Scholarly article on topic 'Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines'

Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines Academic research paper on "Earth and related environmental sciences"

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Abstract of research paper on Earth and related environmental sciences, author of scientific article — Simonas Kecorius, Leizel Madueño, Edgar Vallar, Honey Alas, Grace Betito, et al.

Abstract Ultrafine soot particles (black carbon, BC) in urban environments are related to adverse respiratory and cardiovascular effects, increased cases of asthma and premature deaths. These problems are especially pronounced in developing megacities in South-East Asia, Latin America, and Africa, where unsustainable urbanization ant outdated environmental protection legislation resulted in severe degradation of urban air quality in terms of black carbon emission. Since ultrafine soot particles do often not lead to enhanced PM10 and PM2.5 mass concentration, the risks related to ultrafine particle pollution may therefore be significantly underestimated compared to the contribution of secondary aerosol constituents. To increase the awareness of the potential toxicological relevant problems of ultrafine black carbon particles, we conducted a case study in Metro Manila, the capital of the Philippines. Here, we present a part of the results from a detailed field campaign, called Manila Aerosol Characterization Experiment (MACE, 2015). Measurements took place from May to June 2015 with the focus on the state of mixing of aerosol particles. The results were alarming, showing the abundance of externally mixed refractory particles (soot proxy) at street site with a maximum daily number concentration of approximately 15000 #/cm3. That is up to 10 times higher than in cities of Western countries. We also found that the soot particle mass contributed from 55 to 75% of total street site PM2.5. The retrieved refractory particle number size distribution appeared to be a superposition of 2 ultrafine modes at 20 and 80 nm with a corresponding contribution to the total refractory particle number of 45 and 55%, respectively. The particles in the 20 nm mode were most likely ash from metallic additives in lubricating oil, tiny carbonaceous particles and/or nucleated and oxidized organic polymers, while bigger ones (80 nm) were soot agglomerates. To the best of the authors' knowledge, no other studies reported such high number concentration of ultrafine refractory particles under ambient conditions. Inverse modeling of emission factors of refractory particle number size distributions revealed that diesel-fed public utility Jeepneys, commonly used for public transportation, are responsible for 94% of total roadside emitted refractory particle mass. The observed results showed that the majority of urban pollution in Metro Manila is dominated by carbonaceous aerosol. This suggests that PM10 or PM2.5 metrics do not fully describe possible health related effects in this kind of urban environments. Extremely high concentrations of ultrafine particles have been and will continue to induce adverse health related effects, because of their potential toxicity. We imply that in megacities, where the major fraction of particulates originates from the transport sector, PM10 or PM2.5 mass concentration should be complemented by legislative measurements of equivalent black carbon mass concentration.

Academic research paper on topic "Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines"

Accepted Manuscript

Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines

Simonas Kecorius, Leizel Madueno, Edgar Vallar, Honey Alas, Grace Betito, Wolfram Birmili, Maria Obiminda Cambaliza, Grethyl Catipay, Mylene Gonzaga-Cayetano, Maria Cecilia Galvez, Genie Lorenzo, Thomas Müller, James B. Simpas, Everlyn Gayle Tamayo, Alfred Wiedensohler

PII: S1352-2310(17)30636-2

DOI: 10.1016/j.atmosenv.2017.09.037

Reference: AEA 15575

To appear in: Atmospheric Environment

Received Date: 20 April 2017 Revised Date: 7 September 2017 Accepted Date: 23 September 2017

Please cite this article as: Kecorius, S., Madueno, L., Vallar, E., Alas, H., Betito, G., Birmili, W., Cambaliza, M.O., Catipay, G., Gonzaga-Cayetano, M., Galvez, M.C., Lorenzo, G., Müller, T., Simpas, J.B., Tamayo, E.G., Wiedensohler, A., Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: Case study of Metro Manila, Philippines, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.09.037.

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1000 10000 100000 ■l/dlogDp (#/cm3)

Taft Ave. (Road site)

Volume equivalent diameter (nm)

Aerosol particle mixing state, refractory particle number size distributions and emission factors in a polluted urban environment: case study of Metro Manila, Philippines

Simonas Kecorius1*, Leizel Madueno4, Edgar Vallar2, Honey Alas1'4'6, Grace Betito4'6, Wolfram Birmili1,5, Maria Obiminda Cambaliza4,6, Grethyl Catipay4,6, Mylene Gonzaga-Cayetano3, Maria Cecilia Galvez2, Genie Lorenzo6, Thomas Müller1, James B. Simpas4,6,

Everlyn Gayle Tamayo , and Alfred Wiedensohler

1 Leibniz-Institute for Tropospheric Research, Permoserstr. 15, Leipzig, Germany ARCHERS, CENSER, De La Salle University, Manila, Philippines 3Institute of Environmental Science and Meteorology, University of the Philippines Diliman 4Department of Physics, Ateneo de Manila University, Quezon City, Philippines 5Federal Environment Agency, Berlin, Germany 6Manila Observatory, Quezon City, Philippines

Abstract

Ultrafine soot particles (black carbon, BC) in urban environments are related to adverse respiratory and cardiovascular effects, increased cases of asthma and premature deaths. These problems are especially pronounced in developing megacities in South-East Asia, Latin America, and Africa, where unsustainable urbanization ant outdated environmental protection legislation resulted in severe degradation of urban air quality in terms of black carbon emission. Since ultrafine soot particles do often not lead to enhanced PM10 and PM25 mass concentration, the risks related to ultrafine particle pollution may therefore be significantly underestimated compared to the contribution of secondary aerosol constituents. To increase the awareness of the potential toxicological relevant problems of ultrafine black carbon particles, we conducted a case study in Metro Manila, the capital of the Philippines.

Here, we present a part of the results from a detailed field campaign, called Manila Aerosol Characterization Experiment (MACE 2015). Measurements took place from May to June 2015 with the focus on the state of mixing of aerosol particles. The results were alarming, showing the abundance of externally mixed refractory particles (soot proxy) at street site with a maximum daily number concentration of approximately 15,000 #/cm . That is up to 10 times higher than in cities of Western countries. We also found that the soot particle mass contributed from 55 to 75% of total street site PM25. The retrieved refractory particle number size distribution appeared to be a superposition of 2 ultrafine modes at 20 and 80 nm with a corresponding contribution to the total refractory particle number of 45 and 55%, respectively. The particles in the 20 nm mode were most likely ash from metallic additives in lubricating oil, tiny carbonaceous particles and/or nucleated and oxidized organic polymers, while bigger ones (80 nm) were soot agglomerates. To the best of the authors' knowledge, no other studies reported such high number concentration of ultrafine refractory particles under ambient conditions. Inverse modeling of emission factors of refractory particle number size distributions revealed that diesel-fed public utility Jeepneys, commonly used for public transportation, are responsible for 94% of total roadside emitted refractory particle mass.

The observed results showed that the majority of urban pollution in Metro Manila is dominated by carbonaceous aerosol. This suggests that PM10 or PM25 metrics do not fully describe possible health related effects in this kind of urban environments. Extremely high concentrations of ultrafine particles have been and will continue to induce adverse health related effects, because of their potential toxicity. We imply that in megacities, where the major fraction of particulates originates from the transport sector, PM10 or PM25 mass

concentration should be complemented by legislative measurements of equivalent black carbon mass concentration.

1. Introduction

In year 2050, two thirds of the World's population that is more than 6.5 billion people, will be living in towns and cities (Floater et al., 2012). Asia is predicted to accommodate over half of the planet's population in numerous megacities with more than 10 million inhabitants. Previous studies already highlighted the adverse health risks due to urbanization in New Delhi (India), Beijing (China), Dhaka (Bangladesh) and other cities (e.g. Gurjar et al., 2010 and references therein). As the megacities grow, ranking of the urban areas in terms of their population size, socio-economic, infrastructural and environment-related parameters become increasingly important. It is believed that these rankings could help to develop the mitigation strategies, which are expected to improve the sustainability of megacities worldwide (Gurjar et al., 2008). Environment-related parameters often included ambient concentrations of criteria pollutants, such as total suspended particulate matter (TSP), which became an important subject in air quality legislation that has been implemented globally in the past few decades. For example, the United States Environmental Protection Agency (US EPA) started to regulate the mass concentration of PMi0 in 1988, and PM25 in 1997 (particulate matter with aerodynamic diameter smaller than 10 p,m and 2.5 p,m, respectively) (Aggarwal et al., 2012). In European Union - New Air Quality Directive was enforced on 11 June 2008, which became strictest acts of legislation worldwide concerning PM10 emissions (Marco and Bo, 2013). To improve the living environment and to protect human health, the State Council of China has updated their old environmental legislation in 2012 including standards for never before controlled PM25 values (Zhang and Chao, 2015).

