Combining neural network models to predict spatial patterns of airborne pollutant accumulation in soils around an industrial point emission source

Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies.     

Determining Visitors’ Dispositions Toward the Designation of a Greek National Park

This article presents the findings of the first major visitor survey conducted at the Central Rhodope Mountain Complex, situated in northern Greece. The study area is one of a number of sites planned to become National Parks under a recent initiative of the Greek government. We used a logistic regression method to define significant predictors of visitors’ awareness and attitude toward park designation, as well as their willingness to pay a conditional entrance fee. Study findings demonstrated that the probability of being aware of the park designation initiative is higher for visitors who obtained information from travel agencies or media to make their destination choice, compared to visitors who resorted to word-of-mouth recommendations. Knowledge and attitudes toward animals living in the study area revealed a limited influence on visitors’ dispositions toward park designation. The results of this study call for action to implement education programs for National Park users. These programs should aim at rendering the environmental characteristics of the Central Rhodope Mountain Complex to be significant predictors of visitors’ dispositions toward the study area. This would favor its uniqueness as a destination, as well as contribute to its long-term economic sustainability.     

Arsenic and Other Metal Contamination of Groundwaters in the Industrial Area of Thessaloniki, Northern Greece

In groundwater, used for drinking water supply in the greater industrial area of Thessaloniki, in Northern Greece, concentrations of total arsenic exceeded the WHO provisional guideline value and the EU maximum contaminant level (MCL) of 10 μg/L. The concentration of total arsenic was in the range between 4–130 μg/L, whereas the median value was 36 μg/L and the average concentration 46 μg/L. Nine out of the eleven wells contained total arsenic at concentration higher than 10 μg/L and it should be stressed that 6 of them contain arsenic at concentrations between 10 (new MCL) and 50 μg/L (previous MCL). The examined groundwaters were found to contain elevated concentrations of manganese and phosphate. Arsenic had a positive correlation with the pH, indicating the possible effect of pH on arsenic mobilisation. These findings emerge the problem of contamination from arsenic, since, according to the EU directive 98/83, all drinking water sources within the European Union should have achieved compliance with the new limits by 12/2003, implying that the situation requires urgent remedial action.     

A Combined MOIP–MCDA Approach to Building and Screening Atmospheric Pollution Control Strategies in Urban Regions

This article presents a methodological approach for the formulation of control strategies capable of reducing atmospheric pollution at the standards set by European legislation. The approach was implemented in the greater area of Thessaloniki and was part of a project aiming at the compliance with air quality standards in five major cities in Greece. The methodological approach comprises two stages: in the first stage, the availability of several measures contributing to a certain extent to reducing atmospheric pollution indicates a combinatorial problem and favors the use of Integer Programming. More specifically, Multiple Objective Integer Programming is used in order to generate alternative efficient combinations of the available policy measures on the basis of two conflicting objectives: public expenditure minimization and social acceptance maximization. In the second stage, these combinations of control measures (i.e., the control strategies) are then comparatively evaluated with respect to a wider set of criteria, using tools from Multiple Criteria Decision Analysis, namely, the well-known PROMETHEE method. The whole procedure is based on the active involvement of local and central authorities in order to incorporate their concerns and preferences, as well as to secure the adoption and implementation of the resulting solution.     

Chemical and ecotoxicological assessment of sludge-based biosolids used for corn field fertilization

Environmental Science and Pollution Research - Sludge-based biosolids can be used for crop fertilization and for soil enrichment with organic matter. The prerequisites for their application are...     

The future of transport-related emissions in dense urban areas: an analysis of various policy scenarios with MOLES

Environmental Economics and Policy Studies - Reducing air pollution is a major policy challenge, especially in densely populated urban areas where human exposure to emissions is considerable. This...     

Quality of tube well water intended for irrigation and human consumption with special emphasis on arsenic contamination at the area of Punjab,Pakistan

In the present study, the tube well water quality and the associated health risks, emphasizing on arsenic contamination, were investigated in rural and urban samples from Tehsil Mailsi located in Punjab, Pakistan. Arsenic concentrations (μg/L) were ranged from 12 to 448.5 and which exceeded the WHO recommended limit (10 μg/L) in all cases. The calculated average daily dose (3.3 × 10^?0.4 to 1.2 × 10^?0.2 mg/kg day) and hazard quotient (1.1–40) reflected the potential health risk to local population due to tube well water consumption as drinking purpose. Sodium percent (Na%), sodium absorption ratio, residual sodium carbonate, Kelly’s index and magnesium absorption ratio were also determined to assess the suitability of tube well water for irrigation purpose. The resulting piper plot revealed the Na–Ca–HCO₃ type water chemistry of the area and generally alkaline environment. The spatial distribution of arsenic in the tube well waters pinpoints the significant contribution of anthropogenic activities to arsenic pollution. Nevertheless, different statistical tools, including principal component analysis, hierarchical cluster analysis and correlation matrices, revealed the contribution of both natural and anthropogenic activities and alkaline type of aquifers toward the high level of arsenic contamination.     

A knowledge acquisition process to analyse operational problems in solid waste management facilities.

The available expertise on managing and operating solid waste management (SWM) facilities varies among countries and among types of facilities. Few experts are willing to record their experience, while few researchers systematically investigate the chains of events that could trigger operational failures in a facility; expertise acquisition and dissemination, in SWM, is neither popular nor easy, despite the great need for it. This paper presents a knowledge acquisition process aimed at capturing, codifying and expanding reliable expertise and propagating it to non-experts. The knowledge engineer (KE), the person performing the acquisition, must identify the events (or causes) that could trigger a failure, determine whether a specific event could trigger more than one failure, and establish how various events are related among themselves and how they are linked to specific operational problems. The proposed process, which utilizes logic diagrams (fault trees) widely used in system safety and reliability analyses, was used for the analysis of 24 common landfill operational problems. The acquired knowledge led to the development of a web-based expert system (Landfill Operation Management Advisor, http://loma.civil.duth.gr), which estimates the occurrence possibility of operational problems, provides advice and suggests solutions.     

Optimization of thermo-chemical hydrolysis of kitchen wastes

Municipal Solid Wastes (MSWs) in Greece consist mainly of fermentable organic material such as food scraps (～50%) and paper residuals (～20%). The aim of this work was to study the thermo-chemical pretreatment of the kitchen waste (KW) fraction of MSW focusing on biotechnological exploitation of pretreated wastes for biofuel production. A representative sample of municipal food residues was derived by combining weighted amounts of each individual type of residue recognized in daily samples obtained from the University of Patras’ students restaurant located at the Students Residence Hall (Greece). Chemical pretreatment experiments of the representative KW sample were performed using several types of chemical solutions (i.e. H₂SO₄, HCl, NaOH, H₂SO₃) of different solute concentration (0.7%, 1.5%, 3%) at three temperatures (50, 75, 120 °C) and a range of residence times (30–120 min). Optimized results proved that chemical pretreatment of KW, using either 1.12% HCl for 94 min or 1.17% HCl for 86 min (at 100 °C), increased soluble sugars concentration by 120% compared to untreated KW. The increase of soluble sugars was mainly attributed to the mono-sugars glucose and fructose.