Indian Journal of Public Health

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content="2" /><meta name="wkhealth_article_publication_date" content="June 29, 2024" /><meta name="wkhealth_volume" content="68" /><meta name="wkhealth_ovid_accession_number" content="01586002-202404000-00012" /><meta name="wkhealth_issn" content="0019-557X" /><meta name="wkhealth_title" content="Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India)" /><meta name="wkhealth_authors" content="Pant, Alka;Joshi, Ramesh Chandra;Sharma, Sanjay;Pant, Kamal" /><meta name="wkhealth_doi" content="10.4103/ijph.ijph_279_23" /><meta name="wkhealth_abstract_html_url" content="https://journals.lww.com/ijph/abstract/2024/04000/air_quality_and_public_health_risk_assessment__a.12.aspx" /><meta name="wkhealth_fulltext_html_url" content="https://journals.lww.com/ijph/fulltext/2024/04000/air_quality_and_public_health_risk_assessment__a.12.aspx" /><meta name="wkhealth_toc_section" content="Original Article" /><meta name="wkhealth_toc_sub_section" /><meta name="wkhealth_eJpId" content="ijph:2024:04000:00012" /><meta name="wkhealth_firstpage" content="222" /><meta name="wkhealth_date" content="Apr-Jun 2024" /><meta name="wkhealth_pdf_url" content="https://journals.lww.com/IJPH/_layouts/15/oaks.journals/downloadpdf.aspx?trckng_src_pg=Other&an=01586002-202404000-00012" /><meta name="Description" content="Background: Air pollution is a significant issue for a developing country like India and the air " /><meta property="og:site_name" content="LWW" /><meta property="og:type" content="article" /><meta property="og:title" content="Air Quality and Public Health Risk Assessment: A Case of an ... : Indian Journal of Public Health" /><meta property="og:description" content="ecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. Materials and Methods: Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey–Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values. Results: With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)[12] model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year. Conclusion: The seasonal ARIMA (0,1,0) (1,0,0)[12] model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients’ health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance...." /><meta property="og:image" content="https://images.journals.lww.com/ijph/SocialThumb.01586002-202404000-00012.F1.jpeg" /><meta property="og:image:width" content="741" /><meta property="og:image:height" content="456" /><meta property="og:url" content="https://journals.lww.com/ijph/fulltext/2024/04000/air_quality_and_public_health_risk_assessment__a.12.aspx" /><meta property="twitter:card" content="summary" /><meta name="robots" content="index, follow, noarchive" /><meta name="WT.sv" content="ZUSE1PEJPWEB03" /><meta name="Title" content="Indian Journal of Public Health" /><style id="SPThemeHideForms" type="text/css">body {opacity:0 !important}</style>  <link href="https://fonts.googleapis.com/css?family=Arvo:400,400i,700|Fira+Sans:300,300i,400,400i,500,500i,600,600i,700,700i&display=swap" rel="stylesheet" /> <script> // temporary workaround for Bug 316328:[Stage][Registration][Regression 05.04] - Omniture events are not displayed for article viewer page // prevents JS exception in Adobe launch.js caused by situation when wkhealth_institution_type meta tag is present while wkhealth_institution_name tag is not in the page head // see this check in Adobe launch.js: null != document.querySelector("meta[name='wkhealth_institution_name']") ... // TODO: implement proper code fix after release 05.04.00 deployment var wkhealth_institution_type = document.querySelector("meta[name='wkhealth_institution_type']"); var wkhealth_institution_name = document.querySelector("meta[name='wkhealth_institution_name']"); console.warn(wkhealth_institution_type); console.warn(wkhealth_institution_name); if (wkhealth_institution_type && !wkhealth_institution_name) { console.warn('Workaround in release 05.04.00 - removing wkhealth_institution_type meta tag if wkhealth_institution_name is not present'); 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index).","https://images.journals.lww.com/ijph/Rollover.01586002-202404000-00012.F1.jpeg","https://images.journals.lww.com/ijph/Gallery.01586002-202404000-00012.F1.jpeg","https://journals.lww.com/IJPH/_layouts/oaks.journals/ImageView.aspx?k=ijph:2024:04000:00012&i=F1&year=2024&issue=04000&article=00012&type=Fulltext","https://images.journals.lww.com/ijph/Original.01586002-202404000-00012.F1.jpeg","/IJPH/pages/imagegallery.aspx?year=2024&issue=04000&article=00012", "ijph_2024_04000_00012_F1");images["SF1"]=new image("SF1","SF2","F1","Supplementary Figure 1","Autocorrelation function plot on original time series. 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AQI: Air quality index.","https://images.journals.lww.com/ijph/Rollover.01586002-202404000-00012.SF5.jpeg","https://images.journals.lww.com/ijph/Gallery.01586002-202404000-00012.SF5.jpeg","https://journals.lww.com/IJPH/_layouts/oaks.journals/ImageView.aspx?k=ijph:2024:04000:00012&i=SF5&year=2024&issue=04000&article=00012&type=Fulltext","https://images.journals.lww.com/ijph/Original.01586002-202404000-00012.SF5.jpeg","/IJPH/pages/imagegallery.aspx?year=2024&issue=04000&article=00012", "ijph_2024_04000_00012_SF5");images["SF6"]=new image("SF6","F2","SF5","Supplementary Figure 6","Seasonal autoregressive integrated moving average model forecast with 99% confidence interval. 