Environmental Risk Model Research Articles

Environmental Risk Assessment and Modeling of TPH and PAHS in OBRIKOM 15 Access Road Mini Manifold Spill

From the anthropogenesis operations of petroleum hydrocarbon related activities in the Niger Delta region of Nigeria, the possibilities of environmental crisis and associated hazards is highly expected. BETX and PAHs as major constituents of petroleum hydrocarbon and their impacts on the environment had caused ecological imbalance with contamination/pollution resulting from crude oil spillages. Random sampling method was adopted and soil samples were collected using amber wide-mouth and vial glass bottles with the aid of soil auger at 0-4m depths and analysed using GC-FID, GC-MSD and APHA analytical techniques. The TPH and PAHs decreased with depth. The TPH measured values were up to 1,636.85mg/kg and PAHs as high as 5,702.21mg/kg in soil with below detectable level (BDL) of BTEX. CF values were between 0.059 to 0.327 and the DC value was 2.511 respectively, made Obrikom spill site soil lowly contaminated while the l-geo calculation values of PAHs was between 3.615 to 6.571 indicating strongly contaminated. The hypothesis testing calculation shows that the calculated value is less than the table value and therefore the hypothesis is upheld which states that there is no relationship between TPH and PAHs concentrations in Obrikom soil impacted site. The contour maps show decreasing values of TPH and PAHs from the point of spill and depth while variogram models revealed a continuous behavior having similar trends of identical exponential variograms.

A Multigenerational Model of Environmental Risk for Black, Indigenous, and People of Color (BIPOC) Children and Families.

In recent years, public discourse has increasingly brought institutional and structural racism to the foreground of discussion on the well-being of BIPOC (Black, Indigenous, and People of Color) communities. Environmental toxicity in combination with the social triggers of institutional and structural racism are among the factors that shape the short- and long-term health of BIPOC Americans across multiple lifespans. We outline a Generation Model for examining the mechanisms through which institutional and structural racism promotes the intergenerational transmission of environmental health risk and family and interpersonal relationships across the life course and across multiple generations. We present the model's theoretical underpinnings and rationale, discuss model limitations and needed sources of data, and implications for research, policy, and intervention. Parents and children are not only biologically linked in terms of transmission of environmental toxicities, but they are also linked socially and intergenerationally. The Generation Model foregrounds family and interpersonal relationships occurring within developmental contexts that are influenced by environmental toxicity as well as institutional and structural racism. In sum, the Generation Model highlights the need for an equity-first interdisciplinary approach to environmental health and redirects the burden of risk reduction away from the individual and onto the institutions and structures that perpetuate the racial disparities in exposure. Doing so requires institutional investment in expanded, multigenerational, and multimethod datasets. https://doi.org/10.1289/EHP13110.

Linking conventional activated sludge treatment plant performances for micropollutants removal to environmental risk and SimpleTreat model assessment

The attention to the contaminants of emerging concerns or micropollutants is continuously rising, also stimulated by the new initiatives at European level. The present study focuses on the removal of target micropollutants in a conventional activated sludge wastewater treatment plant which is the most common system and considered a source of micropollutants to the environment. The aims were to evaluate the removal efficiency of different stages of the wastewater treatment plant and to conduct the Environmental Risk Assessment on the final effluent. As indicated by the European’s guidelines on Environmental Risk Assessment, the SimpleTreat4.1 model was applied to this purpose: different values of the biodegradation constant, which represents the key factor for modelling, were applied, and the effects on the quality of prediction were evaluated. The evaluations were carried out thorough 3-year monitoring activity in a full-scale wastewater treatment plant on 10 micropollutants belonging to the classes of illicit drugs, pharmaceuticals and psychoactive. The model was found statistically not satisfactory for most of the investigated micropollutants. A better prediction was obtained when the biodegradation constant was calculated based on the full-scale data. Nonetheless, the results obtained in terms of risk were considered acceptable and comparable among each other.Graphical abstract

