Contaminación Santiago 1989-2001

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Analysis of PM10, PM2.5, and PM2.510Concentrations in

Santiago, Chile, from 1989 to 2001Petros Koutrakis

a, Sonja N. Sax

a, Jeremy A. Sarnat

a, Brent Coull

a, Phil Demokritou

a

, Phil Demokritoua, Pedro Oyola

b, Javier Garcia

c& Ernesto Gramsch

d

aHarvard University , School of Public Health , Boston , MA , USA

bUniversidad de So Paulo , So Paulo , Brazil

cComision Nacional del Medio Ambiente (CONAMA) , Santiago , Chile , USA

dDepartment of Physics , University of Santiago , Santiago , Chile , USA

Published online: 01 Mar 2012.

To cite this article:Petros Koutrakis , Sonja N. Sax , Jeremy A. Sarnat , Brent Coull , Phil Demokritou , Phil

Demokritou , Pedro Oyola , Javier Garcia & Ernesto Gramsch (2005) Analysis of PM10, PM2.5, and PM2.510Concentrations

in Santiago, Chile, from 1989 to 2001, Journal of the Air & Waste Management Association, 55:3, 342-351, DOI:

10.1080/10473289.2005.10464627

To link to this article: http://dx.doi.org/10.1080/10473289.2005.10464627

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Analysis of PM10, PM2.5, and PM2.510 Concentrations inSantiago, Chile, from 1989 to 2001

Petros Koutrakis, Sonja N. Sax, Jeremy A. Sarnat, Brent Coull, and Phil Demokritou

Harvard University, School of Public Health, Boston, MA

Pedro Oyola

Universidad de Sao Paulo, Sao Paulo, Brazil

Javier Garcia

Comision Nacional del Medio Ambiente (CONAMA), Santiago, Chile

Ernesto Gramsch

University of Santiago, Department of Physics, Santiago, Chile

ABSTRACT

Daily particle samples were collected in Santiago, Chile, at

four urban locations from January 1, 1989, through De-

cember 31, 2001. Both fine PM with da2.5 m (PM2.5)

and coarse PM with 2.5da10 m (PM2.510) were col-

lected using dichotomous samplers. The inhalable parti-

cle fraction, PM10, was determined as the sum of fine and

coarse concentrations. Wind speed, temperature and rel-

ative humidity (RH) were also measured continuously.

Average concentrations of PM2.5 for the 19892001 pe-

riod ranged from 38.5 g/m3 to 53 g/m3. For PM2.510levels ranged from 35.848.2 g/m3 and for PM10results

were 74.4 101.2 g/m3 across the four sites. Both annual

and daily PM2.5and PM10concentration levels exceeded

the U.S. National Ambient Air Quality Standards and the

European Union concentration limits. Mean PM2.5levels

during the cold season (April through September) were

more than twice as high as those observed in the warm

season (October through March); whereas coarse particle

levels were similar in both seasons. PM concentration

trends were investigated using regression models,

controlling for site, weekday, month, wind speed, tem-

perature, and RH. Results showed that PM2.5concentra-

tions decreased substantially, 52% over the 12-year period

(19892000), whereas PM2.510 concentrations increased

by 50% in the first 5 years and then decreased by a

similar percentage over the following 7 years. These de-

creases were evident even after controlling for significant

climatic effects. These results suggest that the pollution

reduction programs developed and implemented by the

Comision Nacional del Medio Ambiente (CONAMA) have

been effective in reducing particle levels in the Santiago

Metropolitan region. However, particle levels remain high

and it is thus imperative that efforts to improve air quality

continue.

INTRODUCTION

Santiago, Chile, is one of the most polluted cities in South

America with high levels of nitrogen oxide and dioxide,

CO, ozone, PM2.5, and PM10, polycyclic aromatic hydro-

carbons, and black carbon.111 It is situated in a closed

basin that experiences frequent thermal inversions, espe-

cially in the winter. The combination of these meteoro-

logical and topographical factors and a rapidly growing

economy has given rise to elevated gaseous and particu-

late pollutant concentrations in the Metropolitan area.

Much of the economic growth experienced in Santiago

has been in the industrial sector, which has contributed

to the air pollution problems.6 In addition, the vehicle

fleet in Santiago has doubled between 1990 and 1997.12

Previous work conducted in Chile has shown that the

majority of particles generated outdoors penetrate

indoors because homes in Santiago tend to be well

IMPLICATIONS

This paper examines particle measurements in Santiago,

Chile, from 1989 to 2001. The substantial decrease in par-

ticle levels over the 12-year study period strongly suggests

that the pollution reduction programs implemented by

CONAMA have been successful. However, despite the sig-

nificant improvement in air quality of Santiago, particle

levels still exceed both national and international standards

suggesting that efforts to reduce air pollution should be

continued.

