Next Article in Journal
Examining the Effectiveness of a Pilot Waste Classification Policy in Facilitating the Low-Carbon Transition Regarding Solid Waste in China
Next Article in Special Issue
Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model
Previous Article in Journal
Advances in Air–Sea Interactions, Climate Variability, and Predictability
Previous Article in Special Issue
Influence of Particle Surface Energy and Sphericity on Filtration Performance Based on FLUENT-EDEM Coupling Simulation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM2.5) from an Open-Cast Coal Mining Area

by
Claudia Galeano-Páez
1,
Hugo Brango
2,
Karina Pastor-Sierra
1,
Andrés Coneo-Pretelt
1,
Gean Arteaga-Arroyo
1,
Ana Peñata-Taborda
1,
Pedro Espitia-Pérez
1,
Dina Ricardo-Caldera
3,
Alicia Humanez-Álvarez
1,
Elizabeth Londoño-Velasco
4,
Roger Espinosa-Sáez
5,
Basilio Diaz-Ponguta
6,
Juliana da Silva
7,
Dione Silva Corrêa
8 and
Lyda Espitia-Pérez
1,*
1
Grupo de Investigación Biomédica y Biología Molecular, Facultad de Ciencias de la Salud, Universidad del Sinú, Montería 230001, PC, Colombia
2
Facultad de Educación y Ciencias, Departamento de Matemáticas, Universidad de Sucre, Sincelejo 700003, PC, Colombia
3
Grupo de Investigación en Enfermedades Tropicales y Resistencia Bacteriana, Universidad del Sinú Seccional Montería, Montería 230001, PC, Colombia
4
Departamento de Ciencias Básicas de la Salud, Pontificia Universidad Javeriana Seccional Cali, Cali 760031, PC, Colombia
5
Grupo de Investigación, Evaluación y Desarrollo de Fármacos y Afines, Departamento de Regencia y Farmacia, Universidad de Córdoba, Montería 230002, PC, Colombia
6
Grupo de Investigaciones en Aguas, Pesticidas y Metales Pesados, Universidad de Córdoba, Montería 230002, PC, Colombia
7
Laboratory of Genetics Toxicology, La Salle University, Av. Victor Barreto, 2288, Canoas 92010-000, RS, Brazil
8
Center for Research in Product and Development (CEPPED), Lutheran University of Brazil (ULBRA), Canoas 91712-990, RS, Brazil
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(12), 1420; https://doi.org/10.3390/atmos15121420
Submission received: 23 September 2024 / Revised: 25 October 2024 / Accepted: 2 November 2024 / Published: 26 November 2024
(This article belongs to the Special Issue Characteristics and Control of Particulate Matter)

Abstract

:
This study evaluates the cytotoxic and genotoxic effects of PM2.5 collected from an open-cast coal mining area in northern Colombia. Cyclohexane (CH), dichloromethane (DCM), and acetone (ACE) extracts were obtained using Soxhlet extraction to isolate compounds of different polarities. Human lymphocytes were exposed to the extracted compounds, and cytotoxicity and genotoxicity were assessed using the cytokinesis block micronucleus (CBMN) and comet assays, incorporating FPG and ENDO III enzymes to detect oxidative DNA damage. Chemical analysis revealed that the organic fractions consisted mainly of modified hydrocarbons and volatile organic compounds. The CBMN assay showed a significant increase in micronuclei in binucleated (MNBN) and mononucleated (MNMONO) cells and nucleoplasmic bridges (NPB) in exposed lymphocytes. The comet assay revealed substantial oxidative DNA damage, particularly with the ACE extract, which significantly increased oxidized purines and pyrimidines. DCM induced similar effects, while CH showed moderate effects. CREST immunostaining revealed aneugenic activity, particularly in cells exposed to ACE and DCM extracts. These results suggest that polar fractions of PM2.5, likely containing metals and modified PAHs, contribute to DNA damage and chromosomal instability. The study highlights the need to monitor the composition of PM2.5 in mining regions to implement stricter environmental policies to reduce exposure and health risks.

1. Introduction

PM2.5, defined as particulate matter 2.5 microns or less in diameter, can penetrate deep into the lungs and enter the bloodstream, causing significant adverse health effects [1]. In mining regions, PM2.5 is generated by blasting, drilling, transportation, and coal combustion. This fine particulate matter (PM) can remain suspended in the air, posing an important health risk to nearby communities [2,3]. Exposure to PM in proximity to open-cast coal mines has been associated with increased morbidity from respiratory diseases [4], cardiopulmonary conditions, and lung cancer [5], leading to its classification as a probable human carcinogen by the International Agency for Research on Cancer (IARC) [6].
In vitro studies have demonstrated that PM2.5 exhibits both genotoxic and cytotoxic effects probably caused by its complex chemical composition. Multiple studies have established a strong correlation between DNA damage and fine particulate matter exposure [7,8]. Increasing evidence suggests that the genotoxic effects of PM are primarily due to its organic fraction [9,10], which may contain a mixture of carcinogenic compounds such as polycyclic aromatic hydrocarbons (PAHs). In coal mining areas, PAHs may be released due to incomplete combustion [11] when coal stored in surface mines can spontaneously combust when exposed to direct sunlight and high temperatures; these compounds can form DNA adducts that disrupt normal cellular processes [6], lead to genetic mutations and contribute to oxidative DNA damage by generating free radicals [12,13]. In addition, some studies suggest that chemical elements in the PM, either in the water-soluble or in the insoluble fraction [14], may catalyze reactions that exacerbate oxidative stress-induced DNA damage [14]. These elements generate reactive oxygen species (ROS), leading to oxidative damage to cellular components such as DNA, proteins, and lipids, which in turn results in both genotoxic (DNA damage, mutations, chromosomal instability) and cytotoxic (cell death) effects [9,15]. The cytotoxicity of PM2.5 is manifested through cellular damage or cell death, including apoptosis and necrosis. Apoptosis is often triggered by mitochondrial dysfunction, which involves disrupting mitochondrial membrane potential and releasing pro-apoptotic factors like cytochrome c. This pathway is closely linked to oxidative stress and DNA damage, highlighting the interconnected nature of PM2.5 ’s genotoxic and cytotoxic effects [16,17,18]. Complex airborne particle mixtures released in open-cast coal mines contain high concentrations of chemical elements, including lead (Pb), cadmium (Cd), arsenic (As), chromium (Cr), nickel (Ni), manganese (Mn), titanium (Ti), and zinc (Zn) [19]. These elements, contained mainly in the PM2.5 fraction, can be absorbed by the body through inhalation [20].
Despite growing evidence of the adverse health effects of PM2.5, most existing studies have focused on urban environments, and there is a significant gap in understanding genotoxic and cytotoxic effects, particularly in open pit mines mining regions where PM concentrations are often elevated. Additionally, in open-cast mining, toxic substances contained in the PM2.5 fraction form complex mixtures [2,21] that interact synergistically [22], amplifying their genotoxic potential. Ambient air quality standards have been implemented as a public health measure to mitigate these risks. However, these standards may not adequately address the chemical composition of PM2.5, which can pose significant health risks even at concentrations below the established regulatory thresholds [16]. Some authors argue that the most critical aspect of atmospheric PM that has yet to be fully addressed in ambient air standards is the chemical composition of PM2.5 [23,24,25].
The coal-rich region of northern Colombia is one of the major areas for surface coal mining with most reserves concentrated in La Guajira peninsula, producing approximately of 32 Mtons/year. Colombian environmental regulations establish specific limits for PM10 and PM2.5 concentrations with permissible 24 h exposure limits of 75 μg/m3 for PM10 and 37 μg/m3 for PM2.5 [26]. The current national air quality policy aims to implement stricter targets by 2030, reducing these limits to 30 μg/m3 for PM10 and 15 μg/m3 for PM2.5. However, the chemical characterization of PM is not routinely performed in Colombia [27].
This study aims to address this gap by evaluating the genotoxic and cytotoxic effects of PM2.5 collected from an open-pit coal mining region in northern Colombia on human lymphocytes. PM2.5 extracts of different polarities were analyzed to determine the relative toxicity. The primary objective was to assess chromosomal instability using the cytokinesis-block micronucleus cytome (CBMN-cyt) assay. In addition, oxidative DNA damage was quantified using the modified comet assay with FPG and ENDO III enzymes to assess the oxidative stress induced by these particles. CREST staining was used to distinguish centromere-positive from centromere-negative micronuclei (MNs) and differentiate between aneugenic and clastogenic effects. This approach provides a detailed assessment of the genotoxic and cytotoxic effects of PM2.5 exposure and contributes to understanding the associated health risks in coal mining regions.

2. Materials and Methods

2.1. Location of Sampling Sites and Study Area

The study area is situated in the foothills of the Serranía del Perijá, which is a semi-arid region in the southern portion of the La Guajira department in northeastern Colombia. This region is located between the municipalities of Barrancas, Albania and Hatonuevo [28], is significantly affected by coal mining activities and is home to Wayúu indigenous settlements and several rural communities [29]. The sampling sites for PM2.5 were situated within the direct influence zone of mining operations and near the Puerto Bolívar coal terminal. Based on our previous studies on the effects of mining activities on DNA damage parameters in residentially exposed populations [19,30], five particulate sampling areas were defined in the municipalities of Barrancas, Hatonuevo, and Puerto Bolívar. In Barrancas, three locations were established: the Wayúu communities of Provincial (72°44′10.0″ W; 11°01′24.0″ N) and San Francisco (72°45′58.0″ W; 11°00′08.0″ N) and the Afro-Colombian community of Chancleta (72°40′36.0″ W; 11°03′08.0″ N). As a result of the elevated air pollution levels, the latter community was eventually relocated [31]. In the municipality of Hatonuevo, samples were collected from the Wayúu community of Cerro de Hatonuevo (72°45′40.0″ W; 10°55′38.0″ N); in Puerto Bolívar, the Wayúu community of Media Luna (71°59′49.0″ W; 12°13′33.0″), located near the Puerto Bolívar coal terminal, was selected for sampling. Finally, a reference area was selected for the Manaure community in Mayapo (72°46′60.0″ W; 11°39′00.0″ N). This area is geographically and environmentally distant from the mining operations and serves as a control area to account for baseline levels of PM and any potential background environmental pollution unrelated to coal mining. This reference area allows a more apparent distinction between mining-related pollutants and naturally occurring or regional environmental factors (Figure 1).

