High-resolution bottom-up estimation provides a detailed guide for
city greenhouse gas mitigation options, offering details that can increase
the economic efficiency of emissions reduction options and synergize with
other urban policy priorities at the human scale. As a critical constraint
to urban atmospheric CO2 inversion studies, bottom-up
spatiotemporally explicit emissions data products are also necessary to
construct comprehensive urban CO2 emission information systems useful
for trend detection and emissions verification. The “Hestia Project” is an
effort to provide bottom-up granular fossil fuel (FFCO2) emissions for
the urban domain with building/street and hourly space–time resolution.
Here, we report on the latest urban area for which a Hestia estimate has
been completed – the Los Angeles megacity, encompassing five counties: Los
Angeles County, Orange County, Riverside County, San Bernardino County and
Ventura County. We provide a complete description of the methods used to
build the Hestia FFCO2 emissions data product for the years 2010–2015.
We find that the LA Basin emits 48.06 (±5.3) MtC yr-1, dominated by the
on-road sector. Because of the uneven spatial distribution of emissions,
10 % of the largest-emitting grid cells account for 93.6 %, 73.4 %,
66.2 %, and 45.3 % of the industrial, commercial, on-road, and
residential sector emissions, respectively. Hestia FFCO2 emissions are
10.7 % larger than the inventory estimate generated by the local
metropolitan planning agency, a difference that is driven by the industrial
and electricity production sectors. The detail of the Hestia-LA FFCO2
emissions data product offers the potential for highly targeted, efficient
urban greenhouse gas emissions mitigation policy. The Hestia-LA v2.5
emissions data product can be downloaded from the National Institute of
Standards and Technology repository (10.18434/T4/1502503, Gurney et al., 2019).
Introduction
Driven by the growth of fossil fuel energy demand, the amount of carbon
dioxide (CO2), the most important anthropogenic greenhouse gas (GHG) in
the Earth's atmosphere, recently reached an annual average global mean
concentration of 402.8±0.1 parts per million (ppm), which is on its way to
doubling preindustrial levels (IPCC, 2013; Le Quéré et al., 2018). We have
also witnessed the first time that the majority of the world's inhabitants
reside in urban areas. This trend, like atmospheric CO2 levels, is
intensifying. Projections show city populations worldwide could increase by 2 to 3 billion
this century and triple in area by 2030 (UN DESA
1015; Seto et al., 2012).
These two thresholds are linked – almost three-quarters of energy-related,
atmospheric CO2 emissions are driven by urban activity (Seto et al.,
2014). If the world's top 50 emitting cities were counted as one country
that nation would rank third in emissions behind China and the United States
(World Bank, 2010). Indeed, urbanization is a factor shaping national
contributions to internationally agreed emission reductions, as subnational
governments are playing an increasing role in climate mitigation and
adaptation policy implementation (Bulkeley, 2010; Hsu et al., 2017).
Furthermore, the pace of urbanization continues to increase and
opportunities to avoid carbon “lock-in” – where relationships between
technology, infrastructure, and urban form dictate decades of high CO2
development – are diminishing (Güneralp et al., 2017;
Seto et al., 2016; Erickson and Lazarus, 2015).
Motivated by these numerical realities and the recognition that low-emission
development is consistent with a variety of other co-benefits (e.g., air
quality improvement), cities are taking steps to mitigate their CO2
emissions (Rosenzweig et al., 2010; Hsu et al., 2015; Watts, 2017). For
example, 9120 cities representing over 770 million people (10.5 % of
global population) have committed to the Global Covenant of Mayors (GCoM) to
promote and support action to combat climate change https://www.globalcovenantofmayors.org/about/,
last access: 12 August 2019). Over 90
large cities, as part of the C40 network, have similarly committed to
mitigation actions with demonstrable progress. However, the scale of actual
reductions remains modest, despite the many pledges and initial progress.
For example, a recent study reviewed 228 cities that pledged to reduce 454
megatons of CO2 per year by 2020 (Erickson and Lazarus, 2012). Were
they to meet these commitments, the reduction would account for about 3 %
of current global urban emissions and less than 1 % of total global
emissions projected for 2020. More importantly, there is a need for timely
information to manage and assess the performance of implemented mitigation
efforts and policies (Bellassen et al., 2015).
One of the barriers to targeting a deeper list of emission reduction
activities is the limited amount of actionable emissions information at
scales where human activity occurs: individual buildings, vehicles, parks,
factories, and power plants (Gurney et al., 2015). These are the scales at
which interventions in CO2-emitting activity must occur. Hence, the
emissions magnitude and driving forces of those emissions must be understood
and quantified at the “human” scale to make efficient (i.e., prioritizing
the largest available emitting activities and locales) mitigation choices and to
capture the urban co-benefits that also occur at this scale (e.g., improve
traffic congestion, “walkability”, green space). Similarly, a key obstacle to
assessing progress is a lack of independent atmospheric evaluation (ideally
consistent in space and time with the human-scale emissions mapping) (Duren
and Miller, 2011).
Existing methods and tools to account for urban emissions have been
developed primarily in the nonprofit community (WRI/WBCSD, 2004; Fong et
al., 2014). In spite of these important efforts, most cities lack
independent, comprehensive, and comparable sources of data and information to
drive and/or adjust these frameworks. Furthermore, the existing tools and
methods are designed at an aggregate level (i.e., whole city, whole
province), missing the most important scale – sub-city – and hence provide
limited actionable information. The need for greater granularity and
specificity of emissions promises more efficient policy solutions. As all
cities reach beyond the existing “low-hanging fruit” of emissions
mitigation (i.e., those actions that are already planned for other reasons,
those that are simple, and financially profitable), competition for limited resources and
policy justification will increase. Having information that can isolate the
most efficient and effective emission reduction investments (specific
roadways and intersections, building subdivisions, or commercial building
clusters) will be at a premium.
The scientific community has begun to build information systems aimed at
providing independent assessment of urban CO2 emissions. Through a
combination of atmospheric measurements, atmospheric transport modeling, and
data-driven “bottom-up” estimation, the scientific community is exploring
different methodologies, applications, and uncertainty estimation of these
approaches (Hutyra et al., 2014). Atmospheric monitoring includes
ground-based CO2 concentration measurements (McKain et al., 2012;
Djuricin et al., 2010; Miles et al., 2017; Turnbull et al., 2015; Verhulst
et al., 2017), ground-based eddy flux (i.e., emissions of CO2 into the
atmosphere and/or CO2 being removed from the atmospheric by vegetation)
measurements (Christen, 2014; Grimmond et al.,
2002; Menzer et al., 2015; Velasco and Roth, 2010; Velasco et al., 2005),
aircraft-based flux measurements (Mays et al., 2009; Cambaliza et al., 2014,
2015), and whole column abundances from both ground- and space-based remote-sensing platforms (Wunch et al., 2009; Kort et al., 2012; Wong et al., 2015;
Schwandner et al., 2017).
“Bottom-up” approaches, by contrast, include a mixture of direct flux
measurement, indirect measurement and modeling. Common among the bottom-up
approaches are those that include flux estimation based on a combination of
activity data (population, number of vehicles, building floor area) and
emission factors (amount of CO2 emitted per activity), socioeconomic
regression modeling, or scaling from aggregate fuel consumption (VandeWeghe
and Kennedy, 2007; Shu and Lam, 2011; Zhou and Gurney, 2011; Gurney et al.,
2012; Jones and Kammen, 2014; Ramaswami and Chavez, 2013; Patarasuk et al.,
2016; Porse et al., 2016). Direct end-of-pipe flux monitoring is often used
for large point sources such as power plants (Gurney et al., 2016). Indirect
fluxes (those occurring outside of the domain of interest but driven by
activity within) can be estimated through either direct atmospheric
measurement (and apportioned to the domain of interest) can be modeled
through process-based (Clark and Chester, 2017) or economic input–output
models (Ramaswami et al., 2008).
Integration of bottom-up urban flux estimation with atmospheric monitoring
has been achieved with atmospheric inverse modeling, an approach whereby
surface fluxes are estimated from a best fit between bottom-up estimation
and fluxes inferred, via atmospheric transport modeling, from atmospheric
concentrations (Lauvaux et al., 2013, 2016; Bréon et al.,
2015; Davis et al., 2017). Though the various measurement and modeling
components continue to be tested, integration offers an urban anthropogenic
CO2 information system, which can provide accuracy, emissions process
information, and spatiotemporal detail. This combination of attributes
satisfies a number of urgent requirements. For example, it can offer the
means to evaluate urban emissions mitigation efforts by assessing urban
trends. Space, time, and process details of emitting activity can guide
mitigation efforts, illuminating where efficient opportunities exist to
maximize reductions or focus new efforts. Finally, emissions quantification
is also seen as a potentially powerful metric with which to better
understand the urbanization process itself, given the importance of energy
consumption to the evolution of cities.
