The effects of marine litter pose ecological, economic and optional value threats to the countries and territories on the margins of the Atlantic Basin. As the abundance of transboundary marine litter increases, this in turn has triggered the development of marine litter policy action plans and inter-regional collaboration. The OSPAR Commission and Cartagena Convention of North-East Atlantic (NEA) and the Wider Caribbean Region (WCR) respectively, have established a memorandum of understanding to facilitate collaboration for enhanced marine litter management. In light of this development, this paper conducts a scoping review of management measures of marine litter within these regions as well as a coherence analysis between their regional action plans (RAPs). The analysis uses a proactive framework based on a modified categorization by Chen (2015) and Williams and Rangel-Buitrago (2019) for comparatively analyse management measures. The analytical categorizations include legislation and enforcement measures, prevention, removal, monitoring, research, information management and exchange and education and outreach. Although specific approaches differed among the regions depending upon geographical and individual situations, each categorization included detailed management activities except for prevention in NEA and removal in the WCR. The comparative analysis demonstrates that there are opportunities for inter-regional cooperation, knowledge sharing and overall enhanced and informed marine litter management.

To expand the utility of the Mussel Watch Program, local, regional and state agencies in California partnered with NOAA to design a pilot study that targeted contaminants of emerging concern (CECs). Native mussels (Mytilus spp.) from 68 stations, stratified by land use and discharge scenario, were collected in 2009–10 and analyzed for 167 individual pharmaceuticals, industrial and commercial chemicals and current use pesticides. Passive sampling devices (PSDs) and caged Mytilus were co-deployed to expand the list of CECs, and to assess the ability of PSDs to mimic bioaccumulation by Mytilus. A performance-based quality assurance/quality control (QA/QC) approach was developed to ensure a high degree of data quality, consistency and comparability. Data management and analysis were streamlined and standardized using automated software tools. This pioneering study will help shape future monitoring efforts in California’s coastal ecosystems, while serving as a model for monitoring CECs within the region and across the nation.

‘Collect once, use often’ is a frequently cited principle in both national and international efforts to promote the collection, archiving and sharing of marine monitoring data. Since the implementation of the Marine Conservation Zone (MCZ) evidence collection programme, 67 recommended MCZ sites have been visited and a suite of marine data collected. Here we present how this dataset was utilised outside of the MCZ programme to identify occurrences of non-indigenous species (NIS) around the UK coast. One hundred and thirty-five aquatic species from the Non-native Species Information Portal (NNSIP) register were used to produce a standard list of NIS against which, infauna and epifaunal data records from the MCZ project were compared. A total of 20 NIS were identified across 42 of the 67 sites surveyed. This study demonstrates that with sufficient coordination and management data collected for other purposes can be easily utilised to address additional policy requirements.

Construction 4.0 has become a buzzword since the penetration of building information modeling (BIM), cyber-physical systems, and digital and computing technologies into the construction industry. Among emerging technologies, distributed ledger technology (DLT), or blockchain, is a powerful business enhancer whose potential can disrupt projects, AEC (architecture, engineering, and construction) firms, and construction supply chain, and in a broader sense, the whole construction industry. This technology has not reached the plateau of productivity due to several barriers and challenges. Previous studies have started to investigate the barriers to implementing DLT in various sectors and segmentations. However, we still need further surveys in the construction industry. This study evaluates the applicability of identified challenges and barriers based on a sustainability perspective. Precisely, we will answer which challenges need to be addressed for the sustainability of the construction industry. To meet the research objective, the ordinal priority approach (OPA) in multiple attributes decision-making (MADM) was utilized. This novel method determines the weight of sustainability attributes and barriers simultaneously. The results show that DLT implementation needs (i) infrastructure for data management, (ii) advanced applications and archetypes, and (iii) customers’ demand, interest, and tendency, and (iv) taxation and reporting. Solving high-ranked challenges is the key to social sustainability from the aspects of “supply chain management and procurement”; “transparency, anti-corruption, and anti-counterfeiting”; and “fair operation and honest competition.”

