Sulfur Dioxide Concentrations Research Articles

Long-term exposure to ambient air pollutants and their interaction with physical activity on insomnia: A prospective cohort study

Exposure to air pollution or lack of physical activity (PA) increases the risk of insomnia. However, evidence on joint exposure to air pollutants is limited, and the interaction of joint air pollutants and PA on insomnia is unknown. This prospective cohort study included 40,315 participants with related data from the UK Biobank, which recruited participants from 2006 to 2010. Insomnia was assessed by self-reported symptoms. The annual average air pollutant concentrations of particulate matter (PM2.5, PM10), nitrogen oxides (NO2, NOX), sulfur dioxide (SO2) and carbon monoxide (CO) were calculated based on participants' addresses. We applied a weighted Cox regression model to evaluate the correlation between air pollutants and insomnia and newly proposed an air pollution score to assess joint air pollutants effect using a weighted concentration summation after obtaining the weights of each pollutant in the Weighted-quantile sum regression. With a median follow-up of 8.7 years, 8511 participants developed insomnia. For each 10 μg/m³ increase in NO2, NOX, PM10, SO2, the average hazard ratios (AHRs) and 95% confidence interval (CI) of insomnia were 1.10 (1.06, 1.14), 1.06 (1.04, 1.08), 1.35 (1.25, 1.45) and 2.58 (2.31, 2.89), respectively; For each 5 μg/m³ increase in PM2.5 and each 1 mg/m³ increase in CO, the corresponding AHRs (95%CI) were 1.27 (1.21, 1.34) and 1.83 (1.10, 3.04), respectively. The AHR (95%CI) for insomnia associated with per interquartile range (IQR) increase in air pollution scores were 1.20 (1.15, 1.23). In addition, potential interactions were examined by setting cross-product terms of air pollution score with PA in the models. We observed an interaction between air pollution scores and PA (P = 0.032). The associations between joint air pollutants and insomnia were attenuated among participants with higher PA. Our study provides evidence on developing strategies for improving healthy sleep by promoting PA and reducing air pollution.

Elucidation of spatial disparities of factors that affect air pollutant concentrations in industrial regions at a continental level

Industrial regions and relevant infrastructures are known to contribute to air pollutant emissions; thus, a detailed investigation of the air pollutant concentrations of a region based on specific land uses, with spatial reasoning, can support smart regional planning. However, the current knowledge about the spatial patterns that indicate the relationship between the anthropological or environmental features and the air pollutant concentrations in industrial regions is limited. Thus, in this study, we aimed to identify the factors that affect air-pollutant concentrations due to local spatial impacts in industrial regions across Australia. Considering the large spatial scale, the impact of a global factor can be overwhelmed by another factor due to local spatial impacts, and the phenomenon is a kind of spatial disparity. We developed a novel set of methods, including a point-of-interests-based spatial identification method and geographically weighted regression (with standardised coefficients), to: (i) identify the industrial regions in the study area, (ii) collect the remote sensing factors, and (iii) identify the factors that affect the spatial disparity of air-pollutant concentrations in industrial regions. The results indicated a significant spatial disparity in the air pollutant concentrations in the industrial region, at a continental scale. Anthropogenic factors significantly affected the spatial patterns of air pollutant concentrations in the industrial regions that were remote to cities, whereas meteorological and topographical factors had significant impacts on the air pollutant distributions in urban industrial regions. Furthermore, within the nationwide industrial lands, drives of the relatively high concentrations of ozone and sulphur dioxide, the drivers of the air pollutant concentrations were environmental factors; high concentrations of nitrogen dioxide were more associated with the topographical features of the region. The methods proposed in this study can serve as a reliable framework for analysing the air quality of industrial regions and can also, supplement future studies on emissions reduction in industrial parks.

