Journal of chemical health & safety最新文献

This systematic review was performed according to the PRISMA statement. The search was limited to studies published between 2000 and 2021 using the electronic databases Web of Science, Embase, Scopus, PubMed, BIREME, and SciELO, totaling 919 articles. After removing duplicates, screening articles for eligibility, and adding five articles found from other sources, the final review was composed of 73 articles. The selected articles covered 22 countries, with most studies conducted in Thailand (17.8%), Brazil (16.4%), Italy (15.1%), and Iran (9.6%). The review consolidated a total of 3944 benzene measurements for the exposed group and 1396 for the reference group, with 66% obtained through personal sampling. Benzene concentrations at gas stations ranged from 0.22 μg·m^–3 (0.0007 ppm) in Iran to 35,370 μg·m^–3 (11.07 ppm) in Saudi Arabia, respectively. Statistical comparisons among nine countries showed that the mean benzene exposure was highest in Iran (1020 ± 1252 μg·m^–3) and lowest in Mexico (21 ± 54 μg·m^–3). The lowest mean benzene concentration was observed during Period I (1995–2000) compared to the four subsequent periods for all countries combined. This result can be explained by the prevailing data from studies conducted in gas stations equipped with Vapor Recovery Systems (VRS). We also compared benzene levels across different periods for Brazil, Iran, and Thailand and found no significant differences. Most countries reported mean benzene concentrations lower than 0.5 ppm (TWA-ACGIH). However, despite the limited number of studies, the high levels of benzene found in gas stations from Saudi Arabia (34,140 ± 1740 μg·m^–3) and Egypt (11,790 μg·m^–3) raise significant concerns.

Hazard recognition is crucial for mitigating risks in research laboratory settings where new chemicals and processes are introduced frequently. Where hazards are familiar to the researcher, successful mitigation strategies are likely similarly well-known. While some high energy materials (i.e., chemicals that decompose rapidly with significant energy release) are easily recognized even by nonexperts, the exploratory nature of chemical research can result in the inadvertent formation of high energy materials both known and unknown, putting researchers and their science at risk of a safety incident. In this paper, we discuss approaches at The Dow Chemical Company to enhance hazard recognition and control with a focus on high energy materials. First, researchers are educated in hazard recognition and basic safety decision-making with decision aids made available to help them independently assess their work for hazards. These preliminary hazard assessments guide researchers in identifying and mitigating risks without direct expert engagement. When hazards are identified that researchers cannot resolve independently, reactive chemical subject matter experts and other safety personnel provide additional support. We share cases where hazard identification failed and the lessons, both technical and cultural, that resulted. Overall, the iterative learning process aims to optimize safety and use incidents as learning opportunities for future improvements.

Cyanobacterial algal blooms (CABs) are recognized as an emergent concern globally due to their undesirable impacts on water quality, agricultural ecosystems, and human health. These blooms, fueled by nutrient pollution and climate change, lead to the excessive growth of cyanobacteria, which can release harmful toxins known as cyanotoxins. The current review bestows a comprehensive overview of CABs, their environmental drivers, and the types of cyanotoxins produced. It explores the implications of these toxins for agricultural productivity, ecosystem sustainability, and public health. Furthermore, emerging mitigation approaches are discussed, including physical, chemical, and biological methods aimed at controlling bloom formation and reducing toxin release. In recent years, artificial intelligence (AI) and machine learning (ML) have appeared as powerful tools for environmental monitoring and prediction. Keeping this in mind, the present article highlights for the very first time the integration of AI/ML techniques for enhancing early detection of CABs, predicting the bloom dynamics, and optimizing the mitigation strategies. By combining traditional mitigation strategies with AI-driven insights, this paper provides a roadmap for addressing the challenges posed by CABs in a rapidly changing global environment, offering solutions that balance agricultural productivity with environmental and human safety.

In this study, an experimental platform for the safe operation and leakage monitoring of pipelines in chemical parks was developed, integrating an acoustic emission system for real-time detection. The reliability of the platform was verified through pressure tightness testing, data reproducibility verification, acoustic emission experiments, and CFD simulations. Static and dynamic leakage experiments demonstrated that the effect of initial pressure on leakage is significantly enhanced when the total leakage pore diameter exceeds 4 mm (pore diameter ratio of 0.05). Additionally, the impact of multihole leakage increases substantially when the diameter of a single hole reaches 15 mm (pore diameter ratio of 0.19). The study revealed a positive correlation between the initial pressure and the rate of pressure change, an exponential relationship between leakage hole diameter and pressure change rate, and a threshold effect of leakage hole diameter on the final pipeline pressure. These findings provide empirical support for the development of pipeline safety management strategies and hold significant practical value in enhancing pipeline safety and emergency response capabilities. The platform enables effective monitoring of leakage dynamics through multidimensional validation, offering a scientific basis for pipeline risk prevention and control in chemical parks.

Facilities operated by the US Department of Energy (DOE) are required to have extensive worker safety and health programs by regulation. We describe how Argonne National Laboratory (ANL) has implemented the DOE requirements to bring the Laboratory’s multidisciplinary research risk to an acceptable level to safely execute ANL’s mission to perform cutting-edge research.

Vat photopolymerization (VP) is an additive manufacturing process that uses light to harden resin and build a 3-dimensional shape. Stereolithography (SLA) printing is a variant of VP that uses a laser beam as the light source to initiate a polymerization reaction. During SLA printing, particles and gases can be emitted into the air; however, factors that influence emissions are poorly understood for this technology. Emissions from two brands of SLA printers from different manufacturers (herein termed A and B) were measured using real-time (particle number and size, total volatile organic compound [TVOC] concentration) and time-integrated (aldehydes, acrylates, aromatics, alkanes, butylated hydroxy toluene, and elements) techniques in an environmental test chamber. Three colors of resins (black, clear, and gray), all from the same manufacturer, were tested on each printer. All statistical comparisons used a significance level of 0.05. Printer brand strongly influenced the emission yields. Printer A had significantly higher particle number yield, smaller particle size, and higher 2-hydroxyethyl methacrylate (2-HEMA) and 2-hydroxypropyl methacrylate yields for all resin colors compared with printer B. There were also significant differences between brands in yield values for several aldehydes (acetaldehyde, butyraldehyde, hexaldehyde, isovaleraldehyde, o,m,p-tolualdehyde, and propionaldehyde). Resin color had a minor influence on yields for particle number, some aldehydes, and 2-HEMA for printer A only. The strong influence of printer brand on emissions was partially explained by printer configuration, i.e., printer A had a built-in resin heater, whereas printer B did not. Emission yields of organic chemicals were not always higher for printer A compared with printer B, which indicated that other factors also influenced emissions. Improved understanding of factors that influence emissions from SLA printers is critical for developing exposure mitigation strategies using a hierarchy of controls.