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.     

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.     

Nutrient dynamics between sediment and overlying water in the inner part of Izmir Bay, Eastern Aegean

In this article a concept is described in order to predict and map the occurrence of benthic communities within and near the German Exclusive Economic Zone (EEZ) of the North Sea. The approach consists of two work steps: (1) geostatistical analysis of abiotic measurement data and (2) calculation of benthic provinces by means of Classification and Regression Trees (CART) and GIS-techniques. From bottom water measurements on salinity, temperature, silicate and nutrients as well as from punctual data on grain size ranges (0–20, 20–63, 63–2,000 μ) raster maps were calculated by use of geostatistical methods. At first the autocorrelation structure was examined and modelled with help of variogram analysis. The resulting variogram models were then used to calculate raster maps by applying ordinary kriging procedures. After intersecting these raster maps with punctual data on eight benthic communities a decision tree was derived to predict the occurrence of these communities within the study area. Since such a CART tree corresponds to a hierarchically ordered set of decision rules it was applied to the geostatistically estimated raster data to predict benthic habitats within and near the EEZ.     

喻家湖水质时空分布特征和影响因子分析

通过设计合理的水质监测网,采用多元统计分析,并结合地理信息技术对武汉市喻家湖在2011年-2012年期间12个监测点、13个水质参数监测值进行水质时空分布特征研究.结果表明,喻家湖13个水质指标概括为4个主成分:第一主成分代表喻家湖的重金属污染,第二主成分代表其富营养化水平,第三主成分代表有机污染程度,第四主成分间接指示富营养化程度;在时间和空间变化上都可分为二组,显著性指标的时空差异较明显,水质污染程度从南至北逐渐减弱,湖溪河是喻家湖的最主要污染源.并对水质参数,监测点位和频次进行了优化.     

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.     

Chlorinated solvents in a petrochemical wastewater treatment plant: an assessment of their removal using self-organising maps

The self-organising map approach was used to assess the efficiency of chlorinated solvent removal from petrochemical wastewater in a refinery wastewater treatment plant. Chlorinated solvents and inorganic anions (11 variables) were determined in 72 wastewater samples, collected from three different purification streams. The classification of variables identified technical solvents, brine from oil desalting and runoff sulphates as pollution sources in the refinery, affecting the quality of wastewater treatment plant influent. The classification of samples revealed the formation of five clusters: the first three clusters contained samples collected from the drainage water, process water and oiled rainwater treatment streams. The fourth cluster consisted mainly of samples collected after biological treatment, and the fifth one of samples collected after an unusual event. SOM analysis showed that the biological treatment step significantly reduced concentrations of chlorinated solvents in wastewater.     

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.     

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.     

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.