Home composting (HC) can be a cost-effective strategy for organic solid waste management. This option is also desirable since HC is increasingly automated, with HC machines composting faster than conventional composting in outdoor settings. Besides, HC may reduce organic solid waste management costs, especially for developing countries with scarcer resources. Taking Iran as a study case, the paper examines the influence of variables pertaining to the theory of planned behavior, the value-belief-norm framework, and the technology acceptance model. This study uses data collected from a territory-wide survey (n = 367) of Isfahan's residents to predict HC intentions. The results show that both attitude and subjective norms appear as the most impactful of all variables. These results further vary according to sex, with women being significantly more prone to HC than men. The findings may provide a reference to implement HC in Iran and other developing countries and possibly developed ones.
Citation: Hamid Rastegari Kopaei, Mehdi Nooripoor, Ayatollah Karami, Myriam Ertz. Modeling consumer home composting intentions for sustainable municipal organic waste management in Iran[J]. AIMS Environmental Science, 2021, 8(1): 1-17. doi: 10.3934/environsci.2021001
[1] | Jean-Jacques Kengwoung-Keumo . Competition between a nonallelopathic phytoplankton and an allelopathic phytoplankton species under predation. Mathematical Biosciences and Engineering, 2016, 13(4): 787-812. doi: 10.3934/mbe.2016018 |
[2] | Menghan Chen, Haihong Liu, Ruiqi Wang . Dynamical behaviors of quorum sensing network mediated by combinatorial perturbation. Mathematical Biosciences and Engineering, 2022, 19(5): 4812-4840. doi: 10.3934/mbe.2022225 |
[3] | Gonzalo Robledo . Feedback stabilization for a chemostat with delayed output. Mathematical Biosciences and Engineering, 2009, 6(3): 629-647. doi: 10.3934/mbe.2009.6.629 |
[4] | Blessing O. Emerenini, Stefanie Sonner, Hermann J. Eberl . Mathematical analysis of a quorum sensing induced biofilm dispersal model and numerical simulation of hollowing effects. Mathematical Biosciences and Engineering, 2017, 14(3): 625-653. doi: 10.3934/mbe.2017036 |
[5] | Jinliang Wang, Hongying Shu . Global analysis on a class of multi-group SEIR model with latency and relapse. Mathematical Biosciences and Engineering, 2016, 13(1): 209-225. doi: 10.3934/mbe.2016.13.209 |
[6] | Xiaomeng Ma, Zhanbing Bai, Sujing Sun . Stability and bifurcation control for a fractional-order chemostat model with time delays and incommensurate orders. Mathematical Biosciences and Engineering, 2023, 20(1): 437-455. doi: 10.3934/mbe.2023020 |
[7] | Yueping Dong, Jianlu Ren, Qihua Huang . Dynamics of a toxin-mediated aquatic population model with delayed toxic responses. Mathematical Biosciences and Engineering, 2020, 17(5): 5907-5924. doi: 10.3934/mbe.2020315 |
[8] | Jianquan Li, Zuren Feng, Juan Zhang, Jie Lou . A competition model of the chemostat with an external inhibitor. Mathematical Biosciences and Engineering, 2006, 3(1): 111-123. doi: 10.3934/mbe.2006.3.111 |
[9] | Nancy Azer, P. van den Driessche . Competition and Dispersal Delays in Patchy Environments. Mathematical Biosciences and Engineering, 2006, 3(2): 283-296. doi: 10.3934/mbe.2006.3.283 |
[10] | Frédéric Mazenc, Gonzalo Robledo, Daniel Sepúlveda . A stability analysis of a time-varying chemostat with pointwise delay. Mathematical Biosciences and Engineering, 2024, 21(2): 2691-2728. doi: 10.3934/mbe.2024119 |
Home composting (HC) can be a cost-effective strategy for organic solid waste management. This option is also desirable since HC is increasingly automated, with HC machines composting faster than conventional composting in outdoor settings. Besides, HC may reduce organic solid waste management costs, especially for developing countries with scarcer resources. Taking Iran as a study case, the paper examines the influence of variables pertaining to the theory of planned behavior, the value-belief-norm framework, and the technology acceptance model. This study uses data collected from a territory-wide survey (n = 367) of Isfahan's residents to predict HC intentions. The results show that both attitude and subjective norms appear as the most impactful of all variables. These results further vary according to sex, with women being significantly more prone to HC than men. The findings may provide a reference to implement HC in Iran and other developing countries and possibly developed ones.
