Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China
Abstract
:1. Introduction
2. Methods
2.1. Study Area
2.2. Data
2.2.1. Meteorological Observations
2.2.2. Air Quality Data
2.2.3. Baidu Indices
2.3. Correlation Analysis
2.3.1. Pearson Correlation
2.3.2. Coplot
2.4. Exposure Assessment
2.4.1. Statistical Modeling
logλt = Intercept + βAQIt + DOW + WIND + S(Time,k1) + S(Temp,k2) + S(Humi,k3)
2.4.2. Relative Risk (RR)
2.5. Health AQI
3. Results
3.1. Data Exploration
3.2. Health Impact Evaluation
3.3. RR of Air Pollutants
3.4. Performance of HAQI
4. Discussion
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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IAQI | SO2 (μg/m3) 24 h | NO2 (μg/m3) 24 h | PM10 (μg/m3) 24 h | CO (mg/m3) 24 h | O3 (μg/m3) 8 h | PM2.5 (μg/m3) 24 h |
---|---|---|---|---|---|---|
0 | 0 | 0 | 0 | 0 | 0 | 0 |
50 | 50 | 40 | 50 | 2 | 100 | 35 |
100 | 150 | 80 | 150 | 4 | 160 | 75 |
150 | 475 | 180 | 250 | 14 | 215 | 115 |
200 | 800 | 280 | 350 | 24 | 265 | 150 |
300 | 1600 | 565 | 420 | 36 | 800 | 250 |
400 | 2100 | 750 | 500 | 48 | 1000 | 350 |
500 | 2620 | 940 | 600 | 60 | 1200 | 500 |
Category | Keywords |
---|---|
RTI (respiratory total index) | respiratory system, pulmonary disease, lung cancer, pneumonia, asthma, bronchitis, rheum, cough, sputum, respite, shortness of breath, nasal, congestion, sore throat |
CTI (cardio- and cerebrovascular total index) | cardiovascular, cerebrovascular, cardio- and cerebrovascular, rheumatic, heart disease, coronary heart disease, myocardial infarction, myocardial, ischemia, arrhythmia, heart failure, ischemic stroke, heart valve disease, subarachnoid hemorrhage |
Data | Days | Mean ± SE | Min | P25 | Median | P75 | Max | IQR |
---|---|---|---|---|---|---|---|---|
Air pollution | ||||||||
AQI | 792 | 105.5 ± 2.72 | 15.0 | 50.5 | 83.7 | 135.9 | 475.2 | 85.4 |
PM2.5 (μg/m3) | 792 | 74.8 ± 2.45 | 6.7 | 26.9 | 54.2 | 98.6 | 477.5 | 71.7 |
PM10 (μg/m3) | 792 | 97.1 ± 2.70 | 0.0 | 41.6 | 79.7 | 129.2 | 518.3 | 87.6 |
CO (mg/m3) | 792 | 1.2 ± 0.038 | 0.23 | 0.59 | 0.89 | 1.32 | 8.14 | 0.73 |
O3 (μg/m3) | 792 | 57.5 ± 1.31 | 2.1 | 28.9 | 53.3 | 79.5 | 168.0 | 50.6 |
NO2 (μg/m3) | 792 | 48.5 ± 0.87 | 10.4 | 31.6 | 42.8 | 59.8 | 153.5 | 28.2 |
SO2 (μg/m3) | 792 | 10.3 ± 0.37 | 1.8 | 3.1 | 6.4 | 14.0 | 85.2 | 10.9 |
Meteorological observations | ||||||||
a Wind level | 788 | 1.84 ± 0.021 | 0.75 | 1.43 | 1.72 | 2.09 | 4.67 | 0.66 |
Temperature (°C) | 788 | 13.5 ± 0.37 | -14.5 | 3.6 | 14.9 | 23.1 | 32.4 | 19.5 |
