exploring differences and correlation between thermal ......joseph michael d. racho, gloria r....

Philippine Journal of Science149 (1): 133-144, March 2020ISSN 0031 - 7683Date Received: 17 Jun 2019

Exploring Differences and Correlation Between Thermal-optical Transmittance Elemental

Carbon (EC) and Reflectometer Black Carbon (BC) from an Urban and a Rural Site in the Philippines

Jeff Darren G. Valdez*, Angel T. Bautista VII, Preciosa Corazon B. Pabroa, Joseph Michael D. Racho, Gloria R. Jimenez, and Flora L. Santos

Department of Science and Technology – Philippine Nuclear Research Institute (DOST-PNRI) Commonwealth Ave., Diliman, Quezon City, Philippines

Keywords: BC vs. EC, black carbon, elemental carbon, reflectometry, thermal-optical analysis

*Corresponding Author: [email protected]

INTRODUCTIONBC and EC are types of light-absorbing atmospheric particulate matter that pose significant effects on visibility, climate, and health (Segersson et al. 2017, Anenberg et al. 2012, Li et al. 2011, Baron et al. 2009, Ramanathan

The Philippines has among the highest black carbon (BC) and elemental carbon (EC) concentrations in atmospheric particulate matter in Asia. Despite numerous studies, there is no single, generally accepted measure or method of analysis for this group of atmospheric particulates. Given the high concentrations of BC and EC in the country, the Philippines offers an interesting case to study BC and EC. To gain a better understanding of the similarities and differences of these quantities, BC and EC in an urban (Valenzuela City) and a rural site (Angat, Bulacan) were compared from September 2011 to August 2012. BC was measured using reflectometry, while EC was measured using the thermal-optical (TO) transmittance analysis. Mean concentrations of EC and BC were 5.54 ± 2.1 μg/cm3 and 6.54 ± 2.5 μg/cm3 in Valenzuela City and 1.82 ± 0.7 μg/cm3 and 1.28 ± 0.7 μg/cm3 in Angat, Bulacan. Cluster analysis showed that in both urban and rural sites, EC1 had the highest correlation to BC among the three EC fractions. Additionally, EC2 and EC3 were poorly correlated with BC but were highly correlated with each other. Similarly, conditional probability function (CPF) analysis revealed that BC and EC1 originated from nearly the same directions, while EC2 and EC3 do not. These results suggest that BC and EC1 are more related to each other than EC2 and EC3, providing insights into the similarities and differences between BC and EC. To maximize the comparability of BC and EC, optimal values of ε – used in reflectometry – were determined for the urban and rural sites. Valenzuela and Angat had optimal ε values of 6.31 m2g–1 and 1.89x 10–9m2g–1, respectively. The optimal ε value in Valenzuela is close to the generally used ε value, 7.0 m2g–1, while the optimal ε value in Angat is arguably too small and needs further assessment.

and Carmichael 2008, Ramanathan et al. 2001). These particulates were reported to constitute about 40% to as high as 96% of the total aerosol mass in a variety of sites (Kecorius et al. 2017, Hopke et al. 2008, Oanh et al. 2006, Japar et al. 1986). These particulates were also considered to be the second strongest contributor to global warming next to carbon dioxide (Baron et al. 2009, Ramanathan and Carmichael 2008). Furthermore, mitigation of

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these particulates is estimated to prevent 0.6–5 million premature deaths from health effects by 2030 (Anenberg et al. 2012) and may reduce climate effects from alteration of radiation balance in the atmosphere (Oanh et al. 2006, Baltensperger et al. 1998).

Despite numerous studies, there is no single generally accepted measurement or method of analysis for this group of particulates (Sharma et al. 2017, ICCT 2016, Ahmed et al. 2009, Reisinger et al. 2008, Watson et al. 2005). BC and EC, though often well correlated, are not interchangeable due to their different properties and methods of measurement (Hopke et al. 2012, Jeong et al. 2004). BC can be determined by using optical techniques such as aethalometer and reflectometer, which measure and translate light absorbance or reflectance of the sample to BC concentration using an estimated value of ε (Taha et al. 2007). EC, on the other hand, is determined by the TO method, which directly measures carbon concentrations by varying temperature and atmosphere through the course of analysis (Karanasiou et al. 2015, Bautista et al. 2014, Chow et al. 2007, Birch and Cary 1996). These two methods are accepted and has been the subject of various studies despite their differences, and are being used in several air pollution researches (Bautista et al. 2014, 2015; Pabroa et al. 2011; Hopke et al. 2012; Hopke et al. 2008; Baltensperger et al. 1998; Hopke et al. 1997).

