DSpace at VNU: Modification of uncertainty analysis in adapted material flow analysis: Case study of nitrogen flows in the Day-Nhue River Basin, Vietnam

Resources, Conservation and Recycling 88 (2014) 67–75 Contents lists available at ScienceDirect Resources, Conservation and Recycling journal homepage: www.elsevier.com/locate/resconrec Modification of uncertainty analysis in adapted material flow analysis: Case study of nitrogen flows in the Day-Nhue River Basin, Vietnam Nga Thu Do a , Duc Anh Trinh b , Kei Nishida c,∗ a b c Hanoi University of Science (HUS), Vietnam National University, No 19, Le Thanh Tong, Hoan Kiem, Hanoi, Viet Nam Institute of Chemistry, Vietnam Academy of Science and Technology (VAST), A18 – No 18 Hoang Quoc Viet, Cau Giay, Hanoi, Viet Nam International Research Centre for River Basin Environment (ICRE), University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511, Japan a r t i c l e i n f o Article history: Received 26 September 2013 Received in revised form 19 April 2014 Accepted 22 April 2014 Keywords: Nutrient Material flow analysis (MFA) Monte Carlo simulation Uncertainty Reassessment procedure a b s t r a c t Nitrogen flows impacted by human activities in the Day-Nhue River Basin in northern Vietnam have been modeled using adapted material flow analysis (MFA) This study introduces a modified uncertainty analysis procedure and its importance in MFA We generated a probability distribution using a Monte Carlo simulation, calculated the nitrogen budget for each process and then evaluated the plausibility under three different criterion sets The third criterion, with one standard deviation of the budget value as the confidence interval and 68% as the confidence level, could be applied to effectively identify hidden uncertainties in the MFA system Sensitivity analysis was conducted for revising parameters, followed by the reassessment of the model structure by revising equations or flow regime, if necessary The number of processes that passed the plausibility test increased from five to nine after reassessment of model uncertainty with a greater model quality The application of the uncertainty analysis approach to this case study revealed that the reassessment of equations in the aquaculture process largely changed the results for nitrogen flows to environments The significant differences were identified as increased nitrogen load to the atmosphere and to soil/groundwater (17% and 41%, respectively), and a 58% decrease in nitrogen load to surface water Thus, modified uncertainty analysis was considered to be an important screening system for ensuring quality of MFA modeling © 2014 Elsevier B.V All rights reserved Introduction Modeling of water quality is greatly needed for managing aquatic environments Uncertainty is a critical factor when the model is applied and various types and sources of uncertainty have been identified in previously proposed modeling approaches (Beck, 1987) The first is data inaccuracy caused by unreliable empirical measurements made in the process of data collection The second is data gaps due to shortages of information that occur when employing data from different fields (Björklund, 2002; Steen, 1997; Huijbregts, 1998; Radwan et al., 2004; Antikainen, 2007) Each of these sources of uncertainty is common in developing countries Sources of uncertainty due to inaccuracy and gaps in data were explored to establish a low-waste emission system for the agroindustry (Oenema et al., 2003) A calculation of the nitrogen budget in a case study of the Netherlands using different data sources indicated relatively large uncertainties, including greater than 30% ∗ Corresponding author Tel.: +81 55 220 8593 E-mail addresses: dothu nga2005@yahoo.com (N.T Do), ducta@ich.vast.ac.vn (D.A Trinh), nishida@yamanashi.ac.jp (K Nishida) http://dx.doi.org/10.1016/j.resconrec.2014.04.006 0921-3449/© 2014 Elsevier B.V All rights reserved variation in denitrification and leaching values Walker and Beck (2012) addressed resource management and environmental issues that manipulate nutrients, water and energy flows under data uncertainty condition in the Upper Chattahoochee Watershed in North East Georgia, USA The results showed that the largest degree of uncertainty was 35% and was associated with anthropogenic energy flow The third and most important source of incorrect conclusions is structural bias Such bias can be caused by simplification in material flow analysis (MFA) modeling, especially when temporal or spatial variations are significant (Björklund, 2002) Radwan et al (2004) stressed the importance of and need for investigating uncertainty due to the model structure in modeling of river water quality When water quality model results were compared with measurement results, the errors were 2% for dissolved oxygen, 20% for biochemical oxygen demand, 17% for NH4 –N and 15% for NO3 –N in the case study Reichert and Omlin (1997) stated that ‘neglecting the uncertainty in the model structure leads to an underestimation of the uncertainty in model predictions’ Thus, for quantifying uncertainty, classification of individual uncertainties of the various sources is essential In the context of nutrient management in environmental sanitation systems, the adapted MFA methodology has been proposed as 68 N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 one of the most appropriate methods for reconciling with uncertain and limited data (Montangero and Belevi, 2008) This methodology could also trace critical sources of nitrogen