ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-13121-2016Monthly trends of methane emissions in Los Angeles from 2011 to 2015
inferred by CLARS-FTS observationsWongClare K.wclare@gmail.comclare.wong@csun.eduPongettiThomas J.OdaTomRaoPreetiGurneyKevin R.NewmanSallyDurenRiley M.MillerCharles E.YungYuk L.SanderStanley P.NASA Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, California, USADivision of Geological and Planetary Sciences, California Institute of
Technology, Pasadena, California, USAGoddard Earth Sciences Technology and Research, Universities Space
Research Association, Columbia, Maryland, USAGlobal Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, Maryland, USASchool of Life Sciences, Arizona State University, Tempe, Arizona, USAcurrently at: California State University, Northridge, California,
USAClare K. Wong (wclare@gmail.com, clare.wong@csun.edu)26October20161620131211313016March201629April20166September201610September2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/13121/2016/acp-16-13121-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/13121/2016/acp-16-13121-2016.pdf
This paper presents an analysis of methane emissions from the Los
Angeles Basin at monthly timescales across a 4-year time period – from
September 2011 to August 2015. Using observations acquired by a ground-based
near-infrared remote sensing instrument on Mount Wilson, California, combined
with atmospheric CH4–CO2 tracer–tracer correlations, we observed
-18 to +22 % monthly variability in CH4 : CO2 from the
annual mean in the Los Angeles Basin. Top-down estimates of methane
emissions for the basin also exhibit significant monthly variability
(-19 to +31 % from annual mean and a maximum month-to-month change
of 47 %). During this period, methane emissions consistently peaked in the
late summer/early fall and winter. The estimated annual methane emissions
did not show a statistically significant trend over the 2011 to 2015 time
period.
Introduction
Methane (CH4) is a potent and newly regulated greenhouse gas in
California. However, its emissions are poorly understood. In the South Coast
Air Basin, which holds more than 43 % of state's population, the annual
methane emissions estimates based on atmospheric CH4 observations
indicate that the bottom-up emission inventory was systematically
underestimated by 30 to > 100 % (Wong et al., 2015; Jeong
et al., 2013; Peischl et al., 2013; Wennberg et al., 2012; Wunch et al.,
2009; Wecht et al., 2014; Cui et al., 2015). Methane sources in the basin
can be classified into two categories – biogenic and thermogenic. Biogenic
methane is emitted from anaerobic digestion of organic matter by bacteria in
waste management facilities, and by cattle in dairy farms. Waste management
facilities include landfills, wastewater treatment plants, and manure
management facilities in dairy farms. Thermogenic methane emissions include
natural sources, such as seeps and tar pits, and anthropogenic sources such
as natural gas system leakage and gas/oil fields. Emissions from these
sources are likely to have different seasonal patterns. Quantifying and
tracking the seasonal variability will help to elucidate methane emissions
and are essential for verifying emissions regulation and mitigation
policies. However, most studies to date have been based on data from
short-term measurement campaigns and have provided limited information on
the temporal variability or trends of methane emissions in the basin
(Peischl et al., 2013; Wecht et al., 2014; Cui et al., 2015; Wunch et al.,
2009).
One commonly used approach to estimate CH4 emissions from atmospheric
observations is the tracer–tracer correlation technique. This method uses
the regression slopes between observed trace gas mixing ratios (e.g.,
CH4 : CO2 or CH4 : CO) in the atmosphere to calculate CH4
emissions based on the more accurately known emissions of the correlate
(e.g., CO2 or CO). This method permits the derivation of the relative
emissions of the two trace gases without the use of transport models and
does not require the sources to be co-located (Wong et al., 2015; Peischl et
al., 2013; Wennberg et al., 2012; Hsu et al., 2010; Wunch et al., 2009).
Based on in situ flask observations on Mount Wilson, Hsu et al. (2010) did
not observe any seasonal variability in the CH4 : CO ratio from April
2007 to February 2008. Using column observations from the Total Carbon
Column Observing Network (TCCON) in Pasadena, Wennberg et al. (2012)
observed a ±15 % monthly variability in the CH4 : CO ratio
between August 2007 to June 2008, but the monthly variability in methane
emissions was not reported.
Top: CLARS facility located at 1.67 km above sea level on Mount
Wilson, looking over the Los Angeles Basin. Optical paths from direct sun
beam and basin surface reflection are shown as yellow lines. Bottom:
location of 29 reflection points on Mount Wilson (white square) and in the
basin (yellow triangles).
