Introduction
Tropospheric ozone is a strong oxidant controlling much of the chemistry in
the atmosphere, such as hydroxyl radical production and the lifetime of
atmospheric species (see review in He et al., 2013). Tropospheric ozone is
also a greenhouse gas and acts as an important anthropogenic contributor to
radiative forcing of climate (IPCC, 2007). Lower tropospheric ozone
adversely affects human health (Anderson, 2009; Smith et al., 2009), reduces
crop yields (Avnery et al., 2011; Chameides et al., 1999), and damages
natural ecosystems (Ashmore, 2005; Mauzerall and Wang, 2001). Therefore,
ozone (O3) is one of the six criteria pollutants regulated by the US
Environmental Protection Agency (EPA) through National Ambient Air Quality
Standards (NAAQS). The current NAAQS for O3 concentrations ([O3])
is 75 ppbv, defined as the 3-year average of the annual fourth-highest daily
maximum 8-h average (DMA8) [O3] for each monitoring site within an
airshed. The US EPA has already proposed to lower the standard to 65–70 ppbv
(EPA, 2014) and may also redefine the national O3 secondary standard for
protecting sensitive vegetation and ecosystems (Huang et al., 2013).
Currently, many US cities are classified as NAAQS O3 nonattainment
areas based on the 2008 federal standard (http://www.epa.gov/airquality/greenbook/hnc.html). In addition, sensitive
areas (e.g., national parks and wilderness areas) also experience DMA8
O3 exceedances (http://www.nature.nps.gov/air/Monitoring/exceed.cfm). Therefore, improved
understanding and attribution of [O3] sources in these areas is
necessary to develop effective air quality management strategies to achieve
ever more stringent US air quality standards.
As a secondary pollutant, measured ground-level [O3] is the result of
O3 production/loss due to local sources of precursor emissions, to
transport of O3 and its precursors from nearby and/or remote regions,
and to ozone formed from natural precursor emissions. The direct way to
characterize O3 source attribution is through field measurements (e.g.,
Fast et al., 2002; Kemball-Cook et al., 2009; Nunnermacker et al., 2004).
The other way to identify transported O3 and local generated O3 is
to use trajectory models (e.g., MacDonald et al., 2006; Lanford et al.,
2010).
Transport of ozone and its precursors from one area to another is determined
by flow patterns, which can be obtained by measurement and/or modeling.
However, information on flow alone is insufficient in ozone studies because
of the complexity of the chemistry involved, wherein ozone and precursors
nonlinearly interact with flow, turbulence and sunlight to determine ozone
distributions (Huang et al., 2013; Lee et al., 2003, 2007; Levy II et al.,
1985). Chemical transport models (CTMs) are increasingly common in
simulating atmospheric chemical and transport processes at
regional/continental/global scales because of the detailed physical and
chemical processes which they are capable of simulating. For example, using a
CTM (GFDL AM3), Lin et al. (2012) found that Asian O3 pollutants can
affect surface [O3] in the western US, contributing up to 8–15 ppbv
to the DMA8; and that Asian pollution increases the DMA8 O3 exceedance
days by 53 % in the southwestern US. Huang et al. (2013) combining model
simulations at 12 km resolution (WRF/STEM), remote-sensing, and ground-based
observations, have studied the effect of Southern California anthropogenic
emissions (SoCal) on ozone pollution in southwestern US mountain states.
They found that the SoCal precursor emissions and its transported ozone
increased [O3] up to 15 ppbv in western Arizona. They also
characterized the nonlinear relationship between emissions and [O3].
However, these studies have not examined the impacts of regional emissions
on [O3] in an urban setting (such as Phoenix), at high resolution.
Physical/chemical-based CTM modeling is the only available tool for ozone
transport predictions on finer spatial scales (Lee et al., 2007). Many
studies have investigated ozone transport at urban scales using coupled
meteorological and chemistry models. For example, Lu et al. (1997) found
that ozone and other pollutant concentrations were higher in northern and
eastern Los Angeles (LA) than those in the western and central greater LA,
where strong emission sources are located, due to transport owing to the
persistent onshore sea breeze and mountain-induced upslope flow.
Analogously, that surface [O3] in the Phoenix metropolitan area and its
rural environs are higher in northeastern than in southwestern Phoenix
arises from transport of urban pollutants by prevailing southwest winds
(Fast et al., 2000; Lee et al., 2003, 2007; Lee and Fernando, 2013).
Although these studies have considered both chemistry and transport
processes at the urban scale, they did not try to distinguish between ozone
produced by local emissions and that produced by regional transport, a
principal motivation of this study.
The Phoenix metropolitan area is classified as an O3 nonattainment area
under the 2008 NAAQS primary O3 standard (http://www.epa.gov/airquality/greenbook/hnc.html). Therefore, it is
helpful to separately quantify the relative contributions of local emissions
and regional transport to Phoenix [O3] in order to design feasible and
effective ozone control strategies. Both aircraft observations (Nunnermacker
et al., 2004) and backward trajectory analysis (MacDonald et al., 2006)
indicate that surface [O3] on exceedance days are attributed to both
Arizona local anthropogenic emissions (AZ) and regional and/or continental
transport. Therefore, our focus is to use a CTM to separately quantify the
contributions of local and regional emissions to the ozone distributions in
Phoenix on exceedance days, research which has not been published in
peer-reviewed journals.
