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15 pages, 1493 KiB  
Article
Temporal Variation and Industry-Specific Differences of the Use of Volatile Organic Compounds from 2018 to 2023 and Their Health Risks in a Typical Industrially Concentrated Area in South China
by Yijia Guo, Lihua Zhu, Liyin Zhang, Xinxin Tang, Xinjie Li, Yiming Ge, Feng Li, Jilong Yang, Shaoyou Lu, Jinru Chen and Xiaotao Zhou
Toxics 2024, 12(9), 634; https://doi.org/10.3390/toxics12090634 - 29 Aug 2024
Viewed by 585
Abstract
The risk of occupational exposure to organic solvents varies across industries due to factors such as processing materials, ventilation conditions, and exposure duration. Given the dynamic nature of organic solvent use and occupational exposures, continuous monitoring and analysis are essential for identifying high-risk [...] Read more.
The risk of occupational exposure to organic solvents varies across industries due to factors such as processing materials, ventilation conditions, and exposure duration. Given the dynamic nature of organic solvent use and occupational exposures, continuous monitoring and analysis are essential for identifying high-risk hazards and developing targeted prevention strategies. Therefore, this study aims to analyze the use of organic solvents and volatile organic compounds (VOCs) in different industries in Bao’an District, Shenzhen, China, from 2018 to 2023, to understand their temporal variation and industry-specific differences and to identify high-risk occupational hazards. This study includes 1335 organic solvent samples, used by 414 different industry enterprises, and 1554 air samples. The result shows that the usage of organic solvents in various industries decreased with the outbreak of the pandemic and, conversely, increased as the situation improved. The most frequently detected volatile components in organic solvents were alkanes, followed by aromatic hydrocarbons. The ratios of the detection frequency of VOCs to the total number of detected categories increased year by year after 2020, indicating a tendency towards reduction and concentration of the types of organic solvents used in industrial production. Among the 8 high-risk VOCs, toluene (22.5%), n-hexane (22.0%), xylene (16.1%), and ethylbenzene (15.3%) have relatively high detection rates, suggesting that they need to be focused on in occupational health. Through air samples, the results show that trichloroethylene and xylene pose a high risk to human health (HQ > 1). We recommend that industry should strengthen monitoring of these two VOCs. Full article
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<p>The number (<b>A</b>) and proportion (<b>B</b>) of organic solvent samples across different industries from 2018 to 2023.</p>
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<p>Composition of organic solvents in different industries’ samples.</p>
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<p>The number (<b>A</b>) and detection frequency (<b>B</b>) of VOCs in various organic solvents from 2018–2023.</p>
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15 pages, 4038 KiB  
Article
Health Risk Assessment of Ambient Volatile Organic Compounds in a Border City in Canada
by Taraneh Mihankhah, Yushan Su, Tianchu Zhang, Jonathan Wang, James Gilmore, Michael Noble, Anthony Munoz, Chris Charron and Xiaohong Xu
Atmosphere 2024, 15(9), 1038; https://doi.org/10.3390/atmos15091038 - 28 Aug 2024
Viewed by 511
Abstract
This study characterizes cancer and non-cancer risks due to inhalation exposure to volatile organic compounds (VOCs) in a border city of Windsor in southern Ontario, Canada, using hourly ambient concentrations collected from 17 November 2021 to 17 March 2023. The total incremental lifetime [...] Read more.
This study characterizes cancer and non-cancer risks due to inhalation exposure to volatile organic compounds (VOCs) in a border city of Windsor in southern Ontario, Canada, using hourly ambient concentrations collected from 17 November 2021 to 17 March 2023. The total incremental lifetime cancer risk (CR) due to benzene and ethylbenzene is 4.33 × 10−6, which is in the acceptable risk range of 1 × 10−6 to 1 × 10−4 used by the USEPA. The CR was higher in winter (5.20 × 10−6), followed by fall (4.32 × 10−6), spring (3.86 × 10−6), and summer (2.96 × 10−6), all in the acceptable range. The total chronic non-cancer risk (Hazard Quotient, HQ) of inhalation exposure to 16 VOCs was 0.0488, with a higher value in fall (0.0571), followed by winter (0.0464), and lower in spring (0.0454) and summer (0.0451), all in the safe level of below HQ = 1 used by the USEPA. The hazard index (HI) by organs was higher for the nervous system (0.0213), followed by the hematologic system and immune system (0.0165 each), but much lower for the other five target organs, i.e., the liver/kidney (1.52 × 10−4), developmental system (3.38 × 10−4), endocrine system and urinary system (2.82 × 10−4 each), and respiratory system (9.70 × 10−5). Similar hour-of-day trends were observed in the total CR, total HQ, and HI by organs with higher values in the early morning hours of 5:00–8:00 and lower values during 12:00 to 15:00. Benzene was the major contributor to both total CR (89%) and total HQ (34%) due to its high toxicity and high concentrations. Benzene, toluene, ethylbenzene, and xylenes (BTEX) contributed 100% of the total CR and 51% of the total HQ. Further, BTEX is the sole contributor to the HI for the hematologic system and immune system and the major contributor to the HI for the nervous system (39%) and developmental system (55%). Higher cancer and non-cancer risks were associated with the airmass from the east, southeast, and southwest of Windsor. Full article
(This article belongs to the Special Issue Air Pollution Exposure and Health Impact Assessment (2nd Edition))
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<p>Location of the study area and Windsor West monitoring station in Ontario, Canada.</p>
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<p>Seasonal variation in total cancer risk and cancer risk of benzene and ethylbenzene.</p>
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<p>Diurnal variation in cancer risk (solid circles) and 95% confidence interval (error bars) for (<b>a</b>) benzene, (<b>b</b>) ethylbenzene, and (<b>c</b>) total cancer risk.</p>
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<p>HQ and contribution (%) of each of 16 VOCs to total HQ. “Others” include n-propylbenzene, propene, and cyclohexane.</p>
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<p>Seasonal variation in the total HQ and HI for each organ in Windsor.</p>
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<p>Diurnal variation in total HQ (solid circles) and 95% confidence interval (error bars).</p>
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<p>Directional distribution of total CR: (<b>a</b>) overall, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) fall, and (<b>e</b>) winter.</p>
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<p>Directional distribution of total CR: (<b>a</b>) overall, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) fall, and (<b>e</b>) winter.</p>
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<p>Directional distribution of total HQ: (<b>a</b>) overall, (<b>b</b>) spring, (<b>c</b>) summer, (<b>d</b>) fall, and (<b>e</b>) winter.</p>
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<p>Directional distribution of HI by organ for the (<b>a</b>) nervous system, (<b>b</b>) hematologic system and immune system, (<b>c</b>) liver/ kidney, (<b>d</b>) developmental system, (<b>e</b>) endocrine system and urinary system, and (<b>f</b>) respiratory system.</p>
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16 pages, 3069 KiB  
Article
Source-Oriented Health Risks and Distribution of BTEXS in Urban Shallow Lake Sediment: Application of the Positive Matrix Factorization Model
by Ivana Trajković, Milica Sentić, Jelena Vesković, Milica Lučić, Andrijana Miletić and Antonije Onjia
Water 2024, 16(16), 2302; https://doi.org/10.3390/w16162302 - 15 Aug 2024
Viewed by 540
Abstract
The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released [...] Read more.
