Introduction
In the lower troposphere, ozone (O3) has impacts on both human health
and ecosystems (Pusede et al., 2015), and understanding its mechanisms of
production is essential to managing these impacts. Surface O3 is the
result of both local and regional contributions when measured at any given
location (Berlin et al., 2013). These contributions change in space and time
because of dynamic factors that include emissions of O3 precursors and
meteorology. Understanding these contributions is fundamental to the design
of more efficient controls on anthropogenic O3 precursors to protect
people and ecosystems and to achieve compliance with the National Ambient
Air Quality Standards (NAAQS) for O3.
Regional contributions, often denoted as “regional background” (Berlin et
al., 2013; Cooper et al., 2012), are more challenging to estimate because of
variable influences from regional photochemistry and synoptic air
circulation. In contrast, local contributions (e.g., from urban activities)
are simply the difference between the total measured value and regional
background. In the Houston–Galveston–Brazoria (HGB) area regional background
O3 is not well quantified on the decadal scale, likely due to lack of
information on the spatiotemporal covariance of O3, precursors and
meteorology. Consequently, previous investigations of regional background
O3 in the HGB region were limited by the use of a single variable, the
daily maximum 8 h average (MDA8) O3 (Berlin et al., 2013). No long-term
study exists that quantifies the regional contributions to direct O3
precursors themselves, such as nitrogen oxides (NOx= nitrogen
dioxide (NO2)+ nitric oxide (NO)). Our goal is to better
characterize the trends in regional background O3 and NOx in the
HGB region on the decadal scale.
Volatile organic compounds (VOCs) also are important O3 precursors.
VOCs perturb the photochemical NOx cycle, the governing mechanism of
tropospheric O3 formation, so that O3 mixing ratio increases in
their presence. The relative abundance of NOx and VOCs mediates
O3 production through their individual reactions with the hydroxyl
radical (OH). The products of VOC's reaction with OH (peroxy radicals) react
more rapidly with NO compared to O3, increasing the minimum O3
maintained by the NOx cycle. Therefore, VOC influence is included
implicitly in the measured O3 and NOx mixing ratios. In this
work, we focus on the O3–NOx–meteorology relationship to constrain
regional background O3 and NOx and quantify their trends.
Meteorology influences both transport of pollutants and their chemistry. The
relevant meteorological variables (wind speed (WS) and direction (WD),
temperature (T), boundary layer height, etc.) and air pollution co-vary
synoptically on timescales of days to weeks (Fiore et al., 2015). The
effects of meteorology on tropospheric O3 vary across the United States
(US). Boundary layer height strongly and positively correlates with
tropospheric O3 in the western US (Reddy and Pfister, 2016). The
O3–T relationship is positive in the eastern US but weakens and turns
negative along a north–south gradient, compared to the western US (Camalier
et al., 2007; Tawfik and Steiner, 2013; Rasmussen et al., 2012; Reddy and
Pfister, 2015). Wind speed negatively correlates with O3 (Camalier et
al., 2007; Banta et al., 2011; Reddy and Pfister, 2015). Wind direction can
either enhance or diminish O3, depending on altitude and
topography-induced air circulation (Reddy and Pfister, 2015). More localized
controls on decreasing surface O3 include relative humidity in the
southeast US (Tawfik and Steiner, 2013), shallow and deep convection in the
Houston area (Langford et al., 2010a) and the intensification of southerly
flow in the HGB region (Liu et al., 2015). Recently, Wang et al. (2016)
reported that the location and strength of the Bermuda High (a large-scale
circulation pattern) together drive the interannual variation of the monthly
mean MDA8 O3 in the HGB region and may either increase or decrease
daily MDA8 O3 during summer. Meteorological controls on the scale of
the US also may play a role in the differential decline during recent
decades of summer surface O3 observed in the east, southeast and
midwest (Cooper et al., 2012; Hudman et al., 2009) compared to the west
(Cooper et al., 2012). There are different meteorological controls in the
west (i.e., thermal inversion and orographic lifting; Langford et al.,
2010b), which can either increase O3 locally or transport O3 up
in the free troposphere and towards the east. Additionally, the pollution
transport from Asia contributes to a higher O3 in the western US
compared to the eastern US (Cooper et al., 2012).
Synoptic air circulation contributes to ground-level O3 in the HGB area
in various ways. This region is influenced by the development of high-pressure centers at various altitudes during summer. Analyses of local and
high-altitude winds identified several such centers around the HGB region,
which dictate the predominant WD (compass directions such as SW, S, SE, E,
NE and N refer to the direction from which the wind originates at a given
location) (Nielsen-Gammon et al., 2005; Rappenglück et al., 2008).
Direct tropical storm influences from low-pressure zones also were
identified in the Houston area (Rappenglück et al., 2008). Dry
continental air (higher O3) is advected by northerly flow, industrial
emissions from the Ship Channel and Galveston Bay area are transported by
easterly flow, and marine air (lower O3) enters via southerly flow
(Rappenglück et al., 2008). The land–sea breeze effect complicates this
picture through recirculation of local pollution and formation above the
coast of the Gulf of Mexico (GOM) of stagnant air masses that entrain local
precursors and favor local chemistry and formation of O3 (Banta et al.,
2005; Darby et al., 2005; Nielsen-Gammon et al., 2005; Rappenglück et
al., 2008; Langford et al., 2009).
Two intensive air quality campaigns investigated peak O3 in the HGB
region during 2000 and 2006 (Daum et al., 2003; Ryerson et al., 2003; Daum et
al., 2004; Banta et al., 2005; Rappenglück et al., 2008; Neuman et al., 2009; Parrish et al.,
2009; Pierce et al., 2009; Langford et al., 2010a). The O3 pollution in
this region was likely a result of abundant precursors emitted locally from
urban and industrial sources (particularly, the highly reactive VOCs
(HRVOCs) from the petroleum refineries) and the local chemistry sustained by
the high summer temperature and land–sea breeze effects. However, the
emissions of HRVOCs have been considerably reduced after the first campaign,
resulting in lower local contributions to O3. Texas state controls on
O3 precursor emissions were implemented in 2007, resulting in apparent
decreases in summer O3 levels in the Houston area relative to the
previous 8 h average NAAQS of 75 ppb (Berlin et al., 2013). It is not clear
whether a decline in regional background O3 also contributed (Berlin et al.,
2013).
Regional background O3 in the HGB region has been quantified by many
studies but results vary, depending on the temporal scale, spatial scale and
the altitude of observations used in data analysis (Banta et al., 2005;
Darby et al., 2005; Nielsen-Gammon, 2005; Rappenglück et al., 2008;
Kemball-Cook et al., 2009; Langford et al., 2009; Zhang et al., 2011; Banta
et al., 2011; Berlin et al., 2013; Liu et al, 2015; Souri et al., 2016).
Most of the above studies used the MDA8 O3 to quantify background
O3. Overall, regional (continental) background O3 ranges from 16
to 107 ppb, while marine background has values between 18 and 40 ppb. Local
O3 contributions are quantified between 25 and 80 ppb. Observations
from 1 h average O3 data and using wind patterns resulted in higher
O3 mixing ratios, particularly during stagnation in the afternoon
(> 140 ppb) (Darby et al., 2005). Meteorological variables, such
as wind patterns, were used separately to characterize the transport regime
and its diurnal transition in the HGB region and interpret their findings
from data analysis; their covariance with O3 and NOx was not
considered.
The temporal trend in regional background O3 also is still uncertain.
Previous efforts to quantify the temporal trends in regional background
O3 from decadal surface measurements of MDA8 O3 in the HGB region
were made by Berlin et al. (2013). This study focused on the high O3
season (May–Oct) from 1998 to 2012 and used two methods to extract the
regional background O3: principal component analysis (PCA) and the
Texas Commission on Environmental Quality (TCEQ) method. The former is a
multivariate statistical analysis through which Berlin et al. (2013)
co-varied MDA8 O3 in time and space. The latter is a method used by the
TCEQ and consists of manually selecting the lowest MDA8 O3 measured at
what are considered “background” sites (usually upwind). Using linear
regression of regional background O3 vs. time, Berlin et al. (2013)
estimated the temporal trends and compared them to different wind quadrants.