Air Quality Assessment in an Oil and Gas Field at Atrush Area With Particulare Reference to Sulfur Dioxide and Nitrogen Dioixde

The effectiveness and usability of a diffusion tube for air quality sampling were assessed at 14 sites throughout an oil and gas production area in Atrush sub-district for measurement of Sulfur dioxide (SO2) and nitrogen dioxide (NO2) in ambient air by Palmes diffusion tubes in air during autumn and winter season of 2021-2022. Using triethanolamine as a trapping agent, the passive sampler collects SO2 and NO2, which are then proven to be sulphate and nitrite using ion chromatography and visible spectrometry. Ion chromatography was used to determine the respective dissolved ionic forms of SO2. Additionally, Ultraviolet-Visible (UV-Vis) spectrophotometry used to analyse NO2 as nitrite. Sulfur dioxide (SO2) concentration was high in locations close to flare with high elevation, as well as in winter season were higher than that of autumn season, the highest average of CPF (Central Processing Facility) is (26.39 ppb), however the highest concentration recorded is below Iraqi standard limit 140 ppb for 24 hr and lowest average at ECP3(Entry Check Point 3) (4.37 ppb) respectively. Nitrogen dioxide (NO2) concentrations were high in locations where heavy equipment’s operated and in diesel consumption areas, highest average of NO2 concentration of E pad (East Pad) is (8.94 ppb), and lowest average was in ECP3 (3.09 ppb).

Association between short-term exposure to ambient air pollution and heart failure: An updated systematic review and meta-analysis of more than 7 million participants.

Exposure to air pollution has been linked to the mortality of heart failure. In this study, we sought to update the existing systematic review and meta-analysis, published in 2013, to further assess the association between air pollution and acute decompensated heart failure, including hospitalization and heart failure mortality. PubMed, Web of Science, EMBASE, and OVID databases were systematically searched till April 2022. We enrolled the studies regarding air pollution exposure and heart failure and extracted the original data to combine and obtain an overall risk estimate for each pollutant. We analyzed 51 studies and 7,555,442 patients. Our results indicated that heart failure hospitalization or death was associated with increases in carbon monoxide (3.46% per 1 part per million; 95% CI 1.0233-1.046, P < 0.001), sulfur dioxide (2.20% per 10 parts per billion; 95% CI 1.0106-1.0335, P < 0.001), nitrogen dioxide (2.07% per 10 parts per billion; 95% CI 1.0106-1.0335, P < 0.001), and ozone (0.95% per 10 parts per billion; 95% CI 1.0024-1.0166, P < 0.001) concentrations. Increases in particulate matter concentration were related to heart failure hospitalization or death (PM2.5 1.29% per 10 μg/m3, 95% CI 1.0093-1.0165, P < 0.001; PM10 1.30% per 10 μg/m3, 95% CI 1.0102-1.0157, P < 0.001). The increase in the concentration of all pollutants, including gases (carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone) and particulate matter [(PM2.5), (PM10)], is positively correlated with hospitalization rates and mortality of heart failure. https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021256241.

Eruption of Nyiragongo Volcano On May 22, 2021 Release Of So2 And Co2 Into The Atmosphere

Nyiragongo Volcano is located in the western part of the African Rift. Deep fractures allow gas upwelling during passive and active periods. This study presents the variability of sulfur dioxide (SO2 ) concentration measured at the Rusayo site and carbon dioxide (CO2 ) measured at five separate sites (Bugarura and Munigi) and the seismic activity, most of these measurements were made before and after the eruption. The peak concentrations of SO2 and CO2 correlate well with the tremor measured continuously on the volcano. The Nyiragongo volcano erupted on the evening of Saturday, May 22, 2021 at 6:15 pm local time in Goma. Lava flowed in two directions from the flanks of the Nyiragongo volcano, killing 32 people, destroying 17 villages in the Nyiragongo territory, and cracking the ground in the city of Goma and Gisenyi. The Nyiragongo volcano is infamous for its extremely fluid lava that flows like water when the lava lake flows. Nyiragongo is a major contributor of SO2 and CO2 to the atmosphere. Note that this eruption of 2021 is similar to the one of 2002, which caused the death of a hundred people and the evacuation of many people, the most deadly date of 1977, had killed more than 600 people. The earthquakes were felt, after the eruption May 22, 2021 the strongest had a magnitude of 5.2

