Estimating the health impacts of exposure to Air pollutants and the evaluation of changes in their concentration using a linear model in Iran

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

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

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.

Assessment of the association between ambient air pollution and stillbirth in the UK: Results from a secondary analysis of the MiNESS case-control study

OBJECTIVE

We examined whether the risk of stillbirth was related to ambient air pollution in a UK population.

DESIGN

Prospective case-control study.

SETTING

Forty-one maternity units in the UK.

POPULATION

Women who had a stillbirth ≥28 weeks' gestation (n = 238) and women with an ongoing pregnancy at the time of interview (n = 597).

METHODS

Secondary analysis of data from the Midlands and North of England Stillbirth case-control study only including participants domiciled within 20 km of fixed air pollution monitoring stations. Pollution exposure was calculated using pollution climate modelling data for NO2 , NOx and PM2.5 . The association between air pollution exposure and stillbirth risk was assessed using multivariable logistic regression adjusting for household income, maternal body mass index (BMI), maternal smoking, Index of Multiple Deprivation quintile and household smoking and parity.

MAIN OUTCOME MEASURE

Stillbirth.

RESULTS

There was no association with whole pregnancy ambient air pollution exposure and stillbirth risk, but there was an association with preconceptual NO2 exposure (adjusted odds ratio [aOR] 1.06, 95% CI 1.01-1.08 per microg/m3 ). Risk of stillbirth was associated with maternal smoking (aOR 2.54, 95% CI 1.38-4.71), nulliparity (aOR 2.16, 95% CI 1.55-3.00), maternal BMI (aOR 1.05, 95% CI 1.01-1.08) and placental abnormalities (aOR 4.07, 95% CI 2.57-6.43).

CONCLUSIONS

Levels of ambient air pollution exposure during pregnancy in the UK, all of were beneath recommended thresholds, are not associated with an increased risk of stillbirth. Periconceptual exposure to NO2 may be associated with increased risk but further work is required to investigate this association.

Effects of long-term exposure to outdoor air pollution on COVID-19 incidence: A population-based cohort study accounting for SARS-CoV-2 exposure levels in the Netherlands

Several studies have linked air pollution to COVID-19 morbidity and severity. However, these studies do not account for exposure levels to SARS-CoV-2, nor for different sources of air pollution. We analyzed individual-level data for 8.3 million adults in the Netherlands to assess associations between long-term exposure to ambient air pollution and SARS-CoV-2 infection (i.e., positive test) and COVID-19 hospitalisation risks, accounting for spatiotemporal variation in SARS-CoV-2 exposure levels during the first two major epidemic waves (February 2020-February 2021). We estimated average annual concentrations of PM10, PM2.5 and NO2 at residential addresses, overall and by PM source (road traffic, industry, livestock, other agricultural sources, foreign sources, other Dutch sources), at 1 × 1 km resolution, and weekly SARS-CoV-2 exposure at municipal level. Using generalized additive models, we performed interval-censored survival analyses to assess associations between individuals' average exposure to PM10, PM2.5 and NO2 in the three years before the pandemic (2017-2019) and COVID-19-outcomes, adjusting for SARS-CoV-2 exposure, individual and area-specific confounders. In single-pollutant models, per interquartile (IQR) increase in exposure, PM10 was associated with 7% increased infection risk and 16% increased hospitalisation risk, PM2.5 with 8% increased infection risk and 18% increased hospitalisation risk, and NO2 with 3% increased infection risk and 11% increased hospitalisation risk. Bi-pollutant models suggested that effects were mainly driven by PM. Associations for PM were confirmed when stratifying by urbanization degree, epidemic wave and testing policy. All emission sources of PM, except industry, showed adverse effects on both outcomes. Livestock showed the most detrimental effects per unit exposure, whereas road traffic affected severity (hospitalisation) more than infection risk. This study shows that long-term exposure to air pollution increases both SARS-CoV-2 infection and COVID-19 hospitalisation risks, even after controlling for SARS-CoV-2 exposure levels, and that PM may have differential effects on these COVID-19 outcomes depending on the emission source.

Highly Efficient Room Temperature NO2 Sensor Using Two-Phase TiOx Heterogeneous Nanoparticles

In this study, we successfully synthesized two-phase titanium oxide (TiOx) heterogeneous nanoparticles (NPs) using an advanced sol-gel method, a significant stride in developing efficient, room temperature (RT) NO2 gas sensors. The prepared two-phase TiOx heterogeneous NPs exhibited exceptional sensitivity to low concentrations of NO2 gas at RT. The heightened gas response was attributed to a significant presence of oxygen vacancies, creating intermediate states within the two-phase heterostructures and thus narrowing the band gap. This facilitated electron transport from the valence band (VB) to the conduction band (CB), resulting in increased current at RT. The XPS analysis confirmed a substantial amount of chemisorbed oxygen O2(ads)- within the two-phase heterostructures, providing more chemisorption sites for nitrogen dioxide gas. This increase in chemisorption sites significantly improved the gas response. Furthermore, the introduction of zinc into the TiOx NPs reduced their band gap, enhancing the background resistance signal-to-noise ratio and increasing the response while maintaining remarkable stability. In summary, our work introduces a promising RT NO2 sensor based on two-phase TiOx heterogeneous NPs, holding great potential for applications in environmental monitoring and gas sensing technology. In future work, we aim to delve deeper into the capabilities of the sensor, exploring broader applications and refining its design for enhanced practicality in environmental monitoring.

Enhanced NO2 Sensitivity of Vertically Stacked van der Waals Heterostructure Gas Sensor and Its Remarkable Electric and Mechanical Tunability

Nanodevices based on van der Waals heterostructures have been predicted, and shown, to have unprecedented operational principles and functionalities that hold promise for highly sensitive and selective gas sensors with rapid response times and minimal power consumption. In this study, we fabricated gas sensors based on vertical MoS2/WS2 van der Waals heterostructures and investigated their gas sensing capabilities. Compared with individual MoS2 or WS2 gas sensors, the MoS2/WS2 van der Waals heterostructure gas sensors are shown to have enhanced sensitivity, faster response times, rapid recovery, and a notable selectivity, especially toward NO2. In combination with a theoretical model, we show that it is important to take into account created trapped states (flat bands) induced by the adsorption of gas molecules, which capture charges and alter the inherent built-in potential of van der Waals heterostructure gas sensors. Additionally, we note that the performance of these MoS2/WS2 heterostructure gas sensors could be further enhanced using electrical gating and mechanical strain. Our findings highlight the importance of understanding the effects of altered built-in potentials arising from gas molecule adsorption induced flat bands, thus offering a way to enhance the gas sensing performance of van der Waals heterostructure gas sensors.

Chemresistive Detection of NO2 of ppb Level in Humid Air at 350 K Using Azo-Spaced Polycroconamide

Organic molecules are of great interest for gas sensing applications. However, achieving high-performance gas sensors with high sensitivity, fast response, low consumption, and workability in humid conditions is still challenging. Herein, we report the rational design and synthesis of an ion-in-conjugation polymer, PADC (poly-4,4'-azodianiline-croconamide), obtained by the condensation of croconic acid with 4-4'diaminoazobenzene for gas sensing under humid conditions. The as-fabricated PADC-based gas sensor exhibits ultrahigh sensitivity (802.7 ppm-1 at 1 ppm), subppb detection limit, and high selectivity under humid air with an 80% humidity effect at a temperature down to 350 K. PADC shows good planarity, excellent thermostability, and a narrow band gap of 1.2 eV because of azobenzene fragments spacing previously repulsed biphenyl rings. Compared to previous humidity immunity works, PADC-based sensors realized humidity immunity at a relatively lower temperature, resulting in lower energy consumption.

