Journal of chemical health & safety最新文献

The increasing prevalence of opioid misuse, particularly the adulteration of illicit substances with fentanyl, has heightened the need for effective field detection methods for unknown powders. First responders, including chemical emergency response and Hazardous Materials (HazMat) teams, face significant challenges in assessing chemical threats in real-time without the resources of a controlled laboratory environment. Immunoassay test strips are commonly used for drug detection and are considered potential tools for identifying fentanyl in emergency scenarios. However, the impact of common diluents (substances such as sugar, flour, and other commonplace additives) on the accuracy of these test strips is unexplored. This study evaluates the limit of detection (LOD) of a popular commercially available fentanyl test strip (FTS) in the presence of various diluents commonly found in emergency situations. The experimental LOD was determined to be 0.05 μg/mL, significantly lower than the reported LOD of 0.20 μg/mL. While no false positives were observed with diluents alone, the presence of diluents in fentanyl solutions altered FTS results, requiring higher concentrations of fentanyl to achieve a positive reading. Results were often difficult to interpret, particularly near the LOD, which could pose challenges for first responders in real-world scenarios. This work is the first to assess the application of FTS to chemical emergency response situations and threat assessment of unknown powders. Commercially available FTS are cost-effective, user-friendly, provide rapid results, and detect low concentrations of fentanyl even in the presence of other substances.

The rapid expansion of higher education has introduced new safety challenges in university laboratories, where fire incidents now represent a critical threat to campus safety. In this paper, we propose a method for analyzing the causes of fire accidents by combining Fault Tree Analysis (FTA) and Bayesian Network (BN), which breaks through the limitations of the static analysis of traditional risk assessment. Based on accident cases at home and abroad, the direct and indirect causes of fires in university laboratories were analyzed by using the accident tree. The fire accident tree of the university laboratory, containing 21 basic events, was constructed and mapped into a Bayesian model with dynamic reasoning ability according to the transformation principle. The model was then optimized according to the actual situation. GENIE5.0 software was used for quantitative analysis, forward reasoning of the occurrence probability of fire accidents, and reverse inference of the posterior probability of the root node. The key factors of fires were determined through probability importance analysis and sensitivity analysis. The analysis results show that the probability of laboratory fire is 2.39%, and the three key risk factors are unreasonable storage of combustibles, insufficient or ineffective laboratory fire extinguishing equipment, and chemical reaction. Prevention and control measures such as “five fixed management” and virtual reality emergency drills are proposed to provide a basis for fire analysis and prevention in university laboratories.

Chemical management in university laboratories constitutes a critical component of laboratory safety governance, requiring not only compliance with national chemical regulations but also the establishment of a robust internal management framework. University-level administrative departments play pivotal guiding and supportive roles in this process. This study first outlines China’s current legal framework for chemical safety management, followed by a detailed analysis of Peking University’s initiatives to enhance chemical safety. These initiatives include establishing a multitiered accountability system, refining institutional protocols, implementing comprehensive safety training programs, conducting regular inspections, and enforcing full process management─spanning procurement, usage, storage, and disposal─alongside categorized and tiered risk control strategies. With the advancement of information technologies, emerging tools such as the Internet of Things, artificial intelligence, and virtual reality are anticipated to revolutionize chemical management practices in academic laboratories.

To investigate methane-premixed gas cloud deflagration consequences, we developed a computational fluid dynamics (CFD) model for natural gas deflagration. Validation against large-scale pipeline explosion experiments preceded the examination of characteristic parameters of gas cloud and building presence effects on deflagration fireball thermal radiation and overpressure. Results indicate that open-space deflagration yields increasing external overpressure with equivalence ratios below 1.4; identical-area circular clouds exhibit 10% higher thermal radiation growth rates and greater overpressure increases than square counterparts; nonuniform gas distribution reduces thermal radiation (magnitude dependent on heterogeneity); frontal radiation exceeds lethal limits within 150 m of leakage points; buildings confer significant shielding, establishing safety beyond 200 m; increased building height accelerates surface thermal attenuation; rooftops occupy thermal decay transition zones where attenuation rates intensify with height.

Although various studies have applied numerical experimental design and response surface methodology (RSM) in combination with consequence modeling tools for gas dispersion prediction, there remains a need for a more generalizable and robust approach. Such an approach should account for diverse industrial settings, both offshore and onshore, and variable environmental conditions including atmospheric parameters, wind, and topography. This study integrates numerical experimental design with BREEZE Incident Analyst software to model chlorine gas dispersion in an industrial environment. The effects of five independent variables, which are wind speed, leakage hole diameter, release height, ambient temperature, and surface roughness, on the downwind distance to a specific toxic threshold level were investigated. Significant interactions were identified, while the release height was found to have a minimal influence on gas dispersion. Well-validated predictive models were developed using RSM, followed by optimization to identify the worst-case scenario, defined as the maximum downwind distance to the ERPG-2 level (3 ppm for chlorine). The optimization results indicated that the worst-case scenario occurs under conditions of 1.5 m/s wind speed, 0.076 m leakage diameter, 38 °C ambient temperature, and urban surface roughness. This study demonstrates the effectiveness of integrating numerical experimental design with consequence modeling to enhance the accuracy and reliability of gas dispersion predictions while minimizing uncertainties.

