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Nitrogen removal performance and needed area estimation of surface-flow constructed wetlands using a probabilistic approach
Journal of Environmental Management 255 (2020) 109881
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Research article
Nitrogen removal performance and needed area estimation of surface-flow constructed wetlands using a probabilistic approach Pei Luo a, Feng Liu a, *, Shunan Zhang a, Hongfang Li a, b, Xiang Chen a, b, Xinxing Huang a, b, Runlin Xiao a, Jinshui Wu a, b a
Key Laboratory of Agro-ecological Processes in Subtropical Regions, Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China University of Chinese Academy of Sciences, Beijing, 100049, China
b
A R T I C L E I N F O
A B S T R A C T
Keywords: Constructed wetland Nitrogen removal Probabilistic approach Area estimation Reliability analysis
Pollutant concentrations in influents into constructed wetlands (CWs) are highly fluctuating and may vary over several orders of magnitude, leading to large uncertainties in removal performance assessment when using pollutant concentrations in the influent and effluent directly. Incorporating a probabilistic approach into removal performance assessment and needed area estimation of CWs could advantage decision making regarding wastewater treatment and engineering applications. A series of three-stage surface-flow CWs (SFCWs) were constructed for treating ammonium-rich swine wastewater. The surface removal rate and removal efficiency of ammonium nitrogen in the SFCWs using the probabilistic approach were 0.27–3.23 g m 2 d 1 and 43.0–99.9% (95% confidence interval (CI)), which were consistent with the deterministic approach (95% CI: 0.24–3.18 g m 2 d 1 and 70.4–99.9%). The needed SFCW area was estimated as 6.6 (95% CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for required removal efficiency from 40% to 90% for 0.18 m3 d 1 swine wastewater with different strengthens. For specific removal efficiency of 90%, the needed CW areas was 13.9 (95%CI: 4.9–42.7), 25.1 (95% CI: 5.9–66.0), 33.5 (95%CI: 13.5–87.1), and 40.8 (95%CI: 16.2–89.4) m2 for influent ammonium loading rate of 0.18–2.7, 2.7–14.4, 14.4–36, and 36–60 g d 1, respectively. The first-order removal constant of ammonium nitrogen decreased logarithmically with increasing influent and effluent concentration/loading rate in the SFCW units (p < 0.001), which was responsible for the needed SFCW areas covering a wide range. The reliability analysis confirmed the results from the probabilistic approach were appropriate. The present study shed new lights on the performance evaluation and design of CWs for treating wastewater with highly-fluctuating con centrations using a probabilistic approach.
1. Introduction Due to rapid population and economic growth, issues of water resource shortage and pollution have become increasingly serious, particularly in many developing countries (Addams et al. (2009); United Nations World Water Assessment Programme, 2018). More than 27% of the global population lived in potential severely water-scarce areas during the early-mid 2010s, and water pollution has worsened in almost all rivers in Africa, Asia and Latin America (United Nations World Water Assessment Programme, 2018). Under requirements of low construction and operation costs, constructed wetlands (CWs) have been confirmed to be a robust ecological technique for treating various types of wastewater (Kadlec and Wallace, 2008; Vymazal, 2011; Zhang et al., 2014). The
removal efficiency of total nitrogen and phosphorus ranged 40–60% in the most CWs (Vymazal, 2007). The CWs also plays an important role in water quality improvement at the watershed scale, especially in the intensively agricultural watersheds (Hansen et al., 2018). Performance improvement and optimization design of CWs remain urgent to resolve. Pollutants removal by CWs is affected mainly by water quality, cli matic conditions and operational strategies (Kadlec and Wallace, 2008; Vymazal, 2007). The actual concentrations of pollutants in discharge and wastewater are highly fluctuating and may vary by several orders of magnitude (Addams et al., 2009; Hu et al., 2017; Wen et al., 2017). Removal efficiency or rate of pollutants by CWs can be gauged by the concentration or mass variation between the influent and effluent. The one-to-one correspondence relationship between the influent and
* Corresponding author. E-mail address: [email protected] (F. Liu). https://doi.org/10.1016/j.jenvman.2019.109881 Received 29 July 2019; Received in revised form 5 November 2019; Accepted 16 November 2019 Available online 25 November 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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effluent is weakened when the influent concentrations present large fluctuations and/or the CWs harbor a relative long hydraulic retention time (Kadlec and Wallace, 2008). The removal efficiency or rate will thus have large uncertainties if the influent and effluent concentration or mass is used directly. Probabilistic approaches consider variations in concentrations of pollutants, and may therefore be better than the deterministic ap proaches in scientific assessment of performance (Stamm et al., 2015; Wang et al., 2015). The probabilistic approaches describe variables as a distribution of values, whereas the deterministic approaches present variables as a point value. As such, the probabilistic approaches are suitable in the analysis of highly fluctuating data. Previous studies have found that the probabilistic approaches were reliable to assess the per formance of engineered treatment systems and CWs (Luo et al., 2018a; Page et al., 2011; Vanderzalm et al., 2013). For a specific proven high-efficiency CW system, size or area may be the most important factor since other factors (e.g., aquatic plants and substrates) are pre determined (Jasper et al., 2014; Kadlec and Wallace, 2008; Wu et al., 2015). Although several reports on estimation of needed CWs area using the mean concentration of pollutants in the influent and effluent have been published (Lin et al., 2005; Shi et al., 2011), information about area variability using a probabilistic approach is limited. We hypothesized that integrating probability density functions into a probabilistic approach could be meaningful to design and performance assessment of CWs for wastewater treatment. Due to variations of raw wastewater characteristics and wastewater treatment performance, the probability of failure to meet discharge standards should always be considered (Lombard-Latune et al., 2018; Oliveira and Von Sperling, 2008; Park et al., 2015). Reliability is used to assess the percentage of time that effluent concentration is expected to meet the pre-set discharge limits, and has been widely used to assess wastewater treatment performance (Alderson et al., 2015; Eisenberg ��zwiakowski et al., 2017; Lombard-Latune et al., 2018; et al., 2001; Jo Oliveira and Von Sperling, 2008; Vanderzalm et al., 2013). Reliability analyses of CWs needed to be considered as a primary methodology to assess the design of CWs based on the above-mentioned probabilistic approach. In the present study, a series of three-stage surface-flow CWs (SFCWs) were constructed for treating lagoon-pretreated swine waste water with different strengths. Ammonium nitrogen was selected as an example for discussion. The objectives of the present study were i) to assess the ammonium nitrogen removal performance of the three-stage SFCWs using a probabilistic approach, ii) to estimate the needed CW area in different scenarios and analyze the associated uncertainties, and iii) to conduct reliability analysis and propose design improvement of SFCWs for wastewater treatment. The results will provide valuable in formation on the performance evaluation and design of the SFCWs for treating wastewater with highly fluctuating concentrations.
