PM10 Concentrations Research Articles

Accurate and efficient prediction of atmospheric PM1, PM2.5, PM10, and O3 concentrations using a customized software package based on a machine-learning algorithm.

Particulate matter (PM) and ozone (O3) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O3 are secondary pollutants, and there remain significant challenges in accurately and efficiently predicting their concentrations in the atmosphere. In this study, one-year monitoring data of PM1, PM2.5, PM10, and O3 concentrations as well as meteorological parameters and concentrations of various precursors (i.e., nitrogen oxides, SO2, CO, alkanes, aldehydes, and ketones) are obtained at a monitoring site in central China's Hunan province. The eXtreme Gradient Boosting model is trained and tested to achieve efficient and accurate predictions of the concentrations of the four pollutants. The effects of different datasets, input features, and model parameters on the prediction accuracy are investigated. Principal component analysis is employed to further reduce the dimensions of features, increasing the prediction efficiency. Finally, all model training and prediction processes are incorporated in an executable application using the PyQt5 framework to build a user interface. The customized software supports user-defined modeling. The software can simultaneously predict PM1, PM2.5, PM10, and O3 concentrations, making the prediction process convenient.

Comprehensive analysis of resilience of human airway epithelial barrier against short-term PM2.5 inorganic dust exposure using invitro microfluidic chip and exvivo human airway models.

The updated World Health Organization (WHO) air quality guideline recommends an annual mean concentration of fine particulate matter (PM2.5) not exceeding 5 or 15 μg/m3 in the short-term (24 h) for no more than 3-4 days annually. However, more than 90% of the global population is currently exposed to daily concentrations surpassing these limits, especially during extreme weather conditions and due to transboundary dust transport influenced by climate change. Herein, the effect of respirable <PM2.5 inorganic silica particle exposures on epithelial barrier integrity was simultaneously evaluated within the biomimetic microfluidic platform-based airway epithelial barrier (AEB)-on-a-chip and human bronchoscopic exvivo airway tissue models, comparatively. Silica particles at an average size of 1 μm, referred to as <PM2.5, dose-dependently tested by MTT and LDH analyses. The elicited dose of 800 μg/mL was applied to human airway epithelial cells (Calu-3) seeded to the membrane at air-liquid interface in the AEB-on-a-chip platform, which is operated under static and dynamic conditions and to exvivo human bronchoscopy bronchial tissue slices for 72 h. For both models, healthy and exposed groups were comparatively investigated. Computational fluid dynamics simulations were performed to assess shear stress profiles under different flow conditions. Qualitative and quantitative analyses were carried out to evaluate the resilience of the epithelial barrier via cell survivability, morphology, barrier integrity, permeability, and inflammation. In the AEB-on-a-chip platform, short-term exposure to 800 μg/mL PM2.5 disrupted AEB integrity via increasing barrier permeability, decreasing cell adhesion-barrier markers such as ZO-1, Vinculin, ACE2, and CD31, impaired cell viability and increased the expression levels of proinflammatory markers; IFNs, IL-6, IL-1s, TNF-α, CD68, CD80, and Inos, mostly under dynamic conditions. Besides, decreased tissue viability, impaired tissue integrity via decreasing of Vinculin, ACE2, β-catenin, and E-cadherin, and also proinflammatory response with elevated CD68, IL-1α, IL-6, IFN-Ɣ, Inos, and CD80 markers, were observed after PM2.5 exposure in exvivo tissue. The duration and concentration of PM2.5 that can be exposed during extreme weather conditions and natural events aligns with our exposure model (0-800 μg/mL 72 h). At this level of exposure, the resilience of the epithelial barrier is demonstrated by both AEB-on-a-chip platform emulating dynamic forces in the body and exvivo bronchial biopsy slices. Lung-on-a-chip models will serve as reliable exposure models in this context.

