In recent decades, maritime transport demand has increased along with world population and global trades. This is associated with higher pollution levels, including the emissions of GHG and other polluting gases. Ports are important elements within maritime transport and contribute themselves to pollutant emissions. This paper aims to offer a comprehensive yet technical review of the latest related technologies, explaining and covering aspects that link ports with emissions, i.e., analyzing, monitoring, assessing, and mitigating emissions in ports. This has been achieved through a robust scientific analysis of very recent and significant research studies, to offer an up-to-date and reliable overview. Results show the correlation between emissions and port infrastructures, and demonstrate how proper interventions can help with reducing pollutant emissions and financial costs as well, in ports and for maritime transportation in general. Besides, this review also wishes to propose new ideas for future research: new future experimental studies might spin-off from it, and perhaps port Authorities might be inspired to experiment and implement dedicated technologies to improve their impact on environment and sustainability.

Due to urbanization, large portions of vegetated territory have been replaced by waterproof surfaces. The consequences are greater outflows, lower infiltration, and lower evapotranspiration. Pavement systems made with permeable surfaces allow the infiltration of water, ensuring reduction of runoff volume. In this paper, the methods of analysis of the hydrological and environmental performance of the pavement systems are reviewed in the context of urban drainage and regarding their durability. The purpose is to present an overview of the studies published during the last decade in the field. The Pubmed and Web Science Core Collection electronic databases were used to conduct the scientific literature survey. This generated 1238 papers, of which only 17 met the criteria and were included and discussed in this review. The evidence drawn from the knowledge on which the document is based provides useful critical interpretations of existing studies to progress the current understanding on hydrological performance and environment impacts in terms of conventional pollutant removal efficiency and the current permeable pavement systems.

In the last decade, the growth of the micro-industry in urban areas has produced an increase in the frequency of xenobiotic polluting discharges in drainage systems. Wastewater treatment plants are usually characterized by low removal efficiencies in respect of such pollutants, which may have an acute or cumulative impact on environmental and public health. To facilitate the early isolation of illicit intrusions, this study aims to develop an approach for positioning water quality sensors based on the Bayesian decision network (BDN). The analysis is focused on soluble conservative pollutants, such as metals. The proposed methodology incorporates several sources of information, including network topology, flows and non-formal ‘grey’ information about the possible locations of contamination sources. The methodology is tested using two sewer systems with increasing complexity: a literature scheme from the Storm Water Management Model (SWMM) manual and a real combined sewer in Italy. In both cases, the approach identifies the optimal sensor location gaining advantage from additional information, which reduces the computational effort needed to obtain the solution. In the real case, the application of the method yielded a better solution with regards to the real position of the implemented sensor network.

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal ‘grey’ information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

In integrated urban water systems, energy consumption, and consequently the amount of produced CO₂, depends on many environmental, infrastructural, and management factors such as supply water quality, on which treatment complexity depends, urban area orography, water systems efficiency, and maintenance levels. An important factor is related to the presence of significant water losses, which result in an increase in the supply volume and therefore a higher energy consumption for treatment and pumping, without effectively supplying users. The current European environmental strategy is committed to sustainable development by generating action plans to improve the environmental performance of products and services. The analysis of carbon footprints is considered one such improvement, allowing for the evaluation of the environmental impact of single production phases. Using this framework, the aim of the study is to apply a Life Cycle Assessment (LCA) methodology to quantify the carbon footprint of an overall integrated urban water system referring to ISO/TS 14067 (2013). This methodology uses an approach known as “cradle to grave” and presumes to conduct an objective assessment of product units, balancing energy, and matter flows along the production process. The methodology was applied to a real case study, i.e., the integrated urban water system of the Palermo metropolitan area in Sicily (Italy). Each process in the system was characterized and globally evaluated from the point of view of water loss, energy consumption, and CO₂ production, and some mitigation strategies are proposed and evaluated to reduce the energy consumption and, consequently, the environmental impact of the system.

In recent years, there has been a need to seek adequate preventive measures to deal with contamination in water distribution networks that may be related to the accidental contamination and the deliberate injection of toxic agents. Therefore, it is very important to create a sensor system that detects contamination events in real time, maintains the reliability and efficiency of measurements, and limits the cost of the instrumentation. To this aim, two problems have to be faced: practical difficulties connected to the experimental verification of the optimal sensor configuration efficiency on real operating systems and challenges related to the reliability of the network modelling approaches, which usually neglect the dispersion and diffusion phenomena. The present study applies a numerical optimization approach using the NSGA-II genetic algorithm that was coupled with a new diffusive-dispersive hydraulic simulator. The results are compared with those of an experimental campaign on a laboratory network (Enna, Italy) equipped with a real-time water quality monitoring system and those of a full-scale real distribution network (Zandvoort, Netherlands). The results showed the importance of diffusive processes when flow velocity in the network is low. Neglecting diffusion can negatively influence the water quality sensor positioning, leading to inefficient monitoring networks.