PM10 Concentrations Research Articles

Is It Possible to Breathe Fresh Air in Health Resorts? A Five-Year Seasonal Evaluation of Benzo(a)pyrene Levels and Health Risk Assessment of Polish Resorts

This study examines air quality in Polish health resorts (HR) and its potential health risks, challenging the belief that these environments always provide safe air for recovery. Over five years, air quality was evaluated by measuring concentrations of PM10 and benzo(a)pyrene (B(a)P) in seven resorts with varying environmental conditions. Using data from 3781 daily samples, both non-carcinogenic and carcinogenic risks were assessed for visitors (various age groups) and employees to determine health risks from prolonged exposure. The findings show frequent exceedances of national B(a)P limits, with some resorts, such as HR2 in Rabka-Zdrój and HR3 in Polanica-Zdrój, surpassing permissible levels by up to 320% and 373%, especially in winter. Non-carcinogenic risks exceeded safe limits by up to 40% for visitors in HR2 and 18% for employees in HR6 (Szczawno-Zdrój). Carcinogenic risks were up to 3.74 times higher than acceptable levels for visitors in HR2 and 3.15 times higher for employees in HR5 (Latoszyn), indicating a continuous risk from long-term exposure. These findings underscore the urgent need for measures to improve air quality in HRs. Given the global implications, similar risks could affect resorts worldwide. Therefore, future research should focus on cross-border studies and innovative strategies to manage pollution and protect health.

The Effect of Long-Term Particulate Matter Exposure on Respiratory Mortality: Cohort Study in China.

Particulate matter (PM), which affects respiratory health, has been well documented; however, substantial evidence from large cohorts is still limited, particularly in highly polluted countries and for PM1. Our objective was to examine the potential causal links between long-term exposure to PMs (PM2.5, PM10, and more importantly, PM1) and respiratory mortality. A total of 580,757 participants from the Guangzhou area, China, were recruited from 2009 to 2015 and followed up through 2020. The annual average concentrations of PMs at a 1-km spatial resolution around the residential addresses were estimated using validated spatiotemporal models. The marginal structural Cox model was used to estimate the associations of PM exposure with respiratory mortality, accounting for time-varying PM exposure. Results were stratified by demographics and lifestyle behaviors factors. Among the participants, the mean age was 48.33 (SD 17.55) years, and 275,676 (47.47%) of them were men. During the follow-up period, 7260 deaths occurred due to respiratory diseases. The annual average concentrations of PM1, PM2.5, and PM10 showed a declining trend during the follow-up period. After adjusting for confounders, a 6.6% (95% CI 5.6%-7.6%), 4.2% (95% CI 3.6%-4.7%), and 4.0% (95% CI 3.6%-4.5%) increase in the risk of respiratory mortality was observed following each 1-μg/m3 increase in concentrations of PM1, PM2.5, and PM10, respectively. In addition, older participants, nonsmokers, participants with higher exercise frequency, and those exposed to a lower normalized difference vegetation index tended to be more susceptible to the effects of PMs. Furthermore, participants in the low-exposure group tended to be at a 7.6% and 2.7% greater risk of respiratory mortality following PM1 and PM10 exposure, respectively, compared to the entire cohort. This cohort study provides causal clues of the respiratory impact of long-term ambient PM exposure, indicating that PM reduction efforts may continuously benefit the population's respiratory health.

Does COVID-19 lockdown matter for air pollution in the short and long run in China? A machine learning approach to policy evaluation

