Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection

Abstract

As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation sensing. Firstly, on the basis of regional grid division, the inverse distance spatial interpolation method is used for spatial interpolation to collect regional VOCs data information. Secondly, extreme gradient boosting (XGBoost) is used for spatio-temporal feature selection, combined with graph convolutional neural network (GCN) to construct regional spatial relationships of VOCs, and multiple linear regression (MLR) to process VOCs time series data and predict the VOCs concentration in the grid. Finally, the aggregation potential values of VOCs are calculated based on the prediction results, and the potential perception results are visualised. A VOCs aggregation perception method based on concentration prediction is proposed, using the XGBoost-GCN-MLR method with a scenario-aware approach for VOCs to perceive the VOCs aggregation in the relevant region. VOCs concentration prediction and VOCs aggregation trend perception were carried out in Xi’an, Baoji, Tongchuan, Weinan and Xianyang. The results show that compared with the GCN model, XGBoost model, MLR model and GCN-MLR model, the XGBoost-GCN-MLR model reduces the input variables, achieves the optimisation of the input parameters of the VOCs concentration prediction model, reduces the complexity of the prediction model and improves the prediction accuracy. Intelligent sensing of VOCs aggregation can visualise the regional VOCs. The intelligent sensing of VOCs aggregation can visualise the development trend and status of regional VOCs aggregation and convey more information, which has practical value.

Nanomaterial-based VOC sensing applications and a deep dive into their developmental trends

“…[42][43][44] The aggregation of VOCs emitted from cities and towns increases their concentration, which in turn increases the amount of formation of O 3 and PM2.5. 45,46 The reaction cycle for the formation of ground-level O 3 is clearly depicted in Fig. 2.…”

Exploring influential factors of CO2 emissions in China’s cities using machine learning techniques

“…Due to the intrinsic properties of eXtreme Gradient Boosting (XGBoost), feature selection in this research was based on the importance scores of the features. This technique has been widely used in various applications and has demonstrated satisfactory performance (Chen et al 2020a, Dai et al 2022, Zhang et al 2022. Consequently, the XGBoost model was used to fit the sample data.…”

In-Situ Online Detection Of Atmospheric Volatile Organic Compounds Based On Laser Induced Breakdown Spectroscopy: A Review

“…[4][5][6] Additionally, VOCs exhibit physiological toxicity, leading to cancer, deformity, and organ damage in the human body. 7,8 The sources of VOCs are diverse and can be classified into two categories: natural sources and artificial sources. 9,10 Natural sources include emissions from wetlands, forests, and oceans, while artificial sources include emissions from industry, agriculture, homes, and transportation vehicles.…”