The potential of carbon nanorods (NRs) derived from garbage leachate was assessed in a fixed -bed and batch system replacing the present costly approaches to treat surface water polluted by 4 -Nonylphenol. First, the waste leachate was coated on silica particles and then pyrolysis was performed at 300 to 800 ℃. The optimum temperature for the production of nano -adsorbent was determined by an alyses such as scanning electron microscopy (SEM) for adsorbent morphological characterization, X -ray energy dispersive spectrometry (EDS), and Raman test. Next, Freundlich, Langmuir, and UT isotherm models were used to analyze the batch equilibrium data. The impact of inlet 4 -NP concentration (0.5, 1, and 2 mgL-1), feed flow rate (5, 10, and 20 mLmin-1), bed height (5, 10, and 15 cm), and bottom -up flow on the breakthrough features of the sorption system with a fixed bed was examined. The adsorption data w ere fitted using the well -known column models (i.e., Thomas, Yoon -Nelson, and Adams -Bohart). For all circumstances, the best fitting was obtained for the attained results using Yoon -Nelson and Thomas models, but not the Adams -Bohart model. The experimental data confirmed that increasing the inlet concentration and bed depth resulted in an increase in the adsorption capacity and a decrease in flow rate. After applying a different regeneration process, the results showed the effectiveness of the novel enginee red substance as a reusable adsorbent for water treatment. Keywords : Fixed -bed; 4 -Nonylphenol; Removal; Garbage leachate; Sand particles; Carbon nano -adsorbent 1. Introduction Water is a vital resource for human beings’ life and its quality plays a key role in human health (Guimarães et al., 2018; Zhang et al., 2019; Zhao et al., 2020). Today, water resource has posed a rising concern in the view of socio -economic development (A raujo, Bauerfeldt, & Cid, 2017). Climate change projections have indicated that changing climate affects each component of the climate system with regional differences (Mosadegh, 2013; Mosadegh & Nolin, 2020; IPCC, 2021; Mosadegh et al., 2021). Projected c limate change shows changes in patterns of climate variables such as temperature and precipitation which will consequently affect water resources (Mosadegh et al., 2013; Mosadegh & Babaeian, 2021, Mosadegh & Babaeian, 2021). The primary use of this resourc e or the public supply is compromised by a lack of sufficient basic sanitation (Atlantic & Dias, 2008; Vianna, 2015). Recently, the existence of some pollutants has attracted the attention of environmental experts particularly in aquatic settings (Regadío et al., 2012; Mosadegh et al., 2018; Mejia et al., 2019) . The chemicals including pharmaceuticals and personal care products (PPCPs), endocrine -disrupting chemicals (EDCs), and persistent organic pollutants (POPs) are extensively utilized in different indu stries (Xia, Keller, & Wagner, 2001). The sludge and effluent of industrial and municipal wastewater treatment plants are the entry routes of emerging pollutants to water resources (Geissen et al., 2015). The search for more sources and further expensive and complex chemical treatments for ensuring accessibility of drinking water are other challenges in this regard (Araujo et al., 2017). Current studies have indicated that 4 -NP is a robust estrogenic isomer probably entering the aquatic environment via the treated sewage efﬂuent discharge. Therefore, NP is regularly discovered in the ultimate sewage treatment plant efﬂuents owing to the persistence against complete biodegradation and lack of innovative wastewater(Bouki, C.; Dvorakova & Dvorakova, 2010). Adso rption has been a key option as a result of the exclusive physicochemical features of NP like a high log KOC value and low water solubility (Derco, Dudáš, Valičková, Sumegová, & Murínová, 2017; Ejraei, Ali, & Ziarati, 2019). The presence of NP was reported by Johnson et al.