Long-term exposures to low concentrations of source-specific air pollution, road-traffic noise, and systemic inflammation and cardiovascular disease biomarkers.

OBJECTIVES: Air pollution and traffic noise are detrimental to cardiovascular health. However, the effects of different sources of these exposures on cardiovascular biomarkers remain unclear. We explored the associations of long-term exposure to source-specific air pollution (vehicular exhausts and residential woodsmoke) at low concentrations and road-traffic noise with systemic inflammation and cardiovascular disease biomarkers. MATERIAL AND METHODS: Modeled outdoor exposure to fine particulate matter (aerodynamic diameter ≤2.5 µm; PM2.5) from vehicular exhausts and residential woodsmoke, nitrogen dioxide (NO2) from road traffic, and road-traffic noise were linked to the home addresses of the participants (Finnish residents aged 25-74) in the FINRISK study 1997-2012. The participants were located in the cities of Helsinki, Vantaa, and the region of Turku, Finland. The outcomes were high-sensitivity C-reactive protein (CRP), a biomarker for systemic inflammation, and cardiovascular disease biomarkers N-terminal pro-B-type natriuretic peptide (NT-proBNP) and troponin I. We performed cross-sectional analyses with linear and additive models and adjusted for potential confounders. RESULTS: We found no association between PM2.5 from vehicular exhausts (% CRP difference for 1 µg/m3 increase in PM2.5: -0.9, 95% confidence interval, CI: -7.2, 5.8), or from residential woodsmoke (% difference: -8.1, 95% CI: -21.7, 7.9) and CRP (N = 4147). Road-traffic noise >70 dB tended to be positively associated with CRP (% CRP difference versus noise reference category of ≤45 dB: 18.3, 95% CI: -0.5, 40.6), but the association lacked significance and robustness (N = 7142). Otherwise, we found no association between road-traffic noise and CRP, nor between NO2 from road traffic and NT-proBNP (N = 1907) or troponin I (N = 1951). CONCLUSION: Long-term exposures to source-specific, fairly low-level air pollution from vehicular exhausts and residential woodsmoke, or road-traffic noise were not associated with systemic inflammation and cardiovascular disease biomarkers in this urban area.

Association between active commuting and low-grade inflammation: a population-based cross-sectional study.

BACKGROUND: Prior studies suggest that physical activity lowers circulating C-reactive protein (CRP) levels. However, little is known about the association between regular active commuting, i.e. walking or cycling to work, and CRP concentrations. This study examines whether active commuting is associated with lower CRP. METHODS: We conducted a cross-sectional study using population-based FINRISK data from 1997, 2002, 2007 and 2012. Participants were working adults living in Finland (n = 6208; mean age = 44 years; 53.6% women). We used linear and additive models adjusted for potential confounders to analyze whether daily active commuting, defined as the time spent walking or cycling to work, was associated with lower high-sensitivity (hs-) CRP serum concentrations compared with passive commuting. RESULTS: We observed that daily active commuting for 45 min or more (vs. none) was associated with lower hs-CRP [% mean difference in the main model: -16.8%; 95% confidence interval (CI) -25.6% to -7.0%), and results were robust to adjustment for leisure-time and occupational physical activity, as well as diet. Similarly, active commuting for 15-29 min daily was associated with lower hs-CRP in the main model (-7.4; 95% CI -14.1 to -0.2), but the association attenuated to null after further adjustments. In subgroup analyses, associations were only observed for women. CONCLUSIONS: Active commuting for at least 45 min a day was associated with lower levels of low-grade inflammation. Promoting active modes of transport may lead not only to reduced emissions from motorized traffic but also to population-level health benefits.

Cross-sectional associations of different types of nature exposure with psychotropic, antihypertensive and asthma medication.

