PM10 dysregulates epithelial barrier function in human corneal epithelial cells that is restored by antioxidant SKQ1.

Exposure to airborne particulate <10 µm (PM10) adversely affects the ocular surface. This study tested PM10 on epithelial barrier integrity in immortalized human corneal epithelial cells (HCE-2) and mouse cornea, and whether antioxidant SKQ1 is restorative. HCE-2 were exposed to 100 µg/ml PM10 ± SKQ1 for 24 h. An Electric Cell-Substrate Impedance Sensing (ECIS) system monitored the impact of PM10. RT-PCR, western blotting and immunofluorescence measured levels of barrier and associated proteins, stanniocalcin 2 (STC2), and a kit measured total calcium. In vivo, female C57BL/6 mice were exposed to either control air or PM10 (±SKQ1) in a whole-body exposure chamber, and barrier associated proteins tested. Tight junction and mucins proteins in the cornea were tested. In HCE-2, PM0 vs control significantly reduced mRNA and protein levels of tight junction and adherence proteins, and mucins. ECIS data demonstrated that PM10 vs control cells exhibited a significant decrease in epithelial barrier strength at 4000 Hz indicated by reduced impedance and resistance. PM10 also upregulated STC2 protein and total calcium levels. In vivo, PM10 vs control reduced zonula occludens 1 and mucins. SKQ1 pre-treatment reversed PM10 effects both in vitro and in vivo. In conclusion, PM10 exposure reduced tight junction and mucin proteins, and compromised the seal between cells in the corneal epithelium leading to decreased epithelial barrier strength. This effect was reversed by SKQ1. Since the corneal epithelium forms the first line of defense against air pollutants, including PM10, preserving its integrity using antioxidants such as SKQ1 is crucial in reducing the occurrence of ocular surface disorders.

PM10 exposure induces bronchial hyperresponsiveness by upreguating acetylcholine muscarinic 3 receptor.

Exposure to particulate matter (PM10) can induce respiratory diseases that are closely related to bronchial hyperresponsiveness. However, the involved mechanism remains to be fully elucidated. This study aimed to demonstrate the effects of PM10 on the acetylcholine muscarinic 3 receptor (CHRM3) expression and the role of the ERK1/2 pathway in rat bronchial smooth muscle. A whole-body PM10 exposure system was used to stimulate bronchial hyperresponsiveness in rats for 2 and 4 months, accompanied by MEK1/2 inhibitor U0126 injection. The whole-body plethysmography system and myography were used to detect the pulmonary and bronchoconstrictor function, respectively. The mRNA and protein levels were determined by Western blotting, qPCR, and immunofluorescence. Enzyme-linked immunosorbent assay was used to detect the inflammatory cytokines. Compared with the filtered air group, 4 months of PM10 exposure significantly increased CHRM3-mediated pulmonary function and bronchial constriction, elevated CHRM3 mRNA and protein expression levels on bronchial smooth muscle, then induced bronchial hyperreactivity. Additionally, 4 months of PM10 exposure caused an increase in ERK1/2 phosphorylation and increased the secretion of inflammatory factors in bronchoalveolar lavage fluid. Treatment with the MEK1/2 inhibitor, U0126 inhibited the PM10 exposure-induced phosphorylation of the ERK1/2 pathway, thereby reducing the PM10 exposure-induced upregulation of CHRM3 in bronchial smooth muscle and CHRM3-mediated bronchoconstriction. U0126 could rescue PM10 exposure-induced pathological changes in the bronchus. In conclusion, PM10 exposure can induce bronchial hyperresponsiveness in rats by upregulating CHRM3, and the ERK1/2 pathway may be involved in this process. These findings could reveal a potential therapeutic target for air pollution induced respiratory diseases.

An Ensemble Machine Learning Model to Enhance Extrapolation Ability of Predicting Coarse Particulate Matter with High Resolutions in China.

