Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP2Ex aircraft campaign: chemical composition, size distributions, and the impact of convection

Abstract

The tropical Northwest Pacific (TNWP) is a receptor for pollution sources throughout Asia and is highly susceptible to climate change, making it imperative to understand long-range transport in this complex aerosol-meteorological environment. Measurements from the NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex; 24 August to 5 October 2019) and back trajectories from the National Oceanic and Atmospheric Administration Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) were used to examine transport into the TNWP from the Maritime Continent (MC), peninsular Southeast Asia (PSEA), East Asia (EA), and the West Pacific (WP). A mid-campaign monsoon shift on 20 September 2019 led to distinct transport patterns between the southwest monsoon (SWM; before 20 September) and monsoon transition (MT; after 20 September). During the SWM, long-range transport was a function of southwesterly winds and cyclones over the South China Sea. Low- (high-) altitude air generally came from MC (PSEA), implying distinct aerosol processing related to convection and perhaps wind shear. The MT saw transport from EA and WP, driven by Pacific northeasterly winds, continental anticyclones, and cyclones over the East China Sea. Composition of transported air differed by emission source and accumulated precipitation along trajectories (APT). MC air was characterized by biomass burning tracers while major components of EA air pointed to Asian outflow and secondary formation. Convective scavenging of PSEA air was evidenced by considerable vertical differences between aerosol species but not trace gases, as well as notably higher APT and smaller particles than other regions. Finally, we observed a possible wet scavenging mechanism acting on MC air aloft that was not strictly linked to precipitation. These results are important for understanding the transport and processing of air masses with further implications for modeling aerosol lifecycles and guiding international policymaking to public health and climate, particularly during the SWM and MT.

Full text

Atmos. Chem. Phys., 21, 3777–3802, 2021
https://doi.org/10.5194/acp-21-3777-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Measurement report: Long-range transport patterns
into the tropical northwest Pacific during the CAMP2Ex
aircraft campaign: chemical composition, size
distributions, and the impact of convection
Miguel Ricardo A. Hilario1,a, Ewan Crosbie2,3, Michael Shook2, Jeffrey S. Reid4, Maria Obiminda L. Cambaliza1,5,
James Bernard B. Simpas1,5, Luke Ziemba2, Joshua P. DiGangi2, Glenn S. Diskin2, Phu Nguyen6, F. Joseph Turk7,
Edward Winstead2,3, Claire E. Robinson2,3, Jian Wang8, Jiaoshi Zhang8, Yang Wang9, Subin Yoon10 , James Flynn10,
Sergio L. Alvarez10, Ali Behrangi11,12, and Armin Sorooshian11,13
1Manila Observatory, Quezon City 1108, Philippines
2NASA Langley Research Center, Hampton, VA, USA
3Science Systems and Applications, Inc., Hampton, VA, USA
4Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA
5Department of Physics, Ateneo de Manila University, Quezon City 1108, Philippines
6Department of Civil & Environmental Engineering, University of California Irvine, Irvine, CA 92697, USA
7Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
8Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering,
Washington University in St. Louis, St. Louis, MO 63130, USA
9Department of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology,
Rolla, MO 65409, USA
10Department of Earth and Atmospheric Science, University of Houston, Houston, Texas, 77204, USA
11Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
12Department of Geosciences, University of Arizona, Tucson, AZ 85721, USA
13Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ 85721, USA
anow at: Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA
Correspondence: Miguel Ricardo A. Hilario (hilario@email.arizona.edu) and Armin Sorooshian (armin@email.arizona.edu)
Received: 14 September 2020 – Discussion started: 28 September 2020
Revised: 1 February 2021 – Accepted: 2 February 2021 – Published: 15 March 2021
Abstract. The tropical Northwest Pacific (TNWP) is a re-
ceptor for pollution sources throughout Asia and is highly
susceptible to climate change, making it imperative to
understand long-range transport in this complex aerosol-
meteorological environment. Measurements from the NASA
Cloud, Aerosol, and Monsoon Processes Philippines Exper-
iment (CAMP2Ex; 24 August to 5 October 2019) and back
trajectories from the National Oceanic and Atmospheric Ad-
ministration Hybrid Single Particle Lagrangian Integrated
Trajectory Model (HYSPLIT) were used to examine trans-
port into the TNWP from the Maritime Continent (MC),
peninsular Southeast Asia (PSEA), East Asia (EA), and
the West Pacific (WP). A mid-campaign monsoon shift on
20 September 2019 led to distinct transport patterns between
the southwest monsoon (SWM; before 20 September) and
monsoon transition (MT; after 20 September). During the
SWM, long-range transport was a function of southwesterly
winds and cyclones over the South China Sea. Low- (high-)
altitude air generally came from MC (PSEA), implying dis-
tinct aerosol processing related to convection and perhaps
wind shear. The MT saw transport from EA and WP, driven
by Pacific northeasterly winds, continental anticyclones, and
cyclones over the East China Sea. Composition of trans-
ported air differed by emission source and accumulated pre-
Published by Copernicus Publications on behalf of the European Geosciences Union.

3778 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
cipitation along trajectories (APT). MC air was characterized
by biomass burning tracers while major components of EA
air pointed to Asian outflow and secondary formation. Con-
vective scavenging of PSEA air was evidenced by consid-
erable vertical differences between aerosol species but not
trace gases, as well as notably higher APT and smaller parti-
cles than other regions. Finally, we observed a possible wet
scavenging mechanism acting on MC air aloft that was not
strictly linked to precipitation. These results are important
for understanding the transport and processing of air masses
with further implications for modeling aerosol lifecycles and
guiding international policymaking to public health and cli-
mate, particularly during the SWM and MT.
1 Introduction
As pollution emissions from Asian countries have surpassed
those of countries in Europe and North America (Akimoto,
2003; Smith et al., 2011), Asia has become increasingly im-
portant from a global climate and health perspective. The
tropical Northwest Pacific (TNWP), situated adjacent to
Southeast Asia (Fig. 1), is a receptor for multiple sources of
aerosol particles throughout the region (Bagtasa et al., 2018;
Hilario et al., 2020a; Huang et al., 2019; Reid et al., 2015)
and is one of the most susceptible regions to global climate
change (IPCC, 2014; Reid et al., 2013; Yusuf and Francisco,
2009). Amidst several multi-scale meteorological phenom-
ena ranging from the Asian monsoon system (Akasaka et al.,
2007; Chang et al., 2005), the El Niño–Southern Oscillation
(Cruz et al., 2013), the Madden–Julian Oscillation (Maloney
and Hartmann, 2001; Pullen et al., 2015), and intermittent ty-
phoons (Bagtasa, 2017; Maloney and Dickinson, 2003), the
TNWP hosts arguably one of the most complex meteorologi-
cal environments in the world with likewise intricate relation-
ships to aerosol life cycle and climate impacts (Reid et al.,
2012; Ross et al., 2018).
Owing to atmospheric residence times ranging from days
to weeks (Balkanski et al., 1993; Kritz and Rancher, 1980)
and enabled by the surrounding meteorology, aerosol par-
ticles from multiple sources can undergo long-range trans-
port into the TNWP (Lin et al., 2007; Xian et al., 2013).
These sources include biomass burning from Indonesia and
Malaysia (Hilario et al., 2020a, b; Reid et al., 2015); an-
thropogenic and dust outflow from China, Korea, and Japan
(Bagtasa et al., 2019; Braun et al., 2020; Geng et al., 2019;
Miyazaki, 2003; Oshima et al., 2012; Tan et al., 2012); emis-
sions from countries such as Vietnam, Laos, and Thailand
(Bagtasa et al., 2019; Geng et al., 2019; Huang et al., 2020;
Lin et al., 2009; Nguyen et al., 2020); and marine aerosol
particles from the Pacific Ocean. Such transport is controlled
by the interplay of several factors such as topography, sea
breeze, monsoon flows, and typhoons (Reid et al., 2012;
Wang et al., 2013). Aside from the risk posed by trans-
ported anthropogenic aerosol on public health (Lelieveld
et al., 2015; Zhang et al., 2017), such a diverse set of aerosol
sources and types can result in variable aerosol–cloud–
climate interactions (Hamid et al., 2001; Heald et al., 2014;
Rosenfeld, 1999; Ross et al., 2018; Sorooshian et al., 2009;
Yu et al., 2006; Yuan et al., 2011), which are complicated
further by the spatial inhomogeneity of transported aerosol
particles (Akimoto, 2003). As the influence of aerosol parti-
cles on climate remains one of the largest uncertainties in our
understanding of the atmosphere (IPCC, 2014), investigat-
ing the composition and transport mechanisms of air masses
from different source regions will aid in the future develop-
ment of transport models and lead to a better understanding
of the transport pathways that modulate aerosol particles in
this part of the world.
