Low-Cost IoT Technology for Runoff Water Quality Comparison with Traditional Methods

Repository Created by A.J. Brown, 27 Feb 2024

Principal Investigators: Ansley "AJ" Brown, Erik Wardle, Emmanuel Deleon, and Christina Welch
Affiliation: Colorado State University, Agricultural Water Quality Program, Soil and Crop Sciences Department

Abstract

In Colorado and across the United States, agriculture is identified as a source of nutrient pollution in state and federal waters, with nutrients such as nitrogen and phosphorus running off farmlands into surface waterways, causing quality issues. Despite the lack of regulation for agricultural nonpoint sources in Colorado, initiatives encourage adopting Best Management Practices (BMPs) to protect surface water quality.

The Colorado State University Agricultural Water Quality Program (AWQP) has developed a low-cost, automated water sampler (LCS) with Internet of Things (IoT) technology for scalable, near-real-time water quality research. This development follows from an awarded NRCS Conservation Innovation Grant and shows promise in comparison to commercial-grade equipment and manual collection methods. The project aims to compare water quality and quantity measurements from commercial-grade equipment, the LCS IoT apparatus, and manual data collection methods at a study site in Fort Collins, CO with varying runoff water qualitites due to diverser tillage practices.

A baysian approach is used in this analysis to compare water quality measurements from the four methods due to robust quantification of uncertainty and measurement error from this approach. The results of this study will be used to inform future research and provide a methodology for evaluating other sample collection methods and technologies henceforth.

Objective

To compare and contrast water quality and quantity measurements collected from 4 methods:

1. Commercial-grade equipment: collecting first flush and subsequent hourly samples, composited for each runoff event
2. LCS: collecting first flush and subsequent hourly samples, composited for each runoff event
3. Manual collection 1: water samples collected in a plastic bottle by hand at first flush, then at each susbsequent hour, composited for each runoff event.
4. Manual collection 2: water samples collected in a plastic bottle by hand at first flush, then at the first hour after first flush, then at the last hour of the runoff event, composited.

Table of Contents

• Repository Structure
• Experimental Design and Data Collection
• Data Analysis, Results, and Discussion
• Conclusion
• References

Repository Structure

• 0_docs/ - contains project documentation, including code output reports
• 1_data/ - contains raw data files, including real and simulated data for testing
• 2_code/ - contains source code for data simulation, analysis testing, and real analysis
• figs/ - contains figures needed for the repository readme file
• LICENSE.md - this code is licensed under the GNU General Public License v2.0

Experimental Design and Data Collection

Study Site

The study site is located at the CSU Agricultural Research, Development and Education Center (ARDEC) (40˚40′40″N, 104˚59′51″W) near Fort Collins, CO. Located at 1570 m above sea level, the area has an average annual precipitation of 407 mm with an average monthly maximum and minimum temperatures of 17.6 ˚C and 2.7 ˚C, occurring in July and January, respectively. Soils at the site are dominated by Garrett sandy-loams (fine-loamy, mixed, mesic type of Pachic Argiustoll; Soil Survey, 2016) with an average organic matter content of 1.8%, a pH of 7.8, and a textural profile of 52 % sand, 18 % silt, and 30 % clay. This soil type is representative of a high percentage of soils in Northern Colorado irrigated agriculture.

In 2011, the site was established to compare two conservation tillage treatments, minimum till (MT) and strip till (ST), with a conventional tillage control treatment (CT) that is representative of typical practices in furrow irrigated fields of northern Colorado. The MT and ST treatments were selected in collaboration with a group of advising farmers interested in the feasibility of conservation tillage for furrow-irrigated systems. The field contains relatively large field plots (320 m long × 27 m wide) to realistically represent water movement in furrows and associated challenges with commercial production fields in the region.

During the year of this study (2023), the research field was planted in silage corn in late April and harvested in mid-September. Winter wheat was planted immediately afterward and will be harvested in July 2024.

Edge-of-Field Runoff Monitoring Setup

The edge-of-field (EoF) runoff monitoring setup consists of a commercial-grade automated water sampler, an LCS, a a furrow flume. The placement of each EoF site is located at the bottom of the field, and can be seen in Figure 1:

Figure 1. Plot map of the conservation tillage study site where water samples will be collected, located at CSU ARDEC, Fort Collins, CO.

A more detailed depiction of the equipment and it's orientation at each EoF site can be found in Figure 2:

Figure 2. Edge-of-Field Runoff Monitoring Setup. The LCS and commercial sampler are located at the bottom of the field, near an installed furrow flume. Grab samples are collected by hand from water flowing through the furrow flume. The LCS and commercial sampler use the same flume for flow measurement.

