The use of satellite aerosol optical thickness (AOT) from imaging
spectrometers has been successful in quantifying and mapping high-PM2.5
(particulate matter with a mass <2.5µm diameter) episodes for
pollution abatement and health studies. However, some regions have high
PM2.5 but poor estimation success. The challenges in using AOT
from imaging spectrometers to characterize PM2.5 worldwide
was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's
attendant difficulties of high-albedo surfaces and very shallow, variable
vertical mixing also occur in other significantly polluted regions around
the world. We report on more accurate PM2.5 maps (where cloudiness permits)
for the whole winter period in the SJV (19 November 2012–18 February 2013).
Intensive measurements by including NASA aircraft were made for several
weeks in that winter, the DISCOVER-AQ (Deriving
Information on Surface Conditions from COlumn and VERtically Resolved
Observations Relevant to Air Quality) California mission.
We found success with a relatively simple method based on calibration and
checking with surface monitors and a characterization of vertical mixing,
and incorporating specific understanding of the region's climatology. We
estimate PM2.5 to within ∼7µg m-3
root mean square error (RMSE) and with R values of ∼0.9, based
on remotely sensed multi-angle implementation of atmospheric
correction (MAIAC) observations, and certain further work will improve that
accuracy. Mapping is at 1 km resolution. This allows a time sequence of
mapped aerosols at 1 km for cloud-free days. We describe our technique as a
“static estimation.” Estimation procedures like this one, not dependent on
well-mapped source strengths or on transport error, should help full
source-driven simulations by deconstructing processes. They also provide a
rapid method to create a long-term climatology.
Essential features of the technique are (a) daily calibration of the AOT to
PM2.5 using available surface monitors, and (b) characterization of
mixed layer dilution using column water vapor (CWV, otherwise “precipitable
water”). We noted that on multi-day timescales both water vapor and
particles share near-surface sources and both fall to very low values with
altitude; indeed, both are largely removed by precipitation. The existence
of layers of H2O or aerosol not within the mixed layer adds complexity,
but mixed-effects statistical regression captures essential proportionality
of PM2.5 and the ratio variable (AOT / CWV). Accuracy is much higher than
previous statistical models and can be extended to the whole Aqua satellite
data record. The maps and time series we show suggest a repeated pattern for
large valleys like the SJV – progressive stabilization of the mixing
height after frontal passages: PM2.5 is somewhat more determined by
day-by-day changes in mixing than it is by the progressive accumulation of
pollutants (revealed as increasing AOT).
Introduction
The San Joaquin Valley (SJV) is an important agricultural area,
characterized by poor air quality (Fig. 1). The SJV gives an example of a
region with frequent air pollution episodes, challenged by difficulties as
varied particle characteristics with hard-to-quantify sources from domestic
burning and spatially distinct ammonia and nitrate precursors. The 60 840 km2
area (with approximately 4 million residents) is located southeast of San
Francisco, between the Coastal Mountains to the west and the Sierra
Nevada to the east (Sorek-Hamer et al., 2013). Figure 1 describes the
particularly high particulate pollution characterizing the San Joaquin
Valley. Previous studies in this region reported a range of correlations
between satellite-borne aerosol optical thickness (AOT) and
daily/hourly collocated ground PM2.5 (particulate matter with a mass <2.5µm diameter)
measurements in this region. Using linear tools resulted in little or no
correlation (Engel-Cox et al., 2004; Ballard et al., 2008; Justice et al.,
2009), while applying non-linear methods improved the correlation to
R=0.71 (Sorek-Hamer et al., 2013).
(a) Annual average PM2.5 (24 h average) by county as observed for
2014 (source: EPA: “What is particle pollution and what types of particles
are a health concern?”; https://www.epa.gov/pmcourse/what-particle-pollution, last access: 1 October 2019). The original description
reads as follows: “US counties with high annual mean particle pollution concentrations
in 2015. This map depicts fine particle pollution concentrations by US
county for 2015 based on long-term (annual) average concentrations. The
map's color key is based on categories of the Air Quality Index (AQI) (see
Patient Exposure and the Air Quality Index). All orange and red areas
exceeded the annual ambient air quality standards for fine particle
pollution during 2015.” (b) The 98th percentile concentrations by count for
2014 from the same source. The original description reads as follows: “All orange and red
areas exceeded the 24 h ambient air quality standards for fine particle
pollution during 2015. The map illustrates how likely it may be for a
particular area to experience air quality advisories for particle pollution
based on short-term averaging.” The San Joaquin Valley comprises the area
in red and the adjoining counties to the northeast and southwest; details
are shown in later maps.
(a) Conceptual figure describing the
fair-weather planetary boundary layer (PBL) top for
successive days in a clear-weather PM2.5 episode motivating this study; see
text. (b) Simulation in the National Oceanic and Atmospheric Administration
(NOAA) Rapid Refresh (RAP) model of PBL height for
momentum at three SJV PM2.5 stations (red: Tranquility, gray: Hanford,
green: Bakersfield). Periods from 11:00 to 15:30 UTC approximate the mixed
layer for that period and time following, although advection may change the
concentrations mixing to that height. Maximum PBL-top altitude may not be
accurate for the station, but the shape of diel profile is appropriate.
More broadly, atmospheric PM pollution in the
respirable range, PM2.5, has been recognized as a major threat to human health for
some time (Brunekreef and Holgate, 2002; Dominici et al., 2006; Franklin,
2007; Kloog et al., 2013; Schwartz, et al., 1996; Zanobetti et al., 2009).
Epidemiological studies have been hampered by the availability of relatively
few PM2.5 measurement stations relative to the broad dispersal of
populations affected. A variety of methods have been employed to estimate
exposure, e.g., proximity-based methods using a geographic information system (GIS), interpolation between sparse
monitoring sites, land-use regression models, line- or area-dispersion plume
models, 3-D atmospheric source-and-transport models, and models using
information from imaging satellites, often including also land-use
regression and proximity (Sorek Hamer et al., 2016). Sparse PM2.5 monitoring
spatial networks may limit our ability to accurately assess human exposures
to PM2.5, since concentrations measured at an outdoor site may be less
representative of the subjects' exposures as the distance from the monitor
increases (Bell et al., 2007; Lee et al., 2011).
For this reason, there has been extensive development of techniques to make
the best use of satellite-borne optical extinction, as seen from
moderate-resolution atmospheric imagers. AOT is
typically reported as a vertical column integral of extinction above the
ground footprint observed. Methods using AOT to assess exposure to PM showed
early successes, but certain regions remained very poorly characterized
(Engel-Cox et al., 2004; Liu et al., 2009; Gupta et al., 2006; Koelemeijer
et al., 2006; Hoff and Christopher, 2009). Engel-Cox (2004) found
correlations of AOT with PM2.5 for valleys along the US Pacific coast that ranged
from -0.2 to +0.3; i.e., very little variance is explained. Multi-angle Imaging
SpectroRadiometer (MISR) technology
aided greatly (Liu et al., 2007) but yields mostly monthly averages over
years (van Donkelaar et al., 2010), limiting event and epidemiological
analysis.
