Sulfur Dioxide Research Articles

In situ decorated Mn-FeOx amorphous oxides on filter felt by a polyphenol-assisted method for low-temperature NH3-SCR

The combination of dust filtration technology and selective catalytic reduction of nitrogen oxides can effectively reduce the energy consumption and footprint of the flue gas purification system. To fabricate the high efficiency and firmly bonded catalytic filter material, a novel polyphenol-assisted method was designed in this work for in situ decorating Mn-FeOx amorphous oxides onto the smooth polyphenylene sulfide (PPS) filter felt. The polyphenolic compounds are not only worked as the dispersant, but also the binder for Mn-FeOx catalysts to firmly anchor on PPS filter felts. The introduction of Fe3+ into catalyst precursors can increase the Mn4+ content, surface adsorbed oxygen, as well as surface acidity, which displays satisfactory denitration performance and SO2 tolerance at low temperatures. The Mn5Fe1-TA/PDA@PPS sample shows nearly 100 % NO conversion efficiency at 180 °C with only 10.11 % catalysts loading in the sulfur dioxide free flue gas. Furthermore, the green and efficient synthesis route makes the catalytic filter felts preserve the same gas permeability and thermal stability with the pristine PPS filter felt, which suggests that the Mn5Fe1-TA/PDA@PPS has great prospects for simultaneously removing of NOx and dust in practical applications.

A novel fluorescent probe for fast detection of sulfur dioxide derivatives in water, soil, food samples and its applications in biological imaging

Sulfur dioxide (SO2) has a wide range of applications in food additives and industrial production, and it is one of the main substances that form acid rain, causing serious harm to ecosystems and human health. Hence, it is necessary to construct an effective tool to quickly and accurately detect SO2 derivatives in environmental, food, and biological samples. In this study, fluorescent probe NPMQ was built to detect SO2 derivatives from nopinone with the merits of superior water solubility, high sensitivity (12nM), excellent specificity, large Stokes shift (180nm), and rapid response time (within 5s). NPMQ was used to qualitatively and quantitatively detect SO2 derivatives in environmental water, soil and food samples. In addition, an electrospinning film was prepared with the probe NPMQ to image SO2 derivatives, and test strips are capable of rapidly, sensitively, and selectively detecting SO2 derivatives with the naked eye. Moreover, the probe NPMQ was used to visualize endogenous SO2 derivatives in Arabidopsis thaliana under Cd2+ stress. Furthermore, the probe NPMQ was employed to image exogenous and endogenous SO2 derivatives in living Hela, HepG-2 cells, and zebrafish. This study develops an effective tool for monitoring SO2 derivatives in the environmental, food, and biological systems.

Diverse Effects of SO2-Induced Pt-O-SO3 on the Catalytic Oxidation of C3H6 and C3H8.

The effects of sulfur dioxide (SO2) in the catalytic purification of short-chain hydrocarbons are still controversial, and the exact role of SO2 on adsorption and reaction pathways during the catalytic oxidation of different volatile organic compounds (VOCs) remains unclear. Herein, a three-dimensional ordered macroporous Ce0.8Zr0.2O2 supported Pt nanoparticle monolithic catalyst (Pt/OM CZO) was synthesized to investigate these effects. Our findings uncover the diverse effects of SO2: Upon SO2 treatment, the coupling between the S 3p and Pt 5d orbitals promotes the Pt-O-SO3 structure in situ formed on the catalyst surface. The propene (C3H6) molecule readily binds with the oxygen atom in Pt-O-SO3, resulting in the accumulation of acetone and carbon deposition, thereby hindering C3H6 oxidation. Conversely, a cleaved oxygen atom within the Pt-O-SO3 structure enhances propane (C3H8) adsorption and activates the C-H bond, facilitating C3H8 oxidation. These insights are pivotal for advancing the frontier of sulfur-tolerant catalysts, addressing both economic and environmental challenges.

