In situ investigation of heavy metals at trace concentrations in greenhouse soils via portable X-ray fluorescence spectroscopy

Evaluating the portable X-ray fluorescence reliability for metal(loid)s detection and soil contamination status

Marginalized communities experience barriers that can prevent soil monitoring efforts and knowledge transfer. To address this challenge, this study compared two analytical methods: portable X-ray fluorescence spectroscopy (pXRF, less time, cost) and inductively coupled plasma mass spectrometry (ICP-MS, "gold standard"). Surface soil samples were collected from residential sites in Arizona, USA (N = 124) and public areas in Troy, New York, USA (N = 33). Soil preparation differed between groups to account for community practice. Statistical calculations were conducted, paired t test, Bland-Altman plot, and a two-way ANOVA indicated no significant difference for As, Ba, Ca, Cu, Mn, Pb, and Zn concentrations except for Ba in the t test. Iron, Ni, Cr, and K were statistically different for Arizona soils and V, Ni, Fe, and Al concentrations were statistically different for New York soils. Zinc was the only element with high R2 and low p value. Pollution load index (PLI), enrichment factors (EF), and geo-accumulation index (Igeo) were calculated for both methods using U.S. Geological Survey data. The PLI were > 1, indicating soil pollution in the two states. Between pXRF and ICP-MS, the Igeo and EF in Arizona had similar degree of contamination for most elements except Zn in garden and Pb in yard, respectively. For New York, the Igeo of As, Cu, and Zn differed by only one classification index between the two methods. The pXRF was reliable in determining As, Ba, Ca, Cu, Mn, Pb, and Zn in impacted communities. Therefore, the pXRF can be a cost-effective alternative to using ICP-MS techniques to screen soil samples for several environmentally relevant contaminants to protect environmental public health.

Suitability of XRF for Routine Analysis of Multi-Elemental Composition: A Multi-Standard Verification

This study investigated the suitability of X-ray fluorescence (XRF) analysis for routine multi-elemental composition analysis, checking its analytical capabilities by measuring a wide array of certified reference materials of soil and plant origin. A portable XRF analyzer was used to evaluate 32 soil and 12 plant standard materials, using both the Soil and Geochem mode, with sequential beams, allowing the detection of a wide range of elements. Recovery rates were calculated by comparing XRF measurements with certified values, and their correlations were verified through the Spearman coefficient. The results demonstrated the reliability of XRF measurements for soil samples, with a large number of elements showing a good or very good recovery and strong correlations with certified values. For plant samples, XRF largely overestimated the certified values, but the strong statistically significant correlations for almost all tested elements allowed us to correct this systematic bias, using the reported median value for dividing the value obtained via XRF. The Geochem mode emerged as more reliable for a larger number of elements. It was concluded that XRF may be a suitable alternative to ICP-MS in routine multi-elemental composition analysis.

Trace metal geochemistry sediments from the Dibamba River, SW Cameroon: Implication for heavy metal assessment and origin

Sediment quality and trace metal accumulation are two of the most pressing issues facing the aquatic ecosystem around the world. Twenty-four (24) samples of stream sediments were collected along the Dibamba River, in the economic and industrial capital of Cameroon, to judge the trace metal levels in this river flowing near the city of Douala. Trace metal concentrations were disclosed with an ICP-MS on two different grain size fractions (very fine-grained sand and clay). The sediments indicate possible adverse effects of trace metals on surrounding biota as elements like Cr, Cu, Ni, Zn, Pb, and Hg show positive enrichment of greater than 1 when compared to background values from the upper continental crust (UCC) and sediment quality factors such as TEL (threshold effects level), PEL (probable effects level), ERL (effects range low), and ERM (effects range medium) values. The sediments show values of Degree of contamination (DC = 2-4), Pollution load index (PI = 1-2), and individual potential risk (EI = 92-219) indicating moderate pollution and ecological risk. Statistical and multivariate analyses point to both anthropogenic and geogenic sources for the heavy metals in the Dibamba stream sediments. The geogenic origin of the heavy metals is linked to the weathering of gneiss and migmatite found in the river banks. This study found low to mild levels of metal pollution and toxicity in the sediment, but it also warned that the continued development of nearby industries and businesses, the provision of transportation services, and waste disposal activities could result in a gradual outflow and accumulation of metals in the sediment, endangering the aquatic ecosystem.

