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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (5) : 56    https://doi.org/10.1007/s11783-021-1490-7
RESEARCH ARTICLE
Investigating the impact of air pollution on AMI and COPD hospital admissions in the coastal city of Qingdao, China
Jiuli Yang1, Mingyang Liu2, Qu Cheng3, Lingyue Yang1, Xiaohui Sun4, Haidong Kan5, Yang Liu6, Michelle L. Bell7, Rohini Dasan3, Huiwang Gao1, Xiaohong Yao1, Yang Gao1()
1. Key Laboratory of Marine Environmental Science and Ecology, and Frontiers Science Center for Deep Ocean Multispheres and Earth System (Ministry of Education), Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China
2. Department of Emergency Internal Medicine, The Affiliated Hospital of Qingdao University, Qingdao 266100, China
3. Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
4. Department of Chronic Disease Prevention, Qingdao Municipal Center for Disease Control & Prevention, Qingdao 266100, China
5. School of Public Health, Key Laboratory of Public Health Safety of the Ministry of Education, Fudan University, Shanghai 200433, China
6. Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
7. School of the Environment, Yale University, New Haven, CT 06511, USA
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Abstract

• The impact of air pollution on AMI/COPD hospital admissions were examined.

• Significant connection was found between air pollutants and AMI/COPD in Qingdao.

• Nonlinearity exists between air pollution and AMI/COPD hospital admissions.