Water Quality Analysis

Water samples collected from all sampling methods will be analyzed for the following as per NRCS Conservation Evaluation and Monitoring Activity (CEMA) 201 (NRCS, 2012): Ammonium Nitrogen (EPA 350.1), Nitrate-Nitrite (EPA353.2), Total Phosphorus (EPA365.2), Total Kjeldahl Nitrogen (A4500-NH3), Orthophosphate as P (EPA300), and Total Suspended Solids (EPA160.2). Additionally, the AWQP added the following tests to encompass salinity, pH, and biological measurements: Total Dissolved Solids (EPA 160.1), specific conductance (EPA 120.1), and pH (EPA 150.1).

Total suspended solids, specific conductance, and pH were measured at the CSU AWQP laboratory, whereas the remaining analyte analyses were outsourced to ALS Environmental within proper hold times.

Data Analysis, Results, and Discussion

This analysis will be performed using R via Rstudio in conjunction with the rethinking, cmdstanr, and dplyr packages.

The procedure will be as follows:

1. Simulate a testing dataset to ensure the analysis code is functioning properly
2. Create a causal model to compare the four sampling methods using a directed acyclic graph (DAG)
3. Use the DAG to create a statistical model for the analysis
4. Create the statistical model in R using the rethinking package
5. Test the model using the simulated data and verify model functionality
6. Analyze the real data using the verified model
7. Interpret the results

Simulate data

Simulated data come from an r script specifically built to represent a similar scenario with similar data, but with known influences of each variable on the result (e.g., make each sampler type have it's own bias on analyte concentration). This will allow us to test the model and ensure it is functioning properly before using real data.

The script to generate the simulated can be found in 2_code/data_sim.R, and the results can be found in 0_data/sim_data.csv.

Create a causal model

Produced by dagitty.net, the causal model is a directed acyclic graph (DAG) that represents the relationships between the variables in the study. This model will be used to create the statistical model in R.

Figure 3. DAG representing the causal model for the study.

Where sampler method (S) and tillage treatment (T) influence the unobserved true concentration (C), which in turn influences the observed concentration (C*). The observed concentration is also influenced by measurement error (e).

Here was the code used to generate the DAG:

dag {
"Measurement Error" [pos="0.158,-1.083"]
"Obs. Conc." [outcome,pos="-0.414,-0.561"]
"Sampler Method" [exposure,pos="-1.296,-0.978"]
"Tillage Treatment" [exposure,pos="-1.270,-0.285"]
"True Conc." [latent,pos="-0.918,-0.566"]
"Measurement Error" -> "Obs. Conc."
"Sampler Method" -> "True Conc."
"Tillage Treatment" -> "True Conc."
"True Conc." -> "Obs. Conc."
}

Create the statistical model

The statistical model is created using the DAG and the following assumptions:

$C_i \sim \text{Normal}(\mu_i, \sigma)$

$\mu_i = \alpha_{I} + \gamma_{A} + \theta_{B} + \beta_{S}S_i + \beta_{T}T_i$

Regular priors:

$\beta_{S} \sim \text{Normal}(0, 0.5)$

$\beta_{T} \sim \text{Normal}(0, 0.5)$

Adaptive priors:

$\alpha_{I} \sim \text{Normal}(\overline{\alpha}, \sigma_{\alpha})$

$\gamma_{A} \sim \text{Normal}(\overline{\gamma}, \sigma_{\gamma})$

$\theta_{B} \sim \text{Normal}(\overline{\theta}, \sigma_{\theta})$

Hyper-priors:

$\overline{\alpha} \sim \text{Normal}(0, 0.5)$

$\overline{\gamma} \sim \text{Normal}(0, 0.5)$

$\overline{\theta} \sim \text{Normal}(0, 0.5)$

$\sigma_{\alpha} \sim \text{Exponential}(1)$

$\sigma_{\gamma} \sim \text{Exponential}(1)$

$\sigma_{\theta} \sim \text{Exponential}(1)$

Where:

• $C_i$ is the observed analyte concentration
• $\alpha_{I}$ is the intercept for irrigation event
• $\gamma_{A}$ is the intercept for analyte type
• $\theta_{B}$ is the intercept for replication block
• $\beta_{S}$ is the effect of sampler method
• $\beta_{T}$ is the effect of tillage treatment

A comment on clusters v. features:

Clusters are kinds of groups within the data (e.g., irrigation event count or analyte type). These require adaptive priors to account for the variation within each cluster using hyper-paremeters. In this case, the clusters are irrigation event, analyte type, and replication block.

Features are aspects of the model (parameters) that vary by cluster (e.g., the effect of sampler method or tillage treatment on the observed concentration). These require regular priors to find the expsoure effect on the dependent variable.

Create the statistical model in R

The source code for the statistical model can be found in 2_code/analysis.Rmd and 2_code/analysis_test.Rmd.