Spatial distribution, environmental risks, and sources of potentially toxic elements in soils from a typical abandoned antimony smelting site

The rapid development of the smelting industry increases the release of antimony (Sb) into the soil environment, which threatens human health and ecosystems. A total of 87 samples were collected from an abandoned Sb smelting site to evaluate pollution characteristics and environmental risks of the potentially toxic elements (PTEs). The contents of As, Cu, Ni, Pb, Sb, and Zn in the fresh soils determined by P-XRF were 131, 120, 60, 145, 240, and 154 mg/kg, respectively, whilst following drying, grinding, and sieving pretreatments, the corresponding contents increased to 367, 179, 145, 295, 479, and 276 mg/kg, respectively. There was a significant correlation between the data obtained by P-XRF and ICP-OES in the treated samples, which showed the application feasibility of P-XRF. The average contents of Sb and As were 440.6 and 411.6 mg/kg, respectively, which exceeded the control values of the development land in GB 36600-2018. The ecological risk levels of the six PTEs decreased in the following order: As > Sb > Pb > Zn > Ni > Cu. Non-carcinogenic risk revealed that As, Pb, and Sb posed health risks for children, whilst for carcinogenic risk, the risk values for As and Ni were higher than the limit values for both children and adults. Anthropogenic sources accounted for more than 70.0% of As, Pb, and Sb concentrations in soils, indicating a significant influence on PTEs accumulation. The findings provide a basis for quick determination of the contamination characteristics and risk control of PTEs at Sb smelting sites.

Prediction of Heavy Metal Concentrations in Contaminated Sites from Portable X-ray Fluorescence Spectrometer Data Using Machine Learning

Portable X-ray fluorescence (pXRF) spectrometers provide simple, rapid, nondestructive, and cost-effective analysis of the metal contents in soils. The current method for improving pXRF measurement accuracy is soil sample preparation, which inevitably consumes significant amounts of time. To eliminate the influence of sample preparation on PXRF measurements, this study evaluates the performance of pXRF measurements in the prediction of eight heavy metals’ contents through machine learning algorithm linear regression (LR) and multivariate adaptive regression spline (MARS) models. Soil samples were collected from five industrial sites and separated into high-value and low-value datasets with pXRF measurements above or below the background values. The results showed that for Cu and Cr, the MARS models were better than the LR models at prediction (the MARS-R2 values were 0.88 and 0.78; the MARS-RPD values were 2.89 and 2.11). For the pXRF low-value dataset, the multivariate MARS models improved the pXRF measurement accuracy, with the R2 values improved from 0.032 to 0.39 and the RPD values increased by 0.02 to 0.37. For the pXRF high-value dataset, the univariate MARS models predicted the content of Cu and Cr with less calculation. Our study reveals that machine learning methods can better predict the Cu and Cr of large samples from multiple contaminated sites.

Additional sampling using in-situ portable X-ray fluorescence (PXRF) for rapid and high-precision investigation of soil heavy metals at a regional scale

Traditional soil heavy metal (HM) investigation usually costs a lot of human and material resources. In-situ portable X-ray fluorescence spectrometry (PXRF) is a cheap and rapid HM analysis method, but its analysis accuracy is usually affected by spatially non-stationary field environment factors. In this study, residual sequential Gaussian co-simulation (RCoSGS) was first proposed to incorporate both continuous and categorical auxiliary variables for spatial simulation of soil Cu. Next, additional in-situ PXRF sampling sites (n = 300) were allocated in the subareas with high, medium, and low conditional variances in the proportions of 50%, 33.33%, and 16.67%, respectively. Then, robust geographically weighted regression (RGWR) was established to correct the spatially non-stationary effects of field environmental factors on in-situ PXRF and further compared with the traditionally-used multiple linear regression (MLR) and basic GWR in correction accuracy. Finally, RCoSGS with the RGWR-corrected in-situ PXRF as part of hard data (RCoSGS-PXRF) was established and further compared with the model with one or multiple auxiliary variables in the spatial simulation accuracy. Results showed that (i) RCoSGS effectively incorporated both SOM and land-use types and obtained higher spatial simulation accuracy (RI = 37.52%) than residual sequential Gaussian simulation with land-use types (RI = 19.44%) and sequential Gaussian co-simulation with SOM (RI = 20.92%); (ii) RGWR significantly weakened the spatially non-stationary effects of field environmental factors on in-situ PXRF, and RGWR (RI = 58.96%) and GWR (RI = 39.61%) obtained higher correction accuracy than MLR; (iii) the RGWR-corrected in-situ PXRF (RI = 66.57%) brought higher spatial simulation accuracy than both land-use types and SOM (RI = 37.52%); (iv) RCoSGS-PXRF obtained the highest spatial simulation accuracies (RI = 83.74%). Therefore, the proposed method is cost-effective for the rapid and high-precision investigation of soil HMs at a regional scale.