There are several studies showing the relationship between BC and EC in different settings. Hopke et al. (2012) analyzed BC and EC concentrations in nine countries across Asia and South Pacific and found large differences in BC vs. EC correlation and slopes in both urban and suburban locations with different seasons. Hopke et al. (2004) reported variability in absorption coefficients of light-absorbing particulates from differing origins when BC vs. EC correlation and slope dramatically changed during one forest fire incident at Rochester, NY and Philadelphia, PA in the USA. Reisinger et al. (2008) suggested that another cause of BC and EC discrepancy is due to the presence of humic-like compounds or “brown carbon,” which are non-EC substances that contribute significantly to total light absorption of the sample. In addition, Long et al. (2013) introduced “carbon black”, which resembles a nearly pure EC consisting of fine BC. The effects of these factors on BC and EC in ambient atmospheric particulate samples, however, are still

ambiguous and demand for more in-depth investigation and understanding of matter. Location, sources, weather, and all other factors constitute complexity in these differences. Despite these studies, these particles are still unregulated (Alas et al. 2018). Furthermore, to the best of our knowledge, BC comparisons with different EC (and OC) in the Philippine settings have never been made.

The Philippines has among the highest BC and EC concentrations in atmospheric particulate matter in Asia (Bautista et al. 2014, Hopke et al. 2008). Thus, the country offers a unique and interesting case to study these types of atmospheric particulates. In this regard, this study compares BC and EC (and OC) to better understand the nature of these measures and explore differences between them, in such settings wherein BC and EC are present in very high concentrations. Derivation of the optimal value of ε was done to maximize agreement between the resulting BC and EC. Correlation-based cluster analysis and CPF analysis were employed in an attempt to determine possible relationships between specific carbon fractions of EC (and OC) to BC.

MATERIALS AND METHODSSimultaneous ambient 24-h air particulate samples, one each for OC and EC, and BC analysis were collected from September 2011 to August 2012. Sample collection was performed twice a week during Wednesdays and Sundays in an urban (Valenzuela City, Metro Manila) and a rural site (Angat, Bulacan) in the Philippines (Figure 1). Detailed source apportionment, OC and EC characterization of these sites are reported elsewhere (Bautista et al. 2014, Pabroa et al. 2011). Samples analyzed were in PM2.5 range and were collected using Gent dichotomous samplers (Hopke et al. 1997) with 0.4 μm Nucleopore and Pallflex quartz-fiber (pre-baked at 900 ˚C for 3 hr) filters for BC and OC-EC, respectively. Nucleopore coarse filters (8 μm) were placed before actual sampling filters to remove particulates in the PM2.5–10 range.

Reflectometer BC concentration was measured using a smoke stain reflectometer (model M34D, Diffusion Systems, Ltd) using an average particle ε value of 7 m2g–1. This value of ε was obtained after detailed experiments

Table 1. Minimum, Maximum and Average EC and BC concentrations from the air particulate samples in from Valenzuela and Angat and their ratios.

Location EC (μg/cm3) BC (μg/cm3) BC/EC

Min Max Mean S.Dev Min Max Mean S.Dev Min Max Mean S.Dev

Valenzuela 1.6 10.2 5.5 2.1 1.7 12.6 6.6 2.5 1.1 1.2 1.2 0.4

Angat 0.8 3.4 1.8 0.7 0.4 3.4 1.3 0.7 0.2 1.0 0.8 0.5

Lachica et al.: Trends of Rabies and Impact of IRCP in Davao City

Philippine Journal of ScienceVol. 149 No. 1, March 2019

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analyzing series of carbon samples on various filters (Box et al. 2007). Moreover, this value is widely accepted and used in several air research studies (Nordmann et al. 2014, Bautista et al. 2014, Pabroa et al. 2011). Calibration of this value has been performed both on Nuclepore and Teflon filter media using different types of test BC to determine the value of ε or mass absorption efficiency. Translation to BC concentration was completed using the following formula used by Box et al. (2007) with some modifications:

BC (ng/m3) = [BCsample (ng/cm2) – BCblank (ng/cm2)]*[(12.57 cm2 / volume of air (m3)] (1)

BCsample (ng/cm2) = (1/2ε)ln(Ro/R)*1000 (2)

Ro is the intensity of light reflected from the instrument’s reference target where it is assumed to be 100%, and R is the intensity of light reflected from the sample filter. The experimented exposed area of the sample filter spanned 12.57 cm2.

