Two approaches to mass closure analysis for carbon-rich aerosol in Metro Manila, Philippines

Abstract

In this paper, we investigate physico-chemical properties of particulate matter (PM) at an urban mixed site (UB) and two roadside (RS) sites during the 2015 Metro Manila Aerosol Characterization Experiment (MACE). Aerosol particle number size distributions (0.01–10 μm diameter) were measured using a combination of a mobility particle size spectrometer and aerodynamic particle size spectrometers. PM_2.5 filter samples were analyzed for total mass, organic carbon (OC), elemental carbon (EC), water-soluble inorganic ions, and elemental species. Mass closure between the gravimetric mass, chemical composition, and mass concentration derived from the number size distribution was performed. We found that the bulk PM_2.5 mass was dominated by carbonaceous materials, followed by secondary inorganic aerosols and crustal matter at all sites. The average OC/EC ratios at the RS sites (0.16–1.15) suggest that a major fraction of the aerosol mass at these sites derives from traffic sources, while the OC/EC ratio at the UB site (2.92) is indicative of a more aged aerosol, consistent with greater contribution from secondary organic carbon (SOC) formation. The ultrafine particles (UFPs, diameter < 100 nm) dominated (89–95%) the total particle number concentration at the three sites, highlighting the importance of such measurements in this region. However, UFPs have low mass contribution to PM_2.5 (7–18%), while particles in the accumulation mode (diameter 100–1000 nm) accounted for most of the number-derived PM_2.5 mass concentration (61–67%). On average, strong agreement between the chemically-derived mass and the gravimetric mass was found (slope = 1.02; r² = 0.94). The number-derived mass concentration correlated well with the gravimetric PM_2.5 mass (slope = 1.06; r² = 0.81). These results highlight the need for more comprehensive PM characterization, particularly focusing on size-resolved chemical composition and particle number size distributions. The mass closure approach presented in this work provides a framework for a conversion between number size distributions and PM_2.5 mass concentration in real time in an environment with similar characteristics.

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Environmental significance

Metro Manila, the capital region of the Philippines, is impacted by a diverse range of pollution sources, with road-traffic related emissions of black carbon significantly contributing to PM mass. While previous studies in the region have primarily focused on gravimetric and chemical analyses, recent studies suggest that particle number concentration (especially those of ultrafine particles) is more strongly linked to health effects. Here, we quantify PM mass, PM chemical composition, and particle number concentration to provide a more complete understanding of atmospheric composition in this megacity. Results reveal the importance of black carbon, organic matter, and ultrafine particles at the sampling sites. Mass closure was achieved between gravimetric, chemical composition, and number-derived mass concentrations. This study raises awareness of poor air quality in Metro Manila and motivates further research with longer sample collection periods, covering different seasons and expanding analysis beyond conventional PM metrics.

1 Introduction

Ambient particulate matter (PM) has significant impacts on human health,¹ visibility reduction,² ecosystem degradation,³ and global climate.⁴ Exposure to ambient PM can lead to deleterious health problems such as cardiovascular and respiratory diseases, incidents of lung cancer, and premature deaths.^5,6 The magnitude of the effect of PM on health depends on its chemical composition (toxicity) and deposition efficiency, which is mainly governed by particle size, hygroscopicity, shape, and density.⁷

In terms of PM mass concentration, currently only PM₁₀ and PM_2.5 (aerodynamic particle diameter less than 10 μm and 2.5 μm, respectively) mass are regulated.⁸ Traditional online, and filter-based PM sampling methods for mass concentrations are more common than size spectrometers. However, size spectrometers provide the key advantage of directly measuring particle number concentrations, which are more relevant to health impacts. To be able to relate to current air quality standards, particle number size distribution measurements, which provide comprehensive information about particle properties, must be converted to mass concentration. This conversion requires knowledge of particle density and shape to determine the relationship between mobility and aerodynamic diameters,⁹ and to convert particle number size distributions to particle mass size distributions.^9–11

Typically, particle density is determined indirectly by knowing the dominant chemical composition of PM of a certain size range.¹² However, particle density is affected by both physical and chemical characteristics, as well as generation processes of aerosol particles.¹³ For example, particles from natural sources (e.g., sea salt and mineral dust) have densities relatively higher than 1.0 g cm⁻³ due to a more compact structure, while freshly emitted combustion particles are more loosely aggregated and usually have densities lower than 1.0 g cm⁻³.^7,13 Combustion is a major source of fine and ultrafine particles (UFPs, diameter < 100 nm),^12,14–16 with diesel-powered engines emitting large quantities of agglomerate soot particles.¹⁷ Although UFPs contribute less to total PM mass compared to the fewer but larger particles, they represent the largest portion of the total particle number concentration.^18,19 Studies suggest that particle number concentration, rather than mass concentration, may be more strongly linked to health effects.^20,21 However, some studies in other Southeast Asian cities reported high mass concentration of UFPs^16,22,23 indicating the growing contribution of these tiny particles to total PM mass loading. Thus, it is important to not only quantify PM mass, but also particle number concentration, chemical composition, and the interrelationships among these parameters to better understand PM impacts.

