Daily Ambient PM2 Research Articles

Changes in lipid profiles and blood pressure in response to acute exposure to ambient PM2.5 and its carbonaceous compositions - results from the AIRLESS Study

Background Increasing evidence has shown ambient fine particles (PM2.5) as a key risk factor to cardiovascular and metabolic diseases. However, few human studies examined if the adverse effects were associated carbonaceous particles, the potential toxic species in PM2.5. Methods Based on AIRLESS project, we examined the changes in lipid profiles and blood pressure (BP) in response to acute exposure to ambient PM2.5 and its carbonaceous compositions, namely elemental carbon (EC) and organic carbon (OC). 251 nonsmoking participants from urban (N=123) and peri-urban (N=128) Beijing, China, have completed 2 times clinical visits in winter, and another 2 in summer. During each visit, serum samples were collected for the lipid profile analysis of total cholesterol (TC), High-density lipoprotein cholesterol (HDL), Low-density lipoprotein cholesterol (LDL) and Triglycerides (TG), and BP were measured. Daily ambient PM2.5, EC and OC were measured in the monitoring station nearby the participants’ communities. Associations between exposure and health outcomes were examined with linear mixed-effects model adjusted for demographic, socioeconomic and lifestyle parameters. Results Exposure to EC was significantly associated with all the cholesterol parameters, while PM2.5 and OC were only associated with HDL. For example, an IQR increase in previous 1-day exposure to EC (3.9 μg/m3) was significantly associated with an elevation of HDL [1.4%(95% confidence interval (CI): 0.7-2.1%)], LDL [1.5%(CI: 0.3-2.7%)] and TC [1.3%(CI: 0.4-2.2%)] (p<0.05). All the three pollutants were significantly associated with the increase in systolic and diastolic BP, but not the changes in TG. A clear modified effect between sites were observed that may be partly attributed to the difference in the local emission sources given the significantly differed ratio of OC to EC between the two sites. Conclusions Elemental carbon may play an important role in the adverse cardiovascular and metabolic effect of PM2.5.

Ambient PM2.5Exposure and Mortality in U.S. End Stage Renal Disease Patients Receiving In-center Hemodialysis: 2008-2014

Background/Aim: Air pollution is recognized as a potentially modifiable health risk factor, particularly among susceptible populations. This study examines the effect of short-term exposure to fine particulate matter (PM2.5) among a potentially susceptible population, the end-stage renal disease (ESRD) patients receiving in-center hemodialysis (HD). Methods: A time-series analysis of the association between daily ambient PM2.5 and daily mortality was conducted for 2008-2014 in 1,833 US counties with dialysis clinics. Using the United States Renal Data System, we identified ESRD patients who 1) had Medicare as primary payer, 2) survived first 3 months of HD, and 3) visited dialysis clinics within study counties before death. County-level daily ambient PM2.5 was estimated with a prediction model that incorporates monitored observations, satellite aerosol optical depth, chemical transport model simulations, meteorology, land-use and other variables. We considered non-accidental all-cause deaths and assessed same-day and lagged effects. Rate ratios (RR) are expressed per 10 ug/m3 increase in PM2.5. Additionally, we assess effect modification by pre-existing conditions, age, and time since dialysis to identify vulnerable patient groups within the cohort. Results: A total of 238,621 all-cause deaths were included in the analysis. The average county-level daily PM2.5 concentration was 9.2 ug/m3. For all-cause mortality, an elevated but not significant same-day effect was estimated (RR= 1.005, 95%CI: 0.998-1.013), and elevated lagged-effects were estimated for 0-1, 0-2, and 0-3 lag-periods with RR=1.009 (1.001-1.018), RR=1.010 (1.001-1.019), and RR=1.010 (1.001-1.019), respectively. The results on effect modification will be available at the time of the conference. Conclusions: A 10 ug/m3 increase in ambient PM2.5 exposure was associated with 1% increase in daily mortality rate among HD patients 1-3 days following the exposure. This study highlights an elevated mortality risk for this population with relatively low ambient PM2.5 concentrations. This abstract does not represent EPA policy.

