: To compare the human health risk of heavy metals and As in sewage sludge between adults and children, samples were collected from five wastewater treatment plants (WWTPs) located in the urban district of Taiyuan, the capital of Shanxi. Heavy metals and As in sewage sludge can be ranked according to the mean concentration in the following order: Cu > Cr > Zn > Pb > As > Hg > Cd. Compared with the concentration limit set by different countries, the heavy metals contents in sewage sludge were all within the standard limits, except for the content of As, which was higher than the threshold limit established by Canada. A health risk assessment recommended by the United States Environmental Protection Agency (USEPA) was used to compare the non-cancer risk and cancer risk between adults and children. Based on the mean and 95% upper confidence limit (UCL) of the average daily dose (ADD), heavy metals and As can be ranked in the order of Cu > Cr > Zn > Pb > As > Hg > Cd for adults, and Cu > Cr > Zn > Pb > Hg > As > Cd for children. Moreover, results of ADD_ingest and ADD_inhale indicated that ingestion was the main pathway for heavy metals and As exposure for both adults and children, and the sum of ADD implied that the exposure to all heavy metals and As for children was 8.65 and 9.93 times higher, respectively, than that for adults according to the mean and 95% UCL. For the non-carcinogenic risk, according to the hazard quotient (HQ), the risk of Cu, Hg and Cr was higher than the risk of Zn and Pb. The hazard index (HI) for adults was 0.144 and 0.208 for the mean and 95% UCL, which was less than the limit value of 1; for children, the HI was 1.26 and 2.25, which is higher than the limit value of 1. This result indicated that children had non-carcinogenic risk, but adults did not. Furthermore, ingestion was the main pathway for non-carcinogenic risk exposure by the HQ_ingest and HQ_inhale. For the carcinogenic risk, Cd and As were classified as carcinogenic pollutants. The values of RISK for the mean and 95% UCL for adults and children all exceeded the limit value of 1 × 10⁻⁵, which implied that adults and children had a carcinogenic risk, and this risk was higher for children than for adults. The results of RISK for As and Cd implied that As was the main pollutant for carcinogenic risk. Moreover, the results of RISK_ingest and RISK_inhale indicated that ingestion was the main pathway. Uncertainty analysis was performed, and the risk ranges of it were greater than certainty analysis, which implied that uncertainty analysis was more conservative than certainty analysis. A comparison of the non-carcinogenic risk and carcinogenic risk for adults and children indicated that children were more sensitive and vulnerable than adults when exposed to the same pollutant in the environment.

Accurate identification of plant diseases is a critical task in agricultural production. The existing deep learning crop disease recognition methods require a large number of labeled images for training, limiting the implementation of large-scale detection. To overcome this limitation, this study explores the application of self-supervised learning (SSL) in plant disease recognition. We propose a new model that combines a masked autoencoder (MAE) and a convolutional block attention module (CBAM) to alleviate the harsh requirements of large amounts of labeled data. The performance of the model was validated on the CCMT dataset and our collected dataset. The results show that the improved model achieves an accuracy of 95.35% and 99.61%, recall of 96.2% and 98.51%, and F1 values of 95.52% and 98.62% on the CCMT dataset and our collected dataset, respectively. Compared with ResNet50, ViT, and MAE, the accuracies on the CCMT dataset improved by 1.2%, 0.7%, and 0.8%, respectively, and the accuracy of our collected dataset improved by 1.3%, 1.6%, and 0.6%, respectively. Through experiments on 21 leaf diseases (early blight, late blight, leaf blight, leaf spot, etc.) of five crops, namely, potato, maize, tomato, cashew, and cassava, our model achieved accurate and rapid detection of plant disease categories. This study provides a reference for research work and engineering applications in crop disease detection.

Land use of sewage sludge is the primary disposal method in Shanxi, accounting for 42.66% of all. To determine the ecological risk of heavy metals in sewage sludge, contents of seven heavy metals in sewage sludge from 9 municipal waste water treatment plants (WWTPs) that had the highest application for land use were determined. The order of the measured concentrations was: Zn > Cr > Cu > Ni > Pb > As > Cd, and all heavy metals contents were within the threshold limit values of the Chinese Control Standards for Pollutants in Sludge from Agriculture Use (GB4284-84). Four indices were used to assess the pollution and the ecological risk of heavy metals. By the mean values of the geoaccumulation index (I_geo), heavy metals were ranked in the following order: Cd > Zn > Cu > As > Cr > Ni > Pb. The values showed that the pollution of Zn in station 3 and Cd in station 1, 2, 3, 4, 8 and 9 were heavily; Cu in station 8 and 9, Zn in station 1, 2, 4, 8 and 9 and Cd in station 5 and 7 were moderately to heavily, and the accumulation of other heavy metals were not significant. The single-factor pollution index (PI) suggested that none of the stations had heavy metals contamination, except for Cu in station 9, Zn in station 3 and 8, and Cd in station 1 and 9, which were at a moderate level. According to the results of the Nemerow’s synthetic pollution index (PN), sewage sludge from all stations was safe for land use with respect to heavy metals contamination, except for stations 3, 8 and 9, which were at the warning line. The monomial potential ecological risk coefficient (Eri) revealed that heavy metals ecological risks in most stations were low. However, station 9 had a moderate risk for Cu; station 6 had a moderate risk, stations 5 and 7 had high risk, other stations had very high risk for Cd. According to the results of the potential ecological risk index (RI), station 1, 8 and 9 had high risk; station 2, 3, 4, 5 and 7 had a moderate risk, and station 6 had a low risk. The preliminary results indicated that the potential risk of land exposure to heavy metals in sewage sludge was relatively low, with Zn and Cd as the main contributor to the ecological risk for the applying of sewage sludge on land. Additionally, stations 3, 8 and 9 require more attention regarding the land applications related to heavy metals pollution.

