Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China
Abstract
:1. Introduction
- (1)
- We propose a methodology to better understand the link between environmental changes such as air pollution and citizens’ activities of daily life. It can help government and businesses to understand better the actual effect not the presumed effect of air pollution on the pattern of daily activities of citizens;
- (2)
- This opens up a new perspective for understanding and exploring the interaction between PM2.5 and in more general air pollution and people’s physical behaviour.
- (3)
- This can not only reveal the subtle impact of PM2.5/air pollution on human ADL but can also monitor the indirect impact of PM2.5/air pollution on some human-based business activities, e.g., restaurants. This is challenging to do because different data sets, such as air pollution, human movement, location contexts, etc., with different temporal and spatial characteristics, need to be acquired and fused. Human activities can be complex to characterise. Individual human behaviour in a crowd needs to be identified. Human behaviour is affected by a range of environmental factors, some of which may not be observable, that need to be correlated.
2. Related Works
3. Data and Pre-Processing
3.1. Data Introduction
3.1.1. Call Detail Record (CDR)
3.1.2. Air Pollution
3.2. Data Collection and Accuracy Analysis
3.3. Data Pre-Processing
4. Methodology
4.1. Overview
4.2. Workflow 1
4.2.1. Input Module: Data Preparation and Input Set Up
4.2.2. Processing Module, Output 1: Fixed Effect Model (FEM)
4.2.3. Output 2: Spatial-Temporal Distribution of Behaviour Impacting Indices
4.2.4. Output 3: Estimating the Revenue Change
4.3. Workflow 2
4.3.1. Input Module: Walking and Cycling People Data
4.3.2. Processing Module: Multivariate Linear Regression Model
4.3.3. Output 4: Average Distance Changing of Walking and Cycling People
5. Results and Discussion
5.1. Spatial-Temporal Dataset Description
5.2. Output 1: Fixed Effect Model Results
5.3. Output 2: Spatial-Temporal Behaviour Impacting Indices of Air Pollution on ADLs
5.4. Output 3: Restaurant Business Loss Estimation Due to Air
5.5. Output 4: Changes in the Average Distance Travelled by People Walking and Cycling
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Additional Description of Datasets
Appendix A.1. Additional Materials for CDR Dataset Description
ID | Name | Description |
---|---|---|
1 | Timestamp | Interactive time of users and base station |
2 | CI | Corresponding base station’ identity |
3 | IMSI | The encrypted ID of users |
ID | Name | Description |
---|---|---|
1 | CI | Unique ID of the base station |
2 | Lat, Lon | Latitude and longitude of the base station location |
Appendix A.2. Error Analysis When Estimating Mobile Phone Users’ Density Distributions
Appendix A.3. Error Control Solution When Estimating Mobile Phone Users’ Density Distributions
Appendix A.4. Description of Other Datasets
Appendix A.4.1. Additional Materials for Air Quality Datasets
AQI | PM2.5 | PM10 | SO2 | O3 | NO2 | CO | |
---|---|---|---|---|---|---|---|
Mean concentration (μg/m3) | / | 107.929 | 133.959 | 32.307 | 39.289 | 53.460 | 1.698 |
% hours when it is the primary pollutant | / | 64.29% | 18.65% | 0.00% | 1.79% | 15.48% | 0.00% |
Whether it is easily perceived | / | YES | YES | YES | NO | NO | NO |
Correlation between it and PM2.5 | 0.625 *** | / | 0.703 *** | 0.760 *** | -0.626 *** | 0.723 *** | 0.832 *** |
