Effects of woodland slope on heavy metal migration via surface runoff, interflow, and sediments and associated potential ecological risks following the application of sewage sludge

Effects of woodland slope on heavy metal migration via surface runoff, interflow, and sediments and associated potential ecological risks following the application of sewage sludge | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Effects of woodland slope on heavy metal migration via surface runoff, interflow, and sediments and associated potential ecological risks following the application of sewage sludge Lihua Xian, Dehao Lu, Yuantong Yang, Jiayi Feng, Jianbo Fang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3942079/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The application of sewage sludge (SS) to woodland is an effective approach for the disposal and utilization of SS. However, the woodland slope may determine the risk of heavy metal (HM) migration via runoff. We conducted indoor rainfall simulations and natural rainfall experiments to clarify the effect of slope on the migration of HMs via runoff (including surface and interflow) and sediments. In the simulated rainfall experiment, HMs lost via sediments increased by 9.79–27.28% when the slope increased from 5° to 25°. However, in the natural rainfall experiment, when the slope of forested land increased from 7° to 23°, HMs lost via surface runoff increased by 2.38% to 6.13%. It revealed that the surface runoff water on a high slope (25°) posed high water quality pollution risks. The migration of HMs via surface runoff water or interflow increased as the steepness of the slope increased. The total migration of Cu, Zn, Pb, Ni, Cr and Cd via sediment greatly exceeded that via surface runoff and interflow. Particles ≤0.05 mm contributed the most to the ecological risks posed by sediments. Cd was the main source of potential ecological risks in sediments under both experimental conditions. Sludge utilization soil contamination forest soils surface runoff rainfall Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Urbanization and global industrialization have led to increases in the production of sewage sludge (SS). SS production has thus become a major environmental issue 1-2 . Approximately 120 million tons of SS were generated by the major economies worldwide in 2019 3 , and the amount of SS generated by these economies is projected to be between 150 and 200 million tons in 2025. The composition of SS is highly complex, and previous studies have shown that SS contains various potentially harmful substances, including heavy metals (HMs), organic compounds, pathogens, and pharmaceutical residues 4 . In light of global limitations in the disposal capacity of SS, ensuring the safety and efficiency of the recycling and treatment of SS has become a major focus of research in the environmental science field. SS has been applied as a fertilizer to woodland, which is a sustainable and effective treatment for recycling the abundant organic matter and nutrients in SS 5-8 . As most forest products do not directly enter the human food chain, SS can be applied to forest land to enhance soil fertility and tree growth. However, the risk of contamination with HM elements, such as copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), and nickel (Ni) in SS, poses a major challenge for its use in woodlands at large scales 9-12 . HMs in SS may migrate through surface runoff to neighboring woodlands, where they are absorbed by the soil and accumulate, and this can have deleterious effects on plants, animals, and forest ecosystems 13-14 . Rainfall can facilitate the infiltration of HMs from SS into the subsoil, which affects groundwater quality and can pollute neighboring water resources 15-17 . However, the application of SS to woodlands enhances the physicochemical properties of the soil, which reduces the risk of HM migration in runoff 18 . The high content of organic matter in SS improves soil structure and increases the stability and erosion resistance of soil aggregates, which promotes the immobilization of HMs in the soil and reduces their susceptibility to transport 19 . Galdos et al. 20 found that the application of SS increases the concentration of HMs in surface runoff and sediments and the migration of most HMs mainly occurs through sediments, indicating that the risk of HM migration via sediments is particularly high. Comprehensive assessments of the safety and sustainability of applying SS to woodlands are thus critically important. Recent studies have shown that slope has a major effect on the risks of HM migration via runoff and sediment following the application of SS. Several factors contribute to the effect of slope on HM migration via these pathways. First, soil HMs are mainly in dissolved and particulate forms when they migrate with runoff during rainfall 21 . During this process, increases in slope slow the release of soil HMs via changes in the impact angle of raindrops and gravity, which can alter the form of HMs 22 . In SS, HMs are mainly in a granular state; however, during rainfall, changes in slope might promote the conversion of HMs into the dissolved state, which alters the total amount of HM migration and the relative contributions of different pathways (surface runoff, interflow, and sediments) to HM migration. Second, given that runoff and sediment are important carriers of HMs, runoff and sediment yield are key factors affecting the migration of HMs. Some studies have indicated that the residence time of precipitation on slopes decreases as the slope increases, which leads to decreases in the retention time of water in the soil, increases in the surface runoff on the slope 23 , and decreases in the yield of interflow 24 . However, Other studies have shown that runoff volume reaches a critical threshold as the slope increases; beyond this threshold, runoff volume decreases or remains constant 25 . Furthermore, several studies have found that changes in slope are the main driver of the flushing of sediments by rainfall runoff 26 . High slopes can increase erosion by promoting soil stripping or mitigating the protective effect of SS on the soil surface. In the early stages of flow production, cumulative sediment migration increases with cumulative runoff, and transport-limited erosion is the dominant sediment erosion process. However, segregation-limited erosion becomes more prevalent in subsequent rainfall events 27 . HMs can be transported via surface runoff, interflow, and sediment; however, few studies have examined the relative contributions of all three of these pathways to the transport of HMs. Only a few studies have explored the relative importance of all three of these pathways in experiments aimed at evaluating soil erosion and nutrient loss from agricultural fields 28-29 . Few studies have clarified the effects of slope on the migration of HMs via surface runoff, interflow, and sediments during the application of SS to woodland. Estimates of the magnitude of the potential ecological risks of HM migration via runoff might vary depending on the methodology of rainfall experiments. The effects of slope on the migration of HMs in runoff have generally been estimated via simulated rainfall experiments or natural rainfall experiments in the field 30-31 . However, whether, and to what extent, the risks of HM migration via runoff and sediment estimated using these two experimental approaches varies remains unclear. Here, we studied the migration of the HMs in SS via surface runoff (including surface flow and loam center flow), interflow, and sediment on different slopes simultaneously using an indoor simulated rainfall experiment and natural rainfall experiment in the field. The indoor simulated rainfall experiment was conducted in a homemade steel tank, and the natural rainfall experiment was conducted in a runoff plot in Eucalyptus woodland. We then evaluated (1) the effects of slope on runoff water and sediment yield, (2) differences in the migration patterns of HMs in surface runoff water, interflow, and sediments on different slopes, and (3) the potential ecological risks of HM migration associated with the application of SS to woodland. Overall, the main aim of this study was to identify the optimal slope for enhancing the efficacy and safety of SS application. Results Runoff and sediment yield in simulated rainfall and natural rainfall experiments Runoff and sediment yield in the simulated rainfall experiment. Slope had a major effect on surface runoff, interflow, and sediment yield. Surface runoff yield in the simulated rainfall events was highest in the S25 treatment, followed by the CK, S15, and S5 treatments. Surface runoff yield for all rainfall events in the S15 and CK treatments did not significantly differ (Fig. 1 a). The mean interflow yield was 132.17% and 45.14% higher in the S5 treatment than in the S15 and S25 treatments, respectively (Fig. 1 b). The interflow yield was significantly higher in the S5 treatment than in the S15 and S25 treatments from the fifth (R5) to the tenth (R10) rainfall event. The sediment yield was significantly lower in the S5 treatment than in the other treatments for all rainfall events (Fig. 1 c). Overall, the average yield of surface runoff increased as the slope increased. The average interflow yield was highest in the S5 treatment, which was significantly higher than in the S15 and S25 treatments. The average sediment yield in the S5 was significantly lower than in the other treatments (Fig. 1 d). Runoff and sediment yield in the natural rainfall experiment. The average surface runoff yield in the natural rainfall experiment increased as the slope increased, and the surface runoff yield in S15-F was significantly lower than in the CK-F for R3, R4, R5, R6, R7, and R10 (Fig. 2 a). The sediment yield increased with the slope for both R4 and R5. The sediment yield for R5 was significantly lower in the S15-F treatment than in the CK-F treatment. No sediment was collected in the R1–R3 and R6–R10 rainfall events (Fig. 2 c). The average surface runoff and sediment yield increased as the slope increased, with the mean surface runoff yield and sediment yield in S15-F 25.36% and 20.25% lower than CK-F, respectively, and these differences were significant (Fig. 2 b and 2 d). Cumulative amount of HM migration and the relative contributions of different pathways to HM migration in the simulated rainfall and natural rainfall experiments Cumulative amount of HM migration and the relative contributions of different pathways to HM migration in the simulated rainfall experiment. The cumulative amount of HM migration via surface runoff, interflow, and sediment, as well as the relative contributions of the different pathways to HM migration, were affected by the slope (Fig. 3 ). The cumulative migration amount for each HM in the sediments gradually increased as the slope increased. When the slope increased from 5° to 25°, the loss of HMs in sediments increased by 9.79–27.28%, and the loss of HMs in surface runoff and interflow decreased by 5.47–16.80% and 1.11–16.25%, respectively. Sediment was the main route for the migration of Cd, Cr, Cu, Ni, Pb, and Zn, and migration of these HMs via sediment was an order of magnitude greater than that via surface runoff and interflow. Cumulative amount of HM migration and the relative contributions of different pathways to the migration of HMs in the natural rainfall experiment. Consistent with the results of the simulated rainfall experiment, the cumulative migration of HMs with surface runoff increased as the slope increased (Fig. 4 ). HM migration via sediment increased as the slope increased in the natural rainfall experiment, and this was inconsistent with the results of the simulated rainfall experiment. Nevertheless, sediment was still the main pathway of Cd, Cr, Cu, Ni, Pb, and Zn migration, and the migration of these HMs via sediment was an order of magnitude greater than that via surface runoff. In each treatment, the proportion of HM migration via sediment was greater than 75%. Comparison of the total migration of HMs on different slopes in the simulated rainfall and natural rainfall experiments. The total migration of HMs in surface runoff in the simulated rainfall and natural rainfall experiments varied with slope. Overall, the total migration of HMs in the natural rainfall experiment increased significantly as the slope increased, while the total amount of HMs migration in the simulated rainfall experiment varied with slope and HMs. The total migration of Ni, Pb, and Cu was similar in the simulated indoor rainfall and natural rainfall experiments, and the migration of these elements significantly increased as the slope increased (p < 0.05). The total migration of Cr and Cd first increased and then decreased as the slope increased, and the total migration of Zn first decreased and then increased as the slope increased in both the simulated rainfall and natural rainfall experiments (Fig. 5 ). The total migration of HMs via sediment increased significantly as the slope increased in the simulated indoor rainfall experiment. The total migration of HMs varied with slope in the natural rainfall experiment, and variation in the amount of HMs with slope differed among HMs. The total migration of Cd, Cr, Ni, and Pb increased as the slope increased in the natural rainfall and indoor simulated rainfall experiments. However, the total migration of Cu and Zn first increased and then decreased as the slope increased, and this was inconsistent with the results in the indoor simulated rainfall experiment (Fig. 6 ). Potential ecological risks of HM migration via runoff and sediment in the simulated rainfall and natural rainfall experiments Potential ecological risks of HM migration via runoff and sediment in the indoor simulated rainfall experiment. The WQI was used to evaluate changes in the water quality of surface runoff and interflow during 10 simulated rainfall events (Fig. 7 ). In the 10 rainfall events, the water quality of the surface runoff in the CK and S5 treatments was "excellent." In R1–R2, the surface runoff water quality in the S15 treatment was "excellent", and it changed from "excellent" to "good" as the number of simulated rainfall events increased. In the S25 treatment, the surface runoff water quality was "poor" for R4, but "good" for the other nine rainfall events (Fig. 7 a). The interflow water quality in CK and S5 changed from "good" to "poor" and that in S15 and S25 changed from "good" to "very poor" as number of