Long-term Particulate Matter Research Articles

Particulate Matter and Respiratory Diseases: How Far Have We Gone?

Air pollution is a potential threat to public health worldwide, especially in South Asia. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) reported that most of global deaths attributable to ambient particulate matter occurred in China and India. Particulate matter (PM), as the main air pollutant, is receiving increasing attention due to its specific biological properties. PM is a complicated mixture and varies in sizes, compositions and sources. Increasing epidemiological studies have shown that both short- and long-term PM exposure are associated with the morbidity and mortality of respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, lung cancer, and pneumonia, especially in the elderly and children. Several potential biological mechanisms have been proposed to explain the adverse effect of PM on the respiratory diseases, including oxidant stress, pro-inflammation, epigenetic modifications, DNA damage and carcinogenesis. However, there are still some contradictions with regard to the role of PM in the development of these respiratory diseases. Thus, this review made a summary of results from epidemiological studies about the association between PM and COPD, asthma, lung cancer, and pneumonia, and elucidated its potential biological mechanisms.

Association of air particulate pollution with bone loss over time and bone fracture risk: analysis of data from two independent studies

Air particulate matter (PM) is a ubiquitous environmental exposure associated with oxidation, inflammation, and age-related chronic disease. Whether PM is associated with loss of bone mineral density (BMD) and risk of bone fractures is undetermined. We conducted two complementary studies of: (i) long-term PM <2.5 μm (PM2.5) levels and osteoporosis-related fracture hospital admissions among 9.2 million Medicare enrollees of the Northeast/Mid-Atlantic United States between 2003-2010; (ii) long-term black carbon [BC] and PM2.5 levels, serum calcium homeostasis biomarkers (parathyroid hormone, calcium, and 25-hydroxyvitamin D), and annualized BMD reduction over a 8-year follow-up of 692 middle-aged (46.7±12.3 yrs), low-income BACH/Bone cohort participants. In the Medicare analysis, risk of bone fracture admissions at osteoporosis-related sites was greater in areas with higher PM2.5 levels (Risk ratio [RR] 1.041, 95% Confidence Interval [CI], 1.030, 1.051). This risk was particularly high among low-income communities (RR 1.076; 95% CI, 1.052, 1.100). In the longitudinal BACH/Bone study, baseline BC and PM2.5 levels were associated with lower serum PTH (Estimate for baseline one interquartile increase in 1-year average BC= -1.16, 95% CI -1.93, -0.38; Estimate for baseline one interquartile increase in 1-year average PM2.5= -7.39; 95%CI -14.17, -0.61). BC level was associated with higher BMD loss over time at multiple anatomical sites, including femoral neck (-0.08%/year per one interquartile increase; 95% CI -0.14, -0.02%/year) and ultradistal radius (-0.06%/year per one interquartile increase; 95% CI -0.12, -0.01%/year). Our results suggest that poor air quality is a modifiable risk factor for bone fractures and osteoporosis, especially in low-income communities.

Is long-term particulate matter and nitrogen dioxide air pollution associated with incident monoclonal gammopathy of undetermined significance (MGUS)? An analysis of the Heinz Nixdorf Recall study

