Constructed wetlands as biofuel production systems

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LETTERS PUBLISHED ONLINE: 22 JANUARY 2012 | DOI: 10.1038/NCLIMATE1370

Constructed wetlands as biofuel production systems Dong Liu1 , Xu Wu1 , Jie Chang1,2 *, Baojing Gu1,3 , Yong Min1 , Ying Ge1,2 , Yan Shi1 , Hui Xue1,4 , Changhui Peng5,6 and Jianguo Wu7,8 Clean biofuel production is an effective way to mitigate global climate change and energy crisis1 . Progress has been made in reducing greenhouse-gas (GHG) emissions and nitrogen fertilizer consumption through biofuel production2–4 . Here we advocate an alternative approach that efficiently produces cellulosic biofuel and greatly reduces GHG emissions using waste nitrogen through wastewater treatment with constructed wetlands in China. Our combined experimental and literature data demonstrate that the net life-cycle energy output of constructed wetlands is higher than that of corn, soybean, switchgrass, low-input high-diversity grassland and algae systems. Energy output from existing constructed wetlands is ∼237% of the input for biofuel production and can be enhanced through optimizing the nitrogen supply, hydrologic flow patterns and plant species selection. Assuming that all waste nitrogen in China5 could be used by constructed wetlands, biofuel production can account for 6.7% of national gasoline consumption. We also find that constructed wetlands have a greater GHG reduction than the existing biofuel production systems in a full life-cycle analysis. This alternative approach is worth pursuing because of its great potential for straightforward operation, its economic competitiveness and many ecological benefits. The energy crisis and global warming pose two major challenges to sustainable development worldwide. Biofuels may offer a promising alternative to fossil fuels1,6 , but serious concerns arise about the adverse GHG consequences from using nitrogen fertilizers7 . Attempts have been made to reduce nitrogen use, such as using switchgrass3 , low-input high-diversity (LIHD) grasslands4 and microalgae2 . Waste-nitrogen recycling is an attractive idea and has been attempted through microalgae biofuel trials2,8,9 ; however, the present microalgae systems are still limited by their small production scale because of complex technology10 . Therefore, alternative approaches are needed for fuller use of waste nitrogen. A constructed wetland is an engineering ecosystem with plants and rhizosphere microorganisms living in a physical infrastructure to remove pollutants in waste water11,12 . At present, several thousand full-scale constructed wetlands are in operation for mitigating deteriorating wastewater pollution on this planet. The constructed wetland emerged as an alternative to the wastewater treatment plant (WTP), which is an engineering system that removes pollutants by physical, chemical and biological (using microorganisms) processes13 . The WTP remains the mainstream

approach for centralized and highly efficient treatment in populous areas with high energy consumption and GHG emissions. In contrast, constructed wetlands are economical and provide more ecosystem services such as carbon sequestration, biodiversity conservation and aesthetics12,14 . However, the waste biomass produced by constructed wetlands has become a problem in many places. The idea of using the waste biomass in constructed wetlands to produce biofuel has been put forward in recent years5,15,16 , but detailed research is lacking. Here, we present an approach that, with its related technologies, promotes cellulosic bioenergy production in constructed wetlands, and we carry out a comprehensive analysis of life-cycle energy balance. Meanwhile, we assess the GHG emissions together with other environmental and ecological benefits for using waste nitrogen to produce biofuels in constructed wetlands. We also evaluate the socio-economic feasibility and the biofuel production potential through constructed wetlands in China, where an enormous amount of waste nitrogen is discharged each year. We designed and constructed five experimental constructed wetlands in subtropical China (Supplementary Methods and Fig. S1). We determined bioenergy production in 12 plots, containing 30 plant species. Plots were fed with domestic waste water (for inflow water quality, see Supplementary Table S1). All plots were cleared in early spring to remove above-ground biomass before growth began. Plots were sampled for above-ground biomass production. We also compiled from the literature a data set of 52 comparable constructed wetlands with biomass data for the calculation of biofuel production worldwide. Furthermore, we selected three WTPs near the experimental constructed wetlands (Supplementary Fig. S1 and Table S2). Life-cycle assessment was carried out to estimate the energy balance and GHG emissions during biofuel production through constructed wetlands, from seedling growth to harvest, based on all stages of construction and operation, and energy used in transportation and converting crops to biofuels. Related data for other biofuel production and wastewater treatment systems were collected and compared with constructed wetlands regarding both production and treatment, respectively, and finally ecosystem services were assessed. Our combined experimental and literature results demonstrate that the maximum annual production of above-ground biofuel (dry biomass yield multiplied by energy released on combustion) is lower than that of algae, but much higher than the maximum production of other systems (Table 1). In fact, the bioenergy productivity of existing constructed wetlands worldwide varies over a wide range of

1 College of Life Sciences, Zhejiang University, 388 Yuhangtang Road, Hangzhou 310058, China, 2 Research Centre for Sustainable Development, Zhejiang University, Hangzhou 310058, China, 3 College of Economics, Zhejiang University, Hangzhou 310027, China, 4 International Colleges, Qingdao University, Qingdao 266061, China, 5 Institut des Sciences de L’Environnement, Département des Sciences Biologiques, Université du Quebec à Montréal, Case postale 8888, Montréal, Quebec H3C 3P8, Canada, 6 Laboratory for Ecological Forecasting and Global Change, College of Forestry, Northwest A & F University, Yangling 712100, China, 7 School of Life Sciences and Global Institute of Sustainability, Arizona State University, Tempe, Arizona 85287-4501, USA, 8 Sino-US Center for Conservation, Energy, and Sustainability, Hohhot 010021, Inner Mongolia University, Hohhot, China. *e-mail: [email protected].

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1370

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Table 1 | Comparison of biofuel production and wastewater treatment systems. Item

Bioenergy production (GJ ha−1 yr−1 )

CO2 sequestration*

GHG emission*

Cost–benefit analysis Benefit†

Cost†

28.8 139.7 0.3 0.4 0.7 0.7

184.4 183.3 0.4 1.2 0.4 0.3

13.3 48.9 0.3 0.4 1.0 0.7

0.9 0.1 592.2

NA NA NA

0.37 3.2 4.8–17.3

As biofuel production ecosystems Constructed wetland Microalgae9,30 LIHD grassland4 Switchgrass3 Corn6 Soybean6

1,836.5 4,178.4 88.8 199.1 158.1 45.8

Constructed wetland Microalgae30 WTP

– – –

31.0 0 4.0 16.2 NA NA As wastewater treatment systems 0.8 0 17.1

NA, not available; –, this item does not exist for wastewater treatment. GHGs include CO2 , CH4 and N2 O. *Megagram (1 Mg = 106 g) of CO2 equivalent per hectare per year for biofuel production ecosystems and kilograms of CO2 equivalent per kilogram of nitrogen removal for wastewater treatment systems. † 103 US$ per hectare per year for biofuel production ecosystems and US$ per kilogram of nitrogen removal for wastewater treatment systems.

values (from 11 to 1,836 GJ ha−1 yr−1 (1 GJ = 109 J); Supplementary Tables S1 and S3), and most constructed wetlands maintain a low productivity level, because high biomass yield is not a priority in wastewater treatment. However, the bioenergy yield of constructed wetlands can be substantially increased by taking advantage of discharged waste nitrogen, optimizing hydrologic flow pattern and selecting productive plant species. First, a substantial supply of nitrogen from waste water can promote bioenergy production in constructed wetlands. However, the response curve is logarithmic for the species in our experiment (Fig. 1a), indicating that the increase in bioenergy production per unit of added nitrogen decreases at higher levels of yield—a phenomenon of diminishing return17 . It shows that an optimum nitrogen level should be determined, like the baseline of nitrogenfertilizer application for corn17 . Furthermore, the technology for optimum nitrogen usage (for example, diluting nitrogen by recycling effluent water) should be explored to obtain maximum biofuel production that makes full use of waste nitrogen through constructed wetlands. Second, an optimal hydraulic pattern can promote bioenergy yields in constructed wetlands. The hydraulic pattern can be divided into surface-flow (wastewater flow on the surface passing through constructed wetlands) and subsurface-flow systems (wastewater flow beneath the surface)11 . The average biofuel production in subsurface-flow constructed wetlands was approximately onethird higher than in surface-flow constructed wetlands (P < 0.05; Fig. 1b). The reason for this is that the subsurface-flow constructed wetlands have mesic (not waterlogged) habitats suitable for abundant plant species, whereas surface-flow constructed wetlands have water-saturated (waterlogged) habitats, favouring aquatic plants with a lower biomass than mesophyte plants11 . Hence, constructed wetlands with vertical flow are highly productive and suitable for producing cellulosic biofuel. Third, a proper selection of plant species also plays an important role in biofuel production4,11 . If constructed wetlands are designed with the specific goal of biofuel production, plant species with high productivity can be selected. In our data set, Arundo donax ranked first, with a peak of 1,836 GJ ha−1 yr−1 (Fig. 1c, Supplementary Tables S1 and S3), comparable to the high potential productivity of other biofuel plant species reported (Table 1): 1,628 GJ ha−1 yr−1 for napier grass (Pennisetum purpureum) and 1,850 GJ ha−1 yr−1 for Echinochloa polystachy in tropical regions18 . Therefore, A. donax is a primary candidate species for biofuel production in constructed

