Environ. Sci. Technol. 2010, 44, 7589–7595
Ecosystem-Based Assessment Indices of Restoration for Daya Bay near a Nuclear Power Plant in South China X I A O Y A N C H E N , †,‡ H U I W A N G G A O , * ,† XIAOHONG YAO,† HONGDA FANG,‡ ZHENHUA CHEN,§ AND ZHANZHOU XU‡ Key Laboratory of Marine Environment and Ecology (Ocean University of China), Ministry of Education of China, Qingdao, China, South China Sea Environmental Monitoring Center, South China Sea Branch of the State Oceanic Administration, Guangzhou, China, and College of Physical and Environmental Oceanography, Ocean University of China, Qingdao, China
Received March 17, 2010. Revised manuscript received July 19, 2010. Accepted July 22, 2010.
China has adopted nuclear power generation as one of the strategic energy sources to resolve the dilemma between its evergrowing energy demand and the associated environmental issues. To achieve the latter, a systematic assessment of the state of the ecosystem near nuclear power plants and its restoration via ongoing recovery actions would be highly desirable and much needed. Current assessment methods are mostly based on the individual components of the ecosystem and the methods are therefore not integrated. In this paper, we report a set of system-based assessment indices to study the restoration of Daya Bay in Guangdong, China where a nuclear power plant has been in operation for 15 years. The results show that decades of intensive exploitation by the various coastal activities have pushed Daya Bay’s ecosystem away from its baseline and its structure and functions are impaired; ecosystem restoration does not make up for the weakening of the ecological carrying capacity due to anthropogenic seause, nonetheless, the potential for recovery still exists. The case study suggests that the system-based indices can provide integrated information for ecosystem restoration assessment and management.
1. Introduction Energy use and its associated environmental impact are inseparable. Nuclear power generation has been considered by many as a reasonable balance of the two, and China has adopted it as a strategic energy source in addressing this dilemma. In 2006, the State Council of China issued the “Medium- and long-term nuclear power development plan (2005-2020)” which states the installed capacity of nuclear power will be 12,000 MW in 2010 accounting for 5% of the * Corresponding author e-mail:
[email protected]; phone: +86532-66782977; fax: +86-532-66782810. † Key Laboratory of Marine Environment and Ecology (Ocean University of China), Ministry of Education of China. ‡ South China Sea Environmental Monitoring Center, South China Sea Branch of the State Oceanic Administration. § College of Physical and Environmental Oceanography, Ocean University of China. 10.1021/es1008592
2010 American Chemical Society
Published on Web 08/31/2010
national power generation capacity, and by 2020, it will be 40,000 MW. Seven coastal sites out of 13 sites have been selected for building new nuclear power plants (NPPs) in China while the inland sites are still under consideration. The Daya Bay nuclear power plant (DNPP) is the first commercial nuclear power plant in China. It started construction in 1982 and has been in operation since 1994. The subsequent rapid industrialization and urbanization of the area brought about by DNPP put a severe stress on the ecosystem of the Bay. A systematic assessment of the state of the ecosystem and the natural restoration power of Daya Bay would provide an important benchmark for China in environmental protection and sustainable development. A review suggests that the methods currently used in ecosystem restoration assessment (ERASS) in bays are mostly based on the individual components such as the chemical, physical, or biological properties of the ecosystem, and focused on habitat or target species (1-5). However, the impact of anthropogenic activities on an ecosystem is widespread and complicated. It is not surprising that when simple proxies are used to evaluate the health of ecosystems, some of the results are incomplete and even yield unsuccessful assessment (6). Coastal ecosystems are coupled human-natural and multidimensional eco-complexities (7), and once disturbed, equally and even more complicated restoration efforts have to be taken. An integrated assessment at the system level would better reflect the whole ecosystem. Ecosystem services include not only the direct use of living resources for human, but also the long-term welfare derived from a sustainable ecosystem. Ecosystem services depend on the structure and functions of the ecosystem. When an ecosystem can provide high-quality ecosystem services, it should and would indicate that the ecosystem stays at or has returned to a healthier state with reinstated functions. Thus, a restoration assessment based on the provision of highquality ecosystem services should more fully reflect the restoration efficiency of the mitigation measures. The objectives of this paper are to present an ecosystem approach in establishing a set of multidimensional and integrated indices for ERASS and to illustrate the merits of the method using Daya Bay as a case study.
