Supplementary Information (SI)

for

A review of factors surrounding the air pollution exposure to in-pram babies and

mitigation strategies

Ashish Sharma, Prashant Kumar1

Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental

Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford

GU2 7XH, United Kingdom

This document includes:

Table S1 to Table S7

Section S1 to Section S5

1Corresponding author. Address as above. E-mail addresses: P.Kumar@surrey.ac.uk,

Prashant.Kumar@cantab.net

1

Table S1. Review of previous studies on physico-chemical analysis of air pollution

exposures in roadside environments, directly or indirectly relevant to in-pram babies; r

denotes the coefficient of correlation.

Microenvironment Type of analysis SEM/EDS findings Author (year)Roadside environment

Morphologyand elemental composition quantitative analysis

EDX analyses revealed Cl, Na and Fe as dominant elements in traffic generated PM exposures to in-pram babies.

Kumar et al. (2017)

Road tunnel Elemental analysis Si and Fe as the most abundant elements in the tunnel while Cu, Zn and Ba were found to be the most abundant trace elements

Pant et al. (2017)

Industrial, commercial and residential zones of Costa Rica

Chemical analysis and source identification

PMF (Positive matrix factorization) model identified 8 principle sources for PM10 and PM2.5 in the industrial site (crustal, secondary sulfate, secondary nitrate, secondary organic, traffic, sea–salt aerosols, industrial and oil combustion), 6 and 5 sources in commercial and residential sites, respectively.

Murillo et al. (2013)

School classroom and outdoor

Chemical and morphological properties of PM10 and PM2.5

PM10 (Outdoor air): correlation coefficient (r = 0.94) between Abscoeff

and EC PM10 (indoor air): r =0.41between Abscoeff and EC

Fromme et al. (2008)

Roadside environment

Chemical and physical analysis

Tyre wear is a major contributor of Zn and suspended road dust containing metals such as Si, Fe, Ca, Na, Mg, Al, and Sr.

Thorpe and Harrison (2008)

Roadside environment in Birmingham (UK)

Chemical composition analysis, principal components analysis (PCA) and trace metal analysis)

Correlation with NOx: Cu (r = 0.73), Zn (r = 0.61), Mo (r = 0.71), Ba (r = 0.73) and Pb (r = 0.67)

Correlation with PNC: Cu (r = 0.58), Mo (r = 0.60) and Ba (r = 0.61)

Harrison et al. (2003)

2

Table S2. Ratio of the contribution of specific contents of PM to particle mass indoors and outdoors (median).

Ions PM10 PM2.5

% Proportion Indoor/outdoor % Proportion Indoor/outdoorSulfate Indoor 2.4 0.3 4.4 0.4

Outdoor 8.5 9.0Nitrate Indoor 1.0 0.1 2.4 0.2

Outdoor 8.9 9.8Chloride Indoor 0.6 0.6 0.7 0.5

Outdoor 1.0 1.5Sodium Indoor 0.8 0.9 0.6 0.6

Outdoor 1.2 1.1Ammonium Indoor 0.7 0.1 2.3 0.3

Outdoor 5.5 7.9Potassium Indoor 0.4 0.7

Outdoor 0 0Magnesium Indoor 0.2 0.6 0.2 0.5

Outdoor 0.3 0.4Calcium Indoor 2.8 1.4 1.9 1.6

Outdoor 1.8 1.1ECa Indoor 7.7 0.7 –

Outdoor 10.4 –OCb Indoor 19.2 1.1 –

Outdoor 18.3 –aEC: elemental carbon; bOC: organic carbon; Note. Adapted from “Chemical and morphological properties of particulate matter (PM 10, PM 2.5) in school classrooms and outdoor air”, by Fromme, H, Diemer, J, Dietrich, S, Cyrys, J, Heinrich, J, Lang, W, Kiranoglu, M, and Twardella, D, Atmospheric Environment, 42, Copyright 2008 by “(2008)”.

