Journal of Cleaner Production 69 (2014) 74e82

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Journal of Cleaner Production

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Data uncertainties in anthropogenic phosphorus flow analysis of lake watershed

Huijun Wu a,b, Zengwei Yuan b,*, Yongliang Zhang c, Liangmin Gao a, Shaomin Liu a, Yan Geng a a School of Earth and Environment, University of Science and Technology, 232001, PR b State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, University, Nanjing 210023, PR China c Policy Research Center for Environment and Economy, Ministry of Environmental Protection, Beijing 100029, PR China article info abstract

Article history: The data uncertainty is a crucial limitation for substance flow analysis (SFA) studies. Monte Carlo (MC) Received 5 January 2013 simulation is used to assess the data uncertainty of the anthropogenic phosphorous (P) flow analysis in Received in revised form Watershed. The study selects the key data in crop farming, large-scale breeding, and rural 30 October 2013 consumption subsystems, which are the biggest contributors to P emissions. The results show that in the Accepted 12 January 2014 crop farming subsystem, the P-containing rate of crop, soil deposition rate, harvest of crop, proportion of Available online 23 January 2014 large-scale livestock excrement to field, and the amount of applied chemical fertilizer are the greatest contributors to the output uncertainty. While the amount of feed consumed per large-scale livestock, Keywords: Data uncertainty amount of large-scale livestock, P-containing rate of feed consumed by large-scale livestock, and pro- fi Substance flow analysis portion of large-scale livestock excrement to eld have the greatest uncertainties in the large-scale Monte Carlo simulation breeding subsystem. Moreover, in the rural consumption subsystem, both of the P-containing rate of Phosphorus crop and the amount of crop consumed per rural people have the greatest uncertainties. By analyzing the Chaohu watershed reasons leading to the data uncertainties, the suggestions for minimizing the uncertainty are also pro- posed. The study also shows that the MC methodology is an efficient tool to solve the data uncertainty in SFA study. Crown Copyright Ó 2014 Published by Elsevier Ltd. All rights reserved.

1. Introduction Chen, 2006; Liu et al., 2006). These studies mainly focus on quan- tifying and analyzing the P flows in the socioeconomic system, The high levels of phosphorus (P) occurring through human being lack of analyzing data uncertainty adequately. While the data activities in watersheds contribute to the lake eutrophication uncertainty could lead to assumptions about data accuracy and (Bennett et al.,1999; Carpenter et al.,1998; Drolc and Koncan, 2002; output/results that are not valid and ultimately impact upon the Jeunesse and Elliott, 2004; Smil, 2000; Tangsubkul et al., 2005; corresponding management practices and decisions (Regan et al., Withers and Jarvie, 2008). For mitigating the eutrophication of 2002). lakes, it is critical to trace and quantify the P pathways or flows Dealing with the data uncertainty, (Chen et al., 2008) only throughout socioeconomic system (Kennedy et al., 2007; Wu et al., simplified the model structure and assumed a linear relationship 2012). Substance flow analysis (SFA) is a systematical method to between the P loss into water and the surface P balance. However, analyze the metabolism of a given substance in a geographically the actual relationship is much more complicated. Sokka et al. demarcated system by quantifying input and output flows and (2004) just qualitatively selected several data having high un- stocks (vanderVoet et al., 1995a, b; Zhang et al., 2009), and has been certainties and sought to minimize the uncertainties by using an widely used to quantify the pathways of anthropogenic P at country extensive range of reference sources and make comparisons be- level (Liu, 2005; Antikainen et al., 2005, 2008; Jeong et al., 2009; Ma tween the sources. Thus, a quantitative data uncertainty assess- et al., 2012; Matsubae-Yokoyama et al., 2009; Neset et al., 2008; ment is recommended for use in SFA applying for anthropogenic P Saikku et al., 2007; Sokka et al., 2004), and regional level (Liu and flow analysis to support the interpretation of the results. It is noticed many environmental problems can be directly related to flows of substances, materials and products through the economy. While the two methods of life cycle assessment (LCA) and SFA are * þ Corresponding author. Tel.: 86 (0)25 89680532. both used to study economyematerial interactions and describe E-mail addresses: [email protected], [email protected] (Z. Yuan).