Green Synthesis of Sulphuric Acid Based on "Geber's Method": A Detailed Aspen Plus Simulation

Sulfuric acid (H2SO4) has a wide range of applications, but its current synthesis route via the contact process negatively impacts the atmospheric environment with harmful gaseous pollutants. Thus, based on the non-random two-liquids (NRTL) thermodynamic method, this study presents a detailed Aspen Plus V8.8 simulation of the green synthesis route of H2SO4 based on Geber's method developed already in the 18th century. The research investigates the efficiency and energy dynamics of the process through the analysis of key process parameters such as reactor's heat duty, vapor fraction, and molar extent of reaction in the selected configuration, using green vitriol (FeSO4∙7H2O) as a natural raw material. This study presented a novel manufacturing route that resulted in H2SO4 of 85.76% purity (33.71 kg/h), considering the chosen parameter space. The results highlight the impact of reactant component molar yield and fractional conversion of iron (II) sulfate (FeSO4) on heat duty and the optimal molar extent for maximizing H2SO4 production in a series of equilibrium reactors. In addition, appropriate operational parameters for the synthesis process were carefully specified, offering a pathway towards sustainable and eco-friendly H2SO4 production, which should emit zero greenhouse gases. Further optimization of the reactor conditions, can help maximize the yield of H2SO4, while minimizing energy consumption and byproduct formation. Developing advanced wastewater treatment units to purify the wastewater stream containing trace amounts of H2SO4 and dissolved sulfur trioxide (SO3) can mitigate environmental impact and ensure compliance with regulatory standards.

Study on the Precise Evaluation of Environmental Impacts of Air Pollution in Cold Regions Using the Cost Control Method

Objective: With the acceleration of industrialization, air pollution has become a global environmental issue, particularly in cold regions where the unique climatic and geographical conditions give rise to distinctive types of air pollution and impacts. Considering the economic evaluation of environmental damage is crucial for effective pollution control policies, this study aims to provide a more precise environmental damage assessment method through the Improved Virtual Control Cost Method (IVCCM) to optimize air pollution governance strategies in cold regions. Method: This study utilizes a case study of a major company producing methanol and coal-based natural gas, where the emissions from the boiler exhaust exceeded the prescribed standards for particulate matter, sulfur dioxide, and nitrogen oxides during a specific period. By employing a segmented counting approach that accounts for downtime, precise calculations were conducted for the actual periods of excess emissions. Adjustments were made to the calculation coefficients within the Virtual Control Cost Method to more accurately reflect the ecological damage caused by air pollution. Results: The IVCCM calculations revealed that the total environmental loss caused by the company’s excessive air pollution emissions amounted to USD 1.6844 million, significantly lower than the original calculation method (USD 2.1885 million). Specifically, the environmental losses due to particulate matter, sulfur dioxide, and nitrogen oxides were USD 0.0032 million, USD 0.3600 million, and USD 1.3212 million, respectively. Conclusions: The IVCCM enables a more precise assessment and prediction of ecological environmental damage caused by air pollution in cold regions. Compared to traditional methods, it effectively reduces assessment costs, mitigates disputes arising from unclear parameter values and calculation methods, and facilitates the development of more rational environmental protection policies and measures.

Long term analysis of air quality parameters for Ludhiana, India: sources, trends and health impact.

