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Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign

Journal of Exposure Science & Environmental Epidemiology volume 29, pages 248–257 (2019)Cite this article

Abstract

Sampling campaign design is a crucial aspect of air pollution exposure studies. Selection of both monitor numbers and locations is important for maximizing measured information, while minimizing bias and costs. We developed a two-stage geostatistical-based method using pilot NO2 samples from Lanzhou, China with the goal of improving sample design decision-making, including monitor numbers and spatial pattern. In the first step, we evaluate how additional monitors change prediction precision through minimized kriging variance. This was assessed in a Monte Carlo fashion by adding up to 50 new monitors to our existing sites with assigned concentrations based on conditionally simulated NO2 surfaces. After identifying a number of additional sample sites, a second step evaluates their potential placement using a similar Monte Carlo scheme. Evaluations are based on prediction precision and accuracy. Costs are also considered in the analysis. It was determined that adding 28-locations to the existing Lanzhou NO2 sampling campaign captured 73.5% of the total kriged variance improvement and resulted in predictions that were on average within 10.9 μg/m3 of measured values, while using 56% of the potential budget. Additional monitor sites improved kriging variance in a nonlinear fashion. This method development allows for informed sampling design by quantifying prediction improvement (accuracy and precision) against the costs of monitor deployment.

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Acknowledgments

We acknowledge Qiusheng Jin, Bei Zhang, and Yaqun Zhang for assistance in the field work. L. Jin was supported by a Yale Hixon Center for Urban Ecology Research Fellowship, a Yale Tropical Resources Institute Endowment Fellowship, Yale Global Health Initiative Field Experience Award, and a Yale Graduate School John F Enders Fellowship. This article was developed under Assistance Agreement No. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.

Funding

L. Jin was supported by a Yale Hixon Center for Urban Ecology Research Fellowship, a Yale Tropical Resources Institute Endowment Fellowship, Yale Global Health Initiative Field Experience Award, and a Yale Graduate School John F. Enders Fellowship. This article was developed under Assistance Agreement No. RD835871 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication.

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Authors and Affiliations

  1. Environmental Health Sciences Division, University of Minnesota School of Public Health, Minneapolis, MN, USA

    J. D. Berman

  2. School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA

    L. Jin & M. L. Bell

  3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    F. C. Curriero

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  1. J. D. Berman
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  2. L. Jin
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  4. F. C. Curriero
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Correspondence to J. D. Berman.

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Berman, J.D., Jin, L., Bell, M.L. et al. Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign. J Expo Sci Environ Epidemiol 29, 248–257 (2019). https://doi.org/10.1038/s41370-018-0073-6

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