**2. SNe Ia Cosmology**

SNe Ia are characterized by an intrinsic luminosity that is almost uniform. Because of this, SNe Ia are considered reliable *standard candles*. We compare the theoretical distance moduli *µth* with the observed distance moduli *µobs* of SNe Ia belonging to the PS. The theoretical distance moduli are defined through the luminosity distance *dL*(*z*) which we need to define based on the cosmological model of interest. We here show the CPL parametrization which describes the *w* parameter as a function of redshift (*w*(*z*) = *w*0 + *wa* × *z*/(1 + *z*)) in the *w*0*wa*CDM model. In the usual assumptions *w*0 ∼ −1 and *wa* ∼ 0, and *dL*(*z*) is defined as the following [285]:

$$d\_{\rm L}(z, H\_{0\cdots}) = \frac{c(1+z)}{H\_0} \int\_0^z \frac{dz^\*}{\sqrt{\Omega\_{0m} \left(1+z^\*\right)^3 + \Omega\_{0\Lambda} \left(1+z^\*\right)^3 \left(w\_0 + w\_d + 1\right)} e^{-3w\_d \frac{z^\*}{1+z^\*}}},\tag{1}$$

where Ω0Λ is the Dark Energy component, *c* is the speed of light, and *z* is the redshift. We stress that in this context the relativistic components are ignored. Moreover, since in the present universe the radiation density parameter Ω0*r* ≈ 10−5 , this contribution can be neglected. If we substitute *wa* = 0, *w*0 = −1 in Equation (1) the luminosity distance expression for ΛCDM model is automatically retrieved. According to the distance luminosity expression, the theoretical distance modulus can be written in the following form:

$$
\mu\_{th} = 5 \log\_{10} d\_L(z, H\_{0\prime} \dots) + 25,\tag{2}
$$

which is usually expressed in Megaparsec (Mpc). The observed distance modulus, *µobs* = *m*0 *B* − *M*, taken from PS contains the apparent magnitude in the B-band corrected for statistical and systematic effects (*m*0 *B* ) and the absolute in the B-band for a fiducial SN Ia with a null value of stretch and color corrections (*M*). Considering the color and stretch population models for SNe Ia, in our approach we average the distance moduli given by the [286] (G2010) and [287] (C2011) models. We here remind the reader that *H*0 and *M* are degenerate parameters: in the PS release, *M* = −19.35 such that *H*0 = 70.0.

Ref. [51] obtain information on *H*0 by comparing *µobs* in [284] 1 with *µth* for each SN. Moreover, they fix Ω0*m* to a fiducial value to better constrain the *H*0 parameter. Furthermore, according to [288], we consider the correction of the luminosity distance keeping into account the peculiar velocities of the host galaxies which contain the SNe Ia. To perform our analysis, we define the *χ* 2 for SNe:

$$
\chi\_{\rm SN}^2 = \Delta \boldsymbol{\mu}^T \cdot \mathcal{C}^{-1} \cdot \Delta \boldsymbol{\mu}. \tag{3}
$$

Here ∆*µ* = *µobs* − *µth*, and C denotes the 1048 × 1048 *covariance matrix*, given by [284]. As for the *µobs* values of G2010 and C2011, the systematic uncertainty matrices of the two models have been averaged. After building the C total matrix from Equation (16) in [51], we slice the PS in redshift bins, and then we divide C into submatrices considering the order in redshift. More in detail, starting from the 1048 SNe Ia redshift-ordering, we divide the SNe Ia into 3 equally populated bins made up of ≈349 SNe Ia. Concerning only *Dstat*, it is trivial to build its submatrices considering that the statistical matrix is diagonal. Hence, a single matrix element is related to a given SN of the PS. On the other hand, if the non-diagonal matrix *Csys* is included, a customized code will be used2 to build the submatrices. Our code was developed to select only the total covariance matrix elements related to SNe Ia having redshift within the considered bin.

