高分辨率的地理空间数据库:国家标准-空气污染物度在相邻的美国,2016-2020年
Tianjun Lu1, Sun-Young Kim2, Julian D Marshall3
1Department of Epidemiology and Environmental Health, College of Public Health, University of Kentucky, Lexington, Kentucky, USA.
概括
这项研究为美国 (2016-2020年) 创建了一个高分辨率的空气污染数据库,为公共卫生和环境规划提供了有价值的见解.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 地理空间分析是什么
背景情况:
- 环境空气污染度估计对于环境流行病学,健康影响评估,城市规划和环境公平至关重要.
- 之前的努力为开发详细的空气质量数据库奠定了基础.
研究的目的:
- 开发一个更新的,高分辨率的地理空间数据库,用于六个标准空气污染物的人口加权年平均度.
- 涵盖连续的美国在2016年至2020年的五年期间.
主要方法:
- 土地使用回归 (LUR) 模型是在部分最小平方-通用战争框架内开发的.
- 纳入土地使用,地理空间和基于卫星的预测变量.
- 使用常规和集群交叉验证验证的验证模型,常规方法显示出优异的性能.
主要成果:
- 大多数LUR模型都显示出可靠的性能 (例如,基于MSE的R2>0.8,RMSE<0.1).
- 估计是由人口普查区开发的,在多个地理层面 (区块组,区块,县) 计算了人口加权平均值.
结论:
- 开发的数据库为空气污染动态提供了有价值的见解.
- 该数据库对环境风险评估,公共卫生,政策制定和城市规划有用.
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