Introduction

The numerous and various health effects including increased morbidity, higher mortality, and higher incidence of cardiovascular and respiratory diseases resulting from human exposure to fine particulate matter (PM2.5) are a global public health concern (Brehmer et al. 2020; Guo et al. 2019). Although PM2.5-related toxicity mechanisms are not entirely understood, toxicological evidence supports cellular oxidative stress as an important mechanism (Yang et al. 2021; Brehmer et al. 2020; Guo et al. 2019). Excessive accumulation of reactive species (e.g., H2O2, OH, O2·) in the body can stimulate oxidative stress and start inflammatory responses, which are associated with cellular damage and disease (Anand et al. 2022; Yang et al. 2021; Brehmer et al. 2020; Guo et al. 2019). Therefore, oxidative characteristics, indicated as the ability of particles to originate oxidative stress, are an emerging indicator that can better characterize the health effects of particles (Anand et al. 2022; Yang et al. 2021; Brehmer et al. 2020; Guo et al. 2019).

Several studies have examined the oxidative characteristics of outdoor PM2.5; however, research on indoor oxidative characteristics is scarce (Yang et al. 2021; Szigeti et al. 2015; Sakellaris et al. 2021) . However, humans spend approximately 80% of their time indoors; therefore, indoor environments are a main source of exposure to PM2.5 (Yang et al. 2021). Moreover, metals (e.g., Fe, Mn, Cu, Ni) are chemical components of PM that have been associated with the oxidative characteristics of PM2.5, and they are related to cellular toxicity/damage and diseases (e.g., cardiovascular and pulmonary) (Anand et al. 2022; Brehmer et al. 2020; Guo et al. 2019). Specifically, the abundance of transition metals (Fe, Mn, Zn, Ni, Cu, etc.) could enhance the generation of reactive species via Fenton or Fenton-like reactions, and the intracellular reactive species could exceed an equilibrium threshold determined by available antioxidants within the cell leading to cellular oxidative stress; then, adverse health effects could occur (Anand et al. 2022; Guo et al. 2019). In addition, inorganic and organic functional chemical compounds in particles can influence the attachment of metals onto particles (Fu et al. 2020). These functional groups could also control the mobility and transfer of metals and could be the source of oxidative species including sulfur-oxygen, nitrogen–oxygen, and hydroxides (Fu et al. 2020). Furthermore, elemental ratios like oxygen to carbon (O/C) in the particles can indicate the oxidative potentials, weathering, and hydrophobic characteristics of particles that influence the attachment of organic and inorganic contaminants and biological interactions (Lin et al. 2017). However, these indicators have mostly been disregarded in PM-related studies.

Additionally, several PM2.5 studies demonstrated that PM2.5 have influenced on human health, including cardiovascular and respiratory diseases and adverse neurodevelopmental effects (Pietrogrande et al. 2018). Surprisingly, while many studies have focused mainly on the impact of PM on eukaryotic cells, only a limited number have attempted to examine the effects of PM on bacteria. In fact, bacteria and bacterial responses (survival, growth, activity rate, biochemistry, etc.) are closely related to human health and have the potential to cause diverse health issues including respiratory infections, poor immune responses, and sensitivity to allergens (Bado et al. 2018; Bado et al. 2017; Suraju et al. 2015; Pompilio and Di Bonaventura 2020; White et al. 2020). Moreover, opportunistic bacteria play critical roles in the ecosystem and human health, for instance, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli) cause various serious global diseases (e.g., skin infections and abscesses, pneumonia, meningitis, endocarditis, or toxic shock syndrome), threat to life, and kill millions of people each year (Saygin and Baysal 2020, 2021). Previous studies have shown that PM chemical characteristics and its exposure also influence bacterial properties such as relative abundances, growth, and oxidative stress resistance. (Bado et al. 2017; Suraju et al. 2015; Fan et al. 2019; Zhong et al. 2019; Zhi et al. 2022). For instance, the elemental composition of PM such as sulfur, nitrogen, and elemental carbon are basic elements of life, and their availability promotes pathogenic bacterial survival (Fan et al. 2019; Zhong et al. 2019; Zhi et al. 2022). In addition, higher PM concentrations ensured more surfaces for more opportunistic pathogenic bacteria to adhere (Fan et al. 2019; Zhong et al. 2019).