Metro Manila, the capital region of the Philippines, is impacted by a complex source of particulate pollution, including traffic, aged aerosol, biomass burning, industrial, cooking activities, and waste processing.^24–28 A special feature of PM in Metro Manila is the high concentration and lack of seasonality of black carbon (BC).^29–31 At an urban mixed site, BC is approximately 30% of PM_2.5 mass^24,28 in contrast to 45% at a roadside site,³² and 55–75% for equivalent BC (eBC,³³ a proxy for soot measured optically) at a street site.²⁶ At a roadside site, hourly concentrations of eBC as high as 138 μg m⁻³ were observed.³⁴ Speciated particle mass size distribution measurements by Cruz et al.²⁵ revealed that BC mainly resides in the submicrometer range, peaking between 0.18–0.32 μm diameter, with its total mass accounting for 26.9% of the total PM mass. These studies have highlighted the considerable contribution of BC to the PM loading in the region compared to other polluted neighbouring countries.²⁹

This study tackles the issue of mass concentration closure of ambient PM based on multiple measurements related to particle number size distributions and chemical composition. Mass closure analysis aims to provide a consistent picture of how ambient PM is structured physically and chemically,^35–37 which is highly relevant for a consistent understanding of its sources, transformation processes and subsequent atmospheric and health effects. On an experimental level, mass closure also indicates the compatibility and quality of all the PM measurements involved, and helps estimate parameters such as particle shape and density, which often cannot be measured directly. In this investigation, mass closure was performed in two ways: (i) between gravimetric PM_2.5 mass and the cumulative sum of identified chemical components including organic carbon (OC), elemental carbon (EC), secondary inorganic aerosols, crustal and trace elements; and (ii) between gravimetric PM_2.5 mass and the particle number-derived mass measured from different size spectrometers. The latter addresses the challenge of synthetizing data from a mobility particle size spectrometer (MPSS) and an aerodynamic particle size spectrometer (APSS) that provide different particle metric diameters (e.g., mobility diameter and aerodynamic diameter). This work provides, for the first time, particle number-derived mass distributions and a closure between gravimetric and number-derived mass in the BC-dominated and highly populated Metro Manila area, where PM-related health effects are anticipated to be significant.

2 Experimental methods

2.1 Sampling site

Aerosol particle physico-chemical properties were measured at three sites in Metro Manila from April to June 2015 as part of the Metro Manila Aerosol Characterization Experiment (MACE) campaign (Fig. 1). This campaign was a collaboration between the Leibniz Institute for Tropospheric Research (TROPOS) and the “Researchers for Clean Air” (RESCueAir) consortium including Ateneo de Manila University (ADMU), De La Salle University (DLSU), Manila Observatory (MO), University of the Philippines-Diliman (UPD), and the Department of Science and Technology – Philippine Nuclear Research Institute (DOST-PNRI).

Fig. 1 Study area with locations marked for the three sampling sites during the 2015 MACE campaign. The right panel is the zoomed-in version of sampling sites. Right image source: Google Earth.

The Katipunan Avenue roadside site (14.635° N, 121.075° E) (referred to as “KAT RS”) was situated within the ADMU campus and ∼2 m from Katipunan Avenue, a four-lane (each direction) major road in Quezon City. The site is ∼350 m north of a public utility jeepney (PUJ) vehicle terminal, which is situated along the intersection of Katipunan Avenue and several other main roads that carry heavy traffic, especially during rush hours. Alongside the main road are residential apartment buildings, condominiums, commercial establishments, and fast-food restaurants. Size spectrometers and filter-based PM sampling were performed from 3 April to 5 May 2015.

The MO urban mixed site (14.636° N, 121.078° E), referred to as “MO UB”, was located within the ADMU campus and ∼350 m east of the KAT RS site. This station is surrounded by trees. Size spectrometers and filter-based PM sampling were conducted from 8 to 15 May 2015, coinciding with a period of no summer classes and thus less vehicular traffic in the immediate vicinity as compared to regular school session periods.

Another roadside site (14.566° N, 120.994° E) was situated along Taft Avenue, Manila (referred to as “TAFT RS”) and is adjacent to the DLSU campus. The three-lane (each direction) road under the light-rail transit (LRT) railway is congested by PUJs, public utility vans (PUVs), buses, and taxis. This site resembles a street canyon because of its narrow road that is situated under the LRT railway, with towering condominiums and commercial establishments surrounding this area. The instruments were situated at ∼0.5 m from the street curb. Size spectrometers and filter-based PM sampling were conducted from 18 May to 9 June 2015.

2.2 Meteorological conditions

Meteorological data (wind speed and direction) were collected using an automatic weather station (Davis Vantage Pro2 Plus, Davis Instruments Corp. Inc., California, USA) installed on top of the measurement container. At the KAT RS site, the prevailing wind is from the south-southwest (Fig. S1a†), making KAT RS a downwind site of major thoroughfares (Aurora Boulevard and Marcos Highway) (Fig. 1). Traffic congestion intensifies at the highway during rush hour periods, which typically are 07:00–09:00 and 16:00–18:00 local time.³⁸ At the MO UB site, winds are coming from the west and east (Fig. S1b†), where Katipunan Avenue and the Marikina residential area are situated, respectively. Farther east of the Marikina residential area are the Sierra Madre mountains, which are relatively remote. The prevailing wind at the TAFT RS roadside is coming from the south-southeast direction (Fig. S1c†) where residential areas and large shopping malls are located.

To identify possible impact of long-range transport of PM to the study region, analysis of backward air mass trajectories was conducted using the NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model.^39,40 Trajectories were calculated for an end time of midnight local time and height of 500 m above ground level. Meteorological data were used from the Global Data Analysis System (GDAS1; 1° × 1° resolution). The five-day back trajectory analysis (Fig. S2†) revealed that during the measurement campaign (April–June), the prevailing air masses came from the open ocean suggesting minimal influence from long-range continental sources in neighboring countries.

2.3 Sample collection

Aerosol samples (PM_2.5) were collected using two low-flow air samplers (MiniVol, Airmetrics Inc., Oregon, USA) operating at 5 lpm. The samplers were installed on top of the measurement container approximately 7 m above the ground. Samples were collected using two types of filter substrates: Teflon (PTFE membrane, 2 μm pore size, 46.2 mm diameter, Whatman) and quartz (47 mm diameter Pallflex quartz-fiber, Whatman). The samplers were placed side by side and were programmed to run from midnight for a total of 24 hours. After sampling, Teflon filters were placed inside a temperature (20–23 °C) and relative humidity (30–40%) controlled room for at least 24 hours. Gravimetric analysis was performed using a Sartorius ME5-F microbalance with a sensitivity of ±1 μg (Sartorius, Göttingen, Germany).

