Satellite-based PM concentrations and their application to COPD in Cleveland, OH

Evaluating Data Product Exposure Metrics for Use in Epidemiologic Studies of Dust Storms

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio-temporal coverage, resolution, and accuracy of current data sets. In this work, dust-related metrics from four public areal data products were compared to a monitor-based "gold standard" dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern-Era Retrospective analysis for Research and Applications-Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol-type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi-binomial regression. In general, metrics from CAMS yielded the strongest associations with the "gold standard," followed by the NWS storm database metric. Dust aerosol (0.9-20 μm) mixing ratio, vertically integrated mass of dust aerosol (9-20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine-learning methods to the best-performing metrics to create a public dust storm database suitable for long-term epidemiologic studies.

Using satellite-based AOD and ground-based measurements to evaluate the impact of the DWH oil spill on coastal air quality

The 2010 DWH disaster generated atmospheric pollutants of health concern which reached the Gulf Coast. This study evaluated whether changes in coastal air quality due to the disaster were captured by aerosol optical depth (AOD) estimated using satellite data and by ground-based monitoring of air pollution, including fine particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5), benzene and naphthalene. Mean monthly AOD levels were higher in May 2010 [during oil spill time], (mean AOD = 0.355), than for the prior (mean AOD = 0.258) and following years (mean AOD = 0.252) (p < 0.05). PM_2.5 concentrations and AOD were significantly correlated (R² = 0.59, p < 0.05), for one study area. Elevated PM_2.5, benzene, and naphthalene concentrations coincided with downwind directions from the location of the oil slicks. A fully-coupled oil fate and transport atmospheric transport model of oil spill emissions, integrated with AOD and more extensive ground-based measurements, is recommended to predict coastal population exposures during oil spills.

Environmentally Persistent Free Radical Promotes Lung Cancer Progression by Regulating the Expression Profile of miRNAs

Background: Environmentally persistent free radicals (EPFRs) are generated in the combustion processes of solid waste and can cause adverse influences on human health, especially lung diseases. Lung cancer is one of the most serious malignancies in recent years, which the global deaths rate is about 1.6 million every year. Methods and Results: In this study, we verified that ZnO/MCB EPFRs promote cell proliferation and migration, impedes cell apoptosis in lung cancer. Furthermore, we found that ZnO/MCB could influence the expression of miRNAs (miR-18a and miR-34a). In vivo, ZnO/MCB and ZnO EPFRs can reduce the weight and survival rate of BALB/c male mice more than that of BALB/c female mice. In the ZnO/MCB exposed group, male mice lung became even smaller, while the female mice the lung increased significantly. Taken together, our results provide evidence for assessing the potential health risks of persistent free radicals on fine particles. Conclusions: This study linked toxicity of EPFRs with miRNAs revealed the potential health hazard to human lung cancer.

Air pollution-regulated E-cadherin mediates contact inhibition of proliferation via the hippo signaling pathways in emphysema

Air pollution has been linked to emphysema in chronic obstruction pulmonary disease (COPD). However, the underlying mechanisms in the development of emphysema due to air pollution remain unclear. The objective of this study was to investigate the role of components of the Hippo signaling pathway for E-cadherin-mediated contact inhibition of proliferation in the lungs after air pollution exposure. E-Cadherin-mediated contact inhibition of proliferation via the Hippo signaling pathway was investigated in Sprague-Dawley (SD) rats whole-body exposed to air pollution, and in alveolar epithelial A549 cells exposed to diesel exhaust particles (DEPs), E-cadherin-knockdown, and high-mobility group box 1 (HMGB1) treatment. Underlying epithelial differentiation, apoptosis, and senescence were also examined, and the interaction network among these proteins was examined. COPD lung sections were used to confirm the observations in rats. Expressions of HMGB1 and E-cadherin were negatively regulated in the lungs and A549 cells by air pollution, and this was confirmed by knockdown of E-cadherin and by treating A549 cells with HMGB1. Depletion of phosphorylated (p)-Yap occurred after exposure to air pollution and E-cadherin-knockdown, which resulted in decreases of SPC and T1α. Exposure to air pollution and E-cadherin-knockdown respectively downregulated p-Sirt1 and increased p53 levels in the lungs and in A549 cells. Moreover, the protein interaction network suggested that E-cadherin is a key activator in regulating Sirt1 and p53, as well as alveolar epithelial cell differentiation by SPC and T1α. Consistently, downregulation of E-cadherin, p-Yap, SPC, and T1α was observed in COPD alveolar regions with particulate matter (PM) deposition. In conclusion, our results indicated that E-cadherin-mediated cell-cell contact directly regulates the Hippo signaling pathway to control differentiation, cell proliferation, and senescence due to air pollution. Exposure to air pollution may initiate emphysema in COPD patients.

