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Implications of climate change for thermal discomfort on underground railways
Transportation Research Part D 30 (2014) 1–9
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Implications of climate change for thermal discomfort on underground railways Katie Jenkins a,⇑, Mark Gilbey b, Jim Hall a, Vassilis Glenis c, Chris Kilsby c a
Environmental Change Institute (ECI), University of Oxford, Oxford, UK Parsons Brinckerhoff Ltd, London, UK c School of Civil Engineering and Geosciences, Newcastle University, UK b
a r t i c l e
i n f o
Keywords: Thermal discomfort London Underground Climate change Heat risk
a b s t r a c t Hot weather events, ventilation assets, changing passenger demand and service expectations have all caused increased attention on thermal comfort on London’s Tube. This study provides estimates of the future number of days when passengers travelling on sections of the Tube could be subjected to thermal discomfort under future scenarios of climate change, and the potential number of passengers dissatisfied. A risk based methodology is presented, integrating a spatial weather generator modified for urban areas and a thermal comfort model. The study provides an initial assessment of adaptation options by considering the implications of lowering train temperatures by 2 °C and 4 °C to represent saloon cooling. Median results under a 2050 high scenario indicate that all Tube lines assessed could experience near-complete passenger dissatisfaction with the thermal environment in trains in the unlikely event that nothing else were to change. Adaptation aimed at lowering train temperatures has the potential to provide tangible improvements in thermal comfort. However, this was not projected to be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under high emission scenarios, requiring a combination of other infrastructure cooling measures to be implemented in parallel. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction Underground railway systems generate heat from their operations, raising tunnel and station temperatures above background soil temperatures, potentially causing thermal discomfort to passengers. About 80% of heat generated by underground railway systems is from the operation of the trains, with the remainder from other equipment and passengers (Ampofo et al., 2004a). This, along with other compounding factors such as hot weather conditions, ventilation capacity determined decades before for a lower service intensity railway, and changing passenger expectations, have in recent years increased public and media attention on the thermal comfort of the London Underground (Ampofo et al., 2004b). LU began operation in 1863, and has since expanded to eleven lines and 270 stations. It is comprised of 402 km of line, with 149 km of deeper mined Tube tunnels. The LU primarily serves Greater London carrying approximately three million passengers a day (TfL, 2013). On the LU the tunnel walls form the principal heat sink on a summer day, absorbing heat during warm periods and releasing heat during cooler periods. The next largest heat sink is through ventilation of air which can be ⇑ Corresponding author. Address: Environmental Change Institute and Tyndall Centre for Climate Change Research, University of Oxford, Oxford OX1 3QY, UK. Tel.: +44 01865 275861; fax: +44 01865 275850. E-mail address: [email protected] (K. Jenkins). http://dx.doi.org/10.1016/j.trd.2014.05.002 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved.
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K. Jenkins et al. / Transportation Research Part D 30 (2014) 1–9
caused by the train piston effect or by ventilation fans. Whilst sub-surface lines were designed with natural ventilation in the form of ‘blow holes’ deep Tube lines exchange less air with the outside. The result is that deep Tube lines are warmer yearround than sub-surface lines, with those with the highest service intensity and/or the least ventilation warmest. LU deep Tube trains are also ventilated rather than air conditioned, and stabilise at conditions about 2 °C warmer than the background tunnel and air temperatures. In-car conditions of around 30 °C can typically be observed on deep Tube trains in summer. In contrast the Building Research Establishment (BRE, 2004) reported that the mean temperature range for thermal comfort on the LU varied between 21–26 °C in trains and 17–25 °C on stations. Most metros aim for a temperature of up to 26 °C to deliver thermal comfort. On the LU new stock on sub-surface lines is cooled to achieve thermal conditions of up to 26 °C. However, the deep Tube stock is much smaller than sub-surface rolling stock. Roof mounted saloon cooling is not possible, and the additional heat rejection would also require mitigation. In addition to the heat implications of increasing capacity on the LU network, climate change may also pose an external threat (Botelle et al., 2010). The UK Climate Impacts Programme (UKCIP) projects that summer mean daily maximum temperature in London will increase by 3.7 °C by the 2050s under a medium emission scenario (central estimate) compared to the 1961–1990 baseline (Murphy et al., 2009). Approximately 1.78 °C of this warming was already estimated to have occurred by 2011, since the 1961–1990 baseline period (assuming a linear trend) (Jenkins et al., 2008). Transport infrastructure and residents of urban areas are also particularly vulnerable due to the Urban Heat Island (UHI) effect, whereby temperatures are higher than in surrounding rural areas due to the heat storage of paved and built up areas, reduced radiative cooling efficiency, and waste heat from buildings, transport, and industry. Therefore at a local level the additive impact of the UHI occurring alongside a warming climate is important to understand for urban society (McCarthy et al., 2012). However, there have been limited publications relating to passenger thermal comfort in underground railway environments (Ampofo et al., 2004b), and to the best of the authors knowledge no studies have quantified the potential effects of climate change. The UK Climate Change Risk Assessment (CCRA) report (DEFRA, 2012) highlighted the issue of thermal discomfort on the LU network, however, an understanding of the potential longer-term impacts under future climate change was not quantified. As such, this study uses an integrated approach to estimate the number of days when passengers travelling on the LU could be subjected to thermal discomfort under future scenarios of climate change; the potential number of passengers dissatisfied; and provides an initial assessment of the potential benefits of adaptation aimed at lowering temperatures on the Tube through saloon cooling. Modelling framework To achieve the aims the study utilises platform temperature sensor data from the LU, as well as outputs from a passenger thermal comfort model (Gilbey and Kemp, 2009). The study uses an extended version of the UK Climate Projections (UKCP09) spatial Weather Generator (WG) which provides probabilistic synthetic weather data, and represents local forcing from urban land-use and anthropogenic heat flux to represent the UHI effect. This facilitates a probabilistic analysis of heat events, as well as providing an assessment of underlying climate model uncertainties. An overview of the integrated assessment facilitating the passenger discomfort risk assessment is illustrated in Fig. 1. The study is focused on platform, ticket hall and train carriage temperatures on tunnelled sections of the Bakerloo, Central, Jubilee, Northern, Piccadilly, and Victoria lines. These lines are deep level lines and do not currently have cooled trains. Only stations located below ground are considered with above ground stations identified and excluded (Fig. 2). In addition, the Hainault loop of the Central line and the Heathrow loop of the Piccadilly line are excluded as platform temperatures at these sections are not monitored, and are understood to be considerably cooler than the central London network. Spatial weather generator for urban areas The spatial and temporal scale of climate model outputs is often inconsistent with that required for climate change impact studies. More spatially explicit climate projections can be produced by incorporating downscaling techniques that account for local climatological features. The most recent UK climate scenarios (UKCP09) have been accompanied by a stochastic Weather Generator (WG) which can provide daily and hourly time series of weather variables for present and future conditions at a 5 km2 resolution (Jones et al., 2009). The WG has been well validated against observed data from 1961 to 1990 (Jones et al., 2009). However, some limitations of the UKCP09 WG include that it simulates weather sequences at a single site so does not provide spatial consistency in time across neighbouring grid cells (Jones et al., 2009). The lack of spatial coherence limits the use of the WG for analysing aggregate impacts over several grid cells. In this study a modified version of the UKCP09 WG is used to provide spatially coherent time-series data. In addition the effects of the UHI due to urban land use and anthropogenic heat flux is incorporated as an additional change factor to the WG (Jenkins et al., 2014; McCarthy et al., 2012). Daily maximum temperature (TMax) time-series data for 30-year stationary sequences are taken from the WG for each grid cell in the study area which intersects the Tube lines of interest, and in order to allow probabilistic analysis 100 WG runs were developed. Daily time-series data is generated for the baseline period (1961–1990) and for the 2030s and 2050s under high and low emission scenarios (equivalent to the IPCC SRES B1 and A1FI scenarios). Each future scenario is also run
K. Jenkins et al. / Transportation Research Part D 30 (2014) 1–9
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Fig. 1. Overview of the risk based modelling framework.
