Comparative evaluation of single and multiple exposure to PM2.5 in respirable air on cardiac physiology, structure and function in a Wistar rat model.

Many studies have shown the negative relationship between long term exposure to PM2.5 and cardiac dysfunction. Recently, studies have shown that even a single exposure of PM2.5 from air sample in permissible range can induce very mild cardiac pathological changes. In the present study, we revisited the toxic effect of PM2.5 on rat heart by adopting single and multiple exposure durations. Female Wistar rats were exposed to PM2.5 at a concentration of 250 µg/m3 daily for 3 hr for single (1 day) and multiple (7, 14, 21 days) durations. The major pathological changes noted in 21 days exposed myocardium comprised of an elevated ST segment (the segment between the S wave and the T wave), development of cardiac fibrosis, hypertrophy, cardiac injury, tissue inflammation and declined cardiac function. With 14 days exposed heart, the electrocardiograms (ECG),data showed insignificantly declined heart rate and an increased QT (the time from the start of the Q wave to the end of the T wave) interval along with mild fibrosis, hypertrophy and lesser number of TUNEL positive cells. On the other hand, single- and 7-days exposure to PM2.5 did not impart any significant changes in the myocardium. To determine the reversibility potential of PM2.5 induced cardiotoxicity, a washout period of 24 hours was adopted and all observed changes in the myocardium were reversed till day 7, but not in 14- and 21-days exposed samples. Based on the above findings we concluded that PM2.5 associated cardiac dysfunction is the cumulative outcome of ineffective cardiac adaptive and repair process that accumulate additively over the time due to prolonged exposure durations.

A methodological framework for estimating ambient PM2.5 particulate matter concentrations in the UK.

Scientific evidence sustains PM2.5 particles' inhalation may generate harmful impacts on human beings' health; therefore, their monitoring in ambient air is of paramount relevance in terms of public health. Due to the limited number of fixed stations within the air quality monitoring networks, development of methodological frameworks to model ambient air PM2.5 particles is primordial to providing additional information on PM2.5 exposure and its trends. In this sense, this work aims to offer a global easily-applicable tool to estimate ambient air PM2.5 as a function of meteorological conditions using a multivariate analysis. Daily PM2.5 data measured by 84 fixed monitoring stations and meteorological data from ERA5 (ECMWF Reanalysis v5) reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested approach. Data from January 2017 to December 2020 were employed to build a mathematical expression that related the dependent variable (PM2.5) to predictor ones (sea-level pressure, planetary boundary layer height, temperature, precipitation, wind direction and speed), while 2021 data tested the model. Evaluation indicators evidenced a good performance of model (maximum values of RMSE, MAE and MAPE: 1.80 µg/m3, 3.24 µg/m3, and 20.63%, respectively), compiling the current legislation's requirements for modelling ambient air PM2.5 concentrations. A retrospective analysis of meteorological features allowed estimating ambient air PM2.5 concentrations from 2000 to 2021. The highest PM2.5 concentrations relapsed in the Mid- and Southlands, while Northlands sustained the lowest concentrations.

Silibinin inhibits PM2.5-induced liver triglyceride accumulation through enhancing the function of mitochondrial Complexes I and II.

