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Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.

The consideration of alternative sources of material for construction is imperative to reduce the environmental impacts as two-fifths of the carbon footprint of materials is attributed to the construction industry. One alternative material with improved biodegradable attributes which can contribute to carbon offset is bamboo. The commercialisation of bamboo in modern infrastructures has significant potential to address few of the Sustainable Development Goals (SDGs) itemised by the United Nations, namely SDG 9 about industry, innovation and infrastructure. Other SDGs covering sustainable cities and communities, responsible consumption and production and climate action are also indirectly addressed when utilising sustainable construction materials. Being a natural material however, the full commercialisation of materials such as bamboo is constrained by a lack of durability. Besides fracture mechanisms arising from load-induced cracks and thermal modification, the durability of bamboo material is greatly impaired by biotic and abiotic factors, which equally affect its natural rate of degradation, hence fracture behaviour. In first instance, this chapter outlines the various factors leading to the durability limitations in bamboo material due to load-induced cracks and natural degradation based on recent findings in this field from the author's own work and from past literature. Secondly, part of this chapter is devoted to a new approach of processing the surge of information about the varied aspects of bamboo durability by considering the powerful technique of artificial intelligence (AI), specifically the artificial neural network (ANN) for prediction modelling. Further use of AI-enabled technologies could have an impactful outcome on the life cycle assessment of bamboo-based structures to address the growing challenges outlined by the United Nations.

This chapter proposes a methodology to develop a tool aimed at helping tourists moving sustainably in Rome, focusing on the “last mile” of their transport experience, that is, walking trips. The methodology consists of the development of a stated preference survey, where tourists’ preferences are elicited with respect to alternative configurations of walking paths. This is performed by taking into consideration path accessibility, interference with other modes of transport, and thermal comfort aspects. Besides, georeferenced data are collected and systematized with the overall aim to create a geographical information system of the first municipality of Rome with useful information to evaluate the status of the walking network. The results of the analysis help to understand the relevant factors affecting tourists’ walking behavior. Additionally, the chapter provides the preliminary considerations needed for the definition of a “tourist walking satisfaction indicator” related to their walking experience with two aims: first, it provides useful information for policy-makers on how to design and manage walking networks; second, it provides a framework for a tourist traveler information system (a “StreetsAdvisor”) that can guide them in the city on the base of their heterogeneous preferences.

Ports and port cities play a pivotal role toward the sustainable development of coastal ecosystems. These ecosystems provide their natural capital by offering favorable locations for industry and accessibility to world markets. While port industrial activities have been reactive to pressure from stakeholders, in more recent years ports have adapted inclusive strategies and seek to align their strategic intentions with stakeholders. Around the world, port authorities are aligning their ambitions toward their contribution to the sustainable development goals (SDGs), such as the World Port Sustainability Program (WPSP) for port authorities and AIVP2030 for port cities. The aim of this chapter is to assess to what extent ports have contributed to the implementation of the SDGs. The analysis is based on a content analysis on a portfolio of 212 projects in which port authorities demonstrate leadership in sustainable development. The results indicate that the contributions of port and port city authorities are generally motivated to “do no harm.” Port authorities also have “do good” intentions for their ecosystems, which are mainly focused on reenforcing connections with communities and less to restore their impact on the biosphere. Furthermore, the findings show that linkages between WPSP projects with the SDGs are rather ambiguous. Directions are given toward a methodology for port authorities (PAs) to establish a stronger link between (monitoring) business strategies with the implementation of inclusive port development strategies to prevent using SDG reporting for greenwashing purposes.