Utilisation of sniffer rats as a novel screening technique to accelerate remediation of contaminated soil

Introduction: Soil contaminated with petroleum hydrocarbons (PHCs) poses a major environmental threat, contributing to soil degradation, vegetation loss and broader ecological risks. Yet a major bottleneck in remediation remains the reliable identification of affected areas. Existing methods for delineating PHC-contaminated soil, such as portable instruments and canine teams, are labour intensive, costly, and delayed by logistical challenges.

Methods: We trained African giant pouched rats (Cricetomys ansorgei) in a laboratory environment to perform on-site style screening of soil samples.

Results: After explicit training to delineate soils at a regulatory-relevant threshold of 0.50%, rats reached an overall sensitivity of 100% and specificity of 98.9% individually, and a team-based strategy of six rats achieved perfect discrimination, correctly identifying all PHC-positive samples while rejecting all clean soils. Performance was not significantly affected by crude oil source or background soil variation, including samples from active remediation sites.

Discussion: These results provide the first evidence of threshold-sensitive PHC detection by any species, demonstrating rats’ potential as a rapid, low-cost, and scalable complement to existing techniques. Future work should evaluate deployment of trained rat teams in field conditions and explore integration into mobile testing units.

Introduction

Rodents are known for their excellent sense of smell (Kepecs et al., 2007; Freeman et al., 2020; Rajan et al., 2006; Sato et al., 2017; Uchida and Mainen, 2003) and clever learning abilities (Webb et al., 2020). These traits have allowed African giant pouched rats (Cricetomys ansorgei) to be cost-efficiently (Mulder et al., 2017) deployed for routine scent detection work within both medical and security sectors. The trained rats have saved thousands of lives by sniffing out landmines in former conflict zones (Fast et al., 2017) and helping to diagnose patients afflicted with tuberculosis (Fiebig et al., 2020). The rats have a long lifespan (Cooper, 2008), are quick to train, inexpensive to maintain and transport, and can readily work with multiple handlers, reducing the risk of handler-specific biases. These unique characteristics position them to provide reliable solutions for other pressing global challenges.

One significant challenge involves soil contaminated with petroleum hydrocarbons (PHCs), which can occur in areas of crude oil extraction, transport, and refinement (Fingas, 2016; Mekonnen et al., 2024). At elevated levels, PHC can degrade soil quality, inhibit vegetative growth, and pose health risks (Ansari et al., 2018; Ossai et al., 2020). Therefore, effective remediation depends on not only properly identifying PHC contamination, but also accurately delineating its extent to assess if it exceeds a regulatory threshold (Hyde et al., 2019). Regulatory thresholds also vary across countries, regions, and land uses, making them a critical consideration in development of field detection methods. Missing PHC contaminated soil during a site assessment means it will continue to pose an environmental risk, whereas misclassifying clean soil or soil with PHC levels below the regulatory threshold can lead to unnecessary and costly remediation. Yet, site investigation and characterization remain a major bottleneck in remediation efforts (Alden et al., 2024).

Conventional delineation of PHC within soil is a labour-intensive process that involves collection of soil samples by a trained field sampler. Samples are sent to a certified laboratory for analysis using standardized methodology such as EPA 8015 (USEPA, 1992; USEPA, 2019; ASTM International, 1996; Brewer et al., 2017a; Brewer et al., 2017b; ITRC, 2020). Lab data is then entered into a geographic information system to visually estimate the extent of contamination. Additional assessment is often necessary to further define the contamination but often not conducted due to cost and logistical challenges associated with collecting and shipping samples. For example, waiting for lab results and re-mobilizing field samplers and associated equipment, especially for remote areas.

Portable field instruments have been proposed to solve some of these problems. Some examples include gas chromatography mass spectrometers, gas chromatography photoionization detectors, and infrared spectrometers (Khudur and Ball, 2018). However, PHC consists of a complex mixture of different chemicals and can vary widely between different crude oil types, and analytical measurements are often susceptible to influence from non-PHC chemicals present in the soil (Alden et al., 2024; Bahar et al., 2024; Saari et al., 2007; Yang et al., 2013). Additional disadvantages of portable field instruments that have prevented their widespread use include the need for a trained operator, equipment cost and materials, calibration and significant sample preparation that reduces throughput. Trained scent detection animals may offer a more effective and flexible solution for delineating PHC concentrations at active remediation sites. Scent detection animals have been trained to reliably detect a range of target substances including both biological and synthetic substances (Browne et al., 2006; Desikan, 2013; Edwards et al., 2017; Karpinsky et al., 2024; Kokocińska-Kusiak et al., 2021). In demonstration tests, trained canines proved capable of delineating the horizontal extent of subsurface oil for shoreline and inland oil spills (American Petroleum Institute, 2016a; American Petroleum Institute, 2016b). In other settings, canines have demonstrated their ability to detect spilled oil trapped beneath ice and snow (Brandvik and Buvik, 2017). This suggests canines should also be capable of supporting PHC soil delineation and segregation activities at remediation sites. However, very few Oil Detection Dog Teams currently exist, and many limitations of scent detection canines have been identified, including low cost-efficiency, influence of psychological or physiological states on scent detection accuracy, and susceptibility to human handler cues (Hayes et al., 2018; Kokocińska-Kusiak et al., 2021). To our knowledge, there is no evidence of any species distinguishing PHC concentrations only above a specific threshold, such as a regulatory limit (Dechant et al., 2021; Dechant et al., 2024; Owens and Bunker, 2020). This project therefore aimed to establish proof of principle that rats can be trained to i) detect PHCs in soil, ii) distinguish concentrations of PHC (i.e., delineate contamination levels) in line with a hypothetical regulatory threshold, set at 0.50% for the current study to establish proof-of-principle and iii) apply what they’ve learned to field relevant soil samples. This project provides the foundation for establishing a novel, cost-effective solution to address global PHC contamination challenges.

