Assessment of living quality in Guangdong: A hybrid knowledge-based and data-driven approach

Abstract

Air, water, and radiation in urban living environments are crucial factors affecting human health, societal well-being, and sustainable development. This study developed a novel hybrid knowledge-based and data-driven approach for analyzing air, water, and radiation monitoring data to assess the quality of urban living natural environments (ULNEQ) in Guangdong Province. Fuzzy set-pair analysis was employed for data preprocessing, effectively incorporating domain knowledge into the raw data. Then, a hierarchical clustering algorithm was utilized to evaluate the ULNEQ level. The proposed approach enhanced data interpretability and optimized the clustering process, yielding more robust and reliable analytical results. The results revealed a diverse environmental landscape across the province, with Heyuan and Meizhou consistently maintaining high standards, whereas Dongguan and Jieyang exhibited notably poor conditions, ranking at Level IV or V for over 72% of the observed months. Key factors such as monthly average concentration ozone (0.56), the city water quality index (0.49), and the proportion of days with standard air quality (PDSAQ) (−0.39) significantly influenced the ULNEQ, with PDSAQ showing a negative correlation. Notably, a province-wide observation in June 2022 showed all cities maintaining ULEQ gradings of Level II or better. This innovative approach can be adapted to enhance the management and control of ULNEQ.