Potential Effects of Long-Term Exposure to Air Pollution on Dementia: A Longitudinal Analysis in American Indians Aged 55 Years and Older

(1) Background: American Indians are disproportionately affected by air pollution, an important risk factor for dementia. However, few studies have investigated the effects of air pollution on the risk of dementia among American Indians. (2) Methods: This retrospective cohort study included a total of 26,871 American Indians who were 55+ years old in 2007, with an average follow-up of 3.67 years. County-level average air pollution data were downloaded from land-use regression models. All-cause dementia was identified using ICD-9 diagnostic codes from the Indian Health Service's (IHS) National Data Warehouse and related administrative databases. Cox models were employed to examine the association of air pollution with dementia incidence, adjusting for co-exposures and potential confounders. (3) Results: The average PM2.5 levels in the IHS counties were lower than those in all US counties, while the mean O3 levels in the IHS counties were higher than the US counties. Multivariable Cox regressions revealed a positive association between dementia and county-level O3 with a hazard ratio of 1.24 (95% CI: 1.02-1.50) per 1 ppb standardized O3. PM2.5 and NO2 were not associated with dementia risk after adjusting for all covariates. (4) Conclusions: O3 is associated with a higher risk of dementia among American Indians.

Associations between air pollutants and acute exacerbation of drug-resistant tuberculosis: evidence from a prospective cohort study

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.

Gas Sensing Properties of PLD Grown 2D SnS Film: Effect of Film Thickness, Metal Nanoparticle Decoration, and In Situ KPFM Investigation

This study employs novel growth methodologies and surface sensitization with metal nanoparticles to enhance and manipulate gas sensing behavior of two-dimensional (2D)SnS film. Growth of SnS films is optimized by varying substrate temperature and laser pulses during pulsed laser deposition (PLD). Thereafter, palladium (Pd), gold (Au), and silver (Ag) nanoparticles are decorated on as-grown film using gas-phase synthesis techniques. X-ray diffraction (XRD), Raman spectroscopy, and Field-emission scanning electron microscopy (FESEM) elucidate the growth evolution of SnS and the effect of nanoparticle decoration. X-ray photoelectron spectroscopy (XPS) analyses the chemical state and composition. Pristine SnS, Ag, and Au decorated SnS films are sensitive and selective toward NO2 at room temperature (RT). Ag nanoparticle increases the response of pristine SnS from 48 to 138% toward 2 ppm NO2, which indicates electronic and chemical sensitization effect of Ag. Pd decoration on SnS tunes its selectivity toward H2 gas with a response of 55% toward 70 ppm H2 and limit of detection (LOD) < 1 ppm. In situ Kelvin probe force microscopy (KPFM) maps the work function changes, revealing catalytic effect of Ag toward NO2 in Ag-decorated SnS and direct charge transfer between Pd and SnS during H2 exposure in Pd-decorated SnS.

Green space modified the association between air pollutants and hypertension in China

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

Influence of Different Pt Functionalization Modes on the Properties of CuO Gas-Sensing Materials

The functionalization of noble metals is an effective approach to lowering the sensing temperature and improving the sensitivity of metal oxide semiconductor (MOS)-based gas sensors. However, there is a dearth of comparative analyses regarding the differences in sensitization mechanisms between the two functionalization modes of noble metal loading and doping. In this investigation, we synthesized Pt-doped CuO gas-sensing materials using a one-pot hydrothermal method. And for Pt-loaded CuO, Pt was deposited on the synthesized pristine CuO surface by using a dipping method. We found that both functionalization methods can considerably enhance the response and selectivity of CuO toward NO2 at low temperatures. However, we observed that CuO with Pt loading had superior sensing performance at 25 °C, while CuO with Pt doping showed more substantial response changes with an increase in the operating temperature. This is mainly due to the different dominant roles of electron sensitization and chemical sensitization resulting from the different forms of Pt present in different functionalization modes. For Pt doping, electron sensitization is stronger, and for Pt loading, chemical sensitization is stronger. The results of this study present innovative ideas for understanding the optimization of noble metal functionalization for the gas-sensing performance of metal oxide semiconductors.

Microwave-Assisted Fabrication of Fugus-Based Biocarbons for Malachite Green and NO2 Removal

The aim of the current study was to produce biocarbons through the activation of carbon dioxide with the extraction residues of the fungus Inonotus obliquus. To achieve this goal, a microwave oven was used to apply three different activation temperatures: 500, 600, and 700 °C. Low-temperature nitrogen adsorption/desorption was employed to determine the elemental composition, acid-base properties, and textural parameters of the resulting carbon adsorbents. Subsequently, the produced biocarbons were evaluated for their efficiency in removing malachite green and NO2. The adsorbent obtained by activation of the precursor in 700 °C had a specific surface area of 743 m2/g. In the aqueous malachite green solution, the highest measured sorption capacity was 176 mg/g. Conversely, under dry conditions, the sorption capacity for NO2 on this biocarbon was 21.4 mg/g, and under wet conditions, it was 40.9 mg/g. According to the experimental findings, surface biocarbons had equal-energy active sites that interacted with the dye molecules. A pseudo-second-order kinetics model yielded the most accurate results, indicating that the adsorption of malachite green was driven by chemisorption. Additionally, the study demonstrates a clear correlation between the adsorption capacity of the biocarbons and the pH level of the solution, as it increases proportionately.

Development and validation of a nitrogen dioxide passive sampler

Nitrogen dioxide (NO2) is one of the main indicators of traffic-related air pollution in urban areas. Active sampling methods (common methods) are expensive and need advanced devices. While Passive sampling is a simple and low-cost method for measuring air pollutants, including NO2. Therefore in this study, we developed a passive sampler to measure ambient NO2 and validation its performance by comparing it with active sampling methods. Ambient NO2 was measured for 24 h by both active and passive sampling methods in the same locations (2 m height above grand level and 15 m distance from air pollution sources). Sampling of NO2 was repeated for 18 days to compare ambient NO2 concentrations measured by active and passive methods and validation our developed passive samplers. •To develop passive samplers we used three stainless steel filters impregnated with a combination of triethanolamine and acetone (25:25 mL) in each tube.•Active NO2 sampling was conducted using the modified Satlzman method (standard method).•There was a strong correlation between NO2 concentration obtained from active and passive sampling methods (r = 0.84).

Relationship between air pollutants and spontaneous abortion in a coal resource valley city: a retrospective cohort study

OBJECTIVE

Pollutants in the atmosphere have been linked to poor pregnancy outcomes in women. However, such investigations are scarce in metropolitan northern China. The major exposure window of air pollution affecting pregnant women is also unknown.

METHODS

For the analysis, this retrospective cohort study enrolled 6960 pregnant women recorded at Tongchuan People's Hospital from January 2018 to December 2019. Pollutant concentration values from the nearest monitoring station to the pregnant women were used to estimate exposure doses for each exposure window. Logistic regression models were created to investigate the connection between pollutants and spontaneous abortion while controlling for confounding factors.

RESULTS

PM2.5 was a risk factor for spontaneous abortion in T3 (30-60 days before the first day of the last menstrual period [LMP]), (OR: 1.305, 95% CI: 1.143-1.490) and T4 (60-90 days before the first day of the LMP),(OR: 1.450, 95% CI: 1.239-1.696) after controlling for covariates. In the same window, PM10 was a risk factor (OR: 1.308, 95% CI: 1.140-1.500), (OR: 1.386, 95% CI: 1.184-1.621). In T2 (30 days before the first day of the LMP), T3, and T4, SO2 was a risk factor for spontaneous abortion (OR: 1.185, 95% CI: 1.025-1.371), (OR: 1.219, 95% CI: 1.071-1.396), (OR: 1.202, 95% CI: 1.040-1.389). In T3 and T4, NO2 was a risk factor (OR: 1.171, 95% CI: 1.019- 1.346), (OR: 1.443, 95% CI: 1.259-1.655). In T1 (from the first day of the LMP to the date of abortion), O3 was found to be a risk factor (OR: 1.366, 95% CI: 1.226-1.521).

CONCLUSION

Exposure to high levels of air pollutants before and during pregnancy may be a risk factor for spontaneous abortion in pregnant women. This study further illustrates the importance of reducing air pollution emissions.

