Characterization of PM10 Sampled on the Top of a Former Mining Tower by the High-Volume Wind Direction-Dependent Sampler Using INNA

Pavlíková, Irena; Hladký, Daniel; Motyka, Oldřich; Vergel, Konstantin; Strelkova, Ludmila; Shvetsova, M. S.

doi:10.3390/atmos12010029

Cited by 8 publications

(3 citation statements)

References 35 publications

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“…Based on the results from the previous analysis, quartz filters are the most suitable for INAA (see Discussion). Among other filters commonly used for high-volume sampling are cellulose fiber, glass fiber, mixed fiber, and membrane filters [12]. Filter holders are automatically changed to the sampling position according to the actually evaluated wind conditions.…”

Section: Sampling Strategymentioning

confidence: 99%

Neutron Activation Analysis of PM10 for Air Quality of an Industrial Region in the Czech Republic: A Case Study

et al. 2022

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This work was conducted to focus on pollutant transmission between Poland and Czechia at the most polluted area in the Czech Republic, the Moravian Silesian region. Instrumental neutron activation analysis (INAA) and multivariate statistical analyses were used to determine the mass fractions of inorganic air pollutants accumulated on filters. Particle matters of sizes smaller than 10 µm (PM10) were collected using a high-volume sampler (SAM Hi 30 AUTO WIND). Pollutants PM10 were collected on Whatman QM-A Quartz Microfiber Filters of 150 mm in diameter based on various wind conditions. These filters were irradiated by neutron flux at the experimental reactor IBR-2 at the Joint Institute of Nuclear Research in Dubna, RF. Irradiated samples were measured by gamma spectrometry techniques using HPGe detectors. In total, results are shown for 49 samples (from March to July 2021) and five field blank filters. The mass fractions of 24 elements (Sc, Cr, Fe, Ni, Co, Zn, Se, As, Br, Rb, Mo, Sb, Ba, Cs, La, Ce, Sm, Eu, Tb, Yb, Hf, Au, Th, and U) were determined. The sources of pollution were specified using correlation and exploratory factor analyses and including meteorological conditions. A strong positive correlation was shown between the elements Cr, As, Br, Co, Fe, Sc, Se, Sm, Th, La, and Ce. Elemental exposure to PM10 can be divided based on the factor loadings of common chemical components into three main pollution sources. According to the wind rose, the pollution came from the southeast/west direction; therefore, we can assume that the pollution most likely originated from the metallurgic complex (steel and iron production in the southeast, and a coking plant, metal foundry, and generation plant in the west).

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Section: Sampling Strategymentioning

confidence: 99%

Neutron Activation Analysis of PM10 for Air Quality of an Industrial Region in the Czech Republic: A Case Study

et al. 2022

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“…Their results indicated that the major factors to be blamed for pollution are emissions from residential heating and the burning of solid fuels, followed by the regional primary and secondary aerosol sources, whereas long-range transport and heavy industry appeared to be of only minor importance. Pavlíková et al [7] measured 34 elements in PM 10 sampled in the centre of Ostravsko-karvinská agglomeration at the top of a former mining tower at a height of 90 m above ground to investigate regional air pollution. The elemental composition was found to be seasonal-dependent, and it varied due to sampling conditions.…”

Section: Aerosol Papersmentioning

confidence: 99%

Ambient Air Quality in the Czech Republic

Hůnová

2021

Atmosphere

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“…The Commission advised that coal should be phased out no later than 2038 [12]. The data from April 2018 to March 2019 collected in the Moravian-Silesian Region has verified that during the winter season, the inflow of PM cross-border pollution from Poland is a key factor contributing to air pollution levels [13].…”

Section: Introductionmentioning

confidence: 99%

PM2.5 Estimation in the Czech Republic using Extremely Randomized Trees: A Comprehensive Data Analysis

Ibrahim,

Landa,

Matoušková

et al. 2023

CEJ

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The accuracy of artificial intelligence techniques in estimating air quality is contingent upon a multitude of influencing factors. Unlike our previous study that examined PM2.5 over whole Europe using unbalanced spatial-temporal data, the focus of this study was on estimating PM2.5 specifically over the Czech Republic using more balanced dataset to train and evaluate the model. Moreover, the spatial autocorrelation between the ground-based station was taken into consideration while building the model. The feature importance while developing the Extra Trees model revealed that spatial autocorrelation had greater significance in comparison to commonly used inputs such as elevation and NDVI. We found that R2 of the 10-CV for the new model was 16% higher than the previous one. R2 reached 0.85 when predicting unseen data in new locations. The developed spatiotemporal model was employed to generate comprehensive daily maps covering the entire study area throughout the 2018–2020 years. The temporal analysis showed that the levels of PM2.5 exceeded recommended limits of 20 µg/m3 during the year 2018 in many regions. The eastern part of the country suffered from the highest concentrations especially over Zlín and Moravian-Silesian Regions where in the 2018 winter, the values reached risky average concentrations of 30 µg/m3 and 35 µg/m3 respectively. Air quality improved during the next two years in all regions reaching promising levels in 2020 where almost all regions had average concentrations less than 20 µg/m3. The generated dataset will be available for other future air quality studies.

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Characterization of PM10 Sampled on the Top of a Former Mining Tower by the High-Volume Wind Direction-Dependent Sampler Using INNA

Cited by 8 publications

References 35 publications

Neutron Activation Analysis of PM10 for Air Quality of an Industrial Region in the Czech Republic: A Case Study

Neutron Activation Analysis of PM10 for Air Quality of an Industrial Region in the Czech Republic: A Case Study

Ambient Air Quality in the Czech Republic

PM2.5 Estimation in the Czech Republic using Extremely Randomized Trees: A Comprehensive Data Analysis

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