利用物联网、大数据和机器学习的空气污染监测与预测系统的最新技术水平
The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning
AI总结：
本文综述了基于物联网、大数据和机器学习的空气污染监测与预测研究，梳理数据源、监测及预测模型，并指出相关问题与挑战。
摘要：
空气质量与人类、种植园和野生生物的生活质量密切相关，需要对其进行持续监测和保护。交通运输、工业、建筑工地、发电机、烟花以及废物焚烧在空气质量恶化中占有很大比例，这些来源需要以安全且受控的方式使用。采用传统实验室分析，或每隔几英里安装庞大且昂贵的模型，已不再高效。因此，需要智能设备来收集和分析空气数据。空气质量取决于多种因素，包括位置、交通和时间。近期研究正在使用机器学习算法、大数据技术和物联网（Internet of Things），以为上述目的提出稳定且高效的模型。本综述论文重点研究和汇编该领域的最新研究，并强调数据源、监测模型和预测模型。本文的主要目标是提供对为改进空气污染模型各个方面而开展的研究的深刻理解。此外，本文还阐明了各种研究问题与挑战。
英文摘要：
The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