Ludhiana, a pollution hot spot in North India, has seen a rapid deterioration in air quality over the years due to urbanization and industrialization. This study interprets the variations of particulate matter (PM) and gaseous pollutants (Nitrogen oxide, Nitrogen dioxide, NOX, Sulphur dioxide, Carbon monoxide, Benzene, Toluene, Ozone, and Ammonia) for the data observed from 2017 to 2023 in Ludhiana. This also covers the analysis focused on capturing the changes that occurred at the times of lockdown imposed during the Coronavirus Disease (COVID-19). The maximum 24-h averaged mass concentration values exceeded the National Ambient Air Quality Standards (NAAQS) of 100µg/m3 for PM10 concentration and 60µg/m3 for PM2.5 concentration in 2018 by the factor of 5 and 8. With the onset of the COVID-19 lockdown in 2020year, PM10 and PM2.5 reached the minimum level while CO, T, O3, and NO2 increased by the factor of 3.9, 1.9, 1.4, and 1.3 from their previous year. This NO2 is a precursor of ozone formation, a higher NO2 to NO ratio observed during the lockdown, confirms the role of nitrogen compounds in the higher ozone formation rate. Based on the NO2/NO ratio, the probability rate of ozone formation determined using survival analysis is observed to be 94% from 2017 to 2023. The local sources' contribution to these air pollutants during Pre-Lockdown, Lockdown, and Post-Lockdown are analyzed using principal component analysis. The impact of the lockdown on ozone concentration sources has been observed. During the Pre- and Post-Lockdown phases, three sources (PC1, PC2, and PC3) were positively identified. Ozone levels are linked to PC3 in these phases, but during the lockdown, a negative loading in PC3 and positive loadings in PC1 and PC2 indicate a decrease in ozone from reduced emissions and an increase from secondary reactions involving nitrogen compounds. Moreover, the Toluene to Benzene concentration ratio is > 2, indicating the source of their origin from industrial emission or other non-traffic sources. Health assessment for the years 2017-2019 reveals a significant decrease in the number of cases of all-cause mortality, ischemic heart disease, stroke, and chronic obstructive pulmonary disease associated with reducing PM2.5 concentrations to national and international standards.

Association of environmental and behavioural factors with cardiovascular disease mortality.

Recognizing the rising concern of environmental impacts on health, the study aims to explore how specific environmental factors such as air pollution, humidity, and temperature variations contribute to the prevalence of cardiovascular disease (CVD) mortality, emphasizing the role of air quality, climate variables, and lifestyle factors in the disease mortality specifically. Analysis of province-level data on CVD mortality in Turkey from 2010 to 2019, assessing the correlations with environmental and lifestyle factors like particulate matter, sulfur dioxide, meteorological variables, and smoking and alcohol consumption. The study employs the SAS TRAJ procedure and Ordinal Logistic Regression for statistical analysis. The multiplicity correction was done through Benjamini-Hoechberg false discovery rate (FDR) approach. As expected, both smoking and alcohol consumption were found to be significantly associated with CVD mortality (odds ratio (OR): 1.10, 95% CI: 1.08, 1.11, P-value<0.0001). While median Air Pressure and Humidity were among the most significant markers with OR of 1.10 indicating an increasing CVD mortality, their variability metrics such as coefficient of variation (CV) showed significant protective effects with OR of 0.37 and 0.89, respectively. Temperature and its variability seemed to be protective overall. Our research highlights the significant influence of environmental factors on cardiovascular health, especially air pressure and humidity, beyond the known factors such as smoking and alcohol consumption. These findings suggest the need for comprehensive public health strategies that address both environmental and lifestyle risk factors to effectively reduce the burden of cardiovascular diseases.

Examining the Link between Air Quality (PM, SO2, NO2, PAHs) and Childhood Obesity: A Systematic Review.

Background/Objectives: Childhood obesity has emerged as a global health concern with profound implications for long-term health outcomes. In recent years, there has been increasing interest in the potential role of environmental factors in the development of childhood obesity. This comprehensive review aims to elucidate the intricate relationship between various components of air pollution and childhood obesity. Methods: We systematically analyze the existing literature from the past 5 years to explore the mechanistic pathways linking air pollution, including particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), and polycyclic aromatic hydrocarbons (PAHs), to childhood obesity. This systematic review examines 33 epidemiological studies on the link between air pollution and childhood obesity, published from 1 January 2018, to 31 January 2024. Results: Studies from counties with low overall air pollution noticed only low to no impact of the exposure to childhood obesity, unlike studies from countries with higher levels of pollution, suggesting that the mitigation of air pollutants can reduce the chance of it being a negative factor for the development of obesity. This relationship was noticed for PM2.5, PM1, PM10, NOx, and SO2 but not for PAHs, which showed a negative effect on children's health across 10 out of 11 studies. Conclusions: This review underscores the need for interdisciplinary approaches to address both environmental and socio-economic determinants of childhood obesity. Efforts aimed at reducing air pollution levels and promoting healthy lifestyle behaviors are essential for safeguarding the health and well-being of children worldwide.