In addition, previous studies indicate that coarse-resolution modeling
cannot adequately represent the heterogeneities of ozone and meteorological
fields in Phoenix due to its complex terrain (Fast et al., 2000; Lee et al.,
2003; Lee and Fernando, 2013). That high-resolution CTMs can obtain better
results in modeling urban air quality is also reported for the LA basin,
Mexico City, and other regions (e.g., Tie et al., 2010; Chen at al., 2013;
Lu and Turco, 1995, 1996; Taha, 2008; Klich and Fuelberg, 2014; Stock et
al., 2014). Therefore, employing a high-resolution CTM to address air
pollutant distributions in the Phoenix metropolitan area due to local
emissions and regional transport is our second motivation.
Using WRF-Chem (Grell et al., 2005) at high resolution, we will examine the following: (1) the relative contributions of SoCal and AZ to the ozone episodes in Phoenix,
and (2) how SoCal (emissions) affect Phoenix [O3]. This is a topic that
has received limited research attention to date (Moore, 2014), but requires
investigation because of the metropolitan area's non-attainment ozone status
and because of the need to evaluate the effectiveness of local anthropogenic
emission control strategies necessary to attain the standard.
Results and discussion
Two episodes (14 May 2012 and 19 July 2005) are selected as case studies.
The criterion for selection required observed DMA8 [O3] to exceed 80 ppbv for at least 10 of the reporting stations in the Phoenix metropolitan
area. For both events, the synoptic weather in Southern California and
south-central Arizona was calm, clear, and sunny with light westerly winds
within the lower troposphere for the time periods discussed in this section,
based on NARR 3-hourly data. In addition, these two events represent the
pre-monsoon and monsoon seasons, respectively, two typical climate
circulations (Adams and Comrie, 1997) during the ozone season.
The model (WRF-Chem) is initialized 4 days prior to each episode with the
data of the first 24 h being discarded. In addition, analysis nudging is
applied for the meteorological fields (U, V, T, GPH, and Q) above the PBL in
the outer-most domain for the first 24 h.
Model evaluation
Surface wind comparisons between simulations (bold-red) and
observations (bold black). There are 20 sites in total, including those in CA
and AZ with locations shown in Fig. 1b as circles. The variation ranges of
simulation and observation are correspondingly labeled by thin-red-line and
thin-black-line, respectively. Mean Biases (MB), RMSE and correlation
coefficient (R) are labeled also. CTRL represents WRF-Chem control run.
Figure 2 shows the comparison of surface wind fields (circles in Fig. 1b)
between observations (bold-black) and WRF-Chem simulations (bold-red; i.e.,
running WRF-Chem with appropriate emissions and hereafter referred to as
CTRL) for the selected events. The time periods (labeled in Fig. 2) cover
4 days, concluding with the episode day in the Phoenix metropolitan area. In
comparison with observations, the model appropriately reproduced the diurnal
variation with only a slight overestimate of wind speed during daytime. Note
that each observation represents a single point while the closest simulation
grid cell to the observed latitude/longitude location (representing an area
of 1.333 by 1.333 km) is used for comparison. Although there are some
differences between simulated and observed means, the standard deviations
for both modeled (thin-red) and observed (thin-black) measurements fall in
the same range. Mean Bias (MB), Root Mean Squared Error (RMSE), and
correlation coefficient (R) are also calculated and labeled in each panel.
For the U component of wind speed, MB is less than 1.0 m s-1 and RMSE is about
3.0 m s-1 (indicating wind heterogeneity within the simulation domain).
U component winds for the CTRL runs and the observations exhibit linear
correlations with statistical significance (P<0.01). The MB for
V component wind is less than 0.5 m s-1. Linear correlation indicates that
V component winds from the model and the observations are statistically
significant (P<0.01) for the time periods of 11–14 May 2012 and
16–19 July 2005. The wind and temperature comparisons between WRF-Chem in
Domain 1 and NARR data are also examined. Generally, the simulations are
consistent with NARR data in patterns and magnitudes for the two cases. More
specifically, there were continuously westerly winds between southern
California and central Arizona for both NARR and simulations at 850 hPa.
Figure S1 in the Supplement is an example of the comparisons of wind and temperature at 850 hPa (bottom panel) and 700 hPa (top panel) for the average of 16–19 July 2005. These comparisons, which indicate sufficiently accurate meteorological
simulations, ensure that regional pollutant transport can be adequately
simulated, one of our focuses in this study.
The comparisons of CO, NOx, and O3 concentrations between
observations (bold black) and simulations (bold red) in Domain 4. There are
23 sites for NOx, 20 sites for CO, and 65 sites for O3 observations
during the study time periods. The locations are shown in Fig. 1b. The
variation ranges of simulation and observation are correspondingly labeled
by thin-red-line and thin-black-line, respectively. Missing observation time
(04:00 local time) is masked in the figure. CTRL represents WRF-Chem control
run.
