Prioritization maps: The integration of environmental risks to manage water quality in harbor areas

Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

Water pollution accidents have the characteristics of high uncertainty, rapid evolution and are difficult to control, thus posing great threats to human health, ecological security, and social stability. During the last 10 years, China has faced the occurrence of six extraordinarily serious heavy metal contamination pollution events at the watershed scale. This has alerted governments and enterprises of the significance of emergency decision-making. To quantitatively prioritize risk mitigation strategies for heavy metal emergencies, a Bayesian Decision Network-based probabilistic model is proposed under the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework. A Copula-based exposure risk model is embedded to simulate the fate of heavy metal ions for each risk reduction option, whose joint probability distributions can then be used as input parameters in the Bayesian Decision Network. This method was applied to the emergency response prioritization for acute Cr(VI)-Hg(II) contamination accidents in the Danshui River watershed. The results indicated that comprehensive measure (M5) was the best option for decreasing ecological and human health risks. As for a single risk mitigation strategy, risk source prevention (M1) was the best alternative compared to exposure pathway interruption (M2) and human/ecological receptor protection (M3-M4). This probabilistic method can not only address the uncertainties between certain risk sources and receptors in the BDN structure, but also realize the risk system optimization in a satisfactory/preferred mode under the DPSIR framework. Overall, it provides the probabilistic risk estimates for watershed-scale risk management and policy making for local risk managers and stakeholders.

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr⁶⁺-Hg²⁺ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises.

Environmental Management System for the Analysis of Oil Spill Risk Using Probabilistic Simulations. Application at Tarragona Monobuoy

Oil spill accidents during port operations are one of the main hydrocarbon pollution threats for coastal waters. Appropriate environmental risk assessment and pollution events management tools are needed to achieve sustainability and environmental protection in port activity. Recent developments in monitoring techniques and accurate meteo-oceanographic prediction systems have been implemented in many ports, providing tools for environmental management. A novel method based on meteo-oceanographic operational services, in conjunction with Monte Carlo experiments using an oil spill model, is implemented to perform probabilistic maps of potential pollution events. Tarragona port area was chosen as the study case for three reasons: it accommodates a hub of petrochemical industry, the availability of high-resolution wind and water current data, and previous studies at the area offer the possibility to check the results’ accuracy. The interpretation of the impact probability maps reveals a specific pattern explained by the mean hydrodynamic conditions and the energetic north-westerly wind conditions. The impact probability maps may enhance efficiency in the environmental management of port waters and nearby coastal areas, reducing the negative impact of pollutant discharges.

Mapping the environmental risk assessment of marinas on water quality: The Atlas of the Spanish coast

Recreational sailing sector has an impact on water quality in marinas. This study proposes a standard procedure to assess the environmental risk of marinas on water quality. Risk is assessed through integrating environmental pressures, environmental conditions and societal responses (i.e. the Pressure-State-Response model). Pressures are estimated considering the main driving forces: navigation, port, dredging and external activities. State is estimated through combining the susceptibility, the ecological value and naturalness. Response is estimated through environmental management instruments and adopted measures. Managers and authorities can hierarchically classify marinas from a multi-scale spatial framework. This tool is particularly powerful for generating local, regional or national atlases to prioritize environmental planning actions. The method is applied to 320 marinas along the Spanish coast. This implementation confirms the usefulness, versatility and adaptability of this procedure as a tool for the environmental management of marinas.

Identification and prioritization of areas with high environmental risk in Mediterranean coastal areas: A flexible approach

Interdisciplinarity and transdisciplinarity are the cornerstone for the future management of coastal ecosystems with many vulnerability and hazard indexes developed for this purpose, especially in the engineering literature, but with limited studies that considered ecological implications within a risk assessment. Similarly, the concept of prioritization of sites has been widely examined in biodiversity conservation studies, but only recently as an instrument for territory management. Considering coastal plant diversity at the species and community levels, and their vulnerability to three main potential hazards threatening coastal areas (oil spills, Hazardous and Noxious Substances pollution, fragmentation of natural habitats), the objective of this paper is to define an easy-to-use approach to locate and prioritize the areas more susceptible to those stressors, in order to have a practical instrument for risk management in the ordinary and extra-ordinary management of the coastline. The procedure has been applied at pilot areas in four Mediterranean countries (Italy, France, Lebanon and Tunisia). This approach can provide policy planners, decision makers and local communities an easy-to-use instrument able to facilitate the implementation of the ICZM (Integrated Coastal Zone Management) process in their territory.