While TSP, PM10 and PM2.5 are the most commonly used metrics to address health effects (Pope and Dockery, 2006), there are several major reasons why particulate matter mass may need the complementary measurements in supporting the evidence of the air pollution-related health risks. Firstly, studies shown that exposure to high mass concentration of PM10 sea-salt does not result in any cardiovascular symptoms (Mills et al., 2008). This can be important when cities are located near sea/ocean coast where PM10 aerosol particle mass might be elevated due to high concentrations of airborne sea-salt. Secondly, re-suspended road dust was also shown to contribute to urban PM10 and PM25 concentrations (Abu-Allaban et al., 2003). While the PM metrics would suit well to regulate air quality in situations where the re-suspended coarse mode particles are dominant, the contribution of ultrafine soot particles to health-related effects may be then overlooked. This is because, being less than 100 nm in diameter, the soot particles constitute only a minor fraction of PM mass. It means that even high soot particle number concentrations result in relatively low particulate matter mass concentrations. And yet, high number of these particles can deeply penetrate into the respiratory system increasing the risks of asthma, cancer, heart malformations, and even premature deaths (Ibald-Mulli et al., 2002; Strak et al., 2010). The importance of particle number and surface area in health-related issues was already highlighted by previous studies (Peters et al., 2004; Vinzents et al., 2005; Janssen et al., 2011; Brown et al., 2001). These studies suggest that the supplementary parameters, to evaluate air quality and possible health related risks, are of a great need. Especially in regions with unsustainable urbanization and growing traffic congestions, where controlled PM10 mass concentrations are reported to be within preset limits.

An exemplary case of unsustainable urbanization is the megacity of Metro Manila, the capital of the Philippines. It accounts for more than 12.9 million inhabitants and 2.3 million registered motor vehicles as of 2015 (information available at https://psa.gov.ph/content/population-national-capital-region-based-2015-census-population-0

101 and http://www.lto.gov.ph/transparency-seal/annual-reports/file/19-annual-report-2015.html

102 (accessed March 4th, 2017)). Here, the inefficient public transport system and the rapid

103 increase of vehicular fleet resulted in congested streets being filled with private cars, taxis, old

104 buses and public utility Jeepneys (PUJs). Equipped with pre-EURO diesel engines, PUJs emit

105 high concentration of combustion generated aerosol particles, which in turn became a

106 dominant aerosol in the urban atmosphere. The emission inventories reported that traffic-

107 related sources are responsible for more than 71% of the total country's particulate matter

108 emission in the Philippines (Vergel and Tiglao, 2013). In the National Capital Region (NCR),

109 the local Department of Environment and Natural Resources report that 90% of emissions can

110 be attributed to mobile sources (information available at

111 http://www.aecen.org/sites/default/files/country_report_the_philippines.pdf (accessed March

112 4th, 2017)).

113 To address the air pollution problem, ambient air quality guidelines were set by the

114 Philippine Clean Air Act (CAA) on 23 June 1999 (CAA is available for download at

115 http://emb.gov.ph/wp-content/uploads/2015/09/RA-8749.pdf (accessed March 9th, 2017)).

116 This legislation covered limits for ambient levels of major air pollutants, including PM10.

117 Short-term (24-hr) and long-term (annual) averages of PM10 of 150 and 60 |ig/m3,

118 respectively, were set as National Ambient Air Quality Guideline Values (NAAQGV). Until

119 recently, the PM25 has been also included in NAAQGV, with short-term and long-term

120 guidelines of 50 and 25 |ig/m , respectively, as of January 2016. Since the CAA was declared,

121 long-term measurements (during 2001- 2008) of PM10 showed that annual PM10 is

122 consistently below the NAAQGV (Zhu et al., 2012). In a study by Asian Institute of

123 Technology, the Manila Observatory (MO) performed long-term PM10 and PM2.5

124 measurements from 2001 to 2004 and reported that seasonal average PM10 concentrations in

125 Metro Manila were almost within the World Health Organization (WHO) 24-hour Air Quality

126 Guideline (AQG) of 50 |ig/m . Despite relatively low particulate matter mass values the

127 incidences of lung cancer are rapidly increasing (Laudico et al., 2010). It suggests that the

128 megacity of Metro Manila faces a much different pollution problem than previously reported

129 cases in other, more industrial urban environments.

130 The main objective of this study was to characterize the physical properties of traffic-

131 related carbonaceous particles in a busy street canyon in Metro Manila, Philippines. High-

132 quality emissions data, including number size distributions of particles, is in great demand

133 and is believed to facilitate the improvement of megacities' air quality (Gurjar et al., 2008). In

134 this study, we used equivalent black carbon (eBC) mass concentration and refractory particle

135 number size distributions (r-PNSD) (as a soot proxy) to illustrate that these parameters may

136 be a valuable indicator to evaluate air quality and possible health related risks in urban

137 environments. For the first time in this region, size segregated refractory particle emission

138 factors (EFs) of light duty vehicles (LDVs) and PUJs were calculated. This study is also a

139 continuation of our previous work where we found that the social behavior of the city

140 dwellers in Metro Manila is significantly different than in Western and other Asian countries

141 (Kecorius et al., 2017). It may subject the citizens of Metro Manila to higher personal

142 exposure to carcinogenic species. This work has a potential to bridge the gap between the

143 increasing incidence of lung cancer and the growing air pollution problem in the Philippines.

144 Moreover, results from this study can be applied to other similar environments where the

145 contribution of the transport sector to the worsening air quality and possible health effects on

146 the city population needs to be assessed.

147 2. Experimental methods

149 The measurements were performed as part of an intensive aerosol research experiment

150 called the "Manila Aerosol Characterization Experiment 2015" (MACE 2015). MACE was

151 carried out and jointly organized by the Leibniz Institute for Tropospheric Research

152 (TROPOS) and the Consortium "Researchers for Clean Air" (RESCueAir) from March to

153 June, 2015. The RESCueAir consortium includes researchers and academics from the

154 University of the Philippines (UP) Diliman, the Manila Observatory (MO), Ateneo de Manila

155 University (ADMU), De La Salle University (DLSU), and the Philippine Nuclear Research

156 Institute (PNRI).

158 2.1. Measurement site

160 The aerosol measurement container was situated at the roadside of Taft Avenue

161 (14.56N, 120.99E) in the vicinity of De La Salle University, Manila, Philippines (Fig.1),

162 around 100 meter to a traffic light. Taft Avenue is composed of six lanes, three of which are

163 southbound. The measurement container was placed so that it occupied one of the lanes. One

164 notable feature of the avenue was the suspended railway, which is approximately 7 m high.

165 The aerosol container was air-conditioned to 24 °C to ensure the stability of aerosol

166 instrumentation. PM10 inlet (16.67 l min-1, 5 m a.s.l), followed by two 1.5 m Nafion dryers

167 and an automatic drying chamber (Tuch et al., 2009) secured particle size cut-off at 10

168 micrometers and a constant relative humidity (RH) below 30%. Short and vertical conductive

169 tubes were used inside the container to minimize particle losses in the sampling line. At Taft

170 Avenue, continuous measurements were performed during the period of 16 May to 11 June

171 2015.

172 Driving conditions in the avenue can be described as follows. The lowest fleet count,

173 regardless of the vehicle category, was observed during the early morning hours (03:00 to

174 04:00 LT) (see Fig. 2). During this time period vehicles were moving freely at speeds of

175 approx. 30 to 50 kilometers per hour. At this time, only a traffic light was the main reason for

176 vehicles to brake, idle or accelerate. A rapid increase of fleet density was observed by around

177 05:00 to 07:00 LT, which remained almost constant until 18:00 LT. The situation aggravated

178 during three distinct rush hours (morning, lunch, and afternoon, Fig. 2), when vehicles were

179 standing in one place "bumper-to-bumper" for about 5-10 minutes. In general, we can

180 conclude that the sampled particulate pollution was resulting from braking, idling, and

181 accelerating vehicles as well as free traffic flow (usually at night). We believe that such

182 driving conditions are valid to represent the majority of cases of Metro Manila's traffic flow.

183 An example of traffic flow at Taft Avenue can be found at https://youtu.be/5cT0xSBBasE.

187 Fig. 1. A photo of the instrument container on Taft Avenue (left) and the sketch of the

188 measurement site (right). Green diamond shows the location of rooftop meteorological station

189 and the red mark - instrument container.

191 2.2. Instrumentation

193 2.2.1. Volatility Tandem Differential Mobility Analyzer (V-TDMA)

195 In this study size segregated mixing state of aerosol particles was determined using

196 TROPOS-type V-TDMA system. Detailed instrument description can be found in Philippin et

197 al., (2004) and will not be presented here. Briefly, dried quasi-monodisperse particles,

198 selected by a Differential Mobility Analyzer (DMA-1, Hauke-type, 11 cm effective length,

199 sheath flow rate 5 l min-1) is split into two flows, which are then directed to a Condensation

200 Particle Counter (CPC-1, TSI model 3010) and a thermal conditioning unit (TD). In the TD,

201 the aerosol particle flow is automatically alternated between two temperatures of 25 and

202 300 °C. Lower temperature scans were used to calibrate the kernel function in a data inversion

203 routine presented by Gysel et al., (2009). The volatility distributions were measured by a

204 second DMA in conjunction with a CPC-2 (both identical to the DMA-1 and CPC-1). The

205 total sample flow rate of the system was 1 l min-1 (defined by two CPC flows of 0.5 l/min).

