Potentially toxic metal contamination of urban soils and roadside dust in Shanghai, China

A detailed investigation was conducted to understand the contamination characteristics of a selected set of potentially toxic metals in Shanghai. The amount of Pb, Zn, Cu, Cr, Cd and Ni were determined from 273 soil/dust samples collected within urban area. The results indicated that concentration of all metals except Ni in soils was significant, and metal pollution was even severer in roadside dust. A series of metal spatial distribution maps were created through geostatistical analysis, and the pollution hotspots tended to associate with city core area, major road junctions, and the regions close to industrial zones. In attempt of identifying the source of metals through geostatistical and multivariate statistical analyses, it was concluded as follows: Pb, Zn and Cu mainly originated from traffic contaminants; soil Ni was associated with natural concentration; Cd largely came from point-sourced industrial pollution; and Cr, Ni in dust were mainly related to atmospheric deposition.     

Multivariate Analysis of Interactions Between Phytoplankton Biomass and Environmental Variables in Taihu Lake, China

Phytoplankton variation in large shallow eutrophic lakes is characterized by high spatial and temporal heterogenity. Understanding the pattern of phytoplankton variation and the relationships between it and environmental variables can contribute to eutrophic lakes management. In this study Taihu Lake, one of the largest eutrophic fresh water lake in China, was taken as study area. The water body of Taihu Lake was divided into five regions viz. Wuli bay (WB), Meilian Bay (MB), West Taihu Lake (WTL), Main Body of Taihu Lake (MBTL) and East Taihu Lake (ETL). Concentrations of chlorophyll-a and the related environmental variables were determined in each region in the period 2000–2003. Factor analysis and multivariate analysis were applied to evaluate the interactions between phytoplankton variation and environmental variables. Results showed that the highest average concentrations of TN, TP and Chl-a were observed in WB, followed in a descending order by MB and WTL, and the lowest concentrations of TN, TP and Chl-a were observed in MBTL and ETL. Chl-a and TP concentrations in most regions (except ETL) declined during the study period. It suggested that to some extent the lake was recovering from eutrophication. However, persistent ascending of TN and NH₄–N in all five regions indicated the deteriorating of water quality in the study period. Results of multivariate showed that the relationships between phytoplankton biomass and environmental variables varied among regions. TP illustrated itself a controlling role on phytoplankton in WB, MB, WTL and MBTL according to the significant positive relations to phytoplankton biomass in these regions. Nitrogen could be identified as a limiting factor to phytoplankton biomass in ETL in view of the positive correlations between TN and phytoplankton and between NH₄–N and phytoplankton. Spatial variation of interactions between phytoplankton and environmental parameters suggested proper eutrophication control measures were needed to restore ecological system in each region of Taihu Lake.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

Comprehensive assessment of toxic metals in urban and suburban street deposited sediments (SDSs) in the biggest metropolitan area of China

A set of toxic metals, i.e. As, Hg, Pb, Cd, Cu, Zn, Ni and Cr, in urban and suburban SDSs were investigated comparatively in the biggest metropolitan area of China, Shanghai. Results showed that all of the metals except As were accumulated greatly, much higher than background values. Geo-accumulation index indicated that metal contamination in urban SDSs was generally heavier than that in suburban SDSs. Potential ecological risk index demonstrated that overall risks caused by metals were considerable. Cd contributed 52% to the overall risk. Multivariate statistical analysis revealed that in urban SDSs, Zn, Ni, Cd, Pb, Cu and Cr were related to traffic and industry; coal combustion led to elevated levels of Hg; soil parent materials controlled As contents. In suburban SDSs, Pb, Cu, As and Cd largely originated from traffic pollution; Zn, Ni and Cr were associated with industrial contaminants; Hg was mainly from domestic solid waste.     

Appraisal of measurement methods, chemical composition and sources of fine atmospheric particles over six different areas of Northern Belgium

Daily and seasonal variation in the total elemental, organic carbon (OC) and elemental carbon (EC) content and mass of PM_2.5 were studied at industrial, urban, suburban and agricultural/rural areas. Continuous (optical Dustscan, standard tapered element oscillating micro-balance (TEOM), TEOM with filter dynamics measurement system), semi-continuous (Partisol filter-sampling) and non-continuous (Dekati-impactor sampling and gravimetry) methods of PM_2.5 mass monitoring were critically evaluated. The average elemental fraction accounted for 2-6% of the PM_2.5 mass measured by gravimetry. Metals, like K, Mn, Fe, Cu, Zn and Pb were strongly inter-correlated, also frequently with non-metallic elements (P, S, Cl and/or Br) and EC/OC. A high OC/EC ratio (2-9) was generally observed. The total carbon content of PM_2.5 ranged between 3 and 77% (averages: 12-32%), peaking near industrial/heavy trafficked sites. Principal component analysis identified heavy oil burning, ferrous/non-ferrous industry and vehicular emissions as the main sources of metal pollution.     

Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan

This study identifies the natural background, anthropogenic background and distribution of contamination caused by heavy metal pollutants in soil in Chunghua County of central Taiwan by using a finite mixture distribution model (FMDM). The probabilities of contaminated area distribution are mapped using single-variable indicator kriging and multiple-variable indicator kriging (MVIK) with the FMDM cut-off values and regulation thresholds for heavy metals. FMDM results indicate that Cr, Cu, Ni and Zn can be individually fitted by a mixture model representing the background and contamination distributions of the four metals in soil. The FMDM cut-off values for contamination caused by the metals are close to the regulation thresholds, except for the cut-off value of Zn. The receiver operating characteristic (ROC) curve validates that indicator kriging and MVIK with FMDM cut-off values can reliably delineate heavy metals contamination, particularly for areas lacking background information and high heavy metal concentrations in soil.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Biomarker responsiveness in different tissues of caged Ruditapes philippinarum and its use within an integrated sediment quality assessment

Biomarkers comprising activities of biotransformation enzymes (ethoxyresorufin-O-deethylase -EROD-, dibenzylfluorescein dealkylase -DBF-, glutathione S-transferase -GST), antioxidant enzymes (glutathione reductase -GR- and glutathione peroxidase -GPX), lipid peroxidation -LPO- and DNA strand breaks were analyzed in the clam Ruditapes philippinarum caged at Cádiz Bay, Santander Bay and Las Palmas de Gran Canaria (LPGC) Port (Spain). Sediments were characterized. Digestive gland was the most sensitive tissue to sediment contamination. In Cádiz Bay, changes in LPO regarding day 0 were related with metals. In LPGC Port, DBF, EROD, and GST activity responses suggested the presence of undetermined contaminants which might have led to DNA damage. In Santander Bay, PAHs were related with EROD activity, organic and metal contamination was found to be associated with GR and GST activities and DNA damage presented significant (p < 0.05) induction. R. philippinarum was sensitive to sediment contamination at biochemical level. Biomarkers allowed chemical exposure and sediment quality assessment.     

Substituting missing data in compositional analysis

Multivariate analysis of environmental data sets requires the absence of missing values or their substitution by small values. However, if the data is transformed logarithmically prior to the analysis, this solution cannot be applied because the logarithm of a small value might become an outlier. Several methods for substituting the missing values can be found in the literature although none of them guarantees that no distortion of the structure of the data set is produced. We propose a method for the assessment of these distortions which can be used for deciding whether to retain or not the samples or variables containing missing values and for the investigation of the performance of different substitution techniques. The method analyzes the structure of the distances among samples using Mantel tests. We present an application of the method to PCDD/F data measured in samples of terrestrial moss as part of a biomonitoring study.     

Cognitive attributes and aesthetic preferences in assessment and differentiation of landscapes

The increasing pace and scale of landscape changes involve objective measurements in order to estimate the effects of changes on people's landscape preferences in a meaningful way. In the literature, some attempts have been made to provide a more conceptual base related to landscape preferences. These concepts and their indicators need to be tested empirically in different contexts and landscape types. In the present study, different items related to theoretical concepts of both aesthetic preference and cognitive rating were examined. They were combined in an in situ questionnaire, which was conducted among undergraduate students in geography during two different field excursions. Stimuli consisted of 11 landscape vistas selected during the excursions. All vistas represent rather rural landscapes but they vary with regard to relief, degree of urbanisation, and degree of agricultural land use. Statistical analysis of all data yielded significant correlations between aesthetic and cognitive ratings. However, these correlations did not appear to be very strong. When considering landscape vistas separately, the relations between all cognitive ratings seemed to vary. Further, not all cognitive aspects had an equal predicting value for aesthetic preference. Moreover, this predicting value appeared to vary between different landscape vistas. The groups of interrelated cognitive aspects could not be associated consistently with theoretical concepts. The results demonstrated the inconsistencies existing between the contents of the theoretical concepts and the indicators found within the landscape. The findings argued for the necessity to distinguish between different ratings and landscape types instead of using unitary preference measures and generalized data when studying landscape preference.