In order to achieve the purpose of efficiently removing lead and cadmium as the main heavy metals from wastewater, this paper explores the adsorption properties of cadmium ions and lead ions on biochar under different modified conditions prepared from corn stalks as raw materials and potassium phosphate as surface modifiers. Before preparing biochar (BC), the mass ratios of 1:1 and 1:2 (corn stalks to potassium phosphate) were used for pre-modification, and the oxygen-restricted pyrolysis processes of 350 °C, 550 °C and 750 °C were used for treatment. The study discussed the individual and composite adsorption effects of biochar on Pb²⁺ and Cd²⁺ under different conditions. The experimental results show that phosphorus modification has changed the physical and chemical properties of the original biochar. Among them, the biochar (2PBC550) with an impregnation ratio of 1:2 and a pyrolysis temperature of 550 °C (2PBC550) exhibits excellent adsorption properties. When the pH of the simulated wastewater is 5 and the amount of adsorbent is 30 mg·L⁻¹, the maximum adsorption capacity of Pb²⁺ and Cd²⁺ is 145.48 mg·g⁻¹ and 14.533 mg·g⁻¹, respectively, which are 6.46 times and 3.67 times of the original biochar. The Pb²⁺ and Cd²⁺ adsorption data of 2PBC550 fit well with the quasi-secondary dynamics and Langmuir isothermal models, indicating that the adsorption process is controlled by single-layer chemical adsorption. In the composite metal system of Pb²⁺ and Cd²⁺, 2PBC550 exhibits a stronger affinity for Pb²⁺ than Cd²⁺. Through the analysis of characterization methods such as SEM, FTIR, XRD and XPS, it is proved that the adsorption of Pb²⁺ and Cd²⁺ by 2PBC550 is due to precipitation, complexation and π electron interaction. Therefore, 2PBC550 shows great application potential in the repair of wastewater containing Pb²⁺ or Cd²⁺.

In order to analyse the impact of biochar in terms of reducing the bioavailability of cadmium (Cd) in soil, a study was conducted on the solidification effect of biochar on soil cadmium and its resistance to cadmium contamination in lettuce. In this study, soil which was contaminated with 10 mg/kg cadmium was used as the substrate; corn, rice, and wheat straw biochar were used as solidification and amendment materials; and lettuce (Lactuca sativa L. cv. Grand Rapids) was used as the test plant. The morphological characteristics of the biochar, soil pH, electrical conductivity, cation exchange capacity, and soil-available Cd, as well as lettuce plant height, fresh weight, and leaf Cd content, were measured and analysed. The results showed that all three types of biochar possessed distinct porous structures and functional groups such as hydroxyl, ether, and carbonyl groups. Increases in soil pH, electrical conductivity (EC), cation exchange capacity (CEC), lettuce plant height, and fresh weight were effectively promoted. Additionally, a significant reduction in the available Cd content in the soil and Cd content in lettuce leaves was observed, with the inhibitory effect becoming more pronounced as the biochar application rate increased. When 5% corn straw biochar was added (1 kg of substrate with 50 g of biochar), the best inhibitory effect on Cd contamination was observed, with a cadmium content of 4.63 mg/kg in lettuce leaves. The available Cd in the soil and the Cd content in lettuce leaves decreased by 32.00% and 49.78%, respectively, compared to the CK group (without biochar treatment). Additionally, the plant height and fresh weight of lettuce increased by 25.56% and 31.31%, respectively, compared to the CK group. This indicated that the application of straw biochar can stabilise soil Cd, reduce the availability of Cd in the soil, inhibit the transfer of Cd into lettuce, promote the growth of lettuce, and lower the ecological environmental risk of Cd. These research results can provide a theoretical basis and scientific guidance for the remediation of soil Cd contamination and the safe production of lettuce.

