Air Quality Index Research Articles

Evaluating the Impact of Renewable Energy Integration on Air Quality: A Study of Pollutant Reduction in an Urban city of Calabar

The characterization of air quality parameters was carried out in the coastal city of Calabar with the aim of reducing air pollutants in the atmosphere. Both mobile and stationary measurements were obtained. Mobile data were used for estimating air quality index and creating air quality map. The results show that the average concentration of ozone (O3), carbon monoxide (CO), sulfur dioxide (SO2) and nitrogen Oxides (NOx) was 0.34, 4.52, 0.53 and 0.96 ppm, respectively. The air quality index determined for each station showed that 82% of the stations were classified as “marginally polluted,” 14% were classified as “good,” and the remaining 4% were classified as “unhealthy” according to the U.S. air quality standards. Correlation analysis showed that wind speed had the highest correlation with SO2, R = -0.72, while temperature had a high correlation with ozone, R = -0.68. The 2016 polar plots show that CO sources are located in the south and southeast, NOx sources are located in the south and southwest, SO2 sources are located in the southwest, and O3 sources are located in the southeast. The 2017 polar plots show that CO sources are located in the northeast, NOx sources are located in the northwest, SO2 sources are located in the northeast, and O3 sources are located in the southwest.

Air quality historical correlation model based on time series

Air quality is closely linked to human health and social development, making accurate air quality prediction highly significant. The Air Quality Index (AQI) is inherently a time series. However, most previous studies have overlooked its temporal features and have not thoroughly explored the relationship between pollutant emissions and air quality. To address this issue, this study establishes a historical correlation model for air quality based on a time series model—the Gaussian Hidden Markov Model (GHMM)—using industrial exhaust emissions and historical air quality data. Firstly, a traversal method is used to select the optimal number of hidden states for the GHMM. To optimize the traditional GHMM and reduce error accumulation in the prediction process, the Multi-day Weighted Matching method and the Fixed Training Set Length method are utilized. Both direct and indirect prediction modes are then used to predict the AQI in the Zhangdian District. Experimental results indicate that the improved GHMM with the indirect mode provides higher accuracy and more stable state estimation results (MAE = 13.59, RMSE = 17.59, mean forecasted value = 117.94). Finally, the air quality historical correlation model is integrated with the air quality meteorological correlation model from a previous study, further improving prediction accuracy (MAE = 11.59, RMSE = 14.87, mean forecasted value = 120.88). This study demonstrates that the GHMM’s strong ability to analyze temporal features significantly enhances the accuracy and stability of air quality predictions. The integration of the air quality historical correlation model with the air quality meteorological correlation model from a previous study leverages the strengths of each sub-model in handling different feature groups, leading to even more accurate predictions.

Time Series Forecasting of Air Quality Index in Lahore : A Machine Learning Perspective with Facebook Prophet Model

This research investigates the rising problem of air pollution, which is a serious issue affecting people's health and the environment on a global scale. Focusing on Lahore, Pakistan, a city facing severe pollution, the research examines the Air Quality Index (AQI) from May 2019 to December 2023. Using the Facebook Prophet model for time-series forecasting, the analysis reveals trends at monthly, weekly, and hourly intervals. Peak AQI concentrations are identified in winter months, notably December and January, aligning with hazardous smog levels. Weekly trends show minimum AQI on weekends, particularly Saturday and Sunday. The hourly trend points to peak AQI concentrations between 7:00 and 09:00 AM, correlating with morning traffic and emphasizing the role of vehicular emissions in air quality degradation. These forecasts are crucial for anticipating and mitigating the adverse impact of air pollution on human health. This research underscores the critical need for comprehensive strategies to address air pollution, acknowledging the Lahore High Court and Government's initiatives, such as artificial rain experimentation, closure of commercial markets by 10 PM, ban on cutting trees and smoke-emitting vehicles. In summary, this research significantly contributes to understanding and tackling the increasingly urgent challenges associated with air quality.

Bibliometric analysis of Indian research trends in air quality forecasting research using machine learning from 2007–2023 using Scopus database

Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.

Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.

