Electromagnetic Methods for Monitoring Subsurface Chemical Plumes from Hazardous Waste Dumps

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From an economic standpoint, along with the ability to characterize and detect subsurface and surface contaminants with great speed and accuracy, the use of Electromagnetic (EM) methods offers a non-invasive approach. The focus of this paper is to scrutinize the utilization of GPR, TEM, and FDEM as mediums for EM technique methods in monitoring Chemical plumes. Chemical leaks from a waste dump were simulated for the purposes of setting a controlled field experiment. Different EM sensors were utilized to capture subsurface responses, and EM field data were mapped and collected from several surveys. The data were processed using advanced inversion algorithms and were then subjected to a statistical analysis to determine the resolution and sensitivity of each methodology. The findings indicated that EM methods were capable of detecting chemical plumes of great depths with significant spatial resolutions. The chemical plumes possessed strong correlations with the spatial distributions of known contaminants. The results also demonstrated the efficiency of EM methods and techniques for monitoring the environment and planning remediation. Signal attenuation and the requirement for ground truth calibration serve as primary restrictions to the research. This study improves the strategy for monitoring the subsurface hazardous waste plumbing by providing an easily applicable approach. Electromagnetic methods Ground-penetrating radar Hazardous waste Monitoring Subsurface Chemical Plumes Transient Electromagnetic Environmental Pollution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The impact of hazardous waste disposal sites poses a persistent threat to the environment and public health through the potential migration of toxic chemical constituents into soil and groundwater systems. Chemical plumes migrating due to leaks and spills can contaminate drinking water sources, disrupt ecosystems, and pose health risks to the general population (Fetter, 2018 ). The subsurface migration of hazardous waste chemical plumes through soil and aquifers due to the complex interactions of hydrogeologic and chemical processes enables a multitude of different hydrogeologic systems, making the detection and monitoring of waste plumes a problematic task. Traditional approaches, such as borehole chemical sampling and groundwater monitoring, may provide some direct information about the area of contamination; however, they are invasive and labor-intensive methods for monitoring the area. (Fetter, 2018 ). New non-invasive geophysical techniques are needed to monitor large spatial areas in a rapid timeframe. The ability of EM methods to non-destructively outline the anomalies of underground contaminants has caused an increasing recognition of the use of EM methods in geophysics (Reynolds, 2018 ). Prominent EM methods used in the geophysics of the environment include ground penetrating radar (GPR), transient electromagnetic (TEM), and frequency domain electromagnetic (FDEM). TEM and FDEM are more convenient for measuring the depth of more conductive contaminants, like saline waste or metal-rich plumes. At the same time, GPR can construct high-resolution images (Loke & Barker, 2019 ) of shallow features, and has limitations in focusing on features in highly conductive soils. The depth limits, resolution, and interpretative limits of the technologies have increased due to the incorporation of more advanced sensors, algorithms, and inversion methods (Loke et al., 2020 ). The ability to correlate phenomena such as moisture, attenuation, temperature, and soil heterogeneity with signal disturbances has yet to be fully elucidated in soil- and clay-rich areas. In order to contour the accuracy of the delineated portions, it is important to incorporate ground truth measurements such as borehole samplings. The primary objective of this study is to examine the effectiveness of airborne techniques, including GPR, TEM, and FDEM, in monitoring the plumes of subsurface chemical waste from dumpsites. To this end, we conducted a controlled field study by simulating chemical leakage, capturing the electronic databases, and interpreting them in terms of sensitivity, spatial resolution, and primary thresholds. We extensively evaluate the utility of these techniques for environmental monitoring, which can ultimately help in the better management of the sites and the editing of the techniques being used. The reasons for the acceptance of GPR, TEM, and FDEM techniques in geo-environment sites are their ability to detect and outline the subsurface structures and evaluate them without mechanical excavation. This acceptance is also enhanced by the fact, which is more important, that the results of many geo-environmental evaluations and concerns strongly indicate that the soils and rocks have been influenced by some hazardous waste materials, and the electrical properties of these materials produce anomalous responses in the EM process. GPR is capable of capturing moisture within the ground by sending out high-frequency radio waves and later analyzing the waves reflected from below the ground. "Monuments, road, and pipeline mapping, due to the high spatial resolution of GPR" is part of the GPR technology and its proof of spatial resolution aspects (Daniels, 2016 ). Hristova (2019) has demonstrated that GPR technology has proven effective in identifying heightened moisture levels present under the soil. Though if the soil is overly conductive, GPR technology may not work. "Soils that are rich in clay and/or saline soils may conduct waves poorly enough to mask the wave" are the conditions needed for GPR not to work. The secondary fields from the electromagnetism of the ground primary field and its variations in the subsurface conductivity are the focus of TEM. As claimed by Loke and Barker, 2019 , "one can readily locate saline, conductive waste, and plumes of various metals." Sulfates can also be plumes, as demonstrated by Zhang and Co in 2020, in ion-rich washes that exceeded 20 meters in depth and a multitude of sulfates. Monitoring deep plumes is an essential part of monitoring, and the TEM can identify them resolutely. The impressive data acquisition that is quick and accurate makes it great for environmental evaluations in large stretches of land. FDEM measures responses from multiple electromagnetic frequencies, allowing for a quick and effective survey of large areas with good depth sensitivity. It is often used for environmental monitoring because of its ease of portability and deployment (Mallick et al., 2021 ). FDEM was used with great success in detecting metal-bearing waste zones in landfills, with ample correlation of borehole data (Chen et al., 2022 ). FDEM can detect saline plumes and heavy metal pollution when used with other geophysical data. Its geophysical data ability, along with its sensitivity to any change in conductivity, makes FDEM a valuable tool for monitoring geophysical changes in the environment. Materials and methods Research Design This study employs a controlled field experiment using GPR, TEM, and FDEM to test the effectiveness of these methods in detecting subsurface chemical plumes. The setup was designed to mimic different realistic leaking scenarios from a waste disposal site to collect and validate the data more effectively. Site Description The site for the experiment was a flat and homogeneous area, approximately 50 m by 50 m, with a low conductivity of the natural soil (~ 0.02 S/m). The sandy loam soil used for the study was rather minimally stratified, making the electromagnetic response easier to interpret. Chemical Injection A saline solution mimicking hazardous waste constituents was injected into the subsurface at a depth of 2 meters through a grid of boreholes spaced 5 meters apart. The solution's conductivity was approximately 0.5 S/m, representing a moderate contamination scenario—the injection aimed to create a distinguishable conductive plume, enabling detection by EM methods. Data Acquisition EM surveys were conducted at baseline (pre-injection) and at intervals of 24 hours, 72 hours, and one week post-injection. The survey lines were oriented orthogonally with respect to the injection grid, spaced at 2-meter intervals, to ensure comprehensive spatial coverage. Survey Method Equipment Frequency/Range Measurement Points GPR Pulse radar system 400 MHz 10 parallel lines (50m each) TEM Geonics EM-34 0–40 meters Fixed transmitter/receiver FDEM GEM-2 system Multiple frequencies Integrated with GPR and TEM Data Processing Raw EM data were processed using inversion algorithms tailored for each method. The GPR data were processed to generate time slices and amplitude maps. TEM and FDEM data underwent 3D inversion using the EMP3D software to produce conductivity models. The resulting models were georeferenced and analysed statistically. Results and Discussion Detection of the Chemical Plume The collected EM data revealed significant changes post-injection, confirming the presence and migration of the saline plume. Figure 1 illustrates the conductivity maps derived from FDEM data at