wetlands. When different flow patterns and climate conditions are considered, however, Phragmites australis, Typha spp. and Mischantus spp. are also preferable. The above-mentioned optimization technologies can be easily applied in field operations, making constructed wetlands a feasible and attractive biofuel production system. For example, constructed wetlands in China have expanded rapidly in recent years, promoted both by government and local communities11,14 . Many of these constructed wetlands are located in suburban and rural areas, and bioenergy production from them can be operated by local residents with basic technical training. For constructed wetlands to be an effective and sustainable biofuel production system, several factors should be considered3,4,6,19,20 . First, net energy balance (NEB = energy output − input) and NEB ratio (equal to energy output/input)3,4,6 . Throughout their full life cycles, constructed wetlands have a higher average NEB (253 GJ ha−1 yr−1 ) than that of the other five biofuel production systems (corn, soybean, switchgrass, microalgae and LIHD grassland; Fig. 2). However, the high energy input of constructed wetlands (76.9 GJ ha−1 yr−1 ) led to a mid-level NEB ratio (4.29) among the six systems, lower than LIHD grassland4 and switchgrass3 , but higher than corn/soybean6 and algae systems2 . When considering both NEB and NEB ratios together, constructed wetlands can still be a promising biofuel production system. Second, GHG emissions. In constructed wetlands, CO2 emissions occur during the process of infrastructure construction, biomass production, harvesting and organic matter decomposition. During the production life cycle, fossil-fuel consumption released 6.3 Mg CO2 ha−1 yr−1 , higher than the other biofuel production systems except for microalgae (Supplementary Table S4). In constructed wetlands, the decomposition of organic matter in waste water produces short-cycle CO2 , which is not considered to contribute to the greenhouse effect, according to the Intergovernmental Panel on Climate Change21 , whereas the accumulation of organic matter from waste water and plants is calculated as CO2 sequestration, averaging 31 Mg CO2 ha−1 yr−1 in this study (Table 1). Owing to the high nitrogen concentration, N2 O emission from constructed wetlands (3.76 Mg CO2 equivalent ha−1 yr−1 on average) is ∼4,600% that of microalgae, ∼1,800% that of LIHD grassland and ∼710% that of corn/soybean (Supplementary Table S4). Constructed wetlands and algae systems are a source of CH4 (constructed wetlands emit 18.71 Mg CO2 equivalent ha−1 yr−1 CH4 , ∼1,700% that of microalgae), whereas grassland and corn/soybean are sinks for CH4 (Supplementary

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1370

LETTERS a

b

2,000

2,000

R2 = 0.95, n = 11, P < 0.01

1,500 Productivity (GJ ha¬1 yr¬1)

Productivity (GJ ha¬1 yr¬1)

1,500

1,000 R2 = 0.46, n = 17, P < 0.01

1,000

*

500

500 R2 = 0.83, n = 10, P < 0.01

0

0

20,000

40,000

0

60,000

Subsurface

c

2,000

Productivity (GJ ha¬1 yr¬1)

1,500

Surface

Hydrologic flow pattern

Nitrogen level (kg ha¬1 yr¬1) a

ab ab

ab ab

1,000

ab

ab 500

ab ab b

b

b

b

b

b

b

b

b

b

b

Ph

ra

Ar un gm do d ite on s a ax Ty us ph tra a lis Zi ang za us ni t a c ifo ad lia uc Ca iflo ra M nn isc ai an nd th ica us sin Cy e pe ns ru Ph s p is Sa alar a py i cc ru ha s ar s un ru di m na ar c u ea nd Sa i pi nd nac eu us m m uk or Iri o sp su ssi da co Ju nc ru Tr s ad us b al es tic ca Ca ntia us re re x Ca neb flex a re x d rasc Tr im en ia s or rrh ph is en ole as pi s Ele acc oc har ifl ha Sc or ris ho en pa a op lu Sc str le ho is en ctus op a cu let tu us pu s ng Lo en liu s m pe re nn e

0

Figure 1 | Relationship between nitrogen level, hydrologic flow pattern and plant species and bioenergy productivity in constructed wetlands. a, Nitrogen level: filled circle, A. donax; open diamond, P. australis, open triangle, Typha spp. b, Hydrologic flow patterns: asterisk, significant difference (P < 0.05). c, Plant species: matching lowercase letters indicate non-significant differences (P > 0.05) between plant species. For b and c the line and square within the box represent the median and mean values; bottom bars, bottom and top edges of boxes, and top bars represent 5%, 25%, 75% and 95% of all data, respectively. Open circles outside the boxes represent extreme values.

Table S4). In summary, over a life-cycle assessment, constructed wetlands emit large amounts of GHG, while also sequestrating even more CO2 than they emit, resulting overall in a GHG sink (Table 1). Furthermore, the annual emissions of CH4 , N2 O and fossil fuel CO2 from constructed wetlands were only 0.08%, 3.4% and 7.7% that of WTPs, respectively (Supplementary Tables S4 and S5). Considering that relatively high GHG emissions are associated with the function of wastewater treatment and that it would not lead to extra GHG emissions in biofuel production, then, by taking WTP as the baseline, constructed wetlands could offset GHG emissions by the reduction of 591 kg equivalent CO2 per kilogram of nitrogen removal (Table 1). When microalgae are used for wastewater treatment, they lead to higher net GHG emissions than constructed wetlands. This is because they do not have long-term carbon sequestration, even though they release less GHG than constructed wetlands and WTPs (Table 1). 192

Third, economic feasibility. Constructed wetlands have been economically competitive with WTPs in wastewater treatment and could be more competitive when considering biofuel production. The cost of removing 1 kg nitrogen in a constructed wetland is only one-third to one-half of the cost of a WTP in China (Table 1, Supplementary Table S6) when assuming the life span of both the constructed wetland and WTP to be 20 years. In particular, constructed wetlands have extremely low operating and maintenance costs (∼0.06–0.6 US$ kg−1 N), compared with that for WTPs (1.8–4.8 US$ kg−1 N) (Supplementary Table S6). As the purpose of constructed wetland construction is wastewater treatment, we considered only the cost related to biomass harvesting as the cost for biofuel production, then the cost of the biofuel product could account for only 0.5% of total cost, as low as ∼100 US$ ha−1 yr−1 . Assuming the conversion cost from cellulosic feedstock to ethanol is a constant,

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1370

LETTERS Energy input Energy output NEB NEB ratio

16

600

12

400

8

200

4

0

CW CW ethanol electricity + heat

CW biogas

Microalgae biodiesel

Switchgrass LIHD grassland ethanol ethanol

Corn ethanol

Soybean biodiesel

NEB ratio

Energy input or output (GJ ha¬1 yr¬1)

800

0

Figure 2 | Comparison of energy input, energy output, NEB and NEB ratio for six major biofuels: constructed wetlands, microalgae, switchgrass, LIHD grassland, corn and soybean. Constructed wetlands, CW; error bars, standard error of the mean. For details on the energy inputs for constructed wetlands see Supplementary Table S8.

