Spa io empo al Analysis o PM10 and PM2.5 wi h EBK3D and
Space-Time Cube in he Ci y o Lisbon, Po ugal
João Ma ia Telo Ab eu Ja dine Ne o
ii
Spa io empo al Analysis o PM10 and PM2.5 wi h EBK3D
and Space-Time Cube in he Ci y o Lisbon. Po ugal
Disse a ion supe ised by
PhD Ana C is ina Ma inho da Cos a
Disse a ion co-supe ised by
PhD Jo ge Ma eu
Disse a ion co-supe ised by
PhD Ped o Cab al
Feb ua y, 2024
iii
DECLARATION OF ORIGINALITY
I decla e ha he wo k desc ibed in his documen is my own and no
om someone else. All he assis ance I ha e ecei ed om o he people
is duly acknowledged and all he sou ces (published o no published) a e
e e enced.
This wo k has no been p e iously e alua ed o submi ed o NOVA
In o ma ion Managemen School o elsewhe e.
Lisbon, Feb ua y, 26 h, 2024
João Ma ia Telo Ab eu Ja dine Ne o
[ he signed o iginal has been a chi ed by he NOVA IMS se ices]
i
ACKNOWLEDGMENTS
I would like o hank my main supe iso , P o . Ana C is ina Cos a, o he guidance
h oughou my esea ch. He suppo has been essen ial o my wo k. My g a i ude also
goes o my co-supe iso s, P o . Jo ge Ma eu and P o . Ped o Cab al, o hei
aluable inpu . Special hanks o Nyi Nyi o his assis ance and suppo h oughou
he hesis p ocess. Thank you o my mas e 's coho o hei cama ade ie and suppo
du ing his jou ney. Thanks o Leono Ne o o helping me p oo eading my
manusc ip . Finally, I wan o hank my amily and iends o hei suppo and
encou agemen .
Spa io empo al Analysis o PM10 and PM2.5 wi h EBK3D
and Space-Time Cube in he Ci y o Lisbon. Po ugal
ABSTRACT
This hesis conduc s a spa io empo al analysis o pa icula e ma e
(PM10 and PM2.5) in Lisbon, Po ugal, h ough 2022, u ilizing Empi ical
Bayesian K iging 3D (EBK3D) and Space-Time Cube analysis o explo e
pollu ion dynamics. Focused on how Pa icula e Ma e (PM) le els a y
ac oss Lisbon and iden i ying dis inc pa e ns du ing di e en a ic
pe iods on weekdays and weekends. I employs geos a is ical me hods o
analyze pollu ion le els, o e ing insigh s in o he spa ial and empo al
dis ibu ion o PM concen a ions. Key indings highligh a eas wi h
pe sis en high pollu ion and empo al luc ua ions h oughou he ci y.
This esea ch helps in he unde s anding o Lisbon's PM ela ed ai
pollu ion.
Sus ainable De elopmen Goals (SDG):

i
KEYWORDS
U ban Ai Quali y
PM10
PM2.5
Empi ical Bayesian K iging 3D
Space-Time Cube
Eme ging Ho Spo Analysis
Local Ou lie Analysis
ii
ACRONYMS
ARH - A e noon Rush Hou
EBK3D - Empi ical Bayesian K iging 3D
EEA - Eu opean En i onmen Agency
ESDA - Explo a o y Spa ial Da a Analysis
EU - Eu opean Union
GIS - Geog aphical In o ma ion Sys ems
IDW - In e se Dis ance Weigh ing
MRH - Mo ning Rush Hou
ORH - O Rush Hou
PM – Pa icula e Ma e
PM10 - Pa icula e Ma e up o 10 mic ome e s in size
PM2.5 - Pa icula e Ma e up o 2.5 mic ome e s in size
WHO - Wo ld Heal h O ganiza ion
iii
INDEX OF THE TEXT
DECLARATION OF ORIGINALITY .......................................................................................................... III
ACKNOWLEDGMENTS ........................................................................................................................ IV
ABSTRACT........................................................................................................................................... V
KEYWORDS ........................................................................................................................................ VI
ACRONYMS ...................................................................................................................................... VII
INDEX OF THE TEXT .......................................................................................................................... VIII
INDEX OF TABLES ................................................................................................................................ X
INDEX OF FIGURES ............................................................................................................................. XI
1 INTRODUCTION......................................................................................................................... 1
2 LITERATURE REVIEW ................................................................................................................. 2
2.1 URBAN AIR QUALITY ........................................................................................................................ 2
2.2 PARTICULATE MATTER ..................................................................................................................... 3
2.3 ROAD TRAFFIC RELATED PM.............................................................................................................. 5
2.4 IMPACTS ON HEALTH ........................................................................................................................ 6
2.5 IMPACTS ON ENVIRONMENT .............................................................................................................. 7
2.6 GUIDELINES.................................................................................................................................... 8
2.7 PM DISPERSION .............................................................................................................................. 9
2.8 MITIGATION POLICIES ...................................................................................................................... 9
3 METHODOLOGY ...................................................................................................................... 12
3.1 DATA .......................................................................................................................................... 12
3.2 DATA PREPARATION ...................................................................................................................... 13
3.3 EXPLORATORY SPATIAL DATA ANALYSIS ............................................................................................. 15
3.4 EMPIRICAL BAYESIAN KRIGING 3D .................................................................................................... 15
3.5 SPACE TIME CUBE ......................................................................................................................... 17
3.6 EMERGING HOT SPOT ANALYSIS ....................................................................................................... 18
3.7 LOCAL OUTLIER ANALYSIS ............................................................................................................... 19
4 RESULTS .................................................................................................................................. 20
4.1 EXPLORATORY AND SPATIAL DATA ANALYSIS ...................................................................................... 20
4.1.1 PM10 ............................................................................................................................... 20
4.1.2 PM10 - MRH .................................................................................................................... 20
4.1.3 PM10 - ORH .................................................................................................................... 20
4.1.4 PM10 - ARH ..................................................................................................................... 21
4.1.5 PM2.5 .............................................................................................................................. 22
4.1.6 PM2.5 - MRH ................................................................................................................... 22
4.1.7 PM2.5 – ORH ................................................................................................................... 22
4.1.8 PM2.5 – ARH ................................................................................................................... 23
4.2 EMPIRICAL BAYESIAN KRIGING 3D .................................................................................................... 23
4.3 PM10 EMERGING HOT SPOT .......................................................................................................... 25
4.3.1 PM10 - MRH .................................................................................................................... 25
4.3.2 PM10 - ORH .................................................................................................................... 27
4.3.3 PM10 - ARH ..................................................................................................................... 28
4.4 PM10 LOCAL OUTLIER .................................................................................................................. 30
4.4.1 PM10 - MRH .................................................................................................................... 30
4.4.2 PM10 - ORH .................................................................................................................... 32
4.4.3 PM10 - ARH ..................................................................................................................... 33
4.5 PM2.5 EMERGING HOT SPOT ......................................................................................................... 35
4.5.1 PM2.5 – MRH .................................................................................................................. 35
ix
4.5.2 PM2.5 - ORH ................................................................................................................... 37
4.5.3 PM2.5 ARH ...................................................................................................................... 39
4.6 PM2.5 - LOCAL OUTLIER ............................................................................................................... 41
4.6.1 PM2.5 – MRH .................................................................................................................. 41
4.6.2 PM2.5 – ORH ................................................................................................................... 42
4.6.3 PM2.5 – ARH ................................................................................................................... 43
5 DISCUSSION ............................................................................................................................ 45
6 CONCLUSION .......................................................................................................................... 47
BIBLIOGRAPHIC REFERENCES ........................................................................................................... 49
ANNEX ............................................................................................................................................. 54
APENDIX .......................................................................................................................................... 56
4
ehicle exhaus and powe plan s), indus ial p ocesses, cons uc ion and many o he s.
Pa icles wi h diame e s less han 10 µm can s ay in he ai o many days and be
easily ca ied by wind, ain o be cap u ed by ege a ion o buildings (Ro eli,2017
).
Pa icula e ma e o ma ion can be p ima y o seconda y, in which p ima y pa icles
a e ealized di ec ly o en i onmen h ough a ce ain sou ce (na u al o
an h opogenic) and seconda y sou ces a e o med in he a mosphe e as he esul o
chemical eac ions ha lead o he c ea ion o pa icula e ma e . The main sou ces o
p ima y PM in u ban a eas a e: oad a ic, ixed combus ion (mainly domes ic
chimneys) and, indus ial ac i i ies (Gue a a,2016). Dus and sea a e also impo an
sou ces o p ima y PM, and a e p ima ily in luenced by wind. Black ca bon has a
simila chemical s uc u e o g aphi e, and is mos ly gene a ed h ough imp ope
combus ion o ossil uels (mainly diesel engines) (Zhu, 2002). Examples o
seconda y PM a e sul a es and ni a es which a e o med by he oxida ion o SO2 and
NO2 in he a mosphe e in o acids (Zheng, 2005). Compa ed o p ima y PM, he
chemical p ocesses in ol ed in c ea ing seconda y PM a e ela i ely slow, howe e
hey pe sis longe in he a mosphe e.
Pa icle size is one o he mos impo an cha ac e is ics o PM. The way o measu ing
hei size is by de ining hei ae odynamic diame e , his is de ined by he diame e o
a sphe ical pa icle wi h a olume ic mass o 1 g.cm-3 (which is he same as wa e ).
This me hod is a simple way o ca ego ize he di e en sizes o pa icles wi h
di e en shapes (B ook, 2010).
The e a e se e al ca ego ies o PM depending on hei ae odynamic diame e hese
a e:
- Coa se Pa icles: Be ween 2.5 and 10 µm
- Fine Pa icles: ≤ 2.5 µm
- Ul a ine Pa icles: ≤ 0.1 µm
- Nano Pa icles: < 100 nm
The dimensions o pa icles no only de e mine hei beha io in he a mosphe e bu
also how hey pene a e he human espi a o y sys em. In gene al, ine pa icles end

5
o ha e easie ime pene a ing he al eoli ( iny ai sacs whe e he exchange o oxygen
and ca bon dioxide akes place) and b onchioles (small ai ways ha lead o he
al eoli) han coa se pa icles end o no pass he nasopha ynx ( he uppe pa o he
h oa ha lies behind he nose) (B ooke, 2010).
