Water environments of urban constructed water quality treatment (WQT) wetland waterscapes are strongly related to water quality, whereas scenic beauty values help achieve better aquatic environments. However, correlations between several water quality indicators (WQIs) and scenic beauty indexes (SBIs) have not been thoroughly revealed in the existing studies. In this research, an analytic hierarchy process (AHP)-based on scenic beauty estimation (SBE) per site in two WQT wetlands has been developed. The weights of indicators were determined by the AHP voting, which includes three main criteria, i.e., conditions of vegetation, physical geographical conditions, and human geographical conditions. SBIs are voted and calculated from the professional group and the unprofessional group. WQIs of typical pollutants (i.e., dissolved oxygen (DO), CODCr, NH3-N, pH, and total phosphorus) were sampled in situ simultaneously. Inter-relationships among SBIs and WQIs are indicated by correlative analysis and a regression model, which highlights that DO increase, CODCr removal, and NH3-N removal can explain 68.8% of changes in the SBI. Accordingly, applications for WQT wetland eco-engineered landscaping (EEL) were suggested to be conducted on quantitative estimations in three aspects, i.e., following strategies of EEL, improving WQT techniques, and applying the attention restoration theory.

  • An AHP-based scenic beauty estimation in urban constructed WQT wetland parks has been developed.

  • Quantitative correlation among SBIs and common WQIs are revealed by statistical methods.

  • The priority of influential factors for scenic beauty in constructed wetland parks is analysed.

  • Applicable measures for WQT wetland eco-engineered landscaping are put forward.

With the increasing demand for advanced sewage treatment in cities, urban constructed water quality treatment (WQT) wetlands are widely used to treat lightly polluted waterbodies as one component of nature-based solutions (NBS), which are often effective in improving water quality indicators (WQIs). The pollutant removal rates of constructed WQT wetlands are generally higher than other treatment measures when treating sewage with high-level (80–99%) pollutant concentrations (Girts et al. 2012). Once more attention is given to scenic beauty in the WQT wetlands, it will help to make more efficient use of limited spaces in the city and re-design more spaces for biophilic aesthetic experiences. Constructed WQT wetlands have been extensively utilised in urban built environments due to their impressive geo-ecological and landscape characteristics in eco-engineered landscaping (EEL) projects (Xie et al. 2009; Addo-Bankas et al. 2022; Li et al. 2022a). Therefore, the growth, use, and changes of urban constructed wetlands should be necessarily considered for wetland protection and management in the urban built environment (Geng et al. 2023).

Scenic beauty often relates to cultural, biological, and individual dimensions. Scenic beauty estimation (SBE) has been studied widely for landscape perceptions, which have been conducted on biological, cultural, or social behaviour (Bourassa 1991; Wang et al. 2020; Zhou et al. 2023). Since semi-natural elements have been regarded as one of the significant effectors for optimistic prediction, SBE has often been judged by vegetation and physical and human geographical conditions (Junge et al. 2009). Related studies on SBE have primarily occurred in geography and forestry. SBE judging has been commonly utilised to quantify the scenic beauty index (SBI) voting, while effectors of the SBI vary in existing studies.

Estimations of SBIs can help to achieve a refined landscape design and resource management for the aquatic environments. The SBI is one of the common indices in evaluating geo-sites and villages for conservation (Long et al. 2023), heritage protection (Tessema et al. 2021), and ecological tourism (Jia et al. 2022). The SBE has been used to assess landscape resources in agroecosystems, in which landscape values were found to be affected by conditions of vegetation (van Zanten et al. 2016), land cover structure, vegetation, livestock, and historic architecture (van Zanten et al. 2014). Forestry-related studies indicated that the proportion of green space, canopy coverage, colours, morphology, and the combination of vegetation and the wilderness of the landscape can affect the qualities of forest landscapes (Yang 2014). SBIs have dynamic effects during the process, particularly in the spatial and temporal patterns of change perceived by human beings (Pierskalla et al. 2016). Such understanding enhances the ability to comprehend the real-time experience of the landscape. These over-time dynamic effects of SBIs were also empirically found in a study on forest landscape in a national forest park (Deng et al. 2014).

SBIs can also be integrated with Geographic Information System (GIS) techniques and environmental psychological studies. An SBE-based study indicated that the beauty of urban forests could impact tourists' behaviour (Pierskalla et al. 2016). Based on the expert-based SBI voting and remote sensing (RS) technology, landscape beauty patterns of the surroundings of Alps Mountain have been studied, demonstrating that the complexity of landscape morphology and diversity is positively correlated with visual qualities (Schirpke et al. 2013b). A simple SBI scoring method was proposed to analyse the landscape beauty of semblable forests (Hull & Buhyoff 1983). As for urban landscape open space units, the ‘sequence of penetration’, ‘scale’, and ‘naturalness’ have positive impacts on citizens' scenic beauty preference, whereas ‘complexity’ and ‘rhythm’ are negative (Wang et al. 2020).

Nevertheless, the SBI scoring method has rarely been adapted to evaluate design schemes of urban constructed WQT wetlands. Landscape preferences and physical geographical conditions can affect the aesthetic appreciation of landscapes. Other landscape-oriented factors, i.e., on-site scenery, cultural and educational value, and geoscience knowledge, are often not concerned. Relationships between wetlands' scenic beauty, diverse landscape perceptions, and WQT effects have not been empirically investigated thoroughly. SBIs of constructed WQT wetlands should be further studied. WQIs–SBIs correlations should also be analysed to assist the landscape design of urban constructed WQT wetlands, because the related field is still much more to learn for geographers, landscape architects, and environmental engineers.

Experiment design

The mathematical modelling of SBIs should be applied to landscape dynamics evaluation, which is beneficial to guide decision-making in landscape design (Grêt-Regamey et al. 2007). To evaluate the beauty quantitatively by the SBE method, it is also necessary to integrate the methodology of expert ratings and volunteer SBI voting for practical experiments. The analytic hierarchy process (AHP) method has been widely used as an evaluation algorithm in the multi-criteria decision-making (MCDM) method in the recent decade. However, there have been few studies on a direct comprehensive evaluation of WQT wetland landscape resources based on the SBE.

In this study, we selected two typical urban WQT wetland parks in Hangzhou City, Zhejiang Province, China, as our study sites. These parks are typically designed and constructed for purifying lightly polluted urban sewage and offer an aesthetic value for citizens and tourists. Experiments in the research proceeded as follows (Figure 1):
  • (1)

    Five experts have been invited to vote by questionnaires. Based on AHP methods, weights of three criteria (i.e., conditions of vegetation, physical geographical conditions, and human geographical conditions) and eight sub-criteria (i.e., density, combination, form of vegetation, micro-topology, form of waterbodies, openness of waterbodies, human geographical uniqueness, and the value for natural education) of SBEs are calculated. By testing consistency-by-consistency ratio (CR) values, normalised weights of each criterion for SBE have been set by the AHP model.

  • (2)

    By SBI, judging by two groups of participators (group of Professional and Non-professional), eight first-level indicators of landscape beauty at each site were evaluated by viewing photographs taken in two wetland parks. AHP-based weights calculated by five experts were adapted for weighted calculation for three criterion layers to obtain mean SBIs.

  • (3)

    WQIs of samples, i.e., dissolved oxygen (DO), CODCr, NH3-N, pH, and total phosphorus (TP), were measured during the field research. Correlation analysis among SBIs and WQIs of each site was carried out. Statistical methods are adapted to summarise WQI–SBI relationships, while strategies and guidance for the planning and designing of WQT wetland landscaping were summarised.

Figure 1

Methodology of experiments.

Figure 1

Methodology of experiments.

Close modal

Research sites

The details of two wetland parks in this study are introduced as follows:

Yangbei Lake Wetland

Yangbei Lake Wetland is a constructed WQT wetland that is located in the northwest of Fuyang District, Hangzhou City. Yangbei Lake has been in existence since the Tang Dynasty and has been one of the significant irrigation water sources in the Fuyang District until the 1970s. Later, the aquatic environment of wetlands was gradually degraded for industrialisation of its surroundings. In 2019, a wetland EEL project was conducted. The Fishing Garden built along Yangbei Lake has also been designed with unique local cultural characteristics in the Jiangnan (South-Yangtze) Region in China. At present, Yangbei Lake Wetland is a typical WQT wetland park with some hydro-infrastructures, with retaining ridges of fishponds, arranging revetments, and dredging channels. Also, platforms, huts, and landscape facilities have been set up by the lake. At outlets of different treatment stages in Yangbei Lake Wetlands, four proper sites were selected (Figure 2). Water is charged into wetlands at the inlet and treated primarily at the sediment pond (No. 1), and a series of free surface flow (FSF) wetlands were built at site No. 2. Floating wetland islands (FWIs) were constructed at site No. 3. A large aeration pond (AP) (No. 4) was also built. Detailed introductions of each site are shown in Table 1.
Table 1

Overview of sampling sites in Yangbei Lake Wetlands

Sampling site No.1234
Stages of purification Sedimental pond (SP) FSF wetlands FWIs AP 
Purification stages of WQT wetlands     
Vegetation Phragmites australis, Ceratophyllum demersum, Nymphaea tetragona, Juncellus serotinus Typha orientalis, Sagittaria trifolia, Thalia dealbata, Nelumbo nucifera Nymphaea tetragona, Acorus tatarinowii, Taxodium distichum Ceratophyllum demersum 
Landscape form of the waterbody Artificial ponds Free surface waterbodies A FSF pond with FWIs A narrow pond with influent cascades 
Length of waterbody (m) 444 320 324 171 
Area (m233,600 12,800 45,330 55,000 
Sampling site No.1234
Stages of purification Sedimental pond (SP) FSF wetlands FWIs AP 
Purification stages of WQT wetlands     
Vegetation Phragmites australis, Ceratophyllum demersum, Nymphaea tetragona, Juncellus serotinus Typha orientalis, Sagittaria trifolia, Thalia dealbata, Nelumbo nucifera Nymphaea tetragona, Acorus tatarinowii, Taxodium distichum Ceratophyllum demersum 
Landscape form of the waterbody Artificial ponds Free surface waterbodies A FSF pond with FWIs A narrow pond with influent cascades 
Length of waterbody (m) 444 320 324 171 
Area (m233,600 12,800 45,330 55,000 
Figure 2

Sampling sites of Yangbei Lake Wetlands.

Figure 2

Sampling sites of Yangbei Lake Wetlands.

Close modal

Shuijing Park

Shuijing Park is a waterscape eco-park in Yuhang District, Hangzhou. In the park, hybrid constructed WQT wetlands have been built along the river reach, including vertical flow (VF), free water surface (FSF), and FWI. Also, the park has been well known for its waterscape along its reach in the summer seasons and is an attraction for visitors to enjoy the cool microclimate. At outlets of treatment stages in Shuijing Park, four sampling sites were selected (Figure 3). Detailed introductions of each sampling site are shown in Table 2. Firstly, the river flowed into the reach of the inlet. Secondly, sewage flowed into a VF wetland (site No. 1). Then, there was a narrow FSF wetland set at site No. 2. Later, FWIs were also set at site No. 3. Finally, sewage flowed into an ornamental pond (OP) at site No. 4.
Table 2

Overview of sampling sites in Shuijing Park

Sampling site No.1234
Purification stages of WQT wetlands VF wetland FSF wetlands FWIs OP 
Photograph     
Vegetation Cyperus involucratus Rottboll, Calla palustris Typha orientalis, Lythrum salicaria, Cinnamomum camphora Iris tectorum, Nymphaea tetragona, Lythrum salicaria, Acorus tatarinowii, Juncellus serotinus Phragmites australis, Nymphaea tetragona, Juncellus serotinus, Thalia dealbata Fraser 
Landscape form of the waterbody A substrate bed allows sewage to flow from top to bottom Linear-free surface waterbodies A FSF pond with FWIs Ponds for ornamental use 
Length of waterbody (m) 50 108 90 123 
Area (m21,800 880 1,920 1,860 
Sampling site No.1234
Purification stages of WQT wetlands VF wetland FSF wetlands FWIs OP 
Photograph     
Vegetation Cyperus involucratus Rottboll, Calla palustris Typha orientalis, Lythrum salicaria, Cinnamomum camphora Iris tectorum, Nymphaea tetragona, Lythrum salicaria, Acorus tatarinowii, Juncellus serotinus Phragmites australis, Nymphaea tetragona, Juncellus serotinus, Thalia dealbata Fraser 
Landscape form of the waterbody A substrate bed allows sewage to flow from top to bottom Linear-free surface waterbodies A FSF pond with FWIs Ponds for ornamental use 
Length of waterbody (m) 50 108 90 123 
Area (m21,800 880 1,920 1,860 
Figure 3

Sampling sites of Shuijing Park Wetlands.

Figure 3

Sampling sites of Shuijing Park Wetlands.

Close modal

Water quality sampling

WQI sampling at each sampling site (mentioned in Tables 1 and 2) was conducted in situ on sunny days in June 2021, during which the temperature of all sites varied between 24.5 and 29.5 °C. All samples were taken at 10–20 cm depth underneath water surfaces. DO was determined in situ by the electrochemical probe of 86031 AZ IP67 Combo Water Meter (manufactured by AZ Instrument Corp., P. R. China). The LOHAND water test field kit (manufactured by LOHAND Corp., P. R. China) measured levels of CODcr, NH3-N, and TP. An existing study (Li et al. 2022a) indicated that there was no obvious accuracy difference between measurement methods of in situ monitoring and laboratory methods; therefore, it was appropriate to utilise the water test field kit. Experiments in situ were performed in triplicate; collected data are presented as the mean ± standard deviation, which will be demonstrated in the later section. Simultaneously, the buoy method assessed water flow velocities at each sampling site. The average water velocity was determined by measuring the time per floating buoy cost to pass through a fixed distance of two points above the waterbodies.

SBI judging

Numerous studies (Hull & Stewart 1992; Schirpke et al. 2013a; Deng et al. 2014) have indicated photographs' reliability as an optimal medium for SBE. The photographs were taken with a Canon SLR digital camera to capture the scenic beauty features of sites and landscape resources. The following principles were applied during selecting photographs:

  • (1)

    Photographs were taken from proper perspectives at the eye level (at 1.7 m above ground).

  • (2)

    No non-landscape elements exist in photographs, i.e., people, vehicles, and animals.

  • (3)

    The same shooting conditions were always maintained, while weather conditions were acceptable during the field research. No addictive image processing has been conducted.

Structure and criteria of SBI

As a common decision analysis technique, the AHP was put forward and developed by operation research expert T. L. Saaty. The AHP method has been commonly used to tackle complex, unstructured decision-making problems involving numerous levels, elements, objectives, and criteria (Saaty 1980). The AHP has emerged as one of the most frequently used mathematical tools in studies related to quantitative geography since the 1980s and has been applied as one of the standard methods for evaluating the scenic values of the landscape (Schirpke et al. 2013a; Li & Li 2021; Jia et al. 2022; Li et al. 2022b). The AHP-based decision process consists of four steps, i.e., defining aims, structuring decision hierarchies, constructing pairwise comparison matrices, and adapting the priorities, from comparisons to weighing priorities below the immediate level (Saaty 1980). The AHP-based decision-making process can be determined in terms of the number of attributes, criteria, and alternatives (Nowak 2010). The AHP model is comprised of three-level hierarchical structures, i.e., goal (objective), criteria, sub-criteria, and alternatives.

Considering the wide range of indicators available in the relevant literature and the design expertise of WQT wetlands and urban waterscapes in Southern China (Xie et al. 2009; Li & Li 2021; Zhang et al. 2021; Li et al. 2022a), the SBI model in the research considers three criteria in the criterion layer (Conditions of vegetation (B1), Physical geographical conditions (B2), and Human geographical conditions (B3)) and their eight sub-criteria in the first-level indicator layer (Density (C1), Combination (C2), Form of vegetation (C3), Micro-topology (C4), Form of waterbodies (C5), Openness of waterbodies (C6), Human geographical uniqueness (C7), Value for natural education (C8)), which are primary factors being believed to influence SBIs for WQT wetland landscaping. The ‘openness of water bodies’ refers to the degree of visual openness that would be perceived by a human being when viewing waterbodies in 180° viewsheds (Zhang et al. 2021). The detailed meanings of each criterion are listed in Table 3.

Table 3

Hierarchical structure of the SBI by the AHP method

Goal layerCriterion layerFirst-level indicator layerLower score indicatesHigher score indicates
Scenic beauty estimation (GoalConditions of vegetation (B1Density (C1Sparser vegetations Denser vegetations 
Combination (C2Poorer plant combinations Richer plant combinations 
Form of vegetation(C3More disordered forms More well-ordered forms 
Physical geographical conditions (B2Micro-topology (C4Flatter terrains Wavier terrain 
Form of waterbodies (C5More artificial More semi-natural 
Openness of waterbodies (C6Closer More open 
Human geographical conditions (B3Human geographical uniqueness (C7More normal More unique 
Value for natural education (C8More potential for natural education Less potential for natural education 
Goal layerCriterion layerFirst-level indicator layerLower score indicatesHigher score indicates
Scenic beauty estimation (GoalConditions of vegetation (B1Density (C1Sparser vegetations Denser vegetations 
Combination (C2Poorer plant combinations Richer plant combinations 
Form of vegetation(C3More disordered forms More well-ordered forms 
Physical geographical conditions (B2Micro-topology (C4Flatter terrains Wavier terrain 
Form of waterbodies (C5More artificial More semi-natural 
Openness of waterbodies (C6Closer More open 
Human geographical conditions (B3Human geographical uniqueness (C7More normal More unique 
Value for natural education (C8More potential for natural education Less potential for natural education 

AHP-based SBI weight calculation for expert investigation

Five voluntary experts with experiences in landscape architecture, geography, and ecology are invited for the face-to-face ranking process. Experts are invited to vote for the weights of each factor and these weights will be applied in calculating the SBI voting by judges, as mentioned in Section 3.3. Ranking of the criteria and sub-criteria based on Saaty's 1- to 9-point scale (Saaty 1980), which contains nine intensities, includes five primary levels of intensity (1 = equal importance, 3 = moderate importance, 5 = vital importance, 7 = very vital importance, 9 = extreme importance) and four intermediate levels (2 = weak, 4 = moderate plus, 6 = strong plus, 8 = extreme plus). The class with the most suited and potential in each factor map was given the most weight and vice versa.

A statistic Python programme developed with ‘Scikit-learn’ and ‘Matplotlib’ packages was used for ranking criteria and sub-criteria, during which the most potent factors are given the most significant weight at each level. In contrast, the least ones receive the least weight. The priority value of the sub-criterion was calculated by the arithmetic average of the response of experts, and their answers were ranked in matrices. After the priority value in the criterion layer is calculated, an inverse linear matrix is created. To assess whether the pairwise comparisons were consistent, each weight of factors in the ‘criterion layer’ (Wi) and the CR of all data (Table 4) were also calculated by the statistical programme, according to Saaty (1990). A CR of 0.08 for four compared elements and a CR of 0.05 for three compared features were satisfied, ensuring the reasonable level of consistency of the AHP model (Saaty 1995).

Table 4

Pairwise comparative matrices by the AHP method

Comparison matrices of SBE
B1B2B3Wi
B1 0.6399 1.2172 0.2955 
B2 1.5628 1.9022 0.4618 
B3 0.8216 0.5257 0.2428 
λmax = 3.1035 CR = 0.048    
Comparison matrices of B1
C1C2C3Wi
C1 0.4993 0.4499 0.1912 
C2 2.0026 0.8992 0.3829 
C3 2.2272 1.1121 0.4259 
λmax = 3.0057 CR = 0.031    
Comparison matrices of B2
C4C5C6Wi
C4 0.7614 0.9673 0.2988 
C5 1.3134 1.2705 0.3924 
C6 1.0338 0.7871 0.3089 
λmax = 3.0091 CR = 0.036    
Comparison matrices of B3
C7C8Wi
C7 1.5531 0.6083  
C8 0.6438 0.3917  
λmax = 2.0000 CR = 0.045    
Comparison matrices of SBE
B1B2B3Wi
B1 0.6399 1.2172 0.2955 
B2 1.5628 1.9022 0.4618 
B3 0.8216 0.5257 0.2428 
λmax = 3.1035 CR = 0.048    
Comparison matrices of B1
C1C2C3Wi
C1 0.4993 0.4499 0.1912 
C2 2.0026 0.8992 0.3829 
C3 2.2272 1.1121 0.4259 
λmax = 3.0057 CR = 0.031    
Comparison matrices of B2
C4C5C6Wi
C4 0.7614 0.9673 0.2988 
C5 1.3134 1.2705 0.3924 
C6 1.0338 0.7871 0.3089 
λmax = 3.0091 CR = 0.036    
Comparison matrices of B3
C7C8Wi
C7 1.5531 0.6083  
C8 0.6438 0.3917  
λmax = 2.0000 CR = 0.045    

The CR of the first layer (CR = 0.048) suggested acceptable consistency for weight analysis and was appropriate for identification. CRs of the pairwise comparison matrix are calculated. The weights of each criterion are sufficiently consistent; thus, other re-evaluation processes are not required (CR < 0.05). The normalised weight matrix of each criterion includes ones for the criterion layer (WBi) and first-level indicator (WCi), which have been further set (Table 5). It will be adapted in the SBI and calculated in later sections.

