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
基本信息
- 批准号:10342571
- 负责人:
- 金额:$ 22.11万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AccountingAddressAdoptedAffectAirAir PollutionBaltimoreCalibrationCategoriesComputer softwareDataData CollectionData SetDependenceFrequenciesGeographyGoalsHealthIndividualKnowledgeLeast-Squares AnalysisLeftLinear ModelsLocationMapsMeasuresMethodologyMethodsModelingMonitorNoiseOutcomePeatResearchResolutionRiskRoleSamplingSchemeScientistSeriesSiteSmokingStatistical MethodsSumTechniquesTechnologyTimeTrainingUpdateVariantWorkbasecookingcostcost effectivedata reductiondensityimprovedindexinginnovationinsightinstrumentmachine learning methodmethod developmentnon-Gaussian modelnovelopen sourcepollutantpredictive modelingrandom forestsensorspatiotemporaltime useuptakeuser friendly software
项目摘要
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.
项目总结/文摘
项目成果
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