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The historical (1835–2020) deposition of major air pollutants (SO2, NOx, O3 and PM2.5) indoors, as represented by the monumental Edvard Munch paintings (c. 220 m2) installed in 1916 in the Oslo University Aula in Norway, were approximated from the outdoor air concentrations, indoor to outdoor concentration ratios and dry deposition velocities. The annual deposition of the pollutants to the paintings was found to have been 4–25 times lower than has been reported to buildings outdoors in the urban background in the centre of Oslo. It reflected the outdoor deposition but varied less, from 0.3 to 1.2 g m−2 a−1. The accumulated deposition since 1916, and then not considering the regularly performed cleaning of the paintings, was found to have been 43 ± 13 g m−2, and 110 ± 40 g m−2 in a similar situation since 1835. The ozone deposition, and the PM2.5 deposition before the 1960s, were a relatively larger part of the accumulated total indoor (to the paintings) than reported outdoor deposition. About 18 and 33 times more O3 than NOx and PM2.5 deposition was estimated to the paintings in 2020, as compared to the about similar reported outdoor dry deposition of O3 and NOx. The deposition of PM2.5 to the paintings was probably reduced with about 62% (50–80%) after installation of mechanical filtration in 1975 and was estimated to be 0.011 (± 0.006) g m−2 in 2020.
BioMed Central (BMC)
2022
2024
2022
2022
2015
2004
2015
2020
2008
2011
Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations.
This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications.
2024
2015
Estimating tropospheric and stratospheric winds using infrasound from explosions
The receiver-to-source backazimuth of atmospheric infrasound signals is biased when cross-winds are present along the propagation path. Infrasound from 598 surface explosions from over 30 years in northern Finland is measured with high spatial resolution on an array 178 km almost due North. The array is situated in the classical shadow-zone distance from the explosions. However, strong infrasound is almost always observed, which is most plausibly due to partial reflections from stratospheric altitudes. The most probable propagation paths are subject to both tropospheric and stratospheric cross-winds, and the wave-propagation modelling in this study yields good correspondence between the observed backazimuth deviation and cross-winds from the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA)-Interim reanalysis product. This study demonstrates that atmospheric cross-winds can be estimated directly from infrasound data using propagation time and backazimuth deviation observations. This study finds these cross-wind estimates to be in good agreement with the ERA-Interim reanalysis.
Acoustical Society of America (ASA)
2019
2016
2016
2006
Epidemiological studies have increasingly shown that ambient air pollution is not only associated with mortality but also with the occurrence of a number of long and short-term diseases. Further, the Global Burden of Disease study clearly indicated, that e. g. particulate matter pollution is also associated with a considerable burden of disease related to morbidity effects.
In addition to the most recent EEA’s health risk assessments, this report estimates the morbidity related health burden associated with exposure to the same three key air pollutants: fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3). Years lived with disability (YLDs) or attributable hospitalisation cases are assessed for the year 2019 for numerous European countries, depending on the respective data availability. Besides, the methodological approach as well as reviews on evidence-based health outcomes, health data and concentration-response functions are provided.
For the ten considered risk-outcome pairs, the results showed the highest morbidity related burden of disease in Europe for PM2.5 associated with chronic obstructive pulmonary disease (COPD) with 51.6 YLDs per 100 000 inhabitants ≥25 years. For NO2 the highest morbidity burden resulted from diabetes mellitus (DM) with 54.6 YLDs per 100 000 inhabitants ≥35 years. For short-term O3 exposure hospital admissions due to respiratory diseases were estimated at 18 attributable cases per 100 000 inhabitants ≥65 years.
In addition to the estimates, the report contains suggestions for further sensitivity analyses. These would allow a better assessment of the effects resulting from different input data on the results.
The estimations presented in this report are the first of its kind that are carried out for a wide range of morbidity health outcomes associated with different outdoor air pollutants in Europe, using a consistent methodology and data from European health databases.
ETC/HE
2022
Satellite observations from instruments such as the TROPOspheric Monitoring Instrument (TROPOMI) show significant potential for monitoring the spatiotemporal variability of NO2, however they typically provide vertically integrated measurements over the tropospheric column. In this study, we introduce a machine learning approach entitled ‘S-MESH’ (Satellite and ML-based Estimation of Surface air quality at High resolution) that allows for estimating daily surface NO2 concentrations over Europe at 1 km spatial resolution based on eXtreme gradient boost (XGBoost) model using primarily observation-based datasets over the period 2019–2021. Spatiotemporal datasets used by the model include TROPOMI NO2 tropospheric vertical column density, night light radiance from the Visible Infrared Imaging Radiometer Suite (VIIRS), Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer (MODIS), observations of air quality monitoring stations from the European Environment Agency database and
2024
2013
There are sparse opportunities for direct measurement of upper stratospheric winds, yet improving their representation in subseasonal-to-seasonal prediction models can have significant benefits. There is solid evidence from previous research that global atmospheric infrasound waves are sensitive to stratospheric dynamics. However, there is a lack of results providing a direct mapping between infrasound recordings and polar-cap upper stratospheric winds. The global International Monitoring System (IMS), which monitors compliance with the Comprehensive Nuclear-Test-Ban Treaty, includes ground-based stations that can be used to characterize the infrasound soundscape continuously. In this study, multi-station IMS infrasound data were utilized along with a machine-learning supported stochastic model, Delay-SDE-net, to demonstrate how a near-real-time estimate of the polar-cap averaged zonal wind at 1-hPa pressure level can be found from infrasound data. The infrasound was filtered to a temporal low-frequency regime dominated by microbaroms, which are ambient-noise infrasonic waves continuously radiated into the atmosphere from nonlinear interaction between counter-propagating ocean surface waves. Delay-SDE-net was trained on 5 years (2014–2018) of infrasound data from three stations and the ERA5 reanalysis 1-hPa polar-cap averaged zonal wind. Using infrasound in 2019–2020 for validation, we demonstrate a prediction of the polar-cap averaged zonal wind, with an error standard deviation of around 12 m·s compared with ERA5. These findings highlight the potential of using infrasound data for near-real-time measurements of upper stratospheric dynamics. A long-term goal is to improve high-top atmospheric model accuracy, which can have significant implications for weather and climate prediction.
John Wiley & Sons
2024