SO2 In Air Research Articles

Study on the Mechanism of Temperature Effect on SO2 Electrochemical Gas Sensor

Abstract Temperature can affect the measurement values of electrochemical gas sensors, increasing measurement errors. The influence mechanism of temperature on electrochemical gas sensors was studied based on Fick's first law and the limit diffusion current formula. Temperature affects the sensitive characteristics of sensor by changing the diffusion coefficient Dl1 of SO2 in air, the Henry's coefficient KH of SO2 dissolved in water and the water content of the electrolyte solution. When the temperature increases, the degree of influence of Henry's coefficient KH and the reduction of water content is greater than the degree of influence on the increase in diffusion coefficient, which decreases the sensor measurement value. The results of the temperature experiments show that the optimal temperature range for the sensor is -25 °C to 50 °C, and the average measurement error in this temperature range is less than 20%. When the temperature exceeds 50 ℃, it will cause a reduction in the evaporation of water in the electrolyte solution, leading to a rapid increase in the measurement error. The structure of the sensor can be improved by adding a water retention layer inside the sensor to supplement the electrolyte solution with water, so as to reduce the measurement error.

Evaluation of Machine Learning Application on the Prediction of Particulate Matter Concentrations in Small/Medium-Sized City

Objectives:In this study, Machine Learning (ML) algorithms were evaluated to predict the concentration of particulate matter (PM10 and PM2.5) using air quality and meteorological data in small/medium-sized city.Methods:ML models, including Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), were used to predict PM10 and PM2.5 concentrations. Five air quality variables, including NO2, SO2, CO, PM10 and PM2.5, and seven meteorological variables, including temperature, humidity, vapor pressure, wind speed, precipitation, local atmospheric pressure, and sea-level atmosphere pressure, were collected from three air quality monitoring stations and one meteorological observatory from 2017 to 2022. A total of 52,583 sets of data were used for ML. The prediction accuracies of the applied ML models were evaluated using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2).Results and Discussion:Higher ML performance was obtained when using the data including PM10 and PM2.5 compared to the data excluding these variables. Among five different ML models, the XGB model showed the highest accuracy in predicting PM10 and PM2.5 one hour in the future. However, poorer performance was obtained as the predicted period increased from one hour to 72 hours.Conclusion:The application of ML algorithms for the short-term prediction of PM10 and PM2.5 was successful in this study. However, more input variables and deep learning algorithms are needed for long-term prediction of PM10 and PM2.5.

The effect of outdoor air pollution on Alzheimer’s disease: a systematic review

Abstract Air pollution is one of the largest global environmental risks to public health. Over recent years, neuroimaging techniques have started to uncover the detrimental impact air pollution has had on brain health, including the development of Alzheimer’s Diseases (AD). We systematically reviewed the literature to evaluate the effects of long-term exposure (months to years) to outdoor air pollutants on the development of AD-like neurology. This review was registered on PROSPERO (CRD42023482979) and followed PRISMA guidelines. Four large databases (MEDLINE, Embase, Scopus, and CINAHL) were searched in November 2023 using terms associated with air pollution, neuroimaging, and AD. Only peer-reviewed primary research articles using neuroimaging data to examine AD-like pathology after long-term exposure to air pollutants (Particulate Matter (PM2.5, PM10), SO2, NO2, O3, and/or CO) were included. Titles, abstracts and full-texts were screened, and included articles were quality assessed using the Newcastle Ottawa Scale. A narrative synthesis was conducted to analyse the studies. Our search yielded 397 results, of which eight articles met our inclusion criteria. Articles focused on changes to white matter (n = 5), cortical thickness (n = 6), and grey matter (n = 2). Exposure to PM2.5 was commonly associated with white matter reductions, and PM10 and NO2 exposure was associated with reduced cortical thickness. The effect of exposure to different outdoor pollution on grey matter was inconclusive, with both increases and decreases in grey matter volume observed. This review highlighted how PM2.5, PM10 and NO2 exposure was associated with neurological changes commonly seen in AD. These findings can be used by policymakers and researchers to identify specific pollutants that need greater restrictions and regulations to improve population health. Key messages • PM2.5, PM10 and NO2 exposure are associated with neurological changes commonly seen in AD. • Greater restrictions and regulations are needed on specific pollutants to improve population health.

