Particulate Air Pollution Research Articles

Neurodegenerative effects of air pollutant Particles: Biological mechanisms implicated for Early-Onset Alzheimer’s disease

BackgroundSporadic Alzheimer’s disease (AD) occurs in 99% of all cases and can be influenced by air pollution such as diesel emissions and more recently, an iron oxide particle, magnetite, detected in the brains of AD patients. However, a mechanistic link between air pollutants and AD development remains elusive. AimTo study the development of AD-relevant pathological effects induced by air pollutant particle exposures and their mechanistic links, in wild-type and AD-predisposed models. MethodsC57BL/6 (n = 37) and APP/PS1 transgenic (n = 38) mice (age 13 weeks) were exposed to model pollutant iron-based particle (Fe0–Fe3O4, dTEM = 493 ± 133 nm), hydrocarbon-based diesel combustion particle (43 ± 9 nm) and magnetite (Fe3O4, 153 ± 43 nm) particles (66 µg/20 µL/third day) for 4 months, and were assessed for behavioural changes, neuronal cell loss, amyloid-beta (Aβ) plaque, immune response and oxidative stress-biomarkers. Neuroblastoma SHSY5Y (differentiated) cells were exposed to the particles (100 μg/ml) for 24 h, with assessments on immune response biomarkers and reactive oxygen species generation. ResultsPollutant particle-exposure led to increased anxiety and stress levels in wild-type mice and short-term memory impairment in AD-prone mice. Neuronal cell loss was shown in the hippocampal and somatosensory cortex, with increased detection of Aβ plaque, the latter only in the AD-predisposed mice, with the wild-type not genetically disposed to form the plaque. The particle exposures however, increased AD-relevant immune system responses, including inflammation, in both strains of mice. Exposures also stimulated oxidative stress, although only observed in wild-type mice. The in vitro studies complemented the immune response and oxidative stress observations. ConclusionsThis study provides insights into the mechanistic links between inflammation and oxidative stress to pollutant particle-induced AD pathologies, with magnetite apparently inducing the most pathological effects. No exacerbation of the effects was observed in the AD-predisposed model when compared to the wild-type, indicating a particle-induced neurodegeneration that is independent of disease state.

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review.

Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data. Thirty-six eligible studies were identified. Overall, the studies included in this review establish that an increase in personal exposure to particulate and gaseous air pollutants can significantly increase FeNO. Nine out of the 12 studies reported statistically significant FeNO increases with increasing personal PM2.5 exposures, and up to 11.5% increase in FeNO per IQR increase in exposure has also been reported between FeNO and exposure to gas-phase pollutants, such as ozone, NO2, and benzene. Furthermore, factors such as chronic respiratory diseases, allergies, and medication use were found to be effect modifiers for air pollution-induced FeNO responses. About half of the studies that compared the effect estimates using both personal and ambient air pollution exposure methods reported that only personal exposure yielded significant associations with FeNO response. The evidence from the reviewed studies confirms that FeNO is a sensitive biomarker for air pollutant-induced airway inflammation. Personal air pollution exposure assessment is recommended to accurately assess the air pollution-induced FeNO responses. Furthermore, comprehensive adjustments for the potential confounding factors including the personal exposures of the co-pollutants, respiratory disease status, allergy status, and usage of medications for asthma and allergies are recommended while assessing the air pollution-induced FeNO responses.

Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016

Abstract. We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µm) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s−1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM2.5 during the severe episode. Delhi experiences some of the highest daily mean PM2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM2.5 levels during severe pollution episodes.

