Chemical Composition, Optical Properties and Sources of PM2.5 From a Highly Urbanized Region in Northeastern Mexico

Here, we report the chemical composition and optical properties of the fine particles (PM_2.5) and water-soluble organic carbon (WSOC) of these particles. Additionally, the potential sources of WSOC emission were determined through the study on fluorescence excitation–emission matrix spectra and parallel factor analysis (EEM-PARAFAC). Samples were collected in an urban site of the Monterrey Metropolitan Area in Mexico during summer and winter and characterized using attenuated total reflectance-Fourier-transform infrared spectroscopy (ATR-FTIR), ultraviolet-visible-near infrared-diffuse reflectance spectroscopy (UV–Vis-NIR-DRS), fluorescence spectroscopy, X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) techniques. The ATR-FTIR analyses allowed the identification of inorganic ions (e.g., CO₃²⁻, SO₄²⁻, and NO₃⁻), organic functional groups [e.g., carbonyls (C=O), organic hydroxyl (C-OH), carboxylic acid (COOH)], and aromatic and unsaturated aliphatic hydrocarbons. The results obtained by XRD and XPS revealed the presence of organic and inorganic chemical species in PM_2.5. The diffuse reflectance spectra of PM_2.5 provided the absorption bands in the UV region for CaSO₄, CaCO₃, and aluminosilicates. The absorption coefficient at 365 nm (Abs₃₆₅) and Ångström absorption exponent (AAE) values obtained for the aqueous extracts suggest that many of the water-soluble organic compounds corresponded to brown carbon (BrC) chromophores. The mass absorption efficiency values at 365 nm (MAE₃₆₅) were higher in the winter than summer samples, suggesting the presence of more BrC compounds in the winter samples. The fluorescence indices combined with EEM-PARAFAC analysis showed that the WSOC fraction was mainly composed of humic-like substances (HULIS) which are both of terrestrial and microbial origin.

1 Introduction

The extensive atmospheric pollution occurring in large cities as a result of fast-growing urban and industrial development has caused an increase in the number of people to be continuously exposed to low-quality ambient air. Particulatematter (PM) is a common atmospheric pollutant, which has been widely studied because of its adverse effects on both the public health and global climate. Particularly, fine PM (PM_2.5) has been considered more dangerous because it is able to penetrate the deep parts of the human lung. Indeed, numerous studies have associated the components of PM_2.5 to many respiratory (e.g., lung cancer) and cardiovascular diseases. These components have also been found to cause genetic mutations in animals (Lippmann et al., 2006; Happo et al., 2010). In addition to the adverse effects on animal and human health, PM_2.5 influences both local and global climates. Several PM_2.5 components have been shown to cause the dispersion and absorption of light (including infrared light). These effects on light disturb the radiation balance of the atmosphere, which may then cause unexpected variations in the atmospheric heating-cooling cycles (Jacobson, 2004; Huo et al., 2016; Chen et al., 2019). Our present knowledge related to the influence of PM_2.5 on radiative forcing (change in energy flux in the atmosphere) and climate change is still limited. Moreover, there are difficulties associated with the current atmospheric models, which are used to measure the magnitude of both capture and release of radiation by PM_2.5. For example, different types of aerosols may exert a large range of contrasting effects on the net cooling or heating of an atmosphere. This is caused by the high variability of the PM_2.5 properties in the atmospheric aerosols as a function of the region.

PM_2.5 is composed of a wide variety of chemical substances of both organic and inorganic origin, being carbonaceous material the component that have attracted the most attention because they contribute between 10% and 70% of the total mass of PM_2.5, with concentrations found in selected areas ranged from a few pg/m³ to hundreds of ng/m³ (Tsapakis et al., 2002; Hecobian et al., 2010; Khare et al., 2011). Black carbon (BC) is the main light-absorbing carbonaceous material in the range from visible to near infrared light. However, the fraction of organic carbon (OC) that is often called “Brown Carbon (BrC)” has also been found to significantly absorb ultraviolet (UV) and shorter wavelength visible (Vis) light (Jacobson, 2001; Bond et al., 2004). The contribution of BrC to climate change is not limited to its impact to the atmosphere due to its ability and capacity to absorb solar radiation. Recent studies have shown that BrC has accelerated the melting of snow and decreased the Earth’s albedo (Andreae and Ramanathan, 2013; Qian et al., 2015; Yan et al., 2018; Leskinen et al., 2020). Some researchers have classified OC based on its water solubility, i.e., the water-soluble organic carbon (WSOC) and water-insoluble organic carbon (Kondo et al., 2007; Miyazaki et al., 2009). The analyses of the optical properties of WSOC have allowed the identification of its probable emission sources, and thus provided helpful strategies to control emission. In the literature it has been reported that WSCOC originate from either primary sources (biomass burning, fossil fuel combustion) or atmospheric secondary processes, including gas-to-particle conversion and gas deposition onto the surface of a PM (Graber and Rudich, 2006; Sullivan and Weber, 2006; Duarte et al., 2007).

The study of the optical properties of atmospheric particles requires a variety of analytical techniques, including Fourier-transform infrared (FTIR) and UV–Vis spectroscopy, fluorescence, mass spectrometry (MS), and chromatography. Hecobian et al. (2010) characterized the optical properties of the water-soluble fraction of PM_2.5 samples using various analytical techniques. Their samples were collected in the southeastern United States throughout different seasons of a year. They found that the WSOC absorption was higher in the samples obtained during the cold months. The WSOC content was closely related to the presence of levoglucosan in the sample, suggesting that biomass burning was the main source of the emission. Moreover, Chen et al. (2016a) studied the relationship between the optical properties and chemical composition of total suspended PM in samples using FTIR, UV-Vis, fluorescence spectroscopy, electrospray ionization mass spectrometry (ESI-MS) and high-resolution accurate mass spectrometry (HR-AMS). They found that the mass absorption efficiency (MAE) was higher for the less polar organic compounds in the sample. In addition, the nitrogen and oxygen containing organic compounds contributed significantly to the total absorption of light and fluorescence of PM.

The Monterrey Metropolitan Area (MMA) has been considered the most polluted city in Mexico and one of the most polluted cities in Latin America (Clean Air Institute, 2012; INECC, 2014). The elevated emissions in the MMA originate mainly from the high industrial activity and vehicle traffic. However, studies on the chemical composition of atmospheric PM and optical properties of PM_2.5 in the MMA have been limited (Mancilla et al., 2015; Mancilla et al., 2016; Mancilla et al., 2019; González et al., 2016; González et al., 2017; González et al., 2018; López-Ayala et al., 2019; Longoria-Rodríguez et al., 2020; Longoria-Rodríguez et al., 2021). There is also limited information on the absorption response and light emission of PM_2.5 in Mexico after exposure to variable wavelengths of radiation (Liñán-Abanto et al., 2019; Carmona et al., 2021). Furthermore, studies on the chemical and optical characteristics of WSOC in PM_2.5 in Mexico and other Latin American countries have not been done. Therefore, this research focuses on three main objectives; 1) determine the chemical composition and optical properties of PM_2.5 collected in the MMA during winter and summer, 2) determine the effect of seasonal variation on the chemical composition and optical properties of the PM_2.5, and 3) establish possible sources of WSOC emissions in MMA by applying the parallel factor analysis (PARAFAC) model. The analytical techniques including attenuated total reflection (ATR)-FTIR, UV/VIS-diffuse reflectance spectroscopy (DRS), fluorescence, x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS) were used to determine the optical properties and chemical composition of the PM_2.5 samples.

