Ting Liu1, L.-W. Antony Chen 2, Mi Zhang3, John G. Watson3, Judith C. Chow3, Junji Cao4, Hongyu Chen1, Wei Wang1, Jiaquan Zhang1, Changlin Zhan1, Hongxia Liu1, Jingru Zheng1, Naiwen Chen5, Ruizhen Yao1, Wensheng Xiao1

1 School of Environmental Science and Engineering, Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation, Hubei Polytechnic University, Huangshi 435003, China
2 Department of Environmental and Occupational Health, School of Public Health, University of Nevada Las Vegas, Las Vegas, NV 89154, USA
3 Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA
4 Key Laboratory of Aerosol Chemistry & Physics (KLACP), Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
5 School of Energy and Environment, City University of Hong Kong, Hong Kong 999077, China

Received: November 23, 2018
Revised: January 14, 2019
Accepted: January 15, 2019

Download Citation: ||https://doi.org/10.4209/aaqr.2018.11.0422  

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Cite this article:

Liu, T., Chen, L.W.A., Zhang, M., Watson, J.G., Chow, J.C., Cao, J., Chen, H., Wang, W., Zhang, J., Zhan, C., Liu, H., Zheng, J., Chen, N., Yao, R. and Xiao, W. (2019). Bioaerosol Concentrations and Size Distributions during the Autumn and Winter Seasons in an Industrial City of Central China. Aerosol Air Qual. Res. 19: 1095-1104. https://doi.org/10.4209/aaqr.2018.11.0422


  • Bioaerosol numbers and size distributions were first quantified for Huangshi, China.
  • Bioaerosol number concentrations were higher than other cities in northern China.
  • Bioaerosol number concentrations were dominated by submicron particles.
  • Bioaerosol mass fractions were estimated at 2.4 ± 1.9% in PM2.5 and 4.8 ± 3.2% in PM10.
  • Higher bioaerosol concentrations were observed in winter and on polluted days.


The ambient bioaerosols were measured in PM2.5 and PM10 samples taken in Huangshi City, Hubei Province, China, during autumn and winter from November 2017 to February 2018. Both the bioaerosol number concentration and size distribution (0.37–16 µm) were obtained by direct fluorescent staining coupled with microscopic imaging. The bioaerosol number concentrations ranged from 0.05 to 3.4 # cm–3 for PM2.5 and from 0.17 to 5.7 # cm–3 for PM10, with averages of 0.90 # cm–3 and 1.9 # cm–3, respectively. In terms of particle number, the bioaerosols were dominated by fine particles (0.37–2.5 µm in diameter), with a larger proportion of submicron than supermicron particles. Assuming a unit density of 1 g cm–3 and a spherical shape for the particles, the mass abundances of the bioaerosols were estimated to be 2.4 ± 1.9% and 4.8 ± 3.2% of the PM2.5 and PM10, respectively, as measured by a nearby compliance monitor. Higher bioaerosol concentrations were observed in winter than autumn and on polluted than non-polluted days. During heavily polluted conditions, bioaerosols in the PM2.5 and PM10 were enriched by 6 and 3.7 times, respectively, compared to non-polluted days and contributed up to 15% of the PM10 mass. Rainfall and snowfall appeared to lower the bioaerosol levels. As enhanced emission controls on combustion and dust sources decrease PM levels in China, the bioaerosol fraction in measured PM concentrations will likely increase.

Keywords: Primary biological aerosol particles (PBAP); PM2.5; PM10; Size distribution; Air quality; Fluorescence microscopy.


Bioaerosols, also referred to as primary biological aerosol particles (PBAPs), include fur fibers, dandruff, skin fragments, plant fragments, pollen, spores, bacteria, algae, fungi, and viruses ranging in diameters from tens of nanometers to millimeters (Jaenicke, 2005). Bioaerosols are important because some PBAPs cause human, plant, and animal diseases (Douwes et al., 2003; Griffin, 2007) while others act as cloud condensation nuclei affecting cloud formation and precipitation (Hauspie and Pagezy, 2002; Heidi et al., 2003; Group, 2004; Rosenfeld et al., 2008). Recent studies suggest diverse bioaerosol concentrations and size distributions in both indoor and outdoor environments while calling for a better understanding of how bioaerosols co-vary with other air pollutants, especially PM2.5 and PM10 (airborne particulate matter [PM] with aerodynamic diameter < 2.5 and < 10 µm, respectively), and their effects on climate and health (Wei et al., 2016; Huang et al., 2017; Xie et al., 2018; Zhai et al., 2018).

