Electromagnetic Field (EMF) Mapping in Semi-Urban and Rural Landscapes: A GIS-Based Approach to Environmental and Ecological Assessment

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Sálim Beg This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7949245/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. 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. 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 Mean Root-Mean-Square 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 (%) EMF Class 1 EMF Class 2 EMF Class 3 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 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) 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 Table 5 (a-c) – Band-wise coverage of the seven EMF classes in the three ISAs Classes SA2 E 900 E 1800 E 2100 E 2400 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 E 900 E 1800 E 2100 E 2400 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) Classes SA3 E 900 E 1800 E 2100 E 2400 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 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 ( 0.5 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. Declarations Declaration 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 Acknowledgements: The authors express their sincere gratitude to DST PURSE Program II, for financial assistance provided for field data collection. Data Availability The present study involves primary data generated on field and can be obtained from the corresponding author on request References Adey, W. R. (1996). Biological effects of electromagnetic fields. Journal of Cellular Biochemistry , 62(S24), 16–23. https://doi.org/10.1002/jcb.240621404 Al-Akhras, N. M., Durduran, S. S., & Bekele, A. (2015). Comparison of interpolation methods for spatial distribution of pollutants. Environmental Monitoring and Assessment , 187(6), 345–356. https://doi.org/10.1007/s10661-015-4570-2 Azpurua, M. A., & Ramos, A. (2010). A comparison of spatial interpolation methods for environmental mapping. International Journal of Research and Reviews in Applied Sciences , 5(2), 123–131. Balmori, A. (2003). 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15:49:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1354593,"visible":true,"origin":"","legend":"","description":"","filename":"Siraj1compressed1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7949245/v1_covered_8d72191f-213a-421c-867e-21daa292a486.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eElectromagnetic Field (EMF) Mapping in Semi-Urban and Rural Landscapes: A GIS-Based Approach to Environmental and Ecological Assessment\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe 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, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lu \u0026amp; Wong, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). 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, \u0026amp; Carlo, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMapping 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 \u0026amp; Ramos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lloyd, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Among these, Inverse Distance Weighted (IDW) interpolation has been identified as particularly effective for EMF mapping when sampling points are evenly distributed (Azpurua \u0026amp; Ramos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Nevertheless, methodological inconsistencies in EMF field studies\u0026mdash;including variation in sampling designs, measurement intervals, and analytical frameworks\u0026mdash;have hindered efforts to standardize environmental monitoring (Everaert \u0026amp; Bauwens, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lazaro et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, EMF research has been dominated by studies from Europe and North America, many of which emphasize either point measurements or tower-distance correlations (Balmori, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Balmori \u0026amp; Hallberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). 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 (\u003cem\u003eApis cerana\u003c/em\u003e) to plants such as \u003cem\u003eLepidium sativum\u003c/em\u003e (Cammaerts \u0026amp; Johansson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Taye et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Indian context, EMF mapping remains an underexplored domain. Despite the country\u0026rsquo;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.\u003c/p\u003e\u003cp\u003eThe 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\u0026mdash;900 MHz, 1800 MHz, 2100 MHz, and 2400 MHz\u0026mdash;were measured, interpolated, and analyzed relative to regulatory thresholds and ecological sensitivity levels reported in the literature.\u003c/p\u003e\u003cp\u003eThis paper pursues three main objectives:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo develop and test a replicable GIS-based methodology for EMF mapping at a landscape scale.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate EMF intensity levels in selected Indian landscapes relative to regulatory safety standards.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo discuss the potential ecological implications of observed EMF distributions, with a focus on methodological standardization for future research.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eElectromagnetic Field Measurement and Mapping\u003c/h2\u003e\u003cp\u003eElectromagnetic 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, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Miclaus \u0026amp; Bechet, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and (b) gradient-based sampling in relation to tower distance (Everaert \u0026amp; Bauwens, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). 