Every time a city resident turns on a tap, an invisible network of pipes, pumps, valves, and reservoirs delivers water that is expected to be clean, pressurized, and safe. When a contaminant slips into that network, whether through accidental intrusion, backflow, or deliberate attack, the consequences can reach thousands of people within hours. A new study published in Case Studies in Chemical and Environmental Engineering tackles one of the hardest questions in urban water security: given only the readings from a handful of water-quality sensors scattered across a network, can engineers work backwards to pinpoint exactly where the contamination entered, when it started, and how concentrated it was?

Researchers MohammadReza Najarzadegan and Ramtin Moeini developed and validated an integrated simulation-optimization framework that couples an improved artificial bee colony (IABC) algorithm with EPANET, the widely used hydraulic and water-quality modeling software from the United States Environmental Protection Agency. Rather than introducing a brand-new optimization technique, the authors’ contribution lies in formulating and testing a complete inverse problem: simultaneously estimating the contamination source location, the injection time, and the injection concentration from residual-chlorine observations. These three variables are deeply intertwined through contaminant transport, network hydraulics, and chlorine decay, which makes the search space nonlinear and non-convex, meaning that small changes in one variable can dramatically alter the optimal values of the others.

The artificial bee colony algorithm draws its inspiration from the foraging behavior of honeybees, in which employed bees explore food sources, onlooker bees select promising sources based on shared information, and scout bees search for new regions. In the improved version used in this study, the relative contribution of employed and onlooker bees is adjusted to control how search effort is divided between broad exploration of the solution space and intensive refinement of promising candidates. This balance matters for contamination-source identification because the source location is a discrete variable, while injection time and concentration are continuous. Too much local search risks premature convergence on a wrong node; too much exploration prevents the algorithm from sharpening an accurate estimate of timing and concentration.

The mechanics of the framework are elegantly cyclical. In each iteration, the IABC algorithm generates a candidate contamination scenario consisting of a source node, an injection start time, and an injection concentration. These values are transferred from MATLAB to EPANET through the EPANET-MATLAB Toolkit, which triggers a full hydraulic and water-quality simulation of how the contaminant would propagate through the pipes. The simulated concentrations at the monitoring sensors are then extracted and compared with the observed values, and the objective function, defined as the sum of squared differences between observed and simulated concentrations across all sensors and time steps, is evaluated. The algorithm updates its population of candidate solutions based on these scores, and the cycle repeats until the stopping criterion is met.

Chlorine plays a central role as the diagnostic signal. Utilities maintain free chlorine between roughly 0.5 and 1 milligram per liter throughout their networks, and any contaminant entering the system reacts with and depletes that residual disinfectant. The researchers modeled this depletion using first-order reaction kinetics, in which chlorine and the reactive contaminant decay at rates proportional to their concentrations, with stoichiometric coefficients handled automatically by EPANET. Water age, the hydraulic residence time of water at each node, was also computed as supporting information, since older water tends to carry less residual chlorine and can confound the interpretation of sensor signals.

To test the framework, the team used two case studies of very different scale. The first was the third example network from EPANET, a benchmark containing 92 nodes, 117 conduits, 3 tanks, 2 pumps, and two reservoirs, analyzed over a 24-hour extended period simulation. Two contamination scenarios were examined, with injections at node 100 and node 209 respectively, each beginning at 11 a.m., coinciding with peak demand, at a nominal concentration of 50 milligrams per liter lasting 3 hours. The second case study was far more ambitious: the real water distribution network of Baharestan, Iran, comprising 3,147 consumer nodes, 3,402 pipes, 12 pressure-reducing valves, and 11 quality sensors placed according to actual operating conditions.

Each optimization problem was solved 30 independent times to account for the stochastic nature of the metaheuristic search. In the EPANET benchmark, IABC achieved mean objective-function values of 0.506 and 0.198 for the two scenarios, compared with 0.579 and 0.352 for the shuffled frog algorithm, reductions of 12.60 percent and 43.75 percent respectively. In the Baharestan network, IABC’s mean objective-function value of 6.720 beat the shuffled frog algorithm’s 7.820, a 14.07 percent reduction. When compared with genetic algorithm results from a previous benchmark study, IABC’s mean absolute concentration errors at selected observation points were 92.88 percent and 93.47 percent lower in the two EPANET scenarios. However, the authors are careful to note that Wilcoxon rank-sum tests found no statistically significant difference between IABC and the shuffled frog algorithm at the 5 percent level, so the reported advantages are descriptive rather than proof of superiority.

Robustness was probed by injecting synthetic measurement noise of plus or minus 5, 10, and 20 percent into the observations. As expected, higher uncertainty inflated the objective-function values and degraded identification accuracy, underscoring how much the framework depends on reliable, well-calibrated sensors. A sensitivity analysis over contaminant concentration and injection duration showed that the identification problem becomes harder as events grow larger or longer; for instance, raising the source concentration from 50 to 100 milligrams per liter increased the mean objective-function value in the Baharestan network from 6.930 to 7.510. Notably, in the large Baharestan network, the identified source node varied across the 30 runs, revealing that source localization was not unique under the adopted sensor configuration even when the objective-function values remained low.

The authors are candid about limitations. The validation was conducted under modeled conditions with fixed sensor numbers and locations, and the analysis did not explicitly account for sensor failures, calibration errors, communication delays, missing data, or temporal changes in demand and hydraulics that occur in real systems. The noise perturbations represent a controlled approximation of field uncertainty rather than its full complexity. Broader validation using field-monitoring data, alternative sensor configurations, and dynamically varying hydraulic conditions is recommended before the framework can be generalized to operational networks.

Even with those caveats, the study marks a meaningful step forward for water security. Most previous research on contamination in water distribution networks has concentrated on where to place sensors, how to assess vulnerability, or how to mitigate consequences after detection. Far fewer studies have attempted the full inverse problem of characterizing an unknown contamination event from water-quality observations alone. By demonstrating that a bee-inspired optimizer, coupled to a physically grounded simulation engine, can simultaneously recover source location, timing, and concentration across networks ranging from a 92-node benchmark to a city-scale system of more than 3,000 nodes, the research offers utilities a practical template for turning sparse sensor data into actionable forensic intelligence. In an era when drinking-water infrastructure faces growing threats from emerging contaminants such as microplastics, pharmaceuticals, and antibiotic-resistance pollutants, tools that can rapidly answer where, when, and how much may prove indispensable for protecting public health.

Subject of Research: Inverse identification of contamination source location, injection time, and concentration in water distribution networks using an improved artificial bee colony algorithm coupled with EPANET simulation

Article Title: Using an improved artificial bee colony algorithm to identify contamination sources in water distribution networks

Article References: Najarzadegan, M., & Moeini, R. (2026). Using an improved artificial bee colony algorithm to identify contamination sources in water distribution networks. Case Studies in Chemical and Environmental Engineering, 14, Article 101500. https://doi.org/10.1016/j.cscee.2026.101500

Image Credits: AI Generated

DOI: 10.1016/j.cscee.2026.101500

Keywords: water distribution networks, contamination source identification, artificial bee colony algorithm, EPANET, metaheuristic optimization, residual chlorine, water quality monitoring, inverse problem, shuffled frog algorithm, genetic algorithm, chlorine decay, water security