Nitrogen Dioxide Research Articles

Multi-Sensor Device for Traceable Monitoring of Indoor Environmental Quality.

The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants' health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants' feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

Examining the relationship of major air pollutants with land surface parameters and its monthly variation in Indian cities using satellite data

The relationship of land surface parameters with the air pollutants is worth exploring. The present study investigates the relation of Land Surface Temperature (LST), Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) with six air pollutants such as Aerosol Index (AI), Carbon monoxide (CO), formaldehyde (HCHO), Sulphur Dioxide (SO2), Nitrogen dioxide (NO2) and ozone (O3) from January to December month of year 2022 in four cities situated at varying climatic zones of India. Except for O3, which exhibits lower concentration in winter and greater in summer, the air pollutants concentration showed lower values during monsoon season and higher during summer and post-monsoon seasons. The relationship of LST, NDBI and NDVI with different air pollutants were found to vary throughout the year in the four cities. The magnitude of correlation coefficient (R) was found greater for AI and NO2 as compared to other pollutants depicting greater impact of land surface parameters on concentration of AI and NO2. The surface urban cool island effect in Bikaner showed strong negative relation of LST with NO2 with magnitude of R greater than 0.41. The surface urban heat island formation in Varanasi showed strong positive correlation with the air pollutants such as AI, CO, NO2 and O3 with magnitude of R value greater than 0.61, 0.31, 0.59 and 0.32 for AI, CO, NO2 and O3, respectively. Even though, the correlation of LST with air pollutants varied with seasons and cities, NDVI showed negative correlation with most of the air pollutants in the cities except in Bikaner where vegetation content is negligible. Thus, increasing the amount of vegetation in a city can improve the air quality by lowering the quantity of air pollutants there.

Design of Ce incorporated tricobalt tetroxide Co3O4 sensor with boosted gas sensing performance toward nitrogen dioxide

Design of Ce incorporated tricobalt tetroxide Co3O4 sensor with boosted gas sensing performance toward nitrogen dioxide

Street-scale air quality modelling over the West Midlands, United Kingdom: Effect of idealised traffic reduction scenarios

Air pollution is the major environmental risk to human health. Road transport is one of the major sources for air pollution, particularly nitrogen dioxide, in urban areas, and hence traffic control is an important measure in air quality management. A street-scale air quality model, ADMS-Urban, was configured for a case study of the West Midlands, UK to represent a baseline year (2019). Model outputs were evaluated using hourly air pollutant measurement data, and the model demonstrates good performance overall. This modelling tool was then used to explore the effect of five hypothetical traffic reduction scenarios, ranging from 10% to 90% reduction in traffic activity; scenario impacts were analysed over a range of spatial resolutions. The impacts of traffic reduction are highly dependent on spatial resolution (i.e. street scale, electoral ward level and local authority level), which has to be taken into account when formulating policies for managing air quality on local and city-wide scales. There was an almost linear relationship between the predicted annual concentration and traffic reduction for both NO2 and PM2.5. Traffic reduction would principally reduce NO2 concentrations, with even very substantial changes in traffic having more limited effects on reducing PM2.5 concentrations reflecting the importance of regional and non-traffic PM2.5 sources.

Air pollution and childhood epilepsy diagnosis at a first seizure clinic in The Netherlands: A case-control study

