Impact of Indoor Air Pollutants on the Cardiovascular Health Outcomes of Older Adults: Systematic Review.

Indoor air pollution accounts for approximately 3.8 million inopportune deaths annually at global level. Due to spending more time indoors, children and older adults are especially susceptible to the health risks of indoor air pollution. This review seeks to summarise existing knowledge on the cardiovascular health effects of three common indoor air pollutants, namely carbon monoxide (CO), particulate matter (PM2.5 and PM10), and Nitrogen dioxide (NO2), focusing on older adults. We systematically reviewed the literature (PROSPERO CRD42024479220) on PubMed, Google Scholar, Scopus, Web of Science and Embase. The search yielded 20,914 records. Two independent reviewers screened the articles using titles, abstracts, and full-length articles written in English. Upon a detailed assessment of all the records, the review considered 38 full-length articles. Several studies reported mortality, myocardial infarction, stroke, increased hospitalisation and increased emergency room visits due to exposure to indoor air pollution. A few studies reported arrhythmias, hypertension and Ischaemic heart disease due to exposure to indoor air pollutants. The increased mortality, morbidity, hospitalization, and emergency rooms visits resulting from indoor air pollution associated CVDs makes indoor air pollution a health risk for older adults. There is, therefore, a need to synthesize information on studies relate d to how the selected indoor air pollutants affected the cardiovascular health of older adults.

Impact of urban green spaces on air quality: A study of PM10 reduction across diverse climates.

Urban areas face high particulate matter (PM10) levels, increasing the risk of respiratory and cardiovascular diseases. Green spaces can significantly reduce PM10 concentration, as shown at various scales, from boroughs to whole cities. However, long-term monitoring is needed to understand the specific mechanisms and cumulative impact of green spaces on air quality to changing pollution levels. We investigated the influence of neighbourhood green space percentage, climatic variables, and population density on PM10 deposition during the vegetation period across eight cities in contrasting climate zones over 20 years (2000-2020). We used a correlation matrix, generalized additive model, one-way ANOVA, and Tukey HSD test to analyze the impact of these factors on PM10 deposition rates, assess the role of green space percentage in reducing it, and identify significant differences in PM10 parameters at different proximities to emission sources. Cities with higher population density in warmer, drier climates had higher PM levels, since land surface temperature and wind pressure positively correlated with PM10 deposition, while relative humidity showed a negative correlation. The study found significantly higher PM10 concentrations in industrial areas (36.25 µg/m³) than in roadside areas (25.73 µg/m³) and parks (20.17 µg/m³) (p < 0.01). This highlights the need for targeted interventions in different zones. The study found a complex relationship between green space percentage and PM10 deposition rate onto plant surfaces. Our model suggests that at least 27% of green spaces as land cover can significantly reduce the particulate matter flux, although the minimum threshold can vary depending on the specific urban contexts. The study focused on the proportionate cover of green spaces; still, further investigation including quantitative aspects of urban surface forms, and traffic emissions can comprehend the climatic context and determine the optimal extent of green space required for strategic planning toward future urban sustainability initiatives.

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.

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 ±â€¯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.

Causes of the unremitting high ambient levels of PM10 in a suburban background site in NE Spain.

Atmospheric PM10 and benzo(a)pyrene (BaP) concentrations in Manlleu (NE Spain) have remained high from 2008-2023, frequently exceeding EU limit/target values, and reaching BaP levels up to six times higher than urban averages in Spain. Furthermore, PM speciation campaigns were carried out in 2013, 2014-2015, 2016-2017 and 2021-2022. Chemical mass closure for autumn-winter showed a consistent PM10 composition for the different PM speciation campaigns, comprising 46-53% organic matter (OM), 18-26% secondary inorganic aerosol (SIA), 13-23% mineral matter (MM), and 5-9% elemental carbon (EC). Trend analysis revealed very light decrease and constant concentrations for PM10 and BaP, respectively over the study period, emphasizing the need for compliance with current and forthcoming EU air quality directives, the last aiming to halve PM10 limit values. Source apportionment of samples of the sporadic campaigns identified biomass burning (BB, 17.5 µg m-3, 48%) and MM and industry (16.3 µg m-3, 44%) as the main autumn-winter PM10 contributors, with high SIA concentrations attributed to several factors, including high ammonia (NH3) emissions. Local topography and meteorological conditions contribute to aggravate PM10 pollution. While metal concentrations have decreased since 2013, suggesting reduced industrial emissions, persistently high OM and EC concentrations indicate ongoing issues with BB emissions from domestic, commercial, and agricultural sources. Online analysis of black carbon (BC) and non-refractory PM1 components during winter 2016-2017 confirmed domestic and commercial BB as the primary sources of the BB contributions. These findings highlight the need of the implementation of more effective measures in reducing BB and agricultural/farming NH3 emissions. This study highlights the relevance of these issues for similar towns, the probable unremitting problem over the last decade, and the necessity of enhanced monitoring in small cities and policy actions to meet air quality standards under the new EU directive.

