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Chemomertic Risk Assessment of Soil Pollution

  • Miroslava Nedyalkova EMAIL logo and Vasil Simeonov
Published/Copyright: September 15, 2019

Abstract

In this study, an interpretation and modeling of the soil quality by monitoring data using an intelligent data analysis is presented. On an annual average, values of 12 soil surface chemical parameters as input variables were determined at 35 sampling sites as objects of the study in the region of Burgas, Bulgaria are used as input data set. Cluster analysis (hierarchical and non hierarchical methods abbreviated as HCA and K-means, respectively) and the principal components analysis (PCA) are used as chemometric tools for data interpretation, classification and modeling. Additionally, principal components regression analysis (APCS approach) is introduced to determine the contribution of each identified by PCA latent factor to the total concentration of the chemical parameters. The formation of different patterns of similarity between the variables or the objects of the study by cluster analysis is interpreted with respect to the risk of pollution or spatial conditions. The input data set structure is analyzed by PCA in order to determine the most significant factors responsible for the data structure. Four major patterns of similarity between the chemical parameters measured are found to define soil quality in the region related to industrial and agricultural activity in the region since the objects are separated into two patterns corresponding to each geographical location of the sampling sites. Analogous results were obtained by the use of PCA where the level of explanation of the data set structure is quantitatively assessed by the total explained variance of the system. The apportionment model indicated that the contribution of latent factors (sources of pollution) to the total chemical concentration of the species tested – pH, soil nutrition components, total and organic carbon content and toxic metals.

1 Introduction

The risk assessment and management of soil contamination is usually performed by regular monitoring of different soil parameters at specific sampling sites by forming a sampling net for a certain region of interest. The results obtained by chemical analysis are then compared with the threshold values introduced as allowable levels by national or international directives. Thus, the problem solving and decision making in soil contamination issues is often based on comparison to single set of results solving a particular problem concerning the soil contamination or respective decision making, which is based solely on single set of results instead on classification and modeling of the monitoring output for the region of interest.

The application of multivariate statistical approaches to the soil quality problems makes it possible to reveal hidden relationships within the monitoring data sets both between the sampling locations or between the features characterizing the sites of sampling. This new type of information gets possible as the chemometric (multivariate statistical) approach treats the problem as a system depending simultaneously on many parameters. It becomes possible to find and interpret seasonal, spatial and pollutant factors leading to formation of patterns of similarity in the monitoring the data matrix. In this way, the identification of pollution sources and natural impacts characteristic for the region of study becomes easy and reliable. The modeling of the environmental effect of different factors is achieved and it improves decision making and problem solving. This approach helps in many environmental studies using intelligent data analysis [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] just to mention few out of a large selection.

The major goal of the present study is to assess the soil quality of a region in south-eastern Bulgaria by the application multivariate statistics (cluster analysis, principal components analysis, principal components regression) in order to find pollution and natural impacts on the soil environment in a region located near to the Bulgarian Black Sea costal line characterized by intensive industrial activity and agriculture.

2 Materials and Methods

2.1 Sampling and chemical analysis

The sampling was performed at monthly intervals in 2017. A Total number of 35 locations of the National soil sampling net from the region of Burgas, Bulgaria were chosen. These locations as their names and abbreviations used in the data analysis are given below:

(Jasna Poljana (JP), Drachevo (Dra), Svoboda (Svo), Sarnevo (Sar), Sozopol (Soz), Rudina (Rud), Rechnica (Rec), Biala (Bja), Marinka (Mar), Maglen (Mag), Karageorgievo (Kar), Zvezdets (Zve), Vizitsa (Viz), Kosti (Kos), Malko Tarnovo (MT), Vratitsa (Vra), Krushevets (Kru), Medovo (Med), Kozichino (Koz), Polski Izvor (Piz), Smolnik (Smo), Karnobat (Krb), Podvis (Pod), Terziisko (Ter), Luliakovo (Lul), Topuzevo (Top), Sedlarevo (Sed), Kotel (Kot), Sadovo (Sad), Slivovo (Sli), Zornitsa (Zor), Prohod (Pro), Raklitsa (Rak), Kipilovo (Kip), Samotinovo (Sam)).

The sampling locations are presented in detail on a map (Figure 1).

Figure 1 Sampling site locations.
Figure 1

Sampling site locations.

The procedures for soil sampling, sample preparation and chemical analysis are carried out according to ISO directives by an accredited laboratory from the regional environmental protection agency [11, 12, 13, 14, 15, 16]. The sampling was performed only for the surface soil layer (0 – 20 cm). The total number of samples collected for analysis was 70.

To determine the total content of the metals of interest (As, Cd, Pb, Ni, Cr, Cu, Zn) and phosphorus content, a fraction of a grain size less than 65 mm was used, for pH, total nitrogen (Ntot), and total organic carbon (TOC), a coarse fraction of less than 2 mm was used .

Validated methods created at the Regional analytical laboratory of the Ministry of the Environment and Waters in City of Burgas were applied for the determination of mass contents of As, Cd, Pb, Ni, Cr, Cu, Zn and P. The methods are verified through participation in interlaboratory comparative laboratory tests.

The soil samples were mineralized with aqua regia in a microwave oven at 180oC for 15 min. Metаls were determined after appropriate dilution by ICP MS „Agilent 7500“ in the standard mode of measurement. Total phosphorus content was determined by spectrometric vanadate-molibdate reagent using „Agilent“ UV-VIS Spectroscopy system diode array.