206 The residence time inside the heating column was 0.3 s, which was shown to be enough to

207 evaporate sulfate, nitrate and volatile and semi-volatile organic material shells from black

208 carbon cores (Burtscher et al., 2001, Philippin et al., 2004). We chose six diameters to be

209 selected by DMA-1: 20, 35, 63, 110, 200 and 350 nm. Please note that these are mobility and

210 not volume-equivalent diameters. The methodology used to calculate a volume-equivalent

211 diameter (later used in the text) is described in paragraph 2.3.1. The full scan cycle took less

212 than one hour.

214 2.2.2. Mobility Particle Size Spectrometer (MPSS)

216 Dry (RH<30%) particle number size distributions (PNSD), in a mobility size range from

217 10-800 nm, were measured with a TROPOS-type MPSS (Wiedensohler et al., 2012). The

218 system uses a Hauke-type DMA (effective length of 28 cm) together with a CPC (model

219 3772, TSI Inc., USA, flow rate 1 l min-1). The time resolution of up-and-down scan was 5

220 min. However, we decoupled up-and-down scans to increase the time resolution to 2.5 min.

221 Electrical particle mobility distributions were inverted to PNSDs using the inversion

222 algorithm presented by Pfeifer et al., (2014). The final PNSDs were corrected for transmission

223 losses in the sampling lines using the method of equivalent length and CPC counting

224 efficiencies (Wiedensohler et al., 1997). Sizing accuracy in MPSS and V-TDMA systems

225 were controlled using nebulized polystyrene latex spheres (PSL, Thermo Scientific™, Duke

226 Standards™) of 203 nm. High voltage supply offset calibration, instrument flows and tests for

227 leakage were performed on a regular basis (once per week).

229 2.2.3. Supplementary measurements

231 To check the validity of retrieved refractory particle number size distributions as a proxy

232 for soot, we also used a Multi-Angle Absorption Photometers (MAAP Model 5012, Thermo,

233 Inc., Waltham, MA USA; Petzold and Schönlinner, 2004) to measure equivalent black carbon

234 (eBC) mass concentration. The instrument accounts for multiple scattering effects and

235 absorption enhancement due to reflections from the filter. The operating wavelength of 637

236 nm is chosen to minimize the interference with organic material and mineral dust (Sun et al.,

237 2007, Müller et al., 2009). To save the filter medium in the heavily polluted environment, the

238 MAAP flow was adjusted to 3 l min-1 using a custom made nozzle. The temporal resolution of

239 the instruments was set to 1 min.

240 To be able to model the emission factors (EFs), the vehicles were counted manually from

241 continuously recorded street site videos (an example video is available at

242 https://youtu.be/5cT0xSBBasE). The counts were performed for the whole measurement

243 period and all sequential hours of the day and are presented in Fig. 2. The vehicular fleet was

244 divided into 2 categories of light duty vehicles (LDVs) and public utility Jeepneys (PUJs),

245 each contributing 80% and 20% to the total fleet, respectively. The meteorological

246 parameters, such as wind speed/direction, temperature and relative humidity were measured

247 by the meteorological station at the rooftop of De La Salle University.

Time of day

249 -PUJs WD-LDVs WD----PUJs WE----LDVs WE

251 Fig. 2. The traffic intensity of light duty vehicles (LDVs) and public utility Jeepneys (PUJs)

252 during working days (WD) and weekends (WE).

253 2.2.4. Meteorological conditions

255 The MACE 2015 measurement campaign took place during two seasons - dry and wet.

256 According to Akasaka (2010) and Villafuerte et al. (2014), the dry season lasts from

257 December to May, with the rest of the year being affected by Southwest monsoons and

258 tropical cyclones. Despite this general classification, we did not experience any prolonged

259 rainfall, which may have influenced the quality of our measurements. Short rains occurred on

260 some occasions, and although it did not affect the measurement results, we have eliminated

261 data from those periods in our succeeding analyses.

262 The daily average of temperature, wind speed and RH during the measurement period was

263 31±2 °C, 0.9±0.6 m s-1 and 69±8%, respectively. The maximum temperature and wind speed

264 was recorded in midday reaching 33±1 °C and 1.7±0.9 m s-1. After 1.00 p.m., the values begin

265 to drop gradually and reached the minimum of 29±1 C and 0.5±0.3 m s- at 5.00-6.00 a.m.

266 local time (LT). Maximum (77±4%) and minimum (60±4%) RH values were measured at

267 5.00 a.m. and midday, respectively ("±" shows standard deviation).

269 2.3. Data processing

271 In the next sections, we will briefly explain the procedures used to evaluate measurement

272 data. A more detailed description about the data processing can be found in the supplementary

273 material (SP-1).

275 2.3.1. Retrieval of refractory particle number size distribution (r-PNSD)

277 To retrieve r-PNSD we used the data from the MPSS and V-TDMA systems (Wehner et

278 al., 2004). For the non-spherical particle consideration, we have recalculated the particle

279 mobility diameters (dm) to volume-equivalent diameters (dve) using the formalism presented

280 by Pfeifer et al. (2014) and Park et al. (2004). This includes the division of dm by size-

281 dependent aerodynamic shape factor (c(dve)) as follows:

Cc(dm) "( Cc(dvey ( )

285 where CC is the Cunningham correction. Using the results from Park et al. (2004) we defined

286 the empirical, size-dependent c(dve) as:

288 "-^(¿gny (2)

290 TDMA inversion routine (Gysel et al., 2009) was used to invert volume-equivalent raw V-

291 TDMA scans to Shrink Factor - Probability Density Function (SF-PDF). After analyzing all

292 measured volatility distributions, the refractory particle SF-PDFs were divided into two

293 groups: SF<0.9 and SF>0.9 to determine more-volatile (MV) and less-volatile (LV) particles

294 (Fig. 3). The integration of SF-PDF from SF=0.9 to SF=1.1 gave us the number fraction of

295 LV particles, fN,LV. The r-PNSD can be then reconstructed by multiplying measured PNSD

296 with fN,LV in a measured PNSD size range. The retrieved r-PNSDs were then fitted with log-

297 normal distributions.

o_ ■

0.9 1.0 1.1

selected size

Fig. 3. Averaged Shrink Factor-Probability Density Function (SF-PDF) of size segregated refractory particles.

2.3.2. Estimation of emission factors (EFs)

Size-segregated EFs of refractory particles were estimated utilizing Operational Street Pollution Model (OSPM, Berkowicz et al., 2000). The model's applicability was proven by numerous worldwide studies in the past decade (e.g. Kakosimos et al., 2010). The references to detailed physical principles of the model are provided in Kakosimos et al., (2010). Shortly, the concentration of pollutants in the street canyon depends on both emission and dilution. The general purpose of OSPM is to predict the concentration of pollutants by combining the plume and box models. To run the model, the input of accurate street canyon geometrical configuration (building locations, heights, street orientation against north), rooftop meteorological conditions (wind direction and wind speed), and the emission factors of corresponding pollutants are required. However, in our case, the desired parameter to be estimated is the EFs, while the measured one is particulate pollutant concentration. This is known as inverse modelling approach, widely applied in previous studies to determine the EFs based on in-situ measurements (e.g. Palmgren et al., 1999; Rose et al., 2006; Brizio et al., 2008). The emission factor, qk, for the kth vehicle category can be determined as:

1 1 = . [ve(l

kmvehl V + ) ' k L s J

where F is the dilution function calculated using OSPM, which mainly depend on meteorological parameters and street geometry, Nk is the traffic flow of kth vehicle category and AC is the pollutant concentration increase due to the traffic in a street canyon. The

average, or in other words, total fleet emission factor can then be found by solving the simple linear regression:

Because the total emission along the street is a superposition of separate vehicle group emissions, we can split the emission of the fleet into the emission of separate vehicle groups. If we assume that the total vehicle fleet can be approximated by LDVs and PUJs, we can rewrite equation 4 as:

where qLDVs, .LDVs, #pujs, .pujs are the emission factors and vehicle numbers for the LDVs and PUJs vehicle groups, respectively. Equation 5 can then be solved by bivariate linear regression resulting in a plane, in which the slopes represent EFs of different vehicle fleets. The results of the bivariate linear regression can be found in the supplementary material (SP-

The concentration of pollutants in the street canyon is the superposition of the (1) urban background, which is the result of both regional pollution and the contributions from the city itself, and the (2) direct tailpipe emissions from the bypassing traffic. Several ways to estimate AC were presented in the literature (Kakosimos et al., 2010). It can be calculated using the background concentrations obtained from a nearby measurement station, which is not affected by direct emissions. Urban background and regional pollution models can also be used to calculate the concentration of background pollutants. Brizio et al. (2008) used averaged night-time street level concentrations as a representation for background pollution. However, we find these methods to be limiting as the nearby background measurements may not be available, the regional models may be time consuming and a night-time pollutant concentration does not necessarily represent the daytime background concentration variation. Therefore, we have used instead a new method to determine the background pollution concentrations at a street site. The direct vehicular emission can be easily spotted in MPSS as a sudden high-value particle number concentration increase followed by a consequent steep drop when the vehicles were passing the measurement container. According to Kakosimos et al. (2010, Fig. 5), these high values are the traffic fingerprints, while the values before and after this increase should represent urban background conditions. Following this logic, we have calculated the rolling minimum concentrations with a variable time window over all measurement period. As the rolling minimum window increased, the minimum values were found to get lower and vice versa. The compromise was made to use 1 hour duration for a rolling minimum calculation by a trial and error approach. The results were comparable to a time variation of background factor in a positive matrix factorization (not shown here). This method proved to give a reasonable and smooth diurnal pattern of urban background pollutant concentration. An example case of the application of the proposed method is demonstrated in the supplementary material (SP-3).