Metalloporphyrin-Based Metal-Organic Frameworks for the Ultrasensitive Chemiresistive Detection of NO2: Effect of the Central Metal on Tuning the Sensing Performance

The highly sensitive and selective detection of trace hazardous gases at room temperature is very promising for health protection and environmental safety. Herein, chemiresistive sensors for NO2 were fabricated based on self-assembled films of the four metalloporphyrin (MPor)-based metal-organic frameworks PCN-222-M (M = Cu, Ni, Co, Fe) by the quasi-Langmuir-Shäfer method. It is found that the relative responses of the four PCN-222-M films are linearly related to the NO2 concentration, and the PCN-222-Cu possessed an unprecedented high response to NO2 with a sensitivity of 2209% ppm-1 in the 4-20 ppb range and a low limit of detection (LOD) of 0.93 ppb, achieving the best performance reported so far for NO2 detection at room temperature. Meanwhile, PCN-222-Ni showed the fastest recovery among the four PCN-222-M films, which can be used for the rapid detection of NO2. Excellent reproducibility, stability, selectivity, and moisture resistance are shown for both PCN-222-Cu and PCN-222-Ni. Combining the experimental study and density functional theory (DFT) calculation, the essential roles of MPor units and the MPor/Zr6 cluster hybrid material in tuning the Fermi level and the electron transfer between PCN-222-M and NO2 were further proved. These were less considered topics in previous studies on MOFs. This work explores the application of MPor-based MOFs in gas sensing by selecting appropriate MPor units, thus providing guidance for the development of MOF-based chemiresistive sensors.

Analysis of variation characteristics, transport paths, and influencing factors of atmospheric NO2 pollution in Western Europe

Climate change and air pollution are one of the global environmental problems. It is significant to grasp the air pollution situation of Western Europe in recent 10 years for its or the global pollution control. Based on the OMI tropospheric nitrogen dioxide (NO2) column density data, the spatial and temporal distribution characteristics, variation trend, transmission path, and influencing factors of NO2 in 15 countries in Western Europe from 2011 to 2022 are discussed in this paper. Meanwhile, the annual average spatial and temporal distribution in 2023 is predicted by the random forest (RF) model. The results showed that (1) the 12-year spatial distribution map showed an increasing trend from southwest to northeast, with the border area of the Netherlands and Germany and Milan as two high-value areas, and the overall trend over time was that the high-concentration area gradually shrank, the low-concentration area gradually expanded, and the annual average concentration gradually decreased. (2) The inter-month trend presents a "U" shape, with the mean NO2 pollution ranking in winter > autumn > spring > summer. (3) Natural factors are one of the reasons affecting NO2; for instance, NO2 pollution has a strong positive correlation with the lifted index, relative humidity, and wind speed and a moderately strong negative correlation with precipitable water and air temperature. (4) Exogenous atmospheric transport is another important factor affecting the change of NO2 pollution in Western Europe. The HYSPLIT model is used to analyze the backward trajectory of Milan, Italy, and Nijmegen, Netherlands, in the four seasons of 2022. Both are mainly influenced by westerly airflows, and therefore, the transport effect in the atmosphere brings air pollutants from westerly regions in the atmosphere.

[A Comparison Study on Multiple Modeling Approaches for Air Pollutant Geographic Model Development in Shanghai]

Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai.

Effect of fluorine doping on the NO2-sensing properties of MoS2nanoflowers

The somewhat slow recovery kinetics of NO2sensing at low temperatures are still challenging to overcome. To enhance the gas sensing property, fluorine is doped to MoS2nanoflowers by facile hydrothermal method. Extensive characterization data demonstrate that F was effectively incorporated into the MoS2nanoflowers, and that the microstructure of the MoS2nanoflowers did not change upon F doping. The two MoS2doped with varying concentrations of fluorine were tested for their sensing property to NO2gas. Both of them show good repeatability and stability. A smaller recovery time was seen in the F-MoS2-1 sample with a little amount of F loading, which was three times quicker than that of pure MoS2. The key reason for the quicker recovery time of this material was found to be the fluorine ions that had been adsorbed on the surface of F-MoS2-1 would take up some of the NO2adsorption site. Additionally, the sample F-MoS2-2 with a higher F doping level demonstrated increased sensitivity. The F-MoS2-2 sensor's high sensitivity was mostly due to the lattice fluorine filled to the sulfur vacancy, which generated impurity levels and reduced the energy required for its electronic transition. This study might contribute to the development of new molybdenum sulfide based gas sensor.

Long-term exposure to air pollution and prevalent nonalcoholic fatty liver disease

Background

Nonalcoholic fatty liver disease (NAFLD) is a disease characterized by lipid accumulation within hepatocytes, ranging from simple steatosis to steatohepatitis, in the absence of secondary causes of hepatic fat accumulation. Although air pollution (AP) has been associated with several conditions related to NAFLD (e.g., metabolic syndrome, type 2 diabetes mellitus), few studies have explored an association between AP and NAFLD. The aim of the study was to investigate whether exposure to AP is associated with NAFLD prevalence.

Methods

We used baseline cross-sectional data (2000-2003) of the Heinz-Nixdorf-Recall cohort study in Germany (baseline n = 4,814), a prospective population-based cohort study in the urbanized Ruhr Area. Mean annual exposure to size-fractioned particulate matter (PM10, PM2.5, PMcoarse, and PM2.5abs), nitrogen dioxide, and particle number was assessed using two different exposure models: a chemistry transport dispersion model, which captures urban background AP exposure on a 1 km2 grid at participant's residential addresses, and a land use regression model, which captures point-specific AP exposure at participant's residential addresses. NAFLD was assessed with the fatty liver index (n = 4,065), with NAFLD defined as fatty liver index ≥60. We estimated ORs of NAFLD per interquartile range of exposure using logistic regression, adjusted for socio-demographic and lifestyle variables.

Results

We observed a NAFLD prevalence of 31.7% (n = 1,288). All air pollutants were positively associated with NAFLD prevalence, with an OR per interquartile range for PM2.5 of 1.11 (95% confidence interval [CI] = 1.00, 1.24) using chemistry transport model, and 1.06 (95% CI = 0.94, 1.19) using the land use regression model, respectively.

Conclusion

There was a positive association between long-term AP exposure and NAFLD.

Microwave Heating for Synthesis of Carbonaceous Adsorbents for Removal of Toxic Organic and Inorganic Contaminants

The residues obtained from the extraction of Inonotus obliquus fungus were used to produce carbonaceous adsorbents. The initial material was subjected to pyrolysis in a microwave oven. The adsorbents were characterized through elemental analysis, low-temperature nitrogen adsorption/desorption isotherms, and Boehm titration. The carbonaceous adsorbents were tested for the removal of NO2, methylene blue, and malachite green. The results indicated that the obtained carbonaceous adsorbents exhibited basic characteristics and possessed specific surface areas of 372 and 502 m2/g. The adsorption process of liquid contaminants was modeled using the single-layer Langmuir model. The maximum adsorption capacities were found to be 101 and 109 mg/g for methylene blue, and 75 and 77 mg/g for malachite green. The kinetic study demonstrated that the adsorption of methylene blue and malachite green was better described by a pseudo-second order model. The study affirmed that the adsorption of organic dyes onto the resultant carbonaceous adsorbents was both spontaneous and endothermic. The study also demonstrated that the presence of an air stream during the NO2 adsorption process and prehumidization of the adsorbent with humid air had a beneficial effect on the obtained sorption capacities. In conclusion, the study demonstrated that pyrolysis of the extraction residues from the fungus Inonotus obliquus yields highly effective, environmentally friendly, and cost-efficient carbonaceous adsorbents for the removal of both gaseous and liquid pollutants.

Lagging effects and prediction of pollutants and their interaction modifiers on influenza in northeastern China

BACKGROUND

Previous studies have typically explored the daily lagged relations between influenza and meteorology, but few have explored seasonally the monthly lagged relationship, interaction and multiple prediction between influenza and pollution. Our specific objectives are to evaluate the lagged and interaction effects of pollution factors and construct models for estimating influenza incidence in a hierarchical manner.

METHODS

Our researchers collect influenza case data from 2005 to 2018 with meteorological and contaminative factors in Northeast China. We develop a generalized additive model with up to 6 months of maximum lag to analyze the impact of pollution factors on influenza cases and their interaction effects. We employ LASSO regression to identify the most significant environmental factors and conduct multiple complex regression analysis. In addition, quantile regression is taken to model the relation between influenza morbidity and specific percentiles (or quantiles) of meteorological factors.

