The science of the total environment

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.

openair - An R package for air quality data analysis

Evaluation of waist circumference, waist-to-hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual-energy X-ray absorptiometry, in children aged 3–19 y

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.

A review and evaluation of intraurban air pollution exposure models

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.

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

Electric powered vehicles use energy stored in some form of battery for the vehicle propulsion drive and to power auxiliary instruments such as air conditioning, stereo and in car equipments. Many research initiatives are currently underway to evaluate the potential and performance of electric cars and to identify any barriers to their uptake (being, technical, economic or social) as a greener alternative to the internal combustion engine traditionally powered by fossil fuels. This study provides an early insight into research undertaken by Newcastle University to investigate the performance of electric vehicles through on-road testing, user led trials and the analysis of the data collected from the vehicle. Newcastle University is currently involved in creating a smart ITS infrastructure to analyse the electric vehicle performance by monitoring the vehicles in terms of the power consumed, distance traveled, trip profile, auxiliary loads and driving styles to determine how the battery discharges and recharges under different conditions, By measuring the energy usage on any particular journey the equivalent carbon footprint for the journey can be estimated from the prevailing CO2 per KWh assuming the mean electricity generation profile in the UK. This paper will discuss the results from electric vehicle monitoring infrastructure by analysing the energy regeneration and energy usage, thereby calculating the impact to the environment.

Smart infrastructure for carbon foot print analysis of electric vehicles

A pervasive sensor network for environmental and traffic monitoring

Estimates suggest that there will be between 0.5 and 12.8 million electric vehicles (EVs) on UK roads by 2030. Power grids could overload if large numbers of these EVs are not recharged off peak, between midnight and early morning. This study investigates the recharging patterns of 12 Private, 21 Organization Individual and 32 Organization Pool users over two successive six month periods as part of the Switch EV trials. EVs were monitored using a data loggers and GPS devices. Recharging locations were identified as Home, Work, Public and Other. It was found less than 10% of recharging took place off peak. Work was the most popular location to recharge, and demand peaked between 09:00am and 10:00am. Private individuals peak recharging occurred on an evening at Home, while Organization Individuals users recharged mostly early morning at Work. Organization Pool users recharging peaked at Work, late afternoon. Smart Meters can delay recharging until off peak hours at Home and Work locations. It is recommended that pay as you go access to non-domestic infrastructure is used for individual drivers. This will encourage them to recharge at Home or Work, where Smart Meters can then shift their recharging to off peak.

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Analysis of Electric Vehicle Driver Charging Behaviour and Use of Charging Infrastructure

This article briefly describes the implementation of a new road traffic noise prediction facility, AVTUNE (Airviro Traffic and Urban Noise Evaluator) within the framework of the existing Airviro pollution management system. The joint noise and air quality modelling system forms a major component of the Leicester element of the European Union Fifth Framework project, HEAVEN (Healthier Environment through the Abatement of Vehicle Emissions and Noise).
The noise model itself has been broken down into two major components: the SNEM (Source Noise Emissions Module) that works in quasi real-time and compliments the Airviro system’s ability to acquire directly from Leicester’s UTC (Urban Traffic Control) centre in predicting pollutant emissions, and the SPM (Sound Propagation Module) that provides additional long-term prediction and noise mapping facilities outside of the real time framework.
At the time of writing, the model has just completed a validation exercise covering a section of Leicester’s road network. Initial results from this exercise are presented here. A larger demonstration exercise is planned to run throughout the Summer of 2002.
Keywords: urban traffic control, decision support systems, noise modelling, noise mapping, emissions modelling

Margaret Bell

Papers

Smart infrastructure for carbon foot print analysis of electric vehicles

A pervasive sensor network for environmental and traffic monitoring

Analysis of Electric Vehicle Driver Charging Behaviour and Use of Charging Infrastructure

The AVTUNE Noise Model

Integration of traffic and environmental monitoring and management systems