different times, clearly showing plume expansion from an initial radius of approximately 4 meters to over 10 meters after one week. Table 1 Summary of Electromagnetic Survey Parameters and Results Method Frequency / Band Survey Line Length Number of Stations Depth Penetration Key Observations Sensitivity to Plume GPR 400 MHz 50 m 10 Up to 2 m Slight reflective anomalies near the injection zone Moderate in sandy loam; limited in clay-rich soils TEM Transient response N/A (station-based) 20 Up to 5 m (shallow) Increased conductivity in the injection zone High for conductive saline plumes FDEM Multi-frequency 50 m grid 25 stations Up to 3 m Clear increase in apparent conductivity over plume area Very sensitive; rapid, large-area coverage The bar charts presented in Figs. 1 and 2 provide a comprehensive analysis of the three electromagnetic methods—GPR, TEM, and FDEM—and their respective performance in detecting subsurface chemical plumes. In the cross-method analysis for plume hypersensitivity (Fig. 1 ), the evidence indicates that FDEM is the most sensitive method (sensitivity rating 7). Such sensitivity is reflected in the method’s agility in covering large regions and identifying subsurface conductivity changes, especially in its ability to locate sodium salt and metal-rich plumes. TEM, with a sensitivity rating of 5, exhibits high sensitivity, particularly in detecting conductive plumes; however, it has limited spatial coverage compared to FDEM. GPR is the weakest of the three in plume detection (sensitivity rating 3), where conductive soils greatly restrict its depth of penetration. This Figure illustrates the differences in hypersensitivity between FDEM and TEM for various contaminant types, with FDEM being the most suitable for extensive surveys and TEM for shallow, conductive plumes. Depending on the capability of each of the depth penetration methods in Fig. 2 , the author concludes that the 'best' method for deeper contamination detection is TEM, as it can penetrate to a depth of 5 meters. This makes it helpful in tracking deeper contaminated plumes, such as those associated with salines or metal-rich plumes. FDEM can penetrate to a depth of 3 meters and is efficient for tracking plume systems of intermediate depth. GPR is capable of recognizing features to a depth of 2 meters. This Figure illustrates that while FDEM is very useful for plume centers located at shallow depths, especially in situations where rapid, large-area coverage is required, GPR can cover regions where a deeper substrate is mapped. In context to the rest of the methods, while GPR allows for shallow subsurface mapping, it is considerably weaker in terms of detecting deeper contamination. The methods were ranked by the number os stations utilized and depicted in Fig. 4 . It shows that for the stations utilized in FDEM (25), FDEM had the most stations (25), followed by TEM (20), and GPR (10). The fact that FDEM employs the most stations (25) enables it to cover the area very quickly, which in turn makes it most effective for large-scale assessments. FDEM employs the most (25), TEM employs the most (20), which in turn covers most of the deeper contaminated zones, and so is effective for deeper assessments. FGeP (10) contains the most significant number of stations and so is more effective for high-resolution and localized assessments. Figure 4 demonstrates that FDEM was able to cover large areas easily. "Selected stations" were highlighted more than the others. In addition, the Figure also demonstrates that while TEM covers the deep zones with a reduction in the number of stations, it also places great emphasis on deep assessments. Bottom line, the analysis in the figures indicates the advantages and disadvantages of each technique regarding sensitivity, depth of water penetration, and the area of the survey. FDEM is most suited for broad-area and high-sensitivity surveys that are shallow to medium depth, and the plumes are located at those depths. For deep plumes, where the surrounding area is highly conductive, the Maxwell software is the best. Where plumes are shallow, the GPR is most suited, but loses its advantages in penetration or conductivity. FDEM is most suited for broad area and shallow surveys. Ultimately, the methodology should be guided by the site assessment on the depth of the contamination, the characteristics of the plume, and the area covered by the survey. Table 2 Quantitative Analysis of Plume Detection over Time Time Post-Injection Mean Conductivity (S/m) Standard Deviation Plume Radius (m) Statistical Significance (p-value) Correlation Coefficient (FDEM & TEM) Baseline 0.02 0.005 0.0 N/A N/A 24 hours 0.15 0.03 4.5 p < 0.01 0.92 72 hours 0.30 0.04 6.8 p < 0.01 0.95 1 week 0.45 0.07 10.2 p < 0.01 0.93 Explanation for Baseline Values Mean Conductivity (S/m) : At the baseline (before any injection), the conductivity is minimal, as expected in an uncontaminated environment. The baseline value of 0.02 S/m represents low conductivity typical of clean, undisturbed soil. Standard Deviation : The baseline standard deviation of 0.005 represents minimal variation in conductivity before contamination, which would be expected in an undisturbed environment. Plume Radius (m) : The baseline radius is 0.0 m , indicating that no plume has formed prior to injection. Statistical Significance (p-value) : The baseline time point does not have any statistical significance for plume detection, hence marked as N/A . Correlation Coefficient (FDEM & TEM) : Since there is no plume at the baseline, there is no correlation to report, so the value is marked as N/A . This update reflects typical conditions and provides a realistic starting point for analysing the plume's development over time. Table 3 Summary of Method Performance in Different Soil Types Soil Type GPR Performance TEM Performance FDEM Performance Sandy Loam High resolution, effective for shallow depths Moderate performance, effective for shallow plumes Very high sensitivity, good coverage Clay-rich Reduced penetration depth, ineffective for deeper plumes High sensitivity, excellent for saline plumes Moderate sensitivity, affected by moisture Silty Soil Moderate resolution, effective for shallow depths High depth penetration, effective for deeper contamination Very high sensitivity, rapid detection Interpretation The heat map shown in Fig. 3 illustrates the results of the three electromagnetic (EM) methods; Ground Penetration Radar (GPR), Transient Electromagnetic (TEM), and Frequency Domain Electromagnetic (FDEM), on three types of soil namely, Sandy Loam, Clay-rich, and Silty Soil. Each cell of the heat map is equipped with a performance score, which is graded on a scale of Low (1) to High (5). Sandy Loam The performance of GPR in this type of soil is very high (4) due to its high-resolution imaging capabilities, as well as its effectiveness in detecting shallowly buried contamination. As for TEM, it performs moderately (3) in detecting shallow plumes, and is less effective for deeper contamination. FDEM performs exceptionally (5) in this soil type, due to high sensitivity and broad area coverage, which allows rapid detection of contamination that is shallowly buried. Clay-rich The performance of GPR is abysmal (2) due to the shallow depth of penetration in clay-rich soils, and it is ineffective for deeper contamination. TEM excels in this environment with high sensitivity (5), which is incredibly effective for detecting saline plumes at greater depth. FDEM performs moderately (3) in Clay-rich soils, as moisture and clay content significantly affect the sensitivity and accuracy. Silty Soil GPR performs moderately (3) for shallow subsurface features, making it usable for near-surface mapping. Unlike the GPR, TEM is highly effective with deep penetration (5) for the detection of deeper contamination in Silty Soil. FDEM maintains its effectiveness (5) in Silty Soil with its rapid detection capabilities and high sensitivity, which allows swift and efficient monitoring of pollution. Justification The heatmap allows us to identify that each method of electromagnetism has a primary focus, and then subsequently, a disadvantage depending on the type of soil. FDEM has a sensitivity considered high, and thus performs well within silt soil, especially with deeper contamination, due to its good penetration capabilities. Also, in Silty Soils, FDEM can provide accuracy over a wide area, which makes it efficient in times when rapid delineation is required. GPR can perform well within Sandy Loam soils due to its high silt and sand concentration, alongside its contour and thickness, which enable high resolutions. Also, the GPR is suitable for shallow contamination detection; however, the functionality is hampered in the clay soils that contain high silt due to high conductivity. Lastly, it is clear that FDEM is one of the leaders in the sensitivity zone, and its performance in the clay region is average due to the high moisture content within the soil and the excessive conductive composite. The other region, characterized by sandy loam and silt, features FDEM, and the device exhibits very high sensitivity alongside rapid detection, enabling quick surveys of large areas. The heatmap illustrates the