• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 12, 3948 (2022)
ZHANG Yi1、2, LIU Chuan-yang2、3, CHENG Cheng2、3, SHEN Jian2、3, CHAI Yi-di2、3, LI Fang2、3, CHEN Chong-jun1, MEI Juan1, and WU Jing2、3
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2022)12-3948-06 Cite this Article
    ZHANG Yi, LIU Chuan-yang, CHENG Cheng, SHEN Jian, CHAI Yi-di, LI Fang, CHEN Chong-jun, MEI Juan, WU Jing. Analysis of Fluorescence Fingerprint Characteristics of Water Quality in the Estuary of the Yangtze River[J]. Spectroscopy and Spectral Analysis, 2022, 42(12): 3948 Copy Citation Text show less

    Abstract

    The technique of aqueous fluorescence fingerprint is an emerging technology in water pollution detection in recent years, which can reveal the composition information of the water organic matter and make up for the shortcomings of conventional water quality parameters. The coastwise area of Yangtze River Estuary is an industry-intensive belt in China with high regional urbanization and developed industry. In this basin, water quality change would directly affect economic development and human health. Therefore, it is of great significance to study the water quality change in the Yangtze River Estuary. Different variation trends appeared among pH, conductivity, NH3-N, CODMn, TP, TN and TOC in the study area. However, the four indicators of conductivity, TN, CODMn and TOC, still reflected that there was a certain pollution accumulation from upstream to downstream along the estuary section of the Yangtze River, especially at the downstream sampling points CJ-11 and CJ-12, which may be greatly affected by pollution sources. The results of conventional indicators and TOC could not directly reflect the pollution information, but could only reflect the increase of total pollution from upstream to downstream. Aqueous fluorescence fingerprint in the estuary of the Yangtze River mainly contained three fluorescence peaks, which were recorded as peak A, peak B and peak C. The [excitation wavelength and emission wavelength] were [275, 335] nm, [230, 345] nm and [250, 450] nm respectively. The synchronous variation trend of fluorescence intensity was between peak A and peak B with a high correlation, and the correlation coefficient reached 0.994 8, indicating that the two peaks were likely to come from the same pollution source. Through the similarity comparison algorithm of aqueous fluorescence fingerprints, the similarity of aqueous fluorescence fingerprints between the sampling points of the Yangtze River estuary (CJ-11 and CJ-12) and tributary of the estuary (HPJ-1) were 86% and 88%, respectively. Meanwhile, the similarity to CJ-10 was all less than 60%. It indicated that the change of aqueous fluorescence fingerprints (CJ-11 and CJ-12) in the lower reaches of the Yangtze River Estuary might be caused by the tributary HPJ, which flows into the Yangtze River Estuary. The similarity between the aqueous fluorescence fingerprint of tributary HPJ and the fluorescence fingerprint database of the printing and dyeing industry was approximately 90%, illustrating that the aqueous fluorescence fingerprint of tributary HPJ might be related to the discharge of local printing and dyeing wastewater. Based on the terrific linear positive correlation between the peak intensity, including peak A and peak B, and the concentration of NH3-N in the estuary of the Yangtze River (the correlation coefficient is 0. 8855), the aqueous fluorescence fingerprint technique could have the potential to indicate the concentration of NH3-N in the estuary of the Yangtze River. Aqueous fluorescence fingerprint technology could reveal the composition and source of organic matter in water and have important application value in pollution identification and water quality evaluation.
    ZHANG Yi, LIU Chuan-yang, CHENG Cheng, SHEN Jian, CHAI Yi-di, LI Fang, CHEN Chong-jun, MEI Juan, WU Jing. Analysis of Fluorescence Fingerprint Characteristics of Water Quality in the Estuary of the Yangtze River[J]. Spectroscopy and Spectral Analysis, 2022, 42(12): 3948
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