Fast hybrid methods for modeling landslide susceptibility in Ardal County

Recently, machine learning models have received huge attention for environmental risk modeling. One of these applications is landslide susceptibility mapping which is a necessary primary step for dealing with the landslide risk in prone areas. In this study, a conventional machine learning model called multi-layer perceptron (MLP) neural network is built upon advanced optimization algorithms to achieve a firm prediction of landslide susceptibility in Ardal County, West of Iran. The used geospatial dataset consists of fourteen conditioning factors and 170 landslide events. The used optimizers are electromagnetic field optimization (EFO), symbiotic organisms search (SOS), shuffled complex evolution (SCE), and electrostatic discharge algorithm (ESDA) that contribute to tuning MLP’s internal parameters. The competency of the models is evaluated using several statistical methods to provide a comparison among them. It was discovered that the EFO-MLP and SCE-MLP enjoy much quicker training than SOS-MLP and ESDA-MLP. Further, relying on both accuracy and time criteria, the EFO-MLP was found to be the most efficient model (time = 1161 s, AUC = 0.879, MSE = 0.153, and R = 0.657). Hence, the landslide susceptibility map of this model is recommended to be used by authorities to provide real-world protective measures within Ardal County. For helping this, a random forest-based model showed that Elevation, Lithology, and Land Use are the most important factors within the studied area. Lastly, the solution discovered in this study is converted into an equation for convenient landslide susceptibility prediction.

Research on Enterprise Environmental Risk Management of Industrial Bank under Analytic Hierarchy Process

With the frequent occurrence of corporate environmental incidents and the intensification of bank credit risks, the banking industry urgently needs to strengthen the prevention, control and management of corporate environmental risks. Based on the analytic hierarchy process, this paper takes the three enterprises that have been granted credit by IB as samples, selects the enterprise environmental risk model of four sub-indicators: carbon emissions, basic environmental management, environmental risk management measures, and environmental risk emergency management, and selects the appropriate green credit objects according to the calculation results. It also puts forward suggestions on environmental risk management to improve the collection and transmission of enterprise environmental information, strengthen the supervision of enterprise risk management measures, and formulate a unified enterprise environmental risk rating standard, so as to provide reference for the banking industry to carry out enterprise environmental risk management.

Mapping the Environmental Risk of Beech Leaf Disease in the Northeastern United States.

The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in 2012 in northeast Ohio, U.S.A., BLD had been documented in 10 northeastern states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across northern Ohio, western Pennsylvania, western New York, and northern West Virginia, U.S.A. In the absence of symptoms, an area cannot necessarily be deemed free of BLD (i.e., absence of BLD cannot be certain) due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM), and maximum entropy (Maxent) to predict the spatial pattern of BLD risk based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.

A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks

A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.

Insight into Impact of Phosphate on the Cotransport and Corelease of Eu(III) with Bentonite Colloids in Saturated Quartz Columns

Understanding the fate and transport of radionuclides in porous media reduces the risk of contaminating soils and groundwater systems. While the cotransport of bentonite colloids (BC) with radionuclides in saturated media is well documented, the role of phosphate (P) in the colloid-driven transport of radionuclides in saturated porous media is still unaddressed; in particular, phosphate increases the mobilities of radionuclides in porous media, which should be subjected to an environmental risk assessment and model construction. In this work, the effects of phosphate on the transport and release of Eu(III) in different colloid systems (P-Eu(III), P-BC, P-BC-Eu(III)) was investigated with a fundamental colloid chemistry approach and a range of characterization techniques. The results showed that intrinsic europium colloids with size of 685 nm were formed by precipitation with phosphate, which affected the mobility of Eu(III) due to colloid stability and physical straining. Phosphate enhanced BC and BC-Eu(III) transport, and a high phosphate concentration promoted BC transport by eliminating physical straining and enhancing the electrostatic repulsions. The crystal structure of EuPO4 was not destroyed by the subsequent introduction of BC, which carried EuPO4 for further migration. However, when phosphate, bentonite and Eu(III) coexisted in a colloid suspension, the phosphate promoted Eu(III) transport by preferentially interacting with the BC to form ternary BC-P-Eu(III) pseudo-colloids rather than forming the intrinsic EuPO4 colloids. The synergetic role of P and BC on Eu(III) transport involved a relatively complex process and was not a simply additive effect. The findings in this work highlight the significance of phosphate in controlling the fate and transport of Ln(III)/Am(III) radionuclides in the presence of intrinsic colloids and pseudo-colloids in P-rich colloid-bearing environments.

