Identication of pollution sources and characteristics of atmospheric composition via forward and inverse dispersion modelling

Atmospheric composition has strong influence on human health, ecosystems and also Earth's climate. Among the atmospheric constituents, particulate matter has been recognized as both a strong climate forcer and a significant risk factor for human health, although the health relevance of the specific aerosol characteristics, such as its chemical composition, is still debated. Clouds and aerosols also contribute the largest uncertainty to the radiative budget estimates for climate projections. Thus, reliable estimates of emissions and distributions of pollutants are necessary for assessing the future climate and air-quality related health effects. Chemistry-transport models (CTMs) are valuable tools for understanding the processes influencing the atmospheric composition. This thesis consists of a collection of developments and applications of the chemistry-transport model SILAM. SILAM's ability to reproduce the observed aerosol composition was evaluated and compared with three other commonly used CTM-s in Europe. Compared to the measurements, all models systematically underestimated dry PM10 and PM2.5 by 10-60%, depending on the model and the season of the year. For majority of the PM chemical components the relative underestimation was smaller than that, exceptions being the carbonaceous particles and mineral dust - species that suffer from relatively small amount of available oservational data. The study stressed the necessity for high-quality emissions from wild-land fires and wind-suspended dust, as well as the need for an explicit consideration of aerosol water content in model-measurement comparison. The average water content at laboratory conditions was estimated between 5 and 20% for PM2.5 and between 10 and 25% for PM10. SILAM predictions were used to assess the annual mortality attributable to short-term exposures to vegetation-fire originated PM2.5 in different regions in Europe. PM2.5 emitted from vegetation fires was found to be a relevant risk factor for public health in Europe, more than 1000 premature deaths per year were attributed to vegetation-fire released PM2.5. CTM predictions critically depend on emission data quality. An error was found in the EMEP anthropogenic emission inventory regarding the SOx and PM missions of metallurgy plants on the Kola Peninsula and SILAM was applied to estimate the accuracy of the proposed correction. Allergenic pollen is arguably the type of aerosol with most widely recognised effect to health. SILAM's ability to predict allergenic pollen was extended to include Ambrosia Artemisiifolia - an invasive weed spreading in Southern Europe, with extremely allergenic pollen capable of inducing rhinoconjuctivitis and asthma in the sensitive individuals even in very low concentrations. The model compares well with the pollen observations and predicts occasional exceedances of allergy relevant thresholds even in areas far from the plants' habitat. The variations of allergenicity in grass pollen were studied and mapped to the source areas by adjoint computations with SILAM. Due to the high year-to-year variability of the observed pollen potency between the studied years and the sparse observational network, no clear geographic pattern of pollen allergenicity was detected

Educational Research Association

n/

Identication of pollution sources and characteristics of atmospheric composition via forward and inverse dispersion modelling

Atmospheric composition has strong influence on human health, ecosystems and also Earth's climate. Among the atmospheric constituents, particulate matter has been recognized as both a strong climate forcer and a significant risk factor for human health, although the health relevance of the specific aerosol characteristics, such as its chemical composition, is still debated. Clouds and aerosols also contribute the largest uncertainty to the radiative budget estimates for climate projections. Thus, reliable estimates of emissions and distributions of pollutants are necessary for assessing the future climate and air-quality related health effects. Chemistry-transport models (CTMs) are valuable tools for understanding the processes influencing the atmospheric composition. This thesis consists of a collection of developments and applications of the chemistry-transport model SILAM. SILAM's ability to reproduce the observed aerosol composition was evaluated and compared with three other commonly used CTM-s in Europe. Compared to the measurements, all models systematically underestimated dry PM10 and PM2.5 by 10-60%, depending on the model and the season of the year. For majority of the PM chemical components the relative underestimation was smaller than that, exceptions being the carbonaceous particles and mineral dust - species that suffer from relatively small amount of available oservational data. The study stressed the necessity for high-quality emissions from wild-land fires and wind-suspended dust, as well as the need for an explicit consideration of aerosol water content in model-measurement comparison. The average water content at laboratory conditions was estimated between 5 and 20% for PM2.5 and between 10 and 25% for PM10. SILAM predictions were used to assess the annual mortality attributable to short-term exposures to vegetation-fire originated PM2.5 in different regions in Europe. PM2.5 emitted from vegetation fires was found to be a relevant risk factor for public health in Europe, more than 1000 premature deaths per year were attributed to vegetation-fire released PM2.5. CTM predictions critically depend on emission data quality. An error was found in the EMEP anthropogenic emission inventory regarding the SOx and PM missions of metallurgy plants on the Kola Peninsula and SILAM was applied to estimate the accuracy of the proposed correction. Allergenic pollen is arguably the type of aerosol with most widely recognised effect to health. SILAM's ability to predict allergenic pollen was extended to include Ambrosia Artemisiifolia - an invasive weed spreading in Southern Europe, with extremely allergenic pollen capable of inducing rhinoconjuctivitis and asthma in the sensitive individuals even in very low concentrations. The model compares well with the pollen observations and predicts occasional exceedances of allergy relevant thresholds even in areas far from the plants' habitat. The variations of allergenicity in grass pollen were studied and mapped to the source areas by adjoint computations with SILAM. Due to the high year-to-year variability of the observed pollen potency between the studied years and the sparse observational network, no clear geographic pattern of pollen allergenicity was detected

