Association between Atmospheric Fine Particulate Matter and Hospital Admissions for Chronic Obstructive Pulmonary Disease in Southwestern Taiwan: A Population-Based Study

Spatiotemporal varying coefficient model for respiratory disease mapping in Taiwan

Respiratory diseases have been global public health problems for a long time. In recent years, air pollutants as important risk factors have drawn lots of attention. In this study, we investigate the influence of $\pm2.5$ (particulate matters in diameter less than 2.5 ${\rm{\mu }} m$) on hospital visit rates for respiratory diseases in Taiwan. To reveal the spatiotemporal pattern of data, we propose a Bayesian disease mapping model with spatially varying coefficients and a parametric temporal trend. Model fitting is conducted using the integrated nested Laplace approximation, which is a widely applied technique for large-scale data sets due to its high computational efficiency. The finite sample performance of the proposed method is studied through a series of simulations. As demonstrated by simulations, the proposed model can improve both the parameter estimation performance and the prediction performance. We apply the proposed model on the respiratory disease data in 328 third-level administrative regions in Taiwan and find significant associations between hospital visit rates and $\pm2.5$.

The impact of the synergistic effect of SO2 and PM2.5/PM10 on obstructive lung disease in subtropical Taiwan

Background

Chronic Obstructive lung diseases (COPD) are complex conditions influenced by various environmental, lifestyle, and genetic factors. Ambient air pollution has been identified as a potential risk factor, causing 4.2 million deaths worldwide in 2016, accounting for 25% of all COPD-related deaths and 26% of all respiratory infection-related deaths. This study aims to evaluate the associations among chronic lung diseases, air pollution, and meteorological factors.

Methods

This cross-sectional study obtained data from the Taiwan Biobank and Taiwan Air Quality Monitoring Database. We defined obstructive lung disease as patients with FEV1/FVC < 70%. Descriptive analysis between spirometry groups was performed using one-way ANOVA and the chi-square or Fisher's exact test. A generalized additive model (GAM) was used to evaluate the relationship between SO2 and PM2.5/PM10 through equations and splines fitting.

Results

A total of 2,635 participants were enrolled. Regarding environmental factors, higher temperature, higher relative humidity, and lower rainfall were risk factors for obstructive lung disease. SO2 was positively correlated with PM10 and PM2.5, with correlation coefficients of 0.53 (p < 0.0001) and 0.52 (p < 0.0001), respectively. Additionally, SO2 modified the relative risk of obstructive impairment for both PM10 [β coefficient (β) = 0.01, p = 0.0052] and PM2.5 (β = 0.01, p = 0.0155). Further analysis per standard deviation (per SD) increase revealed that SO2 also modified the relationship for both PM10 (β = 0.11, p = 0.0052) and PM2.5 (β = 0.09, p = 0.0155). Our GAM analysis showed a quadratic pattern for SO2 (per SD) and PM10 (per SD) in model 1, and a quadratic pattern for SO2 (per SD) in model 2. Moreover, our findings confirmed synergistic effects among temperature, SO2 and PM2.5/PM10, as demonstrated by the significant associations of bivariate (SO2 vs. PM10, SO2 vs. PM2.5) thin-plate smoothing splines in models 1 and 2 with obstructive impairment (p < 0.0001).

Conclusion

Our study showed high temperature, humidity, and low rainfall increased the risk of obstructive lung disease. Synergistic effects were observed among temperature, SO2, and PM2.5/PM10. The impact of air pollutants on obstructive lung disease should consider these interactions.

Seasonality of Polyaromatic Hydrocarbons (PAHs) and Their Derivatives in PM2.5 from Ljubljana, Combustion Aerosol Source Apportionment, and Cytotoxicity of Selected Nitrated Polyaromatic Hydrocarbons (NPAHs)

