Huijun Li1,2, Yifan Shan1,2, Yongchao Huang1,2, Zhen An1,2, Guangcui Xu1,2, Fan Wei1,2, Guicheng Zhang 3, Weidong Wu 1,2

School of Public Health, Xinxiang Medical University, Henan 4353003, China
Henan International Laboratory for Air Pollution Health Effects and Intervention, Henan 4353003, China
School of Public Health, Curtin University, Bentley, Western Australia 6102, Australia


Received: December 18, 2018
Revised: May 7, 2019
Accepted: May 13, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.12.0467  


Cite this article:

Li, H., Shan, Y., Huang, Y., An, Z., Xu, G., Wei, F., Zhang, G. and Wu, W. (2019). Bacterial Community Specification in PM2.5 in Different Seasons in Xinxiang, Central China. Aerosol Air Qual. Res. 19: 1355-1364. https://doi.org/10.4209/aaqr.2018.12.0467


HIGHLIGHTS

  • PM2.5 bacterial communities vary with seasons.
  • Bacterial community in PM2.5 exhibits the highest species richness in spring.
  • Bacterial community in PM2.5 exhibits the highest diversity in summer.
  • Ozone concentration is significantly correlated with PM2.5 bacterial communities.
 

ABSTRACT


In China, air pollution has become a significant environmental threat to human health in recent years. Airborne bacteria are critical constituents of microbial aerosols, which contain numerous pathogens. However, the effects of seasonal variations, environmental factors such as air pollution, and meteorological factors on microbial diversity are poorly understood. In this study, fine particulate matter (PM2.5) samples (n = 12) were collected using a high-volume air sampler over 24-hour periods during all four seasons from April 2017 to January 2018. Concurrently, the average daily concentrations of various air pollutants and the meteorological conditions were monitored. High-throughput sequencing of 16s rRNA was then employed to profile PM2.5 bacterial communities. The results showed that the bacterial communities varied significantly by season. Proteobacteria (35.5%), Firmicutes (23.0%), and Actinobacteria (16.2%) were the most abundant bacterial phyla in the PM2.5 samples. At the genus level, the diversity of the bacterial communities was significantly correlated with the ozone (O3) concentration (r = 0.920, p = 0.001) and air temperature (T) (r = 0.534, p = 0.023). The results of this study can be used as a reference by other bioaerosol research that focuses on the health effects of atmospheric particulate matter.


Keywords: PM2.5; Bacterial biodiversity; Pollutants; Meteorological factors; Ozone.


INTRODUCTION


Air pollution is a significant environmental threat to human health. Numerous epidemiological and clinical studies have documented that exposure to particulate matter (PM) is associated with various adverse health effects (WHO, 2013). The 2015 Global Burden of Disease Study showed that exposure to outdoor PM2.5 resulted in 4.241 million deaths in 2015 (GBD 2015 Risk Factors Collaborators, 2016). PM2.5 has a diverse range of components, including sulfates, nitrates, metal ions, organic compounds, and microbes (He et al., 2001; Després et al., 2012). Particles of this size are more likely to penetrate and deposit deeper into the tracheobronchial and alveolar regions (Brook et al., 2004). Biological PM, such as bacteria, fungi, viruses, and plant and animal debris, may account for as much as 25% of airborne aerosols (Jaenicke, 2005). PM2.5 is hypothesized to have a substantial bacterial component, and these microbial elements may play a critical role in causing adverse health effects; however, the composition and relative abundance of PM2.5 bacterial components and the factors that enhance or limit their proliferation and existence are poorly understood.

Airborne microorganisms may be influenced by air pollutants, the season, and other meteorological factors (Fang et al., 2008; Gao et al., 2015; Hu et al., 2015). A temperate climate and/or certain seasons are beneficial for the proliferation and growth of most airborne microorganisms. Li et al. (2011) detected higher airborne bacterial concentrations in autumn in the Qingdao terrestrial region compared with the coastal region, and Fang et al. (2007) reported a higher bacterial concentration in autumn in Beijing. In addition, meteorological factors, such as temperature (T), relative humidity (RH), and wind speed, influence PM2.5 bacteria. Sandstorms have been reported to increase the concentration of culturable bacteria in PM (Li et al., 2011). Moreover, the concentrations of PM2.5 microorganisms decrease with increasing ambient O3 concentration (Dong et al., 2016). O3 can react with olefins and other compounds in the atmosphere, which increases the toxicity of O3, making it toxic to airborne microorganisms (Dong et al., 2016). Although the concentration of airborne microorganisms may be associated with various environmental factors, few studies have examined this phenomenon.

