Particle size distribution and its relationship to black ...

TECHNICAL PAPER

Particle size distribution and its relationship to black carbon in two urban and one rural site in Santiago de Chile 1,⁄ 2 2 3 2 1 4 E. Gramsch, F. Reyes, P. Oyola, M.A. Rubio, G. López, P. Pérez, and R. Martínez 1

Department of Physics, University of Santiago, Santiago, Chile Mario Molina Center for Strategic Studies in Energy and Environment, Santiago, Chile 3 Faculty of Chemistry, University of Santiago, Santiago, Chile 4 Ministry of the Environment, Santiago, Chile ⁄Please address correspondence to: Ernesto Gramsch, University of Santiago de Chile, Department of Physics, Avda. Ecuador 3493, Santiago, Chile; e-mail: [email protected]

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The size distribution of particles has been studied in three sites in the Metropolitan area of Santiago de Chile in the winter of 2009 and a comparison with black carbon was performed. Two sites are located near busy streets in Santiago and the other site is located in a rural area about 40 km west of Santiago with little influence from vehicles, but large influence from wood burning. The campaign lasted 1 or 2 weeks in each site. We have divided the particle size measurements into four groups (10–39 nm, 40–62 nm, 63–174 nm, and 175–700 nm) in order to compare with the carbon monitor. In the sites near the street, black carbon has a high correlation (R 0.85) with larger particles (175–700 nm). The correlation decreased when black carbon was compared with smaller particles, having very small correlation with the smallest sizes (10–39 nm). In the rural site, black carbon also has a high correlation (R ¼ 0.86) with larger particles (175–700 nm), but the correlation between black carbon and the finest particles (10–39 nm) decreases to near 0. These measurements are an indication that wood burning does not generate particles smaller than ~50 nm. In the urban sites, particle size distribution is peaked toward smaller particles (10–39 nm) only during rush hours, but at other times, particles size distribution is peaked toward larger sizes. When solar radiation was high, evidence of secondary particle formation was seen in the rural site, but not in the urban sites. The correlation between the number of secondary particles and solar radiation was R2 ¼ 0.46, indicating that it there may be other variables that play a role in ultrafine particle formation. Implications: A study of the size distribution of particles and black carbon concentration in two street sites and one rural site shows that in the last site the number of particles ultrafine particles (d < 40 nm) is 10 times lower but the number of larger particles is about 2 times lower. Thus, the rural site has less of the particles that are more dangerous to health. The number of ultrafine particles is mostly associated with traffic, while the number of larger particles is associated with wood burning and other sources. Wood burning does not generate particles smaller than ~50 nm.

Introduction Santiago de Chile is a city enclosed between two mountain ranges that generate adverse atmospheric conditions (Rutland and Garreaud, 1995) for dispersion of contaminants. Consequently, large air pollution problems are seen in the city, particularly during winter. The Ministry of the Environment has been working for more than 15 years to improve the air quality, and as a consequence there has been a steady decline in the PM10 and PM2.5 since 1998. A previous study (Koutrakis et al., 2005) found a decline of 52% in PM2.5 and 33% in PM10 between 1989 and 2001. In the last few years, there has been a leveling out of the particulate matter (PM) concentration and even a small increase in the year 2007 (Moreno et al., 2010). Although the quality of vehicles and the combustibles have improved, and the emissions from industry are smaller, there are still several mechanisms that

are playing an increasing role in the air quality that are much harder to control. For instance, the number of vehicles is growing by about 6.5% per year (INE [National Institute for Statistics], 2008), the construction activity by 4.5% per year (INE, 2009), and the number of wood stoves in Santiago is about 60,000, but the increase in their number is not yet known (Conama, 2009). A previous study from Conama (2005) found that there is an increase in the PM10 measured at night (10 p.m.–2 a.m.), which points toward an increase in the use of wood stoves in Santiago. One component of particulate matter that is related to both sources of pollution (diesel vehicles and wood burning) is black carbon (BC), which is generated during incomplete combustion. By studying this fraction of PM and correlating it with particle size, it is possible to improve our understanding of the sources of pollution in the city and the rural site. Most of the BC that is measured is probably graphitic carbon combined with many other organic compounds, but it is operationally defined as the

785 Journal of the Air & Waste Management Association, 64(7):785–796, 2014. Copyright © 2014 A&WMA. ISSN: 1096-2247 print DOI: 10.1080/10962247.2014.890141 Submitted July 21, 2013; final version submitted January 20, 2014; accepted January 22, 2014.


