Air pollution is one of the leading causes for global deaths and understanding pollutant emission sources is key to successful mitigation policies. Air quality data in the urban, suburban, industrial, and rural areas (UA, SA, IA, and RA) of Jining, Shandong Province in China, were collected to compare the characteristics and associated health risks. The average concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , and CO show differences of -3.87, -16.67, -19.24, -15.74, and -8.37% between 2017 and 2018. On the contrary, O 3 concentrations increased by 4.50%. The four functional areas exhibited the same seasonal variations and diurnal patterns in air pollutants, with the highest exposure excess risks (ERs) resulting from O 3 . More frequent ER days occurred within the 25-30°C, but much larger ERs are found within the 0-5°C temperature range, attributed to higher O 3 pollution in summer and more severe PM pollution in winter. The premature deaths attributable to six air pollutants can be calculated in 2017 and 2018, respectively. Investigations on the potential source show that the ER of O 3 ( r of 0.86) had the tightest association with the total ER. The bivariate polar plots indicated that the highest health-based air quality index (HAQI) in IA influences the HAQI in UA and SA by pollution transport, and thus can be regarded as the major pollutant emission source in Jining. The above results indicate that urgent measures should be taken to reduce O 3 pollution taking into account the characteristics of the prevalent ozone formation regime, especially in IA in Jining.
Keyphrases
- particulate matter
- air pollution
- heavy metals
- human health
- risk assessment
- health risk assessment
- public health
- endoplasmic reticulum
- lung function
- healthcare
- breast cancer cells
- estrogen receptor
- climate change
- risk factors
- cardiovascular events
- south africa
- drinking water
- cardiovascular disease
- social media
- coronary artery disease
- chronic obstructive pulmonary disease
- early onset
- health information
- type diabetes
- nitric oxide
- heat stress
- electronic health record
- systemic lupus erythematosus
- big data
- data analysis