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First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

The equilibrium structure of nitrogen dioxide was determined using different methods: experimental, semiexperimental, mass-dependent, using an empirical correction to the effective (r0) structure, and ab initio optimizations. For this goal, recently determined rotational constants of vibrationally excited states have been used. The coupled cluster method, CCSD(T), fails to give an accurate structure. However, an accurate structure can be obtained by taking into account a small correction calculated at the CCSDTQ level of theory. Furthermore, the multireference configuration interaction method, MRCI + Q, gives a satisfactory result which can still be improved by interpolation. The semiexperimental and mass-dependent methods, as well as the corrected r0 structure give accurate results and are furthermore much easier to use than the experimental method. It has to be noted that the different methods give compatible results. The mean of the results gives the following equilibrium structure: r(NO) = 1.1944(6) Å and ∠(ONO) = 133.89(11)°. [ABSTRACT FROM AUTHOR]

In this study, we explore the synthesis and gas-sensing capabilities of zinc stannate (Zn2SnO4) in three morphologiesspherical nanoparticles, urchins, and octahedronsaiming to investigate the influence of morphology on sensing properties. The fabricated devices exhibit a significant resistance decrease upon exposure to NO2 at room temperature (24 °C), indicating p-type sensing behavior. Among these morphologies, the spherical nanoparticle-based sensor exhibits the highest sensor response of 57% to 6 ppm of NO2, outperforming urchins and octahedrons by approximately 1.2 and 4.1 times, respectively. This superior performance, with response and recovery times of 6.3 s and 224 s, is attributed to enhanced redox reactions from a larger surface area and a higher proportion of oxygen interstitials. The spherical nanoparticle-based sensor also demonstrates exceptional selectivity for NO2 over SO2, CO, NH3, and CH4, with a detection limit of 200 ppb. Furthermore, the sensor exhibits excellent reversibility with only 2% variation across 20 consecutive test cycles and demonstrates remarkable long-term stability, with a performance fluctuation of approximately 2.3% over 63 days without significant degradation. [ABSTRACT FROM AUTHOR]

Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development. [ABSTRACT FROM AUTHOR]

Background: Epidemiological and toxicological studies indicate that increased exposure to air pollutants can lead to neurodegenerative diseases. To further confirm this relationship, we evaluated the association between exposure to ambient air pollutants and corneal nerve measures as a surrogate for neurodegeneration, using corneal confocal microscopy. Methods: We used population-based observational cross-sectional data from The Maastricht Study including N = 3635 participants (mean age 59.3 years, 51.6% were women, and 19.9% had type 2 diabetes) living in the Maastricht area. Using the Geoscience and hEalth Cohort COnsortium (GECCO) data we linked the yearly average exposure levels of ambient air pollutants at home address-level [particulate matter with diameters of ≤ 2.5 µm (PM2.5), and ≤ 10.0 µm (PM10), nitrogen dioxide (NO2), and elemental carbon (EC)]. We used linear regression analysis to study the associations between Z-score for ambient air pollutants concentrations (PM2.5, PM10, NO2, and EC) and Z-score for individual corneal nerve measures (corneal nerve bifurcation density, corneal nerve density, corneal nerve length, and fractal dimension). Results: After adjustment for potential confounders (age, sex, level of education, glucose metabolism status, corneal confocal microscopy lag time, inclusion year of participants, smoking status, and alcohol consumption), higher Z-scores for PM2.5 and PM10 were associated with lower Z-scores for corneal nerve bifurcation density, nerve density, nerve length, and nerve fractal dimension [stβ (95% CI): PM2.5 -0.10 (-0.14; -0.05), -0.04 (-0.09; 0.01), -0.11 (-0.16; -0.06), -0.20 (-0.24; -0.15); and PM10 -0.08 (-0.13; -0.03), -0.04 (-0.09; 0.01), -0.08 (-0.13; -0.04), -0.17 (-0.21; -0.12)], respectively. No associations were found between NO2 and EC and corneal nerve measures. Conclusions: Our population-based study demonstrated that exposure to higher levels of PM2.5 and PM10 are associated with higher levels of corneal neurodegeneration, estimated from lower corneal nerve measures. Our results suggest that air pollution may be a determinant for neurodegeneration assessed in the cornea and may impact the ocular surface health as well. [ABSTRACT FROM AUTHOR]

The global industrial development and increase in the number of transportation vehicles, such as automobiles and ships, have led to a steady increase in the issues related to greenhouse gas emissions. NO2 is a greenhouse gas emitted in large quantities from automobiles and factories, and its emission is unavoidable in the modern world. Therefore, a sensor capable of precise detection of NO2 is required. The most commonly reported types of NO2 sensors are those based on metal oxides. However, their operation at room temperature is impossible owing to their high-temperature operating characteristics, and therefore, a heater must be designed inside or installed outside the sensor for heating. Meanwhile, NO2 sensors based on PbS quantum dots (QDs) are advantageous as they can operate at room temperature and can be easily manufactured through a solution process rather than a complicated semiconductor process. Herein, a NO2 sensor was fabricated by doping PbS QDs with poly(3-hexylthiophene) (P3HT). The as-developed sensor exhibited high responsivity to 100–0.4-ppm NO2 gas with a resolution of 200 ppb owing to the stability of the thin film and high hole mobility of P3HT. [ABSTRACT FROM AUTHOR]

