4.1. Meteorology
Basic descriptive statistics for the data used in this work are shown in Table 2.Table 2. Descriptive statistics for the parkrun finishing times and meteorological conditions encountered during the study period.
|
Minimum
|
Maximum
|
Mean
|
parkrun time (minutes)
|
14.42
|
90.50
|
26.62
|
Temperature (oC)
|
-6.60
|
26.00
|
11.38
|
Relative humidity (%)
|
42.50
|
100.00
|
78.30
|
Wind Speed (ms-1)
|
0.00
|
12.86
|
3.61
|
4.1.1 Temperature
Initial analysis shows that the distribution of run times are predominantly between 20 and 30 minutes, with temperatures between 8-15oC. Linear regression analysis across all parkrun events resulted in the regression equation (Equation 1) below, where t is run time in s, and T is temperature in °C. The linear regression gives Equation 1, which is significant at the 99% confidence interval and explains 3% of the variance in run times.
t = -4.83 + (0.42 × T) (Equation 1)
Examination of individual parkrun events shows that five locations have significant relationships between finishing times and temperature (Table 3). Of these, however, Bromley parkrun has a negative relationship with finishing times, suggesting that quicker performances occur under warmer conditions (p=0.05).
Table 3. Individual parkruns and their relationship between finishing times and temperature variations.
Location
|
Intercept
|
Temperature Coefficient
|
F Statistic
|
R2 Value
|
p Value
|
Bedfont
|
-3.78
|
0.24
|
0.4
|
-0.002
|
0.53
|
Brockwell
|
1.11
|
0.01
|
1.00
|
3.111e-05
|
0.32
|
Bromley
|
6.57
|
-0.63
|
3.98
|
0.01
|
0.05
|
Bushy Park
|
1564.36
|
1.2
|
4.12
|
0.05
|
0.05
|
Crystal Palace
|
-0.57
|
0.03
|
0.01
|
-0.004
|
0.93
|
Finsbury Park
|
-10.64
|
0.86
|
5.73
|
0.02
|
0.02
|
Greenwich
|
5.84
|
-0.52
|
1.34
|
0.01
|
0.25
|
Grovelands
|
1.34
|
0.001
|
0.03
|
-0.01
|
0.87
|
Hackney Marshes
|
0.64
|
-0.01
|
0.001
|
-0.004
|
0.98
|
Kingston
|
1509.3
|
-1.41
|
1.35
|
0.01
|
0.25
|
Lloyd
|
-5.00
|
0.4
|
0.4
|
-0.002
|
0.53
|
Old Deer Park
|
7.26
|
-0.87
|
2.24
|
0.01
|
0.14
|
Richmond
|
-9.4
|
0.84
|
8.21
|
0.03
|
<0.01
|
Roundshaw
|
-11.03
|
1.03
|
4.46
|
0.01
|
0.04
|
Wimbledon
|
2.06
|
-0.31
|
0.79
|
-0.001
|
0.38
|
Gender analyses show that at the events where comparable significant relationships are shown, female run times are more influenced than male. Additionally, when all significant relationships between temperature and finishing times are considered, female coefficients are larger and more significant than male. For example, for the complete female subset, temperature coefficients for correlation, linear regression and multiple linear regression are 0.19, 0.56 and 0.75 respectively with p=<0.01, whilst for the male subset the corresponding values are 0.15, 0.32 and 0.41 with p=<0.02.
Examination of age groups showed some interesting results. Increased temperatures and wind speeds were detrimental to finishing times of the age groups shown in Table 4. Temperature shows significant positive relationships with the middle-aged to older age groups, with no apparent influence on the children, youth and young adult competitors in the 25-29 and younger age groups.
Table 4. Significant linear regression results of age group analysis. Wind speed appears to be the
dominant variable regardless of age.
Age Group
|
Explanatory Variable
|
Coefficient
|
P Value
|
20-24
|
Wind Speed
|
2.47
|
<0.01
|
25-29
|
Wind Speed
|
0.03
|
<0.01
|
35-39
|
Temperature
|
0.43
|
0.06
|
40-44
|
Wind Speed
|
1.78
|
<0.01
|
Temperature
|
0.48
|
0.02
|
45-49
|
Wind Speed
|
1.34
|
<0.01
|
temperature
|
0.36
|
0.06
|
50-54
|
Wind Speed
|
1.2
|
<0.01
|
Temperature
|
0.52
|
<0.01
|
55-59
|
Wind Speed
|
1.47
|
<0.01
|
65-69
|
Wind Speed
|
1.66
|
0.02
|
Temperature
|
0.74
|
0.09[JH(G+ESLF1]
|
[JH(G+ESLF1]Reviewer #2 – Merged the age group cells to make table clearer.