Okra (Abelmoschus esculentus) in a refugee context in East Africa: Kitchen gardening helps with mineral provision

Kitchen gardening is considered a way to reconnect with agriculture and complement the cereal-based relief food offered to refugees in East Africa. This work aimed at profiling mineral content of okra in four refugee camps and settlements located in Ethiopia and Uganda and its contribution to adequate intake (AIs) or recommended dietary allowances (RDAs) for young children and pregnant and lactating women (PLW). The study also evaluated the applicability of portable X-ray fluorescence (PXRF) as compared with inductively coupled plasma mass spectrometry (ICP-MS) for mineral profiling of okra powder samples. The contents of minerals (mg kg-1) from the ICP-MS readings were in the following ranges: K (14,385-33,294), Ca (2610-14,090), P (3178-13,248), Mg (3896-7986), Cu (3.81-19.3), Fe (75.7-1243), Zn (33-141) and Mn (23.1-261). Regardless of geographic origin, at low-end consumption probability (17 g day-1 for young children and 68 g day-1 for PLW), okra could contribute ˂ 15% (2.7-12.9%) AI for macro-minerals (K and Ca). In addition, the contributions to RDA values for Fe and Zn, elements of known public health interest, ranged from 4.5 to 34.7% for young children. Interestingly, regression lines revealed strong agreement between ICP-MS and PXRF readings for Mn and Zn, with R2 values > 0.91. This information is useful in support of nutrition-sensitive kitchen gardening programs through scaling culturally important crops in refugee settings.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42452-021-04898-6.

Moving toward a Handheld "Plasma" Spectrometer for Elemental Analysis, Putting the Power of the Atom (Ion) in the Palm of Your Hand

Many of the current innovations in instrument design have been focused on making them smaller, more rugged, and eventually field transportable. The ultimate application is obvious, carrying the instrument to the field for real time sample analysis without the need for a support laboratory. Real time data are priceless when screening either biological or environmental samples, as mitigation strategies can be initiated immediately upon the discovery that contaminant metals are present in a location they were not intended to be. Additionally, smaller "handheld" instruments generally require less sample for analysis, possibly increasing sensitivity, another advantage to instrument miniaturization. While many other instruments can be made smaller just by using available micro-technologies (e.g., eNose), shrinking an ICP-MS or AES to something someone might carry in a backpack or pocket is now closer to reality than in the past, and can be traced to its origins based on a component-by-component evaluation. While the optical and mass spectrometers continue to shrink in size, the ion/excitation source remains a challenge as a tradeoff exists between excitation capabilities and the power requirements for the plasma's generation. Other supporting elements have only recently become small enough for transport. A systematic review of both where the plasma spectrometer started and the evolution of technologies currently available may provide the roadmap necessary to miniaturize the spectrometer. We identify criteria on a component-by-component basis that need to be addressed in designing a miniaturized device and recognize components (e.g., source) that probably require further optimization. For example, the excitation/ionization source must be energetic enough to take a metal from a solid state to its ionic state. Previously, a plasma required a radio frequency generator or high-power DC source, but excitation can now be accomplished with non-thermal (cold) plasma sources. Sample introduction, for solids, liquids, and gasses, presents challenges for all sources in a field instrument. Next, the interface between source and a mass detector usually requires pressure reduction techniques to get an ion from plasma to the spectrometer. Currently, plasma mass spectrometers are field ready but not necessarily handheld. Optical emission spectrometers are already capable of getting photons to the detector but could eventually be connected to your phone. Inert plasma gas generation is close to field ready if nitrogen generators can be miniaturized. Many of these components are already commercially available or at least have been reported in the literature. Comparisons to other "handheld" elemental analysis devices that employ XRF, LIBS, and electrochemical methods (and their limitations) demonstrate that a "cold" plasma-based spectrometer can be more than competitive. Migrating the cold plasma from an emission only source to a mass spectrometer source, would allow both analyte identification and potentially source apportionment through isotopic fingerprinting, and may be the last major hurdle to overcome. Finally, we offer a possible design to aid in making the cold plasma source more applicable to a field deployment.