Inorganic semiconductor gas sensors, being widely utilized in gas‐sensing applications, face significant challenges in attaining mechanical flexibility and humidity resistance in wearable sensing fields. Herein, a highly flexible, breathable, and hydrophobic all‐inorganic self‐supporting nanofiber (NF) gas sensor is developed using electrospinning combined with thermal sulfidation approach. This innovative sensor features a bilayer configuration, with an amorphous SiO2 nanofiber substrate layer and an interwoven SiO2 and SnO2–SnS2 nanofiber active layer. The relatively low elastic modulus of the amorphous SiO2 nanofibers, combined with the three‐dimensional network interwoven structure, endow the SnO2–SnS2–SiO2/SiO2 sensor with superior mechanical flexibility. The sensor exhibits excellent sensitivity, selectivity, moisture resistance, and cycling stability (>10 000 cycles at 140° bending) to both high and low concentration NO2. Notably, an excellent flexible detecting capability of the sensor to NO2, an asthma‐related biomarker, is demonstrated at ultralow concentrations (≈25 ppb) in simulated exhaled breath environments. The enhanced moisture resistance is attributed to the effective inhibition of hydrogen bond formation from H2O molecules by the Sn─S bonds formed through sulfidation of SnO2 nanofibers. This work represents a substantial advancement in the universal fabrication of flexible, breathable and moisture‐resistant inorganic semiconductor sensors for wearable breath sensing applications. [ABSTRACT FROM AUTHOR]

This study explores the estimation of ground-level NO2 concentrations in Mexico City using an integrated approach of machine learning (ML) and remote sensing data. We used the NO2 measurements from the Sentinel-5P satellite, along with ERA5 meteorological data, to evaluate a pre-trained machine learing model. Our findings indicate that the model captures the spatial and temporal variability of NO2 concentrations across the urban landscape. Key meteorological parameters, such as temperature and wind speed, were identified as significant factors influencing NO2 levels. The model's adaptability was further tested by incorporating additional variables, such as atmospheric boundary layer height. In order to compare the model's performance to alternative ML models, we estimated the ground-level NO2 using the state-of-the-art TimeGPT. The results demonstrate that our baseline model has the best performance with a mean normalised root mean square error of 84.47%. This research underscores the potential of combining satellite observations with ML for scalable air quality monitoring, particularly in low- and middle-income countries with limited ground-based infrastructure. The study provides critical insights for air quality management and policy-making, aiming to mitigate the adverse health and environmental impacts of NO2 pollution. [ABSTRACT FROM AUTHOR]

Evidence regarding the combined effects of green space and air pollutants on hypertension remains limited and complex. This study aims to investigate the varying effects of greenness under different air pollution levels in China, using data from the wave 2018 China Health and Retirement Longitudinal Study (CHARLS) involving 17 468 adults (aged ≥ 45 years). As a result, the prevalence rate of hypertension was 42.04%. Logistic regression analyses revealed the positive associations between air pollution concentrations at the city level and prevalent hypertension and the negative associations between NDVI and prevalent hypertension, all of which were more prominent in the populations of the eastern and rural regions. Notably, the negative effect of green space was greater at the lowest quartiles of each air pollutant (OR for PM2.5 quartiles = 0.724, 0.792, 0.740, and 0.931). Improving air quality and greenness could potentially reduce hypertension risk, and minimizing air pollution might optimize the protective effects of greenness. [ABSTRACT FROM AUTHOR]

The Spatiotemporal variability of air quality is influenced by various factors over time. The objectives of this research are to create prediction models for Carbon monoxide (CO) and Nitrogen dioxide (NO 2) and determine the factors which that most impact CO and NO 2 monthly using Random Forest Prediction. The methodology relies on Random Forest Prediction to predict air quality monthly in 2021, incorporating eight variables land surface temperature (LST), normalized difference built-up index (NDBI), built-up index (BU index), normalized difference vegetation index (NDVI), digital elevation model (DEM), relative humidity (RH), wind speed (WS), and wind direction (WD). The results indicate that RH , elevation, WD , and LST are the most significant factors influencing CO concentrations, representing 33%, 24%, 12%, and 10% respectively at annual level in 2021. Similarly, WD, WS, RH , elevation and LST are the most importance factors impacting NO 2 concentrations, representing 24%, 21%, 18%, 12%, and 10% respectively at an annual level in 2021. Furthermore, NDBI and BU index had the lowest impact in on both CO and NO 2 , with BU index showing a slightly higher percentage in NO 2 models compared to CO models. Regarding cross-validation, the MAE values in CO models range from 0.11 to 0.18, and the RMSE values range from 0.14 to 0.23. Additionally, the MAE values in NO 2 models ranges from 3.78 to 7.30, and RMSE values range from 4.93 to 9.23. [ABSTRACT FROM AUTHOR]

This paper aimed to investigate the Spatial and Temporal Variation of the air quality index (AQI) in the Amman and Zarqa Metropolitan Areas during the period 2016-2022 following the method adopted by the Environmental Protection Agency of the United States of America (EPA). Air quality data for PM10, PM2.5, O3, NO2, SO2, and CO recorded at five monitoring stations were downloaded from the official website of the Jordanian Ministry of Environment. Calculated AQI values were generally between the Good class (AQI <50) and the Moderate class (AQI 50-100) at all stations, the AQI calculations for PM10 demonstrated a noticeable increase during autumnal months, likely due to natural dust. PM2.5 demonstrated seasonal variation, with higher values in winter months where residents burn fossil fuel for heating. Stabel air in winter due to the cooled land surface, and the weak natural air mix and ventilation contribute to the deterioration of air quality. Calculated individual AQI for SO2 and NO2 reveals that all extent of the study area falls in the Good AQI class. Similarly, CO and ozone-based AQI values fluctuate within the "Good" class, with occasional episodes of compromised air quality at specific stations. [ABSTRACT FROM AUTHOR]