Predicting Ripe Rot of Grape, Caused by Colletotrichum fioriniae, with Leaf Wetness, Temperature, and the Crop Growth Stage

The role of wetness duration (LWD) and temperature in the development of ripe rot was evaluated with greenhouse grape clusters and detached berries inoculated with Colletotrichum fioriniae. Five classification methods (decision tree, logistic regression, neural network, random forest, and support vector machine) were used to develop environmental risk models for predicting ripe rot from these variables. Additionally, a phenological stage susceptibility model was derived from results of previously published trials. The environmental risk output multiplied by the phenological risk output resulted in the ripe rot disease risk. The models were then tested for their ability to accurately predict the severity of 45 past ripe rot epidemics and were implemented in virtual fungicide application-triggering ripe rot warning systems. In the greenhouse and detached fruit trials, between 27 and 32°C and longer LWD resulted in the highest incidences of ripe rot. The 27°C and 24-h LWD treatment resulted in 100% ripe rot incidence in both greenhouse trials. Four candidate models were proposed for future field efficacy evaluations due to their ability to accurately predict the severity of past ripe rot epidemics while virtually triggering few, an average 2.5 to 3.0, fungicide applications per season. These models could be used to predict ripe rot infection events and inform management decisions for better control with reduced fungicide inputs. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Health Benefit Evaluation for PM2.5 as Well as O3-8h Pollution Control in Chengdu, China from 2016 to 2020

Both particulate matter with aerodynamics of less than 2.5 (PM2.5) and ozone are the two main air pollutants in China, which seriously endanger human health. To estimate the adverse impacts of PM2.5 and ozone on human health during the implementation of air pollution prevention and control actions in Chengdu, both the generalized additive model and the nonlinear distribution lag model of epidemiology were adopted to explore the exposure-response relationship coefficients β of daily ozone 8h maximum concentration average (O3-8h), as well as that of PM2.5 on disease deaths in Chengdu from 2014 to 2016. On this basis, the environmental risk model and environmental value assessment model were both adopted to evaluate the health effects and health benefits in Chengdu from 2016 to 2020, respectively, with the assumption that PM2.5 and O3-8h concentration were reduced to specified air pollution control limits (35 μg·m-3 and 70 μg·m-3, respectively). The results showed 1 the annual concentration of PM2.5 presented gradually decreasing trends in Chengdu from 2016 to 2020. Specifically, ρ(PM2.5) from 63 μg·m-3 in 2016 decreased to 40.92 μg·m-3 in 2020. The average annual decline rate was approximately 9.8%. In contrast, the annual concentration of O3-8h from 155 μg·m-3 in 2016 increased to 169 μg·m-3 in 2020, and the increasing rate was approximately 2.4%. 2 Both PM2.5 and O3-8h had lag effects on three types of disease deaths. Under the maximum lag effect, the corresponding exposure-response relationship coefficients β of PM2.5were 0.0003600, 0.0005001, and 0.0009237 for all-cause, cardiovascular, and respiratory premature deaths, respectively, whereas the corresponding β of O3-8h were 0.0003103, 0.0006726, and 0.0007002, respectively. 3 If ρ(PM2.5) was reduced to the national secondary standard limit (35 μg·m-3), the corresponding number of health beneficiaries and economic benefits declined yearly. Specifically, the health beneficiary number of all-cause, cardiovascular, and respiratory disease deaths were reduced from 1128, 416, and 328 in 2016 to 229, 96, and 54 in 2020, respectively. There were a total number of 3314 avoidable premature deaths for all-cause diseases during the five years, resulting in a total health economic benefit of 7.66 billion yuan. 4 If we assume that ρ(O3-8h) was reduced to the concentration limit specified by the World Health Organization (70 μg·m-3), the corresponding number of health beneficiaries and economic benefits were increasing yearly. Specifically, the health beneficiaries' numbers of all-cause, cardiovascular, and respiratory disease deaths rose from 1919, 779, and 606 in 2016 to 2429, 1157, and 635 in 2020, respectively. The annual average growth rates of avoidable all-cause and cardiovascular mortality were 6.85% and 10.72%, respectively, which was higher than the annual average rise rate of ρ(O3-8h). There were 10790 total avoidable deaths from all-cause diseases during the five years, resulting in a total health economic benefit of 26.62 billion yuan. These findings indicate that PM2.5 pollution in Chengdu had been well controlled, whereas O3 pollution had become more severe and had become another key air pollutant threatening human health. Therefore, the synchronous control of PM2.5 and ozone should be implemented in the future.