A theoretical study on adsorption and sensing of SO2, CS2, CO2, CH2O, H2O, C2H2, and CF3H air pollutant gases by B3S monolayer

A theoretical study on adsorption and sensing of SO2, CS2, CO2, CH2O, H2O, C2H2, and CF3H air pollutant gases by B3S monolayer

Machine learning models for predicting interactions between air pollutants in Tehran Megacity, Iran

Air pollution has significant detrimental impacts on the environmental compartments and human health. The present study investigates the interactions between selected air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, in Tehran megacity, the capital of Iran, using statistical modeling and simulating approaches. Data on selected air pollutants have been extracted from 16 sensors across Tehran during 2013–2022. Using the development of three machine learning models, including XGBoost (XGB), LightGBM (LGBM) and Random Forest (RF), significant relationships between the air pollutants were observed. The comparison of the models demonstrated that the RF model has the highest level of optimality in forecasting the concentrations of CO, O3, NO2, and SO2 (R2CO = 0.63, R2O3 = 0.6, R2NO2 = 0.54, R2SO2 = 0.55). The LGBM model provided higher optimality for NO2 and PM2.5 (R2NO2 = 0.56, R2PM2.5 = 0.88), while the XGB model exhibited higher accuracy for the PM10 (R2PM10 = 0.88). Regarding the results obtained from the statistical models, it can be inferred that the RF model has superior performance in the forecasting of gaseous pollutants. Moreover, the XGB and LGBM models exhibited comparable performance and therefore may be regarded as appropriate options for PM prediction. The findings from the simulations indicated that a rise in an individual pollutant likely leads to an increase in other pollutants. Consequently, implementing air quality management strategies for a specific pollutant meaningfully, directly influences other pollutants, highlighting the significance of considering the chemical aspect of air pollutants interaction and enforcing air pollution management rules.

Impact of Outdoor Air Pollutants Exposure on the Severity and Outcomes of Community-Acquired Pneumonia in Gabes Region, Tunisia.

Background Acute community-acquired pneumonia (CAP) is considered the leading cause of infectious death worldwide. Air pollution and prolonged exposure to airborne contaminants have been implicated in various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). However, the specific impact of air pollution on pneumonia, particularly CAP, remains underexplored. Given the rising levels of urban air pollution and its potential health ramifications, our study aimed to examine the association between exposure to outdoor air pollution and severity as well as the outcomes of pneumonia cases requiring hospitalization. Methodology A cohort analytical study with retrospective data collection was carried out in the pulmonology department of the Gabès University Hospital between January and October 2022. We compared levels of particulate matter less than or equal to 10µm in aerodynamic diameter (PM10),sulfur dioxide (SO2),ozone (O3), moisture and ambient temperature with severity and outcomes ofpneumonia requiring hospitalization. The choice of these specific pollutants and environmental factors was based on their established impact on respiratory health and their prevalence in the study region. Results Increased sulfur dioxide (SO2) levels were associated with increased use of non-invasive ventilation (NIV) (r = 0.400). Higher levels of particulate matter (PM10) were significantly associated with the development of lung abscesses. Similarly, increased humidity and ambient temperature were strongly correlated with the development of lung abscesses. Increased air SO2 levels were correlated with a higher CURB65 score (r = 0.299). High outdoor SO2 levels and increasing moisture content were associated with increased Pneumonia Severity Index (PSI) score (r = 0.303 and = 0.310, respectively). Higher levels of PM10 were associated with an increased risk of pleural effusion, a serious complication of pneumonia. Finally, higher ambient temperatures were correlated with more extensive opacities on chest X-rays (r = 0.706), suggesting the severity of pneumonia. Conclusion This study highlights the significant associations between environmental factors and various clinical parameters in pneumonia patients. The findings underscore the importance of considering environmental exposures, such as air quality and weather conditions, in understanding and managing the severity of pneumonia.

Phographene as a new carbon allotrope for adsorption and detection of SO2, AsH3, NO2, CF3H, and CO2 air pollutant gaseous species.