assuming that (i) the ratio of urban anthropogenic heat (UrbAnth) remains the same as the baseline period (1.0) and (ii) that it increases by 50% from the baseline (1.5). The daily TMax time-series data is used to identify the frequency and spatial pattern of daily heat events which occur, under each scenario, based on the exceedance of a temperature threshold. An external temperature threshold of 27 °C is used to define the temperature above which Tube discomfort may begin to transition from warm to hot for the majority of the underground lines, and become increasingly uncomfortable for passengers. Whilst there are currently no maximum temperature thresholds in place in terms of health and safety regulations for passengers, this threshold is in line with other assessments of tolerable temperatures. For example, the hot weather programme for the LU is triggered when external air temperatures exceed 24 °C for an extended period, and Transport for London (TfL) note that passenger discomfort on the Tube occurs as external air temperatures reach the mid-20s and above. Table 1 outlines the average annual number of days each Tube line will be affected to some extent by external daily maximum temperatures of 27 °C or higher. Given the localised study area the results are relatively homogenous across the lines. The numbers of days increase noticeably from 8 days in the baseline scenario to 26–29 days by the 2030s, and to 37–41 days by the 2050s under the low and high UrbAnth 1.0 scenarios. If the ratio of urban anthropogenic heat also increases by 50% from the baseline, then the number of days could be substantially higher. Estimating temperatures on the LU LU have around 400 sensors recording station and platform temperature and humidity data every 15 min. Sensors are installed at the platform tail walls in many stations, with a number also at ticket hall and outside level. This data has been used by LU to provide an understanding of internal Tube platform temperatures. A series of linear regression coefficients
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K. Jenkins et al. / Transportation Research Part D 30 (2014) 1–9
Fig. 2. Geographical position of Tube lines in Greater London and the underground stations considered in this study.
Table 1 Projected average annual number of days each tube line would be affected to some extent by an external maximum temperature of P27 °C. Line
UrbAnth 1.0
Central Piccadilly Northern Jubilee Victoria Bakerloo
UrbAnth 1.5
Baseline
2030 Low
2030 High
2050 Low
2050 High
2030 Low
2030 High
2050 Low
2050 High
8.3 8.2 8.2 8.1 8.2 8.0
25.9 25.9 26.0 25.8 26.0 25.5
28.6 28.5 28.6 28.4 28.5 28.1
37.0 36.9 37.0 36.7 36.9 36.4
40.9 40.7 40.8 40.7 40.8 40.3
36.3 36.2 36.4 36.1 36.2 35.8
36.7 36.7 36.9 36.6 36.7 36.4
44.7 44.7 44.9 44.5 44.6 44.2
51.6 51.6 51.8 51.5 51.6 51.1
have been generated by plotting temperatures recorded at platform tail walls during the afternoon peak (represented as average temperature calculated between 4 pm and 7 pm across samples taken every 15 min for those hours) from 2006 to 2011 against external temperature data (with R2 values generally between 0.7 and 0.8 (Gilbey, 2011)). The coefficients allow internal platform temperature to be estimated as a function of external temperature (London Underground Limited, 2011). The temperature of ticket halls are assumed to fall midway between the platform and external conditions, and the temperature on trains are assumed to be 2 °C warmer than platform temperatures. This data allows an assessment of changes in internal Tube conditions to general short-term changes in daily temperature (Eqs. (1)–(3)).
T platform ð CÞ ¼ T external ð CÞ slope þ intercept
ð1Þ
T tickethalls ð CÞ ¼ ðT external ð CÞ þ T platform Þ=2
ð2Þ
T trains ð CÞ ¼ T platform ð CÞ þ 2 ð CÞ
ð3Þ
In addition to short-term changes in daily temperature climate change is also expected to influence internal Tube temperatures in the longer-term. The external air and the ground are the major heat sinks that control thermal conditions on the Tube, with deep sink soil temperature understood to be related to the average annual temperature (Ampofo et al., 2004a). There is expected to be regional variations in this effect, caused by soil conditions and groundwater flows, and also changes caused by the built environment above. Changnon (1999) highlighted that soil temperatures taken from more urban areas also reflected increased warming due to the UHI effect. Given the available data it is therefore assumed that as average annual temperatures increase, the deep soil temperature, and inadvertently Tube temperatures, will also increase. This can be calculated for each Tube line by assuming that internal temperatures increase by the same extent as the projected
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K. Jenkins et al. / Transportation Research Part D 30 (2014) 1–9 Table 2 Modelled internal temperatures for an external TMax temperature of 27 °C calculated using the regression coefficients from Gilbey (2011). Line
External TMax
Platform temperature (a)
Ticket hall temperature (b)
On-train temperature (c)
Bakerloo Central (excluding Hainault loop) Jubilee Northern Piccadilly (excluding Heathrow loop) Victoria
27 27 27 27 27 27
31.5 30.8 25.4 28.5 28.9 29.5
29.3 28.9 26.2 27.7 27.9 28.2
33.5 32.8 27.4 30.5 30.9 31.5
warming above the temperatures estimated using the threshold temperature of 27 °C (Table 2 and Eqs. (4)–(7). As the temperature data from the spatial WG incorporates effects of urban land-use and anthropogenic heat the impact of urbanisation on soil temperature is incorporated in the scenarios.
T differernce ð CÞ ¼ T external ð CÞ 27 C
ð4Þ
T platform ð CÞ ¼ a þ T difference
ð5Þ
T tickethalls ð CÞ ¼ b þ T difference
ð6Þ
T trains ð CÞ ¼ c þ T difference
ð7Þ
Using the relationships between external and internal temperatures the corresponding platform, ticket-hall, and train temperatures can be estimated for each daily heat event (defined as a day where one or more grid cells in the study area exceed the threshold of 27 °C) and mapped. When calculating internal Tube temperatures it is noted that TMax values from the spatial WG are likely to reflect daily peaks occurring around 3 pm each day in contrast to the sensor data which represents midday temperature calculated as an average across samples taken every 15 min. Based on sensor data it was estimated that platform temperature at 3 pm would be about 0.25 °C warmer than at midday. The projected platform temperature estimated using equation one was therefore increased by 0.25 °C.