The choice of three bins is justified by the high number of SNe Ia (around hundreds of SNe per bin) that can still constitute statistically illustrative subsamples of the PS and that can properly consider the contribution of systematic uncertainties. Subsequently to the bins division, we focus on the optimal values of *H*0 to minimize the *χ* 2 in Equation (3). *H*0 is regarded as a nuisance parameter, which is free to vary, to better analyze a possible redshift function of *H*0. We follow the assumptions on the fiducial value of *M* = −19.35: while in [51] *M* was estimated assuming a local (*z* = 0) value of *H*0 = 73.5, we here consider the conventional *H*0 value of the PS release, namely *H*0 = 70.0 for three bins. Our choice of a starting value of *H*0 = 70 is dictated by the presence in the current literature of more than 50 papers that are using the PS in combination with other probes to estimate the value of *H*0, see [172,198,204,205,288–307,307–339]. Thus, if an evolutionary effect is present, it is necessary to investigate to which extent this can affect current and future results largely based on the PS sample. Conversely, we fix Ω0*m* = 0.298 ± 0.022 according to [284] for a standard flat ΛCDM model. More specifically, after the minimization of *χ* 2 , we extract the *H*0 value in each redshift bin, via the *Cobaya* code [340]. To this end, we execute an MCMC using the D'Agostini method to obtain the confidence intervals for *H*0 at the 68% and 95% levels, in three bins.


**Author details**

George Mamulashvili and Aleksandre Gurgenidze\* Independent Researcher, Tbilisi, Georgia

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**5. The possibility of transferring bacteria from the oral microbiome of dogs to human**

Except for to the impact on animal health, bacteria from the oral cavity of animals may also have harmful effects on human health in the case of microbial

**253**

**6. Conclusion**

are needed.

1/0081/17.

**Acknowledgements**

*Dental Biofilm as Etiological Agent of Canine Periodontal Disease*

bites by dogs, were isolated from wounds in humans [83].

As transmission of oral bacteria during normal contacts between dogs and humans is also feasible one might expect correlations between the oral microbiota of dogs and humans [17]. Oral-to-oral transfer of *Neisseria shayeganii*,

*Porphyromonas canigingivalis*, *Tannerella forsythia* and *Streptococcus* minor from dogs to humans is suspected. The finding of potentially zoonotic and periodontopathic bacteria in the canine oral microbiome may be a public health concern [15].

Review of literature showed that some bacterial species like *Tannerella forsythia*, *Campylobacter rectus*, *Treponema denticola*, *Fusobacterium nucleatum*, *Parvimonas micra* and *Prevotella intermedia* are the important pathogens for periodontitis in both humans and dogs. On the other hand, *Porphyromonas gulae* is specifically associated with canine periodontal disease. In addition, it is assumed, that oral protozoa such as *Entamoeba gingivalis* and *Trichomonas tenax* play role in canine periodontal disease. In summary, periodontal disease is polymicrobial disease and further analyses of the associated species of periodontitis and their virulence factors in dogs

This publication was supported by the project of the Ministry of education science Research and sport of the Slovak Republic VEGA 1/0788/19 and VEGA

transmissibility, for example, through dog bites. Dog bite wounds are polymicrobial, with a broad combination of aerobic and anaerobic microorganisms. The microbiology of infected bite wounds from dogs is similar to that of the organisms that colonize the dog's oral cavity. Less frequently, isolates may also come from the environment and patients' skin [82]. On average, a dog bite wound contains two to five different species of bacteria [83].  Other common aerobic organisms include *P. multocida*, *P. dagmatis*, *Staphylococcus* spp. (including MRSA), *Streptococcus* spp. (including *S. pyogenes*), *Neisseria* spp., *Capnocytophaga canimorsus*, *Corynebacterium* spp., *Moraxella* spp., *Enterococcus* spp. and *Bacillus* spp. [82, 83]. The most common anaerobic organism isolated from infected dog bite wounds is *Fusobacterium nucleatum* [82]. *Fusobacterium canifelinum* was also isolated from wounds in humans after dog bites [85]. Other common anaerobes include *Prevotella* spp., *Bacteroides* spp., *Porphyromonas* spp., *Propionibacterium* spp. and *Peptostreptococcus* spp. [82, 83]. Several *Porphyromonas* species (*P. macacae*, *P. canoris*, *P. circumdentaria*, *P. cangingivalis*, and *P. cansulci*) [84] and other periodontal pathogens (*Tannerella forsythia*, *Prevotella intermedia* and *Prevotella dentalis*) were also isolated from infected dog bite wounds [82]. In addition, some pathogens such as *Leptospira*, *Rabies virus*, *Clostridium tetani* or *Francisella tularensis*, which can cause systemic infection after