The Hestia Project was begun to estimate bottom-up urban fossil fuel
CO2 (FFCO2) fluxes for use within integrated flux information
systems. Begun in the city of Indianapolis, the Hestia effort is now part of
a larger experiment that includes many of the modeling and measurement
aspects described above. Referred to as the Indianapolis Flux Experiment
(INFLUX), this integrated effort has emerged to test and explore
quantification and uncertainties of the urban CO2 and methane
(CH4) measurement and modeling approaches using Indianapolis as the
test bed experimental environment (Whetstone, 2018; Davis et al.,
2017).
Because urban areas differ in key attributes such as size, geography, and
emission sector composition, multiple cities are now being used to test
aspects of anthropogenic CO2 monitoring and modeling. For example,
ongoing efforts in integration of atmospheric measurements and bottom-up
emissions information are taking place in Paris (Bréeon et al., 2015; Staufer
et al., 2016), Boston (Sargent et al., 2018), Salt Lake City (Mitchell et
al., 2018) and London (Font et al., 2015), to name a few. The Hestia
approach has been used in a number of these urban domains. Here, we provide
the methods and results from one of those urban domains, the Los Angeles
Basin megacity. The Hestia-LA effort was developed under the Megacities
Carbon framework (https://megacities.jpl.nasa.gov/portal/, last access: 12 August 2019). It was designed
to serve the Megacities Carbon Project in a similar capacity to its role in
INFLUX. The Hestia-LA results are unique in that they give us the first
high-resolution spatiotemporally explicit inventory of FFCO2 emissions centered over a megacity. A preliminary version of Hestia-LA
containing only the transportation sector emissions was reported by Rao et
al. (2017). While emphasis thus far has been focused on atmospheric CH4 monitoring analyses in the LA megacity (Carranza et al., 2018; Wong et al.,
2016; Verhulst et al., 2017; Hopkins et al., 2016), work is ongoing to use
the extensive atmospheric CO2 observing capacity in the Los Angeles
domain (e.g., Newman et al., 2016; Feng et al., 2016; Wong et al., 2015;
Wunch et al., 2009) within an atmospheric CO2 inversion (i.e., an
approach whereby CO2 concentration measurements in the atmosphere are
combined with models of wind motions to infer what the emissions emanating
from the surface must be).
In this paper, we describe the study domain, the input data, uncertainty,
and the methods used to generate the Hestia-LA (v2.5) data product and
provide descriptive statistics at various scales of aggregation. We compare
the Hestia results to the metro region planning authority estimate and place
the results in the context of urban greenhouse gas mitigation. We discuss
known gaps and weaknesses in the approach and goals for future work.
MethodsStudy domain
The Los Angeles metropolitan area is the second-largest metropolitan area in
the United States and one of the largest metropolitan areas in the world.
Under the definition of the Metropolitan Statistical Area (MSA) by the U.S.
Office of Management and Budget, Metropolitan Los Angeles consists of Los
Angeles and Orange counties with a land area of 12 562 km2 and a
population of 9 819 000. The Greater Los Angeles Area, as a Combined
Statistical Area (CSA) defined by the U.S. Census Bureau, encompasses the
three additional counties of Ventura, Riverside, and San Bernardino, with a
total land area of 87 945 km2 and an estimated population of
18 550 288 in 2014. The Hestia-LA FFCO2 emissions data product covers
the complete geographic extent of these five counties, including the eastern,
relatively non-urbanized portions of San Bernardino and Riverside counties.
Airport emissions associated with aircraft up to 914 m are included, as
are marine shipping emissions out to 22.2 km from the coastal
boundary. Emissions considered here are carbon dioxide only; other important
greenhouse gases such as methane (CH4) and nitrous oxide (N2O) are
not included.
Input data
Input data to the Hestia-LA data product are supplied by output of the
Vulcan Project (Fig. 2), a quantification of FFCO2 emissions at fine
spatial scales and timescales for the entire US landscape (Gurney et al., 2009). The
Hestia-LA process extracts these results for the five counties within the
Hestia-LA domain and adjusts these estimates where superior local data are
available and further downscales and distributes the Vulcan v3.0 results to
buildings and street segments. Details of the Vulcan v3.0 methodology are
provided elsewhere (Gurney et al., 2018). Here, we summarize the Vulcan v3.0
methods and then provide greater detail regarding the Hestia-LA processing
of that data to high-resolution spatial scales and timescales.
The Vulcan v3.0 input data (the output of which is the input for the
Hestia-LA) are organized following nine economic sector divisions (see Table 1) – residential, commercial, industrial, electricity production, on-road,
non-road, railroad, commercial marine vessel, and airport. Also included are
emissions associated with the calcining process in the production of cement.
The data sources within each sector are either acquired as FFCO2
emissions (the on-road sector and most of the non-road and electricity
production sectors) or as carbon monoxide (CO) emissions (all other sectors)
and transformed to FFCO2 emissions via emission factors. Furthermore,
the data sources are represented geographically as either geocoded emitting
locations (“point”) or as spatial aggregates (“nonpoint” or area-based
emissions). Point sources are stationary emitting entities identified with
geocoded locations such as industrial facilities in which emissions exit
through a stack or identifiable exhaust feature (USEPA, 2015a). Area or
nonpoint source emissions are not inventoried at the facility level but
represent diffuse emissions within an individual US county. Because the
focus of the current study is CO2 emissions resulting from the
combustion of a fossil fuels, fugitive or evaporative emissions are not
included and neither are “process” emissions, for example, associated with
high-temperature metallurgical processes. Similarly, emissions associated
with waste decay (organic or inorganic) are not included.
Total annual FFCO2 emissions for the year 2011 from the
Vulcan v3.0 output.
Much of the input data for Vulcan v3.0 are acquired from the Environmental
Protection Agency's (EPA) National Emission Inventory (NEI) for the year
2011 (referred to hereafter as the “2011 NEI”), which is a comprehensive
inventory of all criteria air pollutants (CAPs) and hazardous air pollutants
(HAPs) across the United States (USEPA, 2015b). All of the individual
record-level reporting in the 2011 NEI comes with a source classification
code (SCC) that codifies the general emission technology, fuel type used,
and sector (USEPA, 1995).
FFCO2 emissions from the electricity production sector are primarily
retrieved from two sources other than the 2011 NEI. The first is the EPA's
Clean Air Markets Division (CAMD) data (USEPA, 2015c), which reports
FFCO2 emissions at geocoded electricity production facility locations.
The second is the Department of Energy's Energy Information Administration
(DOE EIA) reporting data (DOE/EIA, 2003), which reports fuel consumption at
geocoded electricity production facility locations. Some electricity
production emissions are retrieved from the 2011 NEI (as CO emissions).
Overlap between these three data sources is eliminated via preference in the
order listed above. A detailed comparison made between the CAMD and EIA
FFCO2 emissions, along with greater detail regarding data sources,
processing, and procedures can be found in Quick (2014) and Gurney et
al. (2014, 2016, 2018).
The 2011 on-road FFCO2 emissions are retrieved from the EMissions
FACtors 2014 model (EMFAC2014), produced by the California Air Resources
Board (CARB, 2014). On-road transportation represents all mobile transport
using paved roadways and includes both private and commercial vehicles of
many individual classes (e.g., passenger vehicles, buses, light duty trucks,
etc). The non-road sector, by contrast, includes all surface mobile vehicles
that do not travel on designated paved road surfaces and a large
class of vehicles such as construction equipment (e.g., bulldozers,
backhoes, etc.), ATVs, snowmobiles, and airport fueling vehicles. The
non-road emissions are derived from the 2011 NEI reporting of non-road CO
emissions. Airport emissions include all the emissions emanating from
aircraft during their taxi, takeoff, and landing cycles up to 914 m and are
derived from the 2011 NEI point reporting. Other activities occurring at
airports resulting in FFCO2 emissions are captured in the commercial
building sector (building heating) or the non-road sector (baggage vehicles),
sourced to the 2011 NEI nonpoint, 2011 NEI point, and 2011 NEI non-road
reporting. Railroad emissions include passenger and freight rail travel and
are sourced to the 2011 NEI nonpoint and point reporting. Commercial marine
vessels (CMV) include all commercial-based aquatic vessels on either ocean
or freshwater, sourced to the 2011 NEI nonpoint reporting. Personal aquatic
vehicles such as pleasure craft and sailboats are included in the non-road
sector. Emissions associated with cement calcining are included given its
potential size and the tradition of including it with CO2 inventories
and use information from multiple sources (PCA, 2006; USGS, 2003; IPCC,
2006).
