Baiming Ren

Yaqian Zhao

Nathalie Lyczko

Ange Nzihou

Current Status and Outlook of Odor Removal Technologies in Wastewater Treatment Plant

The ever increasing public complaints arising from wastewater treatment plants (WWTPs) grow since sewerage treatment has been associated with nauseous odorous on account of the anaerobic decomposition process and emission. Various physical/ chemical and/or biological methods were used for abating odours worldwide. Thus, an updated comprehensive review for the WWTP odor abatement technologies is urgently required. This study reviews the new achievements of odor abatement technologies (adsorption, chemical scrubbing, biofiltration, biotrickling, bioscrubbing, activated sludge diffusion) in WWTPs and then identifies a new aspect for the future studies. Overall, hybrid technologies (physical/chemical + biotechnologies) attract increasing attention since their highly reliable removal efficiency for various odorants, however, the high costs for investment and O&M (operation & maintenance) of the adsorption part and the complexity and variability of odorants are still the major challenging for wide engineering application and technological innovation. Thus, developing the cost-effective, environmentally friendly odor control technologies, like using the alum sludge (waterworks residue) based adsorbents/media, in terms of using "waste" for waste treatment, could be a highly promising prospect.

Introduction

In recent years, the release of unpleasant odours from wastewater treatment plants (WWTPs) has attracted broad attention, especially among the high population density countries. For technological reasons, WWTPs always occupy a large surface area, ranging from several to more than a dozen hectares and, as a result, are often considered responsible for odor emissions [START_REF] Stellacci | Hygienic sustainability of site location of wastewater treatment plants: a case study. I. Estimating odour emission impact[END_REF]. They not only have a negative impact on the local population but also represent a significant contribution to photochemical smog formation and particulate secondary contaminant emission [START_REF] Estrada | A comparative analysis of odour treatment technologies in wastewater treatment plants[END_REF][START_REF] Latos | Dispersion of odorous gaseous compounds emitted from wastewater treatment plants[END_REF][START_REF] Hayes | The impact of malodour on communities: a review of assessment techniques[END_REF]. Odorous compounds in sewage mainly originate from two processes: anaerobic decomposition of biodegradable materials in the wastewater or direct emission of specific chemicals with wastewater discharges [START_REF] Easter | Odor and air emissions control using biotechnology for both collection and wastewater treatment systems[END_REF][START_REF] Jiang | Odor emissions from domestic wastewater: a review[END_REF]. Furthermore, odours emanating from WWTPs are composed of a mixture of various chemical compounds including ammonia (NH 4 ), hydrogen sulfide (H 2 S) and butanone etc., 78 kinds of main odor-producing compounds relating to wastewater collection and treatment facilities have been reported [START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF][START_REF] Burgess | Developments in odour control and waste gas treatment biotechnology: a review[END_REF][START_REF] Suffet | The anatomy of odour wheels for odours of drinking water, wastewater, compost and the urban environment[END_REF][START_REF] Shaw | Gaseous emissions from wastewater facilities[END_REF], such as various volatile organic compounds (VOCs), indoles, skatoles, mercaptans etc. [START_REF] Zhou | Identification, quantification and treatment of fecal odors released into the air at two wastewater treatment plants[END_REF]. Stuetz and Frechen [START_REF] Stuetz | Odours in Wastewater Treatment[END_REF] had summarized a range of odorous emissions from WWTPs (Table 1). Significantly, H 2 S is considered the most important cause for both odor emission and corrosion in wastewater collection and treatment facilities [START_REF] Stuetz | Odours in Wastewater Treatment[END_REF]. H 2 S is a toxic, flammable, and colourless gas with an unpleasant smell, similar to that of rotten eggs. It can be smelled at low concentrations (about 0.5 ppb) [START_REF] Omri | Performance study of biofilter developed to treat H 2 S from wastewater odour[END_REF]. Based on the European, American, and Chinese standards, the maximum permissible H 2 S emission through a chimney is 5-10 ppm [START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF].

It is worth noting that H 2 S is a key element in sulphide chemical species according to the equilibrium: where, the equilibrium constants K a1 , K a2 , determine the ratio between the concentrations, C, at equilibrium:

The H 2 S release to the atmosphere is thereby strongly dependent on the pH (C H+ in Eqs. ( 2) and ( 3)) [START_REF] Stuetz | Odours in Wastewater Treatment[END_REF]. Simultaneously, another substance that produces odor in WWTPs is ammonia, which is caused by bacterial decomposition of urea produced in the sewage networks. Ammonia possesses a low evaporation temperature, it is therefore easily evaporated and leads to the release of wastewater odours in the environment [START_REF] Liang | Long-term results of ammonia removal and transformation by biofiltration[END_REF][START_REF] Kim | Long-term operation of a biofilter for simultaneous removal of H 2 S and NH 3[END_REF][START_REF] Malhautier | Biological treatment process of air loaded with an ammonia and hydrogen sulfide mixture[END_REF][START_REF] Yu | Isolates identification and characteristics of microorganisms in biotrickling filter and biofilter system treating H 2 S and NH 3[END_REF][START_REF] Jiang | Operational characteristics of efficient coremoval of H 2 S and NH 3 in a horizontal biotrickling filter using exhausted carbon[END_REF].

Consequently, a careful management of WWTP odor is required to avoid the annoyance and to meet the strict regulations. Until now, odor treatment technologies can be classified into physical/chemical and biological methods. Adsorption and chemical scrubbers are among the physical/chemical methods, while biofilters, biotrickling filters, bioscrubbers, and activated sludge diffusion reactors etc. are biological approaches for odor control. Physical/chemical technologies have been broadly implemented since their rapid start-up, low empty bed residence time (EBRT) and extensive experience in design and operation [START_REF] Lagoudianaki | Odor control in evaporation ponds treating olive mill wastewater through the use of Ca(OH) 2[END_REF][START_REF] Zhang | Chemical and biological technologies for hydrogen sulfide emission control in sewer systems: a review[END_REF][START_REF] Anfruns | Removal of odour-causing compounds using carbonaceous adsorbents/catalysts prepared from sewage sludge[END_REF][START_REF] Karageorgos | Treatment of unpleasant odors in municipal wastewater treatment plants[END_REF][START_REF] Rajbansi | Hazardous odor markers from sewage wastewater: a step towards simultaneous assessment, dearomatization and removal[END_REF]. These techniques are often based on the transfer of odorants from the gas emission to either a solid (adsorption) or liquid (absorption) phase. These pollutants can be further (2) transformed into by-products according to their reactivity with the chemicals used. However, in the last decades biological systems have been increasingly implemented due to their ability to efficiently treat malodorous emissions at lower operating costs. The main merits of biotechnologies compared to their physical/chemical counterparts derive from their low generation of secondary wastes and low demand of resources, such as chemicals or adsorbent media.

K a1 = C H + × C HS - C H 2 S(aq) (3) K a2 = C H + × C S 2- C HS -
On the other hand, biological processes often require larger EBRT (2-120 vs. 1-5 s) and associated footprint than physical/chemical alternatives at similar odor removal efficiencies [START_REF] Kim | Removal of odors from swine wastewater by using microbial fuel cells[END_REF][START_REF] Xie | Petrochemical wastewater odor treatment by biofiltration[END_REF][START_REF] Alfonsín | Selection of odour removal technologies in wastewater treatment plants: a guideline based on life cycle assessment[END_REF][START_REF] Lebrero | H 2 S and VOCs abatement robustness in biofilters and air diffusion bioreactors: a comparative study[END_REF].

Although various technologies have been widely reported in the past [START_REF] Estrada | A comparative analysis of odour treatment technologies in wastewater treatment plants[END_REF][START_REF] Lebrero | Odor assessment and management in wastewater treatment plants: a review[END_REF], few comprehensive review was made to analyse and compare complete odor treatment technologies in spite of the recent report of [START_REF] Estrada | A comparative analysis of odour treatment technologies in wastewater treatment plants[END_REF][START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF][START_REF] Lewkowska | Characteristics of odors emitted from municipal wastewater treatment plant and methods for their identification and deodorization techniques[END_REF]. Moreover, a costeffective and environmentally friendly abatement of odours is still urgently needed as the increasingly public concerns as well as the stringent legislation. Thus, the aim of this study is trying to conduct a comprehensive review on the current six main odor treatment technologies emerged in the last 30 years. It is expected to provide an updated guideline for the WWTPs odor management as well as to identify the promising areas of further research and development.

Odor Treatment Technologies in WWTPs

Physical/Chemical Technologies

Physical/chemical technologies consist of two types of reactors, namely adsorption systems and chemical scrubbing, as illustrated in Fig. 1. Either of them is commonly used in practice for odor removal in WWTPs because of their low EBRT, extensive experience in design and operation, and rapid start-up, etc. [START_REF] Estrada | A comparative analysis of odour treatment technologies in wastewater treatment plants[END_REF].