Accurate exposure assessment is important for conducting PM10-2.5-related epidemiological studies, which have been limited thus far. In this study, we aimed to develop an ensemble machine learning method to estimate PM10-2.5 concentrations in mainland China during 2013-2020. The study was conducted in two stages. In the first stage, we developed two methods: the indirect method refers to developing models for PM2.5 and PM10 separately and subsequently calculating PM10-2.5 as the difference between them; and the direct method refers to establishing a model between PM10-2.5 measurements and relevant predictors directly. In the second stage, we employed an ensemble method by integrating predictions from both indirect and direct methods. Internal and external cross-validation (CV) were performed to validate the extrapolation capacity of models. The ensemble method demonstrated enhanced extrapolation accuracy in both internal and external CV compared to indirect and direct methods. The predictions produced by the ensemble method captured the spatiotemporal pattern of PM10-2.5, even in the sand and dust storm seasons. Our study introduces an ensemble strategy leveraging the strengths of both indirect and direct methods to estimate PM10-2.5 concentrations, which holds significant potential to support future epidemiological studies to address knowledge gaps in understanding the health effects of PM10-2.5.

Prenatal and Early Life Exposure to Ambient Air Pollutants Is Associated with the Fecal Metabolome in the First Two Years of Life.

Prenatal and early life air pollution exposure has been linked with several adverse health outcomes. However, the mechanisms underlying these relationships are not yet fully understood. Therefore, this study utilizes fecal metabolomics to determine if pre- and postnatal exposure to ambient air pollutants (i.e., PM10, PM2.5, and NO2) is associated with the fecal metabolome in the first 2 years of life in a Latino cohort from Southern California. The aims of this analysis were to estimate associations between (1) prenatal air pollution exposure with fecal metabolic features at 1-month of age, (2) prior month postnatal air pollution exposure with fecal metabolites from 1-month to 2 years of age, and (3) how postnatal air pollution exposure impacts the change over time of fecal metabolites in the first 2 years of life. Prenatal exposure to air pollutants was associated with several Level-1 metabolites, including those involved in vitamin B6 and tyrosine metabolism. Prior month air pollution exposure in the postnatal period was associated with Level-1 metabolites involved in histidine metabolism. Lastly, we found that pre- and postnatal ambient air pollution exposure was associated with changes in metabolic features involved in metabolic pathways including amino acid metabolism, histidine metabolism, and fatty acid metabolism.

Airborne Aluminum as an Underestimated Source of Human Exposure: Quantification of Aluminum in 24 Human Tissue Types Reveals High Aluminum Concentrations in Lung and Hilar Lymph Node Tissues.

Aluminum (Al) is the most abundant metal in the earth's crust, and humans are exposed to Al through sources like food, cosmetics, and medication. So far, no comprehensive data on the Al distribution between and within human tissues were reported. We measured Al concentrations in 24 different tissue types of 8 autopsied patients using ICP-MS/MS (inductively coupled plasma-tandem mass spectrometry) under cleanroom conditions and found surprisingly high concentrations in both the upper and inferior lobes of the lung and hilar lymph nodes. Al/Si ratios in lung and hilar lymph node samples of 12 additional patients were similar to the ratios reported in urban fine dust. Histological analyses using lumogallion staining showed Al in lung erythrocytes and macrophages, indicating the uptake of airborne Al in the bloodstream. Furthermore, Al was continuously found in PM2.5 and PM10 fine dust particles over 7 years in Upper Austria, Austria. According to our findings, air pollution needs to be reconsidered as a major Al source for humans and the environment.

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.

Air concentrations of trace elements in a municipality under the influence of Tarragona petrochemical complex: Human health risks.