Moreover, various cellular and acellular methods have been applied to characterize the oxidative potential of PM2.5, for example, oxidative stress resistance by H2O2 and monitoring oxidative enzymes (e.g., catalase (CAT), superoxide dismutase (SOD), glutathione (GSH), and lipid peroxidation (LPO)). To investigate the potential role of oxidative stress as a toxicity mechanism of PM, examination of CAT, SOD, GSH, and lipid peroxidation can be a good model (Arora et al. 2008).

Therefore, this paper assessed the oxidative stress and chemical characteristics of indoor PM2.5 using a unique indoor space, a sublevel (-3rd) floor in a university building. The major objectives of this study were, first, to characterize the elements and chemical functional groups in indoor PM2.5 collected from the -3rd floor of the building, and second, to assess the oxidative potential of indoor PM2.5 by bacterial cellular oxidative enzymes and examine its association with PM elemental distribution and chemical functional groups.

Materials and methods

Materials

Bacterial strains were purchased from the American Type Culture Collection (ATCC), which are gram-positive S. aureus ATCC 25,923 and gram-negative E. coli ATCC 35,218. Tryptic soy broth (TSB) and chemicals for the preparation of buffers (ALF and TRIS–HCL) were obtained from Merck (Germany). Neucoprain was acquired from Cayman Chemical (Michigan, USA), and sodium azide, glutathione reduced, glutathione reductase, β-nicotinamide adenine dinucleotide hydrate (NADPH), nitrotetrazolium blue chloride, thiobarbitiric acid, iron (II) sulfate, and ascorbic acid were from BioBasic (Canada). Tris(hydroxymethyl)aminomethane-HCl (TRIS–HCl), Triton X-100, hydroxylamine HCL, sodium carbonate, and H2O2 were obtained from Sigma Aldrich (Germany).

Sampling

Sampling was performed in the -3rd floor of a campus building at Istanbul Aydin University, Istanbul, Turkey (Fig. 1). The underground floor is selected as the sampling indoor area due to the unique sampling area from the indoor places. Moreover, underground spaces have received increasing attention since the land scarcity is the growing urban issue in cities, specifically in high-density populated cities, and pollutants are relatively easy to concentrate in underground buildings that affect the indoor air quality and may cause health issue (Yu et al. 2020). Office, molecular biology- and microbiology-based laboratories, and classroom were located on this floor. In addition to the classroom and office, this underground floor specifically includes microbiology and tissue culture laboratories, a sterilization room, and a storage room. The plan of the sampling area is presented in Fig. 1. Other underground floors are using similar purpose, such as the -2nd and -4th floors, which are located for pathology- and biochemistry-based laboratories with a similar plan. Temperature, air circulation, etc. had been controlled and stabilized; the temperature has been stabilized at 21 ± 3 °C, and all underground air circulation has been controlled. The temperature, humidity, and air circulation had been not changed during the sampling period. The ventilation system of the building for underground floors (-1 to -5) has been located at the top of the building (at the roof) with no permission to smoke. The system includes a separate inlet and outlet with an air filter, and they are located in different places at the roof in a reverse direction. In addition, smoking is not allowed in the building, and also, there is a restricted area for smoking in the campus (Baysal and Ustabasi, 2019). Indoor sampling was conducted from January 2021 to July 2021 during weekdays. During the study period, individual PM samples were collected over 120 h intervals using a low volume PM2.5 air sampling (Mega System Life XP for particulate matter sampling pump and SKC particle sampler, PEM model for sampling head) on quartz fiber filters (Whatman QM-A quartz filters, 47 mm, Merck). The sampler was calibrated for a flow rate of 6.0 L.min−1 at a height of 1.5 m above ground, which stimulates the breathing zone of people (Kenarkoohi et al. 2020). In total, 26 samples were collected and individually analyzed throughout the study to show the temporal variabilities. The sampling procedure was based on the study of Szabados et al. (2021). Precision microbalance (Precisa XR 205 SM) was used to measure the mass concentration, and the filters were weighed before and after sampling. The sampled filters were stored at − 20 C in the dark until chemical and oxidative analysis to prevent evaporation and photodegradation. At least three blank field filters were deployed during the campaigns for particulate matter measurement and analysis quality assurance.