Long-term exposure to indoor air pollution and risk of tuberculosis

Indoor air pollution (IAP) is a recognized risk factor for various diseases. This paper examines the role of indoor solid fuel exposure in the risk of mycobacterium tuberculosis (TB) in Delhi Metropolitan, India. Using a cross-sectional design, subjects were screened for a history of active TB and lifelong exposure to IAP sources, such as solid fuel burning and kerosene. The TB prevalence rate in the study area was 1117 per 100 000 population. Every year, increase in solid fuel exposure was associated with a three percent higher likelihood of a history of active TB. Subjects exposed to solid fuel and kerosene use for both heating home and cooking showed significant associations with TB. Age, household expenditure (a proxy of income), lung function, and smoking also showed significant associations with TB. Smokers and solid fuel-exposed subjects were four times more likely to have a history of active TB than non-smoker and unexposed subjects. These finding calls strategies to mitigate solid fuel exposure, such as use of clean cookstove and ventilation, to mitigate the risk of TB which aligns with the United Nations' goal of "End TB by 2030."

Continuous in-home PM2.5 concentrations of smokers with and without a history of respiratory exacerbations in Iowa, during and after an air purifier intervention

BACKGROUND

Americans spend most of their time indoors. Indoor particulate matter (PM) 2.5 µm and smaller (PM_2.5) concentrations often exceed ambient concentrations. Therefore, we tested whether the use of an air purifying device (electrostatic precipitator, ESP) could reduce PM_2.5 in homes of smokers with and without respiratory exacerbations, compared with baseline.

METHODS

We assessed PM_2.5 concentrations in homes of subjects with and without a recent (≤3 years) history of respiratory exacerbation. We compared PM_2.5 concentrations during 1 month of ESP use with those during 1 month without ESP use.

RESULTS

Our study included 19 subjects (53-80 years old), nine with a history of respiratory exacerbation. Geometric mean (GM) PM_2.5 and median GM daily peak PM_2.5 were significantly lower during ESP deployment compared with the equivalent time-period without the ESP (GSD = 0.50 and 0.37 µg/m³, respectively, p < 0.001). PM_2.5 in homes of respiratory exacerbators tended (p < 0.14) to be higher than PM_2.5 in homes of those without a history of respiratory exacerbation.

CONCLUSIONS

Subjects with a history of respiratory exacerbation tended to have higher mean, median, and mean peak PM_2.5 concentrations compared with homes of subjects without a history of exacerbations. The ESP intervention reduced in-home PM_2.5 concentrations, demonstrating its utility in reducing indoor exposures.

NOVELTY OF STUDY

Our work characterizes PM air pollution concentrations in homes of study subjects with and without respiratory exacerbations. We demonstrate that PM concentrations tend to be higher in homes of participants with respiratory exacerbations, and that the use of an inexpensive air purifier resulted in significantly lower daily average PM concentrations than when the purifier was not present. Our results provide a helpful intervention strategy for purifying indoor air and may be useful for susceptible populations.

