Impact of Environmental Parameters on Marathon
Running Performance
Nour El Helou1,2,3*, Muriel Tafflet1,4, Geoffroy Berthelot1,2, Julien Tolaini1, Andy Marc1,2,
Marion Guillaume1, Christophe Hausswirth5, Jean-Franc¸ois Toussaint1,2,6
1 IRMES (bioMedical Research Institute of Sports Epidemiology), INSEP, Paris, France, 2 Universite´ Paris Descartes, Sorbonne Paris Cite´, Paris, France, 3 Faculte´ de
Pharmacie, De´partement de Nutrition, Universite´ Saint Joseph, Beirut, Lebanon, 4 INSERM, U970, Paris Cardiovascular Research Center – PARCC, Paris, France, 5 Research
Department, INSEP, Paris, France, 6 Hoˆtel-Dieu Hospital, CIMS, AP-HP, Paris, France
Abstract
Purpose: The objectives of this study were to describe the distribution of all runners’ performances in the largest marathons
worldwide and to determine which environmental parameters have the maximal impact.
Methods: We analysed the results of six European (Paris, London, Berlin) and American (Boston, Chicago, New York)
marathon races from 2001 to 2010 through 1,791,972 participants’ performances (all finishers per year and race). Four
environmental factors were gathered for each of the 60 races: temperature (uC), humidity (%), dew point (uC), and the
atmospheric pressure at sea level (hPA); as well as the concentrations of four atmospheric pollutants: NO2 – SO2 – O3 and
PM10 (mg.m23).
Results: All performances per year and race are normally distributed with distribution parameters (mean and standard
deviation) that differ according to environmental factors. Air temperature and performance are significantly correlated
through a quadratic model. The optimal temperatures for maximal mean speed of all runners vary depending on the
performance level. When temperature increases above these optima, running speed decreases and withdrawal rates
increase. Ozone also impacts performance but its effect might be linked to temperature. The other environmental
parameters do not have any significant impact.
Conclusions: The large amount of data analyzed and the model developed in this study highlight the major influence of air
temperature above all other climatic parameter on human running capacity and adaptation to race conditions.
Citation: El Helou N, Tafflet M, Berthelot G, Tolaini J, Marc A, et al. (2012) Impact of Environmental Parameters on Marathon Running Performance. PLoS ONE 7(5):
e37407. doi:10.1371/journal.pone.0037407
Editor: Alejandro Lucia, Universidad Europea de Madrid, Spain
Received February 28, 2012; Accepted April 19, 2012; Published May 23, 2012
Copyright:  2012 El Helou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no support or funding to report.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: nour.elhelou@insep.fr
Introduction
Like most phenotypic traits, athletic performance is multifacto-
rial and influenced
by genetic and environmental factors:
exogenous factors contribute to the expression of the predisposing
characteristics among best athletes [1,2]. The marathon is one of
the most challenging endurance competitions; it is a mass
participation race held under variable environmental conditions
and temperatures sometimes vary widely from start to finish [3–5].
Warm weather during a marathon is detrimental for runners and
is commonly referenced as limiting for thermoregulatory control
[3,6].
More
medical
complaints
of
hyperthermia
(internal
temperature $39uC) occur in warm weather events, while
hypothermia (internal temperature #35uC) sometimes occurs
during cool weather events [3].
In addition, participating in an outdoor urban event exposes
athletes to air pollution which raises concerns for both performance
and health [7]. Runners could be at risk during competitions as they
are subject to elevated ventilation rate and increased airflow velocity
amplifying the dose of inhaled pollutants and carrying them deeper
into the lungs [7–9]. They switch from nasal to mouth breathing,
bypassing nasal filtration mechanisms for large particles. Both might
increase the deleterious effects of pollutants on health and athletic
performance [8,10]. Exposure to air pollution during exercise might
be expected to impair an athlete’s performance in endurance events
lasting one hour or more [7,10].
The relationship between marathon performance decline and
warmer air temperature has been well established. Vihma [6] and
Ely et al. [11,12] found a progressive and quantifiable slowing of
marathon performance as WBGT (Wet Bulb Globe Temperature)
increases, for men and women of wide ranging abilities. Ely et al.