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.

Cleistocalyx nervosum var. paniala mitigates oxidative stress and inflammation induced by PM10 soluble extract in trophoblast cells via miR-146a-5p.

Air pollution poses a significant global concern, notably impacting pregnancy outcomes through mechanisms such as DNA damage, oxidative stress, inflammation, and altered miRNA expression, all of which can adversely affect trophoblast functions. Cleistocalyx nervosum var. paniala, known for its abundance of anthocyanins with diverse biological activities including anti-mutagenic, antioxidant, and anti-inflammatory properties, is the focus of this study examining its effect on Particulate Matter 10 (PM10) soluble extract-induced trophoblast cell dysfunction via miRNA expression. The study involved the extraction of C. nervosum fruit using 70% ethanol, followed by fractionation with hexane, dichloromethane, and ethyl acetate. Subsequent testing for total phenolics, flavonoids, anthocyanins, and antioxidant activity revealed the ethyl acetate fraction (CN-EtOAcF) as possessing the highest phenolic and anthocyanin content along with potent antioxidant activity, prompting its selection for further investigation. In vitro studies on HTR-8/SVneo cells demonstrated that 5-10 µg/mL PM10 soluble extract exposure inhibited cell proliferation, migration, invasion, and induced apoptosis. However, pretreatment with 20-80 µg/mL CN-EtOAcF followed by 5 µg/mL PM10 soluble extract exposure exhibited protective effects against PM10 soluble extract-induced damage, including inflammation inhibition and intracellular ROS suppression. Notably, CN-EtOAcF down-regulated PM10-induced miR-146a-5p expression, with SOX5 identified as a potential target. Overall, CN-EtOAcF demonstrated the potential to protect against PM10-induced harm in trophoblast cells, suggesting its possible application in future therapeutic approaches.

Examining the Link between Air Quality (PM, SO2, NO2, PAHs) and Childhood Obesity: A Systematic Review.

Background/Objectives: Childhood obesity has emerged as a global health concern with profound implications for long-term health outcomes. In recent years, there has been increasing interest in the potential role of environmental factors in the development of childhood obesity. This comprehensive review aims to elucidate the intricate relationship between various components of air pollution and childhood obesity. Methods: We systematically analyze the existing literature from the past 5 years to explore the mechanistic pathways linking air pollution, including particulate matter (PM), nitrogen oxides (NOx), sulfur dioxide (SO2), and polycyclic aromatic hydrocarbons (PAHs), to childhood obesity. This systematic review examines 33 epidemiological studies on the link between air pollution and childhood obesity, published from 1 January 2018, to 31 January 2024. Results: Studies from counties with low overall air pollution noticed only low to no impact of the exposure to childhood obesity, unlike studies from countries with higher levels of pollution, suggesting that the mitigation of air pollutants can reduce the chance of it being a negative factor for the development of obesity. This relationship was noticed for PM2.5, PM1, PM10, NOx, and SO2 but not for PAHs, which showed a negative effect on children's health across 10 out of 11 studies. Conclusions: This review underscores the need for interdisciplinary approaches to address both environmental and socio-economic determinants of childhood obesity. Efforts aimed at reducing air pollution levels and promoting healthy lifestyle behaviors are essential for safeguarding the health and well-being of children worldwide.