Airborne particulate matter (PM) is a vector of many toxic pollutants, including polyaromatic hydrocarbons (PAHs) and their derivatives. Especially harmful is the fine fraction (PM_2.5), which penetrates deep into the lungs during inhalation and causes various diseases. Amongst PM_2.5 components with toxic potential are nitrated PAHs (NPAHs), knowledge of which is still rudimentary. Three of the measured NPAHs (1-nitropyrene (1-nP), 9-nitroanthracene (9-nA), and 6-nitrochrysene (6-nC)) were detected in ambient PM_2.5 from Ljubljana, Slovenia, along with thirteen non-nitrated PAHs. The highest concentrations of pollutants, which are closely linked with incomplete combustion, were observed in the cold part of the year, whereas the concentrations of NPAHs were roughly an order of magnitude lower than those of PAHs throughout the year. Further on, we have evaluated the toxicity of four NPAHs, including 6-nitrobenzo[a]pyrene (6-nBaP), to the human kidney cell line, HEK293T. The most potent was 1-nP (IC₅₀ = 28.7 µM), followed by the other three NPAHs, whose IC₅₀ was above 400 or 800 µM. According to our cytotoxicity assessment, atmospheric 1-nP is the most harmful NPAH among the investigated ones. Despite low airborne concentrations of NPAHs in ambient air, they are generally considered harmful to human health. Therefore, systematic toxicological assessment of NPAHs at different trophic levels, starting with cytotoxicity testing, is necessary in order to accurately evaluate their threat and adopt appropriate abatement strategies.

: Short-term effects of exposure to particulate matter on hospital admissions for asthma and chronic obstructive pulmonary disease

We investigated the effects of particulate matter (PM) factors on hospitalization rates for asthma and chronic obstructive pulmonary disease (COPD). We obtained data on pollutants-PM10, PM2.5-in Seoul, South Korea. We also investigated data for asthma and COPD exacerbation that required hospitalization from 2006 to 2016. We used a time-stratified case-crossover design and generalized additive models with log transformation to assess adjusted risk, and conditional logistic regression was performed to analyze these data. Our study showed that PM10 and PM2.5, on different best lag days, were associated with increased risks of COPD or asthma hospitalization. The odds ratios (ORs) for each per-unit increase in PM10 and PM2.5 were higher in patients with male asthma (PM10: OR, 1.012; 95% confidence interval [CI], 1.008-1.016 and PM2.5: OR, 1.015; 95% CI, 1008-1.023), preschool asthma (PM10: OR, 1.015; 95% CI, 1.006-1.015 and PM2.5: OR, 1.015; 95% CI, 1.009-1.024), male COPD (PM10: OR, 1.012; 95% CI, 1.005-1.019 and PM2.5: OR, 1.013; 95% CI, 1.000-1.026), and senior COPD (PM10: OR, 1.016; 95% CI, 1.008-1.024 and PM2.5: OR, 1.022; 95% CI, 1.007-1.036). Increasing PM levels increased hospitalizations for asthma and COPD. Additionally, the consequences may be different according to age and sex, and PM2.5 may have a more significant effect on airway disease patients than PM10.

Effects of PM2.5 on Chronic Airway Diseases: A Review of Research Progress

The adverse effects of polluted air on human health have been increasingly appreciated worldwide. It is estimated that outdoor air pollution is associated with the death of 4.2 million people globally each year. Accumulating epidemiological studies indicate that exposure to ambient fine particulate matter (PM2.5), one of the important air pollutants, significantly contributes to respiratory mortality and morbidity. PM2.5 causes lung damage mainly by inducing inflammatory response and oxidative stress. In this paper, we reviewed the research results of our group on the effects of PM2.5 on chronic obstructive pulmonary disease, asthma, and lung cancer. And recent research progress on epidemiological studies and potential mechanisms were also discussed. Reducing air pollution, although remaining a major challenge, is the best and most effective way to prevent the onset and progression of respiratory diseases.

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM_2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM_2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM_2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM_2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM_2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM_2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R² up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM_2.5 forecasts and help the decision-maker to take proper action immediately.

Assessing PM2.5-associated risk of hospitalization for COPD: an application of daily excessive concentration hours