BackgroundExposure to air pollution activates the innate immune system and influences the adaptive immune system in experimental settings. We investigated the association of residential long-term exposure to particulate matter (PM) and NO2 air pollution with monoclonal gammopathy of undetermined significance (MGUS) as a marker of adaptive immune system activation. MethodsWe used data from the baseline (2000−2003), 5-year (2006–2008) and 10-year (2011–2015) follow-up examinations of the German Heinz Nixdorf Recall cohort study of 4814 participants (45–75years). Residential exposure to PM size fractions and NO2 was estimated by land-use regression (ESCAPE-LUR, annual mean 2008/2009) and dispersion chemistry transport models (EURAD-CTM, 3-year mean at baseline). We used logistic regression to estimate the effects of air pollutants on incident MGUS, adjusting for age, sex, education, smoking status, physical activity, and BMI. As a non-linear approach, we looked at quartiles (2–4) of the air pollutants in comparison to quartile 1. ResultsOf the 3949 participants with complete data, 100 developed MGUS during the 10-year follow-up. In the main model, only PMcoarse was associated with incident MGUS (OR per IQR (1.9μg/m3): 1.32, 95% CI 1.04–1.67). We further found positive associations between PM size fractions estimated by ESCAPE-LUR and incident MGUS by quartiles of exposure (OR Q4 vs Q1: PM2.5 2.03 (1.08–3.80); PM10 1.97 (1.05–3.67); PMcoarse 1.98 (1.09–3.60)). ConclusionsOur results indicate that an association between long-term exposure to PM and MGUS may exist. Further epidemiologic studies are needed to corroborate this possible link.

Long-term particulate matter modeling for health effect studies in California – Part 2: Concentrations and sources of ultrafine organic aerosols

Abstract. Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM0. 1). Recent epidemiological studies have identified associations between PM0. 1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM0. 1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM0. 1 OA for health effect studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM2. 5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM2. 5 SOA ∕ OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2–3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM0. 1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM0. 1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM0. 1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM0. 1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM0. 1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes.

Abstract P070: Glucose, Insulin, Insulin Resistance and Exposure to Ambient Air Pollution

Background: Numerous studies suggest that long-term exposure to ambient particulate matter (PM) and nitrogen oxide (NOx) air pollution may be diabetogenic, but little is known about the effects of shorter-term exposures on markers of diabetes risk. Therefore, our objective was to determine whether short- and long-term ambient PM and NOx exposures are associated with glucose, insulin, and insulin resistance measures. Methods: We studied a stratified, random minority oversample of non-diabetic Women’s Health Initiative (WHI) clinical trials participants for whom estimates of fasting glucose, fasting insulin, insulin resistance, and geocoded address-specific daily mean concentrations of PM &lt; 10 μm (PM 10 ), PM &lt; 2.5 μm (PM 2.5 ), and NOx were available at screening and up to three follow-up visits between 1993-2004 (total n=4,019; mean age: 62.2 yr; 24% black; 12% Hispanic; 51% white). We measured insulin resistance using the homeostatic model assessment [HOMA], triglyceride to high-density lipoprotein cholesterol ratio, and the triglyceride-glucose index. We log-transformed the glucose, insulin, and insulin resistance measures, then used multi-level, mixed, longitudinal models weighted for sampling design / attrition and adjusted for sociodemographic, clinical, and meteorological covariates to estimate their associations with 2- and 365-day mean PM 10 and NOx and 365-day mean PM 2.5 concentrations. Results: Measures of glucose homeostasis decreased with short-term exposure to ambient PM 10 . For example, we found a -0.2% (95% confidence interval: -0.4%, 0.1%), -0.9% (-1.7%, -0.1%), and -1.1% (-2.0%, -0.3%) change in glucose, insulin, and HOMA per 10 μg/m 3 increase in the 2-day mean PM 10 concentration. In contrast, we found generally null results per 10 μg/m 3 increase in 365-day mean PM 10 concentration. Measures of glucose homeostasis similarly did not appear to be associated with short- and long-term increases in mean NOx or PM 2.5 concentrations. Conclusion: The findings suggest that responses to ambient exposure to PM 10 in women may be adaptive (i.e. capable of re-establishing normal glucose homeostasis and improving insulin sensitivity) over the short-term. Endoplasmic reticulum stress-response mechanisms involving decreases in protein misfolding may help explain this observed phenomena. Contrary to previous studies, results also suggest that sustained exposure to ambient PM and NOx has nominal influence on glucose homeostasis.

Associations between Long-Term Particulate Matter Exposure and Adult Renal Function in the Taipei Metropolis.

