دانلود کتاب علوم داده در پایش کیفیت هوا

As globalization and urbanization continue to accelerate, air pollution has become one of the major challenges affecting human health, the environment, and social development. The rapid expansion of industrialization and transportation has led to a large amount of pollutant emissions, making the trend of air quality deterioration more obvious worldwide. Pollutants such as particulate matter and harmful gases in the air not only pose a direct threat to the human respiratory and cardiovascular systems, but also cause irreversible damage to the ecological environment through climate change, acid rain, and other means. In addition, air pollution also brings hidden costs to economic development, including reduced labor productivity due to health problems, increased medical costs, and the cost of resource depletion and ecological restoration. Air quality monitoring, as an important means of environmental management and public health protection, has received unprecedented attention. With the advancement of science and technology and the surge in data volume, the application of data science and technology in air quality monitoring has gradually shown great potential. The integration of emerging technologies such as big data, the Internet of Things, artificial intelligence, and computational intelligence has enabled air quality monitoring to gradually develop in the direction of digitalization and intelligence. It is in this context that this book innovatively combines computational intelligence with the specific application of air quality monitoring, focusing on the most cutting-edge technologies and methods in the field of data science, and promoting air quality monitoring into a more intelligent and accurate data-driven era. This book analyzes the application of data science in air quality monitoring in detail; explores the importance of data science in this field; introduces the key impact of air quality monitoring on public health, environment, and economy; and points out the important role of data science. Focusing on the application of data science in air quality monitoring, this book systematically introduces a series of advanced technologies from data preprocessing to data decomposition, identification, clustering, prediction, and interpolation. Each chapter not only combines the latest research results, but also demonstrates the actual effects and applications of various technologies through performance comparison and case analysis. Using advanced methods such as machine learning and deep learning, this book explores how to realize automated air quality monitoring and decision support systems and demonstrates the great potential of these technologies in practical applications through a large number of case analyses. This book provides valuable technical references for researchers in the fields of environmental monitoring, data science, artificial intelligence, etc., and also provides operational theoretical support for policy makers and environmental managers. In the process of writing the book, the team members have done a lot of experiments and other work, the authors would like to express heartfelt appreciations.