Existing PM2.5-morbidity studies using daily mean concentration as exposure metric may fail to capture intra-day variations of PM2.5 concentrations, resulting in underestimated health impacts to some extent. This study introduced a novel indicator, daily excessive concentration hours (DECH), defined as sums of per-hourly excessive concentrations of PM2.5 against a specific threshold within a day. PM2.5 DECHs were separately calculated as daily concentration-hours >8, 10, 15, 20, and 25 μg/m3 (abbreviations: DECH-8, DECH-10, DECH-15, DECH-20, and DECH-25). We adopted a time-stratified case-crossover design with conditional logistic regression models to compare risks of hospitalizations for chronic obstructive pulmonary disease (COPD) associated with PM2.5 mean and DECHs in Shenzhen, China. We observed highly comparable PM2.5-COPD associations using exposure metrics of daily mean and DECHs with above-defined thresholds. For instance, PM2.5 mean and DECHs showed similar increases in risks of COPD hospitalization for an interquartile range rise in exposure, with odds ratio estimates of 1.26 (95% confidence interval: 1.06-1.50) for PM2.5 mean, 1.24 (1.05-1.46) for DECH-10 and 1.21 (1.06-1.39) for DECH-25, respectively. Findings remained robust after further adjusting for gaseous pollutants (e.g., SO2, NO2, CO, and O3) and meteorologic factors (e.g., wind speed and air pressure). Our study strengthened the evidence that DECHs could come be as a novel exposure metric in health risk assessments associated with short-term exposure to ambient PM2.5.

Short-Term Effects of Particulate Matter and Its Constituents on Emergency Room Visits for Chronic Obstructive Pulmonary Disease: A Time-Stratified Case-Crossover Study in an Urban Area

Background: PM_2.5 exposure is associated with pulmonary and airway inflammation, and the health impact might vary by PM_2.5 constitutes. This study evaluated the effects of increased short-term exposure to PM_2.5 constituents on chronic obstructive pulmonary disease (COPD)-related emergency department (ED) visits and determined the susceptible groups. Methods: This retrospective observational study performed in a medical center from 2007 to 2010, and enrolled non-trauma patients aged >20 years who visited the emergency department (ED) and were diagnosed as COPD. Concentrations of PM_2.5, PM₁₀, and the four PM_2.5 components, including organic carbon (OC), elemental carbon (EC), nitrate (NO₃⁻), and sulfate (SO₄²⁻), were collected by three PM supersites in Kaohsiung City. We used an alternative design of the Poisson time series regression models called a time-stratified and case-crossover design to analyze the data. Results: Per interquartile range (IQR) increment in PM_2.5 level on lag 2 were associated with increments of 6.6% (95% confidence interval (CI), 0.5–13.0%) in risk of COPD exacerbation. An IQR increase in elemental carbon (EC) was significantly associated with an increment of 3.0% (95% CI, 0.1–5.9%) in risk of COPD exacerbation on lag 0. Meanwhile, an IQR increase in sulfate, nitrate, and OC levels was not significantly associated with COPD. Patients were more sensitive to the harmful effects of EC on COPD during the warm season (interaction p = 0.019). The risk of COPD exacerbation after exposure to PM_2.5 was higher in individuals who are currently smoking, with malignancy, or during cold season, but the differences did not achieve statistical significance. Conclusion: PM_2.5 and EC may play an important role in COPD events in Kaohsiung, Taiwan. Patients were more susceptible to the adverse effects of EC on COPD on warm days.

Effects of Particulate Matter Education on Self-Care Knowledge Regarding Air Pollution, Symptom Changes, and Indoor Air Quality among Patients with Chronic Obstructive Pulmonary Disease

The burden of illness resulting from adverse environmental exposure is significant. Numerous studies have examined self-care behaviors among patients with chronic obstructive pulmonary disease (COPD), but seldom assess these behaviors in relation to air pollution. The study aims to examine the effects of particulate matter (PM) education on prevention and self-care knowledge regarding air pollution, symptom changes, and indoor PM concentration levels among patients with COPD. A longitudinal, quasi-experimental design using a generalized estimating equation examined the effectiveness of the education intervention. Participants were 63 patients with COPD, of whom only 25 received intervention. Levels of PM2.5 and PM10 decreased in the first-month follow-up in the experimental group. Improvement of knowledge and prevention regarding PM in the first and third months were also greater in the experimental group compared to the control. Regarding the COPD assessment test and physical domain scores, the experimental group exhibited a greater improvement in the first-month follow-up. Scores on the psychological domain significantly changed in the sixth-month follow-up. The PM education coordinated by nurses improved the health of participants, maintaining six-month effects. Further studies should evaluate the practice barriers and effects of health education on preventive self-care behaviors regarding indoor PM among patients with COPD.