The FFCO2 emissions input to the Hestia system from the Vulcan v3.0
output is associated with spatial elements represented by points, lines, and
polygons, depending upon the data source, the sector, and the available
spatial proxy data (Table 1). Further spatialization and temporalization
occurs in the Hestia system.
Data sources used in the spatiotemporal distribution of FFCO2 emissions (the text provides acronym explanations and sources).
Sector/typeEmissions data sourceOriginal spatial resolution/informationSpatial distributionTemporal distributionOn-roadEMFACa, EPA NEIb on-roadCounty, road class, vehicle classSCAG AADTcPeMSd, CCSeElectricity productionCAMDfCO2, EIAg fuel, EPA NEI point COLatitude and longitude, fuel type, technologyEPA NEI latitude and longitude, Google EarthCAMD, EIA, and EPAResidential nonpointbuildingsEPA NEI nonpoint COCounty, fuel typeSCAG parcel, floor area, DOE RECS NE-EUIh, LA County building footprinteQUESTiNon-roadNEI nonpoint COCounty, vehicle classEPA spatial surrogates (specific to vehicle class )EPA temporal surrogates (by SCCj)AirportEPA NEI point COLatitude and longitude, aircraft classLatitude and longitudeLAWAkCommercial nonpoint buildingsEPA NEI nonpoint COCounty, fuelSCAG parcel, floor area, DOE CBECS NE-EUIleQUESTCommercial point sourcesEPA NEI point COLatitude and longitude, fuel type, combustion technologyEPA NEI latitude and longitude, Google EartheQUESTIndustrial point sourcesEPA NEI point COLatitude and longitude, fuel type, combustion technologyEPA NEI latitude and longitude, Google EarthEPA temporal surrogates (by SCC)Industrial nonpoint buildingsEPA NEI nonpoint COCounty, fuel typeSCAG parcel, floor area, DOE MECS NE-EUImeQUESTCommercial marinevesselsEPA NEI nonpoint COCounty, fuel type, port and underwayMEMnMEMRailroadEPA NEI nonpoint CO, EPA NEI point COCounty, fuel type, segmentEPA NEI rail shapefile and density distributionEPA temporal surrogates (by SCC)
a Emissions Factors Model.
b Environmental Protection Agency, National Emissions Inventory.
c Southern California Association of Governments, annual average daily
traffic.
d Performance measurement system.
e Continuous count stations.
f Clean Air Markets Division.
g Energy Information Administration.
h Department of Energy Residential Energy Consumption Survey, nonelectric
energy use intensity.
i Quick Energy Simulation Tool.
j Source classification code.
k Los Angeles World Airport.
l Department of Energy Commercial Energy Consumption Survey, nonelectric
energy use intensity.
m Department of Energy Manufacturing Energy Consumption Survey,
nonelectric energy use intensity.
n Marine Emissions Model.
To estimate FFCO2 emissions as a multiyear time series from 2010 to
2015, the results for the year 2011 were scaled using sector, state, and fuel
consumption data (thermal units) from the DOE EIA (DOE/EIA, 2018). The
electricity production sector was an exception to this approach where
year-specific data were available in the CAMD and EIA data sources. Ratios
were constructed relative to the year 2011 in all SEDS sector designations
for each US state. The ratio values are applied to the annual totals in each
of the sector and fuel categories specific to the state FIPS code.
Space and time processingResidential, commercial, and industrial nonpoint buildings
The general approach to spatializing residential, commercial, and
industrial nonpoint FFCO2 emissions is to allocate the county-scale,
fuel-specific annual sector totals to individual buildings (or parcels)
using data on building type, building age, total floor area, energy use
intensity, and location.
A portion of the Hestia-LA building information was provided by the
Southern California Association of Governments (SCAG) (Kimberly
S. Clark and Christine Fernandez, Southern California Association of Governments, personal communication, 2012) and
included building type, age, floor area, and location. The spatial
resolution of this information was at the land parcel scale (larger than the
building footprint). Building footprint data were available in the county of
Los Angeles only, which offered additional building floor area information
needed to correct some floor area values in the SCAG parcel data (LAC,
2016). For example, a large number of commercial parcels with zero floor
area were found in the Riverside County data, which were visually inspected
in Google Earth to include qualifying buildings. These floor area values
were corrected through the combination of the census block-group General
Building Stock (GBS) database from the Federal Emergency Management Agency
(FEMA) (FEMA, 2017) and the National Land Cover Database 2011 (NLCD), which
classifies the US land surface in 30 m pixels (Homer et al., 2015).
Building energy use intensity was derived from data gathered by the DOE EIA
and the California Energy Commission (CEC). The DOE EIA Commercial Buildings
Energy Consumption Survey (CBECS), Manufacturing Energy Consumption Survey
(MECS), and Residential Energy Consumption Survey (RECS) represent regional
surveys of building energy consumption categorized by building type, fuel
type, and age cohort (RECS, 2013; CBECS, 2016; MECS, 2010). Data for the
Pacific West Census Division was used and in the case of the commercial
sector, was appended by the CECs Commercial End-Use Survey (CEUS) data (CEC,
2006).
In the residential sector the nonelectric energy use intensity (NE-EUI) was
calculated from the reported energy consumed and total floor area sampled
specific to five building types (Table 2) in the 2009 RECS survey. This was
additionally categorized by fuel type (natural gas and fuel oil) and two age
cohorts (pre-1980 and post-1979).
Residential NE-EUI survey values by building type from the
Residential Energy Consumption Survey (RECS).
RECS buildingPre-1980Post-1979Pre-1980Post-1979typeNG NE-EUING NE-EUIfuel oil NE-EUIfuel oil NE-EUI(kbtu ft-2) (GJ m-2)(kbtu ft-2) (GJ m-2)(kbtu ft-2) (GJ m-2)(kbtu ft-2) (GJ m-2)Mobile home52.56 (0.5969)22.90 (0.2601)n/a (n/a)n/a (n/a)Single-family detached house24.53 (0.2786)18.00 (0.2044)18.87 (0.2143)7.23 (0.0821)Single-family attached house42.56 (0.4833)32.38 (0.3677)n/a (n/a)n/a (n/a)Apartment building with 2–4 units27.84 (0.3162)42.27 (0.4800)n/a (n/a)n/a (n/a)Apartment building with 5 or more units17.21 (0.1954)30.85 (0.3503)n/a (n/a)n/a (n/a)
“n/a” – not applicable. This indicates that there was no fuel
consumption of this type evident from the survey data.
In the commercial sector, the NE-EUI was similarly calculated from the 2012
CBECS energy consumption micro-data and total floor area sampled specific to
20 building types, two fuel types (natural gas and fuel oil), and two age
cohorts (pre-1980 and post-1979). However, the sampling for the two age
cohorts was insufficient to generate estimates and the age distinction was
eliminated. Furthermore, where the sample sizes remained small, NE-EUI data
from the CEUS was used in place of CBECS estimates (7 of 20 building types
qualified). As the CEUS follows a building typology different from CBECS, a
relationship of building types between the two datasets was necessary (Table 3).
Building type relationship and NE-EUI values for commercial buildings
derived from the CBECS and CUES databases.
* NE-EUI uses the CUES NE-EUI value due to sampling limitations in the CBECS
data.
Unlike the commercial and residential survey data, the 2010 MECS survey data
do not quantify energy consumption for individually sampled buildings but
rather report the sum of the sampled buildings within each census region
categorized by manufacturing sector. The resulting NE-EUI values are shown
in Table 4. Like the commercial data, there was inadequate sampling to
justify two age cohorts.
Industrial NE-EUI survey values from the DOE EIA MECS database.
MECS ClassNG NE-EUIFuel oil NE-EUI(kbtu ft-2) (GJ m-2)(kbtu ft-2) (GJ m-2)Food519.3 (5.897)30.5 (0.346)Beverage and tobacco products162.4 (1.844)8.5 (0.097)Textile mills144.9 (1.646)9.3 (0.11)Textile product mills63.4 (0.720)0 (0)Apparel35.1 (0.399)0 (0)Leather and associated products66.7 (0.757)0 (0)Wood products76.6 (0.870)49.5 (0.562)Paper672.8 (7.641)69.1 (0.785)Printing and related support96.6 (1.10)0 (0)Petroleum and coal products9766.0 (110.9)436.2 (4.954)Chemicals2126.3 (24.15)17.9 (0.203)Plastics and rubber products124.7 (1.416)2.4 (0.027)Nonmetallic mineral products556.0 (6.31)48.9 (0.555)Primary metals895.0 (10.2)16.7 (0.190)Fabricated metal products124.2 (1.410)2.3 (0.026)Machinery78.6 (0.893)3.3 (0.037)Computer and electronic products80.0 (0.91)0 (0)Electrical equip., appliances, and components133.3 (1.514)3.7 (0.042)Transportation equipment100.6 (1.142)4.0 (0.05)Furniture and related products28.6 (0.325)0 (0)Miscellaneous44.7 (0.508)2.8 (0.032)
The NE-EUI values derived from the CBECS/RECS/MECS and CEUS survey data
reflect the total building fuel consumption for a specific fuel in a census
region divided by the total floor area of all buildings in that census
region consuming that fuel. This generates a mean building NE-EUI value.