Previous aircraft campaigns in Asia and the Pacific in-
clude the Transport and Chemical Evolution Over the Pa-
cific (TRACE-P) campaign (Jacob et al., 2003), the Aerosol
Radiative Forcing in East Asia (A-FORCE) campaign (Os-
hima et al., 2012), the Pacific Exploratory Mission – West
A and B (PEM-West) (Hoell et al., 1996, 1997), and the
Oxidant and Particulate Photochemical Processes Above a
South East Asian Rainforest (OP3) project (Hewitt et al.,
2010). These campaigns examined springtime outflow from
the Asian continent (e.g., Koike et al., 2003; Kondo et al.,
2004; Park, 2005) and early-summertime characteristics of
local and transported aerosol over Borneo (e.g., Robinson
et al., 2011, 2012); however, no study to our knowledge has
utilized aircraft data to characterize long-range transport pat-
terns over the TNWP coinciding with the peak agricultural
burning period for Indonesia and Malaysia. Limited ship ob-
servations in association with the 7 Southeast Asian Studies
(7SEAS) program (e.g., Reid et al., 2015, 2016a, b) found a
highly dynamic aerosol environment (Atwood et al., 2017;
Hilario et al., 2020a; Reid et al., 2015).
The NASA Cloud, Aerosol, and Monsoon Processes-
Philippines Experiment (CAMP2Ex) aircraft campaign ex-
amined the influence of meteorology, convection, and radia-
tive effects on gas and aerosol species in the TNWP. Based
at Clark, Luzon, Philippines, from 24 August to 5 Octo-
ber 2019, CAMP2Ex obtained a wide array of measurements
between 0–9 km a.g.l.(above ground level) across 19 re-
search flights (RFs). Some RFs were conducted in coor-
dination with the seaborne research vessel R/V Sally Ride
as part of the Office of Naval Research Propagation of In-
terSeasonal Tropical OscillatioNs (PISTON) project (https:
//onrpiston.colostate.edu/, last access: 27 July 2020). The
CAMP2Ex campaign is unique in that it began during the
peak of the Asian southwest monsoon (SWM) and coincided
with an early monsoon transition (MT), which occurred in
late September instead of the more common time in Octo-
ber (Chang et al., 2005; Matsumoto et al., 2020). The early
MT led to diverse transport patterns (Fig. 2) that offered an
opportunity to examine long-range transport into the TNWP.
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3779
Figure 1. Maps of (a) ground elevation from the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010), with flight tracks in
red and approximate source regions in labeled boxes (peninsular Southeast Asia (PSEA), the Maritime Continent (MC), East Asia (EA), and
the West Pacific (WP)), (b) 2020 population density from the Center for International Earth Science Information Network (CIESIN) Gridded
Population of the World (GPW) v4, (c) MODIS active fire hotspot density (only fires tagged with >80 % confidence) averaged at 0.5◦×0.5◦
resolution from 1 August to 15 October 2019, and (d) OMI-retrieved planetary boundary layer (PBL) SO2averaged over the same period.
Aircraft campaigns allow for vertically resolved measure-
ments of air mass properties, which are essential to better
understand the atmosphere, as aerosol–cloud–climate inter-
actions vary by altitude (Dahutia et al., 2019; Dong et al.,
2017; Hansen, 2005; Mishra et al., 2015) and vertical trans-
port can influence air mass composition (Matsui et al., 2011a;
Moteki et al., 2012; Oshima et al., 2012, 2013). Furthermore,
as the main route of aerosol removal from the atmosphere,
wet scavenging is a crucially important aspect of aerosol ver-
tical profiles and is linked to significant uncertainties in cli-
mate models (Neu and Prather, 2012; H. Wang et al., 2013;
Yu et al., 2019). Vertically resolved in situ observations in
field campaigns targeting aerosol–cloud–meteorology inter-
actions are necessary to advance our understanding of scav-
enging processes to inform the spectrum of models rang-
ing from smaller-scale process models to larger-scale climate
models (MacDonald et al., 2018; Sorooshian et al., 2019).
As Asian emissions continue to increase as a consequence
of rapid economic development, it is imperative to under-
stand the influence of long-range transport on air quality
and aerosol–cloud–climate feedbacks in this region. In this
study, we focus on characterizing transported air masses
from four key regions: the Maritime Continent (MC: 5◦S–
6.8◦N, 94.9–119.5◦E), peninsular Southeast Asia (PSEA:
10–23◦N, 95–109.5◦E), East Asia (EA: 22–44◦N, 100–
122◦E and 30–44◦N, 122–145◦E), and the west Pacific
Ocean (WP: 3–25◦N, 130–145◦E). Using air mass back tra-
jectories to complement the CAMP2Ex data, this study aims
to (1) identify regional transport pathways into the TNWP
and their associated synoptic conditions, (2) characterize air
masses coming from different source regions in terms of
composition and aerosol size distribution, and (3) estimate
the influence of convection and precipitation on transported
air masses. By examining how transport and scavenging
mechanisms impact air mass composition, our results have
implications for improving the modeling of aerosol lifecycles
during the SWM and MT in this meteorologically complex
region. Furthermore, due to the health impacts of biomass
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3780 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
Figure 2. Trajectory densities resolved by monsoon phase and sampling vertical level. Monsoon phases are southwest monsoon (SWM;
before 20 September 2019) and the monsoon transition (MT; after 20 September 2019). Vertical sampling levels are divided into boundary
layer (BL; <2 km) and free troposphere (FT; >2 km). Red star denotes Clark International Airport, Philippines. Number of trajectories (n)
is shown in the upper right of each panel.
burning and anthropogenic emissions, this work is also im-
portant for guiding policymaking related to public health and
climate during the transport-intensive SWM and MT. Due to
the specificity of some abbreviations used in this work, we
have provided a lookup table with definitions (Table 1).
2 Methods
2.1 CAMP2Ex observations
A major goal of the 2019 CAMP2Ex aircraft campaign
was to understand aerosol–cloud–climate feedbacks in the
TNWP (Di Girolamo et al., 2018). Although multiple aircraft
were deployed, this study focused on measurements made
onboard the NASA P-3B Orion (N426NA) aircraft. Aircraft
altitude (m a.g.l.hereafter) was calculated as the difference
between GPS altitude and ground elevation provided by the
Google Maps API, with an uncertainty of ±5 m. Dry opti-
cal size distribution data were collected by the laser aerosol
spectrometer (LAS; TSI Model 3340) and are presented as
an integrated particle number concentration for diameters be-
tween 100 and 1000 nm (N100−1000 nm; cm−3). Uncertainty
of LAS-derived N100−1000 nm is estimated at 20 %. LAS
optical sizing is calibrated using polystyrene latex spheres
(i.e., with a real refractive index of 1.59) and verified in
flight using an onboard nebulizer to ensure a consistent re-
sponse throughout the campaign. During post-flight process-
ing, sizing is corrected using monodisperse ammonium sul-
fate aerosol so that derived size distributions are referenced
to a real refractive index of 1.53 and relevant to ambient
aerosols (Shingler et al., 2016). Carbon monoxide (CO; ppm)
and methane (CH4; ppm) mixing ratios were measured by
a dried-airstream near-infrared cavity ring-down absorption
spectrometer (G2401-m; PICARRO, Inc.), with uncertainties
of 2 % and 1 % and precisions of 0.005 and 0.001 ppm, re-
spectively. Ozone (O3; ppbv) measurements were conducted
with a dual-beam UV absorption sensor (Model 205; 2B
Technologies) with an uncertainty of 6ppbv. A fast inte-
grated mobility spectrometer (FIMS) measured aerosol size
distribution between 10 and ∼600 nm with a concentration
uncertainty of ∼15 % and size uncertainty of ∼3 % (Wang
et al., 2017a, b; Y. Wang et al., 2018).
Non-refractory aerosol composition in the submicrome-
ter range was measured with a high-resolution time-of-flight
aerosol mass spectrometer (AMS; Aerodyne) (Canagaratna
et al., 2007; DeCarlo et al., 2006). The species of relevance
to this study include sulfate (SO2−
4), ammonium (NH+
4), ni-
trate (NO−
3), and organic aerosol (OA), all of which are re-
ported in units of µg m−3with uncertainties up to 50 % to ac-
count for ambiguity in the instrument collection efficiency.
The AMS was operated in 1 Hz Fast-MS mode and aver-
aged to 30 s time resolution for this study. The 1σdetec-
tion limits (in µg m−3) are as follows for the 30 s averaged
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3781
data: 0.169 (OA), 0.039 (SO2−
4), 0.035 (NO−
3), and 0.169
(NH+
4). Detection limits were determined in flight when sam-
pling behind a filter blank or during periods in the free tro-
posphere with constant aerosol concentrations. Mass concen-
trations below these detection limit values, which are some-
times negative due to the AMS difference method, are statis-
tically equal to zero. To avoid a positive bias, negative AMS
values were included in the analysis but were interpreted as
a concentration value of zero (e.g., Table 3). Black carbon
(BC; ngm−3) was measured with a single-particle soot pho-
tometer (SP2) (Moteki and Kondo, 2007, 2010), including
an uncertainty of 15 % based on laboratory intercompari-
son results from Slowik et al. (2007). Lower detection limits
are less than 10 ngm−3based on manufacturer specifications
and confirmed by in-flight filter-blank measurements and ob-
servations in the clean tropical free troposphere. SP2 mass
concentration calibration is accomplished using monodis-
perse nebulized fullerene soot aerosol according to Gysel
et al. (2011). We note that BC data were unavailable dur-
ing one flight covering a major Borneo smoke event (RF9);
thus, we have provided statistics of the AMS total minus RF9
for a more direct comparison with BC (Table 3). Particle size
ranges for SP2 and AMS are reported at 100–700 nm (BC
equivalent), and 60–600 nm (vacuum aerodynamic) diame-
ter, respectively. While quantitative comparison of these in-
struments is complicated by differing sizing techniques, each
is relevant to accumulation-mode aerosol and is assumed
to capture the majority of particle mass in this size range.
