Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

Hu, Bifeng; Chen, Songchao; Hu, Jie; Xia, Fang; Xu, Junfeng; Li, Yan; Shi, Zhou

doi:10.1371/journal.pone.0172438

Cited by 103 publications

(50 citation statements)

References 25 publications

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“…Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the potential threat to food safety and its detrimental effects on human and animal health [ 1 , 2 , 3 , 4 ]. It has also become one of the major environmental problems in China due to continuous industrialization and urbanization [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Traditional laboratory analyses of heavy metals in soils, such as Atomic Fluorescence Spectrometry (AFS), Atomic Absorption Spectrometry (AAS), and Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), are time consuming, laborious, and expensive. Some other methods such as portable X-ray fluorescence (PXRF) [ 4 ], Laser-induced Breakdown Spectroscopy (LIBS) [ 26 ], hyperspectral [ 27 ], visible-near infrared spectroscopy (Vis-NIR), and mid-infrared spectroscopy (MIR) [ 28 ] have been considered as rapid, effective techniques to measure total concentrations of heavy metals in soil. Recently, the combined use of Vis-NIR, MIR, and PXRF technology has also shown bright promise for predicting heavy metal content in soil [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

Jia

et al. 2017

IJERPH

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336

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Heavy metal (HM) contamination and accumulation is a serious problem around the world due to the toxicity, abundant sources, non-biodegradable properties, and accumulative behaviour of HMs. The degree of soil HM contamination in China, especially in the Yangtze River Delta, is prominent. In this study, 1822 pairs of soil and crop samples at corresponding locations were collected from the southern Yangtze River Delta of China, and the contents of Ni, Cr, Zn, Cd, As, Cu, Hg, and Pb were measured. The single pollution index in soil (SPI) and Nemerow composite pollution index (NCPI) were used to assess the degree of HM pollution in soil, and the crop pollution index (CPI) was used to explore the degree of HM accumulation in crops. The bioaccumulation factor (BAF) was used to investigate the translocation of heavy metals in the soil-crop system. The health risks caused by HMs were calculated based on the model released by the U.S. Environmental Protection Agency. The SPIs of all elements were at the unpolluted level. The mean NCPI was at the alert level. The mean CPIs were in the following decreasing order: Ni (1.007) > Cr (0.483) > Zn (0.335) > Cd (0.314) > As (0.232) > Cu (0.187) > Hg (0.118) > Pb (0.105). Only the mean content of Ni in the crops exceeded the national standard value. The standard exceeding rates were used to represent the percentage of samples whose heavy metal content is higher than the corresponding national standard values. The standard exceeding rates of Cu, Hg, and Cd in soil were significantly higher than corresponding values in crops. Meanwhile, the standard exceeding rates of Ni, As, and Cr in crops were significantly higher than corresponding values in soil. The chronic daily intake (CDI) of children (13.8 × 10−3) was the largest among three age groups, followed by adults (6.998 × 10−4) and seniors (5.488 × 10−4). The bioaccumulation factors (BAFs) of all crops followed the order Cd (0.249) > Zn (0.133) > As (0.076) > Cu (0.064) > Ni (0.018) > Hg (0.011) > Cr (0.010) > Pb (0.001). Therefore, Cd was most easily absorbed by crops, and different crops had different capacities to absorb HMs. The hazard quotient (HQ) represents the potential non-carcinogenic risk for an individual HM and it is an estimation of daily exposure to the human population that is not likely to represent an appreciable risk of deleterious effects during a lifetime. All the HQs of the HMs for the different age groups were significantly less than the alert value of 1.0 and were at a safe level. This indicated that citizens in the study area face low potential non-carcinogenic risk caused by HMs. The total carcinogens risks (TCRs) for children, adults, and seniors were 5.24 × 10−5, 2.65 × 10−5, and 2.08 × 10−5, respectively, all of which were less than the guideline value but at the alert level. Ingestion was the main pathway of carcinogen risk to human health.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

Jia

et al. 2017

IJERPH

Self Cite

336

147

View full text Add to dashboard Cite

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“…It calculates various elemental compositions of soil and also generates XRF spectra for analysis. The PXRF spectrometer has been widely used to measure elements for agricultural management, heavy metal detection and pedogenesis studies (Hu et al, ; McLaren et al, ; Stockmann, Cattle, Minasny, & McBratney, ). Additionally, several soil physical and chemical properties (e.g., clay, silt and sand content, SOC, pH, CEC and base saturation percentage (BSP)) have been successfully predicted from elements due to their correlations with elements (Rawal et al, ; Sharma, Weindorf, Man, Aldabaa, & Chakraborty, ; Sharma, Weindorf, Wang, & Chakraborty, ; Weindorf et al, ; Zhu, Weindorf, & Zhang, ).…”