swine wastewater diluted with fresh water at a 1:2 ratio (n ¼ 3). The hydraulic retention time of each unit was 11 d. The SFCWs were oper ated under a sustainable plant harvesting management and the emer gent shoots of M. aquaticum were harvested at 2–4-week intervals (Luo et al., 2018a, 2018b). Water samples of inflow and outflow of each SFCW unit were collected 1–2 time per week in July 2014–February 2016. Water temperature, pH, dissolved oxygen, and oxidation-reduction potential were simultaneously measured with a Mettler Toledo portable multi-parameter water quality meter (SG68-SevenGo, Switzerland). Ammonium nitrogen concentrations of water samples were analyzed using the National Standard Method of China (Ministry of Environmental Protection of China, 2002). 2.2. Data analysis The removal efficiency (ɳ) of ammonium nitrogen by the SFCWs was calculated using the following equation: � � c η ¼ 1 o � 100% (1) ci where ci and c0 are the ammonium nitrogen concentration in the influent and effluent (mg L 1), respectively. Pollutant removal by CWs can generally be described by a first-order plug flow model (Kadlec and Wallace, 2008). Due to the constant hydraulic loading rate in the present study, the first-order removal constant (k; m d 1) of nitrogen was calculated using the first-order plug flow kinetic model as follows (Kadlec and Wallace, 2008): k¼
Q ci � ln A co
(2)
where A is the CW area (m2); and Q is the inlet flow rate (0.18 m3 d 1). The surface areal loading and removal rate (SLR and SRR; g m 2 d 1) of nitrogen by SFCWs were calculated by (Luo et al., 2018a, 2018b): SLR ¼
SRR ¼
ci � Q A ðci
co Þ � Q A
(3) (4)
Based on the first-order plug flow kinetic model, the needed CW area (Ad) for treating wastewater to meet specific discharge requirements was estimated by rearrangement of Eq. (2): � � Q ci Q 1 Ad ¼ � ln ¼ � ln (5) k 1 ηd co k where ɳd is needed removal efficiency of nitrogen by SFCWs. The deterministic method used the actual monitoring data for calculation. To evaluate the uncertainty and variability of nitrogen removal and area estimation of the SFCWs, the ammonium nitrogen concentration and the first-order removal constant were described using probability density functions (Luo et al., 2018a; Page et al., 2011; Vanderzalm et al., 2013). The probability density functions were defined by the log-normal dis tribution with actual mean and standard deviation, which stemmed directly from the actual data fitting results. A Monte Carlo simulation was run 5000 times repeatedly with different input data to evaluate uncertainty and variability of removal efficiency and needed CW area. To assess the probability of influent of the SFCWs meeting the specified discharge standards or treatment targets for a specified period of time, reliability of the SFCWs was estimated using the coefficient of reliability (COR) (Niku et al., 1979; Oliveira et al., 2012; Oliveira and Von Sperling, 2008): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�ffiffi� � pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi COR ¼ CV 2 þ 1 � exp Z1 α � ln CV 2 þ 1 (6)
2. Materials and methods 2.1. Description of the constructed wetlands and sample analysis Detailed information on the SFCWs and sample analysis procedures have been provided previously (Luo et al., 2018a, 2018b), so only a brief description is presented herein. Eleven pilot-scale three-stage SFCWs, i. e., CW1, CW2, and CW3 from inlet to outlet along the wastewater flow direction (Fig. S1 of the Supplementary Material; “S” indicates figures and tables in the Supplementary Material afterwards), were planted with Myriophyllum aquaticum, and their substrate was the local paddy soil with sand, silt and clay content of 31.5%, 36.3%, and 32.2%, respectively. The SFCWs were intermittently fed with lagoon-pretreated swine wastewater at a rate of 0.18 m3 d 1. Three strengths of wastewater were set as follows: i) high strength (HS): raw swine wastewater without dilution (n ¼ 3); ii) medium strength (MS): raw swine wastewater diluted with fresh water at a 2:1 ratio (n ¼ 3); iii) low strength (LS): raw
where CV is the coefficient of variation, which was obtained by dividing 2
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Fig. 1. Ammonium nitrogen concentration and cumulative probability distribution in the influent (a and c) and effluent (b and d) of the three-stage surface flow constructed wetlands treating low-strength (LS), medium-strength (MS) and high-strength (HS) lagoon-pretreated swine wastewater.
the standard deviation by the mean; α is the probability of failing to meet the standards; and Z1 α is standardized normal variate, which was adopted from Oliveira and Von Sperling (2008), listed in Table S1. The design or operational effluent concentrations (cD; mg L 1) of the SFCWs was calculated by the COR value and the discharge standard (cd; mg L 1): cD ¼ COR � cd
pollutants concentration was also observed for other pollutants in the previous studies (Eisenberg et al., 2001; Luo et al., 2018a; Page et al., 2011; Park et al., 2015; Park and Roesner, 2012). The log-normal dis tribution of pollutants in wastewater treatment should be widely considered in wetland design and discharge standards legislation. For example, Oliveira and Von Sperling (2008) proposed that the probabi listic approaches should be realistic and practical in setting discharge standards. The SRRs of ammonium nitrogen were 0.64 (95%CI: 0.24–1.08), 1.21 (95%CI: 0.42–2.13), and 1.72 (95%CI: 0.65–3.18) g m 2 d 1 in the SFCWs fed with LS, MS, and HS wastewater (SLR: 0.65 (95%CI: 0.25–1.11), 1.31 (95%CI: 0.48–2.21), and 1.97 (95%CI: 0.76–3.45) g m 2 d 1), respectively. The 95%CI SRRs of ammonium nitrogen using the probabilistic approach were estimated to be 0.27–1.20, 0.55–2.16, and 0.66–3.23 g m 2 d 1 in the SFCWs for LS, MS, and HS wastewater, respectively. The results from the probabilistic and deterministic approach were consistent. Similarly, the removal efficiencies of nitrogen using the probabilistic approach were estimated to be 98.1% (95%CI: 86.7–99.9%), 92.2% (95%CI: 51.2–99.7%), and 87.4% (95%CI: 43.0–98.6%) in the SFCWs for LS, MS, and HS wastewater, respectively. The results were substantially similar to those calculated using the deterministic values, i.e., 97.9% (95% CI: 88.6%–99.9%), 92.8% (95% CI: 70.4%–99.9%), and 87.7% (95%CI: 70.6%–98.4%) for LS, MS, and HS wastewater, respectively. Seemingly, the removal performance of SFCWs planted with M. aquaticum was little disturbed by large fluctu ations of influent concentrations, probably indicating that the system can resist the wastewater with fluctuating pollutant concentrations. The wetland plant M. aquaticum could tolerate the wastewater with highconcentration ammonium nitrogen (>200 mg L 1) (Liu et al., 2018; Zhang et al., 2017), which may enhance the resistance of SFCWs to shock loading of nutrients. The probabilistic approach could adopt less data input, e.g., mean and standard deviation of pollutant concentration in the influent and effluent, to calculate the removal efficiency of CWs than the deterministic method, implicating the probabilistic approach could be used for predicting future removal performance. Vanderzalm et al. (2013) applied the probabilistic modelling approach to evaluate removal efficiency of nitrogen, phosphorus and organic carbon in an anoxic carbonate aquifer, and proposed that the method was suited for
(7)
The cD higher than the actual effluent concentration of SFCWs sug gests that the CW system is sufficient and reliable for wastewater treatment. If the cD is lower than the actual effluent concentration, the SFCWs need to be adjusted or improved. 3. Results and discussion 3.1. Probabilistic evaluation of ammonium nitrogen removal in the constructed wetlands Ammonium nitrogen concentrations and their cumulative probabil ity in the influent and effluent of the three-stage SFCWs treating LS, MS and HS wastewater are shown in Fig. 1. The ammonium nitrogen con centration in the SFCWs for LS, MS and HS wastewater was 108 (95% confidence interval (CI): 42.8–184), 218 (95%CI: 81.8–366), and 328 (95%CI: 131–572) mg L 1 in the influent, 2.1 (95%CI: 0.06–11.5), 16.9 (95%CI: 0.18–51.7), and 41.3 (95%CI: 4.1–129) mg L 1 in the effluent (Table S2). According to the cumulative probability of ammonium ni trogen concentration, 100%, 96.0%, and 85.5% of the total effluent concentrations in the three-stage SFCWs fed with LS, MS and HS wastewater could meet the wastewater discharge standard for livestock and poultry breeding in China (80 mg L 1; GB 18596-2001). Whereas the mean ammonium nitrogen concentration in the effluent (2.1–41.3 mg L 1) was much lower than the wastewater discharge standard (80 mg L 1; GB 18596-2001). Removal performance of the SFCWs may be overestimated using the arithmetic mean value of wastewater concen tration. The arithmetic mean value of ammonium nitrogen concentra tion was also higher than the median (Table S2). On the other hand, the ammonium nitrogen concentration was well fitted to the log-normal distribution (Fig. S2 and Table S3). The log-normal distribution of 3
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Fig. 2. The needed area estimation of surface-flow constructed wetlands for removal efficiency of 40–90% (a) and for different influent concentration at the removal efficiency of 90% (b).
