What I feel is that by using machine learning, Bayesian models, and spatial-temporal simulations, we can now elucidate intricate pollutant interactions, dynamic baselines, and amalgamate different data sources from satellites to land-use histories. This does not imply that conventional methods like Igeo and EF are outdated; their simplicity and communicative effectiveness still remain significant for policymakers and the public. The genuine future resides in integration: employing predictive models to enhance baselines, quantify uncertainties, and uncover concealed patterns, while conventional indexes serve as rapid, accessible benchmarks. The field should progress towards hybrid frameworks that combine the transparency of traditional indices with the complexity of contemporary predictive methodologies, rather than eliminating them.

Mouataz T. Mostafa

From a professional standpoint, the continued use of traditional pollution indices such as Igeo (Geo-accumulation Index) and EF (Enrichment Factor) is increasingly being questioned, especially in light of modern data analysis methods. These indices were developed in the latter half of the 20th century and rely on statistically static, empirically based formulas that assume:

• Constant natural background values,
• Linear relationships between elements and pollution sources,
• Simplistic classification thresholds that do not account for spatial or temporal variability.

Meanwhile, the development of predictive models, geospatial analyses, and machine learning has enabled a far more dynamic and context-sensitive approach to pollution assessment. These modern approaches:

• Account for nonlinearities and complex interdependencies in ecosystems,
• Allow for causal inference rather than just classification,
• Are adaptable to local conditions and real-time measurements,
• Can incorporate time-series data, socioeconomic factors, and other variables that traditional indices ignore.

Thus, while Igeo and EF still have value for rapid screening assessments and for comparative studies across time and space, their reliability and precision are limited in today’s context, where vastly more data and analytical tools are available.

Yes, the time has come to supplement—or even replace—traditional indices with modern data-driven models, particularly in research that demands high precision, risk prediction, and real-time decision-making.

Conventional indices such as Igeo, EF, and PLI remain fundamental because they provide standardized, comparable, and widely recognized benchmarks

I don’t think we need to completely move beyond indices such as Igeo, EF, or PLI. With today’s tools such as GIS, machine learning, and modeling we should focus on building integrative, predictive frameworks. The Key is not simply replacing conventional indices, but integrating them with modern approaches to generate more effective results.

Conventional pollution indices like Igeo and EF have limitations due to fixed assumptions and background variability, often making them less effective compared to newer, dynamic assessment methods.