Ye Li1,2, Franz Muñoz-Ibañéz4, Ana Maldonado-Alcaíno4, Darby Jack5, Beizhan Yan2, Li Xu3, Marco Acuña6, Manuel Leiva-Guzman7, Ana Valdés8, Dante D. Cáceres This email address is being protected from spambots. You need JavaScript enabled to view it.4,9 

1 Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
2 Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA
3 Woods Hole Oceanographic Institution, Falmouth, MA 02543, USA
4 Programa de Salud Ambiental, Escuela de Salud Pública, Facultad de Medicina, Universidad de Chile, CP 8380463, Santiago, Chile
5 Environmental Health Sciences Department, Columbia University Mailman School of Public Health, New York, NY 10032, USA
6 Secretaria Ministerial de Salud, Región de Aysén, Coyhaique, Carrera 290, Coyhaique, Chile
7 Departamento de Química, Facultad de Ciencias, Universidad de Chile, Ñuñoa, Las Palmeras 3425, Santiago, Chile
8 Departamento de Tecnologías Nucleares, División de Investigación y Aplicaciones Nucleares, Comisión Chilena de Energía Nuclear, Las Condes, Santiago, Chile
9 Facultad de Ciencias de la Salud, Universidad de Tarapacá, Arica y Parinacota, Chile


Received: June 16, 2022
Revised: November 3, 2022
Accepted: November 7, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220247  


Cite this article:

Li, Y., Muñoz-Ibañéz, F., Maldonado-Alcaíno, A., Jack, D., Yan, B., Xu, L., Acuña, M., Leiva-Guzman, M., Valdés, A., Cáceres, D.D. (2022). Cancer Burden Disease Attributable to PM2.5 and Health Risk by PM2.5-bound Toxic Species in Two Urban Chilean Municipalities. Aerosol Air Qual. Res. 22, 220247. https://doi.org/10.4209/aaqr.220247


HIGHLIGHTS

  • PM2.5 daily concentration and its chemical composition were measured in two urban municipalities.
  • Cancer burden disease by PM2.5 exposure and health risk by PM2.5-bound chemicals were estimated.
  • Some cases of lung and cardiopulmonary cancer would be attributable to PM2.5 long-term exposure.
  • A high health risk was determined in Coyhaique for B[a]P and As and for As in Independencia.
 

ABSTRACT


This study aimed to estimate the environmental cancer disease burden in adults attributable to fine particulate matter (PM2.5) exposure using Ostro's function methodology, and health risk indexes for particle-bound toxic chemicals through hazard quotients (HQ, HI) and carcinogenic risk (CR, CRI) indexes from EPA guidelines, of two urban Chilean Municipalities: Coyhaique and Independencia. Quantification of chemical species (OC, EC, metals, and PAHs) was done at the Lamont-Doherty Earth Observatory of Columbia University, USA. Modern carbon in OC and EC analysis showed that the principal source of PM2.5 emission in Coyhaique was firewood burning compared with Independencia. The total PAHs and B[a]P concentrations were 6.3 and 8.9 times higher in Coyhaique than in Independencia. In contrast, As and Pb levels were significantly greater in Independencia. The HI was 14.5 and 2.37 times the limit considered acceptable (HI > 1) in Coyhaique and Independencia, explained 92.45% by B[a]P and 66.99% by As, respectively. CRI exceeded the threshold (1 × 106) in Coyhaique and Independencia, explained by As (75.38%) plus B[a]P (20.30%) and As (97.01%). The attributable fraction (AF) of deaths due to lung cancer from long-term exposure to PM2.5 reached 54% (95% CI: 25–72) in Coyhaique vs. 43% (95% CI: 19–46) in Independencia. The AF for cardiopulmonary cancer were 40% (95% CI: 17–57) and 32% (95% CI: 12–46), respectively. A relevant fraction of the cancer cases and potential expected adverse effects would be attributable to long-term exposure to PM2.5 and the presence of chemical compounds bound to the particles. These results deserve further study to help guide policy in different environments, mainly carcinogenic PM2.5-bound toxic species from other emission sources, particularly firewood burning.


Keywords: PM2.5, Health risk assessment, Burden disease, Attributable fraction, Toxic metals, PAHs


1 INTRODUCTION


Air pollutants, including particulate matter (PM), have become a global environmental issue over several decades (Li et al., 2019a; Molina, 2008). Global burden of disease analysis lists particulate matter as the 5th highest cause of mortality among all health risks, leading to 4.2 million premature deaths in 2015 (Cohen et al., 2017). Many studies have demonstrated that exposure to fine particulate matter PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) increases the risk of respiratory and cardiovascular diseases (Anderson et al., 2012; Dockery, 2009; Liu et al., 2019). Recently, extensive studies have reported a clear and robust association with oral, rectal, liver, skin, breast, gastric and kidney cancers due to exposure to PM2.5 and its metallic components, especially the sulfur fraction (Weinmayr et al., 2018). Air pollution by fine particles is a problem widely observed in cities which experienced rapid economic development (de Jesus et al., 2019). Examples include the London haze in the 1950s, Los Angeles smog in the 1960s and Beijing haze in recent decades, where industrial combustion, vehicle exhausts and secondary aerosol are considered the primary sources of PM2.5 (Li et al., 2019b; Wang et al., 2016). Chile, located in South America, has suffered severe air pollution since 1990. Since then, Chile’s government has implemented a series of policies to control air pollution. As a result, PM10, sulfur, and nitrogen pollution have decreased significantly (Mazzeo et al., 2018). However, PM2.5 remains a problem in central and southern Chile, especially for using wood as a source of energy for house heating and cooking (Díaz-Robles et al., 2014). Currently, 12 of the 15 administrative regions of Chile are decreed as areas with elevated PM levels, and 14 decontamination plans are under development (MMA, 2014).

Previous studies in Chilean cities have reported regional and seasonal variation in estimated short-term health effects of different size distributions of PM, including PM2.5 (Boneberger et al., 2011; Díaz-Robles et al., 2014; Rivas et al., 2008; Sanhueza et al., 2009). The cities of Santiago and Coyhaique are experiencing the most severe air pollution problem. Santiago, the capital of Chile, located in the central zone, is highly populated, with more than 50% of the country's population living in the metropolitan area. The geography and climate of the Santiago basin are generally unfavorable for the diffusion of air pollutants. Consequently, extreme high pollution events occur frequently during the winter season (Bell et al., 2011, 2009; Toro-Araya et al., 2014). It is estimated that the transport sector in Santiago explains about 25% of CO2 emissions and 40% of air pollution by fine particulate matter, also showing high and increasing levels of nitrogen dioxide (NO2) above 28% in the decade 2004–2014 (Barraza et al., 2017; Duncan et al., 2016; Gallardo et al., 2018). Villalobos and collaborators determined that approximately 70% of the mass of PM2.5 is organic matter in the winter season. The primary source of contribution is wood smoke (almost 60%), followed in decreasing order by diesel, gasoline and natural gas emissions (Villalobos et al., 2015). In contrast, in Coyhaique, the southern city most polluted by PM2.5 in the American region, the primary source of emission (99%) of PM2.5 is residential firewood combustion for cooking and heating (Perez et al., 2020). Various studies in developed countries indicate that wood smoke is the most significant source of exposure to particles during the winter months, originating from residential stoves (Maykut et al., 2003; Naeher et al., 2007). It is known that, depending on the quality of combustion, the type and characteristics of the firewood used, a series of compounds that are very harmful to health can be emitted that vary spatially and seasonally, which will determine the potential adverse health effects (Bell et al., 2011, 2009; Toro-Araya et al., 2014). Among the compounds adsorbed to the particles are the Polycyclic Aromatic Hydrocarbons (PAHs) and some heavy metals that are particularly dangerous due to toxicity and their known carcinogenic adverse effects (Scipioni et al., 2012). These are involved in cellular damage and the subsequent inflammatory response to the lung and cardiovascular system (Chang et al., 2019; Diociaiuti et al., 2001; Li et al., 2003; Maykut et al., 2003).

The characterization of public health risk of populations due to exposure to certain environmental agents is essential when risk control and management strategies are to be carried out. Estimation of the health risk due to exposure to air pollutants has generally focused on the use of the mass of particles as a metric (PM2.5 or PM10) to evaluate the possible impacts on health. This has allowed estimating the disease burden and through this generating different public policies, aimed at reducing concentrations to protect the population. Regardless of the achievements that these policies adopted and adapted to the reality of the countries have produced in various parts of the world, one of the most relevant limitations of this metric is that it does not consider the complexity of the chemical composition of the particles, which will vary substantially locally, since the sources that contribute to PM concentrations can be very diverse (Salimbene et al., 2021). Several authors consider that in addition to using mass as a risk assessment metric, the use of indicators such as black carbon (BC), polycyclic aromatic hydrocarbons (PAHs) and metals should be explored as possible metrics (Chowdhury et al., 2022; Krzyzanowski et al., 2014; Grahame et al., 2014; Peng et al., 2013; Sosa et al., 2017), especially in urban areas where quality of the air is dominated by combustion processes with different geographic, topographic, meteorological and social factors, as occurs in the cities of south-central Chile, where the use of firewood for cooking and heating is very intensive (Torres et al., 2021; Salimbene et al., 2021). There are still few studies carried out in Chile that have reported the association between the components of PM2.5 and health effects. Elements such as K, S, Se, V, Cr, Cu, Zn, EC (Elemental Carbon) have been associated with various effects such as respiratory, cardiovascular, and cerebrovascular diseases, both in children and adults (Prieto-Parra et al., 2017; Valdés et al., 2012; Cakmak et al., 2009; Cáceres et al., 2021).