Chlorophyll content is a crucial assessment parameter in the growth monitoring of lettuce, particularly in cases when it is affected by disease. Accurate estimation of chlorophyll content is beneficial for early detection and prevention of diseases and holds significant importance in practical production. To construct a model for estimating the chlorophyll content in lettuce leaves under cadmium stress, this study utilized lettuce as the experimental material. The visible–near-infrared reflectance spectra of lettuce leaves, as well as the relative chlorophyll content of the leaves, were detected and analyzed under different concentrations of cadmium stress. Subsequently, an inversion model for estimating the relative chlorophyll content in lettuce leaves was established. First, to determine the optimal spectral preprocessing method, eight techniques are utilized: Savitzky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variable transformation (SNV), mean normalization (MN), baseline offset (B), detrending (D), gap derivatives—first derivative (FD), and gap derivatives—second derivative (SD). These methods are used to preprocess the spectra and establish a partial least squares regression (PLSR) monitoring model. The optimal spectral preprocessing method is then selected. Next, the feature bands are extracted from the preprocessed spectral data using the correlation coefficient method. Finally, the selected feature bands will be combined with support vector regression (SVR) to establish a chlorophyll content estimation model using a training-to-testing set ratio of 4:1. The results showed that the PLSR model established after preprocessing with detrending (D) had the highest accuracy, with the coefficient of determination (R_v²) and root mean squared error (RMSE_v) values of 0.87 and 1.16, respectively. The feature bands selected by the correlation coefficient method were used to establish SVR models for estimating the chlorophyll content of lettuce leaves under cadmium stress, with the highest accuracy being achieved by the genetic algorithm (GA)–SVR model. It can be seen that near-infrared spectroscopy technology provides a scientific basis for rapid, nondestructive, and accurate detection of lettuce diseases and stress.

In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.

This paper aims to find a fertilization method that better matches the growth characteristics of vegetables and reduces the soil and environmental problems caused by unreasonable fertilization methods, in view of the strong buffering of the clayey black soil in the north and the mismatch between the amount of chemical fertilizer applied and the growth characteristics of vegetables during cultivation. In this experiment, Lactuca sativa L. (Lactuca sativa) was studied in a randomized complete block design in greenhouse and five different fertilizer application methods were arranged: exponential fertilization (EF), linear fertilization (LF), average fertilization (AF), one-time fertilization (OF), and no fertilization as a control group (CG). The effects of different fertilization methods on soil nutrients, agronomic traits of Lactuca sativa growth, and related quality were investigated, and the pattern of effects of different fertilization methods on growth and soil nutrients was obtained. The results of the experiment showed that the exponential fertilization method gradually took advantage as the growth time extended. The plant height (PLH), leaf length (LL), leaf width (LW), yield, soluble protein (SP), soluble sugar (SS), vitamin C (VC), and elemental nitrogen (EN) and potassium (EK) of Lactuca sativa were significantly improved under the exponential fertilization method compared with other fertilization methods, by 29.9 cm, 51.5 cm, 5.96 cm, 22, 2.32 kg/m², 0.23%, 0.44%, 3.93%, 94.66 mg/kg, 1.58 g/kg, and 1.94 g/kg, respectively. The alkali-hydrolyzed nitrogen (SAN), available phosphorus (SAP), and available potassium (SAK) in the soil after fertilization were 139.69 mg/kg, 50.23 mg/kg, and 180.30 mg/kg, respectively. The above results showed that the exponential fertilization method not only improved the quality of Lactuca sativa and thus the quality of the crop growth traits, but also changed the soil nutrients favorably after fertilization, which is of some importance for the protection of black soils.

In order to monitor cadmium contamination in lettuce quickly, non-invasively, and accurately, and to understand the growth status of lettuce under cadmium pollution, lettuce was used as the test material to detect and analyze the visible–near-infrared reflectance spectra and leaf cadmium content under different concentrations of cadmium stress. A model for estimating lettuce leaf cadmium content was established. For model establishment, firstly, the original spectra were preprocessed using smoothing (Savitzky–Golay, SG), SG combined with multiplicative scatter correction (MSC), SG combined with standard normal variable transformation (SNV), SG combined with mean normalization (MN), SG combined with the first derivative (FD), SG combined with the second derivative (SD), SG combined with the baseline offset (B), and SG combined with de-trending (D). Then, the principal component analysis (PCA) was applied to perform dimensionality reduction on the data. Finally, the reduced dataset was divided into training and testing sets in a 2:1 ratio, and separate models for estimating the lettuce leaf cadmium content were built using partial least squares regression (PLSR), the backpropagation neural network (BP-NN), and support vector regression (SVR) in combination. The results showed that the accumulated cadmium content in lettuce leaves increased with an increase in the soil cadmium concentration. In the visible light range, the spectral reflectance of lettuce leaves increased with an increase in the cadmium concentration. In the near-infrared range, the spectral reflectance of the lettuce leaves under 10 mg/kg and 20 mg/kg cadmium stress was lower than that of the control group. The PLSR models established using the SG + MSC and SG + SNV preprocessing methods exhibited the strongest estimation capability for lettuce leaf cadmium content, with R_p² and RMSE_p values of 0.92 and 1.53 mg/kg, respectively, for the testing dataset. This study demonstrated that visible–near-infrared spectroscopy has great potential in monitoring cadmium contamination in lettuce.