AOT may be strongly affected by particles encountered well above the
planetary boundary layer (PBL) and different particulate composition. In addition,
cloud cover severely limits the actual spatial coverage of AOT (Ford and
Heald, 2016). Yet, in spite of these limitations (Jin et al., 2019), AOT has
been employed extensively for assessing PM concentrations (e.g., Liu et al.,
2018; Franklin et al., 2017; van Donkelaar et al., 2015, 2016; Kloog et al.,
2015, 2014; Hu et al., 2014; Sorek-Hamer et al., 2013; Hoff and Christopher
2009).
In regard to the SJV, considerable work has been published, since it was the
site of two major intensive studies: CRPAQS (California Regional PM10/PM2.5
Air Quality Study; Chow et al., 2006) and DISCOVER-AQ California (Deriving
Information on Surface Conditions from COlumn and VERtically Resolved
Observations Relevant to Air Quality;
https://www-air.larc.nasa.gov/missions/discover-aq/discover-aq.html (last access: 1 October 2019); more
references below and on the web site). There was a very useful analysis of
particle composition for a well-instrumented Fresno surface site for this
period (Young et al., 2016). This study added detail to the Watson and Chow
(2002) analysis of an earlier intensive study of the area, in particular,
the striking dominance of nitrate and organic aerosols in a regular diel
pattern. Watson and Chow reference several publications describing that
intensive study. Johnson et al. (2014) made a three-dimensional modeling study of
methane emissions that also helps describe the mixed layer of the specific
DISCOVER-AQ period. Lidar gives a very helpful view of complexities of
submicron particle abundance and properties within the mixed layer and the
uniformity of the mixed layer top (Sawamura et al., 2017).
Application of modeling with satellite AOT columns from different satellite
platforms for the DISCOVER-AQ (included within our study period) was able to
achieve R2∼0.8). These results were achieved for just the
DISCOVER-AQ period of ∼6 weeks and with separate subregions
of the central SJV. They highlight the complexity of composition and
source-driven simulation (Friberg et al., 2018). The Friberg publication is
highly recommended as a comparison to this effort and has extensive
references regarding the SJV and the details required for source-driven
modeling.
There are several related goals in producing PM2.5 maps and assessing their
accuracy. The work of Friberg et al. (2018) primarily aimed to constrain the Community Multi-scale Air Quality model (CMAQ)
downwind of the surface air quality stations and, in particular, to
constrain particle type as much as possible, along with concentration, using
MISR constraints (Ralph Kahn, personal communication, 2019). Our goal was to
produce a large set of maps characterizing one winter in a particular
setting, inland Mediterranean valleys, with the aim of allowing air
pollution professionals to understand particulate episodes and to improve
sources and simulation details (e.g., transport error) for source-driven
models. Goals of the Dalhousie group are to improve annual average exposure:
they see that as the principal driver for health effects (van Donkelaar et
al., 2010, 2015, 2016). A main goal of NASA's Multi-Angle Imager for
Aerosols (MAIA) mission is similarly deliver new data for an each-day mapping of
PM2.5 exposure sufficient for full studies of health effects (Diner et al.,
2018, https://maia.jpl.nasa.gov/, last access: 15 November 2019). In pursuit of that goal for
the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua dataset, we will indicate some preliminary, meteorology-based
ideas for estimating high aerosol concentration when clouds prevent the use
of remote sensing data.
Due to the complex meteorology of the San Joaquin flows and uncertainties
surrounding the sources of ammonia, nitrogen oxides, and residential-burning
smoke, we attempt to separate out some certain aspects of complex 3-D
source-driven modeling (Bey et al., 2001; Nolte, 2015; Appel et al., 2017;
Friberg et al., 2018) with a “static model” which does not attempt to
simulate transport but rather uses observational records related to
vertical mixing and AOT. The spatial maps produced can give a more detailed
check on the 3-D process modeling. They also allow the whole MODIS record of
winters to be analyzed efficiently so as to reveal patterns and trends. We
emphasize this and further extension the interpretation of satellite
radiances, attempting to remain close to physical interpretations by using
both multi-angle implementation of atmospheric
correction (MAIAC) AOT and column water vapor (CWV) retrievals. MAIAC CWV (Lyapustin
et al., 2018) retrievals have been quite acceptability validated with the
AErosol RObotic NETwork (AERONET) CWV measurements in higher CWV environments (Martins et al., 2017,
2018). It has not been previously recognized as a tool for improving ground
PM estimation and, in particular, in the SJV.
DataMAIAC AOT and CWV
MAIAC is an
operational algorithm developed for MODIS Collection 6 (C6) data (Lyapustin
et al., 2011a, b). This algorithm applies a dynamical time series technique
to derive the MODIS surface bidirectional reflectance factor and atmospheric
retrievals at a 1 km resolution, such as AOT, and CWV (Lyapustin et al.,
2008, 2011b). MAIAC AOT retrievals present an expected error within 15 %
and relatively good correlation coefficient (R) with AERONET measurements in
the study area (Lyapustin et al., 2018).
MAIAC data have been used from both Terra and Aqua satellite with a daily
overpass at ∼10:30 and ∼13:30 UTC
(+ ca. 1.5 h), respectively. Data have been obtained for
the period of winter 2012–2013 (November 2012–April 2013). We surveyed this
entire period and included, for estimation, all wintertime high-PM2.5
episodes for this specific winter, selecting 19 November–18 February, as
described in several later figures (discussed in context: Figs. 3, 4, and
7).
AERONET AOT and CWV
AERONET is a global network of automatic
Sun-and-sky radiometers for aerosol monitoring (Holben et al., 1998). Direct
Sun measurements are used to compute the AOT values at seven wavelengths
(340, 380, 440, 500, 675, 870, 1020 nm), while CWV retrievals are derived
from the 940 nm channel (Schmid et al., 2006). The AERONET data were
obtained for the study period with cloud-screened and quality-assured at V3
Level 2 products. The AERONET AOT values were interpolated to 550 nm using
quadratic fits on a log–log scale. Details on instruments and monitoring
sites of the DISCOVER-AQ campaign are available at:
http://www.nasa.gov/mission_pages/discover-aq/instruments/index.html (last access: 1 September 2019).
Archived DISCOVER-AQ data are
available on the NASA LaRC Science Data for Atmospheric Composition website:
http://www-air.larc.nasa.gov/ index.html (last access: 1 September 2019).
Ground PM2.5 concentrations
Hourly ground PM2.5 concentrations were obtained from the US Environmental
Protection Agency (EPA) at ±60 min from the satellite overpass. Data
were obtained from stations that reported non-negative PM values over the
whole study period
(https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw, last access: 15 August 2019).
