Figure 1 - uploaded by Jo Barnes
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Cartograms (areas based on population) showing background concentrations of NO 2 , point-of-use emissions of NOx from road transport and emissions from vehicles registered in each LSOA. Black lines indicate boundaries of the UK regions and Devolved Administrations to aid orientation.
Source publication
This paper will describe a new approach to source apportionment of transport emissions that moves away from traditional approaches which have allocated emissions to point of use, or by journey purpose. Instead, emissions will be attributed spatially to the people responsible for cars that cause the emissions, highlighting how both structural featur...
Contexts in source publication
Context 1
... should be noted that not only is NO 2 generally found to be a good proxy for ultrafine particles (Arain et al. [24], Pekkanen and Kulmala [25]) but local emissions of NOx (particularly from road transport) can be considered as a proxy for much wider health and environmental impacts of motor vehicles (such as noise, vibration, poor quality public space, urban stress etc.). Figure 1 shows maps comparing ambient NO 2 concentrations for LSOAs, point- of-use emissions from road transport and emissions of NOx from private vehicles registered in each area (from the MOT dataset). To be completely clear, the emissions depicted in the right-hand map do not occur within the areas; they may be emitted anywhere. ...
Context 2
... map shows the emissions attributed to the location of the registered keeper -the person best held to be responsible for those emissions, in a similar way to how they would, without evidence to the contrary, be responsible with regard to speeding, parking or other motoring offences). The data in Figure 1 have been presented using cartograms (Tobler [26], Gastner and Newman [27]) in order to better visualise the data. This is because LSOAs are constructed on the basis of roughly equal populations, and therefore, when mapped normally, rural areas, having a much larger area for a given population, tend to dominate the maps. ...
Context 3
... maps provide a spatial context to the inequalities between emissions and exposure described and discussed in Barnes and Chatterton [16]. Figure 1 indicates that there are very significant spatial differences in patterns of exposure and responsibility for emissions. In order to move from a very physical account of why these differences occur (e.g. ...
Context 4
... 2 shows the relationship between exposure to concentrations and responsibility for emissions for each of the subgroups. The strong inverse relationship suggested by the maps in Figure 1 is very clear, with an overall correlation coefficient (Pearson's R) of -0.81. The 'Rural Resident' supergroup clearly stands out as the areas with the greatest emissions and lowest concentrations. ...
Context 5
... there is a strong relationship between type of areas and car ownership/access, it is not just access to a car that determines emissions but how 'clean' the car is (in terms of emissions per km) and how far it is driven. Using the data from the 'MOT' dataset that have been used to calculate the emissions in the right hand plot of Figure 1 and the x-axis of Figure 2, the average distance driven and the average distance driven by vehicles in each LSOA have been plotted in Figure 3, again using the OAC classifications. Again, we see a very similar clustering of the supergroups, and a strong correlation between the variables (R= 0.87). ...
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Citations
... However, in the case of transport, it only considers traffic as a problem at the location where the exceedances are occurring (i.e., in hotspots) and what type of vehicle is creating them [6,12]. This means that any action to deal with traffic often results in micro-scale shuffling and relocating of vehicles, seeing them as problematic only on a particular section of the road rather than recognising this as a local manifestation of symptoms as the result of the entire set of journeys that are being made [13][14][15]. Secondly, this focus on specific areas where acute symptoms are manifested fails to allow air quality management to align itself with other policy areas concerned with overall vehicle flows such as greenhouse gas/carbon emissions reduction [8] or public space/quality of life [16] in order to manage the problem systemically and holistically from a societal perspective [17]. ...
Technological approaches to carbon emission and air pollution data modelling consider where the issues are located and what is creating emissions. This paper argues that more focus should be paid to people—the drivers of vehicles or households burning fossil fuels (‘Who’) and the reasons for doing so at those times (‘Why’). We applied insights from social psychology (social identity theory and social cognitive theory) to better understand and communicate how people’s everyday activities are a cause of climate change and air pollution. A new method for citizen-focused source apportionment modelling and communication was developed in the ClairCity project and applied to travel data from Bristol, U.K. This approach enables understanding of the human dimension of vehicle use to improve policymaking, accounting for demographics (gender or age groups), socio-economic factors (income/car ownership) and motives for specific behaviours (e.g., commuting to work, leisure, shopping, etc.). Tailored communications for segmented in-groups were trialled, aiming to connect with group lived experiences and day-to-day behaviours. This citizen-centred approach aims to make groups more aware that ‘people like me’ create emissions, and equally, ‘people like me’ can take action to reduce emissions.
... Using the latter, it is possible to estimate the annual mileage of each vehicle and from this to calculate the total and average NOx emissions for all private vehicles registered within each LSOA. This methodology is described in more detail in another paper in this volume (Chatterton and Barnes [20]). Figure 1 depicts average annual mean NO 2 concentrations across age categories, based on LSOA rather than ward areas and using 2011 rather than 1991 census and 2011 rather than 2001 pollution data. ...
In 2003, Mitchell and Dorling undertook the first national level environmental justice analysis of air quality in Britain and established that there were clear inequalities in exposure to air pollution based on demography, poverty and car ownership. This paper updates and improves on their work looking at relationships between emissions and exposure a decade later. Using 2011 pollution data (NO2 concentrations and NOx emissions from road transport) in combination with socio-economic and demographic data from the UK Census, we present analyses of patterns of exposure at the level of small area census units. Then, using an enhanced version of the UK Department for Transport’s annual vehicle safety inspection records, we spatially attribute the annual NOx emissions for private motor vehicles to the location of each vehicle’s registered keeper. From this, it is possible to identify who are the highest emitters of traffic related pollution and to explore the relationships between responsibility for causing emissions and exposure to pollution. The research focuses on England and Wales and finds that despite a decade of efforts to reduce air pollution, significant inequalities still characterise exposure. Young children and adults, and households in poverty are much more likely to suffer from the effects of traffic than older people and more affluent households. Furthermore, it is these more affluent households that contribute most to traffic pollution through owning the most vehicles and generating the highest emissions.
The environmental impact of transport is highly significant. In the United Kingdom for example, 23% of greenhouse gas emissions was attributed to transport, the second largest sector after energy supply from the burning of fossil fuels; gas, coal and oil. Emissions from transport are primarily caused by the combustion of fossil fuels to provide energy for generating motion, either directly, as with internal combustion engines (ICEs) in road vehicles or jet engines, or indirectly in power stations to generate electricity for plug-in vehicles or electric trains. Transport emits a wide range of 'conventional' air pollutants (including nitrogen oxides (NOx), particulate matter (PM), Sulphur dioxide (SO2), volatile organic compounds (VOCs) and carbon monoxide (CO)). Various forms of gas are the next most common alternative fuels, predominantly compressed natural gas (CNG) and liquid petroleum gas (LPG). Bio-fuels are being increasingly used in trains, shipping and aviation although levels of uptake are still very low.
