Sulfur Dioxide Concentrations Research Articles

An ultra-sensitive optical H2S sensor based on thermal conversion combined with UV-DOAS: Dynamic detection from ppm to ppb level

Hydrogen sulfide (H2S) as a common gas plays an important role in both industrial production and clinical pathology. Therefore, the realization of dynamic detection of trace H2S gas is of great significance. In this paper, an ultra-sensitive optical H2S sensor based on thermal conversion and ultraviolet differential optical absorption spectroscopy (UV-DOAS) is developed for dynamic measurement of H2S concentration. The H2S concentration can be derived from the measurement of the change in sulfur dioxide (SO2) concentration before and after conversion. First, the evaluation method of SO2 concentration based on UV-DOAS is presented, and the performance indicators of SO2 concentration measurement are given. Second, the conversion characteristics of H2S under different temperatures and flow rates are investigated, and H2S can be efficiently converted to SO2 in the flowing gas state by controlling the conversion conditions. Finally, the performance indicators of H2S sensor are evaluated. The sensor realizes H2S measurements with a detection limit of 14 ppb*m, while its sensitivity and reliability of measuring H2S concentration increase tenfold, making it suitable for dynamic measurements of trace H2S. In addition, the feasibility of the sensor for applications in atmospheric monitoring and human exhaled breath detection is demonstrated by practical tests.

TIME-SERIES ANALYSIS OF SATELLITE-BASED TRACE GASES CONCENTRATIONS IN PORT AREAS IN THE PHILIPPINES USING SENTINEL-5P

Abstract. This study examined the temporal variations of Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2) concentrations retrieved from Sentinel-5P TROPOMI in six port areas in the Philippines, representing different climate types. The results demonstrate that the COVID-19 lockdowns led to a significant decrease in NO2 levels, while SO2 concentrations varied due to factors such as volcanic activity. Seasonal analysis of NO2 and SO2 retrievals, considering the four climate types in the Philippines, revealed that the wet season values for both pollutants were higher than the dry season values for most ports. This disparity contradicted the general trend of higher air pollutant levels during the dry season and lower levels during the wet season. The authors attributed this to the nature of port activities during inclement weather, where ships are more likely to be docked with continuously operating engines, leading to continuous emissions within the port buffer zones. Temporal analysis and time series decomposition analysis revealed patterns and trends, identifying the months of minimum and maximum pollutant concentrations and the overall trend throughout the study period.

Seasonal Variations Assessment of Air Pollutants of Communities in the Vicinity of Scrap Metal Recycling Industries in Ogijo, Shagamu South LGA, Ogun State, SW Nigeria

This manuscript presents a thorough investigation into the seasonal variations of air pollutants in communities surrounding scrap metal recycling industries in Ogijo, Shagamu South LGA, Ogun State, Nigeria utilizing advanced Gary Wolf Environmental Sensing and Particulate Counting devices. The study meticulously measures concentrations of carbon monoxide, nitrogen dioxide, sulfur dioxide, PM2.5, and PM10 during both dry and wet seasons across 20 strategic sampling locations and control. The findings reveal significant seasonal fluctuations in pollutant levels, with some concentrations exceeding Nigerian ambient air quality standards, highlighting a pressing environmental health concern. The study not only underscores the critical need for regulatory oversight and the implementation of safer metal scrapping practices but also advocates for regular environmental monitoring to mitigate the adverse impacts of such industries on local communities and the environment.

Air pollution and diabetes mellitus: Association and validation in a desert area in China.