In e ms o hei composi ion, PM is made up by mul iple componen s, including
black ca bon, o ganic ca bon (CO), ion sul a e (SO4), ion ni a e (NO3), me allic
composi es, ma e ial o igina ing om he ea h c us , and sea sal . The p ope ies o
his complex g oup o pa icles can a y acco ding o hei composi ion and size, as
well as hei impac . Fo example, black ca bon is ela ed wi h a se ies o
en i onmen al impac s, such as ise in empe a u e, due o hei abili y o abso b ligh .
The main componen s o he ma e ial o igina ing om he ea h c us include:
aluminum (Al), silicon (Si), calcium (Ca), i on (Fe), hese a e mos ly associa ed wi h
coa se pa icles. In Eu ope, his ype o pa icles ep esen s almos 20% o he mass o
coa se pa icles. This is mo e p esen in he Sou hwes and Sou heas due o a d ie
and wa me clima e, as well as he in luence o dus pa icles om no h A ica
(Haja , 2015).
In u ban a eas oad a ic is one o he main sou ces o PM, ollowed by ossil uels
combus ion in powe plan s, and indus ial manu ac u ing (Lelie eld,2015). Besides
engine combus ion (which can a y depending on he age o he engine and he ype
o uel used), he e a e o he sou ces o PM ela ed o oad a ic, such as, b ake pads
wea , i e wea , and e-suspension o dus in he oad su ace (Bond, 2013).
2.3 Road T a ic ela ed PM
Mul iple s udies ha e shown ha he majo i y o emissions o PM in u ban a eas a e
ela ed o oad a ic. (Pan ,2013; Ying,2015; Chih-I,2023) The PM emissions ela ed
o oad a ic a e classi ied acco ding o hei o ma ion p ocess. In e nal combus ion
engines, bo h gasoline and diesel a e poin ed as he main mechanism o which PM is
o med in u ban a eas. Addi ionally, oad a ic ela ed ac i i ies can also be he
sou ce o PM (Bond,2013).
Compa ing exhaus emissions o mo o ehicles unning on gasoline and diesel shows
ha diesel engines elease mo e PM in o he a mosphe e, compa ed o gasoline
engines, s udies also show ha diesel hea y comme cial ehicles a e he ones ha
6
p oduce mo e PM in he diesel ca ego y. Analyzing non exhaus ela ed PM in ca s
can be di icul , as hey can be in luenced by many di e en ac o s, such as, he
p ope ies o he ma e ials ( ype o i e, ype o b eak, how ough he pa emen is), and
wea he ac o s ( empe a u e and humidi y). Di e en coun ies o manu ac u e s can
ha e mul iple s anda ds o cons uc ion, and o ha eason analyzing non exhaus
ela ed PM emissions ac oss Eu ope can show conside able a iabili y (Tho pe,2008
).
2.4 Impac s on heal h
P olonged exposu e o PM can ha e ad e se heal h e ec s on human heal h and can
be ela ed o di e en heal h condi ions, such as ca dio ascula diseases, espi a o y
diseases, cance , p egnancy complica ions and may o he s (Lee, 2021). Assessing he
ad e se e ec s o PM can be di icul because he ai composi ion can a y om
place o place and indi iduals can be exposed o a as mix u e o subs ances, ha can
ha e di e en e ec s depending on he pe son and he ime o exposu e (Bas os,
2019).
Ope a o s o mo o ehicles, passenge s, pedes ians, o cyclis s a e o en exposed o
high concen a ions o PM due o he p oximi y o oads in no mal day o day ac i i y.
Ope a o s and passenge s o mo o ized ehicles ha e a highe p obabili y o exposu e
o PM han does ha a el by oo o cycle. Howe e , conside ing ha he ladde is
di ec ly exposed o he ai (no inside a mo o ehicle) as well as longe commu e
imes can lead o highe doses o PM inhaled and deposi ed in he espi a o y sys em
compa ed o does who a el in a mo o ehicle (Bas os, 2019).
The e a e mul iple scien i ic s udies ha demons a e he ela ionship be ween
exposu e o PM and ad e se heal h e ec s, independen o he le el o de elopmen
o he coun y. WHO es ablishes a clea ela ion be ween mul iple and diseases and
e en p ema u e dea hs ela ed o PM, mainly PM10 and PM2.5(WHO, 2018). PM2.5
in pa icula , due o i s size, can easily pene a e he human espi a o y sys em, and
can e en each a eas ou side he pulmona y sys em, such as he ne ous sys em.
E idence also sugges s ha PM can cause i i a ion in he pulmona y sys em and
escala e exis ing ch onic pulmona y diseases, such as as hma. PM is also composed o
7
di e en componen s which a e ha m ul o he heal h, such as hea y me als, ca bon
composi es, and e en cance ogenic elemen s (WHO, 2018).
S udies also epo ed ha sho and long- e m exposu e o PM2.5 can be associa ed
wi h mul iple heal h isks, such as cance . In 2013 he IARC (In e na ional Agency o
Resea ch on Cance ) added PM2.5 o i s lis o cance ogenic subs ances o human
beings (IARC, 2013).
Coa se pa icles (PM10-2.5) igno e he na u al de enses o he body like he nose and
h oa deposi ing hemsel es on he ho ax. These can be expelled by he body
h ough coughing o sneezing, howe e as PM2.5 due o i s size, a e capable o
pene a ing deepe in o he lungs, and can cause p oblems in he lungs and e en
hea h. Mo eo e , hey can in oduce ha m ul subs ances in o he bloods eam and can
s ay in he body o longe pe iods o ime (Chai , 2010).
2.5 Impac s on En i onmen
PM can ha e di e en impac s on he en i onmen such as educed isibili y, mainly
a p oblem in u ban a eas. The le el o impac on he isibili y depends based on
mul iple ac o s, such as densi y o he concen a ed pa icles, he size o he pa icles,
wea he condi ions, ime o day, e c. Rela ed o he lack o isibili y is he educ ion o
sola adia ion caused by he abso p ion o sola ays by he pa icles. This can lead o
a dec ease in empe a u e which can lead o se ious en i onmen al impac s (Dubey,
2018)
PM can also cause ha m in ege a ion and wa e bodies, i i s concen a ions a e high
and oxic o o big dimensions, hey can educe c op p oduc ion blocking plan s
na u al p ocess. I hey a e alkaline o acid hey can change he pH o wa e bodies,
and i he pa icles a e oxic hey can ge in o con ac wi h plan s and animals and
a ec he ood chain (Dubey,2018).
8
2.6 Guidelines
Despi e e o s o imp o e ai quali y wi hin he EU, many coun ies s ill egis e ed
exceed he quali y s anda ds es ablished by he 2008 Ai Quali y Di ec i e, an
ag eemen among all membe s a es se ing pollu an h eshold limi s in he EU. These
legally binding h esholds, ansposed in o na ional law o each membe s a e, o
Po ugal h esholds a e unde he Di ec i e 2008/50/CE o May 21. These alues a e
no ably less s iden han hose ecommended by he Wo ld Heal h O ganiza ion
(WHO). Table 1 delinea es he s anda ds de ined by EU and WHO.
Di ec i e 2008/50/CE o May 21
WHO
Daily Limi
Th esholds (24h)
(a e age)
Annual Limi
Th esholds
(a e age)
Daily Limi
Th esholds (24h)
(a e age)
Annual Limi
Th esholds
(a e age)
PM2.5
-
25 µg.µm³
25 µg.µm³
10 µg.µm³
PM10
50 µg.µm³, no exceed
mo e han 35 days in
one yea
40 µg.µm³
50 µg.µm³
20 µg.µm³
Table 1: EU and WHO Th esholds
As epo ed by (Lopes, 2019), he Eu opean En i onmen al Agency’s Ai Quali y
Repo (EEA,2018) e eals ha 19% o ai quali y moni o ing s a ions eco ded
exceedances o daily PM10 limi . Fu he mo e, annual PM10 concen a ions b eaches
we e de ec ed a 6% o hese s a ions. Rega ding PM2.5 le els, annual limi s we e
exceeded a 5% o s a ions h oughou he EU. When assessed agains WHO’s s ic e
PM10 guidelines, 48% o s a ions om al epo ing coun ies (wi h he excep ions o
Es onia, Iceland, I eland, and Swi ze land) exceeded hese h esholds. Simila ly, o
PM2.5, 68% o s a ions om he epo ing coun ies (excluding Es onia, Finland,
Hunga y, No way, and Swi ze land) exceeded WHO’ ecommended limi s. This
epo also highligh s he s a k con as be ween EU and WHO s anda ds, showing
ha in 2016, 13% o u ban a eas in he EU expe ienced le els su passing EU limi s
o pollu an s, a igu e ha escala es o 42% unde WHO guidelines. Fo PM2.5
exposu e, only 6% o he EU’s u ban popula ion we e subjec ed o concen a ions
abo e EU limi s, a pe cen age ha d ama ically inc eases o 74% when e alua ed
agains WHO s anda ds.
9
The challenge o aligning wi h WHO’s PM guidelines emains signi ican o Eu ope.
Despi e ini ia i es o educe ai pollu ion, nume ous EU na ions s ill epo PM
concen a ions le els abo e he h esholds es ablishes by he 2008 di ec i e. This gap
be ween he EU and WHO s anda ds highligh s he u gen need o enhanced and
mo e s ingen measu es o achie e he 2030 objec i e o mee ing WHO guidelines.
2.7 PM dispe sion
Wea he condi ions hea ily a ec PM dispe sion being composed o wo main
componen s:
- Ve ical componen c ea ed by he u bulence gene a ed by he he mic
e ical g adien be ween he lowe le els o he a mosphe e.
- Ho izon al componen in which he wind is he main componen in bo h
anspo and mix u e.
A mosphe ic p ocesses and he ci cula ion o high-p essu e cen e s de e mine he
wea he abo e con inen s and oceans. High p essu e cen e s also known as
an icyclones a e associa ed wi h g ea s abili y and low e ical mix u e, o ha
eason, he e is low dispe sion o PM. Low p essu e cen e s a e associa ed wi h
condi ions o a mosphe ic ins abili y wi h g ea u bulence which a o s PM
dispe sion.
In win e colde days, he sun du ing he day hea s he ai nex o he su ace o ea h.
A he end o he day, a e he sun se s he e is a quick cooling o he ai which is
mos ly el close o he g ound. When lowe laye s o he a mosphe e ha e lowe
empe a u es han he ones a highe al i udes, i occu s a he mic in e sion, which
c ea es high a mosphe ic s abili y, and he dispe sion condi ions a e lowe . Fo his
eason concen a ions measu ed du ing his ime pe iod migh be highe han he
o he s (Zheng, 2005).
