Differences between conventional and emerging forms of water Governance (based on Tropp, 2007)
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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During this period I strengthened my background in genetic transformation and further developed my tissue culture skills.\nAfter that, I joined a short project on manipulating the life cycle of Streptomyces for commercial applications, including antibiotic production. Here I acquired skills on microbiology and protein activity assays, and developed my interest in applied science. Then I moved back into my career line in plants, and joined a project aimed to understand the molecular and physiological basis of the response of Eucalyptus to drought. The goal was very applied: to find molecular markers for marker-assisted selection or water-stress tolerant plants. During this period I acquired experience in physiological analysis, reversed-phase chromatography, HPLC, radioimmunoassay, field work, organisational and planning skills, and worked with large amounts of data. Very importantly, it was my first real postdoctoral position. 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Increasing demands of water for diverse uses of domestic, agricultural, and industrial consumption, both in quantity and quality, pose tremendous pressure on the need of well-articulated management approaches.Such approaches are expected to address several challenges such as limited supplies, deteriorated quality of available and produced water, conflicting interests of public stakeholders and groups, adverse environmental and ecological impacts, climate changes, etc. Large number of these approaches exist and still evolve and develop for different purpose including data management, operation and governance, conjunctive management of water and energy, asset management, and intelligent systems in water treatment process.
Planning and execution of water treatment processes involve a coordinated effort from different stakeholders. This involves collection of data (water availability data, water demand data, demographic data, water quality data, land use data etc.), identification of appropriate authority for their sources and development of effective communication to these authorities. Logistics involved in establishment of water treatment plants vary from country to country and depend on the country’s socio-political systems. For any city or town, municipality or city council authority is generally responsible for execution of water treatment plants, however their approval process may involve several stakeholders. Government system of any country significantly affects selection of water treatment processes (desalination plant, surface water treatment plant, groundwater treatment and selection of alternative water sources such as stormwater, rainwater and treated greywater). Collection of treatment plants’ performance monitoring data is essential for execution of similar projects. Now-a-days, climate change and their impacts on freshwater availability is a matter of great concern to water professionals. Scientific researches throughout the world anticipate reduction of water availability. Water treatment and water supply schemes require significant amount of energy (electricity) while water is essential for energy and electricity production. This paradox is widely known as “water energy nexus”. It is also known as “climate, water and energy nexus”. Significant progress has been made on uses of intelligent systems in real time monitoring of water quality and water treatment processes. All of these issues can generally be considered as advanced management issues in water treatment and management.
This chapter discusses a number of management approaches associated with different aspects. First, it discusses the data requirements for water treatment and water supply scheme. The management approaches are mostly tailored and oriented to achieve the scheme of Integrated Urban Water Management (IUWM). Second, the chapter discusses the governance in water treatment and water supply scheme. Water governance is defined as the political, social, economic and administrative systems developed to manage water resource and to deliver needed societal water services. Suitable water governance for some sector depends on the societal and environmental conditions prevailing in that sector. Efficient governance system leads to efficient operation of the water system and adequate benefit of the technologies applied in this system. Different classes of water governance, mainly public, public-private, and private systems, are discussed and evaluated. Third, the chapter discusses the joined management of water and energy resources practiced in many cases, known as water energy nexus. Finally, intelligent systems in water treatment process is addressed.
Because of rapidly increasing population, tremendous pressure on water quantity and quality is generally observed across all aspects of urban water cycle. Urban water cycle includes water supply, wastewater, stormwater, groundwater and aquatic ecosystems (Fletcher et al., 2008). Water treatment and water supply cannot be considered as an individual component of urban water cycle, rather this is connected to other components. For example collection, storage, treatment and distribution of stormwater for non-potable domestic consumptions (toilet flushing, gardening) may affect groundwater recharge and water quantity and quality in the downstream of streams. Therefore it is important to manage these interactions between different urban water cycle components. The term Integrated Urban Water Management (IUWM) indicates management of individual water cycle component in an integrated way. Availability of appropriate database is the prerequisite for reliable integrated management of individual water cycle components (water treatment, stormwater, wastewater etc.). Monitoring is a fundamental part for water treatment processes and monitoring data is also important for adaptive management of water projects.
Water treatment process is a component of water supply scheme. Components of water supply scheme are collection of raw water, conveyance of raw (untreated) water, supply reservoir, treatment plant, transportation of treated water, distribution system and finally water consumers. Water treatment process is linked with other components of water supply scheme. Water supply scheme is intrinsically related with other components of IUWM. Wastewater may enter into the water supply scheme through leakage on water reticulation
Data requirements guidelines for water treatment processes as a component of integrated urban water management (based on
pipes. More uses of treated water generate more greywater and wastewater. Monitoring of water quality data is an intrinsic property of water supply scheme. Different agencies may collect and store data on various individual components of IUWM. Therefore data exchange between agencies is significantly important for promoting the IUWM concepts. A guideline for data requirements for an integrated urban water scheme is shown in Figure 1 (Fletcher et al., 2008).
Uncertainty is generally involved in monitoring programs. An ideal monitoring program should consistsof monitoring data, results and/or predictions from these data analyses and analysis of uncertainty involve in data and predicted results. Uncertainty arises from two sources, bias errors such as systematic error from erroneous sensor calibration and random error from natural processes and equipment measurement variations. When uncertainty level is more than allowable limit, the monitoring programs need to be redesigned.
As of now, water sector is driven by investments in technological innovations and development of infrastructures. The goal of such investments is to allow access of large number of people to water supply. However, there are many cases where infrastructures are not operated in an effective manner. In such cases, benefits from appropriate technologies are not fully utilized. This is because of lack of good governance in water management. According to Global Water Partnership (2003), governance can be defined as the range of political, social, economic and administrative systems that are in place to develop and manage water resources, and to deliverwater services to different levels of society. There are two main values on which good governance rests, inclusiveness (ensures that all members of the group receive equal treatment) and accountability (ensures that those in authority answer to the group they serve if things go wrong and are credited when things go well) (Tropp, 2007).