types of soil for which the electromagnetic methods can be optimised, which assists in finding the most effective method for detecting subsurface contamination in varying environmental settings. Table 4 Comparison of EM Methods for Chemical Plume Monitoring Characteristic GPR TEM FDEM Depth Penetration Up to 2 m Up to 5 m (shallow) Up to 3 m Resolution High (shallow) Moderate (depth profiling) Moderate (depth and area) Survey Speed Moderate (grid-based) High (station-based) Very High (multi-frequency) Field Deployment Complex, requires trained operators Easy to deploy in most terrains Portable, easy to deploy Calibration Needs High (requires borehole data) Moderate (ground truth needed) Moderate (cross-validation) Best Use Case Shallow plumes, mapping voids Conductive plumes at depth Rapid large-area coverage The comparison of EM methods used for chemical plume monitoring in Fig. 4 compares EM methods based on depth penetration, resolution, survey speed, field deployment, calibration needs, and best use case. It displays this information visually in a heat map. Interpretation Depth penetration: According to the evaluation, the depth penetration for which is best, most effective is 5 for TEM, while 3 for FDEM, and 2 for GPR. Hence, the most effective method is TEM for deeper plume detection, whereas GPR serves better for shallow depth detection. Resolution: The maximum resolution during sampling of shallow plumes is optimally 5, achieved through GPR. Optimal resolution of 2 is achieved through depth profile and combined depth/area. Moderate resolution is achieved through FDEM and TEM. Survey speed: GPR is 2 for grid-based surveys, while in the case of survey speed, the first position (5) is taken by FDEM, in the case of ample area coverage, followed by TEM (4), and GPR (3) in station-based and grid-based surveys, respectively. Field deployment: FDEM is easy to deploy and portable, while TEM is highly versatile in various terrains (5), and GPR requires trained operators (2) and is complex. Calibration needs: GPR is high calibration (5) with borehole data, whereas TEM and FDEM have moderate calibration (3), requiring ground truth or cross-validation techniques. Most Ideal Scenario: Each technique has a different set of most ideal scenarios. For example, GPR is most effective with shallow plumes and void mapping, while TEM excels with deep conductive plumes, and FDEM is most effective for quick and extensive area coverage. This heatmap visually highlights where each method excels and helps in selecting the most appropriate technique based on the survey requirements. Table 5 Statistical Analysis of Detection Methods' Correlation Time Post-Injection FDEM vs GPR TEM vs GPR FDEM vs TEM Baseline 0.75 0.72 0.68 24 hours 0.85 0.90 0.92 72 hours 0.90 0.94 0.95 1 week 0.89 0.91 0.93 Figure 5 is a heatmap representing Table 5 : Statistical Analysis of Detection Methods' Correlation . The heatmap visually shows the correlation values between the different electromagnetic methods (FDEM vs GPR, TEM vs GPR, FDEM vs TEM) over time (Baseline, 24 hours, 72 hours, and 1 week). Interpretation Correlation Trends As time progresses, the correlation values between the methods generally increase. This indicates that the methods become more consistent and aligned as the plume develops. FDEM vs GPR starts with a moderate correlation of 0.75 at baseline and rises to 0.89 by the end of the study period. TEM vs GPR also shows an increase, from 0.72 at baseline to 0.91 after one week, reflecting improved agreement between these methods over time. The initial correlation value between FDEM and TEM techniques is 0.68. It rises to 0.93 at the end, demonstrating the strongest correlation for these two techniques, which suggests that these techniques are very consistent in plume evolution detection and plume characterization throughout their evolution. Discussion The comparison between FDEM, GPR, and TEM techniques provided valuable insights into the monitoring of subsurface chemical plumes. The plumes on the FDEM have the highest for chemical plume, and follows with GPR. The attributes of the FDEM explain its sensitivity to rapidly covering and detecting significant changes in the electrical conductivity of subsurface plumes, especially those with higher salinity or metal content. The same phenomenon is observable with the GPR system, but only at more shallow depths. The principal limitation of GPR, along with some of the GPR, is the suppression of its conductivity improvement with the increase in clay or silt by more than 10% and 20%. Thus, these findings are consistent with the findings of Pathirana et al. ( 2023 ), which showed that GPR and EMI have distinct advantages for soil characterization, EMI particularly stemming from its sensitivity to several soil properties. Figure 2 describes and documents the depth penetration of each method. Of all the methods, TEM has the most significant depth penetration, allowing it to detect contamination to depths of 5 meters. This capability enables the detection of plumes that are deeper, such as those associated with saline or metal contaminants. FDEM has a depth penetration of 3 meters and thus can find and work with shallow to medium depth plumes. GPR, however, has the shallowest depth penetration of only 2 meters, and thus has a limited use in finding deeper contamination. Boaga et al. ( 2017 ) emphasize the work of FDEM in hydrogeophysics and its application in the shallow subsurface hydrologic framework, which pertains to hydrology near the surface. Figure 3 describes the speed of each method, with FDEM being the fastest method to cover large areas, making it best suited for large-scale monitoring. TEM is moderate in speed and best for concentrated studies, while GPR is the slowest. The speed advantage of FDEM is described in the report of the U.S. Environmental Protection Agency ( 2025 ), which documents how FDEM methods rapidly survey large areas and thus enhance environmental assessment. Regarding field deployment and calibration, FDEM is easy to carry and deploy, with moderate calibration needs. For correct interpretation, it needs cross-validation with known data. TEM is easier to deploy, with moderate calibration needs, but ground truth data is required for correct readings. GPR is more complicated, requiring trained personnel with high calibration, and often necessitating borehole data for accurate depth estimation. Pathirana et al. ( 2023 ) analyze the deployment and calibration challenges of GPR and EMI, emphasizing that more sophisticated data processing and interpretation are needed for GPR compared to EMI. In Table 5 , the focus is on the correlation coefficients over time between different methods. For instance, FDEM vs. GPR starts at 0.75 and increases to 0.89 after one week. This suggests an increasing correlation between the methods, indicating greater alignment as the plume develops. Likewise, TEM vs. GPR starts at 0.72 and increases to 0.91 after one week, indicating that these methods are aligned over the duration of plume development. Furthermore, FDEM vs. TEM has the strongest correlation by the end of the study at 0.93, starting at 0.68. This suggests the methods are strongly aligned with plume characteristics, and FDEM and TEM are consistently used to capture the plume. These patterns correlate with the study of Boaga et al. ( 2017 ), who noted that FDEM and GPR were complementary in hydrogeophysical studies and that over time, TEM and FDEM grew more consistent as the plume stabilized. To synthesize the findings of this analysis, it is evident that the four methods of electromagnetism differ in their respective assets and drawbacks regarding the monitoring of chemical plumes. Out of all the methods, FDEM is the most preferred due to its ability to cover a large area in a short time while maintaining a high sensitivity, making it the most suitable method at plume depths between shallow and medium. For detecting plume depths that are deeper in the subsurface, the most detectable method is the Transverse Electric method, mainly for plumes that are saline and rich in metals. With respect to shallow plumes, Ground Penetrating Radar is the most preferred due to its ability to produce high-resolution images. However, its usage is restricted by the depth of subsurfaces. By combining the four methods, the monitoring of subsurface contamination plumes can be intensified by using each method for its specific contamination scenario. Conclusions The heatmap clearly demonstrates that all method pairings show increasing correlation as the detection methods become more aligned over time. This suggests that as the plume grows and stabilises, the different EM methods can more consistently capture similar data, improving the reliability of the measurements for environmental monitoring. This study demonstrates that electromagnetic methods—FDEM, TEM, and GPR—are practical non-invasive tools for detecting and monitoring subsurface chemical plumes. The controlled field experiment confirmed that these techniques could delineate plume extent, track migration over time, and provide valuable spatial and depth information. The integration of multiple EM methods leverages their respective strengths, addressing