Leading determinants of incident dementia among individuals with and without the apolipoprotein E ε4 genotype: a retrospective cohort study

BackgroundLittle is known regarding the leading risk factors for dementia/Alzheimer’s disease (AD) in individuals with and without APOE4. The identification of key risk factors for dementia/Alzheimer’s disease (AD) in individuals with and without the APOE4 gene is of significant importance in global health.MethodsOur analysis included 110,354 APOE4 carriers and 220,708 age- and sex-matched controls aged 40–73 years at baseline (between 2006–2010) from UK Biobank. Incident dementia was ascertained using hospital inpatient, or death records until January 2021. Individuals of non-European ancestry were excluded. Furthermore, individuals without medical record linkage were excluded from the analysis. Moderation analysis was tested for 134 individual factors.ResultsDuring a median follow-up of 11.9 years, 4,764 cases of incident all-cause dementia and 2065 incident AD cases were documented. Hazard ratios (95% CIs) for all-cause dementia and AD associated with APOE4 were 2.70(2.55–2.85) and 3.72(3.40–4.07), respectively. In APOE4 carriers, the leading risk factors for all-cause dementia included low self-rated overall health, low household income, high multimorbidity risk score, long-term illness, high neutrophil percentage, and high nitrogen dioxide air pollution. In non-APOE4 carriers, the leading risk factors included high multimorbidity risk score, low overall self-rated health, low household income, long-term illness, high microalbumin in urine, high neutrophil count, and low greenspace percentage. Population attributable risk for these individual risk factors combined was 65.1%, and 85.8% in APOE4 and non-APOE4 carriers, respectively. For 20 risk factors including multimorbidity risk score, unhealthy lifestyle habits, and particulate matter air pollutants, their associations with incident dementia were stronger in non-APOE4 carriers. For only 2 risk factors (mother’s history of dementia, low C-reactive protein), their associations with incident all-cause dementia were stronger in APOE4 carriers.ConclusionsOur findings provide evidence for personalized preventative approaches to dementia/AD in APOE4 and non-APOE4 carriers. A mother’s history of dementia and low levels of C-reactive protein were more important risk factors of dementia in APOE4 carriers whereas leading risk factors including unhealthy lifestyle habits, multimorbidity risk score, inflammation and immune-related markers were more predictive of dementia in non-APOE4 carriers.

Ambient particulate matter and surrounding greenness in relation to sleep quality among pregnant women: A nationwide cohort study

BackgroundParticulate air pollution and residential greenness are associated with sleep quality in the general population; however, their influence on maternal sleep quality during pregnancy has not been assessed. ObjectiveThis cross-sectional study investigated the individual and interactive effects of exposure to particulate matter (PM) air pollution and residential greenness on sleep quality in pregnant women. MethodsPregnant women (n = 4933) enrolled in the Korean Children's Environmental Health Study with sleep quality information and residential address were included. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The average concentrations of PM (PM2.5 and PM10) during pregnancy were estimated through land use regression, and residential greenness in a 1000 m buffer area around participants' residences was estimated using the Normalized Difference Vegetation Index (NDVI1000-m). Modified Poisson regression models were used to estimate the associations between PM and NDVI and poor sleep quality (PSQI >5) after controlling for a range of covariates. A four-way mediation analysis was conducted to examine the mediating effects of PM. ResultsAfter adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with a higher risk of poor sleep quality (relative risk [RR]: 1.06; 95% confidence interval [CI]: 1.01, 1.11; and RR: 1.09; 95% CI: 1.06, 1.13, respectively), and each 0.1-unit increase in NDVI1000-m was associated with a lower risk of poor sleep quality (RR: 0.97; 95% CI: 0.95, 0.99). Mediation analysis showed that PM mediated approximately 37%–56% of the association between residential greenness and poor sleep quality. ConclusionsThis study identified a positive association between residential greenness and sleep quality. Furthermore, these associations are mediated by a reduction in exposure to particulate air pollution and highlight the link between green areas, air pollution control, and human health.

