A Multiple Linear Regression Forecast of PM10 Air Contamination

A Multiple Linear Regression Forecast of PM10 Air Contamination | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Multiple Linear Regression Forecast of PM10 Air Contamination Lokesh Kumar, Gaurav Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3953640/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Proper estimation of air pollutants, especially Particulate Matter (PM), is essential for convincing and successful air quality control. A wide range of factors impact PM prediction, and in addition to guarantee the most reliable forecasts, it is imperative to integrate the most pertinent input variables. In order to choose the most relevant input and output variables for an air pollution model, this study uses the multiple linear regression approach. In this work, the concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and the air quality index (AQI) are used to determine the concentration of PM10. PM10 Air Pollution Multiple Linear Regression (MLR) Figures Figure 1 Figure 2 I. INTRODUCTION A number of human activities, as well as the release of particulates, chemicals, or biological resources into the atmosphere that harm or kill people, destroy property, destroy revenue streams, or degrade the environment, are all contributing factors to the fast rising rate of air pollution. When it comes to serious environmental issues in developed and metropolitan areas, air pollution is really the main culprit. Predicting pollution and averting these issues is so crucial. Calculating air pollution is one of the more difficult jobs, thus we provide prediction methods that are utilized to provide air pollution counts for the following day and month in order to prevent issues. The environment is impacted by recent urbanization, which has a negative impact on flora and ecosystems, as well as global climate change. One major source of emissions into the atmosphere is vehicles. Air contaminants that are often present include CO, NO, NO2, PM10, O3, SO2, and a number of organic chemicals. These air pollutants have dangerous impacts on the human ecological system, including sickness, discomfort, or death, harm to other living things including food crops, and destruction of the surrounding natural environment. Thus, it is necessary to keep an eye on air pollution. Although most decision support systems have limitations, many of them are built with data monitoring in consideration. The introduction of chemicals, particulates, or biological materials into the atmosphere that harm humans, other living things like food crops, or the built or natural environment is known as air pollution, and it is growing more and more common because of various human activities. In fact, one of the major environmental issues in industrial and urban areas is air pollution. Therefore, it's essential to forecast pollution and steer clear of these problems. This work aims to investigate the situation regarding PM10 poisoning in the industrial hub of Meerut, Uttar Pradesh, and to illustrate and forecast using the multilinear regression approach. In this regard, data on PM10 from the Uttar Pradesh Pollution Control Board (UPPCB) for the years 2019–2023 was analyzed. The data from 2019 to 2023 was shown in SPSS programming and forecasts were then obtained. II. METHODOLOGY MLR explains the relationship between dependent and independent parameters in a data set by fitting a linear equation. An equation in mathematics describes this relationship. In general, the MLR equation is as follows: $$Y={b}_{0}+\sum _{i=1}^{n}{b}_{i}{X}_{i}+{\in }_{i}$$ Where \({b}_{i}\) are defined as the regression coefficients, \({X}_{i}\) the independent parameters, and the \({\in }_{i}\) is the error for the regression, and \(i\) varies from 1 to n. For many years, the MLR has been used to forecast PM10 concentration, accounting for various gaseous pollutants and climatic conditions. 2.1 Analysis of Data The Pollution Control Board of Uttar Pradesh generates statistics on its website for different metropolitan areas in the state by screening data on four elements of air pollution: PM10, SO2, NO2, and air quality index (AQI). The kesarganj road region of Meerut city is where PM10 is predicted for the city. The level has become serious during the last few years. An increase in PM10 levels would also deteriorate Meerut City's air quality. Data on PM10 levels for Kesarganj Road in Meerut from January 2019 to June 2023 has been collected for analysis. A summary of the data is shown below in Fig. 1 : 2.2 Regression Analysis Using SPSS software, we have created a linear regression model for the city of Meerut. The input factors in this study are SO2, NO2, and AQI, while the output value is PM10. Table 1 lists the values of the various parameters in the multilinear regression model. As we can see R square has a value of 0.996 which means that data is fitted to the regression model up to 99.6%. Since the value of R square is near 1, the regression equation seems to be highly helpful for generating predictions. In this model, the standard error of the estimates is 3.58448. Predictors in the model are constants, air quality index, sulfur dioxide, and nitrogen dioxide. Table 1 Overview of the Model b Model R \({R }^{2}\) Adjusted \({R }^{2}\) Standard Error of Estimate Durbin-Watson 1 0.998 a 0.996 0.996 3.58448 2.134 a. Predictors: (Constant), Air Quality Index, Sulpher Dioxide, Nitrogen Dioxide b. Dependent Variable: Particulate Matter ANOVA (analysis of variance) values are given in Table 2 below which shows the values of mean squares for regression and residual. Table 2 ANOVA Table a Model Sum of Squares Degrees of freedom Mean Square F Significance 1 