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

  1. Martínez, Rosa 1
  2. Sanz-González, Juan Carlos 1
  3. Felis, Ivan 1
  4. Madrid, Eduardo 1
  1. 1 Centro Tecnológico Naval y del Mar
10th International Electronic Conference on Sensors and Applications (ECSA-10)

Datum der Publikation: 2023

Art: Konferenz-Beitrag

DOI: 10.3390/ECSA-10-16159 GOOGLE SCHOLAR lock_openOpen Access editor

Objetivos de desarrollo sostenible


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.

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