Phographene and its family member structures are of the newly proposed semiconductors for detection of chemicals. That is, in this project, the potential of using α-phographene (α-POG) both for adsorption and detection of five types of the most important air pollutant gases containing SO2, AsH3, CF3H, NO2, and CO2 species were investigated. The results of the time dependent density functional theory (TD-DFT) calculations indicate that during the adsorption of NO2, and SO2 by the sorbent, big redshifts occur (up to 866.2 nm, and 936.5, respectively) resulting in considerable changes in the orbitals and the electronic structures of the systems. Moreover, the results of the thermodynamic calculations reveal that α-POG could selectively adsorb SO2, NO2, and AsH3 gases (with different orders), but it could not adsorb the two other gases.Finally, the outcome of the band gap calculations shows that between all mentioned gases, α-POG could selectively detect the presence of SO2, and then NO2; while, this nanosheet could not sense the existence of AsH3, CF3H, or CO2 gases. All of the calculations were carried out by using the Gaussian 03 quantum chemical package. In addition, the physiochemical parameters were extracted from the output files for further calculations. Studies on all saddle points and the following calculations were performed applying the B3LYP/6-311g(d,p) level of theory.

Ambient Air Pollution and Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus: A cohort study.

We aimed to assess the effect of air pollution on incident cardiovascular disease (CVD) in people with type 2 diabetes mellitus (T2DM). We tracked 486 T2DM patients from 2012 to 2021. Cox regression models were applied to assess the hazard of exposure to particulate matter, carbon monoxide (CO), ozone, nitrogen dioxide, and sulfur dioxide (SO2) on incident CVD, revealing hazard ratios (HRs). CVD incidents occurred in 73 individuals. Among men, each 1-ppm increase in CO levels raised the risk of CVD (HR: 2.66 (95% CI: 1.30-5.44). For women, a 5-ppb rise in SO2 increased CVD risk (HR: 1.60 (95% CI: 1.11-2.30). No notable impact of particulate pollutants was found. Persistent exposure to gaseous air pollutants, specifically CO and SO2, is linked to the development of CVD in men and women with T2DM.

Identifying decadal trends in deweathered concentrations of criteria air pollutants in Canadian urban atmospheres with machine learning approaches

Abstract. This study investigates long-term trends of criteria air pollutants, including NO2, CO, SO2, O3 and PM2.5, and Ox (meaning NO2+O3) measured in 10 Canadian cities during the last 2 to 3 decades. We also investigated associated driving forces in terms of emission reductions, perturbations due to varying weather conditions and large-scale wildfires, as well as changes in O3 sources and sinks. Two machine learning methods, the random forest algorithm and boosted regression trees, were used to extract deweathered mixing ratios (or mass concentrations) of the pollutants. The Mann–Kendall trend test of the deweathered and original annual average concentrations of the pollutants showed that, on the timescale of 20 years or longer, perturbation due to varying weather conditions on the decadal trends of the pollutants are minimal (within ±2 %) in about 70 % of the studied cases, although it might be larger (but at most 16 %) in the remaining cases. NO2, CO and SO2 showed decreasing trends in the last 2 to 3 decades in all the cities except CO in Montréal. O3 showed increasing trends in all the cities except Halifax, mainly due to weakened titration reaction between O3 and NO. Ox, however, showed decreasing trends in all the cities except Victoria, because the increase in O3 is much less than the decrease in NO2. In three of the five eastern Canadian cities, emission reductions dominated the decreasing trends in PM2.5, but no significant trends in PM2.5 were observed in the other two cites. In the five western Canadian cities, increasing or no significant trends in PM2.5 were observed, likely due to unpredictable large-scale wildfires overwhelming or balancing the impacts of emission reductions on PM2.5. In addition, despite improving air quality during the last 2 decades in most cities, an air quality health index of above 10 (representing a very high risk condition) still occasionally occurred after 2010 in western Canadian cities because of the increased large-scale wildfires.

