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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

邮发代号 80-973

2018 Impact Factor: 3.883

Frontiers of Environmental Science & Engineering  2024, Vol. 18 Issue (11): 143   https://doi.org/10.1007/s11783-024-1903-5
  本期目录
Pollution characteristics and ecological risk assessment of glucocorticoids in the Jiangsu section of the Yangtze River Basin
Lichao Tan1,2, Keke Xu1,2, Shengxin Zhang1, Fukai Tang1,2, Mingzhu Zhang1,2, Feng Ge1,2(), Kegui Zhang1,2()
1. Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210042, China
2. Key Laboratory of Pesticide Environmental Assessment and Pollution Control, Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210042, China
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Abstract

● Seven glucocorticoids in Jiangsu section of Yangtze River Basin were detected.

● The distributions of those glucocorticoids changed with season and locations.

● The levels of glucocorticoids in Jiangsu are higher than other regions in China.

● Prednison exhibits the highest ecological risk in the studied water environment.

Glucocorticoids, which are one of the most extensively used steroid hormones, are typical endocrine disruptors. In recent years, glucocorticoids have been widely detected in surface waters such as rivers and lakes, but there are relatively few studies focusing on their ecological risk assessment. In this study, the pollution characteristics of seven glucocorticoids were studied in the Jiangsu section of the Yangtze River Basin, and ecological risk assessments were performed using the risk quotient method. The results showed that seven glucocorticoids were detected at different levels at eight sampling sites. Among these glucocorticoids, prednisone had the highest value of 238.27 ng/L in the wet season, with pollution levels significantly higher than those reported in other areas. The ecological risk evaluation showed that prednisone, prednisolone, dexamethasone, and hydrocortisone acetate all had risk quotient values greater than 1 in the studied water environment, posing a high ecological risk. This study provides a scientific foundation for the in-depth study of the pollution characteristics and ecological risk of glucocorticoids in water bodies in the Jiangsu section of the Yangtze River Basin.

Key wordsYangtze River Basin    Glucocorticoids    Pollution characteristics    Ecological risk assessment
收稿日期: 2024-03-27      出版日期: 2024-09-13
Corresponding Author(s): Feng Ge,Kegui Zhang   
 引用本文:   
. [J]. Frontiers of Environmental Science & Engineering, 2024, 18(11): 143.
Lichao Tan, Keke Xu, Shengxin Zhang, Fukai Tang, Mingzhu Zhang, Feng Ge, Kegui Zhang. Pollution characteristics and ecological risk assessment of glucocorticoids in the Jiangsu section of the Yangtze River Basin. Front. Environ. Sci. Eng., 2024, 18(11): 143.
 链接本文:  
https://academic.hep.com.cn/fese/CN/10.1007/s11783-024-1903-5
https://academic.hep.com.cn/fese/CN/Y2024/V18/I11/143
Categories Molecular formula Molecular structure
Prednison (PREN) C21H26O5
Prednisolone (PREL) C21H28O5
Methylprednisolone (MET) C22H30O5
Betamethasone (BET) C22H29FO5
Dexamethasone (DEX) C22H29FO5
Prednisolone acetate (PREA) C23H30O6
Hydrocortisone acetate (HYDA) C23H32O6
Tab.1  
Chemicals Parent ion(m/z) Retention time(min) Daughter ion(m/z) Capillary voltage(kV) Cone voltage(V) Collision energy(eV)
PREN 359.16 7.18 267.19*146.81 3.0 20 1720
PREL 361.19 6.98 343.01*146.99 3.0 20 1215
MET 375.11 9.31 357.09*160.91 3.0 17 1025
BET 393.04 9.71 372.99*355.11 3.0 18 1012
DEX 393.05 9.97 373.05*355.14 3.0 18 815
PREA 403.15 12.30 385.14*289.18 3.0 18 1015
HYDA 405.14 12.63 327.21*309.13 3.0 35 2020
Tab.2  
Chemicals Parent ion(m/z) Retention Time(min) Daughter ion(m/z) Capillary voltage(kV) Cone voltage(V) Collision energy(eV)
PREN 359.16 7.18 267.19*146.81 3.0 20 1720
PREL 361.19 6.98 343.01*146.99 3.0 20 1215
MET 375.11 9.31 357.09*160.91 3.0 17 1025
BET 393.04 9.71 372.99*355.11 3.0 18 1012
DEX 393.05 9.97 373.05*355.14 3.0 18 815
PREA 403.15 12.30 385.14*289.18 3.0 18 1015
HYDA 405.14 12.63 327.21*309.13 3.0 35 2020
Tab.3  
Category Compounds Regression equations R2 LODs (ng/L) LOQs (ng/L) Spiked concentration
10 ng/L 20 ng/L 50 ng/L 100 ng/L
Recovery (%) Rsd (%) Recovery (%) Rsd (%) Recovery (%) Rsd (%) Recovery (%) Rsd (%)
Glucocorticoids PREN Y = 402.99X–32.88 0.9984 2.04 6.80 90.18 10.56 93.66 13.87 83.83 4.85 87.75 2.46
PREL Y = 660.62X–229.86 0.9994 2.41 8.02 100.32 5.67 99.41 11.07 100.86 3.51 100.42 1.75
MET Y = 947.31X–509.77 0.9985 0.69 2.30 98.76 3.76 101.11 4.84 95.35 4.67 94.79 11.82
BET Y = 2057.52X–276.69 0.9976 0.51 1.70 84.71 2.58 78.86 2.46 82.91 3.41 85.69 5.82
DEX Y = 1954.62X+925.15 0.9987 0.35 1.18 89.46 6.17 86.20 12.62 87.74 2.51 92.10 0.48
PDA Y = 641.88X–78.35 0.9996 1.06 3.53 71.84 3.58 70.74 3.25 70.17 1.28 72.48 2.57
HDA Y = 279.16X+22.46 0.9992 1.16 3.88 75.92 11.74 79.63 12.17 74.30 10.46 70.56 10.79
Tab.4  
Fig.1  
Fig.2  
Fig.3  
Glucocorticoids PREN PREL MET BET DEX PREL HYDA
PNEC (ng/L) 100 89.6 100 4.77 13.74 100 4.05
Tab.5  
Fig.4  
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