README.Rmd

---
title: "Bayesian PEC modeling"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This repository contains the data and the R scripts for Wolf & Tollefsen 2021, **"A Bayesian approach to incorporating spatiotemporal variation and uncertainty limits into modeling of predicted environmental concentrations (PECs) from chemical monitoring campaigns"**. The article will be published in *Environmental Science & Technology*, doi\:[10.1021/acs.est.0c06268](https://doi.org/10.1021/acs.est.0c06268).

## Requirements
To analyze the data, the statistical software [R 4.0.1 or higher](https://cloud.r-project.org/) needs to be installed, as well as [Rtools 40](https://cloud.r-project.org/bin/windows/Rtools/) for users operating from Windows. 

The package {brms} needs to be installed as well. Within R, execute the following command to install:
```{r,eval=FALSE}
install.packages("brms")
```

## Information
All scripts are inside the `scripts/` folder of this repository. For each of the three campaigns, a separate R file exists: `sorfjord.R` for the Sørfjord campaign, `kaldvellfjord.R` for the Kaldvellfjord campaign, and `oslofjord.R` for the Oslofjord campaign. 

Additionally, the file `empirical_prior.R` contains a custom function to calculate empirical priors. Detailed information on the modeling procedure and the calculations of the empirical priors are given in the Supporting Information of the publication.