Long-term impact of air pollution on heart failure readmission in unstable angina patients.

Cardiovascular disease remains the leading cause of death worldwide, with air pollution's impact on cardiovascular health being closely monitored. However, the specific effects of air pollution on the risk of hospital readmission for heart failure (HF) in patients with unstable angina (UA) have not been fully explored. We conducted a retrospective study involving 12,857 consecutive patients diagnosed with acute coronary syndrome (ACS) between January 2015 and March 2023. After rigorous screening, we included 8,737 patients with UA in the analysis. Furthermore, we used a Cox proportional hazards regression model to examine the relationship between air quality indicators and hospital readmission for HF in patients with UA. Additionally, a decision tree model identified air quality indicators levels that had the most significant impact on readmission for HF risk. After adjusting for confounding factors, we found that elevated levels of PM10 [hazard ratio (HR) = 1.003, 95% confidence interval (CI): 1.000-1.005, p = 0.04453] and CO (HR = 1.013, 95% CI: 1.005-1.021, p = 0.00216) were associated with an increased risk of hospital readmission for HF in UA patients. Specifically, patients exposed to PM10 levels above 112.5 µ g/m3 had a 1.61-fold higher risk of readmission for HF in UA patients. (HR = 1.609, 95% CI: 1.190-2.176, p = 0.00201), and those exposed to CO levels above 37.5 mg/m3 had a 2.70-fold higher risk of readmission for HF in UA patients. (HR = 2.681, 95% CI: 1.731-4.152, p < 0.00001). Higher concentrations of PM10 and CO significantly increased the risk of HF (HF) readmission in patients with UA after discharge, particularly when PM10 levels exceeded 112.5 ug/m3 and CO levels surpassed 37.5 ug/m3. Besides, female patients with UA, with fewer underlying diseases, were more susceptible to the adverse effects of PM10 and CO.

Airborne particulate matter integral assessment in Magdalena department, Colombia: Patterns, health impact, and policy management.

The relevance of atmospheric particulate matter (PM) to health and the environment is widely known. Long-term studies are necessary for understanding current and future trends in air quality management. This study aimed to assess the long-term PM concentration in the Magdalena department (Colombia). It focused on the following aspects: i) spatiotemporal patterns, ii) correlation with meteorology, iii) compliance with standards, iv) temporal trends over time, v) impact on health, and vi) impact of policy management. Fifteen stations from 2003 to 2021 were analyzed. Spearman-Rho and Mann-Kendall methods were used to correlate concentration with meteorology. The temporal and five-year moving trends were determined, and the trend magnitude was calculated using Teil-Sen. Acute respiratory infection odd ratios and risk of cancer associated with PM concentration were used to assess the impact on health. The study found that the maximum PM10 concentration was 194.5 µg/m3, and the minimum was 3 µg/m3. In all stations, a negative correlation was observed between PM10 and atmospheric water content, while the wind speed and temperature showed a positive correlation. The global trends indicated an increasing value, with five fluctuations in five-year moving trends, consistent with PM sources and socio-economic behavior. PM concentrations were found to comply with national standard; however, the results showed a potential impact on population health. The management regulation had a limited impact on increasing concentration. Considering that national regulations tend to converge towards WHO standards, the study area must create a management program to ensure compliance.

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.

Application of AERMOD dispersion model for assessment PM10 concentrations from mobile sources in Kuala Lumpur Metropolitan City, Malaysia.

Deterioration of air quality in Kuala Lumpur caused by mobile sources and traffic-related activities with interaction with climatic conditions and dispersion in the atmosphere. This study was focused on predicting the averaged 1-h concentration of particulate matter (PM10) that was emitted from private cars in Kuala Lumpur by applying the air dispersion model American Meteorological Society (AMS)/United States Environmental Protection Agency (EPA) Regulatory Model (AERMOD) in 2014. The AERMOD model indicates that private cars in Kuala Lumpur recorded 1-h concentration of PM10 below the Malaysian Ambient Air Quality Guidelines (RMAQG) in 2014. The highest concentration was 59.2 µg/m3 in 2014 and mainly concentrated around the city center and highway networks. The highest concentrations are recorded in early morning and late at night, with the highest concentrations at 4 am and 2 am and 48.68 µg/m3 and 38.42 µg/m3, respectively. Late night to early morning is identified as the dominant time interval for pollutant dispersion. The findings demonstrated that air quality had a significant impact on the 1-h concentration of PM10 emitted from private cars in Kuala Lumpur.