Fig. 3. Average platform, ticket hall and train temperature per daily heat event across the lines studied. Bars represent the median results. The black lines denote the range of values at the 10th and 90th percentile.
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For each daily heat event the platform, ticket hall, and train temperatures were averaged across each of the lines studied. Fig. 3 presents the average temperatures estimated per daily heat event, illustrating the effects of the different climate scenarios, and range of probabilistic results generated. Temperature was projected to increase from the baseline on all Tube lines assessed. Whilst the external temperatures were relatively homogenous across the study area (Table 1), there is more pronounced variation in temperatures across the tube lines, reflecting the pre-existing temperature conditions and characteristics of each line. For the median results changes from the baseline period to the 2030s (high emission scenario) range across the lines from 1.2 °C to 1.5 °C for platforms, 0.8 °C to 1.0 °C for ticket halls and 1.1 °C to 1.3 °C for trains. Assuming that anthropogenic heat emissions also increase in the future results in an additional 0.1–0.2 °C of warming. For the 2050s (high emission scenario) changes from the baseline range in magnitude from 1.5 °C to 1.8 °C, 1.2 °C to 1.3 °C, and 1.4 °C to 1.6 °C for platforms, ticket halls and trains respectively. A future increase in anthropogenic heat emissions increases temperatures by an additional 0.2–0.3 °C (see Supplementary data for full tables of results). Fig. 3 demonstrates the potential impacts of climate change on internal Tube temperatures at an aggregate level. However, detail on the spatial distribution of temperatures across each individual line is also important to consider in terms of adaptation planning and for identifying particular hot spots. Fig. 4 illustrates the spatial distribution of platform and in-train temperatures on each Tube line (for display purposes the Tube lines have been coloured accordingly, however, data reflects platform and train temperatures only and not Tube tunnel temperatures). The maps are unable to capture the same spatial detail as the sensor data as the WG grid cell data still covers relatively large portions of the underground network, and as the temperature coefficients were provided as average values across the Tube lines. However, by using output from the spatial WG, which provides spatial coherence across the grid cells, data for individual days can be assessed. This allows approximate validation of the data to the spatial pattern of monitored internal temperatures on a given day to be made. A comparison was made to measured platform temperature data from TfL averaged over the month of August 2012. Comparing this data to Fig. 4c the same general patterns of temperature were seen, with the Bakerloo line warmest followed by the Central line. The patterns for the Northern and Piccadilly lines also appear similar. The Victoria line appeared to follow a different pattern, being cooler in the measured data compared to modelled data in Fig. 4; however, this line is currently in a thermal transition state. Significant investment was made in the upgrade of 13
Fig. 4. Spatial pattern of maximum daily temperature (°C) in trains (top panels) and platforms (bottom panels) based on median results for the baseline scenario (a and c), and 2050 high UrbAnth 1.5 scenario (b and d).
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mid-tunnel ventilation shafts and the provision of two station cooling schemes, all of which were operational in August 2012. Therefore, the line is currently slightly cooler than the state before the line upgrade changes began to take affect (and from which the regression coefficients were developed). This illustrates the challenge of mapping the temperature at a grid scale to a railway that is also likely to be in a state of change. The proposed upgrades on other lines may result in similar changes and uncertainties in projections, as would be the case for any railway reacting to changes in demand. However, considering the many degrees of freedom, the mapping shown in Fig. 4 is deemed to provide a reasonable level of precision and accuracy to a problem with inherent underlying uncertainty. Passengers at risk of discomfort The estimates of platform, ticket hall, and train temperatures are used to calculate the number of passengers who would feel thermal discomfort on each event day. Thermal sensation and the Percentage of People Dissatisfied (PPD) has typically been calculated for steady-state building applications using the Predicted Mean Vote (PMV) whereby a score of 0 = neutral; 1 = slightly warm; 2 = warm; and 3 = hot (Ampofo et al., 2004b). Alternatively, other indices such as the Relative Warmth Index have been used (Abbaspour et al., 2008) to highlight thermal comfort in underground railways. However, transport environments are not steady-state and therefore the tool developed is based on the prediction of thermal sensation using a Transient Predicted Mean Vote (TPMV) index, which considers factors such as outside conditions, duration in the environment and air movement, specially formulated to capture how thermal sensation may vary across a Tube journey (Gilbey and Kemp, 2009). The PMV and TPMV indices were compared for a large range of environments extended duration exposures (30 min or more), with predictions well clustered around the identity line providing a basic steady-state validation for TPMV. Gilbey and Kemp (2009) also provide a description of other transient thermal comfort indices and evaluation of their performance compared to the TPMV index. None of the indices were found to be highly accurate at predicting transient thermal sensation, which is unsurprising considering the influence of such factors as the preceding thermal environment; the metabolic activity of the individual; the clothing levels; the time in the current location; subjectivity around thermal sensation; and normal inter individual differences. However, the TPMV was found to offer improved performance across a range of thermal environments compared to other thermal indices, and hence has been adopted for use. The TPMV index allows the number of people who are likely to be satisfied or dissatisfied with heat-related thermal conditions to be estimated. Based on LU passenger survey data the relationship determined by the model assumes that 80% of the population would be satisfied at a TPMV of zero. Following this, Eq. (8) illustrates the relationship developed to estimate the PPD based on the TPMV value.
