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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.

We examined associations between prenatal fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) exposures and child respiratory outcomes through age 8-9 years in 1279 ECHO-PATHWAYS Consortium mother-child dyads. We averaged spatiotemporally modeled air pollutant exposures during four fetal lung development phases: pseudoglandular (5-16 weeks), canalicular (16-24 weeks), saccular (24-36 weeks), and alveolar (36+ weeks). We estimated adjusted relative risks (RR) for current asthma at age 8-9 and asthma with recent exacerbation or atopic disease, and odds ratios (OR) for wheezing trajectories using modified Poisson and multinomial logistic regression, respectively. Effect modification by child sex, maternal asthma, and prenatal environmental tobacco smoke was explored. Across all outcomes, 95% confidence intervals (CI) included the null for all estimates of associations between prenatal air pollution exposures and respiratory outcomes. Pseudoglandular PM2.5 exposure modestly increased risk of current asthma (RRadj = 1.15, 95% CI: 0.88-1.51); canalicular PM2.5 exposure modestly increased risk of asthma with recent exacerbation (RRadj = 1.26, 95% CI: 0.86-1.86) and persistent wheezing (ORadj = 1.28, 95% CI: 0.86-1.89). Similar findings were observed for O3, but not NO2, and associations were strengthened among mothers without asthma. While not statistically distinguishable from the null, trends in effect estimates suggest some adverse associations of early pregnancy air pollution exposures with child respiratory conditions, warranting confirmation in larger samples.

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]

It is crucial to develop an acid‐free, mild, and efficient strategy for preparing 1,5‐dinitronaphthalene (1,5‐DNN) from naphthalene (NT). This work presented a one‐step NT nitration method using NO2 conjugated to an O2‐Ac2O system over Fe‐/Cu‐modified S2O82−/ZrO2 catalyst (Sα2FβCγZ). The effects of different nitration systems and Fe‐/Cu‐modified ZrO2 with various sulphur sources on NT nitration have been studied. Under optimal conditions, 99.5% of the NT conversion with 56.9% selectivity to 1,5‐DNN was obtained. Synergistic catalysis between the strong acid site and the current nitration system remarkably improved the 1,5‐DNN selectivity. The characterization results demonstrated that the appropriate Fe/Cu metals loadings combined with the covalent persulphates promoted the formation of more active tetragonal ZrO2. Chemical bonding of Zr4+ with S2O82− species allowed S6+ more electrons to withdraw than in SO42− species, which led to stronger acidity and better catalytic activity in the Sα2FβCγZ catalyst. Moreover, a plausible catalytic nitration reaction mechanism was proposed. The findings revealed that mild reaction conditions, combined with a solid superacid catalyst and NO2‐O2‐Ac2O system, offer significant advantages in reducing acid wastewater discharge and increasing target product selectivity. [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]

Two‐dimensional conjugated metal–organic frameworks (2D c‐MOFs) have emerged as promising candidates in gas sensing, owing to their tunable porous structure and conductivity. Nevertheless, the reported gas sensing mechanisms heavily relied on electron transfer between metal nodes and gas molecules. Normally, the strong interaction between the metal sites and target gas molecule would result poor recovery and thus bad recycling property. Herein, we propose a redox synergy strategy to overcome this issue by balancing the reactivity of metal sites and ligands. A 2D c‐MOF, Zn3(HHTQ)2, was prepared for nitrogen dioxide (NO2) sensing, which was constructed from active ligands (hexahydroxyltricycloquinazoline, HHTQ) and inactive transition‐metal ions (Zn2+). Substantial characterizations and theoretical calculations demonstrated that by utilizing only the redox interactions between ligands and NO2, not only high sensitivity and selectivity, but also excellent cycling stability in NO2 sensing could be achieved. In contrast, control experiments employing isostructural 2D c‐MOFs with Cu/Ni metal nodes exhibited irreversible NO2 sensing. Our current work provides a new design strategy for gas sensing materials, emphasizing harnessing the redox activity of only ligands to enhance the stability of MOF sensing materials. [ABSTRACT FROM AUTHOR]

Background: Air pollution causes many respiratory disorders, especially in children and the elderly. These disorders include asthma exacerbations, bronchiolitis, and pneumonia. Research on the association between air pollution and respiratory disorders helps to reevaluate environmental policies in developing countries. Methods: This descriptive cross-sectional study was conducted on 932 children with respiratory manifestations admitted from December 2017 to December 2019 at the Emergency Department of Akbar Children's Hospital of Mashhad University of Medical Sciences, Mashhad, Iran. Air pollution indices such as concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM) smaller than 2.5 and 10 μm and other parameters, including the air quality index (AQI), air temperature, and humidity level from 2017 to 2019, were retrieved from Mashhad Environmental Pollution Monitoring Center. Demographic and clinical data of patients were collected from patients' hospital documents. We used descriptive analytical methods such as central tendency, variability, and frequency distribution to report and analyze demographic and clinical data through tables and diagrams. The association between air pollution indices and respiratory manifestations was examined by the Spearman correlation test. The correlation between the AQI and total hospital admissions and asthma-related hospital admissions was also evaluated by the Spearman correlation test. Results: Hospital admissions due to respiratory manifestations were not associated with the AQI of each month (p-value = 0.794). The concentration of SO2 was correlated with respiratory-related hospital admissions (correlation coefficient = 0.487, p-value = 0.016) but not asthma attacks. Conclusion: Generally, our cross-sectional study showed no statistically considerable association between air pollution and hospital admissions due to respiratory manifestations and asthma attacks in children. Of the air pollution indices, only SO2 concentration was associated with respiratory-related hospital admissions but not asthma attacks. [ABSTRACT FROM AUTHOR]

As a promising sensing material, Molybdenum disulfide (MoS2) nanosheets is being increasingly studied for Nitrogen dioxide (NO2) gas sensing. However, the MoS2 nanosheets is prone to the stacking effect that compromises the sensing performances. Here, the stacking effect is mitigated by engineering MoS2 nanosheets into a three dimensional (3D) network microstructure, which was fabricated by method of electrostatically self-assembling of MoS2/SiO2 microspheres. The fabricated sensor based on 3D MoS2 network observed a significantly improved response of 15% to 12.3 ppm NO2, which is a 75-fold increase compared to the control sensor with pure MoS2 nanosheets. In addition, the sensitivity of the sensor with 3D MoS2 network was 6.15 times larger than that of the control sensor with pure MoS2 nanosheets. The detection limit of our sensor was 0.297 ppm, lower than most of reported MoS2-based NO2 sensors. The enhanced sensitivity and dynamic response stem from the improved interaction between NO2 molecules and MoS2 network, thanks to its increased surface area per footprint of MoS2 nanosheets compared to pure 2D MoS[sub 2] film (single- or few-layer). This work presents a new approach to enhancing the performance of gas sensors based on 2D materials. [ABSTRACT FROM AUTHOR]