TECHNICAL PAPER ISSN 1047-3289 J. Air & Waste Manage. Assoc. 55:342351

Copyright 2005 Air & Waste Management Association

342 Journal of the Air & Waste Management Association Volume 55 March 2005


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ventilated.13,14 In addition, several studies have found

health effects associated with high air pollution levels in

Santiago including daily mortality15,16 and medical visits

for respiratory illnesses.1719 Reducing exposures to high

levels of air pollution in Santiago, therefore, can have

both short and long-term public health benefits, making

this issue a priority for the Chilean government. A betterunderstanding of factors that influence air pollution lev-

els will aid policy-making and mitigation measures.

In an effort to improve air quality in the Santiago

metropolitan area, a number of control strategies have

been developed and have been steadily implemented over

the last 15 years. In the late 1980s car-use bans were

imposed based on license plate number and mandatory

vehicle inspections were implemented. In the early 1990s,

new cars were required to have catalytic converters, emis-

sion standards were set for industrial and nonindustrial

sources of pollution as well as residential heating, and an

emissions trading program was set up.12 To date, a large

fraction of the old bus fleet has been replaced with cleaner

diesel fuel buses and this effort is expected to continue

until the entire fleet is replaced. Other more recent ac-

tions include: cleaning and paving streets and planting

trees along the valley to reduce road dust, the reduction of

sulfur levels in oil, the reduction of benzene emissions,

and the reduction of olefin emissions.

In addition, the Chilean Ministry of Health has sup-

ported a comprehensive air quality-monitoring network

in Santiago that has been in operation since the mid-

1980s. This network provides the basis for investigatingthe spatial and temporal profile of air pollution levels

throughout the Metropolitan area and aids in the evalu-

ation of the effectiveness of emission control strategies.

Using intervention analysis Jorquera et al.12 analyzed

trends in PM10, PM2.5, and ozone concentrations between

19891998 and found an annual decrease in PM concen-

tration over the course of these years. Our analysis builds

on this previous study by using regression analysis to

examine trends over a longer period while controlling for

corresponding changes in meteorology. In addition, spa-

tial differences in particle concentrations are also ex-

plored. The analysis includes particle data (PM10, [da10

m], PM2.5, [da2.5 m], and, PM2.510, [2.5da10

m]) obtained at four urban sites in Santiago, Chile (Las

Condes, Parque OHiggins, La Paz, and Providencia) from

19892001.

METHODS

Sampling and Analysis

PM2.5and PM2.510 sampling were conducted at four ur-

ban sites in Santiago: Las Condes, Parque OHiggins, La

Paz, and Providencia. Las Condes is situated 10 km

Northeast of downtown Santiago, and 200 m from a busy

road (Avenida Las Condes). It is a sparsely populated and

affluent residential section of Santiago with no major

industrial sources nearby. However, this site is downwind

of emissions from the downtown area, and is character-

ized by high levels of secondary pollutants such as per-

oxyacetyl nitrate, ozone, and organic aerosols.20 Parque

OHiggins is located inside the largest park of the SantiagoMetropolitan area, close to an amusement park, a roller-

blading rink and the University of Chile campus. More

importantly, it is located approximately 1.5 km from a

major highway (the Panamericana), and near many im-

portant pollutant sources such as mechanic shops, metal

works and other small businesses.

The La Paz site is located in a residential area of

Santiago a few kilometers North/Northwest of the down-

town area and is mostly impacted by local traffic emis-

sions. Kerosene and propane are typically used for domes-

tic heating during the winter months in this old area of

Santiago.

The Providencia site is located a few meters from a

major avenue of Santiago, Avenida Providencia, near the

center of the city. This is a commercial/residential area

impacted by heavy traffic, mostly from public transporta-

tion such as buses and taxis. In 2000, monitoring at this

site was terminated because it did not meet the siting

criteria of the surveillance network due to the presence of

trees surrounding the site.

Twenty-four hour particle samples (midnight to mid-

night) were collected daily during the period from Janu-

ary 1, 1989, through December 31, 2001, with the above-noted exception of Providencia, where sampling was

conducted only through 2000. Filter samples were col-

lected every day during the cold season (April through

September) and every other day during the warm season

(October through March). Coarse particles, PM2.510,

(aerodynamic diameter 2.510 m) and fine particles,

PM2.5, (aerodynamic diameter 2.5 m) filter samples

were collected using dichotomous samplers (Andersen;

Anderson Instruments Inc., Smyrna, GA). The total sam-

pling flow rate was 16.7 l/min (15 l/min for fine particles,

major flow, and 1.7 L/min for coarse particles, minor

flow). Sampler inlets were located 3 m above the ground.

Particles were collected on 37 mm Teflon filters. Each

filter was inspected for its integrity before use. Particle

concentrations were determined gravimetrically using an

electronic microbalance, Precisa, with a resolution of 0.01

mg. Both blank and field filter samples were conditioned

at constant temperature (22 3 C) and relative humidity

(RH) (40% 5%) conditions for at least 24 hr before being

weighed. Inhalable particle concentrations, PM10, were

calculated as the sum of coarse and fine particle concen-

trations. Finally, meteorological parameters such as wind

speed, wind direction and temperature were measured for

Koutrakis et al.