Background: The standardized extract of milk thistle seeds, known as silibinin, has been utilized in herbal medicine for over two centuries, with the aim of safeguarding the liver against the deleterious effects of various toxic substances. However, the role of silibinin in Particulate Matter (PM2.5)-induced intrahepatic triglyceride accumulation remains unclear. This study seeks to investigate the impact of silibinin on PM2.5-induced intrahepatic triglyceride accumulation and elucidate potential underlying mechanisms. Methods: A model of intrahepatic triglyceride accumulation was established in male C57BL/6J mice through intratracheal instillation of PM2.5, followed by assessment of liver weight, body weight, liver index, and measurements of intrahepatic triglycerides and cholesterol after treatment with silibinin capsules. Hep G2 cells were exposed to PM2.5 suspension to create an intracellular triglyceride accumulation model, and after treatment with silibinin, cell viability, intracellular triglycerides and cholesterol, fluorescence staining for Nile Red (lipid droplets), and DCFH-DA (Reactive Oxygen Species, ROS), as well as proteomics, real-time PCR, and mitochondrial function assays, were performed to investigate the mechanisms involved in reducing triglycerides. Results: PM2.5 exposure leads to triglyceride accumulation, increased ROS production, elevated expression of inflammatory factors, decreased expression of antioxidant factors, and increased expression of downstream genes of aryl hydrocarbon receptor. Silibinin can partially or fully reverse these factors, thereby protecting cells and animal livers from PM2.5-induced damage. In vitro studies show that silibinin exerts its protective effects by preserving oxidative phosphorylation of mitochondrial complexes I and II, particularly significantly enhancing the function of mitochondrial complex II. Succinate dehydrogenase (mitochondrial complex II) is a direct target of silibinin, but silibinin A and B exhibit different affinities for different subunits of complex II. Conclusion: Silibinin improved the accumulation of intrahepatic triglycerides induced by PM2.5, and this was, at least in part, explained by an enhancement of oxidative phosphorylation in mitochondrial Complexes I and II.

Hourly PM2.5 concentration prediction for dry bulk port clusters considering spatiotemporal correlation: A novel deep learning blending ensemble model.

Accurate prediction of PM2.5 concentrations in ports is crucial for authorities to combat ambient air pollution effectively and protect the health of port staff. However, in port clusters formed by multiple neighboring ports, we encountered several challenges owing to the impact of unique meteorological conditions, potential correlation between PM2.5 levels in neighboring ports, and coupling influence of background pollutants in city zones. Therefore, considering the spatiotemporal correlation among the factors influencing PM2.5 concentration variations within the harbor cluster, we developed a novel blending ensemble deep learning model. The proposed model combined the strengths of four deep learning architectures: graph convolutional networks (GCN), long short-term memory networks (LSTM), residual neural networks (ResNet), and convolutional neural networks (CNN). GCN, LSTM, and ResNet served as the base models aimed at capturing the spatial correlation of PM2.5 concentrations in neighboring ports, the potential long-term dependence of meteorological factors and PM2.5 concentrations, and the effects of urban ambient air pollutants, respectively. Following the blending ensemble technique, the prediction outcomes of three base models were used as the input data for the meta-model CNN, which employs the blending ensemble technique to produce the final prediction results. Based on actual data obtained from 18 ports in Nanjing, the proposed model was compared and analyzed for its prediction performance against six state-of-the-art models. The findings revealed that the proposed model provided more accurate predictions. It reduced mean absolute error (MAE) by 10.59 %-20.00 %, reduced root mean square error (RMSE) by 13.22 %-17.11 %, improved coefficient of determination (R2) by 10 %-35.38 %, and improved accuracy (ACC) by 3.48 %-7.08 %. Additionally, the contribution of each component to the prediction performance of the proposed model was measured using a systematic ablation study. The results demonstrated that the GCN model exerted the most substantial influence on the prediction performance of the GCN-LSTM-ResNet model, followed by the LSTM model. The influence of urban background pollutants can significantly enhance the generalizability of the complete model. Moreover, a comparison with three blended ensemble models incorporating any two base models demonstrated that the GCN-LSTM-ResNet model exhibited superior prediction performance and was particularly excellent in predicting the occurrence of high-concentration events. Specifically, the GCN-LSTM-ResNet model improved MAE and RMSE by at least 12.3% and 9.2%, respectively, but reduced R2 and ACC by 26.1% and 6.8%, respectively. The proposed model provided reliable PM2.5 concentration prediction outcomes and decision support for air quality management strategies in dry bulk port clusters.

