Inﬂuence of Sustainable Biochars Produced from Kitchen Waste, Pig Manure, and Wood on Soil Erosion

: The inﬂuence of biochars on water retention, mitigating nutrient leaching, and pollutant removal in green infrastructure has been explored in the past. However, there is a lack of understanding on how feedstock (i.e., biomass) would affect biochar physicochemical properties and hence, overall erosion control (including inﬁltration, surface, and sub-surface runoff) in green infrastructure. The main purpose of this study was to investigate the effect of biochars produced from three different feedstocks (pig manure, wood, and kitchen waste) on the erosion of granite residual soil. Flume experiments were conducted to measure and analyze soil erosion, runoff, and inﬁltration. The result showed that the runoff and soil erosion of kitchen waste biochar (KWB) samples were reduced by 17.7% and 21.7%, respectively. On the contrary, wood biochar (WB) and pig manure biochar (PMB) were found to enhance runoff and soil erosion. In addition, biochar particles were found in runoff and inﬁltration in erosion experiment. Thus, it is important to note that measures should be taken to prevent biochar loss when using biochar as a soil amendment. Additionally, the effects of different types of biochar on soil hydraulic and hydrophobicity properties should be taken into account as a selection criterion for choosing amendments in green infrastructure. This study ﬁnds that kitchen waste biochar has better performance in improving soil hydraulics and erosion.


Introduction
Soil erosion has been a major environmental concern globally, resulting in a decline of land fertility and secondary environmental problems (e.g., environmental degradation, flooding, water pollution, etc.) [1][2][3]. Erosion can lead to the removal of soil and ultimately decline of vegetation and hence, the performance of green infrastructure. Quinton et al. [4] found that the mass of soil loss is about 28 billion tons per year. The rate of soil erosion in farmland is 10-40 times faster than the rate of soil formation, endangering human food security [5,6]. About 80% of agricultural land degradation is caused by soil erosion [7,8]. Moreover, soil erosion causes global warming through the loss of soil organic carbon [9,10]. More than 20% of the organic carbon produced through soil erosion turns into carbon dioxide, which not only reduces soil fertility but also aggravates the global warming effect [11]. Therefore, the control of soil erosion is necessary for restoring ecosystem services of green infrastructure.
At present, the main methods to control soil erosion are soil modification and vegetation cover. The chemical additives can improve soil aggregate and reinforce soil textile [12][13][14][15], which is considered to be an effective method to reduce soil erodibility. Chemical additives commonly used in soil amendment are fly ash [16,17], lignosulfonates [18], calcite precipitates [19], and polypropylene fibers [20]. However, the use of chemical additives for soil modification can cause leaching and is only effective for fine soils with high compactness. of soil by 18.8% [21]. Further, biochar also enhances vegetation growth, which increases resistance to erosion. Such studies help in selecting suitable biochar as an amendment material in green infrastructure. However, the influence of different biochar types (i.e., from varying feedstocks such as animal and plant origin) on soil erosion has not been considered. Furthermore, there is a lack of interpretation of the erosion control mechanism of biochar using its physicochemical properties [39,[63][64][65][66]. The novelty of this study lies in the comparison of the influence of animal-and plant-based biochars on soil erosion and its interpretation with respect to their physicochemical properties.
The main objective of this study was to assess the impacts of WB, KWB, and PMB on the erosion of granite residual soil. Flume experiments were carried out on bare soil and soil-biochar composites. Each flume experiment lasted 150 min, including 120 min of moderate rain intensity (66.7 mm·h −1 ) and 30 min of heavy rain intensity (91.7 mm·h −1 ). Erosion, runoff, infiltration, and water content were recorded and measured separately during rainfall simulation. Data fitting and integration were used to analyze changes in erosion, runoff, and infiltration for investigating the impact of biochar on erosion control. This study will help in the selection of appropriate biochar depending on feedstock type for usage as amendment material in green infrastructure.