Regional background O3 associated with NW winds increased over time,
while that associated with SW winds remained constant. The only declining
trends were associated with the NE and SE winds, but the quantified slopes
of both linear trends were highly uncertain (> 50 % error),
suggesting that more work is needed to improve estimates of regional
background O3 trends. A very recent study (Souri et al., 2016)
reported long-term linear trends in surface MDA8 O3, which were
interpreted with the help of 900 hPa wind clusters. Hence, the annual trend
in regional background associated with continental air (from ENE and ESE)
shows that MDA8 O3 has declined, while that associated with marine air
(from SSE) has increased slightly, although the latter shows a highly
uncertain slope. When flow was from ENE, it was suggested that local
contributions played an equal role in declining MDA8 O3. The study also
did not consider covariance of MDA8 O3 with meteorology and chemistry.
Regional background NOx also contributes to both surface O3 and
NOx in the HGB region. Through photochemistry, NOx can influence
O3 during transport, but it is unclear whether it enhances or
diminishes the O3 peaks observed locally during spring and summer. A
previous study modeled both local and regional NOx summertime
contributions to surface O3 in southeast Texas and found that both
northern (suburban) and southeastern (coastal) sites were influenced by
upwind sources (Zhang et al., 2011). The study concluded that regional
NOx contributes significantly to local O3 (up to 50 %) and
recommended regional controls on NOx emissions in addition to local
controls. However, their findings are limited to 10 days and do not fully
represent the seasonal and annual variations in regional NOx, O3
and meteorology, suggesting that a longer-term approach would refine the
estimates of regional NOx contributions in the HGB region.
In this work, we estimate regional background O3 and NOx by
spatially and temporally co-varying chemistry and meteorology using up to
17 years of hourly measurements and the PCA method for 8 h levels
(MDA8 O3 and 8 h average NOx). In addition, we use two independent
PCAs on O3 and NOx to separately estimate regional backgrounds and
test for their interaction at both 1 h (i.e., hourly median) and 8 h levels.
By comparing all approaches over a period of 6 months, we could highlight
the effect of co-varying O3 with precursor and meteorology and the
effect of varying the spatial and temporal scales. Using approaches based on
continuous variables only, we quantify the temporal trends in regional
background O3 and NOx. We compare the temporal trend in background
O3 with a previous study and report for the first time a decadal-scale
trend in background NOx.
Methods
Data collection and processing
Public data, representing 1 h average surface measurements of O3,
NOx and meteorology (WD, WS and T), were downloaded from the Texas Air
Monitoring and Information System website owned by TCEQ (see Data
availability). The measurements were taken every second, averaged over 5 min and then averaged over 1 h. Note that, due to the measurement
method (combined chemiluminescence detection–molybdenum conversion), the
monitored total NOx might include traces of other oxidation products
(PAN, HNO3, etc.). The locations of the monitoring sites are mapped in
Fig. S1 (in the Supplement). For each site, we generated and
exported raw data reports (validated data only) for the period of
May–October 1998–2014. Using the hourly measurements, we computed three variables to be
used in the estimation of background O3 and NOx: the hourly median
per month, MDA8 O3 and 8 h average NOx corresponding to MDA8
O3.
The hourly median was used for two purposes: (1) replacement of missing
values, ensuring that multiple parameters are available at the 1 h level for
multivariate data analysis, and (2) use in the analysis as a variable itself
because it is a highly representative value, derived from many replicates of
each daytime hour (i.e., years of observations) at various sites. Overall,
up to 5 % of the missing raw data were replaced by the hourly median (Fig. S2). The protocol for filling data gaps was to replace no more than
6 consecutive hours in a day (i.e., 25 % of the day missing). Therefore,
gaps from 1 to 6 h were identified and replaced with the
corresponding hourly median. Ten sites have data coverage for 13 years, and
five sites have the largest data coverage for 17 years. Therefore it was
possible to observe changes in background O3 and NOx over a
timescale of almost 2 decades, but the spatial coverage was limited to
just five sites. Berlin et al. (2013) also identified six nearly continuous
sites (five identical to those identified in this study) using directly the
MDA8 O3 from the same data source (not hourly data as we used here to
calculate the MDA8 O3). However, in our study, a 1-decade analysis
was also possible by doubling the number of sites, thus increasing slightly
the spatial scale for analysis.
We ran a preliminary bi-variate site correlation analysis from five sites
within the HGB area and found that the timescale of variability in NOx
is much smaller than that of variability in O3, affecting the
correlation of hourly median NOx between sites. Therefore, NOx
appears to be more sensitive than O3 to fast changes in meteorology,
for example. The temporal scale of analysis should be relevant to both
O3 and NOx variabilities in order to test whether there is any chemical
interaction between them during transport, which could influence the
estimation of background levels. An hourly median approach, in combination
with those focused on 8 h averages, would allow for observation of the
effect of temporal scale in the monthly trends of background O3 and
NOx.
Data analysis
We used PCA to analyze single and multiple variables at various sites in the
HGB area. The PCA method is a data reduction technique that uses the
framework of linear algebra (eigenvector and eigenvalues) to reduce a larger
data set to a smaller one, based on common modes of variance or strong
correlations among variables (Wilks, 1995). In PCA, a non-square matrix n×K (i.e., time × space or site value) is converted to a square matrix K×K (variance–covariance or correlation matrix). The off-diagonal elements of
the correlation matrix are important as they reflect the correlations of one
or more variables at each location to any other location, while the diagonal
elements are 1, representing the autocorrelation of each site in terms of
the variable considered. This correlation matrix is transposed to compute an
eigenvector matrix (or component matrix), of which elements are the loadings
or the Pearson's correlation coefficients, if the correlation matrix is used
instead of the variance matrix. The loadings range from -1 to +1 (the
highest correlations possible) with a mean of 0 (no correlation). By summing
the squared loadings of each component (column) we obtain the eigenvalue of
that component. By squaring the loadings and summing them from all
components for each variable (row), we get the maximum variance that could
be explained by all the components, which is 1. This is not always the case,
as not all the components are retained. For example, the maximum number of
the components that can result from PCA equals the number of the original
variables. In general, the first few components explain most of the variance
in the original variables, while the remaining components explain very
little. If only the first components are retained, then their squared
loadings must be normalized by their respective sum (which is less than 1).
These normalized values can be used to convert the PC scores (standardized
regression coefficients) to original variables (Wilks, 1995; Langford et
al., 2009). The PC scores (also negative and positive) are the elements of
the new variables (components) and they have a wider range than the
loadings. The resulting PCs are unique and distinct due to the eigenvectors
being perpendicular to each other. However, the fact that PCs are orthogonal
and distinct is not enough to account for their physical meaning. Therefore,
PCA uses rotation techniques (i.e., Varimax) to rotate the eigenvectors;
thus, in addition to the fact that they are distinct from each other, they
also have a physical meaning based on the association of the significant
elements they contain (i.e., loadings). The output of this rotation is the
rotated component matrix, which has a different composition of loadings than
the unrotated one. The percentage of the variance explained by each
component also changes. We rotated the components in this study. Using the
PCA method implemented in the IBM SPSS Statistics 24 software, we used
different approaches to extract regional background of O3 and
NOx from locally measured values that were converted to hourly median,
MDA8 O3 and 8 h average NOx for analysis, as described below. In
addition to PCA, we used linear regression of season-scale background
O3 and NOx vs. time (year) to quantify temporal trends. We also
used linear regression to test for chemical interaction, to quantify how
much the change in regional background O3 could be explained by the
change in regional background NOx, and to estimate the regional
contributions to locally observed O3 and NOx.
