviour of a blocking. This developed an Ω-structure with a deep cut-off low at the western flank of the blocking high and a trough with highly perturbed trace gas fields on the eastern side. Tropopause heights varied intensively and were bended down to rather low levels in the cut-off low and folds appearing in the eastern trough. Results of artificial tracer experiments are presented which show that polluted air from lower tropospheric levels and – in this case – lower latitudes as well as the North American continent may be lifted up to the UTLS in the anticyclonic part of the Ω-block and transported from there over large distances in streamers. Quasi-periodic variations of CFC 11 flux divergence indicate dynamical coupling of the different domains of the block.

Background Long-term exposure to fine particulate matter (PM2.5) may lead to increased blood pressure (BP). The role of industry- and traffic-specific PM2.5 remains unclear. Objective We investigated the associations of residential long-term source-specific PM2.5 exposure with arterial BP and incident hypertension in the population-based Heinz Nixdorf Recall cohort study. Methods We defined hypertension as systolic BP ≥ 140 mmHg, or diastolic BP ≥ 90 mmHg, or current use of BP lowering medication. Long-term concentrations of PM2.5 from all local sources (PM2.5ALL), local industry (PM2.5IND) and traffic (PM2.5TRA) were modeled with a dispersion and chemistry transport model (EURAD-CTM) with a 1 km2 resolution. We performed a cross-sectional analysis with BP and prevalent hypertension at baseline, using linear and logistic regression, respectively, and a longitudinal analysis with incident hypertension at 5-year follow-up, using Poisson regression with robust variance estimation. We adjusted for age, sex, body mass index, lifestyle, education, and major road proximity. Change in BP (mmHg), odds ratio (OR) and relative risk (RR) for hypertension were calculated per 1 μg/m3 of exposure concentration. Results PM2.5ALL was highly correlated with PM2.5IND (Spearman’s ρ = 0.92) and moderately with PM2.5TRA (ρ = 0.42). In adjusted cross-sectional analysis with 4539 participants, we found positive associations of PM2.5ALL with systolic (0.42 [95%-CI: 0.03, 0.80]) and diastolic (0.25 [0.04, 0.46]) BP. Higher, but less precise estimates were found for PM2.5IND (systolic: 0.55 [−0.05, 1.14]; diastolic: 0.35 [0.03, 0.67]) and PM2.5TRA (systolic: 0.88 [−1.55, 3.31]; diastolic: 0.41 [−0.91, 1.73]). We found crude positive association of PM2.5TRA with prevalence (OR 1.41 [1.10, 1.80]) and incidence of hypertension (RR 1.38 [1.03, 1.85]), attenuating after adjustment (OR 1.19 [0.90, 1.58] and RR 1.28 [0.94, 1.72]). We found no association of PM2.5ALL and PM2.5IND with hypertension. Conclusions Long-term exposures to all-source and industry-specific PM2.5 were positively related to BP. We could not separate the effects of industry-specific PM2.5 from all-source PM2.5. Estimates with traffic-specific PM2.5 were generally higher but inconclusive.

Background In several studies, exposure to fine particulate matter (PM) has been associated with inflammation, with inconsistent results. We used repeated measurements to examine the association of long-term fine and ultrafine particle exposure with several blood markers of inflammation and coagulation. Methods We used baseline (2000–2003) and follow-up (2006–2008) data from the Heinz Nixdorf Recall Study, a German population-based prospective cohort of 4814 participants. A chemistry transport model was applied to model daily surface concentrations of PM air pollutants (PM 10 , PM 2.5 ) and particle number on a grid of 1 km 2 . Applying mixed regression models, we analysed associations of long-term (mean of 365 days prior to blood draw) particle exposure at each participant9s residence with the level of high-sensitivity C reactive protein (hs-CRP), fibrinogen, platelet and white cell count (WCC), adjusting for short-term PM exposure (moving averages of 1–7 days), personal characteristics, season, ambient temperature (1–5 days), ozone and time trend. Results We analysed 6488 observations: 3275 participants with baseline data and 3213 with follow-up data. An increase of 2.4 µg/m 3 in long-term PM 2.5 was associated with an adjusted increase of 5.4% (95% CI 0.6% to 10.5%) in hs-CRP and of 2.3% (95% CI 1.4% to 3.3%) in the platelet count. Fibrinogen and WCC were not associated with long-term particle exposure. Conclusions In this population-based cohort, we found associations of long-term exposure to PM with markers of inflammation (hs-CRP) and coagulation (platelets). This finding supports the hypothesis that inflammatory processes might contribute to chronic effects of air pollution on cardiovascular disease.