The Influence of Socioeconomic Status on the Relationship of Daily Cardiovascular Mortality with Daily Ambient PM2.5 Concentrations in Phoenix, AZ

Background/Aim. Test the suggestion that people with high socioeconomic status (SES) are less susceptible to air pollution than those with low SES. Methods. Standard regression analysis using R. Results. People in different SES populations die on different lag days. By breaking the county into three SES populations, much higher associations are found in the SES populations than in the county. For example, betas for the strongest single lag day association for the low, medium, and high SES populations are 0.063, 0.061, and 0.046 but only 0.030 for the whole county. Since strong associations occur only on 4 or 5 lag days out of 12, and there are not enough deaths on other days to give meaningful associations, an unconstrained distributed lag model is used to estimate the total effect of PM2.5 on mortality. Total increase in % risk for an interquartile increase in PM2.5 (95% confidence interval) (range) is: low, 13.0% (5.1, 21.5) (16.5); middle 7.3% (0.2, 14.8) (14.6); high 11.6% (4.4, 19.3) (7.3); entire county 7.6 % (4.0, 11.3) (7.3). Conclusions. When risks from all lag days with meaningful associations are combined, people with low and high SES have approximately the same % increase in risk for a unit increase in PM2.5. However, it takes longer for the high SES people to die. If on a given lag day, only people in one SES population die due to PM2.5 exposure, the association will be high for that SES population lag day, but much lower for that lag day for the total population, since there will be the same number of deaths due to PM2.5, but within three times the total number of deaths. Thus, by breaking the county population into three SES populations, much higher associations are found in the low and high SES populations than in the county.

CHAIR-consortium: A collaboration for air pollution research across India

Ambient air pollution levels in India are among the highest in the world and contribute to climate change and poor health. Despite extreme levels of air pollution and increased frequency and intensity of heat waves in India, current Indian public policy to reduce emissions has been inadequate and lacked a coordinated and sustained effort. Planned coal power plants will additionally contribute to both more CO2 and air pollution emissions. Moreover, lack of monitoring in rural areas has contributed to a belief that air pollution is only an urban problem. To address this we have developed an exposure assessment methodology employing machine-learning methods and multiple data sources including monitoring data, satellite data, meteorology and land-use data to make reliable high resolution predictions of particulate matter <2.5 µm in diameter (PM2.5) and temperature in areas with sparse monitoring. We will develop a nation-wide exposure model for daily ambient PM2.5 and ambient temperature from 2008-2020 at a spatial resolution of 1 km x 1 km and locally at 200 m x 200 m in India. Our aim is to link our national estimates of PM2.5 and temperature to health data to quantify the associations between PM2.5 and ambient temperature, independently and jointly on major public health endpoints. Our specific intention is to make the exposure model an open resource to accelerate environmental epidemiology and build capacity around the many existing health studies in India to which the exposure model can be applied. We will develop an online interactive environmental database and a strategy for stakeholder communication to significantly facilitate decision-making and increase public awareness and engagement. Successful fulfillment of our aims in India will significantly contribute to sustainable development benefitting not only the very large Indian population but also the global community.

Time-weighted average of fine particulate matter exposure and cause-specific mortality in China: a nationwide analysis

Most previous assessments of the hazardous effects attributable to fine particulate matter (PM2·5) exposure have used ambient PM2·5 as an exposure metric, resulting in substantial bias in effect estimates. We did a study to examine the association between cause-specific mortality and the time-weighted average of PM2·5 exposure after accounting for indoor exposure in 267 cities in China. We did a nationwide study, using Laser Egg air quality monitors in 36 cities to obtain data for indoor PM2·5 concentrations from 18 484 anonymised households between Nov 1, 2015 and July 2, 2018. We developed and validated a nationwide indoor PM2·5 prediction model for a further 302 cities by retrieving raw records of hourly concentrations from residents' air sensors; the model was used to predict indoor PM2·5 during 2013 to 2018. Daily ambient PM2·5 concentration data were estimated by averaging hourly ambient PM2·5 concentrations obtained from China's National Urban Air Quality Real-time Publishing Platform. Daily numbers of deaths from all non-accidental causes were obtained from 324 cities from the Disease Surveillance Point System of China between Jan 1, 2013, to Dec 31, 2017, and calculated for 267 cities that had an average daily mortality above three, and data for PM2·5 concentrations and meteorological information for at least 1 year between 2013 and 2017. We used distributed lag non-linear models to estimate city-specific associations between cause-specific mortality and reconstructed PM2·5 exposure by considering indoor PM2·5 exposure. We combined the city-specific effect estimates at the national level using a random effects meta-analysis. 13 972 records of daily indoor PM2·5 concentrations for 36 cities, extracted from 47 459 183 raw records from the sensors were included for modelling indoor PM2·5 levels. The nationwide indoor PM2·5 concentration was 40 μg/m3 (SD 21) between 2013 and 2017, which was approximately 20% lower than the ambient PM2·5 concentration of 50 μg/m3 (42). An increase of 10 μg/m3 in time-averaged PM2·5 exposure concentrations was associated with increased daily mortality estimates of 0·44% (95% CI 0·33-0·54) for total non-accidental causes, 0·50% (0·37-0·63) for cardiovascular diseases, 0·46% (0·28-0·63) for coronary heart disease, 0·49% (0·32-0·66) for stroke, 0·59% (0·39-0·79) for respiratory diseases, and 0·69% (0·45-0·92) for chronic obstructive pulmonary disease, respectively. Compared with previous estimations based on ambient PM2·5, our estimates approximately doubled the size of the effects related to PM2·5. This nationwide study revealed a higher mortality risk attributed to time-averaged indoor and ambient PM2·5 exposure compared with the risk associated with ambient PM2·5 exposure alone, which indicates that caution should be exercised when using ambient PM2·5 as a surrogate for PM2·5 exposure. National Natural Science Foundation of China (Youth Program) and the Fundamental Research Project of Beihang University.