Exploring Air Quality Dynamics and Predictive Modeling by Using Artificial Intelligence During COVID-19 Lock Down Over the Western Part of India

The lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (&gt;401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.

Enhanced hybrid LSTM and SLAR modeling for in-depth analysis of temporal and spatial patterns in compositional data for environmental monitoring

A novel methodology for analyzing compositional data (CoDa) integrates long short-term memory (LSTM) networks with spatial lag autoregressive (SLAR) models to simultaneously capture temporal and spatial patterns. The proposed approach leverages LSTM’s strengths in handling temporal relationships and SLAR’s ability to capture spatial correlations, employing the centered log-ratio (CLR) transformation to manage CoDa’s characteristics, making it suitable for advanced analysis. The Adam optimizer ensures efficient and stable model training, enhancing convergence speed and precision. The combined approach involves preprocessing CoDa with CLR, initializing LSTM layers, performing forward passes through LSTM, integrating the SLAR model, generating outputs, post-processing data, calculating loss, and performing backpropagation. The methodology’s effectiveness is demonstrated through a case study on pollutant emissions data from a waste incineration plant, focusing on key pollutants such as Dioxins and Furans, Heavy Metals (Hg, Pb, Cd), Nitrogen Oxides (NO), Sulfur Dioxide (SO), Particulate Matter (PM), and Carbon Monoxide (CO). The LSTM network, configured with two hidden layers, dynamically adjusts learning rates for faster convergence. Integrating SLAR improves spatial pattern accounting, significantly enhancing predictive accuracy. Comparative analysis shows substantial performance metric improvements over standard LSTM models, with the LSTM-SLAR model reducing errors and explaining a higher proportion of data variability. In process safety and risk engineering, the proposed methodology enhances pollutant emission monitoring and control, enables proactive risk management, ensures regulatory compliance, and improves risk assessment accuracy, making it invaluable for dynamic risk assessment in environmental monitoring and is broadly applicable to fields requiring detailed temporal and spatial analysis, demonstrating robustness and versatility in handling complex CoDa.

Copper-Catalyzed Oxyfluorosulfonylation of β,γ-Unsaturated Oximes with Sulfur Dioxide and Selectfluor for Isoxazoline-Functionalized Aliphatic Sulfonyl Fluorides.

In this report, we describe a copper-catalyzed cascade reaction involving oxygen radical-induced cyclization/SO2 insertion/fluorination of β,γ-unsaturated oximes with sulfur dioxide and Selectfluor under mild conditions for the synthesis of isoxazoline-functionalized aliphatic sulfonyl fluorides. The synthetic potential of these compounds has been evaluated through diverse SuFEx reactions.

Socio‐economic impact of solar cooking technologies on community kitchens under different climate conditions: A review

AbstractResidential cooking with non‐renewable energy sources, such as firewood, charcoal, natural gas, participate in the emission of more than a gigaton of CO2 per year, which represents 2% of the global CO2 emissions. Additionally, toxic particles including sulfur dioxide, carbon monoxide, and mercury are released leading to elevated levels of indoor air pollution, and adversely affecting the health of the inhabitants. The residential sector's non‐renewable energy cooking devices also pose significant problems, consuming approximately 30%–40% of global energy usage, with over 80% dedicated to cooking applications. To mitigate the negative impacts of traditional cooking on health and the environment, various renewable energy‐based cooking technologies have been developed recently. The primary contributions of our paper are to: (a) present a comprehensive review of concentrated solar thermal cooking technologies, assessing their social, economic, and environmental impact across different climatic zones in developing countries like India; (b) classify and compare different solar cooking technologies, highlighting their advantages and limitations in various scenarios; (c) evaluate the energy efficiency of diverse solar cooking technologies; (d) analyze the impact of solar cookers on communities in developing countries; and (e) identify the challenges and future directions for solar cooker technologies, particularly in solar community kitchens. Our novel findings demonstrate that using solar cooking devices can reduce energy consumption by up to 56% in Indian schools. Moreover, the payback period ranges from 3 to 6 years, contingent on the technology's cost, climatic conditions, and available subsidies. Consequently, significant positive impacts on society, the economy, and the environment are observed when traditional cooking devices are replaced by solar cooking devices. This study provides a unique and thorough analysis, contributing to the growing body of knowledge on sustainable cooking solutions and their potential to transform energy consumption patterns in developing regions.