Background Studies investigating the link between long-term exposure to air pollution and incidence of diabetes are still scarce and results are inconsistent, possibly due to different compositions of the particle mixture. We investigate the long-term effect of traffic-specific and total particulate matter (PM) and road proximity on cumulative incidence of diabetes mellitus (mainly type 2) in a large German cohort. Methods We followed prospectively 3607 individuals without diabetes at baseline (2000–2003) from the Heinz Nixdorf Recall Study in Germany (mean follow-up time 5.1 years). Mean annual exposures to total as well as traffic-specific PM10 and PM2.5 at residence were estimated using a chemistry transport model (EURAD, 1 km2 resolution). Effect estimates for an increase of 1 μg/m3 in PM were obtained with Poisson regression adjusting for sex, age, body mass index, lifestyle factors, area-level and individual-level socio-economic status, and city. Results 331 incident cases developed. Adjusted RRs for total PM10 and PM2.5 were 1.05 (95 %-CI: 1.00;1.10) and 1.03 (95 %-CI: 0.95;1.12), respectively. Markedly higher point estimates were found for local traffic-specific PM with RRs of 1.36 (95 %-CI: 0.98;1.89) for PM10 and 1.36 (95 %-CI: 0.97;1.89) for PM2.5. Individuals living closer than 100 m to a busy road had a more than 30 % higher risk (1.37;95 %-CI: 1.04;1.81) than those living further than 200 m away. Conclusions Long-term exposure to total PM increases type two diabetes risk in the general population, as does living close to a major road. Local traffic-specific PM was related to higher risks for type two diabetes than total PM. Electronic supplementary material The online version of this article (doi:10.1186/s12940-015-0031-x) contains supplementary material, which is available to authorized users.

Background Long-term exposure to fine particulate matter (PM2.5 and PM10) air pollution and high traffic load at residence have been shown to induce systemic inflammation. So far epidemiological in...

A multiple-nesting version of the European Acid Deposition Model (EURAD) has been developed in order to increase the horizontal resolution in a region of enhanced pollution, namely the former German Democratic Republic. This new technique allows the ability to simulate large-scale features together with the development of smaller-scale structures in the nested regions. This multiple-nesting approach was applied to a case that occurred in October 1990, the so-called SANA 1 episode. SANA is a German acronym that stands for “scientific program for the assessment of the air pollution situation in the former German Democratic Republic.” The SANA program was established to observe the rapid change in composition of air pollutants and their concentration levels over the eastern part of Germany due to political and economical changes. Thus, within the SANA program there is a unique chance to observe and control the effect of air quality strategies. Two nested areas are embedded in a coarse domain that co...