0959-6526/$ e see front matter Crown Copyright Ó 2014 Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.jclepro.2014.01.043 H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82 75 such physical flows from the life cycle perspective (Bouman et al., managements to mitigate eutrophication. The study will also 2000). For LCA there exist a number of methods to deal with data outline some implications for future collection of environmental uncertainties (Björklund, 2000; Heijungs and Frischknecht, 2005; data. In particular, the study addresses the following questions: Huijbregts, 1998a, 1998b, 2001; Lo et al., 2005; Maurice et al., 2000; Sonnemann et al., 2003). Contrarily data uncertainties con- (1)How to choose the key data impacting the SFA results for nected with SFA studies are rarely dealt with. uncertainty analysis? A method developed by Hedbrant and Sörme (2001) is designed (2)How uncertainties of the data influence the model output? to be used in SFA, and the method was tested in the subsystem of (3)How to minimize the data uncertainty in the study? food consumption (Neset, 2005). It suggested a method based on uncertainty intervals to consider the data uncertainties. The level of uncertainty is determined for each and every one of the collected 2. Methods data. However, the method is applicative to the situation in which data is limited and the model or calculation formula is simple, and In this study the model, which is developed based on MC not adaptive to that which has many data and complex formula. simulation to determine, present and calculate the data un- Meanwhile, the relation between the uncertainty levels and in- certainties in a SFA study, is applied on data from an earlier case fi tervals was de ed with experience, lacking adequate quantitative study of P flows in the socioeconomic system of Chaohu Watershed ’ analysis. Though in Montangero s study (Montangero et al., 2007), in 2008. the statistical method was applied for quantifying the data uncer- tainty in the P flows in environmental sanitation and agricultural system, only several data were selected for being assessed, and the 2.1. Case study relation between data uncertainty and results was not interpreted. Danius and Burström (2001) slightly modified the uncertainty in- The case study is carried out for the territory of the Chaohu tervals to suit data in their study, and applied them to two situa- Watershed, which is located in the central Anhui province of cen- tions of priority setting and follow-up setting, while they didn’t tral China. The watershed has a population of 9.64 million, which is gain obvious improvement for the previous research. Concerning one of the most densely populated areas. The total watershed of 2 ’ about lake eutrophication, several studies on city (Li et al., 2010; Chaohu Lake is 13,350 km , accounting for 10% of the province s Yuan et al., 2011b), county (Yuan et al., 2011a; Wu et al., 2012) total land area, involving City (the capital of Anhui Province), and lake watershed (Bennett et al., 1999; Drolc and Koncan, 2002; , , , Chaohu City, La Jeunesse and Elliott, 2004; Liu and Chen, 2006a; Liu et al., 2006b) , , , and Wuwei County mainly focus on quantifying the anthropogenic P flows and iden- (Gao et al., 2011). As the 5th largest lake in China, Chaohu Lake is tifying the sources causing eutrophication, rarely include estimates important for local environment and society (Gao et al., 2009). of uncertainties in dada. Though some of the studies analyze the While since 1990s, a great amount of industrial wastewater and data uncertainty (Li et al., 2010; Yuan et al., 2011a, 2011b, 2011c; domestic sewage were discharged into the lake. This has created Wu et al., 2012), they are more qualitative, lacking adequate negative ecological, health, social, and economic effects on the lake quantitative analysis. and its uszhe (Shang and Shang, 2005; Xu et al., 2003, 2007). Monte Carlo (MC) simulation is an effective means to address Eutrophication has become one of the most serious problems of the data uncertainty (Vose, 1996). It is a class of numerical techniques Lake (Wu et al., 2009). In the past, the Chaohu Lake had been based on the statistical characteristics of physical processes, or of extensively studied for a variety of limnological purposes and analogous models that mimic physical processes (Howell, 1998). It related problems (Tu et al., 1990; Yin and Bernhardt, 1992). There is used to predict/simulate measurement results on the basis of were also some measures taken by national and local government probability density functions of the input quantities (Chew and to restrain the eutrophication process in Chaohu Lake. However, the Walczyk, 2012). The realization of MC simulations provides deci- result is not satisfactory, and the heavy eutrophication remains a sion makers with far more information than a single estimate of great problem (Shang and Shang, 2007). damage (Sonnemann et al., 2003). Many applications of this method could be found in the different study areas (Davie, 1960; 2.2. Anthropogenic P flow model Erickson and Stephan, 1988; Lindenschmidt, 2008; Szemesova and Gera, 2010; McLaughlin and Jain, 2011). There are also many A static analytical model for anthropogenic P flow basing on SFA studies applying MC approach to LCA studies for analyzing the data is used to quantify the pathways of anthropogenic P flows and uncertainties (Maurice et al., 2000; McCleese and LaPuma, 2002; identify the sources of P loads into water environment. In the Sonnemann et al., 2003; Yu and Tao, 2009; Mullins and Michael model, the region constituted of all societal activities within the Griffin, 2011). However, the studies analyzing the data un- territory is thought of as an open system exchanging P-containing certainties in SFA studies by using MC simulation are limited. materials and energy with its surrounding via the atmosphere, the Though there’s study (Montangero et al., 2007) applying MC hydrosphere and society. Flows of P between different sectors in simulation to analyze data uncertainty in the model describing society, between from societal sectors to the environment, and material flows, only the probability distributions of some data were from the environment to the societal sectors are recorded. There displayed, other data may being of importance and having un- are nine main sectors or subsystems (ore extraction, product certainties were not analyzed. Moreover, the impacts upon the manufacturing, crop farming, large-scale breeding, domestic results causing by the data displayed were also not analyzed breeding, rural consumption, urban consumption, wastewater further. management, and solid waste management) in society and three This aim of this study is to present MC method to evaluate the main environmental compartments (water, soil and atmosphere), data uncertainty in SFA of P in the socioeconomic system of Chaohu all of which are concluded in the socioeconomic system of the lake Watershed, and analyze how the uncertainty affects the model watershed. P flows are accounted for a period of one year of 2008. results. From analyzing the data uncertainty, some suggestions of The detailed description of the model could be seen in the related minimizing the uncertainty could be proposed. And hence the studies (Li et al., 2010; Yuan et al., 2011a, 2011b, 2011c; Wu, 2012; study could provide the decision makers with more reliable Wu et al., 2012) our research team published previously. 76 H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82

The data used in the case study were gained from many different these data are determined respectively according to their kinds in sources and vary considerably in uncertainty. The data could be Table 1, they are simulated not differentiating their categories for divided into a number of groups: measured data from question- simplifying. naires, face-to-face interviews, statistics at local and regional level, Once the probabilities of all the input data selected have been data from published literature. Here, data uncertainty is divided determined, the probability distributions of the output results of into lack of data, and data inaccuracy. In addition, lack of data is the model, which calculates the P discharging to water of every further specified as a complete lack of data (e.g. some P-containing subsystem in the socioeconomic system of the lake watershed, are rates of materials from inadequate literature), and a lack of repre- calculated with the MC simulation. Here we randomly sample the sentative data (e.g. some data from too early year, or from different data set from their probability distributions in Table 1. The number regions). While the data inaccuracy is mainly resulted from the of runs is set to 1000. The output of this model runs with Minitab interviewers’ inadequate intellectual and education. software is then fitted to data variability though the linear model.