Association between ambient particulate matter and hospital outpatient visits for chronic obstructive pulmonary disease in Lanzhou, China

Until now, a number of epidemiological studies have focused on the association between ambient particulate matter pollution and chronic obstructive pulmonary disease (COPD), especially in developed countries. There are limited evidences on the association between short-term exposure to particulate matters (PM_2.5, PM_C, and PM₁₀) and overall hospital outpatient visits for COPD at the same time in China. Thus, a time-series analysis on the short-term association between three subtypes of PM (PM_2.5, PM_C, and PM₁₀) and daily hospital outpatients for COPD in Lanzhou, China was conducted, from 2014 to 2017.An over dispersed, generalized additive model was used to analyze the associations after controlling for time trend, weather conditions, day of the week, and holidays. Stratified analyses were also performed by age and gender. The results disclosed that a 10-μg/m³ increase in PM_2.5 concentration at a lag of 0-7 days was associated with 1.190% (95% CI 0.176~2.215%). For PM_c, therewere not statistically significant effects at any lag days, but we could find the greatest effect at lag07 that a 10-μg/m³ increase in concentration was associated with 0.014% (95% CI - 0.065~0.093%). PM₁₀ also exerted a high effect for COPD (0.185% increase; 95% CI - 0.046~0.417%) when 6 days of exposures (lag6), however, no significance relationship could be found. For COPD among males, positive results were observed for PM_2.5 with lags of 0-7 days, a 10-μg/m³ increase was 1.184% (95% CI 0.095~2.284%). The effect of PM_2.5 on females was also most significant at lag07, a 10-μg/m³ increase was 1.254% (95% CI 0.053~2.469%). For those aged < 65 years old, PM_2.5 was not statistically significant at all lag days, but it reached the maximum at lag07, a 10-μg/m³ increase was 0.978% (95% CI - 0.139~2.108%). For those aged 65 ≥ years old and older, PM_2.5 had a statistically significant lag effect at lag1, lag2, lag3, lag02, lag03, lag04, lag05, lag06, and lag07, and it was most significant at lag07; a 10-μg/m³ increase was 1.906% (95% CI 0.553~3.277%). Short-term exposure to PM_2.5 was associated with increased risk of hospital visits for COPD. In particular, the elderly (aged ≥ 65 years old) and males were relatively more sensitive to PM_2.5, and were affected right away after the PM_2.5 concentration went up.

Airborne Particulate Matter: Human Exposure and Health Effects

OBJECTIVE

Exposure to airborne particulate matter (PM) is estimated to cause millions of premature deaths annually. This work conveys known routes of exposure to PM and resultant health effects.

METHODS

A review of available literature.

RESULTS

Estimates for daily PM exposure are provided. Known mechanisms by which insoluble particles are transported and removed from the body are discussed. Biological effects of PM, including immune response, cytotoxicity, and mutagenicity, are reported. Epidemiological studies that outline the systemic health effects of PM are presented.

CONCLUSION

While the integrated, per capita, exposure of PM for a large fraction of the first-world may be less than 1 mg per day, links between several syndromes, including attention deficit hyperactivity disorder (ADHD), autism, loss of cognitive function, anxiety, asthma, chronic obstructive pulmonary disease (COPD), hypertension, stroke, and PM exposure have been suggested. This article reviews and summarizes such links reported in the literature.

Vitamin D protects against particles-caused lung injury through induction of autophagy in an Nrf2-dependent manner

Fine particulate matter is a well-known air pollutant threatening public health. Studies have confirmed long-term exposure to the particles could decrease the pulmonary function, induce asthma exacerbation, and chronic obstructive pulmonary disease, as well as increase the incidence and mortality of lung cancer. A clinical study has explored that the prevalence and risks of vitamin D (VD) deficiency in various chronic disease and toxins induced tissue damage. Our current study aimed to explore the mechanism and further therapeutic potential of VD administration to ameliorate fine particles exposure induced pulmonary damage in vivo and in vitro. To elucidate the effects and mechanisms of VD in particles-induced pulmonary damage, a murine model was established with fine particles intratracheal instillation along with VD intramuscular injection. Our study demonstrated that treatment with VD attenuated particles-induced pulmonary damage and promoted tissue repair by repressing of TGFβ1 signaling pathway and upregulation of MMP9 expression. VD treatment could also regulate the autophagy-related signals along with activation of Nrf2 transcription factor. Furthermore, the results from the in vitro study demonstrated that VD protected against particles-induced cells' damage through the induction of autophagy in an Nrf2-dependent manner. VD treatment caused the degradation of P62 and its bound Keap1, which decreased the Nrf2 ubiquitination and increasing its protein stability. Our work explored a novel potential mechanism in the protection of VD in particles-induced pulmonary injury and tissue repair, and could further bring insights into exploring antifine particles exposure caused inflammation among other natural products and contributes to inflammation disease medical therapies.