Volume 55 March 2005 Journal of the Air & Waste Management Association 343


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all four sites during particle sampling. However, complete

data were only available from the La Paz site, and this was

thus the only meteorological data used in the current

analysis.

Data Analysis

Complete particle data for all four sites were availableonly for the years 1989 through 2000, which accordingly

were the years included in the regression analysis. These

regression models allow for the analysis of categorical

variables such as site, year, month, and weekday. The

strength of the association was determined by the signif-

icance of the slope t-values. All continuous data were

converted to categorical variables to capture nonlinear

relationships over time. Meteorological variables were di-

chotomized or trichotomized using the following ranges:

for wind speed, ws 0.8 m/sec, 0.8 ws 1.6 m/sec, ws

1.6 m/sec; for RH, rh 70% and rh 70% and; fortemperature t 20 C and t 20 C. Wind direction data

were also modeled but found to be nonsignificant and

therefore were not included in the analysis.

For each of the categorical variables a reference level

was set, with subsequent effects calculated relative to that

reference variable. For this analysis, Saturday, December,

Providencia and Year 2000 were set as the reference vari-

ables for weekday, month, site, and year, respectively. For

the meteorological variables, the highest ranges were used

as the reference for the corresponding group (ws 1.6

m/sec; rh

70% and; t

20 C).Relationships between PM10, PM2.5, PM2.510, and

meteorological variables were investigated using mixed

regression models to identify specific factors influenc-

ing particle concentrations and quantify their relative

impact (Statistical Applications Systems, SAS, Cary,

NC). Particulate concentrations were log-normally dis-

tributed, thus concentrations were log transformed and

the natural logs (ln[PM]) were regressed against the

categorical values:

ln PM a sj*sjyj*yj mj*mj wj*wj

wsj*wsj tmpj*tmpj rhj*rhj (1 )

where, is the regression intercept and sj, yj, mj, wj,

wsj, tmpj, and rhjare the regression coefficients of the

independent variables: site, sj, (j 14); year, yj, (j

112); month, mj, (j 112); weekday, wj, (j 17); wind

speed, wsj, (j 13); temperature, tmpj, (j 12); and RH,

rhj, (j 12).

Based on eq 1, particle concentrations can be ex-

pressed as the product of the exponential terms:

PM expa*expsj*sj yj*yj mj*mj

wj*wj wsj*wsj tmpj*tmpj

rhj*rhj (2 )

For simplicity eq 2 was transformed as follows:

PM I*fsitej*f*yearj*fmonthlj*fweekdayj*fwsj *ftmpj*frhj

(3)

where fij exp[ij*varij] is the concentration impact

factor of a variable i (e.g., Site) of a category j (e.g., La Paz).

Because the regression coefficient of a reference variable is

0, its concentration impact factor equals 1. Therefore, the

intercept concentration impact factor,I e, corresponds

to the average concentration at the reference level (e.g.,

Providencia, Year 2000, December, Saturday, ws 1.6m/s, rh 70%, and t 20 C). An impact factor greater

than 1 represents a greater concentration of PM relative to

the reference point and a value less than one represents a

concentration that is lower relative to the reference. All

the regression results will be discussed in terms of con-

centration impact factors because this facilitates compar-

isons of the effect of the different parameters on the

concentration levels. The concept of impact factors has

previously been utilized.21

It is worth noting that from eq 3 the effects of the

different parameters are multiplicative rather than addi-

tive. For example, the concentration differences between

Sundays and Mondays may vary each year, season, etc., as

might be expected, but their ratio remains the same.

RESULTS AND DISCUSSION

Concentration Levels

PM2.5Concentration. Mean concentrations corresponding

to the entire sampling period of 19892001 were deter-

mined separately for each of the four sites (except Provi-

dencia for which no data were available in 2001). Also,

mean concentrations across the years sampled were de-

termined separately for each season (Table 1), and the

overall mean was estimated by averaging the means of the

two seasons. Averaging all daily measurements would

lead to an overestimation of mean concentrations be-

cause of the unbalanced seasonal sampling scheme and

the large differences in concentration levels between the

cold and warm season. Figures 1 and 2, present the PM2.5concentration distributions for each of the four sites for

both the cold and warm seasons, respectively. For com-

parison purposes the PM2.5 concentration distribution

plots include the daily U.S. Environment Protection

Agency (EPA) standard of 65 g/m3

, shown as dashed

Koutrakis et al.



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lines. On the PM10distribution plots the standard of 150

g/m3 is also indicated by a dashed line.