Increasing evidence suggests that exposure to air pollution is linked to neurological disorders, but little is known about the association with epilepsy. This study aimed to quantify the association between exposure to ambient air pollutants and the diagnosis of epilepsy in Dutch children. A population-based case-control study was conducted among children presenting to the first seizure clinic at the Wilhelmina Children's Hospital in Utrecht, the Netherlands, from 1 January 2008 to 31 May 2021. Children were assigned to either cases (i.e., diagnosed with epilepsy, n = 406) or controls (n = 737). Levels of ambient air pollution (nitrogen dioxide [NO2], ozone [O3], and particulate matter with aerodynamic diameter < 10 μm [PM10] and < 2.5 μm [PM2.5]) exposure were assigned for the year of presentation to the residential addresses of study participants using EU-wide air pollution metrics. Logistic regression models, adjusted for common confounders, were applied to calculate odds ratios (ORs) with 95 % confidence intervals (CIs) for the association between air pollution and epilepsy. Overall, no association between ambient air pollution and an epilepsy diagnosis was observed, including NO2 (OR: 1.01, 95 % CI: 0.98, 1.03), O3 (OR: 1.01, 95 % CI: 0.98, 1.03), PM2.5 (OR: 0.99, 95 % CI: 0.94, 1.04), and PM10 (OR: 0.99, 95 % CI: 0.95, 1.02). Subgroup analysis was suggestive but ultimately underpowered to draw any meaningful conclusions. Additional work, including a longitudinal evaluation of air pollutants, a closer examination of epilepsy etiologies, and a wider, community-based approach, is needed to explore these findings further.

Impact of haze on potential pathogens in surface bioaerosol in urban environments

Air quality considerably affects bioaerosol dynamics within the atmosphere. Frequent haze events, with their associated alterations in bioaerosol composition, may pose potential health risks. This study investigated the microbial diversity, community structure, and factors of PM2.5 within an urban environment. We further examined the impact of haze on potentially pathogenic bacteria in bioaerosols, and analyzed the sources of haze pollution. Key findings revealed that the highest levels of microbial richness and diversity were associated with lightly polluted air conditions. While the overall bacterial community structure remained relatively consistent across different air quality levels, the relative abundance of specific bacterial taxa exhibited variations. Meteorological and environmental conditions, particularly sulfur dioxide, nitrogen dioxide, and carbon monoxide, exerted a greater influence on bacterial diversity and community structure compared to the physicochemical properties of the PM2.5 particles themselves. Notably, haze events were observed to strengthen interactions among airborne pathogens. Stable carbon isotope analysis suggested that coal combustion and automobile exhaust were likely to represent the primary source of haze during winter months. These findings indicate that adoption of clean energy alternatives such as natural gas and electricity, and the use of public transportation, is crucial to mitigate particle and harmful pollutant emissions, thereby protecting public health.

Residential exposure to air pollution and incidence of leukaemia in the industrial area of Viadana, Northern Italy

BackgroundExposure to air pollution has been proposed as one of the potential risk factors for leukaemia. Work-related formaldehyde exposure is suspected to cause leukaemia. MethodsWe conducted a nested register-based case-control study on leukaemia incidence in the Viadana district, an industrial area for particleboard production in Northern Italy. We recruited 115 cases and 496 controls, frequency-matched by age, between 1999-2014. We assigned estimated exposures to particulate matter (PM10, PM2.5), nitrogen dioxide (NO2), and formaldehyde at residential addresses, averaged over the susceptibility window 3rd to 10th year prior to the index date. We considered potential confounding by sex, age, nationality, socio-economic status, occupational exposures to benzene and formaldehyde, and prior cancer diagnoses. ResultsThere was no association of exposures to PM10, PM2.5, and NO2 with leukaemia incidence. However, an indication of increased risk emerged for formaldehyde, despite wide statistical uncertainty (OR 1.46, 95%CI 0.65-3.25 per IQR-difference of 1.2 μg/m3). Estimated associations for formaldehyde were higher for acute (OR 2.07, 95%CI 0.70-6.12) and myeloid subtypes (OR 1.79, 95%CI 0.64-5.01), and in the 4-km buffer around the industrial facilities (OR 2.78, 95%CI 0.48-16.13), although they remained uncertain. ConclusionsThis was the first study investigating the link between ambient formaldehyde exposure and leukaemia incidence in the general population. The evidence presented suggests an association, although it remains inconclusive, and a potential significance of emissions related to industrial activities in the district. Further research is warranted in larger populations incorporating data on other potential risk factors.