PCA of hourly median to estimate regional background O3 and
NOx and other contributions
To estimate the characteristic hourly regional background O3 and
NOx, we used the hourly median described in Sect. 2.1 for 28 monitoring
sites (Table 1 and Fig. S1) when it could be determined from the available
measurements during 1998–2014. Two independent PCAs of median O3 and
NOx were run using daytime hours (local 10:00–18:00), over a period
from May to October (eight median values for each month). In this approach,
new from the perspective of the metric used in the PCA, we did not co-vary
O3, NOx and meteorology because their respective hourly medians may not
always represent coincident measurements of all of them. Instead, we used
meteorology to interpret the PCA results as previous studies did.
The O3 and NOx sites and their loadings associated with
each principal component using the hourly median approach.
Site name
PC1
PC2
PC3
PC4
PC5
O3
NOx
O3
NOx
O3
NOx
O3
NOx
O3
NOx
Channelview
0.714
0.161
0.501
0.905
0.233
0.075
0.367
-0.042
n/a
0.156
Clinton
0.830
-0.224
0.387
0.178
0.326
0.050
0.130
0.923
n/a
-0.005
Conroe
-0.084
-0.088
0.089
0.382
0.878
0.794
-0.188
0.005
n/a
0.235
Conroe Relocated
0.273
0.212
-0.183
0.233
0.900
0.700
0.076
0.530
n/a
-0.296
Danciger
0.969
0.841
-0.166
0.112
0.045
0.103
0.076
-0.425
n/a
-0.007
Galveston 99 St.
0.925
0.951
-0.279
0.020
0.057
0.190
0.044
-0.031
n/a
-0.062
Galveston Airport
0.960
0.974
0.100
0.043
-0.022
0.052
-0.133
-0.020
n/a
-0.013
Houston Aldine
0.373
0.413
0.549
0.788
0.712
0.368
0.193
-0.011
n/a
0.043
Bayland Park
0.856
0.837
0.272
0.387
0.390
0.260
0.046
-0.073
n/a
0.192
Houston Crawford
-0.055
0.835
0.906
0.441
0.223
-0.126
-0.063
-0.140
n/a
-0.003
Deer Park
0.881
0.871
0.369
0.402
0.274
0.181
0.067
-0.051
n/a
0.045
Houston East
0.460
0.918
0.577
0.341
0.552
0.064
0.290
-0.069
n/a
0.103
Hayden Rd. (HRM3)
0.765
0.324
0.481
0.780
0.334
0.477
0.205
-0.014
n/a
-0.013
Sheldon Rd. (HRM4)
0.044
-0.061
0.921
0.835
0.142
0.213
0.014
0.082
n/a
-0.154
Baytown (HRM7)
0.129
-0.451
0.952
0.135
-0.024
-0.187
0.008
-0.057
n/a
0.749
La Porte (HRM8)
0.405
0.444
-0.336
0.034
-0.131
0.159
0.641
0.009
n/a
0.782
Mont Belvieu (HRM10)
-0.141
-0.736
0.914
0.394
0.035
0.044
-0.237
0.311
n/a
0.257
East Baytown (HRM11)
0.035
-0.727
0.891
0.350
-0.174
0.124
-0.102
-0.094
n/a
-0.042
Lynchburg Ferry
0.827
0.382
0.410
0.773
0.156
0.090
0.194
0.097
n/a
-0.040
Lake Jackson
0.978
0.771
-0.157
0.207
-0.010
0.415
0.037
-0.340
n/a
-0.193
Manvel Croix
0.966
0.847
-0.021
0.336
0.223
0.346
0.087
-0.092
n/a
-0.127
Mustang Bayou
0.977
0.917
-0.162
0.209
0.065
0.149
0.011
0.152
n/a
-0.056
NW Harris
0.653
0.567
0.072
0.499
0.721
0.576
-0.056
0.121
n/a
-0.120
Park Place
0.901
0.829
0.228
0.484
0.311
0.223
0.148
-0.044
n/a
0.029
San Jacinto Monument
0.553
0.808
0.544
0.301
-0.029
0.004
-0.557
-0.120
n/a
0.160
Seabrook Fr. Park
0.971
0.931
0.085
0.236
0.176
0.164
0.064
0.103
n/a
-0.008
Texas City 34 St.
0.982
0.652
-0.104
0.223
0.049
0.632
0.017
-0.178
n/a
-0.108
Wallsville Rd.
0.849
0.171
0.406
0.829
0.123
0.077
0.142
0.263
n/a
0.313
n/a = not applicable
PCA of MDA8 O3 and 8 h average NOx to estimate regional
background O3 and NOx (Approach A)
In this approach, we used two independent PCAs on daily MDA8 O3 and the
corresponding 8 h average NOx to extract the regional backgrounds, but
fewer sites were used than in the hourly median approach (5 vs. 28). Here we
only considered sites with quasi-continuous data for the longest period
possible (17 years) to estimate more accurately the regional background.
These sites are all within Harris County: Aldine, Bayland Park, Deer Park,
Houston East and NW Harris (Fig. S1). Like in the previous approach, we only
used meteorology to interpret the principal components.
The MDA8 O3 was used in previous studies to estimate background O3
(Nielsen-Gammon et al., 2005; Langford et al., 2009; Berlin et al., 2013;
Souri et al., 2016), but no study looked at background NOx using
coincident measurements from the same sites. To compare temporal trends
obtained from this study with other studies (Berlin et al., 2013; Souri et
al., 2016), we separately ran PCA for O3 and NOx. Additionally, we
compared the background estimates from this approach with those obtained
from the hourly median approach to isolate the effect of timescale (which
influences the dynamics of the 6-month trends) and with other approaches in
this study (subsequent sections) to isolate the effect of chemical and
meteorological interaction within the HGB area.
PCA of MDA8 O3 and 8 h average NOx to estimate regional
background O3 and NOx (Approach B)
As a novel approach, we ran five multivariate PCAs for each site (the same
sites and period used in the previous approach) to constrain the estimation
of background O3 in the HGB area with chemistry and meteorology and to
improve the quantification of its temporal trend. This approach is different
from those described in previous sections and studies (single variable,
multiple sites) because it takes into account more variables (multiple
variables, single site). The variables considered at each site are MDA8
O3 and the corresponding 8 h average NOx, WD, WS and T.
PCA of MDA8 O3 and 8 h average NOx to estimate regional
background O3 and NOx (Approach C)
This approach is similar to Approach B except that we used more sites (10)
and a shorter period of time (13 years), based on simultaneous data
availability and continuity at these sites. The five additional sites are
Clinton, Channelview, Manvel Croix, Seabrook Friendship Park and Conroe
Relocated (Fig. S1). Use of larger spatial data coverage could improve the
estimation of regional background, even if the study period is shorter,
because it would capture variations in chemistry and meteorology within the
HGB area.
Results and discussion
Hourly median approach
Main regional contributions to hourly median O3 and
NOx
The PCA resulted in four components for O3 and five components for
NOx. PC5 was not significant for
O3. Only components with eigenvalues greater than 1 were retained. The
first components explained most of the percentage of the variance in
original O3 and NOx (∼ 51 and ∼ 45 %, respectively) and were highly correlated at more than half of the
initial sites (16 out of 28). Among these “PC1 sites,” 12 are common sites
for both O3 and NOx.
Distinct clustering of principal components. The cluster in yellow
is PC1-O3 and PC1-NOx. The cluster in orange is PC2-O3 and
PC2-NOx, and so on. Smaller circles represent NOx clusters.
An interesting cluster-like pattern emerged when we mapped the sites that
highly correlated with any of the PCs (e.g., loadings with absolute values
of 0.5 or higher). The sites associated with these loadings (Table 1) are
mapped in Fig. 1, in which different point sizes are used to show the
overlapping of both O3 and NOx sites, while color is used to show
the correlation of the same component at various sites (i.e., clusters). The
widespread cluster (PC1) suggests a larger-scale control on both O3 and
NOx, while the smaller cluster (PC2) suggests a more localized control.
The proximity to the GOM emphasizes that PC1 is largely influenced by marine
background during summer. The proximity to the Houston Ship Channel
indicates that PC2 likely represents local effects (e.g., chemistry,
emissions). Given the proximity to the rural area in the north of the
HGB region, PC3 might represent a mix between regional (continental) and
local (urban) contributions.