*DOI: http://dx.doi.org/10.5772/intechopen.88305*

### *Dental Biofilm as Etiological Agent of Canine Periodontal Disease DOI: http://dx.doi.org/10.5772/intechopen.88305*

transmissibility, for example, through dog bites. Dog bite wounds are polymicrobial, with a broad combination of aerobic and anaerobic microorganisms. The microbiology of infected bite wounds from dogs is similar to that of the organisms that colonize the dog's oral cavity. Less frequently, isolates may also come from the environment and patients' skin [82]. On average, a dog bite wound contains two to five different species of bacteria [83].  Other common aerobic organisms include *P. multocida*, *P. dagmatis*, *Staphylococcus* spp. (including MRSA), *Streptococcus* spp. (including *S. pyogenes*), *Neisseria* spp., *Capnocytophaga canimorsus*, *Corynebacterium* spp., *Moraxella* spp., *Enterococcus* spp. and *Bacillus* spp. [82, 83]. The most common anaerobic organism isolated from infected dog bite wounds is *Fusobacterium nucleatum* [82]. *Fusobacterium canifelinum* was also isolated from wounds in humans after dog bites [85]. Other common anaerobes include *Prevotella* spp., *Bacteroides* spp., *Porphyromonas* spp., *Propionibacterium* spp. and *Peptostreptococcus* spp. [82, 83]. Several *Porphyromonas* species (*P. macacae*, *P. canoris*, *P. circumdentaria*, *P. cangingivalis*, and *P. cansulci*) [84] and other periodontal pathogens (*Tannerella forsythia*, *Prevotella intermedia* and *Prevotella dentalis*) were also isolated from infected dog bite wounds [82]. In addition, some pathogens such as *Leptospira*, *Rabies virus*, *Clostridium tetani* or *Francisella tularensis*, which can cause systemic infection after bites by dogs, were isolated from wounds in humans [83].

As transmission of oral bacteria during normal contacts between dogs and humans is also feasible one might expect correlations between the oral microbiota of dogs and humans [17]. Oral-to-oral transfer of *Neisseria shayeganii*, *Porphyromonas canigingivalis*, *Tannerella forsythia* and *Streptococcus* minor from dogs to humans is suspected. The finding of potentially zoonotic and periodontopathic bacteria in the canine oral microbiome may be a public health concern [15].


**7. Societal benefits of ocean color approaches to algorithm development for key phytoplankton functional types: Fisheries and habs applications**

A fundamental goal of phytoplankton biogeography is to describe how PFTs are distributed spatially and temporally and how these patterns relate to processes that control primary

Ocean Color Remote Sensing of Phytoplankton Functional Types 117

Ocean Color satellites have had a valuable application for many marine biological processes such as red tides and harmful algal blooms (HABs, reviewed in Stumpf & Tomlinson 2005). HABs are not only harmful to the marine ecosystem but substantive economic losses occur in coastal regions worldwide and the need for remote detection of blooms is critical (Anderson et al. 1997, Schofield et al. 1999, Sellner et al. 2003). HAB events threaten human health, living marine resources, and ecosystem health. Their occurrence has increased in frequency, duration, and severity over the last several decades (Figure 9). Current monitoring efforts of toxic cells and toxin levels in shellfish tissue are relatively slow and lack the synoptic coverage of satellites (Schofield et al. 1999). Nevertheless, HAB monitoring can occur at unprecedented scales with detailed shipboard and drifters to predict arrival, transport time, and possibly toxicity of the bloom (Figure 9, Schofield et al. 1999). Knowledge such as this allows the public to know location, timing of the bloom and close any fisheries that are impacted economically. An excellent example of this type work has been conducted on the mesoscale blooms of *Karenia brevis* in the Gulf of Mexico with ocean color derived chlorophyll a and solar stimulated fluorescence (Stumpf et al. 2003, Hu et al. 2005, Figure 9). Most HAB studies have focused on monitoring with chlorophyll-a as a biomass indicator, however, photo-protective pigments such UV mycosporine-like amino acids and gyroxanthin from other non-toxic organisms (Kahru & Mitchell 1998, Moisan et al. 2011). Several countries all over the world have successfully monitored HABs over broad spatial scales with supplemented satellite imagery and aircraft monitoring in the Gulf of Mexico that allows for tracking of the bloom by satellite and drifters to notify the public.

