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

Activation of iron oxide minerals in an aquifer by humic acid to promote adsorption of organic molecules

In aquifers, the sequestration and transformation of organic carbon are closely associated with soil iron oxides and can facilitate the release of iron ions from iron oxide minerals. There is a strong interaction between dissolved organic matter (DOM) and iron oxide minerals in aquifers, but the extent to which iron is activated by DOM exposure to active iron minerals in natural aquifers, the microscopic distribution of minerals on the surface, and the mechanisms involved in DOM molecular transformation are currently unclear. This study investigated the nonbiological reduction transformation and coupled adsorption of iron oxide minerals in aquifers containing DOM from both macro- and micro perspectives. The results of macroscopic dynamics experiments indicate that DOM can mediate soluble iron release during the reduction of iron oxide minerals, that pH strongly affects DOM removal, and that DOM is more efficiently degraded at low rather than high pH values, suggesting that a low pH is conducive to DOM adsorption and oxidation. Spherical aberration-corrected scanning transmission electron microscopy (SACTS) indicates that the reacted mineral surfaces are covered with large amounts of carbon and that dynamic agglomeration of iron, carbon, and oxygen occurs. At the nanoscale, three forms of DOM are found in the mineral surface agglomerates (on the surfaces, inside the surface agglomerates, and in the polymer pores). The microscopic organic carbon and iron mineral reaction patterns can form through oxidation reactions and selective adsorption effects. Fourier transform ion cyclotron resonance mass spectra indicate that both synergistic and antagonistic reactions occur between DOM and the minerals, that the release of iron is accompanied by DOM decomposition and humification, that large oxygen- and carbon-containing molecules are broken down into smaller oxygen- and carbon-containing compounds and that more molecules are produced through oxidation under acidic rather than alkaline conditions. These molecules provide adsorption sites for sediment, meaning that more iron can be released. Microscopic evidence for the release of iron was acquired. These results improve the understanding of the geochemical processes affecting iron in groundwater, the nonbiological transformation mechanisms that occur at the interfaces between natural iron minerals and organic matter, groundwater pollution control, and the environmental behavior of pollutants.

Prioritizing decision-making of health and well-being response tactics: Incorporating organizational and individual shared demands

As a major energy source in China, the occupational health and well-being (OHW) of miners is a priority. Various statistical techniques have been used to identify factors or assess OHW to provide valuable information for the implementation of health promotion activities. The main bottleneck is the limited focus on solutions that address the demands of both organizations and individuals, and scientific and effective decision-making is pending. Therefore, this study describes the OHW mechanism covering both antecedents and consequences through the driving force-pressure-state-impact-response model. A probabilistic model of management tradeoff analysis was established by using a Bayesian decision network. Causal relationships and dependencies between multiple factors are captured visually. The model was verified and applied with samples of miners (N = 816). The results showed that the comprehensive strategy (R5) was the best tactic, and the management effect of stress (R2) and vulnerability (R3) was prominent. This study provides a valuable tool for managers to identify priority management factors. Prioritizing tactics formulated from dual demands of organizational and individual can ensure project feasibility, operability, and effectiveness. This study is a novel attempt to combine theory with practice, which is timely and necessary for management.

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, "Ecological Conservation Redline" (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

A modeling approach to the efficient evaluation and analysis of water quality risks in cold zone lakes: a case study of Chagan Lake in Northeast China

Due to the influence of complex regional climate, water quality perturbation factors of lakes in cold regions are complicated, and the uncertainty of each factor needs further study. This study coupled two algorithms (clustering and EM) to establish a water quality uncertainty model of Chagan Lake, a typical cold region lake in China. A BN model containing nine influencing factors (including water temperature (WT), total phosphorus (TP), total nitrogen (TN), etc.) was established and optimized, and sensitivity analysis was also performed. The results indicate that the water quality status of the lake is class III and 27.47% risk of exceeding the standard. The water quality of the lake is more susceptible to disturbance during the freezing period (WT < 1 °C). TP is the most sensitive factor for water quality disturbance in the lake followed by chemical oxygen demand (COD), TN, and fluoride (F). Parameter control result displays, and the multifactor synergistic control scheme could reduce the water quality risk of the lake by 36.47%. This study demonstrates that our proposed method can be used to predict both sudden water quality events and the overall trend of water quality fluctuation, which is important for rapid water quality evaluation and management decisions.