Metal Organic Frameworks (MOFs) have received over a decade's worth of attention from different aspects of significance [1,2]. Yet new areas are being explored day by day. Bio-metal organic frameworks (Bio-MOFs) are actually a subclass of MOFs [3]. These materials contain biomolecules as linkers and biocompatible metal cations as connectors [4]. MOFs can be scaled down to the nano-regime to form nanoscale metal-organic frameworks (nano-MOFs). Recently some nanoscale MOFs have been reported [5]. Although a large number of the bulk MOF materials have been synthesized and characterized up till now, but with the passage of time, the number of research reports on the nano-MOFs are also increasing day by day [6]. Nano-bio-MOFs are the nano-sized MOF materials containing biomolecules and biocompatible metal cations. This emerging class of nano-bio-MOFs can be considered as promising candidates for the drug adsorption and controlled release due to their large surface areas, high porosity, and presence of biocompatible linker molecules [7].
Wuttke et al. have reported that MOFs materials can be designed and tuned accordingly and have been used successfully as nano-carriers with more potential compared with previously used drugs carrier materials [8,9,10]. Roder et al. have also reported the successful use of nano-sized MOF materials for drugs delivery. Due to their smaller size and high surface areas, these MOF materials are used as targeted drugs delivery vehicles. Several other research reports also reveal the use of nano-sized MOFs in the field of drugs delivery [11,12,13,14].
Sattar et al. [15] have reported the hydrothermal synthesis of three nano bio-MOFs compounds 6–8 (cobalt argeninate, cobalt asparaginate and cobalt glutaminate respectively). These compounds have been hydrothermally synthesized and characterized by Scanning Electron Microscopic (SEM) studies. Photocatalytic hydrogen production of these three compounds have been evaluated. These compounds can exhibit mulitifunctional properties. The present work comprises of the drugs adsorption studies of these three nano-bio-MOF compounds 6–8. N2 adsorption experiments of all these compounds have been recorded. In vitro drugs adsorption of four different drugs of all these compounds have been evaluated. Thermogravimetric analysis (TGA) and powder X-ray Diffraction Analysis (Powder XRD) patterns of all these compounds in pure form and after drugs adsorption have been recorded. The amounts of adsorbed drugs and its slow release after intervals have been monitored through the High Performance Liquid Chromatography (HPLC).
BET specific surface areas of compounds 6, 7 and 8 have been determined as 2400 m2·g−1, 2500 m2·g−1 and 2200 m2·g−1 respectively with a pore size of 10 Å by using BET-method based on calculations of N2 adsorption isotherm data. Figure 1 represents the N2 adsorption isotherm of compound 6, 7 and 8.
Four types of drugs (terazosine hydrochloride, telmisartan, glimpiride and rosuvastatin) have been adsorbed into compounds 6, 7 and 8. It was also elaborated that some pores were blocked after the drugs adsorptions thus decreasing the specific surface areas of this material as shown in Figure 1.
TGA plots of the as synthesized and drugs adsorbed compounds 6–8 were carried out on a SDT Q600 by heating the compounds from 0 to 600 ℃ at a heating rate of 10 ℃/min.
TGA plot of compound 6 (Figure 2a) in pure form shows first weight loss of 8% at 120 ℃ which is due to the loss of coordinated water molecule. Then up to 300 ℃ the framework shows stability with no weight loss. Second weight loss is observed at 300 ℃, which continues up to 385 ℃ with a 55% weight loss indicating the start of framework decomposition along with ligands. Then slow decomposition of framework continues gradually until the whole framework decomposes at 560 ℃ and finally the metal oxides are left. TGA plot of terazosine adsorbed compound 6 (Figure 2b) shows a weight loss of 7% at 45 ℃, which is attributed to the loss of drug molecules. At 170 ℃, the framework shows 4% weight loss which is due to the loss of coordinated water molecule. Then the framework starts to decompose. At 185 ℃ second loss of mass is observed. After that, the framework remains intact till 280 ℃, and at 285 ℃, the framework starts to decompose and this decomposition continues up to 380 ℃ with a weight loss of 58%. The whole framework decomposes along with remaining terazosine up to 590 ℃. TGA plot of telmisartan adsorbed compound 6 (Figure 2c) shows a weight loss of 5% at 40 ℃ which is due to the loss of telmisartan. At 150 ℃, a second loss of weight of 5% shows the loss of water molecules. At 250 ℃, the framework starts to decompose which continues till 300 ℃ with a maximum weight loss of 59%. TGA plot of glimpiride adsorbed compound 6 at 25 ℃ (Figure 2d) shows first weight loss of 4% which is due to the removal of glimpiride. Then the framework remains intact up to 120 ℃, and at this temperature framework shows a weight loss of 4% up to 190 ℃ which is due to removal of coordinated water molecule. At 280 ℃, the decomposition of the framework and ligands starts which continues till 330 ℃ with weight loss of 58%. The thermogram of rosuvastatin adsorbed compound 6 (Figure 2e) shows first weight loss of 4% at 30 ℃ which is due to the removal of drug. Then the framework shows stability up to 115 ℃ and at this temperature framework shows a weight loss of 4% up to 130 ℃ which is due to removal of coordinated water molecules. After 230 ℃, the decomposition of the framework and ligands starts which continues till 555 ℃ with maximum weight loss of 58%. No further weight loss observed in the remaining range studied.
TGA plot of compound 7 in pure form (Figure 3a) shows first weight loss of 4% at 180 ℃ which is due to the loss of coordinated water molecule. Second weight loss is observed at 280 ℃ due to start of framework decomposition, which gradually and slowly continues up to 580 ℃ with a 30% loss of mass until the whole framework decomposes with the formation of metal oxides. TGA plot of terazosine adsorbed compound 7 (Figure 3b) shows a weight loss of 5% at 60 ℃, which is due to the loss of drug molecules. Then the framework shows stability up to 180 ℃. Second loss of mass at 180 ℃ is observed and this 9% weight loss is due to the loss of coordinated water molecule. After that, the framework remains intact till 280 ℃, and at 280 ℃, the framework starts to decompose and this decomposition continues up to 380 ℃ with a weight loss of 58%. The whole framework decomposes along with remaining terazosine up to 590 ℃. TGA plot of telmisartan adsorbed compound 7 (Figure 3c) shows a weight loss of 4% at 40 ℃ which is due to the loss of telmisartan. Another weight loss of 8% at 150 ℃ shows the loss of coordinated water molecule. Then the framework starts to decompose at 250 ℃ which continues till 300 ℃ with a maximum weight loss of 59%. A plot of glimpiride adsorbed compound 7 (Figure 3d) shows first weight loss of 4% at 50 ℃ which is due to the removal of glimpiride. At 170 ℃, the framework shows a weight loss of 8% up to 190 ℃ which is due to removal of water molecules. At 280 ℃, the decomposition of the framework and ligands starts which continues till 560 ℃ with weight loss of 58%. A TGA plot of rosuvastatin adsorbed compound 7 (Figure 3e) shows first weight loss of 3% at 45 ℃ which is due to the removal of drug. At 175 ℃, framework shows a weight loss of 8% up to 190 ℃ which is due to removal of water molecules. At 285 ℃, the decomposition of the framework and ligands starts which continues till 560 ℃ with weight loss of 58%. No further weight loss observed in the remaining range studied.
The thermogram of compound 8 (Figure 4a) in pure form shows first weight loss of 5% at 129 ℃ which is due to the loss of water. Then up to 270 ℃, the framework remains intact, after that, the decomposition of framework starts along with ligands which continues till 320 ℃ with major weight loss of 60%. Decomposition continues gradually until 600 ℃ which weight loss of 68% shows complete decomposition of framework and formation of metal oxides. The thermogram of the terazosine loaded compound 8 (Figure 4b) indicates the first weight loss of 10% at 53 ℃ which is due to the loss of adsorbed terazosine molecules. Then up to 140 ℃, the framework shows stability, after that, weight loss of 25% can be observed up to 160 ℃ which is due to removal of water molecules. Gradual decomposition of framework and ligands starts after 300 ℃ and continues gradually till 590 ℃ with 60%. Thermogram of telmisartan loaded compound 8 (Figure 4c) indicates the first weight loss of nearly 6% at 30 ℃ which is due to the loss of adsorbed telmisartan molecules. Then up to 150 ℃, the framework shows stability, after that a weight loss of 10% is observed up to 155 ℃ which is due to the loss of coordinated water molecule. Then a gradual decomposition of framework and ligands starts which continues till 590 ℃ with maximum weight loss of 65%. While thermogram of glimpiride loaded compound 8 (Figure 4d) indicates the first weight loss of nearly 5% at 45 ℃ which is due to the loss of adsorbed glimpiride molecules. The framework shows stability for some time and at 175 ℃ the weight loss of 10% is observed which is due to the loss of coordinated water molecule. Then up to 340 ℃, the framework remains intact, after that gradual decomposition of framework and ligands starts which continues till 598 ℃ with maximum weight loss of 45%. The plot of rosuvastatin loaded compound 8 (Figure 4e) indicates the first weight loss of 4% at 50 ℃ which is due to the loss of adsorbed drug molecules. At 150 ℃, a weight loss of 8% is due to the loss of water molecules. Then up to 340 ℃, the framework remains intact, after that gradual decomposition of framework and ligands starts which continues till 556 ℃ with maximum weight loss of 50%. No further weight loss observed for the remaining range studies.
PXRD patterns of compounds 6–8 were recorded as described in Figure 5. Powder XRD patterns of all these new synthesized materials after drugs adsorption have revealed the permanent crystalline integrity of these compounds as these have retained its crystallinity even after soaking in water for several days.