A capability for aggregating risks to aquifers is explored in this paper for cases with sparse data exposed to anthropogenic and geogenic contaminants driven by poor/non-existent planning/regulation practices. The capability seeks ‘Total Information Management’ (TIM) under sparse data by studying hydrogeochemical processes, which is in contrast to Human Health Risk Assessment (HHRA) by the USEPA for using sample data and a procedure with prescribed parameters without deriving their values from site data. The methodology for TIM pools together the following five dimensions: (i) a perceptual model to collect existing knowledge-base; (ii) a conceptual model to analyse a sample of ion-concentrations to determine groundwater type, origin, and dominant processes (e.g. statistical, graphical, multivariate analysis and geological survey); (iii) risk cells to contextualise contaminants, where the paper considers nitrate, arsenic, iron and lead occurring more than three times their permissible values; (iv) ‘soft modelling’ to firm up information by learning from convergences and/or divergences within the conceptual model; and (v) study the processes within each risk cell through the OSPRC framework (Origins, Sources, Pathways, Receptors and Consequence). The study area comprises a series of patchy aquifers but HHRA ignores such contextual data and provides some evidence on both carcinogenic and non-carcinogenic risks to human health. The TIM capability provides a greater insight for the processes to unacceptable risks from minor ions of anthropogenic nitrate pollutions and from trace ions of arsenic, iron and lead contaminants.

The rapid increase in volume and types of hazardous waste (HW) due to China’s continuous economic growth makes it significant to conduct HW safety management. However, due to the information asymmetry between the regulators and regulated institutions, problems such as illegal dumping and statistical fraud are common in hazardous waste transfer (HWT) activities. Moreover, there is a severe problem of information isolated island among waste production, transportation, and treatment companies in the HW market. Promoting the information monitoring and tracking of the whole process of HWT is the main challenge for HW management. Blockchain provides an ideal solution to overcome the above challenges. This study developed a framework for the blockchain-based HWT management system, which could support government regulators to obtain relevant information in HWT and improve the efficiency of HWT. The HWT management system was introduced in detail from different perspectives, including infrastructure, blockchain service, functions, and users, emphasizing six information management functions. Finally, the positive impact and various challenges encountered in implementing blockchain in HWT management were discussed. This study applies blockchain to the research field of HWT management, which expands the application of blockchain in the field of solid waste management with great theoretical and practical significance.

Prenatal exposure to ambient air pollution has been associated with adverse perinatal outcomes in previous studies. However, few studies have examined the interaction between air pollution and the season of conception on term low birth weight (TLBW) or macrosomia. Birth registry data of singleton live births in Wenzhou, China, between January 2015 and December 2016 were accessed from the Wenzhou Maternal and Child Health Information Management platform, and data on the ambient air pollutants in Wenzhou were obtained from the Chinese Air Quality Online Monitoring and Analysis Platform. Single-/two-pollutant binary logistic regression models were used to assess the associations between ambient air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, and O₃) and TLBW/macrosomia, further exploring whether the season of conception interacts with air pollution to impact birth weight. Finally, 213,959 term newborns were selected, including 2452 (1.1%) infants with TLBW and 13,173 (6.1%) infants with macrosomia. In the single-/two-pollutant models, we observed an increased risk of TLBW associated with maternal exposure to PM₂.₅, PM₁₀, SO₂, and NO₂ during the entire pregnancy, especially in the 2nd trimester. Maternal exposure to O₃ during the 1st trimester was associated with increased macrosomia risk, and O₃ exposure during the 3rd trimester was associated with increased TLBW risk. Pregnant women who conceive in the warm season may experience a more adverse ambient air environment that is related to the risks of TLBW. These findings add to the evidence suggesting that air pollution and the season of conception may have synergistic effects on adverse perinatal outcomes, especially TLBW. Further prospective cohort studies are needed to validate our results.

This study contributes to identifying a valid and reliable set of barriers to sustainable solid waste management framework rooted in society 5.0 perspectives in Taiwan. The SSWM-related causal interrelationships within the proposed hierarchical structure, and critical barriers for the practical improvement and enhancement of SSWM performance are identified as preference enriching both literature and practices. In nature, the hierarchical structure is with the causal interrelationships under uncertainties. The perspective empowers the creation of a new biosphere based on technological progress, but in the sustainable solid waste management field, it is difficult to encounter and shape the systematized processes due to barriers and challenges. To address this shortcoming, this study evaluates the technical challenges faced in the field of sustainable solid waste management toward society 5.0. The valid attributes are usually described the qualitative information. The fuzzy Delphi method is applied to acquire the valid and reliable attributes. Fuzzy decision-making trial and evaluation laboratory experiment is to visualize the causal interrelationships among the attributes. Choquet integral with respect to the nonadditive attributes over the valid set provides an overall perspective function. The results establish an understanding of sustainable solid waste management barriers in the perspectives under uncertainties. Community uncertainty, policy and regulation problems, city architecture, and technology interaction are the factors that influence sustainable performance. In practices, (1) diverse disciplines and sectors in local, national, and global communities; (2) a lack of mobility and reliability; (3) mass production and mass consumption; (4) an insufficient level of artificial intelligence application; and (5) failures related to data management and security hinder the improvement of sustainable solid waste management toward society 5.0. The social and technical perspectives are indicated as the top priorities to improve SSWM performance.