There are four competing approaches on how water in different processes should be governed; these are [1] Water (drainage, sanitation, recycling, and reuse) seen as an economic good or commodity, with IWRM focus, [2] Water (and sanitation) seen as a human right and a social good and can be complementary to other approaches, [3] water (drainage and ecological sanitation seen as a socio-ecological good, also can be seen as a h, and human right as well as the right of other living beings and ecosystems, [4] water (and sometimes sanitation) seen as a sector. It is important to realize that for water governance of a city how the territorial dimension of water governance is dealt with and from which perspective. In particular, thre groups here should be distinguished; [1] those mainly looking at water from outside the cities, [2] those mainly looking at water from within the cities, and [3] those mainly looking at water from a multi-scalar perspective combining the global and regional scale with the city, its territory, and the neighborhoods within the cities and vice-versa (Miranda et al., 2011).
Governance processes determine decision making about selection of water source, water storage, regulation of extraction from aquifers, regulation of discharges, and allocation between competing end users including allocations for environmental flow. For example, choice between a desalination plant and a large dam is an issue of water governance. Good water governance is significantly linked to strong policy, legal, and regulatory frameworks; effective implementing organizations; community involvement to improve water governance; and appropriate investments. Good governance ensures appropriate linkages and processes between and within organizations and social groups involved in decision-making, both horizontally across sectors and between urban and rural areas, and vertically from local to international (Rogers and Hall, 2003). Water governance includes private sector and civil society in addition to the government. The differences between conventional and emerging concept of water governance is given in Table 1. Many decision-makers and water managers are currently not prepared enough to deal with new forms of governance issues. The new concept of water governance involves conflict mediation, mobilization of communities, partnership formation, managing processes of stakeholder dialogue and participation. Generation of knowledge and capacity building in water governance is therefore necessary.
There are mainly three forms of governance arrangements. These are public, public-private and private governance. In public governance system, government takes on all of the responsibilities and challenges of water and wastewater services. Throughout the world, about 85% of drinking water supply provision lies under the public governance. Municipal authority or City Corporation is generally responsible for water supply and wastewater schemes. Another form of public management involves cooperatives and user associations.
Emphasizes the government and bureaucracy | Emphasizes to civil society and markets. The government and bureaucracy are still important entities but with reduced authority |
Political power monopoly | Co-steering |
Steering | Steering diversity of actors and power diffusion |
Hierarchical control | Horizontally shared control |
Enforcement of rules and regulations | Inter-organizational relations and coordination |
Control | Formal and informal institutions |
Top-down management | Co-governing (distributed governance); Decentralization/bottom-up management |
Formal institutions | Network governance |
Inter-governmental relations | Process orientation; expansion of voluntary exchange; self-governance and market mechanisms; dialogue and partnership; participation and negotiation |
Disciplinary knowledge based | Multidisciplinary knowledge based |
Differences between conventional and emerging forms of water Governance (based on Tropp, 2007)
In this system, customers have decision-making power through participation in elections for different water authorities. The system is externally audited annually.
A public-private governance mechanism in water sector involves transferring asset management or operations of a public water system into private sectors. Several public-private arrangements are service contracts, management contracts, leases, concessions and build-own-transfer programs. In this governance system, ownership of water systems can be distributed between public and private shareholders in a corporate utility. Majority ownership is usually kept within the public sector. In service contract, a private company is responsible for a specific task, such as meter installation, distribution pipe maintenance or collection of bills. Under management contracts, government transfers certain operation and maintenance activities to a private company. Under the concession and Build Operate Transfer (BOT) models, capital investment, commercial risk, operations and management of the project are undertaken by the private sector. Concessions are usually long-term to allow the private company to recover its investments. At the end of the contract, assets are either transferred back to the public sector or another concession is granted. In the BOT model, the role of government is predominately regulatory. BOT models are usually used for water and wastewater treatment plants.
In the private governance system, government transfers the water business to the private sector through sale of shares or water rights of the public entity. In this system, infrastructure, capital investment, commercial risk, and operations and management become the responsibility of the private provider. This model is not generally adopted.
The close connection between energy and water is generally known as water energy nexus. Significant amount of water is required to create energy. For example, water is used for fuel extraction, refining and production; hydropower generation; and in cooling steam electric power plants fueled by coal, oil, natural gas and nuclear power. In the United States of America, besides agricultural consumption, energy production and power generation systems are major users of freshwater resources (Younos et al., 2009). On the other hand, huge amount of power (energy) is required for water supply schemes – from untreated water collection to treated water distribution. Power is used to operate pumps in water supply scheme and in operating water treatment plants. Irrigation of water requires power. Collection, treatment and disposal of wastewater require power. Uses of alternative water resources need power to operate dual reticulation system. For domestic water heating and cooling purposes, power is required. Shortage of any one or both of water and energy will reduce them. Because of climate change, it is anticipated that available water resources will be reduced in some parts of the world– which will then affect energy production. The impact of climate change on water energy nexus is sometimes called “climate, water and energy nexus”. Interrelationships between water and energy are shown in Figure 2.
Table 3 shows average water consumption for different energy production and power generation techniques. The data presented in Table 3 are predominantly for the United States of America and were compiled by Younos et al. (2009). Natural gas production is found to be the most water efficient. Corn-based and soy-based biofuel production technologies are the most water intensive. Biofuels are also water intensive because significant amounts of water are required during crop growth. In terms of technology, hydroelectric power generation technique is the most water efficient technology. This is because used water is returned to the source.