Optical EC (ECo) and thermal EC (ECt) were determined by TO analysis using Sunset Laboratory OC-EC Aerosol Analyzer. Protocol choice, calculations, and corrections were based on previous site-specific method optimizations reported in Bautista et al. (2014) and are as follows: Analysis followed Interagency Monitoring of PROtected Visual Environments (IMPROVE_A) temperature protocol (Chow et al. 2007) with transmittance and laser correction. Results are given in four OC fractions (OC1, OC2, OC3, and OC4 evolving during pure helium phase at 140 °C, 280 °C, 480 °C, and 580 °C, respectively); three EC fractions (EC1, EC2, and EC3 evolving during 2% oxygen / 98% helium phase at 580 °C, 740 °C, and 840 °C); and a pyrolyzed carbon fraction (portion of OC that underwent pyrolysis and was transformed to EC during analysis; evolves simultaneously with EC1). The following corrections are then applied to respective fractions: Corrected EC1 = EC1 – pyrolyzed carbon; Corrected OC4 (subtracted) and EC2

(added) contain 2.3% and 4.9% of total carbon (sum of all fractions) for Valenzuela and Angat, respectively, as it was found that some OC4 are erroneously counted as EC2 in IMPROVE_A. Taking these corrections into account, OC = OC1 + OC2 + OC3 + OC4 + pyrolyzed carbon and EC = EC1 + EC2 + EC3.

Cluster analysis (classical) was performed using Paleontological Statistics Software (PAST; Hammer et al. 2001) with paired group algorithm and correlation similarity index. The technique follows a Hierarchical Clustering procedure in which data points are arranged and grouped according to how, where, and/or when it evolved from a particular system. By this technique, we can approximate how BC and EC or its fractions are interrelated to each other. CPF was done based on the method described by Kim and Hopke (2004). It is a modeling technique used to identify or estimate sources or directions using wind speed as the third variable. It is defined by Equation 3, where mΔθ is the number of occurrences from wind sector Δθ, that exceeded the threshold criterion and nΔθ is the total number of data from the same wind sector Δθ:

CPFΔθ = mΔθ/ nΔθ (3)

The number of concurrent BC and EC samples (used in comparison analyses i.e., scatter comparison, cluster, and CPF analyses) were 76 and 32 for Valenzuela and Angat, respectively. Measurement errors are in the range of 8.6–18.6%, 6.0–7.8%, and 6.5–10% for BC, OC, and EC, respectively.

RESULTSEC and BC values in urban Valenzuela and rural Angat from September 2011 to August 2012 were studied to determine their relationship and their variations. The

Figure 1. Description (1a) and zoomed-in view (1b) of the sampling sites. Urban Valenzuela sampling is located at 14°40’42.65”N, 120°58’36”E while Rural Angat is located at 14°43’48.0”N, 121°02’11.3”E (1b).



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results were obtained based on a 24-h sampling scheme with a 1-h on / 1-h off settings to prevent overloading of filter the samples. Figure 2 shows ECt, ECo, and BC in Valenzuela and Angat collected throughout the sampling duration. Overall, average concentrations of EC and BC were found to be 5.5 ± 2.1 ug/cm3 and 6.6 ± 2.5 μg/cm3 for Valenzuela; and 1.8 ± 0.7 μg/cm3 and 1.3 ± 0.7 μg/cm3 for Angat. Moreover, the Valenzuela EC and BC ranges exceeded those of Angat by 2–5 times (Table 1). From these values, we can infer that Valenzuela is more polluted than Angat.