For estimation of the drugs in the synthesized materials, High Performance Liquid Chromatography was performed on a Waters 2695 separation module. 0.139 g/g terazosine hydrochloride, 0.090 g/g telmisartan, 0.117 g/g glimpiride and 0.129 g/g rosuvastatin have been estimated in compound 6 with a maximum release time of 3, 3, 3 and 3 d respectively. 0.195 g/g terazosine, 0.055 g/g telmisartan, 0.138 g/g glimpiride and 0.095 g/g rosuvastatin were detected in compound 7. These adsorbed amounts of drugs were slowly released from the compound after different time intervals. The maximum release time of these drugs was 5, 3, 1 and 3 d respectively. The adsorbed amounts of drugs terazosine, telmisartan, glimpiride and rosuvastatin were 0.071 g/g, 0.086 g/g, 0.135 g/g, 0.094 g/g in compound 8. Slow release of these drugs from compound 8 was observed through HPLC. The maximum release times were 1, 3, 3 and 1 d respectively. Table 1 gives summative information about the drugs adsorption capacities of compounds 6–8.
Synthesized porous compounds | Specific Surface areas (m2/g) | Pore sizes (Å) | Names of drugs loaded in materials | Drugs loading capacity (%) | Time of release (days) |
Compound 6 | 2400 | 10 | Terazosine hydrochloride | 0.139 | 3 |
Telmisartan | 0.090 | 3 | |||
Glimpiride | 0.117 | 3 | |||
Rosuvastatin | 0.129 | 3 | |||
Compound 7 | 2500 | 10 | Terazosine hydrochloride | 0.195 | 5 |
Telmisartan | 0.055 | 1 | |||
Glimpiride | 0.138 | 3 | |||
Rosuvastatin | 0.095 | 3 | |||
Compound 8 | 2200 | 10 | Terazosine hydrochloride | 0.072 | 1 |
Telmisartan | 0.086 | 3 | |||
Glimpiride | 0.135 | 3 | |||
Rosuvastatin | 0.094 | 1 |
Figures in Supporting Information indicate the HPLC peaks for the estimation of adsorbed drugs in the channels of compounds 6–8 and their slow release after different time periods. All the details of HPLC parameters used for HPLC studies have been given in Tables S1 and S2 in Supporting Information).
In conclusion, the present work directs towards the multifunctional use of some nanosized bio-MOFs which were previously used for photocatalysis and now have been successfully utilized for the in vitro adsorption of terazosine, telmisartan, glimpiride and rosuvastatin drugs. This work also describes the more potential use of nanoscale bio-MOFs for drugs adsorption compared with previously used large sized carrier materials. Moreover, successful in vitro drug adsorption experiments on these nanosized materials will lead to their use for in vivo drugs adsorption as well as for the welfare of humans in medical grounds.
The authors have no conflict of interest.
[1] |
Tonglet M, Phillips PS, Bates MP (2004) Determining the drivers for householder pro-environmental behavior: Waste minimization compared to recycling. Resour Conserv Recycl 42: 27–48. doi: 10.1016/j.resconrec.2004.02.001
![]() |
[2] |
Abdel-Shafy HI, Mansour MSM (2018) Solid waste issue: Sources, composition, disposal, recycling, and valorization. Egypt J Pet 27: 1275–1290. doi: 10.1016/j.ejpe.2018.07.003
![]() |
[3] |
Price JL, Joseph JB (2000) Demand management–a basis for waste policy: a critical review of the applicability of the waste hierarchy in terms of achieving sustainable waste management. Sustain Dev 8: 96–105. doi: 10.1002/(SICI)1099-1719(200005)8:2<96::AID-SD133>3.0.CO;2-J
![]() |
[4] |
Mosler HJ, Tamas A, Tobias R, et al. (2008) Deriving Interventions on the Basis of Factors Influencing Behavioral Intentions for Waste Recycling, Composting, and Reuse in Cuba. Environ Behav 40: 522–544. doi: 10.1177/0013916507300114
![]() |
[5] |
Llatas C, Osmani M (2016) Development and validation of a building design waste reduction model. Waste Manage 56: 318–336. doi: 10.1016/j.wasman.2016.05.026
![]() |
[6] |
Morone P, Koutinas A, Gathergood N, et al. (2019) Food waste: Challenges and opportunities for enhancing the emerging bio-economy. J Clean Prod 221: 10–16. doi: 10.1016/j.jclepro.2019.02.258
![]() |
[7] |
