Soils Of Different Land-use Types Research Articles

Heavy metals (HMs) in soils of different land-use types in Zhengzhou, China: Occurrence, source and ecological risk

ABSTRACT To understand the impact of urbanization process on heavy metals (HMs) (distribution, sources and risks) in soil in different land-use types, we collected soil samples from 38 sites in Zhengzhou, China, and divided them into industrial, residential and agricultural areas. Various indices, including single factor pollution index (P i ), Nemerow pollution index (P N ) and geo-accumulation index (I geo ) were used to analyze the pollution levels of HMs. The results showed that the average concentrations of Pb, Cd, Cu, Zn, Cr and Mn were 30.26 mg/kg, 0.31 mg/kg, 18.26 mg/kg, 85.85 mg/kg, 76.76 mg/kg and 211.13 mg/kg, respectively. Additionally, Pb, Zn and Cr were the main pollutants and showed serious pollution levels in three land-use types. Remarkably the pollution level of Pb was particularly prominent in industrial areas due to individual sampling points. Then, the principal component analysis (PCA) showed that Pb and Mn came from traffic and natural sources (34.14%), Cd and Zn from metal smelting (28.11%), Cr and Cu from the use of pesticides and fertilizers (17.57%). Furthermore, geographic information system (GIS) and potential ecological risk index () were employed to analyze the risk of HMs. Among them, the value of Cd in the industrial area was shown to be 55.5, which reached moderate pollution. Moreover, Cd has the highest contribution to risk index (RI) among all soil types. The order of RI was industrial area > residential area > agricultural area, which would contribute to the future regional planning of Zhengzhou.

Distribution, ecological risk assessment and source identification of pollutants in soils of different land-use types in degraded wetlands.

BackgroundUrbanization and global warming are generating ecological degradation and land pattern alteration problems in natural wetlands. These changes are greatly affecting the ecological services of wetlands. Therefore, there is an urgent need to explore the relationship between pollutants and land-use type for wetland restoration purposes. Zaozhadian Lake is a freshwater wetland in the North China Plain, which is facing degradation and land-use types changes. An experiment for analyzing soil pollutants was conducted in three land-use types of farmland, lake, and ditch in the Zaozhadian Lake. The aims of this study were to identify the distribution, pollution degree, and sources of pollutants in different land-use types, and to explore the influence of land-use type changes on contamination.MethodsIn this study, surface sediments (0–10 cm) of three land types (farmland, lake, and ditch) in Zaozhadian Lake were collected, and heavy metals (Cu, Ni, Zn, Pb, Cd, Cr, Hg), As, total nitrogen (TN), total phosphorus (TP) and organic matter (OM) were determined. Kriging interpolation was used to visualize the pollutants distribution. The pollution degree of TN and TP was evaluated by the Nemerow pollution index. The pollution of heavy metals and As was evaluated by the geological accumulation index (Igeo) and the potential ecological risk index (RI). Then, dual hierarchical clustering analysis and the principal component analysis were performed to further analyze the impact of land type changes on pollutants.ResultsThe heavy metal contents in the farmland were higher than other areas, while the TN (3.71 ± 1.03 g kg-1) and OM (57.17 ± 15.16 g kg−1) in lake sediments were higher than that in other regions. Farmland, lake, and ditches had low ecological risks, with RI values of 84.21, 71.34, and 50.78, respectively. The primary heavy metal pollutants are Pb, Cu, and Ni. Furthermore, Cu, As, Ni, Pb, and Zn were primarily derived from agriculture pollution, the source of Cd was the industrial pollution, and Cr mainly originated from natural sources. Nutrients primarily came from the decomposition of aquatic animals, plants, and human-related activities. When the lake area was converted into farmland, the heavy metal concentrations in the soils increased and the TN and OM decreased. Based on the results, this study put forward key strategies including the adjustment of the land-use type and restriction of the entry of pollutants into the wetland ecosystems in the Zaozhadian Lake. More attention should be paid to the impact of land-use type change on pollutants in wetlands.

