Sulfur Dioxide Research Articles

Contribution of congested traffic flow condition to air pollution at intersections in Addis Ababa, Ethiopia

Currently, one of the serious problems in the world is transport-related air pollution. Air pollution from vehicles was not considered properly in the plan, design, and management system of the roads in Addis Ababa city, to solve the problem of traffic congestion. It is influenced by different factors such as road geometry, road surface type, traffic congestion, traffic characteristics, fuel type, and meteorological parameters. This study was aimed to investigate the contribution of congested traffic flow conditions on air pollution at intersection points in Addis Ababa city. To carry out the study, seven intersection points were selected in the city randomly. For the measurement technique of carbon monoxide (CO) and sulfur dioxide (SO2) gases, a portable Aeroqual series 500 instrument was exercised, and for the particular matter (PM2.5 and PM10), Light Amplification by Stimulated Emission of Radiation (LASER) was adapted. To count the number of traffic on the site, a video camera method was utilized. For the data analysis techniques, a multiple linear regression method was practiced, which is very powerful for multiple independent variables with a single dependent one. The study revealed that the level of traffic congestion at each selected intersection is at a severe level. During the congested time, as a base of the steady condition, the pollutants concentration of CO, SO2, PM2.5, and PM10 were increased by an average of 19.10, 51.61, 33.83, and 29.07 % respectively. Based on the analyzed results, when the concentration of pollutants increases, the traffic volume, percentage of heavy vehicles, green time, and approach grade are also increased. On the other hand, the concentration of pollutants decreased when the lane width and wind speed increased. Therefore, any concerned bodies in and around Addis Ababa city should take remedial measures to decrease the emission of the concentration of the pollutants. This investigation is very important for policymakers, municipalities, and planners to do any activities in the city. Researchers can also use it as a reference, for further investigations and remedial measurements.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013–2020

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013–2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

Effect of air pollutants particulate matter (PM2.5, PM10), sulfur dioxide (SO2) and ozone (O3) on cognitive health

Impaired cognitive health is the leading cause of various disabilities and disorders. Air pollution has been dramatically increasing over the last few decades and has been identified as a potential risk factor for impaired cognitive health. This study investigates the effect of air pollutants, particulate matter (PM2.5, PM10), sulfur dioxide (SO2), and ground-level ozone, on global cognitive health. The data on environmental pollutants and cognitive health were recorded from PubMed, Web of Science, Scopus, and Google Scholar. Initially, 790 articles were identified after screening for duplicates and applying the inclusion and exclusion criteria, 21 studies were included, and data was synthesized to get a pooled result. The overall results revealed that increased exposure to PM2.5 was positively and significantly associated with cognitive decline (OR 1.49; 95% CI 1.11, 1.99; p = 0.01). The risk of cognitive impairment due to PM10 (OR 1.30; 95% CI 1.00–1.70, p = 0.05), and SO2 (OR 1.39; 95% CI 1.27–1.51; p < 0.01) exposure were also significantly heightened. The study findings show that overall exposure to particulate matter PM2.5, PM10, and SO2 was associated with an increased risk of a decrease in global cognitive functions. The findings suggest that reducing levels of air pollutants could be a strategic approach to mitigate cognitive health risks in populations worldwide.

Land cover object change monitoring and environmental suitability confirmation in Cat Ba Biosphere Reserve of Vietnam

ABSTRACT The study examined the core, buffer, and transition zones within the Cat Ba Biosphere Reserve in Vietnam to assess changes in five land cover categories (water surface, cropland, vacant land, residential land, and forestland) between 2018 and 2022. Additionally, we aimed to evaluate water quality using three indicators (normalized difference turbidity index, normalized difference chlorophyll index, total suspended solids) and air quality using four indicators (nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone) for the 2022 data. Land cover change analysis and monitoring of water and air quality indicators were conducted using the Google Earth Engine platform, employing Sentinel-2 imagery for land cover and water quality assessment and Sentinel-5P images for air quality assessment. The findings revealed correlations between changes in land cover and water and air quality indicators within the Cat Ba Biosphere Reserve. While occasional instances of values surpassing permissible limits were noted, the average values over the 12-month period in 2022 indicated that water and air quality indicators remained within acceptable thresholds. These results suggest effective preservation efforts in the Cat Ba Biosphere Reserve, indicating no significant signs of degradation.

