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.

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.

Sociodemographic inequalities in long-term exposure to air pollution, road traffic noise, and greenness: A population-based cohort study of women

Background

Recent evidence suggests environmental health inequalities both within and between European countries and socially deprived groups may be more susceptible to pollution. However, evidence is still inconclusive and additional studies are warranted. This study aims to investigate sociodemographic inequalities in long-term residential exposure to air pollution, road traffic noise, and greenness, taking lifestyle and degree of urbanization into account.

Methods

In total 20,407 women, born 1914-48 residing in Uppsala County, Sweden, were followed between 1997 and 2017. Time-varying sociodemographic variables were obtained from registers, and questionnaires provided lifestyle information. Generalized estimating equations were used to compute beta-coefficients (β) and 95% confidence intervals (95% CI) for associations between sociodemographic and lifestyle variables and spatial-temporal modeled particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), road traffic noise and greenness. All models were additionally stratified by urbanization type.

Results

Urban area residency was the most important predictor of high exposure to air pollution and noise, and to low greenness. For instance, β for NO2 was -2.92 (95% CI = -3.00, -2.83) and -3.10 (95% CI = -3.18, -3.01) µg/m3 in suburban and rural areas, respectively, compared with urban areas. For greenness, the opposite held true with corresponding β of 0.059 (95% CI = 0.056, 0.062) and 0.095 (95% CI = 0.092, 0.098). Within urban areas, elderly, unmarried and well-educated women had the highest environmental burden. However, less pronounced, and even reversed associations were found in suburban and rural areas.

Conclusion

This study provides evidence of a mixed pattern of environmental health inequalities across sociodemographic groups in urban areas.

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.

Atmospheric heterogeneous reactions on soot: A review

Soot particles, composed of elemental carbon and organic compounds, have attracted widespread attention in recent years due to their significant impacts on climate, the environment and human health. Soot has been found to be chemically and physically active in atmospheric aging processes, which leads to alterations in its composition, morphology, hygroscopicity and optical properties and thus changes its environmental and health effects. The heterogeneous reactions on soot also have a significant impact on the transformation of gaseous pollutants into secondary aerosols. Therefore, the interactions between soot and atmospheric substances have been widely investigated to better understand the environmental behaviors of soot. In this review, we systematically summarize the progress and developments in the heterogeneous chemistry on soot over the past 30 years. Atmospheric trace constituents such as NO2, O3, SO2, N2O5, HNO3, H2SO4, OH radical, HO2 radical, peroxyacetyl nitrate etc., are presented in detail from the aspect of their heterogeneous reactions on soot. The possible mechanisms and the effects of environmental conditions on these heterogeneous reactions are also addressed. Further, the impacts of the heterogeneous reactions of soot on the atmospheric environment are discussed, and some aspects of soot-related research which require further investigation are proposed as well.

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 (PM2.5, PM10, NO2, NOx) 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.