An environmental pressure index proposal for urban development planning based on the analytic network process

This paper introduces a new approach to prioritize urban planning projects according to their environmental pressure in an efficient and reliable way. It is based on the combination of three procedures: (i) the use of environmental pressure indicators, (ii) the aggregation of the indicators in an Environmental Pressure Index by means of the Analytic Network Process method (ANP) and (iii) the interpretation of the information obtained from the experts during the decision-making process.The method has been applied to a proposal for urban development of La Carlota airport in Caracas (Venezuela). There are three options which are currently under evaluation. They include a Health Club, a Residential Area and a Theme Park. After a selection process the experts chose the following environmental pressure indicators as ANP criteria for the project life cycle: used land area, population density, energy consumption, water consumption and waste generation. By using goal-oriented questionnaires designed by the authors, the experts determined the importance of the criteria, the relationships among criteria, and the relationships between the criteria and the urban development alternatives.The resulting data showed that water consumption is the most important environmental pressure factor, and the Theme Park project is by far the urban development alternative which exerts the least environmental pressure on the area. The participating experts coincided in appreciating the technique proposed in this paper is useful and, for ranking ordering these alternatives, an improvement from traditional techniques such as environmental impact studies, life-cycle analysis, etc.     

Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province,Iran

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68 % of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.     

Application of ANP and DEMATEL to evaluate the decision-making of municipal solid waste management in Metro Manila

A municipal solid waste management (MSW) expert group was consulted in order to mirror how government officials might reach an effective solution regarding municipal solid waste management in Metro Manila. A critical issue regarding this is how the expert group can better evaluate and select a favorable MSW management solution using a series of criteria. MSW management solution selection is a multiple criteria decision-making (MCDM) problem, which requires the consideration of a large number of complex criteria. A robust MCDM method should consider the interactions among these criteria. The analytic network process (ANP) is a relatively new MCDM method which can deal with all kinds of interactions systematically. The Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a structural model, but also can be used as a way to handle the inner dependences within a set of criteria. Hence, this paper applies an effective solution based on a combined ANP and DEMATEL method to assist the expert group evaluating different MSW management solutions. According to the results, the best solution is for each city to have its own type of thermal process technology and resource recovery facility before landfill rather than entering a joint venture with enterprises or going into build-operate-transfer projects in order to be able to construct thermal process technologies and resource recovery facilities.     

Effect of data pre-treatment procedures on principal component analysis: a case study for mangrove surface sediment datasets

Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x?+?1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x?+?1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.     

Heavy metal concentration and speciation of seven representative municipal sludges from wastewater treatment plants in Northeast China

The analysis of heavy metals is very important for assessing the feasibility of the agricultural utilization for the municipal sludge. In this paper, a four-step sequential extraction method was applied to extract heavy metals (Cu, Zn, Mn, Cr, and Ni) in municipal sludges from seven individual wastewater treatment plants located in Jilin and Heilongjiang Province, China, for estimating the mobility and bioavailability of the metal ions in the agricultural application. The total concentrations of heavy metals and their chemical fractions after the sequential extraction were determined. Principal component analysis (PCA) was applied to analyze the relations of heavy metals fractions in the municipal sludges. Experimental results indicated that the total concentrations of Cu, Zn, Cr, and Ni in all sludge samples were below the threshold values set out by the Chinese legislation (GB18918-2002). Specially, Zn had a high bioavailability and mobility, Cu and Cr had potential bioavailability, while Mn mainly existed in the residual fraction of municipal sludge. On the other hand, Ni had different mobility in different municipal sludge. PCA results were confirmed by the environmental behavior of heavy metals.     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.     