Interpregnancy interval, air pollution, and the risk of low birth weight: a retrospective study in China

BackgroundBoth interpregnancy intervals (IPI) and environmental factors might contribute to low birth weight (LBW). However, the extent to which air pollution influences the effect of IPIs on LBW remains unclear. We aimed to investigate whether IPI and air pollution jointly affect LBW.MethodsA retrospective cohort study was designed in this study. The data of birth records was collected from the Jiangsu Maternal Child Information System, covering January 2020 to June 2021 in Nantong city, China. IPI was defined as the duration between the delivery date for last live birth and date of LMP for the subsequent birth. The maternal exposure to ambient air pollutants during pregnancy—including particulate matter (PM) with an aerodynamic diameter of ≤ 2.5 μm (PM2.5), PM10, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO)—was estimated using a hybrid kriging-LUR-RF model. A novel air pollution score was proposed, assessing combined exposure to five pollutants (excluding CO) by summing their concentrations, weighted by LBW regression coefficients. Multivariate logistic regression models were used to estimate the effects of IPI, air pollution and their interactions on LBW. Relative excess risk due to interaction (RERI), attributable proportion of interaction (AP) and synergy index (S) were utilized to assess the additive interaction.ResultsAmong 10, 512 singleton live births, the LBW rate was 3.7%. The IPI-LBW risk curve exhibited an L-shaped pattern. The odds ratios (ORs) for LBW for each interquartile range increase in PM2.5, PM10, O3 and the air pollution score were 1.16 (95% CI: 1.01–1.32), 1.30 (1.06–1.59), 1.22 (1.06–1.41), and 1.32 (1.10–1.60) during the entire pregnancy, respectively. An additive interaction between IPI and PM2.5 was noted during the first trimester. Compared to records with normal IPI and low PM2.5 exposure, those with short IPI and high PM2.5 exposure had the highest risk of LBW (relative risk = 3.53, 95% CI: 1.85–6.49, first trimester).ConclusionThe study demonstrates a synergistic effect of interpregnancy interval and air pollution on LBW, indicating that rational birth spacing and air pollution control can jointly improve LBW outcomes.

Antiviral effect of oversulfated kappa-carrageenan derivatives against COVID-19 for spray coating application on facemasks

The COVID-19 pandemic caused by SARS-CoV-2 has spurred the urgent need for effective antiviral strategies. In this work, we explored the potential of oversulfated kappa-carrageenan (OSKC) in spray-coated facemasks for SARS-CoV-2 inhibition pathway. The sulfated derivative was synthesized with sulfur trioxide pyridine complex in dimethylformamide solution. The antiviral efficacy of OSKC at different concentrations and spray-coated facemasks was evaluated using betacoronavirus Murine Hepatitis Virus strain 3, revealing a significant reduction in viral load compared to commercial kappa-carrageenan. Furthermore, the characterization techniques assessed the effect of the position of the introduced sulfate groups on the antiviral activity and on the physicochemical characteristics. OSKC is able to bind specific proteins of enveloped viruses, preventing viral attachment into target cells. Overall, this study demonstrates the feasibility and effectiveness of OSKC spray coating for breathable facemasks with antimicrobial properties, offering a promising approach to enhancing personal protective equipment against viral transmission in healthcare and community settings.

