Differential distributed lag patterns of source-specific particulate matter on respiratory emergency hospitalizations

Exploring the Cellular Impact of Size-Segregated Cigarette Aerosols: Insights into Indoor Particulate Matter Toxicity and Potential Therapeutic Interventions

Exposure to anthropogenic aerosols has been associated with a variety of adverse health effects, increased morbidity, and premature death. Although cigarette smoke poses one of the most significant public health threats, the cellular toxicity of particulate matter contained in cigarette smoke has not been systematically interrogated in a size-segregated manner. In this study, we employed a refined particle size classification to collect cigarette aerosols, enabling a comprehensive assessment and comparison of the impacts exerted by cigarette aerosol extract (CAE) on SH-SY5Y, HEK293T, and A549 cells. Exposure to CAE reduced cell viability in a dose-dependent manner, with organic components having a greater impact and SH-SY5Y cells displaying lower tolerance compared to HEK293T and A549 cells. Moreover, CAE was found to cause increased oxidative stress, mitochondrial dysfunction, and increased levels of apoptosis, pyroptosis, and autophagy, leading to increased cell death. Furthermore, we found that rutin, a phytocompound with antioxidant potential, could reduce intracellular reactive oxygen species and protect against CAE-triggered cell death. These findings underscore the therapeutic potential of antioxidant drugs in mitigating the adverse effects of cigarette aerosol exposure for better public health outcomes.

Application of machine learning in atmospheric pollution research: A state-of-art review

Machine learning (ML) is an artificial intelligence technology that has been used in atmospheric pollution research due to their powerful fitting ability. In this review, 105 articles related to ML and the atmospheric pollution research are critically reviewed. Applications of ML in the prediction of atmospheric pollution (mainly particulate matters) are systematically described, including the principle of prediction, influencing factors and improvement measures. Researchers can improve the accuracy of the prediction model through three main aspects, namely considering the geographical features of the study area into the model, introducing the physical characteristics of pollutants, matching and optimizing ML models. And by using interpretable ML tools, researchers are able to understand the mechanism of the model and gain in-depth information. Then, the state-of-art applications of ML in the source apportionment of atmospheric particulate matter and the effect of atmospheric pollutants on human health are also described. In addition, the advantages and disadvantages of the current applications of ML in atmospheric pollution research are summarized, and the application perspective of ML in this field is elucidated. Given the scarcity of source apportionment applications and human health research, standardized research methods and specialized ML methods are required in atmospheric pollution research to connect these two disciplines.

Introducing activation functions into segmented regression model to address lag effects of interventions

The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions.To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models.OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates.Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model.

The performance of artificial neural networks for modeling daily concentrations of particulate matter from meteorological data

The use of techniques based on artificial intelligence and machine learning for the simulation of many processes is becoming increasingly important in environmental sciences, with applications in the study of time series of atmospheric properties, such as pollution levels. The present work aimed to evaluate the efficiency of a model based on Artificial Neural Networks (ANN) in the simulation PM10 from meteorological data observed between 2018 and 2019 in Guaíba, southern Brazil, thus also having an estimate of the influence of atmospheric conditions on local air pollution. For this purpose, meteorological and PM10 data obtained from the stations Parque 35, sustained by Celulose Riograndense (CMPC), and A-801, sustained by the National Institute of Meteorology (INMET), were used. The ANN used for the simulation was of the Multilayer Perceptron type, trained by the backpropagation algorithm with cross-validation. The results obtained indicate that the simulation was satisfactory with a Nash-Sutcliffe index (NSE) of 0.64, a linear correlation coefficient (R) of 0.81, a relative error (Er) of 26% and a root mean square error (RMSE) of 7.40 µg/m3. Thus, even with some difficulty in estimating extreme concentrations, the model was suitable for the largest range observed, of 10 µg/m3 to 50 µg/m3. For this dataset, the model proved to be an useful assessment tool and has the potential to be applied operationally to contribute to the monitoring and control of air quality levels both in the study area and in other regions of Brazil and the world.

Effects of Ambient Air Pollution on Precocious Puberty: A Case-Crossover Analysis in Nanjing, China

BACKGROUND

Ambient air pollution is closely related to a variety of health outcomes. Few studies have focused on the correlations between air pollution exposure and children's sexual development. In this study, we investigated the potential effects of exposure to air pollution on precocious puberty among children using real-world evidence.

METHODS

We conducted a case-crossover study (n = 2201) to investigate the effect of ambient air pollution exposure on precocious puberty from January 2016 to December 2021. Average exposure levels of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ before diagnosis were calculated by using the inverse distance weighting (IDW) method. Distributed lag nonlinear model (DLNM) was used to assess the effect of air pollutants exposure on precocious puberty.

RESULTS

The mean age of the children who were diagnosed with precocious puberty was 7.47 ± 1.24 years. The average concentration of PM_2.5 and PM₁₀ were 38.81 ± 26.36 μg/m³ and 69.77 ± 41.07 μg/m³, respectively. We found that exposure to high concentrations of PM_2.5 and PM₁₀ might increase the risk of precocious puberty using the DLNM model adjusted for the age, SO₂, NO₂, CO, and O₃ levels. The strongest effects of the PM_2.5 and PM₁₀ on precocious puberty were observed in lag 27 (OR = 1.72, 95% CI: 1.01-2.92) and lag 16 (OR = 1.95, 95% CI: 1.33-2.85), respectively.

CONCLUSION

Our findings supported that short-term exposure to air pollution was the risk factor for precocious puberty. Every effort should be made to protect children from air pollution.

