Yu-Pin Lin

Multivariate analysis of soil heavy metal pollution and landscape pattern in Changhua county in Taiwan

This study applied factor analysis and landscape indices of 55 sampling sites in Changhua county in Taiwan to characterize the factor patterns of eight soil heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb and Zn) and the interrelation patterns of these soil heavy metals, landscape and human activities. The landscape analysis results indicated that landscape indices can elucidate spatial landscape patterns, urbanization and industrialization, demonstrating that higher landscape diversity corresponded to a higher ratio of urban planning area to the number of industrial plants. Factor analyses revealed that soil heavy metals and data concerning landscape data could be grouped into a six-factor model that accounts for 82% of all the variation of data. Moreover, the first factor included the concentration of Cd, Cr, Cu, Ni and Zn, and urbanization and industrialization landscape indices. These variables together explained 34.5% of the variation in the concentration of the soil heavy metals and landscape indices data of this study area. Local urbanization and industrialization caused local soil pollution by heavy metals on the selected sampling sites in Changhua county in Taiwan. Geographic information system can fully display the spatial patterns and relationships among landscape indices and concentration of soil heavy metals in this study area.

Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling-a case study

The objective of this study is to compare the abilities of logistic, auto-logistic and artificial neural network (ANN) models for quantifying the relationships between land uses and their drivers. In addition, the application of the results obtained by the three techniques is tested in a dynamic land-use change model (CLUE-s) for the Paochiao watershed region in Taiwan. Relative operating characteristic curves (ROCs), kappa statistics, multiple resolution validation and landscape metrics were used to assess the ability of the three techniques in estimating the relationship between driving factors and land use and its subsequent application in land-use change models. The validation results illustrate that for this case study ANNs constitute a powerful alternative for the use of logistic regression in empirical modeling of spatial land-use change processes. ANNs provide in this case a better fit between driving factors and land-use pattern. In addition, auto-logistic regression performs better than logistic regression and nearly as well as ANNs. Auto-logistic regression and ANNs are considered especially useful when the performance of more conventional models is not satisfactory or the underlying data relationships are unknown. The results indicate that an evaluation of alternative techniques to specify relationships between driving factors and land use can improve the performance of land-use change models.

Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan

This study identifies the natural background, anthropogenic background and distribution of contamination caused by heavy metal pollutants in soil in Chunghua County of central Taiwan by using a finite mixture distribution model (FMDM). The probabilities of contaminated area distribution are mapped using single-variable indicator kriging and multiple-variable indicator kriging (MVIK) with the FMDM cut-off values and regulation thresholds for heavy metals. FMDM results indicate that Cr, Cu, Ni and Zn can be individually fitted by a mixture model representing the background and contamination distributions of the four metals in soil. The FMDM cut-off values for contamination caused by the metals are close to the regulation thresholds, except for the cut-off value of Zn. The receiver operating characteristic (ROC) curve validates that indicator kriging and MVIK with FMDM cut-off values can reliably delineate heavy metals contamination, particularly for areas lacking background information and high heavy metal concentrations in soil.

Remote Sensing Data with the Conditional Latin Hypercube Sampling and Geostatistical Approach to Delineate Landscape Changes Induced by Large Chronological Physical Disturbances

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

Spatiotemporal trends in oral cancer mortality and potential risks associated with heavy metal content in Taiwan soil.

Central and Eastern Taiwan have alarmingly high oral cancer (OC) mortality rates, however, the effect of lifestyle factors such as betel chewing cannot fully explain the observed high-risk. Elevated concentrations of heavy metals in the soil reflect somewhat the levels of exposure to the human body, which may promote cancer development in local residents. This study assesses the space-time distribution of OC mortality in Taiwan, and its association with prime factors leading to soil heavy metal content. The current research obtained OC mortality data from the Atlas of Cancer Mortality in Taiwan, 1972–2001, and derived soil heavy metals content data from a nationwide survey carried out by ROCEPA in 1985. The exploratory data analyses showed that OC mortality rates in both genders had high spatial autocorrelation (Moran’s I = 0.6716 and 0.6318 for males and females). Factor analyses revealed three common factors (CFs) representing the major pattern of soil pollution in Taiwan. The results for Spatial Lag Models (SLM) showed that CF1 (Cr, Cu, Ni, and Zn) was most spatially related to male OC mortality which implicates that some metals in CF1 might play as promoters in OC etiology.

Geostattstical analysis of soil arsenic content in taiwan

Abstract A combined geostatistical and Computergraphic approach was developed for illustrating the soil Arsenic map of Taiwan. Data were collected from the Environmental Protection Administrations study targeting agricultural soils in Taiwan. The range and arithmetic mean of the As contents in the surface soils (0 to 15 cm) of the study samples are as follows: 0.01 to 16.16, 5.65 (mg/kg dry soil). Variograms (Semivariograms) indicated spatial correlation at distances up to 195.0 km. The data exhibited some anisotropy, but this had little effect on kriging. An exponential variogram model was fitted using least squares and used to krige a grid covering Taiwan. Soils southwest of Taiwan tended to contain higher levels of As than average. The map will be useful in future research to determine the geographic distribution of regional patterns of plants and groundwater As content, the relationship between As and parent soil material, and correlation with of occurrence of blackfoot disease.