CARDIOGENIC SHOCK IN A PATIENT WITH SEVERE AORTIC VALVE STENOSIS AND MULTIVESSEL CORONARY DISEASE: ADVANCED THERAPEUTIC STRATEGIES

Abstract We describe a case of a 58 years old man. He was referred to emergency room for episodes of vomiting and epigastric pain for about a week. Upon admission he was confused with systolic–diastolic blood pressure values ​​70/40 mmHg, heart rate 120 bpm, sinus rhythm. pO2 was 80 mmHg and SO2 in air 97%. Lactate values 10, blood sugar > 300 mg/dl. The electrocardiogram showed sinus tachycardia with the results of antero–septal necrosis and incomplete left bundle branch block. The fast echocardiogram showed a dilated left ventricle with severe depression of the ejection fraction (EF about 10–15%), severe low flow–low gradient aortic valve stenosis (AVA bidimensional was approximately 0.9 cm²), severe mitral valve insufficiency due to a dual mechanism. The dimension of Inferior vena cava was 1.5 cm, collapsible. There was thrombotic stratification in the apex of the left ventricle and VA coupling was 2. The patient was admitted to the ICU with stage C cardiogenic shock according to SCAI classification. We found a value of PVC 3 mmHg. After fluide challenge there was an increase in blood pressure values to 100/60 mmHg. Noradrenaline vasopressor was started and subsequently a low dose of adrenaline. Blood pressure values maintained around 90/60 mmHg and undergo a sudden drop after increasing the dosage of the vasopressor. Diuresis was approximately 50 ml/h, lactates clear completely after approximately 4 hours. After 6 hours, due to a new sudden reduction in blood pressure, the patient was referred for IABP and coronary angiography control. In the context of multivessel coronary artery disease and cardiogenic shock he was therefore a candidate for urgent cardiac surgery of aortic valve replacement, by–pass and mitral anulovalvuloplasty. This case gave us the opportunity to analyze how to proceed in such a critical conditions with the below conclusions: a) the choice of vasopressors and inotropes is limited in case of severe aortic valve stenosis b) the presence of left ventricular intracavitary thrombosis limits the choice of ventricular assistance system c) in case of cardiogenic shock and severe aortic valve stenosis the patient must be a candidate for valve replacement surgery as soon as possible.

HYBRID MODEL AND FRAMEWORK FOR PREDICTING AIR POLLUTANTS IN SMART CITIES

The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations.

Significant influence of urban human activities and marine input on rainwater chemistry in a coastal large city, China

The coastal urban region is generally considered an atmospheric receptor for terrestrial and marine input materials, and rainfall chemistry can trace the wet scavenging process of these materials. Fast urbanization in China's east coastal areas has greatly altered the rainwater chemistry. However, the chemical variations, determinants, and sources of rainfall are unclear. Therefore, the typical coastal city of Fuzhou was selected for 1-year rainwater sampling and inorganic ions were detected to explore above problems. The findings depicted that rainwater ions in Fuzhou were slightly different from those in other coastal cities. Although NO3−, SO42−, Ca2+ and NH4+ dominated the rainwater ions, the marine input Cl− (22 %) and Na+ (11 %) also contributed a considerable percentage to the rainwater ions. Large differences in ion concentrations (2∼28 times) were found in monthly scale due to the rainfall amount. Both natural and anthropogenic determinants influenced the rainwater ions in coastal cities, such as SO2 emission, air SO2 and PM10 content on rainwater SO42−, NO3−, and Ca2+, and soot & dust emission on rainwater SO42−, NO3−, indicating the vital contribution of human activities. Stoichiometry and positive matrix factorization-based sources identification indicated that atmospheric dust/particles were the primary contributor of Ca2+ (83.3 %) and F− (83.7 %), and considerable contributor of SO42− (39.5 %), NO3− (38.3 %) and K+ (41.5 %). Anthropogenic origins, such as urban waste volatilization and fuel combustion emission, contributed 95 % of NH4+, 54.5 % of NO3− and 41.9 % of SO42−, and the traffic sources contribution was relatively higher than fixed emission sources. The marine input represented the vital source of Cl− (77.7 %), Na+ (84.9 %), and Mg2+ (55.3 %). This work highlights the significant influence of urban human activities and marine input on rainwater chemicals and provides new insight into the material cycle between the atmosphere and earth-surface in coastal city.

Gelato: a new hybrid deep learning-based Informer model for multivariate air pollution prediction.

