Dispersion Characteristics of PM10 Particles Identified by Numerical Simulation in the Vicinity of Roads Passing through Various Types of Urban Areas

Pospíšil, Jiří; Huzlík, Jiří; Ličbinský, Roman; Špiláček, Michal

doi:10.3390/atmos11050454

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…PM 10 and NO x values were higher for the Arboretum station located in a park near a busy street than forČerná Pole located in a built-up area. The obtained results correspond to the conclusions published, e.g., in [22].…”

Section: Discussionsupporting

confidence: 92%

“…Due to the fact that the level of PM values in a given place is influenced by climatic factors (especially the effect of wind speed and direction is significant) and these factors are significantly affected by the built-up area geometry, the effect of built-up area geometry will also affect the level of pollution caused by PM 10 particles. Such an influence was studied also in other works, for example in [21,22] or [23], and it should be paid a great deal of attention.…”

Section: Discussionmentioning

confidence: 93%

“…The authors' evaluation was based on PM monitoring in four Brno locations with regard to traffic intensity. PM level modeling in Brno City with regard to wind speed and direction and built-up area geometry around roadways in particular was analyzed in [22]. Furthermore, predictions of local PM 10 pollution that take local traffic intensity in Brno into consideration were discussed in [23].…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Built-Up Area Geometry on PM10 Levels: A Case Study in Brno, Czech Republic

et al. 2020

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This paper presents a statistical comparison of parallel hourly measurements of particulatematter smaller than 10 μm (PM10) from two monitoring stations that are located 560m from each other in the northern part of Brno City. One monitoring station is located in a park, the otherin a built-up area. The authors’ aim is to describe the influence of a built-up area geometryand nearby traffic intensity on modeling of PM10 pollution levels in the respective part of Brno.Furthermore, the purpose of this study is also to examine the influence of meteorological factorson the pollution levels; above all, to assess the influence of wind speed and direction, temperature change, and humidity change. In order to evaluate the obtained data, the following methods of mathematical statistics were applied: descriptive statistics, regression analysis, analysis of variance, and robust statistical tests. According to the results of the Passing–Bablok test, it can be stated that the parallel measurements of PM10 are significantly different. A regression model for PM10 pollution prediction was created and tested in terms of applicability; subsequently, it was used in order to compare measurements from both stations. It shows that in addition to the monitored meteorological factors, pollution levels are influenced mainly by traffic intensity and the geometry of the monitored built-up area.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Impacts of Built-Up Area Geometry on PM10 Levels: A Case Study in Brno, Czech Republic

et al. 2020

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“…Road transport contributes greatly to this fraction—especially non-exhaust PM emissions and road dust resuspension [ 22 , 25 , 27 , 28 , 38 , 40 , 41 , 45 , 54 , 88 , 89 , 90 , 91 ]; the value found in this study was a contribution of 76.7%. This PM fraction is affected evenly throughout the year by road traffic volume, as well as meteorological factors such as wind speed and rainfall [ 92 ]. These factors generate changes in PM concentrations via dry or wet deposition.…”

Section: Resultsmentioning

confidence: 99%

Seasonal Variation, Chemical Composition, and PMF-Derived Sources Identification of Traffic-Related PM1, PM2.5, and PM2.5–10 in the Air Quality Management Region of Žilina, Slovakia

Jandačka

Ďurčanská

2021

IJERPH

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Particulate matter (PM) air pollution in the urban environment is mainly related to the presence of potential sources throughout the year. Road transport is one of the most important sources of PM in the urban environment, because it directly affects pedestrians. PM measurements were performed in the city of Žilina, Slovakia, at various road-traffic-related measurement stations over the course of several years. This paper evaluates changes in the concentration of the fine fraction (PM2.5), the ultrafine fraction (PM1), and the coarse fraction (PM2.5–10) over time. PM concentrations were measured by reference gravimetric method. Significant changes in PM concentrations over time due to the diversification of pollution sources and other, secondary factors can be observed from the analysis of the measured data. PM samples were subjected to chemical analysis inductively coupled plasma mass spectrometry (ICP-MS) to determine the concentrations of elements (Mg, Al, Ca, Cr, Cu, Fe, Cd, Sb, Ba, Pb, Ni, and Zn). The seasonal variation of elements was evaluated, and the sources of PM2.5, PM1, and PM2.5–10 were estimated using principal component analysis (PCA) and positive matrix factorization (PMF). PM2.5 (maximum concentration of 148.95 µg/m3 over 24 h) and PM1 (maximum concentration of 110.51 µg/m3 over 24 h) showed the highest concentrations during the heating season, together with the elements Cd, Pb, and Zn, which showed a significant presence in these fractions. On the other hand, PM2.5–10 (maximum concentration of 38.17 µg/m3 over 24 h) was significantly related to the elements Cu, Sb, Ba, Ca, Cr, Fe, Mg, and Al. High correlation coefficients (r ≥ 0.8) were found for the elements Mg, Ca, Fe, Al, Cd, Pb, and Zn in the PM1 fraction, Cd, Pb, and Zn in PM2.5, and Ba, Sb, Fe, Cu, Cr, Mg, Al, and Ca in PM2.5–10. Using PMF analysis, three major sources of PM (abrasion from tires and brakes, road dust resuspension/winter salting, and combustion processes) were identified for the PM2.5 and PM1 fractions, as well as for the coarse PM2.5–10 fraction. This study reveals the importance of non-exhaust PM emissions in the urban environment.

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“…The most commonly measured property is just mass concentration. It is also most important for health and environmental effects [1,2].…”

Section: Introductionmentioning

confidence: 99%

Aerosol Measurements Before and During Combustion of Various Pellets from Spruce Sawdust and Face Masks FFP2

Kantová

Čaja²,

Hrabovský³

2022

MATEC Web Conf.

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Aerosols in the atmosphere affect our health and quality of life. The most commonly measured property is their mass concentration. In this work was measured the mass concentration of aerosols by optical particle sizer in the range from 0.3 μm to 10 μm. It was realized in the laboratory with an automatic heat source for pellet combustion. Aerosols were firstly measured before the combustion process, then during combustion of three types of samples: pellets from pure sawdust, pellets from 5 % of disintegrated face masks FFP2 blended with spruce sawdust and from10 % of disintegrated face masks FFP2 blended with spruce sawdust. The lowest total mass concentration was measured during the combustion of pellets from pure spruce sawdust. However, results indicate that the combustion process does not have an impact on aerosol mass concentration in the laboratory space for the particle range 0.3-10 μm when an automatic heat source for pellets is used.

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Dispersion Characteristics of PM10 Particles Identified by Numerical Simulation in the Vicinity of Roads Passing through Various Types of Urban Areas

Cited by 11 publications

References 18 publications

Impacts of Built-Up Area Geometry on PM10 Levels: A Case Study in Brno, Czech Republic

Impacts of Built-Up Area Geometry on PM10 Levels: A Case Study in Brno, Czech Republic

Seasonal Variation, Chemical Composition, and PMF-Derived Sources Identification of Traffic-Related PM1, PM2.5, and PM2.5–10 in the Air Quality Management Region of Žilina, Slovakia

Aerosol Measurements Before and During Combustion of Various Pellets from Spruce Sawdust and Face Masks FFP2

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