Marko Kaasik

Health impact assessment of particulate pollution in Tallinn using fine spatial resolution and modeling techniques

BackgroundHealth 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.MethodsTallinn 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).ResultsThe 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.ConclusionSectioning 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.

Elemental and base anions deposition in the snow cover of North-Eastern Estonia.

Snow samples from 18 sites in Estonia were collected in February and March 1996 after 72–110 days of permanent snow cover. Three snow layers corresponding to different snow accumulation periods were separated in each sampling site. Snow water samples were analysed for sulphate (SO42-), nitrate (NO3-), and chloride (Cl-) ions and elemental composition. Deposition fluxes of 27 chemical species were used for factor, cluster and correlation analysis. The effects of cement dust, oil shale fly ash, sulphur dioxide and chlorine from emissions of thermal power plants were distinguished. A large number of trace metals are strongly correlated with each other (R > 0.8) and with macro-components (except NO3-), which refers to a common origin, identified as the mineral part of oil shale. Deposition fluxes of Ca, Mg, SO42- and a number of mineral components exceed near the power plants 1–2 decimal orders the background value. The deposition fluxes in forested sites are up to 2 times higher than in open land sites. This difference may be caused by more efficient turbulent transfer over rougher surface. It is suggested, that NO3- and Zn originate mainly and Pb, Cd and Cu partially from non-local or diffuse sources (traffic, domestic heating, far transport). The results of this research could be used to evaluate the air pollution deposition models and for ecological impact estimations.

Assessment of Urban Air Quality in South Estonia by Simple Measures

In countries having limited resources, it is difficult to assess urban air quality on contemporaneously, due to the absence of on-line information about air pollution levels and emission rates. An alternative approach is recommended for smaller cities with lower demands of resources. The applied scheme consists of a database of air pollution sources (NOx and CO from industry, traffic, and domestic heating), the simple Gaussian-plume model AEROPOL and a series of measurements by passive monitors. This method was used in Tartu, a small city situated in the valley of the river Emajõgi, within a landscape with noteworthy topographical variations. Simulations of annual average and maximal concentrations were performed, and a fair agreement obtained with NO2 monitoring results from passive Palmes monitors. Inventories of pollution sources in 1998 revealed that official statistics of stationary sources covered 64% of SO2,36% of CO, 37% of NOx and 32% of total particulate matter emissions. Recommendations for measures for reducing air pollution levels and for further investigations towards improving air quality assessment and management, are given.

Chronic traffic-induced PM exposure and self-reported respiratory and cardiovascular health in the RHINE Tartu Cohort.

The relationship between exposure to traffic induced particles, respiratory health and cardiac diseases was studied in the RHINE Tartu cohort. A postal questionnaire with commonly used questions regarding respiratory symptoms, cardiac disease, lifestyle issues such as smoking habits, indoor environment, occupation, early life exposure and sleep disorders was sent to 2,460 adults. The annual concentrations of local traffic induced particles were modelled with an atmospheric dispersion model with traffic flow data, and obtained PMexhaust concentrations in 40 × 40 m grids were linked with home addresses with GIS. The relationship between the level of exhaust particles outside home and self-reported health problems were analyzed using a multiple logistic regression model. We found a significant relation between fine exhaust particles and cardiac disease, OR = 1.64 (95% CI 1.12–2.43) for increase in PMexhaust corresponding to the fifth to the 95th percentile range. The associations also were positive but non-significant for hypertension OR = 1.42 (95% CI 0.94–2.13), shortness of breath OR = 1.27 (95% CI 0.84–1.94) and other respiratory symptoms.

An air quality modelling system for a medium-sized town: a case study in Estonia

The air pollution modelling system used in Tartu (population 101,000) is aimed at examining the pollution levels in different meteorological conditions, and determining optimal traffic schemes and strategic development plans for the city. The system consists of: (i) a Gaussian plume model AEROPOL, and (ii) a database on sources of NOx, CO, SO2 and particulates (from industrial, traffic, and domestic heating sources). The calculations of annual average and maximum concentrations are performed and fair agreement with monitoring results is found. A similar scheme is used in Parnu (population 60,000) and Kuressaare (population 15,000).

A Suggested Correction to the EMEP Database, Regarding the Location of a Major Industrial Air Pollution Source in Kola Peninsula

For tracing the sources of aerosol fractions measured in the campaign at Varrio, Finnish Lapland, spring 2003, the SILAM model was applied in two modes: (1) inverse (adjoint) run, with measured aerosol concentrations as the sensitivity source function; and (2) forward run with EMEP database of sulphur dioxide and particulate matter emissions. One of coarse aerosol (0.1–10 μm) peaks resulted in total aerosol concentration exceeding 20 μg/m3 that is very high for polar latitudes. The inverse model for that peak points at the border area between Russia and Norway, but forward run failed to reproduce it. It was found that the well-known metal industries of Apatity-Kirovsk and Monchegorsk, Kola Peninsula, Russia, are represented adequately in EMEP database, but no significant emissions were found at the site of Nikel (a major metal smelting industry), 7 km from the border with Norway. Very high emissions were originated from about 100 km to the east instead. Then the database used to run SILAM was corrected on the basis of satellite images of the region. When running the SILAM model using the revised database, the agreement of measured and modelled peak concentrations was substantially better. A proposal for database correction is made to EMEP. The correction is supposed to improve the quality of air pollution transport calculations.