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities. [ABSTRACT FROM AUTHOR]

Directly coupling N2 and CO2 to synthesize urea by photocatalysis paves a sustainable route for urea synthesis, but its performance is limited by the competition of photogenerated electrons between N2 and CO2, as well as the underutilized photogenerated holes. Herein, we report an efficient urea synthesis process involving photogenerated electrons and holes in respectively converting CO2 and N2 over a redox heterojunction consisting of WO3 and Ni single‐atom‐decorated CdS (Ni1‐CdS/WO3). For the photocatalytic urea synthesis from N2 and CO2 in pure water, Ni1‐CdS/WO3 attained a urea yield rate of 78 μM h−1 and an apparent quantum yield of 0.15 % at 385 nm, which ranked among the best photocatalytic urea synthesis performance reported. Mechanistic studies reveal that the N2 was converted into NO species by ⋅OH radicals generated from photogenerated holes over the WO3 component, meanwhile, the CO2 was transformed into *CO species over the Ni site by photogenerated electrons. The generated NO and *CO species were further coupled to form *OCNO intermediate, then gradually transformed into urea. This work emphasizes the importance of reasonably utilizing photogenerated holes in photocatalytic reduction reactions. [ABSTRACT FROM AUTHOR]

Directly coupling N2 and CO2 to synthesize urea by photocatalysis paves a sustainable route for urea synthesis, but its performance is limited by the competition of photogenerated electrons between N2 and CO2, as well as the underutilized photogenerated holes. Herein, we report an efficient urea synthesis process involving photogenerated electrons and holes in respectively converting CO2 and N2 over a redox heterojunction consisting of WO3 and Ni single‐atom‐decorated CdS (Ni1‐CdS/WO3). For the photocatalytic urea synthesis from N2 and CO2 in pure water, Ni1‐CdS/WO3 attained a urea yield rate of 78 μM h−1 and an apparent quantum yield of 0.15 % at 385 nm, which ranked among the best photocatalytic urea synthesis performance reported. Mechanistic studies reveal that the N2 was converted into NO species by ⋅OH radicals generated from photogenerated holes over the WO3 component, meanwhile, the CO2 was transformed into *CO species over the Ni site by photogenerated electrons. The generated NO and *CO species were further coupled to form *OCNO intermediate, then gradually transformed into urea. This work emphasizes the importance of reasonably utilizing photogenerated holes in photocatalytic reduction reactions. [ABSTRACT FROM AUTHOR]

The release of toxic gases into the atmosphere may reach concentrations that can cause undesirable health, economic, or aesthetic effects. It is therefore important to monitor the amounts of pollutants injected into the atmosphere from various sources. Most countries have a ground network with multiple measuring sites and instruments, that can measure the air quality index (AQI). However, the main challenge with the networks is the low spatial coverage. In this work, satellite data is used to calculate for the first time the spatial distribution of AQI and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. The results that the month of June has the worst air quality distribution throughout the country, while March has the best air quality distribution. Overall, the results clearly show that TROPOMI has the capability to measure air quality at a country and city level. Implications: In this work, satellite data is used to calculate for the first time the spatial distribution of the air quality index (AQI) and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. Currently, South Africa has a ground network of instruments that measure AQ, however, the network does not cover the whole country. In this work, we show that the use of TROPOMI can compliment the current network and provide data for the areas not covered. [ABSTRACT FROM AUTHOR]

Background: Metabolic Syndrome (MetS) is a widely observed metabolic disorder that is increasingly prevalent worldwide, leading to substantial societal consequences. Previous studies have conducted two separate meta-analyses to investigate the relationship between MetS and air pollutants. However, these studies yielded conflicting results, necessitating a thorough systematic review and meta-analysis to reassess the link between different air pollutants and the risk of developing MetS. Methods: We conducted a comprehensive search of relevant literature in databases including PubMed, Embase, Cochrane Library, and Web of Science up to October 9, 2023. The search was specifically restricted to publications in the English language. Following the screening of studies investigating the correlation between air pollution and MetS, we utilized random-effects models to calculate pooled effect sizes along with their respective 95% confidence intervals (CIs). We would like to highlight that this study has been registered with PROSPERO, and it can be identified by the registration number CRD42023484421. Results: The study included twenty-four eligible studies. The results revealed that an increase of 10 μg/m3 in annual concentrations of PM1, PM2.5, PM10, NO2, SO2, and O3 was associated with a 29% increase in metabolic syndrome (MetS) risk for PM1 (OR = 1.29 [CI 1.07–1.54]), an 8% increase for PM2.5 (OR = 1.08 [CI 1.06–1.10]), a 17% increase for PM10 (OR = 1.17 [CI 1.08–1.27]), a 24% increase for NO2 (OR = 1.24 [CI 1.01–1.51]), a 19% increase for SO2 (OR = 1.19 [CI 1.04–1.36]), and a 10% increase for O3 (OR = 1.10 [CI 1.07–1.13]). Conclusion: The findings of this study demonstrate a significant association between exposure to fine particulate matter (PM1, PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and the incidence of metabolic syndrome (MetS). Moreover, the results suggest that air pollution exposure could potentially contribute to the development of MetS in humans. [ABSTRACT FROM AUTHOR]

The quality of economic growth not only increases the size of a country's economy. It also increases the capabilities of human resources and the development of a variety of technologies used in the production process, which can be explained by the Economic Complexity Index (ECI). Many countries accelerate their economic growth through the optimization of the industrial sector, but this sector is still dominated by non-renewable energy such as petroleum and coal, causing an increase in emissions including nitrogen dioxide (N2O) emissions contributed through the combustion of fossil fuels and solid waste. The analysis method Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) panels on 18 G20 member countries from 1995-2020, and uses the concept of the Environmental Kuznets Curve (EKC) approach to see the longterm effect of ECI on N2O emissions. The results show that ECI and renewable energy variables have a significant effect in reducing emissions, while Foreign Direct Investment (FDI) is not significant. The policy implication that country can encourage the increase of complex export products using eco-friendly and renewable materials. [ABSTRACT FROM AUTHOR]