2.3. Uncertainty evaluation by MC 3. Results and discussions

The MC approach is used to highlight effects of data uncertainty 3.1. The uncertainty of key data in crop farming through the model. Probability distribution types (normal, lognormal or uniform) and characteristics (mean and standard Fig. 1 indicates the key data in crop farming subsystem and their deviation or minimum and maximum) are determined for each relative contribution to uncertainty. The wider the spread or width data. Since determinimizing the statistical function of data is time of the output distributions, the greater the uncertainty level is. Of consuming, it is necessary to identify the key input data, which the 13 uncertainty data in the subsystem, P-containing rate of crop, determine the value and uncertainty of the cumulative results. The soil deposition rate, harvest of crop, proportion of large-scale most influential data are considered to be both highly sensitive and livestock excrement to field, and amount of applied chemical fer- at the same time contributing a large share of uncertainty (Böttcher tilizer are the greatest contributors to the output uncertainty. This et al., 2008). From the obvious studies we concluded that the is due to several reasons. Firstly, the data of P-containing rate of largest three contributors to eutrophication in the socioeconomic crop was mainly gained from the literature, while which is few and system are crop farming, large-scale breeding, and rural con- the data have big differences. Secondly, the soil deposition rate sumption. So the data which mostly affected the calculation results relating with the natural environment closely has large difference in these three subsystems are selected to analyze their un- in different regions and with different soil properties, and the data certainties. The key data were selected based on the quantitative source is also scarce. Thirdly, the two data of harvest of crop and evaluation of the P flows in the socioeconomic system in Chaohu amount of applied chemical fertilizer have the great influences on Watershed. From the detailed calculation it could be concluded the calculated results for their big values, and were mainly collected directly the parameters which impact the P flows greatly. Moreover, from questionnaire surveying and interviewing with rural resi- the calculation model and calculated results of the P flows in the dents, whose education level and the communication with in- socioeconomic system of the watershed could be consulted from vestigators would impact the answers and then may influence the the related literature (Li et al., 2010; Yuan et al., 2011a, 2011b, calculated results greatly. Finally, when surveying the large-scale 2011c; Wu et al., 2012) and the author’s doctoral thesis (Wu, breeding enterprises, it is found the staffs’ vague answers about 2012). Meanwhile, we assume the independence of the data, i.e. the enterprises waste disposal and the inadequate knowledge of no correlations between data. environmental protection, would cause deviation to the data of the Based on the related literature, materials, surveys and empirical proportion of large-scale livestock excrement to field. Though the judgments, the probability distribution of data mainly includes answers were revised with related literature, the uncertainty of this triangular distribution, lognormal distribution, and normal distri- data is not to be neglected. bution, etc. When there is very limited sample data of an uncertain While the P containing rate of chemical fertilizer, amount of data, which is obtained in or around the study area, the triangular domestic livestock bred per rural people, amount of large-scale distribution is used, e.g. the P-containing rates of phosphate fer- livestock contribute the least amount of uncertainty in the tilizer and compound fertilizer, amounts of feed consumed per output. On the one hand, the P containing rate of chemical fertilizer large-scale livestock. The lognormal distribution is particularly could be gained from more sufficient literature and statistics. On suitable to represent large uncertainties of data, such as the pro- the other hand, the amount of domestic livestock bred per rural portion of large-scale livestocks’ excrement to field. While the people, and the amount of large-scale livestock could also be more normal distribution is used for the data having smaller sample data, easily obtained from statistics and questionnaires, from which the such as the harvests of crops, P-containing rates of crops, rural interviewees could feel easier to understand and answer the population, amounts of large-scale livestocks. questions about these data. The probability distributions of the data in the MC simulations are presented in Table 1. For additional and more detailed infor- 3.2. The uncertainty of key data in large-scale breeding mation on the data sources, the studies (Li et al., 2010; Yuan et al., 2011a, 2011b, 2011c; Wu et al., 2012) are referred. It is noted that as Fig. 2 describes the frequency distributions of the key output both of the livestocks and crops have no more than one kinds, some variables in large-scale breeding subsystem as the result of the data data such as the harvests of crops, P-containing rates of crops, uncertainties. It is noticed that the amount of feed consumed per amounts of large-scale livestocks, P contents per livestock, amounts large-scale livestock, amount of large-scale livestock, P-containing of domestic livestocks bred per rural people, P contents of excre- rate of feed consumed by large-scale livestock, and proportion of ment discharged per domestic livestock, P contents of excrement large-scale livestock excrement to field have the greatest un- discharged per large-scale livestock, amounts of feed consumed per certainties among the six most influential data in the subsystem. large-scale livestocks, P-containing rates of feed consumed by There are two reasons for this. Firstly, the investigated staffs in the large-scale livestocks, per capita consumptions of crops, per capita large-scale breeding enterprises feel hard to formulate them with consumptions of meat, P-containing rates of meat consumed by accurate data answered the amount of feed consumed, P-contain- people, etc., are the combined data. Though the distributions of ing rate of feed, and the disposal of excrement qualitatively, which H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82 77

Table 1 Probability distributions of model data used to in the MC simulations of Chaohu Watershed.