Fig. 1
figure 1

Map of sampling field, illustration of sampling building, and representative scheme of the underfloor -3

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Chemical analysis

Samples of indoor PM2.5 were analyzed by means of an elemental distribution using an energy dispersive X-ray spectrometer (EDX, QUANTA FEG 250, FEI, Thermo Fisher Scientific, Oregon, USA). The EDX spectra of a blank filter were obtained and then subtracted from the EDX spectra of each ambient particle (Usman et al. 2022; Gupta et al. 2020; Bartz et al. 2021). The oxidative state of the indoor particulates was calculated using the oxygen-to-carbon ratio from the EDX spectrum. To determine the inorganic and organic functional groups, Fourier transform infrared spectroscopy (FTIR, Bruker, InvenioS) was conducted in 400–4000 cm−1. The background correction was applied using an empty filter before the analysis of loaded PM2.5 filter samples. Functional groups were identified according to the studies of Yu et al. (2018) and Usman et al. (2022) and had been calculated by our previous studies (Saygin and Baysal 2022).

The collected 26 samples were individually analyzed by EDX and FTIR, and each loaded PM2.5 filter sample was run at least three times.

Sample preparation and analysis for cellular oxidative characteristics

For the preparation of the indoor PM2.5 to analyze the cellular oxidative characteristics, artificial lung fluid (ALF) and TRIS–HCl soluble fractions of particulates that were extracted from filters respecting pH dependence were used. Buffers are ubiquitously used in in vitro conditions because many biological and chemical systems involve acid–base equilibrium, and pH has been shown to be an important contributor to oxidative characteristics (Cui et al. 2017; Liu et al. 2019; Metrick et al. 2013). The buffer conditions may also alter the stability and aggregation of particulates (Metrick et al. 2013). Therefore, we used two buffer systems, ALF and TRIS–HCl buffers at pH 4.5 and 7.0, respectively. The ALF and TRIS–HCl (1.0 M) buffers were prepared in the laboratory according to the study of Katsury et al. (2018) and Cui et al. (2017), respectively. ALF and TRIS–HCl were freshly made by dissolving constituents detailed in Supplementary Table 1 in 1 L of MilliQ water, and the pHs were adjusted to 4.5 and 7.0 for ALF and TRIS–HCl, respectively. One quarter of each filter was extracted in the ALF and TRIS–HCl buffers. To check the uniform of the pieces, each part was weighted. Indoor PM2.5 samples (26 samples) were individually mixed with 15 mL buffers in a 50 mL centrifuge tube. The mixture was shaken at 45 rpm for 24 h at room temperature, and then filters were removed from tubes and centrifuged at 4000 rpm for 10 min. The supernatant was filtered through a 0.45 μm filter (Kastury et al. 2018). The collected 26 indoor PM2.5 samples were individually extracted in ALF and TRIS–HCL buffers, and these solutions of each PM2.5 filter were used as the sample. Blank and control of the samples and filters were also applied.

ALF- and TRIS–HCl-suspended indoor PM2.5 samples were diluted into a TSB that included saturated cultures of E. coli and S. aerous, grown in TSB broth (3:1 TSB:sample) (Suraju et al. 2015; Bado et al. 2017). For positive and negative controls, only ALF and TRIS–HCl included bacteria without indoor PM2.5, and bacteria in TSB without biological media (ALF and TRIS–HCl) were also applied, respectively. The collected 26 samples and extracted both in ALF and TRIS–HCL buffers separately incubated with bacteria and then analyzed. All growth experiments were conducted in triplicate for each indoor PM2.5 samples. After freshly cultured bacteria were exposed to indoor PM2.5 solution, which was extracted in ALF and TRIS–HCl in selected media as explained above, they were incubated in a dark oven at 37 °C for 24 h. Then, the activity (viability) and oxidative stress assays of the bacteria containing each indoor PM2.5 samples, which were extracted in ALF and TRIS–HCl, were measured using an ultraviolet/visible (UV–VIS) microplate reader (Saygin and Baysal 2022). To measure the activity of bacteria, 200 µL of bacterial suspensions (including extractions of each PM2.5 in ALF and TRIS–HCl, their positive and negative controls) was taken into a 96-well plate and measured using the optical density (OD) method, and the OD measurements were taken at 600 nm using UV–VIS microplate reader (Thermo Scientific Multıskan GO, Finland).