Actual buildings will vary around that mean value for a variety of reasons
including different occupancy schedules, different energy efficiencies (in
the envelope or heating and cooling system), different microclimate, and other
physical and behavioral characteristics. Furthermore, the NE-EUI value applied
in this way will not capture the reality that some buildings do not use
fossil fuel (electricity-only buildings) or that some buildings use one
fossil fuel only versus another or use a mix of fuels in a proportion
different from the county total. Hence, each building will be allocated a
mix of fossil fuel consumption identical to the county total.
Spatial distribution
The county-scale commercial, residential, and industrial nonpoint FFCO2
emissions are allocated to each land parcel in proportion to the product of
the NE-EUI and the total floor area,
EC(b)sf=NE_EUIsfFA(b),
where the energy consumed, EC, in each building, b, is the product of the
NE-EUI value, NE_EUI, and the floor area, FA, for each fuel, f, and each building in
sector, s. The total energy consumed, TEC, within the county for a sector, s, is
the sum of all the EC values across the N buildings in the sector,
TECsf=∑b=1NEC(b)sf.
To convert this to FFCO2 emissions, we first calculate the fraction of
the total energy consumption associated with each building,
F(b)sf=EC(b)sfTECsf,
where F is the fraction of TEC consumed in building, b, of sector s. This is then
used to distribute the county total FFCO2 emissions as follows:
E(b)sf=EsfF(b)sf,
where E is the FFCO2 emissions either for the county or for building, b, and
fuel. In allocating emissions from coal consumptions, however, NE-EUI takes the
value of “1” for all building types so that the allocated emission in a
building is directly proportional to the floor area.
Temporal distribution
The hourly time structure for buildings in the residential and commercial
sectors are created via the use of eQUEST, a building energy simulation tool
run for each of the building classes listed in Tables 2 and 3 and using
only the temporal structure of the energy consumption output (Hirsch and Associates, 2004). The
model domain is specified as the city of Los Angeles for the year 2011 with
Typical Meteorological Year (TMY) weather data from the DOE (Marion and Urban, 1995). The mean building area is provided
by the parcel data as described previously.
For the industrial buildings, a temporal profile representing the mean of
industrial point source temporal surrogates provided by EPA, are used
(USEPA, 2015a). Figure 3 shows the hourly time profile during a 1-week
period in April for a selected building in the residential and commercial
sector.
Energy consumption intensity (hourly fraction) from an eQUEST
simulation of the average week in 2011 for two types of buildings: “single-family detached house” and “office”.
Industrial and commercial point sources
Little space and time processing is required for industrial and commercial point
source emissions since they are geocoded to specific facilities and emitting
stacks or similar identifiable emission points. However, visual inspection
of the point source locations in Geographical Information System (GIS) suggested potential geocoding errors.
Point source locations were reviewed by searching facility names to an
online address search or via the EPA's Facility Registry Service (FRS), which
can link the facility in question to all the reporting made to the federal
government under other environmental regulations (USEPA, 2013). This often
returns a more accurate physical location. The geolocations considered
inaccurate were manually corrected. Out of the total 192 facilities with
corrected locations, 13 were moved a distance of between 924 and 1022 km,
while the remaining 179 were moved 0.5 km or less. The large magnitude
location changes were likely transcription errors when originally recording
the location coordinates.
A given commercial or industrial point source is typically composed of
multiple emission processes or units. For example, in Los Angeles County,
the 2011 NEI reports a total of 3409 emission records at 842 individual
facilities. In some cases, the multiple emitting points at a facility are
not at exactly the same geocoded point but may represent different emitting
points at a facility that occupies a large area of land. Most often,
however, all emitting points at a given facility are geocoded to the same
latitude and longitude.
The sub-annual temporal distribution for the commercial and industrial point
source emissions used temporal surrogate profiles provided by the EPA,
linked according to the SCC of the emission record (USEPA, 2015a).
Electricity production
As described in Sect. 2.2, three different data sources are used to
quantify the FFCO2 emissions in the Hestia-LA domain: the Clean Air
Markets Division (CAMD), the DOE-EIA reporting, and 2011 NEI CO emissions
data. In 2011 there were a total of 34 CAMD facilities, 228 EIA facilities
and 147 NEI facilities (reported through the NEI 2011 point source file set)
in the Hestia-LA domain. Total electricity production emissions in the
domain were 6.21 MtC yr-1 exclusive of biogenic fuels and 6.68 MtC yr-1 with
biogenics included. The CAMD data are reported at an hourly resolution, while
the DOE EIA data are reported at a monthly resolution and the 2011 NEI data are
reported at an annual resolution only. Reduction of all data to an hourly time
increment was achieved by maintaining constant emissions within a month or
year for the DOE EIA and 2011 NEI data, respectively.
On-road emissions
A preliminary version of the Hestia-LA on-road emissions estimates were
presented by Rao et al. (2017). The version presented here uses updated data
and Hestia methodologies.
Temporal distribution
The Hestia-LA on-road FFCO2 emissions input are retrieved from the
Vulcan v3.0 output spatialized to specific road segments in the Hestia-LA
domain and categorized by vehicle class and fuel. Hence, no further
spatialization was required.
Construction of the temporal distribution in the Hestia system relies upon
the California Department of Transportation (CalTrans) Performance
Measurement System (PeMS) (PeMS, 2018). This dataset contains 2011 traffic
count data collected at 5 min intervals at measuring stations along freeways
and principal arterials and along some minor arterials and collectors (major
and minor). Aggregation of the 5 min counts to hourly values are used to
construct hourly fractions for each measurement station.
To apply a time distribution for the FFCO2 on-road emissions on each
road segment, an inverse distance weighting (IDW) spatial interpolation
method was used. A search within a neighborhood of a 10 km radius was
performed from the midpoint of each road segment to locate PeMS sites using
a nearest neighbor searching library (Mount and Arya, 2010). In cases where
more than one station was available, the IDW interpolation was applied; in
cases where only one station was available, the time structure of this
station was directly assigned to the road segment in question. In cases
where no station was available within the 10 km neighborhood, an average
temporal distribution was assigned (an average of all station values in a
county at that hour for that road type). This last case occurred mostly in
the rural portions of predominantly rural counties.
For local roads, PeMS data were not available in any of the counties within
the Hestia-LA domain. Instead, the weekday hourly time fractions were
generated from Annual Average Weekday Traffic (AAWT) data supplied by SCAG
(Mike Ainsworth and Cheryl Leising, Transportation Planning Department,
SCAG Riverside Office, personal communication, 2014). The data contained five distinct time periods within a single 24 h cycle: 06:00–09:00, 09:00–15:00, 15:00–19:00, 19:00–21:00, 21:00–06:00. Hourly time
fractions for weekends were derived from the county average of weekend
hourly time fractions. The weekday and weekend hourly time fractions were
combined to form a complete week, and then replicated for all 52 weeks in
the entire year. This was done because there was no significant seasonality
in weekday and weekend traffic across the year as observed from PeMS data.
Non-road
The non-road Hestia-LA FFCO2 emissions are completely determined in the
Vulcan system, and hence passed to the Hestia-LA domain without further
processing (see Gurney et al., 2018 for details). To summarize the Vulcan
process, California did not report FFCO2 non-road emissions to the NEI
2011 but did report non-road CO emissions. The CO emissions were converted to
FFCO2 using the SCC-specific ratios of CO2/CO derived from all
other states that reported both species (a mean value). The spatial
distribution of the non-road FFCO2 emissions followed two approaches.
Non-road FFCO2 emissions reported through the 2011 NEI point data source
(five locations, 12 % of non-road FFCO2 in the LA megacity) are located
in space according to the provided latitude and longitude. Emissions
reported through the county-scale non-road data source utilize multiple
spatial surrogates provided by the EPA, reflecting a series of spatial
entities such as the mines, golf courses, and agricultural lands. There were
instances in which non-road FFCO2 emissions could not be associated with
a spatial entity due to missing data. These emissions are spatialized by
first aggregating all the offending sub-county emission elements within a
county for a given surrogate shape type (e.g., golf courses, mines) and then
distributing these emissions evenly across the county.
To distribute the non-road FFCO2 emissions from the annual to hourly
timescale, a series of surrogate time profiles provided by the EPA are used.
These temporal surrogates are comprised of three cyclic time profiles
(diurnal, weekly, monthly) specific to SCC that are combined to generate
hourly SCC-specific time fractions for an entire calendar year.