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, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobally, most EMF studies have been conducted in Europe and North America, with limited research in Asia and Africa (Balmori \u0026amp; Hallberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lazaro et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). 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.\u003c/p\u003e\u003cp\u003eMapping EMFs across space is therefore critical to establish exposure baselines. By generating continuous surfaces of EMF intensity, researchers can identify spatial heterogeneity, high exposure \u0026ldquo;hotspots,\u0026rdquo; and gradients that may influence both human and ecological health (Lu \u0026amp; Wong, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGIS-Based Interpolation Techniques for Environmental Monitoring\u003c/h3\u003e\n\u003cp\u003eGeographic 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, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Durduran et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Among these, IDW has gained prominence in EMF mapping studies due to its relative simplicity, robustness, and suitability for evenly distributed sampling points (Azpurua \u0026amp; Ramos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe principle behind IDW is that the influence of a known point decreases with distance, assigning higher weights to closer measurements (Burrough \u0026amp; McDonnell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Compared to kriging, which incorporates spatial autocorrelation models, IDW is computationally less intensive and less reliant on large datasets (Al-Akhras et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Studies have shown IDW to outperform kriging and spline in capturing EMF gradients, particularly when field measurements are evenly spaced (Azpurua \u0026amp; Ramos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond EMFs, interpolation methods have been successfully applied to map chemical pollutants (Ping et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), soil parameters (Bekele et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), and hydrological variables (Mulholland et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The integration of GIS into EMF research thus provides a reliable methodological foundation, enabling comparison across landscapes and supporting environmental management decisions.\u003c/p\u003e\n\u003ch3\u003eBiological and Ecological Impacts of EMF Exposure\u003c/h3\u003e\n\u003cp\u003eWhile 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 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) report that EMF intensities as low as 10⁻\u0026sup3; V/m can interfere with biological systems.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInsects\u003c/strong\u003e\u003cp\u003eHoneybees (\u003cem\u003eApis cerana\u003c/em\u003e) have shown altered foraging behavior at exposure levels of 0.63\u0026ndash;0.189 V/m (Taye et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while ants (\u003cem\u003eMyrmica sabuleti\u003c/em\u003e) experience disrupted olfactory and visual cues at 0.55 V/m (Cammaerts et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given their ecological roles as pollinators and decomposers, such effects could cascade through ecosystems.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eBirds\u003c/strong\u003e\u003cp\u003eStudies by Everaert and Bauwens (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) found reduced abundance of male house sparrows (\u003cem\u003ePasser domesticus\u003c/em\u003e) in areas with EMF levels of 0.822\u0026ndash;1.022 V/m in the 900\u0026ndash;1800 MHz range. This suggests that chronic exposure may influence avian population dynamics, potentially through behavioral or reproductive mechanisms.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePlants\u003c/strong\u003e\u003cp\u003ePlants, being sessile organisms, may be particularly vulnerable to prolonged EMF exposure. Cammaerts and Johansson (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) demonstrated that seeds of \u003cem\u003eLepidium sativum\u003c/em\u003e 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. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported morphological abnormalities in trees exposed to 0.173\u0026ndash;2.213 V/m over long periods.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eCollectively, 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, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Magras \u0026amp; Xenos, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eRegulatory Frameworks and the Need for Standardized Protocols\u003c/h3\u003e\n\u003cp\u003eInternational 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\u0026mdash;particularly those relevant to ecological systems\u0026mdash;are often overlooked in regulatory frameworks (Adey, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The TRAI (2014) guidelines in India, while stringent compared to international standards, similarly focus on human health.\u003c/p\u003e\u003cp\u003eMethodological inconsistencies in EMF measurement further complicate ecological research. Some studies report results based on sporadic point measurements (Balmori, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), while others emphasize tower-distance relationships (Lazaro et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Such disparities hinder comparability and synthesis across studies. Standardized, landscape-scale mapping protocols\u0026mdash;such as those proposed in this study\u0026mdash;are essential for building a coherent body of knowledge on EMF exposure and its ecological implications.