Figure 3 shows the comparison of CO, NOx, and O3 concentrations between
the model (bold-red, i.e., CTRL run) and observations (bold-black) in Domain
4 for the same time periods. Note that only four sites of NOx and CO were
measured (only one site online available) in greater Phoenix while over 20
sites are found in Southern California. On average, the model performed well
for both CO and NOx concentrations for the July case. In contrast, for the
May case, the model overestimated CO and NOx during nighttime but matched
observations during daytime. The standard deviations (thin-red) from the
model are much greater than those from observations (thin-black), indicating
that modeled NOx and CO heterogeneity at sites is greater than that from
observations. The model behavior in the May case indicates that the
anthropogenic emissions could be over-estimated using the NEI05 data due to
emission control strategies enacted in California in the seven intervening
years (Pusede and Cogen, 2012). Figure S2 shows how the emissions changed
between 2005 and 2012 for the South Coast Air Basin, California (http://www.arb.ca.gov/app/emsinv/fcemssumcat2013.php) and 2011 in Maricopa County, Arizona (http://maricopa.gov/aq/divisions/planning_analysis/emissions_ inventory/Default.aspx) Relative to 2005,
anthropogenic emissions of CO, NOx, and VOC are reduced about 40–50 % in
2012 in the South Coast airshed, California. Therefore, the NEI 2005
overestimates [CO] and [NOx]. However, the changes in Maricopa County are
not significant except CO from Mobile.
The [O3] comparison between observations and simulations presented in
Fig. 3 indicates the model performed better in simulating [O3] than
CO or NOx. Both the station average and station standard deviation from the
model and observations matched each other on event and non-event days
(details on site-by-site comparisons in Phoenix will be discussed in the
next section). The simulated average [O3] and their spatial
heterogeneities fall within the range of observations except on 13 May 2012, when modeled average [O3] and the spatial standard deviations
fall out of the observation ranges.
Statistical results of hourly ozone concentrations of WRF-Chem
simulations (CTRL) at 1 and 4 km resolution.
11–14 May 2012
16–19 July 2015
CA
CA
AZ
AZ
CA
CA
AZ
AZ
1 km
4 km
1 km
4 km
1 km
4 km
1 km
4 km
Mean Bias (ppb)
-1.9
-3.4
0.6
-0.4
-2.0
-4.0
-4.8
-4.7
Normalized Mean Bias (NMB)
-7.9
-13.5
2.5
-1.7
-8.6
-16.3
-18.5
-18.4
Normalized Mean Error (%)
16.3
25.0
15.4
16.8
24.2
34.1
24.1
25.6
Mean Normalized Bias (%)
-6.7
-10.7
3.2
-1.2
-3.5
-9.7
-16.4
-18.5
Mean Normalized Gross Error (%)
16.7
24.9
15.9
17.3
23.8
34.0
24.5
26.2
Correlation coefficient
0.75
0.54
0.76
0.65
0.74
0.4
0.75
0.61
Root Mean Square Error (ppb)
16.1
19.9
15.7
15.5
22.9
30.1
15.8
17.2
Figure S3 shows [O3] time series separately for Southern California and
greater Phoenix; corresponding statistics are shown in Table 1. In checking
Fig. 3, and Figs. S2 and S3, although the NEI-2005 over-estimated CO and
NOx emissions in 2012 in the south coast airshed, California, causing [NOx]
and [CO] to be over-estimated as well, the ozone simulations nonetheless
appear to be quite acceptable. One explanation could be that this airshed is
categorized as a VOC-limited ozone environment. Under this condition, ozone
concentrations are restrained by VOC concentrations. In other words,
reducing NOx fails to reduce ozone concentrations (e.g., Taha et al., 1998)
and the same is also found in Phoenix area (Fast et al., 2000; Lee and
Fernando, 2013), which can partly explain why the modeled [O3] matched
the observations, even though the modeled [NOx] and [CO] are highly
overestimated in the May case.
Table 1 presents the statistics of comparisons of surface ozone
concentrations between the model and observations in Southern California
(total 46 sites) and greater Phoenix area (total 24 sites), respectively.
These statistics are widely used in evaluating model performance (Simon et
al., 2012). Our statistics are comparable with those from previous studies
in the two regions. For example, in Southern California, the mean biases,
RSME and correlation coefficients shown in Table 1 are comparable with those
from Huang et al. (2013, their Table 3) and Chen et al. (2013, their Tables 2
and 3). Furthermore, the mean normalized bias and mean normalized gross
error are comparable with those from Taha (2008, in his Table 2). In greater
Phoenix, these statistics are generally comparable with those from Lee et
al. (2007), and Li et al. (2014).
To examine the effects of model resolution on surface ozone concentrations,
we conducted two additional model runs. These two additional runs were set
up and configured exactly the same as the 1.33 km runs; but, with just
running WRF-Chem with Domains 1, 2, and 3, which means the highest
resolution of model output is 4 km. The model performance at 4 km resolution
was also validated against ozone observations and summarized in Table 1. As
shown in Table 1, the model performed much better for the correlation
coefficients, normalized mean gross errors, mean normalized bias, and
normalized mean error at 1.33 km than those at 4 km. For the mean bias and
normalized mean bias, the model performed better in Southern California at
1.33 km than those at 4 km, with similar performance in greater Phoenix.
Therefore, we conclude that WRF-Chem in its present configuration performed
better at 1.33 km resolution than that at 4 km resolution, based on the two
events and on the 2005 NEI. Our results are consistent with previous studies
(e.g., Taha, 2008; Tie et al., 2010). In the following analysis and
discussion, we mainly focus on the model output at 1.33 km resolution.
The evaluation shown in Figs. 2–3, S3, and the statistical analysis
presented in Table 1 demonstrate that the WRF-Chem model, in its current
configuration and set up, produces simulated ozone concentrations comparable
to the observations.
