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 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.

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.

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.

This study investigated improvements in solar energy efficiency in pig houses with different roof types and translucent materials using Ecotect simulation software v2011. An experimental pig house in Qian Guo County, Songyuan City, was selected as the research object. First, the optimal building orientation for pig houses was explored by analysing the solar irradiance received as a primary indicator. Next, the energy consumption of pig houses was simulated using various translucent materials in the roof. Various roof types were designed based on the optimal translucent material. The energy consumption of these designs was compared with that of the experimental pig house. Finally, the economic viability of the different pig houses was analysed. The results showed that the optimal building orientation for a pig house was south by west 25°. Among the pig houses with equal-slope roofs using FRP daylighting boards, double-layer polycarbonate (PC) sheets, flat glass, and organic glass as translucent materials, the pig house using double-layer PC sheets required the least amount of additional heat during the heating season (62,109 kWh). For pig houses with double-layer PC sheets, four roof designs were assessed (equal-slope roof, front and rear roofs with unequal slopes, upper and lower staggered unequal-slope roof, and four-equal-slope roof), and it was found that the pig house with a four-equal-slope roof required the least amount of additional heat during the heating season (48,138 kWh). The economic analysis indicates that the combination of a four-equal-slope roof and double-layer PC sheets is the most cost-effective option, with the lowest total life cycle cost. This design saves 9521 USD compared with the experimental pig housing, effectively improving solar energy utilisation efficiency. These analyses provide a reference for exploring the thermal performance of pig houses.

Light and temperature are important environmental factors for maize growth. Appropriate light and temperature conditions can enhance the net photosynthetic rate of maize leaves, which in turn promotes high-quality and high-yield maize production. This study focuses on maize during the seedling stage and investigates the response patterns of maize leaf net photosynthetic rate, relative chlorophyll content (SPAD value), and visible–near-infrared spectral data under eight light–temperature stress conditions. Spectrally sensitive bands and characteristic wavelengths were extracted, and six different combinations of spectral preprocessing and modeling methods were used to establish inversion models for SPAD and net photosynthetic rate, respectively. Six different spectral preprocessing and modeling combination methods were employed to establish inversion models for the SPAD values and net photosynthetic rate of maize leaves. The research results use the correlation coefficients Rc and Rp, as well as the root mean square errors RMSEC and RMSEP, from the training and validation sets as evaluation metrics for the maize leaf physiological information inversion models. The optimal spectral combination method for the SPAD value inversion model of maize leaves was identified as PLS-MSC-SG, with an Rc value of 0.996 and an Rp value of 0.9743. The optimal combination method for the net photosynthetic rate inversion model was PLS-SNV-SG, with corresponding Rc and Rp values of 0.9848 and 0.9743, respectively. The results deepen the understanding of maize photosynthetic spectral physiological responses to light–temperature environments and provide important guidance for the precise management of maize growth conditions.