Adsorption Systems

Adsorption systems generally consist of static beds of granular materials in vertical cylindrical columns (Fig. 1). Accordingly, the odorous air steam enters the column, process in the direction of air flow, and continues until odor "break through" at the exit end. Its efficiency is severely limited by the high moisture content prevailing in WWTPs malodors emissions. Moreover, capacity, temperature etc. are the vital characteristics of a robustness adsorption system [START_REF] Zarra | Control of odour emission in wastewater treatment plants by direct and undirected measurement of odour emission capacity[END_REF]. Several sorbents have been studied, including fly ash, carbon, activated carbon, polymers, carbon-coated polymers, ceramics, micro-and mesoporous materials, metal organic frameworks, natural zeolites, and synthetic zeolites [START_REF] Gil | VOCs removal by adsorption onto activated carbons from biocollagenic wastes of vegetable tanning[END_REF]. It's worth noting that specific surface area, pore structure together with surface chemical functional groups are three crucial factors of adsorbents, which would directly determine their performance on odorants adsorption [START_REF] Zhang | Adsorption of VOCs onto engineered carbon materials: a review[END_REF]. However, it is difficult to find an adsorbent with all the features of an excellent adsorbent, some of the adsorbent properties must be compromised, such as removal capacity, regeneration, or cost impact [START_REF] Aziz | Adsorptive volatile organic removal from air onto NaZSM-5 and HZSM-5: kinetic and equilibrium studies[END_REF].

Activated carbon (AC) is widely used as adsorbent for odor control in WWTPs. In the initial stage, AC was impregnated with caustics (NaOH or KOH) for H 2 S control. Both NaOH and KOH reacted with atmospheric CO 2 to form the corresponding carbonates, thus facilitating H 2 S removal. Other impregnated sorbents for H 2 S removal are carbons impregnated with heavy metal salts such as copper sulphate or lead acetate. These media are usually classified as hazardous materials because of their content of heavy Fig. 1 Physical/chemical abatement technologies: adsorption system (left), chemical scrubber (right) (Reproduced with permission from [START_REF] Stuetz | Odours in Wastewater Treatment[END_REF]) metals. Among non-carbonaceous sorbents, activated alumina impregnated with potassium permanganate has also been used for oxidative removal of H 2 S [START_REF] Stuetz | Odours in Wastewater Treatment[END_REF]. However, it has been demonstrated by Bandosz et al. [START_REF] Bandosz | Unmodified versus caustics-impregnated carbons for control of hydrogen sulfide emissions from sewage treatment plants[END_REF] that unmodified carbon can provide enough capacity to efficiency removal of H 2 S from effluent gas in WWTPs. It is worth noting that AC adsorption presented the highest operating costs (0.45 € per 1000 m 3 treated) among the odor abatement technologies [START_REF] Estrada | A comparative analysis of odour treatment technologies in wastewater treatment plants[END_REF]. This is because the AC filtration presented the highest annual packed-bed-material requirements and the 6 months replacement short life span as well as the needs for specific management procedures (regeneration or disposal as hazardous waste). Thus, although lots of efforts have been made in terms of the development of different sorbents in WWTPs odor treatment, the high cost of operation continues to be their major drawbacks for wide engineering application of the AC system. As such, generation of high performance sewage sludge-based ACs is continuously studied worldwide, while Yang et al. [START_REF] Yang | One-step synthesis of silicon carbide foams supported hierarchical porous sludge-derived activated carbon as efficient odor gas adsorbent[END_REF] reported a new generated sewage sludge-based ACs, which possesses excellent adsorption capacity (259.9 mg/g) of methyl mercaptan (CH 3 SH). Additionally, Aziz and Kim [START_REF] Aziz | Adsorptive volatile organic removal from air onto NaZSM-5 and HZSM-5: kinetic and equilibrium studies[END_REF] investigated the removal of VOCs (benzene, toluene, ethylbenzene, and p-xylene) by Na-ZSM-5 and H-ZSM-5 (a synthesized material have a high ratios of SiO 2 /Al 2 O 3 ). NaZSM-5 rapidly removed 75% of toluene and ethylbenzene and 50% of Benzene, but xylene was not efficiently removed. On the other hand, HZSM-5 could remove all of the VOCs. Furthermore, Sempere et al. [START_REF] Sempere | Evaluation of a combined activated carbon prefilter and biotrickling filter system treating variable ethanol and ethyl acetate gaseous emissions[END_REF] reported that a AC adsorption combined with biotechnologies to form the hybrid technology becomes a popular odor control approach in WWTPs. Overall, adsorption-based systems provide an excellent performance in the treatment of highly hydrophobic odorants (90-99%) [START_REF] Gil | VOCs removal by adsorption onto activated carbons from biocollagenic wastes of vegetable tanning[END_REF][START_REF] Anfruns | Removal of odourous VOCs using sludge-based adsorbents[END_REF].

Several mechanisms have been proposed for the adsorption reactions. However, it is not yet fully understood. Generally, the mechanism of odor adsorption consists of seven steps as follows: (i) transport of the gas from the bulk of a mixture to a solid particle, (ii) transport of the reactants in the pores of the adsorbent particles to an active site, (iii) adsorption of the reactants to the active site via Van der Waals forces, (iv) reaction of reactants to form an adsorbed product, (v) desorption of the product from the active site, (vi) transport of the products in the pores of the catalytic particle out of the particle, (vii) and transport of the products from the particle to the bulk of the mixture [START_REF] Bamdad | A review on common adsorbents for acid gases removal: Focus on biochar[END_REF].

Chemical Odor Scrubbing

Chemical scrubbers (CS) are among the most commonly employed abatement techniques in WWTPs due to the extensive experience and high robustness as well as the short gas retention time (as low as 1-2.5 s) [START_REF] Lebrero | A comparative assessment of biofiltration and activated sludge diffusion for odour abatement[END_REF]. Various types of CS include the counter-current scrubber, cross-flow scrubber, and venture scrubber have been reported [START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF]. The most common configuration (Fig. 1) is a vertical shell with gas flow going up through packing and the liquid solution (depending on the target compounds) going down. Liquid solution is usually circulated over the packing by pumping from a collection sump in the bottom of the tower, while chemicals are added either in the sump or in the recirculation piping.

For best performance, a multi-stage scrubber is often used. Yang and Chen [START_REF] Yang | Oxidation of nitric oxide in a two-stage chemical scrubber using dc corona discharge[END_REF] investigated the oxidation of 150 ppm nitric oxide with a two-stage chemical scrubber using the dc corona as an alternative for one of the scrubbing chemicals. Bandyopadhyay and Biswas [START_REF] Bandyopadhyaya | Prediction of the removal efficiency of a novel two-stage hybrid scrubber for flue gas desulfurization[END_REF] studied the scrubbing of sulfur dioxide in a two-stage hybrid scrubber using water and dilute sodium alkali. Chen et al. [START_REF] Chen | Treating odorous and nitrogenous compounds from waste composting by acidic chlorination followed by alkaline sulfurization[END_REF] reported a novel two-stage wet scrubbing for composting gases treatment, which consisted of acidic chlorination followed by alkaline sulfurization. However, a water/oil emulsion for VOCs removal has recently been developed by Hariz et al. [START_REF] Hariz | Absorption of toluene by vegetable oilwater emulsion in scrubbing tower: Experiments and modeling[END_REF], it is an alternative that reduces the number of towers and thus reduces the investment and operation costs. Significantly, thermal regeneration of the VOC-saturated oil was reported in the first time to reduce the solvent consumption and to reduce the impact on the environment.

