244 research outputs found

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

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    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

    The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

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    The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

    A novel model for hourly PM2.5 concentration prediction based on CART and EELM

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    Hourly PM2.5 concentrations have multiple change patterns. For hourly PM2.5 concentration prediction, it is beneficial to split the whole dataset into several subsets with similar properties and to train a local prediction model for each subset. However, the methods based on local models need to solve the global-local duality. In this study, a novel prediction model based on classification and regression tree (CART) and ensemble extreme learning machine (EELM) methods is developed to split the dataset into subsets in a hierarchical fashion and build a prediction model for each leaf. Firstly, CART is used to split the dataset by constructing a shallow hierarchical regression tree. Then at each node of the tree, EELM models are built using the training samples of the node, and hidden neuron numbers are selected to minimize validation errors respectively on the leaves of a sub-tree that takes the node as the root. Finally, for each leaf of the tree, a global and several local EELMs on the path from the root to the leaf are compared, and the one with the smallest validation error on the leaf is chosen. The meteorological data of Yancheng urban area and the air pollutant concentration data from City Monitoring Centre are used to evaluate the method developed. The experimental results demonstrate that the method developed addresses the global-local duality, having better performance than global models including random forest (RF), v-support vector regression (v-SVR) and EELM, and other local models based on season and k-means clustering. The new model has improved the capability of treating multiple change patterns

    Developing an early-warning system for air quality prediction and assessment of cities in China

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    © 2017 Elsevier Ltd Air quality has received continuous attention from both environmental managers and citizens. Accordingly, early-warning systems for air pollution are very useful tools to avoid negative health effects and develop effective prevention programs. However, developing robust early-warning systems is very challenging, as well as necessary. This paper develops a reliable and effective early-warning system that consists of air quality prediction and assessment modules. In the prediction module, a hybrid forecasting method is developed for predicting pollutant concentrations that effectively estimates future air quality conditions. In developing this proposed model, we suggest the use of a back propagation neural network algorithm, combined with a probabilistic parameter model and data preprocessing techniques, to address the uncertainties involved in future air quality prediction. Meanwhile, a pre-analysis is implemented, primarily by using optimized distribution functions to examine and analyze statistical characteristics and emission behaviors of air pollutants. The second method, which is developed as part of the second module, is based on fuzzy set theory and the Analytic Hierarchy Process, and it performs air quality assessments to provide a clear and intelligible description of air quality conditions. Using data from the Ministry of Environmental Protection of China and six stages of air quality classification levels, specifically good, moderate, lightly polluted, moderately polluted, heavily polluted and severely polluted, two cities in China, Chengdu and Hangzhou, are used as illustrative examples to verify the effectiveness of the developed early-warning system. The results demonstrate that the proposed methods are effective and reliable for use by environmental supervisors in air pollution monitoring and management

    Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making

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    The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study

    Deep air learning: Interpolation, prediction, and feature analysis of fine-grained air quality

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    The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality

    Spatio-temporal models for air pollution

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    Air pollution is the biggest environmental risk to global health and it is estimated that, globally, 7 million deaths can be attributed to air pollution each year \citep{WHO2018}. The World Bank estimates that, in 2016, the overall cost of ambient air pollution to the global economy was an estimated US \5.7 trillion or 4.4 per cent of global GDP \citep{worldbank}. A number of different air pollutants have been associated with adverse health effects, including fine particulate matter (PM_{2.5}),nitrogendioxideandozone.Instudiesoftheeffectsofairpollution,exposureinformationisoftenobtainedfromafixednumberofmonitoringsiteswithintheregionofinterest.However,anincreasingnumberofmodelsofairpollutionarebeingusedthatprovideestimatesofconcentrations.Theseareusedtorepresentexposuresateverylocationinahealthstudyarea,ratherthanjustatanumberoffixedmeasurementlocations.Anotheruseofmodellingofairpollutionistoprovideshort−termforecaststhatcanbeusedtoinformthebehaviourofvulnerablepeople.Inthisthesis,wedevelopstatisticalapproachestomodelling,andforecasting,dailyconcentrationsof), nitrogen dioxide and ozone. In studies of the effects of air pollution, exposure information is often obtained from a fixed number of monitoring sites within the region of interest. However, an increasing number of models of air pollution are being used that provide estimates of concentrations. These are used to represent exposures at every location in a health study area, rather than just at a number of fixed measurement locations. Another use of modelling of air pollution is to provide short-term forecasts that can be used to inform the behaviour of vulnerable people. In this thesis, we develop statistical approaches to modelling, and forecasting, daily concentrations of \mbox{PM}_{2.5}inurbanareas.Weconsidertwodifferentapproaches,bothintermsofmodelformulationandperforminginference.ThefirstapproachisDynamicSpace−TimeModels(DSTM).Underthisframework,adatamodelrelatesobservations(measurements)toaprocessmodelthatspecifiesthedynamicevolutionofthe"true"underlyingprocess.Thisapproachisimplementedusingtwodifferentmethodsforestimation:methodsofmomentsandexpectation−maximisation.WealsodevelopanapproachusingBayesianHierarchicalSpatio−Temporalmodelling(BHSTM).TheinferenceisdoneusingcomputationalefficientmethodsforBayesianinference(integratednestedLaplaceapproximations).ThismodelallowspredictionsofdailyPM in urban areas. We consider two different approaches, both in terms of model formulation and performing inference. The first approach is Dynamic Space-Time Models (DSTM). Under this framework, a \textit{data} model relates observations (measurements) to a \textit{process} model that specifies the dynamic evolution of the "true" underlying process. This approach is implemented using two different methods for estimation: methods of moments and expectation-maximisation. We also develop an approach using Bayesian Hierarchical Spatio-Temporal modelling (BHSTM). The inference is done using computational efficient methods for Bayesian inference (integrated nested Laplace approximations). This model allows predictions of daily PM_{2.5}overbothspaceandtime,whichcanbeusedtointerpolatebothpastmeasurementsandfuturepredictions.BothapproacheswereimplementedusingdatafromGreaterLondon,withtheirperformanceevaluatedintermsoftheirabilitytopredictdailyconcentrationsofPM over both space and time, which can be used to interpolate both past measurements and future predictions. Both approaches were implemented using data from Greater London, with their performance evaluated in terms of their ability to predict daily concentrations of PM_{2.5}overtimeatdifferentmeasuringsites.BothmethodswereabletoaccuratelypredictfuturevaluesofdailyPM over time at different measuring sites. Both methods were able to accurately predict future values of daily PM_{2.5}$ at different locations, with one-day ahead predictions being more accurate than those used for longer periods, as might be expected. One of the major advantages of the BHSTM approach is that it provides a straightforward method for producing estimates of the uncertainty that is associated with predictions
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