Association between African Dust Transport and Acute Exacerbations of COPD in Miami

Background: Air pollution is increasingly recognized as a risk factor for acute exacerbation of chronic obstructive pulmonary disease (COPD). Changing climate and weather patterns can modify the levels and types of air pollutants. For example, dust outbreaks increase particulate air pollution. Objective: This paper examines the effect of Saharan dust storms on the concentration of coarse particulate matter in Miami, and its association with the risk of acute exacerbation of COPD (AECOPD). Methods: In this prospective cohort study, 296 COPD patients (with 313 events) were followed between 2013 and 2016. We used Light Detection and Ranging (LIDAR) and satellite-based Aerosol Optical Depth (AOD) to identify dust events and quantify particulate matter (PM) exposure, respectively. Exacerbation events were modeled with respect to location- and time-lagged dust and PM exposures, using multivariate logistic regressions. Measurements and main results: Dust duration and intensity increased yearly during the study period. During dust events, AOD increased by 51% and particulate matter ≤2.5 µm in aerodynamic diameter (PM_2.5) increased by 25%. Adjusting for confounders, ambient temperature and local PM_2.5 exposure, one-day lagged dust exposure was associated with 4.9 times higher odds of two or more (2+ hereto after) AECOPD events (odds ratio = 4.9; 95% CI = 1.8–13.4; p < 0.001). Ambient temperature exposure also showed a significant association with 2+ and 3+ AECOPD events. The risk of AECOPD lasted up to 15 days after dust exposure, declining from 10× higher on day 0 to 20% higher on day 15. Conclusions: Saharan dust outbreaks observed in Miami elevate the concentration of PM and increase the risk of AECOPD in COPD patients with recurring exacerbations.

Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland

The fine particulate matter baseline (PMB), which includes PM_2.5 monitor readings fused with Community Multiscale Air Quality (CMAQ) model predictions, using the Hierarchical Bayesian Model (HBM), is less accurate in rural areas without monitors. To address this issue, an upgraded HBM was used to form four experimental aerosol optical depth (AOD)-PM_2.5 concentration surfaces. A case-crossover design and conditional logistic regression evaluated the contribution of the AOD-PM_2.5 surfaces and PMB to four respiratory-cardiovascular hospital events in all 99 12 km² CMAQ grids, and in grids with and without ambient air monitors. For all four health outcomes, only two AOD-PM_2.5 surfaces, one not kriged (PMC) and the other kriged (PMCK), had significantly higher Odds Ratios (ORs) on lag days 0, 1, and 01 than PMB in all grids, and in grids without monitors. In grids with monitors, emergency department (ED) asthma PMCK on lag days 0, 1 and 01 and inpatient (IP) heart failure (HF) PMCK ORs on lag days 01 were significantly higher than PMB ORs. Warm season ORs were significantly higher than cold season ORs. Independent confirmation of these results should include AOD-PM_2.5 concentration surfaces with greater temporal-spatial resolution, now easily available from geostationary satellites, such as GOES-16 and GOES-17.

Chronic obstructive pulmonary disease patients have a higher risk of occurrence of pneumonia by air pollution

Epidemiological evidence has shown that air pollution is associated with chronic obstructive pulmonary disease (COPD). The objective of this study was to investigate the effects of air pollution on patients with COPD and pneumonia. A case-control study of patients who had undergone thoracentesis for pleural effusion drainage in a hospital was recruited for this study. COPD and non-COPD patients with pneumonia respectively served as the case and control groups. Increases in particulate matter of <2.5 μm in aerodynamic diameter (PM_2.5) and NO₂ increased the risk of pneumonia in COPD patients (adjusted odd ratio (OR) = 4.136, 95% confidence interval (CI) = 1.740-9.832 for PM_2.5; adjusted OR = 1.841, 95% CI = 1.117-3.036 for NO₂). COPD patients with pneumonia had higher levels of CD14 in pleural effusion than did non-COPD with pneumonia (p < 0.05). An increase in CD14 of the pleural effusion increased the risk of pneumonia in COPD patients (adjusted OR = 1.126, 95% CI = 1.009-1.256). We further observed that an increase in Cu and a decrease in Zn in the pleural effusion increased the risk of pneumonia in COPD patients (adjusted OR = 1.005, 95% CI = 1.000-1.010 for Cu; adjusted OR = 0.988, 95% CI = 0.978-0.997 for Zn). In conclusion, our results suggest that COPD patients had a high risk of pneumonia occurring due to air pollution exposure.