Distinct microplastic distributions in soils of different land-use types: A case study of Chinese farmlands

Microplastic (MP) pollution poses a huge threat to agroecosystems, but the distribution characteristics of MPs in different types of farmland are still largely unknown. In this work, samples from six land-use types were collected from Chinese farmlands in five provinces. It was found that MP abundances were in the range of 2783-6366 items/kg in all samples. MP distribution results showed that over 80% of particles were less than 1mm, and that MP sizes ranging between 0.02 and 0.2mm represented the greatest proportion. The particle shape classified as fragment (with edges and angular) was the most frequent shape, with an abundance of approximately 54.05%. Polyethylene (PE) and polyamides (PA) were the most abundant polymers in cropped lands; 20.88% and 20.31%, respectively. Statistical analyses showed that lands used for plastic mulching (mulch film and greenhouse crops) had a significantly higher particle abundance, hence identifying plastic mulching as a major contributor to MP pollution in agricultural lands. Furthermore, paddy lands had a significantly higher MP abundance than wheat lands. Variation analyses of MP characteristics revealed that cereal crop farmlands (wheat, paddy land) were more likely to contain fibrous shapes and large MP particles (1-5mm). Economically important tree lands (orchards, woodlands) were likely to contain fragment shapes and pony-size MPs (0.02-0.2mm). Discrepancies among farmlands may depend on various reasons, such as mulching plastic application, irrigation, atmospheric fallout, etc. This study provides firsthand evidences about the characteristics of MP pollution in farmlands and explores some predominant MP sources in agroecosystems.

Factorial Kriging analysis and sources of heavy metals in soils of different land-use types in the Yangtze River Delta of Eastern China.

The objectives of this study are to analyse the pollution status and spatial correlation of soil heavy metals and identify natural and anthropogenic sources of these heavy metals at different spatial scales. Two hundred and twenty-four soil samples (0-20cm) were collected and analysed for eight heavy metals (Cd, Hg, As, Cu, Pb, Cr, Zn and Ni) in soils of different land-use types in the Yangtze River Delta of Eastern China. The multivariate methods and factorial Kriging analysis were used to achieve the research objectives. The results indicated that the human and natural effects of different land-use types on the contents of soil heavy metals were different. The Cd, Hg, Cu, Pb and Zn in soils of industrial area were affected by human activities, and the pollution level of these heavy metals in this area was moderate. The Pb in soils of traffic area was affected by human activities, and eight heavy metals in soils of residential area and farmland area were affected by natural factor. The ecological risk status of eight heavy metals in soils of the whole study area was light. The heavy metals in soils showed three spatial scales (nugget effect, short range and long range). At the nugget effect and short range scales, the Cd, Hg, Cu, Pb and Zn in soils were affected by human and natural factors. At three spatial scales, the As, Cr and Ni in soils were affected by soil parent materials.

Environmental monitoring of organo-halogenated contaminants (OHCs) in surface soils from Pakistan

Extensive monitoring of organo-halogenated contaminants (OHCs) from surface soils of different land-use types from Pakistan was carried out during 2010. The concentrations (ngg−1; dry weight) and profiles clearly indicated the dominance of ∑DDT contaminants followed by the ∑HCHs, ∑30PCBs, chlordanes, and ∑10PBDEs in descending order. Concerning the spatial patterns of occurrence, industrial soils exhibited relatively higher concentration of DDTs, heavy PCBs, and PBDEs (noticeably BDE-47 and -99 congeners), while the urban soils were characterized by high total PCBs (with relatively higher levels of light PCBs), following the agricultural soils. Compared to available criterion guidelines, the current results suggested that 10% of soil samples from industrial sites exhibited slightly higher levels (>50ngg−1) of DDTs. The ∑TEQ levels for mono-ortho DL-PCBs ranged from 0.7 to 5.65 (1.9) pgTEQg−1 dw in all the studied samples and PCB-118 contributed significantly towards the total calculated TEQs. The results of this study will contribute to the environmental management of OHCs contaminated areas of Pakistan.

The molecular marker-based comparison of Azotobacter spp. populations isolated from industrial soils of Cracow-Nowa Huta steelworks (southern Poland) and the adjacent agricultural soils

