Development of a novel and fast XRF instrument for large area heavy metal detection integrated with UAV

Paper-based sap enrichment device combined with laser-induced breakdown spectroscopy for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants

Detecting heavy metals in plants is highly important for diagnosing plant health and understanding the stress mechanisms induced by heavy metals. However, the minimally invasive detection of heavy metals in plants remains a challenge. A novel paper-based sap enrichment device (PBSED), combined with laser-induced breakdown spectroscopy (LIBS) was proposed for the minimally invasive detection of Cd(Ⅱ) and Pb(Ⅱ) in plants. The PBSED included a stainless-steel capillary and heavy metal ion enrichment filter paper (HMIE-FP). The stainless-steel capillary was inserted into the plant stem, where plant sap was transported onto the paper substrate through capillary action. The heavy metal ions (HMIs) in the plants were enriched on the HMIE-FP, and LIBS was used to detect Cd(Ⅱ) and Pb(Ⅱ) on the HMIE-FP to determine the Cd(Ⅱ) and Pb(Ⅱ) concentration within the plant. COMSOL simulations were employed to analyse the flow dynamics of plant sap within the PBSED. To increase the heavy metal enrichment amount, the HMIE-FP was modified with AuAg bimetallic nanoparticles (AuAgBNPs). The PBSED-LIBS method was applied to detect Cd(Ⅱ) and Pb(Ⅱ) in cucumber plants, and the results were strongly correlated with the inductively coupled plasma mass spectrometry (ICP-MS) results (R² = 0.99 for Cd(Ⅱ) and 0.96 for Pb(Ⅱ)). The proposed PBSED-LIBS method demonstrated high sensitivity and minimal invasiveness; thus, it is suitable for rapid, in vivo detection of HMIs in plants. These findings provide valuable insights for the development of efficient, nondestructive tools for environmental applications.

Quantification of greenhouse gas emissions from a municipal solid waste incinerator using an uncrewed aerial vehicle

The accurate quantification of greenhouse gas emissions from waste treatment facilities is critical for effective climate change mitigation and regulatory compliance. Measurement-based methods are increasingly emphasized as essential for addressing uncertainties in emission estimates, with uncrewed aerial vehicles (UAVs) recognized for their flexibility and ability to capture spatially resolved data. This study evaluated CO2 emissions at a municipal solid waste incinerator using UAV monitoring with two quantification methods-the mass balance and inverse Gaussian methods. Ground-based wind data introduced significant uncertainty in CO2 emission quantification. Therefore, this study proposed using a mounted anemometer to capture high-resolution spatially-resolved wind data. The performance of the proposed quantification methods was assessed by comparing UAV-derived fluxes to reference quantification data to calculate errors, which were then compared across methods to evaluate accuracy. The mass balance method, incorporating spatially-resolved wind data, achieved a mean absolute percentage error (MAPE) of 37.34%, which was a marked improvement compared to the 64.32% MAPE using spatially-averaged wind data. Similarly, the inverse Gaussian method showed a lower MAPE of 46.45% using spatially-resolved wind data, compared to 54.97% using spatially averaged wind data. Additionally, the advantages of each method under varying conditions of wind variability were evaluated. This study demonstrates that spatially-resolved wind measurements with a mounted anemometer improve the accuracy of CO2 emission calculations. This approach highlights the importance of UAV-based monitoring of greenhouse gases emitted by waste management facilities.

Improving Soil Heavy Metal Lead Inversion Through Combined Band Selection Methods: A Case Study in Gejiu City, China

Hyperspectral technology has become increasingly important in monitoring soil heavy metal pollution, yet hyperspectral data often contain substantial band redundancy, and band selection methods are typically limited to single algorithms or simple combinations. Multi-algorithm combinations for band selection remain underutilized. To address this gap, this study, conducted in Gejiu, Yunnan Province, China, proposes a multi-algorithm band selection method to enable the rapid prediction of lead (Pb) contamination levels in soil. To construct a preliminary Pb content prediction model, the initial selection of spectral bands utilized methods including CARS (Competitive Adaptive Reweighted Sampling), GA (Genetic Algorithm), MI (mutual information), SPA (Successive Projections Algorithm), and WOA (Whale Optimization Algorithm). The results indicated that WOA achieved the highest modeling accuracy. Building on this, a combined WOA-based band selection method was developed, including combinations such as WOA-CARS, WOA-GA, WOA-MI, and WOA-SPA, with multi-level band optimization further refined by MI (e.g., WOA-GA-MI, WOA-CARS-MI, WOA-SPA-MI). The results showed that the WOA-GA-MI model exhibited optimal performance, achieving an average R2 of 0.75, with improvements of 0.32, 0.11, and 0.02 over the full-spectrum model, the WOA-selected spectral model, and the WOA-GA model, respectively. Additionally, spectral response analysis identified 22 common bands essential for Pb content inversion. The proposed multi-level combined model not only significantly enhances prediction accuracy but also provides new insights into optimizing hyperspectral band selection, serving as a valuable scientific foundation for assessing soil heavy metal contamination.