Wang J, Li Z, Tam VWY (2015) Identifying best design strategies for construction waste minimization. J Clean Prod 92: 237–247. doi: 10.1016/j.jclepro.2014.12.076
![]() |
[8] |
Zorpas AA, Lasaridi K, Voukkali I, et al. (2015) Household waste compositional analysis variation from insular communities in the framework of waste prevention strategy plans. Waste Manage 38: 3–11. doi: 10.1016/j.wasman.2015.01.030
![]() |
[9] |
Yau Y (2012) Stakeholder engagement in waste recycling in a high-rise setting. Sustain Dev 20: 115–127. doi: 10.1002/sd.468
![]() |
[10] |
Meng X, Tan X, Wang Y, et al. (2019) Investigation on decision-making mechanism of residents' household solid waste classification and recycling behaviors. Resour Conserv Recycl 140: 224–234. doi: 10.1016/j.resconrec.2018.09.021
![]() |
[11] |
Ng SL (2019) An assessment of multi-family dwelling recycling in Hong Kong: A managerial perspective. Waste Manage 89: 294–302. doi: 10.1016/j.wasman.2019.04.014
![]() |
[12] |
Yang H, Zhang S, Ye W, et al. (2020) Emission reduction benefits and efficiency of e-waste recycling in China. Waste Manage 102: 541–549. doi: 10.1016/j.wasman.2019.11.016
![]() |
[13] |
Ertz M, Karakas F, Sarigöllü E (2016) Exploring pro-environmental behaviors of consumers: An analysis of contextual factors, attitude, and behaviors. J Bus Res 69: 3971–3980. doi: 10.1016/j.jbusres.2016.06.010
![]() |
[14] |
Ertz M, Durif F, Lecompte A, et al. (2018) Does "sharing" mean "socially responsible consuming"? Exploration of the relationship between collaborative consumption and socially responsible consumption. JCM 35: 392–402. doi: 10.1108/JCM-09-2016-1941
![]() |
[15] | Hoornweg D, Bhada-Tata P (2012) What a waste: a global review of solid waste management. Urban development series; knowledge papers, no. 15. World Bank, Washington, DC. © World Bank. |
[16] |
Xu Z, Elomri A, Pokharel S, et al. (2017) Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint. Waste Manage 64: 358–370. doi: 10.1016/j.wasman.2017.02.024
![]() |
[17] |
Andersen MS (2007) An introductory note on the environmental economics of the circular economy. Sustain Sci 2: 133–140. doi: 10.1007/s11625-006-0013-6
![]() |
[18] | Shams M, Nabipour I, Dobaradaran S, et al. (2013) An environmental friendly and cheap adsorbent (municipal solid waste compost ash) with high efficiency in removal of phosphorus from aqueous solution. Fresenius Environ Bull 22. |
[19] |
Loan LTT, Takahashi Y, Nomura H, et al. (2019) Modeling home composting behavior toward sustainable municipal organic waste management at the source in developing countries. Resour Conserv Recycl 140: 65–71. doi: 10.1016/j.resconrec.2018.08.016
![]() |
[20] |
Edgerton E, McKechnie J, Dunleavy K (2009) Behavioral Determinants of Household Participation in a Home Composting Scheme. Environ Behav 41: 151–169. doi: 10.1177/0013916507311900
![]() |
[21] |
Colón J, Martínez-Blanco J, Gabarrell X, et al. (2010) Environmental assessment of home composting. Resour Conserv Recycl 54: 893–904. doi: 10.1016/j.resconrec.2010.01.008
![]() |
[22] | Bartelings H, Sterner T (1999) Household Waste Management in a Swedish Municipality: Determinants of Waste Disposal, Recycling and Composting. ERE 13: 473–491. |
[23] |
Andersen JK, Boldrin A, Christensen TH, et al. (2012) Home composting as an alternative treatment option for organic household waste in Denmark: An environmental assessment using life cycle assessment-modelling. Waste Manage 32: 31–40. doi: 10.1016/j.wasman.2011.09.014
![]() |
[24] |
Tanaka M (2007) Waste management for a sustainable society. Journal of Material Cycles and Waste Manage 9: 2–6. doi: 10.1007/s10163-006-0164-7
![]() |
[25] | Ueta K, Koizumi H (2001) Reducing Household Waste: Japan Learns from Germany. Environ Sci Policy 43: 20–32. |
[26] | Isfahan Municipal Waste Management Organization (2019) Isfahan Municipal Waste Management Organization. Available from: http://pasmand.isfahan.ir/fa |
[27] | Statistical Centre of Iran (2016) Statistical-Yearbook-2016-2017. Available from: https://www.amar.org.ir/english/Iran-Statistical-Yearbook/Statistical-Yearbook-2016-2017 |