Parameter Description Unit Distribution

r Q1 Amount of applied phosphate fertilizer t Normal distribution (59,684, 2984) r Q2 Amount of applied compound fertilizer t Normal distribution (131,729, 6586) r g1 P-containing rate of phosphate fertilizer % Triangular distribution (41.48, 43.66, 45.84) r g2 P-containing rate of compound fertilizer % Triangular distribution (11.12, 11.71, 12.30) g Harvest of rice t Normal distribution (3,315,219, 331,522) Q1 g Harvest of wheat t Normal distribution (462,450, 46,245) Q2 g Harvest of rapeseed t Normal distribution (445,153, 44,515) Q3 g Harvest of peanut t Normal distribution (61,841, 6184) Q4 g Harvest of sesame t Normal distribution (6270, 627) Q5 g Harvest of cotton t Normal distribution (85,109, 8511) Q6 g Harvest of vegetables and fruits t Normal distribution (2,800,571, 280,057) Q7 g Harvest of maize t Normal distribution (77,264, 7726) Q8 g Harvest of beans t Normal distribution (57368, 5737) Q9 g Harvest of potatoes t Normal distribution (75051, 7505) Q10 p P-containing rate of rice % Normal distribution (0.4, 0.04) g1 p P-containing rate of wheat % Normal distribution (0.5, 0.2) g2 p P-containing rate of rapeseed % Normal distribution (0.9, 0.6) g3 p P-containing rate of peanut % Normal distribution (0.5, 0.2) g4 p P-containing rate of sesame % Normal distribution (0.5, 0.2) g5 p P-containing rate of cotton % Normal distribution (0.78, 0.08) g6 p P-containing rate of vegetables and fruits % Normal distribution (0.1, 0.02) g7 p P-containing rate of maize % Normal distribution (0.4, 0.13) g8 p P-containing rate of beans % Normal distribution (0.6, 0.12) g9 p P-containing rate of potatoes % Normal distribution (0.1, 0.06) g10 Q r Rural population People Normal distribution (6,765,924, 676,592) qh P content of excrement discharged per rural people kg Normal distribution (0.73, 0.14) bh Proportion of rural people’s excrement to field % Normal distribution (94.29, 9.43) f Amount of large-scale chickens Head Normal distribution (126,482,017, 12,648,202) Q1 f Amount of large-scale ducks Head Normal distribution (32,498,360, 3,249,836) Q2 f Amount of large-scale geese Head Normal distribution (16,161,854, 1,616,185) Q3 f Amount of large-scale pigs Head Normal distribution (2,977,398, 297,740) Q4 f Amount of large-scale cattle Head Normal distribution (115,256, 11,526) Q5 f Amount of large-scale sheep Head Normal distribution (14,4117, 14,412) Q6 f Amount of large-scale fishes Head Normal distribution (122,374,887, 12,237,489) Q7 a g1 P content per chicken kg Normal distribution (0.01, 0.003) a g2 P content per duck kg Normal distribution (0.01, 0.003) a g3 P content per goose kg Normal distribution (0.01, 0.003) a g4 P content per pig kg Normal distribution (0.46, 0.12) a g5 P content per cattle kg Normal distribution (2.7, 0.3) a g6 P content per sheep kg Normal distribution (0.28, 0.03) a fi g7 P content per sh kg Normal distribution (0.003, 0.001) d P content of excrement discharged per large-scale chicken kg Normal distribution (0.08, 0.01) b1 d P content of excrement discharged per large-scale duck kg Normal distribution (0.10, 0.01) b2 d P content of excrement discharged per large-scale goose kg Normal distribution (0.02, 0.002) b3 d P content of excrement discharged per large-scale pig kg Normal distribution (1.14, 0.27) b4 d P content of excrement discharged per large-scale cattle kg Normal distribution (10.05, 1.77) b5 d P content of excrement discharged per large-scale sheep kg Normal distribution (1.61, 0.27) b6 ’ bh Proportion of large-scale livestock excrement to field % Lognormal distribution (70.0, 10.5) f ’ Amount of domestic chickens bred per rural people Head Normal distribution (1.23, 0.30) Q1 f ’ Amount of domestic ducks bred per rural people Head Normal distribution (0.58, 0.12) Q2 f ’ Amount of domestic geese bred per rural people Head Normal distribution (0.24, 0.05) Q3 f ’ Amount of domestic pigs bred per rural people Head Normal distribution (0.07, 0.01) Q4 f ’ Amount of domestic cattle bred per rural people Head Normal distribution (0.03, 0.01) Q5 f ’ Amount of domestic sheep bred per rural people Head Normal distribution (0.02, 0.003) Q6 f ’ Amount of domestic fishes bred per rural people Head Normal distribution (3.61, 0.72) Q7 d’ P content of excrement discharged per domestic chicken kg Normal distribution (0.17, 0.05) b1 d0 P content of excrement discharged per domestic duck kg Normal distribution (0.27, 0.16) b2 d’ P content of excrement discharged per domestic goose kg Normal distribution (0.29, 0.13) b3 d’ P content of excrement discharged per domestic pig kg Normal distribution (2.42, 1.01) b4 d’ P content of excrement discharged per domestic cattle kg Normal distribution (10.05, 1.77) b5 (continued on next page) 78 H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82

Table 1 (continued )