Refining Children's exposure assessment to NO2, SO2, and O3: Incorporating indoor-to-outdoor concentration ratios and individual daily routine

Exposure to air pollutants like sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3) is associated with adverse health effects, particularly with exacerbations of asthma symptoms and new asthma cases in both children and adults. While fixed-site monitoring (FSM) stations are commonly used in air pollutant exposure studies, they may not fully capture personal exposures due to limitations such as inadequate consideration of daily routines and indoor/outdoor concentration variations. In this study, to enhance the accuracy of personal exposure calculated by using FSM data, individual's daily activity routine, encompassing both indoor and outdoor environments, were incorporated by using indoor-to-outdoor concentration ratios. Three methodologies were compared to assess the accuracy of exposure calculations: (i) direct exposure determination employing passive samplers (PS), (ii) personal exposure calculated using FSM data alone, and (iii) personal exposure calculated using FSM data refined by integrating local average individual daily activity routines and indoor-to-outdoor ratios. The results demonstrate that the refined method (iii) yields substantial improvements in estimated exposure levels, reducing the average error from 1.4% to 0.4% for NO2, from 72.1% to 12.7% for SO2, and from 323.4% to 24.9% for O3.

Energy production from landfill gas, emissions and pollution indicators–Opportunities and barriers to implementing circular economy

The goal of this research was to test the air emissions resulting from landfill gas combustion in a 250-kW cogeneration unit, in the context of pollutant emission due to the produced electricity. The test results mainly revealed emissions of carbon dioxide, sulfur dioxide, nitrogen oxides, carbon oxides, and dust. The volume of emissions of individual pollutants was compared with the emission values reported from the emission balancing database. The highest emission burdens concerned the production of one MWh of electricity for carbon dioxide 579.80 kgCO2∙MWh−1, carbon monoxide 2.57 kgCO2∙MWh−1, and nitrogen oxides 1.77 kgNOx∙MWh−1. It is worth noting that small cogeneration installations are not covered by emission standards and so they are not equipped with a device for reducing gaseous and particulate pollution. The tests revealed a difference between the actual emissions from the tested installation and the values reported to the national database. The innovation of this study is that it demonstrates the real emission burden of renewable energy in the context of sustainable development. The results can be used as input material for discussion to apply gaseous and particulate pollution reduction systems for cogeneration installations powered by landfill gas, including various types of biogases.

Surface-air exchanges of H2S and SO2 in an urban wetland in eastern China

Wetlands are widely recognized as hot spots for the emission or deposition of biogenic sulfur gases, including hydrogen sulfur (H2S) and sulfur dioxide (SO2), which significantly affect air quality and climate change. With the expansion of urban wetlands, it is critical to know the roles that urban wetlands played in atmospheric H2S and SO2 budget. In this study, the surface-air exchange fluxes of H2S and SO2 were measured by the Dynamic Flux Chamber (DFC) method in a typical urban wetland in eastern China from Sep 2022 to July 2023. It was found that the urban wetland did not have the expected high H2S emission, might be caused by the relatively high pH value and low sulfate concentration in the soil. Although H2S showed emission in the daytime of spring and summer, an overall H2S flux of −0.04 kg S ha−1 yr−1 was observed throughout the year. Meanwhile, the urban wetland presented a net sink of SO2, with a deposition flux of 0.14 kg S ha−1 yr−1. The negative peaks of SO2 flux corresponded to the suddenly elevated SO2 concentration in the ambient air especially in spring and winter. Through linear fitting of SO2 flux and concentration, the concept of SO2 “compensation point” was proposed. The compensation point is the concentration level at which the observed SO2 flux equals zero. The “compensation point” changed with the season and was related to temperature and humidity. The “compensation point” in summer and autumn were larger, being 2.37 ppb and 1.40 ppb, respectively, while they were 1.07 ppb and 0.86 ppb in spring and winter respectively. Our results suggest that the urban wetland expansion may have little risk of increasing air H2S but could act as a significant sink of SO2 with high SO2 concentration in the urban region.

