An ensemble learning based hybrid model and framework for air pollution forecasting

Development of an automated photolysis rates prediction system based on machine learning

Based on observed meteorological elements, photolysis rates (J-values) and pollutant concentrations, an automated J-values predicting system by machine learning (J-ML) has been developed to reproduce and predict the J-values of O1D, NO2, HONO, H2O2, HCHO, and NO3, which are the crucial values for the prediction of the atmospheric oxidation capacity (AOC) and secondary pollutant concentrations such as ozone (O3), secondary organic aerosols (SOA). The J-ML can self-select the optimal "Model + Hyperparameters" without human interference. The evaluated results showed that the J-ML had a good performance to reproduce the J-values where most of the correlation (R) coefficients exceed 0.93 and the accuracy (P) values are in the range of 0.68-0.83, comparing with the J-values from observations and from the tropospheric ultraviolet and visible (TUV) radiation model in Beijing, Chengdu, Guangzhou and Shanghai, China. The hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days, respectively. Compared with O3 concentrations by using J-values from the TUV model, an emission-driven observation-based model (e-OBM) by using the J-values from the J-ML showed a 4%-12% increase in R and 4%-30% decrease in ME, indicating that the J-ML could be used as an excellent supplement to traditional numerical models. The feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values, and the other dominant factors for all J-values were 2-m mean temperature, O3, total cloud cover, boundary layer height, relative humidity and surface pressure.

Quantifying and Forecasting Emission Reductions in Urban Mobility: An IoT-Driven Bike-Sharing Analysis

The growing urgency to address urban air quality and climate change has intensified the need for sustainable mobility solutions that mitigate vehicular emissions. Bike-sharing systems (BSSs) represent a viable alternative; however, their precise environmental impact remains insufficiently explored. This study quantifies and forecasts reductions in CO2 and NOx emissions resulting from BSS usage in Madrid by integrating real-time IoT sensor data with an advanced predictive model. The proposed framework effectively captures nonlinear and seasonal mobility and emission patterns, achieving high predictive accuracy while demonstrating significant energy savings. These findings confirm the environmental benefits of BSSs and provide urban planners and policymakers with a robust tool to extend and replicate this analysis in other cities, fostering sustainable urban mobility and improved air quality.

Forecasting the concentration of the components of the particulate matter in Poland using neural networks

Air pollution is a significant global challenge with profound impacts on human health and the environment. Elevated concentrations of various air pollutants contribute to numerous premature deaths each year. In Europe, and particularly in Poland, air quality remains a critical concern due to pollutants such as particulate matter (PM), which pose serious risks to public health and ecological systems. Effective control of PM emissions and accurate forecasting of their concentrations are essential for improving air quality and supporting public health interventions. This paper presents four advanced deep learning-based forecasting methods: extended long short-term memory network (xLSTM), Kolmogorov-Arnold network (KAN), temporal convolutional network (TCN), and variational autoencoder (VAE). Using data from eight cities in Poland, we evaluate our methods' ability to predict particulate matter concentrations through extensive experiments, utilizing statistical hypothesis testing and error metrics such as mean absolute error (MAE) and root mean square error (RMSE). Our findings demonstrate that these methods achieve high prediction accuracy, significantly outperforming several state-of-the-art algorithms. The proposed forecasting framework offers practical applications for policymakers and public health officials by enabling timely interventions to decrease pollution impacts and enhance urban air quality management.