The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accurate prediction of air pollutants such as CO2, O3, and PM2.5. Most of the models used in the literature are deep learning models with Transformers being the best for time series prediction. However, there is still a need to enhance accuracy in air pollution prediction using Transformers. Alongside the need for increased accuracy, there is a significant demand for predicting a broader spectrum of air pollutants. To encounter this challenge, this paper proposes a new hybrid deep learning-based Informer model called "Gelato" for multivariate air pollution prediction. Gelato takes a leap forward by taking several air pollutants into consideration simultaneously. Besides introducing new changes to the Informer structure as the base model, Gelato utilizes Particle Swarm Optimization for hyperparameter optimization. Moreover, XGBoost is used at the final stage to achieve minimal errors. Applying the proposed model on a dataset containing eight important air pollutants, including CO2, O3, NO, NO2, SO2, PM10, NH3, and PM2.5, the Gelato performance is assessed. Comparing the results of Gelato with other models shows Gelato's superiority over them, proving it is a high-confidence model for multivariate air pollution prediction.

Relationship Between the Incidence of Pulmonary Embolism and the Quantitative Level of Air Pollution (NO2, SO2, CO, O3, and PM10) in Patients Referred to Mashhad Hospitals in 2019

Background: Air pollution is known to be associated with increased levels of inflammatory markers, vascular endothelial damage, and alterations in blood coagulation factors. It represents one of the most significant environmental hazards in urban areas, with adverse effects on the health of the population. Objectives: This study aimed to investigate the relationship between the incidence of pulmonary embolism (PE) and the quantitative levels of various air pollution parameters. Methods: In this descriptive-analytical (cross-sectional) study, we examined all patients diagnosed with PE who were referred to Ghaem and Imam Reza (AS) Hospitals in Mashhad during the year 2019. Daily data on each air pollutant, including NO2, SO2, CO, O3, and PM10, were obtained from the Meteorological Center. For each patient's assessment, we considered the average pollution levels over the past 30 days. The collected data were subsequently analyzed using independent samples t-tests and the Mann-Whitney U test in SPSS 22. Results: The findings of this study revealed a statistically significant association between elevated levels of PM10 and O3 and the occurrence of PE. However, no such relationship was observed for NO2 and CO in relation to the risk of PE. Conclusions: It can be concluded that increased levels of PM10 and O3 are linked to a higher risk of developing PE, while other pollutants showed no evidence of association in this study.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

The effects of short-time air pollution, SO2, and ozone on biochemical, histo-pathological, oxidative stress, and carcinogenesis related genes expressions in the liver of the rats.

Air pollution is a universal issue and has significant deleterious effects on both human health and also environment. The important indicators of air pollution include ozone (O3), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). This research aims to investigate the impacts of ambient air pollution (AAP), SO2, and O3 on oxidative stress parameters, liver tissue histopathology, and expression of some carcinogenesis-related genes in the hepatic tissue of rats. 32 Wistar rats were randomly allocated to four groups: the control group, the AAP group, the SO2 group (10 ppm), and the ozone group (0.6 ppm). Over a period of five consecutive weeks, the rats were exposed to the specified pollutants for 3 h daily; liver tissues were harvested and instantly fixed with formalin. Pathological changes were assessed in the tissue samples. Additionally, the RT-qPCR technique was utilized to investigate Expression alterations of BAX, p-53, BCL2, caspase-3, caspase-8 and caspase-9. Furthermore, 30 milligrams of hepatic tissues were extracted to assess the activities of oxidative stress enzymes. The liver catalase and MDA activity were elevated in the air pollution (p < .05). Also, liver GPx activity in air pollution and ozone groups was significant in comparison to the control group (p < .05). The SO2 group exhibited severe lesions in histopathology examinations. The findings revealed an alteration in liver histopathology, an induction of oxidative stress, and the expression of some apoptosis-related genes in hepatic tissues after exposure to AAP, SO2, and O3.

Ambient air quality monitoring of Chandrapur District, Central India

Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate.