Materials and methods

Subjects

Two cohorts of African giant pouched rats (C. ansorgei) obtained from APOPOs breeding colony served as subjects. The Experienced group (n = 6; mean age 4.4 years, SD = 0.19; 3 females) had prior experience detecting unrelated scents in a similar apparatus; the Naïve group (n = 5; mean age 0.4 years, SD = 0.001; 2 females) was naïve to all procedures. The sample size was constrained by the number of trained animals available within the APOPO breeding program. Although modest, this cohort was sufficient to demonstrate proof-of-principle. Rats were single- or pair-housed with a same-sex littermate within kennels designated for either male or female rats at APOPO’s Training and Research Headquarters on the campus of the Sokoine University of Agriculture in Tanzania. Home-cages were equipped with non-aromatic wood shavings, a clay sleeping pot, and an untreated hardwood climbing/gnawing structure as enrichment. Rats were maintained on a natural (roughly 12:12 h) light cycle with experimental sessions conducted during the daylight period. Water was available ad libitum. Animals were maintained at their free-feeding bodyweights with restricted access to food during daily training sessions. If the rat’s daily ration of food was not earned during training, supplemental food (fruit, vegetables, and 20 g of chow pellets; Specialty Feeds, Maintenance Food, Glen Forrest, Australia) was provided no less than 2 hours after the daily session. Additional protein (sundried fish, peanuts) and produce (e.g., mango, watermelon, tomato) was provided prior to days in which sessions were not conducted (e.g., on Friday). All experiments were conducted under approval of the Institutional Committee for Research Involving Animals at the Sokoine University of Agriculture (SUA/RES/APOPO/2018), following national standards and guidelines. All procedures complied with Tanzanian national guidelines and EU Directive 2010/63/EU. Rats received daily health monitoring with weekly vet oversight, and humane endpoints were strictly applied.

Apparatus

A custom engineered fully-automated line cage (FALCON; L210 × W41 × H52 cm, Figure 1) with glass walls and an aluminium lid and floor, mounted on four 96 cm tall legs was used for all experiments. See Supplementary Video S1 (available at OSF); consent was obtained from all personnel shown in the video, and training sessions posed no health risks requiring PPE. Ten circular sample holes (diameter 30 mm) were evenly spaced along the floor. Aluminium sample bars (L192 × W45 × H8 cm; L × H × W) could be positioned into a hinged bracket directly underneath the sample holes. Each bar had ten circular spaces (diameter 40 mm) which could be loaded with 20 mL borosilicate glass sample containers (Lenz Laborglasinstrumente; diameter 30 mm) and aligned with the ten holes along the floor of the cage. Each sample hole was fitted with an infrared photoelectric sensor which emitted a continuous auditory beep when broken, and an aluminium lid that could slide open to reveal the sample below. The first hole opened upon the start of the session and each subsequent hole opened after the infrared beam was broken by the rat inserting its nose in the hole immediately preceding it. The sample-hole lid was programmed to automatically close 500 ms after the rat removed its nose from the hole. On the left side of the cage, a pellet dispenser and magazine (ENV-203-94, MedAssociates, Georgia, VT) delivered pellets (5TCY OmniTreatTM) via a 20 cm plastic tube. The FALCON floor, walls and pellet dispenser were thoroughly cleaned between each rat session with 70% methylated spirits.

Figure 1

Figure 1. APOPO’s fully automated line cage (FALCON) containing (1) reward dispenser and (2) magazine, (3), sample holder cassette (see B), and (4) sampling holes with metal plates for automatically covering/exposing odour holes.

Oil and soil samples

Material impacted with PHC was used as target samples from four different sources. The first was a Synthetically Aged crude oil sand mixture that was created by mixing 2.7 kg of crude oil with 14 kg of clean quartz ‘Play’ sand sourced from Ace Hardware. During this process the majority of the volatile fraction of the crude oils volatized, leaving a mixture more representative of a past crude oil spill which is fairly stable in both volatility and biodegradability. This resulted in a PHC concentration of 8.40% (gas chromatography analysis using Chevron modified method based on EPA 8015 for Total PHC C4-C44). Samples were extracted using dichloromethane (DCM), and the resulting extracts were then analysed using gas chromatography with flame ionization detection (GC-FID). Targets from the remaining three sources involved material as it was collected (i.e., naturally mixed with sand or soil) from three active remediation sites: California A at 2.10% PHC concentration, California B at 4.30%, and Illinois at 6.50%. Targets were presented in either undiluted form as described above or mixed with additional clean soil (described below) to dilute the PHC concentration (Table 1).

Table 1

Table 1. PHC concentrations within soil samples used for training and tests both above (supra-threshold) and below (sub-threshold) our example threshold of 0.50%.

Clean soil collected from the same field locations served as control and dilutant for California B and Illinois field tests, however, was not available from the California A field location. Non-target samples and dilutant for California A and the Synthetically Aged material were a wide variety of soil types sourced from sixteen locations in Morogoro Region, Tanzania, air-dried and stored in sealed plastic buckets. The different soils were selected based on varying locations, composition, organic matter, soil type, and color. These were used as either i) clean soil, ii) dilutant for PHC samples, or iii) control for the presence of quartz and the tumbling and mixing procedure of diluted target samples by mixing the soil with equivalent volumes of quartz sand (i.e., the same volume of synthetically aged California material used to create target samples was matched for non-PHC controls). An additional control sample (‘Mix’) was created by combining various control mixtures (varying concentrations of quartz sand) within a single storage bucket.

