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Further assessment of forward pressure level for in situ calibration.

ABSTRACT: Quantifying ear-canal sound level in forward pressure has been suggested as a more accurate and practical alternative to sound pressure level (SPL) calibrations used in clinical settings. The mathematical isolation of forward (and reverse) pressure requires defining the Thévenin-equivalent impedance and pressure of the sound source and characteristic impedance of the load; however, the extent to which inaccuracies in characterizing the source and/or load impact forward pressure level (FPL) calibrations has not been specifically evaluated. This study examined how commercially available probe tips and estimates of characteristic impedance impact the calculation of forward and reverse pressure in a number of test cavities with dimensions chosen to reflect human ear-canal dimensions. Results demonstrate that FPL calibration, which has already been shown to be more accurate than in situ SPL calibration, can be improved particularly around standing-wave null frequencies by refining estimates of characteristic impedance. Better estimates allow FPL to be accurately calculated at least through 10 kHz using a variety of probe tips in test cavities of different sizes, suggesting that FPL calibration can be performed in ear canals of all sizes. Additionally, FPL calibration appears a reasonable option when quantifying the levels of extended high-frequency (10-18 kHz) stimuli.

SUBMITTER: Scheperle RA

PROVIDER: S-EPMC3257756 | biostudies-other | 2011 Dec

REPOSITORIES: biostudies-other

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Project description:BackgroundLow-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.ObjectiveBuild 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.MethodsUsing 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.ResultsWe 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.SignificanceWe 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 statementWe 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.

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Further assessment of forward pressure level for in situ calibration.

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