This study aims to investigate how national parks, identified as remarkable and, therefore, worthy of the trip, are visited and experienced. The main objective of this study was to explore the influence of memorable tourism experiences on environmentally responsible behavior, satisfaction, and behavioral intentions as its outcome. By giving an account of how national parks may be experienced by nature-based tourists, its main focus is on the concept of memorable nature-based tourist experience (MNBTE). The data for this research were collected through a survey from tourists visiting national parks in Serbia. The results demonstrate the significance of MNBTE as an important relationship mediator. The study’s key contributions are the extension of the memorable tourism experience construct and the inclusion of tourist satisfaction, intention to revisit, recommend, share the experience, and the environmental responsibility of tourists. Practical implications and potential future research approaches are also highlighted in the conclusion.

This study aims to investigate spatial and temporal dynamics and relationship between air temperature and five air humidity parameters (relative humidity, water vapor pressure, absolute humidity, specific humidity, and vapor pressure deficit) in Novi Sad, Serbia, based on two-year data (December 2015–December 2017). The analysis includes different urban areas of Novi Sad, which are delineated in five built (urban) types of local climate zones (LCZ) (LCZ 2, LCZ 5, LCZ 6, LCZ 8, and LCZ 9), and one land cover (natural) local climate zone (LCZ A) located outside the urban area. Temporal analysis included annual, seasonal, and monthly dynamics of air temperature and air humidity parameters, as well as their patterns during the extreme periods (heat and cold wave). The results showed that urban dry island (UDI) occurs in densely urbanized LCZ 2 from February to October, unlike other urban LCZs. The analysis of the air humidity dynamics during the heat wave shows that UDI intensity is most pronounced during the daytime, but also in the evening (approximately until midnight) in LCZ 2. However, lower UDI intensity is observed in the afternoon, in other urban LCZs (LCZ 6, LCZ 8, and LCZ 9) and occasionally in the later afternoon in LCZ 5. Regression analysis confirms the relationship between air temperature and each of the analyzed air humidity parameters.

Background: Nature-based solutions (NBS) in urban areas offer an opportunity to improve environmental conditions and to reduce CO₂ emissions towards establishing climate-neutral cities in the next few decades. Furthermore, the implementation of NBSs—vertical or horizontal green infrastructures on public facilities—could in particular improve both climate, including outdoor thermal conditions on a micro-scale (especially during the summer season) and the energy demand of buildings as well as save heating energy during the winter period. Results: On both selected buildings, extensive green roofs were implemented as an NBS intervention. The analysed data were obtained using the monitoring systems (from 2019 to 2022) installed on two public buildings in Novi Sad (Republic of Serbia) and Osijek (Republic of Croatia), with a focus on climate/bioclimate characteristics and thermal transmission capacities. Four automatic weather stations (AWS) were used for microclimate monitoring, along with the heat flow meter (HFM) method, to measure the alterations in the thermal transmittance (U value) of a flat concrete roof before and after energy refurbishment and the installation of a green roof. The outcomes of this study show that the air temperatures (Ta) and globe temperatures (Tg) near the green roof are lower by 0–3 °C for Ta and by 0–16.5 °C for Tg than the values captured by the AWSs at other locations. An even more interesting fact is that the green roof has a constant cooling potential during tropical nights, and based upon this research, the cooling value is around 2 °C for Tg (the Ta value is not distinct). The thermal transmittance results show that more savings can be achieved by applying a green roof with an 8 cm thick substrate: U values decreased by 50–69%, as measured by two different heat flux sensors. Conclusions: Nature-based solutions, such as the implementation of an extensive green roof, have positive effects on diverse aspects of urban environments and building energy savings, which are particularly evident in extreme seasons, both summer and winter. Applying the proposed monitoring and assessment system could help local communities in their efforts to reduce carbon-based emissions. This paper provides a good example of the implementation of NBSs on a local- and a micro-scale.

This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (T_a) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014–2018; and secondly - hourly T_a data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the T_a and RH sensors are placed, while the second type contains T_a and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of T_a and RH data, and the hourly datasets contain 17,544 instances of T_a data. The 10-minutes datasets were not quality controlled, but the hourly T_a data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal.