Atmospheric Pollution Research最新文献

{"title":"Assessing capability of Copernicus Atmosphere Monitoring Service to forecast PM2.5 and PM10 hourly concentrations in a European air quality hotspot","authors":"Giovanni Gualtieri ,&nbsp;Lorenzo Brilli ,&nbsp;Federico Carotenuto ,&nbsp;Alice Cavaliere ,&nbsp;Beniamino Gioli ,&nbsp;Tommaso Giordano ,&nbsp;Simone Putzolu ,&nbsp;Carolina Vagnoli ,&nbsp;Alessandro Zaldei","doi":"10.1016/j.apr.2025.102567","DOIUrl":"10.1016/j.apr.2025.102567","url":null,"abstract":"<div><div>The accuracy of Copernicus Atmosphere Monitoring Service (CAMS) European forecasts of PM_2.5 and PM₁₀ hourly concentrations was assessed against hourly observations collected from low-cost stations during the 2022–2023 heating season in the Padana Plain (Italy). The intercomparison of all 11 air quality models integrated into the CAMS framework returned root mean square error (RMSE) values ranging 20.3–37.5 (PM_2.5) and 22.2–37.8 μg/m³ (PM₁₀ concentrations), while hourly variation of observations was poorly captured (<em>r</em> = 0.16–0.41 and 0.25–0.47, respectively). Agreeing with prior research, CAMS models exhibited a marked daily variability in forecasting particulate matter (PM) observations, with the largest discrepancies occurring during the early morning and evening hours. PM_2.5 observations were best predicted by the CHIMERE model, while PM₁₀ observations by the MINNI model. CAMS Ensemble returned the best <em>r</em> values among all models, while, since all (or the majority of) models over-predicted the observations, it failed to best fit their magnitude, returning mean bias of +8.1 for PM_2.5 and +4.0 μg/m³ for PM₁₀ concentrations.</div><div>This study demonstrated that further efforts are still needed to improve the performance of CAMS models in estimating PM concentrations. However, rather than acting on model final output, e.g. by implementing bias-correction techniques, a more robust strategy could be to act upstream, i.e. by adjusting the settings of the individual CAMS models. The latter could include a more region-specific characterisation of the emission input data to avoid unrealistic overweighting of anthropogenic emissions, increasing the number of surface stations used for PM concentration assimilation, or adjusting PM chemical composition.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102567"},"PeriodicalIF":3.9,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol optical properties over three cities of Angola using long term AERONET data","authors":"Pascoal Micolo D. Campos ,&nbsp;Angella G. Moses ,&nbsp;Ke Li ,&nbsp;Bertrand Tchanche ,&nbsp;Anabela A. Leitão ,&nbsp;José C.M. Pires","doi":"10.1016/j.apr.2025.102559","DOIUrl":"10.1016/j.apr.2025.102559","url":null,"abstract":"<div><div>Aerosols can influence the climate, by scattering or absorbing solar radiation. The presence of aerosols may also affect human health as their exposure has been linked to pulmonary and cardiovascular diseases. This paper aims to investigate the spatiotemporal trends of aerosol optical properties, the major types of aerosols, the possible sources of the aerosols, and the aerosol radiative forcing at three locations in Angola, namely, Huambo, Lubango, and Namibe, using the Aerosol Robotic Network (AERONET) dataset from 2016 to 2021. The results revealed that at the three sites, there was a decrease in the aerosol optical depth (AOD) during the warm months, a period characterized by rainfall, and the minimum loadings of AOD varied between 0.073 and 0.132. Huambo registered the highest percentages of biomass-burning aerosols (79 %), followed by Lubango (56 %) and Namibe (21 %). The Ångström exponent (AE) at Lubango ranged from 1.48 to 1.52, denoting the existence of fine-mode aerosol particles. The annual mean of the volume size distribution (VSD) showed that among the three sites, Huambo registered the highest concentration of fine-mode aerosols particles (0.053 μm³/μm²) when compared with Namibe (0.034 μm³/μm²) and Lubango (0.030 μm³/μm²). The mean peak radius of the fine particle over the three sites was about 0.15 μm, while the mean peak radius of the coarse particle registered at both Huambo and Lubango (5.06 μm) was greater than the one at Namibe (3.86 μm). Namibe had the highest annual mean of VSD for coarse-mode aerosol particles among all sites. Further, an investigation revealed that the annual mean aerosol radiative forcing (ARF) at the bottom of the atmosphere (BOA) was the highest at Huambo (−53.63 Wm^-2), while the ARFs at the top of the atmosphere (TOA) varied between −13.91 Wm^-2 and -9.59 Wm^-2, among the three sites. The annual mean ARF efficiencies (ARFE) showed a higher value at BOA at Lubango (−214.57 Wm^-2 per AOD), whilst at TOA, it was recorded at Huambo (−60.27 Wm^-2 per AOD). The findings obtained from the current study provide a summary of the aerosol optical properties at three sites in Angola, which could enrich the knowledge of the influence of aerosol direct radiative impact over parts of the country and so enhance future climate models of this region.