[13] as well as Montain et al. [14] also found that cooler weather
(5–10uC) was associated with better ability to maintain running
velocity through a marathon race compared to warmer conditions
especially by fastest runners; weather impacted pacing and the
impact was dependent on finishing position. Marr and Ely [9]
found significant correlations between the increase of WBGT and
PM10, and slower marathon performance of both men and
women; but they did not find significant correlations with any
other pollutant.
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Previous studies have mostly analysed the performances of the
top 3 males and females finishers as well as the 25th-, 100th-, and
300th- place finishers [11,13–16]. Here we targeted exhaustiveness
and analysed the total number of finishers in order to quantify the
effect of climate on the full range of runners.
The objectives of this study were 1) to analyse all levels of
running
performance
by
describing
the
distribution
of
all
marathons finishers by race, year and gender; 2) to determine
the impact of environmental parameters: on the distribution of all
marathon runners’ performance in men and women (first and last
finishers, quantiles of distribution); and on the percentage of
runners withdrawals. We then modelled the relation between
running speed and air temperature to determine the optimal
environmental conditions for achieving the best running perfor-
mances, and to help, based on known environmental parameters,
to predict the distribution and inform runners on possible
outcomes of running at different ambient temperatures. We tested
the hypothesis that all runners’ performances distributions may be
similar in all races, and may be similarly affected by temperature.
Methods
Data Collection
Marathon race results were obtained from six marathons
included in the « IAAF Gold Labeled Road Races » and « World
Marathon Majors »: Berlin, Boston, Chicago, London, New York
and Paris. From 2001 to 2010 (available data are limited before
2001) the arrival times in hours: minutes: seconds, of all finishers
were gathered for each race. These data are available in the public
domain on the official internet website of each city marathon, and
on marathon archives websites [17] and complementary data
when needed from official sites of each race. Written and informed
consent was therefore not required from individual athletes. The
total number of collected performances was 1,791,972 for the 60
races (10 years 66 marathons), including 1,791,071 performances
for which the gender was known. We also gathered the total
number of starters in order to calculate the number and the
percentage of non-finishers (runner withdrawal) per race.
Hourly weather data corresponding to the race day, time span
and location of the marathon were obtained from ‘‘weather
underground website’’ [5]. Four climatic data were gathered for
each of the 60 races: air temperature (uC), air humidity (%), dew-
point temperatures (uC), and atmospheric pressure at sea level
(hPA). Each of these parameters was averaged for the first 4 hours
after the start of each race. Hourly air pollution data for the day,
time span and location of each race were also obtained through
the concentrations of three atmospheric pollutants: NO2 – SO2 –
O3 (mg.m23) from the Environmental Agency in each state (the
Illinois Environmental Protection Agency for Chicago maratho’n,
the Massachusetts Department of environmental Protection for
Boston marathon and the New York State Department of
Environmental Conservation for New York marathon), and the
Environmental agency websites of the three European cities [18–
20]. All pollutants values were averaged for the first 4 hours after
the start of each race.
Concurrent measurements of air pollution for all ten race years
(2001–2010) were only available for 3 pollutants, because air
pollution monitoring sites typically measure only a subset of
pollutants and may not have been operational in all years. In
addition, particulate matters PM10 were collected in Paris and
Berlin, but there were not enough measurements in the other four
cities races days.
Data Analysis and selection
Men and women performances were analysed separately. For
each race and each gender every year, we fitted the Normal and
log-Normal distributions to the performances and tested the
normality and log normality using the Kolmogorov-Smirnov D
statistic. We rejected the null hypothesis that the sample is
normally or log–normally distributed when p values ,0.01.
The following statistics (performance levels) were determined for
all runners’ performances distribution of each race, every year and
for each gender:
–
the first percentile of the distribution (P1), representing the elite
of each race.
–
the winner.
–
the last finisher.
–
the first quartile of the distribution (Q1), representing the 25th
percentile of best performers of the studied race.
–
the median.
–
the inter quartile range (IQR), representing the statistical
dispersion, being equal to the difference between the third and
first quartiles.
