2017 2nd International Conference on Advanced Materials Science and Environment Engineering (AMSEE 2017) ISBN: 978-1-60595-475-2 Influence of Wind on PM2.5 Distribution in Yangtza Delta Area Based on Geostatistics Jing CHEN 1 , Qing-jie ZHU 2,* and Beata HEJMANOWSKA 3 1 Department of Civil Engineering, Wuxi City College of Vocational Technology, Wuxi 214153, Jiangsu Province, China 2 School of Petroleum Engineering, Changzhou University, Changzhou, 213016, Jiangsu Province, China 3 Department of Geoinformation, Photogrammetry and Remote Sensing of Environment, AGH University of Science and Technology, Cracow 30-059, Poland *Corresponding author Keywords: Geostatistics, Spatial variability, Kriging, GIS analysis, IDRIS. Abstract. Geostatistics include many useful tools to create a perfect and smooth interpolated image for the spatial sample data, and it is different with an ordinary simple interpolating method. Therefore, various methods produce particular and different results. Spatial variability is an important tool of geostatistics in GIS analysis. Spatial variability is described by spatial distance and direction, and can be described as surfaces. Analysis results of spatial variability are the basis of model construction. As an example application, spatial variability’s of environment data are analyzed, models are constructed based on spatial variability with mathematical fitting techniques. With kriging techniques for interpolating surfaces, distribution surfaces of pm 2.5 in Yangtza delta area are obtained. Finally, influence of wind on pm 2.5 concentration in this area is investigated according to the calculating results, and some advice is proposed. Introduction There are many helpful tools in geostatistics, such as kriging interpolation, spherical or gaussian structure of semivariance model. Based on statistics of spatial point data, spatial variability is an important tool to analyze the spatial data values and their locations. Also, it can be combined with some techniques for interpolating surfaces, such as ordinary and universal Kriging [1] . GIS with ingrate tools are applied in decision making, city planning, risk assessment, environment analysis, and so on [2-6] . Geostatistics was also combined with GIS technology to analyze the spatial variability. There are three modules in Idrisi for geostatistics analysis, and many tools are provided in geostatistics for spatial dependency analysis of sample data. As a well-known interpolating technique, kriging are applied in the coal and petroleum industry for geological locations analysis [7] . The model constructed in geostatistics can describe the nature of geological spatial point data with a high flexibility [8] . Ordinary, it is an understanding of spatial data in geostatistics, rather than a correct answer for interpolating surface. The Spatial Dependence Modeler analyzes the spatial continuity or variability for point data. The module Model Fitting is a mathematical fitting technique for model construction. In the module Kriging and Simulation, it is possible to produce different surfaces with different model. Air Pollution Data Because of serious air pollution in China in recent year, more attention has been paid on air contaminants analysis. In this application, 16 cities’ air pollution data in Yangtza delta area that is the most important economic zone in China are used to analyze the distribution and influence factors. 177