Running Head: WEATHER EFFECTS

Oklahoma Weather Effects on E. coli in Surface Water and Produce Safety

Justin McConaghy

Produce Safety Program Coordinator

Oklahoma Department of Agriculture, Food, and Forestry

International Food Protection Training Institute (IFPTI)

Fellowship in Food Protection

Author Note

Justin McConaghy, Produce Safety Program Coordinator

Oklahoma Department of Agriculture, Food, and Forestry

This research was conducted as part of the International Food Protection Training

Institute’s Fellowship in Food Protection, Cohort VII.

Correspondence concerning this article should be addressed to:

Justin McConaghy, Oklahoma Department of Agriculture, Food, and Forestry,

2800 N. Lincoln Blvd, Oklahoma City, OK 73105

Justin.Mcconaghy@ag.ok.gov

*Funding for this statement, publication, press release, etc., was made possible, in part, by the Food and Drug Administration through grant 5U18FD005964-03; views expressed in written materials or publications and by speakers and moderators do not necessarily reflect the official policies of the Department of Health and Human Services; nor does any mention of trade names, commercial practices, or organization imply endorsement by the United States Government.

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Abstract

This study examined the correlation between weather data (rainfall, temperature,

barometric pressure, wind, solar radiation, dew point, and humidity) and the Escherichia

coli (E. coli) counts from surface water sampling in the state of Oklahoma to determine if

weather monitoring is a feasible approach to estimate E. coli count in surface water

supplies used for fresh produce irrigation. If so, such weather monitoring could allow

farmers to determine which days may be safer than other days for irrigation. The study

examined weather data from the Oklahoma Mesonet system from 2001 to 2018 and water

data from the Oklahoma Water Resources Board (OWRB) and the Oklahoma Water

Survey. The study concluded that there were no substantial correlations between the

weather data and surface water E. coli overall. However, E. coli in some individual water

sources could be correlated with a variety of weather factors, the most common being the

amount of rainfall. The study recommended that farmers who regularly test their water

also keep weather records to determine if weather is correlated with individual surface

water contamination, and if so, adjust their irrigation schedule to possibly limit

contaminating fresh produce. The study also recommended that further research should

be conducted to determine how weather may affect water sources not included in this

study, primarily small ponds and irrigation canals. More research also should be

conducted to determine why Oklahoma’s man-made lakes are not impaired by E. coli

even though they capture water from rivers and creeks that were contaminated upstream.

Keywords: surface water, irrigation, fresh produce, Produce Safety Rule, E. coli,

weather, mesonet, rain, temperature, wind, barometric pressure, solar radiation, dew

point, humidity

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Oklahoma Weather Effects on E. coli in Surface Water and Produce Safety

Background

Fresh produce poses a risk of foodborne illness as fresh produce is usually eaten

raw rather than going through a pathogen kill step. Between 1998 and 2008, produce

contamination accounted for 38.1% of hospitalizations and 22.9% of deaths attributed to

foodborne illnesses (Painter et al, 2013). Despite the risk of eating fresh produce, these

foods have important nutritional benefits that suggest increasing their consumption. In an

attempt to reduce the prevalence of foodborne illness outbreaks, the Produce Safety Rule

of the Food Safety Modernization Act (FSMA) was passed in 2011 to regulate the food

safety aspects of the growing, harvesting, packing, and handling of fresh produce on

farms.

One controversial portion of the Produce Safety Rule is the testing of irrigation

water for generic Escherichia coli (E. coli) in order to limit the fecal coliform contamination

of fresh produce due to irrigation (Karst, 2017). This portion of the Rule has been delayed

for two additional years to allow for further research on the topic (FDA, 2019). While this

rule delay was being proposed, it had received criticism from consumer groups, especially

after a recent outbreak associated with romaine lettuce in April 2018 was found to have

been caused by contaminated water in the irrigation canal (Fox, 2019; FDA, 2018).

Section 303(d) of the Clean Water Act (United States Environmental Protection

Agency [EPA], N.D.) requires states to study and develop a list of public waters in the

state that are impaired by various water quality factors. A study of the map of the Section

303(d)-listed surface waters in Oklahoma impaired by E. coli brings up some interesting

observations (see Figure 1). Figure 1 illustrates that the western half of the state has

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considerably more E. coli impaired rivers and streams than the eastern half; additionally,

no lakes in the state are impaired by E. coli. A potential explanation of why the lakes are

not impaired is that the water could be more diluted and is more stationary thus allowing

the E. coli to settle into the sediment on the bottom of the lake.

