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SCIENTIFIC DATA | (2020) 7:128 | https://doi.org/10.1038/s41597-020-0466-y
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An annual time series of weekly
size-resolved aerosol properties
in the megacity of Metro Manila,
Philippines
Connor Stahl
, Melliza Templonuevo Cruz
, Paola Angela Bañaga, Grace Betito
,
Rachel A. Braun
, Mojtaba Azadi Aghdam, Maria Obiminda Cambaliza,
Genevieve Rose Lorenzo, Alexander B. MacDonald, Preciosa Corazon Pabroa,
John Robin Yee, James Bernard Simpas & Armin Sorooshian
✉
City, Metro Manila with samples collected on a weekly basis for water-soluble speciation and mass
during special events such as holidays. The unique aspect of the presented data is a year-long record
with weekly frequency of size-resolved aerosol composition in a highly populated megacity where there
is a lack of measurements. The data are suitable for research to understand the sources, evolution,
and fate of atmospheric aerosols, as well as studies focusing on phenomena such as aerosol-cloud-
The dataset can be used to initialize, validate, and/or improve models and remote sensing algorithms.
Background & Summary
e composition and size distribution of ambient particulate matter (PM) inuence how particles impact air
quality and public health1, climate2, the hydrological cycle3, and geochemical cycling of nutrients and contami-
nants4. Depending on particle size and composition, an inhaled particle can deposit in the extrathoracic (head),
tracheobronchial (TB), or pulmonary (PUL) regions, which can have serious implications for health5–8. Similarly,
size and composition of particles impact their radiative properties, ability to act as cloud condensation nuclei
(CCN), and also the ability to be transported between regions.
Since seasonal changes in meteorology, transport pathways, and emissions can impact a given region, annual
PM cycles are important to characterize. A summary of past long-term (>three-month period) size-resolved
PM substrate-based sampling eorts are provided inOnline-only Table1. ere are a scarcity of annual time
series data with at least weekly frequency regardless of global region. Most substrate-based sampling eorts for
size-fractionated PM cover periods of one to three months with a sample collection duration between 24 to
96 hours per set, which were not included in Online-only Table1. Longer sampling periods for individual sets,
reaching up to a week9,10, are required in regions with less pollution in order to achieve suciently high mass
concentrations (i.e. above limits of detection) for targeted species. e diculty in obtaining long-term records
of size-resolved PM composition with high temporal frequency is largely due to the labor-intensive nature of such
measurements, which include several pre-and post- sampling steps and subsequent chemical analyses.
e megacity of Metro Manila in the Philippines consists of 16 cities containing approximately 12.88 million
people, with a collective population density of about 20,800 km−2 11,12. Quezon City, the location where sampling
1Department of Chemical and Environmental Engineering, University of Arizona, Tucson, Arizona, USA. 2Manila
Observatory, Quezon City, 1108, Philippines. 3Institute of Environmental Science and Meteorology, University of the
Philippines, Diliman, Quezon City, 1101, Philippines. 4Department of Physics, School of Science and Engineering,
Ateneo de Manila University, Quezon City, 1108, Philippines. 5Department of Hydrology and Atmospheric Sciences,
University of Arizona, Tucson, Arizona, USA. 6Department of Science and Technology, Philippine Nuclear Research
Institute, Commonwealth Avenue, Diliman, Quezon City, 1101, Philippines. ✉e-mail: armin@email.arizona.edu
OPEN
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took place, is one of the most populated cities in the region, with a population of about 2.94 million people and
a population density of approximately 17,000 km−2 12, which is among the highest in the world. Metro Manila
is an ideal location for examining locally produced urban anthropogenic PM oen mixed with a host of other
air masses of marine and continental origin that are transported over both short and long distances to Metro
Manila13. One aspect that makes the PM in Metro Manila unique is that black carbon levels are among the highest
in the world11,14,15. e elevated black carbon is mainly due to vehicular emissions, more specically the jeepneys,
large trucks, and outdated vehicles11. e Philippines serves as a representative southeastern Asian country in
terms of high population density, rapid urbanization, outdated vehicle usage and technology, and more lenient
air regulations11.
e goal of this work is to present a 16-month size-resolved PM dataset for Metro Manila, Philippines. e
unique geographic position of Metro Manila coupled to the wide ranging meteorology and transport patterns
makes this dataset highly valuable in terms of examining numerous topics related to PM physics and chemistry
with general implications for other regions: (i) impacts of PM on regional climate, clouds, and monsoonal activ-
ity, (ii) PM removal via wet deposition, (iii) aqueous processing of PM, (iv) source apportionment, (v) eects on
PM properties due to mixing of varying air masses, (vi) catalytic and destructive eects of metals on inorganic/
organic species, (vii) impacts of extreme events (e.g., biomass burning, dust storms, reworks, typhoons) on
regional PM, and (viii) public health implications.
Methods
Field study description. e dataset presented is a 16-month, size-resolved, chemical characterization of
PM as part of a pre-campaign initiative for the Cloud, Aerosol, and Monsoon Processes Philippines Experiment
(CAMP2Ex) titled CAMP2Ex weatHEr and CompoSition Monitoring (CHECSM) study. e CHECSM campaign
took place between July 2018 through October 2019, within which August through October 2019 coincides with
the airborne component of CAMP2Ex.
Study site description. e CHECSM study occurred at the Manila Observatory (MO; 14.64°N, 121.08°E)
located at the Ateneo de Manila campus in Quezon City, Philippines. e site was segregated from surrounding
urban areas, including a major roadway, by a grove of trees circling the campus. However, it was clearly impacted
by local urban emissions and long-range transport based on results from the rst six months of data collected16–18.
Sampling took place on the 3rd oor of the MO oce building, which was approximately 85 m above sea level.