RESULTS

The influenza epidemic in Northeast China has shown an upward trend year by year. The excessive incidence of influenza in Northeast China may be attributed to the suspected primary air pollutant, NO2, which has been observed to have overall low levels during January, March, and June. The Age 15-24 group shows an increase in the relative risk of influenza with an increase in PM2.5 concentration, with a lag of 0-6 months (ERR 1.08, 95% CI 0.10-2.07). In the quantitative analysis of the interaction model, PM10 at the level of 100-120 μg/m3, PM2.5 at the level of 60-80 μg/m3, and NO2 at the level of 60 μg/m3 or more have the greatest effect on the onset of influenza. The GPR model behaves better among prediction models.

CONCLUSIONS

Exposure to the air pollutant NO2 is associated with an increased risk of influenza with a cumulative lag effect. Prioritizing winter and spring pollution monitoring and influenza prediction modeling should be our focus.

Long-Term Exposure of Nitrogen Oxides Air Pollution (NO2) Impact for Coronary Artery Lesion Progression-Pilot Study

BACKGROUND

The potentially harmful effects of air pollution on the human health have been already presented in epidemiological studies, suggesting a strong association with increased morbidity and mortality. The aim of the study was to evaluate a possible relationship between coronary artery lesion progression related to habitation place (cities vs. villages) and air pollution.

METHODS

There were 148 (101 men and 47 women) patients with a median age of 70 (63-74) years enrolled into retrospective analysis based on the coronary angiography results and their habitation place. Patients with stable coronary syndrome, who underwent repeated percutaneous coronary interventions were enrolled into the analysis based on demographical and clinical characteristics combined with annual exposure to air pollution (PM2.5, PM10, and NO2).

RESULTS

The results of multivariable regression analysis showed a significant relationship between coronary artery lesion progression requiring percutaneous intervention and NO2 chronic exposure in patients living in cities of Poland (OR 2.00, 95% CI: 0.41-9.62, p < 0.001). The predictive value of air pollution exposure at habitation place for coronary artery lesion progression requiring percutaneous intervention was evaluated by receiver-operator curve analysis, which revealed an area under the curve of 0.939, yielding a sensitivity of 87.1% and specificity of 90.7%.

CONCLUSIONS

Coronary artery lesion progression can be related to chronic exposure to NO2 air pollution in patients living in cities in Poland.

Association of short-term nitrogen dioxide exposure with hospitalization for urolithiasis in Xinxiang, China: a time series study

Urolithiasis accounts for the highest incidence of all urologic-associated hospitalizations. However, few studies have explored the effect of nitrogen dioxide (NO2) on hospitalizations for urolithiasis. We included 5956 patients with urolithiasis, collected daily meteorological and air pollution data between 2016 and 2021, and analyzed the associations between air pollutants and hospitalization, length of the hospital stay, and hospitalization costs attributable to urolithiasis. NO2 exposure was associated with an increased risk of hospitalization for urinary tract stones. For each 10-μg/m3 increase and 1-day lag of NO2, the maximum daily effect on the risk of hospitalization for urolithiasis was 1.020 (95% confidence interval [CI]: 1.001-1.039), and the cumulative effect peaked on lag day 4 (relative risk [RR]: 1.061; 95% CI: 1.003-1.122). Attribution scores and quantitative analysis revealed that the mean number of hospital days and mean hospital costs were 16 days and 21,164.39 RMB, respectively. Up to 5.75% of all urolithiasis hospitalizations were estimated to be attributable to NO2, and the cost of NO2-related urolithiasis hospitalizations reached approximately 3,430,000 RMB. Stratified analysis showed that NO2 had a more sensitive impact on urolithiasis hospitalizations in women and in those aged ≥65 years. Notably, men and those younger than 65 years of age (exclude people aged 65) incurred more costs for urolithiasis hospitalizations. In the population level, the association between NO2 and risk of urolithiasis hospitalization was more pronounced during the warm season. NO2 can increase hospitalizations for urolithiasis for Xinxiang City residents, and there is a cumulative lag effect. Focusing on air pollution may have practical significance in terms of the prevention and control of urolithiasis.

Effects of low-level air pollution exposures on hospital admission for myocardial infarction using multiple causal models

Myocardial infarctions have been associated with PM2.5, and more recently with NO2 and O3, however counterfactual designs have been lacking and argument continues over the extent of confounding control. Here we introduce a doubly robust, counterfactual-based approach that deals with nonlinearity and interactions in associations between confounders and both outcome and exposure, as well as a double negative controls approach that capture omitted confounders. We used data from over 4 million admissions for myocardial infarction in the US Medicare population between 2000 and 2016 and linked them by ZIP code of residence to high resolution predictions of annual PM2.5, NO2, and O3. We computed the counts of admissions for each ZIP code-year. In the doubly robust approach, we divided each pollutant into deciles, and for each decile, we fitted a gradient boosting machine model to estimate the effects of covariates, including the co-pollutants, on the counts. We used these models to predict, for all ZIP code-years, the expected counts had everyone be exposed in that decile. We also estimated the probability of being in that decile given all covariates, again with a gradient boosting machine, and used inverse probability weights to compute the weighted average rate of MI admission in each decile. In the negative control approach, for each pollutant, we fitted a quasi-Poisson model to estimate the exposure effect, adjusting for covariates including the co-pollutants, and negative exposure and outcome controls to control for unmeasured confounding. Each 1-μg/m3 increase in annual PM2.5 increased the admission for MI by 1.37 cases per 10,000 person-years (95% CI: 1.20, 1.54) in the doubly robust approach, and by 0.69 cases (95% CI 0.60, 0.78) using the negative control approach. Elevated risks were seen even below annual PM2.5 level of 8 μg/m3. Results for NO2 and O3 were inconsistent.

Synergistic analysis of atmospheric pollutants NO2 and PM2.5 based on land use regression models: a case study of the Yangtze River Delta, China

Air pollution is considered one of the greatest threats to human health. This study combines a land use regression (LUR) model with satellite measurements and a distributed-lagged non-linear model (DLNM). It aims to predict high-resolution ground-level concentrations of nitrogen dioxide (NO2) and particulate matter 2.5 (PM2.5) in the Yangtze River Delta (YRD) and reveal the mechanisms of influence between NO2 and PM2.5 and precursors and meteorological factors. Results showed that the annual average NO2 and PM2.5 in the YRD urban agglomeration 2019 were 39.5 µg/m3 and 37.5 µg/m3, respectively. The seasonal variation of NO2 and PM2.5 showed winter > spring > autumn > summer. There is a compelling and complex relationship between NO2 and PM2.5. Predictors indicate that latitude (Y), surface pressure (P), ozone (O3), carbon monoxide (CO), aerosol optical depth (AOD), residential, and rangeland have positive impacts on NO2 and PM2.5. In contrast, temperature (T), precipitation (PRE), and industrial trees hurt NO2 and PM2.5. DLNM model results show that NO2 and PM2.5 had significant associations with the included precursors and meteorological elements, with lagged and non-linear effects observed. Satellite data could help significantly increase the accuracy of LUR models; the R2 of tenfold cross-validation was enhanced by 0.18-0.22. In 2019, PM2.5 will be the dominant pollutant in the YRD, and NO2 showed a high value in the central and eastern parts of the YRD. High concentrations of NO2 and PM2.5 are present in 86% of the YRD, meaning that residents will have difficulty avoiding exposure to these two high pollution levels.