Associations of Exposure to Fine Particulate Matter Mass and Constituents with Systemic Inflammation: A Cross-Sectional Study of Urban Older Adults in China

Systemic inflammation is a key mechanism in the development of cardiovascular diseases induced by exposure to fine particles (particles with aerodynamic diameter ≤2.5 μm [PM_2.5]). However, little is known about the effects of chemical constituents of PM_2.5 on systemic inflammation. In this cross-sectional study, filter samples of personal exposure to PM_2.5 were collected from community-dwelling older adults in Tianjin, China, and the chemical constituents of PM_2.5 were analyzed. Blood samples were collected immediately after the PM_2.5 sample collection. Seventeen cytokines were measured as targets. A linear regression model was applied to estimate the relative effects of PM_2.5 and its chemical constituents on the measured cytokines. A positive matrix factorization model was employed to distinguish the sources of PM_2.5. The calculated source contributions were used to estimate their effects on cytokines. After adjusting for other covariates, higher PM_2.5-bound copper was significantly associated with increased levels of interleukin (IL)1β, IL6, IL10, and IL17 levels. Source analysis showed that an increase in PM_2.5 concentration that originated from tire/brake wear and cooking emissions was significantly associated with enhanced levels of IL1β, IL6, tumor necrosis factor alpha (TNFα), and IL17. In summary, personal exposure to some PM_2.5 constituents and specific sources could increase systemic inflammation in older adults. These findings may explain the cardiopulmonary effects of specific particulate chemical constituents of urban air pollution.

Warm Season and Emergency Department Visits to U.S. Children's Hospitals

BACKGROUND

Extreme heat exposures are increasing with climate change. Health effects are well documented in adults, but the risks to children are not well characterized.

OBJECTIVES

We estimated the association between warm season (May to September) temperatures and cause-specific emergency department (ED) visits among U.S. children and adolescents.

METHODS

This multicenter time-series study leveraged administrative data on 3.8 million ED visits by children and adolescents ≤18 years of age to the EDs of 47 U.S. children's hospitals from May to September from 2016 to 2018. Daily maximum ambient temperature was estimated in the county of the hospital using a spatiotemporal model. We used distributed-lag nonlinear models with a quasi-Poisson distribution to estimate the association between daily maximum temperature and the relative risk (RR) of ED visits, adjusting for temporal trends. We then used a random-effects meta-analytic model to estimate the overall cumulative association.

RESULTS

Extreme heat was associated with an RR of all-cause ED visits of 1.17 (95% CI: 1.12, 1.21) relative to hospital-specific minimum morbidity temperature. Associations were more pronounced for ED visits due to heat-related illness including dehydration and electrolyte disorders (RR= 1.83; 95% CI: 1.31, 2.57), bacterial enteritis (1.35; 95% CI: 1.02, 1.79), and otitis media and externa (1.30; 95% CI: 1.11, 1.52). Taken together, temperatures above the minimum morbidity temperature accounted for an estimated 11.8% [95% empirical 95% confidence interval (eCI): 9.9%, 13.3%] of warm season ED visits for any cause and 31.0% (95% eCI: 17.9%, 36.5%) of ED visits for heat-related illnesses.

CONCLUSION

During the warm season, days with higher temperatures were associated with higher rates of visits to children's hospital EDs. Higher ambient temperatures may contribute to a significant proportion of ED visits among U.S. children and adolescents. https://doi.org/10.1289/EHP8083.

Toxicological effects of personal exposure to fine particles in adult residents of Hong Kong

Toxicological studies have demonstrated the associations between fine particle (PM_2.5) components and various cytotoxic endpoints. However, few studies have investigated the toxicological effects of source-specific PM_2.5 at the individual level. To investigate the potential impact of source-specific PM_2.5 on cytotoxic effects, we performed repeated personal PM_2.5 monitoring of 48 adult participants in Hong Kong during the winter and summer of 2014-2015. Quartz filters were analyzed for carbonaceous aerosols and water-soluble ions in PM_2.5. Teflon filters were collected to determine personal PM_2.5 mass and metal concentrations. The toxicological effects of personal PM_2.5 exposure-including cytotoxicity, inflammatory response, and reactive oxygen species (ROS) production-were measured using A549 cells in vitro. Personal PM_2.5 samples collected in winter were more effective than those collected in summer at inducing cytotoxicity and the expression of proinflammation cytokine IL-6. By contrast, summer personal PM_2.5 samples induced high ROS production. We performed a series of statistical analyses, Spearman correlation and a source apportionment approach with a multiple linear regression (MLR) model, to explore the sources contributing most significantly to personal PM_2.5 bioreactivity. Secondary inorganic species and transition metals were discovered to be weak-to-moderately associated with cytotoxicity (r_s: 0.26-0.55; p < 0.01) and inflammatory response (r_s: 0.26-0.44; p < 0.05), respectively. Carbonaceous aerosols (i.e., organic and elemental carbon; r_s: 0.23-0.27; p < 0.05) and crustal material (Mg and Ca) was positively associated with ROS generation. The PMF-MLR models revealed that tailpipe exhaust and secondary sulfate contributed to ROS generation, whereas secondary nitrate was the major contributor to PM_2.5 cytotoxicity and inflammation. These results improve and variate the arguments for practical policies designed to mitigate the risks posed by air pollution sources and to protect public health.