Poor air quality in China, particularly elevated PM2.5 during cold seasons, is often attributed to extensive coal combustion for heating coupled with unfavorable weather patterns (Cao et al., 2012; Huang et al., 2014; Fu and Chen, 2017). Although PM2.5 chemical compositions have been studied and used for source apportionment (Henry et al., 1984; Watson et al., 2008, 2016; Cheng et al., 2018), bioaerosol contributions to PM2.5are rarely assessed. Several recent studies examined the spatial and temporal variability of bioaerosol concentrations, size distributions, and speciation in China (Cao et al., 2014; Gao et al., 2015; Li et al., 2015; Dong et al., 2016; Wei et al., 2016). Higher bioaerosol concentrations were reported on polluted than on non-polluted days. Xie et al. (2018) found a positive relationship between air quality index (AQI) and bioaerosol concentrations. Most of these studies were conducted in heavily populated megacities of northern China, such as Beijing, Xi’an, and Qingdao (Cao et al., 2014; Wei et al., 2016; Dong et al., 2016; Xie et al., 2018). Bioaerosol characteristics may differ in smaller cities and in central-southern China, owning to different vegetation, land use, and climate conditions.

Huangshi, a medium-size city with an area of 4,583 km2 and a population of ~2.5 million, is located ~90 km southwest of Wuhan in central China. It features a subtropical continental monsoon climate, with a mean annual temperature of 17°C and precipitation of ~1400 mm. The diverse vegetation coupled with mild temperatures and high relative humidity facilitates bioaerosol production. Huangshi is an important industrial source of raw materials with high energy consumption and the accompanying environmental pollution (Liu et al., 2017). Affected by local industries, particularly coal-based ore smelters, and intrusion of regional polluted air, poor air quality often occurs during autumn and winter. Average PM2.5concentrations for the two seasons have exceeded 100 µg m–3 with ~20% from organic carbon (OC) (Zhan et al., 2017). Based on the EC-tracer analysis, Zhan et al. (2017) concluded that a substantial fraction of OC originated from bioaerosols and/or secondary organic carbon (SOC), but they were not able to distinguish between the two. Average OC in PM10 was ~5 µg m–3 higher than in PM2.5 (Zhan et al., 2017) and the difference may be due to bioaerosols such as fungal spores, pollen, and vegetative detritus (Edgerton et al., 2009).

Chen et al. (2019) demonstrated the practicality of using a direct-staining (DS) technique, coupled with epifluorescence microscopy (FM), to quantify bioaerosols collected on filters. The DS-FM method measures bioaerosol number concentration and size distribution, from which the bioaerosol mass can be estimated. Compared to culture-based methods, fluorescence methods detect bioaerosols in viable, non-viable, and viable but non-culturable states (Li and Huang, 2006; Chi and Li, 2007; Li et al., 2011). DS-FM is also more cost-effective than online monitoring with fluorescence analyzers, such as the Waveband Integrated Bioaerosol Sensor (WIBS) and Ultraviolet Aerodynamic Particle Sizer (UV-APS). This study applies the DS-FM method to PM2.5 and PM10 filter samples collected in Huangshi. The concentrations and size distributions of bioaerosols are examined. Results from this work further our understanding of bioaerosol contributions to PM and offer insights to the regional air pollution management.


Sampling Site and Methods

The sampling site (30°12ʹ35.71ʺN, 115°01ʹ30.75ʺE) was on the rooftop of a five-story building (about 15 m AGL) at the Hebei Polytechnic University (HBPU) campus in central Huangshi (Fig. 1). This site is surrounded by trees, greenbelts, and a stadium and is ~400 m from major highways with no nearby industrial activities. Ambient PM2.5 and PM10 samples were acquired daily between November 4, 2017, and February 10, 2018, using a pair of MiniVol samplers (Airmetrics, Springfield, OR, USA) equipped with size-selective inlet sampling at ~5 L min–1. Sampling started at 13:00 LST and lasted for 5–8 hours. Particles were collected onto 47-mm diameter black polycarbonate filters (0.2 µm pore size, PCTE; Whatman, Little Chalfont, UK) as these provide the lowest background for DS-FM.

Fig. 1. Bioaerosol monitoring site and the nearby (~80 m) air quality monitoring station at the Hubei Polytechnic University (HBPU), Huangshi, China.Fig. 1. Bioaerosol monitoring site and the nearby (~80 m) air quality monitoring station at the Hubei Polytechnic University (HBPU), Huangshi, China.