Considering this background, we carried out an exploratory study whose objective is to characterize the health risk due to exposure to fine particulate matter and its components in two urban municipalities, using two strategies, estimation of potentially carcinogenic and non-carcinogenic effects through risk assessment, and estimating the burden of cancer disease attributable to outdoor PM2.5 exposure. The characterization and comparison of these risk assessments will allow us to understand the sources of PM2.5 emission for local actions of environmental and public health.

 
2 METHODS


 
2.1 Urban Municipalities Studied

Coyhaique Municipality (hereafter Coyhaique) is (Fig. 1) located in Chilean Patagonia (45°34′S, 72°04′W), extreme southern Chile with about 58,000 inhabitants, spanning an area of 7290,2 km2, where the urban area is only 7 km2 (Azócar-García et al., 2010). It is surrounded by rivers (Simpson and Coyhaique) and mountains (Mackay, Sombrero, Negro, San Martin), snow-covered throughout the year. Independence Municipality (hereafter Independencia) is located in the north of the metropolitan area of Santiago, with about 100,000 inhabitants in with an area of 7 km2 (BCN, 2021a, 2021b). Santiago is located in a basin between the Andes Range and the Coast Range (33°27′S, 70°40′W). In both localities, the topographic configuration of the basin contributes to the low vertical dispersion of air pollutants during winter, resulting in rapid increases of particulate matter, a phenomenon known as atmospheric blocking (Perez et al., 2020; Yun and Yoo, 2019).

Fig. 1. Location of the AQMS and PM2.5 sampling sites in Coyhaique and North of Santiago (Independencia neighborhood).Fig. 1. Location of the AQMS and PM2.5 sampling sites in Coyhaique and North of Santiago (Independencia neighborhood).

 
2.2 PM2.5 Monitoring and Data Analysis

The hourly data concentrations and the time series of PM2.5 for this study were obtained from the Air Quality Monitoring Stations (AQMS); the AQMS/COY1 located in the center of Coyhaique, and the AQMS/IND located in Independencia close to the Hospital Psiquiatrico of the Universidad de Chile (Fig. 1) (SINCA, 2022). These AQMS continuously measure PM2.5 in the ambient air using specific sensors, reporting the average concentration (µg m–3) for each hour of the day. Each measure of the AQMS has a representativeness area of around 3 km2. For each AQMS, the historical record of hourly concentrations of PM2.5 spanned five years between 01/01/2013 and 31/12/2017. The descriptive and graphic exploratory analysis of temporal data of PM2.5 was done using the R package openair (Carslaw and Ropkins, 2012).

 
2.3 Sampling of Outdoor PM2.5

The outdoor samples of PM2.5 for chemical analysis were collected in the area of representativeness of the AQMS. Twenty samples of PM2.5 were collected, fifteen on Teflon (37 mm and 47 mm) and five on quartz (47 mm) filters. These were pre-weighed and environmentally conditioned for their use at Lamont-Doherty Earth Observatory of Columbia University. These samples were collected during a week in wintertime using two monitor samplers (44XR Universal Sample Pump, SKC Inc., and MiniVol TAS, Air Metrics Corp) between August 24 and 29, 2016, in Coyhaique, and September 13 to 16, 2016, in Independencia. Both monitor samplers were calibrated daily before use and operated with a stable flow rate of 4 L min–1. The sample collection for both kinds of monitors was programmed for 24 h, from 08:00 AM to 08:00 AM the next day. After sampling, all the filters were post-weighed to calculate the PM2.5 concentration for each sample.

 
2.4 Chemical Analysis of PM2.5


2.4.1 PAH analysis on Teflon filters

The Teflon parts of filters were cut and mixed with diatomite and copper powder. The samples were spiked with surrogate standard to monitor the recovery of the procedure. The samples were subsequently extracted by DIONEX accelerated solvent extraction (ASE) with a mixture of 60 mL acetone/dichloromethane (1:1, v/v), then the extracts were purified by silica gel column. Finally, the extracts were converted to hexane and concentrated by gentle N2 to about 1 mL. Before gas chromatography-mass spectrometry (GC-MS) analysis, the internal standards were added to the final extract to calculate the concentrations of PAHs. The parameters of GC-MS were as follows: the oven temperature started at 55°C for 2 min; then it was heated to 280°C at a rate of 20°C min1; finally, it was heated to 300°C at a rate of 3°C min1 and held for 4 min. The injection volume was 2 µL with a split-less mode, and the carrier was helium. Recovery of surrogate and internal standards ranged from 62.3% to 95.6% and from 71.7% to 98.2%, respectively. All PAH data reported in this study were corrected by recoveries. To differentiate PAHs from un-burning fossil fuel and combustion sources, compound-specific carbon stable isotope analysis of PAHs was conducted following a method described in the literature (Yan et al., 2016).


2.4.2 Organic carbon (OC), Elemental carbon (EC) and metals

Quartz filters were used for analysis of radioactive carbon (14C) for calculating the percentage of modern carbon in organic carbon (OC) and elemental carbon (EC) was measured in the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS), Woods Hole Oceanographic Institute, USA (Zencak et al., 2007). For samples less than 30 µgC, accurate amount of CO2 from OX-I was added. The diluted samples were reduced to graphite and measured by AMS. The original 14C of samples was calculated from measured value from the mixture using the dilution factor. Metals cadmium (Cd), nickel (Ni), lead (Pb), and arsenic (As) were measured by energy-dispersive X-ray fluorescence spectrometry on Teflon filters at Lamont-Doherty Earth Observatory of Columbia University.

 
2.5 Human Health Risk Assessment


2.5.1 Exposure concentration (ExpC)

The inhalation dosimetry methodology was used to assess the potential human exposure (Asante-Duah, 2017; U.S. EPA, 2009, 1989). This estimates the exposure concentration (ExpC) for each receptor exposed to contaminants via inhalation using Eq. (1):

 

CA is the contaminant concentration in air (µg m–3), ET is the exposure time (h day–1), EF is the exposure frequency (days year–1), ED is the exposure duration (years) of the exposed subject, and AT is the time (days). According to the EPA, the estimation of the level of exposure is fundamental in calculating the health risks related to air pollutant exposure (Ostro, 2004); concentration and time of exposure to PM2.5 are relevant parameters for this. Short-term exposures are transiently higher air pollution levels that might cause adverse health events, mainly respiratory and cardiovascular illness. Chilean air quality standards consider levels dangerous when the concentration of PM2.5 exceeds the daily norm of 50 µg m–3 (MMA, 2014). To estimate the ET parameter in Eq. (1), we use the average number of hours per day (Short Time Exposure: STE) that the PM2.5 concentration exceeds the value of the daily norm in each city studied (Table 1).

Table 1. Parameters used to calculate the daily exposure concentration (ExpC) dose through the inhalation route, and the non-carcinogenic and carcinogenic risks from PM2.5-bound toxic species.

 
2.5.2 Non-carcinogenic risk

The Hazard Quotient (HQ) was estimated to evaluate the potential non-carcinogenic risk. HQ is the ratio of the potential exposure to a substance to the level at which no adverse effects are expected. The HQ was estimated as the ratio between ExpC coefficient and the reference concentration RfC (mg m–3 day–1) for inhalation of a substance using Eq. (2). RfC is an estimate of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. RfC is derived from the NOAEL (no-observed-adverse-effect level) (U.S. EPA, 1989).

When the pollutants are diverse, the total risk hazard index (HI) can be estimated using Eq. (3), which is the sum of the individual HQs. If the calculated value of HQ or HI is > 1, it indicates a high likelihood of potential non-carcinogenic adverse health effects impacts; otherwise, the probability is minimal.

 


2.5.3 Carcinogenic risk

The carcinogenic risk (CR) was calculated as the product of ExpC and IUR (Eq. (4)), which is the Inhalation Unit Risk (IUR), i.e., as the upper-bound excess lifetime cancer risk estimated to result from continuous exposure to an agent at a concentration of 1 µg m–3 in the air (U.S. EPA, 1989). When the pollutants are diverse, the carcinogenic risk index (CRI) can be calculated using Eq. (5), which is the sum of the CR of each element or compound evaluated. There is no safe threshold level of exposure for carcinogenic agents; the EPA suggests that risk values between 1 × 10–4 and 1 × 10–6 are adequate for protecting human health (Asante-Duah, 2017). Above these values, an excess lifetime cancer risk in exposed populations is expected.

 


2.6 Environmental Cancer Burden Disease by PM2.5 Exposure

We used Ostro’s functions (Ostro, 2004) to estimate the environmental burden of cardiopulmonary mortality and lung cancer disease attributable to long-term exposure to PM2.5. First, the relative risk (RR) for each type of cancer is calculated by comparing the annual concentration of PM2.5 with a background level without anthropogenic air pollution (Eq. (6)).

 

X is the annual average concentration of PM2.5 (µg m–3), X0 is the background concentration of PM2.5 (3 µg m–3), and β is the coefficient of the risk function obtained from the population-based study of Ostro (2004). For RR estimates we used the recommended β coefficient for cardiopulmonary mortality of 0.15515 (95% CI: 0.0562–0.2541), while for lung cancer β mortality is 0.23218 (95% CI: 0.08563–0.37873) (Pope et al., 2002). Once the relative risk (RR) has been determined, the fraction attributable (AF) to PM2.5 exposure is estimated as shown in Eq. (7):

 

The AF estimates the proportion of the deaths that could be avoided if PM2.5 concentrations were reduced to 3 µg m–3. The number of annual deaths (I) was calculated multiplying the annual mortality rate (IR) in both localities by the AF as shown in Eq. (8):

 

I is the number of deaths attributable to long-term exposure to PM2.5, and IR is the total number of annual deaths in the target population. For both localities, the data of age-adjusted (> 30 years) cardiopulmonary and lung-cancer mortality were obtained from the Department of Statistics and Information in Health (DEIS) of the Ministry of Health of Chile. The Tenth International Classification of Diseases (ICD10) code CM I26–I27 was used for cardiopulmonary mortality and code C34 for lung cancer mortality.

