3.1 Basic growth trend
Figure 1 illustrates the amount of CW publications over the previous 34 years (different subject categories of Scopus that classify the nature of different papers is shown in Figure 2). It is clear that CW research has risen steadily over time, peaking in 1999 and 2009, when the number of publications climbed significantly from 14 in 1998 to 31 in 1999 and from 109 in 2008 to 169 in 2009. This developing tendency demonstrates that, whereas CW research has a long history, it has just recently garnered major attention from the scientific world. Unlike other disciplines of research, which experience periods of stagnation and volatility, CW research has been increasing for virtually the entirety of its existence.
This development could be attributed to more than just scientific curiosity; it could also be attributed to the tougher enforcement of environmental regulations. The occurrence of environmental incidents worldwide, of which there are several examples, prompted numerous political responses, including more restrictive legislation and more environmental funding. In the search for efficient and economically viable wastewater treatment methods, constructed wetlands were prioritized due to a number of advantages, including lower costs compared to other treatment technologies, high tolerance for water flow fluctuations common in urban areas, provision of a suitable ecosystem for wetland organisms, and harmonious integration with the surrounding landscape (Scholz and Lee, 2005; Scholz and Trepel, 2004). These benefits and advantages have been investigated in order to better understand the type and function of treatment mechanisms found within these artificial ecosystems.
3.2 Analysis of Journals
The 3842 retrieved publications from Scopus database were published in a total of 503 journals. The top 15 journals for CW research are listed in Table 2. The top 15 journals published 56.74 percent of all publications. "Ecological Engineering" ranks first overall with 554 articles (14.42 percent), followed by "Water Science and Technology" and "Science of The Total Environment," which each publishes 239 and 227 articles in CW research, accounting for 6.22 percent and 5.9 percent of total publications, respectively.
Table 2 Top 15 most productive journals in CW research.
|
Rank
|
Journal Title
|
IF
|
Publisher
|
Number of Publications
(Percent %)
|
1
|
Ecological Engineering
|
4.03
|
Elsevier
|
554
|
(14.41)
|
2
|
Water Science and Technology
|
1.91
|
IWAP*
|
239
|
(6.22)
|
3
|
Science of The Total Environment
|
7.96
|
Elsevier
|
227
|
(5.90)
|
4
|
Bioresource Technology
|
9.64
|
Elsevier
|
202
|
(5.25)
|
5
|
Environmental Science and Pollution Research
|
4.22
|
Springer
|
168
|
(4.37)
|
6
|
Water Research
|
11.24
|
Elsevier
|
130
|
(3.38)
|
7
|
Chemosphere
|
7.09
|
Elsevier
|
108
|
(2.81)
|
8
|
Journal of Environmental Management
|
6.79
|
Elsevier
|
91
|
(2.37)
|
9
|
Desalination and Water Treatment
|
1.25
|
DP*
|
89
|
(2.31)
|
10
|
Water Switzerland
|
3.10
|
MDPI*
|
74
|
(1.92)
|
11
|
Chemical Engineering Journal
|
13.27
|
Elsevier
|
70
|
(1.82)
|
12
|
Water Air and Soil Pollution
|
2.52
|
Springer
|
67
|
(1.74)
|
13
|
Journal of Environmental Science and Health Part A Toxic Hazardous Substances and Environmental Engineering
|
2.69
|
Taylor & Francis
|
59
|
(1.53)
|
14
|
Environmental Technology United Kingdom
|
2.65
|
Taylor & Francis
|
53
|
(1.38)
|
15
|
International Journal of Phytoremediation
|
3.21
|
Taylor & Francis
|
52
|
(1.35)
|
IWAP: International Water Association Publishing, DP: Desalination Publications, MDPI: Multidisciplinary Digital Publishing Institute.
|
3.3 Performance of Countries and Sponsors
The 3842 CW papers analyzed were written by scholars from 98 different countries. However, 15 countries were represented by a single article, while 99 papers were unattributed to any country. The top twenty productive countries in CW research are listed in Table 3. China (far first, 32.09 percent), the United States (15.46 percent), the United Kingdom (5.44 percent), Spain (4.94 percent), and India (4.06 percent) are the five most productive countries. Other indicators can be used to evaluate a country's success. As a result, another column in the table displays the number of publications produced entirely by authors from a specific country (SP). China, the United States, Spain, and India are the top five countries when single-country articles are used to judge country performance, with Brazil in fifth position.
The fifth column of Table 3 does not particularly highlight constructed wetlands. Rather than that, it is a measure of a country's total performance in the SJR topic category "Environmental Engineering." While China has been at the forefront of constructed wetland research, the United States has obtained the highest H-index in this field of study. Because the H-index is influenced by historical data (previous articles), it's worth noting that China has dominated in recent years regardless of the metric used. Figure 3 depicts the growth pattern of the five most active countries in CW research throughout the given time period. While it is true that the researchers in the United States were pioneers of CW in the early years, China has been rapidly surpassing the USA since 2009, following a period of rivalry.
Additionally, Tables 4 and 5 contain sponsorship information for published CW articles. Table 4 shows that China has sponsored the most papers, with 1910 appearances in articles through 61 sponsorship programmes. The European Commission and Brazil are the other major sponsors of CW research. Chinese sponsorship initiatives are also the most active in terms of CW research, as shown in Table 5. China's dominance in this field may be due to the Chinese government's recent commitment to monitoring water resources and cleaning up contaminated water bodies. In certain regions of China, water per capita is lower than that of Middle Eastern countries, compelling the government to invest heavily in water pollution prevention and control through the Water Pollution Prevention and Control Action Plan ("10-Point Water Plan") (Central People’s Government of the People’s Republic of China, 2015). A further 1700 million dollars has been provided by the Chinese government to 20 localities with serious water contamination (Tang and Sillanpää, 2018). Table 5 lists some of the sponsorship schemes that are directly tied to government initiatives. One such initiative is China's 11th five-year plan's "Major Science and Technology Program for Water Pollution Control and Treatment" (Mega Water Projects 2006–2020), which aims to enhance the efficiency of treatment facilities and restore contaminated water supplies (Zhi et al., 2016). As a demonstration project for municipal wastewater treatment, Tianjin established the country's first full-scale CW system in 1987. Around 150 publicly owned CWs for wastewater treatment have been constructed by the end of 2010 (Zhai et al., 2011). The primary reason for China's significant preference for CWs is the country's population dispersal patterns. Today, more than half of China’s population reside in cities of small (less than 200000 inhabitants) and middle size (between 200000 and 500000 inhabitants) (World Population Review, 2022; Zhang et al., 2009). Evidently, centralized water and wastewater treatment facilities will be incapable of adequately addressing the water crisis, sparking interest in less expensive solutions such as artificial wetlands (Brissaud, 2007). The Chinese people's growing awareness of water pollution also contributes to this enormous stride toward the adoption of diverse solutions for water and wastewater treatment. In 2015, a survey found that more than 70% of Chinese were concerned about water pollution, and 56% believed that the situation would not improve, indicating a serious concern in China about water pollution that motivates efforts to incorporate any type of effective methods for achieving a safer water resource (Wike and Parker, 2015). According to survey data, around one-third of Chinese are concerned about water quality in 2020 (Ipsos, 2020). According to a report published by China's Ministry of Ecology and Environment, only 15.9 percent of China's surface water is safe to drink without further treatment, while 86.4 percent of the country's groundwater supplies are contaminated in some way (Ministry of Ecology and Environment, 2020). Therefore, given the critical need for water and wastewater treatment and the widespread public concern about this issue, it is hardly surprising to see a massive expenditure invested on CW studies by Chinese funding sponsors. The same conditions apply to European countries as well. Small treatment facilities are required in the EU because of the population dispersal pattern. According to a 2014 survey, 50% of European inhabitants are concerned about water pollution (European Commission, 2014). Simultaneously, Europe is one of the world's hotspots for wetland loss, which motivates governments to spend more in the ecological and technological benefits of constructed wetlands (Hu et al., 2017). According to the same study, 36% of Europeans expressed concern over the depletion of natural resources, particularly water bodies.
The partnership network of the top twenty countries in CW research is depicted in Figure 4. Several of these countries (for example, China and the United States) are located in the network's heart, suggesting their willingness to collaborate with other countries. Certain countries, such as Singapore, Ireland, and the Czech Republic, on the other hand, have marginal positions, meaning their research is more separated from collaborative effort. In Figure 4, each node represents a country, and the lines between them illustrate how they interact. Consider how many times two countries have partnered; this number is known as the link strength. The total link strength of a country reveals how many times it has collaborated with others. Table 6 also shows the top 20 countries' collaborations, including the number of links, total link strength, total number of citations, and normalized citation score for each country. Because it equals the total number of citations for that country divided by the average number of citations for all papers produced in the same year by all other countries, the normalized citation score is an important factor (it is used to rank countries in Table 6). This is to address for the problem that older documents have received more citations than newer ones.
Table 3 Top 20 most productive countries in constructed wetland research.
|
No.
|
Country
|
Appearances (%)
|
(Rank) SP
|
H-index
|
1
|
China
|
1233
|
(32.07)
|
(1)
|
860
|
243
|
2
|
United States
|
594
|
(15.45)
|
(2)
|
381
|
404
|
3
|
United Kingdom
|
209
|
(5.44)
|
(7)
|
77
|
323
|
4
|
Spain
|
190
|
(4.94)
|
(3)
|
123
|
206
|
5
|
India
|
156
|
(4.06)
|
(4)
|
114
|
151
|
6
|
Canada
|
141
|
(3.67)
|
(8)
|
69
|
236
|
7
|
Germany
|
141
|
(3.67)
|
(12)
|
50
|
243
|
8
|
Brazil
|
130
|
(3.38)
|
(5)
|
104
|
142
|
9
|
France
|
129
|
(3.36)
|
(9)
|
63
|
223
|
10
|
Australia
|
126
|
(3.28)
|
(14)
|
44
|
228
|
11
|
Italy
|
117
|
(3.04)
|
(6)
|
78
|
201
|
12
|
Ireland
|
101
|
(2.63)
|
(17)
|
27
|
94
|
13
|
Czech Republic
|
87
|
(2.26)
|
(11)
|
55
|
91
|
14
|
Greece
|
86
|
(2.24)
|
(10)
|
62
|
130
|
15
|
Denmark
|
79
|
(2.05)
|
(19)
|
6
|
165
|
16
|
Poland
|
74
|
(1.92)
|
(13)
|
48
|
107
|
17
|
Portugal
|
73
|
(1.90)
|
(13)
|
48
|
117
|
18
|
South Korea
|
67
|
(1.74)
|
(15)
|
43
|
121
|
19
|
Netherlands
|
65
|
(1.69)
|
(18)
|
16
|
235
|
20
|
Thailand
|
64
|
(1.66)
|
(16)
|
37
|
81
|
* SP: Single-Country Articles, FA: First-Author Articles
|
Table 4 Top 10 countries funding constructed wetland research.
|
Rank
|
Country
|
Sponsorship Programs
|
Funded Articles
|
1
|
China
|
61
|
1910
|
2
|
European Commission
|
7
|
250
|
3
|
Brazil
|
8
|
155
|
4
|
USA
|
14
|
134
|
5
|
Spain
|
10
|
114
|
6
|
Canada
|
3
|
67
|
7
|
Germany
|
4
|
55
|
8
|
India
|
5
|
45
|
9
|
Portugal
|
3
|
36
|
10
|
Argentina
|
4
|
35
|
Table 5 Top 10 funding sponsors of constructed wetland research.
|
Rank
|
Sponsor Name
|
Country
|
Funded Articles
|
1
|
National Natural Science Foundation of China
|
China
|
621
|
2
|
European Commission
|
-
|
118
|
3
|
Major Science and Technology Program for Water Pollution Control and Treatment
|
China
|
108
|
4
|
Fundamental Research Funds for the Central Universities
|
China
|
102
|
5
|
Ministry of Science and Technology of the People's Republic of China
|
China
|
92
|
6
|
Ministry of Education of the People's Republic of China
|
China
|
80
|
7
|
National Key Research and Development Program of China
|
China
|
72
|
8
|
Ministry of Finance
|
China
|
61
|
9
|
China Scholarship Council
|
China
|
58
|
10
|
Ministry of Housing and Urban-Rural Development
|
China
|
56
|
Table 6 Co-authorship links and strength in top 20 countries
|
No.