3

Table S3. Classification of air filters1 according per EN779:2012 regulations (Camfil, 2017a)Group Class2 Final

pressuredrop (test) Pa

Averagearrestance (Am)of synthetic dust(%)

Averageefficiency (Em) for 0.4 μm particles%

Minimum efficiency3

for 0.4 μm particles (%)

Coarse G1 250 50≤Am<65  -  -

G2 250 65≤Am<80  -  -

G3 250 80≤Am<90  -  -

G4 250 90≤Am  -  -

Medium M5 450 - 40≤Em<60  -

M6 450 - 60≤Em<80  -

Fine F7 450 - 80≤Em<90 35

F8 450 - 90≤Em<95 55

F9 450 - 95≤Em 70

Note 1The characteristics of atmospheric dust vary widely in comparison with those of the synthetic loading dust used

in the tests. Because of this, the test results do not provide a basis for predicting either operational performance

or service life. Loss of media charge or shedding of particles or fibres can also adversely affect efficiency. 2Filter group/class description: G indicates filters for coarse dust particles (i.e., ≥  10 μm) and G1, G2, G3, G4

represents level of filtration; M indicates filters for medium dust particles and M5, M6 represents level of

filtration; F indicates filters for fine dust particles and F7, F8, F9 indicates level of filtration.3Minimum efficiency is the lowest of any of the following three values: initial efficiency, discharged efficiency

or efficiency throughout the test’s loading procedure.

4

Table S4 : Classifications of high-efficiency filters per  EN 1822:2009 (Camfil, 2017b)

Filter Class1

Integral value Local valueCollection Efficiency %

Penetration % Collection Efficiency % Penetration %

E10 85 15 - -

E11 95 5 - -

E12 99.5 0.5 - -

H13 99.95 0.05 99.75 0.25

H14 99.995 0.005 99.975 0.025

U15 99.9995 0.0005 99.9975 0.0025

U16 99.99995 0.00005 99.99975 0.00025

U17 99.999995 0.000005 99.9999 0.0001

Note: 1. 1Filter group/class description: E indicates EPA (Efficient Particulate Air filter) filters for particles

with E10, E11, E12 indicating level of filtration; H indicates HEPA (High Efficiency Particulate Air filters) for

micro particles (i.e., particles ≥ 0.01 μm) with H13, H14 indicating level of filtration; U indicates ULPA (Ultra

Low Penetration Air) filters for micro particles with U15, U16, U17 indicating level of filtration.

5

Table S5. Classification of mechanical coarse filter according to EN 779 : 2003 (BRE, 2012).Particle size Classification according to

EN779cExamples of matter retained per filter class

Coarse >1 µm

G1 EU1 Leaves, insects, textile fibres, human hairs, sand, fly ash, water dropletsG2 EU2

G3 EU3Beach sand, plant spores, pollen, fog

G4 EU4

Fine: 0.4 µm

F5 EU5 Spores, cement dust (coarse fraction), sediment dust

F6 EU6 Bigger bacteria, germs or carrier particles, PM10

F7 EU7 Agglomerated soot, lung damaging dust (PM2.5), cement dustF8 EU8

F9 EU9 Tobacco smoke (coarse fraction), oil smokes, bacteria

Note: 2Filter group/class description: G indicates filters for coarse dust particles (i.e., ≥ 10 μm) and G1, G2, G3,

G4 represents level of filtration; F indicates filters fine coarse dust particles (i.e., =  0.4 µm) and F5, F6, F7, F8,

F9 indicates level of filtration.

6

Table S6. Classification of mechanical fine filter according to EN 779 : 2003 (BRE (2012), BSIGroup (2010)).Particle size Classification according to

EN779 Examples of matter retained per filter class

HEPA (0.3 µm)

H10 EU10 Germs, tobacco smoke, metallurgical fumes, viruses, radioactive particles, carbon BLACKH11 EU11

H12 EU12 Oil fumes, metallurgical fumes, sea salt nuclei, viruses, radioactive particles, all airH13 EU13

H14EU14 Filter cleanroom ISO 4, operating

theatres etc.

UPA (0.12 µm)

U15EU15

Filter cleanroom ISO 3

U16EU16

Filter cleanroom ISO 2

U17EU17

Filter cleanroom ISO 1

U18EU18

-

Note: 1Filter group/class description: H indicates filters H indicates HEPA (High-Efficiency Particulate Air

filters) for micro-particles (i.e., particles ≥ 0.01 µm) with H10, H11, H12, H13, H14 represents the level of

filtration; U indicates ULPA (Ultra Low Penetration Air) filters for micro-particles and U15, U16, U17

represents the level of filtration.

