Public Health Risk Assessment Research Articles

A Review of Wastewater-Based Epidemiology for the SARS-CoV-2 Virus in Rural, Remote, and Resource-Constrained Settings Internationally: Insights for Implementation, Research, and Policy for First Nations in Canada

Wastewater-based epidemiology (WBE) is regarded as a support tool for detecting and assessing the prevalence of infectious diseases at a population level. For rural, remote, and resource-constrained communities with little access to other public health monitoring tools, WBE can be a low-cost approach to filling gaps in population health knowledge to inform public health risk assessment and decision-making. This rapid review explores and discusses unique considerations of WBE in key settings, with a focus on the detection of the SARS-CoV-2 virus, which has rapidly expanded WBE infrastructure globally. To frame our understanding of possibilities for WBE with First Nations in Alberta, we address the following questions: What are the unique considerations and challenges for WBE under similar contexts in rural, remote, or resource-constrained settings? What are the resources and expertise required to support WBE? This review identifies several unique considerations for WBE in rural, remote, and resource-constrained communities, including costs, accessibility, operator capacity, wastewater infrastructure, and data mobilization—highlighting the need for equity in WBE. In summary, most resource-constrained communities require additional support from external research and/or governmental bodies to undertake WBE.

Chemical Characterization and Assessment of Public Health Risk due to Inhalation of PM2.5 in the City of Salamanca, Guanajuato.

In this study, we conducted an analysis of health risks faced by residents of Salamanca, Mexico, who were exposed to fine particulate matter with a diameter of 2.5μm (PM2.5) through inhalation. The characterization and analysis of these particulate matter samples were undertaken. A total of 131 samples were collected from two different sites: 65 from the Red Cross site (RC) and 66 from the Integral Family Development site (DIF) in 2014-2015. These samples were analyzed for a set of chemical components, including metals and ions. Non-cancerous health risk levels associated with PM2.5 exposure through the human respiratory system, as per the WHO benchmark (assigned a value of 1), revealed notable risk values for two elements: Manganese (Mn) with a range of 1.19-2.12 in the adult population and 1.59-2.84 in the child population, and Nickel (Ni) with a uniform risk value of 1.39 for both evaluated population groups. However, concerns arose regarding potential non-cancerous effects as the cumulative risk levels for various assessed elements showed elevated indices. These ranged from 3.81 to 4.4 in adults and 4.48-5.24 in children. This study provided comprehensive data on composition and its potential impact on human health, offering valuable insights for the implementation of mitigation measures aimed at reducing inhalation-related exposure.

Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments.

Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.

Unveiling nitrate contamination and health risks: Insights from groundwater quality assessment and Monte Carlo simulation along the Southern Caspian Sea Coasts

Groundwater resources are at great risk of contamination due to increased industrial and agricultural activities, population growth and urban expansion. This study investigated factors controlling spatio-temporal variability in groundwater quality and nitrate concentration at the southern coast of Caspian Sea, Iran to provide public health risk assessment. Na-Cl (44.8%) and Ca-HCO3 (58.6%) types water were the dominant hydrogeochemical facies in dry and wet seasons, respectively. Most of the examined groundwater samples were found unfit for drinking but appropriate for agricultural irrigation. The chemistry of groundwater predominantly influenced by combination of local lithology and ion exchange in aquifer as well as seawater intrsuin. Nitrate concentration varied from 0.05 to 200 mg/L with a mean value of 33.1 mg/L in which 13.7% and 27.5% of samples showed concentration higher than WHO's recommended value in dry and wet seasons, respectively. The highest nitrate concentrations were observed at locations in proximity to human settlements including cities, villages as well as agricultural lands. The identified pollution hotspots confirm nitrate contributions from un-treated wastewater effluents and agricultural practices with minimum contribution from industrial activities. The result of Monte Carlo simulation revealed that children were at highest risk from drinking of groundwater containing nitrate. This study highlights the urgent need for action to address the growing threat to groundwater quality and public health posed by contamination from various sources in the southern coasts of Caspian Sea.

