Descriptive statistics
The study period was from Dec 6, 2017 to Dec 31, 2018. The average amount of anonymized users included during the study period were 11.1 million for Beijing, 9.6 mil for Shanghai, 2.8 mil for Chongqing, 4.9 mil for Shenzhen and 0.4 mil for Hong Kong. Compared to census data, the study samples in all five cities had a comparable gender distribution with their respective general populations (see Table 1). However, the sample populations tended to be significantly younger than the general population.
Table 2 displays the summary findings. The aggregated average step counts for each of the cities during the study period averaged 6846 steps for Beijing, 6703 for Shanghai, 7540 for Chongqing, 7209 for Shenzhen and 9040 for Hong Kong. The average step count was significantly higher among males than females (T-test, p <0.001). During the study period, daily temperatures averaged from 12.7°C (±SD 12.1) in Beijing, to 23.5°C (±SD 5.3) in Hong Kong.
Table 1 Demographic comparison between sample population and city population of five Chinese cities (Unit 10,000 persons)
City
|
Sample
|
|
|
Population (2018)
|
p-value
|
|
Category
|
Sample size (%)
|
±SD
|
Category
|
N (%)
|
|
Beijing
|
Total
|
1108.9 (100.0)
|
167.3
|
Total
|
2154.2 (100.0)
|
|
|
Gender
|
|
|
|
|
|
|
Male
|
555.2 (50.1)
|
81.7
|
Male
|
1095.6 (50.9)
|
0.879
|
|
Female
|
553.7 (49.9)
|
85.7
|
Female
|
1058.6 (49.1)
|
|
|
Age
|
|
|
Total 15+
|
1927.6
|
|
|
18-64
|
1095.2 (98.8)
|
163.4
|
15-64
|
1686.2 (87.5)
|
<0.001*
|
|
65+
|
13.7 (1.2)
|
4.0
|
65+
|
241.4 (12.5)
|
|
|
|
|
|
|
|
|
Shanghai
|
Total
|
963.5 (100.0)
|
137.3
|
Total
|
1462.4 (100.0)
|
|
|
Gender
|
|
|
|
|
|
|
Male
|
462.5 (48.0)
|
63.9
|
Male
|
724.1 (49.5)
|
0.761
|
|
Female
|
501.0 (52.0)
|
73.4
|
Female
|
738.2 (50.5)
|
|
|
Age
|
|
|
Total 18+
|
1285.9
|
|
|
18-64
|
947.2 (98.3)
|
132.3
|
18-59
|
783.9 (61.0)
|
<0.001*
|
|
65+
|
16.3 (1.7)
|
5.1
|
60+
|
502.0 (39.0)
|
|
|
|
|
|
|
|
|
Chongqing
|
Total
|
286.0 (100.0)
|
46.1
|
Total
|
3101.8 (100.0)
|
|
|
Gender
|
|
|
|
|
|
|
Male
|
131.4 (45.9)
|
21.1
|
Male
|
1563.4 (50.4)
|
0.368
|
|
Female
|
154.6 (54.1)
|
25.1
|
Female
|
1538.4 (49.6)
|
|
|
Age
|
|
|
Total 15+
|
2572.3
|
|
|
18-64
|
282.3 (98.7)
|
44.9
|
15-64
|
2135.0 (83.0)
|
<0.001*
|
|
65+
|
3.6 (1.3)
|
1.2
|
65+
|
437.4 (17.0)
|
|
|
|
|
|
|
|
|
Shenzhen
|
Total
|
489.8 (100.0)
|
66.6
|
Total
|
1302.7 (100.0)
|
|
|
Gender
|
|
|
|
|
|
|
Male
|
269.3 (55.0)
|
37.8
|
Male
|
707.8 (54.3)
|
0.893
|
|
Female
|
220.5 (45.0)
|
28.9
|
Female
|
594.9 (45.7)
|
|
|
Age
|
|
|
Total 18+
|
\
|
|
|
18-64
|
487.4 (99.5)
|
66.2
|
18-64
|
\
|
NA
|
|
65+
|
2.4 (0.5)
|
0.5
|
65+
|
\
|
|
|
|
|
|
|
|
|
Hong Kong
|
Total
|
42.8 (100.0)
|
6.7
|
Total
|
7486.4 (100.0)
|
|
|
Gender
|
|
|
|
|
|
|
Male
|
18.6 (43.5)
|
2.9
|
Male
|
3421.1 (45.7)
|
0.659
|
|
Female
|
24.2 (56.5)
|
3.8
|
Female
|
4065.3 (54.3)
|
|
|
Age
|
|
|
Total 15+
|
6620.2
|
|
|
18-64
|
42.5 (99.3)
|
6.6
|
15 – 64
|
5318.6 (80.3)
|
<0.001*
|
|
65+
|
0.3 (0.7)
|
0.1
|
65+
|
1301.6 (19.7)
|
|
Note: Chi-square test was used to measure the overall difference in proportion between the sample and 2018 population data. Population age group categories were based on the available census data per city; no age group data was found for Shenzhen. Census data sources: (63-67)
\ indicates the absence of data.