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003-2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4-5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009-2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R² = 0.648, as it reaches a population size of 3,5000 per ZIP code and the relationship decreased to 59% as the population size increased above 3,5000 to a maximum of 4,7000 per ZIP code). The observed correlation coefficient (r) between monthly mean O₃ and asthma prevalence was moderately positive during 2009-2011 (r = 0.57). The regression model also indicated that 2011 annual PM_2.5 has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM_2.5 depended on asthma per capita and poverty rate as well. The present study indicates that Jackson urban area and coastal Mississippi are to be observed for disease prevalence in future. The current results and GIS disease maps may be used by federal and state health authorities to identify at-risk populations and health advisory.

Considering spatial heterogeneity in the distributed lag non-linear model when analyzing spatiotemporal data

The distributed lag non-linear (DLNM) model has been frequently used in time series environmental health research. However, its functionality for assessing spatial heterogeneity is still restricted, especially in analyzing spatiotemporal data. This study proposed a solution to take a spatial function into account in the DLNM, and compared the influence with and without considering spatial heterogeneity in a case study. This research applied the DLNM to investigate non-linear lag effect up to 7 days in a case study about the spatiotemporal impact of fine particulate matter (PM_2.5) on preschool children's acute respiratory infection in 41 districts of northern Taiwan during 2005 to 2007. We applied two spatiotemporal methods to impute missing air pollutant data, and included the Markov random fields to analyze district boundary data in the DLNM. When analyzing the original data without a spatial function, the overall PM_2.5 effect accumulated from all lag-specific effects had a slight variation at smaller PM_2.5 measurements, but eventually decreased to relative risk significantly <1 when PM_2.5 increased. While analyzing spatiotemporal imputed data without a spatial function, the overall PM_2.5 effect did not decrease but increased in monotone as PM_2.5 increased over 20 μg/m³. After adding a spatial function in the DLNM, spatiotemporal imputed data conducted similar results compared with the overall effect from the original data. Moreover, the spatial function showed a clear and uneven pattern in Taipei, revealing that preschool children living in 31 districts of Taipei were vulnerable to acute respiratory infection. Our findings suggest the necessity of including a spatial function in the DLNM to make a spatiotemporal analysis available and to conduct more reliable and explainable research. This study also revealed the analytical impact if spatial heterogeneity is ignored.

Characteristics and applications of small, portable gaseous air pollution monitors

BACKGROUND

Traditional approaches for measuring air quality based on fixed measurements are inadequate for personal exposure monitoring. To combat this issue, the use of small, portable gas-sensing air pollution monitoring technologies is increasing, with researchers and individuals employing portable and mobile methods to obtain more spatially and temporally representative air pollution data. However, many commercially available options are built for various applications and based on different technologies, assumptions, and limitations. A review of the monitor characteristics of small, gaseous monitors is missing from current scientific literature.

PURPOSE

A state-of-the-art review of small, portable monitors that measure ambient gaseous outdoor pollutants was developed to address broad trends during the last 5-10 years, and to help future experimenters interested in studying gaseous air pollutants choose monitors appropriate for their application and sampling needs.

METHODS

Trends in small, portable gaseous air pollution monitor uses and technologies were first identified and discussed in a review of literature. Next, searches of online databases were performed for articles containing specific information related to performance, characteristics, and use of such monitors that measure one or more of three criteria gaseous air pollutants: ozone, nitrogen dioxide, and carbon monoxide. All data were summarized into reference tables for comparison between applications, physical features, sensing capabilities, and costs of the devices.