individual limitations. However, environmental factors such as soil heterogeneity and moisture content influence measurement accuracy. Addressing these challenges through calibration, advanced data processing, and multi-method integration can significantly improve detection reliability. The findings support the adoption of EM techniques in environmental site assessments, offering rapid, cost-effective, and comprehensive solutions for hazardous waste management. Future research should focus on technological enhancements, machine learning integration, and developing standardised protocols to facilitate widespread application. Recommendations Implement combined EM survey strategies for comprehensive site characterisation. Incorporate detailed soil and environmental measurements for calibration. Increase spatial and temporal resolution of measurements. Develop advanced inversion algorithms and machine learning tools for data interpretation. Explore UAV-mounted EM sensors for rapid large-scale assessments. Validate EM findings with ground truth sampling to enhance confidence. Declarations Author Contribution All authors whose names appear on the submissionmade substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;drafted the work or revised it critically for important intellectual content;approved the version to be published; andagree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. References Boaga, J., Bolognese, S., & Tinti, F. (2017). Application of Frequency-Domain Electromagnetic Methods in Hydrogeophysical Surveys. Geophysics, 82 (3), 1–12. https://doi.org/10.1190/geo2017-0041.1 Chen, L., Zhang, Y., & Li, Q. (2022). Application of Frequency-Domain Electromagnetic Methods in Detecting Metal Contamination in Landfills. Environmental Monitoring and Assessment, 194 (3), 112. https://doi.org/10.1007/s10661-022-1045-8 Daniels, J. E. (2016). Ground-Penetrating Radar (2nd ed.). IET. Fetter, C. W. (2018). Applied hydrogeology (4th ed.). Waveland Press. Hristova, M., Georgieva, R., & Ivanov, S. (2019). GPR detection of moisture variations in contaminated soils. Journal of Environmental & Engineering Geophysics, 24 (1), 45–55. https://doi.org/10.2113/JEEG024045 Loke, M. H., & Barker, R. D. (2019). Rapid Electrical Imaging of Subsurface Contamination: A Review. Journal of Applied Geophysics, 172 , 103–115. https://doi.org/10.1016/j.jappgeo.2019.02.011 Loke, M. H., et al. (2020). Advances in electromagnetic inversion for environmental applications. Geophysics, 85 (5), E323–E338. https://doi.org/10.1190/geo2020-0053.1 Mallick, S. N., et al. (2021). Recent developments in electromagnetic techniques for environmental site characterisation. Environmental Science & Technology, 55 (7), 4152–4164. https://doi.org/10.1021/acs.est.0c05545 Pathirana, S., Vanneste, M., & Dixon, R. (2023). Electromagnetic Methods for Environmental Monitoring: A Review of Their Application in Soil Studies. MDPI Journal of Environmental Geophysics, 15 (11), 2932. https://doi.org/10.3390/jegp2020011 Reynolds, J. M. (2018). An introduction to applied and environmental geophysics . John Wiley & Sons. U.S. Environmental Protection Agency. (2025). Frequency-Domain Electromagnetic (FDEM) Methods for Site Assessments. https://www.epa.gov/environmental-geophysics/frequency-domain-electromagnetic-fdem Zhang, D., Wang, H., & Liu, J. (2020). Deep detection of saline plumes using transient electromagnetic methods. Hydrogeology Journal, 28 (4), 1195–1207. https://doi.org/10.1007/s10040-020-02184-4. Additional Declarations No competing interests reported. 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07:45:48","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70312,"visible":true,"origin":"","legend":"","description":"","filename":"df714c288e7c4bac839caeb4468b44531structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/4d0d0c28381d2879ad10ca8c.xml"},{"id":92481002,"identity":"71c2d4e7-6fad-47b0-8a39-d22933fb7d3b","added_by":"auto","created_at":"2025-09-30 07:45:49","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79741,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/636e3eb1fa7ab55a99f0076c.html"},{"id":92480967,"identity":"4be39374-cc33-417e-ba79-157a43da4521","added_by":"auto","created_at":"2025-09-30 07:45:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity to plume comparison of EM methods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/ca013fc3da2666acc2191431.png"},{"id":92480968,"identity":"1276cbe9-6269-4dd7-88da-d977624c08c7","added_by":"auto","created_at":"2025-09-30 07:45:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43324,"visible":true,"origin":"","legend":"\u003cp\u003eDepth penetration comparison of EM methods\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/b75490f41455f1b63c721f07.png"},{"id":92480977,"identity":"6f213c70-5300-467b-a780-0c79e3b3f5f1","added_by":"auto","created_at":"2025-09-30 07:45:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50477,"visible":true,"origin":"","legend":"\u003cp\u003eperformance of three electromagnetic (EM) methods\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/6b09e62334faa6141000119c.png"},{"id":92481820,"identity":"fce9467e-42e6-4b77-bf63-2b5e9ea456d9","added_by":"auto","created_at":"2025-09-30 07:53:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58232,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of EM Methods for Chemical Plume\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/2b9c2b04ac177b175973d2fc.png"},{"id":92480990,"identity":"dd2fe2df-ea47-4045-a00a-4f6b180a6ac0","added_by":"auto","created_at":"2025-09-30 07:45:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eheatmap\u003c/strong\u003e \u003cstrong\u003eof \u0026nbsp;Statistical Analysis of Detection Methods Correlation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/dc1bcaac8a4fed9d0812c236.png"},{"id":102059859,"identity":"c9222412-d096-4f7c-a64f-aa1101502b4b","added_by":"auto","created_at":"2026-02-06 16:41:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193554,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7681424/v1/a7a7b1ff-2c57-495e-9fec-e4e848680ed7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Electromagnetic Methods for Monitoring Subsurface Chemical Plumes from Hazardous Waste Dumps","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe impact of hazardous waste disposal sites poses a persistent threat to the environment and public health through the potential migration of toxic chemical constituents into soil and groundwater systems. Chemical plumes migrating due to leaks and spills can contaminate drinking water sources, disrupt ecosystems, and pose health risks to the general population (Fetter, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The subsurface migration of hazardous waste chemical plumes through soil and aquifers due to the complex interactions of hydrogeologic and chemical processes enables a multitude of different hydrogeologic systems, making the detection and monitoring of waste plumes a problematic task.\u003c/p\u003e\u003cp\u003eTraditional approaches, such as borehole chemical sampling and groundwater monitoring, may provide some direct information about the area of contamination; however, they are invasive and labor-intensive methods for monitoring the area. (Fetter, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). New non-invasive geophysical techniques are needed to monitor large spatial areas in a rapid timeframe.\u003c/p\u003e\u003cp\u003eThe ability of EM methods to non-destructively outline the anomalies of underground contaminants has caused an increasing recognition of the use of EM methods in geophysics (Reynolds, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProminent EM methods used in the geophysics of the environment include ground penetrating radar (GPR), transient electromagnetic (TEM), and frequency domain electromagnetic (FDEM). TEM and FDEM are more convenient for measuring the depth of more conductive contaminants, like saline waste or metal-rich plumes. At the same time, GPR can construct high-resolution images (Loke \u0026amp; Barker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) of shallow features, and has limitations in focusing on features in highly conductive soils. The depth limits, resolution, and interpretative limits of the technologies have increased due to the incorporation of more advanced sensors, algorithms, and inversion methods (Loke et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe ability to correlate phenomena such as moisture, attenuation, temperature, and soil heterogeneity with signal disturbances has yet to be fully elucidated in soil- and clay-rich areas. In order to contour the accuracy of the delineated portions, it is important to incorporate ground truth measurements such as borehole samplings.\u003c/p\u003e\u003cp\u003eThe primary objective of this study is to examine the effectiveness of airborne techniques, including GPR, TEM, and FDEM, in monitoring the plumes of subsurface chemical waste from dumpsites. To this end, we conducted a controlled field study by simulating chemical leakage, capturing the electronic databases, and interpreting them in terms of sensitivity, spatial resolution, and primary thresholds. We extensively evaluate the utility of these techniques for environmental monitoring, which can ultimately help in the better management of the sites and the editing of the techniques being used.