7

Table S7. Summary of key studies with assessment of air pollution control technologies

between source and receptors.

Passive Control Technology

Study objectives Findings % Improvement in Air Quality

Author (year)

Controlling source-receptor pathways

Assessing impacts of passive controls on personal exposure to NO concentrations in the street canyon

Field measurements +Numerical modelling (CFD simulations)

Lane distribution, fleet composition and vehicular turbulence all affects pollutant dispersion

15% (Footpaths) Gallagher et al. (2013)

1. Porous barriers such as Green infrastructure (Trees and vegetation)

2. Solid Barriers such as

a. Low Boundary Walls (LBW)

3. Parallel Parked Cars)

Reviewed passive methods for reducing personal exposure in the built Environment

Methods include porous methods such as trees and vegetation and solid methods such as low boundary walls and parked cars

Improves urban air quality by enhancing pollutant dispersion in street canyons

Parallel parked cars provided improvements in air quality in all wind conditions

Tree parameters (crown height, leaf density tree height and spacing) have been found to impact air quality.

LBWs alters localized dispersion patterns.

Improves air quality at street level.

Provide an alternative to pedestrianized streets.

Improve conditions for our urban populations.

Abhijith et al. (2017); Gallagher et al. (2015); McNabola (2010); McNabola et al. (2009)

Roadside vegetation

To review roadside vegetation design characteristics such as height, thickness, coverage, porosity/density, and species characteristics) that promote improved air quality

Roadside vegetation can affect nearby air quality in both a positive and negative way

Properly designed, vegetation barriers can be used to improve near-road air quality, either alone or in combination with solid barriers.

Baldauf (2017)

To characterize the effects of a tree stand on near-road air quality in an open highway environment over a range of meteorological and traffic conditions.

Wind direction and time of day significantly affected pollutant concentrations behind the tree stand.

Reductions in BC concentrations were found.

Vegetation reduced downwind BC concentrations by approximately 12%.

Maximum reductions of upto 22% in BC concentrations during the late afternoon

No change in PNC

Brantley et al. (2014)

Green Roofs To quantify the air pollution mitigation potential of different

The green roof cannot be used as a stand-alone measure in air

Total of 1675 kg of air pollutants was removed by 19.8 ha of green roofs

Yang et al. (2008)

8

types of green roofs in a city and comparison of green roofs with other competing air pollution mitigation technologies.

pollution controls because of its high cost.

in one year with O3

accounting for 52% of the total, NO2 (27%), PM10 (14%), and SO2

(7%). The annual removal per

hectare of green roof was 85 kg ha-1 yr-1

The amount of pollutants removed would increase to 2046.89 metric tons if all rooftops in Chicago were covered with intensive green roofs.

S1. Vertical profile concentration of gaseous pollutants and PNCs

Kumar et al. (2008) investigated the existence of exponential relationship for vertical

profile of PNC samples for three different heights of 0.20 m, 1.0 m and 2.60 m and applied an

exponential variation to the daily averaged PNC data. They derived exponential relationship

(Eq. 1) for vertical profile of PNCs.

(C ¿¿ z−C b)/(C¿¿0−Cb)=exp [−k ( zH

)]¿¿ (1)

Where Cz and Cb are the PNCs at any height z and background respectively, C0 is the PNC at

road level which is assumed equal to the PNC at 0.20 m, H is the canyon height, k/H (= k1) is

the exponential decay coefficient in m− 1. The inverse of k1 indicates the characteristic

dispersion height which corresponds to the height above the road level at which the

dimensionless concentration is e− 1 = 0.37. Other studies conform to these observations for the

gaseous pollutants also (Bauman et al., 1982; Boddy et al., 2005; Zoumakis, 1995). Zoumakis

(1995) studied the average vertical profile concentration of gaseous pollutants through

experimental measurements vertically in the Patision street canyon in Athens. They found

that the height variation of CO followed the general exponential form (Eq. 2):

C ( z ) ≈ Aexp [−B( zh )] (2)

Where A (parts per million, ppm) and B (non-dimensional number; range of 1.18-1.86) are

regression coefficients; z is height (m) of measurement points; and h is the height of the

buildings (m).