The Role of Subsurface Changes and Environmental Factors in Shaping Urban Heat Islands in Southern Xinjiang

The urban heat island (UHI) effect is one of the most prominent surface climate changes driven by human activities. This study examines the UHI characteristics and influencing factors in the Southern Xinjiang urban agglomeration using MODIS satellite data combined with observational datasets. Our results reveal a significant increase in impervious surfaces in the region between 1995 and 2015, with the most rapid expansion occurring from 2010 to 2015. This urban expansion is the primary driver of changes in UHI intensity. The analysis from 2000 to 2015 shows substantial spatial variation in UHI effects across cities. Hotan recorded the highest annual average daytime UHI intensity of 3.7 °C, while Aksu exhibited the lowest at approximately 1.6 °C. Daytime UHI intensity generally increased during the study period, with the highest intensities observed in the summer. However, nighttime UHI trends varied across cities, with most showing an increase in intensity. Temperature, precipitation, and aerosol optical depth (AOD) were identified as the main factors influencing annual average daytime UHI intensity, while PM10 concentration showed a weak and inconsistent correlation with UHI intensity, varying by city and season.

Prediction of Jakarta's Air Quality Using a Stacking Framework of CLSTM, CatBoost, SVR, and XGBoost

Air quality prediction, particularly in estimating PM10 particle concentration, is a significant challenge in major cities like Jakarta, which experience high levels of air pollution. This study aims to develop an air quality prediction model using an innovative stacking framework that combines several machine learning algorithms, namely ConvLSTM, CatBoost, SVR, and XGBoost. The methodology employed in this research is an experimental approach, where each model is trained and tested individually before being integrated into the stacking framework. The dataset used was sourced from the Kaggle platform, containing historical air quality data in Jakarta. Performance evaluation was conducted by measuring the Root Mean Squared Error (RMSE) for each model. The results of the study showed that the ConvLSTM model produced an RMSE of 13.5168, CatBoost with an RMSE of 13.4113, and SVR with an RMSE of 14.2725. To improve prediction accuracy, the researchers employed a stacking approach of the four models (ConvLSTM, CatBoost, SVR, and XGBoost), which yielded a much lower RMSE of 0.8093. Thus, this stacking framework has proven to significantly enhance air quality prediction performance, particularly in predicting PM10 concentrations in Jakarta.

Development of Particulate Matter Concentration Estimation Models for Road Sections Based on Micro-Data

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.

Association Between Particulate Matter Pollutants and Ophthalmology Visits for Ocular Surface Irritation and Allergy.

The World Health Organization has declared climate change to be "the single biggest health threat facing humanity", yet there are limited studies on the impact of climate change-related air pollution on ocular health. To explore associations between ocular surface irritation and allergy-related daily outpatient office visits with daily ambient particulate matter (PM) levels in the Denver Metropolitan Area. Daily visit counts of ophthalmology outpatient offices were obtained from an academic health center (October 1st, 2015 to January 27th, 2023). Daily ambient average concentrations of PM ≤ 10µm in diameter (PM10) and 2.5 micrometers or less in diameter (PM2.5) were obtained. Data were analyzed using distributed lag nonlinear models while accounting for the cumulative lagged effects of PM10 and PM2.5 for visits. Modifications due to temperature were assessed using stratified models. There were 144,313 ocular surface irritation and allergy visits to ophthalmic clinics during the study period. Daily visit counts increased with increasing daily ambient PM10 and PM2.5 concentrations. Five-day cumulative ambient PM10 concentrations at 80, 90, 100, and 110 µg/m3 showed higher rate ratios at each level from 1.77 (95% CI: 1.71, 1.84) at concentration of 80 to 2.20 (95% CI: 2.09, 2.30) for concentration of 110 µg/m3. Similarly, the visit rate ratios increased as the daily PM2.5 concentration increased, but this gradient effect was not significantly higher as the concentration increased. The effect of PM10 was higher on days when the temperature was below the average. Temperature did not affect the association between daily visit counts and PM2.5. This study found that ambient PM increased the rate of ophthalmology visits due to ocular surface irritation and allergies. Most importantly, this association increased in magnitude with higher ambient PM concentrations. Additional studies are required to fully understand the effects of climate change-related stressors on ocular health.