Estimating public health risks of infectious disease events: A Canadian approach to rapid risk assessment.

The COVID-19 pandemic highlighted the need for timely, evidence-based rapid risk assessments (RRA) of infectious disease events to inform public health action during rapidly evolving situations with high uncertainty. In 2022, the Public Health Agency of Canada established a coordinated approach to public health risk assessment, including a methodology for qualitative RRA of infectious disease threats. To describe the RRA methodology and illustrate its use with examples from different infectious hazards of public health concern. The RRA methodology employs the risk pathway to describe the sequence of events leading from a hazard's source to the adverse event of concern and subsequent impacts; define specific questions to be addressed; and identify relevant knowledge gaps, limitations and recommendations. Qualitative likelihood and impact estimates are derived through integration of evidence review and expert opinion and are communicated together with corresponding levels of uncertainty. The impacts of the event are based on an assessment of the most likely spread scenario within Canada, considering individual-level impact on affected individuals, the impact on the general population and, if relevant, sub-groups at higher risk. This RRA approach aligns with well-established international methods and provides flexibility to accommodate a broad range of risk questions. It has been implemented to estimate the risk of various threats of concern to Canada, including mpox, avian influenza A(H5N1) and measles. Given the broad range and complexity of public health hazards, RRAs provide a timely, coordinated and systematic process for characterizing and communicating the risk to inform risk mitigation and decision-making and to guide appropriate public health response.

Challenging unverified assumptions in causal claims: Do gas stoves increase risk of pediatric asthma?

The use of unverified models for risk estimates and policy recommendations can be highly misleading, as their predictions may not reflect real-world health impacts. For example, a recent article states that NO2 from gas stoves “likely causes ∼50,000 cases of current pediatric asthma from long-term NO2 exposure alone” annually in the United States. This explicitly causal claim, which is contrary to several methodology and review articles published in this journal, among others, reflects both (a) An unverified modeling assumption that pediatric asthma burden is approximately proportional to NO2; and (b) An unverified causal assumption that the assumed proportionality between exposure and response is causal. The article is devoid of any causal analysis showing that these assumptions are likely to be true. It does not show that reducing NO2 exposure from gas stoves would reduce pediatric asthma risk. Its key references report no significant associations – let alone causation – between NO2 and pediatric asthma. Thus, the underlying data suggests that the number of pediatric asthma cases caused by gas stoves in the United States is indistinguishable from zero. This highlights the need to rigorously validate modeling assumptions and causal claims in public health risk assessments to ensure scientifically sound foundations for policy decisions.

Effectiveness of Updated 2023-2024 (Monovalent XBB.1.5) COVID-19 Vaccination Against SARS-CoV-2 Omicron XBB and BA.2.86/JN.1 Lineage Hospitalization and a Comparison of Clinical Severity-IVY Network, 26 Hospitals, October 18, 2023-March 9, 2024.

Assessing variant-specific COVID-19 vaccine effectiveness (VE) and severity can inform public health risk assessments and decisions about vaccine composition. BA.2.86 and its descendants, including JN.1 (referred to collectively as "JN lineages"), emerged in late 2023 and exhibited substantial divergence from co-circulating XBB lineages. We analyzed patients hospitalized with COVID-19-like illness at 26 hospitals in 20 U.S. states admitted October 18, 2023-March 9, 2024. Using a test-negative, case-control design, we estimated effectiveness of an updated 2023-2024 (Monovalent XBB.1.5) COVID-19 vaccine dose against sequence-confirmed XBB and JN lineage hospitalization using logistic regression. Odds of severe outcomes, including intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) or death, were compared for JN versus XBB lineage hospitalizations using logistic regression. 585 case-patients with XBB lineages, 397 case-patients with JN lineages, and 4,580 control-patients were included. VE in the first 7-89 days after receipt of an updated dose was 54.2% (95% CI = 36.1%-67.1%) against XBB lineage hospitalization and 32.7% (95% CI = 1.9%-53.8%) against JN lineage hospitalization. Odds of ICU admission (adjusted odds ratio [aOR] 0.80; 95% CI = 0.46-1.38) and IMV or death (aOR 0.69; 95% CI = 0.34-1.40) were not significantly different among JN compared to XBB lineage hospitalizations. Updated 2023-2024 COVID-19 vaccination provided protection against both XBB and JN lineage hospitalization, but protection against the latter may be attenuated by immune escape. Clinical severity of JN lineage hospitalizations was not higher relative to XBB.