This paper presents the first study to quantify total methane emissions from
an urban region at monthly intervals for an extended period of 4
years – from September 2011 to August 2015. Using a unique dataset of
mountaintop remote sensing observations acquired with the California
Laboratory of Atmospheric Remote Sensing Fourier Transform Spectrometer
(CLARS-FTS; Wong et al., 2015; Fu et al., 2014), we have constructed a
series of monthly CH4 : CO2 tracer–tracer correlations to address
the following questions:
What is the monthly variability in methane emissions in the Los Angeles
Basin?
Is there a detectable year-to-year methane emissions change in the basin?
What methane source(s) is (are) responsible for any observed temporal trends?
Methods
Since September 2011, continuous daytime ground-based remote sensing
measurements of CH4 and CO2 have been acquired by a JPL-built
Fourier transform spectrometer on Mount Wilson (Wong et al., 2015; Fu et
al., 2014). The California Laboratory of Atmospheric Remote Sensing (CLARS)
is located at an altitude of 1670 m above sea level with a panoramic view of the
Los Angeles Basin (Fig. 1). CLARS-FTS quantifies atmospheric column CH4
and CO2 using reflected sunlight in the near-infrared region. It
operates in two measurement modes: Spectralon Viewing Observations (SVO) and
Los Angeles Basin Surveys (LABS). In the SVO mode, the instrument quantifies
the background tropospheric column CH4 and CO2 above the Los
Angeles Basin by measuring reflectance from a
Spectralon®
plate located at the CLARS site. In the LABS mode, the instrument samples
the basin slant column CH4 and CO2 by measuring the surface
reflection from 28 geographical locations (or reflection points) in the
basin (Fig. 1). We selected 28 reflection points to achieve an optimal
spatial and temporal coverage of the Los Angeles Basin. The number,
locations and repeat frequencies of the reflection points can be easily
modified to meet specific measurement requirements. In each measurement
cycle, we collect one set of LABS measurements and four SVO measurements.
Four SVO measurements are performed per measurement cycle so that any
variability in the background during each measurement cycle, which typically
lasts for 90 min, can be captured. There are five to eight measurement cycles
per day, depending on the time of the year.
Based on the Beer–Lambert law, the slant column density (SCD) – the total
number of absorbing molecules per unit area along the Sun–Earth–instrument
optical path – is retrieved for CH4 at 1.67 µm, CO2
at 1.60 µm, and O2 at 1.27 µm using a modified version of the
GFIT algorithm developed at JPL (Fu et al., 2014; Wunch et al., 2011). The
retrieved SCDs of CH4 and CO2 are then converted to slant
column-averaged dry air mixing ratio, XCH4 and XCO2, by
normalizing to the retrieved SCD of O2 (SCDO2; Eq. 1).
XGHG=SCDGHGSCDO2×0.2095
Individual retrievals are analyzed with multiple post-processing filters to
ensure data quality. Spectra are removed when the residual root mean square
errors of the fits to the GFIT radiative transfer model exceed a pre-defined
threshold. These are usually associated with aerosols, high and low clouds,
electrical or mechanical noise, and other transient behavior. Details about
the CLARS-FTS design, data retrieval algorithm, and data-filtering process
are described in Fu et al. (2014) and Wong et al. (2015).
Time series of the Los Angeles Basin weighted-average monthly
regression slopes of XCH4(XS)–XCO2(XS) (in unit of ppb ppm-1) and their uncertainties observed by the CLARS-FTS in the basin
from September 2011 to May 2015. Uncertainties are ±1σ of the
regression slopes.
Wong et al. (2015) mapped the spatial distribution of the CH4 : CO2
ratio and derived an annual total CH4 emission for the basin, based on
CLARS-FTS observations from 2011 to 2013. Here we used the same approach but
focused on the temporal trend and quantified the monthly total CH4
emissions for the basin. Therefore, following Wong et al. (2015), we
calculated the excess XCH4 and XCO2, due to the emissions from the
basin, by subtracting the corresponding SVO measurements from the LABS
observations (Eq. 2).
XGHGXS=XGHGLABS-XGHGSVO
We then performed orthogonal distance regression (ODR) analyses of
XCH4(XS) and XCO2(XS) for the 28 reflection points for each month
starting from September 2011 to August 2015. An example of the scatter plot showing
the correlation and the regression slope can be found in Fig. 1S of the supplemental material.
To explore the overall monthly
variability during this period, we calculated the weighted-average
regression slope among the 28 reflection points, R, using Eq. (3). In Eq. (3), ri stands for the regression slope for
reflection point i, wi is the weight which is defined
as the reciprocal of the square of the 1σ uncertainty of the
regression slope, σi.
R|monthlyCLARS=∑i=1i=28riwi∑i=1i=28wi,
where
wi=1σi2.
Monthly patterns of the Los Angeles Basin weighted-average
regression slopes of XCH4(XS)–XCO2(XS) (in unit of ppb ppm-1) and their uncertainties observed by the CLARS-FTS in the basin.