2 Methodology

2.1 Sampling of PM_2.5

The sampling site was in the northwestern region of the MMA in Nuevo Leon, Mexico. PM_2.5 sampling was performed at the routine monitoring station of the Integral Environmental Monitoring System [Sistema Integral de Monitoreo Ambiental (SIMA), the initials in Spanish] located in the San Bernabé suburb (25° 45' 24.86"N, 100° 21' 56.95"W, 551 m. a.s.l.) near the Talleres subway station. This station is surrounded by a highly populated urban area and located alongside the westward winds, which is coming from the highest density industrial area of MMA. In addition, it lies near a natural reserve Mount del Topo Chico. PM_2.5 samples were collected during July to August 2019 and January to February 2020 following the Method IO-2.1 USEPA method (U.S. EPA. Environmental Protection Agency Methods, 1999), where sampling temperature, relative humidity, and atmospheric pressure conditions were recorded (Table 1). The samples were collected every 6 days, collecting a total of 8 samples in each period.

TABLE 1

TABLE 1. Statistical summary of the meteorological variables recorded during both study periods. SD: Standard deviation.

A high-volume sampler HiVol 3000 from Ecotech, which was previously calibrated according to Method IO-2.1 USEPA (U.S. EPA. Environmental Protection Agency Methods, 1999) was placed at the SIMA station. PM_2.5 samples were collected for 24 h on fiberglass filters (EPM 2000 of 8″ × 10″), with a mean airflow of 67 m³/h. For the sampling, glass fiber filters were chosen instead of quartz filters since glass is a material that does not interfere with the analyzes by the XRD technique. The mass of a loaded filter was determined using an analytical balance XS205 dual range from Mettler Toledo under a controlled relative humidity. The filter was stored at−15°C when not in use.

2.2 Optical Analysis of PM_2.5 Samples

2.2.1 ATR-FTIR Analysis

Chemical functional groups present in the PM_2.5 were identified by ATR-FTIR using a Nicolet omnic iS50 FTIR spectrophotometer from Thermo Fisher Scientific. Measurements were made in ATR mode using an ATR Smart Orbit accessory supplied with a diamond crystal. All spectra were recorded at a resolution of 4 cm⁻¹ over the spectral range from 4,000 to 400 cm⁻¹. The absorption path length and total data points were 1 cm and 7,468, respectively. A scan was performed with the sample placed upside down on the ATR crystal (diameter = 3 mm). The background correction was performed by subtracting the absorption spectrum of the filter without sample from that obtained for the loaded filters. Samples did not require any pre-treatment and were analyzed directly on the fiberglass filters. The resulting set of data with baseline correction was analyzed using the SpectraGryph 1.2 Spectroscopy software (Menges, 2020).

2.2.2 UV–Vis Absorption Profiles of PM_2.5

PM_2.5 samples were characterized using the UV–Vis-NIR-DRS technique. The UV–Vis-NIR-DRS spectra were obtained with an Agilent Model Cary 5000 spectrometer equipped with an external integration sphere DRA 2500 from Agilent Technologies. The analysis was carried out directly on a filter at an area of 0.3 cm². The absorption path length was 1 nm. The filter blank was measured before the analysis. Prior to each measurement, baseline corrections were performed at both 0% and 100% transmittance. Samples were analyzed over the range from 200 to 2,500 nm with a resolution of 2 nm, which resulted in 2,301 data points.

2.2.2.1 Analysis of Water-Soluble Organic Carbon Fraction by UV–Vis

For WSOC fraction extraction, 10% of the total area of a filter (and filter blanks) was extracted with 30 ml of purified water in an ultrasonic bath for 15 min. The purified water was obtained from a Milli-Q system from Merck Millipore (Burlington, MA, United States). The extraction procedure was repeated twice. The extract was filtered through a 25-mm outer diameter polytetrafluoroethylene (PTFE) syringe filter of 0.22 µm pore size from Tisch Scientific. Approximately 3 ml of the filtered extract (WSOC solution) was placed in a 1-cm quartz cuvette and scanned from 200 to 800 nm in 1 nm increments using the Agilent Model Cary 5000. Purified water was used as both blank reference and baseline source of data.

The light absorption coefficient (Abs_λ, Mm⁻¹) was determined by Eq. 1 (Teich et al., 2017):

${\mathrm{Abs}}_{\lambda } = (A_{\lambda }-A_{700})\frac{Vl}{Va\times L} \times \text{ln}\left(10\right)(1)$

where A_λ and A₇₀₀ corresponded to the absorption of the extracts at the wavelengths of λ and 700 nm, respectively. Vl was the volume of the solvent that the sample extract removed from the filter, Va was the air volume, and L was the absorption path length (i.e., 1 cm). The light absorption coefficient at 365 nm (Abs₃₆₅) was used as the indicator of BrC (Wu et al., 2019; Mo et al., 2021). Furthermore, the mass absorption efficiency at 365 nm (MAE₃₆₅, m²g⁻¹C) was measured. MAE is a parameter that described the light absorbing ability of WSOC. This parameter was determined by Eq. 2 (Chen et al., 2018):

${\mathrm{MAE}}_{365} = \frac{{\mathrm{Abs}}_{365}}{C_{\mathrm{WSOC}}}(2)$

where Cwsoc represented the WSOC concentration in µgC/m³. The WSOC concentration was determined using a total organic carbon (TOC) analyzer model TOC-VCSH from Shimadzu (Kyoto, Japan). During the analysis, the supply gas pressure, carrier gas flow, and furnace temperature were set at 200–250 kPa, 150 ml min^-1, and 680°C, respectively. The sample was acidified and injected into the analyzer oven. In the oven, the carbon was catalytically oxidized to CO₂ at 680°C and the produced CO₂ was detected by the sensitive Nondispersive Infrared (NDIR) detector (2,500 cm⁻¹). Moreover, the wavelength dependence and light absorption of a water extract was investigated by the Ångström absorption exponent (AAE) in the wavelength range from 300 to 600 nm by Eq. 3 (Moosmüller et al., 2011).