Implementation of BMP Strategies for Adaptation to Climate Change and Land Use Change in a Pasture-Dominated Watershed

Implementing a suite of best management practices (BMPs) can reduce non-point source (NPS) pollutants from various land use activities. Watershed models are generally used to evaluate the effectiveness of BMP performance in improving water quality as the basis for watershed management recommendations. This study evaluates 171 management practice combinations that incorporate nutrient management, vegetated filter strips (VFS) and grazing management for their performances in improving water quality in a pasture-dominated watershed with dynamic land use changes during 1992–2007 by using the Soil and Water Assessment Tool (SWAT). These selected BMPs were further examined with future climate conditions (2010–2069) downscaled from three general circulation models (GCMs) for understanding how climate change may impact BMP performance. Simulation results indicate that total nitrogen (TN) and total phosphorus (TP) losses increase with increasing litter application rates. Alum-treated litter applications resulted in greater TN losses, and fewer TP losses than the losses from untreated poultry litter applications. For the same litter application rates, sediment and TP losses are greater for summer applications than fall and spring applications, while TN losses are greater for fall applications. Overgrazing management resulted in the greatest sediment and phosphorus losses, and VFS is the most influential management practice in reducing pollutant losses. Simulations also indicate that climate change impacts TSS losses the most, resulting in a larger magnitude of TSS losses. However, the performance of selected BMPs in reducing TN and TP losses was more stable in future climate change conditions than in the BMP performance in the historical climate condition. We recommend that selection of BMPs to reduce TSS losses should be a priority concern when multiple uses of BMPs that benefit nutrient reductions are considered in a watershed. Therefore, the BMP combination of spring litter application, optimum grazing management and filter strip with a VFS ratio of 42 could be a promising alternative for use in mitigating future climate change.

Simulated annealing and kriging method for identifying the spatial patterns and variability of soil heavy metal

Abstract Environmental data, including information regarding soil heavy metals, may contain significant uncertainty and exhibit a skewed distribution with a complex and unexplainable spatial variation. This study identified the spatial patterns and variations of soil heavy metals (Cu, Cd, Hg, and Pb) in the northern part of Changhua County in Taiwan to clarify the characteristics and pollution of soil heavy metals. The spatial maps of soil heavy metals were also estimated and simulated using kriging and simulated annealing methods. Correlation analysis indicated that Pearson and Spearman correlation coefficients among these four heavy metals were significant; between Cd and Cu, these two correlation coefficients were strongly significant. As expected, the spatial maps of estimation and simulation of soil heavy metals revealed high concentration areas of Cd, Cu along the main irrigation‐ditch system and surrounding industrial plants in the area of study. In addition to reproducing the spatial variation of the investigated Cd, Cu, Hg and Pb, simulated annealing could also identify the global spatial continuity and discontinuity patterns of soil heavy metals. Kriging and simulated annealing methods can both be applied to identify pollution sources and patterns for monitoring and remedy.

A System Dynamic Model and Sensitivity Analysis for Simulating Domestic Pollution Removal in a Free-Water Surface Constructed Wetland

This work develops a system dynamic simulation model for free-water surface constructed wetlands, as well as provides appropriate values for the parameters of constructed wetland management. The system dynamic model is calibrated and validated by using data from a 1-year study of a constructed wetland in Tainan of southern Taiwan. Additionally, the major parameters that affect the simulation output are obtained via sensitivity analysis by using generalized likelihood uncertainty estimation (GLUE). A high R2 and Nash–Sutcliffe coefficient of efficiency between the simulated and measured outflow values indicate that in addition to reproducing the changing trends of dissolved oxygen (DO), 5-day biological oxygen demand (BOD5), total nitrogen (TN), total suspended sediment (TSS), and total phosphorous (TP) concentrations, the model can simulate the variations of DO, BOD5, and TSS. Taken into account the interactions among parameters, the GLUE method successfully obtained the model sensitive parameters from the Monte Carlo parameter sets. Sensitivity analysis results indicate that the parameters of microorganisms are sensitive factors that affect DO, BOD5, and TN, while sediment diameter largely influences TP and TSS. Further elucidating environmental microorganisms would increase the model accuracy and provide a valuable reference for constructed wetland management and design.

GEOSTATISTICAL SIMULATION AND ESTIMATION OF THE SPATIAL VARIABILITY OF SOIL ZINC

Abstract Collected data in soil heavy metal investigations may contain significant levels of uncertainty, including complex and even unexplainable spatial variations at a small investigation site. Therefore, this study identifies the spatial structure of soil zinc in the northern part of Changhua County in Taiwan to understand the spatial variation and uncertainty of soil zinc. The spatial maps of this heavy metal are simulated by using the geostatistical simulation, and estimated by using ordinary kriging and natural log kriging. The estimation and simulation results indicate that Sequential Gaussian Simulations can reproduce the spatial structure for investigated data. Furthermore, displaying a low spatial variability, the ordinary kriging and natural log kriging estimates can not fit the spatial structure and small‐scale variation for the soil zinc investigated data. The maps of kriging estimates are much smoother than those of simulations. Sequential Gaussian Simulation with multiple realizations has significant advantages at a site with high variation investigated data over ordinary kriging, even natural log kriging techniques. Geographic information systems display these simulation and estimation results.