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Faced with increasingly serious environmental risks, it is necessary to conduct a comprehensive evaluation of the regional environment to provide a solid foundation for environmental policies and actions in the future. This article builds a composite environment risk index that considers spatiotemporal factors and uses annual socio-economic and environmental data of China’s 31 provincial administrative regions from 2004 to 2019 to quantitatively analyze environmental risks. Furthermore, the article employs a panel data model to empirically test the key factors that lead to environmental risks. Moreover, this article employs SVAR models to analyze the dynamics of regional environmental systems in China. The study finds that, at least at this stage, the environmental risks in provincial regions in China are still relatively high, and the key factors of the risks are economic growth, urbanization development, secondary industry growth, and green policy. Therefore, China must adopt more stringent environmental protection policies and actions in the future.

'Extreme' events - including climatic events, such as hurricanes, tornadoes, drought - can cause massive disruption to society, including large death tolls and property damage in the billions of dollars. Events in recent years have shown the importance of being prepared and that countries need to work together to help alleviate the resulting pain and suffering. This volume presents an integrated review of the broad research field of large-scale disasters. It establishes a common framework for predicting, controlling and managing both manmade and natural disasters. There is a particular focus on events caused by weather and climate change. Other topics include air pollution, tsunamis, disaster modeling, the use of remote sensing and the logistics of disaster management. It will appeal to scientists, engineers, first responders and health-care professionals, in addition to graduate students and researchers who have an interest in the prediction, prevention or mitigation of large-scale disasters.

Background While chronic workplace stress is known to be associated with health-related outcomes like mental and cardiovascular diseases, research about day-to-day occupational stress is limited. This systematic review includes studies assessing stress exposures as work environment risk factors and stress outcomes, measured via self-perceived questionnaires and physiological stress detection. These measures needed to be assessed repeatedly or continuously via Ecological Momentary Assessment (EMA) or similar methods carried out in real-world work environments, to be included in this review. The objective was to identify work environment risk factors causing day-to-day stress. Methods The search strategies were applied in seven databases resulting in 11833 records after deduplication, of which 41 studies were included in a qualitative synthesis. Associations were evaluated by correlational analyses. Results The most commonly measured work environment risk factor was work intensity, while stress was most often framed as an affective response. Measures from these two dimensions were also most frequently correlated with each other and most of their correlation coefficients were statistically significant, making work intensity a major risk factor for day-to-day workplace stress. Conclusions This review reveals a diversity in methodological approaches in data collection and data analysis. More studies combining self-perceived stress exposures and outcomes with physiological measures are warranted.

Purpose: The purpose of this study is to presents a structured risk management methodology for managing and controlling the risks in the macro business environments. This paper emphasizes to achieve risk settled environments for industry/ business growth. Design/methodology/approach: In this paper, we present an approach called \"Risk Leveling\" which signifies both a concept and a methodology; it helps to manage risks in the macro business environments. Risk Leveling follows a multi-tier approach towards divergent risks and focuses on balancing the risky macro environs for better industry/ business settings. It recommends the utilization of the available resources in a systematic and efficient way for risk mitigation; such intent is pursued though a structured procedure which urges to reduce the risks to match them with the risk tolerance of the enterprises; it also attempts to diminish the mutual risk disparities giving rise a context in which no risk is seen too big or too small comparative to the others. The Risk Leveling process utilizes logical tools (e.g. AHP, ALARP etc.) and mechanisms to reach the risk settled environments. Findings: Risk Leveling procedure adopted in the study moves through the standard risk management course and tends to pacify the macro risks to achieve risk settled environments for the industry. It suggests exploiting the mitigation efforts in a balanced and efficient way to conserve the resources. Originality/value: The regimes can practice it to pacify and regulate the macro risk forces in the designated industry/ market segments.

Several numerical models have been utilized in water quality assessments for various purposes. Among all the commonly used models, entropy-weighted water quality index (EWQI) has been recognized as the most unbiased model for assessing drinking water quality. Therefore, this paper presents a case study of the application of EWQI in assessing the effect of effluent-derived heavy metals on the groundwater quality in Ajao industrial estate, Nigeria. Three environmental pollution risk assessment tools were integrated to better evaluate the level of heavy metals contamination in the groundwater. Geoaccumulation index ( I geo ) placed 66% of the samples in uncontaminated to moderately contaminated category. However, 19% showed moderate to heavy contamination, whereas 14.29% were heavily contaminated. Similarly, enrichment factor (EF) revealed that 52% of the samples have minimal enrichment, 33% are moderately enriched, while 14.29% were extremely enriched with heavy metals. Vector modulus of pollution index (PI vector ) showed that the majority of the samples (80.9%) have low pollution, 4.76% recorded moderate pollution, while 14.29% had considerable to very high pollution. The EWQI showed that the majority (85.71%) of the groundwater samples are excellent drinking water, while 14.29% are unsuitable for drinking. However, a dendrogram integrating the results of the I geo , EF, PI vector , and EWQI was produced by hierarchical cluster analysis to harmonize and demarcate the groundwater quality in this industrial area. Although this study confirms the suitability of most samples for drinking, more awareness programs towards the protection of the groundwater should be embraced.

