Impacts of the fall 2007 California wildfires on surface ozone:

Integrating local observations with global model simulations

G. G. Pfister,1 C. Wiedinmyer,1 and L. K. Emmons1

Received 20 May 2008; revised 4 September 2008; accepted 9 September 2008; published 9 October 2008.

[1] This study quantifies the impact of the fires in

California in fall 2007 on regional air quality and

especially on surface ozone by analyzing surface

observations of ozone concentrations together with global

chemistry transport model simulations. The latter include a

synthetic tracer providing information about the amount of

ozone produced from the fires. It is shown that the global

model is well suited for simulating the overall fire impact

and a valuable tool for extracting information about the fire

influence from the observations. A clear increase in

observed ozone is found when the model predicts a

strong impact of pollution from the fires, where measured

afternoon 8-hour concentrations increased, on average, by

about 10 ppb. The findings demonstrate that intense

wildfire periods can significantly increase the frequency

of ozone concentrations exceeding current U.S. health

standards, and might cause violations also during

photochemically less active seasons. The study also

demonstrates the far-reaching impact of ozone production

from the fires. Citation: Pfister, G. G., C. Wiedinmyer, and

L. K. Emmons (2008), Impacts of the fall 2007 California

wildfires on surface ozone: Integrating local observations with

global model simulations, Geophys. Res. Lett., 35, L19814,

doi:10.1029/2008GL034747.

1. Introduction

[2] Wildfires are a significant direct source of atmospheric

pollutants such as carbon monoxide (CO), nitrogen oxides

(NOx), volatile organic compounds (VOCs) and particulate

matter. The gaseous pollutants are precursors for ozone (O3)

production and as a result, wildfires have been proposed to

lead to substantial increases in tropospheric O3 concentrations [Wotawa and Trainer, 2000; Cheng et al., 1998;

Crutzen and Andreae, 1990]. Since O3 is toxic in nature,

elevated concentrations can have negative impacts on

human health and plants. In 1997, the U.S. Environmental

Protection Agency (EPA) set the 8-hour standard at

0.08 ppm, and in a recent update, which became effective

in May 2008, lowered the standard to 0.075 ppm. In order

to keep to these limits and help making decisions to reduce

O3 levels in areas where standards are exceeded, it is

crucial to understand the individual contributions to the

ozone budget.

[3] The major factors contributing to elevated pollution

levels in the contiguous US include industry and transportation, but wildfires can also have a significant impact on air

quality [Bravo et al., 2002]. Nationwide, California is one

of the states with the highest wildfire activity. In 2007,

about 13 million acres burned across the US with California

accounting for 10% of the acres burned nationally (http://

www.nifc.gov/fire_info/ytd_state.htm). The fire season in

California typically starts around mid-May and ends around

October, when cooler weather and increased precipitation

conditions prevail. In fall 2007, severe drought conditions

and hot weather contributed to an extremely intense late fire

season. Fires in October were the most destructive of the

year and in addition to severe drought conditions and hot

weather, the unusually strong Santa Ana winds in Southern

California were a major contributor. Numerous fires were

ignited by broken powerlines, and the strong winds further

hindered the progress of fire fighters.

[4] The purpose of this study is to quantify the impact of

the fall 2007 fires in California on surface O3 levels.

Photochemistry is less active later in the year and exceedances of ozone health standards are generally less frequent.

However, extreme events like wildfires might still have

significant impacts on air quality especially when they

occur during periods conducive to ozone formation.

2. Simulations, Observations, and Model

Evaluation

[5] The simulations in this study are performed with

the global chemistry transport model Model for Ozone

and Related Chemical Tracers, version 4 (MOZART-4).

Modifications from Version 2 published by Horowitz et al.

[2003] include, amongst others, a more complete description

of anthropogenic hydrocarbon chemistry, the inclusion of

tropospheric aerosols (extended from the work of Tie et al.

[2001, 2005]), and on-line calculations of photolysis rates,

dry deposition, water vapor, and biogenic emissions. For a

detailed model description we refer to L. K. Emmons et al.

(Impact of Mexico City emissions on regional air quality

from MOZART-4 simulations, manuscript in preparation,

2008) and for model evaluation to Pfister et al. [2005,

2006, 2008].