Interrelationships between water and energy (U.S. Department of Energy, 2006)
Coal | 41 - 164 | Coal mining operation, transport and storage, refining process; lubricate drilling equipment, post-mining activities such as land reclamation and revegetation |
Natural gas | 3 | Drilling operation and gas purification, water exerted during drilling operation are reinjected to aquifer |
Petroleum/Oil | 1200 - 2420 | Drilling operation, refining process, water exerted during drilling operation are reinjected to aquifer |
Corn-Ethanol | 2510 - 29100 | Irrigation water demand for corn production |
Soy-Biodiesel | 14000 - 75000 | Irrigation for Soya bean |
Hydroelectric power generation | 20 | Evaporation water loss |
Fossil fuel thermoelectric power generation | 1100 - 2200 | Steam turbine operation, cooling of turbine exhaust, condenser and reactor cooling |
Nuclear power | 2400 - 5800 | Uranium mining and processing, nuclear reactor |
Geothermal power | 130 | Steam turbine, evaporation loss |
Solar thermoelectric power | 230 - 270 | Steam generation, coolant and cleaning purposes |
Water consumptions for energy production and power generation (based on Younos et al., 2009)
Water supply and wastewater treatment systems are energy intensive. About 35% of total energy used by municipalities is used for operation of water and wastewater treatment plants. In the United States of America, about 1.4% of their total energy is consumed for water and wastewater treatment processes (Elliot, 2005). Water demand reduction can decrease energy uses in pumping and treating water. Reduction of volume of wastewater to wastewater treatment plant can also reduce the energy requirements. Adoption of energy efficient technologies can reduce water consumption, and ultimately energy use. Use of alternative sources of energy in water supply and wastewater schemes will reduce greenhouse gas emissions. For example, uses of solar and wind energy to pump water and to heat household hot water tanks can reduce energy consumption (Thrilwell et al., 2007).
Water treatment processes are governed by a set of complex non-linear relationships between physical, chemical, biological and operational parameters. Traditionally these process relationships are fitted with mathematical models using bench-scale data. These models often perform poorly when two or more key process parameters change simultaneously and in the application of real world treatment plants (Baxter et al., 2001). Plant operators require appropriate tools so that appropriate plant operation conditions are maintained in order to achieve desired effluent quality based on instantaneous monitoring of influent water quality. Coagulation, flocculation and sedimentation processes in water treatment are a prime example. The Jar test is conventionally applied for determination of optimal single coagulant dose. Optimization of multiple treatment chemicals for removal of both particulate matters and organics is a challenging task, particularly if influent water quality changes significantly. Plant operators are generally depending on deterioration of effluent water quality instead of pro-active optimization of treatment chemicals subject to influent water quality.
Artificial Intelligence (AI) is the technique that can control plant operations in a pro-active way. Recent significant development in computing methods opens the door for application of artificial intelligence techniques in water treatment process controls and optimization.
A schematic diagram of SCADA system in water treatment process(based on
Application of Artificial Neural Network (ANN) in water treatment problems is an example of artificial intelligence.
The Supervisory Control and Data Acquisition (SCADA) system is the basic infrastructure for process control of water treatment plants (Grady et al., 1999). The SCADA system is capable of monitoring treatment process, real time collection of process data and works as a communication network between treatment plant and other utilities (Baxter et al., 2001). Typical components of a SCADA water treatment system are (1) communication networks (2) computers (3) Programmable Logic Controllers – PLCs and (4) online instruments. Conceptually the SCADA system has three layers namely hardware layer, network layers and application layer. Figure 3 shows a typical water treatment SCADA structure.
As an example, the EPCOR’s E.L. Smith water treatment plant in Alberta, Canada (http://www. corp.epcor.com/watersolutions/operations/edmonton/Pages/el-smith-water-treatment-plant.aspx) applied neural network raw water quality classifier to monitor real time raw water quality. The online quality analyzer sends raw water quality data to the central process control computer through the SCADA system where the data are analyzed by raw water quality classifiers before entering to central database system. If any new data or error is detected, an alarm system is activated which requires a plant operator’s investigation of sensors. Because of wireless SCADA system, plant operators can monitor the water treatment process operation from a remote computer.
Four management aspects were discussed in this chapter; these are (1) data management for water treatment and water supply scheme, (2) governance in water treatment and water supply scheme, (3) water energy nexus, and (4) intelligent systems in water treatment process.
For data management, the chapter discussed guidelines required in water treatment processes as a component of IUWM. The guidelines include [1] local guidelines originated from municipalities, water authorities, and environmental agencies, [2] monitoring database with different hydraulic, quality, and ecological records, [3] guidelines for water cycle component with relevant standards and operational and environmental data, and [4] data representation techniques. Monitoring programs and uncertainties involved in these programs were also discussed.
For water governance, it is found that lack of good governance in water management results in infrastructures not operated in an effective manner and hence benefits from appropriate technologies can’t be fully utilized. The public governance, even though may not be the best choice in managing and operating some water sectors, it is found to be the most common system applied worldwide (about 85% applied in drinking water systems). Pure private governance system is not generally adopted.
Discussion of water energy nexus and data related to USA indicates that natural gas production is the most water efficient while corn-based and soy-based biofuel production technologies are the most water intensive. Hydroelectric power generation is also found the most water efficient technology from technology point of view where the used water is returned to the source.
As of Artificial Intelligence and its applications in managing water systems, an example of the EPCOR’s E.L. Smith water treatment plant in Alberta, Canada, applied neural network and SCADA system to monitor and control real time raw water quality. Recent advancements in computing methods reflects the growing and significant contribution of applying artificial intelligence techniques in better management and control of water treatment process.
The improvement of effectiveness of pollutant removal from water and sewage using activated carbons has been a subject of numerous studies. The influence of adsorbate, adsorbent, solution properties, and experiment conditions on adsorption process has been analyzed.
Noteworthy is the impact of adsorbate properties on adsorption process such as solubility, molecule dimension, ability to dissociation, and physicochemical properties. Hydrophobicity which can be expressed by solubility is the main driving force of adsorption process of organics from aqueous solutions on activated carbons. The lower solubility of a pollutant is the highest adsorption on hydrophobic carbon is observed. Additionally, analyzing adsorption of pollutants from aqueous solutions on microporous materials, it is necessary to take into account molecule dimension due to a possible sieve effect. Moreover, the influence of functional groups on adsorbate aromatic ring on the differentiation of adsorptive affinity of organic compounds to activated carbon should be also regarded.
The influence of adsorbate substituents on adsorption mechanism is similar to that of surface groups of adsorbent. Depending on their nature, they can attract or repel electrons and affect the dispersive interactions between adsorbate aromatic ring and graphene layers of activated carbon. The adsorbate functional groups that are electron donors activate the aromatic ring by moving electrons toward it, and thereby they enhance the interactions between adsorbate molecule and π electrons of adsorbent graphene planes. On the other hand, the deactivating groups as electron acceptors reduce the electron density of aromatic ring; thus, interactions of adsorbate-adsorbent surface are weakened [1, 2, 3, 4, 5, 6, 7].