3. Results and discussion

In this section we present and discuss the measurement results in the following order: first, in paragraph 3.1 the state of mixing of aerosol particles in terms of volatility; in paragraph 3.2 we present the derived refractory particle number size distributions; and last, the size-segregated emission factors of refractory particles are discussed in paragraph 3.3.

AC = q • (F • Nveh).

AC = qLDVs • F • .LDVs + qPU]s ' F ' .PUJs>

3.1. The state of mixing of aerosol particles

The daily variation of size-segregated Shrink Factor-Probability Density Function (SF-PDF) is shown in Fig. 4. A clear separation of a SF-PDF mode at SF~1 can be seen for particles with volume equivalent diameters greater than 50 nm. This mode gets increasingly dominant with increasing particle size, while smaller particles do not show such clear separation of SF-PDF. Moreover, a rather evident two mode pattern appears in SF-PDF for particles smaller than 50 nm. In this case, the transition region at 50 nm separates two groups of particles: smaller with bimodal volatility properties, and bigger, where particles become more uniform in their thermal properties. Interestingly, in experiments by Rader and McMurry (1986) the boundary between the nucleation and accumulation modes was found at 30 nm. Smaller particles were found to be more volatile than larger ones.

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00:00 03:00 06:00 00:00 12:00 15:00 18:00 21:00 00:00 229, nmi I.........J......J. I........I i

• r. .. • T- . • ----- ' '•< ' i r----1

10.....■-......... J 1........ ................. ..............................

00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00

00:00 03:00

Time of day

'6:00 09:00 12:00 15:00 18:00 21:00 00:00

Time of day

Fig. 4. The daily variation of size segregated particle Shrink-Factor Probability Density Function (SF-PDF)

However, this type of volatility representation as shown in Fig. 4 is rather qualitative. More useful way to discuss particle thermal properties is to use integrated number fraction of less volatile particles (fN,LV) and volume fraction remaining (VFR). The method to retrieve fN,LV is presented in section 2.3, while the VFR was directly extracted from TDMAinv routine "RetrVFRavg" wave (Gysel et al., 2009). The results of fN,LV and VFR are summarized in Table 1 and visually presented in Fig. 5 (as median, mean and the 5, 25, 75, 95-th percentiles). It can be seen that the major number fraction of less-volatile particles resides in a size range from 85 to 229 nm. Similar results were also observed in previous studies with a focus on pollution in urban environment (Wehner et al., 2004; Rose et al., 2006). In this study, the fN,LV and VFR increased up to 90% with increasing particle diameter. On the other hand, these values became as low as 30-50% for particles smaller than 50 nm. As it was

403 already noted before, 50 nm seem to be the transition region between the Aitken and

404 accumulation mode particles with slightly higher variability in particle volatility as

405 represented by higher standard deviation (error bars in Fig. 5). This higher variability is

406 mainly due to daily variation in fN,LV and VFR (not shown here). The fN,LV of 50 nm particle

407 increased to its maximum value of approximately 70% at 8.00 a.m. and 4.00 p.m. At noon

408 these values decreased to as low as 30%. All other size particles, except 50 nm, did not show

409 such behavior and were rather uniform in their fN,LV and VFR values during the hours of day.

410 This suggest that particles in 50 nm size range is most likely a mixture of directly emitted and

411 particles from secondary aerosol production with higher volatility that were transported to the

412 measurement site from the surrounding urban environments. The possible sources and

413 processes determining the observed particle mixing state will be discussed later in this

414 paragraph.

o.o _I_I_I_I_I_I_ 0.0 _I_I_I_I_I_I_

16 nm 28 nm 50 nm 35 nm 145 nm 229 nm 16 nm 28 nm 50 nm 35 nm 145 nm 229 nm

425 due, nm dve, nm

418 Fig. 5. The number fraction of less volatile (fN,LV) and the volume fraction remaining (VFR) of

419 refractory aerosol particles presented as 25th and 75th (box range), 5th and 95th (whiskers)

420 percentiles, median (inner square) and mean (horizontal line inside the box).

422 Another interesting feature characterizing the state of mixing of aerosol particles can be

423 seen from a difference between VFR and fN,LV (Fig. 5). We note that for the fN,LV calculation,

424 we have used only a certain SF range (more details in paragraph 2.3.1.), while the VFR

425 represents the full size range volatility spectrum. For this matter, VFR> fN:LV is always true.

426 From Table 1 one can see that in the size range from 85 to 229 nm particle, fN,LV and VFR

427 agree within 3% (p-value>0.05, see supplementary material SP-4). This result suggests that

428 the VFR in this size range can be almost entirely explained by fN ,LV. In other words, more than

429 97% of VFR in this size range are externally mixed refractory particles with no/or minimal

430 volatile coating. The difference between fN ,LV and VFR starts to increase with decreasing

431 particle size. In a size range from 16 to 50 nm, fN, LV and VFR on average agree within 20% (p-

432 value<0.05). This result suggests that as the particle diameter increased, aerosol particles had

433 almost no volatile coating. While smaller particles seemed to disobey this rule and became

434 more volatile with decreasing particle size.

Table 1. The average size segregated number fraction of less volatile (fN,LV) and the volume fraction remaining (VFR) of refractory aerosol particles.

145 nm

229 nm

Workday Weekend

Workday Weekend

0.30±0.18 0.37±0.20

0.28±0.17 0.34±0.20

0.46±0.14 0.47±0.16

0.46±0.13 0.44±0.16

0.58±0.23 0.74±0.20 0.86±0.16 0.89±0.14

0.50±0.22 0.67±0.20 0.78±0.18 0.82±0.18

0.64±0.18 0.77±0.16 0.86±0.14 0.90±0.13

0.58±0.18 0.71±0.16 0.80±0.15 0.84±0.16

Comparing working days (WD) versus weekends (WE), we found that VFR and fN,LV are from 6 to 9% lower on WE than WD (for particles more than 28 nm, p-value<0.05). Moreover, the difference between VFR and fN, LV is more profound on WE than WD (Fig. 5). One possible explanation for this could be lower traffic intensity on weekends (see Fig. 2). Although the number of heavy duty vehicles during WE did not change significantly compared to WD, the total number of vehicles was found to be somewhat lower. As a result, lower concentrations of pollutants were emitted to the urban environment. This allows higher fraction of volatile material to condense onto a single particle surface during particle ripening process compared to the conditions when higher number of particles would be present. At this stage it is important to discuss which processes and particles might be contributing to our observed thermal particle properties.

In this study, we used the V-TDMA system with a thermal conditioning unit set to 300 oC. Previous studies showed that there is a large variety of species that may remain refractory when heated to 300 oC. For example, inorganic salts were shown to decompose between 400 and 1700 oC, (Knudsen et al., 2004). Low-volatility oxygenated organic aerosol may also contribute to the refractory fraction of atmospheric aerosol particles (Poulain et al., 2014). On the other hand, in polluted urban environments with predominantly diesel emissions, the refractory species that do not decompose at a temperature of 300 oC were attributed to soot particles (e.g. Rose et al., 2006; Ning et al., 2013; Kittelson and Kraft, 2014). These ultrafine particles were found to form a mode with a geometric mean diameter of 80 nm. Non-exhaust-related particles, including re-suspended road dust, particles from mechanical wear of brake pads, tires, etc. may also contribute to measured refractory fractions. However, we do not expect these particles to influence our results because they mostly reside in a supermicrometer size range (Thorpe et al., 2008). The findings from previous studies to some extent agreed with our measured fN, LV and VFR. The main difference was that we were also able to observe non-volatile particles in nucleation mode (in a range of 10 to 20 nm). While the observed refractory species at 80 nm mode are proven to be soot particles and its derivatives, including metal compounds from lubricating oil and engine wear, the origin of the smallest refractory particles (10 to 20 nm), observed in our study, is still not clear. Based on previous studies, we speculate that these particles most likely were the non-volatile and semi-volatile lubricating oil compounds, organic polymers, ash from metallic additives, tiny carbonaceous spheres with different morphology than diesel particulates, as well as newly formed particles from species containing SO3, gaseous sulfuric acid, heavy organics and other compounds that are released after thermal changes from the exhaust systems' surface (Engler et al., 2007; Tiitta et al., 2010; Karjalainen et al., 2014). This would explain our observed less volatile fraction of nucleation mode particles. The more volatile fraction of smallest particles may have been the result of incomplete volatilization of organic compounds (heavy hydrocarbons) due to residence time and/or temperature in the V-TDMA system and/or

internally mixed sulfuric acid with non-volatile cores. Moreover, coagulation products between newly nucleated sulfur particles and refractory ashes may also contribute to more volatile particle number and volume fractions.