Soil and Biochar
WB, KWB, and PMB were used in the soil erosion test. In order to analyze the microstructure of biochar, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis were carried out for various biochar. The specific surface area of biochar was determined using N adsorption isotherms at the relative pressure (P/PO) of 0.075~1. The FTIR of biochar ( Figure 1a) showed that there were abundant hydrophilic functional groups such as -OH and -COOH. In addition, biochar also has -CH 2 , C=C, and -CO hydrophobic functional groups. Therefore, the hydrophilicity of different biochar varies with different raw material types. XRD analysis (Figure 1b) results showed that biochar mainly contained inorganic compounds composed of K, Na, Ca, and other elements. These elements are usually the basic mineral components of biomass. The specific surface area of biochar ( Figure 1c) varies from tens to more than 100. The large specific surface area makes biochar have good adsorption capacity. The area enclosed by the curve and the x-axis is the pore volume of biochar. The pore volumes of WB, PMB, and KWB are 0.169 cm 3 ·g −1 , 0.105 cm 3 ·g −1 , and 0.174 cm 3 ·g −1 , respectively.
The geotechnical characteristics of the soils used in this study are shown in Table 1. The content of coarse grain in the soil is about 40% (>2.36 mm), and the content of silt and clay is about 14%. Plastic and liquid limits of soil are 18.3% and 29.2%, respectively. The maximum dry density (MDD) and optimum moisture content (OMC) of the soil were found as 16.9 kN·m −3 and 18.8%, respectively. The soil specific surface area as measured using BET method is found to be 12.7 m 2 ·g −1 . However, the specific surface area of KWB, PMB, and WB is 76.0 m 2 ·g −1 , 111.5 m 2 ·g −1 , and 66.51 m 2 ·g −1 , respectively. Evidently, the specific surface area of biochar is more than five times that of soil. The soils can be classified as clayey sand (SC) according to ASTM D2487 [67].

Soil Plot Preparation
The setup of the erosion test is shown in Figure 2. The setup was inspired by Cai et al. [68], Mhaske et al. [69], and Römkens et al. [70]. The size of the soil flume was 1.5 m in length, 1.6 m in width (0.8 m in wide for each group), and 0.2 m in height ( Figure 2a). The bottom layer with 5 cm was consisted of gravels to allow infiltration into the collector. The biochar-amended soil and untreated soil were set on top of the 5-cm-thick gravel layer. Between the gravel and soil layer, a geotextile was placed to prevent any mixing of gravel and soil. To achieve a uniform density, the prepared soil-biochar mixture and the soil without biochar addition were packed uniformly into the flume in 3-cm-thick layers to a total depth of 9 cm after compaction of each layer. The prepared soil sample was placed under a rainfall simulation setup at the designed slope gradient of 7 • . Figure 2 indicates the schematic diagram of rainfall simulator setup for flume tests. The major components of the rainfall simulator include a water supply system with spray nozzle, supporting metal frames, flow meters, collectors, and water content sensors. The rainfall simulator used in the current test was a 448-nozzle unit in design and principle of work. The water supply system was 1.5 m in length, 0.8 m in width, and 2 m above the flume. Two control valves were used to control the flow rate. The flow meters were connected to the pipe for monitoring the flow. Adopting the design of Morbidelli et al. [71], the runoff and infiltration during the rainfall event were collected separately by plastic buckets. The moisture content over time was recorded by four water content sensors (EC-5) for each group. The locations of the four sensors are shown in Figure 2b.

Design of Rainfall Simulation
The input flow of the water supply system was set at 80 L·h −1 and 110 L·h −1 , which corresponded to the rainfall intensity of 66.7 mm·h −1 (low rainfall intensity) and 91.7 mm·h −1 (high rainfall intensity), respectively. The relation between the input flow and rainfall intensity is as follows: where I is the intensity of rainfall, Q is the flow indicated by the flow meter, and A is the rainfall area. After each rainfall event, the total volume of rainfall was estimated by the following equation: where V RA is the theoretical total rainfall per unit area, I is the intensity of rainfall, t is the duration of rainfall simulation, and A 1 is the unit area (1 m 2 ).  The input flow of the water supply system was set at 80 L·h −1 and 110 L·h −1 , which corresponded to the rainfall intensity of 66.7 mm·h −1 (low rainfall intensity) and 91.7 mm·h −1 (high rainfall intensity), respectively. The relation between the input flow and rainfall intensity is as follows:

Rainfall Simulation
Soil samples were uniformly mixed with 5% biochar by weight. Three types of biochar, including WB, KWB, and PMB, were mixed with soil. The soil was compacted at 85% degree of compaction (DOC) on the flume by dividing the soil into three layers uniformly. Ten independent rainfall simulations were carried out in this experiment. Each rainfall Water 2021, 13, 2296 7 of 20 event occurred shortly after the sample had been compacted. Before each rainfall event, soil moisture content was recorded using EC-5 moisture sensor (refer to Figure 2).
