Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Gym Data
2.2. PM2.5 Concentration Data
2.3. Land Use Regression (LUR) Model
2.3.1. Predictor Variables
2.3.2. Building the LUR Model
2.4. Statistical Analysis
2.4.1. Building Ordinary Least Squares (OLS) Model
2.4.2. Building Spatial Econometric Models
3. Results
3.1. PM2.5 Concentration Estimates from the LUR Model
3.2. The Impact of PM2.5 Concentration on Gym Visits under COVID-19
4. Discussion
4.1. Discussion of Main Findings
4.2. Implications for Pollution Health and Gym Sports Research
4.3. Research Limitations and Future Research Agenda
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No. | Predictor Variables | Abbreviations | Unit | Buffer Size |
---|---|---|---|---|
Land use | ||||
1 | Cultivated land | Cul_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
2 | Forest | For_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
3 | Grass land | Gra_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
4 | Waterbody | Wat_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
5 | Built-up area | Bui_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
6 | Unused land | Unu_xx | m2 | 100, 300, 500, 1000, 3000, 5000 |
Traffic information | ||||
7 | Trunk road length | Tru_xx | m | 100, 300, 500, 1000, 3000, 5000 |
8 | Primary road length | Pri_xx | m | 100, 300, 500, 1000, 3000, 5000 |
9 | Secondary road length | Sec_xx | m | 100, 300, 500, 1000, 3000, 5000 |
10 | Railroad length | Rai_xx | m | 100, 300, 500, 1000, 3000, 5000 |
POI information | ||||
11 | Bus station number | POI1_xx | - | 100, 300, 500, 1000, 3000, 5000, 7000 |
12 | Gas station number | POI2_xx | - | 100, 300, 500, 1000, 3000, 5000, 7000 |
13 | Polluted enterprise number | POI3_xx | - | 100, 300, 500, 1000, 3000, 5000, 7000 |
14 | Chinese restaurant number | POI4_xx | - | 100, 300, 500, 1000, 3000, 5000, 7000 |
15 | Distance to the nearest bus station | D_bus | m | NA |
16 | Distance to the nearest gas station | D_gas | m | NA |
17 | Distance to the nearest polluted enterprise | D_pol | m | NA |
18 | Distance to the nearest Chinese restaurant | D_res | m | NA |
No. | Predictor Variables | Abbreviations | Unit | Original Spatial Resolution |
---|---|---|---|---|
Population | ||||
1 | Population density | Pop | people/m2 | 1 km |
Geographic information | ||||
2 | Elevation | DEM | m | 30 m |
Vegetation index | ||||
3 | NDVI | NDVI | - | 1 km |
Remote sensing data | ||||
4 | CHAP_PM2.5 | CHAP | μg/m3 | 1 km |
Meteorological data | ||||
5 | Boundary layer height | BLH | m | 0.125° |
6 | 2 m temperature | T2M | K | 0.125° |
7 | Total precipitation | TP | mm | 0.125° |
8 | Surface pressure | SP | 106 Pa | 0.125° |
9 | 10 m u-component of wind | U10m | m/s | 0.125° |
10 | 10 m v-component of wind | V10m | m/s | 0.125° |
Aerosol optical depth | ||||
11 | Optical_Depth_047 | AOD_047 | - | 1 km |
