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Despite the increasing number of studies on industrially contaminated sites (ICS) and their health effects, there are very few studies on perinatal health outcomes in ICSs. In the present study, we examined the perinatal health inequalities by comparing adverse birth outcomes (ABOs) in the oil shale industry region of Ida-Viru County in Estonia with national-level figures and investigated the effects of maternal environmental and sociodemographic factors. Based on the 208,313 birth records from 2004–2018, Ida-Viru ICS has a birth weight 124.5 g lower than the average of 3544 g in Estonia. A higher prevalence of preterm birth (4.3%) and low birth weight (4.8%) in Ida-Viru ICS is found compared to 3.3% on both indicators at the national level. Multiple logistic regression analysis shows the statistically significant association of ABOs with fine particle (PM2.5) air pollution, mother’s ethnicity, and education throughout Estonia. However, in Ida-Viru ICS, the ABOs odds are remarkably higher in these characteristics except for the mother’s ethnicity. Furthermore, the ABOs are associated with the residential proximity to ICS. Thus, the Ida-Viru ICS has unequally higher odds of adverse perinatal health across the environmental and sociodemographic factors. In addition to reducing the air pollutants, policy actions on social disparities are vital to address the country’s unjustly higher perinatal health inequalities, especially in the Ida-Viru ICS.

Residential wood combustion (RWC) is one of the largest sources of fine particles (PMHealth impact assessment (HIA) was employed as the methodology to quantify the health burden. Firstly, the RWC induced annual average PMAmongst the studied city areas, the local RWC induced PMAll four Nordic city areas have a substantial amount of domestic heating, and RWC is one of the most significant sources of PM

Transportation noise is a growing public health concern worldwide, especially in urban areas, causing annoyance, sleep disturbance, cardiovascular diseases and other health effects. Recently, European Commission (EC) has developed a mutual methodology for assessing health impacts of transportation noise in European Union using strategic noise mapping. Applying this methodology, our aim was to quantify the health effects of road, rail and aircraft noise in two Estonian cities, Tallinn and Tartu. We also aimed to assess sensitivity of this methodology, while implementing lower threshold values and employing additional health outcomes. The proportion of highly annoyed residents due to road traffic noise was 11.6% in Tallinn, and 9.2% in Tartu; around 2.5% residents in both cities could have high sleeping disturbances. As exposure to railway and aircraft noise was relatively low in both cities, people with high annoyance and high sleep disturbance caused by railway and aircraft noise was below 1%. Ischemic heart disease (IHD) cases attributable to road traffic noise was estimated to be 122.6 in Tallinn and 21.5 in Tartu. Altogether transportation noise was estimated to cause 1807 disability adjusted life years (DALYs) in Tallinn and 370 DALYs in Tartu. The health costs were calculated as €126.5 and €25.9 million annually, respectively in the two cities. When we included higher number of health outcomes (stroke incidence, IHD deaths) and lowered exposure threshold by 5 dB, the annual burden of disease was doubled. As the latest epidemiological studies showed transportation noise having larger number of effects on lower noise levels, the results with the currently applied European Commission health impact assessment (HIA) methodology were rather conservative. Despite of uncertainties associated to applied methodology, transportation noise, especially road traffic noise, is an important environmental risk factor, that leads to considerable loss of healthy life years and causes large health costs in urban areas.

Introduction Ozone is a highly oxidative pollutant, associated with increasing mortality. All else (e.g. ozone precursors) being equal, summertime ground-level ozone will increase as temperatures i...

Concentrations of nitrogen dioxide and particulate matter were modelled to assess the health benefits of an urban regeneration scenario for the central part of Tallinn, where the traffic on two of the main streets will be reduced to create a more friendly space for active commuters. To model the air quality the stationary Gaussian plume model AEROPOL was used with a 25 m grid resolution. The model was validated against a stationary air quality monitoring station in the domain. The health benefits of the scenarios were calculated based on the changes in air pollution exposures for residents and daily visitors, using methods of health impact assessment. This research predicts that each year the reduction of exhaust (indicated by NO2) and road dust (indicated by PM10) exposure in the city centre would prevent up to 0.29 premature deaths (−27%) among the general population, 0.57 deaths (−3.6%) among daily visitors, 0.18 deaths (−21.2%) among pedestrians, and 0.03 deaths (−24.7%) among people public transport users.