Generally, since a portion of solution is continually wasted to remove the accumulated contaminants, CS required the large annual amounts of chemical reagents and higher quality (preferably softened) potable water. The purchase of chemicals accounts for the highest contribution (69%) to the CS operating cost and followed by the energy consumption (22%) as a result of the high liquid recycling rates [START_REF] Bindra | Technological and life cycle assessment of organics processing odour control technologies[END_REF]. Moreover, NaClO as the commonly used oxidant in CS is likely to form chlorinated by-products which are harmful for human health and to produce another pungent odor, hypochloric acid [START_REF] Yang | Use of peroxymonosulfate in wet scrubbing process for efficient odor control[END_REF][START_REF] Wu | Oxidative scrubbing of DMScontaining waste gases by hypochlorite solution[END_REF]. Alfonsín et al. [START_REF] Alfonsín | Selection of odour removal technologies in wastewater treatment plants: a guideline based on life cycle assessment[END_REF] also pointed out that CS presented the highest impacts in freshwater eutrophication, photochemical oxidant formation, human toxicity and ecotoxicity due to the use of large amounts of chemicals. H 2 O 2 is a promising oxidant except for its instability in basic aqueous solution and the low efficiency of CH 3 SH removal [START_REF] Ding | Removal of CH 3 SH with in-situ generated ferrate(VI) in a wet-scrubbing reactor[END_REF][START_REF] Charron | Use of hydrogen peroxide in scrubbing towers for odor removal in wastewater treatment plants[END_REF]. The emerging oxidants of CS like peroxymonosulfate (PMS) has a similar structure with H 2 O 2 but the oxidation-reduction potential is stronger than H 2 O 2 [START_REF] Couvert | Treatment of odorous sulphur compounds by chemical scrubbing with hydrogen peroxide-application to a laboratory plant[END_REF]. However, Talaiekhozani et al. [START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF] pointed out that Venturi scrubbers can be a highly cost-efficient candidate for many industries, due to their simplicity of construction and usage, the ability to purify large volume of air in a short time and to simultaneously remove particles and gases. Significantly, Venturi scrubbers can even be distinguished from biological methods, such as biofilters, since it can quickly reach the maximum efficiency comparison with biofilters, while the alkaline substances, such as limewater, can convert sulfur into calcium sulfide and inhibit the reproduction of hydrogen sulfide etc. On the other hand, CS showed the lowest geographic dependence worldwide, because chemicals, which constitute the main cost in these systems, were considered to be part of a global market and their prices did not depend on the geographical location [START_REF] Estrada | A sensitivity analysis of process design parameters, commodity prices and robustness on the economics of odour abatement technologies[END_REF]. Overall, despite the high removal efficiencies (> 99%) achieved for H 2 S [START_REF] Couvert | Treatment of odorous sulphur compounds by chemical scrubbing with hydrogen peroxide-application to a laboratory plant[END_REF][START_REF] Biard | Wet scrubbing intensification applied to hydrogen sulphide removal in waste water treatment plant[END_REF], CS presents serious limitations in the elimination of hydrophobic VOCs (high Henry Law constants) as they are finally based on odorant transfer to an aqueous solution of oxidant. In addition, the hazardous nature of the chemical reagents employed and by-products generated represent a serious challenge to its supremacy in a world increasingly devoted to sustainable development.

Biotechnological Treatment

Biotechnologies, particularly hybrid technologies (physical/ chemical + biotechnologies) become the main approach in the last decades for odor control, since their low-cost and environmentally friendly nature. To date, four types of reactors are commonly used in WWTPs [START_REF] Shareefdeen | Biotechnology for Odor and Air Pollution Control[END_REF]: biofilters (BF), biotrickling filters (BTF), bioscrubbers (BS) and activated sludge diffusion reactors (ASD), as illustrated in Fig. 2.

Biofiltration

BF is indisputably the most commonly employed biotechnology for odor treatment in WWTPs [START_REF] Lebrero | Odor assessment and management in wastewater treatment plants: a review[END_REF]. In a biofilter system (Fig. 2a), the humified odorant is forced through a packed bed (compost, peat, bark or a mixture of these) on which the microorganisms are attached as a biofilm [START_REF] Elias | Evaluation of a packing material for the biodegradation of H 2 S and product analysis[END_REF][START_REF] Sakuma | Comparison of different packing materials for the biofiltration of air toxics[END_REF][START_REF] Dumont | Evaluation of a new packing material for H 2 S removed by biofiltration[END_REF][START_REF] Bhaskaran | Dynamics of sustainable grazing fauna and effect on performance of gas biofilter[END_REF][START_REF] Park | Development of a biofilter with tire-derived rubber particle media for hydrogen sulfide odor removal[END_REF][START_REF] Dumont | Biofiltration using peat and a nutritional synthetic packing material: influence of the packing configuration on H2S removal[END_REF]. The pollutants are sorbed by the filter material and degraded by the biofilm. However, to control the key parameters such as pH and moisture content within the packed bed, and to avoid the accumulation of inhibitory by-products are still the technical difficulties and limitations [START_REF] Sempere | Evaluation of a combined activated carbon prefilter and biotrickling filter system treating variable ethanol and ethyl acetate gaseous emissions[END_REF][START_REF] Premkumar | Effect of secondary parameters on biofilter treating industrial effluent[END_REF][START_REF] Zehraoui | Biological treatment of n-hexane and methanol in trickle bed air biofilters under acidic conditions[END_REF].

Performance data of a series of lab-scale BF and engineering applications are summarized in Table 2. An important advantage of biological treatment methods over physical and chemical technologies is that the biological processes can be conducted at moderate temperatures (10-40 °C) and atmospheric pressure. Moreover, microbial degradation processes are generally oxidative in nature and produce compounds such as carbon dioxide, water, sulfate and nitrate that are ecologically safe [START_REF] Elias | Evaluation of a packing material for the biodegradation of H 2 S and product analysis[END_REF]. The microorganisms are the engine of the biotreatment process. Biofilms contain a mixture of bacteria, fungi, yeasts, ciliated protozoa, amoebae, nematodes and algae. Bacteria and fungi are the two dominant microorganisms groups in biofilters, however as bacteria populations (primary degraders) grow they can sustain yeasts and other fungi, algae and higher organisms such as protozoa, rotifers, nematodes, etc., the optimum pH for microorganisms is 7 [START_REF] Shareefdeen | Biotechnology for Odor and Air Pollution Control[END_REF].

By providing support for microbial growth, BF media are very often regarded as the key issue to determine removal efficiencies and BF lifespan [START_REF] Omri | Performance study of biofilter developed to treat H 2 S from wastewater odour[END_REF][START_REF] Oyarzún | Biofiltration of high concentration of hydrogen sulphide using Thiobacillus thioparus[END_REF]. Nontoxic organic or inorganic supports with high specific surface areas and porosities, good water retention capacities, high buffer capacities, and high nutrients content have provided the best odor removal performances. Organic media provide an extra C source necessary to maintain microbial activity, likely challenged by the extremely low C concentrations present at the biofilm-water/air interface [START_REF] Rattanapan | Removal of H 2 S in down-flow GAC biofiltration using sulfide oxidizing bacteria from concentrated latex wastewater[END_REF][START_REF] Chung | Structure of the bacterial community in a biofilter during dimethyl sulfide (DMS) removal processes[END_REF][START_REF] Chouari | Microbial analysis and efficiency of biofiltration packing systems for hydrogen sulfide removal from wastewater off gas[END_REF]. On the other hand, inorganic materials such as ceramic, plastics, lava rock, and activated carbon provide an extra structural stability, which increases BF lifespan [START_REF] Liu | Biofiltration treatment of odors from municipal solid waste treatment plants[END_REF][START_REF] Lee | Biofiltration of a mixture of ethylene, ammonia, n-butanol, and acetone gases[END_REF]. Moisture content of the packing is the most critical parameter to control in BF. Indeed, many references listed in Table 2 mentioned system upsets causing excessive drying of the packed bed and declining performance. Although the relative humidity of the air undergoing treatment is often over 80% at WWTPs, the waste air is frequently humidified in packed towers before entering the BF [START_REF] Yang | Moisture effects on gas-phase biofilter ammonia removal efficiency, nitrous oxide generation, and microbial communities[END_REF].

As shown in Table 2, BFs at industrial applications are operated at EBRTs from 20 to 200 s. Removal of H 2 S is generally between 90 and 100%, indicating the effectiveness of BF. On the other hand, removal of odorous compounds like dimethyl sulfide, DMDS, and methyl mercaptan is often instable, with reported removal efficiencies ranging from about 20 to 100% [START_REF] Yu | Removal of dichloromethane from waste gases by a biotrickling filter[END_REF][START_REF] Zhang | Enhancing and modeling the biofiltration of dimethyl sulfide under dynamic methanol addition[END_REF][START_REF] Abraham | Treatment of odor by a seashell biofilter at a wastewater treatment plant[END_REF][START_REF] Kafle | Field evaluation of wood bark-based down-flow biofilters for mitigation of odor, ammonia, and hydrogen sulfide emissions from confined swine nursery barns[END_REF]. However, Jaber et al. [START_REF] Jaber | Impact of nutrients supply and pH changes on the elimination of hydrogen sulfide, dimethyl disulfide and ethanethiol by biofiltration[END_REF] pointed out the accumulation of sulfuric acid, the most abundant product of the biological oxidation of sulfur compounds in the packing material caused the pH decreasing, led to a reduction of the elimination efficiencies of DMDS etc., while the microorganisms involved in H 2 S degradation appeared active in a large pH range, from 3 to 9.

Because of the need to maintain low pressure drops across the packing bed and the high EBRT needed for efficient odor treatment, BF presented the highest land requirements [START_REF] Wang | Enhanced removal of ethylbenzene from gas streams in biotrickling filters by Tween-20 and Zn(II)[END_REF]. This footprint was 7 and 25 times higher than that of the BTF and CS, respectively, and can limit the application of this biotechnology during plant upgrading in WWTPs when facing land limitations [START_REF] Lebrero | A comparative assessment of biofiltration and activated sludge diffusion for odour abatement[END_REF][START_REF] Alfonsín | Environmental assessment of different biofilters for the treatment of gaseous streams[END_REF][START_REF] Cheng | Challenges and solutions for biofiltration of hydrophobic volatile organic compounds[END_REF].

