Air Pollution Prediction Research Articles

Daily estimation of NO2 concentrations using digital tachograph data.

Traffic information is crucial for estimating NO2 concentrations, but it is static and limited in predicting constantly changing NO2 levels. To overcome these challenges, this study utilized real-time spatial big data to capture both the spatial and temporal fluctuations in traffic. Digital tachograph (DTG) data, sourced from digital devices in all commercial vehicles, are employed to construct a DTG land use regression (LUR) model, and its performance is compared with that of a non-DTG-LUR model. The DTG-LUR model exhibits superior performance, with an explanatory power of 0.46, in contrast to the 0.36 of the non-DTG model. This significant improvement stems from the spatially and temporally dynamic DTG variables such as cargo traffic. This study introduces a novel approach for incorporating DTG data in correlating with NO2 concentrations. It underscores the advantage of DTG data in predicting daily NO2 fluctuations at a precise 200-m grid, which is not feasible with conventional data. The findings of the study highlight the immense potential of spatial big data for fine-grained analyses, which could enable hourly predictions of air pollution.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

A systematic scrutiny of artificial intelligence-based air pollution prediction techniques, challenges, and viable solutions

Air is an essential human necessity, and inhaling filthy air poses a significant health risk. One of the most severe hazards to people’s health is air pollution, and appropriate precautions should be taken to monitor and anticipate its quality in advance. Among all the countries, the air quality in India is decreasing daily, which is a matter of concern to the health department. Many studies use machine learning and Deep learning methods to predict atmospheric pollutant levels, prioritizing accuracy over interpretability. Many research studies confuse researchers and readers about how to proceed with further research. This paper aims to give every detail of the considered air pollutants and brief about the techniques used, their advantages, and challenges faced during pollutant prediction, which leads to a better understanding of the techniques before starting any research related to air pollutant prediction. This paper has given numerous prospective questions on air pollution that piqued the study’s interest. This study discussed various machine and deep learning methods and optimization techniques. Despite all the discussed machine learning and deep learning techniques, the paper concluded that more datasets, better learning techniques, and a variety of suggestions would enhance interpretability while maintaining high accuracy for air pollution prediction. The purpose of this review is also to reveal how a family of neural network algorithms has helped researchers across the globe to predict air pollutant(s).

Predicting Air Pollution Levels in Pune, India using Generative Adversarial Networks

Predicting Air Pollution Levels in Pune, India using Generative Adversarial Networks

A hybrid spatiotemporal model combining graph attention network and gated recurrent unit for regional composite air pollution prediction and collaborative control

Machine learning (ML) models have been extensively applied in air quality prediction. However, many of these models often failed to unveil complex mechanisms and regional spatiotemporal variations of composite air pollution. This brings uncertainties in using ML models for effective composite air pollution control. The present study developed a novel hybrid spatiotemporal model framework combining Graph Attention Network (GAT) and Gated Recurrent Unit (GRU), namely the GAT-GRU model, to foresee composite air pollutions with a focus on PM2.5 and O3. By extracting attention matrices for PM2.5O3 composite pollution and applying the Louvain algorithm, the framework established effective community network divisions for coordinated control of PM2.5O3 composite pollution. The framework was applied and tested in China's “2 + 26″ cities, a city cluster with most heavy PM2.5 and O3 pollution and precursor emission sources. The results demonstrate that the framework successfully captured spatiotemporal evolution of combined PM2.5 and O3 pollution. The attention matrix is autonomously generated during course of the model learning process with the aim to interpret the complex interactions among “2 + 26″ cities. The framework provides a new perspective for the interpretability of artificial intelligence models and offers a methodological support and scientific evidence for formulating regional pollution cooperative governance strategies.

Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves

Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.