The research on effect of adsorbate properties on adsorption process is an extension of the studies already published in the paper [8]. The pesticides belonging to a group of chloride phenoxyacid derivatives were used as adsorbates in view of their common usage in agriculture and hence a high probability of infiltration to surface and underground waters. Their presence in water affects its quality, worsens its properties, and in some cases makes it unsuitable to consume. These pesticides show a carcinogenic activity on living organisms and a relatively long half-life time in the environment; therefore, the intensive study on their removal by adsorption process is very important for practical applications. The experimental studies include measurements of the adsorption isotherms and concentration rate profiles as well as their interpretation on the basis of the generalized Langmuir (GL) equation for equilibrium data and diffusion models (intraparticle diffusion model (IDM) and pore diffusion model (PDM)) and multi-exponential (m-exp) equation for kinetic data. Based on the obtained results, the correlations between adsorbate structure, its properties, and adsorption uptake and rate were analyzed. The evaluation of the theoretical equilibrium and kinetic equations and models based on a fitting quality and consistency with adsorption mechanism was also made.
The selected adsorbates comprise 21 compounds which are structurally differentiated with regard to the number, position, and type of the functional groups and length and spatial arrangement of the hydrocarbon part in a molecule. Not all of these substances are used as pesticides because of their low biological activity. However, due to complexity of the research issues, it seems to be reasonable to include them as a subject of the investigation.
Table 1 presents the physicochemical properties of the adsorbates collected on the basis of literature data or calculations using computer programs generally available. All the substances are analytical reagent grade purity. The chemical names and abbreviations of them are as follows: 4-chlorophenoxyacetic acid (4-CPA); 2,4-dichlorophenoxyacetic acid (2,4-D); 2,4,5-trichlorophenoxyacetic acid (2,4,5-CPA); 2,4,6-trichlorophenoxyacetic acid (2,4,6-CPA); 4-chloro-3-methylphenoxyacetic acid (4-CMPA); 4-chloro-2-methylphenoxyacetic acid (MCPA); 2,4-dibromophenoxypropionic acid (2,4-BrPA); 2,4,6-tribromophenoxypropionic acid (2,4,6-BrPA); 2-(3-chlorophenoxy)propionic acid (3-CPP); 3-(4-chlorophenoxy)propionic acid (4-CP); 2-(4-chlorophenoxy)propionic acid (4-CPP); 2-(2,4-dichlorophenoxy)propionic acid (2,4-CPP); 2-(2,5-dichlorophenoxy)propionic acid (2,5-CPP); 2-(3,4-dichlorophenoxy)propionic acid (3,4-CPP); 2-(2,4,5-trichlorophenoxy)propionic acid (2,4,5-CPP); 2-(4-chloro-2-methylphenoxy)propionic acid (MCPP); 2-(4-chloro-3-methylphenoxy)propionic acid (4-CMPP); 2-(4-chlorophenoxy)-2-methylpropionic acid (CFA); 2-(4-chlorophenoxy)butanoic acid (4-CPB); 4-(4-chloro-2-methylphenoxy)butanoic acid (MCPB); and 2-(4-chloro-2-methylphenoxy)butanoic acid (4-CMPB).
Common name | pKa [9] | Solubility g/dm3 20–25°C | Kow/log D [9] | Kow/log P | Dmin /Å [9] | Dmax /Å [9] | |
---|---|---|---|---|---|---|---|
pH = 1.81 | pH = 10.38 | ||||||
4-CPA | 3.14 | 0.848 [10] | 1.88 | −1.63 | 1.85 [11] | 4.33 | 9.38 |
2,4-D | 2.80 | 0.682 [10] | 2.46 | −1.03 | 2.37 [11] | 4.88 | 9.49 |
2,4,5-CPA | 2.56 | 0.268 [10] | 3.04 | −0.42 | 2.89 [11] | 6.22 | 8.88 |
2,4,6-CPA | 2.57 | 0.247 [10] | 3.04 | −0.42 | 2.89 [11] | 5.42 | 8.84 |
4-CMPA | 3.36 | 0.650 [12] | 2.40 | −1.12 | 5.42 | 8.96 | |
MCPA | 3.36 | 0.825 [10] | 2.40 | −1.12 | 3.25 [13] | 5.47 | 9.52 |
2,4-BrPA | 1.96 | 0.059 [14] | 2.60 | −0.70 | 5.03 | 9 | |
2,4,6-BrPA | 1.50 | 0.015 [15] | 3.12 | 0.07 | 5.71 | 9 | |
3-CPP | 3.27 | 1.200 [16] | 2.45 | −1.06 | 5 | 7.79 | |
4-CP | 3.70 | 0.770 [12] | 2.13 | −1.39 | 4.33 | 9.87 | |
4-CPP | 3.27 | 1.475 [10] | 2.45 | −1.06 | 5.22 | 8.65 | |
2,4-CPP | 2.95 | 0.829 [10] | 3.04 | −0.46 | 5.59 | 9.03 | |
2,5-CPP | 2.95 | 0.181 [14] | 3.04 | −0.46 | 5.62 | 8.63 | |
3,4-CPP | 2.94 | 0.130 [12] | 3.04 | −0.46 | 5.5 | 8.76 | |
2,4,5-CPP | 2.70 | 0.140 [10] | 3.62 | 0.15 | 3.80 [13] | 5.62 | 9 |
MCPP | 3.47 | 0.895 [10] | 2.97 | −0.55 | 3.13 [13] | 5.86 | 8.64 |
4-CMPP | 3.46 | 0.690 [15] | 2.97 | −0.55 | 5.86 | 7 | |
CFA | 3.37 | 0.582 [17] | 2.89 | −0.63 | 2.84 [13] | 4.42 | 9.14 |
4-CPB | 3.42 | 0.315 [14] | 2.98 | −0.54 | 5.22 | 9.7 | |
MCPB | 3.59 | 0.048 [10] | 3.50 | −0.03 | 3.28 [18] | 6.36 | 9.65 |
4-CMPB | 3.59 | 0.17 [12] | 3.50 | −0.03 | 5.86 | 9.28 |
Physicochemical properties of the studied pesticides, where pKa is the value based on partial charge distribution in a molecule, log D is the octanol-water coefficient at a given pH, log P is the partition coefficient of a compound between octanol and water, and Dmin and Dmax are measures between the most distant molecule atoms.