BACKGROUND: Exposure to natural environments is thought to be beneficial for human health, but the evidence is inconsistent. OBJECTIVE: To examine whether exposure to green and blue spaces in urban environments is associated with mental and physical health in Finland. METHODS: The Helsinki Capital Region Environmental Health Survey was conducted in 2015-2016 in Helsinki, Espoo and Vantaa in Finland (n=7321). Cross-sectional associations of the amounts of residential green and blue spaces within 1 km radius around the respondent's home (based on the Urban Atlas 2012), green and blue views from home and green space visits with self-reported use of psychotropic (anxiolytics, hypnotics and antidepressants), antihypertensive and asthma medication were examined using logistic regression models. Indicators of health behaviour, traffic-related outdoor air pollution and noise and socioeconomic status (SES) were used as covariates, the last of these also as a potential effect modifier. RESULTS: Amounts of residential green and blue spaces or green and blue views from home were not associated with medications. However, the frequency of green space visits was associated with lower odds of using psychotropic medication (OR=0.67, 95% CI 0.55 to 0.82 for 3-4 times/week; 0.78, 0.63 to 0.96 for ≥5 times/week) and antihypertensive (0.64, 0.52 to 0.78; 0.59, 0.48 to 0.74, respectively) and asthma (0.74, 0.58 to 0.94; 0.76, 0.59 to 0.99, respectively) medication use. The observed associations were attenuated by body mass index, but no consistent interactions with SES indicators were observed. CONCLUSIONS: Frequent green space visits, but not the amounts of residential green or blue spaces, or green and blue views from home, were associated with less frequent use of psychotropic, antihypertensive and asthma medication in urban environments.

Burden of disease due to transportation noise in the Nordic countries.

BACKGROUND: Environmental noise is of increasing concern for public health. Quantification of associated health impacts is important for regulation and preventive strategies. AIM: To estimate the burden of disease (BoD) due to road traffic and railway noise in four Nordic countries and their capitals, in terms of DALYs (Disability-Adjusted Life Years), using comparable input data across countries. METHOD: Road traffic and railway noise exposure was obtained from the noise mapping conducted according to the Environmental Noise Directive (END) as well as nationwide noise exposure assessments for Denmark and Norway. Noise annoyance, sleep disturbance and ischaemic heart disease were included as the main health outcomes, using exposure-response functions from the WHO, 2018 systematic reviews. Additional analyses included stroke and type 2 diabetes. Country-specific DALY rates from the Global Burden of Disease (GBD) study were used as health input data. RESULTS: Comparable exposure data were not available on a national level for the Nordic countries, only for capital cities. The DALY rates for the capitals ranged from 329 to 485 DALYs/100,000 for road traffic noise and 44 to 146 DALY/100,000 for railway noise. Moreover, the DALY estimates for road traffic noise increased with up to 17% upon inclusion of stroke and diabetes. DALY estimates based on nationwide noise data were 51 and 133% higher than the END-based estimates, for Norway and Denmark, respectively. CONCLUSION: Further harmonization of noise exposure data is required for between-country comparisons. Moreover, nationwide noise models indicate that DALY estimates based on END considerably underestimate national BoD due to transportation noise. The health-related burden of traffic noise was comparable to that of air pollution, an established risk factor for disease in the GBD framework. Inclusion of environmental noise as a risk factor in the GBD is strongly encouraged.

Long-term exposure to traffic noise and risk of incident colon cancer: A pooled study of eleven Nordic cohorts.

Background Colon cancer incidence is rising globally, and factors pertaining to urbanization have been proposed involved in this development. Traffic noise may increase colon cancer risk by causing sleep disturbance and stress, thereby inducing known colon cancer risk-factors, e.g. obesity, diabetes, physical inactivity, and alcohol consumption, but few studies have examined this. Objectives The objective of this study was to investigate the association between traffic noise and colon cancer (all, proximal, distal) in a pooled population of 11 Nordic cohorts, totaling 155,203 persons. Methods We identified residential address history and estimated road, railway, and aircraft noise, as well as air pollution, for all addresses, using similar exposure models across cohorts. Colon cancer cases were identified through national registries. We analyzed data using Cox Proportional Hazards Models, adjusting main models for harmonized sociodemographic and lifestyle data. Results During follow-up (median 18.8 years), 2757 colon cancer cases developed. We found a hazard ratio (HR) of 1.05 (95% confidence interval (CI): 0.99-1.10) per 10-dB higher 5-year mean time-weighted road traffic noise. In sub-type analyses, the association seemed confined to distal colon cancer: HR 1.06 (95% CI: 0.98-1.14). Railway and aircraft noise was not associated with colon cancer, albeit there was some indication in sub-type analyses that railway noise may also be associated with distal colon cancer. In interaction-analyses, the association between road traffic noise and colon cancer was strongest among obese persons and those with high NO2-exposure. Discussion A prominent study strength is the large population with harmonized data across eleven cohorts, and the complete address-history during follow-up. However, each cohort estimated noise independently, and only at the most exposed façade, which may introduce exposure misclassification. Despite this, the results of this pooled study suggest that traffic noise may be a risk factor for colon cancer, especially of distal origin.