Activity and oxidative stress assays were conducted immediately after 24 h using the same sample. The activity of bacteria and other oxidative assays for the samples and positive controls was calculated and presented as a ratio compared to the negative control which was not included a buffer system (bacteria in controlled condition did not include ALF or TRIS–HCl). Blank was included only chemicals and blank filter without bacteria and PM2.5 extractions. All analyses (assays) have been calculated and presented in this concept throughout the work. All experiments for each sample were conducted in triplicate. The cellular oxidative stress responses were determined in cultured species, including ALF- and TRIS–HCl-suspended indoor PM2.5 and controls, and the analyses were measured using UV–VIS microplate reader into 96-well plates (Thermo Scientific Multıskan GO, Finland) throughout the study.

Protein in the bacteria suspensions was estimated by the Bradford (1976) method (Saygin and Baysal 2022; Ustabasi and Baysal 2020; Baysal et al. 2018). After Bradford Reagent (acidified solution of Coomassie G-250) addition (25 µL) into sample bacteria suspension (125 µL), the absorbance of the controls and sample bacteria suspensions, which included ALF- or TRIS–HCl-extracted indoor PM2.5, was measured by the OD595 method with the UV–VIS microplate reader (Thermo Scientific Multıskan GO, Finland).

Cellular oxidative stress induced by indoor PM2.5 was examined by the response of CAT, SOD, GSH, LPO, antioxidant activity, and oxidative challenge by H2O2, adapted from the studies of Arora et al. (2008), Bado et al. (2017), and Suraju et al. (2015). The assays were conducted immediately after 24 h incubation.

Total antioxidant capacities were measured according to the CUPRAC method (Apak et al. 2004; Ustabasi and Baysal 2020). In this method, sample solutions (25 µL) are mixed with 0.01 M CuCl2, 7.5 × 10−3 M neocuproine (25 µL), and 1 M CH3COONH4 (25 µL) and measured at 450 nm, after the 30 min waiting period.

CAT was measured by the method described by Aebi (1984) and Arora et al. (2008). The absorbance at 240 nm of 1 mL reaction mixture containing 0.8 mL H2O2 phosphate buffer (at pH 7.0), 100 µL cell extract, and 100 µL distilled water was recorded for 4 min against blank which was H2O2 phosphate buffer. Change in absorbance after 5 min (i.e., ΔA240) was then calculated and used in the estimation of CAT activity.

SOD activity was measured according to the method described by Kono (1978) and Arora et al. (2008). A sodium carbonate buffer (50 mM, 1924 µL), nitrobluetetrazolium (1.6 mM, 30 µL), Triton X-100 (10%, 6 µL), and hydroxylamine–HCl (100 mM, 20 µL) were mixed with samples (100 µL), and absorbance (560 nm) was read for 5 min against blank. Change in absorbance per minute (i.e., ΔA560) was calculated and used in the estimation of SOD.

GSH activity was measured as described by Flohe and Gunzler (1984) and Arora et al. (2008). The reaction mixture contained potassium phosphate buffer (0.1 M, pH 7, 1200 µL), GSH (10 mM, 200 µL), sodium azide (200 mM, 10 µL), samples (200 µL), and glutathione reductase (2.4 U/mL, 200 µL). Following incubation at 37 °C for 10 min, NADPH (1.5 mM, 200 µL) and H2O2 (1.5 mM, 200 µL) were added. Absorbance (340 nm) was recorded for 4 min against blank, and the change in absorbance per minute was calculated.

The thiobarbituric acid (TBA) method was applied for the LPO activity (Arora et al. 2008; Baysal et al. 2020). In this method, a mixture of Tris buffer (150 mM, pH 7.1, 200 µL), ferrous sulfate (100 mM, 50 µL), ascorbic acid (150 mM, 50 µL), and sample (200 µL) was incubated at 37 °C for 15 min. TBA (0.375%, 2 mL) was then added to the mixture and allowed to react at 100 °C (in a water bath) for 15 min. The absorbance of the supernatant was read at 532 nm against blank.

Oxidative stress was assayed following the study of Bado et al. (2017) and Saruju et al. (2015). ALF- and TRIS-suspended indoor PM2.5 was diluted into the TSB broth that included saturated cultures of E. coli and S. aerous. Following 1 h of growth, H2O2 (100 µL) was added to the subcultures (200 µL) at final concentrations of 0, 20, and 50 mM, and growth was monitored for 24 h.

Statistical analysis

The differences between the control and the samples as well as the differences among samples were analyzed by ANOVA with post hoc Tukey (p < 0.05). SPSS 17.0 software was applied for the significance and Spearman correlation (two-tailed) tests. Standard deviation was used throughout the work.