simulated rainfall events increased (Fig. 7 b). The potential ecological risk assessment revealed (Table 1 ) that the E r of Cd in S15 and S25 was "considerable" in sediments with particle sizes > 0.05 mm and "high" when particle sizes were ≤ 0.05 mm. The E r of Cd in the S5 treatment was "high" when particle sizes were ≤ 0.05 mm and "low" in sediments with particle sizes > 0.05 mm. The potential ecological risks of Cd for the four sediment particle sizes were 74.05–112.85% higher in the S15 treatment than in the CK. The E r of the other five HMs for the four particle sizes in the different treatments was "low." The RI value in the sediment depends on the slope and particle size of the sediment (Table 1 ). In the simulated rainfall experiment, the RI values of the sediments for the four particle sizes were highest in the S15 treatment. The RI values of sediments were highest and lowest in different treatments for particle sizes ≤ 0.05 mm and > 1 mm, respectively. RI values of sediments increased as the particle size decreased. Generally, when the sediment particle size was ≤ 0.25 mm, the RI was "low"; when the sediment particle size was 0.05 ~ 0.25 mm, the RI of the CK and S5 treatment was "low", and the RI of the S15 and S25 treatments was " moderate"; when the sediment particle size was ≤ 0.05 mm, the RI of the CK was "low." The RI of the other three treatments was "moderate." The potential ecological risks of each treatment mainly stemmed from Cd (Table 1 ). Table 1 The potential ecological risks of HMs in sediment of the four particle sizes on different slopes in the simulated rainfall experiment Particle sizes (mm) Treatment Potential ecological risk coefficients ( E r ) of single elements Potential ecological risk index ( RI ) of multiple elements Risk level Cd Cr Cu Ni Pb Zn > 1 CK 62.29 0.87 4.30 1.68 2.54 0.52 72.20 Low S5 23.53 0.25 1.30 0.47 1.17 0.47 27.19 Low S15 110.72 1.23 6.13 2.45 4.95 1.86 127.34 Low S25 107.89 1.07 5.12 2.05 4.42 1.65 122.20 Low 0.25–1 CK 56.49 0.91 4.41 1.68 2.91 0.66 67.06 Low S5 31.38 0.31 1.47 0.59 1.11 0.33 35.19 Low S15 120.24 1.23 5.29 2.34 4.58 1.43 135.11 Low S25 95.11 1.00 4.72 1.98 3.99 1.14 107.94 Low 0.05–0.25 CK 84.08 1.17 6.61 2.46 3.40 0.68 98.40 Low S5 35.48 0.38 2.02 0.86 1.10 0.33 40.17 Low S15 174.81 1.72 8.92 4.32 5.15 1.48 196.40 Moderate S25 133.88 1.35 7.70 3.39 3.67 1.24 151.23 Moderate ≤ 0.05 CK 104.03 1.33 9.07 3.77 3.86 0.76 122.82 Low S5 177.02 1.70 10.32 5.01 4.79 1.62 200.46 Moderate S15 181.06 1.72 10.38 4.97 4.82 1.63 204.58 Moderate S25 177.13 1.72 10.99 5.00 4.98 1.65 201.47 Moderate Note: Data in the table are the average of three replicates. CK: Slope 15°+ no SS application, S5: Slope 5°+ SS application, S15: Slope 15°+ SS application, and S25: slope 25°+ SS application. E r was classified as follows: low < 40; 40 ≤ moderate < 80; 80 ≤ considerable < 160; and 160 ≤ high < 320. RI was classified as follows: low risk < 150; 150 ≤ moderate risks < 300; 300 ≤ considerable risks < 600; and high risk ≥ 600. Potential ecological risks of HM migration via runoff and sediment in the natural rainfall experiment. No significant differences in the WQI of the surface runoff of each treatment were observed for R1–R3. In R4, the WQI of surface runoff was 61.82%, 16.57%, and 92.92% higher in the S7-F, S15-F, and S23-F treatments than in the R3 treatment, respectively. The WQI of each treatment decreased from R5 to R10, and the water quality was "excellent" for R8–R10 (Fig. 8 a). Sediment was collected during only R4 and R5. The potential ecological risks of the three treatments with SS application were significantly higher than CK-F. Consistent with the results of the simulated rainfall experiment, Cd was the main source of potential ecological risks in sediment in the natural rainfall experiment, and the E r value for Cd was significantly higher than that for the other five HMs. The RI values of the sediments for R4 and R5 decreased as the slope increased (Fig. 8 b). Discussion The use of SS as fertilizer for forest soil is a promising SS treatment method. However, HMs can migrate to the external environment via surface runoff, interflow, and sediment following SS application. The risks of HM migration might be particularly high in hot and rainy southern forest regions. Evaluating the risks of HM migration via these different pathways is critically important for ensuring the safety of SS application 32–33 . SS application to forest land might alter the erosion effect of rainfall on soil, and the amount of HM migration via surface runoff, interflow, and sediment varies with slope 20,24 . Indoor simulated rainfall experiments have been widely conducted because variables can be precisely controlled in such experiments, and this helps clarify the mechanism underlying the effect of slope on soil and water erosion 25 . However, the complexity of natural systems, including variation in rainfall patterns, biological characteristics, and soil structure in forest ecosystems, might affect patterns of HM migration inferred in natural rainfall experiments. In our study, the runoff and sediment yields differed in the simulated rainfall and natural rainfall experiments. The migration and potential ecological risks of HMs in runoff and sediment also varied with the slope. The migration and potential ecological risks of HMs in surface runoff and sediment in the indoor simulated rainfall and natural rainfall experiments also differed. Most studies have shown that surface runoff can form more rapidly on steeper slopes, but steeper slopes result in the generation of less interflow 24 . The short residence time of precipitation on slopes reduces the retention time of water in the soil; this in turn reduces the opportunity for water infiltration and increases surface runoff 23 . The results of the indoor simulated rainfall experiments showed that the surface runoff yield increased as the slope increased following SS application. We also found that changes in surface runoff and sediment yield with slope were similar in the natural rainfall and indoor simulated rainfall experiments. This indicates that slope has a major effect on the transport of runoff, even in natural woodlands. Season is one of the main factors affecting surface runoff and sediment yield in natural rainfall experiments in woodlands. In the natural rainfall experiment, 70% of the rainfall events occurred in the summer (from May to August). Furthermore, patterns of variation in rainfall and surface runoff were similar for the 10 natural rainfall events. These findings suggest that surface runoff yield in natural systems is strongly affected by season and the amount of rainfall.We found that the interflow yield was highest in the S5 and lowest in S15 in the simulated rainfall experiment. Zhang et al. 25 showed that permeability decreases as the slope increases in an indoor simulated rainfall experiment. Other studies have shown that the soil infiltration rate decreases significantly as the slope increases when the slope is less than 18°. Slope has a weak effect on the infiltration when it is greater than 18° 34 . This is consistent with the findings of Wu et al. 23 showing that there is a critical slope which makes the runoff reach the maximum. The relationship between slope and infiltration changes as the slope increases 24 . When the slope is 15°, the splashing effect of rainfall on topsoil has a major effect on the physical properties of soil, alters the roughness of topsoil, promotes soil compaction, and reduces the interflow yield. This might also explain why the interflow yield was significantly lower in the S15 treatment than in the S5 and S25 treatments. Sediment production was lower in the S15 treatment than in the CK in the simulated rainfall experiment. This stems from the fact that the large particles prevent the erosion of SS by runoff because they form a protective layer that prevents the erosion of soil particles by precipitation 35 . The sediment yield increased as the slope increased. This might be explained by the small area of rainfall in our experiment; furthermore, the surface water flow velocity and the shear stress increase as the slope increases 36 . However, the retention time of surface water on the slope decreases as the slope increases, which weakens the protective effect of SS on the soil and increases the strength of the splashing effect of rainfall on the topsoil 37 ; this results in the activation and transport of more soil particles. However, the sediment yield was 255.92% and 258.82% lower in the S5 treatment than in the S15 and S25 treatments, respectively. Wang et al. 38 found that surface runoff flow is unable to carry or separate sediments with larger grain sizes on low slopes, and this results in a drastic decrease in sediment yield. This can be explained by the fact that large particle sizes experience greater resistance in raindrops and surface flow, which inhibits their removal. Specifically, the sediment yield first increased and then decreased, suggesting that transport-limited erosion was the dominant erosion process in the early rainfall events 39 . The effect of raindrops on flow transport might also contribute to the mechanism restricting sediment transport 27 . As the number of rainfall events increased, segregation-limited erosion became the dominant erosion process. These results are consistent with those of Ran et al. 40 and Shi et al. 27 . We found that patterns of variation in sediment and surface runoff yield were similar in the natural rainfall and simulated indoor rainfall experiments, and both sediment and surface runoff yield increased as the slope increased. Large sediment particles cannot be activated when the intensity of rainfall is low because of the weak driving force 38 . HMs migrate with runoff via two pathways during rainfall: dissolved state migration, wherein HMs migrate with runoff in their molecular and ionic states, and particulate state migration, wherein HMs adsorb and bind to the surface of sediment particles in their inorganic and organic states and migrate with the sediment 21,41 . Slope did not have a significant effect on the cumulative migration of these metals in runoff; this might be related to several factors. First, the duration and frequency of rainfall are typically higher under natural rainfall conditions compared with indoor simulated rainfall conditions, and prolonged and high-frequency rainfall might promote the leaching of HMs from the soil 42 , especially on steeper slopes. The structure and composition of the soil as well as the activities of the plant's root system in natural systems might also affect the experimental results 10 . Interflow makes a non-negligible contribution to the horizontal migration of HMs 43 , and the release of HMs from interflow stems from interactions between the soil liquid-phase and solid-phase matrices, including processes such as adsorption/desorption and dissolution/precipitation, as well as the complexation of ions in soil 2 . Some previous studies 44 have shown that the mobility of solutes in surface runoff increases as the slope increases, and this promotes the dilution and transport of HMs in runoff. The interflow yield was highest in the S5 treatment at slower flow rates. This likely stemmed from the greater amount of organic matter derived from SS in the S5 treatment, which resulted in anoxic/anaerobic conditions, greatly decreased the redox potential at the soil surface, and facilitated the dissolution and infiltration of HMs in the soil 7,45 . By contrast, Cr and Pb migration via interflow was significantly lower than the migration of these elements via surface runoff, indicating the presence of a barrier to the infiltration of Cr and Pb ions. Cr and Pb have a high cation exchange capacity and strong affinity for organic compounds 46–47 . Cr and Pb ions can be adsorbed by particulate organic matter of different sizes through hydroxyl and carboxyl groups 48 ; Cr and Pb ions are associated with the large amount of organic matter in SS and form stable chelating substances that are immobilized in the soil 49 . HMs in surface soils are mainly present in granular form during erosion, and the amount of migration is positively related to the slope 37 . As the slope increased, more sediment particles were entrained and washed away in the runoff, which led to an increase in the cumulative migration of HMs in the sediments. The cumulative amount of HM migration in the sediments accounted for more than 65% of the total amount of HM migration, indicating that sediment was the main pathway of HM migration; this is consistent with the findings of Galdos et al. 20 and Huang et al. 42 . Although the total amount of each HM transported in both pathways (surface runoff and sediment) increased as the slope increased, the percentage of each HM in sediment and surface runoff decreased and increased, respectively; this finding was inconsistent with the results of the indoor simulated rainfall experiment. Rainfall events in forests typically last for several hours or even days; consequently, the surface soil in the natural rainfall experiment could have been saturated, which would increase the release of HMs via surface runoff. In addition, the splashing of raindrops has been shown to increase the dispersion and transport of soil particles by breaking up and moving aggregates and disrupting soil structure 50 . The presence of a forest canopy in forested areas mitigates the erosion of soil particles by the splashing of raindrops, which slows the transport of HMs via sediments. We found that the risks of HM contamination in surface runoff water were higher when SS was applied compared with the CK; this is consistent with the results of most studies 13 . Furthermore, the water quality in surface runoff from the CK and S5 treatment was "excellent" during the 10 rainfall events, and there was virtually no risk of contamination in surface runoff on lower slopes. The WQI in the S15 and S25 treatments was "good" for most rainfall events, and only poor for the fourth rainfall event in the S25 treatment. This suggests that the risk of surface runoff pollution increases as the slope increases; overall, there was no risk of surface runoff water pollution. No significant change was observed in the risk of surface runoff water pollution as the number of rainfall events increased. Previous studies have shown that the application of sludge will lead to HMS pollution levels in farmland runoff exceeding Class IV water quality standards of China's Surface Water Environmental Quality Standards 