Background: The current understandings of the relationship between air pollution (AP), greenspace exposure and Parkinson's Disease (PD) remain inconclusive. Methods: We engaged 441,462 participants from the UK Biobank who were not diagnosed with PD. Utilizing Cox proportional hazard regression model, relationships between AP [nitrogen dioxide (NO2), and nitrogen oxides (NOX), particulate matter < 2.5 μm in aerodynamic diameter(PM2.5), coarse particulate matter between 2.5 μm and 10 μm in aerodynamic diameter(PM2.5–10), particulate matter < 10 μm in aerodynamic diameter(PM10)], greenspace exposure, and PD risk were determined independently. Our analyses comprised three models, adjusted for covariates, and affirmed through six sensitivity analyses to bolster the robustness of our findings. Moreover, mediation analysis was deployed to discern the mediating effect of AP between greenspaces and PD. Results: During a median follow-up of 12.23 years (5,574,293 person-years), there were 3,293 PD events. Each interquartile (IQR) increment in NO2 and PM10 concentrations were associated with 10% and 8% increase in PD onset risk, while the increases in NOX, PM2.5 and PM2.5–10 were not associated with PD risk. Additionally, greenspace may safeguard by reducing NO2 and PM10 levels, with the effect mediated by NO2 and PM10 in greenspace—PD relationship. Conclusion: Our findings indicate that an IQR increase in ambient NO2 and PM10 concentrations was associated with risk of PD development, while other pollutants (NOX, PM2.5 and PM2.5–10) were not associated with PD risk. Firstly, we find that augmented exposure to greenspace was associated with the lower PD risk by reducing NO2 and PM10 levels. [ABSTRACT FROM AUTHOR]

Concern about air-quality in urban areas has led to the implementation of Low Emission Zones as one of many other initiatives to control it. Recently in Spain, the enactment of a law made this mandatory for cities with a population larger than 50k inhabitants. The delimitation of these areas is not without controversy because of possible negative economic and social impacts. Therefore, clear assessments of how these initiatives decrease pollutant concentrations are to be provided. Madrid Central is a major initiative of Madrid city council for reducing motor traffic and the associated air pollution in the city centre. This Low Emission Zone starts at the end of 2018, but the first fully-operational period corresponds to the second quarter of 2019. In this work, a methodology based on the Gaussian Process to analyse the evolution of Nitrogen Dioxide inside Madrid Central is undertaken. A Gaussian Process is a stochastic process suitable for interpretable model selection and predictions. Due to its probabilistic nature it provides error estimation at predictions. After the activation of Madrid Central, a relevant reduction of Nitrogen Dioxide has been observed. However, the role of the meteorology during this period must be ascertained to correctly evaluate the role of the activation of the Low Emission Zone against a prone weather. In this work, a model based on the Gaussian Process is trained with meteorological information to predict the concentration of Nitrogen Dioxide at Madrid Central, |$[NO_{2}]$|⁠. This probabilistic description allows extracting statistical information on the reduction affected by the meteorological scenario and separately by the Madrid Central activation. [ABSTRACT FROM AUTHOR]

Urban air pollution is a crucial global challenge, mainly originating from urbanization and industrial activities, which are continuously increasing. Vegetation serves as a natural air filter for air pollution, but adverse effects on plant health, photosynthesis, and metabolism can occur. Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX), and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROSs)-scavenging enzymes and hormone signaling pathways. New evidence has shown the central role of antioxidant phenolic compound biosynthesis, via the phenylpropanoid pathway, in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormone signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species, aimed at the development of sustainable nature-based solutions (NBSs) to mitigate urban air pollution. [ABSTRACT FROM AUTHOR]

Air pollution is one of the most critical environmental challenges and threats to humans. Ozone and nitrogen dioxide (their sum is defined as oxidant, OX) are among the criteria air pollutants and have serious health effects. Oxidant is affected by emission sources in local and regional domains. In this work, a systematic and critical review is conducted on the existing studies of the local and regional oxidant contributions. The systematic review focuses on the publication year, journal rank, study location, and keyphrase analysis. The critical review examines the analyzed parameters, and temporal variations and the data analysis of local and regional contributions of oxidant. The systematic review showed that the studies were conducted in 13 different countries and were published in 19 scientific journals. The critical review indicated that O3, NO, NO2, NOx, and OX were used in all the studies. Furthermore, meteorological parameters of temperature and wind speed were mostly employed in the literature. Due to the substantial effects of emission and meteorological parameters on OX, different time scales were defined so that these effects could be evaluated separately. Our findings revealed that the highest OX's local and regional contributions belong to daily (24-h average) and daylight time scales, respectively. The statistical analysis of the present study shows that the regional contribution values have less variations than the local contribution ones. This is due to the fact that the regional contribution values are influenced by a larger spatial domain and are less dependent on emission sources of each location. [ABSTRACT FROM AUTHOR]

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes. [ABSTRACT FROM AUTHOR]

Several studies now document the disproportionate distribution of environmental pollution across different groups, but many are based on aggregated data or subjective pollution measures. In this study, we describe the air quality disadvantage of migrants in Germany using objective pollution data linked to nationally representative individual-level survey data. We intersect 1 × 1 km2 grid geo-references from the German General Social Survey (ALLBUS) 2014, 2016, and 2018 with 2 × 2 km2 estimates of annually averaged air pollution by the German Environment Agency for nitrogen dioxide, ozone, and particulate matter. Respondents with a migration background are exposed to higher levels of nitrogen dioxide and particulate matter than people of German descent. Urbanity of residence partly explains these differences, up to 81 per cent for particulate matter and about 30 per cent for other pollutants. A larger proportion of immigrants live in larger cities, which are more prone to high levels of air pollution. This is especially true for second-generation migrants. Income differences, on the other hand, do not explain the migrant disadvantage. In city fixed effects models, the patterns for migration background point unambiguously in the direction of environmental disadvantage for all pollutants except ozone. However, the within-municipality associations are weak. [ABSTRACT FROM AUTHOR]

This study aimed to investigate whether global air pollution harms human morals beyond physiological and psychological health. To accomplish this, we conducted an original survey involving over 80,000 individuals across 30 countries, inquiring about their recent perceived unethical behaviors. Through regression analyses, we identified global evidence of a positive correlation between local monthly average concentrations of nitrogen dioxide (NO2) and perceptions of unethical behavior. This finding suggests that air pollution may potentially elicit unethical behavior through a complex response mechanism. It is noteworthy that the impact of air pollution on the inclination to perceive unethical behavior is heterogeneous across categories of unethical behavior and countries. For example, the effects of increasing air pollution concentrations vary even within the same European country: an increase in NO2 concentration in Greece and the Netherlands augments the inclination to perceive fatal unethical behaviors such as murder, terrorism, and suicide, while in Germany, NO2 concentration diminishes the inclination to perceive the same types of unethical behaviors. Overall, the societal costs of air pollution may be even more far-reaching than previously acknowledged, and further research is necessary to unveil the intricate response mechanisms underlying this issue. [ABSTRACT FROM AUTHOR]

