Accounting for uncertainty in evaluating water quality impacts of urban development plan

Environmental Impact Assessment Review 30 (2010) 219–228

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Accounting for uncertainty in evaluating water quality impacts of urban development plan Jiquan Zhou ⁎, Yi Liu, Jining Chen The Environmental Science and Engineering Department of Tsinghua University, PR China

a r t i c l e

i n f o

Article history: Received 25 March 2008 Received in revised form 14 August 2009 Accepted 14 August 2009 Available online 13 October 2009 Keywords: Strategic environmental assessment (SEA) Urban development plan Stochastic simulation Uncertainty Environmental sensitivity

a b s t r a c t The implementation of urban development plans causes land use change, which can have significant environmental impacts. In light of this, environmental concerns should be considered sufficiently at an early stage of the planning process. However, uncertainties existing in urban development plans hamper the application of strategic environmental assessment, which is applied to evaluate the environmental impacts of policies, plans and programs. This study develops an integrated assessment method based on accounting uncertainty of environmental impacts. And the proposed method consists of four main steps: (1) designing scenarios of economic scale and industrial structure, (2) sampling for possible land use layouts, (3) evaluating each sample's environmental impact, and (4) identifying environmentally sensitive industries. In doing so, uncertainties of environmental impacts can be accounted. Then environmental risk, overall environmental pressure and potential extreme environmental impact of urban development plans can be analyzed, and environmentally sensitive factors can be identified, especially under considerations of uncertainties. It can help decision-makers enhance environmental consideration and take measures in the early stage of decisionmaking. © 2009 Elsevier Inc. All rights reserved.

1. Introduction As land ownership is held by the State in China, urban plans established by the government have an overwhelming impact on land use changes. Land use changes have significant impacts on ambient surface water quality and spatial distribution of pollutant emissions (Johnes and Heathwaite, 1997; Goonetilleke et al., 2005; Gordon, 2005). Therefore, environmental concerns should be a priority for consideration at an early planning stage. Strategic environmental assessment (SEA) is used as a fundamental approach in the process of improving environmental assessment performance; as an invaluable tool in the integration of environmental concerns in the decision-making process; and in the moving trend toward sustainability goals (Partidário, 1993; Partidário, 1996; Shepherd and Ortolano, 1996). With increases in environmental deterioration come growing public environmental awareness; SEA has gained widespread acceptability as a supporting tool for decisionmaking in planning policy frameworks (Therivel, 1998; Tzilivakis et al., 1999; Arce and Gullon, 2000). The Chinese government recently issued The People's Republic of China Environmental Impact Assessment Law that took effect September 1, 2003. The central concern of this

⁎ Corresponding author. The Environmental Science and Engineering Department of Tsinghua University, No.1 Chengfu Street, Haidian District, Beijing 100084, PR China. Tel.: +86 13683586066. E-mail address: [email protected] (J. Zhou). 0195-9255/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.eiar.2009.08.011

law is to avoid, minimize and control the adverse environmental impacts of urban plans and projects. However, various uncertainties exist in the decision-making process that hampers the application of SEA. Broadly defined, uncertainty can be described as any departure from the unachievable ideal of complete determinism (Walker et al., 2003). There are three distinct dimensions of uncertainty: the location, the nature and the level of uncertainty, all of which are present in urban planning. Since urban plans deal with the future, they possess significant uncertainties and potential risks, which have been discussed in the literature (Liu et al., 2007; Ma et al., 2007; Prato, 2007). Generally, in China, the scale of overall development is explicitly defined in urban development plans in terms of population, GDP and area for construction in the planning year. However, the information about industrial structure and land use layout are likely to be vague and typically do not address environmental concerns. Amount and type of pollution (determined by industry and corresponding land use type) and the spatial distribution of pollution (determined by land use layout) are uncertain. Since environmental impacts are subject to significant uncertainties, we need a systematic approach to forecasting the potential environmental impacts of planned land use changes under such uncertainties, and to identify key factors that planners and decision-makers need to focus on in formulating strategies to alleviate the adverse environmental impacts of land use change. In light of this, and in order to scientifically forecast, analyze and assess the universal trends in changes in environmental quality and their uncertainties, there is a need to quantitatively characterize the

