Search

The detection of nitrogen dioxide (NO2) is essential for safeguarding human health and addressing environmental sustainability. That is why, in the last decades, gas sensors have been developed to detect NO2 to overcome these hazards. This study explores the use of a novel CuO-ZnO composite synthesized through a polyol and sol–gel technique to enhance gas sensing performance. The CuO-ZnO composite offers the advantage of a synergic combination of its properties, leading to improved sensitivity, selectivity, and low detection limit. The innovative polyol technique employed in this research enables the controlled synthesis of hierarchical CuO and porous ZnO structures. The composite formation is achieved using the sol–gel method, resulting in CuO-ZnO composites with different ratios. The structural, morphological, and optical properties of the materials have been characterized using FESEM, X-ray diffraction, and UV-vis spectroscopy. Gas sensing experiments demonstrate enhanced performance, particularly in sensitivity and selectivity for NO2, even at low concentrations. The composites also exhibit improved baseline stability compared to pristine CuO and ZnO. This study explains the influence of humidity on gas sensing properties by examining interactions between water molecules and sensor surfaces. Notably, the developed CuO-ZnO composite displays excellent selectivity towards NO2, attributed to favorable bonding characteristics and acid-base properties. Overall, this research contributes to advancing gas sensor technology, providing a promising potential for sensitive and selective NO2 detection, thereby addressing critical needs for human health and environmental protection. [ABSTRACT FROM AUTHOR]

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 μg m−3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.

Objectives: We report results of a systematic review on the health effects of long-term traffic-related air pollution (TRAP) and diabetes in the adult population. Methods: An expert Panel appointed by the Health Effects Institute conducted this systematic review. We searched the PubMed and LUDOK databases for epidemiological studies from 1980 to July 2019. TRAP was defined based on a comprehensive protocol. Random-effects meta-analyses were performed. Confidence assessments were based on a modified Office for Health Assessment and Translation (OHAT) approach, complemented with a broader narrative synthesis. We extended our interpretation to include evidence published up to May 2022. Results: We considered 21 studies on diabetes. All meta-analytic estimates indicated higher diabetes risks with higher exposure. Exposure to NO2 was associated with higher diabetes prevalence (RR 1.09; 95% CI: 1.02; 1.17 per 10 μg/m3), but less pronounced for diabetes incidence (RR 1.04; 95% CI: 0.96; 1.13 per 10 μg/m3). The overall confidence in the evidence was rated moderate, strengthened by the addition of 5 recently published studies. Conclusion: There was moderate evidence for an association of long-term TRAP exposure with diabetes.

ABSTRACTThis study investigated variations in nitrogen dioxide (NO2) levels in Ukraine during two significant periods: the COVID-19 pandemic lockdown in 2020 and the armed conflict with Russia in 2022. Original and reprocessed Sentinel 5P data products were utilized for the analysis. A machine learning model was employed to generate a business-as-usual NO2 time series that accounted for meteorological variability. For the nine most populous cities in Ukraine, during the lockdown in 2020 we observed a moderation of increases in NO2 levels during the lockdown compared to the pre-lockdown levels. Looking at the same months during the conflict period in 2022, we identified much more significant reductions in NO2 level in these cities, averaging 12.1% for original and 18.1% for reprocessed datasets. Besides our examination of major urban areas, we observed reductions in NO2 levels in areas surrounding coal power plants damaged or destroyed by the conflict. For the major urban areas in Ukraine, we conclude that changes in daily anthropogenic activities due to the conflict-related events had more substantial impacts on NO2 levels than did COVID-19 lockdown.

Abstract Background Short-term exposure to air pollution may trigger symptoms of drug-resistant tuberculosis (DR-TB) through stimulating lung tissue, damaging tracheobronchial mucosa, the key anti-mycobacterium T cell immune function, and production and release of inflammatory cytokines. Objective To investigate the association between acute exacerbations of DR-TB and short-term residential exposure to air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) based on a large prospective cohort in Anhui Province, China. Method Patients were derived from a prospective cohort study of DR-TB in Anhui Province. All DR-TB patients underwent drug-susceptibility testing and prefecture-level reference laboratories confirmed their microbiologies. The case-crossover design was performed to evaluate the association between the risk of acute exacerbations of DR-TB and short-term residential exposure to air pollution. Results Short-term NO2 exposure was significantly related to an elevated risk of first-time outpatient visit due to acute exacerbations of DR-TB(relative risk:1.159, 95% confidence interval:1.011 ~ 1.329). Stratification analyses revealed that the relationship between the risk of acute exacerbations and NO2 exposure was stronger in the elderly (age ≥ 65) DR-TB patients, and in individuals with a history of TB treatment. Conclusions NO2 Exposure was significantly associated with an elevated risk of acute exacerbation of DR-TB in Anhui Province, China.