Figure1 shows a timeline of sampling, which occurred in four identied seasons: the 2018 southwest monsoon/
wet season (18 June–4 October)19,20, a transitional period (5–25 October), the northeast monsoon/dry season (26
October 2018–10 June 2019)21, and the 2019 southwest monsoon/wet season (11 June–7 October)22,23. e south-
west monsoon is characterized by relatively high temperatures, high humidity, frequent and heavy rainfall, and
winds coming predominantly from the southwest. e northeast monsoon is characterized by moderate rainfall,
low humidity, lower temperatures, and winds aecting the eastern side of the country. e characteristics of the
monsoons listed above are general traits, but the major determining factor is rainfall. e measured temperature,
humidity, and rainfall during sampling period collected at MO ranged from 25.4–30.2 and 24.2–30.9 °C, 59–94
and 54–85%, and 0–78.4 and 0–32.6 mm for the southwest and northeast monsoons, respectively, with average
values of 27.6 and 27.7 °C, 72 and 64%, and 18.8 and 2.1 mm. Although the focus of this data descriptor is the
size-resolved PM composition dataset, additional instrumentation co-located at MO during CHECSM is sum-
marized in Table1.
Instrument Parameters
Aerosol Robotic Network (AERONET) Aerosol optical depth (AOD), single-scatter albedo (SSA), absorption angstrom
exponent (AAE), scattering angstrom exponent (SAE), and water vapor
Disdrometer Droplet size and vertical velocity
Arctic High Spectral Resolution Lidar (AHSRL) Backscatter coecient, depolarization ratio, and backscatter ratio
DustTrak and (2) Tactical Air Samplers (TAS) Real-time and 24-hour total PM2.5 mass concentration with chemical speciation
Solar Spectral Flux Radiometer (SSFR) Shortwave and longwave radiance and irradiance
All-Sky Camera Hemispheric sky imaging
Particle Soot Absorption Photometer (PSAP) Black carbon absorption and concentration
Automated Weather Station (AWS) Temperature, relative humidity, wind speed, wind direction, solar radiation,
pressure, and precipit ation
Davis Rotation Uniform-Cut Monitor (DRUM) Size segregated elemental composition of PM
Electronic Beta Attenuation Monitor (e-BAM) Real-time PM2.5 mass concentration
Kipp and Zonen CMP22 Pyranometer S olar radiation (broadband irradiance)
Kipp and Zonen CGR4 Pyrgeometer Solar radiation (infrared irradiance)
SPN1 Shadow Pyranometer Solar radiation (shadow broadband irradiance)
SP1-F Narrowband Shadow Pyranometer Solar radiation (shadow narrowband irradiance)
Tab le 1. List of instruments deployed at Manila Observatory (MO) before and during CAMP2 Ex and the
associated measurement parameters.
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Size-resolved PM was collected using a pair of Micro-Orice Uniform Deposit
Impactors II (MOUDI II 120 R, MSP Corporation, Marple et al.24) on Teon substrate lters (PTFE membrane,
2 μm pores, 46.2 mm diameter, Whatman). e MOUDI-II is a 10-stage impactor with aerodynamic cutpoint
diameters (Dp) of 10, 5.6, 3.2, 1.8, 1.0, 0.56, 0.32, 0.18, 0.10, and 0.056 μm with a pre-impactor (>18 μm) and
an aer-lter (<0.056 μm). Refer to Table2 for the associated stage numbers, collected diameter ranges, and
cutpoint diameters. e instruments operated at a nominal owrate of ~30 L min−1, with measured owrates for
each set reported in Table3. Each stage, except for the pre-impactor and aer-lter, continuously rotates to allow
for uniform deposition of particles. Pressures for each stage were measured and recorded to ensure pressure drops
were within acceptable ranges. An identical pair of MOUDIs were deployed for two reasons: (i) there would be no
delay in sampling when a unit required maintenance, and (ii) simultaneous measurements allowed for additional
analyses of collected PM.
MOUDI sets were collected weekly over a 48-hour period with the exception of sets MO1, MO2, MO3/4,
MO5, MO31/32, and MO51, which were collected for 24, 54, 119, 42, 49, and 50 hours, respectively. MOUDI
sets labeled MO#/# refer to the sets that were simultaneously collected so that both chemical analysis and
Set #
MO 1
MO 2
MO 3/4
MO 5
MO 6
MO 7
MO 8
MO 9
MO 10
MO 11
MO 12
MO 13/14
MO 15
MO 16
MO 17
MO 18
MO 19
MO 20
MO 21
MO 22
MO 23
MO 24
MO 25
MO 26
MO 27
MO 28
MO 29
MO 30
MO 31/32
MO 33
MO 34
MO 35/36
MO 37
MO 38
MO 39/40
MO 41
MO 42
MO 43/44
MO 45
MO 46
MO 47
MO 48/49
MO 50
MO 51
MO 52/53
MO 54
MO 55
MO 56
MO 57/58
MO 59
MO 60
MO 61
MO 62/63
MO 64
MO 65/66
6/19 7/19 8/19 9/19 10/1912/181/192/193/194/195/196/18 7/18 8/18 9/18 10/18 11/18
Fig. 1 Timeline of size-resolved aerosol measurements at the Manila Observatory. Light blue boxes represent
the southwest monsoon/wet seasons, the light green box represents the transitional period, and the orange box
represents the northeast monsoon/dry season. Dark colored boxes represent MOUDI sampling periods and
black boxes represent parallel MOUDI sampling periods.
