In all sites, PM₁₀ and PM_2.5, temperature and humidity were measured with a low cost monitor (LCM) specially designed and built for the studies. Several sensors and an electronic interface are integrated with a microprocessor (ESP32) as shown in Figure 2. The microcontroller, the interface and the sensors were placed in a plastic box of 21 cm³ × 16 cm³ × 7 cm³, weighing approximately 700 g. It is connected to the 220 VAC grid and consumes 1 W. The data is stored in an internal SD memory card and, if there is access to WiFi, it is sent to a website automatically. The equipment works autonomously, but in cases of power failure, the data recording is interrupted. However, if the power returns, the microprocessor restarts automatically. The LCM recorded data every minute, but in order to compare with other instruments, it was averaged to 1 hour. When the number of measurements was less than 50%, the hourly value was reported as missing. The box was placed outside of the monitoring station in each site for the duration of the study. A picture of the low cost instrument installed at Los Caleos site is shown in Figure 3 (red arrow). The sample inlet for the BAM-1020 instrument can be seen to the right of the picture. The white, temperature-controlled container seen in the picture houses the BAM monitor and other reference instruments. In all sites, the LCM was installed in the roof of the regulatory air quality monitoring station.

PM₁₀ and PM_2.5 were measured with Plantower, PMS 7003 (Beijing Plantower Co., Ltd., Houshayu, Beijing, China) and with a SDS011 from Shandong Nova Fitness Co.,Ltd. Jinan., China. Temperature and humidity were measured with an SHT-30 (Sensirion, Staefa ZH, Switzerland). Calibration of the Plantower or Nova Fitness was performed at each site using data from the collocated BAM-1020 for both PM₁₀ and PM_2.5. Data from the Plantower or Nova Fitness was multiplied by a factor which was adjusted to minimize the nRSME (Manikonda et al., 2016) between the instruments.

The Aerosol chemical speciation monitor (Aerodyne Research Inc.,) is able to routinely characterize non-refractory submicron aerosol species organics, nitrate, sulfate, ammonium and chloride with the time resolution from 15 min up (Ng et al., 2011). The instrument consists of a particle sampling inlet, three vacuum chambers and a residual gas analyzer mass spectrometer. Particles are first focused by using an aerodynamic lens system into a narrow particle beam, which is transmitted through three vacuum chambers. In the third chamber the particle beam is directed into a hot tungsten oven (600°C) where particles are flash-vaporized, ionized with a 70 eV electron impact ionizer and detected with a quadrupole mass spectrometer. The ACSM was used to obtain chemical species from particulate matter in Temuco, Talca and Santiago. According to the manufacturer, the sensitivity of the ACSM to Organic matter is 0.3 μg/m³, sulfate: 0.04 μg/m³, nitrate: 0.02 μg/m³, NH4: 0.5 μg/m³, chloride: 0.02.

The BAM 1020 monitor (Beta Attenuation Mass Monitor, Met One Instruments) measures the mass concentration of ambient particulate matter collected onto glass filter tape with a time resolution of 1 minute. The BAM 1020 employs an in-line sampling geometry in which the attenuation of beta rays across filter media is measured and particulate matter is sampled simultaneously. This allows ambient sampling to occur for virtually 60 min each hour. The data resolution is 1 μg/m³.

In Santiago, coarse particles, PM_2.5–10, and fine particles, PM_2.5, were collected using dichotomous samplers (Andersen Instruments, Inc., Smyrna, GA), with a collection time of 24 h. The total sampling 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). Particles were collected on 37 mm Teflon filters.

In Quillota, Los Caleos, Temuco and Talca sites, the Harvard Impactor (Marple and Willeke, 1976; Peters et al., 2001) was used to collect samples in Teflon or quartz filters. A flow rate of 4.0 lt/min was used with a 24 h collection time.

In all cases, each filter was inspected for its integrity before use. Particle concentrations were determined gravimetrically using an electronic microbalance, with a resolution of 0.001 mg. Both blank and field filter samples were conditioned at constant temperature (22 ± 3°C) and relative humidity (40 ± 5%) for at least 24 h before being weighed. Inhalable particle concentrations, PM₁₀, were calculated as the sum of coarse and fine particle concentrations.