 
3 RESULTS



3.1 Concentrations of PM2.5 (µg m–3) and Characteristics of the PM2.5-bound Carcinogenic Species Detected in Independencia and Coyhaique

Fig. 2 shows the time variation for an average of 24 h for PM2.5 (µg m–3) concentrations, and two reference lines representing the annual and daily national standard of the period studied. The average daily concentrations of PM2.5 for Coyhaique and Independencia were 110 µg m–3, and 45 µg m–3, respectively; 2.4 times higher in Coyhaique than in Independencia. The daily (50 µg m–3) and annual (25 µg m–3) AQS were significantly exceeded in Coyhaique compared to Independencia. The highest concentrations are concentrated in the cold months of the year. Table 2 shows the summarized data for season and year as the number of days that the daily average AQS of PM2.5 is exceeded. The average value for the period in Independencia was 45.3 days year–1 and 132.3 days year–1 for Coyhaique, which represents 12.40% and 36.22% of the days the AQS is exceeded in the year, respectively. In winter these values reached 40.60% and 77.00% of days in Independencia and Coyhaique, respectively. It should be noted that in even summer Coyhaique showed values exceeding the daily AQS.

Fig. 2. Time series of PM2.5 concentrations measured in the AQMS/COY (Center of Coyhaique) and AQMS/IND (downtown Independencia) between 01/01/2013–31/12/2017.Fig. 2. Time series of PM2.5 concentrations measured in the AQMS/COY (Center of Coyhaique) and AQMS/IND (downtown Independencia) between 01/01/2013–31/12/2017.

Table 2. Number of days and hours per day that the average 24-hour standard (50 µg m–3) of PM2.5 is exceeded and reached in the Municipalities of Coyhaique and Independencia.

Fig. S1 shows the distribution of the hourly time series decomposition of PM2.5 for each month, per week, for each day of the week, and daily for Independencia and Coyhaique, respectively. As can be seen, in both cases the highest concentrations occur in the cold months, from mid-March to mid-September in Coyhaique and during June in Independencia. The concentrations of PM2.5 were lower on weekends than on weekdays in both cases. The hourly average during the week Coyhaique was relatively similar, with higher concentration on Tuesdays and Fridays. In Independencia this variation was less well defined. Finally, the hourly distribution of PM2.5 during 24 h shows a different pattern for the cities (monitoring sites). There is a clear and marked daily bimodal profile in Coyhaique, whose concentrations began to increase on average from 4:00 to 8:30 and then decreased around midnight. It began to rise around 15:00, reaching a peak around 19:00. This behavior is less evident in Independencia, where the increase in PM2.5 concentration started on average at 5:50, reaching a plateau at 10:00 which progressively decreased until 16:00, finally increasing after 18:00.

Table 3 and Fig. 3 presents the PM2.5 mass-concentrations and PM2.5-bound chemical species collected in both cities during the winter of 2016. The 24 h average concentrations of PM2.5 in the Coyhaique and Independencia sampling sites were 120.58 (78.95–162.82) µg m–3 and 46.08 (40.53–51.98) µg m–3, respectively. The outdoor concentration of PM2.5 was 2.97 times higher in Coyhaique compared to Independencia. The fraction of modern carbon analyzed in EC and OC was almost 4 and 2.5 times higher in Coyhaique than in Independencia. The levels of total PAHs measured in the PM2.5 of the Coyhaique (15.95 ng m–3) were more than 6.35 times the concentration measured in Independencia (2.51 ng m–3). However, 21.8% of PAHs identified in the PM2.5 of the Coyhaique were B[a]P, compared to the 15.5% B[a]P in Independencia. The concentration of B[a]P was 8.92 times higher in Coyhaique than in Independencia. The concentrations of the elements detected in the PM2.5 collected in Coyhaique were, in decreasing order, arsenic (As) [1.80 ng m–3] > lead (Pb) [1.605 ng m–3] > nickel (Ni) [0.573 ng m–3] > cadmium (Cd) [0.153 ng m–3]. In Independencia these were lead (Pb) [9.07 ng m–3] > arsenic (As) [7.00 ng m–3] > nickel (Ni) [0.766 ng m–3] > cadmium (Cd) [0.211 ng m–3]. The concentration of the metallic elements in Independencia was higher than in Coyhaique, with values ranging from 1.3 (Pb) to 5.6 (Ni) times that determined in Coyhaique.

Table 3. PM2.5 mass-concentration and PM2.5-bound chemical species collected in the Municipality of Coyhaique and Independencia in winter of 2016.

 Fig. 3. PM2.5-bound chemical species collected in the Municipality of Coyhaique and Independencia in winter of 2016.Fig. 3. PM2.5-bound chemical species collected in the Municipality of Coyhaique and Independencia in winter of 2016.

 
3.2 Health Risk Assessment of PM2.5-bound Chemical Species

Table 4 and Fig. 4 show both cities' non-carcinogenic (HQ; HI) and carcinogenic (CR; CRI) human health risk indexes. The estimated HQ values for Coyhaique in decreasing order were B[a]P > As > Cd > Ni, and Pb. B[a]P was 13.4 times higher than the safety level (HQ < 1), representing 92.45% of the total risk. In contrast, for Independencia these values were lower than 1 for B[a]P > Cd > Ni, and Pb, but 1.12 for As. As and B[a]P showed the highest percentages of the non-carcinogenic risk. The total hazard index (HI) was almost 6.12 times higher in Coyhaique than in Independencia. For CR, the PM2.5-bound species collected in the Coyhaique were, in decreasing order, As > B[a]P > Cd > Ni > Pb, with As and B[a]P higher than the lower acceptable limit (1 × 10–6). Cd, Ni, and Pb were below this limit. The estimated CR in decreasing order for Independencia was As > Cd > B[a]P > Ni and Pb. All these values were below the risk threshold, except As. Cd, B[a]P, Ni, and Pb were below the lower limit (1 × 10–6). For Coyhaique 20.30% and 75.38% of the CRI (2.72 × 10–5) was explained by B[a]P and As, respectively. For Independencia, these chemicals explained 0.77% and 97.01% of the CRI (2.66 × 10–5).

Table 4. Non-carcinogenic and carcinogenic risk by inhalation via in Municipality of Coyhaique and Independencia.

 Fig. 4. Non-carcinogenic (HQ) and carcinogenic (CR) risk indicators in the Municipality of Coyhaique and Independencia, respectively.Fig. 4. Non-carcinogenic (HQ) and carcinogenic (CR) risk indicators in the Municipality of Coyhaique and Independencia, respectively.

 
3.3 Mortality Attributable to Long-term PM2.5 Exposure

Eq. (8) indicates that an estimate of deaths attributable to long-term exposure to air pollution in a local area can be made by multiplying the attributable fraction (AF) by the total number of deaths annually in the local area (IR). According to Ostro (2004), AF estimates the proportion of deaths that could be avoided if PM2.5 concentrations were reduced to 3 µg m–3.

Table 5 shows the annual mortality rate (IR) for cardiopulmonary and lung cancer, the attributable fraction (AF) and the expected number of deaths attributable (I) to long-term exposure to PM2.5. Coyhaique had a slightly higher IR for lung cancer and a significantly lower IR for cardiopulmonary compared to Independencia (0.05 vs. 1.44 per 10,000 inh). The attributable fraction (AF) estimates show that 54% and 40% of the annual cases of lung cancer and cardiopulmonary mortality in adults over 30 years of age could be attributed to long-term exposure to concentrations of PM2.5 in the city of Coyhaique. According to the communal reports (BCN, 2021a), the population of adults over 30 years is around 35,000 people, so almost 3 (95% CI = 1–4) deaths per year from lung cancer would be attributable to PM2.5, and less than one case per year for cardiopulmonary disease. For Independencia, 43% and 32% of lung cancer and cardiopulmonary mortality cases were attributed to long-term exposure to PM2.5, respectively. Considering that the population over 30 years of age in Independencia is about 56,000 (BCN, 2021b), almost 3 (95% CI = 1–5) and 2 (95% CI = 1–4) new cases per year of lung cancer and cardiopulmonary mortality, respectively, would be expected attributable to long-term exposure to PM2.5.

Table 5. Mortality rate (IR), attributable Fraction (AF) and annual number of deaths (I) from outdoor air pollution by PM2.5 in adults > 30 years.