2.8 Mi iga ion Policies
Due o he inc ease in emissions p oduced by mo o ized ehicles, in pa icula u ban
a eas, he e was an inc ease conce n by go e nmen bodies o con ol his ype o

10
pollu ion. As men ioned abo e he EU di ec i e o 2008 se goals and measu es o
membe s a es o implemen in o de o imp o e Eu ope´s ai quali y.
The concep o sus ainable mobili y is widely used in his documen and is one o he
main s a egies o mi iga e pollu ion le els. The goal is o inc ease he ene gy
e iciency o mo emen in o de o educe i s en i onmen al impac . The main
objec i es o sus ainable mobili y a e as ollows:
- Minimize he use o pe sonal anspo a ion.
- Op imize he use o public anspo a ion.
- Inc ease he use o so modes o anspo a ion ( hese a e ways o
anspo a ion mo e iendly o he en i onmen such as walking o cycling)
The implemen a ion o hese policies equi es deep u ban changes no only in a
physical sense, wi h changes o u ban in as uc u e bu also how he public sees hese
policies and he ad an ages o adop ing o mo e en i onmen ally iendly modes o
anspo a ion. The e needs o be changes o he s uc u e o e i o ies, public
anspo in as uc u e and public places be e design o adop hese changes.
One o he i s s a egies implemen ed by membe s a es o mi iga e his ype o
pollu ion a e he Low Emissions Zones (LEZ), hese zones a e designed o limi o
es ic ed access o ce ain u ban a eas by ce ain ypes o ehicles.
In o de o s udy he capabili y o LEZs a s udy was conduc ed in which analyzed
changes in ai quali y o i e EU membe s a es ha implemen ed LEZ (Denma k,
Ge many, Ne he lands, I aly and UK). (Gue a a, 2016) The s udy showed mixed
esul s, al hough Ge many showed a educ ion in annual PM10 concen a ions o up
p 7% in hose a eas, he same esul s we en’ shown in di e en u ban a eas. These
esul s migh be ela ed o he ype o ehicles ha we e es ic ed in Ge man ZER (i
es ic s he en y o diesel ehicles and passenge ehicles while o he coun ies only
es ic ed diesel ehicles). The low pe cen age o he educ ion o PM10 migh also be
due o he ac ha he ZER limi a ions din cause an impac in PM10 emissions o
o he sou ces (non- ehicula exhaus emissions), which ep esen a signi ican
pe cen age o p ima y emissions o oad a ic.
The Implemen a ion o LEZs is no he only p ojec used o be e ai quali y, he e a e
o he measu es o local, na ional and in e na ional planning and anspo policies
11
being implemen ed o help mi iga e oad a ic emission in u ban a eas. In New Yo k
Ci y axing ees we e used in a eas wi h high oad a ic o inc ease he cos o
ci cula ion in ce ain a eas du ing ce ain pe iods o he day. The goal was di ec
a ic low o less conges ion oads ac oss he ci y. (Schalle , 2010)
In Beijing, China a empo a y es ic ion o d i ing based on license pla e numbe s
was used o con ol he numbe o ca s allowed o d i e e e y day. The sys em was
based on he las digi o he license pla e he e was a day o odd and e en numbe s
h oughou he week. This sys em was used du ing he 2008 Olympic games and
showed p omising esul s in he educ ion o oad a ic emissions. A e he games
we e held a less es ic i e sys em was implemen ed and he es ic ion happen only
once a week. Howe e , i was slowly bypassed by he popula ion as people bough
bo h an e en and odd license pla e (Huijuan, Fujii & Managi, 2013).
12
3 METHODOLOGY
This chap e desc ibes he me hodology used in his hesis, which was di ided in o six
s ages, as seen on Figu e 1.
The i s s age o da a p epa a ion in ol ed o ganizing he collec ed da a, add essing
any missing, inco ec , o inconsis en alues o ensu e da a in eg i y. The nex s age
is he Explo a o y Spa ial Da a Analysis (ESDA), whe e da a was cha ac e ized using
bo h spa ial and desc ip i e s a is ics. The hi d s age in ol ed doing a 3D
in e pola ion o he da a using he Empi ical Bayesian K iging 3D (EBK3D) me hod.
The ou h s age in ol ed aking he ou pu o he EBK3D in e pola ion and
in eg a ing i in a Space-Time Cube using A cGIS P o so wa e. The i h and six
s ages an Eme ging Ho Spo Analysis and Local Ou lie Analysis was made using he
Space-Time Cube c ea ed in he ou h s age.
3.1 Da a
The da a used in his hesis is om an ai quali y moni o ing ne wo k ha is p o ided
by he Lisbon municipali y unde he “Lisboa Abe a” p og am (Lisboa Abe a, 2024).
The s a ions a e managed by he conso ium o MEO/Moni a /QART, and is also pa
o he Eu opean union p og am “Sha ing Ci ies”. This ne wo k has been ope a ional
since augus 2021, and is comp ised o 80 moni o ing s a ions ha moni o di e en
Figu e 1: Me hodology Flow Cha
13
en i onmen al pa ame e s, including PM10 and PM2.5 (Figu e 2). These s a ions a e
equipped wi h senso s ha measu e he hou ly a e age concen a ions o hese wo
ypes o PM. The loca ion o each senso may a y be ween ligh pos o building.
This moni o ing ne wo k se es o een o ce he p e ious ixed moni o ing ne wo k
which a he ime was only composed o 6 s a ions in he ci y o Lisbon. Due o hese
s a ions only been online since augus 2021, o his s udy yea 2022 was selec ed as i
is a he ime o w i ing his hesis he only yea o which he e is ull da a.
The e is no me ada a a ailable ela ed o he ype o s a ions (i hey a e ela ed o
a ic o backg ound), o he exac posi ioning o he s a ions (whe e hey a e
moun ed, a wha heigh , e c.) which can in luence he da a as buildings play a c ucial
ole in PM dispe sion, so his needs o be aken in o accoun when analyzing he
esul s.
Figu e 2: Moni o ing S a ions
3.2 Da a P epa a ion
In his chap e he da a p epa a ion p ocess will be explained. The ini ial s ep in ol ed
ga he ing PM10 and PM2.5 concen a ions da a o he yea 2022 (as men ioned
be o e i was he only a ailable yea ) om all 80 moni o ing s a ions. The collec ed
20
4 RESULTS
4.1 Explo a o y and Spa ial Da a Analysis
4.1.1 PM10
4.1.2 PM10 - MRH
The esul s o he ESDA o PM10 MRH on week days and weekends, a e desc ibed
in Appendix A1.1 and A1.2 espec i ely. Fo week days he mean and s anda d
de ia ion we e g ea e on he mon hs o Janua y and Feb ua y, and Oc obe h ough
Decembe wi h Decembe ha ing he highes alues. The lowes alues we e
egis e ed in he mon hs o Ap il h ough June, o e all, he mean alues o e e y
mon h s ayed below he limi h eshold, al hough in e e y mon h some s a ions had
alues o e he limi h eshold which is e iden on he maximum alues and he IDW,
mainly on he mon hs o Oc obe h ough Decembe . Values we e posi i ely skewed,
excep o Feb ua y, Ma ch, and Oc obe . Ku osis alues we e o Janua y and
Feb ua y, indica ing ha he dis ibu ion had hea ie ails and mo e ex eme alues
han he no mal dis ibu ion. Fo he weekends mean and s anda d de ia ion alues
we e lowe han he week days bu wi h a simila pa e n. Only he mon hs o May
and Augus had nega i e skewness, wi h he es being posi i e. Ku osis was lowe
han 3 in Janua y, Feb ua y, Augus , and No embe , wi h he emaining mon hs being
highe . June and Decembe had much mo e ex eme alue han he es o he mon hs.
O e all alues abo e he limi h eshold we e scien i ically lowe on he weekends
han he week days o he same ime pe iod.
4.1.3 PM10 - ORH
ORH o week days and weekends is desc ibed in Appendix A1.3 and A1.4
espec i ely. Fo week days he mean alues we e highe on he mon hs o Janua y,
Feb ua y, and Sep embe h ough Decembe . The lowes mean alues we e be ween
Ap il and June wi h a sligh inc ease in July and Augus . S anda d de ia ion was
oughly he same, excep o Sep embe which was much highe han he o he s. In
e ms o skewness all mon hs had posi i e alues. Ku osis was lowe han 3 in
Janua y, Feb ua y, Ap il, Augus , No embe , and Decembe . Sep embe had he

21
highes alue due o ha ing e y ex eme alue compa ed wi h he no mal
dis ibu ion. Fo he weekends, simila o he p e ious ime pe iod he mean and
s anda d de ia ion alues a e lowe on he weekends compa ed o week days, wi h
simila end eg ading each mon h. All mon hs showed posi i e skewness. Ku osis
was highe han 3, excep o Janua y and Feb ua y, wi h he highes alues in July
and Decembe . O e all ORH week days and weekends show simila pa e ns
compa ed o he MRH, al hough ORH mean alues a e lowe han MRH on bo h
pe iods, ORH weekends showed a bigge numbe o exceeding le els o PM10 han
MRH on he same days.
4.1.4 PM10 - ARH
ARH is desc ibed in Appendix A1.5 o weekdays and A1.6 o weekends. Fo week
days mean and s anda d de ia ion alues we e highe in Janua y, Feb ua y, Augus ,
Oc obe , No embe and Decembe , wi h Decembe ha ing much highe alues han
he es o he mon hs. The lowes alues we e in he mon hs o Ap il h ough June.
Fo his ime pe iod Augus had bigge alues compa ed o he end o he o he
pe iods analyzed. All mon hs we e posi i ely skewed. Ku osis was highe hen 3,
excep o he mon hs o Janua y, Feb ua y, Oc obe , and No embe . Augus had
much highe ku osis alue han he o he s, due o ha ing highe and mo e ex eme
alues compa ed wi h he no mal dis ibu ion. Fo he weekends mean and s anda d
de ia ion alues we e much lowe han he weekdays, simila o he pa e n obse ed
be o e wi h he excep ion o Sep embe which had much lowe alues, and Decembe
wi h much g ea e alues compa ed wi h he es o he mon hs. The skewness alues
we e posi i e, excep o Augus . Ku osis was highe han 3, excep o Janua y,
indica ing ha he dis ibu ion had mo e ex eme alues han he no mal dis ibu ion.
O e all, ARH on week days is sligh ly lowe han he MRH on mos mon hs excep
o Decembe , and sligh ly bigge han ORH. Fo weekends ARH is gene ally he
lowes o he h ee excep o he mon h o Decembe .
