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.
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