Unveiling urban air quality dynamics during COVID-19: a Sentinel-5P TROPOMI hotspot analysis

In India, the spatial coverage of air pollution data is not homogeneous due to the regionally restricted number of monitoring stations. In a such situation, utilising satellite data might greatly influence choices aimed at enhancing the environment. It is essential to estimate significant air contaminants, comprehend their health impacts, and anticipate air quality to safeguard public health from dangerous pollutants. The current study intends to investigate the spatial and temporal heterogeneity of important air pollutants, such as sulphur dioxide, nitrogen dioxide, carbon monoxide, and ozone, utilising Sentinel-5P TROPOMI satellite images. A comprehensive spatiotemporal analysis of air quality was conducted for the entire country with a special focus on five metro cities from 2019 to 2022, encompassing the pre-COVID-19, during-COVID-19, and current scenarios. Seasonal research revealed that air pollutant concentrations are highest in the winter, followed by the summer and monsoon, with the exception of ozone. Ozone had the greatest concentrations throughout the summer season. The analysis has revealed that NO2 hotspots are predominantly located in megacities, while SO2 hotspots are associated with industrial clusters. Delhi exhibits high levels of NO2 pollution, while Kolkata is highly affected by SO2 pollution compared to other major cities. Notably, there was an 11% increase in SO2 concentrations in Kolkata and a 20% increase in NO2 concentrations in Delhi from 2019 to 2022. The COVID-19 lockdown saw significant drops in NO2 concentrations in 2020; specifically, − 20% in Mumbai, − 18% in Delhi, − 14% in Kolkata, − 12% in Chennai, and − 15% in Hyderabad. This study provides valuable insights into the seasonal, monthly, and yearly behaviour of pollutants and offers a novel approach for hotspot analysis, aiding in the identification of major air pollution sources. The results offer valuable insights for developing effective strategies to tackle air pollution, safeguard public health, and improve the overall environmental quality in India. The study underscores the importance of satellite data analysis and presents a comprehensive assessment of the impact of the shutdown on air quality, laying the groundwork for evidence-based decision-making and long-term pollution mitigation efforts.

Features of Temporal Variability of the Concentrations of Gaseous Trace Pollutants in the Air of the Urban and Rural Areas in the Southern Baikal Region (East Siberia, Russia)

This article presents the results of the automatic monitoring of the concentrations of gaseous impurities of sulfur and nitrogen oxides in the ground-level atmosphere of the urban and rural areas in the Southern Baikal region (East Siberia, Russia). The study was conducted from 2020 to 2023 at the urban Irkutsk station and the rural Listvyanka station located at a distance of 70 km from each other. We calculated the main statistical characteristics of the variations in the concentrations of nitrogen oxides and sulfur dioxide in the ground-level atmosphere and determined a nature of variability in their concentrations on various time scales: annual, weekly, and daily. Annual variabilities of gaseous pollutants in the ground-level atmosphere above the Irkutsk city and the Listvyanka settlement were similar and showed the highest values in winter and the lowest in summer. The daily and weekly dynamics of the nitrogen oxide concentrations in the urban area clearly depended on the increase in the road traffic during rush hours (morning and evening). In the rural area, there was no such dependence. In this area, the daily and weekly variability in the concentrations of nitrogen oxides and sulfur dioxide mainly depended on natural meteorological processes. The work systematizes the meteorological parameters at which the largest amount of anthropogenic impurities enters the air basin of Lake Baikal. The maximum values of acid-forming gas concentrations were observed when the air masses were transferred from the northwest direction, which corresponds to the location of sources in the territory of the Irkutsk–Cheremkhovo industrial hub—the largest concentration of anthropogenic objects in the Irkutsk region.

Preliminary Assessment of Tunic Off-Gassing after Wildland Firefighting Exposure

Evidence has previously shown that outer tunics (turnout coats) worn by firefighters at structural fires are contaminated with harmful chemicals which subsequently off-gas from the material. However, there is limited research on whether this phenomenon extends to wildland firefighter uniforms. This pilot study aimed to explore if the tunics of volunteer bushfire and forestry firefighters in Western Australia off-gas any contaminants after exposure to prescribed burns or bushfires, and whether there is a need to explore this further. Nine tunics were collected from firefighters following nine bushfire and prescribed burn events, with a set of unused tunics serving as a control. Chemical analysis was performed on these tunics to assess levels of acrolein, benzene, formaldehyde, and sulphur dioxide contamination. The assessment involved measuring chemical off-gassing over a 12 h period using infrared spectrometry. Tunics worn by firefighters appear to adsorb acrolein, benzene, formaldehyde, and sulphur dioxide from bushfire smoke and these contaminants are emitted from firefighting tunics following contamination at elevated concentrations. Further investigation of this research with a larger study sample will be beneficial to understand this phenomenon better and to determine the full extent and range of chemical contaminants absorbed by all firefighter clothing.

