Use of cellphone-based time-activity data for air pollutant exposure estimation

描述（由申请人提供）：暴露于空气污染与各种不良健康结果有关。测量人类对环境空气污染物的暴露具有挑战性，特别是在直接监测不可行的大型流行病学研究中。因此，已经开发了几种暴露估计方法，包括土地利用回归和克里格法，以估计城市地区内的个人暴露。这些方法的一个主要局限性是使用居住地址来估计暴露。由于城市地区内空气污染物浓度的变化，住宅暴露可能与离家时经历的暴露有很大不同。我们提出了一种创新的，可行的，具有成本效益的方法来测量时间活动数据，即随着时间的推移，人类的运动，并将这些数据纳入目前的基于居住地的空气污染物暴露估计方法。我们将使用装有全球定位系统（GPS）的手机来测量纽约西部40名使用手机的志愿者的日常活动，为期三个月。手机将测量和记录人的位置，测量为地理坐标，每天几次在整个研究期间。我们还将设计两个模型来估计该地区的细颗粒物（PM2.5）浓度。这些模型，一个土地利用回归模型和克里格模型，都将构建从PM2.5浓度在当地监测站点测量。我们将使用手机测量的地理坐标应用于每个模型，以获得每个参与者基于手机的PM2.5暴露估计。我们将开发技术，以提高这一程序的效率，使其在流行病学研究中的使用是可行的。通过将时间活动数据纳入空气污染物暴露估计模型，我们将提高测量空气污染与健康结果之间关联的准确性。 公共卫生相关性：该项目将开发一种方法，以提高我们估计人类暴露于空气污染物的能力。这种方法将通过允许研究人员更准确地测量人类空气污染物暴露并将这些暴露与健康结果联系起来来改善公众健康。

DESCRIPTION (provided by applicant): Exposure to air pollution is associated with a variety of adverse health outcomes. Measuring human exposures to ambient air pollutants is challenging, particularly in large epidemiologic studies in which direct monitoring is not feasible. Thus, several exposure estimation methods, including land use regression and Kriging, have been developed to estimate individual exposures within urban areas. A major limitation of these methods is their use of residential address to estimate exposures. Because of the variation in air pollutant concentrations within an urban area, a residential exposure may differ substantially from exposures experienced while away from home. We propose an innovative, feasible, and cost-effective method to measure time-activity data, i.e. human movement over time, and incorporate these data into current residence-based methods of air pollutant exposure estimation. We will use cell phones equipped with global positioning system (GPS) to measure the daily movements of 40 cell phone-using volunteers in western New York for a period of three months. The cell phones will measure and record the person's location, measured as geocoordinates, several times a day throughout the study period. We will also design two models to estimate fine particulate matter (PM2.5) concentrations in this region. These models, a land use regression model and a Kriging model, will both be constructed from PM2.5 concentrations measured at local monitoring sites. We will apply the geocoordinates measured using the cell phones to each of the models to obtain a cell phone-based PM2.5 exposure estimate for each participant. We will develop techniques to improve the efficiency of this procedure so that it is feasible for use in epidemiologic studies. By incorporating time-activity data into air pollutant exposure estimation models, we will improve the accuracy with which we can measure the associations between air pollution and health outcomes. PUBLIC HEALTH RELEVANCE: This project will be the development of a method to improve our ability to estimate human exposures to air pollutants. This method will improve public health by allowing researchers to more accurately measure human air pollutant exposures and relate these exposures to health outcomes.