A Spearman correlation test was performed between each
performance level and climate and air pollution parameters, in
order to quantify the impact of weather and pollution on
marathon performances. Spearman correlation tests were also
performed between each environmental parameter. The year
factor was not included because we previously demonstrated that
for the past ten years, marathon performances were now
progressing at a slower rate [21].
Temperature and running speed
We modelled the relation between running speed of each
performance level for each gender and air temperature, using a
second degree polynomial quadratic model, which seems appro-
priate to depict such physiological relations [22–24].
The second degree polynomial equation was applied to
determine the optimal temperature at which maximal running
speed is achieved for each level of performance for each gender,
and then used to calculate the speed decrease associated with
temperature increase and decrease above the optimum.
We similarly modelled the relation between air temperature and
the percentage of runners’ withdrawal.
All analyses were performed using the MATLAB and SAS
software.
Results
The total numbers of starters and finishers of the 6 marathons
increased over the 10 studied years (Figure 1). Marathons
characteristics are described in supplementary data (Table S1).
The race with the least number of finishers was Boston 2001 with
13381 finishers and the highest number was seen in New York
2010 with 44763 finishers.
Three marathons were held in April, the other three during fall.
Air temperatures ranged from 1.7uC (Chicago 2009) to 25.2uC
(Boston 2004) (Table 1).
Performance distribution
For all 60 studied races, the women and men’s performance
distributions were a good approximation of the ‘‘log normal’’ and
‘‘normal’’ distributions (p-values of Kolmogorov-Smirnov statistics
$0.01).
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Figure 2 illustrates examples of 4 races’ performances distribu-
tion fit: men’s performances distribution of two races in Paris
(2002: Tu = 7.6uC; and 2007: Tu = 17.4uC) and Chicago (2002:
Tu = 5.4uC; and 2007: Tu = 25uC).
We notice a stable gap between male and female performances at
all levels in all marathons, women being on average 10.3%61.6%
(mean 6 standard deviation) slower than men (Table S1); mean
female winners are 9.9%61.5% slower than male winners, mean
female median is 9.9%61.6% than male median, and mean female
Q1 are 11.1%61.5% slower that male Q1.
Correlations
Spearman correlations results are displayed in Table 2, detailed
correlations by marathon are available in supplementary data
(Table S2).
The environmental parameter that had the most significant
correlations with marathons performances was air temperature: it
was significantly correlated with all performance levels in both
male and female runners.
Humidity was the second parameter with a high impact on
performance; it was significantly correlated with women’s P1 and
men’s all performance levels.
The dew point and atmospheric pressure only had a slight
influence (p,0.1) in men’s P1 and women’s P1 respectively, and
did not affect the other performance levels.
Concerning the atmospheric pollutants, NO2 had the most
significant correlation with performance: it was significantly
correlated with Q1, IQR and the median for both genders. Sulfur
dioxide (SO2) was correlated with men’s P1 (p,0.01) and had a
slight influence (p,0.1) on men’s Q1. Finally ozone (O3) only had
a slight influence (p,0.1) on men’s Q1. In the marathon by
marathon analysis, ozone (O3) had the most significant correlation
with performance (Table S2): it was significantly correlated with all
performance levels (P1, Q1, IQR and the median) of the Berlin
and Boston (except men’s IQR) marathon for both genders. It also
affected Chicago (men’s P1, Q1, and men’s median), and New
York (women’s Q1) marathons.
Temperature and running speed
When temperature increased above an optimum, performance
decreased. Figure 3 describes the relationship between marathons
running speeds and air temperature, fit through a quadratic
second degree polynomial curve for women’s P1 and men’s Q1 of
all 60 races.
For each performance level the speed decrease associated with
temperature increase and decrease is presented in supplementary
data (Table S3).
For example the optimal temperature at which women’s P1
maximal running speed was attained was 9.9uC, and an increase of
1uC from this optimal temperature will result in a speed loss of
0.03%. The optimal temperatures to run at maximal speed for
men and women, varied from 3.8uC to 9.9uC according to each
level of performance (Table S3).