S2 Photocatalytic oxidation (PCO) technique

Lasek et al. (2013) discussed techniques for removal of NO by photocatalytic

reactions such as, photo selective catalytic reduction (photo-SCR), photo-oxidation and

photo-decomposition. The photocatalysis has real world applications (Lasek et al., 2013) e.g.,

9

utilizing TiO2 mixed concrete in pavement blocks in European cities (Bergamo, Italy;

Antwerp, Belgium and Paris, France) to reduce NOx (Guerrini and Peccati, 2007; Hunger et

al., 2010); photo-active paints such as mineral silicate paint and styrene acrylic paint

(Maggos et al., 2007). Chen and Poon (2009) suggested that TiO2 modified cementitious

materials can be applied onto the external covering of buildings or roads. This can likely

supplement conventional technologies such as catalytic converters fitted on the vehicles for

reducing gaseous exhaust emission (Chen and Poon, 2009). Mamaghani et al. (2017)

reviewed the application of commercial TiO2 photocatalysts for removal of VOC’s in air.

Here, we present a summary of PCO technique as explained in Mamaghani et al. (2017). One

of the important steps in PCO technique is the formation of electron and hole (e−-h+) pairs

which occurs following the following steps. First the semiconductor is illuminated, second,

the photons with sufficient energy are absorbed and third, electrons from the valence band are

transported to the conduction band. The photogenerated charge carriers participate in a series

of reactions with other molecules such as oxygen and water and produce highly reactive

radicals (such as hydroxyl radical). In gas phase PCO, mass transfer of the VOC compounds

from the gas phase (i.e. air stream) to the solid phase plays an important role and greatly

affects the reaction rate and removal efficiency. After the external (from bulk to exterior

surface) and internal diffusions (from exterior surface to internal catalytic surface) and

adsorption onto the surface, pollutant molecules meet the produced reactive species and break

down to lower molecular weight products and eventually to carbon dioxide (CO2), water and

other by-products. The similar PCO technique was previously utilized by Hossain et al.

(1999) for designing a monolith honeycomb reactor (Figure S1).

Hossain et al. (1999) argued that PCO technique minimizes pressure loss of reactor during

operation and thus such reactors have been extensively deployed for mitigating NO emissions

from vehicles and power-plants. The consecutive arrays of UV lamps and honeycomb

monoliths are attached inside the HVAC duct and such UV lamps emit artificial light (range

of 300-400 nm) to irradiate monolith front and back faces. Air flowing in the duct is forced

through the monolith channels, which are coated with the active Titania photocatalyst. Please

note that Titania is the most widely used photocatalyst, but its limited activity under visible

light irradiation has motivated the quest for modified titania materials absorbing visible light

(Primo et al., 2011). Then the next steps include formation of electron hole pairs as discussed

before. Finally, pollutants molecules undergo a series of reactions with reactive radicals and

air is purified.

10

Figure S1. Air purification through multi-stage honeycomb-monolith photocatalytic

reactor (adopted from Hossain et al. (1999); Nath et al. (2016)).

S3. EN779:2012

The new European standard for air filters (EN779:2012) comes into force in 2012. Its

purpose is to classify air filters based on their lowest filtration efficiency. This latter is also

referred to as minimum efficiency (ME). The standard is an initiative that we welcome and a

step towards better indoor environments. The new standard will help to eradicate several

problems. One of these is presented by electrostatic charged synthetic filters. While such

filters can demonstrate good initial filtration efficiency, they discharge extremely rapidly.

This entails a considerable deterioration in their air cleaning ability. Unfortunately, one result

of the foregoing is that far too many European properties are now using F7 class filters that

have ME values of between 5 and 10 percent. This means that as much as 90 to 95 percent of

the contaminants in the outdoor air find their way into buildings and pollute the indoor

environment. By basing classification on ME value, the new standard will force these filters

out of the market. At the same time, it will contribute to the development of synthetic filter

materials offering considerably higher particle separation. Regrettably, the price for this will

include higher pressure drops and increased energy consumption.