The spatial patterns of the components, their extents and locations within
the HGB region all indicate that PC1 represents regional background for both
O3 and NOx. We arrive to this finding by spatially interpolating
the three main clusters from Fig. 1 to reveal continuous patterns of
correlations (Fig. 2). The O3 pattern for the first component (the
square-like pattern in the south of the HGB region) emphasizes the marine
influence because of the higher loadings along the coast, while the lowest
loadings are within the region overlapping with the second component, where
local effects seem to be more important (the smaller rectangle in the
proximity of the Houston Ship Channel). The PC1-derived NOx pattern
shows high correlations in the same area pointed out by PC1-O3, but the
highest correlations appear in the west of the Bay area; lower loadings also
occur in the area controlled by the local effects.
Spatial interpolation of normalized squared loadings from the
highly correlated sites with the first three components in terms of O3
(left) and NOx (right). Range is from 0 to 1.
Meteorology also supports the hypothesis that PC1 describes regional
contributions and reveals that these are mostly marine in summer and
continental in spring and fall. To test whether PC1 is regional background, we
plotted the PC1-O3 and PC1-NOx scores against WD and WS in Fig. S3a–e. Overall, two flow regimes explained the changes in PC1-O3 (Fig. S3a):
summer (marine) flow decreases PC1-O3 (negative scores),
while spring/fall (continental) amplifies it (positive scores). There was no
sign of stagnation in summer (an increase in PC scores at lower WS) from
which we could infer local chemistry (Fig. S3b). The PC1-NOx tells
roughly a similar story in terms of flow regimes (Fig. S3d) and the absence
of stagnation during summer (Fig. S3e). Temperature indicates no consistent
formation of O3 with increasing T at the scale of the entire season
(although the monthly relationship is positive) and very limited chemistry
or some physical effect on NOx, such as dilution at the surface due to
a higher boundary layer (Fig. S3c and f).
The season averaged hourly background O3 and hourly
background NOx. Error bars represent the 95 % confidence interval for
the mean.
The monthly background O3 and NOx trends are consistent between
hours over the entire season. We determined this by converting the PC1
scores to O3 and NOx hourly mixing ratios and plotting them for
each month to assess the 6-month trends (Fig. S4). Background O3 trends
compare well with those from previous estimates of 8 h average background
O3 (Nielsen-Gammon et al., 2005), showing two peaks in spring and
summer/fall, respectively, and a drop in mid-summer, when local chemistry
dominates regional background O3 in the HGB region.
The season-characteristic hourly background O3 and NOx (the most
typical daytime value on 1 h basis in the HGB region averaged over 6
months) points out consistency between hours and no significant chemistry
between O3 and NOx (Fig. 3), particularly during midday, when
important photochemistry occurs. When the 6-month values are also averaged
over 8 h, they compare reasonably well with similar estimates from
previous studies (Nielsen-Gammon et al., 2005; Choi, 2014), ranging from 37
to 38 ppb for background O3 and varying between 4 and 7 ppb for background
NOx.
We further assessed the relationship between regional background O3 and
NOx at both 1 h and 8 h levels (Fig. S5). The positive relationships
suggest that both O3 and NOx are related (possibly through
regional transport) and there is some interaction between them (significant
slopes of 1.89 ± 0.48 and 2.07 ± 1.99, respectively). However,
background NOx only explains ∼ 60 % of the changes in
background O3, at both 1 and 8 h levels, implying that the
unexplained ∼ 40 % might be related to other
processes/sources, such as regional VOC chemistry or from unconsidered VOC
emissions upwind, which can increase both O3 and NOx mixing
ratios. It is also possible that a fraction of background NOx
(including lightning NOx) was converted to PAN and HNO3, which was
accounted for in the total NOx by the measurement method, reducing the
potential of background NOx to explain background O3.
Stratospheric O3 also may explain some of the background O3 in the
HGB. However, stratospheric O3 contributions are either overestimated
at midlatitudes by the global cross-tropopause transport models (Liu et
al., 2016) or the relationship between the cosmogenic beryllium-7 associated
with particulate matter and surface O3 observed in the HGB region is
not conclusive enough (Gaffney et al., 2005). Modeling based estimates of
lightning NOx in the GOM suggest that this source is negligible near
the surface, ranging from near 0 to 50 ppt during 2 summer months
(Pickering et al., 2016).
Other contributions to hourly median O3 and NOx
Here, we report results from the analysis and interpretation of the other
significant components (PC2–PC5) extracted by PCA using the hourly median
approach. The cluster of points localized around the Houston Ship Channel,
where most of the petrochemical industry facilities are located, is likely
related to local chemistry and/or emissions. The cluster of points
representing highly correlated PC3 with both O3 and NOx at
locations in the north of the HGB area (Fig. 1) likely represents a mixed
local/regional (maybe continental) influence. Additionally, it was important
to consider how the other components (PC4 for O3 and PC4 and PC5 for
NOx) may factor into the average of local contributions within the HGB
region, since the sites defining them are in close proximity to the PC2
sites, from which we primarily inferred local contributions.
The second component describes local contributions, given the locations of
the sites and its relationship with meteorological variables. To test for
local influence, we analyzed the PC2-O3 and PC2-NOx scores against
meteorology (Fig. S6). Results revealed that PC2 is insensitive to WD for
both O3 and NOx at the season scale using 1 and 8 h levels.
Within the high O3 season, flow varies from SSW–S–SSE (in summer) to
SE–ESE (in spring and fall). Highest PC2-O3 scores are recorded in July
and August, coinciding with the predominant flow from SSE–SE. A few high
scores are also visible in September, but they appear to be related to
easterly transport. Overall, the spring and fall PC2-O3 scores all
cluster under zero at relatively similar flow direction as observed in
summer. This suggests some local effects, a reverse pattern than that
inferred from PC1-O3 in Fig. S3a. Local effects can also be inferred
from PC2-NOx, with highs and lows in each month (Fig. S6d).
Diurnal variability in PC2-NOx scores is more pronounced for NOx
compared to O3, suggesting that NOx is lost photochemically in the
afternoon hours (i.e., lower scores). With respect to WS, PC2-O3 and
PC2-NOx show different relationships (Fig. S6b and e). Low WS
facilitates the formation of O3 and depletion of NOx. As WS
increases (> 4 m s-1) NOx increases (higher
PC2-NOx scores) but there is no sign of O3 formation (low
PC2-O3 scores).
Relationships with temperature suggest active local chemistry by both month
and season (Fig. S6c and f). A positive PC2-O3 versus T
relationship indicates the build-up of O3 as temperature increases to
favor the chemistry of VOCs. A negative PC2-NOx versus T relationship
may suggest both chemical and physical controls on NOx. However, the
high scores in July and August might be related to NOx and VOCs
chemistry rather than vertical mixing due to a higher boundary layer.
Therefore, we interpreted that PC2 represents mainly local chemistry. To
test whether PCA-inferred local O3 is explained by PCA-inferred local
NOx, the converted PC2 variables are compared in Fig. S7. The negative
relationship is consistent with NOx chemistry and photochemical
production of O3; it also indicates the probability of a VOC-limited
atmosphere. However, NOx only explains about 30 % of the changes in
O3. Note that the 8 h average did not reveal a significant dependence
of O3 on NOx at the season scale (the empty circles), pointing
out the importance of the timescale (1 h) needed to observe relevant
chemistry. The unexplained portion for the 1 h level (70 %) is quite
significant. We believe it is related to rapid VOC chemistry in this area of
the HGB region. Daum et al. (2004) measured various plumes for almost 2
weeks in late summer of 2000 and showed that six of them were different from
typical urban plumes: they were rich in formaldehyde and peroxides,
attributable to hydrocarbon oxidation and photochemistry, respectively. They
also found that O3 formation in these plumes was very efficient (6.4–11 ppbv O3 per ppbv of NOx). These plumes were tracked back to sources of
NOx and hydrocarbons in the proximity of the Houston Ship Channel.
Using zero-dimensional model predictions, they found that O3 formed
very fast (140 ppbv h-1). Compared to urban plumes, the authors found that the
formation of O3 in plumes from the Ship Cannel was more
NOx limited, but uncertainties remain whether the production of O3
in this area is NOx or VOC limited.
