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GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 2, 137–147 https://doi.org/10.1080/24749508.2018.1456851 INWASCON OPEN ACCESS Application of rotary in-vessel composting and analytical hierarchy process for the selection of a suitable combination of flower waste Dayanand Sharma and Kunwar D. Yadav civil engineering d epartment, s V national Institute of Technology, surat, India ABSTRACT ARTICLE HISTORY Received 5 January 2018 The flower waste generated from different sources is either mixed with municipal solid waste a ccepted 21 March 2018 or thrown into the river in India. Flower waste is rich in organic contents and can be converted into nutrient-enriched compost. The aim of the present study was to determine the changes KEYWORDS in physico-chemical and biological changes during the composting of flower waste by using Rotary drum composting; rotary drum technique. For composting the flower, waste was mixed with cow dung, sawdust, flower waste; c:n ratio; and wheat bran. Four different trials were performed, in which 0.5 wt% of sawdust and wheat germination index bran was added in each trial. From the series of trials 1–4, the different proportions of flower waste and cow dung were 5:4, 6:3, 7:2 and 8:1, respectively. Finally, the compost produced by all the trials were found to have pH 7.23–7.51, electrical conductivity 5.5–6.12 mS/cm, reduction in the percentage of total organic carbon 22–33%, the percentage increase in total nitrogen 2.17– 2.66%, C:N ratio 13–17, sodium 2.14–2.60 g/kg and calcium 13.35–15.58 g/kg. The analytical hierarchy process was used for the ranking of the trials to find the best proportions from the different combinations performed in this study. 1. Introduction option for disposal. The compost obtained from flower waste is an alternate for the replacement of chemical e di Th sposal of solid waste is a big challenge both in fertilizers. Chemical fertilizers decrease the fertility of the rural and the urban areas. The waste generation in soils whereas compost will increase the organic content India is 400–600 gram/capita/day (Elango, Thinakaran, of the soil. The compost also increases the water holding Panneerselvam, & Sivanesan, 2009). Due to the huge capacity of the soil and reduces soil erosion (Elango production of waste, the availability of land for landfill- et al., 2009). ing is increasing. Landfilling requires a large area of land Composting is an aerobic process under the thermo- which is very costly and that land can be used for other philic condition for transforming organic matter into purposes. The municipal waste generated contains 70% nutrient-enriched compost. Aeration and moisture con- of organic waste which are the vegetable waste, flower tent play an important role in maintaining the thermo- waste, fruit waste, etc. (Rashad, Saleh, & Moselhy, 2010). philic condition. Aeration is also important to maintain Most of the municipal waste used for landfilling pro- the process of aerobic condition. Flower waste contains duces leachate and pollute the groundwater of the sur- 75–83% moisture which produces leachate and gives rounding area. Landfilling causes air pollution and is a out an unpleasant smell if not managed properly. The threat to the environment. moisture content is very important for the composting The setting for present study happened to be Surat, process to provide proper aeration, increasing the rate one of the fastest growing cities of India in the state of microbial activity and the free air space (Jolanun & Gujarat which is located on the banks of river Tapti. Towprayoon, 2010; Sadaka & El–Taweel, 2003). The The city is situated at 72.83° east longitude and 21.17° moisture content and proper aeration can be con- north latitude. Surat alone generates solid waste of trolled by the addition of the bulking agent. Numerous 1700 ton/day. Among the wastes, the generation of researchers had used various types of bulking agents flower waste is 1500 kg/day (Sharma, Varma, Yadav, & such as leaves (Elwell, Keener, Hoitink, Hansen, & Kalamdhad, 2017). The flower waste contains organic Ho,ff 1994), biochar (Dias, Silva, Higashikawa, Roig, & materials which are easily degradable and are a good Sánchez-Monedero, 2010), and