The weekday and weekend ECt, ECo, and BC comparison in both sites are shown in Figure 3. In Valenzuela, weekday EC exceeded weekend EC from September 2011 to February 2012 and from June 2012 onwards, while the opposite was observed from March to May 2012 (Figure 3a). Valenzuela BC and ECo time series exhibited a similar pattern between weekdays and weekends (Figures 3c and 3e). The correlation between EC and BC values from Valenzuela and Angat is shown in Figures 4 and 5. The number of concurrent BC and EC samples were 76 and 32 for Valenzuela and Angat, respectively. As presented in Figure 4, ECt in both sites were poorly correlated with BC, with low r2 values of 0.34 and 0.03 for Valenzuela and Angat, respectively. This clearly indicates disagreement between these data points. Figure 5 shows the correlation between ECt and ECo, which were both measured using Sunset Laboratory OC-EC Aerosol Analyzer. Valenzuela showed a relatively fair correlation, r2 = 0.76, while Angat still showed poor correlation, r2 = 0.24. Three outliers were observed in Valenzuela and one in Angat.

Variations in the EC and BC correlation can be attributed to a number of factors. Based on the results, the ε value and the difference in the methods of analyses were the most critical factors affecting EC vs. BC relationship in the Philippine air particulate samples. The overall effects of these factors resulted in low r2 values and varying slopes in EC vs. BC regression analysis. Correlation-based cluster analysis was performed to determine which fraction/s of OC and EC correlates to BC. In rural Angat (Figure 6a), cluster analysis shows three distinct groupings: EC2 and EC3, EC1 and BC, and OC fractions; EC2 and EC3 are likewise separately clustered in urban Valenzuela (Figure 6b).

DISCUSSIONSeveral factors can be attributed to the overall emissions observed in these two sites. The spread of the data points shows how EC and BC are varying with respect to each site. Urban Valenzuela is one of the 16 cities of Metropolitan Manila and takes about 7.4% of the total

Metro Manila land area (fourth-largest in Metro Manila) with a population of about 620,000 (sixth-highest in Metro Manila) (PSA 2016). Valenzuela is home to several industrial factories such as recycling plants, smelting plants, battery-reprocessing plants, and even a commercial brewery plant (Pabroa et al. 2011). On the other hand, rural Angat is one of 21 municipalities of Bulacan (Bulacan province has 21 municipalities and three component cities) and has only 60,000 in population size (21st-highest in Bulacan; PSA 2016). It has a land area of about 74 km2 (ninth-largest in Bulacan) with fewer industrial activities as compared to Valenzuela. Furthermore, the Valenzuela air sampling site is located near the busy McArthur Highway, which sees massive volume of traffic from as early as 06:00 A.M. to as late as 10:00 P.M. Moreover, McArthur Highway is connected to the highly traffic-dense Epifanio de los Santos Avenue, which is the main road of the capital city Manila. These major factors likely contributed to the high EC and BC values in urban Valenzuela City. It has also been identified that mobile emission has been the primary contributor to air pollution in Metro Manila since 2008 (EMB 2015).

Statistical analysis was done to determine if there are significant differences in these values. Student’s T-test in Valenzuela BC and ECt showed Pstat < Pcrit – indicating that, in general, ECt is significantly different from BC. In addition, Valenzuela weekday-weekend distributions gave Pstat > Pcrit, indicating that the differences between ECo, ECt, and BC are indistinguishable (Figures 3a, 3c, and 3e). Similarly, Angat weekday-weekend ECt, ECo, and BC showed no statistical difference yielding Pstat > Pcrit, (Figures 3b, 3e, and 3f). However, comparing Valenzuela and Angat EC as well as their BC yielded Pstat < Pcrit, indicating a significant statistical difference between their mean values. This difference may imply that both urban Valenzuela and rural Angat, Bulacan have generally different EC and BC sources (i.e., industrialization, traffic-related, burning, etc.).

From the abovementioned results, it is likely that most emissions came from factories and traffic emissions since these factors were also responsible for the high PM10, PM2.5 as well as Pb levels in Valenzuela (Pabroa et al. 2011). This may be true for Valenzuela since the city is yet to regulate the number and types of vehicle that passes through the area until the start of the Manila truck ban scheme on 2014 aside from some factories along the area that operates 24 hours per day, seven days per week. As in the case of Angat, possible sources include stationary sources and household sources (e.g., biomass burning, solid waste burning, cooking activities, tire pyrolysis, etc).

Typically, combustion sources in the Philippines are less efficient, resulting in EC-dominated emissions (Bautista et al. 2014). In Valenzuela and Angat, carbonaceous



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Figure 2. Optical EC, EC, and BC distribution in Valenzuela (urban) and Angat (rural) from September 2011 to August 2012.