PBL
Momentum-based PBL depth, 10 m wind, and some CWV quantities for the model
were taken from the archive of the National Oceanic and Atmospheric
Administration (NOAA) Rapid Refresh (RAP) model available
for this period. (The choice of MAIAC or RAP estimates is discussed later.) The
model archive had a nominal 13 km resolution resolved at a 1 h time
interval, so that model quantities could be matched closely to the satellite
overpass times. Unreported examination of the AERONET data for the period
suggested that the temporal resolution of the MAIAC AOT was quite accurate.
The High Spectral Resolution Lidar 2 (HSRL2) aerosol data as described by Sawamura et al. (2017) suggested
that depths of afternoon mixing tops were adequately described by a 13 km
resolution model, as were adjacent spirals of the NASA P3-B aircraft as
described by Michael Shook (Shook et al., 2013; see also the Supplement).
AOT could however vary on relatively short distance scales, e.g.,
within 0–2 km of roadways when winds were parallel to the road. We shall
see the consequential variations in estimated PM2.5 later in the processed
results.
Motivating meteorological perspective
Koelenmeijer et al. (2006) give a succinct description of the relationship
between AOT and dry particle mass. We adopt their simplification describing
the relationship of AOT to PM2.5 using a simple equation where all particles
are idealized as evenly mixed throughout a layer mixing to sensors near the
ground, and the thickness of the mixed layer is ΔzML:
PM2.5=fAOT=AOTΔzML⋅MComposition, RH.
The factor in the denominator, M (for “magnification”), describes the
relationship of the optical extinction to “dry particle mass smaller than
2.5 µm aerodynamic diameter” which is the motivated definition of
PM2.5. (PM2.5 also has a definition of a US “Federal Reference
Method” which is formulated to approximate the physical definition as
closely as possible.) The factor M then is a function of particle composition
and the extinction coefficients bExt associated with the components, one
of which may be largely absorbed water. Particle composition and ambient
relative humidity (RH) then interact with each other to determine the water
content. It is significant that RH is a function of temperature and
therefore altitude, with highest RH at the top of a well-mixed layer.
This work emphasizes and attempts to exploit features of regional aerosol
haze palls that parallel features of aerosol mass and a different measure of
water vapor.
(a) Locations of stations in the SJV used; color coding allows
nearby stations to be identified. (b) Matrix plot of PM2.5 as measured at
all instances of overpass at the sites for the period 19 November 2012
through 18 February 2013. The x axis has variable spacing in time; tick marks
indicate successive days. Another view that summarizes the variability of
observed PM2.5 is shown in Fig. 4a.
Figure 2 illustrates a conceptual idea of the fair-weather simulation that we
focus on. Both regional particulate pollution and water vapor originate from
the Earth's surface. Each tends to create relatively well-mixed layers over
several days, transported most significantly by a repeated daily cycle of
mixing. The mixing of momentum is most active from just before noon to
the mid-afternoon, creating an afternoon mixed layer, and water vapor and
aerosol most typically mix well up to this layer. Turbulent mixing depths
vary from day to day, and these can create lofted layers of pollution cut
off from the surface on the day of AOT and CWV observation. Flows in the
SJV can be greatly influenced by the nearby mountains, with flows day
and night, promoting some upslope transport of material which can
recirculate, detached from mixing on following days. Consideration of
subsidence of air into the San Joaquin mixed layer suggests a flow-through
time for aerosol and water of 2–3 d for some situations (Caputi et al.,
2018). Mixing of entrained and mixed layer air allows for continued
accumulation of pollutant aerosol in the valley as Fig. 2 shows.
Particles and water vapor are emitted and accumulate in the same region, and
they are mixed similarly each day at midday and in the afternoon by convective stirring.
The height of mixing can be determined by variations in the buoyancy flux
from the surface and varying vertical subsidence velocities, responding to
larger-scale weather patterns, during successive days. Figure 2 does not
show the effect of particle transport or water vapor transport for a
specific location, but for the PBL top, which is strongly controlled by local
heat and water vapor fluxes at the surface.
If the mixing height is lower on succeeding days, then any water vapor and
any particles at the top of the mixed layer are trapped in an “elevated
layer” which does not mix to the surface. Other common ways in which
elevated layers can be formed are mixing along the side of the valley (small-scale
anabatic and katabatic winds) and by differential transport, i.e.,
wind shear. Fires, power plant plumes, and long-distance synoptic transport
can form layers that are quite separated at higher altitudes in the
troposphere. Eventually, there is removal of both species. Wet removal of
particles is particularly effective, and the specific humidity of the air is
very effectively removed by the condensation accompanying cooling and
rising, according to the Clausius–Clapeyron equation. Similar processes then
limit the vertical spread of particles and specific humidity.
Expected variability of the AOT–PM2.5 relationship
Water vapor molecules also accumulate in the atmosphere over a period of
several days (typically a somewhat longer period), and both aerosols and
water vapor are cleared from a particular place by cloud removal processes
(venting, rainfall) and by air mass replacement. In the case of high-pressure
systems in which air pollution episodes occur, such replacement is a common
feature. If the other variables are available by measurement, e.g., airplane
measurement such as in DISCOVER-AQ
(https://discover-aq.larc.nasa.gov/data.html, last access: 15 August 2019), Eq. (1) can be solved for
ΔzML, defining an equivalent mixing height for particles.
Similarly, we can write equivalent mixing depth of water vapor, ΔzeH2O:
ΔzeH2O=∫0TopρH2O(z)dz/ρ‾H2O(ML, RTP)=CWV/ρH2O(ML, STP),
where CWV is in g cm2, ρH2O(z) and
ρ‾H2OML, RTP correspond to the vertically distributed water vapor and
appropriately average water density of the mixed layer. CWV is available from
the MAIAC analyses yielding AOT. Making the assumption that the heights are
the nearly equivalent for water vapor and aerosol, we may write
PM2.5=fAOT=AOTCWVρ‾H2O(ML, RTP)MComposition, RH.PM2.5 is calculated at EPA reference temperature (25 ∘C)
and pressure (1 atm), water vapor quantities in g cm-3, and ΔzeH2O in centimeters.
Work reported by Shook et al. (2018) described the vertical distribution of
trace species with a vertical coordinate normalized to this estimated
afternoon mixed layer top. This suggested to us that water vapor had
vertical distributions that were usefully similar. The decline of water
vapor was not as sharp, often showing a rapid decrease; the drop in
scattering was dramatically rapid.
We found in ensuing work that approximating ρ‾H2O(ML, RTP)
by ρH2O (z=0,current conditions) added only a small amount to
the variance explained by the regression given other limitations of the
approximation. (Possibly relative humidity effects or the correlation of
water density with temperature could be complicating correlated factors.)