To prepare sample mixtures, soil and PHC were placed in aluminium containers on a rock-tumbler for multiple 15 min cycles and crushed using a metal fork between tumbling rounds to disperse the oil and soil uniformly. Five mL of the mixture was inserted into a glass sample container and sealed prior to presentation to the rats. A subset of Synthetically Aged samples were sent for Total PHC laboratory analysis to Chevron Environmental Analysis Lab in Richmond, California to confirm our procedures achieved the desired PHC concentration (Supplementary Table S1) and validate rat performance results.

Design

The primary independent variable was PHC concentration of soil samples, which was a within-subject factor that varied across training and tests. The dependent variable was the rats’ indication behavior (indicate vs. reject) when exposed to the samples.

Training and test procedures

Rats were trained to detect PHCs in soil through a multi-stage training program. Each group (Naïve and Experienced) progressed through the same training stages, with some adjustments made to account for age, experience and timeline differences. By the end of each stage, rats were able to perform increasingly complex detection tasks with proficient discrimination capabilities.

Experiment 1: can rats be trained to detect the scent of PHCs in soil?

Stage 1. Clicker: Rats were trained to associate a click sound with food delivery. They progressed to the next stage once they consistently approached the food magazine within 2 seconds of the click across at least ten trials in a single session.

Stage 2. Indication: Rats were presented with 30 soil samples with synthetic PHC contamination (3.40%) distributed over three sample holes and were shaped to hold their nose inside the holes. The Experienced group reliably held indications for ≥1500 ms on PHC targets while the Naïve group reached ≥1000 ms to advance to the next stage.

Stage 3. 30-Sample Discrimination (3 holes): Rats were presented with 30 soil samples (10 with PHCs, 20 clean controls) distributed across three sample holes. Samples were pseudo-randomly positioned (within- and across-sessions) with the restriction that no more than two targets appeared per bar and no more than 2 bars contained only non-targets. Targets included five each of synthetic 3.40% PHC mixed with Soils 1 and 2. Non-targets included ten controls each from Soils 1 and 2 mixed with quartz sand at a concentration of 3.40%. Rats advanced to Stage 4 after achieving at least 8 correct indications and ≤7 false alarms (≤5 for Experienced) across two consecutive sessions.

Stage 4. 30-sample Discrimination (10-hole): Rats were again presented with PHC impacted soils randomised among 30 total samples with the same randomisation rules as the previous stage, now spread across 3 bars of ten sample ports. Two target samples were unrewarded (blinds) which were coded to appear as a non-target to the trainer. This was done to test detection reliability when potential extraneous cues were minimised. Once criteria were met (≥8 targets detected, few false alarms as with Stage 3), a novel soil type (Soil 3) was introduced as both target and control. Rats advanced to Stage 5 after all individuals in a group reliably detected at least one blind sample from Soil 3.

Stage 5. 50-sample Discrimination: Only naïve rats completed this phase to more gradually increase sample numbers. Sessions included 50 randomly positioned samples (10 targets with 2 blinds, and 40 controls) from Soils 1, 2 and 3 at 3.40% PHC concentration. New sample varieties were introduced stepwise: ‘Mix’ soil controls, lower PHC concentrations (2.50%), and a fourth soil type (Soil 4). Rats advanced after consistently detecting ≥8 targets with ≤7 false alarms over three consecutive sessions.

Stage 6. 100-Sample Discrimination: Rats evaluated 100 soil samples per session, with ten PHC targets (with two blind samples) and 90 control samples. Targets included three concentrations (2.50%, 3.40%, 4.20%) and all four soil types. Non-targets matched this variation, allowing assessment of concentration and different soil types on detection. From this training stage and for all tests described below, samples were pseudo-randomly positioned (within and between sessions) across ten sample-bars with the restriction that no more than two targets appeared within the same bar and no more than 2 bars could contain only non-targets. Experienced rats began with the same sample types from Stage 4 with new soil types and concentrations gradually added. Naïve rats started with a broader variety and progressed under stricter criteria, including requiring perfect target detection and minimal false alarms in 3 of 5 sessions. Later sessions included unmixed soils to simulate field conditions and reduce preparation time which allowed assessment of how soil background and composition influenced detection accuracy.

Experiment 2: can rats distinguish scents from varying concentrations of PHC in soil?

Spontaneous delineation test (2 sessions)

Before explicitly training rats to reject PHC levels below our example threshold (0.50%), we tested spontaneous generalization across a range of novel concentrations below the minimum training concentration (2.50%). Each test session included ten targets (no blinds): four containing 0.50% PHCs (novel) and two samples each of 2.50, 3.40, and 4.20% PHCs. The 90 non-targets included five each of two novel sub-threshold PHC concentrations (0.10% and 0.01%) and 80 controls (10 each of 0.10% and 0.01% quartz and either fourteen or sixteen samples of 0.50, 2.50, 3.40, and 4.20% quartz sand). Background Soils 1 and 2 were presented during Session 1 and Soils 3 and 4 were used during Session 2 (an equal number of samples from each soil was used for each sample type). The 20 control samples of 0.10% and 0.01% quartz sand were replaced by Mix samples for the Naïve group.¹

Delineation training

We adopted a threshold of 0.50% PHC concentration to mimic a hypothetical regulatory limit. Rats were reinforced for indicating soil samples containing 0.50% PHCs or greater but not for subthreshold samples (<0.50%; Supplementary Table S2). For the experienced group, the first fifteen sessions involved the same sample concentrations as the delineation test, excluding 2.50% concentrations. We provided a roughly equal number of target sample types (3 samples each, and 1 randomly selected blind) and control types (thirteen or fourteen); a roughly equal number of Soils 1 and 2 only were used. Between five to ten sub-threshold 0.10% samples were included per session, and there were fifteen 0.01% concentrations every session. To simplify sample preparation procedures, 4.20% and 0.01% concentrations were dropped after 33 sessions. The ten targets then included two 3.40% and eight 0.50% PHC (one blind sample each). The 90 non-targets were 34 each of 3.40% and 0.50% controls, sixteen 0.10% PHC samples, and six Mix samples.