Mean PM2.5 particle concentrations varied by site,

ranging from 38.5 g/m3 at Las Condes to 53 g/m3 at

Parque OHiggins. The highest concentrations were ob-

served at Parque OHiggins followed by Providencia be-cause of the impact of the Panamericana highway and

downtown traffic on particulate concentrations at these

sites, respectively. Note that concentration levels at the

Providencia site may be underestimated because the site is

surrounded by trees. Lower concentration levels were ob-

served at the La Paz site since this site is less impacted by

traffic. The lowest fine particle concentrations were ob-

served at Las Condes, which is located in the northern

suburbs, and is the least impacted by traffic sources.

PM2.5concentrations exhibited a strong seasonal pat-

tern with mean concentrations 23 times higher during

the cold season (AprilSeptember). The site specific cold/

warm season PM2.5ratio varied between 1.9, at the least

polluted site (Las Condes), and 2.8 at the most polluted

site (Parque OHiggins). Figure 3a depicts the time series

of monthly PM2.5particle concentrations. As can be seen

in this figure, concentrations exhibit a pronounced sea-

sonal pattern throughout the entire study period with

max monthly concentration decreasing each year. This

yearly trend is further discussed below in the presentation

of the results from the regression analysis.

The observed mean PM2.5concentrations levels were

in general higher than annual mean levels measured in

other Chilean cites such as Rancagua 42.6 g/m3, which is

impacted by copper smelting emissions, Temuco 35.2 g/

m3, impacted mostly by biomass burning, and Valparaiso

35.7 g/m3, impacted by a mixture of industrial and traf-

fic emissions.22 Furthermore, the PM2.5 concentrations

found in Santiago were two to three times higher than

those measured in major urban and industrial cities

throughout Western Europe and North America.23 Mean

concentration levels determined for all four sites were

substantially higher than the EPA annual PM2.5standard

of 15 g/m3. Also, 10% of the daily PM2.5 concentra-

tions exceeded the EPA daily limit (65 g/m3) during the

12-year sampling period.

Table 1. Mean PM2.5 and PM2.510, PM10 concentrations, expressed in

g/m3 (19892001).

Size/season Las Condes OHiggins La Paz Providencia

PM2.5

Cold 50.6 (28.3) 78.2 (48.3) 66.4 (41.2) 67.0 (37.8)

Warm 26.4 (12.8) 27.8 (13.7) 27.6 (11.6) 31.5 (13.6)

Year 38.5 53.0 47.0 49.2

Cold/warm 1.9 2.8 2.4 2.1

PM2.510

Cold 33.4 (16.7) 53.6 (29.8) 48.6 (22.6) 41.5 (18.8)

Warm 38.3 (13.4) 42.8 (14.4) 42.4 (15.4) 40.2 (12.7)

Year 35.8 48.2 45.5 40.9

Cold/warm 0.9 1.3 1.1 1.0

PM10

Cold 84.0 (39.5) 131.8 (71.2) 114.3 (54.8) 108.0 (50.6)

Warm 64.7 (19.4) 70.6 (23.5) 70.0 (22.8) 71.6 (22.0)

Year 74.4 101.2 92.2 89.8

Cold/warm 1.3 1.9 1.6 1.5

PM2.5/PM10

Cold 0.60 0.59 0.58 0.62

Warm 0.41 0.39 0.39 0.44

Year 0.52 0.52 0.51 0.55

Figure 1. Distributions of daily concentrations during the cold

season (AprilSeptember) for (a) PM2.5, (b) PM2.510, and (c) PM10.

Dashed line on (a) shows the EPA standard of 65 g/m3 for PM2.5and on 1(c) the EPA standard of 150 g/m3 for PM10.

Koutrakis et al.



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PM10

Concentrations. Table 1 presents the mean PM10concentrations for each of the four sites: Las Condes,

74.4 g/m3, Parque OHiggins, 101.2 g/m3, La Paz,

92.2 g/m3, and Providencia, 89.8 g/m3. As seen for

PM2.5, the highest PM10 concentrations were observed

at Parque OHiggins and the lowest at Las Condes. PM10average concentrations exceeded the Chilean and

United States annual PM10 standard of 50 g/m3 and

more than 10% of the daily concentrations were over

the EPA and Chilean and PM10 standards of 150 and

120 g/m3, respectively. Also, almost 50% of the ob-

served concentrations exceeded the European Union air

quality daily PM10 standard of 75 g/m3 for the years

1999 and 2000. Note that this limit can be exceeded a

max of 35 days per year by European standards. Fur-

thermore, PM10 concentrations exhibited a strong sea-

sonal pattern with levels considerably higher in the

April through September period. The site specific cold/

warm season PM10ratios ranged from 1.3 at Las Condes

to 1.9 at Parque OHiggins. Figure 3c shows the time

series of monthly PM10 concentrations. As can be seen

in this figure, concentrations exhibit a similar seasonal

pattern as PM2.5, although less pronounced but still

decreasing each year.

