Department of Civil Engineering, National Institute of Technology, Srinagar, Jammu and Kashmir, 190006, India.
Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Trivandrum, Kerala, 695547, India.
Environ Monit Assess. 2023 Nov 21;195(12):1502. doi: 10.1007/s10661-023-12001-2.
Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the advances in Air Quality (AQ) monitoring using modern sensors, integrated monitoring systems, remote sensing and the usage of Machine Learning (ML), Deep Learning (DL) algorithms, artificial neural networks, recent computational techniques, hybridizing techniques and different platforms available for AQ modelling. The modern world is data-driven, where critical decisions are taken based on the available and accessible data. Today's data analytics is a consequence of the information explosion we have reached. The current research also tends to re-evaluate its scope with data analytics. The emergence of artificial intelligence and machine learning in the research scenario has radically changed the methodologies and approaches of modern research. The aim of this review is to assess the impact of data analytics such as ML/DL frameworks, data integration techniques, advanced statistical modelling, cloud computing platforms and constantly improving optimization algorithms on AQ research. The usage of remote sensing in AQ monitoring along with providing enormous datasets is constantly filling the spatial gaps of ground stations, as the long-term air pollutant dynamics is best captured by the panoramic view of satellites. Remote sensing coupled with the techniques of ML/DL has the most impact in shaping the modern trends in AQ research. Current standing of research in this field, emerging trends and future scope are also discussed.
环境污染，特别是空气污染，是一种呈指数级增长的威胁，需要立即引起关注，因为它会伴随着健康、经济和生态危机的相关风险而持续存在。本研究的重点特别放在使用现代传感器、综合监测系统、遥感以及机器学习 (ML)、深度学习 (DL) 算法、人工神经网络、最近的计算技术、混合技术和空气质量 (AQ) 建模可用的不同平台来进行空气质量监测方面的进展。现代世界是由数据驱动的，关键决策是基于可用和可访问的数据做出的。当今的数据分析是我们所达到的信息爆炸的结果。当前的研究也倾向于通过数据分析重新评估其范围。人工智能和机器学习在研究场景中的出现，彻底改变了现代研究的方法和方法。本综述的目的是评估数据分析（如 ML/DL 框架、数据集成技术、高级统计建模、云计算平台和不断改进的优化算法）对 AQ 研究的影响。遥感在 AQ 监测中的使用以及提供大量数据集，不断填补地面站的空间空白，因为卫星的全景视图可以最好地捕捉长期的空气污染物动态。遥感与 ML/DL 技术的结合在塑造 AQ 研究的现代趋势方面具有最大的影响。还讨论了该领域当前的研究现状、新兴趋势和未来范围。