constructed-wetland-based biofuel production is competitive among the biofuel production systems. Fourth, land requirement. Constructed wetlands require a land area five to ten times larger than WTPs to remove the same amount of waste nitrogen22 . The total maximum land requirement of constructed wetlands to treat all the waste nitrogen in China is about 4.3×105 km2 (accounting for 17.5% of China’s fallow land5 ), and they do not compete with food production for fertile soils. This indicates that land area may not be a barrier to constructed wetland expansion, taking China as a whole; but land resources may be limited in certain densely populated areas with huge amounts of waste-nitrogen discharge. Furthermore, constructed wetlands can provide a number of ecosystem services in large quantities14 . For monetized ecosystem services, constructed wetlands exhibit a much higher cost–benefit ratio than other biofuel systems (Table 1, Supplementary Table S7). Furthermore, constructed wetlands could also provide several other kinds of ecosystem service, such as biodiversity conservation and recreation, which are not offered by other biofuel production systems (Supplementary Table S7). How much bioenergy can constructed wetlands potentially produce? In China in 2007, domestic wastewater contained an estimated 2.0 Tg N (1 Tg = 1012 g; ref. 23), equal to one-tenth of the nitrogen fertilizer used in China, or about twice the nitrogen fertilizer used in Brazil. If the waste nitrogen is fully used, with biofuel production estimated for two typical biofuel crops—A. donax (for subtropical and tropical regions) and P. australis (for temperate regions)—the total biofuel production from waste nitrogen through constructed wetlands is estimated to be 8.2 × 107 GJ yr−1 , equivalent to 1.2% of gasoline usage in China24 . Furthermore, if 11.5 Tg total waste nitrogen (including waste nitrogen from industrial waste water and agricultural runoff, as well as that in domestic waste water5 ) is used by constructed wetlands, the cellulosic feedstock could be converted to 1.6 × 108 GJ yr−1 ethanol biofuel, equivalent to 6.7% of gasoline usage in China24 . Alternatively, the feedstock could generate 2.4 × 108 GJ yr−1 electricity and heat, which was 1.9% of China’s electricity usage24 . On the basis of our analysis, constructed wetlands have the potential to be another source of cellulosic feedstock for secondgeneration biofuels, with a number of additional significant environmental benefits. Constructed wetlands are scattered in space and small in size. Thus, one of the challenges for using

constructed wetlands for large-scale biofuel production is related to the problems of centralizing operations and transporting liquid fuels. To alleviate these problems, locally converting biomass to electricity with heat25 may be an option. Also, biomass from constructed wetlands can be gasified to produce power for local residents and for running facilities at constructed wetland sites. Therefore, our proposed strategy of using constructed wetlands can help to simultaneously meet the challenges of energy, wastewater treatment and ecological protection.

Methods Cultivation of plants for biofuel production. In the five experimental constructed wetlands, we established 12 monoculture plots, each 2.0 × 2.5 m, for biofuel production. We selected species with high biomass productivity. In April 2006, all plant species were transplanted at a density of 10 seedlings m−2 and with initial seedling heights of 20–30 cm. All plots were weeded by hand during the summer. For measuring the above-ground biomass production, a 0.5 × 2.0 m strip was placed in the centre of each plot, to avoid edge effects, following the method used in LIHD grassland experiments4 . We then cut the plants at 5 cm above the surface of the substrate at the end of September 2007. We sorted the harvested plant material by species, dried the samples at 65 ◦ C to constant weight, and then scaled the weight to the full 2 × 2.5 m plots. Data collection. There have been many studies on constructed wetlands worldwide, but only a few met our analytical criteria for biofuel production in this study. The following four criteria were applied to select appropriate studies, that is, data with above-ground biomass production; plant species; nitrogen-loading rate; and hydraulic flow pattern. Finally, we selected 52 studies that fit these criteria for constructed wetlands from more than 500 published articles distributed worldwide (Supplementary Table S3). Statistical analysis. The normality of the data set was tested by using the Shapio–Wilks test, and data transformation was not required. Correlations between productivity of each plant species and nitrogen-loading rate were tested by using the ordinary least-squares regression analysis. The data set was grouped in terms of flow pattern that included subsurface and surface types, and then one-way analysis of variance was used to test the effect of flow pattern on productivity. The same data set was regrouped in terms of plant species and one-way analysis of variance was used to test the difference in productivity among the species followed by numerous comparisons of means using the least significant difference tests. Significance of all treatment effects was tested at the α = 0.05 level, using SPSS for Windows (SPSS, Chicago, IL, USA; version 16.0). Life-cycle assessment of energy balance and GHG emissions. In experimental constructed wetlands, the energy inputs included the energy used during infrastructure construction, materials production and transportation, labour in construction work, planting of seedlings, operations for wastewater treatment,

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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1370

the energy consumption during biomass growth, harvesting, transportation to factories, and conversion of biomass to biofuel for biorefinery (Supplementary Table S5). The gross energy output of constructed-wetland biofuel production was calculated on the basis of 18.5 MJ kg−1 released on combustion4 . Different biofuel production methods capture different proportions of bioenergy in usable forms (Fig. 2). Biomass can be converted into ethanol, electricity plus heat, and biogas using an efficiency factor of 0.38, 0.6 and 0.75 respectively4,25,26 . To compare the energy balance during the wastewater treatment process, we selected three WTPs in the same region as the experimental constructed wetlands, located in the cities of Hangzhou, Fuyang and Shanghai (Supplementary Fig. S1). For the WTPs, energy consumed in wastewater treatment included initial energy consumption in construction work and successive energy consumption during operation, while there were no energy outputs. We assume that constructed wetlands and WTPs have a life span of 20 years11,22,27–29 ; thus, annual energy consumption in construction work was calculated as divided by 20 years. Further details on the construction and treatment performance of the WTPs are given in Supplementary Table S5. During the wastewater treatment and biofuel production, three principal GHG (CO2 , CH4 and N2 O) emissions were recorded. For the constructed wetlands, CO2 emissions were from the following processes: use of fossil fuel, electricity and labour for seedling plantations; harvesting; operating the constructed wetlands; feedstock transportation; and conversion of crop to biofuel in the factory (Supplementary Table S5). Likewise, for the WTPs, CO2 was emitted from use of fossil fuel, electricity and labour from construction materials, transportation, construction work in WTPs sites, and operating the WTPs. Moreover, we collected CH4 and N2 O emission data during wastewater treatment processes for both constructed wetlands and WTPs from the literature (Supplementary Fig. S3). We used global warming potential conversion factors for CH4 and N2 O to obtain the CO2 equivalents (on a 100-year time horizon, CH4 has a global warming potential of 25 relative to CO2 ; N2 O has a global warming potential of 298 relative to CO2 ). Detailed calculations of GHG emissions are described in the Supplementary Methods.

11. Liu, D. et al. Constructed wetlands in China: Recent developments and future challenges. Front. Ecol. Environ. 7, 261–268 (2009). 12. Vymazal, J. Enhancing ecosystem services on the landscape with created, constructed and restored wetlands. Ecol. Eng. 37, 1–5 (2011). 13. Rosso, D. & Stenstrom, M. K. The carbon-sequestration potential of municipal wastewater treatment. Chemosphere 70, 1468–1475 (2008). 14. Yang, W., Chang, J., Xu, B., Peng, C. & Ge, Y. Ecosystem service value assessment for constructed wetlands: A case study in Hangzhou, China. Ecol. Econ. 68, 116–125 (2008). 15. Ciria, M. P., Solano, M. L. & Soriano, P. Role of macrophyte Typha latifolia in a constructed wetland for wastewater treatment and assessment of its potential as a biomass fuel. Biosyst. Eng. 92, 535–544 (2005). 16. Wrobel, C., Coulman, B. E. & Smith, D. L. The potential use of reed canarygrass (Phalaris arundinacea L.) as a biofuel crop. Acta Agr. Scand. B 59, 1–18 (2009). 17. Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002). 18. Somerville, C., Youngs, H., Taylor, C., Davis, S. & Long, S. P. Feedstocks for lignocellulosic biofuels. Science 329, 790–792 (2010). 19. Goldemberg, J. et al. Ethanol for a sustainable energy future. Science 315, 808–810 (2007). 20. Agusdinata, D. B., Zhao, F., Ileleji, K. E. & DeLaurentis, D. Life cycle assessment of potential bio-jet fuel production in the United States. Environ. Sci. Technol. 45, 9133–9143 (2011). 21. IPCC Climate Change 2007: Synthesis Report (eds Pachauri, R. K. & Reisinger, A.) (Cambridge Univ. Press, 2007). 22. Zhang, D., Gersberg, R. M. & Keat, T. S. Constructed wetlands in China. Ecol. Eng. 35, 1367–1378 (2009). 23. China Ministry of Environmental Protection The First National Census of Pollution Sources (National Bureau of Statistics of China, 2010); available at http://www.stats.gov.cn/tjgb/qttjgb/qgqttjgb/t20100211_402621161.htm. 24. China National Bureau of Statistics China Statistical Yearbook 2009 (China Statistics Press, 2009). 25. Ohlrogge, J. et al. Driving on biomass. Science 324, 1019–1020 (2009). 26. Zeng, X., Ma, Y. & Ma, L. Utilization of straw in biomass energy in China. Renew. Sust. Energ. Rev. 11, 976–87 (2007). 27. Vymazal, J. Long-term performance of constructed wetlands with horizontal sub-surface flow: Ten case studies from the Czech Republic. Ecol. Eng. 37, 54–63 (2011). 28. Zhang, Q. H., Wang, X. C., Xiong, J. Q., Chen, R. & Cao, C. B. Application of life cycle assessment for an evaluation of wastewater treatment and reuse project - case study of Xi’an, China. Bioresour. Technol. 101, 1421–1425 (2010). 29. Zhou, J. B., Jiang, M. M., Chen, B. & Chen, G. Q. Energy evaluations for constructed wetland and conventional wastewater treatments. Commun. Nonlinear Sci. Numer. Simul. 14, 1781–1789 (2009). 30. Liu, J & Ma, X. The analysis on energy and environmental impacts of microalgae-based fuel methanol in China. Energ. Policy 37, 1479–1488 (2009).