This paper leverages a data-driven two-step approach to effectively evaluate the effects of COVID-19 lockdown on air pollution in both the short and long-term in China. Using air pollution, meteorological conditions, and air mass clusters from 34 air quality monitoring stations in Beijing from 2015 to 2022, this study first employs a deweathering machine learning technique to decouple the confounding effects of meteorological on the air pollution. Furthermore, a detrending percentage change indictor is applied to remove the influence of seasonal variations on air pollution. The findings reveal that: (1) Human interventions are the primary drivers of changes in air pollution concentrations, whereas meteorological factors have a relatively minor impact. (2) During the COVID-19 lockdown, significant variations in air pollution levels are observed, with the effects of city lockdown ranging from a decrease of 40.11% ± 14.81% to an increase of 20.28% ± 14.36%. Notably, there is a decline in concentrations of NO2, PM2.5, CO, and PM10, while the levels of O3 and SO2 increase even during the strictest lockdown period. (3) In the year following the COVID-19 lockdown, there is a rebound in overall air pollution levels. However, by the second year, a general decline in air pollution is observed, except for O3. Therefore, it is imperative to integrate the confounding effects of meteorological factors into air quality management policies under various future scenarios: adopt high-intensity control measures for sudden air quality deteriorations, advance green recovery initiatives for long-term emission reductions, and coordinate efforts to reduce composite atmospheric pollution.

Indoor Air Quality at an Urban Primary School in Madrid (Spain): Influence of Surrounding Environment and Occupancy.

Monitoring indoor air quality (IAQ) in schools is critical because children spend most of their daytime inside. One of the main air pollutant sources in urban areas is road traffic, which greatly influences air quality. Thus, this study addresses, in depth, the linkages of meteorology, ambient air pollution, and indoor activities with IAQ in a traffic-influenced school situated south of Madrid. The measurement period was from 22 November to 21 December 2017. Simultaneous measurements of indoor and outdoor PM1, PM2.5, and PM10 mass concentrations, ultrafine particle number concentration (PNC) and equivalent black carbon (eBC) were analyzed under different meteorological conditions. PNC and eBC outdoor concentrations and their temporal trend were similar among the sampling points, with all sites being influenced in the same way by traffic emissions. Strong correlations were found between indoor and outdoor concentrations, indicating that indoor pollution levels were significantly affected by outdoor sources. Especially, PNC and eBC had the same indoor/outdoor (I/O) trend, but indoor concentrations were lower. The time delay in indoor vs. outdoor concentrations varied between 0.5 and 2 h, depending on wind speed. Significant differences were found between different meteorological conditions (ANOVA p-values < 2.14 × 10-6). Atmospheric stability periods led to an increase in indoor and outdoor pollutant levels. However, the highest I/O ratios were found during atmospheric instability, especially for eBC (an average of 1.2). This might be related to rapid changes in the outdoor air concentrations induced by meteorology. Significant variations were observed in indoor PM10 concentrations during classroom occupancy (up to 230 µg m-3) vs. non-occupancy (up to 19 µg m-3) days, finding levels higher than outdoor ones. This was attributed to the scholarly activities in the classroom. Conversely, PNC and eBC concentrations only increased when the windows of the classroom were open. These findings have helped to establish practical recommendations and measures for improving the IAQ in this school and those of similar characteristics.

The Impact of the New Year Celebration on the Air-Pollution in Slovakia

Fireworks and pyrotechnics are an integral part of the New Year celebration that take place both in cities and rural areas as well. Depending on the meteorological conditions, these activities can raise air pollutant concentrations in ambient atmosphere. In order to estimate the size of this increase, air pollutant concentrations from up to 61 air quality stations situated in the Slovak Republic in period from 2010 to 2023 are analysed. Pollutant concentrations of PM10, PM2.5, CO, NO2, SO2, O3 and benzene from New Year's Eve evening to New Year's morning are compared with data from 25 December to 7 January. In case of PM10 557 cases were analysed. Among them in 64 % the significant exceedance in concentrations which can be attributed to the New Year celebration with high probability was observed. The statistical difference between mean PM10 concentrations during the New Year celebration hours and during the surrounding days was strong (p ≤ 0,001). For PM2.5 the situation was similar. The New Year celebration affects other pollutant concentrations studied in this work in less extend. For them it is difficult to attribute the exceedance to the fireworks. This analysis provides clear evidence of the negative impact of fireworks and pyrotechnics on the air quality, which can lead to public health risks. This work can support policy makers to implement more stringent strategies to reduce air pollution and its effects on health.

Long term analysis of air quality parameters for Ludhiana, India: sources, trends and health impact.