Particle number size distribution (PNSD) across the mobility diameter range of 10–800 nm was measured using a TROPOS-type Mobility Particle Size Spectrometer (MPSS)⁴¹ installed inside the measurement container. The MPSS included a Hauke-type Differential Mobility Analyzer (DMA) and TSI 3772 Condensation Particle Counter (CPC) with a flow rate of 1 lpm. Additionally, an aerodynamic particle size spectrometer (APSS, TSI 3321) was used to measure PNSD for the diameter range of 500 nm to 10 μm (aerodynamic diameter; limited by PM₁₀ inlet at aerosol intake line). The MPSS and APSS sizing accuracies were calibrated using nebulized polystyrene latex spheres (PSL) of 203 nm and 2.0 μm (ThermoScientific™, Duke Standards™), respectively.

2.4 Chemical composition analysis

The concentrations of a suite of elements, including Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, and Zn, were determined by Photon Induced X-ray Emission (PIXE). Concentrations obtained from this analysis are expressed in μg cm⁻². To convert it to μg m⁻³, each value is multiplied by the filter area (11.81 cm²) and divided by the volume of air sampled in 24 h (in m³). After the elemental analysis, each Teflon filter was cut in half, with one half extracted in 14 ml of Milli-Q water (18.2 MΩ) through sonication for 1 h in a plastic 15 ml centrifuge vial. The aqueous extracts were analyzed for water-soluble ions (NH₄⁺, K⁺, Mg²⁺, Ca²⁺, Cl⁻, NO₃⁻, SO₄²⁻) using an ion chromatography system (IC; Shimadzu). Pallflex quartz-fiber filters were prebaked at 900 °C for three hours prior to sampling.⁴² OC and EC were then quantified with a Sunset Laboratory OC–EC Aerosol Analyzer following the edited Improve_A protocol (Table S1†).^42,43 The completion date of the chemical analyses was summarized in Table S2.†

2.5 Calculations and error discussion

2.5.1 Gravimetrically-derived mass concentration. The Teflon filters were weighed twice before and after sampling, with differences between duplicate weights being <10 μg. The calculated uncertainties in weighing were negligible, with the highest being ∼0.8%. The PM mass in μg was derived from the average final substrate weight minus the initial substrate weight. The PM mass concentration in μg m⁻³ was calculated by dividing the total PM mass (μg) with the total volume of air sampled (m³). Using the error propagation equation (eqn (S1)†),⁴⁴ the overall uncertainty of the gravimetric mass (due to the uncertainty in weighing, and uncertainty in sampling air volume) was approximately 5%. For our plots, we applied a 10% uncertainty to account for additional unquantified sources of uncertainty.

2.5.2 Chemically-derived mass concentration. The mass concentrations of water-soluble ions were calculated using a manually prepared calibration curve. For this study, five to six calibration standards per analyte were prepared using a reference standard. After creating the calibration curve, two to three check standards were run again to check the accuracy of the calibration. Recoveries were then calculated as the ratio between the mass of a specific measured species and the known amount of that species in that sample.⁴⁵ Recoveries were all above 95% with repeatability ranging from 8% to 20%. Another way to check the validity of the IC analysis is to perform the ion balance to determine potentially missing ionic species.⁴⁶ The molar ratio of cation equivalents (CE) and anion equivalents (AE) are used to infer the acidity of aerosol.⁴⁷ Assuming that aerosol particles are neutral, the ratio of the anion to cation equivalence should be one. For this study, the ratios of AE/CE were higher than one (slope = 1.1; r² = 0.79), indicating cation deficiency. The AE/CE deficiency might be due to unmeasured H⁺ ions.⁴⁸ For the elemental analysis using PIXE, error estimates were calculated using eqn (S2).†⁴⁹ The estimated uncertainty for OC and EC quantification ranged from 7% to 14%.

For the chemical mass balance, the chemical species were divided into eight classes as follows: (1) organic matter (OM), (2) EC, (3) sulfate, (4) nitrate, (5) ammonium, (6) sea salt, (7) crustal matter, and (8) trace elements. To convert OC to OM, correction factors were applied to estimate the average molecular weight per carbon weight for the organic aerosol.^50,51 For this study, a correction factor of 1.4 was used for the urban mixed site data, and 1.3 for the roadside data. These correction factor values were adapted from the study of Taiwo et al.⁵² in the same type of environment. The OC to OM conversion is one of the major sources of uncertainty in chemical mass closure studies.⁵³ Sea-salt was estimated using the formula 1.8·Cl⁻ based on the relative abundance of Cl⁻ and Na⁺ on a mass basis in seawater.⁵⁴ Crustal matter was calculated using the oxide forms of crustal species (2.2·Al + 2.49·Si + 1.63·Ca + 2.42·Fe +1.94·Ti).⁵⁵ Trace elements were calculated by simply adding the concentrations of species Cr, Cu, Mn, Ni and Zn. Similarly, EC and secondary inorganic ions (sulfate, nitrate, and ammonium) were summed up without any extra modifications needed.^56,57 The unexplained mass was referred to as ‘unknown’.

2.5.3 Number-derived particle mass size distribution. The determined PNSDs from the MPSS and APSS are based on different measurement principle. The MPSS classifies particles according to their mobility diameter (d_m) while the APSS does so according to aerodynamic diameter (d_a). To obtain PNSD from 10 nm to 10 μm, MPSS and APSS number size distributions were converted to volume-equivalent diameter (d_ve) using an enhanced inversion algorithm presented by Pfeifer et al.⁵⁸ In the merging process, an input density and dynamic shape factor are required. Selecting the particle density and shape factor is a crucial part in the inversion routine. These two parameters can be manipulated to achieve the best fit between the MPSS and APSS PNSDs. However, simultaneously changing both these parameters is difficult especially when processing thousands of PNSDs. For this study, we used a constant particle density value while varying the shape factors. The density value is usually based on the density of the major chemical components measured within the study area and is not directly obtained from a measuring instrument.^10,59 For this study, we assumed a particle density (1.8 g cm⁻³) based on the dominant species in Metro Manila,^24,25,28 and using published densities for these species (OC – 1.2 g cm⁻³, BC – 2.0 g cm⁻³, (NH₄)₂SO₄ – 1.77 g cm⁻³, NH₄NO₃ – 1.72 g cm⁻³).⁹ From the best fit between MPSS and APSS, a dynamic shape factor ranging from 1.1 to 2.3 was retrieved, falling within the range of shape factor values for soot reported in literature,^60,61 with an average of 1.6 (KAT RS), 1.4 (MO UB), and 2.0 (TAFT RS).

