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.