Background and objective: The association between air pollution and poor respiratory health outcomes is well established. Children are particularly at risk from air pollution, especially during the prenatal period as their organs and systems are still undergoing crucial development. This study investigated maternal exposure to air pollution during pregnancy and oxidative stress (OS), inflammation, and infant lung function at 4 weeks of age. Methods: Data from the Barwon Infant Study were available for 314 infants. The exposure to NO2 and PM2.5 were estimated. Infant lung function (4 weeks) was measured by multiple-breath washout. Glycoprotein acetyls (GlycA) (36 weeks prenatal), cord blood, and OS biomarkers were measured in maternal urine (28 weeks). A genetic pathway score for OS (gPFSox) was calculated. Linear regression was used and potential modification by the OS genotype was tested. Results: There was no relationship between maternal exposure to air pollution and infant lung function, or with GlycA or OS during pregnancy. We found an association in children with a genetic propensity to OS between NO2 and a lower functional residual capacity (FRC) (β = −5.3 mls, 95% CI (−9.3, −1.3), p = 0.01) and lung clearance index (LCI) score (β = 0.46 turnovers, (95% CI 0.10, 0.82), p = 0.01). Conclusion: High prenatal exposure to ambient NO2 is associated with a lower FRC and a higher LCI score in infants with a genetic propensity to oxidative stress. There was no relationship between maternal exposure to air pollution with maternal and cord blood inflammation or OS biomarkers. [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]

As part of the Pandonia Global Network (PGN), Pandora spectrometers are widely deployed around the world. These ground-based, remote-sensing instruments are federated such that they employ a common algorithm and data protocol for reporting on trace gas column densities and lower atmospheric profiles using two modes based on direct-sun and sky-scan observations. To aid users in the analysis of Pandora observations, the PGN standard quality assurance procedure assigns flags to the data indicating high, medium, and low quality. This work assesses the suitability of these data quality flags for filtering data in the scientific analysis of nitrogen dioxide (NO2) and formaldehyde (HCHO), two critical precursors controlling tropospheric ozone production. Pandora data flagged as high quality assures scientifically valid data and is often more abundant for direct-sun NO2 columns. For direct-sun HCHO and sky-scan observations of both molecules, large amounts of data flagged as low quality also appear to be valid. Upon closer inspection of the data, independent uncertainty is shown to be a better indicator of data quality than the standard quality flags. After applying an independent uncertainty filter, Pandora data flagged as medium or low quality in both modes can be demonstrated to be scientifically useful. Demonstrating the utility of this filtering method is enabled by correlating contemporaneous but independent direct-sun and sky-scan observations. When evaluated across 15 Pandora sites in North America, this new filtering method increased the availability of scientifically useful data by as much as 90 % above that tagged as high quality. A method is also developed for combining the direct-sun and sky-scan observations into a single dataset by accounting for biases between the two observing modes and differences in measurement integration times. This combined data provides a more continuous record useful for interpreting Pandora observations against other independent variables such as hourly observations of surface ozone. When Pandora HCHO columns are correlated with surface ozone measurements, data filtered by independent uncertainty exhibits similarly strong and more robust relationships than high-quality data alone. These results suggest that Pandora data users should carefully assess data across all quality flags and consider their potential for useful application to scientific analysis. The present study provides a method for maximizing use of Pandora data with expectation of more robust satellite validation and comparisons with ground-based observations in support of air quality studies. [ABSTRACT FROM AUTHOR]

Trace NO2 detection is essential for the production and life, where the sensing strategy is appropriate for rapid detection but lacks molecular specificity. This investigation proposes a sensing mechanism dominated by surface-scattering to achieve the molecularly-specific detection. Two-dimensional Bi2O2Se is firstly fabricated into a Schottky-junction-based gas-sensor. Applied with an alternating excitation, the sensor simultaneously outputs multiple response signals (i.e., resistance, reactance, and the impedance angle). Their response times are shorter than 200 s at room temperature. In NO2 sensing, these responses present the detection limit in ppt range and the sensitivity is up to 16.8 %·ppb−1. This NO2 sensitivity presents orders of magnitude higher than those of the common gases within the exhaled breath. The impedance angle is involved in the principle component analysis together with the other two sensing signals. Twelve kinds of typical gases containing NO2 are acquired with molecular characteristics. The change in dipole moment of the target molecule adsorbed is demonstrated to correlate with the impedance angle via surface scattering. The proposed mechanism is confirmed to output ultra-sensitive sensing responses with the molecular characteristic. Here, trace detection of NO2 is achieved with a 2D Schottky-junction Bi2O2Se gas sensor. Simultaneous signals allow for detection in the ppt range, and impedance angle analysis allows for molecular specificity. [ABSTRACT FROM AUTHOR]

In this study, a hybrid model, the convolutional neural network-support vector regression model, was adopted to achieve prediction of the NO 2 profile in Nanjing from January 2019 to March 2021. Given the sudden decline in NO 2 in February 2020, the contribution of the Coronavirus Disease-19 (COVID-19) lockdown, Chinese New Year (CNY), and meteorological conditions to the reduction of NO 2 was evaluated. NO 2 vertical column densities (VCDs) from January to March 2020 decreased by 59.05% and 32.81%, relative to the same period in 2019 and 2021, respectively. During the period of 2020 COVID-19, the average NO 2 VCDs were 50.50% and 29.96% lower than those during the pre-lockdown and post-lockdown periods, respectively. The NO 2 volume mixing ratios (VMRs) during the 2020 COVID-19 lockdown significantly decreased below 400 m. The NO 2 VMRs under the different wind fields were significantly lower during the lockdown period than during the pre-lockdown period. This phenomenon could be attributed to the 2020 COVID-19 lockdown. The NO 2 VMRs before and after the CNY were significantly lower in 2020 than in 2019 and 2021 in the same period, which further proves that the decrease in NO 2 in February 2020 was attributed to the COVID-19 lockdown. Pollution source analysis of an NO 2 pollution episode during the lockdown period showed that the polluted air mass in the Beijing-Tianjin-Hebei was transported southwards under the action of the north wind, and the subsequent unfavorable meteorological conditions (local wind speed of < 2.0 m/sec) resulted in the accumulation of pollutants. [ABSTRACT FROM AUTHOR]

Active nanophotonic materials that can emulate and adapt between many different spectral profiles—with high fidelity and over a broad bandwidth—could have a far-reaching impact, but are challenging to design due to a high-dimensional and complex design space. Here, we show that a metamaterial network of coupled 2D-material nanoresonators in graphene can adaptively match multiple complex absorption spectra via a set of input voltages. To design such networks, we develop a semi-analytical auto-differentiable dipole-coupled model that allows scalable optimization of high-dimensional networks with many elements and voltage signals. As a demonstration of multi-spectral capability, we design a single network capable of mimicking four spectral targets resembling select gases (nitric oxide, nitrogen dioxide, methane, nitrous oxide) with very high fidelity ( > 90 % ). Our results could impact the design of highly reconfigurable optical materials and platforms for applications in sensing, communication and display technology, and signature and thermal management. [ABSTRACT FROM AUTHOR]