2. Methodology 2.1. Study Area. Daya Bay (114°29′-114°49′ E, 22°30′-22°50′ N, surrounded by Shenzhen and Huizhou, Guangdong Province), with an area of 516 km2 and shoreline of 150 km, is one of 15 ecological monitoring zones in China coast established by the State Oceanic Administration in 2004 (Figure 1). It is relatively shallow (mean depth of 8.3 m) with no major river discharges and weak tidal exchanges (mean tidal range of 0.85 m). This makes the semiclosed bay poor in water exchange (8). It is a natural reserve of fishery resources of Guangdong Province and is the only spawn site for Chelonia mydas (green sea turtle) on the Chinese continental-shelf (9). DNPP (2 × 1800 MW) has been operating since 1994, and a new plant in nearby Lingdong (1800 MW) is scheduled to start up in 2010. From 1987 to 2004, Shannon-Wiener diversity indices of macrobenthic fauna declined from 3.38 to 2.08 (10). 2.2. Restoration Actions. Table 1 summarizes the ongoing recovery actions used to mitigate the declines as discussed in the following sections. 2.3. Data Collection. The ecosystem of 1985 was used as the baseline because of its exceptional historic and ecological significance: (a) Prior to the establishment of the Pearl River Delta Economic Area in 1985 and the operation of DNPP in VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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the importance of each indicator and the final priorities are listed in Table 2. The local priorities and consistency of the comparisons are listed in Table S3. The computation methods are also detailed in the Supporting Information (Section 2). Structural metrics of ECM comprise species diversity, corresponding to the biotic structure index (BI), and habitat diversity, corresponding to the habitat structure index (HI). Supporting services index (SI), provisioning services index (PI), regulating services index (RI), and cultural services index (CI) were used to assess ecosystem functioning. These indices are ecosystem services according to Millennium Ecosystem Assessment (MA) (21). Using the data listed in Table 2 and formulas S1a, S1b, S2, and S3 in the Supporting Information, the scores of each indicator were computed and are shown in Figure S(1-35, Supporting Information). The trends of the indices are shown in Figure 3. Indices with values of 0 and 1 are assumed to be the worst and the best, respectively. Values 0, 0.4, and 0.9 refer to failure, caution, and success, respectively (22, 23).
3. Results and Discussion
FIGURE 1. Map of Daya Bay. 1994, the ecosystem at Daya Bay was without any significant anthropogenic disturbances and remained relatively stable, e.g., Chl-a, species diversity and sea surface temperature were close to the data in 1987-1988 (the natural variability was within 6-12%) (11). (b) The investigation of the ecosystem made in 1984-1986 was a comparatively systematic monitoring program and the data are reliable and significant. The restoration processes were divided into two periods: before the operation of DNPP (1985-1994), and 1994-2008, when DNPP was in operation and most of the remediation was implemented. Data used were mostly from internal research reports and the literature (Table S1 in the Supporting Information). 2.4. Ecosystem Restoration Assessment Indicators System. We constructed our ecosystem restoration assessment model in a hierarchy similar to the ecological conceptual models (ECM) developed for the South Florida Everglades (12) (Figure 2). The desired outputs are the recovery of ecosystem structure and services. Table 2 summarizes the four themes of the ERASS indices: A is the ecological restoration index (ERI), B includes 6 themes, C consists of 12 subthemes, and D contains 35 indicators. Due to the lack of historical data for some indicators, data collected at Daya Bay closest to 1985 or the best state (defined as the maximum of the positive indicators or the minimum of the negative indicators) of the existing data set were used as reference values (11, 13-19). The Analytic Hierarchy Process (AHP) is used to make pairwise comparisons; the judgmental matrix (20) based on