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carbon fraction that absorbs light. Graphitic carbon consists of pure carbon in several linked structures, forming small spheres of about 30 nm diameter (Seinfeld and Pandis, 2006). The organic compounds are weakly absorbing in the visible and ultraviolet (UV) spectral region, the black component is highly absorbing in the visible and IR region. Black carbon is variously called “elemental”, “graphitic” or “soot” in the literature (Novakov, 1984). In this work, the term black carbon (BC) is used to denote the component that absorbs light. Elemental carbon (EC) and organic carbon (OC) are also defined operationally, and they are measured through the detection of carbon dioxide generated in a hot chamber that contains the sample in an oxygen (O2) atmosphere. The separation of the OC from the EC is achieved by thermal differentiation. There have been several studies that aim to improve our knowledge of BC, in particular its size distribution. However, there are many sources of pollution in a city that have emissions with distinctive particle sizes (Seinfeld and Pandis, 2006). Traffic emissions are characterized by nucleation-mode particles (Dp < 10 nm) and Aitken-mode particles particles (Dp < 100 nm) with a size distribution centered around 20–30 nm (Wåhlin et al., 2001; Watson et al., 2006, and references therein). However, the mean of the size distribution increases as the distance from the source (traffic emissions) increases (Zhu et al., 2002; Chakrabarty et al., 2006). Combustion from biomass in heaters, ovens, and so on is another important source of particles in a city. Mönkkönen et al. (2005) have found in a New Delhi, India, study that the geometrical mean diameter (GMD) increases in the evenings due to biomass and refuse burning. Previous studies of size distribution in wood stoves and fuel test beds have shown a wide variation due to different combustion conditions. Hays et al. (2002) used open combustion of a fuel to simulate burning in the field. They reported very large geometric mean diameters, between 0.1 and 0.2 mm, using a scanning mobility particle sizer (SMPS). However, due to the use of a small chamber (28 m3), their particles may have grown by condensation and coagulation within the enclosure. Chakrabarty et al. (2006) measured particle size distribution from laboratory combustion of several fuels using the SMPS and image analysis. Projected area equivalent diameter peaks ranged from 30 to 200 nm for wet (moisture content [MC] ¼ 20% on dry mass basis) and dry (MC ¼ 5–10%) fuels. Major mode diameter ranged between 40 and 45 nm for three out of six burns (see their Fig. 12) for dry fuels they tested. Le Canut et al. (1996) measured particle size distribution using a laser optical particle counter in their airborne study during a savanna fire. They reported two mass modes: one at 0.2–0.3 mm and the other above 2 mm. Hosseini et al. (2010) have performed controlled biomass burning in a laboratory environment and measured particle size distribution with fast instruments. Their results show that the size distribution of particles is centered around 30–60 nm, smaller than the studies just mentioned. They attribute this difference to due to the relatively slow response rate of the instruments used in those studies, which cannot capture the temporal evolution of the size distribution. However, from the previous studies it seems clear that particle size distribution from biomass burning is larger than ~50 nm. In this work, the relationship between the particle size and black carbon has been studied for three different sites in order to

improve our understanding of the sources of black carbon and ultrafine particles in the Metropolitan Area of Santiago.

Measurements and Methods Measurements were performed for three sites that have different sources of black carbon and particles: an urban site (La Cisterna) located near a busy street about 8 km south of downtown Santiago, another urban site (Alameda) located near the main street in downtown Santiago, and a rural site (Peñaflor) located 30 km south west of the city. The map in Figure 1 shows the measuring sites.

Alameda site The equipment was installed in the balcony of a three story building, about 3 m from the ground and 6 m north from Alameda Avenue. This site is located about 1 km west of downtown Santiago. Alameda is the main avenue in Santiago; it has an east–west direction and it has 12 bus routes. The avenue has five lanes on each direction; three of these lanes are exclusively for buses and two for all other vehicles in each direction. The flux of vehicles is about 60,000 per day. This site has many office buildings and some commercial activity. Measurements were performed continuously at Alameda from July 25 through August 7, 2009, with a differential mobility particle sizer (DMPS) and a black carbon monitor (SIMCA).

La Cisterna site The equipment was installed in the balcony of a two-story building, about 6 m west of Gran Avenida Street and 3 m from the ground. Gran Avenida is a north–south street that has three lanes on each side; it has several bus routes, and a high flux of small trucks, taxis, and private vehicles. The total flux of vehicles in 2009 in La Cisterna was about 10,000 per day. This street has a lot of commercial activity and several office buildings. Measurements were performed continuously at La Cisterna

Figure 1. Map of the city of Santiago de Chile with the location of the monitoring sites.


during August 20–28, 2009, with the same monitors as for Alameda.

Peñaflor site This site is located outside a mid-size town of 90,000 inhabitants, mostly residential, located 30 km southwest of Santiago. There are very few industries but the agricultural activity is high. The town is located upwind from Santiago and little pollution from the city reaches Peñaflor. Measurements were performed continuously at in this site during August 28–September 14, 2009.


