Causality Inference for Mitigating Atmospheric Pollution in Green Ports: A Castellò Port Case Study †

Abstract

Green Ports have emerged due to the increase in air pollution from emissions generated by maritime traffic and the dispersion of particles, as well as water pollution from spills. The primary objective of this study is to anticipate episodes of atmospheric pollution related to cargo-handling activities and assess the quantitative causality between these variables. We employ a causality inference based on time series analysis to investigate the applicability and validity of these techniques in a real-world problem setting. Specifically, methods such as the Granger Test and PCMCI are evaluated and compared with these data. The results demonstrate that cargo handling at the port under study has some causal influence on the PM (particulate matter) measurements. Finally, the PCMCI method is proposed as the most robust among the algorithms considered in this study.