Transportation Emissions and Air Quality: Statistical Modeling and Data Analytics

Overview

Air pollution is an issue of significant importance to public health, climate, visibility, and ecosystem across the world. The literature linking air pollution and its adverse impacts is extensive and continues to increase. Exposure to airborne particulate matter (PM) and ground-level ozone, for example, increases the risk of cardiovascular disease and lung cancer, raises children’s asthma rate, and leads to premature death. Air pollution is also responsible for degradation of visibility and climate on both regional and global scales. Transportation emission is a significant contributor to air pollution. It’s actually the single major air polluter in most urban areas, especially megacities. Numerous studies have found that traffic related air pollution can trigger heart attacks and lead to cancer. In fact, more people are killed by air pollution from vehicles than by traffic accidents. The U.S. EPA attributes thousands of instances of premature mortality, hundreds of thousands of asthma attacks, and millions of lost work days to emissions from transportation. Vehicle emissions are also a major culprit in global warming.

Air pollution is a top global environmental priority, and transportation emission control is crucial to winning the battle against air pollution and climate change. Modeling transportation emissions is at the core of research, education, and decision-making for meeting such challenges. Transportation Emissions and Air Quality: Statistical Modeling and Data Analytics is the first academic book-length treatment of the subject. It aims to contribute to the literature on transportation emissions modeling and the linkage between transportation and air quality via a holistic systems approach through the lens of data analytics with statistical rigor.