From the merged PNSDs (dN/d log D_p), the particle mass size distribution (PMSD; dM/d log D_p) was calculated using eqn (1) from Buonanno et al.:⁶²

where m_mpss/apss,i is the mass concentration calculated from the combined MPSS + APSS number concentration (n_mpss/apss,i) in terms of volume equivalent diameter (d_ve), and particle density (ρ_p). Since the filter-based PM_2.5 is expressed in d_a, the equivalent of PM_2.5 to d_ve was determined using eqn (2) making use of the average shape factor identified at the three sites.where x is the dynamic shape factor, ρ₀ is the reference density (1.0 g cm⁻³), and ρ_p is the particle density (1.8 g cm⁻³). The PMSDs were integrated for d_ve ranges of 0.1–2.5 μm for all sites to obtain the PM_2.5 mass concentration.

3 Results and discussion

3.1 PM_2.5 mass concentration and chemical composition

The average PM_2.5 mass concentration determined via the gravimetric method was 42.8 ± 9.3 μg m⁻³ at TAFT RS, 37.2 ± 11.9 μg m⁻³ at KAT RS, and 23.9 ± 7.2 μg m⁻³ at MO UB (Table 1). EC and OC contributed the most to PM_2.5 mass. Mass concentration of EC was highest at TAFT RS (20.8 ± 6.2 μg m⁻³), followed by KAT RS (12.9 ± 3.3 μg m⁻³), and the lowest at MO UB (4.4 ± 2.2 μg m⁻³). For context, concentrations of EC at a few other sites in the Philippines are as follows: 6.63 μg m⁻³ (urban industrial site);⁴² 2.29 μg m⁻³ (rural municipality area);⁴² 0.67 μg m⁻³ (rural site);⁶³ and 9.0 μg m⁻³ (roadside site).³² One explanation for the high EC concentrations at the roadside sites as compared to the urban mixed site is direct influence of traffic emissions^64,65 since measurements at both TAFT RS and KAT RS were carried out on and near the street. Moreover, the street canyon configuration of Taft Avenue restricts air circulation, leading to the accumulation of vehicular emissions in the area. Although trucks are prohibited along Taft Avenue, the road remains heavily used by private vehicles and PUJs.³⁴ Findings from Kecorius et al.²⁶ provide evidence that PUJ emissions contribute to the elevated eBC mass concentrations along Taft Avenue.

Table 1 Summary (average ± standard deviation, in μg m⁻³) of PM_2.5 mass, carbonaceous species, water-soluble ions, and elemental components analyzed at the three monitoring sites: Katipunan Avenue roadside site (KAT RS), Manila Observatory urban mixed site (MO UB), Taft Avenue roadside site (TAFT RS). Total number of collected filter samples (quartz and Teflon): KAT RS (16), MO UB (7), TAFT RS (9)

Species	KAT RS	MO UB	TAFT RS
PM2.5	37.2 ± 11.9	23.9 ± 7.2	42.8 ± 9.3
OC	14.5 ± 3.8	9.0 ± 2.9	13.7 ± 3.5
EC	12.9 ± 3.3	4.4 ± 2.2	20.8 ± 6.2
Water-soluble ions
NH4^+	0.68 ± 0.66	0.72 ± 0.52	0.77 ± 0.42
K^+	0.33 ± 0.43	0.25 ± 0.12	0.15 ± 0.14
Mg^2+	0.05 ± 0.07	0.02 ± 0.01	0.03 ± 0.00
Cl^−	0.16 ± 0.17	0.08 ± 0.05	0.01 ± 0.00
NO3^−	0.65 ± 0.30	0.63 ± 0.23	0.56 ± 0.32
SO4^2−	2.52 ± 1.83	2.43 ± 1.28	2.87 ± 1.02
Elements
Al	0.11 ± 0.06	0.11 ± 0.00	0.09 ± 0.02
Ca	0.14 ± 0.05	0.09 ± 0.04	0.15 ± 0.07
Cl	0.19 ± 0.15	0.11 ± 0.06	0.05 ± 0.01
Cr	0.02 ± 0.00	0.02 ± 0.00	0.00 ± 0.00
Cu	0.02 ± 0.01	0.01 ± 0.00	0.01 ± 0.00
Fe	0.24 ± 0.08	0.14 ± 0.08	0.21 ± 0.08
K	0.33 ± 0.26	0.22 ± 0.08	0.19 ± 0.08
Mn	0.01 ± 0.00	0.02 ± 0.01	0.02 ± 0.05
Ni	0.01 ± 0.00	0.02 ± 0.00	0.00 ± 0.00
S	0.87 ± 0.54	0.83 ± 0.46	0.97 ± 0.32
Si	0.43 ± 0.13	0.33 ± 0.04	0.34 ± 0.06
Ti	0.03 ± 0.01	0.00 ± 0.00	0.00 ± 0.00
Zn	0.10 ± 0.08	0.08 ± 0.06	0.09 ± 0.07