y ¼ 5:7809x3 þ 28:316x2 6:5638x þ 21:17
ð8Þ
The relationship between internal temperature and the TPMV was determined using the thermal comfort tool (Gilbey, 2012), with temperature considered the key environmental variable. Whilst comfort will also affected by changes in air speed and relative humidity it is expected that these components will remain little changed in the below ground environment. Furthermore, up to a given maximum (normally around 70%), thermal comfort is more sensitive to changes in temperature rather than relative humidity (Streinu-Cercel et al., 2008) so temperature is the key focus of this study. For the estimates of discomfort made here data from the thermal comfort tool on the TPMV was selected at the mid-point for the walk through ticket halls and wait at station, and for minute 24 on the train as this is about halfway through a typical Tube journey. The relationship between temperature and the TPMV were determined for each line. This was applied to the estimates of internal temperature made for each heat event day to provide an estimate of TPMV, and subsequently passenger discomfort for each scenario. It is assumed that the in-train experience is 2 °C warmer than the platforms and tunnels, which is again a source of uncertainty. A carriage with few passengers may be more or less equal to the tunnel/platform temperature whereas a full carriage may warmer. Estimates of the PPD with temperatures on platforms, for a given heat event day, are provided in Fig. 5 for various scenarios. Results are presented for the median, 10th and 90th percentiles to highlight the range in results reflecting the underlying climate model uncertainties. Additionally, as an air conditioned train is expected to be 2–4 °C cooler than a non-air conditioned train the potential benefits of saloon cooling on PPD was also assessed by adjusting estimated train temperatures by 2 °C and 4 °C. Fig. 6 shows the projected effect of choices around saloon cooling for each of the lines for the baseline and 2050H UrbAnth1.0 scenario (see Supplementary data for full tables of results). As expected and based on knowledge of Tube temperature profiles, the results indicate that certain lines could pose more significant challenges to passengers in terms of thermal discomfort. The Central and Bakerloo lines appear particularly problematic in terms of the percentage of passengers in discomfort. Furthermore, for the sections of these lines considered by this study adaptation in the form of saloon cooling is predicted to do little to reduce PPD, even at the higher level of cooling of 4 °C. For the baseline period PPD on the Central line declines by 1.3–3.5% under the higher level of cooling, but PPD still remains close to 100%. On the Bakerloo line the improvement is even more marginal, 0.0–0.6%. In contrast, for certain lines such as the Northern and Jubilee lines, noticeable benefits in terms of reduced PPD could be gained through saloon cooling. The PPD could decline by as much as 36% and 41% on the Northern and Jubilee lines respectively for the 2050H UrbAnth1.0 best case scenario.
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Fig. 5. The PPD due to thermal discomfort on Tube platforms on a heat event day. Bars represent the median results. The black lines denote the range of values at the 10th and 90th percentile.
Fig. 6. The effect of air conditioning on the percentage of people dissatisfied due to thermal discomfort on trains. Median results for the baseline and 2050H UrbAnth1.0 scenario without air conditioning and assuming air conditioning provides in train cooling of 2 °C and 4 °C. The black lines denote the range of values at the 10th and 90th percentile.
Discussion and conclusions The integrated use of a weather generator and thermal comfort model can offer insights into the effects of longer-term climate change for railway operators. A comparatively large volume of weather data can be generated and hence it becomes important to be able to adopt ‘simple’ models or relationships of any railway to allow the impacts of the data on thermal conditions to be appreciated. LU have benefited from their proactive approach in monitoring their railway thermal conditions. Other railway operators, particularly those with large portions underground, might benefit from a similar approach. For example, the PPD index is beneficial in providing outputs which are likely to be more instructive in terms of understanding thermal discomfort for railways than relying on temperature data alone. The spatial WG is able to provide a range of results reflecting underlying climate model uncertainty and spatial coherence across grid cells. Such probabilistic information will be useful for governments and policy makers as they prepare for future climate risks, and aim to improve the resilience of urban areas and their inhabitants. Furthermore, the effects of an intensification of the UHI are also assessed, which will be important in the longer-term so as not to underestimate temperatures, potential impacts, and consequences of adaptation options. For the LU median results under a 2050 high scenario indicate that all lines assessed would experience near-complete dissatisfaction with the thermal environment in trains in the unlikely event that nothing else where to change. The present day warmer lines (Bakerloo and Central) are the most severely affected. The additional amplification of external temperatures due to the UHI effect could also be a factor in the number of passengers feeling thermal discomfort. Although results indicated that a 50% increase in urban anthropogenic heat emissions from the present day would have limited impact on underground temperatures, slight benefits in terms of reduced PPD could be gained as a consequence of stabilising or reducing anthropogenic heat emissions at a city level, for example through urban greening schemes and reduced energy use. The study suggests that saloon cooling alone would not be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under the high emission scenario and higher percentile levels. Indeed, even for the baseline period saloon cooling was shown to have limited impact on the PPD on the Bakerloo and Central lines. Therefore, while climate change is likely to further exacerbate thermal discomfort in the future, additional cooling is already essential on certain
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lines, and cooling of the platforms and tunnels would also be required. Cooling of trains is already being considered by LU for the next lines proposed to undergo major upgrades (Bakerloo, Central and Piccadilly). If feasible this would clearly be a major factor in improving future resilience to climate change, and reducing the risk that climate change would pose to passenger discomfort. Other railways that do not have cooled trains may similarly find that saloon cooling becomes increasingly important as part of climate change resilience. One limitation of the study is that while discontinuity in the data used to create the temperature coefficients and outputs from the spatial WG has been minimised where possible, the method applies TMax data for a baseline period of 1961–1990 to coefficients calculated based on data from 2006 to 2011. Tube lines have also been warming over time and so the coefficients will reflect both warmer external and internal conditions than would have prevailed during the 1961–1990 baseline. As such baseline results on internal temperatures and PPD may be overestimated. Secondly, in applying external thresholds to this case study it is also important to reiterate that the temperature and thermal comfort of any given railway will vary based on a variety of factors, rather than a single indicator. For example, the elevated body temperatures of passengers entering the system from outside on warmer days can cause increased thermal discomfort even when the Tube temperature remains static. Similarly, the general inputs to the thermal comfort tool used to calculate PPD will also have a large impact on the results generated. For example, it is assumed that passengers begin in air conditioned offices and walk to the station over 5 min. Additional cooling and ventilation measures would also likely be put in place before some of the higher predicted temperatures are realised. For example, LU are investigating the ability to cool future deep Tube rolling stock which would have an impact on future passenger conditions. However, overall the spatial pattern of internal platform temperatures estimated here for the baseline scenario were shown to provide a reasonable level of precision and accuracy to a problem with inherent underlying uncertainty. Although results should be considered illustrative, they are nonetheless useful for understanding future vulnerabilities of commuters; to highlight potential benefits of adaptation options; and the general magnitude of people who may benefit or be affected by climate change in the future. The study also builds upon the recent CCRA which highlighted the challenge of thermal discomfort on the LU, by providing a risk-based assessment which explicitly considers the implications of future climate change at a city scale. Acknowledgements This paper has benefited from research undertaken as part of the ARCADIA Project (Adaptation and Resilience in Cities: Analysis and Decisions-making using Integrated Assessment), funded by the Engineering and Physical Sciences Research Council, award number EP/G060983/1. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.trd.2014.05.002. References Abbaspour, M., Jafari, M.J., Mansouri, N., Moattar, F., Nouri, N., Allahyari, M., 2008. Thermal comfort evaluation in Tehran metro using relative warmth index. Int. J. Environ. Sci. Technol. 5 (3), 297–304. Ampofo, F., Maidment, G., Missenden, J., 2004a. Underground railway environment in the UK Part 2: investigation of heat load. Appl. Therm. Eng. 24 (5–6), 633–645. Ampofo, F., Maidment, G., Missenden, J., 2004b. Underground railway environment in the UK Part 1: review of thermal comfort. Appl. Therm. Eng. 24 (5–6), 611–631. Botelle, M., Payne, K., Redhead, B., 2010. Squeezing the heat out of London’s Tube. Proc. Inst. Civil Eng. 163 (3), 114–122. BRE, 2004. Understanding Thermal Comfort on London Underground Trains and Stations – Summer Survey, Report No. 211738. Building Research Establishment, London. Changnon, S.A., 1999. A rare long record of deep soil temperatures defines temporal temperature changes and an urban heat island. Clim. Change 42 (3), 531–538. DEFRA, 2012. The UK Climate Change Risk Assessment 2012: Evidence Report. DEFRA, London. Gilbey, M.J., 2011. Heat Recovery Report. Tfl, London. Gilbey, M.J., 2012. Tube Thermal Sensation with Warming. Tfl, London. Gilbey, M.J., Kemp, S.J., 2009. 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Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Implications of climate change for thermal discomfort on underground railways Katie Jenkins a,⇑, Mark Gilbey b, Jim Hall a, Vassilis Glenis c, Chris Kilsby c a