Parameter Description Unit Distribution

d’ P content of excrement discharged per domestic sheep kg Normal distribution (1.61, 0.27) b6 bb Soil deposition rate (the proposition of fertilizers and excrements) % Triangular distribution (23, 30, 40) f Amount of feed consumed per large-scale chicken kg Triangular distribution (24.01, 39.0, 43.80) b1 f Amount of feed consumed per large-scale duck kg Triangular distribution (30.50, 46.63, 73.0) b2 f Amount of feed consumed per large-scale goose kg Triangular distribution (3.84, 7.88, 40.21) b3 f Amount of feed consumed per large-scale pig kg Triangular distribution (244.0, 418.36, 512.50) b4 f Amount of feed consumed per large-scale cattle kg Triangular distribution (566.70, 755.56, 850.0) b5 f Amount of feed consumed per large-scale sheep kg Triangular distribution (83.7, 93.0, 102.3) b6 f Amount of feed consumed per large-scale fish kg Triangular distribution (3.23, 3.53, 3.57) b7 f P-containing rate of feed consumed by large-scale chicken % Normal distribution (0.44, 0.04) g1 f P-containing rate of feed consumed by large-scale duck % Normal distribution (0.37, 0.04) g2 f P-containing rate of feed consumed by large-scale goose % Normal distribution (1.1, 0.11) g3 f P-containing rate of feed consumed by large-scale pig % Normal distribution (0.46, 0.05) g4 f P-containing rate of feed consumed by large-scale cattle % Normal distribution (1.2, 0.12) g5 f P-containing rate of feed consumed by large-scale sheep % Normal distribution (1.2, 0.12) g6 f P-containing rate of feed consumed by large-scale fish % Normal distribution (0.6, 0.06) g7 o Amount of rice consumed per rural people kg Normal distribution (291.39, 42.96) q1 o Amount of self-farming wheat consumed per rural people kg Normal distribution (7.32, 1.66) q2 o Amount of rapeseed consumed per rural people kg Normal distribution (23.44, 5.46) q3 o Amount of peanut consumed per rural people kg Normal distribution (2.20, 0.57) q4 o Amount of sesame consumed per rural people kg Normal distribution (0.44, 0.09) q5 o Amount of cotton consumed per rural people kg Normal distribution (1.44, 0.64) q6 o Amount of self-farming vegetables and fruits consumed per rural people kg Normal distribution (0.88, 0.17) q7 o Amount of self-farming maize consumed per rural people kg Normal distribution (4.14, 1.34) q8 o Amount of self-farming beans consumed per rural people kg Normal distribution (0.26, 0.15) q9 o Amount of self-farming potatoes consumed per rural people kg Normal distribution (3.3, 2.21) q10 o’ Amount of purchased vegetables consumed per rural people kg Normal distribution (112.79, 4.78) q1 o’ Amount of purchased fruits consumed per rural people kg Normal distribution (39.24, 2.09) q2 p’ P-containing rate of vegetables purchased by rural people % Triangular distribution (0.018, 0.026, 0.034) g1 p’ P-containing rate of fruits purchased by rural people % Triangular distribution (0.008, 0.016, 0.13) g2 b Amount of pigs consumed per rural people kg Normal distribution (12.26, 1.15) q1 b Amount of cattle consumed per rural people kg Normal distribution (0.65, 0.07) q2 b Amount of sheep consumed per rural people kg Normal distribution (0.14, 0.02) q3 b Amount of poultries consumed per rural people kg Normal distribution (6.37, 0.6) q4 b Amount of fishes consumed per rural people kg Normal distribution (5.31, 0.19) q5 b P-containing rate of pigs consumed by people % Normal distribution (0.46, 0.29) g1 b P-containing rate of cattle consumed by people % Normal distribution (0.60, 0.43) g2 b P-containing rate of sheep consumed by people % Normal distribution (0.56, 0.40) g3 b P-containing rate of poultries consumed by people % Normal distribution (0.65, 0.49) g4 b P-containing rate of fishes consumed by people % Normal distribution (0.20, 0.02) g5 qx Living solid wastes discharged per rural people kg Normal distribution (110.14, 22.03) gx P-containing rate of living solid wastes discharged by rural people % Normal distribution (0.15, 0.01) aTriangular distribution (minimum, mode, maximum). bLognormal distribution (arithmetic mean, coefficient of variation). cNormal distribution (arithmetic mean, coefficient of variation). were amended by related literature and materials. Moreover, for 3.3. The uncertainty of key data in rural consumption the limited large-scale breeding enterprises surveyed, it would be hard to obtain the amount of large-scale breeding in the whole In Fig. 3 we present the output distributions for P discharging to studied region. It is noted that the amount of livestock in the sta- surrounding water from rural consumption subsystem for data tistical yearbook is the sum of amount of large-scale breeding and uncertainty. It can be seen that the frequency distributions with P- amount of domestic breeding. Meanwhile, it is found from the containing rate of crop and amount of crop consumed per rural obvious related studies that the amount of domestic breeding was people are the widest. That with amount of meat consumed per gained from interviewing with rural residents, which would rural people, in turn, is most intensive. It is mainly caused by the generate some deviations as discussed in the last paragraph. Thus, very limited sources of P-containing rate of crop and the scattered the deviation would bring about the deviation of the amount of sample data. Meanwhile, the investigated rural residents could only large-scale livestock. It could also be concluded from Fig. 2 that the answer their crop consumed qualitatively and subjectively, hardly P content of excrement discharged per large-scale livestock and the measuring it quantitatively and accurately. As a result, the amount P-content per livestock contribute the least to the output uncer- of crop consumed per rural people could only be calculated from tainty, resulting from the more sufficient referred literature and the subtracting the amount of harvested crop and the amount of sold centralized data in the literature. crop, which would lead to some deviations. On the contrary, the H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82 79

Fig. 1. Monte Carlo simulation results for the key data in the crop farming subsystem. meat consumed by rural residents is smaller than the crop, and is Secondly, collecting more literature and materials to obtain purchased almost from the farmers’ market. Therefore the amount more data samples such as P-containing rate of crop and soil of meat consumed per rural people can be collected more easily and deposition rate, especially some site specific samples such as the reliably. soil deposition rate. Then the uncertainties of the data could be reduced, which could finally raise the accuracy of the P flows 3.4. Minimizing uncertainty suggestions relating with soil deposition, and erosion and runoff of the crop farming subsystem. Based on the results of the study, several suggestions of mini- Meanwhile, raising the awareness of environmental production, mizing the data uncertainty in anthropogenic P flows analysis of cleaner production and circular economy for the staffs in the large- Chaohu Watershed are to be expected. Firstly, increasing the scale breeding enterprises, giving them some suggestions of amount of questionnaires and expanding the range of surveyed establishing the environmental management department of regions, and selecting the surveyed regions and interviewed resi- breeding and the infrastructure of wastes disposal, organizing staffs dents more reasonably. This is the most important measure to to observe and record the amount of feed consumed, P-containing minimize the data uncertainty. The data having high uncertainty rate of feed, and the disposal of excrement. Through these mea- could be clearer from more questionnaires and interviews, thus the sures, the data of the amount of feed consumed per large-scale deviation of the P flows and the P loss of the three subsystems livestock, P-containing rate of feed, proportion of large-scale live- fi would be reduced based on the P flow calculation model described stock excrement to eld would be more accurate, thus the calcu- fl in the previous studies (Li et al., 2010; Yuan et al., 2011a, 2011b, lated results of P ows and the P excrement discharging to water 2011c; Wu, 2012; Wu et al., 2012). would be more credible.

Fig. 2. Monte Carlo simulation results for the key data in the large-scale breeding subsystem. 80 H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82

Fig. 3. Monte Carlo simulation results for the key data in the rural consumption subsystem.

