Application of analytic hierarchy process in a waste treatment technology assessment in Mexico

The high per capita generation of solid waste and the environmental problems in major rural communities of Ensenada, Baja California, have prompted authorities to seek alternatives for waste treatment. In the absence of a selection methodology, three technologies of waste treatment with energy recovery (an anaerobic digester, a downdraft gasifier, and a plasma gasifier) were evaluated, taking the broader social, political, economic, and environmental issues into considerations. Using the scientific literature as a baseline, interviews with experts, decision makers and the community, and waste stream studies were used to construct a hierarchy that was evaluated by the analytic hierarchy process. In terms of the criteria, judgments, and assumptions made in the model, the anaerobic digester was found to have the highest rating and should consequently be selected as the waste treatment technology for this area. The study results showed low sensitivity, so alternative scenarios were not considered. The methodology developed in this study may be useful for other governments who wish to assess technologies to select waste treatment.     

Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP)

River water quality sampling frequency is an important aspect of the river water quality monitoring network. A suitable sampling frequency for each station as well as for the whole network will provide a measure of the real water quality status for the water quality managers as well as the decision makers. The analytic hierarchy process (AHP) is an effective method for decision analysis and calculation of weighting factors based on multiple criteria to solve complicated problems. This study introduces a new procedure to design river water quality sampling frequency by applying the AHP. We introduce and combine weighting factors of variables with the relative weights of stations to select the sampling frequency for each station, monthly and yearly. The new procedure was applied for Jingmei and Xindian rivers, Taipei, Taiwan. The results showed that sampling frequency should be increased at high weighted stations while decreased at low weighted stations. In addition, a detailed monitoring plan for each station and each month could be scheduled from the output results. Finally, the study showed that the AHP is a suitable method to design a system for sampling frequency as it could combine multiple weights and multiple levels for stations and variables to calculate a final weight for stations, variables, and months.     

Assessment of risk factors in forest road design and construction activities with fuzzy analytic hierarchy process approach in Turkey

Forest road design and construction are time-consuming and complicated because various risk factors can be encountered during the process. The aim of this study is to comprehensively assess the risk factors in forest road design and construction using the fuzzy analytic hierarchy process (AHP) method in Turkey, thus contributing to the proper performance of these activities. Within the scope of the study, six main risk factors and 22 sub-risk factors were identified based on literature review. In order to determine the weights of the relevant risk factors, the opinions of three different groups [(group 1: academicians), (group 2: forest engineers (private sector employees + public sector employees)), (group 3: group 1 + group 2)] about the risk factors were obtained. Relevant risk factor weights were determined using the fuzzy AHP method. According to group 3, the most important main risk factors are technical risks and environmental risks. In addition, the most important sub-risk factors for each relevant main risk factor were incorrect road alignment, inadequate work safety in the field, insufficient capital, legal problems on the road alignment, landslide risk during road construction, and illegal logging. Differences were observed between groups 1 and 2 in the weight rankings of relevant risk factor. The results demonstrate that the fuzzy AHP method can be used effectively to assess the risks of forest road design and construction.     

Selection of MSW landfill site for Konya, Turkey using GIS and multi-criteria evaluation

Landfill is a common solution for the final disposal of municipal solid waste (MSW) in Turkey. Landfill siting is an extremely difficult task to accomplish because the site selection process depends on different factors and regulations. To ensure that an appropriate site is chosen, a systematic process should be developed and followed. Unsuccessful landfill siting is typically the result of strong public opposition. In this study, candidate sites for an appropriate landfill area in Cumra County of Konya City are determined by using the integration of geographic information systems (GIS) and multi-criteria evaluation (MCE). ArcGIS 9.0 software and its extensions were used as the GIS tool since it is able to perform suitability analysis using MCE analysis. To identify appropriate landfill areas in the Cumra district, eight input map layers including proximity to municipal and local wells and irrigational canals, distance from transportation routes and rails, distance from archaeological sites, distance from urban areas, land use/land cover, and land slope are used in constraint mapping. A final map was generated which identifies regions showing suitability for the location of the landfill site. According to the map, 6.8% of the study area is most suitable, 15.7% is suitable, 10.4% is moderately suitable, 25.8% is poorly suitable, and 41.3% is unsuitable. At the end of the analyses, three candidate sites are determined. The selection of the final MSW landfill site, however, requires further field research.     

Using the analytical hierarchy process to assess the environmental vulnerabilities of basins in Taiwan

Every year, Taiwan endures typhoons and earthquakes; these natural hazards often induce landslides and debris flows. Therefore, watershed management strategies must consider the environmental vulnerabilities of local basins. Because many factors affect basin ecosystems, this study applied multiple criteria analysis and the analytical hierarchy process (AHP) to evaluate seven criteria in three phases (geographic phase, hydrologic phase, and societal phase). This study focused on five major basins in Taiwan: the Tan-Shui River Basin, the Ta-Chia River Basin, the Cho-Shui River Basin, the Tseng-Wen River Basin, and the Kao-Ping River Basin. The objectives were a comprehensive examination of the environmental characteristics of these basins and a comprehensive assessment of their environmental vulnerabilities. The results of a survey and AHP analysis showed that landslide area is the most important factor for basin environmental vulnerability. Of all these basins, the Cho-Shui River Basin in central Taiwan has the greatest environmental vulnerability.     