The Naïve group were given a break of roughly 5.5 months after completion of the first Delineation Test (and all the supplementary tests in SI) due to onset of the COVID-19 pandemic. No additional training occurred during this break, however the rats continued to receive care and housing, as described above. Therefore, Delineation Training restarted at the Indication stage and progressed through 30-, 50-, and 100-sample Delineation stages. These stages were similar to those described above with training PHC samples sourced from Synthetic only while quartz sand and local background soils served as non-targets as well as background samples. By the end of training, rats were presented with two target concentrations (3.40% and 2.50%), one sub-threshold of 0.10%, and clean control concentrations (3.40%, 2.50%, 0.10% quartz sands, and Mix). Familiar local Soils 1, 2, 4, 5 and 6 were used (Soil 3 was depleted) and novel Soils 13-22 were gradually introduced during 100-sample Discrimination.

Post-training delineation test (experienced group only; 2 sessions)

Identical to the Experienced rat’s Spontaneous Delineation Test (above).

Experiment 3: is reliability of rat delineation less affected by factors that compromise existing methods?

We evaluated rat ability to distinguish PHC from other naturally occurring organic materials, which can result in false-positives on conventional PHC laboratory analysis. An equal number of four soils, including familiar Soils 1 and 2 along with two novel soils pseudo-randomly selected from Soils 7-12 were presented during each of six sessions. Unlike all other training samples, Soils 7-12 were not sifted to remove excess organic matter (see Oil and Soil Samples section) and it was therefore hypothesized that they contained additional baseline quantities of organic materials which was further increased with the addition of random volumes of sticks, mosses, grass, etc. Ten targets were presented per session, including five samples each of 3.40% and 0.50% per soil (either one or two from each concentration and soil type combination, alternated each session; two blinds from familiar soil samples). One sample from each combination of soil type and concentration was mixed with additional organic materials (moss or compost soil). Non-targets were 8 subthreshold PHC 0.01% (two from each of the four soil types), 44 clean soil samples, as collected (not mixed with quartz sand; eleven from each soil type), 20 samples containing 3.40% control (ten each from Soils 1 and 2), and 18 Mix samples.

Experiment 4: can trained scent detection rats accurately and reliably delineate soil from active remediation sites?

In a series of tests, we presented the rats with novel soil samples collected from the three active field sites (see Oil and Soil Samples section). For each field site, rats were first presented with undiluted samples as they were collected from the field, followed by a session in which these samples were diluted with additional soil to produce a range of PHC concentrations.

Only Experienced rats were tested with undiluted field samples sourced from California (California A). This test was conducted in a single session with ten total targets, including five local 3.40% samples (three of Soil 4 and two of Soil 3, one blind) and five undiluted California A (one blind). Non-targets were identical to the pre-test.

During the subsequent test (Test 2), a greater range of dilutions was presented. For Experienced rats, there was one session and the ten targets included two undiluted California A, one sample each of Soils 3 and 4 at 2.50% (1 blind), and six total samples containing 0.50% PHC (four California A, including one blind, and one sample each of Soils 3 and 4). Non-targets included 5 each of Soils 5 and 6; 13 each of controls 0.50% and 0.10% from Soils 3 and 4, 4 Mix; and 8 each of subthreshold 0.10% PHC from Soils 3, 4, and California A. Naïve rats completed two identical test sessions with the ten targets comprised of five undiluted California A (one blind) and five local 2.50% PHC samples (two each from Soils 1 and 2 plus one additional blind, alternated between soil type per session). Non-targets included five California A samples diluted to 0.10% PHC, 34 each of Soils 1 and 2 (sixteen control samples at 50:50 mixtures, sixteen control 3.40%, and two Synthetic sub-threshold 0.01% PHC), and seventeen Mix.

Unanticipated shipping delays caused by the COVID-19 pandemic affected delivery of oil samples used in Test 3. As a result, rats were given a 5.5 months break after completing Test 2 and before conducting Test 3. During this break, no training of any type was conducted while the rats continued to receive care and housing as described above. Following this break, rats completed all training stages in rapid sequence (see SI), however, sub-threshold samples were included. Additional soil types (13–22) were also introduced during this training.

Test 3 then introduced a second field sample from California (California B). Within a single test session, twelve targets were presented, including six familiar (Soil 20) samples of 2.5% PHC (one blind) and six undiluted California B (two blinds). The 88 non-targets included 32 clean California B and 56 samples of Soil 20 (twelve sub-threshold 0.10% PHC, twelve Mix, and eight each of controls 0.00%, 0.10%, 2.50%, and 3.40%). Test 4 immediately followed during the subsequent session and involved identical composition of targets and non-targets, but California B was replaced with the novel Illinois field sample.

Test 5 presented both California B and Illinois field samples (Supplementary Table S3) within a single session. As with Tests 3 and 4, this session included 12 total targets comprised of 6 undiluted field samples (3 from each source) and 6 that had been diluted to approximately 2.50% PHC (3 from each source). The 88 remaining samples within the session were non-targets, including 32 clean field samples from each site and 6 samples from each site that had been diluted to either 0.01% or 0.10% PHCs.

We then evaluated whether additional training could calibrate rats to site-specific differences in PHC scent profiles for greater delineation accuracy (as similarly required with existing technologies). Rats were presented with twelve targets per session, including four undiluted samples from each site (California B and Illinois, one blind each) and four California B diluted to 2.50%. Among the 88 non-targets were 29 clean samples from each site and 30 California B diluted to 0.10%. Combining the Experienced and Naïve groups, seven out of nine rats were required to indicate at least ten targets while committing no more than five false alarms over two consecutive sessions to advance to final testing.