Section: Introductionmentioning

confidence: 99%

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Zhang

Hartemink

2019

European J Soil Science

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We tested one front-end data fusion method to combine visible near-infrared (vis-NIR) and portable X-ray fluorescence (PXRF) spectra for predicting different soil properties and investigated the contribution of different sensor data. A total of 197 soil samples were collected from 25 Alfisols and Mollisols in south-central Wisconsin, USA. Soils were analysed in the laboratory for clay, sand, silt content, total carbon (TC), total nitrogen (TN) and pH. Air-dried soil samples were scanned with vis-NIR and PXRF spectrometers. A principal component analysis (PCA) was applied to each of the vis-NIR and PXRF spectra to extract the first 10 principal components (PCs), which were used in the Cubist model. Five types of input data were compared for constructing the models using a front-end data fusion approach, including: (a) 10 PCs from vis-NIR spectra, (b) 10 PCs from spectra of PXRF beam 1 (XRF40), (c) 10 PCs from spectra of PXRF beam 2 (XRF10), (d) concatenating 20 PCs from two PXRF spectra (XRF40 + 10), and (e) concatenating 30 PCs from two PXRF spectra and vis-NIR spectra (XRF40 + 10 + NIR). In addition, the performances of the preprocessing methods (four smoothing methods with or without background removal) of PXRF spectra were compared. Multiple linear regression was also used to predict soil properties directly from 11 PXRF-estimated elements. Our results suggest that smoothing should be applied to the PXRF spectra prior to developing predictive models.PXRF spectra and elemental data can predict soil texture with validation R 2 > 0.85, better than using solely vis-NIR spectra, and combining vis-NIR and PXRF spectra improved the prediction. Combining PXRF and vis-NIR can also predict TC and TN moderately well. Soil pH cannot be predicted from vis-NIR or PXRF spectra in this dataset due to its weak correlations with other soil properties and elements. It is concluded that PXRF spectra can be solely used to estimate soil texture, whereas combining vis-NIR and PXRF spectra via PCA should be used to estimate TC and TN. Highlights• The use of front-end data fusion method to combine vis-NIR and PXRF spectra for predicting soil properties.

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“…Recently, portable X-ray fluorescence spectroscopy (PXRF) has become increasingly popular for its large sample throughput and minimal sample preparation (Zhu et al, 2011). In general, PXRF is applied for determining heavy metal contamination of soil (Hu et al, 2017). Moreover, elemental data determined with PXRF have been used as an alternative to the measurement of soil texture and pH (Zhu et al, 2011;Sharma et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

Zhao

et al. 2018

European J Soil Science

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Summary Soil organic matter (SOM), total nitrogen (TN), available nitrogen (AN), available phosphorus (AP), available potassium (AK) and pH are key chemical properties for evaluating soil fertility and quality. This study involved the integration of four soil sensors, visible near‐infrared (vis–NIR) spectrometer, mid‐infrared (mid‐IR) spectrometer, portable X‐ray fluorescence (PXRF) analyser and laser‐induced breakdown spectroscopy (LIBS), to achieve rapid measurement of these soil properties. A genetic algorithm and partial least‐squares regression (GA–PLSR) were used to select characteristic bands to reduce data redundancy. We then calibrated models from three aspects: models using partial least‐squares regression (PLSR) based on single sensor data, models using PLSR based on fused sensor data, involving data combined from the four sensors into a new dataset to create a data fusion (DF) model, and models with Bayesian model averaging (BMA) based on prediction results of fused sensor data, involving prediction results combined from the four sensors into a new dataset to form the BMA model. The results showed the following. (i) For the single sensor, the predictive performance decreased as follows: mid‐IR > vis–NIR > LIBS > PXRF. (ii) Compared with the single sensor approach, the DF approach slightly improved or even reduced prediction accuracy and caused a large amount of redundancy. We suggest that this approach is not able to improve predictive ability. (iii) The BMA approach achieved the best prediction for the six soil properties. Our findings suggest that model averaging of vis–NIR, mid‐IR and LIBS could be a reliable and stable approach for the fast measurement of soil properties. Highlights We used four proximal soil sensors to evaluate six key properties for evaluating soil fertility and quality. GA–PLSR was used to select characteristic bands. We compared predictions of six soil properties from single sensor, DF and BMA approaches. BMA predictions were more accurate than predictions from single and fused sensor data.

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Application of portable XRF and VNIR sensors for rapid assessment of soil heavy metal pollution

Cited by 103 publications

References 25 publications

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

Assessment of Heavy Metal Pollution and Health Risks in the Soil-Plant-Human System in the Yangtze River Delta, China

Data fusion of vis–NIR and PXRF spectra to predict soil physical and chemical properties

Multi‐sensor fusion for the determination of several soil properties in the Yangtze River Delta, China

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