The degradation of sediments in urban environments worldwide is driven by population growth, urbanization, and industrialization, highlighting the need for thorough quality assessment and management strategies. As a result of these anthropogenic activities, benzene, toluene, ethylbenzene, xylenes, and styrene (BTEXS) are persistently released into the environment, polluting sediment. This study employed self-organizing maps (SOMs), positive matrix factorization (PMF), and Monte Carlo simulation of source-oriented health risks to comprehensively investigate sediment in an urban shallow lake in a mid-sized city in central Serbia. The results indicated a mean ∑BTEXS concentration of 225 µg/kg, with toluene as the dominant congener, followed by m,p-xylene, benzene, ethylbenzene, o-xylene, and styrene. Three contamination sources were identified: waste solvents and plastic waste due to intensive recreational activities, and vehicle exhaust from heavy traffic surrounding the lake. Both non-carcinogenic and carcinogenic health risks were below the permissible limits. However, children were more susceptible to health risks. Benzene from vehicle exhaust is the most responsible for non-carcinogenic and carcinogenic health risks in both population groups. The results of this study can help researchers to find a suitable perspective on the dynamics and impacts of BTEXS in lake sediments. Full article
(This article belongs to the Special Issue Fate, Transport, Removal and Modeling of Pollutants in Water)
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<p>Location of Bubanj Lake with sampling points.</p>
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<p>Violin plots showing the variations in and probability densities of sediment texture and BTEXS (µg/kg) in the sediment samples of Bubanj Lake.</p>
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<p>Spatial distribution of BTEXS concentration (μg/kg) in Bubanj Lake’s sediments.</p>
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<p>Component planes of BTEXS in the sediment samples of Bubanj Lake.</p>
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<p>Factor profiles and contributions of each factor to BTEXS concentrations in the sediments of Bubanj Lake identified by the PMF model.</p>
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<p>Probability distributions of source-oriented health risks from BTEXS in the sediment of Bubanj Lake.</p>
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19 pages, 7989 KiB  
Article
Impact of Aromatic Hydrocarbons on Emissions in a Custom-Built High-Pressure Combustor
by Qiming Yu and Bhupendra Khandelwal
Energies 2024, 17(16), 3939; https://doi.org/10.3390/en17163939 - 8 Aug 2024
Viewed by 775
Abstract
This study addresses the ongoing demand for increased efficiency and reduced emissions in turbomachinery combustion systems. A custom-built high-pressure combustor was designed and manufactured at the Low Carbon Combustion Centre (LCCC) of the University of Sheffield to investigate the impact of different aromatic [...] Read more.
This study addresses the ongoing demand for increased efficiency and reduced emissions in turbomachinery combustion systems. A custom-built high-pressure combustor was designed and manufactured at the Low Carbon Combustion Centre (LCCC) of the University of Sheffield to investigate the impact of different aromatic hydrocarbons on emission rates. The research involved the comprehensive testing of Jet−A1 fuel and six aromatic species blends under high-pressure conditions of 10 bar. Based on the numerical CFD simulations by ANSYS 19.2, tangential dual air injection and a strategically placed V-shaped baffle plate were utilised to enhance fuel-air mixing and combustion stability. Experimental results demonstrated a negative correlation between combustion temperature and particulate matter (PM) emissions, with higher temperatures yielding lower PM emissions. Unburned hydrocarbons (UHCs), nitrogen oxides (NOx), carbon monoxide (CO), and carbon dioxide (CO2) emissions were also analysed. Ethylbenzene produced the highest UHC and CO emissions, while Indane exhibited the lowest levels of these pollutants, suggesting more complete combustion. O−xylene generated the highest NOx emissions, correlating with its higher combustion temperatures. This research enhances our understanding of gas turbine combustor design and the combustion behaviour of aromatic species, providing valuable insights for developing low-emission, high-efficiency gas turbine combustion technologies. Full article
(This article belongs to the Special Issue Advanced Combustion Technologies and Emission Control)
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<p><math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math> vs. flame temperature [<a href="#B2-energies-17-03939" class="html-bibr">2</a>].</p>
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<p>Six selected aromatic species chemical structures [<a href="#B11-energies-17-03939" class="html-bibr">11</a>].</p>
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<p>Fuel atomiser design and tests.</p>
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<p>Designed high-pressure combustor with a V-shaped baffle plate as flame stabiliser (Unit: mm).</p>
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<p>Manufactured high-pressure combustor.</p>
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<p>Initial CFDsimulations based on ANSYS CFD.</p>
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<p>Temperature and pressure sensors.</p>
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<p>Live air mass flow rate measuring instrument.</p>
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<p>UHC, <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics></math> measuring instruments.</p>
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<p>Initial Jet−A1 tests with visible smoke.</p>
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<p>UHC and <math display="inline"><semantics> <mrow> <mi>N</mi> <msub> <mi>O</mi> <mi>x</mi> </msub> </mrow> </semantics></math> emission results (Jet−A1 with different aromatic species (13% mass blend)).</p>
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<p><math display="inline"><semantics> <mrow> <mi>C</mi> <mi>O</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics></math> emission results (Jet−A1 with different aromatic species (13% mass blend)).</p>
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14 pages, 2196 KiB  
Article
Evaluation of BTEX Pollution and Health Risk for Sustainable Use of a Typical Chemical Pesticide Industrial Site
by Ye Huang, Yangmin Chen, Qingqing Wu, Peili Shi, Bin Yang and Yunfeng Xie
Sustainability 2024, 16(15), 6494; https://doi.org/10.3390/su16156494 - 29 Jul 2024
Viewed by 747
Abstract
BTEX (benzene, toluene, ethylbenzene, and xylenes) are widely used in pesticide manufacturing industries. Due to their high volatility and toxicity, BTEX compounds often leak during production, storage, and transportation, posing significant threats to human health and the environment. In this study, soil and [...] Read more.