Monthly trends are color-coded as follows: 2011 in blue, 2012 in cyan, 2013
in green, 2014 in orange, and 2015 in red. The monthly average ratio and its
standard deviation over the entire observational period are shown in black.
Results
In this section, we describe the monthly and multi-year trends of the basin-average regression slope observed by CLARS-FTS. Figure 2 shows the time
series of the Los Angeles Basin weighted-average monthly
XCH4(XS)–XCO2(XS) regression slopes, R, and their uncertainties
observed by the CLARS-FTS from September 2011 to May 2015. The R values and their
uncertainties are listed in Table S1 of the supplemental material. During this
period, R ranged from 5.4 ± 0.4 (ppm CO2)-1 to
7.7 ± 1.0 ppb CH4 (ppm CO2)-1 with an overall mean and
standard deviation of 6.5 ± 0.5 ppb CH4 (ppm CO2)-1.
This is consistent with previous atmospheric observations and their
uncertainties: 7.8 ± 0.8 ppb CH4 (ppm CO2)-1 from TCCON
in 2007–2008, 6.7 ± 0.6 ppb CH4 (ppm CO2)-1 from ARCTAS
in 2008, and 6.7 ± 0.0 ppb CH4 (ppm CO2)-1
Peischl et al. (2013) reported 6.70 ± 0.01 ppb CH4 (ppm CO2)-1 from CalNex in 2010.
from CalNex
in 2010 (Wunch et al., 2009; Wennberg et al., 2012; Peischl et al., 2013).
CLARS-FTS observations showed significant monthly fluctuations. The monthly
variability in the slope was -8 to +5 % in 2011, -9 to +22 %
in 2012, -13 to +11 % in 2013, -18 to +11 % in 2014, and
-8 to +11 % in 2015. Monthly variability reported here spans the
minimum and maximum deviations from the annual monthly mean for each year.
Monthly variability for 2011 and 2015 was calculated based on partial annual
data (that is, from September to December for 2011 and from January to
August for 2015). In general, we observed peaks in late summer, fall, and
winter: R exceeded 7 ppb CH4 (ppm CO2)-1 in August 2012,
December 2012, November 2013, August 2014, September 2014, November 2014, and
August 2015. The smallest values of R were observed in the spring and early
summer. Typically, R dipped below 6 ppb CH4 (ppm CO2)-1 in
May–June, 2012, June 2013, and March 2013.
Figure 3 compares the year-to-year monthly values of R to the 4-year mean
values. The weighted 4-year mean values showed maxima in August and
September, at 7.0 ppb CH4 (ppm CO2)-1. Minima occurred in
March, when the weighted monthly mean was 5.8 ppb CH4 (ppm CO2)-1. The fall peak was also observed by TCCON observations in
Pasadena from 2007 to 2008 (Wennberg et al., 2012). However, no winter peak
was observed in their study. CLARS observations showed multi-year
variability for some months but not others. To better understand
the seasonal year-to-year trends in R, we plotted the yearly trends for fall
(September, October, and November), winter (December, January, and February),
spring (March, April, and May) and summer (June, July, and August) in Fig. 4.
A 15 % increase in R over Los Angeles was observed in the fall season over
the last few years. R increased from 6.2 ppb CH4 (ppm CO2)-1
in 2012 to 7.1 ppb CH4 (ppm CO2)-1 in 2014. This increasing
trend was also observed in summer from 2012 to 2014. However, the summer
value decreased again from 2014 to 2015. No year-to-year change was observed
in spring. In winter, there were some year-to-year changes but no obvious
increasing or decreasing trend over the study period. The annual average R
value showed no significant trend and less than 4 % year-to-year
variability between 2011 and 2015.
Interannual variability in R (in unit of ppb CH4 (ppm CO2)-1) in fall (orange), winter (blue), spring (green), and summer
(red) from 2011 to 2015. The annual average ratio is shown in black. Also
shown are the ±1σ uncertainties. Note that data for 2011 and
2015 are derived from partial annual observations (that is, September to
December for 2011 and January to August for 2015). The CH4 : CO2
ratio based on the population-scaled bottom-up emission inventory from the
California Resources Board is shown in light blue (California Air Resources
Board, 2013).
For comparison, we also calculated the CH4 : CO2 emission ratio
based on a bottom-up emission inventory. The California Air Resources Board
(CARB) reported statewide total emissions of CH4 and CO2 through
2013 (http://www.arb.ca.gov/app/ghg/2000_2013/ghg_sector.php). For CO2, statewide emissions were
384, 389, and 387 Tg CO2 per year in 2011, 2012, and 2013, respectively.