Prior to discussing the refractory particle physical properties, it is important to clarify how we use the term soot in this work. As it was already mentioned in previous studies, the refractory particles, which predominantly come from diesel vehicles, can be referred to as soot. However, in our case this is only partly equitable, mainly because of the presence of the smallest (10 to 20 nm) refractory particles. Bearing this in mind, we would like to introduce two terms used to distinguish between two different particle populations, derived from V-TDMA measurements. Further in the text, the refractory particles with a diameter >30 nm will be assigned to soot agglomerates, while the particles that are smaller than 30 nm will be cited as refractory particles. A particle number size distribution, in a range of 10 to 1000 nm, retrieved from the measurements of V-TDMA and MPSS will be specified as refractory particle number size distribution (r-PNSD). We believe that such a disclosure will lead to a better distinction between different particle populations. As it will be demonstrated later in section 3.4.2, our derived r-PNSD can be used as a proxy for soot particle number size distribution.

3.2. PM2.5, number concentration and size distribution of refractory particles

In a previous section we presented the results of aerosol particle volatility measurements. Here we will focus on the refractory particle number size distribution (r-PNSD) and the importance of such measurements. As stated before, the Philippine ambient air quality guidelines set by the Clean Air Act, in terms of particulate matter, control only the levels of PM10. This criteria pollutant was shown to be below national guidelines and almost within the World Health Organization (WHO) 24-hour limit (Zhu et al., 2012). There might be several reasons for relatively low PM10 mass concentration in Metro Manila when compared to other megacities. For example, the Philippine Archipelago is unlikely to be affected by dust and sandstorms from remotely located deserts. Coastal location (enhanced dispersion), warm climate (no seasonal heating), low secondary aerosol production and high annual precipitation (particulate removal) may also substantially lower ambient PM10 concentrations (Oanh et al., 2006).

Fig. 6. Equivalent black carbon (eBC, as proxy for soot) mass concentration measured with MAAP (solid black line), gravimetric PM25 mass concentration derived from 24-hour filter samples (red line) and World Health Organization (WHO) guideline value for 24-hour average mass concentration of PM25. Grey shaded area shows variability (standard deviation)

over sampled period of time.

On the other hand, PM25 concentrations that are measured since 2001, but not regulated until 2013, were reported to regularly exceed WHO limits (Zhu et al., 2012). This was also confirmed by our measurements (Fig. 6). PM25 mass concentration determined by gravimetric method showed that PM25 concentration was on average ~1.7 times higher than recommended WHO value. From PM25 measurements and assuming that eBC (measured by MAAP as soot proxy) mainly reside in PM25 size range we estimated that soot particles mass contributed from 55 to 75% of total PM25. Moreover, on some occasion daily soot mass concentration was also much higher than the WHO recommended PM25 value (e.g. 3rd of June, Fig. 6). It suggested that extremely high number concentrations of particles had to be emitted into the urban atmosphere in the form of soot in order to account for its contribution to PM2.5 mass. Bearing this in mind and the fact that particle deposition in the respiratory system is size selective (Heyder et al., 1986), it is useful to determine refractory particle (as a proxy for soot) number size distribution to assess personal exposure to hazardous particulates.

■D 100

1E3 2E5 jD , #lcm3

1.00 x 10a £ n

7.50 x10s ^

5.00 x 10s

00:00 03:00 06:00

09:00 12:00 15:00

Time of day

18:00 21:00 24:00

Fig. 7. Refractory particle (soot proxy) number size distribution (contour plot) and the integrated number concentration (black line).

Size-segregated number concentration of refractory particles can be calculated combining measurements of fN,LV together with ambient PNSD (see section 2.3.1). Because number fractions of refractory species were measured only for 6 preselected diameters, a continuous PNSD was retrieved using a log-normal fit. Refractory particle number concentration in the 10 - 1000 nm size range was then calculated from PNSD. Keeping in mind the terminologies we provided in the last paragraph of section 3.1, the results of refractory particle physical properties are shown in Fig. 7, Fig. 8 and Table 2. Since the difference between workdays and weekends was negligible, we excluded this information from the discussion for redundancy. In Fig. 7 it can be seen that the refractory particle (rp) number concentration (black solid

line) start to increase from its minimum value of 6-10 rp/cm at around 3.00 a.m. The concentration of particles doubled by 5.00 a.m. reaching 1.5-104 rp/cm3. These high values were sustained until around 2.00 p.m. when it started to decline to 1-104 rp/cm3 at around 3.00 to 8.00 p.m. For a comparison, average soot (refractory particles in a 80 nm mode) number

concentration was reported to be between 4-10 and 6-10 rp/cm in a street canyon in

551 Germany (Rose et al. 2006). Our observed steep increase in the refractory particle number

552 concentration is most likely a result of increased traffic intensity (Fig. 2). Diesel powered

553 PUJs and LDVs filled the streets at around 3.00 a.m. By 6.00-7.00 a.m. the number of

554 vehicles was already at its maximum, which when coupled with low wind speeds and stable

555 atmospheric conditions (weak convection mixing due to solar radiation minimum) resulted in

556 the accumulation of pollutants in the street canyon. A slight increase in vehicle flow intensity

557 can also be observed during rush hours (6.00 - 9.00 a.m., noon, and 4.00 - 5.00 p.m.). During

558 the daytime (6.00 a.m.to 6.00 p.m.), the number of vehicles on the street remained rather

559 stable. The intensity of traffic density declined from 6.00 p.m. to its minimum at 3.00 a.m.

561 Table 2. Campaign average of size segregated and normalized refractory particle number

562 (dN/dlogDp, #104/cm3), surface (dS/dlogDp, |m2/cm3) area and mass (dM/dlogDp, |g/m3)

563 concentrations. Variability (standard deviation) is presented after "±" sign.

dve 16 nm 28 nm 50 nm 85 nm 145 nm V 229 nm

Workday

Number conc. 1.79±1.58 1.88±1.46 2.30±1.53 2.31±1.44 1.34±0.85 0.32±0.21

Surface conc. 15±13 47±36 180±120 520±325 890±560 520±342

Mass conc. 0.1±0.08 0.5±0.4 3.3±2.2 16.1±10 46.9±29.8 43.4±28.5

Weekend

Number conc. 1.42±1.24 1.45±1.20 1.63±1.19 1.70±1.20 0.97±0.70 0.23±0.17

Surface conc. 12±10 36±30 127±93 383±271 644±464 372±285

Mass con. 0.07±0.06 0.4±0.3 2.3±1.7 11.8±8.4 34.2±24.6 31.1±23.8

566 The decrease in refractory particle number concentration started at around 2.00 pm, which

567 is approximately four hours before the decline of the traffic intensity. This observation

568 suggests that not only traffic intensity, but also other factors affected diurnal variation of

569 refractory particle number concentration. To address this question, we investigated the

570 relationship between number of particles and the wind speed and direction (see supplementary

571 material SP-5). As expected, the lowest number concentrations of particles were observed

572 when the prevailing winds were from the Southwest, West and Northwest sectors, which are

573 opposite to the roadside. When the winds were coming from the Southeast, East and

574 Northeast sectors (direction of the road), particle number concentrations were determined to

575 be at least two times higher. Diurnal variation in wind speed and direction showed that the

576 lowest wind speed (approx. 0.4 m/s) was observed between midnight and 6.00 a.m. (see

577 supplementary material SP-6). By 8.00 a.m. it gradually built up reaching maximum values of

578 approx. 2 m/s between midday and 5.00 p.m. The strongest winds were consistently

579 prevailing from directions opposite to the roadside (with respect to the measurement

580 container). This specific diurnal variation of the wind speed and direction has created

581 conditions in which the less polluted air from the urban background diluted the street canyon

582 aerosol resulting in observed soot particle number concentration decrease. The dilution,

583 however, did not change r-PNSD shape, only its strength (Fig. 7). This is most likely because

584 the transported background aerosol is a result of the same type of emissions from the

585 surrounding streets.

refractory non-soot ' ' refractory' sbot particles particles

Q. ^ C

O TO TO

3 O N O

dN/dlogDp, #/cm3 Workday Weekend

dS/dlogDp, jim2/cm3 dM/dlogDp, (ig/m3

Fig. 8. Average refractory (yellow shade) and soot (grey shade) particle number, surface area and mass size distributions. Solid lines show distributions after a log-normal fit. Error bars indicate the standard deviation. For example, refractory particle number size distribution is shown by a black solid line with y-axis as dN/dlogDp, #/cm3.