Figure 1. Map of EPA 303(d) E. coli impaired waters in Oklahoma

Potential explanations of why more western rivers are impaired by E. coli include:

lesser rainfall in the western half of the state resulting in the E. coli not being diluted and

washed downstream as often, or different vegetation in the western half of the state such

as shorter grasses and less trees resulting in increased runoff and less natural filtering of

runoff waters.

Another potential explanation of why western rivers are more impaired by E. coli

would be a heavier concentration of livestock. However, the United States Department of

Agriculture (USDA) (2012) National Agricultural Statistics Service Census maps do not

appear to substantially support this hypothesis. Poultry in Oklahoma is concentrated

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along the eastern counties that border Arkansas and cattle in Oklahoma looks evenly

distributed across the entire state. Swine production could explain the E. coli

concentration in the panhandle and portions of the northwest quarter of the state, but

would not explain the impaired waters in the southwest quarter of Oklahoma.

This project sought to determine if monitoring the local weather could help predict

when the E. coli loads in surface waters would increase or decrease, and therefore reduce

the potential for contamination of produce fields that are irrigated using surface water.

Farmers commonly monitor the weather as a major determining factor in deciding when

and what to plant and harvest, which ultimately affects the crop’s profitability. If a farmer

could also monitor certain aspects of local weather that is found to affect the E. coli levels

in surface waters, then such an approach would provide an inexpensive, easy to follow

model that would help reduce the risk of E. coli contamination of fresh produce.

Problem Statement

The relationship between weather events and E. coli load in surface waters in

Oklahoma is unknown at this time.

Research Questions

1. What is the relationship between weather data from the Oklahoma Mesonet

system with the Oklahoma Water Resources Board and Oklahoma Water Survey

water testing data collected from 2001 to 2018?

2. Can the relationships found between weather data and water data provide useful

guidance for farmers?

3. Is the method used in this project applicable to other states?

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Methodology

Data for this study was collected from the Oklahoma Water Resources Board

(OWRB), Oklahoma Water Survey, and Oklahoma Mesonet. The OWRB data used

method 9223B (EPA, 2017) to test for total generic E. coli in 100 ml of water from rivers,

streams, and lakes from across the state on random non-consecutive days during the

months of May through October, the growing and harvesting months for most produce

grown outdoors in Oklahoma, between 2001 and 2015. This water data was one of the

data sources used in the determination of EPA 303(d) impaired waters list for the state of

Oklahoma. Additional water data was used from the Oklahoma Water Survey’s 2018

water tests on 23 various streams across the state with 10 tests per site collected from

May through September.

Daily weather data from the nearest Oklahoma Mesonet station to each water

collection site was compared to the E. coli data to determine correlation. Oklahoma

Mesonet weather data is collected every five minutes from over 110 weather monitoring

sites uniformly scattered across the entire state in locations that minimizes influences

from anthropogenic, water, land slope, and wind obstruction factors, and the data goes

through a quality assurance system to ensure accuracy (Brock et al., 1995) (McPherson

et al., 2007). The weather data that was used in this study as independent variables was

the maximum air temperature (MAXTEMP), minimum air temperature (MINTEMP),

average air temperature (AVGTEMP), maximum wind speed (WINDMAX), minimum wind

speed (WINDMIN), average wind speed (WINDAVG), maximum barometric pressure

(PRESSMAX), minimum barometric pressure (PRESSMIN), average barometric

pressure (PRESSAVG), total solar radiation (TOTSOL), inches of rainfall (RAIN),

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maximum rate of rainfall in five minutes (MAXFIVE), soil temperature under sod at 5 cm

(SODTEMP), dew point (DEWPNT), and average humidity (HUMAVG).

Additional variables that were calculated from the Mesonet weather data included

the change in barometric pressure for the day (PRESSCHG) and the number of days

since a daily rainfall of 0.20 inches or more of rain (RAINDEL). Nevers and Whitman

(2005) found that there was typically a spike in E. coli in the beaches and a tributary of

Lake Michigan following a rainfall of greater than 0.5 cm (.1968 inches) in 24 hours.

Whitman and Nevers (2008) also found that wave height was useful in predicting E. coli

in water of Chicago beaches, and that increase in wave heights can occur during a

change in barometric pressure signifying storm conditions.