Following Wong et al. (2015), we downscaled the statewide CO2 emissions
by fractional population (43 % of state population) to obtain 165, 167, and
166 Tg CO2 per year in 2011, 2012, and 2013, respectively, for emissions
from the South Coast Air Basin. For CH4, bottom-up emissions of 1629,
1636, and 1644 Gg CH4 per year were reported by CARB in 2011, 2012, and
2013, respectively. Following the approach used by Wong et al. (2015), we
estimated the emissions from the South Coast Air Basin by subtracting the
agriculture and forestry emissions from the total emissions and then
apportioning the emissions by population. This gave us emissions of 301, 297
and 300 Gg CH4 per year in the South Coast Air Basin from 2011 to 2013.
The bottom-up estimate of R, the CH4/ CO2 emission ratio, was
calculated from Eq. (5), where
ECH4|annualinventory is the downscaled CARB annual
total CH4 emissions,
ECO2|annualinventory is the downscaled CARB annual
total CO2 emissions, and
MWCO2MWCH4
is the ratio of the molecular weights of CH4 and CO2 (that is,
44gCO2/mole16gCH4/mole).
Rannualinventory=ECH4|annualinventoryECO2|annualinventory×MWCO2MWCH4
Using the downscaled CARB emission estimates for the South Coast Air Basin
yields annual R values of 5.0, 4.9, and 5.0 ppb CH4 (ppm CO2)-1 for 2011, 2012, and 2013, respectively. Figure 4 shows the
annual R values determined from CLARS observations. CLARS annual R values
were 6.4 ± 0.1 (ppm CO2)-1, 6.2 ± 0.1 (ppm CO2)-1, 6.5 ±
(ppm CO2)-1, 6.5 ± 0.1 (ppm CO2)-1, and
6.4 ± 0.1 ppb CH4 (ppm CO2)-1 in 2011, 2012, 2013,
2014, and 2015, respectively. The inventory-based R value systematically
underestimated the observed annual R values by about 20 to 25 % during the
time period from 2011 to 2013.
Discussion
We can rearrange Eq. (5) to estimate monthly CH4 emissions from the
South Coast Air Basin using the CH4/ CO2 regression slope R
determined from CLARS observations and an inventory-based estimate of
monthly CO2 emissions (Wong et al., 2015).
ECH4|monthlytop-down=R|monthlyCLARS×ECO2|monthlyinventory×MWCH4MWCO2
However, this requires estimates of the monthly CO2 emissions from the
South Coast Air Basin.
Time series of the different CO2 monthly emissions (in unit
of Tg per month) from the South Coast Air Basin. Emissions are color-coded
as follows: population-scaled CARB in light blue, Hestia in solid black,
ODIAC in solid red, and FFDAS in solid green. Extrapolated emissions using
annual fuel consumption data are shown in faded solid lines.
Estimating monthly CO2 emissions
This subsection explores the available CO2 emission database
(ECO2|monthly) for the basin. CARB reported annual bottom-up
statewide CO2 emissions from 2011 to 2013. As described in the results
section, we estimated the annual emissions in the South Coast Air Basin by
apportioning the statewide emissions using the ratio of population in the
South Coast Air Basin to the state population. Because there is no monthly
statewide emissions information available, we distributed the annual
CO2 emission evenly over 12 months (shown as solid light-blue line
in Fig. 5). Data in 2014 and 2015 (shown as faded light-blue line) are
extrapolated using statewide annual fuel consumption data provided by the
Energy Information Administration
(http://www.eia.gov/dnav/ng/hist/n9140us2M.htm;
http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=A103450061&f=M).
In addition to the official CARB emission inventory, three CO2 emission
data products provide monthly temporal resolution for the South Coast Air
Basin for our observational period.
Hestia. The Hestia fossil fuel CO2 emissions data product provides
sectoral bottom-up emissions at the building and street level on hourly
timescales (http://hestia.project.asu.edu). Data are available for the South
Coast Air Basin for the years 2011 and 2012. Here, we calculated the monthly
total CO2 emissions for the South Coast Air Basin domain based on the
Hestia 1.3 km × 1.3 km hourly gridded version 1.0 (shown by the solid black
line in Fig. 5). We defined the South Coast Air Basin domain as the
rectangular box bounded by 118.83–116.67∘ W,
33.38–34.77∘ N. Because there are no data after
2012, we extrapolated the emissions from 2012 to 2015 (shown as a faded
black line in Fig. 5) using the same approach described above.