Model | Variable | Coefficient | Std. Error | T Value | p (>|t|) | VIF | Global Statistics |
---|---|---|---|---|---|---|---|
Pre-COVID PM2.5 (μg/m3) | Intercept | 2.34 | 5.552 | 0.421 | 0.676 | NA | Adjusted R2 = 0.68; LOOCV R2 = 0.68 RMSE = 3.10 μg/m3; 10-fold CV R2 = 0.68; RMSE = 3.72 μg/m3. |
CHAP | 0.079 | 0.014 | 7.192 | 0.000 | 1.039 | ||
Cul_3000 | 4.174 × 10−7 | 1.363 × 10−7 | 3.063 | 0.004 | 1.039 | ||
COVID-Lock PM2.5 (μg/m3) | Intercept | 5.868 | 8.578 | 0.684 | 0.499 | NA | Adjusted R2 = 0.73; LOOCV R2 = 0.73 RMSE = 5.12 μg/m3; 10-fold CV R2 = 0.73; RMSE = 6.26 μg/m3. |
CHAP | 2.197 | 0.265 | 8.274 | 0.000 | 1.002 | ||
Cul_300 | 4.23 × 10−4 | 9.5 × 10−5 | 3.063 | 0.000 | 1.002 | ||
COVID-Recover-I PM2.5 (μg/m3) | Intercept | 20.925 | 2.973 | 7.039 | 0.000 | NA | Adjusted R2 = 0.64; LOOCV R2 = 0.64 RMSE = 1.95 μg/m3; 10-fold CV R2 = 0.64; RMSE = 2.34 μg/m3. |
CHAP | 0.398 | 0.093 | 4.297 | 0.000 | 1.048 | ||
Tru_500 | 1198.099 | 345.514 | 3.468 | 0.002 | 1.053 | ||
Cul_300 | 1.17 × 10−4 | 3.3 × 10−5 | 3.577 | 0.001 | 1.017 | ||
Gra_100 | −0.001 | 2.52 × 10−4 | −2.545 | 0.017 | 1.033 | ||
Xinfadi-COVID PM2.5 (μg/m3) | Intercept | 2.032 | 6.088 | 0.334 | 0.741 | NA | Adjusted R2 = 0.73; LOOCV R2 = 0.73 RMSE = 3.36 μg/m3; 10-fold CV R2 = 0.73; RMSE = 3.97 μg/m3. |
AOD_047 | 0.066 | 0.013 | 5.24 | 0.000 | 1.674 | ||
POI3_7000 | 0.215 | 0.046 | 4.723 | 0.000 | 2.854 | ||
Pri_500 | −2640.034 | 445.498 | −5.926 | 0.000 | 1.017 | ||
POI2_7000 | −0.19 | 0.058 | −3.274 | 0.003 | 3.94 | ||
Wat_100 | −49.1 | 1.72 × 10−4 | −2.615 | 0.014 | 1.101 | ||
COVID-Recover-II PM2.5 (μg/m3) | Intercept | 8.407 | 4.257 | 1.975 | 0.057 | NA | Adjusted R2 = 0.54; LOOCV R2 = 0.54 RMSE = 2.89 μg/m3; 10-fold CV R2 = 0.542; RMSE = 3.61 μg/m3 |
AOD_047 | 0.079 | 0.014 | 5.505 | 0.000 | 1.044 | ||
Cul_1000 | 3.7 × 10−6 | 8.407 | 4.257 | 0.045 | 1.044 |
Dependent Variable: Gym Comments | Model 1 | Model 2 | Model 3 | Model 4 |
---|---|---|---|---|
PM2.5 | 0.150 *** | 0.148 *** | 0.119 *** | 0.366 *** |
(0.000) | (0.000) | (0.000) | (0.008) | |
(PM2.5)2 | - | - | - | −0.00165 * |
- | - | - | (0.065) | |
Precipitation | 4.509 | −20.01 *** | −18.05 *** | −19.17 *** |
(0.153) | (0.000) | (0.000) | (0.000) | |
Temperature | 1.486 *** | −0.0322 | 0.0259 | -0.0561 |
(0.001) | (0.897) | (0.916) | (0.823) | |
Constant | 11.16 * | 36.81 *** | 0.327 | 30.61 *** |
(0.075) | (0.000) | (0.939) | (0.000) | |
COVID-19 Wave FEs | Yes | Yes | Yes | Yes |
Gym FEs | No | Yes | Yes | Yes |
Gym Controls | No | No | Yes | Yes |
Observations | 12,260 | 12,260 | 12,260 | 12,260 |
Participant number | 2452 | 2452 | 2452 | 2452 |
Dependent Variable: Gym Comments | Model 1 | Model 2 | Model 3 |
---|---|---|---|
COVID-19 | −0.0272 *** | −0.0246 *** | −0.0246 *** |
(0.000) | (0.000) | (0.000) | |
PM2.5 | 0.171 *** | - | - |
(0.000) | - | - | |
PM2.5 Kriging | - | 0.00305 | - |