How longer can people live by achieving the daily ambient fine particulate pollution standards in the Pearl River Delta region, China?

BackgroundPrevious research has reported the effects of long-term fine particulate matter (PM2.5) pollution on years of life lost (YLL), but these effects may not represent the full impact. This study aims to estimate potential benefits in life time from adhering to daily ambient PM2.5 concentration standards/guidelines. MethodsThis study evaluated the relationship between daily ambient PM2.5 level and YLL using a two-stage approach with generalized additive models and meta-analysis. Potential life expectancy gains were then estimated by presuming that daily PM2.5 levels were in compliance with the Chinese and WHO standards. In addition, the attributable fraction of YLL due to excess PM2.5 exposure was also calculated. ResultsDuring 2013–2016, 459,468 non-accidental deaths were recorded in the six cities of Pearl River Delta, China. Each 10 μg/m3 increment in four-day average (lag03) level of PM2.5 was related to an increment of 13.31 [95% confidence interval (CI): 5.74, 20.87] years of life lost. Implementation of the WHO guidelines might avoid 180,980.83 YLLs (95% CI: 78,116.07, 283,845.60), which corresponded to 0.39 (95% CI: 0.17, 0.62) years of increased life time per death. Additionally, an estimated 0.15% (95% CI: 0.06%, 0.23%) or 2.04% (95% CI: 0.88%, 3.20%) of YLLs could be attributed to PM2.5 exposures higher than the Chinese or WHO guidelines, respectively. ConclusionsThis study suggests that people might live longer by controlling daily PM2.5 concentration and highlights the need to adopt stricter standards in China.

Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization

In this study, a spatiotemporal land use regression (LUR) model using distributed space-time expectation maximization (D-STEM) software was developed. We trained the model using daily mean ambient particulate matter ≤2.5 μm (PM2.5) data measured hourly in 2015 at 30 regulatory monitoring network stations within the megacity of Tehran, Iran. Since a substantial amount of measured data were missing (48% of the total number of daily PM2.5 observations), we used the D-STEM to impute missing data and compared the missing imputation performance between different fitted models and the mean substitution method. We used h-block cross-validation (h-block CV) method in order to account for spatial autocorrelation in the model building and validation. In the imputation of missing data, the D-STEM LUR model had a mean absolute percentage error (MAPE) of 25.3%, outperforming the mean substitution method, which resulted in MAPE of 28.3%. The spatiotemporal R-squared was 0.73 and the average CV R-squared of 2-block and 5-block cross-validations was 0.60. These values were 0.68 and 0.47 when the spatial aspect of the LUR model was assessed, and 0.995 and 0.992 when the temporal aspect of the LUR model was assessed. This study demonstrated the competence of D-STEM software in spatiotemporal modeling, missing data imputation, and mapping of daily ambient PM2.5 at a very high spatial resolution (20 m × 20 m). These estimations are available for future research, especially for epidemiological studies on short- and/or long-term health effects of ambient PM2.5. Generally, we found D-STEM as a promising tool for spatiotemporal LUR modeling of ambient air pollution, especially for those models that rely on regulatory network monitoring stations with a considerable amount of missing data.