Warmer air temperatures were associated with higher percent-
ages of runners’ withdrawal during a race (Figure 4). After testing
linear, quadratic, exponential and logarithmic fits, the quadratic
equation was the best fit (r2 = 0.36; p,0.0001) for modelling the
percentage of runners withdrawals associated with air temperature
(Figure 4):
%withdrawals~{0:59|t0Cz0:02|t0C2z5:75
Discussion
Our study is the first to our knowledge to analyse the
exhaustiveness of all marathon finishers’ performances in the
three major European (Berlin, Paris and London, which were not
previously analysed) and three American marathons. Previous
studies have mostly analysed American marathons including
Chicago, Boston and New York that are analysed in the present
paper [9,11–15], but they have only included the performances of
the top 3 males and females finishers as well as the 25th-, 100th-,
and 300th- place finishers [11,13–15]. In the present study we
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
Finishers
Non finishers
Number of starters
Berlin
Boston
Chicago
London
New York
Paris
2001
2010
2001
2010
2001
2010
2001
2010
2001
2010
2001
2010
Figure 1. Number of starters and finishers by marathon and year (missing data points for Boston, Chicago and Paris marathons).
doi:10.1371/journal.pone.0037407.g001
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Table 1. Average and range values of all weather and pollution parameters for the six marathons.
Marathon
Parameter
N
Mean
Std Dev
Minimum
Maximum
Berlin
Run in September; Starts 9am
Temperature (uC)
10
14.9
3.2
11.3
21.3
Dew Point (uC)
10
10.6
1.8
5.8
12.3
Humidity (%)
10
78.0
14.5
55.0
98.5
Atmospheric pressure (hPA)
10
1017.0
6.3
1003.0
1029.0
NO2 (mg.m23)
10
26.5
4.0
20.8
33.2
O3 (mg.m23)
10
41.0
17.3
21.2
81.8
PM10 (mg.m23)
8
25.1
11.4
7.6
46.5
SO2 (mg.m23)
10
5.0
3.1
1.1
10.7
Boston
Run in April; Starts 10am
Temperature (uC)
10
11.8
5.1
8.0
25.2
Dew Point (uC)
10
3.9
3.8
22.1
10.2
Humidity (%)
10
62.6
19.9
28.3
91.0
Atmospheric pressure (hPA)
10
1013.0
12.4
981.6
1029.0
NO2 (mg.m23)
10
29.3
10.3
14.6
50.5
O3 (mg.m23)
10
73.5
25.7
18.5
122.7
PM10 (mg.m23)
0
SO2 (mg.m23)
10
7.0
2.9
1.6
12.1
Chicago
Run in October; Starts 7:30am
Temperature (uC)
10
12.1
7.5
1.7
25.0
Dew Point (uC)
10
4.9
7.6
25.9
19.0
Humidity (%)
10
62.8
8.1
52.3
79.2
Atmospheric pressure (hPA)
10
1022.0
6.4
1012.0
1031.0
NO2 (mg.m23)
10
27.9
13.0
9.7
52.0
O3 (mg.m23)
10
57.1
15.1
35.9
84.0
PM10 (mg.m23)
2
26.7
11.6
15.3
38.0
SO2 (mg.m23)
9
6.5
3.1
2.1
12.4
London
Run in April; Starts 9:30am
Temperature (uC)
10
12.4
3.2
9.5
19.1
Dew Point (uC)
10
6.0
2.9
0.8
10.7
Humidity (%)
10
66.9
16.7
42.9
86.1
Atmospheric pressure (hPA)
10
1010.0
12.5
976.4
1020.0
NO2 (mg.m23)
10
44.8
14.5
22.8
72.2
O3 (mg.m23)
9
51.4
17.1
35.0
92.3
PM10 (mg.m23)
2
27.8
14.5
13.7
41.9
SO2 (mg.m23)
10
4.5
2.8
0.0
8.8
New York
Run in November; Starts 10am
Temperature (uC)
10
12.5
4.1
7.1
18.4
Dew Point (uC)
10
2.3
6.4
25.6
12.8
Humidity (%)
10
51.1
12.1
36.5
79.8
Atmospheric pressure (hPA)
10
1020.0
7.8
1009.0
1034.0
NO2 (mg.m23)
9
55.1
17.2
21.9
77.3
O3 (mg.m23)
10
32.6
12.3
11.1
53.8
PM10 (mg.m23)
10
5.0
0.0
5.0
5.0
SO2 (mg.m23)
9
19.7
12.2
4.8
42.4
Paris
Run in April; Starts 8:45am
Temperature (uC)
10
9.2
3.2
4.8
17.4
Dew Point (uC)
10
4.2
4.1
23.6
13.4
Humidity (%)
10
72.4
10.1
45.9
85.4
Atmospheric pressure (hPA)
10
1019.0
6.2
1005.0
1026.0
NO2 (mg.m23)
10
43.0
13.7
23.4
73.1
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analysed the total number of finishers in order to exhaustively
quantify the effect of climate on runners from all performance
levels. Updating and extending earlier results, this study still
concludes that the main environmental factor influencing mara-