Association of fine particulate matter on acute exacerbation of chronic obstructive pulmonary disease in Yancheng, China

BACKGROUND

Epidemiological evidence on the association between short-term exposure to fine particulate matter (PM_2.5) air pollution and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is limited in China.

OBJECTIVES

To explore the associations between PM_2.5 and AECOPD in Yancheng, China from 2015 to 2017.

METHODS

In this time-series study, we used a generalized linear model with quasi-Poisson regression to investigate the association between PM_2.5 and AECOPD admitted in two major hospitals in Yancheng. We tested the robustness of the associations using two-pollutant models and examined the potential effect modification by age, gender and season via stratification analyses. Lastly, we fitted the concentration-response curves.

RESULTS

We identified a total of 4761 AECOPD inpatients during the study period. The average daily-mean PM_2.5 concentration was 45.2 μg/m³. Each 10 μg/m³ increase in PM_2.5 concentration on the concurrent day of the onset of AECOPD was associated with a 1.05% (95% confidence interval: 0.14%, 1.96%) increase in AECOPD. The association was robust to the adjustment of ozone, but not to sulfur dioxide, nitrogen dioxide, and carbon monoxide. The association was larger in females, elderly patients, and was restricted within the cold season, but all between-group differences were insignificant. The concentration-response relationship curves were generally linear but flatted at concentrations over 40 μg/m³.

CONCLUSIONS

This study demonstrated a higher risk of AECOPD associated with present-day PM_2.5 exposure in a Chinese city. We further provided important information on lag patterns, susceptible subgroups, sensitive seasons, as well as the characteristics of the concentration-response relationship curves.

Therapeutic potential of bixin in PM2.5 particles-induced lung injury in an Nrf2-dependent manner

Fine particulate matter (PM 2.5) is a well-known air pollutant threatening public health. Studies has confirmed that long-term exposure to the particles could reduce the pulmonary function, cause exacerbation of asthma and chronic obstructive pulmonary disease, and increase incidence and mortality of lung cancer. Bixin is a natural compound that is widely used as a food additive. Our previous studies demonstrated that bixin i.p. administration could protect against particles intratracheal exposure (56 days)-induced lung injury in an Nrf2-dependent manner. But the detail mechanisms are still unclarified. Our current study aimed to explore the further therapeutic potential and mechanism of bixin to slow the progression of lung injury and inflammation in vivo and in vitro. The results from the in vivo study showed that bixin treatment attenuated the accumulation of inflammatory cells, decreased the levels of tissue apoptosis, and increase the ability of cell proliferation. Besides that, bixin also could regulate the expression of MMP9, TGFβ1, and its downstream Fibronectin (FN), along with activation of Nrf2 signals. In vitro experiments in human bronchial epithelial cells demonstrated that Nrf2 activated by bixin contributes to tissue repair by alleviating oxidative stress, increasing proliferation and migration, decreasing apoptosis, which may be partially through modulating the expression of MMP9, TGFβ1, and FN. This study provides convincing experimental evidences that bixin could be used therapeutically to promote tissue repair and improve pulmonary injury induced by particles exposure.

The cumulative effect of air pollutants on the acute exacerbation of COPD in Shanghai, China

BACKGROUND

Epidemiologic studies have shown the effect of air pollutants on acute exacerbation of chronic obstructive pulmonary disease (AECOPD). However, little is known regarding the dose-response relationship. This study aimed to investigate the cumulative effect of air pollutants on AECOPD.

METHODS

We collected 101 patients with AECOPD from November 2010 through August 2011 in Shanghai. Multiple logistic regression was used to estimate associations between air pollutants and AECOPD. Poisson regression was then applied to determine the cumulative effect of air pollutants including particulate matter 10 (PM10), PM2.5, nitrogen dioxide (NO₂), sulphur dioxide (SO₂) and ozone (O₃) on AECOPD, of which the seasonal variation was further explored.