Symptoms intuitively associated with wind turbine infrasound.

In many countries, a certain proportion of individuals living in the vicinity of wind power areas have reported symptoms that they have intuitively associated with infrasound from wind turbines. While the reason for these symptoms remains under debate, this is the first study to describe the phenomenon by assessing the prevalence and severity of these wind turbine infrasound related symptoms as well as factors associated with being symptomatic. Four wind power areas in Finland assessed to have the most problems intuitively associated with wind turbine infrasound were selected for the study. The questionnaire was mailed to 4847 adults in four distance zones (≤ 2.5 km, > 2.5-5 km, > 5-10 km, > 10-20 km from the closest wind turbine), and 28% responded. In the closest distance zone, 15% of respondents reported having symptoms that they have intuitively associated with wind turbine infrasound. In the whole study area, the symptom prevalence was 5%. Many of the symptomatic respondents were annoyed by audible wind turbine sound and associated their symptoms also with vibration or electromagnetic field from wind turbines. One third of the symptomatic respondents rated their symptoms severe, and the symptom spectrum was very broad covering several organ systems. In multivariate models, many factors such as proximity to wind turbines, impaired health status, being annoyed by different aspects of wind turbines and considering wind turbines as a health risk were associated with having wind turbine infrasound related symptoms. Although causal relationships cannot be assessed based on a cross-sectional questionnaire study, it can be speculated that interpretations of symptoms are affected by many other factors in addition to actual exposure.

Wood stove use and other determinants of personal and indoor exposures to particulate air pollution and ozone among elderly persons in a Northern Suburb.

A six-month winter-spring study was conducted in a suburb of the northern European city of Kuopio, Finland, to identify and quantify factors determining daily personal exposure and home indoor levels of fine particulate matter (PM2.5 , diameter <2.5 µm) and its light absorption coefficient (PM2.5abs ), a proxy for combustion-derived black carbon. Moreover, determinants of home indoor ozone (O3 ) concentration were examined. Local central site outdoor, home indoor, and personal daily levels of pollutants were monitored in this suburb among 37 elderly residents. Outdoor concentrations of the pollutants were significant determinants of their levels in home indoor air and personal exposures. Natural ventilation in the detached and row houses increased personal exposure to PM2.5 , but not to PM2.5abs , when compared with mechanical ventilation. Only cooking out of the recorded household activities increased indoor PM2.5 . The use of a wood stove room heater or wood-fired sauna stove was associated with elevated concentrations of personal PM2.5 and PM2.5abs , and indoor PM2.5abs . Candle burning increased daily indoor and personal PM2.5abs , and it was also a determinant of indoor ozone level. In conclusion, relatively short-lasting wood and candle burning of a few hours increased residents' daily exposure to potentially hazardous, combustion-derived carbonaceous particulate matter.

Association Between Short-term Exposure to Ultrafine Particles and Mortality in Eight European Urban Areas.

BACKGROUND: Epidemiologic evidence on the association between short-term exposure to ultrafine particles and mortality is weak, due to the lack of routine measurements of these particles and standardized multicenter studies. We investigated the relationship between ultrafine particles and particulate matter (PM) and daily mortality in eight European urban areas. METHODS: We collected daily data on nonaccidental and cardiorespiratory mortality, particle number concentrations (as proxy for ultrafine particle number concentration), fine and coarse PM, gases and meteorologic parameters in eight urban areas of Finland, Sweden, Denmark, Germany, Italy, Spain, and Greece, between 1999 and 2013. We applied city-specific time-series Poisson regression models and pooled them with random-effects meta-analysis. RESULTS: We estimated a weak, delayed association between particle number concentration and nonaccidental mortality, with mortality increasing by approximately 0.35% per 10,000 particles/cm increases in particle number concentration occurring 5 to 7 days before death. A similar pattern was found for cause-specific mortality. Estimates decreased after adjustment for fine particles (PM2.5) or nitrogen dioxide (NO2). The stronger association found between particle number concentration and mortality in the warmer season (1.14% increase) became null after adjustment for other pollutants. CONCLUSIONS: We found weak evidence of an association between daily ultrafine particles and mortality. Further studies are required with standardized protocols for ultrafine particle data collection in multiple European cities over extended study periods.

