| Abstract [eng] |
The master’s thesis investigates and analyzes air pollution indicators across the territory of Lithuania. The topic of air pollution is relevant to all residents, as it affects the health of every individual. Although this study focuses specifically on Lithuania, its methods are universally applicable to any region of the world. In addition to air pollution metrics, this project also reviews health-related data indicators. The study examines the World Health Organization’s recommended air pollution indices and their threshold values, and takes into account the European Union’s directives on air quality standards. The aim of this research is to conduct a statistical analysis of air pollution indicators within Lithuania. The objectives are to analyze open-source air quality and health data, to perform time-series analyses to identify pollution trends, to test for associations between air pollution and health outcomes, to determine relationships between potential pollution sources and air quality. The thesis employs methods for compensating time-series data and detecting outliers. It also uses statistical tests for normality, correlation analysis, and time-series decomposition techniques to uncover seasonality, cyclicality, and deterministic trends. Additionally, methods are applied to adjust pollutant concentration data for wind direction and speed. Two new approaches are introduced: one for filling missing values in a specific type of multivariate time series (a sum-interpolation with outlier detection and imputation method), and another model that links air pollution indicators to stationary pollution sources, accounting for meteorological conditions, to assess their impact on air quality metrics. The study develops a method for imputing missing time-series values and evaluates the relationship between PM2.5 concentration and increased mortality. It also derives coefficients quantifying the influence of stationary sources on air pollution levels. As a result of this master’s thesis, open air quality and health data sources have been analyzed, their strengths and limitations discussed, and the most useful datasets selected. Both univariate and multivariate time-series analyses were conducted, missing data were imputed, and outliers handled. Pollution trends were identified, and existing links between air pollution and health indicators explored. The findings are compared with similar studies, and relationships between pollution sources and air quality metrics are established. |
