This mini-course introduces statistical modeling techniques for left-censored time series data, with a focus on environmental applications such as air quality monitoring, climate change modeling, and environmental risk assessment. The course combines theoretical foundations with practical computational techniques to handle censored observations in real-world datasets. Students will learn classical and advanced methods, including maximum likelihood and Bayesian approaches, as well as numerical techniques like Monte Carlo simulations and the EM algorithm. The course is suitable for Master’s and PhD students in probability, statistics, and environmental sciences.