Culture-based methods can culture only a fraction of the bacteria in PM because the non-culturable microbiome accounts for > 95% of the total microbiome (Li et al., 2011). Culture-independent methods, especially high-throughput sequencing technology, have been shown to be effective analytical techniques for determining the diversity and composition of airborne microorganisms (Bowers et al., 2013; Cao et al., 2014; Du et al., 2018). In this study, we used high-throughput sequencing to study the bacterial communities in PM2.5 samples collected during four seasons. Thus, this study aimed to determine the diversity of bacterial communities and their composition in PM2.5 in different seasons and their relationship with other atmospheric pollutants and meteorological factors. 


MATERIALS AND METHODS



Sampling Sites and Sample Collection

PM2.5 samples were collected at Xinxiang Medical University from April 2017 to January 2018 in Xinxiang (35°18′13.71°N, 113°55′15.05°E), an industrial city in Central China with a temperate continental climate. The sampling site is nearly 100 m from the nearest major roads. The site is surrounded by trees, greenbelts, and residential and school buildings, with no identified potential industrial pollution sources. To collect PM2.5 samples, a high-volume air sampler (TE-6070VFC; Tisch Environmental, Inc., USA) with a flow rate of 1.17 m3 min–1 was used, and the sampler was placed on the rooftop of a seven-story research building (approximately 25 m high). 3 PM2.5 samples were collected in 24-hour cycles using a high purity glass fiber filter membrane (8 × 10 in.; Safelab, Beijing) during each of the four seasons (spring [SP], summer [SU], autumn [AU], and winter [WI]; total n = 12). Concurrently, the 24-hour average concentrations of PM10 (PM10 refers to the particulates that have an aerodynamic diameter smaller than 10 µm), O3, carbon dioxide (CO), sulfur dioxide (SO2), and nitrogen dioxide (NO2), as well as meteorological factors (T and RH), were monitored continuously. The obtained sampling membranes were stored at –20°C until microbiome analysis. 


DNA Preparation and Sequencing

The total genomic DNA was extracted from the PM2.5 samples using the cetyltrimethylammonium ammonium bromide (CTAB) method. The concentration and purity of DNA were determined through electrophoresis on 1% agarose gel; subsequently, the DNA was diluted with sterile water to a concentration of 1 ng µL–1. For sequencing analyses, the V4 region of the 16s rRNA gene was amplified through polymerase chain reaction (PCR; 98°C for 1 min, followed by 30 cycles at 98°C for 10 s, 50°C for 30 s, and 72°C for 30 s, with a final extension at 72°C for 5 min) that used the primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). PCR reactions were performed in a 30-µL reaction solution containing 2X Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2-µM forward and reverse primers, and approximately 10 ng of template DNA (according to the DNA concentration). PCR products were mixed with the same volume of 1× loading buffer and were detected through electrophoresis on 2% agarose gel. A Gene JET™ Gel Extraction Kit (Thermo Scientific) was used to purify PCR amplification products. High-throughput sequencing of 16s rRNA was conducted on the Ion S5™ XL platform. 


Bioinformatics Analysis

Single-end reads were assigned to DNA samples by identifying their unique barcode; the raw reads were then truncated by cutting off the barcode and primer sequence. According to the Cutadapt quality control process, filtering of the raw reads was performed under specific filtering conditions to obtain high-quality clean reads (Martin, 2011). Finally, to obtain clean reads, the UCHIME algorithm was used to remove chimeric sequences (Edgar et al., 2011; Haas et al., 2011). Sequence analysis was performed using UPARSE (version 7.0.1001; http://drive5.com/uparse/; Edgar, 2013), and sequences with similarity greater than 97% were assigned to the same operational taxonomic units (OTUs). Representative sequences for each OTU were screened for further annotation. To determine alpha diversity, QIIME (version 1.9.1) was used to calculate the Chao1 and abundance-based coverage estimator (ACE) indices to estimate the species abundance, as well as the Shannon and Simpson indices to estimate community diversity. Principal coordinate analysis (PCoA) based on the unweighted-UniFrac distance matrix was conducted using QIIME to test the difference and variation in various bacterial communities in PM2.5 among seasons. R (version 2.15.3) was used to draw PCoA diagrams and analyze the differences between groups.