The presence of nitrogen dioxide (NO2), a hazardous gas emanating from various sources including vehicles, industrial power plants, indoor combustion appliances, and tobacco smoke, underscores the importance of effective monitoring. Early risk assessment and continuous vigilance are vital to mitigate potential respiratory and cardiovascular consequences associated with prolonged exposure. To address this need, we have developed a cost-effective metal–organic framework (MOF)-derived SnO2-ZnO-based gas sensor for NO2 detection. Different combinations of SnO2-ZnO were synthesized by varying the Sn/Zn molar ratio. The sensor SZ 1–0.5, having Sn/Zn = 1/0.5, demonstrated a superior response (Rg/Ra = 7.37) compared to bare SnO2 and ZnO towards 100 ppm of NO2 at 225°C. The fabricated SZ 1–0.5 sensor showed a good response time of 100 s with outstanding selectivity, cyclability, and repeatability. The enhanced gas-sensing characteristics of the sensor are attributed to the formation of heterojunctions. The gas-sensing mechanism is discussed in detail. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite‐based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best‐performing regression models to estimate surface NO2 at 0.01° by 0.01° resolution, and we term this estimate as quasi‐NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross‐validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non‐Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications. Plain Language Summary: Nitrogen dioxide (NO2) is an air pollutant which causes cardiovascular and respiratory illnesses and reacts in the atmosphere to form other harmful pollutants. This necessitates accurate and reliable quantification of NO2 concentrations in the air. Ground monitors directly observe NO2 concentrations near the Earth's surface. However, monitors do not have sufficient spatial coverage to quantify NO2 at large scales. Satellite‐based instruments capture NO2 amounts across the Earth at increasingly high spatial resolution. However, satellite instruments cannot directly observe surface NO2 concentrations. In this study, we compare regression methods for estimating surface NO2 over the continental U.S. using satellite data and auxiliary land‐use variables. We find that NO2 estimated using multivariate regression models with transforms applied to inputs result in the highest agreement with surface NO2 among the regression methods we investigated. We then use the regression models to quantify surface NO2 concentration across the U.S. at 0.01° by 0.01° spatial resolution. Our work leverages the precision of ground observations and the high resolution of satellite data to accurately quantify surface NO2. The interpretable, generalizable, and easily applicable methods used in our study will facilitate the use of satellite data for air quality and human health assessments. Key Points: We compare regression methods to estimate surface nitrogen dioxide concentrations at 0.01° resolution using satellite and land use dataMultivariate linear regression with Anscombe‐transformed inputs has strongest agreement with surface nitrogen dioxide measurementsRegression methods provide accurate, low‐bias concentration estimates with minimal computational and data requirements [ABSTRACT FROM AUTHOR]

Conventional metal sulfide (SnS2) gas-sensitive sensing materials still have insufficient surface area and slow response/recovery times. To increase its gas-sensing performance, MoS2 nanoflower was produced hydrothermally and mechanically combined with SnS2 nanoplate. Extensive characterization results show that MoS2 was effectively integrated into SnS2. Four different concentrations of SnS2–MoS2 composites were evaluated for their NO2 gas sensitization capabilities. Among them, SnS2–15% MoS2 at 170 °C demonstrated the greatest response values to NO2, 7.3 for 1 ppm NO2, which is about three times greater than the SnS2 sensor at 170 °C (2.58). The creation of pn junctions following compositing with SnS2 was determined to be the primary reason for the composite's faster recovery time, while the heterojunction allowed for the rapid separation of hole–electron pairs. Because the MoS2 surface has multiple vacancy defects, the adsorption energy of these vacancies is significantly higher than that of other places, resulting in increased NO2 adsorption. Furthermore, MoS2 can serve as active adsorption sites for SnS2 micrometer sheets during gas sensing. This study may help to build new NO2 gas sensors. [ABSTRACT FROM AUTHOR]

This research aim is to activate water using a plasma jet that works with argon gas and to study the effect of the plasma on the physicochemical properties of the water. The plasma consists of a power supply capable of supplying a high alternating voltage of up to 12 kV in the form of a sinusoidal wave with a frequency of 20 kHz. The temperature of the produced plasma (Te) was (1.028) eV, and its electron density (ne) was (4.392×1017) cm-3 at a flow rate of 1L/min. The physicochemical properties of the water are determined by determining the concentration of each of NO2, NO3, and H2O2, the pH of the water, and the water temperature during exposure to the plasma. The effect of storage time on the properties of the activated water is determined after 48 hours of exposure to plasma. The RONS species were measured using kits purchased from USA (Bartovation) Company manufactured for this purpose. The pH was measured with a pH meter, and the working gas temperature was remotely measured with an IR thermometer. The concentration of the three active species increases with exposure time, but the active species NO2 concentration decreases to zero at an exposure time of 120 minutes. The concentrations of the three components decrease with the decrease in the flow rate. The pH decreases with the exposure time until it reaches 2, and the temperature increases until it reaches 35oC. The concentration of NO3 increases with the increase of the water storage time. In the case of H2O2, the concentration increases slightly, then after 10 hours it begins to decrease exponentially, reaching a value close to zero after the passage of 48 hours. For NO2, its concentration begins to decrease exponentially from the start of storage and reaches a concentration close to zero after 48 hours of storage. The pH increases with the storage time, and the water reaches its natural state with a pH of 7 after 48 hours of storage. From this, it is concluded that the best storage time for PAW is ten minutes after the end of the activation process, and also that the activation time should be short, but not exceeding ten minutes. [ABSTRACT FROM AUTHOR]

Background: There is mounting evidence that diabetic-related cardiac metabolism abnormalities with oxidative stress and inflammatory mechanism activation align with the functional impairments that result in atherosclerotic lesion formation. Among the possible non-traditional coronary lesion risk factors, environmental exposure may be significant, especially in diabetic patients. Methods: A total of 140 diabetic patients (115 (82%) males and 25 (18%) females) with a mean age of 65 (60–71) underwent surgical revascularization due to multivessel coronary disease. The possible all-cause mortality risk factors, including demographical and clinical factors followed by chronic air pollution exposure, were identified. Results: All patients were operated on using the off-pump technique and followed for 5.6 (5–6.1) years. The multivariable model for 5-year mortality prediction presented the nitrogen dioxide chronic exposure (HR: 3.99, 95% CI: 1.16–13.71, p = 0.028) and completeness of revascularization (HR: 0.19, 95% CI: 0.04–0.86, p = 0.031) as significant all-cause mortality risk factors. Conclusions: Ambient air pollutants such as an excessive chronic nitrogen dioxide concentration (>15 µg/m3) may increase 5-year all-cause mortality in diabetic patients following surgical revascularization. [ABSTRACT FROM AUTHOR]