- HJ 655-2013 (China): Technical Specifications for Installation and Acceptance of Ambient Air Quality Continuous Automated Monitoring System for PM10 and PM2.5
- HJ 653-2013 (China): Specifications and Test Procedures for Ambient Air Quality Continuous Automated Monitoring System for PM10 and PM2.5
- HJ 93-2013 (China): Specifications and Test Procedures for PM10 and PM2.5 Sampler
PM | Sensor | Range | Resolution | CTRM | EDR | PoM | Specification |
---|---|---|---|---|---|---|---|
PM2.5 | PM2.5 Sampler | 0~10,000 μg/m3 | 0.1 μg/m3 | Coef. ≥ 0.93 | ≥85% | ≤15% | (1) HJ 655-2013 (2) HJ 653-2013 (3) HJ 93-2013 |
PM10 | PM10 Sampler | 0~10,000 μg/m3 | 0.1 μg/m3 | Coef. ≥ 0.95 | ≥85% | ≤10% |
- HJ 193-2013 (China): Technical Specifications for Installation and Acceptance of Ambient air Quality Continuous Automated Monitoring System for SO2, NO2, O3 and CO
- HJ 654-2013 (China): Specifications and Test Procedures for Ambient Air Quality Continuous Automated Monitoring System for SO2, NO2, O3 and CO
Pollution | Sensor | Measure Method | Range | Resolution | Indication Error | Specification |
---|---|---|---|---|---|---|
SO2 | SO2 Analyzer | Ultraviolet Fluorescent method | 0~500 ppb | 0.1 μg/m3 | ±2%F.S. | (1) HJ 193-2013 (2) HJ 654-2013 |
O3 | O3 Analyzer | Ultraviolet Absorbance method | 0~500 ppb | 0.1 μg/m3 | ±4%F.S. | |
NO2 | NO2 Analyzer | Chemiluminescence Detection method | 0~500 ppb | 0.1 μg/m3 | ±2%F.S. | |
CO | CO Analyzer | Non-dispersive Infrared Absorption method, Gas Filter Correlation Infrared Absorption method | 0~50 ppm | 0.1 mg/m3 | ±2%F.S. |
Appendix A.4.2. Weather Conditions
- GB/T 35221-2017 (China): Specification for surface meteorological observation—General
- GB/T 35226-2017 (China): Specification for surface meteorological observation—Air temperature and humidity
- GB/T 35227-2017 (China): Specification for surface meteorological observation—Wind direction and wind speed
- GB/T 35222-2017 (China): Specification for surface meteorological observation—Cloud
- GB/T 35228-2017 (China): Specification for surface meteorological observation—Precipitation
- GB/T 35229-2017 (China): Specification for surface meteorological observation—Snow depth and snow pressure
Weather Condition | Sensor | Range | Resolution | Accuracy | Specification |
---|---|---|---|---|---|
Temperature | Thermometer | −50~50 °C | 0.1 °C | ±0.2 °C | GB/T 35226-2017 |
Wind | Wind Speed Sensor | 0~60 m/s | 0.1 m/s | ±(0.5 m/s + 0.03 v) | GB/T 35227-2017 |
Cloud Amount | - | 0~100% | - | - | GB/T 35222-2017 |
Precipitation (Rain) | Tipping-Bucket Rain Gauge, Weighing Precipitation Sensor | ≤4 mm/min | 0.1 mm | ±0.4 mm (≤10 mm), ±4% (>10 mm) | GB/T 35228-2017 |
Snow (depth) | Automatic Snow Depth Observation Instrument, Ultrasonic or Laser Sensors | 0~2000 mm | 1 mm | ±10 mm | GB/T 35229-2017 |
- (1)
- POI
- (2)
- Building distribution
Appendix B. An Additional Strategy (S2) for the Eating out ADL
Appendix C. Additional Materials for Results
Appendix C.1. Spatial-Temporal Distributions for Key Datasets
Appendix C.2. The Results of FEM Regressions
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Times | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
1 | 0.0741 *** (0.001) | 0.0215 (0.117) | 0.0005 (0.988) | −0.0006 (0.988) | 0.0854 (0.63) | −0.6276 *** (0.001) |
2 | 0.0289 (0.057) | 0.0067 (0.554) | −0.0065 (0.848) | −0.0153 (0.693) | 0.1172 (0.512) | −0.9823 *** (0) |
3 | 0.0551 ** (0.006) | −0.0011 (0.93) | −0.0113 (0.752) | −0.0462 (0.262) | −0.0747 (0.719) | −0.8426 *** (0) |