Environmental Change Institute (ECI), University of Oxford, Oxford, UK Parsons Brinckerhoff Ltd, London, UK c School of Civil Engineering and Geosciences, Newcastle University, UK b
a r t i c l e
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Keywords: Thermal discomfort London Underground Climate change Heat risk
a b s t r a c t Hot weather events, ventilation assets, changing passenger demand and service expectations have all caused increased attention on thermal comfort on London’s Tube. This study provides estimates of the future number of days when passengers travelling on sections of the Tube could be subjected to thermal discomfort under future scenarios of climate change, and the potential number of passengers dissatisfied. A risk based methodology is presented, integrating a spatial weather generator modified for urban areas and a thermal comfort model. The study provides an initial assessment of adaptation options by considering the implications of lowering train temperatures by 2 °C and 4 °C to represent saloon cooling. Median results under a 2050 high scenario indicate that all Tube lines assessed could experience near-complete passenger dissatisfaction with the thermal environment in trains in the unlikely event that nothing else were to change. Adaptation aimed at lowering train temperatures has the potential to provide tangible improvements in thermal comfort. However, this was not projected to be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under high emission scenarios, requiring a combination of other infrastructure cooling measures to be implemented in parallel. Ó 2014 Elsevier Ltd. All rights reserved.
Introduction Underground railway systems generate heat from their operations, raising tunnel and station temperatures above background soil temperatures, potentially causing thermal discomfort to passengers. About 80% of heat generated by underground railway systems is from the operation of the trains, with the remainder from other equipment and passengers (Ampofo et al., 2004a). This, along with other compounding factors such as hot weather conditions, ventilation capacity determined decades before for a lower service intensity railway, and changing passenger expectations, have in recent years increased public and media attention on the thermal comfort of the London Underground (Ampofo et al., 2004b). LU began operation in 1863, and has since expanded to eleven lines and 270 stations. It is comprised of 402 km of line, with 149 km of deeper mined Tube tunnels. The LU primarily serves Greater London carrying approximately three million passengers a day (TfL, 2013). On the LU the tunnel walls form the principal heat sink on a summer day, absorbing heat during warm periods and releasing heat during cooler periods. The next largest heat sink is through ventilation of air which can be ⇑ Corresponding author. Address: Environmental Change Institute and Tyndall Centre for Climate Change Research, University of Oxford, Oxford OX1 3QY, UK. Tel.: +44 01865 275861; fax: +44 01865 275850. E-mail address: [email protected] (K. Jenkins). http://dx.doi.org/10.1016/j.trd.2014.05.002 1361-9209/Ó 2014 Elsevier Ltd. All rights reserved.
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caused by the train piston effect or by ventilation fans. Whilst sub-surface lines were designed with natural ventilation in the form of ‘blow holes’ deep Tube lines exchange less air with the outside. The result is that deep Tube lines are warmer yearround than sub-surface lines, with those with the highest service intensity and/or the least ventilation warmest. LU deep Tube trains are also ventilated rather than air conditioned, and stabilise at conditions about 2 °C warmer than the background tunnel and air temperatures. In-car conditions of around 30 °C can typically be observed on deep Tube trains in summer. In contrast the Building Research Establishment (BRE, 2004) reported that the mean temperature range for thermal comfort on the LU varied between 21–26 °C in trains and 17–25 °C on stations. Most metros aim for a temperature of up to 26 °C to deliver thermal comfort. On the LU new stock on sub-surface lines is cooled to achieve thermal conditions of up to 26 °C. However, the deep Tube stock is much smaller than sub-surface rolling stock. Roof mounted saloon cooling is not possible, and the additional heat rejection would also require mitigation. In addition to the heat implications of increasing capacity on the LU network, climate change may also pose an external threat (Botelle et al., 2010). The UK Climate Impacts Programme (UKCIP) projects that summer mean daily maximum temperature in London will increase by 3.7 °C by the 2050s under a medium emission scenario (central estimate) compared to the 1961–1990 baseline (Murphy et al., 2009). Approximately 1.78 °C of this warming was already estimated to have occurred by 2011, since the 1961–1990 baseline period (assuming a linear trend) (Jenkins et al., 2008). Transport infrastructure and residents of urban areas are also particularly vulnerable due to the Urban Heat Island (UHI) effect, whereby temperatures are higher than in surrounding rural areas due to the heat storage of paved and built up areas, reduced radiative cooling efficiency, and waste heat from buildings, transport, and industry. Therefore at a local level the additive impact of the UHI occurring alongside a warming climate is important to understand for urban society (McCarthy et al., 2012). However, there have been limited publications relating to passenger thermal comfort in underground railway environments (Ampofo et al., 2004b), and to the best of the authors knowledge no studies have quantified the potential effects of climate change. The UK Climate Change Risk Assessment (CCRA) report (DEFRA, 2012) highlighted the issue of thermal discomfort on the LU network, however, an understanding of the potential longer-term impacts under future climate change was not quantified. As such, this study uses an integrated approach to estimate the number of days when passengers travelling on the LU could be subjected to thermal discomfort under future scenarios of climate change; the potential number of passengers dissatisfied; and provides an initial assessment of the potential benefits of adaptation aimed at lowering temperatures on the Tube through saloon cooling. Modelling framework To achieve the aims the study utilises platform temperature sensor data from the LU, as well as outputs from a passenger thermal comfort model (Gilbey and Kemp, 2009). The study uses an extended version of the UK Climate Projections (UKCP09) spatial Weather Generator (WG) which provides probabilistic synthetic weather data, and represents local forcing from urban land-use and anthropogenic heat flux to represent the UHI effect. This facilitates a probabilistic analysis of heat events, as well as providing an assessment of underlying climate model uncertainties. An overview of the integrated assessment facilitating the passenger discomfort risk assessment is illustrated in Fig. 1. The study is focused on platform, ticket hall and train carriage temperatures on tunnelled sections of the Bakerloo, Central, Jubilee, Northern, Piccadilly, and Victoria lines. These lines are deep level lines and do not currently have cooled trains. Only stations located below ground are considered with above ground stations identified and excluded (Fig. 2). In addition, the Hainault loop of the Central line and the Heathrow loop of the Piccadilly line are excluded as platform temperatures at these sections are not monitored, and are understood to be considerably cooler than the central London network. Spatial weather generator for urban areas The spatial and temporal scale of climate model outputs is often inconsistent with that required for climate change impact studies. More spatially explicit climate projections can be produced by incorporating downscaling techniques that account for local climatological features. The most recent UK climate scenarios (UKCP09) have been accompanied by a stochastic Weather Generator (WG) which can provide daily and hourly time series of weather variables for present and future conditions at a 5 km2 resolution (Jones et al., 2009). The WG has been well validated against observed data from 1961 to 1990 (Jones et al., 2009). However, some limitations of the UKCP09 WG include that it simulates weather sequences at a single site so does not provide spatial consistency in time across neighbouring grid cells (Jones et al., 2009). The lack of spatial coherence limits the use of the WG for analysing aggregate impacts over several grid cells. In this study a modified version of the UKCP09 WG is used to provide spatially coherent time-series data. In addition the effects of the UHI due to urban land use and anthropogenic heat flux is incorporated as an additional change factor to the WG (Jenkins et al., 2014; McCarthy et al., 2012). Daily maximum temperature (TMax) time-series data for 30-year stationary sequences are taken from the WG for each grid cell in the study area which intersects the Tube lines of interest, and in order to allow probabilistic analysis 100 WG runs were developed. Daily time-series data is generated for the baseline period (1961–1990) and for the 2030s and 2050s under high and low emission scenarios (equivalent to the IPCC SRES B1 and A1FI scenarios). Each future scenario is also run
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Fig. 1. Overview of the risk based modelling framework.