Characterization of New Particle Formation in Soft X-ray Radiolysis Reactor: AMCs-to-Secondary Inorganic Aerosols

Airborne molecular contaminants (AMCs) in cleanrooms should be monitored and controlled tightly to reduce yield loss since they can be converted into nanoparticles or surface haze contamination on semiconductor chips or masks. Soft X-ray radiolysis was developed to detect AMCs as low as the ppt-level by forming secondary aerosols from AMCs under soft X-ray irradiation. However, new particle formation (NPF) using soft X-ray radiolysis has not been well investigated. In this study, we have developed a continuous flow tube reactor to understand NPF from AMCs using soft X-ray radiolysis. The reactor was designed to continuously maintain parabolic laminar flows within the tube reactor and to extend the exposure time of the gas molecules to soft X-rays by increasing the number of reactor modules. With the increase in the concentration of sulfur dioxide (SO2), the size distribution of particles formed by soft X-ray radiolysis also showed enhanced NPF and subsequent particle growth. However, the conversion rates of SO2 into particles decreased simultaneously. The NPF and subsequent particle growth in the reactor were also positively affected by the exposure time to soft X-rays and the residence time. The exposure time was controlled by the number of soft X-ray emitters, and the residence time in the reactor was adjusted by the number of reactor modules and the inlet flow rate. The mixture of ammonia (NH3) with SO2 stabilized the nucleation of particles formed from SO2 but suppressed the particle growth. In contrast, nitrogen dioxide (NO2) suppressed both nucleation and growth of particles formed from SO2. Among the parameters for controlling soft X-ray radiolysis, the soft X-ray intensity had the highest effect on the inorganic AMCs-to-nanoparticle conversion.

What are the challenges to producing high quality red wines from interspecific grapes?

In the U.S. Midwest region, interspecific cold hardy grape cultivars have been developed to be resistant to the harsh cold winter, late spring frosts, and hot and humid summer. However, interspecific grape red wines tend to have higher acidity and lower tannins content than Vitis vinifera wines. This leads to unbalanced wines and an increase of the risk of oxidation and therefore impact the overall quality over time. The content and type of phenolic compounds differ in interspecific grapes and wines, depending on the cultivars, the viticultural practices, the environmental conditions and the wine making process. Because the chemical properties of red wines produced from interspecific grapes compared to Vitis vinifera is not well known, it is a challenge to determine the best wine making practices to produce a high quality wine that remains stable over time. This study focuses on evaluating phenolic compounds, oxidation-related compounds, and sulfur dioxide content in Marquette and Frontenac wines aged in bottles for up to 9 years. The goal is to help improve quality of red wines made from interspecific cold-hardy grape cultivars to increase consumer acceptance and develop optimal wine making practices.

Air quality forecasting using convolutional neural networks

Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deaths each year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from the damage which is caused by air pollution is one of the major issues for the global community. The prediction of air pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science to maximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this research is to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, which includes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposed system will be implemented in two steps; the first step will focus on data analysis and pre-processing, including filtering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters of each layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for the developed CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a high accuracy of 86.585%. The overall model is implemented using MATLAB software.

Air quality forecasting using convolutional neural networks

Air pollution is now one of the biggest environmental risks, which causes more than 6 million premature deaths each year from heart diseases, stroke, diabetes, respiratory disease, and so on. Protecting humans from the damage which is caused by air pollution is one of the major issues for the global community. The prediction of air pollution can be done by machine learning (ML) algorithms. ML combines statistics and computer science to maximize the prediction power. ML can be also used to predict the air quality index (AQI). The aim of this research is to develop a convolutional neural network (CNN) model to predict air quality from the unseen data set, which includes concentration of nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). The proposed system will be implemented in two steps; the first step will focus on data analysis and pre-processing, including filtering, feature extraction, constructing convolutional neural network layers, and optimizing the parameters of each layer, while the second step is used to evaluate its model accuracy. The output is predicted as AQI for the developed CNN model. The developed CNN model achieves a root-mean-square error of 13.4150 and a high accuracy of 86.585%. The overall model is implemented using MATLAB software.