Air pollution has been widely associated with adverse effects on the respiratory and cardiovascular systems. We investigated the relationship between acute myocardial infarction (AMI), chronic obstructive pulmonary disease (COPD) and air pollution exposure in the coastal city of Qingdao, China. Air pollution in this region is characterized by inland and oceanic transportation sources in addition to local emission. We examined the influence of PM2.5, PM10, NO2, SO2, CO and O3 concentrations on hospital admissions for AMI and COPD from October 1, 2014, to September 30, 2018, in Qingdao using a Poisson generalized additive model (GAM). We found that PM2.5, PM10, NO2, SO2 and CO exhibited a significant short-term (lag 1 day) association with AMI in the single-pollutant model among older adults (>65 years old) and females, especially during the cold season (October to March). In contrast, only NO2 and SO2 had clear cumulative lag associations with COPD admission for females and those over 65 years old at lag 01 and lag 03, respectively. In the two-pollutant model, the exposure-response relationship fitted by the two-pollutant model did not change significantly. Our findings indicated that there is an inflection point between the concentration of certain air pollutants and the hospital admissions of AMI and COPD even under the linear assumption, indicative of the benefits of reducing air pollution vary with pollution levels. This study has important implications for the development of policy for air pollution control in Qingdao and the public health benefits of reducing air pollution levels.
Keywords AMI      COPD      Air pollution exposure      GAM     
Corresponding Author(s): Yang Gao   
Issue Date: 15 October 2021
 Cite this article:   
Jiuli Yang,Mingyang Liu,Qu Cheng, et al. Investigating the impact of air pollution on AMI and COPD hospital admissions in the coastal city of Qingdao, China[J]. Front. Environ. Sci. Eng., 2022, 16(5): 56.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1490-7
https://academic.hep.com.cn/fese/EN/Y2022/V16/I5/56
Site Longitude Latitude Administrative district
Meteorological data monitoring station 120.3333°E 36.0666°N Qingdao Shibei District
Air pollution data monitoring stations 120.6659°E 36.2403°N Qingdao Laoshan District
120.4587°E 36.0852°N Qingdao Laoshan District
120.4001°E 36.2403°N Qingdao Chengyang District
120.3905°E 36.1851°N Qingdao Licang District
120.3471°E 36.0699°N Qingdao Shibei District
120.3664°E 36.1032°N Qingdao Shibei District
120.4134°E 36.0654°N Qingdao Shinan District
120.2992°E 36.0544°N Qingdao Shinan District
120.1926°E 35.9586°N Qingdao West Coast New Area
Hospital branches 120.4536°E 36.1019°N Qingdao Laoshan District
120.3277°E 36.0670°N Qingdao Shinan District
120.1563°E 35.9871°N Qingdao West Coast New Area
Tab.1  The locations of the hospital branches and air pollutant monitoring stations
Type Variable Mean±SD Min 25th 50th 75th Max IQR
Hospital
admissions		 AMI (Total) 14±8 0 7 14 20 51 13
AMI (Age<65 years) 6±4 0 3 5 8 33 5
AMI (Age≥65 years) 9±5 0 4 8 12 32 8
AMI (Male) 10±6 0 5 9 14 36 9
AMI (Female) 5±3 0 2 4 7 20 5
COPD (Total) 121±72 6 54 115 166 367 112
COPD (Age<65 years) 31±15 3 19 29 41 107 22
COPD (Age≥65 years) 90±59 3 34 85 127 307 93
COPD (Male) 81±49 6 35 77 113 252 78
COPD (Female) 40±24 0 18 38 55 121 37
Air
pollutants		 PM2.5 (μg/m3) 43.3±33.6 5.8 21.2 33.5 54.0 311.7 32.8
PM10 (μg/m3) 86.4±51.3 16.5 51.2 73.4 106.7 454.7 55.5
NO2 (μg/m3) 34.6±16.2 2.5 23.2 32.1 43.1 107.6 19.9
SO2 (μg/m3) 20.4±14.7 3.2 10.4 17.0 24.9 113.0 14.5
CO (mg/m3) 0.8±0.01 0.3 0.5 0.7 0.9 3.2 0.4
O3 (μg/m3) 71.9±29.3 12.5 48.7 69.6 90.8 188.1 42.1
Meteorological
data		 Temperature (°C) 13.9±9.4 −11.5 5.5 14.7 22.0 31.0 16.5
Humidity (%) 68.8±16.7 16 56 71 83 100 27
Tab.2  Distribution of daily hospital admissions of AMI and COPD, air pollutant data and meteorological data in Qingdao, China (from October 1, 2014, to September 30, 2018)
Fig.1  Time series of daily levels of pollutant concentrations during the study period.
Fig.2  Estimated exposure-response curve: relative risk of AMI (a) hospital admissions for lag 1 pollution for daily average NO2, SO2, CO, PM2.5 and PM10 concentrations and COPD (b) for lag 0 pollution for daily average NO2, SO2 and CO concentrations.
Fig.3  Percentage changes (95% CI) in AMI all and subgroups for hospital admissions reported in different study periods (entire period, cold period and warm period) with pollutant concentration increases of 10 μg/m3 at Lag 1. “*” represents P<0.05; “**” represents P<0.01. Adjust P value after Bonferroni correction: season_age: 2.90 × 10−4, season_gender: 0.58.
Fig.4  Percentage change (95% CI) in COPD subgroups for hospital admissions and a NO2 concentration increase of 10 μg/m3 during the entire study period single-day lag and multiday lag. “*” represent P<0.05; “**” represent P<0.01.
Fig.5  Percentage change (95% CI) in COPD subgroups for hospital admissions with a SO2 concentration increase of 10 μg/m3 during the entire study period single-day lag and multiday lag. “*” represent P<0.05; “**” represent P<0.01.
Model Whole period Cold season Warm season
CO 1.038(1.013–1.063)* 1.051(1.021–1.082)* 0.976(0.921–1.035)
+ PM2.5 1.039(0.989–1.091) 1.078(1.006–1.155)* 0.994(0.906–1.089)
+ PM10 1.037(1.001–1.075)* 1.040(0.982–1.101) 0.990(0.923–1.062)
+ NO2 1.031(0.995–1.068) 1.053(1.005–1.103)* 0.959(0.890–1.032)
+ SO2 1.029(0.996–1.064) 1.059(1.016–1.103)* 0.947(0.879–1.021)
+ O3 1.037(1.012–1.062)* 1.050(1.019–1.083)* 0.980(0.921–1.042)
Tab.3  Relative risk (95% CI) at lag 1 day of AMI hospital admissions in two-pollutant models, CO as the main pollutant
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