Environmental influence on coastal phytoplankton and zooplankton diversity: a multivariate statistical model analysis

In a marine ecosystem, the diversity of phytoplankton can influence the diversity of zooplankton, or vice versa, and both can be affected by the environmental factors. In this study, we used principal component analysis (PCA) to identify the major sources of influence on the coastal water near an industrial park, following by construction of structural equation model (SEM) to determine the direct and indirect effect of the factors on phytoplankton and zooplankton diversity. PCA results indicated that the coastal area was mainly affected by riverine discharge (represented by high PC factor loadings of transparency and turbidity) and seasonal change (represented by temperature). SEM further suggested that both riverine discharge and seasonal influences can directly affect phytoplankton diversity, but indirectly affected zooplankton diversity via changes in phytoplankton. Using PCA to determine the sources of influence followed by construction of SEM allowed us to understand the relative importance of the environmental factors, direct or indirect, on phytoplankton and zooplankton diversity. When environmental changes occur, a new SEM could be constructed using the same category of physical and biological data and then compared to the current model to verify whether the environmental changes were the cause of alterations in planktonic communities in the area.     

LCA Integrated ANP Framework for Selection of Sustainable Manufacturing Processes

In recent times, the focus of sustainable development has been shifted toward the process domain to achieve greater environmental benefits. Improved sustainability in the process domain can be achieved by selecting sustainable process or through improvement studies. In this study, sustainability performance of injection molding (IM) and selective laser sintering (SLS) processes are compared to select the sustainable process. Life cycle assessment (LCA) has been widely accepted for assessing the environmental performance of manufacturing process. However, the problem with LCA is the interpretation of impact category scores when comparing alternate processes. Therefore, an effort has been made to integrate analytical network process (ANP) methodology with LCA in which LCA methodology calculates the impact score and ANP computes the Single Point Process Sustainability Score (SPPSS) to compare alternate processes. Based on the inventory data collected for higher production rates, SPPSS for IM and SLS processes are computed as 0.038 and 0.068, respectively. Based on the score, IM has been identified as a sustainable process for higher production volume. Hence, the practical usability of the proposed methodology is validated by the study. This study also appreciates the application of ANP along with LCA for decision making in the real-time scenario.     

Modeling of NO x Emissions from Oil Refinery Furnaces

The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO _x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO _x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO _x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO _x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO _x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper. This revised version was published online in July 2006 with corrections to the Cover Date.     

Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA

To evaluate the significant sources contributing to water quality parameters, we used principal component analysis (PCA) for the interpretation of a large complex data matrix obtained from the Kandla creek environmental monitoring program. The data set consists of analytical results of a seasonal sampling survey conducted over 2 years at four stations. PCA indicates five principal components to be responsible for the data structure and explains 76% of the total variance of the data set. The study stresses the need to include new parameters in the analysis in order to make the interpretation of principal components more meaningful. The PCA could be applied as a useful tool to eliminate multi-collinearity problems and to remove the indirect effect of parameters.     

Evaluation of spatial and temporal variations in marine sediments quality using multivariate statistical techniques

Spatial and temporal variations of sediment quality in Matanzas Bay (Cuba) were studied by determining a total of 12 variables (Zn, Cu, Pb, As, Ni, Co, Al, Fe, Mn, V, CO₃ ^2?, and total hydrocarbons (THC). Surface sediments were collected, annually, at eight stations during 2005–2008. Multivariate statistical techniques, such as principal component (PCA), cluster (CA), and lineal discriminant (LDA) analyses were applied for identification of the most significant variables influencing the environmental quality of sediments. Heavy metals (Zn, Cu, Pb, V, and As) and THC were the most significant species contributing to sediment quality variations during the sampling period. Concentrations of V and As were determined in sediments of this ecosystem for the first time. The variation of sediment environmental quality with the sampling period and the differentiation of samples in three groups along the bay were obtained. The usefulness of the multivariate statistical techniques employed for the environmental interpretation of a limited dataset was confirmed.     

Forest Vegetation Monitoring and Foliar Chemistry of Red Spruce and Red Maple at Acadia National Park in Maine

The USDA Forest Service Forest Health Monitoring (FHM) program indicators, including forest mensuration, crown condition classification, and damage and mortality indicators were used in the Cadillac Brook and Hadlock Brook watershed forests at Acadia National Park (ANP) along coastal Maine. Cadillac Brook watershed burned in a wildfire in 1947. Hadlock Brook watershed, undisturbed for several centuries, serves as the reference site. These two small watersheds have been gauged and monitored at ANP since 1998 as part of the Park Research and Intensive Monitoring of Ecosystems Network (PRIMENet). Forest vegetation at Hadlock Brook was dominated by late successional species such as Acer saccharum, Fagus grandifolia, Betula alleghaniensis, Acer rubrum and Picea rubens. Forest vegetation at Cadillac Brook, on the other hand, was younger and more diverse and included those species found in Hadlock as well as early successional species such as Betula papyrifera and Populus grandidentata. Differences in forest species composition and stand structure were attributed to the severe wildfire that affected the Cadillac Brook watershed. Overall, the forests at these ANP watersheds were healthy with a low percentage (相似文献   