Regression 157419.002 3 52473.001 4083.987 .000 b Residual 603.878 47 12.848 Total 158022.880 50 a. Dependent Variable: Particulate Matter b. Independents: (Constant), Air Quality Index, Sulpher Dioxide, Nitrogen Dioxide Below Table 3 has the values of coefficients that will be used for predictions of PM10 values. Table 3 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Significance B Standard Error Beta 1 (Constant) -31.364 2.050 -15.301 0.000 Sulpher Dioxide -0.021 0.156 -0.001 -0.138 0.891 Nitrogen Dioxide 0.157 0.065 0.042 2.434 0.019 Air Quality Index 1.344 0.026 0.964 52.334 0.000 a. Dependent Variable: Particulate Matter Using Table 3 , one way to define the regression model is PM10=-31.364-0.021*SO2 + 0.157*NO2 + 1.344*AQI Below Fig. 2 describes the graphs of actual and estimated values of PM10. III. CONCLUSION Using multiple linear regression analysis, the PM10 values for Meerut, Uttar Pradesh, India, are predicted. The independent factors provided an interpretation of 99.8% of the changeability, showing how future PM10 values may be somewhat predicted from past data. From Fig. 2 we also see that there is little error in the actual and estimated values of PM10. The model appears to be non-linear. This problem may be solved by using several types of regression, such as log-linear. Models based on artificial neural networks can be developed to handle nonlinearity. Declarations * Ethics approval and consent to participate -YES, I APPROVE AND GIVE CONSENT * Consent for publication -YES, I GIVE CONSENT * Availability of data and material - http://www.uppcb.com/ambient_quality.htm * Competing interests -NOT APPLICABLE * Funding -NOT APPLICABLE * Authors Contributions -Mr. Lokesh Kumar writes the paper and Dr. Gaurav Kumar do corrections. References Ahmad, A.L., Azid, I.A., Yusof, A.R., Seetharamu, K.N.: Emission control in palm oil mills using artificial neural network and genetic algorithm, Computers and Chemical Engineering, 28, pp. 2709–2715 (2004) Asadollahfardi, G., Zangooei, H., Aria, S.H.: Predicting PM2.5 Concentrations using Artificial Neural Networks and Markov Chain, a Case Study Keraj City. Asian J. 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Environ. 39 , 3279–3289 (2005) Kaminski, W., Skrzypski, J., Szakiel, J.E.: Application of Artificial Neural Networks (ANNs) to Predict Air Quality Classes in Big Cities, 19th International Conference on Systems Engineering, 19, pp. 135–140. (2008) Kumar, G., Sharma, R.K.: Air Pollution Evaluation Methods. Int. J. Eng. Res. Dev. 13 (9), 12–17 (2017) Kumar, G.: Time series analysis of PM10 for Bulandhshahr Industrial Area in NCR using Multiple Linear Regression. Int. J. Eng. Res. Dev. 14 (3), 56–62 (2018) Kumar, G.: Time series analysis of PM10 for Noida Sector 1 Industrial Area in NCR using Multiple Linear Regression, Bulletin of Pure and Applied Sciences. Sect. E-Math Stat. 37 (2), 273–277 (2018) Lu, W.Z., Wang, W.J., Fan, H.Y., Leung, A.Y.T., Xu, Z.B., Lo, S.M., Wong, J.C.K.: Prediction of Pollutant Levels in Causeway Bay Area of Hong Kong Using an Improved Neural Network Model. J. Environ. 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Model. 146 , 289–298 (2001) Zaefizadeh, M., Khayatnezhad, M., Gholamin, R.: Comparison of Multiple Linear Regressions (MLR) and Artificial Neural Network (ANN) in Predicting the Yield Using its Components in the Hulless Barley. American-Eurasian J. Agricultural Environ. Sci. 10 (1), 60–64 (2011) SPSS27 software:, SPSS Inc., http://www.spss.com Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3953640","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272743475,"identity":"8b64e0d3-b3ff-414f-8025-39874669a274","order_by":0,"name":"Lokesh Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACPgY2BgbGBgk5Nvbmgw8+AEXY2AloYYNosTDm4zmWbDgDJMJMnJaKxHkSOWbSPCAhglrY2xIf/twhYczGcyxN2ubXNnk+ZgbGDx9z8GjhOXbYmPcM2C+HrXP7bhu2MTMwS87chkeLRHqbNGMb2JbE27k9txmBWtiYefFraf/5s00isU0ix0Dasue2PRFa0o4x8EK0GEkz/LidSFgLMGyleSEOSzbsbbid3MbM2IzXL/zsbYYff7bVycm3A6Pyx5/btvOBjA8f8WhBBYxtYLKBWPUg8IcUxaNgFIyCUTBSAAAPfEvv5An2BQAAAABJRU5ErkJggg==","orcid":"","institution":"NAS College","correspondingAuthor":true,"prefix":"","firstName":"Lokesh","middleName":"","lastName":"Kumar","suffix":""},{"id":272743476,"identity":"8a12ee00-c9d1-4b4d-96b7-1f4989b4ec19","order_by":1,"name":"Gaurav Kumar","email":"","orcid":"","institution":"NAS College","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-02-13 13:50:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3953640/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3953640/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51197936,"identity":"9eff91a3-8055-4fb4-9329-09eb812a071d","added_by":"auto","created_at":"2024-02-15 19:24:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":26723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActual data of PM10\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3953640/v1/7d31ff95d5e58025a1ab7d4f.png"},{"id":51197937,"identity":"ecd14c89-2dc8-4d72-821a-d78169b57ab0","added_by":"auto","created_at":"2024-02-15 19:24:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActual and Estimated values of PM10\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3953640/v1/6b9ebf5ee2320a77afac3a3e.png"},{"id":54616311,"identity":"1d0d2e2e-bb15-42b2-84fc-ab8b2bb585fa","added_by":"auto","created_at":"2024-04-13 08:29:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":292269,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3953640/v1/1fd983c1-6445-495c-a72f-c37e801ea12a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Multiple Linear Regression Forecast of PM10 Air Contamination","fulltext":[{"header":"I. INTRODUCTION","content":"\u003cp\u003eA number of human activities, as well as the release of particulates, chemicals, or biological resources into the atmosphere that harm or kill people, destroy property, destroy revenue streams, or degrade the environment, are all contributing factors to the fast rising rate of air pollution. When it comes to serious environmental issues in developed and metropolitan areas, air pollution is really the main culprit. Predicting pollution and averting these issues is so crucial. Calculating air pollution is one of the more difficult jobs, thus we provide prediction methods that are utilized to provide air pollution counts for the following day and month in order to prevent issues.