Quality of results was controlled by analysis of Certified Reference Materials NIST 2709, BCR 142, having a similar matrix as the studied soils. The recovery of following elements was obtained: As (101-110%), Cd (97-100%), Pb (90-101%), Ni (93-98%), Cr (78%), Cu (94-99%), Zn (95-101%) and Р (90-110%).

The analytical determination of the other parameters needed for the study (pH, total N and TOC) followed the standardized methods. pH was determined in aqueous suspension (1:2.5) with a microprocessor pH meter „pH 3000“ WTW. TC and the TOC were measured by an instrumental method with total carbon/total nitrogen analyzer „Shimadzu“. The content of total nitrogen was determined by modified Kjeldahl method in accordance with ISO. Quality control of results is performed by CRM NCS DC 85104 and the recovery obtained was between 105-108% for Ntot and 90-98% for TOC, recalculated as an organic substance.

2.2 Intelligent data analysis methods

Following chemometric methods were used in the present study: cluster analysis (hierarchical cluster analysis and nonhierarchical cluster analysis by K-means), principal components analysis and principal components regression. Correlation analysis was also applied in the starting stage of the chemometric study. These methods are well known and documented to need an extended description [17,18]. Just some basic principles of the methods used are given below.

The major aim of the hierarchical agglomerative cluster analysis is to spontaneously separate the data from the input data sets into patterns of similarity (clusters) for the objects of the study (sampling locations in our case) in the n-dimensional space of the variables (chemical concentrations of the soil quality parameters. The same spontaneous clustering could be applied to the variables which could form clusters in the space of objects. In general, the hierarchical clustering includes normalization of the raw input data to dimensionless units in order to avoid the impact of the different parameter range; calculation of the similarity between the objects by application of some distance parameter, e.g. Euclidean distance or correlation coefficient; finding a linkage between the objects using different linkage options; plotting the results such as a tree-like structure called dendrogram; formation of the clusters; testing the statistical significance of the patterns formed; interpretation of the clusters either for objects or variables.

The nonhierarchical clustering approach is typically supervised pattern recognition technique. It allows the checking of a priori hypothesis in about a certain number of clusters both for objects and variables. It is also a classified approach serving as an option to separate the data sets into preliminary given number of patterns. The specific part of the analysis is to establish a reasonable number of a priori sought clusters and to interpret their meaning, respectively. K-means algorithm is well developed nonhierarchical clustering procedure.

Principal components analysis is a typical display method which makes it possible to reduce the dimensionality of the space of the variables in the direction of the highest variance of the system. It introduces new variables being linear combinations of the input old variables. The new variables are called latent factors or principal components. The interpretation of the latent factors leads to gaining useful information about specific relationships within the data set. The results of the analysis are usually presented as two outputs - factor scores giving the new coordinates of the factor space with the location of the objects and factor loadings informing on the relationship between the variables. Only statistically significant loadings (> 0.70) are important for the modeling and interpretation procedure.

Each new principal component (latent factor) explains a certain part of the total variance of the system. Usually, the first principal component (PC1) explains the maximal part of the system variation and each additional PC has a lesser contribution to the variance explanation.

A reliable model requires normally such a number of PCs, so that over 75% of the total variation to be explained. In case of presented modeling the Varimax rotated PCA solution was used, that allows a better explanation of the system in consideration. It strengthens the role of the latent factors with higher impact on the variation explanation and diminishes the role of PCs with lower impact.

The principal components regression is a modeling approach which allows it to carry out regression with respect to the impact of several independent variables (principal components in this case) on a dependent variable (e.g. total concentration of any of the chemical parameters used). Since the PCA identifies possible sources of pollution in the shape of latent factors, then the effect of each one of them on the total concentration of the species included in the study is possible. This is a model of source apportionment. In our study we use the absolute principal components score (APCS) methods described by Thurston and Spengler [19]. It is documented that other apportionment approaches are known (Target Transformation Factor Analysis, Positive Matrix Factorization, Unmix, Chemical Mass Balance etc, but they are developed mainly for air pollution modeling). The Thurston – Spengler apportionment proved its importance for source apportionment in water and soil studies.

All calculations were performed by the use of the software package STATISTICA 7.0.

Ethical approval: The conducted research is not related to either human or animal use.

3 Results and Discussion

The input data set consists of 35 objects (sampling locations) described by 12 variables (annual averages of the chemical soil parameters) or [35x12].

In Table 1 the basic statistics of the input data is presented.

Table 1

Basic statistics of the input data (except for pH (log units), TC and TOC given as %, all other variables are in mg/kg).

VariableMeanMedianMinimumMaximumRangeVarianceStd.Dev.
pH6.916.905.458.252.790.560.75
N total1564.651569.33794.662856.002061.33268553518.22
P total744.87728.58268.161920.831652.67110238332.02
TC, %2.282.260.884.423.5310.74
TOC, %1.981.960.933.732.800.10.58
As7.557.033.5916.6313.0482.91
Cu44.8641.2511.7697.4285.6546721.62
Cr71.8743.8418.10230.42212.32319356.50
Ni37.6334.939.4485.1975.7538919.73
Cd0.400.410.220.630.400.10.10
Zn64.0362.8730.58115.0384.4531917.87
Pb20.7820.1811.4137.4226.01345.82

The descriptive statistics table indicates the significant differences in the variable dimensions and lack of normal distribution.