For each rainfall event, the rainfall intensity was set at 66.7 mm·h −1 for 120 min at first. After 120 min, the rainfall intensity was altered immediately to 91.7 mm·h −1 for 30 min. This simulated the transition from gentle rain to heavy rain [72]. During the simulated rainfall, the erosion from the flume was collected using plastic containers. The water content was also recorded during collecting erosion. Within 10 min at the beginning of simulated rain, runoff flow, infiltration flow, and water content were recorded at 2, 5, and 10 min. Subsequently, the data of the experiment were measured every 10 min. For runoff and infiltration, only the total flow over a continuous period of 30 s was collected each time. The flow rate of runoff and infiltration could be determined from these measuring data. It should be noted that all the data need to be collected twice at the end of each rainfall event (i.e., at 120 min). After each rainfall event, the sediment collected in each sample was dried at 105 • C until a negligible change in mass was observed. The constant mass means the eroded mass during the rainfall event. Soil erosion rate could be determined from the measuring data. After measuring the eroded mass, sediments from the mixture of biochar and soil were kept under 1000 • C for 2 h to get the mass of biochar loss in soil erosion [73].

Data Analyses
The flow volume of runoff and infiltration during simulated rainfall can be deduced by integrating the flow rate with time. The formula is as follows: where V t is the flow volume of runoff or infiltration per m 2 during a rainfall event, Q e is the measuring flow rate per m 2 of runoff or infiltration, and t is the duration of the rainfall event. Similarly, the soil erosion during rainfall event can be deduced by integrating the soil erosion rate with time.
where S t is the mass of soil erosion per m 2 during a rainfall event, R e is the soil erosion rate per m 2 , and t is the duration of rainfall event.
One-way analysis of variance (one-way ANOVA) is a method to test the influence of different levels of the same factor on the results [74]. One-way ANOVA was used to analyze the mean values of steady flow rate and erosion rate in ten experiments. The effects of WB, PMB, and KWB on BS were evaluated.

Variation of Runoff Rate and Infiltration Rate during Artificial Rainfall Events
As shown in Figure 3, a simplified model for the variation of runoff and infiltration rate with time is proposed. It is generally believed that the runoff and infiltration rate increase with the increase in time at the beginning of rainfall. After a certain time, the runoff and infiltration rate reach a constant value (peak). This constant value is related to rainfall intensity. According to the results shown in Figure 3a,c, the characteristic curves of runoff and infiltration rate over time under the rainfall conditions are proposed.
