scholarly journals Estimated Long-Term (1981–2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements

2019 ◽  
Vol 53 (9) ◽  
pp. 5071-5079 ◽  
Author(s):  
Jun Meng ◽  
Chi Li ◽  
Randall V. Martin ◽  
Aaron van Donkelaar ◽  
Perry Hystad ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
North America ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Chemical Transport ◽  
Transport Modeling ◽  
Chemical Transport Modeling ◽  
Fine Particulate

scholarly journals An Improved Method for Monitoring Fine Particulate Matter Mass Concentrations via Satellite Remote Sensing

2016 ◽  
Vol 16 (4) ◽  
pp. 1081-1092 ◽  
Author(s):  
Hamed Karimian ◽  
Qi Li ◽  
Chengcai Li ◽  
Lingyan Jin ◽  
Junxiang Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Improved Method ◽  
Fine Particulate ◽  
Mass Concentrations

Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS

2014 ◽  
Vol 35 (17) ◽  
pp. 6522-6544 ◽  
Author(s):  
Yangjie Guo ◽  
Nan Feng ◽  
Sundar A. Christopher ◽  
Ping Kang ◽  
F. Benjamin Zhan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Air Quality ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Fine Particulate

scholarly journals SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications

2015 ◽  
Vol 8 (1) ◽  
pp. 505-521 ◽  
Author(s):  
G. Snider ◽  
C. L. Weagle ◽  
R. V. Martin ◽  
A. van Donkelaar ◽  
K. Conrad ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Particulate Matter ◽  
Health Effects ◽  
Satellite Remote Sensing ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
Water Soluble ◽  
Global Network ◽  
Spatial Coverage

Abstract. Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.

Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis

2019 ◽  
Vol 247 ◽  
pp. 874-882 ◽  
Author(s):  
Yang Yang ◽  
Zengliang Ruan ◽  
Xiaojie Wang ◽  
Yin Yang ◽  
Tonya G. Mason ◽  
...  
Keyword(s):  
Systematic Review ◽  
Particulate Matter ◽  
Meta Analysis ◽  
Fine Particulate Matter ◽  
Short Term ◽  
Fine Particulate

scholarly journals Long‐Term Exposure to Particulate Air Pollution Is Associated With 30‐Day Readmissions and Hospital Visits Among Patients With Heart Failure

2021 ◽  
Author(s):  
Cavin K. Ward‐Caviness, ◽  
Mahdieh Danesh Yazdi, ◽  
Joshua Moyer, ◽  
Anne M. Weaver, ◽  
Wayne E. Cascio, ◽  
...  
Keyword(s):  
Heart Failure ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fine Particulate Matter ◽  
Total Hospital ◽  
Fine Particulate ◽  
Patients With Heart Failure ◽  
Hospital Visits ◽  
Pollution Exposure

Background Long‐term air pollution exposure is a significant risk factor for inpatient hospital admissions in the general population. However, we lack information on whether long‐term air pollution exposure is a risk factor for hospital readmissions, particularly in individuals with elevated readmission rates. Methods and Results We determined the number of readmissions and total hospital visits (outpatient visits+emergency room visits+inpatient admissions) for 20 920 individuals with heart failure. We used quasi‐Poisson regression models to associate annual average fine particulate matter at the date of heart failure diagnosis with the number of hospital visits and 30‐day readmissions. We used inverse probability weights to balance the distribution of confounders and adjust for the competing risk of death. Models were adjusted for age, race, sex, smoking status, urbanicity, year of diagnosis, short‐term fine particulate matter exposure, comorbid disease, and socioeconomic status. A 1‐µg/m 3 increase in fine particulate matter was associated with a 9.31% increase (95% CI, 7.85%–10.8%) in total hospital visits, a 4.35% increase (95% CI, 1.12%–7.68%) in inpatient admissions, and a 14.2% increase (95% CI, 8.41%–20.2%) in 30‐day readmissions. Associations were robust to different modeling approaches. Conclusions These results highlight the potential for air pollution to play a role in hospital use, particularly hospital visits and readmissions. Given the elevated frequency of hospitalizations and readmissions among patients with heart failure, these results also represent an important insight into modifiable environmental risk factors that may improve outcomes and reduce hospital use among patients with heart failure.

scholarly journals Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach

2013 ◽  
Vol 13 (10) ◽  
pp. 26657-26698
Author(s):  
Y. Hu ◽  
S. Balachandran ◽  
J. E. Pachon ◽  
J. Baek ◽  
C. Ivey ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Source Apportionment ◽  
Hybrid Method ◽  
Fine Particulate Matter ◽  
Hybrid Approach ◽  
Chemical Transport ◽  
Measurement Information ◽  
Analysis Tool ◽  
Receptor Model ◽  
Fine Particulate

Abstract. A hybrid fine particulate matter (PM2.5) source apportionment approach based on a receptor-model (RM) species balance and species specific source impacts from a chemical transport model (CTM) equipped with a sensitivity analysis tool is developed to provide physically- and chemically-consistent relationships between source emissions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of source impacts from a much larger number of sources than are typically used in RMs, and provides source-receptor relationships for secondary species. Further, the method addresses issues of source collinearities, and accounts for emissions uncertainties. Hybrid method results also provide information on the resulting source impact uncertainties. We apply this hybrid approach to conduct PM2.5 source apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these receptor sites were apportioned to 33 separate sources. Hybrid method results led to large changes of impacts from CTM estimates for sources such as dust, woodstove, and other biomass burning sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98% when using a weighted least squared error minimization. The rankings of source impacts changed from the initial estimates, revealing that CTM-only results should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved sources. The hybrid method also resolved sources that typical RM methods do not capture without extra measurement information on unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to provide source impact estimates for management- and health-related studies.

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