Hourly PM2.5 and PM10 mass concentrations were measured using a Tapered Element Oscillating Microbalance (1405 TEOM™; Thermal Fisher Scientific, Waltham, MA, USA) located ~80 m from the HBPU site (see Fig. 1), as part of the Ministry of Ecology and Environment’s compliance network to determine Air Quality Index (AQI). According to China’s “Technical Regulation on Ambient Air Quality Index (HJ633-2012),” the AQI value from PM2.5 is used to categorize air pollution. One of the six air quality levels—good (0–50), moderate (51–100), lightly polluted (101–150), moderately polluted (151–200), heavily polluted (201–300), and severely polluted (> 300)—was assigned to each monitoring day. Days with AQI values below 100 (good and moderate) were considered as non-polluted days.

Local meteorological parameters such as wind speed and precipitation were obtained from the China Weather Network (http://www.weather.com.cn/weather1d/101200601.shtml).

Bioaerosol Particle Detection

Bioaerosols were measured using a fluorescence microscope after staining with 4ʹ,6-diamidino-2-phenylindole dihydrochloride (DAPI) following the DS-FM protocol as described in Chen et al. (2019). Briefly, a 13-mm diameter disc was removed from each polycarbonate filter sample and placed with the deposit-side up onto a drop of DAPI working solution (20 µg mL–1). It was incubated at room temperature in the dark for 20 minutes while the stain permeated through the filter to interact with the bioaerosols (Griffin et al., 2001; Prussin et al., 2015). A coverslip was then mounted on the stained sample with a water-soluble, anti-fading adhesive (Mounting Medium; Solarbio, Beijing, China) and stored below 4°C before epifluorescence investigation. DS-FM differs from the extraction-staining fluorescence microscopy (ES-FM) method (Cao et al., 2014; Dong et al., 2016; Wei et al., 2016; Xie et al., 2018) in that the latter stains particles in a liquid after they are extracted from filter samples. The stained particles are then re-deposited onto polycarbonate filters for FM analysis.

The prepared sample slides were examined on a fluorescence microscope (DM2500; Leica, Germany) equipped with a Charge Coupled Device (CCD) camera (DFC450 C; Leica, Germany) and a filter cube containing a 350/50 band pass (BP) excitation filter, a 400 nm dichromatic mirror, and a 460/50 BP emission filter. The DAPI-DNA coupling produces a bright blue fluorescence at ~460 nm when excited with 365 nm light (Porter and Feig, 1980). Under a 400× magnification, about 30 images of view (0.218 × 0.163 mm2, 2560 × 1920 pixels) were captured along a filter dissection to represent the entire 13-mm diameter deposit area.

Controls were prepared from a subset of exposed filters in the same way as the samples except for staining with DAPI. Fluorescing particles were not detected on the controls (see examples in Fig. S1 of the supplementary information). Though non-biological particles such as those containing polycyclic aromatic hydrocarbons (PAHs) auto-fluoresce in the emission region of 440–470 nm (Pan, 2015), the auto-fluorescence may be too weak to be distinguished from the background of polycarbonate filter.

Quantification of Bioaerosol Concentration and Size Distribution

Using the ImageJ® software, the total number and area of bioaerosol particles on each fluorescence image were calculated automatically. The images were converted to binary (black-and-white) pictures with the fluorescent particles, a surrogate for bioaerosols, in black against a white background. The thresholding between particles and background was accomplished with the “Triangle” algorithm (Zack et al., 1977; Seo et al., 2014). Particles smaller than 15 pixels were excluded to suppress false positives. ImageJ® reported the particle number in the image and the projected area (Ap) of each individual particle, from which the equivalent projected area diameter (Deq,A) was calculated:

The cut-off Deq,A was 0.37 µm, corresponding to 15 pixels, which should be sufficient to detect most bacteria, fungal spores, and other major mass-contributing bioaerosol classes. The automatic bioaerosol counts were verified by manual counting for selected samples. Bioaerosol number concentration (# cm–2) was determined from the average particle counts over the 30 fluorescence images taken for each sample, divided by the image area (0.218 × 0.163 mm2), with the standard error representing the uncertainty.