In the first step of the intelligent data analysis correlation analysis was performed in order to get an idea about the possible stochastic relationships between the variables. In Table 2 the correlation matrix for the data set is given.

Table 2

Correlation matrix [12x12] (statistically significant correlations are marked).

pHN totP totTC, %TOC, %AsCuCrNiCdZnPb
pH1.00
N total0.441.00
P total0.520.421.00
TC, %0.520.720.451.00
TOC, %0.370.730.400.891.00
As0.010.13-0.210.04-0.141.00
Cu0.260.290.290.180.29-0.021.00
Cr0.150.410.350.330.57-0.100.411.00
Ni0.280.460.380.330.500.080.550.751.00
Cd0.400.440.250.360.430.380.370.700.691.00
Zn0.340.490.170.340.300.130.590.050.480.241.00
Pb0.160.430.130.350.360.300.500.190.500.270.581.00

Table 2 reveals the good correlation between different variables (pH with N-tot, P-tot, carbon species, Cd, Zn; N-tot with most of the metals and carbon species; Cu with most metals).

The presence of correlation could be indication for the common origin of the correlated species, respectively, for formation of common sources of pollution or of natural origin.

3.1 Cluster analysis

The exploratory data analysis starts with hierarchical cluster analysis. The spontaneous grouping into patterns of similarity (both for sampling locations and chemical variables) gives important clues for the spatial similarity between sampling locations and about the possible common origin of the chemical species.

In Figures 2 and 3 the hierarchical dendrograms (tree diagrams for z-transformed input data, squared Euclidean distances as similarity measure, Ward’s method of linkage and Sneath’s criterion for cluster significance) for

Figure 2 Tree diagram for 12 variables.
Figure 2

Tree diagram for 12 variables.

Figure 3 Tree diagram for 35 sampling locations.
Figure 3

Tree diagram for 35 sampling locations.

clustering of variables (Figure 2) and objects (Figure 3) are presented.

Four significant clusters are seen in Figure 2:

K1: Cd, Ni, Cr

K2: Zn, Cu, Pb

K3 : TC, TOC, N-tot

K4: pH, P-tot

It could be concluded that As is an outlier since it does not link closely to any of the other metals.

This variable linkage indicates that in the region of interest are existing probably several source affecting the soil quality: industrial anthropogenic sources (typical emitters for Cd, Ni, Cr related also with oil burning and refinery activities); aerosol sedimentation sources (pollution by Zn, Cu, Pb – constituents of traffic emissions, rock erosion material, natural soil content); organic material sources (correlation between organic species and total nitrogen) ensuring the soil fertility and soil acidity sources as a results of fertilization activities.

Arsenic has different sources as soil component – both natural (enhanced As content in the regional soils) and anthropogenic (component of herbicides).

Only two patterns of similarity are formed by the hierarchical clustering of the sampling location sites:

K1: DRA, MAR, KOT, KAR, SVO, MED, VRA, PIZ, SLI, ZOR, RAC, SAR, KRB, SMO, MAG, KOZ, KOS, VIZ, ZVE, KRU, MT, SAM

K2: JP, REC, SOZ, BJA, SED, POD, TER, KIP. RUD, TOP, SAD, PRO, LUL

The cluster K1 forms the pattern of sites with rural character and sites which are impacted by anthropogenic industrial sources (near to City of Burgas with oil refinery and heavy industry and traffic). The other pattern of similarity K2 includes sites which are either close to the southern coastal line of the region or the mountainous part. They represent locations with higher level of soil quality since they are tourist resorts or sites with low industrial impact,

These initial results from the hierarchical clustering have lead to two expert hypotheses:

  1. The linkage of the soil quality chemical descriptors for the region of interest leads to definition of four patterns of variables similarly related to the sources of industrial pollution; airborne particle sedimentation; organic impact: soil acidity. It would be interesting to clarify the specific behavior of arsenic as soil quality descriptor as pointed out by hierarchical cluster analysis

  2. Separation of the sampling locations into two classes of similarity related to the geographical position of the sites and the vicinity of additional factors such as industrial and agricultural activity in any of the specific spatial positions – close to the sea side, mountainous or rural region.

The application of non hierarchical clustering (K-means mode) as supervised pattern recognition method makes it possible to verify the expert hypotheses and the results from the spontaneous hierarchical clustering.

K-means clustering of the chemical variables was performed for 5 a priori selected clusters:

Members of cluster 1: Cr, Ni, Cd

Members of cluster 2: N-tot, TC, TOC

Members of cluster 3: pH, P-tot

Members of cluster 4: Cu, Zn, Pb

In cluster 5 only As is found as an object very different from the clusters 1 – 4.

Thus, the results confirm completely the results shown in Figure 2 with four major clusters and one outlier.

The second a priory hypothesis is related to the patterns formed between the sampling sites.

It is expected that the class separation will be in the soil quality for locations impacted by anthropogenic influence and atmospheric depositions and locations with less level of overall pollution from anthropogenic sources (spatially separated from the industrially affected sites). The class separation will be with respect to the differences in the soil quality for locations impacted by anthropogenic influence and atmospheric depositions.