We calibrate the fAOT relationship using data at
official PM2.5 stations and make the calibration daily. It is our
observation that f varies only over a small range when there are several
MODIS observations on the same day, and that it varies in a limited way
between neighboring stations in a local region. The definition of “region”
is based on that similarity, and it suggests similarity of ΔzML
and M, i.e., similar aerosol characteristics and boundary layer behavior.
This similarity does not apply when the wind shifts greatly between times or
between stations, e.g., when a front passes. Fortunately for our
understanding of pollution episodes, frontal passage days tend not to have
high PM2.5.
We distill these understandings when we formulate a regression equation:
PM2.5is=(a+βi)AOTis/CWVis+αi+εis,
where the subscript i describes “instance” or calendar date, and
subscript s describes “station”, so that AOT and PM2.5 form a
two-dimensional table.
Given the independent nature of i and s, the regression must be solved by
mixed-effects methods described below. The subscript s needs to only be
independent of i, so later we will use it to denote “situation” or the hour
of the day when there are many observations made at one station on one day
i. It is not assumed that the consecutive order of the day observations
necessarily describes any continuity in i. Observations show that there is
often continuity, but that the continuity is quickly broken when frontal
passages or rain affect the region.
Writing Eq. (3) in the form used for mixed-effects models, we separate a
general term from the terms that depend on i or calendar date.
PM2.5is=a⋅AOTis/CWVis+c+αi+βi⋅AOTis/CWVis+εis
A commonly used shorthand is the Wilkinson and Rogers (1973) form, accepted
by many software packages:
PM2.5∼AOT/CWV+(AOT/CWV+1|DOY),
where DOY describes the calendar date subscript i. This formalism also
describes the columns of the regression matrix to be solved.
It is tempting to generalize this relationship to recognize that there is
often correlated behavior between stations but with some constant offset:
PM2.5is=a⋅AOTis/CWVis+c+αi+βi⋅AOTis/CWVis+γs+εis.
However, if one allows such variations at monitoring stations, it can be
difficult to decide what values of γs to use between stations.
This is an attempt to describe “subregionality”, that is, similar
behavior within a region modified by slight and geographically coherent
variations which allow spatial interpolation.
For those not familiar with mixed-effects models, we mention that the
procedure is similar to the use of dummy variables, where coefficients
ui multiply a set of discriminating variables, equal to 1 when i takes
on the value of a particular instance/day, and 0 for all other instances.
The mixed-effects techniques similarly solves a much larger regression
equation but has better theoretical development. Note that the number of
observations is Ni times Ns, while the number of parameters is
linear in Ni and Ns, where N
signifies the number of instances. When
Ni and Ns>5, the problem becomes increasingly
overdetermined.
This basic understanding does not fully explain the success of the
mixed-effects model that we observed for the San Joaquin Valley. Furthermore,
analyses of the Baltimore–Washington DC area not described here suggest that it
works more broadly. Both aerosol and especially water vapor often exhibit
layers not in continual contact with surface monitors. These we will call
“elevated layers”. In situ measurements on aircraft and also lidar
measurements from ground lidars looking downward from aircraft (Sawamura et
al., 2017) and satellite lidar (Cloud-Aerosol Lidar and Infrared Pathfinder
Satellite Observation; CALIPSO) reveal aerosol layers with
significant optical thickness above the mixing layer. Similarly, airborne
measurements in the DISCOVER-AQ intensive measurements of 2013 suggest a
fraction of water vapor lies above the mixed layer for water. Allow these
portions of total AOT and CWV layers to be quantified as AOTe and CWVe (“e”
stands for elevated). There can be several individual layers. AOTe and CWVe
refer to the total amounts of extinction and water vapor mass. Thus, there
is an approximate equation upon which to base regression estimation:
PM2.5=(AOT-AOTe)(CWV-CWVe)ρ‾H2O(ML, RTP)M(Composition, RH).
While elevated layers of water and aerosol are common, we will see that it
appears that this regression equation allows rather good fits. This can
happen when AOTe≪AOT and CWVe≪CWV to
a sufficient degree, or else when there are approximate linear (slope plus
intercept) relationships obtaining between the numerator and denominator of
Eq. (8). Essentially, the terms are absorbed into constant parameters for
the day, αi and βi, along with other parameters like M.
AOTe and CWVe are considered to be essentially constant over the region. In
fact, this degree of constancy can be taken to define the “region” of
application. Transforming these terms into constants, αi and
βi, works under an implicit assumption of uniformity in AOTe and CWVe
throughout the region or at least a uniform linear dependence with AOT and
CWV.
Observations and an overview of pollution episode trends
In this section, we will show how components of a mixed-effects model that
utilize CWV contribute to its explanatory power. We examine the
relationships and predictive ability for PM2.5 observed at SJV measurement
stations for the winter season encompassing high-pollution periods,
19 November 2012 to 17 February 2013. Stations from Bakersfield in the south to
Stockton in the north were included. Figure 3b shows several episodes
affecting most of the valley; one period with more stations reporting
includes the DISCOVER-AQ period. This period has additional P3-B aircraft
data which motivated this work but are too lengthy to describe in this
publication.
Figure 3a shows the locations of all stations used in this work; the
stations include much of the valley from Stockton to Bakersfield. Some of
the stations labeled “DAQ” were in operation only during the DISCOVER-AQ
California period. A color wheel was used to assign colors to the stations
on the graph; this allows identification of stations' latitude, longitude,
and proximity in later graphs comparing observations and our fitted values.
Figure 3b describes the rise and fall of PM2.5 pollution using the station
reports. The rows represent stations and are arranged north to south.
Several major episodes are immediately seen, as well as differences in their
intensity and timing of development. The DISCOVER-AQ observations were
limited to the period shown, 8 January through 10 February 2013.
Differences between the PM2.5 values observed at nearby stations, one
DISCOVER-AQ and one California Air Resources Board (labeled “EPA” for the
dataset origin), give an impression of local variability; differences between
observations at Clovis are quite apparent.
(a)PM2.5 as observed at all stations for the winter period
extending from November 2012 to March 2013. The graph has vertical bars
drawn with partial transparency, so that careful inspection of a single day
describes all the observations in the valley for the day. The observations
contributing for each day may be seen in Fig. 3b. (b)PM2.5 as fitted
by the regression with slopes and intercepts, described further below.
Which information contributes to PM2.5 maps
Using the MAIAC 1×1 km estimates for each day for the location of each
aerosol monitoring station and the PM2.5 measured at overpass time for that
day, we may solve the estimation equation (Eq. 6).
The complete simulation of PM2.5 measurement at all stations where MAIAC
data allowed is shown in Fig. 4b. The technique can be used for all
years and the whole area of the SJV where MODIS data are available. We used
the complete model as described in Eq. (6), with “slopes” and “intercepts”
but without any time-independent spatial variation allowed (γs).
Three features deserve immediate comment. First, there are patterns of
gradual increase of PM2.5 up to 45–80 µg m-3, followed by
relatively sudden decrease to levels near 5 µg m-3. Second, the
regression technique using AOT / CWV, as estimated individually for each
day, captures the variation rather well for all days where estimates can be
made.