Results and discussion

Chemical characteristics of indoor PM2.5

Previous studies have shown PM oxidative characteristics to be closely associated with metals, specifically transition metals such as Fe, Zn, Cu, and Mn (Anand et al. 2022; Yang et al. 2021; Guo et al. 2019; Gao et al. 2020). In our study, the elemental distribution was characterized by EDX, and the result is presented in Table 1. The results showed that 10 elements were determined in the indoor PM2.5 samples. The order of the elements in the indoor PM2.5 samples was as follows: F > O > C > Si > Al > Ba > K > Zn > Ca > Ce. Most of the detected elements could be assigned to a crustal origin such as Al, Si, K, and Ca, while Ba, Zn, and Ce are usually considered anthropogenic. F content was found at the highest amount in the indoor samples since F compounds can exist in a gaseous form and can be released into the air naturally via windblown soil, marine aerosols, etc. or anthropogenically via various industrial processes, including steel manufacture, phosphate ore processing, and fertilizer production and use; glass and brick/ceramic manufacturing; and coal combustion (Ozbek et al. 2016). Moreover, the presence of C and Ba can be assigned as the marker species for road dust in previous studies and attributed to fossil-fuel combustion (Lim et al. 2011; Talbi et al. 2018). The occurrence of Ce regarded as the rare earth elements or trace constituents can be determined at the lowest levels, and it can be originated by soil dusts; previous studies have also reported that Ce is a good marker of environmental tobacco smoke in the indoor air environment (Lim et al. 2011; Reynoso-Cruces et al. 2021). Moreover, O and C amounts can be also important, since the O/C ratios characterize the oxidation state of particles, and O and C originated from both the environment (e.g., aerosols) and from engineered sources (Aiken et al. 2008; Saygin & Baysal 2022).

Table 1 Elemental distribution by EDX of indoor PM2.5 collected from -3rd floor (± SD)
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The functional group identification in the particles is important due to the capability of organic and inorganic substances to sorb onto particle surface by functional groups and can also characterize the oxidative variabilities (Fu et al. 2020; El Zokm et al. 2013). Therefore, the FTIR characterization was applied to assess the oxidative species onto PM2.5 and classified upon the main chemical functional groups from FTIR (Fig. 2a). As illustrated in Fig. 2b, the various functional groups were classified and indexed from the FTIR spectrums of indoor PM2.5. The spectra of PM2.5 samples are characterized in the 2915–2925 cm−1 and slight broad absorption 3300–3350 cm−1 intervals attributed to the N–H stretching and O–H groups. The group of C-H with absorption peaks in the frequency range 1455–1463 cm−1 was also visible. The absorption peak at approximately 1540 cm−1 could be attributed to the N = O, and the absorption peak at 2350 and 1030 cm−1 represented the C = O and S = O, respectively. The order of the functional group was S = O > N–H > N = O > C = O > C-H > O–H. The higher abundance of sulfur-related compounds compared to other functional groups indicated that they could be the main source of the oxidative characteristics of the indoor air since sulfur-oxygen compounds of anthropogenic origin signify pollution and oxidative potential (Pokorna and Zabranska 2015). Sulfur-oxides can enhance lipid peroxidation and inhibit antioxidative enzymes as well as be responsible for the generation of strong acidity and chelating of the transition metals (Zhang et al. 2006; Yamaguchi et al. 1986). Nitrogen-related species, like nitrogen–oxygen nitrogen–hydrogen species, have the potential to produce by several endogenous and exogenous processes and can cause oxidative stress; their negative effects are neutralized by antioxidant defenses (Liguori et al. 2018). In addition, the results indicated that the hydroxyl group made a lesser contribution to the oxidative characteristics in indoor PM2.5 compared to other chemical functional groups.

Fig. 2
figure 2

a FTIR spectrums of collected PM2.5 filters. b Box and whisker chart of the identified functional groups in indoor PM2.5 samples collected in -3rd floor. c Box and whisker chart of the mass concentration in indoor PM2.5 samples collected in -3rd floor. (N = 26 indoor PM2.5 samples)

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The indoor concentrations of PM2.5 measured in the 26 samples are illustrated in Fig. 2c. The arithmetic mean concentrations of the particle fraction were 5.94 ± 5.21 μg m−3 (2.31–20.83 μg m−3). The PM2.5 mass concentrations appeared lower when compared to previous studies in the literature and the 24-h and annual WHO Guidelines for Indoor Air Quality (Gemenetzis et al. 2006; Pekey et al. 2010; Hassanvand et al. 2014; Mandin et al. 2017). This result can be originated that this floor has been under controlled condition respecting the temperature, air circulation, and applications in this floor. Moreover, higher concentrations were observed during the period of desert dust convection.