Moreover, strengthening the communication with rural resi- (3) The determination of the probability distributions of the SFA dents, expressing the questions in clear and accessible way to help data is difficult. The determination of the probability distri- them understand more easily. Furthermore, educating and popu- butions for a quantitative uncertainty of the data is the most larizing the rural residents about the knowledge related with the delicate because it determines the quality and the reliability questionnaires, which would improve the accuracy and efficiency of of the quantitative uncertainty analysis. The probability answering questionnaires. Then the key data having high un- distributions chosen for the input data also determine the certainties of the crop farming subsystem and rural residents con- probability distributions of the calculated results of the SFA sumption subsystemwould be clearer. Consequently, the P flows and with any MC simulation. The means and standard deviations the P losses of the two subsystems would be more accurate. In of the data are both quantitative and qualitative, which are addition, improving the calculation accuracy with some special mainly determined by the surveys, literature, materials and software would be helpful to minimizing the data uncertainties. empirical judgments. Moreover, the estimates of un- certainties have not been keyed to geographic domains and fi 3.5. Methodological discussion other site-speci c considerations, it requires a major effort to determine the probability distribution type and the charac- Despite the useful results that have been obtained from teristic of each key data, especially synthesize across many analyzing the data uncertainty in SFA of P in the socioeconomic sources of data uncertainty and use many types of system with the MC methodology, there are several limitations that information. can be stated and would be the subject of future studies in which (4) Correlations among the data are not considered. For simpli- ’ the methodology is improved: fying the calculation, the study assumes there s no correla- tion between data. Where known, large correlations (i.e., (1)It is difficult to quantify the uncertainty of all data which is greater than about 0.5) among inputs should be accounted various. The most influential data in crop farming, large-scale for in the Monte Carlo resampling (Hanna et al., 1998). breeding, and rural consumption subsystems, which are the Actually, in the simulation, the relations between some data largest three contributors to eutrophication in the socioeco- which dependent strongly with each other (i.e., the amounts nomic system are quantified and analyzed. While the uncer- of large-scale livestocks and the amounts of domestic live- tainty of model input data from other subsystems such as ore stocks bred per rural people) are considered. Maybe there are extraction, product manufacturing, domestic breeding, urban other correlations between other key data, while which are fi consumption, wastewater management, and solid waste man- ignored for simpli cation. As a result, the methodology agement, etc. are not considered. Such data uncertainty is hard should be designed so that improbable combinations of data fl to assess and their effects on total data uncertainty exceed the which would in uence the outputs are avoided. scope of the study. (5) Other uncertainties are not taken into account. It may not be (2) Uncertainty assessment is time consuming. An assessment of feasible to address all aspects of uncertainties in all situa- data uncertainty is, so far, too time consuming to be applied tions. Other uncertainties such as model uncertainty, tem- on SFA. While time is necessary to check the probability poral and spatial variability, and environmental changes are distribution that best fits the respective data. On the con- hard to assess at once and their effects on total output un- trary, the operation of the MC simulation itself does not certainty exceed the scope of this study. These need to be demand so much time, due to the use of the special software. expected in the future. To save time, some estimation of probability distributions of input data may be adopted from one study to another. 4. Conclusions However, this need to be done carefully as the uncertainty assessment strongly depends on the study area and The main topic of this paper is to assess the data uncertainty in categories. anthropogenic P flow analysis of Chaohu Watershed by using the H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82 81