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Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India)', '06292024', 'Alka Pant, Ramesh Chandra Joshi, Sanjay Sharma, et al', '68', '2', 'Copyright: \u00A9 2024 Indian Journal of Public Health', '01586002-202404000-00012', '', true, ''); } </script> <div id="ArticleContainer"> <article id="ej-article-view" class="js-article-view ej-article-view" xmlns:ssr="http://ssr-eus-go-csi.cloudapp.net/v1/assets" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xhtml="http://www.w3.org/1999/xhtml"><section class="ejp-r-article-subsection"><div class="ejp-r-article-subsection__text">Original Article</div></section><header class="ejp-article-header"><h1 class="ejp-article-title">Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India)</h1></header><section id="ejp-article-authors"><p id="P7">Pant, Alka<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">1</sup>; Joshi, Ramesh Chandra<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">2</sup>; Sharma, Sanjay<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">3</sup>; Pant, Kamal<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">4</sup></p><section><a href="#" id="ejp-article-authors-link"> Author Information <i class="wk-icon-arrow-down-r"></i></a></section></section><section id="ejp-article-authors-info" class="ejp-article-authors-info ejp-article-authors-info--hidden"><div class="ejp-article-authors-info-holder"> <p id=""><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">1</sup>Associate Professor, Department of Computer Applications, School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand, India</p> <p id=""><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">2</sup>Advisor, Graphic Era (Deemed to be University),Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India</p> <p id=""><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">3</sup>Professor, Department of Computer Applications, School of Computer Applications and Information Technology, Shri Guru Ram Rai University, Dehradun, Uttarakhand, India</p> <p id=""><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">4</sup>Professor, School of Vocational Studies, Graphic Era Hill University, Dehradun, Uttarakhand, India</p> <p id="cor1"><strong xmlns:mrws="http://webservices.ovid.com/mrws/1.0">Address for correspondence:</strong> Dr. Alka Pant, School of Computing, Graphic Era Hill University, Dehradun, Uttarakhand, India. E-mail: <span><a href="/cdn-cgi/l/email-protection#107d71797c647f2a717c7b7160717e6423292250777d71797c3e737f7d" onclick="javascript:window.open('mailto:mailto:alkapant392@gmail.com');return false"><span class="__cf_email__" data-cfemail="d0b1bcbbb1a0b1bea4e3e9e290b7bdb1b9bcfeb3bfbd">[email protected]</span></a></span></p> <p id="">This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.</p> </div></section><section id="article-journal-info"><div class="ej-journal-info"><span class="ej-journal-name">Indian Journal of Public Health </span><span id="ej-journal-date-volume-issue-pg"><a href="/IJPH/toc/2024/04000">68(2):p 222-226, Apr–Jun 2024.</a></span><span> | </span><span class="ej-journal-doi">DOI: </span>10.4103/ijph.ijph_279_23<a id="ej-article-indicators-ccl" onmouseover="javascript:Tooltip_CCL('Creative Commons License Information')" onmouseout="javascript:Tooltip_MouseOut()" href="" target="_blank"><span class="ejp-article-indicators__ccl"></span></a></div></section><section id="ejp-article-indicators" class="ejp-article-indicators"><ul class="ejp-article-indicators__list"> <li id="ej-article-indicators-open" class="ejp-article-indicators__list-item" onmouseover="javascript:Tooltip_Open()" onmouseout="javascript:Tooltip_MouseOut()"><span class="ejp-indicator">Open</span></li> </ul><button id="openArticleMetricsPopupButton" type="button" class="wk-button wk-button-ghost wk-button-icon-left ejp-article-metrics-btn" data-articleMetricsEndpoint="//services.journals.lww.com/IntegrationServices/ArticleMetricsService.svc" data-articleMetricsDetailsUrl="https://wolterskluwer.altmetric.com/details/" data-doi="10.4103/ijph.ijph_279_23" data-suiteId="vrs_woltere_journalsglobalvrs" data-articlePublicationDate="2024-06-29" data-articlePublicationDateString="June 29, 2024" data-receivedDate="2023-02-26" data-revisionReceivedDate="2023-11-26" data-acceptedDate="2024-05-06" data-crossrefRefLinkRoot="https://doi.org/" data-medlineRefLinkRoot="http://www.ncbi.nlm.nih.gov/pubmed/" data-pubMedId="38953809"><i class="wk-icon-filled-chart-r"></i><i class="wk-icon-filled-chart-white-r"></i> Metrics </button></section><section class="ejp-article-tabs" id="abstractWrap"><h2 class="ejp-article-tabs-heading">Abstract</h2> <div class="ejp-article-tabs__tabpanel" id="panel1" role="tabpanel" aria-labelledby="tab1"> <div id="article-abstract-content1"> <div class="ejp-article-text-abstract"> <h3>Background: </h3> <p>Air pollution is a significant issue for a developing country like India and the air quality index (AQI) forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health.</p> <h3>Objectives: </h3> <p>The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model.