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Background: Health impact assessments (HIA) use information on exposure, baseline mortality/morbidity and exposure-response functions from epidemiological studies in order to quantify the health impacts of existing situations and/or alternative scenarios. The aim of this study was to improve HIA methods for air pollution studies in situations where exposures can be estimated using GIS with high spatial resolution and dispersion modeling approaches., Methods: Tallinn was divided into 84 sections according to neighborhoods, with a total population of approx. 390,000 persons. Actual baseline rates for total mortality and hospitalization with cardiovascular and respiratory diagnosis were identified. The exposure to fine particles (PM2.5) from local emissions was defined as the modeled annual levels. The model validation and morbidity assessment were based on 2006 PM10 or PM2.5 levels at 3 monitoring stations. The exposure-response coefficients used were for total mortality 6.2% (95% CI 1.6-11%) per 10 microg/m3 increase of annual mean PM2.5 concentration and for the assessment of respiratory and cardiovascular hospitalizations 1.14% (95% CI 0.62-1.67%) and 0.73% (95% CI 0.47-0.93%) per 10 microg/m3 increase of PM10. The direct costs related to morbidity were calculated according to hospital treatment expenses in 2005 and the cost of premature deaths using the concept of Value of Life Year (VOLY)., Results: The annual population-weighted-modeled exposure to locally emitted PM2.5 in Tallinn was 11.6 microg/m3. Our analysis showed that it corresponds to 296 (95% CI 76528) premature deaths resulting in 3859 (95% CI 10236636) Years of Life Lost (YLL) per year. The average decrease in life-expectancy at birth per resident of Tallinn was estimated to be 0.64 (95% CI 0.17-1.10) years. While in the polluted city centre this may reach 1.17 years, in the least polluted neighborhoods it remains between 0.1 and 0.3 years. When dividing the YLL by the number of premature deaths, the decrease in life expectancy among the actual cases is around 13 years. As for the morbidity, the short-term effects of air pollution were estimated to result in an additional 71 (95% CI 43-104) respiratory and 204 (95% CI 131-260) cardiovascular hospitalizations per year. The biggest external costs are related to the long-term effects on mortality: this is on average euro 150 (95% CI 40-260) million annually. In comparison, the costs of short-term air-pollution driven hospitalizations are small euro 0.3 (95% CI 0.2-0.4) million., Conclusion: Sectioning the city for analysis and using GIS systems can help to improve the accuracy of air pollution health impact estimations, especially in study areas with poor air pollution monitoring data but available dispersion models.

Air pollution is one of the main environmental concerns in urban areas, where anthropogenic emissions strongly affect air quality. This work presents the first spatially resolved detailed characterization of PM2.5 (particulate matter with aerodynamic equivalent diameter daero ≤ 2.5 µm) in two major Estonian cities, Tallinn and Tartu. The measurements were performed in March 2014 using a mobile platform. In both cities, the non-refractory (NR)-PM2.5 was characterized by a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) using a recently developed lens which increases the transmission of super-micron particles. Equivalent black carbon (eBC) and several trace gases including carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4) were also measured. The chemical composition of PM2.5 was found to be very similar in the two cities. Organic aerosol (OA) constituted the largest fraction, explaining on average about 52 to 60% of the PM2.5 mass. Four sources of OA were identified using positive matrix factorization (PMF): hydrocarbon-like OA (HOA, from traffic emissions), biomass burning OA (BBOA, from biomass combustion), residential influenced OA (RIOA, probably mostly from cooking processes with possible contributions from waste and coal burning), and oxygenated OA (OOA, related to secondary aerosol formation). OOA was the major OA source during nighttime, explaining on average half of the OA mass, while during daytime mobile measurements the OA was affected by point sources and dominated by the primary fraction. A strong increase in the secondary organic and inorganic components was observed during periods with transport of air masses from northern Germany, while the primary local emissions accumulated during periods with temperature inversions. Mobile measurements offered the identification of different source regions within the urban areas and the assessment of the extent to which pollutants concentrations exceeded regional background levels (urban increments). HOA, eBC, CO2, and CO showed stronger enhancements on busy roads during the morning and evening traffic rush hours; BBOA had its maximum enhancement in the residential areas during the evening hours and RIOA was enhanced in both the city center (emissions from restaurants) and in the residential areas (emissions from residential cooking). In contrast, secondary components (OOA, sulfate (SO4), nitrate (NO3), ammonium (NH4), and chloride (Cl)) had very homogeneous distributions in time and space. We were able to determine a total PM2.5 urban increment in Tartu of 6.0 µgm-3 over a regional background concentration of 4.0 µgm-3 (i.e., a factor of 2.5 increase). Traffic exhaust emissions were identified as the most important source of this increase, with eBC and HOA explaining on average 53.3 and 20.5% of the total increment, respectively. [ABSTRACT FROM AUTHOR]