loading rate of 0.18 m3 d 1 in the present study. The needed CW area was ranged from 6.6 (95%CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for removal efficiency of 40–90% (Fig. 2a and Table 1). The needed CW areas harbor different probabilities for various removal efficiencies, e.g., a lower removal efficiency required a higher CW area. The large range of the needed CW area for a specific removal efficiency may be ascribed to the highly fluctuating concentrations in the influent and seasonal ef fects. Jasper et al. (2014) found the needed CW area was about five times larger in winter than summer for municipal wastewater treatment due to the temperature variations among the different seasons. The SFCWs required an area premium to meet the treatment goals in winter due to the relatively low removal efficiency of SFCWs in cold climates (Kadlec, 2009). Therefore, application of the probabilistic approach in area estimation of CWs considered the seasonal variations and pollution concentration fluctuations, which will enhance the practicability of CWs in wastewater treatment. To estimate needed CW areas for treating wastewater with different strengths, the ammonium nitrogen concentrations in the influent were divided into four groups: 1–15, 15–80, 80–200 and >200 mg L 1 (Fig. S3). The ammonium nitrogen concentration of 1, 15, and 80 mg L 1 is the limit of Grade III environmental quality standards for surface water (GB 3838-2002), Grade IB discharge standard of pollutants for municipal wastewater treatment plant (GB 18918-2002), and the wastewater discharge standard for livestock and poultry breeding in China (GB 18596-2001), respectively. The ammonium nitrogen con centration of 200 mg L 1 was the highest concentration tolerated by most wetland plants (Zhang et al., 2017). Selecting the removal effi ciency of 90% for treating an inlet loading rate of 0.18 m3 d 1 as an example (Fig. 2b), the needed CW areas were 13.9 (95%CI: 4.9–42.7), 25.1 (95%CI: 5.9–66.0), 33.5 (95%CI: 13.5–87.1), and 40.8 (95%CI: 16.2–89.4) m2 for influent concentration of 1–15, 15–80, 80–200, and >200 mg L 1, respectively (Fig. 2b and Table 1). The differences of the needed areas showed the different area designs of CWs for various types of wastewater treatment, e.g., the less CWs area needed for the lower wastewater concentration. The needed CW areas were 13.9–40.8 m2 for inlet nitrogen loading rates of 0.18–60 g d 1 (Fig. S3). Therefore, the variability of the needed CW area is an important consideration in the selection of engineering parameters for wastewater treatment. Generally, the multiple-stage SFCWs are a robust system and produce stable effluent compared with the single-stage one (Luo et al., 2018a, 2018b). In the present study, a significant linear correlation between the SRR and SLR of SFCWs was observed, with the better linearity (R2) in the three-stage SFCWs than CW units (the single-stage CW) (Fig. 3). Perhaps span of ammonium nitrogen concentration in the influent of the CW
Table 1 Summary of the statistics for the design areas (m2) associated with several scenarios including different removal efficiencies and influent concentrations at a removal efficiency of 90% based on a probabilistic approach. SD represents standard deviation. Scenario
Mean
Median
SD
95% confidence interval Lower
Removal efficiency 40% 6.6 5.1 5.4 1.4 50% 9.0 6.9 7.3 1.9 60% 11.8 9.2 9.6 2.6 70% 15.6 12.0 12.6 3.4 80% 20.8 16.1 16.9 4.5 90% 29.7 23.0 24.2 6.4 Influent concentration (mg L¡1) for 90% removal efficiency 1–15 13.9 8.4 13.6 4.9 15–80 25.1 19.4 20.9 5.9 80–200 33.5 25.1 26.9 13.5 >200 40.8 32.6 26.0 16.2
Upper 17.8 24.1 31.9 41.9 56.0 80.1 42.7 66.0 87.1 89.4
operational assessment of the aquifer treatment performance. 3.2. Area estimation of constructed wetlands Due to space constraint and construction cost, area or size is one of the primary considerations for the application and design of CWs, especially for SFCWs (Kadlec and Wallace, 2008; Wu et al., 2015). For specific wastewater and CW types, the area of CWs determines their removal efficiency. To meet the discharge standards, different removal efficiencies are required for treating various types of wastewater. Generally, a higher removal efficiency is required for the stricter discharge standards (e.g., surface water) and the heavier wastewater loadings (e.g., swine wastewater). The three-stage SFCW units were selected to estimate the needed CWs area in the present study, because ammonium nitrogen concentrations in the SFCW units influent were ranged four orders of magnitude (Range: 0.11–753 mg L 1), which covered almost all types of wastewater in the agricultural watershed including swine wastewater, farmland drainage, and rural decentralized household drainage in China (Hu et al., 2017; Li et al., 2015; Wang et al., 2014; Zhao et al., 2012). Pollutant concentration in the effluent could be generally lower than the discharge standards of local government, when removal efficiency of wastewater treatment engineering was set in the range of 40–90%. Therefore, 40–90% decreases of concentration in the wastewater treat ment were selected to estimate the needed CW areas treating inlet 4
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units was larger than the overall situation. Similarly, a pervious study observed that the multiple-stage CW was robust compared to the single-stage CW for treating highly fluctuating wastewater (Langer graber et al., 2010). The multi-stage arrangement also provided addi tional security for effective operation of CWs (Harrington and McInnes, 2009). For the CWs treating swine wastewater in the present study, either the four-stage CWs will be sufficient or another treatment system should be added at the next unit when the discharge standard of local government or special environmental protection is stricter than the present condition. In other words, the SFCWs area needs optimization and adjustment for different wastewater loadings and wastewater treatment requirements. We suggested the constructed wetlands should be designed as multiple units for improvement of removal efficiency for wastewater. 3.3. Uncertainty of area estimation of constructed wetlands The needed CW area was estimated on the basis of the k value in the present study. As such, the uncertainties of the CW areas stemmed mainly from the variation of the k value (Fig. 4a). For different removal efficiencies, the needed CW areas were a function of the k value. It has been reported that the k value was CW area- or volume-specific, and also depended on the inlet concentration, flow rate, and wastewater quality parameter (Kadlec, 2000; Kadlec and Wallace, 2008). In the present
Fig. 3. Correlation between surface removal rate (SRR) and surface loading rate (SLR) of ammonium nitrogen in the surface-flow constructed wetland. (a) the CW units; (b) the three-stage SFCWs as a whole.