soil infiltration capacity and significantly reduces runoff. On the contrary, WB and PMB significantly inhibited soil infiltration. The effects of different biochar on slope runoff and infiltration rate are different, which may be related to the composition and surface functional groups of biochar. It can be seen from FTIR results that KWB has more hydrophilic groups than WB and PMB (e.g., -OH, C-O, NO3, etc.). Moreover, biochar can promote slope surface solidification due to its enhanced water retention capacity. The surface sealing of bare soil exposed to rainfall affects runoff generation by reducing permeability [75].  The curve of runoff rate over rainfall duration is shown in Figure 3a. The event consists of 120 min of low rainfall and 30 min of heavy rainfall. At 120 min, the simulated rainfall intensity suddenly increases from 66.7 mm·h −1 to 91.7 mm·h −1 . Figure 3b represents the relationship between runoff flow rate and rainfall duration. At first, the runoff flow rates of all samples increase slowly with the duration of rainfall. After about 20-40 min, the runoff flow rates increase rapidly until they reach a peak (point C). After point C, the runoff of all samples reaches a stable value. The sudden increase in rainfall intensity leads to the rapid increase in runoff flow rate. This change occurs almost simultaneously with the change in rainfall intensity. The new runoff flow rates remain stable until the rainfall ends (point D). The characteristic curve model is used to fit the data shown in Figure 3a. The fitting results are shown in Table 2, where c and d respectively represent the flow rate after the runoff and infiltration reach constant. The stable runoff flow rates of KWB are the smallest under two different rainfall intensities, which are 694.2 mL·m −2 ·min −1 and 1129.3 mL·m −2 ·min −1 . The stable runoff of WB is the largest under low rainfall intensity (about 916.7 mL·m −2 ·min −1 ), and the stable runoff of BS is the largest under high rainfall intensity (about 1373.0 mL·m −2 ·min −1 ). Compared with BS, KWB effectively reduces the runoff flow by about 17%. In addition, the stable runoff of PMB, WB, and BS are very close (about 5% difference). Since the pore volume of KWB was 0.174 cm 3 ·g −1 , which was significantly larger than that of PMB (0.105 cm 3 ·g −1 ) and WB (0.169 cm 3 ·g −1 ), the addition of KWB increased the pore volume of soil significantly and improved the hydraulic conductivity. Moreover, the surface hydrophilic functional groups of KWB stabilized the soil aggregates, which alleviated the surface sealing. In contrast, the surface functional groups of PMB and WB are essentially hydrophobic, creating a seal on the soil surface with very low hydraulic conductivity. The existence of the seal blocks the infiltration of rainwater, which increases the surface runoff and promotes soil erosion [53].
Infiltration : where Q e is the flow rate per m 2 of runoff or infiltration, and t is the duration of rainfall event.
The variation curve of infiltration rate with rainfall duration is shown in Figure 3c. The infiltration rate increases linearly with time until point B . After reaching point B , the infiltration rate decreases gradually with time until point C . After point C , the flow rate reaches a steady value. The characteristic curve model is used to fit the data shown in Figure 3d. The fitting results are shown in Table 2. The infiltration rates of all samples increase with the duration of rainfall until they reach a peak. After about 10-30 min, the infiltration rates decrease gradually until they stabilize. β represent the time for the sample to reach the stability of infiltration. It takes about 125 min for the PMB sample to reach the stable infiltration state. However, BS can reach the stable infiltration state the fastest, which takes about 25 min, where D is the flow rate after the infiltration reaches constant. The results show that KWB has the highest stable infiltration rate (about 315.0 mL·m −2 ·min −1 ) and WB has the lowest stable infiltration rate (about 163.6 mL·m −2 ·min −1 ). Compared with BS, KWB increased the infiltration rate by about 20%, while WB significantly reduced the infiltration rate by about 38.0%. The stable infiltration rates of PMB (190.0 mL·m −2 ·min −1 ) and WB are basically the same. After the sudden increase in rainfall intensity, the infiltration rates vary little. The probable reason is that prolonged rainfall has sealed the surface of the exposed soil. Surface sealing leads to a decrease in permeability and tends to be uniform.
One-way ANOVA was used to investigate the significance of different types of biochar on stable runoff and infiltration. The analysis results are shown in Table 3. It can be observed that based on the P-value, the influence of KWB on runoff is significant, and the influence of WB and PMB on infiltration is obvious. The infiltration rate of the KWB sample is the highest, and the runoff rate is the lowest. Compared with BS, KWB improved soil infiltration capacity and significantly reduces runoff. On the contrary, WB and PMB significantly inhibited soil infiltration. The effects of different biochar on slope runoff and infiltration rate are different, which may be related to the composition and surface functional groups of biochar. It can be seen from FTIR results that KWB has more hydrophilic groups than WB and PMB (e.g., -OH, C-O, NO 3 , etc.). Moreover, biochar can promote slope surface solidification due to its enhanced water retention capacity. The surface sealing of bare soil exposed to rainfall affects runoff generation by reducing permeability [75].