sawdust (Iqbal, Shafiq, source of macro and micronutrients. Composting is the & Ahmed, 2010) to maintain proper moisture content, best option for degradation of the flower waste. The free airspace, and aeration. conversion of organic waste available into compost is an CONTACT dayanand sharma firstname.lastname@example.org © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 138 D. SHARMA AND K. D. YADAV Table 1. Initial physico-chemical characteristics and composi- Various methods are available for composting pro- tion of feedstock material. cess. Rotary drum is an efficient and decentralized com - posting technique. It helps in proper mixing, aeration Flowers Wheat Parameters waste Cow dung Saw dust bran and produces stable and matured compost. e Th rotary Mc (%) 80.05 ± 0.85 79.85 ± 2.5 15.84 ± 0.75 7.55 ± 1.25 drum can easily be installed on the site of the organic pH 5.18 ± 0.02 7.2 ± 0.02 5.91 ± 0.1 5.48 ± 0.03 −1 waste generation. Kalamdhad and Kazmi (2008) used ec (ms cm ) 4.40 ± 0.04 3.3 ± 0.01 0.93 ± 0.1 2.14 ± 0.03 Toc (%) 44.78 ± 1.67 32.11 ± 1.67 53.87 ± 1.02 53.44 ± 2.4 the capacity of the 250-litre rotary drum for the com- Tn (%) 2.03 ± 0.07 1.4 ± 0.14 0.63 ± 0.07 2.42 ± 0.1 posting of cow dung, mixed vegetable waste, sawdust, nH –n (%) 1.79 ± 0.04 0.34 ± 0.03 0.22 ± 0.05 1.24 ± 0.06 c:n ratio 22.06 ± 0.34 22.94 ± 0.40 85.51 ± 2.19 22.08 ± 1.98 and compost as bulking agents. Fernández, Sánchez- −1 Tp (g kg ) 3.18 ± 0.01 2.794 ± 0.03 1.144 ± 0.04 4.217 ± 0.02 Arias, Rodríguez, and Villaseñor (2010) used the closed −1 na (g kg ) 0.85 ± 0.07 2.54 ± 0.03 0.62± 0.05 0.47 ± 0.08 −1 K (g kg ) 17.3 ± 0.49 10.03 ± 0.21 1.21 ± 0.14 4.86 ± 0.32 rotary drum reactor with forced aeration of capacity −1 c a (g kg ) 6.43 ± 0.82 9.01 ± 0.53 2.60 ± 0.69 1.15 ± 0.82 100-litre using sewage sludge, olive mill waste, and win- −1 c u (mg kg ) 36.17 ± 0.25 48.52 ± 0.18 18.32 ± 0.14 16.50 ± 0.24 −1 ery waste. The authors concluded that composting was Zn (mg kg ) 129 ± 1.24 178 ± 1.20 6 ± 1.27 110.5 ± 1.34 −1 Mn (mg kg ) 98 ± 0.91 258.5 ± 1.25 55 ± 1.28 103 ± 1.04 successful when sewage sludge used as the bulking agent. −1 Fe (g kg ) 1.76 ± 0.47 1.98 ± 0.37 0.37 ± 0.41 0.49 ± 1.51 −1 Singh and Kalamdhad (2013) used the rotary drum of Mg (g kg ) 2.25 ± 0.07 5.93 ± 0.02 1.03 ± 0.07 1.92 ± 0.09 Trial 1 50 kg 40 kg 5 kg 5 kg capacity 3.5 m for the composting of vegetable waste, Trial 2 60 kg 30 kg 5 kg 5 kg sawdust, and cattle manure. Studies have been carried Trial 3 70 kg 20 kg 5 kg 5 kg Trial 4 80 kg 10 kg 5 kg 5 kg out for rotary drum composting of manure and straw note: Mc – Moisture content; ec – electrical conductivity; T oc – Total co-composting by Rihani et al. (2010) and Rosal, Chica, organic carbon; Tn – Total nitrogen; c:n ratio – c arbon to nitrogen ratio; Arcos, and Dios (2012), and house hold waste using Tp – Total phosphorous; na – s odium, K – potassium; c a – c alcium; c u – c opper; Zn – Zinc; Mn – Manganese; Fe – Iron; Mg – Magnesium. rotary drum by Akinbile and Yusoff (2012). Numerous works had been carried out by rotary drum composting the maximum. Flowers waste was collected from nearby of organic waste using a various bulking agent. There temples and transported to the composting laboratory are no studies available for the flower waste composting of Sardar Vallabhbhai National Institute of Technology, using rotary drum method. Surat, India. Segregation of waste was done manually to Analytical hierarchy process (AHP) is very popular remove the unwanted materials (plastic, threads, incense and widely used decision-making method to solve the sticks, coconut, etc.) before using it for the experiments. problem of various fields. Curiel-Esparza, Cuenca-Ruiz, Cow dung was used as microbial inoculum to enhance Martin-Utrillas, and Canto-Perello (2014) studied the the microbial biomass and biological activities during analytical hierarchy method for selecting a sustaina- the composting process. A dairy farm in village Umra, ble disinfection technique for wastewater reuse pro- Surat provided the cow dung for the study. Sawdust and jects. Karimi, Mehradadi, Hashemian, Bidhendi, and wheat bran were used as bulking agents. Likewise, saw- Moghaddam (2011) used AHP method for selecting dust was collected from a sawmill