The Teflon filters from Quillota and Los Caleos were sent to Chester Lab Net (Garden Pl, Tigard, OR) to determine chemical elements, from Z = 11 to 82 using an “EDXRF Thermo Scientific ™ ARL™ QUANT'X energy-dispersive x-ray fluorescence analyzer” equipment. The Teflon filters from Santiago, Temuco and Talca were sent to the Desert Research Institute (DRI) in Reno, NV, to determine the concentration of elements with XRF.

The Teflon filters from Quillota and Los Caleos were also processed in the Chemistry Department of the University of Santiago to obtain the chemical species. Extraction of the soluble fraction of major trace elements was performed by leaching with 30 ml of ultrapure water inside PVC containers. The containers were then immersed in an ultrasonic bath during 10 min and placed in an oven at 60°C for 6 h. The resultant solution was filtered through a 0.45 μm membrane filter, and finally analyzed by ionic chromatography (high performance liquid chromatography using a WATERS IC-pakTM anion column) for the determination of Cl⁻, NO₃ ⁻ and SO₄ ^2–, and by a specific electrode (WATERS 432 conductivity detector) to obtain NH₄ ⁺ concentrations.

The quartz filters collected in Quillota and Los Caleos were used to obtain organic (OC) and elemental or black carbon (BC) using the thermo-optical method at Chester LabNet (Garden Pl, Tigard, OR). The equipment used to obtain the organic and elemental carbon fraction is the semi-continuous EC-OC Model-4 Analyzer, manufactured by Sunset Laboratory Inc., 10,180 SW Nimbus Avenue, Suite J/5, Tigard, OR 97223, United States. Carbon samples were analyzed with the NIOSH method 5,040 (NIOSH et al., 2016).

As mentioned in the previous paragraph, in some sites (Los Caleos and Quillota) black carbon (BC) concentration was determined from filter measurements. In Temuco, Talca and Santiago, BC was determined from a continuous instrument (Simca) that measures the absorption coefficient of the particles (Gramsch et al., 2004).

Crustal material (natural dust) corresponds to the fraction of particulate matter that is directly resuspended from the ground by wind, traffic or other mechanical means. In this work, an assumption is made that dust (Rudnick et al., 2003) is made up of the most common elements of the earth’s crust, which are the following oxides: SiO₂, Al₂O₃, Fe₂O₃, CaO, MgO, Na₂O, K₂O, TiO₂, Mn₃O₄, SO₃, P₂O₅. According to the methodology outlined by Andrews et al. (2000), and using the concentration of elements–Si, Al, Fe, Ca, Mg, Na, K, Ti, Mn, S and P–measured with the X-Ray Flourescence (XRF) technique and the mass of oxygen, it is possible to estimate the mass of the mentioned compounds assuming that these elements are in the form of the named species. This method tends to underestimate the soil contribution, due to artifacts mentioned in Andrews et al. (2000). However, the method may also overestimate the dust mass if there is anthropogenic contamination of the elements mentioned above. For example, in coastal cities is common to find larger concentrations of Na; in cities with wood burning, it is common to find larger concentrations of K.

Because of the anthropogenic pollutants and it may be difficult to determine dust fraction using all the measured elements. In order to determine the relative fractions of dust coming from the earth crust, that can used as a reference for clean dust in this work, a sample of earth crust in a hill near Santiago (La Cruz) was taken and analyzed. The sample was extracted by digging a 10 cm hole in a place with little organic material. The XRF analysis of the sample is shown in Figure 4A,B, along with the XRF analysis of PM2.5 from the five sites. The concentration of each element was normalized to the concentration of Si, in order to show them in the same plot.

For La Cruz sample, Figure 4 shows high concentrations of Si, Al and Fe; and low concentration of S, K and Cl (relative to Si), which are also commonly found in PM_2.5 or PM₁₀ samples in the air of large cities (Sax et al., 2007; Moreno et al., 2010; Jorquera et al., 2021). The low values of S and Cl indicate that the sample was not contaminated with pollution from the city. The XRF data for the southern sites (Temuco and Talca) show higher concentrations of P, Cl, Ca and K which are an indication of wood burning contamination (Hedberg et al., 2005; Jorquera et al., 2018). In the northern and southern cities, the S concentration is higher than La Cruz, indicating contamination from anthropogenic sources. S may come from many sources, such as diesel engines, boilers, heaters, coal burning, power plants, sea aerosols, etc., and it is commonly found in all cities. In the southern cities, there is relatively less concentration of P, Cl, K and Ca, but higher concentration of S.

