Electromagnetic Field (EMF) Mapping in Semi-Urban and Rural Landscapes: A GIS-Based

Approach to Environmental and Ecological Assessment

SirajUddinMazumder1✉Emailsirajwls@gmail.com

AfifullahKhan1

Mirza.SálimBeg2

1Department of Wildlife SciencesAligarh Muslim UniversityPIN- 202002AligarhUttar PradeshIndia

Department of Electronics EngineeringZHCET, Aligarh Muslim UniversityIndia

Siraj Uddin Mazumder¹*, Afifullah Khan¹, Mirza. Sálim Beg²

1 Department of Wildlife Sciences, Aligarh Muslim University, India

2 Department of Electronics Engineering, ZHCET, Aligarh Muslim University, India

*Corresponding Author:

Siraj Uddin Mazumder,

Department of Wildlife Sciences,

Aligarh Muslim University,

Aligarh, Uttar Pradesh, India,

PIN- 202002

sirajwls@gmail.com

Abstract

Electromagnetic fields (EMFs) emitted from telecommunication towers have become a growing environmental concern, yet mapping and monitoring remain scarce in many regions, including India. This study establishes a replicable GIS-based protocol for EMF mapping using Inverse Distance Weighted (IDW) interpolation. A preliminary study was conducted in a semi-urban area to refine field protocols, followed by a main study across three Independent Sampling Areas (ISAs). Field measurements of electric field intensity (E) across four bands (900, 1800, 2100, 2400 MHz) were taken using a spectrum analyser and interpolated in ArcGIS 10.3. Results showed that EMF intensities across all ISAs were well below national and international safety thresholds for human exposure but overlapped with biologically relevant ranges reported for insects, birds, and plants. The study underscores the need for standardized EMF mapping protocols and highlights the ecological importance of low-intensity, long-term EMF exposure. EMF mapping emerges as a critical tool for environmental monitoring and policy development.

Keywords:

Electrosmog

EMF pollution

mobile telephony

interpolation

IDW

environmental mapping

Introduction

The rapid expansion of telecommunication infrastructure has resulted in a substantial increase in artificial electromagnetic field (EMF) emissions across both urban and rural landscapes. Mobile phone towers, Wi-Fi routers, and associated broadcasting equipment operate across multiple frequency bands, leading to continuous and spatially heterogeneous EMF exposure (Balmori, 2005; Lu & Wong, 2008). While the health implications of EMF exposure for humans remain the subject of considerable debate, the broader environmental effects on ecosystems and non-human organisms have only recently begun to receive systematic scientific attention (Panagopoulos, Johansson, & Carlo, 2015).

Mapping the spatio-temporal distribution of pollutants is a key strategy in environmental science, providing insights into exposure gradients and informing both risk assessment and policy interventions. In this context, Geographic Information System (GIS)-based interpolation methods are widely used due to their efficiency and statistical robustness (Azpurua & Ramos, 2010; Lloyd, 2005). Among these, Inverse Distance Weighted (IDW) interpolation has been identified as particularly effective for EMF mapping when sampling points are evenly distributed (Azpurua & Ramos, 2010). Nevertheless, methodological inconsistencies in EMF field studies—including variation in sampling designs, measurement intervals, and analytical frameworks—have hindered efforts to standardize environmental monitoring (Everaert & Bauwens, 2007; Lazaro et al., 2016).

Globally, EMF research has been dominated by studies from Europe and North America, many of which emphasize either point measurements or tower-distance correlations (Balmori, 2003; Balmori & Hallberg, 2007). However, such approaches often overlook the influence of antenna orientation, tower density, and landscape structure on EMF intensity. Furthermore, most research has been directed toward human health risk assessment, with relatively limited attention to ecological consequences. Studies that do consider ecological impacts have reported that EMF exposure, even at levels far below international human safety thresholds, can influence the behavior, physiology, and survival of species ranging from honeybees (Apis cerana) to plants such as Lepidium sativum (Cammaerts & Johansson, 2015; Taye et al., 2017).