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gravimetric analysis could be performed. A total of 66 sets were collected; 11 of the sets were collected using the
simultaneous sampling approach, 54 of the sets were analyzed using ion chromatography (IC; ermo Scientic
Dionex ICS-2100 system), 47 of the sets were also analyzed using triple quadrupole inductively coupled plasma
mass spectrometry (ICP-QQQ; Agilent 8800 Series), and 1 set (MO25) was collected as a special microscopy
set. Additional MOUDI sets were collected on aluminum substrates for microscopy analysis using a Scanning
Electron Microscope (SEM); however, these sets are not included in the dataset presented here. For more infor-
mation on these sets, please refer to Cruz et al.16.
e MOUDIs were set up in such a way to reduce both particle losses and blockage of the inlet. e inlet tub-
ing connecting the MOUDI to ambient air was constructed of stainless steel. e tubing was bent meticulously
with a large radius such that there were no kinks. e inlet of the tubing was oriented downwards to prevent water
from entering the MOUDI. To further avoid debris from getting into the inlet, a funnel with a mesh covering
was attached securely to the downward facing tube opening exposed to ambient air. e temperature dierential
between the outside air and the tubing was either negligible or the tubing was slightly warmer than the outside
air, thus reducing the possibility of thermal deposition. As the average relative humidity measured onsite over the
sampling period was approximately 68% ranging from 54–94% throughout the year, the diameters of sampled
particles correspond to wet rather than dry diameters and particle bounce was not signicant25. is is addition-
ally supported by particle morphology characterization showing evidence of halo areas, indicative of the particles
being saturated when impacting onto the substrates26–28, surrounding particles in both the ne and coarse size
ranges16,17.
Pre-Sampling processing. e Teon substrates were prepared prior to use by soaking each substrate face
for a minimum of 12 hours in ~7.6 cm of Milli-Q (18.2 M-cm) water in a laminar ow hood and/or covered
container. Once each substrate face was soaked, the substrates were removed and placed in methanol cleaned
Petrislides (Millipore), which were le slightly open in a laminar ow hood to dry any water residue. Once the
substrates were dry, the Petrislides were closed and sealed using Paralm to ensure the substrates were devoid of
any particles or gases that could deposit on them.
Post-Sampling processing. Figure2 summarizes the post-sampling process to reach the nal dataset. Aer
sampling was completed, substrates were rst cut in half using ceramic scissors so one-half could be used for
extraction in Milli-Q water and the other half could be stored in a freezer at −20 °C for future analyses. Ceramic
scissors that were cleaned with methanol were used to cut the substrates in half in order to prevent contami-
nation of heavy metals from other cutting instruments (e.g. metal scissors). e ceramic scissors were subse-
quently cleaned with methanol aer each cut. Substrate extractions were performed using 8 mL of Milli-Q water
(18.2 M-cm) in cleaned 15 mL polypropylene centrifuge tubes that were sonicated for 30 minutes at 25–30 °C.
Samples were extracted in this temperature range to ensure all targeted organics would solubilize. Additionally,
during sampling, temperatures ranged from 28.7–45.7 °C; therefore, any volatile species were expected to be
gone prior to the point of sampling and well before extractions took place. ere have been other papers that
performed similar extractions with temperatures up to 60 °C29–33. Sonicated solutions were then decanted into
two dierent containers for analysis: (i) 0.5 mL polypropylene vial with a lter cap for analysis via IC, and (ii)
a polypropylene centrifuge tube for analysis via ICP-QQQ. e remainder of the solutions were then stored
in a refrigerator at 0 °C. Blank substrates were also processed in a similar manner to serve as background con-
trol samples. e motivation behind using water for extractions was owing to the importance of the results for
health eects and toxicological studies, radiative eects, atmospheric residence time, nucleation eciency, and
bioavailability34–39.
Cationic and anionic water-soluble PM speciation and quantication was conducted using a 2 mm IC
system at a owrate of 0.4 mL min−1. e cationic species measured were Na+, NH4+, Mg2+, Ca2+, dimethylamine
(DMA), trimethylamine (TMA), and diethylamine (DEA) using an eluent of methanesulfonic acid. e anionic
species measured were methanesulfonate (MSA), pyruvate, adipate, succinate, maleate, oxalate, phthalate, Cl−,
Stage # Diameter Range (μm) Cutpoint Diameter (μm)
1>18 18
2 18–10 10
3 10−5.6 5.6
4 5.6–3.2 3.2
5 3.2–1.8 1.8
6 1.8–1.0 1.0
7 1.0–0.56 0.56
8 0.56–0.32 0.32
9 0.32–0.18 0.18
10 0.18–0.10 0.10
11 0.10–0.056 0.056
12 <0.056 <0.056
Tab le 2. List of the stages and the respective collected diameter range and cutpoint diameters.
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Sample ID Avg. Flow
(L/min) Run Time
(hr) Avg. Temp.
(°C) Avg. RH
(%) Days of
the Week Sample ID Avg. Flow
(L/min) Run Time
(hr) Avg. Temp.