INVESTIGATION OF TRANSIENT RESISTANCE AND ELECTRICAL EROSION OF SERIAL CONTACT PARTS RELAY PKL - 2204

In this study, the processes of the occurrence of an electric arc and its duration on the contact details of the PKL-2204 series relay are considered in detail. The patterns of changes in electrical erosion, which is a function of current strength and the number of switchings, were also studied. A detailed analysis of the process of opening the contact details was carried out based on the voltage oscillogram. Studies have shown that this process can be considered in three main stages. The first stage is marked by the separation of the contact parts, and at the very moment of the break of the working surfaces, there is a sharp jump in voltage from 45 mV to 13.5 V, accompanied by the formation of an electric arc. The second stage, lasting about 3.5 ms, is characterized by the weakening of the current to zero, and the arc is completely extinguished on the oscillogram. The third stage is marked by the appearance of a voltage peak on the oscillogram, which occurs when the arc is extinguished and is caused by the inductance L=240 mH. This analysis helps to better understand the process of contact opening and may have practical applications in the field of electrical engineering and electronics. Analysis of the results of our research indicates the presence of metals that have high resistance to erosion, regardless of their role as an anode or cathode. Among these metals, we can note nickel, copper, niobium, molybdenum, silver, and others, which show resistance to erosion even when the polarity changes, that is, they are erosion-resistant both at the anode and at the cathode. In particular, among such metals it is worth noting silver, molybdenum and niobium, which demonstrate extraordinary resistance to erosion in various conditions. These results may have important implications for the development of erosion-resistant materials for electronic devices and other applications. On the basis of the conducted research, it was found that the electroerosion resistance of contact parts made of СрН-10 material depends on several factors, including the current strength, the duration of arc burning, the number of commutations, and the physical and mechanical properties of the contact material. Research into the microstructure of the material during the current switching process revealed thermal, mechanical, and fatigue failure. During this process, the low-melting component of the cathode material melts and intensively evaporates, which leads to the formation of a dispersed and bumpy surface. The working surface of the anode is covered with finely dispersed silver particles, which are transferred from the cathode through the gaseous or liquid phase. The analysis of the spectra of the surface layers indicates a significant occurrence of nickel on the working surface of the cathode, which confirms the priority of evaporation of silver from the surface of the contact part during current switching. The study of the effect of chemically active reagents on transient resistance showed that in humid air with a high content of ammonia, hydrogen sulfide, sulfur dioxide, and carbon dioxide, the transient resistance of contact parts increases by 5-7 times.

Changes of Air Pollutants in Urban Cities during the COVID-19 Lockdown-Sri Lanka

In response to the COVID-19 pandemic in early 2020, Sri Lanka underwent a nationwide lockdown that limited motor vehicle movement, industrial operations, and human activities. This study analyzes the impact of COVID-19 lockdown on carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10, PM2.5) concentrations in two urban cities (Colombo and Kandy) in Sri Lanka, by comparison of data from the lockdown period (March to May 2020) with its analogous period of 2019 and 2021. The results showed that the percentage change of daytime PM10, PM2.5, CO, and NO2 concentration during the lockdown in Colombo (Kandy) is –42.3% (–39.5%), –46% (–54.2%), –14.7% (–8.8%) and –82.2% (–80.9%), respectively. In both cities, the response of NO2 to the lockdown was the most sensitive. In contrast, daytime O3 concentration in Colombo (Kandy) has increased by 6.7% (27.2%), suggesting that the increase in O3 concentration was mainly due to a reduction in NOx emissions leading to lower O3 titration by NO. In addition, daytime SO2 concentration in Colombo has increased by 22.9%, while daytime SO2 concentration in Kandy has decreased by –40%. During the lockdown period, human activities were significantly reduced, causing significant reductions in industrial operations and transportation activities, further reducing emissions and improving air quality in two cities. The results of this study offer potential for local authorities to better understand the emission sources, assess the effectiveness of current air pollution control strategies, and form a basis for formulating better environmental policies to improve air quality and human health.