2.2. Collection of PM2.5 Samples

For PM2.5 collection, a total of 30 atmospheric aerosol samples were collected for 24 h between August and December 2015, starting at 9:00 a.m. Considering that the collection was conducted in a semi-arid region, with minimal seasonal variations, the data obtained are representative of the prevailing conditions throughout the rest of the year. A BGI PQ200 FRM sampler, equipped with a PM2.5 inlet and WINS impactor (as per CFR40 part 50, appendix L), was used with an airflow rate of 16.7 L/min. The sampler was calibrated with a MesaLabs DryCal Definer 220 flow calibrator (Brandt Instruments, Prairieville, LA, USA). On-site calibrations involved verifying the volumetric flow by comparing the calibrated orifice results with volumetric flow controller tables. The samples were collected using 46.2 mm diameter polytetrafluoroethylene (PTFE) filters, supported with polymethyl pentene (PMP) rings (Tisch Environmental Inc., Cincinnati, OH, USA). These hydrophobic, chemically inert filters have low background interference, making them ideal for gravimetric and chemical analysis of particulate matter. With a pore size of 2.0 μm, the filters effectively trapped PM2.5 with minimal flow resistance. Filters were equilibrated in a desiccator under controlled conditions (T = 26.5 ± 1 °C, RH = 30.4 ± 5%) and weighed until consistent values were obtained before and after sampling. The PM samples were tagged, preserved, and transported at 4 °C for additional analysis. The filters were measured using a digital microbalance (Radwag MYA11-3Y) with a precision of 10−6 g.

2.2.1. Chemicals

General cell culture reagents were purchased from (Sigma-Aldrich, St. Louis, MO, USA). Acetone (ACE) was purchased from (Merck K45815214434, Kenilworth, NJ, USA), cyclohexane (CH) from Panreac 131250.0314, Barcelona, Spain), and dichloromethane (DCM) from (Honeywell 10624214, Seelze, Germany). The solvent stock solutions were dissolved in dimethyl sulfoxide (DMS) (Carlo Erba–445103, Chicago, IL, USA) and stored at −20 °C in sealed, sterile glass vials. For the treatments, each stock was dissolved in fetal bovine serum and phenol red-free RPMI 1640 medium (Sigma R-8755, USA).

2.2.2. Extractable Organic Matter (EOM) Determination

Filters from the five sampling sites directly impacted by coal mining activities and from the control area were sequentially extracted using three different solvents. Three quarters of the PTFE filters from each site were extracted together in a Soxhlet apparatus, following the protocol outlined in USEPA Method 3540C (1996). The extraction process for the filters at each site lasted 24 h with each solvent applied in a series of 8 h cycles at a temperature of 59 °C. The solvents were used in the following order: 60 mL of CH from Merck, followed by 60 mL of DCM from Honeywell, and finally 60 mL of ACE from Merck. After extraction, the solutions for each solvent (CH, DCM, and ACE) at each site were concentrated to 1.5 mL using a rotary vacuum evaporator (Heidolph Instruments, Schwabach, Germany). Preliminary analysis of the extractable organic matter (EOM) from the extracts revealed a similar chemical composition across the sampled coal mining areas (see Figure S1). As a result, we pooled the extracts by solvent type to create a representative profile, producing a combined extract for each solvent from the mining-affected sites. This approach ensures a comprehensive overview of the organic fractions across the mining-impacted area, maintaining consistency while reflecting the overall impact. The extract from the reference area was analyzed to distinguish contaminants associated with mining activities from naturally occurring or regional environmental factors. The concentration of extractable organic matter (EOM) in µg/m3 was calculated by dividing the total EOM collected per filter by the total volume of air sampled. The EOM concentration in percentage represents the proportion of EOM in the PM2.5 sample. The concentration of EOM in % represents the percentage of EOM in the PM2.5.

2.2.3. Cleanup

The concentrated extracts were separated using a 20 cm × 1.5 cm column packed with pre-cleaned silica gel (heated for 20 h at 110 °C). The column was initially eluted with 20 mL of hexane: dichloromethane mixture (9:1, v:v), followed by 30 mL of hexane: dichloromethane (4:1, v:v), and finally 10 mL of dichloromethane: methanol (9:1, v:v). The extracts were then concentrated using a rotary evaporator, transferred to vials, and stored at −20 °C until further analysis.
Subsequent analysis of the samples was conducted using high-resolution gas chromatography-mass spectrometry (GC–MS).

2.2.4. High-Resolution Gas Chromatography-Mass Spectrometry (GC–MS)

The GC-MS analysis was conducted using a Shimadzu GC2010 gas chromatograph coupled to a GCMS QP2010 mass spectrometer. The separation was achieved with an Optima 5 MS column, measuring 30 m in length, 0.25 mm in internal diameter, and 0.25 µm in film thickness. The column oven temperature was initially set at 40.0 °C with a temperature ramp up to 260.0 °C at a rate of 10.0 °C/min. The injection temperature was 250.0 °C, and the injection was performed in splitless mode with a sampling time of 1.00 min. The pressure was maintained at 49.5 kPa with a total flow of 4.0 mL/min, column flow of 1.0 mL/min, and purge flow of 3.0 mL/min. The linear velocity was 36.1 cm/sec, and helium was used as the carrier gas in a constant flow mode.
The mass spectrometer was operated with an ion source temperature of 260.0 °C and an interface temperature of 260.0 °C. The solvent cut time was set at 3.00 min. The detector gain mode was set to relative with a gain of 0.00 kV and a threshold of 1000. The mass spectrometric analysis was performed in scan mode with a scanning speed of 666 amu/sec and a mass range from 40.00 to 350.00 m/z. The event time was 0.50 s with the scan starting at 3.00 min and ending at 42.00 min.
The obtained chromatograms in. qgd format files were processed in GCMSsolution software (Shimadzu Corp., Tokyo, Japan) for peak integration to correct any overlapping peaks or inadequate peak separations. The mass spectral search algorithm, integrated with GCMSsolution, was employed to identify the compounds in question. The peaks corresponding to the experimentally obtained mass spectra were then compared with the NIST-05 database. A similarity threshold of 80% was employed to ascertain the reliability of compound identification based on the retention indexes, coded RetIndex, based on the Linear Temperature Programmed Retention Index (LTPRI) for the Shimadzu GC2010.

2.3. Blood Samples and Lymphocyte Isolation

Lymphocytes were obtained from blood samples collected from three healthy volunteers, non-smokers, without recent exposure to X-rays or coal mining residues, ages 30–35 years old. A survey was conducted to collect data on exposure to other factors, such as the use of psychoactive drugs, recent severe viral diseases, and exposure to other pollutants. The participants were previously informed about the study’s purpose. After informed consent was obtained, blood samples were collected aseptically by venipuncture. Approximately 14 mL of peripheral blood from each donor was obtained by venipuncture in tubes with EDTA (Becton-Dickenson, NJ, USA)) for subsequent lymphocyte extraction. The lymphocytes were isolated by centrifugation using the Ficoll–Histopaque method and washed with sterile 1×PBS and RPMI 1640 medium without phenol red (Sigma R8755, USA). The initial viability of the mononuclear cells was determined by the trypan blue exclusion method, considering a viability criterion of 90% per patient [2].

Primary Human Lymphocyte Cultures and Assessment of Cytotoxicity

Approximately 9 × 10⁴ cells per well were cultured in 96-well plates using RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), 2% phytohemagglutinin (PHA), 1% penicillin–streptomycin solution, and 1% L-glutamine. The cells were incubated for 24 h at 37 °C in a humidified atmosphere containing 5% CO2. All primary cultures from donors were processed simultaneously to ensure reproducibility.
After the initial 24 h incubation period, 20 µL of serial dilutions of the evaluated extracts (ACE, CH, DCM) was added, resulting in final concentrations in the culture medium of 0.001, 0.020, 0.045, 0.090, 0.180, 0.360, 0.730, 1.470, 2.940, and 5.88 mM. Two exposure periods were employed to assess cytotoxicity: 4 h for acute cytotoxicity and 72 h for chronic cytotoxicity. ACE, CH, and DCM served as blanks, H2O2 (hydrogen peroxide) was used as a positive control, and untreated cells were used as a negative control.
To prevent cross-contamination due to volatilization between wells, a sterile sheet of Alumna Seal™ (RPI Corp., Mt. Prospect, IL, USA) was placed over the plate before covering it. Following the incubation periods, cell viability was assessed using the XTT assay, which relies on the reduction of the yellow tetrazolium salt XTT to formazan by dehydrogenase enzymes in metabolically active cells, resulting in an orange water-soluble product [28,32]. After each treatment, the wells were washed, and 200 µL of deionized water was added. Absorbance was measured at 595 nm using a BioRad (Hercules, CA, USA) microplate reader. Data were corrected using the blanks with the mean blank-corrected absorbance value of the negative control set to 100% viability. The absorbance for each treatment group well was then expressed as a percentage relative to this concurrent negative control. Results were presented as dose–response curves, and the IC50 values for each extract were calculated using nonlinear regression based on the Hill slope (R2 > 0.95) in GraphPad Prism version 10.