With the exception of Las Condes, Santiago PM10concentrations were higher than concentrations mea-

sured in other Chilean cites that are also impacted by

industrial, traffic, soil dust, sea salt, and biomass burning

sources including Rancagua, 73.8 g/m3, Temuco,

67.7 g/m3

, and Val Paraiso,21

77.5 g/m3

PM10

Figure 2. Distributions of daily concentrations during the warm

season (OctoberMarch) for (a) PM2.5, (b) PM2.510, and (c) PM10.Dashed line on (a) shows the EPA standard of 65 g/m3 for PM2.5and on (c) shows the EPA standard of 150 g/m3 for PM

10.

Figure 3. PM2.5 (a), PM2.510 (b), and PM10 (c) monthly concen-tration time series (all sites).

Koutrakis et al.



7/11

concentrations in Santiago were generally higher than

those observed in downtown Athens, Greece, in a street

with a high density of traffic, 75.5 g/m3, but within the

same range of concentrations measured across Mexico

City in the wintertime (39108 g/m3).21,24 However,

Santiago levels were two to three times higher than those

measured in major urban areas in the United States: Bos-ton, MA (18 g/m3); Philadelphia, PA, Washington, DC,

and Nashville, TN (2526 g/m3).2527 Note that the San-

tiago values represent an average across several years,

whereas for other cities averages are reported only for a

few months or up to 1 year.

PM2.510 Concentrations. Average coarse particle levels for

the four sites are shown in Table 1. The arithmetic means

measured at Las Condes, Parque OHiggins, La Paz, and

Providencia were 35.8, 48.2, 45.5, and 40.9 g/m3, respec-

tively. As with PM2.5, the highest PM2.510concentrations

were observed at Parque OHiggins and the lowest at Las

Condes. There is currently no coarse particle standard, but

the EPA is in the process of developing one to replace the

existing PM10 standard. Despite the lack of a standard,

coarse particle concentrations in Santiago were higher

than concentrations measured in many U.S. cities.22,25

Also, as seen in Table 1, coarse particle concentrations did

not exhibit large seasonal variability with cold/warm sea-

son ratios that only varied from 0.91.1, except at Parque

OHiggins where the ratio was 1.3. Thus, PM10 seasonal

patterns were mostly because of changes in PM2.5concen-

tration. Figure 3b shows the time series of monthly coarseparticle concentrations. Neither a monthly pattern nor a

yearly trend can be easily discerned from this figure. The

effect of these parameters on coarse particle concentra-

tions was further examined using the regression analysis

discussed below.

PM2.5/PM10 Concentration ratios. The relative contribu-

tion of fine and coarse particles to PM10can be assessed

by looking at PM2.5/PM10ratios. These ratios exhibited

a clear seasonal pattern in Santiago, although little

difference was seen across sites (Table 1). During the

warm season, ratios were on average 0.4 and during the

cold period, when PM2.5 particle concentrations are

higher, ratios were higher, ca. 0.6. These findings are

consistent with results of annual mean PM2.5/PM10 ra-

tios measured in a number of urban and semi-rural U.S.

areas where ratios varied between 0.3 and 0.7.25 In the

United States, high ratios were typically seen in the

Northeast, during the summer season when sulfur com-

pounds represent a large fraction of PM10. In contrast,

low PM2.5/PM10 ratios were typical of the semi-arid

Western United States, where a large fraction of PM10

consists mainly of resuspended soil particles. In Athens,

Greece that has a climate like that of Santiago, Chile, a

similar range in ratios was seen (0.450.62) with higher

ratios in the winter.

Regression Model Results

Tables 2 and 3 present the results of the regression

analysis for PM2.5and PM2.510, respectively. Slopes and

Table 2. PM2.5 model results.