Contribution of local and remote emissions to Phoenix [O3]
Next, we investigate impacts of anthropogenic emissions in southern
California (SoCal) and Arizona (AZ) on Phoenix [O3]. To achieve this
goal, we have conducted additional WRF-Chem simulations for the selected
cases with the same model setup as presented and evaluated in Sects. 2.1
and 3.1, and refer to these experiments as “CTRL”, but with (1) exclusion
of SoCal emissions (indicated as the dashed-red-line box in Fig. 1b) and
called “noCA”; (2) exclusion of AZ emissions (indicated as the
dashed-black-line box in Fig. 1b) and called “noAZ”; and (3) exclusion
of all anthropogenic emissions in Domain 4, and called Biogenic emissions
and Lateral Boundary inflow (BILB).
Relative contributions of different emission scenarios to
[O3] at observation sites in Phoenix metropolitan area and surrounding
rural areas. The dates are 11–14 May 2012 (a–f) and 16–19 July 2005 (g–l). Idxxxx corresponds to the EPA AIRS site number in
Maricopa County, Arizona. Black line indicates the [O3] observation.
Red line represents the simulated [O3] for the CTRL run. Blue line
shows the [O3] for the noAZ run. Green line displays the [O3] for
the noCA run. Gray line is the [O3] for the BILB run.
Figure 4 shows the hourly [O3] comparison for observations (Obs), CTRL,
noCA, noAZ, and BILB simulations at selected observation sites in the
Phoenix area on 11–14 May 2012, (Fig. 4a–f) and 16–19 July 2005 (Fig. 4g–l). Figure 4 indicates that hourly [O3] from the CTRL run match the
observations very well in western downtown (ID0019, ID2001), central
downtown (ID3003, ID9997), and east and north suburban areas (ID9508,
ID9702). AZ emissions are the principal contribution to ozone production
over Phoenix during daytime (compare the change in simulated [O3] as
demonstrated by the red contour [CTRL] and dashed-blue contour [noAZ]), with
a maximum magnitude of up to 40–60 ppbv hourly (compare differences between
CTRL and noAZ). The contribution of SoCal emissions to Phoenix [O3]
ranges between 10–40 ppbv during daytime (compare the change in simulated
[O3] as demonstrated by the red contour [CTRL] and green contour
[noCA]). Based on the BILB run (gray contour), the contribution of biogenic
emissions (including larger-scale lateral input) to Phoenix [O3] varies
between 25–35 ppbv, indicating a baseline target for emission reduction
strategies. Following Huang et al. (2013), the contribution of SoCal to
[O3] in the Phoenix area is the difference between the CTRL and noCA
experiments. The relative contributions from SoCal, AZ, and BILB emissions
to hourly [O3] at observation sites for 19 July 2005 and 14 May 2012
are shown in Figs. S4 and S5.
Figures 4 and S4 and S5 indicate the relative contribution of SoCal
and AZ emissions to [O3] vary with time. Physical and chemical
processes at each stage can explain this variation. During nighttime, noCA
[O3] are less than that of the noAZ run. This is because there is no
ozone consumption (or titration) in the noAZ run while transported ozone can
still make its contribution. After sunrise, solar radiation heats the ground
surface, increasing the planetary boundary layer (PBL) height. Ozone
accumulated within a residual layer from previous day(s) is entrained into
the PBL, increasing ground-level [O3]. This process continues until the
PBL height reaches its peak. Simultaneously, ozone production starts with
its precursor emissions in the presence of sunlight, a rate that increases
with increasing sunlight intensity and surpasses the transport rate of
[O3] by mid to late afternoon. Furthermore, the figures indicate that
the peak time of [O3] differs between the CTRL run and the noAZ run at
some locations for some days. These differences of [O3] peak time
indicate the importance of ozone transport. Figure 5 displays the mean
diurnal variation of [O3] for the different emission scenarios for the
two cases. The data are averaged over all urban grid cells (i.e., not solely
over the station sites presented in Fig. 4) in Phoenix for 11–14 May 2012, and 16–19 July 2005, respectively. The relative contribution of
emissions to Phoenix [O3] are clear and the diurnal features are
similar to those shown in Figs. 4, and S4 and S5, emphasizing the
crucial roles of both local and remote emissions.
Simulated diurnal variations of [O3] at Phoenix urban setting
for different emission scenarios: (a) average from 16–19 July 2005, and (b)
average from 11–14 May 2012.
The daily maximum 8 h average (DMA8) [O3] from CTRL and the relative
contributions to DMA8 [O3] from different emission scenarios (BILB,
SoCal, and AZ) are assessed at observation sites and for all urban grid
cells within Phoenix (Fig. 6). The model reproduces observations very well
with a slight underestimation on 19 July 2005, but with an overestimation
on 13 May 2012. The contribution of SoCal to DMA8 [O3] in the Phoenix
area ranges between 20–30 ppbv for the May case and 5–20 ppbv for the
July case. Relative to the CTRL run, the percentage contributions of 26–36 % for the May case and 7–38 % for the July event emphasize
the significant effect of Southern California emissions on Phoenix
metropolitan area air quality. For the two episode days, the contributions
are 28 ppb (36 %) for 14 May 2012, and 11 ppb (16 %) for 19 July 2012.
The relative contributions of AZ local emissions to greater Phoenix
observation sites are also shown in Fig. 6. Overall, the relative
contributions of AZ local emissions to Phoenix [O3] are more than that
of SoCal emissions.
