0.05),不同粒徑粒子所占比例接近且存在粒徑越小所占比例越小的趨勢,粒徑大于2.1μm的粒子約占總數(shù)的80 %。運用隨機森林模型得到了10個影響因子對生物氣溶膠濃度的影響程度,其中風向(WD)、溫度(T)、紫外輻射強度(UV)、懸浮顆粒物濃度(PM100)等對濃度影響較大。為進一步探究影響因子之間的關系,通過層聚類的方法分析各影響因素之間的共線性關系,發(fā)現(xiàn)PM10、PM1與PM2.5之間以及UV與GHI之間具有強共線性關系,可以去除強共線性影響因子,保留組內(nèi)一種環(huán)境因子為代表,以節(jié)約采集成本。本研究可為國內(nèi)牛場空氣環(huán)境污染排放測定提供參考。"/>

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夏季奶牛場生物氣溶膠分布規(guī)律與環(huán)境影響因子研究
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國家自然科學基金項目(32102595)和財政部和農(nóng)業(yè)農(nóng)村部:國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術體系項目(CARS-36)


Dispersion and Environmental Influencing for Bioaerosols in Dairy Farm in Summer
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    為探究典型季節(jié)下的奶牛場生物氣溶膠的分布規(guī)律和多元環(huán)境因子對其影響程度,對天津市某規(guī)?;膛錾餁馊苣z、環(huán)境參數(shù)進行連續(xù)采集,分析了夏季奶牛場生物氣溶膠濃度和載體粒徑的時空分布規(guī)律,同時篩選出對其濃度具有重要影響的環(huán)境因子。結果表明,在空間分布上,牛場近地面1m高度處的生物氣溶膠濃度顯著高于近地面4m高度處濃度(P<0.01)。在粒徑分布上,各采樣點的分布規(guī)律無顯著差異(P>0.05),不同粒徑粒子所占比例接近且存在粒徑越小所占比例越小的趨勢,粒徑大于2.1μm的粒子約占總數(shù)的80 %。運用隨機森林模型得到了10個影響因子對生物氣溶膠濃度的影響程度,其中風向(WD)、溫度(T)、紫外輻射強度(UV)、懸浮顆粒物濃度(PM100)等對濃度影響較大。為進一步探究影響因子之間的關系,通過層聚類的方法分析各影響因素之間的共線性關系,發(fā)現(xiàn)PM10、PM1與PM2.5之間以及UV與GHI之間具有強共線性關系,可以去除強共線性影響因子,保留組內(nèi)一種環(huán)境因子為代表,以節(jié)約采集成本。本研究可為國內(nèi)牛場空氣環(huán)境污染排放測定提供參考。

    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 1m height above the ground was significantly greater than that at 4m (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 2.1μm. 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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汝林,鄧書輝,丁露雨,呂陽,李奇峰,施正香.夏季奶牛場生物氣溶膠分布規(guī)律與環(huán)境影響因子研究[J].農(nóng)業(yè)機械學報,2022,53(5):376-384,399. RU Lin, DENG Shuhui, DING Luyu, Lü Yang, LI Qifeng, SHI Zhengxiang. Dispersion and Environmental Influencing for Bioaerosols in Dairy Farm in Summer[J]. Transactions of the Chinese Society for Agricultural Machinery,2022,53(5):376-384,399.

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  • 收稿日期:2021-11-20
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  • 在線發(fā)布日期: 2022-05-10
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