Biotrickling

To overcome the disadvantages of BF, more sophisticated filtration equipment called biotrickling has been developed. In BTF or fixed-film bioscrubber (Fig. 2b), the odorous gas a Reproduced with permission from [START_REF] Alfonsín | Selection of odour removal technologies in wastewater treatment plants: a guideline based on life cycle assessment[END_REF][START_REF] Iranpour | Literature review of air pollution control biofilters and biotrickling filters for odor and volatile organic compound removal[END_REF][START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF] is forced through a packed bed filled with a chemically inert carrier material which is colonized by microorganism, similar to trickling filters in wastewater treatment. The liquid medium is circulated over the packed bed and the pollutants are first taken up by the biofilm on the carrier material and then degreed by the microorganisms. The liquid medium can be recirculated continuously or discontinuously and in co-or countercurrent to the gas stream. Flow directions will not affect the efficiency of the process. Performance data of BTF in lab and WWTPs are given in Table 3. Various types of packing materials have been used: inorganic salts, polyurethane foam, activated carbon fibers, multi-surface hollow balls etc. The high porosity of these packings causes less headloss compared to that of BF with organic packings, even though BTF are operated at a higher gas velocity [START_REF] Iranpour | Literature review of air pollution control biofilters and biotrickling filters for odor and volatile organic compound removal[END_REF], It is thus noted that BTF footprints are comparable to those of physical/chemical technologies partly as a result of their relative higher media depth. Moreover, a distinctive feature of BTF is the continuous trickling of liquid over the packing, which allows for improved control of nutrient addition, pH, acid product neutralization, end product removal, and (potentially) temperature [START_REF] Talaiekhozani | Removal of formaldehyde from polluted air in a biotrickling filter reactor[END_REF]. The composition of liquid phase can affect the efficiency of BTF process, thus the trickling liquid is continuously enriched with elementary mineral nutrients containing nitrogen, phosphorus, potassium, and trace elements. Usually, BTF is operated in the temperature range between 10 and 40 °C, which is characteristics of the mesophilic microorganisms growth. The efficiency of biotreatment process may be limited by both biological reaction rate and the mass transfer rate. Therefore, it is important to notice that temperature can affect either these limitations [START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF]. Hydrocarbon vapors are removed by aerobic heterotrophic microorganisms that utilize the vapors as a source of carbon and energy [START_REF] Vikromvarasiri | Performance of Paracoccus pantotrophus for H 2 S removal in biotrickling filter[END_REF][START_REF] Wu | Performance and bacterial diversity of biotrickling filters filled with conductive packing material for the treatment of toluene[END_REF].

In the case of odorous waste air containing reduced sulfur compounds, production of sulfuric acid with declining pH and/or accumulation of sodium sulfate (after neutralization with caustic soda) is an important design parameter. Performance data for BTF indicate that these reactors are capable of efficient removal of high concentrations of H 2 S at relatively low EBRTs. Thus, BTF appear to be a good option when the gas to be treated contains high concentrations of H 2 S and possibly other reduced sulfur compounds. BTF performance could be limited by mass transfer of oxygen into the biofilm because oxygen solubility in water is low [START_REF] Salamanca | Removal of cyclohexane gaseous emissions using a biotrickling filter system[END_REF][START_REF] Aguirre | Biofiltration of trimethylamine in biotrickling filter inoculated with Aminobacter aminovorans[END_REF]. However, there are still some drawbacks of BTF, such as the problem of gas transfer arising from the necessity of dissolving the gaseous pollutants in the aqueous phase, the biofilm development on the carrier surface which progressively reduces the empty volume of the filter bed and may lead to excessive pressure drop even the complete clogging of the bed [START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF].

Bioscrubbers

BS or suspended growth BS could be a solution of the limited suitability of BTF for handling high pollutant concentrations and large gas flows. In the early 1980s, the first BS units were applied to the treatment of waste gases [START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF]. In BS (Fig. 2c), the pollutant is adsorbed in an aqueous phase in an absorption tower then converted by the active microorganisms into CO 2 , H 2 O, and biomass in a separate activated sludge unit, the effluent is circulated over the absorption tower in a co-or countercurrent way to the gas stream. Meanwhile, Bowker [START_REF] Bowker | Biological odour control by diffusion into activated sludge basins[END_REF] has proofed that bubble size in activated sludge unit is an important factor, since the reduction of H 2 S and odors in fine-bubble diffusers can be higher (above 99.5%) than in coarse-bubble diffusers (95% for odors and 92% for H 2 S). Most existing BSs are designed for the removal of a single pollutant. For example, Nisola et al. [START_REF] Nisola | NH 3 gas absorption and bio-oxidation in a single bioscrubber system[END_REF] developed a single BS for ammonia removal; Potivichayanon et al. [START_REF] Potivichayanon | Hydrogen sulfide removal by a novel fixed-film bioscrubber system[END_REF] investigated a fixed-film BS for hydrogen sulfide removal. Contrarily, various design modifications have been reported to handling pollutant mixtures, such as sorptive-slurry BS, anoxic BS, two-liquid phase BS, airlift BS, spray column or two-stage BS [START_REF] Mudliar | Bioreactors for treatment of VOCs and odours-a review[END_REF]. Friedrich et al. [START_REF] Friedrich | Odour abatement of waste gases from sludge thickeners in wastewater treatment plant using bioscrubber[END_REF] investigated a 3-stage system (two identical BS + dry chemical scrubber) for the abatement of odors from sludge thickeners. Over 90% abatement efficiency was observed from the first BS and the removal efficiency exceeded 99% when applied two series-connected BS; Liu et al. [START_REF] Liu | Performance evaluation and optimization of field-scale bioscrubbers for intensive pig house exhaust air treatment in northern Germany[END_REF] reported a two-series connected field-scale BS for simultaneous removal of NH 3 and CH 4 from the animal houses. Furthermore, BS offers operational stability and effective control of operating parameters such as pH and nutrients dosage, relatively low gas pressure drop and small space requirement. For example, Hansen and Rindel [START_REF] Hansen | Bioscrubbing, an effective and economic solution to odour control at wastewater treatment plants[END_REF] pointed out the pH values of 8.5-9.0 as the optimal range that facilitates maintaining a high biological activity and at the same time, ensuring effective absorption of H 2 S. Compared to BTF, the risk of clogging of the packing material by growing biomass is avoided, large gas flow rates and high pollutant concentrations can be handled, moreover, as reaction products are removed by washing, concentrations of toxic byproducts generated in the reactor can be maintained at low levels. However, the separate treatment system of the liquid phase increases the initial costs of establishing such kind systems. BS also possess low capacity for treatment in the removal of poorly soluble contaminants, such as hydrogen sulfide [START_REF] Talaiekhozani | Removal of formaldehyde from polluted air in a biotrickling filter reactor[END_REF], thus, adding chemical compounds such as sodium hydroxide or lime water to the circulating liquid provides higher removal rates for such kind of compounds. Alternatively, the choice of microorganisms could be considered, since immobilized cells of Chlorobium limicola have been identified to transform H 2 S into elemental sulfur in an autotrophic reaction, and heterotrophic Xanthomonas species are also known to remove H 2 S from gas streams [START_REF] Ball | Oxidation of hydrogen sulphide in sour gas by Chlorobium limicola[END_REF]. Efficient degradation of sulfur-containing compounds by certain strains of Thiobacillus and Hyphomicrobium has also been reported by Tóth et al. [START_REF] Tóth | Biocatalytic hydrogen sulphide removal from gaseous streams[END_REF]. The mechanisms of odorous compounds removal in BS involves mainly the physical and biochemical processes such as absorption, biodegradation or biotransformation, significantly, biodegradation is the main process for the removal of the pollutants [START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF].

Activated Sludge Diffusion

The economical and practical odor abatement choice is simply moving them from the gaseous phase to the liquid phase [START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF]. A variety of systems can be employed for this purpose, for WWTPs, ASD offers a low-cost alternative. As shown in Fig. 2d, by collection of the odorous gas and its diversion into an activated sludge aeration basin, odours can be eliminated using relatively technology. Complete mixing ensures an adequate food supply for the microbial cells and maximizes the oxygen gradient to optimize mass transfer and disperse the products of metabolism from inside the flocs, wastewater entry displaces mixed liquor into a clarifier, where the flocculated biomass separates into sludge and clarified effluent.