Short-Term Associations of Ambient Fine Particulate Matter (PM2.5) with All-Cause Hospital Admissions and Total Charges in 12 Japanese Cities

The short-term association between ambient particulate matter ≤2.5 microns in diameter (PM_2.5) and hospital admissions is not fully understood. Studies of this association with hospital admission costs are also scarce, especially in entire hospitalized populations. We examined the association between ambient PM_2.5 and all-cause hospital admissions, the corresponding total charges, and the total charges per patient by analyzing the hospital admission data of 2 years from 628 hospitals in 12 cities in Japan. We used generalized additive models with quasi-Poisson regression for hospital admissions and generalized additive models with log-linear regression for total charges and total charges per patient. We first estimated city-specific results and the combined results by random-effect models. A total of 2,017,750 hospital admissions were identified. A 10 µg/m³ increase in the 2 day moving average was associated with a 0.56% (95% CI: 0.14–0.99%) increase in all-cause hospital admissions and a 1.17% (95% CI: 0.44–1.90%) increase in total charges, and a 10 µg/m³ increase in the prior 2 days was associated with a 0.75% (95% CI: 0.34–1.16%) increase in total charges per patient. Short-term exposure to ambient PM_2.5 was associated with increased all-cause hospital admissions, total charges, and total charges per patient.

Mass dose rates of particle-bound organic pollutants in the human respiratory tract: Implications for inhalation exposure and risk estimations

To date, little is known about the effective doses of airborne particulate matter (PM) and PM-bound hazardous organic components to the human respiratory tract (HRT). In the light of this, here we provide particle mass dose rates (dose per hour of exposure) of PM and a suite of PM-bound hazardous organic compounds in the HRT for two population age groups (adults & children). More specifically, the mass dose rates of PM and PM-bound polycyclic aromatic hydrocarbons (PAHs), nitrated-PAH (NPAHs), polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) and polybrominated diphenyl ethers (PBDEs) were estimated at two urban sites using a multiple path particle dosimetry model. We find that, in most cases, the total mass doses are following similar variations across sites and seasons as their ambient total concentrations, however their distribution in the HRT is a function of the particle size distributions and the physiological parameters of each age group. More specifically, the majority of the deposited mass of PM and all the chemical components investigated was accumulated in the upper airways instead of the lungs. We further show that children, due to their different physiology, are more susceptible and receive larger fraction of the total mass doses in the deepest parts of the lungs compared to the adults' group. Comparing the traditional method for estimating the inhalation risk, which is based on the ambient concentration of pollutants, and a modified version using the mass dose in the HRT, we find that the former may overestimate the reported risks. The results presented here provide a novel dataset composed by previously undetermined doses of hazardous airborne particulate organic components in the HRT and demonstrate that alternative health risk estimation approaches may capture some variabilities that are traditionally overlooked.

Characteristics and toxicological effects of commuter exposure to black carbon and metal components of fine particles (PM2.5) in Hong Kong

Epidemiological studies have demonstrated significant associations between traffic-related air pollution and adverse health outcomes. Personal exposure to fine particles (PM_2.5) in transport microenvironments and their toxicological properties remain to be investigated. Commuter exposures were investigated in public transport systems (including the buses and Mass Transit Railway (MTR)) along two sampling routes in Hong Kong. Real-time sampling for PM_2.5 and black carbon (BC), along with integrated PM_2.5 sampling, were performed during the warm and cold season of 2016-2017, respectively. Commuter exposure to BC during 3-hour commuting time exhibited a wider range, from 3.4 to 4.6 μg/m³ on the bus and 5.5 to 8.7 μg/m³ in MTR cabin (p < .05). PM_2.5 mass and major chemical constituents (including organic carbon (OC), elemental carbon (EC), and metals) were analyzed. Cytotoxicity, including cellular reactive oxygen species (ROS) production, was determined in addition to acellular ROS generation. PM_2.5 treatment promoted the ROS generation in a concentration-dependent manner. Consistent diurnal variations were observed for commuter exposure to BC and PM_2.5 components, along with cellular and acellular ROS generation, which marked with two peaks during the morning (08:00-11:00) and evening rush hours (17:30-20:30). Commuter exposures in the MTR system were characterized by higher levels of PM_2.5 and elemental components (e.g., Ca, Cr, Fe, Zn, Ba) compared to riding the bus, along with higher cellular and acellular ROS production (p < .01). These metals were attributed to different sources: rail tracks, wheels, brakes, and crustal origin. Weak to moderate associations were shown for the analyzed transition metals with PM_2.5-induced cell viability and cellular ROS. Multiple linear regression analysis revealed that Ni, Zn, Mn, Fe, Ti, and Co attributed to cytotoxicity and ROS generation. These findings underscore the importance of commuter exposures and their toxic effects, urging effective mitigating strategies to protect human health.

Acute effects of ambient air pollution on hospital outpatients with chronic pharyngitis in Xinxiang, China

We present results on a time-series study that analyzed the acute effects of six criteria air pollutants on hospital outpatient with chronic pharyngitis (CP) in Xinxiang, China. Data on the concentration of air pollutants and CP outpatient records were collected daily in Xinxiang, China, from January 1, 2015 to December 31, 2018. This study identified 62,823 outpatients with CP. The annual average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ are 75.7, 132.1, 33.2, 48.4, 1377, and 59.4 μg/m³, respectively. Further, a 10 μg/m³ increment in the concentration of PM₁₀, SO₂, NO₂, and CO corresponds to an increase of 0.28% (95% confidence interval (CI): 0.03-0.53%), 1.10% (95% CI: 0.09-2.11%), 1.82% (95% CI: 0.84-2.80%), and 0.03% (95% CI: 0.01-0.06%) in daily CP hospital outpatients, respectively. Furthermore, results indicated that outpatients under the age of 15 are more susceptible to the air pollutants, excluding O₃. Meanwhile, males might be more susceptible, and effect estimates appear slightly stronger in the cool season. Therefore, we should implement effective measures to manage air pollutants and reinforce protection of the high-risk population.