4.1.2. Relative Humidity
Results of the relative humidity analysis suggest that in most cases elevated levels reduce performance. Although not significantly different, the mean finishing time (not decomposed) rises from 1584.41 s under relative humidity levels of 40-55% to 1598.14 s when RH is above 85.1%. Interestingly, female participants are slightly more influenced than male, seeing an increase in finishing time 1.3 s more when RH rises from 40-55% to over 85%. For the age groups this descriptive analysis shows that most see increases in finishing time of 5-30 s, although notably the 70-75 and 80-85 age groups show increases of 131.54 and 77.18 s respectively.
Correlation and linear regression analysis for this explanatory variable shows a number of significant relationships. With the exception of the 25-29 age group and the Richmond event (overall and male subset), these all show that increased RH is associated with slower finishing times (p=<0.08).
4.1.3. Wind Speed
Significant results were only found at seven of the fifteen events as well as the overall and male and female subsets (p=<0.08, Fig 4). R2 values ranged from 1-12% and a student’s T-test revealed a significant difference between the mean run time at high (>6 ms-1) and low (<6 ms-1) wind speeds (p=<0.01) for the overall and male datasets. At a number of events, wind speed increases saw correspondingly higher, thus slower, parkrun finishing times. No particular age group showed a greater influence of wind speed on their finishing times compared to the others (Table 4).
Fig 4. Results of linear regression analysis for the overall (row A), female (row B) and male (row C) parkrun subsets with the three meteorological variables examined.
In most cases, male competitors showed a significant relationship with wind speed that was not matched by the corresponding female analysis. For example, at Wimbledon parkrun male finishing times are slower in association with increased wind speeds through correlation (coefficient - 0.16), regression (coefficient - 2.25) and multiple linear regression (coefficient - 2.75) analysis (p = 0.01).
4.1.4. Combined influences
Multiple linear regression was performed using the influence of temperature, relative humidity and wind speed (Equation 2). These three variables explained 10% of the variance in average parkrun finishing times (p=<0.01), with increased values being associated with slower finishing times. Results of the multiple linear regression are shown in Table 5, with 16% of the variance in finishing times at Bushy Park attributed to the three variables.
Average parkrun time = -23.64 + 0.51 T** + 0.13 RH + 1.07 WS**( Equation 2)
**Significance <0.01
Table 5. Meteorology multiple linear regression results for the fifteen parkrun events.
Location
|
Intercept
|
Temperature
|
RH
|
Wind Speed
|
Significance
|
R2
|
Bedfont
|
-23.69
|
0.56
|
0.28
|
-0.6
|
0.24
|
0.004
|
Brockwell
|
1.37
|
0.01
|
-0.003
|
No Data
|
0.52
|
-0.01
|
Bromley
|
-0.26
|
-0.26
|
0.69
|
0.38
|
<0.01
|
0.04
|
Bushy Park
|
1522.16
|
1.08
|
0.42
|
1.39
|
<0.01
|
0.16
|
Crystal Palace
|
-5.57
|
0.06
|
0.03
|
0.26
|
0.96
|
-0.01
|
Finsbury Park
|
-3.15
|
0.78
|
-0.09
|
No Data
|
0.07
|
0.02
|
Greenwich
|
-31.41
|
-0.2
|
0.37
|
0.67
|
0.21
|
0.01
|
Grovelands
|
1.26
|
0.002
|
0.001
|
No Data
|
0.96
|
-0.02
|
Hackney Marshes
|
0.36
|
-0.04
|
-0.02
|
0.22
|
0.99
|
-0.01
|
Kingston
|
1407.99
|
-0.75
|
1.17
|
0.09
|
0.17
|
0.04
|
Lloyd
|
10.34
|
0.18
|
-0.29
|
1.2
|
0.37
|
0.001
|
Old Deer Park
|
-53.91
|
-0.46
|
0.52
|
3.33
|
0.04
|
0.02
|
Richmond
|
5.75
|
0.51
|
-0.24
|
1.5
|
<0.01
|
0.06
|
Roundshaw
|
-57.57
|
1.4
|
0.41
|
0.91
|
0.03
|
0.02
|
Wimbledon
|
-25.59
|
-0.2
|
0.2
|
2.27
|
0.06
|
0.02
|
4.2. Air Quality
Basic descriptive statistics for the data used in this work are shown in Table 6.