\u003c/p\u003e \u003cp\u003eThe environment is impacted by recent urbanization, which has a negative impact on flora and ecosystems, as well as global climate change. One major source of emissions into the atmosphere is vehicles. Air contaminants that are often present include CO, NO, NO2, PM10, O3, SO2, and a number of organic chemicals. These air pollutants have dangerous impacts on the human ecological system, including sickness, discomfort, or death, harm to other living things including food crops, and destruction of the surrounding natural environment. Thus, it is necessary to keep an eye on air pollution.\u003c/p\u003e \u003cp\u003eAlthough most decision support systems have limitations, many of them are built with data monitoring in consideration. The introduction of chemicals, particulates, or biological materials into the atmosphere that harm humans, other living things like food crops, or the built or natural environment is known as air pollution, and it is growing more and more common because of various human activities. In fact, one of the major environmental issues in industrial and urban areas is air pollution. Therefore, it's essential to forecast pollution and steer clear of these problems.\u003c/p\u003e \u003cp\u003eThis work aims to investigate the situation regarding PM10 poisoning in the industrial hub of Meerut, Uttar Pradesh, and to illustrate and forecast using the multilinear regression approach. In this regard, data on PM10 from the Uttar Pradesh Pollution Control Board (UPPCB) for the years 2019\u0026ndash;2023 was analyzed. The data from 2019 to 2023 was shown in SPSS programming and forecasts were then obtained.\u003c/p\u003e"},{"header":"II. METHODOLOGY","content":"\u003cp\u003eMLR explains the relationship between dependent and independent parameters in a data set by fitting a linear equation. An equation in mathematics describes this relationship. In general, the MLR equation is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$Y={b}_{0}+\\sum _{i=1}^{n}{b}_{i}{X}_{i}+{\\in }_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\)\u003c/span\u003e\u003c/span\u003eare defined as the regression coefficients, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{i}\\)\u003c/span\u003e\u003c/span\u003ethe independent parameters, and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\in }_{i}\\)\u003c/span\u003e\u003c/span\u003eis the error for the regression, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(i\\)\u003c/span\u003e\u003c/span\u003e varies from 1 to n. For many years, the MLR has been used to forecast PM10 concentration, accounting for various gaseous pollutants and climatic conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Analysis of Data\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Pollution Control Board of Uttar Pradesh generates statistics on its website for different metropolitan areas in the state by screening data on four elements of air pollution: PM10, SO2, NO2, and air quality index (AQI). The kesarganj road region of Meerut city is where PM10 is predicted for the city. The level has become serious during the last few years. An increase in PM10 levels would also deteriorate Meerut City's air quality. Data on PM10 levels for Kesarganj Road in Meerut from January 2019 to June 2023 has been collected for analysis. A summary of the data is shown below in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Regression Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing SPSS software, we have created a linear regression model for the city of Meerut. The input factors in this study are SO2, NO2, and AQI, while the output value is PM10. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the values of the various parameters in the multilinear regression model. As we can see R square has a value of 0.996 which means that data is fitted to the regression model up to 99.6%. Since the value of R square is near 1, the regression equation seems to be highly helpful for generating predictions. In this model, the standard error of the estimates is 3.58448. Predictors in the model are constants, air quality index, sulfur dioxide, and nitrogen dioxide.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eOverview of the Model \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R }^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R }^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandard Error of Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDurbin-Watson\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.998\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.58448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea. Predictors: (Constant), Air Quality Index, Sulpher Dioxide, Nitrogen Dioxide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eb. Dependent Variable: Particulate Matter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eANOVA (analysis of variance) values are given in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below which shows the values of mean squares for regression and residual.