1. Introduction

2. Materials and Methods

2.3. Tea Bag Plantation in Staircase Wetland

The tea bag index (TBI) method uses code-specific Lipton™ brand, i.e., Pyramid bags – EAN 8,714,100,770,542 and EAN 8,722,700,188,438, shown in Figure 6a. They are distributed by specific European grocery shops, e.g., Dutch supermarkets and Dutch Expat Shop. These particular Lipton tea bags from The Netherlands were used because they are standardized and tested tea bags used in the literature [80]. The tea bags were planted inside the soil in the form of replicates. Each replicate consisted of one green and one rooibos tea bag, making a pair. The primary reason for using two different types of tea bags in a single replicate was to assess the dynamics of two different types of material under the same environment and conditions [62]. Coding was done for the tea bag replicates based on their location, incubation time, and replicate number, e.g., the S1t4R2 code given to a replicate meant Stratum 1, time 4 days, and replicate 2. The stratum (S) refers to the terracotta pot plant, the time frame (t) is the number of days (incubation time) a replicate remained planted, and R2 means the replicate number (pair number) dig out at that particular t. The teabags were taken at incubation times (t) equal to 4, 7, 25, 35 and 246 days. The number of replicates varied for t=4, 7, and 25 days=had only one replicate, while t= 35 and 246 days had three replicates each (mean value was taken with error bars). The tea bags for t = 4 days were not considered because of getting damaged while digging them out.

The tea bags were planted the day the testing of the GW was completed. Before planting, the initial weight of the bags was noted, and a yellow tag was placed on the top of each replica’s plantation place to record the location as shown in Figure 6b below.

Figure 6. (a) Tetrahedron-shaped synthetic tea bags used for Tea Bag Index (TBI) experiments, Rooibos tea (left) and Green tea (right) (b) Yellow stacks placed on the top of tea bag replicates of the planted areas.

Figure 6. (a) Tetrahedron-shaped synthetic tea bags used for Tea Bag Index (TBI) experiments, Rooibos tea (left) and Green tea (right) (b) Yellow stacks placed on the top of tea bag replicates of the planted areas.

The main events of the TBI experimental process are described below:

Day 1- All replicates were weighed and planted in Stratum 1, 2, and 5.

Day 4 - The t4 replicates were taken out and stored in the refrigerator at 4 °C.

Day 7 - The t7 replicates were taken out. To remove the wet soil and the moisture, the replicates were put in the oven for a week at 50 °C.

Day 14 - The replicates were taken out of the oven. The dry soil around the replicates was removed through desiccation, and their final weights were noted.

Day 25 - The t25 replicates were taken out of the soil and stored in the refrigerator.

Day 35 - The t35 replicates were taken out of the soil and placed in the oven alongside the t25 replicates for one week at 50 °C.

Day 42 - The t25 and t35 replicates were taken out of the oven.

Day 246 - The t246 replicates were taken out of the soil and then stored in the oven for seven days before weighing their final weights. After desiccation, their final weights were noted.

The TBI method assumes that any litter incorporated into the soil consists of a labile (decomposable) and a recalcitrant (stable) fraction. Let M₀ be the initial mass of the litter and M_t its mass at time t, to define the mass fraction as m(t)=M₀/M_t. The decomposition is assumed to obey an exponential law with two reaction rates [81]:

$$m=a e^{-kt}+\left(1-a\right) e^{-k\text{'}t}$$

(1)

Where a is the labile fraction, k is the decomposition rate of the labile fraction, (1-a) is the recalcitrant fraction and k’ is the decomposition rate of the recalcitrant fraction. The reaction rate of the recalcitrant fraction k’ is considered to be small incomparison to the labile fraction k, so that for small times$(k\text{'}t<<1)$ Equation (1) can be reduced to

$$m(t)=a{e}^{-kt}+\left(1-a\right)$$

(2)

The TBI method used two different litters: The green tea, a labile litter, and the rooibos, a more recalcitrant litter. They show contrasting decomposition rates. We use subindexes “g” and “r” to encode the parameters of the green tea and the rooibos tea.