$\mathrm{AAE} =\frac{ \ln (\mathrm{Abs}\lambda 2 ) -\ln (\mathrm{Abs}\lambda 1 )}{\ln \left(\lambda 2\right) - \ln (\lambda 1 )}(3)$

2.2.3 Analysis of PM_2.5 by Fluorescence Spectroscopy

The fluorescence of PM_2.5 particles was measured by placing a small portion of a filter (0.5 cm²) in the sample holder of a spectrofluorometer Model FluoroMax-4 from Horiba, which was supplied with a vertically assembled xenon-arc lamp set at 150 W without ozone. The excitation wavelength was 355 nm and the emission wavelength ranged from 400 to 700 nm, with slit set to 5 nm for both the excitation and emission monochromators and using a 0.1 s integration time. The detected signals were provided by a photon-counting detector R928P PMT (185–850 nm). A photodiode array was used to monitor the lamp output.

The excitation–emission matrices (EEMs) of the WSOC fractions were measured in the range from 200 to 600 nm for excitation and 220–600 nm for emission. The band passes for excitation and emission were 20 and 1 nm, respectively. Three fluorescence indices were evaluated including the fluorescence index (FI), biological index (BIX), and humification index (HIX). These indexes have been implemented in studies involving DOM in both soil and aquatic systems and aerosol due to the similarities in the optical aspects of WSOC and aquatic DOM. Indeed, these indexes have been shown to provide some information on the nature of the organic compounds present in the water-soluble fraction of aerosols (McKnight et al., 2001; Huguet et al., 2009; Birdwell and Engel, 2010). The fluorescence parameters FI, BIX, and HIX were evaluated by Eqs 4–6, respectively.

$\mathrm{FI} = \frac{F(\mathrm{Ex} = 370 \mathrm{nm}, \mathrm{Em} = 450 \mathrm{nm})}{F (\mathrm{Ex} = 370 \mathrm{nm}, \mathrm{Em} = 500 \mathrm{nm})}(4)$

$\mathrm{BIX} = \frac{F(\mathrm{Ex} = 310 \mathrm{nm}, \mathrm{Em} = 380 \mathrm{nm})}{F (\mathrm{Ex} = 310 \mathrm{nm}, \mathrm{Em} = 430 \mathrm{nm})}(5)$

$\mathrm{H}\mathrm{I}\mathrm{X} = \frac{\mathrm{F}(\mathrm{E}\mathrm{x} = 255 \mathrm{n}\mathrm{m}, \mathrm{E}\mathrm{m} = 434 - 480 \mathrm{n}\mathrm{m})}{\mathrm{F} (\mathrm{E}\mathrm{x} = 255 \mathrm{n}\mathrm{m}, \mathrm{E}\mathrm{m} = 300 - 345 \mathrm{n}\mathrm{m})}(6)$

where F represents the fluorescence intensity and Ex and Em the excitation and emission wavelengths, respectively. In the literature, FI <1.4 has been attributed to the fluorophores of terrestrial fulvic acid with greater aromaticity, while values higher than 1.9 correspond to fluorophores from microbial emissions of lower aromaticity (McKnight et al., 2001; Wu et al., 2021). The BIX value is used to assess the contributions from microbial activities, BIX >1 indicates the predominant biological or microbial formation, while BIX <0.6 corresponds to an autochthonous component with smaller contribution from biological materials (Huguet et al., 2009; Wen et al., 2021; Wu et al., 2021). HIX value represents the degree of humification of organic matter, higher values (HIX ˃10) are attributed to more humic or aromatic organic matter associated with terrestrial sources, while lower values (HIX ˂5) are associated with organic matter freshly formed (Yang et al., 2020). Finally, to support the information provided by the fluorescence indices, the PARAFAC model was applied. Recently, some researchers have reported the importance of using fluorescence indices and the PARAFAC model to establish possible sources of emission of carbonaceous material in the aqueous fraction. PARAFAC is a statistical model approach that extracts independent fluorophores from EEMs under ideal conditions (Bro, 1997). The determination of the component number and the treatment of the spectroscopic data, which included the biases correction, scattering remove and normalization, was carried out by applying the staRdom software [“spectroscopic analysis of dissolved organic matter (DOM) in R”]. (Pucher et al., 2019).

2.3 Mineralogical and Chemical Analysis

2.3.1 Chemical Surface Analysis of PM_2.5 Using X-Ray Photoelectron Spectroscopy

The XPS technique was used to determine the surface chemical composition of PM_2.5. For this purpose, a Thermo Scientific Escalab 250Xi spectrometer with both a six-channel (channeltron) detector and a monochromatic X-ray Al Kα source (1486.68 eV) was used. A selected 1 cm² piece from the center of each filter was cut and placed on the sample holder using carbon tape. Measurements were read while keeping ultrahigh vacuum conditions (10^–10 torr). Survey spectra for all the samples were collected over the 1100 eV range at a resolution of 1 eV per step, 100 ms, and 150 eV of pass energy. For the chemical species of interest, high-resolution spectra were collected at a resolution of 0.1 eV per step and 20 eV of pass energy. For data treatment, Avantage software (Thermo Scientific version 5.9) was used, and the assignment of signals was made based on the literature reports and the standards database of the National Institute of Standards and Technology (NIST).

2.3.2 Characterization of Crystalline Phases by X-Ray Diffraction

The mineralogical composition of PM_2.5 was determined through XRD by using a PANalytical Empyrean X-ray diffractometer with Cu radiation (Kα = 1.5405 Å), and an X’Celerator ultra-fast detector based on real-time multiple-step technology with Bragg–Brentano geometry. The diffractometer (operating at 45 kV and 40 mA) collected the scans in the 2θ range of 5°–90° with a step size of 0.016° and 59 s per step. For structural refinements, both the Rietveld method and the crystalline phase identification approach were performed by using the High Score Plus software version 3.0.5 and the ICDD PDF+4 plus database as well (ICDD, International Center for Diffraction Data, Newtown Square, PA, United States).

3 Results and Discusion

3.1 PM_2.5 in the Monterrey Metropolitan Area During the Winter and Summer Seasons

The daily variation of PM_2.5 concentrations found in the MMA during the two study periods is shown in Figure 1. The average concentrations of PM_2.5 in winter were much higher (three times higher) than in summer. The concentrations found in winter and summer were 84.39 ± 11.67 and 25.04 ± 3.05 μg/m³, respectively. A similar seasonal trend has been observed in other studies, which were conducted in different cities (Zhou et al., 2020; Duan et al., 2021). According to the regulation by Mexican Norm NOM-025-SSA1-2014 (SSA, 2014), the maximum allowed limit of PM_2.5 in the air is 45 μg/m³ (24 h average). Therefore, the levels found in the winter samples from the San Bernabé station was above the maximum allowed limit, suggesting the poor air quality and potential health risk to the exposed population. The high levels found during the winter season was attributed to the increase in both fossil fuels and biomass burning, which was from the high energy demands by both industries and residences during this season. Similar results have been reported by different researchers (Ravindra et al., 2022; Wang et al., 2022). In addition, low wind speeds and rainfalls were registered during the winter (Table 1). These conditions favored the stagnation of particles in the area.

