地球信息科学学报 ›› 2012, Vol. 14 ›› Issue (4): 432-441.doi: 10.3724/SP.J.1047.2012.00432

• 本期要文(可全文下载) • 上一篇    下一篇

淮河流域上消化道肿瘤与环境污染的模型分析

戚晓鹏1,2, 计伟1, 任红艳1, 郭岩2, 周脉耕2, 杨功焕2, 庄大方*1   

  1. 1. 中国科学院地理科学与资源研究所, 北京 100101;
    2. 中国疾病预防控制中心, 北京 100050
  • 收稿日期:2012-05-13 修回日期:2012-07-12 出版日期:2012-08-25 发布日期:2012-08-22
  • 通讯作者: 庄大方(1963-),男,博士生导师,研究员,研究方向为资源环境数据库建设与土地利用变化动态监测技术的研究。E-mail:zhuangdf@lreis.ac.cn E-mail:zhuangdf@lreis.ac.cn
  • 作者简介:戚晓鹏(1975-),女,黑龙江省哈尔滨人,博士研究生,副研究员,研究方向为公共卫生信息化以及GIS空间分析在公共卫生领域的应用研究。E-mail:caroline_qi@163.com
  • 基金资助:

    "十一五"科技支撑项目"淮河流域水污染与肿瘤的相关性评估研究"(2006BAI19B03)。

Model Analysis of Upper Digestive Tract Cancer and Environmental Pollution in Huaihe River Watershed

QI Xiaopeng1,2, JI Wei1, REN Hongyan1, GUO Yan2, ZHOU Maigeng2, YANG Gonghuan2, Zhuang Dafang*1   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Chinese Center for Disease Control and Prevention, Beijing 102206, China
  • Received:2012-05-13 Revised:2012-07-12 Online:2012-08-25 Published:2012-08-22

摘要:

自20世纪70年代后期以来,淮河流域不断遭受工业点源污染和其他面源污染,媒体也陆续报道了淮河流域"癌症村"的出现。本文探讨了淮河流域14个监测县5810个行政村的消化道肿瘤与环境因子之间的空间分布规律。作者从流域和行政区划等多维空间角度出发,通过全局的最小二乘法线性回归和稳健回归对环境因子进行筛选分析,以局部地理加权回归方法探测各类环境因子,在不同地区对贝叶斯调整的上消化道肿瘤死亡率的影响程度,建立了消化道肿瘤死亡的风险评估模型,其中,包括地表水水质等级、浅层地下水质量分级、河网密度、土壤多环芳烃含量分级、化肥施用量和经济密度等6类环境危险因素。根据局部回归模型中各监测点环境因子的回归系数和统计学检验结果,提取出当地主要的环境影响因素。从14个监测县区总体上看,地表水水质等级和GDP与肿瘤呈负相关,其他环境因子均与肿瘤死亡存在正相关。但从局部角度看,不同地区环境影响因子种类和影响强度有较大差别。其中淮河流域江苏段以化肥施用量、土壤多环芳烃含量、GDP和河网密度为主要影响因子,安徽段以土壤多环芳烃含量和化肥为主,河南段主要是以地下水质量分级、河网密度和化肥为主,同时河南沈丘县地表水水质等级对当地影响较大。山东段虽然也探测出来部分环境危险因子的存在,但没有发现其与肿瘤死亡的关联关系,尚需进一步深化研究。

关键词: 环境因子, 上消化道肿瘤, 模型分析

Abstract:

The Huaihe River watershed has been suffering from industrial point pollution and other non-point pollution since 1970s. The media has reported the emergence of ‘cancer village'. Spatial distribution pattern on upper digestive tract cancer and environmental factors was studied in 14 pilot counties including 5810 villages in Huaihe River watershed. From the multiple perspectives such as watershed and jurisdictional areas, Ordinary Least Squares (OLS) and Robust regression model were used for environmental factors selection. Robust regression model can detect abnormal value and put the weight for each of them. Surface water, phreatic water, river density, soil PAHs, fertilizer, population density and economical density (GDP) were imported into the model. Geographically Weighted Regression (GWR) model was used to locally detect the impact of different environmental factors on Empirical Bayes smoothed upper digestive tract cancer mortality. Through the correlative analysis of them, the risk assessment model of upper digestive tract cancer mortality was developed. Based on the regression coefficient of each environmental factor and statistical test in each pilot, the local main environmental factors were extracted. Even six factors were imported into the global regression model, the impact of each factor was different in distinct areas. In general, all the selected environmental factors show the positive correlation with the upper digestive tract cancer mortality except the surface water quality level and GDP. However the pollution type was diverse in different area based on the regression coefficient of each factor. The main findings were listed as follow: fertilizer amount, soil PAHs, GDP and river density are the main factors in Jiangsu segment in Huaihe River watershed; main factors in Anhui segment included soil PAHs and fertilizer amount; main factors in Henan segment included phreatic water, river density and fertilizer amount, With Shenqiu County, as one of the pilots in Henan, showed the strong positive correlation between surface water and cancer mortality; some kind of environmental risk factors were also detected in Shandong segment, but the result showed no correlation between these risk factors and cancer mortality, which needed to be studied any further.

Key words: upper digestive tract cancer, environmental factors, model analysis