Post-remediation evaluation of a LNAPL site using electrical resistivity imaging

Present understanding of the earth's subsurface is most often derived from samples at discrete points (wells) and interpolations or models that interpret the space between these points. Electrical resistivity imaging techniques have produced an improved capability to map contaminants (especially NAPLs--NonAqueous Phase Liquids) away from traditional wells using actual field data. Electrical resistivity image data, confirmed by drilling, have demonstrated that LNAPL (Light NAPL--less dense than water, such as gasoline) contaminants exist outside of a delineated and remediated area in Golden, Oklahoma. The data also demonstrate that LNAPL exists between monitoring and remediation wells which indicate low contaminant levels when sampled. Additionally, the electrical images provided the drilling location with the highest concentration of hydrocarbon ever found on the site, even after two phases of remediation work had been performed, although the sampling protocols varied. The results indicate that current methods of post-remediation site characterization are inadequate for complete site characterization.     

Establishment of a structural equation model for ground-level ozone: a case study at an urban roadside site

This study established a cause–effect relationship between ground-level ozone and latent variables employing partial least-squares analysis at an urban roadside site in four distinct seasons. Two multivariate analytic methods, factor analysis, and cluster analysis were adopted to cite and identify suitable latent variables from 14 observed variables (i.e., meteorological factors, wind and primary air pollutants) in 2008–2010. Analytical results showed that the first six components explained 80.3 % of the variance, and eigenvalues of the first four components were greater than 1. The effectiveness of this model was empirically confirmed with three indicators. Except for surface pressure, factor loadings of observed variables were 0.303–0.910 and reached statistical significance at the 5 % level. Composite reliabilities for latent variables were 0.672–0.812 and average variances were 0.404–0.547, except for latent variable “primary” in spring; thus, discriminant validity and convergent validity were marginally accepted. The developed model is suitable for the assessment of urban roadside surface ozone, considering interactions among meteorological factors, wind factors, and primary air pollutants in each season.     

Speciation of iron in river water using a specific catalytic determination and size fractionation

A comprehensive speciation scheme was proposed for the speciation of iron in river water (0.45 microm filtrate). The speciation was based on the catalytic determination of reactive iron as Fe3+ and Fe(III)Li/(3 -in)+, where i = 1 or 2 for a unidentate and i = 1 for a bidentate ligand, Ln-. The scheme incudes size fractionation and acid decomposition of the sample and determination of the coexisting species. The proposed scheme was applied to natural and artificial samples and acidified preparations of these. Resulting fractionation patterns of reactive and unreactive iron and of coexisting humic acid were useful for the characterization of iron species in the natural samples. The reactive iron in the molecular weight (Mr) > or = 10(4) fraction was associated with humic iron aggregates and estimated as iron complexed with them. The unreactive iron in this fraction dissociated in 0.1 M HCI and was estimated as iron in the aggregates of iron(III) hydroxide (s) and humic iron. From the fractionation results of the artificial sample, the reactive and unreactive iron in the Mr < 10(3) fraction may be formed by complexing simple anions like F- and C2O4(2-) at their naturally-occurring concentration levels. The fractionation results also suggested that humic acid and Si contributed to the formation of reactive and unreactive iron in the Mr > or = 10(4) fraction.     

Determination of pollution trends in an abandoned mining site by application of a multivariate statistical analysis to heavy metals fractionation using SM&T-SES

The mobility, availability and persistence of Heavy Metals (HMs), As, Cd, Cu, Ni, Pb and Zn, in contaminated soils of a former abandoned mining area were evaluated by means of a sequential extraction scheme (SES) and applying a multivariate statistical analysis to the obtained data. Chemical partitioning of HMs in each sample was determined in four fractions (acid-soluble, reducible, oxidable and residual) following the Standard Measurements and Testing (SM&T) SES, formerly BCR-SES. Statistical evaluation of results by pattern recognition techniques allowed identification of groups of samples with similar characteristics and observations of correlations between variables, determining the pollution trends and distribution of HMs within the studied area. Typical metal-fraction association and metal availability characteristics of heavy metals have been depicted. The obtained results indicate an urgent need to attenuate the hazard in that area posed by high concentrations of toxic metals, which exceed the limits specified by different European legislations on soil reclamation.     