Distribution of Total Flow Volume-Runoff, Infiltration, and Water Retention
Equation (3) is used to calculate the total volume of runoff and infiltration in the whole rainfall event. The total volumes of runoff and infiltration during the rainfall event are shown in Figure 4. Figure 4a shows the total flow volume distribution after 120 min of rainfall, that is, under the condition of low rainfall intensity. The order of the total runoff volume of each sample is WB > PMB > BS > KWB. The total runoff volumes of WB, PMB, BS, and KWB account for 77.2%, 74.3%, 63.2%, and 56.7% of total rainfall volume, respectively. KWB sample has the lowest total runoff volume, which is 75,626.8 mL·m −2 . Compared with BS (84,289.1 mL·m −2 ), KWB reduces the total runoff by 10.3%. WB and PMB increase the total runoff of the samples, which are 103,001.2 mL·m −2 and 99,093.8 mL·m −2 , respectively. On the contrary, the order of the total infiltration volume of each sample is WB < PMB < BS < KWB. The total infiltration volumes of WB, PMB, BS, and KWB account for 12.3%, 16.3%, 25.5%, and 26.4% of the total rainfall volume, respectively. The total volume of infiltration in the KWB sample is the highest, which is 35,229.5 mL·m −2 . The total infiltration volume of BS (33,990.4 mL·m −2 ) is almost the same as that of KWB. Compared with BS, WB and PMB reduce the total volume of infiltration in samples, which are 16,407.5 mL·m −2 and 21,789.0 mL·m −2 , respectively.
The total flow volume distribution after the rainfall lasts for 150 min is shown in Figure 4b. The order of total runoff of each sample is still WB > PMB > BS > KWB. At this time, however, the total runoff of WB, PMB, BS, and KWB account for 79.0%, 77.7%, 69.8%, and 61.2% of the total rainfall volume, respectively. The total infiltration volumes of WB, PMB, BS, and KWB account for 11.4%, 14.7%, 22.9%, and 24.1% of the total rainfall volume, respectively. The increase in rainfall intensity leads to the decrease in the proportion of total infiltration volume and the increase in the proportion of total runoff. It can be observed from Figure 4 that KWB can significantly reduce the total runoff volume and increase the total infiltration volume. It can be predicted that KWB plays an active role in preventing soil erosion. However, WB and PMB lead to an increase in runoff and cause infiltration reduction. Thus, WB and PMB are not recommended to be used as soil amendment materials to prevent erosion. infiltration volume and the increase in the proportion of total runoff. It can be observed from Figure 4 that KWB can significantly reduce the total runoff volume and increase the total infiltration volume. It can be predicted that KWB plays an active role in preventing soil erosion. However, WB and PMB lead to an increase in runoff and cause infiltration reduction. Thus, WB and PMB are not recommended to be used as soil amendment materials to prevent erosion.  The soil water balance model calculates the volume of water in soil, as reported by Littleboy et al. [76]. The relation between the rainfall volume runoff volume, infiltration volume, and water retention volume is as follows: thus, where SWi is the soil water content on current time point (mL), SWi−l is the soil water on previous time point (mL), VRA is rainfall volume (mL), VET is evapotranspiration volume (mL), VRU is runoff volume (mL), VSE is infiltration volume (mL), and WR is water retention volume (mL). Since each rainfall event lasts only 150 min, evapotranspiration can be ignored. Then, the water retention volume of samples can be calculated using Equation (7). Total flow volume distribution (runoff, infiltration, and water retention) of rainfall events at different The soil water balance model calculates the volume of water in soil, as reported by Littleboy et al. [76]. The relation between the rainfall volume runoff volume, infiltration volume, and water retention volume is as follows: thus, where SW i is the soil water content on current time point (mL), SW i−l is the soil water on previous time point (mL), V RA is rainfall volume (mL), V ET is evapotranspiration volume (mL), V RU is runoff volume (mL), V SE is infiltration volume (mL), and WR is water retention volume (mL). Since each