RESULTS

The monthly episodes of AECOPD were associated with the concentrations of PM2.5 (r=0.884, p<0.05) and NO₂ (r=0.763, p<0.05). The cutoff value of PM2.5 and NO₂ for predicting AECOPD was 83.0μg/m³ and 53.5μg/m³, respectively. It showed that per 10μg/m³ increment in PM2.5 increased the relative risks (RR) for AECOPD was 1.09 with 3days cumulative effect in cold season, whereas 7days in warm season. The RR for AECOPD for per 10μg/m³ increment in NO₂ was 1.07, with a 5-day cumulative effect without seasonal variation.

CONCLUSIONS

High consecutive levels of PM2.5 and NO₂ increase the risk of developing AECOPD. Cumulative effect of PM2.5 and NO₂ appears before the exacerbation onset. These gradations were more evident in the PM2.5 during different seasons.

The Burden of COPD Morbidity Attributable to the Interaction between Ambient Air Pollution and Temperature in Chengdu, China

Evidence on the burden of chronic obstructive pulmonary disease (COPD) morbidity attributable to the interaction between ambient air pollution and temperature has been limited. This study aimed to examine the modification effect of temperature on the association of ambient air pollutants (including particulate matter (PM) with aerodynamic diameter <10 μm (PM₁₀) and <2.5 μm (PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO) and ozone (O₃)) with risk of hospital admissions (HAs) for COPD, as well as the associated morbidity burden in urban areas of Chengdu, China, from 2015 to 2016. Based on the generalized additive model (GAM) with quasi-Poisson link, bivariate response surface model and stratification parametric model were developed to investigate the potential interactions between ambient air pollution and temperature on COPD HAs. We found consistent interactions between ambient air pollutants (PM_2.5, PM₁₀ and SO₂) and low temperature on COPD HAs, demonstrated by the stronger associations between ambient air pollutants and COPD HAs at low temperatures than at moderate temperatures. Subgroup analyses showed that the elderly (≥80 years) and males were more vulnerable to this interaction. The joint effect of PM and low temperature had the greatest impact on COPD morbidity burden. Using WHO air quality guidelines as reference concentration, about 17.30% (95% CI: 12.39%, 22.19%) and 14.72% (95% CI: 10.38%, 19.06%) of COPD HAs were attributable to PM_2.5 and PM₁₀ exposures on low temperature days, respectively. Our findings suggested that low temperature significantly enhanced the effects of PM and SO₂ on COPD HAs in urban Chengdu, resulting in increased morbidity burden. This evidence has important implications for developing interventions to reduce the risk effect of COPD morbidity.

Seasonal variation and source apportionment of PM2.5-bound trace elements at a coastal area in southwestern Taiwan

The aim of this study is to investigate the seasonal variations and source apportionment on atmospheric fine particulate matter (PM_2.5) mass and associated trace element concentrations at a coastal area, in Chiayi County of southwestern Taiwan. Particle measurements were conducted in 2015. Twenty-three trace elements in PM_2.5 were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Multiple approaches of the enrichment factor (EF) analysis and positive matrix fraction (PMF) model were used to identify potential sources of PM_2.5-bound trace elements. Daily mean concentration of PM_2.5 in cold season (25.41 μg m^-3) was higher than that in hot season (13.10 μg m^-3). The trace elements contributed 11.02 and 10.74% in total PM_2.5 mass concentrations in cold season and hot season, respectively. The results of EF analysis confirmed that Sb, Mo, and Cd were the top three anthropogenic trace elements in the PM_2.5; furthermore, carcinogenic elements (Cr, Ni, and As) and other trace elements (Na, K, V, Cu, Zn, Sr, Sn, Ba, and Pb) were attributable to anthropogenic emissions in both cold and hot seasons; however, highly enriched Li and Mn were observed only in cold season. The PMF model identified four main sources: iron and steel industry, soil and road dust, coal combustion, and traffic-related emission. Each of these sources has an annual mean contribution of 8.2, 27.5, 11.2, and 53.1%, respectively, to PM_2.5. The relative dominance of each identified source varies with changing seasons. The highest contributions occurred in cold season for iron and steel industry (66.2%), in hot season for traffic-related emission (58.4%), soil and road dust (22.0%), and coal combustion (2.8%). These findings revealed that the PM_2.5 mass concentration, PM_2.5-bound trace element concentrations, and their contributions were various by seasons.

