Short-Term Associations of Ambient Fine Particulate Matter (PM2.5) with All-Cause Hospital Admissions and Total Charges in 12 Japanese Cities.
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ABSTRACT:
SUBMITTER: Hasegawa K
PROVIDER: S-EPMC8070111 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundPositive associations between ambient PM2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error.MethodsThis study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM2.5. We also conducted stratification analyses by age, sex, and season.ResultsA total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m3 increase in PM2.5 (lag0-2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01-1.45%) and 1.95% (95% CI 1.63-2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season.ConclusionsThis study contributes evidence of short-term effects of PM2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.
| S-EPMC8080330 | biostudies-literature
Project description:BackgroundThe Metropolitan Area of São Paulo has a unique composition of atmospheric pollutants, and positive correlations between exposure and the risk of diseases and mortality have been observed. Here we assessed the effects of ambient fine particulate matter (PM2.5) on genotoxic and global DNA methylation and hydroxymethylation changes, as well as the activities of antioxidant enzymes, in tissues of AJ mice exposed whole body to ambient air enriched in PM2.5, which was concentrated in a chamber near an avenue of intense traffic in São Paulo City, Brazil.ResultsMice exposed to concentrated ambient PM2.5 (1 h daily, 3 months) were compared to in situ ambient air exposed mice as the study control. The concentrated PM2.5 exposed group presented increased levels of the oxidized nucleoside 8-oxo-7,8-dihydro-2'-deoxyguanosine in lung and kidney DNA and increased levels of the etheno adducts 1,N6-etheno-2'-deoxyadenosine and 1,N2-etheno-2'-deoxyguanosine in kidney and liver DNA, respectively. Apart from the genotoxic effects, the exposure to PM2.5 led to decreased levels of the epigenetic mark 5-hydroxymethylcytosine (5-hmC) in lung and liver DNA. Changes in lung, liver, and erythrocyte antioxidant enzyme activities were also observed. Decreased glutathione reductase and increased superoxide dismutase (SOD) activities were observed in the lungs, while the liver presented increased glutathione S-transferase and decreased SOD activities. An increase in SOD activity was also observed in erythrocytes. These changes are consistent with the induction of local and systemic oxidative stress.ConclusionsMice exposed daily to PM2.5 at a concentration that mimics 24-h exposure to the mean concentration found in ambient air presented, after 3 months, increased levels of DNA lesions related to the occurrence of oxidative stress in the lungs, liver, and kidney, in parallel to decreased global levels of 5-hmC in lung and liver DNA. Genetic and epigenetic alterations induced by pollutants may affect the genes committed to cell cycle control, apoptosis, and cell differentiation, increasing the chance of cancer development, which merits further investigation.
| S-EPMC6194750 | biostudies-literature
Project description:BackgroundAir pollution and cardiovascular disease are increasing problems in China. However, the short-term association between fine particulate matter (PM2.5) and cardiovascular disease (CVD) is not well documented. The purpose of this study is to estimate the short-term effects of PM2.5 on CVD admissions in Beijing, China.MethodsIn total, 460,938 electronic hospitalization summary reports for CVD between 2013 and 2017 were obtained. A generalized additive model using a quasi-Poisson distribution was used to investigate the association between exposure to PM2.5 and hospitalizations for total and cause-specific CVD, including coronary heart disease (CHD), atrial fibrillation (AF), and heart failure (HF) after controlling for the season, the day of the week, public holidays, and weather conditions. A stratified analysis was also conducted for age (18-64 and ≥ 65 years), sex and season.ResultsFor every 10 μg/m3 increase in the PM2.5 concentration from the previous day to the current (lag 0-1) there was a significant increase in total CVD admissions (0.30, 95% CI: 0.20, 0.39%), with a strong association for older adults (aged ≥65 years), CHD (0.34, 95% CI: 0.22 to 0.45%) and AF (0.29, 95% CI, 0.03 to 0.55%). However, the observed increased risk was not statistically significant for HF hospitalizations. The associations in the single-pollutant models were robust to the inclusion of other pollutants in a two-pollutant model. No differences were found after stratification by sex and season.ConclusionsExposure to PM2.5 increased the risk of hospitalizations from CVD, especially for CHD, and appeared to have more influence in the elderly. Precautions and protective measures and efforts to reduce exposure to PM2.5 should be strengthened, especially for the elderly.
| S-EPMC6670159 | biostudies-literature
Project description:The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June-September) and short rain season (February-April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public's health.
| S-EPMC8583055 | biostudies-literature
Project description:Human health risks caused by PM2.5 raise awareness to the role of trees as bio-filters of urban air pollution, but not all species are equally capable of filtering the air. The objectives of this current study were: (1) to determine the foliar traits for effective PM2.5-capture and (2) explore species-to-species differences in foliar PM2.5-recapture capacity following a rain event. The study concluded that overall, the acicular needle shape made conifers more efficient with PM2.5 accumulation and post-rainfall recapture than broadleaved species. The foliar shape and venation of broadleaved species did not appear to influence the PM2.5 accumulation. However, the number of the grooves and trichomes of broadleaved species were positively related to foliar PM2.5 accumulation, suggesting that they could be used as indicators for the effectiveness of tree PM2.5 capture. Furthermore, the amount of PM2.5 removal by rainfall was determined by the total foliar PM2.5. Not all PM2.5 remained on the foliage. In some species, PM2.5 was resuspended during the growing season, and thus reduced the net particular accumulation for that species. These findings contribute to a better understanding of tree species potential for reducing PM2.5 in urban environments.
