On the development of an intelligent system for particulate matter air pollution monitoring, analysis and forecasting in urban regions

Oprea, Mihaela; Ianache, Cornel; Mihalache, Sanda Florentina; Dragomir, Elia Georgiana; Dunea, Daniel; Iordache, Ştefania; Savu, Tom

doi:10.1109/icstcc.2015.7321377

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…Furthermore, in Phase III, the developed relational database was used in statistical/AI models to establish the relationships between air quality, meteorology and health effects. The same database was used for the development and testing of the air quality forecasts, early warning and decision support system [18]. In Phase IV, the RokidAIR system, including DSS, EWS and forecasting modules will be finalized and presented to the public and other stakeholders at national and international levels.…”

Section: Methodologies and Implementationmentioning

confidence: 99%

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

Liu¹,

Dunea²,

Oprea³

et al. 2017

Rev. Chim.

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This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

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Section: Methodologies and Implementationmentioning

confidence: 99%

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

Liu¹,

Dunea²,

Oprea³

et al. 2017

Rev. Chim.

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“…RNA-AER stands for the Romanian abbreviation of ANN for air pollution. This is a part of a complex system for PM 2.5 forecasting based on various techniques of artificial intelligence (multi-agents, knowledge base system, ANNs, and neurofuzzy) and that is designed to analyze the pollution level of air within ROkidAIR system (http:// www.rokidair.ro/en) [16]. A feed-forward neural network with a single hidden layer was used to perform the tests presented in this work (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

“…The raw data obtained from the eight micro-stations are also used in other modules of the cyber-platform: the ROkidAIR web-based geographic information systems (GIS) geoportal, and the decision support system (DSS) including the early warning module. The DSS system uses artificial intelligence techniques (ANNs and predictive data mining) and hybrid algorithms and models (Neuro-fuzzy ANFIS, and wavelet neural network, WNN) for assessing children's exposure to the pollution with particulate matter, in order to elaborate forecasted values and early warnings [16].…”

Section: A Cyberinfrastructure For the Protection Of Children's Respimentioning

confidence: 99%

Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks

Dunea¹,

Iordache²

2016

Artificial Neural Networks - Models and Applications

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In this study, particulate matter (PM), total suspended particulate (TSP), PM 10 , and PM 2.5 fractions) concentrations were recorded in various cities from south of Romania to build the corresponding time series for various intervals. First, the time series of each pollutant were used as inputs in various configurations of feed-forward neural networks (FANN) to find the most suitable network architecture to the PM specificity. The outputs were evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and Pearson correlation coefficient (r) between observed series and output series. Second, each time series was decomposed using Daubechies wavelets of third order into its corresponding components. Each decomposed component of a PM time series was used as input in the optimal feed-forward neural networks (FANN) architecture established in the first step. The output of each component was re-included to form the modeled series of the original pollutant time series.The final step was the comparison of FANN outputs with wavelet-FANN results to retrieve the wavelet utilization outcomes. The last section of the study describes the ROkidAIR cyberinfrastructure that integrates a decision support system (DSS). The DSS system uses artificial intelligence techniques and hybrid algorithms for assessing children's exposure to the pollution with particulate matter, in order to elaborate PM forecasted values and early warnings.

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“…Our experimental PM 2.5 air pollution early warning system for Ploiesti is based on the architecture of the ROkidAIR intelligent system, described in [13].…”

Section: The Pm25 Air Pollution Early Warning Experimental System Fomentioning

confidence: 99%

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

Oprea¹,

Olteanu²,

Ianache³

2017

Rev. Chim.

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Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

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On the development of an intelligent system for particulate matter air pollution monitoring, analysis and forecasting in urban regions

Cited by 16 publications

References 7 publications

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

Analyzing the Impact of Airborne Particulate Matter on Urban Contamination with the Help of Hybrid Neural Networks

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

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