Environmental Regulation, Foreign Direct Investment and Green Technological Progress-Evidence from Chinese Manufacturing Industries

This study examines the spillover effects of foreign direct investment (FDI) on green technology progress rate (as measured by the green total factor productivity). The analysis utilizes two measures of FDI, labor-based FDI and capital-based FDI, and separately investigates four sets of industry classifications—high/low discharge regulation and high/low emission standard regulation. The results indicate that in the low discharge regulation and low emission standard regulation industry, labor-based FDI has a significant negative spillover effect, and capital-based FDI has a significant positive spillover effect. However, in the high-intensity environmental regulation industry, the negative influence of labor-based FDI is completely restrained, and capital-based FDI continues to play a significant positive green technological spillover effects. These findings have clear policy implications: the government should be gradually reducing the labor-based FDI inflow or increasing stringency of environmental regulation in order to reduce or eliminate the negative spillover effect of the labor-based FDI.

Association of time-serial changes in ambient particulate matters (PMs) with respiratory emergency cases in Taipei's Wenshan District

Ambient air pollution poses a significant risk for a group of common and often debilitating respiratory diseases, but its direct impact on cause-specific respiratory diseases using emergency room visit (ERV) as an indicator remains to be fully explored. In this study, we conducted a time-series study of ambient PM2.5, NO2, SO2 and their association with ERV for asthma, COPD and pneumonia in a four-year time span. Relative risks for ERV as per log increase in the level of ambient pollutants with time lags of up to 10 days were calculated, using a generalized additive model of Poisson regression. Daily 24-h average concentrations of PM2.5 and pollutant gases were obtained from a local Gutting air quality monitoring station. Results showed that the ERVs for pneumonia and asthma were associated with the level of PM2.5. The effects of PM2.5 on the risk of ERV for asthma were found to be significant at lag days 1 and 2 with increasing risk of 4.34% [RR: 1.091; CI: 1.020-1.166 (95%)] and 3.58% [RR: 1.074; CI: 1.007-1.146 (95%)], respectively. The ERV for pneumonia was associated with the level of PM2.5 at lag days 5, 6 and 7, with increasing risk of 1.92% [RR: 1.039; CI: 1.009-1.070 (95%)], 2.03% [RR: 1.041; CI: 1.009-1.075 (95%)], and 1.82% [RR: 1.037; CI: 1.001-1.075 (95%)], respectively. Further, PM2.5, but not NO2 and SO2, posed a significant risk of ERV for asthma during spring at lag days 0, 1 and 2 (17.12%, RR: 1.408, CI: 1.075-1.238; 15.30%, RR: 1.358 CI: 1.158-1.166; 11.94%, RR: 1.165, CI: 1.004-1.121), which was particularly evident for those who were younger than 75 years of age. In contrast, only PM2.5 was a significant risk of ERV for COPD, which was primarily for those who were younger than 75 years of age during summer season at lag days 3, 4 and 5. (26.66%, RR: 1.704, CI: 1.104-2.632; 26.99%; RR: 1.716, CI: 1.151-2.557; 24.09%; RR: 1.619, CI: 1.111-2.360). Collectively, these results suggested significant seasonal variation and differential time lag effects of PM2.5 on ERV for asthma, COPD and pneumonia.

Effects of fine particulate matter and its constituents on emergency room visits for asthma in southern Taiwan during 2008-2010: a population-based study

This population-based study evaluated the short-term association between fine particulate matter (PM_2.5) concentrations and its constituents and hospital emergency room visits (ERVs) for asthma in southern Taiwan during the period 2008-2010. Data on hospital ERVs for asthma and ambient PM_2.5 levels and its constituents were obtained from the National Health Insurance Research database and the Environmental Protection Administration, respectively. The quasi-Poisson generalized additive model was used to explore the associations between PM_2.5 and hospital ERVs for asthma. During the study period, the average daily number of ERVs for asthma and mean 24-h average level of PM_2.5 was 20.0 and 39.4 μg m^-3, respectively. The estimated effects of PM_2.5 on asthma ERVs fluctuated with increasing tendencies after adjusting for O₃ and attenuating tendencies after adjusting for NO₂, SO₂, and CO. Children were more susceptible than other age groups to the effects of PM_2.5 exposure on asthma ERVs, with the relative risks (RRs) for every 10 μg m^-3 increase in PM_2.5 being 1.016 [95% confidence interval (CI) = 1.002-1.030] and 1.018 (95% CI = 1.002-1.034), respectively, at a lag 0 day (i.e., no lag days) and lag 0-1 days. The effect of PM_2.5 concentrations on asthma ERVs was similar in male and female. Furthermore, asthma ERVs was significantly associated with concentrations of nitrate (NO₃^-), with the RR for each 1 μg m^-3 increase in NO₃^- concentrations being 1.004 (95% CI = 1.001-1.007) at lag 0 day. In conclusion, both PM_2.5 concentrations and its chemical constituents are associated with ERVs for asthma; moreover, children were more susceptible to the effects of PM_2.5 in southern Taiwan. PM_2.5 constituent, nitrate, is more closely related to ERVs for asthma.