[6] The simulations were run at T85 (1.4 x 1.4) spatial

resolution using meteorological fields from NCEP-GFS

(National Center for Environmental Prediction, Global

Forecasting System). The vertical resolution of the model

consists of 42 hybrid levels from the surface up to 2 hPa

(45 km). Of those, about 7 are within the lowest kilometer.

[7] Daily emissions of trace gases and particulate matter

from the California fires were estimated using the framework described in detail by Wiedinmyer et al. [2006]. Using

the MODIS thermal anomalies to determine fire location

and timing, the emissions of CO2, CO, NOx, SO2, and NH3

were derived. Emissions of VOCs for the MOZART

GEOPHYSICAL RESEARCH LETTERS, VOL. 35, L19814, doi:10.1029/2008GL034747, 2008

1

Atmospheric Chemistry Division, National Center for Atmospheric

Research, Boulder, Colorado, USA.

Copyright 2008 by the American Geophysical Union.

0094-8276/08/2008GL034747

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chemical mechanism (Emmons et al., manuscript in preparation, 2008; http://gctm.acd.ucar.edu/mozart) were determined from emitted CO2 and the type of land cover burned

in each fire pixel using emission factors from Andreae and

Merlet [2001]. A time series of the total fire emissions over

California is shown in the auxiliary material.1 There were

two main peaks in fire intensity, one in early September

(estimated 5 GgN of emitted NOx) and the other towards

the end of October (estimated 7 GgN of emitted NOx). In

comparison, anthropogenic NOx emissions over California

are 40 GgN/month and global biomass burning emissions

for September through December amount to 1950 GgN.

California fire emissions for CO for this time period are

366 GgC and for VOCs 65 GgC.

[8] Global emissions from biofuel and fossil fuel combustion, aircraft, lightning, ocean, soil and vegetation are

included in the model, primarily from the European Union

project POET (Precursors of Ozone and their Effects in the

Troposphere) [Olivier et al., 2003]. Asian anthropogenic

emissions are from Ohara et al. [2007]. Biomass burning

emissions for outside California are based on a climatology

derived from emission inventories available for previous

years [van der Werf et al., 2006]. While all emission

inventories are subject to large uncertainties, often more

than a factor of 2, [e.g., van der Werf et al., 2006;

Wiedinmyer et al., 2006] and can be a major part of model

uncertainties, their quantification is beyond the scope of

this study.

[9] We incorporated two categories of chemical tracer

schemes in the model. The first keeps track of the amount

of CO emitted from the California fires (COFIRE), the other

follows the O3 production from NO emitted from the

California fires (O3

FIRE). The latter scheme tags the emitted

NO and maintains the tag through all simulated odd

nitrogen species (e.g. PAN, nitrates, HNO3). We also tag

the O3 produced by the photolysis of tagged NO2 (NO2

FIRE).

Except for some minor reactions, O3 in the MOZART

chemical mechanism is only produced through photolysis

of NO2. The scheme is described in greater detail by

Lamarque et al. [2005], Pfister et al. [2006] and Hess

and Lamarque [2007]. The model simulations are started

1 January 2007, and for September through November the

model concentrations of CO, O3, NO2, and the respective

fire tracers COFIRE, O3

FIRE and NO2FIRE are output hourly.

[10] The observational data set includes measured surface O3 concentrations for September–December 2007

provided by the EPA Aeromatic Information Retrieval

System (AIRS) (EPA, U.S. Environmental Protection

Agency air quality system data mart, 2008, available at

http://www.epa.gov/ttn/airs/aqsdatamart). We use hourly

observations from all sites in California and group them

dependent on their location setting into rural sites (55),

urban sites (38) and suburban sites (72). The model data

are interpolated to the time step and location of the

observations and both the modeled and observed 1-hour

values are averaged into 8-hour concentrations, the standard used by the EPA for assessing ozone violations.

[11] Table 1 lists the mean and median observed and

modeled 8-hour concentrations and the mean difference

between the model and the observations for the three

different site categories. We calculate statistics for the full

data set, and also use O3

FIRE to separately derive results for

high (O3

FIRE 5 ppb) and low fire (O3FIRE  0.5 ppb)

impacted subsets. In support of the interpretation, we

include a comparison of modeled and observed NO2 concentrations for monitoring sites where NO2 measurements

are available (30 rural, 21 urban, 46 suburban).

