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A novel seasonal index–based machine learning approach for air pollution forecasting

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Abstract

Novel machine learning models (MLMs) using the seasonal indexing approach that captures the variation in air quality caused due to meteorological changes have been used to provide short-term, real-time forecasts of PM2.5 concentration for one of the most polluted air quality control regions (AQCR) in the capital city of Delhi. Two MLMs—multi-linear regression and random forest—have been developed for using time series data for 1-h and 24-h average PM2.5 concentration. Short-term, real-time forecasts have been made using the developed models. Various model performance evaluation indices indicate satisfactory model performance. R2 values for the hourly and daily models varied between 0.95 and 0.72 and between 0.76 and 0.68 for the 1st to 5th h/day, respectively. The lagged values of PM2.5 concentration (persistence) and the hourly and daily indices are the most influential variables for the forecasts for immediate time steps. In contrast, seasonal indices become more important with the forecasting time horizon. The developed models can be used for making short-term, real-time air quality forecasts and issuing a warning when the pollution levels go beyond acceptable limits.

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Abbreviations

ANN:

Artificial neural network

AQCS:

Air quality control regions

AQMP :

Air quality management plan

ARIMA:

Auto-Regressive Integrated Moving Average

CNG :

Compressed natural gas

CPCB:

Central Pollution Control Board

CV:

Cross-validation

FLT:

Fuzzy logic theory

GPU:

Graphics Processing Unit

GRAP:

Graded Response Action Plan

ISBT:

Inter State Bus Terminal

LSTM:

Long Short Term Memory

MAE:

Mean absolute error

MLM:

Machine learning models

NCR:

National Capital Region

NOAA :

National Oceanic and Atmospheric Administration

OOB:

Out of bag

R2:

Coefficient of determination

RMSE:

Root mean squared error

RSPM:

Respirable suspended particulate matter

WHO:

World Health Organization

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Authors and Affiliations

  1. Council On Energy, Environment and Water, New Delhi, 110016, India

    Adeel Khan

  2. TERI, The Energy and Resources Institute, IHC Complex, Lodi Road, New Delhi, 110003, India

    Sumit Sharma

  3. Accenture Solutions Pvt. Ltd, Gurgaon, Haryana, 122002, India

    Kaushik Roy Chowdhury

  4. TERI School of Advanced Studies, New Delhi, 110070, India

    Prateek Sharma

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  1. Adeel Khan
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  2. Sumit Sharma
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Correspondence to Sumit Sharma.

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Khan, A., Sharma, S., Chowdhury, K.R. et al. A novel seasonal index–based machine learning approach for air pollution forecasting. Environ Monit Assess 194, 429 (2022). https://doi.org/10.1007/s10661-022-10092-x

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