Research on characteristics and influencing factors of road dust emission in a southern city in China.

One of the primary causes of urban atmospheric particulate matter, which is harmful to human health in addition to affecting air quality and atmospheric visibility, is road dust. This study used online monitoring equipment to examine the characteristics of road dust emissions, the effects of temperature, humidity, and wind speed on road dust, as well as the correlation between road and high-space particulate matter concentrations. A section of a real road in Jinhua City, South China, was chosen for the study. The findings demonstrate that the concentration of road dust particles has a very clear bimodal single-valley distribution throughout the day, peaking between 8:00 and 11:00 and 19:00 and 21:00 and troughing between 14:00 and 16:00. Throughout the year, there is a noticeable seasonal change in the concentration of road dust particles, with the highest concentration in the winter and the lowest in the summer. Simultaneously, it has been discovered that temperature and wind speed have the most effects on particle concentration. The concentration of road dust particles reduces with increasing temperature and wind speed. The particle concentrations of road particles and those from urban environmental monitoring stations have a strong correlation, although the trend in the former is not entirely consistent, and the changes in the former occur approximately 1 h after the changes in the latter.

Air quality in a small city: criteria pollutants, volatile organic compounds, metals, and microbes.

This work presents a year-long integral study of air quality parameters in Ciudad Real, a small city in the center of Spain, and its influence on the nearby national park, Las Tablas de Daimiel. The study covers meteorological parameters and criteria pollutants such as O3, NO, NO2, SO2, and PM10. Additionally, for each month, a 1-week campaign was performed sampling air in sorbent tubes with 8-h time resolution to analyze anthropogenic volatile organic compounds and the effects of seasons, daytime, and working-weekend days. During these campaigns, 24-h PM2.5 samples were also collected to measure the load of bacteria and fungi, as well as the trace concentrations of elements.The city and the national park NOx profiles showed that emissions from the town had a non-perceivable effect on the protected area. PM10 levels in Ciudad Real were influenced by Saharan intrusions, as was the national park; however, Ciudad Real had a higher contribution from anthropogenic sources. Ozone levels were lower in the city during the cold season due to the higher concentration of NOx and have not changed significantly in the last decade.The VOCs with higher average concentrations were toluene, m,p-xylene, benzene, methylene chloride, and o-xylene, with traffic being the main source of these pollutants in the city. For benzene and carbon tetrachloride levels, weak carcinogenic risks were estimated. In PM2.5, the most abundant metals were Na, Zn, Mg, Ca, Al, Fe, and K. The carcinogenic and non-carcinogenic risks estimated from the levels of the studied metals were negligible. Bacterial and fungal counts positively correlated with the concentration of PM2.5. Microbial community composition showed seasonal variability, with the dominance of human pathogenic bacteria which correlated with certain pollutants such as SO2. Bacillus and Cutibacterium were the most abundant genera.

Dynamic patterns of particulate matter concentration and size distribution in urban street canyons: insights into diurnal and short-term seasonal variations.

Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller dp particulates (i.e., PM1, PM2.5, and PM4), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM1 and PM2.5) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM7, PM10, and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM2.5, 53.47% lower in PM10, and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments.

[Prediction of PM10 Concentration in Dry Bulk Ports Using a Combined Deep Learning Model Considering Feature Meteorological Factors].