</p> <h3>Materials and Methods: </h3> <p>Three years of existing AQI data points (post-COVID-19) were collected from the Uttarakhand Pollution Control Board for the SIDCUL area of Haridwar City and tried to know the status of AQI values for the following 12 months. Trend and seasonality components were seen through the decomposition process. Further, the augmented Dickey–Fuller test was applied to check the stationarity of the series before finalizing the best-suited time series model for forecasting the AQI values.</p> <h3>Results: </h3> <p>With the help of autocorrelation function (ACF)/partial ACF plots, a seasonal autoregressive integrated moving average (ARIMA) (0,1,0) (1,0,0)<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[12]</sub> model was selected with the minimum akaike information criterion (253.143) and mean absolute percentage error (17.42%). The AQI values have also been forecasted for this industrial area (SIDCUL) for the following year.</p> <h3>Conclusion: </h3> <p>The seasonal ARIMA (0,1,0) (1,0,0)<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[12]</sub> model may be helpful to forecast the AQI values for a nonstationary time series dataset. Research indicates that the air of the SIDCUL area will become moderately polluted and may cause breathing discomfort to asthma patients’ health. The scientists might apply this model to other polluted regions of the country so that the public and the government can take preventive measures in advance.</p> </div> </div> </div></section><div class="ejp-fulltext-content js-ejp-fulltext-content"><section id="ArticleBody"><h2 class="ejp-article-outline-heading" data-level="1" id="sec1-1">I<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">NTRODUCTION</span></h2> <p id="JCL-P-5">Air is an essential element for life, and the air quality is a necessary component of air that makes it fit or unfit for human, animal, and plant consumption. The damage caused to the environment due to lack of proper management of resources or by uncontrolled human activities results in pollution of the atmosphere. Lung cancer, heart disease, and respiratory problems such as asthma are among the long-term health repercussions of air pollution.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R1">1</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Any activity leading to nature’s degradation by violating its original character caused air pollution.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R2">2</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> The Minister for Environment, Forests, and Climate Change, the late Shri Prakash Javadekar, launched the National Air Quality Index (NAQI) in New Delhi. Speaking at the event, Shri Javadekar described the air quality index (AQI) as “<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">one number, one color, one description</em>” so that the average person can figure out how clean the air is around him. Time series forecasting is crucial for data analysis and decision-making in various fields. The air we used to breathe earlier was too pure and fresh. Still, after the Industrial Revolution, things deteriorated considerably, which marked the rapid growth in the number of industries and automobiles that contributed significantly to air pollution. The particulate matter (PM<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">10</sub>/<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">2.5</sub>) is considered one of the most dangerous pollutants emitted from construction, trucks, and smoke and has contributed to increased hospitalization cases, especially for respiratory problems.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R3">3</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">,</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R4">4</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup></p> <p id="JCL-P-6">Based on PM<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">2.5</sub> air pollutant data, India is now the eighth most polluted country in the world. At the same time, the capital of India (New Delhi) is the second-most polluted capital globally.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R5">5</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> However, to tackle this air pollution problem by 2024, the government has launched the National Clean Air Programme along with the Ministry of Environment, Forests, and Climate Change. The AQI is a communicator that tells the clean air status. The NAQI rated air quality as good, satisfactory, moderately polluted, poor, very poor, and severe as per the AQI levels. There was an improvement in air pollution during the lockdown period of the pandemic (COVID-19), but again, it adversely impacts human health.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R6">6</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Now, air pollution is a widespread problem in developing countries like India. The machine learning algorithms<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R7">7</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> and the time series forecasting models<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R8">8</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> are essential tools to predict the AQI category. Based on past values, it is necessary to know the future values of AQI in different regions, and time series analysis makes it easier as it is an application of data science using the time parameter.