Trend Analysis
Water Quality Data Visualization

Early Warning for Exceedances

An ammonia concentration that rises from 2 mg/L to 6 mg/L over twelve months may not trigger an alert on any single sample if the permit limit is 10 mg/L. But the trend is clear: without intervention, an exceedance is coming. Trend analysis identifies these gradual changes and gives operators time to respond proactively.

Treatment Process Performance

Increasing effluent concentrations often signal treatment process issues: aging infrastructure, insufficient chemical dosing, seasonal biological stress, or upstream changes in influent quality. Trend analysis quantifies these changes and helps operators correlate effluent trends with process variables.

Permit Renewal Support

When permits are renewed, regulators evaluate historical data to determine whether current limits are appropriate or should be tightened. Demonstrating stable or improving trends with statistical evidence strengthens your position in permit negotiations.

Mann-Kendall Trend Test

The Mann-Kendall test is the most widely used non-parametric test for detecting monotonic trends in environmental time-series data. It compares every pair of observations to determine whether later values are consistently higher or lower than earlier values. Advantages for water quality data:

• No assumption of normal distribution (water quality data is often skewed)
• Handles non-detect values and censored data
• Resistant to outliers that can distort parametric tests
• Produces a p-value for statistical significance and a Sen’s slope for trend magnitude

Seasonal Mann-Kendall Test

Many water quality parameters exhibit seasonal patterns (e.g., higher temperatures and lower dissolved oxygen in summer, higher nutrient loads during spring runoff). The Seasonal Mann-Kendall variant accounts for these patterns by analyzing trends within each season independently, preventing seasonal variation from masking or falsely indicating long-term trends.

Linear Regression

Simple linear regression fits a straight line to time-series data and calculates the slope (rate of change), R² (goodness of fit), and confidence intervals. While it assumes a linear relationship and normally distributed residuals, it provides an intuitive measure of how fast a parameter is changing — useful for projecting when a value might reach a permit limit.

Control Charts

Control charts display data with upper and lower control limits (typically ±2 or ±3 standard deviations from the mean). Points outside the control limits, runs of consecutive points above or below the mean, and other patterns signal a process shift that warrants investigation. Widely used for tracking treatment process performance.