RESULTS

Recent portable monitoring trends are strongly related to associated applications and audiences. Fundamental research requires monitors with the best individual performance, and thus the highest cost technology. Monitor networking favors real-time capabilities and moderate cost for greater reproduction. Citizen science and crowdsourcing applications allow for lower-cost components; however important strengths and limitations for each application must be addressed or acknowledged for the given use.

Predictive and Prognostic Biomarkers of Respiratory Diseases due to Particulate Matter Exposure

Air pollution is getting severe and concerns about its toxicity effects on airway and lung disease are also increasing. Particulate matter (PM) is major component of air pollutant. It causes respiratory diseases, such as asthma, chronic obstructive pulmonary disease, lung cancer, and so on. PM particles enter the airway and lung by inhalation, causing damages to them. Especially, PM_2.5 can penetrate into the alveolus and pass to the systemic circulation. It can affect the cardiopulmonary system and cause cardiopulmonary disorders. In this review, we focused on PM-inducing toxicity mechanisms in the framework of oxidative stress, inflammation, and epigenetic changes. We also reviewed its correlation with respiratory diseases. In addition, we reviewed biomarkers related to PM-induced respiratory diseases. These biomarkers might be used for disease prediction and early diagnosis. With recent trend of using genomic analysis tools in the field of toxicogenomics, respiratory disease biomarkers associated with PM will be continuously investigated. Effective biomarkers derived from earlier studies and further studies might be utilized to reduce respiratory diseases.

Biomarkers of the health outcomes associated with ambient particulate matter exposure

Epidemiologic evidence supports the positive association of cardiopulmonary morbidity and mortality, and lung cancer risk with exposure to airborne particulate matter (PM). Oxidative stress and inflammation have been proposed to be the major causal factors involved in mediating PM effects on both cardiovascular and pulmonary health outcomes. However, the mechanism whereby PM causes the health effects is not fully elucidated. To evaluate and investigate human exposure to PM, it is essential to have a specific, sensitive and robust characterization of individual exposure to PM. Biomarkers may mark important intermediate steps leading to overt health effects after PM exposure. Thus biomarkers are promising indicators, which could serve as representative measures of the exposure to PM for assessing the health impacts and understanding the mechanism. Indeed, a number of biomarkers are already in use in the field of epidemiological studies and toxicological research. However, we are facing now the challenges to select robust, specific and sensitive biomarkers, which can be employed in large-scale of population to assess the health risk and to monitor the effectiveness of interventions. In this review, we describe a range of biomarkers that are associated with air pollution exposure, particularly markers of oxidative stress, inflammatory factors, and microRNAs, as well as markers of pollutants metabolites. Understanding the nature of the association of these biomarkers with PM exposure may shed some light on the process of selecting biomarkers for large-scale population studies, developing novel preventative and therapeutic strategies.

Chemical composition and source apportionment of size fractionated particulate matter in Cleveland, Ohio, USA

The Cleveland airshed comprises a complex mixture of industrial source emissions that contribute to periods of non-attainment for fine particulate matter (PM_2.5) and are associated with increased adverse health outcomes in the exposed population. Specific PM sources responsible for health effects however are not fully understood. Size-fractionated PM (coarse, fine, and ultrafine) samples were collected using a ChemVol sampler at an urban site (G.T. Craig (GTC)) and rural site (Chippewa Lake (CLM)) from July 2009 to June 2010, and then chemically analyzed. The resulting speciated PM data were apportioned by EPA positive matrix factorization to identify emission sources for each size fraction and location. For comparisons with the ChemVol results, PM samples were also collected with sequential dichotomous and passive samplers, and evaluated for source contributions to each sampling site. The ChemVol results showed that annual average concentrations of PM, elemental carbon, and inorganic elements in the coarse fraction at GTC were ∼2, ∼7, and ∼3 times higher than those at CLM, respectively, while the smaller size fractions at both sites showed similar annual average concentrations. Seasonal variations of secondary aerosols (e.g., high NO₃^- level in winter and high SO₄^2- level in summer) were observed at both sites. Source apportionment results demonstrated that the PM samples at GTC and CLM were enriched with local industrial sources (e.g., steel plant and coal-fired power plant) but their contributions were influenced by meteorological conditions and the emission source's operation conditions. Taken together the year-long PM collection and data analysis provides valuable insights into the characteristics and sources of PM impacting the Cleveland airshed in both the urban center and the rural upwind background locations. These data will be used to classify the PM samples for toxicology studies to determine which PM sources, species, and size fractions are of greatest health concern.