Air quality and health benefits of achieving carbon-neutrality in building sector over Beijing, China.

To meet the goal of the Paris Agreement, China pledges to realize the "Dual Carbon" targets by 2060. As the capital of China, Beijing plays a leading role in becoming zero-emission or carbon neutral in the future. We project the pollutants emissions of building sector based on current strict clean air policies (PO scenario) and China's carbon neutrality target by 2060 (CN scenario) from 2019 to 2050. Results show that PM2.5 concentration will increase by 2.62 µg/m3 under PO scenario; under the CN scenario, ozone concentration will increase by 2.53 µg/m3 but PM2.5 concentration will reduce by 9.04 µg/m3. It is projected that China carbon neutrality goals could avoid 11.12% of PM2.5-related health burden; With strict clean air policies, health burdens of ozone (3.9%) and PM2.5 (4.1%) could be avoided, respectively. This study highlights the importance of achieving co-benefits of air quality and public health.

Evaluating the accuracy of International Classification of Disease Perinatal Mortality (ICD-PM) codes assigned on death certificates before and after expert panel review: a mixed methods observational study.

INTRODUCTION: The present study was conducted with the aim of evaluating the accuracy of International Classification of Disease Perinatal Mortality (ICD-PM) codes assigned on death certificates before and after an expert panel review. METHOD: The present study was a mixed methods observational study conducted at Umm al-Benin Hospital, the sole specialized obstetrics and gynecology center affiliated with Mashhad University of Medical Sciences. The study comprised three distinct stages: (1) Collecting primary ICD-PM codes assigned to perinatal death certificates, along with other relevant information, from October 2021 to March 2022; (2) Examining the circumstances of each perinatal death case and re-identifying the causes of death through a consensus process involving a panel of experts comprising pediatricians, obstetrics and gynecology specialists, and nursing and midwifery experts; presenting the new ICD-PM code; (3) Comparing the ICD-PM codes assigned to perinatal death certificates before and after the expert panel's evaluation. RESULT: During the study period, a total of seven specialized panels were conducted to examine perinatal deaths. Out of the 71 cases, 41 were carefully reviewed by experts. These cases included 32 stillbirths and nine neonatal deaths. The examination process followed specific inclusion and exclusion criteria. The findings revealed that there were no significant changes in the causes of neonatal deaths. However, it was notable that 80% of the previously unknown causes of stillbirths were successfully identified. Notably, the occurrence of stillbirths increased by 78% due to maternal causes and conditions. CONCLUSION: Convening panels of experts to discuss the causes of perinatal deaths can effectively reduce the percentage of unknown causes, as classified by ICD-PM. This approach also guarantees the availability of essential data for implementing effective interventions to decrease preventable perinatal deaths.

Crispr-mediated genome editing reveals a preponderance of non-oncogene addictions as targetable vulnerabilities in pleural mesothelioma.

Pleural mesothelioma (PM) is an aggressive cancer with limited treatment options. In particular, the frequent loss of tumor suppressors, a key oncogenic driver of the disease that is therapeutically intractable, has hampered the development of targeted cancer therapies. Here, we interrogate the PM genome using CRISPR-mediated gene editing to systematically uncover PM cell susceptibilities and provide an evidence-based rationale for targeted cancer drug discovery. This analysis has allowed us to identify with high confidence numerous known and novel gene dependencies that are surprisingly highly enriched for non-oncogenic pathways involved in response to various stress stimuli, in particular DNA damage and transcriptional dysregulation. By integrating genomic analysis with a series of in vitro and in vivo functional studies, we validate and prioritize several non-oncogene addictions conferred by CDK7, CHK1, HDAC3, RAD51, TPX2, and UBA1 as targetable vulnerabilities, revealing previously unappreciated aspects of PM biology. Our findings support the growing consensus that stress-responsive non-oncogenic signaling plays a key role in the initiation and progression of PM and provide a functional blueprint for the development of unprecedented targeted therapies to combat this formidable disease.