To estimate bioaerosol mass, particle volume (V) and density (ρ) are required. The first-order approximation assumes spherical particles, thus: 

Particle density may depend on the bioaerosol type, but an assumption of 1 g cm3 (Matthias-Maser and Jaenicke, 2000; Chow et al., 2015) was used in this study for all bioaerosol particles. The bioaerosol number and mass concentrations were converted to # cm3 and µg m3, respectively, using the MiniVol sampling time and flow rate. Errors from the bioaerosol count and flow rate were propagated to yield the measurement uncertainty. Excel 2013 and SPSS 19.0 software were used to statistically analyze the experimental data. 


Bioaerosol Concentrations and Size Distribution

A total of 51 pairs of PM2.5/PM10 samples were collected. Bioaerosol number concentrations ranged from 0.05 to 3.37 # cm3 for PM2.5 and from 0.17 to 5.73 # cm3 for PM10 (Fig. 2), with averages of 0.90 # cm3 and 1.86 # cm3, respectively. Taking November as autumn and December to January as winter, the concentrations varied significantly by season with higher values found in winter (1.04 # cm3 for PM2.5 and 2.16 # cm3 for PM10) than during autumn (0.60 # cm3 for PM2.5 and 1.25 # cm3 for PM10). Xie et al. (2018) and Dong et al. (2016) observed similar seasonal differences in Xi’an and Qingdao, China, respectively. They attributed the high winter bioaerosol levels to stagnant weather conditions, high PM levels, and hazy days. In Huangshi, severe haze pollution was also observed more frequently in winter.

Fig. 2. Day-to-day variability in PM10 and PM2.5 mass (from TEOM compliance monitors) and bioaerosol number concentrations from November 4, 2017, to February 10, 2018, in Huangshi, China. PM2.5 concentration exceeded PM10 on January 5 and 7, 2018, likely due to interference from heavy rain.Fig. 2. Day-to-day variability in PM10 and PM2.5 mass (from TEOM compliance monitors) and bioaerosol number concentrations from November 4, 2017, to February 10, 2018, in Huangshi, China. PM2.5 concentration exceeded PM10 on January 5 and 7, 2018, likely due to interference from heavy rain.

With respect to the particle size-number distribution, bioaerosols were dominated by fine particles 0.37–2.5 µm in diameter (Fig. 3). Image analysis showed that coarse particles (Deq,A > 2.5 µm) accounted for 2.6% and 6.5% of bioaerosol numbers in PM2.5 and PM10 samples, respectively. The small fraction of coarse particles in PM2.5 samples reflects the imperfect MiniVol size-selective inlet that passes some coarse particles. Bioaerosols with diameters smaller than 1 µm were classified as prokaryotes (e.g., bacteria) and viruses, while supermicrometer particles were classified as eukaryotes (e.g., fungal spores, pollen, and organic debris) (Mayol et al., 2014). The proportions of prokaryotes were 68.7% among PM2.5 and 59.5% among PM10 bioaerosols, and the proportions of eukaryotes were lower with 31.3% among PM2.5 and 40.5% among PM10bioaerosols.

Fig. 3. Average number-size distribution of bioaerosol samples measured by DS-FM. Particles were counted by 7 size bins (0.37–0.42, 0.42–0.56, 0.56–1, 1–2.5, 2.5–5.6, 5.6–10, and 10–16 µm) and averaged over all samples.F
ig. 3. Average number-size distribution of bioaerosol samples measured by DS-FM. Particles were counted by 7 size bins (0.37–0.42, 0.42–0.56, 0.56–1, 1–2.5, 2.5–5.6, 5.6–10, and 10–16 µm) and averaged over all samples.

If determined from the difference of bioaerosol concentrations between PM10 and PM2.5 samples (i.e., 1.86 minus 0.90 # cm3), coarse bioaerosols would account for 0.96 # cm3 or 51.6% of the PM10 bioaerosol numbers, much larger than the 6.5% number fraction inferred solely from the image analysis of PM10 samples. The contrast suggests that substantial fine bioaerosol particles might have been attached to other coarse particles (e.g., fugitive dust) and only captured in PM10 samples. Similar phenomena were reported during dust episodes (Yeo and Kim, 2002; Hallar et al., 2011).

The reconstructed bioaerosol mass from Eq. (2) exhibits a dominant contribution from supermicrometer particles (Fig. 4). As show in Fig. 2, PM2.5 mass ranged from 27 to 190 µg m3 with an average of 85.7 µg m3 while PM10 mass ranged from 34 to 260 µg m3 with an average of 129 µg m3. The bioaerosol component, on average, accounted for 2.1 µg m3 (2.4 ± 1.9% ) of PM2.5 mass and 6.3 µg m3 (4.8 ± 3.2%) of PM10mass with the highest mass fractions being 10% for PM2.5 (December 13, 2017) and 14% for PM10 (December 18, 2017).