Despite the growing evidence pointing to the detrimental effects of air pollution on diabetes mellitus (DM), the relationship remains poorly explored, especially in desert-adjacent areas characterized by high aridity and pollution. We conducted a cross-sectional study with health examination data from over 2.9 million adults in two regions situated in the southern part of the Taklamakan Desert, China. We assessed three-year average concentrations (2018-2020) of particulate matter (PM1, PM2.5, and PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) through a space-time extra-trees model. After adjusting for various covariates, we employed generalized linear mixed models to evaluate the association between exposure to air pollutants and DM. The odds ratios for DM associated with a 10 µg/m3 increase in PM1, PM2.5, PM10, CO, and NO2 were 1.898 (95% CI: 1.741, 2.070), 1.07 (95% CI: 1.053, 1.086), 1.013 (95% CI: 1.008, 1.018), 1.009 (95% CI: 1.007, 1.011), and 1.337 (95% CI: 1.234, 1.449), respectively. Notably, men, individuals aged ≥50 years, those with lower educational attainment, nonsmokers, and those not engaging in physical exercise displayed more susceptible to the adverse effects of air pollution. Multiple sensitivity analyses confirmed the stability of these findings. Our study provides robust evidence of a correlation between prolonged exposure to air pollution and the prevalence of DM among individuals living in the desert-adjacent areas. This research contributes to the expanding knowledge on the relationship between air pollution exposure and DM prevalence in desert-adjacent areas.

2019–2020年西安城市PM2.5、气体污染物及气象数据集

Air pollution in China has improved in recent years, but Central Shaanxi region continues to face great pressure and challenges in pollution control due to its unique geographical location and climatic conditions. The regional and complex air pollution, characterized by ozone (O3) and fine particulate matter (PM2.5), is becoming increasingly serious. More meticulous observation data are needed to clarify the causes of air pollution and provide scientific support for control policies. This dataset comprises the data on the concentrations of nitrogen oxides, sulfur dioxide, ozone and carbon monoxide in the atmosphere obtained by gas analyzer in Gaoxinzi Observation Site of the National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in Guanzhong Plain. Weather stations collected data on temperature, humidity, air pressure, solar radiation, wind speed, wind direction and other meteorological parameters around the station. Offline PM2.5 filter membranes were measured and the mass concentrations were analyzed. The observation site is located in the urban area of Xi 'an, Shaanxi Province. The data covers the period from January 2019 to December 2020 with a resolution of 5 minutes for pollution gas data, one minute for meteorological data, and PM2.5 collected every 24 hours. Online instruments are calibrated monthly to ensure data quality, while the offline PM2.5 sampler is calibrated and maintained weekly to ensure its data quality. This dataset can provide fundamental data records for the atmospheric environment in Central Shaanxi Province and offer data support for further pollution evaluation, control, and treatment. Moreover, it can give researchers scientific insights into the processes, casual mechanisms, as well as prevention and control of atmospheric pollution in Central Shaanxi Province.

Impact of the No-Driving Day Program on Air Quality in a High-Altitude Tropical City: The Case of the Toluca Valley Metropolitan Area

This study addresses the pressing issue of urban air pollution impact, emphasizing the need for emissions control to ensure environmental equity. Focused on the Toluca Valley Metropolitan Area (TVMA), this research employs air quality modeling to examine ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide concentrations during three different periods in 2019. It quantitatively assesses the performance of a state-of-the-art air quality model while evaluating the efficacy of a No-Driving day mitigation measure program, similar to the one which is currently implemented in Mexico City. Using an updated national emissions inventory for 2016, this study highlights the model capability of representing ozone formation and shows that reducing mobile emissions of key pollutants contributes to lowering downwind surface ozone levels, albeit with a minimal local impact. The insights and tools from this work hold potential value for decision-making in the broader Megalopolis context, aligning with global efforts to comprehend and mitigate urban air pollution impacts.

Spatial modeling of air pollution in Kharkiv city


 
 