The scores of the selected 35 indicators and the corresponding six indices at Daya Bay are examined below. (1) Species Diversities of Phytoplankton, Zooplankton, and Benthos. Because of the cooling water discharge from DNPP, the proportion of the warm-water phytoplankton species in the bay is used to identify the retrogressive succession. An increase of the proportion implies a simplification trend of the community structure. The number of species of pelagic eggs is selected as an indicator because of its importance in predicting the recovery in fishery in the Bay. As shown in Figure S(1-5), only the warm-water phytoplankton species approached their original states while the other indices decreased in varied extent; in particular, the number of species of pelagic eggs was very low at 0.09. Low-diversity communities are sensitive to toxic chemicals and are consistently less able to recover from perturbations (24, 25). BI ranged from 0.52 to 0.77 (Figure 3a) indicating that biotic structure was impaired and the species biodiversity responded poorly to the mitigation projects. (2) Habitat Diversity. Spawning sites of selected key species (Chelonia mydas is used as the proxy), area of mangrove, area of coral reefs, and percentage of live cover of coral reefs were used. The first one was close to the original state while the latter two were 2 °C (2004-2008) (13) were observed in the Daya Bay probably caused by the discharge of the cooling water from DNPP since 1993 (28). As the surface waters warm up, biomass, phytoplankton growth, fish species, habitat complexity, and solubility of CO2 decline (29, 30). SST together with pH and SS have been proven to be the dominant factors leading to coral blanching and decrease (31, 32). DSi is a limit factor to carbon fixation because diatoms were the dominant phytoplankton (∼77.4% of species and ∼97.8% of biomass) at Daya Bay (13, 33). 3H was selected as a water quality parameter because 3H is the main production during the operation of light water reactors such as DNPP. An alternate may be 90Sr because of its high toxicity and low distribution coefficient in the sediment (Kd ) 170 L/kg, dry weight) (34). (c) The total organic carbon (TOC) in the sediment is the accumulated organic matter in the marine environment. Artificial radionuclide concentrations in the sediment (average grain-size 1-60 µm, medium diameter >7 φ) (13) is a metric of the long-term discharge from DNPP. 137Cs in sediment is selected because of its high toxicity, long halflife (T1/2 ) 30.17 a), and high distribution coefficient in this bay (Kd ) 2570 L/kg, dry weight) (34). As shown in Figure S(9-19), primary productivity and sediment quality were almost unchanged while the water quality deteriorated. N/P increased rapidly from 1.33 in 1985 to 11.20 in 2004, which could cause algal blooms since the causative species are dominant in the bay. SI ranged from 0.82 to 0.90 (Figure 3c), indicating that the index is nearly at a successful level. Overall, water management seems to have met its goals. (4) To evaluate provisioning services, phytoplankton, zooplankton, and benthic biomass are selected and they are convincing evidence of performance and potential of remediation projects. The biomass loss was caused by the high
mortality (92-99%) of organisms through the DNPP cooling water system (14). The residual contamination in the marine organisms reflects more accurately the level of pollution than the water analysis, and the accumulation of contamination in seafood is a major concern (35). The area near DNPP experienced rapid development including petrochemical, shipping, marine culture, and fishing, thus residual levels of total petroleum hydrocarbon (TPH), heavy metal (Hg) (36), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and artificial radionuclides (e.g., 90Sr or 137 Cs) in benthic molluscs will give a judgment of the main sources of stress to restoration. As shown in Figure S(20-27), sharp fluctuations are seen in both the biomass and seafood qualities. Despite the increase in zooplankton biomass, phytoplankton decreased by 99.4% in 2007, and benthic biomass in 2008 dropped to 27.3% of the baseline. 137Cs in molluscs also decreased. TPH, Hg, PAHs, and PCBs in molluscas increased, but still met China’s national standards. PI ranging from 0.44 to 0.72 (Figure 3d) indicates a caution level. The restoration of fishery resources and the control of industrial emissions were not effective. (5) Regulating services are rarely calculated in the literature even though they have strong effects on the ecosystem (37). In this study, water renewal time, fixing of greenhouse gases, emissions of beneficial gases, and uptake of harmful gases and percentage of harmful algal blooms (HABs) causative species are used to assess the regulating services. Water refresh time is used to evaluate the self-purification ability of the coastal ecosystem, and explore the impact of NPP cooling water discharge on the water cycle. Phytoplankton, the dominant fraction in photosynthetic carbon fixation (38), stabilizes the ambient temperature (39) by producing beneficial gases (dimethyl sulphide, O2), and absorbs harmful gases (H2S, SO2) to purify the air, therefore PP reflects the regulating capacity of the air quality and perhaps the climate: disease regulation is a function of the amount of HABs causative species, which is indicative of the ability of the marine organisms to ingest harmful plankton through phagocytosis. VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 2. Set of Ecosystem-Based Indices for Ecosystem Restoration Assessment in Daya Bay objective (A)
