A cocktail of land-based sources of pollution threatens coral reef ecosystems, and addressing these has become a key management and policy challenge in the State of Hawaiʻi, other US territories, and globally. In West Maui, Hawaiʻi, nearly one quarter of all living corals were lost between 1995 and 2008. Onsite disposal systems (OSDS) for sewage leak contaminants into drinking water sources and nearshore waters. In recognition of this risk, the Hawaiʻi State Department of Health (DOH) is prioritizing areas for cesspool upgrades. Independently, we applied a decision analysis process to identify priority areas to address sewage pollution from OSDS in West Maui, with the objective of reducing nearshore coral reef exposure to pollution. The decision science approach is relevant to a broader context of coastal areas both statewide and worldwide which are struggling with identifying pollution mitigation actions on limited budgets.

Different causes lead to accidental oil spills from fixed and mobile sources in the marine environment. Therefore, it is essential to have a systematic plan for mitigating oil spill consequences. In this research, a general DSS is proposed for passive and active response planning in Persian Gulf, before and after a spill. The DSS is based on NOAA's advanced oil spill model (GNOME), which is now linked with credible met-ocean datasets of CMEMS and ECMWF. The developed open-source tool converts the results of the Lagrangian oil spill model to quantitative parameters such as mean concentration and time of impact of oil. Using them, two new parameters, emergency response priority number (ERPN) and risk index (RI), are defined and used for response planning. The tool was tested in both deterministic and probabilistic modes, and found to be useful for evaluation of emergency response drills and risk-based prioritization of coastal areas.

The ballast water management convention incorporates principles of risk assessment. A new ballast water management risk assessment model was developed to support the implementation of most efficient management measures, which we also present as a flowchart decision support system model. The risk assessment model was tested using data from port baseline surveys where available, and real shipping and ballast water discharges data. The here presented ballast water management risk assessment and possible management options are applicable elsewhere to support and improve complex decision making in the implementation of management requirements according to the ballast water management convention. If needed, the model may easy be adapted to address local specifics in any other region or area.

The air quality is an increasingly concerning topic. Efficient air quality monitoring system is highly required in urban areas due to the existing different types of air pollution sources. Consequently, air pollution profiles of the monitored region should be regularly evaluated to infer the efficiency of the monitoring system. This study applied principal components analysis (PCA) to air quality data from Porto (Portugal), aiming to characterize the spatial distribution of NO₂, O₃ and PM₁₀ concentration profiles. In the analysed period (2006 and 2013), some of the selected monitoring sites have been closed and others were moved to other locations. PCA allowed the evaluation of these changes in geographical distribution of the monitoring sites and the definition of proposals to improve the efficiency of air quality monitoring system. The performed analysis concluded that these last changes were correctly performed, but the reduction of the monitoring sites for NO₂ and O₃is still possible, being optimized the current number for PM₁₀. In addition, the application of computational model HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) allowed analysing the air pollution transport over long distances in high pollutant concentration episodes, having found that the variation of NO₂ and O₃ concentrations may be strongly influenced by air masses from the north of Spain. For PM₁₀, there are two different air mass trajectories: the transport of air masses from the Galicia region and from North Africa.

New optical tools are proposed for the real-time diagnosis of water quality without traditional sampling and laboratory physico-chemical analysis. In particular, the optical decision-making system is developed as an operational tool for in-situ assessment of water quality in natural water areas. Specifically, three versions of this system are analyzed using 8-channel and 35-channel photometric devices and a 128-channel spectroelipsometric device. The spectral images obtained from these devices are the basis for the diagnosis of water quality by using new algorithms to detect these spectral images. In more detail, four algorithms are developed to identify optical spectral images of water objects. The effectiveness of these optical tools is demonstrated in experimental water quality control sites in water systems operating under different climatic conditions. Finally, the results for the determination of the various pollutants at the surface of these water bodies are presented.

Integrated hydrodynamic modelling is an efficient approach for making semi-quantitative scenarios reliable enough for groundwater management, provided that the numerical simulations are from a validated model. The model set-up, however, involves many inputs due to the complexity of both the hydrological system and the land use. The case study of a Mediterranean alluvial unconfined aquifer in the lower Var valley (Southern France) is useful to test a method to estimate lacking data on water abstraction by small farms in urban context. With this estimation of the undocumented pumping volumes, and after calibration of the exchange parameters of the stream-aquifer system with the help of a river model, the groundwater flow model shows a high goodness of fit with the measured potentiometric levels. The consistency between simulated results and real behaviour of the system, with regard to the observed effects of lowering weirs and previously published hydrochemistry data, confirms reliability of the groundwater flow model. On the other hand, accuracy of the transport model output may be influenced by many parameters, many of which are not derived from field measurements. In this case study, for which river-aquifer feeding is the main control, the partition coefficient between direct recharge and runoff does not show a significant effect on the transport model output, and therefore, uncertainty of the hydrological terms such as evapotranspiration and runoff is not a first-rank issue to the pollution propagation. The simulation of pollution scenarios with the model returns expected pessimistic outputs, with regard to hazard management. The model is now ready to be used in a decision support system by the local water supply managers.