20 . This difference might stem from variation in the soil and SS used in the different experiments. Another study has shown that the risk of HM transport is significantly increased in sandy soils 51 , and the risk of HM transport is reduced in lateritic soils rich in iron and aluminium oxides. We found that the pollution risk of surface runoff increased as the slope increased for 10 natural rainfall events, and the WQI was "poor" only in R4 and R5 of the S25 treatment; this was consistent with the results of the indoor simulated rainfall experiment. Sediment was the main pathway of HM transport, and the magnitude of HM loss was an order of magnitude greater via sediment than via surface runoff and interflow. Previous studies have shown that the adsorption/desorption capacities of aggregates might vary for different particle sizes because of differences in soil physicochemical properties 42 . The E r values of all HM elements were “low,” with the exception of that for Cd. In the indoor simulated rainfall experiment, the contribution of Cd to RI was 75% or more in all treatments. Other studies have also found Cd to be the main element contributing to ecological risks, and this might stem from the fact that it has the highest toxicity response factor among all metals tested 1–2,52 . The risk of sediment contamination was highest for two particle sizes: 0.05–0.25 mm and ≤ 0.05 mm. Previous studies have shown that fine particles typically have a higher surface area and pore volume compared with large particles, and this promotes the strong adsorption of HMs. Fine particles can be easily mobilized by water flow, and the transport of larger particles (> 0.25 mm) can only be initiated at higher flow rates 53 . Nevertheless, sediment was the main pathway for the migration of Cd, Cr, Cu, Ni, Pb, and Zn, and the migration of these HMs via sediment was an order of magnitude greater than that via surface runoff. Under laboratory conditions, predictions of HM transport from simulated rainfall experiments can lead to a range of conclusions based on the assumptions made 54 . However, the environmental complexity and variability of forests cannot be easily accounted for in indoor experiments. The results of the simulated and natural rainfall experiments were not completely consistent for different slopes. Thus, achieving an improved understanding of variation in HMs and their bioavailability in woodlands will require comparisons of the results of laboratory and field studies; these studies will also enhance our ability to investigate the mobility of HMs in natural woodlands using laboratory simulation experiments 45 . Conclusions Slope had a significant effect on the runoff and sediment yield. Surface runoff and sediment yields increased as the slope increased in the simulated rainfall experiment, and surface runoff and sediment yields were 11.51% and 258.82% higher in the S25 treatment than in the S5 treatment, respectively. A similar pattern was observed in the natural rainfall experiment, as the surface runoff and sediment yield also increased with slope. Sediment was the main pathway for HM migration in all treatments, and more than 68.70% of the total migration of Cd, Cr, Cu, Pb, and Zn was via sediment. In the simulated rainfall experiment, the proportion of HMs transported via sediments increased as the slope increased, and the proportion of HMs transported via surface runoff decreased as the slope increased. However, the migration of HMs in surface runoff and sediment increased and decreased, respectively, as the slope increased in the natural rainfall experiment. The application of suitable amounts of SS on lower slopes (5°) reduced runoff and soil erosion and did not significantly contribute to the risk of HM contamination in the simulated rainfall experiment. However, the risk of water quality contamination in the interflow was higher in the S15 and S25 treatments during later rainfall events. In the simulated rainfall experiment, the risk of contamination was only observed for sediments with a grain size ≤ 0.25 mm. Our findings revealed significant differences in the effect of slope on the risk of HM migration following SS application on forest land. However, more studies are needed to clarify the factors driving differences in the results of simulated and natural rainfall experiments, as well as their underlying mechanisms, in planted forests in the future due to the high complexity of the hydrological and surface soil conditions in the field. Methods SS and soil properties. SS was obtained from Guangzhou Water Purification Co., Ltd. in Guangdong Province, China. The water content of the treated SS was approximately 40%, and the HM content of the SS was high. SS was anaerobically composted for 60 d prior to its use. The SS was then air-dried, sieved through a 10 mm nylon sieve, and thoroughly mixed. The soil was collected from the Eucalyptus forest in Dalingshan Forest Park, Dongguan City, Guangdong Province, China (22°51′18.99"N, 113°45′22.44"E). The study area has a typical subtropical monsoon climate, with an average annual temperature of 23.3°C and annual precipitation of 2,042.6 mm. The soil is granite red soil and has a sandy loam texture (56.7% sand, 27.7% silt, and 15.6% clay). In November 2020, soil samples were collected from the 0–10 cm, 10–20 cm, and 20–30 cm layers. In the laboratory, soils were sampled using a ring knife, and the bulk weight was measured. The soils were air-dried at room temperature for two weeks, passed through a 10 mm nylon sieve, and mixed thoroughly. The soil and SS samples were then ground and sieved (< 0.15 mm), and the content of organic matter, Cd, chromium (Cr), Cu, Ni, Pb, and Zn, as well as the pH were measured (Table 2 ). Table 2 Basic properties of the SS and soil Variables Sewage sludge Soil pH 9.33 ± 0.05 5.00 ± 0.07 Organic matter (g·kg − 1 ) 204.79 ± 1.14 14.71 ± 0.35 Cu (mg·kg − 1 ) 114.97 ± 0.77 54.12 ± 1.60 Zn (mg·kg − 1 ) 475.55 ± 6.91 120.55 ± 8.88 Pb (mg·kg − 1 ) 41.03 ± 1.79 60.46 ± 2.11 Cr (mg·kg − 1 ) 94.40 ± 4.53 72.90 ± 2.53 Ni (mg·kg − 1 ) 39.05 ± 1.31 18.96 ± 0.07 Cd (mg·kg − 1 ) 1.89 ± 0.12 0.58 ± 0.08 Experimental design of the indoor rainfall simulations. The steel flumes comprise a steel tank and plastic external baffles. They were rectangular and had the following dimensions: 1.0 m × 0.3 m × 0.4 m. The length of the flumes of the soil tank was adjusted for different slopes. The internal part of the steel flumes comprised the soil sample receiving space with a base area of 0.3 m 2 (1.0 m × 0.3 m) and a depth of 0.4 m. Plastic baffles (height of 0.3 m) were fitted around and on top of the steel flumes to prevent soil and water spillage. The bottom of the steel flumes was sealed with a PVC sheet, and the remaining small holes allowed the interflow to drain. The bottom was lined with quartz sand with a thickness of 3 cm, and it was washed with dilute nitric acid and lined with a 100-mesh screen. A V-shaped water-measuring weir was installed at the lower end of the rack to convey surface runoff water through a plastic pipe to a plastic collection bucket. The slope of the rack could be adjusted from 0° to 30°. To enhance the realism of the rainfall simulations, a 2 cm seepage space was installed at the bottom of the steel rack. The soil was raised 2 cm using a plastic mat. The interflow that drained from the soil and flumes was collected and placed at the bottom of the steel rack. Simulated rainfall was used to evaluate the effect of slope on soil erosion and the migration of Cd, Cr, Cu, Ni, Pb, and Zn in surface runoff, interflow, and sediments. The experiment comprised four treatments, S5, S15, and S25 (in which SS was applied and the slope of the soil flume was 5°, 15°, and 25°, respectively) and CK (in which no SS was applied and the slope of the soil flume was 15°) (Table 3 ). Given that the slope altered the contact area between precipitation and the surface of soil troughs, 1.80 kg, 1.87 kg, and 1.98 kg of SS were applied in the S5, S15, and S25 treatments,which corresponds to 60 tons ha − 1 . The SS was thoroughly mixed with the surface soil in the 0–10 cm layer (Table 3 ). Each treatment has three replicates. The simulated rainfall water was municipal tap water. The chemical properties of the water were as follows: pH, 7.4; Cd < 4 µg·L − 1 ; Cr < 4 µg·L − 1 ; Cu < 9 µg·L − 1 ; Pb < 0.07 µg·L − 1 ; and Zn < 1 µg·L − 1 . Table 3 Experimental design Indoor simulated rainfall experiment Natural rainfall experiment Treatment Slope (°) Amount of SS applied (kg) Treatment Slope (°) Amount of SS applied (kg) CK 15 0.00 CK-F 15 0.00 S5 5 1.80 S7-F 7 180.72 S15 15 1.87 S15-F 15 186.30 S25 25 1.98 S23-F 23 198.54 The simulated rainfall experiment was performed in the simulated rainfall hall of the Red Soil Erosion and Flow Hydraulics Laboratory, Institute of Ecology, Environment and Soil, Guangdong Academy of Sciences, China. The rainfall system comprised a variable power pump (0–60 W with a water volume of 0–5 L·min − 1 ) and a water tank (1.2 m × 0.4 m × 0.12 m) with 150 holes (0.8 mm diameter) at the bottom. Water was pumped into the tank and dripped down through the holes at the bottom of the tank to form continuous and steady rainfall. The rainfall intensity was set to 120 mm·h − 1 to simulate heavy rainfall and severe soil erosion, which is consistent with the typical climatic characteristics of South China. Before the formal rainfall experiment, the rainfall intensity was adjusted to rainfall uniformity > 95%. A total of 10 simulated rainfall events were performed over a two-week interval between each rainfall event. The duration of each rainfall event was 60 min, and the total rainfall of the 10 simulations was equivalent to 3/4 of the rainfall in Guangzhou. The simulated rainfall experiment was initiated in March 2022 and ended in December 2022. The simulated rainfall experiment was carried out for two weeks, and there was a two-week interval between each rainfall event. Design of the natural rainfall experiment. The study was conducted in a Eucalyptus plantation in Dalingshan Forest Park, Dongguan City, Guangdong Province, China. The Eucalyptus forest was planted in 1998 with a row spacing of 3 m × 2 m. The runoff plots were arranged according to the actual contours of the site, and the woodland slopes ranged from 5° to 30°. The runoff plots were sloping woodlands with similar characteristics except slope to ensure that rainfall conditions were consistent among plots. Each runoff plot has a horizontal projected area of 20 m² (10 m × 2 m) in the direction of the slope. At the lower end of the runoff plot, there was a collection pond measuring 1 m × 1 m × 1 m, which was connected to the runoff plot. The pond was elevated 20 cm above the ground and covered with PVC plastic sheets to prevent rainwater from entering. The natural rainfall experiment comprised four treatments, S7-F, S15-F, and S23-F (SS was applied and their slopes were 7°, 15°, and 23°, respectively) and CK-F (in which the slope was 15°, and no SS was applied) (Table 3 ). Each treatment has three replicates. The amount of rainfall of the 10 natural rainfall events in the woodland and the amount of rainfall during the 48 h prior to sampling are shown in Fig. 9 . Sample collection and analysis. In the indoor rainfall simulations, surface runoff and interflow were collected using two containers. Only surface runoff was collected in the natural rainfall experiment. When the surface runoff or interflow stopped, measurements were taken. After the collection containers were left to stand for 30 min, the suspensions were collected individually into 200 mL plastic bottles, and 1 mL of 1:10 nitric acid solution was added to inhibit microbial activity and prevent the precipitation of HM ions. The processed samples were stored at 4°C. Using the wet sieving method, sediments precipitated in runoff collection containers during natural and simulated rainfall events were filtered and classified into four different types of agglomerates with different particle sizes: >1 mm (large macro-aggregates), 1–0.25 mm (small macro-aggregates), 0.25–0.05 mm (large micro-aggregates), and < 0.05 mm (small micro-aggregates) 42,55 . Because the amount of sediment produced by the 10th rainfall is very small, the collection of particle size sediment samples was not carried out, and a total of 9 runoff sediment samples were collected in the simulative rainfall experiment. Sediment samples were only washed out and collected during the 4th and 5th natural rainfall experiment. Half of the agglomerates were dried, weighed, and milled separately, and the other half was mixed. Surface runoff, interflow, and sediment samples were digested using the triple-acid method (nitric-hydrofluoric-perchloric acid), and a plasma atomic emission spectrometer (Leeman Prodigy7 model) was used to determine the concentrations of Cd, Cr, Cu, Ni, Pb, and Zn. Calculation of indicators. Three indicators, surface runoff yield (L), interflow yield (L), and sediment yield (g), were used to evaluate the effects of different slopes on runoff and soil erosion. Cumulative lift volume (CLV) was used to evaluate the transport of HMs via three pathways, surface runoff, interflow, and sediment, for all rainfall events. CLV was calculated using equations ( 1 ) and ( 2 ). $$\text{L}\text{V} = \text{S}\text{S}\text{R} (\text{N}\text{S}\text{R}/\text{S}\text{S}\text{L}/\text{N}\text{S}\text{L}/\text{S}\text{S}\text{D}/\text{N}\text{S}\text{D}) \times \text{N}$$ 1 $$\text{C}\text{L}{\text{V}}_{n} = \text{L}{\text{V}}_{1} + \text{L}{\text{V}}_{2} \dots +\text{L}{\text{V}}_{n}$$ 2 where N is the concentration of HMs in surface runoff, interflow, or sediments, which is multiplied by the surface runoff yield (SSR/NSR), interflow yield (SSL/NSL), or sediment yield (SSD/NSD), respectively, for each simulated or natural rainfall event. These values are used to calculate the amount of HM migration via the corresponding pathways (LV). CLV𝑛 was the cumulative migration of HMs in surface runoff, interflow, and sediments during 1st ~ n th rainfall events. The water quality index (WQI) describes water quality via several water quality parameters 56 . We used WQI to evaluate the risk of HM pollution in surface runoff and interflow. WQI was calculated using the following formula: $$WQI=\sum \left[{W}_{i}\times \left(\frac{{C}_{i}}{{S}_{i}}\right)\right]\times 100$$ 3 In the WQI formula, \({W}_{i}\) = \({\text{w}}_{i}/\sum {w}_{i}\) , where \({W}_{i}\) is the weight of each HM, and \(\sum {w}_{i}\) is the sum of the weights of all HMs. The weights