(Shin, Kim, & Park, 2019) in Europe for the efﬂuent of 17 municipal WWTPs with concentrations within the range of 0.05 -1.31 mg/L. Utilizing radiolabeled NP, Cirja et al. (Ejraei et al., 2019) indicated that complete volatilization and min eralization of NP had a role in removing less than 1% of NP from wastewater treated in a membrane bioreactor. The combined impact of bio -oxidation and sludge adsorption is the key removal mechanism for intermediate products. Nonetheless, some reports have indicated the low adsorption capacities of these materials and di ﬃculties or e ﬃciencies in their removal or regeneration(Abbas et al., 2016; Asmaly et al., 2015). Accordingly, it has been attempted to develop a novel adsorbent substance with easy regenerat ion and greater adsorption capacity for wastewater e ﬀective treatment. In this regard, there are some studies on CNTs and their composites, as well as the impact of operating parameters (Ahmad et al., 2017; Rajabi, Mahanpoor, & Moradi, 2017; Shouman & Fath y, 2018). Searching for a cost -effective and novel substance for water treatment is a persistent challenge worldwide. Carbon -based nanostructured materials (CNMs) are among the most versatile and robust contestants for eﬀective water treatment (Selvaraj, Hai, Banat, & Abu, 2020). Today, other carbon structures are produced by the developments in novel technology like nano -adsorbents (Jin, Wang, Sun, Ai, & Wang, 2015). Despite the decent potential of nano -adsorbents in remo ving MP, they have somewhat problematic applications in the slurry state due to their essential removal in the ultimate treatment. The problems at the slurry state are solved by coating silica sand particles with nano -adsorbent. In this regard, the benefic ial properties of silica grains have been enhanced for example in the creation of novel adsorbent NRs. This knowledge is an effective stage in silica grain improvement, although it has still a long way to practical use (Jin et al., 2015). Nanomaterials are extensively utilized as an adsorbent for removing pollutants from water/wastewater as a result of their exclusive features of high surface area to volume ratio. Thus, they are an appropriate candidate for developing future technology in water/wastewater t reatment(Jawed, Saxena, & Pandey, 2020). The composition of garbage leachate is affected by numerous factors such as the type of deposited waste, the technique to exploit the landfill, the availability of oxygen, and the hydrogeological circumstances, and the landfill age(Yang et al., 2016). This issue has increased environmental and health risks (Toro -Vélez et al., 2016). In the present study, the behavior of carbon nanorods (NRs) made by garbage leachate is investigated for adsorption of 4 -NP bearing surf ace water in batch and fixed -bed sorption systems. This adsorbent was synthesized and coated on the sand particles at the same time. The garbage leachate was dried on the surface of the acid -washed sand particles and then pyrolyzed at different temperature s. In the next step, the optimum temperature for nano -adsorbent fabrication was selected, and experiments were performed to remove 4 -NP at different concentrations with different doses of the adsorbent. Column dynamics were assessed using Yoon - Nelson, Thom as, and Adams -Bohart models. Recycling and column regeneration were performed until 7 cycles taking into account the prepared sorbent’s industrial applicability. 2. Methods 2.1. Preparing nano -adsorbent To prepare the nano -sorbent, 500 g sand was acid -washed wit h 500 mL of diluted hydrochloric acid (1:1) and heated for 3 hours. Next, it was rinsed with distilled water several times. The acid -washed sand particles were poured into the beaker containing 500 mL of the leachate. Then they were heated so that the leac hate evaporated and the sand particles were covered with the leachate constituents. Afterward, they were dried in an oven at 100 ℃ for 2 h. In the next step, they were placed in a furnace reaching 700 ℃ for 40 min and keeping at 700 ℃ for 1 h. The particles w ere rinsed with distilled water several times after cooling. Since the temperature can be an effective factor in the formation of carbon nanorods, the pyrolysis was performed at different temperatures ranging from 300 to 800 ℃. 