Single-day and cumulative effects of ambient particulate matter exposure on emergency department visits for respiratory disease in South Korea

Background:

Along with the industrialization, the air pollutants have gained more attention and studies especially about respiratory diseases were conducted. Emergency visit reflects acute aggravation of disease rather than chronic exacerbation.

Objectives:

The objective of this study was to evaluate the relationship between the ambient particulate matter and the emergency visits with respiratory disease in South Korea.

Methods:

Patients diagnosed with respiratory disease in the emergency department in 2018 were enrolled. The data of meteorological factors and air pollutants between 4 December 2017 and 31 December 2018 were acquired. Poisson regression was used with daily emergency visits as the response variable and single-day particulate matter concentration as the explanatory variable.

Results:

A total of 4207 patients were enrolled. In Poisson regression analysis of all respiratory diseases, the effects of [Formula: see text] and [Formula: see text] were strongest at day before 8 and 26. Age older than 65 group and chronic respiratory disease group had earlier lag effect than the all-diseases group. Cumulative effect was peaked at 14 lag day. The split point of prediction was 87 µg/m3 before 3 days for [Formula: see text] and 37 µg/m3 before 8 days for [Formula: see text].

Conclusion:

The results can be used to predict the increase of emergency visits and need for medical resources when the ambient particulate matter concentration rises.

miR-205/IRAK2 signaling pathway is associated with urban airborne PM2.5-induced myocardial toxicity

Exposure to fine particulate matter (PM_2.5) is closely linked with cardiovascular diseases. However, the underlying mechanism of PM_2.5 on cardiac function remains unknown. This study was aimed to investigate the role of microRNA-205 (miR-205) on PM_2.5-induced myocardial inflammation and cardiac dysfunction. PM_2.5 increased the levels of reactive oxygen species (ROS) and malondialdehyde (MDA), following by decreased cell viability and antioxidant enzymes, resulting in apoptosis of cardiomyocytes (AC16). The histopathological and ultrastructural analysis demonstrated that PM_2.5 caused myocardial damage via interstitial edema, inflammatory cell infiltration, and myocardial fiber destruction. PM_2.5 enhanced the release of inflammatory factors in AC16 cells and heart tissue. Microarray analysis and dual-luciferase reporter gene assays demonstrated that PM_2.5-induced down-regulation of miR-205 regulated interleukin 1 receptor-associated kinase 2 (IRAK2), which further activated the TNF receptor-associated factor 6 (TRAF6)/nuclear transcription factor-κB (NF-κB) signaling pathway in vivo. Moreover, the chemical mimics of miR-205 markedly inhibited the IRAK2/TRAF6/NF-κB signaling pathway, whereas the chemical inhibitors of miR-205 amplified PM_2.5-induced activation of the IRAK2 signaling pathway in vitro. In summary, our results found that PM_2.5 could trigger myocardial toxicity via miR-205 negative regulating the IRAK2/TRAF6/NF-κB signaling pathway. Our study suggests that miR-205 could be a promising target molecule for mitigating the hazardous effects of PM_2.5 on the cardiovascular system.

Source-Specific Volatile Organic Compounds and Emergency Hospital Admissions for Cardiorespiratory Diseases

Knowledge gaps remain regarding the cardiorespiratory impacts of ambient volatile organic compounds (VOCs) for the general population. This study identified contributing sources to ambient VOCs and estimated the short-term effects of VOC apportioned sources on daily emergency hospital admissions for cardiorespiratory diseases in Hong Kong from 2011 to 2014. We estimated VOC source contributions using fourteen organic chemicals by positive matrix factorization. Then, we examined the associations between the short-term exposure to VOC apportioned sources and emergency hospital admissions for cause-specific cardiorespiratory diseases using generalized additive models with polynomial distributed lag models while controlling for meteorological and co-pollutant confounders. We identified six VOC sources: gasoline emissions, liquefied petroleum gas (LPG) usage, aged VOCs, architectural paints, household products, and biogenic emissions. We found that increased emergency hospital admissions for chronic obstructive pulmonary disease were positively linked to ambient VOCs from gasoline emissions (excess risk (ER%): 2.1%; 95% CI: 0.9% to 3.4%), architectural paints (ER%: 1.5%; 95% CI: 0.2% to 2.9%), and household products (ER%: 1.5%; 95% CI: 0.2% to 2.8%), but negatively associated with biogenic VOCs (ER%: -6.6%; 95% CI: -10.4% to -2.5%). Increased congestive heart failure admissions were positively related to VOCs from architectural paints and household products in cold seasons. This study suggested that source-specific VOCs might trigger the exacerbation of cardiorespiratory diseases.

The short-term effects of air pollution on respiratory disease hospitalizations in 5 cities in Poland: comparison of time-series and case-crossover analyses

Very few publications have compared different study designs investigating the short-term effects of air pollutants on healthcare visits and hospitalizations for respiratory tract diseases. This study describes, using two different study designs (a case-crossover design and a time-series analysis), the association of air pollutants and respiratory disease hospitalizations. The study has been conducted on 5 cities in Poland on a timeline of almost 4 years. DLNM and regression models were both used for the assessment of the short-term effects of air pollution peaks on respiratory hospitalizations. Both case-crossover and time-series studies equally revealed a positive association between air pollution peaks and hospitalization occurrences. Results were provided in the form of percentage increase of a respiratory visit/hospitalization, for each 10-μg/m3 increment in single pollutant level for both study designs. The most significant estimated % increases of hospitalizations linked to increase of 10 μg/m3 of pollutant have been recorded in general with particulate matter, with highest values for 24 h PM2.5 in Warsaw (6.4%, case-crossover; 4.5%, time series, respectively) and in Białystok (5.6%, case-crossover; 4.5%, time series, respectively). The case-crossover analysis results have shown a larger CI in comparison to the results of the time-series analysis, while the lag days were easier to identify with the case-crossover design. The trends and the overlap of the results occurring from both methods are good and show applicability of both study designs to air pollution effects on short-term hospitalizations.