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R9">9</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> However, the researchers employ various strategies to predict the level of AQI using machine learning algorithms,<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R10 R11 R12">10-12</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> and a few developed different models for AQI prediction.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R13">13</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">,</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R14">14</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> To forecast the AQI, an autoregressive model, i.e. autoregressive-integrated moving average (ARIMA) can also be used.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R15">15</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Hence, this study aims to predict an effective model to forecast the AQI values using time series analysis in Jupyter Notebook (python language) for an industrial area of Haridwar city.</p> <h2 class="ejp-article-outline-heading" data-level="1" id="sec1-2">M<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">ATERIALS AND</span> M<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">ETHODS</span></h2> <p id="JCL-P-7">The foundation of time series forecasting is historical data. AQI forecasting helps to predict air quality levels in advance and allows individuals to take precautionary measures to protect their health. Python libraries such as Pandas, Matplotlib, NumPy, auto-ARIMA and statsmodel have been used for data engineering and visualization purposes in Jupyter Notebook. For predictive modeling purposes, we have taken the past 3 years of AQI values (post-COVID-19) of an industrial area, namely, SIDCUL, Haridwar (Uttarakhand state, India) from the website of the Pollution Control Board of Uttarakhand state,<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R16">16</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> and tried to know the status of AQI values for the following 12 months. We have decomposed our time series dataset to see the trend and seasonality components.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R17">17</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Further, stationarity has been checked out with the help of the augmented Dickey–Fuller (ADF) test on this time series dataset before finalizing the best-suited model for forecasting the AQI values.</p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-1">Decomposition of a time series</h3> <p id="JCL-P-8">A time series refers to the observations taken at regular time intervals. These observations cannot be distinct from other ones. Further, the decomposition process on a time series dataset divides its original dataset into its various parts, such as trend (T), seasonality (S), cyclic (C), and residual (R) and examines each component separately.</p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-2">Stationarity of the time series</h3> <p id="JCL-P-9">The time series forecasting models perform best with a stationary dataset. Before using a time series model for forecasting purposes, it is important to know the stationarity status of the dataset, i.e. whether the series is stationary. An autocorrelation function (ACF) graphic can also determine a time series’ status. If a series is nonstationary, then it can be converted to a stationary series through a differencing process. The ADF test is the most widely used statistical approach for determining the stationarity of a time series dataset.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R18">18</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup></p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-3">Time series modeling techniques</h3> <h4 class="ejp-article-outline-heading" data-level="3" id="sec3-1">Autoregressive</h4> <p id="JCL-P-10">Autoregressive refers (AR) to a model, in which the independent variable is none other than the lag of the dependent variable. AR model considers past values and error terms for predicting the future value. AR models are called the long memory model as the first observation of the series always somehow affects the current value of variables irrespective of the time. The main assumption for estimating the time series is that the correlation with the increase in lags should decrease, so the impact should be highest for the first lag and slowly diminish to zero.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R19">19</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> A time series of “p” past values with error is called AR (p). The model that only depends on the previous value of the previous lag or one lag in the past is called AR.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R1">1</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup></p> <h4 class="ejp-article-outline-heading" data-level="3" id="sec3-2">Moving average</h4> <p id="JCL-P-11">Moving average (MA) refers to that model, in which the variable does not depend on any other variables instead, it depends on the lag of error.