OA gold Two commonly used models to assess air pollution concentration for investigating health effects of air pollution in epidemiological studies are Land Use Regression (LUR) models and Dispersion and Chemistry Transport Models (DCTM). Both modeling approaches have been applied in the Ruhr area, Germany, a location where multiple cohort studies are being conducted. Application of these different modelling approaches leads to differences in exposure estimation and interpretation due to the specific characteristics of each model. We aimed to compare both model approaches by means of their respective aims, modeling characteristics, validation, temporal and spatial resolution, and agreement of residential exposure estimation, referring to the air pollutants PM2.5, PM10, and NO2. Residential exposure referred to air pollution exposure at residences of participants of the Heinz Nixdorf Recall Study, located in the Ruhr area. The point-specific ESCAPE (European Study of Cohorts on Air Pollution Effects)-LUR aims to temporally estimate stable long-term exposure to local, mostly traffic-related air pollution with respect to very small-scale spatial variations (≤100 m). In contrast, the EURAD (European Air Pollution Dispersion)-CTM aims to estimate a time-varying average air pollutant concentration in a small area (i.e., 1 km2), taking into account a range of major sources, e.g., traffic, industry, meteorological conditions, and transport. Overall agreement between EURAD-CTM and ESCAPE-LUR was weak to moderate on a residential basis. Restricting EURAD-CTM to sources of local traffic only, respective agreement was good. The possibility of combining the strengths of both applications will be the next step to enhance exposure assessment.

A diagnostic model (DIAMOD) for the atmosphere over Europe is use at the University of Vienna. Central parameters in each diagnostic column (horizontal resolution 100 km, time resolution 12 hours) are the vertical moisture plus heat flux (the total convective heat fluxh) and the vertical rain flux (r); both are functions of pressure. In this study DIAMOD is applied to validate the output of a forecast model for the simulation of acid deposition (EURAD) which is in use at the University of Cologne. The basic equations of both DIAMOD and EURAD models are summarized with emphasis on the sub-gridscale hydrologic components. First, the nontrivial problem of validating model output versus observations is discussed. Two different validation techniques based upon the budget equations are indentified. The fully prognostic technique compares the forecast of EURAD for the total verification period with the corresponding DIAMOD output. The semiprognostic validation technique involves only one-time-step tendencies. Neither yields an exact correspondence between EURAD and DIAMOD; however, the semiprognostic technique comes somewhat closer to the ideal of an objective validation. The quantities investigated are: The fields, the time tendencies and the fluxesh andr. Second, EURAD is validated versus DIAMOD with both techniques for the EUMAC Joint Wet Case (the Chernobyl episode) in April 1986; the output fields include selected profiles ofh(p) over France (a moist night situation) and over Greece (a dry day situation). The comparison demonstrates for both that the EURAD forecasts are acceptable for ther-fluxes but are relatively poor for theh-fluxes. Reasons for the differences are discussed.

The three-dimensional long-range transport model EURAD has been applied to two episodes in 1986 to study the transport and transformation of air pollutants over Europe under different meteorological conditions. The spring episode is characterized by varying meteorological conditions over Europe and transport of pollutants is complex. The summer episode is suitable to study the enhanced formation of photooxidants as an almost stagnant high pressure system over central Europe favoured the accumulation of pollutants. Available observations from several monitoring networks in Europe are used to evaluate the near surface concentration predictions of the model. This is possible for the sulfur species, O3 and NO2 for the central part of the modelling domain. It is shown that O3 and NO2 trends in the western part of the model domain are estimated reasonably well. The strong bias for underpredicting NO2 in the eastern part of the domain reflects the quality of emission data for the two regions of the modelling area. Typically for regional scale Eulerian transport models when applied on larger grid sizes, EURAD overpredicts the observed minima and underpredicts the high observations. This is particularly true for O3 but also detectable in the sulfate comparisons. Several sensitivity simulations for both episodes were performed to test numerical algorithms, parameterizations or emission data. Results from these simulations clearly show the important role of cloud related processes during the spring time for the sulfur species. Further testing and assessment of cloud parameterizations and emission data for transport models is anticipated.