Spatiotemporal variability and health risk assessment of PM2.5 and NO2 over the Indo-Gangetic Plain: A three years long study (2019-21)

Studying the spatiotemporal variability of pollutants is necessary to identify the pollution hotspots with high health risk and enable the agencies to implement pollution abatement strategies in a targeted manner. Present study reports the spatio-temporal variability and health risk assessment (HRA) of PM2.5 (Particulate matter with aerodynamic diameter <2.5μm) and NO2 over IGP from 2019-2021. The HRA is expressed as passively smoked cigarettes (PSC) for four different health outcomes i.e., low birth weight (LBW), percentage decreased lung function (DLF) in school aged children, lung cancer (LC), and cardiovascular mortality (CM). The findings confirm very high PM2.5 and NO2 mass concentrations and high health risk over middle IGP and Delhi as compared to upper and lower IGP. Within Delhi, north Delhi region is the most polluted and at highest risk as compared to central and south Delhi. The health risk associated with PM2.5 over IGP is highest for DLF, equivalent to 21.63 PSCs daily, followed by CM (11.69), LBW (8.27) and LC (6.94). For NO2, the health risk is highest for DLF (3.09 PSCs) and CM (2.95), followed by LC (1.47) and LBW (1.04). PM2.5 and NO2 concentrations, along with the associated health risks, are highest during the post-monsoon and winter seasons and lowest during the monsoon season.

Low-Regioregularity Polythiophene for a Highly Sensitive and Stretchable Gas Sensor

In this study, we examined how the regioregularity of poly(3-hexylthiophene) (P3HT) affects molecular packing, free volume, charge transport, and gas sensing properties. Our results showed that the presence of regular alkyl side chains on the polymer backbone promoted a high degree of structural order in regioregular P3HT molecules, leading to a compact packing density and reduced free volume. Consequently, it was more challenging for NO₂ molecules to interact with the hole charge carriers in the conductive channel. On the other hand, the regiorandom P3HT films displayed a larger free volume, attributed to the irregular side chains, which facilitated the gas-analyte interaction while impeding efficient charge transport. Thus, these films exhibited greater sensitivity to analyte gas molecules. The molecular order, packing density, and hardness of P3HT films were confirmed through the use of multiple techniques, including UV-vis spectroscopy, atomic force microscopy, and grazing-incidence X-ray diffraction. Additionally, the regiorandom P3HT films showed enhanced mechanical flexibility compared to the regioregular films. In conclusion, our findings emphasize that the regularity of polymer molecules plays a significant role in determining the charge carrier transport and gas adsorption characteristics.

Predicting fine-scale daily NO2 over Mexico City using an ensemble modeling approach

In recent years, there has been growing interest in developing air pollution prediction models to reduce exposure measurement error in epidemiologic studies. However, efforts for localized, fine-scale prediction models have been predominantly focused in the United States and Europe. Furthermore, the availability of new satellite instruments such as the TROPOsopheric Monitoring Instrument (TROPOMI) provides novel opportunities for modeling efforts. We estimated daily ground-level nitrogen dioxide (NO2) concentrations in the Mexico City Metropolitan Area at 1-km2 grids from 2005 to 2019 using a four-stage approach. In stage 1 (imputation stage), we imputed missing satellite NO2 column measurements from the Ozone Monitoring Instrument (OMI) and TROPOMI using the random forest (RF) approach. In stage 2 (calibration stage), we calibrated the association of column NO2 to ground-level NO2 using ground monitors and meteorological features using RF and extreme gradient boosting (XGBoost) models. In stage 3 (prediction stage), we predicted the stage 2 model over each 1-km2 grid in our study area, then ensembled the results using a generalized additive model (GAM). In stage 4 (residual stage), we used XGBoost to model the local component at the 200-m2 scale. The cross-validated R2 of the RF and XGBoost models in stage 2 were 0.75 and 0.86 respectively, and 0.87 for the ensembled GAM. Cross-validated rootmean-squared error (RMSE) of the GAM was 3.95 μg/m3. Using novel approaches and newly available remote sensing data, our multi-stage model presented high cross-validated fits and reconstructs fine-scale NO2 estimates for further epidemiologic studies in Mexico City.

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis

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.

Association between Residential Exposure to Air Pollution and Incident Coronary Heart Disease Is Not Mediated by Leukocyte Telomere Length: A UK Biobank Study

Higher air pollution exposure and shorter leukocyte telomere length (LTL) are both associated with increased risk of coronary heart disease (CHD), and share plausible mechanisms, including inflammation. LTL may serve as a biomarker of air pollution exposure and may be intervened with to reduce the risk of CHD. To the best of our knowledge, we are the first to test the mediation effect of LTL in the relationship between air pollution exposure and incident CHD. Using the UK Biobank (UKB) data (n = 317,601), we conducted a prospective study linking residential air pollution exposure (PM_2.5, PM₁₀, NO₂, NO_x) and LTL to incident CHD during a mean follow-up of 12.6 years. Cox proportional hazards models and generalized additive models with penalized spline functions were used to model the associations of pollutant concentrations and LTL with incident CHD. We found non-linear associations of air pollution exposure with LTL and CHD. Pollutant concentrations in the lower range were decreasingly associated with longer LTL and reduced risk of CHD. The associations between lower pollutant concentrations and reduced risk of CHD, however, were minimally mediated by LTL (<3%). Our findings suggest that air pollution influences CHD through pathways that do not involve LTL. Replication is needed with improved measurements of air pollution that more accurately assesses personal exposure.

Spatial variability of trace gases (NO2, O3 and CO) over Indian region during 2020 and 2021 COVID-19 lockdowns

COVID-19 lockdown has given us an opportunity to investigate the pollutant concentrations in response to the restricted anthropogenic activities. The atmospheric concentration levels of nitrogen dioxide (NO₂), carbon monoxide (CO) and ozone (O₃) have been analysed for the periods during the first wave of COVID-19 lockdown in 2020 (25th March-31st May 2020) and during the partial lockdowns due to second wave in 2021 (25th March-15th June 2021) across India. The trace gas measurements from Ozone Monitoring Instrument (OMI) and Atmosphere InfraRed Sounder (AIRS) satellites have been used. An overall decrease in the concentration of O₃ (5-10%) and NO₂ (20-40%) have been observed during the 2020 lockdown when compared with business as usual (BAU) period in 2019, 2018 and 2017. However, the CO concentration increased up to 10-25% especially in the central-west region. O₃ and NO₂ slightly increased or had no change in 2021 lockdown when compared with the BAU period, but CO showed a mixed variation prominently influenced by the biomass burning/forest fire activities. The changes in trace gas levels during 2020 lockdown have been predominantly due to the reduction in the anthropogenic activities, whereas in 2021, the changes have been mostly due to natural factors like meteorology and long-range transport, as the emission levels have been similar to that of BAU. Later phases of 2021 lockdown saw the dominant effect of rainfall events resulting in washout of pollutants. This study reveals that partial or local lockdowns have very less impact on reducing pollution levels on a regional scale as natural factors like atmospheric long-range transport and meteorology play deciding roles on their concentration levels.

Highly Efficient NO2 Sensors Based on Al-ZnOHF under UV Assistance

Zinc hydroxyfluoride (ZnOHF) is a newly found resistive semiconductor used as a gas-sensing material with excellent selectivity to NO₂ because of its unique energy band structure. In this paper, Al³⁺ doping and UV radiation were used to further improve the gas-sensing performance of ZnOHF. The optimized 0.5 at.% Al-ZnOHF sample exhibits improved sensitivity to 10 ppm NO₂ at a lower temperature (100 °C) under UV assistance, as well as a short response/recovery time (35 s/96 s). The gas-sensing mechanism demonstrates that Al³⁺ doping increases electron concentration and promotes electron transfer of the nanorods by reducing the bandgap of ZnOHF, and the photogenerated electrons and holes with high activity under UV irradiation provide new reaction routes in the gas adsorption and desorption process, effectively promoting the gas-sensing process. The synergistic effect of Al³⁺ and UV radiation contribute to the enhanced performance of Al-ZnOHF.

Tailoring and functionalizing the graphitic-like GaN and GaP nanostructures as selective sensors for NO, NO2, and NH3 adsorbing: a DFT study

CONTEXT

Langmuir adsorption of gas molecules of NO, NO₂, and NH₃ on the graphitic GaN and GaP sheets has been accomplished using density functional theory. The changes of charge density have shown a more important charge transfer for GaN compared to GaP which acts both as the electron donor while gas molecules act as the stronger electron acceptors through adsorption on the graphitic-like GaN surface. The adsorption of NO and NO₂ molecules introduced spin polarization in the PL-GaN sheet, indicating that it can be employed as a magnetic gas sensor for NO and NO₂ sensing.