Fig. 4. The first-order removal constant (k) of ammonium nitrogen in surface-flow constructed wetlands treating lagoon-pretreated swine wastewater. (a) rela tionship between needed constructed wetland area and the k value for the removal efficiency of 40–90%; (b) probability distribution of the k value; (c) relationship between the k value and influent concentration/loading rate; (d) relationship between the k value and effluent concentration/loading rate. The abbreviation LND represents the log-normal distribution; CI represents confidence interval. 5
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3.4. Reliability evaluation of the constructed wetlands
Table 2 Coefficient of reliability, design, and actual effluent concentration of the threestage surface flow constructed wetlands treating low-strength (LS), mediumstrength (MS) and high-strength (HS) lagoon-pretreated swine wastewater. CW1, CW2, and CW3 indicate the first, second, and third unit of the surface flow constructed wetlands, respectively. The data on cofficient of reliability higher than 1.0 and design effluent concentration higher than acutual mean effluent concentration were highlighted. Reliability (1-α)
LS CW1
99% 98% 95% 90% 80% 70% 60% 50% 99% 98% 95% 90% 80% 70% 60% 50%
MS CW2
CW3
CW2
Coefficient of reliability 0.27 0.16 0.12 0.22 0.33 0.21 0.17 0.27 0.42 0.31 0.28 0.37 0.54 0.46 0.44 0.50 0.71 0.71 0.77 0.70 0.88 0.98 1.15 0.90 1.05 1.30 1.62 1.12 1.23 1.68 2.23 1.37 Design effluent concentration (mg L 1) 22 12.6 9.4 17.4 26 16.6 13.2 22 34 25 22 30 43 36 35 40 57 57 61 56 70 79 92 72 84 104 130 90 99 134 178 109 Actual mean effluent concentration (mg L 1) 40.8 10.8 2.1 45.6
Reliability refers to the percentage of time the expected effluent ��zwiakowski et al., 2017; values meet the pre-set discharge limits (Jo Oliveira and Von Sperling, 2008; Park et al., 2015). To assess the reli ability of the SFCWs for nitrogen removal, the coefficient of reliability was calculated for wastewater with different strengths. Herein, the varying reliabilities represent different technical usage scenarios. For example, the reliability was selected as 50–99%, meaning that the number of days that the CWs failed to meet the standards were 3.7–182 d yr 1 (Table S1). Only the effluent concentration of the SFCW unit lower than or comparable to the design effluent concentration was listed in Table 2. All COR values were higher than 1.0 at <60% level of reli ability, meaning that the effluent concentration was lower than the discharge standards during the period of 219 d per year. This could potentially stem from the log-normal distribution of ammonium nitro gen concentration (Oliveira and Von Sperling, 2008). When the COR values were higher than ~0.7, the effluent concentration in the majority of SFCW units could meet the discharge standards in the present study. The COR values were lower for higher reliability levels than lower reliability, indicating that the operation or design of CWs with low COR values needed to be improved. Based on the reliability analysis (Table 2), the present three-stage SFCWs needs to be adjusted to meet the wastewater discharge stan dard for livestock and poultry breeding in China (80 mg L 1; GB 185962001). To meet the wastewater discharge standard, only one and two stage CWs were adequate for the LS wastewater treatment when the reliability was set at 90% and 99%. Similarly, the three-stage CWs could meet the design requirement for the HS wastewater when the reliability was set lower than 90%. If the discharge standard was strict than the present condition, the fourth-stage CW would be required or another treatment system would be added at the next unit. These results were consistent with the above discussion based on the probabilistic approach. This COR approach confirms that it is essential to adopt multiple-stage CWs, i.e., larger CW areas, for various types of waste ��zwiakowski et al. water to meet the regulatory objectives. Similarly, Jo (2017) found that the one-stage horizontal subsurface flow CW over the operation period was insufficient to meet the Polish standards based on reliability analysis, and needed improvement. Our results also confirm that the probabilistic approach is appropriate for the performance evaluation of CWs, i.e., not all effluent concentrations were lower than that in the discharge standard. Based on the above findings, we confirm that the probabilistic approach should be adopted to estimate the needed CW area for wastewater treatment. Firstly, the needed CW areas are estimated by influent and effluent water quality using the probabilistic approach. Generally for the wastewater with greatly fluctuating concentration of pollutants, the estimated needed CW areas present a wide range. It may be overestimated or underestimated if the maximum, mediate or average values are selected. Then the reliability analysis is considered to determine a suitable CW area according to the actual requirements, e.g., discharge standards and seasonal variations of wastewater. Finally, the calculated CW area should be separated into several units, e.g., three to five stages, which is regard as a strategy to enhance the stability and sustainability of CWs. The detailed method for designing multiple-stage CWs needs to be studied in further research.
HS CW3
CW3
0.13 0.18 0.29 0.44 0.74 1.08 1.48 1.99
0.20 0.25 0.35 0.48 0.70 0.92 1.16 1.45
10.4 14.3 23 35 59 86 118 159
15.7 19.8 28 38 56 74 93 116
16.9
41.3
study, the k value of ammonium nitrogen was 30.9 (95%CI: 13.0–43.3), 22.7 (95%CI: 7.3–42.1), and 14.7 (95%CI: 7.3–24.7) mm d 1 in the three-stage SFCWs for LS, MS, and HS wastewater, respectively (Table S2), within the range of 11.2–96 mm d 1 reported by Kadlec and Wallace (2008). The k value was fitted to the log-normal distribution (Fig. 4b), suggesting that the CW area may be overestimated for low-strength wastewater but underestimated for high-strength waste water when the mean k value was selected. Park and Roesner (2012) also observed that the k value in a stormwater best management practice exhibited a log-normal distribution, which may induce the uncertainty of performance using median or mean values. The k value of ammonium nitrogen decreased logarithmically with increasing influent and effluent concentration/loading rate in the SFCW units (p < 0.001; Fig. 4c and d). Higher k values were observed in lower concentrations/loading rate, which was also found in the previous studies (Kadlec and Wallace, 2008; Luo et al., 2017). Previous studies have found that the k value of nitrogen correlated significantly with a number of parameters including temperature, dis solved oxygen, and other factors (Chen et al., 2015; Kadlec and Wallace, 2008). In the present study, however, we did not observe the significant correlation between the k values and water quality parameters (e.g., water temperature, dissolved oxygen, oxidation-reduction potential, and pH) of the SFCWs. The influent ammonium nitrogen concentration with relatively large fluctuation could neutralize the effects of water quality parameters on the k value. The large differences was derived from the removal capacity of SFCWs for different influent concentra tions. The k value is sensitive to ammonium nitrogen concentration of wastewater, and therefore the CW areas needed to be calibrated for the specific type of wastewater. The present study only discussed the CW size for wastewater treatment using the probabilistic approach. The preformation of CWs for wastewater treatment was also dependent on their locations. For example, Hansen et al. (2018) found that the loca tion and size of CWs were important for outlet water quality at the watershed scale. The cost of construction and operation should also be considered during the design of CWs. Further studies are needed to facilitate effective design of CWs at the watershed scale.
4. Conclusions The ammonium nitrogen removal performance of the SFCWs was assessed using the probabilistic approach. The removal performance of the SFCWs may be overestimated using the average values of the wastewater concentration, because ammonium nitrogen concentration was well fitted to the log-normal distribution. The needed CW area was estimated as 6.6 (95%CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for removal efficiencies from 40% to 90%. The k value of ammonium 6
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nitrogen was fitted to the log-normal distribution and decreased loga rithmically with increasing influent and effluent concentration, sug gesting the probabilistic approach is important for CW design. Reliability analysis confirmed the results from the probabilistic approach were appropriate and identified methods of improvement of CWs.
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Acknowledgements The present study was financially supported by the National Key Research and Development Program of China (2018YFD0800504), the Natural Science Foundation of Hunan Province (2019JJ50705), the Key Research Project of Frontier Science of Chinese Academy of Sciences (QYZDJ-SSW-DQC041), and the Youth Innovation Team Project of Institute of Subtropical Agriculture, Chinese Academy of Sciences (2017QNCXTD_LF). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jenvman.2019.109881. References Addams, L., Boccaletti, G., Kerlin, M., Stuchtey, M., 2009. Charting Our Water Future: Economic Frameworks to Inform Decision-Making. Alderson, M.P., dos Santos, A.B., Mota Filho, C.R., 2015. Reliability analysis of low-cost, full-scale domestic wastewater treatment plants for reuse in aquaculture and agriculture. Ecol. Eng. 82, 6–14. Chen, L., Liu, F., Wang, Y., Li, X., Zhang, S., Li, Y., Wu, J., 2015. Nitrogen removal in an ecological ditch receiving agricultural drainage in subtropical central China. Ecol. Eng. 82, 487–492. Eisenberg, D., Soller, J., Sakaji, R., Olivieri, A., 2001. A methodology to evaluate water and wastewater treatment plant reliability. Water Sci. Technol. 43, 91–99. Hansen, A.T., Dolph, C.L., Foufoula-Georgiou, E., Finlay, J.C., 2018. Contribution of wetlands to nitrate removal at the watershed scale. Nat. Geosci. 11, 127–132. Harrington, R., McInnes, R., 2009. Integrated constructed wetlands (ICW) for livestock wastewater management. Bioresour. Technol. 100, 5498–5505. Hu, Y., Cheng, H., Tao, S., 2017. Environmental and human health challenges of industrial livestock and poultry farming in China and their mitigation. Environ. Int. 107, 111–130. J� o�zwiakowski, K., Bugajski, P., Mucha, Z., W� ojcik, W., Jucherski, A., Nastawny, M., Siwiec, T., Mazur, A., Obro�slak, R., Gajewska, M., 2017. Reliability and efficiency of pollution removal during long-term operation of a one-stage constructed wetland system with horizontal flow. Separ. Purif. Technol. 187, 60–66. Jasper, J.T., Jones, Z.L., Sharp, J.O., Sedlak, D.L., 2014. Nitrate removal in shallow, open-water treatment wetlands. Environ. Sci. Technol. 48, 11512–11520. Kadlec, R.H., 2000. The inadequacy of first-order treatment wetland models. Ecol. Eng. 15, 105–119. Kadlec, R.H., 2009. Comparison of free water and horizontal subsurface treatment wetlands. Ecol. Eng. 35, 159–174. Kadlec, R.H., Wallace, S.D., 2008. Treatment Wetlands, second ed. CRC press, Boca Raton, FL. Langergraber, G., Pressl, A., Leroch, K., Rohrhofer, R., Haberl, R., 2010. Comparison of single-stage and a two-stage vertical flow constructed wetland systems for different load scenarios. Water Sci. Technol. 61, 1341–1348. Li, Y., Jiao, J., Wang, Y., Yang, W., Meng, C., Li, B., Li, Y., Wu, J., 2015. Characteristics of nitrogen loading and its influencing factors in several typical agricultural watersheds of subtropical China. Environ. Sci. Pollut. Control Ser. 22, 1831–1840. Lin, Y.-F., Jing, S.-R., Lee, D.-Y., Chang, Y.-F., Chen, Y.-M., Shih, K.-C., 2005. Performance of a constructed wetland treating intensive shrimp aquaculture wastewater under high hydraulic loading rate. Environ. Pollut. 134, 411–421. Liu, F., Zhang, S., Luo, P., Zhuang, X., Chen, X., Wu, J., 2018. Purification and reuse of non-point source wastewater via Myriophyllum-based integrative biotechnology: a review. Bioresour. Technol. 248, 3–11.
