The correct estimation of ambulance travel time is an extremely important issue from the perspective of healthcare and the security of citizens. In some events, the threat to the health or life of an injured person increases with each minute of waiting for an ambulance. The authors of this article analyzed how ambulances travel throughout the entire Lesser Poland voivodeship in southern Poland. Based on the analysis of 300 million GPS records that were collected over several years from 300 ambulances, real ambulance speed characteristics were compiled for the most important cities in the region. The obtained results regarding ambulance speed characteristics were used to understand the correlation between ambulance speed, the density of the road network, and the built-up areas of a given city. Furthermore, the impact on the speed of ambulances of traffic, time of day, day of the week, or the season was also examined. The influence of the use of ambulances’ lights/sirens on travel time was also examined. The culmination of the research was the presentation of the theoretical foundations of coverage maps and a method of implementing them based on the determined speed characteristics. The presented studies show that the speed at which ambulances move is a very local phenomenon. Also, a relatively constant average speed of ambulances throughout the whole week was found. Moreover, a difference in speed between signaled and non-signaled ambulance trips was observed. The speed characteristics that were obtained were used as input data for the development of dynamic coverage maps, which are an invaluable tool for supporting the decisions of ambulance dispatchers.

The study was undertaken in Krakow, which is situated in Lesser Poland Voivodeship, where bad PM₁₀ air-quality indicators occurred on more than 100 days in the years 2010–2019. Krakow has continuous air quality measurement in seven locations that are run by the Province Environmental Protection Inspectorate. The research aimed to create regression and classification models for PM₁₀ and PM_2.5 estimation based on sky photos and basic weather data. For this research, one short video with a resolution of 1920 × 1080 px was captured each day. From each film, only five frames were used, the information from which was averaged. Then, texture analysis was performed on each averaged photo frame. The results of the texture analysis were used in the regression and classification models. The regression models’ quality for the test datasets equals 0.85 and 0.73 for PM₁₀ and 0.63 for PM_2.5. The quality of each classification model differs (0.86 and 0.73 for PM₁₀, and 0.80 for PM_2.5). The obtained results show that the created classification models could be used in PM₁₀ and PM_2.5 air quality assessment. Moreover, the character of the obtained regression models indicates that their quality could be enhanced; thus, improved results could be obtained.