Previous studies have suggested that exposure to air pollutants may be associated with specific blood indicators or anemia in certain populations. However, there is insufficient epidemiological data and prospective evidence to evaluate the relationship between environmental air pollution and specific types of anemia. We conducted a large-scale prospective cohort study based on the UK Biobank. Annual average concentrations of NO2, PM2.5, PM2.5-10, and PM10 were obtained from the ESCAPE study using the Land Use Regression (LUR) model. The association between atmospheric pollutants and different types of anemia was investigated using the Cox proportional hazards model. Furthermore, restricted cubic splines were used to explore exposure-response relationships for positive associations, followed by stratification and effect modification analyses by gender and age. After adjusting for demographic characteristics, 3-4 of the four types of air pollution were significantly associated with an increased risk of iron deficiency, vitamin B12 deficiency and folate deficiency anemia, while there was no significant association with other defined types of anemia. After full adjustment, we estimated that the hazard ratios (HRs) of iron deficiency anemia associated with each 10 μg/m3 increase in NO2, PM2.5, and PM10 were 1.04 (95%CI: 1.02, 1.07), 2.00 (95%CI: 1.71, 2.33), and 1.10 (95%CI: 1.02, 1.20) respectively. The HRs of folate deficiency anemia with each 10 μg/m3 increase in NO2, PM2.5, PM2.5-10, and PM10 were 1.25 (95%CI: 1.12, 1.40), 4.61 (95%CI: 2.03, 10.47), 2.81 (95%CI: 1.11, 7.08), and 1.99 (95%CI: 1.25, 3.15) respectively. For vitamin B12 deficiency anemia, no significant association with atmospheric pollution was found. Additionally, we estimated almost linear exposure-response curves between air pollution and anemia, and interaction analyses suggested that gender and age did not modify the association between air pollution and anemia. Our research provided reliable evidence for the association between long-term exposure to PM10, PM2.5, PM2.5-10, NO2, and several types of anemia. NO2, PM2.5, and PM10 significantly increased the risk of iron deficiency anemia and folate deficiency anemia. Additionally, we found that the smaller the PM diameter, the higher the risk, and folate deficiency anemia was more susceptible to air pollution than iron deficiency anemia. No association was observed between the four types of air pollution and hemolytic anemia, aplastic anemia, and other types of anemia. Although the mechanisms are not well understood, we emphasize the need to limit the levels of PM and NO2 in the environment to reduce the potential impact of air pollution on folate and iron deficiency anemia. [ABSTRACT FROM AUTHOR]

Primary gaseous air pollutants have been associated with death from multiple causes, however, it remains unknown if they play a role in premature mortality among individuals living with HIV/AIDS. Data on HIV/AIDS patients were collected from the Hubei Provincial Center for Disease Control and Prevention, with a total of 1,467 AIDS-related deaths (ARD) between 2013 and 2020. Daily mean sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were generated by artificial intelligence algorithms combined with big data. We employed a time-stratified case-crossover approach and conditional logistical regression models to investigate the acute effects of primary gaseous air pollutants on ARD. Per interquartile range increase in the concentrations of SO2 was significantly linked with ARD, with a corresponding odds ratio (OR) of 1.17 [95% confidence intervals (CIs): 1.01, 1.35] at lag 4 day. Furthermore, our findings indicated that males exhibited a heightened vulnerability to the adverse effects of SO2 and NO2, for example, the ORs were 1.24 (95% CIs: 1.05, 1.47) and 1.16 (95% CIs: 1.01, 1.34), respectively. Moreover, individuals aged over 65 years were more susceptible to SO2 and CO. Additionally, we identified the warm season as a sensitive period for mortality associated with SO2 and NO2. Our study furnished fresh evidence regarding the detrimental effects of primary gaseous air pollutants on ARD. [ABSTRACT FROM AUTHOR]

With the rapid development of industrialization, the emission of nitrogen oxides (NOx) has become a global environmental issue. Uranium is the primary fuel used in nuclear power generation. However, the production of uranium, typically based on the uranyl nitrate method, usually generates large amounts of nitrogen oxides, particularly NO2, with concentrations in the exhaust gas exceeding 10,000 ppm. High concentrations of nitrogen dioxide are also produced during silver electrolysis processing and the treatment of waste electrolyte solutions. Traditional V-W/TiO2 NH3-SCR catalysts typically exhibit high catalytic activity at temperatures ranging from 300 to 400 °C, under conditions of low NOx concentrations and high gas hourly space velocity. However, their performance is not satisfying when reducing high concentrations of NO2. This study aims to optimize the traditional V-W/TiO2 catalysts to enhance their catalytic activity under conditions of high NO2 concentrations (10,000 ppm) and a wide temperature range (200–400 °C). On the basis of 3 wt% Mo/TiO2, various loadings of V2O5 were selected, and their catalytic activities were tested. Subsequently, the optimal ratios of active component vanadium and additive molybdenum were explored. Simultaneously, doping with WO3 for modification was selected in the V-Mo/TiO2 catalyst, followed by activity testing under the same conditions. The results show that: the NOx conversion rates of all five catalysts increase with temperature at range of 200–400 °C. Excessive loading of MoO3 decreased the catalytic performance, with 5 wt% being the optimal loading. The addition of WO3 significantly enhanced the low-temperature activity of the catalysts. When the loadings of WO3 and MoO3 were both 3 wt%, the catalyst exhibited the best denitrification performance, achieving a NOx conversion rate of 98.8% at 250 °C. This catalyst demonstrates excellent catalytic activity in reducing very high concentration (10,000 ppm) NO2, at a wider temperature range, expanding the temperature range by 50% compared to conventional SCR catalysts. Characterization techniques including BET, XRD, XPS, H2-TPR, and NH3-TPD were employed to further study the evolution of the catalyst, and the promotional mechanisms are explored. The results revealed that the proportion of chemisorbed oxygen (Oα) increased in the WO3-modified catalyst, exhibiting lower V reduction temperatures, which are favorable for low-temperature denitrification activity. NH3-TPD experiments showed that compared to MoOx species, surface WOx species could provide more acidic sites, resulting in stronger surface acidity of the catalyst. [ABSTRACT FROM AUTHOR]

BackgroundClonal hematopoiesis of indeterminate potential (CHIP) is an age-related somatic mutation associated with incident hematologic cancer. Environmental stressors which, like air pollution, generate oxidative stress at the cellular level, may induce somatic mutations and some mutations may provide a selection advantage for persistence and expansion of specific clones.MethodsWe used data from the Multi-Ethnic Study of Atherosclerosis (MESA) N = 4,379 and the Women's Health Initiative (WHI) N = 7,701 to estimate cross-sectional associations between annual average air pollution concentrations at participant address the year before blood draw using validated spatiotemporal models. We used covariate-adjusted logistic regression to estimate risk of CHIP per interquartile range increases in particulate matter (PM2.5; 4 μg/m3) and nitrogen dioxide (NO2; 10 ppb) as ORs (95% confidence intervals).ResultsPrevalence of CHIP at blood draw (variant allele fraction > 2%) was 4.4% and 8.7% in MESA and WHI, respectively. The most common CHIP driver mutation was in DNMT3A. Neither pollutant was associated with CHIP: ORMESA PM2.5 = 1.00 (0.68-1.45), ORMESA NO2 = 1.05 (0.69-1.61), ORWHI PM2.5 = 0.97 (0.86-1.09), ORWHI NO2 = 0.98 (0.88-1.10); or with DNMT3A-driven CHIP.ConclusionsWe did not find evidence that air pollution contributes to CHIP prevalence in two large observational cohorts.ImpactThis is the first study to estimate associations between air pollution and CHIP.