 
DISCUSSIONS



4.1 Analysis of PM2.5 Data

The daily profile variation of PM2.5 observed in this, and previous studies suggests that the principal emissions during the day in Independencia are from mobile sources, while in Coyhaique they are closely related to the use of residential firewood for heating and cooking. This distribution was reported by Alvarado-Zúñiga (2006) for downtown Santiago; he analyzed the daily behavior of PM10 during fall and winter. There is an increase in concentration from the beginning of the city's activity from 06:00 to 10:00, decreasing until 15:00–16:00, and then increasing towards the end of the afternoon until 20:00–21:00. Perez and Menares (2018), reported that between April and August in 2014 to 2016 there was a pattern of hourly distribution of PM2.5 concentrations similar to those reported in the present study in two neighborhoods of the south of Santiago, El Bosque and Cerrillos. There was a peak around 09:00 correlated with heavy traffic (rush hour) in the morning. The 23:00 peak would be explained by residential heating and low ventilation during cold months, as in Coyhaique. Another factor that explains this behavior is the effect of the season (seasonal very low temperatures and wind speed), especially in the magnitude of PM2.5 concentration. A study prepared by the Mario Molina center for the Ministry of the Environment in Chile, where the trend of particulate matter in Santiago and the cities of central-southern Chile were studied in an MP time series from 2014 to 2018, concluded that the factor with the greatest influence on the increase in PM2.5 concentration is the seasonal effect, which in Coyhaique accounts for up to 220%, while in Santiago where Independencia is located this increase was 80%. Other relevant factors would be the height of the thermal inversion layer and the synoptic movements of air masses, closely related to the increase in seasonal emission sources such as residential firewood combustion (CMM, 2019). The daily standard was significantly exceeded in Coyhaique; in Independencia it was surpassed especially in the winter months, while the annual standard was exceeded throughout the period studied. Both time series show a seasonal behavior related to an atmospheric phenomenon called the thermal inversion layer that decreases the vertical dispersion of air pollutants during the winter period, increasing the concentrations of fine particles (Perez et al., 2020; Perez and Menares, 2018; CMM, 2019).

These results are in line with what was reported by Molina et al. (2017), who analyzed the daily, monthly, and annual concentrations of PM2.5 at the spatial and temporal level of 16 cities in the center-south of Chile, for a period of 7 years (2007–2014), including Coyhaique, establishing that the limits established by the WHO are systematically exceeded in all cities. The highest concentrations occur in autumn and winter and are significantly higher than those determined in the warm period, estimating that at least one third of the days monitored exceed the limits established by the WHO, the primary source being the combustion of firewood. The highest annual concentrations of PM2.5 range from 57 µg m–3 to less than 20 µg m–3, being the highest in Coyhaique.

 
4.2 Analysis of PM2.5-bound Organic Compounds

The results of the analysis of the carbon origin in the samples of PM2.5 collected in Coyhaique indicate a high percentage of modern carbon in the EC and OC fractions compared to those determined in Independencia. This suggests that the carbonaceous sources of apportionment that prevail in the outdoor air of Coyhaique come mainly from biogenic origin, which would be explained by the use of firewood as combustion energy for residential use. In contrast, the largest source in Independencia would be from the use of fossil fuels; less than 40% would be explained by modern sources probably related to the use of residential firewood during winter, as well as other possible industrial sources that use firewood as a source of energy. These results agree with those reported in different cities of the world with emission sources similar to those described in our study (Marley et al., 2009).

The concentration of total PAHs determined in Coyhaique was within the range reported by Pozo et al. (2015), (4–21 ng m–3), who determined these compounds in Temuco, a city highly contaminated by wood smoke as occurs in Coyhaique. Bravo-Linares et al. (2016), conducted a study in five southern Chilean cities in the Los Ríos region where the primary source of air pollution was the use of wood; they found concentrations of PAHs in PM2.5 of 27.2 ng m–3 to 49.6 ng m–3 during the winters of 2013 and 2014, which were significantly higher than the 1.8 ng m–3 to 7.5 ng m–3 determined in summer. These concentrations were 2 to 3 times higher than those determined in winter in Coyhaique and more than ten times higher than those in Independencia. Pozo et al. (2018), studied the total concentrations of PAHs in outdoor air in eleven different regions and seasons of the year in Chile between January 13, 2016 and March 21, 2017; they reported that average concentrations of total PAHs in Coyhaique were 8.8 times those determined in Santiago downtown in winter, with values of 149 ng m–3 and 17 ng m–3, respectively; these values are significantly higher than those reported in this study. In the same study, the total average total concentrations varied between 4 and 49 ng m–3, the highest value being in the city of Coyhaique. The lowest concentration was in the northern city of Antofagasta. Regarding B[a]P, the concentrations varied between 0.02 ng m–3 and 0.96 ng m–3. The concentration determined in Coyhaique was 0.78 ng m–3.

Various studies have found that the concentrations of PAHs vary depending on the emission sources, the combustion conditions and the types of firewood used (Avagyan et al., 2016). It is important to note that these studies were carried out with different methodologies, monitoring times and determination of PAHs, so they are described referentially; however, they consistently show that PAHs levels are significantly higher in cities where the source of energy for heating is firewood.

 
4.3 Analysis of PM2.5-bound Heavy Metals

The present study found relatively similar concentrations of Ni and Cd in both sites, while the concentrations of Pb and As were significantly higher in Independencia than in Coyhaique These were significantly lower than those that have been reported in different studies in the USA (Chen and Lippmann, 2009) and China (Li et al., 2018a). The possible sources of emission of elements may be related to high vehicular traffic, especially in Santiago, and possible industrial emissions (Jorquera and Barraza, 2012). The United States Environmental Protection Agency (U.S. EPA) classifies Ni refinery dust and nickel subsulfide (Ni3S2) as Group A human carcinogens, and nickel carbonyl (Ni(CO)4) is classified in Group B2, probable human carcinogen (EHC, 1991). The relevance of these toxic species in fine particulate matter is that they have been associated with lung cancer, cardiovascular effects and an increase in the general mortality rate, especially in winter (Grimsrud et al., 2002; Huang et al., 2012; Lippmann et al., 2006; Raaschou-Nielsen et al., 2016). The reference value for environmental nickel in the European Union is 20 ng m–3 (European Commission, 2000). Cadmium is classified by the U.S. EPA as a Group B1 probable human carcinogen. Ambient air Cd concentrations have generally ranged from 2 to 15 ng m–3 in urban areas and 15 to 150 ng m–3 in industrialized areas (EHC, 1992). Aruta et al. (2020) analyzed the spatial distribution of potentially toxic elements including Pb and Cd in soil samples, determining high levels of Pb and Cd in the northern area of Santiago near Independencia, which suggests natural and anthropogenic sources of industrial and urbanized (houses and streets) areas, being lower in the green areas of the city. Similar findings were reported by Rodríguez-Oroz et al. (2018) who determined different metals (Cr, Ni, Cu, Zn, As, Cd, and Pb) in Chile in playground soils in Concepción, a city in south-central Chile, where higher concentrations occurred in industrial sectors than in green areas. These findings may explain the concentrations of these species reported in our study to a certain extent, due to the effect of dust resuspension. Different studies carried out in Santiago, Chile have determined in the PM2.5 analyzed that Pb would be associated with traffic emissions and As to the presence of nearby copper smelters (Ancelet et al., 2014; Dirks et al., 2020; Jorquera and Barraza, 2012; Moreno et al., 2010). The monitoring site in Independencia is just 30 meters from a large avenue with a high traffic flow of public transport buses, which could explain the high levels of Pb (Wróbel et al., 2000). The As concentrations determined in Independencia and Coyhaique were similar to those reported by Jorquera et al. (2018), (5 ng m–3) in a study carried out in the city of Temuco, which is highly polluted with wood smoke. As is an element that has been associated with indoor cooking sources (Abdullahi et al., 2013), and in some studies, high levels have been observed during the winter, when firewood is used for heating; this could be associated with the burning of firewood treated with chemical preservatives that contain As (Dirks et al., 2020). Arsenic in fine air particulate matter has been recently identified as an important factor for lung cancer in China (Wang et al., 2020).

 
4.4 Health Risk Assessment

The non-carcinogenic risk index was greater than 1 for both cities, indicating an increased risk of adverse health effects in the adult population. The HQ of Coyhaique (14.5) was six times more than that of Independencia (2.37), which is mostly explained by the presence of B[a]P in fine particles. The CRI was higher than the safety threshold (1 × 10–6). This would indicate possible carcinogenic effects due to inhalation of these compounds by the exposed population. These values of potential carcinogenic risk would imply an excess of 2 to 3 cases of cancer per year compared to those expected otherwise. These results align with the incidence rate of death from lung cancer and cardiopulmonary mortality recorded in both cities. As reported by the Department of Statistics and Health Information (DEIS) of the Ministry of Health of Chile, the incidence rate (IR) of death due to lung cancer for Coyhaique (1.50 × 10,000 inh) is slightly lower than Independencia (1.3 × 10,000 inh). The incidence rate of cardiopulmonary disease is much higher in Independencia (1.44 × 10,000 inh) than in Coyhaique (0.05 × 10,000 inh). However, the estimated contribution of PM2.5 to lung cancer and cardiopulmonary mortality rates is almost twice as great in Coyhaique as in Independencia.

The high concentrations of As detected in the PM2.5 collected in Independencia could be associated with the higher incidence of lung and cardiopulmonary cancer reported by the DEIS, compared to that Coyhaique, where the concentration of As is lower. As could be determined in our results, the carcinogenic risk index (CRI) in Coyhaique significantly exceeded the lower risk threshold (1 × 10–6), which is mainly explained by As (75.38%) and B[a]P (20.30%), respectively. In Independencia, this risk is mainly explained by As (97.01%).

Both chemical elements have been widely associated with cardiopulmonary and lung cancer (Moorthy et al., 2015; Pope et al., 2002; Taghvaee et al., 2018). In a case-control study carried out in Mexico City, Báez-Saldaña et al. (2021), evaluated the association between the number of hours of daily exposure to wood smoke and lung cancer; they reported an increased risk OR of 2.6 (2.6 (95% CI: 1.05–6.44) for individuals exposed to > 100 hour-years compared to those to fewer hour-years. Similar findings were reported by Hosgood et al. (2010) and Kurmi et al. (2012), who found an association between exposure to coal and wood use and lung cancer risk, supporting the hypothesis of a carcinogenic potential of in-home wood use in a systematic review; they suggest that in-home burning of biomass is consistently associated with an increased risk of lung cancer (OR 1.50, 95% CI: 1.17–1.94). Another systematic review reported a stronger relationship between biomass use, cooking and/or heating and lung cancer (OR 1.17, 95% CI: 1.01–1.37) (Bruce et al., 2015).

