室内悬浮颗粒物浓度与粒径分布测量方法研究
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摘要
据调查,城市居民每天90%的时间是在各种室内环境中度过的。室内悬浮颗粒物是室内最重要的污染源,被吸入人体呼吸道,沉积在咽喉以下呼吸道部位。粒径越小,进入人体呼吸道的位置越深,因而对人体的危害越大,导致人们致病率和死亡率增加。室内悬浮颗粒物的浓度决定了人们吸入的剂量,粒径携带着颗粒物的重要物理化学性质信息,与颗粒物在呼吸道内沉积、滞留和清除有关。因此研究室内悬浮颗粒物的浓度及粒径分布情况,有利于了解颗粒物的影响因素,促进人们采取有利措施,改善室内空气品质,降低和避免颗粒物对人体健康的危害。
     本文研究了一种室内悬浮颗粒物浓度与粒径分布的测量方法,提出了相关的理论和方法,并通过实验加以验证。
     首先提出了一种新的改进遗传算法和最大方差法相结合的图像阈值分割法,对颗粒物图像进行全局优化搜寻获得最优分割阈值,实验证明这种算法具有较强鲁棒性和自适应性等特点,并较好地保留了图像的细节。针对中值滤波的滤波效果将受到噪声强度以及滤波窗口的大小和形状等因素的制约,以及形态学开闭滤波器的滤波效果严重地依赖于结构元素的形状和大小,提出了自适应中值-数学形态学开闭滤波方法,利用遗传算法对其中的邻域模板和结构元素的大小和形状进行自适应调整,达到了既消除噪声又保持图像细节的滤波效果。提出了一种多尺度多结构元素的数学形态学颗粒物图像边缘检测方法,同时采用不同尺度下的多个结构元素的边缘检测算子,实现颗粒物图像的各种边缘检测。
     其次,为了给粒形归纳分类提供了实验数据,研究了单个室内悬浮颗粒物的主要形态学参数如粒径、形状系数和分形维数的识别算法,从颗粒物的延性、多边形态以及边界曲线特征等三个方面分析了颗粒物的形态特征。实验结果表明:颗粒物粒径呈连续多峰曲线形态,以粗模态为主,大多数的颗粒物粒径都在10μm以下,属于最危险的可吸入颗粒物。悬浮颗粒物的总体形态趋近于块状,而尖锐菱角、长方形和长条形的颗粒物较少。
     接着以形状参数和分形维数为每一类的观测数据,提出了一种颗粒物判别分析归类的方法,将待测颗粒物归类,实现颗粒物的粒形、体积和表面积的近似估计;分析颗粒物的来源,估算颗粒物的密度,计算得出室内悬浮颗粒物浓度和粒径分布的实验数据。这种测量方法可以同时获取颗粒物的质量浓度、总表面积和体积浓度与粒径分布情况。
     最后根据实时测量的室内悬浮颗粒物浓度和粒径分布的变化趋势,对室内环境中大气悬浮颖粒物浓度和粒径分布进行了预测和分析。分析影响颗粒物浓度和粒径分布的因素,建立预测室内悬浮颗粒物浓度和粒径分布的质量平衡模型,将颗粒物沉积率模型、穿透因子模型,以及室内风量大小作为质量平衡模型的输入参数,结合实验测量数据、建筑围护结构特征、室外大气悬浮颗粒物浓度等相关数据,进行了仿真研究,预测了室内颗粒物的浓度与粒径分布情况。
According to the survey, the urban residents spend 90% everyday in indoor environment. The suspended particulate matters is one of the most important sources of pollution indoor, which is sucked into the human respiratory tract, and deposit in the lower respiratory tract deposition site under the throat. The smaller the particle size, the deeper the location of respiratory tract, and thus the greater the harm to humans, then the higher morbidity and mortality. The conentration of particles determines the inhaled dose of people, and the particle size carries the important physical and chemical information of particle which is related to its deposition, detention and elimilation in the human respiratory. By researching the concentration and size distribution of indoor suspended particulate matters ,we can not only understand the impact factorsto particles, but also take the positive measures to improve indoor air quality and reduce the harm to the human health.
     This dissertation researches a measurement method to the concentration and size distribution of indoor suspended particulate matters. The relevant theories and methods have been put forward, and validated by experiment.
     Firstly, a new image threshold segmentation method based on the genetic algorithm and Otsu method has been put forward, which can obtain the segmentation threshold by the global optimization. The experimental results show that the algorithm has strong robustness and self-adaptive characteristics, and preserves better image details. Because the filtering effect of median filter is affected by the noise level, and that of opening and closing morphological filter depends on the shape and size of structural elements seriously, a new adaptive filtering method has been put forward based on the binary morphological filtering and median filtering method to adjust the size and shape of the neighborhood template and the structural elements by genetic algorithm. This dissertation also has put forward the mathematical morphology edge detection methods with multi-scale and multi-structural elements combined morphological operations with set operations. It can achieve a variety of particle image edge detection with the edge detection operator of different scales structural elements.
     Secondly,in order to provide the experimental datas to categorize according to the particle shape, this dissertation researched a identification algorithm to the main morphological parameters of a single indoor suspended particulate matter such as size, shape coefficient and the fractal dimension, then analyzed morphological characteristics about the ductility, the multilateral morphology and boundary curve. The experimental results show that the size distribution of indoor suspended particulate matters appears continuous multi-peak curve in rough coarse mode mainly, and most particles with 10μm diameter below are the most dangerous. The overall shape of suspended particulate matters is similar to the block, not the sharp diamond and long rectangular.
     Aftr that, adopting the shape coefficient and the fractal dimension as observational data of each type, a discriminant analysis classified method of particles has been put forward which can estimate the shape, volume and surface area. By analysing the sources of particles and estimating the density of particles, the mass concentration, total surface area and volume concentration and particle size distribution of indoor suspended particulate matters can be obtained at the same time.
     Finally, according to the real-time changes , the concentration and size distribution of indoor suspended particulate matters are predicted and analysed. The dissertation Analysed the factors to effect the concentration and size distribution, established the mass balance model adopting the deposition velocity, penetration factor,as well as air flow indoor as input its parameters. Combining experimental measurement data and building envelope characteristics with the concentrations outdoor particles and other related datas, the concentrations and particle size distribution of indoor suspended particulate matter is predicted by simulation.
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