Deep-learning architecture for PM2.5 concentration prediction: A review

Accurately predicting the concentration of fine particulate matter (PM2.5) is crucial for evaluating air pollution levels and public exposure. Recent advancements have seen a significant rise in using deep learning (DL) models for forecasting PM2.5 concentrations. Nonetheless, there is a lack of unified and standardized frameworks for assessing the performance of DL-based PM2.5 prediction models. Here we extensively reviewed those DL-based hybrid models for forecasting PM2.5 levels according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We examined the similarities and differences among various DL models in predicting PM2.5 by comparing their complexity and effectiveness. We categorized PM2.5 DL methodologies into seven types based on performance and application conditions, including four types of DL-based models and three types of hybrid learning models. Our research indicates that established deep learning architectures are commonly used and respected for their efficiency. However, many of these models often fall short in terms of innovation and interpretability. Conversely, models hybrid with traditional approaches, like deterministic and statistical models, exhibit high interpretability but compromise on accuracy and speed. Besides, hybrid DL models, representing the pinnacle of innovation among the studied models, encounter issues with interpretability. We introduce a novel three-dimensional evaluation framework, i.e., Dataset-Method-Experiment Standard (DMES) to unify and standardize the evaluation for PM2.5 predictions using DL models. This review provides a framework for future evaluations of DL-based models, which could inspire researchers to standardize DL model usage in PM2.5 prediction and improve the quality of related studies.

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning

Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.

Predicting ambient PM2.5 concentrations via time series models in Anhui Province, China

Due to rapid expansion in the global economy and industrialization, PM2.5 (particles smaller than 2.5 µm in aerodynamic diameter) pollution has become a key environmental issue. The public health and social development directly affected by high PM2.5 levels. In this paper, ambient PM2.5 concentrations along with meteorological data are forecasted using time series models, including random forest (RF), prophet forecasting model (PFM), and autoregressive integrated moving average (ARIMA) in Anhui province, China. The results indicate that the RF model outperformed the PFM and ARIMA in the prediction of PM2.5 concentrations, with cross-validation coefficients of determination R2, RMSE, and MAE values of 0.83, 10.39 µg/m3, and 6.83 µg/m3, respectively. PFM achieved the average results (R2 = 0.71, RMSE = 13.90 µg/m3, and MAE = 9.05 µg/m3), while the predicted results by ARIMA are comparatively poorer (R2 = 0.64, RMSE = 15.85 µg/m3, and MAE = 10.59 µg/m3) than RF and PFM. These findings reveal that the RF model is the most effective method for predicting PM2.5 and can be applied to other regions for new findings.

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability

Gas breakthrough pressure is a significant parameter for the gas exploration and safety evaluation of engineering barrier systems in the carbon dioxide storage, remediation of contaminated sites, and deep geological repository for disposal of high-level nuclear waste, etc. Test for determining gas breakthrough pressure is very difficult and time-consuming, due to the low/ultra-low conductivity of the specimen. It is also difficult to get a comprehensive and high-precision model based on limited results obtained through individual experiments, as the measurements of gas breakthrough pressure were influenced by many factors. In this study, a collected database was built that covered a lot of former test data, and then, two models were developed by the random forest (RF) algorithm and multiexpression programming (MEP) method. The MEP model constructed with explicit expressions for the gas breakthrough pressure overcame the drawbacks of common "black box" models. Meanwhile, five significant indicators were selected from ten common features using the permutation importance algorithm. The RF model was interpreted by the Shapley value and the PDP/ICE plots, while the MEP model was analyzed through the proposed explicit expression, showing strong consistence with that in former studies. Finally, robustness analysis was conducted, and stability of the proposed two models was verified.

Dynamic pollution emission prediction method of a combined heat and power system based on the hybrid CNN-LSTM model and attention mechanism

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Background

Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods

All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results

Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion

Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

Prediction of Phase State of Secondary Organic Aerosol Internally Mixed with Aqueous Inorganic Salts

In the presence of inorganic salts, secondary organic aerosol (SOA) undergoes liquid-liquid phase separation (LLPS), liquid-solid phase separation, or a homogeneous phase in ambient air. In this study, a regression model was derived to predict aerosol phase separation relative humidity (SRH) for various organic and inorganic mixes. The model implemented organic physicochemical parameters (i.e., oxygen to carbon ratio, molecular weight, and hydrogen-bonding ability) and the parameters related to inorganic compositions (i.e., ammonium, sulfate, nitrate, and water). The aerosol phase data were observed using an optical microscope and also collected from the literature. The crystallization of aerosols at the effloresce RH (ERH) was semiempirically predicted with a neural network model. Overall, the greater SRH appeared for the organic compounds with the lower oxygen to carbon ratios or the greater molecular weight and the higher aerosol acidity or the larger fraction of inorganic nitrate led to the lower SRH. The resulting model has been demonstrated for three different chamber-generated SOA (originated from β-pinene, toluene, and 1,3,5-trimethylbenzene), which were internally mixed with the inorganic aqueous system of ammonium-sulfate-water. For all three SOA systems, both observations and model predictions showed LLPS at RH <80%. In the urban atmosphere, LLPS is likely a frequent occurrence for the typical anthropogenic SOA, which originates from aromatic and alkane hydrocarbon.