BTEX (benzene, toluene, ethylbenzene, and xylenes) are widely used in pesticide manufacturing industries. Due to their high volatility and toxicity, BTEX compounds often leak during production, storage, and transportation, posing significant threats to human health and the environment. In this study, soil and groundwater samples at a chemical pesticide industrial site in southern China were collected and analyzed. Soil concentrations ranged from 0.05–142 mg/kg for benzene, 0.05–315 mg/kg for toluene, 0.05–889 mg/kg for ethylbenzene, 0.05–2800 mg/kg for m-&p-xylene, and 0.05–668 mg/kg for o-xylene. Groundwater concentrations were 0.7–340,000 μg/L for benzene, 0.9–4070 μg/L for toluene, 0.5–1900 μg/L for ethylbenzene, 1.6–6000 μg/L for m-&p-xylene, and 0.6–1500 μg/L for o-xylene. While the average concentrations were relatively low, there were numerous locations where BTEX levels significantly exceeded national soil and groundwater standards. Despite the minimal health risks from soil BTEX pollution, utilizing groundwater for drinking or bathing could result in unacceptable cancer and non-cancer risks. These findings underscore the urgent need for remediation efforts, particularly concerning benzene contamination in groundwater, to ensure the sustainable utilization of the industrial site in question. Full article
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<p>Layout of the industrial site and distribution of sampling points.</p>
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<p>Detection rates of BTEX compounds in various areas of the site (<b>a</b>) and average concentrations in detected samples (<b>b</b>).</p>
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<p>Concentrations and detection rates of BTEX compounds in the groundwater at the site.</p>
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<p>Exposures of people who work or live in the studied site for different durations by different exposure routes (<b>a</b>): drink; (<b>b</b>): bath; (<b>c</b>): ingest; (<b>d</b>): derma contact; (<b>e</b>): inhale.</p>
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<p>Non-cancer risks of people who work or live in the studied site for different durations by different exposure routes (<b>a</b>): drink; (<b>b</b>): bath; (<b>c</b>): ingest; (<b>d</b>): derma contact; (<b>e</b>): inhale. The red line marks where non-cancer risk (described by the hazard index, HD) is 1.</p>
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15 pages, 5474 KiB  
Article
Comparative Analysis of Machine Learning Models for Predicting Viscosity in Tri-n-Butyl Phosphate Mixtures Using Experimental Data
by Faranak Hatami and Mousa Moradi
Computation 2024, 12(7), 133; https://doi.org/10.3390/computation12070133 - 30 Jun 2024
Viewed by 595
Abstract
Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing [...] Read more.
Tri-n-butyl phosphate (TBP) is essential in the chemical industry for dissolving and purifying various inorganic acids and metals, especially in hydrometallurgical processes. Recent advancements suggest that machine learning can significantly improve the prediction of TBP mixture viscosities, saving time and resources while minimizing exposure to toxic solvents. This study evaluates the effectiveness of five machine learning algorithms for automating TBP mixture viscosity prediction. Using 511 measurements collected across different compositions and temperatures, the neural network (NN) model proved to be the most accurate, achieving a Mean Squared Error (MSE) of 0.157% and an adjusted R2 (a measure of how well the model predicts the variability of the outcome) of 99.72%. The NN model was particularly effective in predicting the viscosity of TBP + ethylbenzene mixtures, with a minimal deviation margin of 0.049%. These results highlight the transformative potential of machine learning to enhance the efficiency and precision of hydrometallurgical processes involving TBP mixtures, while also reducing operational risks. Full article
(This article belongs to the Section Computational Engineering)
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<p>The main steps used in this study to predict the dynamic viscosity of TBP mixtures.</p>
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<p>Distribution of the features used in this study is shown in (<b>a</b>–<b>h</b>). Viscosity has a normal distribution with N (1.47, 0.81) as shown in (<b>i</b>). The width of all the bar charts is uniform. However, in certain charts such as toluene and ethylbenzene, there are clusters of data points that are closely grouped together, appearing as if the bars are wider at first glance. This clustering indicates that there are data points with compositions that are similar.</p>
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<p>Correlation heatmap matrix for input features and the output target.</p>
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<p>(<b>a</b>) Association of temperature with viscosity. (<b>b</b>) Relative viscosity versus TBP concentration (r = 0.8) and temperature (r =−0.5). Each dot represents an individual data point. The histogram along the top displays the distribution of temperature values, while the histogram along the right side shows the distribution of viscosity values. The trend line indicates the general decreasing trend of viscosity with increasing temperature.</p>
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<p>Explained variance (<b>a</b>), eight first features explained almost all variability, (<b>b</b>) distribution of important features. The red dashed line shows the cumulative variance as more components are added. The black horizontal dashed line indicates 100% variance explained, while the black vertical dashed line marks the point where the cumulative variance levels off, suggesting that 7 components are sufficient to capture nearly all the variance in the dataset.</p>
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<p>The optimal structure of the neural network developed in this study.</p>
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<p>Learning curves for the NN for 120 Epochs: (<b>a</b>) accuracy profile; (<b>b</b>) loss profile. The model was converged after 20 epochs. A total of 10% dropout was used to avoid overfitting.</p>
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<p>Evaluating the five developed models in predicting the viscosity of TBP mixtures. The labels of ‘LR’, ‘SVR’, ‘RF’, ’XGB’, and ‘NN’ refer to the respective five image rows, and “Train”, “Test”, and” Box Plot” refer to the respective three columns. A 95% confidence interval was used to plot the data. A 0.05 significance level was used to compare the predicted and observed values. Each blue dot corresponds to a pair of observed and predicted values. The orange line represents the ideal line where predicted values perfectly match the observed values.</p>
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<p>Comparison between the observed and predicted values for the four top features associated with NN performance. TBP + Ethylbenzene showed the best NN performance in predicting viscosity. The boxes represent the interquartile range (IQR), the horizontal line inside each box indicates the median, the whiskers extend to 1.5 times the IQR, and the dots represent outliers.</p>
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<p>The margin of deviation of NN results with respect to the type of solvent (feature): temperature, cyclohexane, n-heptane, ethylbenzene, hexane, dodecane, toluene, and TBP. The red dashed line at 0% indicates perfect agreement between the observed and predicted viscosities. Each box plot shows the range, interquartile range (IQR), median, and outliers of the MOD for each factor.</p>
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12 pages, 3857 KiB  
Article
Groundwater Contamination by Gas Stations in Two Eastern Amazonian Towns (Northern Brazil)
by Pedro Chira, Rosivaldo Mendes, Stephen Ferrari, Cassia Rocha, Elisama da Silva, Jarlana Farias and Raerida do Carmo
Appl. Sci. 2024, 14(13), 5529; https://doi.org/10.3390/app14135529 - 26 Jun 2024
Viewed by 860
Abstract
The present study analyzed the presence of the principal volatile compounds of the BTEX type (benzene, toluene, ethylbenzene, and xylene [o-, m- and p-xylene]) in samples of water from wells located at residences and gas stations in two Amazonian towns—Tracuateua and Augusto Corrêa—in [...] Read more.