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uncertainties of environmental impacts by systematically identifying the relationships between land use changes and environmental quality in the decision-making process. Many researchers focus on the fundamental concepts, guidelines, or SEA framework, but ignore operational methods (Brookes, 1998; Feldmann, 1998; Sadler, 1999; Von Seht, 1999; Annandale et al., 2001; Diaz et al., 2001; Che et al., 2002; Bao et al., 2004; Liou and Yu, 2004; Alshuwaikhat, 2005). In particular, existing SEA approaches are based on qualitative judgments and lack adequate quantitative analysis (Termorshuizen, 1997; Tzilivakis et al., 1999; Finnveden et al., 2003; Oñate et al., 2003; Short et al., 2004; Wang et al., 2004). This article proposes a method that integrates, within an SEA framework, the techniques of uncertainty analysis and the environmental impacts of land use change in a way that allows systematic characterization of the environmental impact uncertainties inherent in urban development plans. The paper is organized as follows. Section 2 provides a description of the study region, Section 3 presents the method, Section 4 discusses the results, and Section 5 provides conclusions. 2. Study area Dalian Municipality was selected as the study area. Dalian Municipality is located on the eastern bank of the Eurasian Continent and the southernmost point of the Liaodong Peninsula in the northeastern part of China (120°58′–123°31′ E, 38°43′–40°10′ N) (Fig. 1). It has a land area of 12,547 km2. The municipality had a population of 5.62 million in 2004, with seven districts and three counties. It is an important regional transportation hub, a regional center for commerce and information, and an important base for electronic and manufacturing industries. Dalian Municipality is one of the first 14 Chinese coastal cities designated as a Special Economic Zone (SEZ) by the Chinese central government in 1984. After becoming a SEZ, Dalian experienced booming development. The Urban Development Plan for Dalian Municipality (2003–2020) (UDPD) aims to help decision-makers formulate a growth strategy, and enhance the city's economic competitiveness relative to other Chinese cities. The UDPD provides a framework for future development of Dalian and is an important blueprint for construction and management. The UDPD also determines the direction of future urban growth in terms of three key aspects: the economic development objectives, population growth, and the general layout of

Dalian Municipality. According to the UDPD, the GDP, population and urbanization rate of Dalian Municipality is planned to increase by 800%, 43% and 70%, respectively, between 2000 and 2020. In summary, the UDPD has significant impacts on land use changes within Dalian Municipality. Dalian Municipality and its surrounding area is a complex space with some of the country's most environmentally valuable features, including being home to 78 nationally protected animals and 60 nationally protected birds. However water quality deterioration of ambient rivers caused by an ever-increasing population, rapidly developing industries, and intense agricultural activities, threatens aquatic biological environment. As a result, it is necessary to assess the impacts of the development envisioned in the UDPD on water quality and ways to integrate environmental, social and economic considerations into the planning process at an early stage in order to minimize potential conflict between urban development and environmental protection. This paper focuses on water quality in the six rivers within Dalian, namely, the Biliu River, Fuzhou River, Yingna River, Zhuang River, Dasha River and Dengsha River. The total catchment area of the rivers is 5332 km2, which equates to 43% of Dalian Municipality. Importantly, these rivers are forecasted to be significantly affected by land use changes resulting from implementation of the UDPD. 3. Method The key idea is to account environmental impact uncertainties of UDPD through an uncertainty analysis on land use change. The area and land use layout for certain industrial sectors indicates the scale and spatial location for the sector in question. On the other hand, land use changes can alter the quantity and spatial distribution of pollutant emissions because different industrial activities in any given land use type will have differing pollutant emission characteristics (i.e., pollutant load per unit area of land use for the chemicals industry is much more than that of the information industry). As such, uncertainty analysis of land use change is the cornerstone of the method. Uncertainty analysis techniques of land use change and its environmental impacts are employed to cope with UDPD uncertainties by combining scenario analysis, stochastic simulation and statistical analysis techniques. Although information, such as total population, GDP and the quantity of land construction in the planning year, are basically sufficient for environmental assessment, the paper instead focuses on the uncertainties associated with the environmental impacts of industrial structure and industrial land use. The integrated environmental assessment method consists of four main steps: (1) designing industrial structure scenarios, which addresses industrial structure uncertainties; (2) sampling for possible land use layouts associated with industrial structure scenarios, which characterizes uncertainties about land use layout; (3) evaluating environmental impacts of land use layouts; and (4) identifying environmentally sensitive industries. A flowchart of the proposed method is shown in Fig. 2. 3.1. Designing future industrial structure scenarios

Fig. 1. Upper-left is Dalian Municipality with 6 main river watersheds and administrative districts and counties, and various shadings represent different watersheds. Bottomright indicates the location of Dalian Municipality on the map of China (intended for reproduction in black-and-white).

Initially, scenario analysis is used to address industrial structure uncertainties in the UDPD. Scenario analysis can describe plausible future scenarios that contain the major uncertainties, and describe potential future developments based on the present situation and logical chains of plausible events and their interactions (Banuls and Salmeron, 2007; Duinker and Greig, 2007). Using scenario analysis in environmental impact assessments has become quite common and increasingly more sophisticated, and has been introduced and discussed in many other articles and researches (Fukushima and Hirao, 2002; Basset-Mens and Van Der Werf, 2005; Abildtrup et al., 2006; Salathe et al., 2007).

J. Zhou et al. / Environmental Impact Assessment Review 30 (2010) 219–228

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Fig. 2. Flowchart showing a methodological approach with main steps outlined.