thon performance remains temperature. The pattern of perfor-
mance reduction with increasing temperature is analogous in men
and women, suggesting no apparent gender differences. In
addition the mean gap between male and female performances
is the same across all marathons and all performance levels
(Table 1). This is consistent with our previous work that showed
that the gender gap in athletic performance has been stable for
more than 25 years, whatever the environmental conditions [25].
The more the temperature increases, the larger the decreases in
running speeds (Table S3). This is supported by the increased
percentage of runners’ withdrawals when races were contested in
very hot weather (Figure 4), and by the significant shift of the
race’s results through the whole range of performance distribution
(Figure 2). The significant effect of air temperature on the median
values (Table 2) also suggests that all runners’ performances are
similarly affected by an increase in air temperature, as seen in
Figure 2 showing performances distribution of races in Paris and
Chicago with different air temperatures: the significant shift of
performance towards the right concerns all runners categories,
from the elite to the less trained competitors. In addition the
percentage of runner’s withdrawals in Chicago 2007 was the
highest (30.74%) among all 60 studied races (Figure 1 and
Figure 4). Roberts [26] reported that organisers tried to interrupt
the race 3.5 h after the start. This was not successful as most of the
finishers crossed the finish line much later (up to 7 h after the
start); 66 runners were admitted to the hospital (12 intensive care
cases with hydration disorders, heat shock syndromes and 1 death).
During the 2004 Boston Marathon (Tu = 22.5uC) more than 300
emergency medical calls were observed, consequently the race’s
start time changed from noon to 10 am in order to decrease heat
stress and related casualties [26]. The 2007 London Marathon was
hot by London standards (air Tu = 19.1uC vs. an average of
11.6uC for the nine other years analysed in our study), 73
hospitalisations were recorded with 6 cases of severe electrolyte
imbalance and one death, the total average time (all participants’
average) was 17 min slower than usual. In contrast, the number of
people treated in London 2008 in cool and rainy conditions
(Tu = 9.9uC), was 20% lower [26]. Our results showed that the
percentage of runners’ withdrawals from races significantly
increases with increasing temperature (Figure 4). The acceptable
upper limit for competition judged by the American College of
Sports Medicine (ACSM) is a WBGT of 28uC, but it may not
reflect the safety profile of unacclimatized, non-elite marathon
runners [3,26–28]. Roberts [26] stated that marathons should not
be allowed to start for non-elite racers at a WBGT of 20.5uC. Our
results suggest that there is no threshold but a continuous process
Table 1. Cont.
Marathon
Parameter
N
Mean
Std Dev
Minimum
Maximum
O3 (mg.m23)
10
66.9
9.8
55.2
82.1
PM10 (mg.m23)
10
37.9
32.6
16.6
132.7
SO2 (mg.m23)
10
6.4
3.7
1.5
12.2
doi:10.1371/journal.pone.0037407.t001
0
2
4
6
8
10
12
2:10
3:10
4:10
5:10
6:10
7:10
8:10
Percent
0
2
4
6
8
10
12
2:10
3:10
4:10
5:10
6:10
7:10
8:10
0
2
4
6
8
10
12
2:10
3:10
4:10
5:10
6:10
7:10
8:10
Percent
Arrival time in hour:minutes
0
2
4
6
8
10
12
2:10
3:10
4:10
5:10
6:10
7:10
8:10
Arrival time in hour:minutes
A - Chicago 2002
B - Paris 2002
C - Chicago 2007
D - Paris 2007
Figure 2. Distribution of performances: example of men’s performances distribution for Chicago (in 2002: T6C = 5.46C; and in 2007:
T6C = 256C); and Paris (in 2002: T6C = 7.66C; and in 2007: T6C = 17.46C).