Spatial distribution of polycyclic aromatic hydrocarbons in sediment deposits in a Seine estuary tributary by hyperspectral imaging

Water bodies allow the storage of sediments from their catchment areas, including sediments containing persistent contaminants. This study used visible and near-infrared hyperspectral imaging to characterize the composition of sediment deposits collected in Martot Pond (France) and to reconstruct the volume of polycyclic aromatic hydrocarbon (PAH) contaminated sediments in the pond. Additionally, combining this method with polychlorinated biphenyl (PCB) analysis enhanced the age model associated with these sediments. To achieve this, indicators of oxides and chlorophyll a (and its derivatives) were employed to correlate various sediment cores, and to propose a sedimentary filling mode for the pond. Furthermore, one sedimentary unit, which appears homogeneous but of variable size within the pond, exhibited repetitive alternations associated with tidal cycles due to a defect in the Martot dam, corresponding to 34 +/- 3 days. A chemometric approach was used to model PAHs with near-infrared hyperspectral imaging data (validation determination coefficient of 0.85, Root Mean Squared Error of Prediction of 1.64 mg/kg). This model was then applied to other cores, coupled with the sedimentary filling mode in the pond, allowing the reconstruction of the volume of PAH contamination. Thus, this study demonstrates that hyperspectral imaging is a powerful tool for estimating various contaminants in sediments: not only is it much faster than conventional chromatographic methods, it also provides a more detailed understanding of a sample, and even of a site through the correlation of multiple core samples.

Inversion of heavy metal content in soil using hyperspectral characteristic bands-based machine learning method

The global concern regarding the adverse effects of heavy metal pollution in soil has grown significantly. Accurate prediction of heavy metal content in soil is crucial for environmental protection. This study proposes an inversion analysis method for heavy metals (As, Cd, Cr, Cu, Ni, Pb) in soil based on hyperspectral and machine learning algorithms for 21 soil reference materials from multiple provinces in China. On this basis, an integrated learning model called Stacked RF (the base model is XGBoost, LightGBM, CatBoost, and the meta-model is RF) was established to perform soil heavy metal inversion. Specifically, three popular algorithms were initially employed to preprocess the spectral data, then Random Forest (RF) was used to select the best feature bands to reduce the impact of noise, finally Stacking and four basic machine learning algorithms were used to establish comparisons and analysis of inversion model. Compared with traditional machine learning methods, the stacking model showcases enhanced stability and superior accuracy. Research results indicate that machine learning algorithms, especially ensemble learning models, have better inversion effects on heavy metals in soil. Overall, the MF-RF-Stacking model performed best in the inversion of the six heavy metals. The research results will provide a new perspective on the ensemble learning model method for soil heavy metal content inversion using data of hyperspectral characteristic bands collected from soil reference materials.

A novel calibration method for continuous airborne metal measurements: Implications for aerosol source apportionment