The use of unverified models for risk estimates and policy recommendations can be highly misleading, as their predictions may not reflect real-world health impacts. For example, a recent article states that NO2 from gas stoves “likely causes ∼50,000 cases of current pediatric asthma from long-term NO2 exposure alone” annually in the United States. This explicitly causal claim, which is contrary to several methodology and review articles published in this journal, among others, reflects both (a) An unverified modeling assumption that pediatric asthma burden is approximately proportional to NO2; and (b) An unverified causal assumption that the assumed proportionality between exposure and response is causal. The article is devoid of any causal analysis showing that these assumptions are likely to be true. It does not show that reducing NO2 exposure from gas stoves would reduce pediatric asthma risk. Its key references report no significant associations – let alone causation – between NO2 and pediatric asthma. Thus, the underlying data suggests that the number of pediatric asthma cases caused by gas stoves in the United States is indistinguishable from zero. This highlights the need to rigorously validate modeling assumptions and causal claims in public health risk assessments to ensure scientifically sound foundations for policy decisions.

Developing general resilience measures that take into account spatio-temporal dynamics to withstand the adverse effects of shocks on the economy is urgent during the COVID-19 pandemic. However, rapid perception of city economic resilience at large scales is currently a challenge during disasters. Using machine learning to massively simulate hourly anthropogenic NO2 emissions from 2016 to 2020, a resilience quantification framework based on an undesired output perspective is proposed to assess the resilience of Chinese cities’ economic operations during the COVID-19 pandemic. The results show that NO2 can characterize economic activity except for the primary industry. Spatially, the economic resilience of Chinese cities at different stages of the pandemic showed a binary pattern of Huanyong Hu line divergence and north-south divergence, respectively. Temporally, economic resilience had a hysteresis effect. Moreover, cities with larger economies recovered more quickly, despite being hit harder. Measurement of economic resilience based on undesired output required integration of information on fluctuations and trends in emissions. Our study provides a new tool for perceiving resilience during disasters from an undesired output perspective to provide support and insight into city management and planning in the post-pandemic era.

ABSTRACTWe investigate the impact of NO2 emissions reduction on O3 and CH4 levels in 14 metropolitan areas of Japan in 2020. To account for meteorological variations, we employ business-as-usual air quality time series generated by machine learning models. Additionally, we use satellite observations and biogeochemical model simulations to analyse air quality changes. During the lockdown period from April 7 to May 25 in 2020, we observed a NO2 reduction that equated to a decrease equivalent to 3.4 and 5 years of the corresponding trends in roadside and ambient air quality recorded from 2010 to 2019. After meteorological normalization, NO2 decreased by 14.5% at ambient air stations and 19.1% at roadside stations. Surprisingly, the NO2 reduction did not immediately lead to increased O3. Instead, O3 levels rose after the lockdown, specifically in August due to favorable sunny conditions. This finding is important for Japan and has not been reported in previous studies. We found that changes in NO2 and CO marginally contributed to variations in CH4 levels across the study areas. It is recommended to simultaneously reduce NOx as well as non-methane volatile organic compounds to mitigate their adverse effects on future policies.

Background: Individuals diagnosed with type 2 diabetes (T2D) frequently exhibit chronic kidney disease (CKD) which may be caused by environmental hazards such as exposure to air pollutants. However, limited research has explored the effects of prolonged exposure to air pollutants on CKD development in this population. This study examines the relationship between long-term exposure to air pollutants and CKD incidence in a longitudinal cohort of individuals with type 2 diabetes in Taiwan Methods: Between 2003 and 2005, we recruited 1316 T2D patients (693 females [52.66 %]; mean age 56.16 ± 8.97 years). Patients were followed until December 31, 2012, with at least two clinical visits. Baseline demographics, medical history, and biomarker levels were collected. The development of CKD was determined by eGFR level < 60 mL/min/1.73 m2. Monthly averages of nitrogen dioxide (NO2) and fine particulate matter [PM ≤ 2.5 μm in aerodynamic diameter (PM2.5)] were acquired from 72 ambient air monitoring stations. The kriging method was employed to estimate the exposure levels to PM2.5, NO2, temperature, and relative humidity in the participants' residential areas. Cox regression with time-dependent covariates regression was applied to assess the impact of long-term exposure to air pollutants and CKD risk. Results: Of 992 patients with normal renal function at baseline, 411 (41.43 %) experienced CKD occurrence over a median follow-up period of 5.45 years. The incidence of CKD was 93.96 cases per 1000 person-years. In multivariable adjusted models, patients exposed to PM2.5 levels above the third quartile of (>33.44 μg/m3) and NO2 levels above the fourth quartile (>22.55 ppb) were found to have an increased risk of CKD occurrence compared to lower exposure levels. Conclusions: This longitudinal study highlights the increased risk of CKD in individuals with type 2 diabetes due to prolonged exposure to NO2 and PM2.5, emphasizing the need for tailored air quality management strategies for this high-risk population.