Individual exotic high values are not captured. Third, there is a pattern
where the end of an air pollution episode, showing very high values, is not
captured by the technique. These are simply days where MODIS observations
were not available, almost always due to cloud cover. We expect that these
are readily explained in terms of weather phenomena especially typical of
the western US during wintertime. Pollution episodes are ended with the
approach of warm fronts with high clouds, followed in a few days by the
cleansing effects of rain, air mass replacement, and higher wind. We will
return to this topic later.
To understand what information is used by the technique and the importance of
that information, based on the series of regression estimates we presented, we
argue that there is a cumulative aspect to the explanation. For example, when we
include one statistical variable, e.g., αi,
describing variation by day but constant for all stations of the day, then
the regression with AOT / CWV becomes much more informative. A general
relation describing the slope of PM2.5 with AOT / CWV becomes more useful when
an appropriate intercept (offset) is provided.
Progressive improvement of PM2.5 simulation showing the roles of
daily calibration and AOT / CWV descriptions of aerosol vertical dispersion.
Station observations (µg m-3) are shown on the y axis;
estimators are shown on the horizontal axis. Note the progressive refinement of R and
remaining root mean square error (RMSE); see text. (a) Use of AOT only, an early methodology.
(b) Some improvement using AOT / CWV but no daily calibration. (c) More
improvement with daily calibration (mixed effects using intercepts αi). (d) Clearly improved linearity when combing intercepts with AOT / CWV.
(e) Estimating daily “random” intercepts and slopes improves RMSE and
R. (f) A simple description of variation within the region (longitude) aids
the estimation slightly (RMSE ∼6.48µg m-3,
R∼0.91).
Consider both Table 1 and Fig. 5 as they describe cumulative effects of
adding information. First, we note that AOT alone is not very informative
about PM2.5. This would seem to follow naturally from Eq. (1), since
variations in mixing depth and composition are not considered. Figure 5a
shows many station observations with high PM2.5 but low AOT, and vice versa.
Slight but significant improvement is made when CWV is
introduced to provide some information on mixing depth and dilution. R
improves to 0.48 but the remaining error is nearly as high. Still, some
linear relationship begins to show for perhaps 60 % of the data.
A side comment regarding significance: R and remaining root mean square error (RMSE)
(in µg m-3) are shown in Table 1. We also performed two other tests that are not
tabulated. An analysis of the Kullback–Liebler divergence (Hastie et al.,
2009), where possible, suggested each successive test in the table clearly
adds information regarding PM2.5. The number of observations justified the
number of additional parameters. While the numerical values are difficult to
compare to other examples of regression, they show similar trends to R and
RMSE; i.e., accuracy becomes increasingly hard to improve as R increases.
Another test was leave-one-out cross validation (Hastie et al., 2009). Each
individual station was omitted, and the regression based on the remaining
stations was tested against observations at that station. The
cross-validated mean squared error was about 7.8 µg m-3 at most
for the most informative regressions shown.
Comparison of results using different terms in the mixed-effects model.
(1) Variables are described in the context of Eqs. (4)–(8) in the text.
(2) In all regressions with random effects (all but the first two regressions),
the inclusion of the α and c variables suggests an overfitting.
Mixed-effects convention emphasizes these “main effects” separately and
therefore specifies there must be a single linear constraint on the terms
such as α and αi (also, c and βi).
Importantly, in Sect. 6 and certain figures below, we describe the (more
intuitive) combination of main and random effects; e.g., we graph
αi←αi+a and
βi←βi+c.
Now consider a popular alternative to the use of satellite data. The third regression shown, labelled (1|DOY), omits satellite data and uses only a single value based on day of year to give a uniform-valued PM2.5 estimate for the whole region. As Table 1 shows, this can do significantly better than the previous non-mixed-effects regressions using satellite data. Color-coded maps of PM2.5 drawn for a region
have a single color which varies from day to day. In many applications of
satellite data to particulate estimation, this has been shown to surpass, or
at least approximate, the results of use of AOT (Sorek-Hamer et al., 2017).
R∼0.78, RMSE ∼10µg m-3. Its
success emphasizes the regional similarity of conditions defining PM2.5
concentrations and their extensive spatial correlation. An explanation is
that respirable PM2.5 is defined by daily weather and orientation to major
sources.
Once the regional similarity of pollutant conditions is recognized, it
becomes appealing to combine information. The fourth estimate, Fig. 5d,
does just this and shows a notable increase in R (0.88) and decrease in
RMSE (8.03). This is an approximately 50 % decrease in error variance. In
our situation, satellite data look to be useful. The scatterplot of Fig. 5d suggests distinctly more linear behavior.
An appealing alternative is to estimate only slope variations,
βi. This is nearly as useful as estimating just
αi, R∼0.85 RMSE
∼10µg m-3. Each is useful. Do the two parameter
estimations give distinct information?
Estimation of varying offsets αi and
sensitivities αi does indeed help, reducing the
variance by another 10 %. Combining the use of AOT, CWV, and individual
daily intercepts and slopes yields R∼0.90 and RMSE
∼6.72µg m-3. Nevertheless, Fig. 5e shows
that certain stations have persistent deviations from the general swarm of
points; Tranquility (pale green) is predicted high, and Porterville and
neighbors (red) are predicted low.
Estimated surface PM2.5 at 1 km indicated overpass times for the
first wintertime episode in the San Joaquin Valley. Winds at 360 m a.g.l. are
also shown. Estimated RMSE is 7 µg m-3 with a similar limit
of detection. Filled circles show station PM2.5. In this episode, the E–W
correction based on the full dataset appears inappropriate, lowering mapped
estimates in the east valley. Error should decrease with improved
understanding of geographic variability. The time stamp at the top of the image
describes the date and time in UTC format.
This analysis of residuals suggests that there may be spatial variations
that can be specified for our stations (γs)
but are general enough that they can be extended to maps. For this
publication, we attempted a very simple variation, an east–west variation
(longitude). This did improve the scatterplot for most stations, especially
when considering values above ∼10µg m-3. RMSE
decreased slightly to 6.48, and the R estimate also rose slightly to
0.911. These changes are close to the range of sample variability. The maps
shown in Fig. 6 also show more convincing (subjective) agreement in
magnitude and pattern. Nevertheless, many of the highest observations are
underestimated by about 20 %.
We used CWV rather than the RAP planetary boundary layer height for
momentum (11:00 to 15:30 UTC). This was available in the 2012–2013 winter
at times within a half hour of overpass time; however, this PBL height is
not always recorded in the high-resolution RAP archive. We compared a
regression very similar to the most detailed regression of Table 1 but
using this PBL height. The formula used was
PM2.5is=a⋅AOTis/PBLis+c+αi+βi⋅AOTis/PBLis+γs+εis.