Sustainable Environmental Management: Evaluating the Effects of the Tail Number Restriction Policy on Air Quality in Lanzhou, China

In recent years, haze pollution in China has garnered significant attention from both the government and the public, with vehicle emissions being identified as a key source of pollution. This study analyzes the impact of the tail number restriction policy (TNRP) on air quality using daily data from Lanzhou, China, in 2020, by employing the regression discontinuity in time (RDiT) method. The results show that the TNRP significantly reduced the air quality index (AQI) and concentrations of PM2.5, PM10, NO2, and CO, indicating a positive effect on air quality, though this is not a long-term standalone solution. Unlike previous policies, the current TNRP restricts traffic during peak hours, yielding consistent effects. This study recommends continuing the restriction policy and developing a comprehensive environmental governance system to promote new energy vehicles and enhance public transportation infrastructure.

The effect of the ultra-low emission zone on PM2.5 concentration in Seoul, South Korea

As studies regarding the positive effect of Low Emission Zones (LEZs) and people's risk perception about air pollution have increased, more powerful and specific traffic regulation policies have been required. London is the first city in the world to implement an Ultra-low Emission Zone (ULEZ) in addition to the existing LEZ. Benchmarking London's ULEZ, a ULEZ policy was implemented in Seoul, South Korea on December 1, 2019. The goal of the policy is to improve air quality by prohibiting entry of vehicles registered nationwide into Seoul's ULEZ that do not meet a specific emission standard including diesel, gasoline, and LPG fuel-based vehicles. This study analyzed the effect of Seoul's ULEZ policy on the five major atmospheric pollutants (PM2.5, PM10, NO2, CO, SO2, O3) concentration in the zone, particularly focusing on PM2.5 concentration. The analysis employs Difference-in-Differences (DD) approach, comparing data from one year before and after the policy's implementation on December 1, 2019. The findings indicate that Seoul's ULEZ policy resulted in a 9.8% increase in PM2.5 concentrations. Conversely, the policy led to reductions in PM10, NO2, CO, and SO2 concentrations by 12.0%, 17.3%, 5.9%, and 10.8%, respectively, while the effect on O3 was statistically insignificant. These empirical results suggest that the ULEZ may need to incorporate more stringent emission standards, expand its coverage, or introduce additional measures to address the unintended increase in PM2.5 concentration.

Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China.

To mitigate the societal impact of the COVID-19 pandemic, China implemented long-term restrictive measures. The sudden liberalization at the end of 2022 disrupted residents' daily routines, making it scientifically intriguing to explore its effect on air quality. Taking Chongqing City in Southwest China as an example, we examined the impact of restriction liberalization on air quality, identified potential sources of pollutants, simulated the effects of abrupt anthropogenic control relaxation using a Random Forest Model, and applied an optimized model to predict the post-liberalization pollutant concentrations. The results showed increases in PM2.5 (72.3%), PM10 (67.7%), and NO2 (21.9%) concentrations, while O3 concentration decreased by 20.5%. Although potential pollution source areas contracted, pollution levels intensified with northeastern Sichuan, interior Chongqing, and northern Guizhou being major contributors to pollutant emissions. Anthropogenic emissions accounted for 26.7 ~ 33% changes in PM2.5 and PM10 concentrations while meteorological conditions contributed to 40.2 ~ 43.3% variations observed during the period. The optimized model demonstrated a correlation between predicted and observed values with R2 ranging from 0.70 to 0.89, enabling accurate prediction of post-liberalization pollutant concentrations. This study can enhance our understanding regarding the impact of sudden social lockdown relaxation events on air quality while providing support for urban air pollution prevention.

Air Pollution Reduces the Individuals’ Life Satisfaction Through Health Impairment