There are quite explicit and quantitative definitions of population, GDP and area of construction land, which are used to define a baseline scale scenario. The latter is based on the UDPD and assumes that the UDPD is 100% implemented as planned (Table 1). The baseline scale scenario can be used as a basis for building other scale scenarios, such as a high scale scenario and a low scale scenario. These other scenarios involve implementing UDPD at a larger or smaller scale than planned. This paper just adopted the baseline scale scenario as an example to demonstrate the method. And the other scenario situation can be addressed in the same way. As the UDPD only defines several key industrial sectors and their approximate growth rates, it is insufficient to determine the whole industrial structure. In accordance with Chinese industrial classification standards, characteristics of pollutant emission from various industrial sectors, and the actual situation in Dalian Municipality, Dalian's industries have been classified into 11 industrial sectors. Based on historical data of industrial growth in Dalian Municipality and other similar regions (with consideration for the leading industries as defined in the UDPD), the baseline structure scenario was built to describe the various industrial growth rates under the constraints of the baseline scale scenario, including GDP and area of construction land as shown in Table 2.

Table 1 Definition of development scale scenario. Scale parameter

Population Construction land (km2) GDP (billion RMB) GDP per capita (US dollar) GDP of primary industry (billion RMB) GDP of secondary industry (billion RMB) GDP of tertiary industry (billion RMB)

2003

Low scale

2020 Baseline

High scale

UDPD's uncertainties are represented by the land area required to achieve a given scale of industrial activity. The land area occupied by various industrial sectors in 2003, and the land area needed for various industrial sectors under the baseline structure scenario in 2020 are listed in Table 2. Data of land area occupied by industrial sectors in 2003 are acquired through field investigations. The land area needed by various industrial sectors in 2020 is determined by experts and local land administration departments, and further approximated with reference to the situation found in economically developed regions and stock land in Dalian Municipality. However, the spatial allocation of land needed by industrial sectors in 2020 is uncertain (i.e. industrial land use layout uncertainty). This problem is addressed in the next step. 3.2. Sampling for possible land use layouts The baseline structure scenario specifies the amount of land required by each industrial sector, but not the spatial distribution of

Table 2 Definition of baseline structure scenario and land area needed by industrial sectors. No

Industrial sectors

GDP in 2003 (billion RMB)

Land area occupied in 2003 (km2)

GDP in 2020 (billion RMB)

Land area needed in 2020 (km2)

1 2 3

Petrochemistry Information Mechanical manufacture Transportation equipment Chemicals Steels Textile and clothing Food processing Building materials Light industry Electricity

42.16 20.23 36.95

87.69 24.68 63.92

311.47 211.90 269.61

174.42 97.47 132.11

11.63

16.17

86.69

22.54

6.84 3.97 5.88

11.35 6.87 16.35

40.34 11.97 15.62

18.15 5.87 7.50

11.01

30.61

26.79

19.56

3.36

11.63

9.98

3.89

6.32 4.71

17.32 8.15

18.24 19.33

10.58 13.34

4

5,601,600 154.9 163.26 3554 13.56

7,200,000 378.8 799.2 13,500 31.97

8,000,000 420.9 888 15,000 35.52

8,800,000 463.0 976.8 16,500 39.07

5 6 7

78.25

247.75

275.28

302.81

9

71.45

519.5

577.2

634.9

10 11

8

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that land in 2020, which directly influences environmental impacts of future industrial development. Therefore, stochastic sampling is used to systematically characterize uncertainties regarding the spatial distribution of land use change. The key idea here is to stochastically generate a large number of samples of the spatial distribution of land use change, referred to here as land use layouts. This process involves defining a large sample space that represents all possible land use layouts, and stochastically choosing samples from the sample space subject to certain sampling rules based on the constraint conditions of the present land use status and planning schemes. Land use change involves other land use types besides industrial land use, such as agricultural, and residential. These other land uses are explicitly specified in the UDPD. Hence, the spatial distribution of these other land uses and their environmental impacts are relatively easy to predict. Therefore, the major source of spatial uncertainty about environmental pollution is the industrial land use layout. Consequently, the uncertainty analysis of land use change only focuses on the spatial uncertainty of industrial land use layout. To start, we divided the entire Dalian Municipality into 6135 grids, where each grid edge length is 1.5 km. The present land use status of these grids is shown in Fig. 3a, and the industrial land sampling space is shown in Fig. 3b. The latter, determined by the local authority according to the constraint conditions of the present land use status and planning schemes, includes 1095 grids, which are used to generate sufficient samples for industrial land use layouts in 2020. These sufficient samples are supposed to represent possible industrial land use layouts in 2020. Monte Carlo spatial sampling is carried out on the industrial land use sampling space to generate possible land use layout schemes. Spatial sampling is subject to limitations imposed by the baseline scenario (i.e. GDP for each industrial sector, or the land area available for each industrial sector). When sampling, the 11 types of industries are affected and restrained by the present land use status. In this study, a total of 50,000 valid samples were generated for uncertainty analysis of land use change and its environmental impacts. Each sample represents one possible industrial land use layout and the 11 industrial sectors differ distinctly from each other by their respective economic scales, land use intensity and pollutant emissions. Therefore, samples differ in terms of their environmental impacts and spatial distribution. Put simply, a land use layout sample consists of two parts. One part is the industrial land use layout, which is uncertain and changes with sampling, and the other part is the layout for other land uses, which is explicitly specified in the UDPD and does not change with sampling.