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R20">20</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> In this model, we try to estimate the future value based solely on the past errors in the series. The value of error is the difference between the expected value and the actual value in the past. It is based on the assumption that the literal error from the period before affects the current value. MA Model is also called the short memory model because these errors do not last long into the future. A time series of q past errors is called MA (q).</p> <h4 class="ejp-article-outline-heading" data-level="3" id="sec3-3">Autoregressive moving average or autoregressive integrated moving average</h4> <p id="JCL-P-12">ARIMA model (Box–Jenkins Methodology) is used when a time series is nonstationary, as the autoregressive MA (ARMA) is used only when the variable in a series is stationary. In ARMA or ARIMA modeling, we can find out how many orders the AR and MA go up to through the ACF (q) and partial ACF (PACF) (p). In ARIMA, “<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">I</em>” means “Integrated,” which recognizes that the original series is already passed out (d = differencing) to make the series stationary. If a time series has a seasonal component, then seasonal ARMA or ARIMA can be used.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R21">21</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> The seasonal ARIMA (SARIMA) model has some additional parameters of ARIMA, i.e. P, D, Q, and m for the seasonal component in the time series dataset where P means seasonal p (autoregressive), D means seasonal order d (differencing), Q means the seasonal q (MA), and m are the seasonal periods.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R22">22</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">,</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R23">23</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup></p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-4">Auto-correlation function and partial auto-correlation function</h3> <p id="JCL-P-13">ACF and PACF are critical functions determining the order of autoregressive (AR) and MA for the ARIMA model. They can also help detect the seasonality component within the dataset. The ACF/PACF often generates key graphs for determining the order of p, q, and r. These values are utilized in the ARIMA/SARIMA model to forecast a time series dataset.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R24">24</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> The ACF gives us values of autocorrelation of time series with its lagged values and with consideration of time series components. ACF and PACF are measures of association between present and past values, which helps predict future values. ACF is used for the estimated parameter value of the MA model, whereas PACF is used for the estimated parameter value of the autoregressive (AR) model.</p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-5">Akaike information criterion and mean absolute percentage error</h3> <p id="JCL-P-14">An akaike information criterion (AIC) has been employed to validate a model.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R25">25</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> The model with the lowest AIC value is preferred. A data scientist can use the AIC value to choose the model that minimizes the expected information loss. Further, the mean absolute error mean absolute percentage error (MAPE) is an evaluation metric of time series forecasting for understanding the model’s goodness of the error value (difference between the predicted and actual values). The variation in MAPE score depends on the type of dataset. Generally, a MAPE lower than 20% is suitable for time series forecasting.</p> <h2 class="ejp-article-outline-heading" data-level="1" id="sec1-3">R<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">ESULTS</span></h2> <p id="JCL-P-15">Autoregressive models implicitly assume that the future will resemble the past, which means predicted future values are based on past values. EDA involves visualizing time series plots and ACFs and identifying seasonality or trends. With the help of the auto-ARIMA function in the Python library, we are trying to pick the best model to forecast the AQI values<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R26">26</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> for the SIDCUL area of Haridwar City. AQI points (post-COVID-19 data) have been collected from the Uttarakhand Government portal of CPCB for research purposes. Using the train-test approach, we also split our data points into training and test datasets.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R27">27</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Before applying any forecasting method, it is essential to understand the underlying patterns in the historical dataset. The autoregressive model is best fitted on a stationary time series dataset. As a preliminary step of our work, we have to check out the stationarity of our dataset, whether our AQI dataset is a static dataset or a nonstationary dataset (evidence of a trend or seasonal effects).