Abstract Background The impact of air pollution on individuals’ happiness and life satisfaction (LS), and its relationship to other factors became the focus of recent research. Though, the underlying mechanism of how air pollution impacts LS remains unclear. In this study, we examined the direct and indirect effect of air pollution on individuals’ LS through health mediation. Methods We used longitudinal individual-level data from “Understanding-Society: the UK Household-Longitudinal Study” on 59,492 individuals with 347,377 repeated responses across 11 years (2009-2019) that was linked to yearly concentrations of NO2, SO2, and particulate-matter (PM10, PM2.5) pollution. Generalized structural equation models with multilevel ordered-logistic regression were used to examine the direct effect of air pollution on LS and the indirect effect from health impairment. Results Higher concentrations of NO2 (coefficient = 0.009, 95%CI = 0.007,0.012, p &amp;lt; 0.001), SO2 (coefficient = 0.025, 95%CI = 0.017,0.034, p &amp;lt; 0.001), PM10 (coefficient = 0.019, 95%CI = 0.013, 0.025, p &amp;lt; 0.001), and PM2.5 (coefficient = 0.025, 95%CI = 0.017,0.033, p &amp;lt; 0.001) pollutants were associated with poorer health, while poorer health was associated with reduced LS (coefficient = -0.605, 95%CI = -0.614,-0.595, p &amp;lt; 0.001). Mediation path analysis showed that air pollution impacted individuals’ LS directly and indirectly. The percent of total effect mediated through health was 44.03% for NO2, 73.95% for SO2, 49.88% for PM10, and 45.42% for PM2.5 and the ratio of indirect to direct effect was 0.79 for NO2, 2.84 for SO2, 0.99 for PM10, and 0.83 for PM2.5. Conclusions Health plays a major mediating role in the relationship between air pollution and LS. To alleviate the impact of air pollution on LS, future strategies should focus on health promotion besides reducing air pollution emissions. Key messages • Air pollution is related to lower life satisfaction directly and indirectly through health impairment. • Strategies and policies aiming at reducing air pollution emissions and better health would improve the individual’s life satisfaction and lower the mental health burden.

Light Absorption Properties of Brown Carbon Aerosol During Winter at a Polluted Rural Site in the North China Plain

Brown carbon aerosols (BrC), a subfraction of organic aerosols, significantly influence the atmospheric environment, climate and human health. The North China Plain (NCP) is a hotspot for BrC research in China, yet our understanding of the optical properties of BrC in rural regions is still very limited. In this study, we characterize the chemical components and light absorption of BrC at a rural site during winter in the NCP. The average mass concentration of PM1 is 135.1 ± 82.3 μg/m3; organics and nitrate are the main components of PM1. The absorption coefficient of BrC (babs,BrC) is 53.6 ± 45.7 Mm−1, accounting for 39.5 ± 10.2% of the total light absorption at 370 nm. Diurnal variations reveal that the babs,BrC and organics are lower in the afternoon, attributed to the evolution of planetary boundary layers. BrC is mainly emitted locally, and both the aqueous phase and the photooxidation reactions can increase babs,BrC. Notably, the babs,BrC is reduced when RH &gt; 65%. During foggy conditions, reactions in the aqueous phase facilitate the formation of secondary components and contribute to the bleaching of BrC. This process ultimately causes a decrease in both the absorption Ångström exponent (AAE) and the mass absorption efficiency (MAE). In contrast, the babs,BrC, along with AAE and MAE, rise significantly due to substantial primary emissions. This study enhances our understanding of the light absorption of BrC in rural polluted regions of the NCP.

Extreme Saharan dust events expand northward over the Atlantic and Europe, prompting record-breaking PM10 and PM2.5 episodes

Abstract. Unprecedented extreme Saharan dust (duxt) events have recently expanded northward from subtropical NW Africa to the Atlantic and Europe, with severe impacts on the Canary Islands, mainland Spain and continental Portugal. These six historic duxt episodes occurred on 3–5 and 22–29 February 2020, 15–21 February 2021, 14–17 January 2022, 29 January–1 February 2022, and 14–20 March 2022. We analyzed data of 341 governmental air quality monitoring stations (AQMSs) in Spain (330) and Portugal (11), where PM10 and PM2.5 are measured with European norm (EN) standards, and found that during duxt events PM10 concentrations are underestimated due to technical limitations of some PM10 monitors meaning that they can not properly measure extremely high concentrations. We assessed the consistency of PM10 and PM2.5 data and reconstructed 1690 PM10 (1 h average) data points of 48 and 7 AQMSs in Spain and Portugal, respectively, by using our novel “duxt-r” method. During duxt events, 1 h average PM10 and PM2.5 concentrations were within the range 1000–6000 µg m−3 and 400–1200 µg m−3, respectively. The intense winds leading to massive dust plumes occurred within meteorological dipoles formed by a blocking anticyclone over western Europe and a cutoff low located to the southwest, near the Canary Islands and Cape Verde, or into the Sahara. These cyclones reached this region via two main paths: by deviating southward from the Atlantic mid-latitude westerly circulation or northward from the tropical belt. The analysis of the 2000–2022 PM10 and PM2.5 time series shows that these events have no precedent in this region. The 22–29 February 2020 event led to (24 h average) PM10 and PM2.5 concentrations within the range 600–1840 and 200–404 µg m−3, respectively, being the most intense dust episode ever recorded on the Canary Islands. The 14–20 March 2022 event led to (24 h average) PM10 and PM2.5 values within the range 500–3070 and 100–690 µg m−3 in southeastern Spain, 200–1000 and 60–260 µg m−3 in central Spain, 150–500 and 75–130 µg m−3 in the northern regions of mainland Spain, and within the ranges 200–650 and 30–70 µg m−3 in continental Portugal, respectively, being the most intense dust episode ever recorded in these regions. All duxt events occurred during meteorological anomalies in the Northern Hemisphere characterized by subtropical anticyclones shifting to higher latitudes, anomalous low pressure expanding beyond the tropical belt and amplified mid-latitude Rossby waves. New studies have reported on recent record-breaking PM10 and PM2.5 episodes linked to dipole-induced extreme dust events from North Africa and Asia in a paradoxical context of a multidecadal decrease in dust emissions, a topic that requires further investigation.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.

