scholarly journals Analysis of Spatial Data from Moss Biomonitoring in Czech–Polish Border

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1237
Author(s):  
Aneta Svozilíková Krakovská ◽  
Vladislav Svozilík ◽  
Inga Zinicovscaia ◽  
Konstantin Vergel ◽  
Petr Jančík
Keyword(s):  
Spatial Data ◽  
Contamination Factor ◽  
Principal Component ◽  
Pollution Load Index ◽  
Anthropogenic Activity ◽  
Long Distance ◽  
The Czech Republic ◽  
Area Of Interest ◽  
Moss Biomonitoring ◽  
Deposition Of Pollutants

The purpose of the study was the analysis of spatial data gained by biomonitoring with the use of mosses. A partial goal was set to characterize the regional atmospheric deposition of pollutants in the air based on the results of the analyses and simultaneously verify the suitability of using mosses as an alternative for monitoring air quality in smaller industrial areas. In total, 93 samples of moss were collected and examined from the area of the Moravian–Silesian Region in the Czech Republic and the area of the Silesian Voivodship in Poland. The samples were analyzed using instrumental neutron activation analysis. Based on the analyses performed, 38 elements, which had been evaluated using principal component analysis, hierarchical clustering on principal components, factor analysis, correlation analysis, contamination factor, geoaccumulation index, enrichment factor, and pollution load index, were determined. The analyses resulted in a division of elements into a group with its concentrations close to the level of the values of the natural background and the second group of elements identified as emission likely originating from anthropogenic activity (Sm, W, U, Tb, and Th). The likely dominant source of emissions for the studied area was identified. Simultaneously, the results pointed to sources of local importance. The area of interest was divided into clusters according to the prevailing type of pollution and long-distance transmission of pollutants was confirmed.

scholarly journals Assessment of heavy metals with ecological risk of soils in the industrial vicinity of Tangail district, Bangladesh

2018 ◽  
Vol 6 (1) ◽  
pp. 108 ◽  
Author(s):  
Ram Proshad ◽  
Md. Saiful Islam ◽  
Tapos Kormoker
Keyword(s):  
Heavy Metals ◽  
Enrichment Factor ◽  
Ecological Risk ◽  
Contamination Factor ◽  
Principal Component ◽  
Potential Ecological Risk ◽  
Pollution Load Index ◽  
Pollution Load ◽  
Cd Contamination ◽  
The Mean

This study was conducted to assess the ecological risk of heavy metals in soils collected from the industrial vicinity of Tangail district in Bangladesh. In this study, the levels of six heavy metals namely chromium (Cr), nickel (Ni), copper (Cu), arsenic (As), cadmium (Cd), and lead (Pb) in 15 sampling sites around the industrial vicinity of Tangail district in Bangladesh were assessed. The mean concentration of Cr, Ni, Cu, As, Cd and Pb in studied soils were 11.56, 23.92, 37.27, 6.11, 2.01, and 17.46 mg/kg, respectively. Certain indices, including the enrichment factor (EF), contamination factor (Cif), geoaccumulation index (Igeo), pollution load index (PLI), toxic unit analysis, and principal component analysis (PCA) were used to assess the ecological risk. The enrichment factor of all the studied metals for all sampling sites were in the descending order of Cd > Cu > As > Pb >Ni > Cr. The contamination factor values revealed that the studied soils were highly impacted by Cd. The pollution load index (PLI) values of Cd were higher than 1, indicating the progressive deterioration of soil due to Cd contamination. In the context of potential ecological risk (PER), soils from all sampling sites showed moderate to very high potential ecological risk.

scholarly journals Comprehensive Evaluation of Metal Pollution in Urban Soils of a Post-Industrial City—A Case of Łódź, Poland

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4350
Author(s):  
Kinga Wieczorek ◽  
Anna Turek ◽  
Małgorzata Szczesio ◽  
Wojciech M. Wolf
Keyword(s):  
Contaminated Soils ◽  
Urban Soils ◽  
Prolonged Exposure ◽  
Comprehensive Evaluation ◽  
Contamination Factor ◽  
Principal Component ◽  
Absorption Spectrometry ◽  
Pollution Load Index ◽  
Soil Extraction ◽  
The City

