Aysegul Aksoy

Application of spatial and non-spatial data analysis in determination of the factors that impact municipal solid waste generation rates in Turkey

In studies focusing on the factors that impact solid waste generation habits and rates, the potential spatial dependency in solid waste generation data is not considered in relating the waste generation rates to its determinants. In this study, spatial dependency is taken into account in determination of the significant socio-economic and climatic factors that may be of importance for the municipal solid waste (MSW) generation rates in different provinces of Turkey. Simultaneous spatial autoregression (SAR) and geographically weighted regression (GWR) models are used for the spatial data analyses. Similar to ordinary least squares regression (OLSR), regression coefficients are global in SAR model. In other words, the effect of a given independent variable on a dependent variable is valid for the whole country. Unlike OLSR or SAR, GWR reveals the local impact of a given factor (or independent variable) on the waste generation rates of different provinces. Results show that provinces within closer neighborhoods have similar MSW generation rates. On the other hand, this spatial autocorrelation is not very high for the exploratory variables considered in the study. OLSR and SAR models have similar regression coefficients. GWR is useful to indicate the local determinants of MSW generation rates. GWR model can be utilized to plan waste management activities at local scale including waste minimization, collection, treatment, and disposal. At global scale, the MSW generation rates in Turkey are significantly related to unemployment rate and asphalt-paved roads ratio. Yet, significances of these variables may diminish at local scale for some provinces. At local scale, different factors may be important in affecting MSW generation rates.

Evaluation of solar sludge drying alternatives by costs and area requirements

Thermal drying is a common method to reach above 90% dry solids content (DS) in sludge. However, thermal drying requires high amount of energy and can be expensive. A greenhouse solar dryer (GSD) can be a cost-effective substitute if the drying performance, which is typically 70% DS, can be increased by additional heat. In this study feasibility of GSD supported with solar panels is evaluated as an alternative to thermal dryers to reach 90% DS. Evaluations are based on capital and O&M costs as well as area requirements for 37 wastewater treatment plants (WWTPs) with various sludge production rates. Costs for the supported GSD system are compared to that of conventional and co-generation thermal dryers. To calculate the optimal costs associated with the drying system, an optimization model was developed in which area limitation was a constraint. Results showed that total cost was minimum when the DS in the GSD (DS(m,i)) was equal to the maximum attainable value (70% DS). On average, 58% of the total cost and 38% of total required area were associated with the GSD. Variations in costs for 37 WWTPs were due to differences in initial DS (DS(i,i)) and sludge production rates, indicating the importance of dewatering to lower drying costs. For large plants, GSD supported with solar panels provided savings in total costs especially in long term when compared to conventional and co-generation thermal dryers.

An evaluation of potential sampling locations in a reservoir with emphasis on conserved spatial correlation structure.

In this study, kernel density estimation (KDE) was coupled with ordinary two-dimensional kriging (OK) to reduce the number of sampling locations in measurement and kriging of dissolved oxygen (DO) concentrations in Porsuk Dam Reservoir (PDR). Conservation of the spatial correlation structure in the DO distribution was a target. KDE was used as a tool to aid in identification of the sampling locations that would be removed from the sampling network in order to decrease the total number of samples. Accordingly, several networks were generated in which sampling locations were reduced from 65 to 10 in increments of 4 or 5 points at a time based on kernel density maps. DO variograms were constructed, and DO values in PDR were kriged. Performance of the networks in DO estimations were evaluated through various error metrics, standard error maps (SEM), and whether the spatial correlation structure was conserved or not. Results indicated that smaller number of sampling points resulted in loss of information in regard to spatial correlation structure in DO. The minimum representative sampling points for PDR was 35. Efficacy of the sampling location selection method was tested against the networks generated by experts. It was shown that the evaluation approach proposed in this study provided a better sampling network design in which the spatial correlation structure of DO was sustained for kriging.

Application of Genetic Algorithms for Calibration and Verification of QUAL2E Model

Traditional trial -and-error methods make the calibration and verification of a model considerably time consuming. In addition, it is doubtful whether the best results will be achieved. However, by use of optimization techniques in calibration and verification, the best kinetic parameter estimates can be obtained in a shorter time period. In this study, a genetic algorithm (GA) is used to determine the reaeration coefficients for QUAL2E. An objective function, defined by sum-of-least-squares, is used in order to describe the difference between the o bserved and simulated dissolved oxygen concentrations. Simultaneous calibration and verificat ion is carried out by treating the verification of the calibrated parameters as a constraint. The efficacy of GAs in predicting the model parameters is evaluated for perfect and biased measurements. The results show that GAs can successfully carry out simultaneous calibration and verification, and estimate the best set of reaeration coefficients to be used in stream water quality modeling with QUAL2E, especially for the accurate observation data.

Challenges in development and implementation of health-risk-based soil quality guidelines: Turkey's experience.