The parameter of the exponential model of Equation (2) were obtained by non linear regression. The range of values for variables ‘a’ and ‘k’ of Equation (2) was generated. Based on that range, the best possible fit was plotted. The generated curve touched most of the experimentally plotted points, and inferred values of a function where no experimental data was available.

If k is assumed constant, it can be obtained by isolating it from Equation (2):

$$k=\frac{1}{t} ln \left[\frac{a}{m(t)-\left(1-a\right)}\right]$$

(3)

In some cases the reaction rate may change with time. This is the case of the fractal kinetics That is characterized by a power-law dependency of the reaction rate with time [81]). OIn this case the reaction rate can be calculated by considering two-time points t₁ and t₂ in Equation (1):

$$m(t_1)=a e^{-k{t}_1}+\left(1-a\right)$$

(4)

$$m(t_2)=a e^{-k{t}_2}+\left(1-a\right)$$

(5)

where m₁ and m₂ are the fractions (m₁ > m₂) of the rooibos biomass that remains after incubation times t₁ and t₂ (t₂ > t₁). The reaction rate ‘k’ can be computed by isolating k and a from Equations (6) and (7). The resulting equations are:

$$k=\frac{1}{t_2-t_1} \ln \left[\frac{m_1-\left(1-a\right)}{m_2-\left(1-a\right)}\right]$$

(6)

$$a=\frac{m_1-m_2}{e^{k{t}_1}-e^{k{t}_2}}$$

(7)

These equations can be solved iteratively by using an initial guess for ‘a’ chosen from the range given by the curve fit in Equation (2); then compute k from Equation (6); next correcting a using Equation (7). The parameters k and a are iterative calculated using Eqs.(6) and (7). By using an appropriate initial guess value of ‘a’ this procedure is applied until k converges to a given value.

During this decomposition, some parts of the labile compounds stabilizes and become recalcitrant tea [82]. Environmental factors play important role in this stabilisation [83] resulting in a deviation of the actual decomposed fraction (i.e. limit value) ‘a’ from the hydrolysable (i.e. chemically labile) fraction H. This aberration can be interpreted as the suppressing effect of the environmental conditions on the decomposition of the labile fraction and will be referred to as stabilisation factor S:

The stabilization factor (S) was calculated as follows [80]:

$$S=1-\frac{a_g}{H_g}$$

(8)

where, H_g is the hydrolysable fraction of the green tea equal to 0.842. This constant value of H_g for green tea was quantified by the method proposed by Van Soest [84], in which the use of two detergents divides the plants cells into less digestible cell walls and mostly digestible cell contents (contains starch and sugars).

The decomposable fraction of rooibos tea a_r was predicted as follows:

$$a_r=H_r \left(1-S\right)$$

(9)

Where $H_r$ hydrolysable fraction constant of rooibos tea.

3. Results and Analysis

3.3. Soil Biomass

3.3.1. Tea Bag Results

This project enabled 30 tea bag index in-field incubations (replicates included). However, not all the tea bag incubations ended in completed or meaningful measurements. The reasons for this incompletion include tea bags getting damaged during the withdrawal process from the soil or remaining inside the soil for too long (296 days) resulting in a complete decomposition. The ratio of the final weight to the initial weight of green and rooibos tea bags (in Control, Stratum 1, and 4) is shown in Figure 13.

The green tea mass loss averaged 62 % in C, 66 %, and 63 % in S1 and S4 respectively. The rooibos tea mass loss average 39 %, 39 %, and 43 % in C, S1, and S4 respectively shown in Figure 13.

The decomposition rate patterns for all the stratums are shown in Figure 14 and Figure 15. The mean decomposition rates for incubation time (t) 0 to 35 in C were 0.05 ± 0.0015, 0.08 ± 0.0033 in S1, and 0.08 ± 0.0030 in S4 respectively, as shown in Figure 14. The decomposition rate decreased non-linearly in C and S4 but increased in S1. As a gradual increase of approximately 3% was noted in the S1 decomposition rate, from 25 to 35 days of incubation, indicating the gradual opening of tissue internal structure by the microflora [81]. The main reason for this increased decomposition with time in S1 is the decrease in the concentration of the GW because the irrigation was stopped. The Carbon: Nitrogen (C: N) ratio increased in S1 from 33:1 to 42:1 in S4. The soil samples were taken before the TBI experiment started, therefore the carbon through filtration of the wetlands got accumulated in S4. If in case, more soil tests had been done later (during the TBI experimentation phase), the C: N would have decreased.

