Atmospheric Environment最新文献

Volatile organic compounds (VOCs) emissions from the vehicle refueling process is an increasingly significant issue. However, the gasoline formulation showed seasonal variation, potentially leading to the alterations of VOC components during the refueling process. Refueling VOCs of different gasoline and diesel were collected at some typical gas stations during both summer and winter to analyze their seasonal emissions characteristics, environmental impacts, and associated health risks. The refueling VOC profiles in summer consisted of a notably higher proportion of small oxygenated VOCs (C3) and a significantly lower content of halocarbons compared to those in winter, which indicated that the refueling VOC profiles in just a single season failed to capture characteristics of the entire year. The OFP values of gasoline vapor in two test seasons were consistent (around 2.9 g O₃/g VOCs), with the dominant species of acrolein (15.7 ± 2.5%) in summer and i-pentane (23.2 ± 2.1%) in winter. Aromatics contributed the most to secondary organic aerosol formation potential (SOAP), with toluene (65.4%, summer; 48.5%, winter) being the overwhelmingly dominant species. The assessment of inhalation health risks indicated that exposure to adverse VOCs resulted in definite non-cancer risk (HQ = 5.9) and cancer risk (ICR = 4.6 × 10⁻⁴) to the gas station attendant, which exceeded the acceptable level, with 1,3-butadiene exhibiting the highest toxicity and a dual toxicity risk. The impact of VOCs on health was more severe during winter compared to summer. It is recommended that seasonal influences should be considered when controlling VOC pollution at gas stations.

In August 2023, a detailed investigation of formaldehyde (HCHO) chemical characteristics on ozone (O₃) polluted days and non-O₃ polluted days in Wuhan was undertaken. The mean value of HCHO on O₃ polluted days (3.02 ± 1.15 ppbv) was 122% higher than that on non-O₃ polluted days (1.35 ± 0.41 ppbv). Utilizing Positive Matrix Factorization (PMF) model revealed secondary formation as the dominant HCHO source (58.3% on non-O₃ polluted days and 66.2% on O₃ polluted days). On O₃ polluted days, the contribution of liquefied petroleum gas (LPG)/solvent usage and industrial emissions to HCHO were 13.7% and 8.2%, respectively, whereas on non-O₃ polluted days, LPG/solvent use and diesel exhaust contributed 15.4% and 14.7%, respectively. The top ten species, with the highest Relative Incremental Reactivity (RIR) to HCHO, were mainly alkenes and aromatics, which remained consistent on both O₃ and non-O₃ polluted days. It is noteworthy that the RIR of isobutane to HCHO is significant in this study. Further reapportionment of secondary HCHO by a photochemical box model indicated that LPG/solvent usage (33.2%) contributed the most to HCHO on O₃ polluted days, while diesel exhaust (31.9%) dominated on non-O₃ polluted days. This research enhances understanding of HCHO in Wuhan, providing a theoretical basis for targeted pollution reduction and supporting efforts to improve air quality and public health.

Particulate Matter (PM), a primary atmospheric pollutant, is characterized by diverse emission sources and complex influencing factors. Particulate matter source profiles (PM-SPs) reported in different studies exhibited significant variability, and their uncertainty is not well documented, highlighting the urgent need for further research. To scientifically evaluate PM-SPs, this study begins with the construction process of PM-SPs, identifying key stages (sampling, weighing, and chemical analysis) that influence their uncertainty. The uncertainty components at each stage, as well as the comprehensive uncertainty, are then quantitatively assessed, resulting in the development of an assessment method that quantifies the uncertainty of PM-SPs. The industrial PM-SPs are evaluated as a case study, which indicate this full-chain method can quantitatively identify the uncertainties introduced at various stages. The sampling uncertainties are mostly below 40%, primarily resulting from errors in repeat sampling. The weighing uncertainties are generally minor, while the impact of the chemical analysis process varies significantly across different components. Among the fifty-three evaluated PM-SPs, 80% have average comprehensive uncertainties below 40%, with only five profiles exhibiting relatively high uncertainties. In practical applications, the uncertainty assessment of PM-SPs should be analyzed on a case-by-case basis, considering the importance of different components, with special attention given to marker components.

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.