TKSTAGNet: A Top-K Spatio-Temporal Attention Gating Network for air pollution prediction

Accurate air pollution prediction is of great significance for human health, travel, and environmental management. As an emerging artificial intelligence technology, Transformer has already shown tremendous potential in air pollution prediction. Unfortunately, existing Transformer-based methods are challenging to accurately predict air pollution due to two limitations: (1) they neglect irrelevant spatial and temporal neighbors (e.g., neighboring sensors and timesteps) when modeling spatio-temporal correlations, and (2) they lack an adaptive mechanism to mine different effects caused by various external factors (e.g., meteorological conditions). Jointly taking these limitations into account, we propose a novel Transformer-based architecture for air pollution prediction, named Top-K Spatio-Temporal Attention Gating Network (TKSTAGNet). In this architecture, to effectively extract spatio-temporal correlations, we first design a TopKpooling Spatial Attention (TopK-SA) and a TopKpooling Temporal Attention (TopK-TA). In TopK-SA, the TopKpooling mechanism is introduced into spatial attention to filter out irrelevant spatial neighbors, accurately capturing spatial dependencies. In TopK-TA, the TopKpooling mechanism is integrated with temporal attention to identify and exclude irrelevant temporal neighbors, precisely extracting temporal dependencies. To adaptively fuse various external factors, a gating fusion module is then proposed. We conduct experiments on three real-world air pollution datasets from urban regions in Beijing, Shanghai, and Chongqing, respectively. Experimental results demonstrate that TKSTAGNet outperforms state-of-the-art baselines in prediction precision and fitting the variation trends of air pollution.

Leveraging long short term memory in air pollution prediction in Nairobi

Leveraging long short term memory in air pollution prediction in Nairobi

Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals

Evaluation of Machine Learning Models in Air Pollution Prediction for a Case Study of Macau as an Effort to Comply with UN Sustainable Development Goals

Applications of machine learning & Internet of Things for outdoor air pollution monitoring and prediction: A systematic literature review

According to the World Health Organization (WHO), air pollution kills seven million people every year. Outdoor air pollution is a major environmental health problem affecting low, middle, and high-income countries. In the past few years, the research community has explored IoT-enabled machine learning applications for outdoor air pollution prediction. The general objective of this paper is to systematically review applications of machine learning and Internet of Things (IoT) for outdoor air pollution prediction and the combination of monitoring sensors and input features used. Two research questions were formulated for this review. 1086 publications were collected in the initial PRISMA stage. After the screening and eligibility phases, 37 papers were selected for inclusion. A cost-based analysis was conducted on the findings to highlight high-cost monitoring, low-cost IoT and hybrid enabled prediction. Three methods of prediction were identified: time series, feature-based and spatio-temporal. This review’s findings identify major limitations in applications found in the literature, namely lack of coverage, lack of diversity of data and lack of inclusion of context-specific features. This review proposes directions for future research and underlines practical implications in healthcare, urban planning, global synergy and smart cities.

Short-term air pollution prediction using graph convolutional neural networks

Pollution is a major concern in the present day, causing multiple illnesses and deaths, specifically in developing countries in Asia and Africa. While it has drawn worldwide attention as governments try to issue laws to meet certain criteria for air pollution levels, pollution concentration forecasting has become a major challenge. Particularly, short term forecasting will help to gain information regarding concentrations of harmful pollutants for the upcoming hours and enable better decision-making with regards to controlling air pollution. In this paper, we investigate spatio-temporal graph-based models to determine the best methods for spatial and temporal analysis of data. The models have the additional capacity to perform multi-variate predictions of correlated data, i.e., predicting multiple pollutant concentrations simultaneously, thus requiring lower computational efforts. A real-world pollution dataset measured over Delhi, India, is used to comparing the proposed models with baselines, which shows the Spatio-Temporal Graph Convolution Neural Network (STGCN) models to be performing better than others. For a better understanding of model architectures with the most effective strategies for spatial and temporal data analysis, three models, namely STGCN-A, STGCN-B, STGCN-C have been developed. The models have been compared with 6 other baselines over multiple forecasting horizons of 1 h, 24 h, and 48 h timesteps using various metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE). On the PM2.5 dataset of Delhi, STGCN-B achieves a performance of 10.53 MAE, 6.92 RMSE and 25.25 MAPE for a 1 h forecast, while STGCN-C achieves 20.18 MAE, 14.73 RMSE and 55.45 MAPE for a 24 h forecast. In general, both structures achieve similar results, with STGCN-C being better in many cases. They are further analysed through observation-prediction graphs and Taylor diagrams, which give an insight into our findings. The models are additionally validated on a benchmark real-world dataset from California, USA for better understanding of the spatio-temporal relations and model performances on a more stable dataset, where STGCN-C performs best for PM2.5 with 4.30 RMSE, 1.98 MAE, 25.96 MAPE for 1 h predictions for univariate data and 3.63 RMSE, 1.88 MAE and 25.91 MAPE in multivariate forecasting. The developed spatio-temporal graph-based models hold promising applications in urban air quality management, aiding policymakers in implementing targeted interventions to mitigate pollution-related health risks. Furthermore, these models can support public health agencies by providing timely and accurate forecasts of pollutant concentrations, enabling proactive measures to safeguard community well-being. Our study showcases the efficacy of spatio-temporal graph-based models in accurately forecasting air pollutant concentrations, with particular emphasis on short-term predictions. By leveraging multi-variate capabilities, our proposed models demonstrate superior performance compared to baseline approaches across various forecasting horizons.