Accurate prediction of PM10 concentration is important for effectively managing PM10 exposure and mitigating health and economic risks posed to humans in dry bulk ports. However, accurately capturing the time-series nonlinear variation characteristics of PM10 concentration is challenging owing to the specific intensity of port operation activities and the influence of meteorological factors. To address such challenges, a novel combined deep learning model （CLAF） was proposed, merging cascaded convolutional neural networks （CNN）, long short-term memory （LSTM）, and an attention mechanism （AM）. This integrated model aimed to forecast hourly PM10 concentration in dry bulk ports. First, using the random forest characteristic importance algorithm, the distinct meteorological factors were identified among the selected five meteorological factors. These selected factors were incorporated into the prediction model along with the PM10 concentration. Subsequently, the CNN layer was employed to extract high-dimensional time-varying features from the input variables, while the LSTM layer captured sequential features and long-term dependencies. In the AM layer, different weights were assigned to the output components of the LSTM layer to amplify the effects of important information. Finally, three evaluation metrics were applied to compare the performance of the CLAF model with three basic models and three commonly used prediction models. Real-case data was collected and used in this study. Comparison results demonstrated that considering the meteorological factors could improve the prediction accuracy and fitting performance of PM10 concentration in ports. The CLAF model reduced the mean absolute error statistic by 13.92%-56.9%, decreased the mean square error statistic by 45.99%-81.02%, and improved the goodness-of-fit statistic by 3.2%-15.5%.

Air pollution exposure during preconception and first trimester of pregnancy and gestational diabetes mellitus in a large pregnancy cohort, Hebei Province, China.

Objective: To explore the relationship between the exposure level of particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10) in the air of pregnant women during preconception and first trimester of pregnancy and the risk of gestational diabetes mellitus (GDM). Methods: The data of pregnant women delivered in 22 monitoring hospitals in Hebei Province from 2019 to 2021 were collected, and the daily air quality data of their cities were used to calculate the exposure levels of PM2.5 and PM10 in different pregnancy stages, and logistic regression model was used to analyze the impact of exposure levels of PM2.5 and PM10 on GDM during preconception and first trimester of pregnancy. Results: 108,429 singleton live deliveries were included in the study, of which 12,967 (12.0%) women had a GDM diagnosis. The prevalence of GDM increased over the course of the study from 10.2% (2019) to 14.9% (2021). From 2019 to 2021, the average exposure of PM2.5 and PM10 was relatively 56.67 and 103.08µg/m3 during the period of preconception and first trimester of pregnancy in Hebei Province. Handan, Shijiazhuang, and Xingtai regions had the most severe exposure to PM2.5 and PM10, while Zhangjiakou, Chengde, and Qinhuangdao had significantly lower exposure levels than other regions. The GDM group had statistically higher exposure concentrations of PM2.5 and PM10 during the period of preconception, first trimester, preconception and first trimester (P<0.05). Multivariate logistic regression analysis showed that the risk of GDM increases by 4.5%, 6.0%, and 10.6% for every 10ug/m3 increase in the average exposure value of PM2.5 in preconception, first trimester, preconception and first trimester, and 1.7%, 2.1%, and 3.9% for PM10. Moreover, High exposure to PM2.5 in the first, second, and third months of preconception and first trimester is associated with the risk of GDM. And high exposure to PM10 in the first, second, and third months of first trimester and the first, and third months of preconception is associated with the risk of GDM. Conclusion: Exposure to high concentrations of PM2.5 and PM10 during preconception and first trimester of pregnancy can significantly increase the risk of GDM. It is important to take precautions to prevent exposure to pollutants, reduce the risk of GDM, and improve maternal and fetal outcomes.

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.

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.

Water-soluble organic compounds (WSOC) from atmospheric aerosols in Bou-Ismail (Algeria).

In this contribution, we report the study of nuclear resonance magnetic spectroscopy techniques (1H-NMR, 13C-NMR, and 2D-NMR) efficiency in the characterisation of the functional composition of water-soluble organic compounds (WSOC) from atmospheric aerosols. The chosen site was our scientific and technical center of research (CRAPC) situated in Algerian Bou-Ismail city. where the concentrations of PM10 were found to be between 15.66 and 142.19 µg.m-3. As results, 1H-NMR analysis showed the coexistence of biological material and emissions from urban and biomass burning. The dominant source was identified by quantitative integration of each 1H NMR spectral region. By using the HSQC technique, many peaks are revealed in biogenic samples including biomass burning. On the other hand, the identification of the source of various organic compounds and their functional composition is possible through specific NMR spectra, which can also be used to adjust the surrounding organic aerosol sources.