AirSense: A Real-Time Monitoring Air Quality Intelligence System

LAKSHAY GIRI; SHRUT JAIN; NIMISH GUPTA; Meenu

doi:10.36948/ijfmr.2026.v08i03.77670

AirSense: A Real-Time Monitoring Air Quality Intelligence System

Author(s)	LAKSHAY GIRI, SHRUT JAIN, NIMISH GUPTA, Meenu
Country	India
Abstract	Air pollution is one of the most severe public health crises of the 21st century, causing an estimated 4.2 million premature deaths annually worldwide [1]. Existing air quality platforms report raw pollutant readings or a single broadcast index without providing personalized health guidance. This paper presents AirSense, a real-time end-to-end air quality intelligence system addressing two distinct problems simultaneously. First, single-pollutant indices systematically misclassify pollution severity when the dominant threat is not PM2.5 but CO, NO2, or O3 [2]; AirSense resolves this by computing the official US EPA AQI across all six criteria pollutants [2] and using an unsupervised K-Means clustering model [13] to independently classify multi-pollutant severity in situations where rule-based formulas alone cannot capture cross- pollutant co-elevation patterns. Second, the same AQI value carries dramatically different health implications for different individuals [10],[11]; AirSense resolves this through a condition-aware personalized health risk engine that delivers tailored advisories, risk scores, and behavioral guidance across thirteen medical conditions including asthma, COPD, heart disease, pregnancy, and childhood. The K-Means model [13] is evaluated using cluster coherence metrics—intra-cluster compactness and inter-cluster separation [14]—and through a novel disagreement analysis against PM2.5-only classification, demonstrating concrete scenarios where ML provides classification value that no deterministic formula can. Experimental evaluation confirms 100% AQI formula accuracy against EPA reference values [2] and a mean end-to-end latency of 2.3 seconds
Keywords	air quality monitoring, AQI computation, K-Means clustering, multi-pollutant classification, personalized health risk, PM2.5, EPA breakpoints, cluster coherence, OpenWeatherMap API, Streamlit, environmental informatics
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-09
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.77670

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AirSense: A Real-Time Monitoring Air Quality Intelligence System

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