[12] The model matches the observed 8-hour O3 values

generally to within 15 ppb, with correlation coefficients in

the range 0.5– 0.6. Both observations and model simulations show largest O3 concentrations for the high fire

impacted subset. As a result, the absolute difference

between modeled and observed concentrations is higher

for this data subset; however, the relative difference does

not depend on the estimated degree of fire impact. Yet, the

comparison does depend on the monitoring site category

(urban, suburban, or rural). The observations show a clear

difference between the individual site categories with

lowest O3 and highest NO2 concentrations at urban sites

and highest O3 and lowest NO2 concentrations at rural

locations. The model in contrast simulates little variability

between the mean concentrations for the different site

categories. The modeled mean concentrations agree more

closely with observations at the rural sites, while they

overestimate O3 and underestimate NO2 values to a greater

extent at urban and suburban sites. This result, however, is

not surprising as the model covers California in 35 grid

cells. Thus, the model is not able to resolve localized

emission sources which are more likely to influence

measurements taken in or near urban centers, but is better

suited to represent conditions farther downwind from

Table 1. Mean, Standard Deviation, Median, and Mean Difference d for Observed and Modeled 8-Hour Concentrations of O3 and NO2

for Individual Types of Monitoring Sitesa

Rural Urban Suburban

Obs Model d Obs Model d Obs Model d

O3

all 44 ± 13 (42) 52 ± 13 (50) 8 ± 12 38 ± 12 (36) 53 ± 16 (50) 15 ± 13 41 ± 13 (39) 54 ± 17 (50) 13 ± 14

O3

FIRE  0.5 ppb 41 ± 11 (40) 49 ± 12 (48) 8 ± 10 36 ± 10 (35) 50 ± 15 (47) 15 ± 12 38 ± 12 (38) 51 ± 16 (48) 13 ± 13

O3

FIRE 5 ppb 51 ± 14 (49) 60 ± 12 (61) 9 ± 13 42 ± 15 (41) 61 ± 15 (62) 15 ± 17 46 ± 17 (45) 62 ± 16 (63) 16 ± 17

NO2

All 7 ± 4 (6) 4 ± 3 (3) 3 ± 5 13 ± 10 (10) 5 ± 4 (4) 8 ± 8 14 ± 10 (11) 5 ± 4 (4) 9±8

O3

FIRE  0.5 ppb 7 ± 4 (6) 4 ± 4 (4) 2 ± 5 13 ± 9 (11) 5 ± 4 (4) 8 ± 8 14 ± 10 (12) 5 ± 4 (4) 9±8

O3

FIRE 5 ppb 6 ± 4 (5) 5 ± 2 (4) 2 ± 4 17 ± 15 (10) 6 ± 4 (5) 11 ± 13 18 ± 16 (11) 6 ± 5 (5) 11 ± 13

a

Observed and model median appears in parentheses. O3 and NO2 concentrations are in ppb. Results are given for all data and for two subsets of 8-hour

O3

FIRE concentrations. Values restricted to averages over 10 – 18 LT, 11 – 19T, and 12 – 20 LT for 1 Sep – 30 Nov.

1

Auxiliary materials are available in the HTML. doi:10.1029/

2008GL034747.

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source regions as is more characteristic of the rural

monitoring sites.

3. Results and Discussion

[13] During the study period there were two main

peaks in fire intensity. Fires during September took place

mostly in the northern part of California, while fires in

October were concentrated in the southern part of the

state. Figure 1 shows the average concentrations of the

model fire tracers O3

FIRE and COFIRE for the two different

time periods. The locations of the EPA monitoring sites

are indicated in the plots.

[14] Our model tracers are restricted to tagging sources

from within California, but it can be clearly seen that the

impact of the fires reaches well beyond the state borders.

During September, the California fires show a clear impact

over large parts of Nevada, adding as much as 5 ppb to the

modeled O3 levels averaged over the fire period. In the

October period, most of the fire pollutants were transported

out over the ocean by the Santa Ana winds, which mitigated

the impact on continental air quality.