\u003c/p\u003e\n\u003ch3\u003eResearch Gaps and Rationale for the Present Study\u003c/h3\u003e\n\u003cp\u003eDespite increasing evidence of ecological sensitivity to EMFs, research remains fragmented and inconsistent. Key gaps include:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLack of \u003cb\u003estandardized EMF mapping protocols\u003c/b\u003e that integrate ecological exposure considerations.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLimited application of \u003cb\u003eGIS-based interpolation techniques\u003c/b\u003e to EMF datasets in India.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eScarcity of \u003cb\u003efield-based ecological exposure assessments\u003c/b\u003e, despite growing evidence of potential impacts.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe present study seeks to address these gaps by developing a \u003cb\u003ereplicable GIS-based mapping framework\u003c/b\u003e, applying it across semi-urban and rural Indian landscapes, and contextualizing the findings within both human safety and ecological relevance frameworks.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eResearch Design\u003c/h2\u003e\u003cp\u003eThis study was conducted in two phases: (a) a \u003cb\u003epreliminary study\u003c/b\u003e to refine the field measurement protocol and evaluate interpolation methods, and (b) a \u003cb\u003emain study\u003c/b\u003e 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.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003ePreliminary Study\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStudy Area and Sampling Design\u003c/h2\u003e\u003cp\u003eA semi-urban area of 1 km\u0026sup2; 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These points were then transferred to a handheld GPS unit (Garmin Map 76S) for field navigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eField Measurements\u003c/h2\u003e\u003cp\u003eMeasurements 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., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), measurements were conducted between 8:00 AM and 6:00 PM, when cellular activity was expected to peak. Following ICNIRP guidelines (Miclaus \u0026amp; Bechet, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), 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.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis and Interpolation\u003c/h2\u003e\u003cp\u003eArcGIS 10.3 was used to generate interpolated EMF surfaces using the Inverse Distance Weighted (IDW) method. Three sampling schemes were tested:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSample 1\u003c/b\u003e: 100 \u0026times; 100 m grid (121 points)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSample 2\u003c/b\u003e: 100 \u0026times; 200 m grid (66 points)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSample 3\u003c/b\u003e: 200 \u0026times; 200 m grid (36 points)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe interpolation assumed a circular search radius with power parameter \u003cem\u003ep\u0026thinsp;=\u0026thinsp;2\u003c/em\u003e. The number of neighbours was set between 10 and 15. Performance was evaluated using Mean Prediction Error (ME) and Root Mean Square Error (RMSE).\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 statistics of IDW (Preliminary study)\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNumber of samples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003ePrediction Errors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNeighbourhood Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMaximum Neighbours\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMinimum Neighbours\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRoot-Mean-Square\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\u003eSample 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10\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\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\u003eSample-wise area coverage of EMF classes (Preliminary Study)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal Area (sq.km.)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTotal (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEMF\u003c/p\u003e\u003cp\u003eClass 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample 1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e92.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample 2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.908\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample 3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e100\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\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eLessons from the Preliminary Study\u003c/h2\u003e\u003cp\u003eThe 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.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eMain Study\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003eReconnaissance and Site Selection\u003c/h2\u003e\u003cp\u003eThree ISAs, each measuring 2.5 \u0026times; 2.5 km (6.25 km\u0026sup2;), 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.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSampling Design and Field Measurements\u003c/h2\u003e\u003cp\u003eA 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\u0026ndash;6:00 PM).