In the Indian context, EMF mapping remains an underexplored domain. Despite the country’s rapid expansion of mobile telephony infrastructure, few systematic attempts have been made to quantify EMF distribution in real-world landscapes. The Telecom Regulatory Authority of India (TRAI, 2014) has set exposure limits at one-tenth of international guidelines to ensure public safety, yet these regulations are predominantly human-centered and may not adequately capture ecological vulnerabilities. Given the diversity of species and ecosystems across India, there is an urgent need for a standardized, landscape-scale EMF mapping protocol that accounts for both environmental complexity and methodological rigor.

The present study addresses this gap by conducting systematic EMF mapping across three Independent Sampling Areas (ISAs) in India, using IDW interpolation within a GIS framework. A preliminary study was first undertaken to refine field measurement protocols and assess analytical procedures, followed by a main study covering three semi-urban and rural landscapes. EMF levels across four operating frequency bands—900 MHz, 1800 MHz, 2100 MHz, and 2400 MHz—were measured, interpolated, and analyzed relative to regulatory thresholds and ecological sensitivity levels reported in the literature.

This paper pursues three main objectives:

To develop and test a replicable GIS-based methodology for EMF mapping at a landscape scale.

To evaluate EMF intensity levels in selected Indian landscapes relative to regulatory safety standards.

To discuss the potential ecological implications of observed EMF distributions, with a focus on methodological standardization for future research.

Literature Review

Electromagnetic Field Measurement and Mapping

Electromagnetic fields (EMFs) are characterized by their frequency, intensity, and spatial distribution. In environmental contexts, field measurement has historically relied on two dominant approaches: (a) point sampling at fixed locations (Balmori, 2005; Miclaus & Bechet, 2007) and (b) gradient-based sampling in relation to tower distance (Everaert & Bauwens, 2007). Both methods provide useful information but suffer from limitations in capturing landscape-level variability. Point measurements often lack spatial resolution, while distance-based studies assume signal attenuation follows an idealized inverse-square law, neglecting antenna azimuths and structural obstructions (Hyland, 2000).

Globally, most EMF studies have been conducted in Europe and North America, with limited research in Asia and Africa (Balmori & Hallberg, 2007; Lazaro et al., 2016). In India, despite rapid growth in mobile telecommunications, empirical data on EMF distribution remain scarce. Regulatory attention has focused primarily on setting exposure thresholds for human populations, with the Telecom Regulatory Authority of India (TRAI, 2014) prescribing limits at one-tenth of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines. This leaves a significant gap in understanding the ecological dimensions of EMF exposure in Indian landscapes.

Mapping EMFs across space is therefore critical to establish exposure baselines. By generating continuous surfaces of EMF intensity, researchers can identify spatial heterogeneity, high exposure “hotspots,” and gradients that may influence both human and ecological health (Lu & Wong, 2008).

GIS-Based Interpolation Techniques for Environmental Monitoring

Geographic Information Systems (GIS) provide powerful tools for transforming point-based field data into continuous surfaces through interpolation. Interpolation techniques such as kriging, spline, and inverse distance weighting (IDW) are widely used in pollution mapping, hydrology, and climatology (Lloyd, 2005; Durduran et al., 2010). Among these, IDW has gained prominence in EMF mapping studies due to its relative simplicity, robustness, and suitability for evenly distributed sampling points (Azpurua & Ramos, 2010).

The principle behind IDW is that the influence of a known point decreases with distance, assigning higher weights to closer measurements (Burrough & McDonnell, 1998). Compared to kriging, which incorporates spatial autocorrelation models, IDW is computationally less intensive and less reliant on large datasets (Al-Akhras et al., 2015). Studies have shown IDW to outperform kriging and spline in capturing EMF gradients, particularly when field measurements are evenly spaced (Azpurua & Ramos, 2010).

