Representativeness of the particulate matter pollution assessed by an official monitoring station of air quality in Santiago, Chile: projection to human health

Abstract

Santiago, capital city of Chile, presents air pollution problems for decades mainly by particulate matter, which significantly affects population health, despite national authority efforts to improve air quality. Different properties of the particulate matter (PM10, PM2.5 and PM1 fractions, particle surface and number) were measured with an optical spectrometer. The sampling was done during spring 2019 at different sites within the official representative area of Independencia monitoring station (ORMS-IS). The results of this study evidence large variations in PM mass concentration at small-scale areas within the ORMS-IS representative zone, which reports the same value for the total area. Results from PM properties such as PM1, particle number and particle surface distribution show that these properties should be incorporated in regular monitoring in order to improve the understanding of the effects of these factors on human health. The use of urban-climate canopy-layer models in a portion of the sampled area around the monitoring station demonstrates the influence of street geometry, building densities and vegetation covers on wind velocity and direction. These factors, consequently, have an effect on the potential for air pollutants concentrations. The results of this study evidence the existence of hot spots of PM pollution within the area of representativeness of the ORMS-IS. This result is relevant from the point of view of human health and contributes to improve the effectiveness of emission reduction policies.

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https://doi.org/10.1007/s10653-022-01390-x
ORIGINAL PAPER
Representativeness oftheparticulate matter pollution
assessed byanofficial monitoring station ofair quality
inSantiago, Chile: projection tohuman health
MargaritaPréndez · PatricioNova ·
HugoRomero · FlávioMendes ·
RaúlFuentealba
Received: 28 December 2021 / Accepted: 4 September 2022 / Published online: 20 September 2022
© The Author(s) 2022
the same value for the total area. Results from PM
properties such as PM1, particle number and parti-
cle surface distribution show that these properties
should be incorporated in regular monitoring in order
to improve the understanding of the effects of these
factors on human health. The use of urban-climate
canopy-layer models in a portion of the sampled area
around the monitoring station demonstrates the influ-
ence of street geometry, building densities and veg-
etation covers on wind velocity and direction. These
factors, consequently, have an effect on the potential
for air pollutants concentrations. The results of this
study evidence the existence of hot spots of PM pol-
lution within the area of representativeness of the
ORMS-IS. This result is relevant from the point of
view of human health and contributes to improve the
effectiveness of emission reduction policies.
Keywords Physical characteristics of
particulate matter· Spatial heterogeneity· Visible
spectrophotometry· Urban geometry· Vegetation
cover· Local sources importance
Introduction
For decades, Santiago de Chile has been affected by
high levels of atmospheric pollution. The outstand-
ing pollutant is the atmospheric aerosol, also called
particulate matter (PM), which has relevant effects,
on the environment and on human health (Jorquera,
Abstract Santiago, capital city of Chile, presents
air pollution problems for decades mainly by par-
ticulate matter, which significantly affects population
health, despite national authority efforts to improve
air quality. Different properties of the particulate mat-
ter (PM10, PM2.5 and PM1 fractions, particle surface
and number) were measured with an optical spec-
trometer. The sampling was done during spring 2019
at different sites within the official representative area
of Independencia monitoring station (ORMS-IS).
The results of this study evidence large variations in
PM mass concentration at small-scale areas within
the ORMS-IS representative zone, which reports
Supplementary Information The online version
contains supplementary material available at https:// doi.
org/ 10. 1007/ s10653- 022- 01390-x.
M.Préndez(*)· P.Nova· R.Fuentealba
Facultad de Ciencias Químicas y Farmacéuticas,
Laboratorio de Química de la Atmósfera y Radioquímica,
Sergio Livingstone 1007, Independencia, Universidad de
Chile, 8380492Santiago, Chile
e-mail: mprendez@ciq.uchile.cl
H.Romero
Facultad de Arquitectura y Urbanismo, Laboratorio de
Medio Ambiente y Territorio, Universidad de Chile,
8320000Santiago, Chile
F.Mendes
Escuela Superior de Agricultura “Luiz de Queiroz”,
Doutorando Em Ciências Florestais, Universidad de Sao
Paulo, Piracicaba, Brasil
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2020). The Health Effects Institute (HEI, 2019) esti-
mates that air pollution (PM2.5 and ozone) is the fifth
risk factor for mortality worldwide, and in 2017, it
was estimated that air pollution contributed to about
5 million deaths worldwide representing almost 10%
of total fatalities. Over 55% of the world’s population
live in urban areas and this is set to rise to 68% by
2050 (WHO, 2022.