in Bhatar, Surat and the best water treatment process. Ghaitidak and Yadav wheat bran was collected from a nearby flour mill in (2014) used AHP method to check the effect of coagulant Surat. Bulking agents (sawdust and wheat bran) was used in greywater treatment and selected the best coagulant to control the excess moisture and reduce the air space condition. At present, the use of multi-attribute decision between the material particles. The initial characteristics method has not used to find the best trials of any method of waste mixtures are shown in Table 1. used for the preparation of compost. The present study gives a step-by-step procedure to find the best trials of flower waste, cow dung and bulking agent (sawdust and 2.1. Rotary drum composter wheat bran) for rotary drum composting using AHP. Figure 1 shows the rotary drum composter of capac- e a Th im of the present study was to investigate phys- ity 0.6 m similar to Kalamdhad and Kazmi (2008) was ico-chemical and maturity parameters of compost pro- made to perform the composting process. The dimen- duced during rotary drum composting of flower waste sions of the drum were length 1.20 m, diameter 0.80 m using cow dung as inoculum, sawdust and wheat bran as and thickness of metal sheet 0.3 mm. The anticorrosive bulking agent at various stages of the composting pro- paint was used to paint the inner portion of the drum cess. The AHP was used to rank the trials by selecting the to prevent the drum from rusting. For the rotation of physico-chemical and maturity indices of the compost. the drum four metal rollers, one handle and a metallic stand were used. For proper mixing of waste mixtures 2. Materials and methods inside the drum 40 mm, angles were welded longitu- dinally. To drain off the leachate from the drum dur - Flower waste was used as main feedstock. Flower waste ing the composting periods two holes were made. A contains a variety of flowers such as rose (Rosa), mar - plastic bucket was used to fill the waste mixture inside igold (Tagetes erecta), lotus (Nelumbo nucifera), and the drum as required for the trials. Aer d ft aily manual siroi lily (Lilium macklinaie). Among these flowers, it turning, opening up both the half side doors was done was observed that the quantity of marigold flowers was GEOLOGY, ECOLOGY, AND LANDSCAPES 139 Figure 1. pictorial view of rotary drum composter. for maintaining the aerobic condition inside the drum. The Petri dish was kept in an incubator for 72 h A sample of about 500–600 g was collected from top, at 25 °C at dark. After seven days the germinated bottom and middle part of the drum at several locations. seeds were counted and determined the relative e s Th ample was collected at intervals of four days. Aer ft seed germination, relative growth rate, and germi- collecting the sample, it was oven dried at 105 ± 2 °C for nation index. The Equations (1) and (2) are used to 24–72 h then ground and sieved. The sample was kept calculate the relative seed germination and germi- for further Physico-chemical parameters analysis aer ft nation index (GI) by the previous researcher Zhang sieving with a 0.2 mm sieve. and Sun (2014), Number of seeds germinated extra × 100 2.2. Sampling and analysis of physico-chemical Relative seed germination(%) = Number of seeds germinated in control parameters of compost (1) During the composting time, the temperature (Relative seed germination) ×(Relative root growth) was recorded by using digital thermometers. The GI(%) = ×100 temperature at the top, middle and the bottom at (2) starting, centre and end locations were monitored. Gravimetric method (BIS No. 10158, 1982) was pH and the electrical conductivity were determined used for finding the availability of moisture content by using pH and EC metre. In 100 mL distilled water into the fresh sample. The moisture content of the 10 g dried sample (1:10 w/v) was mixed and kept for collected fresh sample was determined at the time rotary shaker for a thorough mixture of the sample of turning. Fresh sample was used for determin- into the water for two hours. After two hours, the ing the CO evolution as mentioned by Kalamdhad sample was held for settling down for half an hour, and Kazmi (2008). For determining the germination and then it was filtered