(°C) Avg. RH
(%) Days of
the Week
MO1 29.6 24 30.5 59.0 -F MO34 29.4 48 35.3 57.9 M-W
MO2 29.6 54 31.7 66.8 M-W MO35 (G) 25.6 48 36.6 56.8 T-
MO3 (G) 28.6 119 34.9 69.0 W-M MO36 29.3 48 39.9 56.8 T-
MO4 30.3 119 34.4 69.0 W-M MO37 30.0 48 38.8 55.1 W-F
MO5 28.8 42 33.5 66.7 M-W MO38 29.6 48 36.4 54.0 S-M
MO6 27.1 48 34.6 63.3 M-W MO39 (G) 26.4 48 39.0 57.6 M-W
MO7 27.9 48 34.9 78.3 T- MO40 29.6 48 41.4 57.6 M-W
MO8 29.0 48 35.7 78.2 W-F MO41 29.1 48 38.7 57.7 T-
MO9 27.5 48 34.9 68.4 S-M MO42 29.1 48 40.3 53.7 W-F
MO10 29.0 48 36.7 65.2 M-W MO43 (G) 26.8 48 36.1 59.8 S-M
MO11 27.1 48 35.8 68.3 T- MO44 28.6 48 37.0 59.8 S-M
MO12 27.5 48 37.0 70.9 W-F MO45 28.7 48 37.3 61.8 M-W
MO13 (G) 29.8 48 35.1 73.1 S-M MO46 28.7 48 39.0 72.2 T-
MO14 26.1 48 32.0 73.1 S-M MO47 28.9 48 39.3 64.5 W-F
MO15 29.7 48 37.3 67.6 M-W MO48 28.0 48 38.9 62.6 S-M
MO16 29.2 48 37.6 67.7 T- MO49 (G) 25.5 48 38.1 62.6 S-M
MO17 30.0 48 36.5 60.6 W-F MO50 28.8 48 39.2 64.4 M-W
MO18 29.5 48 36.7 61.9 S-M MO51 27.8 50 36.2 77.1 T-
MO19 31.4 48 35.8 61.4 M-W MO52 (G) 24.9 48 36.6 60.9 W-F
MO20 30.2 48 34.8 60.8 T- MO53 26.9 48 38.8 60.9 W-F
MO21 30.5 48 34.8 72.0 W-F MO54 28.8 48 36.8 66.4 S-M
MO22 29.6 48 32.7 78.5 S-M MO55 28.8 48 38.0 75.4 M-W
MO23 26.4 48 29.7 81.8 M-W MO56 26.7 48 35.0 76.1 T-
MO24 30.2 48 35.8 84.6 M-W MO57 27.5 48 33.0 94.1 W-F
MO25 (AL) N/A 2.75 N/A N/A M-T MO58 (G) 24.5 48 33.4 94.1 W-F
MO26 24.1 48 35.0 77.2 T- MO59 28.2 48 37.8 85.9 S-M
MO27 23.9 48 36.2 65.3 W-F MO60 28.2 48 37.3 92.7 M-W
MO28 25.0 48 33.1 63.5 S-M MO61 29.4 48 36.3 62.1 T-
MO29 29.5 48 34.5 63.3 M-W MO62 27.8 48 36.5 77.0 W-F
MO30 29.8 48 34.4 60.7 T- MO63 (G) 24.4 48 35.0 77.0 W-F
MO31 29.9 49 35.8 65.7 W-F MO64 27.0 48 37.5 67.2 S-M
MO32 (G) 24.4 49 37.0 65.7 W-F MO65 27.2 48 38.4 65.3 M-W
MO33 29.8 48 34.3 58.1 S-M MO66 (G) 23.9 48 37.7 57.9 M-W
Tab le 3. MOUDI sample set operating data. e table includes average owrates, total sample run time, average
operating temperature of the MOUDI cabinet, relative humidity (RH), and the days of the week sampling
occurred. e start/end times varied between 13:00 and 15:00 local time for standard sets and 5:00 local time for
dual gravimetric/IC sets. Sets with a label of (G) are gravimetric sets and the set labeled (AL) was collected for
SEM analysis. All other sets were only measured with IC and/or ICP-QQQ.
Fig. 2 Flow chart of steps leading from MOUDI substrate collection to compilation of nal data. e more
commonly used single MOUDI sampling strategy follows only the top branch aer “MOUDI” while the less
frequent dual MOUDI sampling approach encompasses both the top and bottom branches. Rounded boxes
represent instrument and analytical analyses steps while the standard boxes represent other processing steps.
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NO3−, and SO42− using an eluent of potassium hydroxide (KOH). A 30-minute instrument method was used
for both anion and cation columns with a 5-minute equilibration period giving a total of 35 minutes per sample.
e columns used were the Dionex IonPac AS11-HC 250 mm and CS12A 250 mm models for anion and cation
analysis, respectively. e suppressors used were a Dionex AERS 500e and a CERS 500e for anions and cations,
respectively. For anions, the eluent started at 2 mM, ramped up to 8 mM from 0 to 20 minutes, and then ramped
up from 8 to 28 mM from 20 to 30 minutes using a suppressor current of 28 mA. For cations, the eluent started
at 5 mM, was isocratic from 0 to 13 minutes, ramped up from 5 to 18 mM from 13 to 16 minutes, and nally was
isocratic at 18 mM from 16 to 30 minutes using a suppressor current of 22 mA. e recoveries, limits of detection
(LOD), and limits of quantication (LOQ) for these species can be found in Table4.
Elements. Water-soluble elements were speciated and quantied using ICP-QQQ aer being acidied in 2%
nitric acid. e elements quantied were: Ag, Al, As, Ba, Cd, Co, Cr, Cs, Cu, Fe, Hf, K, Mn, Mo, Nb, Ni, Pb, Rb, Se,
Sn, Sr, Ti, Tl, V, Y, Zn, and Zr. e recoveries, LOD, and LOQ for these species can be found in Table5. For species
that were measured by both IC and ICP-QQQ (Na, Mg, K, and Ca), duplications were not included in the dataset.
IC measurements are provided for Na, Mg, and Ca, while ICP-QQQ measurements for K are provided due to
potential contamination from the eluent (i.e., KOH) used in the IC. e exception to this is for sets MO57-MO65
where K from the IC was used due to lack of ICP-QQQ data.
Gravimetric. Gravimetric analysis was performed using a Sartorius ME5-F microbalance with a sensitiv-
ity of ±1 μg. e microbalance was located in a temperature and humidity-controlled room at 20–23 °C and
30–40% relative humidity with an airlock buer. Clean substrates were weighed prior to sample collection and
then weighed again aer sampling ended. Before weighing took place, the lters were equilibrated in the room
for at least 24 hours. Aer the equilibration time, each substrate was passed near a 210Po antistatic tip for 30 sec-
onds to minimize measurement bias due to electrostatic charge at the surface of the substrate. Each substrate
was weighed twice, once initially and then again 24 hours later. If the dierence between these two weighings
exceeded 10 μg, the substrate was weighed again 24 hours later and this process was repeated until the dierence
between weighings was less than 10 μg. e percent standard deviations for the weighings before and aer sam-
pling, respectively, were relatively negligible, with the highest being 0.005%. e PM mass was derived from the
dierence of the average substrate weight aer sampling minus the average substrate weight before sampling. e
standard deviation of the change in weight was then calculated for each PM substrate using the following error
propagation equation:
=+SD SD SD
(1)
dba
22
where SDd is the standard deviation of the dierence, SDb is the standard deviation of the substrate before sam-
pling, and SDa is the standard deviation of the substrate aer sampling. e percent standard deviation across all
stages and sets averaged out to be approximately 7%.