Oxidative characteristics by cellular responses

As illustrated in Fig. 3, the activity of representative opportunistic bacterial pathogens (E. coli and S. aureus) declined with the presence of indoor PM2.5 extracted in ALF and TRIS–HCl compared to those grown in controlled conditions. The activity of E. coli in the presence of indoor PM2.5 extracted in ALF was at lower levels compared to extraction in TRIS–HCl. However, there were no differences between their controls for E. coli. On the other hand, the activity of S. aureus had significantly lower levels in the presence of indoor PM2.5 extracted both in ALF and TRIS–HCl compared to their negative and positive controls. These results indicated that S. aureus was more sensitive to the indoor PM2.5 extracted both in ALF and TRIS–HCl rather than E. coli. In addition, the results showed that the activity of the pathogens relied on the pH of the medium, and neutral conditions respecting the normal health conditions could be promoting the pathogen activity rather than acidic condition mimicking the inflammatory condition of the lung. Since this acidic medium (e.g., ALF) inhibits the overgrowth of bacteria and other organisms with pathogenic potential. It provides vital natural protection against infections (Sassi et al. 2008). The results were also parallel with the rare study in this field that examined the impact of dust exposure on bacterial growth where it was found that the dust samples inhibited the growth of the tested bacterium like E. coli, Enterococcus faecalis, and Pseudomonas aeruginosa (Suraju et al. 2015). Unfortunately, little is known about the physiological impact on bacterial activity in the presence of PM (Suraju et al. 2015; Bado et al. 2017; Pomplio and Di Bonaventura 2020).

Fig. 3
figure 3

Activity ratio of representative opportunistic bacterial pathogens; a E. coli and b S. aureus in the presence of indoor PM2.5 extracted in ALF and TRIS–HCl, and their controls. The results presented as ratio (A/No) or (Ao/No). A: absorbance of samples of bacterial suspension indoor PM2.5 extracted in ALF or TRIS–HCl, Ao: absorbance of positive control of bacterial suspension in ALF or TRIS–HCl without indoor PM2.5, No: absorbance of negative control of bacterial suspension in controlled condition without indoor PM2.5 and ALF or TRIS–HCl

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In addition to the activity of pathogens, protein and antioxidant activities respecting the biochemical activities were examined with the indoor PM2.5 exposure (Fig. 4). The protein content of the pathogens was decreased with the exposure to indoor PM2.5 extracted in ALF and TRIS–HCl. Moreover, the protein levels were strongly correlated with the activities of the bacterium (rPM2.5+ALF:0.89, rALF: 0.91, rPM2.5+TRIS–HCl:0.86; rTRIS-HCl:0.89 and rPM2.5+ALF:0.78, rALF: 0.83, rPM2.5+TRIS–HCl:0.87; rTRIS-HCl:0.90 for E. coli and S. aureus). This result indicated that the exposure of indoor PM2.5 caused the protein depletion resulting in the inhibition. In this field, a few study was conducted, and none of them examined the protein responses of bacteria. On the other hand, some human cell-based studies observed the impact of PM2.5 on the cell viability and protein responses. These studies indicated that oxidative species due to the oxidative stress can influence intracellular biomacromolecules like protein and lipid. They oxidize and inactivate proteins in the cell and decrease cell viability (Liu et al. 2020; Ray et al. 2012).