A near-infrared ratiometric fluorescent probe for the sensing and imaging of sulfur dioxide derivatives in living systems

As a reactive sulfur species, sulfur dioxide (SO2) and its derivatives play crucial role in various physiological processes, which can maintain redox homeostasis at normal levels and lead to the occurrence of many diseases at abnormal levels. So, the development of a suitable fluorescent probe is a crucial step in advancing our understanding of the role of SO2 derivatives in living organisms. Herein, we developed a near-infrared fluorescent probe (SP) based on the ICT mechanism to monitor SO2 derivatives in living organisms in a ratiometric manner. The probe SP exhibited excellent selectivity, good sensitivity, fast response rate (within 50 s), and low detection limit (1.79 µM). In addition, the cell experiment results suggested that the SP has been successfully employed for the real-time monitoring of endogenous and exogenous SO2 derivatives with negligible cytotoxicity. Moreover, SP was effective in detecting SO2 derivatives in mice.

Environmental regulation and intermediate imports: Firm-product-level evidence

This study examines the effects of domestic environmental regulations on import activity. Using a panel of firm-product-level data and variations in regulatory stringency across products established by China’s Eleventh Five-Year Plan for Environmental Protection (covering 2006–2010), it reveals that tougher regulations on emission-intensive industries at home led to increases in downstream manufacturers’ imports of emission-intensive intermediate inputs. Specifically, a 1% increase in sulfur dioxide emission intensity resulted in a 0.026% increase in intermediate imports after the implementation of the regulation. A back-of-the-envelope calculation suggests that, although the regulation increased emissions in source countries, it reduced global emissions of sulfur oxides and carbon dioxide. This is because the increases in imports caused by the regulation mainly came from countries with lower emission intensity than China. The regulation did not disproportionately increase imports from or emissions in developing countries.

Air pollution and children’s mental health in rural areas: compositional spatio-temporal model

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) is related to behavioural and development disorders, anxiety is related to particulate matter (PM10), O3 and SO2, and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used.

International airport emissions and their impact on local air quality: chemical speciation of ambient aerosols at Madrid–Barajas Airport during the AVIATOR campaign

Abstract. Madrid–Barajas Airport (MAD) is the fourth-busiest airport in Europe. The aerosol chemical composition and the concentrations of other key pollutants were measured at the airport perimeter during October 2021 to assess the impact of airport emissions on local air quality. A high-fidelity ambient instrumentation system was deployed at Madrid–Barajas Airport to measure the following: concentrations of organic aerosols (with their composition), black carbon (eBC), carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NOx), sulfur dioxide (SO2), particulate matter (PM2.5, PM10), total hydrocarbon (THC), and total particle number. The average concentrations of eBC, NOx, SO2, PM2.5, PM10, CO, and THC at the airport for the entire campaign were 1.07 µg m−3, 22.7 µg m−3, 4.10 µg m−3, 9.35 µg m−3, 16.43 µg m−3, 0.23 mg m−3, and 2.30 mg m−3, respectively. The source apportionment analysis of the non-refractory organic aerosol (OA) using positive matrix factorisation (PMF) allowed us to discriminate between different sources of pollution, namely less oxidised oxygenated organic aerosol (LO-OOA), alkane organic aerosol (AlkOA), and more oxidised oxygenated organic aerosol (MO-OOA). The results showed that LO-OOA and MO-OOA account for more than 80 % of the total organic particle mass measured near the runway. Trace gases correlate better with the AlkOA factor than LO-OOA and MO-OOA, indicating that AlkOA is mainly related to primary combustion emissions. Bivariate polar plots were used for pollutant source identification. Significantly higher concentrations of the obtained factors were observed at low wind speeds (&lt;3 m s−1) from the southwest, where two of the runways and all terminals are located. Higher SO2/NOx and CO/eBC ratios were observed when the winds originated from the northeast, where the two northern runways are located. These elevated ratios are attributed to the aircraft activity being the major pollutant source in the northeast area.

