Air Quality Atmosphere and Health最新文献

This study applied Singular Spectrum Analysis (SSA) to investigate the temporal variability of carbon monoxide (CO) and tropospheric ozone (O₃) concentrations in Campo Grande, Brazil, between 2003 and 2018. By decomposing the time series into principal components, it was possible to isolate long-term trends, seasonal cycles, and residual variability. The first principal component explained 87.6% of the variance in CO and 65.2% in O₃, highlighting differences in the pollutants’ temporal structures. Seasonal cycles revealed annual peaks during the dry season (August to October), confirming the influence of biomass burning on CO and the photochemical behavior of O₃. The reconstructed series using nine components showed high fidelity, with performance metrics of R² = 0.93, RMSE = 112.4 ppb, and MAE = 84.7 ppb for CO, and R² = 0.95, RMSE = 6.8 ppb, and MAE = 5.2 ppb for O₃. These results demonstrate SSA’s effectiveness in capturing deterministic patterns and isolating high-frequency noise. The findings provide scientific support for targeted environmental policies, such as stricter traffic control, improved fire management, and integration of SSA-based monitoring into local agencies.

Fine particulate matter (PM_2.5) remains a critical air pollutant with substantial public health risks, particularly in East Asia, where domestic emissions and transboundary transport contribute to elevated concentrations. This study examined the annual and seasonal variations of PM_2.5 and its chemical constituents at Baengnyeongdo Island, South Korea, from 2019 to 2021. The constituents analyzed included carbonaceous components (organic carbon [OC] and elemental carbon [EC]), major inorganic ions (sulfate [SO₄²⁻] and nitrate [NO₃⁻]), crustal elements (e.g., silicon [Si], calcium [Ca], iron [Fe], titanium [Ti]), and various other metallic species. The study also sought to identify potential sources of PM_2.5, with particular emphasis on transboundary influences. Results showed a significant increase in PM_2.5 levels in 2021 (spring mean: 32.657 µgm⁻³), attributed to the resumption of industrial activities following the COVID-19 lockdowns, specific meteorological conditions, such as higher spring relative humidity (74.91%) and increased aerosol water content (32.16 µgm⁻³), and significant transboundary pollution, particularly from China. Seasonal analysis indicated that OC, EC, NO3-, and crustal elements (Si, Ca, Fe, Ti) were the dominant contributors. For example, OC and EC peaked in spring and winter, which was associated with biomass burning, heating, and industrial emissions, which were enhanced by low winter temperatures. NO₃⁻ also exhibited significant winter peaks (5.921 µgm⁻³ in 2021), driven by conditions favoring NH₄NO₃ formation, while SO₄²⁻ levels, highest in 2019 (4.357 µgm⁻³), displayed a more moderate trend. Meteorological parameters, including aerosol water content, relative humidity, temperature, and wind patterns, play a major role in PM_2.5 formation, accumulation, and dispersion. Back-trajectory modeling consistently confirmed air mass transport from the heavily industrialized regions of China, Mongolia, and Russia during high-pollution episodes across all seasons. These findings underscore the complex interplay between local emissions, transboundary transport, and meteorological factors, highlighting the urgent need for coordinated international air quality management policies.

As people spend most of their time indoors, it is vital to assess their exposure to air pollutants considering their adverse impacts on human health. The use of low-cost sensors (LCS) for measuring air quality is becoming increasingly important for providing widespread, real-time exposure assessment, particularly in resource-limited settings. As the accuracy of LCS data is influenced by meteorological factors, data correction using co-location with a reference instrument is essential to ensure good quality data. In this study, we collocated these sensors with a Beta Attenuation Monitor (BAM) reference instrument over a two-month period. Analysis of the co-located data revealed that the sensors tend to overestimate PM_2.5 concentrations as relative humidity (RH) increases. To remove this effect from the data, multiple linear regression technique was applied to adjust the sensor data for RH effects. Post correction, the sensors demonstrated Mean Absolute Bias (MAB) ranging from 6.8 to 7.2 µg/m³, normalized MAB 7.3–7.5%, Root Mean Squared Error (RMSE) of ~ 11 µg/m³, normalized RMSE of ~ 11%, and a Coefficient of Determination (R²) between 0.917 and 0.937 for PM_2.5. To estimate particulate matter exposure in various indoor microenvironments and its relationship with ambient air quality, the sensors were deployed at an outdoor and multiple indoor locations across an academic campus for a semester. We found that air quality in indoor spaces without active emission sources was worse than ambient air quality for half of the times. Indoor air quality in spaces with sources was mostly poorer than the ambient air quality. The ratio of indoor to outdoor PM_2.5 was in the range of 0.6 to 1.2, and 1.0 to 1.9 for the indoor spaces without sources and with sources, respectively. Correlation between indoor and ambient air quality was good, with indoor air quality lagging behind ambient conditions by ~ 90 min. However, indoor environments with active emission sources, such as cooking, soldering, and welding, exhibited poor air quality, posing health concerns. This study underscores the importance of accurate air quality data from LCS and highlights the need for effective indoor air quality management, particularly in environments with emission sources.