One of the largest petrochemical complexes of southern Europe is located in Tarragona County (Catalonia, Spain). Despite environmental monitoring is routinely conducted in the area, the long-term occurrence of airborne trace elements has been poorly investigated. In the present study, the concentrations of arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), lead (Pb) and vanadium (V) were analysed in air samples collected in El Morell, a town potentially impacted by the petrochemical. Air samples were simultaneously collected in the town of Cambrils, as a background site. Meteorological data and retro trajectories analysis were used to evaluate the impact of the petrochemical industry on the levels of trace elements in air. Subsequently, human health risks due to inhalation exposure to the trace elements were also assessed. Except for V, air concentrations were significantly higher near the oil refinery than the background levels. Human health risks were also estimated to be higher in the vicinity of the petrochemical complex. In turn, air inhalation of Pb and V was higher than their dietary intakes. The present data should be considered only as preliminary, since the sampling was taken during only three weeks, which is an insufficient period to extract reliable conclusions. Further long-term studies should be focused on assessing the influence of temporary variables, such as meteorological conditions and fugitive or sporadic emissions.

Particulate matters (PM2.5, PM10) and the risk of depression among middle-aged and older population: analysis of the Korean Longitudinal Study of Aging (KLoSA), 2016-2020 in South Korea.

BACKGROUND: There is a growing concern that particulate matter (PM) such as PM2.5 and PM10 has contributed to exacerbating psychological disorders, particularly depression. However, little is known about the roles of these air pollutants on depression in elderly. Therefore, this study aimed to examine the association between PM2.5 and PM10, and depression in the elderly population in South Korea. METHODS: We used panel survey data, the Korean Longitudinal Study of Aging (KLoSA), administered by the Labor Institute during the study period of 2016, 2018, and 2020 covering 217 districts in South Korea (n = 7674). Annual district-specific PM2.5 and PM10 concentrations were calculated for the study period from the monthly prediction concentrations produced by a machine-learning-based ensemble model (cross-validated R2: 0.87), then linked to the people matching with year and their residential district. We constructed a generalized estimating equation (GEE) model with a logit link to identify the associations between each of the long-term PM2.5 and PM10 exposures and depression (CES-D 10) after adjusting for individual and regional factors as confounders. RESULTS: In single-pollutant models, we found that long-term 10 [Formula: see text] increments in PM2.5 (OR 1.36, 95% CI 1.20-1.56) and PM10 (OR 1.19, 95% CI 1.10-1.29) were associated with an increased risk of depression in the elderly. Associations were consistent after adjusting for other air pollutants (NO2 and O3) in two-pollutant models. In addition, the impacts substantially differed by regions grouped by the tertile of the population density, for which the risks of particulate matters on depression were substantial in the middle- or high-population-density areas in contrast to the low-population-density areas. CONCLUSIONS: Long-term exposure to PM2.5 and PM10 was associated with a higher risk of developing depression in elderly people. The impact was modified by the population density level of the region where they reside.

Human health impacts attributable to inhalation exposure of particulate matter (PM10 and PM2.5) during the Holi festival.

OBJECTIVE: The present study focuses on residential areas of Delhi to identify the elevated levels of ambient PM10 and PM2.5 due to biomass burning followed by the coloring activity in the Holi festival celebrated at the end of the winter season. This study also focuses on the health risk assessment and mortality among different age groups due to the change in particulate matter levels during the Holi festival in Delhi, India. MATERIALS AND METHODS: Secondary data of particulate matters have been procured from the Central Pollution Control Board (CPCB), Delhi Pollution Control Committee (DPCC), and Indian Institute of Tropical Meteorology (IITM), Pune for the period of the pre-, during, and post-Holi period for the year 2018-2020 at four selected residential locations in Delhi, India. The health impacts of particle inhalation were quantified using the AirQ + models. RESULTS: The results indicated the levels of PM10 and PM2.5 rise about 3-4 times higher during the Holi festival than on normal days, resulting in health risks and causing an excess number of mortality and Asthma cases in Delhi. Such cases were also found to be higher in 2018, followed by 2019 and 2020 at all the selected locations in Delhi. CONCLUSIONS: The study linked the increasing particulate levels in the Holi festival with the increased health risk through short-term exposure of the population. The excess number of cases (ENCs) of mortality, all causes of mortality among adults (age > 30 years) associated with short-term exposure to particulate were also identified.

Causal relationships between genetically predicted particulate air pollutants and neurodegenerative diseases: A two-sample Mendelian randomization study.