Mean DMA8 [O3] in Phoenix metropolitan area from observation
(Obs), simulation from CTRL runs (CTRL), BILB runs (BILB), and the relative
contributions of different emission sources. CTRL-noAZ represents the
modeled DMA8 [O3] differences between CTRL run and noAZ run. CTRL-noCA
displays the modeled DMA8 [O3] differences between CTRL run and noCA
run. Observation sites show in Fig. 1b. (a) DMA8 [O3] at observation
sites for 16–19 July 2005, (b) the same as (a) but for that averaged from
Phoenix urban grid cells. (c) and (d), the same as (a) and (b) but for the
case of 11–14 May 2012.
The means of DMA8 [O3] throughout the Phoenix urban area (about 1100 grid cells) arising from the different emission scenarios are shown in
Fig. 6b and d, and indicate similar values to those at observation sites
(Fig. 6a, c). The contribution of SoCal emission to DMA8 [O3] for the
Phoenix metropolitan area ranges between 20–32 ppbv for the 11–14 May 2012, case, and from 6–22 ppbv for the 16–19 July 2005, case. The
percentages, relative to CTRL, are from 27 to 37 % for 11–14 May and
from 9 to 40 % for 16–19 July. Considering only the 2 days with the
maximum ozone concentrations, the contributions are 29 ppb (37 %) and 11 ppb (16 %) for 14 May and 19 July, respectively.
Note that in Fig. 6, the differences of CTRL minus BILB is not equal the
sum of the differences of CTRL minus noCA plus that of CTRL-noAZ. The reason
could be the nonlinear processes among emissions, physical, and/or chemical
mechanisms (Kwok et al., 2015) and the uncertainties of the entire system:
both the emissions and the models themselves.
Figure 6 demonstrates the following results: (1) the impact of AZ emissions
on DMA8 [O3] in the Phoenix area is greater than that of the SoCal's;
(2) even so, SoCal emissions considerably increase DMA8 [O3] in the
Phoenix area by up to 30 ppbv, though this is day and case dependent; (3) the DMA8 [O3] from the BILB experiment are in excess of 30 ppbv,
including the contributions of biogenic emissions and lateral boundary
transport. Based on the diurnal variations shown in Figs. 4 and 5, and
Figs. S4 and S5, [O3] due to biogenic emissions and lateral boundary
inflow could be 10–17 ppbv. In other words, the contribution of BILB to
Phoenix DMA8 [O3] cannot be ignored despite the region's aridity and
lack of dense forests. Note that all of these results are based on the US
EPA 2005 national emissions inventories.
DMA8 [O3] spatial distributions in Greater Phoenix and
surrounding areas on 19 July 2005: (a) CTRL, (b) noAZ, (c) noCA, (d) BILB,
(e) CTRL-noAZ, and (f) CTRL-noCA. Contours represent terrain elevations.
Dots show O3 observation sites. Circle indicates the approximate
location of Phoenix urban area.
Figure 7 depicts the spatial distributions of DMA8 [O3] for different
emission scenarios on 19 July 2005. The CTRL run indicates that higher
[O3] occur in the northeastern urban perimeter, which is consistent
with previous studies (e.g. Lee and Fernando, 2013). The effects of SoCal
emissions and AZ local emissions on DMA8 [O3] are location-dependent.
The case of 14 May 2012, is also examined (see Fig. S6) and a similar
distribution as in Fig. 7 is found, but it differs in magnitude.
In summary, our results demonstrate that removing SoCal emissions would
facilitate attainment of [O3] in Phoenix on some days, but not on
others. In other words, SoCal emissions are an important, if uneven,
contributor to the DMA8 [O3] exceedances for Phoenix. In addition, the
effects of SoCal emissions on Phoenix DMA8 [O3] are location-dependent
(see Figs. 7 and S6). From a pollution control point of view, our
results indicate that reducing the emissions emitted in Phoenix is the key
to attain federal standards. With typical synoptic wind fields, emissions
from Southern California affect ground-level [O3] in the Phoenix
metropolitan area significantly. Therefore, the results indicate that
Phoenix would benefit from regional, in addition to local, emission controls
to reach NAAQS attainment status.
Southern California to Arizona [O3]
transport
Through analysis of [O3] variations with the various emission
scenarios, 10–30 % of [O3] in the Phoenix area can be attributed to
SoCal emissions for the cases presented here. In this section we will
examine pathways characterizing how pollutants in the coastal air basins of
Southern California are transported into Arizona and affect air quality in
the Phoenix area based on 1.33 km resolution model output. The corresponding
analyses of the results from the 4 km resolution output can be found in the
Supplement.
Hovmoller diagram of [O3] differences (CTRL minus noCA) at
13th vertical model layer (about 1100 m a.g.l.) along the cross-section
B'B shown in Fig. 1b for May case (a) and July case (b). Approximate
locations of Phoenix (PHX), desert, mountains (Mnts), and coast are also
labeled in (b). The integrating is counted from 00:00 UTC, 10 May 2012, and
00:00 UTC, 15 July 2005, respectively.
Figure 8a shows a Hovmoller diagram of [O3] differences (CTRL minus
noCA) and the wind vector field (from CTRL run) for the May case at the
model's 13th vertical level (about 1100 m above ground-level, or a.g.l.) of
WRF-Chem along the cross-section B'B (indicated in Fig. 1b). The Hovmoller
diagram is a suitable technique to identify transport and propagating
phenomena in a given field (i.e. Hovmoller, 1949). In Fig. 8a, the y axis is
the model integration time (hours) and the x axis is the location
(longitude) along the B'B transect. The approximate locations of Phoenix
(PHX), desert, mountains (Mnts) and coast are also labeled in this figure.