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis and EMF Mapping\u003c/h2\u003e\u003cp\u003eFollowing guidelines of the Electronics Communications Committee (2002) the sum of electric field intensities across the four frequency bands was calculated as E\u003csub\u003eTotal\u003c/sub\u003e (Mazumder, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). IDW interpolation in ArcGIS 10.3 was used to generate continuous EMF surfaces for each band and for E\u003csub\u003eTotal\u003c/sub\u003e. Each map was classified into seven EMF intensity classes, with particular attention to values near or below 10⁻\u0026sup3; V/m, reported as biologically significant (Panagopoulos et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\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\u003eElectric Field Intensity (E) of different bands in the three ISAs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStudy Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eE \u003csub\u003e900 MHz\u003c/sub\u003e (V/m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eE \u003csub\u003e1800 MHz\u003c/sub\u003e (V/m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eE \u003csub\u003e2100 MHz\u003c/sub\u003e (V/m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eE \u003csub\u003e2400 MHz\u003c/sub\u003e(V/m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eE\u003csub\u003eTotal\u003c/sub\u003e (V/m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eMax\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\u003eSA1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSA2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSA3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.750\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\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\u003eSummary statistics of IDW for different bands in the three ISAs\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBand (MHz)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003ePrediction Errors\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eISA 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eISA 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eISA 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eME\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRMSE\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\u003eE\u003c/b\u003e \u003csub\u003e\u003cb\u003e900\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE\u003c/b\u003e \u003csub\u003e\u003cb\u003e1800\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE\u003c/b\u003e \u003csub\u003e\u003cb\u003e2100\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE\u003c/b\u003e \u003csub\u003e\u003cb\u003e2400\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eE\u003c/b\u003e \u003csub\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.0008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.0003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.131\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\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eME- Mean Prediction Error; RMSE- Root Mean Square Prediction Error\u003c/h2\u003e\u003cp\u003eTable 5 (a-c) \u0026ndash; Band-wise coverage of the seven EMF classes in the three ISAs\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e\u003cp\u003eSA2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eE\u003csub\u003e900\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eE\u003csub\u003e1800\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eE\u003csub\u003e2100\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eE\u003csub\u003e2400\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eE\u003csub\u003eTotal\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e66.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e48.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e31.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e4.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e(b)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eClasses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"10\" nameend=\"c11\" namest=\"c2\"\u003e\u003cp\u003eSA3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eE\u003csub\u003e900\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eE\u003csub\u003e1800\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eE\u003csub\u003e2100\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eE\u003csub\u003e2400\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eE\u003csub\u003eTotal\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eArea (Sq. km)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e% Area\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e82.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e41.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e77.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e6.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section4\"\u003e\u003ch2\u003e(c)\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClasses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSA3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003e900\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003e1800\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003e2100\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003e2400\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003csub\u003eTotal\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Sq. km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Sq. km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Sq. km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Sq. km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (Sq. km)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e51.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e31.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e82.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e43.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e41.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e41.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e16.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e23.