Cushion gas effects on hydrogen storage in porous rocks: Insights from reservoir simulation and deep learning

Underground hydrogen (H2) storage (UHS) is crucial for the H2 economy, offering scalable and long-term solutions vital for reliable and sustainable H2-based energy systems. Cushion gas is a common component in UHS designs, but its impact on H2 storage performance is not yet fully understood. This study bridges this research gap by systematically investigating the effects of various cushion gas scenarios on UHS performance in porous rocks, specifically saline aquifers and depleted gas reservoirs, using reservoir simulations and deep learning. Firstly, we conducted 8,000 multiphase compositional reservoir simulations for UHS operations in two types of storage formations, considering four cushion gas scenarios: no cushion gas, methane (CH4), nitrogen (N2), and carbon dioxide (CO2). These simulations cover a wide range of geological and operational parameters relevant to practical UHS projects. From these simulations, we derived key insights into the physical mechanisms governing UHS processes and proposed critical storage performance metrics, including H2 withdrawal efficiency, produced H2 purity, produced gas–water ratio, and well injectivity. Then, we developed a unified reduced-order model (ROM) using a deep neural network (DNN) based on the comprehensive simulation results. The DNN architecture, with an input layer, five hidden layers, and an output layer, takes 12 geological and operational parameters as inputs, and forecasts the four performance metrics. The ROM accurately predicts the cyclic evolution of performance metrics and is over 5,000,000 times faster than traditional physics-based simulations, allowing for thorough uncertainty quantification of UHS performance prediction under various geological and operational conditions. Key findings of this study include: (a) UHS in porous rocks is technically promising, with improving storage performance over cycles; (b) UHS in saline aquifers has higher withdrawal efficiency and purity but lower gas–water ratio and injectivity compared to depleted gas reservoirs; (c) In saline aquifers, cushion gas reduces withdrawal efficiency and purity but significantly enhances gas–water ratio and injectivity, making it particularly beneficial in scenarios with high water production risks; (d) In depleted gas reservoirs, cushion gas is less important under the current operational conditions, as it barely affects withdrawal efficiency and purity, and these reservoirs inherently exhibit high gas–water ratio and injectivity due to initial/leftover native gas (mostly CH4); (e) The cushion gas type slightly affects storage performance, with N2 slightly outperforming CH4 and CO2 in withdrawal efficiency and purity, CO2 being better for gas–water ratio, and CH4 for injectivity. The findings of this study support cushion gas designs in future UHS projects in both saline aquifers and depleted gas reservoirs. Moreover, the developed ROM offers a highly efficient tool for conducting extensive sensitivity analysis and uncertainty quantification, optimizing operational conditions, and facilitating the screening of potential field sites for future UHS projects.

Photo-transformation of isoproturon under UV-A irradiation: The synergy of nitrite and natural organic matter

Isoproturon (IPU), a widely utilized phenylurea herbicide, is recognized as an emerging contaminant. Previous studies have predominantly attributed the degradation of IPU in natural waters to indirect photolysis by natural organic matter (NOM). Here, we demonstrate that nitrite (NO2-) also serves as an important photosensitizer that induces the photo-degradation of IPU. Through radical quenching tests, we identify hydroxyl radicals (•OH) and nitrogen dioxide radicals (NO2•) originating from NO2- photolysis as key players in IPU degradation, resulting in the generation of a series of hydroxylated and nitrated byproducts. Moreover, we demonstrate a synergistic effect on the photo-transformation of IPU when both NOM and NO2- are present in the reaction mixture. The observed rate constant (kobs) for IPU removal increases to 0.0179±0.0002 min-1 in the co-presence of NO2- (50 μM) and NOM (2.5 mgC/L), surpassing the sum of those in the presence of each alone (0.0135±0.0004 min-1). NOM exhibits multifaceted roles in the indirect photolysis of IPU. It can be excited by UV and transformed to excited triplet states (3NOM*) which oxidize IPU to IPU•+ that undergoes further degradation. Simultaneously, NOM can mitigate the reaction by reducing the IPU•+ intermediate back to the parent IPU. However, the presence of NO2- alters this dynamic, as IPU•+ rapidly couples with NO2•, accelerating IPU degradation and augmenting the formation of mono-nitrated IPU. These findings provide in-depth understandings on the photochemical transformation of environmental contaminants, especially phenylurea herbicides, in natural waters where NOM and NO2- coexist.