Likewise, LAS-integrated number concentrations from 100–
1000 nm optical diameter are used to illustrate variability in
accumulation-mode number concentration.
All aerosol data are reported at standard temperature and
pressure (STP; 273 K, 1013 hPa). All in situ aerosol measure-
ments were placed downstream of a forward-facing shrouded
solid diffuser inlet designed by the University of Hawaii that
efficiently transmits particles (≤5.0 µm aerodynamic diam-
eter) to cabin-mounted instrumentation (McNaughton et al.,
2007). The inlet flow rate is manually controlled to provide
isokinetic sampling over the full range of P-3B airspeeds to
minimize size-dependent biasing of the ambient particle size
distribution. Downstream of the inlet, flow is split to individ-
ual instruments using a custom-designed stainless-steel man-
ifold (Brechtel Manufacturing Inc.).
Only data collected via isokinetic sampling (McNaughton
et al., 2007) were used to eliminate sampling artifacts from
the shattering of large water and ice particles (Murphy et al.,
2004). When the aircraft entered clouds, sampling was man-
ually switched to a counterflow virtual impactor (CVI) in-
let (Brechtel Manufacturing Inc.). Using only data collected
during isokinetic sampling removed 16 % of CAMP2Ex sam-
ples.
Background concentrations of each species were defined
as the lowest 10th percentile of all CAMP2Ex data along
vertical profiles for every 5K range of potential tempera-
ture (Koike et al., 2003; Matsui et al., 2011a). Enhancements
above these background concentrations are denoted by 1.
For species ratios, only data with 1CO >0.02 ppm were in-
cluded similar to past work (Kleinman et al., 2007; Kondo
et al., 2011; Matsui et al., 2011b).
Only data along profiles extending vertically more than
2 km were considered for analysis as they provide a “snap-
shot” of the atmosphere with which we can compare more di-
rectly air mass characteristics across different altitudes. Data
collected when the aircraft sampled directly over urbanized
Luzon (13–15.8◦N, 120–122◦E) were excluded from analy-
sis to minimize the impact of local emissions. Flight tracks
and identified vertical profiles are shown in Fig. S1 in the
Supplement.
2.2 Back trajectory classification
The National Oceanic and Atmospheric Administration
(NOAA) Hybrid Single Particle Lagrangian Integrated Tra-
jectory Model (HYSPLIT) (Rolph et al., 2017; Stein et al.,
2015) was used to generate 120 h back trajectories along
vertical profiles with 1 min temporal resolution. Input me-
teorological data were from the Global Forecast System
(GFS) reanalysis produced by the National Centers for En-
vironmental Prediction (NCEP) at a horizontal resolution of
0.25◦×0.25◦.
Our analysis focused on transport from key source regions
(MC, PSEA, EA, WP). We note here that “source region”
refers to the attributed origin of an air mass as identified by
our trajectory classification scheme and does not preclude
the possibility of entrainment from emission sources during
transport (e.g., shipping) nor small-scale convection along
trajectories unresolved by GFS. Our classification scheme
considered two important environmental factors: (1) the syn-
optic shift that occurred around 20 September 2019, divid-
ing the CAMP2Ex period into the SWM (before 20 Septem-
ber) and MT (after 20 September); and (2) the vertical wind
shear across the region (Fig. 2). To better capture the pro-
nounced effect of the monsoon shift, air masses were only
classified as MC or PSEA (EA or WP) if sampled during the
SWM (MT). For example, instances of EA air sampled dur-
ing the SWM were classified as “other”, while air from EA
sampled during MT was classified as EA. The inclusion of
a monsoon phase filter more explicitly highlights the tempo-
ral aspect of meteorology in the TNWP; however, without
this monsoon phase criterion, resulting air mass classifica-
tions remain largely unchanged (Sect. 3.2). Furthermore, to
account for regional vertical inhomogeneity (Atwood et al.,
2013; Sarkar et al., 2018), our analysis of air mass charac-
teristics differentiates between boundary layer (BL; <2 km)
and free troposphere (FT; >2km) (Sect. 3.3). We use a 2 km
threshold to differentiate between BL and FT air based on cli-
matologically derived BL heights in this region (Chien et al.,
2019). This inherently comes with a degree of uncertainty;
however, we believe a conservative value of 2km is suffi-
cient for an overview study of this kind. An effort to deter-
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3782 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
mine CAMP2Ex BL heights is ongoing and warrants its own
separate study.
For an air mass to be classified as coming from a source
region, its back trajectory must pass within a source re-
gion’s bounding box for more than 6h at an altitude below
2 km (Chien et al., 2019). In addition to excluding data col-
lected over urbanized Luzon (Sect. 2.1), trajectories passing
through the Philippines (12–18.25◦N, 120.5–122.5◦E and
5.1–14.5◦N, 122.5–126.7◦E) under our trajectory classifica-
tion scheme were excluded to further focus our analysis on
long-range transport and associated processes.
Most air masses came from only one of the four source re-
gions: WP (117 occurrences), MC (174 occurrences), PSEA
(88 occurrences), and EA (130 occurrences). Air masses that
passed through both EA and WP (12 occurrences) were re-
garded as EA air due to the considerable influence of EA
outflow on air mass composition (Talbot et al., 1997). Other
transport permutations (e.g., air that passed through both MC
and PSEA) did not meet the requirements of our classifica-
tion scheme and were omitted. Thus, we focus on the four
major transport pathways (MC, PSEA, WP, EA). Focusing
on these major pathways adds robustness to the analysis by
partly compensating for the limits of the trajectory model in
capturing more complex meteorological phenomena such as
wind shear (Freitag et al., 2014), which have been shown to
contribute to trajectory uncertainty (Siems et al., 2000; Stohl
et al., 2002). In addition to requiring that the back trajectories
pass through the source regions, the additional criteria im-
posed (e.g., altitude <2 km over the source region) increase
our confidence that the remaining cases represent instances
of long-range transport. Resulting source-region contribu-
tions per RF are shown in Fig. S2. We emphasize that these
source-region contributions represent frequencies of obser-
vation rather than frequencies of occurrence, as the sampling
location of the aircraft introduces a bias inherent in aircraft
campaigns (Sect. 3.2).
As a consequence of our filtering scheme, a large portion
of trajectories were tagged as “other” (66.8 %). This is at-
tributable to several scenarios, including but not limited to
(1) air masses that passed over source regions, but above our
BL height threshold of 2 km (∼20 %); (2) air masses influ-
enced by the Philippines (i.e., air masses that stayed over the
Philippines at <2 km for more than 6 h) (∼8 %); (3) trans-
port permutations that occurred too infrequently to provide
robust statistics (∼3 %); and (4) stagnant air masses that did
not reach any source region (≤35 %). We note that the first
scenario is equivalent to long-range transport from further
away source regions not considered in this work (e.g., India,
West Asia).
Trajectory clustering was performed using two well-
established methods: kmeans and Ward linkage (Govender
and Sivakumar, 2020) in order to confirm the robustness of
our predefined source regions. K-means clustering classifies
data into kclusters such that the sum of squares per cluster
is minimized (Hartigan and Wong, 1979), with the drawback
that kmust be specified beforehand. Ward linkage is a hier-
archical clustering method that merges clusters such that the
increase in intra-cluster Ward’s distance is minimized (Ward,
1963) and has been described as the method that most closely
accomplishes the goals of clustering (Tufféry, 2011). More
comprehensive descriptions of these clustering methods can
be found elsewhere (Govender and Sivakumar, 2020; Pérez
et al., 2017). Prior to clustering, a weighted distance matrix
was calculated, similar to Taubman et al. (2006): (1) normal-
ized trajectory coordinates to give equal weighting to both
horizontal and vertical transport; (2) weighted time steps lin-
early back in time; and (3) assigned nearby points (time
step <6 h) zero weighting on the clustering to remove the
influence of aircraft position on the clustering.