495 Radon and Fracking Exposures and Lymphoma Risk in a Canine Model of non-Hodgkin Lymphoma

OBJECTIVES/GOALS: The objective of this study was to determine whether residential radon and proximity to horizontal oil and drilling (fracking) are risk factors for the development of multicentric lymphoma in pet dogs, a spontaneous, immunocompetent model for non-occupational risk for NHL in humans. METHODS/STUDY POPULATION: Two case-control populations of dogs with multicentric lymphoma were utilized, with a focus on two dog breeds at high risk for lymphoma. Control dogs were matched for age, breed, and sex. Home addresses were collected for 54 Golden retrievers with lymphoma and 108 Golden retriever controls, and for 56 boxer dogs with lymphoma and 84 unaffected boxer controls. Counties of residence were matched to radon zones and percentage of home radon tests that exceeded the actionable level of 4 pCi/L, available by county through the EPA and the CDC National Environmental Public Health Tracking Network from 2008 to 2017. Locations of horizontal oil and gas wells were obtained from the Enverus Database, and distances from dog homes to the closest well, and well density by county, were calculated for each case and control. RESULTS/ANTICIPATED RESULTS: We found no significant differences in radon zones, county level radon measurements, or residential proximity to active fracking wells between dogs with lymphoma and unaffected controls in either the Golden Retriever or boxer populations. DISCUSSION/SIGNIFICANCE: Canine multicentric lymphoma resembles human NHL and is a valuable model of non-occupational environmental risk for NHL in people. Although we did not find geographic associations between radon and fracking wells, follow-up studies will measure household radon, as well as household air, water, and dog urine for potentially genotoxic chemicals.

Coal mine safety management index system and environmental risk model based on sustainable operation

With the increasing use of coal mine resources, some modern large coal mines have advanced and perfect safety equipment, reasonable personnel structure, and perfect rules and regulations, but why do major accidents start to occur? In addition to the complex geological conditions of China's coal mines, another important factor has attracted our attention, that is, the efficiency of coal mine safety management. This paper aims to study the coal mine safety management index system and environmental risk model based on sustainable operation. Based on the coal mine safety index of sustainable management, this paper proposes a mining algorithm for coal mine safety text classification and hazard source association rules. And this paper analyzes the safety management efficiency of the safety index system based on DEA and DEA-Malmquist. The analysis results prove that the evaluation values of coal mine safety management of the selected 20 coal mining enterprises from 2012 to 2017 were 0.602, 0.645, 0.570, 0.667, 0.771 and 0.705, respectively. The interval range of the data is between [0.55, 0.85], indicating that the evaluation level of safety management efficiency of Chinese coal mining enterprises is general and at a medium level.

