Prediction of Air Quality Influential Factors with AtmosphericAir Present Pollutants Using Multiple

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN:2395-0072

Prediction of Air Quality Influential Factors with AtmosphericAir Present Pollutants Using Multiple Regression Methodology

Chebrolu Ramadevi1 , S Ravindran2

Assistant professor, Computer science Engineering, NRI Institute of Technology, AndhraPradesh, India. SQL Developer, ICT Infracon LLP, 11-Sumangalam Society, B/H Asia School, Drive In Road, Ahmedabad-380054, India ***

ABSTRACT

Globally the temperature and environmental behavior factors has been changing and raising day by day. Change in pollution, air quality causes serious problem to survival of humans, and other living creatures as well. So, an airquality indexisusedbygovernmentagenciestocommunicatetothepublichowmuchtheaircurrentlypolluted.AQIinformationis obtainedbyaveragingreadingsfromanairqualitysensorsandothersourcesaswell.Ourobjectiveistoidentifythemost important parameters thatcausesincreasingoftemperaturemoreandtopredictthevalueoftemperaturebasedonpast dataset.In this paper we implemented regression methodology by considering the Air Quality data of Italian city. The Ground Truth hourly averaged concentrations for CO, Hydrocarbons, Benzene, Nitrogen Oxides and Nitrogen Dioxide pollutantswereused.Afterdoingtheexperimentationcoefficientsofthevariableswereestimatedtoknowhow correlate eachother.R-squaredandadjustedr-squaredmetricsusedtoevaluatethemodel.TheResidualscoresaredescribedafter implementationofthemultipleregressionmethodology.

Keywords: AQI, regression, atmospheric pollutants, Particulate matter, dependent and independent variable

1. Introduction

AQIisthemostimportantindexwhichcanbeusedtoassessthecurrentairqualityaswellaspollutionlevelintheparticular region.PublichealthrisksincreaseastheAQIrises,especiallyaffectingchildren,theelderly,andindividualswithrespiratory orcardiovascularissues.Duringthesetimes,governmentalbodies generally encourage people to reduce physical activity outdoors, or even avoid going out altogether. Air quality index is used by government agencies to communicate to the public how much the air currently polluted [1]. Different countries have their own air quality indices, corresponding to differentnationalairqualitystandards.SomeofthestandardsusedareAirQualityHealthIndexby Canada,AirPollution Index by Malaysia, and the Pollutant Standards Index by Singapore, Air Qualityindex by India etc. AQI information is obtainedbyaveragingreadingsfromanairqualitysensorsandothersourcesaswell[2].

There are several factors causing to increase the more air pollution level pollutants such as ozone, nitrogen dioxide, Sulphur dioxide, among others. The reasons are such as due to vehicle traffic, forest fires, industries, human utilities or anythingthatcanincreaseairpollution[3].

AQIlevelisbasedonthelevelofsixatmosphericpollutants,namely

Sulfurdioxide(SO2),

Nitrogendioxide(NO2),

Suspendedparticulatessmallerthan10μmorPM10,

Suspendedparticulatessmallerthan2.5μmorPM2.5,

Carbonmonoxide(CO),

Ozone(O3)measuredatthemonitoringstationsthroughouteachcity

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Volume: 09 Issue: 12 | Dec 2022 www.irjet.net p-ISSN:2395-0072

1.1. AQI level indicator

ToindicatethelevelofpollutionwillusegenerallyAQIscalebasedontheobtainedorcalculatedvaluewillbecompared withthescalemeasurementindicator.Basedontheindicationrangethestatusorseverityofpollutionwilldetermine. 0-50

Good Moderate

Unhealthy Very unhealthy Danger

Hazardous

SeasonalanddailyAQIcalculationrevealedthatairqualitystatusinthestudyregionundervariousclassesrangingfrom good,moderate,satisfactoryandunacceptableclassfordifferentAQIcalculation.

1.2. Side effects of low AQ levels

Effects of air pollution have impaired human health for a long time [3], [4]. Ever since the industrial revolution, humans have been pumping out large volumes of carbon dioxide and other greenhouse gases. Global warming and greenhouse effect is one of the most notable effects of air pollution at the global level [5]–[7]. On an individual level, air pollution causesthefollowingeffectsonhumanhealth. 

Heart,breathingrelateddisease

Diseases

Harminghealthylife

DamageCentralNervousSystem

GlobalClimateChanges

Effectsonhumans,animals,materials,plantsaswell

Leadstoacidrainsaswell

Thesearethevariousreasonsthatcausemoredamagetoourenvironment,humanlivesaswellsowestartedworkingonair qualitydatasettoestimatehowthevariableswillimpacteachotheraswellasweimplementedpredictionmodelling.