Short-term associations between fine particulate air pollution and cardiovascular and respiratory mortality in 337 cities in Latin America

Ambient air pollution is a health concern in Latin America given its large urban population exposed to levels above recommended guidelines. Yet no studies have examined the mortality impact of air pollutants in the region across a wide range of cities. We assessed whether short-term levels of fine particulate matter (PM2.5) from modeled estimates, are associated with cardiovascular and respiratory mortality among adults in 337 cities from 9 Latin American countries. We compiled mortality, PM2.5 and temperature data for the period 2009–2015. For each city, we evaluated the association between monthly changes in PM2.5 and cardiovascular and respiratory mortality for sex and age subgroups using Poisson models, adjusted for seasonality, long-term trend, and temperature. To accommodate possibly different associations of mortality with PM2.5 by age, we included interaction terms between changes in PM2.5 and age in the models. We combined the city-specific estimates using a random effects meta-regression to obtain mortality relative risks for each sex and age group. We analyzed 3,026,861 and 1,222,623 cardiovascular and respiratory deaths, respectively, from a study population that represents 41 % of the total population of Latin America. We observed that a 10 μg/m3 increase in monthly PM2.5 is associated with an increase of 1.3 % (95 % confidence interval [CI], 0.4 to 2.2) in cardiovascular mortality and a 0.9 % increase (95 % CI -0.6 to 2.4) in respiratory mortality. Increases in mortality risk ranged between −0.5 % to 3.0 % across 6 sex-age groups, were larger in men, and demonstrated stronger associations with cardiovascular mortality as age increased. Socioeconomic, environmental and health contexts in Latin America are different than those present in higher income cities from which most evidence on air pollution impacts is drawn. Locally generated evidence constitutes a powerful instrument to engage civil society and help drive actions to mitigate and control ambient air pollution.

Time-stratified case-crossover studies for aggregated data in environmental epidemiology: a tutorial.

The case-crossover design is widely used in environmental epidemiology as an effective alternative to the conventional time-series regression design to estimate short-term associations of environmental exposures with a range of acute events. This tutorial illustrates the implementation of the time-stratified case-crossover design to study aggregated health outcomes and environmental exposures, such as particulate matter air pollution, focusing on adjusting covariates and investigating effect modification using conditional Poisson regression. Time-varying confounders can be adjusted directly in the conditional regression model accounting for the adequate lagged exposure-response function. Time-invariant covariates at the subpopulation level require reshaping the typical time-series data set into a long format and conditioning out the covariate in the expanded stratum set. When environmental exposure data are available at geographical units, the stratum set should combine time and spatial dimensions. Moreover, it is possible to examine effect modification using interaction models. The time-stratified case-crossover design offers a flexible framework to properly account for a wide range of covariates in environmental epidemiology studies.

Airborne Nanoplastics Exposure Inducing Irreversible Glucose Increase and Complete Hepatic Insulin Resistance.

As an emerging type of pollutant, microplastics have become a global environmental problem. Approximately, a fifth of the global burden of type 2 diabetes can be attributed to air particulate pollution. However, scientific knowledge remains limited about the effects of airborne nanoplastics (NPs) exposure on metabolic diseases. In this experiment, a whole-body exposure system was used to simulate the real atmospheric environment, and three exposure concentrations combined with the actual environmental concentration were selected to explore the effects of airborne NPs on metabolic diseases. Based on histological analyses, metabolic studies, gene expression, metabolites, and molecular signaling analyses, mice exposed to airborne NPs were observed to show a phenotype of systemic inflammation and complete insulin resistance featuring excessive drinking and eating, weight loss, elevated blood glucose, and decreased triglyceride levels. After airborne NPs exposure, mice were intolerant to glucose and tolerant to insulin. In addition, airborne NPs exposure could result in long-term irreversible hyperglycemia. Together, the research findings provide a strong basis for understanding the hazards of airborne nanopollution on metabolic disorders.