Health effects of PM2.5 sources on children's allergic and respiratory symptoms in Fukuoka, Japan

Exposure to fine particulate matter (PM_2.5) is a potential aggravating factor for respiratory and allergic diseases. However, which PM_2.5 sources are associated with such diseases remains unclear. This study aimed to investigate the association of PM_2.5 sources with allergic and respiratory symptoms in schoolchildren. PM_2.5 samples were collected in Fukuoka during the spring in 2014 and 2015. Asian dust was observed in 2014. Ion components, elemental components, and organic components were analyzed. Positive matrix factorization (PMF) was conducted to calculate PM_2.5 concentrations from each source. Mixed logistic regression analysis with a random intercept for each schoolchild was performed to evaluate the association of components and sources with symptoms. Among 2317 schoolchildren, the mean prevalence was 28.9%, 23.6%, 11.2%, and 11.4% for lower respiratory, nasal, ocular, and skin symptoms, respectively. PMF identified the following six PM_2.5 sources "Secondary sulfate and coal combustion", "Secondary nitrate", "Heavy oil combustion", "Sea salt", "Soil" and "Traffic emission". An interquartile range of PM_2.5 mass was associated with nasal (Odds ratios 1.08, 95% confidence interval [1.03, 1.13]), ocular (1.10, [1.04, 1.16]), and skin symptoms (1.13, [1.06, 1.20]). Among the source factors, "Heavy oil combustion" was significantly associated with nasal symptom (1.11, [1.05, 1.18]) while "Sea salt" was associated with nasal (1.06, [1.02, 1.11]) and skin (1.073, [1.01, 1.14]) symptoms. We found "Soil", which might be affected by Asian dust, was associated with ocular (1.07, [1.03, 1.10]) and skin (1.05, [1.01, 1.08]) symptoms. Further studies in other seasons or places are needed to clarify the influence of PM_2.5 sources on children's health.

Risk Analysis of Air Pollution and Meteorological Factors Affecting the Incidence of Diabetes in the Elderly Population in Northern China

BACKGROUND

Research investigating the effect of air pollution on diabetes incidence is mostly conducted in Europe and the United States and often produces conflicting results. The link between meteorological factors and diabetes incidence remains to be explored. We aimed to explore associations between air pollution and diabetes incidence and to estimate the nonlinear and lag effects of meteorological factors on diabetes incidence.

METHODS

Our study included 19,000 people aged ≥60 years from the Binhai New District without diabetes at baseline. The generalized additive model (GAM) and the distributed lag nonlinear model (DLNM) were used to explore the effect of air pollutants and meteorological factors on the incidence of diabetes. In the model combining the GAM and DLNM, the impact of each factor (delayed by 30 days) was first observed separately to select statistically significant factors, which were then incorporated into the final multivariate model. The association between air pollution and the incidence of diabetes was assessed in subgroups based on age, sex, and body mass index (BMI).

RESULTS

We found that cumulative RRs for diabetes incidence were 1.026 (1.011-1.040), 1.019 (1.012-1.026), and 1.051 (1.019-1.083) per 10 μg/m³ increase in PM_2.5, PM₁₀, and NO₂, respectively, as well as 1.156 (1.058-1.264) per 1 mg/m³ increase in CO in a single-pollutant model. Increased temperature, excessive humidity or dryness, and shortened sunshine duration were positively correlated with the incidence of diabetes in single-factor models. After adjusting for temperature, humidity, and sunshine, the risk of diabetes increased by 9.2% (95% confidence interval (CI):2.1%-16.8%) per 10 μg/m³ increase in PM_2.5. We also found that women, the elderly (≥75 years), and obese subjects were more susceptible to the effect of PM_2.5.

CONCLUSION

Our data suggest that PM_2.5 is positively correlated with the incidence of diabetes in the elderly, and the relationship between various meteorological factors and diabetes in the elderly is nonlinear.

Association of emergency room visits for respiratory diseases with sources of ambient PM2.5

Previous studies have reported associations of short-term exposure to different sources of ambient fine particulate matter (PM_2.5) and increased mortality or hospitalizations for respiratory diseases. Few studies, however, have focused on the short-term effects of source-specific PM_2.5 on emergency room visits (ERVs) of respiratory diseases. Source apportionment for PM_2.5 was performed with Positive Matrix Factorization (PMF) and generalized additive model was applied to estimate associations between source-specific PM_2.5 and respiratory disease ERVs. The association of PM_2.5 and total respiratory ERVs was found on lag4 (RR = 1.011, 95%CI: 1.002, 1.020) per interquartile range (76 μg/m³) increase. We found PM_2.5 to be significantly associated with asthma, bronchitis and chronic obstructive pulmonary disease (COPD) ERVs, with the strongest effects on lag5 (RR = 1.072, 95%CI: 1.024, 1.119), lag4 (RR = 1.104, 95%CI: 1.032, 1.176) and lag3 (RR = 1.091, 95%CI: 1.047, 1.135), respectively. The estimated effects of PM_2.5 changed little after adjusting for different air pollutants. Six primary PM_2.5 sources were identified using PMF analysis, including dust/soil (6.7%), industry emission (4.5%), secondary aerosols (30.3%), metal processing (3.2%), coal combustion (37.5%) and traffic-related source (17.8%). Some of the sources were identified to have effects on ERVs of total respiratory diseases (dust/soil, secondary aerosols, metal processing, coal combustion and traffic-related source), bronchitis ERVs (dust/soil) and COPD ERVs (traffic-related source, industry emission and secondary aerosols). Different sources of PM_2.5 contribute to increased risk of respiratory ERVs to different extents, which may provide potential implications for the decision making of air quality related policies, rational emission control and public health welfare.