Figure 5 shows the estimation of the dust composition according to method mentioned before. This estimation is also similar to the composition of earth crust from other places (Chorley, 1969; Rudnick and Gao, 2001). The figure shows that the largest fraction of natural dust is composed of silica particles (SiO₂) which may be relevant to the accuracy of LCMs. With this method, for La Cruz site, the mass concentration of dust is obtained as:

Dust (La Cruz) = 2.14[Si] + 1.13[Al] + 0.81[Fe] + 0.15[Ca] + 0.16[Mg] + 0.07[Na] + 0.06[K] + 0.07[Ti] + 0.021[Mn] + 0.008[S] + 0.012[P]. (1)

where the square brackets denote concentrations of the compounds. In order to obtain the fraction of natural dust in the PM samples from the five sites, the reconstruction is going to be done assuming that dust in all sites has a relative composition similar to La Cruz site, as shown in Eq. 1. The Si concentration from each site is used to find SiO₂ and the remaining compounds are constructed using the relative fractions measured at La Cruz hill and the concentration of SiO₂.

Dust (all sites) = [SiO₂] + 0.53[SiO₂] + 0.38[SiO₂] + 0.068[SiO₂] + 0.073[SiO₂] + 0.034[SiO₂] + 0.030[SiO₂] + 0.033[SiO₂] + 0.010[SiO₂] + 0.004[SiO₂] + 0.006[SiO₂]. (2)

Because in all sites there are elements that have higher concentration than what is found in the reference fractions, the difference is going to be added to a group named “other elements”. S and K are examples of these elements, because they are found in PM_2.5 or PM₁₀ in cities in concentrations much higher than the reference concentration.

In addition to the elements that form earth crust, there are other elements with much lower concentrations, such as: V, Cr, Co, Ni, Cu, Zn, As, Pb, etc., that have been measured, but whose masses are very small compared to the elements that form the dust. These elements have also been added to the “other elements” group.

Measurements in Temuco were done during fall, from June 21 until July 25 of 2019. The time series for PM_2.5 and PM₁₀ are shown in Figure 6A,B. It seem clear that PM₁₀ and PM_2,5 come from the same source because the plots are very similar in shape and the Pearson linear coefficients, R ² is higher than 0.99. In addition the average PM₁₀ was 33.3 μg/m³ and PM_2.5 was 26.4 μg/m³, that is, only 20% difference between them. These numbers indicate that most of the PM₁₀ is composed of fine particles (d < 2.5 μm) and one source present in Temuco that generates mostly fine particles is wood burning (Tsapakis et al., 2002; Díaz-Robles et al., 2014; Villalobos et al., 2017; Jorquera et al., 2018; Jorquera et al., 2021). The correlation between measurements with the Plantower and the BAM monitor is very high, R ² = 0.97 for PM_2.5 and R ² = 0.95 for PM₁₀.

In Talca, measurements were also done in winter, from June 15 until August 7, 2020. A comparison of the measurements with the low cost and the BAM monitor is shown in Figure 6C,D. The plots show that measurements with both monitors are similar and the correlation between the Nova Fitness and the BAM monitor for PM_2.5 is R ² = 0.73 and for PM₁₀ the correlation is R ² = 0.69. The correlation between PM₁₀ and PM_2.5 measured with the BAM monitor is very high R ² = 0.99. The concentration is also very similar, with the average PM₁₀ being 61.4 μg/m³ and PM_2.5 = 53.9 μg/m³.

In Santiago (Parque O’Higgins station), measurements were done during spring of 2020, from October 1st until November 20. In order to compare with the previous measurements, only 1 month is going to be used for the calculations. The times series for the first month is plotted in Figure 7A,B. In this site, there is less correlation between the measurements with the Plantower and the BAM monitor. The shape of PM₁₀ is also different to PM_2.5. The correlation between PM₁₀ and PM_2.5 is R ² = 0.49, the average PM₁₀ was 56.5 μg/m³ and PM_2.5 was 15.7 μg/m³, that is a difference of 3.6 between them. The correlation between the Plantower and the BAM monitor for PM_2.5 is R ² = 0.86 and for PM₁₀ the correlation is R ² = 0.34.