Association of ambient air pollution and Air Quality Index with risk of sudden sensorineural hearing loss: a cross-sectional study

ObjectivesTo explore the associations of air pollutants and Air Quality Index (AQI) with risk of sudden sensorineural hearing loss (SSNHL)DesignCross-sectional studySettingMedical record data and local population data collected between 2014...

Salix humboldtiana as an Indicator of Air Pollution by Trace Metals in the Urban Areas of the City of Loja, Southern Ecuador

Air pollution is the most important environmental problem in urban areas related to vehicular traffic and industrial activities. The widespread presence of common urban trees, such as Salix humboldtiana, and their ability to tolerate diverse environmental conditions make this species an especially promising candidate for assessing environmental metal contamination. Therefore, biomonitoring with vascular plants has been widely used to assess air pollution, especially the accumulation of trace metal concentrations. Therefore, for the first time, we analyzed the concentration of trace metals using Salix humboldtiana in twelve areas with different levels of pollution in a city in Southern Ecuador. For this purpose, samples were taken from each site to assess the accumulation of trace metals such as Zn, Mn, Fe, Cd, Cr, Pb, Cu, Al, and Ni. The results obtained showed significant differences in the concentrations of Zn, Mn, Fe, and Cd between the urban areas and the control area, indicating that the central areas were the most polluted by vehicular traffic. However, these findings suggest that Salix humboldtiana may not be a particularly effective tool for quantifying levels of environmental metal contamination such as Cu and Ni, at least in urban areas in the city of Loja. This study has demonstrated that Salix humboldtiana leaves can effectively monitor trace metals associated with road traffic emissions in areas with varying levels of vehicular activity, indicating that vascular plants can be utilized for this purpose in tropical cities.

Indoor air pollutants and microbes in mass rapid transit (MRT) trains of north-western area of Bangkok, Thailand: Impact on indoor air quality and human health

Most modern cities suffer from urban air pollution caused by human activity such as traffic and other sources. Various pollutants in the environment may pose a threat to the health of commuters. The objective of this study is to assess the indoor air quality (IAQ) in MRT purple line trains serving the north-western area of Bangkok in order to determine any potential impacts on human health. The monitored parameters include total volatile organic compounds (TVOCs), temperature, relative humidity, carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), formaldehyde (CH2O), and fine particulate matter (PM2.5). The indoor air pollutant concentrations in the MRT remained within the permissible thresholds. The results of the correlation analysis reveal that relative humidity (RH) and PM2.5 are positively correlated in both the wintertime and summertime measurement campaigns. In contrast, CO2 correlated with temperature in the wintertime and CO in the rainy season. Bacterial and fungal loads were estimated during various measurement campaigns in MRT. Fungi and bacterial loads were higher in the MRT trains during the wintertime and rainy season campaigns. The IAQ of MRT train in Bangkok is within the satisfactory and excellent category of air quality index (AQI). The inhaled doses of PM2.5, CO, and VOCs were also estimated to identify their health effects on commuters. The inhaled dose of PM2.5 was comparatively higher than other pollutants. In this study, a general perspective of the IAQ in the MRT system was provided. It is recommended to conduct more research studies on various MRT lines in Bangkok to further investigate the levels of bacteria, fungi, TVOCs, O3, and PM. Additionally, the ventilation mode in MRT needs to be investigated as it is the main factor affecting the IAQ.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

Impact of construction activities on the air quality of Agra city, Uttar Pradesh, India

The current research work was carried out to comprehensively assessed the impact of construction activities on the air quality. Seven sites were chosen along the metro line construction in Agra, Uttar Pradesh, India, to meet the study's goals. The monitoring was performed for 24 hours at each site using the respirable dust sampler (RDS) with a gaseous sampling attachment. The raw data was processed to calculate the Air Quality Index (AQI). The data obtained indicate that all the examined sites had PM10 (particulate matter having the diameter less than or equal to 10 micron) values above the National Ambient Air Quality Standards (NAAQ) values of 100 µg/m3, while SS-05 and SS-06 had PM2.5 (particulate matter having the diameter less than or equal to 2.5 micron) values above the NAAQ values of 60 µg/m3. Values of CO, SO2, and NO2 were discovered to be lower than the NAAQ standard limits. Because PM10's sub index (Si) was found to be the greatest across all locations, it was determined to be the criterion pollutant among all the metrics. Based on the AQI value, the research area's overall air quality was determined to be moderately polluted. At every location, a variety of management techniques, including mist guns, water spraying, and planting, are regularly used to reduce air pollution. Effective implementation of applied air pollution control measures is required to make the air clean and safe for breathing.