Ecosystem-service calculation. We have quantified and monetized the ecosystem services, including biofuel production, net GHG emission, treated water provision, water regulation and environmental nitrogen pollution of constructed wetlands12,14 , as well as several biofuel production ecosystems. We also calculated the cost–benefit ratio of these ecosystems. For details, see Supplementary Table S7.

Received 13 June 2011; accepted 6 December 2011; published online 22 January 2012

References 1. Tilman, D. et al. Beneficial biofuels—the food, energy, and environment trilemma. Science 325, 270–271 (2009). 2. Clarens, A. F., Resurreccion, E. P., White, M. A. & Colosi, L. M. Environmental life cycle comparison of algae to other bioenergy feedstocks. Environ. Sci. Technol. 44, 1813–1819 (2010). 3. Schmer, M. R., Vogel, K. P., Mitchell, R. B. & Perrin, R. K. Net energy of cellulosic ethanol from switchgrass. Proc. Natl Acad. Sci. USA 105, 464–469 (2008). 4. Tilman, D., Hill, J. & Lehman, C. Carbon-negative biofuels from low-input high-diversity grassland biomass. Science 314, 1598–1600 (2006). 5. Gu, B. et al. Utilization of waste nitrogen for biofuel production in China. Renew. Sust. Energ. Rev. 15, 4910–4916 (2011). 6. Hill, J., Nelson, E., Tilman, D., Polasky, S. & Tiffany, D. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl Acad. Sci. USA 10, 11206–11210 (2006). 7. Gu, B. et al. The role of technology and policy in mitigating regional nitrogen pollution. Environ. Res. Lett. 6, 014011 (2011). 8. Christenson, L. & Sims, R. Production and harvesting of microalgae for wastewater treatment, biofuels, and bioproducts. Biotechnol. Adv. 29, 686–702 (2011). 9. Brennan, L. & Owende, P. Biofuels from microalgae—a review of technologies for production, processing, and extractions of biofuels and co-products. Renew. Sust. Energ. Rev. 14, 557–577 (2010). 10. Savage, N. The scum solution. Nature 474, 15–16 (2011).

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Acknowledgements We are grateful for the financial support provided by the National Science Foundation of China grants 31170305 and 30970281 and by the Y. C. Tang Disciplinary Development Fund.

Author contributions D.L. compiled the data and carried out the data analysis. J.C. designed the experimental research. X.W., B.G., Y.M., Y.G., Y.S. and H.X. participated in the experiments and contributed to the analysis. C.P. and J.W. contributed to the analysis. All authors contributed to the interpretation of the results and to writing the paper.

Additional information The authors declare no competing financial interests. Supplementary information accompanies this paper on www.nature.com/natureclimatechange. Reprints and permissions information is available online at http://www.nature.com/reprints. Correspondence and requests for materials should be addressed to J.C.

NATURE CLIMATE CHANGE | VOL 2 | MARCH 2012 | www.nature.com/natureclimatechange

NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE1370

LETTERS

Table 1 | Comparison of biofuel production and wastewater treatment systems. Item

Bioenergy production (GJ ha−1 yr−1 )

CO2 sequestration*

GHG emission*

Cost–benefit analysis Benefit†

Cost†

28.8 139.7 0.3 0.4 0.7 0.7

184.4 183.3 0.4 1.2 0.4 0.3

13.3 48.9 0.3 0.4 1.0 0.7

0.9 0.1 592.2

NA NA NA

0.37 3.2 4.8–17.3

As biofuel production ecosystems Constructed wetland Microalgae9,30 LIHD grassland4 Switchgrass3 Corn6 Soybean6

1,836.5 4,178.4 88.8 199.1 158.1 45.8

Constructed wetland Microalgae30 WTP

– – –

31.0 0 4.0 16.2 NA NA As wastewater treatment systems 0.8 0 17.1

NA, not available; –, this item does not exist for wastewater treatment. GHGs include CO2 , CH4 and N2 O. *Megagram (1 Mg = 106 g) of CO2 equivalent per hectare per year for biofuel production ecosystems and kilograms of CO2 equivalent per kilogram of nitrogen removal for wastewater treatment systems. † 103 US$ per hectare per year for biofuel production ecosystems and US$ per kilogram of nitrogen removal for wastewater treatment systems.

values (from 11 to 1,836 GJ ha−1 yr−1 (1 GJ = 109 J); Supplementary Tables S1 and S3), and most constructed wetlands maintain a low productivity level, because high biomass yield is not a priority in wastewater treatment. However, the bioenergy yield of constructed wetlands can be substantially increased by taking advantage of discharged waste nitrogen, optimizing hydrologic flow pattern and selecting productive plant species. First, a substantial supply of nitrogen from waste water can promote bioenergy production in constructed wetlands. However, the response curve is logarithmic for the species in our experiment (Fig. 1a), indicating that the increase in bioenergy production per unit of added nitrogen decreases at higher levels of yield—a phenomenon of diminishing return17 . It shows that an optimum nitrogen level should be determined, like the baseline of nitrogenfertilizer application for corn17 . Furthermore, the technology for optimum nitrogen usage (for example, diluting nitrogen by recycling effluent water) should be explored to obtain maximum biofuel production that makes full use of waste nitrogen through constructed wetlands. Second, an optimal hydraulic pattern can promote bioenergy yields in constructed wetlands. The hydraulic pattern can be divided into surface-flow (wastewater flow on the surface passing through constructed wetlands) and subsurface-flow systems (wastewater flow beneath the surface)11 . The average biofuel production in subsurface-flow constructed wetlands was approximately onethird higher than in surface-flow constructed wetlands (P < 0.05; Fig. 1b). The reason for this is that the subsurface-flow constructed wetlands have mesic (not waterlogged) habitats suitable for abundant plant species, whereas surface-flow constructed wetlands have water-saturated (waterlogged) habitats, favouring aquatic plants with a lower biomass than mesophyte plants11 . Hence, constructed wetlands with vertical flow are highly productive and suitable for producing cellulosic biofuel. Third, a proper selection of plant species also plays an important role in biofuel production4,11 . If constructed wetlands are designed with the specific goal of biofuel production, plant species with high productivity can be selected. In our data set, Arundo donax ranked first, with a peak of 1,836 GJ ha−1 yr−1 (Fig. 1c, Supplementary Tables S1 and S3), comparable to the high potential productivity of other biofuel plant species reported (Table 1): 1,628 GJ ha−1 yr−1 for napier grass (Pennisetum purpureum) and 1,850 GJ ha−1 yr−1 for Echinochloa polystachy in tropical regions18 . Therefore, A. donax is a primary candidate species for biofuel production in constructed 2