Fig. 9. Left figure: Harmful algal bloom image in the Gulf of Mexico while using drifters and ocean color satellite imagery that allows for tracking for responses to the public (Lohrenz et al., unpub.). Right figure: Fluorescence Line Height is a better indicator than the satellitebased Chl for chlrophyll-a biomass in CDOM-rich waters. This image is for a *Karenia brevis* bloom (modified from Hu et al. 2005, Courtesy of Dr. Chuanmin Hu). Please note that

Over the past few decades, the dominant phytoplankton biomass product, chlorophyll-a, has been observed over a long time period on global scales. Recent investigations using ecological provinces have indicated additional regional variability and have provided

**8. Future directions of algorithm development using ocean color**

images from different years.

**b. Monitoring and tracking through the identification of harmful algal blooms**

production/new production. Several theories have been postulated about what governs biological diversity, which stabilizes community dynamics. Future satellites with increased spectral resolution will allow for algorithms of different proxies for diversity whether the products are pigments, size spectra, or biogeochemical indicators. Such phytoplankton diversity indicators will lead to a better understanding of the response of marine ecosystems to climate and human activities (Platt et al. 2008, 2009). To date, the scientific community has been able to describe phytoplankton community structure based on broad size classes, pigmentation, probability of occurrence, or some other index of presence or absence. We have focused on two successful applications including fisheries management and harmful algal blooms.

### **a. Fisheries applications**

Satellite ocean color application on broad scales allow for PFTs, namely diatoms, to be put into an ecological context regarding El Niño events in the Humboldt ecosystem (Jackson et al. 2011, Figure 8). A locally tuned algorithm for detecting diatom distribution allowed for critical observations in shifts in the size structure of the phytoplankton community, which provide a food source for anchovies. Changes in carbon structure will affect food stress on the fish populations and appears to influence landings in the following year. Mapping the distribution and abundance of phytoplankton using remotely sensed data assists in the creation of targeted and quantitative fishing strategies. Satellites can provide strategies and tools together with modeling and other satellite. Both ocean color and other satellite data, coupled with models, can provide resource managers the information of food web components (other than the chlorophyll-a product), which may be used to direct shipboard sampling on regional scales.

Fig. 8. Climate-induced changes in the spatial distribution of diatoms in the Humboldt system. The figure shows a marked reduction in the presence of diatoms during the 1997 El Niño event compared with the control period (December 2000), which represents typical oceanic conditions for the study region. The color bar represents the fraction of pixels identified as diatoms. Modified from Jackson et al. (2011).

production/new production. Several theories have been postulated about what governs biological diversity, which stabilizes community dynamics. Future satellites with increased spectral resolution will allow for algorithms of different proxies for diversity whether the products are pigments, size spectra, or biogeochemical indicators. Such phytoplankton diversity indicators will lead to a better understanding of the response of marine ecosystems to climate and human activities (Platt et al. 2008, 2009). To date, the scientific community has been able to describe phytoplankton community structure based on broad size classes, pigmentation, probability of occurrence, or some other index of presence or absence. We have focused on two successful applications including fisheries management and harmful

Satellite ocean color application on broad scales allow for PFTs, namely diatoms, to be put into an ecological context regarding El Niño events in the Humboldt ecosystem (Jackson et al. 2011, Figure 8). A locally tuned algorithm for detecting diatom distribution allowed for critical observations in shifts in the size structure of the phytoplankton community, which provide a food source for anchovies. Changes in carbon structure will affect food stress on the fish populations and appears to influence landings in the following year. Mapping the distribution and abundance of phytoplankton using remotely sensed data assists in the creation of targeted and quantitative fishing strategies. Satellites can provide strategies and tools together with modeling and other satellite. Both ocean color and other satellite data, coupled with models, can provide resource managers the information of food web components (other than the chlorophyll-a product), which may be used to direct shipboard

Dec 1997 Dec 2000

Fig. 8. Climate-induced changes in the spatial distribution of diatoms in the Humboldt system. The figure shows a marked reduction in the presence of diatoms during the 1997 El Niño event compared with the control period (December 2000), which represents typical oceanic conditions for the study region. The color bar represents the fraction of pixels

identified as diatoms. Modified from Jackson et al. (2011).

algal blooms.

**a. Fisheries applications**

sampling on regional scales.