The microporous activated carbon F300 was used as adsorbent (
Substituents on benzene ring of aromatics have a great influence on adsorption process on activated carbon. It is not directly related to their interaction with an adsorbent but mainly with changes in molecular properties of adsorbed compound [19, 20, 21, 22, 23]. The crucial factors are the nature of the substituents, number, and their mutual position on benzene ring. In the study the adsorption process of compounds with substituents with electron-acceptor (▬Cl, ▬Br) and electron-donor properties (▬CH3) was analyzed. It is well known that substituents affect acid-base properties (pKa), water solubility, hydrophobicity (Kow), dipole moment, molecular weight, and spatial dimension in space of an organic compound. In turn, these properties affect a mechanism and a range of adsorption process. Nevertheless, solubility in water and hydrophobicity of substance most often play a superior role in adsorption process.
As pKa values of the studied pesticides are within the range of 1.50–3.70, thus, the buffers of pH = 1.81 or 10.38 were used to prepare the experimental solutions. Thus, the compounds in solutions were nearly undissociated and completely dissociated, respectively. In the case of the compounds of very low solubility, the studies at alkaline pH conditions were carried out. For the systems for which the adsorption process was carried out in acidic solutions, the isotherms both in the standard linear
In Figures 1 and 2 the comparison of the adsorption isotherms of chlorophenoxyacetic and chlorophenoxypropionic acid derivatives with a different type and number of substituents on aromatic ring on activated carbon F300 at pH = 1.81 is shown. In the case of phenoxyacetic pesticides, relatively small changes in capacities are observed which can be arranged as follows: 2,4-D ∼ 4-CPA > MCPA. The differences in the course of adsorption isotherms for the phenoxypropionic pesticides are greater. Taking into account their affinity to the carbon F300, it looks as follows: 2,4-CPP ∼ 4-CPP > MCPP > 4-CMPP.
The 2,4-D; MCPA; and 4-CPA adsorption isotherms on F300, pH = 1.81 in linear (a) and reduced coordinate system (b).
The 4-CPP; 2,4-CPP; MCPP; and 4-CMPP adsorption isotherms on F300, pH = 1.81 in linear (a) and reduced coordinate system (b).
The solubility values were collected from literature, and partition coefficients between octane and water (
The mechanism of adsorption process is mainly based on the dispersion or donor-acceptor interactions (pH = 1.81, thus both adsorbates and functional groups on active carbon surface are undissociated) and the local repelling electrostatic interactions between positively charged sites on adsorbent surface (as a result of attraction and localization of graphene plane electrons by oxygen functional groups) and a local positive charge in adsorbate molecule (as a result of electron-acceptor properties of chlorine on benzene ring). Generally, the more substituents in a molecule, the greater the local charge and stronger the local electrostatic interactions.
Another aspect concerns a spatial structure of compounds, as the ones with substituents in a para-position have better access to adsorbent micropores. In particular, this applies for halogenophenoxyacetic acids, where the hydrocarbon part is not branched, like in β-halogenophenoxypropionic acids. Therefore, the capacities for 4-CPA and 2,4-D, as well as for 4-CPP and 2,4-CPP, are similar, although comparing the solubility and log D values, one could assume a different situation. A simple explanation is a symmetrical structure of 4-CPA, which means that its molecular volume is smaller than for 2,4-D which enables a better access and greater packing density in the adsorptive space. In a group of phenoxypropionic acid derivatives, the 4-CPP and 2,4-CPP adsorptions are higher than for MCPP and 4-CMPP. The molecules of all these compounds have at least one
In Figure 3, the comparison of the adsorption isotherms of chloro- and bromophenoxyacetic derivatives from solutions at pH = 10.38 is presented. The adsorption decreases in the series: 2,4,6-BrPA > 2,4-BrPA > 2,4,6-CPA ∼ 2,4-D > 4-CPA > MCPA. At alkaline conditions the electrostatic interactions are predominant in adsorption mechanism due to dissociated forms of both the adsorbate and the adsorbent functional groups. The repulsive forces between the pesticide anions and negatively charged adsorbent groups appear. However, a halogen with the electron-withdrawing properties on benzene ring decreases the total electron charge of the aromatic ring and shows a local positive charge. The obtained effect results in the creation of local attractive interactions which decrease the electrostatic repulsion between adsorbate and adsorbent. If a degree of halogen substitution increases, the strength of interactions increases, and it leads to stronger adsorption on active carbon. This explanation is true if substituents in a molecule are of the same type, e.g., bromine or chlorine. Comparing the adsorption affinities in the systems with different substituents, it can be observed that the compounds containing bromine show a stronger affinity for the adsorbent surface compared to compounds with chlorine, regardless of a number of substituents in molecule. In the case of MCPA containing a methyl group with electron-donor properties, the weakest adsorption is observed. Increase of the electron density within aromatic ring raises the electrostatic repulsion with the negatively charged surface of the activated carbon. A similar effect is observed in the case of phenoxypropionic acid derivatives with a different halogen substituent in the para-position at acidic pH.
The 2,4-D; 2,4-BrPA; 2,4,6-CPA; and 2,4,6-BrPA adsorption isotherms on F300, pH = 10.38 (a). The 4-CPA; 2,4-D; and MCPA adsorption isotherms on F300, pH = 10.38 (b).