2.4. High-Throughput Single-Cell Gel Electrophoresis and Modified Comet Assay

The comet assay is another fast, sensitive, and effective method for assessing genetic damage [33].
The alkaline Comet assay was conducted in accordance with the protocol delineated by Espitia-Peréz et al. [2], incorporating several alterations to establish a high-throughput variant proficient in processing numerous samples [34].
After 24 h of primary culture, lymphocytes were washed twice with phosphate buffered saline (PBS) to remove FBS. The ACE, CH and DCM concentration ranges for the SCGE assay did not exceed 30% of acute cytotoxicity [35,36]. H2O2 was used as the positive control, and RPMI 1640 medium without FBS was used as the negative control. All treatment conditions and experimental designs were performed following [37]. After treatment, cells were centrifuged and washed 3 times with PBS. An aliquot of the cell suspension was analyzed for acute cytotoxicity with the trypan blue dye exclusion assay. The remaining cell suspension (10 mL) was mixed with 90 mL of 0.5% low melting point agarose (Sigma) in PBS without Ca2+ and Mg2+.
Additionally, an alkaline comet assay with lesion-specific enzymes (ENDO III and FPG) was used to detect oxidized pyrimidines and purines, respectively [38]. Following lysis, minigels were washed with enzyme buffer and incubated with ENDO III (1:1000 for 30 min) or FPG (1:1000 for 45 min) at 37 °C alongside control gels treated with buffer alone. DNA unwinding, electrophoresis, and staining were conducted as described for the high-throughput comet assay [2]. The contribution of 8-oxoG and oxidized pyrimidines to DNA damage was calculated by subtracting the % Tail DNA values for enzyme-treated gels from those of the buffer-only controls. Significant differences of 7–11% in % Tail DNA were observed for enzyme-treated samples compared to controls.

2.5. Cytokinesis-Block Micronucleus (CBMN) Assay

The CBMN-cyt assay includes key biomarkers such as the frequency of micronuclei in binucleated cells (MNBNs), which indicates chromosome breakage and/or complete chromosome loss; micronuclei in mononucleated cells (MNMONOs), representing chromosome damage induced and expressed in vivo prior to culture; nucleoplasmic bridges (NPBs), markers of DNA misrepair and/or telomere end fusions; and nuclear buds (NBUDs), which are linked to the removal of amplified DNA and/or DNA repair complexes [39]. The CBMN assay was conducted following the methodology previously described by Fenech in 2007 [39] and Pastor et al. in 2023 [40]. Briefly, after 24 h of primary culture, cells were treated with each experimental concentration of ACE, CH, and DCM for 4 h at 37 °C, 5% CO2 in air. Methyl methane sulfonate (MMS) 1 mM was used as the positive control. Cell cultures in complete RPMI 1640 medium were the negative controls. Lymphocytes cultures were established in 4.5 mL of RPMI 1640 medium (Sigma R8758, USA) supplemented with 2 mM l-glutamine (Sigma A5955, USA), 10% fetal bovine serum (Gibco 15000-044, Brazil), 100 µL/mL antibiotic–antimycotic (Sigma A5955, USA) and 2% phytohemagglutinin (Sigma L8754, USA).
Cultures were kept in a 37 °C environment without light for 44 h with 5% CO2. After 44 h, 6 µg/mL of cytochalasin B (Sigma, C6762) was introduced. Following the incubation, lymphocytes were collected through centrifugation, treated with a methanol/acetic acid solution for fixation, and stained with Diff-Quick. Microscopy was used to analyze two thousand binucleated cells for micronuclei (MNs), nucleoplasmic bridges (NPBs), and nuclear buds (NBUDs). The frequency of micronuclei in 1000 mononucleated (MONO) cells was also examined. The cytochalasin B proliferation index (CBPI) was determined based on cell ratios. A blind analysis was conducted, and three independent experiments were carried out per treatment [41].

CREST Immunostaining

The CBMN-cyt assay in combination with CREST antibody distinguishes MN containing one or several whole chromosomes (CREST+), which are positively labeled (centromere positive MN, due to aneugenic effect), or acentric chromosome fragments, which are unlabeled (CREST−) due to the absence of centromeres (centromere-negative MN due to clastogenic effect).
After 24 h of primary culture, cells were treated with each experimental concentration of ACE, CH and DCM for 4 h at 37 °C in a humidified atmosphere containing 5% of CO2. Cells were transferred to a frosted slide and air-dried for 5 min. Then, cells were fixed with methanol and acetic acid pre-chilled at −20 °C for 10 min and air-dried again for 10 min. The CREST immunostaining technique was performed according to the protocol described by [19].

2.6. Statistical Analysis

The statistical analyses were conducted using the R software, version 4.2.3 (R Foundation for Statistical Computing, Vienna, Austria), with the functions from the base stats package. All experiments were independently repeated three times. A pooled-data analysis from all donors was performed for each assay. Mean and median comparisons were performed for CBMN-cyt parameters and % Tail DNA in the Comet assay. Dunnett’s test was conducted to determine if there was a significant difference in DNA damage compared to the negative control. To determine the ACE, CH, and DCM influence over CBMN-cyt parameters, we conducted a Kruskal–Wallis test, since the frequencies were not normally distributed. Additionally, we used the Mann–Whitney U non-parametric test to compare the induced CBMN-cyt parameters frequency to the negative control. The statistical significance was set at p < 0.05.

3. Results

3.1. Chemical Characteristics of ACE, CH and DCM Extracts

Three solvents were chosen for their specific abilities to extract a wide range of pollutants from PM2.5 samples, covering both non-polar and polar fractions. The selected solvents were CH, DCM, and ACE. CH was selected for its effectiveness in extracting non-polar compounds, including aliphatic hydrocarbons, PAHs, and certain non-polar oxidized hydrocarbons. As a result, it represents an optimal solvent for isolating hydrophobic substances commonly found in PM. DCM was selected as the second solvent due to its capability to dissolve a broad spectrum of organic compounds, particularly those with medium and low polarity. Its extraction efficiency is notably superior to benzene, with approximately 26% higher efficacy, indicating its suitability for complex environmental samples. ACE, the third solvent, was expected to serve a dual function: the extraction of both organic compounds and polar or semi-polar inorganic materials, including nitrates, sulfates, and trace metals, as well as water-soluble organic compounds. This multi-solvent approach ensures a comprehensive chemical characterization of the PM2.5 samples, encompassing a diverse range of pollutant types [42].
Notably, the highest EOM concentrations were observed in Provincial, while the lowest concentrations were recorded in Mayapo (Figure S1A). A preliminary analysis of EOM revealed a consistent pattern of organic compounds and an increased percentage of CH-soluble material across mining-affected locations, indicating the predominance of non-polar compounds (Figure S1B). Comprehensive chemical characterization was subsequently performed on extracted materials. Table 1 presents the principal chemical composition of the extracts obtained using ACE, CH, and DCM in coal mining areas. The chemical analysis unexpectedly revealed the absence of PAHs commonly associated with coal mining activities. The lack of PAHs specifically linked to coal mining activities suggests a difference from the anticipated chemical profile. Instead, the analysis showed that most of the detected compounds corresponded to modified hydrocarbons and volatile organic compounds with alkenes being the most dominant. These findings indicate that other hydrocarbon derivatives may be more common than PAHs in the region’s PM2.5.
Among the compounds analyzed in the ACE extracts, several alkenes were identified, including 1-hexadecene (C16H32), E-15-heptadecenal (C17H32O), and 1-nonene (C9H18). The predominant compounds found were 1-hexadecene (C16H32), 3-tridecene (Z) (C13H26), 1-dodecene (C12H24), and 1-octadecene (C18H36). In addition, benzophenone (C13H10O), a polar oxygenated hydrocarbon (Oxy-PAH) commonly associated with PM2.5, was detected. These results suggest that the ACE solvent was particularly effective at extracting volatile and oxygenated compounds, which may be linked to spontaneous combustion processes or industrial activities in the area [43,44].
The DCM extracts revealed the presence of high molecular weight compounds, including 2,4,6-triisopropylbenzene (C15H24), which is recognized as a marker of fossil fuel combustion from energy production, heating, transportation, or industrial applications [45,46,47]. Additionally, compounds such as cyclopentasiloxane (C16H48O10Si9) and 1-heptadecanol (C17H36O) were detected, indicating the presence of organic materials that may originate from industrial processes or lubrication activities [48].
The CH extract demonstrated the presence of non-polar hydrocarbons, including 1-tricosene (C23H46), 1-docosene (C22H44O), and phthalic acid esters such as di-n-octyl phthalate (C24H38O4)[49,50,51]. Phthalates, or phthalic acid esters (PAEs), are pervasive contaminants in diverse environmental compartments and have been linked to adverse health effects due to their capacity to bioaccumulate and their potential to cause reproductive harm [52,53].

3.2. Cytotoxicity of ACE, CH and DCM Extracts in Human Lymphocyte Cultures

As shown in Figure 2, the ACE, CH, and DCM extracts caused a dose-dependent decrease in the viability of human lymphocytes. The ACE extract exhibited the strongest cytotoxic effect, with a mean inhibitory concentration (IC50) of 0.17 mM, as determined by nonlinear regression analysis, indicating it is the most potent at inducing cell death. In contrast, the CH extract showed the lowest cytotoxicity, with an IC50 of 0.47 mM, suggesting a higher concentration is required to achieve the same inhibitory effect as ACE. The DCM extract demonstrated intermediate cytotoxicity, with an IC50 of 0.26 mM, being less toxic than ACE but more potent than CH.