 
4.5 Cancer Burden Diseases

As observed in this study, the adjusted incidence rates of lung cancer are relatively similar between both cities. In contrast, cardiopulmonary mortality rates were significantly higher in Independencia than in Coyhaique. For both diseases, a high percentage of annual cases in the population over 30 years of age would be explained by long-term outdoor exposure to PM2.5, considering a concentration of 3 µg m–3 of PM2.5 as the baseline value. Within the context of the equations used by Ostro to calculate the environmental burden of disease due to long-term exposure outdoor air pollution, these are quite stable since they use a log-linear function for exposure, which allows modulating high concentrations and allows an approximation of risk estimation, with the minimum measurements error (Ostro, 2004; Pope et al., 2002).

The association between exposure to particulate matter and respiratory diseases such as lung cancer and cardiopulmonary diseases has been widely reported in epidemiological studies (Cao et al., 2018; Fu et al., 2015; Wu et al., 2021). The IARC concluded in 2013 that air pollution is carcinogenic to humans. The mechanisms of action involved have to do with epigenetic changes such as DNA deregulation and methylation, microenvironmental alterations related to cellular processes, activation and inactivation of genes, inflammatory processes that produce oxidative stress, among other effects under study (Lee et al., 2020; Li et al., 2018b). In these investigated cities, high concentrations of As and B[a]P were determined, which are known carcinogenic agents for humans. In Chile, Sapunar-Zenteno et al. (2021), studied the association between the incidence of lung cancer and air pollution in 14 districts of Chile served by the Oncology Institute of the Fundation Arturo López Pérez (FALP), reporting a positive association between the incidence of lung cancer lung and the concentration of PM2.5 in a study period of 4 years (2015–2019), adjusting for the human development index.

Ostro´s function was used in this study to calculate the burden of disease resulting from exposure to outdoor fine particulate matter, which is a sufficiently robust methodology based on epidemiological studies in different parts of the world that have been consistent in their findings and that allow estimating parameters that can be used as a reference to estimate disease burden in exposed populations with similar characteristics to those studied. Although the population is indeed exposed to a mixture of solid and liquid particles and chemical compounds that varies temporally and geographically, the data collected allow us to make a quantitative estimate of premature death and the risk attributable to exposure to PM in the general population and the most vulnerable age groups. Like all methodology, it has advantages and disadvantages that must be considered when interpreting these results on their merit (Ostro, 2004). The objective of applying this methodology was to compare the cities with regard to the attributable risks due to exposure to PM. However, the particularities of each place evaluated must be considered. Air quality monitoring stations have geographic and population representation, but they are not capable of discriminating the possible variations due to exposure in hot spots and the fact that the population spends a large amount of time indoors, such as in Coyhaique, so indoor exposure becomes very relevant. The PM2.5 sampling periods for risk assessment were relatively short; the concentrations of the species and the PAH-determined compounds in most cases were similar to or lower than recent studies carried out in Chile as described in this discussion, and therefore our results may be underestimated (Wu et al., 2021).

Estimating the attributable incidence of death for cancer related to long-term PM2.5 exposure using Ostro’s function for Coyhaique and northern Santiago showed different percentages; Coyhaique was worse. Estimates of the burden of disease attributed to outdoor pollution can help prioritize air PM2.5 pollution control over other interventions that improve public health. The burden of disease in Chilean cities will vary due to the amount of fossil fuel used, weather, underlying disease rates, and population size and density. As observed in Coyhaique, the burden of disease estimates will be higher in some areas of the south of Chile, such as those heavily dependent on firewood for fuel use and those with topographic and climatic conditions that limit the dispersion of pollution in winter. PM2.5 is believed to be a greater health threat since the smaller particles are more likely to be deposited deep into the lung. High concentrations of PM2.5-bound species such as B[a]P might be related to the higher attributable incidence of lung cancer in Coyhaique. The function and mechanism of B[a]P exposure to cancer progression remain unclear (Wei et al., 2016). According to the European Environment Agency (EEA), the average B[a]P concentration in Europe in 2012 ranged from 0.12 to 1.5 ng m–3 (Lewandowska et al., 2018). In the urban and mining area of northern Chile, the key pollution sources are copper foundries and coal-burning power plants; in the central zone, as recorded in Independencia, massive vehicular traffic is the principal source of emissions; while in southern urban zones like Coyhaique, residential wood combustion is a key source of particulate matter emission (Molina et al., 2017; Mesías-Monsalve et al., 2018; Torres et al., 2021).

 
5 CONCLUSIONS


The analysis of the time series of the concentration of fine particulate matter suggests that the daily distribution is highly correlated with vehicular traffic activity in Independencia and the use of firewood for heating and cooking in Coyhaique. The PM2.5 concentrations were significantly higher in Cohyaique than in Santiago for the study period, especially in the winter periods, considerably exceeding the former's daily and annual national standards. The total PAH concentrations determined in the PM2.5 collected in winter were more than six times higher for Coyhaique, which would be explained by the extensive use of firewood as an energy source for residential use. The percent of modern carbon observed in the fine particles collected in Coyhaique showed that sources of PM2.5 emission directly relate to firewood burning in houses for heating and cooking. The analysis of potential non-carcinogenic effects (HI) was only significant for Coyhaique, mainly explained by B[a]P, followed by As. Estimating potential carcinogenic chronic effects showed that arsenic, cadmium, nickel, and Benzo[a]pyrene were in the safe range. As and B[a]P explain over 95% of the integrated risk (CRI) of carcinogenic adverse effects in Coyhaique. In contrast, the CRI for Independencia is explained mainly by As (97%). Finally, Ostro’s function estimated that 54% and 43% of lung cancer incidence and between 40% and 32% of the incidence of cardiopulmonary cancer could be attributed to long-term exposure PM2.5 for Coyhaique and Santiago, respectively. PM2.5-bound carcinogenic species might be related to the burden of death for lung and cardiopulmonary cancer in adults > 30 years in both cities. Other studies could elucidate the biomedical basis of the high incidence of cancer in both highly polluted cities.

 
ACKNOWLEDGMENTS


The authors also want to thank the health authorities of Coyhaique city and its inhabitants, who allowed us to carry out this investigation. This study was supported by the Fondo Nacional de Investigación en Salud FONIS Nº 15/20207, Fondo de Innovación para la Competitividad FIC Nº BIP 40010328-0 Gobierno Regional de Aysén and the Fondo Nacional de Investigación de Ciencia y Tecnología FONDECYT Nº 1200674, and Chile-Columbia University “Collaborating to Quantify the Health Benefits of Clean Biomass Combustion in Chile” CGC Office of Global Initiatives Columbia University-Universidad de Chile Fund Budget. Mailman School of Public Health, US. We want to thank Mr. Sebastián Pedrero Quiñones for his support in managing the databases and statistical analysis

 
DISCLAIMER


The authors declare that they have no conflict of interest in this study.


REFERENCES


  1. Abdullahi, K.L., Delgado-Saborit, J.M., Harrison, R.M. (2013). Emissions and indoor concentrations of particulate matter and its specific chemical components from cooking: A review. Atmos. Environ. 71, 260–294. https://doi.org/10.1016/j.atmosenv.2013.01.061

  2. Alvarado-Zúñiga, G.M. (2006). Estimación del aporte de diferentes fuentes a la contaminación atmosférica por partículas en Santiago, mediante un modelo de balance de masas de elementos químicos. Universidad de Chile. Facultad de Ciencias Físicas y Matemáticas. Departamento de ingeniería Mecánica. 

  3. Ancelet, T., Davy, P., Trompetter, W.J. (2014). Hourly concentrations of arsenic associated with Particulate Matter. Proceedings of the 5th International Congress on Arsenic in the Environment, May 11–16, 2014, Buenos Aires, Argentina. https://doi.org/10.1201/b16767-29

  4. Anderson, J.O., Thundiyil, J.G., Stolbach, A. (2012). Clearing the air: A review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8, 166–175. https://doi.org/​10.1007/s13181-011-0203-1

  5. Aruta, A., Daniele, L., Cannatelli, C., De Vivo, B., Lima, A., Albanese, S. (2020). Cadmium, Lead, Tin and Zinc in topsoil of Santiago (Chile): Spatial patterns and health risks for children. Proscience 7, 1–7. https://doi.org/10.14644/ghc2020.001

  6. Asante-Duah, K. (2017). Public Health Risk Assessment for Human Exposure to Chemicals. Springer Netherlands, Dordrecht. https://doi.org/10.1007/978-94-024-1039-6

  7. Avagyan, R., Nyström, R., Lindgren, R., Boman, C., Westerholm, R. (2016). Particulate hydroxy-PAH emissions from a residential wood log stove using different fuels and burning conditions. Atmos. Environ. 140, 1–9. https://doi.org/10.1016/j.atmosenv.2016.05.041

  8. Azócar-García, G., Aguayo-Arias, M., Henríquez-Ruiz, C., Vega-Montero, C., Sanhueza-Contreras, R. (2010). Patrones de crecimiento urbano en la Patagonia chilena: el caso de la ciudad de Coyhaique. Rev. Geogr. Norte Gd. 46, 85–104. https://doi.org/10.4067/S0718-340220100002​00005