Resampling with in situ field portable X-ray fluorescence spectrometry (FPXRF) to reduce the uncertainty in delineating the remediation area of soil heavy metals

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (P_TE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI = 71.5%) than GWR (RI = 59.68%) and OLSR (RI = 25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G = 0.97) than CoSGS-CorFPXRF (G = 0.95) and SGS (G = 0.91) in the spatial simulation; (iii) High-uncertainty area (30%<P_TE<70%) was reduced from 36.55% to 8.7% of the whole study area. It is concluded that the recommended methods are cost-effective to reduce the uncertainty in delineating the remediation areas of soil heavy metals.

Concentrations of selected trace elements in surface soils near crossroads in the city of Bratislava (the Slovak Republic)

It is well known that road transport emits various trace elements into the environment, which are deposited in soils in the vicinity of roads, so-called roadside soils, and thus contributes to the deterioration of their chemical state. The aim of this work was to determine concentrations of some metals and metalloids (arsenic (As), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), antimony (Sb), vanadium (V), and zinc (Zn)) in soils from crossroads with traffic signals, which are characterized by deceleration of vehicles and increased emissions of elements related mainly to brake and tyre wear. The results confirmed a moderate enrichment of soils with Cu, Pb, and Zn (enrichment factor (EF) values > 2) and significant enrichment for Sb (EF > 5), while the other elements showed no or minimal enrichment. The age of crossroads proved to have a positive influence on the accumulation of some elements in soils with the largest differences for Cu, Fe, Pb, Sb, and Zn (p < 0.001). Traffic volumes expressed as the average daily traffic intensity (ADTI) also positively influenced soil concentrations of Cr, Cu, Pb, Sb, and Zn, while distance to the crossroad had a significant negative effect on the soil concentration of Cu, Sb, and Zn (p < 0.001). The stable isotopic ratios of Pb, ²⁰⁶Pb/²⁰⁷Pb and ²⁰⁸Pb/²⁰⁶Pb, ranging from 1.1414 to 1.2046 and from 2.0375 to 2.1246, respectively, pointed to the mixed natural-anthropic origin of Pb in the soils of crossroads with a visible contribution of traffic-related sources. Based on the above findings combined with covariance among the studied elements using statistical methods applied to compositionally transformed data, it was possible to show that Cu, Pb, Sb, and Zn clearly originated from road traffic.

Using pXRF to assess the accumulation, sources, and potential ecological risk of potentially toxic elements in soil under two greenhouse vegetable production systems in North China

Intensive greenhouse vegetable production (GVP) has increased the pollution risk of potentially toxic elements (PTEs) in soils. This study examined the accumulation, sources, and potential ecological risk of six PTEs (Cu, Zn, As, Ni, Pb, and Cr) in soil under two GVP (solar greenhouse (SG) and round-arched plastic greenhouse (RAPG)) systems by portable X-ray fluorescence spectroscopy (pXRF) and conventional laboratory analysis. The results indicated that all PTE concentrations were lower than their corresponding thresholds in GVP soils, presenting a low potential ecological risk in both GVP soils according to risk indices (RI ≤ 40.67). As, Ni, Pb, and Cr were not significantly accumulated in both GVP soils. Although Cu and Zn accumulated in both GVP soils, their accumulation extents in SG soil were both greater than that in RAPG soil. Cu and Zn were mainly originated from anthropogenic activities based on multivariate statistical analysis, which were greatly associated with excessive manure application. Overall, pXRF can identify the accumulation difference of PTEs between the two GVP soils, which is generally consistent with conventional laboratory analysis. Hence, pXRF can be a promising alternative to conventional laboratory analysis for rapid assessment of PTEs accumulation, sources, and the potential ecological risk in the two GVP soils. Although PTEs had a low ecological risk, Cu and Zn accumulation in SG soil was increased with the planting years. Therefore, rational application of livestock manure containing high levels of Cu and Zn should inspire strategies to mitigate the environmental risk in GVP soils, especially in SG soil.