Air Pollution in Jakarta, Indonesia Under Spotlight: An AI-Assisted Semi-Supervised Learning Approach

The air quality in the Jakarta area is examined in this study using artificial intelligence (AI) to assist a semi-supervised learning technique. The clustering approach is used in this article to separate air pollution into three main categories moderate, low, and high levels. This clustering helps identify shared characteristics among measures like PM10, SO2, NO2, and others, even when air quality labels are not always accessible. Using the Random Forest method, the air quality will be categorized in this experiment with an accuracy rate of 93%. Additionally, the results of variable significance analysis are examined on this article to identify the variables with the biggest effects on air quality, notably PM10, SO2, and NO2. This study demonstrates the enormous potential of applying machine learning techniques, particularly semi-supervised learning approaches, to assist sustainable environmental regulations while also monitoring and enhancing Jakarta's air quality. We describe the experimental procedures, the findings, and the implications of our research for comprehending and addressing urban air pollution in this article

Decreasing trend in SO2 concentrations over Durban: 2004 - 2014

The climatology of ambient SO2 air pollution was investigated in Durban, South Africa using data collected by seven air quality monitoring stations (Ferndale, Grosvenor, Jacobs, Wentworth, Settlers School, Southern Works and Prospecton) operated by eThekwini municipality (2004–2014, 2018-2019). These sites constitute a mix of urban and industrial locations. Yearly average trends indicated that no site exceeded the yearly average national guideline (19ppbv) 2004-2014, 2018-2019. Southern Works, Wentworth and Jacobs, recorded highest yearly averages, a consequence of situation within Durban South Industrial Basin (DSIB) while Ferndale recorded lowest yearly averages, a reflection of location in an urban environment. Results of linear fitting to yearly average data (2004-2014) indicated negative trends (all sites). The largest trend was recorded at Jacobs (-0.48ppbv yr-1) and smallest trends observed at Prospecton (-0.12ppbv yr-1), Ferndale (-0.084ppbv yr-1) and Grosvenor (-0.024ppbv yr-1). Using these linear trends, projected SO2 levels were calculated and compared to actual data where it existed (2019 - Wentworth, Settlers, Prospecton), (2018, 2019 Southern Works). Comparison of actual with projected data indicated that except for Prospecton, projected yearly averages are lower than actual yearly averages for these sites. The largest difference between projected and actual data occurred for Southern Works in 2019 (4.10 ppbv). Monthly averages displayed periodic behaviour with maxima recorded in winter and minima in summer. Highest monthly averages were consistently recorded at Wentworth, Jacobs or Southern Works. Maximum monthly average (2004- 2019) was reported at Jacobs, June 2009 (21.71± 2.82ppbv) while minimum monthly average (2004–2019) was reported at Ferndale, December 2012 (0.32±0.04ppbv). Previous Durban SO2 studies are considerably older or of shorter duration than the analysis presented here and are limited as they principally focus on yearly average trends. The determination of SO2 levels over additional averaging periods (monthly average, seasonal variation, seasonal average) coupled with a larger data set and the comparison across sites (industrial vs urban) provides a more detailed exposure profile as experienced by individuals living and working in the eThekwini municipality and thereby expands on previous investigations.

Air quality and characterization of synoptic circulation weather patterns in a South American city from Argentina

The potential cause-effect relationship between synoptic meteorological conditions and levels of criteria air pollutants, including CO, NO2, O3, PM10, PM2.5 and SO2, in Bahia Blanca, Argentina, was assessed for the period of 2018–2019. Daily back-trajectories and global meteorological data fields were employed to characterize the primary transport paths of air masses reaching the study site, and to identify the synoptic meteorological patterns responsible for these atmospheric circulations. Time series of surface-level meteorological parameters and midday mixing layer height were collected to examine the impact of the synoptic meteorological patterns on local meteorology. Furthermore, the NAAPS global aerosol model was utilized to identify days when contributions from long-range transport processes, such as dust and/or biomass burning smoke, impacted air quality. By applying this methodology, it was determined that the air masses coming from the N, NW and W regions significantly contributed to increased mean concentrations of coarse particles in this area through long-range transport events involving dust and smoke. Indeed, the high average levels of PM10 recorded in 2018–2019 (annual mean values of 47 and 52 μg/m3, respectively) represent the main air quality concern in Bahía Blanca. Moreover, PM10, PM2.5 and NO2 emissions should be reduced in order to meet recommended air quality guidelines. On the other hand, the results from this study suggest that the sources and meteorological processes leading to the increase in the concentrations of CO and SO2 have a local-regional origin, although these air pollutants did not reach high values probably as a consequence of the strong wind speed registered in this region during any synoptic meteorological pattern.