Atmospheric aerosols and urban chemistry: assessing the link between air pollution index and traffic volume in Greater Noida

China major cities air quality index forecasting using integrative machine learning models: A perception for sustainable cities

Building evidence regarding nature-based solutions indicators and their implications for policy – the case of air quality

Ambient and personal air monitoring using low-cost sensors to identify elevated air pollution in vulnerable communities.

The role of Indoor Environmental Quality (IEQ) factors in shaping student behavior and emotional states in the classroom, which have been observed as potentially diminishing performance, necessitates objective and continuous assessment to overcome the limitations of subjective methods. This study addressed this need by utilizing a case study approach. We deployed an AI-powered behavioral observation system to anonymously estimate aggregate student behavior metrics (Engagement, Attention, Interaction) in real-time, synchronized with data collected from a custom-built multi-sensor device monitoring IEQ factors, including temperature, humidity, equivalent carbon dioxide (eCO[Formula: see text]), total volatile organic compounds (TVOCs), air quality index (AQI), light variations, and oxygen volume (O[Formula: see text]). Comprehensive statistical and causality analyses included nonparametric correlations, Cross-Correlation Function (CCF) analyses to assess lagged effects, Time-Varying Granger Causality (TV-GC) tests, and categorical analysis with Chi-squared tests. The results revealed that thermal and humidity extremes correlate with increased behavioral volatility. Temperature is the most consistent predictor of student attention; Chi-squared and violin plot analyses demonstrated that attention levels are significantly higher at slightly lower temperatures, specifically below 30.9[Formula: see text]C, within the reported range [29.62 - 31.35[Formula: see text]C]. Besides, a significant association between humidity and attention was observed, although it was significant at the [Formula: see text] level rather than the more stringent [Formula: see text] threshold applied to core findings. Additionally, the study identified a critical TV-GC relationship between O[Formula: see text] volume and engagement, pinpointing specific causal bursts that global correlation measures failed to capture. Standard CCF analyses suggested that lower light levels may be associated with higher interaction levels; however, this pattern was not statistically significant after pre-whitening and bootstrapping the CCF, nor was it supported by the TV-GC analyses. These findings advocate for responsive, automated classroom systems that dynamically adjust IEQ parameters to synchronize with the temporal demands of the learning process.

ABSTRACT The Subak ecosystem, a traditional Balinese irrigation system integrating cultural, social, and ecological functions, is increasingly threatened by watershed degradation and land-use change. This study evaluates the ecological, institutional, and socio-economic impacts of bamboo plantation development across five watershed (DAS) areas in Bali – Oten Sungai, Oos Jinah, Pangi Ayung, Leh Balian, and Sema Bona – using a mixed-methods approach combining interviews, household surveys, field observations, and biophysical measurements. Quantitative results indicate strong institutional autonomy supported by bamboo-based watershed management, with integration into Subak governance reaching 90.2% in Subak Gede and 88.5% in Subak Agung, and high local control of irrigation water (84.3–88.6%). Within 12 months, soil organic carbon increased by 64.0%, soil erosion declined by 53.8%, water turbidity decreased by 48.4%, groundwater recharge increased by 47.4%, and air quality index improved by 21.7%. Socio-economic outcomes were substantial: bamboo-based MSMEs increased by 237.5%, average household income rose by 50.0%, employment expanded by 127.4%, and youth participation increased by 225.0%. Additional benefits included improved water quality classes, biodiversity conservation, and growth of bamboo-based agrotourism aligned with the Tri Hita Karana philosophy. These findings demonstrate that bamboo-based watershed restoration effectively enhances ecological resilience, strengthens Subak governance, and improves local livelihoods.