</p> <p id="JCL-P-16">We have investigated this with the help of the ADF test<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R28">28</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> through the statsmodel library. Here, the <em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P</em> = 0.10 is more than the significance level (0.05), which shows that our AQI dataset is a nonstationary dataset [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('F1', '01586002-202404000-00012');">Figure 1</a>]. It indicates that our AQI values have no constant variance over time. The ACF plot also supports the nonstationarity of the dataset due to the trend in the spikes [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF1', '01586002-202404000-00012');">Supplementary Figure 1</a>]. The seasonal component is an essential factor in choosing the best autoregressive model. Based on the seasonality component of the stationary dataset, the next step is to finalize the order (<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">p, q</em>) and (<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P, Q</em>) of our SARIMA model.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R29">29</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Hence, we have to check the seasonality component in our AQI dataset. For this, we decomposed our time series dataset into trend, seasonal, and residual components [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF2', '01586002-202404000-00012');">Supplementary Figure 2</a>]. Here, we identify the seasonal component in our AQI dataset, which validates that a SARIMA model will best forecast the AQI.</p><section class="ejp-r-article-images"><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('F1', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.F1.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.F1.jpeg 2x" srcset="" alt="F1"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('F1', '01586002-202404000-00012')">Figure 1: </a><div class="ejp-r-article-images__figcaption-text">Original time series dataset (air quality index).</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF1', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF1.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF1.jpeg 2x" srcset="" alt="SF1"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF1', '01586002-202404000-00012')">Supplementary Figure 1: </a><div class="ejp-r-article-images__figcaption-text">Autocorrelation function plot on original time series. ACF: Autocorrelation function.</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF2', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF2.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF2.jpeg 2x" srcset="" alt="SF2"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF2', '01586002-202404000-00012')">Supplementary Figure 2: </a><div class="ejp-r-article-images__figcaption-text">Time series decomposition of the original dataset.</div></figcaption></figure></section><p id="JCL-P-17">Now, our existing dataset requires a differencing process to remove its trend. After applying first-order differencing, the <em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P</em> value becomes 9.56e-08, less than the significance level (0.05), showing that our new dataset is stationary [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF3', '01586002-202404000-00012');">Supplementary Figure 3</a>]. We have again plotted the autocorrelation (ACF) and partial autocorrelation (PACF) plots for our new data to take some ideas to figure out the seasonal order of P (AR) and Q (MA) parameters. <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF4', '01586002-202404000-00012');">Supplementary Figure 4</a> indicates that the differencing <em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">(d)</em> will be one due to the first-order differencing, and P (AR) and Q (MA) can also be 1. We have used the auto-ARIMA function in our Jupyter Notebook (Python coding) to confirm our assumption. This function provides us different two values of (<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">pd, q</em>)(<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P, D, Q</em>)<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sub>m</sub></em> models and their AIC values. Here, many experiments have been performed again and again after splitting our data into train and test datasets (70:30). <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('T1', '01586002-202404000-00012');">Table 1</a> shows the different parameters {(<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">p, d, q</em>) (<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P, D, Q</em>), [<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">m</em>]} of the SARIMA model along with AIC values.</p><section class="ejp-r-article-images"><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF3', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF3.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF3.jpeg 2x" srcset="" alt="SF3"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF3', '01586002-202404000-00012')">Supplementary Figure 3: </a><div class="ejp-r-article-images__figcaption-text">Time series dataset with first-order differencing.</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF4', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF4.