Neighborhood Greenspace and Socioeconomic Risk are Associated with Diabetes Risk at the Sub-neighborhood Scale: Results from the Prospective Urban and Rural Epidemiology (PURE) Study

Greenspace and socioeconomic status are known correlates of diabetes prevalence, but their combined effects at the sub-neighborhood scale are not yet known. This study derives, maps, and validates a combined socioeconomic/greenspace index of individual-level diabetes risk at the sub-neighborhood scale, without the need for clinical measurements. In two Canadian cities (Vancouver and Hamilton), we computed 4 greenspace variables from satellite imagery and extracted 11 socioeconomic variables from the Canadian census. We mapped 5125 participants from the Prospective Urban and Rural Epidemiology Study by their residential address and used age- and sex-dependent walking speeds to estimate individual exposure zones to local greenspace and socioeconomic characteristics, which were then entered into a principal component analysis to derive a novel diabetes risk index (DRI-GLUCoSE). We mapped index scores in both study areas and validated the index using fully adjusted logistic regression models to predict individual diabetes status. Model performance was then compared to other non-clinical diabetes risk indices from the literature. Diabetes prevalence among participants was 9.9%. The DRI-GLUCoSE index was a significant predictor of diabetes status, exhibiting a small non-significant attenuation with the inclusion of dietary and physical activity variables. The final models achieved a predictive accuracy of 75%, the highest among environmental risk models to date. Our combined index of local greenspace and socioeconomic factors demonstrates that the environmental component of diabetes risk is not sufficiently explained by diet and physical activity, and that increasing urban greenspace may be a suitable means of reducing the burden of diabetes at the community scale.

Comparing fine‐scale structural and hydrologic connectivity within unimproved and improved grassland

AbstractGrasslands vary with diverse forms and functions ranging from monocultures of perennial rye grass to more biodiverse unimproved grasslands which cover around 5% of Europe. Despite the broad diversity of grassland types, within environmental and flood risk models grasslands are frequently represented by a singular set of hydrological and structural parameters which belies their diversity and complexity. This study aimed to determine empirically the extent to which improved versus unimproved grasslands exhibit different hydrological connectivity. Working in SW England at neighbouring field sites with comparable slopes and rainfall regimes, we used unpiloted aerial vehicles to survey a tussocky Molinia caerulea dominated unimproved grassland field (MCUG) field and a Lolium perenne dominated improved grassland (LPIG) field. Using digital photogrammetry workflows applied to the overlapping aerial images, we produced a digital surface model (DSM) at 0.03‐m resolution from which flow pathways were modelled using GIS and compared with 1‐m LiDAR and DSM produced by a global navigation satellite system (GNSS). MCUG had longer, tortuous pathways through the dense tussock network with a drainage density of 2.54 m m−2. This was significantly greater than drainage density in the LPIG (1.82 m m−2). As a result of this study, we rescaled the Manning's n value for MCUG according to photogrammetrically‐derived roughness values. We suggest it should lie between 0.075 and 0.09. Our data shows that MCUG can play an important role in reducing overland flow impacts when compared to LPIG through lower connectivity which can delay run‐off to rivers.

A Personal Breast Cancer Risk Stratification Model Using Common Variants and Environmental Risk Factors in Japanese Females.