by determining pollutant stocks and fluxes among environmental processes and human activities by systemizing and reusing applicable results from previous research Therefore, the MFA was applied to visualize and assess environmental quality in terms of nitrogen under the influence of human activities in the old quarter of Hanoi (Montangero et al., 2007) and in two small communes in Ha Nam Province, Vietnam (Do-Thu et al., 2011) Importance of uncertainty analysis in MFA has been demonstrated by various researchers For example, nutrient flows in Danube countries and significance of uncertainties due to data inaccuracy were assessed by applying MFA coupled with a Monte Carlo simulation The uncertainties related to nutrients (N and P) were analyzed by traditional sensitivity analysis, and the relative errors identified in air emissions of N and P from agriculture were about 150–200% (Buzas, 1999) In addition, adapted MFA has been successfully applied in multi-provincial areas, such as the Thachin River Basin in Thailand, to provide an overview of origins and flow paths of point and non-point pollution sources (N and P) for the entire basin (Schaffner et al., 2009, 2010a, 2010b) The results showed that aquaculture (point source) and rice farming (non-point source) were the key sources of N and P in this river basin, and comparison with water-quality and flow measurements revealed that such sources were responsible for approximately 80% of the underestimation caused by gaps and inaccuracies in data Sources of uncertainty were mentioned in these reports, but methods for identifying and resolving the uncertainty were lacking Several approaches have been employed to deal with data uncertainty in MFA studies The simplest is trial and error, i.e comparing results with those of similar studies or with other sources of data to assess reasonableness of the findings (Brunner and Baccini, 1992; Hekkert et al., 2000; Lassen and Hansen, 2000) When solving uncertainty in this manner, inconsistency in data has been considered as an error in budget calculation Weisz et al (1998) developed a cross-checking approach that employed an operating matrix for material inter-relations between the economy and nature This matrix was a helpful tool for establishing MFA on a national scale Although it enables use of a large amount of data and can fill in data gaps, problems remain in applying the matrix to cases of data scarcity Budget calculations in the above-mentioned studies could be revised; however, uncertainty in MFA has not yet been fully analyzed This study aimed to analyze problems related to uncertainty of input data and model structure by using adapted MFA for model improvement For this purpose, on the basis of a method proposed previously, new criterion sets for plausibility tests and a detailed procedure for reassessment were suggested in MFA One of the most severely polluted river systems in Vietnam, the Day-Nhue River Basin (DNRB), was chosen as the case study A number of studies have been conducted on the current status of water quality for the Day and Nhue rivers (Trinh et al., 2006, 2007, 2012a,b; Hanh et al., 2009); however, research that addresses the environment of the entire basin (atmosphere, surface water and soil/groundwater) has not yet been conducted As the key factors in uncertainty analysis, an evaluation of the interactions between different activities and various environmental elements in the entire DNRB is described here, and the critical sources of nitrogen in the system are identified Methodology 2.1 Study area The DNRB covers 7665 km2 of Ha Nam, Nam Dinh, Ninh Binh Provinces, and a part of Hanoi City and Hoa Binh Province (Ministry of Natural Resources and Environment; MONRE, 2006), with a total population of approximately 10.5 million (GSO, 2010) At present, this river system is under considerable pressure from socioeconomic development activities and urbanization, and the basin is experiencing an annual population increase of about 5% (MONRE, 2006) However, the region’s infrastructure is incompatible with rapid development (Ministry of Construction; MOC, 2009) Establishment and operation of industrial zones, craft villages, factories and agricultural areas have caused significant changes to the natural environment, especially to water quality The basin includes more than 156,269 industrial, commercial and service establishments (MOC, 2009) The number of craft villages is increasing in all provinces in the basin, with the largest number located in Hanoi City Agriculture is also an important activity in this basin Approximately 50% of land in the Day–Nhue basin is used for farming and animal production Given the existing infrastructure resources, solid wastes and wastewater are not yet controllable (MOC, 2009) 2.2 Data collection A field survey was conducted in 2010 to collect general background information (social, industrial and agricultural) and environmental conditions for 2008–2010 in the study area The most important data were the data collected from the Vietnam General Statistical Office (GSO, 2008–2010) Other information was in the government reports and documents or results of projects that have been done in the DNRB (MOC, 2009; MONRE, 2006); these were collected from Departments of Natural Resources and Environment of five provinces in the river basin (DARDs, 2010) and from research institutes and non-government organizations Input data such as population, area, number of animals and crop yields is referred to as ‘parameters’ The