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.

项目摘要/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 与地理上稀疏的调节网络所提供的空间和临时尺度的水平更限制 监视器。低成本的空污污染监视器，虽然承诺，但引入了一系列数据功能，例如需要 共同点和校准，以消除噪声，空间相关的大规模数据集，并重复使用 确保暴露。当前的传统空气污染数据收集方案的统计方法 未经优化以正确利用噪声，高通量和空间依赖的低成本数据。 该提案追求由多方面的统计方法开发的开发 低成本监控数据以改善严格性并根据此类数据扩大科学发现的广度。 我们的第一个创新是用于校准噪声低成本数据的空间过滤方法。回归卡利比拉 使用领域与调节监测器的联合置位的低成本网络导致空气污染的低估 高峰 - 从健康角度来看，关键的浮标。当前的实践也无法利用空间相关性 在网络中的曝光水平中。我们提出的过滤方法可以减轻这两个问题，并将被使用 生成污染物的网络范围校准和平滑的高分辨率时空图。 我们的下一组创新涉及从低成本网络中正确利用高通量数据。 大型低成本数据集增加了对数据密集型机器学习（ML）方法的摄取，例如RAN- Dom Forests（RF）用于暴露预测建模。但是，曝光数据是空间相关的 RF遇到了许多问题，导致依赖数据导致准确性丧失。我们提出了RF-GLS， RF的新型扩展，该扩展明确说明了空间相关性，以改善预测。我们将 开发用于空间过滤的RF-GL的扩展，用于预测分类暴露数据（例如空气 质量指数类别），并在考虑混杂因素后估算暴露效果。我们将使用 RF-GL用于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。 我们认识到，从低成本监测器暴露的丰富重复测量数据可以直接 在健康和空气污染之间进行的关联研究，没有任何临时和有损数据降低 使用平均曝光。我们提出了使用整个样品的标量分布分析（SODA） 在关联研究中作为分布值的协变量的暴露。苏打量量身定制为重复措施 协变量，将比通用SOFR（标量 - 功能回归）更有效。苏打会 用于直接评估个人暴露分布的哪些方面与他们的健康最相关， 反过来，这可以帮助重新评估和更新当前的空气质量标准。 此处提出的统计方法将用于分析低成本环境和个人暴露 巴尔的摩的网络。我们还将在公共用户友好的软件中实现所提出的方法。