Beyond EMFs, interpolation methods have been successfully applied to map chemical pollutants (Ping et al., 2004), soil parameters (Bekele et al., 2003), and hydrological variables (Mulholland et al., 1998). The integration of GIS into EMF research thus provides a reliable methodological foundation, enabling comparison across landscapes and supporting environmental management decisions.

Biological and Ecological Impacts of EMF Exposure

While international guidelines primarily assess EMF exposure in terms of human health risks, growing evidence suggests that ecological impacts may occur at levels far below human safety thresholds. For example, Panagopoulos, Johansson, and Carlo (2015) report that EMF intensities as low as 10⁻³ V/m can interfere with biological systems.

Insects

Honeybees (Apis cerana) have shown altered foraging behavior at exposure levels of 0.63–0.189 V/m (Taye et al., 2017), while ants (Myrmica sabuleti) experience disrupted olfactory and visual cues at 0.55 V/m (Cammaerts et al., 2012). Given their ecological roles as pollinators and decomposers, such effects could cascade through ecosystems.

Birds

Studies by Everaert and Bauwens (2007) found reduced abundance of male house sparrows (Passer domesticus) in areas with EMF levels of 0.822–1.022 V/m in the 900–1800 MHz range. This suggests that chronic exposure may influence avian population dynamics, potentially through behavioral or reproductive mechanisms.

Plants

Plants, being sessile organisms, may be particularly vulnerable to prolonged EMF exposure. Cammaerts and Johansson (2015) demonstrated that seeds of Lepidium sativum failed to germinate at exposure levels of 0.175 V/m from GSM phone masts, but germination resumed at much lower intensities (0.003 V/m). Similarly, Waldmann-Selsam et al. (2016) reported morphological abnormalities in trees exposed to 0.173–2.213 V/m over long periods.

Collectively, these findings suggest that EMF intensities well below regulatory limits can exert biologically meaningful effects on multiple taxa. Moreover, chronic low-level exposure, characteristic of real-world environments, may produce cumulative impacts analogous to short-term high-intensity exposures documented in laboratory studies (Lai, 2005; Magras & Xenos, 1997).

Regulatory Frameworks and the Need for Standardized Protocols

International bodies such as ICNIRP and the World Health Organization (WHO) primarily address EMF exposure in terms of thermal effects on human tissue. However, non-thermal biological effects—particularly those relevant to ecological systems—are often overlooked in regulatory frameworks (Adey, 1996). The TRAI (2014) guidelines in India, while stringent compared to international standards, similarly focus on human health.

Methodological inconsistencies in EMF measurement further complicate ecological research. Some studies report results based on sporadic point measurements (Balmori, 2003), while others emphasize tower-distance relationships (Lazaro et al., 2016). Such disparities hinder comparability and synthesis across studies. Standardized, landscape-scale mapping protocols—such as those proposed in this study—are essential for building a coherent body of knowledge on EMF exposure and its ecological implications.

Research Gaps and Rationale for the Present Study

Despite increasing evidence of ecological sensitivity to EMFs, research remains fragmented and inconsistent. Key gaps include:

Lack of standardized EMF mapping protocols that integrate ecological exposure considerations.

Limited application of GIS-based interpolation techniques to EMF datasets in India.

Scarcity of field-based ecological exposure assessments, despite growing evidence of potential impacts.

The present study seeks to address these gaps by developing a replicable GIS-based mapping framework, applying it across semi-urban and rural Indian landscapes, and contextualizing the findings within both human safety and ecological relevance frameworks.

Methodology

Research Design

This study was conducted in two phases: (a) a preliminary study to refine the field measurement protocol and evaluate interpolation methods, and (b) a main study across three Independent Sampling Areas (ISAs) to systematically map EMF intensity across multiple frequency bands. The research design combined field-based measurements with GIS-based interpolation to generate spatially continuous EMF maps.