Simple SummaryBreast cancer remains the most common cancer in females, warranting the development of new approaches to prevention. One such approach is personalized prevention using genetic risk models. Here, we developed a risk model using both genetic and environmental risk factors. Results showed that a genetic risk score defined by the number of risk alleles for 14 breast cancer risk SNPs clearly stratified breast cancer risk. Moreover, the combination of this genetic risk score model with an environmental risk model which included established environmental risk factors showed significantly better C-statistics than the environmental risk model alone. This genetic risk score model in combination with the environmental model may be suitable for stratifying individual breast cancer risk, and may form the basis for a new personalized approach to breast cancer prevention.Personalized approaches to prevention based on genetic risk models have been anticipated, and many models for the prediction of individual breast cancer risk have been developed. However, few studies have evaluated personalized risk using both genetic and environmental factors. We developed a risk model using genetic and environmental risk factors using 1319 breast cancer cases and 2094 controls from three case–control studies in Japan. Risk groups were defined based on the number of risk alleles for 14 breast cancer susceptibility loci, namely low (0–10 alleles), moderate (11–16) and high (17+). Environmental risk factors were collected using a self-administered questionnaire and implemented with harmonization. Odds ratio (OR) and C-statistics, calculated using a logistic regression model, were used to evaluate breast cancer susceptibility and model performance. Respective breast cancer ORs in the moderate- and high-risk groups were 1.69 (95% confidence interval, 1.39–2.04) and 3.27 (2.46–4.34) compared with the low-risk group. The C-statistic for the environmental model of 0.616 (0.596–0.636) was significantly improved by combination with the genetic model, to 0.659 (0.640–0.678). This combined genetic and environmental risk model may be suitable for the stratification of individuals by breast cancer risk. New approaches to breast cancer prevention using the model are warranted.

Ionizing radiation-induced circulatory and metabolic diseases

Risks to health are the prime consideration in all human situations of ionizing radiation exposure and therefore of relevance to radiation protection in all occupational, medical, and public exposure situations. Over the past few decades, advances in therapeutic strategies have led to significant improvements in cancer survival rates. However, a wide range of long-term complications have been reported in cancer survivors, in particular circulatory diseases and their major risk factors, metabolic diseases. However, at lower levels of exposure, the evidence is less clear. Under real-life exposure scenarios, including radiotherapy, radiation effects in the whole organism will be determined mainly by the response of normal tissues receiving relatively low doses, and will be mediated and moderated by systemic effects. Therefore, there is an urgent need for further research on the impact of low-dose radiation. In this article, we review radiation-associated risks of circulatory and metabolic diseases in clinical, occupational or environmental exposure situations, addressing epidemiological, biological, risk modelling, and systems biology aspects, highlight the gaps in knowledge and discuss future directions to address these gaps.

The use of the Monte Carlo method for predicting environmental risk in construction zones

The article analyzes the problem of determining environmental risk. The author took into account the features of natural landscapes when choosing a model of environmental risk in order to predict the level of interaction of the construction project with the environment. The matrix of construction zones distributions and the risk of environmental vulnerability are compiled on the bases of the study of the anthropogenic impact parameters on natural landscapes. Monte Carlo simulation method allows predicting the process of techno genesis relative to the natural environment within the limits of confidence interval. The industrial territories of the Far North and the regions equated to them are characterized by the highest probability and level of risk, as well as vulnerability. The impact of techno genesis on all four components of the environment (atmospheric air, hydrosphere, lithosphere and biosphere) in all areas of construction work normalization will continue to increase. This is indicated by trend graphs of the predicted values of anthropogenic impact within the industrial and residential zones. The quantitative characteristic of possible ecosystem “failures” as a result of anthropogenic interference is analyzed using a point scale. The worst-case scenario can be defined as post-catastrophic (ultra-high risk, emergency measures in emergency situations, score 26-30). The reliability of the simulated forecast is confirmed by the anthropogenic accident in Norilsk in June 2020, the largest and most unprecedented in the history of the Arctic.

Risk assessment simulation for shelf ecosystems based on the ecoscreening and dynamic methods synthesis