Fig. 4. Average mass-size distribution of PM2.5 and PM10 bioaerosols by DS-FM. Particle mass (M) was calculated for 7 size bins between 0.37 and 16 µm in diameter (0.37–0.42, 0.42–0.56, 0.56–1, 1–2.5, 2.5–5.6, 5.6–10, and 10–16 µm) and averaged over all samples.Fig. 4. Average mass-size distribution of PM2.5 and PM10 bioaerosols by DS-FM. Particle mass (M) was calculated for 7 size bins between 0.37 and 16 µm in diameter (0.37–0.42, 0.42–0.56, 0.56–1, 1–2.5, 2.5–5.6, 5.6–10, and 10–16 µm) and averaged over all samples.

The summed mass of bioaerosols with Deq,A < 2.5 µm on PM10 sample images only accounts for 1.8 ± 1.0% of PM2.5 mass, lower than the 2.4 ± 1.9% reconstructed from PM2.5 sample images by combining bioaerosols of all sizes. On the other hand, coarse bioaerosols > 2.5 µm Deq,A would account for 13.5 ± 15.5% of PM2.5-10 determined from the difference of PM10 and PM2.5 mass. The bioaerosol components were more substantial in coarse than fine PM.

Relationships of Bioaerosol, PM, and Meteorological Conditions

Temperature, relative humidity, wind speed, and ambient PM may influence bioaerosol concentrations. PM2.5 and PM10 mass were found to significantly correlate with the bioaerosol numbers (Table 1), consistent with previous research (Haas et al., 2013; Alghamdi et al., 2014; Xie et al., 2018). This suggests that bioaerosols and PM are impacted by similar factors. Coarse particles can act as carriers for microorganisms, and the increasing quantity of PM offers more surface area on which microbes can adhere (Jeon et al., 2011; Xie et al., 2018). On the other hand, ultrafine particles (< 100 nm), including soot particles (30–40 nm), may adhere to bioaerosols.

Table 1. Spearman correlation coefficients between the bioaerosol concentrations and environmental factors for all sampling days. a bioaerosols of PM2.5 (# cm–3). b bioaerosols of PM10 (# cm–3). c PM2.5 mass (µg m–3). d PM10 mass (µg m–3). e The daily mean temperature (°C). f The daily mean relative humidity (%). g The daily mean wind speed (m sec–1). ** p < 0.01 (2-tailed). * p < 0.05 (2-tailed).

Daily mean temperatures varied from 0 to 23°C and daily mean wind speeds varied from 0.84 to 2.88 m s–1 throughout the monitoring period. Neither of them was correlated with bioaerosol levels. Relative humidity was negatively correlated with bioaerosol concentrations (p < 0.05). High relative humidity mainly occurred on rainy and snowy days (Fig. 2), while bioaerosol concentrations during and after the precipitation decreased due to scavenging (Li et al., 2017; Xie et al., 2018).

For example, through a prolonged rainfall from January 1 to January 3, 2018, PM2.5 concentrations were reduced from 170 to 89 µg m3 and PM10concentrations decreased from 239 to 147 µg m3 while bioaerosol number concentrations decreased from 3.37 to 0.10 # cm3 in PM2.5 and from 4.48 to 0.41 # cm3 in PM10. A similar situation was observed for the January 25–26, 2018 snowfall period with low bioaerosol number concentrations (i.e., 0.05–0.09 # cm3 for PM2.5 and 0.17–0.52 # cm3 for PM10) observed after the snow. By contrast, some studies reported higher bioaerosol levels after precipitation, particularly during the spring-summer blooming seasons (Huffman et al., 2013; Kang et al., 2015; Rathnayake et al., 2017).

Bioaerosol versus Air Quality Levels

Based on the AQI, there were 3 good days, 14 moderate days, 27 lightly polluted days, 5 moderately polluted days, and 2 heavily polluted days during the sampling period. Table 2 presents bioaerosol number and PM mass concentrations by AQI level. Average bioaerosol concentrations for non-polluted days were 0.47 ± 0.24 # cm3 for PM2.5 and 1.02 ± 0.53 # cm3 for PM10 and were significantly different from those for the heavily polluted days (t-test: p = 0.002). Average bioaerosol concentrations for moderately and heavily polluted days were 3.6–6 times higher for PM2.5and 3.3–3.7 times higher for PM10 as compared with non-polluted days. There was no appreciable difference in the bioaerosol size distributions among different air quality levels.