[15] An interesting feature seen in Figure 1 is that the

region of maximum impact of fire emitted CO does not

necessarily co-locate with the maximum in O3 produced

from fire precursors. CO is directly emitted and the maximum impact occurs right over the source region. O3,

however, is chemically produced from fire-emitted precursors and the region of maximum impact is shifted downwind of the source region with the distance dependent on

transport times and chemical regime. For October we

actually find the region of maximum O3

FIRE concentrations

off the coast. Many modeling studies use CO tracers or

passive tracers for determining the impacts of selected

sources on trace gas budgets, but these results indicate that

such a method can have significant shortcomings when

looking at photochemically active species and especially

when examining smaller spatial scales.

[16] From Figure 1 we find that the average modelestimated amount of O3 produced from the fires can reach

up to 15 ppb when averaged over the fire period. To

obtain an independent estimate, we analyze observations at

the EPA monitoring sites along with information about the

modeled fire impact. We group the entire data set of

modeled and observed 8-hour values into two categories

based on the timing of the fires: for one we select all data

for 1– 30 September, for the other all data for 8 October –

8 November. Both time periods are chosen to include the

time of most intense burning as well as about 2 weeks

afterwards, or as in the case of October, a week before and a

week after the fires. The separation of these shorter time

periods helps to reduce the impacts of seasonal changes in

photochemistry. We then calculate statistics of the observed

and modeled 8-hour ozone concentrations binned by

values of O3

FIRE concentration, and do this for six subsets

of data grouped by the two time periods stated above and

by site category. The results are included in Figure 2

showing for both observations and model the absolute

deviation from their respective mean concentration. Mean

concentrations are calculated separately for each subset. We

only include 8-hour concentrations centered in the local

afternoon (10 – 18 LT, 11– 19 LT, 12– 20 LT) to preclude

the results being impacted too strongly by the diurnal cycle

in O3 concentrations.

[17] Both model and observations show an overall

increase in O3 concentrations with increasing estimated

fire impact. Best agreement is seen for rural sites, while the

increase at urban and suburban sites is subject to higher

variability and larger discrepancies between modeled and

observed enhancements. This is due to the fact that the

global model is less suited to simulating the urban environment (Section 2, Table 1) and for this reason we focus

our quantitative analysis predominantly on rural monitoring sites. We find that the mean observed (modeled)

enhancement in 8-hour O3 concentrations for O3

FIRE >

10 ppb is 9 ± 14 ppb (8 ± 11 ppb) for September and

8 ± 12 ppb (8 ± 11 ppb) for October. The percentages of

data points in these categories are 10% and 7%, respectively. For data corresponding to O3

FIRE > 20 ppb, the

observed (modeled) enhancements increase to 12 ± 14 ppb

(10 ± 10 ppb) for September and 10 ± 13 ppb (12 ± 9 ppb)

for October. 5% and 3% of the data fall into these

categories, respectively.

[18] Varying conditions of photochemical productivity

(e.g. changes in temperature or cloudiness) can have an

impact on the above statistics and to test this we repeated

the calculations for different limitations of the data subsets

by varying the length of the time periods chosen or selecting

stations for different latitudinal zones. In either case, the

general conclusions remain the same giving confidence that

Figure 1. Mean modeled surface mixing ratios (ppb) for O3

FIRE (filled contours) and COFIRE (contour lines) for (left)

September 1 –20 and (right) October 15– 31. Dots indicate the locations of EPA monitoring sites.

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changes in photochemical conditions have a small impact

on the results. We also examined using COFIRE instead of

O3

FIRE as a tracer (not shown here). In that case we also

detect a positive trend, but we identify fewer statistically

significant results and see a larger variability because of the

lack of co-location of peaks in COFIRE and peaks in O3

FIRE.

[19] At urban and suburban sites, the model shows a

tendency to overestimate the magnitude of fire impact on

O3 concentrations, which is in line with the model underestimate of NO2 at urban and suburban sites (Section 2).

The model dilutes the high pollution concentrations in

urban environments, mainly due to the coarse model

resolution. Adding additional NOx in a less polluted environment causes a larger ozone increase compared to a NOx

and VOC richer environment. In support of this statement,

we show in Figure 3 the modeled relationship between

NO2

FIRE and O3FIRE concentrations for three different NOx

regimes. The latter is estimated by subtracting NO2

FIRE from

total NO2 concentrations. For the same amount of NO2

FIRE,

the NOx poor environment generally gives a larger increase

in O3

FIRE compared to a NOx rich environment. While these

statistics point towards a stronger fire impact at rural versus

urban sites, we cannot provide clear proof with the available data set.