Particle measurements with DMPS The differential mobility particle sizer (DMPS) is similar to what has been described in the work of Winklmayr et al. (1991) and Aalto et al. (2001). The DMPS consists of a 28-cm Viennatype differential mobility analyzer (Winklmayr et al., 1991) using a recirculating flow system (Jokinen and Mäkelä, 1997), in connection with a TSI model 3010 condensation particle counter. This instrument was used to measure particle number concentrations in 25 geometrically equidistant electrical mobility channels corresponding to one-electron charged spherical particles with diameters in the range 10–700 nm. These channels relate to DMPS voltages in the range 10 V–11 kV. Corrections for the DMPS transfer function efficiency, and for zero and multiple electron charging (Wiedensohler, 1988), were made by matrix inversion based on a 13-point spline function of the differential inlet particle number concentration as a function of the particle mobility. Using a known relation between mobility and diameter of spherical particles, differential particle number concentrations as a function of particle diameter were calculated (dN/dlogd). Corrections were made for the drop in the efficiency of the particle counter below 15 nm (Mertes et al., 1995). The scanning time for each spectrum was 2 min, and the average spectra for every hour were calculated for comparison with 1-hr average concentrations of other pollutants. Integrated concentration values over the whole measured size range (10–700 nm) of number (N) have been calculated. In addition to the DMPS and black carbon monitors, we have also used data from the TEOM 1400 monitors from Rupprecht & Patachnick, Albany, NY. These monitors are installed in the stations of the MACAM Network (SINCA 2013), and their data were used to evaluate the background PM2.5 in Santiago.

Black carbon measurements The monitor used in this study (SIMCA) employs a variation of the integrating plate method (Lin et al., 1973; Gramsch et al., 2000; Gramsch et al., 2004) to measure the absorption coefficient of light in the air. This coefficient is multiplied by the mass absorption coefficient (Horvath, 1993, and references therein) to obtain carbon concentration. The SIMCA is made up of a head containing a filter, two light-emitting diodes (LEDs), two photodetectors, and amplifier electronics. A computer controls a pump, the LEDs and photodetectors through an interface box. The design of the head is a variation of the integrating plate method (Lin et al., 1973; Horvath, 1993), which has been used

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previously in Santiago (Horvath, 1993) to measure the absorption coefficient, sa. In this instrument, air is pumped for 1 min through a 25-mm-diameter Nuclepore filter (pore diameter 0.2 mm) that collects particles present in the air, and the intensity of light passing through the filter is measured. This process is repeated every 15 min for 2 to 5 days. In this way, one value of the absorption coefficient is obtained every 15 min using the same filter. The hourly data are obtained by averaging four points per hour. The filter is changed when the intensity of the light reaches about 30% of the intensity with a clean filter, in order to avoid too much carbon accumulation. There is a second detector placed on the side of the filter that monitors the intensity of the lamp. The output from this detector is used to correct for changes in the lamp or gain in the amplifier due to temperature or other effects.

Results and Discussion One the motivations for this work was to investigate the fact that in the last few years the purchasing and installation of wood stoves in Santiago have increased (Conama, 2007) and to study their relationship with the increase in pollution. Although all new stoves sold must have an emissions control mechanism, the increasing numbers could have an influence on air pollution.

Trends in PM2.5 Data from the MACAM Network (SINCA, 2013) from 2001 to 2008 in Santiago de Chile has been used to assess the possible influence of wood burning on PM2.5. In Santiago, it is known that most of the stoves are used during the cold months (May– August) and mostly during the evening and at night. On weekdays, during the daytime most people go to work and the temperature is not very low, and consequently stoves are not used, but at night, the majority of the stoves are in use. Thus, an increase in particle matter would be expected at night. In addition, most stoves are used in peripheral areas that have singlefamily houses. Because wood burning generates mostly PM2.5, the trend of this pollutant may show the effect of the increasing number of wood stoves. An analysis of the monthly average PM2.5 data was performed in two sites near the monitoring stations. The closest MACAM station to the La Cisterna monitoring site is located in a residential area (La Florida) about 7.5 km northeast. The nearest MACAM station to the Alameda site is located in a park about 1 km south of the site. Data were divided into two groups: day, from 3 a.m. to 6 p.m., and night, from 7 p.m. to 2 a.m., only for the colder months in Santiago (May–August). Figure 2a shows the average concentration in La Florida for each cold month and for each year from 2001 until 2008 for both groups. The PM2.5 concentration in La Florida is larger at night, indicating that the height of the mixing layer and lower wind speeds (Muñoz and Undurraga, 2010) play an important role in the observed pollution. The solid lines are a Theil–Sen trend line for each data set. In La Florida, the night trend line (black line) appears to increase over time; however, the Theil–Sen test finds insufficient evidence to identify a significant increasing or decreasing trend even at a 60% level of significance. For the


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Figure 2. (a) PM2.5 concentration at night and day from 2001 to 2008 in La Florida. (b) PM2.5 concentration at night and day from 2001 to 2008 in Parque O’Higgins. The straight lines are Theil–Sen fits to the data.