The Exposure Uncertainty Analysis: The Association between Birth Weight and Trimester Specific Exposure to Particulate Matter (PM2.5 vs. PM10)

Often spatiotemporal resolution/scale of environmental and health data do not align. Therefore, researchers compute exposure by interpolation or by aggregating data to coarse spatiotemporal scales. The latter is often preferred because of sparse geographic coverage of environmental monitoring, as interpolation method cannot reliably compute exposure using the small sample of sparse data points. This paper presents a methodology of diagnosing the levels of uncertainty in exposure at a given distance and time interval, and examines the effects of particulate matter (PM) ≤2.5 µm and ≤10 µm in diameter (PM_2.5 and PM₁₀, respectively) on birth weight (BW) and low birth weight (LBW), i.e., birth weight <2500 g in Chicago (IL, USA), accounting for exposure uncertainty. Two important findings emerge from this paper. First, uncertainty in PM exposure increases significantly with the increase in distance from the monitoring stations, e.g., 50.6% and 38.5% uncertainty in PM₁₀ and PM_2.5 exposure respectively for 0.058° (~6.4 km) distance from the monitoring stations. Second, BW was inversely associated with PM_2.5 exposure, and PM_2.5 exposure during the first trimester and entire gestation period showed a stronger association with BW than the exposure during the second and third trimesters. But PM₁₀ did not show any significant association with BW and LBW. These findings suggest that distance and time intervals need to be chosen with care to compute exposure, and account for the uncertainty to reliably assess the adverse health risks of exposure.

Spatiotemporal analysis of heat and heat wave effects on elderly mortality in Texas, 2006-2011

BACKGROUND

Heat and heat waves have been linked to the increased risk of deaths, hospital admissions, and emergency visits.

OBJECTIVES

This study presents a spatiotemporal analysis of heat and heat wave effects on elderly mortality (≥65years) in Texas.

METHODS

We compiled a six-year daily weather and mortality counts dataset from 254 counties in Texas during 2006-2011. Heat index (HI), a combination of temperature and relative humidity, was used as the exposure metric in this study. Associations between daily all-cause elderly mortality and daily maximum HI and heat waves (≥2days of county-specific daily maximum HI>95th percentiles) were examined using a quasi-Poisson regression. A Markov random field smoother was included in the model to account for spatial autocorrelations and spatial heterogeneity. The model also controlled for long-term trend and seasonality in mortality, and the effects of day of the week.

DISCUSSION

On average, the lag effect of heat on elderly mortality risk lasted up to 10days, and the cumulative heat effects started to increase rapidly when daily maximum HI exceeded the 90th percentile in Texas. Elderly living in Northwest Texas and parts of West Texas were at greater risk of elderly mortality attributable to heat waves, and the highest relative risk for elderly mortality occurred in El Paso County (4.70, 95% Confidence Interval=4.33, 5.10).

CONCLUSIONS

Our study indicates strong geographical variations of heat wave effects on elderly mortality risk in Texas.