Heavy metals (HMs) in sewage sludge have become the crucial limiting factors for land use application. Samples were collected and analyzed from 32 waste water treatment plants (WWTPs) in the Shanxi Province, China. HM levels in sewage sludge were assessed. The multivariate statistical method principal component analysis (PCA) was applied to identify the sources of HMs in sewage sludge. HM pollution classes by geochemical accumulation index I_geo and correlation analyses between HMs were also conducted. HMs were arranged in the following decreasing order of mean concentration: Zn > Cu > Cr > Pb > As > Hg > Cd; the maximum concentrations of all HMs were within the limit of maximum content permitted by Chinese discharge standard. I_geo classes of HMs pollution in order from most polluted to least were: Cu and Hg pollution were the highest; Cd and Cr pollution were moderate; Zn, As and Pb pollution were the least. Sources of HM contamination in sewage sludge were identified as three components. The primary contaminant source accounting for 35.7% of the total variance was identified as smelting industry, coking plant and traffic sources; the second source accounting for 29.0% of the total variance was distinguished as household and water supply pollution; the smallest of the three sources accounting for 16.2% of the total variance was defined as special industries such as leather tanning, textile manufacturing and chemical processing industries. Source apportionment of HMs in sewage sludge can control HM contamination through suggesting improvements in government policies and industrial processes.

Drone multispectral technology enables the real-time monitoring and analysis of soil moisture across vast agricultural lands. overcoming the time-consuming, labor-intensive, and spatial discontinuity constraints of traditional methods. This study establishes a rapid inversion model for deep soil moisture (0–200 cm) in dryland agriculture using data from drone-based multispectral remote sensing. Maize, millet, sorghum, and potatoes were selected for this study, with multispectral data, canopy leaf, and soil moisture content at various depths collected every 3 to 6 days. Vegetation indices highly correlated with crop canopy leaf moisture content (p < 0.01) and were identified using Pearson correlation analysis, leading to the development of linear and nonlinear regression models for predicting moisture content in canopy leaves and soil. The results show a significant linear correlation between the predicted and actual canopy leaf moisture levels for the four crops, according to the chosen vegetation indices. The use of canopy leaf moisture content to predict surface soil moisture (0–20 cm) demonstrated enhanced accuracy. The models designed for the top 20 cm of soil moisture successfully estimated deep soil moisture levels (up to 200 cm) for all four crops. The 20 cm range soil moisture model showed improvements over the 10 cm range model, with increases in Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R²), and Nash–Sutcliffe Efficiency Coefficient (NSE) by 0.4, 0.8, 0.73, and 0.34, respectively, in the corn area; 0.28, 0.69, 0.48, and 0.25 in the millet area; 0.4, 0.48, 0.22, and 0.52 in the sorghum area; and 1.14, 0.81, 0.73, and 0.56 in the potato area, all with an average Relative Error (RE) of less than 10% across the crops. Using drone-based multispectral technology, this study forecasts leaf water content via vegetation index analysis, facilitating swift and effective soil moisture inversion. This research introduces a novel method for monitoring and managing agricultural water resources, providing a scientific basis for precision farming and moisture variation monitoring in dryland areas.

To address the current problem of the difficulty of extracting the phenotypic parameters of tomato plants in a non-destructive and accurate way, we proposed a method of stem and leaf segmentation and phenotypic extraction of tomato plants based on skeleton extraction and supervoxel clustering. To carry out growth and cultivation experiments on tomato plants in a solar greenhouse, we obtained multi-view image sequences of the tomato plants to construct three-dimensional models of the plant. We used Laplace’s skeleton extraction algorithm to extract the skeleton of the point cloud after removing the noise points using a multi-filtering algorithm, and, based on the plant skeleton, searched for the highest point path, height constraints, and radius constraints to separate the stem from the leaf. At the same time, a supervoxel segmentation method based on Euclidean distance was used to segment each leaf. We extracted a total of six phenotypic parameters of the plant: height, stem diameter, leaf angle, leaf length, leaf width and leaf area, using the segmented organs, which are important for the phenotype. The results showed that the average accuracy, average recall and average F1 scores of the stem and leaf segmentation were 0.88, 0.80 and 0.84, and the segmentation indexes were better than the other four segmentation algorithms; the coefficients of determination between the measurement values of the phenotypic parameters and the real values were 0.97, 0.84, 0.88, 0.94, 0.92 and 0.93; and the root-mean-square errors were 2.17 cm, 0.346 cm, 5.65°, 3.18 cm, 2.99 cm and 8.79 cm². The measurement values of the proposed method had a strong correlation with the actual values, which could satisfy the requirements of daily production and provide technical support for the extraction of high-throughput phenotypic parameters of tomato plants in solar greenhouses.