Short-term exposure to sulfur dioxide and the occurrence of chronic obstructive pulmonary disease: An updated systematic review and meta-analysis based on risk of bias and certainty of evidence

Several studies have documented a relationship between short-term exposure to atmospheric sulfur dioxide (SO2) and chronic obstructive pulmonary disease (COPD). However, findings vary across different regions. This meta-analysis employed a random-effects model to calculate the combined risk estimate for each 10-μg/m3 increase in ambient SO2 concentration. Subgroup analysis aimed to identify sources of heterogeneity. To assess potential bias, studies were evaluated using a domain-based assessment tool developed by the World Health Organization. Sensitivity analyses, based on bias risk, explored how model assumptions influenced associations. An evidence certainty framework was used to evaluate overall evidence quality. The study protocol was registered with PROSPERO (CRD42023446823). We thoroughly reviewed 191 full-text articles, ultimately including 15 in the meta-analysis. The pooled relative risk for COPD was 1.26 (95 % CI 0.94–1.70) per 10-μg/m3 increase in ambient SO2. Eleven studies were deemed high risk due to inadequate handling of missing data. Overall evidence certainty was rated as medium. Given SO2's significant public health implications, continuous monitoring is crucial. Future research should include countries in Africa and Oceania to enhance global understanding of atmospheric SO2-related health issues.

Intra-annual variations and determinants of canopy layer urban heat island in China using remotely sensed air temperature and apparent temperature

Canopy layer urban heat island (CLUHI) is closely related to about 4.7 billion urban residents’ well-being. Compared with sparse observation stations, remote sensing can obtain spatially continuous temperature, which is conducive to intra-annual variation and associated factors of CLUHI over large regions. Such research is scarce, especially from apparent temperature perspective. Thus, the intra-annual spatio-temporal variations and twelve determinants of CLUHI intensities (CLUHIIs) were investigated for 917 urban region agglomerations in China’s five ecological regions, using remotely sensed near-surface air temperature, apparent temperature and other multi-source data, and several spatial analysis and mathematical statistic methods. The findings revealed that annual CLUHIIs by mean temperatures were most between 0.5 and 1.0 ℃, higher in the summer and spring, and larger in North China than South China in the summer. The annual CLUHIIs by maximum temperatures were negative in several cities, especially in rainless West China. North China generally had stronger CLUHIIs by minimum temperatures in all seasons compared to South China. The CLUHIIs by mean apparent temperatures were larger in the summer (most between 0.5 and 2.0 ℃) and varied across ecological regions. Significant positive correlations generally existed among CLUHIIs by various air temperature and land surface temperature indices (p < 0.05). Nevertheless, obvious differences existed among them. The CLUHII was significantly negatively partially correlated with total precipitation, urban–rural difference in enhanced vegetation index and albedo, positive with urban–rural difference in nighttime light intensity, population density, sulfur dioxide concentration, insignificant with total population, urban area size and landscape shape index in different degree. These findings provided valuable insights into CLUHI research.

Spillover effects of structure-adjustment pollution reduction measures in China's iron and steel industry

The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China's Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures' role and highlight their potential advantages and limitations.

The temporal-spatial association of respiratory manifestations and air pollution in children referred to the Emergency Department of Akbar Children’s Hospital, Mashhad, Iran

BackgroundAir pollution causes many respiratory disorders, especially in children and the elderly. These disorders include asthma exacerbations, bronchiolitis, and pneumonia. Research on the association between air pollution and respiratory disorders helps to reevaluate environmental policies in developing countries.MethodsThis descriptive cross-sectional study was conducted on 932 children with respiratory manifestations admitted from December 2017 to December 2019 at the Emergency Department of Akbar Children’s Hospital of Mashhad University of Medical Sciences, Mashhad, Iran. Air pollution indices such as concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM) smaller than 2.5 and 10 μm and other parameters, including the air quality index (AQI), air temperature, and humidity level from 2017 to 2019, were retrieved from Mashhad Environmental Pollution Monitoring Center. Demographic and clinical data of patients were collected from patients’ hospital documents. We used descriptive analytical methods such as central tendency, variability, and frequency distribution to report and analyze demographic and clinical data through tables and diagrams. The association between air pollution indices and respiratory manifestations was examined by the Spearman correlation test. The correlation between the AQI and total hospital admissions and asthma-related hospital admissions was also evaluated by the Spearman correlation test.ResultsHospital admissions due to respiratory manifestations were not associated with the AQI of each month (p-value = 0.794). The concentration of SO2 was correlated with respiratory-related hospital admissions (correlation coefficient = 0.487, p-value = 0.016) but not asthma attacks.ConclusionGenerally, our cross-sectional study showed no statistically considerable association between air pollution and hospital admissions due to respiratory manifestations and asthma attacks in children. Of the air pollution indices, only SO2 concentration was associated with respiratory-related hospital admissions but not asthma attacks.