parameters were categorized into two types, certain and uncertain In the subsequent analysis, the probability distribution, mean and standard deviation were assumed for each parameter on the basis of the authors’ knowledge about the data source and the characteristics of the study site (Table 1) The normal distribution provides a good model for parameters; when a parameter has a strong tendency to take a central value, positive and negative deviations from this central value are equally likely The lognormal distribution is appropriate to represent non-negative and positively skewed physical quantities, such as pollutant concentrations, and is particularly suitable for representing large uncertainties (Montangero and Belevi, 2008) For parameters that not have a clear distribution pattern, a uniform distribution is assigned This categorization was originally introduced in this study and would be useful in uncertainty analysis and model revision, because only uncertain parameters are subject to revision when evaluating a model On the other hand, Monte Carlo simulation was run automatically through whole simulation, probability distribution of model output was, therefore, generated as a result 2.3 Model establishment Flows among the environmental and human-related processes in the draft of MFA system were cross-checked with observations and short interviews with local residents and then compared with the proposed flows For example, in Fig 1, eleven processes related to human activities (industrial production, agriculture and solid waste disposal) and three processes corresponded to natural environments (atmosphere, water and soil/groundwater) in the river basin are associated in terms of nitrogen using arrows After drafting model structure, model equations were added or updated on the basis of the collected data Two types of equations were used in this model: balance equations and model equations (Brunner and Rechberger, 2004) Model equations consisted of the certain and uncertain parameters A stock change rate (budget) of N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 69 Table List of parameters directly used in aquaculture process Symbol Description of data Data type Unit Statistical distribution Mean Value Standard deviation (SD) References SD/mean (%) S pond HN Area of fish ponds in Hanoi Certain km2 Normal 129.56 GSO (2008) 10 S pond HNa Certain km2 Normal 58.56 GSO (2008) 10 Certain km Normal 96.77 GSO (2008) 10 Certain km2 Normal 172.22 GSO (2008) 10 Certain km2 Normal 10.28 GSO (2008) 10 Y fi HN Area of fish ponds in Ha Nam Area of fish ponds in Nam Dinh Area of fish ponds in Ninh Binh Area of fish ponds in Hoa Binh Fish yield of Hanoi Certain t/year Normal 31,737 GSO (2008) 10 Y fi HNa Fish yield of Ha Nam Certain t/year Normal 11,400 GSO (2008) 10 Y fi ND Fish yield of Nam Dinh Certain t/year Normal 15,300 GSO (2008) 10 Y fi NB Fish yield of Ninh Binh Certain t/year Normal 9012 GSO (2008) 10 Y fi HB Fish yield of Hoa Binh Certain t/year Normal 756 GSO (2008) 10 aN man pond Certain kg N/km2 Uniform 805a MARD (2008) D pond Nitrogen load in manure supplied for fish pond per time preparation Depth of fish-pond Certain m Uniform 1.50a CN fish Nitrogen content in fish Certain % Normal 3.00 CN fish feed Nitrogen content in commercial food for fish Certain % Normal 7.50 Fd pond Frequency of sludge removals Uncertain – Lognormal 2.00 rN SL fish Nitrogen accumulated in fish pond sludge Percentage of nitrogen release from aquaculture system Fish feed conversion ratio Uncertain – Lognormal 0.10 Uncertain % Lognormal 0.30 Uncertain – Normal 3.00 Uncertain – Lognormal 0.02 S pond ND S pond NB S pond HB rN emis pond rFC e a b Treatment yield in case of sludge removal Field observation and interview local farmers OSPAR (2000) OSPAR (2000) Field observation and interview local farmers OSPAR (2000) Schaffner (2007) OSPAR (2000) OSPAR (2000) 966b 2.50b References Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions Authors’ assumptions MARD (2008) Field observation and interview local farmers 13 OSPAR (2000) 17 OSPAR (2000) 50 Authors’ assumptions 50 OSPAR (2000) 17 Schaffner (2007) 10 OSPAR (2000) OSPAR (2000) Minimum values of the uniform distribution Maximum values of the uniform distribution nitrogen was calculated from balance equation for each process in which zero budget was ideally assumed A well-balanced budget had values of mean and standard deviation that were ±5% of largest flow size Herein, target substance was total nitrogen, but the chemical forms of nitrogen were not differentiated A modified uncertainty analysis procedure was primarily focused, and hence chemical speciation will be considered in the future 2.4 Uncertainty analysis Fig shows a flow chart of the modified uncertainty analysis procedure used in the present MFA study Model quality was improved by adding two additional criteria in the plausibility test and a method for identifying sensitive parameters to the process budget The model was further improved by classifying three types of model uncertainty in parameter, equation and flow regime, detailed as follows: (a) ≥Monte Carlo simulation The Monte Carlo simulation was run on the platform of Excel Visual Basic Macro when conducting plausibility test or sensitivity analysis After defining a standard deviation for each parameter, the Monte Carlo randomly simulated the model parameters using mean, standard deviation and the specified probability distribution The difference between results generated from 1000 iterations and 