Preliminary Study

Study Area and Sampling Design

A semi-urban area of 1 km² in the vicinity of the university campus was selected for the preliminary study. The site was chosen for its manageable size, presence of multiple cellular towers, and accessibility. Using Google Earth and ArcGIS 10.3, a sampling grid was overlaid at 100-metre intervals, resulting in 121 evenly distributed measurement points (Fig. 2). These points were then transferred to a handheld GPS unit (Garmin Map 76S) for field navigation.

Fig. 1

(a) Location map of Aligarh, Uttar Pradesh

(Source: Uttar Pradesh District Fact Book; (b) Google Earth image showing the experimental plot.

Fig. 2

Array of EMF measurement points used in the preliminary study.

Field Measurements

Measurements of electric field intensity (E, in volts/metre) were recorded at 1800 MHz using a handheld spectrum analyser (Spectran HF-6085). To account for traffic-related fluctuations (Mahfouz et al., 2012), measurements were conducted between 8:00 AM and 6:00 PM, when cellular activity was expected to peak. Following ICNIRP guidelines (Miclaus & Bechet, 2007), the analyser was rotated in all directions for six minutes at each sampling location. The hold and peak functions were used to record the maximum E detected during each session.

Data Analysis and Interpolation

ArcGIS 10.3 was used to generate interpolated EMF surfaces using the Inverse Distance Weighted (IDW) method. Three sampling schemes were tested:

Sample 1: 100 × 100 m grid (121 points)

Sample 2: 100 × 200 m grid (66 points)

Sample 3: 200 × 200 m grid (36 points)

The interpolation assumed a circular search radius with power parameter p = 2. The number of neighbours was set between 10 and 15. Performance was evaluated using Mean Prediction Error (ME) and Root Mean Square Error (RMSE).

Table 1
Summary statistics of IDW (Preliminary study)
Sample	Number of samples	Prediction Errors		Neighbourhood Type	Maximum Neighbours	Minimum Neighbours
Sample	Number of samples	Mean	Root-Mean-Square	Neighbourhood Type	Maximum Neighbours	Minimum Neighbours
Sample 1	121	-0.008	0.177	Standard	15	10
Sample 2	66	-0.009	0.225	Standard	15	10
Sample 3	36	-0.019	0.231	Standard	15	10

Table 2
Sample-wise area coverage of EMF classes (Preliminary Study)
Sample	Area (Sq. km)			Total Area (sq.km.)	% Area			Total (%)
Sample	EMF Class 1	EMF Class 2	EMF Class 3	Total Area (sq.km.)	EMF Class 1	EMF Class 2	EMF Class 3
Sample 1	0.922	0.075	0.003	1	92.20	7.46	0.35	100
Sample 2	0.908	0.086	0.006	1	90.81	8.63	0.57	100
Sample 3	0.942	0.049	0.009	1	94.20	4.87	0.93	100

Fig. 3

Interpolated surfaces for preliminary study (a) Sample 1; (b) Sample 2 and (c) Sample 3

Lessons from the Preliminary Study

The preliminary study demonstrated that uniform, equidistant sampling (Sample 1) provided the most reliable interpolation outputs. Sparse sampling (Sample 3) resulted in less accurate predictions despite uniform spacing, due to fewer points across the same area. These insights informed the sampling design of the main study, which emphasized both density and uniformity of measurement points.

Main Study

Reconnaissance and Site Selection

Three ISAs, each measuring 2.5 × 2.5 km (6.25 km²), were selected following reconnaissance surveys. Sites were chosen to maximize accessibility and ensure active operation of four target frequency bands (900, 1800, 2100, and 2400 MHz). Clustered residential areas were avoided to minimize access constraints and interference.

Sampling Design and Field Measurements

A grid of 121 points was established for each ISA, with points spaced at 250-metre intervals. Using the same protocol as the preliminary study, EMF intensity was measured at each point across all four bands. Measurements were conducted between April and June 2017, during peak cellular traffic hours (8:00 AM–6:00 PM).

