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.

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.

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.

Particulate matter affects both the light environment and air quality in greenhouses, obstructing normal gas exchange and hindering efficient physiological activities such as photosynthesis. This study focused on young Chinese cabbage (Brassica rapa L. Chinensis Group) in a greenhouse at harvest time, monitoring and comparing hyperspectral information, net photosynthetic rate, and microscopic leaf structure under two conditions: a quantitative artificial particulate matter environment and a healthy environment. Based on microscopic results combined with spectral responses and changes in photosynthetic physiological information, it is believed that particulate matter enters plant cells through stomata. Through retention and transport pathways, it disrupts the membrane structure, organelles, and other components of plant cells, resulting in adverse effects on the plant’s physiological functions. The study analyzed the mechanisms by which particulate matter influences the photosynthesis, spectral characteristics, and physiological responses of young Chinese cabbage. Physiological Reflectance Index (PRI), Modified Chlorophyll Absorption Ratio Index (MCARI), spectral red-edge position (λr), and spectral sensitive bands were used as spectral feature variables. Through cubic polynomial and 24 combinations of spectral preprocessing and modeling methods, an inversion model of spectral features and net photosynthetic rate was established. The optimal combination of spectral preprocessing and modeling methods was finally selected as SG + SD + PLS + MSC, which consists of Savitzky-Golay smooth (SG), second derivative (SD), partial least squares (PLS), and multiplicative scatter correction (MSC). The coefficient of determination (R²) of the model is 0.9513. The results indicate that particulate matter affects plant photosynthesis. The SG + SD + PLS + MSC combination method is relatively advantageous for processing the photosynthetic spectral physiological information of plants under the influence of particulate matter. The results of this study will deepen the understanding of the mechanisms by which particulate matter affects plants and provide a reference for the physiological information inversion of greenhouse vegetables under particulate matter pollution.

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.

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses under biochar-mediated alleviation of Cd stress remain insufficient. Spectral modeling methods can enable rapid and nondestructive monitoring of crop physiological status. In this preliminary experiment, Zhengdan 958 maize seedlings grown in Cd-contaminated soil were subjected to five biochar application rates: 0, 10, 30, 50, and 70 g/pot, designated as CK, A1, A3, A5, and A7, respectively. The study established a non-destructive spectral detection model for relative chlorophyll content expressed as SPAD values of maize leaves to achieve spectral inversion of leaf physiological information. The alleviating effect of biochar on Cd stress was evaluated by analyzing SPAD values and Cd accumulation in roots, stems, and leaves. The original spectral data underwent preprocessing steps including multivariate scattering correction, standard normal variable transformation, normalization, trend removal, first-order derivative transformation, and second-order derivative transformation. The effectiveness of different preprocessing methods was compared using partial least squares regression. Feature bands were identified via Pearson correlation analysis, and support vector regression models were established based on genetic algorithm (GA), particle swarm optimization (PSO), and grid search optimization. The results demonstrated that biochar application significantly increased the SPAD values of corn leaves (r = 0.879) and reduced the proportion of bioavailable Cd in soil, with the A7 treatment showing the most substantial decrease (30%). This indicates that biochar effectively mitigates Cd’s inhibitory effect on chlorophyll synthesis, with the alleviation effect enhancing as biochar application rates increased. Validation of the partial least squares regression model revealed that detrended spectra achieved optimal predictive performance (R²c = 0.94, RMSEC = 0.82, R²p = 0.88, RMSEP = 1.15), leading to the development of three optimized support vector regression models: GA-SVR, PSO-SVR, and GS-SVR. The GA-SVR model with a sigmoid kernel demonstrated the best internal validation performance for predicting SPAD values in maize leaves (R²c = 0.95, RMSEC = 0.24; R²p = 0.75, RMSEP = 1.63). This study provides preliminary theoretical support and technical reference for rapid spectral detection of the physiological status of maize under biochar-mediated mitigation of cadmium stress.