doi:10.1371/journal.pone.0037407.g002
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on both side of an optimum: the larger the gap from the optimal
temperature, the lower the tolerance and the higher the risk. In
fact, in environments with high heat and humidity, not only is
performance potentially compromised, but health is also at risk
[29]; both are similarly affected. As soon as WBGT is higher than
13uC the rate of finish line medical encounters and on-course
marathon dropouts begin to rise [26] as similarly seen in our study
in Figure 4.
Warm weather enhances the risk of exercise induced hyper-
thermia; its first measurable impact is the reduction of physical
performance [4,14,29–31] as it is detrimental for the cardiovas-
cular, muscular and central nervous systems [32,33]. More recent
work suggested that central fatigue develops before any elevation
in body temperature occurs: evidence supported that subjects
would subconsciously reduce their velocity earlier after the start of
an exercise in hot environment, when internal temperatures are
still lower than levels associated with bodily harm. Exercise is thus
homeostatically regulated by the decrease of exercise intensity
(decrease of running performance and heat production) in order to
prevent hyperthermia and related catastrophic failures [34,35].
On the other hand, cool weather is associated with an improved
ability to maintain running velocity and power output as
compared to warmer conditions, but very cold conditions also
tend to reduce performance [29,36,37].
Among the studied races’ winners, men’s marathon world
record was beaten in Berlin in 2007 and 2008 (Haile Gebrselassie
in 02:03:59), as well as women’s marathon world record, beaten in
London 2003 (Paula Radcliffe in 02:15:25). The winners’ speeds
couldn’t be affected in the same way than the other runners by air
temperature and the other environmental parameters, because top
performances can fluctuate from year to year due to numerous
factors, such as prize money, race strategies, or overall competition
[11]. Another explanation is that, in all of our 60 studied races,
89.5% of male winners were of African origin (57.9% from Kenya;
21.1% from Ethiopia; and 10.5% from Eritrea, Morocco and
South Africa); as well as 54.5% of female winners (27.3% from
Kenya and 27.3% from Ethiopia- data not shown). African
runners might have an advantage over Caucasian athletes,
possibly due to a unique combination of the main endurance
factors such as maximal oxygen uptake, fractional utilization of
VO2max and running economy [38]. They might also perform
better in warm environments as they are usually thinner than
Caucasian runners (smaller size and body mass index) producing
less heat with lower rates of heat storage [38–40]. Psychological
factors may also play a role; some hypothesis suggested that
regardless of the possible existence of physiological advantages in
East African runners, belief that such differences exist may create a
background that can have significant positive consequences on
performance [41,42].
Genetics and training influence the tolerance for hyperthermia
[4,38,43]. Acclimatisation involving repeated exposures to exercise
in the heat also results in large improvements in the time to fatigue.
Optimal thermoregulatory responses are observed in runners who
have been acclimatized to heat and who avoid thirst before and
during the race. Their best performances might be less influenced by
temperature as winners had been more acclimatized to it
[4,29,30,44]. The avoidance of thirst sensation rather than optimum
Table 2. Spearman correlations results between all
marathons performance levels and environmental
parameters: $ = p,0.1; * = p,0.05; ** = p,0.01;
*** = p,0.001.
Parameter
Gender
P1
Median
Q1
IQR
Temperature
Women
0.31*
0.30*
0.35**
0.15
Men
0.48***
0.40***
0.44***
0.25$
Dew Point
Women
0.14
0.18
0.21
0.01
Men
0.25$
0.19
0.20
0.10
Humidity
Women
20.3*
20.16
20.19
20.21
Men
20.34**
20.28*
20.32*
20.19
Atm. Pressure
Women
0.22$
0.06
0.07
0.06
Men
0.13
0.04
0.06
0.06
NO2
Women
0.11
0.40**
0.43***
0.33*
Men
0.25$
0.38**
0.35**
0.27*
O3
Women
0.01
20.15
20.11
20.20
Men
20.05
20.21
20.24$
20.11
PM10
Women
0.08
0.15
0.25
0.03
Men
0.10
0.10
0.09
0.16
SO2
Women
0.21
0.13
0.21
0.02
Men
0.37**
0.20
0.25$
0.04
P1: first percentile, Q1: first quartile, IQR: Inter Quartile Range.