Impact of Atmospheric Parameters on Human Respiratory Tract Infections (RTIs)

Respiratory tract infections (RTIs) are a significant public health issue globally, with varying degrees of severity and implications. Environmental factors, particularly atmospheric parameters, can exacerbate these health conditions. Understanding the relationship between atmospheric conditions and RTIs is crucial for effective public health interventions. This systematic literature review analyzes research on the impact of atmospheric parameters on RTIs through a systematic search across multiple electronic databases using different keyword combinations. The retrieved articles were categorized based on atmospheric parameters investigated, such as temperature, humidity, air pollution, and rainfall. The review findings indicate that various atmospheric factors, including wind speed, air pollution (PM10, PM2.5, CO, and O3), air temperature, atmospheric pressure, and humidity, influence the occurrence and severity of respiratory infections. Low wind speed and high air pollution were identified as risk factors for severe acute respiratory infection (SARI). In children, meteorological factors like air temperature, atmospheric pressure, rainfall, sunlight hours, wind speed, and relative humidity were associated with lower respiratory tract infections. Pollutants like PM2.5, carbon monoxide, sulfur dioxide, and tropospheric ozone directly impact pediatric respiratory infections. Climate variables affect the seasonal activity of pathogens like respiratory syncytial virus (RSV). These findings underscore the importance of considering atmospheric parameters in understanding and preventing RTIs. Addressing these challenges requires a comprehensive approach integrating environmental and public health interventions. This approach can better mitigate the effects of atmosphere-related risks on respiratory health.

Effect of SO2 and NH3 on the corrosion failure of Zn-Al-Mg coatings

This study investigates the corrosion failure mechanism of Zn-Al-Mg (ZAM) coatings in environments containing sulfur dioxide (SO2) and ammonia (NH3) gas through accelerated corrosion tests. A comparison with Galvalume (GL) coatings reveals that ZAM coatings exhibit lower corrosion rates than GL coatings in SO2-containing environments, but display higher corrosion rates in NH3-containing environments. Analysis of the cut-edge corrosion of ZAM-coated steel in these environments reveals noticeable delamination phenomenon, with significant discrepancies observed in the corrosion failure times among different cut-edges of the same sample. This disparity can be attributed to both the uneven distribution of coating caused by processing techniques and the detrimental effects of SO2 and NH3 on the corrosion resistance of surface corrosion products. Notably, the diminished corrosion resistance in NH3-containing environments underscores the pivotal role of environmental factors in coating failure.

Experimental study on the preparation of desulfurizer for low temperature dry desulfurization by digesting CaO in urea solution

Calcium hydroxide (Ca(OH)2) with outstanding sulfur dioxide (SO2) adsorption properties was synthesized through the utilization of calcium oxide (CaO) with a urea solution via a digestion method. This innovative calcium-based desulfurizer aims to meet the needs of low-temperature dry flue gas desulfurization in the steel industry. Fixed-bed experiments were conducted to investigate the impact of different water-ash ratios, urea solution concentrations, and digestion time on the desulfurization efficiency and particle structure of the desulfurizers. Sample CAC-2–0.1 U exhibited outstanding desulfurization performance, achieving 100 % removal efficiency, a sulfur capacity of 34.64 mg/g, and a breakthrough time of 142.6 minutes under target flue gas conditions. The sample also displayed a high specific surface area of 22.10 m2/g and demonstrated adaptability under varying operating conditions. Characterization and reaction mechanisms of the desulfurizer were studied using XRD, SEM, BET, XPS, ICP-OES, and Materials Studio software. Experimental data was analyzed through pseudo-first-order and pseudo-second-order kinetic equations, and intra-particle diffusion model. The product's strong adsorption capacity for SO2 positions it as a highly promising adsorbent for controlling low-temperature flue gas emissions in the steel industry.

