| Abstract [eng] |
Air pollution, particularly fine particulate matter 2.5, has a serious impact on human health and urban environmental quality. In response to growing concerns and the need for more spatially detailed air monitoring, this thesis investigates the use of low-cost particulate matter 2.5 sensors to quantify air pollution trends in urban areas of Kaunas city. The study’s goal was to assess the effectiveness of these sensors, compare particulate matter concentrations in various Taikos avenue areas, and discover the link between outdoor and inside pollution levels. Measurements were taken from February to July 2024, with low-cost sensors displayed in six urban areas of Kaunas city. To examine spatial variation and infiltration potential, one sensor was installed outside and one inside at each site. Furthermore, one sensor was co-located with the official Environmental Protection Agency monitoring station to ensure data quality. Descriptive statistical analysis and Pearson correlation test were performed using IBM SPSS Statistics software. The low-cost sensor’s dependability in measuring PM2.5 concentrations has been demonstrated by a good correlation (r = 0,832, p<0,000) when compared to the Environmental Protection Agency’s monitoring station. PM2.5 levels differed significantly among outdoor locations (p<0,001), with the greatest median values reported at monitoring sites near high-traffic routes like Kaunas College and Vytautas Magnus University residential hostel. In terms of indoor and outdoor dynamics, the Kaunas College, first building (r=0,895) and Vytautas Magnus University residential hostel showed the largest correlations, whereas the library (r=0,752) and Kaunas College, second building (r=0,656) had moderate correlations. The medical station (r=0,488) and residential building (0,357) had the weakest indoor and outdoor correlations, which could be attributed to more restricted ventilation and distance from direct traffic pollution. The findings demonstrate that, low-cost sensors are a good tool for dense urban air monitoring and can help to measure spatial differences in air pollution within urban environments. The study demonstrated that proximity to traffic, building orientation, and ventilation characteristics significantly influence PM2.5 infiltration indoors. The approach applied here presents a viable foundation for assessing fine-scale air quality. These results are particularly relevant for cities seeking cost-effective methods to monitor localized exposure and support data-driven air quality management. |