The third component may be dominated by regional influences, based on the
locations of the associated sites within the HGB region and the comparison
with meteorology. Traditionally, the upwind sites (Conroe, Conroe Relocated,
NW Harris) are considered to be “background” sites. One PC3 site (Houston
Aldine), though, overlaps with a PC2 site resulting in a mixed contribution
within PC3 at this site (Fig. 1). To consider mixed regional/local
influences, the PC3-O3 and PC3-NOx scores were examined with
respect to meteorological variables. In the morning, flow is from the GOM,
which brings already processed air, characterized by low PC-O3 scores
(marine background); PC3-NOx scores vary from positive to negative
within this onshore flow. In the afternoon, flow is from the SSE–SE and
intercepts some local/urban pollution on its way to the PC3 sites (i.e.,
Conroe); here, PC3-O3 increases (continental background), while
PC3-NOx varies largely. Temperature increases PC3-O3 while
decreasing PC3-NOx, suggesting active chemistry by both month and
season. Winds are stable and stagnant in the afternoon, suggesting enhanced
local pollution during that time. At the season scale, the O3–WS
relationship is positive, while the NOx–WS relationship is positive
during spring and summer months only, turning negative in fall. The positive
relationship suggests advection of higher mixing ratios of both O3 and
NOx to the HGB area, while a negative relationship suggests a chemical
or a physical loss of NOx. The former indicates that regional
contributions may dominate the local contributions within this component at
the season scale (for O3) and during spring and summer (for NOx).
Covariance with meteorology would probably better resolve PC3, but this
approach was not possible using the hourly median.
The fourth component likely describes local transport effects. Results from
analysis of PC4-O3 and PC4-NOx while considering meteorology
indicate that the sites associated with this component (Clinton, La Porte)
are influenced by the sea breeze rotation and recirculation of local
pollution (flow is from S–SSE in summer/spring and from SE–ESE in fall),
with higher scores occurring in spring/summer.
The fifth component, which explained a small portion of the variance in
original NOx, appears to be consistent with local VOC chemistry because
its relationship with T is positive over the entire season. Primarily,
NOx increases in summer due to VOC chemistry and/or local emissions. On
a monthly basis, PC5-NOx is negative with increasing T (similar to
PC2-NOx), suggesting physicochemical controls on NOx. Flow is
from SSE–SE–ESE and winds are weak and stable (∼ 3 m s-1)
in summer (increases NOx) and less stable in spring/fall (decreases
NOx). On a monthly basis, PC2-NOx and PC5-NOx are not very
different, as they both may be controlled by physicochemical interactions
involving boundary layer height, solar radiation, VOC chemistry and possibly
other chemistry. However, if we extend the timescale to 6 months, the two
components are very different in terms of the NOx–T relationship: PC2
is negative, while PC5 is positive with increasing T. A possible explanation
is that the two components, when compared to T, are different because of the
averaging over 8 h. These averages are consistent with the 1-hour-based
PC2-T relationships, but are inconsistent with the 1-hour-based PC5-T
relationships. Consequently, the NOx–T relationship turns positive for
PC5 at the season scale. In contrast, in this PCA approach, we did not
use 8 h averages and T but rather the method differentiated between PC2 and PC5. A
possible explanation is that one of the PC5 sites (Baytown) overlaps with
the PC2-defined cluster in Fig. 1, being more exposed to local chemistry and
emissions from the industrial area, an influence standing out at the season
scale only. La Porte is situated south of the Houston Ship Channel and near
the GOM, likely being dominated by marine influences (lower NOx) at the
monthly level. Therefore, PC5 also describes mixed local–regional effects on
surface NOx.
We primarily based our regional background O3 and NOx estimates on
PC1, although some regional contributions could be inferred from other
components (most notably, PC3). Since the components from which we inferred
mixed regional–local contributions explain less variance than PC1
(particularly, PC5), we assumed these contributions are negligible, so we
did not include them in the estimation of regional background O3 and
NOx. Similarly, we estimated local O3 and NOx from the
conversion of PC2 only. However, for estimating the contribution of regional
background to measured hourly median O3 and NOx, we additionally
considered average regional contributions from PC1 and PC3 and compared them
with those estimated from PC1 only.
Regional and local contributions to MDA8 O3 and 8 h average
NOx (Approach A)
The two independent PCAs using fewer sites with nearly continuous data for
which the MDA8 O3 and 8 h average NOx could be calculated resulted
in three components having eigenvalues greater than unity. However, we
retained all five components because they were not significantly different
in explaining the variance in the original variables, particularly for
NOx; their loadings are shown in Table 2.
The loadings or correlations of the components with variables at
each site from Approach A.
Site name
PC1
PC2
PC3
PC4
PC5
O3
NOx
O3
NOx
O3
NOx
O3
NOx
O3
NOx
Houston Aldine
0.609
0.172
0.516
0.209
0.333
0.940
0.259
0.127
0.430
0.163
Bayland Park
0.370
0.209
0.411
0.208
0.445
0.142
0.694
0.884
0.123
0.332
Deer Park
0.305
0.949
0.268
0.067
0.865
0.167
0.272
0.177
0.109
0.185
Houston East
0.775
0.227
0.380
0.173
0.382
0.194
0.320
0.347
0.079
0.872
NW Harris
0.371
0.067
0.814
0.950
0.301
0.203
0.310
0.175
0.114
0.144
Meteorology helped to interpret the components but was insufficient to
clearly distinguish between regional and local contributions. For example,
by looking at how the scores of each component varied with average WD we
found that all sites were influenced by SSE winds (146–155∘), with
the western sites (NW Harris and Bayland Park) experiencing a slightly more
southern WD by 3∘. The flow from GOM encounters local/urban air on
its way to the western sites, while eastern sites experience more direct
marine air from the GOM area. These two patterns were also visible in the
distributions of PC scores vs. average T and WS.
Monthly trends helped to distinguish between regional and local
contributions from the principal components. We used the monthly trends for
each component to observe whether these trends are consistent with expected
regional and local trends from previous studies. Three components (PC2, PC3
and PC4) exhibit monthly trends (Fig. S8a) that are consistent with the
expected bi-modal regional background O3 (Nielsen-Gammon et al.,
2005). The remaining components (PC1 and PC5) show monthly trends (Fig. S8b)
similar to those expected from unimodal local contribution (Nielsen-Gammon
et al., 2005). We found similar monthly trends for 8 h average NOx
(Fig. S8b). Here, regional contributions are suggested by PC1, PC2 and PC5,
while local contributions are denoted by PC3 and PC4. Therefore, we based
our regional and local estimates of O3 and NOx on the components
identified as regional and local from their monthly trends.
The relationship between regional background O3 and NOx (Fig. S9) underscores that NOx explained approximately 20 % of the changes
in background O3, while no significant relationship between
PCA-inferred local O3 and NOx was observed (Fig. S10). These poor
relationships may be the result of using fewer sites, MDA8 O3 and 8 h
average NOx compared to the hourly median approach.
Regional and local contributions to MDA8 O3 and 8 h average
NOx (Approach B)
In this new PCA approach, we co-varied O3 with NOx and meteorology
at the sites used in Approach A. We conditioned the PCA to retain only
components with eigenvalues greater than 1. Two components were retained at
each site. The average eigenvalue was 1.5. Each component explained
approximately 30 % of the variance in the original variables, implying
that they are equally important in explaining the original variables at the
sites used in this approach.