day trend line in La Florida, the Theil–Sen test also finds insufficient evidence to identify a significant increasing or decreasing trend at a 60% level of significance. These tests indicate that the average PM2.5 concentrations for night or day do not change over the period in La Florida. Figure 2b shows the average concentration in Parque O’Higgins for the same months and periods of the day as Figure 2a. As before, the solid lines are a Theil–Sen trend line for each data set. In Parque O’Higgins, the night trend line (black line) decreases over time and the Theil–Sen test finds statistically significant evidence of a decreasing trend at the 90% level of significance. For the day trend line, the Theil–Sen test also finds evidence of a decreasing trend, but at an 80% level of significance. The tests indicate that in Parque O’Higgins the day and night trends are decreasing. Both stations of the MACAM network show a different trend: Parque O’Higgins is decreasing over time and La Florida is not changing. The La Florida station is located about 9.5 km southwest of Parque O’Higgins and it can be assumed that the meteorological conditions are the same in both places. Therefore, the difference between stations has to be related to local emissions, which could be increased use of cars in La Florida or increased use of wood stoves. The decrease of PM2.5 concentrations in Parque O’Higgins may be related to an improvement in the quality of cars, a change in meteorological conditions, or a reduction in other type emissions. The number of cars in Santiago has increased 6.5% per year over the same period (INE, 2008). In order to study the influence of the meteorological variables on PM2.5 in La Florida, a linear regression model was used to identify specific factors influencing the concentrations and to quantify their relative impact (Sax et al., 2007). The regression model included data for year, month, day of week, wind speed, temperature, and relative humidity as categorical variables. Wind speed (ws), was categorized in the following ranges: 0 ws < 0.5 m/sec, 0.5 ws < 1 m/sec, 1 m/sec ws < 1.5 m/sec, 1.5 m/ sec ws < 2 m/sec, 2 m/sec ws. Relative humidity (RH) was

categorized in the following ranges: 0 RH < 25, 25 RH < 50, 50 RH < 75, 75 RH 100. Temperature was categorized as follows: 0 C T < 10 C, 10 C T < 20 C, 20 C T < 30 C, 30 C T. Rainfall, r, was dichotomized as off (no rain) and on (more than 3 mm per day). Wind direction was found to have no effect in the concentrations. As before, PM2.5 data were divided into two groups: day, from 3 a.m. to 6 p.m., and night, from 7 p.m. to 2 a.m., only for the colder months in Santiago (May– August). Following Sax et al. (2007), PM2.5 concentration was modeled as: PM2:5 ¼ I f year ;

j

f ws ;

f month ; j f weekday ; j

fT ;

j

j

fRH ; j fr ;

(1)

j

where P I ¼ exp(a), a is the regression intercept, and fi,j ¼ exp ( byj * var ij) is the concentration impact factor (CIF) for each variable of a category j. A reference level was established for each of the variables in order to study whether there was a relative change. For the year variable, the reference was set to year 2001; for the day of the week, the reference was set to Monday; for the relative humidity, the reference was 0 RH < 25; for rain, the reference was off; for temperature, the reference was 0 C T < 10 C; and for wind speed the reference was 0 ws < 0.5 m/sec. A CIF coefficient of 1 means that the variable has no effect on the concentrations, a CIF coefficient greater than 1 means that the variable has some influence on the increase of the concentration, and a CIF less than 1 represents an influence on the decrease of concentration relative to the reference value. The CIF for the intercept I ¼ ea (Eq. 1) corresponds to the PM2.5 concentration at each of the reference levels, that is, year 2001, Monday, relative humidity RH < 25, for rain off, temperature T < 10 C, and for wind speed the reference was 0 ws < 0.5 m/sec. The results of the regression are shown in Figure 3. The results indicate that there is little difference between day and night in several variables. The day of the week shows a small increase toward the weekend. Friday and Saturday have the

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Figure 3. Regression analysis from 2001 to 2008 showing the influence of several meteorological variables on PM2.5.

highest influence during day and night, respectively. The increase on Saturday night may be due to increase in vehicular flux. As expected, rain has a strong effect in the reduction of PM2.5 concentration, with slightly higher effect at night. The effect of the relative humidity is almost constant except at RH > 75%, which is related to increased condensation of particles and posterior falloff to the ground. The RH effect at night is slightly higher because lower temperatures generate higher relative humidity. The temperature effect shows an increase between 20 and 30 C and a posterior decrease at higher temperatures. Similar increase at mid temperatures was observed by Sax et al, 2007. The decrease at temperatures higher than 30 C may be explained by increased vertical dispersion of pollutants. The missing point in the night curve is due to the fact that at night, in winter, temperatures are always lower than 30 C. As expected, the increase in wind speed has an inverse relationship

to PM2.5 concentration, because of higher dispersion of pollutants. The regression analysis for the year effect does not show a significant change from 2001 to 2006 for the day or night, but during 2007 and 2008 there is a small increase in the night concentration of PM2.5, but not during the day. This difference could be due to an increase in the use of wood for heating at night during the last two years.