In recent years, the development of efficient and environmentally friendly synthesis methods for nanomaterials has gained significant attention in various research fields, particularly in gas‐sensing applications. Among these methods, ultrasonic synthesis stands out for its simplicity, cost‐effectiveness, and ecofriendly nature. Herein, a simple and green ultrasonic synthesis process is used to synthesize the SnO2 nanoparticles‐decorated rGO nanosheets. The obtained X‐ray diffraction reveals the tetragonal rutile‐type crystal structure. The transmission electron microscopy images reveal the decoration of SnO2 nanoparticles on the surfaces of the rGO sheets. SnO2 nanoparticles of size 4–8 nm are identified on the surfaces of the rGO sheets. Furthermore, the optical absorbance and photoluminescence spectra of the nanocomposites validate charge migrations occurring at the interface of SnO2 and the rGO sheets. Compared to the pristine SnO2 nanoparticles, the green ultrasonically synthesized SnO2 nanoparticles‐decorated SnO2/rGO nanocomposite exhibits better sensing performance against NO2 gas and shows selectivity for NO2 gas at 200 °C. The SnO2/rGO nanocomposite demonstrates high NO2 sensing with appealing sensing properties such as excellent responsiveness (67% at 400 °C), rapid reaction time (18 s), and short recovery time (25 s). [ABSTRACT FROM AUTHOR]

The spatial and temporal variations in the atmospheric CO2 concentrations evidently respond to anthropogenic CO2 emission activities. NO2, a pollutant gas emitted from fossil fuel combustion, comes from the same emission sources as CO2. Exploiting the simultaneous emissions characteristics of NO2 and CO2, we proposed an XCO2 prediction approach to reconstruct XCO2 data based on the data-driven machine learning algorithm using multiple predictors, including satellite observation of atmospheric NO2, to resolve the issue of data gaps in satellite observation of XCO2. The prediction model showed good predictive performance in revealing CO2 concentrations in space and time, with a total deviation of 0.17 ± 1.17 ppm in the cross-validation and 1.03 ± 1.15 ppm compared to ground-based XCO2 measurements. As a result, the introduction of NO2 obtained better improvements in the CO2 concentration responding to the anthropogenic emissions in space. The reconstructed XCO2 data not only filled the gaps but also enhanced the signals of anthropogenic CO2 emissions by using NO2 data, as NO2 strongly responds to anthropogenic CO2 emissions (R2 = 0.92). Moreover, the predicted XCO2 data preferred to correct the abnormally low XCO2 retrievals at satellite observing footprints, where the XCO2_uncertainity field in the OCO-2 and OCO-3 products indicated a larger uncertainty in the inversion algorithm. [ABSTRACT FROM AUTHOR]

In this work, silver ferric oxide (AgFeO2) nanoparticles have been successfully synthesized using the co-precipitation method and characterized with various techniques. Highly porous, grain-like AgFeO2 nanoparticles were prepared. Furthermore, this work explores and for the first time proposes the possibility of using low-cost AgFeO2 nanoparticles with a delafossite structure for the low-temperature detection of nitrogen dioxide (NO2) gas. AgFeO2 nanoparticle powder was characterized using X-ray diffraction (XRD). It discloses the delafossite structure indicating the presence of both rhombohedral and hexagonal structures having an average crystallite size of 47 nm. The field emission scanning electron microscopy (FE-SEM) and energy-dispersive spectroscopy (EDS) study give the idea about dense distribution nanoparticles and confirm the elemental composition of AgFeO2, respectively. Transmission electron microscopy (TEM) shows that grain-like nanostructures have a particle size in the range of between 50 and 80 nm. The specific surface area was calculated by Brunauer–Emmett–Teller (BET), and it was found to be 31.9353 ± 0.1551 m2/g. It is found that the AgFeO2 gas sensor shows high selectivity to words NO2 gas at 8 ppm gas concentration at an operating temperature of 50 °C. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, spatio-temporal identification has been analyzed using new probabilistic risk graphs for air quality assessments in Istanbul, Türkiye, for the first time. Detailed assessments can be conducted through the proposed method, which is based on probabilistic models. These models contribute to the objective explanation of variations in air quality. Air quality and its fluctuations have been elucidated using risk graphs across various districts in Istanbul. According to the model results, the air quality at Mecidiyeköy and Göztepe is worse than in the other districts. Conversely, Şile and Sultanbeyli on the Asian side, as well as Sarıyer and Maslak on the European side, exhibit the best air quality in terms of particulate matter (PM10) and NO2 model results. The proposed methodology provides air quality levels at different risk levels, enabling the tracking of temporal variations in air pollution risks. [ABSTRACT FROM AUTHOR]

This study proposes a novel air pollution exposure index (APEI) metric, drawing from traditional elements in rhythmanalysis and public-life studies to help understand how people are exposed to air pollution in their urban environment and when the risks are greatest. It expands on the notion of rhythm as a socially constructed natural phenomenon connected to urban life and spaces. Air quality monitoring data, as well as bus times and in situ traffic and pedestrian counts, from Cable Street and Dalton Square in Lancaster were applied to define the APEI, which combines an air pollution index (NO2 and PM10), a surrogate for ambient air pollution level, with a population index, a surrogate for population flow. The index values were subsequently ranked in numeric order, i.e., a higher ranking shows increased exposure risk and vice versa, to determine total exposure on an individual level. This metric proves valuable in defining air pollution exposure status and recognizing factors associated with high pollution and population levels. Similarly, by comparing APEI values, one could evaluate exposure levels in different locations and seasons to verify when the APEI has increased at a given location and the different rhythms and flows responsible for an individual's exposure. Hence, it has potential use for urban planners and the city council's policymakers who are involved in Lancaster Air Quality Management. [ABSTRACT FROM AUTHOR]

Copyright of Acta Petrologica et Mineralogica / Yanshi Kuangwuxue Zazhi is the property of Acta Petrologica et Mineralogica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development. Graphical Abstract

