Machine learning algorithms to forecast air quality: a survey

Exploring multivariate machine learning frameworks to parallelize PM2.5 simultaneous estimations across the continental United States

Fine particulate matter (PM2.5) comprises diverse chemical components, including elemental carbon (EC), silicon (SI), sulfate (SO4), and calcium (CA), each linked to varied health and environmental impacts. Accurately estimating these components' spatial and temporal distributions is crucial for regulatory policies and public health. This study developed and evaluated multivariate machine learning models, including Random Forest (RF) and XGBoost (XGB), to estimate daily concentrations of EC, SI, SO4, and CA across the contiguous United States from 2000 to 2019. Unlike traditional univariate approaches, multivariate models capture interdependencies among components, improving accuracy and efficiency. Using data from 534 monitoring sites and 187 predictor variables derived from satellite observations, reanalysis datasets, and geographical sources, we implemented univariate and multivariate RF and XGB models (MRF and MXGBoost). Performance was assessed using R-squared metrics, and feature importance was evaluated with SHAP values. MXGBoost outperformed other models, achieving R2 values of 70.2 % for EC, 79.23 % for SO4, 61.57 % for SI, and 59.5 % for CA, with spatial R2 exceeding 93 % and temporal R2 as high as 82.23 % for SO4. Key predictors included wind speed, relative humidity, and aerosol optical depth. The findings highlight the advantages of multivariate modeling in capturing the interdependencies among PM2.5 components, resulting in improved estimation accuracy and computational efficiency. This approach offers valuable applications in air quality management and public health, emphasizing the need to refine multivariate frameworks and explore their applicability to other pollutants.

Design of machine learning-based controllers for speed control of PMSM drive

This study presents machine learning (ML)-based controllers for a surface permanent magnet synchronous motor (PMSM) drive system. The ML-based regression techniques like linear regression (LR), support vector machine regression (SVM), feedforward neural network (NN) and advanced NN like Long Short-Term Memory network (LSTM) are explored here in detail. This paper aims to develop an improved vector controller based on machine learning, and to investigate ML algorithms which are not yet been explored for the current control of a PMSM drive. The proposed machine learning-based control approach, which explores the influence of decoupling terms on vector control, is theoretically investigated and simulated in the vector control environment of the PMSM drive. The performance is also evaluated in real-time using the Opal-RT setup. The proposed control approach demonstrates the ability to fulfill the speed tracking requirements in the closed-loop drive system. A comparison of the simulation results between the PI controller and the suggested control algorithms validates the effectiveness of the proposed control algorithms for speed control applications. The performances of the proposed ML-based controllers improved in terms of evaluation metrics, transient peak levels and current responses, when compared to the conventional PI controller.

Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution

Accurate source apportionment of particulate matter (PM), especially of organic aerosol (OA), is crucial for targeted mitigation efforts. Positive Matrix Factorization (PMF) is powerful in source attribution of primary OA (POA); however, it often struggles to differentiate sources of oxygenated OA (OOA) due to their similar chemical profiles. In this study, a support vector regression machine learning (ML) model was developed to enhance the OOA source apportionment in Dublin from 2016 to 2023. Rolling PMF analysis identified four POA factors and differentiated OOA into less- and more-oxidized (LO-OOA and MO-OOA), highlighting the significant role of the OOA (47-74% of total OA). The ML model further distinguished locally produced OOA (LO-OOAlocal and MO-OOAlocal) from transboundary transport OOA and exhibited robust performance across different pollution scenarios. The relative importance analysis revealed that LO-OOAlocal was more impacted by fossil fuel emissions like hydrocarbon-like OA (20%) and coal (14%), whereas MO-OOAlocal was most influenced by LO-OOA (17%), providing insights into their sources and formation mechanisms. During a mixed pollution episode, the results show that despite the significant contribution of transboundary transport, local heating emissions were more critical sources of OA, with local OA accounting for 68% of total OA and reaching 78% during heating hours. These findings highlight the ongoing need to reduce local emissions to achieve cleaner air in Dublin. The ML model's ability to quantitatively separate local and transboundary OOA offers invaluable insights for future air quality regulations.