Unveiling the organic chemical composition and sources of organic carbon in PM2.5 at an urban site in Greater Cairo (Egypt): A comprehensive analysis of primary and secondary compounds.

This work presents an exhaustive chemical characterization of the organic fraction of fine particulate matter (PM2.5) collected at an urban site in the Greater Cairo Area, Egypt, one of the most polluted megacities in the world. An intensive 2-month sampling campaign was conducted at an urban site in Giza (Dokki), from November 26, 2019, to January 28, 2020. Daily (24-h integrated) PM2.5 filter samples were then analyzed for their carbonaceous (OC, EC) and organic fractions including primary (n-alkanes, phthalates, fatty acids, polycyclic aromatic hydrocarbons, hopanes, sugars, and sugar alcohols) and secondary (isoprene and ß-caryophyllene oxidation products, and dicarboxylic acids) compounds. Average organic (OC) and elemental carbon (EC) concentrations were 17.8 ± 6.6 µg/m3 and 4.4 ± 1.5 µg/m3, respectively. Biomass burning was confirmed by high daily concentration levels of levoglucosan, mannosan, and galactosan (sum equals to 288 ng/m3). Road traffic was also highlighted by the relative abundance of tetracosane and a carbon preference index close to unity as well as by the concentration ratios of PAHs and hopanes. Moreover, phthalates were identified for the first time in Cairo with high concentrations (654 ng/m3) that might be attributable to open waste burning activities. Fatty acids and sugars were also investigated and assigned to cooking activities and primary biogenic sources, respectively. The average concentration of isoprene and ß-caryophyllene oxidation products were 0.89 ± 0.83 ng/m3, and 0.01 ± 0.02 ng/m3, respectively. These low values are expected since no pine trees or even forests exist in Egypt. The macrotracer approach was employed alongside Monte Carlo simulation to identify sources of primary OC and evaluate the uncertainties associated with source attribution and OC reconstruction. The findings revealed a strong contribution from cooking (31% of observed OC) and biomass burning (18%), with median reconstructed OC levels showing significant uncertainty (64%) as expected.

PM2.5 prediction based on modified whale optimization algorithm and support vector regression.

In order to obtain the pattern of variation of PM2.5concentrations in the atmosphere in Nanchang City, we build a Support Vector Regression(SVR) with modified Whale Optimization Algorithm(WOA) hybrid model (namely mWOA-SVR model) that can predict the PM2.5concentration. Firstly, according to the Pearson correlation coefficient (PCC) method to examine the dynamic relationship between air pollutants and meteorological factors together with them, PM10, SO2and CO were selected as air pollutant concentration characteristics, while daily maximum and minimum temperatures, and wind power levels were selected as meteorological characteristics; then, using modified WOA algorithm for parameter selection of SVR model, four sets of better parameter combinations were found; finally, the mWOA-SVR model was built by the four sets parameters to predict PM2.5concentration. The results show that the prediction accuracy of mixed mWOA-SVR model with pollutant concentration plus weather factors as the feature was higher than single pollutant concentration.

Neural correlates of prospective memory in college students with anxiety.

Introduction: Prospective memory (PM) is the ability to create and execute future tasks. It is comprised of two components: cue detection and intention retrieval. PM is essential for performing high-level goals, a proficiency extremely important in college populations. Anxiety is a prevalent psychological experience in college populations that may be associated with impairments in PM. The present study examined PM performance and anxiety in college students, using neurophysiology to measure the mechanism of impairment. Methods: After self-reporting anxiety levels, 80 participants completed an event-based, focal PM task while two event-related potentials were recorded from an electroencephalogram: the N300 to assess cue detection, and the prospective positivity to assess intention retrieval. Results: The results demonstrated that, when controlling for age and gender, higher state anxiety was significantly associated with lower PM accuracy (ß = -0.27, p = 0.020) and lower prospective positivity amplitude (ß = -0.04, p = 0.021). Lower prospective positivity amplitude was significantly associated with lower PM accuracy (ß = 0.27, p = 0.015). Higher state anxiety was significantly indirectly associated with lower PM accuracy mediated by lower prospective positivity amplitude (ab = -0.11, p = 0.047). Discussion: These findings suggest intention retrieval could be a key component in supporting PM for college students with high state anxiety.