The Effects of Short-Term and Very Short-Term Particulate Matter Exposure on Asthma-Related Hospital Visits: National Health Insurance Data

PURPOSE

The purpose of this study was to investigate the effects of short-term and very short-term exposure to particulate matter (PM) exceeding the daily average environmental standards for Korea (≤100 μg/m³ for PM10 and ≤50 μg/m³ for PM2.5) on on asthma-related hospital visits.

MATERIALS AND METHODS

This was a population-based, case-crossover study using National Health Insurance and air pollution data between January 1, 2014 and December 31, 2016. The event day was defined as a day when PM exceeded the daily average environmental standard (short-term exposure) or daily average environmental standard for 2 hours (very short-term exposure). The control day was defined as the same day of the week at 1 week prior to the event day.

RESULTS

Compared with control days, asthma-related hospital visits on the 24-hr event days and 2-hr event days increased by 4.10% and 3.45% for PM₁₀ and 5.66% and 3.74% for PM_2.5, respectively. Asthma-related hospital visits increased from the 24-hr event day for PM₁₀ to 4 days after the event day, peaking on the third day after the event day (1.26, 95% confidence interval, 1.22-1.30). Hospitalizations also increased on the third day after the event. While there was a difference in magnitude, PM_2.5 exposure showed similar trends to PM₁₀ exposure.

CONCLUSION

We found a significant association between short-term and very short-term PM exposure exceeding the current daily average environmental standards of Korea and asthma-related hospital visits. These results are expected to aid in establishing appropriate environmental standards and relevant policies for PM.

Respirable Particulate Constituents and Risk of Cause-Specific Mortality in the Hong Kong Population

Emerging studies examined the associations of particulate matter constituents with nonaccidental and cardiorespiratory diseases, but few have investigated more specific causes of cardiorespiratory diseases or other system diseases, especially in Asia. We estimated the association between respirable particulate matter (PM₁₀) constituents and a spectrum of deaths using a quasi-Poisson time-series model in Hong Kong. Positive associations were identified between cause-specific deaths and elemental carbon, organic carbon (OC), nitrate, and potassium ion (K⁺), but only the associations for OC and K⁺ were robust in the two-constituent models adjusting for other constituents. The estimated effects of OC were strongest on mortality from the respiratory system with cumulative percent excess risk (ER%) of 3.82% (95% CI: 0.96%, 6.92%) per interquartile range (6.7 μg/m³) increase over 7 days prior to death (lag_0-7), especially for pneumonia (ER%: 4.32%; 95% CI: 0.70%, 8.26%). The digestive system was most sensitive to K⁺ with cumulative ER% of 6.74% (95% CI: 0.37%, 14.01%) per interquartile range (0.6 μg/m³) increase. This study indicates that PM₁₀ constituents from biomass burning (OC and K⁺) were more toxic than other constituents for deaths in Hong Kong, especially for mortalities from respiratory and digestive systems. These findings should have potential biological and pollution control implications.

High level of source-specific particulate matter air pollution associated with cardiac arrhythmias

Epidemiological evidence linking source-specific ambient particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) and cardiac arrhythmias is limited. In this study, we investigated the impact of source-specific PM_2.5 on cardiac arrhythmias in a panel of forty-five healthy adults living in Beijing, China, between 2015 and 2016. Repeated measures of 24-hour electrocardiograms were conducted during clinical visits, and daily counts of four arrhythmia events including supraventricular premature beat (SVPB), atrial tachycardia (AT), premature ventricular contraction (PVC) and ventricular tachycardia (VT) were recorded. One hundred forty-seven constituents in PM_2.5 were analyzed for collected particulate samples, in which fifty-six of them above laboratory detection limits were selected for source apportionment analysis using positive matrix factorization. The average contributions of identified five major sources to PM_2.5 were 45.9% from secondary nitrate/sulfate, 18.0% from coal combustion, 16.9% from crustal soil, 13.8% from biomass burning, and 5.4% from cooking. Generalized estimating equation models were used to estimate relative risks (RR) of arrhythmias in association with interquartile-range (IQR) increases in PM_2.5 constituents and specific sources. Total PM_2.5 mass as well as several combustion related constituents were found of significant impacts on increased risks of arrhythmia events. Among the identified sources of PM_2.5, coal burning has been found the major source that associated with increased risks of SVPB, PVC and VT with RR of 1.19 [95% confidence intervals (CI): 1.04, 1.36] to 1.64 (95% CI: 1.35, 2.00). PM_2.5 from combustion related secondary nitrate/sulfate was also found of significant impact on SVPB and AT, followed by PM_2.5 from biomass burning and crustal soil. Our results indicated that PM_2.5 from anthropogenic activity related sources were most responsible for increased risks of arrhythmia events. Our findings enhance the understanding of increased risks of arrhythmias from exposure to PM_2.5, and provide evidence on source-specific PM control priorities.