4 | 0.0402 * (0.039) | 0.0001 (0.991) | −0.0102 (0.781) | −0.0082 (0.83) | 0.1253 (0.555) | −0.5164 (0.056) |
5 | 0.0296 (0.105) | −0.0073 (0.568) | 0.0192 (0.56) | 0.0084 (0.804) | 0.2183 (0.284) | −0.5702 * (0.043) |
6 | 0.0511 * (0.023) | 0.0138 (0.297) | −0.0065 (0.858) | 0.0314 (0.402) | 0.1038 (0.623) | −0.7019 ** (0.003) |
7 | 0.0428 * (0.017) | 0.0067 (0.615) | −0.0026 (0.942) | −0.0149 (0.723) | 0.3725 * (0.034) | −0.6325 ** (0.004) |
8 | 0.0276 (0.146) | −0.0051 (0.678) | −0.0112 (0.735) | −0.0183 (0.603) | 0.1539 (0.382) | −0.7492 ** (0.002) |
9 | 0.0301 (0.076) | −0.0043 (0.732) | 0.0142( 0.627) | −0.0014 (0.966) | −0.0336 (0.874) | −0.9454 *** (0.001) |
10 | 0.0596 *** (0.001) | 0.0111 (0.415) | 0.0237 (0.501) | −0.0056 (0.889) | 0.0243 (0.887) | −0.9433 *** (0) |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | |
---|---|---|---|---|
Times | Period Lunch | Period Lunch | Period Dinner | Period Dinner |
1 | 0.0023 (0.94) | −0.0946 *** (0) | 0.4986 * (0.022) | −0.1509 (0.647) |
2 | 0.0167 (0.528) | −0.0733 *** (0) | 0.3937 (0.086) | 0.4015 (0.251) |
3 | 0.0278 (0.366) | −0.1149 *** (0) | 0.527 * (0.031) | 0.2915 (0.398) |
4 | 0.0305 (0.311) | −0.0857 *** (0) | 0.3457 (0.199) | 0.4059 (0.303) |
5 | −0.0303 (0.223) | −0.1288 *** (0) | 0.45 (0.065) | 0.1942 (0.548) |
6 | 0.0115 (0.7) | −0.0859 *** (0) | 0.2361 (0.363) | −0.3118 (0.391) |
7 | 0.0041 (0.862) | −0.1055 *** (0) | 0.3577 (0.126) | 0.2314 (0.519) |
8 | −0.0067 (0.769) | −0.0943 *** (0) | 0.5685 * (0.012) | 0.2882 (0.412) |
9 | 0.0097 (0.728) | −0.0824 *** (0) | 0.2516 (0.268) | −0.0278 (0.932) |
10 | 0.0037(0.89) | −0.0947 ***(0) | 0.4027(0.058) | 0.1461(0.644) |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Times | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
1 | 0.0667 * (0.014) | 0.0411 ** (0.002) | 0.0742 ** (0.005) | 0.2097 *** (0) | 0.4271 (0.2) | 0.0218 (0.967) |
2 | 0.0643 * (0.014) | 0.0435 ** (0.001) | 0.0726 ** (0.004) | 0.2099 *** (0) | 0.0996 (0.737) | −0.8888 * (0.032) |
3 | 0.0694 ** (0.009) | 0.0452 *** (0) | 0.0453 * (0.034) | 0.2119 *** (0) | 0.3369 (0.273) | −0.1589 (0.716) |
4 | 0.0748 ** (0.007) | 0.0469 *** (0.001) | 0.0923 *** (0.001) | 0.2553 *** (0) | −0.005 (0.987) | −0.4784 (0.267) |
5 | 0.0675 ** (0.01) | 0.0466 *** (0.001) | 0.0896 *** (0.001) | 0.2431 *** (0) | 0.3581 (0.247) | −0.1621 (0.688) |
6 | 0.061 ** (0.008) | 0.0352 ** (0.008) | 0.0771 ** (0.002) | 0.2109 *** (0) | 0.4108 (0.255) | −0.6785 (0.143) |
7 | 0.0963 *** (0.001) | 0.0489 *** (0.001) | 0.0838 *** (0.001) | 0.2181 *** (0) | 0.6312 * (0.043) | −0.5569 (0.162) |
8 | 0.0743 ** (0.002) | 0.0559 *** (0) | 0.0853 *** (0.001) | 0.2267 *** (0) | 0.2482 (0.419) | −0.6419 (0.139) |
9 | 0.0562 * (0.04) | 0.039 ** (0.008) | 0.0566 * (0.015) | 0.1986 *** (0) | 0.1134 (0.625) | −0.5035 (0.128) |
10 | 0.071 ** (0.008) | 0.0441 *** (0.001) | 0.0868 *** (0.001) | 0.2655 *** (0) | 0.5811 * (0.03) | −0.4768 (0.237) |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Times | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
1 | 0.0271 (0.298) | 0.0353 ** (0.007) | −0.0231 (0.43) | 0.197 *** (0) | 0.5775 (0.399) | 0.0459 (0.954) |
2 | 0.0319 (0.262) | 0.0246 (0.235) | 0.0002 (0.997) | 0.0996 (0.18) | 0.1527 (0.744) | −0.2459 (0.746) |
3 | 0.0405 (0.148) | 0.0249 (0.14) | 0.0244 (0.564) | 0.134 * (0.021) | 0.0236 (0.958) | −0.5818 (0.428) |
4 | 0.026 (0.233) | 0.0209 (0.229) | −0.0175 (0.648) | 0.1333 * (0.013) | 0.5704 (0.335) | −0.0521 (0.931) |