3.3. Genotoxicity and Oxidative Damage Induced by ACE, CH and DCM Extracts in Human Lymphocyte Cultures

The concentration–response curves for genomic DNA damage and acute cytotoxicity induced by ACE, CH, and DCM extracts, as well as H2O2, are shown in Figure 3. All extracts caused a dose-dependent increase in DNA damage, which was measured by the DNA percentage in the tail (% Tail DNA). The ACE and DCM extracts showed slightly higher % Tail DNA values compared to the CH extract.
A significant increase in DNA damage was observed at all concentrations of ACE and DCM extracts compared to the negative control (p < 0.05). However, this increase in DNA damage was only significant for the CH extract at the highest concentrations (0.47 and 0.94 mM). Benzene derivatives and oxy-PAHs were more prevalent in the ACE and DCM extracts. Previous research has suggested that organic extracts with moderate and polar polarity may also contain substantial amounts of inorganic materials, such as nitrates, trace metals, and organic compounds. These results suggest that more polar extracts, like ACE and DCM, are more likely to induce DNA damage than less polar extracts. This may be attributed to their higher content of reactive compounds, which increases their interaction with genetic material and other cellular components, leading to a more pronounced toxic effect.
Figure 4 shows the results of oxidative damage induction using the modified comet assay with FPG and ENDO III endonucleases. The ACE extract caused a significant increase in oxidized purines and pyrimidines in exposed lymphocytes compared to the negative control. Similar effects were observed with the DCM extract; however, the oxidative damage induced by DCM and CH was comparable particularly with ENDO III treatment. Additionally, while FPG and ENDO III treatment revealed a slight increase in oxidized purines compared to oxidized pyrimidines in lymphocytes treated with the CH extract, this difference was not statistically significant.

3.4. Effect of ACE, CH and DCM Extracts on CBMN-Cyt Assay Parameters and CREST Staining

The effects of ACE, CH, and DCM extracts on various CBMN-Cyt assay parameters, including MNBN, MNMONO, NBUD, NPB, and nuclear division index (NDI), were evaluated in human lymphocytes (Table 2). As extract concentrations increased, so did the frequency of MNBN for all three extracts with the strongest effects observed at higher concentrations. For the ACE extract, a concentration of 0.34 mM resulted in a significant increase in MNBN (7.53 ± 8.25, p < 0.05) and MNMONO (0.96 ± 1.95, p < 0.05) compared to the negative control. Similarly, for the CH extract, a concentration of 0.94 mM showed a significant increase in MNBN (6.59 ± 4.63, p < 0.05) and MNMONO (0.76 ± 1.69, p < 0.05). The DCM extract at 0.52 mM also resulted in significant increases in MNBN (7.15 ± 7.67, p < 0.05), MNMONO (0.68 ± 1.28, p < 0.05), and NPB (0.40 ± 1.02, p < 0.05) compared to the negative control. These results were consistent with the positive control (MMS at 1 mM), which showed significant increases across all categories. Additionally, as the extract concentrations increased, the NDI decreased, indicating both cytotoxic and genotoxic effects in the exposed lymphocytes.
The analysis of CREST+ and CREST- micronuclei in human lymphocytes exposed to varying concentrations of ACE, CH, and DCM extracts revealed aneuploidy-inducing activities at specific concentrations of ACE and DCM. In lymphocytes treated with the ACE extract, a dose-dependent increase in CREST+ micronuclei were observed, reaching its peak at 0.360 µM, indicating significant aneugenic activity. Similarly, the DCM extract showed a marked increase in CREST+ micronuclei at the same concentration (0.360 µM), suggesting a strong potential to induce aneuploidy. In contrast, the CH extract showed only moderate aneugenic activity, with slight increases in CREST+ micronuclei at 0.009 µM and 0.180 µM, and no significant increase at 0.360 µM compared to the negative control. These findings highlight the greater capacity of the ACE and DCM extracts to induce chromosomal alterations in human lymphocytes compared to the CH extract (Figure 5).

4. Discussion

Our results suggest that the chemical composition of the ACE, CH, and DCM extracts reflects the specific environmental conditions of the coal mining area. Contrary to initial expectations, the absence of PAHs indicates that coal mining activities may not be a direct source of PAHs in this environment. Instead, the presence of modified hydrocarbons and alkenes, particularly in the ACE extracts, points to ongoing combustion processes, possibly linked to spontaneous coal combustion, as suggested in the literature [53].
The detection of Oxy-PAHs in the ACE extracts, associated with PM2.5, highlights the potential health risks posed by these compounds, which are known to be more toxic than their parent PAHs [54,55]. This finding aligns with studies that indicate Oxy-PAHs formation through primary fuel combustion and subsequent reactions with atmospheric pollutants such as ozone (O3) and nitrogen oxides (NOx) [56]. The environmental conditions in La Guajira—marked by high temperatures and intense solar radiation—likely accelerate the formation of Oxy-PAHs, as demonstrated by other studies [57,58].
Furthermore, the detection of phthalates in the CH extracts raises additional concerns due to their widespread presence in the environment and their potential to cause DNA damage and disrupt reproductive hormone levels [59,60,61]. These findings underscore the complexity of pollutant profiles in coal mining areas and highlight the need for comprehensive monitoring and regulation, particularly with regard to the composition of PM2.5, to mitigate potential health risks.
The results of the cytotoxicity assays indicate that the ACE extract demonstrates the highest cytotoxic potential among the three solvents tested, which is followed by DCM and CH. This finding is consistent with previous studies that have proposed a direct correlation between the polarity of the extracting solvents and their cytotoxic effects [62,63]. Highly polar extracts, such as those obtained with ACE, tend to contain nitroaromatics, aromatic amines, and aromatic ketones. These compounds are formed in the atmosphere when organic substances, even those that are non-mutagenic, are exposed to NOx and sunlight [64]. The increased cytotoxicity observed with more polar solvents like ACE and DCM may be attributed to their enhanced ability to extract compounds that amplify the uptake and retention of fine particles within the respiratory system, potentially leading to more severe adverse effects [63]. This highlights the importance of considering solvent polarity in toxicological studies, as it significantly influences the types of compounds extracted and their subsequent biological effects. The findings emphasize the need for further research into the mechanisms driving these effects and the implications for human health, particularly in regions with high levels of atmospheric pollution.
All extracts—ACE, CH, and DCM—induced genotoxic effects in human lymphocyte cultures, as evidenced by the increased % Tail DNA. The higher genotoxicity observed with ACE and DCM extracts aligns with the detection of benzene derivatives and Oxy-PAHs, which are compounds known for their DNA-damaging properties [65,66,67]. These findings are supported by previous research showing that such compounds, particularly when associated with inorganic materials like nitrate and trace metals, can generate ROS, leading to oxidative stress and subsequent DNA damage [68,69].
The use of the Comet assay with FPG and ENDOIII endonucleases provided further insight into the nature of the oxidative damage, showing that the ACE extract induced a significant increase in oxidized purines and pyrimidines compared to the control. Although DCM and CH also induced oxidative damage, the effects were comparable and less pronounced than those of ACE, particularly in the ENDOIII treatment [70,71].
The connection between oxidative stress and carcinogenesis, highlighted by biomarkers such as 8-hydroxy-2′-deoxyguanosine (8-OHdG), underscores the potential long-term health risks associated with exposure to these environmental contaminants [72]. The correlation between FPG-Comet assay changes and cellular ROS, as noted by Zhao et al. [73], further emphasizes the relevance of these findings in understanding the genotoxic and oxidative mechanisms at play. For biomonitoring purposes, the enzymatic detection of DNA oxidized products via the comet assay offers a practical and informative approach to assessing environmental genotoxicity [74].
The results of the CBMN-Cyt assay indicate that although no significant cytostatic effects were observed, there was notable cytogenetic instability at higher concentrations of ACE and DCM extracts. The significant increase in MNMONO frequencies suggests that these extracts may induce genetic damage, leading to cells failing to complete division. This finding aligns with previous studies that have emphasized the utility of MNMONO in biomonitoring due to its effectiveness in detecting in vivo genetic damage [75,76].
The overall results demonstrate a clear dose–response relationship in the genotoxic effects of ACE, CH, and DCM extracts on human lymphocytes. Specifically, there was a significant increase in the frequency of MNBN, MNMONO, and NPB as the extract concentrations increased. These findings are consistent with the effects observed in the positive control (MMS at 1 mM), indicating that the compounds present in these extracts possess significant genotoxic potential [12,13]. Notably, the DCM extract elevated the frequency of MNBN and MNMONO and induced an increase in NPB, suggesting a broader and more severe genotoxic profile for the compounds extracted with this solvent [77,78].
The observed decrease in the NDI with increasing extract concentrations suggests cytotoxic effects, further highlighting the dual risk these compounds pose to cellular health [79,80]. These findings emphasize the importance of considering both cytotoxic and genotoxic effects when assessing the impact of environmental pollutants, especially in high-exposure areas like industrial and mining zones [11,20]. The variability in effects between different solvents indicates that solvent polarity plays a crucial role in their ability to mobilize and extract specific genotoxic compounds, affecting the type and severity of the genetic damage observed [81].
These findings underscore the need for further research to identify the specific compounds responsible for the observed damage and to elucidate the underlying mechanisms. Moreover, the biological and environmental relevance of these results is significant, as human exposure to these solvents or similar compounds could have serious public health implications, especially for vulnerable populations [82,83]. Future studies should focus on assessing these extracts in other biological models and under chronic exposure conditions to obtain a more comprehensive understanding of their risks.
The detection of CREST+ MN at the highest concentrations of ACE and DCM extracts suggests that these extracts contain substances capable of inducing aneuploidy. This indicates potential disturbances during mitosis, which are possibly due to centromere or kinetochore malfunction or disruption of the mitotic spindle, microtubule assembly, or centrosome [84,85]. The polar and moderately polar characteristics of the substances in these extracts, including oxides and metals such as S, Cr, and Cu, could contribute to these effects. Studies have shown that Cr (VI), in particular, is aneugenic as measured by both chromosome assays and centromere-positive micronuclei assays [86].
Overall, the findings underscore the importance of including both MNBN and MNMONO in biomonitoring studies to capture a broad spectrum of genetic damage indicators. The ability of ACE and DCM extracts to induce aneuploidy and cytogenetic instability points to the need for further investigation into the specific components of these extracts and their potential health implications.