The present study analyzed the presence of the principal volatile compounds of the BTEX type (benzene, toluene, ethylbenzene, and xylene [o-, m- and p-xylene]) in samples of water from wells located at residences and gas stations in two Amazonian towns—Tracuateua and Augusto Corrêa—in the Amazon region of northern Brazil. This innovative study is extremely relevant to the Amazonian towns surveyed, given that they lack systematic policies for the environmental control of gas stations and any municipal regulations on the quality of water destined for human consumption. A combination of mass spectrometry (MS) and gas chromatography (CG) techniques was applied to analyze these contaminants in 150 samples of local groundwater collected between 2020 and 2024. One of the four BTEX compounds (toluene) was identified in seven of the samples collected (4.66% of the total) at concentrations of 0.14–2.10 µg L−1. The concentrations of contaminants were low, in general. None of the water samples analyzed here presented any critical loss of water quality for human consumption according to the Brazilian legislation concerning BTEX concentrations. Neither of the two towns surveyed in the present study has remediation programs for environmental contamination. The GC-MS approach produced satisfactory results for the assessment of the contamination of underground water reserves by gas stations in both study towns. Further research (e.g., geophysical methods) will be necessary to determine the source of the contamination and its connection with the levels of toluene identified in the underground water sampled in these Amazonian towns. Full article
(This article belongs to the Section Environmental Sciences)
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<p>Study areas surveyed in two Amazonian towns, in northern Brazil. Sources [<a href="#B41-applsci-14-05529" class="html-bibr">41</a>,<a href="#B42-applsci-14-05529" class="html-bibr">42</a>].</p>
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<p>Chromatogram of sample A06 from gas station P14, showing the BTEX concentrations (Augusto Corrêa, November 2022).</p>
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14 pages, 5670 KiB  
Article
Development of a Smartwatch with Gas and Environmental Sensors for Air Quality Monitoring
by Víctor González, Javier Godoy, Patricia Arroyo, Félix Meléndez, Fernando Díaz, Ángel López, José Ignacio Suárez and Jesús Lozano
Sensors 2024, 24(12), 3808; https://doi.org/10.3390/s24123808 - 12 Jun 2024
Viewed by 3722
Abstract
In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is [...] Read more.
In recent years, there has been a growing interest in developing portable and personal devices for measuring air quality and surrounding pollutants, partly due to the need for ventilation in the aftermath of COVID-19 situation. Moreover, the monitoring of hazardous chemical agents is a focus for ensuring compliance with safety standards and is an indispensable component in safeguarding human welfare. Air quality measurement is conducted by public institutions with high precision but costly equipment, which requires constant calibration and maintenance by highly qualified personnel for its proper operation. Such devices, used as reference stations, have a low spatial resolution since, due to their high cost, they are usually located in a few fixed places in the city or region to be studied. However, they also have a low temporal resolution, providing few samples per hour. To overcome these drawbacks and to provide people with personalized and up-to-date air quality information, a personal device (smartwatch) based on MEMS gas sensors has been developed. The methodology followed to validate the performance of the prototype was as follows: firstly, the detection capability was tested by measuring carbon dioxide and methane at different concentrations, resulting in low detection limits; secondly, several experiments were performed to test the discrimination capability against gases such as toluene, xylene, and ethylbenzene. principal component analysis of the data showed good separation and discrimination between the gases measured. Full article
(This article belongs to the Special Issue Recent Advancements in Olfaction and Electronic Nose)
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<p>Permeation tube diffusion process.</p>
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<p>Smartwatch design and its main menu.</p>
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<p>Smartwatch block diagram (<b>left</b>) and electronic board (<b>right</b>).</p>
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<p>Experimental setup for gas bottles.</p>
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<p>Permeation tube measurement setup.</p>
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<p>SGP40 response: (<b>a</b>) SGP40 CO<sub>2</sub> response; and (<b>b</b>) SGP40 CH<sub>4</sub> response.</p>
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<p>BME688 response: (<b>a</b>) BME688 CO<sub>2</sub> response; and (<b>b</b>) BME688 CH<sub>4</sub> response.</p>
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<p>ENS160 response: (<b>a</b>) ENS160 CO<sub>2</sub> R<sub>4</sub> response; and (<b>b</b>) ENS160 CH<sub>4</sub> R<sub>4</sub> response.</p>
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<p>Lineal regression on CO<sub>2</sub> response.</p>
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<p>Lineal regression on CH<sub>4</sub> response.</p>
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<p>PCA analyses when ethylbenzene, toluene, and xylene are measured.</p>
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<p>Load plots.</p>
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24 pages, 5891 KiB  
Article
Eco-Friendly Inorganic Binders: A Key Alternative for Reducing Harmful Emissions in Molding and Core-Making Technologies
by Angelika Kmita, Rafał Dańko, Mariusz Holtzer, Józef Dańko, Dariusz Drożyński, Mateusz Skrzyński, Agnieszka Roczniak, Daniel Robert Gruszka, Jarosław Jakubski and Sara Tapola
Int. J. Mol. Sci. 2024, 25(10), 5496; https://doi.org/10.3390/ijms25105496 - 17 May 2024
Viewed by 730
Abstract
Many years of foundry practice and much more accurate analytical methods have shown that sands with organic binders, in addition to their many technological advantages, pose risks associated with the emission of many compounds, including harmful ones (e.g., formaldehyde, phenol, benzene, polycyclic aromatic [...] Read more.