Variability of PM concentrations depends on dif-
ferent factors including local emissions, atmospheric
conditions and urban morphology (Buccolieri et al.,
2010; Hofman etal., 2013), causing significant spatial
and temporal differences in air quality in relatively
small areas. Current monitoring network distribution
and its expected spatial resolution do not necessarily
capture PM variability (Karner et al., 2010; Zikova
etal., 2017). Official Representative Monitoring Sta-
tions (ORMSs) installed in Chile have a spatial reso-
lution of 2 km radius. However, the official criteria
are the population size within these two kilometres,
location and sampling conditions (MINSAL, 1998;
MMA, 2017). These criteria ignore spatial and tem-
poral pollutants heterogeneity, the distance from the
sources and magnitude, the variability of local venti-
lation and the complexity of urban morphology, con-
travening the objective of monitoring to determine
population daily exposure to pollutants. Inadequate
monitoring spatial resolution difficult the estima-
tion of individual exposure to PM, hence offering
insufficient information for epidemiological studies
of pollutants effects on health; it also does not pro-
vide an understanding of the effectiveness of emis-
sion reduction policies (Kelly etal., 2017; Tsai etal.,
2019), therefore having negative implications for an
adequate environmental management.
The National Air Quality Information System
(SINCA for its acronym in Spanish) of the Chilean
Environmental Ministry (Ministerio del Medio Ambi-
ente, MMA) registers air quality in the country, and
it is constantly seeking to improve its monitoring and
data management capabilities. In the Metropolitan
Region (RM), in which Santiago city is located, the
air quality monitoring network known as MACAM-
RM has eleven stations (Fig.1) for continuous partic-
ulate matter (PM2.5 and PM10), and gases (SO2, NOx,
CO, O3 and non-methane hydrocarbons (HCNM))
measurements.
The MACAM-RM monitoring network uses two
types of continuous PM measurement instruments
according to national regulations (DS N°59/1998
and DS N°12/2011): Oscillating Conical Element
Microbalance (TEOM) and Beta attenuation monitors
(BAM). These instruments are expensive with costly
operation and maintenance, which limits the possi-
bility of studying the spatial distribution of PM data
(Kelly et al., 2017; Wang et al., 2015). The recent
availability of instruments based on optical princi-
ples of indirect measurement is an interesting alter-
native to the more expensive instruments used by the
MACAM-RM network (Grimm & Eatough, 2009;
Holstius etal., 2014; Kelly etal., 2017; Kumar etal.,
2015). These alternative instruments are portable and
easy to use, which increases the ability to improve
PM characterization with high spatio-temporal reso-
lution at a lower cost.
The Metropolitan area of Santiago is divided in 52
municipalities, 34 located in the urban area (or Grand
Santiago, henceforth Santiago), and 18 located in
rural areas. This study focuses in the urban munici-
palities (Fig. 1). Besides climatological differences
in wind, humidity and temperature (e.g. presence of
heat islands) among the municipalities, there is also a
marked socio-economical differentiation between the
richest areas located to the NE and the rest of the city,
where middle- and low-income classes are dominant
(Sarricolea etal., 2022).
Hitherto, relevant properties of atmospheric aero-
sol that have negative impacts on human health,
especially in polluted areas, such as highly populated
cities, is neither fully investigated nor well under-
stood (Bind et al., 2012; Kuuluvainen et al., 2016;
Tsai etal., 2019). Particle number has been associ-
ated with adverse health effects such as respiratory
diseases among children in urban areas (Li et al.,
2016), effects on fibrinogen, i.e. a type of tumour
marker (Bind etal., 2012), and in inflammatory mark-
ers (Tsai etal., 2019). Systematic studies and meta-
analyses (Dinoi etal., 2017; Forlivesi etal., 2018) of
the effects of exposure to PM on health have found
associations with morbidity and mortality in the pop-
ulation. The above examples complemented with ade-
quate techniques for elemental analysis can be useful
to improve physical and chemical characterization
of PM (Galvão etal., 2018) and to identify station-
ary sources that affect a determined area (Fuentealba,
2018; Leoni etal., 2018; Préndez etal., 2007).
Particle surface distribution is emerging as a use-
ful PM property to investigate health effects due to
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exposure to PM (Kuuluvainen et al., 2016; Ntzi-
achristos etal., 2007). A larger particle surface area
could increase the kinetic and thermodynamic poten-
tial of chemical reactions in the atmosphere, which
can exacerbate pollution. This property is an indica-
tion of the internal structure of the particle, such as
porosity, that increase particle surface, potentially
retaining other pollutants (Cassee etal., 2013; Long
etal., 2013; Guo etal., 2017) or even viruses such as
SARS-2 (Setti etal., 2020; Zoran etal., 2020), within
the particle.
In addition, the monitoring of particle surface
distribution contributes to the assessment of pollu-
tion reduction policies. Reduction of a 67.2% and
65.0% in particle number concentration and particle
surface area has been reported between days with
and without vehicle restriction, respectively (Zhao
& Yu, 2017). The effect of vehicle circulation
restriction on air pollution near a sampling location
depends on the distance between the relative orien-
tation of the sampling site and the traffic-restricted
areas, as well as on meteorological conditions (Zhao
& Yu, 2017).