Life cycle assessment of common urban trees - The environmental performance of three Mediterranean cities.

Given the increasing pressure from extreme events due to climate change, the planting of new trees has become a priority in the political agendas of cities. However, the rush to plant trees often fails to account for the reduced performance and lifespan of trees in heavily urbanized areas and the environmental impact of their production, maintenance, and eventual disposal. By means of the Life Cycle Analysis, this study aims to investigate the potential environmental benefit and impact of trees planted in three European cities located in Mediterranean areas (Perugia, Thessaloniki, and Cascais), that have adhered to the management guidelines of the LIFE Clivut project. The environmental performance of each tree is mainly affected by the tree management operations performed, by the climatic conditions, and by the tree performance in carbon dioxide (CO2) uptake and Particulate matter (PM) capture. The impact assessment obtained by ReCiPe 2016 Endpoint (H) methodology evidenced that the trees' beneficial effects widely overcome the impact of the operations carried out during the tree management. Broadleaved species showed an average environmental performance higher than conifers. The best results have been obtained by Tilia cordata Mill., Quercus ilex L., Populus spp. and Celtis australis L. in the case of broadleaved trees, and by Calocedrus decurrens (Torr.) Florin and Cedrus spp. for Conifers. The results of the Environmental Footprint (EF 3.1) method highlighted the urban trees' potentiality to mitigate Human Health problems and to improve Ecosystem Quality. Future studies may explore other management scenarios to optimize energy and materials use, reduce emissions, hence obtaining an increase of the environmental benefit for the urban areas.

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.

A systematic review and meta-analysis of the globally reported International Classification of Diseases to Perinatal Mortality (ICD-PM).

Introduction: Accurate recording and identification of perinatal mortality causes are crucial to reducing the global burden of perinatal mortality through targeted interventions. However, existing studies on the International Classifications of Diseases to Perinatal Mortality (ICD-PM) are limited by inconsistent results and variations by gestational age. Thus, this review aims to synthesize and document updated data on the causes of death using the ICD-PM classification. Methods: Electronic databases such as the PubMed via MEDLINE, SCOPUS, Web of Sciences, EMBASE, Cochrane Library, and PROSPERO were searched to retrieve studies published from 2016 to February 2024. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of the included studies, and heterogeneity between the studies was assessed using I2 statistics. ICD-PM coded reported data were extracted to Microsoft Excel, and aggregate data of frequencies and percentages were reported. Results: Out of the 23 included studies, 48,596 perinatal mortalities were reported, and approximately 96% (46,816 deaths) were classified according to the ICD-PM. The pooled rate of stillbirths in high-income countries was 23/1,000 births; in low-income countries, it was found to be approximately twice as in high-income countries. Regarding the category of deaths, 25,563 (54.6%) deaths were recorded in the antepartum period, and more than half, 14,887 (58.2%), were classified under unspecified causes (A6). Moreover, 6,148 (13.7%) and 14,835 (31.7%) deaths were coded with intrapartum and neonatal period causes, respectively. The leading causes of perinatal mortality during the intrapartum were acute intrapartum events (I3) 3,712 (57.8%). Furthermore, neonatal death was caused by low birth weight and prematurity (N9) 4,091 (27.6%), congenital malformations, and chromosomal abnormalities (N1) 2,512(16.9%). Conclusion: Congenital malformations, and chromosomal abnormalities contribute to 1 in every 10 perinatal deaths and 1 in every 4 neonatal deaths. Other specified antepartum disorders are responsible for over half of antepartum deaths, while acute intrapartum events are the leading cause of intrapartum deaths, with a significant proportion remaining unexplained. Maternal complications related to the placenta, membranes, cord, labor, and delivery play a significant role in antepartum and intrapartum deaths. Targeted interventions and improved monitoring of high-risk pregnancies are crucial to reducing perinatal mortality rates. Further investigation is needed to enhance understanding and address unexplained perinatal deaths. Systematic review registration: [https://clinicaltrials.gov/], identifier [CRD4202452549].