Ecological risk assessment of heavy metals in sediments and water from the coastal areas of the Bohai Sea and the Yellow Sea

The Yellow Sea Large Marine Ecosystem (YSLME) is an important socioeconomic zone in Asia, but has been deteriorated by various environmental pollutants over the last half century. However, comprehensive coastal pollution assessments, particularly for heavy metals (HMs), have been limited from an international perspective. Here, we first evaluate coastal HM pollution in sediment and water from 119 riverine, estuarine, and marine locations along the BS and Yellow Sea to perform a comparative assessment between the two countries of China and South Korea. The occurrence, distribution, sources, multimedia fate, interactions, associated environmental factors, and potential ecological risks relating to the HM pollution are widely addressed. Eight typical HMs (As, Hg, Zn, Cu, Pb, Cd, Cr, and Ni) were targeted in both sediments and water, and in situ water properties (pH, dissolved oxygen, salinity, and temperature) and sediment properties (pH and organic matter (OM) content) were analyzed. The results indicated that As, Cd, Cr, Cu, Ni, and Pb concentrations in water were higher in the estuarine area than those in riverine and marine areas and that particularly severe HM pollution was evidenced in the BS. The dominant elements in pollution hot spots varied greatly among the countries and regions. According to the geo-accumulation index (I_geo) and pollution load index (PLI) values, the sediments exhibited high Hg and Pb pollution in the BS; high As, Hg, and Pb pollution in the Yellow Sea of China; and high Cd and Hg pollution in the Yellow Sea of South Korea. In general, the sediments were moderately contaminated by HMs based on the high PLI (>1.0) and risk index (RI) values (>160). Ni and Cr in the sediment mainly originated from geogenic sources, while the other elements (Zn, As, Cd, Cu, Hg, and Pb) were primarily linked to anthropogenic sources. Based on the partial redundancy analysis, we found that environmental factors, especially OM, contributed significantly to the concentrations of HMs in both the sediments and water. The sediment HMs significantly contributed to the waterborne HMs due to their release from the sediments to the water column. An overall assessment of the contamination status, spatial distribution, and potential sources of HMs suggested that the water-sediment interaction of HMs and the influence by environmental factors should be subsequently considered for a better understanding of the multimedia fate of HMs in the given dynamic YSLME system or similar environments elsewhere.

Quantitative Analysis of Iron and Silicon Concentrations in Iron Ore Concentrate Using Portable X-ray Fluorescence (XRF)

As a technique capable of rapid, nondestructive, and multi-elemental analysis, portable X-ray fluorescence (pXRF) has applications to mineral exploration, environmental evaluation, and archaeological analysis. However, few applications have been conducted in the smelting industry especially when analyzing the metal concentration in ore concentrate samples. This research analyzed the effectiveness of using pXRF in determining the metal concentration in Fe concentrate. For this proof of concept study, Fe ore samples dominated by Fe and Si were collected from the Northeastern University Mineral Processing Laboratory (Shenyang, China) and directly analyzed using pXRF, laboratory-based XRF, and titration methods. The compactness (density) of the ore concentrate was found to have very little effect on pXRF readings. The pXRF readings for Fe and Si were comparative to laboratory-based XRF results. Based on the strong correlations between the pXRF and XRF results (Fe: R²> 0.99, Si: R²> 0.96), linear calibrations were adopted to improve the accuracy of pXRF readings. Linear regression equations derived from the relations between XRF results and pXRF results of 21 Fe ore concentrate samples were used to calibrate the pXRF, and then validation was performed on five additional samples. Results from this preliminary study suggest that ordinary least squares (OLS) regression improves the accuracy dramatically, especially for Fe with relative errors (REs) decreasing to 0.03%-3.27% from 4.26%-8.32%. Consequently, pXRF shows strong promise for rapid, quantitative analysis of Fe concentration in Fe ore concentrate. Based on the results obtained in this study, a larger, more comprehensive study is warranted to confirm the results obtained.