MC simulation. In crop farming subsystem, the P-containing rate of Chew, G., Walczyk, T., 2012. A Monte Carlo approach for estimating measurement crop, soil deposition rate, harvest of crop, proportion of large-scale uncertainty using standard spreadsheet software. Anal. Bioanal. Chem. 402, 2463e2469. livestock excrement to field, and amount of applied chemical fer- Danius, L., Burström, F., 2001. In: Hilti, L.M., Giligen, P.W. (Eds.), Sustainability in the tilizer contribute the greatest to the output uncertainty. While the Information Society. Part 2: Methods/Workshop Paper. Metropolis Verlag, amount of feed consumed per large-scale livestock, amount of Marburg. Davie, D.H., 1960. Monte Carlo calculation of molecular flow rates through a cy- large-scale livestock, P-containing rate of feed consumed by large- lindrical elbow and pipes of other shapes. J. Appl. Phys. 31 (7), 1169e1176. scale livestock, and proportion of large-scale livestock excrement to Drolc, A., Koncan, J.Z., 2002. Estimation of sources of total phosphorus in a river field have the greatest uncertainties in large-scale breeding sub- basin and assessment of alternatives for river pollution reduction. Environ. Int. 28 (5), 393e400. system. Finally the P-containing rate of crop, and amount of crop Erickson, R.J., Stephan, C.E., 1988. Calculation of the Final Acute Value for Water consumed per rural people have the two greatest uncertainties in Quality Criteria for Aquatic Organisms. Environmental Research Laboratory. US rural consumption subsystem. Environmental Protection Agency, Duluth (MN). EPA/600/3-88/018. Gao, C., Wang, X.Y., Jiang, T., Jin, G.J., 2009. Spatial distribution of archaeological For minimizing the uncertainty of the P-containing rate of crop sites in Lakeshore of Chaohu Lake in China based on GIS. Chin. Geogr. Sci. 19 (4), and soil deposition rate, there needs to collect more literature and 333e340. materials to obtain more data samples. Concerning the amount of Gao, F., Deng, J.C., Xu, Z.B., Ning, Y., Yin, H.B., Gao, J.F., 2011. Ecological characteristics feed consumed, P-containing rate of feed, and the disposal of of macrobenthic communities in the Chaohu Lake Basin and their relationship with environmental factors. J. Animal Veterinary Adv. 10 (5), 627e634. excrement, raising the awareness of environmental production for Hanna, S.R., Chang, J.C., Fernau, M.E., 1998. Monte Carlo estimates of uncertainties in the staffs in the large-scale breeding enterprises, and giving them predictions by a photochemical grid model (UAM-IV) due to uncertainties in e some suggestions of establishing the environmental management input variables. Atmos. Environ. 32 (21), 3619 3628. Hedbrant, J., Sörme, L., 2001. Data vagueness and uncertainties in urban heavy system are need to decrease the uncertainties. For decreasing the metal data collection. Water Air Soil. Pollut. Focus 1, 43e53. uncertainty of the amount of crop consumed per rural people, and Heijungs, R., Frischknecht, R., 2005. Representing statistical distributions for un- the P-containing rate of rural consumption subsystem, educating certain parameters in LCA. Relationships between mathematical forms, their representation in EcoSpold, and their representation in CMLCA. Int. J. Life Cycle the rural residents about more meaning related with the ques- Assess. 10 (4), 248e254. tionnaires, strengthening the communication with rural residents, Howell, J.R., 1998. The Monte Carlo method in radiative heat transfer. Eng. J. Heat. increasing the questionnaires and regions related with question- Transf. 120, 547e560. Huijbregts, M.A.J., 1998a. Application of uncertainty and variability in LCA. Part I: a naire survey are very necessary. general framework for the analysis of uncertainty and variability in life-cycle Thus, the proposed MC methodology appears to have strong assessment. Int. J. Life Cycle Assess. 3 (5), 273e280. potential for solving the data uncertainty in SFA study. While more Huijbregts, M.A.J.,1998b. Part II: dealing with parameter uncertainty and uncertainty ’ due to choices in life cycle assessment. Int. J. Life Cycle Assess. 3 (6), 343e351. studies will be carried out in order to improve the value of SFA s Huijbregts, M.A.J., 2001. Framework for modeling data uncertainty in life cycle in- and provide the decision makers with more efficient environ- ventories. Int. J. Life Cycle Assess. 6 (3), 127e132. mental policy and management. Such as selecting more data to Jeong, Y.S., Matsubae-Yokoyama, K., Kubo, H., Pak, J.J., Nagasaka, T., 2009. Substance fl assess their uncertainties, saving the time and improving the ac- ow analysis of phosphorus and manganese correlated with South Korean steel industry. Resour. Conserv. Recycl. 53 (9), 479e489. curacy of determining the probability distributions of data with Jeunesse, I.L., Elliott, M., 2004. Anthropogenic regulation of the phosphorus balance special software or more related references, considering the in- in the Thau catchment-coastal lagoon system (Mediterranean Sea, France) over e fluence of more correlations with data and other uncertainties on 24 years. Mar. Pollut. Bull. 48, 679 687. Kennedy, C., Cuddihy, J., Engel-Yan, J., 2007. The changing metabolism of cities. the outputs. J. Indust. Ecol. 11, 43e59. La Jeunesse, I., Elliott, M., 2004. Anthropogenic regulation of the phosphorus bal- ance in the Thau catchment-coastal lagoon system (Mediterranean Sea, France) e e Acknowledgments over 24 years. Mar. Pollut. Bull. 48 (7 8), 679 687. Li, S.S., Yuan, Z.W., Bi, J., Wu, H.,J., 2010. Anthropogenic phosphorus flow analysis of Hefei City, China. Sci. Total Environ. 408 (23), 5715e5722. The research was financially supported by the Chinese Water Lindenschmidt, K.E., 2008. Quasi-2D approach in modeling the transport of Pollution Control Program (2008ZX07103-007), the Natural Science contaminated sediments in floodplains during river flooding - model coupling and uncertainty analysis. Environ. Eng. Sci. 25 (3), 333e351. Foundation of China (71303005 & 41222012), and the PhD Fund of Liu, Y., 2005. Phosphorus Flows in China: Physical Profiles and Environmental Anhui University of Science and Technology (12001). We thank the Regulation. Environmental Policy Group, Department of Social Sciences, anonymous reviewers for their comments and suggestions. Wageningen University, Wageningen, the Netherlands. Liu, Y., Chen, J.N., 2006. Substance flow analysis on phosphorus cycle in Dianchi Basin, China. J. Environ. Sci. 27 (8), 1549e1553. Liu, Y., Guo, H.C., Wang, L.J., Dai, Y.L., Zhang, X.M., Li, Z.H., He, B., 2006. Dynamic References phosphorus budget for lake-watershed ecosystems. J. Environ. Sci. 18 (3), 596e 603. Antikainen, R., Haapanen, R., Lemola, R., Nousiainen, J.I., Rekolainen, S., 2008. Ni- Lo, S., Ma, H.W., Lo, S.L., 2005. Quantifying and reducing uncertainty in life cycle trogen and phosphorus flows in the Finnish agricultural and forest sectors, assessment using the Bayesian MonteCarlo method. Sci. Total Environ. 340 (1e 1910e2000. Water Air Soil. Pollut. 194 (1e4), 163e177. 3), 23e33. Antikainen, R., Lemola, R., Nousiainen, J.I., Sokka, L., Esala, M., Huhtanen, P., Ma, D.C., Hu, S.Y., Chen, D.J., Li, Y.R., 2012. Substance flow analysis as a tool for the Rekolainen, S., 2005. Stocks and flows of nitrogen and phosphorus in the elucidation of anthropogenic phosphorus metabolism in China. J. Clean. Prod. Finnish food production and consumption system. Agric. Ecosyst. Environ. 107 29-30, 188e198. (2e3), 287e305. Matsubae-Yokoyama, K., Kubo, H., Nakajima, K., Nagasaka, T.A., 2009. Material flow Bennett, E.M., Reed-Andersen, T., Houser, J.N., Gabriel, J.R., Carpenter, S.R., 1999. analysis of phosphorus in Japan. J. Indust. Ecol. 13 (5), 687e705. A phosphorus budget for the Lake Mendota watershed. Ecosystems 2 (1), 69e Maurice, B., Frischknecht, R., Coelho-Schwirtz, V., Hungerbühler, K., 2000. Uncer- 75. tainty analysis in life cycle inventory. Application to the production of elec- Björklund, A.E., 2000. Survey of Approaches to Improve Reliability in LCA. Manu- tricity with French coal power plants. J. Clean. Prod. 8, 95e108. script submitted to International Journal of Life Cycle Assessment. McCleese, D.L., LaPuma, P.T., 2002. Using Monte Carlo simulation in life cycle Bouman, M., Heijungs, R., van der Voet, E., Bergh, v.d.C.J.M.J., Huppes, G., 2000. assessment for electric andinternal combustion vehicles. Int. J. Life Cycle Assess. Material flows and economic models: an analytical comparison of SFA, LCA and 7 (4), 230e236. partial equilibrium models. Ecol. Econ. 32 (2), 195e216. McLaughlin, D.B., Jain, V., 2011. Using Monte Carlo analysis to characterize the Böttcher, H., Freibauer, A., Obersteiner, M., Schulze, E.D., 2008. Uncertainty analysis uncertainty in final acute values derived from aquatic toxicity data. Integr. of climate change mitigation options in the forestry sector using a generic Environ. Assess. Manag. 7 (2), 269e279. carbon budget model. Ecol. Model. 213, 45e62. Montangero, A., Cau, L.N., Anh, N.V., Tuan, V.D., Nga, P.T., Belevi, H., 2007. Optimising Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N., Smith, V.H., water and phosphorus management in the urban environmental sanitation of 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Hanoi, Vietnam. Sci. Total Environ. 384 (1e3), 55e66. Appl. 8 (3), 559e568. Mullins, K.A., Michael Griffin, W., 2011. Policy implications of uncertainty in Chen, M., Chen, J., Sun, F., 2008. Agricultural phosphorus flow and its environmental modeled life-cycle greenhouse gas emissions of biofuels. Environ. Sci. Technol. impacts in China. Sci. Total Environ. 405 (1e3), 140e152. 45 (1), 132e138. 82 H. Wu et al. / Journal of Cleaner Production 69 (2014) 74e82