[20] Finally, we also use the data sets to investigate the

frequency of ozone exceedances (8-hour average O3 >

0.08 ppm). For this purpose we calculate the maximum

observed 8-hour O3 value and the corresponding O3

FIRE

tracer value for each day at each rural monitoring site.

There are 81 occurrences of O3

FIRE > 15 ppb, and in 17%

of these the observed O3 concentrations exceeded the limit

of 0.08 ppm. For O3

FIRE > 10 ppb (n = 116) the frequency of

exceedances is 12%, and for O3

FIRE > 1 ppb (n = 434) it

is 11%. In comparison, when O3

FIRE < 1 ppb (n = 367),

exceedances occur only 5% of the time.

[21] In total there are 66 exceedances for September

through October. Only three of them occur in October,

the first at the beginning of the month with a small

estimated fire contribution (O3

FIRE1ppb), while the two

later in the month occurred during the time of the fires with

O3

FIRE > 15 ppb. There might have been more violations if

the winds hat not moved a major part of the pollution

offshore (Figure 1).

[22] Revising the calculations for the new public health

standard of 0.075 ppm, the number of exceedances nearly

doubles and measured concentrations exceed the 0.075 ppm

level 32% of the time when O3

FIRE > 15 ppb, 17% of the time

when O3

FIRE > 1 ppb, and 9% of the time when O3FIRE < 1 ppb.

4. Conclusions

[23] We used a combination of surface observations of

ozone and global model simulations to quantify the

impacts of the California fires in autumn 2007 on surface

ozone. For this purpose an O3 fire tracer is incorporated

into the model keeping track of the amount of O3

Figure 2. Observed and modeled 8-hour O3 concentrations for 10– 18 LT, 11 –19LT and 12 –20 LT binned by the fire

tracer O3

FIRE. Shown is the deviation from the mean concentrations (ppb); results are shown for rural, urban and suburban

sites, and for the two main fire periods (Sep 1 – 30 and Oct 8 –Nov 8). The number of data points in each category is

indicated in the upper part of each plot, the total number of data points in the lower left corner. See text for more details.

Figure 3. Relationship between surface concentrations of

the model fire tracers NO2

FIRE and O3FIRE. Mean (&),

median () and standard deviation of NO2

FIRE bins are

plotted over individual data points. Data are grouped into

different NOx regimes estimated by subtracting NO2

FIRE

from total NO2 concentrations. Data set is limited to the

month of September and to local afternoon values to limit

the impact of temporal changes in photochemistry.

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produced from fire-emitted NO and providing essential

information for extracting quantitative information from

the observations.

[24] Even though the spatial resolution of a global model

limits resolving the differences between urban and rural

environments, the combination of the modeled fire impact

with a set of surface observations has been shown to be

valuable in estimating the impact of the fires on surface O3.

We find a clear increase in observed surface O3 when the

model predicts the observations to be impacted by the fires

with, on average, an enhancement of about 10 ppb in

afternoon 8-hour concentrations for cases of high fire

impact. Data and model analysis indicate that the less

polluted areas (i.e. low NOx environment) generally experience a stronger impact, but further studies are needed to

confirm this.

[25] A major part of incidences when observed 8-hour

concentrations exceeded the public health standards are

associated with clearly elevated concentrations of the model

O3 fire tracer: exceedances occurred in 17% of the cases

when the fire tracer was larger than 15 ppb and in 11%

when the fire tracer was greater than 1 ppb. In comparison,

for fire tracer concentrations less than 1 ppb, the frequency

of occurrence of exceedances is 5%. Our findings demonstrate a clear impact of wildfires on surface O3 nearby and

potentially far downwind from the fire location, and show

that intense wildfire periods frequently can cause O3 levels

to exceed current health standards.

[26] Acknowledgments. The authors acknowledge Jean-Franc ̧ois

Lamarque and Mary Barth for valuable input to the manuscript. The work

was supported by NASA grants NNG06GB27G and NNX07AL57G.

NCAR is operated by the University Corporation of Atmospheric Research

under sponsorship of the National Science Foundation.

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L. K. Emmons, G. G. Pfister, and C. Wiedinmyer, Atmospheric

Chemistry Division, National Center for Atmospheric Research, P.O. Box

3000, 1850 Table Mesa Drive, Boulder, CO 80307, USA. (pfister@ucar.

edu)

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