Fig. 4
figure 4

Protein and antioxidant responses of representative opportunistic bacterial pathogens E. coli protein (a) and antioxidant activity (c), and S. aureus protein (b) and antioxidant activity (d) levels in the presence of indoor PM2.5 extracted in ALF and TRIS–HCl, and their controls. The results presented as ratio (A/No) or (Ao/No). A: absorbance of samples of bacterial suspension indoor PM2.5 extracted in ALF or TRIS–HCl, Ao: absorbance of positive control of bacterial suspension in ALF or TRIS–HCl without indoor PM2.5, No: absorbance of negative control of bacterial suspension in controlled condition without indoor PM2.5 and ALF or TRIS–HCl

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Figure 4 also illustrates the antioxidant responses of the pathogens with the exposure of indoor PM2.5 extracted in ALF and TRIC-HCl. The antioxidant response was reduced for both pathogens, and the reduction was greater in ALF extracting indoor PM2.5 than TRIS–HCl extracting indoor PM2.5. The lower antioxidant responses detect the oxidative status with the exposure of indoor PM2.5 (de Paula Ribeiro et al. 2020). Moreover, rising oxidative species can cause to decline in the antioxidant response. The levels of antioxidant response and its enzymes (e.g., GSH, SOD) have negatively correlated with the generation of oxidative species and can cause the oxidative damage (Chen et al. 2018). In addition, the antioxidant reduction was less in E. coli compared to S. aureus in the presence of indoor PM2.5 extracted in ALF and TRIS–HCl, and the reduction in the ALF was greater than in TRIS–HCl. These results indicate that protein depletion is more dominant in the inhibition of E. coli; however, both antioxidant and proteins triggering the oxidative damage promoted the inhibition of S. aureus in the presence of indoor PM2.5 + ALF and ALF as positive control.

Since PM2.5-induced oxidative stress directly interacts with antioxidant enzymes, such as SOD, SOD, CAT, GSH, and LPO, we examined these indicators in the tested bacterium (Deng et al. 2013). A significant reduction of SOD, CAT, GSH, and LPO was observed in the E. coli and S. aureus exposed to indoor PM2.5 ALF and TRIS–HCl extracts when compared to controlled culture conditions since the ratios were < 1 (Fig. 5). However, it is also important to evaluate their behavior in their controls without PM2.5 for understanding the physiological impact on oxidation. The results showed that SOD, CAT, and GSH activities of E. coli were increased by indoor PM2.5 extracted in ALF compared to its control, although the difference in SOD and LPO was not significant, which indicated that the SOD and lipid membrane damage did not influence on the oxidation (de Paula Ribeiro et al. 2020) (Fig. 5a). Moreover, the higher levels of antioxidant enzymes like GSH and CAT consisted with antioxidant responses. On the other hand, increasing antioxidant and antioxidant enzymes indoor PM2.5 extracted in ALF compared to physiological control (ALF control) indicated that indoor PM2.5 have the potential to reduce natural protection against infections of ALF condition since higher antioxidant and antioxidant enzymes can increase the viability of the bacteria (Buettner 2011; Wang et al. 2018; Chen et al. 2018). In addition, the extraction of PM2.5 in TRIS–HCl-exposed E. coli indicated a reverse impact on the antioxidant enzymes compared to ALF exposure that the oxidative potential occurred both with the physiological impact and indoor PM2.5 (Fig. 5c). Since the reduction in the level of antioxidant enzymes observed compared to their TRIS–HCl controls. For example, SOD could catalyze disproportionation of anionic radicals and have a role in eliminating free radical damage, and the GSH is an important metabolic regulator, and it could reduce the damage of free radicals by combining with peroxides and free radicals in the cell (Chen et al. 2018; Zhang et al. 2006). Moreover, an increase in the level of LPO compared to TRIS–HCl control showed that indoor PM2.5 may decline the physiological impact of TRIS–HCl, which means that cell membrane damage was protected in this condition.

Fig. 5
figure 5

SOD, CAT, GSH, and LPO of bacterial cultures. E. coli and S. aureus in the presence of indoor PM2.5 extracted in ALF and TRIS–HCl and their controls. a SOD, CAT, GSH, and LPO of E. coli in the presence of indoor PM2.5 extracted in ALF; b SOD, CAT, GSH, and LPO of S. aureus in the presence of indoor PM2.5 extracted in ALF; c SOD, CAT, GSH and LPO of E. coli in the presence of indoor PM2.5 extracted in TRIS–HCl; d SOD, CAT, GSH, and LPO of S. aureus in the presence of indoor PM2.5 extracted in TRIS–HCl. The results presented as ratio (A/No) or (Ao/No). A: absorbance of samples of bacterial suspension indoor PM2.5 extracted in ALF or TRIS–HCl, Ao: absorbance of positive control of bacterial suspension in ALF or TRIS–HCl without indoor PM2.5, No: absorbance of negative control of bacterial suspension in controlled condition without indoor PM2.5 and ALF or TRIS–HCl. “*” indicates significant importance with their positive control (p < 0.05)