S4. EN 1822

11

This new European standard is based on particle counting methods that cover most

needs for different applications. EN 1822:2009 differs from its previous edition (EN

1822:1998) by including the following: (i) An alternative method for leakage testing of

Group H filters with shapes other than panels; (ii) An alternative test method for using a

solid, instead of a liquid, test aerosol; (iii) A method for testing and classifying of filters made

from membrane-type media; (iv) A method for testing and classifying filters made from

synthetic fibre media. The main difference is related to the classification for the filter classes

H10 - H12, which has now been changed to E10 - E12.

S5. Regulatory instruments

S5.1 Brief historical background and current global state of the regulatory framework

The 1979 Convention on Long-Range Transboundary Air Pollution (CLRTAP)

supported by the United Nations Economic Commission for Europe (UNECE) was a major

regional multilateral environmental agreement (Byrne, 2015). Byrne (2015) assessed the

efficacy of this long range transboundary air pollution regime and revealed that has helped

states to reach agreement on controversial issues and accomplished positive outcomes in air

pollution reduction targets. The CLTRAP led to protocol known as the 1988 protocol signed

in Sofia (Bulgaria) for restricting NOx emissions which was then followed by the 1999

Gothenburg Protocol (UNECE, 1999) for mitigating acidification, eutrophication and

ground-level O3. This protocol outlined maximum limits of national emission from years

2010 till 2020 for selected four pollutants including SO2, NOx, VOCs and ammonia (UNECE,

1999).

Unlike PM2.5, PM10 and NOx, there are no threshold values for the ambient concentration

levels of UFPs and BC and therefore local observation networks do not generally monitor

them. Many technical and practical matters have limited the development of ambient particle

regulations on a number basis. One of the main technical constraints is the lack of standard

methods and instrumentation and the uncertainties in repeatability and reproducibility in

measurements. Practical constraints include lack of sufficient long-term monitoring studies

that include measurements of nanoparticle concentrations and size distributions, insufficient

information on dispersion and transformation behaviour, a shortage of toxicological and

epidemiological evidence (Kumar et al., 2010). Kumar et al. (2010) recommended that the

prospective regulatory frameworks should include the measurement of size distributions

while covering the whole nanoparticle size range to fairly represent all the particle number

population in the ambient environment. Likewise, Keywood et al. (1999) argued for the

12

ambient aerosol standard based on PM2.5 mass concentration rather than the current PM10

mass concentration (Section 3.2). Later, Harrison and Yin (2000) also emphasized on the

limitations of present day ambient air quality standards for PM regulation which are based on

mass concentration. They expressed discontent about the technologies deployed for reducing

the mass of particle emissions as such technologies are effective only in reducing the mean

particle size without significantly reducing the number of particles, and thus may result in

negligent health benefits of enforcing relevant regulations (Harrison and Yin, 2000). A more

recent study by Arnold (2017) stressed the importance of the ambient air quality standards

for UFPs and studying interconnections between UFPs and nanomaterials for utilising

research in nanotoxicology.

S5.2 Impact of regulatory interventions

Regulatory interventions are the actions taken by governments and public bodies to

protect citizens by affecting or interfering with choices made by individuals, groups, or

organizations regarding social and economic matters (Adger et al., 2005). This needs various

tools to drive individual action, including strengthening individual perceptions of air

pollution and facilitating the adoption of practices to cope with air pollution (Ban et al.,

2017). Clancy et al. (2002) quantitatively analysed the effectiveness of the Irish government’s

policy of 1990 which banned the promotion, sales, and supply of bituminous coals within the

city of Dublin (Ireland). They compared the air pollution, weather, and deaths by seasons for

72 months before and after the ban. Mean daily air pollution (black smoke and SO2)

concentrations were measured from six residential monitoring stations in the city of Dublin

(Dublin County Borough) and mean daily temperatures. They found that mean black smoke

concentration dropped by approximately two-thirds (highest reduction during winter season);

SO2 concentrations dropped by one third (highest decline during winter season) after the ban

on coal sales and no change in mean temperatures was observed (Clancy et al., 2002).