Abstract The global industrial development and increase in the number of transportation vehicles, such as automobiles and ships, have led to a steady increase in the issues related to greenhouse gas emissions. NO2 is a greenhouse gas emitted in large quantities from automobiles and factories, and its emission is unavoidable in the modern world. Therefore, a sensor capable of precise detection of NO2 is required. The most commonly reported types of NO2 sensors are those based on metal oxides. However, their operation at room temperature is impossible owing to their high-temperature operating characteristics, and therefore, a heater must be designed inside or installed outside the sensor for heating. Meanwhile, NO2 sensors based on PbS quantum dots (QDs) are advantageous as they can operate at room temperature and can be easily manufactured through a solution process rather than a complicated semiconductor process. Herein, a NO2 sensor was fabricated by doping PbS QDs with poly(3-hexylthiophene) (P3HT). The as-developed sensor exhibited high responsivity to 100–0.4-ppm NO2 gas with a resolution of 200 ppb owing to the stability of the thin film and high hole mobility of P3HT.

Abstract Background Epidemiological and toxicological studies indicate that increased exposure to air pollutants can lead to neurodegenerative diseases. To further confirm this relationship, we evaluated the association between exposure to ambient air pollutants and corneal nerve measures as a surrogate for neurodegeneration, using corneal confocal microscopy. Methods We used population-based observational cross-sectional data from The Maastricht Study including N = 3635 participants (mean age 59.3 years, 51.6% were women, and 19.9% had type 2 diabetes) living in the Maastricht area. Using the Geoscience and hEalth Cohort COnsortium (GECCO) data we linked the yearly average exposure levels of ambient air pollutants at home address-level [particulate matter with diameters of ≤ 2.5 µm (PM2.5), and ≤ 10.0 µm (PM10), nitrogen dioxide (NO2), and elemental carbon (EC)]. We used linear regression analysis to study the associations between Z-score for ambient air pollutants concentrations (PM2.5, PM10, NO2, and EC) and Z-score for individual corneal nerve measures (corneal nerve bifurcation density, corneal nerve density, corneal nerve length, and fractal dimension). Results After adjustment for potential confounders (age, sex, level of education, glucose metabolism status, corneal confocal microscopy lag time, inclusion year of participants, smoking status, and alcohol consumption), higher Z-scores for PM2.5 and PM10 were associated with lower Z-scores for corneal nerve bifurcation density, nerve density, nerve length, and nerve fractal dimension [stβ (95% CI): PM2.5 -0.10 (-0.14; -0.05), -0.04 (-0.09; 0.01), -0.11 (-0.16; -0.06), -0.20 (-0.24; -0.15); and PM10 -0.08 (-0.13; -0.03), -0.04 (-0.09; 0.01), -0.08 (-0.13; -0.04), -0.17 (-0.21; -0.12)], respectively. No associations were found between NO2 and EC and corneal nerve measures. Conclusions Our population-based study demonstrated that exposure to higher levels of PM2.5 and PM10 are associated with higher levels of corneal neurodegeneration, estimated from lower corneal nerve measures. Our results suggest that air pollution may be a determinant for neurodegeneration assessed in the cornea and may impact the ocular surface health as well.

The Spatiotemporal variability of air quality is influenced by various factors over time. The objectives of this research are to create prediction models for Carbon monoxide (CO) and Nitrogen dioxide (NO2) and determine the factors which that most impact CO and NO2 monthly using Random Forest Prediction. The methodology relies on Random Forest Prediction to predict air quality monthly in 2021, incorporating eight variables land surface temperature (LST), normalized difference built-up index (NDBI), built-up index (BU index), normalized difference vegetation index (NDVI), digital elevation model (DEM), relative humidity (RH), wind speed (WS), and wind direction (WD). The results indicate that RH, elevation, WD, and LST are the most significant factors influencing CO concentrations, representing 33%, 24%, 12%, and 10% respectively at annual level in 2021. Similarly, WD, WS, RH, elevation and LST are the most importance factors impacting NO2 concentrations, representing 24%, 21%, 18%, 12%, and 10% respectively at an annual level in 2021. Furthermore, NDBI and BU index had the lowest impact in on both CO and NO2, with BU index showing a slightly higher percentage in NO2 models compared to CO models. Regarding cross-validation, the MAE values in CO models range from 0.11 to 0.18, and the RMSE values range from 0.14 to 0.23. Additionally, the MAE values in NO2 models ranges from 3.78 to 7.30, and RMSE values range from 4.93 to 9.23.

The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

The structure of metal oxide thin films can greatly affect their gas sensing properties. However, the structure-property relationship has not been fully established for the NO 2 sensing properties of WO 3 thin films. In this study, WO 3 thin films with two distinct structures have been prepared by the sol-gel technique via employing two different precursor sols. Dense and porous WO 3 thin films were successfully prepared as tungsten and WCl 6 was used as the precursor source, respectively. Both WO 3 thin films are exclusively sensitive to NO 2 as low as 80 ppb. Porous WO 3 thin films exhibit higher response toward NO 2 but slower response/recovery kinetics as compared to the dense ones. Hall measurement and electrochromic coloration confirm the higher carrier mobility in the porous WO 3 thin films. The response/recovery kinetics is found to be determined by the gas-solid reaction (receptor function), which is combinedly influenced by the porosity and grain size of the WO 3 thin films. [ABSTRACT FROM AUTHOR]