Synthesis of satellite and ground data provide unique perspectives for discovering the air pollution patterns: A case study in Guangdong Province, China

Existing studies have analyzed the spatio-temporal patterns of air pollutants by combining ground and satellite measurements, primarily for cross-validation purposes. However, the unique characteristics and discrepancies between satellite and ground measurements have rarely been leveraged to understand pollution patterns and identify air pollution sources. To our best knowledge, this study is the first to utilize these discrepancies to holistically analyze the spatial and temporal patterns and investigate local biomass-burning effects on the five typical air pollutants: particulate matter (PM2.5)/aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3). Guangdong (GD) province was selected as a case study due to its complex air pollution sources and patterns. Ground-based analysis from 2015 to 2023 shows significant decreases in PM2.5, CO, NO2, and SO2, and a significant increase in O3 in urban areas, indicating the efficacy of stringent air pollution control policies. However, satellite analysis shows significant downtrend only in AOD, while the trends of other pollutants are almost negligible, which are likely to be evidence of industrial migration. Both measurements exhibit regular seasonal patterns for all air pollutants. In-depth time-series comparisons between ground and satellite data reveal seasonal consistency for NO2 but noticeable discrepancies for both AOD and CO, which could be attributed to urban-rural differences and local versus transported pollution sources. Spatially, AOD and NO2 exhibits the most significant regional discrepancies, followed by SO2 and CO, with higher values observed over Pearl River Delta (PRD) compared to non-PRD regions. O3 is more evenly distributed, showing more pronounced seasonal variations than regional differences. The synergetic use of satellite and ground measurements collectively verifies the significant local biomass-burning effects on the five pollutants. These findings can aid in developing more targeted air pollution control policies.

Gas encapsulation technology for large volume press

Abstract For samples in the gaseous state at room temperature and pressure, mature technology has been developed, which can be encapsulated in a diamond anvil cell (DAC). However, large volume press (LVP) can only treat samples with starting materials in solid or liquid form. We have achieved stable encapsulation and reaction treatment of carbon dioxide in centimeter sized sample chamber for a long time (over 10 minutes) under conditions of temperature higher than 1200 ℃ and pressure over 5 GPa through the use of integrated low-temperature freezing and rapid compression sealing method for LVP cell assemblies. This technology can also be applied to the packaging of other gaseous or liquid samples, such as ammonia, sulfur dioxide, water, etc., in LVP devices.

A ratiometric fluorescent sensor based on dual-emitting carbon dots for the rapid detection of sulfite

Sulfur dioxide (SO2) derivatives are typically employed as antioxidants in food and pharmaceutical processing. However, excessive sulfite intake could trigger serious health problems. Hence, it is urgent to establish a rapid and effective system for monitoring SO2. This study adopted a one-step hydrothermal method to synthesize dual-emitting nitrogen-doped carbon quantum dots (CECDs) and developed a ratiometric sensor for sulfite using CECDs-Cr (VI) composites. The emission intensity ratio (I440/I500) of the CECDs-Cr (VI) composites increased considerably with the addition of HSO3−. A method based on the ratiometric sensor was established for SO2 derivatives with advanced efficiency and excellent linearity over a broad concentration range of 0–500 μM (R2 = 0.9946). Four medicine-food homology materials (MFHMs) fumigated with sulfur have been accurately detected using this approach. Furthermore, a portable test tube was prepared to achieve rapid and semi-quantitative detection of SO2 residues and applied to real samples. This work presents an effective approach to develop a rapid on-site detection platform for sulfite residues in food and pharmaceuticals.