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e25.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e4.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e77.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003ePreliminary Study\u003c/h2\u003e\u003cp\u003eThe preliminary study tested the effect of sampling density on the accuracy of IDW interpolation.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e\u0026bull; Prediction Accuracy:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eME values for all three sample designs were close to zero, indicating unbiased predictions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRMSE values were relatively low across all sampling densities, with \u003cb\u003eSample 1 (121 points)\u003c/b\u003e yielding the lowest error and highest accuracy.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e\u0026bull; Area Coverage:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eAcross all sample designs, \u003cb\u003e\u0026gt;\u0026thinsp;90% of the study area fell within the lowest EMF class\u003c/b\u003e, while higher classes occupied progressively smaller proportions.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThis pattern was consistent across Samples 1\u0026ndash;3, though finer sampling resolution (Sample 1) produced smoother interpolation surfaces.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e\u0026bull; Visualization:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eIDW-generated EMF surfaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) clearly showed that increased sampling density improved spatial detail without significantly altering overall distribution patterns.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe 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 \u003cb\u003efrom 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;\u003c/b\u003e Mazumder, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003eMain Study\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003eElectric Field Intensities\u003c/h2\u003e\u003cp\u003eAcross the three ISAs, EMF levels were consistently below national (TRAI, 2014) and international (ICNIRP, 2009) human exposure thresholds.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eISA 1\u003c/b\u003e: ETotal ranged between \u003cb\u003e0.019 and 0.726 V/m\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eISA 2\u003c/b\u003e: ETotal ranged between \u003cb\u003e0.047 and 1.051 V/m\u003c/b\u003e, the highest among all ISAs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eISA 3\u003c/b\u003e: ETotal ranged between \u003cb\u003e0.070 and 0.750 V/m\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhile safe for humans, these values fall within the ranges reported as biologically relevant for insects and birds (0.1\u0026ndash;1.0 V/m).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eIDW Interpolation Accuracy\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eME values were near zero across all ISAs, indicating unbiased interpolation.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRMSE values were higher than in the preliminary study, reflecting greater landscape heterogeneity.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eISA 2 consistently exhibited the highest EMF variability\u003c/b\u003e, while ISA 1 showed the most uniform distribution.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003eArea Coverage by EMF Classes\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLow EMF classes (\u0026lt;\u0026thinsp;0.2 V/m)\u003c/b\u003e covered the majority of land area across all ISAs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntermediate classes (0.2\u0026ndash;0.5 V/m)\u003c/b\u003e occupied smaller but significant portions, particularly in ISA 2.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHigh-intensity zones (\u0026gt;\u0026thinsp;0.5 V/m)\u003c/b\u003e were rare and localized, usually near tower clusters.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003eSpatial Patterns\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eISA 1 exhibited a \u003cb\u003euniform low-level exposure profile\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eISA 2 showed \u003cb\u003eheterogeneous exposure\u003c/b\u003e, with distinct high-intensity pockets near tower installations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eISA 3 displayed \u003cb\u003emoderate variation\u003c/b\u003e, with exposures clustered but less pronounced than in ISA 2.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003eSummary of Results:\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThe preliminary study validated \u003cb\u003eIDW as a robust interpolation method\u003c/b\u003e, with finer sampling improving resolution.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eThe main study demonstrated that \u003cb\u003ehuman safety standards were met\u003c/b\u003e, but \u003cb\u003eecologically relevant exposure levels\u003c/b\u003e were common across all ISAs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSpatial patterns varied: ISA 1 (uniform low-level), ISA 2 (heterogeneous with hotspots), ISA 3 (moderate variation).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAcross the three ISAs, maximum E\u003csub\u003eTotal\u003c/sub\u003e exceeded 1.0 V/m only in ISA 2, with lower values recorded in ISAs 1 and 3 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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 \u0026ldquo;hotspots\u0026rdquo; corresponding to tower density and orientation (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a\u0026ndash;o).\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey Findings\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRegulatory Compliance\u003c/b\u003e: All measured EMF values were substantially below the Indian and international human safety thresholds.