In Figure 4a a comparison of the adsorption isotherms for 2,4-D, 2,4,5-CPA, and 2,4,6-CPA, i.e., the compounds with a differentiated number and position of chlorine atoms on benzene ring, is presented. Similar adsorption is observed for 2,4-D and 2,4,6-CPA, whereas for 2,4,5-CPA the process is significantly greater. 2,4-D shows the weakest hydrophobic properties; its molecule contains two chlorine atoms. The other two compounds 2,4,5-CPA and 2,4,6-CPA are characterized by the similar log D values, but they contain three chlorine atoms. Therefore, the differences in adsorption affinity to adsorbent should be attributed to the steric effect. In the group of chlorophenoxypropionic acids differentiated in terms of a number of substituents in a molecule, the adsorbate affinity to the adsorbent active sites increases in the order 2,4,5-CPP > 2,4-CPP > 2,5-CPP > 4-CPP (Figure 4b). The octanol/water partition coefficient values (log D) for these compounds at pH = 10.38 are 0.15, −0.46, −0.46, and −1.06, respectively. 2,5-CPP is weakly removed at a given pH than its isomeric form 2,4-CPP although its hydrophobicity should promote the adsorption process. Probably an arrangement of the substituents in 2,5-CPP leads to difficulties in diffusion of the adsorbate molecules into small pores of adsorbent that consequently decreases adsorption.
The 2,4-D; 2,4,5-CPA; and 2,4,6-CPA adsorption isotherms on F300, pH = 10.38 (a). The 4-CPP; 2,4-CPP; 2,5-CPP; and 2,4,5-CPP adsorption isotherms on F300, pH = 10.38 (b).
The comparison of the 4-CMPA and 4-CPA (pH = 10.38) adsorption isotherms (Figure 5) shows that removal of the methyl group weakens the adsorption affinity. Similar behavior occurs for their phenoxypropionic and phenoxybutanoic homologs, for which the adsorption of 4-CMPP > 4-CPP and 4-CMPB > 4-CPB (Figures 6 and 7). The observed trend fully coincides with the hydrophobicity of pesticides. For the series of 4-CMPA and 4-CPA, log D is −1.12 and −1.63, respectively; for 4-CMPP and 4-CPP, log D is equal to −0.55 and −1.06, respectively, while for 4-CMPB and 4-CPB, this parameter is −0.03 and −0.54, respectively.
The 4-CPA and 4-CMPA adsorption isotherms on F300, pH = 10.38 in linear (a) and reduced coordinate system (b).
The 4-CPP and 4-CMPP adsorption isotherms on F300, pH = 10.38 in linear (a) and reduced coordinate system (b).
The 4-CPB and 4-CMPB adsorption isotherms on F300, pH = 10.38 in linear (a) and reduced coordinate system (b).
The adsorption affinity of organics depends on a number and type of substituents, as well as on their position on the benzene ring. This is related to the occurrence of the steric and inductive effects of adsorbate. The courses of adsorption isotherms measured at alkaline pH for 2,4,5-CPA and 2,4,6-CPA (Figure 8) and 2,4-CPP and 2,5-CPP (Figure 9a) indicate that the less symmetric distribution of electron charge density in benzene ring is, the stronger adsorption affinity is observed. Therefore, higher affinities for 2,4,5-CPA and 2,4-CPP than for 2,4,6-CPA and 2,5-CPP are found. Analyzing the solubility of these substances, one can say that this parameter is not a main factor controlling the adsorption process.
The 2,4,5-CPA and 2,4,6-CPA adsorption isotherms on F300, pH = 10.38 in linear (a) and reduced coordinate system (b).
The 2,4-CPP and 2,5-CPP adsorption isotherms on F300, pH = 10.38 (a). The 2,4-CPP; 2,5-CPP; and 3,4-CPP adsorption isotherms on F300, pH = 10.38 (b).
As results from Figure 9b at basic conditions for the isomers of dichlorophenoxypropionic acid, their affinity to the activated carbon increases as follows: 3,4-CPP > 2,4-CPP > 2,5-CPP. The strongly adsorbed compound is characterized by two adjacent chlorine atoms with electron-acceptor properties, causing the overlap of local positive charges in the benzene ring. The negatively charged oxygen groups on the carbon surface interact electrostatically with the positive local charge of the pesticide rings, intensifying the adsorption process. The interactions of other compounds with F300 are disturbed by the steric effect due to the position of substituent at the second carbon in the ring and in the case of 2.5-CPP additionally due to a symmetrical distribution of the σ+ charge. This explains the differentiation in adsorption of these compounds.
The comparison of the isotherms for 4-CMPP and MCPP at alkaline pH (Figure 10a) gives information that a shift of methyl group position on aromatic ring from ortho- to meta-position does not affect adsorption process. The same situation is observed for their homologs from the phenoxybutanoic group; the adsorption affinity of both compounds to activated carbon is similar (Figure 10b).
The 4-CMPP and MCPP adsorption isotherms on F300, pH = 10.38 (a). The 4-CMPB and MCPB adsorption isotherms on F300, pH = 10.38 (b).
To analyze the impact of hydrocarbon chain structure on the adsorption process, the systems with chlorogenic phenoxyacetic derivatives and their homologs—phenoxypropionic and phenoxybutanoic derivatives—were studied. The graphs are grouped with regard to hydrocarbon chain structure, while the aromatic part of molecules is the same. In the case of adsorption processes carried out at pH = 10.38, the adsorption affinities of the studied compounds are as follows: 2,4,5-CPA > 2,4,5-CPP (Figure 11a), 2,4-D > 2,4-CPP (Figure 11b), MCPP ∼ MCPB (Figure 12a), 4-CMPA > 4-CMPP > 4-CMPB (Figure 12b), and 4-CPA ∼ 4-CP > 4-CPP ∼ 4-CPB > CFA (Figure 13). For the systems for which it was possible to measure the adsorption process at pH = 1.81, the adsorption affinities of pesticides are 2,4-D ∼ 2,4-CPP (Figure 14), MCPA > MCPP (Figure 15), and 4-CPA > 4-CPP > CFA (Figure 16).
The 2,4,5 CPA and 2,4,5-CPP adsorption isotherms on F300, pH = 10.38 (a). The 2,4-D and 2,4-CPP adsorption isotherms on F300, pH = 10.38 (b).
