Data analytics, statistics, and more
This is a brief tutorial on using R and the trendMK package for the statistical analysis of groundwater monitoring data. The trendMK package is designed to analyze censored data sets containing many sampling locations and monitoring constituents.
Perform geospatial optimization of a groundwater monitoring well network with the objective of minimizing the number of sample locations needed to represent the plume, subject to the constraint that the characteristics of the plume remain comparable.
Use Sequential Gaussian Simulation (SGSIM) conditioned on measured groundwater concentration data to obtain quantitative measures of the uncertainty regarding the extent and severity of contamination at a site.
Costs for groundwater monitoring represent a significant, persistent, and growing burden for environmental remediation projects. This post examines spatial optimization of a groundwater monitoring well network using a geostatistic approach to identify new well locations or redundant locations such that the operational value of the monitoring network is maximized.
The Mann-Kendall test and Theil-Sen regression are typically used to detect temporal trend when analyzing environmental data. Both tests are affected by any value chosen to represent non-detects. Another method that is not affected by non-detects is Akritas-Theil-Sen nonparametric regression.