Few studies investigated the effects of exposure to NO2 on health status in the Chinese floating population. The present cross-sectional study evaluated the association of ambient NO2 with health status in a floating population in China. Data on 168961 floating individuals in 338 cities were obtained from the 2017 China Migrants Dynamic Survey. The association between exposure to NO2 and self-related health (SRH) status was assessed by binary logistic regression analysis, both in the entire subject cohort and in subgroups assorted by socioeconomic levels and demographic characteristics. The robustness of the associations between NO2 exposure and health status was evaluated by sensitivity analyses. Each grade increment of annual average NO2 exposure was found to increase the risk of poor SRH by 2.4% in the floating population (odds ratio [OR] = 1.024, 95% confidence interval [CI]: 1.011–1.038). When subgrouped by age, subjects in the floating population aged 31–49 years had the highest NO2 associated health risk (OR = 1.036, 95% CI: 1.018–1.054). When subgrouped by per capita gross domestic product (PGDP), subjects in regions with mid-level PDGP had the highest NO2 associated SRH (OR = 1.116, 95% CI: 1.091–1.141). These findings indicated that exposure to NO2 increases the risk of poor SRH in the floating population, with individuals aged 31–49 years and those living in mid-level PGDP regions being more sensitive to the adverse effects of NO2. More effective strategies to reduce air pollution may improve the health status of the floating population in China. [ABSTRACT FROM AUTHOR]

Abstract The imperative development of a cutting-edge environmental gas sensor is essential to proficiently monitor and detect hazardous gases, ensuring comprehensive safety and awareness. Nanostructures developed from metal oxides are emerging as promising candidates for achieving superior performance in gas sensors. NO2 is one of the toxic gases that affects people as well as the environment so its detection is crucial. The present study investigates the gas sensing capability of copper oxide-based sensor for 5 ppm of NO2 gas at 100 °C. The sensing material was synthesized using a facile precipitation method and characterized by XRD, FE-SEM, UV–visible spectroscopy, photoluminescence spectroscopy, XPS and BET techniques. The developed material shows a response equal to 67.1% at optimal temperature towards 5 ppm NO2 gas. The sensor demonstrated an impressive detection limit of 300 ppb, along with a commendable percentage response of 5.2%. Under optimized conditions, the synthesized material demonstrated its high selectivity, as evidenced by the highest percentage response recorded for NO2 gas among NO2, NH3, CO, CO2 and H2S.

In big and industrial cities of developing countries, illness and mortality from long-term exposure to air pollutants have become a serious issue. This research was carried out in 2019–2020 to estimate the health impacts of PM10, NO2 and O3 pollutants by using AirQ+ and R statistical programming software in Arak, Isfahan, Tabriz, Shiraz, Karaj, and Mashhad. Mortality statistics, number of people in required age groups, and amount of pollutants were gathered respectively from different agencies like Statistics and Information Technology of the Ministry of Health, Statistical Center, and Department of Environment and by using Excel, the average 24-hour and 1-hour concentration and maximum 8-hour concentration for PM10, NO2 and O3 pollutants were gathered. We used linear mixed impacts model to account for the longitudinal observations and heterogeneity of the cities. The results of the study showed high number of deaths due to chronic bronchitis in adults, premature death of infants, and respiratory diseases in Mashhad. This research highlights the importance of estimation of health impacts from exposure to air pollutants on residents of the studied cities.

The imperative development of a cutting-edge environmental gas sensor is essential to proficiently monitor and detect hazardous gases, ensuring comprehensive safety and awareness. Nanostructures developed from metal oxides are emerging as promising candidates for achieving superior performance in gas sensors. NO2 is one of the toxic gases that affects people as well as the environment so its detection is crucial. The present study investigates the gas sensing capability of copper oxide-based sensor for 5 ppm of NO2 gas at 100 °C. The sensing material was synthesized using a facile precipitation method and characterized by XRD, FE-SEM, UV–visible spectroscopy, photoluminescence spectroscopy, XPS and BET techniques. The developed material shows a response equal to 67.1% at optimal temperature towards 5 ppm NO2 gas. The sensor demonstrated an impressive detection limit of 300 ppb, along with a commendable percentage response of 5.2%. Under optimized conditions, the synthesized material demonstrated its high selectivity, as evidenced by the highest percentage response recorded for NO2 gas among NO2, NH3, CO, CO2 and H2S. [ABSTRACT FROM AUTHOR]

The current study, accurately designed and created a two-dimensional (2D) structure of SnO 2 nano-flowers and In 2 O 3 nanoparticles using a simple hydrothermal technique and sol-gel method, respectively. On the outer surface of SnO 2 nanoflowers the In 2 O 3 nanoparticles were uniformly growing, clearly revealed by field emission scanning electron microscopic. Besides, crystal phase structure, surface area and elementals composition were characterized by XRD, BET, TEM, EDS, XPS and the gas sensing properties of In 2 O 3 @SnO 2 NFs. The results exhibited that 2 wt% In 2 O 3 @SnO 2 NFs sensor is highly sensitive to NO 2 , the response (R g /R a) to 30 ppm NO 2 is 94.5, and at 50 ppb the response measured 0.61 at 150 °C, the response time is 32 and 51s, respectively, along with good moisture resistance. Besides along with excellent selectivity, long-term stability, and relative humidity (RH) in high-humidity environment, the 2 wt% In 2 O 3 @SnO 2 NFs still maintain high response 91 at 30 ppm. More importantly, at room temperature the response was (Rg/Ra = 1.01) at detection limit 300 ppb towards NO 2 , which could use be for trace NO 2 gas detection. The large specific surface areas of SnO 2 , the abundance of oxygen species adsorbed on the surface, the distinctive electron transformation between heterojunction materials, and high electron transmission channel of SnO 2 and In 2 O 3 transition layer were all considered to have a synergistic effect thatin2 contributed to excellent sensing properties of In 2 O 3 @SnO 2 NFs. [ABSTRACT FROM AUTHOR]

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM1 and PM2.5, carbon dioxide (CO2), nitrogen dioxide (NO2), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM1, PM2.5, CO2, NO2, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body's automatic reactions to them. Results: We observed high precision in predicting PM1, PM2.5, and CO2 using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R2) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO2 and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM1, PM2.5, and CO2 with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO2 and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants. [ABSTRACT FROM AUTHOR]

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0–3 days and previous 0–4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations. [ABSTRACT FROM AUTHOR]

