Statistical methods for air-pollution studies using low-cost monitors

使用低成本监测仪进行空气污染研究的统计方法

• 批准号: 10342571
• 负责人: Abhirup Datta
• 金额: $ 22.11万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-10 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10342571
• 关键词: Accounting; Address; Adopted; Affect; Air; Air Pollution; Baltimore; Calibration; Categories; Computer software; Data; Data Collection; Data Set; Dependence; Frequencies; Geography; Goals; Health; Individual; Knowledge; Least-Squares Analysis; Left; Linear Models; Location; Maps; Measures; Methodology; Methods; Modeling; Monitor; Noise; Outcome; Peat; Research; Resolution; Risk; Role; Sampling; Scheme; Scientist; Series; Site; Smoking; Statistical Methods; Sum; Techniques; Technology; Time; Training; Update; Variant; Work; base; cooking; cost; cost effective; data reduction; density; improved; indexing; innovation; insight; instrument; machine learning method; method development; non-Gaussian model; novel; open source; pollutant; predictive modeling; random forest; sensor; spatiotemporal; time use; uptake; user friendly software

Project summary/abstract Air pollution research is increasingly adopting emergent cost-effective technologies to measure pollutant levels at spatial and temporal scales finer than that delivered by the geographically sparse network of regulatory monitors. Low-cost air-pollution monitors, while promising, introduce a series of data features like need for field co-location and calibration to eliminate noise, spatio-temporally correlated massive datasets, and repeated mea- sures on exposures. Current statistical methodology for more traditional air-pollution data collection schemes are not optimized to properly exploit the noisy, high-throughput, and spatio-temporally dependent low-cost data. This proposal pursues multi-faceted statistical methods development motivated by the unique features of the low-cost monitoring data to improve the rigor and widen the breadth of scientific findings based on such data. Our first innovation is a spatial-filtering method for calibration of the noisy low-cost data. Regression calibra- tion of low-cost networks using field co-location with regulatory monitors leads to underestimation of air-pollution peaks – a critical flaw from a health perspective. The current practice also fails to exploit the spatial correlation among exposure levels in the network. Our proposed filtering approach mitigates both issues and will be used to produce network-wide calibrated and smooth high resolution spatio-temporal maps of pollutants. Our next set of innovations concern proper utilization of the high-throughput data from low-cost networks. The large low-cost datasets have increased uptake of data-intensive machine-learning (ML) methods like ran- dom forests (RF) for exposure prediction modeling. However, exposure data are spatio-temporally correlated and RF encounters numerous issues for dependent data leading to loss of accuracy. We proposed RF-GLS, a novel extension of RF that explicitly accounts for spatio-temporal correlation to improve predictions. We will develop extensions of RF-GLS for use in the spatial-filtering, for predicting categorical exposure data (like Air Quality Index category), and for estimating exposure effects after accounting for confounders. We will use RF-GLS for predicting personal exposures using the low-cost ambient and wearable network data in Baltimore. We recognize that the rich repeated measures data on exposures from low-cost monitors can be directly used in association studies between health and air-pollution without any ad-hoc and lossy data reduction like using the mean exposure. We propose a scalar-on-distribution-analysis (SoDA) that uses the entire sample of exposures as a distribution-valued covariate in association studies. SoDA is tailored to repeated measures covariates and will be more efficient than the general-purpose SoFR (scalar-on-function-regression). SoDA will be used to directly assess which aspects of an individual's exposure distribution correlate most with their health, which in turn can help re-evaluate and update current air quality standards. The statistical methods proposed here will be applied to analyze low-cost ambient and personal exposure networks in Baltimore. We will also implement the proposed methods in publicly-available user-friendly software.

项目概要/摘要 空气污染研究越来越多地采用紧急成本效益技术来测量污染物 在空间和时间尺度上的水平比地理上稀疏的监管网络提供的水平更高， 监测器低成本的空气污染监测器虽然前景看好，但引入了一系列数据功能，如需要字段 协同定位和校准，以消除噪声、时空相关的海量数据集和重复测量， 在曝光方面。较传统的空气污染数据收集计划的现行统计方法 没有被优化以适当地利用噪声、高吞吐量和时空相关的低成本数据。 这项建议追求多方面的统计方法的发展，其动机是独特的特点， 低成本的监测数据，以提高严谨性，并扩大基于这些数据的科学发现的广度。 我们的第一项创新是一种空间滤波方法，用于校准噪声低成本数据。回归校准- 使用现场与监管监测器共置的低成本网络的概念导致对空气污染的低估 高峰----从健康角度来看，这是一个关键的问题。目前的做法也未能利用空间相关性 网络中的暴露水平。我们提出的过滤方法可以缓解这两个问题， 制作全网络范围内经过校准的、平滑的高分辨率污染物时空图。 我们的下一组创新涉及适当利用来自低成本网络的高吞吐量数据。 大型低成本数据集增加了数据密集型机器学习（ML）方法的使用，如RAN， dom forests（RF）用于暴露预测建模。然而，曝光数据是时空相关的 并且RF遇到导致精度损失的相关数据的许多问题。我们提出了RF-GLS， 一个新的扩展RF明确占时空相关性，以提高预测。我们将 开发RF-GLS的扩展，用于空间滤波，预测分类暴露数据（如空气 质量指数类别），并在考虑混杂因素后估计暴露效应。我们将使用 使用巴尔的摩的低成本环境和可穿戴网络数据预测个人暴露的RF-GLS。 我们认识到，从低成本监测器获得的关于暴露的丰富的重复测量数据可以直接 用于健康和空气污染之间的关联研究，而无需任何特别和有损数据的减少， 使用平均暴露量。我们提出了一个标量分布分析（SoDA），使用整个样本 暴露作为关联研究中的分布值协变量。SoDA是为重复测量量身定制的 协变量，将比通用SoFR（标量函数回归）更有效。SODA将 用于直接评估个人暴露分布的哪些方面与其健康最相关， 这反过来又可以帮助重新评估和更新目前的空气质量标准。 本文提出的统计方法将用于分析低成本的环境和个人暴露 在巴尔的摩的网络。我们亦会把建议的方法应用于方便市民使用的软件。