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.

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.

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.

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.

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.

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.

Particulate Matter 2.5 Pollution Impact on Comorbid Cancer and Cardiovascular Disease Mortality in the U.S.

Background: Evidence regarding the effect of long-term exposure to particulate matter (PM) 2.5 and comorbid cancer and cardiovascular disease (CVD) mortality is limited. Objectives: In this study, the author report the association between long-term exposure to PM 2.5 and CVD mortality, cancer mortality and comorbid cancer and CVD mortality in the U.S. population. Methods: The Centers for Disease Control and Prevention (CDC) WONDER (Wide-Ranging Online Data for Epidemiologic Research) multiple-cause-of-death database was used to obtain U.S. county-level mortality and population estimates from 2016 to 2020. Data on average daily density of PM 2.5 were abstracted from the 2018 CDC's National Environmental Public Health Tracking system. Counties were divided into quartiles with Q1 representing counties with least average daily density and Q4 representing counties with maximum average daily density of PM 2.5. Age-adjusted mortality rates were abstracted for each quartile, for the overall population and subgroups of population. Results: The age-adjusted mortality rates for CVD, cancer, and comorbid cancer and CVD mortality were 505.3 (range: 505.0-505.7), 210.7 (range: 210.5-210.9), and 62.0 (range: 61.8-62.1) per 100,000 person-years, respectively. CVD mortality had the highest percentage excess mortality in Q4 compared with Q1, followed by comorbid cancer and CVD. Cancer had the least percentage excess mortality. A disproportionate effect of PM 2.5 exposure was noted on vulnerable and minority groups, based on Social Vulnerability Index and race stratification, respectively. Conclusions: Higher levels of long-term PM 2.5 exposure reported increased CVD mortality, cancer mortality and comorbid cancer and CVD disease mortality, with a pronounced detrimental effect in vulnerable and minority population.

Exposure to PM2.5 and Its Constituents in Relation to Thyroid Function of Pregnant Women: Separate and Mixture Analyses.

The relationships between exposure to PM2.5 and its constituents and thyroid hormone (TH) levels in pregnant women are still uncertain, particularly regarding the impact of mixed exposure to PM2.5 constituents on thyroid function during pregnancy. This study aimed to investigate the individual and mixed effects of PM2.5 and its constituents on TH levels during pregnancy. Fluorescence and chemiluminescence immunoassays were utilized to measure serum concentrations of free thyroxine (FT4) and thyroid-stimulating hormone (TSH) in pregnant women participating in the Fujian Birth Cohort Study (FJBCS). PM2.5 and its constituents were obtained from the ChinaHighAirPollutants dataset. Generalized linear regression model and mixture analysis were applied to explore the individual and mixed effects of PM2.5 and its constituents on TH levels. 13711 participants from the FJBCS were taken into the final analysis. In the context of separate exposure, an increase of one interquartile range (IQR) in PM2.5 exposure during the 1st trimester, 2nd trimester, and entire pregnancy was associated with a decrease of -0.042 (-0.050, -0.034), -0.017 (-0.026, -0.009), and -0.011 (-0.017, -0.004) in FT4 levels, respectively. As well, significant negative associations were observed between FT4 levels and PM2.5 constituents. Additionally, PM2.5 and its constituents were in relation to an increased risk of hypothyroxinemia in pregnant women. It is noteworthy that, in the context of mixed exposure, the weighted quantile sum regression (WQS) indices were significantly associated with both FT4 levels (1st trimester: -0.031 (-0.036, -0.026); 2nd trimester: -0.026 (-0.030, -0.023); whole pregnancy: -0.024 (-0.028, -0.020)) and hypothyroxinemia risk (1st trimester: 1.552 (1.312, 1.821); 2nd trimester: 1.453 (1.194, 1.691); whole pregnancy: 1.402 (1.152, 1.713)). PM2.5 and its chemical constituents may affect thyroid function in pregnant women individually and in combination, with the effects observed during early gestational exposure being most pronounced.

Characteristics and health risks assessment of PM2.5-bound heavy metals during winter in urban areas of northern China: A case study in Kaifeng.