The averaged r-PNSD and corresponding surface area and volume size distributions are presented in Fig. 8. It can be seen that r-PNSD is a superposition of two particle modes: refractory non-soot and refractory soot, with the geometric mean diameters of 20 and 80 nm, respectively. Corresponding mode contribution to total refractory particle number was 45 and 55%, respectively. When compared with previous studies, the distinct difference was found in smaller particle mode. For example, using the same methodology, Rose et al. (2006) derived r-PNSD comprising of only bigger mode particles. The same was observed in a study by Ning et al. (2013), who reported that eBC aerosols from diesel vehicles contained mostly unimodal particles at around 80 to 100 nm in measured particle number size distributions. We observed that with daytime the number concentration of particles bigger than 50 nm increased all together, while the number concentration of < 50 nm particles displayed a slightly different pattern (see supplementary material SP-7). These smallest particles formed a unique concentration maximum just around noon, when solar radiation was at its strongest, while a bigger size particle concentration closely followed the trend of vehicle flow intensity. This further suggested that the smallest observed refractory particles might be the result of nucleated low volatility organics, or a subsequent condensation of semi-volatile organics onto nucleated sulfate particles and their later oxidation causing the formation of non-volatile organic polymers.

We find the presence of the smallest refractory particles to be very intriguing not only because they appear to be a signature of a highly polluted urban environment where old

614 technology engines are still in use, but also because of their abundance. Despite their

615 negligible contribution to overall particulate mass, these very fine particles may penetrate

616 deep into the respiratory system. Moreover, because polycyclic aromatic hydrocarbon

617 molecules were found to be a key precursor in carbonaceous particle formation, these smallest

618 particles may play a significant role in health related effects (Kittelson and Kraft, 2014;

619 Abdel-Shafy and Mansour, 2016).

621 3.3. Size-segregated emission factors of refractory particles

623 The derived refractory particle number size distributions from VTDMA - MPSS

624 measurements and meteorological data together with vehicle fleet count allowed us to

625 determine size-segregated refractory particle number, surface area and mass emission factors

626 (EFs). Moreover, apart from calculating the average EFs of refractory particles, we were also

627 able to distinguish EFs between different fleets, LDVs and PUJs. The detailed methodology

628 for determining EFs can be found in section 2.3.2. Information about EFs is important not

629 only for urban air quality modeling, but it also helps to understand, which type of vehicles

630 contribute most to observed particle physical-chemical properties. The results of derived size-

631 segregated EFs are presented in Fig. 9 and summarized in tables 3 and 4. We found average

632 vehicle refractory particle number and mass EFs to be 3.291014 rp/(km veh) and 0.313

633 g/(kmveh), respectively. Separation between different fleets gave corresponding refractory

634 particle number EFs values of 9.7910 and 1.1510 rp/(km veh) and refractory particle

635 mass EFs of 0.027 and 1.618 g/(kmveh) for LDVs and PUJs, respectively. From here, it can

636 be seen that PUJs emit 11.7 times more particle in terms of number and 61.3 times more

637 particles in terms of mass when compared to LDVs. Comparing these values to the European

638 Union (EU) emission standards (Table 3), it is clear that the particle number emission from

639 PUJs in Metro Manila exceeds Euro 6 target value of 6.0-10 particles/(km veh) (6.0-10

640 particles/(kmveh) within the first three years from Euro 6 effective dates) by up to 2000

641 times. We have to note that this difference may be even higher if total, and not only refractory

642 particle number was considered. Until 2014, EU emission regulation did not include particle

643 number as one of the standards, instead, particulate matter mass (PM) was used. In terms of

644 PM, LDVs refractory particle mass EFs in Metro Manila falls in the range of EURO 4

645 emission regulation. While PUJs, with an approximate weight of 3500 kg (Braganza et al.,

646 2007) and being in the category of light commercial vehicles in EU EURO standards, showed

647 6.6 times higher PM emission even when compared to the outdated standard from 1992

648 (EURO 1).

Table 3. Europe Union emission regulations for particulate matter (PM) and particle number (PN) for passenger cars and light commercial vehicles with a comparison to emission factors estimated in this study. Subscripts "diesel" and "diesel+gasoline" stands for the corresponding standard of either diesel or diesel and gasoline vehicles, respectively. PMrefr and PNrefr stands for refractory particle mass and refractory particle number emission factors, respectively. Table was adopted from https://www.dieselnet.com/standards/eu/

Stage Date PM, g/km PN, #/km

Passenger Cars EURO 1 1992 14diesel

EURO 2 1996 0.08-0.10diesel -

EURO 3 2000 0-05diesel -

EURO 4 2005 0.025diesel -

EURO 5-6 2009-2014 0.005diesel+gasoline r Z^v 1 6.0 *10 diesel+gasoline

Light commercial veh. (>1760 kg)

EURO 1 1994 0.25diesel -

EURO 2 1998 0.17-0.20diesel -

EURO 3 2001 0.10diesel -

EURO 4 2006 0.06diesel -

EURO 5-6 2010-2015 0.005diesel+gasoline / A 1 6.0 *10 diesel+gasoline

This study Location PM, g/km PN, #/km

LDV Manila, Philippines 0.027refr 9.79*1013refr

Jeepneys Manila, Philippines 1.618refr 1.15*1015refr

Average fleet Manila, Philippines ^0.313refr 3.29*1014refr

Other studies

LDV a,* Beijing, China 0.027bc 1.60*1014

LDV b,* California, USA 0.0027bc 2.22*1014

LDV c,* California, USA 0.0031bc 4.00*1013

LDV d Leipzig, Germany - 5.80*1013

LDV e Berlin, Germany - 2.40*1013

LDV f Meckenheim, Germany - 2.10*1014

HDV a,* Beijing, China 0.513bc 4.36*1015

HDV b,* California, USA 0.306bc 3.24*1015

HDV c,* California, USA 0.513bc 2.49*1015

HDV d Leipzig, Germany - 2.50*1015

HDV e Berlin, Germany - 2.96*1015

HDV f Meckenheim, Germany - 1.18*1015

aWesterdahl et al. (2009) dRose et al. (2006) refr - only refractory particles bGeller et al. (2005) eBirmili et al. (2009) BC - PM reported as black carbon cKirchstetter et al. (1999) fNickel et al. (2013) * - EFs reported in g/kg of fuel burnt

Furthermore, from Fig. 9, it can be seen that LDVs and PUJs emit not only different number concentrations but also considerably different sizes of particles. Starting with LDVs, in this vehicle fleet most of refractory particles in terms of their number concentration were emitted in a size range from 10 to 200 nm. These findings were consistent with laboratory studies as well as "car chasing" experiments, which reported that not only diesel, but also gasoline driven vehicles emit refractory particles with geometric mean diameter shifted towards smaller particle sizes when compared to diesel emissions (Karjalainen et al., 2014). The larger diameter particles (approx. 200 nm) in this study were most likely soot aggregates emitted by LDVs. The emission of these bigger particles was found to be at least one order of magnitude lower than smaller particle (<80 nm). This can be explained as follows: LDVs in Metro Manila is a mixture of diesel and gasoline powered cars, however, the fraction of gasoline cars is much higher. As the diesel vehicle fraction is lower, so was the emission of bigger (>80 nm) soot particles. Conversely, a higher fraction of gasoline powered vehicles in Metro Manila LDVs' fleet determined the dominant refractory particle emission in the 30 to

675 80 nm size range. The smaller particles in LDVs emission (< 80 nm) most likely were non-

676 volatile and semi-volatile refractory non-soot particles.

680 Fig. 9. Size segregated refractory non-soot (yellow shade) and soot (grey shade) particle

681 number emission factors of different vehicular fleets.

683 PUJs particle number emission size distribution, on the other hand, was different compared

684 to LDVs and comprised of two pronounced particle number modes at around 15 and 80 nm

685 (Fig. 9). We have already discussed in section 3.2 what species possibly contribute to each of

686 the eminent particle modes. Bigger particle mode (at approx. 80 nm) was also observed by

687 Rose et al. (2006) in a similar study in Germany, however, such pronounced emission of

688 smaller and non-volatile particles from diesel vehicles has not been seen before. To retrieve

689 non-volatile PUJs nucleation mode particle number EFs we have fitted values from Table 4

690 with a log-normal function (geometric mean diameter of 12 nm and sigma of 1.58). This

691 procedure resulted in a non-volatile nucleation mode particle EF of 4.111014 rp/(kmveh).