This study thoroughly examined indoor air quality in six chain stores in Isfahan, Iran and involved the measurement of air pollutants (PM2.5, PM10, NO2, SO2, CO2, bacteria and fungi), alongside noise levels and thermal comfort over four seasons (2023-2024). To address the diversity of pollutants and enhance decision-making, an indoor air quality index (IAQI) was proposed, based on the importance coefficient and pollutant duration of presence. The results revealed that concentration of PMs in the autumn was relatively higher than in other seasons, with a strong correlation (R2 = 0.926) observed between PM2.5 and PM10. For other parameters, levels remained within acceptable limits; however, noise levels in all seasons and stores exceeded standards. Airborne bacteria (312-1,007 CFU/m³), dominated by Rhodococcus and Micrococcus, and airborne fungi (639-1,332 CFU/m³), mainly represented by Aspergillus and Penicillium, were detected. Overall, most stores exhibited intermediate biological contamination levels across all seasons. The proposed IAQI for all seasons and stores, with some allowance for variability, falls into class D, corresponding to an intermediate/acceptable indoor air quality level. In conclusion, the air quality in studied stores does not pose an immediate concern for customers and visitors, continuous monitoring and the implementation of ventilation equipment are recommended to ensure the well-being of store personnel.

Ambient air pollution contributes substantially to human morbidity and mortality, and athletes are recognised as a particularly vulnerable group. However, little is known about the impact of air pollution on equine athletes. To explore the relationship between air pollution exposure during the pre-competition training period and race day performance among Thoroughbred racehorses that competed on California racetracks. A retrospective longitudinal study. For each winning horse, pollutant exposure during the 21-day pre-competition training period was assigned using data from the nearest EPA air quality monitoring site to the racetrack where horses trained and competed. Exposure was characterised using the threshold Air Quality Index (AQI), with additional analyses evaluating fine particulate matter (PM2.5) and ozone (O3). A distributed lag non-linear model was applied to estimate associations between pollutant exposure during the training period and winning speed. Horses exposed to higher pollutant levels (80th percentile AQI = 58) during the pre-competition period had slower winning speeds when compared with those exposed to lower levels (20th percentile AQI = 32), with statistically significant decreases observed approximately 2-17 days before competition. Over the 21-day pre-competition exposure window, daily exposure to an AQI of 58, compared to an AQI of 32, was associated with a decrease in winning speed of 0.044 m/s (95% CI: -0.056, -0.032). Limitations include the use of data from regional air quality monitors, which may not accurately reflect the quality of air horses are actually breathing, and the inclusion of only California racetracks, limiting generalisability. Pre-competition air pollution exposure was associated with slower winning speeds in Thoroughbred racehorses, highlighting the importance of systematic air quality measurement at equine racetracks.

Chemical complexity and enrichment in wildland-urban interface fire emissions: A case study of particulate matter, gas-phase pollutants, and ash from the 2025 Los Angeles fires.

Abstract Introduction Majority of world population resides in areas with air quality index (AQI) exceeding the acceptable standards set by World Health Organization. Research from areas with poor AQI have shown association of sleep disorders with air pollutants (particulate matter [PM10 and PM2.5], ozone, carbon monoxide, nitrogen dioxide and sulphur dioxide) that contribute towards air quality index (AQI). This ongoing study of 18 months duration from June 2025 is evaluating the association between AQI and sleep disorders in normally developing 2-18-year-olds from New Delhi, India. Methods Subjects are being screened for sleep disorders using the Childhood and Adolescent Sleep Evaluation Questionnaire (CASEQ) at a tertiary-care teaching hospital by systematic random sampling. Screen-positives for sleep disorders are evaluated by overnight polysomnography and assigned an ICSD-3 based diagnosis. Daily AQI and the predominant air pollutant are noted from SAMEER, the official mobile application of Indian Central Pollution Control Board. Monthly prevalence of sleep disorders is being correlated with the combined mean AQI of Delhi for that month and the preceding month. Results Overall, 212 cases have been enrolled from June till November 2025 (mean age 9.1 ± 3.5 years; 62.1% males, 59/212 [27.8%] with final diagnosis of sleep disorder). The most common ICSD-3 diagnoses are periodic limb movement disorders (75%) and obstructive sleep apnea (54.5%). Multiple diagnoses were seen in 63.6%. The most common symptoms on initial CASEQ screening are sleep related movement disorder (77.8%) and insomnia (59.3%). The prevalence of sleep disorders is higher during June (29.3%), October (42.9%) and November (48.5%) with higher combined mean AQI noted for the corresponding time periods (May-June:155.4, September-October:165.3, and October-November:287.7) and lower during July, August and September with corresponding lower AQIs (11.8%,109; 17.4%, 83.5; 96.8, 25%) (r=0.86). The predominant pollutants during higher AQI months are PM10 and ozone whereas carbon monoxide predominates lower AQI months. Conclusion Significant correlation exists between prevalence of sleep disorders and AQI. PM10 and ozone may be central to health hazards of air pollution. Future studies should focus on causality establishment exploring biological pathways, longitudinal follow-ups, and controlling for multiple environmental determinants. Support (if any)