2.3 Accumulated precipitation along individual
trajectories
Accumulated precipitation along individual trajectories
(APT) was calculated using data from satellite precipita-
tion products (SPPs): (1) the Precipitation Estimation from
Remotely Sensed Information using Artificial Neural Net-
works – Climate Data Record (PERSIANN-CDR) (Ashouri
et al., 2015; Nguyen et al., 2018); (2) the Integrated Multi-
satellitE Retrievals for the Global Precipitation Measure-
ment (GPM) mission (IMERG) (Huffman et al., 2019); and
(3) the Tropical Rainfall Measuring Mission (TRMM) Multi-
satellite Precipitation Analysis (TMPA) 3B42-V7 (Huffman
et al., 2007). The purpose of utilizing multiple SPPs is to ac-
count for the uncertainties inherent in satellite retrievals, par-
ticularly during very light or heavy rainfall conditions, pro-
viding us with an ensemble of estimates rather than relying
on a single SPP (S. Chen et al., 2020; Liu, 2016; Maggioni
et al., 2016; Mahmud et al., 2017; Tan and Santo, 2018). Fur-
thermore, although these SPPs measure surface precipitation
and do not fully capture scavenging effects aloft, we use APT
as an indicator of whether an air mass passed through a con-
vectively active region.
PERSIANN-CDR (0.25◦×0.25◦, daily resolution) uses
a modified PERSIANN algorithm utilizing NCEP Stage
IV hourly precipitation and monthly precipitation from the
Global Precipitation Climatology Project (GPCP) to main-
tain monthly amounts consistent with GPCP (Ashouri et al.,
2015). PERSIANN-CDR data are available at the Center
for Hydrometeorology and Remote Sensing (CHRS) Data
Portal (http://chrsdata.eng.uci.edu, last access: 8 June 2020)
(Nguyen et al., 2019).
IMERG (0.1◦×0.1◦, 30 min resolution) integrates mul-
tiple satellite retrievals from the passive microwave (MW)
precipitation retrievals provided by the suite of GPM
constellation passive microwave radiometers (Kummerow
et al., 2015), the Climate Prediction Center MORPH-
ing technique (CMORPH) from NOAA, and PERSIANN-
Cloud Classification System (PERSIANN-CCS; Hong et al.,
2004) from the University of California, Irvine. These
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3783
data are available from the NASA Precipitation Process-
ing System (Skofronick-Jackson et al., 2018). For inter-
calibrating various MW precipitation products, IMERG uses
the GPM_2BCMB product (Olson, 2018) that utilizes the
GPM Microwave Imager (GMI) and Dual-frequency Pre-
cipitation Radar (DPR) instruments on the GPM core satel-
lite IMERG (Hou et al., 2014; Kidd et al., 2020). For
this study, we use IMERG Final Run data, available at
https://pmm.nasa.gov/data-access/downloads/gpm (last ac-
cess: 6 July 2020).
TMPA (0.25◦×0.25◦, 3 h resolution) similarly combines
data from multiple satellites such as TRMM (pre-2015),
NASA’s Aqua, and the NOAA satellite series, involving cali-
bration with gauge data when feasible (Huffman et al., 2007).
Though TRMM ended its service in 2015, the TMPA 3B42
algorithm was continued in parallel with IMERG through
December 2019 and had been based on a climatological cali-
bration since 2014. As TMPA is climatologically calibrated,
TMPA may be less sensitive to interannual variability in pre-
cipitation; thus, including TMPA in this study may provide a
better idea of the spread among SPPs. TMPA data are acces-
sible through https://pmm.nasa.gov/data-access/downloads/
trmm (last access: 28 January 2020).
Precipitation along each trajectory was integrated from the
trajectory spawn point (i.e., P3-B sampling location) to the
point when it reaches the boundary of a source region. An
additional 24 h along the trajectory after reaching a source
region was included in the APT integration to account for
precipitation effects within the source region (Matsui et al.,
2011a, b). No significant APT differences were found be-
tween using 0, 24, or 48 h for the APT calculation, suggesting
that our results are robust with regard to the added duration.
Furthermore, APT comparisons demonstrate that our results
are independent of chosen SPP in terms of APT ranking (i.e.,
all SPPs agreed on which source regions are associated with
the highest or lowest APTs).
2.4 Other data
Elevation data (Fig. 1a) were acquired from the United States
Geological Survey (USGS) Global Multi-resolution Terrain
Elevation Data 2010 (GMTED2010) (Danielson and Gesch,
2011). Population density data for 2020 (Fig. 1b) were re-
trieved from the Gridded Population of the World (GPW),
v4 (Center for International Earth Science Information
Network, 2018) (https://sedac.ciesin.columbia.edu/data/set/
gpw-v4-population-density- rev11/data-download, last ac-
cess: 3 July 2020). Depicted in Fig. 1c, active fires tagged
with high confidence (>80 %; Bhardwaj et al., 2016) were
obtained from the Moderate Resolution Imaging Spectro-
radiometer (MODIS) Collection 6 algorithm (https://firms.
modaps.eosdis.nasa.gov/, last access: 27 June 2020) (Levy
et al., 2013) and converted into average fire density at
0.5◦×0.5◦resolution. Planetary BL (PBL) sulfur diox-
ide (SO2) was retrieved by the Ozone Monitoring Instru-
ment (OMI) and obtained from NASA Goddard Earth Sci-
ences Data and Information Services Center (GES DISC)
(Li et al., 2015). The OMI SO2data were then resam-
pled to 1◦×1◦resolution and averaged between 1 Au-
gust and 15 October 2019 (Fig. 1d). Reanalysis data from
NCEP/NCAR (2.5◦×2.5◦) were used to examine synoptic
conditions (Kalnay et al., 1996).
3 Results and discussion
3.1 Observed transport patterns during CAMP2Ex
Figure 1 provides an overview of the general source re-
gions impacting the TNWP. The TNWP is surrounded by
areas of high population density in EA, MC, and PSEA
(Fig. 1b). Burning was prevalent mainly in the MC (Fig. 1c),
although fires were also detected along the eastern PSEA
coast. Satellite retrievals of PBL SO2reveal possible point
sources (Fig. 1d), perhaps from volcanoes, shipping, burning,
and industry (Fioletov et al., 2016; Guttikunda et al., 2001;
Zhang et al., 2019); however, we caution that cloud contam-
ination may influence the SO2retrievals, and it is used here
to demonstrate the variety of sources in Asia.
Trajectories from each source region show distinct path-
ways (Fig. 3a, d, g, and j), indicative of differences in ac-
companying synoptic-scale circulation. These pathways are
generally corroborated by both kmeans and Ward linkage
clustering methods (Figs. S3 and S4), confirming the ro-
bustness of our predefined source regions (Fig. 1a). Prior
to further discussion, we emphasize the temporal aspect of
these observed transport patterns (Figs. 2 and 3), in partic-
ular, their dependence on both synoptic (e.g., SWM) and
mesoscale meteorology (e.g., typhoons), which varied dur-
ing CAMP2Ex in terms of phase and frequency, respectively.
Consequently, a specific transport pattern may be more domi-
nant in one monsoon phase and less so in another while being
enhanced (or suppressed) by intermittent mesoscale phenom-
ena.
3.1.1 Southwest monsoon
Beginning with transport during the SWM prior to
20 September 2019, PSEA air is advected by uniform wester-
lies (Fig. 3a) associated with cyclonic activity over the north-
ern South China Sea (SCS) (Fig. 3b) (Cheng et al., 2013;
Huang et al., 2020; Lin et al., 2009). In comparison, al-
though MC transport also occurs during the SWM, the mech-
anism behind MC transport is driven instead by southwest-
erlies originating across the MC (Fig. 3d) (Ge et al., 2014;
J. Wang et al., 2013; Xian et al., 2013). Transport from the
MC is further promoted by well-developed cyclones enter-
ing PSEA (Fig. 3e), as previously highlighted by observa-
tional (Hilario et al., 2020a; Reid et al., 2015) and model-
ing studies (J. Wang et al., 2013). The similar cyclonic ac-
tivity over northern SCS/PSEA may explain the confluence
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3784 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
Figure 3. Classified trajectories and synoptic conditions associated with transport from (a–c) peninsular Southeast Asia (PSEA), (d–f) the
Maritime Continent (MC), (g–i) East Asia (EA), and (j–l) the West Pacific (WP). Left column: trajectory density normalized to the mean per
source region, with the number of trajectories classified as coming from each source region annotated on the lower left of each panel. Middle
column: NCEP 850 mb geopotential height anomaly from the mean for 2019, overlaid with horizontal winds (≥2 m s−1). Right column:
PERSIANN-CDR average precipitation overlaid with NCEP 850mb ω, where red (blue) contour lines represent ωvalues above 0.05 Pa s−1
(below −0.05 Pa s−1).
of air masses from PSEA and MC (e.g., RF6), indicated by
the frequent sampling of MC and PSEA air in the same RF
(Fig. S2).
A key difference between PSEA and MC air is that PSEA
air passed through convective areas over the PSEA (Taka-
hashi et al., 2010), the SCS (Fig. 3a and c) (Chen et al., 2017),
and along the western coast of the Philippines (Akasaka
et al., 2007; Chen et al., 2017; Cruz et al., 2013; Hilario
et al., 2020c), while MC air passed through areas with less
precipitation (Fig. 3d and f). As a result, PSEA air showed
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3785
Table 1. Definitions of frequently used abbreviations in this work.