* p ≤ 0.05 indicates significant difference.
Table 2 Summary findings of five Chinese cities
Variables
|
Beijing (BJ)
|
Shanghai (SH)
|
Chongqing (CQ)
|
Shenzhen (SZ)
|
Hong Kong (HK)
|
p-value
|
Observation days
|
391
|
391
|
391
|
391
|
391
|
|
Physical activity
|
|
Total avg. daily step count, mean (±SD)
|
6846.0 (478.4)
|
6702.5 (463.3)
|
7540.1 (507.6)
|
7209.4 (402.7)
|
9039.6 (429.1)
|
<0.001*
|
Males, mean (±SD)
|
7594.3 (518.2)
|
7465.3 (500.9)
|
8079.8 (461.9)
|
7862.8 (415.2)
|
9635.2 (430.4)
|
<0.001*
|
Females, mean (±SD)
|
6095.1 (454.1)
|
5998.1 (444.3)
|
7081.4 (561.2)
|
6412.1 (416.4)
|
8580.1 (443.4)
|
<0.001*
|
Age group 18-64, mean (±SD)
|
6850.0 (477.6)
|
6707.7 (461.2)
|
7535.6 (505.7)
|
7208.0 (401.8)
|
9047.7 (429.2)
|
<0.001*
|
Age group 65+, mean (±SD)
|
6505.9 (614.4)
|
6385.3 (648.2)
|
7899.5 (708.8)
|
7485.1 (620.6)
|
7966.0 (461.9)
|
<0.001*
|
Meteorological
|
|
Station ID
|
54511
|
58362
|
57516
|
59493
|
HKO
|
|
Climate (Köppen-Geiger classification)
|
Dwa
|
Cfa
|
Cfa
|
Cwa
|
Cwa
|
|
Temperature, range
|
-9.2 to 32.5
|
-1.0 to 32.6
|
4.5 to 36.5
|
6.6 to 30.8
|
9.0 to 31.2
|
|
Temp, °C mean (±SD)
|
12.7 (12.1)
|
17.0 (9.3)
|
18.7 (8.4)
|
23.0 (5.5)
|
23.5 (5.3)
|
<0.001*
|
Apparent temp, °C mean (±SD)
|
13.5 (12.6)
|
18.0 (12.5)
|
19.6 (11.1)
|
26.0 (8.7)
|
26.8 (8.3)
|
<0.001*
|
Relative humidity, mean (±SD)
|
48.0 (19.2)
|
73.4 (12.6)
|
75.23 (12.0)
|
74.7 (14.0)
|
76.2 (10.8)
|
<0.001*
|
Rainfall days, non-zero (%)
|
58 (14.8)
|
140 (35.8)
|
173 (44.2)
|
122 (31.4)
|
226 (57.8)
|
<0.001*
|
Rainfall, mean (±SD)
|
1.4 (7.3)
|
3.7 (11.3)
|
3.1 (8.0)
|
5.0 (15.3)
|
5.5 (16.0)
|
<0.001*
|
Windspeed, m/s (±SD)
|
2.0 (0.8)
|
2.6 (1.0)
|
1.3 (0.4)
|
1.9 (0.7)
|
6.6 (3.0)
|
<0.001*
|
Pressure, mean (±SD)
|
1013.9 (10.6)
|
1016.8 (9.5)
|
983.6 (9.2)
|
1005.7 (7.0)
|
1013.1 (7.0)
|
<0.001*
|
Sunshine, mean (±SD)
|
6.8 (3.7)
|
5.1 (4.2)
|
3.1 (4.1)
|
5.3 (3.8)
|
5.3 (3.9)
|
<0.001*
|
Precision variables
|
|
AQI, mean (±SD)
|
82.7 (48.7)
|
64.9 (32.7)
|
65.1 (31.5)
|
48.3 (17.0)
|
\
|
<0.001*
|
AQHI, mean (±SD)
|
\
|
\
|
\
|
\
|
3.5 (1.1)
|
NA
|
Holiday (%)
|
25 (6.4)
|
25 (6.4)
|
25 (6.4)
|
25 (6.4)
|
19 (4.9)
|
0.863
|
Extra workdays (%)
|
7 (1.8)
|
7 (1.8)
|
7 (1.8)
|
7 (1.8)
|
0 (0.0)
|
NA
|
Typhoon (%)
|
0 (0.0)
|
5 (1.3)
|
0 (0.0)
|
2 (0.5)
|
9 (2.3)
|
0.001*
|
Super typhoon (%)
|
0 (0.0)
|
0 (0.0)
|
0 (0.0)
|
1 (0.3)
|
1 (0.3)
|
0.557
|
Marathon (%)
|
0 (0.0)