Mikrokraadide väljatöötamine kõrghariduses Eesti Lennuakadeemia näitel

https://www.ester.ee/record=b5510355*es

Constraining the Atmospheric Limb of the Plastic Cycle

Plastic pollution is one of the most pressing environmental and social issues of the 21st century. Recent work has highlighted the atmosphere’s role in transporting microplastics to remote locations [S. Allen et al., Nat. Geosci. 12, 339 (2019) and J. Brahney, M. Hallerud, E. Heim, M. Hahnenberger, S. Sukumaran, Science 368, 1257–1260 (2020)]. Here, we use in situ observations of microplastic deposition combined with an atmospheric transport model and optimal estimation techniques to test hypotheses of the most likely sources of atmospheric plastic. Results suggest that atmospheric microplastics in the western United States are primarily derived from secondary re-emission sources including roads (84%), the ocean (11%), and agricultural soil dust (5%). Using our best estimate of plastic sources and modeled transport pathways, most continents were net importers of plastics from the marine environment, underscoring the cumulative role of legacy pollution in the atmospheric burden of plastic. This effort uses high-resolution spatial and temporal deposition data along with several hypothesized emission sources to constrain atmospheric plastic. Akin to global biogeochemical cycles, plastics now spiral around the globe with distinct atmospheric, oceanic, cryospheric, and terrestrial residence times. Though advancements have been made in the manufacture of biodegradable polymers, our data suggest that extant nonbiodegradable polymers will continue to cycle through the earth’s systems. Due to limited observations and understanding of the source processes, there remain large uncertainties in the transport, deposition, and source attribution of microplastics. Thus, we prioritize future research directions for understanding the plastic cycle

Aerosol optical properties over Europe: an evaluation of the AQMEII Phase 3 simulations against satellite observations

Abstract. The main uncertainties regarding the estimation of changes in the Earth's energy budget are related to the role of atmospheric aerosols. These changes are caused by aerosol–radiation (ARIs) and aerosol–cloud interactions (ACIs), which heavily depend on aerosol properties. Since the 1980s, many international modeling initiatives have studied atmospheric aerosols and their climate effects. Phase 3 of the Air Quality Modelling Evaluation International Initiative (AQMEII) focuses on evaluating and intercomparing regional and linked global/regional modeling systems by collaborating with the Task Force on the Hemispheric Transport of Air Pollution Phase 2 (HTAP2) initiative. Within this framework, the main aim of this work is the assessment of the representation of aerosol optical depth (AOD) and the Ångström exponent (AE) in AQMEII Phase 3 simulations over Europe. The evaluation was made using remote-sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors aboard the Terra and Aqua platforms, and the instruments belonging to the ground-based Aerosol Robotic Network (AERONET) and the Maritime Aerosol Network (MAN). Overall, the skills of AQMEII simulations when representing AOD (mean absolute errors from 0.05 to 0.30) produced lower errors than for the AE (mean absolute errors from 0.30 to 1). Regardless of the models or the emissions used, models were skillful at representing the low and mean AOD values observed (below 0.5). However, high values (around 1.0) were overpredicted for biomass burning episodes, due to an underestimation in the common fires' emissions, and were overestimated for coarse particles – principally desert dust – related to the boundary conditions. Despite this behavior, the spatial and temporal variability of AOD was better represented by all the models than AE variability, which was strongly underestimated in all the simulations. Noticeably, the impact of the model selection when representing aerosol optical properties is higher than the use of different emission inventories. On the other hand, the influence of ARIs and ACIs has a little visible impact compared to the impact of the model used

Loogika: mõtlemisest tõestamiseni

http://tartu.ester.ee/record=b1019331~S