Affordable particulate matter (PM) monitors suitable for use on horses will facilitate the evaluation of PM mitigation methods and improve the management of equine asthma. Calibrate a real-time wearable PM monitor (Black Beauty [BB]) and compare the PM exposures of horses fed dry or soaked hay. Laboratory calibration; complete cross-over feed trial. Side-by-side sampling with BB monitors and tapered element oscillating microbalances (TEOMs) was performed under varying concentrations of PM from alfalfa hay. Linear regression was used to derive a calibration formula for each unit based on TEOM PM measurements. Precision was evaluated by calculating the coefficient of variation and pairwise correlation coefficients between three BB monitors. PM exposure was measured at the breathing zone of 10 horses for 8 h after they were fed dry or soaked hay. Repeated measures generalised linear models were constructed to determine the effect of hay treatment and measurement duration (initial 20-min vs. 8-h) upon exposure to PM with diameters smaller than or equal to 10 μm (PM10) and 2.5 μm (PM2.5). BB monitor PM2.5 and PM10 measurements were linearly correlated with TEOM data (coefficient of determination r2 > 0.85 and r2 > 0.90 respectively), but underestimated PM2.5 mass concentrations by a factor of 4 and PM10 concentrations by a factor of 44. Measures from the three BB monitors had a coefficient of variation <15% and pairwise r > 0.98. Feeding soaked hay significantly reduced average PM2.5 exposures (20-min: dry: 160 μg/m3, soaked: 53 μg/m3, p < 0.0001; 8-h: dry: 76 μg/m3, soaked: 31 μg/m3, p = 0.0008) and PM10 exposures (20-min: dry: 2829 μg/m3, soaked: 970 μg/m3, p < 0.0001; 8-h: dry: 1581 μg/m3, soaked: 488 μg/m3, p = 0.008). No health outcome measures were collected. With appropriate corrections, the BB monitor can be used to estimate horse PM exposure. While 20-min measurements yielded higher estimates of exposure than 8-h measurements, both intervals demonstrate that soaking hay reduces PM exposures by more than 50%.

Air quality in different urban hotspots in a metropolitan city in India and the environmental implication.