wetlands. When different flow patterns and climate conditions are considered, however, Phragmites australis, Typha spp. and Mischantus spp. are also preferable. The above-mentioned optimization technologies can be easily applied in field operations, making constructed wetlands a feasible and attractive biofuel production system. For example, constructed wetlands in China have expanded rapidly in recent years, promoted both by government and local communities11,14 . Many of these constructed wetlands are located in suburban and rural areas, and bioenergy production from them can be operated by local residents with basic technical training. For constructed wetlands to be an effective and sustainable biofuel production system, several factors should be considered3,4,6,19,20 . First, net energy balance (NEB = energy output − input) and NEB ratio (equal to energy output/input)3,4,6 . Throughout their full life cycles, constructed wetlands have a higher average NEB (253 GJ ha−1 yr−1 ) than that of the other five biofuel production systems (corn, soybean, switchgrass, microalgae and LIHD grassland; Fig. 2). However, the high energy input of constructed wetlands (76.9 GJ ha−1 yr−1 ) led to a mid-level NEB ratio (4.29) among the six systems, lower than LIHD grassland4 and switchgrass3 , but higher than corn/soybean6 and algae systems2 . When considering both NEB and NEB ratios together, constructed wetlands can still be a promising biofuel production system. Second, GHG emissions. In constructed wetlands, CO2 emissions occur during the process of infrastructure construction, biomass production, harvesting and organic matter decomposition. During the production life cycle, fossil-fuel consumption released 6.3 Mg CO2 ha−1 yr−1 , higher than the other biofuel production systems except for microalgae (Supplementary Table S4). In constructed wetlands, the decomposition of organic matter in waste water produces short-cycle CO2 , which is not considered to contribute to the greenhouse effect, according to the Intergovernmental Panel on Climate Change21 , whereas the accumulation of organic matter from waste water and plants is calculated as CO2 sequestration, averaging 31 Mg CO2 ha−1 yr−1 in this study (Table 1). Owing to the high nitrogen concentration, N2 O emission from constructed wetlands (3.76 Mg CO2 equivalent ha−1 yr−1 on average) is ∼4,600% that of microalgae, ∼1,800% that of LIHD grassland and ∼710% that of corn/soybean (Supplementary Table S4). Constructed wetlands and algae systems are a source of CH4 (constructed wetlands emit 18.71 Mg CO2 equivalent ha−1 yr−1 CH4 , ∼1,700% that of microalgae), whereas grassland and corn/soybean are sinks for CH4 (Supplementary

NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange

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SUPPLEMENTARY INFORMATION DOI: 10.1038/NCLIMATE1370

Constructed wetlands as biofuel production systems Dong Liu, Xu Wu, Jie Chang*, Baojing Gu, Yong Min, Ying Ge, Yan Shi, Hui Xue, Changhui Peng and Jianguo Wu *To whom correspondence should be addressed. E-mail: [email protected] This PDF file includes: Methods: Site description for CWs and Calculation of GHG emissions Figure S1 to S3 Tables S1 to S8

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1

1

METHODS Site description for the experimental constructed wetlands. Five experimental constructed wetlands (CW) were designed and constructed in Zhejiang Province, subtropical China (Fig. S1). The Zhoushan CW is described here as an example. The system was filled with three layers of material: 0.5 m of 50–120 mm size gravel in the bottom, 0.2 m of 6–12 mm gravel in the middle, and 0.4 m of 1–2 mm coarse sand at the top. Wastewater was supplied three times per day intermittently, and the water level was controlled by an outlet valve (Fig. S2). Calculation of greenhouse gas emissions. We considered three principle GHG emissions from CWs and wastewater treatment plants (WTP): CO2, CH4 and N2O. We measured the life-cycle environmental impacts of CO2, which equals net ecosystem CO2 sequestration (in soil or sludges1) minus fossil CO2 released into the atmosphere during the operations of CWs and WTPs. Carbon fixed by way of herbaceous biomass in CWs, as in croplands2, was harvested per year and released back to the atmosphere within one year, thus it does not contribute to a long-term carbon sink and was excluded in the calculation of net ecosystem CO2 sequestration in this paper. There is no carbon fixation as no green plants grow in the WTP. Net ecosystem CO2 sequestration includes CO2 storage in substrates of CWs from wastewater and plants, and in the sludge1 of WTP from wastewater. According to IPCC, CO2 released from biogenic organic matter in wastewater (not originating from fossil fuels) would not contribute to the greenhouse effect3 and thus was excluded from the calculation of net ecosystem CO2 sequestration. We transformed estimates of per hectare energy use into per hectare CO2 emission using the emission factor of 81.56 g CO2 MJ-1 from literature3. To compare the GHG emissions during the wastewater treatment process, we excluded GHG emissions from refinery processes for CWs and WTPs and compared GHG emissions in terms of g CO2 equivalent per kg nitrogen (N) removal. Data in the unit of Mg CO2 equivalent ha-1 yr-1 were converted by two steps: firstly, using average hydraulic loading rate of CWs (464 L m-2 d-1) and WTPs (1,235 L m-2 d-1) to get Mg CO2 equivalent treating per m3 wastewater; secondly, using average nitrogen removal rate of CWs (0.0213 kg N removal treating per m3 wastewater) and WTPs (0.035 kg N removal treating per m3 wastewater) to get g CO2 equivalent kg-1 nitrogen (N) removal. These conversion parameters are calculated from Table S1 and S2. Detailed CO2 emission during wastewater treatment including construction and operation stages of CWs are shown in Table S5.

2

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Supporting figures and tables

Figure S1. The field location of the five CWs managed for biofuel production experiment (filled circles) and three WTPs (open circles) located in subtropical China. The words ‘Zhejiang’ and ‘Shanghai’ represent the names of provinces.

Figure S2. Physical structure and hydrologic flow pattern design for vertical subsurface CWs for experiment.

3

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Figure S3. Literature data of CH4 (left) and N2O (right) fluxes from WTP, microalgae and CW (in three seasons). All data are transformed base on Log 10 and 95% confidence intervals are shown.

4

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Table S1. Information in five experimental CWs in subtropical region, China Location

Hangzhou Botanic Garden, Zhejiang Province (30°N)

Dongyang, Zhejiang Province (29.16°N) Ningbo University, Zhejiang Province (29.52°N) Houcongyuan residential Ningbo, Zhejiang Province (29.5°N) Zhujiajian, Zhoushan, Zhejiang Province (29.5°N)

Built year

2001

Area (ha)

0.06

Wastewater type

Polluted pond water

Hydraulic Loading rate (m3 d-1)

160

Treatment performance (mg L-1) Total N (TN) COD Inflow

1.7

Outflow

0.9

Inflow

2.8

Outflow

1.1

BOD5

Inflow

4.8

Plant species

Outflow

1.19

Bioenergy yield (GJ ha-1 yr-1)

Canna indica Miscanthus sinensis Saccharum arundinaceum Phragmites australis

323.8 223.1 169.6 167.2

2008

4.0

Domestic and medicine wastewater

2002

0.1

Domestic wastewater

100

16.0

4.3

24.9

2.2

50

5

Canna indica

227.6

2001

0.16

Domestic wastewater

124

57.7

15.3

251.8

22.5

126.8

11.7

Canna indica

217.3

Saccharum arundinaceum

321.9

0.3

Domestic wastewater

Arundo donax

1836.5

2005

15000

40

20

80

16





Arundo donax

1665.0

800

39.3

7.5

13.2

3.036

12.6

2.9

5

© 2012 Macmillan Publishers Limited. All rights reserved.

Table S2. Location and production information in three WTPs for treat domestic and industrial wastewater in subtropical region, China Location

Built year

Hangzhou WTP, Zhejiang 2009 Province (30°18´N, 120°12´E) Bayi WTP, Fuyang, Zhejiang 2002 Province (30°06´N, 119°59´E) Shanghai suburban WTP (31° 1998 13´N, 121° 28´E)

Area (ha)

Performance (mg L-1)

Loading rate (m3 d-1)

TN

COD

BOD5

Inflow

Outflow

Inflow

Outflow

Inflow

Outflow

1.7

20000

50

15

400

60

200

10

13.3

150000

60

15

500

60

220

10

0.9

10000

45

20

500

120

200

30

6

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Table S3. Compiled dataset on CWs in China and other countries worldwide CW type*

Mass loading rate (g N m-2 d-1)

Plant species

Bioenergy yield (GJ ha-1 yr-1)

Tianjin (38°N), China

SF

1.08

NA

157.3

4

Beijing (40°N), China

SF

0.68

NA

543.9

4

Qinghe, Beijing (40°N), China

SF

0.06

NA

277.5

4

Taihu, Wuxi, Jiangsu Province (30.5°N), China

SF

3.08

Typha angustifolia

370.0

5

Taihu, Wuxi, Jiangsu Province (30.5°N), China

SF

NA

Typha angustifolia

573.5

5

Taihu, Wuxi, Jiangsu Province (30.5°N), China

SF

NA

Typha angustifolia

425.5

5

Liaohe Oilfield, Liaoning Province (41°N), China

SSF

NA

Phragmites communis

95.1

6

Liaohe Oilfield, Liaoning Province (41°N), China

SSF

NA

Phragmites communis

108.6

6

Liaohe Oilfield, Liaoning Province (41°N), China

SF

NA

Phragmites communis

138.9

6

Liaohe Oilfield, Liaoning Province (41°N), China

SF

NA

Phragmites communis

121.5

6

Kunming, Yunnan Province (24°N), China

SF

NA

Zizania caduciflora

768.2

7

Kunming, Yunnan Province (24°N), China

SF

NA

Phragmites australis

430.4

7

Kunming, Yunnan Province (24°N), China

SF

NA

Zizania caduciflora

440.3

7

Kunming, Yunnan Province (24°N), China

SF

NA

Phragmites australis

356.7

7

Guangzhou, Guangdong Province (23°N), China

SSF

NA

Phragmites australis

1500.4

8

Miyun, Beijing (40°N), China

SSF

NA

Phragmites australis

232.8

9

Guangzhou, Guangdong Province (23°N), China

SSF

5.10

Canna indica

1063.8

10

Shatianhu, Shanghai (31°N), China

SSF

0.07

Scirpus validus

23.3

11

Location

Source

7

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Shatianhu, Shanghai (31°N), China

SSF

0.11

Scirpus validus

23.3

11

Nanjing, Jiangsu Province (32°N), China

SF

NA

Canna indica

392.2

12

Nanjing, Jiangsu Province (32°N), China

SF

NA

Phragmites australis

790.0

12

Nanjing, Jiangsu Province (32°N), China

SF

NA

Zizania caduciflora

173.9

12

Nanjing, Jiangsu Province (32°N), China

SF

NA

Acorus calamus

144.3

12

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

0.44

Canna indica

178.9

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Typha spp.