doi:10.1371/journal.pone.0037407.t002
0
5
10
15
20
25
30
3.4
3.5
3.6
3.7
3.8
3.9
4.0
Temperature (°C)
speed (m.s−1)
0
5
10
15
20
25
30
2.6
2.8
3.0
3.2
3.4
3.6
3.8
4.0
Temperature (°C)
A - Women P1
B - Men Q1
Figure 3. Quadratic second degree polynomial fit for Women’s P1 running speeds vs. air temperature, r2 = 0.27; p,0.001;
max = 9.96C. B) Men’s Q1 running speeds vs. air temperature, r2 = 0.24; p,0.001; max = 66C.
doi:10.1371/journal.pone.0037407.g003
Environmental Parameters and Marathon Running
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hydration prevents the decline in running performance [45];
contradicting the idea that dehydration associated with a body
weight loss of 2% during an exercise will impair performance, recent
studies reported that Haile Gebrselassie lost 10% of his body weight
when he established his world record [45–47].
Previous studies suggested that the impact of weather on speed
might depend on running ability, with faster runners being less
limited than slower ones [6,13,14,29]. This could be attributable
to a longer time of exposition to the environmental conditions of
slower runners during the race [11]. Also, slower runners tend to
run in closer proximity to other runners with clustering formation
[48,49], which may cause more heat stress as compared with
running solo [50]. These elements, however, are not supported
after analyzing the full range of finisher’s data; at a population
level, temperature causes its full effect whatever the initial
capacity. Differences in fitness relative to physiological potential
may also contribute to differences in performance times and ability
to cope with increasing heat stress [11,48,49].
There was a strong correlation of running speed with air
temperature (Figure 3). The maximal average speeds were
performed at an optimal temperature comprised between 3.8uC
and 9.9uC depending on the performance level (Table S3); small
increases in air temperatures caused marathon performances to
decline in a predictable and quantifiable manner. On the other
hand, large decreases in air temperatures under the optimum also
reduce performances. These optimal temperatures found in the
present study are comprised in the optimal temperature range of
5–10uC WBGT found in previous studies [14]; other studies stated
that a weather of 10–12uC WBGT is the norm for fast field
performance and reported a decrease of performance with
increasing WBGT [12,27,51,52]. Best marathon times and most
marathon world records were achieved in cool environmental
temperatures (10–15uC) and have been run in the early morning
during spring and fall [12]. Analysing Gebrselassie’s performances
in Berlin reveals that they follow the same trend, with both World
Records obtained at the lowest temperatures (14uC in 2007 and
13uC in 2008, vs. 18uC in 2009 and 22uC in 2006 when he also
won these two races without beating the world record).
The relationship between running speed and air temperature
defined in our study (Figure 3) is similar to the relationship found
between mortality and air temperature (asymmetrical U-like
pattern) in France defined by Laaidi et al [53], where mortality
rates increase with the lowest and the highest temperatures. A
‘‘thermal optimum’’ occurs in between, where mortality rates are
minimal [53]. The great influence that temperature has on
performance is comparable to the influence it has on mortality,
suggesting that both sports performance and mortality are
thermodynamically regulated. This also emphasizes the utility of
prevention programs, the assessment of public health impacts and
acclimatization before participating in hot marathons [53]. Similar
correlations were also found between temperature and swimming
performance in juvenile southern catfish [22], and between
increases in summer water temperature and elevated mortality
rates of adult sockeye salmon [23]; suggesting that physiological
adaptations to temperature, similarly occur in various taxons, but
vary within specific limits that depend on species and will modify
performances.