Table 5

Weights of each criterion for SBE calculated by the AHP model

Criterion layerWeight (WBi)First-level indicator layerWeight (WCi)
Conditions of vegetations (B1) 0.2955 Density (C1) 0.0565 
Combination (C2) 0.1131 
Form (C3) 0.1258 
Physical geographical conditions (B2) 0.4618 Micro-topology (C4) 0.1380 
Form of waterbodies (C5) 0.1812 
Openness of waterbodies (C6) 0.1426 
Human geographical conditions (B3) 0.2428 Human geographical uniqueness (C7) 0.1477 
Value for natural education (C8) 0.0951 
Criterion layerWeight (WBi)First-level indicator layerWeight (WCi)
Conditions of vegetations (B1) 0.2955 Density (C1) 0.0565 
Combination (C2) 0.1131 
Form (C3) 0.1258 
Physical geographical conditions (B2) 0.4618 Micro-topology (C4) 0.1380 
Form of waterbodies (C5) 0.1812 
Openness of waterbodies (C6) 0.1426 
Human geographical conditions (B3) 0.2428 Human geographical uniqueness (C7) 0.1477 
Value for natural education (C8) 0.0951 

SBI voting by judges

Judges' voting for evaluation indicators can directly indicate the landscape beauty. It has been indicated that there were no significant statistical differences in aesthetic attitudes between distinct groups or evaluators with different cultural backgrounds (Buhyoff et al. 1984). It was also indicated that on-site-based SBE with spatial–temporal views of context was slightly different from photo-based SBE. Otherwise, the group of in situ SBE and photo-based SBE were very similar for most of the studies (Hull & Buhyoff 1983). A set of eight photographs was often used for SBI judging, which is a proper amount of volunteer work (Franco et al. 2003). Photographs for estimations are presented in Figure 4. To obtain first-hand data, participating students on a university campus voluntarily signed up for the voting. Thirty students majoring in landscape architecture (group of ‘Professionals’) and 30 students majoring in unrelated majors, i.e., computer science and civil engineering (group of ‘Non-professionals’) were randomly recruited as volunteers to participate in the SBI judging, respectively, including 34 males and 26 females. Among them, students majoring in landscape architecture had comprehensive educational experience for more than 3 years. All participants were well-informed and consented to participate. Participators have been informed in detail of the definitions of eight first-level indicators before the voting procedure. All participants completed their judging according to the design of the SBI voting experiment, and 60 questionnaires were collected.
Figure 4

Photographs for SBE.

Figure 4

Photographs for SBE.

Close modal
Volunteers were asked to vote for each first-layer indicator (sub-criteria) based on Saaty's 1- to 9-point scale. Then, the matrix was adopted for weighted linear combination (WLC) by combining factors in AHP-aided decision-making. The mean values of the SBIs of each site were calculated by Equation (1).
formula
(1)
in which m is the sum of participants, is the weight of ith first-level indicator, and is the criterion voting of indicator by the nth participant.

SBIs of each site are listed in Table 6.

Table 6

SBI mean scores of each site voted by distinct groups (M ± SD)

Site No.Yangbei 1Yangbei 2Yangbei 3Yangbei 4Shuijing 1Shuijing 2Shuijing 3Shuijing 4
Non-professional 6.0098 ± 0.5098 5.7162 ± 0.5587 5.7333 ± 0.7126 4.9331 ± 0.5238 4.2229 ± 0.6989 5.0504 ± 0.5810 6.4592 ± 0.4566 5.6804 ± 0.5860 
Professional 6.3405 ± 0.6811 5.9131 ± 0.5922 5.7223 ± 0.5998 4.9374 ± 0.6474 4.5705 ± 0.5062 5.0443 ± 0.5205 6.5224 ± 0.4396 5.7395 ± 0.5918 
All 6.2252 ± 0.5868 5.8146 ± 0.5794 5.7278 ± 0.6031 4.9352 ± 0.5838 4.3967 ± 0.6289 5.0473 ± 0.5446 6.4908 ± 0.4458 5.7099 ± 0.5847 
Site No.Yangbei 1Yangbei 2Yangbei 3Yangbei 4Shuijing 1Shuijing 2Shuijing 3Shuijing 4
Non-professional 6.0098 ± 0.5098 5.7162 ± 0.5587 5.7333 ± 0.7126 4.9331 ± 0.5238 4.2229 ± 0.6989 5.0504 ± 0.5810 6.4592 ± 0.4566 5.6804 ± 0.5860 
Professional 6.3405 ± 0.6811 5.9131 ± 0.5922 5.7223 ± 0.5998 4.9374 ± 0.6474 4.5705 ± 0.5062 5.0443 ± 0.5205 6.5224 ± 0.4396 5.7395 ± 0.5918 
All 6.2252 ± 0.5868 5.8146 ± 0.5794 5.7278 ± 0.6031 4.9352 ± 0.5838 4.3967 ± 0.6289 5.0473 ± 0.5446 6.4908 ± 0.4458 5.7099 ± 0.5847 

At Yangbei Lake Wetland, Site No. 1 achieved the highest mean SBI (6.2252) due to its dense and abundant vegetation along the lake, highly naturalised waterbodies, and a wooden bridge. In contrast, Site No. 2 reached the lowest mean SBI (4.9352) because of its highly modified landscapes, linear embankments, fewer types and densities of vegetation, and a stone bridge. For Shuijing Park, Site No. 3 reached the highest level of well-managed vegetation along buffers, wooden boardwalks, and pavilions. However, a subsurface flow wetland at Site No. 1 has the lowest SBI.

Statistical analysis of SBIs

The statistical paired t-test proceeded to conduct differences between SBI voting by a group of ‘Non-professionals’ and ‘Professionals’ (Table 7). The accuracy and stability of the group of ‘Professionals’ and the group of ‘Non-professionals’ were adjacent to each other (paired mean difference = −0.12; p = 0.052 > 0.05), and no deviation was observed. In general, SBI voted by the group of ‘Professionals’ was only slightly higher than that of ‘Non-professionals’.

Table 7

Results of paired t-test

ItemPaired (M ± SD)
Paired mean differencetp
Paired 1Paired 2
Non-professionals paired Professionals 5.48 ± 0.70 5.60 ± 0.69 −0.12 −2.336 0.052 
ItemPaired (M ± SD)
Paired mean differencetp
Paired 1Paired 2
Non-professionals paired Professionals 5.48 ± 0.70 5.60 ± 0.69 −0.12 −2.336 0.052 

The aesthetic values of ecological landscape should be considered by landscape architects and engineers when designing constructed WQT wetlands. To determine the contributions of multi-effectors to SBIs, the mean SBIs of each criterion layer are shown in Table 8. Distinctive properties of different criteria have been indicated at other sites, which will be analysed in detail later.

Table 8

Mean SBIs of each criterion layer (B1–3)

Yangbei 1Yangbei 2Yangbei 3Yangbei 4Shuijing 1Shuijing 2Shuijing 3Shuijing 4
Mean SBI of B1 6.2631 6.2450 6.4422 5.4181 4.2842 5.5007 6.8319 6.2262 
Mean SBI of B2 5.9926 5.7603 5.6855 5.0835 4.9655 5.5640 6.9707 5.8485 
Mean SBI of B3 6.5442 5.3953 4.9403 3.5836 3.4526 3.5141 5.1644 4.8197 
Yangbei 1Yangbei 2Yangbei 3Yangbei 4Shuijing 1Shuijing 2Shuijing 3Shuijing 4
Mean SBI of B1 6.2631 6.2450 6.4422 5.4181 4.2842 5.5007 6.8319 6.2262 
Mean SBI of B2 5.9926 5.7603 5.6855 5.0835 4.9655 5.5640 6.9707 5.8485 
Mean SBI of B3 6.5442 5.3953 4.9403 3.5836 3.4526 3.5141 5.1644 4.8197 

Results of WQI monitoring

The constructed WQT wetland is one of the most effective media to purify domestic and industrial wastewater. Following the design process of eco-landscaping engineering as ‘design–management–monitoring–evaluation–adjustment’ (Xiang et al. 2022), first-hand in situ WQT monitoring is essential for comprehending WQI–SBI correlation to summarise design strategies. In situ monitoring of each WQI per site was conducted in June 2021 (Table 9). Detailed methods have been introduced in Section 2.3. It is indicated that WQIs were improved after successive purification processes of WQT wetlands. However, slightly adverse purification effects for CODCr have existed.