This study evaluates if an ORMS has the capabil-
ity to capture i) spatial heterogeneity in PM meas-
urements and ii) urban characteristics of various
sampling sites within the station representative area,
using a portable visible spectrophotometer to measure
PM properties that are not currently quantified by the
national monitoring authority.
Fig. 1 Grand Santiago (grey area), its municipalities (light
grey delimitations), and main geographical features around
the sampling area (zoomed in box): San Cristobal and Blanco
hills, Mapocho River, open market (La Vega) and public
services (hospitals, underground subway station, thermo-
electric plant and cemeteries) marked with 1–8 numbers, and
MACAM-RM network monitoring stations indicated with
A–K letters
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Materials andmethods
Study area
The Metropolitan Region of Santiago is located
within the basin conformed by Maipo and Mapo-
cho river valleys that descend from the Andes Cor-
dillera, and concentrates about 40% of the country’s
population (19828 563 inhabitants, projection to 30
June 2022, INE, 2022). West of Santiago, the basin
is constrained by a coastal mountain range that sep-
arates the city from the Pacific Ocean by approxi-
mately 130km. Transversal hills join both mountain
ranges substantially reducing atmospheric ventilation
at the basin. Since the city is located at a subtropical
latitude (33°S), the Mediterranean type of regional
climate is under the prevalence of anticyclone influ-
ences, and a marked seasonal temperature and rain-
fall variation, with warm and dry summers and cool
and wet winters. Climate at the region, however, has
shown a tendency to drier conditions over the last
decade due to the ongoing mega-drought that affects
central Chile The presence of a persistent radiative
thermal inversion layer above the basin explain the
remarkable stable atmospheric conditions present
over Santiago during the year, which intensify dur-
ing the winter months. (DMC, 2021). In addition to
a large city population, Santiago concentrates most
of the country’s industries and services, representing
more than 50% of the national Gross Domestic Prod-
uct (GDP).
The air pollution monitoring station used in
this work is located in the Independencia munici-
pality that has around 100 000 inhabitants, posi-
tioned in the middle rank of the human develop-
ment and social indicators (Romero et al., 2010).
The monitoring station (ORMS-IS) is part of the
MACAM-RM monitoring network. The station is
representative of an area that includes parts of Inde-
pendencia, Recoleta and Santiago municipalities.
It is located north of the historical centre of San-
tiago city and includes many important urban fea-
tures, for instance, the Mapocho River, the main
metropolitan park and zoo (San Cristóbal hill), the
largest open market (La Vega) and large cemeter-
ies. Two high traffic roads (Independencia and La
Paz avenues) cross the area from north to south
and another one (Santa Maria Avenue) from west
to east. These avenues connect other municipalities
with the city centre and also allow access to several
large services such as hospitals, a university cam-
pus and an underground subway station. In addition,
a thermoelectric plant is located about 1.5km west
of Independencia. During recent years, the pre-
dominant middle-class one-story houses have been
replaced by tall apartment buildings occupied both
by national and migrant population (Fig.1).
The selected sampling sites in this study locate
within a radius of 2km (Fig. 2) from the ORMS-IS
and have the purpose to characterize a population
area similar to the area represented by the ORMS-IS.
Sampling and characteristics of the sampled sites
Sampling was performed during two weeks between
4 and 6 September, and between 10 and 12 Septem-
ber 2019. This month of the year corresponds to the
period that follows the critical PM pollution episodes
that occur during the austral autumn–winter months
(April–August). These episodes relate to various fac-
tors that generate low atmospheric PM dispersion
(Préndez et al., 2011; Toro et al., 2014). Figure 2
shows the 15 sampling sites reported in this study,
labelled according to cardinal direction and distance
from the ORMS-IS.
Sampling was performed by a GRIMM spectrom-
eter, Mini-LAS 11-E model with GRIMM Spectrom-
eter 1158-EE Sensor, located 1.75 m above ground
level according to the protocol for population expo-
sure to urban pollution (USEPA, 2004). A time reso-
lution of 1min was used for two periods of 30min
to measure PM10, PM2.5 and PM1 fractions during
one period, and the surface distribution and the par-
ticle number during the other. The spectrometer has
a laser diode with a wavelength in the visible range
of 660nm. The intensity of the laser beam is modu-
lated to detect particles between 0.25μm and 32 μm
and classify them into 31 channels within the range
(Grimm & Eatough, 2009).
Vehicle traffic of the study area was obtained using
Google Traffic to determine the typical condition or
real-time condition in each sampling area. The system
detects traffic information, non-moving vehicles, road
accidents, and road constructions, using GPS signals
of smartphones within each car on the streets. Subse-
quently, the sampling sites were classified according
to vehicle traffic, as shown in Table1.
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Statistics and data
PM10, PM2.5 and PM1 mass concentrations were
analysed using Box Plots obtained from the "R"
statistical software. Particle number and parti-
cle surface distribution were obtained using the
Spectrometer_V7-1 Software provided by the
Grimm spectrometer and plotted using Excel.