InGaN/GaN multi-quantum well nanowires: Enhanced trace-level NO2 detection for environmental and breath analysis

The World Health Organization highlights nitrogen dioxide (NO2) as a key atmospheric pollutant impacting air quality and human health. To date, several sensors have been proposed for NO2 detection and disease monitoring, but their effectiveness is limited because of low sensitivity and high operational temperatures. Herein, we propose a selective and sensitive NO2 sensor based on indium gallium nitride/gallium nitride multiple quantum wells (InGaN/GaN-MQWs). We explored different InGaN/GaN-MQWs configurations (InGaN/GaN-MQW1, InGaN/GaN-MQW2, InGaN/GaN-MQW3), developed through metal–organic chemical vapor deposition (MOCVD), to analyze their impact on gas sensing performance. Our findings reveal that the InGaN/GaN-MQW2 sensor at 150 °C exhibits a response 4.0, 3.1, and 1.65 times greater than GaN-NWs, InGaN/GaN-MQW1, and InGaN/GaN-MQW3 sensors, respectively. The limit of detection (LOD) for the InGaN/GaN-MQW2 sensor is 3 parts per billion (ppb), significantly lower than the NO2 threshold value of 53 ppb. Under ultraviolet (UV) light, the response of InGaN/GaN-MQW2 is 5.51-fold, 4.18-fold, and 1.82-fold higher than that of the GaN-NWs, InGaN/GaN-MQW1, and InGaN/GaN-MQW2 sensors, respectively. Importantly, our hybrid nanocomposite-based electronic nose (e-nose) sensor arrays could distinguish between healthy and simulated unhealthy breaths with high accuracy, promising a new era of efficient, non-invasive disease detection through breath analysis.

Air pollutants, genetic susceptibility, and incident schizophrenia in later life: A prospective study in the UK Biobank

ObjectiveAir pollution has been linked to multiple psychiatric disorders, but little is known on its long-term association with schizophrenia. The interaction between air pollution and genetic susceptibility on incident schizophrenia has never been reported. We aimed to explore the associations between long-term air pollution exposure and late-onset schizophrenia and evaluate whether genetic susceptibility could modify the association. MethodsThis population-based prospective cohort study included 437,802 middle-aged and elderly individuals free of schizophrenia at baseline in the UK Biobank. Land use regression models were applied in the estimation of the annual average concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) at residence. The associations between air pollutants and schizophrenia were evaluated by using Cox proportional hazard models. A polygenic risk score of schizophrenia was constructed for exploring potential interaction of air pollutants with genetic susceptibility. ResultsAn interquartile range increase in PM2.5, PM10, NO2, and NOx was associated with the hazard ratios (HR) for incident schizophrenia at 1.19, 1.16, 1.22, and 1.09, respectively. The exposure-response curves for the association of air pollution with incident schizophrenia were approximately linear. There are additive interactions of air pollution score (APS), PM10, NO2, and NOx with genetic risk. Specifically, compared with participants with low genetic susceptibility and low APS, the HR was 3.23 for individuals with high genetic risk and high APS, among which 0.49 excess risk could be attributed to the additive interaction, accounting for 15 % of the schizophrenia risk. ConclusionThis large-scale, prospective cohort study conveys the first-hand evidence that long-term air pollution exposure could elevate schizophrenia incidence in later life, especially for individuals with higher genetic risks. The findings highlight the importance of improving air quality for preventing the late-onset schizophrenia in an aging era, especially among those with high genetic risks.

