Soil and groundwater remediation act has been enacted and executed since year 2000 in Taiwan.  It has been ten good years till today where lots of remediation techniques progressively employed to improve Taiwan soil and groundwater resource quality.  Regulatory agencies, academia, remediation consulting firms, on-site professional engineers all have contribute the proud ten years in terms of soil and groundwater clean-up contribution.  However, some of technologies were un-environmental friendly even detrimental and damage to Taiwan precious soil and groundwater resources.  In Article one of the current Taiwan soil and groundwater Act, it clearly stated that soil is a precious nature resources.  Soil definitely is not a waste, shame on us most of current most commonly employed remediation are unlawful and merely aiming to save time and money consideration without any care to our land.  Dig-and-dump and soil acid washing are damaged employed in almost every single local environment agency soil clean-up project.  Lot of money, effort and time has been spent during past ten years.  Most of the spending is not improving soil quality using Green approach.

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.

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

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.