Watershed vulnerability and the characterization of potential risk are important inputs for decision support tools in assessing watershed health. Most previous studies have focused on the assessment of the environmental risk using physicochemical properties of surface water and mathematical models to predict the health of a watershed. Here, we present a new methodology for evaluating watershed vulnerability using the analytic hierarchy process (AHP) and weighted overlay analysis. The new methodology provides an inexpensive approach for assessing areas that need more investigation based on known factors such hydrogeological, geological, and climate parameters without the need for site-specific physicochemical data. The proposed method was implemented using six main factors that influence water quality: land use, soil type, precipitation, slope, depth to groundwater, and bedrock type. Vulnerability was predicted for ten sub-watersheds within the Eagle Creek Watershed in Indiana using publicly available data input into geographic information system. Combination of watershed susceptibility assessment and GIS spatial analysis tools was used to produce the maps that show the susceptible zones within a watershed. A comparison of the resulting vulnerability estimates showed the expected significant positive correlations with measurements of nitrate, phosphate, temperature, and electrical conductivity. Likewise, the vulnerability estimates negatively correlated with dissolved oxygen and E. coli. Furthermore, the validation of the proposed approach revealed that the areas predicted to have high vulnerability did have lower water quality indices; the results showed a high negative correlation (r² = 0.77, p < 0.05) between water quality index (WQI) and vulnerability which strongly suggests this method can be used successfully to assess a watershed’s susceptibility.

Over the last few years, intensive agriculture has often been denounced as a source of negative effects, particularly at the environmental and health level (overexploitation of natural resources, degradation of their quality, appearance and development of several diseases, etc.). Reducing the excessive use of agricultural inputs for the protection of the environment and the preservation of human and animal health is a social requirement nowadays. Investing in more sustainable agricultural models which make it possible to reduce, or even eliminate the risks, has become urgent. A possible solution may be to resort to agroecological systems. In order to be sustainable, these new systems must be performant at the agronomic, economic, social, and environmental levels. There is a multitude of tools for assessing the sustainability of agricultural systems. These tools are inappropriate for organic and agroecological systems, and do not make it possible to measure the agroecological transition performance of farms (Trabelsi et al. Environ Sci Pollut Res 23:139–156, 2016; Trabelsi 2017). This research project aims to design a decision support tool in order to help farms throughout the agroecological transition process, to assess the performance of this transition, and to put forward improvement scenarios. Contrary to other assessment methods, ESSIMAGE (Evaluation and Simulation of Agroecological Systems) is based on both pressure and impact indicators, and takes the specificities of agricultural production systems into account. It is a dynamic tool which not only makes it possible to assess farm performance at the present moment but also to consider the future by putting forward possible alternative improvement scenarios and by simulating their consequences at a later stage. ESSIMAGE is based on the interaction of two elements: agro-environmental, social, and economic indicators, and the GIS (Geographic Information System) software. This tool has been tested as part of a CASDAR “Post-MAET Gimone” (agriculture.gouv.fr/ministere/mobilisation-collective-pour-lagroecologie.) project on the subject of “Collective mobilization for agroecology” by using farm data, most of the farms having been involved in an agro-environmental measure for the progressive reduction of phytosanitary treatments since 2008. It has made it possible to compare the agroecological performances of these farms with an optimal situation, as well as with each other. Considering the research objectives and the approaches discussed, this study is an original step in the development of agricultural management strategies in favor of agroecology.

The main aim of this study was to develop a decision support system for sustainable municipal solid waste management (MSWM) in Hoi An city (HAC), Vietnam. A face-to-face interview was conducted with local experts including authorities, citizens, waste collection contractors and recyclers to identify main objectives of waste management system and appropriate treatment methods. A multi-objective optimization model was proposed using non-linear programming approach. An interactive method known as reference point method (RPM) was applied for solving the problem with three objectives including cost minimization, landfill minimization and emission minimization. As a result, the efficient waste-flow-allocation and the optimal capacity of disposal facilities were determined by intense discussion and agreement among decision-makers. Waste incineration, anaerobic digestion, sanitary landfill and current recycling activities should be applied with a cost of about 2300 US$ daily. Also, the daily emission of various pollutants was about 35 metric tons, and the greenhouse gas (GHG) is 313 tons CO₂₋ₑq.

At the global scale, urban agriculture is increasingly developing in cities due to demographic growth and sustainable food concerns. But, urban soils are frequently polluted with metals. In urban gardens, organic matter is also commonly added both to valorize organic household waste and to promote biophysicochemical fertility. As earthworms promote the decomposition and the recycling of soil organic matter, they can also influence the biogeochemical cycle of metals in urban polluted soils. In order to produce safe vegetables in urban areas, it is crucial to highlight the mechanisms involved in complex soil-earthworm-plant ecosystems. An experiment was set up to examine these relationships using lettuce cultivated in controlled conditions with RHIZOtest® devices. Thanks to the RHIZOtest® devices, metal transfer and bioaccessibility were for the first time compared for urban polluted soil without (1—urban soil polluted with Pb, Cd, Cu, and Zn: essential or toxic metals currently found in environment, SNB) and with bioturbation (2—this metal-polluted soil subjected to earthworm bioturbation, SB) and earthworm casts (3—earthworm casts produced in this polluted soil and naturally enriched in organic matter and microorganisms, T). Metal concentration, phytoavailability, and human gastric bioaccessibility were determined in the different samples. Results showed that earthworm bioturbation increased the phytoavailability of all the metals. For the experimental condition SB, the phytoavailability of metals was increased up to 75% compared to SNB. In addition, surprisingly, metal phytoavailability was always superior in SB compared to earthworm casts (T). Moreover, earthworms led to an increase in Zn gastric bioaccessibility up to 10% in the soils in the same way as for phytoavailability, meaning Zn bioaccessibility in SB > T > SNB, whereas it remained unchanged in the lettuces. These data are important to promote sustainable agriculture activities in urban areas; actually, databases concerning different experimental conditions are needed to develop decision support tools.