 One of the most pressing problems facing cities, which negatively affects public health. There is a global practice of online monitoring of the content of pollutants in the air of settlements and informing the population about dangerous periods of increased pollution concentrations for certain categories of diseases and the population as a whole. It is also likely that one of the factors of pollution spread is the landscape differentiation of the city’s territory. The purpose of the study presented in this article is to develop statistical and cartographic models of the spatial distribution of air pollutants within the city of Kharkiv and to establish the relationship between the degree of air pollution and types of urban landscapes. Methods. Based on the analysis of the main methodological approaches and tasks of cartographic modeling of the spatial distribution of air pollution, the study substantiates the choice of the modeling method in this study; the features of a large city as an object of cartographic modeling of air pollution are assessed. Results. Based on the results of long-term monitoring by the Kharkiv Regional Center for Hydrometeorology, a statistical analysis of the results of the maximum and average content of pollutants in the atmosphere of Kharkiv was made and the spatial distribution of air pollution in Kharkiv (including (dust, carbon monoxide, nitrogen dioxide, sulfur dioxide, formaldehyde) was modeled, and patterns of pollution distribution were identified according to landscape types. Conclusions. In general, the spatial analysis of pollutant concentrations in the territories of Kharkiv showed that, in terms of maximum concentrations, the southeastern part of Kharkiv is most affected by carbon monoxide, nitrogen dioxide, and formaldehyde. The reason for this is the large area of industrial facilities. The northwestern part of the city has the highest concentrations of dust and sulfur dioxide. There is a lowering of the relief in the floodplain type of landscape and dense development. Intensive traffic and lowered relief (floodplain landscape) cause high average concentrations of dust, carbon monoxide, and sulfur dioxide in the southern part of the city. Nitrogen dioxide concentrations exceed the TLK in the northwest. The reason is the lowering of the relief on the outskirts of the city (floodplain landscape). The central part of the city has a high average concentration of formaldehyde. In general, a pattern of high concentrations of pollutants was found on the floodplains of the Lopan and Kharkiv rivers, with the lowest concentrations confined to watershed types of terrain between the Kharkiv and Lopan rivers and the Udy and Lopan rivers.
 
 

Long-term trends in British Columbia lower mainland air quality: Criteria air pollutants and VOC

ABSTRACT The lower mainland of British Columbia is a geographic region that comprises the districts of Metro Vancouver and the Lower Fraser Valley. It is situated in a complex topographical and coastal location in southwestern British Columbia. Metro Vancouver is Canada’s third largest population center. Accessing the Canadian National Air Pollution Surveillance Program (NAPS) database we calculated air pollutant statistics using the Canadian Ambient Air Quality Standards (CAAQS) averaging times, numerical forms, and numerical levels for the years 2001to 2020. Man Kendall and Sen statistical methods were used to test for the presence of trends and the slope of those trends in fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and volatile organic compound (VOC) ambient air concentrations. We did not determine a significant trend in 98th percentile of the daily 24-hr average PM2.5 concentrations. We did determine significant negative trends in the annual average of the daily 24-hr average PM2.5 concentrations at 6 of the 9 locations. Episodic, multi-day duration elevated PM2.5 concentrations related to forest fires were a significant influence on PM2.5 ambient concentrations. Annual 4th highest daily maximum 8-hr average O3 concentrations showed no trend at 14 of 18 locations, declined at 3 locations, and increased at one location. We determined statistically significant declines in peak and average NO2 and SO2 concentrations, and in time-integrated annual VOC concentrations.

Determining food safety in canned food using fuzzy logic based on sulphur dioxide, benzoic acid and sorbic acid concentration

Canned food market demand has arisen due to the higher need for instant and ready-to-eat food. Food preservatives are often added to canned and processed foods to prolong their shelf life and help to sustain the quality, taste, color, and food texture. However, excessive usage of such food preservatives can lead to various diseases and health issues including palpitations, allergies, and cancer. Therefore, food preservative detection in food samples is essential for safe consumption and health well-being. This paper proposed a fuzzy logic framework to determine the safety of food products based on the concentration of sulphur dioxide (SD), benzoic acid (BA), and sorbic acid (SA) in five different food categories as referred to the Food Acts 1983 and Food Regulations 1985 in Malaysia. The fuzzy logic framework comprises of Mamdani inference system design with 90 fuzzy rules, 15 and 5 membership functions for both the input and output parameters respectively. 50 random values and 10 lab analysis results based on the industrial samples were used to validate the developed algorithms in ensuring the safety of the food products. The membership functions generated for the three inputs (SD, BA, and SA) during the fuzzification steps are based on the maximum allowable limit from the food acts. The defuzzification of fuzzy logic gave an average output value of 0.1565, 0.1350, 0.1150, 0.1100, and 0.1550 for chicken curry with potatoes, satay sauce, sardine in tomato sauce, anchovies paste, and sardine spread accordingly. Results obtained from the fuzzy logic framework concluded that all the industrial samples are safe to be eaten and comply with the Sixth Schedule, Regulation 20 in both Acts.