assuming that (i) the ratio of urban anthropogenic heat (UrbAnth) remains the same as the baseline period (1.0) and (ii) that it increases by 50% from the baseline (1.5). The daily TMax time-series data is used to identify the frequency and spatial pattern of daily heat events which occur, under each scenario, based on the exceedance of a temperature threshold. An external temperature threshold of 27 °C is used to define the temperature above which Tube discomfort may begin to transition from warm to hot for the majority of the underground lines, and become increasingly uncomfortable for passengers. Whilst there are currently no maximum temperature thresholds in place in terms of health and safety regulations for passengers, this threshold is in line with other assessments of tolerable temperatures. For example, the hot weather programme for the LU is triggered when external air temperatures exceed 24 °C for an extended period, and Transport for London (TfL) note that passenger discomfort on the Tube occurs as external air temperatures reach the mid-20s and above. Table 1 outlines the average annual number of days each Tube line will be affected to some extent by external daily maximum temperatures of 27 °C or higher. Given the localised study area the results are relatively homogenous across the lines. The numbers of days increase noticeably from 8 days in the baseline scenario to 26–29 days by the 2030s, and to 37–41 days by the 2050s under the low and high UrbAnth 1.0 scenarios. If the ratio of urban anthropogenic heat also increases by 50% from the baseline, then the number of days could be substantially higher. Estimating temperatures on the LU LU have around 400 sensors recording station and platform temperature and humidity data every 15 min. Sensors are installed at the platform tail walls in many stations, with a number also at ticket hall and outside level. This data has been used by LU to provide an understanding of internal Tube platform temperatures. A series of linear regression coefficients
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Fig. 2. Geographical position of Tube lines in Greater London and the underground stations considered in this study.
Table 1 Projected average annual number of days each tube line would be affected to some extent by an external maximum temperature of P27 °C. Line
UrbAnth 1.0
Central Piccadilly Northern Jubilee Victoria Bakerloo
UrbAnth 1.5
Baseline
2030 Low
2030 High
2050 Low
2050 High
2030 Low
2030 High
2050 Low
2050 High
8.3 8.2 8.2 8.1 8.2 8.0
25.9 25.9 26.0 25.8 26.0 25.5
28.6 28.5 28.6 28.4 28.5 28.1
37.0 36.9 37.0 36.7 36.9 36.4
40.9 40.7 40.8 40.7 40.8 40.3
36.3 36.2 36.4 36.1 36.2 35.8
36.7 36.7 36.9 36.6 36.7 36.4
44.7 44.7 44.9 44.5 44.6 44.2
51.6 51.6 51.8 51.5 51.6 51.1
have been generated by plotting temperatures recorded at platform tail walls during the afternoon peak (represented as average temperature calculated between 4 pm and 7 pm across samples taken every 15 min for those hours) from 2006 to 2011 against external temperature data (with R2 values generally between 0.7 and 0.8 (Gilbey, 2011)). The coefficients allow internal platform temperature to be estimated as a function of external temperature (London Underground Limited, 2011). The temperature of ticket halls are assumed to fall midway between the platform and external conditions, and the temperature on trains are assumed to be 2 °C warmer than platform temperatures. This data allows an assessment of changes in internal Tube conditions to general short-term changes in daily temperature (Eqs. (1)–(3)).
T platform ð CÞ ¼ T external ð CÞ slope þ intercept
ð1Þ
T tickethalls ð CÞ ¼ ðT external ð CÞ þ T platform Þ=2
ð2Þ
T trains ð CÞ ¼ T platform ð CÞ þ 2 ð CÞ
ð3Þ
In addition to short-term changes in daily temperature climate change is also expected to influence internal Tube temperatures in the longer-term. The external air and the ground are the major heat sinks that control thermal conditions on the Tube, with deep sink soil temperature understood to be related to the average annual temperature (Ampofo et al., 2004a). There is expected to be regional variations in this effect, caused by soil conditions and groundwater flows, and also changes caused by the built environment above. Changnon (1999) highlighted that soil temperatures taken from more urban areas also reflected increased warming due to the UHI effect. Given the available data it is therefore assumed that as average annual temperatures increase, the deep soil temperature, and inadvertently Tube temperatures, will also increase. This can be calculated for each Tube line by assuming that internal temperatures increase by the same extent as the projected
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K. Jenkins et al. / Transportation Research Part D 30 (2014) 1–9 Table 2 Modelled internal temperatures for an external TMax temperature of 27 °C calculated using the regression coefficients from Gilbey (2011). Line
External TMax
Platform temperature (a)
Ticket hall temperature (b)
On-train temperature (c)
Bakerloo Central (excluding Hainault loop) Jubilee Northern Piccadilly (excluding Heathrow loop) Victoria
27 27 27 27 27 27
31.5 30.8 25.4 28.5 28.9 29.5
29.3 28.9 26.2 27.7 27.9 28.2
33.5 32.8 27.4 30.5 30.9 31.5
warming above the temperatures estimated using the threshold temperature of 27 °C (Table 2 and Eqs. (4)–(7). As the temperature data from the spatial WG incorporates effects of urban land-use and anthropogenic heat the impact of urbanisation on soil temperature is incorporated in the scenarios.
