Analysis of influencing factors of microbial aerosol concentration in centralized air conditioning and ventilation system of subway platforms
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摘要:
目的 了解城市地铁车站集中空调通风系统微生物气溶胶的特征，探讨超大城市地铁车站集中空调通风系统微生物气溶胶的影响因素，并对真菌气溶胶浓度与相关影响因素进行分析。 方法 基于随机抽样抽取覆盖某大型城市15条地铁线路的120座车站，运用Andersen撞击式分级采样法采集599个地铁车站集中空调通风系统中6级粒径段(≥7.00 μm, 4.70~＜7.00 μm, 3.30~＜4.70 μm, 2.10~＜3.30 μm, 1.10~＜2.10 μm和0.65~＜1.10 μm)的气溶胶样本，并采用培养法检测细菌与真菌气溶胶浓度。运用Spearman秩相关性、Mann-Whitney U检验及Kruskal-Wallis检验分析微生物气溶胶浓度的影响因素。 结果 某大型城市地铁车站集中空调通风系统细菌气溶胶和真菌气溶胶的中位数浓度(四分位距)分别为163(148)CFU/m3和346(205)CFU/m3。Spearman秩相关性分析的结果显示不同影响因素与微生物气溶胶浓度的相关性有所不同。其中，环境温度与总细菌气溶胶浓度(r=0.22, P＜0.001)和总真菌气溶胶浓度(r=0.17, P＜0.001)均呈正相关。可吸入颗粒物(inhalable particulate matter, PM10)质量浓度与总细菌气溶胶浓度呈正相关(r=0.10, P＜0.05)，与总真菌气溶胶浓度的相关性较低(r=0.06, P＞0.05)。Spearman秩相关性分析的结果还显示站点类型、站台开通年份、季节及气象条件等因素均可能影响车站集中空调通风系统的微生物气溶胶浓度。 结论 温度、相对湿度、PM10质量浓度、清洗间隔、采样时经过人数、站点类型、气象条件等因素均会影响车站集中空调通风系统的微生物气溶胶浓度，未来仍需对这些因素予以重视，做好地铁车站集中空调通风系统的微生物风险防控工作。 Abstract:Objective To characterize the microbial aerosols in the centralized air conditioning and ventilation system of urban subway stations, and to explore the influencing factors of microbial aerosol concentration in the centralized air conditioning and ventilation system of subway stations in mega cities, and conduct analysis on fungal aerosol concentration and related influencing factors. Methods Applying random sampling strategy, this study selected 120 subway stations across 15 subway lines in a mega city. For each station, Andersen stage impactors were used to collect aerosol samples, covering 6 particle size segments (≥7.00 μm, 4.70- < 7.00 μm, 3.30- < 4.70 μm, 2.10- < 3.30 μm, 1.10- < 2.10 μm, 0.65- < 1.10 μm). The concentration of bacterial and fungal aerosols was detected using culture method. Spearman rank correlation analysis, Mann Whitney U test, and Kruskal Wallis test were conducted to identify the potential influencing factors of microbial aerosol concentration. Results The median concentrations (interquartile range, IQR) of bacterial and fungal aerosols in the centralized air conditioning and ventilation system were 163 (148) CFU/m3 and 346 (205) CFU/m3, respectively. The results of the Spearman rank correlation analysis indicated that the correlation between different influencing factors and microbial aerosol concentration varied. For instance, we found a significant positive correlation of temperature with total bacteria aerosol concentration (r=0.22, P < 0.001) and total fungal aerosol concentration (r=0.17, P < 0.001). Inhalable particulate matter (PM10) mass concentration was significantly and positively correlated with total bacterial aerosol concentration (r=0.10, P < 0.05); however, its correlation with total fungal aerosol concentration was weak and did not reach statistical significance (r=0.06, P > 0.05). The results of Spearman rank correlation analysis showed that factors such as station type, platform opening year, season, and meteorological conditions might significantly affect the microbial aerosol concentration of the station′s centralized air conditioning and ventilation system. Conclusions Temperature, relative humidity, PM10 mass concentration, cleaning interval, number of people passing through during sampling, station type, meteorological conditions may significantly affect the microbial aerosol concentration in the centralized air conditioning and ventilation system of subway stations. In the future, it is urgent to pay attention to these factors. Our findings may support the microbial risk prevention and control for subway stations. -
图 1 地铁车站集中空调通风系统颗粒物、采样点经过人数和微气候统计学描述
A：地铁车站集中空调通风系统PM10 μg/m3；B：采样时经过人数；C：温度/℃；D：相对湿度/%; 地铁线路采样点数从左至右依次为14、55、5、60、5、80、46、85、55、20、25、30、70、15和34。
Figure 1. Statistical description of particulate matter, number of people passing through sampling, and microclimate in the centralized air conditioning and ventilation system of subway stations
A: PM10 μg/m3; B: number of people passing through during sampling; C: temperature /℃; D: relative humidity/%; The sampling points for subway lines from left to right are 14, 55, 5, 60, 5, 80, 46, 85, 55, 20, 25, 30, 70, 15, and 34, respectively.
图 2 地铁车站集中空调通风系统细菌及真菌气溶胶浓度分布
A：细菌气溶胶浓度分布；B：真菌气溶胶浓度分布。
Figure 2. Aerosol concentrations distribution of bacterial and fungal in the centralized air conditioning and ventilation system of subway stations
A: aerosol concentrations distribution of bacterial; B: aerosol concentrations distribution of fungal.
