Aether 360 Launches to Unmask the Hidden Health Crisis: AI to Quantify Air Pollution’s Link to Acute Hospital Admissions in New Delhi

RMN Research Report Highlights:

• 💡 The Aether 360 project launched as a Proof-of-Concept (PoC) to develop the world’s first Artificial Intelligence (AI) model capable of calculating the Attribution Rate (A-Rate).
• 📈 The A-Rate quantifies the probability that a patient’s acute respiratory or cardiac hospital admission is directly caused by a recent spike in air pollution.
• 💻 Aether 360 utilizes a proprietary Explainable AI (XAI) algorithm to integrate granular air quality data with de-identified patient data to establish the Pollution Probability Link (PPL).
• 🤝 Advancement to a live Beta deployment is critically dependent on securing institutional partnerships from agencies like UNEP, WHO, or academic medical centers to validate the AI with real, anonymized data.

By Rakesh Raman
New Delhi | October 29, 2025

NEW DELHI, INDIA – October 29, 2025 – Rakesh Raman, a journalist, environmental activist, and founder of RMN News Service and RMN Foundation, today announced the launch of the Aether 360 project. This self-initiated Proof-of-Concept (PoC) aims to develop the world’s first Artificial Intelligence (AI) model capable of calculating the Attribution Rate (A-Rate). The A-Rate quantifies the probability that a patient’s acute respiratory or cardiac hospital admission is directly caused by a recent spike in air pollution.

Air pollution represents the most significant environmental health crisis in India, yet public health systems currently lack the necessary tools to measure its direct, acute impact on hospital resources. Aether 360 is pioneering an AI-driven system designed to bridge this critical gap between environmental data and clinical records.

The project introduces a novel, quantifiable metric for health agencies and governments to measure the true public health burden of air pollution. This AI-driven Clinical Decision Support System (CDSS) Augment works by:

• Analyzing granular air quality data (e.g., PM2.5, NO2) with de-identified patient data (time of admission, location).
• Attributing causality using a proprietary Explainable AI (XAI) algorithm to calculate the Pollution Probability Link (PPL) for a given acute health event.
• Quantifying the output to produce the A-Rate, a key metric for hospitals and public health agencies to quantify the burden of pollution-related disease (PRD).

Proof-of-Concept Success and Next Steps

The current PoC phase is being executed as a solo, self-funded, simulated pilot by the lead developer to prove the technical feasibility of the core A-Rate algorithm. The initial architectural design and framework development are being conducted with strategic and technical assistance from Google AI technologies.

Key deliverables of the PoC phase include functional Python code, a trained Explainable AI (XAI) model, and a Dashboard Wireframe showcasing the simulated A-Rate. This phase will utilize publicly available historical CPCB AQI Data from high-density Delhi stations and a synthetic (mock) dataset of 1,000 patient records to avoid the complexities associated with handling real patient data and complying with privacy regulations like HIPAA, GDPR, or India-specific laws.

While the technical PoC aims to prove that the A-Rate can be calculated, its advancement to a live Beta deployment is critically dependent on securing institutional partnerships. The model and its outputs require rigorous, independent verification by qualified clinicians, epidemiologists, and government data bodies.

Call for Collaboration

Aether 360 is actively seeking institutional support and collaboration to advance to the clinically validated Beta Phase. This innovation is positioned as a vital tool for policy change, offering the first robust, quantifiable metric needed to justify, fund, and track the effectiveness of pollution control measures.

Targeted partners include:

1. Government/Agency Partnerships: Seeking strategic endorsement and funding from agencies like the UNEP, WHO, and major Indian government departments. A successful partnership would integrate this crucial metric into public health policy.
2. Academic/Clinical Partners: Seeking access to anonymized, real clinical data (in compliance with all regulations) from institutions like AIIMS or IITs for the Beta Phase. Partners would receive a state-of-the-art AI-powered research and clinical tool, generating novel, publishable insights.

The ultimate success, stability, and clinical reliability of the Aether 360 project are entirely dependent on securing formal collaborations to validate its clinical utility with real data.

Access and Archival

This research paper is available for public access and archival. A number of the author’s research publications, including this one, are archived on Zenodo, developed under the European OpenAIRE program and operated by CERN. It provides permanent DOI-based citations. This paper may be cited permanently using the following Digital Object Identifier (DOI): DOI: 10.5281/zenodo.17474705

Download: You can click here to download the project proposal, which is also given below.

Contact Information

We are ready to partner. Contact us to discuss the Aether 360 project.

By Rakesh Raman, who is a national award-winning journalist and social activist. He is the founder of a humanitarian organization RMN Foundation which is working in diverse areas to help the disadvantaged and distressed people in the society.

As a technology and AI expert, his professional focus is on applying emerging AI and digital technologies to enhance decision-making, operational efficiency, transparency, and democratic participation in governance, media, and business systems. You can click here to view his full profile.

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