北京地区土壤污染物二氯乙烷含量检测数据

通过检测数据分析研判，我们可以判断北京地区土壤污染物中二氯乙烷是否超标，避免因二氯乙烷持续污染而产生的污染问题，有以下几点作用。一、进行土壤污染治理可以减少农作物中的该有害物质含量，确保食品的质量和安全；二、根据检测结果可有针对的改善士壤质量，提高土壤的生产力，可以为农业发展提供可持续的基础，同时也有利于保护和改善环境。另外可结合地理信息系统（GIS）技术，将各地点的土壤地理数据和二氯乙烷污染物含量信息进行深度整合和分析，绘制地理位置-污染物含量地图，以直观的可视化形式呈现给用户，增强地理位置与污染物含量关系的理解，构建起一个包含污染源、污染物种类、污染程度、污染扩散路径等多维度信息的地理图谱。这一图谱不仅能够提供实时的监测数据，还能够通过数据之间的关联性，揭示潜在的污染风险和趋势。1数据采集：每天早上10点对北京地区的不同地点，在各个地点的方圆1米直径内随机采集3个土壤；2数据处理：将数据去噪、优化、补全；3数据加工：通过检测仪设备对3个土壤进行二氯乙烷污染物含量检测，得出3个采样点的土壤二氯乙烷污染物含量数据，分别为P1、P2和P3，则该地点的土壤二氯乙烷污染物含量平均值P4=（P1+P2+P3）/3，3个采样点二氯乙烷的含量方差s^2={(P1-P4)^2+(P2-P4̅)^2+(P3-P4̅)^2}/3；4数据应用：根据土壤二氯乙烷污染物含量平均值P4有助于了解该地区土壤中二氯乙烷的污染状况和潜在的污染风险趋势。若s2大于0.01则该采集地点为异常，否则为不异常，对于检测结果为异常的采集地点，需重点关注，查找出引起异常的原因，分析问题解决问题。

Through data analysis and testing-based judgment, we can determine whether the concentration of 1,2-dichloroethane in soil pollutants in Beijing exceeds the standard, so as to avoid pollution problems caused by sustained 1,2-dichloroethane contamination. This dataset has the following core functions:
1. Implementing soil pollution remediation can reduce the content of this harmful contaminant in crops, ensuring food quality and safety;
2. Targeted soil quality improvement can be carried out based on test results, enhancing soil productivity, providing a sustainable foundation for agricultural development, and simultaneously contributing to environmental protection and improvement.
In addition, combined with Geographic Information System (GIS) technology, in-depth integration and analysis can be conducted on soil geographic data and 1,2-dichloroethane pollutant concentration information of each location, generating location-pollutant concentration maps presented to users in an intuitive visual format, enhancing the understanding of the relationship between geographic location and pollutant concentrations, and constructing a geographic knowledge graph containing multi-dimensional information such as pollution sources, pollutant types, pollution levels, and pollution diffusion paths. This knowledge graph not only provides real-time monitoring data but also reveals potential pollution risks and trends through the correlation between different datasets.

1. Data Collection: At 10:00 AM every day, collect 3 soil samples randomly within a 1-meter diameter circle at various locations across Beijing;
2. Data Preprocessing: Denoise, optimize, and complete the collected data;
3. Data Processing and Calculation: Use testing equipment to measure the 1,2-dichloroethane concentrations of the 3 soil samples, obtaining the concentration data of the three sampling sites as P1, P2, and P3. Then the average concentration of 1,2-dichloroethane in soil at this location is P4 = (P1 + P2 + P3)/3, and the variance of the 1,2-dichloroethane concentrations of the 3 sampling sites is s² = [(P1-P4)² + (P2-P4)² + (P3-P4)²]/3;
4. Data Application: The average concentration P4 of 1,2-dichloroethane in soil helps understand the local soil contamination status and potential pollution risk trends. If the variance s² is greater than 0.01, the sampling site is deemed abnormal; otherwise, it is normal. For sampling sites with abnormal test results, priority attention shall be paid to identifying the causes of the abnormality, analyzing the issues, and resolving them.

搜集汇总

数据集介绍

特点

该数据集记录了北京地区土壤中二氯乙烷污染物的检测数据，包含时间、地点、污染物含量等信息，数据规模为13902条，每日更新。主要用于土壤污染治理、食品安全保障及环境监测等场景，结合GIS技术可进行深度分析和可视化展示。