rainfall event lasts only 150 min, evapotranspiration can be ignored. Then, the water retention volume of samples can be calculated using Equation (7). Total flow volume distribution (runoff, infiltration, and water retention) of rainfall events at different times are summarized in Table 4. As shown in Figure 4 and Table 4, the water retention volumes of all samples are related to the runoff and infiltration. In general, high infiltration leads to a higher water retention volume. The total volume of biochar-soil amendment retained water accumulates gradually with an increase in rainfall duration. This change is also picked up by installed soil moisture sensors (see Figure 5). Figure 5 shows that the soil moisture content of most samples increases gradually with rainfall duration and then remains stable after reaching the peak. The water content increment of KWB is the largest in rainfall (from 13.4% to 25.7%). Correspondingly, its total water retention volume is also the largest (see Table 4 and Figure 4). In summary, biochar, runoff, and infiltration all affect the water retention in the soil during rainfall. The water retention of the incubated soil is reduced by the decreased infiltration rate due to surface solidification. In addition, soil internal water content and water retention are also different due to the heterogeneity of the soil medium. As shown in Figure 5, the water content captured by the upper soil moisture sensor and the bottom soil moisture sensor is often different. times are summarized in Table 4. As shown in Figure 4 and Table 4, the water retention volumes of all samples are related to the runoff and infiltration. In general, high infiltration leads to a higher water retention volume. The total volume of biochar-soil amendment retained water accumulates gradually with an increase in rainfall duration. This change is also picked up by installed soil moisture sensors (see Figure 5). Figure 5 shows that the soil moisture content of most samples increases gradually with rainfall duration and then remains stable after reaching the peak. The water content increment of KWB is the largest in rainfall (from 13.4% to 25.7%). Correspondingly, its total water retention volume is also the largest (see Table 4 and Figure 4). In summary, biochar, runoff, and infiltration all affect the water retention in the soil during rainfall. The water retention of the incubated soil is reduced by the decreased infiltration rate due to surface solidification. In addition, soil internal water content and water retention are also different due to the heterogeneity of the soil medium. As shown in Figure 5, the water content captured by the upper soil moisture sensor and the bottom soil moisture sensor is often different.
(a) Top layer (b) Bottom layer    Figure 6 shows the variation of erosion rate and biochar loss rate. The event consists of 120 min of low rainfall and 30 min of heavy rainfall. At 120 min, the simulated rainfall intensity suddenly increases from 66.7 mm·h −1 to 91.7mm·h −1 . Figure 6a represents the relationship between erosion rate in runoff and rainfall duration. At first, the erosion rates of all the samples increase with rainfall duration until they reach the peak. After about 50-60 min, the erosion rates gradually decrease until they stabilize. It takes around 90 min for the erosion rate to reach stability. KWB has the lowest peak erosion rate (about 1.75 g·m −2 ·min −1 ). Conversely, PMB has the highest peak erosion rate (about 2.83 g·m −2 ·min −1 ). Compared with BS, KWB effectively decreases the peak erosion rate by about 20.0%. However, PMB increases the peak erosion rate by about 30.4%. The erosion rates of all samples increase after the sudden increase of rainfall intensity. In particular, the stable erosion rate of KMB is 1.50 g·m −2 ·min −1 , which is the lowest of all the samples. Furthermore, the biochar loss rates of WB, PMB, and KWB account for 27.4%, 25.6%, and 24.0% of the total runoff erosion, respectively. Figure 6b represents the relationship between erosion rate in infiltration and rainfall duration. At first, the erosion rates of all the samples increase with rainfall duration until they reach the peak. After about 30-40 min, the erosion