To reduce the amount of data to a feasible work load, the water sample data was

reduced to only the samples that contained 100 coliform forming units (cfu) or more of E.

coli. This decision was made based on the recommendation of Dr. James Bidlack,

Professor of Biology at the University of Central Oklahoma (J. Bidlack, personal

communication, December 10, 2018). This level is below the geometric mean decision

level of 126 cfu that is established in the Produce Safety Rule. This choice of criteria

reduced the water data points from over 10,000 water tests to 2,036. In addition, some

weather data points were excluded from the analysis due to a loss of data from non-

functioning sensors.

Since the 303(d) map (Figure 1) of E. coli impaired water bodies looked to favor

the Western half of Oklahoma, a variable was added to the data to account for whether

the water test site was on the West or East side of the Indian Meridian, which is a

geographic central dividing line of the state.

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Analysis of the data was completed by using the Pearson Correlation in the SAS

computer program to determine the linear correlation between the E. coli cfu and each

independent variable of weather.

Results

A statewide correlation using all of the data had significant p-values in WINDMAX,

PRESSMAX, PRESSMIN, PRESSAVG, TOTSOL, RAIN, and MAXFIVE. A p-value of

0.05 or less is considered in most research as the threshold to consider a variable to be

statistically significant, and the lower the p-value the less likely of having a false positive.

While the p-values of these variables were all 0.05 or less, they all had very low

correlations. The highest correlation coefficient was RAIN with 0.1355, while the rest of

the variables had correlation coefficients less than 0.1. A positive correlation in rainfall

was expected since runoff from surrounding land could pick up fecal material from

domestic animals and wildlife and deposit the E. coli bacteria into the tested water supply.

This increase in E. coli bacteria from rainfall was short-lived as the number of days since

a rainfall 0.2 inches or greater was not a significant variable. Therefore, the data analysis

suggests that a rain event only caused an increase in E. coli on the date that the location

had rainfall, and the bacteria was likely dispersed downstream, settled out of the water,

or had died by the next day.

Examination of the data for western Oklahoma found no weather variable on the

more arid western half of the state to be statistically significant (p-value < 0.05). However,

in eastern Oklahoma there were several significant weather factors with only WINDMIN,

all barometric pressure measurements, and DEWPNT not being statistically significant.

The correlations were higher in the East than in the statewide analysis. RAIN in the East

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once again had the highest correlation coefficient with a value of 0.24366, with TOTSOL

and MAXFIVE at -0.14666 and 0.14579, respectively. These coefficients for eastern

Oklahoma suggest that the more volume of rain in a day and a higher rate of rainfall would

result in slightly higher E. coli counts in the water and that solar radiation had a slight

negative effect on the E. coli. All air temperature variables and SODTEMP had negative

correlations, meaning a higher temperature resulted in lower E. coli amounts.

Dividing the water sources by stream or lake resulted in streams having significant

but very low correlation (under 0.1) in MAXTEMP, AVGTEMP, WINDMAX, PRESSMAX,

PRESSMIN, PRESSAVG, TOTSOL, and MAXFIVE. RAIN was once again the highest

correlated variable with a coefficient of 0.16479 and a p-value of <0.0001. Lakes were

unique in that the only significant variables were PRESSCHG and HUMAVG with

coefficients of 0.17128 and -0.19623, respectively. The increase in pressure change

resulting in a slight increase of E. coli is similar to what was found from the Lake Michigan

study by Whitman and Nevers (2008), which showed changes in pressure change and

wave height were interrelated (which may occur during storm conditions) and a greater

wave height increased the E. coli in the water along the lake’s beaches (Nevers and

Whitman, 2005).

Additionally, correlations were conducted on the 24 individual sample collection

sites that had 20 or more samples with an E. coli cfu of 100 or greater. Narrowing down

the data by site resulted in many highly correlated weather variables, but each site was

definitely affected by different factors. One example of a site highly correlated with

weather factors was the Big Cabin Creek (see Table 1) with significant variables (p-value

< 0.05) being WINDMAX, PRESSCHG, TOTSOL, RAIN, MAXFIVE, and HUMAVG, and

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the respective correlation coefficients being 0.63286, 0.57492, -0.48977, 0.92785,

0.89297, and 0.42327. The correlation coefficients being closest to +1 or -1 have the most

impact on the E. coli cfu. Therefore, RAIN and MAXFIVE with the respective correlation

coefficients at 0.92785 and 0.89297 and very low p-values of < 0.0001 implies that both

an increase in the amount and rate of rainfall are very likely to increase the E. coli in the

water at the Big Cabin Creek test site.