The R statistical computing environment was used for all community statistics and visualizations. The relationship between environmental factors, and bacterial relative abundance and diversity indices was determined using canonical correspondence analysis (CCA). Furthermore, Spearman’s rank correlation analysis was used to examine the correlations between environmental factors (meteorological factors and other pollutants) and the 35 most abundant bacterial genera. The p values were corrected for multiple testing with the Holm method by using R vegan. A p value of < 0.05 was considered statistically significant. Bioinformatics analysis was conducted by Beijing Novogene Bioinformatics Technology Co., Ltd., under the authors’ supervision. 


RESULTS AND DISCUSSION



Comparison of Bacteria
l Compositions among the Four Seasons

We acquired a total of 916,229 raw reads and 787,923 high-quality clean reads after specific filtering conditions. Sequences with ≥ 97% similarity were clustered into the same OTUs; thus, 744,044 effective reads were clustered into the same OTUs. The representative sequence for each OTU was screened for further annotation. Approximately 50 bacterial phyla and 900 bacterial genera were detected from the PM2.5 samples. The top 10 phyla and genera in the four seasons are listed in Tables 1 and 2, respectively. Proteobacteria, Firmicutes, and Actinobacteria were the most abundant bacterial phyla in all the samples. Their ranking in spring was as follows: Proteobacteria, Actinobacteria, and Firmicutes, with relative abundances of 37.35%, 24.26%, and 23.90%, respectively. This ranking differs from that in the other seasons (summer, autumn, and winter), in which the ranking was as follows: Proteobacteria, Firmicutes, and Actinobacteria. At the genus level, the most abundant genera were Chloroplast and Lactobacillus, which belong to the phyla Cyanobacteria and Firmicutes, respectively, followed by Pseudomonas and unidentified_Mitochondria, which belong to the phylum Proteobacteria. The 3 dominant bacterial phyla were Proteobacteria, Firmicutes, and Actinobacteria, which is consistent with a relevant study conducted in Beijing (Du et al., 2018). In order to make a valid comparison with Du’s study, the same sampling and analytical methods as Du’s were used in our study. However, at the genus level, a significant difference was observed in bacterial compositions between our samples and the samples from Beijing (Fig. S1). In our study, we identified Lactobacillus in spring and summer, unidentified_Mitochondria in autumn, and unidentified_Chloroplast in winter at the genus level. By contrast, the study in Beijing identified Kocuria as the dominant genus in all four seasons. This finding indicates a significant geographical variation of PM2.5 bacteria because the capital city of Beijing is 600 km from Xinxiang. Consistent with the results of other studies, this study mainly identified plant-, soil-, and fecal-associated bacteria. For example, unidentified_Chloroplast is a plant-associated and soil-inhabiting bacterium (Gao et al., 2017), Pseudomonas widely inhabits soil and water (Qu et al., 2016), and Escherichia is a fecal-associated bacterium (Hu et al., 2007). These findings indicate that plants, soil, and feces are likely the main sources of bacteria in PM (Sun et al., 2018).


Table 1. Relative abundance (%) of top 10 PM2.5 bacterial phyla among the four seasons (mean ± SD).


Table 2. Relative abundance (%) of top 10 PM2.5 bacterial genera among the four seasons (mean ± SD).

The seasonal differences in the bacterial community of PM2.5 were evaluated further through analysis of molecular variance. The bacterial community structure was markedly different between spring and summer (p = 0.016). In addition, we used t-tests to determine the differential species between the two seasons for each phylogenetic level. Tables S1–S6 show the significant difference in abundant PM2.5 bacteria at the phylum and genus levels. In brief, our study demonstrated that considerable seasonal variations existed in PM2.5 bacterial profiles.

