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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 Sao 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.