Background:Studies on the effect of air pollutions on kidney diseases are still limited.Objective:We aimed to investigate the associations between particulate matter (PM) exposures and renal function among adults.Methods:We recruited 21,656 adults as participants from 2007 to 2009. The Taiwanese Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to derive the estimated glomerular filtration rate (eGFR). Subjects with an eGFR lower than 60 mL/min/1.73 m2 were defined as having chronic kidney disease (CKD). Land use regression (LUR) models were used to estimate individual exposures to PM with an aerodynamic diameter < 10 μm (PM10), coarse particles (PMCoarse), fine particles (PM2.5), and PM2.5Absorbance. Generalized linear and logistic regression models were used to estimate the associations between PM exposure and renal function.Results:An IQR increase in PM10 (5.83 μg/m3) was negatively associated with eGFR by –0.69 (95% CI: –0.89, –0.48) mL/min/1.73 m2 and positively associated with the prevalence of CKD with adjusted OR = 1.15 (95% CI: 1.07, 1.23). An IQR increase in PMCoarse (6.59 μg/m3) was significantly associated with lower eGFR by –1.07 (95% CI: –1.32, –0.81) mL/min/1.73 m2 and CKD with OR = 1.26 (95% CI: 1.15, 1.38). In contrast, neither outcome was significantly associated with PM2.5 or PM2.5Absorbance. Stratified analyses indicated that associations of CKD with both PM10 and PMCoarse were limited to participants < 65 years of age, and were stronger (for PM10) or limited to (PMCoarse) women. Associations also appeared to be stronger in those without (vs. with) hypertension, and in normal versus overweight participants.Conclusions:Exposure during the previous year to PM10 and PMCoarse, but not PM2.5 or PM2.5Absorbance, was associated with reduced renal function among Taiwanese adults.Citation:Yang YR, Chen YM, Chen SY, Chan CC. 2017. Associations between long-term particulate matter exposure and adult renal function in the Taipei metropolis. Environ Health Perspect 125:602–607; http://dx.doi.org/10.1289/EHP302

The contributions to long-term health-relevant particulate matter at the UK EMEP supersites between 2010 and 2013: Quantifying the mitigation challenge

Human health burdens associated with long-term exposure to particulate matter (PM) are substantial. The metrics currently recommended by the World Health Organization for quantification of long-term health-relevant PM are the annual average PM10 and PM2.5 mass concentrations, with no low concentration threshold. However, within an annual average, there is substantial variation in the composition of PM associated with different sources. To inform effective mitigation strategies, therefore, it is necessary to quantify the conditions that contribute to annual average PM10 and PM2.5 (rather than just short-term episodic concentrations). PM10, PM2.5, and speciated water-soluble inorganic, carbonaceous, heavy metal and polycyclic aromatic hydrocarbon components are concurrently measured at the two UK European Monitoring and Evaluation Programme (EMEP) ‘supersites’ at Harwell (SE England) and Auchencorth Moss (SE Scotland). In this work, statistical analyses of these measurements are integrated with air-mass back trajectory data to characterise the ‘chemical climate’ associated with the long-term health-relevant PM metrics at these sites. Specifically, the contributions from different PM concentrations, months, components and geographic regions are detailed. The analyses at these sites provide policy-relevant conclusions on mitigation of (i) long-term health-relevant PM in the spatial domain for which these sites are representative, and (ii) the contribution of regional background PM to long-term health-relevant PM.At Harwell the mean (±1 sd) 2010–2013 annual average concentrations were PM10=16.4±1.4μgm−3 and PM2.5=11.9±1.1μgm−3 and at Auchencorth PM10=7.4±0.4μgm−3 and PM2.5=4.1±0.2μgm−3. The chemical climate state at each site showed that frequent, moderate hourly PM10 and PM2.5 concentrations (defined as approximately 5–15μgm−3 for PM10 and PM2.5 at Harwell and 5–10μgm−3 for PM10 at Auchencorth) determined the magnitude of annual average PM10 and PM2.5 to a greater extent than the relatively infrequent high, episodic PM10 and PM2.5 concentrations. These moderate PM10 and PM2.5 concentrations were derived across the range of chemical components, seasons and air-mass pathways, in contrast to the highest PM concentrations which tended to associate with specific conditions. For example, the largest contribution to moderate PM10 and PM2.5 concentrations – the secondary inorganic aerosol components, specifically NO3− – were accumulated during the arrival of trajectories traversing the spectrum of marine, UK, and continental Europe areas. Mitigation of the long-term health-relevant PM impact in the regions characterised by these two sites requires multilateral action, across species (and hence source sectors), both nationally and internationally; there is no dominant determinant of the long-term PM metrics to target.