Can Low-Carbon City Pilot Policies Reduce Environmental Pollution? Evidence From Prefecture-Level Data in China

Based on the second batch of low-carbon city pilot policies in 2013 as a quasi-natural experiment, this study adopts the panel data of 212 cities in China from 2007 to 2017, and uses the difference-in-differences model (DID) to carry out a policy evaluation of the pollution reduction and emission reduction effects of the low-carbon pilot policies and further analyzes the transmission mechanism. Based on the DID method, the actual impacts of the government's low-carbon call policy and low-carbon governance policy on the urban ecological environment are examined. It is found that compared with non-pilot cities, low-carbon governance construction in low-carbon pilot cities significantly reduces pollutant emissions in the region. Analysis of the transmission mechanism reveals that urban low-carbon governance strengthens the strength of environmental policies and reduces urban carbon and pollutant emissions through the low-carbon treatment of urban waste and the low-carbon industrial structure. The test of regional heterogeneity found that the effect of pollution reduction and emission reduction is more significant in the eastern cities, high population concentrated cities and more economically developed cities. The results are still significant after replacing the explanatory variables with sulphur dioxide content and excluding extreme outliers. Therefore, the emission reduction policy has a significant contribution to improving the urban environment and reducing pollution emissions.

Quantifying the indirect effects of different air pollutants on crop yields in North China Plain

High concentrations of air pollutants could affect crop yield directly via influencing crop physiology and indirectly via regulating climate conditions. How multiple air pollutants affect crop yields directly and indirectly remains elusive in the North China Plain (NCP), which is the hotspot of air pollution and crop production. Here, we construct data-driven models to quantify the indirect effects of four major air pollutants on the yields of winter wheat and summer maize through modulating climate variations in the NCP during 2005–2020. Our results show that wheat has a greater negative sensitivity to absorbing aerosol optical thickness (AAOD), ozone concentration (O3), sulfur dioxide concentration (SO2) and nitrogen dioxide concentration (NO2) than maize. The indirect effects of AAOD, O3 and SO2 in November and O3 in April control wheat yield variation, while the indirect effects of AAOD and O3 in June, O3 and SO2 in July, O3 and NO2 in August, and O3 in September dominate maize yield variation. Our results emphasize the indirect effects of air pollutants on crop yield via regulating climate variations, which has great implications for improving our understanding of air pollution-climate-crop interactions and guiding targeted adaptation and mitigation efforts.

Intercomparison of Air Quality Models in a Megacity: Toward an Operational Ensemble Forecasting System for São Paulo

AbstractAn intercomparison of four regional air quality models is performed in the tropical megacity of São Paulo with the perspective of developing a forecasting system based on a model ensemble. Modeled concentrations of the main regulated pollutants are compared with combined observations in the megacity center, after analyzing the spatial scale of representativeness of air monitoring stations. During three contrasting periods characterized by different types of pollution events, the hourly concentrations of carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter (PM2.5 and PM10) modeled by the ensemble are in moderate agreement with observations. The median of the ensemble provides the best performance (R ≈ 0.7 for CO, 0.7 for NOx, 0.5 for SO2, 0.5 for PM2.5, and 0.4 for PM10) because each model has periods and pollutants for which it has the best agreement. NOx concentration is modeled with a large inter‐model variability, highlighting potential for improvement of anthropogenic emissions. Pollutants transported by biomass burning events strongly affect the air quality in São Paulo and are associated with significant inter‐model variability. Modeled hourly concentration of ozone (O3) is overestimated during the day (≈20 ppb) and underestimated at night (≈10 ppb), while nitrogen dioxide (NO2) is overestimated at night (≈20 ppb). The observed O3 concentration is best reproduced by the median of the ensemble (R ≈ 0.8), taking advantage of the variable performance of the models. Therefore, an operational air quality forecast system based on a regional model ensemble is promising for São Paulo.