2.2. Characterization Scann ing electron microscopy (SEM, Raman spectroscopy, and X -ray energy dispersive spectroscopy (EDS) were performed to characterize the chemical and physical features of the produced nano -adsorbent. 2.3. Reagents The solvents, reagents, and other chemicals u tilized in this work were bought from Merck, Germany. The concentration of the materials applied in the tests was obtained using the stock solution. 2.4. Determination of 4 -NP To determine the residual concentration of 4 -NP, an HPLC (Agilent 1100) equipp ed with a C18 column (4.6 × 250 mm, 5 µm) and a UV detector was used in an isocratic elution with the constant composition of the mobile phase. The mobile phase included an acetonitrile -water mixture (80:20) with a flow rate of 1 mL/min. The samples were f iltered before injection into the column using PTEF syringe filters (0.2 µm). 2.5. Batch adsorption investigation The bath tests were performed by the addition of 0.1g of NRs) in 100 mL of 4 -NP solution (0.5, 1, and 2 mgL- 1) and shaking at 120 rpm until reaching equilibrium. The concentration of the remaining cations was studied followed by a set of time intervals until reaching equilibrium via an atomic absorption spectrophotometer (PerkinElmer Model 3100). At the equilibrium state, the quantity of adsor bed 4 -NP, q e (mg/g), was determined through Eq. (1): 𝑞𝑒 =(𝐶0 − 𝐶𝑒 ) 𝑊 (1) Where q e (mgg-1) is the quantity of 4 -NP adsorbed at equilibrium. C e and C 0 (mgL-1) represent the liquid -phase concentrations of 4 -NP at equilibrium and initial circumstances, respectively. V(L) shows the solution volume and W(g) represents the mass of applied NRs. To determine the removal efficacy of the 4 -NP, the residual 4 -NP concent ration was divided followed by equilibrium through primary 4 -NP concentration, and the outcome is determined on a percentage basis. 2.6. Column data analysis Dynamic column investigations were performed within a glass column with the height and internal diameter of 30 cm and 2.54 cm, respectively. The preferred flow rate was maintained using a peristaltic pump. To avoid any loss of adsorbent and give mechanical support to the adsorbent bed, a ball of 0.05 -cm thick glass wool was set on the bottom side. Th e entire test were performed at room temperature (26 ± 2 ℃). The procedure parameters’ effects including flow rate (5, 10, and 20 mLmin-1), bed depth (5, 10, and 15 cm), and concentration (0.5, 1, and 2 mgL-1) were assessed. The specimens were gathered ever y 5 min from the bottom of the column and were examined for the 4 -NP concentration. The column behavior was studied by determining the adsorption capacity and breakthrough time. The adsorption capacity at the breakthrough of 10% was determined as(Biswas & Mishra, 2015): 𝑞𝐵=(𝑥 𝑚) 𝐵=𝑥𝐵 𝑚𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡=𝑄𝑣 (𝐶0−𝐶𝐵 2)𝑡𝐵 𝑚𝑎𝑑𝑠𝑜𝑟𝑏𝑒𝑛𝑡 (2) Where C 0 is the influent concentration (mgL-1), 𝑥𝐵 shows the mass of adsorbed in the column at breakthrough (mg), 𝑚adsorbent represents the mass of the adsorbent in the column (g), Q V is the flow rate (mLmin-1), C B is the breakthrough concentration (mgL-1), and 𝑡𝐵 shows the time to breakthrough (min). 3. Results 3.1. The adsorbent features SEM tests were performed fo r morphological detection of possible changes in sand -covered with waste garbage leachate and sand coated with carbon nanoparticles. According to the SEM images ( Fig. 1c and 1 h), no specific changes were observed in the raw and acid -washed sand particles, but in the sand coated with carbon nanoparticles, short nanorods, and a considerable alteration in the structural integrity of the adsorbent was observed. As can be seen in Fig.1 , the nanoparticles, which were planar, formed at 400 ℃. However, they tend to be rod -shaped with increasing pyrolysis temperature. Fig.1. (a) Sand particles covered with waste garbage leachate and (b) silica -coated with Nanoparticles (NRs); SEM images of nano -adsorbent at (c) 300 ℃, (d) 400 ℃, (e) 500 ℃, (f) 600 ℃, (g) 700 ℃, and (h) 800 ℃ Raman spectroscopy was applied to study chemical and intramolecular bonds(Goldstein & All, 2003). EDS is an analytical method for the chemical characterization or elemental analysis of a specimen. This technique works based on the interaction of a sample a nd some X -ray excitation sources. This method relies on the fundamental principle where each element possesses an exclusive atomic structure, permitting an exclusive set of peaks on its electromagnetic emission spectrum(Asmaly et al., 2015). Fig. 2a shows the results of the Raman test. As can be seen, sand particles were covered with garbage leachate, leading to different peaks that may be due to different elements existing in the garbage leachate and their coverage at the sand surface. Moreover, the peaks showed the formation of NRs on the sand particles’ surface. Fig. 2b illustrates the EDS results of the adsorbents produced in this study. As can be noticed, due to the leaching of sand on the sand, several elements deposit on the sand surface. Fig.2. Silica -coated with NRs : (a) Raman test, ( b) EDS test 3.2. Batch adsorption study Freundlich and Langmuir’s isotherms are the models commonly applied for modeling heterogeneous and homogenous surfaces, respectively. According to the Langmuir model, the adsorption capacity is correlated with the initial concentration of the adsorbent. Furthermore, the exponential distribution and energies of the active sites and the expression related to surface heterogeneity are represented through the Freundlich is otherm(Ayawei, Ebelegi, & Wankasi, 2017). To examine batch equilibrium data, the linear form of the Langmuir isotherm equation was used as: 𝐶𝑒 𝑞𝑒=1 𝐾𝐿𝑞𝑚+𝐶𝑒 𝑞𝑚 (1) Where q max (mgg-1) shows the maximum quantity of the 4 -NP per unit weight of the NRs to create a complete monolayer over the surface. Also, 𝐾L(Lmg-1) represents the Langmuir constant associated with the a ﬃnity of binding sites. 𝑅𝐿=1 1+𝐾𝐿𝐶0 (5) The essential features of the Langmuir equation are described based on a separation factor. Here, 𝑅𝐿 is obtained as follows. The linear form Freu ndlich isotherm is: 𝑙𝑛𝑞𝑒=𝑙𝑛𝐾𝐹+1 𝑛𝑙𝑛𝐶𝑒 (6) Where 𝐾(mgg-1) denotes the multilayer adsorption capacity or a ﬃnity factor, and 1/ 𝑛 represents the adsorption intensity, respectively. UT isotherm model (Amini et al., 2008)is a dose -dependent model capable of estimating the highest adsorption capacity of adsorbent with the heterogeneous surface. There is a relation between the adsorption capacity obt ained utilizing the UT model and multi -layer adsorption. Here, the adsorption capacity attained from the Langmuir model is related to the monolayer adsorption over the adsorbent’s surface. Table 1 represents the errors and parameters of the three isotherm models for 4 -NP adsorption. Based on the R2 and error values, the Langmuir and Redlich -Peterson models were the most proper model to explain the 4 -NP adsorption procedure. As previously mentioned, RL denotes adsorption nature. For 4 -NP, the quantity of R L was within the range of 0.72 -0.98 (0 < R L<1), indicating satisfactory adsorption. Table 1. The isotherm parameters were obtained utilizing error values and linear regression for adsorbing 4 -NP o n the adsorbent Isotherm Equation R2 RMSE χ2 Paramete r Value Freundlich 1ln ln lne F eFq K Cn=+ 0.9995 0.0183 0.0020 nF 1.055 KF:mgg1) (L.mg)1/n 1.057 Langmuir 1 ee e L m mCC q K q q=+ 0.9999 5.22×1 0-5 2.11×1 0-8 qm: mgL-1 6.447 KL: L mg-1 0.1887 01 1L LRKC=+ RL 0.72 -0.98 UT Isotherm ln( ) ln lnedme adm eCKb C b CC C− =− + 0.9808 0.0046 0.0001 qm: mg g-1 1.4523 Kd: mg L 0.0085 b 0.1012 FCF 0.0059 3.3. Column data analysis 3.3.1. The impact of 4 -NP inlet concentration The impact of 4 -NP concentration on the column behavior was investigated by altering the inlet concentration ( 0.5, 1, and 2 mgL-1) when using the feed flow rate of 20 mLmin-1 and the same adsorbent bed height of 5 cm. Fig. 3 represents the breakthrough cu rve where the NRs beds were fatigued sooner at a higher 4-NP inlet concentration of 2 mgL-1. Hence, the former breakthrough point was obtained at higher concentrations. It was found that the breakpoint time was reduced by increasing 4 -NP inlet concentratio n because of saturating the binding sites more rapidly in the column. A prolonged breakthrough curve was attained by a reduced