|
0 (0.0)
|
0 (0.0)
|
0 (0.0)
|
1 (0.3)
|
NA
|
Note: Chi-square test was used to measure the overall difference in proportion between the cities.
\ indicates the absence of data.
* p ≤ 0.05 indicates significant difference.
Main model
The final models for Beijing, Chongqing, and Hong Kong underwent a stepdown process, in which atmospheric pressure and relative humidity were removed (see Table 3). For Shanghai and Shenzhen, no changes were required from the full models.
Table 3 Stepdown models of five Chinese cities
|
Beijing (BJ)
|
Shanghai (SH)
|
Chongqing (CQ)
|
Shenzhen (SZ)
|
Hong Kong (HK)
|
|
AIC (df)
|
AIC (df)
|
AIC (df)
|
AIC (df)
|
AIC (df)
|
Full model
|
4833.6 (34.0)
|
4851.0 (38.6)
|
4930.0 (39.7)
|
4795.1 (38.9)
|
5369.0 (32.6)
|
Stepdown 1
|
4828.1 (33.8)
|
Process stopped
|
4931.6 (38.2)
|
Process stopped
|
5367.1 (31.6)
|
Stepdown 2
|
Process stopped
|
|
Process stopped
|
|
5365.2 (30.6)
|
Variables removed
|
Removed pressure
|
|
Removed pressure
|
|
Removed pressure, RH
|
Note: AIC = Akaike information criterion; df = degrees of freedom
Overall, three of five cities (Beijing, Shanghai, and Chongqing) had significant inverse U-shaped associations between temperature and daily step count in high temperatures (see Table 4 and Figure 1). During periods of high temperature, populations in Beijing, Shanghai, and Chongqing had significantly lower physical activity compared to optimal temperatures, while no significant associations were found in Shenzhen and Hong Kong. In periods of low temperatures, while populations in Beijing, Shanghai, and Shenzhen also found significantly lower step counts compared to optimal temperatures, the amount of decrease was less than in hot temperatures. The optimal temperature of peak step counts varied slightly between cities. In Beijing, the estimate of optimal temperature was at 19.3°C, with a change in -386.0 steps (95% CI: -626.6, -145.5) for a 10°C increase from optimal temperature. In Shanghai, the optimal temperature was 17.9°C, with a change in -432.7 steps (95% CI: -636.2, -229.1) and in Chongqing, the optimal temperature was 16.1°C, with a -321.7 decrease (95% CI: -526.6, -116.8) in average step count for 10°C increase from optimal temperature. On days with extremely hot temperatures, step counts decreased, by -820 steps at 32.6°C in Shanghai, and -1494 steps at 36.5°C in Chongqing when compared to their respective optimal temperatures.