Black Carbon Concentration in the Urban and Rural Sites Black carbon is an important fraction of PM2.5, and in Santiago it can vary from 10 to 20% (Artaxo et al., 1999). In cities, it is emitted mainly from diesel combustion but also from wood smoke, and it can contribute to 5–20% of the particulate


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mass in wood smoke (Glasius et al., 2006; Hedberg et al., 2002). Thus, BC could be used as a tracer for these sources. The measurements in the downtown urban site (Alameda) were performed during July 25–August 7, 2009, measurements in the southern urban site (La Cisterna) were performed during August 20–28, 2009, and the measurements in the rural site were performed during August 28–September 14, 2009. A simple inspection of the data from the MACAM Network shows that the meteorological conditions during the measurement period could be considered normal. During this time, the wind speed fluctuated between ~0.1 and 2.5 m/sec every day, with only 2 days reaching speeds above 3 m/sec. The temperature fluctuated between 4 and 22 C, with 1 day reaching 0 C and 3 days with maximum temperatures above 25 C. These temperatures and wind speeds are normal for this time of year and do not represent extreme conditions. There were only two days with rain during the measuring period (September 5 and 6), which affected the measurements in Peñaflor, but they can also be considered normal. Because measurements were not simultaneous, only a relative comparison between sites is performed and a very simple comparison between the averages of the sites is done. Black carbon concentrations for all three sites are shown in Figure 4. For each point in the curves, the standard deviation is shown. The Student t-test was applied to La Cisterna and Alameda to determine the probability that the averages are the same. For all points the test gives a probability higher than 50% that the average in Alameda and the average in La Cisterna are the same. Thus, the two curves cannot be differentiated. However, a comparison between Peñaflor and Alameda or Peñaflor and La Cisterna indicates that the averages are different (Figure 4). The Student t-calculation also indicates that the averages are different. Figure 4 also shows that all sites have similar trends: There is an increase in concentration during morning rush hour; there is a decrease during the afternoon (2–6 p.m.), which coincides with higher wind speeds (Muñoz and Undurraga, 2010), and an increase in BC at night. The rural site has lower average black carbon concentration than the other sites (the inset shows the BC

Table 1. Ratio of the average BC at night (7 p.m.–1 a.m.) to the average BC, during the whole day

Ratio BCnight/BCday Rural site (Peñaflor) Urban site south (La Cisterna) Urban site downtown (Alameda)

1.23 1.15 1.11

plot of Peñaflor), but the BC concentration at night seems to be slightly higher than the concentration during rush hour. This site has less traffic and less commercial activity than the other sites because it is mainly a residential area located in a rural sector. However, many houses have wood burners for heating, which may be responsible for the slightly higher relative concentrations at night. The urban site in the south of Santiago (La Cisterna) has a higher BC concentration than the rural site, but similar BC to the downtown urban site, indicating that traffic and commercial activity of the city influence heavily the BC pollution because both urban sites have much more activity than the rural site. The BC concentration at night in La Cisterna is almost as high as the concentration during rush hour, but Alameda has slightly lower concentration at night than during rush hour. La Cisterna is located close to residential areas, with single houses that can have wood burners that may explain the larger relative BC concentration at night. However, more studies are needed to determine whether La Cisterna has larger relative concentrations at night. In order to compare the sites, the ratio of the night concentration (from 7 p.m.–1 a.m.) to the average for the whole day has been calculated. Table 1 shows the ratio for the three sites. It is clear that the rural site has a higher ratio because the relative BC emissions at night are higher than emissions at other times of the day. The opposite happens for the urban site with fewer night emissions. If the increase in BC at night were only due to the thermal inversion, the increase should be similar for all sites, because all sites are in a zone with the same topographical characteristics. But the fact that the increase is higher in a zone with more single-family houses and few pollution sources indicates that the increase in BC is due to wood burning. The urban site in the southern part of Santiago also seems to have emissions from wood burning, but in this site there are many sources that may also generate BC.

Particle size distribution at the urban and rural sites Particle size distribution was measured simultaneously with BC at the three sites. To facilitate and simplify the examination of the data, the 2-min values for each size have been averaged in three periods:

Figure 4. Average black carbon concentration in the rural and urban sites. The inset shows BC in the rural site (Peñaflor).

From 7:00 to 10:00 a.m., corresponding to morning rush hour. During this period, the main sources of particles are vehicles, the wind speed is low (~1 m/sec). From 3:00 to 6:00 p.m., corresponding to the afternoon period. During this period there are fewer vehicles and there is a clean breeze from the west.