Since both CTRL and noCA experiments include the same emissions except over
California, the difference in ozone between these experiments offsets the
chemical ozone production east of California and west of Phoenix. Thus, the
residual ozone perturbation field in these regions is dominated by
transport. The pattern of this field exhibits tilted ozone bands with phase
lines that have consistent positive slopes (Fig. 8a), indicating that a
perturbation of ozone in California will eventually reach Arizona. This
demonstrates that the residual ozone field shown in Fig. 8a is caused by
transport from California to Arizona. The Hovmoller diagram of [O3]
differences for the July case also exhibits patterns of residual ozone with
positive slopes indicating transport (Fig. 8b). These slopes are, however,
less pronounced than the May case.
The data within each model vertical layer are examined. It is found that
peak transport occurs in different model layers depending on the event. For
the July event, there is ozone transport from the 5th model layer
(about 150 m a.g.l.) to the 13th model layer (1100 m a.g.l.). For
the May event, ozone transport occurs from the 5th to 17th (2000 m a.g.l.) model layers. The Hovmoller diagrams for NOx and VOCs indicate
that most air masses of NOx and VOCs are horizontally confined near emission
source areas and are vertically restricted to below about 1500 m a.g.l.
(figure not shown), compared to the magnitude presented in Fig. 8.
We next examine how pollutants from Southern California are transported into
south-central Arizona and discuss the physical-chemical mechanisms
responsible. Analysis of anthropogenic emission distributions indicates that
emissions mainly originate from coastal areas in Southern California (also
see their Fig. 1 in Chen et al. , 2013 for emission distribution).
Therefore, we first explain how the pollutants cross the coastal mountains
and reach the inland desert regions in Southern California.
Wind vector field at 40 m above surface layer in southern
California coastal area. Data are averaged from 20:00 to 02:00 UTC, 16–20 July 2005.
As discussed in Sect. 1, wind fields are paramount in pollutant transport
(Lee et al., 2007). Figure 9 displays the daytime averaged (20:00 to 02:00 UTC) wind
vector field at 40 m a.g.l. in the Southern California coastal area of
16–19 July 2005 (for 4 km resolution plots, see Fig. S7). The wind
patterns exhibit a combination of on-shore ocean breezes and
mountain-induced upslope winds, similar to features reported by Lu and Turco (1996) and Lu et al. (1997). The wind field distribution shown in Fig. 9
propels pollutants emitted in coastal areas towards the coastal mountains.
The polluted air masses can be lofted up to 3–4 km a.g.l. over the mountains
through the Mountain Chimney Effect (MCE, Lu and Turco, 1996). The
pollutants above mountain-top height might either be transported into the
free atmosphere over the coast (Lu and Turco, 1996) and/or be transported
towards the inland desert and affect the air quality in the desert of
Southern California (Huang et al., 2013; VanCuren, 2015) and of nearby
mountain states (Langford et al., 2010; Huang et al., 2013).
The entire transport path, from the Southern California coast to
south-central Arizona, and the associated ozone vertical distributions along
cross-sections A'A, B'B, D'D and E'E, is described here in this subsection.
First, vertical distributions of [O3] along cross-sections A'A and B'B
are checked from 21:00 to 24:00 UTC each day and Fig. 10 is an example of vertical
distributions of [O3] along cross-section A'A and B'B at 22:00 UTC on 17 July 2005 (for 4 km resolution plots, see Fig. S8). Results presented in
Fig. 10 are similar to those reported by Lu and Turco (1996, in their
Figs. 4 and 6) from modeling and Langford et al. (2010; in their Fig. 3)
from observations, indicating that WRF-Chem adequately simulates the
Mountain Chimney Effect (MCE). Note the distribution of potential
temperature contours in Fig. 10, illustrating that ozone-laden air masses
above mountain peak height may be directly transported into the desert PBL
under appropriate flow at these levels. This pattern differs from that of
transport back to the free atmosphere over coastal basins (note the tongue
of high [O3] to the west of the peak in Fig. 10a). This is because of
the particularly high PBL height (in excess of 3–4 km a.g.l.) in the
desert during daytime due to strong solar radiation. At nighttime, ozone air
masses subsequently subside into the residual layers and/or stable PBL in
the desert, and are continuously advected by westerly winds (part of the
near-surface ozone will be consumed by titration from NOx and by deposition
during nighttime). Importantly, Fig. 9 indicates the presence of strong
winds from the coast flowing through the mountain passes. For example, there
are southerly winds flowing along the Cajon Pass (see location in Fig. 1b)
and strong westerly winds flowing along the San Gorgonio Pass (see location
Fig. 1b), which are realistic and consistent with the immense fields of
wind turbines there. With the wind pattern shown in Fig. 9, ozone in low
air layers can be directly transported into the southern Mojave Desert Air
Basin (SMDAB, see Fig. 1b) from the greater Los Angeles Air Basin (GLAAB)
through the Cajon Pass. Ozone can also be transported eastward to the Salton
Sea Air Basin (SSAB) from the GLAAB through the San Gorgonio Pass and from
the San Diego Air Basin (SDAB) through other passes (see Fig. 9 for the
locations and wind vectors).
Vertical distributions of ozone along cross-section A'A (a) and B'B (b) shown in Fig. 1b at 22:00 UTC of 17 July 2005. The
contours are potential temperature starting at 280 K with 1 K interval.