5 | 0.0329 (0.195) | 0.0378 * (0.018) | −0.011 (0.736) | 0.1571 *** (0.001) | −0.3182 (0.518) | −0.6631 (0.348) |
6 | 0.0815 * (0.011) | 0.0437 * (0.028) | −0.0069 (0.834) | 0.1378 ** (0.01) | −0.3302 (0.579) | −0.6254 (0.446) |
7 | 0.0206 (0.542) | 0.0072 (0.752) | 0.0054 (0.883) | 0.1078 (0.066) | 0.033 (0.956) | −0.8677 (0.276) |
8 | 0.0273 (0.316) | 0.0299 (0.137) | 0.0095 (0.807) | 0.161 *** (0.001) | 0.0735 (0.925) | −1.2288 (0.199) |
9 | 0.0364 (0.166) | 0.0394 * (0.039) | 0.0441 (0.17) | 0.1548 ** (0.002) | −0.4657 (0.408) | −1.0819 (0.256) |
10 | 0.0406 (0.112) | 0.0339 (0.073) | −0.0193 (0.607) | 0.0894 (0.171) | 0.6506 (0.309) | 0.0991 (0.907) |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Times | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
1 | 0.0399 * (0.046) | 0.0117 (0.525) | 0.0948 ** (0.001) | 0.1623 *** (0) | −0.2442 (0.793) | −1.0232 (0.466) |
2 | 0.0521 * (0.032) | 0.0304 (0.076) | 0.0376 (0.112) | 0.1408 *** (0.001) | −0.7651 (0.109) | −0.5469 (0.444) |
3 | 0.0112 (0.628) | −0.0325 (0.082) | 0.0581 * (0.021) | 0.1575 *** (0) | 0.6758 (0.329) | 0.4968 (0.654) |
4 | 0.0439 * (0.041) | 0.0321 * (0.042) | 0.0367 (0.086) | 0.1616 *** (0) | −0.4887 (0.371) | −0.2133 (0.776) |
5 | 0.0651 *** (0.008) | 0.0341 (0.057) | 0.0159 (0.482) | 0.1752 *** (0) | 0.2509 (0.65) | −0.872 (0.185) |
6 | 0.0146 (0.469) | −0.0079 (0.652) | 0.0307 (0.208) | 0.161 *** (0) | 0.1355 (0.827) | 0.8016 (0.263) |
7 | 0.0479 * (0.022) | −0.0045 (0.824) | 0.0491 (0.095) | 0.1653 *** (0) | 0.3038 (0.671) | 0.1252 (0.897) |
8 | 0.0049 (0.825) | 0.0094 (0.563) | 0.0394 (0.105) | 0.2048 *** (0) | 0.5727 (0.345) | 0.9211 (0.263) |
9 | 0.0342 (0.142) | 0.0132 (0.475) | 0.0617 * (0.027) | 0.18 *** (0) | −0.6704 (0.354) | −1.2534 (0.162) |
10 | 0.0431 (0.061) | 0.0177 (0.298) | 0.0746 * (0.011) | 0.1702 *** (0) | −0.0914 (0.881) | 0.2375 (0.722) |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Dependent variables | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
AQI | 0.0570 *** | 0.00195 | 0.0744 | |||
(0.0145) | (0.0242) | (0.1343) | ||||
PM2.5 | 0.00949 | −0.00784 | −0.794 *** | |||
(0.0093) | (0.0269) | (0.1658) | ||||
Weather variables | ||||||
TEMP | 2.264 | 2.446 | 206.5 *** | 201.9 *** | 100.5 ** | −19.3 |
(2.3320) | (2.1031) | (18.3392) | (23.3036) | (31.1725) | (20.4207) | |
(TEMP)2 | −0.0769 ** | −0.0754 ** | −2.701 *** | −2.642 *** | −2.227 *** | 0.19 |
(0.0254) | (0.0229) | (0.2376) | (0.2982) | (0.6226) | (0.4093) | |
WIND | 2.534 *** | 2.790 *** | 15.58 *** | 15.56 *** | 54.01 *** | 15.93 * |
(0.2617) | (0.2194) | (0.9970) | (0.9738) | (5.7752) | (7.4903) | |
CLOUD | 7.730 *** | 8.616 *** | −21.49 *** | −21.31 *** | 0 | 0 |
(0.8331) | (0.7992) | (1.5167) | (1.3588) | − | − | |
Constant | 402.6 *** | 398.0 *** | −3436 *** | −3346 *** | −632.4 | 1088.0 *** |
(52.2414) | (47.5121) | (351.9851) | (450.4228) | (416.5775) | (298.0420) | |
POI fixed effects | YES | YES | YES | YES | YES | YES |
Type of day fixed effects | YES | YES | YES | YES | YES | YES |
N | 4000 | 4000 | 4000 | 4000 | 2200 | 2200 |
R2 | 0.4525 | 0.452 | 0.277 | 0.277 | 0.3775 | 0.3795 |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | |
---|---|---|---|---|
Dependent variables | Lunch | Lunch | Dinner | Dinner |
AQI | 0.00374 | 0.620 *** | ||
(0.0198) | (0.1661) | |||
PM2.5 | −0.0994 *** | 0.391 | ||
(0.0144) | (0.2482) | |||
Weather variables | ||||
TEMP | −376.0 *** | −378.7 *** | −244.8 *** | −210.0 *** |
(15.8275) | (16.0107) | (17.3950) | (29.7944) | |