|
Country
|
Link
|
Total Link Strength
|
Citations
|
Norm. Citation Score
|
1
|
China
|
17
|
392
|
26657
|
1445.37
|
2
|
United States
|
19
|
209
|
21931
|
574.21
|
3
|
Spain
|
12
|
51
|
8196
|
287.29
|
4
|
United Kingdom
|
17
|
144
|
5974
|
197.59
|
5
|
Australia
|
16
|
97
|
3937
|
171.85
|
6
|
Czech Republic
|
6
|
26
|
5099
|
152.52
|
7
|
India
|
15
|
55
|
2255
|
144.95
|
8
|
Germany
|
16
|
84
|
4737
|
139.34
|
9
|
Canada
|
12
|
79
|
4779
|
137.52
|
10
|
Ireland
|
9
|
93
|
2874
|
124.73
|
11
|
Denmark
|
16
|
84
|
2988
|
119.17
|
12
|
Italy
|
9
|
24
|
3204
|
114.22
|
13
|
France
|
16
|
60
|
2798
|
113.48
|
14
|
Netherlands
|
13
|
45
|
1614
|
66.93
|
15
|
Japan
|
10
|
35
|
1619
|
57.83
|
16
|
Belgium
|
13
|
41
|
1507
|
55.00
|
17
|
Singapore
|
7
|
40
|
895
|
52.43
|
18
|
South Korea
|
10
|
34
|
1386
|
50.36
|
19
|
Thailand
|
9
|
25
|
1547
|
45.16
|
20
|
Vietnam
|
10
|
30
|
462
|
25.93
|
3.4 Authorship Analysis
In total, 7833 individuals have contributed to CW research has. On average, 7.4 authors have taken part in each article. Around half of the authors have stepped into the field after 2014 (in the past 7 years). The participation of such a large number of authors in such a short period of time indicates that CW studies have garnered enormous attention from the research community. Additionally, Figure 6 illustrates the trend of authors entering the field of CW research. 5270 (67.28 percent) of the 7833 writers have only published one article, and 132 papers have a single author. Table 10 summarizes data on the top twenty productive authors in CW research. According to the table, Jian Zhang is the most productive author with 74 publications, followed by Jan Vymazal and Joan García with 59 and 57 articles, respectively. It's worth noting that academics from a variety of countries took the top five spots for most productive CW authors, with Chinese authors dominating the top twenty.
A technique for examining how researchers in a certain area interact with one another is the collaboration network of different authors. The co-authorship network of the top 50 CW researchers is depicted in Figure 5. It is worth noting that, in contrast to other fields of study, where some of the top authors work independently, all of the authors in CW studies were related. Table 8 also includes information regarding the co-authorship of the top 20 authors. Table 8 reorders the twenty most productive authors from Table 7 according to their normalized citation scores.
Table 7 Top 20 most active authors in CW research.
|
Rank
|
Author
|
Current Affiliation
|
Country
|
TP (%)
|
H-index
|
1
|
Zhang, Jian
|
Shandong University
|
China
|
74
|
(1.92)
|
55
|
2
|
Vymazal, Jan
|
Czech University of Life Sciences Prague
|
Czech Republic
|
59
|
(1.53)
|
47
|
3
|
García, Joan
|
Universitat Politècnica de Catalunya
|
Spain
|
57
|
(1.48)
|
51
|
4
|
Scholz, Miklas
|
Wrocław University of Environmental and Life Sciences
|
Poland
|
54
|
(1.40)
|
38
|
5
|
Zhao, Yaqian
|
Xi'an University of Technology
|
China
|
42
|
(1.09)
|
43
|
6
|
Song, Xinshan
|
Donghua University
|
China
|
39
|
(1.01)
|
25
|
7
|
Wu, Haiming
|
Shandong University
|
China
|
39
|
(1.01)
|
27
|
8
|
Brix, Hans
|
Aarhus Universitet
|
Denmark
|
37
|
(0.96)
|
63
|
9
|
Chang, Jie
|
College of Life Sciences, Zhejiang University
|
China
|
37
|
(0.96)
|
31
|
10
|
Ge, Ying
|
College of Life Sciences, Zhejiang University
|
China
|
37
|
(0.96)
|
30
|
11
|
Hu, Zhen
|
Shandong University
|
China
|
37
|
(0.96)
|
30
|
12
|
Liang, Shuang
|
Shandong University
|
China
|
37
|
(0.96)
|
36
|
13
|
Tsihrintzis, Vassiliοs Andrew
|
National Technical University of Athens
|
Greece
|
36
|
(0.94)
|
39
|
14
|
Xie, Huijun
|
Shandong University
|
China
|
36
|
(0.94)
|
29
|
15
|
Kuschk, Peter
|
Helmholtz Zentrum für Umweltforschung
|
Germany
|
32
|
(0.83)
|
38
|
16
|
Molle, Pascal
|
National Research Institute for Agriculture, Food and Environment (INRAE)
|
France
|
32
|
(0.83)
|
21
|
17
|
Wang, Yuhui
|
Donghua University
|
China
|
31
|
(0.81)
|
23
|
18
|
Guo, Wenshan
|
University of Technology Sydney
|
Australia
|
30
|
(0.78)
|
68
|
19
|
Wu, Zhenbin Bing
|
Institute of Hydrobiology, Chinese Academy of Sciences
|
China
|
30
|
(0.78)
|
32
|
20
|
Ngo, Huu Hao
|
University of Technology Sydney
|
Australia
|
28
|
(0.73)
|
75
|
* TP: Total Publications.