Giles et al. (2011) emphasized the role of various interventions such as individual,

community and regulatory interventions. Their work included case studies of London’s

congestion charge zone (CCZ) and low-emission zone (LEZ) programs.  The congestion

charge scheme (CCS) of London was implemented 2003 to address the issues of traffic

congestion in the central city zone. These resulted in multiple co-benefits (Tonne et al., 2008)

such as: (i) reduction of up to 0.73 μg m-3 of annual average concentration of NO2 in the CCZ

area; (ii) life expectancy savings of as much as 183 years of life per 100,000 population in

the CCZ area and 1888 years of life in London; and (iii) reduction in socioeconomic

inequalities in exposure to traffic related air pollution and mortality rates.

13

Atkinson et al. (2009) also assessed the impacts of implementing CCS on the concentrations

in certain pollutants in London. They analysed pollutants such as: NOx, NO2, nitric oxide,

PM10, CO and O3. They found that implementation of CCS could be linked with small

temporal changes in air pollution concentrations in central London relative to outer

areas. Similar to the CCS scheme, LEZ programs for the mitigation of air and noise pollution

have been implemented across London, Japan, and Sweden (Giles et al., 2011). Johansson et

al. (2009) evaluated the quantitative effects of a road charge system (Stockholm Trial) in

Stockholm in terms of pollutant concentrations, population exposure and health impacts of

this scheme. The pollutants assessed included PM10 and NOx. They found that the annual

average concentrations of PM10 and NOx were reduced by 7% and 12%, respectively. The

overall population exposure to NOx was declined by 0.23 μg m-3 which was converted into

savings of premature mortality of the order of 27 premature deaths avoided per year which

further corresponded to life expectancy gains of 206 years over a period of 10 years per 100

000 people. The targeted policies to reduce the concentrations of PM10, NOx, CO and O3 in

pollution hotspot areas is helpful in attaining overall air quality benefits and reduced

exposure to communities (see Section 8) including young children and in-pram babies. The

reduced exposures will lead to long-term health (such as a decline in premature mortality, life

expectancy gains) and socio-economic (such as reduced environmental inequalities in air

pollution exposure) benefits.

14

Sulfate33%

Nitrate18%

Chloride5%

Sodium5%

Ammonium

18%

Potassium5%

Magnesium2%

Calcium 14%

INDOOR PM2.5

Sulfate29%

Nitrate32%

Chloride5%

Sodium3%

Ammonium26%

Potassium0%

Magnesium1%

Calcium 4%

OUTDOOR PM2.5

Sulfate27%

Nitrate11%

Chloride7%

Sodium9%

Ammonium8%

Potassium5%

Magnesium2%

Calcium 31%

INDOOR PM10

Sulfate31%

Nitrate33%

Chloride4%

Sodium4%

Ammonium20%

Potassium0%

Magnesium1%

Calcium 7%

OUTDOOR PM10

(a)

(b)

Figure S2. Contribution of ions to total ion concentration in (a) Indoor PM2.5 versus outdoor

PM2.5 (median; particle mass concentrations) and (b) Indoor PM10 versus outdoor PM10

(median; particle mass concentrations); adapted from Fromme et al. (2008).

S6. References

Abhijith, K., Kumar, P., Gallagher, J., McNabola, A., Baldauf, R., Pilla, F., Broderick, B., Di Sabatino, S., Pulvirenti, B., 2017. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments-A review. Atmos. Environ.

Adger, W.N., Arnell, N.W., Tompkins, E.L., 2005. Successful adaptation to climate change across scales. Global environmental change 15, 77-86.

Atkinson, R.W., Barratt, B., Armstrong, B., Anderson, H.R., Beevers, S.D., Mudway, I.S., Green, D., Derwent, R.G., Wilkinson, P., Tonne, C., Kelly, F.J., 2009. The impact of the congestion charging scheme on ambient air pollution concentrations in London. Atmospheric Environment 43, 5493-5500.

15

Baldauf, R., 2017. Roadside vegetation design characteristics that can improve local, near-road air quality. Transportation Research Part D: Transport and Environment 52, 354-361.