Background: Health impact assessments (HIA) use information on exposure, baseline mortality/morbidity and exposureresponse functions from epidemiological studies in order to quantify the health impacts of existing situations and/or alternative scenarios. The aim of this study was to improve HIA methods for air pollution studies in situations where exposures can be estimated using GIS with high spatial resolution and dispersion modeling approaches. Methods: Tallinn was divided into 84 sections according to neighborhoods, with a total population of approx. 390 000 persons. Actual baseline rates for total mortality and hospitalization with cardiovascular and respiratory diagnosis were identified. The exposure to fine particles (PM2.5) from local emissions was defined as the modeled annual levels. The model validation and morbidity assessment were based on 2006 PM10 or PM2.5 levels at 3 monitoring stations. The exposure-response coefficients used were for total mortality 6.2% (95% CI 1.6-11%) per 10 μg/m3 increase of annual mean PM2.5 concentration and for the assessment of respiratory and cardiovascular hospitalizations 1.14% (95% CI 0.62-1.67%) and 0.73% (95% CI 0.47 0.93%) per 10 μg/m3 increase of PM10. The direct costs related to morbidity were calculated according to hospital treatment expenses in 2005 and the cost of premature deaths using the concept of Value of Life Year (VOLY). Results: The annual population-weighted-modeled exposure to locally emitted PM2.5 in Tallinn was 11.6 μg/m3. Our analysis showed that it corresponds to 296 (95% CI 76528) premature deaths resulting in 3859 (95% CI 10236636) Years of Life Lost (YLL) per year. The average decrease in life-expectancy at birth per resident of Tallinn was estimated to be 0.64 (95% CI 0.17- 1.10) years. While in the polluted city centre this may reach 1.17 years, in the least polluted neighborhoods it remains between 0.1 and 0.3 years. When dividing the YLL by the number of premature deaths, the decrease in life expectancy among the actual cases is around 13 years. As for the morbidity, the short-term effects of air pollution were estimated to result in an additional 71 (95% CI 43-104) respiratory and 204 (95% CI 131-260) cardiovascular hospitalizations per year. The biggest external costs are related to the long-term effects on mortality: this is on average €150 (95% CI 40-260) million annually. In comparison, the costs of short-term air-pollution driven hospitalizations are small €0.3 (95% CI 0.2-0.4) million. Conclusion: Sectioning the city for analysis and using GIS systems can help to improve the accuracy of air pollution health impact estimations, especially in study areas with poor air pollution monitoring data but available dispersion models. [ABSTRACT FROM AUTHOR]

The levels of priority air pollutants (NOX, CO, SO2, O3, particulate matter) were investigated at three sites in Tallinn (Estonia). The concentration of particles with diameters less than 2.5 μm (PM2.5), reaching human alveoli in lungs, was also measured. The collected samples of particulate matter were extracted with the accelerated solvent extraction method and analysed with high performance liquid chromatography. The chemical analysis indicated the occurrence of polycyclic aromatic hydrocarbons in the airborne particulate matter samples. The data received were grouped into four categories for annual, seasonal, diurnal, and day-of-the-week trends. The winter concentrations of NOX and CO in the urban area were twice as high as in the summer period. Annual and diurnal variations were studied to find out the main sources of pollution. Increasing traffic intensity and industrial activity were suggested to be associated with changes in air quality. [ABSTRACT FROM AUTHOR]