Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

Effect of traffic dataset on various machine-learning algorithms when forecasting air quality

© Emerald Publishing Limited. This is the accepted manuscript version of an article which has been published in final form at https://10.1108/JEDT-10-2021-0554Purpose (limit 100 words) Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic datasets on air quality predictions has not been clearly investigated. This research investigates the effects traffic dataset have on the performance of Machine Learning (ML) predictive models in air quality prediction. Design/methodology/approach (limit 100 words) To achieve this, we have set up an experiment with the control dataset having only the Air Quality (AQ) dataset and Meteorological (Met) dataset. While the experimental dataset is made up of the AQ dataset, Met dataset and Traffic dataset. Several ML models (such as Extra Trees Regressor, eXtreme Gradient Boosting Regressor, Random Forest Regressor, K-Neighbors Regressor, and five others) were trained, tested, and compared on these individual combinations of datasets to predict the volume of PM2.5, PM10, NO2, and O3 in the atmosphere at various time of the day. Findings (limit 100 words) The result obtained showed that various ML algorithms react differently to the traffic dataset despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%. Research limitations/implications (limit 100 words) This research is limited in terms of the study area and the result cannot be generalized outside of the UK as many conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research. Therefore, leaving out a few other ML algorithms. Practical implications (limit 100 words) This study reinforces the belief that the traffic dataset has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form traffic dataset in the development of an air quality prediction model. This implies that developers and researchers in air quality prediction need to identify the ML algorithms that behave in their best interest before implementation. Originality/value (limit 100 words) This will enable researchers to focus more on algorithms of benefit when using traffic datasets in air quality prediction.Peer reviewe

Prediction of PM10 concentrations using Fuzzy c-Means and ANN

Salamanca has been considered among the most polluted cities in Mexico. The vehicular park, the industry and the emissions produced by agriculture, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Particulate Matter less than 10 μg/m3 in diameter (PM10). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables (wind speed, wind direction, temperature and relative humidity) and air pollutant concentrations of PM10. Before the prediction, Fuzzy c-Means clustering algorithm have been implemented in order to find relationship among pollutant and meteorological variables. These relationship help us to get additional information that will be used for predicting. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of PM10 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results shown that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hour

Performance Comparison Between HMLP, MLP And Recurrent Networks With Applications To Carbon Monoxide Concentrations Forecasting.

This paper compares the performance of Hybrid Multilayered Perceptron (HMLP) network, Multilayered Perceptron (MLP) network and Recurrent network. These networks are used to model and forecast carbon monoxide (CO) concentration

A Review of 21st-Century Studies

PM10 prediction has attracted special legislative and scientific attention due to its harmful effects on human health. Statistical techniques have the potential for high-accuracy PM10 prediction and accordingly, previous studies on statistical methods for temporal, spatial and spatio-temporal prediction of PM10 are reviewed and discussed in this paper. A review of previous studies demonstrates that Support Vector Machines, Artificial Neural Networks and hybrid techniques show promise for suitable temporal PM10 prediction. A review of the spatial predictions of PM10 shows that the LUR (Land Use Regression) approach has been successfully utilized for spatial prediction of PM10 in urban areas. Of the six introduced approaches for spatio-temporal prediction of PM10, only one approach is suitable for high-resolved prediction (Spatial resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon the LUR modeling method, short-term dynamic input variables are employed as explanatory variables alongside typical non-dynamic input variables in a non- linear modeling procedure

Model Prediction Of Pm2.5 And Pm10 Using Machine Learning Approach

This study was done to develop a multi-input-single-output (MISO) and multi-input-multi-output (MIMO) models using an artificial neural network by MATLAB software to predict the concentrations of PM2.5 and PM10 respectively based on meteorological parameters. For the purpose of this research, the historical dataset is obtained from the Beijing Municipal Environmental Monitoring Centre to be used as the case study. The model was developed as a generic use where data pre-processing using two separate methods of calculating a correlation coefficient and variable importance in projection (VIP) scores managed to select significant input toward output for model development. Both methods of feature selection produced similar results where gaseous pollutants of Carbon Monoxide (CO), Nitrogen Dioxide (NO2) and Sulfur Dioxide (SO2) demonstrated the highest correlation towards the output target. Based on the feature selection, model development was built with and without input selection using the Nonlinear Autoregressive with Exogeneous Input (NARX) neural network model which made use of 10 number of hidden neurons and 2 number of delays, implementing Levenberg-Marquardt as training algorithm. The performance of the prediction model was evaluated by measuring Means Square Error (MSE), Root Mean Square Error (RMSE), Regression Number (R), and Coefficient of Determination (R2) values as a performance validation. Models developed with and without input selections were studied and compared where MISO Model 1, without input selection obtained the best performance having MSE, RMSE, R and R2 with values of 0.0594, 0.2437, 0.9704 and 0.9417 respectively for testing. Meanwhile, with input selection the values obtained 0.0589, 0.2428, 0.9709 and 0.9427. It was found that taking into account the removal of the irrelevant variables does not increase precision significantly nor does it reduce the performance tremendously. Instead, knowing the key parameters with the most relation with PM2.5 and PM10 would guarantee a better predicament of the concentration. Prediction of PM2.5 and PM10 concentration using machine learning is achieved and useful not only to improve public awareness but the air quality management in Malaysia as well as other parts of the world

Improving the prediction of air pollution peak episodes generated by urban transport networks

This paper illustrates the early results of ongoing research developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope is to integrate the new models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas.This first stage concerns the relationship between the hourly mean concentration of nitrogen dioxide (NO2) and explanatory factors reflecting the NO2 mean level one hour back, along with traffic and weather conditions. Particular attention is given to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two model frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated.The analysis of findings points out that the prediction of extreme concentrations is best performed by integrating the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the concentration's dependency upon wind characteristics. So, the Neural Network can be exploited to highlight the involved functional forms and improve the ARIMAX model specification. In the end, the study shows that the ability to forecast exceedances of legal pollution limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a lower threshold than the normative one