5. Conclusions

Our findings demonstrate that the PM2.5 organic fraction collected from the open-pit coal mining area in northern Colombia is primarily composed of modified hydrocarbons and volatile organic compounds, particularly alkenes. The study provides evidence of the cytotoxic and genotoxic effects of these fractions on human lymphocytes, as shown through the significant increases in micronuclei formation, NPB, and oxidative DNA damage, particularly from the ACE and DCM extracts. The Comet and CBMN assays, combined with the use of FPG and ENDO III enzymes, highlighted the oxidative DNA damage caused by these polar fractions. Moreover, the CREST immunostaining confirmed the aneugenic potential of the ACE and DCM extracts, underlining their capacity to induce chromosomal instability.
These results underscore the importance of monitoring the chemical composition of PM2.5 in mining regions to assess potential health risks. The presence of harmful compounds, such as metals and modified PAHs, calls for stricter environmental regulations and public health interventions to minimize exposure. Future studies should focus on identifying the specific compounds responsible for the observed damage and further exploring the mechanisms driving the toxic effects.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/atmos15121420/s1, Figure S1. Results for extractable organic matter (EOM) determination described for sampling sites. Solvents used in Soxhlet extraction are described using colors and acronyms. ACE (acetone); DCM (dichloromethane); CH (cyclohexane).

Author Contributions

Conceptualization, methodology, and supervision, L.E.-P.; C.G.-P., K.P.-S. and P.E.-P.; investigation and methodology, R.E.-S., G.A.-A., E.L.-V., D.S.C. and B.D.-P.; data curation A.H.-Á. and E.L.-V.; software, formal analysis and visualization H.B., D.R.-C. and A.C.-P.; writing—review and editing L.E.-P., J.d.S. and A.P.-T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by COLCIENCIAS/Colombia (Grant number 751/2013), Universidad del Sinú/Colombia (UNISINU), Universidad del Cauca/Colombia (UNICAUCA), Universidad Luterana do Brasil (ULBRA) and Universidade Federal do Rio Grande do Sul/Brasil (UFRGS).

Institutional Review Board Statement

The Ethics Committee of the University of Sinú approved all the protocols described in this study (Act 002/2012). In addition, authorization and participation in the survey were obtained from the governors of each resguardo, community representatives, and municipal authorities.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available in the article.

