Assessing the impact of fine particulate matter (PM 2.5 ) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

Weber, Stephanie; Insaf, Tabassum Z.; Hall, Eric S.; Talbot, Thomas O.; Huff, Amy K.

doi:10.1016/j.envres.2016.07.012

Cited by 101 publications

(103 citation statements)

References 33 publications

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“…These effects were substantially higher than those seen from similar studies examining ambient air pollution measures of fine particulate matter and asthma outcomes. Weber et al (2016) used the USEPA's hierarchical baysian model (HBM) of fine particulate matter and linked daily measures preceding hospital and emergency room visits related to asthma and found a 10-unit change in PM 2.5 resulted in odds ratios of inpatient and emergency room visits slightly over 1.00 for those older than 14 years of age. Nachman and Paker (2012) assessed fine particulate matter and respiratory outcomes in adults using two national cross-sectional datasets and annual average PM 2.5 data derived from USEPA AirData and found an adjusted odds ratio for current asthma of 0.97 (95% CI=0.87, 1.07).…”

Section: Discussionmentioning

confidence: 99%

Allergic disease associations with regional and localized estimates of air pollution

Schultz

Schauer

Malecki

2017

Environmental Research

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Background: Exposure to multiple types of air pollution may contribute to and exacerbate allergic diseases including asthma and wheezing. However, few studies have examined chronic air pollution exposure and allergic disease outcomes among an adult population. Associations between potential estimates of annual average fine particulate matter (PM2.5), traffic related air pollution, and industrial source air emissions and three allergic disease outcomes (asthma, allergies and wheezing) were examined in a state-wide general population of adults. Methods: The study includes a representative sample of 3381 adult Wisconsin residents who participated in the 2008–2013 Survey of the Health of Wisconsin (SHOW) program. Participant data were geographically linked to The United States Environmental Protection Agency (USEPA) Baysian space-time downscaler air pollution model for PM2.5, the United States Census roadway, and USEPA's Toxic Release Inventory data. Self-report and lung function (FEV1) estimates were used to define prevalence of asthma, allergies and wheezing symptoms. Results: Annual mean exposure to fine particulate matter (PM2.5) was between 6.59 and 15.14 μg/m3. An increase of 5 μg/m3 in the annual mean PM2.5 resulted in a 3.58 (2.36, 5.43) increase in the adjusted odds (95% CI) of having asthma. Exposure to vehicle traffic increased the odds of both current allergies [OR (95% CI)=1.35 (1.07, 1.35)] and current asthma [OR (95% CI)=1.51 (1.14, 2.00)]. Living within 300 m of an Interstate roadway was associated with a 3-fold increase in the odds of asthma. Those living within 800 m of an industrial site were 47% more likely to have asthma. No significant associations were seen with wheezing. Conclusions: Within this population exposed to overall annual average levels of estimated low level chronic exposure to fine particulate matter (PM2.5) at or near 12 μg/m3, the USEPA standard for air quality, significant association between both modeled PM2.5 exposure and proximity to roadways with asthma and allergies but not wheezing were found. Industrial source emissions were not associated with any allergic disease outcomes.

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Section: Discussionmentioning

confidence: 99%

Allergic disease associations with regional and localized estimates of air pollution

Schultz

Schauer

Malecki

2017

Environmental Research

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“…Ambient air monitors are usually found in urban areas. In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26].…”

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confidence: 99%

“…In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26]. To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31].…”

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confidence: 99%

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

et al. 2020

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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 km 2 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.In urban areas, PMB gives more "weight" to PM 2.5 monitor readings than CMAQ PM 2.5 model predictions. In rural areas, CMAQ PM 2.5 model predictions exert more influence than PM 2.5 monitor readings on PMB, since there are fewer monitors or no monitors. Ambient air monitors are usually found in urban areas. In the last 15 years, PMB has turned out to be a more representative PM 2.5 concentration surface, compared to the interpolation of PM 2.5 monitor data, as a method to resolve spatial gaps between ambient air monitors [16,18,22]. CDC subsequently incorporated PMB into its Environmental Public Health Tracking (EPHT) network of state and New York City partners [16,18,22,26]. To date, PMB has been used by federal and state epidemiologists completing EPHT projects in different parts of the US [16,18,22,26].Within this decade, the availability and use of satellite AOD data have become more routine [6,16,[27][28][29][30][31]. Newer generation satellite instruments measure AOD with increased temporal accuracy and finer spatial resolution [27,[32][33][34][35][36][37]. AOD is a unitless measure of the scattering and absorption of visible light by aerosols (particles) in the atmosphere [38][39][40]. AOD data are, by definition, actual physical measurements, an improvement over CMAQ PM 2.5 model predictions. Once AOD unitless measurements have been calibrated with actual PM 2.5 readings from on-the-ground ambient air monitors, it is then possible to utilize the derived AOD-PM 2.5 concentration readings to estimate actual ambient PM 2.5 concentration in areas where there are no on-the-ground air monitors. The relationship between AOD measurements and on-the-ground measurements of PM 2.5 concentration readings has b...

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“…Respirable dust is a main factor which has led to coal workers’ pneumoconiosis 3 . PM 2.5 is classified as a respirable dust, and is known to have toxic effects on the cardiovascular system by directly entering into the human pulmonary alveolus 4 . Therefore, in order to ensure the health and safety of China’s miners, more attention should be paid to the PM 2.5 dust particles generated in underground roadway blasting processes.…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation of Particulate Matter 2.5 Distribution in a Roadway

Feng

Liao

2018

Sci Rep

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Large amounts of dust particles pose serious hazards to the health and safety of China’s coal miners during roadway blasting processes. It is known that among these dust particles, Particulate Matter 2.5 (PM2.5) does the greatest amount of harm. In order to study the distributions of the PM2.5 in roadway blasting processes, a mathematical model of the gas-solid two-phase flow was established in this study, which was based on a Direct Simulation Monte Carlo Method (DSMC). Then, a multiphase flow program was developed. This study’s results indicated that following the blasting processes, fine dust particles gradually floated up and were suspended for long durations in the underground roadway space. The medium-sized dust particles slowly sink to the ground and were eventually expelled before settling to the floor of the roadway. The coarse particles were rapidly settled to the roadway floor. It was determined that the PM2.5 particles in the front end of the dust group could not be quickly diluted, and the concentrations were high until it is expelled from the roadway, whereas the PM2.5 dust particles in the back end of the underground roadway were found to be gradually diluted. Eventually, the PM2.5 concentrations exhibited an alternating thin to dense phase distribution. When compared with the Particulate Matter 5 (PM5), it was found that the PM2.5 was more difficult to discharge, and easily formed serious PM2.5 dust air pollution. This study’s results were determined to be conductive to the future control of PM2.5 in the underground roadway blasting processes.

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Assessing the impact of fine particulate matter (PM 2.5 ) on respiratory-cardiovascular chronic diseases in the New York City Metropolitan area using Hierarchical Bayesian Model estimates

Cited by 101 publications

References 33 publications

Allergic disease associations with regional and localized estimates of air pollution

Allergic disease associations with regional and localized estimates of air pollution

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

Numerical Simulation of Particulate Matter 2.5 Distribution in a Roadway

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