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Mass concentrations of OC were 14.5 ± 3.8 μg m⁻³ (KAT RS), 9.0 ± 2.9 μg m⁻³ (MO UB), and 14.5 ± 3.8 μg m⁻³ (TAFT RS). Concentrations of OC are as follows for other studies in the country: 8.00 μg m⁻³ (urban industrial site);⁴² 4.08 μg m⁻³ (rural municipality area);⁴² and 1.15 μg m⁻³ (rural site).⁶³ OC is a complex mixture of different compounds produced via either direct emissions or produced by secondary processes (i.e., gas-to-particle conversion).⁶⁶ Biomass burning and fossil fuel combustion are some of the major anthropogenic sources of OC.^64,67 There was a strong correlation between OC and K⁺ at MO UB (r = 0.83) and TAFT RS (r = 0.65); these species were found to be associated with biomass burning (Table S3†).^68,69 Possible sources of these species at MO UB could be local biomass burning emission, especially coming from the east of the sampling area where the Sierra Madre mountains are located. At TAFT RS, aside from traffic, residential wood burning is also a likely source of OC, as well as meat cooking⁷⁰ since there are several grilling/barbecue restaurants operating near the sampling area.

The OC/EC ratio has been used as an indicator of the emission source and the transformation of carbonaceous aerosol. Values of OC/EC lower than 1 suggest that emissions of carbonaceous particles are dominated by traffic, greater than 2 implies secondary organic carbon (SOC) formation,⁵⁶ and between 4 and 6 signifies impacts from biomass burning or long-range transport from urban areas.⁷¹ For this study, the mean OC/EC ratio was highest at MO UB (2.92), followed by KAT RS (1.15), and lowest at TAFT RS (0.68). An OC/EC ratio of 2.92 was also observed inside a university campus in Athens, Greece.⁴⁶ For TAFT RS, similar OC/EC ratios (<1) were observed in roadway tunnel studies in some regions of the world⁷² such as Los Angeles, USA (0.76), southern Taiwan (0.4–0.6), and Marseille, France (0.53). The elevated OC/EC ratio at the MO UB indicates a clear prevalence of organic carbonaceous species over EC suggestive of possible SOC formation in that area. This can be attributed to the greater aging of the sampled air masses at MO UB site as compared to the other two sites. Aging helps generate secondary organic aerosols (SOA), a key player in OC formation.⁷³ On the other hand, since EC is directly emitted and not secondarily produced, aging does not produce it but instead dilutes its concentration.

To know the extent of the contribution of SOC to total OC, the equation proposed by Castro et al.⁷⁴ was used. The calculation uses the minimum OC/EC ratio of each site and assumes that EC × (OC/EC)_min represents primary OC. The SOC fraction is then calculated by:

SOC = OC − EC × (OC/EC)_min (3)

The highest SOC contribution was estimated at MO UB (52%) and is comparable to the value observed at a rural site (56.1%) in the Philippines (Angat, Bulacan).⁴² This supports the assumption that the higher OC/EC ratio at MO UB was due to SOC formation. The SOC contributions to the total OC at the roadside sites were lower (KAT RS: 38%, TAFT RS: 28%) suggesting that there were more freshly emitted aerosols coming from traffic emissions.

Based on average ionic concentrations, SO₄²⁻ (2.43–2.87 μg m⁻³) was the most abundant species for all sites, followed by NH₄⁺ (0.68–0.77 μg m⁻³), and NO₃⁻ (0.56–0.65 μg m⁻³). Sulfate is formed secondarily from the oxidation of its precursor, SO₂, which is emitted via incomplete combustion of sulfur-containing fuels in vehicles and industries, and biomass burning.^64,75 Ammonium is derived from its precursor NH₃, which usually comes from agricultural activities and animal waste, but there is also some contribution arising from vehicle exhaust.^76,77 Nitrate is formed from the oxidation of NO_x with major local sources including gasoline- and diesel-powered engines, natural gas, and coal combustion.⁷⁸ Excellent correlation between SO₄²⁻ and NH₄⁺ (r = 0.90–0.96) at the three sites suggests that NH₄⁺ can be a neutralizing agent of SO₄²⁻ (Table S3†). For SO₄²⁻ to be completely neutralized, the NH₄⁺/SO₄²⁻ molar ratio should be equal to 2 or higher.⁴⁶ For this study, the mean NH₄⁺/SO₄²⁻ molar ratio (1.21–1.47) was lower than 2, which means that there was insufficient NH₄⁺ to completely neutralize SO₄²⁻. On the other hand, the correlation between NH₄⁺ and NO₃⁻ (r = 0.35–0.58) is lower, suggesting that the formation of NH₄NO₃ was likely not favorable. At MO UB, nitrate is mainly in the coarse mode (1–3.2 μm) as it partitions to large aerosol types like salt and dust rather than making secondary salts like ammonium nitrate.²⁵

Strong correlations between crustal species (Al, Si, Ca, and Fe) were also found at KAT RS (r = 0.68–0.85), MO UB (r = 0.94–0.97), and TAFT RS (r = 0.39–0.79) (Table S3†). An appreciable amount of a combination of trace elements (Cr, Cu, Ni, Mn, and Zn) was also measured at KAT RS (0.5 ± 0.3 μg m⁻³), MO UB (0.3 ± 0.1 μg m⁻³), and TAFT RS (0.3 ± 0.2 μg m⁻³). It is known that Ca and Fe are associated with resuspended dust,^79,80 while Zn is commonly used in brake linings.^81,82 Another source of Ca is construction,⁷⁵ and we noted some construction activities near the roadside sites during the sampling period. The higher percentage of crustal matter at KAT RS and MO UB compared to TAFT RS is attributed partly to significant soil coverage in these sites, whereas, TAFT RS mostly has cemented/asphalted areas. The El Niño in 2015 ⁸³ heightened the dry conditions in these areas, so the soil is less covered with vegetation, and consequently had more efficient dust resuspension.