104.5

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Phragmites australis

167.2

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Tradescantia reflexa

133.2

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Saccharu arundinaceum

175.8

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Lolium perenne

64.4

13

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Juncus spp.

64.9

13

Longhongjian, Hangzhou, Zhejiang Province (30°N), China

IVSSF

NA

Lolium perenne

22.6

14

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Canna indica

323.8

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Miscanthus sinensis

223.1

15

8

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Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Sapindus mukorossi

216.4

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Saccharum arundinaceum

175.8

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Iris psudacorus

170.1

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Tradescantia reflexa

124.7

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Typha spp.

91.4

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Triarrhena sacchariflora

83.6

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Zizania caduciflora

56.5

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Reineckia carnea

40.1

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Phalaris arundinacea

39.9

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Lolium perenne

39.3

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Iris psudoacorus

35.0

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Carex dimorpholepis

32.6

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Carex dimorpholepis

19.4

15

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Acorus calamus

16.2

15

9

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Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Iris psudacorus

131.9

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Saccharum arundinaceum

169.6

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Canna indica

138.6

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Acorus calamus

107.1

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Carex dimorpholepis

109.3

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Tradescantia reflexa

100.1

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Reineckia carnea

96.0

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Zizania caduciflora

85.8

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Typha spp.

69.0

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Triarrhena sacchariflora

55.5

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Sapindus mukorossi

45.9

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Zizania caduciflora

45.5

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Lolium perenne

30.5

16

Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Iris psudoacorus

24.2

16

10

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Hangzhou Botanic Garden, Zhejiang Province (30°N), China

IVSSF

NA

Commelina communis

27.4

16

Hong Kong (22°N), China

SSF

NA

Typha spp.

1091.9

17

Hong Kong (22°N), China

SSF

NA

Typha spp.

277.5

17

Dianchi, Kunming, Yunnan Province (24°N), China

SF

NA

Zizania caduciflora

1259.9

18

Dianchi, Kunming, Yunnan Province (24°N), China

SF

NA

Phragmites australis

664.2

18

Dianchi, Kunming, Yunnan Province (24°N), China

SF

NA

Zizania caduciflora

769.6

18

Dianchi, Kunming, Yunnan Province (18°N), China

SF

NA

Phragmites australis

564.3

18

Tianjin (38°N), China

SSF

NA

Typha spp.

796.6

19

Tianjin (38°N), China

SSF

NA

Typha spp.

942.7

19

Tianjin (38°N), China

SSF

NA

Typha spp.

1197.2

19

Tianjin (38°N), China

SSF

NA

Typha spp.

1319.8

19

Tianjin (38°N), China

SSF

NA

Typha spp.

956.8

19

Tianjin (38°N), China

SSF

NA

Typha spp.

1069.9

19

Taihu, Wuxi, Jiangsu Province (30°N), China

SF

NA

Typha spp.

397.8

5

Taihu, Wuxi, Jiangsu Province (30°N), China

SF

NA

Typha spp.

573.5

5

Taihu, Wuxi, Jiangsu Province (30°N), China

SF

NA

Typha spp.

416.3

5

Czech Republic (50°N)

SSF

NA

Phragmites australis

402.7

20

Czech Republic (50°N)

SSF

NA

Phragmites australis

375.6

20

Czech Republic (50°N)

SSF

NA

Phragmites australis

144.5

20

Czech Republic (50°N)

SSF

NA

Phragmites australis

378.9

20

11

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Czech Republic (50°N)

SSF

NA

Phragmites australis

460.7

20

Czech Republic (50°N)

SSF

NA

Phragmites australis

781.3

20

Czech Republic (50°N)

SF

NA

Phragmites australis

145.8

21

Germany (51°N)

SF

NA

Phragmites australis

251.6

21

Czech Republic (50°N)

SF

NA

Phragmites australis

386.3

21

Czech Republic (50°N)

SF

NA

Phragmites australis

401.8

21

Poland (53°N)

SF

NA

Phragmites australis

435.3

21

The Netherlands (52°N)

SF

NA

Phragmites australis

527.3

21

Austria (47°N)

SF

NA

Phragmites australis

573.5

21

Alabama (32°N), USA

SF

NA

Phragmites australis

748.5

21

Czech Republic (50°N)

SSF

NA

Phalaris arundinacea

135.2

21

Alabama (32°N), USA

SF

NA

Phalaris arundinacea

153.7

21

Minnesota (44°N), USA

SF

NA

Phalaris arundinacea

226.8

21

New York (40°N), USA

SF

NA

Phalaris arundinacea

316.9

21

England (55°N)

SF

NA

Phalaris arundinacea

454.7

21

Asian Institute of Technology (13°N), Thailand

SF

NA

Canna spp.

578.7

21

100.8

21

703.0

22

Asian Institute of Technology (13°N), Thailand

SF

NA

Heliconia psittacorum and spontaneous plants

Raukura Reserch centre, Hamilton (43°N), Ireland

SF

NA

Zizania latifolia

12

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Raukura Reserch centre, Hamilton (43°N), Ireland

SF

NA

Schoenopectus validus

305.3

22

Raukura Reserch centre, Hamilton (43°N), Ireland

SF

NA

Phragmites australis

333.0

22

Raukura Reserch centre, Hamilton (43°N), Ireland

SF

NA

Glyceria maxima

610.5

22

Florida, Orlando's wastewater division easterly wetlands (27°N), USA

SF

NA

Schoenopectus validus

27.6

23

Florida, Orlando's wastewater division easterly wetlands (27°N), USA

SF

NA

Schoenopectus validus

64.8

23

Florida, Orlando's wastewater division easterly wetlands (27°N), USA

SF

NA

Typha spp.

49.2

23

Florida, Orlando's wastewater division easterly wetlands (27°N), USA

SF

NA

Typha spp.

46.6

23

Thailand (13°N)

SF

NA

Cyperus papyrus

433.1

24

Thailand (13°N)

SF

NA

Cyperus papyrus

436.4

24

Thailand (13°N)

SF

NA

Cyperus papyrus

469.5

24

Thailand (13°N)

SF

NA

Cyperus papyrus

576.3

24

Montreal Botanical Garden, Montreal, Québec (45°N), Canada.

SF

NA

Typha spp.

458.8

25

Montreal Botanical Garden, Montreal, Québec (45°N), Canada.

SF

NA

Typha spp.

408.9

25

Montreal Botanical Garden, Montreal, Québec (45°N), Canada.

SF

NA

Phragmites australis

170.2

25

Montreal Botanical Garden, Montreal, Québec (45°N), Canada.

SF

NA

Phragmites australis

235.0

25

Montreal Botanical Garden, Montreal, Québec (45°N), Canada.