Bauman, S.E., Williams, E.T., Finston, H.L., Ferrand, E.F., Sontowski, J., 1982. Street level versus rooftop sampling: carbon monoxide and aerosol in New York City. Atmospheric Environment (1967) 16, 2489-2496.

Boddy, J., Smalley, R., Dixon, N., Tate, J., Tomlin, A., 2005. The spatial variability in concentrations of a traffic-related pollutant in two street canyons in York, UK—Part I: the influence of background winds. Atmospheric Environment 39, 3147-3161.

Brantley, H.L., Hagler, G.S.W., J. Deshmukh, P., Baldauf, R.W., 2014. Field assessment of the effects of roadside vegetation on near-road black carbon and particulate matter. Science of The Total Environment 468, 120-129.

BRE, U., 2012. Final Report Task 1 Product Definition, Standards and Legislation Ventilation Systems for non residential and collective residential applications.

BSIGroup, 2010. High efficiency air filters (EPA, HEPA and ULPA) Part 5: Determining the efficiency of filter elements

British Standards, BSI Group Headquarters 389 Chiswick High Road, London, W4 4AL, UK.Byrne, A., 2015. The 1979 convention on long-range transboundary air pollution: assessing

its effectiveness as a multilateral environmental regime after 35 years. Transnational Environmental Law 4, 37-67.

Camfil, 2017a. Filter Technology : Specifications and Regulations : EN 779 : 2012 : The new standard forces air filters to perform better. Camfill, Knowsley Road, Haslingden, Lancashire BB4 4EG.

Camfil, 2017b. Filter Technology : Specifications and Regulations : EN 1822:2009 : The new standard forces air filters to perform better. . Camfil, Camfill, Knowsley Road, Haslingden, Lancashire BB4 4EG.

Chen, J., Poon, C.-s., 2009. Photocatalytic construction and building materials: from fundamentals to applications. Building and environment 44, 1899-1906.

Clancy, L., Goodman, P., Sinclair, H., Dockery, D.W., 2002. Effect of air-pollution control on death rates in Dublin, Ireland: an intervention study. The lancet 360, 1210-1214.

Colbeck, I., 2014. Aerosol Science: Technology and Applications. John Wiley & Sons.Fromme, H., Diemer, J., Dietrich, S., Cyrys, J., Heinrich, J., Lang, W., Kiranoglu, M.,

Twardella, D., 2008. Chemical and morphological properties of particulate matter (PM 10, PM 2.5) in school classrooms and outdoor air. Atmospheric Environment 42, 6597-6605.

Gallagher, J., Baldauf, R., Fuller, C.H., Kumar, P., Gill, L.W., McNabola, A., 2015. Passive methods for improving air quality in the built environment: A review of porous and solid barriers. Atmospheric Environment 120, 61-70.

Gallagher, J., Gill, L., McNabola, A., 2013. The passive control of air pollution exposure in Dublin, Ireland: A combined measurement and modelling case study.

Giles, L.V., Barn, P., Künzli, N., Romieu, I., Mittleman, M.A., van Eeden, S., Allen, R., Carlsten, C., Stieb, D., Noonan, C., Smargiassi, A., Kaufman, J.D., Hajat, S., Kosatsky, T., Brauer, M., 2011. From Good Intentions to Proven Interventions: Effectiveness of Actions to Reduce the Health Impacts of Air Pollution. Environmental Health Perspectives 119, 29-36.

Guerrini, G.L., Peccati, E., 2007. Photocatalytic cementitious roads for depollution, International RILEM symposium on photocatalysis, environment and construction materials. RILEM Florence, pp. 179-186.

16

Harrison, R.M., Tilling, R., Romero, M.a.S.C., Harrad, S., Jarvis, K., 2003. A study of trace metals and polycyclic aromatic hydrocarbons in the roadside environment. Atmospheric Environment 37, 2391-2402.

Harrison, R.M., Yin, J., 2000. Particulate matter in the atmosphere: which particle properties are important for its effects on health? Science of the total environment 249, 85-101.

Hossain, M., Raupp, G.B., Hay, S.O., Obee, T.N., 1999. Three‐dimensional developing flow model for photocatalytic monolith reactors. AIChE Journal 45, 1309-1321.