ODIAC. Open-source Data Inventory for Anthropogenic CO2 (ODIAC)
provides global emission fields of fossil fuel CO2 emission with 1 km × 1 km spatial sampling on a monthly basis. ODIAC is based on
CO2 emission estimates from the Carbon Dioxide Information and Analysis
Center (CDIAC), fuel consumption statistics published by British Petroleum,
satellite-observed nightlights and a global power plant database (Oda and
Maksyutov, 2011). The monthly CO2 emissions for the South Coast Air
Basin domain from September 2011 to December 2014 are shown as the solid red
line in Fig. 5. Data in 2015 (shown as the faded red line) are projected
using the same approach used to extrapolate the Hestia emissions.
FFDAS. Fossil Fuel Data Assimilation System (FFDAS) provides global
monthly/hourly sectoral fossil fuel CO2 emission with 0.1∘× 0.1∘ (approx. 10 km × 10 km) spatial sampling
(Asefi-Najafabady et al., 2014). This data product is derived from an
optimization of the Kaya identity constrained by national fossil fuel
CO2 emissions from the International Energy Agency, satellite-observed
nightlights, population, and the Ventus power plant dataset. Emissions are
available through 2012 (shown as the solid green line). Data from 2013 and
onwards (shown as the faded green line) are extrapolated using the same
method described previously for CARB, Hestia, and ODIAC.
As shown in Fig. 5, there are differences as large as 3 Tg CO2 per
month among the three gridded datasets: Hestia, ODIAC, and FFDAS. The
differences result from (1) emission calculation methods, (2) the underlying
dataset used in the emission calculations, and (3) spatial modeling. Hestia
is derived primarily from local data in the South Coast Air Basin, while
ODIAC and FFDAS are based primarily on national and global proxy approaches.
It has been shown that the use of a global dataset may underestimate
emissions in Los Angeles by up to 18 % (Brioude et al., 2013). Despite the
systematic differences, all three gridded emission datasets show very
similar monthly variability, with peaks in summer and winter. Based on the
source apportionment in Hestia, the summer peak is due to electricity usage
(air conditioning) and the winter peak is due to space heating. In all three
datasets, fossil fuel CO2 emissions in the basin show -9 to +14 %
monthly fluctuations about the annual mean.
Time series of CLARS-FTS inferred monthly CH4 emissions (in
unit of Gg per month) and their 1σ uncertainties from the Los
Angeles Basin from September 2011 to August 2015. Overall uncertainties are
propagated from the uncertainties in CLARS-FTS
XCH4(XS)–XCO2(XS) regression slopes and CO2
emissions.
We believe the Hestia data product provides the most accurate CO2
emission estimates for the South Coast Air Basin among all available
databases. Therefore, we used the Hestia CO2 emissions in our
calculations to estimate CH4 emissions. We did not use the CARB
CO2 emissions in our calculation because the official CARB emission
inventories are annual statewide estimates. To derive the monthly CO2
emissions for the basin from the CARB inventory, we have to first scale it
to regional emissions by population and then apply the monthly variability
from Hestia. Through these steps, we will introduce additional uncertainties
in the derived emissions.
Deriving top-down monthly CH4 emissions
This subsection explains the monthly and annual trends of our methane
emission estimates.
Figure 6 shows the time series of monthly methane emissions computed from
Eq. (6). Shaded areas represent the 1σ uncertainties in the derived
emissions. Uncertainties are propagated from the uncertainties in CLARS-FTS
XCH4(XS)–XCO2(XS) regression slopes and CO2 emissions. For
CO2 emissions, we assumed a 10 % uncertainty in the Hestia monthly
CO2 emissions. The values of the derived monthly methane emissions and
their uncertainties can be found in Table S1 of the supplemental material.
Derived methane emission estimates ranged from 23 to 39 Gg CH4 per
month. Methane emission peaks occurred in late summer/early fall and winter
months. Distinct peaks of methane emission occurred in December 2011, August
2012, and December 2012, when methane emissions exceeded 33 Gg per month. In
2013 and 2014, the summer and fall peaks were less prominent than in 2012.
Minimum methane emissions occurred in late spring/early summer when
emissions dropped below 27 Gg per month. The monthly variability in methane
emissions was -12 to +16 % in 2011, -13 to +31 % in 2012,
-19 to +14 % in 2013, -16 to +17 % in 2014 and -14 to
+17 % in 2015. Monthly variability reported here is the minimum and
maximum percent difference from the annual average. Note that monthly
variability in 2011 and 2015 was calculated based on partial annual data,
that is, from September to December in 2011 and from January to August in
2015.
Monthly patterns of derived CH4 emissions (in unit of Gg per
month). Error bars represent the ±1σ uncertainties. Derived
CH4 emissions are color-coded as follows: 2011 in blue, 2012 in cyan,
2013 in green, 2014 in orange, and 2015 in red. Average monthly emissions and
their standard deviations over the entire observational period are shown in
black.