K-means clustering of sampling sites with a priori requirement for formation of two clusters gave the following results:

Members of cluster 1(13 sites): JP, RUD, SOZ, REC, BJA, POD, LUL, TOP, SED, TER, SAD, PRO, KIP

Members of cluster 2 (22 sites): DRA, SVO, SAR, MAR, MAG, KAR, ZVE, VIZ, KOS, MT, VRA, KRU, MED, KOZ, PIZ, SMO, KRB, KOT, SLI, ZOR, RAC, SAM

Again, the same distribution as in hierarchical clustering is confirmed. The conditional classification of the sampling locations into classes “industrial rural” (K2) and “mountainous coastal” (K1) is proven.

In Figure 4 the average values (z-standardized input data) that those for K2 of each chemical variable for each one of the identified patterns of similarity are shown.

Figure 4 Plot of mean values for each chemical parameter for each of the clusters found.
Figure 4

Plot of mean values for each chemical parameter for each of the clusters found.

It is readily seen that the two patterns of locations are clearly separated by the levels of the chemical variables. All average values for K2 are significantly higher than those of K1. This is convenient proof for the higher industrial pollution of the soil in the linked in K2 sampling sites. The levels for all tested chemical components for K1 are lower (as mean values). The mountainous coastal locations (tourist and recreation activities and low scale industries) are free of pollution as compared to the industrially affected locations.

As expected, only arsenic reveals a more special behavior. The industrially impacted areas have an average minimum level as compared to the relatively constant averages for the rest of chemical descriptors (K 2). On contrary, the arsenic levels for the locations in K1 indicate a local average maximum. Still the absolute average value of the industrially polluted pattern of sites are higher that of the non polluted pattern of sites. It might indicate that specific soil properties contribute to the formation of the total arsenic concentration for both patterns and not only anthropogenically influenced sources are responsible for the total As concentration. Some previous studies on the arsenic distribution in Bulgarian soils confirm that at many locations enhanced natural concentration of As is observed [20,21].

It is important to note that the difference between the average values for both clusters is statistically significant.

3.2 Principal components analysis

The input data set was also subject to principal components analysis (Varimax rotation mode of standardized values).

In Table 3 factor loadings for four identified latent factor which explain over 75% of the total variance of the system are presented.

Table 3

Factor loadings (statically significant loadings are marked by bold).

VariablePC- 1PC- 2PC- 3PC- 4
pH0.6660.0630.185-0.061
N total0.7850.2260.2740.142
P total0.5620.2400.117-0.428
TC0.9250.1230.0950.063
TOC0.8030.3650.113-0.091
As-0.0220.0670.1170.930
Cu0.0470.3860.775-0.210
Cr0.2210.9270.024-0.153
Ni0.2200.7700.466-0.013
Cd0.2860.8010.1200.347
Zn0.285-0.0240.8640.071
Pb0.2270.1280.7490.283
Expl. Var %26.6021.1019.2011.30

In Figure 5 the scree plot of the eigenvalues is presented. It is readily seen that four latent factors have eigenvalues higher than 1 and it explains the choice of 4 latent factors for data mining.

Figure 6 shows clearly the grouping of the chemical variables in the space of the first two principal components. From Table 3 and Figure 6 is readily seen that each one of the identified latent factors has its own physical meaning and the grouping confirms the results from cluster analysis.

PC1 (principal component 1) explains over 26% of the total variance and registers the good correlation between pH, P-tot, N-tot, TC and TOC. PC1 could be conditional named “nutrients and acidic” factor revealing the close linkage between soil nutrients, organic matter and pH of the soil. In fact, the same interpretation could be achieved by hierarchical cluster analysis (Figure 2) where the same chemical parameters could be considered as belonging to one and the same cluster if a cluster significance is chosen (2/3 Dmax instead of 1/3Dmax).

The second latent factor PC 2 explains over 21% of the total variance and indicates for the impact of a conditional “industrial” factor due to the high factor loadings of three chemical components – Cr, Ni, Cd [10, 20, 23, 26]. In the references cited the metal soil pollution sources are assumed to be of industrial origin – metal smelters, ore production, combustion processes. As already discussed, their close relationship is a sign for their common anthropogenic origin (source).

Next principal component PC 3 counts for explanation of more than 19% of the total variance of the system and represents the influence of aerosol sedimentation effects by strong correlation between Cu, Zn and Pb. The conditional name “aerosol sedimentation” factor corresponds to the role of this specific source on the soil quality.

Figure 5 Plot of eigenvalues.
Figure 5

Plot of eigenvalues.

Figure 6 Biplot for factor loadings (PC 1 vs PC 2).
Figure 6

Biplot for factor loadings (PC 1 vs PC 2).

The last latent factor PC 4 (over 11% explanation of the total variance) underlines the specific role of arsenic in assessment of the soil quality of the region. The only significant factor loading in PC 4 is that for As.

It is of substantial interest to determine the contribution of each identified latent factor to the formation of the total concentration of each of the chemical variables. This could be achieved by the apportioning procedure known as principal components regression (APCS approach of Thurston and Spengler). The results of the regression analysis are summarized in Table 4.

Table 4

Source apportionment for the soil quality parameters in region of Burgas, Bulgaria (in %).