through Whatman filter paper index test, the compost sample was taken from the for finding the pH and EC. For determining the total rotar y drum at the inter val of 7 days. For mixing the nitrogen, the sieved sample of 0.2 g was digested using compost into water 50-g compost was mixed into heating digester (Cupric sulphate and potassium sul- 100 mL distilled water and was kept in for shaker phate in 1:5 ratios and 10 mL H SO4). The digested for 6 h for the complete mixing. After that, the sam- sample was used for determining the total nitrogen. ple was centrifuged at 8000 rpm for 20 min. Ten Pelican Kelplus Distyl ems instrument was used for Petri dish was having 10 cm diameter lined with total nitrogen determination. KCl extraction methods fast speed qualitative filter paper. The centrifuged were used for ammoniacal nitrogen estimation and sample of 5 mL quantity was put into each Petri followed the phenate methods (APHA, 2005). Dry dish, and for control 5 mL deionized water was put solid weighing 10 g was kept at 550 °C for two hours into the Petri dish. In each Petri dish, 10 radish in a muffle furnace to determine the volatile solids seeds were sown with 10 replicates per treatment. of the sample. Carbon content was calculated similar 140 D. SHARMA AND K. D. YADAV to Adhikari, Barrington, Martinez, and King (2009) 2.4. Statistical analysis by dividing the volatile solids 1.83. Heating digester ANOVA of physico-chemical parameters is given in (Velp Scientifica DK 20) was used for digesting the Table 3. The average of the equivalent is represented 0.2 g sample 10 mL H SO4 and HClO mixture (5:1) 2 4 by monitored physico-chemical parameters tested for at 300 °C for two hours. The total phosphorous in the each trial. One way ANOVA was used to trial those digested sample was determined using Stannous chlo- distinctions for every of the measured physicochemical ride methods (APHA, 2005). The flame photometer parameters throughout composting. SPSS 13.0 was used was used to determine the concentrations of sodium, for each single parameter in all trials for computing the potassium, and calcium (Systronics 128 μ). Analysis of variance (i.e., ANOVA p < 0.05). The aim of statistical Ca, Cu, Zn, Mn, Fe, and Mg was done by the Atomic analysis was significant variation between all the param - Absorption Spectroscopy (Agilent). eters analysed for distinct combinations. 2.3. Analytical hierarchy process 3. Result and discussion AHP is used to perform qualitative and quantitative 3.1. Temperature, moisture content, pH and numbers. Appropriate scales are used to convert the qual- electrical conductivity (EC) itative values into absolute numbers. Different attributes have different dimensions, so comparisons of attributes Temperature is the key parameter for the composting with different dimensions are difficult. Normalization process which shows the correlation with microbial is used to make the attributes dimensionless (Table 2). activities. Figure 2(a) shows the variation of tempera- e P Th resent study shows the geometric mean methods ture which indicates that all the trials had reached up of AHP. This method is mostly applied since it is easy to to the thermophilic phase within 2 days. Trials 1, 2, 3, understand. Eigenvalue can be determined very easily and 4 stayed in the thermophilic phase up to 4, 6, 8, and thereby reducing the inconsistency in the judgement 4 days, respectively. It shows that the rate of microbial (Rao, 2007). The decision for intensity of attributes is activity is higher during the composting period. All trials found by the survey the opinion of expert. were achieved the highest temperature of 50.54, 52.65, 55.21, and 52.21 °C, respectively. Temperature > 50 °C up to 3 days shows the sanitization of compost (Awasthi, Table 2. d escription and importance of nine-point intensity Pandey, Bundela, & Khan, 2015; Sharma & Yadav, 2017; scale. Zhang, Xiao, Peng, Su, & Tan, 2013). The ambient tem- Definition Intensity of importance perature ranges from 19 to 25 °C. In all the trials the equally preferred 1 temperature had reached near the ambient temperature one is Moderately preferred over another 3 one is strongly preferred over another 5 ae ft r 15 days. Varma and Kalamdhad ( 2014) and Sharma one is very strongly