Penalized distributed lag interaction model: Air pollution, birth weight, and neighborhood vulnerability

AbstractMaternal exposure to air pollution during pregnancy has a substantial public health impact. Epidemiological evidence supports an association between maternal exposure to air pollution and low birth weight. A popular method to estimate this association while identifying windows of susceptibility is a distributed lag model (DLM), which regresses an outcome onto exposure history observed at multiple time points. However, the standard DLM framework does not allow for modification of the association between repeated measures of exposure and the outcome. We propose a distributed lag interaction model that allows modification of the exposure‐time‐response associations across individuals by including an interaction between a continuous modifying variable and the exposure history. Our model framework is an extension of a standard DLM that uses a cross‐basis, or bi‐dimensional function space, to simultaneously describe both the modification of the exposure‐response relationship and the temporal structure of the exposure data. Through simulations, we showed that our model with penalization out‐performs a standard DLM when the true exposure‐time‐response associations vary by a continuous variable. Using a Colorado, USA birth cohort, we estimated the association between birth weight and ambient fine particulate matter air pollution modified by an area‐level metric of health and social adversities from Colorado EnviroScreen.

Comparison of Air Pollution-Mortality Associations Using Observed Particulate Matter Concentrations and Reanalysis Data in 33 Spanish Cities.

Air pollution poses a health hazard in all countries. However, complete data on ambient particulate matter (PM) concentrations are not available in all world regions. Reanalysis data is already a valuable source of exposure data in epidemiological studies examining the relationship between temperature and health. Nevertheless, the performance of reanalysis data in assessing the short-term health effects of particulate air pollution remains unclear. We assessed the performance of CAMS reanalysis (EAC4) data from the European Centre for Medium-Range Weather Forecasts, compared with daily PM concentrations from field monitoring stations, to estimate short-term exposure to PM with an aerodynamic diameter less than 10 μm (PM10) on daily mortality in 33 Spanish provincial capital cities using a two-stage time series regression design. The shape of the PM10 distribution varied substantially between PM observations and CAMS global reanalysis of atmospheric composition (EAC4) reanalysis data, with correlation ranging from 0.21 to 0.58. The pooled mortality risk for a 10 μg/m3 increase in PM10 showed similar estimates using PM concentrations {relative risks (RR) = 1.007, 95% confidence intervals (95% CI) = [1.002, 1.011]} and EAC4 reanalysis data (RR = 1.011, 95% CI = [1.006, 1.015]). However, the city-specific PM10 beta coefficients estimated using PM concentrations and EAC4 reanalysis data showed a low correlation (r = 0.22). The use of reanalysis data should be approached with caution when assessing the association between particulate matter air pollution and health outcomes, particularly in cities with small populations.

Particulate Matter Emission and Air Pollution Reduction by Applying Variable Systems in Tribologically Optimized Diesel Engines for Vehicles in Road Traffic

Regardless of the increasingly intensive application of vehicles with electric drives, internal combustion engines are still dominant as power units of mobile systems in various sectors of the economy. In order to reduce the emission of exhaust gases and satisfy legal regulations, as a temporary solution, hybrid drives with optimized internal combustion engines and their associated systems are increasingly being used. Application of the variable compression ratio and diesel fuel injection timing, as well as the tribological optimization of parts, contribute to the reduction in fuel consumption, partly due to the reduction in mechanical losses, which, according to test results, also results in the reduction in emissions. This manuscript presents the results of diesel engine testing on a test bench in laboratory conditions at different operating modes (compression ratio, fuel injection timing, engine speed, and load), which were processed using a zero-dimensional model of the combustion process. The test results should contribute to the optimization of the combustion process from the aspect of minimal particulate matter emission. As a special contribution, the results of tribological tests of materials for strengthening the sliding surface of the aluminum alloy piston and cylinder of the internal combustion engine and air compressors, which were obtained using a tribometer, are presented. In this way, tribological optimization should also contribute to the reduction in particulate matter emissions due to the reduction in fuel consumption, and thus emissions due to the reduction in friction, as well as the recorded reduction in the wear of materials that are in sliding contact. In this way, it contributes to the reduction in harmful gases in the air.