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF4.jpeg 2x" srcset="" alt="SF4"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF4', '01586002-202404000-00012')">Supplementary Figure 4: </a><div class="ejp-r-article-images__figcaption-text">(a) Autocorrelation function plot after differencing, (b) Partial autocorrelation function plot after differencing. ACF: Autocorrelation function, PACF: Partial autocorrelation function.</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('T1', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.T1.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.T1.jpeg 2x" srcset="" alt="T1"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('T1', '01586002-202404000-00012')">Table 1: </a><div class="ejp-r-article-images__figcaption-text">Seasonal ARIMA model’s results</div></figcaption></figure></section><p id="JCL-P-18">According to the AIC criterion, the lowest the AIC value, the better the model’s fitting effect. Hence, it has been easy to choose our best model based on its minimum AIC. The result of <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('T1', '01586002-202404000-00012');">Table 1</a> shows that the SARIMA (0, 1, 0) (1, 0, 0)<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[12]</sub> model has the lowest AIC value of 253.143 comprising the best parameters (<em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">p, q</em> and <em xmlns:mrws="http://webservices.ovid.com/mrws/1.0">P, Q</em>) to forecast the AQI. It is time to validate our model for predicting the AQI values. Here, we have considered the data points up to December 2022 as a training dataset and January 2023 onward as test data for validation purposes.<sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[</sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0"><sup><a class="ejp-citation-link js-ejp-citation-link" data-reference-links="R30">30</a></sup></sup><sup xmlns:mrws="http://webservices.ovid.com/mrws/1.0">]</sup> Using the SARIMA {(0,1,0) (1,0,0)<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[12]</sub>} model, values have been predicted on the test dataset. We have also plotted the predicted values (SARIMA Forecast) with our actual dataset along with the 99% confidence interval [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF5', '01586002-202404000-00012');">Supplementary Figures 5</a> and <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('SF6', '01586002-202404000-00012');">6</a>], and it seems quite acceptable to refit the entire dataset. Further, using the stats model of the Python library, mean absolute error (MAPE) has also been calculated for the acceptability of this model. The MAPE of this model is 17.42% (below 20%), indicating that our time series model is good and can be used to forecast the AQI values for the following year.</p><section class="ejp-r-article-images"><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF5', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF5.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF5.jpeg 2x" srcset="" alt="SF5"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF5', '01586002-202404000-00012')">Supplementary Figure 5: </a><div class="ejp-r-article-images__figcaption-text">Actual versus predicted values of air quality index using seasonal autoregressive integrated moving average model. AQI: Air quality index.</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('SF6', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.SF6.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.SF6.jpeg 2x" srcset="" alt="SF6"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('SF6', '01586002-202404000-00012')">Supplementary Figure 6: </a><div class="ejp-r-article-images__figcaption-text">Seasonal autoregressive integrated moving average model forecast with 99% confidence interval. SARIMA: Seasonal autoregressive integrated moving average.</div></figcaption></figure></section><p id="JCL-P-19">After the model validation, the last step is forecasting the AQI values for the following year. <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('F2', '01586002-202404000-00012');">Figure 2</a> shows the forecasted values of the AQI (100–200) for the year 2024 [<a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('ST1', '01586002-202404000-00012');">Supplementary Table 1</a>].</p><section class="ejp-r-article-images"><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('F2', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.F2.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.F2.jpeg 2x" srcset="" alt="F2"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('F2', '01586002-202404000-00012')">Figure 2: </a><div class="ejp-r-article-images__figcaption-text">Forecast of air quality index values using seasonal autoregressive integrated moving average model. AQI: Air quality index.</div></figcaption></figure><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('ST1', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.ST1.