3.3. Evaluating environmental impact for samples In the third step, the environmental impacts of each sample generated in the second step are simulated and predicted. Since each sample represents a possible implementation of the UDPD, all factors pertaining to human activity and land use layout are defined explicitly and clearly. Therefore, it is possible — and straightforward — to estimate the environmental pressures and impacts for each sample. An assessment is made to determine whether or not each sample is environmentally acceptable. This is done by comparing the simulated pollutant loads to the corresponding environmental capacity of a river. This study considers three sources of water pollution: sewage; agricultural nonpoint sources; and industries, which are the main problems to solve for the local government. A sample's pollutant load j to a river can be calculated as: j

j

j

j

LΩ = LiΩ + LsΩ + LaΩ

ð1Þ

Ω represents the set of grids encompassing the catchment area of a river; Li jΩ represents the pollutant load of j expelled from industrial land within Ω; Ls jΩ represents the pollutant load j expelled from residential land (sewage) within Ω; La jΩ represents pollutant load j expelled from agricultural non point sources within Ω. Estimation of sewage and agricultural nonpoint source is not the focus of this paper because there are many studies that have already explored this subject (Chandler, 1994; Johnes, 1996; Soranno et al., 1996). Furthermore, population and the layout of residential and agricultural land use in 2020 are quite explicitly and quantitatively defined in the UDPD, which makes it easy to estimate pollutant load expelled from sewage and agricultural nonpoint sources by the unit evaluation method (Chen et al., 2006). Here we focus on the calculation of Li jΩ (i.e. the pollutant load j expelled from industrial land use within Ω), which is uncertain and can be denoted as j

j

LiΩ = ∑ Li i∈Ω

Lji is industrial pollutant load j expelled to rivers from grid i, which is calculated as follows: Li = Ai Wlui Clui ; lui ∈f1; 2; …; 11g; i = 1; …; n j

j

Ai is the area of the No. i grid; Wlui is the volume of waste water yielded per unit area of land use type lui, obtained from empirical data

Fig. 3. (a) Grid partitions of present land use and (b) sampling space for industrial land use in Dalian Municipality.

J. Zhou et al. / Environmental Impact Assessment Review 30 (2010) 219–228 Table 3 Volume of waste water yielded per unit land area and discharge standard of pollutant expelled from various land use types. lui

1 2 3 4 5 6 7 8 9 10 11

Sectors upon correspondent land use types

Wlui (104 m3/km2)

Petrochemistry Information Mechanical manufacturing Transportation equipment Chemicals Steels Textile and clothing Food processing Building materials Light industry Electricity

104 30 37 96 400 224 79 58 308 66 27

j Clu (mg/L) i

COD

NH3–N

120 100 60 100 80 120 60 80 20 120 120

5.00 5.00 5.00 5.00 5.00 5.00 15.00 25.00 5.00 5.00 5.00

223

GB3838-2002; available at: http://www.sepa.gov.cn/tech/hjbz/bzwb/ index.htm. Each river in the study area has been assigned a water quality standard as specified by NEQSSW (Table 4). The upper reaches and lower reaches of some long rivers can have different water quality standards. This is the case for the Zhuang River. For surface water, based on definitions of surface water criterion, a continuous stirred-tank reactor (CSTR) model is used to estimate the environmental capacities of each river (Ni et al., 2002; Chen and Deng, 2006). The results are shown in Table 5. According to the watershed boundary of each river, it is easy to calculate all pollutants discharged from industrial land use and other land uses to the river, and to determine whether or not the pollutant load exceeds the water environmental capacities. 3.4. Identifying environmentally sensitive industries

j of the study area, as shown in Table 3; Clu is the discharge standard of i pollutant j of the land use type lui, as determined by the industrial sector correspondent to the land use type lui, which can be obtained from Integrated wastewater discharge standard (GB8978-1996) and Discharge standard of pollutants for municipal wastewater treatment plant (GB18918-2002) issued by the Chinese government (Table 3). A complete list of the standards is available at: http://www.sepa.gov.cn/ tech/hjbz/bzwb/index.htm. The National Environmental Quality Standard for Surface Water (GB3838-2002) (NEQSSW) issued by Chinese government includes the following five water classifications:

Category I. It is mainly applicable to the source of the water bodies and the national nature preserves. This is the highest water quality classification and includes water that requires only simple disinfection. Such water can be used for drinking, recreation, fish production, agricultural irrigation, industrial uses, etc. Category II. It is mainly applicable to water source protection area for centralized drinking water supply, sanctuaries for rare species of fish, spawning grounds of fishes and shrimps, etc. This water quality classification identifies slightly polluted water. This water can only become part of the water supply after pertinent treatment, i.e., treatment specific to the particular pollutant. Category III. It is mainly applicable to water source protection area for centralized drinking water supply, sanctuaries for common species of fish, and swimming zones. Water in this classification can only enter the water supply after a series of physical, chemical, and biological treatments. Category IV. It is mainly applicable to water bodies for general industrial water supply and recreational waters in which there is no direct contact of the human body with the water. Water in this category represents polluted water and requires complex treatment before being used for agricultural irrigation and industrial uses except for those like food and textile industries, which need a higher quality of water. Category V. It is mainly applicable to water bodies for agricultural water supply and for the general landscape requirements. It is seriously polluted water. In addition to the above five water quality classifications, a sixth classification, V+, is commonly used by government reporting agencies to indicate water quality worse than the national standard. In other words, it is used where water quality results far exceed the criteria set for classifying water within Category V. The above qualitative descriptions of water quality classifications are based on a series of quantitative indices and formulas developed by the Chinese Government. For a complete list of standards, see

Given the environmental impacts of all possible development in regard to economic scale, industrial structure and spatial distribution, SEA should be able to distinguish which developments are environmentally acceptable or unacceptable. By applying HSY(Young et al., 1978; Hornberger and Spear, 1980; Spear and Hornberger, 1980) algorithm, all samples (Xi) are classified into two subspaces: one consists of those samples that can satisfy a river's environmental constraints (AΩ), and the other is those cannot – (AΩ): Ω

j

j

ð2Þ

A = fXi j LΩ ≤ CapΩ ; i = 1; 2; …; Ng; Ω j j A = fXi j LΩ N CapΩ ; i = 1; 2; …; Ng;

Where Cap jΩ is the capacity of a river with regard to pollutant j and the definition of LjΩ is the same as in Eq. (1). – The number of all samples, samples in AΩ and AΩ are denoted N, NAΩ and NΩ , respectively. A

3.4.1. Pollution load ratio Pollution load ratio (PLR) is defined as the proportion of pollutant emission to the given environmental capacity. This indicator is introduced to measure if the pollution load of one development scenario can satisfy the given environmental constraint. The pollution load

Table 4 Rivers and their water quality standard as specified by the National Environmental Quality Standard for Surface Water (GB3838-2002). Level

COD (mg/L)

NH3–N (mg/L)

Study river

I II

≤15 ≤15

≤ 0.15 ≤ 0.5

III

≤20

≤ 1.0

IV

≤30

≤ 1.5

V

≤40

≤ 2.0

None Yingna River, upper reaches of Zhuang River, Fuzhou River, upper reaches of Dasha River, upper reaches of Dengsha River Lower reaches of Zhuang River, lower reaches of Dasha River Biliu River, lower reaches of Dengsha River

Table 5 Environmental capacities of rivers in the study. River

COD (t/a)

NH3–N (t/a)

Biliu River Fuzhou River Yingna River Zhuang River Dasha River Dengsha River

2570 3060 1310 2250 1320 830

130 150 60 140 70 40

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J. Zhou et al. / Environmental Impact Assessment Review 30 (2010) 219–228

ratio of a single sample (PLRi), and the average (PLRavg) and maximum (PLRmax) pollution load ratio of all samples can be defined respectively, as below. While the average pollution load ratio represents the overall environmental pressure of the plan, the maximum load ratio indicates the extraordinary development. PLRi =

LjΩ CapΩj

× 100%; PLRavg =

1 N ∑ PLRi ; PLRmax = maxfPLRi ; i = 1; 2; …; Ng N i=1

ð3Þ 3.4.2. Violation probability Violation probability is defined as the proportion of the number of environmentally unacceptable samples to the total, as described below. By doing so, the areas with high environmental risks can be easily identified. Ω

VP = ðN Ω = NÞ × 100%

ð4Þ

A

The cumulative distribution function of the pollution load ratio, FPLR, can be applied to indicate the distribution of violation probability. FPLR ðPLR ≤ γÞ = ffPLRi j PLRi ≤ γ; i = 1; 2; …; Ng = N

ð5Þ

where γ is a given pollution load ratio. The calculation of FPLR is worthy to examine the probability of pollution load ratio within a given extent, or within a certain confidential degree. Furthermore, for a river's catchment area, a joint probability distribution of the pollution load ratio and the industrial output value can be calculated to explore the environmentally acceptable economic scale at given limitation of pollution load and vice versa. This analysis can provide substantial suggestions on environmental regulation on industrial development for specific locations for decision-makers. Ω

Ω

FPLR;V ðPLR ≤ γ; V

Ω

Ω

where VΩ is the output value of industry within a river's catchment area; χ is a given output value. 3.4.3. Environmentally sensitive industry Focusing on a certain river's catchment area Ω, the output value of each individual industrial sector can vary significantly among the samples. The cumulative distribution functions of the value of an – industrial sector in those two subspaces (AΩ and AΩ) are defined as: Ω