T differernce ð CÞ ¼ T external ð CÞ 27 C
ð4Þ
T platform ð CÞ ¼ a þ T difference
ð5Þ
T tickethalls ð CÞ ¼ b þ T difference
ð6Þ
T trains ð CÞ ¼ c þ T difference
ð7Þ
Using the relationships between external and internal temperatures the corresponding platform, ticket-hall, and train temperatures can be estimated for each daily heat event (defined as a day where one or more grid cells in the study area exceed the threshold of 27 °C) and mapped. When calculating internal Tube temperatures it is noted that TMax values from the spatial WG are likely to reflect daily peaks occurring around 3 pm each day in contrast to the sensor data which represents midday temperature calculated as an average across samples taken every 15 min. Based on sensor data it was estimated that platform temperature at 3 pm would be about 0.25 °C warmer than at midday. The projected platform temperature estimated using equation one was therefore increased by 0.25 °C.
Fig. 3. Average platform, ticket hall and train temperature per daily heat event across the lines studied. Bars represent the median results. The black lines denote the range of values at the 10th and 90th percentile.
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For each daily heat event the platform, ticket hall, and train temperatures were averaged across each of the lines studied. Fig. 3 presents the average temperatures estimated per daily heat event, illustrating the effects of the different climate scenarios, and range of probabilistic results generated. Temperature was projected to increase from the baseline on all Tube lines assessed. Whilst the external temperatures were relatively homogenous across the study area (Table 1), there is more pronounced variation in temperatures across the tube lines, reflecting the pre-existing temperature conditions and characteristics of each line. For the median results changes from the baseline period to the 2030s (high emission scenario) range across the lines from 1.2 °C to 1.5 °C for platforms, 0.8 °C to 1.0 °C for ticket halls and 1.1 °C to 1.3 °C for trains. Assuming that anthropogenic heat emissions also increase in the future results in an additional 0.1–0.2 °C of warming. For the 2050s (high emission scenario) changes from the baseline range in magnitude from 1.5 °C to 1.8 °C, 1.2 °C to 1.3 °C, and 1.4 °C to 1.6 °C for platforms, ticket halls and trains respectively. A future increase in anthropogenic heat emissions increases temperatures by an additional 0.2–0.3 °C (see Supplementary data for full tables of results). Fig. 3 demonstrates the potential impacts of climate change on internal Tube temperatures at an aggregate level. However, detail on the spatial distribution of temperatures across each individual line is also important to consider in terms of adaptation planning and for identifying particular hot spots. Fig. 4 illustrates the spatial distribution of platform and in-train temperatures on each Tube line (for display purposes the Tube lines have been coloured accordingly, however, data reflects platform and train temperatures only and not Tube tunnel temperatures). The maps are unable to capture the same spatial detail as the sensor data as the WG grid cell data still covers relatively large portions of the underground network, and as the temperature coefficients were provided as average values across the Tube lines. However, by using output from the spatial WG, which provides spatial coherence across the grid cells, data for individual days can be assessed. This allows approximate validation of the data to the spatial pattern of monitored internal temperatures on a given day to be made. A comparison was made to measured platform temperature data from TfL averaged over the month of August 2012. Comparing this data to Fig. 4c the same general patterns of temperature were seen, with the Bakerloo line warmest followed by the Central line. The patterns for the Northern and Piccadilly lines also appear similar. The Victoria line appeared to follow a different pattern, being cooler in the measured data compared to modelled data in Fig. 4; however, this line is currently in a thermal transition state. Significant investment was made in the upgrade of 13
Fig. 4. Spatial pattern of maximum daily temperature (°C) in trains (top panels) and platforms (bottom panels) based on median results for the baseline scenario (a and c), and 2050 high UrbAnth 1.5 scenario (b and d).
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mid-tunnel ventilation shafts and the provision of two station cooling schemes, all of which were operational in August 2012. Therefore, the line is currently slightly cooler than the state before the line upgrade changes began to take affect (and from which the regression coefficients were developed). This illustrates the challenge of mapping the temperature at a grid scale to a railway that is also likely to be in a state of change. The proposed upgrades on other lines may result in similar changes and uncertainties in projections, as would be the case for any railway reacting to changes in demand. However, considering the many degrees of freedom, the mapping shown in Fig. 4 is deemed to provide a reasonable level of precision and accuracy to a problem with inherent underlying uncertainty. Passengers at risk of discomfort The estimates of platform, ticket hall, and train temperatures are used to calculate the number of passengers who would feel thermal discomfort on each event day. Thermal sensation and the Percentage of People Dissatisfied (PPD) has typically been calculated for steady-state building applications using the Predicted Mean Vote (PMV) whereby a score of 0 = neutral; 1 = slightly warm; 2 = warm; and 3 = hot (Ampofo et al., 2004b). Alternatively, other indices such as the Relative Warmth Index have been used (Abbaspour et al., 2008) to highlight thermal comfort in underground railways. However, transport environments are not steady-state and therefore the tool developed is based on the prediction of thermal sensation using a Transient Predicted Mean Vote (TPMV) index, which considers factors such as outside conditions, duration in the environment and air movement, specially formulated to capture how thermal sensation may vary across a Tube journey (Gilbey and Kemp, 2009). The PMV and TPMV indices were compared for a large range of environments extended duration exposures (30 min or more), with predictions well clustered around the identity line providing a basic steady-state validation for TPMV. Gilbey and Kemp (2009) also provide a description of other transient thermal comfort indices and evaluation of their performance compared to the TPMV index. None of the indices were found to be highly accurate at predicting transient thermal sensation, which is unsurprising considering the influence of such factors as the preceding thermal environment; the metabolic activity of the individual; the clothing levels; the time in the current location; subjectivity around thermal sensation; and normal inter individual differences. However, the TPMV was found to offer improved performance across a range of thermal environments compared to other thermal indices, and hence has been adopted for use. The TPMV index allows the number of people who are likely to be satisfied or dissatisfied with heat-related thermal conditions to be estimated. Based on LU passenger survey data the relationship determined by the model assumes that 80% of the population would be satisfied at a TPMV of zero. Following this, Eq. (8) illustrates the relationship developed to estimate the PPD based on the TPMV value.