Figure 3. Weekday and weekend comparison from September 2011 to August 2012: Valenzuela EC (3A), Angat EC (3B), Valenzuela BC (3C), Angat BC (3D), Valenzuela optical EC (3E), and Angat BC (3F).



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Figure 4. BC (4A) and thermal EC (ECt) (4B) regression analysis in Valenzuela and Angat. The number of concurrent samples was 76 and 32 for Valenzuela and Angat, respectively.

Figure 5. Optical EC (ECo) and thermal EC (ECt) regression analysis in Valenzuela (5A) and Angat (5B). The number of concurrent samples was 82 and 35 for Valenzuela and Angat, respectively.

Figure 6. Mean and standard deviation plot of the distributions of BC across the Asian Region (ASFID Database; ANSTO, with permission). The horizontal line in the upper portion of the column represents the mean concentration. The line extending upward of the column represents the standard deviation.



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particulates account for 38.9% and 19.7% of the total PM2.5 mass, respectively. Moreover, Valenzuela has an EC level of 6.63 μg/cm3 (17.7% of total PM2.5 mass), which is higher than the Angat EC level of 2.29 μg/cm3 (7.0% of total PM2.5 mass). Remarkably, Philippine BC values are among the highest in the Asian Region (Bautista et al. 2014, Hopke et al. 2008, Oanh et al. 2006) and remain unregulated (Alas et al. 2018). It has been studied that EC and BC are linked to various health issues (Segersson et al. 2017, Olstrup et al. 2016, Rappazzo et al. 2015, WHO 2012, Pabroa et al. 2011, Ramanathan et al. 2008, Hopke et al. 2008). A better picture of the situation in Valenzuela is given by source apportionment, which identified the primary sources of PM2.5 as vehicular/biomass (53.1%), heavy oil burning (26.4%), and industrial (14.2%) activities constituting a total of 93.7% of PM2.5 mass (Pabroa et al. 2011). In addition, BC emissions in the Philippines – particularly in Valenzuela and Ateneo de Manila University (Hopke et al. 2008) were among the highest in Asia and in some Pacific neighbors (Figure 6).

One reason causing poor correlation of ECo, ECt, and BC for both sites is more likely due to the difference in the methods used to measure these parameters. ECt and ECo were determined using the TO method, which considers both the chemical and optical properties of the samples. BC, on the other hand, was measured by reflectometry, a technique based only on light reflectance of filter samples. Sample optical property is translated to BC concentration using ε, which in this study was equal to 7 m2g–1. This deviates from the Beer-Lambert’s Law as several factors cause non-linearity of the light absorbance. Factors such as the nature of the substrate, amount of deposited sample, and the sample matrix composition cause variations in the absorptivity rate of the sample matrix (Fuller et al. 2017, Lack et al. 2014, Moosmuller et al. 2009, Virkkula et al. 2007). Consequently, reflectometry does not regard individual chemical identities, and it assumes that all particulate species have acceptably comparable or equal absorption coefficients (degrees of light absorbance). Regardless, the latter is widely accepted due to its ease of usage and practicality, providing a useful measure of the BC.

Both sites showed a better correlation for ECt vs. ECo than for ECt vs. BC. This improvement can be attributed to how ECt and ECO were measured. The apparent attenuation coefficient in ECo was estimated using a second-degree polynomial fit of the specific absorption cross-section obtained automatically by the OC-EC analyzer (Hopke et al. 2004). This way, the absorption coefficient is derived from actual samples, unlike the single value of ε used to translate BC concentration in reflectometry. However, in the case of rural Angat, the poor ECo vs. ECt correlation may be explained by the aerosol chemical composition

and/or the mixing state, which is different for Valenzuela and Angat. Furthermore, Bautista et al. (2014) reported the OC/EC ratio in Angat to be greater than that of Valenzuela (Angat: OC/EC = 1.78, Valenzuela: OC/EC = 1.20) – indicating that rural Angat to be more OC dominated than the urban Valenzuela. Rural areas, in general, are more affected by light-absorbing organic materials that came from biomass burning, wood burning, and other related activities (Chiappini et al. 2014).

