To reveal the impact of cadmium stress on the physiological mechanism of lettuce, simultaneous determination and correlation analyses of chlorophyll content and photosynthetic function were conducted using lettuce seedlings as the research subject. The changes in relative chlorophyll content, rapid chlorophyll fluorescence induction kinetics curve, and related chlorophyll fluorescence parameters of lettuce seedling leaves under cadmium stress were detected and analyzed. Furthermore, a model for estimating relative chlorophyll content was established. The results showed that cadmium stress at 1 mg/kg and 5 mg/kg had a promoting effect on the relative chlorophyll content, while cadmium stress at 10 mg/kg and 20 mg/kg had an inhibitory effect on the relative chlorophyll content. Moreover, with the extension of time, the inhibitory effect became more pronounced. Cadmium stress affects both the donor and acceptor sides of photosystem II in lettuce seedling leaves, damaging the electron transfer chain and reducing energy transfer in the photosynthetic system. It also inhibits water photolysis and decreases electron transfer efficiency, leading to a decline in photosynthesis. However, lettuce seedling leaves can mitigate photosystem II damage caused by cadmium stress through increased thermal dissipation. The model established based on the energy captured by a reaction center for electron transfer can effectively estimate the relative chlorophyll content of leaves. This study demonstrates that chlorophyll fluorescence techniques have great potential in elucidating the physiological mechanism of cadmium stress in lettuce, as well as in achieving synchronized determination and correlation analyses of chlorophyll content and photosynthetic function.

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.

Particulate matter (PM) emitted from livestock and poultry production contributes to atmospheric aerosol loading, affecting animal health and the surrounding atmospheric environment. However, developing and optimizing remediation technologies require a better understanding of air pollutant concentrations, the emission plumes, and the mechanism of emission. Previous studies have primarily focused on indoor air pollution research, while outdoor research is relatively rare. Field test research is not only costly but also consumes extensive amounts of time. The application of computational fluid dynamics (CFD) technology can save a lot of measurement time and repetitive labor, in order to better understand the diffusion fundamentals and spatial and temporal distribution differences of PM. This study monitored the PM concentrations of different particle sizes inside and outside a layer house with negative pressure ventilation in Northeast China during the summer of 2021. These data were also used to validate the three-dimensional simulation of the PM concentrations inside and outside of the layer house in various scenarios of wind directions at different times by CFD technology. Through correlation analysis, it is found that temperature was positively correlated with PM₁ and PM_2.5, and relative humidity and wind speed were negatively correlated with PM, which has a greater impact on PM₁₀ and total suspended particulate (TSP). The particle size was proportional to the diffusion distance and diffusion height, time, and wind direction both have an impact on the spread of PM. Considering the environmental conditions in Northeast China, increasing the height of the fan by 1 m was suggested to reduce the diffusion of PM concentration. In addition, the diffusion patterns and transport paths in this study provide valuable information for improving control measures to minimize the influence of PM on both animal health and air quality.

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.