Non-Linear Probabilistic Calibration of Low-Cost Environmental Air Pollution Sensor Networks for Neighborhood Level Spatiotemporal Exposure Assessment

Details

• Personal Author:
  Buehler C ; Datta A ; Gentner DR ; Koehler K ; Patton A ; Xiong F ; Zamora ML
• Description:
  Background: Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment. Objective: Build direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level. Methods: Using data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore. Results: We demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 µg/m3, and also on monitors not included in the training set. Significance: We provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD. Impact statement: We demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process. [Description provided by NIOSH]
• Subjects:
  Air Pollution Occupational Health Particulate Matter Safety
• Keywords:
  Air Pollution Author Keywords: Exposure Modeling Environmental Health Monitoring Exposure Assessment Geospatial Analyses Monitoring Systems Particulates Sensors
• ISSN:
  1559-0631
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  California ; Connecticut ; Maryland ; OSHA Region 1 ; OSHA Region 3 ; OSHA Region 9
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  32
• Issue:
  6
• NIOSHTIC Number:
  nn:20067803
• Citation:
  J Expo Sci Environ Epidemiol 2022 Nov; 32(6):908-916
• Contact Point Address:
  Andrew Patton, Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615N. Wolfe St., Baltimore, MD, 21205, USA
• Email:
  anpatton@usfca.edu
• Federal Fiscal Year:
  2023
• Performing Organization:
  Johns Hopkins University
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Journal of Exposure Science and Environmental Epidemiology
• End Date:
  20280630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:0a17b6bbbd0f7082a00e71e51727e78b7720dfe71382eef97c86943f0b1684e8555722db29907fe76898ecb68ce9cea70f7503fdc7e40d5747b0f923ed9ffe56
• Download URL:
  https://stacks.cdc.gov/view/cdc/230059/cdc_230059_DS1.pdf
• File Type:
  [PDF - 1.89 MB ]