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eANOVA Table \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSum of Squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean Square\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157419.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52473.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4083.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e603.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158022.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea. Dependent Variable: Particulate Matter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eb. Independents: (Constant), Air Quality Index, Sulpher Dioxide, Nitrogen Dioxide\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBelow Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e has the values of coefficients that will be used for predictions of PM10 values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eCoefficients \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-15.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSulpher Dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen Dioxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir Quality Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ea. Dependent Variable: Particulate Matter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, one way to define the regression model is\u003c/p\u003e \u003cp\u003ePM10=-31.364-0.021*SO2\u0026thinsp;+\u0026thinsp;0.157*NO2\u0026thinsp;+\u0026thinsp;1.344*AQI\u003c/p\u003e \u003cp\u003eBelow Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the graphs of actual and estimated values of PM10.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"III. CONCLUSION","content":"\u003cp\u003eUsing multiple linear regression analysis, the PM10 values for Meerut, Uttar Pradesh, India, are predicted. The independent factors provided an interpretation of 99.8% of the changeability, showing how future PM10 values may be somewhat predicted from past data. From Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e we also see that there is little error in the actual and estimated values of PM10. The model appears to be non-linear. This problem may be solved by using several types of regression, such as log-linear. Models based on artificial neural networks can be developed to handle nonlinearity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e* Ethics approval and consent to participate\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-YES, I APPROVE AND GIVE CONSENT\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;* Consent for publication\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-YES, I GIVE CONSENT\u003cbr\u003e\u0026nbsp;* Availability of data and material\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-\u003c/strong\u003e \u003cstrong\u003ehttp://www.uppcb.com/ambient_quality.htm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e* Competing interests\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-NOT APPLICABLE\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;* Funding\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-NOT APPLICABLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e* Authors Contributions\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;-Mr. Lokesh Kumar writes the paper and\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u003c/p\u003e\u003cp\u003eDr. Gaurav Kumar do corrections.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad, A.L., Azid, I.A., Yusof, A.R., Seetharamu, K.N.: Emission control in palm oil mills using artificial neural network and genetic algorithm, Computers and Chemical Engineering, 28, pp. 2709\u0026ndash;2715 (2004)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadollahfardi, G., Zangooei, H., Aria, S.H.: Predicting PM2.5 Concentrations using Artificial Neural Networks and Markov Chain, a Case Study Keraj City. 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Sci. \u003cb\u003e10\u003c/b\u003e(1), 60\u0026ndash;64 (2011)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSPSS27 software:, SPSS Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.spss.com\u003c/span\u003e\u003cspan address=\"http://www.spss.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PM10, Air Pollution, Multiple Linear Regression (MLR)","lastPublishedDoi":"10.21203/rs.3.rs-3953640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3953640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProper estimation of air pollutants, especially Particulate Matter (PM), is essential for convincing and successful air quality control. A wide range of factors impact PM prediction, and in addition to guarantee the most reliable forecasts, it is imperative to integrate the most pertinent input variables. In order to choose the most relevant input and output variables for an air pollution model, this study uses the multiple linear regression approach. In this work, the concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and the air quality index (AQI) are used to determine the concentration of PM10.\u003c/p\u003e","manuscriptTitle":"A Multiple Linear Regression Forecast of PM10 Air Contamination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-15 19:24:19","doi":"10.21203/rs.3.rs-3953640/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd676e83-641b-4170-b067-aa4688ed6220","owner":[],"postedDate":"February 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-13T08:20:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-15 19:24:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3953640","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3953640","identity":"rs-3953640","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}