of Cd, Cr, Cu, Ni, Pb, and Zn were defined as 5, 5, 2, 4, 5, and 1, respectively, following a previous study 57 . \({C}_{i}\) corresponds to HM concentrations, and \({S}_{i}\) indicates the Chinese drinking water standard (Chu et al., 2023). WQI was classified as excellent(WQI < 50), good(50 ≤ WQI < 100), poor(100 ≤ WQI < 200), very poor( 200 ≤ WQI < 300), and unfit for drinking(WQI ≥ 300) 57 . The potential ecological risk index ( RI ) was used to evaluate the ecological risk level of HMs in soil and evaluate the combined toxicity of HMs 58 . RI can be calculated using equations ( 4 ) and ( 5 ); $${E}_{i}={T}_{i}\times \frac{{C}_{i}}{{C}_{0}}$$ 4 $$RI=\sum _{i=1}^{n}{E}_{i}$$ 5 where \({E}_{i}\) is the risk factor for the given HM; \({T}_{i}\) is the toxicity response factor for the given pollutant ( \({T}_{i}\) for Cd, Cr, Cu, Ni, Pb, and Zn was defined as 30, 2, 5, 5, 5, and 1, respectively); \({C}_{i}\) is the concentration of each HM in the soil, and \({C}_{0}\) is the background concentration of HMs in the study area. Chu et al. 11 found that the background concentrations of Cu, Zn, Pb, Cd, Cr, and Ni in Guangzhou were 28.7, 77.8, 57.6, 0.13, 87.0, and 23.5 mg·kg − 1 , respectively. Values of \({E}_{i}\) were classified as follows: low < 40; 40 ≤ moderate < 80; 80 ≤ considerable < 160; and 160 ≤ high < 320. Values of RI were classified as follows: low risk < 150; 150 ≤ moderate risk < 300; 300 ≤ considerable risk < 600; and high risk ≥ 600. Statistical analysis. SPSS 19.0 (SPSS Inc., USA) was used to conduct all statistical analyses. One-way analysis of variance, followed by Duncan’s test, was used to evaluate the significance of differences among treatments (p < 0.05). Origin Pro 2019 software (Origin Lab Corporation, Northampton, MA) was used to make plots. Declarations Acknowledgments This work was supported by the Forestry Science and Technology Innovation Project of Guangdong Province (2022KJCX015) and the National Natural Science Foundation of China (31971629) . Author contributions L.H.X. and D.H.L. conceived and designed the experiments, and wrote the first draft of the manuscript. L.H.X. and Y.T.Y. conducted the experiments. L.H.X., D.H.L., J.Y.F. and J.B.F. analyzed the data. Y.T.Y., D.M.W. and S.C.Z. supervised the research and contributed to the discussion of the results. L.H.X. and D.H.L. wrote the manuscript. D.F.J., D.M.W., and S.C.Z. contributed to the guidance of this study and reviewed the manuscript. 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Geomorphology . 45 , 261-275. https://doi.org/10.1016/S0169-555X(01)00158-1 (2002). Brazauskiene, D.M., Paulauskas, V., Sabiene, N. Speciation of Zn, Cu, and Pb in the soil depending on soil texture and fertilization with sewage sludge compost. J. Soils Sediments . 8 , 184-192. https://doi.org/10.1007/s11368-008-0004-6 (2008). Zuo, W.G., Bai, Y.C., Lv, M., Tang, Z.H., Ding, C., Gu, C.H., Shan, Y.H., Dai, Q.G., Li, M. Sustained effects of one-time sewage sludge addition on rice yield and heavy metals accumulation in salt-affected mudflat soil. Environ. Sci. Pollut. Res. 2 8 , 7476-7490. https://doi.org/10.1007/s11356-020-11115-1 (2021). Tansel, B., Rafiuddin, S. Heavy metal content in relation to particle size and organic content of surficial sediments in Miami River and transport potential. Int. J. Sediment Res. 31 , 324-329. https://doi.org/10.1016/j.ijsrc.2016.05.004 (2016). Fang, W., Wei, Y.H., Liu, J.G. Comparative characterization of sewage sludge compost and soil: Heavy metal leaching characteristics. J. Hazard. Mater. 310 , 1-10. https://doi.org/10.1016/j.jhazmat.2016.02.025 (2016). Xiao, R., Zhang, M.X., Yao, X.Y., Ma, Z.W., Yu, F.H., Bai, J.H. Heavy metal distribution in different soil aggregate size classes from restored brackish marsh, oil exploitation zone, and tidal mud flat of the Yellow River Delta. J. Soil. Sediment . 16 , 821-830. https://doi.org/10.1007/s11368-015-1274-4 (2016). Li, M.Z., Hu, Y.J., Zhou, N., Wang, S.R., Sun, F.F. Hydrothermal treatment coupled with pyrolysis and calcination for stabilization of electroplating sludge: speciation transformation and environmental risk of heavy metals. J. Hazard. Mater. 438 , 9. https://doi.org/10.1016/j.jhazmat.2022.129539 (2022). Li, F.Y., Yu, T., Huang, Z.Z., Jiang, T.Y., Wang, L.X., Hou, Q.Y., Tang, Q.F., Liu, J.L., Yang, Z.F. Leaching experiments and risk assessment to explore the migration and risk of potentially toxic elements in soil from black shale. Sci. Total Environ . 844 , 156922. https://doi.org/10.1016/j.scitotenv.2022.156922 (2022b). Hakanson, L. An ecological risk index for aquatic pollution control: a sedimentological approach. Water Res. 14 , 975-1001. https://doi.org/10.1016/0043-1354(80)90143-8 (1980). Additional Declarations No competing interests reported. Supplementary Files rawdata.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Mar, 2024 Reviews received at journal 21 Mar, 2024 Reviews received at journal 04 Mar, 2024 Reviewers agreed at journal 24 Feb, 2024 Reviewers invited by journal 23 Feb, 2024 Editor assigned by journal 23 Feb, 2024 Editor invited by journal 21 Feb, 2024 Submission checks completed at journal 21 Feb, 2024 First submitted to journal 09 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3942079","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":274224586,"identity":"5f599d54-753c-40ae-be0f-4774172de71f","order_by":0,"name":"Lihua Xian","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Xian","suffix":""},{"id":274224587,"identity":"5d73f4cd-4b22-4ef1-84c5-7ae4c6e8e937","order_by":1,"name":"Dehao Lu","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Dehao","middleName":"","lastName":"Lu","suffix":""},{"id":274224588,"identity":"cf5ac467-a6ed-4de7-a38b-63401ab73597","order_by":2,"name":"Yuantong Yang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yuantong","middleName":"","lastName":"Yang","suffix":""},{"id":274224589,"identity":"1123adcc-ab8a-4687-835d-9017972fd571","order_by":3,"name":"Jiayi Feng","email":"","orcid":"","institution":"Guangdong Eco-Engineering Polytechnic","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Feng","suffix":""},{"id":274224590,"identity":"b6b9c6ae-87bb-488c-b6df-6a56a7a763b0","order_by":4,"name":"Jianbo Fang","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Fang","suffix":""},{"id":274224591,"identity":"329ec52d-79c1-4939-a99d-473ffa2a3ba2","order_by":5,"name":"Douglass F. Jacobs","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Douglass","middleName":"F.","lastName":"Jacobs","suffix":""},{"id":274224592,"identity":"ccedb320-ea81-4d15-a19e-b09096465e64","order_by":6,"name":"Daoming Wu","email":"","orcid":"","institution":"South China Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Daoming","middleName":"","lastName":"Wu","suffix":""},{"id":274224593,"identity":"09ceb5bc-35cc-4ff5-b30b-9db0e7cc8267","order_by":7,"name":"Shucai Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYPACGyjNRryWNNK1HCZBi3z78YePC36dt+dvP2PA8KHsMAP/7Ab8WgzOJCQbz+y7nTjjTI4B44xzhxkk7hwgoEWC4Zg0b8/tBAOGHANm3rbDQJEEAg6bwdj+m7fnnL0B/xsD5r/EaGG4wczGzPPjAOMGCaAtjMRoMTiTxizN25CcOOPGs4KDPefSeSRuEHIYMMQ+8/yxs+fvT9744EeZtRz/DEIOAwHGNgh9AIh5iFAPAn+IVDcKRsEoGAUjEwAA4zpAG99z8REAAAAASUVORK5CYII=","orcid":"","institution":"South China Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Shucai","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2024-02-09 05:44:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3942079/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3942079/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51534547,"identity":"95d097e7-a393-41f9-b2ba-bfeb0b1e9ed7","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":39132,"visible":true,"origin":"","legend":"\u003cp\u003eSurface runoff yield (SSR, L) (a), interflow yield (SSL, L) (b), and sediment yield (SSD,·g) (c) in each simulated rainfall event (R1–R10) (sediment was collected only in R1–R9). Total SSR (L), total SSL (L), and total SSD (g) of all 10 or 9 simulated rainfall events (CR10, CR9) (d). The data in the figure are the average of three replicates, and different letters indicate significant differences between treatments for the same simulated rainfall event. The threshold for statistical significance was \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. CK: Slope 15°+ no SS application, S5: Slope 5°+ SS application, S15: slope 15°+ SS application, and S25: slope 25°+ SS application.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/0b95b066267b02884355e6c1.png"},{"id":51534548,"identity":"6420126e-132e-4bac-a16e-6205b13b21dc","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28066,"visible":true,"origin":"","legend":"\u003cp\u003eSurface runoff yield (NSR, L) (a), interflow yield (NSR, L) (b), and sediment yield (NSD, g) (c) for each natural rainfall event (R1–R10) (sediment was collected only in R4 and R5). Total NSR (L) and total NSD (g) of the 10 or 2 natural rainfall events (CR10, CR2) (d) The data in the figure are the average of three replicates, and different letters indicate significant differences between treatments for the same natural rainfall event. The threshold for statistical significance was \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. CK: Slope 15°+ no SS application, S5: Slope 5°+ SS application, S15: slope 15°+ SS application, and S25: slope 25°+ SS application.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/fd1b001a23b2a03f36ebdfb0.png"},{"id":51534549,"identity":"9ac657c2-45a8-4a81-bf8c-e1626bb91595","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47071,"visible":true,"origin":"","legend":"\u003cp\u003ea, b, c, d, e, and f are the cumulative migration amount (mg) and proportion (%) of Cd, Cr, Cu, Ni, Pb, and Zn with surface runoff (SSR), interflow (SSL), and sediment (SSD) in simulated rainfall events, respectively. CK: slope 15°+ no SS application, S5: slope 5°+ SS application, S15: slope 15°+ SS application, and S25: slope 25°+ SS application.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/4f50bdcbf0946a6ab6f94582.png"},{"id":51534550,"identity":"0fbb5bf1-bec4-4af1-8a4a-49c854abcead","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42180,"visible":true,"origin":"","legend":"\u003cp\u003ea, b, c, d, e, and f show the cumulative migration amount (mg) and proportion (%) of Cd, Cr, Cu, Ni, Pb, and Zn via surface runoff (NSR) and sediment (NSD) in natural rainfall events, respectively. CK-F: slope 15°+ no SS application, S7-F: slope 7°+ SS application, S15: slope 15°+ SS application, and S23: slope 23°+ SS application.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/a4e15ff18843a4a97a3076ed.png"},{"id":51534556,"identity":"84da3d56-d34b-4698-af63-bacc648b0dde","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50448,"visible":true,"origin":"","legend":"\u003cp\u003ea, b, c, d, e, and f show the total migration of Cd, Cr, Cu, Ni, Pb, and Zn via surface runoff for 10 simulated rainfall events and 10 forest natural rainfall events, respectively. CK: Slope 15°+ no SS application, S5: Slope 5°+ SS application, S15: Slope 15°+ SS application, and S25: slope 25°+ SS application (simulated rainfall experiment). CK-F: Slope 15°+ no SS application, S7-F: slope 7°+ SS application, S15-F: Slope 15°+ SS application, and S23-F: slope 23°+ SS application (natural rainfall experiment). The data in the figure are the average of three replicates, and different letters indicate significant differences between treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/ab1aa646090206a5592f0f72.png"},{"id":51534552,"identity":"11fc099c-30ea-4dfd-9a26-a355c2abf692","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":51997,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;a, b, c, d, e, and f show the total migration of Cd, Cr, Cu, Ni, Pb, and Zn via sediment for 10 simulated rainfall events and 10 forest natural rainfall events, respectively. CK-F: Slope 15°+ no SS application, S7-F: Slope 7°+ SS application, S15-F: Slope 15°+ SS application, and S23-F: slope 23°+ SS application (simulated rainfall experiment). CK-F: Slope 15°+ no SS application, S7-F: slope 7°+ SS application, S15-F: Slope 15°+ SS application, S23-F: slope 23°+ SS application (natural rainfall experiment). The data in the figure are the average of three replicates, and different letters indicate significant differences between treatments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/e38006438e372aa58e6fba5f.png"},{"id":51534554,"identity":"2570fb22-bacc-4edc-9505-a40703b6a6f3","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41734,"visible":true,"origin":"","legend":"\u003cp\u003ea and b show WQI values of surface runoff (SSR) and interflow (SSL) in simulated rainfall events, respectively. CK: Slope 15°+ no SS application, S5: Slope 5°+ SS application, S15: Slope 15°+ SS application, and S25: slope 25°+ SS application. WQI \u0026lt; 50, 50 ≤ WQI \u0026lt; 100, 100 ≤ WQI \u0026lt; 200, 200 ≤ WQI \u0026lt; 300, and WQI ≥ 300 correspond to excellent, good, poor, extremely poor, and unfit for drinking, respectively.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/78c1b74cf4ea804650373de3.png"},{"id":51534553,"identity":"9db1dcd4-a38b-4821-8c00-6b17108700ab","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27019,"visible":true,"origin":"","legend":"\u003cp\u003eWQI value of surface runoff (NSR) in 10 natural rainfall events (a). \u003cem\u003eRI\u003c/em\u003evalue of sediment (NSD) for two natural rainfall events in forest land (b). CK-F: Slope 15°+ no SS application, S7-F: slope 7°+ SS application, S15-F: slope 15°+ SS application, and S23: slope 23°+ SS application.\u003cem\u003e RI\u003c/em\u003e can be categorized as follows: low risk \u0026lt; 150; 150 ≤ moderate risks \u0026lt; 300; 300 ≤ considerable risks \u0026lt; 600; and high risk ≥ 600. WQI \u0026lt; 50, 50 ≤ WQI \u0026lt; 100, 100 ≤ WQI \u0026lt; 200, 200 ≤ WQI \u0026lt; 300, and WQI ≥ 300 correspond to excellent, good, poor, extremely poor, and unfit for drinking, respectively.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/9b4c3af0d49e9125e25fa86d.png"},{"id":51534555,"identity":"65938f3c-fdc8-4351-8c52-8b43801e47eb","added_by":"auto","created_at":"2024-02-23 09:01:24","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":22752,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of natural rainfall in the forest land plots during the experimental period.