A framework for city-specific air quality health index
A comparative assessment of Delhi and Varanasi, India
Bibliographic Data
| ID | 15544344 |
|---|---|
| Authors | Franciosalgeo George (0009-0009-5765-3954, Manipal Academy of Higher Education, corresponding author), Pallavi Joshi (0000-0002-5888-3921, Amity University), Sagnik Dey (0000-0002-0604-0869, Korea University), R K Mall (0000-0002-3118-096X, Banaras Hindu University), Santu Ghosh (0000-0002-9373-9570, Manipal Academy of Higher Education) |
| Year | 2025 |
| Volume | 20 |
| Issue | 8 |
| Pages | 084067-084067 |
| Publication date | 2025-07-10 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/aded26 |
| OpenAlex | W4412176703 |
| Language | EN |
| References cited | 31 |
Air quality index (AQI) is a crucial tool for communicating the health risks of air quality to the public. However, the current AQI in India does not consider the health impacts of exposure to air pollutants, necessitating the development of air quality health index (AQHI). Here, we proposed a framework for developing city-specific AQHI that better reflects local air quality and associated health risks using air pollution and health data from two polluted and densely populated cities in north India—Delhi and Varanasi (Delhi: 2013–2017; Varanasi: 2009–2016). We also constructed a pooled AQHI by combining data from both cities. Using concentrations of ambient fine particulate matter (PM 2.5 ), nitrogen dioxide, and ozone, we applied generalized additive models with a quasi-Poisson link, using daily mortality counts as the outcome, excluding suicide and accidental deaths. A 10 μ g m −3 increase in PM 2.5 was associated with mortality increases of 0.17% (95% CI: 0.01–0.34) in Varanasi, 0.20% (95% CI: 0.1–0.29) in Delhi, and 0.16% (95% CI: 0.08–0.24) in the pooled model. The city-specific AQHI classified 21.8% of days in Delhi as ‘Satisfactory’ versus 18.2% by the pooled index, while 24.2% of days were ‘Poor’ compared to 30.1% under the pooled index. In Varanasi, 6.8% of days were ‘Good’ under the city-specific AQHI, compared to 9.3% by the pooled index, with 15.7% of days classified as ‘Poor’ versus 19.3% by the pooled index. Our results suggest that a single-pooled AQHI may misrepresent local air quality and associated health risks. Since AQHI values are derived from excess mortality risk estimates, a city-specific AQHI ensures a more accurate reflection of local pollution-related health impacts, supporting targeted public health interventions. We recommend accessibility of health data to enable developing AQHI for non-attainment cities in India and use it to track progress towards cleaner air
Air quality index · Archaeology · Environmental health · Geography · Index (typography · Meteorology · Metropolitan area · New delhi · Water resource management · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Computer Science · COVID-19 impact on air quality · Environmental Science · Medicine
| Highly cited | No |
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