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on 30 December 2022 and 5 January 2023, covering heavily polluted areas east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points. [ABSTRACT FROM AUTHOR]

In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding. [ABSTRACT FROM AUTHOR]

Enhancing the stability and selectivity of flexible room-temperature gas sensors has been of extensive concern for their practical application. However, it is very challenging to combine the integration of excellent mechanical flexibility and outstanding gas sensing properties. This study presents the design of a parallel structure of ZnO//In2O3 bicomponent nanofibers using a homemade V-type dual-channel electrospinning technique. Compared with as-electrospinning pure ZnO nanofibers and pure In2O3 nanofibers, the ZnO//In2O3-2 composite nanofibers showed higher response and low detection line NO2 at room temperature. The ZnO//In2O3-2 nanofibers have a maximum high response of 109.84 to 60 ppm of NO2 at room temperature, which is about 8.44 times higher than that of the pure In2O3 nanofibers. Moreover, the theoretical detection limit for NO2 is determined to be 1.99 ppb. We have investigated the excellent flexibility of the gas sensor for potential application. These findings indicate that the flexible ZnO//In2O3-2 nanofiber sensors hold great prospect in the application of portable and room-temperature ppb NO2 detection equipment. [ABSTRACT FROM AUTHOR]

The Outer Continental Shelf of the Gulf of Mexico (GOM) is populated with numerous oil and natural gas (ONG) platforms which produce NOx (NOx = NO + NO2), a major component of air pollution. The Bureau of Ocean Energy Management (BOEM) is mandated to ensure that the air quality of coastal states is not degraded by these emissions. As part of a NASA‐BOEM collaboration, we conducted a satellite data‐based analysis of nitrogen dioxide (NO2) patterns and trends in the GOM. Data from the OMI and TROPOMI sensors were used to obtain 18+ year records of tropospheric column (TrC) NO2 in three GOM regions: (a) Houston urban area, (b) near shore area off the Louisiana coast, and a (c) deepwater area off the Louisiana coast. The 2004–2022 time series show a decreasing trend for the urban (−0.027 DU/decade) and near shore (−0.0022 DU/decade) areas, and an increasing trend (0.0019 DU/decade) for the deepwater area. MERRA‐2 wind and TROPOMI NO2 data were used to reveal several NO2 hotspots (up to 25% above background values) under calm wind conditions near individual platforms. The NO2 signals from these deepwater platforms and the high density of shallow water platforms closer to shore were confirmed by TrC NO2 anomalies of up to 10%, taking into account the monthly TrC NO2 climatology over the GOM. The results presented in this study establish a baseline for future estimates of emissions from the ONG hotspots and provide a methodology for analyzing NO2 measurements from the new geostationary TEMPO instrument. Plain Language Summary: Oil and natural gas operations emit nitrogen oxides (NOx), which are major air pollutants and precursors to ground‐level ozone. The Bureau of Ocean Energy Management (BOEM) agency is responsible for managing planned oil and natural gas (ONG) activity on the outer continental shelf, and is mandated to ensure related emissions do not degrade air quality of coastal states. In collaboration with BOEM, we used satellite data from the OMI and TROPOMI sensors to construct an 18+ year record of tropospheric nitrogen dioxide (NO2), a proxy for NOx, in the Gulf Coast region. These time series focused on three areas: (a) Houston urban, (b) off the Louisiana coast, and (c) deepwater Gulf off Louisiana. These regions experienced changes in tropospheric column NO2 of −13.7%, −5.8%, and +5.4% per decade, respectively. We also identified NO2 hotspots from ONG platforms using TROPOMI NO2 averages under calm wind conditions. The ONG deepwater platforms enhance NO2 background amounts by 7%–13% on average, and up to 25% for the Mars and Olympus platforms combined. The results presented here will facilitate our work on emissions estimates from these sources and on applications to the recently launched TEMPO instrument. Key Points: Satellite NO2 records and trends of urban, coastal and deep water areas from 2005 to 2022, are presentedClassifying NO2 over the Gulf of Mexico (GOM) under various wind conditions highlights typical patterns in average NO2 valuesGOM NO2 hotspots from deepwater platforms were identified by TROPOMI under calm wind conditions, the largest of which is over Mars/Olympus [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is one of the pollutants that can cause potential damage to the ecosystem. NO2 emitted from vehicles forms the primary precursor for ground-level ozone. In this study, an analysis of the daily average of NO2 concentration with meteorology measured for two years 2021 and 2022 is being carried out. It is evident from the analysis that NO2 concentration followed an apparent diurnal pattern with a maximum value in the morning hours and a minimum during the afternoon hours. Summer months recorded the highest, and North East Monsoon (NEM) recorded the lowest values of NO2. A statistically significant positive correlation was found between NO2 and Temperature. An autoregressive model was formulated to forecast the daily average values of NO2 concentration. Unit root test was performed to check the stationarity of the data points, which is important in determining trends and seasonal changes. From the model procedure, the order that best fits the data was identified as AR (4), in which the process has the current value based on the previous three values. The Akaike Information Criterion (AIC) and Schwartz Criterion (SC), which are estimators of prediction error for AR (4), are low. The Jarque confirmed the normal distribution-Bera test, which again approves the satisfactoriness of the model.. [ABSTRACT FROM AUTHOR]

