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首页> 外文会议>Annual conference of the International Society of Exposure Science >Predicting personal exposure to air pollutants in children living in a high risk area. A Universal Bayesian Kriging approach
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Predicting personal exposure to air pollutants in children living in a high risk area. A Universal Bayesian Kriging approach

机译：预测生活在高风险区域的儿童的空气污染物上的个人暴露。 一个通用的贝叶斯克里格方法

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Background: A core challenge in epidemiological analysis of the impact of air pollution exposure on health is assigning exposures to individuals subjects at risk.GIS-based (Geographical Information Systems) pollution mapping has become one of the main tools for exposure assessment to ambient pollutants, which interpolation techniques, such as Kriging, have helped to utilize routine monitoring data to estimate levels of ambient air pollutants at unmeasured locations. Aim:to estimate the individual exposure to gaseous air pollutants of asthmatic children living in the area of Milazzo-Valle del Mela (Sicily, Italy) by Universal Bayesian Kriging approach. Methods:Weekly measurements for sulphur dioxide (SO2) and nitrogen dioxide (N02)were obtained from 21 passive dosimeters located at each school yards of the study area (November 2007-April 2008). The residences of 113 asthmatic children were geo-referenced. A Universal Bayesian Kriging approach was performed to predict individual exposure levels at each residential address, using as covariates land use information, altitude, distance to main roads and population density. Results: A large geographical heterogeneity in air quality was recorded suggesting complex exposure patterns. With an effective sample size of, we obtained a predicted mean level of 25.78 (± 10.61) μg/m3 NO2 and 4.10 (± 2.71) μg/m3 SO2 at 1,682 children's residential addresses, with a normalized root squared mean of 28% and 25%, respectively. The spatio-temporal distribution of SO2 concentrations showed a point source effect with a plume consistent with prevalent winds, whereas NO2 patterns were more stable and reflected mostly diffuse traffic emissions. Conclusion: Universal Bayesian kriging may be useful to predict residential concentrations from monitoring data. Heterogeneity of the spatio-temporal distribution of pollutants should be considered in order to estimate the real exposure to them.
机译：背景：空气污染暴露对健康影响的流行病学分析中的核心挑战是将曝光为风险的个人科目分配。基于GIS的（地理信息系统）污染绘图已成为对环境污染物的接触评估的主要工具之一，哪种插值技术（例如Kriging）有助于利用常规监测数据来估计未测量位置的环境空气污染物的水平。目的：通过普遍的贝叶松克里明方法估算居住在Milazzo-Valle Mela（西西里岛（西西里岛）的哮喘儿童的气态空气污染物的个人暴露。方法：从研究区域的每个学校码（2007年11月）的21个无源剂量计中，获得每周二氧化硫（SO2）和二氧化氮（NO 2）的每周测量。 113名哮喘儿童的住宿是地理参考。进行了一个通用的贝叶斯克里格治疗方法，以预测每个住宅地址的单个曝光率，用作协变者土地利用信息，海拔高度，到主要道路和人口密度的距离。结果：记录了空气质量的大型地理异质性，表明复杂的曝光模式。具有有效的样本量，我们在1,682名儿童住宅地址中获得了预测的平均水平为25.78（±10.61）μg/ m3 no2和4.10（±2.71）μg/ m3 so2，标准化的根平均平均为28％和25 ％， 分别。 SO2浓度的时空分布显示了与普遍风均一致的羽流的点源效应，而No2图案更稳定并且主要反射大多数漫射交通排放。结论：通用贝叶克克里格可能有助于预测监测数据的住宅浓度。应考虑污染物的时空分布的异质性，以估计对它们的真正接触。

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