SF

NA

Phalaris arundinacea

357.1

25

13

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Tänassilma (25°N)

SF

NA

Typha latifolia

255.3

26

Põltsamaa (25°N)

SF

NA

Typha latifolia

325.6

26

Põltsamaa (25°N)

SF

NA

Typha latifolia

61.1

26

Põltsamaa (25°N)

SF

NA

Typha latifolia

68.5

26

Häädemeeste (24°N)

SF

NA

Phragmites australis

112.9

26

Häädemeeste (24°N)

SF

NA

Phragmites australis

188.7

26

Häädemeeste (24°N)

SF

NA

Phragmites australis

244.2

26

San Michele di Ganzaria, Eastern Sicily (37°N), Italy

SF

NA

Phragmites australis

869.7

26

San Michele di Ganzaria, Eastern Sicily (37°N), Italy

SF

NA

Phragmites australis

571.3

26

San Michele di Ganzaria, Eastern Sicily (37°N), Italy

SF

NA

Spontaneous plants

314.5

26

San Michele di Ganzaria, Eastern Sicily (37°N), Italy

SF

NA

Spontaneous plants

44.4

26

Ondrejov (50°N), Czech Republic

SSF

NA

Phragmites australis

557.4

27

SpálenéPorící(50°N), Czech Republic

SSF

NA

Phragmites australis

386.7

27

Monegros area (40°N), Spain

SSF

NA

Phragmites australis

292.3

28

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Carex nebrascensis

67.7

29

American Falls Reservoir,southeastern Idaho (44°N), USA

SF

NA

Carex nebrascensis

96.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Carex nebrascensis

95.5

29

14

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American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Carex nebrascensis

125.3

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Eleocharis palustris

51.7

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Eleocharis palustris

18.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Eleocharis palustris

65.7

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Eleocharis palustris

79.7

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Juncus balticus

140.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Juncus balticus

153.4

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Juncus balticus

147.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Juncus balticus

67.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus maritimus

73.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus maritimus

28.3

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus maritimus

73.2

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus maritimus

107.6

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus acutus

10.5

29

15

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American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus acutus

38.6

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus acutus

46.1

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenoplectus acutus

78.6

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenopletus pungens

13.4

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenopletus pungens

20.8

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenopletus pungens

30.1

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Schoenopletus pungens

69.4

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Typha latifolia

21.7

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Typha latifolia

36.4

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Typha latifolia

51.3

29

American Falls Reservoir, southeastern Idaho (44°N), USA

SF

NA

Typha latifolia

68.9

29

Slavošovice, South Bohemia (49°N), Czech Republic

SSF

NA

Phragmites australis

259.0

30

King Mongkut' s University of Technology Thonburi, Bangkok (13°N), Thailand

SF

NA

C. siamensis

1050.8

31

Putrajaya (2°N), Malaysia

SF

NA

Phragmites karka

195.0

32

Putrajaya (2°N), Malaysia

SF

NA

Phragmites karka

35.2

32

16

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Putrajaya (2°N), Malaysia

SF

NA

Lepironia articulata

86.9

32

Putrajaya (2°N), Malaysia

SF

NA

Lepironia articulata

45.2

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

188.5

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

110.8

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

23.6

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

25.9

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

35.4

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

141.4

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

485.5

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

563.3

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

671.7

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

511.4

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

459.6

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

553.8

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

560.9

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

758.9

32

17

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The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

697.6

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

1140.6

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

947.4

32

The university of Edinburgh (55°N), Scotland

SSF

NA

Phragmites australis

784.8

32

Santo Tomé, Santa Fe (31°S), Argentina

SF

NA

P. elephantipes

92.5

33

Santo Tomé, Santa Fe (31°S), Argentina

SF

NA

P. elephantipes

77.7

33

Santo Tomé, Santa Fe (31°S), Argentina

SF

NA

T. domingensis

231.3

33

Santo Tomé, Santa Fe (31°S), Argentina

SF

NA

T. domingensis

218.3

33

South Bohemia (50°N), Czech Republic

SSF

NA

Phalaris arundinacea

148.0

34

South Bohemia (50°N), Czech Republic

SSF

NA

Phalaris arundinacea

157.3

34

Czech Republic (50°N)

SF

NA

Phragmites australis

938.0

22

Czech Republic (50°N)

SF

NA

Phalaris arundinacea

351.5

22

Honghu park, Shenzhen (22°N), Guandong Province, China

IVSSF

12.10

Arundo donax

1684.5

our data

Tonglu (30°N), Zhejiang Province, China

IVSSF

10.33

Arundo donax

1647.5

our data

Tonglu (30°N), Zhejiang Province, China

IVSSF

0.46

Arundo donax

1104

our data

Tonglu (30°N), Zhejiang Province, China

IVSSF

0.16

Arundo donax

833.5

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.55

Phragmites australis

233

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.44

Phragmites australis

352.4

our data

18

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Gongshu, Hangzhou (30°N), Zhejiang Province, China

SSF

0.79

Phragmites australis

416.0

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

IVSSF

8.25

Phragmites australis

790.0

our data

Dongyang (29°N), Zhejiang Province, China

IVSSF

15.02

Phragmites australis

923.6

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

IVSSF

6.31

Phragmites australis

712.3

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

IVSSF

3.99

Phragmites australis

557.3

our data

Shenzhen, Guangdong Province (22°N), China

SSF

14.16

Phragmites australis

1156.7

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.43

Typha angustifolia

105.5

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.43

Typha angustifolia

69.3

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.44

Typha angustifolia

91.1

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

0.44

Typha angustifolia

105.2

our data

Xixi, Hangzhou (30°N), Zhejiang Province, China

SF

2.10

Typha angustifolia

107.4

our data

Zhujiajian (29°N), Ningbo, Zhejiang Province, China

IVSSF

3.08

Typha angustifolia

370.7

our data

Dongyang (29°N), Zhejiang Province, China

IVSSF

6.31

Typha angustifolia

318.0

our data

Dongyang (29°N), Zhejiang Province, China

IVSSF

15.02

Typha angustifolia

453.2

our data

Note: NA means not available; SF means surface flow CW, SSF means subsurface flow CW, IVF-SSF means integrated vertical flow CW35.

19

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Table S4. Greenhouse gas (GHG) emission for four current biofuel production systems and constructed wetlands (CW) and wastewater treatment plants (WTP) GHG emissions Item CH4

N2O

CO2

As biofuel production ecosystems (Mg CO2 equivalent ha-1 yr–1) CW cellulose Microalgae36,37 LIHD grassland38 Switchgrass38

18.71 0.86 –0.2 NA

3.76 0.05 0.2 NA

6.27a 138.83 0.3 0.4

Corn/soybean38

–0.04

0.52

0.23

As wastewater treatment systems (g CO2 equivalent kg-1 N removal ) CW Microalgaeb WTP

469.5 0.4 585279.3

103.3 1.9 3069.2

295.8 124.5 3848.6

Note: NA- data not available; a Value of CO2 emission is seen in Supplementary Table S8 and methods; b Calculated based on ref 36 and 39. Conversion factor for transforming Mg CO2 equivalent ha-1 yr-1 into g CO2 equivalent kg-1 N removal was calculated as 15,147 kg N ha-1 yr-1.

20

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Table S5. Energy consumption and CO2 emission of total input for WTPs and CWs for wastewater treatment Item

Construction Construction material Steel Cement metal pipe Timber gravel Sand PC liner PE pipe Geotextile Transportation Construction work Seedling plant Operation Electricity and fuel Chemical Labor Total input

CO2 emissiona (Mg CO2 ha-1 yr-1)

Energy consumption (GJ ha-1 yr-1) SSF-CW

SF-CW

WTP

SSF-CW

SF-CW

WTP

124.4 85.1 0.5 33.0 — — 17.7 4.2 20.7 4.6 4.5 27.5 — 11.8 58.2 32.3 — 25.9 182.6

21.6 7.1 — 6.6 — — — — — 0.5 — 2.7 — 11.8 58.2 32.3 — 25.9 79.9

1733.9 886.8 719.4 77.6 62.4 2.2 22.4 2.9 — — — 55.7 791.4 — 5708.6 3193.1 2449.6 65.9 7442.5

10.1 6.9 0.0 2.7 — — 1.4 0.3 1.7 0.4 0.4 2.2 — 1.0 4.8 2.6 — 2.1 14.9

1.8 0.6 — 0.5 — — — — — 0.0 — 0.2 — 1.0 4.8 2.6 — 2.1 6.5

141.5 72.4 58.7 6.3 5.1 0.2 1.8 0.2 — — — 4.5 64.6 — 465.8 260.6 199.9 5.4 607.3

a

Energy consumption is converted to CO2 emission by the emission factor: 81.6g CO2 equivalent MJ-1 ethanol produced. b For transportation of construction materials from the location of purchase to the construction site, a unit of energy consumption of 1,836 kJ ton–1 km–1 and an average distance of 20 km were used.