Accumulating observational studies have linked particulate air pollutants to neurodegenerative diseases (NDDs). However, the causal links and the direction of their associations remain unclear. Therefore, we adopted a two-sample Mendelian randomization (TSMR) design using the GWAS-based genetic instruments of particulate air pollutants (PM2.5 and PM10) from the UK Biobank to explore their causal influence on four common neurodegenerative diseases. Estimates of causative relationships were generated by the Inverse variance weighted (IVW) method with multiple sensitive analyses. The heterogeneity and pleiotropy tests were additionally performed to verify whether our findings were robust. Genetically predicted PM2.5 and PM10 could elevate the occurrence of AD (odds ratio [OR] = 2.22, 95 % confidence interval [CI] 1.53-3.22, PIVW = 2.85×10-5, PFalsediscovery rate[FDR]= 2.85×10-4 and OR = 2.41, 95 % CI: 1.26-4.60, PIVW = 0.008, PFDR=0.039, respectively). The results were robust in sensitive analysis. However, no evidence of causality was found for other NDDs. Our present study suggests that PM2.5 and PM10 have a detrimental effect on AD, which indicates that improving air quality to prevent AD may have pivotal public health implications.

Early-life exposure to ambient air pollutants and kidney function in adolescents: a cohort study based on the 'Children of 1997' Hong Kong birth cohort.

OBJECTIVES: Air pollution is increasingly linked to impaired kidney function in adults. However, little is known about how early-life exposure to air pollutants affects kidney function in adolescents. STUDY DESIGN: Cohort study. METHODS: We leveraged data from the 'Children of 1997' Hong Kong population-representative birth cohort (N = 8327). Residential exposure to average ambient levels of four air pollutants, including inhalable particle (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and nitrogen monoxide (NO), during in utero, infancy, and childhood periods was estimated using the inverse distance weighting. Kidney function was assessed using estimated glomerular filtration rate (eGFR) calculated from age-adjusted equations for adolescents. Generalized linear regression was used to examine the association of air pollutant exposure in each period with kidney function at 17.6 years. Two-pollutant models tested the robustness of the association. RESULTS: Of the 3350 participants included, 51.4% were boys. Exposure to PM10 was associated with poorer kidney function. Each interquartile range increment in PM10 was inversely associated with eGFR (ß: -2.933, 95% confidence interval -4.677 to -1.189) in utero, -2.362 (-3.992 to -0.732) infancy, -2.708 (-4.370 to -1.047) childhood, and -2.828 (-4.409 to -1.247) overall. Exposure to PM10 and SO2in utero had a stronger inverse association with kidney function in males. The associations were robust to PM10 exposure in two-pollutant models. CONCLUSIONS: Our findings suggest that early-life exposure to ambient PM10 and SO2 is associated with reduced kidney function in adolescents, especially exposure in utero.

The impact of air pollutants on spontaneous abortion: a case-control study in Tongchuan City.

OBJECTIVES: Studies related to air pollutants and spontaneous abortion in urban northwestern China are scarce, and the main exposure windows of pollutants acting on pregnant women are unclear. STUDY DESIGN: Case-control study. METHODS: Data were collected from pregnant women in Tongchuan City from 2018 to 2019. A total of 289 cases of spontaneous abortion and 1156 cases of full-term labor were included and analyzed using a case-control study. Logistic regression models were developed to explore the relationship between air pollutants and spontaneous abortion after Chi square analysis and Air pollutant description. RESULTS: O3 (odds ratio [OR] = 1.028) is a risk factor for spontaneous abortion throughout pregnancy. PM2.5 (OR = 1.015), PM10 (OR = 1.010), SO2 (OR = 1.026), and NO2 (OR = 1.028) are risk factors for spontaneous abortion in the 30 days before the last menstrual period. PM2.5 (OR = 1.015), PM10 (OR = 1.013), SO2 (OR = 1.036), and NO2 (OR = 1.033) are risk factors for spontaneous abortion in the 30-60 days before the last menstrual period. PM2.5 (OR = 1.028), PM10 (OR = 1.013), SO2 (OR = 1.035), and NO2 (OR = 1.059) are risk factors for spontaneous abortion in the 60-90 days before the last menstrual period. CONCLUSION: Exposure to high levels of air pollutants may be a cause of increased risk of spontaneous abortion, especially in the first trimester of the last menstrual period.