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Research article
Nitrogen removal performance and needed area estimation of surface-flow constructed wetlands using a probabilistic approach Pei Luo a, Feng Liu a, *, Shunan Zhang a, Hongfang Li a, b, Xiang Chen a, b, Xinxing Huang a, b, Runlin Xiao a, Jinshui Wu a, b a
Key Laboratory of Agro-ecological Processes in Subtropical Regions, Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, 410125, China University of Chinese Academy of Sciences, Beijing, 100049, China
b
A R T I C L E I N F O
A B S T R A C T
Keywords: Constructed wetland Nitrogen removal Probabilistic approach Area estimation Reliability analysis
Pollutant concentrations in influents into constructed wetlands (CWs) are highly fluctuating and may vary over several orders of magnitude, leading to large uncertainties in removal performance assessment when using pollutant concentrations in the influent and effluent directly. Incorporating a probabilistic approach into removal performance assessment and needed area estimation of CWs could advantage decision making regarding wastewater treatment and engineering applications. A series of three-stage surface-flow CWs (SFCWs) were constructed for treating ammonium-rich swine wastewater. The surface removal rate and removal efficiency of ammonium nitrogen in the SFCWs using the probabilistic approach were 0.27–3.23 g m 2 d 1 and 43.0–99.9% (95% confidence interval (CI)), which were consistent with the deterministic approach (95% CI: 0.24–3.18 g m 2 d 1 and 70.4–99.9%). The needed SFCW area was estimated as 6.6 (95% CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for required removal efficiency from 40% to 90% for 0.18 m3 d 1 swine wastewater with different strengthens. For specific removal efficiency of 90%, the needed CW areas was 13.9 (95%CI: 4.9–42.7), 25.1 (95% CI: 5.9–66.0), 33.5 (95%CI: 13.5–87.1), and 40.8 (95%CI: 16.2–89.4) m2 for influent ammonium loading rate of 0.18–2.7, 2.7–14.4, 14.4–36, and 36–60 g d 1, respectively. The first-order removal constant of ammonium nitrogen decreased logarithmically with increasing influent and effluent concentration/loading rate in the SFCW units (p < 0.001), which was responsible for the needed SFCW areas covering a wide range. The reliability analysis confirmed the results from the probabilistic approach were appropriate. The present study shed new lights on the performance evaluation and design of CWs for treating wastewater with highly-fluctuating con centrations using a probabilistic approach.
1. Introduction Due to rapid population and economic growth, issues of water resource shortage and pollution have become increasingly serious, particularly in many developing countries (Addams et al. (2009); United Nations World Water Assessment Programme, 2018). More than 27% of the global population lived in potential severely water-scarce areas during the early-mid 2010s, and water pollution has worsened in almost all rivers in Africa, Asia and Latin America (United Nations World Water Assessment Programme, 2018). Under requirements of low construction and operation costs, constructed wetlands (CWs) have been confirmed to be a robust ecological technique for treating various types of wastewater (Kadlec and Wallace, 2008; Vymazal, 2011; Zhang et al., 2014). The
removal efficiency of total nitrogen and phosphorus ranged 40–60% in the most CWs (Vymazal, 2007). The CWs also plays an important role in water quality improvement at the watershed scale, especially in the intensively agricultural watersheds (Hansen et al., 2018). Performance improvement and optimization design of CWs remain urgent to resolve. Pollutants removal by CWs is affected mainly by water quality, cli matic conditions and operational strategies (Kadlec and Wallace, 2008; Vymazal, 2007). The actual concentrations of pollutants in discharge and wastewater are highly fluctuating and may vary by several orders of magnitude (Addams et al., 2009; Hu et al., 2017; Wen et al., 2017). Removal efficiency or rate of pollutants by CWs can be gauged by the concentration or mass variation between the influent and effluent. The one-to-one correspondence relationship between the influent and
* Corresponding author. E-mail address: [email protected] (F. Liu). https://doi.org/10.1016/j.jenvman.2019.109881 Received 29 July 2019; Received in revised form 5 November 2019; Accepted 16 November 2019 Available online 25 November 2019 0301-4797/© 2019 Elsevier Ltd. All rights reserved.
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effluent is weakened when the influent concentrations present large fluctuations and/or the CWs harbor a relative long hydraulic retention time (Kadlec and Wallace, 2008). The removal efficiency or rate will thus have large uncertainties if the influent and effluent concentration or mass is used directly. Probabilistic approaches consider variations in concentrations of pollutants, and may therefore be better than the deterministic ap proaches in scientific assessment of performance (Stamm et al., 2015; Wang et al., 2015). The probabilistic approaches describe variables as a distribution of values, whereas the deterministic approaches present variables as a point value. As such, the probabilistic approaches are suitable in the analysis of highly fluctuating data. Previous studies have found that the probabilistic approaches were reliable to assess the per formance of engineered treatment systems and CWs (Luo et al., 2018a; Page et al., 2011; Vanderzalm et al., 2013). For a specific proven high-efficiency CW system, size or area may be the most important factor since other factors (e.g., aquatic plants and substrates) are pre determined (Jasper et al., 2014; Kadlec and Wallace, 2008; Wu et al., 2015). Although several reports on estimation of needed CWs area using the mean concentration of pollutants in the influent and effluent have been published (Lin et al., 2005; Shi et al., 2011), information about area variability using a probabilistic approach is limited. We hypothesized that integrating probability density functions into a probabilistic approach could be meaningful to design and performance assessment of CWs for wastewater treatment. Due to variations of raw wastewater characteristics and wastewater treatment performance, the probability of failure to meet discharge standards should always be considered (Lombard-Latune et al., 2018; Oliveira and Von Sperling, 2008; Park et al., 2015). Reliability is used to assess the percentage of time that effluent concentration is expected to meet the pre-set discharge limits, and has been widely used to assess wastewater treatment performance (Alderson et al., 2015; Eisenberg ��zwiakowski et al., 2017; Lombard-Latune et al., 2018; et al., 2001; Jo Oliveira and Von Sperling, 2008; Vanderzalm et al., 2013). Reliability analyses of CWs needed to be considered as a primary methodology to assess the design of CWs based on the above-mentioned probabilistic approach. In the present study, a series of three-stage surface-flow CWs (SFCWs) were constructed for treating lagoon-pretreated swine waste water with different strengths. Ammonium nitrogen was selected as an example for discussion. The objectives of the present study were i) to assess the ammonium nitrogen removal performance of the three-stage SFCWs using a probabilistic approach, ii) to estimate the needed CW area in different scenarios and analyze the associated uncertainties, and iii) to conduct reliability analysis and propose design improvement of SFCWs for wastewater treatment. The results will provide valuable in formation on the performance evaluation and design of the SFCWs for treating wastewater with highly fluctuating concentrations.