Seasonal variation, formation mechanisms and potential sources of PM2.5 in two typical cities in the Central Plains Urban Agglomeration, China

In order to probe the seasonal variation, formation mechanisms as well as geographical origins of fine particles and its chemical components in two cities (Zhengzhou, ZZ and Xinxiang, XX) in Central Plains Urban Agglomeration, daily PM2.5 aerosol samples were collected for four consecutive seasons during 2017–2018. The annual average concentrations of PM2.5 (particulate matter with an aerodynamic diameter smaller than 2.5 μm) were calculated at 70.5 ± 50.8 and 69.0 ± 46.3 μg m−3 at ZZ and XX, respectively. Daily ambient PM2.5 concentrations ranged from 18.2 to 303.0 μg m−3, among which >81% of the total sampling days exceeded the National Ambient Air Quality Standard of China (NAAQS, 35 μg m−3 as an annual average). Additionally, concentrations of PM2.5 and its major chemical components were seasonally dependent, usually with the highest mass concentration in winter. Compared with previous studies, higher NO3−/SO42− were observed in this study depicted that air pollution caused by motor vehicle exhaust cannot be ignored. OC concentration was higher at ZZ than XX during sampling campaign likely partially caused by larger number of motor vehicles, chemical pesticide and solvent used in ZZ. Both homogeneous and heterogeneous reactions played an important role in the formation of nitrate, while heterogeneous reactions dominated the formation of sulfate. We also found a faster increase in nitrate than in sulfate during the evolution of haze. The characteristics of long-range transportation of PM2.5 and its major chemical components and gaseous precursors were observed at both sites through back trajectories and WPSCF analysis, suggesting the complexity of air pollution and the multi-influence among cities.

Associations of PM2.5 and aspergillosis: ambient fine particulate air pollution and population-based big data linkage analyses

A substantial amount of reports has been published on the association between the environment Aspergillus conidia and fine particulates, while limited studies have focused on the association between invasive aspergillosis and ambient fine particulate air pollution. The aim of this study is to explore the population-level association between ambient fine particulate PM2.5 air pollution and invasive aspergillosis. We designed multiple powerful systems applied to a unique collection of long-term and nationwide database of daily ambient PM2.5 levels and invasive aspergillosis incidence in Taiwan. Two data sets were leveraged for this study; the National Health Insurance Research Database and the Taiwan air quality monitoring network. The National Health Insurance Research Database was used to define invasive aspergillosis while the Taiwan air quality monitoring network was used to profile the PM2.5 concentration in Taiwan. We adopted the cases of invasive aspergillosis infection from the system with PM2.5 levels. A total of 1,000,000 patients during the study period (1999–2009) were included in the data set.The findings of this study suggest positive association between PM2.5 concentration and incidence of aspergillosis. Furthermore, monthly-wise invasive aspergillosis case number potentially demonstrated lagged pattern following peaking of PM2.5 concentration.

Source Appointed Ambient Fine Particulate in Beijing, China and Circulating Biomarkers, Blood Pressure, and Pulmonary Function in Healthy Adults

Background: Ambient particulate chemical constituents have been associated with adverse cardiopulmonary health effects. However, specific pollution sources behind the cardiopulmonary health effects of ambient particles are uncertain. Objective: We examined the cardiopulmonary health effects of fine particles (PM2.5) from different pollution sources in Beijing, China among a panel of 40 healthy university students. Methods: Study subjects were examined for a series of cardiopulmonary health indicators including circulating biomarkers, blood pressure and pulmonary function over 6 months before and after relocating from a suburban campus to an urban campus with changing air pollution levels and contents. Daily ambient PM2.5 mass samples were collected over the study and measured for 29 chemical constituents in the laboratory. Source appointment for ambient PM2.5 was performed using USEPA Positive Matrix Factorization 3.0, and mixed-effects models were used to estimate the cardiopulmonary effects associated with source-specific PM2.5 concentrations. Results: Seven PM2.5 sources were identified as traffic emissions, coal combustion, secondary sulfate/nitrate, metallurgical emission, dust/soil, industry, and secondary organic aerosol. Ambient PM2.5 in the suburban campus had larger contributions from secondary sulfate/nitrate (41.8%) and metallurgical emission (0.7%), whereas PM2.5 in the urban campus had larger contributions from traffic emissions (13.0%-16.3%) and coal combustion (21.0%-30.7%). Potential key sources were identified for PM2.5 effects on inflammatory biomarkers (secondary sulfate/nitrate and dust/soil), blood pressure (coal combustion and metallurgical emission), and pulmonary function (dust/soil and industry). Conclusion: Ambient PM2.5 in Beijing suburban and urban areas has two distinct patterns of source contributions, and PM2.5 from different sources may play important roles on different aspects of PM2.5-related cardiopulmonary health effects.

