An Optimal Spatial Sampling Design for Intra-Urban Population Exposure Assessment.
An Optimal Spatial Sampling Design for Intra-Urban Population Exposure Assessment.
复制标题
DOI：
10.1016/j.atmosenv.2008.10.055
复制
发表时间：
2009-02
影响因子：
5
通讯作者：
Kumar, Naresh
中科院分区：
文献类型：
--
作者：
Kumar, Naresh
本文提供了一种最佳的空间抽样设计，可以用最小的样本量捕获最大的方差。拟议的抽样设计解决了Kanaroglou等人（2005年）在加拿大多伦多确定100个地点以获取人群暴露于NO2的抽样设计的弱点。他们的采样设计存在许多弱点，未能有效地捕捉NO2的空间变异性。他们使用的需求曲面是空间自相关的，并由人口规模加权，这导致了冗余站点的选择。商业软件包提供的位置分配模型（LAM），他们用来确定他们的采样点，并不是为了解决空间采样问题，使用空间自相关数据。一个计算机应用程序（用C++编写），利用空间搜索算法来实现所提出的抽样设计。该设计在三个不同的城市环境中实施-即俄亥俄州克利夫兰市、印度德里市和爱荷华州爱荷华州市-以确定监测空气中颗粒物的最佳采样点。
This article offers an optimal spatial sampling design that captures maximum variance with the minimum sample size. The proposed sampling design addresses the weaknesses of the sampling design that Kanaroglou et al. (2005) used for identifying 100 sites for capturing population exposure to NO2 in Toronto, Canada. Their sampling design suffers from a number of weaknesses and fails to capture the spatial variability in NO2 effectively. The demand surface they used is spatially autocorrelated and weighted by the population size, which leads to the selection of redundant sites. The location-allocation model (LAM) available with the commercial software packages, which they used to identify their sample sites, is not designed to solve spatial sampling problems using spatially autocorrelated data. A computer application (written in C++) that utilizes spatial search algorithm was developed to implement the proposed sampling design. This design was implemented in three different urban environments - namely Cleveland, OH; Delhi, India; and Iowa City, IA - to identify optimal sample sites for monitoring airborne particulates.
登录
查看更多内容
影响因子：
5
作者：
Ott, Darrin K.;Kumar, Naresh;Peters, Thomas M.
通讯作者：
Peters, Thomas M.
影响因子：
5
作者：
Kanaroglou, PS;Jerrett, M;Brook, JR
通讯作者：
Brook, JR
影响因子：
2.4
作者：
Kumar, Naresh
通讯作者：
Kumar, Naresh
影响因子：
5.4
作者：
Kumar, N
通讯作者：
Kumar, N