A high decomposition rate (k) was found in S4 initially, the main reason is the high TOC and pH values in S4 (shown in Figure 14) compared to S1 and C. The degradation processes increase between pH 6.5 and 8 [106]. The k value decreases by day 35 in S4 because it was not receiving any GW (the irrigation was stopped). The decomposition was always low in the C compared to S1 and S4, showing a clear effect of the GW on the decomposition rates.

Time-dependent reaction rates have been observed in experimental studies of reaction kinetics (reaction rate) [107]. The reaction rate ranged from 0.05 to 0.01 in C (0 - 246 days), 0.07 to 0.03 in S1 (0 - 35 days), and 0.15 to 0.01 in S4 (0 - 35 days), as shown in Figure 15. This decrease in the reaction rate with time across all stratum was also shown in Keuskamp et al. [62,108]. The faster reaction rate in S4 can be related to the high pH value because S4 reached 0.01 in only 35 days compared to C (246 days). It also means the gravity-actioned filtering of the stair-case wetland not only increased the TOC levels in S4 but also increased the reaction rate. As the GW kept on getting purified from S1 to S4 increasing the decomposition rate but also increased the stabilization as shown in Figure 16. The stabilization factor of S4 (0.17) was greater than C (0.14) and S1 (0.09). These results indicate that filtered GW absorbed in the soil stabilizes the soil better compared to tap water-absorbed soil or GW-absorbed soil.

3.3.2. Soil DNA Results

Figure 17a shows a comparison of α-diversity index between the Control (C) soil samples and all the other treatment soil samples taken from all the remaining strata(S1,S2,S3,S4). One soil sample was taken from each stratum and its diversity was examined. A T-test/ANOVA statistical method was used, and the taxonomic level was Feature-level. Chao 1 is an estimator based on abundance because it requires referring to the abundance of individuals belonging to a certain class in a sample [109]. An increase in the number of species was noticed for all the samples including the control (C). The highest increase was noticed for C at 38%, followed by S1 (32%), S2 (23%), S4 (19%), and S3 (12%). This showed that the tap water caused the maximum increase in species compared to GW. Also, the specie numbers decreased as the GW filtered from S1 to S2 and on, which means, concentrated levels of GW increases species richness in soil. The difference between S1 and C was only 6%, if there were more filtering mediums of C soil, then a decreasing trend in the soil species of tap water absorbed soil would have been noticed in a sample like the S stratums.

Null hypothesis test was used to show that there is no significant difference in the α-diversity of the Control and Treatment soil samples. The ANOVA test result showed the p-value (0.66) turned out to be greater than the level of significance 0.05) [110]. Therefore, the null hypothesis was accepted (p-value > 0.05: the null hypothesis is accepted; p-value ≤ 0.05: the null hypothesis is rejected). The statistical test of the α-diversity index (measure with Chao1 index) determined that the GW treatment didn’t change the α-diversity.

The results for β-diversity, Figure 17b were analyzed using the index of Bray - Curtis coupled with Principal Coordinate Analysis (PCA) [111]. The two main components are the ordination axes represented in the 2D PCoA graph as Axis 1 and Axis 2, representing the most representative proportion of the total variance of the data (41.3% in total). Two cluster formations are noted. In the Before treatment samples; S1, S4 and S2 (Orange, Green and Pink). In the After treatment samples; S2,S3 and S4 (Green, Yellow and Pink) are closely spaced. The ‘S1’ After samples is not part of any cluster because of high concentration of GW. The β-diversity data generated by PCA method was further studied using UMAP and t-SNE methods as shown in the Figure 18.

Low Similarity scores are noted in samples S1, S2 and S4 (Before use of GW/ or week 1) in the form of a cluster as shown in Figure 18. It means the similarity is high between the species of these samples. But after the treatment of GW, they form another cluster with high similarity score indicating dissimilarities between the species. This means that GW increases the β-diversity index or the dissimilarities of the species in the soil samples. The C sample had low scores even after GW treatment, showing the clear effect of the tap water use. As S1 and C had similar plant species but still the GW effect is significant. S3 results were hard to interpret because it’s the only sample whose index becomes low after GW use in t-SNE and goes up in UMAP.