y ¼ 5:7809x3 þ 28:316x2 6:5638x þ 21:17
ð8Þ
The relationship between internal temperature and the TPMV was determined using the thermal comfort tool (Gilbey, 2012), with temperature considered the key environmental variable. Whilst comfort will also affected by changes in air speed and relative humidity it is expected that these components will remain little changed in the below ground environment. Furthermore, up to a given maximum (normally around 70%), thermal comfort is more sensitive to changes in temperature rather than relative humidity (Streinu-Cercel et al., 2008) so temperature is the key focus of this study. For the estimates of discomfort made here data from the thermal comfort tool on the TPMV was selected at the mid-point for the walk through ticket halls and wait at station, and for minute 24 on the train as this is about halfway through a typical Tube journey. The relationship between temperature and the TPMV were determined for each line. This was applied to the estimates of internal temperature made for each heat event day to provide an estimate of TPMV, and subsequently passenger discomfort for each scenario. It is assumed that the in-train experience is 2 °C warmer than the platforms and tunnels, which is again a source of uncertainty. A carriage with few passengers may be more or less equal to the tunnel/platform temperature whereas a full carriage may warmer. Estimates of the PPD with temperatures on platforms, for a given heat event day, are provided in Fig. 5 for various scenarios. Results are presented for the median, 10th and 90th percentiles to highlight the range in results reflecting the underlying climate model uncertainties. Additionally, as an air conditioned train is expected to be 2–4 °C cooler than a non-air conditioned train the potential benefits of saloon cooling on PPD was also assessed by adjusting estimated train temperatures by 2 °C and 4 °C. Fig. 6 shows the projected effect of choices around saloon cooling for each of the lines for the baseline and 2050H UrbAnth1.0 scenario (see Supplementary data for full tables of results). As expected and based on knowledge of Tube temperature profiles, the results indicate that certain lines could pose more significant challenges to passengers in terms of thermal discomfort. The Central and Bakerloo lines appear particularly problematic in terms of the percentage of passengers in discomfort. Furthermore, for the sections of these lines considered by this study adaptation in the form of saloon cooling is predicted to do little to reduce PPD, even at the higher level of cooling of 4 °C. For the baseline period PPD on the Central line declines by 1.3–3.5% under the higher level of cooling, but PPD still remains close to 100%. On the Bakerloo line the improvement is even more marginal, 0.0–0.6%. In contrast, for certain lines such as the Northern and Jubilee lines, noticeable benefits in terms of reduced PPD could be gained through saloon cooling. The PPD could decline by as much as 36% and 41% on the Northern and Jubilee lines respectively for the 2050H UrbAnth1.0 best case scenario.
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Fig. 5. The PPD due to thermal discomfort on Tube platforms on a heat event day. Bars represent the median results. The black lines denote the range of values at the 10th and 90th percentile.
Fig. 6. The effect of air conditioning on the percentage of people dissatisfied due to thermal discomfort on trains. Median results for the baseline and 2050H UrbAnth1.0 scenario without air conditioning and assuming air conditioning provides in train cooling of 2 °C and 4 °C. The black lines denote the range of values at the 10th and 90th percentile.
Discussion and conclusions The integrated use of a weather generator and thermal comfort model can offer insights into the effects of longer-term climate change for railway operators. A comparatively large volume of weather data can be generated and hence it becomes important to be able to adopt ‘simple’ models or relationships of any railway to allow the impacts of the data on thermal conditions to be appreciated. LU have benefited from their proactive approach in monitoring their railway thermal conditions. Other railway operators, particularly those with large portions underground, might benefit from a similar approach. For example, the PPD index is beneficial in providing outputs which are likely to be more instructive in terms of understanding thermal discomfort for railways than relying on temperature data alone. The spatial WG is able to provide a range of results reflecting underlying climate model uncertainty and spatial coherence across grid cells. Such probabilistic information will be useful for governments and policy makers as they prepare for future climate risks, and aim to improve the resilience of urban areas and their inhabitants. Furthermore, the effects of an intensification of the UHI are also assessed, which will be important in the longer-term so as not to underestimate temperatures, potential impacts, and consequences of adaptation options. For the LU median results under a 2050 high scenario indicate that all lines assessed would experience near-complete dissatisfaction with the thermal environment in trains in the unlikely event that nothing else where to change. The present day warmer lines (Bakerloo and Central) are the most severely affected. The additional amplification of external temperatures due to the UHI effect could also be a factor in the number of passengers feeling thermal discomfort. Although results indicated that a 50% increase in urban anthropogenic heat emissions from the present day would have limited impact on underground temperatures, slight benefits in terms of reduced PPD could be gained as a consequence of stabilising or reducing anthropogenic heat emissions at a city level, for example through urban greening schemes and reduced energy use. The study suggests that saloon cooling alone would not be sufficient to maintain comfortable thermal conditions for many of the lines in the 2050s under the high emission scenario and higher percentile levels. Indeed, even for the baseline period saloon cooling was shown to have limited impact on the PPD on the Bakerloo and Central lines. Therefore, while climate change is likely to further exacerbate thermal discomfort in the future, additional cooling is already essential on certain
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lines, and cooling of the platforms and tunnels would also be required. Cooling of trains is already being considered by LU for the next lines proposed to undergo major upgrades (Bakerloo, Central and Piccadilly). If feasible this would clearly be a major factor in improving future resilience to climate change, and reducing the risk that climate change would pose to passenger discomfort. Other railways that do not have cooled trains may similarly find that saloon cooling becomes increasingly important as part of climate change resilience. One limitation of the study is that while discontinuity in the data used to create the temperature coefficients and outputs from the spatial WG has been minimised where possible, the method applies TMax data for a baseline period of 1961–1990 to coefficients calculated based on data from 2006 to 2011. Tube lines have also been warming over time and so the coefficients will reflect both warmer external and internal conditions than would have prevailed during the 1961–1990 baseline. As such baseline results on internal temperatures and PPD may be overestimated. Secondly, in applying external thresholds to this case study it is also important to reiterate that the temperature and thermal comfort of any given railway will vary based on a variety of factors, rather than a single indicator. For example, the elevated body temperatures of passengers entering the system from outside on warmer days can cause increased thermal discomfort even when the Tube temperature remains static. Similarly, the general inputs to the thermal comfort tool used to calculate PPD will also have a large impact on the results generated. For example, it is assumed that passengers begin in air conditioned offices and walk to the station over 5 min. Additional cooling and ventilation measures would also likely be put in place before some of the higher predicted temperatures are realised. For example, LU are investigating the ability to cool future deep Tube rolling stock which would have an impact on future passenger conditions. However, overall the spatial pattern of internal platform temperatures estimated here for the baseline scenario were shown to provide a reasonable level of precision and accuracy to a problem with inherent underlying uncertainty. Although results should be considered illustrative, they are nonetheless useful for understanding future vulnerabilities of commuters; to highlight potential benefits of adaptation options; and the general magnitude of people who may benefit or be affected by climate change in the future. The study also builds upon the recent CCRA which highlighted the challenge of thermal discomfort on the LU, by providing a risk-based assessment which explicitly considers the implications of future climate change at a city scale. Acknowledgements This paper has benefited from research undertaken as part of the ARCADIA Project (Adaptation and Resilience in Cities: Analysis and Decisions-making using Integrated Assessment), funded by the Engineering and Physical Sciences Research Council, award number EP/G060983/1. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.trd.2014.05.002. References Abbaspour, M., Jafari, M.J., Mansouri, N., Moattar, F., Nouri, N., Allahyari, M., 2008. Thermal comfort evaluation in Tehran metro using relative warmth index. Int. J. Environ. Sci. Technol. 5 (3), 297–304. Ampofo, F., Maidment, G., Missenden, J., 2004a. 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