Evaluation of 3-D spatial distribution of dissolved oxygen concentrations in a eutrophic lake.

In this study, three-dimensional (3-D) ordinary kriging of dissolved oxygen (DO) concentrations was performed for a eutrophic reservoir based on 81 sampling points using Stanford Geostatistical Modeling Software (SGeMs). Potential hotspots (problematic zones in terms of water quality with high/low DO concentrations) not only at the surface but also in deeper layers of Porsuk Dam Reservoir (PDR) were evaluated. Moreover, 3-D distributions of DO, and specific conductivity (SC) were examined against the thermocline layer identified using the 3-D temperature data.

A pollution fate and transport model application in a semi-arid region: Is some number better than no number?

Fate and transport models are powerful tools that aid authorities in making unbiased decisions for developing sustainable management strategies. Application of pollution fate and transport models in semi-arid regions has been challenging because of unique hydrological characteristics and limited data availability. Significant temporal and spatial variability in rainfall events, complex interactions between soil, vegetation and topography, and limited water quality and hydrological data due to insufficient monitoring network make it a difficult task to develop reliable models in semi-arid regions.

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.

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.