Various geostatistical models have been used in epidemiological research to evaluate ambient air pollutant exposures at a fine spatial scale. Few studies have investigated the performance of different exposure models on population-weighted exposure estimates and the resulting potential misclassification across various modeling approaches. This study developed spatial models for NO2 and PM2.5 and conducted exposure assessment in Beijing, China. It explored three spatial modeling approaches: variable dimension reduction, machine learning, and conventional linear regression. It compared their model performance by cross-validation (CV) and population-weighted exposure estimates. Specifically, partial least square (PLS) regression, random forests (RF), and supervised linear regression (SLR) models were developed based on an ordinary kriging (OK) framework for NO2 and PM2.5 in Beijing, China. The mean squared error-based R2 (R2mse) and root mean squared error (RMSE) in leave-one site-out cross-validation (LOOCV) were used to evaluate model performance. These models were used to predict the ambient exposure levels in the urban area and to estimate the misclassification of population-weighted exposure estimates in quartiles between them. The results showed that the PLS-OK models for NO2 and PM2.5, with the LOOCV R2mse of 0.82 and 0.81, respectively, outperformed the other models. The population-weighted exposure to NO2 estimated by the PLS-OK and RF-OK models exhibited the lowest misclassification in quartiles. For PM2.5, the estimates of potential misclassification were comparable across the three models. It indicated that the exposure misclassification made by choosing different modeling approaches should be carefully considered, and the resulting bias needs to be evaluated in epidemiological studies. [ABSTRACT FROM AUTHOR]

The effects of air pollution on physical health are well recognized, with many studies revealing air pollution's effects on vision disorder, yet no relationship has been established. Therefore, a meta-analysis was carried out in this study to investigate the connection between vision disorder and ambient particles (diameter ≤ 2.5 µm (PM2.5), diameter ≤ 10 µm (PM10)) and gaseous pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), Ozone (O3)). Twelve relevant studies published by 26 February 2024 were identified in three databases. A pooled odds ratios (ORs) of 95% confidence intervals (CIs) were obtained using random-effects meta-analysis models. Meta-analysis results revealed that for every 10 µg/m3 increase in PM2.5 and NO2 exposure, a substantially higher incidence of vision disorder was observed (OR = 1.10; 95% CI: 1.01, 1.19; OR = 1.08, 95% CI: 1.00, 1.16). No significant correlation existed between exposure to PM10, SO2 and CO and vision disorder. However, O3 exposure was negatively associated with vision disorder. In addition, subgroup analyses revealed that PM2.5 exposure was significantly correlated with the risk of glaucoma and age-related macular degeneration and that children and adolescents were more susceptible to NO2 and PM2.5 than adults. Overall, exposure to air pollutants, especially PM2.5 and NO2, may increase the incidence of vision disorder. [ABSTRACT FROM AUTHOR]

This study demonstrates that few-layer two-dimensional (2D) CrCl3 transition-metal trihalides (TMTHs; MX3, where M = Ti, V, Cr, Mo, Fe, Ru, and X = Cl, Br, or I) exhibit promising capabilities as chemoresistive sensors for humidity and NO2, H2, and NH3 gases, representing suitable 2D interfaces for gas-sensing applications. Liquid-phase-exfoliated 2D-CrCl3 flakes spin-coated over interdigital substrates exhibit higher chemical stability than CrI3 and VI3 with an excellent reproducible and long-term stable electrical response at an operating temperature (OT) of 100 °C in dry/wet air environments. Results show that with an increase/decrease in resistance, 2D-CrCl3 exhibits p-type responses to NO2 (400 ppb to 1 ppm) and H2O (10–80% relative humidity (RH) at 25 °C), H2 (10–250 ppm), and NH3 (10–250 ppm) oxidizing/reducing gases, respectively. The humidity cross-response of 2D-CrCl3 to NO2 reveals that an environment with 40% RH improves the relative responses and limits of detection of exfoliated CrCl3. The humidity- and gas-sensing response mechanism of 2D-CrCl3 sensor explains its reversal in resistance from an ionic (OT = 25 °C) to an electronic conduction regime (OT = 100 °C) under humid air conditions. These findings provide insights and possibilities for using 2D-CrCl3 TMTHs as humidity- and gas-sensing interfaces. [ABSTRACT FROM AUTHOR]

The research aims to find out the crucial factors in the design phase of packaging products, which are related to the determination of environmental influences on sustainable materials. The paper presents the results of research into the influence of environmentally friendly cardboard and the separation of yellow offset ink on the optical properties of prints exposed to thermal aging without and with exposure to NO2. The samples were obtained under real conditions on a Roland 705 printing machine. The colorimetric characteristics of the print and its stability were determined in the research. The research is significant for graphic reproduction in the domain of testing the quality of the print itself, which is defined by certain raster characteristics. The research covers prints in 100%, 70% RTV, 50% RTV, and 30% RTV. The intensity of the tonal experience will depend on the interaction of the substrate with the raster and different types of inks in offset printing as a function of the experimental conditions. The ink characteristics of prints ∆L*, ∆a*, ∆b*, and ∆E were determined. The research results show that ink I1, with about 80% renewable raw materials, achieves the best stability under the specified experimental conditions. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the economy, increasing level of urbanization, and industrialization, air pollution has transformed from the traditional coal-fired type to the composite pollution of coal-fired type and motor vehicle exhaust. With this transformation, the excessive release of many gases results in drastic climatic changes, global warming, ozone layer depletion, and more, which dramatically affect human lives in many sectors including sports. Based on the above, this research employs the Generalized Additive Model (GAM) to study and predict the effect of atmospheric pollution on the health of soccer players. First, the paper evaluates the impact of air pollution on the physical health of soccer players. It starts with a rigorous evaluation of the influence of various atmospheric pollutants on the health of athletes. Second, it investigates data analysis using numerous tests, including the Augmented Dickey–Fuller test, to evaluate the stationarity of the data and choose the most suitable models for further examination. Third, the research explores the complex correlations between meteorological factors and atmospheric pollutants by considering the dynamic interplay of environmental variables on health results. This analysis offers a nuanced understanding of how meteorological conditions affect pollutant concentrations and their potential health implications. Finally, the research constructs a prediction model that incorporates these complicated associations by employing GAMs with cubic spline functions to capture the non-linear interactions among multiple factors influencing soccer players' health. This advanced modeling technique offers a full examination of air pollution's complex effects, opening the way for useful findings in the disciplines of public health, environmental management, and sports medicine. The experimental results show that the proposed GAM outperforms the other models in terms of accuracy (85%), precision (88%), F1-Score (87%), consistency (90%), recall (86%), specificity (91%), and sensitivity (82%). These remarkable achievements highlight the importance of the proposed GAM in predicting the effect of air pollution on soccer players' health. [ABSTRACT FROM AUTHOR]