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/d3180fd604ead01bad69de66.png"},{"id":51535012,"identity":"63bedd24-92ba-48cb-845b-d87d942d6b65","added_by":"auto","created_at":"2024-02-23 09:17:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1587385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/64b3a884-b136-4539-858a-2989ece82625.pdf"},{"id":51534753,"identity":"4952f4d4-e6f2-4d2b-bd93-47ea31af2f17","added_by":"auto","created_at":"2024-02-23 09:09:24","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":380745,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3942079/v1/0fb4674121ac5df1af6fbeb3.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of woodland slope on heavy metal migration via surface runoff, interflow, and sediments and associated potential ecological risks following the application of sewage sludge","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUrbanization and global industrialization have led to increases in the production of sewage sludge (SS). SS production has thus become a major environmental issue\u003csup\u003e1-2\u003c/sup\u003e. Approximately 120 million tons of SS were generated by the major economies worldwide in 2019\u003csup\u003e3\u003c/sup\u003e, and the amount of SS generated by these economies is projected to be between 150 and 200 million tons in 2025. The composition of SS is highly complex, and previous studies have shown that SS contains various potentially harmful substances, including heavy metals (HMs), organic compounds, pathogens, and pharmaceutical residues\u003csup\u003e4\u003c/sup\u003e. In light of global limitations in the disposal capacity of SS, ensuring the safety and efficiency of the recycling and treatment of SS has become a major focus of research in the environmental science field.\u003c/p\u003e\n\u003cp\u003eSS has been applied as a fertilizer to woodland, which is a sustainable and effective treatment for recycling the abundant organic matter and nutrients in SS\u003csup\u003e5-8\u003c/sup\u003e. As most forest products do not directly enter the human food chain, SS can be applied to forest land to enhance soil fertility and tree growth. However, the risk of contamination with HM elements, such as copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), and nickel (Ni) in SS, poses a major challenge for its use in woodlands at large scales\u003csup\u003e9-12\u003c/sup\u003e. HMs in SS may migrate through surface runoff to neighboring woodlands, where they are absorbed by the soil and accumulate, and this can have deleterious effects on plants, animals, and forest ecosystems\u003csup\u003e13-14\u003c/sup\u003e. Rainfall can facilitate the infiltration of HMs from SS into the subsoil, which affects groundwater quality and can pollute neighboring water resources\u003csup\u003e15-17\u003c/sup\u003e. However, the application of SS to woodlands enhances the physicochemical properties of the soil, which reduces the risk of HM migration in runoff\u003csup\u003e18\u003c/sup\u003e. The high content of organic matter in SS improves soil structure and increases the stability and erosion resistance of soil aggregates, which promotes the immobilization of HMs in the soil and reduces their susceptibility to transport\u003csup\u003e19\u003c/sup\u003e. Galdos et al.\u003csup\u003e20\u003c/sup\u003e found that the application of SS increases the concentration of HMs in surface runoff and sediments and the migration of most HMs mainly occurs through sediments, indicating that the risk of HM migration via sediments is particularly high. Comprehensive assessments of the safety and sustainability of applying SS to woodlands are thus critically important.\u003c/p\u003e\n\u003cp\u003eRecent studies have shown that slope has a major effect on the risks of HM migration via runoff and sediment following the application of SS. Several factors contribute to the effect of slope on HM migration via these pathways. First, soil HMs are mainly in dissolved and particulate forms when they migrate with runoff during rainfall\u003csup\u003e21\u003c/sup\u003e. During this process, increases in slope slow the release of soil HMs via changes in the impact angle of raindrops and gravity, which can alter the form of HMs\u003csup\u003e22\u003c/sup\u003e. In SS, HMs are mainly in a granular state; however, during rainfall, changes in slope might promote the conversion of HMs into the dissolved state, which alters the total amount of HM migration and the relative contributions of different pathways (surface runoff, interflow, and sediments) to HM migration. Second, given that runoff and sediment are important carriers of HMs, runoff and sediment yield are key factors affecting the migration of HMs. Some studies have indicated that the residence time of precipitation on slopes decreases as the slope increases, which leads to decreases in the retention time of water in the soil, increases in the surface runoff on the slope\u003csup\u003e23\u003c/sup\u003e, and decreases in the yield of interflow\u003csup\u003e24\u003c/sup\u003e. However, Other studies have shown that runoff volume reaches a critical threshold as the slope increases; beyond this threshold, runoff volume decreases or remains constant\u003csup\u003e25\u003c/sup\u003e. Furthermore, several studies have found that changes in slope are the main driver of the flushing of sediments by rainfall runoff\u003csup\u003e26\u003c/sup\u003e. High slopes can increase erosion by promoting soil stripping or mitigating the protective effect of SS on the soil surface. In the early stages of flow production, cumulative sediment migration increases with cumulative runoff, and transport-limited erosion is the dominant sediment erosion process. However, segregation-limited erosion becomes more prevalent in subsequent rainfall events\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHMs can be transported via surface runoff, interflow, and sediment; however, few studies have examined the relative contributions of all three of these pathways to the transport of HMs. Only a few studies have explored the relative importance of all three of these pathways in experiments aimed at evaluating soil erosion and nutrient loss from agricultural fields\u003csup\u003e28-29\u003c/sup\u003e. Few studies have clarified the effects of slope on the migration of HMs via surface runoff, interflow, and sediments during the application of SS to woodland. Estimates of the magnitude of the potential ecological risks of HM migration via runoff might vary depending on the methodology of rainfall experiments. The effects of slope on the migration of HMs in runoff have generally been estimated via simulated rainfall experiments or natural rainfall experiments in the field\u003csup\u003e30-31\u003c/sup\u003e. However, whether, and to what extent, the risks of HM migration via runoff and sediment estimated using these two experimental approaches varies remains unclear. Here, we studied the migration of the HMs in SS via surface runoff (including surface flow and loam center flow), interflow, and sediment on different slopes simultaneously using an indoor simulated rainfall experiment and natural rainfall experiment in the field. The indoor simulated rainfall experiment was conducted in a homemade steel tank, and the natural rainfall experiment was conducted in a runoff plot in \u003cem\u003eEucalyptus\u003c/em\u003e woodland. We then evaluated (1) the effects of slope on runoff water and sediment yield, (2) differences in the migration patterns of HMs in surface runoff water, interflow, and sediments on different slopes, and (3) the potential ecological risks of HM migration associated with the application of SS to woodland. Overall, the main aim of this study was to identify the optimal slope for enhancing the efficacy and safety of SS application.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eRunoff and sediment yield in simulated rainfall and natural rainfall experiments\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRunoff and sediment yield in the simulated rainfall experiment.\u003c/em\u003e Slope had a major effect on surface runoff, interflow, and sediment yield. Surface runoff yield in the simulated rainfall events was highest in the S25 treatment, followed by the CK, S15, and S5 treatments. Surface runoff yield for all rainfall events in the S15 and CK treatments did not significantly differ (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The mean interflow yield was 132.17% and 45.14% higher in the S5 treatment than in the S15 and S25 treatments, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The interflow yield was significantly higher in the S5 treatment than in the S15 and S25 treatments from the fifth (R5) to the tenth (R10) rainfall event. The sediment yield was significantly lower in the S5 treatment than in the other treatments for all rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Overall, the average yield of surface runoff increased as the slope increased. The average interflow yield was highest in the S5 treatment, which was significantly higher than in the S15 and S25 treatments. The average sediment yield in the S5 was significantly lower than in the other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRunoff and sediment yield in the natural rainfall experiment.\u003c/em\u003e The average surface runoff yield in the natural rainfall experiment increased as the slope increased, and the surface runoff yield in S15-F was significantly lower than in the CK-F for R3, R4, R5, R6, R7, and R10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The sediment yield increased with the slope for both R4 and R5. The sediment yield for R5 was significantly lower in the S15-F treatment than in the CK-F treatment. No sediment was collected in the R1\u0026ndash;R3 and R6\u0026ndash;R10 rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The average surface runoff and sediment yield increased as the slope increased, with the mean surface runoff yield and sediment yield in S15-F 25.36% and 20.25% lower than CK-F, respectively, and these differences were significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCumulative amount of HM migration and the relative contributions of different pathways to HM migration in the simulated rainfall and natural rainfall experiments\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCumulative amount of HM migration and the relative contributions of different pathways to HM migration in the simulated rainfall experiment.\u003c/em\u003e The cumulative amount of HM migration via surface runoff, interflow, and sediment, as well as the relative contributions of the different pathways to HM migration, were affected by the slope (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The cumulative migration amount for each HM in the sediments gradually increased as the slope increased. When the slope increased from 5\u0026deg; to 25\u0026deg;, the loss of HMs in sediments increased by 9.79\u0026ndash;27.28%, and the loss of HMs in surface runoff and interflow decreased by 5.47\u0026ndash;16.80% and 1.11\u0026ndash;16.25%, respectively. Sediment was the main route for the migration of Cd, Cr, Cu, Ni, Pb, and Zn, and migration of these HMs via sediment was an order of magnitude greater than that via surface runoff and interflow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eCumulative amount of HM migration and the relative contributions of different pathways to the migration of HMs in the natural rainfall experiment.\u003c/em\u003e Consistent with the results of the simulated rainfall experiment, the cumulative migration of HMs with surface runoff increased as the slope increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). HM migration via sediment increased as the slope increased in the natural rainfall experiment, and this was inconsistent with the results of the simulated rainfall experiment. Nevertheless, sediment was still the main pathway of Cd, Cr, Cu, Ni, Pb, and Zn migration, and the migration of these HMs via sediment was an order of magnitude greater than that via surface runoff. In each treatment, the proportion of HM migration via sediment was greater than 75%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eComparison of the total migration of HMs on different slopes in the simulated rainfall and natural rainfall experiments.\u003c/em\u003e The total migration of HMs in surface runoff in the simulated rainfall and natural rainfall experiments varied with slope. Overall, the total migration of HMs in the natural rainfall experiment increased significantly as the slope increased, while the total amount of HMs migration in the simulated rainfall experiment varied with slope and HMs. The total migration of Ni, Pb, and Cu was similar in the simulated indoor rainfall and natural rainfall experiments, and the migration of these elements significantly increased as the slope increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The total migration of Cr and Cd first increased and then decreased as the slope increased, and the total migration of Zn first decreased and then increased as the slope increased in both the simulated rainfall and natural rainfall experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe total migration of HMs via sediment increased significantly as the slope increased in the simulated indoor rainfall experiment. The total migration of HMs varied with slope in the natural rainfall experiment, and variation in the amount of HMs with slope differed among HMs. The total migration of Cd, Cr, Ni, and Pb increased as the slope increased in the natural rainfall and indoor simulated rainfall experiments. However, the total migration of Cu and Zn first increased and then decreased as the slope increased, and this was inconsistent with the results in the indoor simulated rainfall experiment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePotential ecological risks of HM migration via runoff and sediment in the simulated rainfall and natural rainfall experiments\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePotential ecological risks of HM migration via runoff and sediment in the indoor simulated rainfall experiment.\u003c/em\u003e The WQI was used to evaluate changes in the water quality of surface runoff and interflow during 10 simulated rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In the 10 rainfall events, the water quality of the surface runoff in the CK and S5 treatments was \"excellent.