Assessing the Impact of Aviation Emissions on Air Quality at a Regional Greek Airport Using Machine Learning

Aviation emissions significantly impact air quality, contributing to environmental degradation and public health risks. This study aims to assess the impact of aviation-related emissions on air quality at Alexandroupolis Regional Airport, Greece, and evaluate the role of meteorological factors in pollution dispersion. Using machine learning models, we analyzed emissions data, including CO2, NOx, CO, HC, SOx, PM2.5, fuel consumption, and meteorological parameters from 2019-2020. Results indicate that NOx and CO2 emissions showed the highest correlation with air traffic volume and fuel consumption (R = 0.63 and 0.67, respectively). Bayesian Linear Regression and Linear Regression emerged as the most accurate models, achieving an R2 value of 0.96 and 0.97, respectively, for predicting PM2.5 concentrations. Meteorological factors had a moderate influence, with precipitation negatively correlated with PM2.5 (-0.03), while temperature and wind speed showed limited effects on emissions. A significant decline in aviation emissions was observed in 2020, with CO2 emissions decreasing by 28.1%, NOx by 26.5%, and PM2.5 by 35.4% compared to 2019, reflecting the impact of COVID-19 travel restrictions. Carbon dioxide had the most extensive percentage distribution, accounting for 75.5% of total emissions, followed by fuels, which accounted for 24%, and the remaining pollutants, such as NOx, CO, HC, SOx, and PM2.5, had more minor impacts. These findings highlight the need for optimized air quality management at regional airports, integrating machine learning for predictive monitoring and supporting policy interventions to mitigate aviation-related pollution.

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.

Enhancing urban air quality prediction using time-based-spatial forecasting framework

Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model's ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.

Application of the Lasso regularisation technique in mitigating overfitting in air quality prediction models

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM2.5 and PM10), CO, NO2, SO2, and O3 while decreasing overfitting. The outputs were compared using the R-squared (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and normalised mean square error (NMSE) indices. Despite the preliminary findings revealing that Lasso dramatically enhances model reliability by decreasing overfitting and determining key attributes, the model's performance in predicting gaseous pollutants against PM remained unsatisfactory (R2PM2.5 = 0.80, R2PM10 = 0.75, R2CO = 0.45, R2NO2 = 0.55, R2SO2 = 0.65, and R2O3 = 0.35). The minimal degree of missing data presumably explained the strong performance of the PM model, while the high dynamism of gases and their chemical interactions, in conjunction with the inherent characteristics of the model, were the primary factors contributing to the poor performance of the model. Simultaneously, the successful implementation of the Lasso regularisation approach in mitigating overfitting and selecting more important features makes it highly suggested for application in air quality forecasting models.

Predictive modeling of diazinon residual concentration in soils contaminated with potentially toxic elements: a comparative study of machine learning approaches

The widespread use of pesticides, including diazinon, poses an increased risk of environmental pollution and detrimental effects on biodiversity, food security, and water resources. In this study, we investigated the impact of Potentially Toxic Elements (PTE) including Zn, Cd, V, and Mn on the degradation of diazinon in three different soils. We investigated the capability and performance of four machine learning models to predict residual pesticide concentration, including adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), radial basis function (RBF), and multi-layer perceptron (MLP). We employed a 10-fold cross-validation mechanism to evaluate the models. Moreover, performance validation of selected algorithms through the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and mean square error (MSE) confirm that the SVR and ANFIS with lower RMSE, MSE, and a higher R2 can simulate the degradation process better than other models. The result showed that both SVR and ANFIS approaches worked well for the data set, but the SVR technique is more accurate than the fuzzy model for estimating pesticide concentration in soil in the presence of PTE. Vanadium appeared to be the best option for the degradation of diazinon. The models predicted the performance of V2+ for diazinon degradation with R2 and RMSE of 0.99 and 2.18 m g . k g - 1 for SVR and, 0.99, and 1.30 for the ANFIS model for the training set. Finally, the high accuracy of the models was confirmed.