Relative humidity (%) | 788 | 52.1 ± 0.73 | 8.0 | 35.8 | 52.7 | 68.5 | 98.6 | 32.7 |
Baidu indices | ||||||||
RTI | 792 | 3511.9 ± 17.79 | 2336.0 | 3144.0 | 3460.5 | 3837.5 | 5800.0 | 693.5 |
CTI | 792 | 1935.4 ± 12.30 | 928.0 | 1792.5 | 1948.5 | 2071.0 | 8750.0 | 278.5 |
Pollutant | RTI | CTI | ||
---|---|---|---|---|
r (95% CIs) | p−value | r (95% CIs) | p-value | |
AQI | 0.21 (0.14,0.28) | 9.84 ×10−10 | 0.05(−0.02, 0.12) | 0.1288 |
PM2.5 | 0.23 (0.16,0.30) | 1.57 ×10−11 | 0.05(−0.02, 0.12) | 0.1781 |
PM10 | 0.23 (0.16, 0.29) | 3.212 ×10−11 | 0.07(−0.00, 0.14) | 0.0589 |
CO | 0.33 (0.26,0.39) | <2.2 ×10−16 | 0.05 (−0.02, 0.12) | 0.1683 |
O3 | −0.40(−0.45, −0.33) | <2.2 ×10−16 | 0.07 (0.00,0.14) | 0.03644 |
NO2 | 0.35 (0.29,0.41) | <2.2 ×10−16 | 0.04 (−0.03, 0.11) | 0.2233 |
SO2 | 0.28 (0.21,0.34) | 1.485 ×10−15 | 0.08 (0.01, 0.14) | 0.03383 |
Lag (Day) | 0 | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|
β for RTI (10−4) | 2.178 | 2.489 | 2.835 | 3.05 | 3.073 | 2.468 |
β for CTI (10−4) | 1.151 | 1.867 | 2.496 | 3.157 | 2.991 | 2.408 |
BI | β | R2 | Deviance Explained | Reserved |
---|---|---|---|---|
respiratory system | 3.204 ×10−4 | 0.0845 | 8.27 | * |
pulmonary disease | 5.437 ×10−4 | 0.0847 | 8.04 | ** |
lung cancer | 6.017 ×10−4 | 0.267 | 30.8 | *** |
pneumonia | 3.385 ×10−4 | 0.694 | 71 | - |
asthma | 0.773 ×10−4 | 0.226 | 26 | - |
bronchitis | 3.181 ×10−4 | 0.631 | 65.1 | * |
rheum | 3.350 ×10−4 | 0.386 | 42.7 | * |
cough | 1.942 ×10−4 | 0.737 | 75.8 | - |
sputum | 0.142 ×10−4 | 0.037 | 5.06 | - |
respite | −3.04 ×10−4 | 0.0197 | 3.2 | - |
shortness of breath | 0.819 ×10−4 | 0.0863 | 9.6 | - |
nasal congestion | 3.927 ×10−4 | 0.384 | 40 | *** |
sore throat | 1.618 ×10−4 | 0.318 | 33.2 | * |
cardiovascular | 3.277 ×10−4 | 0.11 | 10.2 | ** |
cerebrovascular | 4.877 ×10−4 | 0.216 | 19.7 | ** |
cardio- and cerebrovascular | 1.621 ×10−4 | 0.0975 | 9.37 | - |
rheumatic heart disease | 0.932 ×10−4 | 0.0432 | 5.05 | - |
coronary heart disease | 2.844 ×10−4 | 0.438 | 45.8 | * |
myocardial infarction | 0.43 ×10−4 | 0.105 | 12.4 | - |
myocardial ischemia | 1.332 ×10−4 | 0.285 | 30 | - |
arrhythmia | 0.368 ×10−4 | 0.0883 | 9.96 | - |
heart failure | 1.8 ×10−4 | 0.0815 | 9.1 | * |
ischemic stroke | −2.56 ×10−4 | 0.0765 | 7.09 | - |
heart valve disease | 5.455 ×10−4 | 0.109 | 10.1 | ** |
subarachnoid hemorrhage | 6.747 ×10−4 | 0.0179 | 13.6 | ** |
Total Index | Pollutant | IQR | β | RR (95% CIs) | (RR − 1) × 100% (95% CIs) |