Ω

Ω

IS =

 E −E 3σ

ð8Þ

where σ is the standard variation of PVΩ, for standardizing the difference. Four points, i.e. ±σ and ±3σ, are used to divided IS into 5 intervals to indicate the levels of environmental sensitivity (cf. Fig. 4), e.g.: 8 9 ð−∞; −1Þ SL1: high friendly > > > > > > > ½−1; −03Þ SL2: friendly > < = IS ∈ ½−0:3; 0:3 SL3: non  sensitive ð9Þ > > > > SL4: sensitive > > > ð0:3; 1 > : ; ð1;+ ∞Þ SL5: high sensitive

Ω

≤ χÞ≈ffXi j PLRi ≤ γ; Vi ≤ χ; i = 1; 2; …; Ng = N ð6Þ

FV ðV

If the development of the industrial sector is sensitive to the environment constraint within the catchment area, the distributions –Ω of FΩ V and F V should be statistically different. The question thus turns into how to identify effectively two probability distributions that come from the same one. In this regard, some nonparametric test methods can be applied, mainly involving Kolmogorov–Smirnov test, Mann– Whitney U test, Kruskal–Wallis test and Wilcoxon test. Besides, this article proposes a simple and intuitive test to estimate the degree of sensitivity. –Ω Noting that when N is large enough, FΩ V and F V can be approximately treated as normal distributions, and their probability density –Ω functions are denoted PΩ V and PV , shown in Fig. 4. As shown in Fig. 4, the difference between two distributions can be simply defined by the distance between the mathematical expecta– – tions (E, E ) of the two normal distributions. For instance, if E N E, i.e. average scale of the industrial sector in unacceptable samples is significantly larger than its scale in acceptable samples, it implies that the industrial sector is environmentally sensitive for the area: the larger scale, the higher environmental risk probability. Thus, the sensitivity index can be defined as:

Ω

≤ χÞ = ffXi j Vi ≤ χ; ∀Xi ∈A g = NAΩ ;

Ω Ω Ω Ω F V ðV ≤ χÞ = ffXi j Vi ≤ χ; ∀Xi ∈ A g = NΩ : A

ð7Þ

Obviously, the higher IS is, the more attention should be paid on the development of the corresponding industrial sector at the specific area. Moreover, the environmental sensitivity analysis enables to identify the most sensitive industrial sector(s) over the planning area and thus contributes to a reasonable optimization of overall industrial development. 4. Results and discussion 4.1. Identification of environmental risk under the uncertainties It is impossible to predict the exact environmental impact of the UDPD because of uncertainties. However we can identify the environmental risk by accounting the uncertainties of environmental impact. It is very important to identity the rivers that are a high

Fig. 4. Normal distribution based environmentally sensitive industry analysis.

J. Zhou et al. / Environmental Impact Assessment Review 30 (2010) 219–228

environmental risk as a result of implementation of the UDPD because it helps decision-makers to develop, in advance, effective and practical measures for mitigating adverse future environmental impacts of the UDPD. Referencing the river's watershed boundaries makes it straightforward to estimate its pollutant load resulting from all land use layout samples under the baseline scenario. Based on these data, we can determine whether or not a particular river's pollutant load is within environmental capacities. In this study, pollution standards for COD and NH3–N are used for this purpose, because COD and NH3–N are the major pollutants of the rivers within Dalian according to the water quality data supplied by local environmental administration. The probability distributions of pollutant load ratios in the whole sample space for all rivers evaluated in the study are shown in Fig. 5. These probability distributions are useful for analyzing each river's environment risk from land use layout uncertainties. The probability that the pollutant load ratio is less than 100% indicates the degree of uncertainty with regard to the pollution impacts of the land use

225

layout. In particular, the higher (lower) the probability, the lower (greater) is the uncertainty. The higher (lower) the violation probability (i.e., the probability the pollutant load ratio exceeds 100%), the higher (lower) the river's environmental risk. This study considers a river to have high environmental risk when its violation probability exceeds 20% (i.e. the probability the water quality in the river exceeds the water quality standard). In practice, thresholds of violation probability can be determined according to the environmental objective. Each river's violation probability and environmental risk are shown in Table 6. The results indicate that all six rivers will probably exceed their environmental capacities. For COD, Fuzhou River has the highest violation probability of 89.63%; Zhuang and Dasha Rivers also have high violation probabilities of 39.86% and 41.70%, respectively, whereas the other three rivers have relatively low violation probabilities. For NH3–N, all rivers except the Yingna and Dengsha rivers have violation probabilities of 100%. The Dengsha and Yingna Rivers have violation probabilities of 40.59% and 54.51%, respectively. In Chinese

Fig. 5. Probability distribution of pollution load ratio of COD and NH3–N for rivers in the study in 2020. The abscissa refers to pollution load ratio.