Abstract Background Air pollution causes many respiratory disorders, especially in children and the elderly. These disorders include asthma exacerbations, bronchiolitis, and pneumonia. Research on the association between air pollution and respiratory disorders helps to reevaluate environmental policies in developing countries. Methods This descriptive cross-sectional study was conducted on 932 children with respiratory manifestations admitted from December 2017 to December 2019 at the Emergency Department of Akbar Children’s Hospital of Mashhad University of Medical Sciences, Mashhad, Iran. Air pollution indices such as concentration of sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter (PM) smaller than 2.5 and 10 μm and other parameters, including the air quality index (AQI), air temperature, and humidity level from 2017 to 2019, were retrieved from Mashhad Environmental Pollution Monitoring Center. Demographic and clinical data of patients were collected from patients’ hospital documents. We used descriptive analytical methods such as central tendency, variability, and frequency distribution to report and analyze demographic and clinical data through tables and diagrams. The association between air pollution indices and respiratory manifestations was examined by the Spearman correlation test. The correlation between the AQI and total hospital admissions and asthma-related hospital admissions was also evaluated by the Spearman correlation test. Results Hospital admissions due to respiratory manifestations were not associated with the AQI of each month (p-value = 0.794). The concentration of SO2 was correlated with respiratory-related hospital admissions (correlation coefficient = 0.487, p-value = 0.016) but not asthma attacks. Conclusion Generally, our cross-sectional study showed no statistically considerable association between air pollution and hospital admissions due to respiratory manifestations and asthma attacks in children. Of the air pollution indices, only SO2 concentration was associated with respiratory-related hospital admissions but not asthma attacks.

Introduction: Environmental factors such as temperature, precipitation, and various other pollutants in the air like nitrogen dioxide (NO2), CO2, etc., have been studied for sustenance and transmission of Coronavirus disease-19 (COVID-19). Studies from India are lacking about the distribution of COVID-19 cases according to air pollutants and meteorological factors. Hence, the present study was conducted to study the relationship between COVID-19 and environmental factors with the objectives of Cluster mapping of the COVID-19 cases district-wise for the state of Maharashtra. Methodology: The air pollution variables NO2 column density (mol/m2), sulfur dioxide column density (mol/m2), Aerosol absorbing index (Ai), Carbon monoxide (CO) column density (mol/m2), and Ozone column density (mol/m2) were extracted from Sentinel-5P datasets available in Google Earth Engine (GEE) (https://code.earthengine.google.com/). The meteorological variables precipitation (m), temperature (t), and humidity (k) were extracted from the European Environmental Agency 5-Land reanalysis dataset in GEE, which provides data at a resolution of 1113.2 m. The preprocessing and retrieval of data were carried out in GEE. These values were correlated with daily cases, density, and other demographic factors. Results: The study identified a cluster of cases in few districts of Maharashtra. These districts were Pune, Ahmednagar, Mumbai, Nashik, and Nagpur. Population density and NO2 were important factors in most of the monthly data. Conclusion: The present study used data available on public platforms to assess the association between COVID-19 and environmental factors. The study found that various environmental factors such as NO2, CO, and Ai may be associated with increase cases of COVID-19 in the district. However, ecological bias may be kept in mind.

Abstract Although previous studies have suggested that meteorological factors and air pollutants can cause dry eye disease (DED), few clinical cohort studies have determined the individual and combined effects of these factors on DED. We investigated the effects of meteorological factors (humidity and temperature) and air pollutants [particles with a diameter ≤ 2.5 $$\upmu$$ μ m (PM2.5), ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO)] on DED. A retrospective cohort study was conducted on 53 DED patients. DED was evaluated by Symptom Assessment in Dry Eye (SANDE), tear secretion, tear film break-up time (TBUT), ocular staining score (OSS), and tear osmolarity. To explore the individual, non-linear, and joint associations between meteorological factors, air pollutants, and DED parameters, we used generalized linear mixed model (GLMM) and Bayesian kernel machine regression (BKMR). After adjusting for all covariates, lower relative humidity or temperature was associated with a higher SANDE (p

BackgroundNitrogen dioxide (NO2) is one of the main air pollutants, and though China's NO2 pollution has been improving year by year, it maintains at a high level, threatening the health of the population. ObjectiveTo investigate the effect of short-term exposure to atmospheric NO2 on the coagulation indexes in obese and normal-weight young individuals and potential modification effect of temperature. MethodsBased on a parallel control panel study design, this study recruited 53 normal-weight and 44 obese young individuals. Three prospective follow-ups were conducted. Air pollution data were obtained from the fixed monitoring station closest to the participant's residences, and personal air pollution exposure was simulated based on time-activity log and infiltration factor for the week before every follow-up. Temperature was collected from China Meteorological Data Service Center. Venous blood samples were taken to measure platelet (PLT) count, mean platelet volume (MPV), soluble CD40 ligand (sCD40L), soluble P-selectin (sP-selectin), platelet aggregation rate (PAgT), and plasminogen activator inhibitor type-1 (PAI-1) during every follow-up. A linear mixed-effect model was used to assess the association between short-term atmospheric NO2 exposure and the coagulation indexes of weight grouped young individuals, and a stratified analysis was used to explore potential modification effect of temperature. ResultsThe median [interquartile range (IQR)] of personal atmospheric NO2 exposure concentrations was 21.47 (8.01) µg·m−3. Short-term exposure to atmospheric NO2 was significantly associated the increase of sCD40L and PAgT in the obese individuals, while the most significant association appeared at 5 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, sCD40L increased by 27.4% (95%CI: 4.2%, 56.6%) and PAgT increased by 37.5% (95%CI: 12.2%, 68.6%); short-term exposure to atmospheric NO2 was significantly associated with the decrease of PLT and PAgT in the normal-weight individuals, while the most significant association appeared at 5 d lag or 7 d lag, and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 5 d lag, PLT decreased by 11.8% (95%CI: −17.8%, −5.3%), and for each IQR increase in the average sliding exposure concentration of atmospheric NO2 with a 7 d lag, PAgT decreased by 16.8% (95%CI: −30.6%, −0.4%). We didn't find statistically significant association of short-term exposure to atmospheric NO2 with PLT in the obese individuals or sCD40L in the normal-weight individuals, nor statistically significant association between short-term exposure to atmospheric NO2 and PAI-1, MPV, and sP-selectin in different weight grouped individuals. The stratified analysis found that short-term exposure to atmospheric NO2 was significantly associated with PAgT in the normal-weight individuals, or with PLT, sCD40L, and sP-selectin in the obese individuals only at high temperature. ConclusionsShort-term exposure to atmospheric NO2 has adverse effects on the coagulation indexes of different weight grouped young individuals, and the obese individuals are more sensitive to it than the normal-weight individuals. High temperature can enhance the adverse health effect of short-term exposure to atmospheric NO2.