preferred over another 7 and Yadav (2017), observed the similar trends of temper- one is extremely preferred over another 9 ature profile during the composting of vegetable waste Intermediate values 2,4,6,8 and flower waste. s ource: Ghaitidak and yadav (2014). Table 3. anova of physico-chemical parameters. Parameters Anova Sum of squares Degree of freedom (DF) Mean square F-value p-value pH Between groups 0.201 3 0.067 0.143 0.048 Within groups 48.769 104 0.469 Total 48.970 107 electrical conductivity Between groups 193.414 3 64.471 91.687 0.0001 Within groups 73.130 104 0.703 Total 266.544 107 Total organic carbon Between groups 179.316 3 59.772 2.865 0.040 Within groups 2169.742 104 20.863 Total 2349.059 107 c:n ratio Between groups 7780.852 3 2593.617 105.209 0.0001 Within groups 2341.278 104 22.512 Total 10122.130 107 ammoniacal nitrogen Between groups 2872.379 3 957.460 1.724 0.049 Within groups 57771.561 104 555.496 Total 60643.940 107 Total nitrogen Between groups 1.937 3 0.646 8.032 0.001 Within groups 8.358 104 0.080 Total 10.294 107 Germination index Between groups 677.580 3 225.860 2.720 0.048 Within groups 8634.617 104 83.025 Total 9312.197 107 co evolution Between groups 42.030 3 14.010 1.453 0.039 Within groups 1002.910 104 9.643 Total 1044.940 107 GEOLOGY, ECOLOGY, AND LANDSCAPES 141 Figure 2. (a) Variation of temperature (b) moisture content (c) pH and (d) electrical conductivity during the composting period. th The moisture content is a significant parameter for the increase in pH value. After the 8 day, neutral pH the growth of microbes as well as in the physiochemical value was observed because the flower waste was a good properties of the compost. Figure 2(b) illustrates the source of potassium concentration which reacts to the variations of moisture during the first 30 days. In trial bi-carbonic acid (HCO –) and forms strong base KOH 4, the quantity of flower waste was high. Therefore, the at the organic matter degrades. The initial pH values of moisture content on the final day of composting was trials 1–4 were 5.91, 5.84, 5.74, 5.65 and it increased to high compared to other trials. The moisture reduction 7.31, 7.23, 7.51, and 7.34 on the final day of compost- in trial 1 was less due to proportions of 50 kg flower ing. Awasthi et al. (2015), reported that under aero- waste and 40 kg cow dung. In trial 2 and trial 3 the bic conditions, organic N is transformed into NH or reduction of moisture was 58.64 and 60%, respectively. NH during ammonification which was responsible for Availability of moisture ae ff cts the temperature of the increasing the pH of the compost. The stable pH value drum it can be understood by comparing Figure 2(a) during co-composting is due to the buffering capacity and (b). Higher the moisture content reduces the rise in of hummus, which was synthesized during the matu- the temperature. But with the increase in the quantity ration phase of composting. The range of pH for the of cow dung in the composition, temperature decreases. matured compost was between 6 and 8 (Wong et al., Cow dung is a good source of microbes which increases 2001). the activity and the degradation rate of organic mat- Electrical conductivity is the indicator of the degree ter. An adequate proportion of flower waste and cow of salinity. It indicates the phytotoxicity effect on the dung is to be used as it significantly ae ff cts the rate of growth of the plant. Initial electrical conductivity in all composting. the trials was lower, but it increased aer co ft mposting. Figure 2(c) shows the variation in pH during com- Figure 2(d) shows the initial electrical conductivity of posting period. pH plays an important role in the evalu- trials 1–4 were 3.61, 3.95, 3.94 and 3.74 mS/cm which ation of the overall efficiency of the composting process. aer 30 ft days increased to 5.50, 5.86, 6.02, and 6.12 mS/ It was observed that initially, the pH of all the trials cm, respectively. The degradation of organic matter was below 6, but on the final day, all trials were hav- in thermophilic phase releases mineral salts such as ing pH > 7. The rotation of the drum provides proper ammonium, phosphate and thus increases the electri- aeration which was responsible for increasing the pH cal conductivity. Similar trends