Acknowledgments

The authors thank the National Board of SINTRACARBÓN for their logistical support, CORPOGUAJIRA for their technical assistance during sampling, and the communities of Provincial, San Francisco, Chancleta, Cerro de Hatonuevo, Media Luna, and Mayapo. We also thank Ofelia Olivero at Laboratory of Cancer Biology and Genetics, National Institutes of Health (NIH) for her assistance in standardizing the CREST immunostaining technique.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Turner, M.C.; Andersen, Z.J.; Baccarelli, A.; Diver, W.R.; Gapstur, S.M.; Pope, C.A., 3rd; Prada, D.; Samet, J.; Thurston, G.; Cohen, A. Outdoor air pollution and cancer: An overview of the current evidence and public health recommendations. CA Cancer J. Clin. 2020. [CrossRef]
  2. Espitia-Pérez, L.; da Silva, J.; Brango, H.; Espitia-Pérez, P.; Pastor-Sierra, K.; Salcedo-Arteaga, S.; de Souza, C.T.; Dias, J.F.; Hoyos-Giraldo, L.S.; Gómez-Pérez, M.; et al. Genetic damage in environmentally exposed populations to open-pit coal mining residues: Analysis of buccal micronucleus cytome (BMN-cyt) assay and alkaline, Endo III and FPG high-throughput comet assay. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2018, 836, 24–35. [Google Scholar] [CrossRef]
  3. Liu, Y.; Wang, R.; Zhang, Y.; Zhao, T.; Wang, J.; Wu, H.; Hu, P. Temporal and spatial distributions of particulate matters around mining areas under two coal mining methods in arid desert region of northwest China. Environ. Technol. Innov. 2020, 19, 101029. [Google Scholar] [CrossRef]
  4. Prasad, S.; Gao, C.X.; Borg, B.; Broder, J.; Brown, D.; Ikin, J.F.; Makar, A.; McCrabb, T.; Hoy, R.; Thompson, B.; et al. Chronic Obstructive Pulmonary Disease in Adults Exposed to Fine Particles from a Coal Mine Fire. Ann. Am. Thorac. Soc. 2022, 19, 186–195. [Google Scholar] [CrossRef] [PubMed]
  5. He, S.; Lu, Y.; Li, M. Probabilistic risk analysis for coal mine gas overrun based on FAHP and BN: A case study. Environ. Sci. Pollut. Res. 2022, 29, 28458–28468. [Google Scholar] [CrossRef]
  6. Cohen, A.J.; Ross Anderson, H.; Ostro, B.; Pandey, K.D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K.; Pope, A.; Romieu, I.; Samet, J.M.; et al. The global burden of disease due to outdoor air pollution. J. Toxicol. Env. Health A 2005, 68, 1301–1307. [Google Scholar] [CrossRef] [PubMed]
  7. Çakmak, G.; Ertürk Arı, P.; Emerce, E.; Arı, A.; Odabaşı, M.; Schins, R.; Burgaz, S.; Gaga, E.O. Investigation of spatial and temporal variation of particulate matter in vitro genotoxicity and cytotoxicity in relation to the elemental composition. Mutat. Res. Genet. Toxicol. Env. Mutagen. 2019, 842, 22–34. [Google Scholar] [CrossRef]
  8. Ahmad, M.; Chen, J.; Panyametheekul, S.; Yu, Q.; Nawab, A.; Khan, M.T.; Zhang, Y.; Ali, S.W.; Phairuang, W. Fine particulate matter from brick kilns site and roadside in Lahore, Pakistan: Insight into chemical composition, oxidative potential, and health risk assessment. Heliyon 2024, 10, e25884. [Google Scholar] [CrossRef]
  9. Badran, G.; Ledoux, F.; Verdin, A.; Abbas, I.; Roumie, M.; Genevray, P.; Landkocz, Y.; Lo Guidice, J.-M.; Garçon, G.; Courcot, D. Toxicity of fine and quasi-ultrafine particles: Focus on the effects of organic extractable and non-extractable matter fractions. Chemosphere 2020, 243, 125440. [Google Scholar] [CrossRef]
  10. Romano, S.; Perrone, M.R.; Becagli, S.; Pietrogrande, M.C.; Russo, M.; Caricato, R.; Lionetto, M.G. Ecotoxicity, genotoxicity, and oxidative potential tests of atmospheric PM10 particles. Atmos. Environ. 2020, 221, 117085. [Google Scholar] [CrossRef]
  11. Arregocés, H.A.; Bonivento, G.J.; Ladino, L.A.; Beristain-Montiel, E.; Restrepo, G.; Miranda, J.; Alvarez-Ospina, H.; Rojano, R. Human health risk assessment of PM10-bound heavy metals and PAHs around the Latin America’s Largest opencast coal mine. Environ. Sci. Pollut. Res. 2023, 30, 125915–125930. [Google Scholar] [CrossRef] [PubMed]
  12. Jarvis, I.W.H.; Enlo-Scott, Z.; Nagy, E.; Mudway, I.S.; Tetley, T.D.; Arlt, V.M.; Phillips, D.H. Genotoxicity of fine and coarse fraction ambient particulate matter in immortalised normal (TT1) and cancer-derived (A549) alveolar epithelial cells. Environ. Mol. Mutagen. 2018, 59, 290–301. [Google Scholar] [CrossRef]
  13. Bukowska, B.; Mokra, K.; Michałowicz, J. Benzo[a]pyrene—Environmental Occurrence, Human Exposure, and Mechanisms of Toxicity. Int. J. Mol. Sci. 2022, 23, 6348. [Google Scholar] [CrossRef]
  14. Gao, D.; Mulholland, J.A.; Russell, A.G.; Weber, R.J. Characterization of water-insoluble oxidative potential of PM2.5 using the dithiothreitol assay. Atmos. Environ. 2020, 224, 117327. [Google Scholar] [CrossRef]
  15. Valko, M.; Morris, H.; Cronin, M.T. Metals, toxicity and oxidative stress. Curr. Med. Chem. 2005, 12, 1161–1208. [Google Scholar] [CrossRef] [PubMed]
  16. Sidwell, A.; Smith, S.C.; Roper, C. A comparison of fine particulate matter (PM2.5) in vivo exposure studies incorporating chemical analysis. J. Toxicol. Env. Health B Crit. Rev. 2022, 25, 422–444. [Google Scholar] [CrossRef] [PubMed]
  17. Wu, T.; Xu, S.; Chen, B.; Bao, L.; Ma, J.; Han, W.; Xu, A.; Yu, K.N.; Wu, L.; Chen, S. Ambient PM2.5 exposure causes cellular senescence via DNA damage, micronuclei formation, and cGAS activation. Nanotoxicology 2022, 16, 757–775. [Google Scholar] [CrossRef]
  18. Li, N.; Xia, T.; Nel, A.E. The role of oxidative stress in ambient particulate matter-induced lung diseases and its implications in the toxicity of engineered nanoparticles. Free Radic. Biol. Med. 2008, 44, 1689–1699. [Google Scholar] [CrossRef]
  19. Espitia-Pérez, L.; da Silva, J.; Espitia-Pérez, P.; Brango, H.; Salcedo-Arteaga, S.; Hoyos-Giraldo, L.S.; de Souza, C.T.; Dias, J.F.; Agudelo-Castañeda, D.; Valdés Toscano, A.; et al. Cytogenetic instability in populations with residential proximity to open-pit coal mine in Northern Colombia in relation to PM10 and PM2.5 levels. Ecotoxicol. Environ. Saf. 2018, 148, 453–466. [Google Scholar] [CrossRef]
  20. Zhang, J.; Zhou, X.; Wang, Z.; Yang, L.; Wang, J.; Wang, W. Trace elements in PM2.5 in Shandong Province: Source identification and health risk assessment. Sci. Total Environ. 2018, 621, 558–577. [Google Scholar] [CrossRef]
  21. Sun, K.; Song, Y.; He, F.; Jing, M.; Tang, J.; Liu, R. A review of human and animals exposure to polycyclic aromatic hydrocarbons: Health risk and adverse effects, photo-induced toxicity and regulating effect of microplastics. Sci. Total Environ. 2021, 773, 145403. [Google Scholar] [CrossRef] [PubMed]
  22. Lin, H.; Chen, Q.; Wang, M.; Chang, T. Oxidation potential and coupling effects of the fractionated components in airborne fine particulate matter. Environ. Res. 2022, 213, 113652. [Google Scholar] [CrossRef] [PubMed]
  23. Sun, L.; Lin, Z.; Liao, K.; Xi, Z.; Wang, D. Adverse effects of coal combustion related fine particulate matter (PM2.5) on nematode Caenorhabditis elegans. Sci. Total Environ. 2015, 512–513, 251–260. [Google Scholar] [CrossRef] [PubMed]
  24. Aneja, V.P.; Isherwood, A.; Morgan, P. Characterization of particulate matter (PM10) related to surface coal mining operations in Appalachia. Atmos. Environ. 2012, 54, 496–501. [Google Scholar] [CrossRef]
  25. Longhin, E.; Holme, J.A.; Gutzkow, K.B.; Arlt, V.M.; Kucab, J.E.; Camatini, M.; Gualtieri, M. Cell cycle alterations induced by urban PM2.5 in bronchial epithelial cells: Characterization of the process and possible mechanisms involved. Part. Fibre Toxicol. 2013, 10, 63. [Google Scholar] [CrossRef]
  26. MINAMBIENTE. Resolución 2254. 2017. Available online: https://www.minambiente.gov.co/documento-normativa/resolucion-2554-de-2017/ (accessed on 22 June 2024).
  27. Sun, J.; Fang, R.; Wang, H.; Xu, D.-X.; Yang, J.; Huang, X.; Cozzolino, D.; Fang, M.; Huang, Y. A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics. Environ. Int. 2022, 158, 106941. [Google Scholar] [CrossRef]
  28. Kumbıçak, Ü.; Çavaş, T.; Çinkılıç, N.; Kumbıçak, Z.; Vatan, Ö.; Yılmaz, D. Evaluation of in vitro cytotoxicity and genotoxicity of copper–zinc alloy nanoparticles in human lung epithelial cells. Food Chem. Toxicol. 2014, 73, 105–112. [Google Scholar] [CrossRef]
  29. Ulloa, A. The rights of the Wayúu people and water in the context of mining in La Guajira, Colombia: Demands of relational water justice. Hum. Geogr. 2020, 13, 6–15. [Google Scholar] [CrossRef]
  30. Espitia-Pérez, L.; Arteaga–Pertuz, M.; Soto, J.S.; Espitia-Pérez, P.; Salcedo-Arteaga, S.; Pastor–Sierra, K.; Galeano–Páez, C.; Brango, H.; da Silva, J.; Henriques, J.A.P. Geospatial analysis of residential proximity to open-pit coal mining areas in relation to micronuclei frequency, particulate matter concentration, and elemental enrichment factors. Chemosphere 2018, 206, 203–216. [Google Scholar] [CrossRef]
  31. Deluquez, E.P. Cooperación Internacional y derechos humanos frente a la minería en Colombia. Rev. Int. Coop. Y Desarro. 2015, 2, 125–152. [Google Scholar]
  32. Berridge, M.V.; Herst, P.M.; Tan, A.S. Tetrazolium dyes as tools in cell biology: New insights into their cellular reduction. In Biotechnology Annual Review; Elsevier: Amsterdam, The Netherlands, 2005; Volume 11, pp. 127–152. [Google Scholar]
  33. Azqueta, A.; Langie, S.A.S.; Boutet-Robinet, E.; Duthie, S.; Ladeira, C.; Møller, P.; Collins, A.R.; Godschalk, R.W.L. DNA repair as a human biomonitoring tool: Comet assay approaches. Mutat. Res. Rev. Mutat. Res. 2019, 781, 71–87. [Google Scholar] [CrossRef] [PubMed]
  34. Londoño-Velasco, E.; Martínez-Perafán, F.; Carvajal-Varona, S.; García-Vallejo, F.; Hoyos-Giraldo, L.S. Assessment of DNA damage in car spray painters exposed to organic solvents by the high-throughput comet assay. Toxicol. Mech. Methods 2016, 26, 238–242. [Google Scholar] [CrossRef] [PubMed]
  35. Henderson, L.; Wolfreys, A.; Fedyk, J.; Bourner, C.; Windebank, S. The ability of the Comet assay to discriminate between genotoxins and cytotoxins. Mutagenesis 1998, 13, 89–94. [Google Scholar] [CrossRef] [PubMed]
  36. Kryston, T.B.; Georgiev, A.B.; Pissis, P.; Georgakilas, A.G. Role of oxidative stress and DNA damage in human carcinogenesis. Mutat. Res. Fundam. Mol. Mech. Mutagen. 2011, 711, 193–201. [Google Scholar] [CrossRef]
  37. Olive, P.L.; Banáth, J.P. The comet assay: A method to measure DNA damage in individual cells. Nat. Protoc. 2006, 1, 23–29. [Google Scholar] [CrossRef]
  38. Muruzabal, D.; Collins, A.; Azqueta, A. The enzyme-modified comet assay: Past, present and future. Food Chem. Toxicol. 2021, 147, 111865. [Google Scholar] [CrossRef]
  39. Fenech, M. Cytokinesis-block micronucleus cytome assay. Nat. Protoc. 2007, 2, 1084–1104. [Google Scholar] [CrossRef]
  40. Pastor-Sierra, K.; Espitia-Pérez, L.; Espitia-Pérez, P.; Peñata-Taborda, A.; Brango, H.; Galeano-Páez, C.; Bru-Cordero, O.E.; Palma-Parra, M.; Díaz, S.M.; Trillos, C.; et al. Micronuclei frequency and exposure to chemical mixtures in three Colombian mining populations. Sci. Total Environ. 2023, 901, 165789. [Google Scholar] [CrossRef]
  41. Fenech, M.; Chang, W.P.; Kirsch-Volders, M.; Holland, N.; Bonassi, S.; Zeiger, E. HUMN project: Detailed description of the scoring criteria for the cytokinesis-block micronucleus assay using isolated human lymphocyte cultures. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2003, 534, 65–75. [Google Scholar] [CrossRef]
  42. Daisey, J.M. ORGANIC COMPOUNDS IN URBAN AEROSOLS. Ann. New York Acad. Sci. 1980, 338, 50–69. [Google Scholar] [CrossRef]
  43. Picot, A. Exploration and Exploitation of Shale Gas and Shale Oil (Parent Rock Hydrocarbon) by Fracking; Toxicology–Chemistry Association: Brussels Belgium, 2012. [Google Scholar]
  44. Wang, X. Fine Particle and Mercury Formation and Control During Coal Combustion; Washington University: St. Louis, MO, USA, 2014. [Google Scholar]
  45. Wu, Q.; Su, Q.; Simpson, S.L.; Tan, Q.G.; Chen, R.; Xie, M. Isotopically Modified Bioassay Bridges the Bioavailability and Toxicity Risk Assessment of Metals in Bedded Sediments. Env. Sci. Technol. 2022, 56, 16919–16928. [Google Scholar] [CrossRef] [PubMed]
  46. Meepage, J.N.; Welker, J.K.; Meyer, C.M.; Mohammadi, S.; Stanier, C.O.; Stone, E.A. Advances in the Separation and Detection of Secondary Organic Aerosol Produced by Decamethylcyclopentasiloxane (D(5)) in Laboratory-Generated and Ambient Aerosol. ACS EST Air 2024, 1, 365–375. [Google Scholar] [CrossRef] [PubMed]
  47. Walgraeve, C.; Demeestere, K.; Dewulf, J.; Zimmermann, R.; Van Langenhove, H. Oxygenated polycyclic aromatic hydrocarbons in atmospheric particulate matter: Molecular characterization and occurrence. Atmos. Environ. 2010, 44, 1831–1846. [Google Scholar] [CrossRef]
  48. Allen, J.O.; Dookeran, N.M.; Smith, K.A.; Sarofim, A.F.; Taghizadeh, K.; Lafleur, A.L. Measurement of Polycyclic Aromatic Hydrocarbons Associated with Size-Segregated Atmospheric Aerosols in Massachusetts. Environ. Sci. Technol. 1996, 30, 1023–1031. [Google Scholar] [CrossRef]
  49. Lammel, G. Polycyclic Aromatic Compounds in the Atmosphere—A Review Identifying Research Needs. Polycycl. Aromat. Compd. 2015, 35, 316–329. [Google Scholar] [CrossRef]
  50. Dasgupta, S.; Cao, A.; Mauer, B.; Yan, B.; Uno, S.; McElroy, A. Genotoxicity of oxy-PAHs to Japanese medaka (Oryzias latipes) embryos assessed using the comet assay. Environ. Sci. Pollut. Res. 2014, 21, 13867–13876. [Google Scholar] [CrossRef]
  51. Zhang, W.; Wei, C.; Yan, B.; Feng, C.; Zhao, G.; Lin, C.; Yuan, M.; Wu, C.; Ren, Y.; Hu, Y. Identification and removal of polycyclic aromatic hydrocarbons in wastewater treatment processes from coke production plants. Environ. Sci. Pollut. Res. 2013, 20, 6418–6432. [Google Scholar] [CrossRef]
  52. Guntupalli, S.; Thunuguntla, V.; Reddy, K.S.; Newton, M.I.; Rao, C.; Bondili, J. Enhanced degradation of carcinogenic PAHs benzo (a) pyrene and benzo (k) fluoranthene by a microbial consortia. Indian. J. Sci. Technol. 2016, 9, 35. [Google Scholar] [CrossRef]
  53. Net, S.; Sempéré, R.; Delmont, A.; Paluselli, A.; Ouddane, B. Occurrence, Fate, Behavior and Ecotoxicological State of Phthalates in Different Environmental Matrices. Environ. Sci. Technol. 2015, 49, 4019–4035. [Google Scholar] [CrossRef]