Airport
Emissions of FFCO2 from airports retrieved from the Vulcan system for
the Hestia-LA domain are specific to geocoded airport locations. Hence, the
Hestia-LA system performs the temporal distribution only. There are 374
commercial airports and helipads in the Hestia-LA domain totaling 0.77 MtC yr-1,
dominated by Los Angeles County (0.39 MtC yr-1), and LAX in particular.
The annual airport FFCO2 emissions are distributed in time utilizing
airport-specific flight volume data from four datasets:
the Operations Network (OPSNET) data from the Federal Aviation
Administration (FAA), which reports total date-specific daily flight volume
(365 values) at specific airports for specific aircraft classes (FAA,
2018a);
“AIRNAV” data that reports average daily percentage flight volume for
aircraft class at US airports and facilities (AirNav, http://www.airnav.com/airports/, last access: 1 August 2018);
the Enhanced Traffic Management System Count (ETMSC) daily flight volume
data from the FAA for two airports' in the Hestia-LA domain (NTD and RIV)
with mostly military operations (FAA, 2018b);
the Los Angeles World Airport (LAWA) data, which reports hourly flight
volume for Los Angeles International Airport (LAX), Ontario Airport (ONT),
and Van Nuys Airport (VNY) (Norene Hastings, Environmental Supervisor, Environmental Services division, Los Angeles World Airports, personal communication, 2014).
For three large airports (LAX, ONT, VNY), the daily aircraft class-specific
flight volume (from OPSNET) and the hourly data of flight volume (from LAWA)
were combined to create hourly aircraft class-specific time profiles (Figs. 4–6). All of the flight volume data are specific to four aircraft classes:
Military (MIL), Air Carrier (AC), General Aviation (GA), and Air Taxi (AT).
Average hourly flight volume at LAX for (a) total, (b) AC, (c) AT, (d) GA, and (e) MIL aircraft classes for each day of the week. The plots
represent the mean diurnal cycle for all Mondays, Tuesday, Wednesdays, and
so on, given a full year of data.
Same as Fig. 4 but for the Ontario (ONT) airport.
Same as Fig. 4 but for the Van Nuys (VNY) airport.
To generate hourly time profiles for all other airports in the Hestia-LA
domain for which this type of detailed hourly data were not available,
airports first were categorized based on average daily flight volumes and
average aircraft class proportions from the OPSNET, AIRNAV and ETMSC data.
Each airport was categorically matched to one of the two non-international
airports with hourly data (ONT, VNY) and the hourly time fractions adopted.
LAX was unique in terms of its volume and aircraft class proportions and
hence was not used for any other airports. For helipads and very small
airports, a flat time structure was used.
Railroad
Railroad FFCO2 emissions are similarly distributed in space within the
Vulcan system and passed through to the Hestia-LA landscape without
alteration (see Gurney et al., 2018 for additional details). The Vulcan
process treats railroad point records somewhat differently from the railroad
nonpoint records. The point source railroad emissions are associated with
rail yards and related geo-specific locales and are placed in space
according to the provided latitude and longitude. The railroad FFCO2
emissions associated with the nonpoint 2011 NEI reporting contain an ID
variable that links to a spatial feature (rail line segment) in the EPA
railroad GIS shapefile. Nearly two-thirds of the railroad emitting segments
have no segment link. The sum of these “unlinked” railroad FFCO2
emissions are distributed to rail line within the given county according to
freight statistics. The annual railroad FFCO2 emissions are distributed
to the hourly timescale with no additional temporal structure (a “flat”
time distribution).
Commercial marine vessels
The commercial marine vessel (CMV) FFCO2 emissions retrieved from the
Vulcan system are specific to county and SCCs, which are subsequently
aggregated by the Hestia-LA system into emissions associated with two
activity categories: “port” emissions and “underway” emissions. For the port CMV
emissions (Fig. 7), a port shapefile from the EPA was used as a reference
along with a visual inspection of the coastline (USEPA, 2015a).
Allocation of the FFCO2 emissions designated as “underway” used a
polyline shapefile (Fig. 8) of commercial shipping lanes in California
provided by CARB (Andy Alexis, California Air Resources
Board, personal communication, 2011, https://www.arb.ca.gov/ports/marinevess/ogv/ogv1085.htm, last access: 6 July 2011). The shipping lanes for each county were
bounded so that only lanes between the exterior of ports and a distance of
38.6 km from the port exterior were included. County total FFCO2
emissions were then distributed evenly to these shipping lanes on a per unit
length basis individually for each of the three counties. Each shipping lane
segment receives its length fraction of the annual total of underway
emissions.
The time profile was based on the Marine Emissions Model (MEM) developed by
CARB. MEM had marine vessel activity data, which includes the arrival time of
ocean-going vessels for all ports in California spanning the 2004 to 2006
time period (Andy Alexis, California Air Resources
Board, personal communication, 2011). This hourly dataset was analyzed using a Fourier
time series, which allowed for an isolation of the dominant cycles of ship
traffic in the data. Results from the Fourier fit were then used to fill in
the missing hours. Weekday hours were examined separately from weekend hours
to isolate potential differences in traffic volume. Three cycles resulted: a
24 h diurnal cycle, a weekly cycle, and a monthly cycle. These were
applied to all years of the annual FFCO2 emissions to create an hourly
distribution at each of the CMV ports within the domain.
Cement
Emissions of FFCO2 from cement production facilities retrieved from the
Vulcan system for the Hestia-LA domain are specific to geocoded facility
locations. CO2 is emitted from cement manufacturing as a result of fuel
combustion and as process-derived emissions (Andrew, 2018). The emissions
from fuel combustion are captured in the industrial sector. The
process-derived CO2 emissions result from the chemical process that
converts limestone to calcium oxide and CO2 during “clinker”
production (clinker is the raw material for cement, which is produced by
grinding the clinker material). These emissions are reported as cement
sector emissions.
These emissions are fully calculated, spatialized and temporalized in the
Vulcan v3.0 system and passed directly to the Hestia-LA landscape. The
cement facilities are geocoded with some corrections to provide more
accurate placement of the emission stacks.
Gridding
The county-level FFCO2 emissions inventory, which has been distributed
into the point, line, and polygon features by sector, is rasterized into a
sector-specific and time-resolved into gridded form under a common grid
reference. This grid reference divides the entire Hestia-LA domain into
509 by 342 1 km × 1 km grid cells on the California State Plane Coordinate
System. The grid reference is made into “fishnet” in the shapefile format
with 509 × 342 grid cells.
The first step of the gridding procedure is to perform a spatial
intersection operation between the fishnet and each of the sectoral
emissions layers in ArcGIS. The output of an intersection operation is a new
set of features common to both input layers. The emissions value of each
feature in the intersection output was scaled by the ratio of the spatial
footprint of the feature to that of the original feature in the sectoral
emissions layer. For line-sourced and polygon-sourced emissions layers, the
spatial footprint represents the line length and polygon area, respectively.
For point-sourced layers, the footprint is equal to 1.
Uncertainty
Uncertainty estimation for Hestia results is challenging owing to the fact
that many of the datasets used to construct the flux results are not
accompanied by uncertainty or traceable to transparent sources or methods.
The approach taken for the Hestia-LA v2.5 results was to conservatively
estimate the uncertainty based on available comparisons to Hestia results
and exploration of the dominant components of the Hestia output. The first
of these is a comparison of the Hestia-Indianapolis (Hestia-Indy) results to
an inverse estimation of fluxes in the INFLUX project (Gurney et al., 2017).
In that study, it was shown that the Hestia-Indy whole-city FFCO2
emissions result agreed with an inverse estimate (Lauvaux et al., 2016)
within 3.3 % (CI: -4.6 % to +10.7 %). This suggests both potential
bias (3.3 %) and an estimation uncertainty (∼7.5 %). This
comparison was accomplished by estimating portions of the carbon budget,
included in the inverse estimate but not explicitly included in the
Hestia-Indy result. Most importantly, biosphere respiration estimated from
chamber studies at commensurate urban latitudes was combined with a
remote-sensing based approach to quantifying the available vegetated
landscape. This comparison, it should be noted, is for a single city
(Indianapolis) for a single time period. We directly sum the random and
systematic error and use this in the current study to represent the
Hestia-LA whole-city uncertainty (a 95 % CI), rounded up to 11 %.
The next element for consideration with a conservative uncertainty estimate
is the work done to compare two different electricity production FFCO2
estimates in the US. This work (Gurney et al., 2016) found that one-fifth of
the facilities had monthly FFCO2 emission differences exceeding
-6.4%--+6.8% for the year 2009 (the closest analyzed year to the 2011
analysis examined here). The distributions of emissions of the two datasets
were not normally distributed and neither were the differences. Hence, a typical
Gaussian uncertainty estimate cannot be made – rather, the difference
distribution was represented by quintiles of percentage difference. Hence,
these values cannot be cast within the context of other normally distributed
errors. However, we conservatively consider the quintile value (the positive
and negative tails) as a 1σ value and 13 % as a 2σ value.