Four final test sessions were then conducted to assess the impact of training with two different field sites on delineation accuracy for those sites. As with all prior tests, each session included 100 soil samples with only twelve representing targets. Sessions 1 and 2 presented a greater range of PHC concentrations within California B soil. Among the twelve targets were two undiluted samples from each site (California B and Illinois), two California B diluted to 2.50%, and three each (including one blind) of California B diluted to 1.00% and 0.50%. The 88 non-targets included 22 clean samples from each site, 22 California B diluted to 0.10%, and 22 California B diluted to 0.01%. Similar samples were presented during Sessions 3 and 4, however, diluted field samples only contained Illinois soil and only undiluted and clean California B samples were included.

Statistical analyses

All data were analyzed using R Studio. Generalized linear mixed models (GLMMs) were used to analyse the rats’ responses, with Indication (indicate vs. reject) as the outcome variable and Rat ID included as a random effect. Depending on the distribution of the data, models assumed either a binomial or Poisson structure, with arcsine transformation applied where necessary to address complete separation. When multiple rat groups were included, Rat ID was nested within Group. Model assumptions were checked through inspection of residuals and simplified random effects structures were used if singular fit issues occurred (Barr, 2013). Fixed effects varied according to the hypothesis tested. For example, models included Concentration when assessing detection thresholds, Stage (pre-vs. post-training) when comparing learning effects, Source (organic vs. PHCs or field vs. familiar soils) when comparing different sample types, and Blinding (blind vs. non-blind) when assessing potential experimenter influence. Interactions between factors were included when relevant. Post hoc comparisons were carried out using generalized linear hypothesis tests (GLHT) for main effects and emmeans for interactions, with Tukey’s Honestly Significant Difference (HSD) used to correct for multiple comparisons.

Results

Experiment 1: rats can be trained to detect the scent of PHCs in soil

We first trained two cohorts of rats to determine if they could reliably distinguish between the smell of clean (non-PHC) soil and soil containing a range (2.50%–4.20%) of PHC concentrations (Table 2; Figure 2). After an average of 78 daily training sessions (SD = 57.13; or approximately 3.6 months; Figure 3), rats reliably evaluated 100 soil samples in a line-up within an average of 23 min (SD = 7.92). Across the final three training sessions, rats indicated 94.85% (SD = 0.22) of PHC samples and only 1.68% (SD = 0.13) of clean samples.

Table 2

Table 2. Number of sessions to reach criterion for each group and training stage. Only Naïve rats completed 50-sample training to gradually increase the total number of samples evaluated within a single session.

Figure 2

Figure 2. Example sample pots containing clean local soil (left) and the same soil source mixed with synthetically aged crude oil (right).

Figure 3

Figure 3. Soil samples indicated by the rats as containing petroleum hydrocarbons (PHCs) across all stages of training for Experienced rats (Left) and Naïve rats (Right). While the Experienced rats discriminated between soil samples with and without PHCs more rapidly across sessions, both groups achieved similar levels of mastery at the conclusion of training. Number of independent data points (IDP) = 827.

A general linear mixed model (GLMM) suggested no significant differences in rat detection behavior to control concentrations of quartz sand in clean soil (ranging from 0.00%–4.20% and Mix; GLMM: X² = 1.85, p = 0.76) therefore all controls were grouped into a single category (clean 0.00% PHC). A significant difference in detection accuracy was found between PHC concentrations (0.00%, 2.50%, 3.40% and 4.20%; GLMM X² = 351.64, p < 0.001), with all target concentrations significantly differing from 0.00% (Emmeans: all p <0 .001) but no differences in detection accuracy between concentrations of PHC impacted samples (p ≥ 0.47).

Trainer knowledge did not significantly affect detection accuracy (blinded petroleum hydrocarbon impacted soil [PHIS]: M = 93.33%, SD = 0.25; known PHIS: M = 97.92%, SD = 0.14; GLMM: F(1, 298) = 3.48, p = 0.06). Additionally, an outlier analysis (defined as no more than two standard deviations from the mean) did not identify any subjects.

Background soil type (Soils 1–4) also did not influence rat detection accuracy (GLMM: X² = 2.34, p = 0.50) nor did it interact with Concentration (0.00%–4.20%; X² = 11.13, p = 0.27). Thus soil composition had little effect on detection. Taken together, these results provide strong evidence rats can be trained to reliably signal the presence of PHCs within soil (see Supplementary Table S2 for individual scores).

Experiment 2: rats can distinguish scents from varying concentrations of PHCs in soil

To determine if rats can distinguish between concentrations of PHCs, we then introduced soil samples containing less than or equal to 0.50% PHCs. Control concentrations did not differ (GLMM: X² = 9.90, p = 0.13) and were grouped as 0.00%. There was a main effect of Concentration (GLMM: X² = 5192.30, p < 0.001; Figure 4). Rats readily detected PHIS at 0.50% PHCs (M = 98.33%, SD = 0.13; Figure 1D) with accuracy comparable to higher concentrations (Emmeans: all p = 0.99). Detection declined at 0.10% (M = 84.0%, SD = 0.37; p ≤ 0.01 vs. higher concentrations), though still significantly above lower levels (all p < 0.001). Finally, we found significantly fewer indications of 0.01% (M = 25.33%, SD = 0.44) compared to all other PHC samples but that they still indicated these samples significantly more than clean soil (0% PHC; M = 1.75%, SD = 0.13; all p < 0.001).

