Spatiotemporal Analysis of PM10 and PM2.5 with EBK3D and Space-Time Cube in the City of Lisbon, Portugal

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 Lisboa Abe a, 2024. 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A mosphe ic En i onmen , 36, 4323–4335. h ps://doi.o g/10.1016/S1352-2310(02)00354-0 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