\" In R1\u0026ndash;R2, the surface runoff water quality in the S15 treatment was \"excellent\", and it changed from \"excellent\" to \"good\" as the number of simulated rainfall events increased. In the S25 treatment, the surface runoff water quality was \"poor\" for R4, but \"good\" for the other nine rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). The interflow water quality in CK and S5 changed from \"good\" to \"poor\" and that in S15 and S25 changed from \"good\" to \"very poor\" as number of simulated rainfall events increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe potential ecological risk assessment revealed (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that the \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e of Cd in S15 and S25 was \"considerable\" in sediments with particle sizes\u0026thinsp;\u0026gt;\u0026thinsp;0.05 mm and \"high\" when particle sizes were \u0026le;\u0026thinsp;0.05 mm. The \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e of Cd in the S5 treatment was \"high\" when particle sizes were \u0026le;\u0026thinsp;0.05 mm and \"low\" in sediments with particle sizes\u0026thinsp;\u0026gt;\u0026thinsp;0.05 mm. The potential ecological risks of Cd for the four sediment particle sizes were 74.05\u0026ndash;112.85% higher in the S15 treatment than in the CK. The \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e of the other five HMs for the four particle sizes in the different treatments was \"low.\"\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eRI\u003c/em\u003e value in the sediment depends on the slope and particle size of the sediment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the simulated rainfall experiment, the \u003cem\u003eRI\u003c/em\u003e values of the sediments for the four particle sizes were highest in the S15 treatment. The \u003cem\u003eRI\u003c/em\u003e values of sediments were highest and lowest in different treatments for particle sizes\u0026thinsp;\u0026le;\u0026thinsp;0.05 mm and \u0026gt;\u0026thinsp;1 mm, respectively. \u003cem\u003eRI\u003c/em\u003e values of sediments increased as the particle size decreased. Generally, when the sediment particle size was \u0026le;\u0026thinsp;0.25 mm, the \u003cem\u003eRI\u003c/em\u003e was \"low\"; when the sediment particle size was 0.05\u0026thinsp;~\u0026thinsp;0.25 mm, the \u003cem\u003eRI\u003c/em\u003e of the CK and S5 treatment was \"low\", and the \u003cem\u003eRI\u003c/em\u003e of the S15 and S25 treatments was \" moderate\"; when the sediment particle size was \u0026le;\u0026thinsp;0.05 mm, the \u003cem\u003eRI\u003c/em\u003e of the CK was \"low.\" The \u003cem\u003eRI\u003c/em\u003e of the other three treatments was \"moderate.\" The potential ecological risks of each treatment mainly stemmed from Cd (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe potential ecological risks of HMs in sediment of the four particle sizes on different slopes in the simulated rainfall experiment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParticle sizes\u003c/p\u003e \u003cp\u003e(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003ePotential ecological risk\u003c/p\u003e \u003cp\u003ecoefficients (\u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e) of single elements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePotential ecological risk index (\u003cem\u003eRI\u003c/em\u003e) of multiple elements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e122.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.25\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e67.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e135.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e107.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.05\u0026ndash;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e196.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e151.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e122.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e200.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e204.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e177.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e201.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: Data in the table are the average of three replicates. CK: Slope 15\u0026deg;+ no SS application, S5: Slope 5\u0026deg;+ SS application, S15: Slope 15\u0026deg;+ SS application, and S25: slope 25\u0026deg;+ SS application. \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e was classified as follows: low\u0026thinsp;\u0026lt;\u0026thinsp;40; 40\u0026thinsp;\u0026le;\u0026thinsp;moderate\u0026thinsp;\u0026lt;\u0026thinsp;80; 80\u0026thinsp;\u0026le;\u0026thinsp;considerable\u0026thinsp;\u0026lt;\u0026thinsp;160; and 160\u0026thinsp;\u0026le;\u0026thinsp;high\u0026thinsp;\u0026lt;\u0026thinsp;320. \u003cem\u003eRI\u003c/em\u003e was classified as follows: low risk\u0026thinsp;\u0026lt;\u0026thinsp;150; 150\u0026thinsp;\u0026le;\u0026thinsp;moderate risks\u0026thinsp;\u0026lt;\u0026thinsp;300; 300\u0026thinsp;\u0026le;\u0026thinsp;considerable risks\u0026thinsp;\u0026lt;\u0026thinsp;600; and high risk\u0026thinsp;\u0026ge;\u0026thinsp;600.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003ePotential ecological risks of HM migration via runoff and sediment in the natural rainfall experiment.\u003c/em\u003e No significant differences in the WQI of the surface runoff of each treatment were observed for R1\u0026ndash;R3. In R4, the WQI of surface runoff was 61.82%, 16.57%, and 92.92% higher in the S7-F, S15-F, and S23-F treatments than in the R3 treatment, respectively. The WQI of each treatment decreased from R5 to R10, and the water quality was \"excellent\" for R8\u0026ndash;R10 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Sediment was collected during only R4 and R5. The potential ecological risks of the three treatments with SS application were significantly higher than CK-F. Consistent with the results of the simulated rainfall experiment, Cd was the main source of potential ecological risks in sediment in the natural rainfall experiment, and the E\u003csub\u003er\u003c/sub\u003e value for Cd was significantly higher than that for the other five HMs. The RI values of the sediments for R4 and R5 decreased as the slope increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of SS as fertilizer for forest soil is a promising SS treatment method. However, HMs can migrate to the external environment via surface runoff, interflow, and sediment following SS application. The risks of HM migration might be particularly high in hot and rainy southern forest regions. Evaluating the risks of HM migration via these different pathways is critically important for ensuring the safety of SS application\u003csup\u003e32\u0026ndash;33\u003c/sup\u003e. SS application to forest land might alter the erosion effect of rainfall on soil, and the amount of HM migration via surface runoff, interflow, and sediment varies with slope\u003csup\u003e20,24\u003c/sup\u003e. Indoor simulated rainfall experiments have been widely conducted because variables can be precisely controlled in such experiments, and this helps clarify the mechanism underlying the effect of slope on soil and water erosion\u003csup\u003e25\u003c/sup\u003e. However, the complexity of natural systems, including variation in rainfall patterns, biological characteristics, and soil structure in forest ecosystems, might affect patterns of HM migration inferred in natural rainfall experiments. In our study, the runoff and sediment yields differed in the simulated rainfall and natural rainfall experiments. The migration and potential ecological risks of HMs in runoff and sediment also varied with the slope. The migration and potential ecological risks of HMs in surface runoff and sediment in the indoor simulated rainfall and natural rainfall experiments also differed.\u003c/p\u003e \u003cp\u003eMost studies have shown that surface runoff can form more rapidly on steeper slopes, but steeper slopes result in the generation of less interflow\u003csup\u003e24\u003c/sup\u003e. The short residence time of precipitation on slopes reduces the retention time of water in the soil; this in turn reduces the opportunity for water infiltration and increases surface runoff\u003csup\u003e23\u003c/sup\u003e. The results of the indoor simulated rainfall experiments showed that the surface runoff yield increased as the slope increased following SS application. We also found that changes in surface runoff and sediment yield with slope were similar in the natural rainfall and indoor simulated rainfall experiments. This indicates that slope has a major effect on the transport of runoff, even in natural woodlands. Season is one of the main factors affecting surface runoff and sediment yield in natural rainfall experiments in woodlands. In the natural rainfall experiment, 70% of the rainfall events occurred in the summer (from May to August). Furthermore, patterns of variation in rainfall and surface runoff were similar for the 10 natural rainfall events. These findings suggest that surface runoff yield in natural systems is strongly affected by season and the amount of rainfall.We found that the interflow yield was highest in the S5 and lowest in S15 in the simulated rainfall experiment. Zhang et al.\u003csup\u003e25\u003c/sup\u003e showed that permeability decreases as the slope increases in an indoor simulated rainfall experiment. Other studies have shown that the soil infiltration rate decreases significantly as the slope increases when the slope is less than 18\u0026deg;. Slope has a weak effect on the infiltration when it is greater than 18\u0026deg;\u003csup\u003e34\u003c/sup\u003e. This is consistent with the findings of Wu et al.\u003csup\u003e23\u003c/sup\u003e showing that there is a critical slope which makes the runoff reach the maximum. The relationship between slope and infiltration changes as the slope increases\u003csup\u003e24\u003c/sup\u003e. When the slope is 15\u0026deg;, the splashing effect of rainfall on topsoil has a major effect on the physical properties of soil, alters the roughness of topsoil, promotes soil compaction, and reduces the interflow yield. This might also explain why the interflow yield was significantly lower in the S15 treatment than in the S5 and S25 treatments.\u003c/p\u003e \u003cp\u003eSediment production was lower in the S15 treatment than in the CK in the simulated rainfall experiment. This stems from the fact that the large particles prevent the erosion of SS by runoff because they form a protective layer that prevents the erosion of soil particles by precipitation\u003csup\u003e35\u003c/sup\u003e. The sediment yield increased as the slope increased. This might be explained by the small area of rainfall in our experiment; furthermore, the surface water flow velocity and the shear stress increase as the slope increases\u003csup\u003e36\u003c/sup\u003e. However, the retention time of surface water on the slope decreases as the slope increases, which weakens the protective effect of SS on the soil and increases the strength of the splashing effect of rainfall on the topsoil\u003csup\u003e37\u003c/sup\u003e; this results in the activation and transport of more soil particles. However, the sediment yield was 255.92% and 258.82% lower in the S5 treatment than in the S15 and S25 treatments, respectively. Wang et al.\u003csup\u003e38\u003c/sup\u003e found that surface runoff flow is unable to carry or separate sediments with larger grain sizes on low slopes, and this results in a drastic decrease in sediment yield. This can be explained by the fact that large particle sizes experience greater resistance in raindrops and surface flow, which inhibits their removal. Specifically, the sediment yield first increased and then decreased, suggesting that transport-limited erosion was the dominant erosion process in the early rainfall events\u003csup\u003e39\u003c/sup\u003e. The effect of raindrops on flow transport might also contribute to the mechanism restricting sediment transport\u003csup\u003e27\u003c/sup\u003e. As the number of rainfall events increased, segregation-limited erosion became the dominant erosion process. These results are consistent with those of Ran et al. \u003csup\u003e40\u003c/sup\u003e and Shi et al.\u003csup\u003e27\u003c/sup\u003e. We found that patterns of variation in sediment and surface runoff yield were similar in the natural rainfall and simulated indoor rainfall experiments, and both sediment and surface runoff yield increased as the slope increased. Large sediment particles cannot be activated when the intensity of rainfall is low because of the weak driving force\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHMs migrate with runoff via two pathways during rainfall: dissolved state migration, wherein HMs migrate with runoff in their molecular and ionic states, and particulate state migration, wherein HMs adsorb and bind to the surface of sediment particles in their inorganic and organic states and migrate with the sediment\u003csup\u003e21,41\u003c/sup\u003e. Slope did not have a significant effect on the cumulative migration of these metals in runoff; this might be related to several factors. First, the duration and frequency of rainfall are typically higher under natural rainfall conditions compared with indoor simulated rainfall conditions, and prolonged and high-frequency rainfall might promote the leaching of HMs from the soil\u003csup\u003e42\u003c/sup\u003e, especially on steeper slopes. The structure and composition of the soil as well as the activities of the plant's root system in natural systems might also affect the experimental results\u003csup\u003e10\u003c/sup\u003e. Interflow makes a non-negligible contribution to the horizontal migration of HMs\u003csup\u003e43\u003c/sup\u003e, and the release of HMs from interflow stems from interactions between the soil liquid-phase and solid-phase matrices, including processes such as adsorption/desorption and dissolution/precipitation, as well as the complexation of ions in soil\u003csup\u003e2\u003c/sup\u003e. Some previous studies\u003csup\u003e44\u003c/sup\u003e have shown that the mobility of solutes in surface runoff increases as the slope increases, and this promotes the dilution and transport of HMs in runoff. The interflow yield was highest in the S5 treatment at slower flow rates. This likely stemmed from the greater amount of organic matter derived from SS in the S5 treatment, which resulted in anoxic/anaerobic conditions, greatly decreased the redox potential at the soil surface, and facilitated the dissolution and infiltration of HMs in the soil\u003csup\u003e7,45\u003c/sup\u003e. By contrast, Cr and Pb migration via interflow was significantly lower than the migration of these elements via surface runoff, indicating the presence of a barrier to the infiltration of Cr and Pb ions. Cr and Pb have a high cation exchange capacity and strong affinity for organic compounds\u003csup\u003e46\u0026ndash;47\u003c/sup\u003e. Cr and Pb ions can be adsorbed by particulate organic matter of different sizes through hydroxyl and carboxyl groups\u003csup\u003e48\u003c/sup\u003e; Cr and Pb ions are associated with the large amount of organic matter in SS and form stable chelating substances that are immobilized in the soil\u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHMs in surface soils are mainly present in granular form during erosion, and the amount of migration is positively related to the slope\u003csup\u003e37\u003c/sup\u003e. As the slope increased, more sediment particles were entrained and washed away in the runoff, which led to an increase in the cumulative migration of HMs in the sediments. The cumulative amount of HM migration in the sediments accounted for more than 65% of the total amount of HM migration, indicating that sediment was the main pathway of HM migration; this is consistent with the findings of Galdos et al.