In order to explore future air quality in Europe at the 2030 horizon, two emission scenarios developed in the framework of the Global Energy Assessment including varying assumptions on climate and energy access policies are investigated with an ensemble of six regional and global atmospheric chemistry transport models. A specific focus is given in the paper to the assessment of uncertainties and robustness of the projected changes in air quality. The present work relies on an ensemble of chemistry transport models giving insight into the model spread. Both regional and global scale models were involved, so that the ensemble benefits from medium-resolution approaches as well as global models that capture long-range transport. For each scenario a whole decade is modelled in order to gain statistical confidence in the results. A statistical downscaling approach is used to correct the distribution of the modelled projection. Last, the modelling experiment is related to a hind-cast study published earlier, where the performances of all participating models were extensively documented. The analysis is presented in an exposure-based framework in order to discuss policy relevant changes. According to the emission projections, ozone precursors such as NOx will drop down to 30% to 50% of their current levels, depending on the scenario. As a result, annual mean O3 will slightly increase in NOx saturated areas but the overall O3 burden will decrease substantially. Exposure to detrimental O3 levels for health (SOMO35) will be reduced down to 45% to 70% of their current levels. And the fraction of stations where present-day exceedences of daily maximum O3 is higher than 120 μg m−3 more than 25 days per year will drop from 43% down to 2 to 8%. We conclude that air pollution mitigation measures (present in both scenarios) are the main factors leading to the improvement, but an additional cobenefit of at least 40% (depending on the indicator) is brought about by the climate policy.

Air quality within the European Union (EU) is controlled by the Member States' monitoring networks. In this study, measured data is compared with the EURAD (EURopean Air pollution Dispersion) model system diagnostic output. Simulations for the German state North Rhine-Westphalia (NRW) with a horizontal grid resolution of 5 km × 5 km are analyzed. The comparison is performed for NO2, O3, and PM10, for the 4-year time period from 2002 through 2005. Although the spatial representativity of data of the two systems differs, the analyzed temporal variability of the averages shows good agreement of modeled and observed concentrations for all three parameters. This confirms the applicability of the EURAD model to mesoscale air quality assessment. Discrepancies between the model and observed data occurred at low concentrations close to the detection limits of the analyzers, for high O3 concentrations in summer, and for PM10 before 2004, when earlier versions of the MM5 meteorological module and of the emission inventory were used. Possible causes of O3 overprediction and NO2 underprediction preferably showing up in summer are considered. It is found that modeled Ox = O3 + NO2 leads to better representation of the observations than the individual species themselves. The model performance can probably be increased by further development of the emission inventories and more accurate land use data. These input data seem to be the main reason for deviations between observations and model results.

Numerical models that combine weather forecasting and atmospheric chemistry are here referred to as chemical weather forecasting models. Eighteen operational chemical weather forecasting models on regional and continental scales in Europe are described and compared in this article. Topics discussed in this article include how weather forecasting and atmospheric chemistry models are integrated into chemical weather forecasting systems, how physical processes are incorporated into the models through parameterization schemes, how the model architecture affects the pre dicted variables, and how air chemistry and aerosol processes are formulated. In addition, we discuss sensitivity analysis and evaluation of the models, user operational requirements, such as model availability and documentation, and output availability and dissemination. In this manner, this article allows for the evaluation of the relative strengths and weaknesses of the various modelling systems and modelling approaches. Finally, this article highlights the most prominent gaps of knowledge for chemical weather forecasting models and suggests potential priorities for future research directions, for the following selected focus areas: emission inventories, the integration of numerical weather prediction and atmospheric chemical transport models, boundary conditions and nesting of models, data assimilation of the various chemical species, improved understanding and parameterization of physical processes, better evaluation of models against data and the construction of model ensembles. We acknowledge the funding of COST ES0602, ES1004, 728, and EU TRANSPHORM. The research leading to these results has received funding from the European Union’s Seventh Framework Programme FP/2007-2011 within the project MEGAPOLI, grant agreement no. 212520. David Schultz is partially funded by Vaisala Oyj. We thank the following people for their contributions to this manuscript: Paul Agnew, Gregory Carmichael, Lucy Davis, Oriol Jorba, Mark Lawrence, Dimitros Melas, Arjo Segers, William Skamarock, and Christos Zerefos. Kenneth Schere and an anonymous reviewer provided detailed comments that improved this article. Kenneth Schere (US Environmental Protection Agency) is thanked for a thorough review of this article, and for his many valuable comments. published

