Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques

Fernández-Camacho, R.; Cabeza, I. Brito; Aroba, J.; Gómez-Bravo, Fernando; Rodrı́guez, Sergio; Rosa, J. de la

doi:10.1016/j.scitotenv.2015.01.036

Cited by 12 publications

(8 citation statements)

References 43 publications

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“…At those times a critical amount of noise was added to the sample so that the ANN models, using the considered input variables, were consequently not able to reproduce observations. Overall, the results presented for the "Aachen-Karlsgraben" campaign reflect the findings that correlations between sound pressure levels and aerosol concentrations are generally higher for small particle fractions [7,[10][11][12][13][14], here represented by PNC(0.25-2.5) in comparison to coarse particle fractions where the correlation in general was found to be weak [12,14,15]. Good model performance regarding the prediction of PM(0.25-2.5), PM(0.25-10) and PNC(0.25-2.5) within the "Münster-Aasee" test case was expected due to the spatial variation of measurement locations (cf.…”

Section: Interpretation Of Ann Model Resultssupporting

confidence: 72%

“…Generally speaking, a relation could be proved between particle concentrations and noise levels; however, the statistical correlation between both is complex and different for various metrics [9]. Recent studies highlight that the correlation between equivalent sound pressure levels (A-weighted or non-weighted) and aerosol concentrations is generally higher for either small particle fractions like PM1 [10] or ultrafine particle metrics like the particle number concentration (PNC), respectively [7,[11][12][13][14]. The correlation tends to increase with decreasing particle sizes [13].…”

Section: Introductionmentioning

confidence: 99%

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Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

et al. 2017

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Air quality assessment is an important task for local authorities due to several adverse health effects that are associated with exposure to e.g., urban particle concentrations throughout the world. Based on the consumption of costs and time related to the experimental works required for standardized measurements of particle concentration in the atmosphere, other methods such as modelling arise as integrative options, on condition that model performance reaches certain quality standards. This study presents an Artificial Neural Network (ANN) approach to predict atmospheric concentrations of particle mass considering particles with an aerodynamic diameter of 0.25-1 μm (PM(0.25-1)), 0.25-2.5 μm (PM(0.25-2.5)), 0.25-10 μm (PM(0.25-10)) as well as particle number concentrations of particles with an aerodynamic diameter of 0.25-2.5 μm (PNC(0.25-2.5)). ANN model input variables were defined using data of local sound measurements, concentrations of background particle transport and standard meteorological data. A methodology including input variable selection, data splitting and an evaluation of their performance is proposed. The ANN models were developed and tested by the use of a data set that was collected in a street canyon. The ANN models were applied furthermore to a research site featuring an inner-city park to test the ability of the approach to gather spatial information of aerosol concentrations. It was observed that ANN model predictions of PM(0.25-10) and PNC(0.25-2.5) within the street canyon case as well as predictions of PM(0.25-2.5), PM(0.25-10) and PNC(0.25-2.5) within the case study of the park area show good agreement to observations and meet quality standards proposed by the European Commission regarding mean value prediction. Results indicate that the ANN models proposed can be a fairly accurate tool for assessment in predicting particle concentrations not only in time but also in space.

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Section: Interpretation Of Ann Model Resultssupporting

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

et al. 2017

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“… Bobb et al [ 31 ] 2014 Mixture of chemicals Multiple chemicals, neurodevelopment, hemodynamics Bayesian kernel machine regression (BKMR) Identifying mixtures (e.g., metals) and components responsible for various health effects (e.g., neurodevelopment) Gass et al [ 20 ] 2014 Outdoor air pollution CO, NO2, O3, PM Classification and regression trees Apply classification and regression trees to generate hypothesis about exposure to mixtures of pollutants and health effects. They work with children’s asthma emergency visit Fernández-Camacho et al [ 51 ] 2015 Urban air and noise pollution by traffic NOx, O3, SO2, Black Carbon Fuzzy clustering Find the relationship of noise to the traffic emission Bell et al [ 63 ] 2015 General chemical exposure 219 chemicals ARM Find relationships between chemicals and health biomarkers or diseases Qin et al [ 53 ] 2015 Outdoor air pollution PM ARM Exploring relationships of PM spatial-temporal variations and how cities influence each other Reid et al [ 50 ] 2016 Outdoor air quality with wildfire PM2.5 Respiratory diseases Generalized estimating equation and generalized boosting model Finding the relationship between wildfire and associated increment in PM2.5 affects people with respiratory diseases Toti et al [ 36 ] 2016 Outdoor air pollution, pediatric asthma …”