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China

Atmospheric ozone (O3) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological conditions, plateau regions contain high concentrations of atmospheric O3. However, traditional experimental methods for determining O3 concentrations using automatic monitoring stations cannot predict O3 trends. In this study, two machine learning models (a nonlinear auto-regressive model with external inputs (NARX) and a temporal convolution network (TCN)) were developed to predict O3 concentrations in a plateau area in the Kunming region by considering the effects of meteorological parameters, air quality parameters, and volatile organic compounds (VOCs). The plateau O3 prediction accuracy of the machine learning models was found to be much higher than those of numerical models that served as a comparison. The O3 values predicted by the machine learning models closely matched the actual monitoring data. The temporal distribution of plateau O3 displayed a high all-day peak from February to May. A correlation analysis between O3 concentrations and feature parameters demonstrated that humidity is the feature with the highest absolute correlation (-0.72), and was negatively correlated with O3 concentrations during all test periods. VOCs and temperatures were also found to have high positive correlation coefficients with O3 during periods of significant O3 pollution. After negating the effects of meteorological parameters, the predicted O3 concentrations decreased significantly, whereas they increased in the absence of NOx. Although individual VOCs were found to greatly affect the O3 concentration, the total VOC (TVOC) concentration had a relatively small effect. The proposed machine learning model was demonstrated to predict plateau O3 concentrations and distinguish how different features affect O3 variations.

Exploring action-law of microplastic abundance variation in river waters at coastal regions of China based on machine learning prediction

Surface waters, particularly the river systems, constitute a vital freshwater resource for human beings and aquatic life on Earth. In economically developed and densely populated coastal regions, river water is facing severe microplastic pollution, posing a threat to public health and ecological safety. Reliable prediction of microplastic abundance (MPA) can significantly reduce the costs associated with microplastic field sampling and analysis. This study employed spatial correlation, geographical detector, principal component analysis and five mainstream machine learning models to analyze 79 datasets of MPAs in seven coastal areas of China and performed correlation, regression and attribution analyses based on 19 terrestrial influencing factors that potentially affect the MPA life cycle processes (generation, aging, and migration). The results showed that the Neural Network (NN) and the Gaussian Process Regression (GPR) models achieved the best prediction performance, with the predicted R2 close to 1. Principal component analysis and Shapley additive explanations concluded that meteorological factors, in particular the annual geotemperature, surface solar radiation, and annual relative humidity, had a key influence on the aging of microplastics. The second key factor in improving the MPA prediction ability was the dynamic description of microplastic migration, which was primarily governed by hydrological factors such as annual precipitation and average terrain slope. Unexpectedly, the effects of land use and level of urbanization were relatively small in describing the generation of microplastics. Only the percentage of built areas was strongly correlated with the MPA levels. Note that the MPA prediction and its contribution factors may vary across different basins. Nevertheless, the findings of this study are applicable to predicting and analyzing the distribution of microplastics in other coastal rivers, and for indicating the main contributing factors, ultimately serving as a basis for guiding microplastic pollution control strategies in different river basins.

Advances and future directions of environmental risk research: A bibliometric review

Environmental risk is one of the world's most significant threats, projected to be the leading risk over the next decade. It has garnered global attention due to increasingly severe environmental issues, such as climate change and ecosystem degradation. Research and technology on environmental risks are gradually developing, and the scope of environmental risk study is also expanding. Here, we developed a tailored bibliometric method, incorporating co-occurrence network analysis, cluster analysis, trend factor analysis, patent primary path analysis, and patent map methods, to explore the status, hotspots, and trends of environment risk research over the past three decades. According to the bibliometric results, the publications and patents related to environmental risk have reached explosive growth since 2018. The primary topics in environmental risk research mainly involve (a) ecotoxicology risk of emerging contaminants (ECs), (b) environmental risk induced by climate change, (c) air pollution and health risk assessment, (d) soil contamination and risk prevention, and (e) environmental risk of heavy metal. Recently, the hotspots of this field have shifted into artificial intelligence (AI) based techniques and environmental risk of climate change and ECs. More research is needed to assess ecological and health risk of ECs, to formulize mitigation and adaptation strategies for climate change risks, and to develop AI-based environmental risk assessment and control technology. This study provides the first comprehensive overview of recent advances in environmental risk research, suggesting future research directions based on current understanding and limitations.