5000, × 104 , × 104 or × 104 iterations was only 1%, which is acceptable for an uncertainty range Then, 1000 iterations were chosen for the following quantification By using the parameters with 1000 iterations, a set of 1000 budget values were calculated for each process in the whole system This budget set was then subjected to a subsequent analysis using plausibility criteria (b) ≥Plausibility test Montangero and Belevi (2008) primarily proposed parameter-based criteria for improving model quality of MFA by referring to the environmental state reports of Hanoi City and information from previous research conducted in Vietnam However, even though all parameters were 70 N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 Fig Material flow analysis (MFA) system for the Day–Nhue River basin Dashed lines represent new nitrogen flows added after field observations Note: livestock process (12) contains pig process (12a), poultry process (12b) and cattle process (12c) processes; field crop process (14) contains paddy (14a) and vegetable–fruit (14b) examined within the corresponding plausible range, the quality of whole model may not be assured A budget-based criterion introduced by Montangero (2006) is an alternative to solve this problem This criterion was developed to check model plausibility with the assumption that there was no N stock within the household process; to pass the plausibility test, at least 68% of the 1000 iterated budget values (i.e the confidence level) should be in the range of ±15% (i.e the confidence interval) for the total input in the process (Criterion 1) Montangero et al (2007) assumed that a population’s standard deviation was 15% of the mean by considering unregistered inhabitants in the old quarter of Hanoi Thus, the confidence interval might be assumed as 15% of total input Do-Thu et al (2011) showed that the budget value of the household process was highly controlled by the parameter ‘population’ However, in the case of a larger target area with more complex systems in a river basin, the budget value cannot be controlled by a single influential factor such as population, but must account for an increased number of parameters, such as number of animals or paddy area Therefore, two other criteria were Input data Running Monte Carlo simulation Plausibility test with proposed criteria Fail Running Monte Carlo simulation Sensitivity analysis for process budget Checking impact of parameters on both total input and total output in the process No parameter reassessment is possible Checking model equations No equation reassessment is possible If any reassessment is possible Pass Checking flow regimes MFA results No flow reassessment is possible Fig Flow chart of modified uncertainty analysis in MFA Gray boxes represent for new proposals in the procedure N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 71 Table List of detailed balance equations and model equations for aquaculture process before and after model revision Nitrogen flow dMN(13)/dt Input AN2–13 AN3–13 AN6–13 AN12a–13 AN12b–13 AN15–13 AN17–13 Output AN13–6 AN13–14 AN13–15 AN13–15 a AN13–16 AN13–16 a AN13–17 AN13–17 a S pond Y fish a Equation (Unit: tN/year) =AN2–13 + AN3–13 + AN6–13 + AN12–13 + AN15–13 + AN17–13 − AN13–6 − AN13–14 − AN13–15 − AN13–16 − AN13–17 See Onsite sanitation (2) See Drainage (3) =Y fish × rFC × CN fish feed × 10−2 =aN man pond × S pond × Fd pond × 10−3 See Poultry (12b) =S pond × D pond × (1 + ET/P) × CN river =S pond × P × CN rain × 10−3 =Y fish × CN fish × 10−2 =(AN2–13 + AN6–13 + AN12–13 – AN13–6 ) × rN SL fish × Fd pond =(AN6–13 − AN13–6 ) × (1 − e) =(AN2–13 + AN6–13 + AN12–13 − AN13–6 ) × (1 – rN SL fish × Fd pond – rN leach paddy – rN emis pond) =AN6–13 × rN leach paddy =(AN2–13 + AN6–13 + AN12–13 –AN13–6 ) × rN leach paddy =AN6–13 × rN emis pond =(AN2–13 + AN6–13 + AN12–13 – AN13–6 ) × rN emis pond =S pond HN + S pond HNa + S pond ND + S pond NB + S pond HB =Y fi HN + Y fi HNa + Y fi ND + Y fi NB + Y fi HB Equation after reassessment newly proposed and compared with the previously proposed one In MFA, output was traditionally estimated on the basis of input, as shown in Table (AN13–16 and AN13–17 ), but output should periodically be independently quantified, as presented in Do-Thu et al (2011) Therefore, it was necessary to assess the uncertainty of output and input simultaneously In the second criteria, the confidence interval was ±15% of the averaged value of input and output, and the confidence level was 68% (Criterion 2) Therefore, this criterion could assess the flow-size-based budget balance of inputs and outputs in the process Criteria and examined the budget value based on flow size of a process, total input or both total input and output, respectively However, large-sized flows may allow the process budget to pass the plausibility test even though the budget value is imbalanced because of problems in the model structure The 68% confidence level, which was initially set by Montangero et al (2007), corresponded to one standard deviation of the theoretical normal distribution Therefore, the third criterion was proposed, with one standard deviation of the budget value as the confidence interval and 68% as the confidence level (Criterion 3) The error of each parameter accumulated in the standard deviation of the budget value would also be considered, thus, this criterion could better identify hidden problems in the model Finally, Criteria 1, and would be tested in this study as an example of a model-improvement method (c) ≥Sensitivity analysis for process budget In traditional concept of adapted MFA, sensitivity analysis for environmental impacts is needed for identifying the influential parameters on outflows to environments, and ultimately, for reducing requirements for data collection (Montangero and Belevi, 2008) Herein, the sensitivity analysis was applied to reduce uncertainty in parameter that caused imbalance of process budgets and failures in the plausibility test This analysis was performed to quantify the effect of a 10% increase in the mean value of each parameter on the respective process’ total input and total output and to identify the parameters that were sensitive to total input and total output for reassessment in the following step (d) ≥Reassessment of model If any process failed the plausibility test, that process was reassessed by verifying uncertain parameter as determined by literature reviews, model equations and flow regimes Firstly, the sensitive and uncertain parameters were identified by sensitivity analysis for each individual process To improve the pass rate in the plausibility test, the sensitive parameters were replaced by the ones from area which had similar social–physical conditions as the study site in the parameter reassessment If parameter reassessment was not possible, the model equation and flow regime were reassessed accordingly This procedure was repeated until the process passed the plausibility test or no further reassessment was possible All of the nitrogen flows would be finalized with mean and standard deviation values after the reassessment procedure Results 3.1 Model establishment The draft of the MFA system for the entire DNRB was carefully cross-checked by field survey As can be observed in Fig 1, 14 new flows were added, and flows were updated A water supply process (5) was removed for model simplification because of its minor impacts to surrounding environments and to other processes of the entire system in terms of nitrogen load Also, the grassland process (7) and forest process (8) were combined into forest–grassland process (8), and craft village (9) and industry (10) were combined into industry (10) because of their similar roles in the MFA system The collected data and detailed equations to simulate nitrogen flows, for example, between the aquaculture process and the natural environments (atmosphere, surface water and soil/groundwater) or other processes, are shown in Tables and 2, respectively 3.2 Uncertainty analysis (a) ≥Plausibility test results for three different criterion sets Table shows pass rates for all processes before and after reassessment For Criteria 1, and 3, the confidence intervals were ±15% of the total input, ±15% of the average input and output and ±one standard deviation of the nitrogen budget, respectively Unlike Criteria and 2, the forest–grassland process passed the test for Criterion without need for revision because the standard deviation of its budget (638 ± 1153 t N/year) was about twenty times the value of 15% of the total input (384 ± 20 t N/year) Therefore, the pass rate increased naturally 72 N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 Table Results of the plausibility test for all processes: percentages of the estimated budget within confidence intervals (pass rate) before (a) and after (b) reassessment Process Household (1) On-site sanitation system (2) Drainage (3) Solid waste collection (4) Market (6) Forest–grassland (8) Industry (10) Solid waste disposal place (11) Pigs (12a) Poultry (12b) Cattle (12c) Aquaculture (13) Paddy (14a) Vegetable-fruit (14b) Criterion Criterion Criterion (a) (b) (a) (b) (a) (b) 86 100 100 22 28 60 49 76 57 86 100 100 22 29 77 74 76 96 82 92 86 100 100 24 28 40 46 74 33 86 100 100 24 29 77 74 74 94 82 88 77 100 33 42 75 24 42 46 70 69 77 88 33 42 75 24 69 68 70 74 71 82 Notes: Criteria 1, and represent three different criterion sets, where confidence intervals were ± 15% of total input, ± 15% of averaged input and output and ± one standard deviation of the budget, respectively The confidence level was 68% in all cases Pass rates of processes greater than 68% are indicated in bold Fig and Table show probability distributions of the budgets and pass rates of the processes in the plausibility tests, respectively The budget for the drainage process was well balanced (−2771 ± 1319 t N/year), indicating that the process could pass the test for Criterion without reassessment Owing to a large flow size (e.g wastewater flow from onsite sanitation process to drainage process, 36,760 ± 4699 t N/year), the drainage process was able to pass Criterion without reassessment However, the standard deviation of the budget was also small, and the budget value was negatively skewed; therefore, the drainage process had less chance to pass the test for Criterion Similarly, the budget for the solid waste collection process was well balanced (2040 ± 1467 t N/year) However, this process did not pass the test for any criterion because of a small flow size In addition, the pass rate for Criterion was higher than those for Criterion or 2, implying that Criterion could better evaluate the quality of the model structure without being impacted by flow size Therefore, the uncertainty analysis under Criterion would be considered as representative In short, out of 14 processes failed the plausibility test for Criterion before reassessment (Table 3) The drainage, solid waste collection, market, industry and solid waste disposal place processes could not be thoroughly