Ludhiana, a pollution hot spot in North India, has seen a rapid deterioration in air quality over the years due to urbanization and industrialization. This study interprets the variations of particulate matter (PM) and gaseous pollutants (Nitrogen oxide, Nitrogen dioxide, NOX, Sulphur dioxide, Carbon monoxide, Benzene, Toluene, Ozone, and Ammonia) for the data observed from 2017 to 2023 in Ludhiana. This also covers the analysis focused on capturing the changes that occurred at the times of lockdown imposed during the Coronavirus Disease (COVID-19). The maximum 24-h averaged mass concentration values exceeded the National Ambient Air Quality Standards (NAAQS) of 100µg/m3 for PM10 concentration and 60µg/m3 for PM2.5 concentration in 2018 by the factor of 5 and 8. With the onset of the COVID-19 lockdown in 2020year, PM10 and PM2.5 reached the minimum level while CO, T, O3, and NO2 increased by the factor of 3.9, 1.9, 1.4, and 1.3 from their previous year. This NO2 is a precursor of ozone formation, a higher NO2 to NO ratio observed during the lockdown, confirms the role of nitrogen compounds in the higher ozone formation rate. Based on the NO2/NO ratio, the probability rate of ozone formation determined using survival analysis is observed to be 94% from 2017 to 2023. The local sources' contribution to these air pollutants during Pre-Lockdown, Lockdown, and Post-Lockdown are analyzed using principal component analysis. The impact of the lockdown on ozone concentration sources has been observed. During the Pre- and Post-Lockdown phases, three sources (PC1, PC2, and PC3) were positively identified. Ozone levels are linked to PC3 in these phases, but during the lockdown, a negative loading in PC3 and positive loadings in PC1 and PC2 indicate a decrease in ozone from reduced emissions and an increase from secondary reactions involving nitrogen compounds. Moreover, the Toluene to Benzene concentration ratio is > 2, indicating the source of their origin from industrial emission or other non-traffic sources. Health assessment for the years 2017-2019 reveals a significant decrease in the number of cases of all-cause mortality, ischemic heart disease, stroke, and chronic obstructive pulmonary disease associated with reducing PM2.5 concentrations to national and international standards.

Air quality in a small city: criteria pollutants, volatile organic compounds, metals, and microbes.

This work presents a year-long integral study of air quality parameters in Ciudad Real, a small city in the center of Spain, and its influence on the nearby national park, Las Tablas de Daimiel. The study covers meteorological parameters and criteria pollutants such as O3, NO, NO2, SO2, and PM10. Additionally, for each month, a 1-week campaign was performed sampling air in sorbent tubes with 8-h time resolution to analyze anthropogenic volatile organic compounds and the effects of seasons, daytime, and working-weekend days. During these campaigns, 24-h PM2.5 samples were also collected to measure the load of bacteria and fungi, as well as the trace concentrations of elements.The city and the national park NOx profiles showed that emissions from the town had a non-perceivable effect on the protected area. PM10 levels in Ciudad Real were influenced by Saharan intrusions, as was the national park; however, Ciudad Real had a higher contribution from anthropogenic sources. Ozone levels were lower in the city during the cold season due to the higher concentration of NOx and have not changed significantly in the last decade.The VOCs with higher average concentrations were toluene, m,p-xylene, benzene, methylene chloride, and o-xylene, with traffic being the main source of these pollutants in the city. For benzene and carbon tetrachloride levels, weak carcinogenic risks were estimated. In PM2.5, the most abundant metals were Na, Zn, Mg, Ca, Al, Fe, and K. The carcinogenic and non-carcinogenic risks estimated from the levels of the studied metals were negligible. Bacterial and fungal counts positively correlated with the concentration of PM2.5. Microbial community composition showed seasonal variability, with the dominance of human pathogenic bacteria which correlated with certain pollutants such as SO2. Bacillus and Cutibacterium were the most abundant genera.

Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM

Accurate prediction of dust concentration is essential for effectively preventing and controlling mine dust. The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. Additionally, model evaluation metrics and testing methods are established. Compared with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other prediction models, the GA-LSSVM model demonstrates a final fitting degree of 0.872 for PM2.5 concentration data and 0.913 for PM10 concentration data. The GA-LSSVM model clearly demonstrates a strong predictive performance with low error and high fitting. The research results can serve as a foundation for developing dust control measures in opencast mines.