VariableInterceptNutrition-acidic sourceIndustrial sourceAerosol sedimentation sourceAs sourceR2
pH11.572.3-16.2-0.91
N total4.081.15.26.43.30.83
P total18.064.79.113.64.60.81
TC6.381.212.4--0.88
TOC5.484.310.3--0.86
As12.8---87.20.84
Cu10.5-10.679.9-0.86
Cr8.210.481.4--0.81
Ni6.53.876.812.9-0.89
Cd2.68.675.59.14.20.90
Zn12.85.8-81.4-0.82
Pb8.89.17.369.25.60.79

The regression intercept indicated the unexplained contribution by the identified sources. The multiple correlation coefficient is a measure for the model validity (comparison of calculated by the model total concentrations and the experimentally found ones). The difference 1 – R2 is showing (in %) the value of the model prediction error. The error of prediction is between 9 and 21%.

The results of the intelligent data analysis carried out are in good agreement with the major conclusions of a series of similar studies dedicated to soil pollution and risk management in different countries [22, 23, 24, 25, 26, 27] performed recently.

4 Conclusion

The multivariate statistical data treatment made it possible to assess in a reliable way the risk of soil pollution in the region studied and to contribute for proper risk management and decision-making concerning soil quality.

The chemometric expertise revealed and quantitatively assessed four major possible sources of pollution in the region of Burgas, Bulgaria. These sources are related to the impact of industrial activity, soil specific properties and aerosol sedimentation processes. The respective apportionment regression models are suggested for quantitative description of the contribution of each identified source to the formation of the total chemical parameter concentrations used for monitoring of the soil quality.

Additionally, a spatial analysis of the sampling locations was performed showing the separation of the region sampling net into two patterns of similarity: industrially impacted area with higher level of pollution and relatively less polluted area of recreation locations close to the coastal line and locations from mountainous areas.

Acknowledgements

The authors would like to express their sincere gratitude to the Project Bulgarian Science Fund, DCOST 01/6 – 2017 for the financial support.

This work was supported by the project „Information and Communication Technologies for a Single Digital Market in Science, Education and Security“ of the Scientific Research Center, NIS-3317.

  1. Conflict of interest

    Authors declare no conflict of interest.

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Received: 2019-01-24
Accepted: 2019-03-03
Published Online: 2019-09-15