FIGURE 1

FIGURE 1. Temporal evolution of PM_2.5 during the summer and winter seasons at the San Bernabé station.

3.2. Functional Groups, Light-Absorbing and Fluorescent Properties of PM_2.5

3.2.1 Identification of Chemical Functional Groups by ATR-FTIR

The infrared spectra of the PM_2.5 samples that were collected in winter and summer are shown in Figure 2. In the two study period, the absorption bands between 612 and 615 cm⁻¹and 1,080 and 1,135 cm⁻¹was attributed to the sulfate ions, which were likely as CaSO₄ (Allen et al., 1994; Maria et al., 2002; Radulescu et al., 2017; Yu et al., 2018; Zeb et al., 2018). It is of further importance to notice that CaSO₄ particles has a cooling effect on the atmosphere, which causes a negative radiative forcing and alters the troposphere temperature balance (Buseck and Pósfai, 1999; Martin et al., 2004). A summary of the chemical species that were found in the samples is found in Table 2. In majority of the collected samples, CO₃²⁻signals (i.e., at 1,412 cm⁻¹ and 872 cm⁻¹) were predominant in the PM_2.5 samples (Shaka’ and Saliba, 2004; Bahadur et al., 2010; Siciliano et al., 2018). Moreover, the band found between 710 and 719 cm⁻¹ was related to the asymmetric vibrations of CaCO₃ (Varrica et al., 2019). Recent studies indicated that both carbonate ion and its products from atmospheric aging can affect the climate directly by dispersing and absorbing solar and terrestrial radiation (Crowley et al., 2010; Tang et al., 2015). They can affect the climate indirectly by changing the microphysics, albedo, and useful life of clouds.

FIGURE 2

FIGURE 2. ATR-FTIR spectrum of selected PM_2.5 samples collected on fiberglass filters, (A) winter, samples: 1(W6), 2(W5), 3(W3), 4(W8) and (B) summer, samples: 1(S1), 2(S3), 3(S6), 4(S8).

TABLE 2

TABLE 2. Summary of the functional groups identified by FT-IR on the PM_2.5 samples collected in both seasons.

The weak shoulder signals between the ranges from 1,320 to 1,330 cm⁻¹ and 820–830 cm⁻¹ in some samples suggested the presence of NO₃⁻ species (Figure 2B). These signals overlapped with the strong CO₃²⁻ signals between 1,412 and 870 cm⁻¹. In addition, in some summer and winter samples, the absorption signals between 1,760 and 1,800 cm⁻¹ were observed, which indicated the presence of inorganic nitrate Previous studies on the MMA have reported the presence of NO₃⁻ in PM_2.5 (Martinez et al., 2012; Mancilla et al., 2019). Meanwhile, the peaks between 1,000 and 1,044 cm⁻¹ and peak at 800 cm⁻¹ were due to Si-O bonds (samples: S1, S3, S6, W6, W5, and W3) The peak between 1,000 and 1,044 cm⁻¹was due to the asymmetric stretching vibrations of the O-Si-O, while the peak at 800 cm⁻¹was due to bending vibrations of the O-Si-O bond in the silicates (Maria et al., 2002; Shaka’ and Saliba, 2004; Simonsen et al., 2009; Radulescu et al., 2017; Zeb et al., 2018). In addition, in both summer and winter samples, a peak was observed between 900 and 920 cm⁻⁻¹, which was indicative of Al-(OH) bond vibrations. Compounds with Al-(OH) are usually present in aluminosilicate minerals such as kaolinite (Al₂Si₂O₅(OH)₄) (Ravisankar et al., 2010). It should be noted that this peak appeared more intense in the summer samples and in some cases this peak overlapped with the carbonate band at ∼877 cm⁻¹. The absorption signals found between 3,618 and 3,700 cm⁻¹as well as 3,695 and 3,700 cm⁻¹ corresponded to the vibrations of the OH functional groups, which are typically located between the tetrahedral and octahedral layers of clay minerals (aluminosilicates). These functional groups have been shown to interact with the Si-O-Si layers to produce bonds, which produce characteristic absorptions at 3,620 and 3,695 cm⁻¹ (Davarcioglu, 2011; Varrica et al., 2019). Additionally, the absorption bands at lower frequencies of around 540 cm⁻¹ and the small peak at 750 cm⁻¹, which appeared in some samples were attributed to the Al-O-Si bonds in silicates (Chou et al., 2005; Coury and Dillner, 2008).

The OH stretching vibrations around 3,400 cm⁻¹observed in our samples (Figure 2) indicated the presence of water and/or hydroxylated compounds such as alcohols and polyols from biogenic sources and biomass burning (Gipson et al., 2015; Siciliano et al., 2018; Zapata-Hernandez et al., 2020). The broad band observed between 1,617 and 1,640 cm⁻¹confirmed the presence of water in the samples. The broad band signal between 1,617 and 1,640 cm⁻¹ indicated of an overlap of the signals from different chemical functional groups including the stretching vibration absorptions for C=C aromatic and C=O bonds. Some authors have attributed this broad band signal to the functional groups found in organonitrates, aromatic amides, and organic amines (Ji et al., 2015; Radulescu et al., 2017; Siciliano et al., 2018; Zapata-Hernandez et al., 2020). Meanwhile, the appearance of the weak shoulder signals at around 1,220 cm⁻¹ in most of the samples supported the presence of organ nitrated compounds (Shaka’ and Saliba, 2004).

The broad band observed between 3,534 and 3,560 cm⁻¹ in our samples (Figure 2) were due to the vibrations of the O-H groups in alcohols, phenols, and carboxylic acids (Allen et al., 1994; Coury and Dillner, 2008; Shaka’ and Saliba, 2004). A weak peak between 1,798 and 1,800 cm⁻¹ was observed in three and five of the summer and winter samples, respectively. This peak corresponded to the vibrations of the carbonyl functional group (C=O) in carboxylic acids. The amounts of carboxylic acids seemed to be higher in the winter samples suggested in Figure 2A (Siciliano et al., 2018). In most of the collected samples, peaks were observed between 2,920 and 2,850 cm⁻¹, which were associated to the CH₂ in aliphatic compounds (Shaka’ and Saliba, 2004; Zapata-Hernández et al., 2020).