The effects of a dissolved gas on the behavior of liquid in cylindrical nanopores are investigated in the framework of Gibbsian composite system thermodynamics and classical nucleation theory. An equation is derived relating the phase equilibrium of a mixture of a subcritical solvent and a supercritical gas to the curvature of the liquid–vapor interface. Both the liquid and the vapor phases are treated nonideally, which is shown to be important for the accuracy of the predictions in the case of water with dissolved nitrogen or carbon dioxide. The behavior of water in nanoconfinement is found to be only affected when the gas amount is significantly more than the saturation concentration of these gases at atmospheric conditions. However, such concentrations can be easily reached at high pressures during intrusion if there is sufficient gas present in the system, especially considering gas oversolubility in confinement. By including an adjustable line tension term in the free energy equation (−44 pJ/m for all points), the theory can make predictions in line with the few data points available from recent experimental work. However, we note that such a fitted value empirically accounts for multiple effects and should not be interpreted as the energy of the three-phase contact line. Compared to molecular dynamics simulations, our method is easy to implement, requires minimal computational resources, and is not limited to small pore sizes and/or short simulation times. It provides an efficient path for first-order estimation of the metastability limit of water–gas solutions in nanopores. [ABSTRACT FROM AUTHOR]

Inhaled particles and gases can harm health by promoting chronic inflammation in the body. Few studies have investigated the relationship between outdoor air pollution and inflammation by race and ethnicity, socioeconomic status, and lifestyle risk factors. We examined associations of particulate matter (PM) and other markers of traffic-related air pollution with circulating levels of C-reactive protein (CRP), a biomarker of systemic inflammation. CRP was measured from blood samples obtained in 1994-2016 from 7,860 California residents participating in the Multiethnic Cohort (MEC) Study. Exposure to PM (aerodynamic diameter ≤2.5 μm [PM2.5], ≤10 μm [PM10], and between 2.5 and 10 μm [PM10-2.5]), nitrogen oxides (NOx, including nitrogen dioxide [NO2]), carbon monoxide (CO), ground-level ozone (O3), and benzene averaged over one or twelve months before blood draw were estimated based on participants' addresses. Percent change in geometric mean CRP levels and 95% confidence intervals (CI) per standard concentration increase of each pollutant were estimated using multivariable generalized linear regression. Among 4,305 females (55%) and 3,555 males (45%) (mean age 68.1 [SD 7.5] years at blood draw), CRP levels increased with 12-month exposure to PM10 (11.0%, 95% CI: 4.2%, 18.2% per 10 μg/m3), PM10-2.5 (12.4%, 95% CI: 1.4%, 24.5% per 10 μg/m3), NOx (10.4%, 95% CI: 2.2%, 19.2% per 50 ppb), and benzene (2.9%, 95% CI: 1.1%, 4.6% per 1 ppb). In subgroup analyses, these associations were observed in Latino participants, those who lived in low socioeconomic neighborhoods, overweight or obese participants, and never or former smokers. No consistent patterns were found for 1-month pollutant exposures. This investigation identified associations of primarily traffic-related air pollutants, including PM, NOx, and benzene, with CRP in a multiethnic population. The diversity of the MEC across demographic, socioeconomic, and lifestyle factors allowed us to explore the generalizability of the effects of air pollution on inflammation across subgroups.

Nitrogen dioxide (NO2) plays a pivotal role in the production of secondary pollutants, most importantly ozone (O3) and particulate matter. Regulatory controls have greatly reduced NO2 in cities, where most of the surface monitoring occurs, but the change in rural environments is less certain. Here, we present summertime (June-September) spatio-temporal patterns of NO2 concentrations using satellite and ground observations across California from 2009-2020, quantifying the differences in NO2 trends for five distinct land cover classes: urban, forests, croplands, scrublands (shrublands, savannas, and grasslands), and barren (minimally vegetated) lands. Over urban environments, NO2 columns exhibited continued but weakening downward trends (−3.7 ± 0.3%a−1), which agree fairly well with contemporaneous trends estimated from the surface air quality network (−4.5 ± 0.5%a−1). In rural (i.e., non-urban) parts of the state, however, secular trends are insignificant (0.0-0.4 ± 0.4%a−1) or in the case of remote forests are rapidly on the rise (+4.2 ± 1.2%a−1). Sorting the NO2 columns by air temperature and soil moisture reveals relationships that are commensurate with extant parameterizations but do indicate a stronger temperature dependence. We further find that rapidly rising temperatures and, to a lesser extent, decreasing precipitation in response to climate change are acting to increase soil NO x emissions, explaining about one-third of the observed NO2 rise in non-urban regions across California. Finally, we show that these trends, or their absence, can be attributed predominantly to the dramatic rise in wildfire frequency, especially since the turn of the 21st century.

PURPOSE: Exposures to ambient air pollutants may prime the lung enhancing risk of acute respiratory distress syndrome (ARDS) in sepsis. Our objective was to determine the association of short-, medium-, and long-term pollutant exposures and ARDS risk in critically ill sepsis patients. METHODS: We analyzed a prospective cohort of 1858 critically ill patients with sepsis, and estimated short- (3 days), medium- (6 weeks), and long- (5 years) term exposures to ozone, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter

INTRODUCTION: Traffic-related air pollution can trigger myocardial infarction (MI). However, the hourly hazard period of exposure to nitrogen dioxide (NO2), a common traffic tracer, for incident MI has not been fully evaluated. Thus, the current hourly US national air quality standard (100 ppb) is based on limited hourly-level effect estimates, which may not adequately protect cardiovascular health. OBJECTIVES: We characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015. METHODS: For nine cities in NYS, we obtained data on MI hospitalizations from the NYS Department of Health Statewide Planning and Research Cooperative System and hourly NO2 concentrations from the US Environmental Protection Agencys Air Quality System database. We used city-wide exposures and a case-crossover study design with distributed lag non-linear terms to assess the relationship between hourly NO2 concentrations over 24 h and MI, adjusting for hourly temperature and relative humidity. RESULTS: The mean NO2 concentration was 23.2 ppb (standard deviation: 12.6 ppb). In the six hours preceding MI, we found linearly increased risk with increasing NO2 concentrations. At lag hour 0, a 10 ppb increase in NO2 was associated with 0.2 % increased risk of MI (Rate Ratio [RR]: 1.002; 95 % Confidence Interval [CI]: 1.000, 1.004). We estimated a cumulative RR of 1.015 (95 % CI: 1.008, 1.021) for all 24 lag hours per 10 ppb increase in NO2. Lag hours 2-3 had consistently elevated risk ratios in sensitivity analyses. CONCLUSIONS: We found robust associations between hourly NO2 exposure and MI risk at concentrations far lower than current hourly NO2 national standards. Risk of MI was most elevated in the six hours after exposure, consistent with prior studies and experimental work evaluating physiologic responses after acute traffic exposure. Our findings suggest that current hourly standards may be insufficient to protect cardiovascular health.

