Pollution characteristics and health risk of polycyclic aromatic hydrocarbons in atmospheric PM2.5 of Nanjing,China,2022
Objective To investigate the pollution characteristics and health risk of polycyclic aromatic hydrocarbons(PAHs)in fine particulate matter(PM2.5)of Nanjing,China.Methods PM2.5 and PAHs samples were collected at two monitoring sites in Jiang-bei new district and Jiangning district from the 10th to 16th days of each month in 2022.The weighing method and high-performance liquid chromatography were used to measure the concentrations of PM2.5 and PAHs,and a health risk assessment model was used to e-valuate the carcinogenic risk of PAHs.Results There were significant differences in the concentrations of PM2.5,PAHs,and B[a]P across different seasons(P＜0.01);the concentrations of PM2.5 and PAHs in winter and spring were higher than those in summer and autumn,and the concentration of B[a]P in spring was higher than that in summer and autumn.There were significant differences be-tween the two monitoring sites in the mean annual concentrations of PAHs and B[a]P and their concentrations in spring and summer(P＜0.05),and Jiangbei new district showed higher concentrations than Jiangning District.In both monitoring sites,5-ring PAHs showed the highest concentration,followed by 4-ring PAHs and 6-ring PAHs.There were significant differences in the mean annual concentrations of 3-,4-,5-,and 6-ring PAHs between the two monitoring sites(P＜0.05),and Jiangbei new district showed higher concentrations than Jiangning District.The median annual concentration of PAHs had an excess cancer risk(ECR)of＞1.0×10-6 for the populations in the two monitoring sites.The 95th percentile of the annual concentration of PAHs showed an ECR of＞1×10-6 for all age groups except the population aged 0-2 years.Conclusion The distribution of PAHs in PM2.5 of Nanjing shows marked seasonal and regional variations,with a potential excess carcinogenic risk.
fine particulate matter(PM2.5)polycyclic aromatic hydrocarbons(PAHs)pollution characteristicshealth risk as-sessment
NETL
NSTL
万方数据