IMPACT OF SULPHUR DIOXIDE EXPOSURE ON PHOTOSYNTHETIC PIGMENTS IN VIGNA MUNGO

The present studies carried out on Vigna mungo regarding impacts of SO2 exposure on photosynthetic pigments clearly revealed that sulphur dioxide has deleterious effects on different photosynthetic pigments. These effects had become more significant when the plants were exposed to higher doses of sulphur dioxide. During the present investigation four concentrations, i.e. 0.1, 0.3, 0.5 and 0.7 ppm of sulphur dioxide were taken in consideration and their impact on biochemical contents i.e chlorophyll ‘a’, chlorophyll ‘b’, total chlorophyll and carotenoids in Vigna mungo was studied. The observations obtained clearly revealed that a significant reduction was observed in photosynthetic pigments of Vigna mungo. This impact was of great extent in such plants which were exposed to 0.7 ppm of sulphur dioxide as compared to those exposed to lower concentration of sulphur dioxide. It was also observed that the impact was not only dose dependent but also age dependent.

Prediction of SO2 Concentration Based on AR-LSTM Neural Network.

Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor's time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R2, RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.

Балқаш қаласының атмосфералық ауасының ауыр металдармен ластануы

Мақалада Балқаш қаласының атмосфералық ауасының химиялық элементтермен ластану жағдайы талқыланған. Қалқыма бөлшектердің мөлшері РМ-10 стационарлық бақылау нәтижелері бойынша сынамалар 99,6% шаң бөлшектерінің шоғырлануы (құрамы бойынша сараланбаған шаң/аэрозоль) кезінде — 0,8 РШК о.т құрады, РШК о.т. нормасы 0,05 мг/м3, Балқаш қаласының атмосфералық ауасында қатты бөлшектердің бір реттік концентрациясының ең жоғары мөлшері 0,2 РШК болды. Қалқыма бөлшектердің концентрациясының мөлшері РМ-2.5. Нормалар бойынша РМ 2.5 орташа тәуліктік деңгейі 0,25 мг/м3 аспауы керек. Мәліметтер бойынша РМ 2.5 орташа тәуліктік деңгейінің асу еселігі 0,7 мг/м3қатты бөлшектердің бір реттік шоғырлануының ең жоғары РМ-2,5-0,2 РШК болды. Жалпы қала бойынша озонның орташа концентрациясы 1,5 РШК о.т. құрады; күкірт диоксиді-5,4 РШК м.бр.ш.; көміртегі оксиді-2,2 РШК м.бр.ш; азот диоксиді — 2,0 РШК м.бр.ш.; күкіртсутегі-20,1 РШК м.бр.ш. рұқсат етілген ең жоғары бір реттік шоғырлану 0,5 мг/м3 кезінде қолайсыз метеорологиялық жағдайлар кезеңінде күкіртті ангидридтің нақты концентрациясы тиісті РШК-дан 23,7 есе асып түскендігі анықталды.

Using Multispectral Imaging to Reveal the Relationship between UV Absorbance and Sulphur Dioxide Concentration

The sulphur dioxide (SO2) concentration can be monitored via its absorbance in the ultraviolet (UV) region. To address the absence of a systematic study on the UV absorbance of varying SO2 concentrations, a multispectral imaging method was implemented in the present study. SO2 concentrations and various environmental conditions were controlled by a purpose-built environmental control system with a light path of 4.2 m. Five narrow-band-pass filters in front of a UV camera were used to implement multispectral imaging. Regression models of the relationship between UV absorbance and SO2 concentration at each wavelength are presented. Their residuals were kept relatively small by adopting Jenks natural breaks analysis, with a maximum standard deviation of 0.086. Temperature and SO2 concentrations were significantly associated with UV absorbance. Furthermore, smoothed look-up tables were developed to explicitly elucidate the correlation between UV absorbance and SO2. These have been represented as heat maps, showcasing an example of library construction using a combination of abundant research data.