The pollution of urban soils by metals is a global problem. Prolonged exposure of habitants who are in contact with metals retained in soil poses a health risk. This particularly applies to industrialized cities with developed transport networks. The aim of the study was to determine the content and spatial distribution of mobile metal fractions in soils of the city of Łódź and to identify their load and sources. Multivariate statistical analysis (principal component analysis (PCA), cluster analysis (CA)), combined with GIS, were used to make a comprehensive evaluation of the soil contamination. Hot-spots and differences between urban and suburban areas were also investigated. Metals were determined by atomic absorption spectrometry (AAS) after soil extraction with 1 mol L−1 HCl. In most sites, the metal content changes in the following order: Zn > Pb > Cu > Ni > Cd. About one-third of the samples are considerably (or very highly) contaminated, (contamination factor, CF > 3) with Cu, Pb, or Zn. In almost 40% of the samples, contaminated soils were found (pollution load index, PLI > 1). All metals have a strong influence on the first principal component (PC1), whereas second principal component (PC2) is related to pH. Polluted soils are located in the downtown, in the south and east part of the city. The distribution of contamination coincides with the urban layout, low emission sources and former industrial areas of Łódź.

scholarly journals Determination and Statistical Analysis of Atmospheric Deposition of Heavy Metals in Kosovo. A Moss Survey

2021 ◽  
Author(s):  
Flamur Sopaj ◽  
Trajče Stafilov ◽  
Krste Tašev ◽  
Robert Šajn ◽  
Musaj Paçarizi
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Atmospheric Deposition ◽  
Contamination Factor ◽  
Principal Component ◽  
Pollution Load Index ◽  
Industrial Sites ◽  
Polluted Sites ◽  
High Concentrations ◽  
Contamination Levels

Abstract Atmospheric deposition of heavy metals on the territory of Kosovo was studied using the already widely used technique of mosses as biomonitors. This is a very convenient method as it uses natural samples, and thus avoids many difficulties associated with man-made samplers. Nine heavy metals (Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were determined in 45 moss samples. Statistical analysis was performed to better present and explain the data. High concentrations of Pb, Zn, Cd, and Ni were found near industrial sites and near more densely populated areas. Principal component analysis (PCA) identified more polluted sites such as Zveqan, Stanterg, Prapashticë, Siboc, and Lupç. It was also found that Pb, Zn, Cd, Cr, and Ni are the heavy metals that affect these polluted sites the most. The contamination factor (CF) and the polluted load index (PLI) were calculated. CF showed that only Mn, Cu, and Zn, had no or almost no contamination no contamination levels over the range of moss samples, while Cd and particularly Pb gave extremely high values for CF, indicating extreme contamination levels. Pollution load index also showed that only a few samples are slightly polluted, while most of the samples shown considerable and very high levels of pollution.

scholarly journals “Assessment of Heavy Metal Pollution in Soil of Chattogram Hill Tracts, Bangladesh and Concomitant Health Risk”

2021 ◽  
Author(s):  
Troyee Barua ◽  
AKM Saiful Islam Bhuian ◽  
Mayeen Uddin Khandaker ◽  
Nipa Deb ◽  
Shahadat Hossain ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Risk Index ◽  
Hazard Index ◽  
Significant Risk ◽  
Contamination Factor ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Potential Ecological Risk ◽  
Pollution Load Index ◽  
Toxic Heavy Metals

Abstract The increased human population and associated activities may create a risk in the ecological balance of Chattogram Hill Tracts (CHT), Bangladesh via contamination of soil with toxic heavy metals. Thus, the present study was conducted to assess the concentration of heavy metals (Lead, Cadmium, Copper, Zinc, Iron, Manganese, Chromium and Nickel) in forest soils of the CHT area by using an atomic absorption spectrometer. The degree of contamination of soil was evaluated by five indices: geo-accumulation index (I geo ), enrichment factor (EF), contamination factor (CF), pollution load index (PLI) and potential ecological risk index (PERI). According to these criteria, these soils can be classified as moderately contaminated with some metals. Furthermore, the identification of pollution sources based on principal component analysis and hierarchical cluster analysis have revealed that all analyzed metals are anthropogenic except Fe. Calculated hazard index>1 indicates the possibility of noncarcinogenic effect due to higher value of Fe. Carcinogenic risks through the ingestion, inhalation and dermal pathway for carcinogenic elements (Pb, Cd, Cr, and Ni) shows a non-significant risk (CR<10 -6 ) for both children and adults living in the studied area. Measured data may help the policymakers to reduce the potential effects of soil contamination on eco-environment and human health.

scholarly journals Determination And Statistical Analysis of Atmospheric Deposition of Heavy Metals In Kosovo. A Moss Survey

2021 ◽  
Author(s):  
Flamur Sopaj ◽  
Trajče Stafilov ◽  
Robert Šajn ◽  
Krste Tašev ◽  
Musaj Paçarizi
Keyword(s):  
Heavy Metals ◽  
Statistical Analysis ◽  
Atmospheric Deposition ◽  
Contamination Factor ◽  
Principal Component ◽  
Pollution Load Index ◽  
Pollution Levels ◽  
Polluted Sites ◽  
High Concentrations ◽  
Contamination Levels