In this work, we used X-ray photoelectron spectroscopy (XPS) to perform a comparative study of the interaction of NO2 with two samples of highly oriented pyrolytic graphite (HOPG), on the surfaces of which rhodium was preliminarily deposited by evaporation in a vacuum, at room temperature and a pressure of 10–5 mbar. Before metal deposition, one of the HOPG samples was annealed in a vacuum at 600°C, and the other was bombarded with argon ions followed by exposure to air at room temperature for 1 h in order to introduce strongly bound oxygen atoms into the surface composition. After the deposition of rhodium onto the two HOPG samples, two model catalysts designated as Rh/C and Rh/C(A)–O were prepared. It was found that the interaction of NO2 with Rh/C led to the oxidation of graphite with the destruction of the surface layer. The Rh particles remained in a metallic state, but they were introduced into the near-surface layer of the carbon support. On the contrary, when the Rh/C(A)–O sample was treated with NO2, the deposited rhodium was partially converted into Rh2O3, while the graphite was oxidized to an insignificant degree and retained its original structure. The role of surface oxygen in the stabilization of graphite with respect to oxidation in NO2 was discussed. [ABSTRACT FROM AUTHOR]

Studies related to air pollutants and spontaneous abortion in urban northwestern China are scarce, and the main exposure windows of pollutants acting on pregnant women are unclear. Case-control study. Data were collected from pregnant women in Tongchuan City from 2018 to 2019. A total of 289 cases of spontaneous abortion and 1156 cases of full-term labor were included and analyzed using a case–control study. Logistic regression models were developed to explore the relationship between air pollutants and spontaneous abortion after Chi square analysis and Air pollutant description. O 3 (odds ratio [OR] = 1.028) is a risk factor for spontaneous abortion throughout pregnancy. PM 2.5 (OR = 1.015), PM 10 (OR = 1.010), SO 2 (OR = 1.026), and NO 2 (OR = 1.028) are risk factors for spontaneous abortion in the 30 days before the last menstrual period. PM 2.5 (OR = 1.015), PM 10 (OR = 1.013), SO 2 (OR = 1.036), and NO 2 (OR = 1.033) are risk factors for spontaneous abortion in the 30–60 days before the last menstrual period. PM 2.5 (OR = 1.028), PM 10 (OR = 1.013), SO 2 (OR = 1.035), and NO 2 (OR = 1.059) are risk factors for spontaneous abortion in the 60–90 days before the last menstrual period. Exposure to high levels of air pollutants may be a cause of increased risk of spontaneous abortion, especially in the first trimester of the last menstrual period. [ABSTRACT FROM AUTHOR]

The adsorption of four CO, CO2, NO, NO2 gas molecules in the atmosphere on titanium (Ti)‑doped monolayer SiC surface was investigated using the density functional theory (DFT) with equilibrium geometries optimized at the CAM-B3LYP/6-311+G(d, p) level of theory. The thermochemical, electric and magnetic properties data recommend that the adsorption of these gas molecules on Ti-embedded SiC sheet (SiC_sh) monolayer is more energetically desired than that on the pristine ones. Gas molecules of CO, CO2, NO, NO2 have been adsorbed on the Ti site of doped SiC monolayer through the formation of covalent bonds. Moreover, after the adsorption, the orientations of the gas molecules exhibited a tendency to orient in the inclined and parallel forms to monolayer SiC_sh. The DFT analysis explored that the monolayer SiC_sh with C and Ti-doped atoms possessing one satisfied valency increased the Van der Waal interactions between the gas molecules and the monolayer SiC_sh. Furthermore, the assumption of chemical adsorptions has been approved by the projected density of states (PDOS) and charge density difference plots. Charge density difference calculations also indicate that the electronic densities were mainly accumulated on the adsorbate of CO, CO2, NO, NO2 gas molecules. The results in this investigation can indicate the competence of transition metal doped silicon carbide nanosheet in sensor devices. The overall analysis showed that the adsorption strength of monolayer SiC_sh towards the chosen gas molecules follows the order: NO2 > CO2 > NO > CO. [ABSTRACT FROM AUTHOR]