Therestofthepaperisstructuredasfollows,section2briefedaboutmethodologyanddatasetdescription.Insection3we putupobtainedresultsandgraphs.Conclusionandfutureworkaredefinedinsection4.

2. Methodology Data Set Information:

ThedatasetformulatedwithanAirQualityChemicalMulti-SensorDeviceembeddedwith5metal oxidechemicalsensors whichrecordshourlyaveragedresponses.Thedevicewaslocatedonthefield inasignificantlypollutedarea,atroadlevel, withinanItaliancity.Thedatasethas9358instanceswhichwererecordedfromMarch2004toFebruary2005[8].

Attribute Information:

TruehourlyaveragedconcentrationCOinmg/m3

PT08.S1(tinoxide)hourlyaveragedsensorresponse

NonMetanicHydrocarbonsconcentrationinmicrog/m3

Benzeneconcentrationinmicrog/m3

PT08.S2(titania)

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 NOxconcentrationinppb

PT08.S3(tungstenoxide)  NO2concentrationinmicrog/m3  PT08.S4(tungstenoxide)  PT08.S5(indiumoxide)

Figure 2: ProposedapproachforTemperaturepredictionfromAirQualitydata

Initially the data collected we collected from data repository. We filtered the data by removing irrelevant features.We considered the features of pollutants which are important and needed for analysis. By considering the data we defined dependentandindependentvariablescorrespondingtoimplementregressionmethodology[9].

3. Experimental Results

The analysis of regression methodology [10] done over the air quality data set and we evaluated using different evaluationmetrics.ThemetricsweusedareR-squareandadjustedR-square[11]. Thebelowtable1representsvarious regressionstatisticsandalongwithactual,predictedvaluesandresidualdifferencesaswell.

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Regression Statistics

Observation s Actualvalues Predicted Residuals

MultipleR 0.997436 1 13.6 15.8931157 3 -2.593115734 RSquare 0.994880 2 13.3 13.8664881 9 -1.96648819

AdjustedRSquare 0.994873 3 11.9 12.7070096 7 -1.707009668 StandardError 3.093455 : :::::: :::::: :::::: Observations 9356 9 10 10.7 10.7 10.1934577 3 7.44726592 4

0.106542268 2.652734076 11 10.3 8.24124191 5 2.758758085 : :::::: :::::: 4739 21.1 18.2165671 8 2.183432821 4740 20.4 20.7649490 1 0.035050993 4741 20.8 23.5556113 9 -2.255611387 4742 21.3 24.7784132 4 -2.878413241 : :::::: :::::: :::::: 9347 11.8 9.22835480 7 1.171645193 9348 10.4 10.4075887 5 -0.907588755

9349 9.5 14.4188576 2 -4.71885762 : :::::: :::::: ::::::

Table 1: Representationofregressionstatisticsandshowingactual,predictedvalueswithresiduals

Basedonpredicted,actual,residualscoresweplottedthegraphwhichisrepresentedinfigure3.Thecurvesof predicted and actual 1 represents various regression statistics and along with actual, predicted values and residualdifferences as well.

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Actual vs predicted chart

Actual Residuals

Figure 3: GraphrepresentingActualvspredictedfromAirQualitydata

Coefficients StandardError tStat

Lower95% Upper95% Intercept 12.0934958 0.845781 14.29861 10.43558 13.75141 2.6 0.001400334 0.000567 2.467717 0.000288 0.002513 1360 -0.00184685 0.000451 -4.09433 -0.00273 -0.00096 150 -0.008192661 0.000275 -29.8438 -0.00873 -0.00765 11.9 -0.734283107 0.027051 -27.1445 -0.78731 -0.68126 1046 0.001137903 0.00095 1.19837 -0.00072 0.002999 166 0.00646517 0.000329 19.65011 0.00582 0.00711 1056 -0.006610725 0.000277 -23.8778 -0.00715 -0.00607 113 -0.011212918 0.000575 -19.5137 -0.01234 -0.01009 1692 0.024733971 0.000191 129.7509 0.02436 0.025108 1268 -0.004077349 0.000234 -17.4133 -0.00454 -0.00362 48.9 -0.316230013 0.002449 -129.143 -0.32103 -0.31143 0.7578 2.108685511 0.022997 91.69399 2.063606 2.153765 Table 2: Representationofestimatedregressioncoefficients,lower,upperboundaries

4. Conclusion & Future work

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References

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[9] S.S.Ganesh,S.H.Modali,S.R.Palreddy,andP.Arulmozhivarman,“Forecastingairquality indexusingregression models: Acase study onDelhi and Houston,” Proceedings - InternationalConference on Trends in Electronics and Informatics, ICEI 2017,vol.2018-January,pp.248–254,Feb.2018,doi:10.1109/ICOEI.2017.8300926.

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