Chemistry transport models often ignore the cloud parameters that can be provided by meteorological pre-processors like mesoscale meteorological models. They often recalculate these parameters with algorithms that differ from those used in the meteorological preprocessors. Hence, inconsistencies can occur between the treatment of clouds in the meteorological and chemical part of the model package. In this study the influence of five different cloud parameterization schemes used in a well-known mesoscale meteorological model on the results of a stand-alone version of a cloud and scavenging module is illustrated. The differences between the results provided by five model runs with different cloud modules and those recalculated by the stand-alone version are discussed. Such differences occur due to the inconsistencies between the different cloud parameterization schemes in the meteorological model and the cloud and scavenging module. The results of the cloud and scavenging module differ due to the d...

Methods that include a combination of weather forecasting and atmospheric chemistry simulations are here referred to as chemical weather forecasting (CWF). We have selected 18 operational CWF models on regional and continental scales in Europe for a more detailed analysis. We have collected the information in a structured form, and inter-compared and evaluated the mathematical structure of these models. This information makes it possible to evaluate the relative advantages and limitations of the various modeling systems, modeling approaches and sub-models. We have also surveyed the most prominent gaps of knowledge in this field, and suggested potential priorities for future research directions. There are substantial gaps of knowledge, especially in the following fields: emission inventories, the availability and exchange of observations, the evaluation of the boundary conditions for the CWF models, the integration of numerical weather prediction and atmospheric chemical transport models, the data assimilation of the various chemical species, the understanding of several chemical and physical processes, the construction of model ensembles, and the scientific evaluation of the CWF models, including their evaluation against data.

On 24 March 2007 an extraordinary dust plume was observed in the troposphere over Central Europe. Satellite observations revealed its origins in a dust storm in Southern Ukraine, where large amounts of soil were resuspended from dried-out farmlands by wind gusts up to 30 m/s. Maximum particulate matter (PM 10 ) mass concentrations between 200 and 1400 μg m −3 occurred in Slovakia, the Czech Republic, Poland, and Germany along the pathway of the plume from East to West. In order to achieve a better understanding of the processes involved and their interaction the numerical simulation of this event was done by using a new parameterization of dust emission under certain conditions. A simplified dust emission scheme, proposed by Shao (2004), is implemented in the EURAD –Model (European Air Pollution Dispersion Model). A possible way for simulation of other similar dust events in the future is described. The obtained results are compared with the available measurement data and are commented.

Background: Recent studies have shown an association of short-term exposure to fine particulate matter (PM) with transient increases in blood pressure (BP), but it is unclear whether long-term exposure has an effect on arterial BP and hypertension. Objectives: We investigated the cross-sectional association of residential long-term PM exposure with arterial BP and hypertension, taking short-term variations of PM and long-term road traffic noise exposure into account. Methods: We used baseline data (2000–2003) on 4,291 participants, 45–75 years of age, from the Heinz Nixdorf Recall Study, a population-based prospective cohort in Germany. Urban background exposure to PM with aerodynamic diameter ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10) was assessed with a dispersion and chemistry transport model. We used generalized additive models, adjusting for short-term PM, meteorology, traffic proximity, and individual risk factors. Results: An interquartile increase in PM2.5 (2.4 μg/m3) was associated with estimated increases in mean systolic and diastolic BP of 1.4 mmHg [95% confidence interval (CI): 0.5, 2.3] and 0.9 mmHg (95% CI: 0.4, 1.4), respectively. The observed relationship was independent of long-term exposure to road traffic noise and robust to the inclusion of many potential confounders. Residential proximity to high traffic and traffic noise exposure showed a tendency toward higher BP and an elevated prevalence of hypertension. Conclusions: We found an association of long-term exposure to PM with increased arterial BP in a population-based sample. This finding supports our hypothesis that long-term PM exposure may promote atherosclerosis, with air-pollution–induced increases in BP being one possible biological pathway.