swine wastewater diluted with fresh water at a 1:2 ratio (n ¼ 3). The hydraulic retention time of each unit was 11 d. The SFCWs were oper ated under a sustainable plant harvesting management and the emer gent shoots of M. aquaticum were harvested at 2–4-week intervals (Luo et al., 2018a, 2018b). Water samples of inflow and outflow of each SFCW unit were collected 1–2 time per week in July 2014–February 2016. Water temperature, pH, dissolved oxygen, and oxidation-reduction potential were simultaneously measured with a Mettler Toledo portable multi-parameter water quality meter (SG68-SevenGo, Switzerland). Ammonium nitrogen concentrations of water samples were analyzed using the National Standard Method of China (Ministry of Environmental Protection of China, 2002). 2.2. Data analysis The removal efficiency (ɳ) of ammonium nitrogen by the SFCWs was calculated using the following equation: � � c η ¼ 1 o � 100% (1) ci where ci and c0 are the ammonium nitrogen concentration in the influent and effluent (mg L 1), respectively. Pollutant removal by CWs can generally be described by a first-order plug flow model (Kadlec and Wallace, 2008). Due to the constant hydraulic loading rate in the present study, the first-order removal constant (k; m d 1) of nitrogen was calculated using the first-order plug flow kinetic model as follows (Kadlec and Wallace, 2008): k¼
Q ci � ln A co
(2)
where A is the CW area (m2); and Q is the inlet flow rate (0.18 m3 d 1). The surface areal loading and removal rate (SLR and SRR; g m 2 d 1) of nitrogen by SFCWs were calculated by (Luo et al., 2018a, 2018b): SLR ¼
SRR ¼
ci � Q A ðci
co Þ � Q A
(3) (4)
Based on the first-order plug flow kinetic model, the needed CW area (Ad) for treating wastewater to meet specific discharge requirements was estimated by rearrangement of Eq. (2): � � Q ci Q 1 Ad ¼ � ln ¼ � ln (5) k 1 ηd co k where ɳd is needed removal efficiency of nitrogen by SFCWs. The deterministic method used the actual monitoring data for calculation. To evaluate the uncertainty and variability of nitrogen removal and area estimation of the SFCWs, the ammonium nitrogen concentration and the first-order removal constant were described using probability density functions (Luo et al., 2018a; Page et al., 2011; Vanderzalm et al., 2013). The probability density functions were defined by the log-normal dis tribution with actual mean and standard deviation, which stemmed directly from the actual data fitting results. A Monte Carlo simulation was run 5000 times repeatedly with different input data to evaluate uncertainty and variability of removal efficiency and needed CW area. To assess the probability of influent of the SFCWs meeting the specified discharge standards or treatment targets for a specified period of time, reliability of the SFCWs was estimated using the coefficient of reliability (COR) (Niku et al., 1979; Oliveira et al., 2012; Oliveira and Von Sperling, 2008): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�ffiffi� � pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi COR ¼ CV 2 þ 1 � exp Z1 α � ln CV 2 þ 1 (6)
2. Materials and methods 2.1. Description of the constructed wetlands and sample analysis Detailed information on the SFCWs and sample analysis procedures have been provided previously (Luo et al., 2018a, 2018b), so only a brief description is presented herein. Eleven pilot-scale three-stage SFCWs, i. e., CW1, CW2, and CW3 from inlet to outlet along the wastewater flow direction (Fig. S1 of the Supplementary Material; “S” indicates figures and tables in the Supplementary Material afterwards), were planted with Myriophyllum aquaticum, and their substrate was the local paddy soil with sand, silt and clay content of 31.5%, 36.3%, and 32.2%, respectively. The SFCWs were intermittently fed with lagoon-pretreated swine wastewater at a rate of 0.18 m3 d 1. Three strengths of wastewater were set as follows: i) high strength (HS): raw swine wastewater without dilution (n ¼ 3); ii) medium strength (MS): raw swine wastewater diluted with fresh water at a 2:1 ratio (n ¼ 3); iii) low strength (LS): raw
where CV is the coefficient of variation, which was obtained by dividing 2
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Fig. 1. Ammonium nitrogen concentration and cumulative probability distribution in the influent (a and c) and effluent (b and d) of the three-stage surface flow constructed wetlands treating low-strength (LS), medium-strength (MS) and high-strength (HS) lagoon-pretreated swine wastewater.
the standard deviation by the mean; α is the probability of failing to meet the standards; and Z1 α is standardized normal variate, which was adopted from Oliveira and Von Sperling (2008), listed in Table S1. The design or operational effluent concentrations (cD; mg L 1) of the SFCWs was calculated by the COR value and the discharge standard (cd; mg L 1): cD ¼ COR � cd
pollutants concentration was also observed for other pollutants in the previous studies (Eisenberg et al., 2001; Luo et al., 2018a; Page et al., 2011; Park et al., 2015; Park and Roesner, 2012). The log-normal dis tribution of pollutants in wastewater treatment should be widely considered in wetland design and discharge standards legislation. For example, Oliveira and Von Sperling (2008) proposed that the probabi listic approaches should be realistic and practical in setting discharge standards. The SRRs of ammonium nitrogen were 0.64 (95%CI: 0.24–1.08), 1.21 (95%CI: 0.42–2.13), and 1.72 (95%CI: 0.65–3.18) g m 2 d 1 in the SFCWs fed with LS, MS, and HS wastewater (SLR: 0.65 (95%CI: 0.25–1.11), 1.31 (95%CI: 0.48–2.21), and 1.97 (95%CI: 0.76–3.45) g m 2 d 1), respectively. The 95%CI SRRs of ammonium nitrogen using the probabilistic approach were estimated to be 0.27–1.20, 0.55–2.16, and 0.66–3.23 g m 2 d 1 in the SFCWs for LS, MS, and HS wastewater, respectively. The results from the probabilistic and deterministic approach were consistent. Similarly, the removal efficiencies of nitrogen using the probabilistic approach were estimated to be 98.1% (95%CI: 86.7–99.9%), 92.2% (95%CI: 51.2–99.7%), and 87.4% (95%CI: 43.0–98.6%) in the SFCWs for LS, MS, and HS wastewater, respectively. The results were substantially similar to those calculated using the deterministic values, i.e., 97.9% (95% CI: 88.6%–99.9%), 92.8% (95% CI: 70.4%–99.9%), and 87.7% (95%CI: 70.6%–98.4%) for LS, MS, and HS wastewater, respectively. Seemingly, the removal performance of SFCWs planted with M. aquaticum was little disturbed by large fluctu ations of influent concentrations, probably indicating that the system can resist the wastewater with fluctuating pollutant concentrations. The wetland plant M. aquaticum could tolerate the wastewater with highconcentration ammonium nitrogen (>200 mg L 1) (Liu et al., 2018; Zhang et al., 2017), which may enhance the resistance of SFCWs to shock loading of nutrients. The probabilistic approach could adopt less data input, e.g., mean and standard deviation of pollutant concentration in the influent and effluent, to calculate the removal efficiency of CWs than the deterministic method, implicating the probabilistic approach could be used for predicting future removal performance. Vanderzalm et al. (2013) applied the probabilistic modelling approach to evaluate removal efficiency of nitrogen, phosphorus and organic carbon in an anoxic carbonate aquifer, and proposed that the method was suited for
(7)
The cD higher than the actual effluent concentration of SFCWs sug gests that the CW system is sufficient and reliable for wastewater treatment. If the cD is lower than the actual effluent concentration, the SFCWs need to be adjusted or improved. 3. Results and discussion 3.1. Probabilistic evaluation of ammonium nitrogen removal in the constructed wetlands Ammonium nitrogen concentrations and their cumulative probabil ity in the influent and effluent of the three-stage SFCWs treating LS, MS and HS wastewater are shown in Fig. 1. The ammonium nitrogen con centration in the SFCWs for LS, MS and HS wastewater was 108 (95% confidence interval (CI): 42.8–184), 218 (95%CI: 81.8–366), and 328 (95%CI: 131–572) mg L 1 in the influent, 2.1 (95%CI: 0.06–11.5), 16.9 (95%CI: 0.18–51.7), and 41.3 (95%CI: 4.1–129) mg L 1 in the effluent (Table S2). According to the cumulative probability of ammonium ni trogen concentration, 100%, 96.0%, and 85.5% of the total effluent concentrations in the three-stage SFCWs fed with LS, MS and HS wastewater could meet the wastewater discharge standard for livestock and poultry breeding in China (80 mg L 1; GB 18596-2001). Whereas the mean ammonium nitrogen concentration in the effluent (2.1–41.3 mg L 1) was much lower than the wastewater discharge standard (80 mg L 1; GB 18596-2001). Removal performance of the SFCWs may be overestimated using the arithmetic mean value of wastewater concen tration. The arithmetic mean value of ammonium nitrogen concentra tion was also higher than the median (Table S2). On the other hand, the ammonium nitrogen concentration was well fitted to the log-normal distribution (Fig. S2 and Table S3). The log-normal distribution of 3
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Fig. 2. The needed area estimation of surface-flow constructed wetlands for removal efficiency of 40–90% (a) and for different influent concentration at the removal efficiency of 90% (b).