Acronym Definition
AMS Aerosol mass spectrometer
APT Accumulated precipitation along individual trajectories
BC Black carbon
BL Boundary layer
CAMP2Ex Cloud, Aerosol, and Monsoon Processes Philippines Experiment
EA East Asia
FIMS Fast integrated mobility spectrometer
FT Free troposphere
HYSPLIT Hybrid Single Particle Lagrangian Integrated Trajectory Model
IMERG Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission
LAS Laser aerosol spectrometer
MC Maritime Continent
MT Monsoon transition (after 20 September)
N100−1000 nm Number concentrations between 100 to 1000 nm; derived from LAS
OA Organic aerosol
OMI Ozone Monitoring Instrument
PERSIANN-CDR Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record
PH Philippines
PSEA Peninsular Southeast Asia
RF Research flight
SCS South China Sea
SPP Satellite precipitation product
SWM Southwest monsoon (before 20 September)
TMPA Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis
TNWP Tropical Northwest Pacific
WP West Pacific
Table 2. Statistics for accumulated precipitation along trajectories (APT, mm) for peninsular Southeast Asia (PSEA), the Maritime Continent
(MC), East Asia (EA), and the West Pacific (WP), calculated with IMERG, TMPA, and PERSIANN-CDR. Median values are presented along
with the 25th and 75th percentiles provided in parentheses.
IMERG TMPA PERSIANN-CDR
PSEA 34.11 (19.47–49.13) 39.83 (22.25–60.08) 34.74 (28.89–43.84)
MC 1.70 (0.34–11.78) 2.15 (0.00–12.64) 6.10 (3.11–17.30)
EA 0.54 (0.04–1.48) 1.23 (0.14–2.94) 5.14 (2.67–12.13)
WP 1.31 (0.12–7.45) 3.17 (0.00–13.17) 14.30 (4.78–20.05)
much higher APT than MC air (Table 2) and was more likely
to have been processed by clouds. The transport pathway
of PSEA through convective regions may lead to wet scav-
enging and aqueous-phase processing (MacDonald et al.,
2018; Moteki et al., 2012; Sorooshian et al., 2006, 2007;
Wonaschuetz et al., 2012), affecting both air mass compo-
sition and particle size distributions (Sect. 3.3).
In terms of sampled air masses, PSEA and MC showed
contributions of 5.7 % and 11.3%, respectively (Fig. 4a), and
they differ in terms of vertical distribution (Fig. 4b). PSEA
air was sampled across a wide range of altitudes with the ma-
jority of observations over 900hPa, similar to Kondo et al.
(2004), explainable by convection-related lofting (Fig. 5a).
Very few observations of PSEA air were made near the sur-
face. The lofting of PSEA air can occur over the PSEA itself
(Fig. 3c) (Kondo et al., 2004), through mechanisms like oro-
graphic effects (Lin et al., 2009). Convection over the SCS
trough likely also contributes to lofting (Fig. 3c) (Chen et al.,
2017). Lofting of PSEA air into the FT has important down-
stream ramifications as it modulates both aerosol composi-
tion and size distribution (Matsui et al., 2011a; Moteki et al.,
2012; Oshima et al., 2013). However, we note that vertical
motion is an important source of uncertainty in trajectory
models (Harris et al., 2005) and should be interpreted with
caution.
In contrast to PSEA air, sampled MC air was well-mixed
within the BL (Fig. 5b), but the observation frequency of MC
air dropped sharply above 750 hPa, consistent with previous
modeling studies (e.g., Xian et al., 2013). Such distinct verti-
cal distributions between MC and PSEA air are perhaps due
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3786 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
Figure 4. Relative contributions (in percentages) of air masses arriving from study regions averaged over (a) all altitudes and (b) pressure
levels (hPa). Total number of trajectories per pressure bin is provided at the right end of (b). Source regions are peninsular Southeast Asia
(PSEA), the Maritime Continent (MC), East Asia (EA), and the West Pacific (WP).
Figure 5. Vertical motion during transport from (a) peninsular Southeast Asia (PSEA), (b) the Maritime Continent (MC), (c) East Asia (EA),
and (d) the West Pacific (WP). Color corresponds to density as a function of trajectory altitude (pressure) and time step. Example trajectories
are plotted in black to show actual vertical motion.
to a highly sheared environment during the SWM, generally
contributing to distinct air mass sources across different alti-
tudes (Atwood et al., 2013; Sarkar et al., 2018) and varying
degrees to which these air masses are processed (Sect. 3.3).
3.1.2 Monsoon transition
The arrival of the MT period after 20 September 2019 led
to a synoptic-scale shift (Fig. 2), allowing the sampling of
air from EA and WP (Fig. 3g and j). Transport from EA
was observed across several MT flights (Fig. S2) and orig-
inated mainly from southeastern China, Korea, and Japan
(Fig. 3g), suggesting the entrainment of anthropogenic emis-
sions (Sect. 3.3) (Cheng et al., 2013; Hatakeyama et al.,
2001, 2004; Kim et al., 2009; Wang et al., 2016). Depicted
in Fig. 3h, Asian outflow was promoted by the pairing of
a well-developed cyclone passing over the East China Sea
(Hatakeyama et al., 2001, 2004; Uno et al., 1998) and an
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3787
anticyclone over the Asian continent (Honomichl and Pan,
2020). In comparison, WP transport was observed mainly
towards the end of the campaign (Fig. S2), likely a conse-
quence of sampling location, and was driven by Pacific north-
easterlies (Fig. 3j–k). Transport from the WP, similar to that
of EA, coincided with an anticyclone over the Asian conti-
nent (Fig. 3k); however, WP transport is marked by the ab-
sence of the East China Sea cyclone that promoted southward
transport of EA air (Fig. 3h). This difference may explain
why EA and WP air were usually sampled in separate RFs
(Fig. S2), in contrast to PSEA and MC air, which tended to
be sampled together.
Air from EA and WP show similarly low APT (Ta-
ble 2), explainable by the generally lower precipitation in
MT (Fig. 3i and l) compared to SWM (Fig. 3c and f) (Mat-
sumoto et al., 2020), as well as the lower number of cyclone
occurrences after 20 September 2019. Although EA transport
was driven by a well-developed cyclone (Fig. 3h), trajecto-
ries suggest that EA air traveled through the outer bands of
the cyclone (Fig. 3g), largely avoiding high-precipitation ar-
eas (Fig. 3i). This suggests that anthropogenic emissions en-
trained in EA air experienced low levels of scavenging and
were more likely to be sampled, in contrast with high APT
urban source regions like PSEA (Sect. 3.3).
Transport from EA and WP were quite similar in terms of
relative contribution (8.5% and 7.6 %, respectively; Fig. 4a)
and vertical sampling distribution (Fig. 4b). Sampled EA and
WP air were largely constrained to the BL, though EA air
was sampled almost entirely below the 900 hPa level, while
WP air was more evenly sampled. In terms of vertical motion
during transport, some EA trajectories exhibited downward
motion (Fig. 5c), likely due to subsidence from the continen-
tal anticyclone (Fig. 3h), contrasting the vertical motion of
PSEA air, which generally experienced upward motion asso-
ciated with convection (Fig. 5a).
In summary, important transport features over the TNWP
include the following: (1) SWM transport from the MC and
PSEA was driven by southwesterlies and cyclonic activity
over northern SCS/PSEA, while MT transport from EA and
the WP was driven by Pacific northeasterlies, anticyclones
over the Asian continent, and well-developed cyclones over
the East China Sea; (2) EA and MC air were sampled largely
within the BL, did not exhibit significant upward motion, and
experienced low APT, suggesting that they likely carry ur-
ban/continental or biomass burning emissions; in contrast,
(3) PSEA air may have undergone a high degree of aerosol
scavenging over convective regions (e.g., SCS), indicated by
high APT and upward motion of trajectories.
3.2 Sensitivity analysis
Due to the complex nature of long-range transport and the
limited resolution of the meteorological input data, there is
inherent uncertainty in the generated trajectories. In order
to assess the effect of this uncertainty on our results, we
evaluated the effect of modifying the following variables on
our source-region distribution: (1) trajectory height threshold
over source regions; (2) back trajectory run time; (3) vertical
profile filtering; (4) monsoon phase; and (5) aircraft sampling
location. For example, to test the sensitivity of our results to
trajectory height threshold (i.e., 2 km over source regions),
we varied this threshold (e.g., 0.5, 1, 3 km over source re-
gions), reclassified trajectories according to the new thresh-
old, and compared the new source-region distribution to the
original result, which was presented in Sect. 3.1. Except for
aircraft sampling location, independently changing any of
these variables had little effect on the resulting source-region
distribution (Table S1 in the Supplement). The relative con-
tributions of source regions did vary significantly with sam-
pling location, though areas surrounding Luzon (e.g., east of
Luzon, north of Luzon) showed some degree of similarity.