of increased electrical value during the degradation process. Aeration helped conductivity were observed by Awasthi et al. (2014) to release more hydrogen ions, which contributed to during the composting of municipal waste. Electrical 142 D. SHARMA AND K. D. YADAV Figure 3. (a) Variation of total organic carbon (b) total nitrogen and (c) ammoniacal nitrogen during the composting period. conductivity values from 3 to 12 mS/cm show the matu- because of degradation of organic matter, the loss of rity of the compost. Similar values of electrical conduc- carbon as CO and the contribution of nitrogen-fix- tivity in water hyacinth and municipal waste composting ing bacteria. The rate of increasing overall nitrogen in were also reported by Kalamdhad and Kazmi (2008) and the present study found similar to the previous result Awasthi et al. (2015). The significant difference (p < 0.05) obtained by Jolanun and Towprayoon (2010), Awasthi in the variation of pH, as well as electrical connectivity, et al. (2014) and Sharma et al. (2017) for the organic is indicated by ANOVA analysis in all the trials. waste composting. e co Th mpost maturity can be determined when the ammoniacal nitrogen concentration decreases at the 3.2. Total organic carbon (TOC), total nitrogen + end of the composting period. It was observed that the and ammoniacal nitrogen (NH − N) biological decomposition slowed down and the compost Total organic carbon (TOC) is generally used to check had become mature. Figure 3(c) indicates the variation the stability of compost. TOC reduction in trials 1, 2, 3, of ammoniacal nitrogen. It was observed that initially and 4 were 33, 29, 26, and 22%, respectively. Reduction up to 12 days ammoniacal nitrogen was increased and of total organic carbon with time is shown in Figure 3(a). aer t ft hat, it started decreasing and stabilized within e m Th aximum TOC reduction was observed in trial 1 30 days. It was also observed that the change in ammo- which was possibly due to the presence of more quantity niacal nitrogen also depended on the variation of pH. of cow dung. Elango et al. (2009) and Adhikari et al. e co Th ncentrations of ammoniacal nitrogen were 101.23, (2009) also observed the similar trend of reduction in 96.85, 102.57 and 104.54 mg/kg which were decreased to total organic carbon while composting the municipal 53.92, 73.60, 78.87 and 75.21 mg/kg in trial 1, 2, 3 and 4, solid waste and food waste. respectively. Huang, Wong, Wu, and Nagar (2004) and Figure 3(b) indicates the variation in the overall nitro- Rashad et al. (2010) also observed similar decreasing gen. It was observed that there was a rise in the overall trends of ammoniacal nitrogen during the organic waste nitrogen at the time of composting. The initial concen- composting. ANOVA analysis indicates the significant tration of total nitrogen in trials 1–4 was 1.54–1.75% difference (p < 0.05) in the variation of total organic and increased to 2.17–2.66% at the final day of the com - carbon, total nitrogen and ammoniacal nitrogen in all posting period. The total nitrogen raises concentration the trials. GEOLOGY, ECOLOGY, AND LANDSCAPES 143 Figure 4. (a) Variation of c:n ratio (b) germination index and (c) co evolution during the composting period. 3.3. Carbon to nitrogen ratio (C:N), germination As per conclusion of Sun, Wang, Lu, and Wang (2012), index (GI) and carbon dioxide (CO ) evolution the optimum percentage of germination index appropri- ate for mature compost are greater than 80%. A similar The C :N ratio is a very useful criterion for evaluating trend of germination index was observed by Sharma et the maturity rate, the intensity of microbial growth, the al. (2017) during the agitated (windrow) pile composting compost quality, and presence of nutrients in the final of flower waste. compost. The living organisms present in the compost CO evolution is one of the best methods for deter- utilize the carbon as a source of energy and the nitrogen 2 mining the maturity and stability of the compost as it for building cell structures. The maturity of the compost measures the carbon derived directly from the com- was linearly dependent on the preliminary C:N ratio. post. CO evolution directly synthesizes to aerobic res- Figure 4(a) indicates that the preliminary