Программный комплекс для дифференциальной оценки риска здоровью населения от воздействия шума железнодорожного и автомобильного транспорта

Objective: in the modern world, the number of vehicles on the roads is growing exponentially, and the infrastructure either remains unchanged or is changing, but it is not happening as fast as we would like [1]. Long-term effects of noise exposure, such as disorders of the cardiovascular system (high blood pressure, myocardial infarction, coronary heart disease, etc.), the nervous system (sleep disorder, cognitive impairment, vegetative vascular dystonia), diseases of the ear and mastoid process (conductive and sensorineural hearing loss), depression and other mental disorders [2, 3, 4], are more related to indicators that sum up the noise exposure over a long time. Both when conducting tests by accredited specialists and when choosing housing for buyers, you have to rely on an analytical or practical way to assess the noise pollution of a room. In order to provide a person with a simple and objective way to assess noise pollution, a software package has been developed that allows assessing the impact of noise pollution from transport on public health. Methods: quantitative risk assessment was carried out in accordance with the methodological recommendations of MP 2.1.10.0059-12 “Assessment of public health risk from exposure to traffic noise”. Results: in order to automate calculations, unify the evaluation of results and reduce time costs, the authors propose a software package for differential assessment of public health risks from exposure to rail and road transport. The end product is a user-friendly, cross-platform application for operational risk assessment in the field for employees of test centers who measure noise from known sources of automobile, railway or combined traf fic flows. Practical importance: convenience and optimization of risk processing by noise measurements by employees of noise measurement laboratories using the application.

Assessment of ecological and public health risks associated with heavy metals on farmland in Wonosobo Regency, Indonesia

The deposition of metallic substances on farmland is of critical importance owing to its possible detrimental impacts on the surroundings and human wellness. Heavy metals can adversely affect the physicochemical properties of soil and plant health. Prolonged exposure to heavy metals in humans can lead to both carcinogenic and non-carcinogenic effects. The present research intended to determine the dispersion condition of toxic metals on cropland and assess the attendant environmental and health dangers in the Wonosobo Regency. This study used a soil sample survey encompassing 312 soil sampling sites. The completed analyses comprise geographical analysis, index of geo-accumulation (IG), contamination index (CI), Nemerow comprehensive contamination index (NCCI), risk analysis, and health risk analysis. The research findings reveal that cadmium material is the primary contributor to contamination from heavy metals in farmland in Wonosobo Regency, as shown by IG analysis. The CI and environmental hazards assessments suggest considerable pollution levels. Kids are more prone to illnesses than adults due to their daily intake amount, so it is necessary to pay more attention to the daily intake sources in children to minimize exposure to heavy metals.

Chemical Characteristics and Public Health Risk Assessment of PM2.5-Bound Elements in Sheohar, India

Chemical Characteristics and Public Health Risk Assessment of PM2.5-Bound Elements in Sheohar, India

On alert for Ebola: public health risk assessment of travellers from Uganda to the USA during the 2022 outbreak.