Air pollution and performance
The measured levels of pollution had no impact on perfor-
mance, except for ozone (Table S2) and NO2 (Table 2). Assessing
the effect of any single air pollutant separately is not simple; it is
not isolated in the inhaled air, but rather combined with other
parameters. Therefore any possible influence might probably be
due to a combination of components. In addition most marathons
are held on Sunday mornings, when urban transport activity and
its associated emissions are low, and photochemical reactions
driven by solar radiation have not yet produced secondary
pollutants such as ozone [9]. This is the most probable explanation
to our results, confirming previous studies. Among the air
pollutants analysed in the present study, ozone and NO2 had
the greatest effect on decreasing marathon performances (Ta-
ble S2). Ozone concentrations on the ground increase linearly
with air temperature [7,8,10]; thus the effect of ozone in our study
may be mainly associated with the temperature effect, as seen in
Berlin and Chicago. However ozone and other pollutants effects
are known to be detrimental to exercise performance only when
exposure is sufficiently high. Many studies showed no effect of air
pollutants on sports performance [9]. Some of them showed that
PM2.5 and aerosol acidity were associated with acute decrements
in pulmonary function, but these changes in pulmonary function
were unlikely to result in clinical symptoms [54]. Others showed
that chronic exposure to mixed pollutants during exercise may
result in decreased lung function, or vascular dysfunction, and may
compromise performance [55]. During the marathons studied
here, concentrations of air pollutants never exceeded the limits set
forth by national environmental agencies (US Environmental
Protection Agency- EPA; AirParif; European Environmental
Agency- EEA) or the levels known to alter lung function in
laboratory situations [9].
Conclusions
Air temperature is the most important factor influencing
marathon running performance for runners of all levels. It greatly
influences the entire distribution of runners’ performances as well
as the percentage of withdrawals. Running speed at all levels is
linked to temperature through a quadratic model. Any increase or
decrease from the optimal temperature range will result in running
speed decrease. Ozone also has an influence on performance but
its effect might be linked to the temperature impact. The model
developed in this study could be used for further predictions, in
order to evaluate expected performance variations with changing
weather conditions.
Temperature (°C)
Withdrawals (%)
0
5
10
15
20
25
30
35
0
5
10
15
20
25
30
Figure 4. Relationship between air temperature and the
percentage of runners’ withdrawals, modeled with a quadratic
fit (blue curve, r2 = 0.36; p,0.0001). The green curve represents the
quadratic fit without the maxima (Chicago 2007: 30.74% withdrawals at
a race temperature of 25uC).
doi:10.1371/journal.pone.0037407.g004
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May 2012 | Volume 7 | Issue 5 | e37407
Supporting Information
Table S1
Time values of different descriptive statistics
and their variability by marathon and gender. 1 Value of
the described statistic for all performances of all year together,
hour:min:sec 2 Standard deviation of the described statistic for all
performances of each year, hour:min:sec 3 IQR: Inter Quartile
Range.
(DOCX)
Table S2
Spearman correlations results between each
marathon performance levels and environmental pa-
rameters: $ = p,0.1; * = p,0.05; ** = p,0.01; *** =
p,0.001. P1: first percentile, Q1: first quartile, IQR:
Inter Quartile Range.
(DOCX)
Table S3
Optimal temperatures for maximal running
speeds of each level of performance, with speed losses
associated with each temperature increase.
(DOCX)
Acknowledgments
We thank the Centre National de De´veloppement du Sport and the Ministry
of Health,Youthand Sport.We thankINSEPteamsfor theirfullsupport. We
thank Mrs Karine Schaal for carefully reviewing the manuscript.
Author Contributions
Conceived and designed the experiments: JFT NEH GB AM. Analyzed the
data: JT GB AM NEH MG MT. Wrote the paper: NEH GB JFT.
Reviewed the paper: CH JFT.
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Impact of environmental parameters on marathon running performance.
El Helou, Nour,Tafflet, Muriel,Berthelot, Geoffroy,Tolaini, Julien,Marc, Andy,Guillaume, Marion,Hausswirth, Christophe,Toussaint, Jean-François