Air pollution remains a significant environmental and public health issue in Malaysia, with notable effects on the economy and climate. Despite long-standing monitoring systems and regulations, comprehensive assessments that combine multiple data sources with health impact analysis are still lacking. This study reviews peer-reviewed studies, haze events from 1983 to 2024, regulatory documents from the Department of Environment, Malaysia, and data from air quality monitoring stations across regions. and presents an integrated assessment of air quality across strategic and important urban, industrial, coastal, and residential areas. It analyses trends in the Air Pollutant Index and PM2.5 levels from 2013 to 2024, and uses exposure-response models to estimate related deaths and economic impacts. Satellite data (MODIS and AERONET) help understand spatial variation and cross-border pollution. Average PM2.5 levels ranged from 8.66-16.77µg/m3, consistently above WHO guidelines from 2021. In 2020, PM2.5 exposure was linked to 1.419 early deaths in four urban areas, costing about MYR 2.46 billion (USD 524 million). Population-attributable fractions ranged from 2.6-7.1%, with risk rising by 2.7-7.6% per 10µg/m3 increase. The study identified 12 major haze events, notably in 1997 and 2015. COVID-19 restrictions temporarily reduced emissions, but levels quickly rebounded afterward. Conclusively, Malaysia's air quality issues are mainly due to transport, industry, dust, and recurring transboundary haze. Solutions include better source tracking, enhanced secondary pollutant monitoring, integrating health data more effectively, adopting advanced technologies, and aligning policies with WHO standards. Pollution effects are more pronounced in metropolitan regions due to proximity to dense sources.

Abstract - Air pollution is one of the most critical environmental challenges, significantly affecting human health and ecosystems. The Air Quality Index (AQI) is widely used to measure pollution levels based on pollutants such as PM2.5, PM10, NO₂, SO₂, CO, and O₃. Traditional AQI estimation methods fail to capture complex, non-linear relationships between pollutants and cannot provide accurate future predictions. This study presents a Machine Learning-based AQI prediction system using regression models including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. The dataset consists of real-world air pollution data containing multiple pollutant parameters across different cities. Data pre-processing, feature engineering, and Exploratory Data Analysis (EDA) were performed to improve model performance. Among all models, the Random Forest Regressor achieved the best results with an R² score of 0.92, demonstrating superior capability in handling non-linear relationships and noisy environmental data. The system provides accurate AQI predictions and can be extended for real-time monitoring and smart city applications. Key Words: Air Quality Index, Machine Learning, Random Forest, Pollution Prediction, Time Series Data, Environmental Monitoring.