Background, aim, and scope The need for global and integrated approaches to water resources management, both from the quantitative and the qualitative point of view, has long been recognized. Water quality management is a major issue for sustainable development and a mandatory task with respect to the implementation of the European Water Framework Directive as well as the Swiss legislation. However, data modelling to develop relational databases and subsequent geographic information system (GIS)-based water management instruments are a rather recent and not that widespread trend. The publication of overall guidelines for data modelling along with the EU Water Framework Directive is an important milestone in this area. Improving overall water quality requires better and more easily accessible data, but also the possibility to link data to simulation models. Models are to be used to derive indicators that will in turn support decision-making processes. For this whole chain to become effective at a river basin scale, all its components have to become part of the current daily practice of the local water administration. Any system, tool, or instrument that is not designed to meet, first of all, the fundamental needs of its primary end-users has almost no chance to be successful in the longer term. Materials and methods Although based on a pre-existing water resources management system developed in Switzerland, the methodological approach applied to develop a GIS-based water quality management system adapted to the Romanian context followed a set of well-defined steps: the first and very important step is the assessment of needs (on the basis of a careful analysis of the various activities and missions of the water administration and other relevant stakeholders in water management related issues). On that basis, a conceptual data model (CDM) can be developed, to be later on turned into a physical database. Finally, the specifically requested additional functionalities (i.e. functionalities not provided by classical commercial GIS software), also identified during the assessment of needs, are developed. This methodology was applied, on an experimental basin, in the Ialomita River basin. Results The results obtained from this action-research project consist of a set of tangible elements, among which (1) a conceptual data model adapted to the Romanian specificities regarding water resources management (needs, data availability, etc.), (2) a related spatial relational database (objects and attributes in tables, links, etc.), that can be used to store the data collected, among others, by the water administration, and later on exploited with geographical information systems, (3) a toolbar (in the ESRI environment) offering the requested data processing and visualizing functionalities. Lessons learned from this whole process can be considered as additional, although less tangible, results. Discussion The applied methodology is fairly classical and did not come up with revolutionary results. Actually, the interesting aspects of this work are, on the one hand, and obviously, the fact that it produced tools matching the needs of the local (if not national) water administration (i.e. with a good chance of being effectively used in the day-to-day practice), and, on the other hand, the adaptations and adjustments that were needed both at the staff level and in technical terms. Conclusions This research showed that a GIS-based water management system needs to be backed by some basic data management tools that form the necessary support upon which a GIS can be deployed. The main lesson gained is that technology transfer has to pay much attention to the differences in existing situations and backgrounds in general, and therefore must be able to show much flexibility. The fact that the original objectives could be adapted to meet the real needs of the local end-users is considered as a major aspect in achieving a successful adaptation and development of water resources management tools. Time needed to setup things in real life was probably the most underestimated aspect in this technology transfer process. Recommendations and perspectives The whole material produced (conceptual data model, database and GIS tools) was disseminated among all river basin authorities in Romania on the behalf of the national water administration (ANAR). The fact that further developments, for example, to address water quantity issues more precisely, as envisaged by ANAR, can be seen as an indication that this project succeeded in providing an appropriate input to improve water quality in Romania on the long term.

Adopting methodologies utilizing exogenous data from ancillary stations for determining crop water requirement is a suitable approach to exempt local shortcomings due to the lack of meteorological data/stations. Meanwhile, soft computing techniques might be suitable tools to be used with such data management scenarios. The present paper aimed at evaluating the generalizability of the gene expression programming (GEP) technique for estimating reference evapotranspiration (ET₀) through cross-station assessment and exogenous data supply, using data from Turkey and Iran. The GEP-based models were established and learnt using data from 10 stations in Turkey, and then the developed models were tested (validated) in 18 stations of Iran with considerable latitude differences. Different time periods (beginning and the end of time series) were selected for the training and testing stations so that there was no overlap among the dates of the events in both the groups. A comparison was also performed between the GEP models and the corresponding commonly used empirical equations. The obtained results revealed that the generalized GEP models presented promising outcomes in simulating daily ET₀ values when they were trained and tested in quite distant stations with different chronological periods of the applied parameters. The performance accuracy of the empirical equations calibrated using exogenous data was reduced in comparison with their original (non-calibrated) versions. Further, although the generalization ability of the GEP models was reduced when the climatic context of the training-testing stations was different, the overall performance accuracy of those models was higher than those of the commonly used classic empirical equations.