#### **b. Monitoring and tracking through the identification of harmful algal blooms**

Ocean Color satellites have had a valuable application for many marine biological processes such as red tides and harmful algal blooms (HABs, reviewed in Stumpf & Tomlinson 2005). HABs are not only harmful to the marine ecosystem but substantive economic losses occur in coastal regions worldwide and the need for remote detection of blooms is critical (Anderson et al. 1997, Schofield et al. 1999, Sellner et al. 2003). HAB events threaten human health, living marine resources, and ecosystem health. Their occurrence has increased in frequency, duration, and severity over the last several decades (Figure 9). Current monitoring efforts of toxic cells and toxin levels in shellfish tissue are relatively slow and lack the synoptic coverage of satellites (Schofield et al. 1999). Nevertheless, HAB monitoring can occur at unprecedented scales with detailed shipboard and drifters to predict arrival, transport time, and possibly toxicity of the bloom (Figure 9, Schofield et al. 1999). Knowledge such as this allows the public to know location, timing of the bloom and close any fisheries that are impacted economically. An excellent example of this type work has been conducted on the mesoscale blooms of *Karenia brevis* in the Gulf of Mexico with ocean color derived chlorophyll a and solar stimulated fluorescence (Stumpf et al. 2003, Hu et al. 2005, Figure 9). Most HAB studies have focused on monitoring with chlorophyll-a as a biomass indicator, however, photo-protective pigments such UV mycosporine-like amino acids and gyroxanthin from other non-toxic organisms (Kahru & Mitchell 1998, Moisan et al. 2011). Several countries all over the world have successfully monitored HABs over broad spatial scales with supplemented satellite imagery and aircraft monitoring in the Gulf of Mexico that allows for tracking of the bloom by satellite and drifters to notify the public.

Fig. 9. Left figure: Harmful algal bloom image in the Gulf of Mexico while using drifters and ocean color satellite imagery that allows for tracking for responses to the public (Lohrenz et al., unpub.). Right figure: Fluorescence Line Height is a better indicator than the satellitebased Chl for chlrophyll-a biomass in CDOM-rich waters. This image is for a *Karenia brevis* bloom (modified from Hu et al. 2005, Courtesy of Dr. Chuanmin Hu). Please note that images from different years.

### **8. Future directions of algorithm development using ocean color**

Over the past few decades, the dominant phytoplankton biomass product, chlorophyll-a, has been observed over a long time period on global scales. Recent investigations using ecological provinces have indicated additional regional variability and have provided

Ocean Color Remote Sensing of Phytoplankton Functional Types 119

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**4.3 Comparsion of angular changes in knee joint in transversal plane**

As a result of the disease external rotation is marked on the operated side during the initial contact and loading response periods and throughout the swing phase. After the treatment, on the other hand, the crus undergoes external rotation throughout the stance phase and the internal rotation is insufficient at the swing phase.

Distinct external rotation appeared on the unoperated side throughout the gait cycle, crus rotation occurred after the procedure towards proper internal rotation (Fig. 5).

Ext – external rotation, Int – internal rotation

Fig. 5. Angular changes in the knee joints in the transversal plane


**Photocatalytic TiO2: From Airless Jet Spray Technology to Digital Inkjet Printing** Photocatalytic TiO2: From Airless Jet Spray Technology to Digital Inkjet Printing

DOI: 10.5772/intechopen.72790

Claudia L. Bianchi, Carlo Pirola, Marta Stucchi, Giuseppina Cerrato, Federico Galli, Alessandro Di Michele, Serena Biella, Wen-Fan Chen, Pramod Koshy, Charles Sorrell and Valentino Capucci Claudia L. Bianchi, Carlo Pirola, Marta Stucchi, Giuseppina Cerrato, Federico Galli, Alessandro Di Michele, Serena Biella, Wen-Fan Chen, Pramod Koshy, Charles Sorrell and Valentino Capucci

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.72790

#### Abstract

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TiO2 powders can be employed as both photocatalytic and structural materials, leading to applications in external coatings or in interior furnishing devices, including cement mortar, tiles, floorings, and glass supports. The technology of photocatalytic building materials is connected with the widespread production of photocatalytic active tiles. All the techniques proposed in the study involve the employment of nanosized TiO2: this represents a new problem to be dealt with, as inhaling nanoparticles exposes workers during industrial production and people in everyday locations to their dangerousness. Only very recently the employment of microsized TiO2 has been proposed, and the authors in this manuscript report the use of micrometric titania materials, but employing a new deposition technique, which is digital inkjet printing. It represents an improvement of the classical spray coating methods, as it requires piezoelectric heads to precisely direct the deposition of the suspension with an electrostatic field. The mixture contains aqueous/ organic components containing micrometric TiO2: to form a suspension, which is printed onto the surface of porcelain grès, large slabs using a digital printer. Many advantages are immediately evident, namely rapid and precise deposition, (almost) no waste of raw materials, thereby highlighting the economy, environmental friendliness, and sustainability of the process. All the materials we obtained have been thoroughly characterized by means of several experimental physico-chemical techniques, such as Raman microspectroscopy and scanning electron microscopy coupled with elemental analysis. Two different model VOCs, ethanol and toluene, and NOx have been selected to test the