In this work, CdS thin films were prepared on glass substrates using the chemical bath deposition method. The samples were used as a gas sensor for NO2 in the range of 5–50 ppm and H2S in the range of 10–50 ppm. The sensor was operated at room temperature and activated by UV LED illumination of 395 nm. The morphology, optical, and electrical properties of this nanostructure were examined using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction, spectroscopic ellipsometry, UV–Vis spectroscopy and 3D optical profilometry. The films are highly sensitive to NO2 and H2S with a response of 75 and 52% at 50 ppm respectively. Furthermore, they showed good long duration stability and short response/recovery times. The sensor response to NO2 gas increases linearly with increasing gas concentration, while for H2S gas the rate of increase of the sensor response becomes greater from 20 ppm. The results obtained suggest that the developed sensor may have great application potential to detect toxic gases such as H2S and NO2 at room temperature. [ABSTRACT FROM AUTHOR]

Bilayer graphene (Bl-Gr) and sulphur-doped graphene (S-Gr) have been integrated with LiTaO3 surface acustic wave (SAW) sensors to enhance the performance of NO2 detection at room temperature. The sensitivity of the Bl-Gr SAW sensors toward NO2, measured at room temperature, was 0.29º/ppm, with a limit of detection of 0.068 ppm. The S-Gr SAW sensors showed 0.19º/ppm sensitivity and a limit of detection of 0.140 ppm. The origin of these high sensitivities was attributed to the mass loading and elastic effects of the graphene-based sensing materials, with surface changes caused by the absorption of the NO2 molecules on the sensing films. Although there are no significant differences regarding the sensitivity and detection limit of the two types of sensors, the measurements in the presence of interferent gases and various humidity conditions outlined much better selectivity and sensing performances towards NO2 gas for the Bl-Gr SAW sensors. [ABSTRACT FROM AUTHOR]

The serious densification of human settlements necessitates an increase in the role and importance of green infrastructures in the overall functioning of urban ecosystems. Accordingly, the aim of the present study was to (1) assess the efficiency of air pollutant removal (potentially toxic elements) of three common ornamental trees (Tilia tomentosa Moench, Fraxinus excelsior L. and Pinus nigra J.F. Arnold) and (2) model the air quality regulatory services (removal of PM10, PM2.5 and NO2). Three different approaches were applied—enrichment factor (EF) and metal accumulation factor (MAI) per tree species, as well as simulation modeling for the whole urban forest. The MAI values of the three studied species were found to be very similar, in the range of 22.35 to 23.08, which suggests that these species could be a good choice for planting in urban areas with worsened air quality. The highest EF values were observed for U (3–18), followed by As (1.6–2.56) and Sr (0.87–2.46). The potential of urban forests in countering air pollution was highlighted by three simulated scenarios for PM10, PM2.5 and NO2 removal. The highest removal efficiency was calculated for evergreen species, followed by the mixed composition of deciduous (90%) and evergreen trees (10%), and the scenario with wholly deciduous trees had the lowest one. The contribution of nature-based solutions in meeting air quality standards and enhancing resilience in urban areas was clearly demonstrated. The functional complementarity of the different functional tree groups (coniferous, evergreen and deciduous broad-leaved species) was proven to be crucial for the support of both functional stabilities of the phytocenosis under diverse climatic conditions and during the change of seasonal cycles in the vegetation. [ABSTRACT FROM AUTHOR]

We report the design and development of simple and economical Arduino-based four-channel data acquisition system with digital temperature control that is suitable for sensor parameter optimization involved in the search of appropriate chemiresistive gas sensing material for a particular gas. The developed system is capable of simultaneously capturing the data as well as controlling the desired operating temperature (up to 300 °C) of four sensing elements. This is achieved by employing sixteen analog and digital channels of the Arduino nano microcontroller. The sensing circuit is capable of recording the resistance changes from 20 kΩ to 5 MΩ at a rate of 15 kHz. The developed system has been tested rigorously, validated and demonstrated for its effectiveness towards investigating the gas sensing properties of chemiresistive NO2 sensors realized using ZnO nanowires. The present simple and low-cost alternative finds its application for developing the target specific chemiresistive sensors in particular for investigating the gas sensing properties of metal oxide semiconductors. [ABSTRACT FROM AUTHOR]

Considering the advancement of contemporary industrialization and urban development, a consequential escalation of air quality deterioration has led to the emergence of diseases and disasters. In this research, we endeavor to harness the properties of MgNiO for the fabrication of a gas sensor designed specifically for the detection of NO2. In contrast to conventional NiO gas sensors, our findings indicate a noticeable enhancement in the sensor's NO2 gas responsiveness with increasing proportion of Mg. Remarkably, the utilization of a Mg0.5Ni0.5O film exhibits the most promising outcomes, that is, heightened responsiveness and commendable selectivity. This improvement of performance can be attributed to the increase in oxygen vacancies within the film and consequent alterations in film resistance, which collectively contribute to the overall improvement of sensor responsivity. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2) exposure can have several adverse health impacts on people, especially the respiratory system. Low selectivity, a lack of long-term stability, and structural and morphological optimization are some challenges associated with using ZnO for NO 2 gas sensing. Therefore, we intended copper (Cu) doping in ZnO to alter its shape and gas-sensing characteristics. This study explores the synthesis, properties, and gas-sensing capabilities of bitter gourd-shaped Cu-doped zinc oxide (ZnO) nanostructures for the detection of nitrogen dioxide (NO 2). A facile hydrothermal synthesis method was employed to synthesize bitter gourd-shaped Cu-doped ZnO nanostructures and comprehensively characterized by several techniques. Comprehensive characterizations of our produced Cu-doped ZnO nanomaterial demonstrated by X-ray diffraction (XRD), Scanning electron microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), and different energy spectrum. The material was annealed at 400 °C in a controlled dry air environment to improve its suitability for gas sensor applications. A series of temperature-ranging experiments were conducted to get gas-sensing readings from 25 °C to 300 °C. The resulting sensor, founded upon the distinctive morphology of Cu-doped ZnO structures reminiscent of the intricate shape of bitter gourd, unveiled good selectivity with a pronounced affinity for detecting NO 2 gas. Notably, the zenith of its performance was attained at an operating temperature of 200 °C, where its selectivity and sensitivity were most pronounced. Even at a low concentration of 1 ppm, the sensors displayed a maximum response of 3.7, highlighting their high sensitivity. The sensors demonstrated excellent reproducibility, selectivity, and a low detection limit. These findings position bitter gourd-shaped Cu-doped ZnO nanostructures as promising candidates for NO 2 sensing applications across diverse environments. [ABSTRACT FROM AUTHOR]