On 20 September 2022, the Ugandan Ministry of Health declared an outbreak of Ebola disease caused by Sudan ebolavirus. From 6 October 2022 to 10 January 2023, Centers for Disease Control and Prevention (CDC) staff conducted public health assessments at five US ports of entry for travellers identified as having been in Uganda in the past 21days. CDC also recommended that state, local and territorial health departments ('health departments') conduct post-arrival monitoring of these travellers. CDC provided traveller contact information, daily to 58 health departments, and collected health department data regarding monitoring outcomes. Among 11 583 travellers screened, 132 (1%) required additional assessment due to potential exposures or symptoms of concern. Fifty-three (91%) health departments reported receiving traveller data from CDC for 10 114 (87%) travellers, of whom 8499 (84%) were contacted for monitoring, 1547 (15%) could not be contacted and 68 (1%) had no reported outcomes. No travellers with high-risk exposures or Ebola disease were identified. Entry risk assessment and post-arrival monitoring of travellers are resource-intensive activities that had low demonstrated yield during this and previous outbreaks. The efficiency of future responses could be improved by incorporating an assessment of risk of importation of disease, accounting for individual travellers' potential for exposure, and expanded use of methods that reduce burden to federal agencies, health departments, and travellers.

Effectiveness of Updated 2023-2024 (Monovalent XBB.1.5) COVID-19 Vaccination Against SARS-CoV-2 Omicron XBB and BA.2.86/JN.1 Lineage Hospitalization and a Comparison of Clinical Severity - IVY Network, 26 Hospitals, October 18, 2023-March 9, 2024.

Assessing COVID-19 vaccine effectiveness (VE) and severity of SARS-CoV-2 variants can inform public health risk assessments and decisions about vaccine composition. BA.2.86 and its descendants, including JN.1 (referred to collectively as "JN lineages"), emerged in late 2023 and exhibited substantial genomic divergence from co-circulating XBB lineages. We analyzed patients hospitalized with COVID-19-like illness at 26 hospitals in 20 U.S. states admitted October 18, 2023-March 9, 2024. Using a test-negative, case-control design, we estimated the effectiveness of an updated 2023-2024 (Monovalent XBB.1.5) COVID-19 vaccine dose against sequence-confirmed XBB and JN lineage hospitalization using logistic regression. Odds of severe outcomes, including intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) or death, were compared for JN versus XBB lineage hospitalizations using logistic regression. 585 case-patients with XBB lineages, 397 case-patients with JN lineages, and 4,580 control-patients were included. VE in the first 7-89 days after receipt of an updated dose was 54.2% (95% CI = 36.1%-67.1%) against XBB lineage hospitalization and 32.7% (95% CI = 1.9%-53.8%) against JN lineage hospitalization. Odds of ICU admission (adjusted odds ratio [aOR] 0.80; 95% CI = 0.46-1.38) and IMV or death (aOR 0.69; 95% CI = 0.34-1.40) were not significantly different among JN compared to XBB lineage hospitalizations. Updated 2023-2024 COVID-19 vaccination provided protection against both XBB and JN lineage hospitalization, but protection against the latter may be attenuated by immune escape. Clinical severity of JN lineage hospitalizations was not higher relative to XBB lineage hospitalizations.

Physicochemical and heavy metal pollution level in Hindon River ecosystem: An implication to public health risk assessment

Physicochemical and heavy metal pollution level in Hindon River ecosystem: An implication to public health risk assessment

The impact of bioaerosol trajectories on microbial community assembly and physicochemical dynamics in the atmosphere

This study explored the assembly mechanisms and physicochemical dynamics of microbial communities within atmospheric bioaerosols, focusing on the influence of different aerial trajectories. Over two years, samples near Seoul were classified into ‘North’, ‘Southwest’, and ‘Others’ categories based on their aerial trajectories. Physicochemical analysis of the PM2.5 particles revealed distinct ion compositions for each cluster, reflecting diverse environmental influences. Microbial community analysis revealed that shared dominant bacterial phyla were present in all clusters. However, distinct taxonomic profiles and biomarkers were also evident, such as coastal bacteria in the ‘Southwest’ cluster correlating with wind speed, and arid soil-originated bacteria in the ‘North’ cluster correlating with cations. These findings demonstrate that biomarkers in each cluster are representative of the distinct environments associated with their aerial trajectories. Notably, cluster ‘Southwest’ the highest microbial diversity and a strong alignment with the neutral community model, suggesting a large influence of passive dispersal from marine environments. Contrarily, ‘North’ and ‘Others’ were more influenced by niche-dependent factors. This study highlights the complex interplay between environmental factors and microbial dynamics in bioaerosols and provides important insights for environmental monitoring and public health risk assessment.

