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Heavy metal lead (Pb) accumulation in agricultural soil has become a serious threat to human health through the food chain in recent decades. Wheat, being one of the most important crops, is a crucial source of food for humans. Pb in soil can pose a significant risk to the wheat grain, as it can be transported through the root and stem. This transportation not only increases the Pb concentration in wheat, but also threatens the wheat's overall food security. Therefore, it is imperative to investigate the effects of Pb stress on wheat seedlings. Double-pulse laser-induced breakdown spectroscopy (DP-LIBS) and surface-enhanced Raman spectroscopy (SERS) as promising enhanced spectroscopy techniques were coupled to estimate Pb stress in wheat seedlings. Wheat seedling samples under Pb stress at 6 concentration gradients (0(c.k.), 500, 1000, 2000, 3000, and 5000 mg L−1) were cultivated with the same substrate and management. The SERS and DP-LIBS spectra of the root, stem and leaf were obtained by using spectrometers in an enhanced way. Principal component analysis (PCA) was employed for preliminary analysis of DP-LIBS and SERS data to investigate the clustering of samples, including substrates and the wheat seedling root and leaf, under different Pb concentration stresses. However, while PCA enabled the rough differentiation of the samples, the classification accuracy requires improvement. The pre-processed SERS, DP-LIBS, and fusion spectral data (LIBS-Raman) were analyzed using discriminant models such as least squares support vector machines (LS-SVM), multi-layer perceptron-artificial neural network (MLP-ANN), radial basis function-artificial neural network (RBF-ANN) and probabilistic neural networks (PNN). The accuracy of the model built by LIBS-Raman was significantly better than the model built based on SERS and DP-LIBS data alone. LIBS-Raman increased the accuracy of the discrimination model, which highlights the effectiveness of collaborative spectroscopic methods for heavy metal detection. This study investigated the migration of Pb in the substrate-root-leaf. At 0 concentration (c.k.), Pb was not detected in the substrate and wheat seedling root and leaf. At the same concentration, the level of Pb stress in the root was higher than that in the leaf. This might be attributed to the self-protection mechanism of wheat seedlings, which had a weak ability to transport Pb ions. This work provides theoretical support for collaborative spectral data, furnishes a reference for the development of a combined spectrometer, and offers a valuable method for monitoring heavy metal contamination in plants. [ABSTRACT FROM AUTHOR]

The three‐point bending test was conducted and the digital image correlation (DIC) technique was used to observe the full‐field displacement and strain of the notched beam. The initial cracking load and the crack mouth opening displacement were determined based on the deformation field near the tip and mouth of the notch. The initial cracking toughness Kini and the unstable fracture toughness Kun calculated according to the double‐K fracture model, the fracture energy GF and the ductility index Du determined with a modified method, and the equivalent fracture toughness K at four different deformation stages, were analyzed under different temperatures and basalt fiber contents. The results showed that Kini exhibited a monotonic decrease trend with the increasing temperature, indicating that the notched beam was prone to initial cracking after high temperatures. However, Kini rose slowly with the increase of the fiber content, suggesting that the basalt fiber had little influence on Kini. At the temperatures below 400°C, the Kun, GF, Du, and K at each deformation stage increased with the rise of temperature. At the temperatures above 400°C, the Kun, GF, Du, and K at each deformation stage apparently decreased with the increase of temperature. Under the current test conditions, the basalt fiber content of 0.4% by volume had the greatest improvement on the fracture performances of concrete after high temperatures. [ABSTRACT FROM AUTHOR]

The evolution of pore structure is very important to the frost resistance of concrete. In order to study the effect of nano-TiO2 on pore structure and frost resistance of concrete, the nano-TiO2 concrete with substitution rates of 0%, 1%, 3%, 5% (mass fraction, the same below) were selected as research object, and the quick freezing test, compressive strength test, nuclear magnetic resonance test and scanning electron microscope test were carried out. The changes of mass, dynamic elastic modulus, compressive strength, pore structure and micro-morphology of nano-TiO2 concrete before and after freeze-thaw cycle were analyzed to evaluate the freeze-thaw damage at - 18 ~ 5 °C and to analyze the improvement mechanism of nano-TiO2 on concrete properties. At the same time, the response models of compressive strength, relative dynamic modulus, free water saturation and crack ratio were established to determine the optimum content of nano-TiO2. The results show that an appropriate content of nano-TiO2 effectively improves the micro-morphology of concrete, increases the proportion of micropores and medium pores of concrete, and reduces the proportion of macropores and cracks, while excessive nano-TiO2 increases the proportion of macropores and cracks in concrete, which is not conducive to the improvement of frost resistance of concrete. When the content of nano-TiO2 is 1%, the frost resistance of concrete is the best. Based on the frost resistance performance index and pore test results, the response model is established, and the optimal content of nano-TiO2 is close to 1%, which is consistent with the test results. [ABSTRACT FROM AUTHOR]

Tree diseases endanger forestry and fruit tree plantations seriously worldwide in the past decades, leading to significant economic losses for the agricultural production sector. Rapid and accurate detection of tree diseases is crucial in tree protection. Despite molecular biological detection methods have prominent specificity, they are time-consuming and laborious, and are not suitable for large-scale detection of tree diseases. Spectroscopy with nondestructive, rapid, and high throughput characteristics has been applied to plant disease detection. Spectral detection systems are divided into three categories according to the spectrometer's carrying platform: portable hand-held spectrometer, airborne vehicle-mounted spectrometer, and large laboratory spectrometer. This review summarized three main spectral detection systems and their advantages and disadvantages in detecting various diseases of forestry and fruit trees: including detection of the single disease, multiple stress, and early disease using Visible/near-infrared, Raman, and hyperspectral imaging. Finally, spectroscopy detection technology applications of challenges were summarized, highlighting future trends. [ABSTRACT FROM AUTHOR]

Spatial land use conflicts (SLUCs) are a critical issue worldwide due to the scarcity of land resources and diversified human demand. Despite many time-series studies of SLUCs, comprehensive research on SLUCs and their driving factors over a long period remain limited. This study was conducted in the Pearl River Delta urban agglomeration, Guangdong Province, China. We constructed a landscape ecological risk assessment model to calculate annual SLUC values and analyze their spatiotemporal distribution over 30 years. K-means clustering analysis was used to cluster SLUC values for 1990–2005 and 2006–2020, yielding comprehensive conflict intensity data for each period. The major factors driving the spatial differentiation of SLUCs and their interactions in each period were identified using an optimal parameter-based geographical detector model. The results show that SLUCs varied significantly over time, with an overall decreasing trend and distinct spatial heterogeneity. Comprehensive conflict intensity for each period was characterized by low values in the peripheral regions and high values in central parts of the study area, which tended to decrease from 1990–2005 to 2006–2020. SLUCs were heavily dependent on topographical (slope and elevation) and environmental (normalized difference vegetation index) factors. Socio-economic factors (gross domestic product and population density) were also major contributors to the spatial differentiation of SLUCs. The explanatory power of multiple interacting factors on SLUCs was enhanced compared with that of individual factors. The explanatory power of the driving factors varied, and their interactions decreased over time. The results may facilitate the rational government planning of regional land use and thus effectively mitigate SLUC intensity at the macro level. [ABSTRACT FROM AUTHOR]