21

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Table S6. Building cost and operation/maintenance cost of building WTPs and CWs Item

Design capacity (m3 day-1)

Total cost (Million USD)

Unit capital cost (USD m-3)

Operation/ Maintenance cost (USD m-3)

Source

30000

104.24

474

0.09

our data

3300

14.24

570

0.08

our data

13000

42.80

428

0.08

our data

13000

71.13

711

0.10

our data

3200

16.26

678

0.08

our data

13000

46.37

464

0.07

our data

7000

15.25

305

0.05

our data

4000

14.23

474

0.08

our data

100000 200 2600

8.2 0.03 1.87

82 146 72

0.01 0.01 0.01

40 40 our data

WTP Liedu WTP phase I, Guangdong Guangzhou economic development zone WTP, Guangdong Zhennan WTP in Foshan, Guangdong Luofang WTP phase I, Guangdong Pinghu WTP in Shenzhen, Guangdong Zhongshan WTP, Guangdong Zhaoqing WTP, Guangdong Shekou WTP in Shenzhen, Guangdong CW Dongying, Shandong Longdao River, Beijing Dongyang, Zhejiang

22

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Table S7. The ecosystem services and cost-benefit analysis of several biofuel ecosystems (USD ha-1 yr-1) Item

LIHD grassland

Corn

Soy-bean

0

0

0

0

3238

1148

369

392

299

40

-2115

41

NA

NA

NA

4029

4029

-

-

-

-

CW

Microalgae

178098

178098

2189

Switchgrass

Provisioning services Treated watera Biofuel production

b

Regulating services Net GHG sequestrationc Water regulation

d



-

-



-

-

Recreation



-

-

-

-

-

Aesthetics



-

-

-

-

-

Education





-

-

-

-

0

0

-14

0

-4

0

183250

1189

369

392

299

46377 2523 3.75

1982 469 0.49

2850 863 0.10

968 431 0.28

687 306 0.30

Biodiversity conservation Cultural services

Dis-services Nitrogen water pollutione Economic benefit 40-42

Farming cost

40-42

Refinery cost Cost-benefit ratio

184356 f

13272 15642 6.38

Note: NA, not available; “-”, no this service; √, have this service but cannot monetized. a Treated water price was 1.5yuan m-3. b From biomass energy to electricity, and the price of electricity is 0.158yuan GJ-1. c Carbon price is 10 Euro t-1CO2. d The price of water regulation (0.48 USD m-3)43 is the cost of middle size reservoir. e The price for nitrogen water pollution is 239 USD ha-1 yr-1 f Farming cost of CWs includes land rent, production and transportation costs of construction materials and fuel, labor costs for construction work and biomass harvests. We excluded refinery costs in the total costs for CW biofuel production in main text table 1, for the biomass are directly combusted to get the highest energy value and are not convert into ethanol, electricity and heat or biogas.

23

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Table S8. Energy inputs of CWs for biofuel production system Items Inputs (GJ ha-1) Seedling plant 11.8 Labor for harvest 0.3 Feedstock transportation 2.4 a Conversion from feedstock to biofuel 4.1 Labor for operating 25.9 Electricity and fuel for operating 32.3 Total 76.9 a 44 Note: Following the method used for switchgrass , we took into account these parameters during the biorefinery process: diesel use (0.06 MJ L-1), plant capital and equipment (0.44 MJ L-1), water usage in the conversion process (0.29 MJ L-1), and sewage effluent (0.29 MJ L-1).

24

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19. Tang, X., Huang, S., Scholz, M. & Li, J. Nutrient removal in pilot-scale constructed wetlands treating eutrophic river water assessment of plants, intermittent artificial aeration and polyhedron hollow polypropylene balls. Wat. Air Soi. Pollut. 197, 61-73 (2009). 20. Vymazal, J., Kröpfelová, L., švehla, J., Chrastnŷ, V. & Štíchová, J. Trace elements in Phragmites australis growing in constructed wetlands for treatment of municipal wastewater. Ecol. Engin. 35, 303-309 (2009). 21. Vymazal, J. & Kröpfelová, L. Growth of Phragmites australis and Phalaris arundinacea in constructed wetlands for wastewater treatment in the Czech Republic. Ecol. Engin. 25, 606-621 (2005). 22. Tanner, C. C. Plants for constructed wetland treatment systems –– a comparison of the growth and nutrient uptake of eight emergent species. Ecol. Engin. 7, 59-83 (1996). 23. Malecki - Brown, L. M., White, J. R. & Brix, H. Alum application to improve water quality in a municipal wastewater treatment wetland: Effects on macrophyte growth and nutrient uptake. Chemosphere 79, 186-192 (2010). 24. Perbangkhem, T. & Polprasert, C. Biomass production of papyrus (Cyperus papyrus) in constructed wetland treating low-strength domestic wastewater. Bioresour. Technol.101, 833-835 (2010). 25. Maltais-Landry, G., Maranger, R., Brisson, J. & Chazarenc, F. Nitrogen transformations and retention in planted and artificially aerated constructed wetlands. Wat. Res. 43, 535-545 (2009). 26. Maddison, M., Soosaar, K., Mauring, T. & Mander, ü. The biomass and nutrient and heavy metal content of cattails and reeds in wastewater treatment wetlands for the production of construction material in Estonia. Desalination 246, 120-128 (2009). 27. Vymazal, J. Horizontal sub-surface flow constructed wetlands Ondrřejov and Spálené Poříčí in the Czech Republic –– 15 years of operation. Desalination 246, 226-237 (2009). 28. Moreno, D., Pedrocchi, C., Comín, F. A., García, M. & Cabezas, A. Creating wetlands for the improvement of water quality and landscape restoration in semi-arid zones degraded by intensive agricultural use. Ecol. Engin. 30, 103-111 (2007). 29. Ray, A. M. & Inouye, R. S. Development of vegetation in a constructed wetland receiving irrigation return flows. Agr. Ecosyst. Environ. 121, 401-406 (2007). 30. Picek, T., Čížková, H. & Dušek, J. Greenhouse gas emissions from a constructed wetland—Plants as important sources of carbon. Ecol. Engin. 31, 98-106 (2007). 31. Sohsalam, P., Englande, A. J. & Sirianuntapiboon, S. Seafood wastewater treatment in constructed wetland tropical case. Bioresour. Technol. 99, 1218-1224 (2008). 32. Sim, C. H., Yusoff, M. K., Ho, B. C. & Mansor, M. Nutrient removal in a pilot and full scale constructed wetland, Putrajaya city, Malaysia. J. Environ. Manage. 88, 307-317 (2008). 33. Hadad, H.R., Maine, M. A. & Bonetto, C.A. Macrophyte growth in a pilot-scale constructed wetland for industrial wastewater treatment. Chemosphere 63, 26

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1744-1753 (2006). 34. Edwards, K. R., Čižková, H., Zemanová, K. & Šantrůčková, H. Plant growth and microbial processes in a constructed wetland planted with Phalaris arundinacea. Ecol. Engin. 63, 1744-1753 (2006). 35. Liu D. et al. Constructed wetlands in China: recent developments and future challenges. Front. Ecol. Environ. 7, 261-268 (2009). 36. Liu, J. & Ma, X. The analysis on energy and environmental impacts of microalgaebased fuel methanol in China. Energ. Pol. 37, 1479-1488 (2009). 37. Sander, K. & Murthy, G. S. Life cycle analysis of algae biodiesel Int. J. Life Cycle Assess. 15, 704-714(2010). 38. Hill, J., Nelson, E., Tilman, D., Polasky, S. & Tiffany, D. Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. Proc. Natl. Acad. Sci. U.S.A. 10, 11206-11210 (2006). 39. Christenson, L & Sims, R. Production and harvesting of microalgae for wastewater treatment, biofuels, and bioproducts. Bioresour. Technol. 29, 686-702 (2011) 40. Zhang, D., Gersberg, R. M. & Keat, T. S. Constructed wetlands in China. Ecol. Engin. 35, 1367-1378 (2009). 41. Agusdinata, D. B., Zhao, F., Klein, E. Ileleji & DeLaurentis D. Life cycle assessment of potential biojet fuel production in the United States. Environ. Sci. Technol. doi: 10.1021/es202148g (2011). 42. State Development Planning Commission of China Price Department. China Agricultural Cost and Benefit Data Compilation (China Statistics Press, 2009) (in Chinese). 43. Lv, Y., Gu, S. & Guo D. Valuing environmental externalities from rice –– wheat farming in the lower reaches of the Yangtze River. Ecol. Econ. 69, 1436-1442 (2010). 44. Schmer, M. R., Vogel, K. P., Mitchell, R. B. & Perrin, R. K. Net energy of cellulosic ethanol from switchgrass. Proc. Natl. Acad. Sci. U.S.A. 105, 464-469 (2008).

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