Thus, we emphasize that, as with any aircraft campaign, ob-
served transport is to some degree dependent on aircraft lo-
cation.
To reduce the effect of local emissions, we excluded trajec-
tories classified as influenced by the Philippines (PH). This
filter screened approximately 8 % of the data. To evaluate our
filter for Philippine-influenced trajectories (hereafter, PH fil-
ter), air mass characteristics were compared between trans-
ported air unaffected by PH (no PH air; e.g., MC) and trans-
ported air mixed with PH air (with PH; e.g., MC-PH). A lo-
cal signal was observed for N100−1000 nm, suggested by dif-
ferences in the histograms of N100−1000 nm between non-PH
and PH-mixed air (Fig. S5), particularly for MC and PSEA
air. Differences in the species concentration histograms of
non-PH and PH-mixed air were also observed for other an-
thropogenic species (BC, OA, SO2−
4; not shown), confirming
the effectiveness of the PH filter.
3.3 Chemical composition of transported air masses
A convenient opportunity afforded by having conducted the
air mass classification presented above was to examine how
gas and aerosol properties vary for each source region based
on vertically resolved in situ aircraft measurements. To ac-
count for regional vertical wind shear (Fig. 2) while consider-
ing the generally lower classification frequency at higher al-
titudes (Fig. 4b), air mass characterization was resolved into
BL and FT subsets and composited by source region (Ta-
ble 3). The delineation between BL and FT composition is
demonstrated by selected species (Fig. 6), which generally
dropped in concentration above the BL (/850 hPa). Prior to
further discussion, we note here that shipping is a major re-
gional source (Streets et al., 1997, 2000) and may contribute
appreciably to all air masses.
Significant differences in composition were observed in
the same monsoon season (e.g., SWM) depending on air
mass origin. Air from PSEA had much lower species con-
centrations than MC (Table 3) due to decreased emissions
and increased potential for wet scavenging. Sampled MC
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3788 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
Figure 6. Vertical median profiles of composition for peninsular Southeast Asia (PSEA), the Maritime Continent (MC), East Asia (EA), and
the West Pacific (WP). (a) N100−1000 nm (cm−3), (b) CO (ppm), (c) SO2−
4(µg m−3), (d) OA (µg m−3). Left and right sides of shaded areas
refer to 25th and 75th percentiles, respectively.
Table 3. Comparisons of boundary layer (BL; <2 km) and free troposphere (FT; >2km) mean values across source regions. Uncertainty
values are presented as 1 SD. Bold values denote when significant statistical differences are found (p < 0.05). Corresponding pvalues are
provided in Table S2. The EA FT column was left blank due to the infrequent sampling of EA air in the FT. AMS total is provided minus
RF9 for better comparison to BC (no RF9 SP2 data). Statistics exclude RF18 (local pollution flight).
EA MC PSEA WP
BL FT BL FT BL FT BL FT
N100−1000 nm (cm−3) 839.11 ±507.34 – 818.43 ±571.90 223.87 ±316.26 272.55 ±125.29 35.88 ±41.23 71.18 ±66.13 14.72 ±5.99
CO (ppm) 0.16 ±0.07 – 0.18 ±0.12 0.11 ±0.07 0.10 ±0.02 0.10 ±0.02 0.08 ±0.00 0.08 ±0.01
O3(ppbv) 45.29 ±28.89 – 31.22 ±10.62 23.69 ±7.62 24.03 ±3.44 29.69 ±5.52 12.73 ±3.27 18.81 ±7.79
CH4(ppm) 1.96 ±0.06 – 1.86 ±0.01 1.85 ±0.01 1.88 ±0.03 1.87 ±0.01 1.88 ±0.00 1.88 ±0.01
SO2−
4(µg m−3) 5.14 ±2.40 – 2.43 ±1.33 0.69 ±0.75 1.03 ±0.54 0.17 ±0.15 0.79 ±0.98 0.23 ±0.09
NO−
3(µg m−3) 0.19 ±0.45 – 0.24 ±0.32 0.08 ±0.20 0.04 ±0.04 0.00 ±0.03 0.01 ±0.04 −0.01 ±0.05
NH+
4(µg m−3) 1.32 ±1.19 – 0.86 ±0.75 0.28 ±0.43 0.23 ±0.24 −0.01 ±0.18 0.10 ±0.30 0.00 ±0.20
OA (µg m−3) 2.68 ±3.54 – 7.23 ±8.80 2.10 ±4.52 0.96 ±1.00 0.07 ±0.24 0.13 ±0.32 0.01 ±0.21
BC (ng m−3) 87.29 ±98.53 – 71.81 ±41.79 16.06 ±17.40 24.90 ±15.83 2.79 ±5.59 1.03 ±2.92 0.20 ±0.91
AMS total (µg m−3) 9.35 ±7.14 – 7.26 ±5.19 1.38 ±1.46 2.10 ±1.41 0.19 ±0.41 1.03 ±1.48 0.24 ±0.33
air showed statistically significant differences between BL
and FT concentrations for both gas and aerosol species (Ta-
ble S2), indicative of emissions constrained to the BL, and
exhibited strong biomass burning signals in its composition
profile (e.g., N100−1000 nm, CO, NO−
3, OA, and BC) (Maudlin
et al., 2015; Pósfai et al., 2003; Reid et al., 1998, 2005;
Theodoritsi et al., 2020; Yadav et al., 2017). We note that
peaks of CO (Fig. 6b) and CO2(not shown) were observed
in MC samples at around 650 hPa, suggestive of MC burning
emissions lofted into the FT; however, this feature consisted
of few samples and did not appear in other gases (e.g., SO2)
and thus warrants caution in its interpretation.
In contrast, PSEA air was generally characterized by
concentration magnitudes between those of MC and WP.
Aerosol concentrations of PSEA air in the FT were lower
by at least 1 order of magnitude than those in the BL (SO2−
4,
NH+
4, OA, BC) while trace gases (CO, CH4) showed more
similar BL and FT concentrations (Tables 2 and S2; Fig. S6).
These aerosol–gas differences point to (1) the lofting of
PSEA air into the FT, as suggested by the similarity of
trace gas concentrations between BL and FT, and (2) the
consequent scavenging of aerosol particles, explaining the
much lower aerosol concentrations in the FT (Oshima et al.,
2012, 2013; Sievering et al., 1984). For comparison, MC air
showed large BL and FT differences in both aerosol and gas
species, the latter of which indicates the infrequent lofting
of MC air (Figs. 4b and 5b). Since PSEA air came from a
populated region (Fig. 1b) and likely originally contained an-
thropogenic aerosol particles, these unique characteristics of
PSEA air compared to MC and EA air support the likelihood
of aerosol scavenging in PSEA air. These observations are ro-
bust due to the relatively even sampling frequency of PSEA
across altitudes (Fig. 4b).
Transport during the MT season showed similarly dis-
tinct composition profiles depending on air mass origin. Air
from EA exhibited higher concentrations of SO2−
4, O3, CH4,
and NH+
4, owing to urban emissions in continental outflow
(Chuang et al., 2014; Talbot et al., 1997; Thornton et al.,
1999; Umezawa et al., 2014; Wang et al., 2007) and exten-
sive secondary aerosol formation (Hatakeyama et al., 2001,
2004, 2011; Krupa and Manning, 1988; Matsui et al., 2014).
In contrast, WP air is characterized as pure marine due to
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M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3789
Figure 7. Linear regressions of (a) 1CO2/1CO, (b) 1CH4/1CO, (c) 1OA/1CO, (d) 1SO2−
4/1CO, and (e) 1BC/1CO for the Maritime
Continent (MC; red) and East Asia (EA; purple). Annotated are species ratios (slope) per region and standard error (SE) as a measure of
uncertainty (slope ±SE). Pearson’s Rvalues are provided in parentheses. Only data with 1CO >0.02 ppm were included to better identify
combustion-related ratios. Peninsular Southeast Asia and West Pacific data were not plotted due to low correlations.
composition similar to those previously reported in Pacific
marine environments (Davis et al., 1996; Matsumoto et al.,
1998; Talbot et al., 1997).
3.3.1 Species ratios
Composition profiles between regions (Table 3) reveal clear
differences as a function of (1) emission source and (2) pas-
sage through convective regions indicated by APT (Table 2).
The role of emission source was most evident when com-
paring air masses of low APT (EA, MC). Though EA and
MC had similar BL values for N100−1000 nm (Table 3), they
showed distinct chemical differences: MC air was charac-
terized by higher concentrations of biomass burning trac-
ers (e.g., CO, CH4, NO−
3, OA), while EA air showed influ-
ence from urban/continental sources and secondary forma-
tion (e.g., O3, CH4, NH+
4, SO2−
4). Such differences in com-
position are corroborated by species ratios derived with lin-
ear regression (Fig. 7). Prior to a discussion on the species
ratios, we note that the reported species ratios in this study
are difficult to compare directly to at-source measurements of
the same quantity because the composition of air masses was
likely influenced by sources and sinks during transport (e.g.,
Choi et al., 2019; Conte et al., 2019; Gruber et al., 2019; Yang
et al., 2009); however, differences in species ratios can still
aid in air mass characterization and point to possible emis-
sion sources.