Abstract Atmospheric deposition of heavy metals on the territory of Kosovo was studied using the already widely used technique of mosses as biomonitors. This method is very convenient as it uses natural samples and thus avoids many difficulties associated with artificial samplers. Eight heavy metals (Al, Cd, Cr, Cu, Fe, Ni, Pb, and Zn) were determined in 45 moss samples. Statistical analysis was performed to better present and explain the data. High concentrations of Pb, Zn, Cd, and Ni were found near industrial sites and in more densely populated areas. Principal component analysis (PCA) identified more polluted sites such as Zveqan, Stanterg, Prapashticë, Siboc, and Lupç. It was also found that Pb, Zn, Cd, Cr, and Ni are the heavy metals that affect these polluted sites the most. High contents of Cd were found in Kaçanik and Paldemicë, Te Kalaja, Çikatovë, and Shalc, all sampling points found around industrial sights. The contamination factor (CF) and the polluted load index (PLI) were calculated. CF showed that only Cu, and Zn, had no or almost no contamination levels over the range of moss samples, while Cd and mainly Pb gave extremely high values for CF, indicating extreme contamination levels. The pollution load index also showed that only a few samples were slightly polluted, while most samples showed considerable and very high pollution levels.

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

Spatial clustering analysis of green economy based on knowledge graph

2021 ◽  
pp. 1-10
Author(s):  
Shiyuan Zhou ◽  
Xiaoqin Yang ◽  
Qianli Chang
Keyword(s):  
Spatial Data ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Principal Component ◽  
Green Economy ◽  
Membership Degree ◽  
Two Dimensional ◽  
Dimensional Graph ◽  
Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

scholarly journals Change detection and building spatial geodatabase for Iraqi marshes

2018 ◽  
Vol 162 ◽  
pp. 03021
Author(s):  
Oday Jasim ◽  
Noor Hamed ◽  
Tamarra Abdulgabar
Keyword(s):  
Data Collection ◽  
Spatial Data ◽  
Satellite Images ◽  
The Other ◽  
Image Mosaic ◽  
Economic Potential ◽  
Second Stage ◽  
Area Of Interest ◽  
Natural Factors ◽  
Three Stages

The Iraqi Marshlands has natural and economic potential through its environment rich in various forms of lives. This region has suffered numerous setbacks due to human and natural factors, especially in the last two decades of the last century, which led to significant environmental degradation. The purpose of this paper is to prepare spatial data for the area of the marshes in Iraq as a base (Hour-al Hoveizah and central marshes and Hammar). Also, the other aim is to produce a digital geodatabase for the marshes for the years 1973, 1986, 1999, 2006 and 2016 by using ArcGIS. The process of building geodatabase has been through done in three stages: the first stage is including data collection. The second stage will be by merging the satellite images covering the Iraqi marshes and add to images in order to get the image mosaic process. Also, a georeferencing of satellite images is to be done with all the traditional maps of the same area of the marsh. Finally, complete the full geodatabase for the area of interest by using ArcGIS as the in Cartography Design. The results of this research would be a geodatabase for the Iraqi marshes.

scholarly journals GEOCHEMISTRY AND ENVIRONMENTAL ASSESSMENT OF HEAVY METALS IN SURFACE SOIL IN AL-HAWIJA, SOUTHWEST KIRKUK

2020 ◽  
Vol 53 (2E) ◽  
pp. 36-61
Author(s):  
Ahmed Al-Obeidi
Keyword(s):  
Heavy Metals ◽  
Soil Pollution ◽  
Ecological Risk ◽  
Inductively Coupled Plasma ◽  
Surface Soil ◽  
Risk Index ◽  
Size Fraction ◽  
Contamination Factor ◽  
Pollution Load Index ◽  
Anthropogenic Sources

Soil pollution adversely affects the safety and health of the human being. The main objective of the study is to determine the concentrations of heavy metals (As, Cd, Co, Cr, Cu, Fe, Mn, Mo, Ni, Pb and Zn) in surface soil in Al-Hawija, southwestern Kirkuk. Twenty-one samples were collected and analyzed by inductively coupled plasma-mass spectrometry (ICP-MS) to measure the content of heavy metals and assess the soil pollution by using the contamination factor, degree of contamination, geoaccumulation index, pollution load index and ecological risk index (RI). The results indicate that there is high pollution by lead, chromium and copper (78.8, 87.4 and 53.8 mg/kg) respectively, in industrial areas due to anthropogenic sources with the presence of significant ecological risk (Er) of the lead (116) in site S7, due to its high concentrations, while size fraction analysis indicated that all heavy metals are concentrated in the fine parts as a result of adsorption processes by clay minerals.

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