Particulate Matter Sampling Techniques and Data Modelling Methods

Particulate matter with 10 μm or less in diameter (PM10) is known to have adverse effects on human health and the environment. For countries committed to reducing PM10 emissions, it is essential to have models that accurately estimate and predict PM10 concentrations for reporting and monitoring purposes. In this chapter, a broad overview of recent empirical statistical and machine learning techniques for modelling PM10 is presented. This includes the instrumentation used to measure particulate matter, data preprocessing, the selection of explanatory variables and modelling methods. Key features of some PM10 prediction models developed in the last 10 years are described, and current work modelling and predicting PM10 trends in New Zealand—a remote country of islands in the South Pacific Ocean—are examined. In conclusion, the issues and challenges faced when modelling PM10 are discussed and suggestions for future avenues of investigation, which could improve the precision of PM10 prediction and estimation models are presented

Analysis of sensory data using graph signal processing

Air pollution monitoring is an important topic that has been researched in the past few years thanks to the massive deployment of IoT platforms, as it affects the lives of both children and adults, and it kills millions of people worldwide every year. A new framework of tools called Graph Signal Processing was presented recently and it allows, among other things, the ability to predict data on a node that belongs to a network of sensors using both the data itself and the topology of the graph, which is based on the Laplacian matrix. This thesis is a comparative study on different prediction techniques for pollutant signals, such as Linear Combination, Multiple Linear Regression and GSP and it presents the results of all three methods in different scenarios, using RMSE and R2 indicators, and focusing the efforts on the understanding of how different parameters (such as the distance between nodes) affect the performances of these new tools. The results of the study show that pollutants O3 and NO2 are lowpass signals, and as the number of edges between nodes increases, GSP obtains a close performances to MRL. For PM10, we conclude that is not a low-pass signal, and the performance of the indicators drop massively compared with the previous ones. Linear combination is the worst of all three and MLR has a stable performance during all the scenarios

Predicción temporal de calidad del aire en Lima a partir de datos de estaciones de bajo costo y Aprendizaje Automático: una revisión de literatura

El presente trabajo explora los estudios en los cuales se utilizan técnicas de aprendizaje profundo para realizar predicción temporal de calidad del aire, de manera que se pueda comprender que características tendrían los modelos de aprendizaje profundo que tienen un mejor rendimiento con para realizar esta tarea y puedan utilizarse como línea base para desarrollar modelos similares en el contexto de la ciudad de lima. Esta revisión de literatura se realiza con el objetivo de poder obtener los modelos de aprendizaje profundo que estén teniendo un mejor rendimiento en la actualidad al predecir temporalmente la calidad del aire mediante un procedimiento que garantice objetividad y reproducción de resultados. Para ello, se realiza una revisión sistemática de literatura que garantiza el uso de procedimientos estructurados y definidos para conocer las preguntas de investigación que guían la exploración de los estudios de predicción temporal de calidad del aire, los motores de búsqueda considerados para la revisión y las cadenas de búsqueda asociadas tanto a las preguntas de investigación como los motores de búsqueda, de manera que estas se puedan ejecutar y reproducir la obtención de estudios. Las respuestas se reportan en un formulario de extracción con datos relacionados a las arquitecturas de aprendizaje profundo, limitaciones de los modelos empleados y el rendimiento obtenido por cada modelo en cada estudio. Al finalizar el estudio, se concluye que se puede desarrollar un modelo basado en una arquitectura adecuada de aprendizaje profundo para poder atacar el problema de la predicción inadecuada de calidad del aire en Lima al percatar su efectividad reportada en la literatura para otras localidades en el mundo, considerando que dichos modelos deben tomarse únicamente como una línea base y que deben ajustarse a la localidad de Lima para obtener predicciones adecuadas a su entorno.Trabajo de investigació

A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlations Air Pollutions Global Risk Assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this pa-per proposes a new air pollution global risk assessment (APGRA) model for predicting spatial correlations air quality index risk assessment to address these issues. The APGRA model incorporates autoregressive integrated moving average (ARIMA), Monte-Carlo simulation, and collaborative multi-agent system, and prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models

Pronóstico de las concentraciones de SO2 y NO2 en Ecuador a partir de imágenes satelitales Sentinel 5P, mediante técnicas de Machine Learning