Data Analysis and EMF Mapping

Following guidelines of the Electronics Communications Committee (2002) the sum of electric field intensities across the four frequency bands was calculated as E_Total (Mazumder, 2020). IDW interpolation in ArcGIS 10.3 was used to generate continuous EMF surfaces for each band and for E_Total. Each map was classified into seven EMF intensity classes, with particular attention to values near or below 10⁻³ V/m, reported as biologically significant (Panagopoulos et al., 2015).

Table 3
Electric Field Intensity (E) of different bands in the three ISAs
Study Area	E _{900 MHz} (V/m)		E _{1800 MHz} (V/m)		E _{2100 MHz} (V/m)		E _{2400 MHz}(V/m)		E_Total (V/m)
Study Area	Min	Max	Min	Max	Min	Max	Min	Max	Min	Max
SA1	0.005	0.452	0.004	0.485	0.007	0.528	0.003	0.310	0.019	0.726
SA2	0.013	0.478	0.014	0.759	0.007	0.658	0.005	0.523	0.047	1.051
SA3	0.031	0.718	0.021	0.399	0.015	0.687	0.004	0.263	0.070	0.750

Table 4
Summary statistics of IDW for different bands in the three ISAs
Band (MHz)	Prediction Errors
	ISA 1		ISA 2		ISA 3
	ME	RMSE	ME	RMSE	ME	RMSE
E ₉₀₀	0.0008	0.059	0.0000	0.071	0.0004	0.095
E ₁₈₀₀	0.0018	0.064	-0.0001	0.007	0.0000	0.059
E ₂₁₀₀	-0.0007	0.0915	-0.0008	0.104	-0.0027	0.122
E ₂₄₀₀	0.0001	0.0401	0.0008	0.080	0.0004	0.055
E _Total	-0.0008	0.115	0.0004	0.129	-0.0003	0.131

ME- Mean Prediction Error; RMSE- Root Mean Square Prediction Error

Classes	SA2
	E₉₀₀		E₁₈₀₀		E₂₁₀₀		E₂₄₀₀		E_Total
	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area
1	0	0	0	0	0	0	0	0	0	0
2	3.84	61.44	3.96	63.36	4.14	66.24	5.37	85.92	0.44	7.04
3	1.25	20	1.24	19.84	1.49	23.84	0.75	12	3.02	48.32
4	1.06	16.96	0.72	11.52	0.59	9.44	0.12	1.92	1.96	31.36
5	0.1	1.6	0.28	4.48	0.03	0.48	0.01	0.16	0.5	8
6	0	0	0.05	0.8	0	0	0	0	0.26	4.16
7	0	0	0	0	0	0	0	0	0.07	1.12
Total	6.25	100	6.25	100	6.25	100	6.25	100	6.25	100

(b)

Classes	SA3
	E₉₀₀		E₁₈₀₀		E₂₁₀₀		E₂₄₀₀		E_Total
	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area	Area (Sq. km)	% Area
1	0	0	0	0	0	0	0	0	0	0
2	2	32	3.22	51.52	1.97	31.52	5.17	82.72	0.03	0.48
3	2.73	43.68	2.61	41.76	2.57	41.12	1.02	16.32	0.95	15.2
4	1.46	23.36	0.42	6.72	1.61	25.76	0.06	0.96	4.84	77.44
5	0.04	0.64	0	0	0.09	1.44	0	0	0.4	6.4
6	0.02	0.32	0	0	0.01	0.16	0	0	0.03	0.48
7	0	0	0	0	0	0	0	0	0	0
Total	6.25	100	6.25	100	6.25	100	6.25	100	6.25	100

(c)

Fig. 4

(a-o): EMF maps of the three ISAs across four bandwidths

Results

Preliminary Study

The preliminary study tested the effect of sampling density on the accuracy of IDW interpolation.

