Temporal trends in mean PM_2.5 concentrations within venues of the Hellenic Air Monitoring Study, Greece, 2010–2011.

<p>Temporal trends in mean PM_2.5 concentrations within venues of the Hellenic Air Monitoring Study, Greece, 2010–2011.</p

Factors associated with indoor PM_2.5 exposure attributable to SHS exposure in venues (N = 455) throughout Greece, the Hellenic Air Monitoring Study, 2010–2011.

<p><b>Abbreviations</b>: Adj. Beta = Adjusted Beta coefficient; St. Error = Standard error; Ref = Reference Category; PM_2.5 = Particulate matter ≤2.5 microns in diameter; SHS = secondhand smoke; N = total number of sampled sites.</p>1<p>Mixed effects Linear regression analyses adjusted for all other variables in the table.</p

Temporal trends in mean indoor PM<i>_2.5</i> measurements (µg/m³) and unadjusted pair-wise comparisons between Waves of the Hellenic Air Monitoring Study, Greece 2010–2011.

<p><b>Abbreviations</b>: St. Error = Standard error; PM_2.5 = Particulate matter ≤2.5 microns in diameter; n = number of wave-specific sampled sites; 95%CI = 95% Confidence Interval.</p

Adjusted¹ and crude linear regression models for the relationship between venue and measurement characteristics and average number of cigarettes² within hospitality venues in Greece (N = 445), 2010–2011.

<p><b>Abbreviations</b>: Adj. Beta = Adjusted Beta coefficient; St. Error = Standard error; Ref = Reference Category; N = total number of sampled sites; BC/100 m³ = Burning cigarettes per 100 m^3.</p>1<p>Adjusted for all other variables in the table.</p>2<p>Average number of cigarettes per measurement.</p

Adjusted¹ and crude linear regression models for the relationship between venue and measurement characteristics and smoker density² levels, within hospitality venues in Greece (N = 445), 2010–2011.

<p><b>Abbreviations</b>: Adj. Beta = Adjusted Beta coefficient; St. Error = Standard error; Ref = Reference Category; N = total number of sampled sites; BC/100 m³ = Burning cigarettes per 100 m³.</p>1<p>Adjusted for all other variables in the table.</p>2<p>Average number of cigarettes/100 m³ of venue air volume.</p

The relationship between ashtrays and signage within venues (n = 151) and adherence to a smoke-free legislation, within Waves 3 and 4 of the Hellenic Air Monitoring Study Greece, 2010–2011.

<p><b>Abbreviations</b>: Adj. Beta = Adjusted Beta coefficient; St. Error = Standard error; n = number of sampled sites.</p>1<p>Adjusted for venue type and city in a mixed effects linear model accounting for repeated measures in Waves 3 and 4.</p>2<p>Includes both factory made receptacles as well as improvised ashtray equivalents.</p>3<p>Refers to presence of any signage either outdoors or indoors against smoking (signs on doors, walls, bar tops and table tops against smoking).</p

Characteristics of the Hellenic Air Monitoring Study by wave and site, Greece, 2010–2011.

<p><b>Abbreviations</b>: n = number of sampled venues.</p>1<p>In total 19 venues were lost due to closure (Athens = 11, Crete = 6, Thessaloniki = 2), while 53 were lost due to the inability to assess those cities in Wave 4 (Larissa = 28, Serres = 25).</p

Development of Land Use Regression Models for PM_2.5, PM_2.5 Absorbance, PM₁₀ 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, PM₁₀, 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 (<i>R</i>²) was 71% for PM_2.5 (range across study areas 35–94%). Model <i>R</i>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 <i>R</i>² was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation <i>R</i>² results were on average 8–11% lower than model <i>R</i>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