Inequities in air pollution on stroke mortality among Older Americans: a U.S. nationwide analysis.

Background: Air pollution is a known risk factor for cardiovascular diseases, including stroke. This study examines the impact of county-level air pollution on ischemic and hemorrhagic stroke mortality among U.S. individuals aged 65 and older, emphasizing racial and socioeconomic disparities. Methods: Using data from the Center for Disease Control (CDC) Interactive Atlas of Heart Disease and Stroke, we analyzed county-level ischemic stroke mortality rates for older residents between 2016 and 2020. The data on air pollution at the county level, specifically particulate matter (PM2.5) levels, were obtained from the CDC. We applied multivariable linear and logistic regression models to examine the association between PM2.5 levels and stroke mortality, as well as the probability of meeting the Environmental Protection Agency (EPA) air quality standards. Results: County-level analysis revealed a significant correlation (R = 0.68, R2 = 0.48, p < 0.001) between PM2.5 levels and overall stroke mortality. For every 1 µg/m3 increase in PM2.5, there was an increase of 1.89 ischemic stroke deaths per 100,000 residents. Racial and socioeconomic disparities were evident. Counties with predominantly Black populations exhibited a stark disparity, with each 1 µg/m3 increase in PM2.5 correlating with a significant rise in mortality, amounting to 5.81 additional deaths per 100,000 residents. Persistently poor counties displayed vulnerability, experiencing a 4.05 increase in ischemic stroke deaths per 100,000 residents for every 1 µg/m3 increase in PM2.5 levels. Conversely, in counties with a White majority and counties without a persistent state of poverty, the associated increases in stroke mortality per 100,000 residents for every 1 µg/m3 rise in county-level PM2.5 were 1.85 and 1.60, respectively. Counties with a majority of Black residents were over twice as likely to be non-compliant with EPA air quality standards compared to predominantly White counties (aOR 2.36 95% CI: 1.27-4.38, p = 0.006). Conclusion: This study underscores the significant impact of county-level air pollution, particularly PM2.5, on ischemic stroke mortality among older U.S. residents. Our findings indicate that counties with predominantly Black populations and those experiencing persistent poverty not only suffer from higher mortality rates but also are more likely to be non-compliant with EPA air quality standards. Targeted interventions and policies are urgently needed to reduce air pollution in these vulnerable communities and promote equitable public health outcomes.

Bacterial and fungal aerosols in poultry houses: PM2.5 metagenomics via single-molecule real-time sequencing.