© 2019 Miroslava Nedyalkova, Vasil Simeonov, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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  8. Development of a validated spectrofluorimetric method for assay of sotalol hydrochloride in tablets and human plasma: application for stability-indicating studies
  9. Topological Indices of Hyaluronic Acid-Paclitaxel Conjugates’ Molecular Structure in Cancer Treatment
  10. Thermodynamic properties of the bubble growth process in a pool boiling of water-ethanol mixture two-component system
  11. Critical Roles of the PI3K-Akt-mTOR Signaling Pathway in Apoptosis and Autophagy of Astrocytes Induced by Methamphetamine
  12. Characteristics of Stable Hydrogen and Oxygen Isotopes of Soil Moisture under Different Land Use in Dry Hot Valley of Yuanmou
  13. Specific, highly sensitive and simple spectrofluorimetric method for quantification of daclatasvir in HCV human plasma patients and in tablets dosage form
  14. Chromium-modified cobalt molybdenum nitrides as catalysts for ammonia synthesis
  15. Langerhans cell-like dendritic cells treated with ginsenoside Rh2 regulate the differentiation of Th1 and Th2 cells in vivo
  16. Identification of Powdery Mildew Blumeria graminis f. sp. tritici Resistance Genes in Selected Wheat Varieties and Development of Multiplex PCR
  17. Computational Analysis of new Degree-based descriptors of oxide networks
  18. The Use Of Chemical Composition And Additives To Classify Petrol And Diesel Using Gas Chromatography–Mass Spectrometry And Chemometric Analysis: A Uk Study
  19. Minimal Energy Tree with 4 Branched Vertices
  20. Jatropha seed oil derived poly(esteramide-urethane)/ fumed silica nanocomposite coatings for corrosion protection
  21. Calculating topological indices of certain OTIS interconnection networks
  22. Energy storage analysis of R125 in UIO-66 and MOF-5 nanoparticles: A molecular simulation study
  23. Velvet Antler compounds targeting major cell signaling pathways in osteosarcoma - a new insight into mediating the process of invasion and metastasis in OS
  24. Effects of Azadirachta Indica Leaf Extract, Capping Agents, on the Synthesis of Pure And Cu Doped ZnO-Nanoparticles: A Green Approach and Microbial Activity
  25. Aqueous Micro-hydration of Na+(H2O)n=1-7 Clusters: DFT Study
  26. A proposed image-based detection of methamidophos pesticide using peroxyoxalate chemiluminescence system
  27. Phytochemical screening and estrogenic activity of total glycosides of Cistanche deserticola
  28. Biological evaluation of a series of benzothiazole derivatives as mosquitocidal agents
  29. Chemical pretreatments of Trapa bispinosa's peel (TBP) biosorbent to enhance adsorption capacity for Pb(ll)
  30. Dynamic Changes in MMP1 and TIMP1 in the Antifibrotic Process of Dahuang Zhechong Pill in Rats with Liver Fibrosis
  31. The Optimization and Production of Ginkgolide B Lipid Microemulsion
  32. Photodynamic Therapy Enhanced the Antitumor Effects of Berberine on HeLa Cells
  33. Chiral and Achiral Enantiomeric Separation of (±)-Alprenolol
  34. Correlation of Water Fluoride with Body Fluids, Dental Fluorosis and FT4, FT3 –TSH Disruption among Children in an Endemic Fluorosis area in Pakistan
  35. A one-step incubation ELISA kit for rapid determination of dibutyl phthalate in water, beverage and liquor
  36. Free Radical Scavenging Activity of Essential Oil of Eugenia caryophylata from Amboina Island and Derivatives of Eugenol
  37. Effects of Blue and Red Light On Growth And Nitrate Metabolism In Pakchoi
  38. miRNA-199a-5p functions as a tumor suppressor in prolactinomas
  39. Solar photodegradation of carbamazepine from aqueous solutions using a compound parabolic concentrator equipped with a sun tracking system
  40. Influence of sub-inhibitory concentration of selected plant essential oils on the physical and biochemical properties of Pseudomonas orientalis
  41. Preparation and spectroscopic studies of Fe(II), Ru(II), Pd(II) and Zn(II) complexes of Schiff base containing terephthalaldehyde and their transfer hydrogenation and Suzuki-Miyaura coupling reaction
  42. Complex formation in a liquid-liquid extraction-chromogenic system for vanadium(IV)
  43. Synthesis, characterization (IR, 1H, 13C & 31P NMR), fungicidal, herbicidal and molecular docking evaluation of steroid phosphorus compounds
  44. Analysis and Biological Evaluation of Arisaema Amuremse Maxim Essential Oil
  45. A preliminary assessment of potential ecological risk and soil contamination by heavy metals around a cement factory, western Saudi Arabia
  46. Anti- inflammatory effect of Prunus tomentosa Thunb total flavones in LPS-induced RAW264.7 cells
  47. Collaborative Influence of Elevated CO2 Concentration and High Temperature on Potato Biomass Accumulation and Characteristics
  48. Methods of extraction, physicochemical properties of alginates and their applications in biomedical field – a review
  49. Characteristics of liposomes derived from egg yolk
  50. Preparation of ternary ZnO/Ag/cellulose and its enhanced photocatalytic degradation property on phenol and benzene in VOCs
  51. Influence of Human Serum Albumin Glycation on the Binding Affinities for Natural Flavonoids
  52. Synthesis and antioxidant activity of 2-methylthio-pyrido[3,2-e][1,2,4] triazolo[1,5-a]pyrimidines
  53. Comparative study on the antioxidant activities of ten common flower teas from China
  54. Molecular Properties of Symmetrical Networks Using Topological Polynomials
  55. Synthesis of Co3O4 Nano Aggregates by Co-precipitation Method and its Catalytic and Fuel Additive Applications
  56. Phytochemical analysis, Antioxidant and Antiprotoscolices potential of ethanol extracts of selected plants species against Echinococcus granulosus: In-vitro study
  57. Silver nanoparticles enhanced fluorescence for sensitive determination of fluoroquinolones in water solutions
  58. Simultaneous Quantification of the New Psychoactive Substances 3-FMC, 3-FPM, 4-CEC, and 4-BMC in Human Blood using GC-MS