Unconventional oil and gas development (UOGD) has experienced substantial growth in recent years with currently unconstrained impacts on air quality. In this work, we describe ambient aerosol and gas-phase measurements during spring 2021 to examine air quality in the Eagle Ford Shale (EFS), a region in southeast Texas with thousands of UOGD wells. NO_x, O₃, H₂S, PM₁, and several VOCs were measured at concentrations above expected levels for non-urban regions. Hydrocarbon measurements from gas chromatography (GC) and proton transfer reaction (PTR) mass spectrometry show periodic high concentrations of variable compositions consisting of both saturated and unsaturated (polycyclic and/or aromatic) hydrocarbons, including larger (C _> 10) unsaturated hydrocarbons that are often not quantified by GC measurements but are still present at substantial concentrations and relevant for air quality. Hydrocarbon VOCs are expected pollutants from anthropogenic activity and, based on ratios of i-pentane to n-pentane, xylene to benzene, and toluene to benzene, measured VOC ratios were characteristic of UOGD activity. Wind data and back-trajectory analyses show that air predominantly arrived from the S/SE, consistent with UOGD sources as well as shipping emissions from the Gulf of Mexico impacting air quality in the EFS. Analysis of diurnal trends at the site show hydrocarbon and NO_x accumulation overnight and highlight diurnal differences in oxidative conditions, with important implications for both SOA and O₃ formation. UOGD emissions impact the air quality in Karnes City and more broadly the EFS as well as the nearby urban area of San Antonio, which struggles to meet national air quality standards set by the Environmental Protection Agency.

The short-, medium- and long-chain chlorinated paraffins (SCCPs, MCCPs, and LCCPs) were measured in outdoor fine particulate matter (PM_2.5) 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 PM_2.5 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 PM_2.5 in spring, and it showed shorter carbon chain and lower chlorinated CPs dominated in SCCPs in gas phase (C₁₀, Cl_5,6) compared with in PM_2.5 (C₁₁, Cl_6,7). The absorption was the main mechanism for the partitioning of CP in particle and gaseous phase. The shorter carbon chain CPs (C_10,11, C₁₄) with lower chlorinated (Cl_6,7) CPs dominated in SCCPs and MCCPs, while the longer carbon chain (C₂₅ or C₂₉) with Cl_6,12 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.

The detrimental impacts of fine particulate matter (PM_2.5) on human health, climate, ecosystems, crops, and building materials are well-established. However, there remain unresolved inquiries regarding the precise location of the sources of PM_2.5. This study is the first attempt to use a calibrated sensors-based ambient air quality monitoring network (SAAQM network) and regulatory government monitors to train micro-satellite images for high spatial-resolution air pollution field determination of PM_2.5 in Lucknow, Uttar Pradesh, India. A hybrid approach is developed to integrate three different datasets that include microsatellite images, PM_2.5 ground measurements, and supporting information (meteorological parameters and geographical coordinates), to be fed into a Random Trees-Random Forest- Convolutional Neural Network (RT-RF-CNN) joint model to estimate PM_2.5 concentrations at a sub-km level. The RT-RF-CNN joint model can derive PM_2.5 concentrations at a spatial resolution of 500 m with statistically significant indicators such as spatial r of 0.9, a low root-mean-square error of 26.9 μg/m³ and a mean absolute error of 17.2 μg/m³. Based on our approach, the PM_2.5 prediction maps using micro-satellite images (spatial resolution of 3m/pixel) and RT-RF-CNN joint model were generated for each day throughout the study period (December 2021–December 2022). The inter-grid comparison of these maps revealed the intra-urban local hotspots and coolspots at a fine-granular level seasonally, monthly, and daily. It is observed that the monsoon season has the highest number of coolspots (67%), while winter (0.1%), post-monsoon (0.5%) and summer (11%) have fewer. It is noted that the high temporal-spatial information of PM_2.5 estimates from our integrated approach is not achievable by ground-based measurements and other existing satellite-based estimates alone. The findings of this study have potential applications on a diverse array, encompassing near real-time daily PM_2.5 predicted maps, specific air pollution hotspot identification, PM_2.5 exposure assessment at the neighbourhood level, and integration of remote sensing-based micro-satellite images and ground-based measurements.