  54. Wang, Q.; Wang, L.; Li, X.; Xin, J.; Liu, Z.; Sun, Y.; Liu, J.; Zhang, Y.; Du, W.; Jin, X.; et al. Emission characteristics of size distribution, chemical composition and light absorption of particles from field-scale crop residue burning in Northeast China. Sci. Total Environ. 2020, 710, 136304. [Google Scholar] [CrossRef]
  55. Han, Y.; Chen, Y.; Feng, Y.; Song, W.; Cao, F.; Zhang, Y.; Li, Q.; Yang, X.; Chen, J. Different formation mechanisms of PAH during wood and coal combustion under different temperatures. Atmos. Environ. 2020, 222, 117084. [Google Scholar] [CrossRef]
  56. Hill, S.C.; Douglas Smoot, L. Modeling of nitrogen oxides formation and destruction in combustion systems. Prog. Energy Combust. Sci. 2000, 26, 417–458. [Google Scholar] [CrossRef]
  57. Han, M.; Kong, J.; Yuan, J.; He, H.; Hu, J.; Yang, S.; Li, S.; Zhang, L.; Sun, C. Method development for simultaneous analyses of polycyclic aromatic hydrocarbons and their nitro-, oxy-, hydroxy- derivatives in sediments. Talanta 2019, 205, 120128. [Google Scholar] [CrossRef] [PubMed]
  58. Lara, S.; Villanueva, F.; Cabañas, B.; Sagrario, S.; Aranda, I.; Soriano, J.; Martin, P. Determination of policyclic aromatic compounds,(PAH, nitro-PAH and oxy-PAH) in soot collected from a diesel engine operating with different fuels. Sci. Total Environ. 2023, 900, 165755. [Google Scholar] [CrossRef] [PubMed]
  59. Rocha, B.A.; Asimakopoulos, A.G.; Barbosa Jr, F.; Kannan, K. Urinary concentrations of 25 phthalate metabolites in Brazilian children and their association with oxidative DNA damage. Sci. Total Environ. 2017, 586, 152–162. [Google Scholar] [CrossRef]
  60. Gurdemir, G.; Erkekoglu, P.; Balci, A.; Sur, U.; Ozkemahli, G.; Tutkun, E.; Yilmaz, H.; Asci, A.; Kocer-Gumusel, B. Oxidative stress parameters, selenium levels, DNA damage, and phthalate levels in plastic workers. J. Environ. Pathol. Toxicol. Oncol. 2019, 38, 253–270. [Google Scholar] [CrossRef]
  61. Sedha, S.; Lee, H.; Singh, S.; Kumar, S.; Jain, S.; Ahmad, A.; Bin Jardan, Y.A.; Sonwal, S.; Shukla, S.; Simal-Gandara, J.; et al. Reproductive toxic potential of phthalate compounds—State of art review. Pharmacol. Res. 2021, 167, 105536. [Google Scholar] [CrossRef]
  62. Velali, E.; Papachristou, E.; Pantazaki, A.; Besis, A.; Samara, C.; Labrianidis, C.; Lialiaris, T. In vitro cellular toxicity induced by extractable organic fractions of particles exhausted from urban combustion sources—Role of PAHs. Environ. Pollut. 2018, 243, 1166–1176. [Google Scholar] [CrossRef] [PubMed]
  63. Thepnuan, D.; Yabueng, N.; Chantara, S.; Prapamontol, T.; Tsai, Y.I. Simultaneous determination of carcinogenic PAHs and levoglucosan bound to PM2.5 for assessment of health risk and pollution sources during a smoke haze period. Chemosphere 2020, 257, 127154. [Google Scholar] [CrossRef]
  64. Krug, J.D.; Riedel, T.P.; Lewandowski, M.; Lonneman, W.A.; Turlington, J.M.; Zavala, J.; Warren, S.H.; Kleindienst, T.E.; DeMarini, D.M. Mutagenic atmospheres generated from the photooxidation of NOx with selected VOCs and a complex mixture: Apportionment of aromatic mutagenicity for reacted gasoline vapor. Atmos. Environ. 2024, 334, 120668. [Google Scholar] [CrossRef]
  65. Libalova, H.; Zavodna, T.; Elzeinova, F.; Barosova, H.; Cervena, T.; Milcova, A.; Vankova, J.; Paradeisi, F.; Vojtisek-Lom, M.; Sikorova, J. The Genotoxicity of Organic Extracts from Particulate Emissions Produced by Neat Gasoline (E0) and a Gasoline–Ethanol Blend (E15) in BEAS-2B Cells. J. Xenobiotics 2023, 14, 1–14. [Google Scholar] [CrossRef] [PubMed]
  66. Kenneth, S.K.; Nneka, O.F.; Kalu, A.K.; Joseph, A.O. Radioprotective Potencies of Allium Cepa Extract (ACE) against Radiation-Induced Hepatoxicity in Wistar Rats. Int. J. Med. Phys. Clin. Eng. Radiat. Oncol. 2023, 12, 59–83. [Google Scholar] [CrossRef]
  67. Molinelli, A.R.; Santacana, G.E.; Madden, M.C.; Jiménez, B.D. Toxicity and metal content of organic solvent extracts from airborne particulate matter in Puerto Rico. Environ. Res. 2006, 102, 314–325. [Google Scholar] [CrossRef] [PubMed]
  68. Valko, M.; Rhodes, C.J.; Moncol, J.; Izakovic, M.; Mazur, M. Free radicals, metals and antioxidants in oxidative stress-induced cancer. Chem. Biol. Interact. 2006, 160, 1–40. [Google Scholar] [CrossRef] [PubMed]
  69. Wang, C.; Liu, X.; Zhai, J.; Zhong, C.; Zeng, H.; Feng, L.; Yang, Y.; Li, X.; Ma, M.; Luan, T. Effect of oxidative stress induced by 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin on DNA damage. J. Hazard. Mater. 2024, 472, 134485. [Google Scholar] [CrossRef]
  70. Collins, A.R. Measuring oxidative damage to DNA and its repair with the comet assay. Biochim. Biophys. Acta (BBA)-Gen. Subj. 2014, 1840, 794–800. [Google Scholar] [CrossRef]
  71. Zhao, J.; Li, H.; Zhai, Q.; Qiu, Y.; Niu, Y.; Dai, Y.; Zheng, Y.-x.; Duan, H. [Endonuclease modified comet assay for oxidative DNA damage induced by detection of genetic toxicants]. Zhonghua yu fang yi xue za zhi [Chin. J. Prev. Med.] 2014, 48, 208–212. [Google Scholar]
  72. Valavanidis, A.; Vlachogianni, T.; Fiotakis, C. 8-hydroxy-2′ -deoxyguanosine (8-OHdG): A critical biomarker of oxidative stress and carcinogenesis. J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 2009, 27, 120–139. [Google Scholar] [CrossRef]
  73. Duan, H.; Jia, X.; Zhai, Q.; Ma, L.; Wang, S.; Huang, C.; Wang, H.; Niu, Y.; Li, X.; Dai, Y.; et al. Long-term exposure to diesel engine exhaust induces primary DNA damage: A population-based study. Occup. Environ. Med. 2016, 73, 83–90. [Google Scholar] [CrossRef]
  74. ESCODD; Gedik, C.M.; Collins, A. Establishing the background level of base oxidation in human lymphocyte DNA: Results of an interlaboratory validation study. FASEB J. 2005, 19, 82–84. [Google Scholar] [CrossRef]
  75. Espitia-Pérez, L.; Jiménez-Vidal, L.; Espitia-Pérez, P. Particulate matter exposure: Genomic instability, disease, and cancer risk. In Environmental Health: Management and Prevention Practices; Makan, A., Ed.; IntechOpen: London, UK, 2020; Volume 13, p. 126. [Google Scholar]
  76. Cao, X.; Padoan, S.; Binder, S.; Bauer, S.; Orasche, J.; Rus, C.-M.; Mudan, A.; Huber, A.; Kuhn, E.; Oeder, S. A comparative study of persistent DNA oxidation and chromosomal instability induced in vitro by oxidizers and reference airborne particles. Mutat. Res./Genet. Toxicol. Environ. Mutagen. 2022, 874, 503446. [Google Scholar] [CrossRef] [PubMed]
  77. Li, A.J.; Pal, V.K.; Kannan, K. A review of environmental occurrence, toxicity, biotransformation and biomonitoring of volatile organic compounds. Environ. Chem. Ecotoxicol. 2021, 3, 91–116. [Google Scholar] [CrossRef]
  78. Sisto, R.; Cavallo, D.; Ursini, C.L.; Fresegna, A.M.; Ciervo, A.; Maiello, R.; Paci, E.; Pigini, D.; Gherardi, M.; Gordiani, A. Direct and oxidative DNA damage in a group of painters exposed to VOCs: Dose–response relationship. Front. Public Health 2020, 8, 445. [Google Scholar] [CrossRef]
  79. Garcia, A.L.; Picinini, J.; Silveira, M.D.; Camassola, M.; Visentim, A.P.; Salvador, M.; da Silva, J. Environmental Mutagenesis. Mutat. Res.-Genet. Toxicol. Environ. Mutagen. 2021, 861, 503297. [Google Scholar] [CrossRef] [PubMed]
  80. Ladeira, C.; Araújo, R.; Ramalhete, L.; Teixeira, H.; Calado, C.R. The use of effect biomarkers in chemical mixtures risk assessment—Are they still important? Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2024, 896, 503768. [Google Scholar] [CrossRef] [PubMed]
  81. Silva, T.D.; Alves, C.; Oliveira, H.; Duarte, I.F. Biological Impact of Organic Extracts from Urban-Air Particulate Matter: An In Vitro Study of Cytotoxic and Metabolic Effects in Lung Cells. Int. J. Mol. Sci. 2023, 24, 16896. [Google Scholar] [CrossRef]
  82. Abdel-Shafy, H.I.; Mansour, M.S. A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation. Egypt. J. Pet. 2016, 25, 107–123. [Google Scholar] [CrossRef]
  83. Soni, V.; Singh, P.; Shree, V.; Goel, V. Effects of VOCs on human health. In Air Pollution and Control; Sharma, N., Ed.; Springer: Singapore, 2018; pp. 119–142. [Google Scholar]
  84. Luzhna, L.; Kathiria, P.; Kovalchuk, O. Micronuclei in genotoxicity assessment: From genetics to epigenetics and beyond. Front. Genet. 2013, 4, 131. [Google Scholar] [CrossRef]
  85. Kirsch-Volders, M.; Bonassi, S.; Knasmueller, S.; Holland, N.; Bolognesi, C.; Fenech, M.F. Commentary: Critical questions, misconceptions and a road map for improving the use of the lymphocyte cytokinesis-block micronucleus assay for in vivo biomonitoring of human exposure to genotoxic chemicals—A HUMN project perspective. Mutat. Res. Rev. Mutat. Res. 2014, 759, 49–58. [Google Scholar] [CrossRef]
  86. García-Rodríguez, M.D.C.; Santiago-Moreno, Y.; Molina-Alvarez, B.; Altamirano-Lozano, M. Evaluation of clastogenic/aneugenic damage using the fish micronucleus assay in mice exposed to chromium (VI). In Vivo 2023, 37, 1666–1671. [Google Scholar] [CrossRef]
Figure 1. Study area and sampling sites. (a) Open pit coal-mining areas, (b) Puerto Bolívar coal terminal, (c) general location of the study area.
Figure 1. Study area and sampling sites. (a) Open pit coal-mining areas, (b) Puerto Bolívar coal terminal, (c) general location of the study area.
Atmosphere 15 01420 g001
Figure 2. A log-linear plot of the lymphocyte cell cytotoxicity of ACE, CH and DCM extracts. The concentration was considered cytotoxic when cell survival was 50%. The results are shown as the mean of three independent experiments.
Figure 2. A log-linear plot of the lymphocyte cell cytotoxicity of ACE, CH and DCM extracts. The concentration was considered cytotoxic when cell survival was 50%. The results are shown as the mean of three independent experiments.
Atmosphere 15 01420 g002
Figure 3. SCGE analysis of ACE, CH and DCM extracts in lymphocytes. (A) Acute toxicity of lymphocytes from treatment groups used in the SCGE assay. (B) Log-linear plot of the genotoxic concentration–response curves. H2O2 was used as the positive control.
Figure 3. SCGE analysis of ACE, CH and DCM extracts in lymphocytes. (A) Acute toxicity of lymphocytes from treatment groups used in the SCGE assay. (B) Log-linear plot of the genotoxic concentration–response curves. H2O2 was used as the positive control.
Atmosphere 15 01420 g003
Figure 4. Analysis of the oxidative damage caused by ACE, CH and DCM extracts exposure in lymphocytes after treatment with ENDOIII and FPG enzymes in the modified SCGE. Right (Upper panel) (A) Acute toxicity of lymphocytes from treatment groups used in the SCGE assay. Right (Lower panel) (B) Log-linear plot of the genotoxic concentration–response curves. H2O2 was used as the positive control.
Figure 4. Analysis of the oxidative damage caused by ACE, CH and DCM extracts exposure in lymphocytes after treatment with ENDOIII and FPG enzymes in the modified SCGE. Right (Upper panel) (A) Acute toxicity of lymphocytes from treatment groups used in the SCGE assay. Right (Lower panel) (B) Log-linear plot of the genotoxic concentration–response curves. H2O2 was used as the positive control.
Atmosphere 15 01420 g004
Figure 5. Percentages of CREST+/CREST- micronuclei induced by exposure to ACE, CH and DCM extracts. * Significant difference in relation to the negative control; p ≤ 0.05; ** Significant difference in relation to the negative control; p ≤ 0.01.
Figure 5. Percentages of CREST+/CREST- micronuclei induced by exposure to ACE, CH and DCM extracts. * Significant difference in relation to the negative control; p ≤ 0.05; ** Significant difference in relation to the negative control; p ≤ 0.01.
Atmosphere 15 01420 g005
Table 1. Main chemical constituents of ACE, CH and DCM extracts in coal mining areas determined by GC/MS.
Table 1. Main chemical constituents of ACE, CH and DCM extracts in coal mining areas determined by GC/MS.
ExtractFormulaNameCAS NumberChemical Structure
ACEC16H321-Hexadecene629-73-2Atmosphere 15 01420 i001
C17H32OE-15-Heptadecenal700381-35-7Atmosphere 15 01420 i002
C9H181-Nonene124-11-8Atmosphere 15 01420 i003
C13H263—Tridecene (Z)41446-53-1Atmosphere 15 01420 i004
C12H241-Dodecene112-41-4Atmosphere 15 01420 i005
C13H10OBenzophenone119-61-9Atmosphere 15 01420 i006
C18H361-Octadecene112-88-9Atmosphere 15 01420 i007
DCMC15H242,4,6-triisopropylbenzene717-74-8Atmosphere 15 01420 i008
C16H48O10Si9Cyclopentasiloxane145344-72-5Atmosphere 15 01420 i009
C17H36O1-Heptadecanol1454-85-9Atmosphere 15 01420 i010
CHC23H461-Tricosene18835-32-0Atmosphere 15 01420 i011
C24H38O4Di-n-octyl phthalate117-84-0Atmosphere 15 01420 i012
C22H44O1-Docosene1599-67-3Atmosphere 15 01420 i013
Table 2. CBMN-Cyt parameters frequencies after ACE, CH and DCM exposure in lymphocytes.
Table 2. CBMN-Cyt parameters frequencies after ACE, CH and DCM exposure in lymphocytes.
Extract ConcentrationsMNBN MNMONONBUDNPBNDI
(mM)
ACE02.52 ± 2.12 0.05 ± 0.240.80 ± 1.600.10 ± 0.312.13 ± 0.14
0.054.34 ± 1.670.86 ± 0.09 0.55 ± 0.330.28 ± 0.622.33 ± 0.11
0.084.45 ± 3.670.98 ± 0.16 0.35 ± 0.780.31 ± 0.212.05 ± 0.08
0.176.30 ± 6.230.33 ± 0.710.43 ± 0.940.53 ± 1.541.63 ± 0.28
0.347.53 ± 8.25 *0.96 ± 1.95 *0.68 ± 1.110.56 ± 1.981.61 ± 0.45
CH01.41 ± 0.28 0.15 ± 0.360.43 ± 0.14 0.17 ± 0.892.16 ± 0.11
0.052.13 ± 0.84 0.19 ± 0.610.51 ± 0.40 0.19 ± 0.512.0 ± 0.31
0.233.16 ± 0.520.18 ± 0.590.48 ± 0.950.22 ± 0.541.68 ± 0.18
0.474.28 ± 2.560.21 ± 0.610.60 ± 1.060.33 ± 0.661.62 ± 0.28
0.946.59 ± 4.63 *0.76 ± 1.69 *0.83 ± 0.38 0.65 ± 0.171.52 ± 0.15
DCM02.08 ± 0.38 0.50 ± 0.40 0.61 ± 0.17 0.20 ± 0.562.0 ± 0.09
0.052.68 ± 0.30 0.49 ± 0.23 0.55 ± 0.65 0.33 ± 0.621.87 ± 0.13
0.134.75 ± 3.720.47 ± 1.07 0.67 ± 0.780.09 ± 0.301.96 ± 0.78
0.265.54 ± 3.980.55 ± 1.650.70 ± 0.13 0.95 ± 0.22 1.70 ± 0.19
0.527.15 ± 7.67 *0.68 ± 1.28 *0.90 ± 0.350.40 ± 1.02 *1.69 ± 0.20
Positive control MMS18.15 ± 1.67 *1.15 ± 0.96 *1.35 ± 1.78 *1.23 ± 0.80 *1.30 ± 0.11*
* Significant difference compared with the negative control group within a column. p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Galeano-Páez, C.; Brango, H.; Pastor-Sierra, K.; Coneo-Pretelt, A.; Arteaga-Arroyo, G.; Peñata-Taborda, A.; Espitia-Pérez, P.; Ricardo-Caldera, D.; Humanez-Álvarez, A.; Londoño-Velasco, E.; et al. Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM2.5) from an Open-Cast Coal Mining Area. Atmosphere 2024, 15, 1420. https://doi.org/10.3390/atmos15121420

