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.

In order to optimize the environmental factors for cucumber growth, a fertilizer and water control system was designed based on the Internet of Things (IoT) system. The IoT system monitors environmental factors such as temperature, light and soil Ec value, and uses image processing to obtain four growth indicators such as cucumber stem height, stem diameter size, number of leaves and number of fruit set to establish a single growth indicator model for temperature, light, soil Ec value and growth stage, and the four growth indicators were fused to obtain the comprehensive growth indicator I_c for cucumber, and calculates its deviation to determine the cucumber growth status. Based on the integrated growth index I_c of cucumber, a soil Ec control model was established to provide the optimal environment and fertilizer ration for cucumber at different growth stages to achieve stable and high yield of cucumber.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The impact of human activities on the quality of urban environment has become increasingly prominent and urban soil pollution problems on the health of local residents also gradually prominent. In addition, the study of heavy metal pollution in urban surface soil is an important part of the evolution model of urban geological environment so it is necessary to analyze the heavy metal pollution in urban soil. In this paper, the data of the given samples are processed and analyzed by MATLAB software and EXCEL spreadsheet. The three - dimensional image model and the planar model of metal element space are established by interpolation method. The spatial distribution of eight kinds of heavy metal elements in the city is presented in detail. For the urban environment, especially the macro-grasp of soil pollution, regulation provides a simple and accurate three-dimensional spatial distribution model of pollutants. Combined with data analysis of the urban area of different areas of heavy metal pollution to make a preliminary judgment. The data show that in the five types of cities, heavy soil pollution is the most serious in industrial areas. A method of imagination of the data analysis is boldly used and then combined with the distribution map, they found a source of pollution. For the spatial distribution of heavy metal elements, this paper uses EXCEL to calculate the data and MATLAB to map the data which showed a detailed and intuitive distribution map according to the distribution map can be analyzed in different areas of pollution; For the second question, this paper uses a method of design to deal with the data, part of the data for the results of the more effective show to determine the cause of pollution. For the third question, this article will be more serious pollution or a wider range of local screening, analysis, and then speculate the location of pollution sources. For other pollution information, this article is based on the modeling process encountered in the thought of the factors given.

Relying on the D-Vine copula model, this paper delves into the hedging capabilities of Brent crude oil against the exchange rate of oil-exporting and oil-importing nations. The results affirm Brent crude oil’s role as a safeguard and a refuge against the fluctuations of major currencies. Furthermore, we reaffirm that oil retains its robust hedging and safe-haven attributes during times of crisis, with currency co-movements across all countries exhibiting greater correlation than during the entire dataset. Additionally, our empirical findings highlight an unusually positive correlation between Brent crude oil and the Russian exchange rate during the Russia-Ukraine conflict, demonstrating that oil functions as a less effective hedge and a less dependable refuge for the Russian exchange rate in such geopolitical turbulence.

The provided material presents a priority article on the scientific discovery titled “The phenomenon of simultaneous destruction of water-oil and oil-water emulsions”. The authors propose the corresponding formula: the previously unknown phenomenon of simultaneous destruction of water-oil and oil-water emulsions occurs when polynanostructured surfactant demulsifiers with characteristics akin to crystalline liquids, intramolecular interblock activity, and enduring intramolecular nanomotors (such as block copolymers of ethylene and propylene oxides, which act as sources of oligomer homologues of oxyethylene ethers) are added to crude oil during primary oil processing. This phenomenon is attributed to the redistribution of oligomer homologues, with the most hydrophobic oxyethylene ethers being dispersed in water-oil emulsions and the most hydrophilic ones in oil-water emulsions, resulting in robust nanodispersed phases with crystalline liquid properties.