These variables were interpolated in ArcGIS Pro 3.4, allowing the team to generate spatially continuous comfort maps rather than relying on single-station readings, and the TCI formula was then applied to classify bioclimatic comfort levels across the province. The results reveal pronounced seasonal and spatial contrasts in tourism climatic comfort. From March to November, the province's classifications fell within the categories of Marginal, Acceptable, Good, and Very Good, while the winter months of December, January, and February were characterized by Marginal, Unfavourable, Very Unfavourable, and Extremely Unfavourable conditions.

Notably, the highest categories on the index, Excellent and Ideal, were not observed in any month of the year anywhere in the province. Within that overall pattern, the seasonal distribution was far from uniform. Areas classified as Very Unfavourable predominated in December, marking the low point of the comfort calendar.

By contrast, Good and Very Good conditions predominated in April, September, and October, making these months the strongest performers in Hatay's tourism climate profile. March emerged as a transition month, during which the Acceptable class was the most widespread across the study area as conditions improved from winter toward spring. Because the comfort classifications were mapped across the province rather than averaged for a single location, the findings allow planners to identify which destination areas within Hatay offer the most suitable climatic conditions in each period.

This kind of spatially explicit information is central to the study's stated aim of determining the most suitable periods and destination areas for sustainable tourism activities. The timing of these findings is significant for the province's development strategy. The researchers note that tourism represents an important potential component of Hatay's economy in the post-earthquake reconstruction process, and that identifying comfort conditions on the basis of long-term data can guide planning and design decisions.

Climate parameters such as temperature, humidity, wind, and precipitation directly affect bioclimatic comfort, and in turn influence the quality of urban life, the harmony of living areas with nature, and patterns of energy consumption. According to the study's discussion, December, January, and February represent the lowest levels of tourism comfort in Hatay, whereas the period from March through November provides relatively higher levels of climatic comfort. The authors frame the findings as a contribution to the development and diversification of tourism strategies in Hatay Province and to sustainable tourism planning grounded in long-term climatic conditions.

Because the analysis rests on more than six decades of climate records, the comfort maps reflect established seasonal patterns rather than short-term weather variability, which strengthens their value as a planning baseline. Nevertheless, the study provides a clear, data-driven picture of when and where Hatay's climate is most supportive of outdoor tourism, and it establishes a spatial framework that planners can use as the province rebuilds and seeks to position itself within Türkiye's Eastern Mediterranean tourism landscape.

Subject of Research: Evaluation of bioclimatic comfort conditions in Hatay using the tourism climate index: a spatial analysis for sustainable tourism planning

Article Title: Evaluation of bioclimatic comfort conditions in Hatay using the tourism climate index: a spatial analysis for sustainable tourism planning

Article References: Vural, E., Dinç, Y., Doğan, M., Bozdoğan, E., & Arslan, F. (2026). Evaluation of bioclimatic comfort conditions in Hatay using the tourism climate index: a spatial analysis for sustainable tourism planning. Frontiers in Environmental Science, 14, Article 1962497. https://doi.org/10.3389/fenvs.2026.1962497

Image Credits: AI Generated

DOI: 10.3389/fenvs.2026.1962497

Keywords: Evaluation, bioclimatic, comfort, conditions, Hatay, tourism, climate, index, spatial, analysis, sustainable, planning