ZnO nanoparticles (NPs) were prepared by green synthesis using plant leaf extraction of Ochradenus baccatus and characterized by XRD, FESEM, HRTEM, and Raman spectroscopy techniques. Since elevated CO levels have been associated with inflammatory conditions, cardiovascular diseases, and respiratory disorders and the methane gas primarily produced by gut microbiota and linked to gastrointestinal disorders and other abnormal methane levels in breath samples, the nanoparticles were applied for gas sensor fabrication. Thus, the gas sensors fabricated using ZnO nanoparticles were investigated for CH4, H2, CO, and NO2 gases. The gas sensing was performed for the fabricated sensors at various operating temperatures and gas concentrations. Interestingly, leaf-extracted green synthesized ZnO NPs were more sensitive to CH4, CO, and NO2 gases than to H2. The results of sensing studies revealed that the nanoparticles exhibit a selectivity toward gas depending on the gas type. The sensor response was also studied against the humidity. These findings bridge between the laboratory and industry sectors for future gas sensors development, which can be used for exhaled breath analysis and serve as potential diagnostic tools for highly sensitive contagious diseases. [ABSTRACT FROM AUTHOR]

In this work, we used X-ray photoelectron spectroscopy (XPS) to perform a comparative study of the interaction of NO2 with two samples of highly oriented pyrolytic graphite (HOPG), on the surfaces of which rhodium was preliminarily deposited by evaporation in a vacuum, at room temperature and a pressure of 10–5 mbar. Before metal deposition, one of the HOPG samples was annealed in a vacuum at 600°C, and the other was bombarded with argon ions followed by exposure to air at room temperature for 1 h in order to introduce strongly bound oxygen atoms into the surface composition. After the deposition of rhodium onto the two HOPG samples, two model catalysts designated as Rh/C and Rh/C(A)–O were prepared. It was found that the interaction of NO2 with Rh/C led to the oxidation of graphite with the destruction of the surface layer. The Rh particles remained in a metallic state, but they were introduced into the near-surface layer of the carbon support. On the contrary, when the Rh/C(A)–O sample was treated with NO2, the deposited rhodium was partially converted into Rh2O3, while the graphite was oxidized to an insignificant degree and retained its original structure. The role of surface oxygen in the stabilization of graphite with respect to oxidation in NO2 was discussed. [ABSTRACT FROM AUTHOR]

Background: Short-term exposure to air pollution may trigger symptoms of drug-resistant tuberculosis (DR-TB) through stimulating lung tissue, damaging tracheobronchial mucosa, the key anti-mycobacterium T cell immune function, and production and release of inflammatory cytokines. Objective: To investigate the association between acute exacerbations of DR-TB and short-term residential exposure to air pollutants (PM10, PM2.5, SO2, NO2, CO and O3) based on a large prospective cohort in Anhui Province, China. Method: Patients were derived from a prospective cohort study of DR-TB in Anhui Province. All DR-TB patients underwent drug-susceptibility testing and prefecture-level reference laboratories confirmed their microbiologies. The case-crossover design was performed to evaluate the association between the risk of acute exacerbations of DR-TB and short-term residential exposure to air pollution. Results: Short-term NO2 exposure was significantly related to an elevated risk of first-time outpatient visit due to acute exacerbations of DR-TB(relative risk:1.159, 95% confidence interval:1.011 ~ 1.329). Stratification analyses revealed that the relationship between the risk of acute exacerbations and NO2 exposure was stronger in the elderly (age ≥ 65) DR-TB patients, and in individuals with a history of TB treatment. Conclusions: NO2 Exposure was significantly associated with an elevated risk of acute exacerbation of DR-TB in Anhui Province, China. [ABSTRACT FROM AUTHOR]

Due to their high surface-to-volume ratio and material structure, two-dimensional transition-metal dichalcogenides (TMDs) exhibit excellent characteristics on gas sensors. NO2 detection was investigated in the liquid-phase exfoliated MoTe2 nanoparticle cluster with different 2H to 1T′ component ratios. These ratios were controlled via thermal annealing processes at different temperatures. The detection response was found to be sensitive to the 2H to 1T′ component ratios. The response was measured to be 0.2% at 25 ppb from the 2H structure, but no detection response was taken from the 1T′ structure. The response was then enhanced by two orders through an appropriate 2H and 1T′ structural mixture and was higher than most reported values in two-dimensional transition-metal dichalcogenides under similar conditions. This enhancement originates from reducing effective contact resistance from the metallic 1T′ structure and carrier contribution from the semiconducting 2H structure. [ABSTRACT FROM AUTHOR]

The ZnO−SnO2 nanocomposite has been synthesized using the simple and low‐cost co‐precipitation method. The physicochemical characterization of the prepared nanocomposites was carried out by using X‐ray diffraction (XRD), Field Emission Scanning Electron Microscope (FE‐SEM), High‐resolution Transmission Electron Microscopy (HR‐TEM), Energy Dispersive X‐ray analysis (EDAX) and UV‐visible spectroscopy techniques. In XRD confirmed that, hexagonal wurtzite phase of ZnO with the tetragonal rutile phase of SnO2 was found to be in the pure form. The particle size of the ZnO−SnO2 nanocomposite was fund to be ~23 nm in size. The nano cubes like surface morphology of the ZnO−SnO2 nanocomposite has been investigated using FESEM. Based on this various characterization, the ZnO−SnO2 nanocomposite has been used for the development of the chemiresistive gas sensor application. The gas sensitivity is found to be 29.30 % for 100 ppm NO2 gas at 200 °C operating temperature. This work has been used for the development of gas sensor devices for practical application. [ABSTRACT FROM AUTHOR]

Regarding two-dimensional (2D) nanomaterials as gas sensors, we have studied the adsorption of gas molecules (NH3, NO2, NO) on the graphitic GaN sheet (PL-GaN) using density functional theory calculations. "Langmuir" adsorption of gas molecules of NH3, NO2, and NO on the graphitic GaN sheet has been accomplished using density functional theory. The changes in charge density have shown a more important charge transfer on the hexagonal honeycomb nanosheet of gallium nitride (GaN) which acts as the electron acceptor while gas molecules act as the stronger electron donors through adsorption on the graphitic-like GaN surface. The adsorption of NH3, NO 2 , and NO, respectively, on GaN nanosheet has more contribution with high expansion curves of hydrogen, nitrogen and oxygen. The results extracted from PDOS curves after optimization show the electron transferring from nitrogen and oxygen of gas molecules (adsorbate/donor) towards gallium in the active site of GaN surface (adsorbent/acceptor). GaN nanosheet represents enough capability for adsorbing gases of NH3, NO2, and NO through charge transfer from nitrogen and oxygen atoms to the gallium atom regarding the intra-atomic and interatomic interactions. Concerning the change of physicochemical properties of the PL-GaN sheet before and after molecule adsorption, the PL-GaN nanosheet can be applied as an appropriate selective gas sensor for NH3, NO2 and NO detection. [ABSTRACT FROM AUTHOR]

We investigate the impact of NO2 emissions reduction on O3 and CH4 levels in 14 metropolitan areas of Japan in 2020. To account for meteorological variations, we employ business-as-usual air quality time series generated by machine learning models. Additionally, we use satellite observations and biogeochemical model simulations to analyse air quality changes. During the lockdown period from April 7 to May 25 in 2020, we observed a NO2 reduction that equated to a decrease equivalent to 3.4 and 5 years of the corresponding trends in roadside and ambient air quality recorded from 2010 to 2019. After meteorological normalization, NO2 decreased by 14.5% at ambient air stations and 19.1% at roadside stations. Surprisingly, the NO2 reduction did not immediately lead to increased O3. Instead, O3 levels rose after the lockdown, specifically in August due to favorable sunny conditions. This finding is important for Japan and has not been reported in previous studies. We found that changes in NO2 and CO marginally contributed to variations in CH4 levels across the study areas. It is recommended to simultaneously reduce NOx as well as non-methane volatile organic compounds to mitigate their adverse effects on future policies. [ABSTRACT FROM AUTHOR]

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. [ABSTRACT FROM AUTHOR]

In the research field on severe accidents in nuclear power plant, a specific scenario correspond to accident with oxidative conditions for which damaged fuel can be highly oxidised with significant releases of ruthenium oxides. Ruthenium chemistry is complex, and the current knowledge has to be deepened to better assess ruthenium source term with potential radioactive releases to the environment as volatile ruthenium tetroxide. In this work, experimental studies are focused on ruthenium behaviour along a stainless-steel thermal gradient tube, with maximum temperature of 1200 °C, simulating the reactor cooling system in oxidizing conditions with mainly steam/air gas mixtures. Results showed that few % of ruthenium oxides (< 10% with SS tube) can reach low temperature, representative of containment temperature, even with low oxygen content in the carrier gas. The ruthenium revaporization process from the Ru deposits along the tube on mid-term was studied. Influence of carrier gas composition (steam %), flow rate and NOx feed are discussed. [ABSTRACT FROM AUTHOR]

Abstract Background Air pollution is a serious worldwide issue, where both outdoor and indoor air quality have a substantial impact on the health of children. Children are more vulnerable to the effects of air pollution due to their developing respiratory systems and higher respiration rates than adults. These children dedicate a substantial amount of time to the preschool setting. The study sought to evaluate the concentration of NO2 and SO2 in both outdoor and indoor environments of preschool facilities. Methods Concurrently, the levels of NO2 and SO2 were measured indoors and outdoors at 13 randomly selected preschools in 7 districts in Mpumalanga Province, South Africa. Radiello passive air samplers were employed to collect gaseous samples. Results The mean levels of NO2 indoors and outdoors were within the recommended criteria outlined by the World Health Organization (WHO), specifically target levels 1–3. Additionally, these values were also below the air quality guides outlined by South Africa, both for yearly and 1‐h mean measurements. Nevertheless, both levels remained above the suitable threshold of 10 µg m−3 outlined by the WHO air quality guidelines. Conclusions It is important to implement proactive measures such as enhancing airflow systems, employing air filters and reducing potential sources of air pollution in preschools, to aid in reducing exposure to indoor and outdoor NO2 and SO2.