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and eproduction in any medium, provided the original work is properly cited.

photocatalytic performances of the abovementioned tiles. Moreover, the antibacterial properties of the tiles have been determined, using Escherichia coli as example. Life cycle assessments (LCAs) for the two processes were modeled for 1 m2 of tiles produced in Modena, Italy. The impact assessments revealed that jet spraying exhibited uniformly greater impacts than digital inkjet printing and that the principal impacts were in human toxicity, cancer effects, freshwater ecotoxicity, and climate change. Most of the impacts were associated with the energy required for the production processes. Further considerations revealed that jet spraying is projected to generate twice as much CO2 and 30% more NOx than digital inkjet printing.

1.2. Photocatalytic TiO2 and its applications

zation of contamination from the substrate.

gas concentration and residence time [22].

respirability of nanoparticulates [26].

Semiconductor photocatalysis involves the activation of a photocatalytic material, such as titania (TiO2), by the action of light to produce charge carriers that undergo redox reactions with water and oxygen molecules [11]. This property allows these materials to decompose organic contaminants ideally into CO2 and water, thus allowing applications in self-cleaning, anti-fogging, corrosion prevention, and air and water purification [12]. Although there are many scientific and technical requirements for effective photocatalytic performance [13], there are also numerous practical challenges that must be met. One of these is catalyst immobilization [14] because it generally must balance the requirements of achievement of a robust chemical bond to a substrate, the retention of sufficient exposed surface area, and the minimi-

Photocatalytic TiO2: From Airless Jet Spray Technology to Digital Inkjet Printing

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There has been an increase in the sales of TiO2-based products for self-cleaning and air and water purification owing to growing environmental concerns [15]. One of the key product developments was photocatalytic building materials, which appeared on the market at the beginning of the 1990s [16]. TiO2 can be applied either in exterior construction or in interior furnishing materials, including concrete, mortar, tiles, flooring, and glass. In the early 2000s, the sales of photocatalytic building materials accounted for ~60% of the total market for photocatalytic materials [17]. Recent market projections to 2020 foreshadow increasing demand in the construction sector for self-cleaning coatings and other technologies for improving air quality, leading to a global market for photocatalyst-based products of ~\$2.9 billion [18]. At present, TiO2-based photocatalyst products constitute 97.6% of the market, and this dominance is a result of TiO2's low cost, ready availability, and well-documented performance data. Many studies have demonstrated the effectiveness of photocatalytic materials for pollution abatement of the volatile organic compounds (VOCs) and nitrogen oxides (NOx) that represent widespread risks to health. For example, concrete paving blocks with TiO2 added directly to the mix were able to remove 4.01 mg/h/m2 of NO [19]. However, similar work demonstrated that the efficiency of NOx photodegradation varied significantly according to preparation method and resultant microstructure, with some products achieving 40% photodegradation while others showed almost no effect [20]. A comparison between pure TiO2 and TiO2 embedded in neat hydrated cement revealed that the photocatalytic activity was three to 10 times lower for the latter, which was attributed to the agglomeration of the TiO2 powder [21]. TiO2 also has been applied as surface coatings on roofing tiles and corrugated sheet and found to achieve gaseous toluene removal efficiencies as high as 78 2%, although this was limited by

It is clear that the permanence and performance of TiO2-containing construction products depend significantly on the method used to immobilize the photocatalyst, whether in the bulk or on the surface. Other factors of importance include the type of substrate and the method of TiO2 application [23]. Hence, there has been a commensurate focus on the design of such building products and their manufacture in order to obtain maximal lifetimes and efficiencies. An additional consideration is the potential alteration of the photocatalyst by fouling, which can block the active sites and reduce or prevent photocatalysis [24]. Finally, there are also potential health issues associated with the formation of chemical intermediates [25] and the

Keywords: TiO2, titania, digital inkjet printing, jet spraying, VOCs, NOx, E. coli, life cycle assessment, impact assessments