We discuss the capability of current state-of-the-art chemistry and transport models to reproduce air quality trends and interannual variability. Documenting these strengths and weaknesses on the basis of historical simulations is essential before the models are used to investigate future air quality projections. To achieve this, a coordinated modelling exercise was performed in the framework of the CityZEN European Project. It involved six regional and global chemistry-transport models (BOLCHEM, CHIMERE, EMEP, EURAD, OSLOCTM2 and MOZART) simulating air quality over the past decade in the Western European anthropogenic emissions hotspots. Comparisons between models and observations allow assessing the skills of the models to capture the trends in basic atmospheric constituents (NO2, O3, and PM10). We find that the trends of primary constituents are well reproduced (except in some countries – owing to their sensitivity to the emission inventory) although capturing the more moderate trends of secondary species such as O3 is more challenging. Apart from the long term trend, the modelled monthly variability is consistent with the observations but the year-to-year variability is generally underestimated. A comparison of simulations where anthropogenic emissions are kept constant is also investigated. We find that the magnitude of the emission-driven trend exceeds the natural variability for primary compounds. We can thus conclude that emission management strategies have had a significant impact over the past 10 yr, hence supporting further emission reductions.

The EURAD model has been applied to simulate the transport and chemical transformations of atmospheric constituents over central Europe for a 3 week late winter episode in February/March 1982. The TADAP model (the European version of ADOM) carried out simulations for the same episode with the same emission data set but with different meteorological input. The episode is characterized by two typical European meteorological conditions: a relatively quiet blocking situation at the beginning of the episode is followed by rather disturbed conditions with frontal systems passing through central European. The evolution of the pollutant fields is markedly different during these two conditions. Evaluation studies with the focus on the sulfur species were performed using air quality data from the EMEP monitoring network for the comparison of model predictions with observations. The first simulation with predicted meteorology and the PHOXA emission data set revealed an underprediction of airborne sulfate and wet deposition, while SO 2 was overpredicted. A simulation with meteorological data created by a so-called nudging technique corrected the transport of SO 2 to remote regions and drastically improved the predictions of rainfall and subsequently the amount of wet deposition. However, the ambient sulfate aerosol concentrations were still underpredicted. As the same bias in sulfate was also observed for TADAP simulations of the same episode a hypothesis for the underprediction is a very low primary sulfate emission inherent in the emission database. By artificially increasing the primary sulfate emission to an obviously too high value, the underprediction of sulfate was changed into a strong overprediction for the major source regions and into a reasonable range as observed for the remote regions. An analysis of the precipitation amounts, the aqueous concentrations and the resulting wet deposition showed that during this episode the aqueous were precipitation limited.

Daily to monthly variations in fine particulate matter have been linked to systemic inflammatory responses. It has been hypothesized that smaller particles resulting from combustion processes confer higher toxicity. We aim to analyze the association between short-term exposure to ultrafine and fine particles and systemic inflammation. We use baseline data (2000-2003) of the Heinz Nixdorf Recall Study, a population-based cohort study of 4,814 participants in the Ruhr Area in Germany. A chemistry transport model was applied to model daily surface concentrations of particulate air pollutants on a grid of 1 km(2). Exposure included particle number (PN) and particulate matter mass concentration with an aerodynamic diameter < or = 2.5 microm (PM(2.5)) and < or = 10 microm (PM(10)). Generalized additive models were used to explore the relation of air pollutants using single day lags and averaging times of up to 28 days with high-sensitivity C-reactive protein (hs-CRP). We adjusted for meteorology, season, time trend, and personal characteristics. Median hs-CRP level in the 3,999 included participants was 1.5 mg/l. Median daily concentration of PN was 8,414 x 10(4)/ml (IQR 4,580 x 10(4)/ml), of PM(2.5) 14.5 microg/m(3) (IQR 11.5 microg/m(3)) and of PM(10) 18.5 microg/m(3) (IQR 13.9 microg/m(3)). A positive association between PN and hs-CRP could be observed only for single day lags and for averaged PN concentrations with higher estimates for longer averaging times. The highest hs-CRP-increase of 7.1% (95%-CI: 1.9, 12.6%) was found for the 21-day average. These results support the hypothesis that short-term exposure to traffic-related particles might lead to detrimental cardiovascular health effects via an inflammatory mechanism.