Table 9

WQIs of two wetlands

SitepHDO (mg/L)CODCr (mg/L)Temperature (°C)NH3-N (mg/L)TP (mg/L)V (m/s)
Yangbei Lake Wetland The inlet 8.41 ± 0.05 5.50 ± 0.26 81.67 ± 2.52 29.37 ± 0.06 0.45 ± 0.16 0.45 ± 0.04 0.3 
Site No. 1 7.95 ± 0.08 5.60 ± 0.17 90.33 ± 4.40 28.80 ± 0.00 0.45 ± 0.02 0.35 ± 0.04 0.15 
Site No. 2 7.83 ± 0.01 6.02 ± 0.17 84.67 ± 3.79 29.47 ± 0.12 0.36 ± 0.01 0.21 ± 0.01 0.15 
Site No. 3 7.22 ± 0.03 7.03 ± 0.06 66.77 ± 1.76 29.43 ± 0.06 0.13 ± 0.02 0.10 ± 0.02 0.28 
Site No. 4 7.71 ± 0.01 7.10 ± 0.20 67.67 ± 1.53 30.03 ± 0.06 0.12 ± 0.01 0.05 ± 0.04 0.33 
Shuijing Park The inlet 9.27 ± 0.01 6.13 ± 0.15 93.06 ± 1.53 32.33 ± 0.12 0.37 ± 0.01 0.32 ± 0.02 0.1 
Site No. 1 8.26 ± 0.03 6.60 ± 0.36 70.03 ± 4.00 30.43 ± 0.06 0.28 ± 0.02 0.26 ± 0.02 0.15 
Site No. 2 7.92 ± 0.03 7.37 ± 0.06 53.67 ± 3.21 32.34 ± 0.31 0.19 ± 0.01 0.13 ± 0.02 0.35 
Site No. 3 7.58 ± 0.02 7.73 ± 0.15 40.33 ± 1.53 31.03 ± 0.12 0.13 ± 0.04 0.11 ± 0.01 0.3 
Site No. 4 8.00 ± 0.04 8.01 ± 0.17 44.67 ± 2.52 30.30 ± 0.10 0.11 ± 0.01 0.12 ± 0.01 0.2 
SitepHDO (mg/L)CODCr (mg/L)Temperature (°C)NH3-N (mg/L)TP (mg/L)V (m/s)
Yangbei Lake Wetland The inlet 8.41 ± 0.05 5.50 ± 0.26 81.67 ± 2.52 29.37 ± 0.06 0.45 ± 0.16 0.45 ± 0.04 0.3 
Site No. 1 7.95 ± 0.08 5.60 ± 0.17 90.33 ± 4.40 28.80 ± 0.00 0.45 ± 0.02 0.35 ± 0.04 0.15 
Site No. 2 7.83 ± 0.01 6.02 ± 0.17 84.67 ± 3.79 29.47 ± 0.12 0.36 ± 0.01 0.21 ± 0.01 0.15 
Site No. 3 7.22 ± 0.03 7.03 ± 0.06 66.77 ± 1.76 29.43 ± 0.06 0.13 ± 0.02 0.10 ± 0.02 0.28 
Site No. 4 7.71 ± 0.01 7.10 ± 0.20 67.67 ± 1.53 30.03 ± 0.06 0.12 ± 0.01 0.05 ± 0.04 0.33 
Shuijing Park The inlet 9.27 ± 0.01 6.13 ± 0.15 93.06 ± 1.53 32.33 ± 0.12 0.37 ± 0.01 0.32 ± 0.02 0.1 
Site No. 1 8.26 ± 0.03 6.60 ± 0.36 70.03 ± 4.00 30.43 ± 0.06 0.28 ± 0.02 0.26 ± 0.02 0.15 
Site No. 2 7.92 ± 0.03 7.37 ± 0.06 53.67 ± 3.21 32.34 ± 0.31 0.19 ± 0.01 0.13 ± 0.02 0.35 
Site No. 3 7.58 ± 0.02 7.73 ± 0.15 40.33 ± 1.53 31.03 ± 0.12 0.13 ± 0.04 0.11 ± 0.01 0.3 
Site No. 4 8.00 ± 0.04 8.01 ± 0.17 44.67 ± 2.52 30.30 ± 0.10 0.11 ± 0.01 0.12 ± 0.01 0.2 

To assess the WQT effects of each purification stage in constructed WQT wetland systems, the removal (or increase) contributions (%) of WQIs of each water quality purification stage have also been computed, respectively (Table 10).

Table 10

Removal (or increase) contributions of each sort of pollutant in each site

Site No.DO increase (%)CODCr removal (%)NH3-N removal (%)TP removal (%)
Yangbei Lake No. 1 1.41 −10.60 0.00 22.22 
Yangbei Lake No. 2 5.92 6.93 20.00 31.10 
Yangbei Lake No. 3 14.23 21.92 51.11 24.44 
Yangbei Lake No. 4 0.99 1.10 2.22 11.10 
Shuijing Park No. 1 5.87 24.75 24.32 18.75 
Shuijing Park No. 2 4.49 17.58 24.32 9.38 
Shuijing Park No. 3 3.50 −4.19 16.22 6.25 
Shuijing Park No. 4 3.50 4.66 5.44 3.13 
Site No.DO increase (%)CODCr removal (%)NH3-N removal (%)TP removal (%)
Yangbei Lake No. 1 1.41 −10.60 0.00 22.22 
Yangbei Lake No. 2 5.92 6.93 20.00 31.10 
Yangbei Lake No. 3 14.23 21.92 51.11 24.44 
Yangbei Lake No. 4 0.99 1.10 2.22 11.10 
Shuijing Park No. 1 5.87 24.75 24.32 18.75 
Shuijing Park No. 2 4.49 17.58 24.32 9.38 
Shuijing Park No. 3 3.50 −4.19 16.22 6.25 
Shuijing Park No. 4 3.50 4.66 5.44 3.13 

WQI–SBI correlation analysis

To figure out relations among WQIs for indicating pollutant removal contributions (i.e., DO, CODCr, NH3-N, and TP) and mean SBIs, correlation analysis has been processed by Python 3.0 with the ‘NumPy’ package. Firstly, Pearson correlation test analysis (Table 11) proceeded.