• Prediction Accuracy:

ME values for all three sample designs were close to zero, indicating unbiased predictions.

RMSE values were relatively low across all sampling densities, with Sample 1 (121 points) yielding the lowest error and highest accuracy.

• Area Coverage:

Across all sample designs, > 90% of the study area fell within the lowest EMF class, while higher classes occupied progressively smaller proportions.

This pattern was consistent across Samples 1–3, though finer sampling resolution (Sample 1) produced smoother interpolation surfaces.

• Visualization:

IDW-generated EMF surfaces (Fig. 3) clearly showed that increased sampling density improved spatial detail without significantly altering overall distribution patterns.

The preliminary study confirmed that IDW is a reliable interpolation method for EMF mapping and that sampling density affects spatial resolution more than predictive accuracy. Based on these results, the denser grid design was adopted for the main study. The recorded EMF values ranged from 0.014 V/m to 1.387 V/m, well below the Indian regulatory threshold of 18.4 V/m for 1800 MHz (TRAI, 2014; Mazumder, 2020). Summary statistics indicated that Sample 1 produced the most accurate interpolation (lowest ME and RMSE), whereas Sample 3 showed slight over-prediction of EMF classes (Tables 1 and 2; Fig. 3).

Main Study

Electric Field Intensities

Across the three ISAs, EMF levels were consistently below national (TRAI, 2014) and international (ICNIRP, 2009) human exposure thresholds.

ISA 1: ETotal ranged between 0.019 and 0.726 V/m.

ISA 2: ETotal ranged between 0.047 and 1.051 V/m, the highest among all ISAs.

ISA 3: ETotal ranged between 0.070 and 0.750 V/m.

While safe for humans, these values fall within the ranges reported as biologically relevant for insects and birds (0.1–1.0 V/m).

IDW Interpolation Accuracy

ME values were near zero across all ISAs, indicating unbiased interpolation.

RMSE values were higher than in the preliminary study, reflecting greater landscape heterogeneity.

ISA 2 consistently exhibited the highest EMF variability, while ISA 1 showed the most uniform distribution.

Area Coverage by EMF Classes

Low EMF classes (< 0.2 V/m) covered the majority of land area across all ISAs.

Intermediate classes (0.2–0.5 V/m) occupied smaller but significant portions, particularly in ISA 2.

High-intensity zones (> 0.5 V/m) were rare and localized, usually near tower clusters.

Spatial Patterns

ISA 1 exhibited a uniform low-level exposure profile.

ISA 2 showed heterogeneous exposure, with distinct high-intensity pockets near tower installations.

ISA 3 displayed moderate variation, with exposures clustered but less pronounced than in ISA 2.

Summary of Results:

The preliminary study validated IDW as a robust interpolation method, with finer sampling improving resolution.

The main study demonstrated that human safety standards were met, but ecologically relevant exposure levels were common across all ISAs.

Spatial patterns varied: ISA 1 (uniform low-level), ISA 2 (heterogeneous with hotspots), ISA 3 (moderate variation).

Across the three ISAs, maximum E_Total exceeded 1.0 V/m only in ISA 2, with lower values recorded in ISAs 1 and 3 (Table 3). Band-specific maxima were consistently below 1 V/m, indicating compliance with national and international exposure limits. However, values exceeded known ecological sensitivity thresholds, warranting further investigation. Interpolated surfaces displayed spatial variability, with EMF “hotspots” corresponding to tower density and orientation (Figs. 4, a–o).

Key Findings

Regulatory Compliance: All measured EMF values were substantially below the Indian and international human safety thresholds.

Spatial Variability: EMF intensities showed heterogeneity across landscapes, with localized hotspots linked to tower placement and orientation.

Ecological Relevance: Although values were within regulatory safety margins, many exceeded thresholds reported in ecological studies for behavioral or physiological interference in non-human organisms (e.g., 0.1–0.6 V/m).

