Characterising the ground level concentrations of harmful organic and inorganic substances released during major industrial fires, and implications for human health

Griffiths, Simon; Entwistle, Jane; Kelly, Frank J.; Deary, Michael E.

doi:10.1016/j.envint.2022.107152

Cited by 18 publications

(24 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is important to note that the selected subset of spectral data used for CV may not fully represent the overall scenario. However, the model exhibited lower accuracy for ammonium ions, possibly due to spectral feature overlap with other components, leading to signal cross‐interference (Griffiths et al, 2022). Limitations in the application of Raman spectroscopy to quantify ammonium ions in solution is consistent with prior literature reports (Dimiev et al, 2012).…”

Section: Resultsmentioning

confidence: 99%

Genetic algorithm‐based semisupervised convolutional neural network for real‐time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

Liu,

Zhou,

Wang

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

As a non‐destructive sensing technique, Raman spectroscopy is often combined with regression models for real‐time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time‐consuming and labor‐intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm‐based semi‐supervised convolutional neural network (GA‐SCNN). GA‐SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA‐SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross‐validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

show abstract

Section: Resultsmentioning

confidence: 99%

Genetic algorithm‐based semisupervised convolutional neural network for real‐time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

Liu,

Zhou,

Wang

et al. 2024

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…The Emergency Response Plan Guidelines (ERPG), established by the American Industrial Hygiene Association (AIHA), are presently the recommended standards for exposure concentration. These guidelines have been widely adopted to assess the severity of a toxic gas spill [24,25]. The guidelines specify three concentration ranges, which are outlined in Table 4.…”

Section: Gas-diffusion Toxicity Consequence Modeling Based On Gaussia...mentioning

confidence: 99%

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

Li,

Chen,

Dou

et al. 2023

Sustainability

View full text Add to dashboard Cite

In order to achieve the prediction and early warning of city gas pipe network leakage accidents, as well as to provide rapid and precise support for emergency response to such accidents, this study focuses on a Gaussian diffusion model applied to a large urban gas pipeline network. Specifically, it investigates the gas gate wells, which are key nodes in the pipeline network, to develop a leakage model. The objective is to analyze the variation in internal gas concentration in the gate wells and determine the range of danger posed by external gas diffusion from the gate wells. In addition, Fluent simulation is utilized to compare the accuracy of the model’s calculations. The findings of this study indicate that the gas concentration inside the gate well, as predicted by the model fitting results and Fluent simulation, exhibit a high level of agreement, with coefficient of determination (R2) values exceeding 0.99. Moreover, when predicting the hazardous distance of gas leakage outside the gate well, the model’s results show an average relative error of 0.15 compared to the Fluent simulation results. This demonstrates that the model is highly accurate and meets the practical application requirements.

show abstract

“…Two hours after the first leak, the monomer air concentration in the plant was 36 ppm, and the level became undetectable after 5 h. No SM was detected in a nearby village 5 h after the first exposure. The United States Environmental Protection Agency (EPA) Acute Exposure Guideline Level (AEGL) was level 1: “Notable discomfort, irritation, or certain asymptomatic non-sensory effects” [ 6 , 7 , 8 , 9 , 10 ]. Acute inhalation of >50 ppm of styrene (AEGL-2) may irritate the nasal mucosa and eyes; a level > 100 ppm may cause skin irritation and central nervous system symptoms [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Prior Mental Health on Persistent Physical Symptoms after Exposure to a Chemical Disaster

Min

Kim²,

Choi

et al. 2023

Healthcare

View full text Add to dashboard Cite

A styrene monomer (SM) oil vapor leak occurred at a chemical plant in Seosan, South Korea on 17 May 2019. A bad odor developed, and many residents complained of various symptoms and visited nearby medical institutions. We analyzed the demographic and clinical characteristics of patients treated at local hospitals and clinics for symptoms related to SM exposure, and identified factors affecting symptom persistence in any organ. Data were collected by the main Seosan office, and 1201 (33.0%) subjects agreed to participate in this study. We used the Assessment of Chemical Exposure toolkit of the Agency for Toxic Substances and Disease Registry. Logistic regression was performed to determine whether mental health symptoms prior to the accident were risk factors for symptom persistence. The strongest risk factor for persistence of at least one symptom in any organ was a preexisting mental health symptom (odds ratio [OR] = 5.47, 95% confidence interval [CI]: 2.57–11.65). Persistent symptoms of the nervous (OR = 1.54), musculoskeletal (OR = 1.92), and gastrointestinal (OR = 1.45) systems were observed. Prior mental health symptoms are risk factors for persistent physical symptoms after a chemical disaster. After a disaster, management of individuals with preaccident mental symptoms or disease is needed.

show abstract

Characterising the ground level concentrations of harmful organic and inorganic substances released during major industrial fires, and implications for human health

Cited by 18 publications

References 49 publications

Genetic algorithm‐based semisupervised convolutional neural network for real‐time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

Genetic algorithm‐based semisupervised convolutional neural network for real‐time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

Leakage Diffusion Modeling of Key Nodes of Gas Pipeline Network Based on Leakage Concentration

The Effect of Prior Mental Health on Persistent Physical Symptoms after Exposure to a Chemical Disaster

Contact Info

Product

Resources

About