Black carbon. e subsequently weighed substrates were then analyzed using a Multi-wavelength Absorption
Black Carbon Instrument (MABI; Australian Nuclear Science and Technology Organisation). e MABI is an
optical instrument used to quantify the mass concentration of black carbon by detecting the absorption for seven
Ion Recovery ± SD (%) LOD (ppb) LOQ (ppb) LOD (μgm−3)LO Q (μgm−3)
Adipate 101 ± 4 22.655 75.517 2.10E-03 6.99E-03
Ammonium 100 ± 17 42.434 141.447 3.93E-03 1.31E-02
Calcium 100 ± 5 45.229 150.763 4.19E-03 1.40E-02
Chloride 103 ± 7 2.144 7.147 1.99E-04 6.62E-04
DMA 100 ± 2 52.709 175.697 4.88E-03 1.63E-02
Magnesium 104 ± 8 36.925 123.083 3.42E-03 1.14E-02
Maleate 100 ± 3 6.970 23.233 6.45E-04 2.15E-03
MSA 102 ± 6 12.316 41.053 1.14E-03 3.80E-03
Nitrate 106 ± 12 8.917 29.723 8.26E-04 2.75E-03
Oxalate 100 ± 2 12.312 41.040 1.14E-03 3.80E-03
Phthalate 99 ± 2 20.685 68.950 1.92E-03 6.38E-03
Pyruvate 102 ± 6 63.754 212.513 5.90E-03 1.97E-02
Sodium 104 ± 8 43.476 144.920 4.03E-03 1.34E-02
Succinate 98 ± 9 11.046 36.820 1.02E-03 3.41E-03
Sulfate 101 ± 3 11.982 39.940 1.11E-03 3.70E-03
TMA & DEA 102 ± 4 315.164 1050.550 2.92E-02 9.73E-02
Tab le 4. Water-soluble species analyzed with their respective recoveries ± standard deviations (SD), limits of
detection (LOD), and limits of quantication (LOQ) in aqueous concentration units. Species were quantied
using IC (ions). LODs and LOQs in ppb are aqueous concentrations while LODs and LOQs in μgm−3 are air
equivalent concentrations.
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wavelengths (405, 465, 525, 639, 870, 940, and 1050 nm). e following equation was used to calculate black
carbon concentration:
ε
=
−−
BC ng m
Acm
mg Vm
ln
I
I
()
10 [( )]
[()
][ ()](2)
3
52
21 30
where ϵ is the mass absorption coecient, A is the substrate collection area, V is the volume of air sampled, I0
is the measured light transmission through the blank substrate, and I is the measured light transmitted through
the sample substrate. e mass absorption coecient was provided in the MABI manual, collection area was
retrieved based on impaction rings on the substrates, volume was calculated from owrate and sample time, and
light transmission was produced directly from the MABI.
Data processing. IC and ICP-QQQ areas were converted to concentrations using Excel sheets formatted to
use calibration curves, unit operations, and sampling information. e concentration les were then organized
using an assortment of MATLAB codes to produce the data into the published state with gravimetric and black
carbon data. Excel and MATLAB processing les are available upon request.
Data Records
The dataset, located on figshare40, is in a specialized format used by the National Aeronautics and Space
Administration (NASA) for eld data, which is referred to as the ICARTT le format. e le name consists of
the associated campaign, instrument used, sampling method, start date, revision number, and the end date. e
format includes data notes in a README tab. ese notes include the data principal investigator (PI), aliated
institution, mission name, the start date of data collection, the last data revision date, the number of variables,
data ags, sampling platform and location, instrument information, brief description of the data, and revision log.
e revision log states what revision the data is currently on and lists the previous revisions and their relative sta-
tus. Additional tabs include the MOUDI stage cutpoints and size ranges, uncertainties and LODs, and the variable
list and units. Data include ions, elements, gravimetric weights, and MABI measurements separated by stages in
air equivalent mass concentrations (µg m−3). Note that the reported data are in air equivalent concentrations and
typically are converted to dC/dlog Dp to properly look at the size distributions.