theme (B) 1. biotic structure index (BI)
2. habitat structure index (HI)
3. supporting services index (SI)
subtheme (C) 1. species diversity
2. habitat diversity
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9
reference
overall priority
241
-
8
0.0382
83
-
8
0.0368
506
-
16
0.0311
33.3
%
14
0.0479
64
ind
15
0.0609
58
ind
13
0.0564
0.836
km2
8
0.0493
76.60
%
13
0.0323
317
mg C/m2 d
8
0.0437
4. water quality
10. concentration of dissolved oxygen 11. N/P ratio 12. pH value 13. surface sea temperature 14. concentration of suspended substance 15. concentration of dissolved silicates 16. chemical oxygen demand 17. concentration of 3 H/90Sr in seawater
5
mg/L
8
0.0261
1.30 8.22 26.34
°C
8 8 8
0.0266 0.0161 0.0509
3.44
mg/L
11
0.0194
39.24
µmol/L
8
0.0224
0.37
mg/L
18
0.0236
6. biomass
3
2.20
mBq/dm
19
0.0485
18. total organic carbon 19. concentration of 137 Cs in coastal sediment
0.82
%
13
0.0545
1.86
Bq/kg
13
0.0694
20. phytoplankton biomass
63
106 cell/m3
8
0.0227
21. zooplankton biomass 22. benthos biomass 23. residual level of heavy metal (Hg) in benthic molluscs 24. residual level of polychlorinated biphenyls in benthic molluscs 25. residual level of polycyclic aromatic hydrocarbon in benthic molluscs 26. residual level of total petroleum hydrocarbon in benthic molluscs 27. residual level of 90 Sr/ 137Cs in benthic molluscs
185.70
mg/m3
8
0.0214
72.43 0.0025
2
g/m 10-6 ww
8 16
0.0162 0.0125
0.20
10-6 ww
13
0.0128
2.69
10-6 ww
13
0.0108
0.66
10-9 ww
13
0.0166
0.025
Bq/kg
19
0.0211
8. water regulation
28. water renewal time
86
day
8
0.0385
9. climate and air
29. fixation of greenhouse gases (CO2 etc) 30. emissions of beneficial gases (O2) and uptake of harmful gases (SO2, H2S etc)
1.76
mg/m3
13
0.0051
1.76
mg/m3
13
0.0051
regulation ecosystem restoration index (ERI)
6. spawning sites of elected key species (Chelonia mydas (green sea turtle)) 7. area of mangrove swamps 8. area of coral reefs and percentage of live cover
unit
9. primary productivity
7. seafood quality
5. regulating services index (RI)
1. phytoplankton diversity (species of phytoplankton) 2. zooplankton diversity (species of zooplankton) 3. benthos diversity (species of benthos) 4. proportion of warm-water species in phytoplankton 5. species of pelagic egg
reference value
3. productivity
5. sediment quality
4. provisioning services index (PI)
indicator (D)
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TABLE 2. Continued objective (A)
theme (B)
6. cultural services index (CI)
subtheme (C)
indicator (D)
reference value
unit
reference
overall priority
10. disease regulation
31. percentage of harmful algal blooms causative species
6.05
%
13
0.0243
11. cultural heritage
32. proportion of key area protected
14
%
13
0.0100
12. recreation
33. concentration of total petroleum hydrocarbon in seawater 34. presence of Enterococci in seawater 35. presence of Vibrio in seawater
0.026
mg/L
18
0.0111
1.81
log/dm3
14
0.0088
3.97
log/dm3
14
0.0101
The water refresh time remains practically unchanged. The regulating capacity of climate and air increased steadily except for a decrease in 2007 (Figure S(28-31)). RI ranged from 0.65 to 0.74 (Figure 3e), suggesting that the regulating functions were weakened; the percentage of HABs causative species was mainly responsible for the decrease of RI. The recovery from eutrophication and mitigation of biodiversity were unsuccessful. (6) Very few ERASS studies used cultural services as an index and quantitatively addressed its role in ecosystem restoration (40). For policy makers, cultural services often are more useful than other services which only provide direct factors (37). Cultural services range from aesthetic value, education, and recreation to cultural heritage. The selected
FIGURE 3. Trends of ecosystem restoration indices at Daya bay. (a)-(g) Biotic structure index, Habitat structure index, Supporting services index, Provisioning services index, Regulating services index, Cultural services index, and Ecosystem restoration index, respectively.