In Fig. 7a, the higher 1CO2/1CO ratio of EA air vs.
MC air is indicative of an inefficient combustion signature
in MC air (Halliday et al., 2019), attributable to the predomi-
nantly smoldering phase of MC fires (Gras et al., 1999; Reid
et al., 2013). We note that the poor 1CO2–1CO correla-
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3790 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
tion for MC air indicates that our reported ratio does not re-
flect expected emission ratios (Andreae, 2019; Hurst et al.,
1994). This further suggests (1) our source-region classifica-
tion (i.e., MC, EA) may not perfectly capture air mass differ-
ences and (2) additional sources of CO or CO2during trans-
port. The contribution of other CO or CO2sources within the
MC besides biomass burning may also explain the low corre-
lation. Thus, it is necessary to use multiple species ratios to
supplement air mass chemical characterization.
The strong relationship between CH4and CO in MC air
is a good indication of biomass burning influence (Andreae,
2019; Hecobian et al., 2011). The ratio of 1CH4/1CO
(Fig. 7b) was higher by 1 order of magnitude in EA air com-
pared to MC air, indicating the contribution of CH4from EA
residential and industrial activity (Geng et al., 2019; He et al.,
2019; Tohjima et al., 2014) as well as from rice cultivation in
EA (Xia et al., 2020).
As indicators of aerosol hygroscopicity (Kreidenweis and
Asa-Awuku, 2014; Malm et al., 2005; Svenningsson et al.,
2006), 1OA/1CO (Fig. 7c) and 1SO2−
4/1CO (Fig. 7d) ra-
tios point to higher hygroscopicity in EA air than MC air
(Cheung et al., 2020; Saxena et al., 1995; X. Wang et al.,
2017; Z. Wang et al., 2018, Wang et al., 2019). Interest-
ingly, though peat burning in the MC has been associated
with SO2−
4(Ikegami et al., 2001; Reid et al., 2013), the el-
evated 1OA/1CO and lower 1SO2−
4/1CO in MC air are
indicative of lower hygroscopicity. This is explainable by the
high levels of OA emitted during biomass burning (Radzi
bin Abas et al., 2004; Theodoritsi et al., 2020) or produced
through gas-to-particle conversion during transport (Cappa
et al., 2020; Mardi et al., 2018; Zhou et al., 2012). In con-
trast, lower 1OA/1CO and higher 1SO2−
4/1CO ratios of
EA air signal hygroscopicity that is attributable to the high
levels of SO2in EA (Fig. 1d), facilitating the secondary for-
mation of SO2−
4(e.g., Hatakeyama et al., 2011).
Although MC was calculated to have low APT (Table 2),
a comparison of BL and FT air from MC (Figs. S6 and S7)
allows for speculation on a possible scavenging mechanism
acting on FT air. Vertically resolved linear regressions of
1SO2−
4/1CO reveal a reduction in 1SO2−
4in the FT com-
pared to the BL (Fig. S7a), while 1OA/1CO (Fig. S7b) in-
dicated no such effect on OA. Considering the higher hy-
groscopicity and therefore scavenging susceptibility of SO2−
4
compared to OA (e.g., Kreidenweis and Asa-Awuku, 2014),
we speculate the removal of SO2−
4to be related to wet scav-
enging. Comparisons of BL and FT aerosol concentrations
(Fig. S6) and size distributions (Sect. 3.4; Fig. 8b) further
support this possibility, as aerosol and particle concentrations
have significantly lower values in the FT compared to BL, a
difference not observed for trace gases (Fig. S6). The dis-
crepancy between 1SO2−
4/1CO and 1OA/1CO (Fig. S7)
implies externally mixed particles, which is surprising given
the aged nature of these air masses (Gorkowski et al., 2020).
Further analysis is required involving m/z, O :C ratios to ac-
Figure 8. Median FIMS number size distributions (dN/d log Dp;
cm−3) as a function of geometric mean particle diameter (Dp; nm),
resolved by source region and sampling altitude in the boundary
layer (BL; <2 km) and free troposphere (FT; >2 km). Source re-
gions are peninsular Southeast Asia (PSEA), the Maritime Conti-
nent (MC), East Asia (EA), and the West Pacific (WP). Upper and
lower bounds of the shaded areas refer to 25th and 75th percentiles,
respectively. The size distribution of EA air in the FT was not plot-
ted due to the infrequent sampling of EA air in the FT.
count for aging effects (e.g., internal mixing, oxidation) and
determine the exact mechanism behind the difference. We
emphasize that the hypothesized scavenging of MC air in the
FT is largely speculation for now and mainly introduces op-
portunities for future work.
We note that the speculated wet scavenging is not appar-
ent from APT, which suggested dry conditions for MC air
(Table 2). This disagreement with APT stems from the us-
age of SPPs which typically describe surface precipitation,
and, consequently, our APT may not fully capture potential
wet scavenging effects aloft. Thus, the speculated scavenging
mechanism of MC air in the FT may occur aloft and may not
be linked to surface precipitation. Perhaps this mechanism is
related to processes such as in-cloud scavenging (Sievering
et al., 1984; Yang et al., 2021; Yu et al., 2020), but, indeed,
this requires a deeper investigation in future work.
Due to BC’s lack of secondary sources, the ratio of
1BC/1CO has been used to gauge transport efficiency as
affected by physical removal processes in air masses (Matsui
et al., 2011b; Moteki et al., 2012; Oshima et al., 2012) and as
an indicator of combustion efficiency, which increases with
1BC/1CO (Kondo et al., 2011). The ratio of 1BC/1CO
was much higher in EA air than in MC air (Fig. 7e), similar to
observations by Pani et al. (2019) in Taiwan. This difference
is explainable by burning in industrial and residential areas
Atmos. Chem. Phys., 21, 3777–3802, 2021 https://doi.org/10.5194/acp-21-3777-2021

M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3791
in EA (Bond et al., 2004; Geng et al., 2019) and the predom-
inance of smoldering fires in the MC (Gras et al., 1999; Reid
et al., 2013), which yield a lower 1BC/1CO than flaming
fires (Kondo et al., 2011).
3.4 Size distributions of transported air masses
To more deeply characterize the air masses from different
source regions, we examine the differences in normalized
FIMS number (Fig. 8) and volume (Fig. S8) size distribu-
tions between BL and FT, which can also offer insights into
convection-related removal. We note that these findings re-
quire further investigation in future research, which is out-
side the scope of this work focused on overall transport dur-
ing CAMP2Ex. In-cloud processing during transport may in-
fluence particle sizes in these air masses, whereby a com-
bination of the following processes can occur (e.g., Ervens,
2015; Sorooshian et al., 2007; Wonaschuetz et al., 2012), fol-
lowed by detrainment from the cloud or wet removal: (1) col-
lisions between interstitial aerosol and droplets; (2) coales-
cence among droplets; and (3) aqueous-phase processing in
droplets. However, comparisons between size distributions
between regions and between BL and FT may still lend valu-
able insights into transport-related processes (e.g., Moteki
et al., 2012).
EA air in the BL (Fig. 8a) had a wide peak (40–200 nm),
suggestive of contributions from multiple sources (e.g., in-
dustrial, rice cultivation) (Y. Chen et al., 2020; Geng et al.,
2019; Wang et al., 2016; Xia et al., 2020). The width of the
accumulation-mode peak and the absence of an Aitken mode
peak may indicate aged aerosol that has been shifted towards
larger modes during transport (Zhang et al., 2005).
MC air in the BL (Fig. 8b) had a single peak centered
at 100 nm. Biomass burning emissions have been shown
to greatly influence air mass composition (Engling et al.,
2014; Fujii et al., 2015; Santoso et al., 2011), and, by exten-
sion, such a dominant emission source in addition to growth
processes during transport explains the large accumulation-
mode peak (Figs. 8b and S8b). The Aitken mode peak in
MC FT air supports the possibility of new particle forma-
tion (NPF) and growth (Williamson et al., 2019), promoted
by the removal of aerosols and transport of gases during loft-
ing into the FT (Fig. S6). Significant differences between the
size distributions of FT and BL air from the MC (Fig. 8b)
point to a potential scavenging mechanism acting on MC air
lofted into the FT (Sect. 3.3.1).
