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夏季奶牛场生物气溶胶分布规律与环境影响因子研究

Dispersion and Environmental Influencing for Bioaerosols in Dairy Farm in Summer

  • 摘要: 为探究典型季节下的奶牛场生物气溶胶的分布规律和多元环境因子对其影响程度,对天津市某规模化奶牛场生物气溶胶、环境参数进行连续采集,分析了夏季奶牛场生物气溶胶浓度和载体粒径的时空分布规律,同时筛选出对其浓度具有重要影响的环境因子。结果表明,在空间分布上,牛场近地面1 m高度处的生物气溶胶浓度显著高于近地面4 m高度处浓度(P<0.01)。在粒径分布上,各采样点的分布规律无显著差异(P>0.05),不同粒径粒子所占比例接近且存在粒径越小所占比例越小的趋势,粒径大于2.1μm的粒子约占总数的80%。运用随机森林模型得到了10个影响因子对生物气溶胶浓度的影响程度,其中风向(WD)、温度(T)、紫外辐射强度(UV)、悬浮颗粒物浓度(PM100)等对浓度影响较大。为进一步探究影响因子之间的关系,通过层聚类的方法分析各影响因素之间的共线性关系,发现PM10、PM1与PM2.5之间以及UV与GHI之间具有强共线性关系,可以去除强共线性影响因子,保留组内一种环境因子为代表,以节约采集成本。本研究可为国内牛场空气环境污染排放测定提供参考。

     

    Abstract: Bioaerosol and environmental parameters were continuously collected in a large-scale dairy farm in Tianjin of China to explore the distribution of bioaerosols and the influence of multiple environmental factors on its concentration for dairy farm in typical a season. The temporal and spatial variations of concentration and particle size for bioaerosols in the dairy farm in summer were analyzed, and the importance of measured environmental factors on the concentration of bioaerosols was illustrated. Results showed that the concentration of bioaerosols at 1 m height above the ground was significantly greater than that at 4 m(P<0.01). As for the dispersion of carrier particle size, there was no significant difference between varied sampling points(P>0.05). A smaller proportion was observed at the smaller particle size of carriers. About 80% of carriers had the particle size over 2.1 μm. The importance of 10 environmental factors were analyzed on affecting the concentration of bioaerosols based on the random forest algorithm. Wind direction(WD), temperature(T), ultraviolet radiation intensity(UV) and suspended particulate matter concentration(PM100) showed a greater influence than other factors. The collinearity relationship among different influencing factors were analyzed through the hierarchical clustering method. There was a strong collinearity relationship among PM10, PM1 and PM2.5, and so did that between UV and GHI. This suggested that the strong collinearity influence factor can be removed to leave one factor within the group to saving the sampling cost. The conclusions obtained can provide a reference for the determination of air pollution emissions from domestic dairy farms.

     

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