Microbial aerosol contamination is a common problem in poultry farms, posing potential health risks to poultry and their caretakers. Exploring the distribution and diversity of the microbial community in poultry farm aerosols is crucial for effective mitigation strategies. The composition of bacterial and fungal aerosols is poorly understood, particularly the prevalence of potential pathogenic microorganisms in fine particulate matter (PM2.5) in various types of poultry houses. In this study, 27 PM2.5 samples were collected from 5 chicken houses and 4 duck houses in Shandong Province, China. Species-level diversity of bacterial and fungal components in PM2.5 samples was determined using advanced single-molecule real-time sequencing (SMRT) technology, based on the 16S and internal transcribed spacer 1 (ITS) ribosomal genes. Microbial diversity and community composition varied significantly across the different poultry house. Notably, duck houses had higher concentrations (p < 0.01) of PM2.5 (92.8-143.1 µg/m3) than chicken houses (42.0-56.4 µg/m3). Furthermore, microbial variation in PM2.5 samples was associated with the type of poultry facility. The predominant pathogenic microorganisms included Aspergillus sydowii, Penicillium sp., Aspergillus insolitus, Cladosporium sp., Aspergillus sp., Aspergillus pseudoglaucus, Cladosporium sp. C4092-2-PD1, and Colletotrichum sp., all of which were classified as second category of pathogens. Aspergillus sydowii and Penicillium sp. were the dominant species in chicken houses, while Cladosporium sp., Aspergillus sp., and Aspergillus pseudoglaucus were the dominant species identified in duck houses. To our knowledge, this study is the first to investigate bacterial and fungal diversity in PM2.5 samples collected from various types of poultry houses. These findings advance our understanding of poultry environmental microbiology and have important implications for safeguarding the health of both poultry and their caretakers.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57 µg/m3 and PM10 at 39.58 µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Exposure to mixture particulate contaminants in the air and the risk of oral cancer: An updated systematic review and meta-analysis.

The mixture of contaminants in the air (e.g., PM2.5, smoke) is a part of air pollutants that has become a hot environmental issue. Previous epidemiological studies have reported the relationship between wood smoke and PM2.5 exposure and oral cancer, but findings have been inconsistent. Therefore, this work designed to find out the relationship between mixture contaminants in air exposure and oral cancer. Fourteen studies were included through research in three databases before February 2024. Before analysis, the Newcastle-Ottawa Scale was applied to examine the quality of all selected studies. Then, the meta-analysis was carried out by meta-regression analysis, sensitivity analysis and subgroup analysis. The results showed that exposure to PM2.5 may have a positive association with oral cancer (pooled OR = 1.13, 95 % confidence interval: 1.06, 1.20). In contrast, no significant association was found between indoor air pollution and oral cancer. However, the result of the subgroup analysis indicated there is a significant association of indoor air pollution and oral cancer in developing countries (pooled OR = 2.5, 95 % confidence interval: 1.7, 3.6). In addition, the heterogeneity among studies of indoor air pollution exposure and oral cancer may caused by studies carried out in developed countries according to the subgroup and meta-regression analyses. In conclusion, the studies about indoor air pollution exposure and oral cancer are discrepant. The effects of mixed air contaminants for people's health are not simple and more studies are demanded to find out it in the future.

From silos to synergy: a consortium approach to air pollution and public health in Abu Dhabi.

Financial resources alone cannot guarantee effective public health policy. In Abu Dhabi, massive economic growth in the desert climate resulted in concentrated urbanization and led to challenges in the regulation of air pollution. The Environment Agency in Abu Dhabi commissioned us to scope the regulatory challenges for air pollution. Part of this project relied on the participation and involvement of key stakeholders. We found three barriers: (1) limited appreciation of uncertainties in risk estimates and discussion on the importance of considering control costs and the societal trade-offs between health and wealth inherent in such decisions, (2) compartmentalization of efforts, and (3) challenges to decide how to prioritize risks in policy agendas. We propose a consortium-like approach that brings stakeholders together and places risk, uncertainty, and tradeoffs between health and wealth at the forefront of decision-making. Expected outcomes include improved collaboration and information sharing, strategic prioritization of emission controls, and a better understanding and consideration of uncertainty to guide future public health research.

Exposure to submicron particulate matter and long-term survival: Cross-cohort analysis of 3 Chinese national surveys.