An enhanced approach for predicting air pollution using quantum support vector machine

The essence of quantum machine learning is to optimize problem-solving by executing machine learning algorithms on quantum computers and exploiting potent laws such as superposition and entanglement. Support vector machine (SVM) is widely recognized as one of the most effective classification machine learning techniques currently available. Since, in conventional systems, the SVM kernel technique tends to sluggish down and even fail as datasets become increasingly complex or jumbled. To compare the execution time and accuracy of conventional SVM classification to that of quantum SVM classification, the appropriate quantum features for mapping need to be selected. As the dataset grows complex, the importance of selecting an appropriate feature map that outperforms or performs as well as the classification grows. This paper utilizes conventional SVM to select an optimal feature map and benchmark dataset for predicting air quality. Experimental evidence demonstrates that the precision of quantum SVM surpasses that of classical SVM for air quality assessment. Using quantum labs from IBM’s quantum computer cloud, conventional and quantum computing have been compared. When applied to the same dataset, the conventional SVM achieved an accuracy of 91% and 87% respectively, whereas the quantum SVM demonstrated an accuracy of 97% and 94% respectively for air quality prediction. The study introduces the use of quantum Support Vector Machines (SVM) for predicting air quality. It emphasizes the novel method of choosing the best quantum feature maps. Through the utilization of quantum-enhanced feature mapping, our objective is to exceed the constraints of classical SVM and achieve unparalleled levels of precision and effectiveness. We conduct precise experiments utilizing IBM’s state-of-the-art quantum computer cloud to compare the performance of conventional and quantum SVM algorithms on a shared dataset.

Enhancing air pollution prediction: A neural transfer learning approach across different air pollutants

Air pollution stands out as one of the most alarming environmental challenges. It poses significant risks to human health and the environment. Accurate forecasting of air pollutant concentration levels is crucial for effective air quality management and timely implementation of mitigation strategies. In this paper, the transfer learning technique is investigated using the artificial neural network (ANN), also called multi-layer perception (MLP), to transfer knowledge across different air pollutants forecasting, and therefore, to generalize over a large set of air pollutants in the same air monitoring station. By leveraging the knowledge learned from a source forecasting task, transfer learning allows us to reduce the data requirements, speed up the training of the models, and enhance the predictive performance for different air pollutants for the target forecasting task. We present a comprehensive analysis of the transfer learning across different air pollutants in the same air monitoring station on a large dataset of air quality measurements. Our results demonstrate that transfer learning significantly improves forecasting accuracy with fewer fine-tuning data, particularly when limited labeled data is available for the target task. The findings of this study contribute to the advancement of air pollution forecasting methodologies, facilitating better decision-making processes and proactive air quality management.

Development and comparison of land use regression, dispersion and hybrid models for predicting air pollution levels

Development and comparison of land use regression, dispersion and hybrid models for predicting air pollution levels

A novel hybrid optimization model for evaluating and forecasting air quality grades.