This research concentrates on an introduction of a multi-model approach integrating Bayesian Networks (BN), Machine Learning (ML) models, Natural Language Processing (NLP) with Sentiment Analysis, Agent-Based Modeling (ABM), and Survival Analysis to improve predictive modelling of accident causation in high-risk steel industries. The significance of the artificial intelligence (AI) based models is that every approach complements other substantiating the hypothesis. Also, the augmentation of prediction accuracy could be achieved through AI approaches contrary to conventional methods. Results reveal that the application of AI model improves the prediction accuracy compared to conventional approaches. BN application uncovers the machine conditions and human errors responsible for causing accidents. Gradient Boosting Machines discussed equipment-related incidents, while NLP analysis demonstrated negative sentiment due to non-compliance with safety protocols. Moving forward, ABM simulations in accidents focus on personal protective equipment (PPE) compliance and machine maintenance. Survival analysis indicated the role of timely interventions in reducing severe accidents. Additionally, temporal insights aid in timing interventions, improving safety strategy efficacy. The outcome of this research discusses advancements in proactive accident prediction and risk management in high-risk steel industrial environments by addressing latent risk factors.

Women engaged in sex work (WESW) who use drugs are a key population in Kazakhstan’s HIV epidemic. Global research suggests susceptibility to HIV varies by sex work environment. This study aims to identify evidence-based typologies of WESW and examine their associations with HIV risk. We surveyed 400 WESW who use drugs in two Kazakhstani cities, including questions on sociodemographic characteristics, social, physical, and economic risk environments, and sexual risk behaviors. Latent class analysis identified four distinct typologies of sex work practice: occasional sex work (n = 61, 15%), professional sex work for money (n = 187, 47%), sex work in exchange for drugs, goods, or other services (n = 117, 29%), and managed sex work under a boss/pimp/madam (n = 35, 9%). We then used logistic regression to examine associations between typologies and risk behaviors. Compared to professional sex work, occasional sex work was associated with lower odds of multiple sexual partners (aOR:0.46[95%CI:0.24,0.90]), of multiple paid clients (aOR:0.25[0.13,0.49]), and of > 1 instance of unprotected sex with a paying partner (aOR:0.33[0.17,0.63]). Compared to professional sex work, sex work for nonmonetary items was associated with higher odds of multiple sexual partners (aOR:1.85[0.96,3.67]) and of > 1 instance of unprotected sex with a paying partner (aOR:1.71[1.01,2.93]). Results suggest heterogeneity among WESW who use drugs in Kazakhstan, and that typologies of sex work are associated with varying HIV risk environment factors and risk behaviors. Effective HIV prevention efforts must be tailored to address these varying risk environments and the resulting variety of needs.

Research documents significant management bias and opportunism around the discretionary inputs of audited complex estimates, including fair value measurements (FVMs), which raises questions about auditors' ability to test these estimates. We examine how the degree of quantification in client evidence and client control environment risk influence auditors' planned substantive testing of management's discretionary inputs to FVMs. We find that auditors allocate a lower proportion of effort to testing the subjective inputs to the fair value estimate when the degree of quantification in the client evidence and level of client risk are both high. Further, this tendency persists even after auditors receive a regulatory practice alert reminding them to focus more audit effort on testing fair value (FV) inputs that are susceptible to management bias, and despite the auditors increasing their overall audit effort. Qualitative analyses of the procedures auditors selected indicate that inapt attention to the degree of quantification in evidence is a potential root cause of the difficulty auditors encounter when testing complex estimates. Our results imply that in situations where both quantified and non-quantified data are important to the audit, there is the potential for management to manipulate the evidence they provide to auditors to distract auditors from testing the discretionary inputs to complex estimates that are susceptible to management opportunism.

The COVID-19 pandemic represents a type of force majeure that significantly and unexpectedly affected all human lifestyles. This study includes an integrative review of articles published across Scopus and Web of Science journals and compiled using the systematic review methodology based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Statement and VOSreview (visualization of similarities) software by defining keywords that include “construction industry” and “force majeure” and “environmental risks” as a starting point. Moreover, the research years and the countries covered by this research were determined in a second stage. Finally, the abstracts of selected studies were reviewed in order to extract factors similar to the pandemic conditions of COVID-19 along with the brief results of the research. Out of 6384 publications identified and 56 publications reporting, 20 studies fulfilled the inclusion criteria with full text. Based on our findings, there has been a continuous growth of publications on construction risk and environmental research since 2010. Malaysia had the greatest contribution to the research topic of the countries covered by the study, followed by Egypt. The Engineering, Construction and Architectural Management journal published the greatest number of publications related to the research topic. In this review, the most important previous studies are classified according to their handling of force majeure and environmental risks and the most important factors mentioned in these studies are identified. In addition, recommendations are made for dealing with the COVID-19 pandemic and for mitigating its effects on the construction industry in the Arab world and Malaysia. The results of this review will benefit researchers and construction companies alike in furthering research on reducing the risks of COVID-19 to construction projects and avoiding the significant economic loss that results from stopping these projects.

Fipronil is a broad potent insecticide that belongs to the phenylpyrazole chemical family. Its action mode acting in the presynaptic and postsynaptic blocking the chlorine ions by the neurotransmitters GABA. It is considered highly toxic, and in some countries, its use has been prohibited. The objective of this review is to perform a scientometric analysis for global measurement of the research on the insecticide fipronil. All information in this study was searched in the Web of Science (WoS) database in December 2021. The search was carried using the term “fipronil.” Thus, 2362 studies were selected. Most selected articles showed toxicity effects of fipronil on non-target organisms, analytical methods to detect the insecticide, environmental degradation processes, and efficiency in reducing insects through its use. The H index for this dataset was 91. The cooperation network of the authors among countries showed the USA as the most notorious, with 30.6% of studies, followed by China (15.7%) and Brazil (10.9%). There are many studies on the toxicity of fipronil in bees, forms of degradation, and effectiveness of this insecticide. The present work presents suggestions pointed out in the articles for further research and highlights the importance of studies involving fipronil, as well as studies of alternative pest control.