•Metabolomic profiling is a powerful tool for mechanistic understanding of PM2.5 exposure impact.•PM2.5 exposure has metabolomic signatures related to oxidative stress, immunity, & nucleic acid ...damage.•Little is known about the specific PM2.5 species (hence sources) that drive these signatures.•Ultrafine particles were the main significant species of short-term PM2.5 exposure followed by Ni and V then Si, Al, and K.•Black carbon, Ni, V, Zn, Cu, Fe, and Se were the main significant species of long-term PM2.5 exposure.
The metabolomic signatures of short- and long-term exposure to PM2.5 have been reported and linked to inflammation and oxidative stress. However, little is known about the relative contribution of the specific PM2.5 species (hence sources) that drive these metabolomic signatures.
We aimed to determine the relative contribution of the different species of PM2.5 exposure to the perturbed metabolic pathways related to changes in the plasma metabolome.
We performed mass-spectrometry based metabolomic profiling of plasma samples among men from the Normative Aging Study to identify metabolic pathways associated with PM2.5 species. The exposure windows included short-term (one, seven-, and thirty-day moving average) and long-term (one year moving average). We used linear mixed-effect regression with subject-specific intercepts while simultaneously adjusting for PM2.5, NO2, O3, temperature, relative humidity, and covariates and correcting for multiple testing. We also used independent component analysis (ICA) to examine the relative contribution of patterns of PM2.5 species.
Between 2000 and 2016, 456 men provided 648 blood samples, in which 1158 metabolites were quantified. We chose 305 metabolites for the short-term and 288 metabolites for the long-term exposure in this analysis that were significantly associated (p-value < 0.01) with PM2.5 to include in our PM2.5 species analysis. On average, men were 75.0 years old and their body mass index was 27.7 kg/m2. Only 3% were current smokers. In the adjusted models, ultrafine particles (UFPs) were the most significant species of short-term PM2.5 exposure followed by nickel, vanadium, potassium, silicon, and aluminum. Black carbon, vanadium, zinc, nickel, iron, copper, and selenium were the significant species of long-term PM2.5 exposure. We identified several metabolic pathways perturbed with PM2.5 species including glycerophospholipid, sphingolipid, and glutathione. These pathways are involved in inflammation, oxidative stress, immunity, and nucleic acid damage and repair. Results were overlapped with the ICA.
We identified several significant perturbed plasma metabolites and metabolic pathways associated with exposure to PM2.5 species. These species are associated with traffic, fuel oil, and wood smoke. This is the largest study to report a metabolomic signature of PM2.5 species’ exposure and the first to use ICA.
Particulate matter with aerodynamic diameter <2.5 μm (PM2.5) is associated with asthma exacerbation. In the Children's Air Pollution Asthma Study, we investigated the longitudinal association of ...PM2.5 and its components from indoor and outdoor sources with cough and wheeze symptoms in 36 asthmatic children. The sulfur tracer method was used to estimate infiltration factors. Mixed proportional odds models for an ordinal response were used to relate daily cough and wheeze scores to PM2.5 exposures. The odds ratio associated with being above a given symptom score for a SD increase in PM2.5 from indoor sources (PMIS) was 1.24 (95% confidence interval: 0.92-1.68) for cough and 1.63 (1.11-2.39) for wheeze. Ozone was associated with wheeze (1.82, 1.19-2.80), and cough was associated with indoor PM2.5 components from outdoor sources (denoted with subscript "OS") bromine (BrOS: 1.32, 1.05-1.67), chlorine (ClOS: 1.27, 1.02-1.59) and pyrolyzed organic carbon (OPOS: 1.49, 1.12-1.99). The highest effects were seen in the winter for cough with sulfur (SOS: 2.28, 1.01-5.16) and wheeze with organic carbon fraction 2 (OC2OS: 7.46, 1.19-46.60). Our results indicate that exposure to components originating from outdoor sources of photochemistry, diesel and fuel oil combustion is associated with symptom's exacerbation, especially in the winter. PM2.5 mass of indoor origin was more strongly associated with wheeze than with cough.
