@article{1432481,
      recid = {1432481},
      author = {Cox, Louis A.,},
      title = {Quantitative risk analysis of air pollution health effects  /},
      pages = {1 online resource},
      abstract = {This book highlights quantitative risk assessment and  modeling methods for assessing health risks caused by air  pollution, as well as characterizing and communicating  remaining uncertainties. It shows how to apply modern data  science, artificial intelligence and machine learning,  causal analytics, mathematical modeling, and risk analysis  to better quantify human health risks caused by  environmental and occupational exposures to air pollutants.  The adverse health effects that are caused by air  pollution, and preventable by reducing it, instead of  merely being statistically associated with exposure to air  pollution (and with other many conditions, from cold  weather to low income) have proved to be difficult to  quantify with high precision and confidence, largely  because correlation is not causation. This book shows how  to use recent advances in causal analytics and risk  analysis to determine more accurately how reducing  exposures affects human health risks. Quantitative Risk  Analysis of Air Pollution Health Effects is divided into  three parts. Part I focuses mainly on quantitative  simulation modelling of biological responses to exposures  and resulting health risks. It considers occupational risks  from asbestos and crystalline silica as examples, showing  how dynamic simulation models can provide insights into  more effective policies for protecting worker health. Part  II examines limitations of regression models and the  potential to instead apply machine learning, causal  analysis, and Bayesian network learning methods for more  accurate quantitative risk assessment, with applications to  occupational risks from inhalation exposures. Finally, Part  III examines applications to public health risks from air  pollution, especially fine particulate matter (PM2.5) air  pollution. The book applies freely available browser  analytics software and data sets that allow readers to  download data and carry out many of the analyses described,  in addition to applying the techniques discussed to their  own data.},
      url = {http://library.usi.edu/record/1432481},
      doi = {https://doi.org/10.1007/978-3-030-57358-4},
}