In Shenzhen, a curvilinear association was found albeit non-significant at higher temperatures. At the highest temperature in the dataset (30.8°C), there was a non-significant decrease of -204.8 step counts (95% CI: -514.5, 104.8) compared to the optimal temperature (24.2°C). On the other hand, a weak non-significant negative linear temperature association was found for Hong Kong (Change in steps at 10°C from pre-set temperature 20°C: -105.4; 95% CI -268.5, 57.6).
For other meteorological variables, higher relative humidity was negatively associated with step counts in Shanghai, Chongqing, and Shenzhen in a non-linear manner (see Figure S1). High relative humidity in Beijing had a non-significant association with average step count. Rainfall and windspeeds were negatively associated with daily step count in all five cities, while daily sunshine hours were positively associated with step count, with a particularly strong association observed in inland Chongqing. Where atmospheric pressure remained in the model, it was found to be positively associated with step counts in Shenzhen and Shanghai. The air pollution index was significantly associated with physical activity levels in all cities except Beijing. Overall, the final model of these cities explained 73% to 88% of the variance in daily mean step counts (see Table 4 for model information).
Table 4 Mean temperature associations on daily average step count, by city
City
|
Beijing
|
Shanghai
|
Chongqing
|
Shenzhen
|
Hong Kong
|
Optimal temperature
|
19.3
|
17.9
|
16.1
|
24.2
|
20 a
|
Change in steps at OptT - 10C (95% CI)
|
-342.8 *
(-452.2, -233.4)
|
-251.6 *
(-423.0, -80.1)
|
-19.1
(-293.1, 254.9)
|
-351.7*
(-614.8, -88.6)
|
-3.0
(-331.8, 325.8)
|
Change in steps at OptT + 10C (95% CI)
|
-386.0 *
(-626.6, -145.5)
|
-432.7 *
(-636.2, -229.1)
|
-321.7 *
(-526.6, -116.8)
|
-204.8 b
(-514.5, 104.8)
|
-105.4
(-268.5, 57.6)
|
N
|
366
|
366
|
366
|
361
|
391
|
Adjusted R squared
|
0.88
|
0.86
|
0.86
|
0.80
|
0.73
|
AIC
|
4828.1
|
4856.0
|
4931.6
|
4795.1
|
5365.2
|
Note: BJ = Beijing, SH = Shanghai, CQ = Chongqing, SZ = Shenzhen, HK = Hong Kong; OptT = optimal temperature; CI = confidence interval; AIC = Akaike information criterion. The model for each city was adjusted for relative humidity#, precipitation, windspeed, pressure#, sunshine, AQI/AQHI, month, day of week, public holiday, extra workdays, typhoon, super typhoon, and marathon (#some cities had these variables removed in the stepdown process).
aWhere association was not curvilinear, the optimal temperature was pre-set to 20°C
bA change from 30.8°C was used for Shenzhen, since this was the upper limit of temperature data in the city.
* p ≤ 0.05 indicates significant difference.
Stratified analyses
When stratified by gender, a lower optimal temperature was found among females than males in all four cities with curvilinear associations (Beijing, Shanghai, Chongqing, and Shenzhen) (see Table 5). A slightly larger decline in step counts was found in Beijing among females at 10°C above the optimal temperature (28.7°C, change in steps: -405.4; 95% CI: -641.1, -169.6). Alternately, in Shenzhen a slightly larger effect was found among females at 10°C colder temperatures from optimal (13.5°C, change in steps: -338.1; 95% CI: -629.9, -46.4). In Hong Kong, the associations among both males and females remained non-significant.
When stratified by age group, a lower optimal temperature was also found for the elderly over 65 in all cities with curvilinear associations (Beijing, Shanghai, Shenzhen, Hong Kong). In Chongqing, the association among the elderly was no longer inverse U-shaped, but had a steep significant negative slope. Additionally, in warmer temperatures, the elderly were associated with a markedly larger decrease in step counts compared with the adult age group (aged 18-64), with an approximate additional reduction of ~70 steps in Beijing and Shanghai and ~1200 steps in Chongqing. Furthermore, in Shenzhen and Hong Kong, the association of decreased step counts in warm temperatures was found significant among the elderly, while still remaining non-significant among the adults. In cold temperatures, there was no clear difference between the elderly and adults in most cities, except in Shenzhen, where the elderly were associated with a larger decrease in step counts with an approximate additional 130~ steps.