We partially inferred the meaning of the components by considering how
variables and their respective loadings (absolute values nearly or greater
than 0.5) are associated within each component (Table 3). The first
component (PC1) associated O3 with WS and, sometimes, with NOx
at three sites (Bayland Park, Deer Park and NW Harris), while the same
component combined NOx with T at other sites (Houston Aldine and
Houston East). In contrast, the second component (PC2) associated
O3 with WS at two sites (Houston Aldine and Houston East) and combined
NOx with T at the remaining sites (Bayland Park, Deer Park and NW
Harris). Overall, two patterns emerged from each component:
“O3–NOx–WS” sites and “NOx–T” sites. The association of
O3 with WS could indicate a physical control (i.e., advection or
stagnation), while the NOx–T relationship may suggest a chemical
control (T-mediated chemical reactions). In the first component, O3 and
WS also associate with NOx (with lower loadings), suggesting either
some chemical interaction sustained by a lower WS or a similar transport
source for both O3 and NOx. Temperature and NOx at Houston
Aldine confirmed that NOx–T in the first component describes
chemistry, possibly local formation of O3 (Fig. S11). Ozone, NOx
and WS at Bayland Park together confirmed that O3–NOx–WS
represents regional transport of O3 and NOx and/or local VOC
chemistry, because both O3 and NOx increase with PC1 while WS
decreases (Fig. S12). Local chemistry might be possible at lower WS, which
causes an increase in PC1 scores.
The loadings or correlations of the components with variables at
each site from Approach B.
Site name
PC1
PC2
O3
NOx
T
WD
WS
O3
NOx
T
WD
WS
Houston Aldine
0.065
-0.794
0.802
0.310
0.223
0.813
0.183
0.319
-0.107
-0.771
Bayland Park
0.805
0.463
0.267
-0.160
-0.787
-0.075
-0.698
0.810
0.541
0.057
Deer Park
0.820
0.648
0.123
-0.159
-0.779
0.053
-0.549
0.929
0.330
0.167
Houston East
0.118
-0.823
0.798
0.439
0.295
0.804
0.200
0.344
-0.284
-0.763
NW Harris
0.825
0.498
0.147
-0.508
-0.605
0.097
-0.573
0.892
0.278
-0.013
By mapping how the input variables are partitioned between the two
components we more clearly discriminated between regional and local
contributions at each site (Fig. S13). For instance, O3 is well
represented by PC1 at three sites (NW Harris, Bayland Park and Deer Park).
At these sites, some NOx is also distributed in PC1, suggesting that
O3 and NOx are related either through transport or chemistry.
However, WS shows a pattern strongly similar to that of O3 and less
strongly to that of NOx in PC1, reinforcing that PC1 at these sites is
dominated by regional transport. At Houston Aldine and Houston East, O3
shows an opposite partition compared to NOx, indicating that PC1 at
these sites is local chemistry, which also is supported by T and WS.
Regional background O3 and NOx were determined by averaging the
converted PC scores from O3–NOx–WS sites, while local
contributions were quantified by averaging the converted PC scores from
NOx–T sites. The conversion method (Langford et al., 2009) differs
slightly from Approach A because in Approach B multiple variables defined
one component at a particular site as opposed to a single variable at many
sites. Therefore, the normalized relative contribution (in %) of the
variable of interest in each component was used instead of the total
variance (in %) explained by the component.
Regional and local contributions to MDA8 O3 and 8 h average
NOx (Approach C)
The simultaneous effect of increasing the spatial scale and reducing the
temporal scale of the analysis (constrained by the availability of
continuous data) was studied using Approach C. Therefore, results in this
section were driven by the use of five more sites and a shorter study period
compared to Approach B. The same variables were used in PCA as in Approach
B. For each site, there were two components retained (average eigenvalues of
1.3–1.6) and each explained, on average, 31 and 27 % of the variance
in MDA8 O3 and 8 h average NOx, respectively. Similar to
Approach B, we also identified two modes of variance among the original
data: O3–NOx–WS (denoting a physical control) and
NOx–T (denoting a chemical control) based on loadings in Table 4
(those with absolute values nearly or greater than 0.5). Therefore, we
obtained the regional background O3 and NOx by averaging the
corresponding PC scores and using the adjusted equation from Langford et al. (2009) as described previously.
The loadings or correlations of the components with variables at
each site from Approach C.
Site name
PC1
PC2
O3
NOx
T
WD
WS
O3
NOx
T
WD
WS
Houston Aldine
0.780
0.319
0.145
-0.243
-0.804
0.236
-0.773
0.835
0.086
0.127
Bayland Park
0.807
0.481
0.288
-0.203
-0.772
-0.031
-0.684
0.823
0.461
0.124
Deer Park
0.821
0.554
0.161
-0.392
-0.701
-0.030
-0.681
0.886
-0.168
0.358
Houston East
0.272
-0.794
0.859
0.223
0.155
0.736
0.344
0.149
-0.399
-0.814
NW Harris
0.784
0.451
0.130
-0.535
-0.668
0.082
-0.697
0.900
0.159
0.060
Channelview
0.625
0.484
0.047
0.271
-0.843
0.106
-0.627
0.709
0.567
-0.030
Conroe Relocated
0.741
0.560
-0.007
-0.015
-0.844
-0.207
-0.664
0.723
0.666
-0.139
Manvel Croix
-0.825
0.625
-0.042
0.627
0.717
0.103
0.065
0.941
0.510
0.074
Clinton
-0.220
0.117
0.254
0.694
0.785
0.792
0.035
0.736
0.007
-0.016
Seabrook Fr. Park
0.480
0.871
-0.602
0.278
-0.451
-0.578
-0.160
-0.040
0.833
0.634
Similarities and differences between monthly trends of regional
background O3 and NOx from all approaches
The 6-month trends in background O3 from different
approaches. Points represent the monthly average background values derived
from the hourly median O3 and MDA8 O3. Error bars represent the
95 % confidence interval for the mean.
We compared the monthly trends from all approaches used to estimate regional
O3 and NOx contributions. We found that the use of MDA8 O3
(approaches A–C) estimated larger background contributions for the entire
season compared to the hourly median approach (either from PC1 only or from
PC1 adjusted by PC3), as shown in Fig. 4. This likely is due not only to the
difference in the number of sites used in the PCA (5–10 vs. 28,
respectively) but also to the fact that the highest 8 h average was selected
for each day in approaches A–C, compared to the hourly median (the 50th
percentile of the hourly measurements), which was averaged over 8 h for
comparison. In Fig. 4, the hourly median approach also reveals a stronger
onshore effect than the MDA8 O3 approach. This could be because of the
smaller timescale of observations, which allows the median to capture
better the influence of the onshore flow in terms of O3. Approach A
follows the trend described by the hourly median (although smoothed) because
it was derived using a similar PCA (single variable/multiple sites).
Approaches B and C deviate from this trend because they were derived using a
different PCA (single site/multiple variables). Regardless of the approach,
background O3 drops in July, which is consistent with the bimodal
variation of the annual 8 h average background O3 (Nielsen-Gammon et
al., 2005) and with the less intense and a more easterly Bermuda High during
July (Wang et al., 2016). The three approaches (A–C) yield similar values
for July, when local chemistry is expected to be more important
(Nielsen-Gammon et al., 2005). The sudden increase from July to August is
consistent in all approaches (significant regional summertime chemistry),
but background O3 starts decreasing earlier for approaches B and C
compared to the hourly median and Approach A, likely the result of changes
in meteorology after August (less influence from sea breeze effects).
Because meteorology was not used to estimate regional background O3 in
the hourly median approach or in Approach A, the enhancement of background
O3 continues until September and starts declining only after, as a
result of changing regional transport and chemistry. Interestingly,
approaches B and C agree with the hourly median approach in May and October,
suggesting that the timescale of observations (1 h) is small enough to
capture rapid changes in NOx concentration and fluctuations in WS,
which are reflected in the 8 h average regional background O3.
The 6-month trends in background NOx from different
approaches. Points represent the monthly average background values derived
from the hourly median NOx and the 8 h average NOx. Error bars
represent the 95 % confidence interval for the mean.
A similar analysis was done for regional background NOx (Fig. 5). Here,
estimation of larger background NOx resulted from approaches A to C until
mid-August, when compared to the hourly median approach based on PC1 only.
All approaches intersect this hourly median approach sometimes between
August and September. However, when the regional background from the hourly
median approach is adjusted by PC3 (average of PC1 and PC3), approaches A–C
all gave higher estimates than the hourly median over the entire season.