Contributions of dry and wet depositions of polychlorinated dibenzo-p-dioxins and dibenzofurans to a contaminated site resulting from a penetachlorophenol manufacturing process

The soils at a factory for manufacturing pentachlorophenol were heavily contaminated by polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). In order to verify the contributions of dry and wet deposition of PCDD/Fs from the ambient air, the concentration of PCDD/Fs in ambient air and soil were measured, the partition of particle- and gas-phases of atmospheric PCDD/Fs was calculated, and the annual fluxes of total dry and wet PCDD/F depositions were modeled. Average atmospheric PCDD/F concentration was 1.24 ng Nm^???3 (or 0.0397 ng I-TEQ Nm^???3). Moreover, over 92.8% of total PCDD/Fs were in the particle phase, and the dominant species were high chlorinated congeners. The total PCDD/F fluxes of dry and wet deposition were 119.5 ng m^???2 year^???1 (1.34 ng I-TEQ m^???2 year^???1) and 82.0 ng m^???2 year^???1 (1.07 ng I-TEQ m^???2 year^???1), respectively. By scenario simulation, the total fluxes of dry and wet PCDD/F depositions were 87.1 and 68.6 ng I-TEQ, respectively. However, the estimated PCDD/F contents in the contaminated soil were 839.9 ?? g I-TEQ. Hence, the contributions of total depositions of atmospheric PCDD/F were only 0.02%. The results indicated that the major sources of PCDD/F for the contaminated soil could be attributed to the pentachlorophenol manufacturing process.     

Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

气相色谱直接进样法测定水中的N-甲基哌嗪-应急监测分析方法

建立了水中N-甲基哌嗪的应急监测分析方法,在3分钟内完成整个分析过程。采用直接进水样的方法,经毛细管柱分离水中的N-甲基哌嗪,用GC/FID测定,以保留时间定性,峰高定量。在浓度范围为8.44～80.32mg/L时,相对标准偏差在1.8％～2.9％之间,标准偏差在0.19～1.5之间。样品的加标回收率可达81％,标准曲线的相关系数为0.9992。检出限为0.08mg/L     

Remediation of drinking water contaminated with arsenic by the electro-removal process using different metal electrodes

The purification of water by the electro-removal process using different metal electrodes is widely used in different spheres of science and industry. The comparative characteristics under galvanostatic conditions of zinc (Zn), brass (Cu-Zn), copper (Cu) and iron (Fe) anodes for arsenic (As) removal from water by the electro-removal process in laboratory scale experiments were determined at current densities of 1.5, 3 and 12 mA cm(-2) for 60 min, from a solution containing different concentrations of As(v) (from 70 to 130 microg L(-1)). The results at these different current densities indicated that rapid arsenic removal was achieved at higher current densities (12 mA cm(-2)), with the chemical precipitation of arsenate complexes. The removal of As was relatively efficient, with the following tendency (at 1.5 mA cm(-2)): Fe (>93%) congruent with Zn (>93%) > Cu-Zn (>73%) >Cu (>67%), these efficiencies were relatively independent of the removal rate for all the initial arsenic concentrations investigated. This behaviour is attributed to the electrochemical intrinsic properties of the most active metals, and to the chemical precipitation reactions following the electrochemical process, iron being the most attractive metal for arsenic removal for practical applications.     

A study on the waste metal remediation using floriculture at East Calcutta Wetlands, a Ramsar site in India

Use of specific plant species in remediation of heavy metal-contaminated soil and water was a promising eco-friendly technology. The present study indicated the possibilities of phytoremediation of metal-contaminated (namely Ca, Cr, Mn, Fe, Cu, Zn, and Pb) soil by using plant species important for floriculture of East Calcutta Wetlands, a Ramsar site at the eastern fringe of Calcutta city. Plant species like sunflower (Helianthus annuus), marigold (Tagetes patula), and cock's comb (Celocia cristata) grew on soil contaminated by industrial sludge and irrigated regularly with wastewater accumulated different metals in different plant parts in varied concentrations. Pot culture study in the laboratory setup was also done to ascertain the efficiency of these plants for ameliorating contaminated soil. It was found that general accumulation patterns of metals concerned in different plant parts were root > leaf > stem > flower. This work indicated the importance of cultivation of economically important, non-edible, ornamental plant species as an alternative cost-effective practice to remediate heavily contaminated farmlands of East Calcutta Wetlands.     

Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data

This study aims at the classification and water quality assessment of Harike wetland (Ramsar site) in India using satellite images from the Indian Remote Sensing satellite, Resourcesat (IRS P6). The Harike wetland is a converging zone of two rivers, Beas and Sutlej. The satellite images of IRS Linear Imaging Self Scanner (LISS) IV multispectral sensor with three bands (green, red, and near infrared (NIR)) and a spatial resolution of 5.8 m were classified using supervised image classification techniques. Field points for image classification and water sampling were recorded using a Garmin eTrex Global Positioning System. The water quality parameters assessed were dissolved oxygen, conductivity, pH, turbidity, total and suspended solids (SS), chemical oxygen demand, and Secchi disk transparency (SDT). Correlations were established between turbidity and SS, SS and SDT, and total solids and turbidity. Using reflectance values from the green, red, and NIR bands, we then plotted the water quality parameters with the mean digital number values from the satellite imagery. The NIR band correlated significantly with the water quality parameters, whereas, using SDT values, it was observed that the green and the red reflectance bands were able to distinguish the waters from the two rivers, which have different water qualities.     

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.