Reductions in premature deaths from heat and particulate matter air pollution in South Asia, China, and the United States under decarbonization

Following a sustainable development pathway designed to keep warming below 2 °C will benefit human health. We quantify premature deaths attributable to fine particulate matter (PM2.5) air pollution and heat exposures for China, South Asia, and the United States using projections from multiple climate models under high- and low-emission scenarios. Projected changes in premature deaths are typically dominated by population aging, primarily reflecting increased longevity leading to greater sensitivity to environmental risks. Changes in PM2.5 exposure typically have small impacts on premature deaths under a high-emission scenario but provide substantial benefits under a low-emission scenario. PM2.5-attributable deaths increase in South Asia throughout the century under both scenarios but shift to decreases by late century in China, and US values decrease throughout the century. In contrast, heat exposure increases under both scenarios and combines with population aging to drive projected increases in deaths in all countries. Despite population aging, combined PM2.5- and heat-related deaths decrease under the low-emission scenario by ~2.4 million per year by midcentury and ~2.9 million by century's end, with ~3% and ~21% of these reductions from heat, respectively. Intermodel variations in exposure projections generally lead to uncertainties of <40% except for US and China heat impacts. Health benefits of low emissions are larger from reduced heat exposure than improved air quality by the late 2090s in the United States. In contrast, in South and East Asia, the PM2.5-related benefits are largest throughout the century, and their valuation exceeds the cost of decarbonization, especially in China, over the next 30 y.

Black carbon particles in human breast milk: assessing infant's exposure.

Human breast milk is the recommended source of nutrition for infants due to its complex composition and numerous benefits, including a decline in infection rates in childhood and a lower risk of obesity. Hence, it is crucial that environmental pollutants in human breast milk are minimized. Exposure to black carbon (BC) particles has adverse effects on health; therefore, this pilot study investigates the presence of these particles in human breast milk. BC particles from ambient exposure were measured in eight human breast milk samples using a white light generation under femtosecond illumination. The carbonaceous nature of the particles was confirmed with BC fingerprinting. Ambient air pollution exposures (PM2.5, PM10, and NO2) were estimated using a spatial interpolation model based on the maternal residential address. Spearman rank correlation coefficients were obtained to assess the association between human breast milk's BC load and ambient air pollution exposure. BC particles were found in all human breast milk samples. BC loads in human breast milk were strongly and positively correlated with recent (i.e., 1 week) maternal residential NO2 (r = 0.79; p = 0.02) exposure and medium-term (i.e., 1 month) PM2.5 (r = 0.83; p = 0.02) and PM10 (r = 0.93; p = 0.002) exposure. For the first time, we showed the presence of BC particles in human breast milk and found a robust association with ambient air pollution concentrations. Our findings present a pioneering insight into a novel pathway through which combustion-derived air pollution particles can permeate the delicate system of infants.

Association between Ambient Particulate Air Pollution and Soluble Biomarkers of Endothelial Function: A Meta-Analysis.

The burden of cardiovascular diseases caused by ambient particulate air pollution is universal. An increasing number of studies have investigated the potential effects of exposure to particulate air pollution on endothelial function, which is one of the important mechanisms for the onset and development of cardiovascular disease. However, no previous study has conducted a summary analysis of the potential effects of particulate air pollution on endothelial function. To summarize the evidence for the potential effects of short-term exposure to ambient particulate air pollution on endothelial function based on existing studies. A systematic literature search on the relationship between ambient particulate air pollution and biomarkers of endothelial function including endothelin-1 (ET-1), E-selectin, intercellular cell adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1) was conducted in PubMed, Scopus, EMBASE, and Web of Science up to 20 May 2023. Subsequently, a meta-analysis was conducted using a random effects model. A total of 18 studies were included in this meta-analysis. A 10 μg/m3 increase in short-term exposure to ambient PM2.5 was associated with a 1.55% (95% CI: 0.89%, 2.22%) increase in ICAM-1 and a 1.97% (95% CI: 0.86%, 3.08%) increase in VCAM-1. The associations of ET-1 (0.22%, 95% CI: -4.94%, 5.65%) and E-selectin (3.21%, 95% CI: -0.90% 7.49%) with short-term exposure to ambient PM2.5 were statistically insignificant. Short-term exposure to ambient PM2.5 pollution may significantly increase the levels of typical markers of endothelial function, including ICAM-1 and VCAM-1, suggesting potential endothelial dysfunction following ambient air pollution exposure.