22
4.1.5 PM2.5
4.1.6 PM2.5 - MRH
MRH is desc ibed in Appendix A2.1 and A2.2 o week days and weekends
espec i ely. Fo weekdays he mean and s anda d de ia ion we e g ea e in Janua y,
Feb ua y, and Sep embe h ough Decembe , wi h Decembe ha ing he highes alue
e y close o passing he limi h eshold, bu o e all, all mon hs we e below he limi .
The skewness was posi i e ac oss all mon hs. Ku osis was lowe han 3, excep o
Sep embe , meaning ha he dis ibu ion had ligh e ails and less ex eme alues han
he no mal dis ibu ion. Fo he weekends s anda d de ia ion was s able ac oss all
mon hs and he mean was also e y simila , June had he lowes mean alue and
Decembe he highes , howe e he di e ence is no ha signi ican . All mon hs had a
posi i e skewness. Ku osis was highe han 3, excep o Feb ua y, Ap il, and
Sep embe , which indica es he p esence o mo e ex eme alues han he no mal
dis ibu ion. O e all MRH on week days is highe when compa ed wi h o he ime
pe iods ac oss almos e e y mon h. On weekends MRH is highe han o he ime
pe iods in Ma ch, Ap il, May, July, and Augus .
4.1.7 PM2.5 – ORH
ORH week days and weekends, is desc ibed in Appendix A2.3 and A2.4, espec i ely.
On week days he s anda d de ia ion is s able ac oss e e y mon h. Mean alues a e
highes be ween Janua y, Feb ua y Ma ch, and Oc obe h ough Decembe . All
mon hs a e posi i ely skewed. Ku osis alues a e lowe han 3, excep o Decembe ,
which means he dis ibu ion had ligh e ails and ewe ex eme alues compa ed
wi h he no mal dis ibu ion. On weekends he s anda d de ia ion is s able ac oss
e e y mon h as well as he mean, wi h he highes alue being Decembe bu o e all
he mean end emains he same as o he ime pe iods wi h week days ha ing highe
mean alues han weekends. Skewness alues we e all posi i e. Ku osis alues
highe hen 3, excep o Ap il and May, simila o MRH weekend alues end o ha e
a highe ku osis. Compa ing ORH wi h o he ime pe iods, we can say ha ORH is
he lowes o he h ee on bo h week days and weekends.
23
4.1.8 PM2.5 – ARH
ARH on week days and weekends is desc ibed in Appendix A2.5 and A2.6. Fo he
week days mean alues a e below he limi h eshold wi h Decembe coming e y
close o exceeding, alues he lowes be ween Ap il and July. S anda d de ia ion
alues a e s able and posi i ely skewed ac oss all mon hs. Ku osis is lowe han 3,
excep o he mon h o May and Sep embe , simila o he o he wo ime pe iods
whe e on week days he ku osis is lowe han h ee wi h some excep ions. Fo
weekends he mean and s anda d de ia ion a e highe in Janua y and Decembe , wi h
a o e all end o he mean simila o o he ime pe iods. Skewness is posi i e ac oss
all mon hs. Ku osis is highe han 3 in he mon hs o Feb ua y, Ma ch, May, Augus ,
Sep embe , and Oc obe , indica ing highe o mo e ex eme alues in hese mon hs
compa ed o he o he s. O e all compa ing ARH is e y simila wi h he MRH pe iod
sligh ly lowe on a e age bu shows e y simila esul s on week days. Du ing he
weekends ends o be highe han MRH pe iod du ing he mon hs o Janua y, Oc obe ,
No embe , and Decembe .
4.2 Empi ical Bayesian K iging 3D
EBK3D c oss- alida ion s a is ics can be seen in Table 2. O e all, he me hod
pe o med eliably wi h mino changes o he de aul ad anced model pa ame e s.
The e we e some di e ences in he pe o mance o he wo di e en PM, bu bo h
achie ed good esul s and had mino di e ences o esul s o each o he ime
pe iods. Di e en sea ch neighbo hoods we e used o PM10 and PM2.5 mainly
based on he isual aspec o he model and no based on he c oss- alida ion esul s
which showed insigni ican changes. Using di e en sec o ypes mainly impac ed he
isual aspec o he in e pola ed su ace, and we e selec ed based on hese c i e ia
alone. Looking a Table 2 he pe o mance o each model was sligh ly di e en on
each case, when se ing he model pa ame e s and analyzing he c oss- alida ion
esul s mino changes could be made o each, al hough hey we en’ signi ican and
o ha eason i was be e o use a simila model o each case, changing he sec o
and neighbo hood i signi ican changes we e isualized. On mos cases he ME and
SME we e bo h close o 0, showing a unbiased p edic ion. ASE was close o he
RMSE and he RMSSE was close o 1 o PM10 weekends MRH, ORH, and ARH,
week days didn’ show as good esul s bu i was s ill e y close o ha a ge , PM2.5
24
pe o med he bes on bo h week days and weekends, wi h he excep ion o week days
ARH. The pe cen age poin s inside he 95 pe cen in e al we e, wi h ew excep ions,
close o 95% sugges ing ha he model had and o e all good pe o mance. A e age
CRPS esul s showed sligh ly wo se esul s o PM10 week days on he h ee pe iods
and o PM2.5 weekends ORH. Analyzing he esul s, we can obse e ha gene ally
he numbe o samples had a sligh impac on he model pe o mance, al hough he
di e ences in he numbe o samples o each model is no signi ican .
MRH
ORH
ARH
PM10
Week
Days
A e age CRPS
1,9253186
2,954744
0,001139868
Inside 90 Pe cen In e al
93,628088
94,72991
92,53112033
Inside 95 Pe cen In e al
95,968791
96,31094
95,02074689
Mean
-0,345395
-0,56507
-0,193787353
Roo -Mean-Squa e
1,6717398
1,707321
2,730620898
Mean S anda dized
-0,034116
-0,06365
-0,014231785
Roo -Mean-Squa e
S anda dized
0,5605864
0,976796
1,10657277
A e age S anda d E o
0,3723336
0,171405
0,121982943
Weekends
A e age CRPS
0,377323
1,634583
0,678411438
Inside 90 Pe cen In e al
91,961853
93,21383
91,25326371
Inside 95 Pe cen In e al
95,776567
96,15877
96,08355091
Mean
-0,064457
-0,10534
-0,182743435
Roo -Mean-Squa e
0,0265319
0,738225
0,674823928
Mean S anda dized
-0,003361
-0,01519
-0,016973458
Roo -Mean-Squa e
S anda dized
0,6375992
0,774436
1,162878174
A e age S anda d E o
0,0350026
0,116884
0,995211389
PM2.5
Week
Days
A e age CRPS
0,6879175
2,066574
0,05082201
Inside 90 Pe cen In e al
91,454082
91,21172
91,28686327
Inside 95 Pe cen In e al
95,642857
94,94008
95,30831099
Mean
-0,088983
-0,15541
-0,232171836
Roo -Mean-Squa e
0,7507313
0,218055
1,273589524
Mean S anda dized
-0,029168
-0,02872
-0,050844966
Roo -Mean-Squa e
S anda dized
0,2295961
1,059213
0,128480813
A e age S anda d E o
0,3556574
1,262782
0,23568247
Weekends
A e age CRPS
1,0612272
0,981601
0,234333473
Inside 90 Pe cen In e al
90,087829
89,94845
90,33942559
Inside 95 Pe cen In e al
95,483061
94,71649
95,03916449
Mean
-0,147496
-0,1545
-0,081631622
Roo -Mean-Squa e
0,9669044
0,07903
0,506472409
Mean S anda dized
-0,039727
-0,03429
-0,018424825
Roo -Mean-Squa e
S anda dized
0,0813832
0,015033
0,103117343
A e age S anda d E o
0,970639
0,424993
0,493957064
Table 2: EBK3D Model Pa ame e s
25
4.3 PM10 Eme ging Ho Spo
4.3.1 PM10 - MRH
Based on he space- ime cube o PM10 MRH (Figu e 4), se e al ho and cold spo s
we e iden i ied (Table 3). Fo week days 10 ypes o spa ial- empo al cold and Ho
Spo s we e iden i ied. Ho Spo s we e dis ibu ed mainly in he middle no h and
sou h o Lisbon c ea ing a co ido o Ho Spo s, while cold spo s we e iden i ied in
he eas and wes o he ci y. The ca ego ies Ho Spo s include: consecu i e,
in ensi ying, new, oscilla ing, pe sis en , and spo adic. The numbe o In ensi ying
Ho Spo s was he la ges , meaning ha hese loca ions end o inc ease o e ime and
ha i s inc ease is s a is ically signi ican (Es i, 2022d). Pe sis en Ho Spo s we e he
second highes , concen a ed mainly in he no h o Lisbon in he pa ishes o San a
Cla a, Lumia , pa o Oli ais (a ound he Lisbon Ai po ), and also nea Penha de
F ança, which indica es ha hese a eas pe sis en ly a ho spo bu s a is ically s able
h ough ime. Consecu i e Ho Spo s appea o bo de he In ensi ying Ho Spo s and
ep esen a loca ion gene ally a en’ s a is ically signi ican Ho Spo s un il he las
h ee ime-s ep in e als (Oc obe , No embe and Decembe ). The a eas ha we e
iden i ied as cold spo s a e mainly loca ed in he wes e n egions o Lisbon such as
Belém, Ajuda, and Ben ica, as well in he eas a ound Ma ila. The ypes o cold
spo s included: diminishing, In ensi ying, pe sis en . Diminishing cold spo s we e he
la ges , meaning hose a eas a e always s a is ically signi ican cold spo s and keep
dec easing h ough ime. Pe sis en and In ensi ying Cold Spo s iden i y a eas ha a e
always s a is ically signi ican cold spo s o we e o he wise no signi ican un il he
las ime-s ep in e al.
Rega ding he weekends du ing he MRH, he o e all egion o cold and Ho Spo s
emains he same, howe e he ype o ho spo changes, wi h oscilla ing ho spo
being he la ges , meaning ha has been a s a is ically signi ican ho spo in less han
90 pe cen o he ime and oscilla ing be ween he wo. Spo adic ho spo is he second
la ges which is simila o he oscilla ing in ha is ypically and on-again o -again ho
spo .

26
O e all, he main di e ences be ween MRH week days and weekends, is ha he ho
and cold spo s end o a y in ime mo e on he weekends han he weekdays, meaning
ha on he week days he pa e n ends o be mo e consis en h ough ime.