Element Recovery ± SD (%) LOD (ppt) LOQ (ppt) LOD (μgm−3)LO Q (μgm−3)
Ag 100 ± 11 0.743 2.477 6.88E-08 2.29E-07
Al 96 ± 7 29.474 98.247 2.73E-06 9.10E-06
As 98 ± 10 7.945 26.483 7.36E-07 2.45E-06
Ba 97 ± 11 3.698 12.327 3.42E-07 1.14E-06
Cd 102 ± 11 4.194 13.980 3.88E-07 1.29E-06
Co 98 ± 8 0.722 2.407 6.69E-08 2.23E-07
Cr 97 ± 9 1.150 3.833 1.06E-07 3.55E-07
Cs — 0.733 2.443 6.79E-08 2.26E-07
Cu 99 ± 8 1.127 3.757 1.04E-07 3.48E-07
Fe 97 ± 9 1.191 3.970 1.10E-07 3.68E-07
Hf — 0.963 3.210 8.92E-08 2.97E-07
K 93 ± 18 10.480 34.933 9.70E-07 3.23E-06
Mn 97 ± 9 1.624 5.413 1.50E-07 5.01E-07
Mo 96 ± 11 2.258 7.527 2.09E-07 6.97E-07
Nb — 0.522 1.740 4.83E-08 1.61E-07
Ni 97 ± 8 2.837 9.457 2.63E-07 8.76E-07
Pb 99 ± 8 0.503 1.677 4.66E-08 1.55E-07
Rb — 1.566 5.220 1.45E-07 4.83E-07
Se 97 ± 10 82.393 274.643 7.63E-06 2.54E-05
Sn 97 ± 7 1.772 5.907 1.64E-07 5.47E-07
Sr 98 ± 9 1.102 3.673 1.02E-07 3.40E-07
Ti 101 ± 10 39.046 130.153 3.62E-06 1.21E-05
Tl 100 ± 8 0.383 1.277 3.55E-08 1.18E-07
V 95 ± 9 1.353 4.510 1.25E-07 4.18E-07
Y — 0.523 1.743 4.84E-08 1.61E-07
Zn 96 ± 8 5.880 19.600 5.44E-07 1.81E-06
Zr — 1.008 3.360 9.33E-08 3.11E-07
Tab le 5. Same as Table4 but species were quantied using ICP-QQQ (elements). Species marked with ‘—’ in
their respective recovery and standard deviation columns were not measured for recovery purposes. LODs and
LOQs in ppt are aqueous concentrations while LODs and LOQs in μgm−3 are air equivalent concentrations.
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Technical Validation
A number of experimental and data processing techniques were implemented to validate and better characterize
the nal data. e owrate for each set was measured using a owmeter (Mesa Labs Dener 220 series) three
times both prior to and aer each sampling period. e overall average of these values was used as the owrate
for each set. Additionally, pressures for each stage were measured at the beginning and end of sampling to ensure
there was no signicant change in the pressure drop. To keep the owrate as close to 30 L min−1 as possible, the
MOUDI nozzle plates were removed and cleaned regularly and especially if the owrate dropped below 27 L
min−1. e nozzle plates were cleaned by soaking the plates in either a methanol-water solution or in pure meth-
anol for 24 hours or more. ey were then removed and rinsed with methanol, followed by placement in a clean
area to let the methanol evaporate. However, towards the ending of the sampling campaign the owrate dropped
to about 24 L min−1 and subsequent cleanings did not alleviate the problem. e issue was likely due to one or
both of the lower nozzle plates (0.056 and 0.1 μm cutpoint diameters) being heavily clogged with the black carbon
rich air and unable to be cleared without a more aggressive cleaning method.
Chromatogram peaks were automatically drawn by the IC and ICP-QQQ system soware. However, for the
IC only, the operator would view each chromatogram to adjust peak areas and add in missing species. LOD and
LOQ were calculated using 3 Sab−1 and 10 Sab−1 methods, respectively, where Sa is the standard deviation of the
response and b is the slope of the calibration curve41. Recoveries were calculated by taking the ratio of the mass of
a specic measured species to the known amount of that species in that sample42. Recoveries for IC and ICP-QQQ
were all above 93% with repeatability ranging from 2% to 18% (Tables4 and 5). During data analysis, dC/dlog Dp
plots (stages 2–11) were examined to ensure a normal distribution was obtained. If the rst (stage 2) or last stage
(stage 11) was higher than the next (stage 3) or previous stage (stage 10), respectively, then that stage was not con-
sidered for a particular set and viewed as having unreliable data. If a species was not measured for a stage, a value
of −9999 was inputted. Similarly, if a species was below the LOD for a stage, a value of −8888 was inputted. A
summary of the relative number of data points either missing (i.e., no ICP-QQQ data for last seven sets) or below
the LOD for a specic species and stage can be seen in Table6.
A charge balance was also performed by converting each species to moles, multiplying by their respective
charges, and then summing up all cations and all anions in a stage. It should be noted that only IC species, with
the exception of K from ICP-QQQ, were used to measure the overall charge balances. e reasons for these are
(i) the majority of the ICP-QQQ species are transition metals which have varying oxidation states and, without
pH measurements, the proper charge cannot be assigned, and (ii) the majority of these species are very low in
concentration and do not signicantly aect the overall charge balances. All the stages were then plotted per set
and a trend line was applied to test if there was a linear correlation. e charge balance R2 values in Table7 reveal
strong linear correlations (>0.90), verifying that the data are valid. Additionally, Fig.3 shows the overall charge
balance for every set. All of the sets agree with the trend, with the exception of set 24, which can be seen deviating
from the rest of the data. is set coincided with New Year’s reworks, which produce a large amount of anionic
species such as sulfate and nitrate as well as cationic metals, such as Fe and Cu. e combination of large anionic
concentrations and the presence of cationic metals not included in the calculation lead to a charge balance slope
below unity (i.e. more anions than cations).
e data provided can be used to conduct various studies to improve understanding of regional PM eects
and implications. e dataset can be synchronized up with the other CHECSM instruments set up by the Air
Quality Dynamics-Instrumentation and Technology Development (AQD-ITD) laboratory, the AErosol RObotic
NETwork (AERONET) station43, and meteorological and precipitation chemistry data collected by MO (Table2).
ere are a host of previous (7 SouthEast Asian Studies (7-SEAS) 2010–2018; Biomass-burning Aerosols
& Stratocumulus Environment: Lifecycles and Interactions Experiment (BASELInE) 2013–2015) and ongoing
(CAMP2Ex) research activities in southeast Asia from which this dataset can provide additional context. e
dataset also has relevance for all global regions in that process-level understanding can be improved using a
dataset with such a wide range of pollution scenarios in one of the most polluted cities of the world with diverse
meteorological characteristics.