Long-term Particulate Matter Exposures during Adulthood and Risk of Breast Cancer Incidence in the Nurses' Health Study II Prospective Cohort.

There is increasing concern that environmental exposures, such as air pollution, may be related to increasing rates of breast cancer; however, results from cohort studies have been mixed. We examined the association between particulate matter (PM) and measures of distance to roadway with the risk of incident breast cancer in the prospective nationwide Nurses' Health Study II (NHSII) cohort. Incident invasive breast cancer from 1993 to 2011 (N = 3,416) was assessed among 115,921 women in the NHSII cohort. Time-varying Cox proportional hazards models were used to calculate HRs and 95% confidence intervals (95% CI) for increases in ambient exposures to PM10, PM2.5-10, and PM2.5 and residential roadway proximity categories. In multivariable adjusted models, there was little evidence of an increased risk of breast cancer (or any of the receptor-specific subtypes) overall or by menopausal status with PM exposure. There was, however, a suggestion of increased risks among women living <50 m of the largest road type (HR = 1.60; 95% CI, 0.80-3.21) or within <50 m of the two largest road types (1.14; 95% CI, 0.84-1.54) compared with women living farther (≥200 m) away. Among women in the NHSII, we found no statistically significant associations between particulate matter exposures and incidence of breast cancer overall, by menopausal status, or by hormone receptor subtype. There was, however, a suggestion that residential proximity to major roadways may be associated with increased risk. These results suggest no elevated breast cancer risk with increasing exposures to particulate matter air pollution, but that other traffic-related exposures may be important. Cancer Epidemiol Biomarkers Prev; 25(8); 1274-6. ©2016 AACR.

Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: Environmental justice applications of downscaled numerical model output

BackgroundResearchers and policymakers are increasingly focused on combined exposures to social and environmental stressors, especially given how often these stressors tend to co-locate. Such exposures are equally relevant in urban and rural areas and may accrue disproportionately to particular communities or specific subpopulations. ObjectivesTo estimate relationships between racial isolation (RI), a measure of the extent to which minority racial/ethnic group members are exposed to only one another, and long-term particulate matter with an aerodynamic diameter of <2.5μ (PM2.5) and ozone (O3) levels in urban and nonurban areas of the eastern two-thirds of the US. MethodsLong-term (5year average) census tract-level PM2.5 and O3 concentrations were calculated using output from a downscaler model (2002–2006). The downscaler uses a linear regression with additive and multiplicative bias coefficients to relate ambient monitoring data with gridded output from the Community Multi-scale Air Quality (CMAQ) model. A local, spatial measure of RI was calculated at the tract level, and tracts were classified by urbanicity, RI, and geographic region. We examined differences in estimated pollutant exposures by RI, urbanicity, and demographic subgroup (e.g., race/ethnicity, education, socioeconomic status, age), and used linear models to estimate associations between RI and air pollution levels in urban, suburban, and rural tracts. ResultsHigh RI tracts (≥80th percentile) had higher average PM2.5 levels in each category of urbanicity compared to low RI tracts (<20th percentile), with the exception of the rural West. Patterns in O3 levels by urbanicity and RI differed by region. Linear models indicated that PM2.5 concentrations were significantly and positively associated with RI. The largest association between PM2.5 and RI was observed in the rural Midwest, where a one quintile increase in RI was associated with a 0.90μg/m3 (95% confidence interval: 0.83, 0.99μg/m3) increase in PM2.5 concentration. Associations between O3 and RI in the Northeast, Midwest and West were positive and highest in suburban and rural tracts, even after controlling for potential confounders such as percentage in poverty. ConclusionRI is associated with higher 5year estimated PM2.5 concentrations in urban, suburban, and rural census tracts, adding to evidence that segregation is broadly associated with disparate air pollution exposures. Disproportionate burdens to adverse exposures such as air pollution may be a pathway to racial/ethnic disparities in health.