METHODS

The partial electron density states based on "PDOS" graphs have explained that the NO and NO₂ states in both of GaN and GaP nanosheets, respectively, have more of the conduction band between - 5 and - 10 eV, while expanded contribution of phosphorus states is close to gallium states, but nitrogen and oxygen states have minor contributions. GaN and GaP nanosheets represent having enough capability for adsorbing gases of NO, NO₂, and NH₃ through charge transfer from nitrogen atom and oxygen atom to the gallium element owing to intra-atomic and interatomic interactions. Ga sites in GaN and GaP nanosheets have higher interaction energy from Van der Waals' forces with gas molecules.

[Long-term exposure to ambient air pollution and the incidence of SARS-CoV-2 infections in Italy: The EpiCovAir study]

BACKGROUND

after the outbreak of the SARS-CoV-2 pandemic in 2020, several waves of pandemic cases have occurred in Italy. The role of air pollution has been hypothesized and investigated in several studies. However, to date, the role of chronic exposure to air pollutants in increasing incidence of SARS-CoV-2 infections is still debated.

OBJECTIVES

to investigate the association between long-term exposure to air pollutants and the incidence of SARS-CoV-2 infections in Italy.

DESIGN

a satellite-based air pollution exposure model with 1-km2 spatial resolution for entire Italy was applied and 2016-2019 mean population-weighted concentrations of particulate matter < 10 micron (PM10), PM <2.5 micron (PM2.5), and nitrogen dioxide (NO2) was calculated to each municipality as estimates of chronic exposures. A principal component analysis (PCA) approach was applied to 50+ area-level covariates (geography and topography, population density, mobility, population health, socioeconomic status) to account for the major determinants of the spatial distribution of incidence rates of SARS-CoV-2 infection. Detailed information was further used on intra- and inter-municipal mobility during the pandemic period. Finally, a mixed longitudinal ecological design with the study units consisting of individual municipalities in Italy was applied. Generalized negative binomial models controlling for age, gender, province, month, PCA variables, and population density were estimated.

SETTING AND PARTICIPANTS

individual records of diagnosed SARS-2-CoV-2 infections in Italy from February 2020 to June 2021 reported to the Italian Integrated Surveillance of COVID-19 were used.

MAIN OUTCOME MEASURES

percentage increases in incidence rate (%IR) and corresponding 95% confidence intervals (95% CI) per unit increase in exposure.

RESULTS

3,995,202 COVID-19 cases in 7,800 municipalities were analysed (total population: 59,589,357 inhabitants). It was found that long-term exposure to PM2.5, PM10, and NO2 was significantly associated with the incidence rates of SARS-CoV-2 infection. In particular, incidence of COVID-19 increased by 0.3% (95%CI 0.1%-0.4%), 0.3% (0.2%-0.4%), and 0.9% (0.8%-1.0%) per 1 μg/m3 increment in PM2.5, PM10 and NO2, respectively. Associations were higher among elderly subjects and during the second pandemic wave (September 2020-December 2020). Several sensitivity analyses confirmed the main results. The results for NO2 were especially robust to multiple sensitivity analyses.

CONCLUSIONS

evidence of an association between long-term exposure to ambient air pollutants and the incidence of SARS-CoV-2 infections in Italy was found.

Significant association between increased risk of emergency department visits for psychiatric disorders and air pollutants in South Korea

BACKGROUND

The association between air pollutants and psychiatric disorders has been investigated in many countries. However, results for the association between air pollutants and emergency room (ER) visits for psychiatric disorders are inconsistent. Further, systematic large-scale studies relating to the same are lacking, especially in South Korea.

OBJECTIVE

We aimed to investigate the acute and short-term cumulative effect of air pollutants on ER visits for psychiatric disorders in South Korea.

METHODS

The data on nitrogen dioxide (NO2) and particulate matter (PM2.5 and PM10) and ER visits due to nine representative psychiatric disorders were collected from eight major cities in South Korea for three years. We estimated the relative risk (RR) at lag 0 and a cumulative 11-day RR by increasing a 10-unit for PM and 0.01-unit for NO2 using the Distributed Lag Nonlinear Model.

RESULTS

During the study period, a total of 79,092 ER visits for psychiatric disorders were identified and tested for association with NO2, PM2.5, and PM10. The RR at lag 0 of depression per 0.01-unit increase in NO2 was the highest (3.127; 95% confidence interval [CI] 2.933 to 3.332) among the psychiatric disorders. The RRs at lag 0 of anxiety disorders per 10-unit increase in PM2.5 (1.709; 95% CI 1.424 to 2.053) and PM10 (2.168; 95% CI 1.957 to 2.403) were the highest among the psychiatric disorders.

SIGNIFICANCE

Air pollutants increased ER visits for psychiatric disorders with the highest RR of depression due to NO2 and anxiety disorder due to PM2.5 and PM10. These results contribute evidence to the positive association between ambient exposure to air pollution and aggravation of psychiatric disorders, indicating air pollution may be a modifiable risk factor in mental health management.

IMPACT STATEMENT

We investigated the effect of air pollution on emergency room visits caused by major psychiatric disorders in prominent cities in South Korea. Using the Distributed Lag Nonlinear Model, an advanced analysis method, we calculated the acute effect and short-term cumulative effect. Air pollutants increased ER visits for psychiatric disorders with the highest relative risk of depression due to NO2 and anxiety disorder due to PM2.5 and PM10. These results reveal an association between ambient exposure to air pollution and aggravation of psychiatric disorders and suggest that air pollution may be a modifiable risk factor in mental health management.

Controllable Synthesis of Sheet-Flower ZnO for Low Temperature NO2 Sensor

ZnO is a wide band gap semiconductor metal oxide that not only has excellent electrical properties but also shows excellent gas-sensitive properties and is a promising material for the development of NO₂ sensors. However, the current ZnO-based gas sensors usually operate at high temperatures, which greatly increases the energy consumption of the sensors and is not conducive to practical applications. Therefore, there is a need to improve the gas sensitivity and practicality of ZnO-based gas sensors. In this study, three-dimensional sheet-flower ZnO was successfully synthesized at 60 °C by a simple water bath method and modulated by different malic acid concentrations. The phase formation, surface morphology, and elemental composition of the prepared samples were studied by various characterization techniques. The gas sensor based on sheet-flower ZnO has a high response value to NO₂ without any modification. The optimal operating temperature is 125 °C, and the response value to 1 ppm NO₂ is 125. At the same time, the sensor also has a lower detection limit (100 ppb), good selectivity, and good stability, showing excellent sensing performance. In the future, water bath-based methods are expected to prepare other metal oxide materials with unique structures.

Photocatalytic Oxidation of NO2 on TiO2: Evidence of a New Source of N2O5

N2O5 is an important intermediate in the atmospheric nitrogen cycle. Using a flow tube reactor, N2O5 was found to be released from the TiO2 surface during the photocatalytic oxidation of NO2, revealing a previously unreported source of N2O5. The rate of N2O5 release from TiO2 was dependent on the initial NO2 concentration, relative humidity, O2/N2 ratio, and irradiation intensity. Experimental evidences and quantum chemical calculations showed that NO2 can react with the surface hydroxyl groups and the generated electron holes on the TiO2, followed by combining with another NO2 molecule to form N2O5. The latter was physisorbed on TiO2 and had a low adsorption energy of -0.13 eV. Box model simulations indicated that the new source of N2O5 released from TiO2 can increase the daytime N2O5 concentration by up to 20% in urban areas if abundant TiO2-containing materials and high NOx concentrations were present. This joint experimental/theoretical study not only demonstrates a new chemical mechanism for N2O5 formation but also has important implications for air quality in urban areas.

