Prediction of air pollutant concentrations based on the long short-term memory neural network
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In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM 2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R 2 reaching over 88%.
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Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have been extensively employed for temporal data analysis, enabling the capture of dependencies and patterns over time.
“Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have been extensively employed for temporal data analysis, enabling the capture of dependencies and patterns over time [42–44].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [43]
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- Authors
- Wu, Z., Tian, Y., Li, M., Wang, B., Quan, Y., & Liu, J.
- Affiliations
- Northeast Forestry University (CN)
- Corresponding author
- Li, Mingze
- Wang, Bin
- Venue
- Journal of Hazardous Materials
- Publisher
- Elsevier BV
- Published
- 2024-03
- Volume
- 465
- Pages
- 133099
- ISSN
- 0304-3894 (ISSN-L)
- OpenAlex
- W4389071026
- Cited by
- 35
- References
- 89
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 9
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Hazardous Materials (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- International Science and Technology Cooperation Programme (2020YFC1511603)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- air pollutant concentration prediction, PM2.5, long short-term memory neural network, forest fires, biomass combustion, long-term time series forecasting, air quality, Heilongjiang
- MeSH terms
- Air Pollutants, Air Pollution, Environmental Monitoring, Memory, Short-Term, Neural Networks, Computer, Particulate Matter
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