BACKGROUND: Cohort evidence linking increased mortality with airborne fine particulate matter (PM2.5, particulate matter [PM] with aerodynamic diameter ≤2.5 µm) exposure was extensively validated worldwide. Nevertheless, long-term survival associated with submicron particulate matter (PM1, PM with aerodynamic diameter ≤1 µm) exposure remained largely unstudied, particularly in highly exposed populations. METHODS: We performed a population-based investigation involving 86844 adults aged 16+ years from 3 national dynamic cohorts spanning from 2005 to 2018. Residential annual exposure to PM1 and PM2.5 was assigned for each follow-up year using satellite-derived spatiotemporal estimates at a 1-km2 resolution. The concentration of PM1-2.5 (PM with aerodynamic diameter between 1 and 2.5 µm) was calculated by subtracting PM1 from PM2.5. Time-independent Cox proportional hazards regression models were applied to assess the associations of all-cause mortality with long-term exposure to size-specific particles. To investigate the effect of PM1 on PM2.5-mortality associations, we categorized participants into low, medium, and high groups based on PM1/PM2.5 ratio and examined the risk of PM2.5-associated mortality in each stratum. Effect modifications were checked via subgroup analyses. RESULTS: A total of 18722 deaths occurred during 497069.2 person-years of follow-up (median 5.7 years). Participants were exposed to an average annual concentration of 31.8 µg/m³ (range: 7.6-66.8 µg/m³) for PM1, 56.3 µg/m³ (range: 19.8-127.2 µg/m³) for PM2.5, and 24.5 µg/m³ (range: 7.3-60.3 µg/m³) for PM1-2.5. PM1, PM2.5, and PM1-2.5 were consistently associated with elevated mortality risks, with a hazard ratio (HR) of 1.029 (95% confidence interval [CI]: 1.013-1.046), 1.014 (95% CI: 1.005-1.023), and 1.019 (95% CI: 1.001-1.038) for each 10-µg/m3 increase in exposure, respectively. Compared with low (HR = 0.986, 95% CI: 0.967-1.004) and medium (HR = 1.015, 95% CI: 1.002-1.029) PM1/PM2.5 ratio groups, PM2.5-related risk of mortality was more pronounced in high PM1/PM2.5 ratio stratum (HR = 1.041, 95% CI: 1.019-1.064). Greater risks of mortality associated with size-specific particles were found among the elderly (>80 years old), southeastern participants, and those living in warmer areas. CONCLUSIONS: This study demonstrated that long-term exposure to PM1, PM2.5, and PM1-2.5 was associated with heightened mortality, and PM1 may play a predominant role in PM2.5-induced risk. Our results emphasized the population health implications of establishing ambient PM1 air quality guidelines to mitigate the burden of premature mortality stemming from particulate air pollution.

Risk assessment of PM2.5 from fossil energy consumption on the respiratory health of the elderly.

Air pollution mainly comes from fossil energy consumption (FEC), and it brings great threat to public health. The respiratory system of the elderly is highly susceptible to the effects of air pollution due to the decline in body functions. PM2.5 is a major component of air pollution, so the study of the impact of PM2.5 generated by FEC on the respiratory health of the elderly is of great significance. The existing studies have focused more on the effect of PM2.5 on mortality, and this paper is a useful addition to the existing studies by examining the effect of PM2.5 from FEC on the health of the elderly from the perspective of prevalence. In this paper, the binary Logistic regression model was used to calculate the exposure-response relationship coefficient for respiratory health in older adults using the data in 2018 from the Chinese Longitudinal Healthy Longevity Survey. And referring to the Dynamic Projection model for Emissions in China, the changes in the number of older persons suffering from respiratory diseases due to PM2.5 from FEC in the baseline scenario, the clean air scenario, and the on-time peak-clean air scenario were predicted. The results indicated that: (1) PM2.5 from FEC mainly came from coal; (2) PM2.5 from FEC was detrimental to the respiratory health of the elderly, and older seniors were more affected as they age; (3) In the on-time peak-clean air scenario, the number of elderly people suffering from respiratory diseases due to PM2.5 from FEC was growing at the slowest rate. Based on the above results, this paper raised recommendations for reducing the effect of PM2.5 from FEC on the health of the elderly.