Air Pollution: A Challenge to Public Health and Healthcare Systems

Air Pollution: A Challenge to Public Health and Healthcare Systems

Intrauterine and early postnatal exposures to submicron particulate matter and childhood allergic rhinitis: A multicity cross-sectional study in China

BackgroundAirborne particulate matter pollution has been linked to occurrence of childhood allergic rhinitis (AR). However, the relationships between exposure to particulate matter with an aerodynamic diameter ≤1 μm (PM1) during early life (in utero and first year of life) and the onset of childhood AR remain largely unknown. This study aims to investigate potential associations of in utero and first-year exposures to size-segregated PMs, including PM1, PM1–2.5, PM2.5, PM2.5–10, and PM10, with childhood AR. MethodsWe investigated 29286 preschool children aged 3–6 years in 7 Chinese major cities during 2019–2020 as the Phase II of the China Children, Families, Health Study. Machine learning-based space-time models were utilized to estimate early-life residential exposure to PM1, PM2.5, and PM10 at 1 × 1-km resolutions. The concentrations of PM1–2.5 and PM2.5–10 were calculated by subtracting PM1 from PM2.5 and PM2.5 from PM10, respectively. Multiple mixed-effects logistic models were used to assess the odds ratios (ORs) and 95% confidence intervals (CIs) of childhood AR associated with per 10-μg/m3 increase in exposure to particulate air pollution during in utero period and the first year of life. ResultsAmong the 29286 children surveyed (mean ± standard deviation, 4.9 ± 0.9 years), 3652 (12.5%) were reported to be diagnosed with AR. Average PM1 concentrations during in utero period and the first year since birth were 36.3 ± 8.6 μg/m3 and 33.1 ± 6.9 μg/m3, respectively. Exposure to PM1 and PM2.5 during pregnancy and the first year of life was associated with an increased risk of AR in children, and the OR estimates were higher for each 10-μg/m3 increase in PM1 than for PM2.5 (e.g., 1.132 [95% CI: 1.022–1.254] vs. 1.079 [95% CI: 1.014–1.149] in pregnancy; 1.151 [95% CI: 1.014–1.306] vs. 1.095 [95% CI: 1.008–1.189] in the first year of life). No associations were observed between AR and both pre- and post-natal exposure to PM1–2.5, indicating that PM1 rather than PM1–2.5 contributed to the association between PM2.5 and childhood AR. In trimester-stratified analysis, childhood AR was only found to be associated with exposure to PM1 (OR = 1.077, 95% CI: 1.027–1.128), PM2.5 (OR = 1.048, 95% CI: 1.018–1.078), and PM10 (OR = 1.032, 95% CI: 1.007–1.058) during the third trimester of pregnancy. Subgroup analysis suggested stronger PM-AR associations among younger (<5 years old) and winter-born children. ConclusionsPrenatal and postnatal exposures to ambient PM1 and PM2.5 were associated with an increased risk of childhood AR, and PM2.5-related hazards could be predominantly attributed to PM1. These findings highlighted public health significance of formulating air quality guideline for ambient PM1 in mitigating children's AR burden caused by particulate air pollution.

Boosted income, busted environment: a tradeoff in the wider economic impacts of transport corridor investments?