This research study investigates hourly data on concentrations of five major air pollutants such as particulate matter (PM10, PM2.5) and gaseous pollutants (SO2, NO2, CO) measured during 2022 at four hotspot sites (industrial site, traffic site, commercial site, harbour, and one residential site) in Chennai, India. The analysis encompasses temporal variations spanning annual, seasonal, and diurnal variations in the pollutants. Notably, PM10 and CO emerge as the predominant pollutants, with the highest concentrations at industrial and traffic sites (PM10: 67.64 ± 40.77µg/m3, CO: 1.41 ± 0.84mg/m3; traffic site: PM10: 58.67 ± 20.05µg/m3, CO: 0.99 ± 0.57mg/m3). Seasonal dynamics reveal prominent winter spikes in particulate matter (PM10, PM2.5) and carbon monoxide (CO) concentrations, while nitrogen dioxide (NO2) and sulphur dioxide (SO2) levels peak during the summer season, particularly in the harbour area. The proximity to roadways exerts a discernible influence on diurnal patterns, with traffic sites showcasing broader rush hour peaks compared to sharper spikes observed at other sites. Furthermore, distinct bimodal patterns are evident for PM10 and PM2.5 concentrations in residential and harbour areas. A common lognormal distribution pattern is identified across the studied sites, suggesting consistent air quality trends despite contrasting locations. The conditional probability function (CPF) is used in conjunction with local meteorological conditions for identifying key pollution sources in each location. The implementation of polar plots emphasizes industries as principal local sources of pollution, at industrial sites significantly contributing to PM10, SO2, and NO2 concentrations under specific wind conditions. The main objective of the present study is to facilitate a good understanding of pollutant dynamics, pollution sources, and their intricate interplay with meteorological factors, thereby contributing to the formulation and implementation of effective air pollution control and mitigation strategies.

The Formaldehyde, TVOCs, PM2.5 and PM10 Concentrations at Outdoor Street Food in Kalasin, Thailand

Human health and well-being are directly impacted by air quality. Cardiovascular and respiratory problems have been linked to exposure to pollutants such as formaldehyde, total volatile organic compounds (TVOCs), and particulate matter 2.5 and 10 micrometers (PM2.5 and PM10). This study aimed to quantify formaldehyde, TVOCs, PM2.5, and PM10 levels in the outdoor air at street food vendors in Kalasin, Thailand. The study was conducted at street food vendors in the province of Kalasin. Formaldehyde values at the top five food stalls in Kalasin ranged from 0.58 to 1.41 mg/m3. The PM2.5 values ranged from 2.29 to 9.43 ug/m3, and the TVOC values ranged from 2.91 to 7.02 ug/m3, with PM10 concentrations ranging from 2.86 to 7.29 ug/m3. Drawing on data about these pollutant concentrations across five distinct locations, the following recommendations can be put forth: Elevated levels of formaldehyde, TVOCs, PM2.5, and PM10 in certain areas can have detrimental effects on the environment and human well-being.

Vertical distribution of ambient air pollutants (PM2.5, PM10, NOX, and NO2); A systematic review

BackgroundNumerous investigations have explored variations in vertical air pollutant concentrations, yielding diverse findings. So, we have conducted this systematic review to gain a more comprehensive understanding of the vertical distribution of air pollutant concentrations (PM2.5, PM10, NOX, and NO2) and the influencing factors. MethodsRelevant studies were identified by searching the three central electronic databases, PubMed, Web of Science, and Scopus, from the beginning of 2000 to the end of 2023. This study included original articles published in English that examine the pollutant concentration variations below 500 m. FindingOf 3614 articles, 57 studies met our criteria. Our findings showed a decline in PM2.5 and NOX concentrations as altitude increased, while NO2 concentrations exhibited an increase. Conversely, no statistically significant relationship was identified between altitude and PM10 concentrations. The regression analysis yielded coefficients for the relationship between concentration and altitude (0–500 m) as follows: PM2.5 (−0.11), PM2.5/PM10 (0.0008), NOX (−0.11), and NO2 (0.13). Conducting additional research on this topic and investigating the impact of meteorological parameters on pollutant concentrations at different altitudes enhances our understanding of the vertical distribution of pollutant concentrations.

A Study on Ambient Air Quality and Management inCommercial Areas of Gorakhpur City