rates gradually decrease until they stabilize. It takes around 90 minutes for the erosion rate to reach stability. The peak soil loss rates of BS, WB, PMB, and KWB are 1.08 g·m −2 ·min −1 , 0.58 g·m −2 ·min −1 , 2.17 g·m −2 ·min −1 , and 0.75 g·m −2 ·min −1 , respectively. The erosion rates of all samples increase after the sudden increase of rainfall intensity. Furthermore, the biochar loss rates of WB, PMB, and KWB account for around 44.72%, 49.87%, and 42.23% of the total infiltration erosion, respectively. It can be obtained from Figures 1 and 6 that the erosion rate is related to the flow rate. A high peak flow rate leads to a high erosion rate. After the peak flow rate, sediment concentration and amplitude variation are decreased. KWB significantly reduces the runoff rate and hence the erosion rate. However, it is worth noting that the erosion rate in infiltration does not increase despite the increase in infiltration rate by KWB. On the contrary, WB and PMB enhance the runoff rate and hence the erosion rate. Nevertheless, WB and PMB also lead to an increase in erosion rate in infiltration. The results of the ANOVA are summarized in Table 5. It can be observed that based on the P-value, KWB has a significant impact on the erosion rate of runoff, and PMB significantly increases the erosion rate of infiltration. Thus, it is evident that KWB is a favorable amendment of soil for preventing erosion. WB and PMB, however, are not suggested to use for preventing soil erosion. It can be obtained from Figures 1 and 6 that the erosion rate is related to the flow rate. A high peak flow rate leads to a high erosion rate. After the peak flow rate, sediment concentration and amplitude variation are decreased. KWB significantly reduces the runoff rate and hence the erosion rate. However, it is worth noting that the erosion rate in infiltration does not increase despite the increase in infiltration rate by KWB. On the contrary, WB and PMB enhance the runoff rate and hence the erosion rate. Nevertheless, WB and PMB also lead to an increase in erosion rate in infiltration. The results of the ANOVA are summarized in Table 5. It can be observed that based on the P-value, KWB has a significant impact on the erosion rate of runoff, and PMB significantly increases the erosion rate of infiltration. Thus, it is evident that KWB is a favorable amendment of soil for preventing erosion. WB and PMB, however, are not suggested to use for preventing soil erosion. Equation (4) is used to calculate the total erosion of runoff and infiltration in the whole rainfall event. Figure 7 shows the total erosion of samples during rainfall events. As shown in Figure 7, KWB has the lowest runoff erosion and infiltration erosion under low rainfall intensity, which are 189.5 g·m −2 and 60.5 g·m −2 , respectively. Compared with BS (270.0 g·m −2 and 49.2 g·m −2 ), KWB effectually reduces the runoff erosion by 29.8%. On the contrary, PMB increases runoff erosion and infiltration erosion by 12.7% and 274.8%, respectively. It is interesting that WB increases the runoff erosion of the sample but basically did not affect the infiltration erosion. It can be observed that KWB can significantly reduce the total erosion by 21.7%. On the contrary, both WB and PMB increase the total amount of erosion in the two rainfall events by 7.3% and 53.1%, respectively.  Equation (4) is used to calculate the total erosion of runoff and infiltration in the whole rainfall event. Figure 7 shows the total erosion of samples during rainfall events. As shown in Figure 7, KWB has the lowest runoff erosion and infiltration erosion under low rainfall intensity, which are 189.5 g·m −2 and 60.5 g·m −2 , respectively. Compared with BS (270.0 g·m −2 and 49.2 g·m −2 ), KWB effectually reduces the runoff erosion by 29.8%. On the contrary, PMB increases runoff erosion and infiltration erosion by 12.7% and 274.8%, respectively. It is interesting that WB increases the runoff erosion of the sample but basically did not affect the infiltration erosion. It can be observed that KWB can significantly reduce the total erosion by 21.7%. On the contrary, both WB and PMB increase the total amount of erosion in the two rainfall events by 7.3% and 53.1%, respectively.