AbstractThe literature on the impacts of transport corridors points to a tradeoff between income and environmental quality. We estimate the impacts of India's Golden Quadrilateral and North-South-East-West highways on income and environmental quality to test this tradeoff hypothesis. Applying the difference-in-difference method to district level data, we find that the highways increased both local income and particulate matter air pollution. The estimated increase in air pollution is robust to using an instrumental variables approach, while that in income is not. Examining heterogeneity in these impacts, we find that the income–environment tradeoff was less steep in districts with initially higher educational attainment rates because they experienced a smaller increase in air pollution due to the highways.

Associations between fine particulate air pollution with small-airway inflammation: A nationwide analysis in 122 Chinese cities

Alveolar nitric oxide is a non-invasive indicator of small-airway inflammation, a key pathophysiologic mechanism underlying lower respiratory diseases. However, no epidemiological studies have investigated the impact of fine particulate matter (PM2.5) exposure on the concentration of alveolar nitric oxide (CANO). To explore the associations between PM2.5 exposure in multiple periods and CANO, we conducted a nationwide cross-sectional study in 122 Chinese cities between 2019 and 2021. Utilizing a satellite-based model with a spatial resolution of 1 × 1 km, we matched long-term, mid-term, and short-term PM2.5 exposure for 28,399 individuals based on their home addresses. Multivariable linear regression models were applied to estimate the associations between PM2.5 at multiple exposure windows and CANO. Stratified analyses were also performed to identify potentially vulnerable subgroups. We found that per interquartile range (IQR) unit higher in 1-year average, 1-month average, and 7-day average PM2.5 concentration was significantly associated with increments of 17.78% [95% confidence interval (95%CI): 12.54%, 23.26%], 8.76% (95%CI: 7.35%, 10.19%), and 4.00% (95%CI: 2.81%, 5.20%) increment in CANO, respectively. The exposure-response relationship curves consistently increased with the slope becoming statistically significant beyond 20 μg/m3. Males, children, smokers, individuals with respiratory symptoms or using inhaled corticosteroids, and those living in Southern China were more vulnerable to PM2.5 exposure. In conclusion, our study provided novel evidence that PM2.5 exposure in long-term, mid-term, and short-term periods could significantly elevate small-airway inflammation represented by CANO. Our results highlight the significance of CANO measurement as a non-invasive tool for early screening in the management of PM2.5-related inflammatory respiratory diseases.

Polyvinylidene fluoride/graphene oxide/polyimide composite high-efficiency PM2.5 filtration nanofiber membranes.

Particulate air pollution is a global environmental problem, with PM2.5 being the primary pollutant. One of the most effective ways to remove particles from the air is through filtration. Therefore, high-performance air filters are urgently needed to combat the harm caused by PM2.5. This study uses an electrospinning technique to prepare high-efficiency polyvinylidene fluoride/graphene oxide/polyimide nanofiber membranes. These composite nanofiber membranes demonstrate high filtration efficiency (99.6%), low pressure drop (123 Pa), remarkable thermal stability (450 °C), and excellent mechanical strength (7 MPa). Considering the advantages, these highly efficient nanofiber membranes can find advanced applications in industrial and civil infrastructures.

Particulate air pollution in Central Serbia and some proposed measures for the restoration of degraded and disturbed mining areas

Mining causes soil degradation, particle emission, and air and water quality deterioration. This study estimates some health risks in districts of Central Serbia affected by surface mining activities, and proposes measures for land restoration. The epidemiological risk assessment was based on data for seven cancers and three cardiovascular diseases during 2010 - 2020. Results showed a statistically significant increase in the incidence of lung and bronchial cancer in critical districts. Borski district stood out with the highest incidence rates for cardiovascular diseases. The suspected role of particulate air pollution from the mining industry in health deterioration calls for intensified air quality monitoring and development of mitigation technologies. A restoration strategy called the Pan-Life-Carpet technology is proposed for the restoration of mining areas and for air and water pollution control.