Conventional TOPSIS approaches for comprehensive air pollution assessment are often constrained by their reliance on pre-assigned weights and high sensitivity to outliers. To address these limitations, an Un-weighted TOPSIS (UW-TOPSIS) method was applied to evaluate the air quality of Zhenjiang Port from September 2021 to September 2024 based on six criteria pollutants (PM2.5, PM10, SO2, NO2, CO, and O3).Meanwhile, AHP-TOPSIS (incorporating expert-derived weights) and EW-TOPSIS (using entropy weight determination) were also employed for comparative analysis. Model performance was quantitatively evaluated against normalized Air Quality Index (AQI) scores using the relative error (ε). Analysis revealed that Zhenjiang Port's pollution was predominantly driven by PM10 (37.93% of days) and PM2.5 (36.75% of days), exhibiting a distinct seasonal pattern of winter highs and summer lows. Methodologically, AHP method yielded a combined weight of 0.6282 for PM, showing a pronounced bias, whereas the EW produced a more balanced weight structure (0.3334 combined) but was highly sensitivity to gaseous pollutants, particularly NO2 and CO. Using July 2023 (low pollution) and January 2024 (high pollution) as representative periods, UW-TOPSIS demonstrated superior stability. During the low-pollution period, 68.75% of days exhibited ε < 0.06 under UW-TOPSIS, outperforming AHP (54.84%) and EW (48.39%); during the high-pollution period, UW-TOPSIS maintained stability with only 32.3% 32.3% exceeded ε = 0.1, significantly outperforming AHP (64.5%) and avoiding the extreme error peaks (up to 0.463) observed in the EW method. Ultimately, AHP's reliance on subjective weighting amplified errors during shifts in pollution composition, while the EW method proved excessively sensitive to outliers, yielding volatile evalution outputs. By eliminating explicit weight assignment in favor of bounded weight constraints, UW-TOPSIS substantially enhanced stability and robustness. These findings confirm its reliability of UW-TOPSIS under multi-pollutant conditions, presenting a robust framework for developing composite pollution indices and the evaluating regional air quality management efficacy.

-Air pollution poses a severe threat to public health and the environment due to the presence of pollutants such as PM2.5, PM10, NOₓ, CO, and volatile organic compounds (VOCs). Conventional air purification systems are primarily limited to indoor environments and lack scalability for outdoor applications. This paper proposes a solar-powered outdoor air purification system integrated with real-time air quality monitoring. The system operates using renewable solar energy, ensuring sustainable and energy-efficient performance. A multi-stage filtration mechanism, comprising HEPA filters, activated carbon, and photocatalytic oxidation layers, is employed for effective removal of particulate matter and gaseous pollutants. An IoT-enabled sensing module continuously monitors key air quality parameters and computes the Air Quality Index (AQI). The collected data is transmitted to a cloud-based platform for real-time analysis, visualization, and public accessibility. Furthermore, the system incorporates a carbon credit generation framework based on quantified pollutant reduction metrics, enabling potential economic benefits. The proposed solution offers a scalable, cost-effective, and environmentally sustainable approach to improving outdoor air quality and supporting climate action initiatives. Keywords— Air Pollution, Solar Energy, Air Purifier, IoT, Air Quality Monitoring, AQI, Carbon Credits, Sustainable Systems, Smart Environment, Renewable Energy.

Purpose: This study evaluates the performance of distributed lag, autoencoder, and LSTM autoencoder methods in detecting point anomalies in simulated data generated from Jakarta's Air Quality Index (AQI). The evaluation is conducted across several simulation scenarios that represent factors that may influence anomaly detection performance. Methods: Simulation scenarios were constructed by varying two anomaly characteristics: anomaly percentage (0.3%, 0.5%, and 1.0%) and anomaly depth (4.4σ, 4.7σ, and 5.0σ), yielding 90 datasets generated via repeated experiments. Anomaly detection was performed using a forecasting-based approach with a 4σ threshold on prediction errors. Model performance was evaluated using mean absolute percentage error (MAPE) for forecasting accuracy and balanced accuracy for anomaly detection. Result: Increasing anomaly percentage significantly degrades both forecasting and anomaly detection performance across all methods. In contrast, anomaly depth has no significant effect on forecasting accuracy but strongly influences detection performance. Among the evaluated methods, the distributed lag model consistently shows the most robust anomaly detection performance across scenarios, outperforming the autoencoder and LSTM autoencoder, particularly at higher anomaly percentages and depths. Novelty: This study introduces a more rigorous and structured evaluation framework for anomaly detection by integrating three key contributions. First, it provides a unified comparison of distributed lag, autoencoder, and LSTM autoencoder methods within a single experimental setting, a limitation in prior studies. Second, it employs a controlled simulation design that systematically varies the anomaly percentage and depth while preserving key characteristics of empirical AQI data, enabling a more objective assessment of model performance across diverse anomaly conditions. Third, it uses ANOVA and interaction analysis to formally examine the effects of anomaly characteristics on both forecasting and detection performance, moving beyond purely descriptive comparisons commonly used in previous research.