Children spend over 6 h a day in schools and have higher asthma morbidity from school environmental exposures. The present study aims to determine indoor and outdoor possible sources affecting indoor ...PM2.5 in classrooms. Weeklong indoor PM2.5 samples were collected from 32 inner-city schools from a Northeastern U.S. community during three seasons (fall, winter and spring) during the years 2009 to 2013. Concurrently, daily outdoor PM2.5 samples were taken at a central monitoring site located at a median distance of 4974 m (range 1065–11,592 m) from the schools. Classroom indoor concentrations of PM2.5 (an average of 5.2 μg/m3) were lower than outdoors (an average of 6.5 μg/m3), and these averages were in the lower range compared to the findings in other schools' studies. The USEPA PMF model was applied to the PM2.5 components measured simultaneously from classroom indoor and outdoor to estimate the source apportionment. The major sources (contributions) identified across all seasons of indoor PM2.5 were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM2.5 across all seasons were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). Secondary pollution was the greatest contributor to indoor and outdoor PM2.5 over all three seasons, with the highest contribution during spring with 53% to indoor PM2.5 and 45% to outdoor PM2.5. Lower contributions of this source during fall and winter are most likely attributed to less infiltration indoors. In contrast, the indoor contribution of motor vehicles source was highest in the fall (29%) and winter (25%), which was presumably categorized by a local source. From the relationship between indoor-to-outdoor sulfur ratios and each source contribution, we also estimated the local and regional influence on indoor PM2.5 concentration. Overall, the observed differences to indoor PM2.5 are related to seasonality, and the distinct characteristics and behavior of each classroom/school.
•Relative source contributions of indoor and outdoor PM2.5 were determined for inner-city school classrooms;•Four outdoor sources and two indoor sources were identified as contributors to indoor PM2.5 concentrations, and;•Regional sources were the greatest contributor to indoor and outdoor PM2.5 in all seasons.
Between March and April 2020, Cyprus and Greece health authorities enforced three escalated levels of public health interventions to control the COVID-19 pandemic. We quantified compliance of 108 ...asthmatic schoolchildren (53 from Cyprus, 55 from Greece, mean age 9.7 years) from both countries to intervention levels, using wearable sensors to continuously track personal location and physical activity. Changes in 'fraction time spent at home' and 'total steps/day' were assessed with a mixed-effects model adjusting for confounders. We observed significant mean increases in 'fraction time spent at home' in Cyprus and Greece, during each intervention level by 41.4% and 14.3% (level 1), 48.7% and 23.1% (level 2) and 45.2% and 32.0% (level 3), respectively. Physical activity in Cyprus and Greece demonstrated significant mean decreases by - 2,531 and - 1,191 (level 1), - 3,638 and - 2,337 (level 2) and - 3,644 and - 1,961 (level 3) total steps/day, respectively. Significant independent effects of weekends and age were found on 'fraction time spent at home'. Similarly, weekends, age, humidity and gender had an independent effect on physical activity. We suggest that wearable technology provides objective, continuous, real-time location and activity data making possible to inform in a timely manner public health officials on compliance to various tiers of public health interventions during a pandemic.
Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and ...topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies.
Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative.
109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources.
Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location.
The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research.
•A framework for spatial clustering of sites based on PM2.5 composition is presented.•Sites are grouped using k-means cluster analysis.•Clustering process is guided by specific air pollution characteristics.•Validity based on chemical, geographical and weather characteristics.•Sensitivity analysis confirmed that clustering was robust.
The mechanism for spread of SARS-CoV-2 has been attributed to large particles produced by coughing and sneezing. There is controversy whether smaller airborne particles may transport SARS-CoV-2. ...Smaller particles, particularly fine particulate matter (≤ 2.5 µm in diameter), can remain airborne for longer periods than larger particles and after inhalation will penetrate deeply into the lungs. Little is known about the size distribution and location of airborne SARS-CoV-2 RNA.
As a measure of hospital-related exposure, air samples of three particle sizes (> 10.0 µm, 10.0-2.5 µm, and ≤ 2.5 µm) were collected in a Boston, Massachusetts (USA) hospital from April to May 2020 (N = 90 size-fractionated samples). Locations included outside negative-pressure COVID-19 wards, a hospital ward not directly involved in COVID-19 patient care, and the emergency department.
SARS-CoV-2 RNA was present in 9% of samples and in all size fractions at concentrations of 5 to 51 copies m
. Locations outside COVID-19 wards had the fewest positive samples. A non-COVID-19 ward had the highest number of positive samples, likely reflecting staff congregation. The probability of a positive sample was positively associated (r = 0.95, p < 0.01) with the number of COVID-19 patients in the hospital. The number of COVID-19 patients in the hospital was positively associated (r = 0.99, p < 0.01) with the number of new daily cases in Massachusetts.