|
Table 8 Co-authorship information of top 20 countries of CW research.
|
No.
|
Author
|
Link
|
Total Link Strength
|
Citations
|
Norm. Citation Score
|
1
|
Zhang J.
|
36
|
290
|
2884
|
158.41
|
2
|
Vymazal J.
|
17
|
34
|
4625
|
138.12
|
3
|
Zhao Y.
|
28
|
95
|
1996
|
127.17
|
4
|
Wu H.
|
20
|
116
|
1876
|
102.18
|
5
|
Wang Y.
|
31
|
130
|
1659
|
98.20
|
6
|
Zhang Y.
|
35
|
131
|
1283
|
76.73
|
7
|
Li X.
|
25
|
73
|
1072
|
75.04
|
8
|
Brix H.
|
5
|
22
|
2171
|
72.93
|
9
|
Chen Y.
|
18
|
55
|
1101
|
70.86
|
10
|
Yang Y.
|
28
|
74
|
721
|
68.91
|
11
|
Wang X.
|
26
|
63
|
873
|
68.56
|
12
|
Song X.
|
14
|
88
|
1169
|
67.42
|
13
|
Guo W.
|
28
|
160
|
1393
|
65.62
|
14
|
Wang J.
|
32
|
96
|
1321
|
63.00
|
15
|
Wu S.
|
22
|
38
|
1141
|
59.91
|
16
|
Chen J.
|
22
|
53
|
650
|
59.44
|
17
|
Liu Y.
|
28
|
86
|
945
|
58.40
|
18
|
Li X.
|
25
|
79
|
1078
|
58.24
|
19
|
Wu J.
|
25
|
79
|
1118
|
57.07
|
20
|
Zhou Q.
|
21
|
89
|
1344
|
56.17
|
3.5 Keywords analysis
Author-designated keywords (often referred to as author keywords) are one way for researchers to communicate what they are searching for and working on in their study. As a result, author keyword analysis could be a useful indicator of research trends and in data visualization. Only author keywords that were appeared at least ten times were investigated in this study, yielding 245 unique keywords. To enhance the analysis and provide more precise results, two additional procedures were implemented:
- To begin, keywords that had the same sense but were classified differently were combined. For instance, the first data extraction labeled "hydraulic residence time" and "hydraulic retention time" as distinct keywords, despite the fact that they refer to the same thing. Another example is the segregation of the terms "heavy metal" and "heavy metals," which required to be united into a single entry. The complete dataset was thoroughly inspected in order to consolidate similar items and words with various spellings. This is a critical issue since, if not addressed, it has the potential to significantly reduce the quality of keyword analysis, as VOSviewer connects and identifies nodes based on keyword co-occurrence in the same article.
- Second, there are some broad terms such as "constructed wetland," "design," "developing countries," and "hydraulics" that, if used for the analysis, would provide no additional insight into research trends, but would instead distort and alter the true structure of the co-occurrence network. Additionally, some keywords were manually omitted from network analysis.
In all, 129 identical keywords (out of 245) were merged into 43 different entries during data cleansing. In addition, the network analysis excluded 38 generic keywords with no clear informational value. Finally, the software was used to analyze the 121 refined author keywords (each with at least ten occurrences). The co-occurrence network in Figure 6 depicts the relationship between the imported author keywords and their importance (weighting). Each node (circle) corresponds to a distinct term. The diameter of the circles represents the frequency with which each keyword appeared. Curves connect the circles, and a value represents the "link strength" of each connection (curve) between two nodes. Increased link strength results in thicker curves.
The research determined that the keywords could be categorized into four fundamental clusters (1–4), which are shown by the colors red, green, blue, and yellow. It should be emphasized that the “LinLog/modularity" achieved the most relevant clustering when compared to the other four normalization methods, namely "no normalization," "association strength," and "fractionalization." Figure 7 displays the locations of these four clusters inside the network to provide a clearer and more accurate image.
Generally, cluster 1, which includes the terms "vertical flow," "domestic wastewater," "horizontal subsurface flow," "phragmites australis," "hybrid constructed wetland," "Typha," and "reed bed," is concerned with determining the efficacy of various plant species in treating domestic wastewater and landfill leachate in particular. This cluster contains the majority of plant species that are regularly employed in constructed wetlands.
Cluster 2 keywords are mostly concerned with determining the effect of various elements on phytoremediation. The most often appearing keywords of this cluster include "nutrient," "phytoremediation," "water quality," "wetland plant," "heavy metal," "salinity," and "pH." Clusters 1 and 2 are strikingly similar in terms of their primary focus on plant uptake. However, the thin line separating these two foci is that the latter examines strategies for increasing the efficiency of plant species in removing specific pollutants from domestic wastewater, whereas the former primarily evaluates the viability of different species in removing specific pollutants from domestic wastewater. Cluster 3 is clearly devoted to the removal of nitrogen and phosphorus from wastewater using a variety of novel and classic technologies. "Nitrogen," "phosphorus," "aeration," "organic matter," "COD," and "BOD" are examples of keywords indicating the goal of studies in this cluster. Nitrogen and phosphorus, along with COD and BOD, are present due to their importance in effluent requirements and their abundance in agricultural runoff that is a main influent source for constructed wetlands due to their abundancy in rural areas. Additionally, the phrase "electrolysis" is included in this cluster due to the fact that several studies have demonstrated that this technology is quite effective at increasing the performance of constructed wetlands in terms of nitrogen and phosphorus removal (Dong et al., 2020; Gao et al., 2019; Ju et al., 2014). Cluster 4 contains the keywords "microbial community," "microbial fuel cell," "adsorption," "pharmaceutical," "substrate," "enzyme activity," "biofilm," and "bacteria," which all refer to the incorporation of microbial mechanisms in the treatment of various types of pollutants, with an emphasis on micropollutants and emerging contaminants such as pharmaceuticals. Simultaneously, electricity generation via microbial fuel cells (MFCs) is a significant area of research in this sector.
Table 9 shows the top five most frequently appearing keywords in each cluster, together with their occurrence and appearance histories, to provide more understanding for the clusters. Table 10 shows the relationship between the top five keywords from each cluster to help illustrate how they are connected. Each value in Table 10 represents the number of articles in which the vertically and horizontally connected keywords appeared together, with a bigger value indicating a stronger association between the two terms. Additionally, smaller squares denote the boundaries of each of the two clusters. The greater the sum of the values inside each square, the more significant they are, and cells in this table are highlighted in various hues ranging from blue (the weakest connection) to red (the strongest). As a result of Table 10, cluster 3 is determined to be the primary target of CW investigations due to its 507 co-occurrences with other clusters. In other words, experts in this discipline have focused their efforts on nitrogen and phosphorus removal. Additionally, among all top cluster keywords, "nitrogen," "phosphorous," and "vertical flow" are the three most frequently used terms to connect diverse topics of CW research, indicating the vertical flow constructed wetland's supremacy over other types of constructed wetlands. As previously said, nitrogen has received the most attention in the literature, most likely because to its abundance in various forms of wastewater. Although vertical flow constructed wetlands have an advantage in terms of oxygen transfer, which enhances their ability in nitrification, in order for a CW system to maintain an acceptable nitrification-denitrification performance, a combination of vertical flow and horizontal flow CW is the ideal combination (Nuamah et al., 2020; Tilley et al., 2014). This is because it is well recognized that a high dissolved oxygen (DO) content inhibits denitrification capacity by increasing the nitrate to ammonium ratio to levels that are occasionally greater than the absorption capacity of plant species. Another advantage of vertical constructed wetlands is the low breeding rate of mosquitos owing to the absence of still water. Significant removal rates of total coliform bacteria (98 percent), faecal coliform bacteria (95.61 percent), ammonium (69.69 percent), BOD (80.69 percent), COD (69.87 percent), and total phosphorous (50 percent) were obtained in a small scale vertical flow constructed wetland system (García-Ávila et al., 2019). The literature indicates that when an emphasis is placed on nitrification for plant uptake, vertical constructed wetland systems are recommended (Nuamah et al., 2020). Three terms emerged in the top five keywords in cluster 1: "vertical flow," "horizontal flow," and "municipal wastewater."