AMA Style

Galeano-Páez C, Brango H, Pastor-Sierra K, Coneo-Pretelt A, Arteaga-Arroyo G, Peñata-Taborda A, Espitia-Pérez P, Ricardo-Caldera D, Humanez-Álvarez A, Londoño-Velasco E, et al. Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM2.5) from an Open-Cast Coal Mining Area. Atmosphere. 2024; 15(12):1420. https://doi.org/10.3390/atmos15121420

Chicago/Turabian Style

Galeano-Páez, Claudia, Hugo Brango, Karina Pastor-Sierra, Andrés Coneo-Pretelt, Gean Arteaga-Arroyo, Ana Peñata-Taborda, Pedro Espitia-Pérez, Dina Ricardo-Caldera, Alicia Humanez-Álvarez, Elizabeth Londoño-Velasco, and et al. 2024. "Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM2.5) from an Open-Cast Coal Mining Area" Atmosphere 15, no. 12: 1420. https://doi.org/10.3390/atmos15121420

APA Style

Galeano-Páez, C., Brango, H., Pastor-Sierra, K., Coneo-Pretelt, A., Arteaga-Arroyo, G., Peñata-Taborda, A., Espitia-Pérez, P., Ricardo-Caldera, D., Humanez-Álvarez, A., Londoño-Velasco, E., Espinosa-Sáez, R., Diaz-Ponguta, B., da Silva, J., Silva Corrêa, D., & Espitia-Pérez, L. (2024). Genotoxicity and Cytotoxicity Induced In Vitro by Airborne Particulate Matter (PM2.5) from an Open-Cast Coal Mining Area. Atmosphere, 15(12), 1420. https://doi.org/10.3390/atmos15121420

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop