在美国东北部和中西部的空间随机森林中预测月度社区级度
Longxiang Li1, Rebeca Ariel Stern1, Eric Garshick2,3,4
1Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, Massachusetts 02114, United States.
Environmental science & technology
|October 15, 2023
概括
数以百万计的历史测量现在可以准确估计人口暴露. 一个新的空间随机森林模型为流行病学研究提供了高分辨率的纵向度数据.
科学领域:
- 环境健康 环境健康
- 流行病学 流行病学
- 地理空间建模的应用
背景情况:
- 是一种致癌气体,需要在148 Bq/m3以上进行缓解.
- 数以百万计的短期测试存在,但在人口暴露评估中未得到充分利用.
- 以前的国家模型提供了粗略的,不变的估计,限制了对健康影响的研究.
研究的目的:
- 开发一种用于准确,高分辨率估计历史室内度的新型模型.
- 为了利用大量的历史短时间测量数据集,改进人口暴露评估.
主要方法:
- 收集了超过280万个历史的短期子测量结果.
- 开发了一个空间随机森林 (SRF) 模型,结合了地质,建筑,社会经济和气象因素.
- 美国东北部和中西部 (2001-2020年) 的邮政编码表格区域 (ZCTAs) 的估计月度度.
主要成果:
- 在SRF模型预测中,与观察到的度有很高的相关性 (R2 = 0.70).
- 随着测量数据的增加,预测准确度有所提高;平均绝对误差为24.6 Bq/m3,测量≥15.
- 模型准确估计了ZCTA水平的纵向暴露.
结论:
- 历史的短期子测量对于详细的暴露评估是有价值的.
- 该SRF模型为估计人口暴露提供了前所未有的分辨率和准确性.
- 这种方法提高了研究对健康的影响的能力,包括急性暴露.
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