Hunger, M., Hüsken, G., Brouwers, H., 2010. Photocatalytic degradation of air pollutants—From modeling to large scale application. Cement and Concrete Research 40, 313-320.

Johansson, C., Burman, L., Forsberg, B., 2009. The effects of congestions tax on air quality and health. Atmospheric Environment 43, 4843-4854.

Keywood, M., Ayers, G., Gras, J., Gillett, R., Cohen, D., 1999. Relationships between size segregated mass concentration data and ultrafine particle number concentrations in urban areas. Atmospheric Environment 33, 2907-2913.

Kumar, P., Fennell, P., Britter, R., 2008. Measurements of particles in the 5–1000 nm range close to road level in an urban street canyon. Science of the Total Environment 390, 437-447.

Kumar, P., Rivas, I., Sachdeva, L., 2017. Exposure of in-pram babies to airborne particles during morning drop-in and afternoon pick-up of school children. Environmental Pollution 224, 407-420.

Kumar, P., Robins, A., Vardoulakis, S., Britter, R., 2010. A review of the characteristics of nanoparticles in the urban atmosphere and the prospects for developing regulatory controls. Atmospheric Environment 44, 5035-5052.

Lasek, J., Yu, Y.-H., Wu, J.C., 2013. Removal of NOx by photocatalytic processes. Journal of Photochemistry and Photobiology C: Photochemistry Reviews 14, 29-52.

Maggos, T., Bartzis, J., Liakou, M., Gobin, C., 2007. Photocatalytic degradation of NOx gases using TiO2-containing paint: a real scale study. Journal of hazardous materials 146, 668-673.

Mamaghani, A.H., Haghighat, F., Lee, C.-S., 2017. Photocatalytic oxidation technology for indoor environment air purification: The state-of-the-art. Applied Catalysis B: Environmental 203, 247-269.

McNabola, A., 2010. New Directions: Passive control of personal air pollution exposure from traffic emissions in urban street canyons. Atmospheric Environment 44, 2940-2941.

McNabola, A., Broderick, B.M., Gill, L.W., 2009. A numerical investigation of the impact of low boundary walls on pedestrian exposure to air pollutants in urban street canyons. Science of The Total Environment 407, 760-769.

Murillo, J.H., Roman, S.R., Marin, J.F.R., Ramos, A.C., Jimenez, S.B., Gonzalez, B.C., Baumgardner, D.G., 2013. Chemical characterization and source apportionment of PM10 and PM2. 5 in the metropolitan area of Costa Rica, Central America. Atmospheric Pollution Research 4, 181-190.

Nath, R.K., Zain, M.F.M., Jamil, M., 2016. An environment-friendly solution for indoor air purification by using renewable photocatalysts in concrete: A review. Renewable and Sustainable Energy Reviews 62, 1184-1194.

Pant, P., Shi, Z., Pope, F.D., Harrison, R.M., 2017. Characterization of traffic-related particulate matter emissions in a road tunnel in Birmingham, UK: Trace metals and organic molecular markers. Aerosol Air Qual. Res 17, 117-130.

Primo, A., Corma, A., García, H., 2011. Titania supported gold nanoparticles as photocatalyst. Physical Chemistry Chemical Physics 13, 886-910.

17

Thorpe, A., Harrison, R.M., 2008. Sources and properties of non-exhaust particulate matter from road traffic: a review. Science of the total environment 400, 270-282.

Tonne, C., Beevers, S., Armstrong, B., Kelly, F., Wilkinson, P., 2008. Air pollution and mortality benefits of the London Congestion Charge: spatial and socioeconomic inequalities. Occupational and Environmental Medicine 65, 620-627.

UNECE, 1999. The 1999 Gothenburg Protocol to Abate Acidification, Eutrophication and Ground-level Ozone, Convention on Long-range Transboundary Air Pollution.

Yang, J., Yu, Q., Gong, P., 2008. Quantifying air pollution removal by green roofs in Chicago. Atmospheric environment 42, 7266-7273.

Zoumakis, N., 1995. A note on average vertical profiles of vehicular pollutant concentrations in urban street canyons. Atmospheric Environment 29, 3719-3725.

18