3.2.2 Analysis of PM_2.5 Samples by X-Ray Diffraction and X-Ray Photoelectron Spectroscopy

3.2.2.1 Chemical Analysis of the Crystalline Phases by X-Ray Diffraction

The XRD diffractograms obtained for the PM_2.5 samples in the two study periods are shown in Figure 3. Several reflections with different intensities were observed in the diffractograms. Most of the signals corresponded to the crystalline phases of CaCO₃ (index number or ICDD No. 010721937), SiO₂ (ICDD No. 010781252), and CaSO₄·2H₂O (ICDD No. 010741433). Signals with lower intensities corresponded to the aluminosilicates including kyanite (ICDD No. 000110046), albite (ICDD No. 010831658), leucite (ICDD No. 010768736), sillimanite (ICDD No. 000380471), pyrophyllite dehydroxylated (ICDD No. 010734051), and NaCl (ICDD No. 010763457). In the literature it is reported that in the study region there are some mineral deposits related to these crystalline phases (such as muscovite, sillimanite, kaolin, albite, quartz, plagioclase, feldspars, and micas), only some of them are commercially exploited (nl.gob.mx, 2020). On the other hand, it is known that these mineral phases are used in the refractory and ceramic industry, which are abundant in the area and therefore it is not ruled out that they also contribute to the emission of these crystalline compounds (Bradt, 2008). Adjustments on the reflection profiles were carried out by the Rietveld method, which allowed us to perform semiquantitative analysis on each of the crystalline phases detected (Rietveld, 1969). The refinement of the signals provided the mass percentage of each crystalline phase in the diffractograms. The results showed that the most abundant crystalline phases were CaCO₃ and SiO₂ (Table 3). CaSO₄ was also abundant while aluminosilicates and NaCl were the least abundant. These phases have been extensively reported in the literature (Ahmady-birgani et al., 2015; Zarasvandi et al., 2011). The average percentage of CaCO₃ was slightly higher in summer (73.9%) than in winter (71.1%). These findings were consistent with the ATR-FTIR results, which showed the carbonate absorption bands at 1,412, 872, and 710–719 cm⁻¹, in all samples collected in both seasonal periods. These crystalline phases may have emerged from natural sources (resuspension of crust material) and anthropogenic sources. We note that MMA is a semiarid region with many ceramic, glass and, cement industrial facilities, as well as quarries inserted within the expanding metropolitan area.

FIGURE 3

FIGURE 3. X-ray diffraction patterns of PM_2.5 collected in (A) winter and (B) summer.

TABLE 3

TABLE 3. Contents of the most important crystalline mineral phases present in the PM_2.5 samples, which were obtained by x-ray powder diffractometry.

On the other hand, the average percentage of CaSO₄ found was significantly higher in winter (10.2%; min: 0% and max: 30%) than in summer (4.5%; min: 0% and max: 12%), which was consistent with the findings by the ATR-FTIR technique. In ATR-FTIR, we found more frequent and greater intensity signals for SO₄⁻² (1,080–1,135 and 612–615 cm⁻¹) in the winter samples. This finding is relevant as most of the previous studies in the MMA have assumed that sulfate associated to the PM_2.5 is mainly in the form of (NH₄)₂SO₄ (Martinez et al., 2012; Mancilla et al., 2019). The higher percentage of CaSO₄ found in the winter samples is likely more related to the meteorological parameters and concentration of SO₂ in the atmosphere, since we have considered CaSO₄ as a phase of secondary origin. Sulfates are formed by photochemical reactions that occur between their main precursor (SO₂) and oxidizing species such as O₃ and NOx (Xu et al., 2016). Unfortunately, SO₂ is not monitored in the San Bernabé station. However, further exploration of available ground-level SO₂ data from the local air quality monitoring network resulted in identifying that the nearby North 2 station had a complete dataset for the periods of interest. The North 2 station is approximately 6 km to the southeast of the Northwest (San Bernabé) station, in a very similar landuse and main emission sources setup. Assuming that the North 2 station is a good proxy for the Northwest station, we retrieved SO₂ data for the North 2 station for the months of interest (Supplementary Table S2). The data confirms that SO₂ levels are higher during the winter period, which could contribute to the formation of sulfates in this season. Another factor to consider is the changes in the meteorological parameters, where several studies have shown the dependency of sulfate levels to relative humidity and wind speed (Banerjee et al., 2015; Pant et al., 2015; Lu et al., 2016). For example, Song et al. (2014) attributed the high percentages of sulfate found in their PM₁₀ samples to the higher humidity and low wind speed in the collection/sampling area (Henan, China). Moreover, Yue et al. (2022) found that the reaction between SO₂ and CaCO₃ particles in the atmosphere was favored by relative humidity values of greater than 40%. Therefore, the low wind speed, high relative humidity, as well as the abundance of SO₂ y CaCO₃ during the winter sampling (Table 1) in the MMA explained the higher proportions of CaSO₄ found in the winter samples. It should be noted that there is no known primary source of CaSO₄ near the study area. In addition, in some samples it is not possible to detect the presence of this mineral phase, it is only detected when the meteorological parameters are favorable for the photochemical reaction to take place. For everything described above, it can be thought that the CaSO₄ found in the PM is of secondary origin

Similar amounts of SiO₂ were found in all the samples. Several reports have associated SiO₂ with natural sources (Adamo et al., 2008; Saitoh et al., 2008; Song et al., 2014). Thus, it was not surprising to find SiO₂ in our samples, since the overall soil composition of the MMA falls within a sandy profile with numerous deposits that are rich with SiO₂. Nonetheless, the contributions from anthropogenic sources were not overlooked. For example, there is a large glass industry in MMA, which uses crystalline SiO₂ as raw material. Finally, we note that the XRD and ATR-FTIR results of this investigation complemented each other.

3.2.2.2 Determination of Surface Elemental Composition by X-Ray Photoelectron Spectroscopy

High-resolution XPS spectra of the elements identified in the survey scan were corrected using the C_1s signal (285 eV) as reference. The assignment of signals was carried out by considering the data from the literature and National Institute of Standards and Technology (NIST) database. The atomic percentage of silicon was corrected by the method described by Atzei and Rossi (2004) and Gilham et al. (2008). In both winter and summer samples, high-intensity bands related to C and O was found along with low-intensity bands related to Si, Na, N, and S. Additional elements identified through their high- and low-resolution spectra were Ca, Mg, Zn, Cl, and F. The surface elemental composition of the PM_2.5 samples are shown in Figure 4. The most abundant elements found were C, O, and Si followed by Na, S, N, and Ca. Similar results have been reported in studies by Guascito et al. (2015), Atzei et al. (2014) and Huertas et al. (2012). In these previous studies, C and O were the predominant elements found on the PM_2.5 samples. The average percentage of C were higher in the winter sample, which can be attributed to the higher consumption of fossil fuels due to e.g., increased use of heating both at the industrial and residential level in the MMA (Figure 4).

FIGURE 4

FIGURE 4. Surface elemental composition obtained by XPS of PM_2.5 samples for the two study periods.