  59. Biodiesel Production by Lipids From Indonesian strain of Microalgae Chlorella vulgaris
  60. Miscibility studies of polystyrene/polyvinyl chloride blend in presence of organoclay
  61. Antibacterial Activities of Transition Metal complexes of Mesocyclic Amidine 1,4-diazacycloheptane (DACH)
  62. Novel 1,8-Naphthyridine Derivatives: Design, Synthesis and in vitro screening of their cytotoxic activity against MCF7 cell line
  63. Investigation of Stress Corrosion Cracking Behaviour of Mg-Al-Zn Alloys in Different pH Environments by SSRT Method
  64. Various Combinations of Flame Retardants for Poly (vinyl chloride)
  65. Phenolic compounds and biological activities of rye (Secale cereale L.) grains
  66. Oxidative degradation of gentamicin present in water by an electro-Fenton process and biodegradability improvement
  67. Optimizing Suitable Conditions for the Removal of Ammonium Nitrogen by a Microbe Isolated from Chicken Manure
  68. Anti-inflammatory, antipyretic, analgesic, and antioxidant activities of Haloxylon salicornicum aqueous fraction
  69. The anti-corrosion behaviour of Satureja montana L. extract on iron in NaCl solution
  70. Interleukin-4, hemopexin, and lipoprotein-associated phospholipase A2 are significantly increased in patients with unstable carotid plaque
  71. A comparative study of the crystal structures of 2-(4-(2-(4-(3-chlorophenyl)pipera -zinyl)ethyl) benzyl)isoindoline-1,3-dione by synchrotron radiation X-ray powder diffraction and single-crystal X-ray diffraction
  72. Conceptual DFT as a Novel Chemoinformatics Tool for Studying the Chemical Reactivity Properties of the Amatoxin Family of Fungal Peptides
  73. Occurrence of Aflatoxin M1 in Milk-based Mithae samples from Pakistan
  74. Kinetics of Iron Removal From Ti-Extraction Blast Furnace Slag by Chlorination Calcination
  75. Increasing the activity of DNAzyme based on the telomeric sequence: 2’-OMe-RNA and LNA modifications
  76. Exploring the optoelectronic properties of a chromene-appended pyrimidone derivative for photovoltaic applications
  77. Effect of He Qi San on DNA Methylation in Type 2 Diabetes Mellitus Patients with Phlegm-blood Stasis Syndrome
  78. Cyclodextrin potentiometric sensors based on selective recognition sites for procainamide: Comparative and theoretical study
  79. Greener synthesis of dimethyl carbonate from carbon dioxide and methanol using a tunable ionic liquid catalyst
  80. Nonisothermal Cold Crystallization Kinetics of Poly(lactic acid)/Bacterial Poly(hydroxyoctanoate) (PHO)/Talc
  81. Enhanced adsorption of sulfonamide antibiotics in water by modified biochar derived from bagasse
  82. Study on the Mechanism of Shugan Xiaozhi Fang on Cells with Non-alcoholic Fatty Liver Disease
  83. Comparative Effects of Salt and Alkali Stress on Antioxidant System in Cotton (Gossypium Hirsutum L.) Leaves
  84. Optimization of chromatographic systems for analysis of selected psychotropic drugs and their metabolites in serum and saliva by HPLC in order to monitor therapeutic drugs
  85. Electrocatalytic Properties of Ni-Doped BaFe12O19 for Oxygen Evolution in Alkaline Solution
  86. Study on the removal of high contents of ammonium from piggery wastewater by clinoptilolite and the corresponding mechanisms
  87. Phytochemistry and toxicological assessment of Bryonia dioica roots used in north-African alternative medicine
  88. The essential oil composition of selected Hemerocallis cultivars and their biological activity
  89. Mechanical Properties of Carbon Fiber Reinforced Nanocrystalline Nickel Composite Electroforming Deposit
  90. Anti-c-myc efficacy block EGFL7 induced prolactinoma tumorigenesis
  91. Topical Issue on Applications of Mathematics in Chemistry
  92. Zagreb Connection Number Index of Nanotubes and Regular Hexagonal Lattice
  93. The Sanskruti index of trees and unicyclic graphs
  94. Valency-based molecular descriptors of Bakelite network BNmn
  95. Computing Topological Indices for Para-Line Graphs of Anthracene
  96. Zagreb Polynomials and redefined Zagreb indices of Dendrimers and Polyomino Chains
  97. Topological Descriptor of 2-Dimensional Silicon Carbons and Their Applications
  98. Topological invariants for the line graphs of some classes of graphs
  99. Words for maximal Subgroups of Fi24
  100. Generators of Maximal Subgroups of Harada-Norton and some Linear Groups
  101. Special Issue on POKOCHA 2018
  102. Influence of Production Parameters on the Content of Polyphenolic Compounds in Extruded Porridge Enriched with Chokeberry Fruit (Aronia melanocarpa (Michx.) Elliott)
  103. Effects of Supercritical Carbon Dioxide Extraction (SC-CO2) on the content of tiliroside in the extracts from Tilia L. flowers
  104. Impact of xanthan gum addition on phenolic acids composition and selected properties of new gluten-free maize-field bean pasta
  105. Impact of storage temperature and time on Moldavian dragonhead oil – spectroscopic and chemometric analysis
  106. The effect of selected substances on the stability of standard solutions in voltammetric analysis of ascorbic acid in fruit juices
  107. Determination of the content of Pb, Cd, Cu, Zn in dairy products from various regions of Poland
  108. Special Issue on IC3PE 2018 Conference
  109. The Photocatalytic Activity of Zns-TiO2 on a Carbon Fiber Prepared by Chemical Bath Deposition
  110. N-octyl chitosan derivatives as amphiphilic carrier agents for herbicide formulations
  111. Kinetics and Mechanistic Study of Hydrolysis of Adenosine Monophosphate Disodium Salt (AMPNa2) in Acidic and Alkaline Media
  112. Antimalarial Activity of Andrographis Paniculata Ness‘s N-hexane Extract and Its Major Compounds
  113. Special Issue on ABB2018 Conference
  114. Special Issue on ICCESEN 2017
  115. Theoretical Diagnostics of Second and Third-order Hyperpolarizabilities of Several Acid Derivatives
  116. Determination of Gamma Rays Efficiency Against Rhizoctonia solani in Potatoes
  117. Studies On Compatibilization Of Recycled Polyethylene/Thermoplastic Starch Blends By Using Different Compatibilizer