Associations of autophagy with lung diffusion capacity and oxygen saturation in severe COPD: effects of particulate air pollution

Although traffic exposure has been associated with the development of COPD, the role of particulate matter <10 μm in aerodynamic diameter (PM₁₀) in the pathogenesis of COPD is not yet fully understood. We assessed the 1-year effect of exposure to PM₁₀ on the pathogenesis of COPD in a retrospective cohort study. We recruited 53 subjects with COPD stages III and IV and 15 healthy controls in a hospital in Taiwan. We estimated the 1-year annual mean levels of PM₁₀ at all residential addresses of the cohort participants. Changes in PM₁₀ for the 1-year averages in quintiles were related to diffusion capacity of the lung for carbon monoxide levels (r=−0.914, P=0.029), changes in the pulse oxygen saturation (ΔSaO₂; r=−0.973, P=0.005), receptor for advanced glycation end-products (r=−0.881, P=0.048), interleukin-6 (r=0.986, P=0.002), ubiquitin (r=0.940, P=0.017), and beclin 1 (r=0.923, P=0.025) in COPD. Next, we observed that ubiquitin was correlated with ΔSaO₂ (r=−0.374, P=0.019). Beclin 1 was associated with diffusion capacity of the lung for carbon monoxide (r=−0.362, P=0.028), ΔSaO₂ (r=−0.354, P=0.032), and receptor for advanced glycation end-products (r=−0.471, P=0.004). Autophagy may be an important regulator of the PM₁₀-related pathogenesis of COPD, which could cause deterioration in the lung diffusion capacity and oxygen saturation.

Short-term population-based non-linear concentration-response associations between fine particulate matter and respiratory diseases in Taipei (Taiwan): a spatiotemporal analysis

Fine particulate matter <2.5 μm (PM2.5) has been associated with human health issues; however, findings regarding the influence of PM2.5 on respiratory disease remain inconsistent. The short-term, population-based association between the respiratory clinic visits of children and PM2.5 exposure levels were investigated by considering both the spatiotemporal distributions of ambient pollution and clinic visit data. We applied a spatiotemporal structured additive regression model to examine the concentration-response (C-R) association between children's respiratory clinic visits and PM2.5 concentrations. This analysis was separately performed on three respiratory disease categories that were selected from the Taiwanese National Health Insurance database, which includes 41 districts in the Taipei area of Taiwan from 2005 to 2007. The findings reveal a non-linear C-R pattern of PM2.5, particularly in acute respiratory infections. However, a PM2.5 increase at relatively lower levels can elevate the same-day respiratory health risks of both preschool children (<6 years old) and schoolchildren (6-14 years old). In preschool children, same-day health risks rise when concentrations increase from 0.76 to 7.44 μg/m(3), and in schoolchildren, same-day health risks rise when concentrations increase from 0.76 to 7.52 μg/m(3). Changes in PM2.5 levels generally exhibited no significant association with same-day respiratory risks, except in instances where PM2.5 levels are extremely high, and these occurrences do exhibit a significant positive influence on respiratory health that is especially notable in schoolchildren. A significant high relative rate of respiratory clinic visits are concentrated in highly populated areas. We highlight the non-linearity of the respiratory health effects of PM2.5 on children to investigate this population-based association. The C-R relationship in this study can provide a highly valuable alternative for assessing the effects of ambient air pollution on human health.

Pollutional haze and COPD: etiology, epidemiology, pathogenesis, pathology, biological markers and therapy

In recent years, serious pollutional haze occurs in the mainland of China thanks to the development of urbanization and industrialization. There is a close relationship between air pollution and the occurrence and development of chronic obstructive pulmonary disease (COPD), but there are some new characteristics in some aspects of COPD associated with pollutional haze compared with COPD induced by traditional physical and chemical factors. This article attempts to summarize the new progress from these new features of COPD related to pollutional haze, focus on etiology, epidemiology, pathogenesis, pathology, biological markers and therapy.