\u003csup\u003e20\u003c/sup\u003e and Huang et al.\u003csup\u003e42\u003c/sup\u003e. Although the total amount of each HM transported in both pathways (surface runoff and sediment) increased as the slope increased, the percentage of each HM in sediment and surface runoff decreased and increased, respectively; this finding was inconsistent with the results of the indoor simulated rainfall experiment. Rainfall events in forests typically last for several hours or even days; consequently, the surface soil in the natural rainfall experiment could have been saturated, which would increase the release of HMs via surface runoff. In addition, the splashing of raindrops has been shown to increase the dispersion and transport of soil particles by breaking up and moving aggregates and disrupting soil structure\u003csup\u003e50\u003c/sup\u003e. The presence of a forest canopy in forested areas mitigates the erosion of soil particles by the splashing of raindrops, which slows the transport of HMs via sediments.\u003c/p\u003e \u003cp\u003eWe found that the risks of HM contamination in surface runoff water were higher when SS was applied compared with the CK; this is consistent with the results of most studies\u003csup\u003e13\u003c/sup\u003e. Furthermore, the water quality in surface runoff from the CK and S5 treatment was \"excellent\" during the 10 rainfall events, and there was virtually no risk of contamination in surface runoff on lower slopes. The WQI in the S15 and S25 treatments was \"good\" for most rainfall events, and only poor for the fourth rainfall event in the S25 treatment. This suggests that the risk of surface runoff pollution increases as the slope increases; overall, there was no risk of surface runoff water pollution. No significant change was observed in the risk of surface runoff water pollution as the number of rainfall events increased. Previous studies have shown that the application of sludge will lead to HMS pollution levels in farmland runoff exceeding Class IV water quality standards of China's Surface Water Environmental Quality Standards\u003csup\u003e20\u003c/sup\u003e. This difference might stem from variation in the soil and SS used in the different experiments. Another study has shown that the risk of HM transport is significantly increased in sandy soils\u003csup\u003e51\u003c/sup\u003e, and the risk of HM transport is reduced in lateritic soils rich in iron and aluminium oxides. We found that the pollution risk of surface runoff increased as the slope increased for 10 natural rainfall events, and the WQI was \"poor\" only in R4 and R5 of the S25 treatment; this was consistent with the results of the indoor simulated rainfall experiment.\u003c/p\u003e \u003cp\u003eSediment was the main pathway of HM transport, and the magnitude of HM loss was an order of magnitude greater via sediment than via surface runoff and interflow. Previous studies have shown that the adsorption/desorption capacities of aggregates might vary for different particle sizes because of differences in soil physicochemical properties\u003csup\u003e42\u003c/sup\u003e. The \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003er\u003c/em\u003e\u003c/sub\u003e values of all HM elements were \u0026ldquo;low,\u0026rdquo; with the exception of that for Cd. In the indoor simulated rainfall experiment, the contribution of Cd to \u003cem\u003eRI\u003c/em\u003e was 75% or more in all treatments. Other studies have also found Cd to be the main element contributing to ecological risks, and this might stem from the fact that it has the highest toxicity response factor among all metals tested\u003csup\u003e1\u0026ndash;2,52\u003c/sup\u003e. The risk of sediment contamination was highest for two particle sizes: 0.05\u0026ndash;0.25 mm and \u0026le;\u0026thinsp;0.05 mm. Previous studies have shown that fine particles typically have a higher surface area and pore volume compared with large particles, and this promotes the strong adsorption of HMs. Fine particles can be easily mobilized by water flow, and the transport of larger particles (\u0026gt;\u0026thinsp;0.25 mm) can only be initiated at higher flow rates\u003csup\u003e53\u003c/sup\u003e. Nevertheless, sediment was the main pathway for the migration of Cd, Cr, Cu, Ni, Pb, and Zn, and the migration of these HMs via sediment was an order of magnitude greater than that via surface runoff. Under laboratory conditions, predictions of HM transport from simulated rainfall experiments can lead to a range of conclusions based on the assumptions made\u003csup\u003e54\u003c/sup\u003e. However, the environmental complexity and variability of forests cannot be easily accounted for in indoor experiments. The results of the simulated and natural rainfall experiments were not completely consistent for different slopes. Thus, achieving an improved understanding of variation in HMs and their bioavailability in woodlands will require comparisons of the results of laboratory and field studies; these studies will also enhance our ability to investigate the mobility of HMs in natural woodlands using laboratory simulation experiments\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSlope had a significant effect on the runoff and sediment yield. Surface runoff and sediment yields increased as the slope increased in the simulated rainfall experiment, and surface runoff and sediment yields were 11.51% and 258.82% higher in the S25 treatment than in the S5 treatment, respectively. A similar pattern was observed in the natural rainfall experiment, as the surface runoff and sediment yield also increased with slope. Sediment was the main pathway for HM migration in all treatments, and more than 68.70% of the total migration of Cd, Cr, Cu, Pb, and Zn was via sediment. In the simulated rainfall experiment, the proportion of HMs transported via sediments increased as the slope increased, and the proportion of HMs transported via surface runoff decreased as the slope increased. However, the migration of HMs in surface runoff and sediment increased and decreased, respectively, as the slope increased in the natural rainfall experiment. The application of suitable amounts of SS on lower slopes (5\u0026deg;) reduced runoff and soil erosion and did not significantly contribute to the risk of HM contamination in the simulated rainfall experiment. However, the risk of water quality contamination in the interflow was higher in the S15 and S25 treatments during later rainfall events. In the simulated rainfall experiment, the risk of contamination was only observed for sediments with a grain size\u0026thinsp;\u0026le;\u0026thinsp;0.25 mm. Our findings revealed significant differences in the effect of slope on the risk of HM migration following SS application on forest land. However, more studies are needed to clarify the factors driving differences in the results of simulated and natural rainfall experiments, as well as their underlying mechanisms, in planted forests in the future due to the high complexity of the hydrological and surface soil conditions in the field.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eSS and soil properties.\u003c/b\u003e SS was obtained from Guangzhou Water Purification Co., Ltd. in Guangdong Province, China. The water content of the treated SS was approximately 40%, and the HM content of the SS was high. SS was anaerobically composted for 60 d prior to its use. The SS was then air-dried, sieved through a 10 mm nylon sieve, and thoroughly mixed. The soil was collected from the Eucalyptus forest in Dalingshan Forest Park, Dongguan City, Guangdong Province, China (22\u0026deg;51\u0026prime;18.99\"N, 113\u0026deg;45\u0026prime;22.44\"E). The study area has a typical subtropical monsoon climate, with an average annual temperature of 23.3\u0026deg;C and annual precipitation of 2,042.6 mm. The soil is granite red soil and has a sandy loam texture (56.7% sand, 27.7% silt, and 15.6% clay). In November 2020, soil samples were collected from the 0\u0026ndash;10 cm, 10\u0026ndash;20 cm, and 20\u0026ndash;30 cm layers. In the laboratory, soils were sampled using a ring knife, and the bulk weight was measured. The soils were air-dried at room temperature for two weeks, passed through a 10 mm nylon sieve, and mixed thoroughly. The soil and SS samples were then ground and sieved (\u0026lt;\u0026thinsp;0.15 mm), and the content of organic matter, Cd, chromium (Cr), Cu, Ni, Pb, and Zn, as well as the pH were measured (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic properties of the SS and soil\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSewage sludge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganic matter (g\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e204.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e114.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e54.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e475.55\u0026thinsp;\u0026plusmn;\u0026thinsp;6.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e120.55\u0026thinsp;\u0026plusmn;\u0026thinsp;8.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e41.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e60.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e94.40\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e72.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e39.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd (mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eExperimental design of the indoor rainfall simulations.\u003c/b\u003e The steel flumes comprise a steel tank and plastic external baffles. They were rectangular and had the following dimensions: 1.0 m \u0026times; 0.3 m \u0026times; 0.4 m. The length of the flumes of the soil tank was adjusted for different slopes. The internal part of the steel flumes comprised the soil sample receiving space with a base area of 0.3 m\u003csup\u003e2\u003c/sup\u003e (1.0 m \u0026times; 0.3 m) and a depth of 0.4 m. Plastic baffles (height of 0.3 m) were fitted around and on top of the steel flumes to prevent soil and water spillage. The bottom of the steel flumes was sealed with a PVC sheet, and the remaining small holes allowed the interflow to drain. The bottom was lined with quartz sand with a thickness of 3 cm, and it was washed with dilute nitric acid and lined with a 100-mesh screen. A V-shaped water-measuring weir was installed at the lower end of the rack to convey surface runoff water through a plastic pipe to a plastic collection bucket. The slope of the rack could be adjusted from 0\u0026deg; to 30\u0026deg;. To enhance the realism of the rainfall simulations, a 2 cm seepage space was installed at the bottom of the steel rack. The soil was raised 2 cm using a plastic mat. The interflow that drained from the soil and flumes was collected and placed at the bottom of the steel rack.\u003c/p\u003e \u003cp\u003eSimulated rainfall was used to evaluate the effect of slope on soil erosion and the migration of Cd, Cr, Cu, Ni, Pb, and Zn in surface runoff, interflow, and sediments. The experiment comprised four treatments, S5, S15, and S25 (in which SS was applied and the slope of the soil flume was 5\u0026deg;, 15\u0026deg;, and 25\u0026deg;, respectively) and CK (in which no SS was applied and the slope of the soil flume was 15\u0026deg;) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Given that the slope altered the contact area between precipitation and the surface of soil troughs, 1.80 kg, 1.87 kg, and 1.98 kg of SS were applied in the S5, S15, and S25 treatments,which corresponds to 60 tons ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The SS was thoroughly mixed with the surface soil in the 0\u0026ndash;10 cm layer (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Each treatment has three replicates. The simulated rainfall water was municipal tap water. The chemical properties of the water were as follows: pH, 7.4; Cd\u0026thinsp;\u0026lt;\u0026thinsp;4 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Cr\u0026thinsp;\u0026lt;\u0026thinsp;4 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Cu\u0026thinsp;\u0026lt;\u0026thinsp;9 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Pb\u0026thinsp;\u0026lt;\u0026thinsp;0.07 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; and Zn\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026micro;g\u0026middot;L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperimental design\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIndoor simulated rainfall experiment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eNatural rainfall experiment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlope (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmount of SS applied (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSlope (\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAmount of SS applied (kg)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCK-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS7-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS15-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e186.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS23-F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e198.