Discussion
The average value of repeated experiments is used to reduce the influence of error on the experimental results. It can be found that although the results of each measurement are different, the values fluctuate within the normal range. Table 6 summarizes the research results of biochar on soil erosion resistance (the biochar application rate is 5%). As shown in the table, the effect of biochar on erosion control varies with the types of biochar, the particle size of biochar, and soil properties. It can be reasonably concluded that the physical and chemical properties (i.e., shape properties and surface properties) of biochar affected by raw materials mainly determine the performance of biochar in soil. The same type of biochar has varying effects on different types of soil. In this study, 5% wood biochar enhances the erosion of sandy soil. However, as per Jien and Wang (2013) [49], 5% wood biochar can significantly reduce the erosion of highly weathered soil. In addition to the raw materials of biochar, curing time and biochar particle size also affect the effect of soil erosion resistance. However, few studies have systematically studied the effects of different types of biochar on the same type of soil. Future work should study in more detail the potential of different kinds of biochar to control the erosion of one kind of soil.  [77] Hutton soil 5% biochar from maize residue 14.2% reduction in soil erosion Li et al. [56] Silt loam 5% biochar from apple branches 9.1% decrease in soil erosion 10.8% decrease in runoff rate The biochar particle size is 1-2 mm 5.1% increase in soil erosion 8.6% decrease in runoff rate The biochar particle size is 0.25-1 mm 12.5% increase in soil erosion No effect on runoff rate The biochar particle size is less than 0. 25  The main reason for soil erosion is the destruction of soil structure and the formation of surface seal caused by rainfall [53,78,79]. Rainfall disperses the clay on the soil surface, which further destroys soil aggregates and clogs soil pores [80]. Previous studies have shown that biochar can effectively improve soil physicochemical properties and inhibit soil erosion [49,55,68,81,82]. However, Li et al. [56] indicated that high biochar application rate and small biochar particle size have an adverse effect on soil erosion resistance. Slope gradient, slope length, soil compaction degree, and rainfall intensity also affect soil erosion resistance except for biochar [68]. This study investigated the influences of biochar type on runoff, infiltration, and erosion. The results suggested that the addition of 5% KWB could reduce the runoff and infiltration rate and restrain erosion. However, WB and PMB increased the flow rate and erosion under the same application rate. Different effects on soil erosion can be attributed to different biochar surface characteristics. The pores inside the KWB particles and pores between the biochar and soil particles can increase the soil porosity, which increases hydraulic conductivity [81][82][83]. Hydrophilic functional groups make soil aggregates retain water by bridging cation and soil particles under the action of Coulomb force and van der Waals force [25]. Moreover, KWB is beneficial to the formation and stabilization of soil aggregates because it has many hydrophilic function groups (-OH, C-O, NO 3 , and SO 4 2− ) on the surface [49,84]. Conversely, the FTIR indicated a clear presence of many hydrophobic functional groups in WB and PMB. The hydrophobic functional groups can break cohesive bonds formed between soil particles.
Current experiment results show that KWB inhibits soil erosion, while WB and PMB increase soil erodibility under the same conditions. The properties (e.g., surface area, function group, and pore volume, etc.) of biochar that affect water retention should be considered before its application [85]. Great care must be taken in the choice of biochar types because feedstock has a strong influence on the properties of biochar. The characteristics of biochar, such as pore volume and functional groups, affect the water and nutrients absorption capacity of biochar [83]. Biochar particles are found in runoff and infiltration in this experiment, similar to other studies [54,83]. Methods including straw covering, manure, or polyacrylamide should be taken to prevent biochar loss during rainfall [9]. In addition to soil erosion, further studies need to be conducted to assess the impact of biochar on surface runoff water quality. Further studies, particularly on biochars produced at low pyrolysis temperature, need to be conducted.

Conclusions
The role of biochars produced from kitchen waste, wood, and pig manure in controlling runoff and erosion is highlighted in this study. Flume experiments were conducted on bare soil and soil mixed with biochars produced from kitchen waste (KWB), wood (WB), and pig manure (PMB). One-way ANOVA revealed the influence of three types of biochar on the erosion resistance of clayey sand. The experiment results show that KWB significantly reduces the runoff and soil erosion, whereas WB and PMB have an opposite trend. It can be observed that the runoff and soil erosion of the KWB sample were reduced by 17.7% and 21.7%, respectively. Meanwhile, the addition of KWB had a significant effect on water retention (increased by 49.1%). Thus, it is evident that KWB is a favorable amendment of soil for preventing erosion. The main reasons are the highly porous structure and hydrophilic functional groups, which are abundant in KWB. In addition, biochar particles were observed in collected runoff and infiltration in this study. The biochar loss rate in runoff and infiltration erosion is around 20-30% and 40-50%, respectively. It is important to note that measures should be taken to prevent biochar loss when using biochar as a soil amendment.
This study will assist in narrowing down the selection of biochar type for its usage in green infrastructure for erosion control. Further studies are needed to study the influence of different biochar types on surface runoff and infiltration with the presence of vegetation  Data Availability Statement: Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.