Figu e 4: PM10 Mo ning Rush Hou , Eme ging Ho Spo Analysis
WEEK DAYS
COUNT
%
WEEKENDS
COUNT
%
CONSECUTIVE HOT SPOT
42
7,29
Consecu i e Ho Spo
26
4,52
DIMINISHING COLD SPOT
77
13,37
Diminishing Cold Spo
39
6,78
INTENSIFYING COLD SPOT
60
10,42
His o ical Cold Spo
12
2,09
INTENSIFYING HOT SPOT
105
18,23
In ensi ying Cold Spo
35
6,09
NEW HOT SPOT
12
2,08
New Ho Spo
18
3,13
NO PATTERN DETECTED
119
20,66
No Pa e n De ec ed
160
27,83
OSCILLATING HOT SPOT
1
0,17
Oscilla ing Ho Spo
99
17,22
PERSISTENT COLD SPOT
59
10,24
Pe sis en Cold Spo
61
10,61
PERSISTENT HOT SPOT
81
14,06
Pe sis en Ho Spo
49
8,52
SPORADIC HOT SPOT
20
3,47
Spo adic Cold Spo
3
0,52
SUM
576
100%
Spo adic Ho Spo
73
12,70
Sum
576
100%
Table 3: PM10 Mo ning Rush Hou , Eme ging Ho Spo Analysis Classes
27
4.3.2 PM10 - ORH
The esul s o he eme ging ho spo analysis o ORH can be ound in Figu e 5, as
well as Table 4 wi h he coun s o each class. Du ing he weekdays, he esul s show
di e en ypes o ho and cold spo s. A signi ican numbe o In ensi ying Ho Spo s
(26,7%) we e obse ed, pa icula ly in he sou h-cen al pa ishes o San a Ma ia
Maio , São Vicen e, Penha de F ança, A oios, as well as he cen e pa s o Lisbon
such as A enidas No as, pa s o Al alade, and a he no h in Lumia and San a
Cla a, indica ing a ise in PM10 le els o e ime. Pe sis en Ho Spo s concen a ed
mainly in he pa ish o Mise ico dia, e lec a eas wi h s able bu ele a ed PM10
alues ac oss ime. Rega ding he cold spo s, pe sis en cold spo s we e p edominan ly
ound in he wes e n egions o he ci y, in a eas such as Belém, Ajuda, Alcân a a, and
Ben ica, sugges ing hese a eas ha e consis en ly lowe PM10 alues.
Figu e 5: PM10 O Rush Hou , Eme ging Ho Spo Analysis
On he weekends, PM10 pa e ns shi sligh ly. The mos p ominen spo s a e
In ensi ying Cold Spo s in he pa ishes o Belém and Ajuda, indica ing a g owing
28
end owa ds lowe alues. In ensi ying Ho Spo s a e obse ed in Mise ico dia,
San a Ma ia Maio , and San o An onio, sugges ing hese a eas ha e an inc ease in
PM10 du ing he weekends. Addi ionally, pe sis en Ho Spo s in Lumia and San a
Cla a, and pe sis en cold spo s on he wes e n edge o Ajuda and pa s o Ben ica
indica e a eas whe e PM10 alues emain consis en ly high o low h ough ime,
h oughou he weekends. Spo adic Ho Spo s in A enidas No as ep esen loca ions
wi h luc ua ing PM10 alues, ypically no consis en enough o o m a pa e n.
O e all, he speci ic ypes o ho and cold spo s may a y be ween week days and
weekends, he egions o Lisbon expe iencing hese phenomena emain consis en ,
wi h cen al a eas mo e p one o Ho Spo s and pe iphe al a eas o cold spo s.
4.3.3 PM10 - ARH
Eme ging ho spo analysis esul s o ARH on week days and weekends can ound in
Figu e 6 and Table 5. Du ing he week days, In ensi ying Ho Spo s a e p e alen ,
especially in he no he n pa ishes o Lumia , Oli ais, San a Cla a, and sou he n a eas
such as San a Ma ia Maio , São Vicen e, and San o An ónio whe e PM10 le els end
o ise. The pe sis en Ho Spo s, a e loca ed be ween A oios, San o An ónio, and São
WEEK DAYS
COUNT
%
WEEKENDS
COUNT
%
CONSECUTIVE HOT SPOT
13
2,26
Consecu i e Cold Spo
2
0,35
DIMINISHING COLD SPOT
34
5,90
Consecu i e Ho Spo
5
0,87
HISTORICAL COLD SPOT
1
0,17
Diminishing Cold Spo
45
7,81
INTENSIFYING COLD SPOT
28
4,86
Diminishing Ho Spo
1
0,17
INTENSIFYING HOT SPOT
154
26,74
His o ical Cold Spo
2
0,35
NEW HOT SPOT
5
0,87
In ensi ying Cold Spo
47
8,16
NO PATTERN DETECTED
131
22,74
In ensi ying Ho Spo
95
16,49
PERSISTENT COLD SPOT
140
24,31
New Ho Spo
9
1,56
PERSISTENT HOT SPOT
57
9,90
No Pa e n De ec ed
112
19,44
SPORADIC COLD SPOT
2
0,35
Pe sis en Cold Spo
113
19,62
SPORADIC HOT SPOT
11
1,91
Pe sis en Ho Spo
111
19,27
SUM
576
100%
Spo adic Cold Spo
3
0,52
Spo adic Ho Spo
31
5,38
Sum
576
100%
Table 4: PM10 O Rush Hou , Eme ging Ho Spo Analysis Classes
29
Vicen e signi ying loca ions ha a e always high compa ed o i s neighbo s. Spo adic
Ho Spo s a e sca e ed ac oss A enidas No as and a eas bo de ing he In ensi ying
Ho Spo s, indica ing a eas wi h a iable bu occasionally high PM10 alues.
Pe sis en cold spo s make up a signi ican po ion o he esul s, pa icula ly in he
wes e n egions such as Belém, Ajuda, and sou h o Ben ica, indica ing consis en ly
lowe PM10 alues. Diminishing cold spo s loca ed in he eas in Ma ila and he
wes in Alcân a a, and Ben ica show a eas whe e PM10 alues a e dec easing in ime.
Figu e 6: PM10 A e noon Rush Hou , Eme ging Ho Spo Analysis
On he weekends, he pa e n shi s sligh ly. The la ges ca ego y o Ho Spo s is he
spo adic Ho Spo s, indica ing ha cen al a eas like Penha de F ança, São Vicen e,
San a Ma ia Maio , A eei o, A enidas No as, and Al alade expe ience a iable
PM10 alues h ough ime, and a e occasionally s a is ically signi ican Ho Spo s.
Pe sis en Ho Spo s a e mo e p ominen in he a eas o A oios, San o An onio,
Lumia , and nea he ai po indica ing ha hese a e always signi ican ly s a is ical
ho spo s. His o ical cold spo s in Campo de Ou ique and nea Campolide, as well as,
pe sis en cold spo s in Ajuda and Ben ica indica e a eas whe e lowe PM10 alues
a e a end in ime.
36
Figu e 10: PM2.5 Mo ning Rush Hou , Eme ging Ho Spo Analysis
Weekends, pe sis en cold spo s a e e y simila in e ms o a eas han he week days.
Pe sis en Ho Spo s eme ge in he weekends in São Vicen e, Penha de F ança, and
A oios, whe e high alues ha e been consis en ly obse ed o end o inc ease o e
ime. Cen al and no he n egions expe ience In ensi ying Ho Spo s bu a a lowe
numbe han he week days, indica ing a high bu s able end h ough ime. No
pa e n de ec ed emains simila o he week days.
Compa ing MRH pe iod ac oss weekdays and weekends, i is clea ha ce ain a eas
exhibi pe sis en ends, ei he ho o cold, indica ing s able alues h oughou he
yea .
Week days
Coun
%
Weekends
Coun
%
Consecu i e Cold Spo
3
0,520833
Consecu i e Cold Spo
10
1,736111
Consecu i e Ho Spo
3
0,520833
Consecu i e Ho Spo
2
0,347222
Diminishing Cold Spo
18
3,125
Diminishing Cold Spo
15
2,604167
Diminishing Ho Spo
11
1,909722
Diminishing Ho Spo
12
3,819444
His o ical Cold Spo
4
0,694444
His o ical Cold Spo
2
0,347222
In ensi ying Cold Spo
42
7,291667
His o ical Ho Spo
1
0,173611
In ensi ying Ho Spo
93
16,14583
In ensi ying Cold Spo
31
5,381944

37
4.5.2 PM2.5 - ORH
Eme ging ho spo analysis o PM2.5 ORH a e p esen ed in Figu e 11 and Table 10.
Du ing he week days he pe sis en cold spo s a e la gely si ua ed in Alcân a a,
Belém, Ben ica, and Ca nide. These loca ions consis en ly exhibi lowe PM2.5
alues compa ed wi h i s spa io empo al neighbo s, In ensi ying Ho Spo s (18,06%),
eme ge s ongly in he cen al and down own pa ishes such as Bea o, São Vicen e,
Penha de F ança, San a Ma ia, Mise ico dia, A oios, A enidas No as, and ex end o
he no h a eas like Lumia and Oli ais no h nea he Lisbon Ai po , indica ing no
only pe sis en ly high bu also g owing le els compa ed wi h i s neighbo s in ime.
Diminishing cold spo s (3,82%) a e p esen in loca ions such as Ben ica and
Alcân a a, and show low alues ha a e diminishing o e ime compa ed o i s
su oundings. No pa e n de ec ed (24,31%), spli he s udy a ea in wo be ween cold
and Ho Spo s, indica ing egion whe e no signi ican end has been iden i ied.
New Ho Spo
3
0,520833
In ensi ying Ho Spo
27
4,6875
No Pa e n De ec ed
159
27,60417
New Cold Spo
2
0,347222
Pe sis en Cold Spo
165
28,64583
New Ho Spo
7
1,215278
Pe sis en Ho Spo
62
10,76389
No Pa e n De ec ed
110
19,09722
Spo adic Ho Spo
13
2,256944
Pe sis en Cold Spo
172
29,86111
Sum
576
100%
Pe sis en Ho Spo
145
25,17361
Spo adic Cold Spo
9
1,5625
Spo adic Ho Spo
21
3,645833
Sum
576
100%
Table 9: PM2.5 Mo ning Rush Hou , Eme ging Ho Spo Analysis Classes
38
Figu e 11: PM2.5 O Rush Hou , Eme ging Ho Spo Analysis
On he weekends he pa e n adjus s sligh ly, wi h pe sis en cold spo s now
accoun ing o 37,50%, in he same a eas as week days bu wi h addi ional loca ions
in he cen al a eas. In ensi ying Ho Spo s (14,58%) a e p esen in he no h in he
pa ishes o Lumia , San a Cla a, and Oli ais, as well as sou h a eas, indica ing a end
o inc easing le els h ough ime in ela ion o i s spa io empo al neighbo s. No
pa e n de ec ed (22,22%) is p esen in a eas such as Pa que das Nações and ce ain
cen al egions ha ypically exhibi some pa e n. Diminishing cold spo s (4,69%)
and spo adic Ho Spo s (2,43%) a e s ill p esen bu in smalle pe cen ages sugges ing
localized and less consis en pa e ns du ing he weekends.
