Literature DB >> 28936112

Trends in PM2.5 emissions, concentrations and apportionments in Detroit and Chicago.

Chad Milando1, Lei Huang1, Stuart Batterman1.   

Abstract

PM2.5 concentrations throughout much of the U.S. have decreased over the last 15 years, but emissions and concentration trends can vary by location and source type. Such trends should be understood to inform air quality management and policies. This work examines trends in emissions, concentrations and source apportionments in two large Midwest U.S. cities, Detroit, Michigan, and Chicago, Illinois. Annual and seasonal trends were investigated using National Emission Inventory (NEI) data for 2002 to 2011, speciated ambient PM2.5 data from 2001 to 2014, apportionments from positive matrix factorization (PMF) receptor modeling, and quantile regression. Over the study period, county-wide data suggest emissions from point sources decreased (Detroit) or held constant (Chicago), while emissions from on-road mobile sources were constant (Detroit) or increased (Chicago), however changes in methodology limit the interpretation of inventory trends. Ambient concentration data also suggest source and apportionment trends, e.g., annual median concentrations of PM2.5 in the two cities declined by 3.2 to 3.6 %/yr (faster than national trends), and sulfate concentrations (due to coal-fired facilities and other point source emissions) declined even faster; in contrast, organic and elemental carbon (tracers of gasoline and diesel vehicle exhaust) declined more slowly or held constant. The PMF models identified nine sources in Detroit and eight in Chicago, the most important being secondary sulfate, secondary nitrate and vehicle emissions. A minor crustal dust source, metals sources, and a biomass source also were present in both cities. These apportionments showed that the median relative contributions from secondary sulfate sources decreased by 4.2 to 5.5% per year in Detroit and Chicago, while contributions from metals sources, biomass sources, and vehicles increased from 1.3 to 9.2% per year. This first application of quantile regression to trend analyses of speciated PM2.5 data reveals that source contributions to PM2.5 varied as PM2.5 concentrations decreased, and that the fraction of PM2.5 due to emissions from vehicles and other local emissions has increased. Each data source has uncertainties, but emissions, monitoring and PMF data provide complementary information that can help to discern trends and identify contributing sources. Study results emphasize the need to target specific sources in policies and regulations aimed at decreasing PM2.5 concentrations in urban areas.

Entities:  

Keywords:  Positive Matrix Factorization; Quantile Regression; Receptor Modeling; Source Apportionment

Year:  2016        PMID: 28936112      PMCID: PMC5603263          DOI: 10.1016/j.atmosenv.2016.01.012

Source DB:  PubMed          Journal:  Atmos Environ (1994)        ISSN: 1352-2310            Impact factor:   4.798


  17 in total

1.  Sources of fine urban particulate matter in Detroit, MI.

Authors:  Amy E Gildemeister; Philip K Hopke; Eugene Kim
Journal:  Chemosphere       Date:  2007-05-29       Impact factor: 7.086

2.  Source identifications of airborne fine particles using positive matrix factorization and U.S. Environmental Protection Agency positive matrix factorization.

Authors:  Eugene Kim; Philip K Hopke
Journal:  J Air Waste Manag Assoc       Date:  2007-07       Impact factor: 2.235

3.  Temporal variation of traffic on highways and the development of accurate temporal allocation factors for air pollution analyses.

Authors:  Stuart Batterman; Richard Cook; Thomas Justin
Journal:  Atmos Environ (1994)       Date:  2015-04       Impact factor: 4.798

4.  U.S. national PM2.5 Chemical Speciation Monitoring Networks-CSN and IMPROVE: description of networks.

Authors:  Paul A Solomon; Dennis Crumpler; James B Flanagan; R K M Jayanty; Ed E Rickman; Charles E McDade
Journal:  J Air Waste Manag Assoc       Date:  2014-12       Impact factor: 2.235

5.  Long-term trends in California mobile source emissions and ambient concentrations of black carbon and organic aerosol.

Authors:  Brian C McDonald; Allen H Goldstein; Robert A Harley
Journal:  Environ Sci Technol       Date:  2015-03-31       Impact factor: 9.028

6.  Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5).

Authors:  Steven J Dutton; Sverre Vedal; Ricardo Piedrahita; Jana B Milford; Shelly L Miller; Michael P Hannigan
Journal:  Atmos Environ (1994)       Date:  2010-07-01       Impact factor: 4.798

7.  The design and field implementation of the Detroit Exposure and Aerosol Research Study.

Authors:  Ron Williams; Anne Rea; Alan Vette; Carry Croghan; Donald Whitaker; Carvin Stevens; Steve McDow; Roy Fortmann; Linda Sheldon; Holly Wilson; Jonathan Thornburg; Michael Phillips; Phil Lawless; Charles Rodes; Hunter Daughtrey
Journal:  J Expo Sci Environ Epidemiol       Date:  2008-10-22       Impact factor: 5.563

8.  Composition and sources of fine particulate matter across urban and rural sites in the Midwestern United States.

Authors:  Shuvashish Kundu; Elizabeth A Stone
Journal:  Environ Sci Process Impacts       Date:  2014-05       Impact factor: 4.238

9.  A Bayesian Multivariate Receptor Model for Estimating Source Contributions to Particulate Matter Pollution using National Databases.

Authors:  Amber J Hackstadt; Roger D Peng
Journal:  Environmetrics       Date:  2014-11-01       Impact factor: 1.900

10.  Spatial and temporal variation in PM(2.5) chemical composition in the United States for health effects studies.

Authors:  Michelle L Bell; Francesca Dominici; Keita Ebisu; Scott L Zeger; Jonathan M Samet
Journal:  Environ Health Perspect       Date:  2007-07       Impact factor: 9.031

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  3 in total

1.  Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan.

Authors:  Sheena E Martenies; Chad W Milando; Guy O Williams; Stuart A Batterman
Journal:  Int J Environ Res Public Health       Date:  2017-10-19       Impact factor: 3.390

2.  A number-based inventory of size-resolved black carbon particle emissions by global civil aviation.

Authors:  Xiaole Zhang; Xi Chen; Jing Wang
Journal:  Nat Commun       Date:  2019-02-01       Impact factor: 14.919

3.  Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City.

Authors:  Wen-Yuan Ho; Kuo-Hsin Tseng; Ming-Lone Liou; Chang-Chuan Chan; Chia-Hung Wang
Journal:  Int J Environ Res Public Health       Date:  2018-06-21       Impact factor: 3.390

  3 in total

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