seasonal and regional variability in the relationship between ground-level ozone...
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![Page 1: Seasonal and Regional Variability in the Relationship between Ground-Level Ozone …amfiore/presentations/pdfs/GuoJ... · 2015. 9. 2. · found for each site across the years 2000-2013](https://reader035.vdocument.in/reader035/viewer/2022071213/6038182f4f201c58b776b1df/html5/thumbnails/1.jpg)
EPA Region 3
Site
s
2000 2013 20132000 20132000Year
• Daily O3 and PM2.5 concentrations positively correlated over much
of the U.S. (Figure 3)
• Highest in summer: r ≈ 0.7 to 0.9
• Spring and fall generally lower: r ≈ -0.2 to 0.4.
• Exception to the trend:
• Region 6 (South Central – TX and surrounding states)
• Little difference in correlation between seasons: r ≈ 0 to 0.4
• Daily correlations for sites with at least 10 years of data (Figure 4):
• Positive for the years 2000-2013 when averaged over each
season
• Spring – 20 out of 28 sites (2 significant at 95% confidence
level, p-value < 0.05)
• Summer – 41 out of 46 sites (11 significant)
• Fall – 26 out of 31 sites (3 significant)
• Though the average correlation is not significant at many of
the sites, the trend may become significant at longer
timescales.
• Fresno and Bakersfield, CA are consistently negative
California is VOC limited and decreasing NOx in a VOC-
limited regime increases O3 formation.
• Correlation coefficients decreasing over time (Figure 4):
• Spring – 17 out of 20 sites
• Summer – 37 out of 41 sites
• Fall – 19 out of 26 sites
• Average negative slope of the best fit line of the correlation over
time: m = -0.03, -0.03, -0.02 over the years 2000-2013 for
spring, summer, fall respectively
• Decreasing trend in correlations may be driven by emissions
reductions from air pollution control programs in the last decades
• Decrease in NOx – Affects both O3 and PM2.5
• Decrease in SO2 – Mainly affects only PM2.5
Results and Discussion
Seasonal and Regional Variability in the Relationship between
Ground-Level Ozone (O3) and Fine Particulate Matter (PM2.5) in the
United StatesJean Guo, Department of Earth and Env. Science, Columbia University
Arlene Fiore, Lamont Doherty Earth Observatory, Columbia University
Martin Stute, Department of Env. Science, Barnard College
4/23/2015
• Daily data from EPA’s Air Quality Monitoring sites (2000-2013)
• O3 – Max daily 8 hour average
• PM2.5 – 24-hour sample (Total mass of filter is divided by 24 to obtain
one value representing the average 24-hr concentration)
• Sites required to contain data for both O3 and PM2.5 and to have at least
75% of the daily data for each season: Spring (March, April, May), summer
(June, July, August), and fall (September, October, November).
• Linear correlations (R-value), calculated separately for each season, were
found for each site across the years 2000-2013
• The ordinary least squares regression for O3:PM2.5 between the years 2000
and 2013 was calculated
• Requirements:
• At least 10 years of data out of a possible 14 years
• The R-value, when averaged over all years, had to be positive
Methods
Ozone (O3) and fine particulate matter (PM2.5) are the top two air pollutants in the U.S.