Particulate matter (PM) is the major air pollution problem with health impacts in Estonia. The prevailing sources of particles are traffic and local heating. In this study, we quantified the health effects of PM in neighbourhoods of five main cities with a health impact assessment (HIA) approach that uses information on exposure, baseline mortality/morbidity and exposure–response relationships from previous epidemiological studies. The exposure was defined as modelled PM2.5 annual levels and daily averages of PM10 (monitoring data in Tallinn and Kohtla-Jarve and modelled levels in Tartu, Narva and Parnu). The modelled results were validated with data from monitoring stations and additional measuring programmes. The annual average concentration of PM2.5 in the neighbourhoods studied varied from 7.6 to 23.6 μg m−3. The analysis indicated that the exposure above natural background corresponds to 462 [95% confidence interval (CI) 120–815] premature deaths, resulting in 6,034 (95% CI 1,583–10,309) years of life lost per year. The average decrease in life-expectancy at birth per resident of Tallinn was estimated to be 0.63 (95% CI 0.16–1.08) years. In the polluted city centres, this average decrease may reach >1 year and in Parnu, it may reach 0.95 year. However, in the least polluted neighbourhood, the decrease of life expectancy was only 0.17 years. In addition, 231 (95% CI 145–306) respiratory and 338 (95% CI 205–454) cardiovascular hospitalisations per year could be expected. The majority of the external costs are related to the long-term effects on mortality and amount to €270 (95% CI 190–350) million annually. In comparison, the costs of hospitalisations contribute just €1.1 (95% CI 0.6–1.6) million. The main differences in health impacts were mostly driven by differences in the pollution sources, the magnitude of such sources and distribution patterns in the atmosphere. The smallest health effects, with the exception of the green residential areas, were observed in the industrial cities Kohtla-Jarve and Narva (due to the small share contributed by local residential heating and relatively little car traffic). However, it is questionable whether the mass of fine particles is the best indicator of air pollution risk in such areas.

The aim of this study is to assess the performance of single-pass X-band bistatic SAR interferometric forest height estimation of boreal and temperate deciduous forests under variable seasonal conditions. For this, twelve acquisitions of single-and dual-polarized TanDEM-X coherence images over 118 forest stands were analyzed and compared against LiDAR forest height maps. Strong correlations were found between interferometric coherence magnitude and LiDAR derived forest stand height for pine forests (r2=0.94) and spruce forest (r2=0.87) as well as for deciduous trees (r2=0.94) during leaf-off conditions with temperatures below 0°C. It was found that coherence magnitude based forest height estimation is influenced by leaf-on and leaf-off conditions as well as daily temperature fluctuations, height of ambiguity and effective baseline. These factors alter the correlation and should be taken into account for accurate coherence-based height retrieval. Despite the influence of the mentioned factors, generally a strong relationship in regression analysis between X-band SAR coherence and LiDAR derived forest stand height can be found. Moreover, a simple semi empirical model, derived from Random Volume over Ground model, is presented. The model takes into account all imaging geometry dependent parameters and allows to derive tree height estimate without a priori knowledge. Our results show that X-band SAR interferometry can be used to estimate forest canopy height for boreal and deciduous forests in both summer and winter, but the conditions should be stable.

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.

This study is aimed to assess the health impacts of outdoor PM2.5 concentrations to entire population of Estonia. As the air quality monitoring network in Estonia is rather sparse (six urban, three rural and a few industrial sites), the exposure assessment was based on long-term modelling, controlled by monitoring data from existing stations. The model runs were performed with IairViro modelling system, including the Eulerian MATCH model for country-wide run with 5 km grid resolution and AirViro urban model with 200 m grid resolution for five major cities. The database of pollution sources includes industrial, transport and domestic heating emissions. The average annual PM2.5 concentrations were found 7–9 μg/m3 at most of rural and in 9–13 μg/m3 typically at urban areas, up to 30 μg/m3 in some parts of capital city Tallinn. To estimate the health risks, the base-line national health statistics and exposure-response coefficients from previous epidemiological studies (ACS, APHEIS), were applied. It was found that on average 600 (CI 95 % 155–1,061) premature deaths per year are caused by PM2.5 pollution in Estonia, which has population nearly 1.3 million. On average 5 months of life are lost, maximum 14 months in some parts of Tallinn. About 900 additional hospitalizations due to pulmonary and cardiovascular diseases occur per year.

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 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.