Compa ing he weekdays o weekdays he mos no iceable di e ence be ween he wo
is ha du ing he weekends cold spo s end o ake o e a la ge a ea o he ci y while
Ho Spo s a e dense wi h a end o inc easing h ough ime.
Week days
Coun
%
Weekends
Coun
%
Consecu i e Cold
Spo
3
0,52
Consecu i e Cold Spo
8
1,39
Consecu i e Ho Spo
10
1,74
Diminishing Cold Spo
27
4,69
Diminishing Cold Spo
22
3,82
His o ical Cold Spo
1
0,17
His o ical Cold Spo
1
0,17
In ensi ying Cold Spo
3
0,52
39
4.5.3 PM2.5 ARH
Du ing week days Figu e 12 and Table 11 pe sis en cold spo s (30,56%) a e
p edominan ly loca ed in he wes e n egions o Alcân a a, Ajuda, belém, and
Ca nide.
Figu e 12: PM2.5 A e noon Rush Hou , Eme ging Ho Spo Analysis
In ensi ying Cold Spo
19
3,30
In ensi ying Ho Spo
84
14,58
In ensi ying Ho Spo
104
18,06
New Cold Spo
1
0,17
New Ho Spo
4
0,69
New Ho Spo
2
0,35
No Pa e n De ec ed
140
24,31
No Pa e n De ec ed
128
22,22
Pe sis en Cold Spo
177
30,73
Oscilla ing Ho Spo
1
0,17
Pe sis en Ho Spo
78
13,54
Pe sis en Cold Spo
216
37,50
Spo adic Cold Spo
2
0,35
Pe sis en Ho Spo
78
13,54
Spo adic Ho Spo
16
2,78
Spo adic Cold Spo
13
2,26
Sum
576
100%
Spo adic Ho Spo
14
2,43
Sum
576
100%
Table 10: PM2.5 O Rush Hou , Eme ging Ho Spo Analysis Classes
40
These a eas consis en ly show lowe PM2.5 le els, han i s space ime neighbo s.
Pe sis en Ho Spo s (23,44%), a e concen a ed in he cen al a eas such as
Mise ico dia, San a Ma ia Maio , São Vicen e, Penha de F ança, A oios, San o
An ónio, A enidas No as, Campolide, and in he no h a eas o Lumia , San a Cla a,
and Oli ais, indica ing a eas whe e PM2.5 a e high bu s able o e ime. No Pa e n
Dec ec ed (32,64%), includes a eas like Ca nide, Al alade, and Oli ais, indica ing
a eas whe e alues don’ exhibi any pa e n h ough space and ime.
On he weekends, he pa e n o cold and ho spo s shi s sligh ly. Pe sis en cold spo s
a e subs an ial (32,81%), wi h a dis ibu ion simila o week days bu ex ending o he
wes e n a eas o Ma ila, Pa que da Nações, Oli ais. In ensi ying Ho Spo s a e in a e
la ge he pe sis en Ho Spo s and a e loca ed in he no h, sou h a eas as well as
Belém, which du ing he week days shows in ensi ying o pe sis en cold spo s. No
pa e n de ec ed (28,99%) inc eases on he weekends wi h a bigge change being in
he wes pa ish o Belém and Ajuda.
Compa ing he wo, he main di e ence is he shi om pe sis en Ho Spo s du ing
week days o In ensi ying Ho Spo s on he weekends. This ansi ion indica es ha
Ho Spo s du ing he week end o be mo e s able du ing ime while on he weekends,
hey end o inc ease h ough ime.
Week days
Coun
%
Weekends
Coun
%
Consecu i e Cold Spo
1
0,17
Consecu i e Cold Spo
15
2,60
Diminishing Cold Spo
7
1,22
Consecu i e Ho Spo
8
1,16
Diminishing Ho Spo
11
1,91
Diminishing Cold Spo
2
0,35
His o ical Cold Spo
3
0,52
Diminishing Ho Spo
1
0,17
In ensi ying Cold Spo
15
2,60
His o ical Cold Spo
2
0,35
In ensi ying Ho Spo
17
3,65
In ensi ying Cold Spo
6
1,04
New Ho Spo
3
0,52
In ensi ying Ho Spo
101
17,53
No Pa e n De ec ed
188
32,64
New Ho Spo
6
1,04
Pe sis en Cold Spo
176
30,56
No Pa e n De ec ed
167
28,99
Pe sis en Ho Spo
135
23,44
Oscilla ing Ho Spo
7
1,22
Spo adic Ho Spo
16
2,78
Pe sis en Cold Spo
189
32,81
Sum
576
100%
Pe sis en Ho Spo
47
8,16
Spo adic Cold Spo
2
0,35
Spo adic Ho Spo
18
3,13
Sum
576
100%
Table 11: PM2.5 A e noon Rush Hou , Eme ging Ho Spo Analysis Classes
41
4.6 PM2.5 - Local Ou lie
4.6.1 PM2.5 – MRH
Analyzing he MRH (Figu e 13, and Table 12) week days esul s o he local ou lie
analysis, we can obse e ha he la ges ca ego y is he only Low-Low clus e
(43,40%), in he wes e n pa o he s udy a ea in pa ishes such as Alcân a a, Ajuda,
Belém, Ben ica, and Ca nide, along wi h eas e n egions o Pa que das Nações, and
Ma ila. These clus e s indica e a eas whe e PM2.5 le els a e low ela i e o hei
immedia e empo al and spa ial con ex . Only High-High clus e s (28,65%) ound in
he sou he n pa ishes o he ci y like Bea o, São Vicen e, San a Ma ia Maio , Penha de
F ança, and A oios, and in he no h pa ishes o Lumia , San a Cla a, and Oli ais,
ep esen s a eas whe e le els a e high and simila o hei su oundings.
Figu e 13: PM2.5 Mo ning Rush Hou , Local Ou lie
On weekends, he dis ibu ion pa e n is somewha simila . The only Low-Low clus e
again o ms he la ges g oup (44,79%), sugges ing ha hese a eas main ain lowe
alues ela i e o hei neighbo s h ough ime. The only High-High clus e inc eases
sligh ly on he weekends sugges ing ha ce ain a eas con inue o expe ience high
ales compa ed o hei neighbo s, consis en wi h he weekday pa e n.

42
The Mul iple Types o week days (2,08%) and weekends (6,08%) indica es a eas ha
do no i in o a single ca ego y and ha e luc ua ing and less p edic able alues.
O e all, he pe sis ence o he Low-Low and High-High clus e s sugges s ha he e
a e a eas in Lisbon wi h consis en ly low o highe le els in hei local con ex s.
4.6.2 PM2.5 – ORH
ORH (Figu e 14, and Table 13) week days only Low-Low clus e s (43,58%) a e he
la ges . They a e p esen in he wes pa o Lisbon in he pa ishes o Alcân a a,
Ajuda, Belém, Ben ica, and Ca nide, and in he wes in Ma ila, Pa que da Nações,
and Oli ais. These clus e s indica e a eas whe e PM2.5 le els a e consis en ly lowe
han i s space ime neighbo s.
Figu e 14: PM2.5 O Rush Hou , Local Ou lie
Week Days
Coun
%
Weekends
Coun
%
Mul iple Types
12
2,08
Mul iple Types
35
6,08
Ne e Signi ican
140
24,31
Ne e Signi ican
65
11,28
Only High-High Clus e
165
28,65
Only High-High Clus e
180
36,28
Only High-Low Ou lie
2
0,35
Only Low-High Ou lie
8
1,39
Only Low-High Ou lie
7
1,22
Only Low-Low Clus e
258
44,79
Only Low-Low Clus e
250
43,40
Sum
576
100%
Sum
576
100%
Table 12: PM2.5 Mo ning Rush Hou , Local Ou lie Classes
43
Only High-High clus e s (33,68%) a e loca ed in he pa ishes o Penha de F ança,
São Vicen e, San a Ma ia, A oios, and San o An ónio, as well in he no h in Lumia ,
Oli ais, and San a Cla a. Indica ing ha PM2.5 alues a e signi ican ly highe and
simila o hei su ounding a eas.
On he weekends, he pa e n shi s sligh ly. Only Low-Low clus e (50,17) is bigge
in his pe iod in simila a eas as he weekends, wi h he addi ion o cen al pa ishes
like Al alade, A eei o, and pa s o Lumia . Only High-High clus e s (39,73%)
dec ease sligh ly, bu emain signi ican , especially in he down own a eas and no h
o Lisbon.
Compa ing he wo pe iods, while Low-Low clus e emain he p edominan ca ego y,
he e is a no able inc ease in hese clus e s du ing weekends. The sligh dec ease in
High-High clus e s in weekends could indica e ha ac i i ies con ibu ing o highe
le els o PM2.5 a e mo e p e alen du ing week days, howe e , he a eas ep esen ed
by hese clus e s emain la gely consis en .
4.6.3 PM2.5 – ARH
On week days, only Low-Low clus e s (41,84%) emain he la ges , in wes e n a eas
such as Alcân a a, Ajuda, Belém, Ben ica, and Ca nide, and eas Al alade, Ma ila,
Pa que das Nações, and Oli ais. These clus e s iden i y a eas whe e PM2.5 le els a e
consis en ly lowe han hei space ime neighbo s. Only High-High clus e s
(30,90%), loca ed in down own and cen al pa ishes. These a eas a e cha ac e ized by
ha ing PM2.5 le els ha a e signi ican ly highe and simila o hei space ime
su oundings.