(TEMP)2 | 4.711 *** | 4.748 *** | 6.258 *** | 5.588 *** |
(0.1979) | (0.1999) | (0.4188) | (0.6805) | |
WIND | 23.60 *** | 23.57 *** | 93.60 *** | 89.30 *** |
(1.4692) | (1.4384) | (5.6235) | (5.6046) | |
CLOUD | −41.08 *** | −38.94 *** | 0 | 0 |
(2.8149) | (2.6028) | − | − | |
Constant | 7867.2 *** | 7926.1 *** | 2564.5 *** | 2177.8 *** |
(301.4089) | (306.0135) | (156.5763) | (341.0804) | |
POI fixed effects | YES | YES | YES | YES |
Type of day fixed effects | YES | YES | YES | YES |
N | 3780 | 3780 | 2268 | 2268 |
R2 | 0.5421 | 0.5425 | 0.6658 | 0.6655 |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Dependent variables | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
AQI | 0.0614 *** | 0.0704 *** | 0.375 | |||
(0.0175) | (0.0171) | (0.2090) | ||||
PM2.5 | 0.0393 *** | 0.225 *** | −0.403 | |||
(0.0091) | (0.0144) | (0.2895) | ||||
Weather variables | ||||||
TEMP | 14.22 *** | 13.35 *** | 342.6 *** | 451.4 *** | 240.5 *** | 114.0 ** |
(1.7454) | (1.6242) | (29.4455) | (34.5974) | (44.3278) | (37.9603) | |
(TEMP)2 | −0.217 *** | −0.206 *** | −4.424 *** | −5.799 *** | −5.067 *** | −2.511 ** |
(0.0192) | (0.0179) | (0.3826) | (0.4478) | (0.8937) | (0.7672) | |
WIND | 2.656 *** | 2.996 *** | 19.01 *** | 19.78 *** | 93.94 *** | 66.43 *** |
(0.2548) | (0.2106) | (1.3030) | (1.3243) | (8.8098) | (15.2918) | |
CLOUD | 12.40 *** | 12.00 *** | −18.56 *** | −22.73 *** | 0 | 0 |
(1.0094) | (1.0000) | (1.3265) | (1.3514) | − | − | |
Constant | 279.9 *** | 296.8 *** | −5923 *** | −8050 *** | −2277.7 *** | −528.7 |
(40.8030) | (38.2207) | (562.706) | (664.212) | (591.1757) | (549.7147) | |
POI fixed effects | YES | YES | YES | YES | YES | YES |
Type of day fixed effects | YES | YES | YES | YES | YES | YES |
N | 4000 | 4000 | 4000 | 4000 | 2200 | 2200 |
R2 | 0.5943 | 0.5943 | 0.4793 | 0.4814 | 0.5618 | 0.5618 |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Dependent variables | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
AQI | 0.036 | 0.0142 | 0.17 | |||
(0.0193) | (0.0263) | (0.3953) | ||||
PM2.5 | 0.0217 | 0.147 *** | −0.342 | |||
(0.0136) | (0.0383) | (0.5427) | ||||
Weather variables | ||||||
TEMP | 5.526 | 5.049 | 296.6 *** | 373.7 *** | 190.8 * | 112 |
(3.9977) | (3.6908) | (31.9462) | (38.7465) | (82.4009) | (68.1898) | |
(TEMP)2 | −0.129 ** | −0.123 ** | −3.832 *** | −4.808 *** | −4.059 * | −2.469 |
(0.0433) | (0.0402) | (0.4170) | (0.4995) | (1.6581) | (1.3709) | |
WIND | 3.668 *** | 3.860 *** | 18.86 *** | 19.31 *** | 83.43 *** | 63.88 ** |
(0.4218) | (0.3523) | (1.6356) | (1.6282) | (12.6869) | (24.2800) | |
CLOUD | 12.28 *** | 12.11 *** | −22.85 *** | −25.77 *** | 0 | 0 |
(1.2568) | (1.0827) | (2.1693) | (1.9706) | − | − | |
Constant | 522.5 *** | 532.0 *** | −5028.9 *** | −6535.0 *** | −1620.6 | −517.1 |
(88.3302) | (81.8273) | (609.2183) | (745.6948) | (1100) | (996.0337) | |
POI fixed effects | YES | YES | YES | YES | YES | YES |
Type of day fixed effects | YES | YES | YES | YES | YES | YES |
N | 1980 | 1980 | 1980 | 1980 | 1089 | 1089 |
R2 | 0.5687 | 0.5686 | 0.4249 | 0.426 | 0.5603 | 0.5603 |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | (v—AQI) | (vi—PM2.5) | |
---|---|---|---|---|---|---|
Dependent variables | Period1 | Period1 | Period2 | Period2 | Period3 | Period3 |
AQI | 0.031 | 0.0567 ** | −0.00762 | |||
(0.0159) | (0.0184) | (0.4551) | ||||
PM2.5 | 0.00557 | 0.167 *** | −0.18 | |||
(0.0127) | (0.0272) | (0.6530) | ||||
Weather variables | ||||||
TEMP | 10.44 ** | 10.53 ** | 333.8 *** | 411.8 *** | 183.4 | 161.1 |