Figure 7 plots the monthly patterns of CLARS-FTS inferred methane emissions
for each year. The inferred methane emission estimates showed a bimodal
distribution with peaks during the winter and the late summer/early fall.
The weighted monthly average over this period showed maxima in January,
August and December at 31, 33, and 32 Gg CH4 per month. The weighted
monthly average gradually decreased from January to June, when methane
emission reached a minimum of 25 Gg CH4 per month. No statistically
significant interannual seasonal variability was observed.
Yearly trends in top-down CH4 emissions
Figure 8 shows the estimated CH4 annual emissions for the South Coast
Air Basin from 2011 to 2015. The annual methane emission derived for the
South Coast Air Basin was 345 Gg CH4 per year in 2011. Derived
emissions
increased to 356 Gg CH4 per year in 2013. Since then, there has been a
decreasing trend, reaching 325 Gg CH4 per year in 2015. Due to the large
uncertainty propagated mainly from CO2 emissions, we derived a
decreasing trend of -5 ± 4 Gg CH4 per year with only 25 %
confidence level.
CLARS-FTS inferred annual CH4 emission estimates (in unit of
Gg per month), based on Hestia CO2 emissions. The red line indicates the
regression slope and the shaded area is the 25 % confidence interval.
Figure 9 compares all reported CH4 annual total emission estimates for
the South Coast Air Basin in the past 10 years. These estimates were
derived based on in situ ground observations (Hsu et al., 2010), column
measurements (Wunch et al., 2009; Wennberg et al., 2012; Wong et al., 2015)
and aircraft measurements (Peischl et al., 2013; Wennberg et al., 2012;
Wecht et al., 2014; Cui et al., 2015) in the Los Angeles Basin. Among all
the previous studies, only one study (Wong et al., 2015) estimated methane
emissions for the period between 2011 and 2015. Our estimates for 2011 to
2013 were lower but within uncertainties of the estimates reported by Wong
et al. (2015). The difference in the estimated methane emissions between the
present study and Wong et al. (2015) is due to differences in the CO2
reference emissions used in the calculations. Hestia CO2 emissions used
in the present calculations were lower than the population-scaled CARB
emissions used in Wong et al. (2015). The rest of the studies were based on
methane observations from 2007 to 2010. Despite the different study periods,
methane emission estimates from our study are in consistent with previous
top-down estimates. About half of previously reported methane emission
estimates were focused on the CALNEX field experiment in May and June 2010.
The annual methane emission estimates from these studies could be
underestimated as we observed that methane emissions tend to be lowest
during these months. When our results are compared to the bottom-up inventory, the
scaled CARB CH4 emissions from 2011 to 2013 were 2–31 % lower than
our estimates.
Comparison of annual CH4 emission estimates (in unit of Gg
per month) reported in the past 10 years. The Mount Wilson estimate
reported by Wennberg et al. (2012) was derived for the South Coast Air Basin
using the emission estimates based on Hsu et al., 2010.
Analysis assumptions
In this subsection, we discuss the analysis assumptions used to derive
CH4 emissions for the South Coast Air Basin using CLARS-FTS
observations.
Spatial and temporal representation based on CLARS-FTS measurement
technique. We assumed that the CLARS-FTS measurement domain is
representative of the South Coast Air Basin. The CLARS-FTS measurement
domain covers 67 % of CO2 emissions in the South Coast Air Basin
spatial domain according to the Hestia CO2 data product. Therefore, the
CLARS-FTS observations are more representative of the sampled area in the
South Coast Air Basin than the entire basin. In addition, our methane
emission estimates were based on daytime-only observations.
Spatial and temporal bias due to data filtering. CLARS-FTS samples the Los
Angeles Basin using its standard measurement sequence. However, as described
in Wong et al. (2015), certain months of the year are more prone to cloud
and aerosol interference in the Los Angeles Basin. This may introduce biases
in the monthly sampling of post-filtered data. The number of post-filtered
observations did not have a strong diurnal bias, however. To accurately
estimate the Los Angeles Basin value, we used the weighted-average
XCH4(XS)–XCO2(XS) regression slope, because of the statistical weight for
each reflection point is based on the number of samples passing through the
data quality filters. We also performed a bootstrap analysis to ensure that
there is no sampling bias in the regression slopes (Efron and Tibshirani,
1993).
