Tarja Yli-Tuomi

Development of Land Use Regression Models for PM2.5, PM2.5 Absorbance, PM10 and PMcoarse 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.

A simple procedure for correcting loading effects of aethalometer data

A simple method for correcting for the loading effects of aethalometer data is presented. The formula BC(CORRECTED) = (1 + k x ATN) x BC(NONCORRECTED), where ATN is the attenuation and BC is black carbon, was used for correcting aethalometer data obtained from measurements at three different sites: a subway station in Helsinki, an urban background measurement station in Helsinki, and a rural station in Hyytiälä in central Finland. The BC data were compared with simultaneously measured aerosol volume concentrations (V). After the correction algorithm, the BC-to-V ratio remained relatively stable between consequent filter spots, which can be regarded as indirect evidence that the correction algorithm works. The k value calculated from the outdoor sites had a clear seasonal cycle that could be explained by darker aerosol in winter than in summer. When the contribution of BC to the total aerosol volume was high, the k factor was high and vice versa. In winter, the k values at all wavelengths were very close to that obtained from the subway station data. In summer, the k value was wavelength dependent and often negative. When the k value is negative, the noncorrected BC concentrations overestimated the true concentrations.

Urban Air Pollution And Asthma And Copd Hospital Emergency Room Visits

Background: There is little previous information of the effects of size fractioned particulate air pollution and source specific fine particles (PM2.5; <2.5 μm) on asthma and chronic obstructive pulmonary disease (COPD) among children, adults and the elderly. Objectives: To determine the effects of daily variation in levels of different particle size fractions and gaseous pollutants on asthma and COPD by age group. Methods: Levels of particulate air pollution, NO2 and CO were measured from 1998 to 2004 at central outdoor monitoring sites in Helsinki, Finland. Associations between daily pollution levels and hospital emergency room visits were evaluated for asthma (ICD10: J45+J46) in children <15 years old, and for asthma and COPD (ICD10: J41+J44) in adults (15–64 years) and the elderly (⩾65 years). Results: Three to 5 day lagged increases in asthma visits were found among children in association with nucleation (<0.03 μm), Aitken (0.03–0.1 μm) and accumulation (0.1–0.29 μm) mode particles, gaseous pollutants and traffic related PM2.5 (7.8% (95% CI 3.5 to 12.3) for 1.1 μg/m3 increase in traffic related PM2.5 at lag 4). Pooled asthma–COPD visits among the elderly were associated with lag 0 of PM2.5, coarse particles, gaseous pollutants and long range transported and traffic related PM2.5 (3.9% (95% CI 0.28 to 7.7) at lag 0). Only accumulation mode and coarse particles were associated with asthma and COPD among adults. Conclusions: Among children, traffic related PM2.5 had delayed effects, whereas among the elderly, several types of particles had effects that were more immediate. These findings suggest that the mechanisms of the respiratory effects of air pollution, and responsible pollutants, differ by age group.

Particulate Air Pollution and Acute Cardiorespiratory Hospital Admissions and Mortality Among the Elderly

Background: It is known that particulate air pollution affects cardiorespiratory health; however, it is unclear which particle size fractions and sources of particles are responsible for the health effects. Methods: Daily levels of nucleation (<0.03 &mgr;m), Aitken (0.03–0.1 &mgr;m), accumulation (0.1–0.29 &mgr;m), and coarse mode (2.5–10 &mgr;m) particles, particles with diameter <2.5 &mgr;m (PM2.5), and gaseous pollutants were measured at central outdoor measurement sites in Helsinki, Finland between 1998 and 2004. We determined the associations of particles with daily cardiorespiratory mortality and acute hospital admissions among the elderly (≥65 years). For the analyses we used Poisson generalized additive models and for the source apportionment of PM2.5 we used the EPA positive matrix factorization method. Results: There was a suggestion of an association of hospital admissions for arrhythmia with Aitken mode particles and PM2.5 from traffic. Otherwise few associations were observed between various sizes and types of particles and either cardiovascular admissions or mortality. In contrast, most particle fractions had positive associations with admissions for pneumonia and asthma-chronic obstructive pulmonary disease (COPD). The strongest and most consistent associations were found for accumulation mode particles (3.1%; 95% confidence interval = 0.43–5.8 for pneumonia over the 5-day mean, and 3.8%; 1.3–6.3 for asthma-COPD at lag 0, for an interquartile increase in particles). We also found a positive association of respiratory mortality mainly with accumulation mode particles (5.1%; 1.2–9.0 at lag 0). Conclusions: All particle fractions including Aitken, accumulation, and coarse mode had especially adverse respiratory health effects among the elderly. Overall associations were stronger for respiratory than for cardiovascular outcomes.