Observational and modeling studies reveal that an appreciable fraction of mesospheric and lower thermospheric variability in space and time can be attributed to planetary waves. Measurements indicate that this is also true for summer conditions when vertically propagating stationary and travelling planetary waves experience strong attenuation in the lower part of the middle atmosphere. Possible causes including in-situ generation of normal modes are discussed. The methods of spherical 2-d turbulence analysis are used to investigate temporal and vertical changes of large—scale variability employing satellite radiance data. A relative increase of the variance related to intermediate—scale transient structures is found at high altitudes. This seems to reflect the special propagation conditions for travelling planetary waves at heights around the mesopause.

A tropopause fold and cut-off low developed over Europe at the end of April 1982 and enhanced the exchange of air between the stratosphere and troposphere. The episode has been simulated using the EURAD model which has been designed for long-range transport simulation for European conditions. Applying a linear relationship between potential vorticity and ozone, concentration fields of the tracer and their changes due to outflow of ozone-rich air from the stratosphere could be simulated. A considerable decrease of stratospheric ozone was also obtained. This indicates noticeable mixing of tropospheric air with reduced ozone content into the stratosphere during the episode. Strong downward fluxes more than an order of magnitude larger than normal when averaged over the model domain show up around levels close to the tropopause. Obviously, dynamical processes fovern teh ozone budget o the upper troposphere during the vigorous intrusion event. Drastic increases of ozone also occur in the lower troposphere but are probably underestimated since vertical mixing by clouds is not taken into account in the simulation experiments. It is intended to explore the interaction of ozone of stratospheric and tropospheric (anthropogenic) origin in further experiments.

The Eur opean A cid D eposition Model (EURAD) is used to investigate the long-range transport (LRT) and deposition of radioactive material in Europe during the first week after the Chernobyl accident. Emphasis is laid on using the model system in a forecast mode as possibly would be done shortly after such an event. Thus, meteorological fields are predicted with the PSU/NCAR mososcale model MM4. The multilayer Eulerian model CTM ( C hemistry T ransport Model ) is applied to compute transport and deposition of Cs-137 and I-131 using the predicted meteorological fields. However, the accident scenario was estimated using published data. The model results and performance are discussed by comparison with observations. It is demonstrated that the model can reproduce certain observed characteristics of the radioactive cloud, i.e. trends in surface air concentrations, arrival times and wet deposition patterns. This leads to the suggestion that the predictive capability of the EURAD-system has a relatively high level considering the fact that several simple approaches were used.

The EU directive 1999/30 allow for air quality modelling as a tool to plan strategies for air pollution reduction in Europe. A comprehensive air quality model, including ozone, NO 2 and atmospheric particle (PM 10 ) is applied to estimate the future development of air quality in Europe. As a base year 2002 is selected, model calculations are performed for 2005 and 2010 for Europe as well as for North Rhine-Westphalia (NRW) as a strongly polluted core region in central Europe. The results show an improvement in air quality, in particular in western and central Europe. However, major problems to fulfil the limit values as given in the EU directives might occur with respect to PM 10 .

Numerical simulations of aerosols in the atmospheric boundary can computationally be highly demanding since processes governing the formation and dynamics of atmospheric particles are extremely complex. It is necessary to apply reasonable simplifications to make the computations tractable. This paper gives an outline of the approach to the problem chosen for the EURAD model system. A special module MADOC (Modal Aerosol Dynamics with Organic compounds and Clouds) has been developed for aerosol simulations employing EURAD. Annual runs for a region in Central Europe are shown and discussed with regard to model content, performance and reliability.