Neset, T.S.S., 2005. Environmental imprint of human food consumption: Linköping, Withers, P.J.A., Jarvie, H.P., 2008. Delivery and cycling of phosphorous in rivers: a Sweden 1870e2000. Linköping Stud. Arts Sci.. ISSN: 0282-9800, 333. review. Sci. Total Environ. 400, 379e395. Neset, T.S.S., Bader, H.P., Scheidegger, R., Lohm, U., 2008. The flow of phosphorus in Wu, H.J., Yuan, Z.W., Zhang, L., Bi, J., 2012. Eutrophication mitigation strategies: food production and consumption e Linkoping, Sweden, 1870e2000. Sci. Total perspectives from the quantification of phosphorus flows in socioeconomic Environ. 396 (2e3), 111e120. system at county level. J. Clean. Prod. 23 (1), 122e137. Regan, H.M., Colyvan, M., Burgman, M.A., 2002. A taxonomy and treatment of un- Wu, H.J., 2012. P Flow Analysis in the Socioeconomic System of Lake Watershed certainty for ecology and conservation biology. Ecol. Appl. 12 (2), 618e628. and System Optimizition (PhD thesis). Nanjing University, Nanjing (in Saikku, L., Antikainen, R., Kauppi, P.E., 2007. Nitrogen and phosphorus in the Finnish Chinese). energy system, 1900e2003. J. Indust. Ecol. 11 (1), 103e119. Wu, M., Zhang, W., Wang, X.J., Luo, D.G., 2009. Application of MODIS satellite data in Shang, G.P., Shang, J.C., 2005. Causes and control countermeasures of eutrophication monitoring water quality parameters of Chaohu Lake in China. Environ. Monit. in Chaohu lake, China. Chin. Geogr. Sci. 15 (4), 348e354. Assess. 148 (1e4), 255e264. Shang, G.P., Shang, J.C., 2007. Spatial and temporal variations of eutrophication in Xu, F.L., Tao, S., Dawson, R.W., Xu, Z.R., 2003. The distributions and effects of nu- western Chaohu Lake, China. Environ. Monit. Assess. 130 (1e3), 99e109. trients in the sediments of a shallow eutrophic Chinese lake. Hydrobiologia 429, Smil, V., 2000. Phosphorus in the environment-natural flows and human in- 85e93. terferences. Annu. Rev. Energy Environ. 25 (1), 53e88. Xu, J., Zhang, M., Xie, P., 2007. Stable carbon isotope variations in surface bloom Sokka, L., Antikainen, R., Kauppi, P., 2004. Flows of nitrogen and phosphorus scum and subsurface seston among shallow eutrophic lakes. Harmful Algae 6 in municipal waste: a substance flow analysis in Finland. Prog. Indust. Ecol. 1 (5), 679e685. (1e3), 165e186. Yin, C., Bernhardt, K., 1992. Ecological effects of pollution in Chaohu Lake, China. Sonnemann, G.W., Schuhmacher, M., Castells, F., 2003. Uncertainty assessment by a J. Environ. Sci. 4 (2), 1e128. Monte Carlo simulation in a life cycle inventory of electricity produced by a Yu, S., Tao, J., 2009. Economic, energy and environmental evaluations of biomass- waste incinerator. J. Clean. Prod. 11 (3), 279e292. based fuel ethanol projects based on life cycle assessment and simulation. Szemesova, J., Gera, M., 2010. Uncertainty analysis for estimation of landfill Appl. Energy 86, S178eS188. emissions and data sensitivity for the input variation. Clim. Change 103 (1e2), Yuan, Z.W., Liu, X., Wu, H.J., Zhang, L., Bi, J., 2011a. Anthropogenic phosphorus flow 37e54. analysis of Lujiang County, Anhui Province, Central China. Ecol. Model. 222, Tangsubkul, N., Moore, S., Wait, T.D., 2005. Incorporating phosphors management 1534e1543. considerations into wastewater management practice. Environ. Sci. Policy 8, 1e15. Yuan, Z.W., Shi, J.K., Wu, H.J., Zhang, L., Bi, J., 2011b. Understanding the anthropo- Tu, H., Gu, D., Yin, C., Xu, Z., Han, J., 1990. Chaohu Lake e Study on Eutrophication. genic phosphorus pathway with substance flow analysis at the city level. University of China Science and Technology Press, Hefei, China, pp. 1e226. J. Environ. Manage. 92 (8), 2021e2028. vanderVoet, E., et al., 1995a. Substance flows through the economy and environ- Yuan, Z.W., Sun, L., Bi, J., Wu, H.J., Zhang, L., 2011c. Phosphorus flow analysis of the ment of a region. 2. Model. Environ. Sci. Pollut. Res. 2, 137e144. socioeconomic ecosystem of Shucheng County, China. Ecol. Appl. 92 (8), 2822e vanderVoet, E., et al., 1995b. Substance flows through the economy and environ- 2832. ment of a region.1. Systems definition. Environ. Sci. Pollut. Res. 2, 90e96. Zhang, L., Yuan, Z.W., Bi, J., 2009. Substance flow analysis (SFA): a critical review. Vose, D., 1996. Quantitative Risk Analysis. A Guide to Monte Carlo Simulation Acta Ecol. 29, 6189e6198. Modelling. John Wiley &: Sons, West Sussex.