Moss biomonitoring and air pollution modelling on a regional scale: delayed reflection of industrial pollution in moss in a heavily polluted region?

Motyka, Oldřich; Pavlíková, Irena; Bitta, Jan; Frontasyeva, M. V.; Jančík, Petr

doi:10.1007/s11356-020-09466-w

Cited by 13 publications

(9 citation statements)

References 38 publications

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“…Ma et al (2020) described a LUR modelling and pollution visualisation software called PyLUR that uses Python-based GDAL/OGR libraries and can create a LUR model and produce efficient maps of pollutants concentration. Motyka et al (2020) examined the air quality model SYMOS 97 in comparison to the biomonitoring survey findings and vice versa; this model is also based on Python language programming. Wyrwa (2015) used the ðESA model to evaluate the concentration of PM 2.5 .…”

Section: Gis Toolsmentioning

confidence: 99%

Common Analysis Methods for Gaseous and Solidcontaminants Emitted From Different Pollutionsources: A Review Paper

AJAJ¹,

SHAFRI²,

WAYAYOK³

et al. 2022

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Cement is widely used for building construction and infrastructural purposes. In the last thirteen years, this productâs global annual production has increased by 78 percent, to 4,200 Mtpa. However, cement factories have been found to be harmful to air quality, plantations, and houses. Dust emission occurs through all the stages of cement production, such as storage, frying, packing, and even in the transportation stage. Cement factories cause environmental contamination worldwide, including in developed countries facing problems related to managing and controlling pollution, and also enforcing stringent legislation to prevent it. By reviewing different studies concerning cement plant emissions, we observed that various mathematical and statistical models have been applied to analyse and determine the effect of these emissions on the environment. Our study is focused on these analytical and statistical methods for analysing gaseous pollutant and particulate matter emissions from various pollution sources, especially cement plants. Identifying the elements of emissions and the development stages of other authorsâ analysis methods allowed us to conduct the most precise report possible. Our study also depicts the determinants of these strategies and their advantages, limitations, and implementation variables. The findings of these other studies were discussed, and it was discovered that the output differed depending on the technique used. The frameworks utilised ranged from using a single model to two or more models, in order to compare or combine them. GIS was used as an additional tool to improve the modelâs functionality in several studies. Some authors used programming languages such as Python and R to make their models more realistic.

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Section: Gis Toolsmentioning

confidence: 99%

Common Analysis Methods for Gaseous and Solidcontaminants Emitted From Different Pollutionsources: A Review Paper

AJAJ¹,

SHAFRI²,

WAYAYOK³

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Sci. 2021, 11, x FOR PEER REVIEW 3 of 27 heating season, would decrease with the distance from the factory along the prevailing wind direction [33,[42][43][44] and would be significantly correlated to the modelled PM10 concentrations [45,46].…”

Section: Experiments 21 Study Areamentioning

confidence: 99%

“…Pearson correlations calculation, Principal Component Analysis (PCA) and the visualisations of the results were performed in the R environment [65], packages compositions [66], robCompositions [67,68], FactoMineR [69,70] Because the multivariate analysis has to be performed on complete datasets and zero values or fractions of detection limits are inappropriate [75], measurements under the detection limit were imputed using expectation-maximisation-based replacement by the ImpRZilr algorithm. Prior to the PCA, the data were transformed according to the Compositional data analysis (CoDa) principles [76,77] using the centred log-ratio (clr) transformation, the approach that was proven to be appropriate recently [45,78,79]. Only the clr-transformed elemental concentration data were used for the construction of the model.…”

Section: Statistical Analyses and Visualisationmentioning

confidence: 99%

“…Measured concentrations of respective elements in a moss tissue are supposed to be the function of their atmospheric deposition; they are usually correlated with the Euro-pean Monitoring and Evaluation Programme (EMEP) deposition modelling results [80][81][82][83]. However, the annual average EMEP data on atmospheric deposition have the resolution of a 0.1 • × 0.1 • longitude-latitude grid, which is not sufficient for such a local study and-as it was proven previously-the particulate matter concentrations originating from anthropogenic sources are reflected in moss [45,46]; modelling of PM 10 concentrations was performed. The Czech reference methodology SYMOS'97 [84,85] based on Sutton's theory of turbulent diffusion [71] was used for modelling.…”

Section: Modelling Of Pm Concentrationsmentioning

confidence: 99%

“…Additionally, the relationship between the composition of the moss analysed and modelled seasonal atmospheric particulate matter concentrations (PM 10 ) originating in anthropogenic sources were explored. We hypothesised that the element concentrations in moss tissues would be influenced by the heating season, would decrease with the distance from the factory along the prevailing wind direction [33,[42][43][44] and would be significantly correlated to the modelled PM 10 concentrations [45,46].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring of Heavy Metals and Nitrogen Concentrations in Mosses in the Vicinity of an Integrated Iron and Steel Plant: Case Study in Czechia

et al. 2021

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A biomonitoring study using terrestrial mosses was performed in the vicinity of an Integrated Iron and Steel plant near the Czech–Polish border. Moss samples were collected in two seasons (June, October) in order to embrace the effect of the heating season on the pollution levels. The contents of metals (Al, V, Cr, Mn, Fe, Ni, Cu, Zn, Cd, Pb, As, Sb and Hg) were determined using the Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES), Atomic Absorption Spectroscopy (AAS) and contents of N, C, H via elemental analysis. The influence of the proximity of the factory, the heating season and modelled concentrations of particulate matter <10 µm (PM10) on determined concentrations of elements were studied via multivariate statistical methods using clr-transformed data. This approach led to the first-time demonstration that not only the distance from the industrial source but also the sampling season and PM10 concentrations significantly affect the elemental content in mosses; the association of the emissions from the source and the determined concentrations of elements in moss samples were more evident outside the heating season (October). The analyses of transformed data revealed the association of Fe, Cr, V, As and Al with the coarse particles and their dominant spatial distribution depending on the prevailing wind directions. The spatial distribution of Mn, Zn and Cd, which are carried by fine particles, appears to depend more on atmospheric dispersion and long-range transport, and, thus, these metals should be considered weak markers of the pollution load in the close surroundings of an industrial source.

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Moss Biomonitoring of Atmospheric Trace Element Pollution in the Republic of Moldova

Zinicovscaia

Chaligava

Yushin

et al. 2022

Arch Environ Contam Toxicol

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Moss biomonitoring and air pollution modelling on a regional scale: delayed reflection of industrial pollution in moss in a heavily polluted region?

Cited by 13 publications

References 38 publications

Common Analysis Methods for Gaseous and Solidcontaminants Emitted From Different Pollutionsources: A Review Paper

Common Analysis Methods for Gaseous and Solidcontaminants Emitted From Different Pollutionsources: A Review Paper

Monitoring of Heavy Metals and Nitrogen Concentrations in Mosses in the Vicinity of an Integrated Iron and Steel Plant: Case Study in Czechia

Moss Biomonitoring of Atmospheric Trace Element Pollution in the Republic of Moldova

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