Table 6. Descriptive statistics of the air quality conditions encountered by parkrun participants during the study period in comparison to the UK air quality standards.
|
Minimum
|
Maximum (UK standard)
|
Mean (UK standard - yearly average)
|
O3 (ugm-3)
|
1.14
|
76.91 (120)
|
33.62 (N/A)
|
NO2 (ugm-3)
|
10
|
95.38 (200)
|
33.54 (40)
|
PM2.5 (ugm-3)
|
1.4
|
86 (N/A)
|
13.33 (25)
|
4.2.1. Ozone
Examination of the O3 data showed only two close to significant relationships with finishing times. This was for the male subset with correlation and linear regression suggesting a correlation coefficient of 0.11 and 0.08 respectively (p=0.09). Both the overall and female subsets showed no significant relationships between the variables. However, all analyses despite not being significant, showed O3 to have positive relationship with finishing times, thus suggesting that run times are getting slower. At individual parkrun events, most showed positive relationships with O3, with the most notable significant relationships at the Bushy Park, Crystal Palace and Lloyd Park events (p=<0.05). In contrast, however, Greenwich, Kingston and Wimbledon parkruns all showed negative relationships, although these weren’t significant. The 55-59 age group also showed a significant (p=<0.09) positive relationships with ozone whilst the 40-44 and 45-49 were close to significant with p=0.07 and 0.09 respectively.
4.2.2. Nitrogen Dioxide
NO2 for the overall data and two gender subsets shows no significant relationships with performance. However, all results show a negative trend, suggesting a potential for improved performances under elevated NO2 conditions. Similarly to the larger subsets, most individual parkrun events showed a negative relationship between finishing times and NO2 levels, with close to significant results shown at Bushy Park, Lloyd and Richmond (p=<0.09). Interestingly, events at Bromley and Finsbury showed positive relationships between the two variables, particularly for the overall and female subsets. Similarly to ozone, age group analysis showed the same demographics, 40-49 and 55-59, had significant (p=<0.05) negative relationships with NO2.
4.2.3. PM2.5
PM2.5 showed no significant relationships with the overall or subset run times. Unlike the O3 and NO2 results, which if not consistently significant show clear trends in their relationship with finishing times for both the overall and individual parkrun events, there isn’t a clear trend in the PM2.5 data (Fig 5.). At individual parkrun events, Bushy, Bromley and Lloyd are the only significant results, which are negative relationships. At the remaining twelve events, three shown positive trends, five are negative and the other four have both positive and negative relationships depending on the subset examined. Only the 45-49 age group had a significant (p=0.01) relationship with PM2.5, which was again negative.
Multiple linear regression analysis that included the three pollutants showed only one significant relationship with finishing times (Table 7).
Table[JH(G+ESLF1] 7. Air quality multiple linear regression results for the fifteen parkrun events.
Location
|
Intercept
|
O3
|
NO2
|
PM2.5
|
Significance
|
R2
|
Bedfont
|
-27.83
|
0.13
|
7.25
|
11.7
|
0.52
|
0.01
|
Brockwell
|
1.18
|
0.003
|
-0.04
|
No data
|
0.67
|
-0.02
|
Bromley
|
-11.7
|
0.11
|
6.66
|
-2.68
|
0.8
|
-0.01
|
Bushy Park
|
3.93
|
0.13
|
-1.02
|
-7.16
|
0.12
|
0.03
|
Crystal Palace
|
-11.15
|
0.25
|
-11.15
|
12.35
|
0.09
|
0.02
|
Finsbury Park
|
0.96
|
0.002
|
0.14
|
No data
|
0.84
|
-0.02
|
Greenwich
|
23.17
|
-0.31
|
-22.41
|
14.83
|
0.12
|
0.02
|
Grovelands
|
1.32
|
0.003
|
-0.04
|
No data
|
0.36
|
0.001
|
Hackney Marshes
|
16.48
|
-0.01
|
-10.83
|
No data
|
0.79
|
-0.01
|
Kingston
|
2.12
|
-0.01
|
-0.16
|
-0.3
|
0.71
|
-0.04
|
Lloyd
|
17.2
|
0.29
|
-26.73
|
1.15
|
0.01
|
0.04
|
Old Deer Park
|
0.89
|
0.003
|
0.25
|
0.16
|
0.85
|
-0.07
|
Richmond
|
-13.96
|
0.08
|
13.75
|
-10.13
|
0.52
|
-0.01
|
Roundshaw
|
-28.33
|
0.22
|
5.52
|
10.00
|
0.92
|
-0.03
|
Wimbledon
|
3.68
|
-0.03
|
-0.54
|
-3.19
|
0.99
|
-0.03
|
[JH(G+ESLF1]Reviewer #1 MLR results included as a table as requested.