Relevant studies have demonstrated that bacterial richness is considerably higher in winter than in other seasons (Kumari and Choi, 2014; Du et al., 2018). However, in our study, we found that the bacterial community in PM2.5 showed the highest species richness in spring and the highest diversity in summer (Table 3). The discrepancy between our results and the results of previous studies may be explained by the different geographical locations and their corresponding climatic conditions. Xinxiang has a temperate continental climate characterized by four seasons. Northwesterly winds prevail in winter because of the influence of the Siberian monsoon, whereas no dominant wind direction exists in spring. As previously reported, the wind speed reduces air pollution levels and strongly influences airborne bacterial diversity (Kumari and Choi, 2014). A previous study showed that wind speed is correlated with the diversity and composition of bacterial communities (Kembel et al., 2012). A strong wind is beneficial for the diffusion of PM and its microorganisms (Du et al., 2018). Even within the same season, variations in microbial community richness and diversity as well as variations in the total concentration of PM caused by wind are apparent (Du et al., 2018).


Table 3. Operational taxonomic units (OTUs) and alpha diversity indices for each season.


Bacterial Diversity in PM2.5

Table 3 summarizes the number of observed OTUs and diversity of bacterial communities in PM2.5 (mean ± standard deviation). To determine alpha diversity, the Shannon and Simpson indices were calculated to compare community diversity, and the ACE and Chao1 indices were estimated to compare community richness (Table 3). The spring samples showed the highest species richness, with ACE and Chao1 indices of 2310 ± 189 and 2257 ± 165, respectively, whereas the autumn samples had the lowest species richness, with ACE and Chao1 indices of 1330 ± 634 and 1320 ± 629, respectively. The Wilcoxon rank sum test showed that bacterial community richness was significantly different between autumn and spring (p = 0.039 for ACE). Furthermore, the highest Shannon and Simpson indices, community diversity indices, were observed in summer (9.17 ± 0.57 and 1.00 ± 0.001), followed by spring (8.63 ± 0.35 and 0.99 ± 0.002), winter (7.91 ± 1.27 and 0.97 ± 0.042), and autumn (7.07 ± 2.14 and 0.94 ± 0.080). The Wilcoxon rank sum test showed that the diversity of the bacterial community was significantly different between the summer and autumn (p = 0.049 for the Shannon indices). The number of OTUs in spring was higher than that in other seasons. These results indicated that the abundance and diversity of the bacterial community differed between seasons. Furthermore, we detected a relationship between environmental factors and alpha diversity indices. The Spearman’s rank test showed that both T (r = 0.66, p = 0.02) and O3 (r = 0.75, p = 0.005) were correlated with the Simpson index.

The variation in bacterial communities in PM2.5 collected during different seasons was analyzed using PCoA; the closer the samples were, the more similar the species compositions were. PCoA analysis of bacterial communities showed that PM2.5 samples collected in spring and winter were clustered, whereas those collected in summer and autumn were relatively scattered (Fig. 1). Therefore, the PCoA results demonstrated that microbial community variation was significantly correlated with the season.


Fig. 1. Principal coordinate analysis of the samples using the unweighted-UniFrac distance matrix: The colored shape labels of red squares, green circles, upper blue triangles, and sky blue diamonds correspond to spring (SP), summer (SU), autumn (AU), and winter (WI) samples, respectively. The numbers next to PC 1 and PC 2 explain the percentages of community variations that are attributed to each PC.Fig. 1. Principal coordinate analysis of the samples using the unweighted-UniFrac distance matrix: The colored shape labels of red squares, green circles, upper blue triangles, and sky blue diamonds correspond to spring (SP), summer (SU), autumn (AU), and winter (WI) samples, respectively. The numbers next to PC 1 and PC 2 explain the percentages of community variations that are attributed to each PC.