Effect Estimate SE pvalue

CI

factor

Intercept 2.54 0.04 0.001 12.65

Las Condes 0.22 0.01 0.001 0.80

Parque OHiggins 0.05 0.01 0.001 1.05

La Paz 0.06 0.01 0.001 0.94

Providencia 0.00 1.00

Year: 1989 0.75 0.03 0.001 2.11

1990 0.64 0.03 0.001 1.90

1991 0.54 0.03 0.001 1.71

1992 0.55 0.03 0.001 1.73

1993 0.50 0.02 0.001 1.66

1994 0.38 0.02 0.001 1.47

1995 0.25 0.02 0.001 1.28

1996 0.20 0.03 0.001 1.23

1997 0.13 0.02 0.001 1.13

1998 0.24 0.03

0.001 1.27

1999 0.17 0.03 0.001 1.18

2000 0.00 1.00

January 0.11 0.03 0.001 1.11

February 0.24 0.03 0.001 1.27

March 0.46 0.03 0.001 1.59

April 0.68 0.03 0.001 1.98

May 1.03 0.03 0.001 2.81

June 1.03 0.03 0.001 2.81

July 1.13 0.03 0.001 3.09

August 0.92 0.03 0.001 2.51

September 0.51 0.03 0.001 1.66

October 0.23 0.03 0.001 1.26

November 0.05 0.03 0.065 1.05

December 0.00 1.00

Sunday 0.13 0.02 0.001 0.87

Monday 0.01 0.02 0.608 1.01

Tuesday 0.08 0.02 0.001 1.08

Wednesday 0.08 0.02 0.001 1.08

Thursday 0.10 0.02 0.001 1.11

Friday 0.08 0.02 0.001 1.09

Saturday 0.00 1.00

Wind speed (ws 0.8 m/s) 0.29 0.02 0.001 1.33

(0.8 ws 1.6 m/s) 0.13 0.02 0.001 1.14

(ws 1.6 m/s) 0.00 1.00

Relative humidity (rh 70%) 0.06 0.01 0.001 1.06(rh 70%) 0.00 1.00

Temperature (t 20 C) 0.04 0.02 0.045 1.04

(t 20 C) 0.00 1.00

Koutrakis et al.



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corresponding standard errors are presented for models

examining the influence of the categorical variables,

site, year, month, weekday, wind speed, temperature,

and RH. Note that a regression model was not devel-

oped for PM10, since investigating the effect of the

different parameters on its components, coarse and fine

particles, is sufficient.

Intercept. The intercepts for PM2.5and PM2.510were 12.6

and 19.5, respectively. This corresponds to an average

concentration in g/m3 across sampling days for the ref-

erence variables; site Providencia, year 2000, month De-

cember, day Saturday, wind speed 1.6 m/s, rh 70%,

and temperature 20 C.

Site-Specific Differences. Differences among sites were as-

sessed using regression analysis, which allowed for con-

trol of time and meteorological impacts. The concentra-

tion trends shown by the regression impact factors follow

the same patterns as shown in Table 1. For PM2.5, Las

Condes had the lowest concentration impact factor of 0.8,

a 20% difference from the reference site. La Paz had a

lower concentration by 6%, whereas Parque OHiggins

had the largest concentration impact of 1.05. For PM2.510,

OHiggins and La Paz showed higher concentration im-

pacts compared with Providencia, 20 and 11%, respec-

tively. As with PM2.5 particle concentrations, Las Condes

exhibited the lowest concentration impact, 0.80. Similar

concentration trends among sites are also shown in Table 1

for coarse particles.

Yearly Trend. One of the main objectives of this study was

to determine whether particle concentrations have de-

creased over the past 12 years as a result of source emis-

sion control strategies that have been implemented in

Santiago. As shown in Figure 3a and c, PM2.5 and PM10levels have decreased over the study period. However, as

discussed above, it is not possible to discern a yearlyconcentration trend for PM2.510 by simply looking at

Figure 3b. In addition, a simple comparison of the yearly

means or medians may not be sufficient to examine con-

centration trends, and thus a more sophisticated statisti-

cal analysis was employed. Using regression modeling,

the variable year expresses the concentration trend while

controlling for other parameters. Concentrations depend

not only on emission sources, but also on meteorological

conditions that can vary year to year. Thus, the inclusion

of meteorological parameters in the model is necessary to

distinguish trends because of changes in source emissions

from those related to weather. This is especially important

for studying trends over a relatively short period of time,

where a few atypical years can make it difficult to compare

annual mean concentrations.

The regression analysis results (Tables 2 and 3) show

that PM2.5 and PM2.510 concentrations significantly

changed over time. In Figure 4a, yearly concentration

impact factors for both PM2.5and PM2.510 are graphed.

Concentration impact factors for PM2.5 decreased from

2.111.00 over the period 19892000 or 6.3% per year

(based on the average of the yearly percentage decreases),

but up to a 15% decrease in some years. For PM2.510,

Table 3. PM2.510 model results.