Signal speciation of C, N, S, and Ca was performed by deconvolution of the high-resolution spectrum of each element. For example, consider the high-resolution spectra of C, N, and S shown in Figure 5. For C_1s (280–295 eV), the deconvolutions showed eight different states of oxidized carbon found in all samples. These were the aliphatic (C-C, 285 eV), aromatic (C=C, 284.7 eV), EC (284 eV), carbonyl (C=O, 288 eV), carbides (283.6 eV), carboxylics (O-C=O, 289 eV), carbonates (289.5 eV), and ether/alcohol (C-O, 296.5 eV). Several researchers have reported these contributing functional groups on PM (Gaddam and Vander Wal, 2013; Atzei et al., 2014). The atomic percentages of each contributing functional group to the C_1s signal are summarized in Table 4. The aromatic hydrocarbons in all the samples were considered as the highest component to the C_1s signals. This was followed by the EC and aliphatic hydrocarbons chains. The least contributing component in the winter and summer samples were carbonates and carbides, respectively. The atomic percentages of highly oxidized species such as ether, alcohol, carboxylic acids and/or carbonyl groups were significantly higher in the winter samples. These were consistent with the results of the ATR-FTIR spectra. The higher proportion of these functional groups in winter could be related to an increase in coal burning, in addition to a greater atmospheric oxidation caused by changes in meteorological parameters (Domínguez et al., 1999; Ahlers et al., 2000; Siciliano et al., 2018). In contrast, the decrease in oxidized species observed in summer could explain the higher proportion of aromatic and aliphatic groups recorded in this season. On the other hand, average area percentage of EC in all samples did not show any significant variation.

FIGURE 5

FIGURE 5. Deconvolution of high-resolution spectra in the region of the signal (A,B) C_1s, (C,D) S_2p, and (E,F) N_1s of PM_2.5 collected in winter and summer.

TABLE 4

TABLE 4. Atomic percentage of each contributing functional group to the C1s signals (n = 8).

For the observed N_1s signal in the samples, three contributors were identified including amide/pyrrole groups, nitrites, and nitrates, positioned at bonding energies of 399.2 and 402 eV and 407, respectively. The results obtained for the winter and summer samples showed a similar trend, where amide/pyrrole groups predominated at the surface level on PM_2.5 followed by nitrates and nitrites. These were consistent with the ATR-FTIR findings where organic amides and amines, as well as inorganic nitrate were found in the samples. The abundance of amide/pyrrole groups was associated to the burning of fossil fuels. The nitrates and nitrites were of secondary origin from the photo-oxidation reactions of gaseous nitrogenous precursors in the atmosphere. In the literature, it has been reported that some ions of secondary origin, such as nitrites, are more stable under conditions of low solar radiation, which is consistent with the lower solar radiation recorded in winter(Paoletti et al., 2003). The atomic percentages obtained for the amide/pyrrole groups and nitrates in the two seasons were similar. This confirmed that seasonal variation had no significant effect on the amounts of these species in the atmosphere. Interestingly, the concentrations of nitrite were significantly higher in the winter samples (Table 5), which was attributed to the typically lower solar radiation during the cold months (Hoek et al., 1996; Lee et al., 1999; Paoletti et al., 2003).

TABLE 5

TABLE 5. Atomic percentage of each contributing functional group to the (a) N_1s, (b) Ca_2p, and (c) S_2p signals.

Two components for the Ca_2p signal were identified, where one was located at a binding energy of 347 eV, which corresponded to CaCO₃. The other was at 347.8 eV, which was associated with CaSO₄ (Guascito et al., 2015). Our results showed that CaCO₃ was the main signal contributor in all samples, while the highest percentages of CaCO₃ were found in the summer samples (e.g., 74.6%). Notably, the deconvolution of the C_1s signals suggested a higher percentage of CaCO₃ in the summer samples. This was explained by the summer activities related to the extraction of limestone from local quarries and its use as a raw material in the cement binder industry. The deconvolution of S_2p signals showed that the sulfates and organic sulfur compounds dominated the winter (68.4%) and summer (65.9%) samples, respectively. The higher levels of CaSO₄ found in the winter samples were related to the changes in meteorological conditions and possibly higher concentrations of SO₂ in the atmosphere during the sampling. In summer, the meteorological conditions did not favor the formation of the CaSO₄, which led to an increase in the percentages of organic sulfur in this season.

3.2.3 Diffuse Reflectance Spectroscopy Analysis

The DRS technique has been widely used for the characterization of solid materials (Zwinkels et al., 2014; Bock et al., 2019), since it is a tool useful to determine the electronic transitions from the valence to conduction band (band gap) of semiconductor oxide powders (Escobedo-Morales et al., 2019). In the environmental sciences, has been applied mainly for the analysis of minerals in soils (Sellitto et al., 2009; Szalai et al., 2013), however, we have found a few studies that used DRS for the characterization of atmospheric particulate material. For example, Wonaschütz et al. (2009) used this technique for the characterization of BC and BrC in atmospheric aerosols. In another study, Morozzi et al. (2021) used DRS to characterize the diverse mineral phases of the Sahara dust that were present in the PM samples obtained from the Mediterranean area.

The diffuse reflectance spectra obtained from the PM_2.5 samples are shown in Figures 6A,B. Significant attenuation of the signal response in the visible region (400–800 nm) were observed in all the spectra, indicating the presence of chromophores that were likely from the carbonaceous materials in the samples. On the other hand, the UV region spectra showed some noticeable changes in the slopes of the curves. These were associated to the electronic transitions in the inorganic materials present in the samples (Makuła et al., 2018). The spectra were converted by the Kubleka–$132#? Munk function. To better define the processes involved, the second derivative was obtained by following the methodology proposed by Torrent and Barrón (2015) and the results are shown in Figures 6C,D. In the spectra, the absorption band near 250 nm was related to the presence of CaSO₄, whose absorption range was previously reported to be between 250 and 350 nm (Nagabhushana et al., 2010). The absorption band between 210 and 230 nm corresponded to the CaCO₃ electronic transition from the valence to conduction band (Al Omari et al., 2016). The additional absorptions observed between 330–400 nm in some samples are possibly related to electronic transitions of the aluminosilicates containing transition metals (Zent et al., 2008; Tarantola et al., 2019). The above demonstrates the application of the DRS technique to characterize the inorganic fraction present in PM. In addition, it is versatile, simple and low cost since the analysis of the sample is done directly in the filter, without the need to use reagents or solvents for its

FIGURE 6

FIGURE 6. Diffuse reflectance spectra of representative PM_2.5samples obtained during (A) winter and (B) summer. Derivative of second order of the Kubelka–Munk function of the same samples collected in (C) winter and (D) summer.