Discussion

Methodological Contributions

This study demonstrates the utility of a GIS-based IDW interpolation framework for EMF mapping at a landscape scale. The preliminary study underscored the importance of sampling density and uniformity, showing that evenly spaced 100 × 100 m grids (Sample 1) yielded the most reliable interpolations. Sparse sampling (Sample 3) produced statistically acceptable outputs but generated misleading class shifts when visualized, highlighting the need for careful consideration of sampling resolution.

Compared to previous EMF research, which often relied on point-based measurements (Balmori, 2003; Balmori & Hallberg, 2007) or tower-distance correlations (Everaert & Bauwens, 2007), the present approach provides several advantages. By decoupling measurements from tower azimuths and orientations, this mapping protocol captures the composite EMF environment experienced by organisms in real landscapes. This methodological independence is particularly important in India, where tower-sharing practices create overlapping radiation lobes with complex spatial patterns.

The consistently low ME and RMSE values across bands and ISAs affirm the robustness of IDW for EMF mapping. While kriging has been widely promoted in environmental interpolation, its reliance on spatial autocorrelation models requires larger datasets and is less suited to landscapes with limited or uneven sampling points (Azpurua & Ramos, 2010). This study therefore validates IDW as a reliable and cost-effective method for EMF landscape assessments in resource-constrained contexts.

EMF Intensity Levels: Human Health Perspective

Field measurements across all three ISAs showed EMF levels well below the regulatory thresholds set by the Telecom Regulatory Authority of India (2014) and international standards (ICNIRP, 2009). The maximum E_Total recorded (1.051 V/m in ISA 2) was an order of magnitude below the Indian safety limits for the relevant frequency bands (13.02 V/m for 900 MHz, 18.41 V/m for 1800 MHz, and 19.29 V/m for 2400 MHz). From a public health perspective, the findings suggest negligible immediate risks to human populations within the study areas.

However, regulatory frameworks typically emphasize thermal effects of EMF exposure on human tissues (Adey, 1996), with limited consideration of non-thermal biological effects. Growing evidence indicates that low-intensity, long-term exposure may still produce subtle physiological and neurological responses in humans (Lai, 2005; Hyland, 2000). Although not the primary focus of this study, the persistence of EMF exposure in populated environments warrants continued monitoring and epidemiological research.

Ecological Implications of EMF Exposure

While EMF intensities observed in this study fall within human safety margins, they overlap with exposure ranges known to disrupt biological systems in non-human organisms. For example, EMF levels of 0.63–0.189 V/m negatively affected honeybee foraging behavior (Apis cerana) (Taye et al., 2017), while intensities as low as 0.175 V/m inhibited germination of Lepidium sativum seeds (Cammaerts & Johansson, 2015). Similarly, house sparrows (Passer domesticus) avoided areas with EMF intensities of 0.822–1.022 V/m (Everaert & Bauwens, 2007), values consistent with maxima recorded in ISA 2.

Plants, due to their sedentary nature, may be particularly vulnerable to long-term EMF exposure. Waldmann-Selsam et al. (2016) reported morphological damage in trees exposed to 0.173–2.213 V/m over extended periods. Such effects may indirectly influence animal populations by reducing habitat quality. The present study’s finding that substantial portions of all three ISAs fall within 0.3–0.6 V/m (Class 3) intensities suggests that ecological risks cannot be dismissed, even if human exposure remains within safe limits.

Moreover, the chronic exposure characteristic of field conditions may produce cumulative effects equivalent to acute high-intensity exposures in laboratory settings (Magras & Xenos, 1997; Balmori, 2005). This underscores the need to move beyond regulatory frameworks focused solely on human thermal thresholds toward broader ecological risk assessments.

Policy and Conservation Relevance

The results highlight a critical disjunction between human-centered safety standards and the ecological realities of EMF exposure. While TRAI’s precautionary limits are stricter than ICNIRP’s, they do not account for species-specific sensitivities at much lower thresholds. In biodiversity-rich countries such as India, where pollinators, birds, and vegetation form the backbone of ecosystems and agriculture, overlooking ecological dimensions of EMF exposure may have unintended consequences.