Application of AERMOD dispersion model for assessment PM10 concentrations from mobile sources in Kuala Lumpur Metropolitan City, Malaysia.

Deterioration of air quality in Kuala Lumpur caused by mobile sources and traffic-related activities with interaction with climatic conditions and dispersion in the atmosphere. This study was focused on predicting the averaged 1-h concentration of particulate matter (PM10) that was emitted from private cars in Kuala Lumpur by applying the air dispersion model American Meteorological Society (AMS)/United States Environmental Protection Agency (EPA) Regulatory Model (AERMOD) in 2014. The AERMOD model indicates that private cars in Kuala Lumpur recorded 1-h concentration of PM10 below the Malaysian Ambient Air Quality Guidelines (RMAQG) in 2014. The highest concentration was 59.2µg/m3 in 2014 and mainly concentrated around the city center and highway networks. The highest concentrations are recorded in early morning and late at night, with the highest concentrations at 4 am and 2 am and 48.68µg/m3 and 38.42µg/m3, respectively. Late night to early morning is identified as the dominant time interval for pollutant dispersion. The findings demonstrated that air quality had a significant impact on the 1-h concentration of PM10 emitted from private cars in Kuala Lumpur.

Dynamic patterns of particulate matter concentration and size distribution in urban street canyons: insights into diurnal and short-term seasonal variations.

Time-varying characteristics of particulate matter (PM) pollution play a crucial role in shaping atmospheric dynamics, which impact the health and welfare of urban commuters. Previously published studies on the diurnal patterns of PMs are not consistent, especially in the context of field experiments in central China, and most field studies have only focused on particles with a single particle size. This study conducted regional-scale studies across 72 street canyon sets in Wuhan, China, investigated diurnal and seasonal PM concentration variations while also evaluating various PM size and the key driving factors. During summer (July, August, and September), evergreen tree-lined street canyons maintained a stable linear trend for smaller dp particulates (i.e., PM1, PM2.5, and PM4), while deciduous street canyons exhibited a bimodal distribution. In winter (January and February), fine particulates (i.e., PM1 and PM2.5) remained a linear trend in evergreen street canyons, while deciduous street canyons show a slightly wavy fluctuating pattern. Meanwhile, it exhibited quadrimodal-peak and triple-trough patterns in both PM7, PM10, and TSP concentrations. The lowest PM concentrations were observed between 14:00 and 16:00 for all particle sizes, with decreased summer pollution (7.81% lower in PM2.5, 53.47% lower in PM10, and 50.3% lower in TSP) noted in our seasonal analysis. Among the various meteorological factors, relative humidity (RH) was identified as the dominant influencing PM factor in both summer and winter. Results from this study will help us better understand field-based air pollutant dispersion processes within pedestrian spaces while laying the groundwork for future research into street PM experiments.

Characterization of indoor airborne particulate matter and bioaerosols in wood-fired pizzeria kitchens

Indoor air quality can be affected by the presence of atmospheric pollutants, which impact severely in human health. The objectives were to show the characterization of particle number (PN0.3–PN2.5), particles mass (PM1.0, PM2.5, PM10), Black Carbon (BC), Brown Carbon (BrC) and bioaerosols (fungi and bacteria) in indoor environments of three pizzerias that have wood ovens as fuel at Londrina, Brazil. Mean Particle mass concentration varied amongst the pizzerias (28.9–42.4 µg m−3 for PM2.5) with the levels of BC and BrC reaching up to 42.9 µg m−3 and 29.4 µg m−3, respectively. These were correlated with the air ventilation of the place, the amount of wood burned, and service mode (e.g., all-can-eat). Furthermore, an average of 934 ± 404 CFU m−3 of bacteria was found in all places and Aspergillus, Penicillium, and Cladosphialophora were the most important fungal genera found. Inside kitchens, the concentrations of PM2.5, BC, BrC, bacteria and fungi were 29.6, 3.0, 2.8 µg m−3, 876, and 1261 CFU m−3, with a mean ratio of BrC/PM2.5 of 0.06, that indicates this as a significant source for BrC. Finally, the concentrations of PM and bioaerosols found inside kitchens demonstrate pollution levels much higher than those recommended by national and international guidelines. Consequently, problems associated with poor indoor air quality will only be solved when adequate kitchen designs are considered. From the results of this work is possible to conclude that the building characteristics of kitchen and the burning process carried out are important factors for the concentrations of pollutants analyzed in the indoor air of wood-burning kitchens.