La contaminación del aire se ha convertido en uno de los principales problemas ambientales a nivel mundial debido a su afección tanto en el medio ambiente como en la salud en general. Los gobiernos tanto nacionales como internacionales han implementado esfuerzos para medir y controlar las emisiones de contaminantes al aire proveniente de fuente antrópicas instalando redes de monitorización atmosférica. Sin embargo, no todas las ciudades y países cuentan con estas herramientas de monitoreo. Por ello, el uso de las imágenes satelitales ha ido tomando fuerza en los últimos años ya que nos permite obtener información satelital de áreas que no cuentan con monitoreo terrestre y poder utilizar estos datos para fines de control, prevención e investigación. Por medio de dicha información podemos realizar análisis y modelado de las emisiones y comportamiento de los contaminantes atmosféricos. Debido a la necesidad de poder prevenir a la sociedad y tomar medidas preventivas de las emisiones de contaminantes atmosféricos, la comunidad científica en los últimos años ha propuesto diferentes modelos matemáticos y modelos de aprendizaje no supervisado que permitan predecir las emisiones de los contaminantes atmosféricos. Para ello, es necesario tomar en cuenta las variables externas que afectan al comportamiento de los contaminantes dependiendo de la zona de estudio, ya que la ubicación geográfica, la topografía, y condiciones meteorológicas influyen directa o indirectamente en este comportamiento, por esta razón generalmente los investigadores diseñan modelos para regiones específicas. No existe un método para establecer qué variables meteorológicas deben ser usadas en la predicción de los contaminantes, los antecedentes a usar son los estudios previos realizados, observando los resultados obtenidos para saber las influencias de estas variables en el comportamiento de los contaminantes. El presente trabajo propone dos modelos de predicción de la concentración de NO2 y SO2 para las tres ciudades más importantes del Ecuador Tomando como base la información de imágenes satelitales Sentinel-5P, Giovanni NASA y ERA 5. El primer modelo propuesto utiliza redes Neuronales Recurrentes utilizando el número de retrasos o variables ficticias creadas que se utilizan para encontrar relaciones entre la concentración y las variables meteorológicas, las cuales proporcionan información a la red neuronal para realizar la Bryam Montesdeoca Jara Dayana Ortiz Morocho ii predicción. Se propuso predecir la contaminación atmosférica hasta 5 días hacia adelante con el uso de diferentes estructuras buscando la mejor para el pronóstico. El segundo modelo propuesto utiliza el método de Random Forest teniendo en cuenta dos características importantes, la profundidad máxima de cada árbol y el número mínimo de muestras para considerarse Nodos Hoja. Estas dos características nos dan dos perspectivas acerca de los bosques aleatorios buscando el mejor modelo de predicción. Se puede decir que la predicción a través del algoritmo de Regresión de Random Forest fue el que mejor rendimiento R2=0,98 mostró y las métricas de error MAPE, RMSE y PBIAS fueron más bajas en este método con valores de 7, 3,67, 0,68, respectivamente, haciendo énfasis en los distintos conjuntos de datos, la predicción para la ciudad de Cuenca fue la mejor seguida de la ciudad de Guayaquil que supera ligeramente a las predicciones de Quito. Esto demuestra que la predicción de la calidad del aire es efectiva mostrando resultados satisfactorios y abriendo puertas a nuevas investigaciones con la finalidad de poder prever las medidas de concentraciones de gases contaminantes al aire y así poder tomar decisiones preventivas tanto para la salud como el medio ambiente.Air pollution has become one of the main environmental problems worldwide due to its effects on both the environment and health in general. Both national and international governments have implemented efforts to measure and control air pollutant emissions from anthropogenic sources by installing atmospheric monitoring networks. However, not all cities and countries have these monitoring tools. For this reason, the use of satellite images has been gaining strength in recent years as it allows us to obtain satellite information from areas that do not have terrestrial monitoring and to be able to use this data for control, prevention and research purposes. Through this information we can perform analysis and modeling of emissions and behavior of atmospheric pollutants. Due to the need to be able to prevent society and take preventive measures regarding the emissions of atmospheric pollutants, the scientific community in recent years has proposed different mathematical models and unsupervised learning models that allow predicting the emissions of atmospheric pollutants. For them it is necessary to take into account the external variables that affect the behavior of pollutants depending on the study area, since the geographical location, topography, and meteorological conditions directly or indirectly influence this behavior, for this reason researchers generally design models for specific regions. There is no method to establish which meteorological variables should be used in the prediction of pollutants, the background to be used are the previous studies carried out, observing the results obtained to know the influences of these variables on the behavior of pollutants. The present work proposes two prediction models for the concentration of NO2 and SO2 for the three most important cities of Ecuador, based on information from Sentinel-5P, Giovanni NASA and ERA 5 satellite images. The first proposed model uses Recurrent Neural Networks using the number of lags or dummy variables created that are used to find relationships between concentration and meteorological variables, which provide information to the neural network to make the prediction. It was proposed to predict air pollution up to 5 days ahead with the use of different structures looking for the best one for the forecast. The second proposed model uses the Random Forest method taking into account two important characteristics, the maximum depth of each tree and the minimum number of samples to be considered Leaf Nodes. These two features give us two Bryam Montesdeoca Jara Dayana Ortiz Morocho iv perspectives about random forests looking for the best prediction model. It can be said that the prediction through the Random Forest Regression algorithm was the one that showed the best performance R2=0.98 and the error metrics MAPE, RMSE and PBIAS were lower in this method with values of 7, 3.67, 0.68, respectively. , emphasizing the different data sets, the prediction for the city of Cuenca was the best, followed by the city of Guayaquil, which slightly exceeds the predictions for Quito. This shows that the prediction of air quality is effective, showing satisfactory results and opening doors to new research in order to be able to anticipate the measurements of concentrations of polluting gases in the air and thus be able to make preventive decisions for both health and the environment.Ingeniero AmbientalCuenc

Data mining methods for prediction of air pollution

The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other—a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way

Data mining methods for prediction of air pollution

The paper discusses methods of data mining for prediction of air pollution. Two tasks in such a problem are important: generation and selection of the prognostic features, and the final prognostic system of the pollution for the next day. An advanced set of features, created on the basis of the atmospheric parameters, is proposed. This set is subject to analysis and selection of the most important features from the prediction point of view. Two methods of feature selection are compared. One applies a genetic algorithm (a global approach), and the other-a linear method of stepwise fit (a locally optimized approach). On the basis of such analysis, two sets of the most predictive features are selected. These sets take part in prediction of the atmospheric pollutants PM10, SO2, NO2 and O3. Two approaches to prediction are compared. In the first one, the features selected are directly applied to the random forest (RF), which forms an ensemble of decision trees. In the second case, intermediate predictors built on the basis of neural networks (the multilayer perceptron, the radial basis function and the support vector machine) are used. They create an ensemble integrated into the final prognosis. The paper shows that preselection of the most important features, cooperating with an ensemble of predictors, allows increasing the forecasting accuracy of atmospheric pollution in a significant way