- | (0.952) | - | |
PM2.5 US Embassy | - | - | 2.341 *** |
- | - | (0.000) | |
Precipitation | −19.14 *** | −17.14 *** | −17.04 *** |
(0.000) | (0.000) | (0.000) | |
Temperature | 0.116 | 0.0712 | 0.0707 |
(0.626) | (0.771) | (0.773) | |
Constant | −2.985 | 4.568 | −94.63 *** |
(0.479) | (0.307) | (0.000) | |
Gym FEs | YES | YES | YES |
COVID-19 Wave FEs | YES | YES | YES |
Gym Controls | YES | YES | YES |
Observations | 12,260 | 12,260 | 12,260 |
Participant Number | 2452 | 2452 | 2452 |
Dependent Variable: Gym Comments under P1–P5 | I | z | p Value |
---|---|---|---|
Pre-COVID | 0.181 *** | 6.137 | 0.000 |
COVID-Lock | 0.062 *** | 2.159 | 0.015 |
COVID-Recover-I | 0.100 *** | 3.394 | 0.000 |
Xinfadi-COVID | 0.102 *** | 3.490 | 0.000 |
COVID-Recover-II | 0.122 *** | 4.229 | 0.000 |
Dependent Variable: Gym Comments under P1–P5 | OLS | SLM | SEM | SDM |
---|---|---|---|---|
COVID-19 | −0.027 *** | −0.027 *** | −0.028 *** | −0.024 *** |
(0.000) | (0.000) | (0.000) | (0.008) | |
PM2.5 | 0.171 *** | 0.205 ** | 0.197 ** | 0.110 |
(0.000) | (0.017) | (0.025) | (0.233) | |
Precipitation | −19.14 *** | −14.128 | −13.897 | 5.397 |
(0.000) | (0.223) | (0.253) | (0.757) | |
Temperature | 0.116 | 0.164 | 0.196 | 0.347 |
(0.626) | (0.671) | (0.614) | (0.376) | |
ρ | - | 0.078 *** | - | - |
- | (0.000) | - | - | |
λ | - | - | 0.078 *** | 0.076 *** |
- | - | (0.000) | (0.000) | |
Gym FEs | YES | YES | YES | YES |
COVID-19 Wave FEs | YES | YES | YES | YES |
Observations | 12,260 | 12,260 | 12,260 | 12,260 |
Participant Number | 2452 | 2452 | 2452 | 2452 |
Dependent Variable: Gym Comments under P1–P5 | Direct | Indirect | Total |
---|---|---|---|
COVID-19 | −0.024 *** | −0.003 | −0.027 *** |
(0.006) | (0.788) | (0.001) | |
PM2.5 | 0.118 | 0.394 *** | 0.512 *** |
(0.210) | (0.003) | (0.000) | |
Precipitation | 4.025 | −45.799 ** | −41.773 *** |
(0.810) | (0.028) | (0.007) | |
Temperature | 0.367 | −1.412 * | -1.045 |
(0.328) | (0.065) | (0.215) | |
Gym FEs | YES | YES | YES |
COVID-19 Wave FEs | YES | YES | YES |
Observations | 12,260 | 12,260 | 12,260 |
Participant Number | 2452 | 2452 | 2452 |
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Dong, X.; Yang, S.; Zhang, C. Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China. Int. J. Environ. Res. Public Health 2022, 19, 12614. https://doi.org/10.3390/ijerph191912614
Dong X, Yang S, Zhang C. Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China. International Journal of Environmental Research and Public Health. 2022; 19(19):12614. https://doi.org/10.3390/ijerph191912614
Chicago/Turabian StyleDong, Xin, Shili Yang, and Chunxiao Zhang. 2022. "Air Pollution Increased the Demand for Gym Sports under COVID-19: Evidence from Beijing, China" International Journal of Environmental Research and Public Health 19, no. 19: 12614. https://doi.org/10.3390/ijerph191912614