图 3 地铁车站集中空调通风系统细菌及真菌气溶胶粒径分布
A: 细菌气溶胶粒径/μm分布；B: 真菌气溶胶粒径/μm分布；Ⅰ~Ⅵ为微生物气溶胶粒径分级。
Figure 3. Particle size distribution of bacterial and fungal aerosols in the centralized air conditioning and ventilation system of subway stations
A: particle size distribution of bacterial /μm; B: particle size distribution of fungal /μm; Ⅰ~Ⅵ represent the particle size classification of microbial aerosols.
图 4 细菌和真菌气溶胶浓度的站点类型、建设年份、季节、晴雨状况、温度及相对湿度多因子箱线图
A：站点类型；B：建设年份；C：季节；D：晴雨状况；E：温度/℃；F：相对湿度/%；a：P＜0.001；b：P＜0.01；c：P＜0.05。
Figure 4. Box plot of bacterial and fungal aerosol concentration distribution in subgroups of station type, station construction year, season, whether conditions, temperature, and relative humidity
A: station type; B: station construction year; C: season; D: whether conditions; E: temperature/℃; F: relative humidity/%; a: P < 0.001; b: P < 0.01; c: P < 0.05.
图 5 地铁车站空调送风系统微生物气溶胶粒级分布与环境因素相关性分析
A：Spearman相关性系数对应的P值；B：Spearman相关性系数; Ⅰ级~Ⅵ级的细菌气溶胶浓度记为BⅠ~BⅥ，Ⅰ级~Ⅵ级的真菌气溶胶浓度记为FI~FⅥ，TB、TF分别代表细菌总数、真菌总数；a：P＜0.001；b：P＜0.01；c：P＜0.05。
Figure 5. Correlation analysis between the particle size distribution of aerosols and environmental factors in the air conditioning system of subway stations
A: the significance of Spearman correlation coefficient; B: the right figure shows Spearman correlation coefficient; The concentration of bacterial aerosols in grades Ⅰ to Ⅵ is recorded as BⅠ to BⅥ, and the concentration of fungal aerosols in grades Ⅰ to Ⅵ is recorded as FⅠ~FⅥ; TB and TF representing the total number of bacteria and fungi, respectively; a: P < 0.001; b: P < 0.01; c: P < 0.05.
图 6 换乘站点基于多元线性回归的真菌气溶胶浓度回归模型的拟合效果
Ⅰ级至Ⅵ级的真菌气溶胶浓度记为F Ⅰ~F Ⅵ，TF代表真菌总数。
Figure 6. Fitting effect of fungal aerosol concentration regression model based on multiple linear regression at interchange stations
The concentration of fungal aerosols in grades Ⅰ to Ⅵ is recorded as F Ⅰ~F Ⅵ; TF representing the total number of fungi.
图 7 普通站点基于多元线性回归的真菌气溶胶浓度回归模型的拟合效果
Ⅰ级至Ⅵ级的真菌气溶胶浓度记为F Ⅰ~F Ⅵ，TF代表真菌总数。
Figure 7. Fitting effect of fungal aerosol concentration regression model based on multiple linear regression at common stations
The concentration of fungal aerosols in grades Ⅰ to Ⅵ is recorded as F Ⅰ~F Ⅵ; TF representing the total number of fungi.
表 1 基于多元线性回归的真菌气溶胶浓度回归模型
Table 1. Regression model of fungal aerosol concentration based on multiple linear regression
站点类型
Station type变量
Variable常量
Constant站点开通年份
Construction year清洗间隔/天
Cleaning interval/dayPM10质量浓度/(μg·m-3)
PM10 mass concentration/(μg·m-3)采样时经过人数
People passed through温度/℃
Temperature/℃相对湿度/%
Relative humidity
/%Radjusted2值value RMSE 换乘站点
Interchange stationFⅠ -724.72 0.38 0.05 -0.02 0.17 ① -1.77 ① 0.24 0.18 26.83 FⅡ 331.31 0.12 0.12 ① 0.40 0.49 ① -5.26 ① 0.33 0.30 54.41 FⅢ -7.21 0.05 0.12 ① 0.44 0.64 ① -4.62 ① 0.80 0.44 56.04 FⅣ -9 560.67 ① 4.71 ① 0.02 1.43 ① 0.79 ① 1.14 1.28 0.48 87.10 FⅤ -12 671.61 ① 6.29 ① -0.09 0.55 0.73 ① -2.60 1.42 ① 0.47 79.30 TF -22 686.34 ① 11.35 ① 0.23 2.73 ① 2.84 ① -13.93 ① 4.25 ① 0.61 206.69 普通站点
Common stationFⅠ -1 107.23 0.54 0.03 -0.07 0.34 ① 2.97 ① -0.28 0.06 31.29 FⅡ -2 478.97 ① 1.18 ① 0.04 ① 0.22 0.72 ① 5.24 ① -0.09 0.12 36.37 FⅢ -5 978.69 ① 2.90 ① 0.09 ① 0.36 ① 0.47 ① 9.18 ① -0.22 0.15 51.95 FⅣ -7 638.87 ① 3.63 ① 0.13 ① 0.65 ① 0.01 17.89 ① 0.05 0.14 92.35 FⅤ -5 572.00 ① 2.65 ① -0.08 ① -0.12 0.12 10.40 ① 0.92 ① 0.16 55.99 TF -22 946.39 ① 10.98 ① 0.22 1.03 1.75 ① 45.76 ① 0.38 0.17 210.77 注：Ⅰ级至Ⅵ级的真菌气溶胶浓度记为F I~F Ⅵ，TF代表真菌总数; RMSE，均方根误差。
① P＜0.05。
Note：The concentration of fungal aerosols in grades Ⅰ to Ⅵ is recorded as F I~F Ⅵ; TF representing the total number of fungi; RMSE, root mean squared error.
① P＜0.05.
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