From 10:00 p.m. to 1:00 a.m., corresponding to the night period. This period is characterized by low winds, thermal inversion, and some wood burning. Figure 5a shows the size distribution data for Alameda during the time interval from July 25 until August 6, 2009, for the three time periods mentioned before. The profiles shown are characteristic of a site with high traffic load in the center of a city. The line with data from 7:00 to 10:00 a.m., which corresponds to morning rush hour in Santiago, has a clear peak at ~25 nm. During this time of day traffic emissions are predominant with a distribution peaked toward ultrafine particles coming from vehicle exhaust (Wåhlin et al., 2001). But there is also a secondary peak centered around 80 nm. In Santiago, the maximum of the size distribution at this time of the day (25 nm) is slightly higher than for studies in other cities. Kittelson et al. (2004) have found a size distribution centered around 10 nm near a highway, but larger particles near residential areas. Wåhlin et al. (2001) also have found distributions centered around 20 nm in streets of Copenhagen. Thomas and Morawska (2001) have found particle size distributions peaked at ~15 nm from vehicular combustion in the city of Brisbane, Australia, but, after a biomass burning episode, the maximum of the distribution was shifted to ~70 nm. Our measurements are done near a street with high traffic, but there are also many other sources of particles that can shift the distribution toward larger particles. The number of particles during this period of the day is higher than the number of particles at other

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periods, because the traffic is higher (Gramsch et al., 2008) at this time of day. During the afternoon, the number of particles is lowest because at this time of day a clean breeze enters the city from the southwest (Muñoz and Undurraga, 2010). The size distribution is broad, which indicates that there are no predominant sources of pollution. The size distribution of particles at night peaks at larger sizes (~80 nm), indicating that traffic emissions are not predominant at this time of day. Larger particles could be produced by agglomeration and coagulation of smaller particles generated during the day. They could be also generated by wood burning or other sources of large particles at night (Mönkkönen et al., 2005; Hosseini et al., 2010). The size distribution of particles measured in La Cisterna is shown in Figure 5b. The distribution during rush hour peaks toward small sizes (~25 nm) and the shape is very similar to that for Alameda, indicating a strong influence from vehicle emissions. During the afternoon, the shape is broad with no clear peak. The number of particles is lower at this time of day because of the afternoon breeze (Muñoz and Undurraga, 2010). At night, the curve is clearly peaked toward larger sizes (~80 nm), and the number of particles is almost the same as in the morning rush hour. As mentioned before, the large particles could be generated by coagulation and agglomeration of smaller particles or by other sources of fresh particles. It is likely that larger particles are generated by other processes, because if the larger particles were generated only by coagulation and agglomeration the distribution would be similar to what is seen in Alameda at night

Figure 5. (a) Particle size distribution in the downtown urban site (Alameda), for three time periods. (b) Size distribution in the urban site south (La Cisterna). (c) Size distribution in the rural site (Peñaflor).


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and the height of the peak would not be as large as in Figure 5b. The most likely primary source for the larger particles is wood burning because this area is also residential. The size distribution of particles in the rural site (Peñaflor) is shown in Figure 5c. The shape of the curve during the rush hour is very different than for the other sites because no ultrafine particles peak (10–40 nm) is seen in any of the curves and there is a clear dominance of large particles (~100 nm) all day. These results are similar to what was observed by Thomas and Morawska (2001) after a biomass burning episode, with a distribution peaking at ~70 nm. The reason for not observing small particles in Peñaflor could be that the influence of traffic is so small that ultrafine particles are hidden by the larger particles. Another reason could be the large distance from the nearest street to the measuring site. The distance is about 200 m, which means that particles can coagulate and grow before reaching the DMPS inlet. The fact that the curve during the morning is peaked toward large particles indicates that there may be influence from the previous night or that there are sources of fresh large particles during the morning. The curve during the afternoon is also peaked toward large particles and it is very similar to the morning curve. It is likely that the sources of pollution are the same in the morning and afternoon. At night, the shape of the curve is also similar to the others, but the total number of particles is higher than in the morning or afternoon. This indicates that there are more sources emitting at night. It is known that in rural areas like Peñaflor, many houses use wood stoves for heating and the main source of pollution is wood burning. The fact that at night, when wood burning is highest, there are very few of the smallest particles indicates that this process does not generate particles smaller than ~70 nm.

Correlation between black carbon and particle size A comparison between the time series for black carbon and several groups of particles allows a better understanding of the sources of pollution. In order to simplify the analysis, data from the DMPS have been divided into 4 groups: 10–39 nm, 40–62 nm, 63–174 nm, and 175s–700 nm. The first group, 10–39 nm, contains the nucleation and Airken mode particles, which are primary particles emitted by the exhaust of vehicles or created by photochemical processes in the atmosphere (Young and Keeler,

