The impact of seasonality and meteorological conditions on PM2.5 carbonaceous fractions coupled with carbon isotope analysis: Advantages, weaknesses and interpretation pitfalls

Górka, Maciej; Stojanowska, Agnieszka; Lewandowska, Anita; Drzeniecka-Osiadacz, Anetta; Miazga, Beata; Rybak, Justyna; Wîdory, David

doi:10.1016/j.atmosres.2023.106800

Cited by 6 publications

(1 citation statement)

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“…However, these models primarily focus on utilizing historical data from air quality monitoring stations for predictions, largely ignoring the consideration of other influencing factors, particularly meteorological variables [35]. Meteorological conditions like temperature, humidity, wind speed, and direction significantly impact PM 2.5 concentrations by influencing and shaping the diffusion, mixing, and deposition processes of particulates in the air [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

MSAFormer: A Transformer-Based Model for PM2.5 Prediction Leveraging Sparse Autoencoding of Multi-Site Meteorological Features in Urban Areas

Wang

Zhang

2023

Atmosphere

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The accurate prediction of PM2.5 concentration, a matter of paramount importance in environmental science and public health, has remained a substantial challenge. Conventional methodologies for predicting PM2.5 concentration often grapple with capturing complex dynamics and nonlinear relationships inherent in multi-station meteorological data. To address this issue, we have devised a novel deep learning model, named the Meteorological Sparse Autoencoding Transformer (MSAFormer). The MSAFormer leverages the strengths of the Transformer architecture, effectively incorporating a Meteorological Sparse Autoencoding module, a Meteorological Positional Embedding Module, and a PM2.5 Prediction Transformer Module. The Sparse Autoencoding Module serves to extract salient features from high-dimensional, multi-station meteorological data. Subsequently, the Positional Embedding Module applies a one-dimensional Convolutional Neural Network to flatten the sparse-encoded features, facilitating data processing in the subsequent Transformer module. Finally, the PM2.5 Prediction Transformer Module utilizes a self-attention mechanism to handle temporal dependencies in the input data, predicting future PM2.5 concentrations. Experimental results underscore that the MSAFormer model achieves a significant improvement in predicting PM2.5 concentrations in the Haidian district compared to traditional methods. This research offers a novel predictive tool for the field of environmental science and illustrates the potential of deep learning in the analysis of environmental meteorological data.

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