Ulaanbaatar, the capital city of Mongolia, is the one of polluted cities in the world. The negative health effects of air pollution such as mortality, morbidity and other symptoms have been widely reported. Particulate matter (PM) is major contributing factor for health issues and is mainly produced from coal and biomass combustion, and vehicle emissions. This study aims to evaluate the concentrations of PM_2.5, PM₁₀ and polycyclic aromatic hydrocarbons (PAHs) in the samples collected in Ulaanbaatar from 2017 to 2022. The seasonal change of concentrations and the primary source of PAHs were obtained. The primary sources of PAHs in air particulates were found to be biomass and coal combustion. In addition, the cancer risk assessment associated with PAHs in PM_2.5 and PM₁₀ has been carried out. It should be well noted from this study that PM₁₀ poses higher cancer risk than PM_2.5 in the winter in Ulaanbaatar city.

Thailand experiences significant haze pollution. Aerosol optical depth (AOD) and Ångström exponent (α) are closely associated with fine particulate matter (PM_2.5) a critical atmospheric factor that significantly influences the global climate. The primary objective of this study is to examine the relationship between AOD and α to classify aerosols into five distinct categories. The study area was divided into two distinct regions each with unique climate and geographical characteristics, namely Chiang Mai (urban area) and Songkhla (coastal area). The data collected from 2020 to 2023 were categorized into two seasons: the dry season and the wet season. The findings suggested that the most severe particulate pollution typically occurs in urban areas in the North during the dry season. The most prevalent aerosol type was Clean Continental/Urban Industrial (CC/UI), accounting for approximately 96% of all particles, while the Biomass Burning (BB) type accounted for only 4%. In contrast, the Clean Maritime (CM) type was the predominant characteristic of aerosols in the coastal region. Of the CM type particles, 52% were observed during the dry season and 73% during the wet season.The majority of these particles are aerosols derived from natural sources, such as sea salt emissions. Therefore, the classification of aerosols based on the relationship between AOD and α serves only as a preliminary assessment of their sources and types. This method cannot precisely identify the specific characteristics of each aerosol type. However, these findings indicate that aerosol types may vary by season and region.

Fine particulate matter (PM_2.5) is a major air pollutant that significantly affects both human health and atmospheric chemistry. Although it has been extensively researched in urban and industrial areas, studies focusing on its characteristics in agricultural regions remain limited. This study aimed to analyze the chemical composition of PM_2.5 in the agricultural areas of Jeonbuk-do, South Korea, and assess the seasonal variations in its components. Air quality monitoring stations were established in a paddy field (Buan) and an upland field (Gochang) from June 2022 to May 2024. Continuous measurements of PM_2.5, PM₁₀, and gaseous precursors such as NH₃, NO_x, and SO₂ were performed, and the concentrations of organic carbon, elemental carbon, and major ions (Na⁺, NH₄⁺, K⁺, Ca₂⁺, Mg₂⁺, Cl⁻, NO₃⁻, SO₄²⁻) in PM_2.5 were analyzed in PM_2.5 were analyzed. Results indicated that ionic species were the dominant component, accounting for > 40% of the PM_2.5 mass. SO₄²⁻ and NO₃⁻ concentrations varied seasonally, with SO₄²⁻ levels peaking in summer and NO3 − concentrations peaking in winter. NH₄⁺ concentrations were consistently higher in the agricultural areas than in urban and industrial sites, highlighting the contribution of agricultural emissions. These findings underscore the necessity of dedicated air quality monitoring in agricultural regions to elucidate the mechanisms of PM_2.5 formation and determine their implications for rural air quality management.

Volatile organic compounds (VOCs) are key contributors to urban air pollution, photochemical smog, and associated health risks. Skopje, one of Europe’s most polluted capitals, faces significant VOC emissions from traffic, industry, and domestic heating. This study presents a year-long monitoring of 82 VOCs at five urban sites in Skopje, assessing concentrations, seasonal variability, emission sources, and ozone formation potential (OFP). Aliphatic hydrocarbons dominated, followed by aromatics (notably BTEX (benzene, toluene, ethylbenzene and xylenes)), terpenes, and hydrocarbon derivatives. Winter showed higher VOC levels due to heating, traffic, and industrial activity, while summer levels declined due to photochemical degradation and atmospheric dispersion. BTEX analysis, diagnostic ratios of toluene/benzene (T/B), and correlation patterns indicated traffic as the primary source, with industrial and waste-related contributions at specific sites. Principal component analysis identified six source-related factors: mixed traffic, solvent/industrial emissions, biogenic sources, fuel evaporation, personal care products, and industrial/petroleum-related VOCs. Meteorological factors also influenced VOC dynamics: aromatics and aliphatics correlated negatively with temperature, UV radiation, and ozone, while PM_2.5 correlated positively with aromatics and terpenes, linking VOC emissions to fine particulate matter. OFP analysis revealed that highly reactive species – toluene, xylenes, C₆-C₃ substituted benzenes, and n-tetradecane – disproportionately drive ozone formation, despite lower abundance. This work provides the first comprehensive VOC dataset for Skopje, revealing source contributions, seasonal patterns, and compounds with the greatest OFP. Findings support targeted air quality management, emphasizing control of high-OFP VOCs and integrated strategies considering both emissions and atmospheric chemistry to reduce urban pollution and associated health risks.