loading rate of 0.18 m3 d 1 in the present study. The needed CW area was ranged from 6.6 (95%CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for removal efficiency of 40–90% (Fig. 2a and Table 1). The needed CW areas harbor different probabilities for various removal efficiencies, e.g., a lower removal efficiency required a higher CW area. The large range of the needed CW area for a specific removal efficiency may be ascribed to the highly fluctuating concentrations in the influent and seasonal ef fects. Jasper et al. (2014) found the needed CW area was about five times larger in winter than summer for municipal wastewater treatment due to the temperature variations among the different seasons. The SFCWs required an area premium to meet the treatment goals in winter due to the relatively low removal efficiency of SFCWs in cold climates (Kadlec, 2009). Therefore, application of the probabilistic approach in area estimation of CWs considered the seasonal variations and pollution concentration fluctuations, which will enhance the practicability of CWs in wastewater treatment. To estimate needed CW areas for treating wastewater with different strengths, the ammonium nitrogen concentrations in the influent were divided into four groups: 1–15, 15–80, 80–200 and >200 mg L 1 (Fig. S3). The ammonium nitrogen concentration of 1, 15, and 80 mg L 1 is the limit of Grade III environmental quality standards for surface water (GB 3838-2002), Grade IB discharge standard of pollutants for municipal wastewater treatment plant (GB 18918-2002), and the wastewater discharge standard for livestock and poultry breeding in China (GB 18596-2001), respectively. The ammonium nitrogen con centration of 200 mg L 1 was the highest concentration tolerated by most wetland plants (Zhang et al., 2017). Selecting the removal effi ciency of 90% for treating an inlet loading rate of 0.18 m3 d 1 as an example (Fig. 2b), the needed CW areas were 13.9 (95%CI: 4.9–42.7), 25.1 (95%CI: 5.9–66.0), 33.5 (95%CI: 13.5–87.1), and 40.8 (95%CI: 16.2–89.4) m2 for influent concentration of 1–15, 15–80, 80–200, and >200 mg L 1, respectively (Fig. 2b and Table 1). The differences of the needed areas showed the different area designs of CWs for various types of wastewater treatment, e.g., the less CWs area needed for the lower wastewater concentration. The needed CW areas were 13.9–40.8 m2 for inlet nitrogen loading rates of 0.18–60 g d 1 (Fig. S3). Therefore, the variability of the needed CW area is an important consideration in the selection of engineering parameters for wastewater treatment. Generally, the multiple-stage SFCWs are a robust system and produce stable effluent compared with the single-stage one (Luo et al., 2018a, 2018b). In the present study, a significant linear correlation between the SRR and SLR of SFCWs was observed, with the better linearity (R2) in the three-stage SFCWs than CW units (the single-stage CW) (Fig. 3). Perhaps span of ammonium nitrogen concentration in the influent of the CW
Table 1 Summary of the statistics for the design areas (m2) associated with several scenarios including different removal efficiencies and influent concentrations at a removal efficiency of 90% based on a probabilistic approach. SD represents standard deviation. Scenario
Mean
Median
SD
95% confidence interval Lower
Removal efficiency 40% 6.6 5.1 5.4 1.4 50% 9.0 6.9 7.3 1.9 60% 11.8 9.2 9.6 2.6 70% 15.6 12.0 12.6 3.4 80% 20.8 16.1 16.9 4.5 90% 29.7 23.0 24.2 6.4 Influent concentration (mg L¡1) for 90% removal efficiency 1–15 13.9 8.4 13.6 4.9 15–80 25.1 19.4 20.9 5.9 80–200 33.5 25.1 26.9 13.5 >200 40.8 32.6 26.0 16.2
Upper 17.8 24.1 31.9 41.9 56.0 80.1 42.7 66.0 87.1 89.4
operational assessment of the aquifer treatment performance. 3.2. Area estimation of constructed wetlands Due to space constraint and construction cost, area or size is one of the primary considerations for the application and design of CWs, especially for SFCWs (Kadlec and Wallace, 2008; Wu et al., 2015). For specific wastewater and CW types, the area of CWs determines their removal efficiency. To meet the discharge standards, different removal efficiencies are required for treating various types of wastewater. Generally, a higher removal efficiency is required for the stricter discharge standards (e.g., surface water) and the heavier wastewater loadings (e.g., swine wastewater). The three-stage SFCW units were selected to estimate the needed CWs area in the present study, because ammonium nitrogen concentrations in the SFCW units influent were ranged four orders of magnitude (Range: 0.11–753 mg L 1), which covered almost all types of wastewater in the agricultural watershed including swine wastewater, farmland drainage, and rural decentralized household drainage in China (Hu et al., 2017; Li et al., 2015; Wang et al., 2014; Zhao et al., 2012). Pollutant concentration in the effluent could be generally lower than the discharge standards of local government, when removal efficiency of wastewater treatment engineering was set in the range of 40–90%. Therefore, 40–90% decreases of concentration in the wastewater treat ment were selected to estimate the needed CW areas treating inlet 4
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units was larger than the overall situation. Similarly, a pervious study observed that the multiple-stage CW was robust compared to the single-stage CW for treating highly fluctuating wastewater (Langer graber et al., 2010). The multi-stage arrangement also provided addi tional security for effective operation of CWs (Harrington and McInnes, 2009). For the CWs treating swine wastewater in the present study, either the four-stage CWs will be sufficient or another treatment system should be added at the next unit when the discharge standard of local government or special environmental protection is stricter than the present condition. In other words, the SFCWs area needs optimization and adjustment for different wastewater loadings and wastewater treatment requirements. We suggested the constructed wetlands should be designed as multiple units for improvement of removal efficiency for wastewater. 3.3. Uncertainty of area estimation of constructed wetlands The needed CW area was estimated on the basis of the k value in the present study. As such, the uncertainties of the CW areas stemmed mainly from the variation of the k value (Fig. 4a). For different removal efficiencies, the needed CW areas were a function of the k value. It has been reported that the k value was CW area- or volume-specific, and also depended on the inlet concentration, flow rate, and wastewater quality parameter (Kadlec, 2000; Kadlec and Wallace, 2008). In the present
Fig. 3. Correlation between surface removal rate (SRR) and surface loading rate (SLR) of ammonium nitrogen in the surface-flow constructed wetland. (a) the CW units; (b) the three-stage SFCWs as a whole.