Particulate matter retention and removal efficiency in ten tree species of semi-arid environment.

Modern era has witnessed particulate matter (PM) become one of the biggest threats for the existence of biological species. Therefore, a study was performed in Faisalabad city to evaluate PM retention and removal efficiency of ten local tree species. Branch samples were collected from urban, sub-urban and rural areas in September 2020 (183 days after rain), and in August 2021 (30 days after rain). Results showed that total PM load, PM>10 and PM10-2.5 retention was the highest in urban followed by sub-urban and rural area. PM>10, PM10-2.5, total PM, and PM deposition rate decreased significantly in the following order, F. benghalensis > T. arjuna > S. cumini > A. scholaris > F. religiosa > E. camaldulensis > D sissoo > C lancifolius > B. ceiba > M. alba during both years 2020, and 2021. During the artificial rainfall experiment, total PM removed by the species also followed the same order however, PM removal efficiency was the highest in B. ceiba and M. alba followed by E. camaldulensis, C. lancifolius, D. sissoo, T. arjuna, S. cumini, A. scholaris, F. religiosa and F. benghalensis. Therefore, it can be concluded that species selection must be done skillfully for congested urban environment.

Owing to excessive industrialization and motorization, optimizing particulate matter (PM) removal from the atmosphere has become imperative. Since, trees can help in controlling the atmospheric PM therefore, current study aimed to evaluate the efficiency of ten tree species in controlling the atmospheric PM. These species belong to arid and semi-arid regions have not been characterized for the efficiency in removing PM from the atmosphere. The results will help in the selection of efficient local tree species for controlling atmospheric PM especially in the congested urban environment.

Indication Variability of the Particulate Matter Sensors Dependent on Their Location.

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.

Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy).

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM2.5 and PM10 daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM2.5 and 20 for PM10 concentrations were compared vs. regulatory-grade stations during a full "heating season" (15 October 2022-15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM10 than PM2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R2. AirQino stations under-estimated both PM2.5 and PM10 reference concentrations (MB = -4.8 and -2.9 µg/m3, respectively), while PurpleAir stations over-estimated PM2.5 concentrations (MB = +5.4 µg/m3) and slightly under-estimated PM10 concentrations (MB = -0.4 µg/m3). PurpleAir stations were finer than AirQino at capturing the time variation of both PM2.5 and PM10 daily concentrations (R2 = 0.68-0.75 vs. 0.59-0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM2.5/PM10 ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM2.5 vs. PM10 outputs.

Mechanisms of PM10 Disruption of the Nrf2 Pathway in Cornea.

We have previously shown that PM10 exposure causes oxidative stress and reduces Nrf2 protein levels, and SKQ1 pre-treatment protects against this damage in human corneal epithelial cells (HCE-2). The current study focuses on uncovering the mechanisms underlying acute PM10 toxicity and SKQ1-mediated protection. HCE-2 were pre-treated with SKQ1 and then exposed to 100 µg/mL PM10. Cell viability, oxidative stress markers, programmed cell death, DNA damage, senescence markers, and pro-inflammatory cytokines were analyzed. Nrf2 cellular location and its transcriptional activity were determined. Effects of the Nrf2 inhibitor ML385 were similarly evaluated. Data showed that PM10 decreased cell viability, Nrf2 transcriptional activity, and mRNA levels of antioxidant enzymes, but increased p-PI3K, p-NFκB, COX-2, and iNOS proteins levels. Additionally, PM10 exposure significantly increased DNA damage, phosphor-p53, p16 and p21 protein levels, and ß-galactosidase (ß-gal) staining, which confirmed the senescence. SKQ1 pre-treatment reversed these effects. ML385 lowered the Nrf2 protein levels and mRNA levels of its downstream targets. ML385 also abrogated the protective effects of SKQ1 against PM10 toxicity by preventing the restoration of cell viability and reduced oxidative stress. In conclusion, PM10 induces inflammation, reduces Nrf2 transcriptional activity, and causes DNA damage, leading to a senescence-like phenotype, which is prevented by SKQ1.

