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A description of methods for deriving air pollution land use regression model predictor variables from remote sensing data in Ulaanbaatar, Mongolia

机译:蒙古乌兰巴托的遥感数据推导空气污染土地利用回归模型预测变量的方法描述

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摘要

Air pollution is a major risk factor for death and disease, particularly in low-and middle-income countries (LMIC) where concentrations are relatively high and large populations are exposed. High-quality exposure assessment is integral to both air pollution epidemiologic studies and impact assessments. Land use regression (LUR) modelling is a powerful exposure assessment technique that uses the relationships between air pollution concentrations at discrete monitoring locations and the surrounding characteristics of those locations to model small-scale spatial concentration gradients within cities. Regardless of whether they are calibrated based on local measurements or transferred from another location, LUR models require spatially resolved data on predictor variables that may be unavailable or of insufficient quality in many settings. We describe methods for deriving LUR model predictors, including land cover, road locations, and ger (Mongolian yurt) locations, from satellite data and high-resolution imagery in Ulaanbaatar, Mongolia. These methods may allow LUR models to be developed for more locations in LMIC, potentially improving the quality of air pollution exposure assessments in those settings.
机译:空气污染是造成死亡和疾病的主要危险因素,特别是在中低收入国家(LMIC)浓度相对较高且人口众多的情况下。高质量的接触评估是空气污染流行病学研究和影响评估不可或缺的一部分。土地利用回归(LUR)建模是一种功能强大的暴露评估技术,它使用离散的监视位置处的空气污染浓度与这些位置的周围特征之间的关系来对城市中的小规模空间浓度梯度进行建模。无论是根据本地测量值对它们进行校准还是从其他位置转移,LUR模型都需要在许多设置下可能不可用或质量不足的预测变量上进行空间解析的数据。我们描述了从蒙古乌兰巴托的卫星数据和高分辨率图像中得出LUR模型预测变量的方法,包括土地覆盖,道路位置和ger(蒙古包)位置。这些方法可能允许针对LMIC中的更多位置开发LUR模型,从而有可能在那些环境中改善空气污染暴露评估的质量。

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