Search

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.

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

Monitoring of when, where and in which quantity peat is harvested is currently based on manual declarations. Synthetic Aperture Radar (SAR) is a powerful tool for change detection and monitoring. The aim of this study was to evaluate whether Sentinel-1 6-day interferometric SAR (InSAR) temporal coherence could allow peat extraction monitoring from satellite. We demonstrate that temporal median coherence enables to detect harvest related surface altering works and therefore also spatially explicitly determine active and inactive extraction areas. A polygon-based multi-orbit time series approach is sufficient for the task. Hereby, vertical–vertical polarisation (VV) is more sensitive to the changes compared to vertical-horizontal (VH). During the main harvest season the peat extraction area has median VV coherence lower than 0.2 while the abandoned area and open bog which serve as reference for undisturbed extraction area have close to 0.6. Also, the potential for coherence based milled peat extraction intensity estimation is demonstrated and an indication is given how partially extracted areas could be distinguished from fully harvested and not harvest areas, by the use of coherence standard deviation. Regarding the influence of rainfall, only heavy rain on one of the acquisitions of the image pair whereas the other is from dry conditions seems to cause decorrelation comparable to surface altering works. Moreover, deploying images from multiple consecutive orbits or introducing backscatter intensity σ 0 or reference polygons of undisturbed area helps to reduce risk for rain induced false positives. Developing an operational algorithm for peat extraction identification could be undertaken in future studies.

Climate change is expected to increase to extreme temperatures and lead to more intense formation of near-surface ozone. Higher temperatures can cause heat stress and ozone is a highly oxidative pollutant; both increase cardiorespiratory mortality. Using greenhouse gas and ozone precursor emission scenarios, global and regional climate and chemistry-transport models, epidemiological data, and population projections, we projected ozone- and heat-related health risks under a changing climate. European near-surface temperature was modelled with the regional climate model (RCA4), forced by the greenhouse gas emission scenario RCP4.5 and the global climate model EC-EARTH, and near-surface ozone was modelled with the Multi-scale Atmospheric Transport and Chemistry (MATCH) model. Two periods were compared: recent climate in 1991–2000 and future climate in 2046–2055, projecting around a 2° increase in global temperatures by that time. Projections of premature mortality considered future climate, future population, and future emissions separately and jointly to understand the relative importance of their contributions. Ozone currently causes 55 000 premature deaths annually in Europe due to long-term exposure, including a proportion of the estimated 26 000 deaths per year due to short-term exposures. When only taking into account the impact of a changing climate, up to an 11% increase in ozone-associated mortality is expected in some countries in Central and Southern Europe in 2050. However, projected decreases in ozone precursor emissions are expected to result in a decrease in ozone-related mortality (−30% as EU average). Due to aging and increasingly susceptible populations, the decrease in 2050 would be smaller, up to −24%. During summer months, ozone risks could combine with increasing temperatures, especially during the hottest periods and in densely populated urban areas. While the heat burden is currently of the same order of magnitude as ozone, due to increasing temperatures and decreasing ozone precursor emissions, heat-related mortality could be twice as large as ozone-related mortality in 2050.

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.

Locally computed statistics of image texture and a case-based reasoning (CBR) system were evaluated for mapping of forest attributes. Cluster analysis was preferred to regression models, as a pre-selection method of features. The best stand-based accuracy using satellite sensor images was 74.64 m−3 ha−1 (36%) RMSE for stand volume, 1.98 m−3 ha−1 a−1 (49%) for annual increase in stand volume, where κ = 0.23 for stand growth classes and κ = 0.41 for dominant tree species in stands. The top pixel-based accuracy using orthophotos was 76.54 m−3 ha−1 (41%) RMSE for stand volume, 1.87 m−3 ha−1 a−1 (44%) for annual increase in stand volume, where κ = 0.24 for stand growth classes and κ = 0.38 for dominant tree species in stands. Mean saturation in 30 m radius was the most useful feature when orthophotos were used, and standard deviation of Landsat ETM 6.2 values in 80 m radius was the best when satellite sensor images were used. The most valuable feature components (radii, channels and local statistics) for orthophotos were: 30 m kernel radius, lightness and the mean of pixel values; for satellite sensor images: 80 m kernel radius, near-infrared channel (ETM 4) and the mean of pixel values. Locally computed statistics.

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.

It can be assumed that future climate changes may have diverse impact on different parts of Estonia. For the regionalization of climate change impacts we used outputs of the EURO-CORDEX climate model ensemble and added its relative changes for temperature and precipitation to the values of the model’s reference period 1971–2000. In order to reduce the ambiguity of spatially detailed outputs, the clusters of future climate variables for seasons were produced. We conclude that three climatic regions – the mainland, the coast and the western Estonian islands – will most likely persist in the future climate. Major regional changes may occur at the end of the 21st century according to the pessimistic scenario of Representative Concentration Pathways RCP8.5. In the rest of the cases, although the boundaries of regions may shift, the warming as well as the increase in precipitation will mainly occur within the regions. Partially this will result in the growth of contrasts between regions.

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.

In this study, the interferometric coherence calculated from 12-day Sentinel-1 image pairs was analysed in relation to mowing events on agricultural grasslands. Results showed that after a mowing event, median VH (vertical transmit, horizontal receive) and VV (vertical transmit, vertical receive) polarisation coherence values were statistically significantly higher than those from before the event. The shorter the time interval after the mowing event and the first interferometric acquisition, the higher the coherence. The coherence tended to stay higher, even 24 to 36 days after a mowing event. Precipitation caused the coherence to decrease, impeding the detection of a mowing event. Given the three analysed acquisition geometries, it was concluded that afternoon acquisitions and steeper incidence angles were more useful in the context of this study. In the case of morning acquisitions, dew might have caused a decrease of coherence for mowed and unmowed grasslands. Additionally, an increase of coherence after a mowing event was not evident during the rapid growth phase, due to the 12-day separation between the interferometric acquisitions. In future studies, six-day pairs utilising Sentinel-1A and 1B acquisitions should be considered.