  118. Liquid−Liquid Extraction of Linalool from Methyl Eugenol with 1-Ethyl-3-methylimidazolium Hydrogen Sulfate [EMIM][HSO4] Ionic Liquid
  119. Synthesis of Graphene Oxide Through Ultrasonic Assisted Electrochemical Exfoliation
  120. Special Issue on ISCMP 2018
  121. Synthesis and antiproliferative evaluation of some 1,4-naphthoquinone derivatives against human cervical cancer cells
  122. The influence of the grafted aryl groups on the solvation properties of the graphyne and graphdiyne - a MD study
  123. Electrochemical modification of platinum and glassy carbon surfaces with pyridine layers and their use as complexing agents for copper (II) ions
  124. Effect of Electrospinning Process on Total Antioxidant Activity of Electrospun Nanofibers Containing Grape Seed Extract
  125. Effect Of Thermal Treatment Of Trepel At Temperature Range 800-1200˚C
  126. Topical Issue on Agriculture
  127. The effect of Cladophora glomerata exudates on the amino acid composition of Cladophora fracta and Rhizoclonium sp.
  128. Influence of the Static Magnetic Field and Algal Extract on the Germination of Soybean Seeds
  129. The use of UV-induced fluorescence for the assessment of homogeneity of granular mixtures
  130. The use of microorganisms as bio-fertilizers in the cultivation of white lupine
  131. Lyophilized apples on flax oil and ethyl esters of flax oil - stability and antioxidant evaluation
  132. Production of phosphorus biofertilizer based on the renewable materials in large laboratory scale
  133. Human health risk assessment of potential toxic elements in paddy soil and rice (Oryza sativa) from Ugbawka fields, Enugu, Nigeria
  134. Recovery of phosphates(V) from wastewaters of different chemical composition
  135. Special Issue on the 4th Green Chemistry 2018
  136. Dead zone for hydrogenation of propylene reaction carried out on commercial catalyst pellets
  137. Improved thermally stable oligoetherols from 6-aminouracil, ethylene carbonate and boric acid
  138. The role of a chemical loop in removal of hazardous contaminants from coke oven wastewater during its treatment
  139. Combating paraben pollution in surface waters with a variety of photocatalyzed systems: Looking for the most efficient technology
  140. Special Issue on Chemistry Today for Tomorrow 2019
  141. Applying Discriminant and Cluster Analyses to Separate Allergenic from Non-allergenic Proteins
  142. Chemometric Expertise Of Clinical Monitoring Data Of Prolactinoma Patients
  143. Chemomertic Risk Assessment of Soil Pollution
  144. New composite sorbent for speciation analysis of soluble chromium in textiles
  145. Photocatalytic activity of NiFe2O4 and Zn0.5Ni0.5Fe2O4 modified by Eu(III) and Tb(III) for decomposition of Malachite Green
  146. Photophysical and antibacterial activity of light-activated quaternary eosin Y
  147. Spectral properties and biological activity of La(III) and Nd(III) Monensinates
  148. Special Issue on Monitoring, Risk Assessment and Sustainable Management for the Exposure to Environmental Toxins
  149. Soil organic carbon mineralization in relation to microbial dynamics in subtropical red soils dominated by differently sized aggregates
  150. A potential reusable fluorescent aptasensor based on magnetic nanoparticles for ochratoxin A analysis
  151. Special Issue on 13th JCC 2018
  152. Fluorescence study of 5-nitroisatin Schiff base immobilized on SBA-15 for sensing Fe3+
  153. Thermal and Morphology Properties of Cellulose Nanofiber from TEMPO-oxidized Lower part of Empty Fruit Bunches (LEFB)
  154. Encapsulation of Vitamin C in Sesame Liposomes: Computational and Experimental Studies
  155. A comparative study of the utilization of synthetic foaming agent and aluminum powder as pore-forming agents in lightweight geopolymer synthesis
  156. Synthesis of high surface area mesoporous silica SBA-15 by adjusting hydrothermal treatment time and the amount of polyvinyl alcohol
  157. Review of large-pore mesostructured cellular foam (MCF) silica and its applications
  158. Ion Exchange of Benzoate in Ni-Al-Benzoate Layered Double Hydroxide by Amoxicillin
  159. Synthesis And Characterization Of CoMo/Mordenite Catalyst For Hydrotreatment Of Lignin Compound Models
  160. Production of Biodiesel from Nyamplung (Calophyllum inophyllum L.) using Microwave with CaO Catalyst from Eggshell Waste: Optimization of Transesterification Process Parameters
  161. The Study of the Optical Properties of C60 Fullerene in Different Organic Solvents
  162. Composite Material Consisting of HKUST-1 and Indonesian Activated Natural Zeolite and its Application in CO2 Capture
  163. Topical Issue on Environmental Chemistry
  164. Ionic liquids modified cobalt/ZSM-5 as a highly efficient catalyst for enhancing the selectivity towards KA oil in the aerobic oxidation of cyclohexane
  165. Application of Thermal Resistant Gemini Surfactants in Highly Thixotropic Water-in-oil Drilling Fluid System
  166. Screening Study on Rheological Behavior and Phase Transition Point of Polymer-containing Fluids produced under the Oil Freezing Point Temperature
  167. The Chemical Softening Effect and Mechanism of Low Rank Coal Soaked in Alkaline Solution
  168. The Influence Of NO/O2 On The NOx Storage Properties Over A Pt-Ba-Ce/γ-Al2O3 Catalyst
  169. Special Issue on the International conference CosCI 2018
  170. Design of SiO2/TiO2 that Synergistically Increases The Hydrophobicity of Methyltrimethoxysilane Coated Glass
  171. Antidiabetes and Antioxidant agents from Clausena excavata root as medicinal plant of Myanmar
  172. Development of a Gold Immunochromatographic Assay Method Using Candida Biofilm Antigen as a Bioreceptor for Candidiasis in Rats
  173. Special Issue on Applied Biochemistry and Biotechnology 2019
  174. Adsorption of copper ions on Magnolia officinalis residues after solid-phase fermentation with Phanerochaete chrysosporium
  175. Erratum
  176. Erratum to: Sand Dune Characterization For Preparing Metallurgical Grade Silicon
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