Biomolecular Markers within the Core Axis of Aging and Particulate Air Pollution Exposure in the Elderly: A Cross-Sectional Study.

Background:Telomere length and mitochondrial DNA (mtDNA) content are markers of aging and aging-related diseases. There is inconclusive evidence concerning the mechanistic effects of airborne particulate matter (PM) exposure on biomolecular markers of aging.Objective:The present study examines the association between short- and long-term PM exposure with telomere length and mtDNA content in the elderly and investigates to what extent this association is mediated by expression of genes playing a role in the telomere–mitochondrial axis of aging.Methods:Among 166 nonsmoking elderly participants, we used qPCR to measure telomere length and mtDNA content in leukocytes and RNA from whole blood to measure expression of SIRT1, TP53, PPARGC1A, PPARGC1B, NRF1, and NFE2L2. Associations between PM exposure and markers of aging were estimated using multivariable linear regression models adjusted for sex, age, BMI, socioeconomic status, statin use, past smoking status, white blood cell count, and percentage of neutrophils. Mediation analysis was performed to explore the role of age-related markers between the association of PM exposure and outcome. Annual PM2.5 exposure was calculated for each participant’s home address using a high-resolution spatial–temporal interpolation model.Results:Annual PM2.5 concentrations ranged from 15 to 23 μg/m3. A 5-μg/m3 increment in annual PM2.5 concentration was associated with a relative decrease of 16.8% (95% CI: –26.0%, –7.4%, p = 0.0005) in telomere length and a relative decrease of 25.7% (95% CI: –35.2%, –16.2%, p < 0.0001) in mtDNA content. Assuming causality, results of the mediation analysis indicated that SIRT1 mediated 19.5% and 22.5% of the estimated effect of PM2.5 exposure on telomere length and mtDNA content, respectively.Conclusions:Our findings suggest that the estimated effects of PM2.5 exposure on the telomere–mitochondrial axis of aging may play an important role in chronic health effects of PM2.5.Citation:Pieters N, Janssen BG, Dewitte H, Cox B, Cuypers A, Lefebvre W, Smeets K, Vanpoucke C, Plusquin M, Nawrot TS. 2016. Biomolecular markers within the core axis of aging and particulate air pollution exposure in the elderly: a cross-sectional study. Environ Health Perspect 124:943–950; http://dx.doi.org/10.1289/ehp.1509728

Spatial and temporal variations of the concentrations of PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt;, PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; and PM&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt; in China

Abstract. Concentrations of PM10, PM2.5 and PM1 were monitored at 24 CAWNET (China Atmosphere Watch Network) stations from 2006 to 2014. The highest particulate matter (PM) concentrations were observed at the stations of Xian, Zhengzhou and Gucheng, on the Guanzhong Plain and the Huabei Plain (HBP). The second highest PM concentrations were observed in northeast China, followed by southern China. According to the latest air quality standards of China, 14 stations reached the PM10 standard, and only 7 stations, mainly rural and remote stations, reached the PM2.5 standard. The ratios of PM2.5 to PM10 showed a clear increasing trend from northern to southern China, because of the substantial contribution of coarse mineral aerosol in northern China. The ratios of PM1 to PM2.5 were higher than 80 % at most stations. PM concentrations tended to be highest in winter and lowest in summer at most stations, and mineral dust influenced the results in spring. A decreasing interannual trend was observed on the HBP and in southern China for the period 2006 to 2014, but an increasing trend occurred at some stations in northeast China. Bimodal and unimodal diurnal variation patterns were identified at urban stations. Both emissions and meteorological variations dominate the long-term PM concentration trend, while meteorological factors play a leading role in the short term.