Fig. 4. The first-order removal constant (k) of ammonium nitrogen in surface-flow constructed wetlands treating lagoon-pretreated swine wastewater. (a) rela tionship between needed constructed wetland area and the k value for the removal efficiency of 40–90%; (b) probability distribution of the k value; (c) relationship between the k value and influent concentration/loading rate; (d) relationship between the k value and effluent concentration/loading rate. The abbreviation LND represents the log-normal distribution; CI represents confidence interval. 5
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3.4. Reliability evaluation of the constructed wetlands
Table 2 Coefficient of reliability, design, and actual effluent concentration of the threestage surface flow constructed wetlands treating low-strength (LS), mediumstrength (MS) and high-strength (HS) lagoon-pretreated swine wastewater. CW1, CW2, and CW3 indicate the first, second, and third unit of the surface flow constructed wetlands, respectively. The data on cofficient of reliability higher than 1.0 and design effluent concentration higher than acutual mean effluent concentration were highlighted. Reliability (1-α)
LS CW1
99% 98% 95% 90% 80% 70% 60% 50% 99% 98% 95% 90% 80% 70% 60% 50%
MS CW2
CW3
CW2
Coefficient of reliability 0.27 0.16 0.12 0.22 0.33 0.21 0.17 0.27 0.42 0.31 0.28 0.37 0.54 0.46 0.44 0.50 0.71 0.71 0.77 0.70 0.88 0.98 1.15 0.90 1.05 1.30 1.62 1.12 1.23 1.68 2.23 1.37 Design effluent concentration (mg L 1) 22 12.6 9.4 17.4 26 16.6 13.2 22 34 25 22 30 43 36 35 40 57 57 61 56 70 79 92 72 84 104 130 90 99 134 178 109 Actual mean effluent concentration (mg L 1) 40.8 10.8 2.1 45.6
Reliability refers to the percentage of time the expected effluent ��zwiakowski et al., 2017; values meet the pre-set discharge limits (Jo Oliveira and Von Sperling, 2008; Park et al., 2015). To assess the reli ability of the SFCWs for nitrogen removal, the coefficient of reliability was calculated for wastewater with different strengths. Herein, the varying reliabilities represent different technical usage scenarios. For example, the reliability was selected as 50–99%, meaning that the number of days that the CWs failed to meet the standards were 3.7–182 d yr 1 (Table S1). Only the effluent concentration of the SFCW unit lower than or comparable to the design effluent concentration was listed in Table 2. All COR values were higher than 1.0 at <60% level of reli ability, meaning that the effluent concentration was lower than the discharge standards during the period of 219 d per year. This could potentially stem from the log-normal distribution of ammonium nitro gen concentration (Oliveira and Von Sperling, 2008). When the COR values were higher than ~0.7, the effluent concentration in the majority of SFCW units could meet the discharge standards in the present study. The COR values were lower for higher reliability levels than lower reliability, indicating that the operation or design of CWs with low COR values needed to be improved. Based on the reliability analysis (Table 2), the present three-stage SFCWs needs to be adjusted to meet the wastewater discharge stan dard for livestock and poultry breeding in China (80 mg L 1; GB 185962001). To meet the wastewater discharge standard, only one and two stage CWs were adequate for the LS wastewater treatment when the reliability was set at 90% and 99%. Similarly, the three-stage CWs could meet the design requirement for the HS wastewater when the reliability was set lower than 90%. If the discharge standard was strict than the present condition, the fourth-stage CW would be required or another treatment system would be added at the next unit. These results were consistent with the above discussion based on the probabilistic approach. This COR approach confirms that it is essential to adopt multiple-stage CWs, i.e., larger CW areas, for various types of waste ��zwiakowski et al. water to meet the regulatory objectives. Similarly, Jo (2017) found that the one-stage horizontal subsurface flow CW over the operation period was insufficient to meet the Polish standards based on reliability analysis, and needed improvement. Our results also confirm that the probabilistic approach is appropriate for the performance evaluation of CWs, i.e., not all effluent concentrations were lower than that in the discharge standard. Based on the above findings, we confirm that the probabilistic approach should be adopted to estimate the needed CW area for wastewater treatment. Firstly, the needed CW areas are estimated by influent and effluent water quality using the probabilistic approach. Generally for the wastewater with greatly fluctuating concentration of pollutants, the estimated needed CW areas present a wide range. It may be overestimated or underestimated if the maximum, mediate or average values are selected. Then the reliability analysis is considered to determine a suitable CW area according to the actual requirements, e.g., discharge standards and seasonal variations of wastewater. Finally, the calculated CW area should be separated into several units, e.g., three to five stages, which is regard as a strategy to enhance the stability and sustainability of CWs. The detailed method for designing multiple-stage CWs needs to be studied in further research.
HS CW3
CW3
0.13 0.18 0.29 0.44 0.74 1.08 1.48 1.99
0.20 0.25 0.35 0.48 0.70 0.92 1.16 1.45
10.4 14.3 23 35 59 86 118 159
15.7 19.8 28 38 56 74 93 116
16.9
41.3
study, the k value of ammonium nitrogen was 30.9 (95%CI: 13.0–43.3), 22.7 (95%CI: 7.3–42.1), and 14.7 (95%CI: 7.3–24.7) mm d 1 in the three-stage SFCWs for LS, MS, and HS wastewater, respectively (Table S2), within the range of 11.2–96 mm d 1 reported by Kadlec and Wallace (2008). The k value was fitted to the log-normal distribution (Fig. 4b), suggesting that the CW area may be overestimated for low-strength wastewater but underestimated for high-strength waste water when the mean k value was selected. Park and Roesner (2012) also observed that the k value in a stormwater best management practice exhibited a log-normal distribution, which may induce the uncertainty of performance using median or mean values. The k value of ammonium nitrogen decreased logarithmically with increasing influent and effluent concentration/loading rate in the SFCW units (p < 0.001; Fig. 4c and d). Higher k values were observed in lower concentrations/loading rate, which was also found in the previous studies (Kadlec and Wallace, 2008; Luo et al., 2017). Previous studies have found that the k value of nitrogen correlated significantly with a number of parameters including temperature, dis solved oxygen, and other factors (Chen et al., 2015; Kadlec and Wallace, 2008). In the present study, however, we did not observe the significant correlation between the k values and water quality parameters (e.g., water temperature, dissolved oxygen, oxidation-reduction potential, and pH) of the SFCWs. The influent ammonium nitrogen concentration with relatively large fluctuation could neutralize the effects of water quality parameters on the k value. The large differences was derived from the removal capacity of SFCWs for different influent concentra tions. The k value is sensitive to ammonium nitrogen concentration of wastewater, and therefore the CW areas needed to be calibrated for the specific type of wastewater. The present study only discussed the CW size for wastewater treatment using the probabilistic approach. The preformation of CWs for wastewater treatment was also dependent on their locations. For example, Hansen et al. (2018) found that the loca tion and size of CWs were important for outlet water quality at the watershed scale. The cost of construction and operation should also be considered during the design of CWs. Further studies are needed to facilitate effective design of CWs at the watershed scale.
4. Conclusions The ammonium nitrogen removal performance of the SFCWs was assessed using the probabilistic approach. The removal performance of the SFCWs may be overestimated using the average values of the wastewater concentration, because ammonium nitrogen concentration was well fitted to the log-normal distribution. The needed CW area was estimated as 6.6 (95%CI: 1.4–17.8) to 29.7 (95% CI: 6.4–80.1) m2 for removal efficiencies from 40% to 90%. The k value of ammonium 6
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nitrogen was fitted to the log-normal distribution and decreased loga rithmically with increasing influent and effluent concentration, sug gesting the probabilistic approach is important for CW design. Reliability analysis confirmed the results from the probabilistic approach were appropriate and identified methods of improvement of CWs.
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