  9. Báez-Saldaña, R., Canseco-Raymundo, A., Ixcot-Mejía, B., Juárez-Verdugo, I., Escobar-Rojas, A., Rumbo-Nava, U., Castillo-González, P., León-Dueñas, S., Arrieta, O. (2021). Case-control study about magnitude of exposure to wood smoke and risk of developing lung cancer. Eur. J. Cancer Prev. 30, 462–468. https://doi.org/10.1097/CEJ.0000000000000644

  10. Barraza, F., Lambert, F., Jorquera, H., Villalobos, A.M., Gallardo, L. (2017). Temporal evolution of main ambient PM2.5 sources in Santiago, Chile, from 1998 to 2012. Atmos. Chem. Phys. 17, 10093–10107. https://doi.org/10.5194/acp-17-10093-2017

  11. Biblioteca del Congreso Nacional de Chile (BCN) (2021a). Coyhaique, Reporte Comunal  (accessed 5 May 2022).

  12. Biblioteca del Congreso Nacional de Chile (BCN) (2021b). Independencia, Reporte Comunal  (accessed 5 May 2022).

  13. Bell, M.L., Ebisu, K., Peng, R.D., Samet, J.M., Dominici, F. (2009). Hospital admissions and chemical composition of fine particle air pollution. Am. J. Respir. Crit. Care Med. 179, 1115–1120. https://doi.org/10.1164/rccm.200808-1240OC

  14. Bell, M.L., Cifuentes, L.A., Davis, D.L., Cushing, E., Telles, A.G., Gouveia, N. (2011). Environmental health indicators and a case study of air pollution in Latin American cities. Environ. Res. 111, 57–66. https://doi.org/10.1016/j.envres.2010.10.005

  15. Boneberger, A., Haider, D., Baer, J., Kausel, L., Von Kries, R., Kabesch, M., Radon, K., Calvo, M. (2011). Environmental risk factors in the first year of life and childhood asthma in the Central South of Chile. J. Asthma Off. J. Assoc. Care Asthma 48, 464–469. https://doi.org/10.3109/​02770903.2011.576740

  16. Bravo-Linares, C., Ovando-Fuentealba, L., Orellana-Donoso, S., Gatica, S., Klerman, F., Mudge, S.M., Gallardo, W., Pinaud, J.P., Loyola-Sepulveda, R. (2016). Source identification, apportionment and toxicity of indoor and outdoor PM2.5 airborne particulates in a region characterised by wood burning. Environ. Sci. Process. Impacts 18, 575–589. https://doi.org/10.1039/C6EM00148C

  17. Bruce, N., Dherani, M., Liu, R., Hosgood, H.D., Sapkota, A., Smith, K.R., Straif, K., Lan, Q., Pope, D. (2015). Does household use of biomass fuel cause lung cancer? A systematic review and evaluation of the evidence for the GBD 2010 study. Thorax 70, 433–441. https://doi.org/​10.1136/thoraxjnl-2014-206625

  18. Cáceres, D.D., Flores-Jimenez, P., Hernández, K., Peres, F., Maldonado, A.K., Klarián, J., Cáceres, D.A. (2021). Health risk due to heavy metal(loid)s exposure through fine particulate matter and sedimented dust in people living next to a beach contaminated by mine tailings. Rev. Int. Contam. Ambient. 37, 211–226. https://doi.org/10.20937/RICA.53830

  19. Cakmak, S., Dales, R.E., Vida, C.B. (2009). Components of particulate air pollution and mortality in Chile. Int. J. Occup. Environ. Health 15, 152–158. https://doi.org/10.1179/oeh.2009.15.2.152

  20. Cao, Q., Rui, G., Liang, Y. (2018). Study on PM2.5 pollution and the mortality due to lung cancer in China based on geographic weighted regression model. BMC Public Health 18, 925. https://doi.org/10.1186/s12889-018-5844-4

  21. Carslaw, D.C., Ropkins, K. (2012). openair — An R package for air quality data analysis. Environ. Modell. Software 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008

  22. Chang, J., Shen, J., Tao, J., Li, N., Xu, C., Li, Y., Liu, Z., Wang, Q. (2019). The impact of heating season factors on eight PM2.5-bound polycyclic aromatic hydrocarbon (PAH) concentrations and cancer risk in Beijing. Sci. Total Environ. 688, 1413–1421. https://doi.org/10.1016/j.​scitotenv.2019.06.149

  23. Chen, L.C., Lippmann, M. (2009). Effects of metals within ambient air particulate matter (PM) on human health. Inhalation Toxicol. 21, 1–31. https://doi.org/10.1080/08958370802105405

  24. Chowdhury, S., Pozzer, A., Haines, A., Klingmüller, K., Münzel, T., Paasonen, P., Sharma, A., Venkataraman, C., Lelieveld, J. (2022). Global health burden of ambient PM2.5 and the contribution of anthropogenic black carbon and organic aerosols. Environ. Int. 159, 107020. https://doi.org/10.1016/j.envint.2021.107020

  25. Centro Mario Molina Chile (CMM) (2019). Análisis de Tendencia del Material Particulado en la Región Metropolitana y la Regiones Centro Sur. Informe Final. Licitación ID 698897-LP19. Ministerio del Medio Ambiente. Gobierno de Chile. 

  26. Cohen, A.J., Brauer, M., Burnett, R., Anderson, H.R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C.A., et al. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 389, 1907–1918. https://doi.org/​10.1016/S0140-6736(17)30505-6

  27. de Jesus, A.L., Rahman, M.M., Mazaheri, M., Thompson, H., Knibbs, L.D., Jeong, C., Evans, G., Nei, W., Ding, A., Qiao, L., Li, L., Portin, H., Niemi, J.V., Timonen, H., Luoma, K., Petäjä, T., Kulmala, M., Kowalski, M., Peters, A., Cyrys, J., et al. (2019). Ultrafine particles and PM2.5 in the air of cities around the world: Are they representative of each other? Environ. Int. 129, 118–135. https://doi.org/10.1016/j.envint.2019.05.021

  28. Díaz-Robles, L.A., Fu, J.S., Vergara-Fernández, A., Etcharren, P., Schiappacasse, L.N., Reed, G.D., Silva, M.P. (2014). Health risks caused by short term exposure to ultrafine particles generated by residential wood combustion: A case study of Temuco, Chile. Environ. Int. 66, 174–181. https://doi.org/10.1016/j.envint.2014.01.01

  29. Diociaiuti, M., Balduzzi, M., De Berardis, B., Cattani, G., Stacchini, G., Ziemacki, G., Marconi, A., Paoletti, L. (2001). The two PM2.5 (fine) and PM2.5–10 (coarse) fractions: Evidence of different biological activity. Environ. Res. 86, 254–262. https://doi.org/10.1006/enrs.2001.4275

  30. Dirks, K.N., Chester, A., Salmond, J.A., Talbot, N., Thornley, S., Davy, P. (2020). Arsenic in hair as a marker of exposure to smoke from the burning of treated wood in domestic wood burners. Int. J. Environ. Res. Public. Health 17, 3944. https://doi.org/10.3390/ijerph17113944

  31. Dockery, D.W. (2009). Health effects of particulate air pollution. Ann. Epidemiol. 19, 257–263. https://doi.org/10.1016/j.annepidem.2009.01.018

  32. Duncan, B.N., Lamsal, L.N., Thompson, A.M., Yoshida, Y., Lu, Z., Streets, D.G., Hurwitz, M.M., Pickering, K.E. (2016). A space-based, high-resolution view of notable changes in urban NOx pollution around the world (2005–2014). J. Geophys. Res. 121, 976–996.https://doi.org/​10.1002/2015JD024121

  33. Environmental Health Criteria (EHC) (1991). Nickel. International Programme on Chemical Safety. Environmental Health Criteria (EHC) 108. (accessed 5 May 2022).

  34. Environmental Health Criteria (EHC) (1992). Cadmium. International Programme on Chemical Safety. Environmental Health Criteria (EHC) 134.  (accessed 3 May 2022).

  35. European Commission (2000). Ambient Air Pollution by As, Cd and Ni Compounds—Position Paper on As, Cd and Ni. European Union, Office for Official Publications of the European Communities, Brussels, Belgium. 