The contribution of electricity production is important to urban FFCO2
emissions uncertainty given how large power production can be within the
total urban FFCO2 context. For example, in the Los Angeles megacity,
electricity production accounts for 19 % of the total FFCO2
emissions. The percentage differences can act as a form of uncertainty at
the point-wise or (conservatively) the grid-cell scale, though only
representative of the type of uncertainties represented by electricity
production point sources.
Finally, an initial assessment of the range of two critical parameters in
the Vulcan–Hestia estimation is included as part of the conservative
uncertainty estimation. The two critical parameters are the CO emissions
factor and the CO2 emissions factor. Primarily for the CO EF, there is
a range of potential values for each application (combination of fuel
category and combustion technology) though that range is not represented by
a well-populated distribution of values but rather a discrete set of values
within the data sources described in Gurney et al. (2009). Furthermore, the
expectation is that the CO EFs would not be normally distributed even if there were
to be a well-populated distribution of values (i.e., many literature
estimates of the same fuel and combustion technology) owing to the nature of CO
emissions from fuel combustion. This is driven by both the variation in
combustion conditions for a given fuel–technology combination and the
variation is CO EF values across combustion technology. The distribution
would likely be a positively skewed “heavy-tailed” or “long-tailed”
distribution. For the current study, a range of the CO and CO2 EF
values culled from the literature are conservatively assigned a 1σ
uncertainty of 10 % or a 2σ value of 20 %. Like the electricity
production analysis in the previous paragraph, the uncertainty associated
with the CO and CO2 emission factors is a grid-cell-scale uncertainty
(as opposed to whole city where error cancelation occurs) and is independent
of the electricity production uncertainty estimate (the CO and CO2 EF
values are not used in the electricity production sector but in the other
point sources and nonpoint sources).
These latter two uncertainties are more representative of grid-cell-scale
uncertainties and sum them in quadrature to arrive at a grid-cell-scale
uncertainty (95 % CI) of 23.4 % or conservatively rounded to 25 %.
Work is underway that includes a complete input parameter range for the
Hestia emissions data results to more formally assign uncertainty at
multiple scales.
Results
The total 2011 emissions for the Hestia-LA domain are 48.06±5.3 MtC yr-1 (Fig. 9, Table 5). Transportation accounts for the largest share
(24.27±2.7 MtC yr-1) of the total, and within the transportation
sector, on-road emissions account for the largest portion (20.81±2.3 MtC yr-1). The next largest sectors are the industrial (11.65 MtC yr-1±1.3) and electricity production (5.88±0.76 MtC yr-1) sectors. On-road, electricity production, residential, and industrial
FFCO2 emissions make up 86 % of the total. Petroleum accounts for
almost 75 % of the total LA megacity fuel consumption for direct
FFCO2 emissions, consistent with the dominance of the transportation and
industrial sectors, which are mostly reliant on petroleum fuels. Los Angeles
County dominates emissions in the five counties of the Hestia-LA domain,
accounting for 55 % of the total FFCO2 emissions. This is followed by
San Bernardino, Orange, Riverside, and Ventura counties, respectively. Los
Angeles and San Bernardino counties are dominated by on-road and industrial
FFCO2 emissions, while on-road emissions account for the largest share in the remaining three counties by far. Not surprisingly, Los Angeles
county has the largest CMV FFCO2 emissions among the five counties,
owing to the port of Los Angeles, which hosts a large amount of international
commercial shipping. At 0.61±0.067 MtC yr-1, it rivals in emission
magnitude the combination of residential and commercial building emissions
in each of the other four counties.
Total FFCO2 emissions proportions for the Hestia-LA domain:
(a)FFCO2 emission proportions by sector and (b)FFCO2 emission
proportions by fuel category.
Sectoral FFCO2 emissions in the five Hestia-LA domain counties
for the year 2011.
Total emissions in the LA megacity show a small downward trend over the
2010–2015 time period of 0.44 % yr-1, which is a statistically significant
trend (slope: -0.21 MtC yr-1; CI: -0.397, -0.023). Individual sectors show
greater variation and there are compensating temporal changes among the
individual sectors (Fig. 10). The residential sector showed a relatively
large decline in 2014, though due to its relatively small portion of total
emissions, has limited impact on the total temporal variation from
2010–2015. Similarly, 2015 showed a large increase in commercial sector
emissions, which also do not translate to large changes in the total
FFCO2 emissions time series. The relative temporal stability of the
industrial and on-road FFCO2 emissions sectors, combined with their large
share of the total FFCO2 emissions, are reflected in the total emissions
trend. When categorized by fuel type, natural gas FFCO2 emissions
exhibited the greatest variation with a maxima in 2012 and to a lesser
extent 2013, driven primarily by consumption in the electricity production
sector.
Fractional changes over the 2010 to 2015 timeframe in LA Basin
FFCO2 emissions: (a) by fuel category and (b) by sector. Whole-city error
is provided for the total FFCO2 emissions only.
Spatial distribution of the Hestia-LA FFCO2 emissions demonstrates the
importance of the populated areas and road-intensive portions of the domain
in the overall emissions (Fig. 11). The constant emissions that appear
over large areas, particularly in San Bernardino and Riverside counties, are
due to the non-road FFCO2 emissions, which have relatively simple spatial
distribution proxies with considerable areal extent.
Hestia-LA v2.5 FFCO2 emissions for the year 2011 represented
on a 1 km × 1 km grid: (a) total FFCO2 emissions, (b) on-road
FFCO2 emissions, (c) residential FFCO2 emissions, and (d) commercial
FFCO2 emissions. Units: natural logarithm of kgC yr-1.
Figure 12 shows the cumulative FFCO2 emissions across four of the
sectors for which the 1 km2 grid-cell accumulation is most appropriate:
the commercial, industrial, on-road, and residential sectors. The other
FFCO2 emission sectors (airport, electricity production, cement) are
not included in Fig. 12 because they are dominated by a few points, have
limited spatial distribution (railroad), or no spatial variance (non-road).
The accumulation of FFCO2 emissions at the threshold by which 10 % of
the grid cells are accumulated is noted in the figure. For the industrial
sector, 10 % of the largest-emitting grid cells account for 93.6 % of the
total industrial sector emissions. For the commercial sector, this occurs at
73.4 % of the accumulated grid cells. For the on-road and residential
sectors this occurs at 66.2 % and 45.3 %, respectively. This
demonstrates two important points about the FFCO2 emissions in the Los
Angeles megacity (and most cities). First, the emissions have very high
spatial variance with few grid cells accounting for a large portion of the
total FFCO2 emissions. Second, this is particularly true for the
industrial sector, driven by the fact that it is comprised of a large
proportion of point emitters. This is somewhat true of the commercial sector,
which does have some point-wise data within the original NEI reporting. Of
the remaining two sectors, which contain no point-wise spatial emitters, the
majority (66.2 %) of the on-road emissions are captured in the largest
10 %, while the residential sector, being less concentrated, shows an
accumulation just short of the 50 % threshold at a 10 % grid-cell
accumulation threshold.
Cumulative FFCO2 emissions according to key sectors in the
Hestia-LA FFCO2 emissions data product. The dashed line at 10 %
cumulative grid cells is given for reference. See text for details.
An important attribute of estimating urban emissions at fine spatial scales and timescales is the resulting clustering in space (and time) of the emissions and
the varying patterns of the clustering across the emitting sectors. Figure 13 provides an analysis of spatial clustering using the Getis–Ord Gi statistic, which
provides a score that measures statistically significant departures from
random local clustering (Getis and Ord, 1992). The three sectors included in this
analysis are the residential, commercial, and on-road sectors. The on-road
sector shows a more widely dispersed clustering pattern with local
“hotspots” generated by high traffic flow points and traffic congestion,
primarily on the interstate network, coincident with a greater density of
commercial and residential activity. The residential sector exhibits less
extensive emissions compared to the on-road FFCO2 emissions clustering but with
larger individual hotspot areas. Particularly large clustering occurs from
the coast centered on Santa Monica and Marina del Rey and extending east and
north through West Hollywood on to Pasadena and Alhambra. Other hotspots
occur in the Manhattan Beach to Redondo Beach corridor, the Burbank and
Glendale area, and the coastal portion of Orange county (e.g., Huntington
Beach, Newport Beach). The commercial sector shows a similar overall
extensivity to the residential sector but with less extensive individual
hotspots associated with commercial building clusters.
The Getis–Ord Gi z score for Hestia-LA FFCO2 emissions across three
sectors: (a) commercial, (b) on-road, and (c) residential.