PSEA air in the BL had a bimodal size distribution, peak-
ing around 50 and 200 nm (Fig. 7c). Comparing PSEA and
MC air in the BL reveals much smaller particle sizes in PSEA
air (Fig. 8c), explainable by differences in source emissions
as well as by the susceptibility of larger particles to scaveng-
ing (Moteki et al., 2012). A comparison of BL and FT air
from PSEA points to scavenging during lofting into the FT:
the FT size distribution of PSEA air showed a sharp drop
in particle number concentrations above 50 nm, resembling
Figure 9. 1BC/1CO ratio (1CO >0.02 ppm) as a function of
accumulated precipitation along individual trajectories (APT) re-
solved by source region. APT was calculated with PERSIANN-
CDR. Medians and 25th/75th percentiles (error bars) are shown for
each source region: peninsular Southeast Asia (PSEA), the Mar-
itime Continent (MC), and East Asia (EA). The West Pacific (WP)
was not plotted because of few data points where 1CO >0.02 ppm.
The number of observations plotted per source region is provided in
parentheses.
background WP air (Fig. 7d), while the BL size distribution
of PSEA air had an additional peak at 200 nm. The 30 nm
peaks from PSEA and WP may originate from NPF events
(Williamson et al., 2019), associated with high APT in ma-
rine environments (Ueda et al., 2016).
3.5 Influence of convection on transported air masses
The relationship between composition and convection was
further investigated through scatterplots of the 1BC/1CO
ratio, an indicator of physical removal processes (Moteki
et al., 2012), as a function of APT, an indicator of convec-
tion. For simplicity, APT in Fig. 9 is derived solely from
PERSIANN-CDR, as Table 2 shows no significant qualita-
tive difference between SPPs. The decrease in 1BC/1CO
ratio with higher APT (Fig. 9) indicates that convection dur-
ing transport indeed contributes to scavenging in the TNWP,
though we note that source emission ratios also play an im-
portant role in the 1BC/1CO ratio. Both EA and MC air
showed very high 1BC/1CO ratios compared to PSEA,
suggestive of more efficient transport and higher source
emission ratios. Lower APT along EA and MC pathways (Ta-
ble 2) likely allowed for a clear transport signal as shown by
the high concentrations of anthropogenic or burning species
in these air masses (Sect. 3.3). In contrast to MC and EA air,
air from PSEA was characterized by a lower 1BC/1CO ra-
tio, particularly at higher APT, pointing to particle scaveng-
ing over convective areas (e.g,. SCS). Higher 1BC/1CO at
https://doi.org/10.5194/acp-21-3777-2021 Atmos. Chem. Phys., 21, 3777–3802, 2021

3792 M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific
lower APT suggests that the higher BL concentrations of an-
thropogenic species in MC and EA air were likely enabled by
lower levels of wet removal. Deviations from this trend indi-
cate that Fig. 9, though useful in showing wet deposition, is
unable to capture scavenging unrelated to precipitation, such
as in-cloud scavenging (Sect. 3.4).
4 Summary and conclusions
Utilizing airborne CAMP2Ex measurements between 24 Au-
gust and 5 October 2019 and HYSPLIT back trajectories,
we examined transport patterns into TNWP from key source
regions (PSEA, MC, EA, WP). Key conclusions from this
study include the following points.
1. Meteorological phenomena driving transport as well
as the origins of transported air masses shifted sig-
nificantly with the monsoon phase. During the SWM,
MC and PSEA transport was associated with monsoon-
driven southwesterly winds and cyclonic activity over
the northern SCS. Wind shear was associated with pre-
dominantly BL (FT) sampling of MC (PSEA) air, im-
plying distinct aerosol processing between these two
source regions. In comparison, transport during the MT
period from EA and WP was driven by northeasterly
winds from the Pacific, anticyclones over the Asian con-
tinent, and well-developed cyclones passing through the
East China Sea. These transport differences led to vary-
ing degrees of convection experienced by transported air
masses. PSEA air generally passed through convective
regions (SCS, west of Luzon, and over the PSEA itself)
and was lofted into the FT, which led to the scavenging
of aerosols. In contrast, air masses from the MC and EA
underwent relatively little convection, indicated by low
APT, and mainly were confined to the BL, enabling the
transport of anthropogenic emissions.
2. Characteristics of transported air masses differed pri-
marily by emission source and passage through con-
vective regions. Due to low APT and high 1BC/1CO,
transported air from MC and EA exhibited characteris-
tic emissions: MC air from biomass burning (CO, well-
correlated CO and CH4, NO−
3, OA) and EA air from
anthropogenic outflow and secondary formation (O3,
CH4, NH+
4, SO2−
4). Key species ratios corroborated dis-
tinct sources between MC and EA air. Aerosol size dis-
tributions in EA air suggested multiple primary sources
(industry, residential emissions, rice cultivation) as well
as secondary formation, indicated by its relatively broad
peak; in contrast, the narrower peak in the size distribu-
tion of MC air pointed to the predominance of biomass
burning emissions.
3. Air from the PSEA showed strong evidence of particle
scavenging: passage over high-precipitation areas, con-
vective lofting, high APT, low 1BC/1CO, relatively
low levels of anthropogenic species, and a size distri-
bution shifted towards smaller particle sizes. Aerosol
concentrations of PSEA air in the FT were lower by
at least 1 order of magnitude than those in the BL, a
difference that was not observed for trace gases, which
pointed to the scavenging of aerosol particles in the FT.
Furthermore, PSEA air in the FT lacked the larger peak
(Dp=200 nm) observed in BL air and instead peaked at
much smaller sizes (Dp=30 nm), suggesting large par-
ticle removal during convective lofting. The fine-mode
peak (Dp=30 nm) for PSEA FT air also resembled that
of WP air, suggestive of new particle formation during
transport from the PSEA, perhaps occurring over the
convective SCS.
4. A possible wet scavenging mechanism for MC FT air
was inferred from 1SO2−
4/1CO and 1OA/1CO ra-
tios between BL and FT and corroborated by size dis-
tributions, which showed significant BL and FT differ-
ences for larger particles (>50 nm). The disagreement
with APT was attributed to SPP limitations in captur-
ing scavenging effects aloft, hinting that the scavenging
mechanism acts at higher layers and may not be linked
to surface precipitation. However, we emphasize that
the exact scavenging mechanism is for now speculative
and warrants its own investigation in the future.
Recommendations for future work include (1) investigating
the hypothesized scavenging mechanism of MC air aloft us-
ing vertically resolved moisture and convection retrievals;
(2) examining deep convection periods to further evaluate
wet scavenging effects on transported air; (3) characterizing
aerosol hygroscopicity as a function of air mass source region
and transport processes; and (4) comparing different sam-
pling areas over the Philippines as impacted by transported
air masses.
Data availability. The CAMP2Ex dataset can be found at
https://doi.org/10.5067/Suborbital/CAMP2EX2018/DATA001;
NASA, 2020a). HYSPLIT data are accessible through the
NOAA READY website (http://www.ready.noaa.gov, last ac-
cess: 13 July 2020) (NOAA Air Resources Laboratory, 2020).
Global elevation data from GMTED2010 are available at
http://temis.nl/data/gmted2010/ (last access: 12 March 2020)
(USGS and NGA, 2020). Population density data are provided
by the Center for International Earth Science Information
Network, available at https://sedac.ciesin.columbia.edu/data/
set/gpw-v4-population-density- rev11/data-download (last ac-
cess: 3 July 2020) (CIESIN, 2020). MODIS active fire data
can be downloaded through the Fire Information for Resource
Management System (https://firms.modaps.eosdis.nasa.gov/,
last access: 29 June 2020) (NASA, 2020b). OMI data
were retrieved from the NASA GES DISC website
(https://doi.org/10.5067/Aura/OMI/DATA3008) (Li et al.,
2020). NCEP/NCAR Reanalysis data were provided by the
NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their
Atmos. Chem. Phys., 21, 3777–3802, 2021 https://doi.org/10.5194/acp-21-3777-2021

M. R. A. Hilario et al.: Measurement report: Long-range transport patterns into the tropical northwest Pacific 3793
website (https://www.esrl.noaa.gov/psd/, last access: 13 June 2020)
(NOAA Physical Sciences Laboratory, 2020).
Supplement. The supplement related to this article is available on-
line at: https://doi.org/10.5194/acp-21-3777-2021-supplement.
Author contributions. MRAH performed the analysis and prepared
the paper. EC, MS, LZ, JPDG, GSD, EW, CER, JW, JZ, YW, SY,
JF, SLA, and AS collected and prepared the data. EC, MS, JSR,
MOLC, JBBS, LZ, JPDG, GSD, PN, FJT, EW, JW, JZ, YW, AB,
and AS provided input and feedback on the paper.
Competing interests. The authors declare that they have no conflict
of interest.
Special issue statement. This article is part of the special is-
sue “Cloud Aerosol and Monsoon Processes Philippines Experi-
ment (CAMP2Ex) (ACP/AMT inter-journal SI)”. It is not associ-
ated with a conference.
Acknowledgements. This research was funded by NASA
grant 80NSSC18K0148 in support of CAMP2Ex. The research by
FJT was carried out at the Jet Propulsion Laboratory, California
Institute of Technology, under a contract with NASA.
Financial support. This research has been supported by
the National Aeronautics and Space Administration (grant
no. 80NSSC18K0148).
Review statement. This paper was edited by Roya Bahreini and re-
viewed by two anonymous referees.
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