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Table 6 Violation probability and environmental risk of the rivers in the study in 2020. Rivers

Biliu River Fuzhou River Yingna River Zhuang River Dasha River Dengsha River

Violation probability COD (%)

NH3–N (%)

13.67 89.63 2.99 39.86 41.70 14.30

100 100 54.51 100 100 40.59

Environmental risk

High High High High High High

environmental management practice, a river is considered polluted if any pollutant discharged to the river exceeds water quality standard. So all the six rivers have high environmental risk and should be further analyzed in order to identity their critical pollutants and critical pollution sources. 4.2. Identification of critical pollutants Table 6 indicates that the violation probability is higher for NH3–N than COD for all the rivers. The violation probability of NH3–N is up to 100% for the Biliu, Fuzhou, Zhuang and Dasha rivers, whereas the lowest violation probability for NH3–N exceeds 40%. Therefore, NH3–N appears to be the most critical pollutant to control in surface water in Dalian Municipality between 2003 and 2020. However, the violation probability only indicates the extent to which water quality exceeds the water quality standard, and not the degree of water quality degradation (i.e., overall environmental pressure). The statistical characteristics of all samples' pollutant load ratios, such as average and maximums, can help to describe and analyze the overall environmental pressure and extreme environmental impact of possible land use layouts on the rivers. The results of the statistical analysis of environmental pressure are summarized in Table 7. They describe the overall environment pressure and extreme impact from the UDPD taking into account land use layout uncertainties based on the scale and structure scenarios. With the exception of the Fuzhou River (PLR = 125%), COD for the rivers satisfy the standard for average pollutant load. However, the Zhuang River (PLR = 85%) and Dasha River (PLR = 94%) are close to their environmental capacities. Although most of the rivers' pollutant loads are within their environmental capacity, they still pose extreme water quality impacts, with maximum pollutant load ratios ranging from 166% to 324%. More attention should be paid to extreme environmental impacts of possible land use layouts, especially for the Dengsha River where the maximum pollutant load ratio is as high as 324%. Pollution potential from NH3–N is much more serious than from COD. Based on average pollutant load, only the Yingna River (PLR = 109%) and Dengsha River (PLR = 109%) appear to be close to the standard. Pollutant loads for all other rivers far exceed their environmental capacity, and all rivers pose extreme environmental risks as evidenced by maximum pollution ratios of 268% for the Yingna River and 484% for the Dasha River. Table 7 Statistical analysis of environmental pressure for all samples. Rivers

Pollution load ratio (PLR) NH3–N

COD

Biliu River Fuzhou River Yingna River Zhuang River Dasha River Dengsha River

Average (%)

Max (%)

Average (%)

Max (%)

58 125 49 85 94 52

166 271 199 189 272 324

171 289 109 180 277 109

310 464 268 274 484 442

Table 8 Average source contribution of rivers in the study in 2020. Rivers

Biliu River Fuzhou River Yingna River Zhuang River Dasha River Dengsha River

NH3–N contribution

COD contribution Sewage (%)

Agriculture (%)

Industry (%)

Sewage (%)

Agriculture (%)

Industry (%)

32

14

54

19

61

20

43

5

52

40

30

30

37

7

56

30

42

28

57

5

38

51

23

26

42

13

46

25

56

19

36

6

59

30

32

38

In summary, both NH3–N and COD pollution of surface water needs to be controlled, with NH3–N being more significant with regard to environmental risk, overall environmental pressure and extreme impact. As a result, the regulatory authorities should consider implementing efficient mitigation measures to reduce the water pollution risk posed by NH3–N. 4.3. Identification of critical pollution sources and mitigation measures The average pollution source contribution of the whole sample space is calculated to identify the critical pollution source (Table 8). For most rivers, industrial activity is a major source of COD emission, whereas sewage and agriculture are more significant sources of NH3–N emission. For COD, industrial activities are critical pollution sources for all rivers except the Zhuang River for which sewage is the major source. For NH3–N, agriculture is the critical pollution source for the Biliu, Yingna and Dasha rivers; sewage is the critical pollution source for the Fuzhou and Zhuang rivers; and industrial activity is the critical pollution source for the Dengsha River. The average source contribution and critical pollution source are different between the rivers in the study. Consequently, control strategy should be tailored to each river. 4.4. Environmentally sensitive industries and suggestions for land use spatial distribution Identification of the environmentally sensitive industrial sector via the HSY algorithm and sensitivity analysis approach enables precautionary prevention of the industrial pollution in specific areas. Taking Fuzhou River's catchment area as an example, it reveals that Overall industry (IS = 1.17), Fine chemicals (IS = 2.01) and Fine steels (IS = 1.65) are highly sensitive to this area (Table 9). This implies that there Table 9 Identifying the environmentally sensitive industries in Fuzhou River's catchment. Industry

ISa

Levelb

Petrochemistry Electronic information Mechanical manufacture Transportation equipment Fine chemicals Fine steels Textile and clothing Food processing Building materials Light industry Electricity Overall industry

0.11 −0.01 −0.02 0.02 2.01 1.65 0.03 0.25 0.12 − 0.02 0.26 1.17

SL3 SL3 SL3 SL3 SL5 SL5 SL3 SL3 SL3 SL3 SL3 SL5

a b

Environmentally sensitive index. Environmentally sensitive level.