Table 5. Stratification results of the temperature-physical activity associations in five Chinese cities
City
|
Stratifi-cation
|
OptTa
|
OptT – 10C
|
Change in steps
|
95% CI
|
Sig.
|
OptT
+ 10C
|
Change in steps
|
95% CI
|
Sig.
|
BJ
|
Male
|
20.0
|
10.0
|
-344.9
|
-453.2, -236.6
|
*
|
29.9
|
-353.4
|
-614.0, -92.7
|
*
|
|
Female
|
18.7
|
8.7
|
-339.2
|
-456.4, -222.0
|
*
|
28.7
|
-405.4
|
-641.1, -169.6
|
*
|
|
18-64
|
19.3
|
9.3
|
-344.1
|
-453.0, -235.2
|
*
|
29.3
|
-383.8
|
-624.1, -143.5
|
*
|
|
65+
|
16.5
|
6.5
|
-314.9
|
-517.0, -112.8
|
*
|
26.5
|
-466.4
|
-762.7, -170.1
|
*
|
SH
|
Male
|
18.6
|
8.6
|
-249.6
|
-422.6, -76.5
|
*
|
28.6
|
-427.4
|
-656.2, -198.7
|
*
|
|
Female
|
17.3
|
7.3
|
-250.6
|
-427.5, -73.7
|
*
|
27.3
|
-418.7
|
-609.1, -228.4
|
*
|
|
18-64
|
18.0
|
8.0
|
-251.9
|
-422.1, -81.8
|
*
|
28.0
|
-430.1
|
-635.1, -225.1
|
*
|
|
65+
|
13.5
|
3.5
|
-318.6
|
-657.2, 20.1
|
|
23.5
|
-501.3
|
-708.4, -294.2
|
*
|
CQ
|
Male
|
18.0
|
8.0
|
16.1
|
-200.4, 232.6
|
|
28.0
|
-336.2
|
-551.6, -120.9
|
*
|
|
Female
|
15.6
|
5.6
|
-76.6
|
-383.3, 230.1
|
|
25.6
|
-376.7
|
-589.0, -164.5
|
*
|
|
18-64
|
16.2
|
6.2
|
-20.4
|
-289.2, 248.4
|
|
26.2
|
-327.2
|
-533.0, -121.4
|
*
|
|
65+
|
20a
|
10.0
|
-2.4
|
-292.9, 288.2
|
|
30.0
|
-1541.8
|
-1927.0, -1156.7
|
*
|
SZ
|
Male
|
24.3
|
14.3
|
-291.8
|
-536.9, -46.6
|
*
|
30.8b
|
-185.8
|
-486.0, 114.5
|
|
|
Female
|
23.5
|
13.5
|
-338.1
|
-629.9, -46.4
|
*
|
30.8b
|
-242.2
|
-571.0, 86.5
|
|
|
18-64
|
24.2
|
14.2
|
-350.8
|
-613.5, -88.0
|
*
|
30.8b
|
-203.5
|
-513.1, 106.0
|
|
|
65+
|
22.5
|
12.5
|
-487.5
|
-903.1, -71.9
|
*
|
30.8b
|
-462.4
|
-901.8, -23.0
|
*
|
HK
|
Male
|
20a
|
10.0
|
-4.5
|
-312.7, 303.7
|
|
30.0
|
-128.9
|
-281.8, 24.1
|
|
|
Female
|
20a
|
10.0
|
-3.4
|
-358.4, 351.6
|
|
30.0
|
-96.7
|
-272.9, 79.5
|
|
|
18-64
|
20a
|
10.0
|
-3.6
|
-333.1, 325.8
|
|
30.0
|
-104.0
|
-267.5, 59.5
|
|
|
65+
|
17.1
|
9.0 b
|
-41.6
|
-348.9, 265.7
|
|
27.1
|
-193.2
|
-290.1, -96.4
|
*
|
Note: BJ = Beijing, SH = Shanghai, CQ = Chongqing, SZ = Shenzhen, HK = Hong Kong; OptT = optimal temperature; CI = confidence interval. The model for each city was adjusted for relative humidity#, precipitation, windspeed, pressure#, sunshine, AQI/AQHI, month, day of week, public holiday, extra workdays, typhoon, super typhoon, and marathon (#some cities had these variables removed in the stepdown process).
aWhere association was not curvilinear, the optimal temperature was pre-set to 20°C.
bThe upper or lower limit of temperature was reached for that city’s dataset. The upper limit of temperature in the Shenzhen dataset was at 30.8°C. The lower limit of temperature in Hong Kong was at 9.0°C.