The MCPP and MCPB adsorption isotherms on F300, pH = 10.38 (a). The 4-CMPA; 4-CMPP; and 4-CMPB adsorption isotherms on F300, pH = 10.38 (b).
The 4-CPA; 4-CPP; 4-CP; 4-CPB; and CFA adsorption isotherms on F300, pH = 10.38 (a). The MCPP and CFA adsorption isotherms on F300, pH = 1.81 (b).
The 2,4-D and 2,4-CPP adsorption isotherms on F300, pH = 1.81 in linear (a) and reduced coordinate system (b).
The MCPA and MCPP adsorption isotherms on F300, pH = 1.81 in linear (a) and reduced coordinate system (b).
The 4-CPA; 4-CPP; and CFA adsorption isotherms on F300, pH = 1.81 in linear (a) and reduced coordinate system (b).
Analyzing the coefficient
All the studied experimental systems were analyzed by the generalized Langmuir isotherm equation (GL) [24, 25, 26] that for the specific values of heterogeneity parameters are reduced to four equations: Langmuir (L, m = n = 1), Langmuir-Freundlich (LF, 0 < m = n ≤ 1), generalized Freundlich (GF, 0 < m ≤ 1, n = 1), and Tóth isotherm (T, m = 1, 0 < n ≤ 1). These isotherms were obtained from the global integral equation assuming that the Langmuir isotherm is a local one. In the optimization procedure, a method of the minimal sum of square deviations of adsorption values was used assuming the limitation of adsorption capacity (am ≤ 20 mmol/g) and the equilibrium constant K (K ≤ 105 l/mmol). Table 2 presents the parameters of this equation, the correlation coefficients R2, and standard deviations SD(a).
System, pH (type of isotherm) | am | m | n | log K | R2 | SD(a) |
---|---|---|---|---|---|---|
4-CPA/F300, pH 1.81 (T) | 3.34 | 1 | 0.40 | 1.59 | 0.957 | 0.087 |
4-CPA/F300, pH 10.38 (GL) | 1.25 | 0.25 | 0.78 | −0.35 | 0.940 | 0.043 |
2,4-D/F300, pH 1.81 (T) | 3.69 | 1 | 0.35 | 1.81 | 0.968 | 0.079 |
2,4-D/F300, pH 10.38 (GF) | 1.19 | 0.35 | 1 | 0.14 | 0.960 | 0.035 |
2,4,5-CPA/F300, pH 10.38 (GF) | 1.29 | 0.25 | 1 | 0.01 | 0.907 | 0.054 |
2,4,6-CPA/F300, pH 10.38 (GF) | 1.07 | 0.38 | 1 | 0.46 | 0.923 | 0.036 |
4-CMPA/F300, pH 10.38 (GF) | 15.11 | 0.17 | 1 | −7.25 | 0.936 | 0.030 |
MCPA/F300, pH 1.81 (GF) | 3.09 | 0.34 | 1 | −0.45 | 0.937 | 0.110 |
MCPA/F300, pH 10.38 (GF) | 20 | 0.20 | 1 | −7.04 | 0.766 | 0.064 |
2,4-BrPA/F300, pH 10.38 (GF) | 1.83 | 0.33 | 1 | −0.49 | 0.957 | 0.046 |
2,4,6-BrPA/F300, pH 10.38 (GL) | 20 | 0.32 | 0.29 | −3.71 | 0.949 | 0.056 |
3-CPP/F300, pH 1.81 (L) | 1.92 | 1 | 1 | 0.93 | 0.948 | 0.082 |
4-CP/F300, pH 10.38 (GF) | 20 | 0.24 | 1 | −5.53 | 0.955 | 0.033 |
4-CPP/F300, pH 1.81 (L) | 2.02 | 1 | 1 | 0.96 | 0.977 | 0.058 |
4-CPP/F300, pH 10.38 (GF) | 20 | 0.18 | 1 | −7.73 | 0.939 | 0.031 |
2,4-CPP/F300, pH 1.81 (T) | 2.41 | 1 | 0.48 | 1.79 | 0.96 | 0.081 |
2,4-CPP/F300, pH 10.38 (GF) | 1.02 | 0.32 | 1 | 0.181 | 0.916 | 0.035 |
2,5-CPP/F300, pH 10.38 (T) | 1.32 | 1 | 0.29 | 2.94 | 0.896 | 0.032 |
3,4-CPP/F300, pH 10.38 (GF) | 1.03 | 0.22 | 1 | 0.05 | 0.937 | 0.026 |
2,4,5-CPP/F300, pH 10.38 (GL) | 1.07 | 0.56 | 0.75 | 0.83 | 0.942 | 0.041 |
MCPP/F300, pH 1.81 (T) | 2.83 | 1 | 0.40 | 1.67 | 0.951 | 0.083 |
MCPP/F300, pH 10.38 (GF) | 1.12 | 0.11 | 1 | −1 | 0.822 | 0.034 |
4-CMPP/F300, pH 1.81 (GF) | 2.60 | 0.34 | 1 | −0.41 | 0.961 | 0.070 |
4-CMPP/F300, pH 10.38 (GF) | 12.90 | 0.16 | 1 | −7.35 | 0.949 | 0.026 |
CFA/F300, pH 1.81 (GF) | 2.02 | 0.66 | 1 | 0.53 | 0.995 | 0.061 |
CFA/F300, pH 10.38 (GF) | 20 | 0.21 | 1 | −7.03 | 0.928 | 0.030 |
4-CPB/F300, pH 10.38 (GF) | 10.42 | 0.15 | 1 | −7.81 | 0.952 | 0.022 |
MCPB/F300, pH 10.38 (GL) | 4.65 | 0.45 | 0.08 | 5 | 0.934 | 0.025 |
4-CMPB/F300, pH 10.38 (T) | 1.02 | 1 | 0.59 | 1.54 | 0.923 | 0.027 |
Values of parameters of the generalized Langmuir equation characterizing the experimental systems.