Temperature and relative humidity (RH) were
measured using an 1158-EE sensor from the Grimm
spectrometer. The temperature effect on the spectrom-
eter’s performance is negligible in the range from
Fig. 2 Sampling sites within the representative area of the ORMS-IS
Table 1 Level of the traffic at each sampling site within the representative area of ORMS-IS. Source: Google Traffic
High traffic Indicates slow traffic in the sector due to the large number of vehicles circulating at that moment
Medium traffic Indicates an intermediate level of traffic in the sector
Medium/low traffic Indicates that sectors with medium and low traffic were observed near the sampling site
Low traffic Indicates a low level of traffic in the sector and an expeditious travel speed
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5 °C to 32 °C (Holstius et al., 2014; Zikova et al.,
2017). High levels of RH affect the light scattering
principle of measurement due to PM absorbing water
vapour, therefore changing the dispersion and absorp-
tion coefficients (Dinoi etal., 2017). Wind speed and
wind direction were obtained from the MMA, (2019)
for each sampling day. Figure3 shows the wind speed
and wind direction at 4–5m height. Wind data were
processed using the Open Air extension of the “R”
software.
The lognormal function is the most widely used
fit to plot PM mass, size and surface distribution
because it gives better adjustments for experimen-
tal results, being especially useful for the number of
particle size ranges greater than 10 units (TSI, 2012).
Particle number distribution and particle surface dis-
tribution (dN/dlogD) were calculated for the 15 sam-
pling sites during the two sampling periods. X-axis
and y-axis are in logarithmic (base 10) scale (details
can be found in the SI.
Land use and cover, and urban morphology
Land use and cover, and the urban morphology
around the ORMS-IS were analysed using the
ENVI-met model (Mendes etal., 2020). This model
considers the spatial distribution of streets, houses
and buildings, according to type, position, height,
Fig. 3 Wind roses according to frequency (%) and wind speed (m s−1) during the daytime period 4–6 September and 10–12 Septem-
ber 2019 from 7:00 a.m. to 7:00 p.m. local time
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and vegetation cover and density obtained from
2015 Google Street View images. The tallest build-
ing in the sampling area reaches 20 floors (~ 60 m
height) at Santos Dumont Street near Blanco Hill,
but most of the buildings have only one or two floors
(between 5 and 6m height). Vegetation corresponds
to palms of 15 m height and lower canopy density
(i.e. trees ~ 10m height), such as Robinia pseudoaca-
cia, Liquidambar styraciflua, and Prunus cerasifera,
dense shrubs (~ 6m height), and grass. Predominant
land cover corresponds to sand, followed by asphalt,
concrete, and water. The modelled selected area, lim-
ited by computational restrictions, corresponds to
400 × 400m and is located within a 2km radius from
the ORMS-IS (Fig.2). Figure4 shows an aerial view
of the area (from Google Earth).
Results
Table 2 shows the main characteristics of the sam-
pling sites.
The 15 sampling sites were classified according to
typical traffic conditions and sampling time (Table1).
There are two sites with high traffic, two sites with
medium traffic, nine sites with medium/low traffic,
and two sites with a low level of traffic. It is probable
that different levels of traffic at each site contribute
to pollutant concentration heterogeneity within the
representative area of the ORMS-IS. During the sam-
pling period, temperature ranged from 14.4 ± 0.3 °C
to 26.7 ± 2.1 °C, and relative humidity ranged from
31.6% ± 0.7% to 76.0% ± 1.8%. During the sam-
pling period, only one day (10 September) presented
a significant amount of rainfall. Wind speed ranged
between 0.2 m s−1 and 2.3 m s−1, with SW, S and
Was predominant wind directions.
PM10, PM2.5 and PM1 mass concentration
Figure 5 (a, b, and c) shows mass concentration of
PM10, PM2.5 and PM1. It can be noted that 11 of the
15 sampled sites present outliers. These outliers can
be interpreted as high concentrations in time (sin-
gle data points) due to the high temporal resolution
(1min) of the spectrometric technique employed.
Figure 5a and 5b shows that the highest concen-
trations of PM10 and PM2.5 correspond to 6 Sep-
tember at site NW1, with 339 ± 214 μg m−3 and
70 ± 13 μg m−3, respectively. The minimum concen-
trations of PM10 and PM2.5 occurred in 11 Septem-
ber at SW2, with 39 ± 7μg m−3 (almost 9 times lower
Fig. 4 Aerial view and modelled land use and cover at the Independencia municipality (location of the ORMS-IS). Building blocks
(grey) cover 33% of the surface, bare land (white) 47.6%, trees (dark green) 15.8%, and grass (light green) 3.3%, respectively
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than the maximum) for PM10, and 14 ± 2 μg m−3 for
PM2.5. Site SW2 is located at a vegetated area, which
could explain the minimum values obtained on 10
and 11 September. A rainfall event occurred in the
early morning of the 10 September.