The occurrence of Azotobacter spp., which has beneficial effects on plant development, is related to various soil properties, such as pH and fertility. This study evaluated the prevalence of Azotobacter spp. in industrial (H) and agricultural soils (P) in Nowa Huta, Cracow and determined the phenotypic and genetic diversity of these bacteria. The examined bacteria were present in 40% of H and in 50% of P soils. Taxonomic identification of the bacterial isolates indicated the presence of three species—A. salinestris, A. chroococcum and A. vinelandii. The genetic diversity, determined using two fingerprinting methods—Random Analysis of Polymorphic DNA (RAPD) and Rep-PCR (BOX) revealed high level of population diversity. In AMOVA analysis most of diversity was attributed to within-population variation (76–85%), and only 3.78–6.18% was associated with among-group H and P variation. Global test of differences revealed distinct population structure within bacterial strains isolated from H and P areas only for BOX markers (Fst = 0.05732, P = 0.00275). Phenetic analyses: UPGMA and DCA better discriminated H and P groups based on RAPD data. Both BOX and RAPD methods provided an insight into the genetic complexity of Azotobacter spp. variation in soils of different land-use types.

Chemometric interpretation of heavy metal patterns in soils worldwide

Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs < 100%, and the set of polluted soils with SMIs > 100%. Additionally, univariate descriptive statistics and the Spearman’s non-parametric rank correlation coefficients were calculated for these three sets. A Box–Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the “masking” effect of the polluted soil patterns over the non-polluted ones.

P.332 Methotrexate-related lymphoproliferative disorder in the gum

Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs < 100%, and the set of polluted soils with SMIs > 100%. Additionally, univariate descriptive statistics and the Spearman’s non-parametric rank correlation coefficients were calculated for these three sets. A Box–Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the “masking” effect of the polluted soil patterns over the non-polluted ones.

Greenhouse gas fluxes from soils of different land-use types in a hilly area of South China

The magnitude, temporal, and spatial patterns of greenhouse gas (hereafter referred to as GHG) fluxes from soils of plantation in the subtropical area of China are still highly uncertain. To contribute towards an improvement of actual estimates, soil CO 2, CH 4, and N 2O fluxes were measured in two different land-use types in a hilly area of South China. This study showed 2 years continuous measurements (twice a week) of GHG fluxes from soils of a pine plantation and a longan orchard system. Impacts of environmental drivers (soil temperature and soil moisture), litter exclusion and land-use (vegetation versus orchard) were presented. Our results suggested that the plantation and orchard soils were weak sinks of atmospheric CH 4 and significant sources of atmospheric CO 2 and N 2O. Annual mean GHG fluxes from soils of plantation and orchard were: CO 2 fluxes of 4.70 and 14.72 Mg CO 2–C ha −1 year −1, CH 4 fluxes of −2.57 and −2.61 kg CH 4–C ha −1 year −1, N 2O fluxes of 3.03 and 8.64 kg N 2O–N ha −1 year −1, respectively. Land use types had great impact on CO 2 and N 2O emissions. Annual average CO 2 and N 2O emissions were higher in the orchard than in the plantation, while there were no clear differences in CH 4 emissions between two sites. Our results suggest that afforestation could be a potential mitigation strategy to reduce GHG emissions from agricultural soils if the observed results were representative at the regional scale. CO 2 and N 2O emissions were mainly affected by soil temperature and soil moisture. CH 4 uptakes showed significant correlation with soil moisture. The seasonal changes in soil CO 2 and N 2O fluxes followed the seasonal weather pattern, with high CO 2 and N 2O emission rates in the rainy period and low rates in the dry period. In contrast, seasonal patterns of CH 4 fluxes were not clear. Removal of surface litter reduced soil CO 2 effluxes by 17–25% and N 2O effluxes by 34–31% in the plantation and orchard in the second sampling year but not in the first sampling year which suggested micro-environmental heterogeneity in soils. Removal of surface litter had no significant effect on CH 4 absorption rates in both years. This suggests that microbial CH 4 uptake was mainly related to the mineral soil rather than in the surface litter layer.

Th-P15:236 Combining ACAT inhibition and cholesterol esterase enhancement results in marked export of macrophage cholesterol in vivo

Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs < 100%, and the set of polluted soils with SMIs > 100%. Additionally, univariate descriptive statistics and the Spearman’s non-parametric rank correlation coefficients were calculated for these three sets. A Box–Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the “masking” effect of the polluted soil patterns over the non-polluted ones.

T03-P-002 Targeting cardiovascular risk plunge in metabolic syndrome: The targets study

Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs < 100%, and the set of polluted soils with SMIs > 100%. Additionally, univariate descriptive statistics and the Spearman’s non-parametric rank correlation coefficients were calculated for these three sets. A Box–Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the “masking” effect of the polluted soil patterns over the non-polluted ones.