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An integrated framework for predicting air quality index using pollutant concentration and meteorological data

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Abstract

Everyone wishes to lead a healthy life, but the tremendous increase in air pollution affects our lives. The current tools can give updates on the Air Quality Index (AQI), but we can get further insights into air pollution by applying knowledge discovery and analytics techniques. Our research proposes a novel and integrated framework to predict AQI using pollutant concentration data and meteorological data. The framework comprises four modules for insights into air pollution. The first module (AQIfp) computes air quality by forecasting pollutant concentrations of particulate matter and harmful gases. The second module (AQIp) predicts air quality using pollutant data and historical air quality index data. The third module (AQIm) predicts AQI using meteorological features (temperature, surface pressure, cloud cover, humidity, wind speed, wind direction, and precipitation) and extra features (month, year, time, day of month, day of week). The fourth module (AQIc) computes AQI by combining the results of AQIp and AQIm. Various methods are used for forecasting pollutants and predicting AQI, viz artificial neural networks (ANN), random forest regression, XGradientboost, long-short term memory, vector autoregression, auto-regressive integrated moving average (ARIMA), decision tree regression, support vector regression, multiple linear regression, and K-nearest neighbour. The results show that different methods are best suited for various pollutants. However, ARIMA and ANN successfully predict almost all the pollutants. The results are promising, with Mean Absolute Error to predict AQI being 7.09 only when combining AQIp and AQIm. AQI can be predicted best with both pollutant and meteorological data. Since the modules are independent and if one data is missing, any module can be utilized to obtain an approximation of the AQI with minimal error. The framework will assist in providing decisions at the individual and administration levels to mitigate air pollution.

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Data availability

Data sharing does not apply to this article as no new data is created. However, we analyzed the data from the website Central Pollution Control Board.

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Acknowledgements

The authors thank CPCB, New Delhi, for providing us with the website to get the air quality data.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Plot No. FA7, ZoneP1, GT Karnal Road, Delhi, 110036, India

    Shelly Sachdeva & Hitendra Singh

  2. Department of Computer Science and Engineering, SRM University, 39, Rajiv Gandhi Education City, Sonipat, Haryana, 131029, India

    Shailee Bhatia & Puneet Goswami

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  1. Shelly Sachdeva
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All authors equally contributed to the study conception, methodology and result analysis. All authors read and approved the final manuscript.

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Correspondence to Shelly Sachdeva.

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Sachdeva, S., Singh, H., Bhatia, S. et al. An integrated framework for predicting air quality index using pollutant concentration and meteorological data. Multimed Tools Appl 83, 46967–46996 (2024). https://doi.org/10.1007/s11042-023-17432-0

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