Abstract PR011: Investigating the interplay among allostatic load, social determinants of health, and treatment outcomes in chronic myeloid leukemia patients

Abstract Introduction. The advent of tyrosine kinase inhibitors (TKIs) has transformed chronic myeloid leukemia (CML) into a chronic condition, allowing many patients (pts) to achieve survival rates comparable to the general population. Yet, most pts patients require lifelong therapy, regular follow-up, and management of TKI side effects, posing challenges within the healthcare system alongside structural and financial barriers. We have previously found that CML pts with an allostatic load (AL) burden indicating cumulative stress, experience worse treatment outcomes. In light of this, we investigated whether AL is a biological indicator of social determinants of health (SDH) and its correlation with CML treatment outcomes. Methods. We conducted a single-institution retrospective study, enrolling CML pts and assessing 23 biomarkers across four domains (cardiovascular, metabolic, immunologic, and neuroendocrine) required to calculate the AL score at the time of the first visit. Pts were sorted into low and high AL groups using the cohort's median score of 5 as the threshold. SDH was evaluated at both individual and population levels by integrating data from medical records with the SDH database from the Agency for Healthcare Research and Quality. Statistical analyses, including chi-square and Fisher’s exact tests, were conducted with significance set at p<0.05. Results. Between 2003 and 2023, data from 108 CML pts undergoing first-line treatment were retrieved. Pts with high AL were more likely to be older (mean age 53.8 vs. 47.2 years, p=0.03), and a greater proportion were identified as Black compared to those in the low AL cohort (25.5% vs. 12.3%, p=0.01). We identified an association between high AL and overall mortality (13.7% vs. 1.8%, p=0.02), with effects trending towards a higher risk of CML-specific death (6.4% vs. 0, p=0.05). Higher AL was correlated with adverse SDH, such as reliance on public health insurance (32% vs. 52.2%, p=0.04), living alone (67.7% vs. 86.8%, p=0.05), and lack of regular exercise (38.9% vs. 7.4%, p=0.01). Geographic inequality was identified, as patients with high AL were more likely to reside in Georgia (GA) compared to South Carolina and other states (66.7% vs. 27.5% vs. 5.9%, p=0.01). Indeed, pts in GA who traveled shorter distances to the treatment facility and resided in areas with higher particulate matter levels, as an indicator of air pollution, were less likely to achieve major molecular response (p=0.011 and p=0.008, respectively). SDH such as regular exercise, home environment, and health insurance were associated with overall and CML-specific survival. However, birth country, language, household income, and education, were not associated with either AL or treatment outcomes. Conclusion. Adverse SDH significantly amplifies the burden of AL, serving as a comprehensive indicator of chronic stress, health behaviors, and environmental conditions. Our findings highlight the intricate interplay between social, economic, and environmental factors in shaping treatment outcomes among CML patients. Citation Format: Marisol Miranda-Galvis, Muhannad Sharara, Kellen C. Tjioe, McKenzie Maloney Maloney, Vamsi Kota, Anand Jillella, Locke Bryan, Amany R. Keruakous, Jorge E. Cortes. Investigating the interplay among allostatic load, social determinants of health, and treatment outcomes in chronic myeloid leukemia patients [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr PR011.

Exploring Air Quality Dynamics and Predictive Modeling by Using Artificial Intelligence During COVID-19 Lock Down Over the Western Part of India

The lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (>401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.

How Effective is the Regional Joint Environmental Policy in China? Evidence from Inverse Difference-in-Differences

AbstractThis paper aims to evaluate the effects of the “2+26” policy launched by the Chinese government to improve air quality in Beijing–Tianjin–Hebei and its surrounding region. In order to evaluate such policy interventions, we propose a new inverse difference-in-differences (IDID) framework, which can identify the average quasi-causal effect under the inverse common trend condition. Using a baseline IDID regression model, we find that the “2+26” policy has a significant effect on improving the air quality, since the policy would reduce the value of Air Quality Index by 9.338 on average, if it had been implemented in the pre-treatment period. In the supplementary materials, we also investigate the co-existing spatial–seasonal policy effects by using several heterogeneous IDID regression models and show the remarkable long-run quasi-benefits over the quasi-costs of the “2+26” policy in Beijing and Tianjin through a cost–benefit analysis. Supplementary materials accompanying this paper appear online.