Estimated Sulfur Dioxide Pollutant Concentrations In Medan City Using Ordinary Kriging and Inverse Distance Weighting Approaches

Sulfur dioxide (SO2) pollution is a serious problem that negatively affects air quality and human health. So2 is generated by various human activities, especially the combustion of fossil fuels, as well as from natural sources such as volcanic eruptions. The emission of SO2 in urban areas, including Medan, Indonesia, has raised concerns as it is associated with respiratory diseases and the formation of acid rain. This research aimed to estimate comparative SO2 concentrations in Medan City using geostatistical methods, specifically ordinary kriging and inverse distance weighted (IDW). Air quality monitoring data from the Dinas Lingkungan Hidup Kota Medan were collected during a certain period. The collected data were analyzed and then interpolated using ordinary kriging and IDW methods. Furthermore, the Ordinary Kriging method involves testing the OK assumption, calculation of the experimental semivariogram, calculation of the theoretical semivariogram, structural analysis, and calculation of Root Mean Square Error (RMSE). Meanwhile, the Inverse Distance Weighted method involves calculating the Euclidean distance, determining the weights based on the power parameter, calculating the RMSE value, and estimating the SO2 concentration. The comparison results show that the OK method is more accurate in determining SO2 concentration compared to the IDW method in Medan City. In the estimation of sulfur dioxide (SO2) concentration, the OK method used the best theoretical semivariogram model, namely the exponential model, and in the estimation process of the IDW method, the power parameter 1 was used.

Time evolution of sulphur dioxide concentration generated by solid fuel combustion systems during the cold periods of the year

About 99% of sulphur dioxide present in the atmosphere comes from anthropogenic activities, the main source being industrial activity that processes sulphur materials, including burning fuels (coal, oil or gas), thus releasing sulphur dioxide into the air. The purpose of this paper is to monitor the concentration of sulphur dioxide during two seasons, in the ambient air of Petroșani, with the aim of highlighting the possible atmospheric pollution caused by thermal power plants that use solid fuel during the winter period. The monitoring periods were distributed over a period of 6 months (three in summer and three in winter), namely the summer months (May, June, July of 2022), the winter months of December (2022), January and February of 2023. The main objective of the measurements is to identify if there is an increase in sulphur dioxide concentration in the atmosphere, resulting from the burning of solid fuels from household activities during the winter period. Results indicate that there is a higher degree of SO2 pollution in the winter period, but the recorded values are far below the limits set by national legislation. The paper presents some measures to limit sulphur dioxide pollution in the context of climate change.

PROBLEM STATEMENT AND MODELING OF AN ENERGY TECHNOLOGY SYSTEM FOR THE JOINT PRODUCTION OF SULFURIC ACID AND CONDENSED SULFUR DIOXIDE

The article develops a mathematical model for the quantitative assessment of technological parameters of energy-technological systems for the production of sulfuric acid and condensed sulfur dioxide. When modeling, the system was considered as energy-technological, taking into account the need to reduce its negative impact on the environment, due to both industrial emissions of harmful substances and emissions of unused thermal energy. In this regard, it was envisaged to process part of the sulfur dioxide according to a cyclic scheme. To increase the intensity of the technological system and the processes occurring in it, the use of oxygen and the provision of a high concentration of sulfur dioxide in the gas mixture before the stage of its catalytic oxidation in the contact apparatus was envisaged. To reduce the total volume of reactants, an increase in pressure in the system was assumed. The carried out formalization and object-oriented decomposition of the simulated system made it possible to identify 6 functional subsystems (blocks), each of which can be modeled and investigated independently. The accepted high level of formalization made it possible to include a minimum set of real system parameters in the model. The model is capable of development and can be concretized by introducing additional parameters and mathematical models of technological functions into consideration. The developed model is recommended to be used for operational modeling of combined systems for the production of sulfuric acid and condensed sulfur dioxide in the preparation and optimization of initial data for their design. After testing and setting up the model in practice, it can be used in an automated process control system, including when creating adaptive control systems.