Management of contaminated sites is a critical environmental issue around the world due to the human health risk involved for many sites and scarcity of funding. Moreover, clean-up costs of all contaminated sites to their background levels with existing engineering technologies may be financially infeasible and demand extended periods of operation time. Given these constraints, to achieve optimal utilization of available funds and prioritization of contaminated sites that need immediate attention, health-risk-based soil quality guidelines should be preferred over the traditional soil quality standards. For these reasons, traditional soil quality standards are being replaced by health-risk-based ones in many countries and in Turkey as well. The need for health-risk-based guidelines is clear, but developing these guidelines and implementation of them in contaminated site management is not a straightforward process. The goal of this study is to highlight the problems that are encountered at various stages of the development process of risk-based soil quality guidelines for Turkey and how they are dealt with. Utilization of different definitions and methodologies at different countries, existence of inconsistent risk assessment tools, difficulties in accessing relevant documents and reports, and lack of specific data required for Turkey are among these problems. We believe that Turkeys experience may help other countries that are planning to develop health-risk-based guidelines achieve their goals in a more efficient manner.

Satellite-linked sensor networks for planetary scale monitoring

Sensor networks continue to attract significant interest in various research communities. An individual sensor system can provide important observations within a local area. However, local observations alone are not sufficient for some applications that require a global coverage. In this paper, we introduce our interdisciplinary project PLASMA (planetary scale monitoring architecture). PLASMA aims at providing an integrated platform for in-situ and remote sensing. Our focus includes environmental monitoring, disaster monitoring, and emergency response systems, enabled by reliable and high performance networking infrastructures. This paper discusses motivating applications and the research challenges of our project in its early stages.

A Fuzzy Rule Based Remedial Priority Ranking System for Contaminated Sites

Contaminated site remediation is generally difficult, time consuming, and expensive. As a result ranking may aid in efficient allocation of resources. In order to rank the priorities of contaminated sites, input parameters relevant to contaminant fate and transport, and exposure assessment should be as accurate as possible. Yet, in most cases these parameters are vague or not precise. Most of the current remediation priority ranking methodologies overlook the vagueness in parameter values or do not go beyond assigning a contaminated site to a risk class. The main objective of this study is to develop an alternative remedial priority ranking system (RPRS) for contaminated sites in which vagueness in parameter values is considered. RPRS aims to evaluate potential human health risks due to contamination using sufficiently comprehensive and readily available parameters in describing the fate and transport of contaminants in air, soil, and groundwater. Vagueness in parameter values is considered by means of fuzzy set theory. A fuzzy expert system is proposed for the evaluation of contaminated sites and a software (ConSiteRPRS) is developed in Microsoft Office Excel 2007 platform. Rankings are employed for hypothetical and real sites. Results show that RPRS is successful in distinguishing between the higher and lower risk cases.

Change in optimal pump-and-treat remediation design and cost for different correlation lengths of spatially variable hydraulic conductivity field

Abstract In this study, variations in optimal pump-and-treat (P&T) remediation designs and costs for a contaminated and mass-transfer-limited aquifer are investigated for different hydraulic conductivity (K) heterogeneity conditions with focus on the influence of the correlation length (λ) of spatially variable K values. Several heterogeneous K fields with diverse λ values and variances (σ2) are considered. The impact of λ on optimal remediation design selection is analysed considering different relative locations of low and high K regions. Furthermore, optimal designs obtained for different initial contaminant plume configurations are evaluated. Optimal designs are determined using a simulation–optimization approach. Results show that the locations of low and high K zones within an aquifer, and their respective areas defined through λ, affect remediation design and cleanup cost noticeably. It is observed that in addition to typical geostatistical parameters (λ and σ2), better determination of both the spatial distribution of low and high K regions and the initial contaminant mass is critical for better P&T design.

Dynamic Optimal Design of Groundwater Remediation Using Genetic Algorithms

The use of genetic algorithms for the dynamic optimal design of pump-and-treat groundwater remediation systems is demonstrated through two new dynamic formulations. In the first formulation in which the contaminant sorption was assumed to be in equilibrium, the lengths of management periods were decision variables. The second formulation assumed a pulsed pumping approach to remove a contaminant with mass-transfer-limited sorption. While the genetic algorithm could successfully solve these dynamic problems, only small percentage reductions in the overall remediation costs were achieved. However, the savings in the operational costs were more significant with the mass transfer-limited pulsed pumping example saving up to 10% compared to continuous pumping and the flexible-length management periods saving up to 3% compared to fixed-length periods. With the high costs of remediation, even a small percentage of savings in operational costs could be significant. For instance, the 3% savings with flexible management periods corresponded to a cost reduction of more than

Effect of Heterogeneity Correlation Scale on Pump-and-Treat Remediation Design

26,000 that was achieved by allowing for variable length management periods, a relatively simple change within the GA algorithm. Dynamic pumping that is adapted over time to the unique site conditions may be an option to improve the cost-effectiveness of a remediation design, especially for mass-transfer limited sites.