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Prediction of air pollutant concentrations is currently one of the most important methods for the prevention and control of urban air pollution in most countries, and accurate and timely prediction of pollutant concentrations is of great significance for urban pollution control. Using Taiyuan, China, as a case study, this study examines how to pred...
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... datasets used in this study were hourly air quality data and meteorological data from 1 January 2018 to 31 October 2021 at seven monitoring stations in the urban area of Taiyuan City, as shown in Figure 1. The data used in this study are all local monitoring station monitoring data. ...
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Citations
... The model Environmental Similarity Improved Inverse Distance Weighted Interpolation and Informer Model (EIDW-Informer) has been proposed recently for hourly prediction of PM2.5, NO 2 , and O 3 concentrations (Lai et al. 2023). In this research, a combination of environmental similarity and inverse distance weighted interpolation methods is used. ...
The increase in air pollutants and its adverse effects on human health and the environment has raised significant concerns. This implies the necessity of predicting air pollutant levels. Numerous studies have aimed to provide new models for more accurate prediction of air pollutants such as CO2, O3, and PM2.5. Most of the models used in the literature are deep learning models with Transformers being the best for time series prediction. However, there is still a need to enhance accuracy in air pollution prediction using Transformers. Alongside the need for increased accuracy, there is a significant demand for predicting a broader spectrum of air pollutants. To encounter this challenge, this paper proposes a new hybrid deep learning-based Informer model called “Gelato” for multivariate air pollution prediction. Gelato takes a leap forward by taking several air pollutants into consideration simultaneously. Besides introducing new changes to the Informer structure as the base model, Gelato utilizes Particle Swarm Optimization for hyperparameter optimization. Moreover, XGBoost is used at the final stage to achieve minimal errors. Applying the proposed model on a dataset containing eight important air pollutants, including CO2, O3, NO, NO2, SO2, PM10, NH3, and PM2.5, the Gelato performance is assessed. Comparing the results of Gelato with other models shows Gelato’s superiority over them, proving it is a high-confidence model for multivariate air pollution prediction.
... This approach enhances traditional statistical models, including linear and nonlinear machine capturing algorithms [4], with deep learning techniques. These AI-driven models excel in extracting relevant features and capturing temporal dependencies in air quality data [5,6].The explosion of time series data, fueled by technological advancements, industrialization, and the proliferation of sensors, has been a game changer in this field. Time series data, a chronological sequence of data points, is instrumental in various aspects of life, including environmental monitoring [7,8]. ...
The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R² (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.
... Atmosphere 2023, 14,1817 16 of 18 models in certain time horizons, indicating that each model has its own strengths and weaknesses in prediction. (2) If the PM 2.5 concentration remains above 35 µg/m 3 for an extended period, there is a higher probability that the predicted results will be underestimated. ...
With the rapid urbanization and industrialization in Taiwan, pollutants generated from industrial processes, coal combustion, and vehicle emissions have led to severe air pollution issues. This study focuses on predicting the fine particulate matter (PM2.5) concentration. This enables individuals to be aware of their immediate surroundings in advance, reducing their exposure to high concentrations of fine particulate matter. The research area includes Keelung City and Xizhi District in New Taipei City, located in northern Taiwan. This study establishes five fine prediction models based on machine-learning algorithms, namely, the deep neural network (DNN), M5’ decision tree algorithm (M5P), M5’ rules decision tree algorithm (M5Rules), alternating model tree (AMT), and multiple linear regression (MLR). Based on the predictive results from these five models, the study evaluates the optimal model for forecast horizons and proposes a real-time PM2.5 concentration prediction system by integrating various models. The results demonstrate that the prediction errors vary across different models at different forecast horizons, with no single model consistently outperforming the others. Therefore, the establishment of a hybrid prediction system proves to be more accurate in predicting future PM2.5 concentration compared to a single model. To assess the practicality of the system, the study process involved simulating data, with a particular focus on the winter season when high PM2.5 concentrations are prevalent. The predictive system generated excellent results, even though errors increased in long-term predictions. The system can promptly adjust its predictions over time, effectively forecasting the PM2.5 concentration for the next 12 h.
... In addition, Taiyuan has conducted a series of ecological projects which have played an important role for the ecological restoration. According to the Taiyuan Environmental Status Bulletin (Lai et al. 2023), the water quality of Jinyang Lake after comprehensive treatment has improved yearly, which is consistent with the results assessed in this study. In addition, under the background that the SDDs of other major water bodies in Taiyuan were in various degrees of decline, the SDD of Jinyang Lake has a significant improvement trend, which may be closely related to the Jinyang Lake Comprehensive Management Project. ...
Remote sensing technology plays a critical role in evaluating large-scale ecological restoration efforts. However, previous studies tend to focus on specific aspects of ecological restoration, such as water conservation, vegetation restoration, or land rehabilitation. As a sustainable development demonstration city in China, Taiyuan has undertaken numerous ecological projects to mitigate environmental degradation caused by its long-standing energy-driven economic expansion. To comprehensively evaluate Taiyuan’s ecological restoration efforts, this study used remote sensing technology to assess its progress from the SDGs perspectives, such as water conservation (SDG 6.3.2), air quality improvement (SDG 11.6.2), and ecosystem preservation (SDG 6.6.1, 15.1.1, 15.3.1, 15.4.2). Firstly, ecological restoration indicators were selected from the aforementioned SDGs perspectives. Secondly, a new vegetation growth condition index (VGCI) was developed to accurately monitor changes in vegetation growth. Finally, Taiyuan’s ecological restoration efforts were evaluated based on the selected indicators. The results showed that Taiyuan has made significant progress in ecological restoration, including improved water and air quality, increased forest coverage, and restored mine’s vegetation, likely due to its ecological projects. This study provides a useful framework for evaluating ecological restoration efforts and advancing sustainability goals.