Seasonal bias due to transport variability. Changes in meteorology patterns
in summer vs. winter can lead to a seasonal dependence on the observations'
footprint, which is the sensitivity of the observations to changes in
emissions. In the Los Angeles Basin, the prevailing winds are typically
northwesterly and onshore throughout the year, except for Santa Ana events
(Conil and Hall, 2006). During Santa Ana events, which typically occur
during the period from October to March, the wind patterns in the basin
shift to easterly and offshore flow (Hughes and Hall, 2010). We investigated
the impact of Santa Ana events on our results using the Santa Ana index to
remove observations during Santa Ana events (Hughes and Hall, 2010; Conil
and Hall, 2006; http://meteora.ucsd.edu/weather/). A correlation analysis
showed that applying the Santa Ana index filter did not cause any
statistically significant bias on the CLARS monthly CH4 : CO2
ratios. This insensitivity is likely due to the effect of spatial averaging
over 28 slant column measurements that span a 50 km × 100 km spatial
domain in the Los Angeles Basin, mitigating the effect of transport
variability, especially when compared with measurements from individual
tower sites. A more diagnostic approach involving the application of a
high-resolution tracer transport model to investigate potential
transport-induced biases on CLARS-FTS results will be carried out in the
future.
Exploring seasonal variability from major CH4 emission
sources
Currently, there are no monthly-resolved inventories available for us to
compare with our top-down results. When these data become available in the
future, we hope to better understand the role of each CH4 source in the
monthly variability we observed in total CH4 emissions in Los Angeles.
In this subsection, we review previous studies of the seasonal
emissions variability from major methane sources (landfills, dairies,
wastewater treatment plants, and natural gas system leakage) to understand
possible contributions to the observed monthly variability in total CH4
emission in the South Coast Air Basin.
Landfills. Landfills are major emitters of CH4 in the basin. Previous
studies suggested that landfills could contribute 41–63 % of total annual
methane emissions (Peischl et al., 2013; Wennberg et al., 2012; Hsu et al.,
2010). The seasonal variability in landfill CH4 emissions is poorly
understood, however. Peischl et al. (2013) estimated the emissions from two
of the largest landfills in the basin – Olinda Alpha landfill and Puente
Hills landfill – based on aircraft measurements in May and June 2010. Based
on observations taken from four flights in May and one flight in June, their
studies found that CH4 emissions from Olinda Alpha landfill was almost
double in June relative to May, while Puente Hills landfill (which was closed
in 2012) showed less than 15 % changes in monthly emissions in 2010. Using
a landfill model, Spokas et al. (2015) found that the statewide landfill
emissions were largest in October and smallest in April in 2010. Other
observational studies found that CH4 emissions from landfills peak in
July and August (Shan et al., 2013; Spokas et al., 2011; Tratt et al., 2014;
Goldsmith et al., 2012). These studies suggest that landfills can contribute
to the late summer/early fall peak in the total CH4 emissions observed
by CLARS-FTS but are unlikely to explain the winter peaks.
Dairies. Previous observations suggested that dairy farms could contribute
32–76 Gg CH4 per year in the South Coast Air Basin (Peischl et al.,
2013; Wennberg et al., 2012). This corresponds to 8 to 36 % of the
reported total annual CH4 emissions in the studies. In general, studies
on dairies focus on mitigation strategies rather than quantifying temporal
changes in emissions. Limited studies of dairy emissions report peaks in
CH4 emissions in summer and early fall (from June to September) and
steady minima in spring and winter (VanderZaag et al., 2009, 2010, 2013, 2014; Ulyatt et al.,
2002; Kaharabata et al., 1998). These findings imply that dairies can also
be contributing to the summer/early fall peaks in the CLARS-FTS inferred
CH4 emissions.
Wastewater treatment. This sector is suggested to be responsible for 33 %
of Los Angeles County and 9.4 % of the South Coast Air Basin (Hsu et al.,
2010; Wennberg et al., 2012). Daelman et al. (2012, 2013) measured CH4
emissions from a wastewater treatment facility for 1 year (2010–2011) and
reported up to 40 % monthly fluctuations from the mean, with a maximum in
June.
Fossil fuel sources. Recent studies based on mobile, stationary, and airborne
measurements of methane in Los Angeles have indicated that fossil fuel sources
contribute 47 to 90 % of the total CH4 emissions in the basin
(Wennberg et al., 2012; Townsend-Small et al., 2012; Peischl et al., 2013;
Hopkins et al., 2016). Wennberg et al. (2012) and Peischl et al. (2013)
suggested that fugitive emission from natural gas distribution system
leakage contributes to the gaps between bottom-up and top-down total
CH4 emissions in the South Coast Air Basin. McKain et al. (2014) found
little seasonal dependence (< 10 %) on the emissions from the
natural gas system in Boston, Massachusetts. Their studies showed a leakage
rate of 2.7 ± 0.6 % from the natural gas system. Wennberg et al. (2012) reported a consistent leakage rate from the natural gas system in Los
Angeles and suggested that most of the leakages from such systems are likely
to occur in residential/commercial areas where the distribution system ends.