(3.3324) | (3.1121) | (42.0320) | (48.6495) | (93.7985) | (81.4032) | |
(TEMP)2 | −0.186 *** | −0.185 *** | −4.306 *** | −5.291 *** | −3.935 * | −3.486 * |
(0.0346) | (0.0325) | (0.5481) | (0.6306) | (1.8950) | (1.6519) | |
WIND | 3.938 *** | 4.072 *** | 20.71 *** | 21.30 *** | 89.90 *** | 82.09 ** |
(0.3957) | (0.3453) | (1.9061) | (1.9226) | (14.7067) | (30.4025) | |
CLOUD | 13.99 *** | 14.47 *** | −26.44 *** | −29.32 *** | 0 | 0 |
(1.2415) | (1.1751) | (2.7577) | (2.5480) | − | − | |
Constant | 472.7 *** | 470.3 *** | −5656.0 *** | −7181.8 *** | −1374.6 | −1049.3 |
(78.7130) | (73.8032) | (800.5169) | (931.2908) | (1200) | (1200) | |
POI fixed effects | YES | YES | YES | YES | YES | YES |
Type of day fixed effects | YES | YES | YES | YES | YES | YES |
N | 1960 | 1960 | 1960 | 1960 | 1078 | 1078 |
R2 | 0.5725 | 0.5725 | 0.4474 | 0.4482 | 0.55 | 0.55 |
Appendix C.3. The Results of the Mutilative Linear Regression
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | |
---|---|---|---|---|
Number | Number | Distance | Distance | |
AQI | −19.54 * | −7.271 * | ||
(8.6284) | (3.5220) | |||
PM2.5 | −2.839 | −1.015 | ||
(6.5185) | (2.7622) | |||
TEMP | 255.9 | −37.73 | 116.7 | −3.994 |
(277.4387) | (292.0201) | (123.4601) | (128.0484) | |
(TEMP)2 | −6.352 | −2.794 | −2.831 | −1.403 |
(3.5965) | (3.8598) | (1.6012) | (1.6949) | |
WIND | 124.4 | 95.25 | 56.04 * | 44.06 |
(62.5282) | (63.9672) | (27.6811) | (28.0982) | |
CLOUD | −1147.5 ** | −830.4 | −497.8 * | −360.2 |
(425.6531) | (505.6191) | (192.2372) | (226.7534) | |
Constant | 7563.8 | 9745.2 | 2855.4 | 3988 |
(5700) | (5700) | (2500) | (2500) | |
Type of day controls | YES | YES | YES | YES |
Hour controls | YES | YES | YES | YES |
N | 73 | 73 | 73 | 73 |
R2 | 0.3971 | 0.3983 | 0.3838 | 0.3989 |
(i—AQI) | (ii—PM2.5) | (iii—AQI) | (iv—PM2.5) | |
---|---|---|---|---|
Number | Number | Distance | Distance | |
AQI | −7.466 * | −1.201 * | ||
(3.3690) | (0.5445) | |||
PM2.5 | −0.661 | −0.207 | ||
(2.3764) | (0.4006) | |||
TEMP | 30.77 | −35.41 | 7.162 | −3.113 |
(84.8118) | (90.3629) | (14.7112) | (15.6714) | |
(TEMP)2 | −1.202 | −0.343 | −0.23 | −0.103 |
(1.1018) | (1.1933) | (0.1909) | (0.2069) | |
WIND | 25.21 | 22.78 | 5.529 | 5.127 |
(19.4659) | (20.3324) | (3.3585) | (3.5008) | |
CLOUD | −360.4 ** | −293.3 | −59.49 ** | −44.84 |
(126.2527) | (151.0873) | (22.0692) | (26.2204) | |
Constant | 3506.3 | 3132.2 | 542.1 | 506.2 |
(1800) | (1800) | (309.7733) | (316.8005) | |
Type of day controls | YES | YES | YES | YES |
Hour controls | YES | YES | YES | YES |
N | 73 | 73 | 73 | 73 |
R2 | 0.4087 | 0.3661 | 0.404 | 0.3682 |
Number of People | Distance of Movement | |||
---|---|---|---|---|
AQI | PM2.5 | AQI | PM2.5 | |
Walk | 1.818 | 1.778 | 1.889 | 1.818 |
(0.423) | (0.394) | (0.415) | (0.391) | |
Riding bike | 1.929 | 1.856 | 1.893 | 1.838 |
(0.404) | (0.376) | (0.397) | (0.372) |
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ADL Category | Time (Minutes) | Percentage | If Is LD-ADL |
---|---|---|---|
Total | 1440 | . | - |
1. Personal Physiologically Necessary Activities | 713 | 49.51% | - |
Sleeping | 559 | 38.82% | YES |
Personal Hygiene Care | 50 | 3.47% | NO |
Meals or Other Diet | 104 | 7.22% | YES |
2. Paid Labour | 264 | 18.33% | - |
Employment Work | 177 | 12.29% | NO |
Family Production and Business Activities | 87 | 6.04% | NO |
3. Unpaid Work | 162 | 11.25% | - |
Housework | 86 | 5.97% | YES |
Accompanying and Caring for Family | 53 | 3.68% | NO |
