Soil and groundwater remediation Act was enacted in year 2000. More than ten years has already passed,  Monitoring project has been completed,pollution status has been defined,contaminated sites depollution have been launched,a great progress has been made. This paper majorly to depict the extensive farmland soil qauality monittoring which is unpredent in Taiwan and believe has never been done worldwide.

This project was initiated from February 8^th, 2002 to August 8^th, 2002. The project tasks including digitalization of cadastre, farmland listing, basic information collecting, field investigation, sampling & analysis planning, field sampling, soil sample analysis, data evaluation, suggestion of contaminated farmland control, and analysis of potential pollution sources and transfer routes.

2,251 soil samples,had been sampled from Chang-Hwa County, Yun-Lin County, Nan-Tao County, and Chia-Yi City, and been analyzed in this project. 44% of these samples concentration exceed the soil pollution control standard (Table 1), including 492 farmlands (125.65 ha registered) with total contaminated farming area of 108.38 ha in Chang-Hwa, and 6 farmlands (0.39 ha registered) with total contaminated farming area of 0.39 ha in Nan-Tao County. However, the concentration of samples from Ynu-Lin County and Chia-Yi City do not exceed the soil pollution control standard.

To coordinate with the investigation results of the relative project regarding to water and sediment quality of irrigation channels in Chang-Hwa area, the pollution sources are preliminary concluded to be the irrigation channels surrounding the farmlands in Chang-Hwa area. As to the Nan-Tao County, the abandoned brick furnace plants neighboring the farmland are suspected to be the pollution sources.

The results show that the soil of the investigation area in Chang-Hwa County is the most polluted. Base on the Geostatistics study and the distribution of the irrigation channels; the area neighboring the investigated farmland in this project is suspected being polluted. For the farmlands exceeding soil control standard, Geostatistics method is suggested to coordinate with the information of the irrigation system to clarify the contaminated area so as to be the basis of land control and remediation work. As to the farmlands, not being investigated in this project but with high pollution potential according to the Geostatistics study, detail investigations are suggested. Regarding to soil pollution remediation, it is suggested to coordinate with the effluent control and irrigation channel remediation to achieve an all-out success.

Theoretically, within the diatomic model, the relative stability of most abundant boron clusters B₁₁, B₁₂, and B₁₃ with planar structures in neutral, positive and negative charged-states is studied. According to the specific (per atom) binding energy criterion, B₁₂⁺ (6.49 eV) is found to be the most stable boron cluster, while B₁₁^– + B₁₃⁺ (5.83 eV) neutral pair is expected to present the preferable ablation channel for boron-rich solids. Obtained results would be applicable in production of boron-clusters-based nanostructured coating materials with super-properties such as lightness, hardness, conductivity, chemical inertness, neutron-absorption, etc., making them especially effective for protection against cracking, wear, corrosion, neutron- and electromagnetic-radiations, etc.

This article describes a classification tool to cluster SARAL/AltiKa waveforms. The tool was made using Python scripts. Radar altimetry systems (e.g., SARAL/AltiKa) measures the distance from the satellite centre to a target surface by calculating the satellite-to-surface round-trip time of a radar pulse. An altimeter waveform represents the energy reflected by the earth’s surface to the satellite antenna with respect to time. The tool clusters the altimetric waveforms data into desired groups. For the clustering, we used evolutionary minimize indexing function (EMIF) with k-means cluster mechanism. The idea was to develop a simple interface which takes the altimetry waveforms data from a folder as inputs and provides single value (using EMIF algorithm) for each waveform. These values are further used for clustering. This is a simple light weighted tool and user can easily interact with it.

In this study, the development of rinnenkarren systems is analyzed. During the field studies, 36 rinnenkarren systems were investigated. The width and depth were measured at every 10 cm on the main channels and then shape was calculated to these places (the quotient of channel width and depth). Water flow was performed on artificial rinnenkarren system. A relation was looked for between the density of tributary channels and the average shape of the main channel, between the distance of tributary channels from each other and the shape of a given place of the main channel. The density and total length of the tributary channels on the lower and upper sections of the main channels being narrow at their lower end (11 pieces) and being wide at their lower end (10 pieces) of the rinnenkarren systems were calculated as well as their average proportional distance from the lower end of the main channel. The number of channel hollows was determined on the lower and upper sections of these main channels. It can be stated that the average shape of the main channel calculated to its total length depends on the density of the tributary channels and on the distance of tributary channels from each other. The main channel shape is smaller if less water flows on the floor for a long time because of the small density of the tributary channels and the great distance between the tributary channels. In this case, the channel deepens, but it does not widen. The width of the main channel depends on the number and location of the rivulets developing on channel-free relief. The main channel becomes narrow towards its lower end if the tributary rivulets are denser and longer on the upper part of the main rivulet developing on the channel-free, plain terrain and their distance is larger compared to the lower end. The channel hollows develop mainly at those places where the later developing tributary channels are hanging above the floor of the main channel. Thus, the former ones are younger than the latter ones. It can be stated that the morphology of the main channels (shape, channel hollows, and width changes of the main channel) is determined by the tributary channels (their number, location and age).