Approach A appears consistent with the hourly median “adjusted by PC3”,
for the same reasons described previously for background O3. The effect
of spatial scale is more visible between approaches B and C from August to
September, when local influences likely dominate within the HGB region.
Quantification of temporal trends in regional background O3 and
NOx
The goal in this portion of the work was to quantify the temporal trends in
the final background O3 and NOx and to investigate if the
background O3 and NOx have declined over the past decades. In
addition, we wanted to assess the effects of co-varying chemistry and
meteorology on these trends. We used linear regression of the season-averaged background O3 and NOx in each year vs. time to quantify
temporal trends.
Weak and negative linear trends resulted from Approach A
Temporal trends in background O3 (approaches A–C) and average
wind direction. Error bars represent the 95 % confidence interval for the
mean.
The temporal trend quantified from Approach A (Fig. 6) suggests that
background O3 has declined; corresponding average WD also is shown for
the five sites. The linear model is statistically significant, yielding a
slope of -0.13 ± 0.10 ppb yr-1, comparable in magnitude but
smaller than that reported in a previous study and irrespective to WD
(Berlin et al., 2013) using a similar approach (-0.33 ± 0.39 ppb yr-1). Compared to the SE wind-constrained slopes from Berlin et al.
(2013) (-0.92 ± 0.74 ppb yr-1 or -0.79 ± 0.65 ppb yr-1),
our slope is much smaller but closer to that from Souri et al. (2016) (0.09 ± 0.40 ppb yr-1). The mean background O3 over the
17
years is 46.74 ± 0.58 ppb and compares well with the 14 and 15 year
means from Berlin et al. (2013) and Souri et al. (2016) (42.5 ± 6.3 ppb and 57 ± 19 ppb, respectively), representing SE influences only.
The decadal timescale explained about 27 % of the changes in background
O3 in this study, similar to Berlin et al. (23 %).
The decline in background NOx is better explained by this approach
(R2=0.53) compared to O3 due to less scatter in the data after
2003, while the slope is similar compared to that for O3 (Fig. 7). On
average, the 17-year background NOx is 6.86 ± 0.19 ppb. Note that
due to potential biases in background NOx (p. 15–16 in the Supplement), this
value represents the upper bound in background NOx. After taking into
account the overall bias, we also estimated a lower bound in background
NOx of 4.49 ± 0.12 ppb (see Table 5 for all approaches). The
linear trends for all approaches were shifted to lower ranges by ca. 2 ppb,
on average (Fig. S22).
Temporal trends in upper-bound background NOx (approaches
A–C) and average wind direction at various sites. Error bars represent the
95 % confidence interval for the mean.
Comparison between all approaches in this study and
literature.
Method
Average regional background
Temporal trends in regional background
O3
NOx
O3
NOx
ppb
(or NO2) ppb
slope (ppb yr-1)
R2
slope (ppb yr-1)
R2
Approach A (17 years)
46.74 ± 0.58a
6.86 ± 0.19a 4.49 ± 0.12h
-0.13 ± 0.10
0.27
-0.06 ± 0.03 -0.04 ± 0.02h
0.53 0.53h
Approach B (17 years)
46.72 ± 2.08a
6.80 ± 0.13a 4.45 ± 0.08h
-0.68 ± 0.27
0.63
-0.04 ± 0.02 -0.03 ± 0.01h
0.58 0.58h
Approach C (13 years)
44.71 ± 1.28a
6.03 ± 0.05a 3.95 ± 0.03h
-0.49 ± 0.24
0.62
-0.013 ± 0.012 -0.009 ± 0.008h
0.30 0.30h
Hourly median (up to 17 years)
37.60 ± 1.55b
5.75 ± 0.62b 4.05 ± 0.44h
Adjusted hourly median (up to 17 years)
37.67 ± 0.80c
5.74 ± 0.32c 4.03 ± 0.09h
Berlin et al. (2013) (14 years)
42.5 ± 6.3d
-0.33 ± 0.39
0.23
-0.21 ± 0.39
0.12
-0.92 ± 0.74d
-0.79 ± 0.65d
Souri et al. (2016) (15 years)
107 ± 27e
(10 ± 3)e
-1.0 ± 0.5e
77 ± 27f
(8 ± 3)f
-0.9 ± 0.86f
57 ± 19g
(6 ± 3)g
0.09 ± 0.40g
a The average values were obtained by averaging the yearly values
over the respective study period; the yearly values represent the season
means (May–October) and account for daytime hours only.
b The hourly background values (daytime hours during May–October) were
averaged over 8 h for each month to get the season mean that is
comparable with the other approaches. This background is based on a single
component (PC1).
c The hourly background was adjusted to include average
regional contributions from two components (PC1 and PC3).
d Constrained by wind direction from southeast.
e Constrained by wind direction from east-northeast.
f Constrained by wind direction from east-southeast.
g Constrained by wind direction from south-southeast.
h Lower bound of background NOx (corrected for time-averaging
and/or measurement bias, see p. 15–16 in the Supplement).
() Regional background NO2 (average of both daytime and nighttime).
The negative trend significantly improved for O3 using Approach
B
When background O3 is adjusted by NOx and meteorology, its decline
over time is stronger and more significant than in Approach A (Fig. 6),
though still of the same order of magnitude. The resulting slope is -0.68 ± 0.27 ppb yr-1, while the 17-year mean of background O3 is
46.72 ± 2.08 ppb, in agreement with the previous approach. Relative to
a previous study (Berlin et al., 2013), the slope is less steep (-0.69 vs. -0.92 ppb yr-1 or -0.79 ppb yr-1), but its error is halved (42 %
vs. 80 %, respectively). Our slope, though smaller, compares well in terms
of absolute error with the slope from Souri et al. (2016), describing
continental regional background O3 (-1.0 ± 0.55 ppb yr-1);
however, as Souri et al. (2016) suggested, local sources may have
contributed half to the observed O3 within the ENE wind cluster, which
could explain the steeper slope observed in their study. They also reported
a weaker slope for regional background O3 from the ESE (-0.9 ± 0.86 ppb yr-1). As observed in Fig. 6, a slight shift in WD over the
past 7 years (more southerly flow) might have also played a role in the
decline of background O3, which is consistent with the findings in
Liu et al. (2015). Also, State of Texas controls on precursor emissions
implemented in 2007 (Berlin et al., 2013) may also have contributed to
reduced background O3 after that.
The slope of background NOx versus time is slightly smaller compared to
Approach A (-0.04 ppb yr-1 vs. -0.06 ppb yr-1), but the linear model
performed better (R2=0.58 versus R2=0.53), highlighting the
effect of spatial and temporal covariance of chemistry and meteorology (Fig. 7). The 17-year mean of background NOx (6.80 ± 0.13 ppb),
representing the upper bound, is in good agreement with Approach A. The
average value corresponding to the lower bound of background NOx is
4.45 ± 0.08 ppb.
The negative trends did not improve using Approach C (spatial
extension of Approach B)
By extending the spatial scale (from 5 to 10 sites) and lowering the period
of analysis (from 17 to 13 years), the effect of co-varying O3 with
NOx and meteorology within the HGB area did not make a significant
difference in the temporal trend of background O3 (Fig. 6), but it
weakened the temporal trend in background NOx (Fig. 7). It is possible
that NOx from additional sites was more sensitive to local influences
(i.e., meteorology) than O3 or that the years left out from analysis
had higher 8 h average NOx mixing ratio. The 13-year mean of background
O3 is 44.71 ± 1.28 ppb, while of mean upper-bound background
NOx is 6.03 ± 0.05 ppb. The lower-bound estimate of mean
background NOx represents 3.95 ± 0.03 ppb.