To demonstrate the model performance in simulating [O3] in the passes,
Fig. 11 presents the hourly comparison of [O3] between observations
and simulations (CTRL) at Crestline, near the Cajon Pass, and Banning
Airport, near the San Gorgonio Pass. Figure 11 shows that the simulations
and the observations are comparable from 17 to 19 July 2005. In Fig. 11, model simulations with 12 km resolution are also plotted to characterize
resolution-dependency. It is clear that with higher resolution, simulated
results are improved above those of coarser resolution, a feature likely due
to more accurate ozone transport through the passes.
Ground-level ozone concentration comparisons between observations
and simulations at (a) Banning Airport (ID0650012, 33.92077∘,
-116.85841∘) located in the San Gorgonio Pass and (b) Crestline
(ID060710005, 34.24313∘, -117.2723∘) near the Cajon Pass from 17–19 July 2005. Obs indicates the observation. CTRL represents the simulations
from CTRL run and M12km is the model simulations at 12 km resolution.
Figure 12 shows the horizontal distribution of the integrated fluxes of
ozone differences (∫([O3]CTRL-[O3]noCA)VCTRLdz) from the surface to 1400 m a.g.l. averaged from (a)
18:00
to 02:00 UTC and (b) 03:00 to 17:00 UTC, 16–20 July 2005 (data from the other case 11–15 May 2012 are similar and for 4 km resolution plot, see Fig. S9). Figure 12 emphasizes two key aspects of this transport:
There were stronger fluxes in the mountain passes, especially in the San
Gorgonio Pass, than any other location, indicating the important
contributions of mountain passes to ozone transport. Most recently, VanCuren (2015), based on analysis of ozone observations, also suggests the
importance of ozone transported into the MDAB through the passes and has
confirmed our model results.
Ozone fluxes are present, originating from the coasts and mountains in
Southern California, extending southeastward along the SSAB and the SMDAB
(Fig. 12b), crossing the California-Arizona border near the southern
Colorado River, then moving northeastward (Fig. 12b) along the Lower Gila
river basin, and finally reaching the Phoenix area.
Integrated fluxes of ozone differences (CTRL-noCA) from surface
to 1400 m above ground-level: (a) average from 18:00 to 02:00 UTC, 16 to 20 July 2005, and (b) average from 03:00 to 17:00 UTC, 16 to 20 July 2005.
The vertical distribution of pollutants is also evaluated along
cross-section D'D in the Salton Sea Valley and cross-section E'E in the Gila
River Valley (locations are labeled in Fig. 1b). Presenting vertical
distributions of VOC, NOx and O3 along D'D on 18 July from CTRL, Fig. 13 depicts the transport of the pollutants from late afternoon to midnight,
as indicated by the location of high-concentration fronts (for the
corresponding 4 km resolution plots, see Fig. S10). The NOx masses are
vertically confined to below 1 km above sea level (a.s.l.) with concentrations
of 5–15 ppbv. VOC plumes are confined below 2 km a.s.l. with concentrations of
10–20 ppbv. We also evaluated the vertical distribution of VOC from the BILB
emissions experiment: the vertical distribution is similar to the VOC shown
in Fig. 13, but the concentrations are about 10 ppbv (figure not shown).
In other words, there are about 10 ppbv of VOC that are transported from
coastal anthropogenic emissions to this region. Similar to NOx
concentrations, the highest concentrations of VOC are near the ground
surface.
The vertical distribution of VOC (top), NOx (middle), and O3
(bottom) along the cross-section D'D (shown in Fig. 1b) in the Salton Sea
Basin at 01:00, 03:00, and 06:00 UTC, 18 July 2005. Contours are potential
temperature with 1 K interval.
Ozone vertical distributions reach up to 2–3 km a.s.l. with concentrations
as high as 90 ppbv. The high [O3] is centered 1–2 km a.s.l. during
nighttime while [O3] is low near ground-level due to the chemical
titration by NOx and dry deposition (Fig. 13). In other words, among the
three pollutants, ozone is most “long-lived” and NOx has the shortest
span, which is consistent with their atmospheric chemistry and previous
results (e.g., Lee and Fernando, 2013).
The diurnal variation of a pollutant is, in part, a consequence of diurnal
variation of flow (the other principal influence is the diurnal variation of
the emissions themselves). During daytime, southeasterly winds (valley
winds) at lower layers in the northern Salton Sea basin hinder the
pollutants from being transported southeastward along the Salton Sea Basin
(See Figs. 12a and 9). Therefore, a portion of the pollutants,
transported from the GLAAB through the San Gorgonio Pass, accumulate over
the northern Salton Sea basin (as shown at 01:00 UTC in Fig. 13), while a
different portion of the pollutants crossed the Little San Bernardino
Mountains and reached the SMDAB due to upslope flow (see Figs. 12a and
9). During nighttime, basin-scale mountain downslope winds transport
the pollutants southeastward along the SSAB basin (Figs. 12b and
13).
The vertical distribution of VOC (top), NOx (middle), and O3
(bottom) along the cross-section D'D (shown in Fig. 1b) in the Gila River
Basin, Arizona at 05:00, 11:00, and 18:00 UTC, 18 July 2005. Contours are potential
temperature with 1 K interval.