Table 13: PM2.5 O Rush Hou , Local Ou lie Classes
Week Days
Coun
%
Weekends
Coun
%
Mul iple Types
16
2,78
Mul iple Types
14
4,69
Ne e Signi ican
98
17,01
Ne e Signi ican
77
13,37
Only High-High Clus e
194
33,68
Only High-High Clus e
177
30,73
Only Low-High Ou lie
17
2,95
Only High-Low Ou lie
2
0,35
Only Low-Low Clus e
251
43,58
Only Low-High Ou lie
4
0,69
Sum
576
100%
Only Low-Low Clus e
289
50,17
Sum
576
100%
44
Du ing weekends, he only Low-Low clus e (41,49%) emains he la ges , expanding
sligh ly mo e om eas o wes in he cen al a ea o he ci y. The High-High clus e s
(30,03%) dec eases sligh ly, howe e , no he n a eas emain consis en wi h he
weekday pa e n.
Figu e 15: PM2.5 A e noon Rush Hou , Local Ou lie
Compa ing he wo, Low-Low clus e s emain he mos p ominen ca ego y. The
sligh inc ease in Low-Low clus e du ing weekends sugges s mino imp o emen s in
ai quali y in some cen al a eas. High-High clus e s emain consis en in he no he n
a eas in bo h pe iods as well as down own a eas.
Week Days
Coun
%
Weekends
Coun
%
Mul iple Types
13
2,26
Mul iple Types
36
6,25
Ne e Signi ican
135
23,44
Ne e Signi ican
113
19,62
Only High-High Clus e
178
30,90
Only High-High Clus e
173
30,03
Only High-Low Ou lie
1
0,17
Only High-Low Ou lie
5
0,87
Only Low-High Ou lie
8
1,39
Only Low-High Ou lie
10
1,74
Only Low-Low Clus e
241
41,84
Only Low-Low Clus e
239
41,49
Sum
576
100%
Sum
576
100%
Table 14: PM2.5 A e noon Rush Hou , Local Ou lie Classes
45
5 DISCUSSION
Analyzing he concen a ions o bo h pollu an s in Lisbon du ing 2022, we obse e
ha colde mon hs, gene ally om Oc obe o Feb ua y, end o ha e highe
concen a ion han du ing he ho e mon hs. This pa e n aligns wi h he e ec s o
a mosphe ic s abili y, which is mo e p esen du ing colde mon hs, whe e he e a e
less op imal dispe sion condi ion which can be a con ibu ing ac o o he highe
concen a ions measu ed (Zheng, 2005). Looking a he spa ial dis ibu ion o PM
concen a ions in he ci y, we can see ha mo e cen al and down own a eas end o
ha e highe concen a ions han he su ounding a eas, his could be he esul o he
e ec ha he u ban in as uc u e has on wind, which is he main componen o
ho izon al dispe sion, he p esence o buildings, especially i hey a e dense as in
hese cases, i could impede he p ope dispe sion and esul in highe concen a ion in
hose a eas. Ano he a ea wi h cons an ly high alues is in he no he n egion o he
s udy a ea in he pa ishes o Lumia , Oli ais, and San a Cla a, which a e loca ed nea
he Lisbon Ai po which can explain he high concen a ions o PM measu ed in
hese a eas.
Compa ing week days and weekends esul s o he spa io empo al analysis o PM10
highligh s ce ain pa e ns in di e en ime pe iods. Week days end o show a mo e
consis en spa io empo al pa e n, his is can be seen by he dominance o one o wo
ypes o ho and cold spo s, as well as dominance o High-High o Low-Low clus e s
wi h ew ou lie s. This can be explained by he mo e linea a ic beha io s du ing
week days. In con as , he weekend pa e ns show mo e di e se spa ial pa e ns, he
p esence o a highe numbe o di e en ho and cold spo s ypes, as a bigge numbe
o ou lie s and Mul iple Types, highligh a mo e di e se o less p edic able pa e n,
which can be he esul o he less s uc u ed na u e o weekend ac i i ies.
Analyzing he spa io empo al esul s o he di e en ime pe iods, ce ain pa e ns
can be iden i ied. Du ing week days MRH and ORH seem o be mo e consis en
h ough ime, domina ed by In ensi ying Ho Spo s and pe sis en cold spo s as well as
ewe numbe o ou lie s compa ed wi h ARH pe iod. ARH is ep esen ed wi h highe
numbe o ypes o ho and cold spo s, as well as, sligh ly mo e ou lie s. This shows
52
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54
ANNEX
Table 15A: S a ion ID and Loca ion
ID
Loca ion
ID
Loca ion
QAPM1000
01
Calçada da Ajuda
QAPM1000
41
Ja dim do B aço de P a a
QAPM1000
02
Res elo - Rua Gonçalo Velho Cab al
QAPM1000
42
T a essa de F ancisco Rezende
QAPM1000
03
Cais do Sod é
QAPM1000
43
A enida Almi an e Gago Cou inho
QAPM1000
04
Alcân a a - Rua dos Lusíadas
QAPM1000
44
A enida do San o Condes á el
QAPM1000
05
A enida Vin e e Qua o de Julho
QAPM1000
45
Rua F ei Ca los
QAPM1000
06
A enida In an e San o
QAPM1000
46
En ecampos
QAPM1000
07
A In an e Dom Hen ique (Cha a iz Del
Rei)
QAPM1000
47
A enida dos Es ados Unidos da Amé ica
QAPM1000
08
Baixa - Rua do Ou o
QAPM1000
48
A enida Lusíada
QAPM1000
09
P aça do Comé cio
QAPM1000
49
A enida de Roma
QAPM1000
10
Al o da Ajuda - Rua Sá Noguei a
QAPM1000
50
Chelas - Rua D . José Espi i o San o
QAPM1000
11
A enida de Ceu a
QAPM1000
51
A enida José Régio
QAPM1000
12
Rua de São Ben o
QAPM1000
52
A enida Lusíada / Q a da G anja
QAPM1000
13
Rua Damasceno Mon ei o
QAPM1000
53
Rua Lúcio de Aze edo
QAPM1000
14
P aça Ma im Moniz
QAPM1000
54
A enida Ma echal Gomes da Cos a
QAPM1000
15
Campo de San a Cla a
QAPM1000
55
A enida do B asil
QAPM1000
16
Cemi é io dos P aze es
QAPM1000
56
A enida Gene al No on de Ma os
QAPM1000
17
Ja dim Bo ânico
QAPM1000
57
Campo G ande - Museu da Cidade
QAPM1000
18
Pa que de Campismo de Lisboa
QAPM1000
58
Ja dim P o esso An ónio F anco
QAPM1000
19
Monsan o - Alameda Keil do Ama al
QAPM1000
59
Pa que da Vinha - Es ação Me eo ológica
QAPM1000
20
A enida da Libe dade - Rua Manuel Jesus
Coelho
QAPM1000
60
Oli ais Sul - Quin a Pedagógica
QAPM1000
21
Rua dos Sapado es
QAPM1000
61
Quin a das Conchas - A enida Ma ia Helena
Viei a da Sil a
QAPM1000
22
Campo de Ou ique
QAPM1000
62
Es ada do Paço do Lumia
QAPM1000
23
A enida Almi an e Reis
QAPM1000
63
Es ada Mili a
QAPM1000
24
Rua B aamcamp
QAPM1000
64
Alameda da Enca nação
QAPM1000
25
Monsan o - Pa que Ecológico
QAPM1000
65
A enida Dou o Al edo Bensaúde
QAPM1000
26
Pa ada Al o de São João
QAPM1000
66
Rua Ilha dos Amo es
QAPM1000
27
Ma quês de Pombal - Alameda Edga
Ca doso
QAPM1000
67
Rua Vasco da Gama Fe nandes
QAPM1000
28
Bea o - A enida In an e Dom Hen ique
QAPM1000
68
Labo a ó io de B oma ologia e Águas
QAPM1000
29
A enida Fon es Pe ei a de Melo
QAPM1000
69
Calçada de Ca iche
QAPM1000
30
A enida An ónio Augus o de Aguia
QAPM1000
70
Rua Chen He
QAPM1000
31
La go da Mad e de Deus
QAPM1000
71
Es ada Mili a às Galinhei as
55
QAPM1000
32
Rua de Campolide
QAPM1000
72
Rua Má io Bo as
QAPM1000
33
La go do Leão
QAPM1000
73
Rua Al e es Malhei o
QAPM1000
34
A enida da Républica
QAPM1000
74
Rua da Venezuela
QAPM1000
35
P aça São F ancisco de Assis
QAPM1000
75
Alm. P. Ál a o P oença EMQA
QAPM1000
36
Es ada de Monsan o
QAPM1000
76
Res au ado es - A enida da Libe dade
QAPM1000
37
P aça de Espanha
QAPM1000
77
Rua da A alaia
QAPM1000
38
Ma ila - Rua Ped o de Aze edo
QAPM1000
78
Ja dim da Es ela
QAPM1000
39
Es ada de Ben ica
QAPM1000
79
A enida Dou o F ancisco Luís Gomes / EMQA
QAPM1000
40
A enida João XXI
QAPM1000
80
Rua Nau Ca ine a c uz Rua No a dos Me cado es
56
APENDIX
A
A1 - PM10
A1.1 Mo ning Rush Hou Week Days