BC determination by reflectometer significantly depends on the value of ε. It is evident that ε is critical in translating the optical absorption of particles to mass considering optical absorption varies depending on the size distributions, types of aerosol mixtures, and the amount of sample deposition per unit time (Hopke et al. 2004, Ballach et al. 2001, Babich et al. 2000, Hitzenberger et al. 1999, Petzold et al. 1997, Horvath 1993, Liousse et al. 1993). Using the Solver Tool (Find Value) in Microsoft Office Excel, we derived the value of ε that will give the best agreement between BC and ECt in Valenzuela and Angat. It shows that the optimized value of ε for urban Valenzuela is 6.31 m2g–1, which is slightly lower than ε used in this study. This, thus verify the use of ε = 7 m2g–1

(Box et al. 2007) to be a good approximate to observe BC in Valenzuela. Nonetheless, the correlation is still low, with r2 = 0.34. However, the optimal ε for rural Angat was ε = 1.89x 10–9 m2g–1, which is arguably too small and needs further reassessment. The value of ε obtained the optimization should not, however, be taken as a general value for translating urban or rural BC as this averaged all the parameters throughout the sampling collection, thus rendering ε to be a site-specific value. Further study must be conducted to determine and optimize the more suitable ε value in a particular area.

A study by Hopke et al. (2012) reported four out of nine sites giving a good EC vs. BC correlation, ranging from r2 = 0.82 to r2 = 0.93 using ε = 9.31 m2g–1. This indicates a good estimate of ε for the four sites. However, for the remaining five sites, there might be other sources contributing to the concentration of BC in the area. Valenzuela gave an ECt vs. BC relation of BC = 0.78*EC (r2 = 0.34), while Angat gave BC = 0.19*EC (r2 = 0.03). Hopke et al. (2012) suggest that a low correlation and slope may mean that there are at least two EC sources with different light absorptivity present in the area. Moreover, another factor to consider is the performance of the reflectometer as if it reaches its saturation point. When this happens, samples cannot be fully analyzed and thus may deviate from the true value. Hence, differences between EC and BC might arise if: (1) different EC species (i.e., EC1, EC2, and EC3 fractions) and/or sources have different absorption coefficients; (2) if there are non-EC particulates that significantly contribute to overall



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absorbance of the sample; and (3) if the reflectometer reaches its saturation point.

The groupings observed in the correlation-based cluster in rural Angat shows that BC concentration is more correlated to EC1. EC2 and EC3 are grouped in a separate cluster, indicating that these are independent of EC1 and BC, and are possibly from a different source (Figure 7a). Given that all three EC fractions are light-absorbing (based on TO thermograms, not shown) and that EC2 is present in almost equal concentrations to that of EC1 (Figure 8), this clustering suggests that EC1 and EC2 have different absorption coefficients, resulting to unequal correlations with BC. More specifically, EC1 and BC may have more similar absorption coefficients and are thus more correlated with each other. On the other hand, EC2 and EC3 may have significantly different – possibly lower – absorption coefficients compared to that of EC1 and BC; as a result, EC2 and EC3 have lower correlations with BC. This explanation, if correct, should lead to the observed discrepancy between total EC and BC concentrations and may explain why EC (sum of EC1, EC2, and EC3) is generally higher than BC in Angat. Moreover, cluster analysis shows that all OC fractions are separately grouped from EC and BC, which likewise suggest that these come from a different source – most likely natural or secondary formation – in agreement with the findings of Bautista et al. (2014).

EC2 and EC3 are likewise separately clustered in urban Valenzuela (Figure 7b). In contrast with Angat, however, this will have a smaller effect on the total EC vs. BC correlation since Valenzuela is dominated by EC1, which is the supposed primary contributor to BC. Even so, this correlation value is lower than expected, considering that EC1 is dominant. Moreover, as noted earlier, BC in Valenzuela is generally higher than EC – unlike in Angat. Discounting systematic errors and with the assumption that EC1 in Valenzuela and Angat have comparable absorption coefficients, the remaining significant possibility is that non-EC particulates contribute to the overall light absorbance of the sample. These can be non-carbonaceous particulates (neither OC nor EC) and/or the likewise dominant OC2 and OC3 fractions (Figure 8), whose concentrations increase with sample loading. It is, however, difficult to ascertain OC2 and OC3 contribution to light absorbance since: (1) in TO thermograms, evolution of OC2 and OC3 coincide with formation of pyrolyzed carbon resulting to net decrease in light absorption and making it difficult to see changes in filter light absorbance as OC2 and OC3 evolve; and (2) in cluster analysis (Figure 7b), OC2, OC3, OC4, and EC1 are grouped together in this case, making it unable to determine fraction contributions to BC. From this grouping, it shows that – in contrast to Angat – OC

fractions in Valenzuela (together with EC1 and BC) mainly come from primary combustion sources. These results are explored further through CPF analysis.