3.2.3.1 UV–Vis Analyses of Water-Soluble Organic Carbon

To fully understand the optical properties of the water-soluble fraction, UV–Vis analyses were performed on the aqueous extracts of the PM_2.5 samples collected in the San Bernabé monitoring station. The variation of the Abs_λ with respect to the wavelength of radiation in the range of 300–600 nm is shown in Figure 7A. In both seasons studied, the absorption intensities decreased notably at longer wavelengths, suggesting a strong dependence with the radiation wavelength, which is characteristic of BrC (Cheng et al., 2016; Wu et al., 2020). Furthermore, the absorption coefficients of the winter samples were higher than the summer samples. Similar results have been reported by Chen et al. (2018) for samples obtained in Nanjing, China. Prior studies have associated the Abs₃₆₅ with the absorption coefficient of BrC (not inorganic salts) (Hecobian et al., 2010). The values for Abs₃₆₅ and temporal variation are shown in Table 6 and Figure 7B, respectively. The Abs₃₆₅ values were higher in the winter (1.15 ± 0.49 Mm⁻¹) than summer (0.66 ± 0.19 Mm⁻¹) samples, which has been a seasonal trend confirmed by other researchers (Kim et al., 2016; Chen et al., 2020). The values we found were comparable to those reported by Liu et al. (2018) for samples obtained in Guangzhou City, China, and by Liu et al. (2013) in Atlanta, United States. However, our values were lower than those reported by Satish et al. (2017) for samples obtained from the cities of Indo-Gangeti Plain, India Satish et al. (2017) and Huang et al. (2018) for samples obtained from Xi’an, China. Strong correlations of the Abs₃₆₅ of water extracts with WSOC concentrations for both winter (r = 0.89) and summer (r = 0.93) were found (Figure 7C), which is indicative that WSOC contains BrC (Chen et al., 2020). The difference in the Abs₃₆₅ values found in both seasons could be attributed to differences in the emission sources that predominate in each season (Chen et al., 2018; Zhan et al., 2022).

FIGURE 7

FIGURE 7. (A) Averaged light absorption spectra of the WSOC for the samples obtained in winter and summer. (B) Time series of light absorption coefficient at 365 nm (Abs₃₆₅) for the two study periods. (C) Scatter plots of Abs365 versus WSOC.

TABLE 6

TABLE 6. WSOC mass concentration and light absorption parameters (Abs₃₆₅, MAE₃₆₅, and AAE), measured during winter and summer in the two study periods.

On the other hand, the average MAE₃₆₅ values in the winter and summer samples were 0.23 ± 0.05 m²g⁻¹ and 0.19 ± 0.04 m²g⁻¹, respectively (Table 6 and Figure 8A). These results indicate that BrC in winter has a higher absorption capacity compared to summer. This could be attributed to a higher concentration of BrC chromophores in winter and/or to differences in the chemical nature of chromophores that predominate in the cold season as a result of differences in emission sources. In this study, the observed MAE₃₆₅ values were lower than those reported for cities with higher pollution indices such as Beijing, China (Yan et al., 2015), Xi’an, China (Yuan et al., 2020), and New Delhi, India (Kirillova et al., 2014). In addition, our values were closer to the values reported for Seoul, Korea (Kim et al., 2016) and Atlanta, United States (Hecobian et al., 2010).

FIGURE 8

FIGURE 8. (A) Time series of mass absorption efficiency at 365 nm (MAE₃₆₅) for the two study periods, (B) linear regression fit log(abs₃₆₅) versus log(λ) in the wavelength range of 300–600 nm for both study periods.

The AAE is a very important parameter that provides information on the spectral dependence of the aerosol and the absorption of light in bulk (e.g., extracts) (Moosmüller et al., 2011). In the literature, it has been reported that BC from the burning of fossil fuels generally has AAE values that were close to 1 (Fraund et al., 2020). This means that the refractive index values of BC are independent of the wavelength. AAE values slightly higher than one indicated the presence of BC or a mixture of BrC and BC (Pokhrel et al., 2017). Meanwhile, high AAE values between 5 and 9 revealed the predominance of BrC in the aerosol samples (Chakraborty et al., 2016; Lin et al., 2017). The AAE values calculated for WSOC of our samples in the wavelength range from 300 to 600 nm are summarized in Table 6. The average values obtained for the winter and summer samples were 6.32 ± 0.78 and 7.33 ± 1.02, respectively (Figure 8B). The high AAE values in all samples as well as the high correlations found between Abs₃₆₅ and WSOC concentration demonstrated an important contribution of BrC chromophores in the WSOC (Lin et al., 2017; Yuan et al., 2020). The AAE values were higher in the summer samples, indicating a greater contribution of compounds that absorb at shorter wavelengths in these samples. Similarly, Chen et al. (2018) reported that the highest and lowest values of AAE for PM_2.5 samples collected throughout the year from Nanjing, China was obtained from the summer and winter samples, respectively. The results were explained by the difference in the chemical composition or emission sources in these seasons. Likewise, Kim et al. (2016) reported a similar seasonal variation, where the higher values of AAE in summer was attributed to the presence of metals that come from the Earth’s crust

3.2.4 Analysis of PM_2.5 by Fluorescence Spectroscopy

The fluorescence emission spectra (with excitation at 355 nm) of the filters collected during winter and summer are shown in Supplementary Figure S1. The maximum wavelength intensity of the spectra from the winter and summer samples ranged from 440 to 464 nm and 439–449 nm, respectively. Similar ranges have been reported by other researchers for highly oxygenated carbonaceous and humic-like substances (HULIS) (McKnight et al., 2001; Chen et al., 2016b; Qin et al., 2021). Barsotti et al. (2016) reported that HULIS derived from the oligomerization of phenolic compounds exhibited a strong fluorescence emission in the vicinity of 450 nm. On the other hand, it is known that ozone and hydroxyl radicals are able to cause the oxidation of volatile organic compounds (VOCs) such as limonene and α-pinene. The oxidation generated HULIS that showed strong fluorescence emissions within the range of 437–513 nm. The average of maximum emission intensities was higher in the winter (1.6 × 10⁶ ± 7.8 × 10⁵ a.u.) than summers (1.4 × 10⁶ ± 6.3 × 10⁵ a.u.) samples. This was attributed to the greater amounts of oxygenated carbonaceous species found in the winter samples. Through XPS analyses, we also found slightly higher values for the total percentage of carbon and extent of functionalization by oxidized species on the C_1s signal in the winter samples.

An exploratory EEM study was conducted to gain insights on the fluorescent compounds found in the samples. The aqueous extracts of the samples obtained during both seasons were used. The fluorescence indexes HIX, BIX, and FI were determined. These indexes have been implemented in studies involving DOM in both soil and aquatic systems and aerosol due to the similarities in the optical aspects of WSOC and aquatic DOM. Indeed, these indexes have been shown to provide some information on the nature of the organic compounds present in the water-soluble fraction of aerosols (McKnight et al., 2001; Huguet et al., 2009; Birdwell and Engel, 2010). The FI values are summarized in Supplementary Table S1, where 40% of the samples showed FI ∼ 1.5 values. This suggested the presence of fulvic acid-like substances with high aromaticity and of terrestrial origin. Meanwhile, 60% of the remaining samples produced FI values >2. This indicated the strong presence of fluorescent compounds with low aromaticity and of microbial origin. Similar results have been reported by Lee et al. (2013) in their EEM analysis of fresh and aged organic aerosols. They suggested that aerosols with FI values near 1.4 and 1.9 were associated with fulvic acids of terrestrial and microbial origin, respectively. The presence of this acid in our samples was verified by the FTIR and XPS results, where C = O (1798–1800 cm⁻¹) and OH (3500–3650 cm⁻¹) vibrations for the carboxylic acid group and OC = O, 289 eV to the C_1s signal for the carboxyl group, respectively were observed. The autochthonous contribution index value (BIX near 0.4) also suggested that fulvic acid came from nonbiological sources. In addition, the HIX index found in the aerosols of ∼0.6 can be associated with the low-grade humification fluorescent components.