DFT study of adsorbing SO2, NO2, and NH3 gases based on pristine and carbon-doped Al24N24 nanocages

The adsorption of SO₂, NO₂, and NH₃ toxic gases on Al₂₄N₂₄ and Al₂₄N₂₃C nanocages was investigated by using density functional theory (DFT) calculations. The adsorption energies, frontier orbitals, charge transfer using natural bonding orbital (NBO) analysis, dipole moment, the partial density of states (PDOS), thermodynamic relationships, non-covalent interaction (NCI), and quantum theory of atoms in molecules (QTAIM) were considered. The results reveal that carbon-doped Al₂₄N₂₄ nanocage increases the adsorption energies for SO₂ and NO₂ gases while decreasing the adsorption energy of NH₃ gas. The ΔG for all configurations were negative except the configurations A1 and G2 confirming the weak adsorption of these two complexes. In conclusion, Al₂₄N₂₄ and Al₂₄N₂₃C nanocages are in general promising adsorbents for the removal of SO₂, NO₂, and NH₃ toxic gases. The Al₂₄N₂₄ and Al₂₄N₂₃C nanocages are ideal electronic materials.

Template Based Synthesis of Porous Graphdiyne Nanosheet for Reversible and Fast NO2 Detection by UV Irradiation

Graphdiyne (GDY) formed by sp and sp2 hybridized carbon has been proved to be potential novel 2D material for efficiency toxic gas sensing material by density functional theory (DFT). However, almost no relevant research in experimental concerning its gas sensing capability has been reported due to the complex preparation process and harsh experimental conditions. Herein, porous GDY nanosheets are successfully synthesized through a facile solvothermal synthesis technique by using CuO microspheres (MSs) as both template and source of catalyst. The porous GDY nanosheets exhibit a broadband optical absorption, which are suitable for the light-driven optoelectronic gas sensing applications. The GDY-based gas sensor demonstrates excellent reversible to NO2 behaviors at 25 ℃ for the first time.  More importantly, higher response value and faster response-recovery times once exposed to NO2 gas molecules are achieved by the illumination of UV light. In this way, our work paves the way for the exploration of GDY-based gas detection experimentally.

Early-phase pandemic in Italy: Covid-19 spread determinant factors

Although the Covid-19 pandemic is still ongoing, the environmental factors beyond virus transmission are only partially known. This statistical study has the aim to identify the key factors that have affected the virus spread during the early phase of pandemic in Italy, among a wide set of potential determinants concerning demographics, environmental pollution and climate. Because of its heterogeneity in pollution levels and climate conditions, Italy provides an ideal scenario for an ecological study. Moreover, the selected period excludes important confounding factors, as different virus variants, restriction policies or vaccines. The short-term relationship between the infection maximum increase and demographic, pollution and meteo-climatic parameters was investigated, including both winter-spring and summer 2020 data, also focusing separately on the two seasonal periods and on North vs Centre-South. Among main results, the importance of population size confirmed social distancing as a key management option. The pollution hazardous role undoubtedly emerged, as NO₂ affected infection increase in all the studied scenarios, PM_2.5 manifested its impact in North of Italy, while O₃ always showed a protective action. Whereas higher temperatures were beneficial, especially in the cold season with also wind and relative humidity, solar irradiance was always relevant, revealing several significant interactions with other co-factors. Presented findings address the importance of the environment in Sars-CoV-2 spread and indicated that special carefulness should be taken in crowded areas, especially if they are highly polluted and weakly exposed to sun. The results suggest that containment of future epidemics similar to Covid-19 could be supported by reducing environmental pollution, achieving safer social habits and promoting preventive health care for better immune system response, as an only comprehensive strategy.

Nitrogen dioxide gas-sensing properties of hydrothermally synthesized WO3 · nH2O nanostructures

Nitrogen dioxide (NO₂) has been identified as a serious air pollutant that threats to our environment, human life and world ecosystems. Therefore, detection of this air pollutant is crucial. Metal oxide semiconductor is one of the best approaches frequently used to detect NO₂ at relatively low temperatures. Hydrated tungsten trioxide (WO₃ · H₂O), an n-type semiconductor, is regarded to be a promising material for fabricating gas sensors, which are widely used in environmental and safety monitoring. In this work, WO₃ · nH₂O nanoparticles have been synthesized using a polyfunctional surfactant-mediated hydrothermal approach in the addition of H₂C₂O₄ and K₂SO₄ at a molar ratio of 1 : 1. This paper has also reported the effect of reaction temperature (120°C to 200°C) on morphological changes and gas-sensing performance. The characterization of these synthesized nanostructures was carried out by UV-Vis absorption spectroscopy, X-ray diffraction and field-emission scanning electron microscopy (FESEM). The UV absorption peak was obtained around 300 nm. FESEM analysis showed sheet-like structures come together to form flower-type morphology. The synthesized WO₃ · nH₂O flower-like structures was then used for NO₂ gas-sensing application. The prepared sensors showed considerably better sensor response (R _g/R _a = 17.48) at 185°C for 25 ppm NO₂.

Transition metal (X = Mn, Fe, Co, Ni, Cu, Zn)-doped graphene as gas sensor for CO2 and NO2 detection: a molecular modeling framework by DFT perspective

CONTEXT

In this research, CO₂ and NO₂ adsorption on doped nanographene (NG) sheets with transition metals (Fe, Ni, Zn) and (Mn, Co, Cu), respectively, have been applied for scavenging of these toxic gases as the environmental pollutants. The values of changes of atomic charge density have illustrated a more significant charge transfer for Ni-doped C-NG through CO₂ adsorption and a more remarkable charge transfer for Co-doped C-NG through NO₂ adsorption. The data of NMR spectroscopy has depicted several fluctuations around the graph of Zn-doped on the nanographene surface. The thermodynamic results from IR spectroscopy have indicated that [Formula: see text] values are almost similar for doped metal transitions of Mn, Co, and Cu on the C-NG nanosheet, while [Formula: see text] has the largest gap of Gibbs free energy adsorption with dipole moment.

METHODS

The Langmuir adsorption model with a three-layered ONIOM using CAM-B3LYP functional accompanying LANL2DZ, EPR-III and 6-31 + G (d,p) basis sets due to Gaussian 16 revision C.01 program on the complexes of CO₂ → (Fe, Ni, Zn) and NO₂ → (Mn, Co, Cu) doped on the C-NG has been accomplished. Then, NMR and IR spectroscopy, nuclear quadrupole resonance, and natural bond orbital analysis have been accomplished for evaluating chemical shielding tensors, thermodynamic properties, electric potential, and occupancy fluctuation through bond orbitals, respectively. In addition, frontier orbitals of LUMO, HOMO, and also a series of chemical reactivity parameters have been calculated. Finally, time-dependent-DFT method due to UV-VIS spectrums has been accomplished to discern the low-lying excited states of CO₂ and NO₂ adsorption on the (Fe, Ni, Zn) and (Mn, Co, Cu), respectively, doped C-NG sheet.

MAX-DOAS Measurements of Tropospheric NO2 and HCHO Vertical Profiles at the Longfengshan Regional Background Station in Northeastern China

The vertical profiles of nitrogen dioxide (NO₂) and formaldehyde (HCHO) in the troposphere at the Longfengshan (LFS) regional atmospheric background station (127°36' E, 44°44' N, 330.5 m above sea level) from 24 October 2020 to 13 October 2021 were retrieved from solar scattering spectra by multi-axis differential optical absorption spectroscopy (MAX-DOAS). We analyzed the temporal variations of NO₂ and HCHO as well as the sensitivity of ozone (O₃) production to the concentration ratio of HCHO to NO₂. The largest NO₂ volume mixing ratios (VMRs) occur in the near-surface layer for each month, with high values concentrated in the morning and evening. HCHO has an elevated layer around the altitude of 1.4 km consistently. The means ± standard deviations of vertical column densities (VCDs) and near-surface VMRs were 4.69 ± 3.72 ×10¹⁵ molecule·cm^-2 and 1.22 ± 1.09 ppb for NO₂, and they were 1.19 ± 8.35 × 10¹⁶ molecule·cm^-2 and 2.41 ± 3.26 ppb for HCHO. The VCDs and near-surface VMRs for NO₂ were high in the cold months and low in the warm months, while HCHO presented the opposite. The larger near-surface NO₂ VMRs appeared in the condition associated with lower temperature and higher humidity, but this relationship was not found between HCHO and temperature. We also found the O₃ production at the Longfengshan station was mainly in the NO_x-limited regime. This is the first study presenting the vertical distributions of NO₂ and HCHO in the regional background atmosphere of northeastern China, which are significant to enhancing the understanding of background atmospheric chemistry and regional ozone pollution processes.