More frequent detection of positive samples in non-COVID-19 than COVID-19 hospital areas indicates effectiveness of COVID-ward hospital controls in controlling air concentrations and suggests the potential for disease spread in areas without the strictest precautions. The positive associations regarding the probability of a positive sample, COVID-19 cases in the hospital, and cases in Massachusetts suggests that hospital air sample positivity was related to community burden. SARS-CoV-2 RNA with fine particulate matter supports the possibility of airborne transmission over distances greater than six feet. The findings support guidelines that limit exposure to airborne particles including fine particles capable of longer distance transport and greater lung penetration.
Short-term exposure to ambient air pollution has been associated with lower lung function. Few studies have examined whether these associations are detectable at relatively low levels of pollution ...within current U.S. Environmental Protection Agency (EPA) standards.
To examine exposure to ambient air pollutants within EPA standards and lung function in a large cohort study.
We included 3,262 participants of the Framingham Offspring and Third Generation cohorts living within 40 km of the Harvard Supersite monitor in Boston, Massachusetts (5,358 examinations, 1995-2011) who were not current smokers, with previous-day pollutant levels in compliance with EPA standards. We compared lung function (FEV1 and FVC) after previous-day exposure to particulate matter less than 2.5 μm in diameter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in the "moderate" range of the EPA Air Quality Index to exposure in the "good" range. We also examined linear relationships between moving averages of pollutant concentrations 1, 2, 3, 5, and 7 days before spirometry and lung function.
Exposure to pollutant concentrations in the "moderate" range of the EPA Air Quality Index was associated with a 20.1-ml lower FEV1 for PM2.5 (95% confidence interval CI, -33.4, -6.9), a 30.6-ml lower FEV1 for NO2 (95% CI, -60.9, -0.2), and a 55.7-ml lower FEV1 for O3 (95% CI, -100.7, -10.8) compared with the "good" range. The 1- and 2-day moving averages of PM2.5, NO2, and O3 before testing were negatively associated with FEV1 and FVC.
Short-term exposure to PM2.5, NO2, and O3 within current EPA standards was associated with lower lung function in this cohort of adults.
The health burden from exposure to air pollution has been studied in many parts of the world. However, there is limited research on the health effects of air quality in arid areas where sand dust is ...the primary particulate pollution source.
Study the risk of mortality from exposure to poor air quality days in Kuwait.
We conducted a time-series analysis using daily visibility as a measure of particulate pollution and non-accidental total mortality from January 2000 through December 2016. A generalized additive Poisson model was used adjusting for time trends, day of week, and temperature. Low visibility (yes/no), defined as visibility lower than the 25th percentile, was used as an indicator of poor air quality days. Dust storm events were also examined. Finally, we examined these associations after stratifying by gender, age group, and nationality (Kuwaitis/non-Kuwaitis).
There were 73,748 deaths from natural causes in Kuwait during the study period. The rate ratio comparing the mortality rate on low visibility days to high visibility days was 1.01 (95% CI: 0.99–1.03). Similar estimates were observed for dust storms (1.02, 95% CI: 1.00–1.04). Higher and statistically significant estimates were observed among non-Kuwaiti men and non-Kuwaiti adolescents and adults.
We observed a higher risk of mortality during days with poor air quality in Kuwait from 2000 through 2016.
•The health effects of air quality in arid areas are not well studied.•Studied the acute effect of poor air quality and dust storms on mortality in Kuwait•Time-series analysis using 2000-16 daily visibility and all nonaccidental mortality•People are in higher risk of dying during days with poor air quality in Kuwait.•Non-Kuwaiti people especially men and adults are in higher risk.
To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial ...resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample “ten-fold” cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels.
•We investigate the spatial variability of the AOD-PM2.5 relationship.•The model-predicted PM2.5 mass concentrations are highly correlated with the actual observations (R2 = 0.89).•The model captures the pollution levels along highways.•High accuracy of PM2.5 estimates enables to examine PM2.5 levels within cities.
Many studies have reported significant associations between exposure to PM(2.5) and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have ...relied on a limited number of PM(2.5) monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias.
Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM(2.5) exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM(2.5) exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk.
We observed associations between both short-term and long-term exposure to PM(2.5) and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every 10-µg/m(3) increase in short-term PM(2.5) exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every 10-µg/m(3) increase in long-term PM(2.5) exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75).
As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.
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