Particulates and noise exposure during bicycle, bus and car commuting: A study in three European cities.

BACKGROUND: In order to curb traffic-related air pollution and its impact on the physical environment, contemporary city commuters are encouraged to shift from private car use to active or public transport modes. However, personal exposures to particulate matter (PM), black carbon and noise during commuting may be substantial. Therefore, studies comparing exposures during recommended modes of transport versus car trips are needed. METHODS: We measured personal exposure to various-sized particulates, soot, and noise during commuting by bicycle, bus and car in three European cities: Helsinki in Finland, Rotterdam in the Netherlands and Thessaloniki in Greece using portable monitoring devices. We monitored commonly travelled routes in these cities. RESULTS: The total number of one-way trips yielding data on any of the measured parameters were 84, 72, 94 and 69 for bicycle, bus, closed-window car and open-window car modes, respectively. The highest mean PM2.5 (85µg/m3), PM10 (131µg/m3), black carbon (10.9µg/m3) and noise (75dBA) levels were recorded on the bus, bus (again), open-window car and bicycle modes, respectively, all in Thessaloniki, PM and soot concentrations were generally higher during biking and taking a bus than during a drive in a a car with closed windows. Ratios of bike:car PM10 ranged from 1.1 in Thessaloniki to 2.6 in Helsinki, while bus:car ratios ranged from in 1.0 in Rotterdam to 5.6 in Thessaloniki. Higher noise levels were mostly recorded during bicycle rides. CONCLUSION: Based on our study, active- and public-transport commuters are often at risk of higher air pollution and noise exposure than private car users. This should be taken into account in urban transportation planning.

Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data.

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR2: 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.

Impact of wood combustion for secondary heating and recreational purposes on particulate air pollution in a suburb in Finland.

Little information is available on the concentrations of ambient fine particles (PM2.5) in residential areas where wood combustion is common for recreational purposes and secondary heating. Further, the validity of central site measurements of PM2.5 as a measure of exposure is unclear. Therefore, outdoor PM2.5 samples were repeatedly collected at a central site and home outdoor locations from a panel of 29 residents in a suburb in Kuopio, Finland. Source apportionment results from the central site were used to estimate the contributions from local sources, including wood combustion, to PM2.5 and absorption coefficient (ABS) at home outdoor locations. Correlations between the central and home outdoor concentrations of PM2.5, ABS, and their local components were analyzed for each home. At the central site, the average PM2.5 was 6.0 µg m(-)(3) during the heating season, and the contribution from wood combustion (16%) was higher than the contribution from exhaust emissions (12%). Central site measurements predicted poorly daily variation in PM2.5 from local sources. In conclusion, wood combustion significantly affects air quality also in areas where it is not the primary heating source. In epidemiological panel studies, central site measurements may not sufficiently capture daily variation in exposure to PM2.5 from local wood combustion.

Source-specific fine particulate air pollution and systemic inflammation in ischaemic heart disease patients.

OBJECTIVE: To compare short-term effects of fine particles (PM2.5; aerodynamic diameter <2.5 µm) from different sources on the blood levels of markers of systemic inflammation. METHODS: We followed a panel of 52 ischaemic heart disease patients from 15 November 2005 to 21 April 2006 with clinic visits in every second week in the city of Kotka, Finland, and determined nine inflammatory markers from blood samples. In addition, we monitored outdoor air pollution at a fixed site during the study period and conducted a source apportionment of PM2.5 using the Environmental Protection Agency's model EPA PMF 3.0. We then analysed associations between levels of source-specific PM2.5 and markers of systemic inflammation using linear mixed models. RESULTS: We identified five source categories: regional and long-range transport (LRT), traffic, biomass combustion, sea salt, and pulp industry. We found most evidence for the relation of air pollution and inflammation in LRT, traffic and biomass combustion; the most relevant inflammation markers were C-reactive protein, interleukin-12 and myeloperoxidase. Sea salt was not positively associated with any of the inflammatory markers. CONCLUSIONS: Results suggest that PM2.5 from several sources, such as biomass combustion and traffic, are promoters of systemic inflammation, a risk factor for cardiovascular diseases.