With this, the R value was 0.917 and the RMSE was 6.25 µg m-3;
these are only insignificantly better than the CWV-based estimate
R of 0.912; the RMSE was 6.43 µg m-3. Mid-afternoon
PBL depth is consequently useful. However, the CWV-based estimate may be used
with all years of the MODIS data, while the best-available meteorology for
PBL depth varies considerably, as high-resolution NOAA models advanced
through the years.
Results: maps of estimated PM2.5
The major purpose of this work is to combine AOT, CWV, and daily
calibration in order to allow maps of estimated PM2.5 for all regions where
MODIS can provide optical thickness data. Results using the full model with
αi, βi, and
γs are shown (Fig. 5f). Out of the 42 d
in the calibration set, we consider 6 d of single major air pollution
episode during middle of January 2013, a period that was largely sampled by
the DISCOVER-AQ ground and airplane samples. Detailed comparisons of the
DISCOVER-AQ data would expand this work beyond a manageable size; such
analysis is desirable. Winds are shown with streamlines and are obtained by
interpolation from the RAP wind analyses.
We created 39 maps, six of which are shown in Fig. 6. Their accuracy is good.
RMSE is ∼7µg m-3.
This dictated the 5 µg m-3 contour colors used: similar colors
or neighboring colors show expected agreement. Winds at 360 m for the hour
of sampling have been superimposed on the maps.
There follows the description of just of one episode: on 14 January 2013,
the valley is clean (see also Figs. 3 and 4). By 16 January 2013, light
regional haze is accumulating, and the winds and mapped levels suggest some
accumulation towards the south. On 18 January 2013, winds have veered: in
the central valley, pollution accumulates towards the east; in the south,
transport is towards Bakersfield. On 20 January 2013, winds press the
accumulating PM2.5 back towards the more populated east valley. Several days
following have increasing clouds (no maps). The first day, with advancing
clouds overhead but no low clouds and no front nor rain, retains high PM2.5 at
the monitors. This pattern is seen for several wintertime pollution episodes
in this region. When the clouds clear, the valley is as clean as it was on 14 January. In
the maps for 18, 19, and 20 January, the maps underestimate the highest
values of PM2.5 by about 20 %, as noted above.
Intensification of PM2.5 episodes: pollutant accumulation vs. confinement
The well-performing mixed-effects models (Eqs. 5 and 6) led us to
examine the repeated development of air pollution episodes to a maximum,
striking patterns seen in Figs. 3, 4, and 6. How did the independent
values for various models in Table 1 vary within episodes and between
episodes? Our description of the development leads to some answers in Fig. 7.
Time evolution of PM2.5 and related variables for approximately eight
intensifying particulate episodes during the winter of 2012–2013. Dots and
vertical bars indicate variable values at individual stations when
available. Blank regions reflect periods of cloud cover. (a)PM2.5
(µg m-3) as observed at stations (all dates) and (b) fitted PM2.5 on days
and locations when MAIAC was available. (c) MAIAC AOT, 11:30 to 15:30 UTC.
Note that the increase is less pronounced than PM2.5 and varies between
episodes. (d) CWV in g cm-2 or, colloquially, “cm of precipitable
(liquid) water.” (e) PBL height for the noon–afternoon observations in this
dataset. Morning PBL heights are much lower. (f) Ratio of CWV to PBL height
(cm(H2O-liq) km-1). The ratio
remains relatively constant over several days, consistent with a commonly close
meteorological relationship of CWV and PBL.
Figure 7a and b describe the development of the episodes. The time series
of observed PM2.5 and fitted PM2.5 are repeated from Fig. 4. The times
with no data are essentially cloudy times. After periods of cloudiness,
particulate values typically rise until the next period of clouds. There are
seven to eight such periods of rising, or weather episodes.
(“Episodes” can also refer to periods of highest particulate matter.) High values typically
remain for 1–4 d after cloud obscuration. Figure 7b and c show the
values fitted by our mixed-effects regression and the values that are
available for fitting. The time sequence as well as the magnitudes are in
expected agreement, but the variability between stations is smaller on
some occasions (e.g., 17 January and 15 February). Figure 7d shows that the time series
of the AOT / CWV ratio develops from day to day as PM2.5 does but suggests
that these are modulated by differences in amplitude between weather
episodes and sometime over several days within the weather episodes, e.g.,
13 January to 17 January and 4 February to 8 February. These explain the low overall
correlation. In contrast, AOT shows little resemblance in the time series.
CWV shows some tendency to decline during weather
episodes (Fig. 7e); notably, the values at different stations are more
similar than those for AOT. Region-wide similarity in CWV within and above
the afternoon mixed layer is an appealing explanation. Note the limited
variability of the ratio of CWV to PBL over 3–5 d and between stations. The
afternoon PBL height itself is shown in Fig. 7g. Note that it is often
very low at the end of a cloudy period and then rises to high values
∼1 km at the end of the cloudy period. We suggest that this
reflects overcast skies and very limited convective mixing, followed by rain
and the introduction of new air masses with deeper mixing of water vapor in
a less stable atmosphere.
Figure 7 describes differing causes of repeated PM2.5 buildup during
cloud-free weather episodes. Progressive restriction of vertical mixing
during clear-weather episodes acts to concentrate the effects of accumulated
and recent pollution sources. The less stable air following a frontal
passage feels increasing effects of strong subsidence, diminishing the
mixing height. The 3-fold reduction in PBL height during major episodes
(Fig. 7d) nearly matches the 4-fold increases in PM2.5 during these periods
(Fig. 7a). MAIAC AOT shows variability between stations and is reflected
in local PM2.5. Winds redistribute particles and AOT. Figure 7 does not make
clear the fate of aerosol, but it likely escapes with mountainside winds
along the valley. The entire set of maps suggests a flow to the south and
stronger outflow near the Tejon Pass east of Bakersfield. These mountainside
winds likely may facilitate water vapor and aerosols escape the prevalent
mixed layer.
This suggests a typical behavior for the SJV and similar regions in
winter. A cloudy disturbance (new air mass, rain, wind) stirs the lower
troposphere. This initiates a high PBL mixing on the first clear days.
Typical fair-weather subsidence begins. The surface buoyancy flux is too
weak to maintain these relatively high mixed layer tops. Afternoon PBL
depths and mixed layer depths decrease day by day until a depth of 300–400 m
is reached (Fig. 7d). Escape from the valley may slow, allowing
accumulation of pollution from within the region or from upwind. This
further increases the surface PM2.5. Relatively local sources add to both
AOT and PM2.5, and can transport them 50–100 km downwind, occasionally from
east-valley sources to west-valley pollution hotspots (the map of Fig. 6d).
Both subsidence and surface buoyancy flux are broad-scale weather
phenomena (∼300 km) , and so AOT–PM2.5 relationships are
similar on a given day with a given history of weather. Finally,
warm-frontal rain approaches the region.
