Heavy metal contamination in lake sediments is a serious problem, particularly in developing countries such as China. To evaluate heavy metal pollution and risk of contamination in lake sediments on a national scale in China, we collated available data in the literature of the last 10 years on lake sediments polluted with heavy metals from 24 provinces in China. Based on these data, we used sediment quality guidelines, geoaccumulation index, and potential ecological risk index to assess potential ecological risk levels. The results showed that approximately 20.6% of the lakes studied exceeded grade II level in Chinese soil quality standards for As, 31.3% for Cd, 4.6% for Cu, 20.8% for Ni, 2.8% for Zn, and 11.1% for Hg, respectively. Besides, the mean concentrations for As in 10.3% of lakes, Hg in 11.9% of lakes, and Ni in 31.3% of lakes surpassed the probable effect level. The potential ecological risk for toxic metals decreased in the order of Cd > Hg > As > Cu > Pb > Ni > Cr > Zn, and there were 21.8% of the lakes studied in the state of moderate risk, 10.9% in high risk, and 12.7% in very high risk. It can be concluded that Chinese lake sediments are polluted by heavy metals to varying degrees. In order to provide key management targets for relevant administrative agencies, based on the results of the pollution and ecological risk assessments, Cd, Hg, As, Cu, and Ni were selected as the priority control heavy metals, and the eastern coastal provinces and Hunan province were selected as the priority control provinces. This article, therefore, provides a comprehensive assessment of heavy metal pollution in lake sediments in China, while providing a reference for the development of lake sediment quality standards.

In recent years, horticultural plants have frequently suffered significant heat damage due to excessive temperatures. In this study, a horticultural spray cooling system was designed, consisting mainly of a jet fan and spraying system. CFD simulation technology and response surface methodology were used to optimize the design of the jet fan, which improved the thrust of the fan. The length of the inlet section was 300 mm, the length of the outlet section was 300 mm, the length of the cone section was 450 mm, and the diameter of the outlet was 950 mm, where the thrust of the jet fan was 225.06 N. By establishing the CFD model of spray cooling in a tea field and designing a L₉ (3⁴) orthogonal experiment, the effect of the spray parameters on the maximum temperature drop and effective cooling distance was studied, and the best parameters were selected. The simulation results show that the optimum parameters are a spray flow rate of 4.5 kg/s, a droplet diameter of 15–45 μm, a droplet temperature of 298.15 K, and a nozzle double circle layout. Based on the simulation results of the optimized jet fan and spray parameters selected, a spray cooling test bench was established. Field test results show that when the initial ambient temperature was 310.05 K–310.95 K, the maximum temperature drop of the spray cooling fan was 9.1 K, and the cooling distance was approximately 36.0 m. The temperature drop decreased with increasing distance from the fan. This study is of great significance to protect horticultural plants from extremely high temperatures.

Accurate estimation of reference evapotranspiration (ET_o) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ET_o. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ET_o estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ET_o at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ET_o estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R²), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ET_o estimation. Among the applied ML models, the ET_o tree boost (TB) ML model outperformed the other ML models in estimating ET_o in diverse climatic conditions based on statistical indicators (R², NSE, r, RMSE, and MAE). Moreover, the observed R², NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ET_o point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ET_o maps. Based on the ET_o maps, this study suggests mainly a focus on areas with high ET_o values and proper irrigation scheduling of crops to ensure water sustainability.

The Hongze Lake serves as the first important transfer and storage path along the East Route of the South-to-North Water Diversion Project (SNWDP-ER). After the Project began operating in 2013, eutrophication in the lake has created increasing public concern regarding the direct effect on water quality from the project. In this paper, we used multivariate statistical methods to examine the variances between water quality variables at four site groups (inlets and outlets in the study area) and during three seasons (wet, normal, and dry seasons) in Eastern Hongze Lake. We also used redundancy analysis (RDA) to analyze environmental factors’ impact on water quality. We found that (1) the concentrations of nitrogen and phosphorus were exceeding the standard values; (2) No significant spatial heterogeneity regarding nitrogen and phosphorus among the inlet/outlet of Eastern Hongze Lake existed in normal and wet seasons, and there was no evidence demonstrating that SNWDP-ER could improve eutrophication or even contribute to eutrophication in the dry season; (3) Environmental factors (land use, water diversion, and population) have limited influences on water quality, which was possibly caused by the overly-high river input and artificial input of nitrogen and phosphorus. Thus, it is critical to reinforce the control of pollution sources and enhance the ecological restoration.

Leaves are used extensively as an indicator in research on tree growth. Leaf area, as one of the most important index in leaf morphology, is also a comprehensive growth index for evaluating the effects of environmental factors. When scanning tree surfaces using a 3D laser scanner, the scanned point cloud data usually contain many outliers and noise. These outliers can be clusters or sparse points, whereas the noise is usually non-isolated but exhibits different attributes from valid points. In this study, a 3D point cloud filtering method for leaves based on manifold distance and normal estimation is proposed. First, leaf was extracted from the tree point cloud and initial clustering was performed as the preprocessing step. Second, outlier clusters filtering and outlier points filtering were successively performed using a manifold distance and truncation method. Third, noise points in each cluster were filtered based on the local surface normal estimation. The 3D reconstruction results of leaves after applying the proposed filtering method prove that this method outperforms other classic filtering methods. Comparisons of leaf areas with real values and area assessments of the mean absolute error (MAE) and mean absolute error percent (MAE%) for leaves in different levels were also conducted. The root mean square error (RMSE) for leaf area was 2.49 cm². The MAE values for small leaves, medium leaves and large leaves were 0.92 cm², 1.05 cm² and 3.39 cm², respectively, with corresponding MAE% values of 10.63, 4.83 and 3.8. These results demonstrate that the method proposed can be used to filter outliers and noise for 3D point clouds of leaves and improve 3D leaf visualization authenticity and leaf area measurement accuracy.