Particle deposition in the human lung: Health implications of particulate matter from different sources

Although ambient particulate matter or particles have been found to be associated with morbidity and mortality all over the world, specific health effects of particles from different sources need further elucidation. The objective of this work is to predict the deposition of particles from different sources in the human lung. The whole lung, consisting of 24 generations of branches from trachea to alveoli, was approximated using a one-dimensional lumped "trumpet" model with a variable cross-sectional area. The aerosol dynamics equation was numerically solved using a finite difference method to investigate the transport and deposition of particles in the lung model. Particles from various sources were assumed to be different in both size and density. We found that in general, coarse particles (> 2.5 µm) were mainly deposited in the tracheobronchial (TB) region by impaction, and fine particles (< 2.5 µm) were mainly deposited in the pulmonary (P) region by sedimentation and diffusion. However, the coarse particles with low density can be deposited in P region by sedimentation. As a comparison, our results found that soil particles, which are coarse with low density, were deposited in the deep lung more than traffic particles, which are fine with high density. Modeling of particle deposition in the human lung indicated that coarse particles generated by crustal sources may have adverse health effects as strong as those resulting from fine particles generated from combustion sources.

Short-Term Effects of Ambient Air Pollution on Hospitalization for Respiratory Disease in Taiyuan, China: A Time-Series Analysis

In this study, we estimated the short-term effects of ambient air pollution on respiratory disease hospitalization in Taiyuan, China. Daily data of respiratory disease hospitalization, daily concentration of ambient air pollutants and meteorological factors from 1 October 2014 to 30 September 2017 in Taiyuan were included in our study. We conducted a time-series study design and applied a generalized additive model to evaluate the association between every 10-μg/m³ increment of air pollutants and percent increase of respiratory disease hospitalization. A total of 127,565 respiratory disease hospitalization cases were included in this study during the present period. In single-pollutant models, the effect values in multi-day lags were greater than those in single-day lags. PM_2.5 at lag02 days, SO₂ at lag03 days, PM₁₀ and NO₂ at lag05 days were observed to be strongly and significantly associated with respiratory disease hospitalization. No significant association was found between O₃ and respiratory disease hospitalization. SO₂ and NO₂ were still significantly associated with hospitalization after adjusting for PM_2.5 or PM₁₀ into two-pollutant models. Females and younger population for respiratory disease were more vulnerable to air pollution than males and older groups. Therefore, some effective measures should be taken to strengthen the management of the ambient air pollutants, especially SO₂ and NO₂, and to enhance the protection of the high-risk population from air pollutants, thereby reducing the burden of respiratory disease caused by ambient air pollution.

Determinants of personal exposure to fine particulate matter (PM2.5) in adult subjects in Hong Kong

Personal monitoring for fine particulate matter (PM_2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM_2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM_2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM_2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM_2.5 was the dominant predictor of (R²=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM_2.5 mass and most components. For all subjects, a one-unit (2.72μg/m³) increase in ambient PM_2.5 was associated with a 0.75μg/m³ (95% CI: 0.59-0.94μg/m³) increase in personal PM_2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM_2.5 components in personal exposure (R²_β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM_2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM_2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM_2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.

Fine particulate matter on hospital admissions for acute exacerbation of chronic obstructive pulmonary disease in southwestern Taiwan during 2006-2012

This study explored the effects of PM_2.5 on hospital admissions (HAs) for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) in southwestern Taiwan. Data on HAs for AECOPD, pollutants, and meteorological variables were obtained from the National Health Insurance Research Database and Environmental Protection Administration. The relative risks (RRs) of HAs for AECOPD was estimated using the Quasi-Poisson generalized additive model. A total of 38,715 HAs for AECOPD were recorded. The average daily HAs for AECOPD and mean 24-h average level of PM_2.5 were 15.2 and 38.8 µg/m³, respectively. For both single and multiple pollutant (adjusted for O₃ and NO₂) models, increased AECOPD admissions were significantly associated with PM_2.5 during cold season, with the RRs for every 10 µg/m³ increase in PM_2.5 being 1.02 (95% CI = 1.007-1.040) at lag 0-1 in single-pollutant, and 1.02 (95 % CI = 1.001-1.042) at lag 0 day in multiple pollutant model. People 65 years of age and older had higher risk of HAs for AECOPD after PM_2.5 exposure. The RRs of PM_2.5 on HAs for AECOPD were robust after adjusting for O₃ and NO₂. Findings reveal an association between PM_2.5 and HAs for AECOPD in southwestern Taiwan, particularly during cold season.

Comparison of Health Impact of Air Pollution Between China and Other Countries

Air pollution is the world's largest single environmental risk according to the World Health Organization (WHO), which caused around seven million deaths in 2012. Extensive epidemiological studies have been carried out worldwide to examine the health impacts of ambient air pollution, consistently demonstrating significant health impacts of ambient air pollution. Air pollution problem in China is especially serious; it has become the fourth biggest threat to the health of the Chinese people. In this review, we summarized existing literature, compared health impact of air pollution between China and other countries, and found substantial heterogeneity in the risk estimates of air pollution. The effect heterogeneities may be due to the differences in the characteristics of populations (e.g., the proportion of the elder population and people with preexisting diseases), exposure profile (e.g., air pollution concentrations and composition), and regional climate. Although the magnitude of relative risk estimates of air pollution is generally similar with that in other parts of the world, air pollution is one of China's most serious environmental health problems given the huge number of people exposed to high concentration levels of air pollution in China.