C:N ratio of piration, which shows the direct measure of respiration trials 1–4 was 28.84–30.30 which decreased linearly to and aerobic biological activity. CO evolution decreased 13–17. C:N ratio 10–15 indicates the compost maturity. with time. Figure 4(c) shows the CO evolution in trials Similar values (13–17) of C:N ratio was observed during 1–4 were 7.99, 7.86, 7.74, and 7.99 mg/g VS/day which the composting of organic waste by several researchers decreased to 0.27, 0.44, 0.17 and 0.39 mg/g VS/day dur- (Guo et al., 2012; Zhang & Sun, 2014). Germination index is one of the widely used tools for ing the composting process. Similar decreasing trends checking the maturity and phytotoxicity effects of the of CO evolution rate during the rotary drum com- compost during the composting period. Germination posting of vegetable waste was observed by Varma and index assesses the fittingness of compost for agricul- Kalamdhad (2014). tural purposes. Initially, the germination indexes were 75, 73, 74, and 68% in trials 1–4 which increased to 91, 3.4. Macro and micronutrients 94, 98, and 91%, respectively (refer Figure 4(b)). It was observed that in all trials the germination index was e p Th resence of nutrients in the compost shows the found to be greater than 80% which displays that for compost quality as macro and micronutrients are indis- the plant growth, the flower waste compost is suitable. pensable for the growth of plants. Table 4 indicates the 144 D. SHARMA AND K. D. YADAV Table 4. presence of macro (p, K, na, c a and Mg) and micronutrients (Fe, Mn, Zn and c u) at initial and final day into compost. Parameter Day Trial 1 Trial 2 Trial 3 Trial 4 −1 p (g kg ) 0 8.65 ± 0.06 7.02 ± 0.01 4.96 ± 0.03 5.58 ± 0.02 30 13.06 ± 0.05 12.01 ± 0.03 11.25 ± 0.06 10.22 ± 0.04 −1 K (g kg ) 0 9.37 ± 0.01 10.87 ± 0.04 11.33 ± 0.02 11.54 ± 0.03 30 17.01 ± 0.02 18.61 ± 0.01 17.69 ± 0.01 18.34 ± 0.04 −1 na (g kg ) 0 1.57 ± 0.05 1.49 ± 0.07 1.52 ± 0.03 1.47 ± 0.04 30 2.44 ± 0.06 2.60 ± 0.08 2.52 ± 0.02 2.14 ± 0.01 −1 c a (g kg ) 0 7.92 ± 0.08 8.15 ± 1.36 6.95 ± 0.97 7.53 ± 1.22 30 14.84 ± 1.07 15.58 ± 136 13.86 ± 0.51 13.35 ± 1.31 −1 Mg (g kg ) 0 3.66 ± 0.02 3.36 ± 0.04 2.96 ± 0.02 2.51 ± 0.01 30 6.38 ± 0.01 6.94 ± 0.05 5.25 ± 0.04 7.03 ± 0.01 −1 Fe (g kg ) 0 1.95 ± 0.57 1.84 ± 0.48 1.96 ± 0.14 1.21 ± 0.37 30 4.25 ± 0.63 4.63 ± 0.41 4.894 ± 0.38 3.83 ± 0.25 −1 Mn (mg kg ) 0 103.09 ± 61 94.37 ± 54.5 85.13 ± 48.26 75.78 ± 41.09 30 211.45 ± 1.1 186.64 ± 0.99 160.31 ± 0.93 158.76 ± 0.89 −1 Zn (mg kg ) 0 142.33 ± 0.8 161.27 ± 1.06 159.51 ± 0.61 144.74 ± 0.5 30 186.66 ± 1.3 197.41 ± 0.83 190.45 ± 1.9 178.2 ± 1.32 −1 c u (mg kg ) 0 41.41 ± 0.18 39.73 ± 0.15 36.7 ± 0.17 34.61 ± 0.08 30 45.39 ± 0.15 43.4 ± 0.15 42.51 ± 0.15 41.29 ± 0.04 Figure 5. The hierarchy structure used for ranking of drum composting. presence of an initial and final concentration of nutrients 3.5. Rank of drum composting using AHP in the compost. The concentration of potassium content e hiera Th rchy structure used for ranking of the trials is was high in all the trials. It shows the presence of high shown in Figure 5. Seven important criteria suitable for inherent content in flower waste, pointing that the com - the maturation of compost had been taken for deciding post might be the good source of potassium. The con- the rank of the trials by using AHP method. (1) No of centration of sodium and calcium gradually increased days the trials stay in thermophilic phase (2) pH (3) EC which means the net loss in dry mass because of the (4) TOC (5) germination index (6) C:N and (7) CO degradation of organic matter and the release of CO 2 evolution rate. The final aer (30 ft days) composting value mineralization during the composting period. Calcium had been taken for ranking from each trial. Keeping and sodium are essential nutrients for plant growth. these parameters as the source of quality of good com- When the compost is mixed with soil, it increases the post, the ranking of the trials has been optimized using soil acidification and makes the nutrient easily availa- AHP. Alternatives and attributes are significant for the ble to the plant. The order of concentration of macro decision-making (refer matrix A1). Aer t ft he decision and micronutrients in the flowers waste