Lebrero et al. [START_REF] Lebrero | A comparative assessment of biofiltration and activated sludge diffusion for odour abatement[END_REF] compared BF with ASD for the synthetic odor (H 2 S, butanone and toluene) removal. The results confirmed ASD being a robust and efficient technology with over 95% removal efficiency for synthetic odor. Recently, Rodríguez et al. [START_REF] Rodríguez | Microbial community changes during different empty bed residence times and operational fluctuations in an air diffusion reactor for odor abatement[END_REF] investigated the microbial community and bioreactor function relationships at different EBRTs in an ASD, when treating a synthetic malodorous (H 2 S, toluene, butanone and alphapinene). A stable and efficient abatement performance of H 2 S, butanone and toluene was observed, regard less of the EBRT and fluctuations applied, while no clear positive or negative relationship between community characteristics and bioreactor functions was confirmed. The most abundant groups namely Actinobacteria, Proteobacteria and Fungi (Hypocreales, Chaetothyriales) play a putative key role in the degradation of butanone and toluene. On the other hand, Barbosa et al. [START_REF] Barbosa | Investigating the capacity of an activated sludge process to reduce volatile organic compounds and odor emissions[END_REF] reported the effects of H 2 S diffusion into ASD on odor and VOC concentrations in offgas, indicating a negative effect of H 2 S on VOC removal and minimal effect on odor emissions. Moreover, Barbosa and Stuetz [START_REF] Barbosa | Performance of activated sludge diffusion for biological treatment of hydrogen sulphide gas emissions[END_REF] identified hydrogen sulfide can act as an energy source for microorganisms, while the carbon sources can be provided by compounds such as glucose, methanol or untreated domestic sewage. Since the solubility of hydrogen sulfide in water with pH of 7 and temperature of 19 °C is 4 g/l [START_REF] Talaiekhozani | An overview of principles of odor production, emission, and control methods in wastewater collection and treatment systems[END_REF], ASD are recommended in treatment of this contaminant [START_REF] Moussavi | The removal of H 2 S from process air by diffusion into activated sludge[END_REF]. Additionally, Blonda et al. [START_REF] Blonda | Activated sludge diffusion for odour removal-effects of H 2 S on the biomass[END_REF] pointed out that there is no significant effects of sulphide on COD removal efficiencies as well as nitrification and denitrification in ASD. Contaminant removal mechanisms in ASD include absorption, adsorption (high molecular mass compounds with low solubility adsorb onto flocs) or condensation (VOCs in warm air condense on contact with the cooler mixed liquor), followed by biodegradation [START_REF] Burgess | Developments in odour control and waste gas treatment biotechnology: a review[END_REF]. ASD is used as an alternative to more established bioreactors for waste gas treatment, such as BF, BS, BTF. Despite ASD systems have been used for over 30 years with high H 2 S removal efficiencies, their widespread implementation is still limited by the lack of reliable data concerning its performance during the treatment of odorous volatile organic compounds (VOCs).

Comparison of Odor Control Technologies in WWTPs

Summary of Different Odor Control Technologies

The benefits and disadvantages of various odor treatment technologies in WWTPs are compared in Table 4. It should be noted that none of the six main odor abatement technologies could be "one-size-fits-all". In the last decades, lot of efforts have been done in the selection of various odor control technologies. Among the efforts, life cycle assessment (LCA) could be a promising solution which has been proofed by [START_REF] Agus | A framework for identifying characteristic odor compounds in municipal wastewater effluent[END_REF][START_REF] Alfonsín | Selection of odour removal technologies in wastewater treatment plants: a guideline based on life cycle assessment[END_REF][START_REF] Bindra | Technological and life cycle assessment of organics processing odour control technologies[END_REF] etc.

Comparison of Different Odor Removal Performance

The range of 37 kinds of odorant removal performance in AC adsorption, BF and BTF are reviewed and summarised in Fig. 3. The single point represents the only one available data from the literature, indicating that there are less studies on the relevant odorant.

Generally, the performance data of odorant removal in AC adsorption were less reported than the relevant biotechnologies, 19 kinds of odorants were investigated by AC adsorption in the lab and/or field tests, H 2 S and methyl mercaptan are well removed by AC, however, dimethyl disulfide was poorly removed since its generation from the breakdown of methyl mercaptan on AC systems [104]. Moreover, the removal of benzene, xylene, toluene, TCE and PCE reduced with the active lifetime decreased of the AC systems. Although AC has a longer history than BTF, in-depth studies on the performance of field units generally rely on one or two samples to justify removal. It's worth to note that there is little data available on the long-term performance of these units, particularly with respect to removal efficiency other than H 2 S. The majority odorants could be removed by BF and nearly 35 kinds of different odorants were reported. BF is good at removing H 2 S (RE range from 90 to 100%), Toluene (20-100%) while dimethyl disulfide and carbonyl sulfide exhibit poor removal efficiency, higher retention times might facilitate dimethyl disulfide and carbonyl sulfide removal. Twenty-one kinds of odorants were removed by BTF in the past. BTF was able to remove H 2 S at lower EBRT than in BF due to the high solubility of the pollutant. They were widely used in the higher load of H 2 S. However, VOC removal is poorer in BTF than BF which could be due to the low solubility of many VOCs and the continuous trickling water layer acting as a barrier to VOC bioreaction in BTF.

Perspective

In the last 30 years, hybrid technologies (physical/chemical with biological technologies) have become popular, which agrees with the robustness evaluation [START_REF] Estrada | A sensitivity analysis of process design parameters, commodity prices and robustness on the economics of odour abatement technologies[END_REF] as well as the life LCA study [START_REF] Alfonsín | Selection of odour removal technologies in wastewater treatment plants: a guideline based on life cycle assessment[END_REF]. Although it presents a highly reliable removal efficiency for various odorants, a large amount of media replacement cost in hybrid technologies, significantly, the replacement of AC in adsorption system still the major concerns. Consequently, development of low-cost and environmental friendly adsorbents/media for odor adsorption system/ biotechnologies is urgently needed in the future study. Notwithstanding various media/sorbents for odor treatment have been reported in previous studies, such as peat, zeolite, silicon, sewage sludge derived sorbents, etc. To the best of our knowledge, none of the studies ever chose the waterworks residue (alum sludge) as the low-cost adsorbent even the media in odor control of WWTPs, even it has been intensively studied and demonstrated to be a good material for wastewater treatment [105,106].

Alum sludge is an inescapable by-product of the processing of drinking water in waterworks where aluminium salt is used as the coagulant for purifying raw water [107]. It has been advocated that waterworks sludge could be a potential recyclable product, offering promising potential as a lowcost adsorbent for various pollutants immobilization with unique feature of using "waste" for wastewater treatment [108, 109]. Thus, it has been widely reused in wastewater treatment, building & construction material, etc. moreover, alum sludge is a locally, easily and largely available material in towns, cities and metropolis and free of charge for the moment, unfortunately treated as a waste for landfilling [106]. Table 5 summaries the major characterization of two sources alum sludge which have been collected from Dublin (Ireland) and Carmaux (France), it is worth to note that alum sludge has a large specific surface area (40-300 m 2 /g) and porous structure, contains various metal elements like Al, Ca, Fe, etc., particularly it has been successfully reused in constructed wetlands as substrate for high P-containing wastewater treatment and our group is a leading group in the study of alum sludge reuse [110][111][112].

Therefore, it is highly expected and reasonable to believe that alum sludge could be a good material/adsorbent for adsorption system. Preliminary trials have been conducted, two sources of alum sludges collected from Carmaux (France) and Dublin (Ireland) have demonstrated the H 2 S adsorption effectiveness, significantly, the H 2 S adsorption breakthrough capacity of Carmaux sludge (78 mg/g after 90 h) at ambient condition is several times than the capacity of calcium carbonate based solid wastes, sewage sludge based sorbents, even few active carbons [113][114][115]. Therefore, developing the alum sludge based adsorbent and figuring out the potential effectiveness for various odorants removal even the mechanisms and kinetics for WWTPs odor control should be a promising perspective.

Conclusion

Six odor abatement technologies were reviewed. Initially, adsorption systems and CS were developed and gradually replaced by the low-cost and environmental friendly biotechnologies. BF has dominated odor treatment applications and later, more sophisticated types of filtration equipment such as BTF and BS have been developed. However, the usage of water and the regular replacement of media is still a big concern. Particularly, the high costs of adsorption system limited their wide engineering application. Subsequently, hybrid technologies (adsorption + biotechnologies) present an environmentally friendly solution.

Further research is required on the selection of low-cost sorbent to ensure a high efficiency of pollutant removal and satisfactory process robustness. Alum sludge due to its inherent characteristics could be a promising replacement to the traditional activated carbon in the adsorption system even to the media in the biofilters and biotrickling. Developing the alum sludge-based adsorbents/media for WWTPs odor treatment could be a vital innovative aspect of the environmentally friendly development as well as the "Blue Economics". It not only opens the new prospects for the waterworks sludge management but also fits in the circular approach of using a waste for waste gas control. It is hoped in this review, a clear overview of the state of the art of odor treatment technologies as well as a future study prospect was provided.