reassessed owing to data shortage However, the agricultural processes, pig, poultry, aquaculture and vegetable–fruit, could be reassessed (b) ≥Sensitivity analysis and reassessment of parameters As can be seen in Table 3, the pig process had to be reassessed owing to a pass rate of less than 68% of the confidence level (42%) Therefore, sensitivity analysis was conducted for this process The most sensitive parameters were number of pigs and daily nitrogen load in pig manure However, these two parameters were certain type; and thus were not subject to revision The most sensitive parameter among all the uncertain parameters in this process was the ratio of nitrogen gas losses to the nitrogen content in pig manure The statistical value of this parameter was replaced with a value obtained from a literature review (Ruettimann and Menzi, 2001) The pass rate then increased to 69% and passed the plausibility test Similar to the pig process, the statistical value of the parameter represents the ratio of nitrogen gas losses to the nitrogen in chicken manure in the poultry process was replaced with a value obtained from another study (Schaffner, 2007) The pass rate of this process then increased from 46% to 68% and passed the plausibility test (c) ≥Reassessment of model equations The 0% pass rate for all criteria indicated that the model equations should be used to reassess the aquaculture process because the pass rate could not be increased by parameter revision after reassessing the uncertain parameters in this process Besides, the estimated mean budget was very positive (31,204 t N/year) and was much higher than the standard deviation (4676 t N/year) Therefore, it was assumed that the total nitrogen output from aquaculture was underestimated Of the five outputs from aquaculture, the three relevant to the environment, runoff (AN13–15 ), leaching (AN13–16 ) and evaporation (AN13–17 ), were reassessed In this reassessment, the following flows were considered in the output: wastewater leaching from the drainage system (AN3–13 ) to aquaculture, sludge from onsite sanitation systems (AN2–13 ), manure from pigs and poultry used as fish feed (AN12a–13 , AN12b–13 ) and river water and rain (AN15–13 , AN17–13 ) Drainage wastewater, river water and rain, as liquid states, amounted to 4898 ± 903 t/year, contributing only 15% of the total N input in aquaculture The Fig Results of the plausibility test for drainage process and solid waste collection process after all model revisions The values between broken lines represent confidence intervals of three criteria: dashed, solid and bold lines denote Criteria 1, and 3, respectively N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 nitrogen load from aquaculture to surface water (AN13–15 ) was calculated on the basis of the method of OSPAR (2000), by incorporating commercial fish feed (AN6–13 ) and treatment efficiency of the pond (e) Only commercial feed was used for fish at the OSPAR pilot scale, but fishes farmed in the river basin were also fed manure Therefore, AN2–13 , AN12a–13 and AN12b–13 were required in addition to AN6–13 , and the equations for AN13–15 , AN13–16 and AN13–17 would be revised as described in Table Instead of using treatment efficiency, nitrogen loss through runoff was quantified on the basis of the law of conservation of mass After making revisions as shown in Table 2, the aquaculture process had a 74% pass rate and passed the plausibility test (d) ≥Reassessment of flow regimes The pass rate was 6% for the vegetable–fruit process; thus, this process failed the plausibility test Parameter reassessment was then conducted, but no improvement of the pass rate was possible by parameter or equation only; thus, the flow regimes were reconsidered To include an additional output flow from residues of soybean, peanut, corn, vegetables and fruit remaining in the field (AN14b–16 ), five corresponding parameters were obtained from the literature (IFA, 2006) After revision, the vegetable–fruit process passed the test with an 82% pass rate 73 respectively Although aquaculture contributed only 7% of the total load to atmosphere, it contributed 61% of the total nitrogen load to surface water Field crop contributed only 8% of the total load to surface water, however, it discharged 60% of the total load to the soil/groundwater environment while aquaculture and livestock contributed only 5% and 19%, respectively The five processes that failed the plausibility test (drainage, solid waste collection, market, industry and solid waste disposal place) were examined Solid wastes or wastewater from the solid waste collection process was transported to solid waste disposal place and wastewater from market was transported to drainage Their outflows to environments were then assessed in these destination processes Industrial solid waste was burnt and then emitted to the atmosphere, and it contributed to 5% of the total annual N discharged Industrial wastewater (2758 ± 407 t N/year) was entirely connected to drainage and contributed to only 4% of the total N input to drainage annually The outflows from solid waste disposal place to the atmosphere or to soil/groundwater were 327 ± 23 or 0.39 ± 0.4 t N/year, respectively The drainage process was responsible for 26%, 31% and 8% of total N load to the atmosphere, surface water and soil/groundwater, respectively Discussion 4.1 Uncertainty analysis 3.3 Effect of uncertainty analysis on outflows to environments The outflows to environmental processes were determined after the