An examination of HSRL2 data for the DISCOVER-AQ period (Sawamura et al.,
2017) suggests that there can be considerable vertical variability of
aerosol extinction; the fact that AOT tends to average the whole afternoon
mixed layer allows our generalized description to hold nevertheless.
Finally, we venture on to some ideas for filling in afternoon PM2.5 on days when
MAIAC did not allow mapping due to cloud cover. Young et al. (2016) provided
a thorough microphysical and chemical analysis for just the time period of
13 January to 11 February 2013 – essentially the DISCOVER-AQ period –
and just the fully instrumented UC Davis site deployed in Fresno. Their
Fig. 2a, b, and e show time series of temperature, wind
speed and direction, and particle mass for the period, respectively.
Their measurements include periods of cloud cover and clearly show air mass
transitions during rain and frontal passage (seen as wind shifts, commonly N
to W to S). These time series suggest a meteorological plausible method to
interpolate PM2.5 maps into cloud-covered days. These do compare to
our Fig. 7a, b, and c, describing observed and statistically
estimated particulate mass at all stations including Fresno. PM2.5 drops to
values below 10–15 µg m-3 whenever wind speeds rise to above
∼2 m s-1 and the wind direction is from a quadrant (90∘
sector) centered on the north–northwest direction. Their Fig. 2a
also describes rainfall at the Fresno site. Particulate matter does drop by
∼50 % from the highest observed/estimated values at the end
of the clear-sky period and further when the winds rise to 2 m s-1 or
higher. This behavior is most clearly observed in their graphs for the
period of 23–27 January. Similar behavior is observed in the period
of 6–11 February, although the episode has a more complex increase than
the earlier, most intense episodes. The short spike up to 80 µg m-3
on the night of 10 February is not explained and not reflected in
the afternoon-only data of our Fig. 7. Nevertheless, the averages shown by
Young et al. (2016) in their Fig. 2e do repeat the general observation that daily average
PM2.5 and afternoon PM2.5 do tend to correlate well. For best-estimate maps
of PM2.5, we suggest that the end-of-retrieval values of PM2.5 reduce
gradually over a day or two. Maps of precipitation (e.g., from radar or other
analyses) allow more detail. Estimates for a region should then fall to
∼7µg m-3 whenever sustained winds rise to
>2 m s-1 from the NNW or >3 m s-1 from any
direction. Such wind speeds are held to mark air mass replacement (e.g.,
frontal passage). These ideas remain suggestions since our analysis for a
single winter may not provide enough instances. The whole Aqua MAIAC period
is available but currently beyond NASA's resources.
Roles of slopes and intercepts in a regression fit. (a) A “stork
plot” for the clear-sky air-pollution episode mapped in Fig. 6. Vertical
blue lines indicate the contribution of the random intercept αi
to the total PM2.5 fitted in the model. These are the same for all
geographical locations including the observation stations for any given day.
The slope parameter βi is the same for all geographical locations.
(See note to Table 1.) Values of PM2.5 evaluated at the stations are shown
by red dots along a line. Large values of AOT / CWV have wide vertical extent,
and the corresponding high values of PM2.5 are shown as red dots at the
upper right of each day's plot. Highly sloped lines indicate high βi. (b) A stork plot for the whole wintertime interval evaluation, showing
several clear-day episodes. (c) The values of βi vary
considerably. These slopes are shown as a time series.
Variation of random-effects model parameters
The preceding section gives some background so that we may understand the
parameters for the random-effects model. We will discuss the full Eq. (4);
results with mild spatial dependence (Eq. 5) are very similar. The
intercept αi and the slope for βi×AOTis/CWVis are the same for each day and determine the fitted
PM2.5 for the regression (Eq. 4). We exploit this to produce a “stork
plot” like that in Fig. 8. High αi is shown by tall blue lines; high
βi is shown as a high slope. Variation in AOT / CWV contributes
∼30 %–70 % to the estimate on almost all days.
The stork plot in Fig. 8a illustrates a puzzling progression of parameter
estimates day by day. For the first days (10–14 January), the slope parameter
accounts for the largest contribution to PM2.5. For the second part of the
period (15–19 January), the intercept term becomes progressively more important
compared to the AOT / CWV dependence. The regression equation fit (Fig. 7c)
has difficulty in matching the observed PM2.5 (Fig. 7b) variability
between stations on these days although AOT (Fig. 7e) shows moderate
variability around low values (0.03–0.05). (A side note: MAIAC AOT estimates
should be particularly challenged at these low values.) Then, from 20 to
22 January, the intercept contribution diminishes and the AOT / CWV dependence
becomes rather larger than typical. Referring back to Fig. 7e, f, and g,
these variations seem explainable: the mixed layer decreases rapidly during
the first period, then reaches a minimum at ∼300 m. In the
last 3 d, the AOT increases rapidly, though the mixing depth changes
little. The following weather episode is notable for high and quite variable
AOT (Fig. 7e), and the fitting procedure does well.
Value of improved CWV data
At this point, concerns about the quality of the CWV estimate should be
addressed. In our analysis of the difficult San Joaquin Valley, MAIAC CWV
can be frequently low compared to AERONET CWV, some error can arise from the
presence of clouds in neighboring footprints. In the figures and results,
the shown CWV was based on the MAIAC data interpolated and extrapolated where
cloud contamination made the retrieval of lower accuracy (Lyapustin et al.,
2018). Figure 6 shows that some small-scale variability RAP analyses of CWV
could also be used at their 13 km model-imposed width with similar results,
since CWV does not vary as rapidly spatially as AOT. A better direct use of
the MAIAC CWV could uses spatial averaging with a width of 3 to 6 km. Random
errors in the MAIAC CWV due to the low radiances used would be reduced;
considerations of source patterns suggests that CWV might not truly vary at
such small scales. Improved PM2.5 values could result. We are implementing
this averaging.
As understanding of MAIAC CWV improves, its role in determining daily
AOT–PM2.5 relationships should improve; calibration of MAIAC using
Sun photometer measurements can be useful in the meantime (Just et al.,
2019). Note also that assimilated CWV from the National Weather Service models
is constrained empirically by satellite and surface observations, and
therefore CWV is not as reliant on transport descriptions as is aerosol. Here, some constraints are surface-station
humidity measurements constraining CWV below 0.4–1 km; thermal-radiation
sounders on the GOES (Geostationary Operational Environmental Satellite)
satellites describe water vapor partial above that; radiosonde and GPS
humidity sensors give further constraints. This allows GOES AOT estimates to
be used with assimilated CWV, even though GOES lacks a reflective water
vapor channel (Shobha Kondragunta, personal communication, 2018).
Conclusions
As our goals, we sought broadly applicable methods to estimate PM2.5 maps from
satellite AOT for very polluted regions poorly described by satellite data.