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.

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 &mgr;m (PM2.5), less than 10 &mgr;m (PM10), and 10 &mgr;m to 2.5 &mgr;m (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 &mgr;g/m3 and for PM10, 1.22 (0.91–1.63) per 10 &mgr;g/m3. 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.

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 &mgr;m (PM2.5), less than 10 &mgr;m (PM10), and 10 &mgr;m to 2.5 &mgr;m (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 &mgr;g/m3 and for PM10, 1.22 (0.91–1.63) per 10 &mgr;g/m3. 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.

Low-level exposure to ambient particulate matter is associated with systemic inflammation in ischemic heart disease patients

Short-term exposure to ambient air pollution is associated with increased cardiovascular mortality and morbidity. This adverse health effect is suggested to be mediated by inflammatory processes. The purpose of this study was to determine if low levels of particulate matter, typical for smaller cities, are associated with acute systemic inflammation. Fifty-two elderly individuals with ischemic heart disease were followed for six months with biweekly clinical visits in the city of Kotka, Finland. Blood samples were collected for the determination of inflammatory markers interleukin (IL)-1β, IL-6, IL-8, IL-12, interferon (IFN)γ, C-reactive protein (CRP), fibrinogen, myeloperoxidase and white blood cell count. Particle number concentration and fine particle (particles with aerodynamic diameters <2.5 μm (PM(2.5))) as well as thoracic particle (particles with aerodynamic diameters <10 μm (PM(10))) mass concentration were measured daily at a fixed outdoor measurement site. Light-absorbance of PM(2.5) filter samples, an indicator of combustion derived particles, was measured with a smoke-stain reflectometer. In addition, personal exposure to PM(2.5) was measured with portable photometers. During the study period, wildfires in Eastern Europe led to a 12-day air pollution episode, which was excluded from the main analyses. Average ambient PM(2.5) concentration was 8.7 μg/m(3). Of the studied pollutants, PM(2.5) and absorbance were most strongly associated with increased levels of inflammatory markers; most notably with C-reactive protein and IL-12 within a few days of exposure. There was also some evidence of an effect of particulate air pollution on fibrinogen and myeloperoxidase. The concentration of IL-12 was considerably (227%) higher during than before the forest fire episode. These findings show that even low levels of particulate air pollution from urban sources are associated with acute systemic inflammation. Also particles from wildfires may exhibit pro-inflammatory effects.

Atmospheric aerosol over Finnish Arctic: source analysis by the multilinear engine and the potential source contribution function

Week-long samples of total suspended particles were collected between 1964 and 1978 from Kevo at the Finnish Arctic and analyzed for a number of chemical species. The chemical composition data was analyzed using a mixed 2-way/3-way model. The results of receptor modeling were connected with the back trajectory data in a Potential Source Contribution Function analysis to determine the likely source areas. Nine sources, namely silver emissions, coal/oil shale combustion, biomass burning, non-ferrous smelters (two sources), crustal elements from remote sources, excess silicon from local sources, sea salt particles and biogenic sulfur emissions from marine algae were found. Although the emissions from industrial areas in the Kola Peninsula had an effect on the concentration of anthropogenic pollutants at Kevo, the highest concentrations during winter were transported from the sources in the mid-latitudes. The yearly strength of the biogenic sulfur emissions showed no dependence on the Northern Hemisphere temperature anomaly and thus, a climatic feedback loop could not be confirmed.

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

BACKGROUND Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods. OBJECTIVES Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5. METHODS The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area. RESULTS The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5. CONCLUSIONS LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.