Validation of Gaussian plume model Aeropol against Cabauw field experiment

The Gaussian dispersion model AEROPOL is validated against the Cabauw (1977-1978) dataset, applying the parameters and rules from the Model Validation Kit. The purpose to revisit this experiment is preparation for fast response to buoyant accidental releases. In the AEROPOL model, the classical Pasquill-Gifford stability and a scheme based on Lagrangian time scales (the Gryning scheme) are used as alternatives. Validation is based on correlation, fractional bias, fractional sigma, NMSE and fraction in factor 2, applying these statistics to maximal arc-wise, near-centreline and cross-wind integrated concentrations. Both parameterisations are found to be fairly adequate. The Gryning scheme results in moderately underestimated concentrations, which are well correlated with the experimental ones. The Pasquill parameterisation, in contrast, represents the concentrations well on average, but with larger scatter. The average wind direction and speed between the lowest measurement level and release level are found to be good approximations to represent the position of the Gaussian plume.

Association Between Health Symptoms and Particulate Matter from Traffic and Residential Heating " Results from RHINE III in Tartu

Background: Traffic and residential heating are the main sources of particulate matter (PM) in Northern Europe. Wood is widely used for residential heating and vehicle numbers are increasing. Besides traffic exhaust, studded tires produce road dust that is the main source of traffic-related PM10. Several studies have associated total PM mass with health symptoms; however there has been little research on the effects of PM from specific sources. Objective: To study the health effects resulting from traffic and local heating PM. Methods: Data on respiratory and cardiac diseases were collected within the framework of RHINE III (2011/2012) in Tartu, Estonia. Respondents’ geocoded home addresses were mapped in ArcGIS and linked with local heating-related PM2.5, traffic-related PM10 and total PM2.5 concentrations. Association between self-reported health and PM was assessed using multiple logistic regression analysis. Results: The annual mean modelled exposure for local heating PM2.5 was 2.3 μg/m3, for traffic PM10 3.3 μg/m3 and for all sources PM2.5 5.6 μg/m3. We found relationship between traffic induced PM10 as well as all sources induced PM2.5 with cardiac disease, OR=1.45 (95% CI 1.06−1.93) and 1.42 (95% CI 1.02−1.95), respectively. However, we did not find any significant association between residential heating induced particles and self-reported health symptoms. People with longer and better confirmed exposure period were also significantly associated with traffic induced PM10, all sources induced PM2.5 and cardiac diseases. Conclusion: Traffic-related PM10 and all sources induced PM2.5 associated with cardiac disease; whereas residential heating induced particles did not.

The Porosity Concept Applied to Urban Canopy Improves the Results of Gaussian Dispersion Modelling of Traffic-Dominated Emissions

The Gaussian plume model AEROPOL 5 is applied to estimate the yearly average NO2 concentrations in Tartu, the second largest town of Estonia with about 100,000 inhabitants, for RHINE study. We apply the porosity concept by Genikhovich E, Gracheva I, Filatova E (Modelling of urban air pollution: principles and problems. In: Borrego C, Schayes G (eds) Air pollution modelling and its application, XV. Kluwer, New York, pp 275–283, 2002) in post-processing of modelled ground-level concentrations: the area under buildings is excluded from dispersion volume in each grid cell, thus the concentration is divided to the fraction of porosity, i.e. non-built-up area. It appears that porosity correction substantially enhances the site-wise correlations between model-estimated and measured concentrations, bringing the underestimated levels in particular monitoring sites closer to reality. Moreover, correlations are even higher, when dividing the “raw” modelled concentrations by squared porosities. We suppose that reason of non-linearity is in slowing down the wind between the buildings.

The Origins and Formation Mechanisms of Aerosol during a Measurement Campaign in Finnish Lapland, Evaluated Using the Regional Dispersion Model SILAM

This paper is intended to clarify the geographical extent of processes leading to a nucleation event and the role of atmospheric transport in it. The study is based on the inverse (adjoint) runs of atmospheric advection-diffusion model SILAM and general knowledge on basic mechanisms and time scales of nanometer particle formation in the atmosphere. Results of an aerosol measurement campaign carried out in Varrio, Finland, Eastern Lapland, April–May 2003, were used as sensitivity source data for backward tracing. The footprint areas of three observed nucleation events suggest that (1) spatial scale of a nucleation event may reach about 1,000 km, and (2) impact of atmospheric transport to the aerosol processes recorded by a Eulerian (ground-based) observer may be significant. Formation of an intense event over extensive forested areas supports the theory on the role of biogenic VOC emissions. Need for coupling the models of atmospheric transport and aerosol dynamics was stressed.