Potential Bacterial Pathogens in PM2.5

Pathogenic bacteria have previously been found in airborne PM (Fröhlich-Nowoisky et al., 2009; Gou et al., 2016). Using the directory of pathogenic microorganisms infecting humans promulgated by the National Health Commission of the People’s Republic of China to detect pathogens, in our study, we found 18 pathogenic bacteria in the PM2.5 samples that belonged to 9 genera. Escherichia coli (19.66%), Acinetobacter lwoffii (11.73%), and Pseudomonas aeruginosa (5.27%) were the major pathogenic bacteria in all PM2.5 samples collected in Xinxiang. E. coli was the most abundant bacteria in summer (2.9%), autumn (10.24%), and winter (6.03%). In spring, A. lwoffii was the most abundant bacteria, accounting for 8.18%. A. lwoffii is a Gram-negative aerobic bacillus present in the normal oropharynx and skin, and it is an opportunistic pathogen that can cause infections in humans with impaired immune systems (Ku et al., 2000; Regalado et al., 2009; Singh et al., 2016). P. aeruginosa is a Gram-negative bacillus found in warm, moist environments and can be identified from soil, water, and normal human skin; ozone has high germicidal effectiveness against P. aeruginosa (Zuma et al., 2009). In our study, ozone levels were the highest in summer while P. aeruginosa was at the lowest relative abundance of 0.09%. In this study, Clostridium novyi and Campylobacter jejuni were detected in autumn. C. novyi is an opportunistic pathogen that can cause infection in devitalized/hypoxic tissues; it also can foster spore germination and proliferation with the concomitant expression of virulence factors and toxins (Aronoff and Kazanjian, 2018). C. jejuni, a Gram-negative bacterium, is commonly associated with gastroenteritis and infects humans mainly through the food chain or other routes from the environment (Giallourou et al., 2018; Oh et al., 2018). PM2.5 can be deposited in the respiratory bronchioles and alveoli through the upper airways, and it induces various inflammatory responses and cellular immune impairment (Guan et al., 2016). The high concentration of PM2.5 as well as its pathogenic bacteria may play a role in the development of respiratory diseases. 


Relationship between Environmental Factors and PM2.5 Bacterial Community

Table S7 lists the air pollutant concentrations and corresponding meteorological factors for each sample. The highest average concentration of PM2.5 was detected in winter; the highest average concentrations of PM10, SO2, and CO were detected in spring; and the highest average concentration of O3 was detected in summer. Moreover, the highest average NO2 concentration was found in autumn.

Assessing the effects of environmental factors on bacteria is necessary because they influence bacterial survival, growth, and diffusion (Hwang et al., 2010; Zhong et al., 2016). In the present study, CCA was used to analyze the relationships between the bacterial community composition and environmental factors (Fig. 2). The CCA biplot shows that certain environmental factors were distinctly correlated to different samples (Fig. 2). The first axis explains 27.31% of the variation in the genus-environment relationship, and the second axis accounts for 16.98%. The order of the degree of influence of these environmental factors was as follows: T > O3 > RH > PM10 > CO > PM2.5 > SO2 > NO2. In addition, the most critical drivers of the bacterial community structure were T and O3, which accounted for 82.63% and 75.83% of the variation, respectively. This result is consistent with those reported by Lu et al. (2018), who found that T, O3, and NO2 had more significant effects on the bacterial community than did other environmental factors. However, in our study, the influence of NO2 was the lowest.


Fig. 2. Canonical correspondence analysis shows the relationships between environmental factors and the PM2.5 bacterial community composition; environmental factors are represented by arrows. The length of the arrow represents the correlation between the environmental factor and the distribution of the bacterial community; the longer the arrow is, the greater the correlation. An acute angle between two environmental factors indicates that they are positively correlated, whereas an obtuse angle indicates that they are negatively correlated.Fig. 2. Canonical correspondence analysis shows the relationships between environmental factors and the PM2.5 bacterial community composition; environmental factors are represented by arrows. The length of the arrow represents the correlation between the environmental factor and the distribution of the bacterial community; the longer the arrow is, the greater the correlation. An acute angle between two environmental factors indicates that they are positively correlated, whereas an obtuse angle indicates that they are negatively correlated.

We hypothesized that analyzing the correlation between environmental factors, and bacterial relative abundance and diversity indices would be a more effective and precise method of distinguishing bacteria that might be significantly related to environmental factors. Spearman’s correlation analysis was used to investigate which bacteria are more closely related to environmental factors. The result revealed that bacterial genera presented significant associations with many environmental factors, especially T and O3. Bacteroides, Subdoligranulum, and Clostridium_sensu_stricto_1 were significantly and negatively correlated with both T and O3 (Table 4 and Fig. 3).


Table 4. Bacterial genera significantly correlated with environmental factors.


