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

项目摘要/摘要 空气污染研究越来越多地采用新出现的高性价比技术来测量污染物 空间和时间尺度的fi水平低于地理稀疏的监管网络提供的水平 监视器。低成本的空气污染监测仪虽然前景看好，但它引入了一系列数据功能，如Need forfield 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。 曝光后的光泽。更传统的空气污染数据收集方案的当前统计方法 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。 这一建议追求多方面的统计方法的发展推动了独特的特点和 低成本的监测数据，以提高严谨性和扩大基于这些数据的科学ficfi编码的广度。 我们的fi第一次创新是一种空间fi测量方法，用于校准有噪声的低成本数据。回归校准- 使用field与监管监控器共用的低成本网络导致低估了空气污染 高峰-从健康角度来看，这是一种严重的fl。目前的做法也未能利用空间相关性 在网络中的暴露水平中。我们建议的fiLing方法缓解了这两个问题，并将使用 制作全网校准和平滑的高分辨率污染物时空图。 我们的下一组创新涉及正确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对数据密集型机器学习(ML)方法的吸收，如RAN- 用于曝光预测建模的DOM森林(RF)。然而，曝光数据是时空相关的 而射频遇到了许多相关数据的问题，导致了精度的损失。我们提出了RF-GLS， 一种新的RF扩展，它显式地解释了时空相关性，以改进预测。我们会 开发用于空间fi测量的RF-GLS扩展，用于预测分类暴露数据(如空气 质量指数类别)，以及在考虑混杂因素后估计暴露影响。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，来自低成本监测仪的丰富的重复测量数据可以直接 在健康和空气污染之间的关联研究中使用，而不需要任何特别的和有损的数据减少，如 使用平均曝光量。我们提出了一种使用整个样本的标量分布分析(SODA 在关联性研究中暴露作为分布值协变量的可能性。苏打水是根据重复的措施量身定做的 协变量，并将比通用的SOFR(标量-函数-回归)更有效的fi。苏打水就行了 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当前的空气质量标准。 这里提出的统计方法将被应用于分析低成本的环境和个人接触 巴尔的摩的电视网。我们还将在公开提供的用户友好软件中实施建议的方法。

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.