The paper simulates ecological risk assessment for shelf ecosystems based on the ecoscreening and dynamic methods synthesis. Both approaches and environmental risk model are described, which results make it possible to estimate probability of acceptable impact on the shelf ecosystem. In contrast to existing approaches related to ranking and compiling risk matrices, the proposed method based on probabilistic ecoscreening risk assessment takes into account not only the altering conditions of external influence on the shelf ecosystem, but also the intra-annual dynamics of its main components. This makes it possible to calculate the ecological risk not as a fixed value for the ecosystem under study, but as its component changing over the course of a year along with the intra-annual functioning dynamics. The latter is especially important for the coastal basins, where processes of various genesis are concentrated, high gradients of biomass values and concentrations of components containing it are presented. To simulate various scenarios of combining the effect of external stressors with the intra-annual dynamics of the ecosystem functioning, the Northern Caspian shelf basin was selected, which is subject to intense river flow and to exposure to mineral resources development technologies, including oil and gas. Ecological risk values were calculated taking into account combination of adverse natural factors and implementation of emergency and regular anthropogenic impacts at different periods of the shelf ecosystem components natural dynamics. Results made it possible to assess probabilities of the allowable exposure of shelf ecosystem, to identify areas with the 100% probability of allowable impact and especially critical areas with less than 5% of the impact probability. Calculations show not only efficiency of the proposed approach, but also open possibility of harmonizing ecological and economic requirements in the development of shelf resources. For areas with high allowable impact probability, it is possible to reduce the economic costs of ensuring ecological safety. Ecological risk areas, which are characterized by low estimate of allowable impact probability require an increase in the safety economic expenses. The proposed approach to estimating the intra-annual risk dynamics is of interdisciplinary nature and could be useful both to specialists in the ecology area, in the related fields, and to management personnel in making decisions on redistributing economic expenses and minimizing them while maintaining the priority of environmental safety.

Source apportionment and source-oriented risk assessment of heavy metals in the sediments of an urban river-lake system

Heavy metal pollution in lakes has attracted concerns worldwide since long retention times in lakes allow metals to accumulate and may pose significant threat to ecosystem health. For designing targeted risk mitigation strategies, it is necessary to identify the source-specific risks of heavy metals in the environment. Although previous studies have addressed either risk assessment or source identification of heavy metals in the environment, few have attempted to establish a link between them. In the study, we perform a combination of source apportionment and risk assessment for characterizing the pollution sources and source-specific risks of heavy metals in the sediments of an urban river-lake system. To this end, positive matrix factorization (PMF) was employed to apportion the potential sources of heavy metals, combined with a support vector machine classifier and the referential source fingerprints of metals in the study area. Then, the apportionment results were incorporated into the environmental risk models to evaluate the ecological and human health risks posed by heavy metals from the identified pollution sources. Results showed the river-lake system was contaminated by Cd, Cu, Zn and other metals in varying degree. Particularly, the element of Cd presented moderate to heavy pollution level. In relative, the industrial activities were identified as the largest contributor (48.0%) of heavy metals in the river-lake sediments, mainly associating with electroplating and paper making, followed by the agricultural activities (27.3%) and mix source (24.7%). Overall, the non-carcinogenic and carcinogenic risks posed by the heavy metals were acceptable, however, the element of Cd showed moderate ecological effect. Further, source-oriented risk evaluation suggested industrial processes made higher contributions to the ecological risk of heavy metals in the river-lake system. The study will provide regulators help to update the information by adding apportionment analysis in the context of risk assessment to facilitate subsequent mitigation strategies.

Evaluation of ship collision risk assessments using environmental stress and collision risk models

In Korea, ship collisions represent the majority of marine accidents and are substantially more likely in port areas where ship traffic is dense. To improve the safe navigation of vessels, the Korea Maritime Safety Audit (KMSA) has been conducting maritime traffic flow evaluations based on the Environmental Stress (ES) model since 2009. The ES model quantifies the subjective stress felt by the navigator; however, it does not reflect the relative bearing between ships. Risk assessment models using the ship domain are designed to consider the conditions of a potential encounter with a target ship and the possibility of avoidance action. However, even if the target ship does not invade the domain of the own ship, collision may occur at the closest point of approach (CPA). Therefore, in this study, the Collision RIsk (CoRI) model which moves the existing ship domain to the CPA is applied to compare the collision risk according to the navigator's awareness of the vessel encounter conditions. The results demonstrate that the CoRI model is a better risk assessment method than the ES model because it sensitively reflects the risks due to collision avoidance action, such as course alteration and speed changes.