The effects of particulate matters on allergic rhinitis in Nanjing, China.
Haiyan ChuJunyi XinQi YuanMeilin WangLei ChengZhengdong ZhangMeiping LuPublished in: Environmental science and pollution research international (2019)
Particulate matter pollution is a serious environmental problem. Individuals exposed to particulate matters have an increased prevalence to diseases. In the present study, we performed an epidemiological study to investigate the effects of particulate matter less than 10 μm in aerodynamic diameter (PM10) and particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) on allergic rhinitis in Nanjing, China. Daily numbers of allergic rhinitis patients (33,063 patients), PM10, PM2.5, and weather data were collected from January 2014 to December 2016 in Nanjing, China. Generalized additive models (GAM) were used to evaluate the effects of PM10 and PM2.5 on allergic rhinitis. We found that the interquartile range (IQR) increases in PM10 (difference of estimates, 5.86%; 95% CI, 3.00-8.81%; P = 4.72 × 10-5) and PM2.5 (difference of estimates, 5.39%; 95% CI, 2.73-8.12%; P = 5.67 × 10-5) concentrations were associated with the higher increased numbers of allergic rhinitis patients with 3-day cumulative effects in single-pollutant model. In addition, we found that the IQR increase in PM10 (age ≥ 18 years: 7.37%, 3.91-10.96%, 2.14 × 10-5; 0-17 years: 0.83%, - 4.00-5.91%, 0.740) and PM2.5 (age ≥ 18 years: 7.00%, 3.78-10.32%, 1.40 × 10-5; 0-17 years: 0.40%, - 4.10-5.10%, 0.866) increased the number of allergic rhinitis patients in adults, but not in children. In summary, our findings suggested that exposure to PM10 and PM2.5 was associated with the risk of allergic rhinitis.
Keyphrases
- particulate matter
- allergic rhinitis
- air pollution
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- prognostic factors
- heavy metals
- young adults
- physical activity
- electronic health record
- risk assessment
- risk factors
- polycyclic aromatic hydrocarbons
- big data
- water soluble
- climate change
- deep learning
- drinking water