Soil salinization is a crucial type in the degradation of coastal land, but its spatial distribution and drivers have not been sufficiently explored especially at the depth scale owing to its multidimensional nature. In this study, we proposed a multi-depth soil salinity prediction model (0–10 cm, 10–20 cm, 20–40 cm, and 40–60 cm) fully using the advantages of satellite image data and field sampling to rapidly estimate the multi-depth soil salinity in the Yellow River Delta, China. Firstly, a multi-depth soil salinity predictive factor system was developed through correlation analysis of soil sample electrical conductivity with a series of remote-sensing parameters containing heat, moisture, salinity, vegetation indices, spectral value, and spatial location. Then, three machine learning methods including back propagation neural network (BPNN), support vector machine (SVM), and random forest (RF) were adopted to construct a coastal soil salinity inversion model. By using the best inversion model, we obtain the spatial distribution of soil salinity in the Yellow River Delta. The results show the following: (1) Environmental variables in this study are all effective variables for soil salinity prediction. The most sensitive indicators to multi-depth soil salinity are GDVI, ENDVI, SI-T, NDWI, and LST. (2) The RF model was chosen as the optimal approach for predicting and mapping soil salinity based on performance at four soil depths. (3) The soil salinity profiles exhibited intricate coexistence of two distinct types: surface aggregated and homogeneous. The former was dominant in the east, where salinity was higher. The central and southwestern parts were mostly homogeneous, with lower soil salinity. (4) The soil salinity throughout the four depths examined was found to be most elevated in saltern and bare land and lowest in wetland vegetation and farmland, according to land-cover type. This study proposed a remote sensing prediction method for salinization in multiple soil layers in the coastal plain, which could provide decision support for spatial monitoring of land salinization and achieving land degradation neutrality targets.

To explore the purification effect of biochar-constructed wetlands on rural domestic sewage, six types of biochar-constructed wetlands were constructed for experiments. Under different hydraulic conditions, the removal effects of each biochar-constructed wetland on chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus in sewage were analyzed. The results showed that the removal rates of the four types of pollutants in each biochar-constructed wetland first increased and then decreased with the increase in hydraulic retention time, and the optimal hydraulic retention time range was 36–48 h. The highest removal rates of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus in the wetland were 97.34 ± 0.84%, 95.44 ± 1.29%, 98.95 ± 0.52%, and 97.78 ± 0.91%, respectively. The chemical oxygen demand (COD) removal rate of each biochar-constructed wetland increased first, then decreased with the increase in hydraulic load, and the optimal hydraulic load was 10 cm/d. The removal efficiency of ammonia nitrogen, total nitrogen, and total phosphorus of each biochar-constructed wetland gradually weakened with the increase in hydraulic load, and the optimal hydraulic load range was between 5 and 10 cm/d. Under these conditions, the highest removal rates of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus in the wetland were 92.15 ± 2.39%, 98.32 ± 0.48%, 96.69 ± 1.26%, and 92.62 ± 2.92%, respectively. Coconut shell and shell-constructed wetlands with the highest proportion of biochar in the matrix have the best removal effect on pollutants under different hydraulic conditions, and the wastewater purification effect is stronger, indicating that the addition of biochar is helpful for the removal of pollutants in constructed wetlands.

Water shortages due to low precipitation and seawater intrusion in the Lower China Yellow River Delta have occurred in recent years. Exploiting underground brackish water through well drilling is a potential alternative way to satisfy the demand for agricultural irrigation. However, how to successfully use brackish water for irrigation has become a new problem to solve. A two-year field experiment was conducted in this typical saline-alkaline region to investigate the effects of irrigating with brackish water on the soil water-salt dynamics, and the physiological response of winter wheat to drought-salt stress. The experiment was laid out in a randomized block design with three replications according to the quantity (160 mm and 240 mm) and quality (fresh water and brackish water with a salt concentration of 3 g L⁻¹) of irrigation water: T1 was 240 mm of fresh water, T2 was 160 mm of fresh water, T3 was 80 mm of fresh water and 160 mm of brackish water, and T4 was 80 mm of fresh water and 80 mm of brackish water. The results showed that the soil moisture of T3 was almost the same as T1 after the harvest of winter wheat each year, therefore, irrigating with brackish water can maintain soil moisture while saving fresh water resources. After two years, the soil salinity of each treatment increased by 0.307, 0.406, 0.383, and 0.889 g kg⁻¹, respectively. During the jointing-flowering stage, salt stress has a significant inhibitory effect on photosynthesis; T3 and T4 were lower than T1 and T2 in terms of plant height and dry weight. During the filling stage, because the effect of drought stress is more serious than that of salt stress, the photosynthesis of T3 was greater than that of T2 and T4. For both years, the yield of crops followed the rank order T1 > T3 > T2 > T4. Compared with irrigating with fresh water in T1, T3 changed the second and third irrigation into brackish water, however we did not find that soil salinity increased significantly, and this treatment was able to ensure crop growth during the filling stage. Therefore, the combination of fresh water (80 mm), then brackish water (80 mm), then brackish water (80 mm) is a feasible irrigation strategy in China’s Yellow River Delta for winter wheat.

The adsorption properties of synthetic zeolite from coal bottom ash (CBA) were investigated for Cd and Ni removal from simulated acidic wastewater. The results showed that alkaline fusion synthetic zeolite significantly increased the specific surface area, pore volume, and cation exchange capacity (CEC) compared with CBA. The effects of various experimental parameters such as coexisting ion competition, pH, contact time, and temperature on the adsorption capacity and adsorption mechanism were investigated. Compared with single-metal ion adsorption, the adsorption capacity for Cd or Ni decreased in multiple-metal ions adsorption due to the competition effect of coexisting ion. Adsorption efficiency increased with increasing pH and the equilibrium was achieved at around 5.5 (>pH_pzc 4.5). The adsorption equilibrium was achieved within 4 h and followed the pseudo-second-order kinetic model. The result concluded that ion exchange was the primary adsorption mechanism. Moreover, zeolite exhibited strong neutralizing capacity, which could be attractive for acidic wastewater treatment.