Monitoring of air emissions in maritime ports

Cammin, Philip; Yu, Jingjing; Heilig, Leonard; Voß, Stefan

doi:10.1016/j.trd.2020.102479

Cited by 32 publications

(12 citation statements)

References 26 publications

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“…Despite the previous concentration on emission inventory techniques, little research has been conducted on their implementation in information systems. Data confidentiality and ineffective information systems are important roadblocks that obstruct the generation of high-quality emission inventories and contribute to expenditures, thus appropriate information systems are needed to assist the establishment of high-quality emission inventories [25]. Information systems have become critical to ports' competitiveness, improving communication and decision-making in order to improve visibility, efficiency, dependability, and security in port operations under a variety of scenarios [26].…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

et al. 2023

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Maritime ports play a pivotal role in fostering the growth of domestic and international trade and economies. As ports continue to expand in size and capacity, the impact of their operations on air quality and climate change becomes increasingly significant. While nearby regions may experience economic benefits, there are significant concerns regarding the emission of atmospheric pollutants, which have adverse effects on both human health and climate change. Predictive modeling of port emissions can serve as a valuable tool in identifying areas of concern, evaluating the effectiveness of emission reduction strategies, and promoting sustainable development within ports. The primary objective of this research is to utilize machine learning frameworks to estimate the emissions of SO2 from ships during various port activities, including hoteling, maneuvering, and cruising. By employing these models, we aim to gain insights into the emission patterns and explore strategies to mitigate their impact. Through our analysis, we have identified the most effective models for estimating SO2 emissions. The AutoML TPOT framework emerges as the top-performing model, followed by Non-Linear Regression with interaction effects. On the other hand, Linear Regression exhibited the lowest performance among the models evaluated. By employing these advanced machine learning techniques, we aim to contribute to the body of knowledge surrounding port emissions and foster sustainable practices within the maritime industry.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

et al. 2023

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“…More and more automation and integration of data sources are required, which support decision-making in green or smart ports [28]. The intelligent use of data can generate a different business logic with novel economic models to improve planning and control of productivity management [29].…”

Section: Monitoring Technologiesmentioning

confidence: 99%

Influence of Strategic Interrelationships and Decision-Making in Chilean Port Networks on Their Degree of Sustainability

et al. 2021

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An extensive literary review is carried out to determine the strategic and business advantages, and difficulties that non-smart ports must face to develop sustainability. Based on a two-port case study, the strategic text of the corporate missions of port administrators and operators is analyzed and classified in order to understand to what extend economic, social and environmental aspects are fulfilled. A conceptual model is designed for an information system based on indicators that can determine the state or degree of sustainability in the critical operational activities of the ports studied. A system is proposed that is based on a data warehouse core and a multidimensional database, which can be implemented in the ROLAP mode, allowing taking advantage of the good characteristics of relational databases without losing the OLAP approach. A discussion of the strategic feasibility of implementing this conceptual model of case study monitoring and its long-term benefits is delivered. The conclusions state that the development of this green technology requires cultural changes, public policy initiatives and the incorporation of new actors. In addition, more research is needed in this area to identify other relevant sustainable variables.

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“…For this reason, port authorities have developed plans and programs to reduce atmospheric emissions. Programs have been created to monitor and form inventories of atmospheric emissions from the perspective of information systems (e.g., the clean trucks program, emission control systems technology demonstrations, the vessel speed reduction program, the idling reduction program, expansion of shore side electricity, and the air emissions inventor in several ports: Antwerp, Belgium; Bremerhaven, Germany; Dalian, China; Livorno, Italy; Long Beach, USA; Stockholm, Sweden; Vancouver, Canada) [55]. Along this line of research are the works developed by Lee et al [56], Zhu et al [57], and Wang et al [58].…”

Section: Clean Energies In Port Environmentsmentioning

confidence: 99%

How to Measure Environmental Performance in Ports

et al. 2021

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Oceans, seas, and marine resources are highly relevant for achieving the Sustainable Development Goals. Such relevance has given rise to the blue economy approach, where scholars and policymakers see activities carried out in cargo ports from a different perspective. The blue economy approach stresses the emergence of multiple transnational networks in relation to these topics and the development of green ports plus environmental measurements at seaports in general. In this context, our study aimed to review the various scientifically documented methods for measuring environmental performance in ports, ports companies, or port authorities in the maritime transport and inland waterways framework. The study followed a scientometric meta-analytic methodology to accomplish its goals. The study strictly referred to the Environmental Performance in Ports (EPP) and extracted the corpus to analyze data held in five databases embedded in the Web of Science Core Collection. Then, the selection was processed and refined with the PRISMA guidelines to establish the eligibility criteria for articles with the PICOS (Population, Interventions, Comparators, Outcomes, and Study) tool. A limited study set was identified. This included port environmental performance indicators and studies that were strongly influenced by the European Sea Ports Organization and Green Marine networks. These were compared based on the ecological Sustainable Development Goals (SDGs), scale structure, and diversity in application. Finally, we assessed two measurement forms documented in the scientific research on the subject at a global level and discussed their pros and cons.

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Monitoring of air emissions in maritime ports

Cited by 32 publications

References 26 publications

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning

Influence of Strategic Interrelationships and Decision-Making in Chilean Port Networks on Their Degree of Sustainability

How to Measure Environmental Performance in Ports

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