Abstract
Fine particulate matter (PM2.5) is a complex air pollutant with numerous gaseous and solid constituents. PM2.5 possesses a significant hazard due to its ability to penetrate deep into the lungs, corrode the alveolar wall, and impair lung functions. Modeling the non-linear and dynamic time series of daily PM2.5 concentration remains a challenge. This study proposes a deep LSTM neural network to forecast accurate PM2.5 concentration in the Kathmandu valley. Correlation analysis illustrates that dew, minimum ambient temperature, maximum ambient temperature, and pressure are strongly correlated with PM2.5 concentration. Hence, five models are developed based on different input parameter combinations and are eventually evaluated to determine the best performing model. Model 2 with single-step prediction is the best performing deep LSTM model with RMSE of 13.04 μg/m3 and MAE of 10.81 μg/m3. The SARIMA model applied to the univariate PM2.5 data series illustrates the RMSE of 19.54 μg/m3 and MAE of 15.21 μg/m3 for the test data. Hence, the deep LSTM model with past PM2.5 data and dew as inputs is recommended to predict future PM2.5 concentration in the Kathmandu valley. The negative impact of PM2.5 concentration on public health can be minimized with efficient forecasting.
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Availability of data and material
Raw data of PM2.5 concentration, maximum and minimum ambient temperature, dew, humidity, pressure, and wind speed of Kathmandu valley is freely available on The World Air Quality Project’s open data platform (The World Air Quality Project 2019). Data will be made available upon request to the corresponding author.
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The authors would like to express sincere gratitude to The World Air Quality Project for providing necessary data to complete the study.
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Keras library with TensorFlow backend is utilized to implement the proposed deep LSTM neural network. The code is executed in the Google Colaboratory. The code will be made available upon request to the corresponding author.
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Dhakal, S., Gautam, Y. & Bhattarai, A. Exploring a deep LSTM neural network to forecast daily PM2.5 concentration using meteorological parameters in Kathmandu Valley, Nepal. Air Qual Atmos Health 14, 83–96 (2021). https://doi.org/10.1007/s11869-020-00915-6
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DOI: https://doi.org/10.1007/s11869-020-00915-6