Effect Estimate

Std

Error pvalue

CI

Factor

Intercept 2.97 0.04 0.001 19.49

Las Condes 0.23 0.01 0.001 0.80

Parque OHiggins 0.18 0.01 0.001 1.20

La Paz 0.11 0.01 0.001 1.11

Providencia 0.00 1.00

Year: 1989 0.04 0.03 0.172 0.96

1990 0.14 0.03 0.001 1.15

1991 0.29 0.03 0.001 1.33

1992 0.27 0.03 0.001 1.31

1993 0.39 0.03 0.001 1.48

1994 0.33 0.03 0.001 1.39

1995 0.29 0.03 0.001 1.34

1996 0.31 0.03 0.001 1.36

1997 0.21 0.03 0.001 1.24

1998 0.23 0.03

0.001 1.26

1999 0.14 0.03 0.001 1.15

2000 0.00 1.00

January 0.12 0.03 0.001 1.13

February 0.12 0.03 0.001 1.13

March 0.24 0.03 0.001 1.27

April 0.21 0.03 0.001 1.24

May 0.23 0.03 0.001 1.25

June 0.15 0.03 0.001 1.16

July 0.18 0.03 0.001 1.20

August 0.11 0.03 0.001 1.12

September 0.04 0.03 0.160 0.96

October 0.04 0.03 0.162 1.05

November 0.09 0.03 0.004 1.09

December 0.00 1.00

Sunday 0.20 0.02 0.001 0.82

Monday 0.08 0.02 0.001 1.09

Tuesday 0.15 0.02 0.001 1.16

Wednesday 0.12 0.02 0.001 1.13

Thursday 0.15 0.02 0.001 1.16

Friday 0.15 0.02 0.001 1.16

Saturday 0.00 1.00

Wind speed (ws 0.8 m/s) 0.26 0.02 0.001 1.30

(0.8 ws 1.6 m/s) 0.14 0.02 0.001 1.15

(ws 1.6 m/s) 0.00 1.00

Relative humidity (rh 70%) 0.21 0.01 0.001 1.24(rh 70%) 0.00 1.00

Temperature (t 20 C) 0.05 0.02 0.008 0.95

(t 20 C) 0.00 1.00

Koutrakis et al.



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concentration impact factors increased from 0.96 in 1989

to 1.48 in 1993, 50%. For the subsequent seven years

(19942000), there was a decrease from 1.48 to 1.00, at an

average rate of 5% per year. Jorquera et al. (2000)12 deter-

mined particle concentration trends on the same data for

the period 1989 through 1998. The results from that

study, showed trends for PM10 and PM2.5 that ranged

from 1.5 to 3.3% and 5 to 7% per annum across

different monitoring sites, respectively. The results for

PM2.5 are in close agreement with our findings. Their

analysis also showed a net increase in concentration for

the coarse fraction, whereas here we show that more

recently these concentrations have actually decreased.

Month Effect. As suggested by the regression analysis (Ta-

ble 2) and the seasonal trends depicted in Figure 4b, PM2.5monthly concentrations exhibit pronounced seasonal

variability. Concentration impact factors varied from

unity in December to 3.09 in July. These differences are

because of poor vertical mixing of air masses within the

Santiago basin encountered in the cold season. During

the winter season a coastal low-pressure system frequently

forms between two high-pressure systems, the semi-per-

manent Pacific high and the migratory high located in

central/north Argentina. Eruption of the mid-tropospherecreates a warm ridge above central Chile resulting in sta-

ble conditions that favor reduction of the mixing layer

and results in poor ventilation of the Santiago basin.29

It is worth noting that a significant month effect

was found even when controlling for the effect of wind

speed, temperature, and RH. This effect increased when

these three meteorological parameters were excluded

from the regression model, indicating that these varia-

bles do explain some of the variability in PM con-

centrations. However, because there is still a significant

amount of unexplained variation in PM concentrations,

additional meteorological parameters may also be of im-

portance, including mixing height and synoptic air mass

movements.

Monthly PM2.510 impact factors varied little by sea-

son with slightly higher values, 20 25% higher, during

the period of March through July. These results agree with

the monthly PM2.510 concentration patterns depicted in

Figure 3b. Poor air mass mixing favors the accumulation

of combustion-generated PM2.5that can remain airborne

for hours to days. In contrast, the long residence of air

masses within the basin does not increase coarse particle

concentrations significantly. This is because of the rela-tively shorter life time of coarse particles (minutes to a few

hours), which are effectively removed by sedimentation

and impaction.

Effects of Wind Speed, Temperature, and RH. The regression

analysis results suggest strong associations between parti-

cle concentrations and wind speed, and to a lesser extent,

temperature and RH (Tables 2 and 3). PM2.5and PM2.510concentrations were 30% and 15% higher for wind

speed values, ws 0.8 m/s and 0.8 ws 1.6 m/s, respec-

tively, compared with higher wind speeds (ws 1.6 m/s).

The effect of RH and temperature on both PM2.5 and

PM2.510 concentrations was significant but negligible

(less than 6%), with the exception of low RH (rh 70%)

for coarse particle concentrations (26%) possibly because

of the effect of dryness on particle generation.

As previously reported air pollutant concentrations

are considerably higher during the cold season because of

the frequent stagnation of air masses in the Santiago

basin.4 Particulate levels decreased only during strong

wind events that managed to push the air pollution west

of Santiago or east above the Andes. Similar relation-

ships between pollutant concentrations and weather

Figure 4. (A) PM2.5 and PM2.510 yearly concentration impact

factors; (B) PM2.5

and PM2.510

monthly concentration impact fac-

tors; (C) PM2.5and PM2.510 weekday concentration impact factors.

Koutrakis et al.



10/11

conditions in Santiago have been reported previously us-

ing more comprehensive weather measurements such as

wind trajectory analysis and boundary layer height.29

Studies have also shown an inverse relationship between

wind speed and concentrations of locally emitted pollut-

ants. PM2.5 concentrations measured near a road with

heavy traffic in Paris, for example, were inversely propor-tional to wind speed.30 Also, PM10 concentrations mea-

sured in downtown Athens decreased with higher wind

speeds.21 Cheng and Lam (1998)31 investigated the im-

pact of wind on total suspended particulates concentra-

tions in Hong Kong and found a similar relationship

between concentrations and wind speed.