Анализ стандартов Евразийского экономического союза на кондитерские изделия

A barrier-free trade in the Eurasian Economic Union (EAEU) market is impossible without a unified approach to standardization. The research objective was to analyze and develop proposals that would make it possible to unify the standards of the Eurasian Economic Commission (EAEC) on the content of sulfur dioxide and sulfites in confectionery products.&#x0D; The study featured technical regulations and standards for confectionery products, methods for determining sulfur dioxide, and EAEU resources on the results of control oversight activities. The methods included comparison, analogy, analysis, synthesis, peer review, integrated standardization, and system analysis.&#x0D; The analysis involved the official websites of the authorized bodies of the EAEU states on control and supervisory measures for compliance with technical regulations. It revealed the main reasons behind the limited confectionery turnover associated with excessive sulfur dioxide and sulfites. The standards showed discrepancies with Technical Regulations of Customs Union TR CU 022/2011 and TR CU 029/2012. The content standards for sulfur dioxide and sulfites in finished products were different. The analysis detected no unified approach to indicators, measurement units, and detection methods. The authors also assessed the advantages and disadvantages of the existing domestic State Standards for methods of sulfur dioxide and sulfite detection in confectionery products. The state standards have to be adapted to the international standards and EU Directives, in particular, by certifying the Monier-Williams method.&#x0D; The results can help to unify national and international regulations in order to remove technical barriers in the EAEU market and increase the export volume. They also can help confectionery producers to improve the quality and safety of confectionery products.

Spatio-Temporal analysis of sensor based air pollutants in Raipur city, India

&#x0D; &#x0D; &#x0D; &#x0D; Introduction: Particulate matter pollutants and gaseous pollutants are some of the most hazardous pollutants released into the atmosphere. In order to determine how these pollutants impact the living being at breathing height, the current study measures these pollutants using wireless sensors at a height of 1 m above the ground.&#x0D; Materials and methods: In this study, horizontal monitoring of the air pollutants using wireless sensors at twenty-four locations covering four zones including industrial, residential, public-place and transportation of the Raipur city has been evaluated. Spatial variation has been obtained using Inverse Distance Weighting (IDW) method in Geographic Information System (GIS).&#x0D; Results: The obtained results indicate that as compared to the monsoon and post-monsoon seasons, the concentrations of air pollutants are highest in the winter. It was observed that Particulate Matters (PM2.5 and PM10) are the main causes of declining air quality, but Nitrogen dioxide (NO2) and Sulfur dioxide (SO2) concentrations were below Central Pollution Control Board (CPCB) guidelines except for NO2 in winter. The Carbon monoxide (CO) concentration has been above the standard limit in all three seasons. The main finding of this study is to evaluate how air contaminants vary in space and time near the ground surface which is not possible through the static monitoring instruments in the study area.&#x0D; Conclusion: The primary benefit of the obtained results is their great resolution in a compact area, effectively addressing the air quality issue. The findings suggest that seasonality has a substantial impact on the amount of pollutants in the city. According to the temporal distributions of the air pollutants, monsoon had the best air quality, followed by post-monsoon while the winter season has the highest concentration of pollution.&#x0D; &#x0D; &#x0D; &#x0D;