Long-term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts.

BACKGROUND: Air pollution has been associated with cardiovascular mortality, but it remains unclear as to whether specific pollutants are related to specific cardiovascular causes of death. Within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE), we investigated the associations of long-term exposure to several air pollutants with all cardiovascular disease (CVD) mortality, as well as with specific cardiovascular causes of death. METHODS: Data from 22 European cohort studies were used. Using a standardized protocol, study area-specific air pollution exposure at the residential address was characterized as annual average concentrations of the following: nitrogen oxides (NO2 and NOx); particles with diameters of less than 2.5 µm (PM2.5), less than 10 µm (PM10), and 10 µm to 2.5 µm (PMcoarse); PM2.5 absorbance estimated by land-use regression models; and traffic indicators. We applied cohort-specific Cox proportional hazards models using a standardized protocol. Random-effects meta-analysis was used to obtain pooled effect estimates. RESULTS: The total study population consisted of 367,383 participants, with 9994 deaths from CVD (including 4,992 from ischemic heart disease, 2264 from myocardial infarction, and 2484 from cerebrovascular disease). All hazard ratios were approximately 1.0, except for particle mass and cerebrovascular disease mortality; for PM2.5, the hazard ratio was 1.21 (95% confidence interval = 0.87-1.69) per 5 µg/m and for PM10, 1.22 (0.91-1.63) per 10 µg/m. CONCLUSION: In a joint analysis of data from 22 European cohorts, most hazard ratios for the association of air pollutants with mortality from overall CVD and with specific CVDs were approximately 1.0, with the exception of particulate mass and cerebrovascular disease mortality for which there was suggestive evidence for an association.

Development of land use regression models for elemental, organic carbon, PAH, and hopanes/steranes in 10 ESCAPE/TRANSPHORM European study areas.

Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NOx), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16-40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R(2)) was found for EC - 84%. The median R(2) was 51% for OC, 67% for benzo[a]pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies.

Long-term exposure to transportation noise and obesity: A pooled analysis of eleven Nordic cohorts.

Background: Available evidence suggests a link between exposure to transportation noise and an increased risk of obesity. We aimed to assess exposure-response functions for long-term residential exposure to road traffic, railway and aircraft noise, and markers of obesity. Methods: Our cross-sectional study is based on pooled data from 11 Nordic cohorts, including up to 162,639 individuals with either measured (69.2%) or self-reported obesity data. Residential exposure to transportation noise was estimated as a time-weighted average Lden 5 years before recruitment. Adjusted linear and logistic regression models were fitted to assess beta coefficients and odds ratios (OR) with 95% confidence intervals (CI) for body mass index, overweight, and obesity, as well as for waist circumference and central obesity. Furthermore, natural splines were fitted to assess the shape of the exposure-response functions. Results: For road traffic noise, the OR for obesity was 1.06 (95% CI = 1.03, 1.08) and for central obesity 1.03 (95% CI = 1.01, 1.05) per 10 dB Lden. Thresholds were observed at around 50-55 and 55-60 dB Lden, respectively, above which there was an approximate 10% risk increase per 10 dB Lden increment for both outcomes. However, linear associations only occurred in participants with measured obesity markers and were strongly influenced by the largest cohort. Similar risk estimates as for road traffic noise were found for railway noise, with no clear thresholds. For aircraft noise, results were uncertain due to the low number of exposed participants. Conclusion: Our results support an association between road traffic and railway noise and obesity.

Agreement of land use regression models with personal exposure measurements of particulate matter and nitrogen oxides air pollution.

Land use regression (LUR) models are often used to predict long-term average concentrations of air pollutants. Little is known how well LUR models predict personal exposure. In this study, the agreement of LUR models with measured personal exposure was assessed. The measured components were particulate matter with a diameter smaller than 2.5 µm (PM2.5), soot (reflectance of PM2.5), nitrogen oxides (NOx), and nitrogen dioxide (NO2). In Helsinki, Utrecht, and Barcelona, 15 volunteers (from semiurban, urban background, and traffic sites) followed prescribed time activity patterns. Per participant, six 96 h outdoor, indoor, and personal measurements spread over three seasons were conducted. Soot LUR models were significantly correlated with measured average outdoor and personal soot concentrations. Soot LUR models explained 39%, 44%, and 20% of personal exposure variability (R(2)) in Helsinki, Utrecht, and Barcelona. NO2 LUR models significantly predicted outdoor concentrations and personal exposure in Utrecht and Helsinki, whereas NOx and PM2.5 LUR models did not predict personal exposure. PM2.5, NO2, and NOx models were correlated with personal soot, the component least affected by indoor sources. LUR modeled and measured outdoor, indoor, and personal concentrations were highly correlated for all pollutants when data from the three cities were combined. This study supports the use of intraurban LUR models for especially soot in air pollution epidemiology.