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSpatial Variability\u003c/b\u003e: EMF intensities showed heterogeneity across landscapes, with localized hotspots linked to tower placement and orientation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEcological Relevance\u003c/b\u003e: 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\u0026ndash;0.6 V/m).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec34\" class=\"Section2\"\u003e\u003ch2\u003eMethodological Contributions\u003c/h2\u003e\u003cp\u003eThis study demonstrates the utility of a \u003cb\u003eGIS-based IDW interpolation framework\u003c/b\u003e for EMF mapping at a landscape scale. The preliminary study underscored the importance of sampling density and uniformity, showing that evenly spaced 100 \u0026times; 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.\u003c/p\u003e\u003cp\u003eCompared to previous EMF research, which often relied on \u003cb\u003epoint-based measurements\u003c/b\u003e (Balmori, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Balmori \u0026amp; Hallberg, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or \u003cb\u003etower-distance correlations\u003c/b\u003e (Everaert \u0026amp; Bauwens, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the present approach provides several advantages. By decoupling measurements from tower azimuths and orientations, this mapping protocol captures the \u003cb\u003ecomposite EMF environment\u003c/b\u003e 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.\u003c/p\u003e\u003cp\u003eThe 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 \u0026amp; Ramos, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This study therefore validates IDW as a reliable and cost-effective method for EMF landscape assessments in resource-constrained contexts.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEMF Intensity Levels: Human Health Perspective\u003c/h3\u003e\n\u003cp\u003eField measurements across all three ISAs showed EMF levels well below the regulatory thresholds set by the Telecom Regulatory Authority of India (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e and international standards (ICNIRP, 2009). The maximum E\u003csub\u003eTotal\u003c/sub\u003e 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.\u003c/p\u003e\u003cp\u003eHowever, regulatory frameworks typically emphasize \u003cb\u003ethermal effects\u003c/b\u003e of EMF exposure on human tissues (Adey, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), with limited consideration of \u003cb\u003enon-thermal biological effects\u003c/b\u003e. Growing evidence indicates that low-intensity, long-term exposure may still produce subtle physiological and neurological responses in humans (Lai, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hyland, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Although not the primary focus of this study, the persistence of EMF exposure in populated environments warrants continued monitoring and epidemiological research.\u003c/p\u003e\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e\u003ch2\u003eEcological Implications of EMF Exposure\u003c/h2\u003e\u003cp\u003eWhile 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 \u003cb\u003e0.63\u0026ndash;0.189 V/m\u003c/b\u003e negatively affected honeybee foraging behavior (\u003cem\u003eApis cerana\u003c/em\u003e) (Taye et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while intensities as low as \u003cb\u003e0.175 V/m\u003c/b\u003e inhibited germination of \u003cem\u003eLepidium sativum\u003c/em\u003e seeds (Cammaerts \u0026amp; Johansson, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Similarly, \u003cb\u003ehouse sparrows (\u003c/b\u003e\u003cb\u003ePasser domesticus\u003c/b\u003e\u003cb\u003e) avoided areas with EMF intensities of 0.822\u0026ndash;1.022 V/m\u003c/b\u003e (Everaert \u0026amp; Bauwens, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), values consistent with maxima recorded in ISA 2.\u003c/p\u003e\u003cp\u003ePlants, due to their sedentary nature, may be particularly vulnerable to long-term EMF exposure. Waldmann-Selsam et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported morphological damage in trees exposed to \u003cb\u003e0.173\u0026ndash;2.213 V/m\u003c/b\u003e over extended periods. Such effects may indirectly influence animal populations by reducing habitat quality. The present study\u0026rsquo;s finding that substantial portions of all three ISAs fall within \u003cb\u003e0.3\u0026ndash;0.6 V/m (Class 3)\u003c/b\u003e intensities suggests that ecological risks cannot be dismissed, even if human exposure remains within safe limits.\u003c/p\u003e\u003cp\u003eMoreover, the \u003cb\u003echronic exposure characteristic of field conditions\u003c/b\u003e may produce cumulative effects equivalent to acute high-intensity exposures in laboratory settings (Magras \u0026amp; Xenos, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Balmori, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This underscores the need to \u003cb\u003emove beyond regulatory frameworks focused solely on human thermal thresholds toward broader ecological risk assessments.\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec37\" class=\"Section3\"\u003e\u003ch2\u003ePolicy and Conservation Relevance\u003c/h2\u003e\u003cp\u003eThe results highlight a critical disjunction between \u003cb\u003ehuman-centered safety standards\u003c/b\u003e and the \u003cb\u003eecological realities of EMF exposure\u003c/b\u003e. While TRAI\u0026rsquo;s precautionary limits are stricter than ICNIRP\u0026rsquo;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.