The health impact of heavy metals in atmospheric PM2.5 has garnered increasingly widespread attention. We have collected PM2.5 samples in a typical city (Kaifeng) within the Central Plains Urban Agglomeration in China during winter and measured the mass concentration of PM2.5-bound heavy metals. The pollution of As and Cr in the urban atmosphere requires significant attention. As PM2.5 concentrations increased, the enrichment factors (EFs) of Mn, Cu, Zn, As, and Pb also rose, suggesting a growing contribution from anthropogenic emissions. The analysis showed that the hazard quotient (HQ) for the non-heating and heating periods (HQ < 1) did not result in a cumulative non-carcinogenic health risk to humans. Regarding carcinogenic effects, As and Cr exhibit significant carcinogenic impacts on both children and adults (ELCR>1 × 10-6), indicating that the carcinogenic risks posed by As and Cr under PM2.5 exposure in Kaifeng could not be overlooked. It was found that industrial and biomass combustion are the primary sources of carcinogenic risk in Kaifeng city. From the non-heating to the heating period, the industrial carcinogenic risk increased from 37.12 % to 43.39 %, while the contribution of biomass burning remained at 25 %. This result was strongly correlated with the high proportions of heavy metal elements such as As, Mn, Pb, and Ni from the metal refinery industry. The results of this study revealed the equally important source of heavy metals, compared to coal combustion in North China Plain. In addition to residential coal combustion, industrial emissions are a major source of PM2.5-bound heavy metals in Kaifeng, contributing significantly to overall air pollution and providing a useful reference to mitigating human health risks in the area.

Modifying factors and temporal trends of adverse health effects of short-term exposure to PM2.5 in Canada (2001-2018).

Considerable evidence has been accumulated on serious acute health outcomes associated with short-term exposure to ambient fine particulate matter (PM2.5). Modifying factors of those associations, however, have been less explored and need further analyses. In this national study, we investigated whether short-term effects of PM2.5 are modified according to region, cause of mortality/hospitalization, season, age, and sex. PM2.5-related adverse health effects were estimated by an ecological time-series study, covering about 80 % of the Canadian population for 18 years (2001-2018). We estimated city-specific associations using daily averages of PM2.5 and temperature, and daily counts of hospitalizations and mortality (non-accidental all-cause, circulatory, and respiratory). National and regional associations were then estimated with a 2-stage model. We considered potential modifying factors of PM2.5-related adverse health effects, and examined linear trends in the annual associations. Nationally, PM2.5 exposure was associated with both hospitalizations and mortality, and there was evidence of differences by the modifying factors. Of the various causes, circulatory mortality and respiratory hospitalization were more attributable to PM2.5 exposure. We found regional differences for both all-cause hospitalization and all-cause mortality, and seasonal differences for respiratory hospitalization (warm season) and circulatory hospitalization (cold season). Circulatory mortality risk was significant for seniors and females. All-cause hospitalizations appeared to gradually decrease over time, but annual all-cause mortality remained constant at 0.6 % of the population. Adverse health effects of PM2.5 exposures may depend on not only PM2.5 concentration, but also other factors (region, cause, season, age, sex). National estimates for the baseline (age ≥ 1 year, both sexes) risk cannot be interpreted without consideration of the differences by modifying factors. Study findings can be used by seniors, women, and those who have pre-existing health conditions to make informed decisions regarding their health risks from daily exposure to ambient PM2.5.

Prenatal Exposure to Source-Specific Fine Particulate Matter and Autism Spectrum Disorder.

In this study, associations between prenatal exposure to fine particulate matter (PM2.5) from 9 sources and development of autism spectrum disorder (ASD) were assessed in a population-based retrospective pregnancy cohort in southern California. The cohort included 318,750 mother-child singleton pairs. ASD cases (N = 4559) were identified by ICD codes. Source-specific PM2.5 concentrations were estimated from a chemical transport model with a 4 × 4 km2 resolution and assigned to maternal pregnancy residential addresses. Cox proportional hazard models were used to estimate the hazard ratios (HR) of ASD development for each individual source. We also adjusted for total PM2.5 mass and in a separate model for all other sources simultaneously. Increased ASD risk was observed with on-road gasoline (HR [CI]: 1.18 [1.13, 1.24]), off-road gasoline (1.15 [1.12, 1.19]), off-road diesel (1.08 [1.05, 1.10]), food cooking (1.05 [1.02, 1.08]), aircraft (1.04 [1.01, 1.06]), and natural gas combustion (1.09 [1.06, 1.11]), each scaled to standard deviation increases in concentration. On-road gasoline and off-road gasoline were robust for other pollutant groups. PM2.5 emitted from different sources may have different impacts on ASD. The results also identify PM source mixtures for toxicological investigations that may provide evidence for future public health policies.