Air pollution has a significant global impact on natural resources and public health. Accurate prediction of air pollution is crucial for effective prevention and control measures. However, due to regional variations, different cities may have varying primary pollutants, posing new challenges for accurate prediction. In this paper, we propose a novel method called FP-RF, which integrates clustering algorithms to categorize multiple cities according to their air quality index values. Subsequently, we apply functional principal component analysis to extract the primary components of air pollution within each cluster. Furthermore, an enhanced random forest algorithm is utilized to predict air quality grades for each city. We conduct experimental evaluations using authentic historical data from Anhui Province spanning from 2018 to 2023. The results unequivocally establish the effectiveness of our model, with an average accuracy rate of 98.6% in forecasting six pollution grades and 96.04% accuracy in predicting 16 prefecture-level cities, surpassing performance compared to other baseline models.

Hyper-local black carbon prediction by integrating land use variables with explainable machine learning model

This research introduces an innovative framework that effectively integrates the Land Use Regression (LUR) model with an explainable machine learning methodology, thereby enhancing the causal understanding of how land use variables impact Black Carbon (BC) concentrations in complex urban landscapes. Through the development and application of a LUR-based Bayesian Network (BN) model to detailed, mobile-based BC measurements, we have successfully decoded the causal relationships and conditional probabilities linking diverse urban land use variables to BC levels. The BN model demonstrates notable prediction accuracy on the test set, with an R2 of 0.59 and an NRMSE of 0.65. Among the varied land use variables analyzed, proximity to the port is identified as the dominant factor contributing to BC hotspots. Moreover, the synergistic effect of port proximity and truck routes emerges as a key driver of spatially clustered BC hotspots, a complex phenomenon not adequately captured by conventional machine learning and deep learning models. The insights revealed through this study are vital not just for enhancing air pollution prediction and exposure analysis but also for informing urban planning and policymaking, offering valuable, actionable insights.

Investigation of models predicting NOx level in the sample region and the use of intelligent transportation system

Artificial intelligence (AI), unlike natural intelligence, possesses the ability to problem-solving activities by machines. As AI-based models increasingly provide robust approaches to predicting air pollution, they are becoming more widespread. Intelligent transportation systems (ITS) are poised to be significant solutions for sustainable mobility. These systems, by appropriately enhancing mobility, will prevent the concentration of air pollution in a region through transportation. This study aims to examine AI-based models used in air pollution prediction and demonstrate the effectiveness of intelligent transportation systems in improving transportation-related air pollution. As a sample region, Kocaeli Province, which has highly polluted air, the amounts of transportation-related NOx pollutants emitted from light and heavy vehicles passing through the Dilovası district were modeled using Adaptive Neuro-Fuzzy (ANFIS) and Artificial Neural Networks (ANN). The results were compared with the outputs of the Calculations of Emissions from Road Transport (COPERT4) program. The evaluations revealed that ANFIS performed better in modeling NOx pollutants. Based on the prediction results, in case of exceeding the NOx limit, an intelligent transportation system redirecting vehicles to alternative routes was suggested. For the use of this system, scenarios proposing the redirection of cars in varying proportions, including single-plate, double-plate, and light vehicles, depending on route redirection, were proposed and evaluated. The evaluation of scenario results showed that redirecting a large number of cars to alternative routes with the assistance of ITS resulted in a significant decrease in emissions.

Predicting PM2.5 concentrations in Ba Ria - Vung Tau province using an artificial neural network model

Abstract Air pollution has increased rapidly and significantly recently, particularly in big cities. Various methods for predicting air pollution are available, including traditional air quality models, statistical techniques, and artificial intelligence. In this study, the author developed a model using a Feed Forward Neural Network with multivariate statistical methods to predict air pollution. Data from three automated air monitoring locations in Ba Ria-Vung Tau province were gathered between 2020 and 2022 to forecast the concentration of PM2.5. The results demonstrated that the FFNN model with an I(6)-HL(5)-O(1) structure outperformed other models in predicting PM2.5 concentration. The training, validation, and testing phases yielded mean squared error values of 9.2×10−6, 8.2×10−6, and 8.6×10−6, respectively. The regression coefficient obtained consistently high values across a range of experiments (above 0.99). The MSE value of the FFNN model of the prediction set was lower than that of the NSE value, which was higher than those obtained from the multiple linear regression.

Hga-lstm: LSTM architecture and hyperparameter search by hybrid GA for air pollution prediction

Hga-lstm: LSTM architecture and hyperparameter search by hybrid GA for air pollution prediction