3.2 Particle number and number-derived mass size distribution

The combined number size distributions from the MPSS and APSS covered the total size range between 0.01–10 μm. In general, the shape of PNSD was similar at the three sites (Fig. 2a). Three modes can be observed: the nucleation mode (10–20 nm), accumulation mode (around 100 nm), and coarse mode (above 1000 nm). The average UFP number concentration was 9.6 × 10⁴ cm⁻³ (KAT RS), 2.9 × 10⁴ cm⁻³ (MO UB), and 7.7 × 10⁴ cm⁻³ (TAFT RS) (Table 2). UFP concentration is higher at the roadside sites, reflecting the influence of traffic emissions. The average PNC at MO UB is at least two times lower than at RS sites because number concentrations of fine and UFPs decrease rapidly with distance from the sources due to coagulation and dilution.^84,85 The roadside average PNCs for this study are at least two times higher than those reported at European cities,¹¹ but an order of magnitude lower than other polluted Asian cities.^86–88

Fig. 2 Average (a) PNSD and (b) PMSD at KAT RS (black line), MO UB (blue line), and TAFT RS (red line) calculated from hourly data between 9 April–3 June 2015. The shaded area represents one standard deviation over the days sampled. d_ve represents volume equivalent diameter.

Table 2 Measured particle number concentrations (PNCs) (particle density: 1.8 g cm⁻³) at KAT RS, MO UB, and TAFT RS for a specific size interval (size range units: nm), and number-derived mass concentration for those same size ranges

	KAT RS	MO UB	TAFT RS
(a) PNC (# cm^−3)
N10–100	9.6 × 10^4 ± 1.5 × 10^4	2.9 × 10^4 ± 1.2 × 10^4	7.7 × 10^4 ± 2.1 × 10^4
N100–1000	4.8 × 10^3 ± 1.8 × 10^3	3.4 × 10^3 ± 1.3 × 10^3	3.9 × 10^3 ± 1.1 × 10^3
N1000–2500	7.0 ± 1.6	5.5 ± 1.3	4.8 ± 1.1
N2500–10000	0.8 ± 0.2	0.6 ± 0.2	0.5 ± 0.1
N10–100/N10–2500	0.95	0.89	0.95
N100–1000/N10–2500	0.05	0.1	0.05
(b) Mass (μg m^−3)
PM10–100	6.1 ± 1.4	2.0 ± 0.5	7.5 ± 2.0
PM100–1000	28.5 ± 11.3	19.4 ± 8.8	25.4 ± 7.1
PM1000–2500	9.4 ± 2.3	7.6 ± 2.0	8.5 ± 2.0
PM2500–10000	16.9 ± 4.7	11.8 ± 3.3	17.6 ± 5.5
PM10–100/PM10–2500	0.14	0.07	0.18
PM100–1000/PM10–2500	0.65	0.67	0.61

---

The PMSD across all sites (Fig. 2b) shows bimodal distributions with peaks in the accumulation mode (185–234 nm) and coarse mode (2790–3318 nm) (Table S4†). Particles in the accumulation mode are the result of primary emissions (combustion particles like BC), gas-to-particle conversion (sulfate, ammonium, organics), and coagulation between particles.⁶⁶ A recent study at MO UB using size-segregated PM measurements showed that BC was more dominant in the accumulation mode, peaking between 180 and 320 nm.^25,31 Secondarily produced species (NH₄⁺, SO₄²⁻) peaked between 320 and 560 nm, while NO₃⁻ peaked between 1.8 and 3.2 μm.²⁵ Aside from NO₃⁻, particles larger than 1 μm likely consist of non-exhaust traffic emissions, including resuspended road dust species like Al, Ca, Si, and Ti,⁸⁰ as well as particles from tire and metal brake wear enriched with Fe and Zn.⁶⁹

A slight shift in PMSD was observed between the three sites (Fig. 2b). Compared with KAT RS, the accumulation mode at TAFT RS is slightly shifted towards the smaller size range, while the coarse mode is shifted towards the larger size range. At MO UB, particles in the accumulation mode shifted towards the larger size range; this is because particles changed in size when moving away from traffic due to coagulation.⁸⁴ The particles at MO UB are composed of more aged species compared to the freshly emitted particles at the roadside sites.

The mass concentration of UFPs ranged between 2.0 and 7.5 μg m⁻³ (7–18% of PM_2.5 mass) depending on the sampling site (Table 2). For context, UFP mass concentrations in other Southeast Asian cities summarized in Phairuang et al.¹⁶ ranged between 1.71 and 17.1 μg m⁻³ (Hanoi, Vietnam), 5.4–16.8 μg m⁻³ (different sites in Indonesia), 7.7–25.2 μg m⁻³ (Bangkok, Thailand), 18.9 μg m⁻³ (Phnom Phen, Cambodia), 9.3 μg m⁻³ (Kuala Lumpur, Malaysia), and 3.1 ± 0.91 μg m⁻³ (Sumatra, Indonesia).⁸⁹ In Delhi, India, UFP mass concentrations ranged between 2.1 and 10.3 μg m⁻³ (3–10% of PM_0.56) depending on season and time of day.⁹⁰ Other reported UFP mass concentrations in cleaner urban environments are as follows: <3 μg m⁻³ (Dresden, Germany),⁹¹ <2 μg m⁻³ (Taipei, Taiwan),⁹² and <0.2 μg m⁻³ (Los Angeles, East Oakland, San Pablo, and Fresno, California).⁹³ It is also clear in Table 2 that the particles in the accumulation mode have a substantial impact on particle mass, contributing as much as 61–67% to the number-derived PM_2.5 mass concentration.