Assessment of trace element contamination in sediment cores from the Pearl River and estuary,South China: geochemical and multivariate analysis approaches

Twenty-four major and trace elements and the mineralogical composition of four sediment cores along the Pearl River and estuary were analyzed using ICP-AES, ICP-MS, and X-ray diffraction (XRD) to evaluate contamination levels. The dominant minerals were quartz, kaolinite, and illite, followed by montmorillonite and feldspars, while small amounts of halite and calcite were also observed in a few samples. Cluster analysis (CA) and principal component analysis (PCA) were performed to identify the element sources. The highest metal concentrations were found at Huangpu, primarily due to wastewater treatment plant discharge and/or the surreptitious dumping of sludge, and these data differed from those of other sources. Excluding the data from Huangpu, the PCA showed that most elements could be considered as lithogenic; few elements are the combination of lithogenic and anthropogenic sources. An antagonistic relationship between the anthropogenic source metals (K, Ba, Zn, Pb, Cd, Ag, Tl, and U) and marine source metals (Na, Mg, Ti, V, and Ca) was observed. The resulting normalized Al enrichment factor (EF) indicated very high or significant pollution of Cd, Ag, Cu, Zn, Mo, and Pb at Huangpu, which may cause serious environmental effects. Conflicting results between the PCA and EF can be attributed to the background values used, indicating that background values must be selected carefully.     

Assessment of Metal Contamination in Doñana National Park (Spain) using Crayfish (Procamburus Clarkii)

Water quality assessment in the Aznalcollar area was attempted using multivariate methods based on heavy metal concentrations in red swamp crayfish (Procamburus clarkii). Trace levels of four heavy metals, copper (Cu), zinc (Zn), cadmium (Cd) and lead (Pb), were detected in crayfish from eleven different stations. Principal component analysis (PCA) highlighted a gradient of contamination between the sampling stations. Cluster analysis (CA) distinguished three groups of stations. Discriminant analysis also differentiated three groups. The group centroids of the first discriminant function were used to devise an index that varies according to the source of the crayfish. These standardized values are proposed for use as a water quality index. The ability of this index to successfully predict environmental quality was proved with random samples.     

Evaluating cumulative effects of small scale hydropower development using GIS modelling and representativeness assessments

Assessing cumulative effects are a vital task for strategic environmental assessments (SEA) but lack of consistent methodology has hampered the development and implementation of useful tools. We present a model for GIS and multivariate analysis to assess the effects on a valued ecosystem type at a regional scale based on the sum of impacts of local projects. We demonstrate application of the model by assessing how hydropower developments would generate cumulative impacts on river gorges for a county in northern Norway. We use principal component analyses (PCA) of spatially-explicit variables from the region to describe the diversity of river gorge ecology with a mathematical low-dimensional bioclimatic space. We then calculate cumulative effects of hydropower development as the proportions of subspaces of the multidimensional bioclimatic PCA that are affected by either existing infrastructure or planned and possible hydropower developments. The results showed that adding development of all potential sites for small-scale hydropower would have substantial impacts on over half of all bioclimatic segments where gorges were registered and more than 70% of all segments with forested river gorges. By demonstrating these possible cumulative effects we can illustrate the need for caution in hydropower planning to avoid reducing river gorge representativeness and diversity. The method can be applied for other types of development projects and other valued ecosystems, provided the assessed ecosystems and development installations can be mapped or modelled over a sufficiently large area.     