Table 11

Results of the Pearson correlation tests

WQI–SBI itemsRp
DO–SBI 0.6310* 0.0000 
CODCr–SBI 0.7697** 0.0003 
NH3-N–SBI 0.7619** 0.0001 
TP–SBI −0.0278 0.0000 
WQI–SBI itemsRp
DO–SBI 0.6310* 0.0000 
CODCr–SBI 0.7697** 0.0003 
NH3-N–SBI 0.7619** 0.0001 
TP–SBI −0.0278 0.0000 

** of high correlations; * of significant correlations.

SBIs were found to be correlated with the WQIs, as listed in Table 11. High correlations have been found among CODCr–SBI (R = 0.7697, p < 0.001) and NH3-N − SBI (R = 0.7619, p < 0.001), while significant correlations have been found among DO–SBI (R = 0.6310, p < 0.001). No correlation existed among TP–SBI (R = −0.0287, p < 0.001). Based on the existing research on landscape perception, SBIs of EEL projects are often related to strategies of ecological landscaping (Chen et al. 2014). Firstly, a linear regression model was performed by a Python programme with the package Scikit-learn, with a single variable among correlative items, i.e., DO–SBI, CODCr–SBI, and NH3-N–SBI (Tables 7 and 10). Plot graphs are drawn in Figure 5. DO increase rate (), CODCr removal rate (), NH3-N removal rate (), and SBIs () were indicated to follow the empirical prediction Equation (2).
formula
formula
formula
(2)
Figure 5

Linear regression of SBI–WQI relationships.

Figure 5

Linear regression of SBI–WQI relationships.

Close modal
Secondly, the Python programme sets a linear regression model with multiple variables among SBIs–WQIs. As seen from Table 12, DO increase, CODCr removal, and NH3-N removal are independent variables, and the SBI was used as the dependent variable for linear regression analysis. The linear regression formula has been solved in Equation (3):
formula
(3)
Table 12

Results of linear regression with multiple variables among SBIs–WQIs

Unstandardised coefficients
Standardised coefficientstpVIFR²Adj. R²F
BetaStd. errorBeta
Constant 5.097 0.297 – 17.133 0.000** – 0.688 0.453 F (3,4) = 2.936
p = 0.163 
DO increase −15.754 15.625 −0.945 −1.008 0.37 11.263 
CODCr removal 1.27 1.858 0.263 0.684 0.532 1.9 
NH3-N removal 6.258 4.085 1.48 1.532 0.2 11.953 
Unstandardised coefficients
Standardised coefficientstpVIFR²Adj. R²F
BetaStd. errorBeta
Constant 5.097 0.297 – 17.133 0.000** – 0.688 0.453 F (3,4) = 2.936
p = 0.163 
DO increase −15.754 15.625 −0.945 −1.008 0.37 11.263 
CODCr removal 1.27 1.858 0.263 0.684 0.532 1.9 
NH3-N removal 6.258 4.085 1.48 1.532 0.2 11.953 

Dependent variable: SBI.

D–W: 1.553.

*p < 0.05; **p < 0.01.

DO increase, CODCr removal, and NH3-N removal can explain 68.8% of the changes in the SBI with R2 = 0.688 (F = 2.936, p = 0.163). These phenomena were listed as follows.

A significant positive correlation between the DO increase rate and the SBI is found in both Yangbei Lake and Shuijing Park. DO levels related to variations in the intensity of nitrification and denitrification in substrates. FWIs at site Shuijing No. 3 were planted with more amounts and types of aquatic vegetation than other sites, while high different water levels are led by weirs, which contribute to the highest DO increase rate and the highest SBI. Other sites presented less DO increase contributions.

In addition, a relatively high positive correlation between CODCr and NH3-N removal was indicated. CODCr in the constructed WQT wetland was believed to be removed by the absorption and bio-metabolic degradation effects of plants' roots. At the same time, substrates and microorganisms grown on surfaces played a vital role in NH3-N removal (Cao et al. 2020). Noticeable results for CODCr and NH3-N have been shown at specific sites. The most apparent NH3-N reduction was observed at site Yangbei No. 3 (51.11%), at which SBI was also the highest. Meanwhile, a VF wetland at Shuijing No.1 attained the lowest SBI (4.3967). At the same time, improvements in WQIs (24.75% of CODCr and 24.32% of NH3-N removal) have occurred due to the lack of diverse vegetation, highly homogenised physical geographical conditions, as well as insufficiencies of human geographical elements. In addition, Site Yangbei No. 1 (SP) and Shuijing No. 3 (FWI) present negative removal contributions of NH3-N, which might be influenced by uncertain external pollution, although they obtained a higher SBI. TP removal was believed to be accomplished by adsorption, complexion, and precipitation by plants and the biochemical action of microorganisms (Ebrahimi et al. 2022). However, no obvious TP–SBI correlations have been indicated (R= − 0.0278, p < 0.01) on account of in situ conditions during the field research.

Effects of SBI effectors for WQT wetland landscaping

Two aspects are commonly admitted for integrations of scenic beauty and ecological effects. On the one hand, knowledge of environmental geography plays a role in moderating beauty cognitions to be ecologically aware. On the other hand, it was believed that landscape aesthetic experience was a psychological process with perceptual, affective, and cognitive characteristics, which were often obtained from patterns of waterbodies, vegetation, and micro-topologies (Gobster et al. 2007). Phenomena and rules can be indicated from SBI voting as follows.

Relationships between vegetation conditions and SBIs

Plants were often beautifully shaped for sites with higher scores, while dense vegetation promoted higher green-looking ratios. The high density of arbours contributed more to SBIs. Scenes with multi-layered vegetation (e.g., Yangbei No. 1, SBI = 6.2252) were more favoured by participants than those with a medium density of shrubs (e.g., Yangbei No. 2, SBI = 5.8146) or floating-leaf plants (e.g., Yangbei No. 3, SBI = 5.7278). Based on these phenomena, arbours, shrubs, herbaceous plants, and floating vegetations should be arranged to maintain proper vegetation structures. Sites planted with homotypic deciduous vegetations were often attributed to lower SBIs, while sites vegetated with greater amounts of evergreen perennial aquatic plants reached higher SBIs. Neat Cinnamomum camphora groves planted further up the bank as the backview woods can lead to emotions of fatigue or dullness (e.g., Shuijing No. 2, SBI = 5.0473). Also, substrate beds planted with high-density and single-typed Cyperus involucratus Rottboll were the most unattractive scene to participants (SBI = 4.3967). Thus, the landscape design of SSF wetlands should focus on improving varieties and integrations of vegetation.

Relationships between physical geographical conditions and SBIs

Sites with semi-natural geo-features were often rated higher SBIs, which is influenced by micro-topology and the form of the waterbody. Sites with slope revetments and well-vegetated buffers were often more ornamentally valuable than those with linear embankments (e.g., Yangbei No. 3 and 4). Sites with vegetated wavy micro-topologies were often more attractive than the others (e.g., Shuijing No. 2 and 4), more multi-layered spaces could be created by setting appropriate micro-topologies and wetland EEL measures can be formed delicately. Sites with high degrees of waterbody openness were rated higher for SBIs. Waterscapes with a higher degree of openness demonstrated a higher level of potential than those of lower openness. This phenomenon can be explained by psychological perceptions of human beings. Empirical research (Völker & Kistemann 2013) demonstrated that open waterscapes can improve citizens' mental health, probably because waterscapes were often regarded as clean, peaceful, and worth preserving. Well-designed waterscapes are a primary factor contributing to the improved restorative potential of the urban environment. Also, shape, flow velocity, and transparency of water can affect people's perceptions of aesthetics in semi-natural environments (Yamashita 2002). Semi-natural waterscapes were supported for rendering visitors' attention. Waterscapes at those sites of higher waterbody openness are beneficial to improve participators' attention restoration into senses of relaxation, immersion, and calmness (Kaplan 1995). However, those with vast and empty water were often non-attractive (e.g., the sediment pond at Yangbei No. 4), which should be avoided in practical design as much as possible.