Abstract As global urbanization intensifies, there is an increasing need for highly sensitive and accurate environmental monitoring devices that can meet the demands of specific gas sensing applications with low power consumption. This study focuses on enhancing the sensitivity of MXene‐based chemiresistive sensors for detecting CO2(g) and NO2(g) under zero‐bias operation. This study shows that lignin hybridization effectively improves the sensitivity of a Ti3C2Tx MXene‐based chemiresistive sensor; under zero‐bias operation, lignin hybridization increases the sensitivity to 15 ppm NO2(g) and CO2(g) by 157.38% and 297.95%, respectively. When deposited on a flexible substrate, the MXene/lignin flexible sensor shows a similar response and sensitivity to 15 ppm NO2(g) and CO2(g) under 38° curvature compared to the planar sensor. Consequently, the MXene/lignin hybrid sensor is attractive for room temperature and zero‐bias NO2(g) and CO2(g) detection. The MXene/lignin flexible sensor serves as a model system for advanced solid‐state sensory platforms suitable for curved structures.

Background: Environmental pollution is a highly challenging global health concern, affecting the natural ecosystem and human health. The obesity risk is allied to diverse factors, including genetics, behaviour, food, and socio-cultural conditions, however, literature is lacking in exploring the effect of environmental pollution on obesity and the causal pathways are not fully understood. Therefore, this study aimed to investigate the effect of environmental pollutants particulate matter PM2.5 μm, PM10 μm, Nitrogen Dioxide (NO2), and ground level Ozone (O3) on obesity. Methods: This study recorded data on air pollutants and obesity using the electronic platforms Pub Med, Web of Science, Scopus, and Google Scholar. The keywords included for the literature search were based on a combination of two main aspects, which were used to represent exposure (air pollutants), and outcome (obesity). Initially, 324 articles and reports were identified, and after revising the abstracts and full articles, 12 studies were selected for a detailed analysis and discussion. The Odds Ratio (OR) and 95 % confidence intervals (CIs) were extracted to investigate the impact between air pollutants and obesity. The Cochrane chi-squared test (Chi2), fixed-effects design was used when I2 0.05; otherwise, a random-effects model was adopted. Results: Environmental pollutants, particulate matter PM2.5 (OR = 1.18; 95 % CI: 1.10–1.25p

India is experiencing a rapid urban growth in recent decades modifying the regional air quality around urban agglomerations. Hyderabad, the capital city of Telangana state in India, has been experiencing significant urbanization of about 17 % growth in urban agglomeration over the past two decades. We investigated the long-term pollution characteristics along with the meteorology in and around Hyderabad (300 km × 300 km) using satellite-based remote sensing, and reanalysis data. Columnar aerosol loading was highest during the Spring while the positive trend was more during the Winter. The northeastern and southeastern parts of the study domain experienced higher aerosol loading. A significant increasing linear trend in AOD and PM2.5 is observed over the urban region as well as the northern and eastern parts. The NO2 and SO2 columnar concentrations showed considerable enhancement over the northeast sub-region where numerous thermal power plants are located, and over the urban centre. The SO2 concentration and SSA values were higher during the Autumn, while the NO2 values peaked along with lower SSA values during the Spring. The observed spatio-temporal features in air pollutants are further investigated using rainfall information, transport pathways, vegetation index, and fire events. Higher surface temperature and the polluted northeasterlies caused the comparative enhancement of NO2 concentration during Spring. The investigation on the NDVI and the fire events in different sub-regions points to the possibility of enhanced human settlement, and thereby the associated anthropogenic activities are notable over the West and South parts of Hyderabad. However, the presence of thermal power plants in the northeast and natural gas plants along the coast act as persistent regional sources for aerosols and pollutant gases irrespective of the wet removal.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Governments worldwide have established lockdowns to limit the spread of COVID-19 during the pandemic. The restrictions on travel and reduction of economic activity have led to a temporary improvement in air quality in several countries, especially in urban areas. This study investigates the changes in concentration levels of air pollutants (PM10, PM2.5, SO2, NO2, and bacterial aerosol) in the Upper Silesia Region of Southern Poland, during three periods: pre (March 2018 and 2019), during (March 2020, and 2021) and post-COVID-19 lockdown period (March 2022 and 2023). Our findings indicate that COVID-19 restrictions had a moderate impact on PM10 levels in comparison to pre- and post-COVID-19 periods. PM2.5 during lockdown was significantly lower than in the pre-COVID period and not significantly higher after COVID. PM10 and PM2.5 average concentrations decreased during COVID-19 restrictions by 27.8% and 12.7%, respectively. Compared with the results from the pre-COVID-19 phase, the reductions in NO2 and SO2 during the lockdown were 9.5% and 34.0%. Among other pollutants, bacterial aerosol (BA) concentrations also decreased during the lockdown by 23.0%, compared to the results from the pre-COVID-19 period.

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.

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.

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.

Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO2 and PM2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO2 levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO2 and PM2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.