In Quillota the measurements were done at the end of winter, from August 26 until September 30, 2020. The comparison between the Plantower and the BAM monitor is plotted in Figure 7C,D. In this site, the PM_2.5 measurement with the Plantower monitor shows better agreement with the BAM monitor than the PM₁₀ measurement. The correlation between PM₁₀ and PM_2.5 is R ² = 0.65, the average PM₁₀ was 36.8 μg/m³ and PM_2.5 was 19.4 μg/m³. The correlation between the Plantower and the BAM monitor for PM_2.5 is R ² = 0.65 and the correlation for PM₁₀ is R ² = 0.34.

In Los Caleos the measurements were done in spring of 2020, from September 24 until November 14. The comparison between the Plantower and the BAM monitor is plotted in Figure 7E,F. In this site, the PM_2.5 measurement with the Plantower monitor also shows better agreement with the BAM monitor than the PM₁₀ measurement. However, for PM₁₀ there is almost no agreement. The correlation between the Plantower and the BAM monitor for PM_2.5 is R ² = 0.68 and the correlation for PM₁₀ is R ² = 0.04. This last number indicates that in some places, LCMs cannot be used to measure PM₁₀.

A summary of this information is presented in Table 2. In the sites with winter measurements (Temuco and Talca) the LCM show a very high correlation between PM₁₀ and PM_2.5, with the concentration of PM_2.5.

The sites in which the measurements were done in spring or at the end of winter (Santiago, Quillota and Los Caleos) with the LCM, show a lower correlation between PM₁₀ and PM_2.5. Spring has higher temperatures and higher wind speed than winter and there is more re-suspended dust, which is a source of PM₁₀. In these cities PM₁₀ is much larger than PM_2.5 and the correlation between them is lower (Table 2). In contrast to the cities with winter measurements, for PM₁₀ the correlation between the LCM and the BAM is much lower, and for Los Caleos, there is no correlation between both instruments.

The difference between these cities, seem to be the sources of the particles. Cities with high correlation for PM₁₀ and PM_2.5 between the LCM and BAM have mainly one source of particles; wood burning. For cities with lower or null correlation there are several sources of particles. In the case of Los Caleos, because it is a rural site, a larger fraction of the particles are generated from resuspension of dust.

In each site, chemical species (sulfate, nitrate, ammonium, chlorine), black carbon, organic carbon, natural dust and chemical elements in PM_2.5 were determined. The measurements were done in the same period as the PM_2.5 monitoring with the low cost instrument. These species form most of the PM_2.5 found in cities (Seinfeld and Pandis, 2006). As shown in Table 2, there are large variations in total PM_2.5 concentration in the sites; thus, the concentration of the species has been normalized to the average for the period. The results are plotted in Figure 8, with the cities ordered from south to north. The figure shows that there are trends in the composition of the particles that depend on the location of the city. Sulfate increases from south to north, which is most likely related to the industrial activities of the area. Talca and Temuco do not have industries that emit SO₂ to the atmosphere and have a colder climate that reduce photochemical activity that can form sulfate. In addition, the main source of pollution in Temuco and Talca (Pozo et al., 2015; Jorquera et al., 2018) is wood burning, which generates mostly fine and ultrafine particles without sulfate (Rau, 1989; Kleeman et al., 1999). Quillota and Los Caleos are located in an industrial zone that has several large sources of SO₂ (Hedberg et al., 2005) and because the climate is drier there is more photochemical activity than the south. Santiago is a large city that also has many sources of SO₂ and has large photochemical activity. Nitrate also has a clear trend from south to north. Nitrate is formed partly from NO₂ emissions that undergo photochemical reactions in the atmosphere creating nitrate (Seinfeld and Pandis, 2006). For the same reasons as before there is more nitrate in the northern cities. Natural dust has also a trend from south to north, with exception from Santiago. Temuco and Talca have larger amounts of rain (DMC, 2021) which keep the ground moist, reducing the resuspension of dust (Nicholson, 1988). The cities in the north have drier climates and have more resuspension of particulate material (Nicholson, 1988). The relative larger amount of dust in Santiago is probably related to the size of the city (1.7 million inhabitants compared to 90,000 in Quillota and 10,000 in Los Caleos), consequently there is more traffic and more activities that can resuspend dust into the air. Several studies have found that when there is little rain, materials that were previously deposited on road surface can be more easily resuspended (Amato et al., 2009; Karanasiou et al., 2011; Rienda and Alves, 2021). In addition Earlier work in Santiago has characterized the elemental composition of PM₁₀ and PM_2.5 with XRF. Using source apportionment analysis, it has been found that about 25% of the elements in PM_2.5, come from dust, except for a period in winter 2002 which had specially rainy months and fell to 5% (Moreno et al., 2010).