CPF is a tool that uses wind speed and direction data to determine probable direction/s from which BC and EC are coming from. It estimates the likelihood that these carbonaceous particulates, from a given direction, will exceed a predetermined threshold criterion, which was set at the upper 25th percentile for this study. CPF results for Valenzuela BC and EC show BC and EC have common major sources located at 30° (NE), 150° (SE), and 210° (NW) and a sole EC source at 300–330° (NW) from the site (Figure 9). BC CPF yielded directions similar to OC and EC results pointing to sources in the 30° (NE), 150° (SE), 210° (SW), and 300° (NW) direction (Figure 9a–c). These are further verified by CPF analysis of individual fractions OC3, OC4, EC1, EC2, and EC3 (Figure 9d–e), while EC1 yielded similar results to Figures 9b and 9c. EC2 and EC3, meanwhile, give entirely different trends pointing towards 150° (SE), 210° (SW), 270° (NW), and 330° (NW) for EC2 and 60–90° (NE), 240–270° (SW), and 330–360° (NW) for EC3. These results strongly suggest that BC and EC1 in Valenzuela generally may come from the same sources and are related to each other, while EC2 and EC3 have entirely different origins.

CONCLUSIONThe Philippines is among the countries in Asia that have the highest EC and BC concentrations. Though often used interchangeably, EC and BC are different entities due to the methods these were measured. EC and BC concentrations were measured from an urban site (Valenzuela) and a rural site (Angat) in the Philippines from September 2011 to August 2012. Results show that the average concentrations of these values in Valenzuela were 3–5 times higher than those in Angat, indicating the effect of industrialization on the air quality of the area. Regression analysis gave EC and BC correlation to be r2 = 0.34 and r2 = 0.03 for Valenzuela and Angat, respectively – indicating disagreement between these values.

In summary, variations on EC and BC were attributed to the overall effects of their methods of analyses and of the ε value. EC was determined by the TO process, which considers both chemical and optical properties while BC was determined by a reflectometer and translated to BC concentration using a single value of ε, which only accounts for optical properties on the assumption that all samples have the same absorption properties. Derivation of the optimal value of ε using the Solver Tool (Find Value) in Excel showed ε = 6.31 m2g–1 in Valenzuela, relatively comparable to ε = 7m2g–1 by Box et al. (2007) used in



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Figure 9. Results of CPF analysis for Valenzuela: BC (a), OC (b), EC (c), and specific fractions OC3 (d), OC4 (e), EC1 (f), EC2 (g), and EC3 (h).

Figure 8. Box plot summary of the individual OC and EC fractions (in µg/m3) for Valenzuela (V) and Angat (A) (Bautista et al. 2014, with permission). The median by the horizontal bar; middle 50% of the data by the hatched boxes; the largest and smallest observations by the whiskers; outliers – 1.5 times or 3 times the interquartile range above or below the limits of the box – by square symbol and square symbol with plus sign, respectively.

Figure 7. Correlation-based cluster analysis of rural Angat (A) and urban Valenzuela (B). The graph shows which variables are clustered according to similarity and the horizontal line connecting the grouped variables indicates the correlation.



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this study. For Angat, the optimized value of ε was found to be 1.89x 10–9 m2g–1, which is arguably too small and needs to be further reassessed.

Cluster analysis showed that different EC fractions have different influences on BC and, thus, unequal contribution to total BC for Valenzuela. CPF analysis revealed both EC and BC sources came from 30° (NE), 150° (SE), and 210° (SW) – with sole BC and EC at 300° (NW) and 300–330° (NW), respectively. CPF analysis was not performed for the Angat site as there were fewer sampling points, and appropriate wind direction and wind speed data were not available. More information is given by source apportionment (Pabroa et al. 2011) at the Valenzuela site, which identified primary sources of PM2.5.

NOTES ON APPENDICESPermission to reprint Figures 7 and 8 with some modifications were granted by the respective authors.

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