---|---|---|---|---|---|
RTI | PM2.5 | 71.7 | 0.00044 | 1.0317 (1.0297–1.0338) | 0.45% (0.42%–0.48%) |
PM10 | 87.6 | 0.000389 | 1.0353 (1.0332–1.0374) | 0.40% (0.37%–0.42%) | |
CO | 0.7 | 0.0320 | 1.0227 (1.0212–1.0241) | 3.24% (3.03%–3.44%) | |
O3 | 50.6 | 0.000721 | 1.0375 (1.0333–1.0417) | 0.73% (0.65%–0.82%) | |
NO2 | 28.2 | 0.00135 | 1.0388 (1.0362–1.0414) | 1.37% (1.28%–1.46%) | |
SO2 | 10.9 | 0.00141 | 1.0156 (1.0135–1.0176) | 1.42% (1.23%–1.61%) | |
CTI | PM2.5 | 71.7 | 0.000522 | 1.0378 (1.0351–1.0405) | 0.53% (0.49%–0.57%) |
PM10 | 87.6 | 0.000393 | 1.0356 (1.0329–1.0384) | 0.40% (0.37%–0.43%) | |
CO | 0.7 | 0.0373 | 1.0265 (1.0246–1.0284) | 3.78% (3.51%–4.05%) | |
O3 | 50.6 | 0.00197 | 1.1056 (1.0999–1.1113) | 2.07% (1.96%–2.18%) | |
NO2 | 28.2 | 0.00135 | 1.0390 (1.0355–1.0424) | 1.38% (1.26%–1.50%) | |
SO2 | 10.9 | 0.00312 | 1.0349 (1.0322–1.0376) | 3.17% (2.93%–3.41%) |
RRTotal | PSI | SO2 (μg/m3) 24 h | NO2 (μg/m3) 24 h | PM10 (μg/m3) 24 h | CO (mg/m3) 24 h | O3 (μg/m3) 8 h | PM2.5 (μg/m3) 24 h |
---|---|---|---|---|---|---|---|
1.0000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
1.0223 | 50 | 15.74 | 16.25 | 56.40 | 0.44 | 30.36 | 50 |
1.0446 | 100 | 31.48 | 32.50 | 112.80 | 0.89 | 60.71 | 100 |
1.0670 | 150 | 47.22 | 48.75 | 169.20 | 1.33 | 91.07 | 150 |
1.0893 | 200 | 62.95 | 65.00 | 225.60 | 1.77 | 121.42 | 200 |
1.1116 | 250 | 78.69 | 81.26 | 282.00 | 2.21 | 151.78 | 250 |
1.1339 | 300 | 94.43 | 97.51 | 338.40 | 2.66 | 182.13 | 300 |
1.1562 | 350 | 110.17 | 113.76 | 394.80 | 3.10 | 212.49 | 350 |
1.1786 | 400 | 125.91 | 130.01 | 451.21 | 3.54 | 242.84 | 400 |
1.2009 | 450 | 141.65 | 146.26 | 507.61 | 3.98 | 273.20 | 450 |
1.2232 | 500 | 157.38 | 162.51 | 564.01 | 4.43 | 303.55 | 500 |
aj | 3.18 | 3.08 | 0.89 | 112.97 | 1.65 | 1.00 |
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Zhong, S.; Yu, Z.; Zhu, W. Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China. Int. J. Environ. Res. Public Health 2019, 16, 1014. https://doi.org/10.3390/ijerph16061014
Zhong S, Yu Z, Zhu W. Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China. International Journal of Environmental Research and Public Health. 2019; 16(6):1014. https://doi.org/10.3390/ijerph16061014
Chicago/Turabian StyleZhong, Shaobo, Zhichen Yu, and Wei Zhu. 2019. "Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China" International Journal of Environmental Research and Public Health 16, no. 6: 1014. https://doi.org/10.3390/ijerph16061014