A GIS - environmental justice analysis of particulate air pollution in Hamilton, Canada
A GIS - environmental justice analysis of particulate air pollution in Hamilton, Canada
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DOI：
10.1068/a33137
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发表时间：
2001-06-01
期刊：
影响因子：
--
通讯作者：
Brook, JR
中科院分区：
文献类型：
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作者：
Jerrett, M;Burnett, RT;Brook, JR
作者提出了两个研究问题：（1）在加拿大安大略的汉密尔顿，与社会经济地位较高的人相比，社会经济地位较低的人更有可能暴露于较高水平的颗粒物空气污染中吗？(2)颗粒空气污染水平和社会经济地位之间的关联对暴露估计或统计模型的说明有多敏感？总悬浮颗粒物（TSP）的数据从23个监测站在汉密尔顿（1985-94年）内插与通用克里格程序开发一个估计可能的污染值在整个城市的基础上，每年的几何平均值和极端事件。比较最高和最低暴露区，插值表面显示TSP浓度增加了两倍以上，暴露于极端事件的概率增加了20倍以上。通过使用普通最小二乘法和同步自回归（SAR）模型，将暴露估计值与人口普查区的社会经济和人口数据联系起来。通过控制SAR模型中的空间自相关性，可以检验特定社会经济变量在预测污染暴露方面的可靠性。居住价值显着负相关的污染暴露，结果稳健的统计分析方法。低收入和失业也是暴露的重要预测因素，尽管结果因分析方法而异。统计模型中相对较小的变化改变了重要变量。这一结果强调了地理信息系统（GIS）和空间统计技术在模拟暴露方面的价值。研究结果还表明，在未来的司法-健康研究中考虑空间自相关的重要性。
The authors address two research questions: (1) Are populations with lower socioeconomic status, compared with people of higher socioeconomic status, more likely to be exposed to higher levels of particulate air pollution in Hamilton, Ontario, Canada? (2) How sensitive is the association between levels of particulate air pollution and socioeconomic status to specification of exposure estimates or statistical models? Total suspended particulate (TSP) data from the twenty-three monitoring stations in Hamilton (1985-94) were interpolated with a universal kriging procedure to develop an estimate of likely pollution values across the city based on annual geometric means and extreme events. Comparing the highest with the lowest exposure zones, the interpolated surfaces showed more than a twofold increase in TSP concentrations and more than a twentyfold difference in the probability of exposure to extreme events. Exposure estimates were related to socioeconomic and demographic data from census tract areas by using ordinary least squares and simultaneous autoregressive (SAR) models. Control for spatial autocorrelation in the SAR models allowed for tests of how robust specific socioeconomic variables were for predicting pollution exposure. Dwelling values were significantly and negatively associated with pollution exposure, a result robust to the method of statistical analysis. Low income and unemployment were also significant predictors of exposure, although results varied depending on the method of analysis. Relatively minor changes in the statistical models altered the significant variables. This result emphasizes the value of geographical information systems (GIS) and spatial statistical techniques in modelling exposure. The result also shows the importance of taking spatial autocorrelation into account in future justice-health studies.