Figure 14 is similar to Fig. 13 but presents results for the cross-section
E'E in the Gila River basin in Arizona (location shown in Fig. 1b) on 18 July (corresponding 4 km resolution plots, see Fig. S11). During this time
period, although concentrations of pollutants continued to decrease along
this transport pathway, the ozone transport phenomenon was still very clear
along the Gila River basin due to the prevailing nighttime southwesterly
winds (see Fig. 12). These southwesterly winds can result from either the
low-level jet from the northern Gulf of California during monsoon season
(mid-July to mid-September, Adams and Comrie, 1997) or by the inertia from
a remnant of daytime westerly winds during pre-monsoon season (from May to
mid-July, Lee and Fernando, 2013). At about 18:00 UTC, the ozone in the residual
layer mixes with PBL ozone generated by local photochemical reactions, and
finally affects the ground-level concentrations in Phoenix and its
surrounding rural areas.
The results presented in this section are mainly based on model simulations.
In past decades, there were a few field experiments conducted to measure the
vertical distributions of meteorological fields and trace gases in southern
California (e.g., the Southern California Air Quality Study in 1987; Lawson,
1990; the Southern California Ozone Study in 1997; Croes and Fujita, 2003
and CALNEX-2010; www.esrl.noaa.gov/csd/calnex/) as well as in the Phoenix
area (e.g., Phoenix Air Flow Experiment II in 1998; Fast et al., 2000;
Nunnermacker et al., 2004). Some of the events during the experiments have
been used to address ozone transport (e.g., Huang et al., 2013; Langford et
al., 2010) from the Southern California coast. No aloft measurements could
be found for May 2010 that would be of help in the present model performance
evaluation. In addition, satellite-retrieved data may be used to demonstrate
the vertical distributions and even distant transport (e.g., Huang et al.,
2013), although these data are hampered by limitations such as
coarse-resolution, accuracy, etc. (e.g., Bowman, 2013). To quantitatively
examine the transport and vertical distribution from Southern California
coasts to Phoenix, field observations, especially measurements aloft, along
the inland California desert region and within western Arizona are needed.
Conclusions
As with other cities, Phoenix's ozone concentrations on exceedance days can
be attributed to both local precursor emissions and to the transport of
ozone and its precursors from remote regions. In this study, WRF-Chem at
high resolution (∼ 1.333 km grid spacing) is employed to
investigate surface ozone distributions in Southern California and
south-central Arizona for two selected Phoenix episodes. Model simulations
have been compared with surface observations of hourly ozone, CO, NOX
and wind fields in Southern California and Arizona. The results indicate
that the WRF-Chem configuration in this study can adequately simulate the
spatial distribution, the magnitude, and the variability of the
observations. The modeled ozone concentrations ([O3]) are comparable
with previous studies in the focus region.
Three sensitivity studies have been conducted to separate the contributions
of Southern California anthropogenic emissions (SoCal), of the Arizona local
anthropogenic emissions (AZ), and of biogenic emissions and lateral boundary
input to Phoenix [O3] on the exceedance days: (1) running WRF-Chem as
CTRL but excluding SoCal emissions (noCA), (2) running WRF-Chem as the
Control simulation but excluding AZ emissions (noAZ) and (3) running
WRF-Chem as the Control simulation but excluding all anthropogenic emissions
in domain 4 areas, leaving the Biogenic emissions and Lateral Boundary input
(BILB). Our simulations indicate that AZ emissions play the key role in
formation of the elevated [O3] in Phoenix for the selected cases (see
Figs. 4, 5, and 6). Based on the US EPA 2005 emissions inventories, SoCal
emissions contribute to DMA8 [O3] in the Phoenix area, and this impact
varies between 5–30 ppbv at various observation sites and from 6–32 ppbv
throughout the urban setting. In addition, our model simulations indicate
the effects of SoCal emissions on DMA8 [O3] in Phoenix are location and
event dependent, but not negligible. The effects of BILB contributions to
Phoenix DMA8 [O3] are also significant in spite of the region's
aridity. Our future research will distinguish biogenic and lateral boundary
inflow contribution to this area through model simulations and observations.
The model results are based on the 2005 US National Emissions Inventories
(NEI, 2005). With more stringent emission control strategies in California,
the effects of the pollutants transported from California could be reduced.
The time series of [O3] of the relative contributions to Phoenix
[O3] from SoCal and AZ emissions exhibit a diurnal variation. During
nighttime hours, the transported ozone increases [O3] while local NOx
emissions consume it. The reverse occurs during afternoon hours when locally
generated emissions predominate.
WRF-chem's high resolution resolves all pertinent topographical features,
especially the critical low-elevation mountain passes, capturing the
pollutant transport through them. Therefore, the pollutant's (mainly ozone)
transport pathway in the lower troposphere is identified: the pollutants
(mainly ozone) are first transported to the southern Mojave Desert Air Basin
(SMDAB) and the Salton Sea Air Basin (SSAB) through both the Mountain
Chimney Effect (MCE) and Mountain Pass Channel Effect (PCE) during daytime,
affecting DMA8 [O3] in these two air basins. The following physical
transport paths (based on the two events) are: the pollutants are first
transported southeastward along the two air basins (the SSAB and the SMDAB)
in CA during nighttime, then northeastward along the Gila River basin in AZ
during nighttime, and finally reach the Phoenix area and mix with the local
air mass by turbulent mixing during daytime. The entire transport path is
determined by a combination of local and synoptic circulations.
Since the PBL height can extend in excess of 3–4 km a.g.l. in desert air
basins, pollutants may be directly transported into the daytime desert PBL
from coasts by both PCE and MCE. Therefore, regional transport in the desert
is accomplished in the PBL (daytime), and residual layer and stable PBL
(nighttime).