Association between Outdoor Air Pollution and Fatal Acute Myocardial Infarction in Lithuania between 2006 and 2015: A Time Series Design

BACKGROUND

Air pollution has a significant effect on human health and there is a broad body of evidence showing that exposure to air pollution is associated with an increased risk of adverse health effects. The main objective of this study was to assess the association of traffic-related air pollutants with fatal AMI during the ten-year period.

METHODS

The study was conducted in Kaunas city, where the WHO MONICA register included a total of 2273 adult cases of fatal AMI cases during the 10-year study period. We focused on the period between 2006 and 2015. The associations between exposure to traffic-related air pollution and the risk of fatal AMI were evaluated by using a multivariate Poisson regression model, RR presented per an increase in IQR.

RESULTS

It was found that the risk of fatal AMI was significantly higher in all subjects (RR 1.06; 95% CI 1.00-1.12) and women (RR 1.12; 95% CI 1.02-1.22) when the concentration of PM₁₀ in the ambient air was increased 5-11 days before the onset of AMI, adjusting for NO₂ concentration. The effect was stronger during spring in all subjects (RR 1.12; 95% CI 1.03-1.22), in men (RR 1.13; 95% CI 1.01-1.26), in younger-aged (RR 1.15; 95% CI 1.03-1.28), and in winter in women (RR 1.24; 95% CI 1.03-1.50).

CONCLUSIONS

Our findings show that ambient air pollution increases the risk of fatal AMI, and this pertains to PM₁₀ specifically.

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO₂ levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO₂ concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

NO(2) Sensing Capability of Pt-Au-SnO(2) Composite Nanoceramics at Room Temperature

Composite ceramics of metal oxides and noble metals have received much attention for sensing reducing gases at room temperature. Presently, composite ceramics of SnO₂ and noble metals have been prepared and investigated for sensing oxidizing NO₂ at room temperature. While dramatic increases in resistance were observed for both 1 wt% Pt-SnO₂ and 5 wt% Au-SnO₂ composite nanoceramics after being exposed to NO₂ at room temperature, the largest increase in resistance was observed for 1 wt% Pt-5 wt% -Au-SnO₂ composite nanoceramics among the three composites. The response to 0.5 ppm NO₂--20% O₂-N₂ was as high as 875 at room temperature, with a response time of 2566 s and a recovery time of 450 s in the air of 50% relative humidity (RH). Further investigation revealed that water molecules in the air are essential for recovering the resistance of Pt-Au-SnO₂ composite nanoceramics. A room temperature NO₂-sensing mechanism has been established, in which NO₂ molecules are catalyzed by Pt-Au to be chemisorbed on SnO₂ at room temperature, and desorbed from SnO₂ by the attraction of water molecules in the air. These results suggest that composite ceramics of metal oxides and noble metals should be promising for room temperature sensing, not only reducing gases, but also oxidizing gases.

Exposure to PM10, PM2.5, and NO2 and gross motor function in children: a systematic review and meta-analysis

Air pollution exposure has been related to negative gross motor development in children. However, there is currently a lack of conclusive evidence for such a relationship. We carried out a systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria to examine whether exposure to air pollution has an impact on children's gross motor development. Of the 9746 papers found, 7 studies examined the impact of air pollutant characteristics, such as PM_2.5, PM₁₀, and NO₂, on children's gross motor development. The results of the study reveal a significant association between air pollutants and an increased likelihood of negative gross motor development. PM₁₀ was discovered to be considerably riskier for children's gross motor development (effect: - 1.83, 95% CI: - 3.04, - 0.62, p value = 0.002). Additionally, NO₂ exhibited indications of a tendency to be connected to a detrimental impact on children's gross motor development (effect - 0.18, 95% CI: - 0.42, 0.07, p value = 0.097).  Conclusion: Our study indicates that exposure to PM₁₀, PM_2.5, and NO₂, especially PM_2.5 and PM₁₀, is negatively associated with children's gross motor development. However, further research is required to determine how exposure to prenatal air pollution affects children's gross motor development. What is Known - What is New: • In this study, we provide an overview of emerging data related to PM10, PM2.5, and NO2 exposure in child development, especially on the gross motor function that continues to emerge, and key findings are highlighted. • Additionally, we summarize the evidence on the underlying effect of air pollution on gross motor function from human studies.. • Overall, we emphasize that evidence from human studies is critical in suggesting detrimental child health outcomes of an action to promote preventive strategies that will effectively protect children's health..

Lockdown effects of the COVID-19 on the spatio-temporal distribution of air pollution in Beijing, China

To prevent the spread of COVID-19, China enacted a series of strict policies, which reduced anthropogenic activities to a near standstill. This provided a precious window to explore its effects on the spatio-temporal distribution of air pollution in Beijing, China. In this study, continuous wavelet transforms and spatial interpolation methods were used to explore the spatiotemporal variations in air pollutants and their lockdown effects. The results indicate that except O₃, the annual average concentration of NO₂, PM_2.5 and SO₂ showed a decreasing trend during 2016 and 2019; NO₂, PM_2.5 and SO₂ show a trend of "low in summer and high in winter"; the diurnal variation of NO₂ concentration was mainly related to the rush hours of traffic volume, with the first peak at the morning peak (7:00), and then accumulating gradually to second peak (22:00). The continuous wavelet analysis shows that PM2.5, SO₂ and NO₂ had four primary periods, while O₃ only had two primary periods. The high NO₂ concentration areas were mainly in Dongcheng, Xicheng, Chaoyang and Fengtai, while the low concentration areas were located in the northern areas, such as Miyun and Huairou; the PM_2.5 concentration decreased from south to north; this characteristic presented more obviously in winter. Compared to the pre-lockdown, NO₂ and SO₂ decreased considerably during lockdown, whereas PM_2.5 and O₃ increased dramatically. The contribution rates of transportation activities to the NO₂, O₃, PM_2.5 and SO₂ were estimated be 9.4 % ∼ 17.2 %, -76.4 % ∼ -42.9 %, -39.5 % ∼ -22.8 % and 5.7 % ∼ 43.7 %, respectively; the contribution rates of industrial activities were 19.9 % ∼ 26.7 %, 7.8 % ∼ 30.9 %, 1.6 % ∼ 36.2 % and -10.5 % ∼ 15.9 %, respectively. Considering meteorological factors, we inferred that pauses in anthropogenic activities indeed help improving air pollution, but it is difficult to offset the impact of extreme weather. These findings can enhance our understanding on the sources of air pollution, and can therefore provide insights on urban air pollution mitigation.

Fine Particle Adsorption Capacity of Volcanic Soil from Southern Kyushu, Japan

"Akahoya" is a volcanic soil classified as a special soil deposited in Kyushu, Japan. Many of its properties are not yet clearly understood. We found that Akahoya had the potential to adsorb bacteria in cattle feces, which prompted us to investigate its material properties and perform experiments to comprehensively evaluate its adsorption performance for various fine particles such as acidic and basic dyes, NO_x/SO_x gas, and phosphoric acid ions, in addition to bacteria. Akahoya had a very high specific surface area owing to the large number of nanometer-sized pores in its structure; it exhibited a high adsorption capacity for both NO₂ and SO₂. Regarding the zeta potential of Akahoya, the point of zero charge was approximately pH 7.0. The surface potential had a significant effect on the adsorption of acidic and basic dyes. Akahoya had a very high cation exchange capacity when the sample surface was negatively charged and a high anion exchange capacity when the sample surface was positively charged. Akahoya also exhibited a relatively high adsorption capacity for phosphoric acid because of its high level of Al₂O₃, and the immersion liquid had a very high Al ion concentration. Finally, filtration tests were performed on Escherichia coli suspension using a column filled with Akahoya or another volcanic soil sample. The results confirmed that the Escherichia coli adhered on the Akahoya sample. The results of the Escherichia coli release test, after the filtration test, suggested that this adhesion to Akahoya could be phosphorus-mediated.