2004). This group is centered on the maximum of the distribution found in Alameda and La Cisterna in the morning (see Figures 5a and 5b). Nucleation-mode particles usually consist of volatile organic and sulfur compounds that form during exhaust dilution and cooling, and may also contain solid carbon and metal compounds. A small fraction of the mass (1–20%) is contained in the nucleation mode (Kittelson, 1998). The second group, 40–62 nm, is an intermediate group between nucleation mode particles and accumulation mode. The third group, 63–174 nm, is centered on the secondary maximum found in Alameda and La Cisterna (see Figures 5a and 5b), and is also centered around the maximum of the distribution found in Peñaflor (see Figure 5c). This group corresponds approximately to the condensation mode as described in Seinfeld and Pandis (2006). This group probably has particles from the previous groups that condense and agglomerate, and primary particles emitted from sources other than vehicles. The fourth group, 175–700 nm, corresponds to larger particles, which belong to the accumulation mode. This group has primary particles from different sources. Figure 6a shows a time-series plot of the 10–39 nm group of particles and black carbon in the downtown urban site (Alameda). It can be seen that the two curves are very different and have very low correlation, indicating that the sources are different. This also indicates that black carbon in the atmosphere of the city is not composed of particles smaller than ~40 nm. Figure 6b shows the time-series plot of the 175–700 nm group of particles and black carbon. In this case the curves resemble each other, indicating that the correlation between black carbon and these types of particles is much higher. A study in Fresno (Watson et al., 2002) found that larger particles and “higher BC concentrations during weekday evenings may be related to a combination of emissions from residential wood combustion and motor vehicles.” Similar relationships between particle sizes and black carbon can be found for the La Cisterna and Peñaflor sites. If the Pearson correlation function is calculated between black carbon and different groups of particles in the three sites, a trend between them can be found (Figure 7). In the rural site (Peñaflor) there is no correlation (0.08) between BC and the group of particles with sizes 10–39 nm, indicating that these particles are not black. The largest source of ultrafine black particles in

Figure 6. (a) Time series between black carbon and ultrafine particles (10–39 nm) in Alameda. (b) Time series between black carbon and large particles (175–700 nm) in the same place.



Figure 7. Correlation coefficient between black carbon and different groups of particles in the three sites.

a city is diesel engines (Apte et al., 2011, and references therein); thus, the data indicate that there are few diesel vehicles in Peñaflor during the morning and the ultrafine black particles do not reach the inlet of the DMPS monitor. The correlation between BC and particles increases as the size of the particles increases, reaching 0.86 with the 175–700 nm group of particles. This curve of Figure 7 indicates that black particles are mostly of larger sizes (d 175 nm) in the rural site. In the urban site in downtown Santiago (Alameda), the correlation between the 10–40 nm size and BC is relatively high (0.5), indicating that many of the finer particles are black, which is expected, because in the city there are many diesel vehicles. The correlation coefficient between BC and larger particles increases, reaching the same value as in Peñaflor for the 175–700 nm group. The high correlation between BC and the 175–700 nm group indicates that these particles are mostly black. A similar situation occurs in La Cisterna, a site near a large street but also near residential areas. In La Cisterna, the correlation between the 10–39 nm group and BC is lower than in Alameda although the site is very close to the street. Thus, the most probable reason for the lower correlation is that the night hours have lower correlation with BC because the traffic is lower.

Secondary particle formation in the three sites Secondary particle formation has been found to occur in many different situations, and it can mislead the interpretation of the data because this can be assumed as coming from traffic. An analysis is done to separate them from particles coming from traffic, wood burning or other sources. Secondary particles are associated with high solar radiation (Young and Keeler, 2004) and the presence of precursor gases such as sulfur dioxide (SO2), hydroxyl radical (OH), and nitrogen oxides (NOx). A peak in hourly average nanoparticle (3–10 nm) concentrations of ~9000 cm3 was observed by Lowenthal, Borys, and Wetzel (2002) following the diurnal variation in solar irradiance at a site in the Colorado Rocky Mountains. A nucleation event in the

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remote marine boundary layer was observed by Covert et al. (1992) that appeared to be related to an increase in sulfur dioxide (SO2) concentration. Birmili et al. (2000) observed new particle production at noon in conjunction with solar radiation and increases in H2SO4 and hydroxyl radical (OH) concentrations. O’Dowd et al. (1998) observed increases in nanoparticle numbers at a remote coastal site in Ireland, attributed to photochemical conversion of gaseous biogenic precursors. In general, photochemical formation of secondary particles is favored by low ambient temperature (Gidhagen et al., 2004) and low aerosol surface area of the diluting air (Sturm et al., 2003). Increased surface area increases the coagulation of the newly formed nanoparticles, and constrains the formation of new nanoparticles. An assessment of the likelihood of secondary particle formation was performed on the rural and urban sites during the measuring period in the Metropolitan Region. In the rural site, the formation of secondary particles has been observed in days with high solar irradiance. Figure 8 shows measurements of ultrafine particles (10–39 nm) along with solar radiation. The top left graph shows a clear day with high solar radiation. The number of nanoparticles (10–39 nm) plotted in the left axis shows a peak between ~11:00 and 15:00 hr. This peak is probably not coming from vehicle emissions because it does not occur during rush hour; hence, it indicates new particle formation due to photochemical processes. Figure 8b shows measurements in the same place, but on a day with little solar radiation. In this case there is no ultrafine particle peak during the afternoon—that is, there is no secondary particle formation. In the urban site (La Cisterna), where a large number of particles of all sizes are present, secondary particle formation is not seen. Figure 8c shows the urban site (La Cisterna) on a clear day with high radiation, and Figure 8d shows a day with low radiation in the urban site; in each case, no secondary particle formation is seen during the afternoon. The inhibition of secondary particle formation has also seen in other polluted cities. Results from Dunn et al. (2004) in Mexico City suggested that particle formation events occur when PM10 mass concentrations were at a significantly lower level than their averages, hence decreasing condensational surface area. Mönkkönen et al. (2005), in New Delhi, India, have found that even though the ultrafine particle events could be detected, the formation and growth of nucleation mode particles are disturbed by high aerosol background concentration. In their studies all events occurred usually at noon or in the afternoon when the solar radiation was most intensive. The results also indicate that secondary particle formation has little effect in the shape of the particle distribution of the city, even for days with high solar radiation, during the period of the study. In the rural site, it may affect the correlation between BC and the 10–39 nm group of particles. The correlation coefficient for all days in the rural site (Peñaflor) has been calculated between the solar radiation and the area of the afternoon peak of the ultrafine particles (10–40 nm). The start of the peak was defined when the number of ultrafine particles started to increase after 11 a.m. and before 6 p.m. (each peak had a different stating hour). The background was subtracted from the integral, and when the net area was negative, zero was used. Figure 9 shows the correlation