A snapshot mosquito field survey reveals the occurrence of Aedes geminus, Anopheles daciae, and Culex torrentium in the Republic of Ireland

Abstract A snapshot mosquito field survey was conducted in the Republic of Ireland in April–May 2017 and 2018 (for the VectorNet project) to provide mosquito distribution data as there is a dearth of recent data available for that region. The occurrence data obtained serve as a baseline for a preliminary public and veterinary health risk assessment and to promote further targeted vector surveillance. Morphological identification was complemented by genetic analyses to determine the species identity of members of the Anopheles Maculipennis Complex and the Culex Pipiens Assemblage. A total of 52 samples, including 50 immature aquatic samples and 2 adult catches, were collected from 72 locations inspected and distributed over 30 of the 34 vice counties across the country. Seventeen mosquito taxa were collected, and here we provide the first records of Aedes geminus, Anopheles daciae and Culex torrentium for the Republic of Ireland. We also confirm the occurrence of both Anopheles algeriensis and Culiseta alaskaensis which have only been found once before in Ireland.

Molecular and histopathological survey of Francisella spp., Borrelia spp. and Leptospira spp. in ornamental birds of four provinces of Iran

Leptospirosis, tularemia, and Lyme disease are among the most important infectious diseases affecting humans, animals, and birds. The present study aimed to identify these infections in exotic birds in Iran using conventional PCR and histopathology methods. The samples consisted of 51 fecal samples and 70 tissue samples collected from 10 different species of ornamental birds in four cities in Iran (Tehran, Tabriz, Ahvaz, and Yazd), each with distinct geographical locations. The presence of bacterial diseases caused by Francisella, Borrelia, and Leptospira in birds was investigated using the 16SrRNA, 5S-23SrRNA, and 16SrRNA genes, respectively. Histopathological examination was conducted on the brain, lung, trachea, kidney, and liver. The results showed a positive rate of 9.91 % for Francisella spp. and 16.52 % for Leptospira spp. DNA in ornamental birds. However, Borrelia species were not identified based on the 5S-23SrRNA gene. Furthermore, the molecular prevalence based on the 16SrRNA gene was 9.9 % (12 out of 121 samples) for Francisella spp. and 16.5 % (20 out of 121 samples) for Leptospira spp. Histopathological studies revealed tracheitis, pneumonia, nephritis, and hepatitis in the PCR-positive birds. To the best of our knowledge, this report represents the first isolation of Leptospira spp. and Francisella spp. from birds in Iran. Considering that both identified bacteria are zoonotic agents, these findings demand a public health risk assessment of exotic birds, which could be important reservoirs for these infections.

Competitive-like binding between carbon black and CTNNB1 to ΔNp63 interpreting the abnormal respiratory epithelial repair after injury

Airway epithelium is extraordinary vulnerable to damage owning to continuous environment exposure. Subsequent repair is therefore essential to restore the homeostasis of respiratory system. Disruptions in respiratory epithelial repair caused by nanoparticles exposure have been linked to various human diseases, yet implications in repair process remain incompletely elucidated. This study aims to elucidate the key stage in epithelial repair disturbed by carbon black (CB) nanoparticles, highlighting the pivotal role of ΔNp63 in mediating the epithelium repair. A competitive-like binding between CB and beta-catenin 1 (CTNNB1) to ΔNp63 is proposed to elaborate the underlying toxicity mechanism. Specifically, CB exhibits a remarkable inhibitory effect on cell proliferation, leading to aberrant airway epithelial repair, as validated in air-liquid culture. ΔNp63 drives efficient epithelial proliferation during CB exposure, and CTNNB1 was identified as a target of ΔNp63 by bioinformatics analysis. Further molecular dynamics simulation reveals that oxygen-containing functional groups on CB disrupt the native interaction of CTNNB1 with ΔNp63 through competitive-like binding pattern. This process modulates CTNNB1 expression, ultimately restraining proliferation during respiratory epithelial repair. Overall, the current study elucidates that the diminished interaction between CTNNB1 and ΔNp63 impedes respiratory epithelial repair in response to CB exposure, thereby enriching the public health risk assessment on CB-related respiratory diseases.