There are very few estimates that can serve as an assessment of the accuracy
of the Hestia FFCO2 emissions, as few inventory efforts have been
accomplished at the sub-state spatial scale in the United States. However,
the Southern California Association of Governments (SCAG) has completed a
regional greenhouse gas emissions inventory for a base year period of
1990–2009 with projections out to the year 2035 (Kimberly
S. Clark and Christine Fernandez, Southern California Association of personal communication, 2012). The SCAG
inventory reflects two components that make a comparison to the Hestia-LA
FFCO2 emissions data product imperfect. First, the domain considered in
the SCAG inventory includes Imperial county, a county not included in the
Hestia-LA domain. However, Imperial county is estimated to be less than a
few percent of the SCAG domain total. For example, Imperial county on-road
VMT is 1.9 % of the SCAG domain total. The Imperial county retail sales of
electricity is 1.1 % of the SCAG domain total. The other distinction is
that the SCAG inventory reports total GHGs, inclusive of both methane
(CH4) and nitrous oxide (N2O). However, in the sectors and
activities used for comparing the SCAG inventory to the Hestia-LA FFCO2
emissions data product, both CH4 and N2O are negligibly small.
Hence, small differences (<5 %) could be due to these categorical
discrepancies. We use only the reported Scope 1 emissions which were based
on the approach adopted by CARB based on guidelines from the
Intergovernmental Panel on Climate Change (CARB, 2010).
Figure 14 shows a 2010 comparison between the two estimates using the
comparable sector divisions. The Hestia-LA FFCO2 emissions estimate is
10.7 % larger than the SCAG estimate, 95 % of the difference (4.46 MtC yr-1) owing to the larger industrial and electricity production FFCO2
emissions in the Hestia estimate. We have included the non-road sector in the
on-road category as the SCAG inventory did not explicitly include a non-road
sector. SCAG documentation suggests that the non-road sector is included in
the forecasts for the residential, commercial, and industrial sectors (Kimberly
S. Clark and Christine Fernandez, Southern California Association of Governments, personal communication, 2012, Parcel Data GIS
shapefiles p. C-10), but further details on the base year estimates could not be
found and no mention is made in the report where these sectors are
described. If the Hestia non-road estimate (1.56 MtC yr-1) was not allocated
to the on-road sector but distributed to the residential, commercial, and industrial
sectors it would exacerbate the difference in the on-road, commercial, and
industrial sectors.
Comparison of sector-specific FFCO2 emissions for the year
2010 between the Hestia-LA and SCAG estimates. Units: MtC yr-1.
The California Energy Commission archives energy consumption data for both
natural gas and electricity (http://ecdms.energy.ca.gov/, last acces: 12 August 2019). The data are
archived as specific to the residential sector and the nonresidential
sector. Because of ambiguities regarding the nonresidential sector
definition, we compare the reported values by county for the residential
only (Table 6). Good agreement for natural gas FFCO2 emissions is
achieved for the Los Angeles megacity as a whole (<1 %) with some
variation at the scale of the individual counties. Agreement with the CEC
estimate is better than that found for the comparison with the SCAG
inventory (Hestia being 3.1 % lower than the SCAG residential NG
FFCO2 estimate).
Average hourly variations in FFCO2 emissions are sensitive to both the
sector and spatial location. Figure 15 presents annual mean diurnal patterns
specified by county and sector (the railroad or cement sectors were
constructed with no diurnal cycle and hence are not shown). As noted
previously, Los Angeles county shows the greatest emissions overall,
particularly for the commercial marine vessel sector where the port of Los
Angeles dominates. The commercial, residential, on-road, and CMV sectors
exhibit two maxima, one in the morning (∼05:00–10:00 local time)
and another in the afternoon–evening. In the commercial sector, this
afternoon–evening maximum occurs later in this time period centered on 21:00
local time, coinciding with retail closing schedules. The maximum CMV
emissions are shifted by roughly 2 h earlier in the day for both the
morning and afternoon–evening peaks. The afternoon–evening maximum for the
on-road sector shows an afternoon–evening maximum that is of longer duration
than that in the morning, with emissions gradually rising after the midpoint
of the day, local time. In addition to large daily variations, the on-road
sector contains a significant weekly temporal pattern with emissions largest
on Monday and smallest on Saturday (Fig. 16).
Diurnal patterns in on-road and airport FFCO2 emissions have a single
maximum at the middle of the day but broadly extending across all daylight
hours. In the case of the non-road emissions, this is simply a reflection of
the EPA temporal surrogate applied. In the case of the airport FFCO2
emissions, the time structure reflects the reported air traffic volume at
the major airports in the LA megacity. Finally, the industrial and
electricity production sectors maintain relatively constant emissions across
the entire 24 h. In the case of the industrial sector, this reflects the
integration of industry-specific EPA temporal surrogates within a given
county. For the electricity production sector, the time structure is
primarily driven by the stack-monitored emissions and shows slightly
greater emissions in the evening hours compared to all other hours.
Residential natural gas FFCO2 emissions in the five Hestia-LA
domain counties for the year 2011 compared to estimates from the California
Energy Commission (CEC). Units: MtC yr-1.
The diurnal patterns are consistent across all five counties with the
exception of the commercial sector where there are small differences in the
maximum point of the morning emissions in San Bernardino and Ventura
counties compared to the other LA megacity counties.
Average daily FFCO2 emissions in the Hestia-LA v2.5 data
product for five counties across eight sectors: (a) residential, (b) on-road,
(c) commercial, (d) airport, (e) commercial marine vessel, (f) electricity
production, (g) industrial, and (h) non-road. Note the different scale ranges on each
plot. Units: kgC h-1.
Average weekly on-road FFCO2 emissions from the Hestia-LA
v2.5 data product for five counties. Units: kgC d-1.
Discussion
The first Hestia urban FFCO2 emissions data product was produced for
the Indianapolis domain (Gurney et al., 2012). As an outcome of the Hestia
effort, a large multifaceted effort, the Indianapolis Flux Experiment
(INFLUX), emerged (Whetstone, 2018; Davis et al., 2017). INFLUX aims
to advance quantification and associated uncertainties of urban CO2 and
CH4 emissions by integrating a high-resolution bottom-up emission data
product, such as Hestia, with atmospheric concentration measurements
(Turnbull et al., 2015; Miles et al., 2017; Richardson et al., 2017), flux
measurements (Cambaliza et al., 2014, 2015; Heimburger et al., 2017), and
atmospheric inverse modeling. In addition to its use as a key constraint in
the INFLUX atmospheric inverse estimation (Lauvaux et al., 2016), Hestia has
been informed by atmospheric observations making it useable as a stand-alone
high-resolution flux estimate offering a detailed space-time understanding
of urban emissions. Begun in the late 2000s, INFLUX has explored many
aspects of the individual elements of a scientifically driven urban flux
assessment (e.g., Wu et al., 2018), in addition to demonstrating potential
reconciliation between Hestia and the atmospheric measurements (Gurney et
al., 2017; Turnbull et al., 2015). Similar efforts are ongoing in the Salt
Lake City (Mitchell et al., 2018; Lin et al., 2018) and Baltimore (Martin et
al., 2018) domains with a different arrangement of atmospheric monitoring
and modeling. As with INFLUX, a Hestia FFCO2 emissions data product was
produced in each domain (Patarasuk et al., 2016; Gurney et al., 2018).
The Hestia Los Angeles megacity effort was developed under the Megacities
Carbon Project framework (https://megacities.jpl.nasa.gov/portal/, last access: 12 August 2019). It was
designed to serve the Megacities Carbon Project in a similar capacity to its
role in INFLUX. The Hestia-LA results are unique in that it is the first
high-resolution spatiotemporally explicit inventory of FFCO2 emissions centered over a megacity. Presented here at the 1 km2
spatial and hourly temporal resolution, the emissions can be represented at
finer spatial scales down to the individual building, though with higher
uncertainty. While policy emphasis in California thus far has been focused
on CH4 emissions (Carranza et al., 2018; Wong et al., 2016; Verhulst et
al., 2017; Hopkins et al., 2016), work is ongoing to use the extensive
atmospheric CO2 observing capacity in the Los Angeles domain (e.g.,
Newman et al., 2016; Wong et al., 2015; Wunch et al., 2009) within an
atmospheric CO2 inversion. This will offer an important evaluation of
the Hestia-LA emissions for which limited independent evaluation is
currently available.
The potential of the Hestia-LA FFCO2 emissions to enable or assist with
policymaking in the city, county, or metropolitan planning domain of the
overall Southern California area is considerable. The traditional urban
inventory approach, such as that accomplished by many cities as part of their
climate action plans, are whole-city accounts, often specific to sector,
that follow one of a few inventory protocols. Given the challenges of data
acquisition and the idiosyncrasies of protocol choice and needs, the
traditional urban inventories are difficult to compare across cities and
hence aggregate reliably in a metropolitan domain such as the LA megacity.