The ANOSIM test was then used to test statistically the visual graphical results by confirming the effect of GW treatment. In the ANOISM test, the null hypothesis of the β-diversity was studied which compared the variation in the abundance and composition of species (or any other taxon) between sampling units [110] in terms of the experimental treatment(S1, S2, S3, S4) and control group (C). The null hypothesis statement here was “there are no differences between the members of the treatment and control groups”. The comparison revealed a significant β-diversity between the groups (ANOSIM R: 0.35776; p < 0.039), which suggests a related effect of the treatment with the composition of the soil microbiome. The p-value was <0.05 and a positive R-value means that the intergroup variation between groups (treatment and control) is significant [110], therefore null hypothesis was rejected.

Table 6. R value in the ANOISM test, comparing all the treatment samples (S1, S2, S3, S4) with the control (C) and within the group. If R is positive: The variation between the groups is significant. If R = 0: The dissimilarities between and within the groups are the same on average. If R is negative: The variation within the group is greater than the variation between the groups).

Table 6. R value in the ANOISM test, comparing all the treatment samples (S1, S2, S3, S4) with the control (C) and within the group. If R is positive: The variation between the groups is significant. If R = 0: The dissimilarities between and within the groups are the same on average. If R is negative: The variation within the group is greater than the variation between the groups).

	C	S1	S2	S3	S4
C	-	1	0.25	1	1
S1	1	-	0	1	0
S2	0.25	0	-	0	-0.25
S3	1	1	0	-	0.5
S4	1	0	-0.25	0.5	-

It is important to note that for the control group there were only two samples, unlike the treatment group with eight samples, which could cause bias in the analysis. For future research, it is recommended to take a similar number of samples for analysis. The highest number of sequencings was done for S4. The most richness was found in the S3 sample.

Figure 19 shows the actual abundance of different types of phyla in each soil sample (before and after) from every stratum. An average of about 150,000 OTUs (operational taxonomic units) were retrieved per sample. At a phylum level, the total number of phyla identified was 24.

The phylum taxonomy is mostly dominated by Proteobacteria followed by Bacteroidetes, which has also been the case in the GW study of small-diameter gravity sewers carrying GW [112]. Proteobacteria and Bacteroidetes were also found dominant in tap water studies [113], which has been seen in C soil samples that received tap water for eight weeks. Proteobacteria is typically observed in soil libraries [114]. Proteobacteria are a phylum of Gram-negative bacteria, very common in soil environments, and are related to a wide range of functions involved in carbon, nitrogen, and sulfur cycling [115]. Their relative richness, which increases with high organic carbon availability in soils, is in line with findings from previous studies [116,117]. The highest percentage of Proteobacteria in the After samples was found in S2.2, up to 61%. It is interesting to note the percentage increase of Proteobacteria in all the strata except for S4, as it increases by 4% from S1.1 to S1.2, 3% in C, 1% in S2, and 6% in S4, but in S4 it dips by approximately 9%. The number of Bacteroidetes was high in (30%). The observation of Bacteroidetes being the second most abundant phylum in this study is compatible with the wetland study done in [118]. S4 showed a surprising increase of 1% in Bacteroidetes from S4.1 to S4.2 compared to an approximate 8% decrease each in S1 and respectively, 13% decrease in S2 and 10% in S4. Bacteroidetes are ecologically important for proper soil functioning [118]. This change in behavior of S4 soil compared to the rest can be related to the plant ecotype rather than the use of the GW, because S1, S2, and S4 have long-leaved plants.

Firmicutes experienced the greatest increase in a specific phylum when comparing Before and After samples, as they jumped from 0.5% to 7.7% in S2.2. As S2 received the GW from S1 and has a different plant community to S1, the S2 readings are mostly changed compared to S1. But S1 and C, having the same plant community, have a mostly matched phylum abundance distribution. The Actinobacteria abundance hasn’t fluctuated overall and remained steady by ±1% in all the strata.

4. Conclusions

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