EMF mapping provides an actionable tool for environmental governance. By identifying low- and high-exposure zones, researchers and policymakers can prioritize monitoring of vulnerable taxa, incorporate EMF assessments into environmental impact evaluations, and guide tower placement to minimize ecological disruption. Importantly, standardized mapping protocols would enable comparative studies across landscapes and temporal monitoring of EMF dynamics, contributing to evidence-based regulation.

Limitations and Future Directions

While the study establishes a robust methodological framework, certain limitations must be acknowledged. First, measurements were limited to daytime hours during peak traffic, potentially underrepresenting nocturnal exposure patterns. Second, only three ISAs were included, limiting generalizability across India’s diverse ecosystems. Third, the study did not directly assess biological responses, relying instead on thresholds from existing literature.

Future research should expand the spatial and temporal scope of EMF mapping, incorporating nocturnal and seasonal measurements. Integrating ecological monitoring (e.g., pollinator activity, plant germination, avian behavior) with EMF gradients would provide direct evidence of exposure-response relationships. Additionally, comparative testing of interpolation methods across heterogeneous landscapes could refine methodological best practices.

Conclusion

This study presents one of the first systematic attempts to map electromagnetic field (EMF) intensity in Indian landscapes using a GIS-based interpolation framework. By combining a preliminary pilot study with a larger-scale main study across three Independent Sampling Areas (ISAs), the research demonstrates both the methodological feasibility and ecological relevance of EMF mapping.

The key findings can be summarized as follows:

Methodological Rigor: The study validates Inverse Distance Weighted (IDW) interpolation as a robust, efficient, and cost-effective approach for EMF mapping, particularly when sampling grids are evenly distributed and sufficiently dense. This contributes a replicable protocol for environmental monitoring that is independent of tower azimuths and orientations.

Human Safety Perspective: EMF intensities across all ISAs remained well below national (TRAI, 2014) and international (ICNIRP, 2009) exposure thresholds. From a human health standpoint, this suggests negligible immediate risks under current operating conditions.

Ecological Relevance: Despite compliance with human safety standards, EMF values often fell within ranges known to affect insects, birds, and plants (0.1–1.0 V/m). This highlights the importance of considering non-thermal ecological effects that are not currently addressed by regulatory frameworks.

Policy and Conservation Implications: EMF mapping enables the identification of exposure hotspots and provides a baseline for ecological risk assessments. Incorporating EMF mapping into environmental impact evaluations could support biodiversity conservation and sustainable urban planning.

The study underscores the need for standardized protocols in EMF research to ensure comparability across regions and taxa. By extending mapping efforts across varied ecosystems and integrating them with ecological monitoring, researchers can build a more comprehensive understanding of EMF impacts.

In conclusion, this research offers both a methodological contribution—through the development of a replicable EMF mapping protocol—and an ecological perspective, emphasizing that environmental monitoring must extend beyond human-centred safety standards. Periodic EMF mapping, combined with biodiversity assessments, will be essential to assess long-term ecological impacts in an increasingly electrified world.

Acknowledgements:

The authors express their sincere gratitude to DST PURSE Program II, for financial assistance provided for field data collection.

Declarations

of Generative AI and AI-Assisted Technologies:

The corresponding author used ChatGPT’s polish paragraph request for elimination of wordiness in preparation of this manuscript. The corresponding author thoroughly reviewed and edited the content generated and takes full responsibility for the content of the publication.

Author Contribution

Mazumder, S.U. was the PhD research scholar and is responsible for the document text, figures and tables; Khan, A. was the PhD guide and reviewer for the ecological component of the study; Beg, M. S. was the co-guide and reviewer for the electronics component of the study

Data Availability

The present study involves primary data generated on field and can be obtained from the corresponding author on request

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Yes