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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: [email protected],
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).
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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.
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16
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