inlet concentration, representing the possibility to treat a higher volume of solution. The lower concentration gradient resulted in slower transport because of a reduction in mass transfer coe ﬃcient or diﬀusion coe ﬃcient(Biswas & Mishra, 2015). Fig. 3. The preliminary concentration versus breakthrough (flow rates 20 mLmin-1; bed depth 5cm) 3.3.2. The impacts of NRs bed height Fig. 4 represents the breakthrough curve achieved for adsorbing 4 -NP over NRs for 3 di ﬀerent bed height of 5, 10, and 15 cm (38.49, 76.98, and 115.47 g of NRs) at constant 4 -NP inlet concentration of 2 mgL-1 and 4-NP feed flow rate of 20 mLmin-1. As can be seen from Fig. 4 , the exhaustion time ( 𝑡𝑒) and breakthrough time ( 𝑡b) increased by increasing the bed height. The plots show that by varying the bed depth, the gradient and shape of the breakthrough curves slightly vary. At higher bed height, a larger uptake was found owing to the increased quantity of the NRs providing fu rther fixations of the cations by active binding sites. The mass transfer zone is augmented by increasing bed height. In a column, the mass transfer zone travels from the bed entrance toward the exit. Therefore, for the fixed bed system and similar influen t concentration, the increased bed height would make the long -distance for moving the mass transfer zone toward the exit leading to a prolonged breakthrough time. For larger bed depths, the increased adsorbent mass offers a greater service area increasing the volume of the treated solution(Ramesh, Gandhimathi, Nidheesh, Badabhagni, & Bharathi, n.d.). Fig. 4. The impact of bed depth on the breakthrough curve (flow rate: 20 mLmin-1 and concentration: 2mgL-1) 3.3.3. The impact of feed flow rate The impa ct of feed flow rate on adsorbing 4 -NP over NRs was studied by changing the feed flow rate (5, 10, and 20 mLmin-1) with the 4 -NP concentration of 2 mgL-1 and a constant adsorbent bed height of 5 cm ( Fig. 5) . According to the breakthrough curve, at relative ly high flow rates, the adsorption zone rapidly reached the top of the column, indicating the quick saturation of the column. The lower flow rate led to higher contact time and a shallow adsorption zone. Less adsorption uptake was obtained at a higher flow rate, by a steeper curve with comparatively less exhaustion time and early breakthrough. Fig. 5. The impact of flow rate on the breakthrough curve (concentration: 2mgL-1 and bed depth: 5 cm) 3.3.4. Column dynamics study The 4 -NP’s sorption behavior in the column was analyzed using Adams -Bohart, Thomas, and Yoon -Nelson models. The initial parameters of the models are concentration ratio, 𝐶𝑡/𝐶0>0.1 as a 10% breakthrough until 𝐶𝑡/𝐶0>0.90, and 90% breakthrough for 4 -NP through a column’s operation limit of mass transfer zone(Lin, Huang, Qi, Xiong, & Huang, 2017; Rafati, Ehrampoush, Rafati, Mokhtari, & Mahvi, 2019). 3.3.4.1. Thomas model Thomas’s model is based on the Langmuir kinetic s of adsorption -desorption without any axial dispersion. It describes that the rate -driving force follows the second -order reversible reaction kinetics(Biswas & Mishra, 2015; Charumathi & Das, 2012). The model is linearized as: 𝑙𝑛𝑙𝑛 (𝐶0 𝐶𝑡−1) =𝑘𝑇ℎ𝑞0𝑚 𝑄−𝑘𝑇ℎ𝐶0𝑡 (1) Where 𝑘Th represents the Thomas kinetic coefficient (mLmin-1mg-1), 𝑡 shows the overall flow time (min), and 𝑄 indicates the volumetric flow rate (mLmin-1). The mass of the adsorbent and adsorption capacity is represented as (g) and 𝑞0 (mgg-1), respectively. The plot of ln[( 𝐶0/𝐶𝑡) −1] versus 𝑡 provides the value of 𝑞0 and 𝑘Th represented in Table 2. The obtained values indicate the increase in 𝑞0 values by increasing the influent concentration; however, by incrementing the bed depth, the 𝑞0 values decline. Conversely, 𝑘Th reduces by increasing the concentration and flow rate while in increases by i ncreasing the bed depth. 𝑅2 value is within the range of 0.85 -0.98, indicating the acceptable fitting between the model and the empirical breakthrough curve. 