This study focused on the whole polluted winter season of the SJV
(19 November 2012 to 18 February 2013). We sought to fulfill
the overarching goal of the whole DISCOVER-AQ mission – to find general
relationships between extended satellite data observations and surface air
pollutant concentrations and to evaluate their success. We found success
with a simple methodology that follows the meteorology of regions like the
SJV. This success recommends an approach to the remote sensing to PM2.5
analysis, investigating important pollution regions in terms of their
meteorology and sources but carrying over methods from similar regions. For
example, the Po Valley of Italy and the Indo-Gangetic Plain of India
may respond similarly to analyses based on detailed mixing height data and
related distribution indicators.
As direct results, we found that a combination of information utilizing (1) optical
depth, (2) measures of vertical dispersion (e.g., CWV), and (3) daily
calibration of PM2.5 to predictors produced significantly better
quantification of PM2.5 than a competitive no-satellite-use method which we
named “regional correlation” since it produces unfeatured maps of PM2.5
which vary only from day to day. Our maps of estimated PM2.5 extend for all
cloud-free periods from 19 November 2012 to 18 February 2013, which is essentially the
whole pollution season for this winter. For that whole period, this first
published attempt found good predictive value of R∼0.9 and
RMSE of 6.5 µg m-3. Cross validation suggested an RMSE of
7 µg m-3. Analysis of residuals suggested that better
RMSEs could be achieved if further work allowed for subregionality (use
of smaller regions or a geographic characterization incorporating some
spatial variation). Local variations in PM2.5 on the order of 1–3 km were
noted using our method but only when particulate accumulation could occur
along-wind. Still, in order to estimate PM2.5 at 1 km scales, we expect
that it will be necessary to use refined geographic information system
methods (Kloog et al., 2014).
DISCOVER-AQ comparisons are advisable. Our analyzed winter 2012–2103 period did
include the more limited DISCOVER-AQ California 2013 airborne-intensive
study period, primarily focused on the area around Fresno. Analysis of that
intensive period suggested ideas (Shook et al., 2013) that motivated this work. The
shorter DISCOVER-AQ period does deserve more detailed comparison to our
results. Aircraft in situ profiles of gas and particle composition, lidar
profiles, very detailed surface measurements of particulate composition, and
source-and-transport modeling all deserve comparison. The distribution of
atmospheric particles and precursor gases is more complex than this work
might suggest. Somehow averaging appears to allow our general methods. The
development of concepts and the length of this work do not allow for such
comparison. We hope that research will be encouraged.
A major finding was that the usefulness of
CWV does not become apparent unless there is daily calibration of the
AOT / CWV relationship to PM2.5. We attribute this primarily to (a) details of
CWV (e.g., CWV's dependence on mixed layer temperature on the timescale of
days), (b) CWV above the mixed layer for aerosols, presumably responding
to other H2O sources upwind, and (c) variations in composition (the
relation of PM2.5 to light extinction). We believe that allowing for a full
linear relationship each day for AOT / CWV to PM2.5, both slope and intercept
effects, in a daily calibration allows regression to exploit portions of
the PM2.5 vs. f(AOT / CWV) scatterplots that reveal proportionality.
High-spatial-resolution estimates of the 11:00–15:30 UTC PBL heights for
momentum may be as helpful as CWV when available and when the PBL estimation
has been examined for accuracy; this could be explored. Such PBL data are not
available for the whole MODIS Aqua period (2004–present), while CWV data are.
In terms of accompanying insights on pollution episodes, we found that this approach
allowed a broad description of the buildup of six air pollution episodes and
the balance of the roles of accumulation of pollutants vs. limited
vertical mixing. Episodes were as in earlier descriptions (Watson and Chow,
2002). Each appears important in different phases of repetitive
PM2.5-increase cycles. PM2.5–AOT relationships suggest a few days'
residence time for particles (actually particulate extinction) in the valley.
The first 1–3 d after MODIS described full cloud cover could still show
high, slowly decreasing PM2.5. Unpublished analysis (see Young et al., 2018)
suggests that this high PM2.5 dropped precipitously when surface winds rose
to >4 m s-1 from a quadrant centered on the NNW.
Best-estimate extensions to cloudy periods of the remote-sensing-based
record can be made using the typical meteorology of the SJV or
presumably other areas, and verified by extensive checks. Widely available
data mapping surface winds and precipitation suffice and do not require
detailed meteorological modeling to be available.
In terms of the role of “static” models, our estimation approach aimed to avoid the use of
modeling driven by source estimation and transport simulation. Principally,
we wished to provide datasets that allowed independent comparison to such
3-D atmospheric chemistry models (e.g., Friberg et al., 2018). When we
used RAP-model CWV rather than spatially averaged or calibrated (Just et
al., 2019) MAIAC CWV, that goal was not fully
reached, although RAP CWV is strongly constrained by surface, satellite, and
other observations. An aspirational goal is to provide an economical,
accurate, and calibrated estimation of PM2.5 for the whole MODIS Aqua period
to date and then beyond. The opportunities to use MISR, Visible Infrared Imaging Radiometer Suite (VIIRS), MAIA, and
even geostationary imaging are appealing!
Data availability
MODIS data with MAIAC processing is accessible via Lyapustin et al. (2018). The database of PM2.5 measurements (Federal Reference Method) is accessible via Environmental Protection Agency (United States), 2019. The estimation method for the mixed effects estimation is available from GitHUB: 10.5281/zenodo.3625240, Chatfield, and Esswein, 2019.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-4379-2020-supplement.
Author contributions
RBC was responsible for conceptualization. AL and RE were responsible for data collection.
RBC and RE were responsible for data analysis and modeling. RBC and RE were responsible for visualizations.
RBC and MSH were responsible for writing and validation.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We gratefully acknowledge the support from NASA's DISCOVER-AQ mission,
followed by very encouraging continued interest and some partial support
from the Health and Air Quality program management of NASA's Earth Science
Applications Division. This allowed fulfillment of the prime DISCOVER-AQ
objective to demonstrate the relevance of remote sensing to specific air
pollution problems. We appreciate the advice from individuals in that program's
Applied Science Team and from the GEO-CAPE mission formulation effort
(aerosol focus). Aid from Yujie Wang (NASA GSFC) regarding the MAIAC
processing of MODIS data was helpful. Michael Shook's analysis suggested
the use of CWV. Recent comments on the draft paper by Qian Tan and Frank
Freedman are also appreciated.
Financial support
This research has been partially supported by the US NASA Earth
Sciences Research program (NNH09ZDA001N-EV1) for the DISCOVER-AQ suborbital mission,
with additional support by the NASA Earth Sciences Applications Health and Air Quality
program. The work of Alexei I. Lyapustin was funded by the NASA Science of Terra, Aqua, and SNPP programs (17-TASNPP17-0116; solicitation NNH17ZDA001NTASNPP).
Review statement
This paper was edited by Michael Schulz and reviewed by two anonymous referees.
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