reassessment of parameters, equations and flow regimes in MFA Version was the result prior to reassessment Versions 2.1, 2.2 and 2.3 resulted from a reassessment of pig and poultry, aquaculture and vegetable–fruit processes, respectively Fig demonstrates the variability of nitrogen load to the surrounding environments in the three versions of the modified model By revising parameters in pig and poultry, the nitrogen load to the atmosphere, surface water and soil/groundwater decreased by 5%, 2% and 2%, respectively, compared to Version Altering the model equations was the secondary choice used to compensate for the variability of target results By revising the equations for aquaculture, in Version 2.2, the nitrogen loads to the atmosphere and soil/groundwater increased by 17% and 41%, respectively, and the load to surface water decreased by 58% In Version 2.3, the nitrogen loads to the atmosphere and soil/groundwater decreased by 4% and 1%, respectively, and increased by 2% to surface water by revising the flow regimes for the vegetable–fruit process After all revisions were complete, outflows to environments were quantified again Field crop and livestock contributed 33% and 29% of the total nitrogen load to the atmosphere every year, Fig Nitrogen load to the environment (t/year) in four model versions White, gray and black bars represent nitrogen loads to the atmosphere, surface water and soil/groundwater, respectively Version shows results before reassessment; Version 2.1 includes the revised parameters for both pig and poultry processes; Version 2.2 includes the revised equations for aquaculture process; Version 2.3 includes the revised flow regime for vegetable–fruit process (a) ≥Setting criteria In the case study of the old quarter of Hanoi, the market process was defined as a ‘platform’ for nitrogen exchange in the entire MFA system, where inter-boundary flows were not yet determined (Montangero et al., 2007) Similarly, herein, the drainage and solid waste collection processes may be considered as ‘platforms’ for internal exchange of nitrogen in the system Therefore, it was acceptable for these processes not to pass the plausibility test because their inputs and outputs were indirectly estimated, and as a result contained many uncertainties from other processes As shown in Table 3, the pass rates of processes for Criterion were smaller than those for Criterion prior to reassessment in most cases, implying that Criterion was stricter than Criterion In general, Criteria and shared the concept of considering the impact of flow size on the balance of the nitrogen budget Therefore, in the cases of solid waste collection, forest–grassland and solid waste disposal place processes, flow sizes were small and resulted in poorer pass rates In contrast, the pass rate for Criterion was significantly higher than those of Criteria and for those three processes The pass rate for Criterion was smaller than those for Criteria and for almost all of the processes such as household, onsite sanitation system, drainage, pig, poultry, cattle, aquaculture, paddy and vegetable–fruit; pass rates for processes such as market and industry were similar for the three criteria These results demonstrated that Criterion could better evaluate model quality, because the pass rate was not impacted by flow size (b) ≥Reassessment of the model For improving model quality, parameters would initially be reassessed by applying sensitivity analysis to the process budget Parameters were classified into two types: certain and uncertain on the basis of the authors’ knowledge about the data source and the characteristics of the study site The certain parameters, population, number of animals and paddy area, were mostly collected from governmental offices (GSO, MOC, MONRE, MARD) The uncertain parameters, including ratio of wastewater from drainage system to aquaculture and ratio of nitrogen leaching to soil to the total nitrogen applied in a paddy, were collected from field observation, interviews or 74 N.T Do et al / Resources, Conservation and Recycling 88 (2014) 67–75 other research and websites Only the most sensitive parameters among uncertain types were revised Parameter reassessment was possible for four processes: pig, poultry, aquaculture and vegetable–fruit However, only two processes, pig and poultry, passed the test; further reassessment of model equations would be required for the remaining two processes As can be seen in the revision of the aquaculture process in Table 2, equations used to estimate outflows to environments (air, surface water and soil/groundwater) were re-evaluated Nitrogen flows from aquaculture to surrounding environments were all quantified on the basis of net nitrogen input and output as solid states, e.g fish feed and fish production Liquid states, such as drains, rivers and rainwater, were not included in this version of the reassessment; however, their contribution to the total N input in aquaculture was very small, contributing to only 15% of the total input of aquaculture Therefore, outflow was slightly underestimated Table revealed that the pass rates of the aquaculture and vegetable–fruit processes prior to reassessment were very small, 0% and 6%, respectively This implied that the pass rate could not be improved by the parameters but that the equation and flow regime would need to be reassessed hidden uncertainties in the MFA system Sensitivity analysis was conducted for revising parameters, followed by model structure reassessment by revising