with adverse health impacts. PM2.5 is a complex mixture of different chemical
components, some of which, like O3, form secondarily in the atmosphere from precursor
emissions. The National Research Council, in a comprehensive review of air quality in
the United States, has suggested that controlling for groups of pollutants that share
sources, precursors, or products, or have similar effects on human health or the
ecosystem would be more effective than the current single-pollutant approach. Currently,
a quantitative description of how often multiple pollutants like O3 and PM2.5 co-vary is
lacking. In this thesis, I analyze the seasonal and regional variability in the
relationship between ground level ozone (O3) and fine particulate matter (PM2.5) in
the United States by examining data from the EPA’s Air Quality System monitoring
sites. Understanding the relationship between these two pollutants can shed light on
which sources most strongly influence both pollutants, and how emissions controls have
been affecting the relationship between these pollutants. I find that most sites with at
least 10 years of data between the years 2000 and 2013 were, on average, positively
correlated in daily concentrations across all sites during each season. However, the trend
in the correlation coefficient, as determined by the ordinary least squares regression of
the R-values as a function of time, has been decreasing. I suggest that this decreasing
trend is likely due to a decline in precursor emissions driven by recent air pollution control
programs put into effect to lower O3 and PM2.5 levels. This trend may suggest that the
composition of PM2.5 is changing from one dominated by secondary components to one
dominated by primary ones. A decrease in precursor emissions implies that as emission
levels decline, fewer pollutants will form photochemically even under favorable
meteorological conditions.
Abstract
• Investigate if the reduction in correlation is driven by emissions
controls:
• Does the timing of the emissions controls fit with the timing of the
change in correlation?
• Use models to examine whether the change in the correlation can
be explained by other factors, such as a change in meteorology,
or if emissions are driving the change.
• Examine if the decreasing correlation between PM2.5 and O3 reflects
a change in the dominant pollutant driving PM2.5 composition.
• Is the decrease in secondary PM2.5 components causing the
decreasing trend?
• Compare models with observational data to see whether the
relationships revealed in this data analysis is represented in the
models used to determine how air pollution will respond to emission
controls.
• Study health responses:
• Is the health impact higher when both PM2.5 and O3 are high as
opposed if only one is high?
• How will the changing correlation between the two affect the
health response?
Recommendations
• What is the seasonal and regional variability in the relationship
between ground-level O3 and PM2.5 in the United States?
• O3 and PM2.5 share some sources and are affected by some of the same
meteorology (Figure 2, Table 1)
• Both SO2 and NOx are emitted from power plants
• SO2 emissions Increases secondary PM2.5.
• NOx emissions Increases secondary PM2.5 and O3.
• VOCs, CH4, and CO can create O3.
• Dust contributes to PM2.5 with little influence on O3
• Complex mechanisms affect O3 and PM2.5 and their formation and
accumulation through changing radiation, atmospheric chemistry, and
the climate system.
Introduction
Variable PM2.5 O3
Wildfires + ~
Dust + ~
NOx + +
SO2 + ~
Humidity + –
Regional stagnation + +
Wind speed – –
Mixing depth – ~
Precipitation – ~
Temperature + or – +
Table 1. General effects of meteorology and
emissions on regional O3 and PM2.5
concentrations. Symbols: positive (+),
negative (–), and unclear relationship (~).
(Adapted from Jacob and Winner, 2009 and
Fiore et al., 2012).
References
• Fiore, A.M., Naik, V., Spracklen, D. V, Steiner, A., Unger, N., Prather, M., Bergmann, D., Cameron-Smith, P.J., Cionni, I., Collins, W.J., Dalsøren, S., Eyring, V.,
Folberth, G. a, Ginoux, P., et al., 2012, Global air quality and climate.: Chemical Society reviews, v. 41, no. 19, p. 6663–83, doi: 10.1039/c2cs35095e.
• Jacob, D.J., and Winner, D.A., 2009, Effect of climate change quality: Atmospheric Environment,.
• U.S. Environmental Protection Agency, 2013, Air Quality Trends.
• U.S. Environmental Protection Agency, 2014a, Air Trends: Air and Radiation, no. 3/27/2015.
Figure 3. Seasonal correlation (R-value) between O3 and PM2.5 for each season from the years 2000 to 2013. Different EPA
regions are shown separately. Only sites with data available for both pollutants for at least 69 out of the possible 92 days in a given
season for each year were included. Correlations were strongest in the summer (r ≈ 0.7 to 0.9) for most regions (examples
above show regions 2 and 3). However, region 6 showed relatively low correlations throughout the year (r ≈ 0 to 0.4).