692 These smallest particles were found to account for approximately 36% of overall PUJs

693 emitted refractory particles in terms of particle number while their contribution to total

694 emitted refractory particle mass was found to be less than 0.2%. Despite their little mass,

695 these smallest particles may have a significant effect on urban air quality in terms of

696 pollution-related adverse health effects.

Table 4. Size segregated and normalized refractory particle number emission factor (EFs, 1014) for different vehicular fleets: light duty vehicles (LDVs); public utility Jeepneys (PUJ); and emission factor per average vehicle. We were not able to retrieve EFs of LDVs at 85 nm and 229 nm.

dve 16 nm 28 nm 50 nm 85 nm 145 nm 229 nm

dqN/dlogDp, #/veh*km

Light Duty Vehicles (LDVs) 0.782 1.770 0.894 --- 0.111 ---

Public Utility Jeepneys (PUJs) 7.730 3.860 8.020 13.300 7.360 2.000

Average vehicle 2.580 2.420 2.630 2.520 1.500 ^ 0.353

From the results discussed previously, it can be seen that emission of refractory particle number and mass from PUJs is a serious urban pollution problem in Metro Manila. Despite the fact that these vehicles emit as much as lorry-like trucks in western countries, they neither encounter any stringent, environment sustainable technical requirements, nor are banned from the "green zones" (park, school, university, hospital environments, etc.) and are widely used as a means of public transportation. The need to improve emissions from this particular fleet is obvious and urgent.

3.4. General remarks 3.4.1. Emission factors (EFs)

The comparison between emission factors (EFs) determined in this and other studies can be found in Table 3. Only a sparse amount of studies exist in which EFs of soot particles were estimated. Since, not all studies reported EFs of same measured quantities, in the table we have also included subscripts to indicate whether it was soot, black carbon (BC) or the total particles (no subscript). The closest to our study in terms of the applied measurement techniques is the study by Rose et al. (2006). They found that in Leipzig, Germany, average fleet and LDV EFs of soot particle number was 2.2 and 1.7 times lower (when comparing to total refractory particles) than the values we observed in Metro Manila, respectively. In a study in Berlin, Germany, by Birmili et al. (2009), average fleet soot mode (geometric mean

diameter of 71 nm) particle EFs was found to be 7.8-10 sp/(kmveh) that is 4.2 times lower than the values observed in our study. Comparing EFs that were reported from studies in Germany - Berlin particle number EFs of LDV was found to be 2 times lower than soot particle EFs determined in Leipzig. On the other hand, in yet another study in Germany by Nickel et al. (2013), reported EFs of particle number was closer to the ones observed in Metro Manila. When comparing our soot particle number EFs to those reported from the United States (US), we found that in some cases EFs of LDV was lower in Manila than in the US (e.g. Geller et al., 2005), while Kirchstetter et al. (1999) reported EFs of LDV to be 2.5 times lower in the US than in Manila. All suggesting that some kind of variability exist even within the studies conducted in the same country. Please note that in most of the previous studies the total particle number EFs and not only soot or refractory particle number EFs were reported. The closest match to Manila's carbonaceous particle mass EFs of LDV was found to be China (0.027 g/(km-veh) in both countries, Westerdahl et al., 2009).

It is important to understand that direct comparison between our derived EFs and those reported from other countries must be viewed critically due to several reasons. Firstly, EFs depend on the pollutant concentration difference between urban street and background sites (delta C, AC) as described by Eq. 3, thus proper estimation of pollutant increment due to traffic emission is very important. For example, in a study by Rose et al. (2006), they used

742 only one VTDMA-system to determine AC and thus, the measurements at urban and

743 background sites were accomplished in different time periods. This might result in higher

744 uncertainties when estimating AC. Secondly, measured particle number concentration greatly

745 depends on the measurement technique and the instrumentation used. Several studies

746 indicated that the higher emission factors for particle number concentration were found when

747 instruments with lower detection limits were used (e.g. Nickel et al., 2013). From a study by

748 Birmili et al. (2009) it is clear that there is a large variability of instruments with different

749 detection limits used to evaluate EFs. Moreover, vehicle speeds for which EFs were estimated

750 also span over a large range of values. It is known that different engine operating regimes

751 (idling, accelerating, stopping etc.) do influence EFs and the comparison between values from

752 different studies becomes even more vague (Karjalainen et al., 2013).

753 For the reasons mentioned above, the comparison between EFs in different studies must be

754 taken with a grain of salt. Nevertheless, we were able to show that traffic related pollutant

755 emissions in Metro Manila and other low-income countries are/may be far from accepted

756 norms and limits.

758 3.4.2. Possible errors in estimating soot particle concentration from refractory particle

759 fractions

761 Finally we would like to discuss how close the retrieved refractory particle number and

762 mass could represent actual soot particles. The retrieval of soot particle number distribution,

763 when VTDMA and MPSS systems are used, is based on the assumption that refractory

764 species at a road side are predominantly soot particles and its constituents (e.g. Rose et al.,

765 2009). This assumption, however, might not be always true due to the fact that not only soot

766 particles remain refractory at 300 oC. For example, fine dust particles, particles from

767 mechanical wear of tires, braking pads, some inorganic salts and non-volatile organic material

768 transported from the background or formed on the site may also form externally mixed mode

769 in particle volatility distribution. These particles then might be misleadingly interpreted as

770 soot particles even though their related health effects could be much different. Although in

771 previous studies (e.g. Rose et al., 2009) this step was neglected, we have conducted s

772 refractory particle mass closure to make sure that our retrieved refractory particle number size

773 distribution is a good proxy for soot particle physical properties. Calculated refractory particle

774 mass size distribution allowed us to obtain refractory particle mass in the size range of 10 to

775 800 nm, which we then compared to MAAP measurements (Fig. 10).

Derived via f.

800 801 802

MAAPeBC, (ig/m

Fig. 10. A Comparison between retrieved refractory particle and measured soot particle mass concentrations.

From here it can be seen that refractory particle mass concentration derived from fN,LV and equivalent black carbon mass (as soot proxy) directly measured with MAAP is in a reasonable agreement (slope of 0.992 and R2 = 0.85). It is worth reminding that our retrieved refractory particle number size distribution is a superposition of refractory non-soot and refractory soot particle modes (as described in section 3.1). For this reason the calculated refractory particle mass is a product of these two modes. However, as can be seen from Fig. 8, the mass of refractory particles is mostly driven by the larger size mode particles (mainly ultrafine soot at 80 nm), and thus, it is correct to assume that our derived refractory particle mass is a good proxy for soot mass. The closure between retrieved refractory and measured soot particles would also be a useful tool to elucidate the origin of the smallest refractory particles. Unfortunately, this type of closure study was not possible at the time of experiment.

4. Summary and conclusions

In this study, we characterized the traffic emissions from a street canyon at a polluted urban environment in Metro Manila, Philippines, during a time period from 16 May to 11 June 2015. A Volatility Tandem Differential Mobility Analyzer (V-TDMA) was used to measure refractory number fraction of sub-micrometer particles at Taft Avenue by

evaporating volatile material at 300 C temperature. In addition, particle number size distribution (PNSD), equivalent black carbon mass concentration (eBC, as soot proxy) and PM25 measurements were performed with a Mobility Particle Size Spectrometer (MPSS), Multi-Angle Absorption Photometer (MAAP) and a mini-volume sampler (gravimetric method), respectively. The refractory particle number concentration was then extracted from a combination of the externally mixed particle number fraction and the measured PNSD. Supplementary information on meteorological parameters was collected using an autonomous

weather station and a sonic anemometer at the heights of the measurement container and the rooftop of the nearby building, respectively. This data was later used to calculate emission factors (EFs) with Operational Street Pollution Model (OSPM).

Our observed size-segregated number fractions of externally mixed particles were noticeably higher (90% versus 60%) than in other similar studies. Moreover, the highest number fraction of refractory particles was found to be higher than in previous studies (229 versus 80 nm). We assume that this is because variability in engine technologies and/or fuel composition. Another difference to previous studies was that, despite some randomness, we were not able to observe defined diurnal variation of particle volatility properties. The number fractions of refractory species neither followed the diurnal cycle of the traffic, nor it changed when the roadside aerosol was diluted with cleaner air from urban background. These observations suggest that at road side traffic was a dominant pollutant source, which also determined very distinct physical and chemical aerosol particle properties in an urban background environment.

In contrast to refractory number fractions, derived refractory particle number concentration showed a pronounced diurnal cycle, which was found to be the result of both the increase in traffic density and change in local meteorological conditions (wind speed and direction). Refractory particle number concentration reached as high as 15000 particles/cm during morning rush hours and where at least 3 times higher than values reported from western countries. When roadside aerosol was diluted with cleaner background air, refractory particle number concentration decreased to 6000 particles/cm3. We also found that the World Health Organization limit value for PM25 was exceeded on all measurement days by an average of 2 fold with refractory particles contributing to total PM25 from 55 to 75%. Convection-enhanced vertical mixing appeared to be of minor importance in decreasing soot particle number concentration.