Many years of foundry practice and much more accurate analytical methods have shown that sands with organic binders, in addition to their many technological advantages, pose risks associated with the emission of many compounds, including harmful ones (e.g., formaldehyde, phenol, benzene, polycyclic aromatic hydrocarbons, and sulfur), arising during the pouring of liquid casting alloys into molds, their cooling, and knock-out. The aim of this research is to demonstrate the potential benefits of adopting inorganic binders in European iron foundries. This will improve the environmental and working conditions by introducing cleaner and more ecological production methods, while also ranking the tested binders studied in terms of their harmful content. The article pays special attention to the analysis of seven innovative inorganic binders and one organic binder, acting as a reference for emissions of gases from the BTEX (benzene, toluene, ethylbenzene, and xylenes) and PAHs (polycyclic aromatic hydrocarbons) groups and other compounds such as phenol, formaldehyde, and isocyanates (MDI and TDI) generated during the mold pouring process with liquid metals. The knowledge gained will, for the first time, enrich the database needed to update the Reference Document on The Best Available Techniques for the Smitheries and Foundries Industry (SF BREF). Full article
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<p>Chemical reaction between binder components in the PUNB organic binder system.</p>
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<p>Examples of the most often applied aromatic isocyanates [<a href="#B1-ijms-25-05496" class="html-bibr">1</a>].</p>
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<p>Emissivity of gases from the investigated molding sands and influence of the hardening method (red dashed line—the contractual time for completing the examined process).</p>
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<p>Emissivity of gases from the investigated molding sands and influence of the protective coating (red dashed line—the contractual time for completing the examined process).</p>
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<p>Gas release rate from the investigated molding sands: influence of the applied hardening technology.</p>
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<p>Total emission of substances from the BTEX group, calculated per 1 g of a binder (I red group (code: no. 4, no. 4 + coating)—very high content of compounds from the BTEX group; II blue group (code: no. 3 + coating, 5, 8, 7, 2)—small content of compounds from the BTEX group; III green group (code: no. 3, 6, 1)—trace content of compounds from the BTEX group).</p>
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<p>Contents of individual substances from the BTEX group emitted during pouring the molding sand (code no. 4) bonded by an organic binder: (<b>a</b>) per 1 g of a binder (red group (Benzene, Toluene)—scale on the left; blue group (Ethylbenzene, Xylenes)—scale on the right) and (<b>b</b>) percentage composition of gases emitted from the BTEX group.</p>
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<p>Contents of individual substances from the BTEX group emitted during pouring the molding sand (code no. 4) bonded by an organic binder: (<b>a</b>) per 1 g of a binder (red group (Benzene, Toluene)—scale on the left; blue group (Ethylbenzene, Xylenes)—scale on the right) and (<b>b</b>) percentage composition of gases emitted from the BTEX group.</p>
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<p>Contents of individual substances from the BTEX group emitted during pouring the molding sand (code no. 1) bonded by an inorganic binder: (<b>a</b>) per 1g of a binder (red group (Benzene, Toluene)—scale on the left; blue group (Ethylbenzene, Xylenes)—scale on the right) and (<b>b</b>) percentage composition of gases emitted from the BTEX group.</p>
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<p>Contents of individual substances from the BTEX group emitted during pouring the molding sand (code no. 1) bonded by an inorganic binder: (<b>a</b>) per 1g of a binder (red group (Benzene, Toluene)—scale on the left; blue group (Ethylbenzene, Xylenes)—scale on the right) and (<b>b</b>) percentage composition of gases emitted from the BTEX group.</p>
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<p>Total PAHs emission, calculated per 1 g of a binder (I red group (code: no. 4 + coating, no. 4)—very high content of substances from the PAHs group; II blue group (code: no. 5, 3 + coating, 7, 2, 8)—small content of substances from the PAHs group; III green group (code: no. 3, 1, 6)—trace content of substances from the PAHs group).</p>
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<p>Formaldehyde emission, calculated per 1 g of a binder (red group (code: no. 4 + coating, no. 4)—the highest formaldehyde emission among those tested; blue group (code: no. 2, 7, 3, 6, 1)—trace emission of formaldehyde among those tested).</p>
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<p>Total isocyanates (MDI/TDI) emission, calculated per 1 g of molding sand and per 1 g of a binder.</p>
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<p>Concentration changes in CO, CO<sub>2</sub>, NO<sub>x</sub>, O<sub>2,</sub> and T<sub>voc</sub> in gases released from the molding sand code no. 1 (red dashed line—minimum O<sub>2</sub> concentration in emitted gases).</p>
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<p>Concentration changes in CO, CO<sub>2</sub>, NO<sub>x</sub>, SO<sub>2,</sub> and T<sub>voc</sub> in gases released from the investigated molding sand (code no. 4) (red dashed line—minimum O<sub>2</sub> concentration in emitted gases).</p>
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<p>Concentration changes in CO, CO<sub>2</sub>, NO<sub>x</sub>, SO<sub>2,</sub> and T<sub>voc</sub> in gases released from the investigated molding sand (code no. 4 + coating) (red dashed line—minimum O<sub>2</sub> concentration in emitted gases).</p>
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<p>Examples of the results of the grain size distribution of dusts generated during pouring molds of the tested molding sands: (<b>a</b>) no. 1; (<b>b</b>) no. 3; (<b>c</b>) no. 4; and (<b>d</b>) no. 4 + coating.</p>
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<p>Examples of the results of the grain size distribution of dusts generated during pouring molds of the tested molding sands: (<b>a</b>) no. 1; (<b>b</b>) no. 3; (<b>c</b>) no. 4; and (<b>d</b>) no. 4 + coating.</p>
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34 pages, 3225 KiB  
Article
Plant-Wide Models for Optimizing the Operation and Maintenance of BTEX-Contaminated Wastewater Treatment and Reuse
by Dániel Bencsik, Tanush Wadhawan, Ferenc Házi and Tamás Karches
Environments 2024, 11(5), 88; https://doi.org/10.3390/environments11050088 - 25 Apr 2024
Cited by 1 | Viewed by 1523
Abstract
Benzene, toluene, ethylbenzene and xylenes, collectively known as BTEX compounds, are significant emerging contaminants in municipal wastewater. Stricter effluent quality regulations necessitate their removal, especially with concerns about organic micropollutant concentrations. Water scarcity further underscores the need for wastewater treatment to ensure safe [...] Read more.