Purchase Goods or Services (including Medical Treatment) | 21 | 1.46% | NO |
Charitable Activities | 3 | 0.21% | NO |
4. Personal Discretionary Activity | 236 | 16.39% | - |
Fitness Exercise | 31 | 2.15% | YES |
Listening to Radio or Music | 6 | 0.42% | NO |
Watching TV | 100 | 6.94% | YES |
Reading Books, Newspapers and Periodicals | 9 | 0.63% | NO |
Leisure and Entertainment | 65 | 4.51% | NO |
Social Interaction | 24 | 1.67% | NO |
5. Learning and Training | 27 | 1.88% | NO |
6. Transportation | 38 | 2.64% | YES |
Other: Use the Internet | 162 | 11.25% | NO |
Abbreviation | Explanation | Abbreviation | Explanation |
---|---|---|---|
ADL | Activities of daily living | MEP | Ministry of Environmental Protection |
AQ | Air quality | MPUD | Mobile phone users’ density |
AQI | Air quality index | NCDC | National Climatic Data Centre |
CAR | Change in average revenue | NOAA | National Oceanic and Atmospheric Administration |
CDR | Call Detail Record | OLS | Ordinary Least Squares |
CI | Cell Identity | P1 | Period 1 |
CNAAQS | Chinese National Ambient Air Quality Standard | P2 | Period 2 |
CNY | Chinese Yuan | P3 | Period 3 |
CSV | Comma-Separated Values | PCC | Per Capita Consumption |
CTRM | Comparison Test of Reference Method | P-Dinner | Period dinner |
EDR | Effective Data Rate | P-Lunch | Period lunch |
EPE | Empty-positive-empty | PM | Particulate Matter |
EPN | Empty-positive-negative | PNN | positive-negative-negative |
FEM | Fixed Effect Model | POI | Point of Interest |
GIS | Geographic Information Science | PoM | Parallelism of Monitors |
GSM | Global System for Mobile Communication | PURT | Panel Unit Root Test |
HT | Harris-Tzavalis | S1 | Strategy 1 |
IDW | Inverse Distance Weighting | S2 | Strategy 2 |
IMSI | International Mobile Subscriber Identification Number | SIM | Subscriber Identity Module |
IoB | Internet of Behaviours | SPSA | Specific place with a specific activity |
IoT | Internet of Things | TEOM | Tapered Element Oscillating Microbalance |
IPS | Im-Pesaran-Shin | UWB | Ultra Wide Band |
KDE | Kernel Density Estimation | VP | Voronoi polygon |
LD-ADL | Location-driven ADL |
Author(s) | Detected ADL(s) | Data Collection | Analysis Method | Limitation(s) |
---|---|---|---|---|
De Freitas [16] | Beach user behaviour | Questionnaire survey | Two-dimensional regression analysis | Single ADL; Traditional survey method |
Lin et al. [17] | Stay in behaviours of elder people | Multi-sensors | Traditional machine learning methods | Single ADL; Small spatial scale; |
Jiang et al. [18] | Maximum number of park visits | On-line and off-line survey | Quantile regression analysis | Single ADL; Small spatial scale; cannot consider unobservable variables |
R-Toubes et al. [19] | Tourist number on beaches | Webcam images in combination with real-time weather | Pearson relatsionship anaylsis | Single ADL; Small spatial scale; cannot consider unobservable variables |
Zhao et al. [20] | Cycling behaviour | Survey in various locations during different periods | Conceptualize the relationship via perceptions | Single ADL; Small spatial scale; cannot consider unobservable variables |
Hu et al. [21] | Outdoor exercise (running, biking, and walking) | APP Tulipsport users’ data | Multivariate analyses of variance | Too few samples; cannot consider unobservable variables |
Gao et al. [24]; Zheng et al. [25] | Dining-out activities | Third-party website (dianping.com), (accessed on 26 July 2021) | FEMs | Data objectivity and model robustness have not been tested |