  36. Fu, J., Jiang, D., Lin, G., Liu, K., Wang, Q. (2015). An ecological analysis of PM2.5 concentrations and lung cancer mortality rates in China. BMJ Open 5, e009452. https://doi.org/10.1136/​bmjopen-2015-009452

  37. Gallardo, L., Barraza, F., Ceballos, A., Galleguillos, M., Huneeus, N., Lambert, F., Ibarra, C., Munizaga, M., O’Ryan, R., Osses, M., Tolvett, S., Urquiza, A., Véliz, K.D. (2018). Evolution of air quality in Santiago: The role of mobility and lessons from the science-policy interface. Elem. Sci. Anth. 6, 38. https://doi.org/10.1525/elementa.293

  38. Grahame, T.J., Klemm, R., Schlesinger, R.B. (2014). Public health and components of particulate matter: The changing assessment of black carbon. J. Air Waste Manage. Assoc. 64, 620–660. https://doi.org/10.1080/10962247.2014.912692

  39. Greene, N.A., Morris, V.R. (2006). Assessment of public health risks associated with atmospheric exposure to PM2.5 in Washington, DC, USA. Int. J. Environ. Res. Public. Health 3, 86–97. https://doi.org/10.3390/ijerph2006030010

  40. Grimsrud, T.K., Berge, S.R., Haldorsen, T., Andersen, A. (2002). Exposure to different forms of nickel and risk of lung cancer. Am. J. Epidemiol. 156, 1123–1132. https://doi.org/10.1093/aje/​kwf165

  41. Hosgood, H.D., Boffetta, P., Greenland, S., Lee, Y. C.A., McLaughlin, J., Seow, A., Duell, E.J., Andrew, A.S., Zaridze, D., Szeszenia-Dabrowska, N., Rudnai, P., Lissowska, J., Fabiánová, E., Mates, D., Bencko, V., Foretova, L., Janout, V., Morgenstern, H., Rothman, N., Hung, R.J., et al. (2010). In-home coal and wood use and lung cancer risk: A pooled analysis of the International Lung Cancer Consortium. Environ. Health Perspect. 118, 1743–1747. https://doi.org/10.1289/​ehp.1002217

  42. Huang, W., Cao, J., Tao, Y., Dai, L., Lu, S. E., Hou, B., Wang, Z., Zhu, T. (2012). Seasonal variation of chemical species associated with short-term mortality effects of PM2.5 in Xi'an, a Central City in China. Am. J. Epidemiol. 175, 556–566. https://doi.org/10.1093/aje/kwr342

  43. Jorquera, H., Barraza, F. (2012). Source apportionment of ambient PM2.5 in Santiago, Chile: 1999 and 2004 results. Sci. Total Environ. 435–436, 418–429. https://doi.org/10.1016/j.scitotenv.​2012.07.049

  44. Jorquera, H., Barraza, F., Heyer, J., Valdivia, G., Schiappacasse, L.N., Montoya, L.D. (2018). Indoor PM2.5 in an urban zone with heavy wood smoke pollution: The case of Temuco, Chile. Environ. Pollut. 236, 477–487. https://doi.org/10.1016/j.envpol.2018.01.085

  45. Krzyzanowski, M., Apte, J.S., Bonjour, S.P., Brauer, M., Cohen, A.J., Prüss-Ustun, A.M. (2014). Air pollution in the mega-cities. Curr. Environ. Health Rep. 1, 185–191. https://doi.org/10.1007/​s40572-014-0019-7

  46. Kurmi, O.P., Arya, P.H., Lam, K. B.H., Sorahan, T., Ayres, J.G. (2012). Lung cancer risk and solid fuel smoke exposure: A systematic review and meta-analysis. Eur. Respir. J. 40, 1228–1237. https://doi.org/10.1183/09031936.00099511

  47. Lee, C.W., Vo, T.T.T., Wu, C. Z., Chi, M.C., Lin, C.M., Fang, M.L., Lee, I.T. (2020). The inducible role of ambient particulate matter in cancer progression via oxidative stress-mediated reactive oxygen species pathways: A recent perception. Cancers 12, E2505. https://doi.org/10.3390/​cancers12092505

  48. Lewandowska, A.U., Staniszewska, M., Witkowska, A., Machuta, M., Falkowska, L. (2018). Benzo(a)pyrene parallel measurements in PM1 and PM2.5 in the coastal zone of the Gulf of Gdansk (Baltic Sea) in the heating and non-heating seasons. Environ. Sci. Pollut. Res. Int. 25, 19458–19469. https://doi.org/10.1007/s11356-018-2089-9

  49. Li, H., Wan, Y., Chen, X., Cheng, L., Yang, X., Xia, W., Xu, S., Zhang, H. (2018a). A multiregional survey of nickel in outdoor air particulate matter in China: Implication for human exposure. Chemosphere 199, 702–708. https://doi.org/10.1016/j.chemosphere.2018.01.114

  50. Li, N., Hao, M., Phalen, R.F., Hinds, W.C., Nel, A.E. (2003). Particulate air pollutants and asthma: A paradigm for the role of oxidative stress in PM-induced adverse health effects. Clin. Immunol. 109, 250–265. https://doi.org/10.1016/j.clim.2003.08.006

  51. Li, R., Zhou, R., Zhang, J. (2018b). Function of PM2.5 in the pathogenesis of lung cancer and chronic airway inflammatory diseases. Oncol. Lett. 15, 7506–7514. https://doi.org/10.3892/ol.2018.​8355

  52. Li, X., Jin, L., Kan, H. (2019a). Air pollution: A global problem needs local fixes. Nature 570, 437–439. https://doi.org/10.1038/d41586-019-01960-7

  53. Li, Y., Chiu, Y., Lin, T. Y. (2019b). The impact of economic growth and air pollution on public health in 31 Chinese cities. Int. J. Environ. Res. Public Health 16, 393. https://doi.org/10.3390/​ijerph16030393

  54. Lippmann, M., Ito, K., Hwang, J. S., Maciejczyk, P., Chen, L. C. (2006). Cardiovascular effects of nickel in ambient air. Environ. Health Perspect. 114, 1662–1669. https://doi.org/10.1289/​ehp.9150

  55. Liu, C., Chen, R., Sera, F., Vicedo-Cabrera, A.M., Guo, Y., Tong, S., Coelho, M.S.Z.S., Saldiva, P.H.N., Lavigne, E., Matus, P., Valdes Ortega, N., Osorio Garcia, S., Pascal, M., Stafoggia, M., Scortichini, M., Hashizume, M., Honda, Y., Hurtado-Díaz, M., Cruz, J., Nunes, B., et al. (2019). Ambient particulate air pollution and daily mortality in 652 cities. N. Engl. J. Med. 381, 705–715. https://doi.org/10.1056/NEJMoa1817364

  56. Marley, N.A., Gaffney, J.S., Tackett, M., Sturchio, N.C., Heraty, L., Martinez, N., Hardy, K.D., Marchany-Rivera, A., Guilderson, T., MacMillan, A., Steelman, K. (2009). The impact of biogenic carbon sources on aerosol absorption in Mexico City. Atmos. Chem. Phys. 9, 1537–1549. https://doi.org/10.5194/acp-9-1537-2009

  57. Maykut, N.N., Lewtas, J., Kim, E., Larson, T.V. (2003). Source apportionment of PM2.5 at an urban IMPROVE site in Seattle, Washington. Environ. Sci. Technol. 37, 5135–5142. https://doi.org/​10.1021/es030370y

  58. Mazzeo, A., Huneeus, N., Ordóñez, C., Orfanoz-Cheuquelaf, A., Menut, L., Mailler, S., Valari, M., Denier van der Gon, H., Gallardo, L., Muñoz, R., Donoso, R., Galleguillos, M., Osses, M., Tolvett, S. (2018). Impact of residential combustion and transport emissions on air pollution in Santiago during winter. Atmos. Environ. 190, 195–208. https://doi.org/10.1016/j.atmosenv.2018.06.043

  59. Mesías-Monsalve, S., Martínez, L., Yohannessen Vásquez, K., Alvarado Orellana, S., Klarián Vergara, J., Martín Mateo, M., Costilla Salazar, R., Fuentes Alburquenque, M., Cáceres Lillo, D.D. (2018). Trace element contents in fine particulate matter (PM2.5) in urban school microenvironments near a contaminated beach with mine tailings, Chañaral, Chile. Environ. Geochem. Health 40, 1077–1091. https://doi.org/10.1007/s10653-017-9980-z

  60. Ministerio del Medio Ambiente (MMA) (2014). Gobierno de Chile. Planes de Descontaminación Atmosférica Estrategia 2014-2018. 

  61. Molina, C., Toro A, R., Morales S, R.G.E., Manzano, C., Leiva-Guzmán, M.A. (2017). Particulate matter in urban areas of south-central Chile exceeds air quality standards. Air Qual. Atmos. Health 10, 653–667. https://doi.org/10.1007/s11869-017-0459-y

  62. Molina, M. (2008). Air pollution is a global problem with local solutions. Nature 456, 19–19. https://doi.org/10.1038/twas08.19a

  63. Moorthy, B., Chu, C., Carlin, D.J. (2015). Polycyclic aromatic hydrocarbons: from metabolism to lung cancer. Toxicol. Sci. Off. J. Soc. Toxicol. 145, 5–15. https://doi.org/10.1093/toxsci/kfv040

  64. Moreno, F., Gramsch, E., Oyola, P., Rubio, M.A. (2010). Modification in the soil and traffic-related sources of particle matter between 1998 and 2007 in Santiago de Chile. J. Air Waste Manage. Assoc. 1995 60, 1410–1421. https://doi.org/10.3155/1047-3289.60.12.1410

  65. Naeher, L.P., Brauer, M., Lipsett, M., Zelikoff, J.T., Simpson, C.D., Koenig, J.Q., Smith, K.R. (2007). Woodsmoke health effects: A review. Inhalation Toxicol. 19, 67–106. https://doi.org/10.1080/​08958370600985875

  66. Ostro, B. (2004). Outdoor air pollution: Assessing the environmental burden of disease at national and local levels. Geneva, World Health Organization, Environmental Burden of Disease Series, No. 5 

  67. Peng, C., Ouyang, Z., Wang, M., Chen, W., Li, X., Crittenden, J.C. (2013). Assessing the combined risks of PAHs and metals in urban soils by urbanization indicators. Environ. Pollut. 178, 426–432. https://doi.org/10.1016/j.envpol.2013.03.058

  68. Perez, P., Menares, C. (2018). Forecasting of hourly PM2.5 in South-West zone in Santiago de Chile. Aerosol Air Qual. Res. 18, 2666–2679. https://doi.org/10.4209/aaqr.2018.01.0029

  69. Perez, P., Menares, C., Ramírez, C. (2020). PM2.5 forecasting in Coyhaique, the most polluted city in the Americas. Urban Clim. 32, 100608. https://doi.org/10.1016/j.uclim.2020.100608

  70. Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287, 1132–1141. https://doi.org/10.1001/jama.287.9.1132

  71. Pozo, K., Estellano, V.H., Harner, T., Diaz-Robles, L., Cereceda-Balic, F., Etcharren, P., Pozo, Katerine, Vidal, V., Guerrero, F., Vergara-Fernández, A. (2015). Assessing Polycyclic Aromatic Hydrocarbons (PAHs) using passive air sampling in the atmosphere of one of the most wood-smoke-polluted cities in Chile: The case study of Temuco. Chemosphere 134, 475–481. https://doi.org/10.1016/j.chemosphere.2015.04.077

  72. Pozo, K., Cortes, S., Jorquera, H., Torres, M., Lanos, Y. (2018). Programa de Vigilancia y Caracterización de Compuestos Tóxicos en la Atmósfera de Chile. 

