Jordan Frengut, Anwesha Tomar, Andrew Burwell, R. Francis
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引用次数: 2
Abstract
The objective of this paper is to report the results of a generalized additive model used to predict local particulate matter concentrations at a Washington, DC Department of Energy and Environment (DOEE) federal regulatory monitoring station. While the DOEE uses state-of-the-art federal equivalent method (FEM) equipment to demonstrate compliance with the clean air act for regulatory purposes, these measurements reflect regional, not neighborhood air quality. A GW student-led living lab project—Fresh Air DC—has been testing uRAD INDUSTRIAL low-cost air quality sensors that can be used to collect air quality data at the neighborhood level using LoRaWAN based smart city technology. Because low-cost sensors often lack the accuracy and sensitivity of FEM equipment, research indicates that low-cost sensor (LCS) monitoring networks require post- processing and data modelling in order to apply findings to educational and policy goals. Although LCS data processing has been conducted using linear and nonlinear models, nonlinear models tend to have a greater ability to capture the nuanced relationships between air pollutants and meteorological influences. In this paper, we post-process uRAD PM 2.5 sensor data using DOEE FEM equipment as a reference instrument in the development of three models to adjust uRAD data to the DOEE FEM data—ordinary least squares linear regression, generalized linear models (GLMs), and generalized additive models (GAMs). Our model includes meteorological variables such as temperature, humidity, and wind speed. Our statistical models for post-processing are evaluated on the basis of deviance and Akaike Information Criterion (AIC). We expect that the GLM and GAM will be useful for capturing nonlinear relationships between the PM2.5 measurements and meteorological variables.
本文的目的是报告在华盛顿特区能源和环境部(DOEE)联邦监管监测站用于预测当地颗粒物浓度的广义相加模型的结果。虽然doe使用最先进的联邦等效方法(FEM)设备来证明符合清洁空气法案的监管目的,但这些测量反映的是区域空气质量,而不是社区空气质量。华盛顿大学学生领导的生活实验室项目fresh Air dc一直在测试uRAD INDUSTRIAL低成本空气质量传感器,该传感器可用于使用基于LoRaWAN的智能城市技术收集社区一级的空气质量数据。由于低成本传感器往往缺乏FEM设备的准确性和灵敏度,研究表明,低成本传感器(LCS)监测网络需要后处理和数据建模,以便将研究结果应用于教育和政策目标。虽然LCS数据处理是使用线性和非线性模型进行的,但非线性模型往往更能捕捉空气污染物与气象影响之间的细微关系。本文以DOEE FEM设备为参考工具,对uRAD pm2.5传感器数据进行后处理,建立了三种模型,将uRAD数据调整为DOEE FEM数据——普通最小二乘线性回归、广义线性模型(GLMs)和广义加性模型(GAMs)。我们的模型包括气象变量,如温度、湿度和风速。基于偏差和赤池信息准则(Akaike Information Criterion, AIC)对后处理统计模型进行了评价。我们期望GLM和GAM将有助于捕捉PM2.5测量值与气象变量之间的非线性关系。