Extracting regional and temporal features to improve machine learning for hourly air pollutants in urban India

India is suffering from severe particulate matter (PM, including PM2.5 and PM10) pollution, while limited ground observations are insufficient to support a comprehensive understanding of its health risks. Machine learning (ML) has the potential to improve the estimation of PM distribution and exposure efficiently. Regional transport as well as accumulation and dispersion processes of PM and its components, which have significant impacts on PM concentrations, are crucial when building ML models, especially for sparsely observed regions like India. Here, geographic and temporal-rolling weighting methods were used to separately extract regional and temporal features for improving the performance of the ML model. The incorporation of temporal and regional features into the ML model significantly improved ML model performance, with root mean square error (RMSE) reduced by 21 % and 19% for PM2.5 and PM10 estimation, as well as an improvement in model underestimation for the heavy pollution scenarios. The spatial-temporal model shows out-of-sample test CV coefficients of determination (R2) of 0.87 and 0.88 for hourly PM2.5 and PM10. The ML model predicts an annual nationwide concentration of 68.3 μg/m3 for PM2.5 with a north (high, especially in Indo-Gangetic Plain) to south (low) distribution, which is consistent with high satellite aerosol optical depth (AOD) values. Boundary layer height is identified as the main meteorological factor influencing PM2.5 concentrations in winter. Characterizing the regional transport and cumulative dispersion processes of pollutants by extracting features can help in machine learning training, and this method can be further improved and applied to other studies.

In-Vehicle Air Pollutant Exposures from Daily Commute in the San Francisco Bay Area, California

With urbanization and increased vehicle usage, understanding the exposure to air pollutants inside the vehicles is vital for developing strategies to mitigate associated health risks. In-vehicle air quality influences the comfort of the driver during long commutes and has gained significant interest. This study focuses on studying in-vehicle air quality in the San Francisco Bay Area in California, an urban setting with significant traffic congestion and varied emission sources and road conditions. Each trip is about 80.5 km (50 miles) in length, with commute times of approximately one hour. Two low-cost portable sensors were employed to simultaneously measure in-vehicle pollutants (PM2.5, PM10, and CO2) during morning and evening rush hours from May 2023 to December 2023. Seasonally averaged PM2.5 varied from 5.07 µg/m3 to 6.55 µg/m3 during morning rush hours and from 4.38 µg/m3 to 4.47 µg/m3 during evening rush hours. In addition, the impacts of local PM2.5, vehicle ventilation settings, and speed of the vehicle on in-vehicle PM concentrations were also analyzed. CO2 buildup in vehicles was studied for two scenarios: one with inside recirculation enabled (RC on) and the other with circulation from outside (RC off). With RC off, CO2 concentrations are largely within the 1100 ppm range recommended by many organizations, while the average CO2 concentrations can be three times high under recirculation mode. This research suggests that low-cost sensors can provide valuable insights into the dynamics of air pollution in the in-vehicle microenvironment, which can better help commuters reduce health risks.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Extreme particulate matter exposure at traffic intersections in a densely populated city

Commuters are exposed to significantly high pollution levels at traffic intersections. This study utilized the generalized Pareto distribution to model commuters’ exposure to extreme particulate matter (PM) levels across 36 traffic intersections in Varanasi, India. A Bayesian hierarchical framework was deployed to account for the seasonal variation. The monthly return levels for extreme PM2.5 (PM10) were 589 (1127), 474 (961), and 429 (902) µg/m3 during winter, spring, and summer, respectively. The extreme PM2.5 and PM10 concentrations exceeded the NAAQS severe category for all three seasons. There is a 0.72 % (1.47 %) chance that during winter, PM2.5 (PM10) levels would exceed that of the Delhi smog event (PM2.5: 585 µg/m3, PM10: 989 µg/m3). These findings raise concerns about public health and the environment, particularly in winter. The results would guide policymakers in enforcing stringent measures to reduce extreme exposures at traffic intersections in densely populated cities.