Long-Term Exposure to Particulate Matter Air Pollution Is a Risk Factor for Stroke: Meta-Analytical Evidence.

Epidemiological studies suggest an association between stroke incidence and stroke mortality and long-term exposure to particulate matter (PM) air pollution. However, the magnitude of the association is still unclear. We searched the Pubmed citation database for epidemiological studies and reviews on stroke and PM exposure. Then, we carried out a meta-analysis to quantify the pooled association between stroke incidence and mortality and long-term exposure to PM. Meta-analyses were performed for stroke events and stroke mortality and for PM10 and PM2.5 separately and jointly. We identified 20 studies, including a total of >10 million people, on long-term PM exposure and stroke event or stroke mortality. For exposure to PM10 (including estimated exposure to PM10 from studies using PM2.5), the pooled hazard ratio for each 10-μg/m3 increment in PM10 was 1.061 (95% confidence interval, 1.018-1.105) and 1.080 (0.992-1.177) for overall stroke events and stroke mortality, respectively. A stratified analysis by continent revealed that the association between stroke and long-term PM10 exposure was positive in North America (1.062 [1.015-1.110]) and Europe (1.057 [0.973-1.148]), but studies in Asia (1.010 [0.885-1.153]) showed a high degree of heterogeneity. Considering exposure to PM2.5 (Europe and North America combined), the hazard ratios for a 5-μg/m3 increment were 1.064 (1.021-1.109) and 1.125 (1.007-1.256) for stroke events and mortality, respectively. The scientific evidence of the past decade identifies long-term exposure to PM, and PM2.5 in particular, as a risk factor for stroke. However, we found some currently unexplained geographical variability in this association.

Prospective Study of Ambient Particulate Matter Exposure and Risk of Pulmonary Embolism in the Nurses' Health Study Cohort.

BackgroundPulmonary embolism (PE) is the most serious manifestation of venous thromboembolism and a leading cause of sudden death. Several studies have suggested associations of venous thromboembolism with short-term particulate matter (PM) exposure; evidence on long-term PM and traffic exposure is mixed.ObjectivesWe examined the association of long-term exposure to PM2.5, PM2.5–10, and PM10 (PM with diameter of ≤ 2.5, 2.5–10, and ≤ 10 μm) and distance to roadways with overall incident PE and with PE subtypes in a cohort of U.S. women.MethodsThe study included 115,745 women from the Nurses’ Health Study, followed from 1992 through 2008. Incident PE cases were self-reported biennially. Nonidiopathic PE were cases for which the medical record revealed an underlying health condition related to PE (i.e., surgery, trauma, or malignancy); idiopathic PE were cases with no such history. We used spatiotemporal models combining spatial smoothing and geographic covariates to quantify exposure at residential addresses, and Cox proportional hazards models to calculate hazard ratios (HR) and 95% confidence intervals (CIs).ResultsPM2.5 averaged over 1 month (HR = 1.22; 95% CI: 1.04, 1.44) or 12 months (HR = 1.17; 95% CI: 0.93, 1.48) was associated with incident PE, after adjusting for known risk factors and PM2.5–10. Equivalent analyses restricted to PE subtypes showed a positive association for PM2.5 with nonidiopathic PE, but not with idiopathic PE. We did not find evidence of an association between distance to roadways and PE risk.ConclusionsWe provide evidence that PM in the prior 1 and 12 months is associated with PE risk. Our results also suggest that women with underlying health conditions may be more susceptible to PE after PM exposure.CitationPun VC, Hart JE, Kabrhel C, Camargo CA Jr, Baccarelli AA, Laden F. 2015. Prospective study of ambient particulate matter exposure and risk of pulmonary embolism in the Nurses’ Health Study cohort. Environ Health Perspect 123:1265–1270; http://dx.doi.org/10.1289/ehp.1408927