Regional background contributions to locally measured O3 and
NOx from all approaches
We quantified the regional background contributions to locally measured
O3 and NOx via linear regression for all the approaches in this
study (Figs. S14 to S21). Based on slope values, these contributions
ranged from 1.16 to 5.65 (mole measured per mole of background) for measured
O3 (hourly median and MDA8) and varied from 0.33 to 4.06 for measured
NOx (hourly median and 8 h average). Compared to the analogous slope
from Berlin et al. (2013) (1.22 ± 0.04), our slope value for O3
using approach A is about 5 times steeper (5.65 ± 0.15), while
those from approaches B and C are slightly lower (0.91 ± 0.02) or
slightly higher (1.47 ± 0.06), respectively. The intercept
coefficients were significant in all approaches. Background O3
explained between 57 and 98 % of the variation in spatially averaged
hourly median and MDA8, while background NOx explained about 16–62 %
of the changes in spatially averaged hourly median and 8 h average. In
general, the linear model performed less well for NOx (all approaches)
compared to O3. This could be explained by its smaller temporal scale
of variability compared to O3 but also by the fact that the
corresponding 8 h average NOx to MDA8 O3 was used in the PCA. It
is possible that this approach makes it more difficult to extract background
NOx if MDA8 O3 is mainly the result of local chemistry (see p. 15–16 in the Supplement for potential biases). The larger estimates of background
NOx compared to measured median values from May through October could
be the result of a stronger intra-seasonal variability for NOx (Fig. S15). For example, the measured median relates negatively with background
NOx from May to July (the cluster around 5 ppb); it only turns positive
after that, from July to October. As a consequence, hourly background
NOx is overestimated in spring compared to summer and fall and relative
to measured values. A separate analysis of hourly median NOx within the
PCA for spring vs. summer/fall potentially could improve the estimates of
the upper bound of background NOx using the hourly median approach.
Also, it should be noted that background NOx was not adjusted by
meteorology, as their covariance was not possible using the hourly median.
Summary
Comparison between the slopes of temporal trends in regional
background O3 in the HGB region.
Approach B is our best estimate of the temporal trend in background O3.
Results from all approaches are summarized in Table 5, along with values
from Berlin et al. (2013) and Souri et al. (2016). Overall, the slope we
report in our study (-0.68 ± 0.27 ppb yr-1) is larger but more
certain compared to the slopes reported by Berlin et al. (2013), which were
quantified regardless of the WD (-0.33 ± 0.39 ppb yr-1 and -0.21 ± 0.39 ppb yr-1). Compared to the value reported by Berlin et al.
(2013), which represents the trend associated with SE winds only (-0.92 ± 0.74 ppb yr-1 or -0.75 ± 0.55 ppb yr-1), our slope
derived from Approach B is smaller but twice as certain (-0.68 ± 0.27 ppb yr-1) and compares better with that reported by Souri et al. (2016)
in terms of absolute error (-1.1 ± 0.55 ppb yr-1). Overall, the
slopes from different approaches in this study and other studies are not
significantly different (Fig. 8). The average background O3 in this
study is slightly larger (by 2–4 ppb) compared to that reported by Berlin et
al. (2013), in any of the approaches except for the hourly median approach,
which is smaller by up to 5 ppb. However, compared to Souri et al. (2016)
the average estimates from our study and Berlin et al. (2013) are all much
smaller, with differences ranging from 10 to 69 ppb (Table 5).
Both upper and lower bounds of background NOx, also declined in all approaches, with significant slopes (see Table 5). No other long-term background
NOx studies exist, making comparison impossible. Additionally, there is
no long-term and season-scale evidence of the effect of NOx conversion
to PAN and HNO3 that could affect its temporal decline. Considering
that the majority of the sites used to derive background NOx are urban
sites or sites that are affected by fresh emissions, we could assume that
conversion to PAN and HNO3 might have had a minor effect on the
temporal trends in background NOx and at the 6-month scale. However,
we estimated a bias of ca. 30 % due to detection of PAN, HNO3 and
other nitrogen species as NOx (see p. 15–16 in the Supplement). This, combined
with the bias due to 8 h averaging of NOx, has shifted the annual
trends to lower ranges by 2 ppb. Regional background contributions to
measured MDA8 O3 are consistent with previously reported contributions
from Berlin et al. (2013), with the closest estimate of slope values
spanning unity (from linear regression of measured MDA8 versus regional
background) resulting from the approaches in which chemistry and meteorology
were co-varied spatially and temporally; a higher estimate of slope value
(by a factor of 5) resulted from the approach in which MDA8 O3 was not
constrained by NOx and meteorology.
Conclusions
The overall goals of this study were to estimate regional background O3
and NOx in the HGB area and to quantify their temporal trends over the
past decades. To design more efficient controls on local pollution, we need
an improved understanding of regional contributions from a long-term
perspective, and also better constraints on O3 mixing ratio. We used up
to 17 years of hourly measurements of O3 and NOx mixing
ratios in different multivariate analysis approaches, including one that
allowed covariance of O3 with NOx and meteorology (T, WD and WS).
Because we used ground-monitoring data, both background O3 and NOx
determined in this study represent the ground-level backgrounds, describing
influences from regional chemistry and transport.
We found that the observed decline in regional background O3 is real
and quantifiable, regardless of the approach used to analyze the changes in
regional background O3 on the longest term possible. This is consistent
with results from two previous studies (Berlin et al., 2013; Souri et al.,
2016). Similarly, we detected and quantified a decline in the upper and
lower bounds of background NOx in all approaches.
By accounting for the space–time covariance of O3 with NOx and
meteorology, we could better resolve the temporal trend of background
O3, with a more significant slope and improved coefficient of
determination (R2 of 0.62–0.63) on both timescales: 17 and 13 years, respectively. Similarly, the temporal trend of background NOx
resulted in a better performance of the linear model (R2=0.58
compared to R2=0.53) when the covariance of variables was used
for the longest term, although the associated slope decreased slightly.
Our findings support the claim of Berlin et al. (2013) that changes in
regional background O3 also contributed to a local decline in MDA8
O3. However, in our study, regional contributions to average MDA8
O3 are underestimated when the space–time covariance of meteorology and
chemistry is not considered (Fig. S16 vs. Fig. S18). When this covariance is
accounted for in the analysis (our Approach B), the associated temporal
trend in background O3 (or NOx) reflects both the effects of
controlling precursor emissions and changes in meteorology. For instance,
local chemistry was much more important in earlier years (prior to 2007) due
to high emissions of O3 precursors from petrochemical facilities,
making it difficult to extract the regional background from surface data
during those years. The trend became steadier after 2007 probably as an
effect of emissions controls and a prevailing SSE flow; this latter is
consistent with the observed increased frequency of the southerly flow from
the GOM (Liu et al., 2015). Based on a previous study (Wang et al., 2016),
variations in the intensity and location of the Bermuda High could also
explain some of the temporal behavior in summertime MDA8 O3, causing a
drop in mid-July, when southerly flow from the GOM is allowed to enter the
region; this is marine background O3 and also contributes to the
decline in regional background O3 over time. We also observed this
effect in regional background O3 during July, particularly when using
the hourly median approach.
Our estimates of 8 h based average background O3 and NOx are both
slightly overestimated compared to the hourly median approach, likely due to
constraining the 8 h average NOx (and meteorology) by the MDA8 O3.
Future studies might consider refining these estimates by using a smaller
time-averaging scale for NOx, O3 and meteorology. Although we
estimated a bias of 18 % due to 8 h averaging of NOx, future
refinements of background NOx would probably reduce this bias. In
addition, corrections of NOx measurements that are representative for
the region and the time periods analyzed in this study are highly
recommended to further improve the lower-bound estimate of background
NOx; the average value of ca. 4 ppb still appears to be large compared
to the short-term aircraft “non-plume” NOx of 1–1.5 ppb observed in the
region.
To test the linearity of the temporal trends in background O3 and
NOx and to continuously determine the effectiveness of control
measures, and identify regulatory changes that need to be made, new studies
should extend the trends in this study into future years. Additionally,
wherever VOCs data are available, the extraction of background O3 and
NOx should be constrained over that period by VOCs as well and possibly
by solar radiation. The related temporal trends should be compared over that
period with those estimated from this study to highlight the effect of
including VOCs and an additional meteorological variable in the multivariate
analysis. Coincident solar radiation and NOx could also be used to test
the conversion of NOx to oxidation products (PAN, HNO3, etc.) and
asses the magnitude of this effect on the declining background NOx in
the HGB region.