Changes in urban residential and agricultural sources induce a decrease in PM10 levels in cold clean area: A thirteen-year monitoring at the Longfengshan WMO/GAW regional background station in Northeast China

Atmospheric background monitoring provides insights into the long-term changes in atmospheric composition resulting from human activities on both global and regional scales. It is important to understand the local characteristics of key air pollutants to evaluate and manage air quality control policies effectively. This study analyzed 13 years of continuous data of PM10 (particulate matter with aerodynamic diameter ≤10 μm) concentration from the only atmospheric background station in Northeastern China, the Longfengshan (LFS) World Meteorological Organization (WMO)/Global Atmosphere Watch (GAW) regional station. The results revealed a significant decrease of 58.02% in PM10 concentrations in the LFS from 2007 to 2019, which was primarily influenced by transport from Changchun and Harbin. Over periods exceeding eight months, the correlation between PM10 levels at the LFS atmospheric background station and those in Changchun and Harbin became more pronounced. The observed decline in PM10 concentrations at the LFS atmospheric background station was largely attributed to reduced emissions from urban residential and agricultural sources. Emissions of carbon monoxide and ammonia from these sources, along with temperature and relative humidity (RH), significantly affected the PM10 concentrations. Using machine learning (ML) methods and PM10 data from the LFS atmospheric background station, this study effectively predicted PM10 concentrations in neighboring cities, demonstrating that the LFS atmospheric background station can effectively assess the trend of PM10 changes in Northeast China. This study quantified the influence of urban areas on atmospheric background stations, highlighting the effectiveness of existing environmental policies, and offering a reference for similar studies and forecasts in other regions.

Development of a photocell-based system supported by simulations for road vehicle counting and particle emissions estimation

This work presents the design and implementation of an innovative system that utilizes photocells for road vehicle counting and computational simulations to estimate traffic-related particulate (PM10) emissions. The integration of photocell technology has resulted in a robust system with reduced counting errors, ensuring high accuracy in correlating traffic counts with emission values. The system was evaluated under various environmental conditions, demonstrating its effectiveness in providing reliable real-time data on the impact of road traffic on particle emissions. The methodology included the use of the ADMS-Urban model for emission estimation and simulations with Ansys Fluent, enabling the acquisition of dependable real-time data. The results indicate a strong agreement between measured and calculated PM10 concentrations, with the system maintaining precision comparable to direct observations. The observed values of 5.0 µg/m3 and 19.2% for absolute and relative errors, respectively, demonstrate the system's remarkable performance, supported by a robust correlation represented by R2 = 0.88. This strong correlation underscores the reliability of the employed model, suggesting its capability to capture and explain a substantial portion of the variability in PM10 concentrations. Validation campaigns conducted on a street in Portugal confirmed the system's ability to accurately count vehicles and estimate particle emissions. The analysis revealed a significant agreement between measured and calculated average PM10 concentrations, with standard deviation values of 7.0 µg/m3 for measured concentrations and 7.5 µg/m3 for calculated concentrations, suggesting consistency between the datasets. These conclusions confirm that the system is a valuable tool for researchers and policymakers, providing crucial insights for the management and improvement of urban air quality, contributing to the development of urban planning policies and environmental sustainability.