According to Table 9, the most frequently used keywords in each cluster may be classified into two basic groups: "contaminants" and "strategies and mechanisms." In order of frequency of occurrence, common target pollutants include nitrogen, phosphorous, heavy metals, COD, BOD, pharmaceuticals, pesticides, and emerging contaminants; additionally, popular strategies and mechanisms employed by researchers include vertical flow, horizontal subsurface flow, microbial community, phytoremediation, phragmites australis, aeration, microbial fuel cell, hybrid constructed wetland, Typha, and bacteria. The co-occurrence network's close proximity of all clusters (Figure 6) shows that neither process nor cluster is truly unique. This is also true in fact, because no single miraculous procedure can guarantee the highest treatment rate, and no single pollutant is the primary source of CWs.
Nitrogen, as illustrated in Figure 6, has the largest node size. Additionally, a look at Table 10 demonstrates that "nitrogen" (the first term in cluster 3) is a contagious term for all other clusters' top keywords. This indicates that numerous studies have concentrated on nitrogen removal by CWs. Following nitrogen, phosphorus is the most frequent keyword.
Numerous researchers working on a variety of pollutants (e.g., nitrogen, phosphorus, heavy metals, etc.) have extensively employed phragmites australis for phytoremediation. According to Table 9, the keyword "phragmites australis" has a lengthy history in CW research and a constant increasing trend, with an average publication year of 2012.
Table 9 Top 5 most-occurred author keywords of clusters 1-4.
|
|
No.
|
Author Keyword
|
OC
|
BY
|
APY
|
EY
|
Cluster 1
|
1
|
Vertical flow
|
67
|
1995
|
2014.55
|
2021
|
2
|
Domestic wastewater
|
73
|
2000
|
2014.03
|
2021
|
3
|
Horizontal subsurface flow
|
74
|
1995
|
2013.79
|
2021
|
4
|
Phragmites australis
|
63
|
1995
|
2012.48
|
2021
|
5
|
Hybrid constructed wetland
|
43
|
2003
|
2015.32
|
2021
|
Cluster 2
|
6
|
Nutrient
|
77
|
1992
|
2012.29
|
2021
|
7
|
Phytoremediation
|
70
|
2001
|
2015.43
|
2021
|
8
|
Water quality
|
40
|
1989
|
2011.11
|
2021
|
9
|
Wetland plant
|
50
|
1996
|
2014.11
|
2021
|
10
|
Heavy metal
|
48
|
1995
|
2014.51
|
2021
|
Cluster 3
|
11
|
Nitrogen
|
110
|
1993
|
2013.89
|
2021
|
12
|
Phosphorus
|
78
|
1995
|
2012.16
|
2021
|
13
|
Aeration
|
49
|
1999
|
2014.98
|
2021
|
14
|
Organic matter
|
55
|
1995
|
2014.06
|
2021
|
15
|
COD
|
45
|
1999
|
2013.22
|
2021
|
Cluster 4
|
16
|
Microbial community
|
162
|
1998
|
2018.06
|
2021
|
17
|
Microbial fuel cell
|
81
|
2012
|
2018.47
|
2021
|
18
|
Adsorption
|
60
|
1995
|
2013.63
|
2021
|
19
|
Pharmaceutical
|
58
|
2008
|
2015.72
|
2021
|
20
|
Substrate
|
49
|
2000
|
2015.08
|
2021
|
* OC: Occurrence; BY: Beginning Year; APY: Average Publication Year; EY: Ending Year
|
Table 11 contains information on the three most productive authors from each cluster in order to identify notable authors. As expected, Miklas Scholz appears in the most clusters, which corresponds to his presence in Table 7, albeit in a secondary position. Although Joan García is listed as the most productive author in Table 7, he appears to be concentrating his efforts on clusters 1 and 4, where he is the dominant author. It's worth noting that, as shown in Table 11, some institutes are more focused on specific areas of CW study. Shandong University, for example, is more active in nitrogen removal research, with two of the cluster's top three authors affiliated with the university.