[28] | Ando A W, Gosselin A Y (2005) Recycling in Multifamily Dwellings: Does Convenience Matter? Econ Inq 43: 426–438. |
[29] | Weinstein D, Norton C (2014) Recycling and Waste Reduction—Center for Environmental Policy and Management. Available from: https://louisville.edu/cepm/project-areas-1/recycling-and-waste-reduction |
[30] | Foley M (2009) Indoor Composting Systems. Chowhound. Available from: https://www.chowhound.com/food-news/54730/indoor-composting-systems/ |
[31] | McCandless SG (2010) How Indoor Automatic Composting Systems Work. HowStuffWorks. Available from: https://home.howstuffworks.com/indoor-automatic-composting-system.htm |
[32] | Na W, Gang L, Hongming Z (2019) Design and Research of Home Automatic Kitchen Waste Composting device. In E3S Web of Conferences 136: 04013. EDP Sciences. |
[33] | Fishbein M (1979) A theory of reasoned action: Some applications and implications. Nebr Symp Motiv 27: 65–116. |
[34] | Fishbein M, Ajzen I (2011) Predicting and Changing Behavior: The Reasoned Action Approach (1st Ed.). Psychology Press. |
[35] |
Chase K, Reicks M, Jones J M (2003) Applying the theory of planned behavior to promotion of whole-grain foods by dietitians. J Am Diet Assoc 103: 1639–1642. doi: 10.1016/j.jada.2003.09.026
![]() |
[36] |
Liou D, Bauer K (2007) Exploratory Investigation of Obesity Risk and Prevention in Chinese Americans. J Nutr Educ Behav 39: 134–141. doi: 10.1016/j.jneb.2006.07.007
![]() |
[37] |
Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50: 179–211. doi: 10.1016/0749-5978(91)90020-T
![]() |
[38] |
Ajzen I (2002) Residual Effects of Past on Later Behavior: Habituation and Reasoned Action Perspectives. Pers Soc Psychol Rev 6: 107–122. doi: 10.1207/S15327957PSPR0602_02
![]() |
[39] |
Taylor S, Todd P (1997) Understanding the Determinants of Consumer Composting Behavior1. J Appl Soc Psychol 27: 602–628. doi: 10.1111/j.1559-1816.1997.tb00651.x
![]() |
[40] |
Thøgersen J (1999) Spillover processes in the development of a sustainable consumption pattern. J Econ Psychol 20: 53–81. doi: 10.1016/S0167-4870(98)00043-9
![]() |
[41] |
Barr S (2003) Strategies for sustainability: Citizens and responsible environmental behaviour. Area 35: 227–240. doi: 10.1111/1475-4762.00172
![]() |
[42] |
Chen MF, Tung PJ (2010) The Moderating Effect of Perceived Lack of Facilities on Consumers' Recycling Intentions. Environ Behav 42: 824–844. doi: 10.1177/0013916509352833
![]() |
[43] | Tucker P, Speirs D (2003) Attitudes and Behavioural Change in Household Waste Management Behaviours. J Environ Plan. Manag. 46: 289–307. |
[44] |
Meng X, Wen Z, Qian Y (2018) Multi-agent based simulation for household solid waste recycling behavior. Resour Conserv Recycl 128: 535–545. doi: 10.1016/j.resconrec.2016.09.033
![]() |
[45] |
Valle POD, Rebelo E, Reis E, et al. (2005) Combining Behavioral Theories to Predict Recycling Involvement. Environ Behav 37: 364–396. doi: 10.1177/0013916504272563
![]() |
[46] |
Knussen C, Yule F, MacKenzie J, et al. (2004) An analysis of intentions to recycle household waste: The roles of past behaviour, perceived habit, and perceived lack of facilities. J Environ Psychol 24: 237–246. doi: 10.1016/j.jenvp.2003.12.001
![]() |
[47] |
Kim TG, Lee JH, Law R (2008) An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tour Manag 29: 500–513. doi: 10.1016/j.tourman.2007.05.016
![]() |
[48] | Benbasat I, Barki H (2007) Quo vadis TAM? J Assoc Inf Syst 8: 211–218. |
[49] | Saade R, Kira D (2006) The Emotional State of Technology Acceptance. ⅡSIT 3: 529–539. |
[50] |
Broman TM, Schuitema G, Thøgersen J (2014) Responsible technology acceptance: Model development and application to consumer acceptance of Smart Grid technology. Appl Energy 134: 392–400. doi: 10.1016/j.apenergy.2014.08.048
![]() |
[51] |
Davies J, Foxall GR, Pallister J (2002) Beyond the Intention–Behaviour Mythology. Mark Theory 2: 29–113. doi: 10.1177/1470593102002001645
![]() |
[52] |