equations or flow regime, if necessary It is reasonable to conclude that uncertainty analysis played an important role in evaluating accuracy of the model structure and reliability of input data, which were very important in the cases of data scarcity and uncertainty Prioritizing reassessment of sensitive and uncertain parameters, equations and flow regimes is effective for saving time and expense in developing countries Based on the modification of uncertainty analysis, outflows to environments were calculated by MFA Agricultural processes were the most significant nitrogen sources for the atmosphere, surface water and soil/groundwater, probably because of excessive application of fertilizer or misappropriate treatment of manure Drainage process could also have a large impact on outflow to environment The results imply practical usefulness when the model is applied for river basin management, thus the environmental effects, as well as the chemical speciation, should be further explored in the future In summary, uncertainty analysis is a screening system for ensuring quality of MFA modeling Model accuracy may be validated in more direct ways, such as by comparison to observation-based data, but this requires additional efforts for data collection and analysis 4.2 Effect of uncertainty analysis on outflows to environments Acknowledgements Radwan et al (2004) concluded that model input needed the greatest attention, followed by model parameters and model structure In the case study of Molenbeek sub-catchment, Belgium, the results showed that the percentage of model input contribution to the total uncertainty was 61% for dissolved oxygen (DO), 56% for biochemical oxygen demand (BOD), 56% for NH4 –N and 72% for NO3 –N Schaffner et al (2009, 2010a, b) applied adapted MFA in the Thachin River Basin in Thailand The results showed that point and non-point pollution sources from the entire basin were responsible for approximately 80% of the underestimation caused by gaps and inaccuracies in input data In this study, the effects of uncertainty analysis on outflows to environments were clearly shown in Fig Version was the result prior to reassessment Versions 2.1, 2.2 and 2.3 resulted from a reassessment of parameters, model equations and flow regimes, respectively However, in contrast to the conclusions of Radwan et al (2004) or Schaffner et al (2009, 2010a,b), Fig demonstrates that equation reassessment affected the variability of the results the most and that parameter or flow-regime reassessment was less effective in the case study of the DNRB Modified uncertainty analysis was the first screening for model quality, and was useful to identify the problems of both parameter and model structure in the MFA system Outflows to environments from the five processes that failed the plausibility test were quantified Solid wastes or wastewater from the solid waste collection or market was transported to the solid waste disposal place and to drainage, respectively Their outflows were then assessed in these destination processes Impacts of industry and solid waste disposal place to surrounding environments were small However, the drainage process was the only process among the five that made a major contribution to the nitrogen load to surrounding environments Platform process could have a large impact if it has direct outflows to environments despite of the higher uncertainty Conclusions and recommendations This paper proposed a modified uncertainty analysis procedure in MFA and its importance for assessing the obtained results Among three different criterion sets, the third criterion with a standard deviation could be applied to effectively identify We gratefully acknowledge Dr Ishidaira Hiroshi for helpful advice about uncertainty analysis We are grateful to Dr Kazama Futaba for basic information on nitrogen cycles in field crops We also acknowledge Dr Sakamoto Yasushi and Dr Shindo Junko for their assessments of the research methodology We acknowledge local authorities in the Chuong My district, Ha Noi, Cao Phong District, Hoa Binh Province, Kim Bang district, Ha Nam Province, and Vu Ban district, Nam Dinh Province for their cooperation in collecting data and interviewing local residents The study presented here was supported by the Global COE Program ‘Evolution of Research and Education on Integrated River Basin Management in Asian Region’ from the Ministry of Education, Culture, Sport, Science and Technology of Japan Appendix A Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.resconrec 2014.04.006 References Antikainen R Substance flow analysis in Finland – four case studies on N and P flows Monographs of the boreal environmental research Finland: Finnish Environment Institute; 2007 Beck MB Water quality modeling: a review of the analysis of uncertainty Water Resour Res 1987;23(8):1393–442 Björklund AE Survey 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Monitoring, planning and managing solid wastes in provinces of Day-Nhue River Basin toward year 2020 Vietnam Ministry of Construction;2009 MONRE State of environment in Vietnam, 2006 Vietnam Ministry... systems in Vietnam, the Day-Nhue River Basin (DNRB), was chosen as the case study A number of studies have been conducted on the current status of water quality for the Day and Nhue rivers (Trinh... that addresses the environment of the entire basin (atmosphere, surface water and soil/groundwater) has not yet been conducted As the key factors in uncertainty analysis, an evaluation of the interactions