Additional high concentration of PM2.5 occurred
at site W2 sampled on 12 September, with
(131 ± 16) μg m−3 and (48 ± 3) μg m−3 for PM10
and PM2.5, respectively. The W2 site is the closest
site from the highway Autopista Central and the
Renca thermoelectric power plant (see Fig.1). On
12 September, NW winds were registered. Three
other sites sampled on 4 September show similar
levels of PM2.5 (29 μg m−3 to 33μg m−3). Concen-
trations of PM2.5 on the other sampling days show
higher variation depending on location, with a
greater spatial variability at the area of representa-
tiveness of the ORMS-IS. Figure 5c shows that
the W2 site registered the highest concentration of
PM1, with (36 ± 2) μg m−3, followed by NW1, the
site with greatest mass concentrations of PM10 and
PM2.5 (Figs.5a and 5b). PM1 and PM2.5 have simi-
lar sources associated with fossil fuel combustion.
The NE2 site, located in a residential sector with no
other recorded sources than a medium/low level of
vehicular traffic, shows the lowest concentration for
PM1, with (8 ± 2)μg m−3.
Table 2 Sites sampled description of the local sources and the environment within the representative area of the ORMS-IS between
4 and 12 September 2019
Sites Traffic t (°C) RH (%) Local sources Green areas
E1 Mid 22.2 ± 0.1 41.6 ± 0.9 no observation Cerro San Cristóbal (~ 400m). Trees on the
streets, approximately 3m near the site
SE1 Mid 22.1 ± 0.6 46.5 ± 0.5 High pedestrian flow (Near a College) Trees on the streets, approximately 15
mnear the site
NE1 Mid/low 15.1 ± 0.4 76.0 ± 1.8 Gas station (~ 30m). La Recoleta Cinerario
(~ 500m)
Cerro Blanco (~ 70m) -Cerro San Cristóbal
(~ 650m) Located in a square, approxi-
mately 10 mnear the site
SW1 Mid/low 15.1 ± 0.3 67.3 ± 1.1 Motor vehicles repair (~ 20m) Los Reyes Park (~ 700m)
NW1 High 14.4 ± 0.3 74.2 ± 0.8 High pedestrian flow (metro entrance).
Building (~ 70m). Cinerario of the Gen-
eral Cemetery (~ 700m)
no observation
W1 Mid/low 18.9 ± 0.7 59.9 ± 1.5 La Estampa Mill (~ 500m). Three gas sta-
tion (~ 150m / ~ 150m / ~ 60m)
Trees on the streets, approximately 10m
near the site
N1 Mid/low 15.8 ± 0.3 68.4 ± 1.0 Building (~ 450m). Cinerario of the Gen-
eral Cemetery (~ 600m)
Cerro Blanco (~ 300m) Trees on the streets,
approximately 10m near the site
S1 Mid/low 15.5 ± 0.4 66.5 ± 2.2 La Vega Market (~ 150m). Street cooking
(~ 100m)
Río Mapocho Slope (~ 30m) Trees on the
streets, approximately 5m near the site
SE2 Mid/low 20.4 ± 1.1 54.1 ± 3.0 no observation Entrance fee of Parque Metropolitano Park
(~ 20m)
SW2 Mid/low 20.6 ± 1.3 50.3 ± 3.5 Gas station (~ 50m) Located in a square, approximately 20
mnear the site. Los Reyes Park(~ 400m)
S2 High 15.5 ± 0.4 63.3 ± 1.7 High pedestrian flow (Pedestrian walk) no observation
N2 Low 26.7 ± 2.1 31.6 ± 0.7 Automotive workshop (~ 50m) Trees on the streets, approximately 2 mnear
the site
W2 Low 16.5 ± 0.7 59.6 ± 1.5 Closest site to Renca thermoelectric power
plant (~ 1.7km). Pharmaceutical industry
(~ 700m)
Enel Stadium (~ 100m). Los Reyes Park
(~ 400m). Located in a square, approxi-
mately 20m near the site
NE2 Mid/low 26.2 ± 1.2 33.8 ± 0.8 no observation Trees on the streets, approximately 7m near
the site
NW2 Mid/low 24.6 ± 2.6 42.9 ± 3.4 Pharmaceutical industry (~ 600m) Trees on the streets, approximately 5 mnear
the site
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Particle number distribution
Figure 6 shows that particle number distribution is
mostly below 0.5μm, i.e. correspond to ultra-fine par-
ticles. Figure 6a shows particle number distribution
sampled between 4 September and 6 September. The
sub-micrometric fraction shows similar distributions
for all sampling sites, with NE1 and W1 register-
ing the lowest values, in the range between 0.25μm
and 0.70 μm. Particle number of the PM1 fraction
fluctuates between 99.5% in W1 to 99.8% in E1 and
SE1. The fraction of PM1-2.5 shows similar distribu-
tions in all sites, with site NW1 being the highest and
SE1 the lowest, and in the range between 1.0μm to
2.0μm, and the site N1 in the range between 2.0μm
to 2.5μm. The site N1 located at ~ 700m from NW1
and sampled the same day shows the lowest par-
ticle number concentration, which evidences the
spatial variability of concentration in mass and in
particle number, in sites located at a short distance.