J. Zhou et al. / Environmental Impact Assessment Review 30 (2010) 219–228

are no possibilities to satisfy the water quality by only appropriate adjustment of the industrial structure and layout, without limiting the growth scale of overall industrial and sensitive industries. According to the joint distribution of pollution load ratio and sensitive industries' scale within this area, given the confidential degree of 85%, the overall industry scale in catchment areas of Fuzhou River, Zhuang River, Dasha River and Dengsha River should be separately limited within 45 billion RMB, 40 billion RMB, 20 billion RMB and 20 billion RMB. Industries of fine chemicals and fine steel, which are of high environmentally sensitive, should be limited within 3 billion RMB totally in those four rivers' catchment areas. 5. Conclusions This paper presents an integrated method for assessing uncertainty about the environmental impacts of land use change. The method combines a scenario analysis approach, stochastic simulation technique and statistics. Scenario analysis is used to address the uncertainties of industrial scale and structure. Based on using Monte Carlo sampling a large number of land use layout samples are generated. The environmental impact uncertainties caused by inherent uncertainties of the UDPD are then analyzed within the sample space using statistics algorithms. The proposed method provides useful information that decisionmakers can use to formulate comprehensive proposals for mitigating water pollution from future changes in land use under conditions of uncertainty. Statistical analysis of the sample space derived from the current land use plan for the study area indicates that the present plan has potentially adverse impacts on water quality in major rivers in the study area. For instance, the average pollutant load ratio of the samples represents the overall environmental pressure of the plan, the violation probability of the samples reveals the potential environmental risk of the plan, and the maximum pollutant load ratio represents potential extreme and adverse environmental impacts of the plan. Furthermore, the method allows critical pollutants and critical pollution sources to be identified, which helps decisionmakers to select appropriate strategies and set priorities for pollution control. This paper tests the method using the Urban Development Plan for Dalian Municipality (2003–2020) as a case study. The results of this study are being used by the Dalian Municipality Administration to guide on-going construction in the area. The method of uncertainty analysis proposed here offers some new perspectives in the field of SEA. Through land use change, the study related the environment with activities prescribed in development plans. Various human activities involved in development plans cause land use change to a certain extent, and the area and layout of the land occupied by a certain activity, such as chemicals production, represent the scale and spatial layout of chemicals production. Therefore, through uncertainty analysis of land use change, we can quantitatively analyze uncertainties inherent in development plans. Along with the process of establishing the method, it can be seen that the application of the proposed method is feasible and flexible. Although the method is developed directly for the UDPD, it can be used to evaluate spatial uncertainties regarding water pollution impacts of other plans in other regions. Acknowledgments Financial supports from the National Natural Science Foundation of China (NSFC) (Grant no. 4070157) are highly appreciated. References Abildtrup J, Audsley E, Fekete-Farkas M, Giupponi C, Gylling M, Rosato P, et al. Socioeconomic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach. Environ Sci Policy 2006;9 (2):101–15.

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Tzilivakis J, Broom C, Lewis KA, Tucker P, Drummond C, Cook R. A strategic environmental assessment method for agricultural policy in the UK. Land Use Policy 1999;16(4):223–34. von Seht H. Requirements of a comprehensive strategic environmental assessment system. Landsc Urban Plan 1999;45(1):1–14. Walker W, Harremoes P, Rotmans J, Van der Sluijs J, Van Asselt M, Janssen P, et al. Defining uncertainty: a conceptual basis for uncertainty management in modelbased decision support. J Integr Asses 2003;4(1):5–17. Wang JH, Guo HC, Liu L, Hao MJ, Zhang M, Lu XJ, et al. An inexact multi-objective programming approach for strategic environmental assessment on regional development plan. Prog Nat Sci 2004;14(11):950–9. Young PC, Hornberger GM, Spear RC. Modelling badly defined systems — some further thoughts. Canberra, Australia: SIMSIG Simulation Conference; 1978. Jiquan Zhou is presently a doctoral candidate at the Environmental Science and Engineering Department of Tsinghua University, PR China. His research areas include land use change, environmental impact assessment, environmental simulation and uncertainty analysis. He has published 3 papers since 2004. Yi Liu is presently a Lecturer at the Environmental Science and Engineering Department, Tsinghua University. He received his Ph.D. in Environmental Science (Environmental Sociology) from Wageningen University, the Netherlands in 2005. Since then he joined Tsinghua University.Dr. Liu's main research areas include strategic environmental assessment, environmental system analysis, environmental simulation and uncertainty analysis. He has published one book and more than 20 papers since 2001.

Jining Chen, vice President of Tsinghua University of PR China, is presently a Professor at the Environmental Science and Engineering Department, Tsinghua University. He also is member of the editorial committee of Water Science & Technology, and vice Director of National Environmental Science and Technology Committee of China. Professor Chen received his Ph.D. in Civil Engineering from Imperial College, UK in 1992. After postdoctoral fellowships at Imperial College, he joined Tsinghua University in 1998. His main research areas include environmental system analysis, uncertainty analysis and modeling. Especially his work on quality assurance of multi-media modal was adopted by USEPA, and published as USEPA report (EPA/600/R-98/106). He has published more than 80 papers since 1999.