BackgroundThe molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences.ObjectivesTo assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles.MethodsOur study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis.ResultsWe found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease.ConclusionOur results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

Nitrogen dioxide (NO2) is a prevalent air pollutant in urban areas, originating from outdoor sources, household gas consumption, and secondhand smoke. The limited evaluation of the disease burden attributable to NO2, encompassing different health effects and contributions from various sources, impedes our understanding from a public health perspective. Based on modeled NO2 exposure concentrations, their exposure–response relationships with lung cancer, chronic obstructive pulmonary disease, and diabetes mellitus, and baseline disability-adjusted life years (DALYs), we estimated that 1,675 (655–2,624) thousand DALYs were attributable to NO2 in urban China in 2019 [138 (54–216) billion Chinese yuan (CNY) economic losses]. The transition from gas to electricity for household cooking was estimated to reduce the attributable economic losses by 35%. This reduction falls within the range of reductions achieved when outdoor air meets the World Health Organization interim target 3 and air quality guidelines for annual NO2, highlighting the significance of raising awareness of gas as a polluting household energy for cooking. These findings align with global sustainable development initiatives, providing a sustainable solution to promote public health while potentially mitigating climate change.

Abstract Higher concentrations of air pollutants, such as nitrogen dioxide (NO $$_\text {2}$$ 2 ) and particulate matter (PM), are observed in streets compared to the urban background. These concentrations could be predominantly attributed to road traffic, which remains an important source despite relentless efforts. In this study, performed over the Paris area, the impacts of the evolution of the fleet composition on urban air quality down to the street scale is assessed with two scenarios assuming the introduction of very-low-emission vehicles. Exhaust emission factors for these vehicles are based on the improvement of engine and after-treatment technologies, leading to factors lower than those proposed for the European emission standard Euro 7. Using the year 2014 as the baseline, very-low-emission vehicles are introduced up to the year 2030 for the Paris region. NO $$_\text {2}$$ 2 emissions are thus reduced by 68 % in streets, and concentrations by 53 %. However, PM concentration reduction is limited to 18 % in streets as non-exhaust emissions from tyre and brake wear and road abrasion are preponderant. PM emissions from non-exhaust sources represent 59 % of the total road-traffic emission of PM in 2014 and 89 % in 2030. Non-regulated pollutant concentrations are also reduced, by 42 % for black carbon and 30 % for organic matter. Considering only very-low-emission and electric vehicles in the fleet further reduce NO $$_\text {2}$$ 2 emissions and concentrations by 99.5 % and 80 % respectively. PM concentrations are only reduced by 22 %. This study highlights the high reduction potential of NO $$_\text {2}$$ 2 concentrations with very-low-emission and electric vehicles, because of reduction of exhaust emissions. However, difficulties remain to reduce PM concentrations in urban areas, with the large majority of PM traffic emissions coming from non-exhaust sources.

Abstract Background Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures. Methods Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables. Results We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr

Weiyi Wang,1,2 John Gulliver,3 Sean Beevers,4,5 Anna Freni Sterrantino,1,6 Bethan Davies,1,2 Richard W Atkinson,3 Daniela Fecht1,2 1UK Small Area Health Statistics Unit, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; 2National Institute for Health and Care Research Health Protection Research Unit in Chemical and Radiation Threats and Hazards, School of Public Health, Imperial College London, London, UK; 3Population Health Research Institute, St George’s, University of London, London, UK; 4MRC Centre for Environment and Health, Environmental Research Group, School of Public Health, Imperial College London, London, UK; 5National Institute for Health and Care Research Health Protection Research Unit in Environmental Exposures and Health, School of Public Health, Imperial College London, London, UK; 6The Alan Turing Institute, London, UKCorrespondence: Weiyi Wang, MRC Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, W2 1PG, UK, Email weiyi.wang14@imperial.ac.ukBackground: There is an increasing body of evidence associating short-term ambient nitrogen dioxide (NO2) exposure with asthma-related hospital admissions in children. However, most studies have relied on temporally resolved exposure information, potentially ignoring the spatial variability of NO2. We aimed to investigate how daily NO2 estimates from a highly resolved spatio-temporal model are associated with the risk of emergency hospital admission for asthma in children in England.Methods: We conducted a time-stratified case-crossover study including 111,766 emergency hospital admissions for asthma in children (aged 0– 14 years) between 1st January 2011 and 31st December 2015 in England. Daily NO2 levels were predicted at the patients’ place of residence using spatio-temporal models by combining land use data and chemical transport model estimates. Conditional logistic regression models were used to obtain the odds ratios (OR) and confidence intervals (CI) after adjusting for temperature, relative humidity, bank holidays, and influenza rates. The effect modifications by age, sex, season, area-level income deprivation, and region were explored in stratified analyses.Results: For each 10 μg/m³ increase in NO2 exposure, we observed an 8% increase in asthma-related emergency admissions using a five-day moving NO2 average (mean lag 0– 4) (OR 1.08, 95% CI 1.06– 1.10). In the stratified analysis, we found larger effect sizes for male (OR 1.10, 95% CI 1.07– 1.12) and during the cold season (OR 1.10, 95% CI 1.08– 1.12). The effect estimates varied slightly by age group, area-level income deprivation, and region.Significance: Short-term exposure to NO2 was significantly associated with an increased risk of asthma emergency admissions among children in England. Future guidance and policies need to consider reflecting certain proven modifications, such as using season-specific countermeasures for air pollution control, to protect the at-risk population.Keywords: asthma, nitrogen dioxide, children, hospital admissions, case-crossover

Designing a transparent carbon nanotube (CNT) gas sensor for nitrogen dioxide (NO2) detection at room temperature (RT), which is unaffected by humidity, is a critical challenge in various technologies. To solve this issue, a filament‐based memristor heater (MH) embedded low‐power CNT gas sensor is proposed to address humidity‐related issues, and dynamic response with a low power consumption of 6.15 µW and swift recovery of <1 ms/30.8 µW is successfully demonstrated, without any serious effects on humidity. This study reveals that the MH can effectively heat the surface of the active region due to the joule heating effect, which improves the absorption and desorption of the target gases and mitigates the influence on the humidity, which is in a response that is below 7.5%. As a result, it is believed that the proposed MH‐embedded transparent CNT gas sensors can address the humidity issues and slow response/recovery with ultralow‐power consumption and high‐accuracy gas sensing characteristics. [ABSTRACT FROM AUTHOR]