Dataset | Sensor | Range | Resolution | Accuracy |
---|---|---|---|---|
PM2.5 | PM2.5 | 0~10,000 μg/m3 | 0.1 μg/m3 | ≥85% |
PM10 | PM10 | 0~10,000 μg/m3 | 0.1 μg/m3 | ≥85% |
SO2 | SO2 | 0~500 ppb | 0.1 μg/m3 | ±2%F.S. |
O3 | O3 | 0~500 ppb | 0.1 μg/m3 | ±4%F.S. |
NO2 | NO2 | 0~500 ppb | 0.1 μg/m3 | ±2%F.S. |
CO | CO | 0~50 ppm | 0.1 mg/m3 | ±2%F.S. |
Temperature | Thermometer | −50~50 °C | 0.1 °C | ±0.2 °C |
Wind | Wind Speed | 0~60 m/s | 0.1 m/s | ±(0.5 m/s + 0.03 v) |
Cloud Amount | - | 0~100% | - | - |
Precipitation (Rain) | Tipping-Bucket Rain Gauge, Weighing Precipitation | ≤4 mm/min | 0.1mm | ±0.4 mm (≤10 mm), ±4% (>10 mm) |
Snow (depth) | Automatic Snow Depth Observation Instrument, Ultrasonic or Laser Sensors | 0~2000 mm | 1 mm | ±10 mm |
Variable | Definition | Obs. | Mean | Std. |
---|---|---|---|---|
Mobile phone users’ density (MPUD) variables (Person/Km2) | ||||
Sightseeing | MPUD for the sampled sightseeing POIs | 92,232 | 529.287 | 469.586 |
Eating out | MPUD for the sampled restaurant POIs | 95,256 | 519.974 | 440.782 |
Stay in | MPUD for the sampled house POIs | 100,593 | 539.684 | 452.293 |
Bus Stop | MPUD for the sampled bus stop POIs | 49,811 | 562.951 | 418.586 |
Subway Station | MPUD for the sampled underground station POIs | 49,310 | 620.532 | 509.309 |
Sources: CDR data is from China Mobile Limited Company (Beijing Branch), POI dataset is from AutoNavi Software Limited Company. | ||||
Pollution variables | ||||
AQI | Hourly air quality index | 17,640 | 153.025 | 93.099 |
PM2.5 | Hourly PM2.5 concentration (μg/m3) | 17,640 | 107.929 | 89.774 |
Source: Ministry of Environmental Protection of the People’s Republic of China | ||||
Weather variables | ||||
TEMP | Mean temperature of the site in Beijing (°F) | 504 | 34.381 | 8.642 |
WIND | Mean wind speed of the site in Beijing (m/s) | 504 | 6.871 | 5.412 |
CLOUD | Cloud coverage score (0 to 3), 3 = full, 0 = none | 504 | 0.325 | 0.741 |
RAIN | One-hour liquid precipitation of the site (inches) | 504 | 0 | 0 |
SNOW | One-hour snow depth of the site (inches) | 504 | 0 | 0 |
Source: Daily weather data are collected from the National Oceanic and Atmospheric Administration | ||||
Type of day variables | ||||
Spring Festival | 1 = today is in Spring Festival Holiday, 0 = otherwise | - | - | - |
Weekend | 1 = today is weekend, 0 = otherwise | - | - | - |
Valentine’s Day | 1 = today is Valentine’s Day, 0 = otherwise | - | - | - |
Source: None |
Number of People | Distance of Movement | |||
---|---|---|---|---|
AQI | PM2.5 | AQI | PM2.5 | |
Walk | −7.466 * | −0.661 | −1.201 * | −0.207 |
(0.031) | (0.782) | (0.032) | (0.608) | |
Riding bike | −19.540 * | −2.839 | −7.271 * | −1.015 |
(0.028) | (0.665) | (0.044) | (0.715) |
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Zhang, G.; Poslad, S.; Rui, X.; Yu, G.; Fan, Y.; Song, X.; Li, R. Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China. Sensors 2021, 21, 5569. https://doi.org/10.3390/s21165569
Zhang G, Poslad S, Rui X, Yu G, Fan Y, Song X, Li R. Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China. Sensors. 2021; 21(16):5569. https://doi.org/10.3390/s21165569
Chicago/Turabian StyleZhang, Guangyuan, Stefan Poslad, Xiaoping Rui, Guangxia Yu, Yonglei Fan, Xianfeng Song, and Runkui Li. 2021. "Using an Internet of Behaviours to Study How Air Pollution Can Affect People’s Activities of Daily Living: A Case Study of Beijing, China" Sensors 21, no. 16: 5569. https://doi.org/10.3390/s21165569