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.

Exposure to Indoor Particulate Matter Worsens the Symptoms and Acute Exacerbations in Chronic Obstructive Pulmonary Disease Patients of Southwestern Taiwan: A Pilot Study

Ambient particulate matter (PM) can trigger adverse reactions in the respiratory system, but less is known about the effect of indoor PM. In this longitudinal study, we investigated the relationships between indoor PM and clinical parameters in patients with moderate to very severe chronic obstructive pulmonary disease (COPD). Indoor air quality (PM_2.5 and PM₁₀ levels) was monitored in the patients’ bedroom, kitchen, living room, and front door at baseline and every two months for one year. At each home visit, the patients were asked to complete spirometry and questionnaire testing. Exacerbations were assessed by chart review and questionnaires during home visits. Generalized estimating equation (GEE) analysis (n = 83) showed that the level of wheezing was significantly higher in patients whose living room and kitchen had abnormal (higher than ambient air quality standards in Taiwan) PM_2.5 and PM₁₀ levels. Patients who lived in houses with abnormal outdoor PM_2.5 levels had higher COPD Assessment Test scores (physical domain), and those who lived in houses with abnormal PM₁₀ levels in the living room and kitchen had higher London Chest Activity of Daily Living scores. Increased PM levels were associated with worse respiratory symptoms and increased risk of exacerbation in patients with moderate to very severe COPD.

Airborne Infectious Agents and Other Pollutants in Automobiles for Domestic Use: Potential Health Impacts and Approaches to Risk Mitigation

The world total of passenger cars is expected to go from the current one billion to >2.5 billion by 2050. Cars for domestic use account for ~74% of the world's yearly production of motorized vehicles. In North America, ~80% of the commuters use their own car with another 5.6% travelling as passengers. With the current life-expectancy of 78.6 years, the average North American spends 4.3 years driving a car! This equates to driving 101 minutes/day with a lifetime driving distance of nearly 1.3 million km inside the confined and often shared space of the car with exposure to a mix of potentially harmful pathogens, allergens, endotoxins, particulates, and volatile organics. Such risks may increase in proportion to the unprecedented upsurge in the numbers of family cars globally. Though new technologies may reduce the levels of air pollution from car exhausts and other sources, they are unlikely to impact our in-car exposure to pathogens. Can commercial in-car air decontamination devices reduce the risk from airborne infections and other pollutants? We lack scientifically rigorous protocols to verify the claims of such devices. Here we discuss the essentials of a customized aerobiology facility and test protocols to assess such devices under field-relevant conditions.

Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China

Recent studies in PM_2.5 sources show that anthropogenic emissions are the main contributors to haze pollution. Due to their essential roles in establishing policies for improving air quality, socioeconomic drivers of PM_2.5 levels have attracted increasing attention. Unlike previous studies focusing on the annual PM_2.5 concentration (C_year), this paper focuses on the accumulation phase of PM_2.5 during the pollution episode (PMAE) in the Yangtze River Delta in China. This paper mainly explores the spatial variations of PMAE and its links to the socioeconomic factors using a geographical detector and simple linear regression. The results indicated that PM_2.5 was more likely to accumulate in more developed cities, such as Nanjing and Shanghai. Compared with C_year, PMAE was more sensitive to socioeconomic impacts. Among the twelve indicators chosen for this study, population density was an especially critical factor that could affect the accumulation of PM_2.5 dramatically and accounted for the regional difference. A 1% increase in population density could cause a 0.167% rise in the maximal increment and a 0.214% rise in the daily increase rate of PM_2.5. Additionally, industry, energy consumption, and vehicles were also significantly associated with PM_2.5 accumulation. These conclusions could serve to remediate the severe PM_2.5 pollution in China.