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe simulated rainfall experiment was performed in the simulated rainfall hall of the Red Soil Erosion and Flow Hydraulics Laboratory, Institute of Ecology, Environment and Soil, Guangdong Academy of Sciences, China. The rainfall system comprised a variable power pump (0\u0026ndash;60 W with a water volume of 0\u0026ndash;5 L\u0026middot;min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and a water tank (1.2 m \u0026times; 0.4 m \u0026times; 0.12 m) with 150 holes (0.8 mm diameter) at the bottom. Water was pumped into the tank and dripped down through the holes at the bottom of the tank to form continuous and steady rainfall. The rainfall intensity was set to 120 mm\u0026middot;h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to simulate heavy rainfall and severe soil erosion, which is consistent with the typical climatic characteristics of South China. Before the formal rainfall experiment, the rainfall intensity was adjusted to rainfall uniformity\u0026thinsp;\u0026gt;\u0026thinsp;95%. A total of 10 simulated rainfall events were performed over a two-week interval between each rainfall event. The duration of each rainfall event was 60 min, and the total rainfall of the 10 simulations was equivalent to 3/4 of the rainfall in Guangzhou. The simulated rainfall experiment was initiated in March 2022 and ended in December 2022. The simulated rainfall experiment was carried out for two weeks, and there was a two-week interval between each rainfall event.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDesign of the natural rainfall experiment.\u003c/b\u003e The study was conducted in a \u003cem\u003eEucalyptus\u003c/em\u003e plantation in Dalingshan Forest Park, Dongguan City, Guangdong Province, China. The \u003cem\u003eEucalyptus\u003c/em\u003e forest was planted in 1998 with a row spacing of 3 m \u0026times; 2 m. The runoff plots were arranged according to the actual contours of the site, and the woodland slopes ranged from 5\u0026deg; to 30\u0026deg;. The runoff plots were sloping woodlands with similar characteristics except slope to ensure that rainfall conditions were consistent among plots. Each runoff plot has a horizontal projected area of 20 m\u0026sup2; (10 m \u0026times; 2 m) in the direction of the slope. At the lower end of the runoff plot, there was a collection pond measuring 1 m \u0026times; 1 m \u0026times; 1 m, which was connected to the runoff plot. The pond was elevated 20 cm above the ground and covered with PVC plastic sheets to prevent rainwater from entering.\u003c/p\u003e \u003cp\u003eThe natural rainfall experiment comprised four treatments, S7-F, S15-F, and S23-F (SS was applied and their slopes were 7\u0026deg;, 15\u0026deg;, and 23\u0026deg;, respectively) and CK-F (in which the slope was 15\u0026deg;, and no SS was applied) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Each treatment has three replicates. The amount of rainfall of the 10 natural rainfall events in the woodland and the amount of rainfall during the 48 h prior to sampling are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSample collection and analysis.\u003c/b\u003e In the indoor rainfall simulations, surface runoff and interflow were collected using two containers. Only surface runoff was collected in the natural rainfall experiment. When the surface runoff or interflow stopped, measurements were taken. After the collection containers were left to stand for 30 min, the suspensions were collected individually into 200 mL plastic bottles, and 1 mL of 1:10 nitric acid solution was added to inhibit microbial activity and prevent the precipitation of HM ions. The processed samples were stored at 4\u0026deg;C. Using the wet sieving method, sediments precipitated in runoff collection containers during natural and simulated rainfall events were filtered and classified into four different types of agglomerates with different particle sizes: \u0026gt;1 mm (large macro-aggregates), 1\u0026ndash;0.25 mm (small macro-aggregates), 0.25\u0026ndash;0.05 mm (large micro-aggregates), and \u0026lt;\u0026thinsp;0.05 mm (small micro-aggregates)\u003csup\u003e42,55\u003c/sup\u003e. Because the amount of sediment produced by the 10th rainfall is very small, the collection of particle size sediment samples was not carried out, and a total of 9 runoff sediment samples were collected in the simulative rainfall experiment. Sediment samples were only washed out and collected during the 4th and 5th natural rainfall experiment. Half of the agglomerates were dried, weighed, and milled separately, and the other half was mixed. Surface runoff, interflow, and sediment samples were digested using the triple-acid method (nitric-hydrofluoric-perchloric acid), and a plasma atomic emission spectrometer (Leeman Prodigy7 model) was used to determine the concentrations of Cd, Cr, Cu, Ni, Pb, and Zn.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCalculation of indicators.\u003c/b\u003e Three indicators, surface runoff yield (L), interflow yield (L), and sediment yield (g), were used to evaluate the effects of different slopes on runoff and soil erosion. Cumulative lift volume (CLV) was used to evaluate the transport of HMs via three pathways, surface runoff, interflow, and sediment, for all rainfall events. CLV was calculated using equations (\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and (\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{L}\\text{V} = \\text{S}\\text{S}\\text{R} (\\text{N}\\text{S}\\text{R}/\\text{S}\\text{S}\\text{L}/\\text{N}\\text{S}\\text{L}/\\text{S}\\text{S}\\text{D}/\\text{N}\\text{S}\\text{D}) \\times \\text{N}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{C}\\text{L}{\\text{V}}_{n} = \\text{L}{\\text{V}}_{1} + \\text{L}{\\text{V}}_{2} \\dots +\\text{L}{\\text{V}}_{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere N is the concentration of HMs in surface runoff, interflow, or sediments, which is multiplied by the surface runoff yield (SSR/NSR), interflow yield (SSL/NSL), or sediment yield (SSD/NSD), respectively, for each simulated or natural rainfall event. These values are used to calculate the amount of HM migration via the corresponding pathways (LV). CLV\u0026#119899; was the cumulative migration of HMs in surface runoff, interflow, and sediments during 1st\u0026thinsp;~\u0026thinsp;n\u003csup\u003eth\u003c/sup\u003e rainfall events.\u003c/p\u003e \u003cp\u003eThe water quality index (WQI) describes water quality via several water quality parameters \u003csup\u003e56\u003c/sup\u003e. We used WQI to evaluate the risk of HM pollution in surface runoff and interflow. WQI was calculated using the following formula:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$WQI=\\sum \\left[{W}_{i}\\times \\left(\\frac{{C}_{i}}{{S}_{i}}\\right)\\right]\\times 100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the WQI formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{w}}_{i}/\\sum {w}_{i}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({W}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the weight of each HM, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum {w}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the sum of the weights of all HMs. The weights of Cd, Cr, Cu, Ni, Pb, and Zn were defined as 5, 5, 2, 4, 5, and 1, respectively, following a previous study\u003csup\u003e57\u003c/sup\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i}\\)\u003c/span\u003e\u003c/span\u003e corresponds to HM concentrations, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({S}_{i}\\)\u003c/span\u003e\u003c/span\u003e indicates the Chinese drinking water standard (Chu et al., 2023). WQI was classified as excellent(WQI\u0026thinsp;\u0026lt;\u0026thinsp;50), good(50\u0026thinsp;\u0026le;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;100), poor(100\u0026thinsp;\u0026le;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;200), very poor( 200\u0026thinsp;\u0026le;\u0026thinsp;WQI\u0026thinsp;\u0026lt;\u0026thinsp;300), and unfit for drinking(WQI\u0026thinsp;\u0026ge;\u0026thinsp;300)\u003csup\u003e57\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe potential ecological risk index (\u003cem\u003eRI\u003c/em\u003e) was used to evaluate the ecological risk level of HMs in soil and evaluate the combined toxicity of HMs\u003csup\u003e58\u003c/sup\u003e. \u003cem\u003eRI\u003c/em\u003e can be calculated using equations (\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and (\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e);\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${E}_{i}={T}_{i}\\times \\frac{{C}_{i}}{{C}_{0}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$RI=\\sum _{i=1}^{n}{E}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the risk factor for the given HM; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the toxicity response factor for the given pollutant (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{i}\\)\u003c/span\u003e\u003c/span\u003e for Cd, Cr, Cu, Ni, Pb, and Zn was defined as 30, 2, 5, 5, 5, and 1, respectively); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the concentration of each HM in the soil, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{0}\\)\u003c/span\u003e\u003c/span\u003e is the background concentration of HMs in the study area. Chu et al.\u003csup\u003e11\u003c/sup\u003e found that the background concentrations of Cu, Zn, Pb, Cd, Cr, and Ni in Guangzhou were 28.7, 77.8, 57.6, 0.13, 87.0, and 23.5 mg\u0026middot;kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Values of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}\\)\u003c/span\u003e\u003c/span\u003e were classified as follows: low\u0026thinsp;\u0026lt;\u0026thinsp;40; 40\u0026thinsp;\u0026le;\u0026thinsp;moderate\u0026thinsp;\u0026lt;\u0026thinsp;80; 80\u0026thinsp;\u0026le;\u0026thinsp;considerable\u0026thinsp;\u0026lt;\u0026thinsp;160; and 160\u0026thinsp;\u0026le;\u0026thinsp;high\u0026thinsp;\u0026lt;\u0026thinsp;320. Values of \u003cem\u003eRI\u003c/em\u003e were classified as follows: low risk\u0026thinsp;\u0026lt;\u0026thinsp;150; 150\u0026thinsp;\u0026le;\u0026thinsp;moderate risk\u0026thinsp;\u0026lt;\u0026thinsp;300; 300\u0026thinsp;\u0026le;\u0026thinsp;considerable risk\u0026thinsp;\u0026lt;\u0026thinsp;600; and high risk\u0026thinsp;\u0026ge;\u0026thinsp;600.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis.\u003c/b\u003e SPSS 19.0 (SPSS Inc., USA) was used to conduct all statistical analyses. One-way analysis of variance, followed by Duncan\u0026rsquo;s test, was used to evaluate the significance of differences among treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Origin Pro 2019 software (Origin Lab Corporation, Northampton, MA) was used to make plots.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Forestry Science and Technology Innovation Project of Guangdong Province (2022KJCX015) and the National Natural Science Foundation of China (31971629) .\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eL.H.X. and D.H.L. conceived and designed the experiments, and wrote the first draft of the manuscript. L.H.X. and Y.T.Y. conducted the experiments. L.H.X., D.H.L., J.Y.F. and J.B.F. analyzed the data. Y.T.Y., D.M.W. and S.C.Z. supervised the research and contributed to the discussion of the results. L.H.X. and D.H.L. wrote the manuscript. D.F.J., D.M.W., and S.C.Z. contributed to the guidance of this study and reviewed the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to S.C. Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026apos;s note\u0026nbsp;\u003c/strong\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afliations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi, Z.W., Yu, D., Liu, X.J., Wang, Y. 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An ecological risk index for aquatic pollution control: a sedimentological approach. \u003cem\u003eWater Res. \u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 975-1001. https://doi.org/10.1016/0043-1354(80)90143-8 (1980).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sludge utilization, soil contamination, forest soils, surface runoff, rainfall","lastPublishedDoi":"10.21203/rs.3.rs-3942079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3942079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The application of sewage sludge (SS) to woodland is an effective approach for the disposal and utilization of SS. However, the woodland slope may determine the risk of heavy metal (HM) migration via runoff. We conducted indoor rainfall simulations and natural rainfall experiments to clarify the effect of slope on the migration of HMs via runoff (including surface and interflow) and sediments. In the simulated rainfall experiment, HMs lost via sediments increased by 9.79–27.28% when the slope increased from 5° to 25°. However, in the natural rainfall experiment, when the slope of forested land increased from 7° to 23°, HMs lost via surface runoff increased by 2.38% to 6.13%. It revealed that the surface runoff water on a high slope (25°) posed high water quality pollution risks. The migration of HMs via surface runoff water or interflow increased as the steepness of the slope increased. The total migration of Cu, Zn, Pb, Ni, Cr and Cd via sediment greatly exceeded that via surface runoff and interflow. Particles ≤0.05 mm contributed the most to the ecological risks posed by sediments. Cd was the main source of potential ecological risks in sediments under both experimental conditions.","manuscriptTitle":"Effects of woodland slope on heavy metal migration via surface runoff, interflow, and sediments and associated potential ecological risks following the application of sewage sludge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 09:01:19","doi":"10.21203/rs.3.rs-3942079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-28T13:44:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-21T13:59:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-05T00:43:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44e21165-cf05-4b92-982b-2fcc1d4a0993","date":"2024-02-25T01:44:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-23T07:55:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-23T07:51:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-22T04:20:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-22T04:18:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-09T05:31:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"16955eee-8375-474b-94dc-3006d1bbda83","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-05T13:06:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 09:01:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3942079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3942079","identity":"rs-3942079","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}