Low-temperature NO2 sensor based on γ-In2Se3/In2O3 nanoflower heterojunction

The γ-In2Se3/In2O3 nanoflower-like heterojunctions were synthesized by solvothermal treatment followed by thermal oxidation in Air. The nanoflower-like γ-In2Se3/In2O3-based chemiresistive-type sensor showed a significantly enhanced response to NO2 at an operating temperature of 170°C. The sensor achieves a response of 2.69 to 0.2 ppm NO2 at 170°C. The sensor exhibits remarkable selectivity to several possible interferents such as carbon monoxide, ammonia, formaldehyde, acetone, methanol, and toluene, and good stability at 170°C. The nanoflower structure increases the specific surface area and introduces more active sites, which improves the sensitivity of the sensor to NO2. The heterojunction formed between γ-In2Se3 and In2O3 improves the charge carrier transfer between NO2 gas molecules and sensing materials, thereby increasing the response of the sensor to NO2. Density functional theory was used to elucidate this sensing mechanism, and it was found that improved sensing performance is mainly due to heterojunction formation, which increases the adsorption energy of nitrogen dioxide molecules on a sensing material surface and facilitates charge transfer between molecules.

Air and Noise Pollution Exposure in Early Life and Mental Health From Adolescence to Young Adulthood.

Growing evidence associates air pollution exposure with various psychiatric disorders. However, the importance of early-life (eg, prenatal) air pollution exposure to mental health during youth is poorly understood, and few longitudinal studies have investigated the association of noise pollution with youth mental health. To examine the longitudinal associations of air and noise pollution exposure in pregnancy, childhood, and adolescence with psychotic experiences, depression, and anxiety in youths from ages 13 to 24 years. This cohort study used data from the Avon Longitudinal Study of Parents and Children, an ongoing longitudinal birth cohort founded in 1991 through 1993 in Southwest England, United Kingdom. The cohort includes over 14 000 infants with due dates between April 1, 1991, and December 31, 1992, who were subsequently followed up into adulthood. Data were analyzed October 29, 2021, to March 11, 2024. A novel linkage (completed in 2020) was performed to link high-resolution (100 m2) estimates of nitrogen dioxide (NO2), fine particulate matter under 2.5 μm (PM2.5), and noise pollution to home addresses from pregnancy to 12 years of age. Psychotic experiences, depression, and anxiety were measured at ages 13, 18, and 24 years. Logistic regression models controlled for key individual-, family-, and area-level confounders. This cohort study included 9065 participants who had any mental health data, of whom (with sample size varying by parameter) 51.4% (4657 of 9051) were female, 19.5% (1544 of 7910) reported psychotic experiences, 11.4% (947 of 8344) reported depression, and 9.7% (811 of 8398) reported anxiety. Mean (SD) age at follow-up was 24.5 (0.8) years. After covariate adjustment, IQR increases (0.72 μg/m3) in PM2.5 levels during pregnancy (adjusted odds ratio [AOR], 1.11 [95% CI, 1.04-1.19]; P = .002) and during childhood (AOR, 1.09 [95% CI, 1.00-1.10]; P = .04) were associated with elevated odds for psychotic experiences. Pregnancy PM2.5 exposure was also associated with depression (AOR, 1.10 [95% CI, 1.02-1.18]; P = .01). Higher noise pollution exposure in childhood (AOR, 1.19 [95% CI, 1.03-1.38]; P = .02) and adolescence (AOR, 1.22 [95% CI, 1.02-1.45]; P = .03) was associated with elevated odds for anxiety. In this longitudinal cohort study, early-life air and noise pollution exposure were prospectively associated with 3 common mental health problems from adolescence to young adulthood. There was a degree of specificity in terms of pollutant-timing-outcome associations. Interventions to reduce air and noise pollution exposure (eg, clean air zones) could potentially improve population mental health. Replication using quasi-experimental designs is now needed to shed further light on the underlying causes of these associations.