| S-EPMC5466687 | biostudies-literature
Project description:Specification of PM 2.5 transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM 2.5 contributors and PM 2.5 transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM 2.5 concentrations. We aim to reveal PM 2.5 mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM 2.5 pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM 2.5 are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM 2.5 transport.
| S-EPMC5643331 | biostudies-literature
Project description:This study presents one of the first long term datasets including a statistical summary of PM2.5 concentrations obtained from one-year monitoring in 190 cities in China. We found only 25 out of 190 cities could meet the National Ambient Air Quality Standards of China, and the population-weighted mean of PM2.5 in Chinese cities are 61 μg/m(3), ~3 times as high as global population-weighted mean, highlighting a high health risk. PM2.5 concentrations are generally higher in north than in south regions due to relative large PM emissions and unfavorable meteorological conditions for pollution dispersion. A remarkable seasonal variability of PM2.5 is observed with the highest during the winter and the lowest during the summer. Due to the enhanced contributions from dust particles and open biomass burning, high PM2.5 abundances are also found in the spring (in Northwest and West Central China) and autumn (in East China), respectively. In addition, we found the lowest and highest PM2.5 often occurs in the afternoon and evening hours, respectively, associated with daily variation of the boundary layer depth and anthropogenic emissions. The diurnal distribution of the PM2.5-to-CO ratio consistently displays a pronounced peak during the afternoon periods, reflecting a significant contribution of secondary PM formation.
| S-EPMC4606739 | biostudies-literature
Project description:Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
| S-EPMC9723408 | biostudies-literature
Project description:Ambient PM2.5 was collected during the winter season from Taiyuan, Shanxi, China; Jinan, Shandong, China; and Sacramento, California, USA, and used to create PMSX, PMSD, and PMCA extracts, respectively. Time-lag experiments were performed to explore the in vivo and in vitro toxicity of the PM extracts. In vivo inflammatory lung responses were assessed in BALB/c mice using a single oropharyngeal aspiration (OPA) of PM extract or vehicle (CTRL) on Day 0. Necropsies were performed on Days 1, 2, and 4 post-OPA, and pulmonary effects were determined using bronchoalveolar lavage (BAL) and histopathology. On Day 1, BAL neutrophils were significantly elevated in all PM- versus CTRL-exposed mice, with PMCA producing the strongest response. However, histopathological scoring showed greater alveolar and perivascular effects in PMSX-exposed mice compared to all three other groups. By Day 4, BAL neutrophilia and tissue inflammation were resolved, similar across all groups. In vitro effects were examined in human HepG2 hepatocytes, and U937 cells following 6, 24, or 48 h of exposure to PM extract or DMSO (control). Luciferase reporter and quantitative polymerase chain reaction assays were used to determine in vitro effects on aryl hydrocarbon receptor (AhR) activation and gene transcription, respectively. Though all three PM extracts activated AhR, PMSX produced the greatest increases in AhR activation, and mRNA levels of cyclooxygenase-2, cytochrome P450, interleukin (IL)-8, and interleukin (IL)-1β. These effects were assumed to result from a greater abundance of polycyclic aromatic hydrocarbons (PAHs) in PMSX compared to PMSD and PMCA.
| S-EPMC7641014 | biostudies-literature
Project description:Chiang Mai encounters severe pollution during the wildfire season. Wildland firefighters encounter various hazards while engaged in fire suppression operations, which encompass significant exposure to elevated concentrations of air pollutants resulting from combustion, especially particulate matter. The adverse effects of wildfire smoke on respiratory health are a significant concern. The objective of this study was to examine the potential adverse effects of PM2.5 exposure on the respiratory function and DNA damage of wildland firefighters. This prospective cohort study conducted in Chiang Mai from January to May 2022 planned to evaluate the health status of wildland firefighters during the pre-peak, peak, and post-peak ambient air pollution seasons. The measurement of PM2.5 was done at every forest fire station, as well as utilizing data from the Pollution Control Department. Participants received general health examinations, spirometry evaluations, and blood tests for DNA damage analysis. Pair t-tests and multiple regression models were used to examine the connection between pulmonary function parameters (FVC, FEV1) and PM2.5 concentration, with a significance level of P < 0.05. Thirty-three peak-season and twenty-one post-peak-season participants were enrolled. Four pre-peak-season wildland firefighters had FVC and FEV1 declines of > 15%. Multiple regression analysis showed a negative association between PM2.5 exposure and FVC% predicted (- 2.81%, 95% CI - 5.27 to - 0.34%, P = 0.027) and a marginally significant negative correlation with FVC (- 114.38 ml, 95% CI - 230.36 to 1.59 ml, P = 0.053). The remaining pulmonary measures showed a statistically insignificant decline. There were no significant changes in DNA damage detected. Wildland firefighters suffered a significant decline in pulmonary function associated with PM2.5 exposure. Spirometry is crucial for monitoring and promptly identifying respiratory issues that occur during wildfire seasons. Further research is recommended to explore DNA damage alterations and their potential association with PM2.5.
| S-EPMC10994925 | biostudies-literature