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

Abstract. For the first time, a ~ decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution over populated regions and daily time resolution has been conducted for California to provide air quality data for health effect studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, elemental carbon (EC), organic carbon (OC), nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95, 100, 71, 73, and 92% of the simulated months, respectively. The base data set provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated 1 day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to overpredict wind speed during stagnation events, leading to underpredictions of high PM concentrations, usually in winter months. The WRF model also generally underpredicts relative humidity, resulting in less particulate nitrate formation, especially during winter months. These limitations must be recognized when using data in health studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/ .

Long-term particulate matter exposure: Attributing health effects to individual PM components

While most in the scientific community are of the opinion that the composition of fine particulate matter (PM2.5) is an important driver of resultant health effects, there is still some degree of uncertainty regarding those components considered to be most harmful. Reviews of the subject from several perspectives have been published, but to our knowledge a comprehensive review of the epidemiological and toxicological literature related to long-term exposure to PM2.5 components does not exist. We reviewed published epidemiological studies that were of a cohort design, included at least one PM component as well as PM2.5 mass, and included quantitative analysis to relate health outcomes to individual components. Toxicological studies were included if they were ≥5 months in duration and either included at least one PM component as well as PM mass or focused on a specific PM or emissions type. Overall, we find that epidemiological and toxicological evidence for long-term effects of PM components is limited, in contrast to the short-term literature, which is more plentiful. Epidemiological literature suggests that a number of components are associated with health effects, and that no component is unequivocally not so associated. Toxicological studies that can more easily identify potentially causal components are generally limited to long-term studies using concentrated ambient particles (CAPs), of which few long-term studies exist. Epidemiological study designs that utilize existing monitoring data routinely collected by the U.S. Environmental Protection Agency would be valuable additions to the literature, as would novel toxicological studies that incorporate innovative designs to separate components or groups of components, such as denuders, filtration, or other approaches. From a policy perspective, it is important to more comprehensively investigate this issue so that if particular constituents are determined to be more potent in inducing health effects, their sources can be controlled.Implications: Understanding the components of PM2.5 that are most harmful to human health is a critical policy issue. This review examined the epidemiological and toxicological literature related to long-term exposure to PM components and found that, unlike the literature on short-term health effects, there is insufficient information to make clear inferences about causal components. There is a need for further research in this area to exploit existing PM monitoring data in epidemiological studies and to design experimental studies that are able to tease out the effects of multiple constituents.

Shape of concentration-response curves between long-term particulate matter exposure and morbidities of chronic bronchitis: a review of epidemiological evidence.

Numerous studies have assessed the concentration-response (C-R) relationships between long-term exposure to particulate matter (PM) and mortality from cardiopulmonary diseases, but few studies have evaluated the C-R relationships between PM exposure and morbidity of chronic respiratory diseases or their symptoms, and to date no systematic review has been published on the characteristics of the C-R curves between PM exposure and respiratory disease morbidity. Screening of all available studies in Medline identified ten studies with figures or scatter plots showing the C-R relationships between PM exposure and chronic bronchitis or chronic cough/phlegm. The C-R relationships showed ballistic 'S' shaped curves, linear in the low to moderate PM range and flattening out in the high PM range. Moreover, the shape and level of the C-R curves differed markedly between susceptible and nonsusceptible populations. New evidence from a prospective cohort study confirmed that the C-R relationship between PM reduction and beneficial effects on respiratory health may be due to the decreased incidence of respiratory symptoms and increased recovery in individuals with symptoms of bronchitis. Additional studies are needed to assess the C-R relationships between different PM contents and chronic health parameters, especially in geographic areas with high PM pollution and in more susceptible populations. Evidence from prospective cohort studies in developing countries with areas of high PM pollution may help evaluate the burden of chronic respiratory disease attributable to PM pollution and air quality standards.