Relationships between human geographical conditions and SBIs

Sites with human geographical elements have more potential for natural education, which is often attributed to higher SBIs. As for Yangbei Lake Wetlands, the bridge with ornamental boats (Site No. 1) and waterwheels (Site No. 2) attributed to higher human geographical characteristics, which highlighted features of hydraulic landscapes. As for Shuijing Park, boardwalks and pavilions (Site No. 3) and a wooden hut at the OP (Site No. 4) supported cultural aesthetics. All sites in both wetlands owned specific potentials for activities of natural education, especially for presentations of water purification processes. However, no related appliances or signals have been set in situ.

Applicable EEL measures in design and management

Following strategies of ecological engineering landscaping

Humans both perceive and experience landscapes, which demonstrate an intertwined relationship. In urban environments, people's perceptions of landscapes are often correlated to a sense of ‘place attachment’ and their ‘perceived restoration’ (Chen et al. 2014; Goetcheus et al. 2016). The perception realm theory (PRT) by Gobster et al. (2007) suggests that humans often cannot perceive large-scale ecological phenomena that occur in natural ecosystems. Humans are limited to perceiving small-scale cognitive landscape characters and patterns. Therefore, EEL projects should focus on human's landscape aesthetic experience at a specific scale, which would encompass broader ecological processes. EEL measures can be integrated into landscaping, i.e., re-designing buffer zones, constructing green infrastructures, and re-arranging vegetation structures (Li et al. 2022b). Also, waterscapes in different sections of WQT wetlands are recommended to be designed for higher cognitions. Wetland cells should be instructed to design in shapes with length/width ratios of less than 4:1 for treatment purposes. Curvilinear shapes that follow existing topographical contours should be used. Also, WQT wetlands should be designed to balance the friendly biodiversity and the WQT benefits (Addo-Bankas et al. 2022; Liu et al. 2023). Overall, when planning and designing WQT wetlands, it is crucial to incorporate both aesthetic design factors and technical engineering considerations of EEL (Huang et al. 2022). Embankments, shoals, ponds, and pools can be innovated in practical design for purifying waterbodies and reducing flow rates.

Improving WQT techniques

Constructed WQT wetland EEL integrates a highly complex WQT system, which normally consists of vegetation, microorganisms, and substrates. Among them, microorganisms played a vital role in improving water qualities, whereas substrates' WQT capabilities often relate to vegetation and physical geographical conditions. Suggestions for further improvements of WQT techniques are advocated as follows. For the two wetlands mentioned in the research, EEL measures were indicated to be vital for arousing visitors' biophilic attention to the physical geographical phenomena and EEL principles of WQT processes. Both physical and human geographical conditions should be optimised, especially for SPs and VFs. Node design of the SP and VF wetlands was found to be omitted by landscape architects. By adapting waterbodies with higher openness, greater amounts of natural-form micro-topologies at buffers can contribute to more significant WQI improvements and SBIs. Vegetation can remove multiple pollutants by assimilation and oxidation, e.g., Phragmites australis and Canna indica L. are effective for CODCr and NH3-N removal. Increasing vegetation density and planting more floating-leaf vegetation at APs and FSF wetlands are recommended. Otherwise, the constructions of siltation ponds are not appropriate in urban environments, as they are inclined to be polluted significantly.

Applying the attention restoration theory in the WQT wetland landscaping practices

Exposure to semi-natural environments may enhance attention performance through two distinct mechanisms: the modulation of alertness and the connection between humans and nature (Johnson et al. 2022). According to the attention restoration theory (ART) suggested by Kaplan (1995), four types of restorative natural environments, including ‘Being away’, ‘Fascination’, ‘Compatibility’, and ‘Extent’, can be attributed to semi-natural wetland waterscapes. Waterscapes with higher naturalness levels usually demonstrate a better effect of participating in restoration than those of lower naturalness (Yamashita 2002; Nassauer & Faust 2013). Among them, ‘being away’ means to be distant from highly modified artificial landscape elements. Sites with characteristics of ‘fascination’ are often arranged with abundant landscape elements. Sites with spaces for developing diverse educational and recreational activities are regarded with high ‘compatibility’. High and medium naturalness can create restorative semi-natural environments of ‘being away’ and ‘extent’ (Kaplan 1995). In practical wetland design, waterscapes of higher naturalness and openness with semi-natural micro-topologies can significantly improve WQTs and scenic beauty (Li et al. 2022a). Therefore, wetland landscapes should be designed for better attention to restorative achievements by designing waterscapes with naturalised characters, proper vegetation, and abundant landscape elements, which was mentioned by Sevenant & Antrop (2009). Also, the urban constructed wetlands are important for the management of the urban water environment and the promotion of the water-sensitive urban design (WSUD) because they are resilient to the challenges of climate change and were often the surrounding places where citizens potentially want to live. Urban constructed WQT wetlands designed and constructed with integrated urban water management solutions were often developed in the context of social and aesthetic values.

Further improvements of experiments

So far, two main methods have been widely adopted in the SBE, i.e., expert-based evaluations (Sowińska-Świerkosz & Chmielewski 2016) and the perception-based method (Peng & Han 2018). In terms of cost and time, expert-based methods outperformed perception-based methods. In the study, we found that it was easier to verify the reliability and validity of perception-based methods with AHP voting than expert-based methods. Further improvements in the following can be adapted in the experiment.

  • (1)

    The SBE process can be disturbed by weather conditions and participants' viewpoints of the scenery. If states were permitted, panoramic photographs or virtual reality (VR) presentations could be adapted for the SBI judging.

  • (2)

    Apart from the visual sense, other senses, i.e., hearing, smell, and touch, can also affect the perception of the voluntary judges. Soundscapes have intensive relationships with human perception. Fountains with waterfalls or jects can produce acoustic aversions (Patón et al. 2020). Also, integrating the soundscape with the visual landscape in urban outdoor spaces can increase the aesthetic attractiveness for citizens and tourists. Therefore, multi-sensory perception and the related SBE scoring should further proceed to improve the credibility of experiments.

  • (3)

    Some changes of hydrological and vegetation conditions in urban WQT wetland parks due to temperature may affect visitors' visual perceptions and the results of the SBI voting for landscapes (Ma et al. 2023). The experiment in this study measured water temperature between 24.5 and 29.5 °C. It remains uncertain whether the SBI–WQI relationships found in the study remain valid for colder water temperatures (0–10 °C), and further studies are required.

This research focuses on the scenic beauty values of the EEL measures for the design and management of WQT wetlands. During processes of the quantitative AHP-based SBE, with investigations by experts and SBI voting by participators, SBIs at each site have been obtained. WQI–SBI correlations have been summarised. DO increase, CODCr, and NH3-N removal contributions are significantly related to SBIs, while no obvious TP–SBI correlations have been indicated. Adapting SBIs to constructed WQT wetlands can offer proper guidance for both landscape architects and engineers. EEL strategies for wetlands have been concluded, i.e., following strategies of EEL, improving WQT techniques, and applying ART in landscape design. Adjusted vegetation and physical and human geographical conditions will enable the creation of sustainable landscapes that promote eco-tourism and natural education. A deeper understanding of the SBIs will enhance the practical design, management, and maintenance of urban WQT wetlands.

Y.H. and T.L. wrote the main manuscript text, Y.J. and T.L. prepared some data, and W.W. reviewed the paper and offered some advice. All authors reviewed the manuscript.