Figure 4

Figure 4. Proportion of samples indicated by the Naïve (striped) and Experienced (solid) groups on each PHC concentration before (light shaded colors, both groups) and after (dark shaded colors, (Experienced only) explicit delineation training. Rat team represents post-training performance of Experienced rats applying a 4 -rat cut-off. IDP = 2700. Green-colored bars appearing to the left of the x-axis represent control and sub-threshold PHC samples (non-targets), while blue-shaded bars appearing on the right side of the x-axis represent soil samples containing supra-threshold PHC concentrations (targets). Vertical dashed lines separate non-targets (LEFT) and targets (RIGHT); all error bars represent the Standard Error of the Mean (SEM). Control concentrations binned for illustration purposes.

As found at the conclusion of training, there was no interaction between Soil Type and Concentration (GLMM: X² = 6.11, p = 0.98) nor main effect of Soil Type (X² = 2.82, p = 0.42), thus soil type again had minimal influence.

Following 48 training sessions in which rats were reinforced for indicating soil containing PHC levels ≥ 0.50% but no others, the same delineation test was repeated with the Experienced group only. During this second (post-training) test, all rats indicated 100% (SD = 0.00) of PHIS containing ≥0.50% PHCs while the average rat also hit 1.11% (SD = 0.10) or just 2 out of 180 soil samples that would otherwise be considered clean by our example threshold. Control concentrations again showed no differences (X² = 4.60, p = 0.47), and were grouped as 0.00%. A significant interaction between Stage and Concentration (GLMM: X² = 1008.91, p < 0.001) reflected a decrease in sub-threshold indications compared to the first delineation test: 0.01% (M = 0.00%; Emmeans: p < 0.001; Figure 2) and 0.10% (M = 16.67, SD = 0.38; p <0 .001). Subtle, though non-significant improvement in accuracy was also shown for 0.00% (M = 0.21, SD = 0.05; p = 0.09) and 4.20% (M = 100%; p = 0.09) concentrations. Soil Type again had no effect (LM: F (3, 1976) = 0.61, p = 0.61) nor interaction with Concentration (F (15, 1976) = 0.19, p = 0.99).

Adopting a rat team strategy (≥4/6 rats in agreement), the team of six rats achieved perfect delineation: 100% of the 20 PHIS samples identified and none of the 180 clean samples incorrectly flagged. Dilution accuracy was validated by lab analysis (mean deviation = 0.093%, range −0.79%–0.29%; Supplementary Table S1) with no misclassification. Collectively, these results suggest explicit training enabled accurate delineation of PHC thresholds.

Experiment 3: rat delineation reliability is less affected by factors that compromise existing methods

We evaluated if additional hydrocarbons from non-petroleum organic sources influenced rat delineation accuracy by introducing organic material (moss, grasses, compost material) to both clean and impacted samples prior to rat evaluation. Control conditions did not differ (X² = 4.29, p = 0.12), and were grouped as 0.00%.

Overall, rats indicated 97.22% (SD = 0.16) targets and 0.86% (SD = 0.09) of nontargets with only PHC added, and 90.42% (SD = 0.29) targets and 2.12% (SD = 0.14) nontargets with additional organic material (Figure 5). There was a main effect of Concentration (GLMM: X² = 721.29, p < 0.001). All concentrations significantly differed except 0.00% vs. 0.01% (p = 0.76), with lower concentrations eliciting fewer hits (GLHT: p ≤ 0.006).

Figure 5

Figure 5. Average rat indications of samples containing extra organic material (light shade colors) or PHCs-only (dark shade colors). Team approach represents performance when agreement between 4 rats was required. IDP = 6000. Green-colored bars appearing to the left of the x-axis represent control and sub-threshold PHC samples (non-targets), while blue-shaded bars appearing on the right side of the x-axis represent soil samples containing supra-threshold PHC concentrations (targets). Vertical dashed lines separate non-targets (LEFT) and targets (RIGHT); all error bars represent SEM. Control concentrations binned for illustration purposes.

Rats hit more 0.00% samples when organics were added (Emmeans: b = 1.10, SE = 0.28, z = 3.89, p = 0.002). At the 0.50% threshold, the reverse occurred: fewer hits with organics vs. PHC only (b = 1.48, SE = 0.42, z = 3.52, p < 0.001). No differences emerged at 0.01% or 3.40% (both p ≥ 0.99).

Applying the same rat team strategy with a 4-rat cut-off, all false indications to 0.00% and 0.01% samples were eliminated. Indications on 3.40% were increased to 100% and there was also a slight increase in indications of 0.50%–83.33%. Thus while organics increased false positives for individuals, a team approach mitigated this effect.

Experiment 4: trained scent detection rats can accurately and reliably delineate soil from active remediation sites

We tested if rat accuracy was influenced by crude oil source using soil from three remediation sites (Figures 6, 7). Sample Type (source/concentration) predicted responses in all cases.

Figure 6

Figure 6. Field generalization tests. IDP = 2400. Vertical dashed lines separate non-targets (LEFT) and targets (RIGHT); all error bars represent SEM.

Figure 7

Figure 7. HIS (left) and clean (right) samples from California B (top) and Illinois (bottom).

Field site 1: California A

All control concentrations were grouped into a single category as there were no significant differences (F (3,440) = 0.19, p = 0.90). Overall, rats indicated 100% of both familiar and California A targets and 2.22% (SD = 0.15) familiar nontargets (Figure 6). There was a main effect of Concentration (GLMM: X² = 207.71, p < 0.001). Specifically, rats correctly indicated 100% of targets (including blinds), regardless of whether they contained HIS from the California A field location or synthetically aged crude oil introduced into locally sourced soil as used for training (p = 1.00). They also indicated more 0.10% samples relative to 0.00% (p = 0.004).