The > PM10 fraction shows a greater variability in the
distributions than the other fractions.
Figure 6b shows particle number distribution of
samples taken on 10 September to 12 September
2019. Particle number percentage registered in the
sub-micrometric fraction was between 99.4% (at
NE2) and 99.9% (at the sites S1, SE2 and N2) of the
total, as in the first sampling week. The sub-micro-
metric fraction shows that the sites with the highest
number of particles are the site S1, in the range of
0.25μm to 0.45μm, site N2, in the range of 0.45μm
to 0.60 μm, and site W2, in the range 0.60 μm to
1.00μm. The < 1.1μm range (i.e. ultra-fine particles)
is linked to fossil fuel combustion such as thermoe-
lectric power plant emissions. As mentioned before,
a thermoelectric power plant is located ~ 1.7km west
from the sampling sites. The lowest concentration,
between 0.25 μm and 0.60 μm range, was obtained
Fig. 5 Boxplot diagrams in μg m−3 for the 15 sampling sites: a PM10; b PM2.5;and c PM1
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at NE2, without observed local sources and with a
medium–low level of traffic. The 0.60μm to 5.00μm
range shows the lowest concentrations at sites SE2
and SW2, where similar distributions were obtained,
which can be attributed to vegetate areas proximity.
In the PM1–2.5 range, the W2 site shows a particle
number higher than other sites. In the PM2.5–10 range,
W2 shows the highest number of particles between
2.5μm and 6.5μm. W2 was exceeded by NE2 in the
6.5μm to 10μm range.
Particle surface distribution
Figure 7 shows particle surface distribution of sam-
ples taken between 4 September and 12 September.
The figure shows two peaks at almost all sites. The
first peak is present at all sites, with particles less than
0.3μm; the second peak is located between 0.28μm
and 0.4μm. Figure 7a shows the results for the sam-
ples taken on 4 September to 6 September. The larg-
est peak is found at all sites at 0.28μm; a second peak
appears at 0.35 μm. There is high distribution vari-
ability in the 0.25μm to 0.65μm range followed by a
convergence of all distribution curves. In the PM1–2.5
range, something similar is found. In the PM2.5–10
range, the distributions become different again, which
may be due to particle number differences (Fig.6).
Figure 7b shows particle surface distributions
for the second sampling period. The highest surface
percentage is found at the sub-micrometric fraction,
accumulating between 55.2% (siteW1) and 85.4%
(site N1), of the total PM surface. The largest sur-
face area is concentrated in the sub-micrometric frac-
tion, which accumulates between 47.1% (site NE2)
and 92.4% (site S1) of the total PM surface, a more
extended range than PM collected during the first
sampling period. The surface contribution from the
larger aerodynamic diameter ranges is smaller than
the observed in Fig.8a.
The surface maximum is reached at 0.28μm at all
the sites. The observed variability in particle surface
distribution is attributable to differences in traffic
levels. Particle surface distributions converge in the
0.80–1.30 μm range with minimal surface contribu-
tion. Subsequently, the PM2.5–10 and PM10–32 ranges
have a lower surface concentration than the first sam-
pling period. The peak observed at SE2 was also reg-
istered in particle number distribution (Fig.6b).
Fig. 6 Particle number distribution (dN/dlogD) at the 15 sampling sites: a 4 to 6 September 2019; b 10 to 12 September 2019.
X-axis and y-axis are in logarithmic (base 10) scale
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Discussion
There is a pharmaceutical industry and a mill located
to the NW and W direction from the ORMS-IS
(Fuentealba, 2018; Préndez et al., 2007); in addition,
the General Cemetery crematorium and La Recoleta
Cinerary locate at N and NE from the ORMS-IS,
respectively. The above-mentioned industries and ser-
vices are associated with < 2.5µm particles emissions
(Figs.1 and 2). The Renca thermoelectric power plant
and the industries located in the SW sector of the city
(Fuentealba, 2018) can also affect the sampling sites
with ultra-fine particles. Previous studies report the
presence of Co, Cu, Fe, Na, Sb and Zn in the fine
particle size range linked with vehicular emissions
(Fuentealba, 2018) in samples collected near the
highway Autopista Central. Miler, (2021) in the city
of Ljubljana (Slovenia) also found some of those ele-
ments coming from vehicle exhaust emissions, brake
disc dust and road sediment. Thursday September
5 presented a predominant SE wind direction and
lower frequency regimes varying during the day as
NW, NE and N (Fig. 3). To note that ultra-fine PM
(PM less than 1 μm) can penetrate deeper into the
respiratory system (Chen etal., 2016; Shiraiwa etal.,
2017), clearly affecting respiratory and cardiovascular
systems.