Table 11 Top 3 productive authors in each cluster.
|
Cluster
|
Rank
|
Name
|
Affiliation
|
Cluster 1. Phytoremediation for municipal wastewater treatment
|
|
1
|
García, Joan
|
Universitat Politècnica de Catalunya
|
|
2
|
Tsihrintzis, Vassiliοs Andrew
|
National Technical University of Athens
|
|
3
|
Scholz, Miklas
|
Wrocław University of Environmental and Life Sciences
|
Cluster 2. Effect of different factors on phytoremediation
|
|
1
|
Vymazal, Jan
|
Czech University of Life Sciences Prague
|
|
2
|
Scholz, Miklas
|
Wrocław University of Environmental and Life Sciences
|
|
3
|
Brix, Hans
|
Aarhus Universitet
|
Cluster 3. Nitrogen and phosphorous removal
|
|
1
|
Zhang, Jian
|
Shandong University
|
|
2
|
Wu, Haiming
|
Shandong University
|
|
3
|
Scholz, Miklas
|
Wrocław University of Environmental and Life Sciences
|
Cluster 4. Microbial mechanisms for removal of emerging contaminants
|
|
1
|
García, Joan
|
Universitat Politècnica de Catalunya
|
|
2
|
Zhao, Yaqian
|
Xi'an University of Technology
|
|
3
|
Zhang, Jian
|
Shandong University
|
3.6 Research trend
The environmentally destructive phenomena of wastewater creation is a byproduct of human existence and will continue for many years to come in all human settlements. Among the various techniques of wastewater treatment, the CW system has attracted considerable interest in recent years due to its cost-effectiveness and ability to provide a variety of ancillary services. To identify hot subjects in CW research, one method is to examine the time duration between the first and final appearances of top keywords. Figure 8 depicts a different sort of co-occurrence network (an overlay network), in which more yellowish colors indicate more recent keywords and more bluish ones indicate older keywords. Cluster 4 contains more yellowish hues, as depicted in the image. Three of the top five most recent keywords in the entire network (for example, "microbial fuel cell," "sulfamethoxazole," and "electricity generation") belong to cluster 4. This figure equals respectively 2014.21, 2012.73, 2014.38, and 2016.68 for clusters 1-4 when the average publication year is calculated. By examining the final publication year in Table 9, it is established that no study in any of the clusters is dormant, although cluster 2 appears to be slightly less active.
Finally, a more in-depth examination of the overlay network reveals trends. For instance, it can be shown that studies on reducing the energy demands of CW systems are spearheading studies into the future. Additionally, studies examining the treatment of new pollutants such as antibiotics will be of relevance in the coming years. Energy generation by microbial fuel cells integrated into CWs is a powerful weapon in the fight for more sustainable solutions. The addition of electron acceptors in this process is an effective way to accelerate anaerobic treatment in CWs. In this approach, which was pioneered by (Potter, 1911), electron exchange between an acceptor anode and a donor cathode generates a difference via separate oxidation and reduction processes. This difference can then be used to generate energy. This enables us to use MFCs to simultaneously remove substrates and generate energy. As a result, various research have examined the use of MFCs in CWs for the treatment of a variety of substrates. Domestic and municipal wastewater can be a significant source of substrate for MFCs, as evidenced by the literature and several authors (Pandey et al., 2016; Pant et al., 2010). Despite the large number of studies examining the mechanics underlying this technology as well as its use for pollutant removal, there is still a considerable gap that needs to be filled through additional research. MFCs are extremely reliant on oxygen as the primary oxidant in the cathodes due to its high reduction potential. However, one of the disadvantages of MFCs is the low kinetics of oxygen reduction at low-cost cathode materials such as carbon (Rismani-Yazdi et al., 2008). This has provided an excellent motivation for them to investigate ways to improve their performance.
As illustrated in Figure 8, traditional contaminants such as phosphorus, BOD, and suspended solids are losing favor. Rather than that, new pollutants that have recently raised concerns are driving future CW research. The challenge with these types of pollutants is the difficulty of eliminating them from aquatic environments by treatment methods. This could be another reason for focusing on the process of these pollutants rather than on traditional pollutants.