The PARAFAC model was applied to identify the possible sources of emission for the water-soluble carbonaceous substances that were present in the collected samples (Murphy et al., 2013). Recently, this model has been used to characterize the water-soluble organic matter in atmospheric aerosols (Mladenov et al., 2011; Matos et al., 2015; Chen et al., 2021) and organic matter in various aqueous matrices (Ishii and Boyer, 2012; Dittmar and Stubbins, 2014; Wünsch and Murphy, 2021). It is important to mention that in Mexico and possibly in Latin America, fluorescence indices and the PARAFAC model had not yet been used in the study of atmospheric aerosols. The results obtained are shown in Figure 9. The model was fixed for three components and helped determine 99.1% the data set variance and delivered central consistency values higher than 87%. Additionally, the model was validated through the analysis of divided halves (Supplementary Figure S2) and Tucke r´s congruence coefficient for both excitation and emission spectra The coefficients were found to be higher than the similarity threshold (0.95) proposed by Murphy et al. (2014). Component 1 (C1) revealed the primary and secondary signals (λex/λem) of 340/433 nm and 340/394 nm, respectively. Component (C2) gave the primary and secondary signals of 300/410 nm and 360/410 nm, respectively. Component 3 (C3) showed one signal of 360/398 nm. The signals for C1 were similar to those reported for highly oxygenated HULIS of terrestrial and dust origin, which were found in aerosol samples from urban areas and organic matter dissolved in water samples (Fu et al., 2015; Yan and Kim, 2017; Qin et al., 2018; Wang et al., 2020). Moreover, Wen et al. (2021) applied the PARAFAC model to samples collected from three sites with different environments in northern China. One of the components presented signals of 245 and 360/476 nm, which were associated with highly oxygenated HULIS from terrestrial sources. In addition, Murphy et al. (2011) attributed the signal of 349/431 nm to terrestrial humic- and fulvic-like fluorescent compounds in their samples of organic matter dissolved in water.

FIGURE 9

FIGURE 9. Comparison of the final models with three normalized components according to the maximum fluorescence (Fmax) emission.

In the case of C2, the fluorescence was associated to HULIS of microbial origin (Søndergaard et al., 2003; Zhuo et al., 2010; Murphy et al., 2011). Similar fluorescent compounds have been identified in the DOM of groundwater (Chen et al., 2010). On the other hand, C3 was similar to those found for humic fluorophores present in wastewater (Stedmon and Markager, 2005; D’Andrilli et al., 2013). Here, the results of the PARAFAC study were fully consistent with those obtained from the fluorescence indices study. These studies revealed the contribution of HULIS of terrestrial and microbial origin in the samples. It is important to emphasize that the emission sources and chemical composition of the fluorophores in the PM may not be the same as those found in the DOM. Moreover, only a few studies have used the fluorescence indices and PARAFAC method to characterize the fluorophore components in PM samples. Therefore, it was not possible to compare the detected components that may be found in different sources of atmospheric aerosols, such as biomass burning, fossil sources, secondary aerosols of biogenic, and anthropogenic VOCs, among others. In this sense, the present work, although an exploratory study, may serve as a basis to deepen the study of the fluorophore components present in atmospheric aerosols in Mexico.

4 Conclusion

This study investigated the light absorption characteristics and chemical composition of PM_2.5 particles and their water-soluble fraction. In winter, the concentration of PM_2.5 recorded at the San Bernabé station exceeded the limits in Mexico (NOM-025-SSA1-2014), which was a potential health risk to the exposed population. The presence of inorganic species in the samples collected during winter and summer, such as CaCO₃, CaSO₄, SiO₂, and aluminosilicates were identified by ATR-FTIR, XRD, and XPS techniques. The DRS spectra showed that most of the detected constituents of the sample absorbed mainly in the UV region (200–350 nm). The highest percentages of CaSO₄ were found in the winter samples (both at the surface level and in the bulk). This was attributed to the higher concentration of SO₂ in the atmosphere, as well as the higher humidity and low wind speed that were recorded during the winter months of sample collection. The speciation of the C_1s signal showed eight different oxidation states for carbon in all samples, with aromatic hydrocarbons, EC, and aliphatic hydrocarbons as the main contributors to the overall signal.

The light absorption by the water extracts exhibited a strong dependence with the wavelengths in the ranges 300 and 600 nm. Optical parameters, such as ABs₃₆₅ and AAE, as well as the high correlation found between Abs₃₆₅ and WSOC, showed that a fraction of WSOC corresponds to BrC chromophores. The Abs₃₆₅ and MAE₃₆₅ were found to be higher in the winter than in summer samples. This was attributed to the differences in the chemical nature of the BrC compounds and/or to a higher concentration of these compounds in the winter samples. EEM-PARAFAC analysis showed that the water-soluble BrC compounds were mainly HULIS that come from terrestrial (C1), microbial origin (C2) and humic fluorophores (C3). A high correlation was found between the fluorescence indices (FI, HIX, and BIX) and EMM-PARAFAC, which suggested the importance of both approaches in establishing the possible sources of emission for the BrC compounds in the aqueous extracts. We believe that it is necessary to continue the global studies on the molecular characterization of BrC compounds and fluorescence properties of atmospheric WSOC. These studies will allow us to establish a database on atmospheric aerosols for more accurate identification of possible emission sources.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Author Contributions

Conceptualization, LG; Writing—original draft preparation, LG, AM, FL, and KA; Writing—review and editing, LG, JA, AM, and FL; Characterization, LB-C, DL-S, AR-G, and AC; Data curation, EL, Investigation, JA-B and OK; Supervision, LG.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors thank Ing. Armandina Valdez for the use of the monitoring stations of the Sistema Integral de Monitoreo Ambiental (SIMA). The valuable support of Dr. Alonso Concha and M. C. Gerardo Silva Vidaurri from CIMAV.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2022.929449/full#supplementary-material

Abbreviations

ATR, Attenuated total reflection; DOM, Dissolved organic matter; DRS, Diffuse reflectance spectroscopy; EC, Elemental carbon; FI, Fluorescence index; HULIS, Humic-like substances; MAE, Mass absorption efficiency; MMA, Monterrey metropolitan area; MS, Mass spectrometry; NIST, National institute of standards and technology; OC, Organic carbon; PM, Particulate matter; SIMA, Sistema integral de monitoreo ambiental; TOC, Total organic carbon; VOC, Volatile organic compounds; WSOC, Water-soluble organic carbon; XPS, X-ray photoelectron spectroscopy; XRD, X-ray diffraction.

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