Section: Resultsmentioning

confidence: 99%

A systematic review of data mining and machine learning for air pollution epidemiology

et al. 2017

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BackgroundData measuring airborne pollutants, public health and environmental factors are increasingly being stored and merged. These big datasets offer great potential, but also challenge traditional epidemiological methods. This has motivated the exploration of alternative methods to make predictions, find patterns and extract information. To this end, data mining and machine learning algorithms are increasingly being applied to air pollution epidemiology.MethodsWe conducted a systematic literature review on the application of data mining and machine learning methods in air pollution epidemiology. We carried out our search process in PubMed, the MEDLINE database and Google Scholar. Research articles applying data mining and machine learning methods to air pollution epidemiology were queried and reviewed.ResultsOur search queries resulted in 400 research articles. Our fine-grained analysis employed our inclusion/exclusion criteria to reduce the results to 47 articles, which we separate into three primary areas of interest: 1) source apportionment; 2) forecasting/prediction of air pollution/quality or exposure; and 3) generating hypotheses. Early applications had a preference for artificial neural networks. In more recent work, decision trees, support vector machines, k-means clustering and the APRIORI algorithm have been widely applied. Our survey shows that the majority of the research has been conducted in Europe, China and the USA, and that data mining is becoming an increasingly common tool in environmental health. For potential new directions, we have identified that deep learning and geo-spacial pattern mining are two burgeoning areas of data mining that have good potential for future applications in air pollution epidemiology.ConclusionsWe carried out a systematic review identifying the current trends, challenges and new directions to explore in the application of data mining methods to air pollution epidemiology. This work shows that data mining is increasingly being applied in air pollution epidemiology.The potential to support air pollution epidemiology continues to grow with advancements in data mining related to temporal and geo-spacial mining, and deep learning. This is further supported by new sensors and storage mediums that enable larger, better quality data. This suggests that many more fruitful applications can be expected in the future.

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“…Additional activities included the participation in the first epidemiological study of exposure to ultrafine particles in urban areas of Spain, based on records in Santa Cruz de Tenerife, Huelva and Barcelona cities (Tobias et al, 2017). This study used a data set obtained in previous research projects led by the IARC team, which had focused on the sources (Fernández-Camacho et al, 2015;Gonález et al, 2011;González and Rodríguez, 2013;Rodríguez et al, 2007) and the impact of ultrafine particles on cardiovascular disease (Domínguez- Rodríguez et al, , 2015.…”

Section: Participation In Scientific Projects and Studies/experimentsmentioning

confidence: 99%

Izaña Atmospheric Research Center. Activity Report 2017-2018

2020

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The designations employed in WMO publications and the presentation of material in this publication do not imply the expression of any opinion whatsoever on the part of WMO concerning the legal status of any country, territory, city or area, or of its authorities, or concerning the delimitation of its frontiers or boundaries.The mention of specific companies or products does not imply that they are endorsed or recommended by WMO in preference to others of a similar nature which are not mentioned or advertised.The findings, interpretations and conclusions expressed in WMO publications with named authors are those of the authors alone and do not necessarily reflect those of WMO or its Members. This publication has been issued without formal editing.

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Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques

Cited by 12 publications

References 43 publications

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

Modelling of Urban Near-Road Atmospheric PM Concentrations Using an Artificial Neural Network Approach with Acoustic Data Input

A systematic review of data mining and machine learning for air pollution epidemiology

Izaña Atmospheric Research Center. Activity Report 2017-2018

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