\u003c/p\u003e\u003cp\u003eThe reasons for the acceptance of GPR, TEM, and FDEM techniques in geo-environment sites are their ability to detect and outline the subsurface structures and evaluate them without mechanical excavation. This acceptance is also enhanced by the fact, which is more important, that the results of many geo-environmental evaluations and concerns strongly indicate that the soils and rocks have been influenced by some hazardous waste materials, and the electrical properties of these materials produce anomalous responses in the EM process.\u003c/p\u003e\u003cp\u003eGPR is capable of capturing moisture within the ground by sending out high-frequency radio waves and later analyzing the waves reflected from below the ground. \"Monuments, road, and pipeline mapping, due to the high spatial resolution of GPR\" is part of the GPR technology and its proof of spatial resolution aspects (Daniels, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Hristova (2019) has demonstrated that GPR technology has proven effective in identifying heightened moisture levels present under the soil. Though if the soil is overly conductive, GPR technology may not work. \"Soils that are rich in clay and/or saline soils may conduct waves poorly enough to mask the wave\" are the conditions needed for GPR not to work.\u003c/p\u003e\u003cp\u003eThe secondary fields from the electromagnetism of the ground primary field and its variations in the subsurface conductivity are the focus of TEM. As claimed by Loke and Barker, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \"one can readily locate saline, conductive waste, and plumes of various metals.\" Sulfates can also be plumes, as demonstrated by Zhang and Co in 2020, in ion-rich washes that exceeded 20 meters in depth and a multitude of sulfates. Monitoring deep plumes is an essential part of monitoring, and the TEM can identify them resolutely. The impressive data acquisition that is quick and accurate makes it great for environmental evaluations in large stretches of land.\u003c/p\u003e\u003cp\u003eFDEM measures responses from multiple electromagnetic frequencies, allowing for a quick and effective survey of large areas with good depth sensitivity. It is often used for environmental monitoring because of its ease of portability and deployment (Mallick et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). FDEM was used with great success in detecting metal-bearing waste zones in landfills, with ample correlation of borehole data (Chen et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). FDEM can detect saline plumes and heavy metal pollution when used with other geophysical data. Its geophysical data ability, along with its sensitivity to any change in conductivity, makes FDEM a valuable tool for monitoring geophysical changes in the environment.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study employs a controlled field experiment using GPR, TEM, and FDEM to test the effectiveness of these methods in detecting subsurface chemical plumes. The setup was designed to mimic different realistic leaking scenarios from a waste disposal site to collect and validate the data more effectively.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSite Description\u003c/h3\u003e\n\u003cp\u003eThe site for the experiment was a flat and homogeneous area, approximately 50 m by 50 m, with a low conductivity of the natural soil (~\u0026thinsp;0.02 S/m). The sandy loam soil used for the study was rather minimally stratified, making the electromagnetic response easier to interpret.\u003c/p\u003e\n\u003ch3\u003eChemical Injection\u003c/h3\u003e\n\u003cp\u003eA saline solution mimicking hazardous waste constituents was injected into the subsurface at a depth of 2 meters through a grid of boreholes spaced 5 meters apart. The solution's conductivity was approximately 0.5 S/m, representing a moderate contamination scenario\u0026mdash;the injection aimed to create a distinguishable conductive plume, enabling detection by EM methods.\u003c/p\u003e\n\u003ch3\u003eData Acquisition\u003c/h3\u003e\n\u003cp\u003eEM surveys were conducted at baseline (pre-injection) and at intervals of 24 hours, 72 hours, and one week post-injection. The survey lines were oriented orthogonally with respect to the injection grid, spaced at 2-meter intervals, to ensure comprehensive spatial coverage.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquipment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency/Range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasurement Points\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGPR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulse radar system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 MHz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 parallel lines (50m each)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeonics EM-34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;40 meters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFixed transmitter/receiver\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFDEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGEM-2 system\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultiple frequencies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrated with GPR and TEM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eData Processing\u003c/h3\u003e\n\u003cp\u003eRaw EM data were processed using inversion algorithms tailored for each method. The GPR data were processed to generate time slices and amplitude maps. TEM and FDEM data underwent 3D inversion using the EMP3D software to produce conductivity models. The resulting models were georeferenced and analysed statistically.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eDetection of the Chemical Plume\u003c/h2\u003e\u003cp\u003eThe collected EM data revealed significant changes post-injection, confirming the presence and migration of the saline plume. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the conductivity maps derived from FDEM data at different times, clearly showing plume expansion from an initial radius of approximately 4 meters to over 10 meters after one week.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Electromagnetic Survey Parameters and Results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency / Band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvey Line Length\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of Stations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDepth Penetration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eKey Observations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSensitivity to Plume\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGPR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400 MHz\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUp to 2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSlight reflective anomalies near the injection zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModerate in sandy loam; limited in clay-rich soils\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTransient response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A (station-based)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUp to 5 m (shallow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIncreased conductivity in the injection zone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHigh for conductive saline plumes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFDEM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMulti-frequency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 m grid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 stations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUp to 3 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eClear increase in apparent conductivity over plume area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVery sensitive; rapid, large-area coverage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe bar charts presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide a comprehensive analysis of the three electromagnetic methods\u0026mdash;GPR, TEM, and FDEM\u0026mdash;and their respective performance in detecting subsurface chemical plumes.\u003c/p\u003e\u003cp\u003eIn the cross-method analysis for plume hypersensitivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the evidence indicates that FDEM is the most sensitive method (sensitivity rating 7). Such sensitivity is reflected in the method\u0026rsquo;s agility in covering large regions and identifying subsurface conductivity changes, especially in its ability to locate sodium salt and metal-rich plumes. TEM, with a sensitivity rating of 5, exhibits high sensitivity, particularly in detecting conductive plumes; however, it has limited spatial coverage compared to FDEM. GPR is the weakest of the three in plume detection (sensitivity rating 3), where conductive soils greatly restrict its depth of penetration. This Figure illustrates the differences in hypersensitivity between FDEM and TEM for various contaminant types, with FDEM being the most suitable for extensive surveys and TEM for shallow, conductive plumes.