Abstract
Air pollution is one of the most pressing modern-day issues in cities around the world. However, most cities have adopted air quality measurement devices that only measure the past pollution levels without paying attention to the influencing factors. To obtain preliminary pollution information with regard to environmental factors, we developed a variational autoencoder and feedforward neural network-based embedded generative model to examine the relationship between air quality and the effects of environmental factors. In the model, actual SO2, NO2, PM2.5, PM10, and CO measurements from 2016 to 2020 were used, which were assembled from 15 differently located ground monitoring stations in Ulaanbaatar city. A wide range of weather and fuel measurements were used as the data for the influencing factors, and were collected over the same period as the air pollution data were recorded. The prediction results concerned all measurement stations, and the results were visualized as a spatial–temporal distribution of pollution and the performance of individual stations. A cross-validated R2 was used to estimate the entire pollution distribution through the regions as SO2: 0.81, PM2.5: 0.76, PM10: 0.89, and CO: 0.83. Pearson’s chi-squared tests were used for assessing each measurement station, and the contingency tables represent a high correlation between the actual and model results. The model can be applied to perform specific analysis of the interdependencies between pollution and environmental factors, and the performance of the model improves with long-range data.
Highlights
Serious environmental issues are common in densely populated large cities in developing countries
The application of a neural network (NN) for air pollution is associated with the development of an artificial neural network (ANN), with mainly machine learning and variational autoencoder (VAE) being applied in prediction works
The embedded generative model c4o. nCsoisntcsluofsiaoVnsAE and a feedforward NN to discover the relationship between the air pol
Summary
Serious environmental issues are common in densely populated large cities in developing countries. The application of a neural network (NN) for air pollution is associated with the development of an artificial neural network (ANN), with mainly machine learning and VAE being applied in prediction works. Machine learning (ML) approaches have been applied for more automatic and accurate air pollution predictions from a large amount of input data with numerous outputs. Zhang et al studied the superior predictive ability of ML methods based on six years of Hong Kong air-quality index measurements [18]. For UB city, PM2.5 concentration prediction has been studied using two different ML approaches, and high-performance results have shown the spatiotemporal variations in the high-emission areas [24]. The spatiotemporal prediction of air pollution based on deep learning approaches has been studied widely for many different purposes. DueDtuoeUtloaaUnblaaaantabra’astaairr’spoailrluptioolnluitsisounesis, stuheesre, tihs eareneiseda tnoeeexdpatondextphaenndettwheornkebtwy ork equibpypienqguiitpwpiinthg aitutwomithataicu,tcoomntaitnicu,ocuosnmtineausouursemmeenatscuarpemabeinlittiecsapaanbdiliimtiepsroavnidngimitspcroav- ing paciittys.cTahpeaaciirty-q. uTahlietyaimr-oqnuiatloirtyinmg noentiwtoorrinkgconnestiwstosrokf cthoensAisitrsPooflltuhteioAniRr ePdoullcuintigonDeRpeadrut-cing Atmosphere 2022, 13, x FOR PEEmlRisRehEneDeVtdsIeotEaptfWhbaCelriatsAmphPieetdRanltDtChoeNfitCyAeat(PwApRioPtDarRklNDCwe)ittiNywthe(oAtfrwikvPoeRwrDskitt)ahatNnifiodevnttewshseiotnarNkt2iAo0a0nnM8sd,EiatnMhn2ed0NN0teh8At,ewMaNnoErdAMktM.hANeEeGMNtweANroMmerkatEwn.MAogrNrGkae,netwrtwmeiotsahrtnak1b,6g0-wroafinth2t0 stati1o0nsst,awtiaosnes,stwabalsisehsetadbbliyshmedeabnys omfeaanFrseonfcah Floraenncihn l2o0a1n0.inCu2r0r1e0n.tlCyu, r1r2enautltyo,m12ataicutsotam- atic tionsstaartieomnseaasreurminegassuurlfiunrgdsiuolxfuidred, inoixtriodgee, nniotrxoidgeesn, coaxribdoens, mcaornbooxnidmeo, onzooxnidee, ,PoMzon,ea,nPdM10, PoxMidad.eopnifo(deFsxrvteiiPegedprMrueriyeor(2esrF1.5e53idnge–)it.uvs3iTtner0rerhgimybe3tu)1ihc.nt5oeiT,–ondha3ncne0aestdncm[ao4t3ri1nan]nct.,eiloaoonnnnwt-rdaiasdu3tiditomnoenmoteeninars-smtadiicuoientntcoeeoamrdlnmctauroitosniniclentsdicgnoouufansotsciruunhoslgelfsmulaaoritcfcedhansieluotmslxvfoiuiadlcuraeitdlaiaobsinoonledxl.usidntwieiotirnatohn. gdiennntridtarcio-tgaebnle
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