Stochastic Models Applied to Air Pollution Studies

Stochastic Models Applied to Air Pollution Studies: A Bayesian Approach offers a comprehensive and accessible guide to the stochastic methods that underpin modern environmental analysis. Grounded in real-world data and decades of research, this book presents a unified framework for modeling pollutant concentrations, exceedances, temporal variability, and spatial dependence. Bridging foundational concepts with advanced applications, the book explores: Discrete-time Markov chains , including homogeneous, non-homogeneous, and higher-order formulations for forecasting pollution levels. Homogeneous and non-homogeneous Poisson processes , with and without change-points, for studying exceedance frequencies and event clustering. Stochastic volatility models adapted from financial mathematics to characterize environmental variability. Spatio-temporal models that capture how pollutants evolve across both time and geographic regions. Bayesian inference and MCMC techniques , providing robust parameter estimation even in complex or data-limited scenarios. Drawing on extensive ozone and particulate matter data from Mexico City and São Paulo, the book demonstrates how these models inform environmental policy, health-risk assessment, and scientific understanding. Detailed case studies show how thresholds are exceeded, how clusters of high-pollution events form, and how legislative interventions alter long-term behavior. Complete with appendices featuring R , this volume provides readers with ready-to-use tools for their own research. It serves as an essential resource for statisticians, environmental scientists, data analysts, atmospheric researchers, and graduate students seeking a rigorous yet application-oriented treatment of stochastic environmental modeling. Insightful, methodologically rich, and deeply practical—this book equips researchers to confront the complexities of air pollution with clarity and mathematical power.