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Similarly with E. coli, the response of S. aureus exposed to indoor PM2.5 ALF and TRIS–HCl extracts showed the greater reduction in the oxidation enzymes compared to the controlled culture condition (Fig. 5b and d). However, their comparison with their control indicated that the main oxidative pathways can occur under the impact of ALF and TRIS–HCl media. The behavior of the indicators was similar for both ALF and TRIS–HCl, and the CAT and GSH activities were higher in these extracts of indoor PM2.5 compared to their controls without indoor PM2.5, although SOD and LPO were reduced. These results indicated that the indoor PM2.5 induced the formation of oxidative species through eliminating free radical and lipid damage (Chen et al. 2018; de Paula Ribeiro et al. 2020). However, the higher levels in CAT and GSH with indoor PM2.5 can break the natural protection of ALF and TRIS–HCl.

The sensitivity of cells to oxidative stress from the hydrogen peroxide challenge is also important in evaluating the oxidation potential of air particulates (Suraju et al. 2015; Bado et al. 2017). The result indicated that E. coli exhibited increasing sensitivity only to 50 mM H2O2 when exposed to both ALF and TRIS–HCl forms of indoor PM2.5 (Fig. 6). However, the S. aureus sensitivity response to the H2O2 when exposed to indoor PM2.5 was varied according to the exposure media, and S. aureus exhibited increasing sensitivity in 20 mM H2O2 when exposed to ALF forms of indoor PM2.5. Since it was at 50 mM H2O2 when exposed to the TRIS–HCl forms of indoor PM2.5. The higher sensitivity of E. coli can be obtained at 50 mM H2O2 when exposed to the ALF and TRIS–HCl forms of indoor PM2.5. The higher responses can be explained by envelope stress response factors, chemical stresses, and antimicrobial resistance (Raivio 2005; Bado et al. 2017; Suraju et al. 2015). This response also correlated with the lower inhibition of E. coli compared to S. aureus. In addition, it may be helpful to understand both biofilm formation and antibiotic resistance mechanism.

Fig. 6
figure 6

Oxidative stress resistance of bacterial cultures. E. coli and S. aureus were exposed to 0, 20, and 50 mM H2O2 in the presence of indoor PM2.5 extracted in ALF and TRIS–HCl and their controls. a Oxidative stress resistance of E. coli in the presence of indoor PM2.5 extracted in ALF. b Oxidative stress resistance of S. aureus in the presence of indoor PM2.5 extracted in ALF. c Oxidative stress resistance of E. coli in the presence of indoor PM2.5 extracted in TRIS–HCl. d Oxidative stress resistance of S. aureus in the presence of indoor PM2.5 extracted in TRIS–HCl. The results presented as ratio (A/No) or (Ao/No). A: absorbance of samples of bacterial suspension indoor PM2.5 extracted in ALF or TRIS–HCl, Ao: absorbance of positive control of bacterial suspension in ALF or TRIS–HCl without indoor PM2.5, No: absorbance of negative control of bacterial suspension in controlled condition without indoor PM2.5 and ALF or TRIS–HCl. “*” indicates significant importance with their positive control (p < 0.05)

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Conclusions

The indoor PM2.5 samples were collected from the -3rd floor in a university building and aimed to show oxidative chemical characteristics with various elements and chemical functional groups and bacteria-based cellular oxidative stress. The PM2.5 mass concentrations were lower levels in the -3rd floor compared to the other studies. Chemical characteristics showed that both natural and anthropogenic elements were detected, as well as oxidative characteristics in the functional groups such as S = O, N = O, and N–H. The cell-based oxidative characteristics indicated that the bacteria were influenced by indoor PM2.5 and physiological fluids due to disparities in pHs. The cellular oxidative indicators showed that PM2.5 induced the change of oxidative responses. These results are also important to evaluate the oxidative impact of the indoor PM2.5 on human health using physiological conditions and on bacterial interactions. Since bacterial infections are the main cause of respiratory diseases, antibiotic resistance, and higher morbidity and mortality. Future study in this topic is still necessary to deeper understand the complex relations between physiological conditions, bacteria, and indoor PM. Moreover, the interaction of bacteria and various PM fractions, their chemicals, their interaction pathways, and their co-impact on antibiotic resistance can be investigated in the future to enlighten their impact on human health.