All sampled sites are affected by high, medium or
low traffic of personal and public traffic. The highest
surface and particle number vales for the site NW1
were found at the PM2.5–10 range, which have also the
highest PM concentrations.
On the other hand, 11 of the 15 sampling sites are
near vegetated areas, including some densely veg-
etated covers such as San Cristóbal and Blanco hills
(located E and NE from the sampling sites) and Los
Reyes and Forestal parks (located S and SW from
the sampling sites) (Fig. 1). Vegetation contributes
to improve air quality due to the capture of PM and
the absorption of gases (Escobedo et al., 2011; Gao
etal., 2015; Nowak etal., 2013; Préndez etal., 2019).
On the > PM10 fraction, a peak was observed at SE2
which coincides with the pollen size range (Ramli
et al., 2020). Pollen varies in shape and size (from
10µm to 100µm) depending on the species. Two dif-
ferent tree species present at the Metropolitan Park
have their pollination period during September–Octo-
ber: Platanus x acerifolia and Acer negundo. The
Fig. 7 Particle surface distribution (dN/dlogD) at the 15 sampling sites during: a 4 September to 6 September 2019; b 10 September
to 12 September 2019. X-axis and y-axis are depicted in logarithmic (base 10) scale
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Cupressus sempervirens has its pollination period
during August–September (PARQUEMET, 2017). In
addition to the emission of pollen, site W2 is inter-
esting since it shows the highest number of particles
between 2.5µm and 6.5μm, and the lowest number
of particle surface.
Trees also constitute a natural source of fine PM
due to the emission of volatile organic compounds,
potential ozone precursors and fine secondary organic
compounds (SOA), having a complex chemical com-
position (Nault etal., 2018; Préndez etal., 2013).
The highest surface percentage of the particle sur-
face distributions is found at the sub-micrometric
fraction, which is mostly influenced by anthropic
emissions (Gietl et al., 2010; Perrone et al., 2014;
Sinha etal., 2011; Zhao & Yu, 2017). However, the
percentages are not as high as those observed in the
number of particles (99.5% to 99.8%). This is because
at the MP2.5–10 range, the particles are few in number;
however, they represent a significant contribution to
particle surface.
Figure 8 shows the spatial analysis of PM con-
centrations: a) PM10; b) PM2.5 and c) PM1. Figure8a
shows that the highest concentration of PM10 and
PM2.5 corresponds to site NW1 (on 6 September).
High concentrations at this site are probably due to
the influence of local sources such as high vehicu-
lar traffic, re-suspended dust deposited on the pave-
ment, abrasion of vehicle brakes, tyres and pavement
degradation, and building construction (Gietl et al.,
2010; Thorpe et al., 2007). In addition, the sam-
pling site is in front of Hospitales subway station
that presents a large flow of people during daytime.
This site also shows the second highest concentra-
tion of PM1 and a high number of particles of large
surface, thus constituting a dangerous site for human
health in case of prolonged exposure. However, dur-
ing the sampling hours, the ORMS-IS station report
Fig. 8 Spatial analysis of the concentrations of PM sampled in the representative area of the ORMS-IS: a PM10; b PM2.5 and c PM1
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concentrations of 74μg m−3 and 23μg m−3 for PM10
and PM2.5, respectively, 4.6 and 3.0 times lower than
the observed values in this work ((339 ± 214)μg m−3
and (70 ± 13) μg m−3, respectively). Concentrations
of PM10 and PM2.5 at site SW1 are lower than NW1,
with (230 ± 89) μg m−3 and (43 ± 8) μg m−3, respec-
tively (Fig.8b). At the other sites, there are also dif-
ferences between concentrations of PM10 and PM2.5.
Mean concentrations for 24h for the daily report by
ORMS-IS are 43μg m−3 and 13μg m−3 for PM10 and
PM2.5, respectively (MMA, 2021a, 2021b).
Minimum concentrations of PM10, PM2.5 and
PM1 occurred on 11 September at SW2, with
(39 ± 7)μg m−3, (14 ± 2) μg m−3 and (11 ± 1)μg m−3.
This day registered a mild rain (Figure SI-2), with
rainfall often contributing to decrease atmospheric
PM concentration (Nowak et al., 2013; Préndez
etal, 2014). In the case of ORMS-IS station, SINCA
reports concentrations of 23 μg m−3 and 3 μg m−3
for PM10 and PM2.5, respectively, during the sampled
hours of this work, but does not report accumulative
values for 24h (MMA, 2021a).
PM1 concentrations show a completely different
spatial distribution than PM10 and PM2.5. Sites SW1
and W2 show the highest PM concentrations. It is
interesting to note that the site W2 increases their
mass relative concentration in the sense inverse to the
diameter of the particles (Fig.8c).
