Margaret Bell

Bio: Margaret Bell is an academic researcher from Newcastle University. The author has contributed to research in topics: Air quality index & Traffic congestion. The author has an hindex of 34, co-authored 209 publications receiving 3864 citations. Previous affiliations of Margaret Bell include University of Nottingham & University of Leeds.

UTMC compliant database to support technologies of the future

Abstract: This paper will present the results of an in depth investigation of the standard database format defined by the Common Database project, UTMC10, funded by the Department for Transport on the mid 1990s applied to diverse datasets. It outlines the difficulties encountered in implementing the database platform to embrace different sources, (namely accident, pollutant concentrations, registration plate, journey time etc.) and to include both dynamic (vehicle tracking, tailpipe emissions, bus stop arrival times, car park occupancies etc) and static (public transport time tables, car parking charges etc.) data with different dimensions, sampling frequencies and accuracies. The paper will look at the requirements of Urban Traffic Management and Control compliant database infrastructure in the context of the delivery of wider policy objectives to those relating to traffic. In particular, to create the statistical processing capability that defines the current and changing status of the network, to provide the platform upon which to build a historic picture of the network to allow a statistical forecasting capability and to develop the foundations for model validation and evaluation. In this way the infrastructure needed to deliver policy objectives which not only embrace traffic management but also public transport, air quality, health impacts and climate change will be formulated.

Prediction of air pollution peaks generated by urban transport networks

Abstract: This paper illustrates the first results of an ongoing research for developing novel methods to analyse and simulate the relationship between trasport-related air pollutant concentrations and easily accessible explanatory variables. The final scope of the analysis is to integrate the new models in traditional traffic management decision-support systems for a sustainable mobility of road vehicles in urban areas. This first stage concerns the relationship between the mean hourly concentration of nitrogen dioxide and explanatory factors like traffic and weather conditions, with particular reference to the prediction of pollution peaks, defined as exceedances of normative concentration limits. Two modelling frameworks are explored: the Artificial Neural Network approach and the ARIMAX model. Furthermore, the benefit of a synergic use of both models for air quality forecasting is investigated. The analysis of findings points out that the prediction of extreme pollutant concentrations is best performed by the integration of the two models into an ensemble. The neural network is outperformed by the ARIMAX model in foreseeing peaks, but gives a more realistic representation of the relationships between concentration and wind characteristics. So, it can be exploited to direct the ARIMAX model specification. At last, the study shows that the ability at forecasting exceedances of pollution regulative limits can be enhanced by requiring traffic management actions when the predicted concentration exceeds a threshold that is pretty high but lower than the normative one.

openair - An R package for air quality data analysis

Abstract: openair is an R package primarily developed for the analysis of air pollution measurement data but which is also of more general use in the atmospheric sciences. The package consists of many tools for importing and manipulating data, and undertaking a wide range of analyses to enhance understanding of air pollution data. In this paper we consider the development of the package with the purpose of showing how air pollution data can be analysed in more insightful ways. Examples are provided of importing data from UK air pollution networks, source identification and characterisation using bivariate polar plots, quantitative trend estimates and the use of functions for model evaluation purposes. We demonstrate how air pollution data can be analysed quickly and efficiently and in an interactive way, freeing time to consider the problem at hand. One of the central themes of openair is the use of conditioning plots and analyses, which greatly enhance inference possibilities. Finally, some consideration is given to future developments.

A review and evaluation of intraurban air pollution exposure models

Abstract: The development of models to assess air pollution exposures within cities for assignment to subjects in health studies has been identified as a priority area for future research. This paper reviews models for assessing intraurban exposure under six classes, including: (i) proximity-based assessments, (ii) statistical interpolation, (iii) land use regression models, (iv) line dispersion models, (v) integrated emission-meteorological models, and (vi) hybrid models combining personal or household exposure monitoring with one of the preceding methods. We enrich this review of the modelling procedures and results with applied examples from Hamilton, Canada. In addition, we qualitatively evaluate the models based on key criteria important to health effects assessment research. Hybrid models appear well suited to overcoming the problem of achieving population representative samples while understanding the role of exposure variation at the individual level. Remote sensing and activity-space analysis will complement refinements in pre-existing methods, and with expected advances, the field of exposure assessment may help to reduce scientific uncertainties that now impede policy intervention aimed at protecting public health.

An updated roadmap for the integration of metal–organic frameworks with electronic devices and chemical sensors

Abstract: Metal-organic frameworks (MOFs) are typically highlighted for their potential application in gas storage, separations and catalysis. In contrast, the unique prospects these porous and crystalline materials offer for application in electronic devices, although actively developed, are often underexposed. This review highlights the research aimed at the implementation of MOFs as an integral part of solid-state microelectronics. Manufacturing these devices will critically depend on the compatibility of MOFs with existing fabrication protocols and predominant standards. Therefore, it is important to focus in parallel on a fundamental understanding of the distinguishing properties of MOFs and eliminating fabrication-related obstacles for integration. The latter implies a shift from the microcrystalline powder synthesis in chemistry labs, towards film deposition and processing in a cleanroom environment. Both the fundamental and applied aspects of this two-pronged approach are discussed. Critical directions for future research are proposed in an updated high-level roadmap to stimulate the next steps towards MOF-based microelectronics within the community.