composting is of hierarchy, the weight criteria are calculated by using K > Ca > Mg > Na and Fe > Mn > Zn > Cu and these AHP method. The acceptance of weights depends on the concentrations of nutrients are suitable for plant growth. consistency ratio. The normalized value of the quantita- Similarly, it was observed in the literature that the quan- tive data is shown below. The normalized data with the tity of nutrients increases in the compost due to the deg- weight of attributes show the ranking of alternatives. radation of organic matter (Awasthi et al., 2014; Singh Equations (3) and (4) show the steps for the calculation & Kalamdhad, 2014). of consistency ratio by using AHP. GEOLOGY, ECOLOGY, AND LANDSCAPES 145 Attributes► CI =( − M)∕(M − 1) (3) max Alternatives▼ Temp pH EC TOC GI C/N CO where = maximum eigenvalue of the matrix. = Trial 1 max max Trial 2 6 7.2 5.9 29 94 14 0.44 Average of matrix A4 (see matrix A2 section) and Trial 3 8 7.5 6 26 98 13 0.17 M = order of matrix (Here, = 7.748andM = 7). max Trial 4 4 7.4 6.1 22 91 17 0.39 e co Th nsistency ratio (CR) was calculated as Equation (4). CR = CI∕RI (4) 3.5.2. Selection index Selection index shows the ranking of the piles. The where RI = random index. RI depends upon the size of higher values of SI indicate, the better alternative. the relative importance matrix (Here, RI= 1.35). Selection index was obtained by multiplying normal- e va Th lue of CR should be less than 0.1, which satisfies ized data by weight (MAT A2). The normalization of the pairwise comparison matrix for criteria and validates the alternative and attributes used for the ranking of the the weights. In this case, CR = 0.092; which is CR < 0.1; piles are shown below. hence the matrix satisfies the consistent and validated resultant weights. Matrix A1 shows the pair wise com- parison matrix for the criteria shown below. Attrib- C:N utes Temp pH EC TOC GI ratio CO Mat Temp 1.00 7.00 7.00 5.00 3.00 5.00 7.00 (a1) = pH 0.14 1.00 2.00 0.20 0.14 0.33 0.14 ec 0.14 0.50 1.00 0.20 0.14 0.14 0.33 Toc 0.20 5.00 5.00 1.00 1.00 3.00 3.00 GI 0.33 7.14 7.14 1.00 1.00 3.00 3.00 c:n 0.20 3.03 7.14 0.33 0.33 1.00 3.00 ratio co 0.14 7 3.0 0.33 0.33 0.33 1.00 Calculation of consistency ratio and the result obtained using AHP. Mat Temp 0.4096 Mat (a3) 3.2213 Mat (a4) = 7.8643 (a2) pH 0.0311 = Mat(a1) 0.2447 Mat(a3)/ 7.8582 λ = max = × Mat (a2) Mat (a2) 7.748 ec 0.0255 0.1976 7.7570 cI = 0.124 Toc 0.1654 1.2416 7.5072 RI = 1.35 GI 0.1970 1.4175 7.1957 cR = 0.09 c:n 0.1012 0.7909 7.8170 ratio co 0.0702 0.5784 8.2339 3.5.1. Normalized data Normalized data of the attributes are shown below. Attributes temp was the main effects of composting. Hence, temp was the beneficial attribute and was the case of maximization. The attributes pH, electrical con- ductivity, total organic carbon, germination index, C:N and CO evolution rate increases or decreases due to the variation in temperature. Hence, these attributes are minimized. For instance, piles stay in no of days in ther- mophilic phase was 8 in alternative trial 3. Therefore, all temp value was divided by 8 so that the normalized temp value at trial 3 will be 1 and that in another alternative will be <1 (refer normalized data and selection index), respectively. e Th minimum value of pH was 7.2 in alter - native trial 2 and normalized as 1, and the other normal- ized value of pH was obtained by dividing 7.2 by each pH (7.2/7.3 = 0.986). Alternatives and attributes used for ranking of the piles are shown below. Temperatures mentioned in days indicate that the piles stayed in that particular number of days in the thermophilic phase. Attributes ► Alternatives▼ Temp pH EC TOC GI C:N ratio CO Mat(A2) Rank Trial 1 0.5000 0.9863 1.0000 1.0000 0.9286 0.9286 0.6296 0.4096 0.7475 Trial 2 0.7500 1.0000 0.9322 0.8788 0.9592 0.9286 0.3864 0.0311 0.8175 Trial 3 1.0000 0.9600 0.9167 0.7879 1.0000 1.0000 1.0000 0.0255 0.9616 Trial 4 0.5000 0.9730 0.9016 0.6667 0.9286 0.7647 0.4359 0.1654 0.6592 0.1970 0.1012 0.0702 146 D. SHARMA AND K. D. 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Geology Ecology and Landscapes – Taylor & Francis
Published: Apr 3, 2018
Keywords: Rotary drum composting; flower waste; C:N ratio; germination index
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