⇔ H 2 S

 2 (aq) pK a1 =7.0 ⇔ HS -pK a2 =14 ⇔ S 2-

Fig. 2

 2 Fig. 2 Biotechnological treatment: a open biofilter; b biotrickling filter; c bioscrubber; d activated sludge diffusion (Reproduced with permission from [12])

Fig. 3

 3 Fig.3The range of various odorant removal efficiency by AC adsorption, BT, BTF in WWTPs from literature[START_REF] Iranpour | Literature review of air pollution control biofilters and biotrickling filters for odor and volatile organic compound removal[END_REF][START_REF] Barbusinski | Biological methods for odor treatment-a review[END_REF][START_REF] Lee | Biofiltration of a mixture of ethylene, ammonia, n-butanol, and acetone gases[END_REF][START_REF] Jaber | Impact of nutrients supply and pH changes on the elimination of hydrogen sulfide, dimethyl disulfide and ethanethiol by biofiltration[END_REF][START_REF] Talaiekhozani | Removal of formaldehyde from polluted air in a biotrickling filter reactor[END_REF][START_REF] Mudliar | Bioreactors for treatment of VOCs and odours-a review[END_REF][100][101][102][103]

Table 1

 1

	A range of odorous emissions from WWTPs. Reproduced with permission from [12, 14]	Compound Ammonia Butanone Dimethyl sulfide Geosmin Hydrogen sulfide Indole Methyl mercaptan Skatole Sulfur dioxide Thiophenol	Odor description Sharp, pungent Sweet, minty, green apple Decayed vegetables Earthy, musty Rotten eggs Fecal, repulsive Decayed cabbage, garlic Fecal Pungent, acidic Garlic, stench	Chemical formula NH 4 CH 3 C(O)CH 2 CH 3 (CH 3 ) 2 S C 12 H 22 O H 2 S C 8 H 7 N CH 4 S C 9 H 9 N SO 2 C 6 H 6 S	Odor threshold (ppb) 130-15300 270 0.12-0.4 4 0.5 0.3-1.4 0.0014-18 0.006 9 0.064

Table 2

 2 Performance of BF in lab and on-site systems

	Reference/loca-tion a	Media	Parameters EBRT (s) pH	Temp. (°C) Nutrients	Odorant	Concentration (mg/m 3 )	RE (%)
	[69] [75] [70]	Perlite, polyu-rethane foam, compost, wood chips, straw Pine bark, com-posted wood mulch Compost & wood-chip	-70-290 34	7-7.5 20 2.7-8.7 Room temp K 2 HPO 4 NaNO 3 , KH 2 PO 4 , Na 2 HPO 4 , MgSO 4 , CaCl 2 •2H 2 O, FeSO 4 •7H 2 O, ZnSO 4 •H 2 O, MnSO 4 •H 2 O, CuSO 4 •5H 2 O, CoCl 2 •6H 2 O, H 3 BO 3 , Na 2 MoO 7 •2H 2 O (NH 4 ) 2 SO 4 Na 2 CO 3 6.1-8.1 Room temp None	Acetone, n-butanol methane ethylene ammonia H 2 S DMDS ethanethiol Ammonia	23.9 7.0 21.7 ppm 20.2 ppm 10-30 ppm 3.8-7.6 40 ppm	100 100 0 95 85-98 100 95 82 35-63
	[74] Los Angeles, U.S Compost, perlite, Wood bark oyster shell	1.6-3.1 14-69	-7-9	11.3-30.8 None -None	Ammonia H 2 S Benzene Xylenes Toluene Dichloro-Benzene H 2 S Carbon disulfide, Methyl mer-captan Carbonyl sulfide Dimethyl sulfide	0.25-75.00 ppm 0.05-8.00 ppm 0.002-0.003 0.18-0.66 0.077-0.23 0.024-0.049 10-50 0.02-0.03 0.30-0.33 0.02-0.03 0.05-0.13	95.2-97.9 95.8-100 0-50 40-75 42-86 43-60 > 99 32-36 91-94 0-21 30-35
	Ojai Valley, U.S Lava rock Carson, U.S Compost, wood chips, oyster shell, perlite Yarmouth, U.S Compost, bark mulch, wood chips	18-54 45-180 45	7.9-8.1 ----Low temp Chemical misting α-Pinene Nitrogen Phosphorous Potassium MTBE Acetone Toluene Xylenes Dichloro-methane Chloro-form H 2 S None Benzene Toluene m,p-Xylene o-Xylene H 2 S β-Pinene Dimethyl sulfide DMDS D-Limonene Carbon disulfide Methyl mercaptan	1.8 1.6 2.3 1.3 3.5 0.3 0.01-42 3.0 4.0 1.1 0.4 13.9 675 ppb 345 0.02 0.16 70 0.01 0.006	20 80 60 40 30 15 > 90 83-95 88-97 88-93 88-91 > 99 100 100 100 100 97 100 100
	Tampa, U.S	Top soil, peat, mulch	60	-	-	None	H 2 S	7-120	100
	Albany, U.S	-	150	-	-	-	H 2 S Dimethyl sulfide Methyl mercaptan	200 8.8 22	100 21 66
	Hillsborough, U.S	Pine bark	115	-	-	None	H 2 S DMDS Carbon disulfide Methyl mercaptan	140 936 618 330	99.5 97 82 100

Table 2 (

 2

	continued)
	Reference/loca-tion a	Media	Parameters EBRT (s) pH	Temp. (°C) Nutrients	Odorant	Concentration (mg/m 3 )	RE (%)
	Boca-Grande, U.S	Peat, wood chips, top soil	130	-	-	None	H 2 S	140	100
	Charlotte, U.S	Wood chips, compost, perlite, granular fill	111	-	-	-	Dimethyl sulfide Carbon disulfide	625 448	100 100
	Fountain Valley, U.S Martinez, U.S	Two units with GAC and yard waste compost Wood chips, yard waste, compost, lime	17-70 38	1-2.7 ---	--	Benzene Toluene Xylenes Dimethyl sulfide Chloroform TCE PCE Total gaseous nonmethane organics H 2 S H 2 S Dimethyl sulfide DMDS Methyl mercaptan	0.01 0.1 0.08 0.07 0.06 0.01 0.37 26 ppm 4.3 0.11 0.03 0.01 0.054	36-93 24-99 96 35 11 82 98 99 99 95 68 41 90
	Renton, U.S	Bark, topsoil, compost, peat, moss, oyster shells	40-60	-	-	-	Acetone H 2 S Benzene Mercaptans Amines TCE PCE Chloroform	0.03-0.09 1.5-34 0.01-0.25 0.16-3.8 ppm 2.5-6 ppm 0.02-0.05 0.02-0.5 0.10-0.21	55 97 25 62 > 60 44 40

Table 3

 3 Performance of BTF in lab and on-site systems

	a Reproduced with permission from [27, 62, 63]

Table 4

 4 Summary of different odor control technologies in WWTPs

	Technology	Benefits	Disadvantages
	Adsorption system	-High odor removal efficiency -Simplicity of mechanism	-Short life span -Air humidity and small molecule size -High reduction costs -Efficient only in small concentrations of contaminant
	Chemical scrubbing	-Simple, effective, efficient, reliable & proper utilization -Low maintenance costs	-High costs than biotechnologies -Secondary treatment of leftover sludge
	Biofilter system	-Low investment & operating costs -Absence of secondary waste streams -Low pressure drop & suitability for treating large volume of low concentration odorous gases	-Low efficiency of the treatment of high concentration pollutants -Difficult control of moisture and pH -Filter bed replacement every 2-5 years & risk of bed clogging by particulate matter
	Biotrickling	-Simple and low-cost technology -Medium capital, low operating costs -Effective removal of pollutants including acid-produc--Low pressure drop ing ones	-When treating high pollutant concentrations, too high nutrient doses may lead to filter-bed clogging by grow-ing biomass
	Bioscrubber	-Stability of operation -Proper biological parameters control, including pH, temperature, nutrients -High pressure drop -Avoids inhibitory effects nated -Harmful environmental compounds are easily elimi-	-Low efficiency in case of poorly soluble substances -Treatment efficiency is reduced by special contact area of gas/liquid -High pressure drop
	Activated sludge diffusion -Utilization of available equipment (lower costs) & easy operation -Higher management capability for larger loadings -Simultaneous treatment of air and wastewater	-Complex steering by experts -Tower efficiency due to the limits of gas/liquid trans-ference -Possibility of corrosion in equipment

Table 5

 5

	Characterization of two sources alum sludges	Sludge	S P (BET) (m 2 /g)	pH	Al (mg/kg)	Ca	K, Fe, Mo	Si, P
		Dublin	47	6.9	17,983	2673	4210	106
		Carmaux	257	10.0	17,581	21,156	9483

Acknowledgements

The first author greatly acknowledges the PhD scholarship received jointly from University College Dublin, Ireland and China Scholarship Council (CSC). The RAPSODEE research centre (CNRS UMR 5302), France is also gratefully acknowledged for the technological support.


El-Hachemi Guerrout
email: eguerrout@esi.dz

Samy Ait-Aoudia
email: saitaoudia@esi.dz

Dominique Michelucci
email: dominique.michelucci@u-bourgogne.fr

Ramdane Mahiou
email: rmahiou@esi.dz

Conjugate Gradient method for Brain Magnetic Resonance Images Segmentation

Keywords: Brain image segmentation, Hidden Markov Random Field, The Conjugate Gradient algorithm, Dice Coefficient metric

Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. Conjugate Gradient algorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear Conjugate Gradient algorithm (CG) for image segmentation, in combination with the Hidden Markov Random Field modelization. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.

