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Data Integration Technology for Industrial and Environmental Research via Air Quality Monitoring Network

Data Integration Technology for Industrial and Environmental Research via Air Quality Monitoring Network

Tianxing Cai
Copyright: © 2015 |Pages: 35
ISBN13: 9781466664937|ISBN10: 1466664932|EISBN13: 9781466664944
DOI: 10.4018/978-1-4666-6493-7.ch007
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MLA

Cai, Tianxing. "Data Integration Technology for Industrial and Environmental Research via Air Quality Monitoring Network." Enhancing Qualitative and Mixed Methods Research with Technology, edited by Shalin Hai-Jew, IGI Global, 2015, pp. 158-192. https://doi.org/10.4018/978-1-4666-6493-7.ch007

APA

Cai, T. (2015). Data Integration Technology for Industrial and Environmental Research via Air Quality Monitoring Network. In S. Hai-Jew (Ed.), Enhancing Qualitative and Mixed Methods Research with Technology (pp. 158-192). IGI Global. https://doi.org/10.4018/978-1-4666-6493-7.ch007

Chicago

Cai, Tianxing. "Data Integration Technology for Industrial and Environmental Research via Air Quality Monitoring Network." In Enhancing Qualitative and Mixed Methods Research with Technology, edited by Shalin Hai-Jew, 158-192. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6493-7.ch007

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Abstract

Industrial and environmental research will always involve the study of the cause-effect relationship between emissions and the surrounding environment. Qualitative and mixed methods researchers have employed a variety of Information and Communication Technology (ICT) tools, simulated or virtual environments, information systems, information devices, and data analysis tools in this field. With the collection and representation of information in a range of ways, software tools have been created to manage and store this data. This data management enables more efficient searching ability of various types of electronic and digitized information. Various technologies have made the work of research more efficient. The results of the qualitative or mixed methods research may be integrated to reach the research target. Right now, a lot of software tools are available for analysis to identify patterns and represent new meanings. The programs extend the capabilities of the researcher in terms of information coding and meaning-making. Machine-enhanced analytics has enabled the identification of aspects of interest such as correlations and anomalies from large datasets. Chemical facilities, where large amounts of chemicals and fuels are processed, manufactured, and housed, have high risks to originate air emission events, such as intensive flaring and toxic gas release caused by various uncertainties like equipment failure, false operation, nature disaster, or terrorist attack. Based on an available air-quality monitoring network, the data integration technologies are applied to identify the scenarios of the possible emission source and the dynamic pollutant monitor result, so as to timely and effectively support diagnostic and prognostic decisions. In this chapter, several systematic methodologies and preliminary data integration system designs for such applications are developed according to the real application purpose. It includes two stages of modeling and optimization work: 1) the determination of background normal emission rates from multiple emission sources and 2) single-objective or multi-objective optimization for impact scenario identification and quantification. They have the capability of identifying the potential emission profile and spatial-temporal characterization of pollutant dispersion for a specific region, including reverse estimation of air quality issues. The chapter provides valuable information for accidental investigations and root cause analysis for an emission event, and it helps evaluate the regional air quality impact caused by such an emission event as well. Case studies are employed to demonstrate the efficacy of the developed methodology.

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