The effect of COVID-19 epidemic and sandstorm on distribution of short-, medium-, and long-chain chlorinated paraffins in outdoor atmosphere of Xi'an, Northwest China

The short-, medium- and long-chain chlorinated paraffins (SCCPs, MCCPs, and LCCPs) were measured in outdoor fine particulate matter (PM2.5) and gas-phases in Xi'an in spring, autumn and winter of 2021, which containing two special events during this time, the sandstorms happened in spring and the COVID-19 epidemic in winter. The CPs concentrations in PM2.5 were seasonal variated, with spring (mean: 17.5 ng/m3) > autumn (mean: 8.6 ng/m3) > winter (mean: 5.8 ng/m3). The CPs concentrations in gas phases (mean:12.6 ng/m3) were lower than in PM2.5 in spring, and it showed shorter carbon chain and lower chlorinated CPs dominated in SCCPs in gas phase (C10, Cl5,6) compared with in PM2.5 (C11, Cl6,7). The absorption was the main mechanism for the partitioning of CP in particle and gaseous phase. The shorter carbon chain CPs (C10,11, C14) with lower chlorinated (Cl6,7) CPs dominated in SCCPs and MCCPs, while the longer carbon chain (C25 or C29) with Cl6,12 dominated in the LCCPs. So, more researches about the LCCPs should be concerned. Additionally, the CPs concentrations of all samples during the sandstorms in spring were increased 30.4%–136.5% and the proportion of SCCPs was obviously elevated. CPs concentrations obviously decreased during the COVID-19 epidemic in winter, while SCCPs, MCCPs attributed comparable in autumn and winter. Potential source analysis showed that during the sandstorms in spring, atmospheric pollutants were mainly transported over a long distance from northern and northeast of Shaanxi Province, while it was mainly from local emission during the COVID-19 epidemic in winter. The health risk assessment of CPs displayed that there had no or very low inhalation exposure risks of CPs for all age groups, but the younger age groups (younger than 6 years old) should be played more attention for their not fully development of their immune and respiratory systems.

Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation

Air pollution is a situation that negatively affects the health of humans and all living things in nature and causes damage to the environment. The most important cause of air pollution is the amount and density of substances called “particulate matter” above guidelines. Particulate matter (PM) are mixed liquid droplets and solid particles with advective diameters less than 2.5 μm (PM2.5—fine particles) and between 2.5 and 10 μm (PM2.5–10—coarse particles). PM10 is defined as one that can remain in the air for a long time and settle in the respiratory tract, damaging the lungs. It is important to identify the underlying causes of air pollution caused by PM10. In this context, these criteria need to be evaluated to minimize the negative effects of PM10. In the study, monthly average PM10 data obtained from the Air Quality Monitoring Station in Kocaeli, Türkiye, between 2017 and 2023 are used. After determining the criteria for PM10, the criteria are prioritized with the Intuitionistic Fuzzy AHP (IF-AHP) method by taking decision-maker opinions. The proposed decision-making model aims to guide obtaining and focusing on the important causes of out-of-limit and dangerous PM10 concentrations in the air. Additionally, PM10 data is analyzed in the context of COVID-19 and a statistical analysis is conducted. One-way Analysis of Variance (ANOVA) is used to evaluate whether there is a significant difference in average monthly data over the years. The Games–Howell test, one of the post-hoc tests, is used for determining differences between groups (years). In addition, monthly PM10 values for the future are estimated using the Expert Modeler tool in the software IBM® SPSS® Statistics 22. The study is important in that it provides a focus on the criteria affecting PM10 with an intuitionistic fuzzy perspective, along with statistical analysis.

Ionic Components of Particulate Matter 2.5 May Influence Daily Prevalence of Skin Symptom Exacerbations in Allergy Sufferers

(1) Background: To date, little research has epidemiologically examined whether the concentration of particulate matter (PM) 2.5 and its ionic components is related to the prevalence of skin symptom exacerbations. Therefore, this study aimed to clarify this association in patients with allergic diseases. (2) Methods: From 1 February to 31 May 2020, we evaluated the daily prevalence of skin symptoms in outpatients with allergic diseases being treated at Fukuoka National Hospital, Fukuoka, Japan, and measured the concentration of PM2.5 and its ionic components. (3) Results: Univariate analysis showed a statistically significant association between skin symptoms and the concentration of PM2.5 and the ionic components SO42−, NH4+, K+, and Mg2+; multivariate analysis showed a statistically significant association between the daily prevalence of skin symptom and the concentration of the ionic components SO42− or Mg2+. (4) Conclusions: Our findings indicate that the concentration of some PM2.5 ionic components may affect skin symptom in patients with allergic diseases.