Although UFP mass concentration was only 7–18% of the number-derived PM_2.5 mass, the UFP number concentration contributed as much as 89–95% within the 2.5 μm size range (Table 2). This result reveals that UFPs represent the highest share of the total number of particles at these sampling sites. This highlights the importance of long term particle number concentration measurements for health risk assessment studies.⁹⁴

3.3 Mass closure

3.3.1 Gravimetric versus chemically-derived mass concentration. Aerosol chemical mass closure calculations were performed for the PM_2.5 aerosol collected at the three sites. Fig. 3 summarizes the contribution of the identified chemical species to the gravimetric PM_2.5 mass, which includes OM, EC, sulfate, nitrate, ammonium, crustal matter, and trace elements. As the chemical mass closure calculations require an estimation of OM contribution, OC was multiplied by a correction factor of 1.3 for the roadside sites and 1.4 for the urban mixed site as already noted in Section 2.5.2. OM accounted for 53.3% (MO UB), 51.9% (KAT RS), and 41.7% (TAFT RS) of the PM_2.5 mass concentration (Fig. 3). The contribution of OM at MO UB is nearly equal to the unaccounted PM_2.5 mass (52%) reported in Simpas et al.²⁸ for the same study site, suggesting that the unaccounted mass from the previous study was likely OM. For comparison, the OM contribution to PM_2.5 mass in other studies in Asia are as follows: 33.3% (roadside: Delhi, India);⁹⁵ 42% (kerb site: Mumbai, India);⁷⁵ 35% (urban background: Europe);⁹⁶ 27% (roadside: Hongkong);⁹⁷ and 14% (urban area: Jinan City, China).⁹⁸

Fig. 3 Average speciated breakdown (in percentage) of the PM_2.5 gravimetric mass concentration at individual sites. Total mass concentration is shown at the top of each pie chart.

EC contributed the most to PM_2.5 at TAFT RS (47.9%), followed by KAT RS (35.8%), and MO UB (18.7%). For the same measurement campaign, Kecorius et al.²⁶ reported that eBC in Manila contributed 55–75% to total PM_2.5 mass. Compared with EC, the measured BC via reflectometry at TAFT RS contributed to 50–71% of the total PM_2.5 mass, which is near the measured value reported by Kecorius et al.²⁶ Overall, BC levels for this study were on average 20% higher than EC, which is close to the 17.39% obtained by Salako et al.⁹⁹ who studied the variability between EC and BC at different locations in Asia and the South Pacific. At MO UB, since the site was only 350 m away from the main road, Katipunan traffic is expected to influence the measured BC especially when it was downwind of Katipunan. Furthermore, MO UB was also impacted by the exhaust from vehicles coming in and out of the ADMU campus. However, because the school calendar shift coincided with the campaign period, we expected reduced traffic both on-campus and main road, and consequently less EC.

Secondary inorganic aerosols (sum of sulfate, nitrate, and ammonium) accounted for 9.5% (KAT RS), 14.4% (MO UB), and 9.8% (TAFT RS) of the total PM_2.5 mass. The higher percentage of secondarily produced aerosol at MO UB is suggestive that the air masses transported in the area were more aged as compared to the roadside site's more freshly emitted particles. Contribution of crustal species and trace elements were 5.6 and 1.2% (KAT RS), 5.9 and 1.3% (MO UB), and 3.9 and 0.7% (TAFT RS), respectively. Sea salt accounted for less than 1% of the total PM mass at the three sites.

The sums of all the included chemical species to total PM_2.5 mass were 104% for the two roadside sites, and 95% for the urban mixed site. The measured PM_2.5 mass accounted for in other closure studies are as follows: 115% (traffic area: Delhi, India);⁹⁵ 79–106% (six urban background sites: Europe);⁹⁶ 75% (urban site: Athens, Greece);⁴⁶ 98% (roadside: Hongkong).⁹⁷ Although the closure was quite close to 100%, potential sources of uncertainty and/or reasons for the slight over- and under-estimation were possibly due to: (i) adsorption of volatile organic carbons on quartz filters;^100,101 (ii) use of a fixed OC multiplier for the quantification of the OM contribution;⁹⁶ (iii) conversion factors for the crustal elements and; (iv) volatilization during and after sample collection.¹⁰¹ To assess sensitivity of closure to different conversion factors, we varied OM : OC ratios from 1.1 to 2.0.⁵⁰ By looking at the difference between the sum of the identified chemical components and the gravimetric mass, the upper limit of the OM : OC ratio can be estimated.¹⁰² At the MO UB site, the OM : OC upper limit was 1.6, whereas in the KAT RS and TAFT RS an OM : OC ratio upper limit of 1.2 was obtained. Our use of 1.3 conversion factor in the roadside sites resulted in an overestimation of approximately 5% while our use of 1.4 in the MO UB site resulted in an underestimation of around 5%. Overall, linear regression between gravimetric mass and chemically-derived mass showed excellent correlation (r² = 0.94) indicating a well-explained total PM_2.5 mass (Fig. 4).

Fig. 4 Linear regression analysis of the gravimetric mass versus chemically-derived mass for KAT RS (black line), MO UB (blue line), and TAFT RS (red line) with the 1 : 1 line shown as a black dashed line. The linear equation statistics correspond to sites based on color, where the italicized equation (in green) is for all data. The error bars for the chemically-derived mass concentrations were taken to be ±20%. For the gravimetric mass, an error value of ±10% was used.

3.3.2 Gravimetric versus number-derived mass concentration. The PMSD was derived from the merged MPSS and APSS data. From the single PMSD, the hourly number-derived mass concentration of PM_2.5 was calculated, which was then summed daily for comparison with the PM_2.5 gravimetric mass concentration. Sources of uncertainties for this method may include: (i) instruments' concentration and sizing errors, which may contribute up to 10% of the uncertainty;^58,103 (ii) inversion routine, which provides 5% deviation with respect to the resulting PNSD⁵⁸ and; (iii) assumed particle density used to convert the aerodynamic diameter to Stokes diameter, which is estimated to have a systematic error of 5–10%.¹⁰⁴

