Wu Jianfa, Peng Dahao, Ma Jianhao, Zhao Li, Sun Ce, Ling Huanzhang
College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China.
College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China.
PLoS One. 2015 Apr 29;10(4):e0123766. doi: 10.1371/journal.pone.0123766. eCollection 2015.
To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions KA (xj) and KB (xj), which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.
为有效监测小规模区域的大气质量，有必要优化监测站点的位置。本研究将GIS提取地理参数与模糊物元分析相结合。通过GIS提取地理坐标并转换为直角坐标。将这些坐标输入高斯烟羽模型以计算每个站点的污染物浓度。用于解决不相容问题的模糊物元分析被用于选择站点位置。根据浓度参数建立物元矩阵。针对每个站点求解反映监测指标间相关程度的综合关联函数KA(xj)和KB(xj)，并绘制站点散点图，根据函数确定站点的最终位置。站点可通过散点图进行分类并最终选定。通过实际案例测试，结果表明有5个位置可用于监测，且这些位置符合技术标准。在本文结果中，采用层次聚类方法对这些方法进行了改进。将站点分为5类，选定了7个位置。7个位置中的5个与模糊物元分析确定的站点完全相同。这两种方法的选择相似，可结合使用。与传统方法相比，本研究监测小范围内的孤立点污染源，可降低监测成本。
