摘 要
城市空气质量监测与评估是保障居民健康和实现可持续发展的重要环节，随着工业化和城市化进程的加快，大气污染问题日益严重，传统的监测与评估技术难以满足当前需求。为此，本研究旨在构建一套高效、精准的城市空气质量监测与评估体系。研究基于物联网、大数据分析等先进技术，整合多源数据，建立了一个涵盖污染物浓度预测、来源解析及风险预警等功能的综合平台。通过在典型城市区域布设高密度传感器网络，实现了对主要空气污染物如PM2.5、SO2、NOx等的实时动态监测，并利用机器学习算法优化了数据处理流程，提高了监测精度与时效性。结果表明，该系统能够准确反映不同季节、时段的空气质量变化特征，为环境管理部门提供了科学决策依据。创新点在于首次将边缘计算应用于空气质量监测领域，有效解决了海量数据传输瓶颈问题；同时提出了基于深度学习的污染物溯源方法，可精确识别污染源位置及其贡献率，为制定针对性治理措施提供了技术支持，显著提升了城市空气质量管理水平。
关键词：空气质量监测 物联网 大数据分析
Abstract
Urban air quality monitoring and evaluation are critical components for safeguarding public health and achieving sustainable development. As industrialization and urbanization accelerate, atmospheric pollution has become increasingly severe, and traditional monitoring and assessment technologies struggle to meet current demands. This study aims to develop an efficient and accurate urban air quality monitoring and evaluation system. Leveraging advanced technologies such as the Internet of Things (IoT) and big data analytics, this research integrates multi-source data to establish a comprehensive platform that includes functions for pollutant concentration prediction, source apportionment, and risk warning. By deploying a high-density sensor network in typical urban areas, real-time dynamic monitoring of major air pollutants such as PM2.5, SO2, and NOx is achieved. Machine learning algorithms are utilized to optimize data processing workflows, enhancing both the accuracy and timeliness of monitoring. The results demonstrate that the system can accurately reflect the characteristics of air quality changes across different seasons and time periods, providing a scientific basis for decision-making by environmental management departments. Innovations include the first application of edge computing in air quality monitoring, effectively addressing the bottleneck of massive data transmission; and the introduction of a deep learning-based method for pollutant source tracing, which can precisely identify the location and contribution rate of pollution sources, offering technical support for targeted control measures and significantly improving urban air quality management.
Keyword:Air Quality Monitoring Internet Of Things Big Data Analysis
目 录
1绪论 1
1.1城市空气质量监测背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2空气质量监测技术体系 2
2.1监测站点布局优化 2
2.2主要污染物监测方法 3
2.3新型监测技术应用 3
3空气质量评估模型构建 4
3.1指标体系建立原则 4
3.2数学模型选择依据 4
3.3模型验证与优化 5
4空气质量管理决策支持 6
4.1数据分析与可视化 6
4.3政策建议与实施路径 7
结论 7
参考文献 9
致谢 10
城市空气质量监测与评估是保障居民健康和实现可持续发展的重要环节，随着工业化和城市化进程的加快，大气污染问题日益严重，传统的监测与评估技术难以满足当前需求。为此，本研究旨在构建一套高效、精准的城市空气质量监测与评估体系。研究基于物联网、大数据分析等先进技术，整合多源数据，建立了一个涵盖污染物浓度预测、来源解析及风险预警等功能的综合平台。通过在典型城市区域布设高密度传感器网络，实现了对主要空气污染物如PM2.5、SO2、NOx等的实时动态监测，并利用机器学习算法优化了数据处理流程，提高了监测精度与时效性。结果表明，该系统能够准确反映不同季节、时段的空气质量变化特征，为环境管理部门提供了科学决策依据。创新点在于首次将边缘计算应用于空气质量监测领域，有效解决了海量数据传输瓶颈问题；同时提出了基于深度学习的污染物溯源方法，可精确识别污染源位置及其贡献率，为制定针对性治理措施提供了技术支持，显著提升了城市空气质量管理水平。
关键词：空气质量监测 物联网 大数据分析
Abstract
Urban air quality monitoring and evaluation are critical components for safeguarding public health and achieving sustainable development. As industrialization and urbanization accelerate, atmospheric pollution has become increasingly severe, and traditional monitoring and assessment technologies struggle to meet current demands. This study aims to develop an efficient and accurate urban air quality monitoring and evaluation system. Leveraging advanced technologies such as the Internet of Things (IoT) and big data analytics, this research integrates multi-source data to establish a comprehensive platform that includes functions for pollutant concentration prediction, source apportionment, and risk warning. By deploying a high-density sensor network in typical urban areas, real-time dynamic monitoring of major air pollutants such as PM2.5, SO2, and NOx is achieved. Machine learning algorithms are utilized to optimize data processing workflows, enhancing both the accuracy and timeliness of monitoring. The results demonstrate that the system can accurately reflect the characteristics of air quality changes across different seasons and time periods, providing a scientific basis for decision-making by environmental management departments. Innovations include the first application of edge computing in air quality monitoring, effectively addressing the bottleneck of massive data transmission; and the introduction of a deep learning-based method for pollutant source tracing, which can precisely identify the location and contribution rate of pollution sources, offering technical support for targeted control measures and significantly improving urban air quality management.
Keyword:Air Quality Monitoring Internet Of Things Big Data Analysis
目 录
1绪论 1
1.1城市空气质量监测背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2空气质量监测技术体系 2
2.1监测站点布局优化 2
2.2主要污染物监测方法 3
2.3新型监测技术应用 3
3空气质量评估模型构建 4
3.1指标体系建立原则 4
3.2数学模型选择依据 4
3.3模型验证与优化 5
4空气质量管理决策支持 6
4.1数据分析与可视化 6
4.3政策建议与实施路径 7
结论 7
参考文献 9
致谢 10