* p ≤ 0.05 indicates significant difference.
Sensitivity analyses
Several sensitivity analyses were conducted including 1) examining the effect of apparent temperature and 2) examining the effect of percentile temperature, 3) removal of air pollution index, and 4) removal of outlier data caused by Typhoon Mangkhut in Shenzhen and Hong Kong on Sept 16, 2018. The results were largely consistent with the primary findings (see Table 6).
For apparent temperature models, the AIC was higher than for the original models in all cities aside from Hong Kong. A slightly higher optimal apparent temperature was found in Beijing, Shanghai, and Shenzhen. A slightly lower optimal apparent temperature was found in Chongqing, although the effect at +10°C was no longer significant. In Hong Kong, the effect at +10°C from 20°C was to significantly decrease step counts by -83.7 (95% CI: -150.4, -17.1).
Optimal percentile temperature was found at the 48th percentile in Chongqing, 54th percentile in Shanghai, 58th percentile in Shenzhen, and 68th percentile in Beijing. Similar to the main model, no optimal percentile temperature was found for Hong Kong. The model AIC improved when using percentile temperature for all cities except Chongqing.
Without the pollution index, the results remained consistent in Beijing, Shanghai, and Shenzhen, although the model AIC had a substantial increase from each city’s original model. In Chongqing, the optimal temperature increased from 16°C to 19.3°C. Additionally, a curvilinear association was found in Hong Kong, with optimal temperatures at 21.9°C and a marginally significant decrease of -348.0 (95% CI: -697.8, 1.8) for a 10°C increase from optimal temperature.
The results remained consistent when removing the typhoon outlier for Shenzhen and Hong Kong, while the model AIC improved from the original. When the two cities were hit by Typhoon Mangkhut on Sept 16, 2018, the aggregated daily step counts on that date dropped significantly to 3992 and 4682, respectively compared to average step counts.
Table 6. Sensitivity analyses of the temperature-physical activity associations in five Chinese cities
City
|
Model
|
OptTa
|
OptT
- 10C
|
Change in steps
|
95% CI
|
Sig.
|
OptT
+ 10C
|
Change in steps
|
95% CI
|
Sig.
|
df
|
AIC
|
BJ
|
Original (Mean Temp)
|
19.3
|
9.3
|
-342.8
|
-452.2, -233.4
|
*
|
29.3
|
-386.0
|
-626.6, -145.5
|
*
|
33.8
|
4828.1
|
|
Apparent Temperature
|
22.1
|
12.1
|
-250.2
|
-362.7, -137.7
|
*
|
32.1
|
-344.7
|
-610.3, -79.2
|
*
|
33.3
|
4836.1
|
|
Percentile Temp
|
68th
|
10th
|
-767.1
|
-965.4, -568.7
|
*
|
90th
|
-353.8
|
-581.8, -125.7
|
*
|
34.8
|
4814.3
|
|
Without pollution index
|
18.8
|
8.8
|
-310.5
|
-419.7, -201.3
|
*
|
28.7
|
-377.0
|
-613.1, -140.9
|
*
|
31.9
|
5161.6
|
SH
|