Evaluation of land use regression models for NO2 and particulate matter in 20 European study areas: the ESCAPE project.

Land use regression models (LUR) frequently use leave-one-out-cross-validation (LOOCV) to assess model fit, but recent studies suggested that this may overestimate predictive ability in independent data sets. Our aim was to evaluate LUR models for nitrogen dioxide (NO2) and particulate matter (PM) components exploiting the high correlation between concentrations of PM metrics and NO2. LUR models have been developed for NO2, PM2.5 absorbance, and copper (Cu) in PM10 based on 20 sites in each of the 20 study areas of the ESCAPE project. Models were evaluated with LOOCV and "hold-out evaluation (HEV)" using the correlation of predicted NO2 or PM concentrations with measured NO2 concentrations at the 20 additional NO2 sites in each area. For NO2, PM2.5 absorbance and PM10 Cu, the median LOOCV R(2)s were 0.83, 0.81, and 0.76 whereas the median HEV R(2) were 0.52, 0.44, and 0.40. There was a positive association between the LOOCV R(2) and HEV R(2) for PM2.5 absorbance and PM10 Cu. Our results confirm that the predictive ability of LUR models based on relatively small training sets is overestimated by the LOOCV R(2)s. Nevertheless, in most areas LUR models still explained a substantial fraction of the variation of concentrations measured at independent sites.

Development of land use regression models for particle composition in twenty study areas in Europe.

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM2.5) explaining on average between 67 and 79% of the concentration variance (R(2)) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R(2) ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R(2) under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Exposure to long-term source-specific transportation noise and incident breast cancer: A pooled study of eight Nordic cohorts.

BACKGROUND: Environmental noise is an important environmental exposure that can affect health. An association between transportation noise and breast cancer incidence has been suggested, although current evidence is limited. We investigated the pooled association between long-term exposure to transportation noise and breast cancer incidence. METHODS: Pooled data from eight Nordic cohorts provided a study population of 111,492 women. Road, railway, and aircraft noise were modelled at residential addresses. Breast cancer incidence (all, estrogen receptor (ER) positive, and ER negative) was derived from cancer registries. Hazard ratios (HR) were estimated using Cox Proportional Hazards Models, adjusting main models for sociodemographic and lifestyle variables together with long-term exposure to air pollution. RESULTS: A total of 93,859 women were included in the analyses, of whom 5,875 developed breast cancer. The median (5th-95th percentile) 5-year residential road traffic noise was 54.8 (40.0-67.8) dB Lden, and among those exposed, the median railway noise was 51.0 (41.2-65.8) dB Lden. We observed a pooled HR for breast cancer (95 % confidence interval (CI)) of 1.03 (0.99-1.06) per 10 dB increase in 5-year mean exposure to road traffic noise, and 1.03 (95 % CI: 0.96-1.11) for railway noise, after adjustment for lifestyle and sociodemographic covariates. HRs remained unchanged in analyses with further adjustment for PM2.5 and attenuated when adjusted for NO2 (HRs from 1.02 to 1.01), in analyses using the same sample. For aircraft noise, no association was observed. The associations did not vary by ER status for any noise source. In analyses using <60 dB as a cutoff, we found HRs of 1.08 (0.99-1.18) for road traffic and 1.19 (0.95-1.49) for railway noise. CONCLUSIONS: We found weak associations between road and railway noise and breast cancer risk. More high-quality prospective studies are needed, particularly among those exposed to railway and aircraft noise before conclusions regarding noise as a risk factor for breast cancer can be made.

Development of Land Use Regression models for PM(2.5), PM(2.5) absorbance, PM(10) and PM(coarse) in 20 European study areas; results of the ESCAPE project.

Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.