This article presents the impact of vehicle traffic intensity and the closure of access roads for wheeled vehicles to selected schools in Warsaw at selected hours of the day on changes in air quality. This study focused primarily on analyses related to the pollutant typical of road traffic, i.e., nitrogen dioxide, but also took into account PM10 and PM2.5 concentrations, which are some of the key factors determining the quality of atmospheric air, the sustainability of human beings, and sustainable development. Some kinds of relationships were found between vehicle traffic intensity and air pollutant concentrations, in particular—nitrogen dioxide. Analyses of the pollutant concentrations in the periods before and after the introduction of restrictions on vehicle traffic on working days indicate that, in the case of two of the schools during the morning traffic rush hours, significantly lower NO2 concentrations were observed in the period after the introduction of road traffic restrictions, compared with the period before they were introduced. NO2 concentrations during the morning peak hours (on working days) after introducing restrictions were more than 30% lower than the concentrations recorded during the same hours in the period before introducing restrictions. [ABSTRACT FROM AUTHOR]

Objective: We examined whether the risk of stillbirth was related to ambient air pollution in a UK population. Design: Prospective case–control study. Setting: Forty‐one maternity units in the UK. Population: Women who had a stillbirth ≥28 weeks' gestation (n = 238) and women with an ongoing pregnancy at the time of interview (n = 597). Methods: Secondary analysis of data from the Midlands and North of England Stillbirth case–control study only including participants domiciled within 20 km of fixed air pollution monitoring stations. Pollution exposure was calculated using pollution climate modelling data for NO2, NOx and PM2.5. The association between air pollution exposure and stillbirth risk was assessed using multivariable logistic regression adjusting for household income, maternal body mass index (BMI), maternal smoking, Index of Multiple Deprivation quintile and household smoking and parity. Main Outcome Measure: Stillbirth. Results: There was no association with whole pregnancy ambient air pollution exposure and stillbirth risk, but there was an association with preconceptual NO2 exposure (adjusted odds ratio [aOR] 1.06, 95% CI 1.01–1.08 per microg/m3). Risk of stillbirth was associated with maternal smoking (aOR 2.54, 95% CI 1.38–4.71), nulliparity (aOR 2.16, 95% CI 1.55–3.00), maternal BMI (aOR 1.05, 95% CI 1.01–1.08) and placental abnormalities (aOR 4.07, 95% CI 2.57–6.43). Conclusions: Levels of ambient air pollution exposure during pregnancy in the UK, all of were beneath recommended thresholds, are not associated with an increased risk of stillbirth. Periconceptual exposure to NO2 may be associated with increased risk but further work is required to investigate this association. [ABSTRACT FROM AUTHOR]

Results of a series of spectroscopic measurements of the tropospheric NO2 content carried out on a closed route of the Ring Road of St. Petersburg in different seasons in 2012, 2014, 2015, and 2016 are considered. A unified approach to the interpretation of all experimental data using numerical modeling of air pollution spread and a priori information about the spatial distribution of its anthropogenic sources has significantly improved the accuracy of determining the integral NOx emissions. The total NOx volume, reduced to gross annual anthropogenic emissions from the territory of St. Petersburg, amounted to 81 000 ± 17 000 t. This value slightly exceeds the air pollution city inventory official data (from 61 000 to 63 000 t per year), consistent within the error margin with the estimate obtained earlier on the basis of similar mobile measurements in the spring of 2019 (75 000 ± 26 000 t per year). [ABSTRACT FROM AUTHOR]

Potential health concerns from cooking with natural gas appliances include short-term exposure to nitrogen dioxide (NO2). Published, one-hour concentrations measured in a few kitchens during simulated cooking events have been cited as justification for moving to electric alternatives by certain advocates. These estimates, however, are not believed to adequately represent typical potential exposure concentrations. In this paper, the various challenges in evaluating potential exposures based on measured indoor air concentrations of NO2 associated with cooking are identified and discussed. These include the lack of standards for indoor air in the U.S., the relatively short duration of cooking activities, and the likelihood of bias in indoor NO2 measurements due to the instrument response to other nitrogen-containing compounds. Correction factors for making more meaningful comparisons are suggested, including correction for duration of cooking time and correction for presence of nitrous acid (HONO). [ABSTRACT FROM AUTHOR]

This paper aims to assess the impact of individual measures for NOx emission reduction on NO2 concentrations at very high spatial resolution in an urban district of Madrid City (Spain). A methodology based on a set of Computational Fluid Dynamics simulations for 16 meteorological scenarios combined with the CHIMERE model for background pollution is used to obtain annual NO2 concentration maps. Two scenarios included in the Spanish National Air Pollution Control Programme are investigated: NOx emission reductions from the installation of more efficient boilers for domestic heating (ECOBOIL) and from the partly substitution of passenger cars with combustion engines by electric cars (EC). This analysis is extended to 9 additional scenarios of more ambitious implementation of electric vehicles in order to determine what the NOx emission reduction required for the annual mean NO2 concentration EU limit value not being exceeded is. The ECOBOIL scenario has a very weak impact on the NO2 concentrations. However, the EC scenario implies a more significant reduction of the NO2 concentrations, but not enough to fully remove NO2 limit value exceedances in the study area. A small additional (compared with the EC scenario) implementation of electric vehicles seems to fulfil that the spatially averaged NO2 concentration be lower than the EU limit value, but the area with exceedances is still very large. However, stronger traffic emission reductions (80%) corresponding to the most ambitious scenarios are needed in order to reach that at least 95% of the domain is free of EU limit value exceedances. [ABSTRACT FROM AUTHOR]