A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data-Reference-Cited by-同舟云学术

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A dynamic spatial filtering approach to mitigate underestimation bias in field calibrated low-cost sensor air pollution data

Published:2023-12-01 Issue:4 Volume:17 Page:
ISSN:1932-6157
Container-title:The Annals of Applied Statistics
language:
Short-container-title:Ann. Appl. Stat.

Author:

Heffernan Claire¹,Peng Roger²,Gentner Drew R.³,Koehler Kirsten⁴,Datta Abhirup¹

Affiliation:

1. Department of Biostatistics, Johns Hopkins University

2. Department of Statistics and Data Sciences, University of Texas, Austin

3. Department of Chemical & Environmental Engineering, Yale University

4. Department of Environmental Health and Engineering, Johns Hopkins University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Modeling and Simulation,Statistics and Probability

Reference64 articles.

1. DAVISON, A. C. and SMITH, R. L. (1990). Models for exceedances over high thresholds. J. Roy. Statist. Soc. Ser. B 52 393–425.

2. van der Vaart, A. W. and van Zanten, J. H. (2008). Rates of contraction of posterior distributions based on Gaussian process priors. Ann. Statist. 36 1435–1463.

3. FINLEY, A. O., DATTA, A., COOK, B. D., MORTON, D. C., ANDERSEN, H. E. and BANERJEE, S. (2019). Efficient algorithms for Bayesian nearest neighbor Gaussian processes. J. Comput. Graph. Statist. 28 401–414.

4. APTE, J. S., MESSIER, K. P., GANI, S., BRAUER, M., KIRCHSTETTER, T. W., LUNDEN, M. M., MARSHALL, J. D., PORTIER, C. J., VERMEULEN, R. C. H. et al. (2017). High-resolution air pollution mapping with Google street view cars: Exploiting big data. Environ. Sci. Technol. 51 6999–7008.

5. ARDON-DRYER, K., DRYER, Y., WILLIAMS, J. N. and MOGHIMI, N. (2020). Measurements of PM 2.5 with PurpleAir under atmospheric conditions. Atmos. Meas. Tech. 13 5441–5458.

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