GEOG 566






         Advanced spatial statistics and GIScience

April 9, 2018

Predicting Produce Safety Rule compliance through spatiotemporal analysis of publicly-available water quality data

Filed under: 2018,My Spatial Problem @ 11:05 am
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Research Question

Because of the numerous foodborne illness outbreaks associated with fresh produce, the Food and Drug Administration finalized the Produce Safety Rule in November 2015. This rule implements a variety of new food safety practices on the farm to prevent foodborne pathogens from reaching the consumer. As part of this new rule, growers of fresh produce are required to meet water testing requirements for all water used in the growing, handling, and harvesting of produce. Growers are expected to test their surface water source a minimum of 20 times to establish a baseline Water Quality Profile (WQP). The WQP is then to be updated annually with 5 additional samples. The WQP consists of the geometric mean and statistical threshold value of generic E. coli in the water.

My objectives with this dataset are twofold:

  1. Determine whether Oregon produce growers will face difficulty in meeting the water quality requirements based on historical trends
  2. Explore whether produce growers who share a common surface water source can pool their data to collectively establish a WQP to meet the requirements

Dataset

Oregon Department of Environmental Quality maintains a public database (Ambient Water Quality Monitoring System) of statewide surface water testing for a variety of contaminants. I will analyze the dataset for generic E. coli. I have also acquired data for pH and temperature as potential explanatory variables for the data set. These data exist as point data at DEQ monitoring stations that are adjacent to a water source (river/stream), with the data spanning from January 1, 2013 through December 21, 2016. Each monitoring station has different temporal spans (for example: one monitoring station only contains data for 2015, while another covers the entire three-year span).

Hypotheses

 I hypothesize that generic E. coli concentrations will correlate most strongly to time of year for sampling. I predict that pH and temperature variations will contribute insignificantly to the fluctuations of generic E. coli. Additionally, I predict that trends will be consistent within each watershed, but vary greatly between.

Approaches

 I will test the dataset within ArcGIS and R to determine statistically significant factors in generic E. coli concentrations within watersheds in the state of Oregon.

Expected Outcome

The outcome of this research will help inform food safety extension work. Additionally, this data may help growers alleviate the water-testing burden if we identify that current testing regimes by government agencies is sufficient to meet the requirements of the rule, or if the data within a watershed can be collectively shared to meet compliance standards.

Significance

This study will help guide the direction of future food safety extension work related to the Produce Safety Rule to prevent foodborne illness outbreaks associated with fresh produce.

Preparation

I am very comfortable working with the suite of ArcInfo software. I have limited beginner level experience with R, Python, Modelbuilder, and image-processing software (ENVI Classic).

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