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

Award Number: R01ES033739
ORGANIZATION: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 02/10/2022
PERIOD OF PERFORMANCE END DATE: 11/30/2026

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $221,063 )
2026	2026	THE JOHNS HOPKINS UNIVERSITY	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	000	5	12/10/2025	NON-COMPETING CONTINUATION	$221,063
														Subtotal = $221,063

Issue Date FY: 2025 ( Subtotal = $221,063 )
2025	2025	THE JOHNS HOPKINS UNIVERSITY	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	000	4	11/15/2024	NON-COMPETING CONTINUATION	$221,063
														Subtotal = $221,063

Issue Date FY: 2024 ( Subtotal = $331,594 )
2024	2024	THE JOHNS HOPKINS UNIVERSITY	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	000	3	11/9/2023	NON-COMPETING CONTINUATION	$331,594
														Subtotal = $331,594

Issue Date FY: 2023 ( Subtotal = $331,594 )
2023	2023	JOHNS HOPKINS UNIVERSITY, THE	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	001	2	11/30/2022	NON-COMPETING CONTINUATION	$0
2023	2023	JOHNS HOPKINS UNIVERSITY, THE	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	000	2	11/15/2022	NON-COMPETING CONTINUATION	$331,594
														Subtotal = $331,594

Issue Date FY: 2022 ( Subtotal = $221,062 )
2022	2022	JOHNS HOPKINS UNIVERSITY, THE	3400 N CHARLES ST	BALTIMORE	MD	21218	BALTIMORE CITY	USA	Environmental Health	000	1	2/9/2022	NEW	$221,062
														Subtotal = $221,062

Grand Total All Awards = $1,326,376

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

Award Number: R01ES033739

ORGANIZATION: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 02/10/2022

PERIOD OF PERFORMANCE END DATE: 11/30/2026

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