Mukerjee Shaibal, Smith Luther, Neas Lucas, Norris Gary
National Exposure Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Mail Code E205-03, Research Triangle Park, NC 27711, USA.
ScientificWorldJournal. 2012;2012:865150. doi: 10.1100/2012/865150. Epub 2012 Nov 25.
Spatial analysis studies have included the application of land use regression models (LURs) for health and air quality assessments. Recent LUR studies have collected nitrogen dioxide (NO(2)) and volatile organic compounds (VOCs) using passive samplers at urban air monitoring networks in El Paso and Dallas, TX, Detroit, MI, and Cleveland, OH to assess spatial variability and source influences. LURs were successfully developed to estimate pollutant concentrations throughout the study areas. Comparisons of development and predictive capabilities of LURs from these four cities are presented to address this issue of uniform application of LURs across study areas. Traffic and other urban variables were important predictors in the LURs although city-specific influences (such as border crossings) were also important. In addition, transferability of variables or LURs from one city to another may be problematic due to intercity differences and data availability or comparability. Thus, developing common predictors in future LURs may be difficult.
空间分析研究包括将土地利用回归模型（LURs）应用于健康和空气质量评估。最近的LUR研究在得克萨斯州埃尔帕索和达拉斯、密歇根州底特律以及俄亥俄州克利夫兰的城市空气监测网络中，使用被动采样器收集二氧化氮（NO₂）和挥发性有机化合物（VOCs），以评估空间变异性和源影响。成功开发了LURs来估计整个研究区域的污染物浓度。本文对这四个城市LURs的开发和预测能力进行了比较，以解决LURs在不同研究区域统一应用的问题。交通和其他城市变量是LURs中的重要预测因子，尽管特定城市的影响（如边境过境）也很重要。此外，由于城市间差异以及数据可用性或可比性，变量或LURs从一个城市转移到另一个城市可能存在问题。因此，未来在LURs中开发通用预测因子可能会很困难。