As seen in Table 2, the correlation between the low cost instruments and the standard instruments used for regulatory purposes decreases from south to north. The PM₁₀ correlation coefficient (R ²) between the LCM and a collocated standard instrument varied from to 0.95 in Temuco to 0.042 in Los Caleos. PM_2.5 correlation varied from 0.97 to 0.68 in the same places.

Figure 8 shows that the fraction of OC in particle matter decreases from south to north, and Wang et al., 2015, observed that several LCMs had a larger response for organic carbon particles (OC) than for inorganic salt particles. Thus the relative response of the Plantower in this work could be lower in the northern cities that have less OC. However, it is also known that OC is not constant throughout the day (Seinfeld and Pandis, 2006; Carbone et al., 2013), as a consequence the Plantower response may change along the day.

He et al., 2019 used the Plantower to test ammonium nitrate and sodium chloride particles and found 1) that particle number distributions from the six channels of the sensor may not reflect actual size distributions and can be significantly erroneous; 2) the reported PM values may be underestimated, especially for larger sized particles. In this work, chloride increases from south to north (Figure 8), so the Plantower could have a larger response in the northern cities due to the relatively larger amount of sodium chloride. Zou et al. (2021) and Northcross et al. (2013) also found a different response for sulfate particles than other particles. In this work, sulfate has a clear increase from the northern to the southern cities and the relative response of the Plantower could change.

Zou et al. (2021) found that the Plantower underestimated the concentration of Arizona Test Dust; Sayahi et al. (2019) found and underestimation for aluminum oxide (a component of natural dust). As shown in Figure 8, the relative concentration of natural dust increases from south to north, so the Plantower could underestimate the concentration in the northern cities. Natural dust concentration in PM_2.5 is also higher in the northern cities. Most of this component is made of SiO₂ which has a scattering coefficient that has a strong dependence with the particle size (Seinfeld and Pandis 2006). Similar to sulfate and nitrate, SiO₂ can generate electronics pulses in the photodiode that are not proportional to particle size, leading to incorrect classification of particles. In addition, natural dust tends to have particles with larger sizes which fall in geometric scattering regime that has a strong dependency on the orientation of the particle. Thus, measuring with low cost monitors in cities that have high concentration of natural dust may induce large errors in the results.

The under- or over-estimation of the concentration would not be a problem if it were constant, because the sensor is calibrated at each site. However, the concentration of the species mentioned is not constant throughout the day, week or season, and it is not possible to find a calibration factor that accounts for this type of changes. As shown in Figure 9 there are larger under- and over-estimations of PM₁₀ in Los Caleos, a site with a relatively large proportion of dust, sulfate and chloride in PM₁₀ and PM_2.5.

Table 2 shows that in the three cities in the north (Santiago, Quillota and Los Caleos), PM₁₀ is larger than PM_2.5 (PM₁₀/PM_2.5 ratios of 3.6, 1.9 and 2.5) which indicates that there are many large particles. Some of these particles fall into the geometric scattering regime which means that the light received by the photodiode depends on the orientation of the particle with respect to the laser beam. In this case, large particles can generate electronic pulses of different sizes that can be interpreted incorrectly, thus larger uncertainties in the measurement are obtanined. In contrast, in the southern cities (Talca and Temuco) most particles are smaller (PM₁₀/PM_2.5 ratios of 1.26 and 1.14), the scattered light has less dependence with the orientation of the particle and there is smaller uncertainty in the measurement.

Figure 8 shows that in the southern cities (Temuco and Talca) the relative concentration of OC and BC is larger than the northern cities. These species have small sizes (d < 1 μm) but also they come from the same source and most of them have similar composition (Tsapakis et al., 2002; Jorquera et al., 2018) consequently the scattering coefficient is probably similar for most particles. Thus OC and BC are more likely to be classified in the correct size range.