The parameters for two systems correspond to the simple L isotherm equation which is characteristic for energetically homogeneous system. For the other systems, satisfying optimization using the T and GF equations or the general form of the GL isotherm was obtained. The GL equation corresponds to the quasi-Gaussian function of adsorption energy distribution; the heterogeneity parameters characterize the extension of this function toward the higher (
Analyzing the heterogeneity parameters which characterize the experimental systems, one can conclude that in most cases they achieve values much lower than one. It indicates a significant impact of the energetic heterogeneity. Attention should be paid to high values of the adsorption capacity for several experimental systems. In the case of experimental isotherms with a narrow range of relative adsorption
Model dependencies of the adsorption isotherm slope in logarithmic coordinates,
Analyzing the calculated curves, one can observe the different nature of the GF equation in relation to the other isotherms being special cases of the GL isotherm. For the LF equation (
The above observations are confirmed by the results of model calculations presented in Figure 18 as the dependences of relative slope
Model dependences of relative slope
Referring to a general problem of accuracy of the determination of adsorption isotherm parameters, it should be emphasized that a key problem is to determine the adsorption capacity
It should be stated that a fully reliable optimization of the theoretical model can be obtained if the experimental data are distributed evenly in a range of both low and high concentration/adsorption regions. It is important that the range of relative adsorption is symmetrical in relation to
In order to investigate an effect of the adsorbate properties on adsorption rate, the measurements of adsorption kinetics for selected systems were conducted. As the activated carbon is characterized by high porosity, it seemed reasonable to include diffusion effects in a kinetic description. The Crank model (intraparticle diffusion model) in a standard form as well as in a simplified one is a fundamental model of intraparticle diffusion process. The full IDM model takes into account the changes of adsorbate concentration during adsorption process and a spherical shape of adsorbent particles with a radius
where
However, for the analyzed experimental kinetics, the full IDM equation describes the data very poorly (the curves and points are marked as “Crank” in Figure 19). The Crank model analysis for MCPP/F300 at pH = 2 was chosen and presented as representative for all experimental systems; the quality of optimization of all of them was very similar. If effective concentration of the adsorbate in a surface layer is different than in a volume phase, it can be assumed that
The comparison of the optimization using the Crank model (Crank) and the optimization neglecting a relation of a parameter ueq of the Crank model with concentration change during adsorption process (Crank, opt. Ueq) for the MCPP adsorption data on F300 at pH = 2. The figure shows deviations for both optimizations (triangles).
As one can see, the adsorption data can be described quite satisfactorily by using the simplified form of Crank model. It should be assumed that as a result of the rapid initial adsorption stage, the outer part of the granules becomes an adsorbate reservoir providing constant conditions for adsorbate penetration inside the granules. The standard deviation of concentration for the simplified Crank model decreased 3–5 times compared to the full IDM model. In turn, the obtained values of parameter D/r2 of the order 10−3–10−5 are acceptable, while those determined on the basis of the full model IDM were unsatisfactory. Nevertheless, nonlinearity of experimental data in the initial range in c ∼ t1/2 coordinates (linearity is the fundamental assumption of all variants of the Crank model) proves that this model is not suitable for the description of the adsorption process for the studied systems
In practice, a somewhat better choice was the McKay model (pore diffusion model) [31]. It additionally takes into account a resistance during adsorbate transfer through adsorbent surface layer and assumes that the adsorbate evenly penetrates the granules, and a clear boundary between an area in which adsorption equilibrium is established, and the one without adsorbate is formed:
Here,
As shown in Figure 20, the McKay model describes quite well the kinetics of experimental systems in the initial range, and it does not exhibit the same feature as the IDM model. The initial part of curve is a characteristic for typical adsorption systems. However, in the subsequent stage of experiment, one can see significant discrepancies between the model and data, because this model predicts that the system comes to equilibrium after a finite time (curve bend corresponds to reaching the equilibrium state). It is reflected in the increase of the standard deviations with time, inversely to the IDM model. It should be added that obtaining an acceptable optimization using the McKay model was associated with treating a parameter
Comparison of the optimization procedure using the McKay model to the adsorption pesticides data on F300 at pH = 2.
Very good results in the analysis of the kinetic data were obtained for the multi-exponential equation (Figure 21). This equation describes kinetics as an independent parallel series of first-order processes or approximation of follow-up processes [8, 32, 33, 34]. Generally, the multi-exponential equation characterizes well enough the experimental systems with a heterogenous pore structure or a complex pore system with differentiated pore distribution. A mathematical form of the multi-exponential equation is as follows:
Comparison of the optimization using the multi-exponential equation to the experimental data in the coordinates: relative concentration c/co ∼ time and c/co ∼ time1/2.
where
Both the experimental and optimized concentration profiles for the studied systems are presented in two coordinate systems: the relative concentration (
Analyzing the values of adsorption half-time and time needed to reach a nearby equilibrium state, one can state that the kinetics of studied pesticides on a given type of carbon material is relatively slow as a result of the micropore predominant contribution in the adsorbent structure. The kinetic curves for the selected adsorbates can be set from the fastest to the slowest ones in order: 2,4-CPP > MCPP > MCPA > 3-CPP > 2,4-D > 4-CPP. The differences are not very significant, but it is clearly noticeable that adsorption of compounds with hydrocarbon chain of propionic acid is faster compared to their homologs of acetic acid, i.e., 2,4-CPP > 2,4-D and MCPP > MCPA. This is undoubtedly a result of the increase of the hydrophobic interactions between the adsorbate and activated carbon surface (pHpzc ∼ 9.8). These interactions along with dispersive ones are the main mechanism of adsorption process in aqueous systems at fixed pH = 2. One can say that in the case of these pairs of compounds, differences in a length of the hydrocarbon chain (a molecule dimension as well) do not affect differentiation of the diffusion rate in the microporous structure of activated carbon. Completely reverse trend in the adsorption capacity for these compounds determined on the basis of equilibrium isotherms was observed. The shorter hydrocarbon chain of adsorbate, the greater adsorption capacity for a given compound due to possibility of greater packing in the adsorbent structure.
Additionally a comparison of the adsorption kinetics for the studied systems is presented as spectrum of m-exp optimized parameters in the coordinate system: the adsorbed amounts
Distributions of adsorbed amounts versus adsorption half-times determined from the multi-exponential equation.
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