Bamberg S, Moser G M (2007) Twenty years after Hines, Hungerford, and Tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. J Environ Psychol 27: 14–25. doi: 10.1016/j.jenvp.2006.12.002
![]() |
[53] |
Zeweld W, Van Huylenbroeck G, Tesfay G, et al. (2017) Smallholder farmers' behavioural intentions towards sustainable agricultural practices. J Environ Manage 187: 71–81. doi: 10.1016/j.jenvman.2016.11.014
![]() |
[54] |
Yuan Y, Nomura H, Takahashi Y, et al. (2016) Model of Chinese Household Kitchen Waste Separation Behavior: A Case Study in Beijing City. Sustainability 8: 1083–1083. doi: 10.3390/su8101083
![]() |
[55] |
Bamberg S, Hunecke M, Blöbaum A (2007) Social context, personal norms and the use of public transportation: Two field studies. J Environ Psychol 27: 190–203. doi: 10.1016/j.jenvp.2007.04.001
![]() |
[56] | Byun J, Jang S (2019) Can signaling impact customer satisfaction and behavioral intentions in times of service failure? Evidence from open versus closed kitchen restaurants. J Hosp Mark Manag 28: 785–806. |
[57] |
Onwezen MC, Antonides G, Bartels J (2013) The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J Econ Psychol 39: 141–153. doi: 10.1016/j.joep.2013.07.005
![]() |
[58] |
Park J, Ha S (2014) Understanding Consumer Recycling Behavior: Combining the Theory of Planned Behavior and the Norm Activation Model. Fam Consum Sci Res J 42: 278–291. doi: 10.1111/fcsr.12061
![]() |
[59] |
Han H, Hwang J, Lee M J, et al. (2019) Word-of-mouth, buying, and sacrifice intentions for eco-cruises: Exploring the function of norm activation and value-attitude-behavior. Tour Manag 70: 430–443. doi: 10.1016/j.tourman.2018.09.006
![]() |
[60] |
Liao C, Zhao D, Zhang S, et al. (2018) Determinants and the moderating effect of perceived policy effectiveness on residents' separation intention for rural household solid waste. Int J Environ Res Public Health 15: 726. doi: 10.3390/ijerph15040726
![]() |
[61] |
Oztekin C, Teksöz G, Pamuk S, et al. (2017) Gender perspective on the factors predicting recycling behavior: Implications from the theory of planned behavior. Waste Manage 62: 290–302. doi: 10.1016/j.wasman.2016.12.036
![]() |
[62] |
Zhou Y, Zhou Q, Gan S, et al. (2018) Factors affecting farmers' willingness to pay for adopting vegetable residue compost in North China. Acta Ecologica Sinica 38: 401–411. doi: 10.1016/j.chnaes.2018.04.001
![]() |
[63] | Pedhazur EJ (1997) Multiple Regression in Behavioral Research: Explanation and Prediction, 3rd Ed. New York: Holt, Rinehart and Winston, 1997. (p. 1058). Wadsworth. |
[64] |
Chu PY, Chiu J (2003) Factors Influencing Household Waste Recycling Behavior: Test of an Integrated Model. J Appl Soc Psychol 33: 604–626. doi: 10.1111/j.1559-1816.2003.tb01915.x
![]() |
[65] | Miles J, Shevlin M (2001) Applying Regression and Correlation: A Guide for Students and Researchers (First edition). SAGE Publications Ltd. |
[66] | Hair JF, Hult GTM, Ringle C M, et al. (2017) Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. J Acad Mark. 45: 616–632. |
[67] |
Fornell C, Larcker DF (1981) Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. J Mark Res 18: 382–388. doi: 10.1177/002224378101800313
![]() |
[68] |
Tenenhaus M, Vinzi V E, Chatelin Y-M, et al. (2005) PLS path modeling. Comput Stat Data An, 48: 159–205. doi: 10.1016/j.csda.2004.03.005
![]() |
[69] | Ipsos (2018) Global views on the environment–2018. Available from: https://www.ipsos.com/sites/default/files/Global_Views_on_the_Environment.pdf (accessed on 17-02-2020). |
[70] |
Stern PC (2000) New Environmental Theories: Toward a Coherent Theory of Environmentally Significant Behavior. J Soc Issues 56: 407–424. https://doi.org/10.1111/0022-4537.00175 doi: 10.1111/0022-4537.00175
![]() |
[71] |
de Leeuw A, Valois P, Ajzen I, et al. (2015) Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: Implications for educational interventions. J Environ Psychol 42: 128–138. doi: 10.1016/j.jenvp.2015.03.005
![]() |
[72] |
Ertz M, Huang R, Jo MS, et al. (2017) From single-use to multi-use: Study of consumers' behavior toward consumption of reusable containers. J Environ Manage 193: 334–344. doi: 10.1016/j.jenvman.2017.01.060