Kilning with smoke-affected air impacts the chemical and aromatic qualities of Cascade hops.

Accurately predicting hospitalization risk in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive outpatients is critical for triage and resource planning. We developed and externally validated a clinical prediction model for 30-day COVID-19 hospitalization using data from symptomatic adults who tested positive in the outpatient setting. The derivation cohort included 22 859 patients from 185 outpatient clinics in a large Utah health care network between December 2021 and March 2023, during Omicron variant predominance. Among these patients, 281 (1.2%) were hospitalized for COVID-19 within 30 days. We fit random forest and multivariable logistic regression models incorporating clinical variables (vital signs, comorbidities, and vaccination status), social determinants of health, seasonality, and air quality indices. We externally validated our model using data from 10 670 patients in a Pennsylvania healthcare network who tested positive between October 2021 and November 2022, among whom 166 (1.6%) were hospitalized. In cross-validation, a random forest model including only clinical predictors performed similarly to an expanded model that also included social, environmental, and seasonal predictors (area under the receiver operator characteristic curve [AUC]: 0.83, 95% CI: 0.77-0.88 for both). A parsimonious logistic regression model with only 3 clinical predictors (respiratory rate, age, and pulse oximetry) achieved an AUC of 0.79 (95% CI: .72-.86) on internal validation and an AUC of 0.88 (95% CI: .85-.90) on external validation. Calibration was robust, and decision curve analysis demonstrated clinical utility at low-risk thresholds. We conclude that a parsimonious 3-predictor model can effectively stratify hospitalization risk in SARS-CoV-2 positive outpatients, offering a practical tool to support clinical decision-making and optimize resource allocation during current and future COVID-19 surges.

Policymakers require accurate and localized data on the burden of chronic respiratory diseases (CRDs) to allocate resources and design targeted interventions. Our objective was to explore longitudinal patterns in CRD metrics across Iranian provinces over three decades, leveraging recent subnational estimates from Global Burden of Disease (GBD) 2021 to derive actionable public health insights. We used longitudinal K-means clustering (KmL) to identify provincial-level trends in age-standardized rates (ASRs) of mortality (ASMR), prevalence (ASPR), and incidence (ASIR) attributable to CRDs, including asthma and COPD. Risk-attributed burden estimates were also examined for major environmental and occupational exposures. CRD-related ASMR, ASPR, and ASIR decreased nationally between 1990 and 2021. Sistan-Baluchestan and South Khorasan provinces showed the highest cluster profiles for ASIR and ASPR, while Kerman province alone formed the highest mortality (ASMR) cluster. Among modifiable exposures, tobacco use dominated male-attributed CRD burden, whereas air quality indicators, particularly particulate pollutants, were more prominent among females. Kerman also had the highest burden of CRD mortality due to occupational risks and air pollution. Geographical inequalities and distinct risk profiles underline the need for region-specific prevention strategies. National-level interventions to reduce exposure to modifiable risk factors, particularly smoking and air pollution, are essential to further reduce the burden of CRDs in Iran.

Climate-sensitive hazards-heat, wildfire smoke, floods, and hurricanes-increase morbidity and mortality and disrupt routine care, yet US policy centers on disaster declarations rather than day-to-day hazards. We outline a practical framework to integrate climate adaptation into health insurance coverage, using public indicators such as heat alerts and air quality indices to trigger regional activations that last for the duration of the hazard window. Actions follow two pathways: reduce exposure during short high-risk periods by providing supports such as cooling access and indoor air filtration, and when routine channels fail, preserve care by expanding access during hazard windows using exceptions such as early refills and temporary network flexibilities. Near-term implementation channels include Medicaid Section 1115 demonstrations and "in lieu of services" provisions (which allow a state to substitute cost-effective services outside of its federally approved state Medicaid plan); Medicare Advantage supplemental benefits and Special Needs Plans; existing emergency authorities; and commercial plan flexibilities, following comparable domestic and international precedents. As climate risks grow, embedding adaptation in health insurance systems may be among the most practical and scalable strategies to protect population health.

Atmospheric microplastic deposition in 24 Chinese cities with different socio-economic development levels.