title: Application of validation data for assessing spatial interpolation methods for 8-h ozone or other sparsely monitored constituents.
abstract: The adverse health effects of high concentrations of ground-level ozone are well-known, but estimating exposure is difficult due to the sparseness of urban monitoring networks. This sparseness discourages the reservation of a portion of the monitoring stations for validation of interpolation techniques precisely when the risk of overfitting is greatest. In this study, we test a variety of simple spatial interpolation techniques for 8-h ozone with thousands of randomly selected subsets of data from two urban areas with monitoring stations sufficiently numerous to allow for true validation. Results indicate that ordinary kriging with only the range parameter calibrated in an exponential variogram is the generally superior method, and yields reliable confidence intervals. Sparse data sets may contain sufficient information for calibration of the range parameter even if the Moran I p-value is close to unity. R script is made available to apply the methodology to other sparsely monitored constituents.
authors.affiliation: The University of Texas at San Antonio, Department of Civil and Environmental Engineering, BSE 1.202, One UTSA Circle, San Antonio, TX 78249, USA. john.joseph@utsa.edu
authors.forename: John
authors.initials: J
authors.lastname: Joseph
authors.forename: Hatim O
authors.initials: HO
authors.lastname: Sharif
authors.forename: Thankam
authors.initials: T
authors.lastname: Sunil
authors.forename: Hasanat
authors.initials: H
authors.lastname: Alamgir
mesh_terms: D000393:Air Pollutants / Q000633:toxicity*; D000397:Air Pollution / Q000706:statistics & numerical data*; D004784:Environmental Monitoring; D010126:Ozone / Q000633:toxicity*; D062211:Spatio-Temporal Analysis
chemical_list: D000393:Air Pollutants; D010126:Ozone