MRH
WK
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
6,61
5,10
5,00
6,44
6,82
5,09
10,90
10,3
9
6,83
6,63
10,9
5
13,73
Maximum
43,7
4
41,9
5
36,5
5
47,5
8
38,4
5
71,2
1
105,0
5
41,0
0
63,5
5
48,5
0
53,8
3
129,3
4
Mean
24,3
4
26,5
6
24,2
5
16,9
5
18,8
8
18,8
7
22,07
21,9
0
22,5
2
28,3
1
27,4
8
30,96
S aDe
7,97
8,91
6,57
6,82
6,36
9,36
11,53
6,06
9,44
7,83
8,60
16,35
Median
24,4
0
29,1
8
25,2
3
17,0
0
19,0
5
18,3
4
20,81
21,4
1
20,3
3
27,8
2
27,1
7
27,52
Skewness
0,29
-0,30
-0,49
2,08
0,78
3,23
5,70
0,80
1,57
-0,15
0,70
3,97
Ku osis
2,99
2,40
3,68
10,0
8
4,22
17,5
3
41,39
4,14
6,94
3,23
4,30
24,22
Table 16A: PM10 Mo ning Rush Hou Week Days Summa y S a is ics
57
Figu e 16A: PM10 Mo ning Rush Hou Week Days His og am

58
Figu e 17A: PM10 Mo ning Rush Hou Week Days Sca e Plo s
Figu e 18: PM10 Mo ning Rush Hou Week Days IDW
59
A1.2 – PM10 Mo ning Rush Hou Weekends
MRH WE
Jan
Fe
b
Ma
Ap
Ma
y
Ju
n
Jul
Au
g
Se
p
Oc
No
Dec
Minimum
2,80
3,67
2,70
2,42
2,50
7,57
8,28
3,58
3,22
7,56
3,92
Maximum
41,6
9
48,0
6
37,3
0
37,0
0
85,3
6
38,4
4
36,2
2
39,5
6
72,0
0
39,9
4
108,1
8
Mean
19,9
1
20,3
5
17,0
9
20,4
8
17,2
8
17,3
6
22,0
3
15,7
9
22,1
7
23,5
2
24,86
S anda dDe ia i
on
9,21
10,9
5
7,19
7,07
10,7
0
5,80
6,62
6,73
9,14
7,81
13,93
Median
18,6
5
20,8
1
17,1
0
22,1
4
16,6
8
17,0
7
22,7
2
14,4
4
21,2
3
23,8
3
22,90
Skewness
0,58
0,59
1,04
-
0,33
3,92
1,14
-
0,14
1,31
2,24
0,16
3,39
Ku osis
2,77
2,74
4,62
3,41
25,1
3
5,15
2,74
5,67
13,5
4
2,73
21,21
Table 17: PM10 Mo ning Rush Hou Weekends Summa y S a is ics
60
Figu e 19A: PM10 Mo ning Rush Hou Weekends His og ams
61
Figu e 20A: PM10 Mo ning Rush Hou Weekends Sca e Plo s
Figu e 21A: PM10 Mo ning Rush Hou Weekends IDW
68
A1.5 PM10 A e noon Rush Hou Week Days
ARH WK
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
4,00
4,50
7,87
6,39
3,13
10,00
10,1
4
6,41
4,43
9,41
12,2
3
Maximum
44,0
0
48,2
6
49,9
8
44,3
0
66,2
1
103,7
3
68,0
0
57,4
4
52,0
2
50,1
6
67,4
0
Mean
24,3
2
23,5
5
18,4
5
18,2
5
17,9
8
21,88
22,3
1
21,0
1
25,6
8
25,0
7
31,3
1
S anda dDe ia io
n
10,1
6
10,5
6
8,15
7,67
10,4
6
12,58
9,33
11,4
7
9,55
9,28
12,5
3
Median
25,0
0
25,7
2
17,2
5
17,0
2
15,8
6
19,14
20,4
2
17,2
1
24,2
3
23,9
4
28,1
8
Skewness
0,08
0,11
1,86
1,53
2,05
4,31
2,29
1,35
0,31
0,49
0,75
Ku osis
2,32
2,25
6,80
5,62
8,71
27,37
10,4
0
3,93
2,81
2,62
3,23
Table 20A: PM10 A e noon Rush Hou Week Days Summa y S a is ics

69
Figu e 28A: PM10 A e noon Rush Hou Week Days His og ams
70
Figu e 29A: PM10 A e noon Rush Hou Week Days Sca e Plo s
Figu e 30A: PM10 A e noon Rush Hou Week Days IDW
71
A1.6. PM10 A e noon Rush Hou Weekends
ARH WE
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
2,00
5,17
2,23
2,00
1,00
6,13
7,70
2,39
3,33
2,00
3,17
Maximum
47,7
6
38,3
3
32,0
8
47,0
4
48,6
1
27,8
2
26,7
0
39,1
7
38,3
0
41,7
8
164,0
0
Mean
21,4
9
17,1
3
12,3
0
15,8
9
14,9
7
14,0
1
16,9
1
10,9
4
18,4
3
19,5
1
27,31
S anda dDe ia io
n
10,3
3
6,02
4,65
6,50
8,14
3,89
3,97
5,74
6,07
7,31
20,70
Median
22,6
3
18,5
6
12,5
7
16,8
9
14,9
4
14,0
0
17,3
0
10,2
5
18,4
5
19,1
5
25,04
Skewness
0,07
0,37
1,02
1,31
1,91
0,95
-0,25
3,08
0,57
0,45
4,98
Ku osis
2,28
4,08
7,41
9,24
8,47
5,26
3,28
15,9
3
4,70
4,12
32,50
Table 21A: PM10 A e noon Rush Hou Weekends Summa y S a is ics
72
Figu e 31A: PM10 A e noon Rush Hou Weekends His og ams
73
Figu e 32A: PM10 A e noon Rush Hou Weekends Sca e Plo s
Figu e 33A: PM10 A e noon Rush Hou Weekends IDW

74
A2. PM2.5
A2.1. PM2.5 Mo ning Rush Hou Week Days
MRH WK
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
6,46
3,03
5,68
7,08
4,00
6,17
7,45
5,07
6,69
2,13
7,98
7,38
Maximum
28,7
5
29,4
1
29,9
5
29,9
2
29,0
0
30,5
5
34,7
6
28,5
5
59,8
5
42,2
5
33,5
5
37,0
5
Mean
16,6
9
16,2
5
15,1
2
15,3
1
15,0
2
14,6
0
14,9
7
14,0
1
16,2
1
16,4
2
17,5
3
19,4
6
S anda dDe ia io
n
5,19
6,15
5,91
7,35
8,53
8,22
7,12
7,32
8,72
7,36
6,41
6,88
Median
14,4
5
14,1
8
12,5
0
10,9
2
10,0
0
9,82
11,5
5
11,1
9
12,5
5
13,9
4
15,1
9
16,5
8
Skewness
0,50
0,38
0,82
0,81
0,72
0,77
0,97
0,75
2,13
0,90
0,89
0,63
Ku osis
2,18
2,14
2,62
2,03
1,80
1,89
2,67
2,12
10,1
9
3,71
2,65
2,37
Table 22A: PM2.5 Mo ning Rush Hou Week Days Summa y S a is ics
75
Figu e 34: PM2.5 Mo ning Rush Hou Week Days His og ams
76
Figu e 35A: PM2.5 Mo ning Rush Hou Week Days Sca e Plo s
Figu e 36A: PM2.5 Mo ning Rush Hou Week Days IDW
77
A2.1.2 PM2.5 Mo ning Rush Hou Weekends
MRH WE
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
2,20
2,08
3,50
4,35
1,08
1,05
3,70
5,22
2,17
2,22
5,25
5,14
Maximum
29,2
5
28,5
6
29,8
9
29,4
5
40,8
9
34,8
2
27,6
7
28,4
4
28,7
5
29,1
1
28,7
2
44,4
1
Mean
12,6
7
11,7
6
12,7
8
12,1
0
12,6
3
10,6
4
11,8
1
12,8
3
11,4
6
12,2
9
12,5
1
13,3
6
S anda dDe ia io
n
6,82
7,21
5,91
7,05
7,04
7,10
6,48
5,67
6,90
6,20
6,13
8,42
Median
9,85
8,75
11,0
0
8,30
9,39
7,45
9,61
10,8
9
9,25
10,7
1
10,0
0
9,91
Skewness
1,17
0,84
1,45
0,87
1,64
1,37
1,02
0,94
0,82
1,12
1,25
1,95
Ku osis
3,29
2,57
4,50
2,55
5,97
4,17
3,21
3,15
2,79
3,63
3,61
6,60
Table 23: PM2.5 Mo ning Rush Hou Weekends Summa y S a is ics
84
Figu e 43A: PM2.5 O Rush Hou Weekends His og am

85
Figu e 44A: PM2.5 O Rush Hou Weekends Sca e Plo s
Figu e 45A: PM2.5 O Rush Hou Weekends IDW
86
A2.5. PM2.5 A e noon Rush Hou Week Days
ARH WK
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
3,50
3,98
4,96
4,80
3,89
6,07
4,82
4,03
1,61
4,57
8,52
Maximum
31,1
9
32,8
3
29,1
5
47,9
2
34,7
9
43,7
7
36,3
8
43,6
2
34,1
7
35,7
1
39,7
2
Mean
16,2
0
16,3
4
14,6
5
15,3
1
13,0
6
14,3
6
13,9
0
15,1
3
16,3
1
16,0
9
19,5
8
S anda dDe ia io
n
7,60
7,16
9,33
9,54
9,12
8,87
8,75
9,54
8,01
9,04
8,91
Median
12,5
6
13,4
8
8,35
9,88
7,51
9,47
9,09
10,1
3
13,1
0
11,1
3
15,8
2
Skewness
0,57
0,61
0,60
1,03
0,81
1,13
0,89
1,08
0,57
0,70
0,54
Ku osis
2,04
2,29
1,44
3,16
2,00
3,17
2,23
2,86
2,04
1,90
1,84
Table 26A: PM2.5 A e noon Rush Hou Week Days Summa y S a is ics
87
Figu e 46A: PM2.5 A e noon Rush Hou Week Days His og ams
88
Figu e 47A: PM2.5 A e noon Rush Hou Week Days Sca e Plo s
89
A2.6. PM2.5 A e noon Rush Hou Weekends
ARH WE
Jan
Feb
Ma
Ap
May
Jun
Jul
Aug
Sep
Oc
No
Dec
Minimum
5,70
3,28
2,60
1,17
2,56
3,30
3,33
4,15
2,04
4,30
4,11
5,53
Maximum
27,6
3
28,3
3
22,5
3
26,9
7
42,8
0
27,0
9
28,9
7
39,6
7
39,2
1
42,3
9
37,3
7
39,0
3
Mean
13,6
3
11,4
8
11,6
4
10,1
2
11,3
2
10,9
2
10,8
9
11,8
9
11,3
6
12,1
7
13,3
5
14,4
0
S anda dDe ia io
n
5,44
7,15
4,42
7,30
8,32
7,25
7,47
7,57
8,21
7,73
8,16
7,99
Median
12,5
7
8,17
10,3
3
6,10
8,00
7,30
6,80
8,93
7,42
9,15
10,1
1
12,1
7
Skewness
0,79
1,18
1,23
0,94
1,54
1,08
1,02
1,40
1,42
1,60
1,31
1,07
Ku osis
2,91
3,24
4,39
2,64
5,42
2,81
2,82
4,41
4,63
5,37
3,61
3,20
Table 27A: PM2.5 A e noon Rush Hou Weekends Summa y S a is ics

90
Figu e 48A: PM2.5 A e noon Rush Hou Weekends His og ams
91
Figu e 49A: PM2.5 A e noon Rush Hou Weekends Sca e Plo s
92
APPENDIX – B
93
Figu e 50B: 3D Visualiza ion o PM10 Eme ging Ho Spo Analysis