  73. Prieto-Parra, L., Yohannessen, K., Brea, C., Vidal, D., Ubilla, C.A., Ruiz-Rudolph, P. (2017). Air pollution, PM2.5 composition, source factors, and respiratory symptoms in asthmatic and nonasthmatic children in Santiago, Chile. Environ. Int. 101, 190–200. https://doi.org/10.1016/​j.envint.2017.01.021

  74. Raaschou-Nielsen, O., Beelen, R., Wang, M., Hoek, G., Andersen, Z.J., Hoffmann, B., Stafoggia, M., Samoli, E., Weinmayr, G., Dimakopoulou, K., Nieuwenhuijsen, M., Xun, W.W., Fischer, P., Eriksen, K.T., Sørensen, M., Tjønneland, A., Ricceri, F., de Hoogh, K., Key, T., Eeftens, M., et al. (2016). Particulate matter air pollution components and risk for lung cancer. Environ. Int. 87, 66–73. https://doi.org/10.1016/j.envint.2015.11.007

  75. Rivas, E., Barrios, S., Dorner, A., Osorio, X. (2008). Association between indoor contamination and respiratory diseases in children living in Temuco and Padre Las Casas, Chile. Rev. Med. Chil. 136, 767–774. https://doi.org//S0034-98872008000600013

  76. Rodríguez-Oroz, D., Vidal, R., Fernandoy, F., Lambert, F., Quiero, F. (2018). Metal concentrations and source identification in Chilean public children’s playgrounds. Environ. Monit. Assess. 190, 703. https://doi.org/10.1007/s10661-018-7056-x

  77. Salimbene, O., Morreale, S., Pilla, F. (2021). Health Risk Assessment and Black Carbon: State of Art and New Prospectives. Presented at the Air Pollution 2021, Santiago de Compostela, Spain, pp. 149–159. https://doi.org/10.2495/AIR210141

  78. Sanhueza, P.A., Torreblanca, M.A., Diaz-Robles, L.A., Schiappacasse, L.N., Silva, M.P., Astete, T.D. (2009). Particulate air pollution and health effects for cardiovascular and respiratory causes in Temuco, Chile: A wood-smoke-polluted urban area. J. Air Waste Manage. Assoc. 1995 59, 1481–1488. https://doi.org/10.3155/1047-3289.59.12.1481

  79. Sapunar-Zenteno, J., Ferrer-Rosende, P., Caglevic, C. (2021). Incidence of lung cancer and air pollution in boroughs of Chile: An ecological study. ecancer 15, 1247. https://doi.org/10.3332/​ecancer.2021.1247

  80. Scipioni, C., Villanueva, F., Pozo, K., Mabilia, R. (2012). Preliminary characterization of polycyclic aromatic hydrocarbons, nitrated polycyclic aromatic hydrocarbons and polychlorinated dibenzo-p-dioxins and furans in atmospheric PM10 of an urban and a remote area of Chile. Environ. Technol. 33, 809–820. https://doi.org/10.1080/09593330.2011.597433

  81. SINCA (2022). Estado de calidad del aire en línea. Sistema de Información Nacional de Calidad del Aire. https://sinca.mma.gob.cl/mapainteractivo/index.html (accessed 5 May 2022).

  82. Sosa, B.S., Porta, A., Colman Lerner, J.E., Banda Noriega, R., Massolo, L. (2017). Human health risk due to variations in PM10-PM2.5 and associated PAHs levels. Atmos. Environ. 160, 27–35. https://doi.org/10.1016/j.atmosenv.2017.04.004

  83. Taghvaee, S., Sowlat, M.H., Hassanvand, M.S., Yunesian, M., Naddafi, K., Sioutas, C. (2018). Source-specific lung cancer risk assessment of ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in central Tehran. Environ. Int. 120, 321–332. https://doi.org/10.1016/j.​envint.2018.08.003

  84. Toro-Araya, R., Flocchini, R., Morales Segura, R.G.E., Leiva Guzmán, M.A. (2014). Carbonaceous aerosols in fine particulate matter of Santiago Metropolitan Area, Chile. Sci. World J. 2014, 794590. https://doi.org/10.1155/2014/794590

  85. Torres, R., Baker, N., Bernal, G., Peres, F., Maldonado, A., Caceres, D.D. (2021). The effect of short-term of fine particles on daily respiratory emergency in cities contaminated with wood smoke. Global J. Environ. Sci. Manage. 7, 15–32. https://doi.org/10.22034/gjesm.2021.01.02

  86. U.S. Environmental Protection Agency (U.S. EPA) (1989). Risk Assessment Guidance for Superfund (RAGS): Volume I: Human Health Evaluation Manual: Part F, Supplemental Guidance for Inhalation Risk Assessment. Part A. EPA/540/1-89/002. 

  87. U.S. Environmental Protection Agency (U.S. EPA) (2009). Risk Assessment Guidance for Superfund (RAGS). Volume I: Human Health Evaluation Manual: Part F, Supplemental Guidance for Inhalation Risk Assessment. EPA/540/R/070/002. 

  88. Valdés, A., Zanobetti, A., Halonen, J.I., Cifuentes, L., Morata, D., Schwartz, J. (2012). Elemental concentrations of ambient particles and cause specific mortality in Santiago, Chile: A time series study. Environ. Health 11, 82. https://doi.org/10.1186/1476-069X-11-82

  89. Villalobos, A.M., Barraza, F., Jorquera, H., Schauer, J.J. (2015). Chemical speciation and source apportionment of fine particulate matter in Santiago, Chile, 2013. Sci. Total Environ. 512, 133–142  https://doi.org/10.1016/j.scitotenv.2015.01.006

  90. Wang, G., Zhang, R., Gomez, M.E., Yang, L., Levy Zamora, M., Hu, M., Lin, Y., Peng, J., Guo, S., Meng, J., Li, J., Cheng, C., Hu, T., Ren, Y., Wang, Yuesi, Gao, J., Cao, J., An, Z., Zhou, W., Li, G., et al. (2016). Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. 113, 13630–13635. https://doi.org/10.1073/pnas.1616540113

  91. Wang, J., Wan, Y., Cheng, L., Xia, W., Li, Y., Xu, S. (2020). Arsenic in outdoor air particulate matter in China: Tiered study and implications for human exposure potential. Atmos. Pollut. Res. 11, 785–792. https://doi.org/10.1016/j.apr.2020.01.006

  92. Wei, Y., Zhao, L., He, W., Yang, J., Geng, C., Chen, Y., Liu, T., Chen, H., Li, Y. (2016). Benzo[a]pyrene promotes gastric cancer cell proliferation and metastasis likely through the Aryl hydrocarbon receptor and ERK-dependent induction of MMP9 and c-myc. Int. J. Oncol. 49, 2055–2063. https://doi.org/10.3892/ijo.2016.3674

  93. Weinmayr, G., Pedersen, M., Stafoggia, M., Andersen, Z.J., Galassi, C., Munkenast, J., Jaensch, A., Oftedal, B., Krog, N.H., Aamodt, G., Pyko, A., Pershagen, G., Korek, M., De Faire, U., Pedersen, N.L., Östenson, C. G., Rizzuto, D., Sørensen, M., Tjønneland, A., Bueno-de-Mesquita, B., et al. (2018). Particulate matter air pollution components and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts of Air Pollution Effects (ESCAPE). Environ. Int. 120, 163–171. https://doi.org/10.1016/j.envint.2018.07.030

  94. Wróbel, A., Rokita, E., Maenhaut, W. (2000). Transport of traffic-related aerosols in urban areas. Sci. Total Environ. 257, 199–211. https://doi.org/10.1016/S0048-9697(00)00519-2

  95. Wu, X., Zhu, B., Zhou, J., Bi, Y., Xu, S., Zhou, B. (2021). The epidemiological trends in the burden of lung cancer attributable to PM2.5 exposure in China. BMC Public Health 21, 737. https://doi.org/​10.1186/s12889-021-10765-1

  96. Yan, B., Passow, U., Chanton, J.P., Nöthig, E. M., Asper, V., Sweet, J., Pitiranggon, M., Diercks, A., Pak, D. (2016). Sustained deposition of contaminants from the Deepwater Horizon spill. PNAS 113, E3332–E3340. https://doi.org/10.1073/pnas.1513156113

  97. Yun, S., Yoo, C. (2019). The effects of spring and winter blocking on PM10 concentration in Korea. Atmosphere 10, 410. https://doi.org/10.3390/atmos10070410

  98. Zencak, Z., Elmquist, M., Gustafsson, Ö. (2007). Quantification and radiocarbon source apportionment of black carbon in atmospheric aerosols using the CTO-375 method. Atmos. Environ. 41, 7895–7906. https://doi.org/10.1016/j.atmosenv.2007.06.006 


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