3.3.4.2. Yoon -Nelson Model The breakthrough performance of 4 -NP on NRs was investigated using a simple theoretical model provided by Yoon -Nelson. The model hypothesizes that the reduction rate in the adsorption probability for each adsorbate molecule is proportionate to the adsorbat e adsorption probability and the adsorbate breakthrough probability on the adsorbent(Biswas & Mishra, 2015). The model is linearized for a single -component system as: 𝑙𝑛(𝐶𝑡 𝐶0−𝐶𝑡 )=𝑘𝑌𝑁𝑡− 𝜏𝑘𝑌𝑁 (3) Where 𝜏 is the tim e needed for 50% adsorbate breakthrough (min), 𝑘YN represents the rate constant (min−1), and 𝑡 shows the sampling time (min). The values of 𝑘YN and 𝜏 are obtained by drawing the plot of the graph between ln ( 𝐶𝑡/ (𝐶0 −𝐶 𝑡)) and 𝑡 (Table 4). It was found that the values of k YN reduced by decreasing the bed height while the equivalent values of 𝜏 increased by the higher bed height; 𝜏 and 𝐾YN declined by increasing the primary cation concentration; and k YN increased but 𝜏 reduce d by increasing the flow rate. For column mode sorption, there was a similar trend for the sorption of 4 -NP. Table 2 represents the 𝜏 and 𝐾YN values and other statistical parameters. 3.3.4.3. Adams -Bohart model This model is based on the surface reactio n theory assuming an instantaneous equilibrium. Hence, the adsorption rate was proportionate to the NRs’ residual capacity and the 4 -NP concentration. The model can be mathematically expressed as Eq. (2)(Song et al., 2015): 𝑙𝑛(𝐶𝑡 𝑐0)=𝑘𝐴𝐵 𝐶0𝑡−𝑘𝐴𝐵 𝑁0(𝑍 𝑈0) (2) Where 𝐶0 and 𝐶𝑡 denote influent and effluent concentrations (mg L-1), respectively. 𝑁0 represents the saturation concentration (mg L-1), 𝑘AB shows the kinetic constant (L mg-1min-1), 𝑡 is the flow time (min), 𝑈0 shows the superficial velocity (cm min-1), and 𝑍 is the bed depth of the fixed -bed column (cm). To obtain the values of 𝑅2, 𝑘AB, and 𝑁0, the plot of ln( 𝐶𝑡/𝐶0) versus 𝑡 is use d. Table 2 represents the values of 𝑘AB, 𝑅2, and 𝑁0. This method estimates the features like the mass transfer coe ﬃcient (k AB) and the highest adsorption capacity ( 𝑁0). The linear plots of ln ( 𝐶𝑡/𝐶0) were drawn against time 𝑡 at various flow rates, initial cation concentrations, and bed heights (figures are not presented). The intercept and slope of the linear curves give the saturation concentration ( 𝑁0) and mass transfer coe ﬃcient (k AB), respecti vely ( Table 2 ). Meanwhile, a comprehensive and simple method was obtained by the Adams -Bohart model to evaluate column dynamics with the validity restricted to the used condition range. Therefore, the less applicability of this model is reflected by the po or correlation coe ﬃcient(Asmaly et al., 2015). Table 2 shows the experimental uptake capacity and mass transfer coe ﬃcient alongside the 𝑁0 and k AB and other statistical parameters. In this table, the mass transfer coeﬃcient increases by incrementing the f low rate and bed height while it declines by the primary concentration. This indicated that the external mass transfer dominates the total system kinetics. Nevertheless, the sorption capacity 𝑁0 increased by the increased primary concentration, flow rate, and bed height(Chowdhury, Zain, Rashid, Ra, & Khalid, 2013). By the incremented flow rate and concentration, 𝑘AB values were reduced, however, the increased bed depth raised them. The performance of the adsorption column at various flow rates was studie d. By reducing the flow rate from 5 to 20 mLmin-1, the exhaustion time and constant inlet breakthrough time were increased for 4 -NP adsorption onto NRs ( Fig. 5 ). This result is consistent with the results reported by other researchers(Mondal, 2009). The contact time of the adsorbate with adsorbent was reduced by an increase in flow rate, leading to a reduction in the service time of the bed and adsorption capacity. The breakthrough curves were attained at various bed depths (5, 10, and 15 cm) with a constan t influent concentration of 2 mgL-1 and a flow rate of 20 mLmin-1 (Fig. 4) . It can be seen that the exhaustion time and breakthrough time incremented by the increase in bed depth. Based on Table 2, by increasing the bed depth, the adsorption capacity is also increased at a 10% breakthrough, which is in line with the results repo rted in(Mondal, 2009). No sufficient time exists for 4 -NP at the lowest bed depth for diffusing into the NRs holes. The concentration of 4 -NP significantly affects the breakthrough curve ( Fig. 5) . Based on the curves, it is demonstrated that by increasing the primary 4 -NP concentration, the exhaustion time and breakthrough time are declined. Based on the empirical results in Table 2, by increasing the concentration from 0.5 to 2 mgL-1, the adsorption capacity increased. The outcomes are consistent with thos e of other researchers(Naveen & Malik, 2019). 