APPLICATION OF MACHINE LEARNING TO FILL IN THE MISSING MONITORING DATA OF AIR QUALITY

In this paper, three machine learning models have been applied to predict and fill in the missing monitoring data of air quality for Gia Lam and Nha Trang stations in Hanoi and Khanh Hoa respectively, including Autoregressive Moving Average (ARMA), Artificial Neural Network (ANN), and Support Vector Regression (SVR). Two air pollutants being NO2 and PM10 were selected for this study. The experimental results showed that the performance of all three studied models is better than that of some traditional approaches, including Multiple Linear Regression (LR) and Spline interpolation. Besides that, ARMA, ANN and SVR can capture the fluctuation of concentrations of the selected pollutants. These results indicated that the machine learning is a feasible approach to deal with the missing of data which is one of the biggest problems of air quality monitoring stations in Viet Nam.

A comparison of forecasting the results of road transportation needs

Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winter's method for seasonal problems – a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error). The results of calculations were compared and the best example was presented

Assessing Atmospheric Pollution and Its Impacts on the Human Health

This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

Spatial and Temporal Analysis of Big Dataset on PM2.5 Air Pollution in Beijing, China, 2014 to 2018

Air particulate matter (PM2.5) pollution is a critical environment problem worldwide and also in Beijing, China. We gathered five-year PM2.5 contaminate concentrations from 2014 to 2018, from the Beijing Municipal Environmental Monitoring Center and China Air Quality Real-time Distribution Platform. This is a big dataset, and we collected with crawler technology from Python programming. After examining the quality of the recorded data, we determined to conduct the temporal and spatial analysis using 27 observation stations located in both urban and suburb area in the municipality of Beijing. The big dataset of five-year hourly PM2.5 concentrations was sorted to actionable datasets (Selected Datasets and Seasonal Average Selected Datasets) with the help of Python programming. Linear Regression based Fundamental Data Analysis was conducted as the first part of temporal analysis in R studio to gather the temporal patterns of five-year seasonal PM2.5 contaminant concentrations on each observation sites. As the second part of temporal analysis, the Principal Component Analysis (PCA) was conducted in MATLAB to gather the patterns of variations of entire five-year PM2.5 contaminant concentration on each of the sites. Geographic Information System (GIS) was utilized to study the spatial pattern of air pollution distribution from the selected 27 observation sites during selected time periods. The results of this research are, 1) PM2.5 pollutions in winter are the most severe or the highest in each of the natural years. 2) PM2.5 pollution concentrations in Beijing were gradually decrease during 2014 to 2018. 3) In terms of a five-year time perspective, the improvements of air quality and reduction of PM2.5 contaminant appeared in all the seasons based on Fundamental Data Analysis. 4) PM2.5 contaminant concentrations in summer are significantly less than other seasons. 5) The least PM2.5 pollutant influenced area is north and northwest regions in Beijing, and the most PM2.5 pollutant influenced area is south and southeast areas in Beijing. 6) Vehicle concentration and traffic congestion is not the significant impact factor of PM2.5 pollutions in Beijing. 7) Heating supply of buildings and houses generated great contributions to the PM2.5 contaminant concentration in Beijing. While, in the background of rigorous emission reduction policy and management operations by the municipal government, contribution of heating supplies is gradually decreasing. 8) Human activities have limited contributions to the PM2.5 contaminants in Beijing. Meanwhile, type and quantity of fossil fuel energy consumptions might contribute large amount of air pollutions

Low cost Internet of things based sensor networks for air quality in cities

Air pollution is a major public health concern, with over 7 million deaths globally attributed to it annually, as stated by the World Health Organization (WHO) in 2018. Existing real-time Air Quality (AQ) monitoring stations are expensive to install and maintain; therefore, such air quality monitoring networks are sparsely deployed and lack the measurement density to develop high-resolution spatiotemporal air pollutant monitoring. The data generated also lacks accuracy, but still, they have great potential to complement the existing air quality assessment framework. Therefore, this thesis aims to propose a comprehensive architecture for utilizing low-cost sensors in air pollution monitoring. The thesis presents a novel approach to deploy a low-cost sensor network in a city and use a hybrid convolutional-long short-term memory (Conv-LSTM) model for spatiotemporal prediction of air pollution. This approach utilizes both convolutional layers to capture spatial patterns in the sensor data and LSTM layers to capture temporal dependencies. The use of a hybrid model allows for the simultaneous capture of both spatial and temporal patterns in the data, resulting in more accurate predictions compared to models that only utilize one or the other. The research also explores the use of statistical models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) and Nonlinear Autoregressive with exogenous inputs (NARX) models for air quality forecasting, presenting a comparison of the proposed hybrid model with other such state-of-the-art statistical and machine learning models. The results show that the proposed Conv-LSTM model outperforms these approaches in terms of prediction accuracy and robustness and, therefore, is a promising approach for spatiotemporal prediction of air pollution using low-cost sensor data. Additionally, the thesis proposes a general solution to analyze how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty of low-cost sensor data. The thesis further presents an extensive evaluation of the proposed hybrid model using real-world data from the low-cost sensor network deployed in Sheffield, and the results demonstrate the effectiveness of the proposed approach. Finally, the real-world studies present the integration of low-cost sensor data into a decision-making system, social and behavioural changes driven by such sensors and the impact of these results on driving policy changes to achieve the World Health Organization’s (WHO) 2021 target for air quality

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

An evaluation of selected estimation methods for the processing of differential absorption lidar data

This work examines the application of selected estimation methods to path integrated direct detection CO₂ lidar data, with the objective of improving the precision in the estimates of the log power, and log power ratios. Particular emphasis is given to the optimal estimation techniques of Kalman filtering theory, and to the consequent requirements for system and measurement model identification. A dual wavelength system was designed and constructed, employing two hybridised TEA lasers, a co-axial transceiver, and direct detection.Over a period of several months, a database of differential absorption measurements was accumulated, each consisting of 10,000 dual wavelength lidar returns. Various wavelength pairs were used, including those recommended for the monitoring of H₂O, CO₂, NH₃ and C₂H₄. A subset of this database is used to evaluate the above mentioned estimation methods. The results are compared with simulated data files in which it was possible to control precisely process models which are believed to form an approximation to the real processes latent in the actual lidar data