李永振, 方志伟, 鲁煜建, 梁超, 施正香, 王朝元. 大型自然通风奶牛舍空气颗粒物浓度监测方法中测点数和位置优化[J]. 农业工程学报, 2023, 39(9): 201-209. DOI: 10.11975/j.issn.1002-6819.202302038
    引用本文: 李永振, 方志伟, 鲁煜建, 梁超, 施正香, 王朝元. 大型自然通风奶牛舍空气颗粒物浓度监测方法中测点数和位置优化[J]. 农业工程学报, 2023, 39(9): 201-209. DOI: 10.11975/j.issn.1002-6819.202302038
    LI Yongzhen, FANG Zhiwei, LU Yujian, LIANG Chao, SHI Zhengxiang, WANG Chaoyuan. Sampling points number and location optimization of particulate matter concentration monitoring in a large naturally ventilated dairy barn[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(9): 201-209. DOI: 10.11975/j.issn.1002-6819.202302038
    Citation: LI Yongzhen, FANG Zhiwei, LU Yujian, LIANG Chao, SHI Zhengxiang, WANG Chaoyuan. Sampling points number and location optimization of particulate matter concentration monitoring in a large naturally ventilated dairy barn[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2023, 39(9): 201-209. DOI: 10.11975/j.issn.1002-6819.202302038

    大型自然通风奶牛舍空气颗粒物浓度监测方法中测点数和位置优化

    Sampling points number and location optimization of particulate matter concentration monitoring in a large naturally ventilated dairy barn

    • 摘要: 对奶牛舍颗粒物浓度和分布进行实时连续监测,是评估其环境风险和制定科学减排措施的前提。随着大型奶牛舍的快速发展,对尽可能少且科学地布置测点数量和位置并能够对舍内颗粒物浓度进行精准监测,提出了新的挑战。为探究测点优化布置方案,该研究基于物联网技术在大型自然通风奶牛舍内3个相邻饲养区域布置17个采样点,对总悬浮颗粒物(total suspended particle, TSP)和细颗粒物(PM2.5)浓度进行了连续6个月监测,得到舍内浓度平均值(视为“真值”);利用系统聚类和误差分析方法得到优化的测点方案,并将其颗粒物浓度结果与6种传统测点方案进行对比,以确定最优的测点数量和位置方案。结果表明,舍内3个饲养区域内颗粒物浓度分布均匀,PM2.5浓度在不同采样高度上无统计差异(P>0.05),而TSP浓度在屋顶通风口下方(9.0 m)明显低于1.5和2.5 m高度(P<0.05)。与真值相比,优化测点方案的TSP和PM2.5浓度监测误差绝对值之和分别为6.4%~22.6%和4.7%~14.2%,均低于传统测点方案。综合考虑优化方案的科学性和传统方案的易操作性,确定最优测点方案为:在奶牛舍中央屋顶通风口下方1.0~2.0 m处布置1个测点,在奶牛卧床上方2.5 m高度布置2个测点,上述3个测点按奶牛舍斜对角线进行布置;另外,分别在挤奶通道、饲喂通道、清粪通道上方2.5 m高度处各布置1个测点;共计6个测点。该优化测点方案可满足监测准确性和易操作性要求。

       

      Abstract: Particulate matter (PM) concentration can be real-time monitored to assess the environmental risks and make emission reduction measures in dairy barns. However, a great challenge is remained on arranging as few sampling points as possible to accurately monitor the PM concentration in an intensive barn, particularly with the rapid development of large dairy barns in China. This study aims to design an appropriate monitoring layout with the optimal PM sampling number and location. An online monitoring system was built to continuously detect the PM concentration inside a naturally ventilated dairy barn using the Internet of Things (IoT) and sensing technologies. A total of 17 sampling points were set inside three relatively independent sections of the investigated barn. The total suspended particle (TSP) and PM2.5 concentrations were monitored in real time for the six months during the field test. The uniformity of PM concentration was evaluated on the spatial distribution of PM concentration among the three sections and the difference in sampling heights. The systematical clustering and error analysis were also performed on the sampling of PM concentration. The optimal sampling was determined to compare the measurement with the six regular ones under three environmental controls (namely, EC1: Fans and spraying, EC2: Fans, EC3: No fans and no spraying). The average PM concentration from the 17 sampling points was treated as the true value during data analysis. Results showed that no significant difference was found for the TSP and PM2.5 concentration among the three measuring sections of the barn (P>0.05). TSP concentration sampled at the height of 9.0 m was significantly lower than that at the 1.5 and 2.5 m heights (P<0.05). There was no statistical difference in the PM2.5 concentration among different sampling heights (P>0.05). The concentrations of TSP and PM2.5 sampled at the height of 2.5 m were uniformly distributed among three sections of the barn. The sampling point setting down the ridge opening (approximately 9.0 m above the floor surface) was necessary for the TSP concentration monitoring. In TSP and PM2.5 concentrations, the sum of absolute errors between the true values and the optimized sampling under three ECs were 6.4%-22.6% and 4.7%-14.2%, respectively, indicating all smaller than those of six regular monitoring (P<0.05). Generally, the number of PM sampling points was appropriately reduced to consider the monitoring costs and practical operability. The final PM monitoring was determined with the optimized sampling number and location in a naturally ventilated dairy barn. Six PM sampling points were set inside a dairy barn: one sampling point 1.0-2.0 m down the ridge openings in the central of the barn, two sampling points at a 2.5 m height above the cubicles, and three sampling points distributed at milking alley, feed delivery alley and manure alley at a 2.5 m height, respectively. Among them, the three sampling points down the ridge opening and above the cubicles should be diagonally arranged along the barn. The final PM sampling can be expected to achieve both the accuracy and economy of PM real-time monitoring for a naturally ventilated dairy barn.

       

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