Investigating the causes and reduction approaches of nocturnal ozone increase events over Tai'an in the North China Plain

China's O3 pollution events continue to be frequent, and accurately understanding the sources and sinks of O3 pollution is crucial. Previous research predominantly focused on controlling O3 pollution through daytime photochemical production, overlooking the significant impact of nocturnal high O3 values on the subsequent days' O3 concentrations. Understanding the causes of nocturnal O3 pollution and implementing effective control measures are essential for O3 pollution alleviation. In light of this, we utilized clustering analysis, combined with meteorological and chemical factors to comprehend the nocturnal variation characteristics and mechanisms of nocturnal O3 increase (NOI) events in Tai'an from July 2021 to June 2022. The results qualitatively identified that three clusters have distinct influencing factors: horizontal transport predominantly drives NOIflucts, meanwhile, NOIdecline events, mainly occurring in summer, experienced more intense photochemical reactions. For NOIstable events, primarily observed in spring, there was greater downward transport of high-altitude O3 and more intense titration reactions. Further, using interpretable machine learning models, we quantitatively assessed the contributions of several factors to nocturnal O3. The results reveal that nocturnal titration is the predominant factor, followed by the accumulation of daytime photochemical generation. Additionally, we explored approaches for reducing daytime nitrogen dioxide (NO2) and formaldehyde (HCHO), quantifying their impact on nocturnal O3 concentrations. The results indicate that reducing emissions of HCHO and NO2 can effectively lower O3 levels in NOIdecline and focusing on reducing HCHO emissions proves to be more beneficial in NOIstable events, with NOIflucts events falling in between these two clusters. Targeted implementation of diverse reduction approaches and proportions in different NOI clusters can more effectively mitigate the occurrence of nocturnal O3 pollution, thereby influencing the O3 levels of the following day.

Synergistic tailoring of adsorption and vacancy enrichment in lamellar stacked layers of α-MoO3 nanorods by Mg2+ for NO2 gas sensor

Extremely toxic nitrogen dioxide (NO2) was detected using lamellar layered α-MoO3. The stable orthorhombic phase of MoO3 (α-MoO3) with preferential growth along (02k0) and the nano-rectangular rods (NRs) like morphology were observed. The incorporation of Mg2+ in α-MoO3 influenced the morphology and the oxygen vacancies (VO), which were analyzed through Raman, photoluminescence, and X-ray photoelectron spectroscopy. The core level spectra provided direct evidence for VO enrichment in 3%Mg-α-MoO3 via emergence of peak at 531.02eV with an area of 45.15%. NO2 response was analyzed at 150-200 °C with various NO2 concentrations. 3%Mg-α-MoO3 NRs provided maximum response of 133.9% for 50 ppm with low limit of detection (LOD) of 250 ppb with good response (193s) and recovery (260s) transient behaviors. The fabricated sensor shows high selectivity to NO2 among H2S, SO2, N2O, and NH3, also good repeatability suggesting potential suitability for NO2 detection.

Heatwave exacerbates air pollution in China through intertwined climate–energy–environment interactions

Climate change is increasing the frequency and intensity of heatwaves, raising concerns about their detrimental effects on air quality. However, a role for heatwave–human–environment interactions in air pollution exacerbation has not been established. In the summer of 2022, record-breaking heatwaves struck China and Europe. In this study, we use integrated observational data and machine learning to elucidate the formation mechanism underlying one of the most severe ozone pollution seasons on record in central eastern China, an area that encompasses approximately half of China’s total population and sown land. Our findings reveal that the worsened ozone and nitrogen dioxide pollution resulted from a mismatch between energy demand and supply, which was driven by both heatwaves and energy policy-related factors. The observed adverse heatwave–energy–environment feedback loop highlights the need for the diversification of clean energy sources, more resilient energy structures and power policies, and further emission control to confront the escalating climate challenge in the future.