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Elevated pollution levels and associated health effects are primary concerns of the modern world. This study examines the short-term effect of particulate matter (PMx= 2.5 and 10) and ozone (O₃) on emergency room (ER) visits for respiratory diseases (J00 – J99) in Shihezi from January 2021 to December 2022. The short-term effects of exposure to PMx and O₃ on respiratory morbidity at different lags were assessed by employing the generalized linear time-series model with the negative binomial distribution (GLM-NBD). The results showed that during the study period, 3778 patients visited the ER, of which 50.9% were >70 years old. The daily number of ER visits ranged from 1 to 21 ER visits per day, with an average of 5.44±3.31 ER visits per day. In 2022, a potential aging trend was observed. Male susceptibility, hospitalization expenses, and length of stay experienced slight decreases. In the case of PM_2.5, a marginal increase in the relative risk (RR) of ER visits on the day of exposure was observed, reaching its highest point on Lag Day 5, indicating a delayed impact. In case of PM_2.5, the RR (gender specific) of ER visits increased for male population, while increased for female in case of PM₁₀. The younger (15 – 35 years) and middle-aged (36 – 55 years) adults showed a higher RR of ER visits for respiratory diseases. These findings provide valuable insights into the healthcare burden of respiratory diseases in Shihezi, highlighting the need for targeted healthcare strategies in managing such conditions on large scale.

The Yellow River Basin (YRB) is the core region for promoting the comprehensive green transformation of the economy and society in Northern China. This paper comprehensively employs the pollutant reduction and carbon reduction (PR-CR) synergy coefficient and multiple regression models to analyze the spatiotemporal characteristics of PR-CR synergistic effects and explore PR-CR paths for different types of cities, based on panel data of 60 prefecture-level cities in the YRB from 2005 to 2020. The results show that: (1) The atmospheric pollutant emission intensity (PEI) and CO₂ emission intensity (CEI) of each city showed a decreasing trend, with more than 85% of the cities experiencing decreases of 90% and 35%, respectively. (2) The number of cities that did not synergize in PR-CR was gradually increasing, but the gap was gradually narrowing. The synergistic effects of pollutant and carbon reduction vary by region. (3) The scale effect is a key factor common to all three types of cities, while other influencing factors exhibited spatial heterogeneity. Based on the key factors of PR-CR, this paper proposed the emission reduction path of “Tailored Policy for Each Category” in order to provide a reference for promoting the realization of the strategic goal of green, low-carbon, and high-quality development in the YRB.

Airborne particulate matter generated during wheat threshing is a major environmental pollutant as well as occupational health risk to farm workers. Dust control system is dire need of hour to reduce particulate matter concentration. Current study was conducted to ascertain effectiveness of three developed dust control systems for wheat thresher to reduce airborne PMs during wheat threshing for two consecutive years. Concentrations of PM_1.0, PM_2.5, PM₁₀ and total suspended particulate (TSP) were recorded at four distances (20, 40, 60, and 80 m) from wheat thresher’s straw outlet and three heights (0.91, 1.37, and 1.83 m) from ground. Results revealed that System-I reduced concentration of PM_1.0, PM_2.5, PM₁₀ and TSP by 36.0, 64.3, 58.1 and 66.0% respectively during 2023. After improvements in developed systems during 2024, PMs reduction through system-I was recorded as 60.2, 71.1, 64.8 and 74.0% for PM_1.0, PM_2.5, PM₁₀ and TSP respectively. However, system-II and system-III were found relatively less effective compared to system-I. System-I outperformed other systems by achieving maximum percent reduction for all recorded PMs during both years. Remarkable reduction in PMs concentration was observed through use of dust control systems as distance from the wheat thresher’s straw outlet increased. Moreover, PMs concentration was found to increase with height having maximum mean values recorded at height of 1.83 m leading to increased exposure risk at general public breathing zone. Current findings emphasized use of effective dust control system (system-I) to improve air quality and safeguard health of farm workers.