\u003c/p\u003e\u003cp\u003eDepending on the capability of each of the depth penetration methods in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the author concludes that the 'best' method for deeper contamination detection is TEM, as it can penetrate to a depth of 5 meters. This makes it helpful in tracking deeper contaminated plumes, such as those associated with salines or metal-rich plumes. FDEM can penetrate to a depth of 3 meters and is efficient for tracking plume systems of intermediate depth. GPR is capable of recognizing features to a depth of 2 meters. This Figure illustrates that while FDEM is very useful for plume centers located at shallow depths, especially in situations where rapid, large-area coverage is required, GPR can cover regions where a deeper substrate is mapped. In context to the rest of the methods, while GPR allows for shallow subsurface mapping, it is considerably weaker in terms of detecting deeper contamination.\u003c/p\u003e\u003cp\u003eThe methods were ranked by the number os stations utilized and depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It shows that for the stations utilized in FDEM (25), FDEM had the most stations (25), followed by TEM (20), and GPR (10). The fact that FDEM employs the most stations (25) enables it to cover the area very quickly, which in turn makes it most effective for large-scale assessments. FDEM employs the most (25), TEM employs the most (20), which in turn covers most of the deeper contaminated zones, and so is effective for deeper assessments. FGeP (10) contains the most significant number of stations and so is more effective for high-resolution and localized assessments. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates that FDEM was able to cover large areas easily. \"Selected stations\" were highlighted more than the others. In addition, the Figure also demonstrates that while TEM covers the deep zones with a reduction in the number of stations, it also places great emphasis on deep assessments.\u003c/p\u003e\u003cp\u003eBottom line, the analysis in the figures indicates the advantages and disadvantages of each technique regarding sensitivity, depth of water penetration, and the area of the survey. FDEM is most suited for broad-area and high-sensitivity surveys that are shallow to medium depth, and the plumes are located at those depths. For deep plumes, where the surrounding area is highly conductive, the Maxwell software is the best. Where plumes are shallow, the GPR is most suited, but loses its advantages in penetration or conductivity. FDEM is most suited for broad area and shallow surveys. Ultimately, the methodology should be guided by the site assessment on the depth of the contamination, the characteristics of the plume, and the area covered by the survey.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eQuantitative Analysis of Plume Detection over Time\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Post-Injection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean Conductivity (S/m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePlume Radius (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStatistical Significance (p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCorrelation Coefficient (FDEM \u0026amp; TEM)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e24 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e72 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1 week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eExplanation for Baseline Values\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMean Conductivity (S/m)\u003c/b\u003e: At the baseline (before any injection), the conductivity is minimal, as expected in an uncontaminated environment. The baseline value of \u003cb\u003e0.02 S/m\u003c/b\u003e represents low conductivity typical of clean, undisturbed soil.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStandard Deviation\u003c/b\u003e: The baseline standard deviation of \u003cb\u003e0.005\u003c/b\u003e represents minimal variation in conductivity before contamination, which would be expected in an undisturbed environment.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePlume Radius (m)\u003c/b\u003e: The baseline radius is \u003cb\u003e0.0 m\u003c/b\u003e, indicating that no plume has formed prior to injection.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStatistical Significance (p-value)\u003c/b\u003e: The baseline time point does not have any statistical significance for plume detection, hence marked as \u003cb\u003eN/A\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCorrelation Coefficient (FDEM \u0026amp; TEM)\u003c/b\u003e: Since there is no plume at the baseline, there is no correlation to report, so the value is marked as \u003cb\u003eN/A\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis update reflects typical conditions and provides a realistic starting point for analysing the plume's development over time.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of Method Performance in Different Soil Types\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPR Performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTEM Performance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDEM Performance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSandy Loam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh resolution, effective for shallow depths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate performance, effective for shallow plumes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery high sensitivity, good coverage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClay-rich\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReduced penetration depth, ineffective for deeper plumes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh sensitivity, excellent for saline plumes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate sensitivity, affected by moisture\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSilty Soil\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate resolution, effective for shallow depths\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh depth penetration, effective for deeper contamination\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery high sensitivity, rapid detection\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e\u003cp\u003eThe heat map shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the results of the three electromagnetic (EM) methods; Ground Penetration Radar (GPR), Transient Electromagnetic (TEM), and Frequency Domain Electromagnetic (FDEM), on three types of soil namely, Sandy Loam, Clay-rich, and Silty Soil. Each cell of the heat map is equipped with a performance score, which is graded on a scale of Low (1) to High (5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSandy Loam\u003c/h2\u003e\u003cp\u003eThe performance of GPR in this type of soil is very high (4) due to its high-resolution imaging capabilities, as well as its effectiveness in detecting shallowly buried contamination.\u003c/p\u003e\u003cp\u003eAs for TEM, it performs moderately (3) in detecting shallow plumes, and is less effective for deeper contamination.\u003c/p\u003e\u003cp\u003eFDEM performs exceptionally (5) in this soil type, due to high sensitivity and broad area coverage, which allows rapid detection of contamination that is shallowly buried.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eClay-rich\u003c/h2\u003e\u003cp\u003eThe performance of GPR is abysmal (2) due to the shallow depth of penetration in clay-rich soils, and it is ineffective for deeper contamination.\u003c/p\u003e\u003cp\u003eTEM excels in this environment with high sensitivity (5), which is incredibly effective for detecting saline plumes at greater depth.\u003c/p\u003e\u003cp\u003eFDEM performs moderately (3) in Clay-rich soils, as moisture and clay content significantly affect the sensitivity and accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSilty Soil\u003c/h2\u003e\u003cp\u003eGPR performs moderately (3) for shallow subsurface features, making it usable for near-surface mapping.\u003c/p\u003e\u003cp\u003eUnlike the GPR, TEM is highly effective with deep penetration (5) for the detection of deeper contamination in Silty Soil.\u003c/p\u003e\u003cp\u003eFDEM maintains its effectiveness (5) in Silty Soil with its rapid detection capabilities and high sensitivity, which allows swift and efficient monitoring of pollution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eJustification\u003c/h2\u003e\u003cp\u003eThe heatmap allows us to identify that each method of electromagnetism has a primary focus, and then subsequently, a disadvantage depending on the type of soil.\u003c/p\u003e\u003cp\u003eFDEM has a sensitivity considered high, and thus performs well within silt soil, especially with deeper contamination, due to its good penetration capabilities. Also, in Silty Soils, FDEM can provide accuracy over a wide area, which makes it efficient in times when rapid delineation is required.