Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China.
Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, One Forestry Drive, Syracuse, New York, NY 13210, USA.
Int J Environ Res Public Health. 2018 Oct 19;15(10):2300. doi: 10.3390/ijerph15102300.
: The purpose of this study was to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse (NY, USA), using quantile regression models. : Global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to model the relationships between children's lead poisoning and three environmental factors at different quantiles (25th, 50th, 75th, and 90th). The response variable was the incident rate of children's blood lead level ≥ 5 µg/dL in each census block, and the three predictor variables included building year, town taxable values, and soil lead concentration. : At each quantile, the regression coefficients of both global QR and GWQR models were (1) negative for both building year and town taxable values, indicating that the incident rate of children lead poisoning reduced with newer buildings and/or higher taxable values of the houses; and (2) positive for the soil lead concentration, implying that higher soil lead concentration around the house may cause higher risks of children's lead poisoning. Further, these negative or positive relationships between children's lead poisoning and three environmental factors became stronger for larger quantiles (i.e., higher risks). : The GWQR models enabled us to explore the full distribution of children's lead poisoning and identify "high risk" locations or areas in the neighborhood of the inner city of Syracuse, which would provide useful information to assist the government agencies to make better decisions on where and what the lead hazard treatment should focus on.
: 本研究旨在探索儿童铅中毒的完整分布情况，并利用分位数回归模型，确定美国锡拉丘兹市（纽约州）内城社区的“高危”地点或区域。: 采用全局分位数回归（QR）和地理加权分位数回归（GWQR）对儿童铅中毒与三种环境因素在不同分位数（第 25 百分位、第 50 百分位、第 75 百分位和第 90 百分位）之间的关系进行建模。因变量为每个普查块中儿童血铅水平≥5μg/dL 的事件发生率，三个预测变量包括建筑物年代、城镇应税价值和土壤铅浓度。: 在每个分位数上，全局 QR 和 GWQR 模型的回归系数均为（1）建筑物年代和城镇应税价值均为负，表明建筑物越新且/或房屋应税价值越高，儿童铅中毒的发生率越低；（2）土壤铅浓度为正，表明房屋周围土壤铅浓度越高，儿童铅中毒的风险可能越高。此外，这些儿童铅中毒与三种环境因素之间的负相关或正相关关系在较大分位数（即较高风险）下变得更强。: GWQR 模型使我们能够探索儿童铅中毒的完整分布情况，并确定锡拉丘兹市（纽约州）内城社区的“高危”地点或区域，这将为政府机构提供有用的信息，帮助其做出更好的决策，确定需要关注的地点和铅危害处理的重点。