Importantly, without explicit space and time emissions information, they are
difficult to calibrate with atmospheric measurements and hence evaluate
against this important scientific constraint. The Hestia-LA FFCO2
emissions approach attempts to overcome these limitations to traditional
inventory work. By quantifying emissions at the scale of individual
buildings and road segments, with process detail such as the sector, fuel,
and combustion technology, Hestia results can be organized according to most
of the protocols in use by cities. This explicit space and time detail also
allows for calibration to atmospheric measurements, for which emission
location and time structure is essential.
The state of California continues to lead the nation in climate policy with
numerous legislative and executive orders outlining both general reduction
goals and specific policy instruments. The California Global Warming
Solutions Act (Assembly Bill 32) passed in 2006, specifies a statewide
reduction in greenhouse gas emissions to 1990 levels by the year 2020
(https://www.arb.ca.gov/cc/ab32/ab32.htm, last access: 12 August 2019). Furthermore, the bill requires
reporting and verification of reductions in order to demonstrate compliance.
Executive order B-30-15 and Senate Bill SB 32 have built on this with an
aim to reduce emissions 40 % below 1990 levels by 2030 and 80 % below
1990 levels by 2050, respectively
(https://www.ca.gov/archive/gov39/2015/04/29/news18938/, last access: 12 August 2019;
https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201520160SB32, last access: 12 August 2019). Ultimately, much of the specific action needed to meet
these goals will rest upon local governments and authorities. Given that
87 % of the state population resides in urban areas and nearly half of
state population resides in the Los Angeles megacity, the cities and
counties that comprise the Los Angeles metropolitan area have a central role
to play in achieving the statewide climate change policy goals. The city of
Los Angeles, the largest individual city in the metro region, has specified
goals consistent with the state commitments, expecting to reduce greenhouse
gas emissions to 35 % below 1990 levels by the year 2030
(https://coolcalifornia.arb.ca.gov/story/city-of-los-angeles, last access: 12 August 2019). To meet these reduction goals, policy actions will become
increasingly difficult to achieve at no or low cost and economic efficiency
will become central to making policy choices.
The most important attribute of the Hestia-LA approach, therefore, is the
potential it offers for targeting urban CO2 reduction policy more
efficiently. As shown in Figs. 12 and 13, FFCO2 emissions are highly
variable in space and typically cluster in concentrated areas. In choosing
specific policy approaches and instruments, this offers Los Angeles
policymakers the ability to target specific neighborhoods, road segments, or
commercial hubs, where policies will achieve the greatest reduction for
resources expended. This rests on the argument that specificity leads to
efficiency. As all cities, including those in the Los Angeles megacity, move
towards those aspects of carbon emission reductions that are not part of the
“low-hanging fruit” policy instruments, competition for limited resources
and policy justification will increase. Having information that targets the
most efficient and effective emission reduction investments, established by
independent rigorous scientific information, will be at a premium. For
example, if a small proportion of the commercial sector buildings in the LA
megacity account for a large proportion of the FFCO2 emissions, knowing
the location of these buildings and targeting energy efficiency programs to
those buildings, may offer the most economically efficient route to
emissions reductions in the commercial sector. A similar argument can be
made in the on-road sector due to the clustering of large on-road emitting
grid cells and specific road-class attributes (see Rao et al., 2017).
A number of caveats are worth mentioning in association with the Hestia-LA
v2.5 FFCO2 emissions results. With Vulcan v3.0 as the starting point
for the quantification in Hestia, errors in Vulcan will be passed to Hestia,
with a few exceptions. Of particular note are the industrial sector and, more
specifically, refining operations which have limited emissions reporting.
These remain difficult to quantify due to the range of CO emission factors
representing many of the combustion processes undertaken at these large and
complex facilities. The uncertainty estimation described remains limited and
there are additional sources of uncertainty that must be quantified such as
categorical errors (e.g., misspecification of fuel category or road class),
errors in spatial accuracy and spatial error correlation. Quantifying these
contributions to the overall uncertainty presented here remain a task for
future work.
Data availability, policy, and future updates
The Hestia-LA v2.5 emissions data product can be downloaded from the data
repository at the National Institute of Standards and Technology (https://doi.org/10.18434/T4/1502503, Gurney et al., 2019) and is distributed under Creative
Commons Attribution 4.0 International (CC-BY 4.0,
https://creativecommons.org/licenses/by-nc-sa/4.0/, last access: 12 August 2019). The Hestia-LA v2.5
FFCO2 emissions data product is provided as annual and hourly (local
and UTC versions) 1 km × 1 km NetCDF file formats, one file for each of the
6 years (2010–2015). The hourly files are approximately 2.9 GB each. The
annual files are 0.34 GB each.
Attempts will be made to update the Hestia-LA FFCO2 emissions on a
roughly bi-annual basis, depending upon support, the availability of updates
to the Vulcan FFCO2 emissions data product, and updates to the
additional data sources described in this study.
Conclusions
The Hestia Project quantifies urban fossil fuel CO2 emissions at high
space- and time-resolution with application to both scientific and policy
arenas. We present here the Hestia-LA version 2.5 FFCO2 emissions data
product, which represents hourly, 1 km2, sector-specific emissions for
the five counties of the Los Angeles metropolitan area for the 2010 to 2015
time period. The methodology relies on the results of the Vulcan Project
(version 3.0) further enhancing and distributing emissions to the scale of
individual buildings and road segments with local data sources acquired from
local government agencies. Each sector is quantified using data sources and
spatial and temporal distribution approaches distinct to the sector
characteristics. The results offer a detailed view of FFCO2 emissions
across the LA megacity and point to the extreme spatial variance of
emissions. For example, 10 % of the 1 km2 emitting grid cells account
for 93.6 %, 73.4 %, 66.2 %, and 45.3 % of the emissions in the
industrial, on-road, commercial, and residential sectors, respectively. We
find that the LA megacity emitted 48.06±5.3 MtC yr-1 in the year 2011,
dominated by Los Angeles county (26.42±2.9 MtC yr-1) and from a
sector-specific viewpoint, dominated by the on-road sector (20.81±2.3 MtC yr-1). Hestia FFCO2 emissions are 10.7 % larger than the inventory
estimate generated by the local metropolitan planning agency, a difference
that is driven by the industrial and electricity production sectors. Good
agreement is found (<1 %) when comparing residential natural gas
FFCO2 emissions to utility-based reporting at the county spatial scale.
The largest temporal variations are found in the diurnal cycle with the
residential, commercial, on-road, and commercial marine vessel emissions
showing two maxima, one in the morning and a second in the afternoon–evening.
Airport and non-road emissions, by contrast show broad maxima across the
daylight hours. Finally, the industrial and electricity production sectors
show little diurnal variation across 24 h. The on-road sector also
exhibits variation in the weekly distribution of emissions with maximum
FFCO2 emissions on Monday and minimum emissions on Saturday.
The Hestia-LA v2.5 FFCO2 emissions data product offers the scientific
and policymaking communities unprecedented spatially and temporally resolved
information on FFCO2 emission sources in the Los Angeles megacity. As
part of the Megacities Carbon Project, future work includes incorporation
into atmospheric CO2 inversion research to further evaluate the
Hestia-LA data product and improve estimation. Policymakers can use the
Hestia-LA results to better understand FFCO2 emissions at the human
scale, offering the potential for improved targeting of FFCO2 reduction
policy instruments. Finally, urban researchers can use Hestia-LA to explore
a number of important urban science questions such as how emissions
intersect with other urban sociodemographic variables such as income,
education, housing size, or vehicle ownership.
The Hestia-LA data product is publicly available and will be updated with
future years as data becomes available.
Author contributions
KRG conceived of the content, performed portions of the analysis, and wrote the paper. RP, JL, and PR contributed data product development and analysis. YS and DO'K contributed to development and analysis. JW, RD, AE, and CM contributed conceptual content.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We thank Lan Nguyan from the City of Los Angeles, Andy Alexis from the California Air Resources Board, and Cheol-Ho Lee at the Southern California Association of Governments for guidance on data use.
Financial support
This research was made possible through support from the National Aeronautics and Space Administration Carbon Monitoring System program, the Understanding User Needs for Carbon Information project (subcontract no. 1491755), the National Aeronautics and Space Administration (grant no. NNX14AJ20G), the National Institute of Standards and Technology (grant nos. 70NANB14H321 and 70NANB16H264), the Jet Propulsion Laboratory’s Strategic University Research Partnership program, and the Trust for Public Land.
Review statement
This paper was edited by Scott Stevens and reviewed by two anonymous referees.
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