Original (Mean Temp)
|
17.9
|
7.9
|
-251.6
|
-423.0, -80.1
|
*
|
27.9
|
-432.7
|
-636.2, -229.1
|
*
|
38.6
|
4851.0
|
|
Apparent Temperature
|
18.6
|
8.6
|
-145.2
|
-297.8, 7.4
|
|
28.6
|
-187.3
|
-362.5, -12.1
|
*
|
36.6
|
4878.3
|
|
Percentile Temp
|
54th
|
10th
|
-356.9
|
-571.7, -142.0
|
*
|
90th
|
-514.0
|
-744.1, -283.9
|
*
|
37.5
|
4845.4
|
|
Without pollution index
|
17.9
|
7.9
|
-237.9
|
-408.2, -67.5
|
*
|
27.9
|
-402.2
|
-597.9, -206.5
|
*
|
35.0
|
5193.6
|
CQ
|
Original (Mean Temp)
|
16.1
|
6.1
|
-19.1
|
-293.1, 254.9
|
|
26.0
|
-321.7
|
-526.6, -116.8
|
*
|
38.2
|
4931.6
|
|
Apparent Temperature
|
15.3
|
5.3
|
1.8
|
-253.6, 257.1
|
|
25.3
|
-138.3
|
-319.8, 43.2
|
|
35.8
|
4984.7
|
|
Percentile Temp
|
48th
|
10th
|
-51.3
|
-284.2, 181.5
|
|
90th
|
-812.0
|
-1080.0, -544.0
|
*
|
38.3
|
4949.1
|
|
Without pollution index
|
19.3
|
9.3
|
-126.1
|
-398.6, 146.4
|
|
29.3
|
-501.0
|
-808.0, -194.0
|
*
|
36.4
|
5273.9
|
SZ
|
Original (Mean Temp)
|
24.2
|
14.2
|
-351.7
|
-614.8, -88.6
|
*
|
30.8
|
-204.8
|
-514.5, 104.8
|
|
38.9
|
4795.1
|
|
Apparent Temperature
|
27.0
|
17.0
|
-96.0
|
-274.8, 82.9
|
|
37.0
|
-126.5
|
-367.8, 114.7
|
|
36.2
|
4832.2
|
|
Percentile Temp
|
58th
|
10th
|
-279.7
|
-509.9, -49.5
|
*
|
90th
|
-171.8
|
-390.3, 46.7
|
|
38.0
|
4794.8
|
|
Without Pollution index
|
23.8
|
13.8
|
-310.1
|
-577.8, -42.5
|
*
|
30.8
|
-213.4
|
-511.8, 85.2
|
|
36.1
|
5137.4
|
|
Without Typhoon
|
24.2
|
14.2
|
-352.6
|
-613.6, -91.6
|
*
|
30.8
|
-204.6
|
-513.2, 104.0
|
|
37.9
|
4781.1
|
HK
|
Original (Mean Temp)
|
20a
|
10.0
|
-3.0
|
-331.8, 325.8
|
|
30.0
|
-105.4
|
-268.5, 57.6
|
|
30.6
|
5365.2
|
|
Apparent Temperature
|
20a
|
10.0
|
-9.5
|
-273.0, 254.1
|
|
30.0
|
-83.7
|
-150.4, -17.1
|
*
|
30.7
|
5365.0
|
|
Percentile Temp
|
50tha
|
10th
|
-10.4
|
-221.3, 200.6
|
|
90th
|
-173.1
|
-382.9, 36.8
|
|
30.7
|
5363.3
|
|
Without Pollution index
|
21.9
|
11.9
|
-128.8
|
-523.0, 265.3
|
|
31.2
|
-348.0
|
-697.8, 1.8
|
|
34.7
|
5373.7
|
|
Without Typhoon
|
20a
|
10.0
|
-3.7
|
-328.9, 321.6
|
|
30.0
|
-104.2
|
-261.1, 52.7
|
|
29.9
|
5350.4
|
Note: BJ = Beijing, SH = Shanghai, CQ = Chongqing, SZ = Shenzhen, HK = Hong Kong; OptT = optimal temperature; CI = confidence interval; df = degrees of freedom; AIC = Akaike information criterion. The model for each city was adjusted for relative humidity#, precipitation, windspeed, pressure#, sunshine, AQI/AQHI, month, day of week, public holiday, extra workdays, typhoon, super typhoon, and marathon (#some cities had these variables removed in the stepdown process). Percentile temperatures were set to 10th and 90th percentiles for analysis.
aWhere association was not curvilinear, the optimal temperature was pre-set to 20°C or 50th percentile.
* p ≤ 0.05 indicates significant difference.