Publicly available natural gas consumption data from residential and
commercial sectors in the South Coast Air Basin show a significant seasonal
cycle with a maximum in winter due to heating
(https://energydatarequest.socalgas.com/). Wennberg et al. (2012) and McKain
et al. (2014) observed that the leakage rate from the natural gas system is
constant throughout the year and suggested that the majority of leakage
occurs in the distribution system to the residential and commercial sectors.
This conclusion is reasonable since the natural gas distribution pipeline
system is pressure-regulated at several points, and leakage should be
independent of consumption to first order. However, this is not the case for
natural gas storage facilities,
which are pressurized to higher levels in the
summer and late fall in Southern California to respond to increased demands
for summertime electric power generation for air conditioning and wintertime
space heating. In October 2015, a massive leak began at an underground well
pipe at the Aliso Canyon (Los Angeles) natural gas storage facility as it
was being pressurized to provide wintertime reserves. While this leak was
unprecedented in scale, it raises the question of whether smaller fugitive
leaks in the storage infrastructure from this and numerous other above- and
belowground reservoirs contribute to the seasonal variability observed in
CLARS-FTS data. The Aliso Canyon leak resulted in very large increases (as
much as a factor of 10) in the observed instantaneous values of
XCH4(XS)/XCO2(XS) throughout the entire CLARS-FTS field of regard. Since CLARS-FTS is capable of resolving CH4
enhancements that are significantly smaller than those caused by the Aliso
Canyon leak, perhaps seasonally varying fugitive emissions from natural gas
storage facilities and associated infrastructure are partially responsible
for the observed monthly variability. Enhanced long-term monitoring for
fugitive emissions will be required to test this hypothesis.
Summary and conclusions
Using CLARS-FTS mountaintop remote sensing observations from Mount Wilson
along with tracer–tracer CH4 : CO2 correlation analyses, we
estimated the monthly variability in CH4 : CO2 and top-down CH4
emissions from the South Coast Air Basin from 2011 to 2015. Significant monthly
variability (-18 to +22 %) in CH4 : CO2 was observed. Double
peaks in late summer/early fall and winter occurred consistently during the
study period. The fall peak in the CH4 : CO2 ratios was also
observed by TCCON (Wennberg et al., 2012). The CLARS-FTS
XCH4(XS)–XCO2(XS) regression slopes showed -7 to 10 %
year-to-year seasonal variability, with an increasing trend in the fall
season from 2012 to 2014. The annual average
XCH4(XS)–XCO2(XS)
regression slopes showed less than 4 % year-to-year variability between
2011 and 2015.
Using the best available estimates of CO2 emissions, top-down estimates
of CH4 emissions were determined using the emission ratio method.
Repeatable peaks in late summer/early fall and winter were observed between
2011 and 2015. There were significant monthly fluctuations (-19 to
+31 % from annual mean and a maximum month-to-month change of 47 %) in
the inferred methane emissions in the basin. Based on previous studies on
the seasonal variability in CH4 emissions from CH4 sources, we
concluded that landfills, dairies, and wastewater treatment facilities are
likely sources of the peak CH4 emissions in late summer/early fall.
Fugitive emissions from natural gas storage facilities and associated
infrastructure may contribute to both the late summer and late fall peaks.
No significant trend in CH4 emissions (-5 ± 4 Gg CH4 per
year with a 25 % confidence level due to the uncertainty in CO2
emissions) could be discerned over the 2011 to 2015 time period. The
population-scaled bottom-up CH4 emissions from 2011 to 2013 were
2–31 % lower than our top-down estimates. These results are consistent
with previous studies (Wunch et al., 2009; Hsu et al., 2010; Wennberg et
al., 2012; Peischl et al., 2013; Wong et al., 2015). A combination of
several measurement and modeling strategies are necessary to further
disentangle the monthly variability in methane sources in the Los Angeles
Basin.
Data availability
The CLARS-FTS measurements are available upon request. A portion of the data are available on the
Megacities Carbon Project data portal (https://megacities.jpl.nasa.gov/portal).
The Supplement related to this article is available online at doi:10.5194/acp-16-13121-2016-supplement.
Acknowledgements
The research in this study was performed at the Jet Propulsion Laboratory,
California Institute of Technology, under a contract with the National
Aeronautics and Space Administration. Clare K. Wong thanks the California Air
Resources Board, NIST GHG and Climate Science Program, and the W. M. Keck
Institute for Space Studies for support. The authors would like to
acknowledge our colleagues at JPL and California Institute of Technology,
and Risa Patarasuk at Arizona State University for helpful comments and
suggestions.
Edited by: R. Harley
Reviewed by: two anonymous referees
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