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists. In addressing this challenge, deep learning models have garnered substantial attention in research due to their outstanding performance across diverse applications. However, the optimization of hyperparameters and features remains a challenging task. This study seeks to leverage historical data to construct the long short-term memory-based model for forecasting multistep PM10. To refine its architecture, a modified genetic algorithm is employed for automatic design. Furthermore, we explore principal component analysis and exhaustive feature selection to identify the optimal feature set. This paper introduces a novel hybrid deep learning model named EFS-GA-LSTM, tailored for multistep hourly PM10 forecasting. To assess its performance, we compare it with other hyperparameter optimization algorithms, including particle swarm optimization, variable neighborhood search, and Bayesian optimization with Gaussian process. The input dataset comprises hourly PM10 concentrations, meteorological variables, and time variables. The results reveal that for 3-h-ahead forecasting tasks, the EFS-GA-LSTM network demonstrates improvements in root mean square error, mean absolute percentage error, correlation coefficient, and coefficient of determination.

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.

Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment

Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

Probabilistic classification of the severity classes of unhealthy air pollution events

Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events will exert a disastrous effect on the environment. Therefore, public health and policy-making authorities must be able to determine the characteristics of these events. This study proposes a probabilistic machine learning technique for predicting the classification of extreme and non-extreme events on the basis of data features to address the above issue. The use of the naïve Bayes model in the prediction of air pollution classes is proposed to leverage its simplicity as well as high accuracy and efficiency. A case study was conducted on the air pollution index data of Klang, Malaysia, for the period of January 01, 1997, to August 31, 2020. The trained naïve Bayes model achieves high accuracy, sensitivity, and specificity on the training and test datasets. Therefore, the naïve Bayes model can be easily applied in air pollution analysis while providing a promising solution for the accurate and efficient prediction of extreme or non-extreme air pollution events. The findings of this study provide reliable information to public authorities for monitoring and managing sustainable air quality over time.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Constructing transferable and interpretable machine learning models for black carbon concentrations

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Applications of remote sensing vis-à-vis machine learning in air quality monitoring and modelling: a review

Environmental contamination especially air pollution is an exponentially growing menace requiring immediate attention, as it lingers on with the associated risks of health, economic and ecological crisis. The special focus of this study is on the advances in Air Quality (AQ) monitoring using modern sensors, integrated monitoring systems, remote sensing and the usage of Machine Learning (ML), Deep Learning (DL) algorithms, artificial neural networks, recent computational techniques, hybridizing techniques and different platforms available for AQ modelling. The modern world is data-driven, where critical decisions are taken based on the available and accessible data. Today's data analytics is a consequence of the information explosion we have reached. The current research also tends to re-evaluate its scope with data analytics. The emergence of artificial intelligence and machine learning in the research scenario has radically changed the methodologies and approaches of modern research. The aim of this review is to assess the impact of data analytics such as ML/DL frameworks, data integration techniques, advanced statistical modelling, cloud computing platforms and constantly improving optimization algorithms on AQ research. The usage of remote sensing in AQ monitoring along with providing enormous datasets is constantly filling the spatial gaps of ground stations, as the long-term air pollutant dynamics is best captured by the panoramic view of satellites. Remote sensing coupled with the techniques of ML/DL has the most impact in shaping the modern trends in AQ research. Current standing of research in this field, emerging trends and future scope are also discussed.

Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic

BACKGROUND

A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic.

RESULTS

The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity.

CONCLUSIONS

The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.