Introduction

Automatic segmentation of medical images has become a crucial task due to the huge amount of data produced by imaging devices. Many popular tools as FSL [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm[END_REF] and Freesurfer [START_REF] Fischl | [END_REF] are dedicated to this aim. There are several techniques to achieve the segmentation. We can broadly classify them into thresholding methods [START_REF] Kumar | Skull stripping and automatic segmentation of brain MRI using seed growth and threshold techniques[END_REF][START_REF] Natarajan | Tumor detection using threshold operation in MRI brain images[END_REF][START_REF] Zhao | Automatic threshold level set model applied on MRI image segmentation of brain tissue[END_REF], clustering methods [START_REF] Pham | Current methods in medical image segmentation 1[END_REF][START_REF] Wu | An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation[END_REF][START_REF] Chuang | Fuzzy c-means clustering with spatial information for image segmentation[END_REF], edge detection methods [START_REF] Perona | Scale-space and edge detection using anisotropic diffusion[END_REF][START_REF] Senthilkumaran | Edge detection techniques for image segmentation-a survey of soft computing approaches[END_REF][START_REF] Canny | A computational approach to edge detection[END_REF], region-growing methods [START_REF] Lin | Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing[END_REF][START_REF] Roura | Marga: multispectral adaptive region growing algorithm for brain extraction on axial MRI[END_REF], watersheds methods [START_REF] Benson | Brain tumor extraction from MRI brain images using marker based watershed algorithm[END_REF][START_REF] Masoumi | Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network[END_REF], model-based methods [START_REF] Chan | Active contours without edges. Image processing[END_REF][START_REF] Ho | Level-set evolution with region competition: automatic 3-D segmentation of brain tumors[END_REF][START_REF] Mcinerney | Deformable models in medical image analysis: a survey[END_REF][START_REF] Wang | Segmentation of neonatal brain MR images using patch-driven level sets[END_REF] and Hidden Markov Random Field methods [START_REF] Zhang | Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm[END_REF][START_REF] Held | Markov random field segmentation of brain MR images[END_REF][START_REF] Panjwani | Markov random field models for unsupervised segmentation of textured color images[END_REF][START_REF] Guerrout | Hidden Markov random field model and BFGS algorithm for brain image segmentation[END_REF][START_REF] Guerrout | Hidden Markov random fields and swarm particles: a winning combination in image segmentation[END_REF][START_REF] Ait-Aoudia | Medical image segmentation using particle swarm optimization[END_REF][START_REF] Guerrout | Hidden Markov random field model and BFGS algorithm for brain image segmentation[END_REF][START_REF] Guerrout | Medical image segmentation on a cluster of PCs using Markov random fields[END_REF][START_REF] Guerrout | Medical image segmentation using hidden Markov random field a distributed approach[END_REF][START_REF] Guerrout | Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation[END_REF]. Threshold-based methods are the simplest ones that require only one pass through the pixels. They begin with the creation of an image histogram. Then, thresholds are used to separate the different image classes. For example, to segment an image into two classes, foreground and background, one threshold is necessary. The disadvantage of threshold-based techniques is the sensitivity to noise. Region-based methods assemble neighboring pixels of the image in non-overlapping regions according to some homogeneity criterion (gray level, color, texture, shape and model). We distinguish two categories, region-growing methods and split-merge methods. They are effective when the neighboring pixels within one region have similar characteristics. In model-based segmentation, a model is built for a specific anatomic structure by incorporating prior information on shape, location and orientation. The presence of noise degrades the segmentation quality. Therefore, noise removal phase is generally an essential prior. Hidden Markov Random Field (HMRF) [START_REF] Geman | Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images[END_REF] provides an elegant way to model the segmentation problem. It is based on the MAP (Maximum A Posteriori) criterion [START_REF] Wyatt | MAP MRF joint segmentation and registration of medical images[END_REF]. MAP estimation leads to the minimization of an objective function [START_REF] Szeliski | A comparative study of energy minimization methods for Markov random fields with smoothness-based priors[END_REF]. Therefore, optimization techniques are necessary to compute a solution. Conjugate Gradient algorithm [START_REF] Møller | A scaled conjugate gradient algorithm for fast supervised learning[END_REF][START_REF] Powell | Restart procedures for the conjugate gradient method[END_REF][START_REF] Shewchuk | An introduction to the conjugate gradient method without the agonizing pain[END_REF] is one of the most popular optimization methods.

This paper presents an unsupervised segmentation method based on the combination of Hidden Markov Random Field model and Conjugate Gradient algorithm. This method referred to as HMRF-CG, does not require preprocessing, feature extraction, training and learning. Brain MR image segmentation has attracted a particular attention in medical imaging. Thus, our tests focus on BrainWeb3 [START_REF] Cocosco | Brainweb: Online interface to a 3D MRI simulated brain database[END_REF] and IBSR4 images where the ground truth is known. Segmentation quality is evaluated using Dice Coefficient (DC) [START_REF] Dice | Measures of the amount of ecologic association between species[END_REF] criterion. DC measures how much the segmentation result is close to the ground truth. This paper is organized as follows. We begin by introducing the concept of Hidden Markov Field in section 2. Section 3 is devoted to the well known Conjugate Gradient algorithm. Section 4 is dedicated to the experimental results and section 5 concludes the paper.

Hidden Markov Random Field (HMRF)

Let S = {s 1 , s 2 , . . . , s M } be the sites, pixels or positions set. Both image to segment and segmented image are formed of M sites. Each site s ∈ S has a neighborhood set V s (S) (see an example in Figure 1).

A neighborhood system V (S) has the following properties:

∀s ∈ S, s / ∈ V s (S) ∀{s, t} ∈ S, s ∈ V t (S) ⇔ t ∈ V s (S) (1)
A r-order neighborhood system V r (S) is defined by the following formula: where distance(s, t) is the Euclidean distance between pixels s and t. This distance depends only on the pixel position i.e., it is not related to the pixel value (see examples in Figure 2). For volumetric data sets, as slices acquired by scanners, a 3D neighborhood system is used. A clique c is a subset of S where all sites are neighbors to each other. For a non single-site clique, we have:

V r s (S) = {t ∈ S| distance(s, t) 2 ≤ r ∧ s = t} (2)
1 1 s 1 1 (a) V 1 s (S) 2 2 2 2 s 2 2 2 2 (b) V 2 s (S)
∀{s, t} ∈ c, s = t ⇒ (t ∈ V s (S) ∧ s ∈ V t (S)) (3)
A p-order clique noted C p contains p sites i.e. p is the cardinal of the clique (see an example in Figure 3). x . Figure 4 shows an example of segmentation into three classes.

The image to segment

The segmented image Fig. 4. An example of segmentation using FSL tool.

The segmentation of the image y consists of looking for a realization x of X. HMRF models this problem by maximizing the probability P

[X = x | Y = y]. x * = arg x∈Ωx max {P [X = x | Y = y]} (4)      P [X = x|Y = y] = A exp (-Ψ (x, y)) Ψ (x, y) = s∈S ln(σ xs ) + (ys-µx s ) 2 2σ 2 xs + B T c2={s,t} (1 -2δ(x s , x t )) A is a positive constant
where B is a constant, T is a control parameter called temperature, δ is a Kronecker's delta and µ xs , σ xs are respectively the mean and standard deviation of the class x s . When B > 0, the most likely segmentation corresponds to the constitution of large homogeneous regions. The size of these regions is controlled by the B value.

Maximizing the probability P

[X = x | Y = y] is equivalent to minimizing the function Ψ (x, y). x * = arg x∈Ωx min {Ψ (x, y)} (5)
The computation of the exact segmentation x * is practically impossible [START_REF] Geman | Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images[END_REF]. Therefore optimization techniques are necessary to compute an approximate solution x.

Let µ = (µ 1 , . . . , µ j , . . . , µ K ) be the means and σ = (σ 1 , . . . , σ j , . . . , σ K ) be the standard deviations of the K classes in the segmented image x = (x 1 , . . . , x s , . . . , x

M ) i.e.,        µ j = 1 |Sj | s∈Sj y s σ j = 1 |Sj | s∈Sj (y s -µ j ) 2 S j = {s | x s = j} (6)
In our approach, we will minimize Ψ (µ) defined below instead of minimizing Ψ (x, y). We can always compute x through µ by classifying y s into the nearest mean µ j i.e., x s = j if the nearest mean to y s is µ j . Thus instead of looking for x * , we look for µ * . The configuration set of µ is

Ω µ = [0 . . . 255] K .        µ * = arg µ∈Ωµ min {Ψ (µ)} Ψ (µ) = K j=1 f (µ j ) f (µ j ) = s∈Sj [ln(σ j ) + (ys-µj ) 2 2σ 2 j ] + B T c2={s,t} (1 -2δ(x s , x t )) (7)
where S j , µ j and σ j are defined in the equation [START_REF] Chan | Active contours without edges. Image processing[END_REF].

