Linking a tourism-transport suitability map with multi-pollutant air pollution using GIS-based spatial interpolation and seasonal analysis

This thesis aims to characterize the spatial and seasonal variability of air pollution in coastal provinces where tourism and transport activities co-intensify, and to quantify the relationship between tourism-transport pressure and combined pollution levels. Antalya, Muğla, Aydın, and İzmir are selected as the study area. In the first stage, 11 criteria are defined to represent tourism attractiveness, accommodation and recreation intensity, transport network and accessibility, as well as population and topography. Criteria weights are derived using the Analytic Hierarchy Process (AHP) and Fuzzy SIWEC, based on the assessments of a four-member expert panel. Weighted criteria are integrated in a Geographic Information Systems (GIS) environment to generate the Tourism-Transport Suitability Index (SUI) surfaces. In the second stage, station observations for PM10, SO₂, NO₂, O₃, and CO are interpolated into continuous seasonal surfaces using Inverse Distance Weighting (IDW). Surfaces are normalized by dividing by pollutant-specific reference thresholds. Since fixed reference values are not available for NO and NOx, province-level 95th percentile (N95) thresholds are computed from seasonal station medians and used for normalization. Normalized sub-indices are then aggregated into a multi-pollutant combined metric using the Root Mean Square (RMS) approach, and seasonal difference surfaces (Δ) are calculated. In the third stage, SUI is classified into eight classes and class-based RMS statistics are computed using ArcMap Zonal Statistics. The association between SUI and the summer, winter, and Δ multi-pollutant indices was evaluated using Spearman's rank correlation coefficient (ρ), while Pearson's correlation coefficient (r) was reported as part of a sensitivity analysis.Results indicate that tourism-transport pressure concentrates along coastal zones and corridor-like structures, whereas pollution surfaces exhibit province-specific seasonal patterns. RMS maps improve comparability by synthesizing pollutant-specific and sometimes opposing seasonal behaviours into a single metric. Zonal statistics and correlation outcomes suggest that the SUI-pollution relationship is generally weak and context dependent, with seasonal change (Δ) providing a clearer signal in some provinces. Overall, the thesis proposes a GIS-based framework that jointly addresses multi-pollutant effects and seasonality, enabling province-level comparison and more generalizable interpretation of tourism-transport pressure and air pollution dynamics