Characterization of Aerosol and CO2 Co-Emissions around Power Plants through Satellite-Based Synergistic Observations

The tracking of carbon and aerosol co-emissions is essential for environmental management. Satellite-based atmospheric synoptic observation networks provide large-scale and multifaceted data to help resolve emission behaviors. This study employs a comprehensive analysis of atmospheric dynamics, combustion byproducts, and aerosol characteristics around power plants. Strong correlations between Aerosol Optical Depth (AOD) at 500 nm and the column-averaged dry-air mole fraction of carbon dioxide (XCO2) were observed, revealing synchronous peaks in their emission patterns. The investigation into combustion completeness utilized metrics such as the ratio of carbon monoxide (CO)/XCO2 and Black Carbon Extinction (BCEXT)/Total Aerosol Extinction (TOTEXT). Discrepancies in these ratios across cases suggest variations in combustion efficiency and aerosol characteristics. Nitrogen dioxide (NO2) distributions closely mirrored XCO2, indicating consistent emission patterns, while variations in sulfur dioxide (SO2) distributions implied differences in sulfide content in the coal used. The influence of coal composition on AOD/XCO2 ratios was evident, with sulfide content contributing to variations besides combustion efficiency. This multifactorial analysis underscores the complex interplay of combustion completeness, aerosol composition, and coal components in shaping the air quality around power stations. The findings highlight the need for a nuanced understanding of these factors for effective air quality management.

Investigation of Air Quality for Malegaon Region

Abstract: The purpose of this study is to examine the quality of the air in the Malegaon region by identifying, quantifying, and evaluating contaminants and their effects on the environment and public health. The Malegaon region's air quality has been a rising topic of concern because of industrial operations, vehicle emissions, and other human-caused factors. elevated concentrations of many pollutants, including Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Particulate matter (PM2.5, PM10), and Ozone (O3).It's an intriguing problem to comprehend the mechanics of air pollution in a semi-urban location like Malegaon. The dispersion and concentration patterns of pollutants are influenced by various factors, including terrain, meteorological circumstances, and industrial expansion. The challenge is further complicated by evaluating the combined effects of several pollution sources on air quality. In order to thoroughly evaluate the state of the air in the Malegaon region, this study uses a multidisciplinary method that combines statistical analysis, atmospheric modeling, and air quality monitoring.

Association between ambient air pollution and age-related macular degeneration: a meta-analysis.

Several epidemiological studies have investigated the association between ambient air pollution and age-related macular degeneration (AMD). However, a consensus has not yet been reached. Our meta-analysis aimed to clarify this association. Databases, including PubMed, EMBASE, and Web of Science, were searched for relevant studies from 01 January 2000 to 30 January 2024. English-language, peer-reviewed studies using cross-sectional, prospective, or retrospective cohorts and case-control studies exploring this relationship were included. Two authors independently extracted data and assessed study quality. A random-effects model was used to calculate pooled covariate-adjusted odds ratios. Heterogeneity across studies was also tested. We identified 358 relevant studies, of which eight were included in the meta-analysis. Four studies evaluated the association between particulate matter less than 2.5μm in diameter (PM2.5) and AMD, and three studies explored the relationship between nitrogen dioxide (NO2) or ozone (O3) and AMD. The pooled odds ratios were 1.16 (95% confidence interval [CI]: 1.11-1.21), 1.17 (95% CI: 1.09-1.25), and 1.06 (95% CI: 1.05-1.07), respectively. Current evidence suggests a concomitant positive but not causal relationship between PM2.5, NO2, or O3 and AMD risk.

IoT Based Air Pollution Monitoring System

Abstract: In light of the growing concern for air quality and its impact on public health, there is an increasing demand for air pollution monitoring systems that are both effective and efficient. This article introduces an IoT-based Air Pollution Detection System that utilizes the power of Internet of Things (IoT) technology to provide real-time monitoring and analysis of air quality parameters. The system incorporates a network of advanced air quality sensors that can measure particulate matter (PM) concentrations, as well as various gas levels such as carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). These sensors are integrated with a microcontroller that enables data processing and communication capabilities.