The current study evaluates the city of Gorakhpur's ambient air quality using an air quality index. For the period January 2018 to November 2022, the 24-hour average concentration of the three primary pollutants SO2, NO2, andPM10 in a commercial area close to Jalkal Bhawan in Gorakhpur city was measured. Pollutant measurements were made between January 2018 and November 2022. The IND-AQI technique was implemented to calculate the Air Quality Index. In July, the highest monthly average concentrations of PM10, SO2, and NO2 were recorded at 401.13, 36.41, and 54.02, respectively. The yearly average SO2 concentration was found to be within the CPCB- recommended limit, while NO2 was found to be slightly above the maximum allowable level. However, PM10 wasconsistently found to be above the CPCB-recommended limit. The average monthly concentration of NO2 is higherthan SO2, and the overall AQI rating was determined to be moderate. The global experiences clearly demonstratedthat international action had been city specific rather than country oriented, and as a result, statistics show a reductionin PM2.5 of 35%–40% in five years for cities like Beijing and Seoul, whereas cities like Santiago and Mexico Cityhave shown reductions in PM2.5 and PM10 concentration of 73% and 61% in 20 to 25 years, respectively. Recent research published in the Lancet found that over the past five years, CO and Sulphur dioxide levels decreased by 28.2% and 54.1%, respectively, while NO2 and O3 concertation showed no considerable improvement. The August2018 TERI and ARAI report examined several interventions and predicted their possible effects. PM2.5 and PM10 levelsin Delhi and the NCR are above average. A different scenario has been developed in light of the intervention that can improve air quality the most. The NCAP produces an estimated national level target of 20%-30% decreasesin PM2.5 and PM10 concentration by 2024 after taking into consideration the existing international and national studies. This keeps 2018 as the baseline year for concentration comparisons. Gorakhpur City has recently experienced an increase in development activity, which has led to particulate matter.

Quantifying the noncarcinogenic and carcinogenic risks resulting from the inhalation of PM2.5-bound metals: A multicity analysis and implications for public health

Toxic metals in PM2.5 represent a growing public health concern because of their potential health effects. Ambient PM2.5 samples were collected from five provinces around Bangkok from December 2020 to December 2021. The concentrations of PM2.5 and metals (arsenic, cadmium, chromium, cobalt, nickel, lead, and vanadium) were analyzed. The noncarcinogenic and carcinogenic risks were evaluated via Monte Carlo simulations in four age groups (0 to <6, 6 to <12, 12 to <18, 18–70 years), with a dataset comprising 2282 heavy metal assays within the particulate matter. Significantly higher PM2.5 concentrations were detected in provinces with higher factory densities, as well as higher concentrations during haze periods than during non-haze periods. The HI values for all age groups and provinces were lower than 1, indicating acceptable noncarcinogenic risks. The carcinogenic risks from PM2.5-bound metals varied between 7.08×10−7 and 4.29×10−6, indicating increased cancer risks, for preschool children and adults. Cancer risk was highest in industrial areas, followed by areas with dense traffic, and lowest in agricultural areas. The results indicated that Thailand's current air quality regulations, which primarily provide only lead limits, are insufficient considering the high carcinogenic potential of other metals. The Monte Carlo simulation results confirmed that lowering Thailand’s 24-h PM2.5 standard from 37.5 to either 25 or 15 µg m−3, as specified in the 2021 World Health Organization guidelines, would decrease cancer risk by 9.63–43.18 %.

About Air Quality Assessment using Time Series Analysis Techniques on Air Particles Data

This study investigates the dynamics of air pollution caused by particulate matter (PM10 and PM2.5) in Omsk, Russia. Data collected from the Sensor.Community network between 2021 and 2023 were analyzed using time series analysis methods. The findings reveal a subtle weekly seasonality in the data, potentially attributed to fluctuations in industrial activity and traffic patterns throughout the week. Additionally, a concerning trend of deteriorating air quality was identified, characterized by increasing annual average concentrations of PM10 and PM2.5. The study employed various time series analysis techniques, including Empirical Cumulative Distribution Function (ECDF), Seasonal and Trend decomposition using Loess (STL), and autocorrelation function (ACF) analysis. These methods allowed for the visualization and examination of data distribution, trend identification, and seasonality detection. Furthermore, the Mann-Kendall test confirmed a statistically significant trend of increasing pollution levels over the three-year period. To enhance the accuracy of spatial analysis, data interpolation was performed using kriging with a Gaussian model. This method, compared to simple interpolation, provides a more precise assessment of the spatial distribution of pollutants. The study also implemented Long Short-Term Memory (LSTM) for forecasting hourly pollution values. LSTM exhibited promising results, demonstrating its potential for developing early warning systems, particularly when combined with meteorological data such as wind forecasts. The results highlight the need for effective measures to mitigate air pollution in Omsk and emphasize the importance of utilizing data from diverse sources, including official air quality monitoring stations, to ensure accuracy and reliability in air quality assessments.