Spatialized Analysis of Air Pollution Complaints in Beijing Using the BERT+CRF Model

Abstract

(1) Background: To better carry out air pollution control and to assist in accurate investigations of air pollution, in this study, we fully explore the spatial distribution characteristics of air pollution complaint results and provide guidance for air pollution control by combining regional air monitoring data. (2) Methods: By selecting the air pollution complaint information in Beijing from 2019 to 2020, in this study, we extract the names and addresses of complaint points, as well as the complaint times and types by adopting the BERT (bidirectional encoder representations from transformers) + CRF (conditional random field) model deep learning method. Moreover, through further filtering and processing of the complaint points’ address information, we achieve address matching and spatial positioning of the complaint points, and realize the regional spatial representation of air pollution complaints in Beijing in the form of a heat map. (3) Results: The experimental results are compared and analyzed with the ranking data of total suspended particulate (TSP) concentration of townships (streets) in Beijing during the same period, indicating that the key areas of air pollution complaints have a high correlation with the key polluted township (street) areas. The distribution of complaints and the types of complaints in each township (street) differ according to the population density in each township (street), the level of education, and economic activity. (4) Conclusions: The results of this study show that the public, as the intuitive perceiver of air pollution, is sensitive to the air pollution situation at a smaller spatial scale; furthermore, complaints can provide guidance and reference for the direction of air pollution control and law enforcement investigations when coupled with geographical features and economic status.

ENEX-FP: A BERT-Based Address Recognition Model

“…In 2015, Baidu [10] published a paper concerning the BILSTM-CRF model, which combines a CRF and a bidirectional long short-term memory network (LSTM); this addresses the issue of text annotation. The BERT-CRF model has been utilized for Beijing air pollution complaints to aid in the [11] addition of text data from responses to public complaints about air pollution in Beijing from 2019 to 2020. Lin Junting et al [12] used a CNN-BILSTM-CRF model for NER of underground onboard equipment, the accuracy of this model on the marked metro vehicle-mounted fault data is up to 0.95, which is higher than other entity recognition models.…”

Spatial variation of air pollutants by using GIS modelling

“…This approach can aid in the development of effective strategies to control emissions and mitigate the impacts on human health and the environment (Jie Chen, 2023). By visualizing and analyzing multi-source spatio-temporal data related to air pollution, researchers can improve the efficiency of air pollution law enforcement and control methods, as well as provide valuable insights for air pollution investigation and monitoring (Xiaoshuang Wang, 2022). Additionally, GIS modelling can assist in the establishment of air pollution measurement stations in areas where pollutants are concentrated, enabling better monitoring and assessment of air quality and its potential impacts on the population (Naoum, 2002).…”