Precision measurement for effective pollution mitigation by evaluating air quality monitoring systems in linguistic Pythagorean fuzzy dombi environment

Air quality is a major concern for human health, with pollutants linked to respiratory problems and chronic illnesses. Air quality monitoring systems are essential for measuring and tracking pollutants in indoor and outdoor environments. In the various disciplines of fuzzy environments, the aggregation operators are indispensable components of the decision-making process and possess a significant capacity to manage unpredictable and ambiguous data. This study utilizes the linguistic Pythagorean fuzzy set to address the aforementioned environmental scenarios, which improve comprehension of air quality through the application of AOs. This work introduces two new aggregation operators: the linguistic Pythagorean fuzzy Dombi ordered weighted averaging (LPFDOWA) and the language Pythagorean fuzzy Dombi ordered weighted geometric (LPFDOWG), and examines their structural properties. Furthermore, we develop a novel scoring function for multiple attribute decision-making (MADM) issues within the context of linguistic Pythagorean fuzzy knowledge. We provide a systematic mathematical procedure to address MADM issues within the context of the linguistic Pythagorean fuzzy Dombi framework. Furthermore, we effectively employ these approaches to address the MADM issue of selecting an efficient Air quality monitoring systems for air pollution monitoring. Additionally, we present a thorough comparative analysis to demonstrate the effectiveness of the proposed methodology relative to conventional techniques.