metrics include proportion of protected key areas, concentration of TPH and pathogens (e.g., enterococci) in swimming beach water according to the recreational water quality criteria by USEPA (2007). Some harmful mesophile, e.g., Vibri (17), represents a realistic measure of cultural services since thermal pollution from DNPP enhanced the bacterial production rate (0.5-30.2 µg · dm-3 · h-1) (41). CI ranged from 0.80 to 0.99 (Figure 3f) and the sharp decrease due to TPH in 2008 was likely an exterior unidentified reason/source (Figure S(32-35)). The control of pollutant emissions from ballast water discharges, fugitive petroleum leakages, and the treatment of domestic sewage from adjacent cities were effective. On the whole, an ERI ranging from 0.68 to 0.76 was scored (suggesting “caution”) (Figure 3g). Biotic structure was partially repaired, leading to an improvement on supporting and cultural functions. On the other hand, recovery of habitat failed. Providing and regulating abilities thereby deteriorated. Although there was an obvious decrease of ERI during phase I (1985-1994), the ERI stabilized during phase II (1994-2008) as shown in Figure 3g. The latter suggests that the restoring projects have been partially successful. In the following, four scenarios, associated with practical measures to remove stresses and based on the relationship of the ecological effects and stress sources presented in ECM (Figure 2), are outlined to help improve the Daya Bay ecosystem. They are (1) habitat, (2) wastewater, (3) coastal plants, and (4) fishing management. As summarized in Table S4, 6-11% of the indicators scored less than 0.9 and needed to be restored. The four scenarios are further divided into 12 subscenarios based on 3 targets, i.e., low, moderate, and high efficiency of restoration inputs, which assumed that current stressors would be eliminated, and their scores would increase up to 0.4, 0.6, and 0.9, respectively. Then, change degree of ERI under scenarios 1-4 from that of 2008 are 3.21-10.14%, 0.91-5.11%, 3.08-7.69%, and 2.46-10.92%, respectively (Figure 4). The result suggests that potential for recovery still exists. The results also suggest that the priority of the restoration inputs is (1) habitat reconstruction, (2) fisheries, (3) coastal plants, and (4) wastewater management. When the scenarios were analyzed separately, the ERI was still below 0.8, but the combined effect of the four scenarios is much more effective, and ranged from 0.81 to 0.95 (Table S5). This indicates that a multidisciplinary and multisector management is essential in coastal restoration. However, adaptive assessment, continuous identifying, and quantifying of suitable indicators are still needed. The merits of our method are now summarized. Dynamic or process indicators or parameters were used in addition to the common individual parameters and a combination of VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Literature Cited
FIGURE 4. Ecosystem restoration index at Daya Bay under four series of scenarios. water quality and nutrition conditions used in the conventional approach (42). Our method focused on the total bundle of services provided by the ecosystem which results in an objective-oriented assessment to guide future restoration projects. The method also emphasized the limiting factors imposed by specific local stress or pressure (DNPP in this study) on the conservation targets, e.g., SST, pH, SS on coral reefs, spawning site on Chelonia mydas, and the proportion of warm-water phytoplankton species, etc. Since removing stressors is an effective means of restoration, the indices used in this study can serve as an appraisal tool. Although most indicators used are measurable, some are still missing data, e.g., nuclide concentration, water exchange rate, pCO2 data for air-sea carbon flux, and swimming beach health. These indices used in this study can therefore be a guide for ecological monitoring program. However, due to anthropogenic disturbance, it is still a challenge in designing appropriate sampling schemes to investigate natural variability. In addition, the MA was reported to still lack a manageable ecosystem indicator consistency applied to providing information for maintenance or restoring of ecosystem function and restoration is context-specific (37). No fixed parameter can be used unequivocally in ERASS (43). This study has developed an analytical toolsthe index base can serve in projecting trends and evaluating the success of interventions in a generic bay, while the NPP-related indices allow differentiated assessment in a typical NPP-influenced bay. Further, the index system built on the ecosystem-based conceptual model, applying to a coastal bay is also a reference for a wide range of ecosystems, freshwater or terrestrial ones, which are generally measured by variables of indicator species (44-46), nutrient input (45), or land-use patterns (47).
Acknowledgments This study is supported by the Special Fund for Public Welfare Industry (Oceanography) of China (200805080, 200805015), the key project of International Science and Technology Cooperation program of China (2006DFB21250). We thank Prof. Dewen Ding for his constructive suggestion. And we thank Prof. Ming Fang for his valuable comments. We are grateful to Prof. Duan Lin, Chuguang Huang, and Dongsheng Ke of South China Sea Branch, State Oceanic Administration for their valuable help, and we thank Dr. Honghua Shi and Master Guangliang Liu of Ocean University of China for their useful advice.
Supporting Information Available Detailed description and tables of data, statistical methods, and scenarios assessment, and figure of indicators scores. This information is available free of charge via the Internet at http://pubs.acs.org. 7594
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