A few papers have been produced using portions of this dataset already. Cruz et al.16 looked at size-resolved
PM composition during the 2018 southwest monsoon season and conducted positive matrix factorization (PMF)
to identify PM sources, which were attributed to aged PM, sea salt, combustion emissions, vehicular/resuspended
dust, and waste processing emissions. Braun et al.18 presented case examples of long-range transport of PM from
east and southeast Asia, such as biomass burning from the Maritime Continent and transport from continental
East Asia. ey also presented examples of dierent transport pathways of pollution to the study site which
yielded concentration dierences for species such as K, Rb, Ba, V, Pb, Mo, and Sn. AzadiAghdam et al.17 analyzed
sea salt PM in Metro Manila and found that sea salt concentrations varied during the wet season and appeared
to be contaminated by crustal and anthropogenic sources. Building o these limited examples using just a subset
of the overall dataset, there are a signicant number of topics that this dataset can be used to address, such as the
following:
• Impacts of PM on regional climate, clouds, and monsoon activity by (i) comparing PM composition to other
cities around the world with and without monsoon seasons, (ii) combining the dataset with meteorological
data from satellites and models to understand inuences on aerosol composition via mechanisms such as
photochemical processing, and (iii) relating surface PM concentrations to AOD from AERONET and satellite
sensors to examine the vertical nature of aerosol in the region as has been done in other regions (e.g. ref. 44).
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• Removal of PM via wet deposition by looking at what species are most eectively scavenged using precipita-
tion data (e.g. refs. 45,46).
• Aqueous processing of PM by looking at the changes of PM concentrations in the dry vs the wet season and
additionally as a function of cloud coverage and aerosol liquid water amounts (e.g. refs. 47,48).
• Source apportionment of PM by (i) observing seasonal changes in emissions (e.g. ref. 49) and (ii) comparing
the emission sources determined by techniques such as PMF for the 2018 southwest monsoon season versus
the 2019 southwest monsoon season.
• Eects associated with mixing of varying air masses (e.g. ref. 50) by identifying (i) what air masses inuence the
city and during what times of year, (ii) if synergistic eects of mixing air masses can be seen year round, and (iii) if
satellites and models that speciate aerosol can capture the behavior of mixing air masses in the region as reected
in the MOUDI data.
Species Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 Stage 7 Stage 8 Stage 9 Stage 10 Stage 11 Stage 12
Ag 7(39) 7(42) 7(31) 7(32) 7(30) 7(30) 7(30) 7(29) 7(27) 7(33) 7(40) 7(40)
Al 7(5) 7(5) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(1) 7(1) 7(23) 7(19)
As 7(41) 7(44) 7(39) 7(35) 7(34) 7(21) 7(4) 7(5) 7(5) 7(8) 7(37) 7(40)
Ba 7(5) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(16) 7(33) 7(26)
Cd 7(36) 7(41) 7(35) 7(30) 7(27) 7(6) 7(0) 7(0) 7(0) 7(8) 7(33) 7(37)
Co 7(24) 7(31) 7(18) 7(11) 7(7) 7(11) 7(12) 7(12) 7(11) 7(25) 7(40) 7(35)
Cr 7(27) 7(27) 7(14) 7(14) 7(14) 7(14) 7(6) 7(12) 7(14) 7(14) 7(16) 7(14)
Cs 7(47) 7(46) 7(37) 7(23) 7(23) 7(11) 7(3) 7(0) 7(0) 7(2) 7(41) 7(46)
Cu 7(28) 7(28) 7(8) 7(8) 7(7) 7(7) 7(3) 7(2) 7(7) 7(8) 7(14) 7(13)
Fe 7(18) 7(21) 7(12) 7(4) 7(2) 7(4) 7(1) 7(2) 7(10) 7(16) 7(25) 7(17)
Hf 7(45) 7(47) 7(41) 7(34) 7(31) 7(40) 7(37) 7(41) 7(44) 7(42) 7(47) 7(47)
K 0(14) 0(12) 0(1) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(15) 0(15)
Mn 7(3) 7(1) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(15) 7(15)
Mo 7(37) 7(40) 7(23) 7(12) 7(8) 7(6) 7(4) 7(4) 7(4) 7(8) 7(34) 7(38)
Nb 7(43) 7(44) 7(35) 7(28) 7(23) 7(30) 7(17) 7(25) 7(34) 7(44) 7(47) 7(41)
Ni 7(26) 7(26) 7(3) 7(3) 7(1) 7(1) 7(0) 7(0) 7(0) 7(2) 7(10) 7(17)
Pb 7(25) 7(24) 7(3) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(13) 7(13)
Rb 7(10) 7(8) 7(2) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(8) 7(12)
Se 7(37) 7(43) 7(27) 7(25) 7(14) 7(13) 7(11) 7(11) 7(16) 7(24) 7(39) 7(39)
Sn 7(38) 7(40) 7(35) 7(22) 7(18) 7(8) 7(4) 7(0) 7(1) 7(5) 7(36) 7(36)
Sr 7(1) 7(1) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(7) 7(14) 7(23) 7(18)
Ti 7(12) 7(8) 7(0) 7(0) 7(0) 7(0) 7(2) 7(3) 7(2) 7(6) 7(24) 7(21)
Tl 15(35) 15(36) 15(34) 15(35) 15(34) 15(31) 15(20) 15(18) 15(21) 15(27) 15(34) 15(34)
V 7(41) 7(41) 7(33) 7(26) 7(23) 7(16) 7(4) 7(0) 7(1) 7(18) 7(40) 7(40)
Y 7(33) 7(35) 7(22) 7(15) 7(14) 7(22) 7(26) 7(32) 7(29) 7(33) 7(39) 7(40)