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEMF mapping provides an actionable tool for environmental governance.\u003c/b\u003e By identifying \u003cb\u003elow- and high-exposure zones\u003c/b\u003e, 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.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec38\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003eWhile the study establishes a robust methodological framework, certain limitations must be acknowledged. \u003cb\u003eFirst\u003c/b\u003e, measurements were limited to daytime hours during peak traffic, potentially underrepresenting nocturnal exposure patterns. \u003cb\u003eSecond\u003c/b\u003e, only three ISAs were included, limiting generalizability across India\u0026rsquo;s diverse ecosystems. \u003cb\u003eThird\u003c/b\u003e, the study did not directly assess biological responses, relying instead on thresholds from existing literature.\u003c/p\u003e\u003cp\u003eFuture research should expand the spatial and temporal scope of EMF mapping, incorporating nocturnal and seasonal measurements. Integrating \u003cb\u003eecological monitoring (e.g., pollinator activity, plant germination, avian behavior)\u003c/b\u003e 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.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents one of the first systematic attempts to \u003cb\u003emap electromagnetic field (EMF) intensity in Indian landscapes\u003c/b\u003e using a GIS-based interpolation framework. By combining a \u003cb\u003epreliminary pilot study\u003c/b\u003e with a \u003cb\u003elarger-scale main study across three Independent Sampling Areas (ISAs)\u003c/b\u003e, the research demonstrates both the methodological feasibility and ecological relevance of EMF mapping.\u003c/p\u003e\u003cp\u003eThe key findings can be summarized as follows:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMethodological Rigor\u003c/b\u003e: The study validates \u003cb\u003eInverse Distance Weighted (IDW) interpolation\u003c/b\u003e 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 \u003cb\u003eindependent of tower azimuths and orientations\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHuman Safety Perspective\u003c/b\u003e: EMF intensities across all ISAs remained \u003cb\u003ewell below national (TRAI, 2014) and international (ICNIRP, 2009) exposure thresholds\u003c/b\u003e. From a human health standpoint, this suggests negligible immediate risks under current operating conditions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEcological Relevance: Despite compliance with human safety standards\u003c/b\u003e, EMF values often fell within ranges known to affect insects, birds, and plants (0.1\u0026ndash;1.0 V/m). This highlights the importance of considering \u003cb\u003enon-thermal ecological effects\u003c/b\u003e that are not currently addressed by regulatory frameworks.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePolicy and Conservation Implications\u003c/b\u003e: 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 \u003cb\u003ebiodiversity conservation and sustainable urban planning\u003c/b\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe study underscores the need for \u003cb\u003estandardized protocols\u003c/b\u003e 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.\u003c/p\u003e\u003cp\u003eIn conclusion, this research offers both a \u003cb\u003emethodological contribution\u003c/b\u003e\u0026mdash;through the development of a \u003cb\u003ereplicable\u003c/b\u003e EMF mapping protocol\u0026mdash;and an \u003cb\u003eecological perspective\u003c/b\u003e, 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of Generative AI and AI-Assisted Technologies:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author used ChatGPT\u0026rsquo;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.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMazumder, 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\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eThe authors express their sincere gratitude to DST PURSE Program II, for financial assistance provided for field data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe present study involves primary data generated on field and can be obtained from the corresponding author on request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdey, W. R. (1996). 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Radiofrequency radiation injures trees around mobile phone base stations. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, 572, 554\u0026ndash;569. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2016.08.045\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2016.08.045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"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":"Electrosmog, EMF pollution, mobile telephony, interpolation, IDW, environmental mapping","lastPublishedDoi":"10.21203/rs.3.rs-7949245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7949245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectromagnetic 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.\u003c/p\u003e","manuscriptTitle":"Electromagnetic Field (EMF) Mapping in Semi-Urban and Rural Landscapes: A GIS-Based Approach to Environmental and Ecological Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:35:13","doi":"10.21203/rs.3.rs-7949245/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":"10cec6f9-b554-46cf-9b89-bd03959a633b","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-11T16:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:35:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7949245","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7949245","identity":"rs-7949245","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}