Lary D J, Lary T, Sattler B
Hanson Center for Space Sciences, University of Texas at Dallas, Dallas, TX, USA.
Environ Health Insights. 2015 May 12;9(Suppl 1):41-52. doi: 10.4137/EHI.S15664. eCollection 2015.
With the increasing awareness of health impacts of particulate matter, there is a growing need to comprehend the spatial and temporal variations of the global abundance of ground-level airborne particulate matter (PM2.5). Here we use a suite of remote sensing and meteorological data products together with ground based observations of PM2.5 from 8,329 measurement sites in 55 countries taken between 1997 and 2014 to train a machine learning algorithm to estimate the daily distributions of PM2.5 from 1997 to the present. We demonstrate that the new PM2.5 data product can reliably represent global observations of PM2.5 for epidemiological studies. An analysis of Baltimore schizophrenia emergency room admissions is presented in terms of the levels of ambient pollution. PM2.5 appears to have an impact on some aspects of mental health.
随着人们对颗粒物对健康影响的认识不断提高，越来越需要了解全球地面空气中颗粒物（PM2.5）丰度的时空变化。在此，我们使用一套遥感和气象数据产品，以及1997年至2014年间在55个国家8329个测量站点对PM2.5的地面观测数据，训练一种机器学习算法，以估计1997年至今PM2.5的每日分布情况。我们证明，新的PM2.5数据产品能够可靠地代表用于流行病学研究的全球PM2.5观测结果。本文根据环境污染水平对巴尔的摩精神分裂症急诊入院情况进行了分析。PM2.5似乎对心理健康的某些方面有影响。