![]() |
[73] | Dutta S, Bhaskar S (2018) Bengaluru's All Women Trio Teaches Composting Via WhatsApp To Learners Across The World | Women's Day. NDTV-Dettol Banega Swasth Swachh India. Available from: https://swachhindia.ndtv.com/bengalurus-all-women-trio-teaches-composting-via-whatsapp-to-learners-across-the-world-17788/ |
[74] | CCAP (2015) Tackling waste through community-based composting – Bangladesh. Available from: https://ccap.org/assets/CCAP-Booklet_BangladeshCompost.pdf (accessed on 17-02-2020). |
[75] | Sutta S, Bhaskar S (2018) Bengaluru's All Women Trio Teaches Composting via WhatsApp to Learners across the World. Available from: https://swachhindia.ndtv.com/bengalurus-all-women-trio-teaches-composting-via-whatsapp-to-learners-across-the-world-17788/ (accessed on 17-02-2020). |
[76] | Pearson AR, Ballew MT, Naiman S, et al. (2017) Race, Class, Gender and Climate Change Communication. Oxford Research Encyclopedia of Climate Science Available from: https://doi.org/10.1093/acrefore/9780190228620.013.412 |
[77] | Ballew M, Marlon J, Leiserowitz A, et al. (2018) Gender Differences in Public Understanding of Climate Change. Yale Program on Climate Change Communication. Available from: https://climatecommunication.yale.edu/publications/gender-differences-in-public-understanding-of-climate-change/ |
![]() |
![]() |
1. | Fahimeh Asadi, Hamid Forootanfar, Mehdi Ranjbar, Ali Asadipour, Eco friendly synthesis of the LiY(MoO4)2 coral-like quantum dots in biotemplate MOF (QD/BioMOF) for in vivo imaging and ibuprofen removal from an aqueous media study, 2020, 13, 18785352, 7820, 10.1016/j.arabjc.2020.09.013 | |
2. | Baoting Sun, Muhammad Bilal, Shiru Jia, Yunhong Jiang, Jiandong Cui, Design and bio-applications of biological metal-organic frameworks, 2019, 36, 0256-1115, 1949, 10.1007/s11814-019-0394-8 | |
3. | Alireza Hashemzadeh, Gregor P. C. Drummen, Amir Avan, Majid Darroudi, Majid Khazaei, Ruhollah Khajavian, Abdolrasoul Rangrazi, Masoud Mirzaei, When metal–organic framework mediated smart drug delivery meets gastrointestinal cancers, 2021, 2050-750X, 10.1039/D1TB00155H | |
4. | Navid Rabiee, Monireh Atarod, Maryam Tavakolizadeh, Shadi Asgari, Mohsen Rezaei, Omid Akhavan, Ali Pourjavadi, Maryam Jouyandeh, Eder C. Lima, Amin Hamed Mashhadzadeh, Ali Ehsani, Sepideh Ahmadi, Mohammad Reza Saeb, Green metal-organic frameworks (MOFs) for biomedical applications, 2022, 335, 13871811, 111670, 10.1016/j.micromeso.2021.111670 | |
5. | Agni Puentes Ossa, Diego Julian Rodriguez, Julian Andres Salamanca Bernal, Computational Modeling of Light Scattering in Polymer Nanoparticles for Optical Characterization, 2024, 34, 1909-7735, 63, 10.18359/rcin.7276 |
Synthesized porous compounds | Specific Surface areas (m2/g) | Pore sizes (Å) | Names of drugs loaded in materials | Drugs loading capacity (%) | Time of release (days) |
Compound 6 | 2400 | 10 | Terazosine hydrochloride | 0.139 | 3 |
Telmisartan | 0.090 | 3 | |||
Glimpiride | 0.117 | 3 | |||
Rosuvastatin | 0.129 | 3 | |||
Compound 7 | 2500 | 10 | Terazosine hydrochloride | 0.195 | 5 |
Telmisartan | 0.055 | 1 | |||
Glimpiride | 0.138 | 3 | |||
Rosuvastatin | 0.095 | 3 | |||
Compound 8 | 2200 | 10 | Terazosine hydrochloride | 0.072 | 1 |
Telmisartan | 0.086 | 3 | |||
Glimpiride | 0.135 | 3 | |||
Rosuvastatin | 0.094 | 1 |
Synthesized porous compounds | Specific Surface areas (m2/g) | Pore sizes (Å) | Names of drugs loaded in materials | Drugs loading capacity (%) | Time of release (days) |
Compound 6 | 2400 | 10 | Terazosine hydrochloride | 0.139 | 3 |
Telmisartan | 0.090 | 3 | |||
Glimpiride | 0.117 | 3 | |||
Rosuvastatin | 0.129 | 3 | |||
Compound 7 | 2500 | 10 | Terazosine hydrochloride | 0.195 | 5 |
Telmisartan | 0.055 | 1 | |||
Glimpiride | 0.138 | 3 | |||
Rosuvastatin | 0.095 | 3 | |||
Compound 8 | 2200 | 10 | Terazosine hydrochloride | 0.072 | 1 |
Telmisartan | 0.086 | 3 | |||
Glimpiride | 0.135 | 3 | |||
Rosuvastatin | 0.094 | 1 |