Dynamic monitoring of SO2 changes during ferroptosis using a light-controlled lipid droplets-targeting probe

During the process of ferroptosis, the generation of toxic lipid peroxides triggers severe inflammatory responses. Despite sulfur dioxide (SO2) being recognized as an important signal molecule and antioxidant, its specific physiological mechanism in ferroptosis remains unclear. Here, we developed a novel probe, SP-TB, tailored for detecting SO2 within lipid droplets (LDs). SP-TB features a triphenylamine moiety for LD targeting, coupled with a spiropyran serving as both the SO2 reaction site and a light-responsive switch. Under normal conditions, SP-TB exhibits yellow fluorescence at 570 nm with its SO2 reaction site closed. Upon UV light activation, the spiropyran moiety undergoes isomerization to the merocyanine form (MC-TB), resulting in red fluorescence emission at 634 nm. During this photoisomerization, the exposed CC-CN+ fragment enables specific Michael addition reactions with SO2, quenching the red fluorescence of MC-TB and enhancing yellow fluorescence. The probe exhibits stable photoreversibility and excellent LDs targeting, along with remarkable SO2 selectivity with a low detection limit of 3.5 μM. Notably, the controllability of the SO2 reaction site minimizes false positives. This probe was successfully used to monitor the changes of SO2 concentrations in LDs during ferroptosis.

Air Quality Analysis through IoT Device and Risk Prediction of Asthma Attack using ML Techniques

Background: The air quality of any area depends upon the various PMs (particulate matter) and hazardous gases present in the air. Low-cost PM sensors and gas sensors are present in different target places to monitor the air quality, read the environmental data, and transmit it to local servers through the IoT device. The low-cost sensor is not reliable due to its low sensing capacity; therefore, the read data is calibrated with the meteorological data presented by the nearby meteorological Centre of that particular area. The calibrated reading data sent to the server could be analyzed through some Machine Learning [ML] models. The ML models help to predict the risk of asthma in a particular area. The risk of asthma is directly related to the air quality of the surroundings. It is observed that the air quality of the industrial area is much worse than the non-industrial belt. Air quality monitoring of industrial areas is always a challenging task due to the ununiformed pollution in some segregated places around the industry, emitting pollutants mostly from chimneys. The air quality of any area depends upon the PM (PM), i.e., PM2.5 and PM10.0, as well as the gasses like NO2(Nitrogen Dioxide), NH3 (Ammonia), SO2(Sulfur dioxide), CO(Carbon monoxide), O3(Ozone) and Benzene. These are the most hazardous gases generally emitted by common heavy industries like iron and steel. In this article, the researchers considered the industrial belt of the Asansol- Durgapur region of West Bengal, India, and predicted the risk of asthma attacks for the test dataset. The experiment was carried out on 10 different supervised machine learning [SML] models as well as semi-supervised machine learning (SSML) models. The SML models have been further refined through hyper-parameter tuning, and better results have been obtained in the case of some ML models. The result has been compared with the existing literature considering the same external environment from where the meteorological data was collected, and similar ML models have been used. The research outperformed the existing literature, which is depicted in the result and analysis section of the article. Methods: The study evaluated ML models, both supervised and semi-supervised, to assess pollution levels. Relevant features were selected while less relevant ones were discarded. Accuracy levels of different ML algorithms werecompared in the results. The most effective model for an IoT system was chosen to maximize accuracy. In semi-supervised learning, feature selection followed supervised learning, but testing was akin to unsupervised learning. Results were compared with supervised learning data, enhancing reliability. Results: The result employing various classifiers werepresented across tables after the independent parameter Ozone was removed. Following the output of several classifiers, the results were verified using the k-fold validation method, with k being set to 5 or 10, accordingly. Tables display the best outcome, which is indicated in bold characters. method: In this research work the researcher considered 9 different ML models and used them as supervised as well as semi supervised model to determine the pollution level of the certain area. In this research work the researcher also selected the most relevant features and discarded the less relevant features. In case of SML algorithm, the accuracy level of the different ML algorithm has been determined and depicted in the result analysis section. The most effective ML model has been chosen for the proposed embedded system so that accuracy could be achieved at most. In case of semi supervised algorithm the feature selection is done as per the supervised algorithm. In this case the training is done same as the SML algorithm, but the testing phase is done like unsupervised machine learning algorithm where the decision parameter is predicted and ultimately matched with the previously achieved data of SML algorithm. The reliability of this approach is much more effective than simple SML algorithm. Conclusion: This study focused on predicting asthma risk in the Asansol-Durgapur industrial belt, India, using low-cost PM and gas sensors. Data calibration with meteorological inputs enhanced accuracy. ML models predicted risk and were refined through hyper-parameter tuning. Comparative analysis showed superior performance, emphasizing the importance of precise air quality monitoring. While offering a robust framework for future research, the study’s limitation lies in its area-specific dataset.