An Improved Air Health Index Based on Short-Term Cardiovascular Effects in Tianjin, China.

To construct an improved air health index (AHI) based on cardiovascular years of life lost (YLL) in Tianjin and assess its utility. We derived the exposure-response coefficients from time-series models and calculated the excess YLL (EYLL) for simultaneous exposure to air pollution and non-optimum temperature. The AHI was developed using the EYLL at the WHO 2021 Air Quality Guideline annual mean values and optimum temperature as a reference. We assessed the validity of AHI by comparing the correlations and model fit between the AHI, air quality health index (AQHI), and air quality index (AQI) with cause-specific YLLs. Each inter quartile range (IQR) increase in AHI was associated with 256.31 (95%CI: 183.05, 329.57), 150.34 (95%CI: 108.23, 192.46), 90.41 (95%CI: 64.80, 116.02) and 60.80 (95%CI:33.41, 88.18) person-year increments for non-accidental, cardiovascular, ischaemic, and cerebrovascular YLL, respectively. The AHI, in contrast to the AQHI and AQI, showed the strongest correlations with the risks of cause-specific YLLs, both in the total population and subpopulations. The AHI based on cardiovascular YLL has a greater predictive ability for health risks.

Spatiotemporal distribution of air quality dynamics and health risks study during 2019–2021 lockdown in urban and industrial cities of West Bengal, India

Globally, air pollution is the second most common cause of mortality from non-communicable diseases (NCDs), after tobacco smoking. Breathing polluted air has potential to cause a diverse array of health problems. This study comprehensively investigates the health risk of different areas of the population mainly industrial and urban belt by using AIRQ + health risk assessment model. This study also analyzes the spatiotemporal distribution of air quality in industrial and urban areas and correlation study was conducted between air pollutants and meteorological factors with episodic influences mainly lockdown phases. In Kolkata, Howrah, Asansol and Siliguri, the average Air quality index (AQI) varied from satisfactory (51–100) to moderate (101–200) values. The poor (201−300) to very poor (301−400) air quality observed in January, February, November and December month of the year. The higher AQI observed in the study area in winter season as compared to summer season. In the lockdown period overall air quality was improved. The meteorological factors, including rainfall and temperature played a crucial role in influencing air quality in this area. The regression analysis indicates a significant negative association between temperature and the following pollutants: PM2.5 (β = − 4.91, p < 0.01), PM10 (β = − 8.27, p < 0.01), NO2 (β = − 2.10, p < 0.01), NOx (β = − 4.05, p < 0.01), CO (β = − 0.04, p < 0.01), and SO2 (β = − 0.45, p < 0.01). The number of attributable cases of long-term PM2.5 exposure mortality due to lung cancer, stroke, IHD and COPD in four cities were 1132, 3969, 4381 and 974 in Kolkata; 1454, 4913, 5327 and 1221 in Howrah; 154, 531, 581 and 131 for Asansol; 137, 474, 520 and 117 in Siliguri respectively. IHD is the leading cause for mortality in the study area. The long-term effects of PM10 such as chronic bronchitis in adults and post-neonatal infant mortality were 30,860 and 738,450 in Kolkata, 53,961 and 898,920 in Howrah, 3782 and 89,460 in Asansol, and 3293 and 76,331 in Siliguri respectively. These study findings help policymakers, stakeholders and governments to take early action to reduce the effects of air pollution and implement interventions for the well-being of the population.