The Dynamics of the Concentration and Speciation of Arsenic in Private Drinking Water Wells in Eastern Wisconsin, USA

About 2.5 billion people rely on groundwater as their main drinking water source, and arsenic pollution within the groundwater system can cause serious short- and long-term health issues. Within the natural environment, arsenic generally exists as oxyanions which have two oxidation states, As(III) and As(V). Under ambient pH conditions, As(V) is primarily present as an anion (i.e., H2AsO4−) while As(III) tends to be uncharged (i.e., H3AsO3), making it much more difficult to remove As(III) through existing treatment techniques such as adsorption and reverse osmosis (RO). In Eastern Wisconsin, the dolomite and dolomite/sandstone aquifers represent a major drinking water source and high arsenic concentrations have been observed. Previous studies showed that arsenic can be released into private drinking water wells due to the oxidation of sulfide-bearing minerals with arsenic impurities that are usually dispersed within the dolomite and sandstone bedrock. However, there is a lack of information on the concentrations of each arsenic species as well as arsenic dynamics during water pumping/usage. The primary goals of this research were (1) to quantify the baseline concentrations of each arsenic species within selected Eastern Wisconsin private drinking water wells, and (2) to determine how the arsenic concentrations and species could be affected by continuous water usage. Our results showed that As(III) was the dominant species of arsenic, and during continuous water usage, there could be an upward trend in arsenic concentration (there was minimal change in arsenic speciation). Upon the completion of water pumping, arsenic concentrations decreased over time and returned to pre-pumping levels. Our findings highlighted the importance of quantifying the speciation and dynamics of arsenic during water use to the assessment of public health risks and the design of appropriate water treatment techniques.

The Interplay Between Individual Mobility, Health Risk, and Economic Choice: A Holistic Model for COVID-19 Policy Intervention

This paper was motivated by the need to simultaneously address two competing policy objectives during the course of the COVID pandemic: namely, the public health objective, which required people to be less mobile, and the economic objective, which aimed to ensure that the economy was not adversely affected by the constraints imposed by the first objective. To realize these objectives, we developed a data-informed approach to model human mobility, health risk, and economic activity jointly. This approach computes equilibrium between epidemic models of public health and economic activity under policy interventions that could be used to change people’s mobility behavior. Our approach is distinctive in its capacity to assemble proprietary data sets from public and private sectors at the individual and the zip code levels, which heretofore had not been used together. These data enabled customization of the population-level epidemic models widely used in public health (e.g., the SIR model) with individual-level data traces of mobility behaviors for assessment of public health risks. The outputs of the proposed model enabled parameterization of economic choice models of individuals’ economic decision-making. Various policy interventions and their capacities to shift the equilibrium between economic activity and public health were investigated in this study. Whereas the data-informed joint modeling approach was developed and tested in the pandemic context, it is generalizable for the evaluation of any counterfactual policy interventions. History: Olivia R. Liu Sheng and W. Nick Street served as senior editors for this article. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.4390192.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0013 ).

Air Quality and Public Health Risk Assessment: A Case of an Industrial Area in Haridwar City, Uttarakhand (India).

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. The study aimed to forecast the AQI for an industrial area (SIDCUL, Haridwar City) using a time series regression model. 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. 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. 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.