The derived refractory particle number size distribution (r-PNSD, soot proxy) was found to be a superposition of two ultrafine modes at 20 and 80 nm. That is noticeably different compared to previous studies, which usually reported only a bigger refractory particle mode. To better understand the origin of nearly nucleation mode particles, we have also calculated size segregated emission factors (EFs) of refractory particles for different vehicle fleets. The separation of the emissions between light duty vehicles (LDVs) and public utility Jeepneys (PUJs) led not only to overall higher EFs, but also substantially different shaped EFs size distribution when comparing PUJs and LDVs. While LDVs EFs could be characterized by much stronger mode at around 30 nm and weaker mode at around 100 to 200 nm, PUJs EF-SP comprised of two, nearly equally strong modes at 10 to 20 nm and 80 to 100 nm. The smallest particles were most likely ash from metallic additives in lubricating oil, tiny carbonaceous particles and/or nucleated and oxidized organic polymers, while bigger ones were probably soot agglomerates. Observed extremely high concentrations of smallest particles, which have little to no contribution to particulate mass, may play an important role when assessing health related effects.

Calculated EFs of refractory particle number and mass were

1.15 - 1015, 9.79- 1013 and

3.29-1014 rp/km-veh and 1.618, 0.027 and 0.313 g/km-veh for PUJs, LDVs and average fleet, respectively. The important message from the EFs analysis is that old technology diesel PUJs, being only 20% of total vehicular fleet, contribute up to 75 and 94% of total roadside emitted refractory particle number and soot mass, respectively. It is also worth mentioning that PUJs, not like heavy duty vehicles, do not encounter traffic bans and are able to drive streets of Metro Manila with no restrictions regarding environmental protection. The reduction of PUJs emission by improving fuel quality and engine technology, using particulate filters or banning this type of vehicles from the streets may contribute significantly to the reduction of carbonaceous particulate pollutants in urban Metro Manila. Reinforcement of more stringent

857 environmental legislation and technical requirements are also advisable.

858 Due to the complexity, such studies are rare in low-income countries and yet are much

859 desired. The unique findings presented here can be used in urban air quality models and

860 health studies as well as to demonstrate that a better indicator to evaluate air quality and

861 possible health related risks than PMi0 or PM25 is very much needed. Especially in the

862 developing regions where unsustainable urbanization has led to traffic related pollution

863 problems. Our future work will focus on accessing individual exposure to high levels of

864 ultrafine soot particulates in polluted urban environments.

866 Acknowledgement

868 This study was supported by the Partnership for Clean Air Inc., the Researchers for Clean

869 Air, Inc., the Metro Manila Development Authority, the City Office of Manila, De La Salle

870 University (through URCO project number 35FU2TAY11-3TAY12 and the visiting professor

871 grants of the Vice-Chancellor for Academics), CHED (COE research grant of Dr. Vallar and

872 Dr. Galvez) and Leibniz-Institut für Troposphärenforschung e.V. (TROPOS). This study was

873 also supported by the Ministry of Science, ICT and Future Planning in South Korea through

874 the International Environmental Research Center and the UNU & GIST Joint Programme on

875 Science and Technology for Sustainability in 2015. We would also like to acknowledge Mr.

876 Alberto Suansing, Kristine Loise Simangan, Catrina Urbiztondo, Red Castilla, Floyd Rey

877 Plando, Neil Matthew Anore, Kevin Apoloan, Sanmiguel Arkanghel Dichoso, and Sherwin

878 Movilla for their participation in this phase of the campaign.

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Table 1. The average size segregated number fraction of less volatile (fN,Lv) and the volume fraction remaining (VFR) of refractory aerosol particles.

Workday Weekend

Workday Weekend

0.30±0.18 0.28±0.17

0.46±0.14 0.46±0.13

0.37±0.20 0.34±0.20

0.47±0.16 0.44±0.16

0.58±0.23 0.50±0.22

0.64±0.18 0.58±0.18

0.74±0.20 0.67±0.20

0.77±0.16 0.71±0.16

145 nm

0.86±0.16 0.78±0.18

0.86±0.14 0.80±0.15

229 nm

0.89±0.14 0.82±0.18

0.90±0.13 0.84±0.16

Table 2. Campaign average of size segregated and normalized refractory particle number

4 3 2 3 3

(dN/dlogDp, #104/cm3), surface (dS/dlogDp, |m /cm ) area and mass (dM/dlogDp, |g/m3) concentrations. Variability (standard deviation) is presented after "±" sign.

dve 16 nm 28 nm 50 nm 85 nm 145 nm 229 nm

Workday

Number conc. 1.79±1.58 1.88±1.46 2.30±1.53 2.31±1.44 1.34±0.85 0.32±0.21

Surface conc. 15±13 47±36 180±120 520±325 890±560 520±342

Mass conc. 0.1±0.08 0.5±0.4 3.3±2.2 16.1±10 46.9±29.8 43.4±28.5

Weekend

Number conc. 1.42±1.24 1.45±1.20 1.63±1.19 1.70±1.20 0.97±0.70 0.23±0.17

Surface conc. 12±10 36±30 127±93 383±271 644±464 372±285

Mass con. 0.07±0.06 0.4±0.3 2.3±1.7 11.8±8.4 34.2±24.6 -31.1±23.8

Table 3. Europe Union emission regulations for particulate matter (PM) and particle number (PN) for passenger cars and light commercial vehicles with a comparison to emission factors estimated in this study. Subscripts "diesel" and "diesel+gasoline" stands for the corresponding standard of either diesel or diesel and gasoline vehicles, respectively. PMrefr and PNrefr stands for refractory particle mass and refractory particle number emission factors, respectively. Table was adopted from https://www.dieselnet.com/standards/eu/

Stage Date PM, g/km PN, #/km

Passenger Cars

EURO 1 1992 14diesel -

EURO 2 1996 0.08-0.10diesel -

EURO 3 2000 0-05diesel -

EURO 4 2005 0.025diesel -

EURO 5-6 2009-2014 0.005diesel+gasoline r Z^v 1 6.0 *10 diesel+gasoline

Light commercial veh. (>1760 kg)

EURO 1 1994 0.25diesel -

EURO 2 1998 0.17-0.20diesel -

EURO 3 2001 0.10diesel -

EURO 4 2006 0.06diesel -

EURO 5-6 2010-2015 0.005diesel+gasoline 6.0*10 diesel+gasoline

This study Location PM, g/km PN, #/km

LDV Manila, Philippines 0.027refr 9.79*1013refr

Jeepneys Manila, Philippines 1.618refr 1.15*1015refr

Average fleet Manila, Philippines ^0.313refr 3.29*1014refr

Other studies

LDV a'* Beijing, China 0.027bc 1.60*1014

LDV b'* California, USA 0.0027bc 2.22*1014

LDV c'* California, USA 0.0031bc 4.00*1013

LDV d Leipzig, Germany - 5.80*1013

LDV e Berlin, Germany - 2.40*1013

LDV f Meckenheim, Germany - 2.10*1014

HDV a'* Beijing, China 0.513bc 4.36*1015

HDV b'* California, USA 0.306bc 3.24*1015

HDV c'* California, USA 0.513bc 2.49*1015

HDV d Leipzig, Germany - 2.50*1015

HDV e Berlin, Germany - 2.96*1015

HDV f Meckenheim, Germany - 1.18*1015

aWesterdahl et al. (2009) dRose et al. (2006) refr - only refractory particles

bGeller et al. (2005) eBirmili et al. (2009) BC - PM reported as black carbon

cKirchstetter et al. (1999) fNickel et al. (2013) * - EFs reported in g/kg of fuel burnt

Table 4. Size segregated and normalized refractory particle number emission factor (EFs, 1014) for different vehicular fleets: light duty vehicles (LDVs); public utility Jeepneys (PUJ); and emission factor per average vehicle. We were not able to retrieve EFs of LDVs at 85 nm and 229 nm.

dve 16 nm 28 nm 50 nm 85 nm 145 nm 229 nm

dqx/dlogDp #/veh*km

Light Duty Vehicles (LDVs) 0.782 1.770 0.894 --- 0.111 ---

Public Utility Jeepneys (PUJs) 7.730 3.860 8.020 13.300 7.360 2.000

Average vehicle 2.580 2.420 2.630 2.520 1.500 ^ 0.353

• Aerosol particles in urban Metro Manila are mixed exclusively externally.

• Urban aerosol is dominated by refractory ultrafine particles.

• Soot emission from Jeepneys contributes up to 94% of total urban soot mass.

• In developing countries PM10 is not sufficient metric to evaluate air quality.