Critical windows for exposure to chemical composition of ambient particulate matter and human semen quality decline.

BACKGROUND: Critical windows for exposure to chemical components of particulate matter (PM <2.5 µm in diameter [PM2.5]) associated with the human semen quality decline remain unclear. OBJECTIVES: To address this gap, we developed a new analytical framework by integrating a Linear Mixed Model (LMM) with subject- and center-specific intercepts and a Distributed Lag Model (DLM) to fully account for correlations between finely vulnerable exposure windows based on complete profile of the spermatogenesis cycle. METHODS: We constructed a multicenter cohort involving 33,234 sperm donors with 78,952 semen samples covering 6 representative regions across China from 2014 to 2020 to investigate the week-scale critical windows for the exposure. Daily exposure to PM2.5 chemical components of donors was derived from grid data based on 1-km spatial resolution surface measurements. RESULTS: Decreased sperm count was significantly associated with NO3- and SO42- at 9-10 weeks (e.g., ß: -0.05 %, 95%CI: [-0.10 %, -0.00 %] at the 9th week) and 0-2 weeks (e.g., ß: -0.66 %, 95%CI: [-1.24 %, -0.07 %] at the 1st week), respectively. Critical windows of progressive motility decline were 0-10 weeks for BC (e.g., ß: -0.07 %, 95%CI: [-0.11 %, -0.03 %] at the 5th week), Cl- at 1-4 weeks (e.g., ß: -2.21 %, 95%CI: [-3.77 %, -0.66 %] at the 2nd week), 0-6 weeks and 9-10 weeks for NO3- (e.g., ß: -0.05 %, 95%CI: [-0.09 %, -0.01 %] at the 4th week), 1-3 weeks and the 8th week for NH4+ (e.g., ß: -0.06 %, 95%CI: [-0.11 %, -0.01 %] at the 2nd week). Total motility is significantly negatively associated with BC at entire windows, Cl- at 0-3 weeks, the 5th week and 9-10 weeks. CONCLUSIONS: There are week-scale vulnerable windows of exposure to PM2.5 chemical components for human semen quality. This highlights the need for more targeted pollution control strategies addressing PM2.5 and its chemical components.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77 µg/m3, CO: 1.41 ± 0.84 mg/m3; traffic site: PM10: 58.67 ± 20.05 µg/m3, CO: 0.99 ± 0.57 mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

Differences in cellular and molecular processes in exposure to PM2.5 and O3.

Epidemiological and toxicological studies have shown that PM2.5 and O3 could pose significant risks to human health, such as an increased incidence of respiratory and cardiovascular diseases. Usually, the adverse health outcomes induced by PM2.5 and O3 exposure are similar. However, PM2.5 and O3 have distinct physical and chemical properties, with PM2.5 being a solid-liquid mixture and O3 being a strongly oxidizing gaseous pollutant. Therefore, we speculated that there are some differences in biological processes induced by PM2.5 and O3 exposure. In the present study, we investigated the differences induced by PM2.5 and O3 exposure from the perspective of cellular and molecular processes. Firstly, the pulmonary epithelial cells (BEAS-2B) were exposed to different concentrations of PM2.5 or O3 at different durations. Then, we chose experimental models with the concentrations and duration at which the cell survival rate was 50 % after exposure to PM2.5 and O3, which were 100 µg/mL for 24 h for PM2.5, and 200 ppb for 4 h for O3. Our findings indicate that PM2.5 infiltrates cells via endocytosis without causing significant damage to cell membranes, while O3 induces lipid peroxidation at the cell surface. Moreover, the detection of mitochondrial function showed that the content of ATP was significantly reduced after exposure to both PM2.5 and O3. However, we found a significant difference in mtDNA copy number. PM2.5 exposure increased the mtDNA copy number by up-regulating the expression of fission genes (Fis1, Mff, Dnm1). O3 exposure decreased it by up-regulating the expression of fusion gene (Mfn1, Mfn2) and down-regulating the expression of fission gene (Fis1, Dnm1). These results indicate that although both PM2.5 and O3 exposure induced almost exactly similar adverse health outcomes, significant differences do exist in cellular and molecular processes.

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.

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches.

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 µg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.