Since measurements of particle density are non-existent in Metro Manila, we based the particle density used in the inversion algorithm on prior studies of PM chemical composition in the study area.^25,28 A constant particle density (1.8 g cm⁻³) was used for this study. An uncertainty of ±20% was adopted from Khlystov et al.¹⁰ and was incorporated into the daily number-derived mass concentration (Fig. 5). The estimated uncertainty is due to both the changes in aerosol composition (density change) as well as the aerosol shape.¹⁰⁵ For this study, the majority of the sampling days have number-derived mass with ±20% uncertainty. The use of constant particle density resulted in an under- and over-estimation of the daily number-derived mass concentration as particle density varies day-to-day.^7,104 For individual sites, on average, the number-derived mass overestimated the gravimetric mass at KAT RS (slope = 1.11, r² = 0.91) and MO UB (slope = 1.17, r² = 0.84), while underestimating gravimetric mass at TAFT RS (slope = 0.97, r² = 0.58). Combining samples from all sites, the linear regression analysis revealed a strong correlation between the number-derived mass and gravimetric mass concentration (slope = 1.06, r² = 0.81) (Fig. 5b). This value exhibits excellent agreement with values obtained from other mass closure studies.^59,104 On average, the estimated mass from the combined MPSS + APSS was 6% higher than the gravimetric mass.

Fig. 5 Same as Fig. 4 except for comparing gravimetric mass versus number-derived mass for (a–c) different assumed particle densities.

To determine the best value of the input density parameter, aside from 1.8 g cm⁻³, we also explored two other particle density values (1.6 g cm⁻³ and 2.0 g cm⁻³) in fitting the MPSS and APSS PNSDs. The use of 1.6 g cm⁻³ particle density improved the mass closure at KAT RS and MO UB, while the use of 2.0 g cm⁻³ improved the closure at TAFT RS (Fig. 5a and c). This result suggests that a particle density of 2.0 g cm⁻³ may be more appropriate to use at TAFT RS due to larger contribution of EC at this site compared to KAT RS and MO UB where OM was more dominant.

4 Conclusions

In this study, we comprehensively characterize the chemical composition, as well as the physical properties of PM_2.5 obtained from the three different sampling sites in Metro Manila. Our results showed that the major components in PM_2.5 mass at MO UB, KAT RS, and TAFT RS are organic matter (53.3, 51.9, 41.7%), elemental carbon (18.7, 35.8, 47.9%), secondary inorganic aerosols (14.4, 9.5, 9.8%), crustal matter (5.9, 5.6, 3.9%), and trace elements (1.3, 1.2, 0.7%), respectively.

The average OC/EC ratios at the roadside sites suggest freshly emitted aerosols (soot) from traffic sources, while the ratio exceeding 2 at the urban mixed site points to more aged aerosols associated with secondary organic carbon (SOC) formation. Consistently, the estimated SOC contribution was highest at MO UB (52%), followed by KAT RS (38%) and TAFT RS (28%), reinforcing the influence of secondary formation processes at the urban mixed site compared to the traffic-dominated roadside sites.

The PNCs measured at the roadside sites (8.1 × 10⁴ – 1 × 10⁵ cm⁻³) are at least two times higher than at the urban mixed site (3.2 × 10⁴ cm⁻³). The ultrafine particles make up the majority of the total particle number concentration (89–95%); however, they only accounted for 7–18% of the number-derived PM_2.5 mass. Unlike UFPs, particles in the accumulation mode contributes as much as 61–67% of the total PM_2.5 mass.

Results of mass closure reveal that the chemically-derived mass agreed well with the gravimetric mass (slope = 1.02; r² = 0.94). The identified chemical components accounted for 95% at the urban mixed site and 104% at both the roadside sites. The over- and under-estimation was largely attributed to the use of conversion factors, especially the OC-to-OM conversion factor. The number-derived PM_2.5 mass concentration also correlated well with gravimetric mass (slope = 1.06; r² = 0.81). The over- and under-estimation for this method was possibly due to measurement uncertainties and assumptions, especially the use of assumed particle density in the inversion routine. Overall, mass closure was achieved, and our method can be used to quantify the PM mass concentration using the continuous PNSD measurement.

The high fraction of carbonaceous and combustion particles makes it pivotal to control the corresponding sources if PM pollution is to be significantly reduced in the Metro Manila area. Our results also suggest investigating not only the mass concentration but also the number concentration as the latter is also relevant for epidemiological studies and studies of cloud microphysics that are concerned more with number concentrations of aerosol particles and cloud droplets. Greater emphasis on measuring the chemical and number concentrations of UFPs, as well as assessing the health risks associated with UFP-bound toxic carbon and trace elements,^106,107 should be prioritized in future studies. This study provides an incremental and valuable contribution to the understanding of the atmospheric composition of an emerging megacity in Southeast Asia and serves as an important input to the development of air pollution mitigation strategies, as well as for future epidemiological studies, which are still lacking in this region.

Data availability

The datasets used in this manuscript are accessible at https://doi.org/10.5281/zenodo.14913715. HYSPLIT data are available through the NOAA READY website (http://www.ready.noaa.gov).

Author contributions

GB and GC-J conducted the data analysis and drafted the original manuscript. All other authors contributed to the campaign design, funding acquisition, measurement phase, and the discussion, review, and editing of the manuscript. All authors have approved the final version of the manuscript.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This research was supported by the Researchers for Clean Air, Inc., and the Leibniz-Institut für Troposphärenforschung e.V. (TROPOS). This research was also partly funded by the German Federal Ministry of Education and Research in the framework of TAME-BC (project number 01LE1903A). Funding for the ion chromatography (IC) analysis was provided by the ADMU Research Council Grant No. 16-13. We would like to acknowledge the DOST-PNRI for allowing us to use their IC system for the water-soluble ion analysis. G. Betito and G. Catipay-Jamero acknowledge support from the DOST-Accelerated Science and Technology Human Resource Development Program (ASTHRDP) scholarship. A. Sorooshian was supported by NASA grant 80NSSC18K0148 in support of CAMP²Ex. The authors acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (https://www.ready.noaa.gov/) used in this publication. We thank Connor Stahl for providing feedback on the initial draft of this paper. Special thank you to Jackie Ibañez who participated in PM_2.5 measurements throughout the campaign.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5ea00028a

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