Evaluating the effectiveness of routine water quality monitoring in Miyun reservoir based on geostatistical analysis

This study used geographic information system techniques and geostatistics methods to evaluate the effectiveness of routine water quality monitoring in the western segment of the Miyun reservoir in Beijing. Methodologies as well as the sampling design are evaluated. The single-layer evaluation and three integrated evaluation methods including principal component analysis (PCA), ordinary kriging (OK)_Mean, and Mean_Layers were used to validate the effectiveness of evaluation methods, and the effectiveness of each sampling design was validated by comparing their errors. Results indicated that, while a single-layer evaluation only shows the trophic state of water at a specific level, an integrated evaluation synthetically analyzes and evaluates the trophic state of the entire water body. Furthermore, results of the integrated analysis show that a PCA method is more accurate and can represent the trophic state of the entire water body. The OK_Mean and Mean_Layers methods are only able to represent the mean level for trophic state of the entire water body but cannot reflect local trophic state and distribution details. Although methods used in the routine monitoring of Miyun reservoir have some similarities to the OK_Mean and Mean_Layers methods, their range of errors and uncertainty are greater because of a lack of detailed spatial continuous information. The analysis on the number of sampling points shows that, within a certain range of error, minor changes of sampling points will have no obvious impact on the monitoring results. For the routine monitoring of western Miyun reservoir, using only three to five sampling points for monitoring is inadequate. According to our analysis, it is more appropriate to use at least ten sampling points for monitoring these areas.     

Use of cluster and principal component analyses to profile areas in California where ground water has been contaminated by pesticides

An empirical approach to profiling areas of ground water contamination by pesticides was devised that did not rely upon determining the level of vulnerability between land areas and that did not assume any particular pathway for ground water contamination. Climatic and soil data were obtained for 1-square mile sections of land in California where pesticide residues had been found in well water samples and the detection was attributed to legal agricultural applications. These sections were designated as known contaminated (KC) sections. Climate and soil data were also obtained for sections which lacked either well sampling data or a positive pesticide detection. These sections were designated as candidate sections. Statistical procedures were used to cluster groups of KC sections first with respect to climate characteristics and then with respect to soil characteristics. Principal components analysis (PCA) was used to construct a statistical profile of soil variables for each cluster of KC sections. A method based on the PCA was developed to compare the similarity of soil profiles derived for each KC section cluster to individual candidate sections. Since the profiling scheme was based only on data from KC sections, candidate sections that did not match any KC cluster profile could only be considered dissimilar to contaminated sections, receiving a status of not-classified. This profiling method is flexible and it can be revised to incorporate updated well sampling information.     

Development of a robust chromatographic method for the detection of chlorophenols in cork oak forest soils

A major concern for the cork and wine industry is ‘cork taint’ which is associated with chloroanisoles, the microbial degradation metabolites of chlorophenols. The use of chlorophenolic compounds as pesticides within cork forests was prohibited in 1993 in the European Union (EU) following the introduction of industry guidance. However, cork produced outside the EU is still thought to be affected and simple, robust methods for chlorophenol analysis are required for wider environmental assessment by industry and local environmental regulators. Soil samples were collected from three common-use forests in Tunisia and from one privately owned forest in Sardinia, providing examples of varied management practice and degree of human intervention. These provided challenge samples for the optimisation of a HPLC-UV detection method. It produced recoveries consistently >75 % against a soil CRM (ERM-CC008) for pentachlorophenol. The optimised method, with ultraviolet (diode array) detection is able to separate and quantify 16 different chlorophenols at field concentrations greater than the limits of detection ranging from 6.5 to 191.3 μg/kg (dry weight). Application to a range of field samples demonstrated the absence of widespread contamination in forest soils at sites sampled in Sardinia and Tunisia.     

Adaptive Consensus Principal Component Analysis for On-Line Batch Process Monitoring

As the regulations of effluent quality are increasingly stringent, the on-line monitoring of wastewater treatment processes becomes very important. Multivariate statistical process control such as principal component analysis (PCA) has found wide applications in process fault detection and diagnosis using measurement data. In this work, we propose a consensus PCA algorithm for adaptive wastewater treatment process monitoring. The method overcomes the problem of changing operating conditions by updating the covariance structure recursively. The algorithm does not require any estimation compared to typical multiway PCA models. With this method process disturbances are detected in real time and the responsible measurements are directly identified. The presented methodology is successfully applied to a pilot-scale sequencing batch reactor for wastewater treatment.