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.ST1.jpeg 2x" srcset="" alt="ST1"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('ST1', '01586002-202404000-00012')">Supplementary Table 1: </a><div class="ejp-r-article-images__figcaption-text">Forecast values of air quality index using SARIMA</div></figcaption></figure></section><p id="JCL-P-20">The above process to forecast the AQI values for a nonstationary dataset (having a seasonal component) has been figured out in <a href="javascript:void(0)" onclick="javascript:showSlideShowByImageID('F3', '01586002-202404000-00012');">Figure 3</a>.</p><section class="ejp-r-article-images"><figure class="ejp-r-article-images__figure"><a href="javascript:void(0)" class="ejp-r-article-images__image-link" onclick="showSlideShowByImageID('F3', '01586002-202404000-00012')"><img class="ejp-r-article-images__img js-lazy-load lazy-load" src="javascript:void(0);" data-src="https://images.journals.lww.com/ijph/ArticleViewerPreview.01586002-202404000-00012.F3.jpeg" data-srcset="https://images.journals.lww.com/ijph/ArticleViewerPreview@2.01586002-202404000-00012.F3.jpeg 2x" srcset="" alt="F3"></a><figcaption class="ejp-r-article-images__figcaption"><a href="javascript:void(0)" class="ejp-r-article-images__figcaption-link" onclick="showSlideShowByImageID('F3', '01586002-202404000-00012')">Figure 3: </a><div class="ejp-r-article-images__figcaption-text">Predictive model for air quality index forecasting. AQI: Air quality index, ADF: Augmented Dickey–Fuller, ACF: Autocorrelation function, AIC: Akaike information criterion, MAPE: Mean absolute percentage error, SARIMA: Seasonal autoregressive integrated moving average, AI: Artificial intelligence.</div></figcaption></figure></section><h2 class="ejp-article-outline-heading" data-level="1" id="sec1-4">C<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">ONCLUSION</span></h2> <p id="JCL-P-21">Polluted air can be seen in every state of India, but we can forecast the upcoming trends of air pollution using historical information and experiences. For predicting air quality, the train-test approach of machine learning has made incredible technological breakthroughs. The present work focused on forecasting the AQI values using a time series regression technique for a nonstationary dataset having a seasonal component. The auto-ARIMA function and statsmodel libraries are used to find a SARIMA model of {(0,1,0) (1,0,0)<sub xmlns:mrws="http://webservices.ovid.com/mrws/1.0">[12]</sub>}. This model was selected based on its lowest AIC (253.143) and MAPE (17.42%) to forecast the AQI values for the following months. The research indicates that the air will be moderately polluted and may cause breathing discomfort to people with lung disease (asthma patients), heart disease, and older adults living in the SIDCUL area of Haridwar City. The model can be helpful for a scientist, and the forecasted values of AQI for this region can help the government and the general public to take preventive measures in advance.</p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-6">Financial support and sponsorship</h3> <p id="JCL-P-22">This research work was funded by the Uttarakhand State Council for Science and Technology, Government of Uttarakhand, India under the order number UCS&T/R&D-25/23-24/25096.</p> <h3 class="ejp-article-outline-heading" data-level="2" id="sec2-7">Conflicts of interest</h3> <p id="JCL-P-23">There are no conflicts of interest.</p></section><section id="article-references" class="article-references js-article-references"><h2 class="ejp-article-outline-heading" data-level="1" id="JCL-L-0">R<span style="font-variant: small-caps;" xmlns:mrws="http://webservices.ovid.com/mrws/1.0">EFERENCES</span></h2> <div id="R1" class="article-references__item js-article-reference">1. 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}); Sys.Application.add_init(function() { $create(AjaxControlToolkit.ModalPopupBehavior, {"BackgroundCssClass":"Popup-Overlay","PopupControlID":"ctl00_ctl29_g_37a86981_c8fc_47ee_aeb4_9158ddffeb47_ctl00_emailToColleague_pnlEmailToColleague","dynamicServicePath":"/ijph/pages/articleviewer.aspx","id":"emailToColleaguePopupExtender_1428380213"}, null, null, $get("ctl00_ctl29_g_37a86981_c8fc_47ee_aeb4_9158ddffeb47_ctl00_emailToColleague_hypEmailToColleague")); }); //]]> </script> </form> <span id="DeltaPlaceHolderUtilityContent"> </span> <script type="text/javascript">// <![CDATA[ var g_Workspace = "s4-workspace"; // ]]> </script> <div id="ctl00_ctl64_divAdConfig" data-adconfig="{"adTitle":"Advertisement","slotClass":"ad-slot-container","topBanner":{"sticky":"false","stickyDuration":"5000"},"lazyLoadingEnabled":false,"npiHash":"","pageTargeting":{"ArticleId":"01586002-202404000-00012"}}" data-videoadconfig="{"serverURL":"https://pubads.g.doubleclick.net/gampad/ads","sizes":"320x180|640x360","overlayUnitPath":"/15726702761/Desktop/IJPH/Video/overlay","prerollUnitPath":"/15726702761/Desktop/IJPH/Video/pre-roll","postrollUnitPath":"/15726702761/Desktop/IJPH/Video/post-roll"}" class="js-ad-config"> </div> <div id="ctl00_ctl64_divScripts"><script type="text/plain" async="async" src="https://securepubads.g.doubleclick.net/tag/js/gpt.js" class="optanon-category-C0004"></script> <script type="text/javascript" src="https://lww.com/_layouts/15/oaks.journals/core/ads.js?v=05.24.00.023" defer="true"> </script> </div>  <script type="text/javascript"> (function($) { if (false) { $(".top-microbar-container").addClass('top-microbar-container-moved').insertAfter(".advertisement-content_top"); 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