Дистанционная оценка качества атмосферного воздуха в населенных пунктах нефтегазовой специализации Ямало-Ненецкого автономного округа

The remote assessment of methane (CH4), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations in the atmosphere of oil-and-gas profile settlements within the Yamalo-Nenets Autonomous Okrug for 2019–2022 was made for the first time. Data analysis from the TROPOMI spectrometer revealed that the highest levels of atmospheric pollution are observed near industrial centers and major cities (Novy Urengoy, Salekhard, Nadym). Increased impurities were identified along gas pipelines (near compressor stations). The highest average values of nitrogen dioxide content were recorded in Novy Urengoy (1,34 mol/m², with contamination peaks occurring in winter months). Excessive concentrations of sulfur dioxide were found in urban-type settlements. The highest average values were recorded in Stary Nadym (20,35 mol/m²). The maximum methane content (1866,2 ppb) was reported in Novy Urengoy. Urban air, in general, has increased methane content. However, focuses of its spread are also observed beyond cities, which may be a consequence of global warming

Selective Wine Aroma Enhancement through Enzyme Hydrolysis of Glycosidic Precursors.

Selective enhancement of wine aroma was achieved using a broad spectrum of exogenous glycosidases. Eight different enzyme preparations were added to Verdejo wine, resulting in an increase in the levels of varietal volatile compounds compared to the control wine after 15 days of treatment. The enzyme preparations studied were robust under winemaking conditions (sulfur dioxide, reducing sugars, and alcohol content), and no inhibition of β-glucosidase activity was observed. Significant differences were detected in four individual terpenes (α-terpineol, terpinen-4-ol, α-pinene, and citronellal) and benzyl alcohol in all the treated wines compared to the control wine, contributing to the final wine to varying degrees. In addition, a significant increase in the other aromatic compounds was observed, which showed different patterns depending on the enzyme preparation that was tested. The principal component analysis of the data revealed the possibility of modulating the different aromatic profiles of the final wines depending on the enzyme preparation used. Taking these results into account, enhancement of the floral, balsamic, and/or fruity notes of wines is possible by using a suitable commercial enzyme preparation.

Estimating PM2.5 utilizing multiple linear regression and ANN techniques

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg–Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Study on indoor pollutants emission in Akure, Ondo State, Nigeria

PurposeThis study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM2.5 and PM10) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.Design/methodology/approachThe evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.FindingsCO2 (430.34 ppm), NO2 (93.31 ppb), O3 (19.94 ppb), SO2 (40.87 ppb), PM1 (29.31 µg/m3), PM2.5 (43.56 µg/m3), PM10 (50.70 µg/m3), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO2 (18%) and low PM10 (49%) contributions, NO2 and PM10 were the most common pollutants in all of the months. The mean air quality index (AQI) for NO2 indicated that the AQI was “moderate” (51–100). In contrast to SO2, whose AQI ranged from “moderate” to “very unhealthy,” O3's AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM1, PM2.5 and PM10 made up the majority of PC1’s contribution, both PM2.5 and PM10 were deemed “hazardous.”Practical implicationsThe practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.Originality/valueLack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.

SO2 Mitigation via Catalytic Oxidation using Carbonaceous Materials and Metal Oxides for Environmental Sustainability

The high concentration of sulfur dioxide (SO2) in the air that contributes to increasing health and environmental issues has caught the attention of all countries. Numerous tactics to regulate and lower the SO2 levels in the environment that have been applied through regulations and promising technology, progress has been obtained to decrease the SO2 concentration. Among methods for SO2 removal, one of the promising techniques used is the catalytic oxidation of SO2 to SO3, which not only reduces the SO2 concentration in the environment but also produces sulfuric acid (H2SO4). Thus, the performance of the catalysts that can promote the catalytic oxidation of SO2 to SO3 for environmental sustainability is reviewed in this study. The types of catalysts evaluated in this study are carbon-based materials and metal oxides. Worth noting that these catalysts are feasible to catalytically converting SO2 hazardous material to resources, viz. SO3 and H2SO4 for industrial use. The findings of this study can serve as a foundation for devising an innovative method for SO2 mitigation through catalytic oxidation. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).