Chemical components of respirable particulate matter associated with emergency hospital admissions for type 2 diabetes mellitus in Hong Kong

BACKGROUND

Epidemiological studies have shown that short-term exposure to particulate matter (PM) mass is associated with diabetes morbidity and mortality, although inconsistencies still exist. Variation of chemical components in PM may have contributed to these inconsistencies. We hypothesize that certain components of respirable particulate matter (PM₁₀), not simply PM₁₀ mass, can exacerbate symptoms or cause acute complications for type 2 diabetes mellitus (T2DM).

METHODS

We used a Poisson time-series model to examine the association between 17 chemical components of PM₁₀ and daily emergency hospital admissions for T2DM among residents aged 65years or above from January 1998 to December 2007 in Hong Kong. We estimated excess risk (ER%) for T2DM hospitalizations per interquartile range (IQR) increment in chemical component concentrations of days at lag₀ through lag₃, and the moving average of the same-day and previous-day (lag_0-1) in single-pollutant models. To further evaluate the independent effects of chemical components on T2DM, we controlled for PM₁₀ mass, major PM₁₀ chemical components, and gaseous pollutants in two-pollutant models.

RESULTS

In the single-pollutant models, PM₁₀ components associated with T2DM admissions include: elemental carbon, organic carbon, nitrate, and nickel. The ER% estimates per IQR increment at lag_0-1 for these four components were 3.79% (1.63, 5.95), 3.74% (0.83, 6.64), 4.58% (2.17, 6.99), and 1.91% (0.43, 3.38), respectively. Risk estimates for nitrate and elemental carbon were robust to adjustment for co-pollutant concentrations.

CONCLUSIONS

Short-term exposure to some PM₁₀ chemical components such as nitrate and elemental carbon increases the risk of acute complications or exacerbation of symptoms for the T2DM patients. These findings may have potential biological and policy implications.

Differential health effects of short-term exposure to source-specific particles in London, U.K

BACKGROUND

There is ample evidence of adverse associations between short-term exposure to ambient particle mass concentrations and health but little is known about the relative contribution from various sources.

METHODS

We used air particle composition and number networks in London between 2011 and 2012 to derive six source-related factors for PM₁₀ and four factors for size distributions of ultrafine particles (NSD). We assessed the associations of these factors, at pre-specified lags, with daily total, cardiovascular (CVD) and respiratory mortality and hospitalizations using Poisson regression. Relative risks and 95% confidence intervals (CI) were expressed as percentage change per interquartile range increment in source-factor mass or number concentration. We evaluated the sensitivity of associations to adjustment for multiple other factors and by season.

RESULTS

We found no evidence of associations between PM₁₀ or NSD source-related factors and daily mortality, as the direction of the estimates were variable with 95% CI spanning 0%. Traffic-related PM₁₀ and NSD displayed consistent associations with CVD admissions aged 15-64years (1.01% (95%CI: 0.03%, 2.00%) and 1.04% (95%CI: -0.62%, 2.72%) respectively) as did particles from background urban sources (0.36% for PM₁₀ and 0.81% for NSD). Most sources were positively associated with pediatric (0-14years) respiratory hospitalizations, with stronger evidence for fuel oil PM₁₀ (3.43%, 95%CI: 1.26%, 5.65%). Our results did not suggest associations with cardiovascular admissions in 65+ or respiratory admissions in 15+ age groups. Effect estimates were generally robust to adjustment for other factors and by season.

CONCLUSIONS

Our findings are broadly consistent with the growing evidence of the toxicity of traffic and combustion particles, particularly in relation to respiratory morbidity in children and cardiovascular morbidity in younger adults.

Emergency room visits associated with particulate concentration and Asian dust storms in metropolitan Taipei

This study evaluated risks of emergency room visits (ERV) for all causes, circulatory diseases, and respiratory diseases associated with concentrations of particulate matter (PM10 and PM2.5) and Asian dust storms (ADS) from 2000 to 2008 in metropolitan Taipei. Cumulative 4-day (lag 0-3) relative risks (RR) and confidence intervals (CI) of cause-specific ERV associated with daily concentrations of PM10 or PM2.5 and ADS based on study period (ADS frequently inflicted period: 2000-2004 and less-inflicted period: 2005-2008) were estimated using a distributed lag non-linear model with Poisson distribution. Risks associated with ADS-inflicted season (winter and spring), strength (ratio of stations with Pollutant Standard Index above 100 is < 0.5 or ≥ 0.5), and duration (ADS lasting for 1-3 days or ≥ 4 days) were especially evaluated. In non-linear models, an increase in PM10 from 10 μg/m(3) to 50 μg/m(3) was associated with increased risk of ERV for all causes and respiratory disease with cumulative 4- day RR of 1.18 (95% CI: 1.13, 1.24) and 1.37 (95% CI: 1.23, 1.54), respectively. From 2005 to 2008, the cumulative 4-day RR for an ERV related to an increase in PM2.5 from 5 μg/m(3) to 30 μg/m(3) is 1.21 (95% CI: 1.03, 1.41) for respiratory diseases, and 1.15 (95% CI: 1.08, 1.22) for all causes. In comparison with normal days, elevated ERV of all causes and respiratory diseases was also associated with winter ADS (with corresponding RRs of 1.10 (95% CI: 1.07, 1.13) and 1.14 (95% CI: 1.08, 1.21)) and shorter and less area-affected ADS (with corresponding RRs of 1.07 (95% CI: 1.01, 1.10) and 1.09 (95% CI: 1.03, 1.14)) from 2000 to 2004. Results of this study demonstrate that population health risk varies not only with PM concentration, but also with the ADS characteristics.