Zn 7(11) 7(13) 7(5) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(0) 7(12) 7(12)
Zr 7(31) 7(36) 7(14) 7(5) 7(1) 7(7) 7(9) 7(21) 7(36) 7(30) 7(42) 7(34)
Adipate 4(39) 4(42) 4(22) 4(30) 4(30) 4(30) 4(32) 4(29) 4(25) 4(20) 4(39) 4(37)
Ammonium 0(28) 0(37) 0(11) 0(8) 0(6) 0(2) 0(1) 0(0) 0(0) 0(0) 0(10) 0(8)
Calcium 0(15) 0(14) 0(2) 0(0) 0(0) 0(0) 0(1) 0(5) 0(8) 0(15) 0(41) 0(33)
Chloride 0(11) 0(8) 0(1) 0(0) 0(0) 0(0) 0(0) 0(2) 0(2) 0(7) 0(39) 0(30)
DMA 0(52) 0(53) 0(43) 0(47) 0(47) 0(39) 0(27) 0(25) 0(29) 0(41) 0(46) 0(44)
Magnesium 0(12) 0(9) 0(0) 0(0) 0(0) 0(0) 0(0) 0(1) 0(1) 0(12) 0(34) 0(34)
Maleate 0(54) 0(54) 0(53) 0(49) 0(51) 0(48) 0(24) 0(16) 0(23) 0(52) 0(54) 0(53)
MSA 0(49) 0(51) 0(44) 0(42) 0(28) 0(22) 0(7) 0(4) 0(8) 0(11) 0(48) 0(49)
Nitrate 0(20) 0(19) 0(2) 0(0) 0(0) 0(0) 0(1) 0(1) 0(1) 0(5) 0(31) 0(21)
Oxalate 0(14) 0(13) 0(5) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(6) 0(20)
Phthalate 0(47) 0(51) 0(38) 0(19) 0(20) 0(31) 0(24) 0(24) 0(23) 0(42) 0(51) 0(44)
Pyruvate 0(48) 0(53) 0(50) 0(50) 0(48) 0(51) 0(51) 0(54) 0(53) 0(52) 0(54) 0(51)
Sodium 0(13) 0(12) 0(1) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(1) 0(24) 0(19)
Succinate 0(48) 0(50) 0(43) 0(41) 0(38) 0(42) 0(35) 0(35) 0(41) 0(47) 0(52) 0(47)
Sulfate 0(11) 0(8) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(1) 0(9)
TMA & DEA 0(54) 0(54) 0(54) 0(54) 0(54) 0(52) 0(39) 0(32) 0(34) 0(46) 0(53) 0(54)
Tab le 6. Summary of the number of data points either missing (outside parenthesis) or below the LOD (inside
parenthesis) for a given species and MOUDI stage. Note that there were a total of 54 possible data points for
each species and stage. ese counts exclude gravimetric and microscopy sets where chemical analysis was not
performed. Refer to Table3 for cutpoint diameters and diameter ranges.
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• Catalytic and destructive eects of metals on inorganic (e.g. refs. 51–53) and organic species (e.g. refs. 54–56).
• Impacts of extreme events on regional PM by examining (i) sets where holidays occurred (e.g. New Year’s)
and (ii) sets inuenced by typhoons, which have been shown to impact aerosol in the general region, such as
was shown in previous studies in Taiwan57.
• Public health implications related to PM by examining the characteristic size distributions of species posing
negative eects such as heavy metals and their general prevalence in Metro Manila.
Set # Slope R2Set # Slope R2
MO1 0.89 0.92 MO31/32 1.19 0.94
MO2 1.42 0.99 MO33 1.26 0.95
MO3/4 1.21 1.00 MO34 1.43 0.98
MO5 1.36 0.99 MO35/36 1.36 1.00
MO6 1.32 0.98 MO37 1.37 0.94
MO7 1.36 0.99 MO38 1.29 0.95
MO8 1.36 1.00 MO39/40 1.50 0.97
MO9 1.26 0.99 MO41 1.50 0.99
MO10 1.35 1.00 MO42 1.46 0.96
MO11 1.26 0.84 MO43/44 1.44 1.00
MO12 1.33 0.99 MO45 1.35 1.00
MO13/14 1.29 1.00 MO46 1.47 1.00
MO15 1.30 0.99 MO47 1.60 0.99
MO16 1.42 0.98 MO48/49 1.70 0.97
MO17 1.39 0.96 MO50 1.94 0.99
MO18 1.33 0.98 MO51 1.43 0.94
MO19 1.47 0.98 MO52/53 1.63 0.94
MO20 1.29 0.95 MO54 1.46 1.00
MO21 1.30 0.97 MO55 1.38 0.98
MO22 1.27 0.97 MO56 1.57 0.94
MO23 1.27 0.94 MO57/58 1.24 0.96
MO24 0.82 1.00 MO59 1.45 1.00
MO26 1.46 0.91 MO60 1.29 0.96
MO27 1.55 1.00 MO61 1.39 0.97
MO28 1.17 0.97 MO62/63 1.24 0.97
MO29 1.50 0.87 MO64 1.36 1.00
MO30 1.66 0.91 MO65/66 1.44 0.99
Tab le 7. Slope and coecient of determination (R2) of the water-soluble charge balance for each MOUDI
set. Values above 1 indicate there is an anion decit. Only IC species and K from ICP-QQQ are taken into
consideration for the charge balance calculations.
Fig. 3 Charge balance plot for the cumulative MOUDI dataset using individual stages of all MOUDI sets. Red
dots represent every stage of every set, with the exclusion of set 24, which is represented as green squares. e
blue dashed line represents the line of best t with a slope of 1.38 ± 0.01 and a R2 value of 0.97, excluding set 24,
which was associated with New Year’s reworks containing elevated anions and cationic transition metals.
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Received: 19 February 2020; Accepted: 30 March 2020;
Published: xx xx xxxx
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Acknowledgements
e authors acknowledge support from NASA grant 80NSSC18K0148.
Author contributions
C.S. organized the dataset and led the conception and writing of the manuscript. All authors contributed to eld
data collection and quality control of dataset. C.S. conducted a nal round of quality control on all datasets. All
authors helped in the editing of the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to A.S.
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