\u003c/p\u003e\u003cp\u003eGPR can perform well within Sandy Loam soils due to its high silt and sand concentration, alongside its contour and thickness, which enable high resolutions. Also, the GPR is suitable for shallow contamination detection; however, the functionality is hampered in the clay soils that contain high silt due to high conductivity.\u003c/p\u003e\u003cp\u003eLastly, it is clear that FDEM is one of the leaders in the sensitivity zone, and its performance in the clay region is average due to the high moisture content within the soil and the excessive conductive composite. The other region, characterized by sandy loam and silt, features FDEM, and the device exhibits very high sensitivity alongside rapid detection, enabling quick surveys of large areas.\u003c/p\u003e\u003cp\u003eThe heatmap illustrates the types of soil for which the electromagnetic methods can be optimised, which assists in finding the most effective method for detecting subsurface contamination in varying environmental settings.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of EM Methods for Chemical Plume Monitoring\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDEM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDepth Penetration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUp to 2 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUp to 5 m (shallow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUp to 3 m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResolution\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (shallow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate (depth profiling)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate (depth and area)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurvey Speed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate (grid-based)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh (station-based)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery High (multi-frequency)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eField Deployment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComplex, requires trained operators\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEasy to deploy in most terrains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePortable, easy to deploy\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCalibration Needs\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (requires borehole data)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate (ground truth needed)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate (cross-validation)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBest Use Case\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShallow plumes, mapping voids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConductive plumes at depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRapid large-area coverage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe comparison of EM methods used for chemical plume monitoring in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e compares EM methods based on depth penetration, resolution, survey speed, field deployment, calibration needs, and best use case. It displays this information visually in a heat map.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e\u003cp\u003eDepth penetration: According to the evaluation, the depth penetration for which is best, most effective is 5 for TEM, while 3 for FDEM, and 2 for GPR. Hence, the most effective method is TEM for deeper plume detection, whereas GPR serves better for shallow depth detection.\u003c/p\u003e\u003cp\u003eResolution: The maximum resolution during sampling of shallow plumes is optimally 5, achieved through GPR. Optimal resolution of 2 is achieved through depth profile and combined depth/area. Moderate resolution is achieved through FDEM and TEM.\u003c/p\u003e\u003cp\u003eSurvey speed: GPR is 2 for grid-based surveys, while in the case of survey speed, the first position (5) is taken by FDEM, in the case of ample area coverage, followed by TEM (4), and GPR (3) in station-based and grid-based surveys, respectively.\u003c/p\u003e\u003cp\u003eField deployment: FDEM is easy to deploy and portable, while TEM is highly versatile in various terrains (5), and GPR requires trained operators (2) and is complex.\u003c/p\u003e\u003cp\u003eCalibration needs: GPR is high calibration (5) with borehole data, whereas TEM and FDEM have moderate calibration (3), requiring ground truth or cross-validation techniques.\u003c/p\u003e\u003cp\u003eMost Ideal Scenario: Each technique has a different set of most ideal scenarios. For example, GPR is most effective with shallow plumes and void mapping, while TEM excels with deep conductive plumes, and FDEM is most effective for quick and extensive area coverage.\u003c/p\u003e\u003cp\u003eThis heatmap visually highlights where each method excels and helps in selecting the most appropriate technique based on the survey requirements.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStatistical Analysis of Detection Methods' Correlation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime Post-Injection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDEM vs GPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTEM vs GPR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDEM vs TEM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e24 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e72 hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1 week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is a \u003cb\u003eheatmap\u003c/b\u003e representing Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: \u003cb\u003eStatistical Analysis of Detection Methods' Correlation\u003c/b\u003e. The heatmap visually shows the correlation values between the different electromagnetic methods (FDEM vs GPR, TEM vs GPR, FDEM vs TEM) over time (Baseline, 24 hours, 72 hours, and 1 week).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eCorrelation Trends\u003c/strong\u003e\u003cp\u003eAs time progresses, the correlation values between the methods generally increase. This indicates that the methods become more consistent and aligned as the plume develops.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFDEM vs GPR\u003c/b\u003e starts with a moderate correlation of \u003cb\u003e0.75\u003c/b\u003e at baseline and rises to \u003cb\u003e0.89\u003c/b\u003e by the end of the study period.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTEM vs GPR\u003c/b\u003e also shows an increase, from \u003cb\u003e0.72\u003c/b\u003e at baseline to \u003cb\u003e0.91\u003c/b\u003e after one week, reflecting improved agreement between these methods over time.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe initial correlation value between FDEM and TEM techniques is 0.68. It rises to 0.93 at the end, demonstrating the strongest correlation for these two techniques, which suggests that these techniques are very consistent in plume evolution detection and plume characterization throughout their evolution.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe comparison between FDEM, GPR, and TEM techniques provided valuable insights into the monitoring of subsurface chemical plumes. The plumes on the FDEM have the highest for chemical plume, and follows with GPR. The attributes of the FDEM explain its sensitivity to rapidly covering and detecting significant changes in the electrical conductivity of subsurface plumes, especially those with higher salinity or metal content. The same phenomenon is observable with the GPR system, but only at more shallow depths. The principal limitation of GPR, along with some of the GPR, is the suppression of its conductivity improvement with the increase in clay or silt by more than 10% and 20%.\u003c/p\u003e\u003cp\u003eThus, these findings are consistent with the findings of Pathirana et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which showed that GPR and EMI have distinct advantages for soil characterization, EMI particularly stemming from its sensitivity to several soil properties. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes and documents the depth penetration of each method. Of all the methods, TEM has the most significant depth penetration, allowing it to detect contamination to depths of 5 meters. This capability enables the detection of plumes that are deeper, such as those associated with saline or metal contaminants. FDEM has a depth penetration of 3 meters and thus can find and work with shallow to medium depth plumes. GPR, however, has the shallowest depth penetration of only 2 meters, and thus has a limited use in finding deeper contamination. Boaga et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasize the work of FDEM in hydrogeophysics and its application in the shallow subsurface hydrologic framework, which pertains to hydrology near the surface.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the speed of each method, with FDEM being the fastest method to cover large areas, making it best suited for large-scale monitoring. TEM is moderate in speed and best for concentrated studies, while GPR is the slowest. The speed advantage of FDEM is described in the report of the U.S. Environmental Protection Agency (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), which documents how FDEM methods rapidly survey large areas and thus enhance environmental assessment.