Weekday Effect. As shown in Tables 2 and 3 and Figure 4c

weekday is an important determinant for both PM2.5and

PM2.510 concentrations. The PM2.510 concentration im-

pact factors for Monday through Friday were 9 16%

higher than the reference (Saturday). The concentration

factor for Sunday was 18% lower than Saturday. A less

pronounced weekday effect was found for PM2.5possibly

because of longer lifetimes within the basin compared

with PM2.510. PM2.5 concentration impact factors were

0.87, 1.01, 1.08, 1.08, 1.11, 1.09, and 1.00 for Sunday

through Saturday, respectively. These results are expected

because traffic and other human particle-generating activ-

ities are reduced on weekends and to a greater extent on

Sundays. Furthermore, our analysis suggests that on Mon-

days particle levels are lower compared with the rest of

the workdays. Although Monday is a working day, it

appears that a lagged effect exists and pollution levels

accumulate over the course of several days.

CONCLUSIONS

Particle concentration levels measured at all four sites in

the Santiago Metropolitan region exceeded the corre-

sponding Chilean, United States, and European Union air

quality standards. Fine and coarse particle levels were the

highest at Parque OHiggins, which is impacted by vehic-

ular emissions and small industrial sources. The lowest

levels were observed at the Las Condes site that is the leastimpacted by downtown traffic. PM2.5particle concentra-

tions exhibited strong seasonal patterns because of the

distinct differences in climatologic conditions during the

cold (April through September) and warm (October

through March) seasons. Mean PM2.5 concentration levels

during the cold season were approximately two times

higher than those observed during the warm season. In

contrast to PM2.5, similar coarse particle levels were ob-

served in both the warm and cold season. Finally, model

results showed that particle levels are lower on Sundays

and to a lesser extent Saturdays and Mondays. This is

because of the fact that a large fraction of particles are

associated with traffic emissions.

Furthermore, the results of the regression analyses

suggested that for PM2.5, and to a lesser extent PM2.510,

wind speed is an important determinant of concentra-

tions. Most of the highest particle levels occur during low

wind speed events when air masses stagnate for severaldays over the metropolitan region and air pollution emis-

sions concentrate in the valley. Levels decrease when

wind velocity increases to move pollution west or east

above the Andes.

Concentration trends were investigated using regres-

sion models, while controlling for the effect of site, week-

day, month, wind speed, temperature, and RH on particle

concentrations. PM2.5 concentrations decreased signifi-

cantly, 52% over the 12-year period, 19892000.

PM2.510concentrations initially increased by 50% over

the first 5 years and then decreased by a similar percent-

age over the following 7 years. Therefore, the pollution

reduction programs that included removal of old buses,

the introduction of vehicles with catalytic converters,

paving and cleaning streets, among others, implemented

by CONAMA, have been effective in reducing fine particle

levels within the Santiago Metropolitan region. Despite

these concentration decreases, particle levels still exceed

both national and international standards, justifying con-

tinuing efforts to improve air quality.

ACKNOWLEDGMENTS

The filter samples were collected by the Ministry of Health(SESMA). The data analysis was supported by the Comis-

sion Nacional del Medio Ambiente (CONAMA). The au-

thors thank Yolanda Silva Cerna and Ignacio Olaeta Un-

dabarrena for their assistance in the collection and

analysis of filter samples.

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About the Authors

Petros Koutrakis, Ph.D., is Professor of Environmental Sci-

ences in the Department of Environmental Health at the

Harvard School of Public Health. He is also the Head of the

Exposure, Epidemiology, and Risk Program and the Direc-

tor of the EPA/Harvard University Center for Ambient Par-

ticle Health Effects. Sonja Sax, Sc.D., and Jeremy Sarnat,

Sc.D., are Research Fellows in the Department of Environ-

mental Health at the Harvard School of Public Health. Brent

Coull, Ph.D., is Assistant Professor of Biostatistics at the

Harvard School of Public Health in the Department of Bio-

statistics. Philip Demokritou is Assistant Professor of Aero-

sol Physics in the Department of Environmental Health at

the Harvard School of Public Health. Pedro Oyola, Ph.D., is

a visiting professor at the University of Sao Paulo, Brazil in

the Faculty of Public Health. He is also working as re-

searcher at the Technical University Federico Santa Mara,

Valparaso, Chile. Javier Garcia is a Project Engineer at theComision National del Medio Ambiente in Santiago, Chile.

Ernesto Gramsch is associate professor in the Department

of Physics at the University of Santiago, Chile. Address

correspondence to: Dr. Sonja Sax, Harvard School of Pub-

lic Health, Department of Environmental Health, Landmark

Center 4th Floor West, 401 Park Drive, Boston, MA 02115;

phone: 1-617-384-8827; fax: 1-617-384-8859; e-mail:

[email protected].

Koutrakis et al.


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