Benzene, toluene, ethylbenzene and xylenes, collectively known as BTEX compounds, are significant emerging contaminants in municipal wastewater. Stricter effluent quality regulations necessitate their removal, especially with concerns about organic micropollutant concentrations. Water scarcity further underscores the need for wastewater treatment to ensure safe agricultural or drinking water supplies. Although biological treatment partially reduces BTEX levels through processes like biodegradation and sorption, additional purification using physico-chemical methods is crucial for substantial reduction. This paper aims to outline plant-wide simulation methods for treating BTEX-contaminated sewage and facilitating reuse, adhering to IWA Good Modelling Practice Guidelines. The model, built upon the MiniSumo process model, incorporates equations detailing BTEX metabolism and removal kinetics, informed by an extensive literature review. Using a variant of the Benchmark Simulation Model with granular activated carbon for water reuse, the study examines strategies for improving effluent quality and minimizing operational costs. These strategies include adjusting the sludge retention time and airflow to enhance BTEX degradation and stripping, respectively, and comparing maintenance approaches for the GAC tower. Full article
(This article belongs to the Special Issue Advanced Technologies of Water and Wastewater Treatment)
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<p>Modified BSM1 test configuration for modelling examples of BTEX removal. Arrows represent the direction of the fluid flow.</p>
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<p>Combined BTEX component removal rate profile.</p>
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<p>Specific AC demand and blower energy demand for SRT-based scenarios.</p>
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<p>Effluent quality concerning BTEX compounds for SRT-based scenarios.</p>
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<p>Contribution of BTEX removal processes for SRT-based scenarios.</p>
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<p>Specific AC demand and blower energy demand for air flux-based scenarios.</p>
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<p>Effluent quality concerning BTEX compounds for air flux-based scenarios.</p>
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<p>Contribution of BTEX removal processes for air flux-based scenarios.</p>
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<p>Illustration of GAC tower operational strategies.</p>
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20 pages, 5292 KiB  
Article
One-Step Production of Highly Selective Ethylbenzene and Propylbenzene from Benzene and Carbon Dioxide via Coupling Reaction
by Tianyun Wang, Yingjie Guan, Haidan Wu, Zhaojie Su, Jianguo Zhuang, Siyan Yan, Xuedong Zhu and Fan Yang
Catalysts 2024, 14(5), 288; https://doi.org/10.3390/catal14050288 - 24 Apr 2024
Viewed by 1043
Abstract
Utilizing carbon dioxide as a carbon source for the synthesis of olefins and aromatics has emerged as one of the most practical methods for CO2 reduction. In this study, an improved selectivity of 85% for targeting products (ethylbenzene and propylbenzene) is achieved [...] Read more.
Utilizing carbon dioxide as a carbon source for the synthesis of olefins and aromatics has emerged as one of the most practical methods for CO2 reduction. In this study, an improved selectivity of 85% for targeting products (ethylbenzene and propylbenzene) is achieved with a benzene conversion of 16.8% by coupling the hydrogenation of carbon dioxide to olefins over the bifunctional catalyst “Oxide-Zeolite” (OX-ZEO) and the alkylation of benzene with olefins over ZSM-5. In addition to investigating the influence of SAPO-34 and ZSM-5 zeolite acidity on product distribution, catalyst deactivation due to coke formation is addressed by modifying both molecular sieves to be hierarchical to extend the catalyst lifespan. Even after 100 h of operation at 400 °C, the catalysts maintained over 80% selectivity towards the target products, with benzene conversion over 14.2%. Furthermore, the pathway of propylbenzene formation is demonstrated through simple experimental design, revealing that the surface Brønsted acid sites of SAPO-34 serve as its primary formation sites. This provides a novel perspective for further investigation of the reaction network. Full article
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Graphical abstract
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<p>XRD patterns of Zn/Ga/Al oxides.</p>
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<p>XPS spectra of (<b>a</b>) Zn 2p, (<b>b</b>) Al 2p, (<b>c</b>) Ga 3d, and (<b>d</b>) O 1s orbitals.</p>
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<p>SEM micrographs of (<b>a</b>–<b>e</b>) hierarchical SAPO-34 with different Si/Al ratios, (<b>f</b>) commercial SAPO-34, (<b>g</b>,<b>h</b>) hierarchical ZSM-5, and (<b>i</b>) commercial ZSM-5.</p>
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<p>NH<sub>3</sub>-IR spectra of hierarchical SAPO-34 with different Si/Al ratios measured at (<b>a</b>) 100 °C and (<b>b</b>) 300 °C.</p>
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<p>Conversions of benzene and composition of the liquid phase with different Zn/Ga/Al oxides. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Conversions and traces of CO<sub>2</sub> with different Zn/Ga/Al oxides. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Conversions of benzene and composition of the liquid phase with different metal oxides to SAPO-34 ratios. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Conversions of benzene and composition of the liquid phase in SAPO-34 with different Si/Al ratios. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Selectivity of ethylbenzene and propylbenzene (EB and PB) in SAPO-34 with different Si/Al ratios.</p>
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<p>Conversions of benzene and composition of the liquid phase in ZSM-5 with different Si/Al ratios. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Comparison of commercial and hierarchical ZSM-5 (Si/Al = 65) in catalytic performance. Reaction conditions: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Stability of the Zn<sub>2</sub>Ga<sub>0.5</sub>Al<sub>1.5</sub>O<sub>x</sub>/H-S34(0.2) +H-Z5(65) catalyst in the reaction. Reaction condition: 400 °C, 3 MPa, H<sub>2</sub>:CO<sub>2</sub>:Benzene = 12:4:1, GHSV = 22,500 mL·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> ·h<sup>−1</sup>, WHSV = 0.9 g<sub>benzene</sub>·<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mi mathvariant="normal">c</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math>·h<sup>−1</sup>.</p>
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<p>Effect of the series combination of catalysts on the product distribution and benzene conversion rate.</p>
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<p>Proposed generation sites for propylbenzene.</p>
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9 pages, 608 KiB  
Study Protocol
Surveillance of Occupational Exposure to Volatile Organic Compounds at Gas Stations: A Scoping Review Protocol
by Tatiana de Medeiros Carvalho Mendes, Juliana Pontes Soares, Pétala Tuani Cândido de Oliveira Salvador and Janete Lima de Castro
Int. J. Environ. Res. Public Health 2024, 21(5), 518; https://doi.org/10.3390/ijerph21050518 - 23 Apr 2024
Viewed by 1042
Abstract
Health surveillance guides public policies, allows for the monitoring of occupational exposures that may cause health risks, and can prevent work-related diseases. The scoping review protocol herein is designed to map studies on the surveillance of occupational exposure to volatile organic compounds (VOCs) [...] Read more.