Mercury is a global problem for both human and environmental health. The Minamata Convention on Mercury entered into force in 2017, joining efforts of 151 countries, mostly in the Global South. Many of them are still in the process of incorporating these international concepts into domestic practice. This includes Brazil, which has been a signatory of the Convention since 2013. Since the country includes 70% of the Amazon, which is the region responsible for approximately 40% of the global emissions and 80% of South America′s emissions, it plays quite an important role in the Convention. For the successful implementation of the Convention, efforts must be made to bridge the gap between policymakers and academic knowledge when setting mercury limits. As a first step, this study followed the PRISMA-ScR guidelines and used references exclusively within this scope to perform a systematic search of guidelines with recommended limits for mercury in soil, water, and air. The documents retrieved by this approach were compared, bringing together insights and recommendations, especially for Brazil and the Amazonian context. While not exhaustive, the number, geographical diversity, and recency of the documents (guidelines) allow for the establishment of benchmarks through comparative analysis and the formulation of recommendations that could potentially be adopted by other Amazonian countries (or even other countries in the Global South), while respecting their specific differences.

Emerging battery technologies are transforming the landscape of energy storage. Within this domain, flow batteries are increasingly seen as critical enablers for the integration and deployment of renewable energy systems. Nevertheless, the electrolytes utilized in these systems present potential risks to both human health and environmental safety. Over the past five decades, vanadium–vanadium flow batteries have become a commercially viable solution; however, several distinct electrolyte compositions have been proposed for asymmetric vanadium flow batteries (V-X FB: X = Ce, Br, Fe, Mn, Zn, H₂, O₂), each driven by unique technical and commercial motivations. This study aims to evaluate their risks, prioritize further research investments, and identify gaps in current efforts to advance safer and more sustainable energy storage technologies. This research builds on our prior work, entitled Chemical Hazard Assessment of Vanadium–Vanadium Flow Battery Electrolytes in Failure Mode, [ Khaje, K. ACS Chem. Health Saf. 2025, 32, 449–460]. But shifts the focus to asymmetric vanadium flow batteries that are at a lower technology readiness level and are earlier in the commercialization pathway. Overcharging of batteries has been identified as one of the primary potential failure modes, directly leading to electrolyte degradation. This condition poses significant hazards due to the potential generation of toxic gases. Depending on the electrolyte composition, overcharging may result in the release of gases, such as Cl₂, Br₂, SO₂, H₂S, PH₃, NO₂, CO₂, NH₃, or HCN, each carrying immediate risks to human health. This study shows that electrolytes containing bromide, chloride, and cyanide ions are particularly concerning, as they present the most severe toxicity hazards during failure modes. Future experimental work is needed to evaluate conditions under which gases are produced by these flow batteries under both normal and severe overcharging conditions and to quantify the associated hazards. This will provide critical insights for improving battery safety and guiding future research and development in energy storage technologies.

As the operating years of the underground integrated pipe increase and the aging of various pipelines progresses, as well as coal mining operations, the potential risks of fire and explosion gradually rise. To effectively alleviate the harm caused by explosion accidents, a synergistic suppression method is proposed. This paper aimed to study the synergistic effects of porous sliding devices and water mist on methane/air explosions with different equivalent ratios by changing the porosity of porous medium and spray pressure. The results indicate that the combined use of porous sliding devices and water mist yields better synergistic suppression effects in terms of the explosion overpressure and the flame propagation velocity. Based on the orthogonal test results, the optimal synergistic combination was obtained, that is, a porosity of 60 PPI and a spray pressure of 0.1 MPa. The flame propagation velocity with Φ = 1.0 decreases by 17.91%, and the peak overpressure rate of explosion decreases by 34.72%. In addition, the relationship between explosion energy, flame propagation velocity, and explosion overpressure is established according to the Buckingham Π theorem. The establishment of the model and the experimental results have important theoretical significance for evaluating the degree of explosion risk after the addition of synergistic devices. Finally, the suppression mechanism of the synergistic device in the methane/air explosion process was elucidated by combining the key free radicals and basic reactions involved in the methane explosion process through chemical kinetics simulations. This study can provide numerical data support and a theoretical reference for active suppression systems applied in realistic scenarios.

To prevent explosion accidents arising from urban gas pipeline leaks, it is crucial to understand both the factors that lead to pipeline failure and the mechanisms that govern the evolution of accident consequences, thereby enabling the development of comprehensive preleak risk management and postleak emergency response strategies. This study employs Bayesian networks (BN) and influence diagrams (ID) to establish a robust risk control model for urban gas pipeline leak and explosion incidents. Based on pipeline installation scenarios, the pipelines are segmented to identify failure risk factors in each section, resulting in the construction of a BN model for pipeline failures; subsequently, event sequence diagrams (ESD) are utilized to analyze the evolutionary pathways of accident consequences, which are then translated into a BN model and integrated with the pipeline failure BN model to form a complete framework that captures both failure mechanisms and accident outcomes. Key risk factors are identified and corresponding control measures are proposed, leading to the development of a Bayesian ID model that comprehensively considers the effectiveness and cost implications of these measures in order to select the optimal risk control strategy.