EPA Region 6
20132000 201320002013
Spring (MAM) Summer (JJA) Fall (SON)
Site
s
2000 Year
AcknowledgementsI would like to acknowledge my research mentor, Dr. Arlene Fiore, for all the support and guidance on the science behind my project. I am indebted to the entire Fiore
Atmospheric Chemistry Group for their guidance and advice throughout this whole process. I would like to thank my thesis advisor, Dr. Martin Stute for offering feedback
and commentary on how to tell the story behind my project convincingly. I would also like to thank Dr. Nick Mangus at EPA for answering my questions pertaining to
understanding the data and how it is used in regulation.
Production Schematic for O3 and PM2.5
Figure 2. Formation schematic showing the interconnected nature of the formation
of O3 and PM2.5. The photochemical components that can lead to the formation of
secondary PM2.5 can also lead to O3 formation. However, primary PM2.5 is partly
created through processes unrelated to O3 formation. Therefore, it can be
expected that the secondary components of PM2.5 would be more strongly
correlated with O3 than the primary components. Note: SO4 can be produced
through both gas-phase chemistry and aqueous chemistry; however, only the gas-
phase chemistry formation pathway (sunlight dependent) would be expected to
correlate with O3.
Regional Correlation for PM2.5 and O3
Site
s
EPA Region 2
2000 2013 20132000 20132000Year
Seaso
nal C
orrelatio
n (R
-valu
e)
NO2 29%
SO2 62%
O3
PM2.5 34%
18%
Figure 5. Percent decrease in several air pollutants between 2000 and 2013 in the U.S.
NO2: annual 98th percentile of daily max 1-hr average (92 sites). SO2: Annual 99th percentile
of daily max 1-hr average (235 sites). O3: Annual 4th highest max daily 8-hr average (466
sites). PM2.5: Seasonally-weighted annual average (537 sites) (EPA 2014).
EPA, 2013
Correlations Between O3 and PM2.5 as a Function of Time (2000-2013)(Sites with at least 10 years of data)
Figure 4. Slope of the line of best fit for O3:PM2.5 correlation over the years 2000 and 2013. Only sites with at least 10 years of
data were included. Sites that had a negative correlation when averaged over all years for each season are grayed out to
separate sites with a negative correlation between PM2.5 and O3 from sites showing a decreasing trend in the correlation. Most
sites were positively correlated between PM2.5 and O3 during all the seasons when R-values were averaged over 2000 and 2013.
However, the correlations have been decreasing for the 2000 to 2013 time span.
Number of People (millions) Living in Counties that Exceed National
Ambient Air Quality Standards (NAAQS) (2013)
One or more NAAQS
Ozone (8-hour)
PM2.5 (annual/24-hr)
PM10 (24-hr)
SO2 (1-hr)
Lead (3-month)
NO2 (annual/1-hr)
CO (8-hr)
Health Effects: 1) Decreased lung function
2) Respiratory problems
3) Premature death (PM2.5)
• O3: 470 thousand premature respiratory deaths
annually and globally
• PM2.5: 1.3 to 3.0 million deaths from
cardiopulmonary diseases (93%) and lung cancer (7%)
75.4M
53.1M
33.1M
17.8M
5.5M
2.9M
0M
0M
• On average, daily concentrations of O3 and PM2.5 are
positively correlated
• Strongest correlations in the summer (r ≈ 0.7 to 0.9)
• Spring and fall: r ≈ -0.2 to 0.4
• However, there is a decreasing trend in the correlation
coefficient between O3 and PM2.5 between 2000 and 2013
Spring (MAM) Summer (JJA) Fall (SON)
Spring (MAM) Summer (JJA) Fall (SON)
EPA Regions
Figure 1. O3 and PM2.5 are the top two pollutants in the U.S. that adversely affect health. Over
86 million people live in areas that exceed the air quality standard for O3 and/or PM2.5.