Continuous metal monitors have been widely used in environmental monitoring due to the high temporal resolution, high detection limit, and necessity for near real-time source apportionment. However, the reliability of the conventional calibration method, the deviation caused by uncalibrated monitoring data, and the subsequent impact on source identification results are rarely discussed. In this study, a reliable multi-point calibration approach by Primary Standard Aerosol Mass Concentration Calibration System (PAMAS) for the Xact625i Ambient Metals Monitor was developed and applied. The measured data was almost meaningless in the low-concentration range with bias even exceeding 100 % by using the conventional single-point calibration method based on thin-film standards. PAMAS was utilized to generate aerosols with known concentrations of the 20 metal elements including Al, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, As, Sr, Cd, Sn, Sb, Ba, Tl, Pb, and Bi, in two concentration ranges of 150-1200 ng m-3 and 2.5-30 ng m-3 to validate the Xact625i Monitor. The results showed that the elemental concentrations were underestimated, especially in the low-concentration range, only for Cr, As, and Sr with slopes close to unity (1.00 ± 0.03). After calibration by PAMAS, the slopes of the linear relationships between measured and standard concentrations were all unity for the 19 elements in the high-concentration range, and close to unity for the 15 elements in the low-concentration range, and the accuracy of the remaining elements was also improved. After considering the calibration of aerosol metal data, it was found the number of source factors and their contributions to metals and PM2.5 in Chongming Dongtan, China, based on the PMF model significantly changed. This study highlighted the need of developing reliable calibration methods for online aerosol monitoring instruments and implied that the source apportionment results could be biased without careful data calibration.

Rapid Estimation of Soil Pb Concentration Based on Spectral Feature Screening and Multi-Strategy Spectral Fusion

Traditional methods for obtaining soil heavy metal content are expensive, inefficient, and limited in monitoring range. In order to meet the needs of soil environmental quality evaluation and health status assessment, visible near-infrared spectroscopy and XRF spectroscopy for monitoring heavy metal content in soil have attracted much attention, because of their rapid, nondestructive, economical, and environmentally friendly features. The use of either of these spectra alone cannot meet the accuracy requirements of traditional measurements, while the synergistic use of the two spectra can further improve the accuracy of monitoring heavy metal lead content in soil. Therefore, this study applied various spectral transformations and preprocessing to vis-NIR and XRF spectra; used the whale optimization algorithm (WOA) and competitive adaptive re-weighted sampling (CARS) algorithms to identify feature spectra; designed a combination variable model (CVM) based on multi-layer spectral data fusion, which improved the spectral preprocessing and spectral feature screening process to increase the efficiency of spectral fusion; and established a quantitative model for soil Pb concentration using partial least squares regression (PLSR). The estimation performance of three spectral fusion strategies, CVM, outer-product analysis (OPA), and Granger-Ramanathan averaging (GRA), was discussed. The results showed that the accuracy and efficiency of the CARS algorithm in the fused spectra estimation model were superior to those of the WOA algorithm, with an average coefficient of determination (R2) value of 0.9226 and an average root mean square error (RMSE) of 0.1984. The accuracy of the estimation models established, based on the different spectral types, to predict the Pb content of the soil was ranked as follows: the CVM model > the XRF spectral model > the vis-NIR spectral model. Within the CVM fusion strategy, the estimation model based on CARS and PLSR (CARS_D1+D2) performed the best, with R2 and RMSE values of 0.9546 and 0.2035, respectively. Among the three spectral fusion strategies, CVM had the highest accuracy, OPA had the smallest errors, and GRA showed a more balanced performance. This study provides technical means for on-site rapid estimation of Pb content based on multi-source spectral fusion and lays the foundation for subsequent research on dynamic, real-time, and large-scale quantitative monitoring of soil heavy metal pollution using high-spectral remote sensing images.

Recent advances in instrumental techniques for heavy metal quantification

Heavy metals (HMs) are ubiquitous; they are found in soil, water, air, and all biological matrices. The toxicity, bioaccumulation potential, and deleterious effects of most of these metals on humans and the environment have been widely documented. Consequently, the detection and quantification of HMs in various environmental samples have become a pressing issue. The analysis of the concentrations of HMs is a vital component of environmental monitoring; hence, the selection of the most suitable analytical technique for their determination has become a topic of great interest in food, environment, and human health safety. Analytical techniques for the quantification of these metals have evolved. Presently, a broad range of HM analytical techniques are available with each having its outstanding merits as well as limitations. Most analytical scientists, therefore, adopt complementation of more than one method, with the choice influenced by the specific metal of interest, desired limits of detection and quantification, nature of the interference, level of sensitivity, and precision among others. Sequel to the above, this work comprehensively reviews the most recent advances in instrumental techniques for the determination of HMs. It gives a general overview of the concept of HMs, their sources, and why their accurate quantification is pertinent. It highlights various conventional and more advanced techniques for HM determination, and as one of its kind, it also gives special attention to the specific merits and demerits of the analytical techniques. Finally, it presents the most recent studies in this regard.