# create data list for ulam models
dlist <- list(
    C_obs = dat$result_ctr, #use standardized results
    #C_sd = d$Divorce.SE / sd( d$Divorce ), # we don't have SE from our sample
    S = as.numeric(as.factor(dat$method.name)),
    TRT = as.numeric(as.factor(dat$treatment)),
    I = as.numeric(as.factor(dat$event.count)),
    A = as.numeric(as.factor(dat$analyte_abbr)),
    B = as.numeric(as.factor(dat$block)),
    N = nrow(dat)
)

# fit model 2.3
# using partial pooling around irrigation # and Analyte, and block (non-centered)
m2.3 <- ulam(
    alist(
        # model for C* (observed results)
        C_obs ~ dnorm(mu, sigma),
        sigma ~ dexp(1),
        # model
        mu <- a_bar + 
              z[I]*sigma_a + # irrigation intercepts
              x[A]*sigma_g + # analyte intercepts
              b[B]*sigma_j + # block intercepts
              bS[S] + # sampler method effect
              bTRT[TRT], # treatment effect
        # regular priors
        a_bar ~ dnorm(0,0.5),
        bS[S] ~ dnorm(0,0.5),
        bTRT[TRT] ~ dnorm(0,0.5),
        z[I] ~ dnorm(0,1),
        x[A] ~ dnorm(0,1),
        b[B] ~ dnorm(0,1),
        sigma_a ~ dexp(1),
        sigma_g ~ dexp(1),
        sigma_j ~ dexp(1),
        gq> vector[I]:a <<- a_bar + z*sigma_a,
        gq> vector[A]:g <<- x*sigma_g,
        gq> vector[B]:j <<- b*sigma_j
        # adaptive priors
          # none
        # hyper-priors
          # none
        
    ), data=dlist, chains=4, cores=4 )

Test the model using the simulated data

Using the simulated data, the model was tested to ensure that the model functions as intented. The results can be found in 0_docs/analysis_test.html.

Artificial values were added to the simulated data to represent the effect of sampler method, tillage treatment, irrigation event, and replication block on the observed concentration. The model was then used to create a modified average that was then pushed through a normal distribution to represent a simulated observed concentration. This was done twice for each combination of sampler method, tillage treatment, irrigation event, and replication block in congruence with real sampling protocol.

The modeled summary data looked like this:

Figure 4. Simulated data summary for reference. Bars represent average analyte concentration over all irrigations, treatments, and blocks with black error bars representing the standard deviation around the mean.

The statistical model was then verified by running simulated data in the model where the impacts of sampler method, tillage treatement, irrigation event, and replication block were known. The results were then compared to the known values to ensure the model was functioning properly.

Figure 5. Effects of Sampler Method on Observed Concentration

Figure 6. Effects of Tillage Treatment on Observed Concentration

Analyze the real data

The real data was analyzed using the verified model. The results can be found in 0_docs/analysis_real.html.

Figure 7. Raw data summary for reference. Bars represent average analyte concentration over all irrigations, treatments, and blocks with black error bars representing the standard deviation around the mean.

Figure 8. Effects of Sampler Method on Observed Concentration

Figure 9. Effects of Tillage Treatment on Observed Concentration

Interpret the results

Looking at sampler effect distributions, the results show that the sampler method over all analytes did not have a significant effect on the observed concentration (i.e., Figure 8 shows distributions centered around 0, meaning no effect). This is good news, as it means that the sampler method is not a significant source of bias in the observed concentration.

Looking at tillage effect distributions, the results show that the tillage treatment over all analytes did not have a significant effect on the observed concentration (i.e., Figure 9 shows distributions centered around 0, meaning no effect). This is unexpected, as the tillage treatment was expected to have significant impacts on the observed water quality concentration.

Conclusion

Water quality is an important aspect of agriculture, and it is important to understand the impacts of different sampling methods and tillage treatments on agricultural water quality measurements for scalable decision making and water resource management in general. This study used a bayesian approach, using a generative model, to estimate the impacts of sampler method and tillage treatment on observed water quality concentration. The results showed that neither the sampler method nor the tillage treatment had significant impacts on the observed water quality concentration. Regarding sampling method, this is good news, because it means that multiple sampling methods can be used in the field without significantly impacting the observed water quality concentration. Regarding tillage, however, the results are unexpected, as the tillage treatment was expected to have significant impacts on the observed water quality concentration. Further research is needed to understand the impacts of tillage treatment on observed water quality concentration. Future work will include a more detailed analysis of the tillage treatment impacts over multiple years, and not just 2023 data as used in this study.

For more information, please contact me!

References

• McElreath R (2023). rethinking: Statistical Rethinking book package. R package version 2.40.

• McElreath R (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman and Hall/CRC.