Experienced group: 100% of all targets were indicated, while 4.07% (SD = 0.06) familiar and 18.75% (SD = 0.39) novel nontargets were indicated (Figure 8). Concentration was significant (X² = 488.72, p < 0.001), with targets indicated more than nontargets (all p < 0.001). Source interacted with Concentration (X² = 19.88, p < 0.001), driven by more false indications at 0.10% in novel samples. A 4-rat team cut-off yielded perfect delineation.

Figure 8

Figure 8. Field Delineation; California B separated by Group (Experienced, Naïve). IDP = 2800. Vertical dashed lines separate non-targets (LEFT) and targets (RIGHT); all error bars represent SEM.

Naïve rats: 85% (SD = 0.36) of familiar and 92.50% (SD = 0.27) of novel targets were indicated, while 2.94% (SD = 0.17) familiar and 42.50% (SD = 0.50) novel nontargets were indicated.

Concentration significantly predicted responses (X² = 96.58, p < 0.001). A 4-rat team cut-off reduced false indications and yielded 80% correct for 2.10% PHC.

Field site 2: California B

Overall, 94.44% (SD = 0.23) familiar and 66.67 (SD = 0.48) of novel targets were indicated, and 6.75 (SD = 0.25) familiar and 1.74 (SD = 0.13) novel nontargets (Figure 6). Concentration was a significant predictor (GLMM: X² = 205.70, p < 0.001). No differences between Illinois and familiar targets (p = 0.91). A 3-rat team again achieved perfect delineation.

Field site 3: Illinois

For Illinois samples, 96.30% (SD = 0.19) familiar and 81.48% (SD = 0.39) novel targets were successfully indicated, while 2.78% (SD = 0.16) familiar and 2.08% (SD = 0.14) novel nontargets were indicated (Figure 6).

As with the prior field sites, Sample Type was a significant predictor (GLMM: X² = 205.70, p < 0.001). Greater hits were recorded on all target samples relative to non-targets (controls, clean Illinois, and synthetic 0.10%; all p < 0.001). No differences between Illinois and familiar targets (p = 0.91). A 3-rat team again achieved perfect delineation.

Diluted field samples

Two rats were excluded from analysis (one for failing to complete both sessions, one as an outlier >2 SD below mean performance).

For the remaining rats, when diluted samples were introduced, 98.81% (SD = 0.11) California B and 92.86% (SD = 0.26) Illinois targets were indicated, and 11.04% (SD = 0.31) California B and 2.92% (SD = 0.17) Illinois nontargets were indicated (Figure 8).

Concentration significantly predicted responses (X² = 2988.92, p < 0.001): higher concentrations were indicated more than lower ones (p ≤ 0.03) (Figure 9), except undiluted and 2.50% which did not differ (p = 0.98). An interaction with Source (X² = 234.54, p < 0.001) was driven by significantly more false indications to 0.10% California B than Illinois samples (p < 0.001). A 5-rat cut-off eliminated most errors but still produced 25% false indications to 0.10% California B, while maintaining perfect discrimination at all other concentrations.

Figure 9

Figure 9. Post-delineation training on California B (top) and Illinois (bottom) samples; IDP = 5000. Vertical dashed lines separate non-targets (LEFT) and targets (RIGHT); all error bars represent SEM.

This discrepancy could reflect differences in either VOC profiles or soil substrate. GCMS analysis confirmed clear differences in volatiles (Supplementary Table S3): Illinois samples contained more light hydrocarbons, but California B may have had a higher proportion of the specific volatiles rats learned during training. Alternatively, sandy California B soils may have released volatiles more readily than clay-based Illinois soils, which would slow diffusion.

To test these alternatives, rats were explicitly trained on California B before being re-tested across additional dilutions (0.50, 1.00%). Post-training, indications to California B samples decreased overall (pre: 21.6%, post: 15.0%; X² = 101.70, p < 0.001), with a Stage × Concentration interaction (X² = 284.46, p < 0.001). Indications to 0.10% fell significantly (p < 0.001), though they remained higher than 0.01% and 0.00% (both p < 0.001). By contrast, indications to Illinois samples increased after California B training (pre: 13.7%, post: 23.6%; X² = 9.94, p = 0.002), driven by false positives at 0.10%.

Thus, while explicit training reduced errors on California B, it increased errors on Illinois, supporting the explanation that soil substrate and VOC release dynamics—rather than source-specific odor signatures alone—contributed to performance differences.

Discussion

This study not only demonstrated that African giant pouched rats can be trained to discriminate PHC impacted soil from clean soil, but also provided the first evidence of any species to delineate concentration according to a specific threshold: concentrations greater than or equal to the threshold were reliably detected, while concentrations below this threshold were ignored. Whereas dogs have proven reliable at detecting the presence of oil (e.g., Karpinsky et al., 2024) there is little evidence suggesting they can distinguish levels above versus below a specific limit. This remarkable capacity was possible with rats previously trained on other scent-detection tasks, and with very young, inexperienced rats. Here we used an example threshold of 0.50% to establish proof-of-principle, however, considering the regional variation in acceptable thresholds, future research would benefit from exploring to what extent animals can be trained to delineate other limits. Additionally, if an animal can be trained to flexibly adjust the threshold limit based on the project or regional requirements, this would further demonstrate their flexibility for deployment across areas where regulatory limits may vary. It is important to note that all testing was conducted under controlled laboratory conditions; as such, the present results should be interpreted as proof-of-principle rather than evidence of on-site performance. An important limitation of the present study is that all testing was conducted with topsoil-level samples. In practice, PHC contamination may occur at depths below the accessible surface, and volatiles may diffuse differently through overlying soil layers. Future work should evaluate rat detection accuracy at greater depths under realistic field conditions, to determine whether trained teams can provide reliable guidance in subsurface screening.