Functions of Hemp-Induced Exosomes against Periodontal Deterioration Caused by Fine Dust.

Although fine dust is linked to numerous health issues, including cardiovascular, neurological, respiratory, and cancerous diseases, research on its effects on oral health remains limited. In this study, we investigated the protective effects of mature hemp stem extract-induced exosomes (MSEIEs) on periodontal cells exposed to fine dust. Using various methods, including microRNA profiling, PCR, flow cytometry, immunocytochemistry, ELISA, and Alizarin O staining, we found that MSE treatment upregulated key microRNAs, such as hsa-miR-122-5p, hsa-miR-1301-3p, and hsa-let-7e-5p, associated with vital biological functions. MSEIEs exhibited three primary protective functions: suppressing inflammatory genes while activating anti-inflammatory ones, promoting the differentiation of periodontal ligament stem cells (PDLSCs) into osteoblasts and other cells, and regulating LL-37 and MCP-1 expression. These findings suggest that MSEIEs have potential as functional biomaterials for applications in pharmaceuticals, cosmetics, and food industries.

Mobility, bacterial hosts, and risks of antibiotic resistome in submicron bioaerosols from a full-scale wastewater treatment plant.

Antibiotic resistome could be loaded by bioaerosols and escape from wastewater or sludge to atmosphere environments. However, until recently, their profile, mobility, bacterial hosts, and risks in submicron bioaerosols (PM1.0) remain unclear. Here, metagenomic sequencing and assembly were employed to conduct an investigation of antibiotic resistome associated with PM1.0 within and around a full-scale wastewater treatment plant (WWTP). More subtypes of antibiotic resistant genes (ARGs) with higher total abundance were found along the upwind-downwind-WWTP transect. ARGs in WWTP-PM1.0 were mainly mediated by plasmids and transposases were the most prevalent mobile genetic elements (MGEs) co-occurring with ARGs. A contig-based analysis indicated that very small proportions (15.32%-19.74%) of ARGs in WWTP-PM1.0 were flanked by MGEs. Proteobacteria was the most dominant host of ARGs. A total of 28 kinds of potential pathogens, such as Pseudomonas aeruginosa and Escherichia coli, carried multiple ARG types. Compared to upwind, WWTP and corresponding downwind were characterized by higher PM1.0 resistome risk. This study emphasizes the vital role of WWTPs in discharging PM1.0-loaded ARGs and antibiotic resistant pathogens to air, and indicates the need for active safeguard procedures, such as that employees wear masks and work clothes, covering the main emission sites, and collecting and destroying of bioaerosols.

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.

Ambient air fine particulate matter (PM10 and PM2.5) and risk of type 2 diabetes mellitus and mechanisms of effects: a global systematic review and meta-analysis.

Type 2 diabetes causes early mortality worldwide. Air pollution's relationship with T2DM has been studied. The association between them is unclear because of inconsistent outcomes. Studies on this topic have been published since 2019, but not thoroughly evaluated. We conducted a systematic review and meta-analysis using relevant data. The study protocol was registered in PROSPIRO and conducted according to MOOSE guidelines. In total, 4510 manuscripts were found. After screening, 46 studies were assessed using the OHAT tool. This meta-analysis evaluated fine particles with T2DM using OR and HR effect estimates. Evaluation of publication bias was conducted by Egger's test, Begg's test, and funnel plot analysis. A sensitivity analysis was conducted to evaluate the influence of several studies on the total estimations. Results show a significant association between PM2.5 and PM10 exposure and T2DM. Long-term exposure to fine air particles may increase the prevalence and incidence of T2DM. Fine air pollution increases the chance of developing T2DM mainly via systemic inflammation, oxidative stress, and endoplasmic reticulum stress.