3.3.4.4. Error function analysis The most effective optimization technique is the error function analysis to investigate the best -fitted model for the investigational data. The studied models were evaluated b y applying two common error functions: χ2 and RMSE (Baghdadi, 2017): ()2 11n exp caliiRMSE Y Ynp==−− (7) ()2 nexp cal 2 i1 calYY Y =−= (8) where p shows the number of parameters, n represents the experimental data number, Y cal shows the calculated data, and Y exp is the experimental outcomes(Jafari, Rahimi, Ghaedi, Javadian, & Asfaram, 2017). Table 4 presents the error function results indicating the Thomas model as the best -fitted model for the current adsorption system. 3.4. Regenerating the adsorbent Regeneration is a very imperative parameter specifying the cost -effectiveness of an adsorbent(Qu, Alvarez, & Li, 2013). Due to the high cost of the nano -adsorbent, the desorption of 4 -NP from the NRs surface is a key step. To regenerate the adsorbent, 5 g r of adsorbent was added to 100 mL of 4 -NP solution (2 mgL-1) and agitated for 20 min by a magnetic stirrer and the residue of 4 -NP in solution was determined by HPLC. Then the adsorbent was washed with methanol to remove the adsorbates from the surface o f the adsorbent, and they were used again. After the replication of this procedure up to 7 times, the value of q e decreased from 0.32 to 0.16 mgg-1 (Fig.7 .). This reduction could be owing to the incomplete desorption of the 4 -NP from the NRs surface. Base d on a previous study(Su & Lu, 2007), “it is anticipated that the unit cost of the nano -adsorbent can decrease more in the future. Hence, nano adsorbents can be possibly profitable adsorbents. Fig. 7. Regeneration and removal efficiency of 4 -NP adsorpt ion 4. Conclusions This paper concludes that removing 4 -NP in a packed -bed system utilizing NRs is a feasible and effective technique. The nano -adsorbent synthesized in this research was produced through the pyrolysis of the waste leachate film on silicon at 700 ℃. The column performance and the 4 -NP adsorption capacity were highly affected by influent concentration, flow rate, and bed depth. The breakthrough time prolongs by a greater bed depth, a lower influent concentration, and a lower flow rate. The adsorption capacities were high at the flow rate of 15 mL.min-1, the influent concentration of 30 mg L-1, and a bed depth of 7 cm. The column adsorption process is associated with the highest adsorption capacity of 38.56 mg g-1. The break was predicted through curves plotted us ing the Adams -Bohart, Yoon -Nelson, and Thomas models. Nevertheless, the Thomas model predicted the total breakthrough curve best. This assessment showed that the obtained NRs from garbage leachate were promising materials for removing 4 -NP from contaminate d water using a fixed -bed sorption column. At a constant concentration, larger bed height, and a lower feed flow rate, the column provides better performance. The empirical data obeyed the Langmuir isotherm. Furthermore, the best consistency was between th e experimental column data and Yoon -Nelson and Thomas models. The adsorbed 4 -NP was desorbed using methanol and the adsorbent was frequently utilized without considerable sorption capacity loss, reflecting the feasibility for commercial use. Abbreviations : MPs : Micropollutants BPA : Bisphenol A 4-OP : 4-Octylphenol 4-NP : 4-Nonylphenol NPs : Nonylphenols NOM : Natural organic material NRs : Carbon nanorods UT : University of Tehran Acknowledgments We appreciate the supports of the Nanotechnology Research Center of the Faculty of Environment, the University of Tehran. Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.