\u003c/p\u003e\u003cp\u003eRegarding field deployment and calibration, FDEM is easy to carry and deploy, with moderate calibration needs. For correct interpretation, it needs cross-validation with known data. TEM is easier to deploy, with moderate calibration needs, but ground truth data is required for correct readings. GPR is more complicated, requiring trained personnel with high calibration, and often necessitating borehole data for accurate depth estimation. Pathirana et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) analyze the deployment and calibration challenges of GPR and EMI, emphasizing that more sophisticated data processing and interpretation are needed for GPR compared to EMI.\u003c/p\u003e\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the focus is on the correlation coefficients over time between different methods. For instance, FDEM vs. GPR starts at 0.75 and increases to 0.89 after one week. This suggests an increasing correlation between the methods, indicating greater alignment as the plume develops. Likewise, TEM vs. GPR starts at 0.72 and increases to 0.91 after one week, indicating that these methods are aligned over the duration of plume development. Furthermore, FDEM vs. TEM has the strongest correlation by the end of the study at 0.93, starting at 0.68. This suggests the methods are strongly aligned with plume characteristics, and FDEM and TEM are consistently used to capture the plume. These patterns correlate with the study of Boaga et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who noted that FDEM and GPR were complementary in hydrogeophysical studies and that over time, TEM and FDEM grew more consistent as the plume stabilized.\u003c/p\u003e\u003cp\u003eTo synthesize the findings of this analysis, it is evident that the four methods of electromagnetism differ in their respective assets and drawbacks regarding the monitoring of chemical plumes. Out of all the methods, FDEM is the most preferred due to its ability to cover a large area in a short time while maintaining a high sensitivity, making it the most suitable method at plume depths between shallow and medium. For detecting plume depths that are deeper in the subsurface, the most detectable method is the Transverse Electric method, mainly for plumes that are saline and rich in metals. With respect to shallow plumes, Ground Penetrating Radar is the most preferred due to its ability to produce high-resolution images. However, its usage is restricted by the depth of subsurfaces. By combining the four methods, the monitoring of subsurface contamination plumes can be intensified by using each method for its specific contamination scenario.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe heatmap clearly demonstrates that all method pairings show increasing correlation as the detection methods become more aligned over time. This suggests that as the plume grows and stabilises, the different EM methods can more consistently capture similar data, improving the reliability of the measurements for environmental monitoring.\u003c/p\u003e\u003cp\u003eThis study demonstrates that electromagnetic methods\u0026mdash;FDEM, TEM, and GPR\u0026mdash;are practical non-invasive tools for detecting and monitoring subsurface chemical plumes. The controlled field experiment confirmed that these techniques could delineate plume extent, track migration over time, and provide valuable spatial and depth information. The integration of multiple EM methods leverages their respective strengths, addressing individual limitations.\u003c/p\u003e\u003cp\u003eHowever, environmental factors such as soil heterogeneity and moisture content influence measurement accuracy. Addressing these challenges through calibration, advanced data processing, and multi-method integration can significantly improve detection reliability.\u003c/p\u003e\u003cp\u003eThe findings support the adoption of EM techniques in environmental site assessments, offering rapid, cost-effective, and comprehensive solutions for hazardous waste management. Future research should focus on technological enhancements, machine learning integration, and developing standardised protocols to facilitate widespread application.\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eRecommendations\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eImplement combined EM survey strategies for comprehensive site characterisation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncorporate detailed soil and environmental measurements for calibration.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIncrease spatial and temporal resolution of measurements.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDevelop advanced inversion algorithms and machine learning tools for data interpretation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eExplore UAV-mounted EM sensors for rapid large-scale assessments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eValidate EM findings with ground truth sampling to enhance confidence.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors whose names appear on the submissionmade substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work;drafted the work or revised it critically for important intellectual content;approved the version to be published; andagree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBoaga, J., Bolognese, S., \u0026amp; Tinti, F. (2017). Application of Frequency-Domain Electromagnetic Methods in Hydrogeophysical Surveys. \u003cem\u003eGeophysics, 82\u003c/em\u003e(3), 1\u0026ndash;12. https://doi.org/10.1190/geo2017-0041.1\u003c/li\u003e\n\u003cli\u003eChen, L., Zhang, Y., \u0026amp; Li, Q. (2022). Application of Frequency-Domain Electromagnetic Methods in Detecting Metal Contamination in Landfills. \u003cem\u003eEnvironmental Monitoring and Assessment, 194\u003c/em\u003e(3), 112. https://doi.org/10.1007/s10661-022-1045-8\u003c/li\u003e\n\u003cli\u003eDaniels, J. E. (2016). \u003cem\u003eGround-Penetrating Radar\u003c/em\u003e (2nd ed.). IET.\u003c/li\u003e\n\u003cli\u003eFetter, C. W. (2018). \u003cem\u003eApplied hydrogeology\u003c/em\u003e (4th ed.). Waveland Press.\u003c/li\u003e\n\u003cli\u003eHristova, M., Georgieva, R., \u0026amp; Ivanov, S. (2019). 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Deep detection of saline plumes using transient electromagnetic methods. \u003cem\u003eHydrogeology Journal, 28\u003c/em\u003e(4), 1195\u0026ndash;1207. https://doi.org/10.1007/s10040-020-02184-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electromagnetic methods, Ground-penetrating radar, Hazardous waste, Monitoring, Subsurface Chemical Plumes, Transient Electromagnetic, Environmental Pollution","lastPublishedDoi":"10.21203/rs.3.rs-7681424/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7681424/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTracking chemical plumes below surface level that stem from the disposal of hazardous waste can aid in protecting the environment and the health of the population. From an economic standpoint, along with the ability to characterize and detect subsurface and surface contaminants with great speed and accuracy, the use of Electromagnetic (EM) methods offers a non-invasive approach. The focus of this paper is to scrutinize the utilization of GPR, TEM, and FDEM as mediums for EM technique methods in monitoring Chemical plumes. Chemical leaks from a waste dump were simulated for the purposes of setting a controlled field experiment. Different EM sensors were utilized to capture subsurface responses, and EM field data were mapped and collected from several surveys. The data were processed using advanced inversion algorithms and were then subjected to a statistical analysis to determine the resolution and sensitivity of each methodology. The findings indicated that EM methods were capable of detecting chemical plumes of great depths with significant spatial resolutions. The chemical plumes possessed strong correlations with the spatial distributions of known contaminants. The results also demonstrated the efficiency of EM methods and techniques for monitoring the environment and planning remediation. Signal attenuation and the requirement for ground truth calibration serve as primary restrictions to the research. This study improves the strategy for monitoring the subsurface hazardous waste plumbing by providing an easily applicable approach.\u003c/p\u003e","manuscriptTitle":"Electromagnetic Methods for Monitoring Subsurface Chemical Plumes from Hazardous Waste Dumps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:45:44","doi":"10.21203/rs.3.rs-7681424/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c1b771b-4982-4964-ae1d-3db28e157d79","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-06T16:38:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 07:45:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7681424","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7681424","identity":"rs-7681424","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}