Health surveillance guides public policies, allows for the monitoring of occupational exposures that may cause health risks, and can prevent work-related diseases. The scoping review protocol herein is designed to map studies on the surveillance of occupational exposure to volatile organic compounds (VOCs) in gas stations and identify the governmental agencies and public health measures in different countries. This review protocol is based on the Joanna Briggs Institute manual and guided by the PRISMA Extension for Scoping Reviews. It includes research articles, theses, dissertations, and official documents on surveillance measures for occupational exposure to VOCs (i.e., benzene, ethylbenzene, toluene, and xylene) in gas stations from different countries. All languages and publication dates will be considered, and a spreadsheet will be used to extract and analyze qualitative and quantitative data. The final version will present the main surveillance measures implemented, responsible entities, results, challenges, limitations, and potential gaps in gas stations. Full article
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<p>Stages of the scoping review protocol.</p>
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20 pages, 3555 KiB  
Article
BTEX Assessment among Informal Charcoal-Burning Food Traders for Cleaner and Sustainable Environment
by Lebogang Phama, Goitsemang Keretetse, Thokozani Mbonane, Phoka Rathebe, Robert Makae and Masilu Daniel Masekameni
Sustainability 2024, 16(8), 3336; https://doi.org/10.3390/su16083336 - 16 Apr 2024
Viewed by 998
Abstract
This study assessed the cleaner and sustainable environment by measuring emission levels of benzene, toluene, ethylbenzene, and xylene (BTEX) from informal food traders using charcoal as the primary source of energy at a flea market in Fordsburg, Johannesburg. Volatile organic compounds (VOCs) were [...] Read more.
This study assessed the cleaner and sustainable environment by measuring emission levels of benzene, toluene, ethylbenzene, and xylene (BTEX) from informal food traders using charcoal as the primary source of energy at a flea market in Fordsburg, Johannesburg. Volatile organic compounds (VOCs) were measured using a real-time monitor (MiniRae 3000 photoionization detector); an indoor air quality (IAQ) monitor was used to monitor environmental parameters and passive samplers in the form of Radiello badges, which were used to determine BTEX emissions from charcoal used during food preparation. Measurements were taken at 1.5 m above ground assuming the receptor’s breathing circumference using PID and Radiello. PID data were downloaded and analyzed using Microsoft Excel (Version 2019). Radiellos were sent to the laboratory to determine the BTEX levels from the total VOCs. The total volatile organic compound (TVOC) concentration over the combustion cycle was 306.7 ± 62.8 ppm. The flaming phase had the highest VOC concentration (547 ± 110.46 ppm), followed by the ignition phase (339.44 ± 40.6 ppm) and coking with the lowest concentration (24.64 ± 14.3). The average BTEX concentration was 15.7 ± 5.9 µg/m3 corresponding to the entire combustion cycle. BTEX concentrations were highest at the flaming phase (23.6 µg/m3) followed by the ignition (13.4 µg/m3) and coking phase (9.45 µg/m3). Ignition phase versus the flaming phase, there was a significant difference at 95% at a p-value of 0.09; ignition phase versus the coking phase, there was a significant difference at 95% at a p-value of 0.039; and coking phase versus the flaming phase, there was a significant difference at 95% at a p-value of 0.025. When compared to the occupational exposure limits (OELs), none of the exposure concentrations (BTEX) were above the 8 h exposure limit. The findings of this study suggest that charcoal, as a source of energy, can still be a useful and sustainable fuel for informal food traders. Shortening the ignition and flaming phase duration by using a fan to supply sufficient air can further reduce exposure to VOCs. Full article
(This article belongs to the Special Issue Environmental Pollution and Impacts on Human Health)
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<p>Geographic map for study: Fordsburg, Johannesburg.</p>
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<p>Schematic diagram of Fordsburg Square Flea Market.</p>
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<p>Flow diagram of activities observed at the flea market.</p>
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<p>Photoionization detector (PID), MiniRae 3000 (RAE Systems, Inc., Kent, WA, USA).</p>
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<p>Q-Trak I IAQ meter for monitoring temperature, humidity, CO, and CO<sub>2</sub> (TSI Inc., Shoreview, USA).</p>
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<p>TVOCs during the ignition phase.</p>
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<p>TVOCs during the flaming phase.</p>
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<p>TVOCs during the coking phase.</p>
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13 pages, 2978 KiB  
Article
Urinary Volatile Organic Compound Metabolites Are Associated with Reduced Lung Function in U.S. Children and Adolescents
by Angelico Mendy, Sara Burcham, Ashley L. Merianos, Tesfaye B. Mersha, Kimberly Yolton, Aimin Chen and E. Melinda Mahabee-Gittens
Toxics 2024, 12(4), 289; https://doi.org/10.3390/toxics12040289 - 16 Apr 2024
Viewed by 1403
Abstract
(1) Background: Volatile organic compounds (VOCs) are indoor pollutants absorbed by inhalation. The association of several VOCs with lung function in children and adolescents is unknown. (2) Methods: We analyzed 505 participants, 6–17-year-olds from the 2011–2012 National Health and Nutrition Examination Survey. Multiple [...] Read more.