Aromatherapy can relieve stress and lower blood pressure (BP) and heart rate (HR) in humans. However, whether exposure to ambient aromas in the workplace has positive or negative cardiovascular effects remains unclear. We recruited 356 healthy office workers from 10 companies in Northern Taiwan to conduct a prospective observational study from 2019 to 2022. Six repeated visits were conducted for each worker to measure HR, systolic BP (SBP), diastolic BP (DBP), perceived stress scale (PSS), and exposure to particulate air pollution and total volatile organic compounds (TVOCs) at the workplace. Mixed-effects models were used to explore the association between air pollution exposure and cardiovascular effects. The results showed significant associations between TVOCs, particulate air pollution and adverse cardiovascular effects, including increased HR and BP among office workers with heavy use of essential oils (office workers who used and/or were exposed to essential oils for more than 1 h per day in the workplace and home). No significant association between air pollution exposure and cardiovascular effects was observed among workers without essential oils usage. However, an association between TVOCs exposure and beneficial cardiovascular effects (decreased HR and BP) was observed among workers with light use of essential oils. Moreover, workers with heavy use of essential oils were significantly associated with increased PSS scores. We concluded that workers with heavy use of essential oils exposed to TVOCs in the workplace may lead to adverse cardiovascular effects and increased self-reported stress.

This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM_2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM_2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022. The bilinear interpolation method was applied to the data resampling from 0.75° × 0.75°–0.25° × 0.25° spatial resolution, and subsequently, the climatological analysis was performed on seasonal and annual scales of PM_2.5 concentrations for the 20-year series. The assessment comprised three main stages: 1) descriptive statistics, 2) seasonal and annual climatology patterns, and 3) trend analysis (Mann-Kendall - MK and Pettitt tests). Results indicated that the largest annual PM_2.5 concentrations were found in the Amazon region and northern Paraguay due the El-Niño South Oscillation events (2003–2005, 2007, 2010, 2020, and 2022). The annual average PM_2.5 concentrations exceeded the WHO recommended limit (5 μg m⁻³), ranging from 15.57 μg m⁻³ (1999) and 22.74 μg m⁻³ (2007 and 2010). The highest PM_2.5 concentrations were observed in the Amazon and Chile (Santiago), reaching 160.4 μg m⁻³ annually and 177.8 μg m⁻³ seasonally (spring). Elevated seasonal PM_2.5 concentrations were attributed to increased deforestation and wildfires associated with meteorological systems such as the Bolivian high and subtropical jets during dry periods (winter and spring). The MK test revealed a significant reduction in PM_2.5 concentrations between 2003 and 2022, observed in Argentina (north), Brazil (arc of reforestation), Bolivia (south), and Chile (north). Reduction values varied between 0.5 and 10 μg m⁻³.year⁻¹ annually and during winter, attributed to no-tillage practices in regions cultivating commodities. The Pettitt test also identified significant structural changes occurring in 2006–2008 and 2010–2011, aligning with areas highlighted in the MK test. This study provides valuable insights for air quality and environmental monitoring managers to mitigate PM_2.5 pollution in SA.

Economic development has historically led to environmental challenges, notably in China where fine particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM_2.5), has significantly influenced human health and social issues. However, the scarcity and uneven distribution of ground-based PM_2.5 observation sites hinder studies about air pollution impacts at regional and national scales. Although PM_2.5 datasets based on remote sensing retrieval algorithms have provided long-term and high-resolution gridded near surface PM_2.5 concentration data recently, comparisons on accuracy between datasets were not conducted by previous studies. This study evaluated eight publicly accessible PM_2.5 datasets (i.e., CHAP, GHAP, GWRPM25, HQQPM25, LGHAP v1, LGHAP v2, MuAP, and TAP) across China using independent records at 1020 monitoring sites from 2017 to 2022 at monthly and annual granularities. Mean Absolute Errors (MAEs) showed a seasonal trend, with higher errors in winter and lower in summer. Datasets exhibited a bias towards overestimation or underestimation based on concentration levels. CHAP, GWRPM25, and HQQPM25 had better estimation control. Additionally, the incorporation of spatiotemporal features into original machine learning based algorithms was likely credited to the outperformance compared to conventional PM_2.5 simulation methods. Overall, this study contributed to comprehensive references for PM_2.5 concentration dataset users and potential explanations to the variations within and among datasets.