Optimized ensemble deep random vector functional link with nature inspired algorithm and boruta feature selection: Multi-site intelligent model for air quality index forecasting

Air quality index (AQI) forecasting is complex due to its variability, instability, and inconsistent trends resulting from dynamic atmospheric conditions, various contaminants, and interactions between environmental factors. Advanced modeling techniques are needed to accurately forecast AQI values to capture subtle patterns and variations in air quality data. Thus, a new forecasting model is suggested in this study to improve the accuracy of AQI forecasting. The model integrates three-phase decomposition technique, a feature selection approach, and ensemble Deep Random Vector Functional Link (EDRVFL), optimized using adaptive teaching-learning-based optimization and differential evolution (ATLDE). The AQI series was first broken down into a group of intrinsic mode functions (IMFs) with different frequencies using multivariate variational mode decomposition (MVMD). Subsequently, a feature selection method based on the Boruta technique was applied to identify the most significant input variables. Finally, for daily AQI levels forecasting, ATLDE optimized the EDRVFL model (EDRVFL-ATLDE). Three daily AQI series gathered from Chengdu, Wuhan, and Taiyuan in China from January 1, 2018, to December 30, 2022, were used to test and confirm the proposed model via empirical research. Based on the results, the proposed model can yield the superior results for three cities (Chengdu: correlation coefficient (R = 0.987), root mean square error (RMSE = 5.583), Wuhan: (R = 0.987), (RMSE = 3.299), and Taiyuan: (R = 0.996), (RMSE = 4.521)) in China. The experimental findings demonstrated the feasibility of the three-phase hybrid methodology, outperforming all other models regarding forecast accuracy.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

Performance analysis of machine learning models for AQI prediction in Gorakhpur City: a critical study.

Air pollution and climate change are two complementary forces that directly or indirectly affect the environment's physical, chemical, and biological processes. The air quality index is a parameter defined to cope with this effect of air pollution. This study delves deeper into predicting this AQI parameter using multiple machine learning-based models. The AQI pollutants considered for this study are particulate matter (PM10, PM2.5), SO2, and NO2. It also tries to develop a comparative analysis of two different machine learning (ML) models viz. a viz. XGBoost and Lasso regression. An ever-changing emission concentration of pollutants is displayed by this study conducted in the urban city of Gorakhpur Uttar Pradesh, India. The validation of prediction accuracies of models was done over several statistical metrics. The value of the R2 metric for XGBoost (0.9985) is comparatively more than the R2 value for Lasso regression (0.9218) indicating lesser variance and higher accuracy of XGBoost in predicting AQI. Various statistical measures are taken into consideration in this study, including mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), T-test and p-values, and confidence intervals (CI). An increased degree of model accuracy is suggested as XGBoost's MAE, MSE, and RMSE values are significantly lower than Lasso's. Statistically significant performance differences between the XGBoost and Lasso regression models are demonstrated by T-statistics and p-values for MAE, MSE, RMSE, and R2.

Domain knowledge-enhanced multi-spatial multi-temporal [formula omitted] forecasting with integrated monitoring and reanalysis data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Predictors of Pediatric Asthma Management: Identifying Actionable Results With Geographic Determinants.

Pediatric asthma remains one of the most prominent chronic health conditions among US youth. Geographic determinants such as air pollutants have been identified as playing a role in asthma development and exacerbation. The purpose of this study was to determine geospatial predictors of pediatric asthma exacerbation events and to prioritize housing remediation resources. Electronic medical records were abstracted from a health plan in Southern California. The inclusion criteria that created a sample of 51 557 members were those aged 21years and younger, who had at least 1 asthma-related encounter between January 2019 and December 2021. Diagnoses, age, number of clinic and emergency department visits, and home addresses were included. The air quality index from the closest monitoring station during the study period, residential distance from a primary roadway, and residential distance from manufacturing sites were included in the spatial analysis. The average number of asthma-related clinic visits was 2 across the sample. Individuals with more asthma-related clinic visits residing in public housing were more likely to live within 4 km of industrial manufacturing locations (P <.001), reside closer to a major roadway (P <.001), and experience a higher number of poor air quality days (P <.001). Modeling results show these factors were also significantly predictive of an increase of asthma-related health care encounters. The findings of this study were consistent with previous studies linking asthma and poor air quality and further highlighted some of the additive and potentially exponential challenges that public housing, major roadways, and manufacturing sites provide communities in their proximity. This research can guide environmental interventions, including the frequency of public housing inspections, community outreach, and the development of communication strategies, to reduce asthma-related experiences across neighborhoods.