(1) Background: Volatile organic compounds (VOCs) are indoor pollutants absorbed by inhalation. The association of several VOCs with lung function in children and adolescents is unknown. (2) Methods: We analyzed 505 participants, 6–17-year-olds from the 2011–2012 National Health and Nutrition Examination Survey. Multiple linear regression models were fitted to estimate the associations of VOC metabolites with spirometry outcomes adjusting for covariates. (3) Results: Urinary metabolites of xylene, acrylamide, acrolein, 1,3-butadiene, cyanide, toluene, 1-bromopropane, acrylonitrile, propylene oxide, styrene, ethylbenzene, and crotonaldehyde were all detected in ≥64.5% of participants. Forced expiratory volume in 1 s (FEV1) % predicted was lower in participants with higher levels of metabolites of acrylamide (β: −7.95, 95% CI: −13.69, −2.21) and styrene (β: −6.33, 95% CI: −11.60, −1.07), whereas the FEV1 to forced vital capacity (FVC) ratio % was lower in children with higher propylene oxide metabolite levels (β: −2.05, 95% CI: −3.49, −0.61). FEV1 % predicted was lower with higher crotonaldehyde metabolite levels only in overweight/obese participants (β: −15.42, 95% CI: −26.76, −4.08) (Pinteraction < 0.001) and with higher 1-bromopropane metabolite levels only in those with serum cotinine > 1 ng/mL (β: −6.26, 95% CI: −9.69, −2.82) (Pinteraction < 0.001). (4) Conclusions: We found novel associations of metabolites for acrylamide, propylene oxide, styrene, 1-bromopropane and crotonaldehyde with lower lung function in children and adolescents. Full article
(This article belongs to the Section Human Toxicology and Epidemiology)
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<p>Correlation heatmap and coefficients of creatinine-corrected urinary VOC metabolites.</p>
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<p>Restricted cubic splines (solid lines) and their corresponding 95% confidence interval (dashed lines) for the associations of acrylamide (AAM + AMC) and styrene (MAD) urinary metabolites with FEV<sub>1</sub> % predicted and of propylene oxide (HP2) urinary metabolite with FEV<sub>1</sub>/FVC. Models adjusted for age, sex, race/ethnicity, the poverty income ratio, body mass index, height, log-transformed serum cotinine, and urinary creatinine. * = <span class="html-italic">p</span> &lt; 0.05, ** = <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Univariate exposure-outcome relationship for each VOC metabolites association with FEV<sub>1</sub> % predicted and FEV<sub>1</sub>/FVC, with all other VOC metabolites fixed at their median value. Bayesian Kernel Machine Regression models adjusted for age, sex, race/ethnicity, the poverty income ratio, body mass index, height, log-transformed serum cotinine, and urinary creatinine. Rectangle indicates a significant association. The blue line indicate the cubic splines and the shadows indicate the 95% confidence intervals.</p>
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18 pages, 6551 KiB  
Article
Investigating the Role of Cs Species in the Toluene–Methanol Side Chain Alkylation Catalyzed by CsX Catalysts
by Zhihui Zhang, Qingwei Wang, Wenxiu Gao, Chunxiang Ma and Miaomiao Yang
Catalysts 2024, 14(4), 256; https://doi.org/10.3390/catal14040256 - 12 Apr 2024
Viewed by 931
Abstract
The side chain alkylation of toluene with methanol was studied on a series of CsX catalysts prepared by varying the Cs species and ion exchange conditions. The effects of various parameters, such as the exchanging temperatures and times on the adsorption/activation properties of [...] Read more.
The side chain alkylation of toluene with methanol was studied on a series of CsX catalysts prepared by varying the Cs species and ion exchange conditions. The effects of various parameters, such as the exchanging temperatures and times on the adsorption/activation properties of different CsX catalysts, were investigated by combining a variety of characterization means for understanding the role of Cs species in the side chain alkylation reaction. On the basis of the various characterization results and their related literature results, it can be proposed that the Cs ions located on the ion-exchanged sites of X zeolites could effectively adsorb and activate toluene molecularly through modifying the basicity of framework oxygen, whereas the cluster of cesium oxide (Cs2O) could ensure the effective conversion of methanol into formaldehyde. Additionally, Cs ions can promote the production of monodentate formate, which enhances the selectivity of styrene. However, too much Cs2O will lead to the excessive decomposition of methanol into CO2, CO, and H2, thus inhibiting the production of styrene. In summary, the presence of suitable amounts of Cs ions and Cs2O clusters plays a significant role in the formation of the side chain products of styrene and ethylbenzene. Full article
(This article belongs to the Section Catalytic Materials)
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Graphical abstract
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<p>XRD patterns of the catalysts.</p>
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<p>Distribution of Cs elements on CsX_10 and CsX_31.</p>
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<p>SEM images of NaX and CsX zeolites.</p>
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<p>NH<sub>3</sub>-TPD spectra of zeolites: (a) CsX_37; (b) CsX_31; (c) CsX_24; and (d) CsX_10.</p>
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<p>CO<sub>2</sub>-TPD profiles of zeolites: (a) CsX_37; (b) CsX_31; (c) CsX_24; and (d) CsX_10.</p>
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<p>Cs 3d<sub>5/2</sub> spectra in different depths of CsX_10, CsX_24, and CsX_31.</p>
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<p>O 1s spectra in different depths of CsX_10, CsX_24, and CsX_31.</p>
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<p>FT-IR spectra of methanol on desorbed CsX. Methanol pre-adsorbed on CsX at 50 °C and then evacuated at 140 °C: (a) CsX_37; (b) CsX_31; (c) CsX_24; (d) CsX_10; and (e) NaX.</p>
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<p>TPD decomposition of methanol over CsX: (<b>A</b>) CsX_37; (<b>B</b>) CsX_31; (<b>C</b>) CsX_24; (<b>D</b>) CsX_10; and (<b>E</b>) NaX.</p>
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<p>Topology structure of faujasite zeolite and its three cations sites (SI, SII, and SIII) [<a href="#B25-catalysts-14-00256" class="html-bibr">25</a>].</p>
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