Table 1

Big Cabin Creek Test Site Variable Correlations

Statistic Weather Variables

MAXTEMP MINTEMP AVGTEMP

Correlation Coefficient -0.40185 -0.18706 -0.28718

P value 0.0573 0.3927 0.184

WINDMAX WINDMIN WINDAVG

Correlation Coefficient 0.63286 0.01484 0.20075

P value 0.0012 0.9464 0.3584

PRESSMAX PRESSMIN PRESSAVG

Correlation Coefficient 0.06524 -0.1785 -0.01788

P value 0.7674 0.4151 0.9355

PRESSCHG TOTSOL SODTEMP

Correlation Coefficient 0.57492 -0.48977 -0.17761

P value 0.0041 0.0242 0.4175

RAIN MAXFIVE RAINDEL

Correlation Coefficient 0.92785 0.89297 -0.38777

P value <.0001 <.0001 0.0675

HUMAVG DEWPNT

Correlation Coefficient 0.42327 0.00295

P value 0.0442 0.9893

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Six sites of the 24 used in this targeted analysis resulted in having no significant

correlations with any weather variables. The weather variables with the most significant

correlations were RAIN and MAXFIVE with 7 sites that were all positively correlated,

which once again suggests that increased rainfall volume and an increase in the

maximum rate of rainfall on the day of testing is likely increasing E. coli in water at select

sites by picking up fecal material through runoff from the surrounding land. TOTSOL had

6 significant sites that were all negatively correlated, suggesting that solar radiation could

be eliminating the E. coli in the water at select locations. While MAXTEMP, WINDMAX,

and HUMAVG also had 6 significant sites each, they all had a mix of both positive and

negative correlations.

Conclusions

This study has shown that a statewide recommendation on monitoring weather to

predict when E. coli concentrations in surface water would not be advisable since the

correlation coefficients were quite low. However, individual sites may be highly correlated

with weather, and therefore monitoring could be used on certain individual sites. If a farm

were to perform a similar in-depth study with each individual water source, there is

potential that weather monitoring guidelines could be created for each site to limit the

potential of contaminating fresh produce with fecal coliforms.

The most significant and highest correlated weather variable throughout the study

was rainfall volume on the day of testing, suggesting that farmers may want to avoid

irrigating produce for one day after a rain. The number of days since a 0.2 inch or greater

rain was rarely a significant variable, suggesting that waiting more than one day is likely

not necessary unless an individual site’s analysis suggests waiting longer. Not irrigating

WEATHER EFFECTS 12

within 24 hours of a rain should already be a common practice as irrigation is not typically

needed when there is significant rainfall.

With the Eastern half of Oklahoma having many significant variables and the more

arid climate in the Western half of the state having no significant variables, these

differences between East and West in this study show that using weather as a guidance

in reducing the potential of fecal coliforms (E. coli) in surface water may be more feasible

in Eastern states than in the more arid locations found in Western states.

Recommendations

Farmers that are regularly testing surface water should keep weather records to

determine if there are weather events that affect each individual surface water source. If

there are any correlations found, then that information could be used to adjust their

irrigation schedule to limit contaminating fresh produce.

Further research should be conducted to determine how weather may affect small

ponds and irrigation canals, not included in this study, which are commonly used as

irrigation sources.

In addition, research should also be conducted to determine why Oklahoma’s man-

made lakes are not impaired by E. coli even though they capture water from rivers and

creeks that were contaminated upstream.

Acknowledgments

First, I greatly appreciate the Oklahoma Water Resources Board, Oklahoma Water

Survey, and Oklahoma Mesonet for their work in collecting the data over the years and

providing it for use in my research. I also would like to thank Dr. James E. Bidlack of the

University of Central Oklahoma for his direction and assistance in programming and

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analyzing of the data in SAS. I want to recognize the IFPTI staff, mentors, and Paul

Dezendorf for their assistance in this project and development of an excellent fellowship

program. A special thanks to my mentor, Dan Gump, for his guidance. Finally, I would

like to thank my boss, Bryan Buchwald, for recommending and allowing me to participate

in the fellowship program, and a special recognition to all of the members of Cohort 7.

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