Long-Term Exposure to Indoor Particulate Matter and Blood Pressure among Elder Population

The association of short-term exposure to Indoor particulate matter (PM) with elevated blood pressure (BP) has been well documented in previous studies. The literature related to long-term indoor PM exposure and BP variation is limited. The aim of this study is to explore the association between long-term (annual mean) exposure to indoor fine PM (PM2.5) and changes in systolic BP (SBP) and diastolic BP (DBP) among elder population. We recruited 60 elder retired volunteers to participate in our cross sectional study. Four repeated measurements of daytime (0800-2000 hours) average BP, indoor PM2.5 and noise levels were conducted for each participant per month. Each of the 60 participants received 48 home visits in 2012 to generate annual mean indoor PM2.5 and BP for data analysis. We used generalized additive models to investigate the association between indoor PM2.5, SBP and DBP adjusting for cardiovascular disease risk factors. The results showed that participants had significantly higher exposures to indoor PM2.5 and higher SBP in houses with stir-frying and burning incense than in houses without above household activities. Moreover, a 10 µg/m3 increase in indoor PM2.5 was associated with 2.4 mmHg higher systolic BP (95%CI: 1.5, 3.3; p&lt;0.001) for the overall effect of indoor PM2.5 exposure. The effects were greater among participants in houses with stir-frying and burning incense. We concluded that long-term indoor PM2.5 exposure was associated with higher SBP among elder population. Such association could be modified by household activities, including stir-frying and burning incense.

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities

Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed. Results Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics. Keywords: Exposure prediction, Health effect, Kriging, Long-term exposure, Particulate matter

Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities.

ObjectivesCohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10) concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability.MethodsWe obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2) statistics were computed.ResultsMean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively). Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36) than those for the city-specific models.ConclusionsIn general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.

Associations between particulate matter elements and early-life pneumonia in seven birth cohorts: results from the ESCAPE and TRANSPHORM projects.

Evidence for a role of long-term particulate matter exposure on acute respiratory infections is growing. However, which components of particulate matter may be causative remains largely unknown. We assessed associations between eight particulate matter elements and early-life pneumonia in seven birth cohort studies (N total=15,980): BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), INMA (Spain), MAAS (United Kingdom) and PIAMA (The Netherlands). Annual average exposure to copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc, each respectively derived from particles with aerodynamic diameters ≤ 10 μm (PM10) and 2.5 μm (PM2.5), were estimated using standardized land use regression models and assigned to birth addresses. Cohort-specific associations between these exposures and parental reports of physician-diagnosed pneumonia between birth and two years were assessed using logistic regression models adjusted for host and environmental covariates and total PM10 or PM2.5 mass. Combined estimates were calculated using random-effects meta-analysis. There was substantial within and between-cohort variability in element concentrations. In the adjusted meta-analysis, pneumonia was weakly associated with zinc derived from PM10 (OR: 1.47 (95% CI: 0.99, 2.18) per 20 ng/m(3) increase). No other associations with the other elements were consistently observed. The independent effect of particulate matter mass remained after adjustment for element concentrations. In conclusion, associations between particulate matter mass exposure and pneumonia were not explained by the elements we investigated. Zinc from PM10 was the only element which appeared independently associated with a higher risk of early-life pneumonia. As zinc is primarily attributable to non-tailpipe traffic emissions, these results may suggest a potential adverse effect of non-tailpipe emissions on health.