To apply unconstrained optimization techniques, we redefine the function Ψ (µ) for µ ∈ R K instead of µ ∈ [0 . . . 255] K as recommended by [START_REF] Boyd | Convex optimization[END_REF]. Therefore, the new function Ψ (µ) becomes as follows:

Ψ (µ) = K j=1 F (µ j ) where µ j ∈ R (8)
F (µ j ) =      f (0) -u j * 10 3 if µ j < 0 f (µ j ) if µ j ∈ [0 . . . 255] f (255) + (u j -255) * 10 3 if µ j > 255 (9)

Hidden Markov Random Field and Conjugate Gradient algorithm (HMRF-CG)

To solve the minimization problem expressed in equation 7, we used the nonlinear conjugate gradient method. This latter generalizes the conjugate gradient method to nonlinear optimization. The summary of the algorithm is set out below. Let µ 0 be the initial point and d 0 = -Ψ (µ 0 ) be the first direction search.

Calculate the step size α k that minimizes ϕ k (α). It is found by ensuring that the gradient is orthogonal to the search direction d k .

ϕ k (α) = Ψ (µ k + αd k ) ( 10
)
At the iteration k + 1, calculate µ k+1 as follows:

µ k+1 = µ k + α k d k (11)
Calculate the residual or the steepest direction:

r k+1 = -Ψ (µ k+1 ) (12)
Calculate the search direction d k+1 as follows:

d k+1 = r k+1 + β k+1 d k (13)
In conjugate gradient method there are many variants to compute β k+1 , for example:

1. The Fletcher-Reeves conjugate gradient method:

β k+1 = r k+1 T r k+1 (r k ) T r k (14)
2. The Polak-Ribière conjugate gradient method:

β k+1 = max r k+1 T r k+1 -r k (r k ) T r k , 0 (15)
To use conjugate gradient algorithm, we need the first derivative Ψ (µ)) = (∆ 1 , . . . , ∆ i , . . . , ∆ K ). Since no mathematical expression is available, it is approximated with finite differences [START_REF] Eberly | Derivative approximation by finite differences[END_REF]. In our tests, we have used a centered difference approximation to compute the first derivative as follows:

∆ i = Ψ (µ 1 , . . . , µ i + ε, . . . , µ n ) -Ψ (µ 1 , . . . , µ i -ε, . . . , µ n ) 2ε (16)
The good approximation of the first derivative relies on the choice of the value of the parameter ε. Through the tests conducted, we have selected 0.01 as the best value. In practice, the application is implemented in the cross-platform Qt creator (C++) under Linux system. We have used the GNU Scientific Library implementation of Polak-Ribière conjugate gradient method [START_REF] Polak | Note sur la convergence de méthodes de directions conjuguées[END_REF][START_REF] Grippo | A globally convergent version of the Polak-Ribière conjugate gradient method[END_REF]. The HMRF-CG method is summarized hereafter.

Input: y the image to segment, K the number of classes, B the constant parameter of HMRF, T the control parameter of HMRF, µ 0 the initial point, ε the parameter used by the first derivative.

Initialization:

we define s as the minimizer structure of gsl multimin fdfminimizer type and we initialize it by: K the size problem, Ψ the function to minimize using the equation 8, Ψ the first derivative of the function to minimize using the equation 16, µ 0 the start point, gsl multimin fdfminimizer conjugate pr (Polak-Ribière conjugate gradient) the minimizer function.

Iterations: we perform one iteration to update the state of the minimizer using the function gsl multimin fdfminimizer iterate(s) and after that, we test s for convergence.

The stopping criterion: in our case, the minimization procedure should stop when the norm of the gradient ( Ψ ) is less than 10 -3 . Output: an approximation μ of µ * ∈ R n , x the segmented image using μ.

Experimental Results

In this section, we show the effectiveness of HMRF-CG method. To this end, we will make a comparison with some methods that are: improved k-means and MRF-ACO-Gossiping [START_REF] Yousefi | Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms[END_REF]. Next, we will show, the robustness of HMRF-CG method against noise, by doing a comparison with FAST FSL(FMRIBs Automated Segmentation Tool) and LGMM (Local Gaussian Mixture Model) [START_REF] Liu | Image segmentation using a local gmm in a variational framework[END_REF]. To perform a fair and meaningful comparison, we have used a metric known as Dice Coefficient [START_REF] Dice | Measures of the amount of ecologic association between species[END_REF]. Morey et al. [START_REF] Morey | A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes[END_REF] used interchangeably Dice coefficient and Percentage volume overlap. This metric is usable only when the ground truth segmentation is known (see section 4.1). The image sets and related parameters are described in section 4.2. Finally, section 4.3 is devoted to the yielded results.

Dice Coefficient metric

Dice Coefficient (DC) measures how much the result is close to the ground truth. Let the resulting class be  and its ground truth be A * . Dice Coefficient is given by the following formula:

DC = 2| Â ∩ A * | | Â ∪ A * | (17)

The image sets and related parameters

To evaluate the quality of segmentation, we use four volumetric (3D) MR images, one obtained from IBSR (real image) and the others from BrainWeb (simulated images).

Results

Table 2 shows the mean DC values using IBSR image. The parameters used by HMRF-CG are described in Table 1. The parameters used by the other methods are given in [START_REF] Yousefi | Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms[END_REF]. Table 3 shows the mean DC values using BrainWeb images. The parameters used by HMRF-CG are described in Table 1. The parameters used by the LGMM method are given in LGMM [START_REF] Liu | Image segmentation using a local gmm in a variational framework[END_REF]. The implementation of LGMM is built upon the segmentation method [START_REF] Ashburner | Unified segmentation[END_REF] of SPM 8 (Statistical Parametric Mapping5 ), which is a well known software for MRI analysis. As reported by [START_REF] Liu | Image segmentation using a local gmm in a variational framework[END_REF], LGMM has better results than SPM 8.

Figure 5 shows a sample of slices to segment obtained from IBSR image, their ground truths and their segmentation using HMRF-CG method.

Figure 6 shows the slices number #95 with different noise and intensity nonuniformity from BrainWeb images and their segmentation using HMRF-CG.

Discussion and Conclusion

In this paper, we have described a method which combines Hidden Markov Random Field (HMRF) and Conjugate Gradient (GC). The tests have been carried out on samples obtained from IBSR and BrainWeb images, the most commonly used images in the field. For a fair and meaningful comparison of methods, the segmentation quality is measured using the Dice Coefficient metric. The results depend on the choice of parameters. This very sensitive task has been conducted by performing numerous tests. From the results obtained, the HMRF-GC method outperforms the methods tested that are: LGMM, Classical MRF, MRF-ACO-Gossiping and MRF-ACO. Tests permit to find good parameters for HMRF-CG to achieve good segmentation results. To further improve performances a preprocessing step can be added to reduce noise and inhomogeneity using appropriate filters.

Fig. 1 .

 1 Fig.1. An example of a lattice S, n is the set of sites neighboring s.

Fig. 2 .

 2 Fig.2. First, second, fourth and fifth order neighborhood system of the site s.

Fig. 3 .

 3 Fig.3. Cliques associated to the second order neighborhood system for the site s.

Fig. 5 .

 5 Fig. 5. (a) Slices number, (b) slices to segment from IBSR, (c) ground truth slices, (d) segmented slices using HMRF-CG.

Fig. 6 .

 6 Fig. 6. The slices number #95 of BrainWeb images. (a) Noise and intensity nonuniformity, (b) slices to segment from BrainWeb images, (c) segmented slices using HMRF-CG.

Table 1 .

 1 Related parameters to images used in our tests.

	Three components were considered: GM (Grey Matter), WM (White
	Matter) and CSF (Cerebro Spinal Fluid). IBSR image has the following charac-
	teristics: dimension is 256 × 256 × 63, with voxel=1 × 3 × 1mm and T1-weighted

Table 2 .

 2 Mean DC values (the best results are given in bold type).

	Methods	Dice Coefficient GM WM CSF Mean
	K-means	0.500 0.607 0.06	0.390
	MRF-ACO-Gossiping 0.778 0.827 0.262 0.623
	HMRF-CG	0.859 0.855 0.381 0.698

Table 3 .

 3 Mean DC values (the best results are in bold type).

	Tissue Method	Dice Coefficient BrainWeb1 BrainWeb2 BrainWeb3
	HMRF-CG	0.970	0.945	0.921
	GM	LGMM	0.697	0.905	0.912
	FSL FAST	0.727	0.737	0.735
	HMRF-CG	0.990	0.971	0.954
	WM	LGMM	0.667	0.940	0.951
	FSL FAST	0.877	0.862	0.860
	HMRF-CG	0.961	0.942	0.926
	CSF	LGMM	0.751	0.897	0.893
	FSL FAST	0.635	0.647	0.643
	IBSR 1-24/18
	IBSR 1-24/34

http://www.bic.mni.mcgill.ca/brainweb/

https://www.nitrc.org/projects/ibsr

http://www.fil.ion.ucl.ac.uk/spm/software/spm8/