Links between the concentrations of gaseous pollutants measured in different regions of Estonia.

The factors that determine the concentrations of air pollutants (NO, NO2, SO2, O3), measured in 8 monitoring stations (4 rural background, 3 urban, and 1 industrial) in Estonia, are studied applying the factor analysis. The factor analysis reveals remarkable impact of COVID-19 lockdown, effects caused by dramatic decrease in oil-shale based energy production in Estonia provoked by new socio-economic conditions such as elevated price for CO2 emission quota, differences between rural and urban stations, maritime-continental difference for NO2 and ozone, and specific industrial impact in case of SO2. The multiple regression analysis to predict the ozone concentration in one rural background station at Tahkuse was performed, based on the ozone concentrations measured in other stations and the concentrations of NO, NO2, and CO2, recorded in the same station. It was found that the ozone concentration at Tahkuse is rather well predictable (determination coefficient, i.e., correlation coefficient squared, R 2 = 0.714), using only the concentrations from another rural station at Saarejärve that is about 110 km away from Tahkuse. Adding all the available data into the list of regression analysis arguments, the model predictability is improved moderately (determination coefficient R 2 = 0.795). Large model residuals above all tend to occur with the values measured and predicted at summer nights. Surprisingly, neither NO nor NO2 concentration measured in the Tahkuse station did appear a good predictor for ozone (R 2 = 0.02 and 0.05, respectively), possibly long-range transport of ozone (that has also experienced NO and/or NO2 influence during transport) overrides the local effects of NO and/or NO2.

A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission conditions.

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015-2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015-2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples' mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015-2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015-2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.

Uncertainties in the use of concentration ratios for modelling NORM waste sites.

The activity concentrations of 238U, 226Ra and 210Pb were modelled in Pinus sylvestris (Scots pine trees) on a uniform CaF2 sludge heap in Belgium. The aim of this work is to enhance the knowledge of how transfer factors behave in NORM landfills. The simplest possible model in radioecology is used, which is based on Concentration Ratios (CR-s) measured in equilibrium and activity concentrations of the above-mentioned radionuclides measured in the substrate where pine trees grow. Two alternative CR-s were used: (1) international CR compilations by the IAEA (2014) and (2) CR-s specifically determined for pine trees studied in British Columbia (Mahon and Mathews, 1983). Both CR-s were applied assuming lognormal distributions fitted from data reported in the literature. The results were compared with activity concentrations measured in trees sampled on-site. Modelled concentrations match the measured ones best in the case of 238U. For the studied NORM waste site, the approach using generic IAEA concentration ratios does not fulfill the conservatism requirement in the cases of 238U and 226Ra, as the concentration of radionuclides in trees is underestimated. On the other hand, the ratios from Mahon and Mathews, (1983) produce wide distributions, ensuring conservatism due to larger CR-s. The measured concentrations are narrowly distributed in general, which can be expected on a small sampling site on a uniform substrate. The generic approach outlined here is practical but, as a result of the uniqueness of the site considered, should be applied cautiously in other NORM situations.

Long-term modelling of fly ash and radionuclide emissions as well as deposition fluxes due to the operation of large oil shale-fired power plants.

Two of the world's largest oil shale-fired power plants (PPs) in Estonia have been operational over 40 years, emitting various pollutants, such as fly ash, SOx, NOx, heavy metals, volatile organic compounds as well as radionuclides to the environment. The emissions from these PPs have varied significantly during this period, with the maximum during the 1970s and 1980s. The oil shale burned in the PPs contains naturally occurring radionuclides from the 238U and 232Th decay series as well as 40K. These radionuclides become enriched in fly ash fractions (up to 10 times), especially in the fine fly ash escaping the purification system. Using a validated Gaussian-plume model, atmospheric dispersion modelling was carried out to determine the quantity and a real magnitude of fly ash and radionuclide deposition fluxes during different decades. The maximum deposition fluxes of volatile radionuclides (210Pb and 210Po) were around 70 mBq m-2 d-1 nearby the PPs during 1970s and 1980s. Due to the reduction of burned oil shale and significant renovations done on the PPs, the deposition fluxes were reduced to 10 mBq m-2 d-1 in the 2000s and down to 1.5 mBq m-2 d-1 in 2015. The maximum deposition occurs within couple of kilometers of the PPs, but the impacted area extends to over 50 km from the sources. For many radionuclides, including 210Po, the PPs have been larger contributors of radionuclides to the environment via atmospheric pathway than natural sources. This is the first time that the emissions and deposition fluxes of radionuclides from the PPs have been quantified, providing the information about their radionuclide deposition load on the surrounding environment during various time periods.

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.

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

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.

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 PM(exhaust) concentrations in 40 x 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 PM(exhaust) 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.