Assessment of Population Exposure to Coarse and Fine Particulate Matter in the Urban Areas of Chennai, India

Research outcomes from the epidemiological studies have found that the course (PM₁₀) and the fine particulate matter (PM_2.5) are mainly responsible for various respiratory health effects for humans. The population-weighted exposure assessment is used as a vital decision-making tool to analyze the vulnerable areas where the population is exposed to critical concentrations of pollutants. Systemic sampling was carried out at strategic locations of Chennai to estimate the various concentration levels of particulate pollution during November 2013–January 2014. The concentration of the pollutants was classified based on the World Health Organization interim target (IT) guidelines. Using geospatial information systems the pollution and the high-resolution population data were interpolated to study the extent of the pollutants at the urban scale. The results show that approximately 28% of the population resides in vulnerable locations where the coarse particulate matter exceeds the prescribed standards. Alarmingly, the results of the analysis of fine particulates show that about 94% of the inhabitants live in critical areas where the concentration of the fine particulates exceeds the IT guidelines. Results based on human exposure analysis show the vulnerability is more towards the zones which are surrounded by prominent sources of pollution.

Inter-alpha-trypsin inhibitor heavy chain 4: a novel biomarker for environmental exposure to particulate air pollution in patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease that is correlated with environmental stress. Particulate matter ≤10 μm (PM₁₀) is considered to be a risk factor for COPD development; however, the effects of PM₁₀ on the protein levels in COPD remain unclear. Fifty subjects with COPD and 15 healthy controls were recruited. Gene ontology analysis of differentially expressed proteins identified immune system process and binding as the most important biological process and molecular function, respectively, in the responses of PM₁₀-exposed patients with COPD. Biomarkers for PM₁₀ in COPD were identified and compared with the same in healthy controls and included proteoglycan 4 (PRG4), inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4), and apolipoprotein F (APOF). PRG4 and ITIH4 were associated with a past 3-year PM₁₀ exposure level. The receiver operating characteristic curve analysis showed that ITIH4 is a sensitive and specific biomarker for PM₁₀ exposure (area under the curve [AUC] =0.690, P=0.015) compared with PRG4 (AUC =0.636, P=0.083), APOF (AUC =0.523, P=0.766), 8-isoprostane (AUC =0.563, P=0.405), and C-reactive protein (CRP; AUC =0.634, P=0.086). ITIH4 levels were correlated with CRP (r=0.353, P=0.005), suggesting that ITIH4 may be involved in an inflammatory mechanism. In summary, serum ITIH4 may be a PM₁₀-specific biomarker in COPD and may be related to inflammation.

Exposure to combustion generated environmentally persistent free radicals enhances severity of influenza virus infection

Background

Exposures to elevated levels of particulate matter (PM) enhance severity of influenza virus infection in infants. The biological mechanism responsible for this phenomenon is unknown. The recent identification of environmentally persistent free radicals (EPFRs) associated with PM from a variety of combustion sources suggests its role in the enhancement of influenza disease severity.

Methods

Neonatal mice (< seven days of age) were exposed to DCB230 (combustion derived PM with a chemisorbed EPFR), DCB50 (non-EPFR PM sample), or air for 30 minutes/day for seven consecutive days. Four days post-exposure, neonates were infected with influenza intranasally at 1.25 TCID₅₀/neonate. Neonates were assessed for morbidity (% weight gain, peak pulmonary viral load, and viral clearance) and percent survival. Lungs were isolated and assessed for oxidative stress (8-isoprostanes and glutathione levels), adaptive immune response to influenza, and regulatory T cells (Tregs). The role of the EPFR was also assessed by use of transgenic mice expressing human superoxide dismutase 2.

Results

Neonates exposed to EPFRs had significantly enhanced morbidity and decreased survival following influenza infection. Increased oxidative stress was also observed in EPFR exposed neonates. This correlated with increased pulmonary Tregs and dampened protective T cell responses to influenza infection. Reduction of EPFR-induced oxidative stress attenuated these effects.

Conclusions

Neonatal exposure to EPFR containing PM resulted in pulmonary oxidative stress and enhanced influenza disease severity. EPFR-induced oxidative stress resulted in increased presence of Tregs in the lungs and subsequent suppression of adaptive immune response to influenza.

Electronic supplementary material

The online version of this article (doi:10.1186/s12989-014-0057-1) contains supplementary material, which is available to authorized users.

Exposure prediction approaches used in air pollution epidemiology studies: key findings and future recommendations

Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.