Table 2. Summary of bioaerosol and PM mass concentrations by air quality level in Huangshi, China (November 4, 2017–February 10, 2018). a Air quality levels were categorized by AQI: Good (0–50), Moderate (51–100), Lightly polluted (101–150), Moderately polluted (151–200), Heavily polluted (201–300), and Severely polluted (> 300). b Number of days for each air quality level.

AQI values were consistently above 100 and exceeded 200 (i.e., polluted) during the December 21, 2017–January 1, 2018 period with PM10 bioaerosol numbers > 2.25 # cm3, except on December 28, 2017 (1.08 # cm3), when precipitation of ~1.3 mm occurred (Fig. 2). Elevated bioaerosol concentrations (2.6 # cm3 for PM2.5 and 5.4 # cm3 for PM10), PM2.5 (123 µg m3), and PM10 (144 µg m3) returned on December 30, 2017, right after the precipitation. The haze episode on December 30, 2017 covered part of Hubei Province (e.g., Wuhan and Huangshi) while also affecting nearby Anhui and Jiangshu Provinces. Back trajectory analysis (Fig. S2) reveals possible transport of polluted air masses from northern China. Bioaerosols contributed to ~10% of PM10 mass during the episode.

In an urban environment such as Huangshi, conditions that engender high PM episodes, such as stagnation and shallow boundary layers, might also lead to the accumulation of bioaerosols, while precipitation would lower bioaerosol and other PM components simultaneously. It is noteworthy that bioaerosol concentrations did not always coincide with the deteriorating air quality. The highest PM10 bioaerosol concentration, 5.73 # cm3, was found on December 23, 2017, with an AQI of 144 (i.e., lightly polluted). Xie et al. (2018) reported the highest bioaerosol levels under the moderately polluted condition, whereas Wei et al. (2016) found that upon the occurrence of haze the number concentration of fluorescent particles ascended briefly, and then started to decrease as the haze progressed over time during a short monitoring period. The differences in source and formation mechanism between secondary aerosol and bioaerosols may explain their distinct temporal trends during the pollution episodes.

Table 3 compares the bioaerosol concentrations of polluted days. The average PM10 bioaerosol concentration in this study was 3–4 times higher than those measured in Beijing, Xi’an, and Qingdao. The Xi’an and Qingdao bioaerosols were measured using the ES-FM method with DAPI stain. ES-FM likely yields lower bioaerosol counts than DS-FM because of particle losses during the wash-off and re-deposition steps (Chen et al., 2019). Beijing’s bioaerosols were quantified by UV-APS, though real-time auto-fluorescence methods including UV-APS and WIBS are known to underestimate bioaerosol counts as not every bioaerosol particle fluoresces under the experimental conditions (Huffman et al., 2010; Després et al., 2012). Different analytical methods partly explain the higher bioaerosol concentrations measured in this study. Other factors include different climate conditions among these cities.

Table 3. Comparison of bioaerosol concentrations for different cities Bioaerosols of in China during polluted periods.


The DS-FM method was applied to quantify not only the bioaerosol number concentration but also the size distribution. Higher concentrations were observed in winter than autumn and on polluted than non-polluted days. However, these levels did not peak at the climax of haze episodes, implying different sources and/or subtle interactions between the bioaerosols and the PM. The results also confirm the important role of precipitation in removing bioaerosols. The bioaerosol number concentration was higher in the PM2.5 fraction, but the mass concentration was higher in the PM2.5-10 fraction. Bioaerosols contributed substantially to the mass of airborne particles in Huangshi, especially on polluted days. As a result of enhanced emission controls that have been reducing PM from combustion and dust sources in China, bioaerosols are expected to constitute a growing fraction of measured PM concentrations. Investigations of bioaerosol contributions to air pollution should be continued and extended to other regions of China.


The authors appreciate financial support from the Research Project of Hubei Provincial Department of Education (D20184502), the Hubei Universities of Outstanding Young Scientific and Technological Innovation Team Plans (T201729), and the Special Scientific Research Funds for National Basic Research Program of China (2013FY112700).

The University of Nevada, Las Vegas, and Desert Research Institute provided additional financial support for their participants. 

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