Currently in Chile, there are no official standards
for the PM sub-micrometric fraction. The lowest PM1
concentrations were obtained at the sites SW2, S2,
SE2, and NE2, sampled at the ORMS-IS area bound-
ary. The W2 site showed the highest concentration of
PM1 probably due to its closeness to Autopista Cen-
tral and Renca thermoelectric power plant, as dis-
cussed by Wang et al. (2005) in relation to combus-
tion of fossil fuels.
In the sites located within 1km from the ORMS-
IS, the lowest PM1 concentration and particle number
was recorded at W1 (at 0.28µm). This site presents
medium/low traffic, without vegetated areas close by,
but potentially affected by three gas stations. Using
a similar optical spectrophotometer as the one used
in this study, Dahari et al., 2021 observed a peak
of the number of particles in the submicron range
with percentages of the order of more than 95% and
attributed these findings mainly to vehicular traffic.
Site S1 shows mass concentrations higher than the
PM1 average (between 17.7% and 58.7%) and the
highest number of particles in the range of 0.25μm
to 0.45 μm. This site is the closest to local sources
of street cooking that use different combustion types,
such as gas stoves for frying or open charcoal grills.
The < 1.1 μm range (i.e. ultra-fine particles) is linked
to combustion of fossil fuels (Rajput et al., 2016;
Wang et al., 2005). In addition, Buonanno et al.
(2009) reported the highest percentage of particles
from such outdoor cooking coming from activities
in the (0.1–1.0) μm range, followed by PM within
the (1.0–2.5) μm range. The precariousness of the
facilities for street cooking leads to poor combustion;
therefore, for this reason, additional research in this
topic is necessary to assess the contributing of this
PM source.
Independencia municipality, as all the peri-central
areas, has increased its urban functionality, density
and mobility, during recent years, and it is expected
that this trend would continue in the near future.
Hospitals, in addition to the largest open market of
the city (La Vega), large cemeteries, a large subway
station, and heavy traffic roads for private and public
transportation have not been the object of systematic
environmental planning and management. Vegetation
cover is poor and not distributed according to their
ecological services. The eventual arrival of clean air
masses from nearby hills and parks is constrained by
the roughness of an increasing number and density of
high-rise buildings. It is necessary for Santiago and
other large Latin American cities to incorporate air
pollution spatial and temporal detailed information in
their sustainable urban development. The presence of
such a large number of sources without counterbal-
ancing actions contributes air pollution and increases
the population’s risk of contracting respiratory and/or
cardiovascular diseases.
Conclusions
New characteristics of aerosol not measured at
present by the official air quality monitoring net-
work were quantified within the ORMS-IS area. All
15-sites show the highest values of particle number
distributions in the range of ultra-fine particles. Sur-
face distributions at all sites showed that particle size
was mostly below 0.4 μm (i.e. nanometric particles),
with high potential to absorb other pollutants.
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Differences in PM mass concentration, parti-
cle number and particle surface distribution were
observed within the representative area of the ORMS-
IS. PM at the area was most likely due to local
sources around the microenvironment, e.g. vehicular
traffic, building construction, street cooking and pol-
len emissions. PM hot spots can be individualized
within the studied area, with important implications
for human health, including seasonal allergies.
PM10 data provided by the ORMS-IS represent
well the sites near the station. On the contrary, PM2.5
and PM1 are not well represented by the ORMS-IS.
The importance of ultra-fine PM (< PM1) and
other characteristics of the aerosol such as particle
surface and particle number distribution evidence the
different quality of air within the monitored area and
potential effects on population health. This heteroge-
neity is the result of local sources, in addition to spe-
cific urban conditions and/or the lack of management
policies.
The complementary setting of a monitoring net-
work based on instruments assessing the different
properties of the aerosol could contribute to improve
air pollution policy making at Santiago city.
Acknowledgements To REDES consolidation project of the
Universidad de Chile URC-026/17 for financial support. To
CPV for text editing and revision.
Author contributions All authors contributed to the study
conception and design. Material preparation, data collection
and analysis were performed by PN and MP. The first draft of
the manuscript was written by PN, MP and HR, and all authors
commented on previous versions of the manuscript. All authors
read and approved the final manuscript.
Funding The authors have not disclosed any funding.
Data availability See web page https:// figsh are. com/ artic les/
datas et/_/ 16441 707
Declarations
Conflict of interest The authors declare that they have no
known competing financial interests or personal relationships
that could have appeared to influence the work reported in this
paper.
Consent for publication The authors also declare that the
work sent for publication does not include material from third
parties that are subject to copyright (figures, tables, photos or
others similar).
Ethical approval The corresponding author Margarita Prén-
dez, and on its behalf the rest of the authors, declares that
the individualized article above represents results of original
research, that is has not been published nor is being consid-
ered for publication in another journal, and that it complies
with international ethical standards of intellectual property and
authorship.
Open Access This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits
use, sharing, adaptation, distribution and reproduction in any
medium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Crea-
tive Commons licence, and indicate if changes were made. The
images or other third party material in this article are included
in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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