Correction of in-situ portable X-ray fluorescence (PXRF) data of soil heavy metal for enhancing spatial prediction

Heavy metal data measured by portable X-ray fluorescence (PXRF), especially by in-situ PXRF, are usually affected by multiple soil factors, such as soil moisture (SM), soil organic matter (SOM), and soil particle size (SPZ). Thus, a correction may be needed. However, traditionally-used correction methods, such as non-spatial linear regression (LR), cannot effectively correct the spatially non-stationary influences of the related soil factors on PXRF analysis. Moreover, these correction methods are not robust to outliers. In this study, robust geographically weighted regression (RGWR) was used to correct in-situ and ex-situ PXRF data of soil Pb in a peri-urban agricultural area of Wuhan City, China. The accuracy of the corrected PXRF data by RGWR was compared with those by non-spatial and spatial but non-robust methods (i.e., LR and GWR). In addition, to find an appropriate method of using the corrected PXRF data for more accurate spatial prediction, we compared robust ordinary kriging with the corrected PXRF data as part of hard data (ROK-CPXRF) and robust ordinary cokriging with the corrected PXRF data as auxiliary soft data (RCoK-CPXRF). Results showed that (i) RGWR obtained higher correction accuracy than LR and GWR on both the in-situ and ex-situ PXRF data; (ii) the accuracy of the RGWR-corrected in-situ PXRF data was increased nearly to that of the RGWR-corrected ex-situ PXRF data; (iii) given the same amount of sample data, ROK-CPXRF obtained higher prediction accuracy than RCoK-CPXRF. It is concluded that the methods suggested in this study may largely promote the application of in-situ PXRF technique for rapid and accurate soil heavy metal investigation in large-scale areas.

Cd, Cu, and Zn Accumulations Caused by Long-Term Fertilization in Greenhouse Soils and Their Potential Risk Assessment

The intense management practices in greenhouse production may lead to heavy metal (HM) accumulations in soils. To determine the accumulation characteristics of HM and to evaluate possible HM sources in greenhouse soils, thirty typical greenhouse soil samples were collected in Shouguang District, Shandong Province, China. The results indicate that the Cd, Cu, and Zn concentrations are, respectively, 164.8%, 78.6%, and 123.9% higher than their background values. In the study area, Cd exhibits certain characteristics, such as wide variations in the proportion of its exchangeable form and the highest mobility factor and geo-accumulation index, which are indicative of its high bioavailability and environmental risk. In addition, there is a significant positive correlation between pairs of Cd, P, soil organic carbon, and cultivation age. Combined with principal component analysis, the results indicate the clear effects that agricultural activities have on Cd, Cu, and Zn accumulation. However, Cr, Ni, and Pb have a significant correlation with soil Fe and Al (hydr)-oxides, which indicates that these metals mainly originate from parent materials. This research indicated that long-term intensive fertilization (especially the application of chemical fertilizers and livestock manure) leads to Cd, Cu, and Zn accumulation in greenhouse soils in Shouguang. And the time required to reach the maximum permeable limit in agricultural soils for Cd, Cu, and Zn is 23, 51, and 42 years, respectively, based on their current increasing rates.

Monitoring metal pollution in soils using portable-XRF and conventional laboratory-based techniques: Evaluation of the performance and limitations according to metal properties and sources

Large variability in the spatial distribution and content of metals is generally recognised in anthropogenically-polluted soils, hence, a detailed site investigation implying the collection and analysis of a large number of soil samples is often necessary. To this regard, the selection of a rapid, cost-effective and accurate analytical technique to assess the concentration of metals in soil is of paramount importance. The overall objective of this work was to evaluate the possibility of assessing the aqua regia-extractable (AR) content of metals in soil from the multi-element profile of the soil obtained by a portable X-ray fluorescence analyser (pXRF). To this objective, we attempted: (i) to establish, by simple linear regressions, the relations occurring between the metal contents measured by pXRF and AR in laboratory setting on air-dried and 2 mm-sieved soil samples from two case studies (A-agricultural and B-industrial sites); (ii) to define metal-based linear models predicting metal AR contents from pXRF measurements; (iii) to assess the influence of metal properties and sources on relations found between the two analytical methods. Very satisfying correlations (R² > 0.90) were observed between the AR and pXRF contents of Ca, Cu, Cr, Ni, Pb and Zn in the site A, and of Cd, Cu, Pb and Zn in the site B. For the majority of metals, lower AR than pXRF contents were measured, as result of the AR incomplete dissolution of metal-bearing silicates. This was not observed when metals - of anthropogenic origin - occurred in soil in very high concentrations (i.e., Cr for A and Pb for B). In both sites, the comparison among different regression parameters revealed a strong metal-dependence. Moreover, for most of the metals, the parameters of each metal-regression line significantly differed between the two case studies, indicating site-dependence of regression fits.