Association of Cardiopulmonary Health Effects with Source-Appointed Ambient Fine Particulate in Beijing, China: A Combined Analysis from the Healthy Volunteer Natural Relocation (HVNR) Study

Previous studies have associated ambient particulate chemical constituents with adverse cardiopulmonary health effects. However, specific pollution sources behind the cardiopulmonary health effects of ambient particles are uncertain. We examined the cardiopulmonary health effects of fine particles (PM2.5) from different pollution sources in Beijing, China, among a panel of 40 healthy university students. Study subjects were repeatedly examined for a series of cardiopulmonary health indicators during three 2-month-long study periods (suburban period, urban period 1, and urban period 2) in 2010-2011 before and after relocating from a suburban campus to an urban campus with changing air pollution levels and contents. Daily ambient PM2.5 mass samples were collected over the study and measured for 29 chemical constituents in the laboratory. Source appointment for ambient PM2.5 was performed using Positive Matrix Factorization, and mixed-effects models were used to estimate the cardiopulmonary effects associated with source-specific PM2.5 concentrations. Seven PM2.5 sources were identified as traffic emissions (12.0%), coal combustion (22.0%), secondary sulfate/nitrate (30.2%), metallurgical emission (0.4%), dust/soil (12.4%), industry (6.9%), and secondary organic aerosol (9.9%). Ambient PM2.5 in the suburban campus had larger contributions from secondary sulfate/nitrate (41.8% vs. 22.9%-26.0%) and metallurgical emission (0.7% vs. 0.3%) as compared to that in the urban campus), whereas PM2.5 in the urban campus had larger contributions from traffic emissions (13.0%-16.3% vs. 5.1%), coal combustion (21.0%-30.7% vs. 10.7%), and secondary organic aerosol (9.7%-12.0% vs. 8.7%) as compared to that in the suburban campus. Potential key sources were identified for PM2.5 effects on inflammatory biomarkers (secondary sulfate/nitrate and dust/soil), blood pressure (coal combustion and metallurgical emission), and pulmonary function (dust/soil and industry). Analyses using another source appointment tool Unmix yielded a similar pattern of source contributions and associated health effects. In conclusion, ambient PM2.5 in Beijing suburban and urban areas has two distinct patterns of source contributions, and PM2.5 from different sources may play important roles on different aspects of PM2.5-related cardiopulmonary health effects.

Impacts of biomass burning smoke on the distributions and concentrations of C2–C5 dicarboxylic acids and dicarboxylates in a tropical urban environment

Daily ambient PM2.5 was collected during 8 September to 5 October (Julian Days, JDs 252–279), 2008 in Singapore to investigate impacts of transboundary biomass burning smoke on distribution among C2–C5 dicarboxylic acids (DCAs) and corresponding dicarboxylate salts (DCS) in the tropical urban atmosphere. Quantification of DCAs and DCS were performed using solvent and water extraction followed by chromatography analyses via GC–MS and ion chromatography. The averaged PM2.5 and PM10 concentrations from September 19–30 (JDs 263–274) were concurrently elevated by ∼40% due to the transboundary smoke. During this same period, C2–C5 total dicarboxylates (TDCAS, summation of DCAs and DCS), on average, increased more than two times, with C2-TDCAS accounted for in average 80% of the C2–C5 TDCAS. This demonstrates that the transboundary smoke enriched C2–C5 TDCAS more than PM2.5 in the urban environment. In the presence of the transboundary smoke, the averaged concentration ratios of C2–C5 DCS to corresponding DCA were 13.4, 2.9, 1.0, and 1.4 with oxalate salts exhibiting the highest concentration (355.0 ng m−3). The transboundary smoke increased malic acid concentration more than 3.5 times, which is the largest relative increase among the quantified TDCAS. Considering that malic acid is mainly generated through ambient photooxidation, such significant increase in malic acid demonstrates more prominent photooxidation incurred by the smoke.