Fig. 3. Spearman’s correlation analysis performed between the relative abundance of bacterial genera and environmental factors. The value heat map shows the Spearman correlation coefficient r, which is between –1 and 1. A negative r value signifies a negative correlation, whereas a positive value signifies a positive correlation. * indicates that the significance test was p < 0.05, and ** indicates the significance test was p < 0.001.Fig. 3. Spearman’s correlation analysis performed between the relative abundance of bacterial genera and environmental factors. The value heat map shows the Spearman correlation coefficient r, which is between –1 and 1. A negative r value signifies a negative correlation, whereas a positive value signifies a positive correlation. * indicates that the significance test was p < 0.05, and ** indicates the significance test was p < 0.001.

Tropospheric O3 is a ubiquitous and highly reactive photochemical oxidant gas that results from the chemical reaction of ultraviolet radiation with nitrogen oxides and volatile organic compounds, which mainly originate from the burning of fossil fuels and from industrial sources (Brook et al., 2004; Wu et al., 2011). This photochemical production is stimulated by intensive sunlight and high T, and the tropospheric O3 concentration is the highest in summer and the lowest in winter (Wang et al., 2018). Exposure to tropospheric O3 has been shown to induce inflammation, airway hyper-responsiveness, and oxidative DNA damage (Williams et al., 2008; Palli et al., 2009; Feng et al., 2016). Epidemiological evidence has shown a strong association between increasing levels of tropospheric O3 and the occurrence of respiratory diseases, such as asthma and lung inflammation (Zmirou et al., 2004; Wu et al., 2011; Feng et al., 2016). Furthermore, O3 has a toxic effect on microorganisms in PM2.5 (Hameed et al., 2012), and high concentrations of O3 slow down the growth of the bacteria in PM2.5 (Xu et al., 2017). The synergistic effect of O3 and PM2.5 has been reported to cause serious cardiac and systematical injury, with an apparent dose response tendency (Wang, 2013); however, the relative abundance of pathogenic bacteria was found to be negatively correlated with the O3 concentration, which may be caused by the phototoxic oxidant effect of O3 (Tiedemann et al., 2000; Dong et al., 2016; Liu et al., 2018). In addition, this study identified that Gram-negative bacteria (e.g., Bacteroides, Subdoligranulum, and Parabacteroides), which possess a thin peptidoglycan lamella that leads to weaker resistance (Zuma et al., 2009), were more susceptible to O3.

Moreover, we found that T was correlated with PM2.5 microorganisms. Increased T may promote bacterial growth and accelerate convective air movements, thereby enhancing bacterial dispersal in the atmosphere (Smets et al., 2016). PM2.5 is largely caused by traffic activity and contains hydrocarbons and other chemical compounds. T enhances chemical reactions on the particle surface and may help to form compounds that are more toxic to microorganisms (Alghamdi et al., 2014). Although a relationship was found between airborne bacterial communities and environmental factors, the underlying mechanisms of these interactions remain unclear.


CONCLUSIONS


This study demonstrated that the PM2.5 bacterial community in Xinxiang, Central China, possessed the highest species richness during spring and the highest diversity during summer and analyzed the relationship between the microbial community structure and environmental factors. The results showed that the variations in the bacterial community composition and structure were significantly related to the season. Both the O3 concentration and the air T showed a significant correlation with the PM2.5 bacterial community composition. Our findings serve as a critical reference for studies evaluating the characteristics and effects of bioaerosols as well as those focusing on the effects of atmospheric PM on human health. However, this study has a few limitations. First, it used only a single sample site in an industrial city. Second, a small number (3) of samples per season was collected, and the results were influenced by the specific variations in temperature and humidity on the sampling days. Therefore, these results should be interpreted with caution. Additionally, the present study evaluated only the bacterial diversity and communities in PM2.5. Although bacteria account for nearly 80% of atmospheric microorganisms, fungi and viruses in PM2.5 should also be investigated, considering their potential adverse effects on human health. Therefore, future studies should examine fungal and viral communities in PM as well as their relationships with the chemical composition of PM2.5. 


ACKNOWLEDGMENTS


This study was supported by the National Key Research and Development Program of China (2016YFC0900803 and 2017YFD0400301) and National Natural Science Foundation of China (81573112). The authors also wish to acknowledge Beijing Novogene Bioinformatics Technology Co., Ltd. This manuscript was edited by Wallace Academic Editing.


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