Prediction of heavy metals in soils of an arid area based on multi-spectral data

Improving river water quality monitoring using satellite data products and a genetic algorithm processing approach

The increase in toxic metal concentration leads to environmental pollution. The pollution level can be estimated by measuring the concentration of these heavy metals. This work was conducted to determine the level of heavy metal contamination in the water bodies due to the activities of industries. This paper discusses the concentrations of few heavy metals which were measured in Tiruppur District, Tamil Nadu, India, known for dyeing activities. The heavy metal concentrations have been investigated using water samples and remote sensing data (Cartosat-2) with the relevant water standards. Recursive linear regression analysis is applied for estimating the concentration from remote sensing data. Based on these estimated concentrations of the pollution level, mapping was done using clustering and band-max mechanism. Finally, the heavy metal concentration estimated from remote sensing data was correlated with in situ heavy metal concentration data and results are analyzed.

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

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, [4–6], 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 River estuary in August, October, November, December 2009 and February, March, July and October 2010, respectively. 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' concentration is reasonable.

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

Copper ion is one of the hazardous pollutants often present in industrial wastewater or acid mine drainage that is regarded as a primary environmental challenge. Hyperspectral remote sensing has a long tradition in water quality monitoring. However, its application in heavy metal detection is relatively similar, and the detection is highly influenced by water turbidity or total suspended matter (TSM), requiring research efforts to improve accuracy and generalize the applicability of this technique. In this study, the use of simple filtration (pore size of 0.7 μm) for sample pretreatment to improve hyperspectral remote sensing of copper ion concentrations (Cu, 100-1000 mg/L) in water samples is proposed. A wide variety of water samples, including as-prepared and field (fish pond and river water) samples, were investigated to validate the developed method. Spectral data containing sensitive bands characterized in the range of 900-1100 nm were first preprocessed with logarithm transformation, followed by quantitative prediction model development using stepwise multivariate linear regression (SMLR) with the most sensitive wavebands at around 900 nm and 1080 nm. Satisfactory prediction performance for Cu ions was found for turbid water samples (TSM greater than approximately 200 mg/L) after simple filtration pretreatment, suggesting that pretreatment removed suspended solids in the mixtures and enhanced the spectral features of Cu ions in the model. Moreover, good agreement between the laboratory results and the field samples (adjusted R2 > 0.95 and NRMSE <0.15) highlights the suitability of the developed model and filtration pretreatment for obtaining relevant information for the rapid determination of Cu ion concentrations in complex water samples.

An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy.

The heavy metals in waters cannot be decomposed but can be transferred and accumulated with food chains. Many heavy metals are toxic to human beings. It is very important to measure concentration of heavy metals in coastal waters for water quality research, monitoring, and environmental management. On consideration of geochemistry behavior of heavy metals, their distribution is related with water components which determined waters' optical properties. The in-situ remote sensing reflectance data and heavy metal concentration data at 48 sampling points collected from three cruises in the Pearl River estuary were analysed. For single band among all the 57 bands ranging from 365 to 935 nm, the band centered at 711 nm (B 711 ) has highest correlation coefficient (R=0.51) with concentration of both Cu and Zn. The band ratio, B 711 / B 406 has the highest correlation coefficient with Cu (R=0.749), and band ration, B 711 / B 416 has the highest correlation coeficient with Zn (R=0.804). The band and band ratio were employed for algorithm development using the symbolic regression method, and the results showed the possiblity to retrieve concentration of heavy metal from remotely-sensed data.

/pdf/the-possibility-on-estimation-of-concentration-of-heavy-l3l1j2f6sk.pdf

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

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