Assessing Environmental Pollution: Quality Control Using CRITIC-WASPAS Methods under Intuitionistic Fuzzy Z-Numbers

Abstract

 Industrial pollution is still a major worldwide issue. Strong
decision-making frameworks are required to evaluate and reduce its detri
mental effects on the environment. An extensive case study assessing dif
ferent industrial pollution produced by the energy industry, textile indus
try, chemical industry, manufacturing industry, agriculture industry, and
construction industry and its environmental impact is presented in this pa
per. We present a structured evaluation of environmental degradation by
analyzing key environmental criteria, including waste generation, noise
pollution, soil contamination, air pollution, and water pollution. In or
der to accomplish a systematic and objective evaluation, we provide an
advanced multi-criteria decision-making (MCDM) framework that com
bines the CRITIC-WASPAS and CRITIC-EDAS approaches with intu
itionistic fuzzy Z-number (IFZN). This integration ensures a more reli
able, flexible, and data-driven assessment of industrial pollution sources
by addressing uncertainty, imprecision, and inconsistency in the data ef
fectively. In order to determine the objective weights of environmental
criteria , the CRITIC (Criteria Importance Through Intercriteria Correla
tion) method is applied here. These weights undergo additional processing
using the WASPAS and EDAS approaches, which combine aggregated
sum product weighting (WASPAS) and positive and negative distance
based evaluation (EDAS) to check the ranking of given criteria. Addition
ally, sensitivity analysis is used to assess the robustness and dependabil
ity of the suggested fuzzy model by altering input parameters, including
weights, membership values, and decision-maker preferences. Moreover, a comparison with earlier approaches is carried out to evaluate the consis
tency and accuracy of the proposed model’s ranking.