The possibility on estimation of concentration of heavy metals in coastal waters from remote sensing data

Chen, Chuqun; Liu, Fenfen; He, Quanjun; Shi, Heyin

doi:10.1109/igarss.2010.5648845

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…Furthermore, previous research has been conducted on the remote sensing monitoring of heavy metal pollution in water quality. It is believed that this limitation will be effectively addressed in the future [69][70][71]. It should be noted that there is still a lack of a globally harmonized and fixed standard for water quality parameters, and local water quality standards need to be used for practical applications.…”

Section: Discussionmentioning

confidence: 99%

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Chen,

Tan,

Peng

et al. 2024

Water

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Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.

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Section: Discussionmentioning

confidence: 99%

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Chen,

Tan,

Peng

et al. 2024

Water

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“…As the heavy metals in water exist in three forms [13][14], each of which is respectively controlled by optically-significant parameters, suspended sediments, dissolved organic matters and phytoplankton. Previous work showed the possibility of detecting heavy metal concentration from remote sensing data [15].Eight cruises were conducted for in-situ data collection in the Pearl An above-water method was used for the measurement of remote sensing reflectance (Rrs). The water-leaving radiance, the radiance reflected by a reference panel with 25% reflectance and the sky radiance were measured in turn for 3 times using the Ocean Optics USB4000 spectrometer (wavelength range from 346 to 1037 nm with a spectral resolution of 0.22 nm) at 128 sampling points, where the water samples were synchronously collected for lab analysis of concentration for three heavy metals (Cu, Pb and Zn) and other water components (suspended sediments, chlorophyll-a and CDOM).…”

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confidence: 99%

mentioning

confidence: 99%

Estimation of heavy metal concentration in the Pearl River estuarine waters from remote sensing data

Chen

Liu

Tang

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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With the increase of population and the development of light industry in the Pearl River Delta area, a great deal of industrial and household waste waters with heavy metals are discharged to the ocean via the river channels. The heavy metals cannot be decomposed but can be transferred and accumulated with food chains [1,2]. Many heavy metals are toxic to human beings. It is very important to measure the heavy metal concentration in the coastal waters for water quality investigation, environmental management and for security of aquaculture products. The remote sensing technology has been successfully applied for estimation of many parameters of water quality, such as the suspended sediments [3], chlorophyll a, [4][5][6], CDOM (colored dissolved organic matter) [7-8], eutrophication [9-10], salinity [11], and water quality grades [12]. The remote sensing technology has many advantages over the conventional investigation of water quality for its high spatial and temporal resolution, low cost, big coverage of data and synchronization. With its development, the remote sensing technique is expected to retrieve more parameters of water quality. As the heavy metals in water exist in three forms [13][14], each of which is respectively controlled by optically-significant parameters, suspended sediments, dissolved organic matters and phytoplankton. Previous work showed the possibility of detecting heavy metal concentration from remote sensing data [15].Eight cruises were conducted for in-situ data collection in the Pearl An above-water method was used for the measurement of remote sensing reflectance (Rrs). The water-leaving radiance, the radiance reflected by a reference panel with 25% reflectance and the sky radiance were measured in turn for 3 times using the Ocean Optics USB4000 spectrometer (wavelength range from 346 to 1037 nm with a spectral resolution of 0.22 nm) at 128 sampling points, where the water samples were synchronously collected for lab analysis of concentration for three heavy metals (Cu, Pb and Zn) and other water components (suspended sediments, chlorophyll-a and CDOM). The in-situ measured Rrs data were calculated to match the bands of EO-1 Hyperion and the Landsat-TM band 1 to 4. The relationships between the concentration of the three heavy metals (Cu, Pb and Zn) and the remote sensing reflectance were analysed. The results showed that all the three heavy metals have good correlative relation with Rrs data. The correlative relation could be improved if it was analyzed respectively from data collected in dry season or wet season, and the correlative relation could be improved with the band ratio of Rrs. Algorithms for estimation of heavy metal concentration were developed using the ratio band combination, which has the maximum correlative coefficient with heavy metals. Then the algorithms were applied to Landsat TM data for estimation of the heavy metals (Cu, Pb, Zn) concentration. The results showed that the distribution of the three heavy metals' 2575 978-1-4673-1159-5/12/$31.00 ©2012 IEEE IGARSS 20...

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Estimating the Pollution Level Based on Heavy Metal Concentration in Water Bodies of Tiruppur District

Rajesh

Jiji²,

Raj³

2019

J Indian Soc Remote Sens

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The possibility on estimation of concentration of heavy metals in coastal waters from remote sensing data

Cited by 7 publications

References 10 publications

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Estimation of heavy metal concentration in the Pearl River estuarine waters from remote sensing data

Estimating the Pollution Level Based on Heavy Metal Concentration in Water Bodies of Tiruppur District

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