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Figure 8. (a) Number of ultrafine particles (10–39 nm) in a day with high solar radiation in Peñaflor. (b) Number of particles in a day with low solar radiation in Peñaflor. (c). Number of particles with high solar radiation in the urban site (La Cisterna). (d) Number of particles with low solar radiation in La Cisterna.

not the only factor. In addition, the correlation plot shows that formation of particles starts when solar radiation is greater than approximately 10 MJ/m2, which points to the possible existence of an energy threshold for the formation of new particles in the atmosphere. However, more studies are needed to understand this occurrence.

Conclusions Black carbon and particle number concentrations were measured in three sites, with a sampling period of 1 or 2 weeks depending on the site. Two of the sites were located in a large city near busy streets and one site was in a rural environment. The size distribution of particles is similar to that observed in other large and polluted cities. The distribution has two peaks in the city (~25 and 80 nm) but only one peak in the rural site (~80 nm), which may be related to the dominant source of particles at the respective sites. We have divided the particle size measurements into four groups (10–39 nm, 40–62 nm, 63–174 nm, and 175–700 Figure 9. Correlation coefficient between the number of ultrafine particles and nm) in order to compare with the carbon monitor. In the urban total solar energy in Peñaflor. sites, near the street, the data show that black carbon has a high correlation (R 0.85) with larger particles (175–700 nm), and thus coefficient between the total solar radiation for the day and the most large particles are black. The correlation decreased when number of ultrafine particles as defined before. The correlation black carbon was compared with smaller particles, having very coefficient (R2 ¼ 0.46) is an indication that solar radiation plays small correlation with 10–39 nm group of particles (R ¼ 0.4 for a role in the formation of ultrafine secondary particles but it is one site and R ¼ 0.5 for the other site).



In the rural site, the number of particles in the 175–700 nm group was two times smaller than for the street site but the number of particles in the 10–39 nm group was 10 times smaller. This fact may be important for city planners, because smaller particles are more harmful to health and the rural site has less of those. In the rural site, the correlation between large particles (175–700 nm) and black carbon was also high, but it decreased when black carbon was compared with the smaller particles. The correlation between black carbon and the smallest particles (10–39 nm group) was very low (R ¼ 0.08). The null correlation indicates that these particles are not black; thus, ultrafine particles in the rural site may originate from photochemical processes, not from diesel vehicles. These measurements are also an indication that wood burning does not generate particles smaller than ~50 nm; therefore the size distribution is peaked toward ~100 nm. In both urban sites, particle size distribution is peaked toward smaller particles (10– 39 nm) only during rush hours. At other times, particles size distribution is peaked toward larger sizes. Evidence of secondary particle formation was seen for the rural site, but not for the urban sites. The correlation between the number of secondary particles and solar energy was R2 ¼ 0.46, indicating that solar radiation is not the only variable that plays a role in ultrafine particle formation.

Funding This work was supported by the University of Santiago (Dicyt) project numbers 09-1031GL and 091331PJ, the National Commission for the Environment (CONAMA), and Fondecyt project number 1120672.

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About the Authors Ernesto Gramsch and Patricio Pérez are research professors at the Physics Department of the University of Santiago de Chile, Santiago, Chile. Pedro Oyola and Gianni López are directors and Felipe Reyes is a staff member at the Mario Molina Center for strategic studies of energy and environment, Santiago, Chile. María Angélica Rubio is a research professor at the Faculty of Chemistry, University of Santiago, Santiago, Chile. Roberto Martínez is a staff member at Ministry for the Environment, Santiago, Chile.