Performance was unaffected when exposed to a range of 22 different background soils and other organic (non-petroleum) hydrocarbons, demonstrating generalisation across soil matrices and confounds. This robustness is remarkable, as portable field instruments often show matrix interference (Alden et al., 2024; Bahar et al., 2024). Although dogs can also generalise, they remain more expensive to train and deploy (Hayes et al., 2018), whereas the rats achieved accurate detection across diverse conditions, highlighting their efficiency and comprehensive utility for PHC detection. African giant pouched rats have already been successfully deployed at scale in real-world contexts, including landmine detection (Fast et al., 2017) and tuberculosis diagnosis (Fiebig et al., 2020), demonstrating their operational feasibility for large-scale scent detection tasks.

Rats were also accurate and reliable in generalizing to PHC from novel crude oil sources and compositions, a feat which remains a challenge for analytical instruments (Chen and Tien, 2020). In addition, this has only been shown in dogs when explicit training was provided (DeChant et al., 2024). Although our results suggest calibration will be necessary to achieve optimal performance when transferring the rats between field sites, this can be easily implemented and included within the standardized operating procedure for rat deployment in this capacity. Additionally, although the current study found differences in detection accuracy between rats, a team-based scoring procedure reduced noise from inter-individual variability and improved reliability. These findings suggest that, although demonstrated here with a modest sample size, the use of rat teams can be scaled effectively for field deployment, since performance improves with pooled responses and training protocols are readily transferable across individuals.

These results demonstrate that African giant pouched rats may offer a sophisticated, practical and inexpensive screening solution for screening for PHC soil contamination. In contrast to conventional laboratory analysis that require costly logistics and are prone to delays, and dogs that demand greater resources, rats can screen large batches of samples within minutes. This capability provides near real time decision making about which soil sites require remediation, reducing cost, time and unnecessary excavations of clean soil. The comprehensive battery of background material used, and controlled sample preparation in the current study suggest the specificity of the rats would be maintained across a greater range of samples of varying compositions. To translate these findings into practise, future work should assess rat performance under diverse field conditions, as well as directly compare the rats to established, widely used field tools. Relatedly, development of a mobile rat deployment system, building on APOPO’s automated FALCON apparatus could further streamline operational field use. An automated evaluation chamber would provide rats with a working environment closely aligned to that used in the current study, ensuring consistency while enabling on-site application. Furthermore, soil samples to be screened by the rats can be collected from topsoil, from drilled soil borings at various depths, or from excavated soil. Samples can be collected following conventional soil sampling procedures to seamlessly integrate into existing processes. The advantage of using the rats is that information on presence of contamination can be obtained near real-time. This will decrease the time it takes to delineate contaminated sites, allowing for screening soil more efficiently during excavations to decrease the amount of clean soil that is often inadvertently disposed of, reducing greenhouse gas emissions associated with landfilling.

Future research should also assess the limits of this generalization by examining performance under conditions such as weathering, mixed hydrocarbons, and heterogeneous substrates. Mapping their boundaries can determine the extent of the rats flexibility. Further, direct comparative studies of rat and dog performance in PHC detection would provide valuable insights into the relative strengths of each species and inform decisions about their optimal deployment in environmental monitoring.

Finally, this study also demonstrated the utility of APOPO’s custom-engineered apparatus for training and testing scent detection rats. This automated apparatus enabled rapid learning of the target PHC scent while also bolstering reliability by minimizing trainer influence. By reducing handler bias, the apparatus ensured objective assessment of rat performance, similar to double-blind protocols used in canine detection studies. This innovative scent detection apparatus could easily be adapted into a mobile, on-site testing unit which could be transported to relevant remediation sites for near real-time analysis and high throughput. This potential increase in efficiency would enable more accurate, cost effective, and less wasteful remediation of PHC soil.

African giant pouched rats demonstrated reliable, threshold-based detection of PHCs, with performance unaffected by soil background, oil source, or prior training history. Their robustness and efficiency, combined with low cost and scalability, position them as a practical complement to existing laboratory instruments and canine detection. Integration with APOPO’s automated training apparatus offers the prospect of mobile, near real-time field deployment. Together, these advances highlight rats as a uniquely flexible and economical tool for guiding efficient and targeted soil remediation.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://osf.io/fm35q/?view_only&equals;c7b336a0e6d74750aedcf5836b5f05b1.

Ethics statement

The animal study was approved by Institutional Committee for Research Involving Animals at the Sokoine University of Agriculture. The study was conducted in accordance with the local legislation and institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

DK: Investigation, Resources, Writing – review and editing, Conceptualization, Writing – original draft, Data curation, Project administration, Methodology, Supervision, Formal Analysis. BL: Writing – review and editing, Methodology, Investigation. SM: Writing – review and editing, Methodology, Supervision. WM: Investigation, Writing – review and editing, Methodology. MS: Conceptualization, Project administration, Supervision, Writing – review and editing, Methodology, Investigation. DS: Resources, Conceptualization, Funding acquisition, Methodology, Writing – review and editing, Validation. CC: Writing – review and editing, Funding acquisition. CF: Methodology, Funding acquisition, Writing – original draft, Investigation, Resources, Conceptualization, Supervision, Writing – review and editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by Chevron, U.S.A. Inc. a Pennsylvania Corporation.

Acknowledgements

The authors wish to thank the APOPO staff and rodent trainers for their assistance, especially Alexander Iyungu and Miraji Jack for preparing soil sample mixtures.

Conflict of interest

The authors declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2025.1667451/full#supplementary-material

Footnotes

¹Two Experienced rats (Siri and Ellen) were not tested, and one Naïve rat (Sammy-Jo) did not complete the first test session due to differences in training timelines from the rest of the group

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