Abstract
Abstract The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively. This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.