610 EMERGING TRIGGERS OF ARRHYTHMIC EVENTS IN REMOTE MONITORED PATIENTS

Abstract Background and aims Worldwide, air pollution is the fourth leading cause of death. Both brief and long-term exposures to air pollution have been associated with marked increases in cardiovascular-related morbidity and mortality. Previous studies have focused on the short-term influence of particulate pollutants on ventricular arrhythmia (VA) development, primarily expressed as 24-hour Holter ECG monitoring arrhythmic burden or as presentation as life-threatening ventricular arrhythmias. The aim of our study was to investigate the association between air pollutant exposure and VA burden in remotely-monitored patients. Methods We enrolled retrospectively 86 patients carriers of implantable cardioverter-defibrillators (ICD), loop recorders (ILR) or pacemaker devices with remote monitoring via CarelinkTM Medtronic or Merlin.netTM PCN Abbott softwares. Intracavitary and ILR tracings were reviewed by EP personnel at our centre to define VA burden and the number of appropriate shocks and ATPs occurred in 2021. Demographic and clinical characteristics (e.g. cardiovascular history, ejection fraction, smoking, antiarrhythmic drugs (AADs)) were collected through telephone interviews. Each patient's residential and working address were used to obtain information on pollutant exposure in the years 2017-2021 in terms of annual mean concentrations of PM10, PM2.5, ozone, sulfur dioxide, carbon dioxide, carbon monoxide and benzene, as determined by European Environmental Agency air quality reports. The primary endpoint was a composite of ventricular fibrillation, sustained and nonsustained ventricular tachycardia, appropriate ATP or shocks in patients with various levels of air pollutants exposure. Results Of 86 patients (58 male, mean age 63.2±18.7 years) enrolled in this study, 44 (51.6%) had received an ICD, 32 of whom (37.2%) with a primary prevention indication. 31 patients (36.1%) had been receiving at least one AAD. History of coronary artery disease (CAD) was present in one third of the study population and baseline mean EF was 49.8±13.8%. VA events occurred in 17 patients (19.8%), all with a significantly lower baseline EF (40.3±12.2% vs 51.5±13.5% p=0.005). Average mean values of benzene concentration in the previous 4 years were significantly higher in patients suffering from a VA event (1.64±0.82 μg/m3 vs 1.12±0.48 μg/m3, p=0.001), as were PM2.5 values (11.64±1.98 vs 10.74±1.28 μg/m3, p=0.023). A weak but positive correlation was found between median PM2.5 concentration, benzene and VAs (p=0.023, p=0.001 respectively). A multivariate regression model was built, including long-term exposure to benzene and PM2.5 and baseline EF, and it would explain 29.8% of the variance in VAs, correctly classifying 78.1% of cases. Conclusions This study was the first to evaluate the relationship between long-term exposure to a wide array of air pollutants and VAs in a European population. In our sample, we found a weak but significant correlation between a higher chronic exposure to benzene and recorded VA events. Possible mechanisms may include oxidative stress and a direct, nonspecific membrane action due to benzene lipophilic properties. Hence, we highlighted the possible role of outdoor benzene exposure in ventricular arrhythmogenesis for the first time, a finding that requires validation through subsequent studies aimed at elucidating the role of air pollution as a ubiquitous, potentially modifiable, population-based risk factor.

Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network.

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).

Association between Cough and Ambient Polycyclic Aromatic Hydrocarbons in Patients with Chronic Cough: An Observational Study in Two Regions of Japan

Ambient polycyclic aromatic hydrocarbons’ (PAHs) specific components are likely involved in respiratory disease development and exacerbation in children and adults. Airborne PAH exposure’s effects on cough symptoms in children and adults with chronic coughs in Kanazawa and Fukuoka, Japan, were investigated in this longitudinal study. A total of 98 patients with chronic coughs were enrolled and followed up between 1 April and 31 May 2020. The enrolled patients were non-smoking adults and children aged 3–83 years. Cough diaries were used to record and collect daily cough symptoms. High-performance liquid chromatography coupled with a fluorescence detector was used to determine the particulate PAH content in daily total suspended particles collected on quartz fiber filters. Ambient concentrations of fine particulate matter, nitrogen dioxide, and sulfur dioxide were obtained from local monitoring sites. Generalized estimated equations were used to estimate the association between daily PAHs and cough symptoms. Among nine PAHs measured, benz[a]anthracene (BaA) was significantly associated with cough symptoms for both lag4 and lag5 PAH exposure. These findings suggest that airborne specific PAHs, especially BaA, affect cough symptoms in children and adults with chronic cough. Further studies are needed to develop effective measures to prevent respiratory diseases against specific PAHs.