The Process and Platform for Predicting PM2.5 Inhalation and Retention during Exercise

In recent years, people have been increasingly concerned about air quality and pollution since a number of studies have proved that air pollution, especially PM2.5 (particulate matter), can affect human health drastically. Though the research on air quality prediction has become a mainstream research field, most of the studies focused only on the prediction of urban air quality and pollution. These studies did not predict the actual impact of these pollutants on people. According to the researchers’ best knowledge, the amount of polluted air inhaled by people and the amount of polluted air that remains inside their body are two important factors that affect their health. In order to predict the quantity of PM2.5 inhaled by people and what they have retained in their body, a process and a platform have been proposed in the current research work. In this research, the experimental process is as follows: (1) First, a personalized PM2.5 sensor is designed and developed to sense the quantity of PM2.5 around people. (2) Then, the Bruce protocol is applied to collect the information and calculate the relationship between heart rate and air intake under different activities. (3) The amount of PM2.5 retained in the body is calculated in this step using the International Commission on Radiological Protection (ICRP) air particle retention formula. (4) Then, a cloud platform is designed to collect people’s heart rate under different activities and PM2.5 values at respective times. (5) Finally, an APP is developed to show the daily intake of PM2.5. The result reveals that the developed app can show a person’s daily PM2.5 intake and retention in a specific population.

Modeling and forecasting of monthly PM2.5 emission of Paris by periodogram-based time series methodology

In this study, monthly particulate matter (PM_2.5) of Paris for the period between January 2000 and December 2019 is investigated by utilizing a periodogram-based time series methodology. The main contribution of the study is modeling the PM_2.5 of Paris by extracting the information purely from the examined time series data, where proposed model implicitly captures the effects of other factors, as all their periodic and seasonal effects reside in the air pollution data. Periodicity can be defined as the patterns embedded in the data other than seasonality, and it is crucial to understand the underlying periodic dynamics of air pollutants to better fight pollution. The method we use successfully captures and accounts for the periodicities, which could otherwise be mixed with seasonality under an alternative methodology. Upon the unit root test based on periodograms, it is revealed that the investigated data has periodicities of 1 year and 20 years, so harmonic regression is utilized as an alternative to Box-Jenkins methodology. As the harmonic regression displayed a better performance both in and out-of-sample forecasts, it can be considered as a powerful alternative to model and forecast time series with a periodic structure.

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.

Short-term prediction of urban PM2.5 based on a hybrid modified variational mode decomposition and support vector regression model

PM_2.5 (particulate matter with a size/diameter ≤ 2.5 μm) is an important air pollutant that affects human health, especially in urban environments. However, as time-series data of PM_2.5 are non-linear and non-stationary, it is difficult to predict future PM_2.5 distribution and behavior. Therefore, in this paper, we propose a hybrid short-term urban PM_2.5 prediction model based on variational mode decomposition modified by the correntropy criterion, the state transition simulated annealing (STASA) algorithm, and a support vector regression model to overcome the disadvantages of traditional forecasting techniques which consider different environmental factors. Two experiments were performed with the model to assess its effectiveness and predictive ability: in experiment I, we verified the performance of STASA on benchmark functions, while in experiment II, we used PM_2.5 data from different epochs and regions of Beijing to verify its forecasting performance. The experimental results showed that the proposed model is robust and can achieve satisfactory prediction results under different conditions compared with current forecasting techniques.