The research was supported by the National Natural Science Foundation of China (Project #51208467) and the Scientific Research Foundation for the High-level Talent Introduction, Zhejiang University of Technology (Project #118001929).

This study recruited some voluntary participants from the campus of the Zhejiang University of Technology. Free and informed consent of the participants or their legal representatives was obtained. All the research protocol of the study was approved by the Ethics Committee of Zhejiang University of Technology (Zhejiang Province, P. R. China). All participants gave their verbal informed consent before participating in the experiment. The authors would like to express their sincere gratitude to them.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Bourassa
S. C.
1991
The Aesthetics of Landscape
.
Belhaven Press
,
London and New York, UK
.
Buhyoff
G. J.
,
Gauthier
L. J.
&
Wellman
J. D.
1984
Predicting scenic quality for urban forests using vegetation measurements
.
Forest Science
30
,
71
82
.
Cao
T. S.
,
Giang
N. T. H.
,
Thao
T. P.
,
Nui
N. H.
,
Lam
N. T.
&
Cong
V. H.
2020
Assessment of Cau River water quality assessment using a combination of water quality and pollution indices
.
AQUA
69
(
2
),
160
172
.
Ebrahimi
E.
,
Asadi
H.
,
Rahmani
M.
,
Farhangi
M. B.
&
Ashrafzadeh
A.
2022
Effect of precipitation and sediment concentration on the loss of nitrogen and phosphorus in the Pasikhan River
.
AQUA
71
(
2
),
211
228
.
Franco
D.
,
Franco
D.
,
Mannino
I.
&
Zanetto
G.
2003
The impact of agroforestry networks on scenic beauty estimation
.
Landscape and Urban Planning
62
,
119
138
.
Geng
Z.
,
Jiang
W.
,
Peng
K.
,
Deng
Y.
&
Wang
X.
2023
Wetland mapping and landscape analysis for supporting international wetland cities: case studies in Nanchang City and Wuhan City
.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
99
,
1
14
.
Girts
M. A.
,
David
G.
&
Mary
J. K.
2012
Integrated water and ecosystem service management as complementary utility-beneficial approaches
.
Proceeding of the Water Environment Federation
.
doi:10.2175/193864712811704305
.
Gobster
P.
,
Nassauer
J.
,
Daniel
T.
&
Fry
G.
2007
The shared landscape: what does aesthetics have to do with ecology
.
Landscape Ecology
22
,
959
972
.
Goetcheus
C.
,
Karson
R.
&
Carr
E.
2016
Designing living landscapes: cultural landscapes as landscape architecture
.
Landscape Journal
35
,
6
15
.
Grêt-Regamey
A.
,
Bishop
I. D.
&
Bebi
P.
2007
Predicting the scenic beauty value of mapped landscape changes in a mountainous region through the use of GIS
.
Environmental Planning B: Planning and Design
34
,
50
67
.
Huang
Y.
,
Lange
E.
&
Ma
Y. C.
2022
Living with floods and reconnecting to the river – landscape planning and design for delta plains
.
Journal of Environmental Engineering and Landscape Management
30
,
206
219
.
Hull
R.
&
Buhyoff
G.
1983
Distance and scenic beauty
.
Environment and Behavior
15
,
77
91
.
Hull
R.
&
Stewart
W.
1992
Validity of photo-based scenic beauty judgments
.
Journal of Environmental Psychology
12
,
101
114
.
Johnson
K.
,
Pontvianne
A.
,
Ly
V.
,
Jin
R.
,
Januar
J.
,
Machida
K.
,
Sargent
L.
,
Lee
K.
,
Williams
N.
&
Williams
K.
2022
Water and meadow views both afford perceived but not performance-based attention restoration: results from two experimental studies
.
Frontiers in Psychology
13
,
809629
.
Junge
X.
,
Jacot
K. A.
,
Bosshard
A.
&
Lindemann-Matthies
P.
2009
Swiss people's attitudes towards field margins for biodiversity conservation
.
Journal of Nature Conservation
17
,
150
159
.
Kaplan
S.
1995
The restorative benefits of nature: toward an integrative framework
.
Journal of Environmental Psychology
15
,
169
182
.
Nassauer
J.
&
Faust
C.
2013
Cultural sustainability: Aligning aesthetics and ecology
. In:
Placing Nature: Culture and Landscape Ecology
.
(Nassauer, J., ed.)
Bibliovault OAI Repository, the University of Chicago Press
,
Chicago, IL
.
Patón
D.
,
Delgado
P.
,
Galet
C.
,
Muriel
J.
,
Méndez Suárez
M.
&
Hidalgo-Sánchez
M.
2020
Using acoustic perception to water sounds in the planning of urban gardens
.
Building and Environment
168
,
1
10
.
Saaty
T. L.
1980
The Analytic Hierarchy Process
.
Mc Graw-Hill Company
,
London
.
Saaty
T. L.
1990
How to make a decision the analytic hierarchy process
.
European Journal of Operational Research
48
,
9
26
.
Schirpke
U.
,
Tasser
E.
&
Tappeiner
U.
2013a
Predicting scenic beauty of mountain regions
.
Landscape and Urban Planning
111
,
1
12
.
Schirpke
U.
,
Hölzler
S.
,
Leitinger
G.
,
Bacher
M.
,
Tappeiner
U.
&
Tasser
E.
2013b
Can we model the scenic beauty of an alpine landscape?
Sustainability
5
,
1080
1094
.
Sevenant
M.
&
Antrop
M.
2009
Cognitive attributes and aesthetic preferences in assessment and differentiation of landscapes
.
Journal of Environmental Management
90
(
9
),
2889
2899
.
Sowińska-Świerkosz
B. N.
&
Chmielewski
T. J.
2016
A new approach to the identification of Landscape Quality Objectives (LQOs) as a set of indicators
.
Journal of Environmental Management
184
,
596
608
.
Van Zanten
B. T.
,
Verburg
P. H.
,
Koetse
M. J.
&
van Beukering
P. J. H.
2014
Preferences for European agrarian landscapes: a meta-analysis of case studies
.
Landscape and Urban Planning
132
,
89
101
.
Van Zanten
B. T.
,
Zasada
I.
,
Koetse
M. J.
,
Ungaro
F.
,
Häfner
K.
&
Verburg
P. H.
2016
A comparative approach to assess the contribution of landscape features to aesthetic and recreational values in agricultural landscapes
.
Ecosystem Services
17
,
87
98
.
Wang
Y.
,
Zlatanova
S.
,
Yan
J.
,
Huang
Z.
&
Cheng
Y.
2020
Exploring the relationship between spatial morphology characteristics and scenic beauty preference of landscape open space unit by using point cloud data
.
Environment and Planning B: Urban Analytics and City Science
48
.
doi:10.1177/2399808320949885
.
Xiang
L.
,
Zhang
H.
,
Chen
Z.
,
Chen
C.
,
Chen
P.
,
Sang
K.
&
Hua
G.
2022
Purification effect of aquatic plant communities in constructed wetland park: a comparative experiment of Tianhe wetland park in China
.
Environmental Research Communications
4
(
5
),
055007
.
Xie
B.
,
Cui
Y.
,
Yuan
Q.
&
Li
J.
2009
Pollutants removal and distribution of microorganisms in a reed wetland of Shanghai Mengqing Park
.
Environmental Progress & Sustainable Energy
28
,
240
248
.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).