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102559"},"PeriodicalIF":3.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid machine learning to enhance PM2.5 forecasting performance by the WRF-Chem model","authors":"Laddawan Noynoo ,&nbsp;Perapong Tekasakul ,&nbsp;Thanathip Limna ,&nbsp;Chidchanok Choksuchat ,&nbsp;Korakot Wichitsa-Nguan Jetwanna ,&nbsp;Chuen-Jinn Tsai ,&nbsp;Thi-Cuc Le ,&nbsp;Panwadee Suwattiga ,&nbsp;John Morris ,&nbsp;Racha Dejchanchaiwong","doi":"10.1016/j.apr.2025.102558","DOIUrl":"10.1016/j.apr.2025.102558","url":null,"abstract":"<div><div>The weather research and forecasting with chemistry (WRF-Chem) model had been widely used in PM_2.5 concentration forecasting. However, uncertainties in global emission inventories, meteorological data, and simplified chemical parameterizations continue to pose challenges. We evaluated the performance of the original WRF-Chem model and three models augmented with machine learning (ML) algorithms, i.e. Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost) and XGBoost-LSTM (Hybrid) approaches, to enhance forecasting accuracy. The ML models were trained and tested using dataset from WRF-Chem-simulated meteorological and pollutant data at four monitoring stations in southern Thailand during the year 2019–2020. The WRF-Chem-Hybrid model significantly improved all metrics in the original WRF-Chem results - with R² increasing from insignificant to 0.90 or more, RMSE decreasing from 7.00-15.17 μg/m³ to 1.34–3.47 μg/m³, and MAE decreasing from 5.52-10.55 μg/m³ to 0.79–1.49 μg/m³. The validation test during the entire 2021 performed well, with R² = 0.94–0.96, RMSE = 1.56–2.48 μg/m³ and MAE = 1.01–1.56 μg/m³. The WRF-Chem-Hybrid model forecast PM_2.5 concentrations for 72 h in advance, with R² = 0.70–0.89, RMSE = 4.64–11.99 μg/m³, and MAE = 3.07–8.38 μg/m³. Thus, the hybrid model is suggested for forecasting PM_2.5 concentrations over southern Thailand and other regions up to 72 h in advance. Overall, this study demonstrated the advantages of augmenting the WRF-Chem model to form hybrid ML models to more accurately forecast PM_2.5 levels, their distribution and evolution over time, particularly in regions where PM_2.5 levels were affected by open biomass burning from both local and cross-border emissions.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102558"},"PeriodicalIF":3.9,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sleep and indoor air quality: an exploratory polysomnographic evaluation of potential associations","authors":"Joana Belo ,&nbsp;Miguel Meira e Cruz ,&nbsp;Carla Viegas ,&nbsp;Joana Lage ,&nbsp;Susana Marta Almeida ,&nbsp;Sandra Cabo Verde ,&nbsp;Célia Alves ,&nbsp;Nuno Canha","doi":"10.1016/j.apr.2025.102557","DOIUrl":"10.1016/j.apr.2025.102557","url":null,"abstract":"<div><div>This exploratory pilot study examines the potential impact of indoor environmental exposures on sleep quality, with a particular focus on a comprehensive characterization of indoor air quality (IAQ) parameters and their association with sleep architecture assessed through polysomnography. The study was conducted during the cold seasons of 2016 and 2017 with a small sample of 10 subjects from the urban area of Lisbon, Portugal. Polysomnography was performed over two consecutive weeknights, while IAQ monitoring took place over three consecutive nights using typical real-time instruments. Additionally, bioburden was assessed in each bedroom before and after the sleep period using active methods. The analysis was based on correlations between the environmental parameters and the sleep data from these 10 subjects. Parametric and non-parametric statistics were employed to examine potential associations, with a significance level set at α = 0.05. The findings showed that higher bedroom temperatures during sleep were associated with a decrease in REM sleep. Both minimum and mean heart rates (HR) increased with higher levels of CO and CO₂, while post-sleep bacteria levels were linked to a decrease in maximum HR. Fungal levels in the bedrooms were associated with a reduction in NREM2, and higher formaldehyde exposure was found to increase REM sleep latency. Exposure to PM_2.5 negatively impacted NREM1, RDI, and snoring, while PM₁₀ levels were negatively correlated with WASO and RDI. Although these findings provide a preliminary baseline, they are based on a small sample and may not be representative, highlighting the need for future studies to confirm the effects of various IAQ parameters on sleep quality in a larger and more diverse population.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102557"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indoor air quality assessment of particulate matter levels in urban homes in India","authors":"Prince Vijay ,&nbsp;Vinayak Sahota ,&nbsp;Rajdeep Singh ,&nbsp;Shreya Dubey ,&nbsp;Sonali Borse ,&nbsp;Harish C. Phuleria","doi":"10.1016/j.apr.2025.102553","DOIUrl":"10.1016/j.apr.2025.102553","url":null,"abstract":"<div><div>Indoor air pollution with respect to particulate matter (PM) levels have not been investigated as intensively as those of outdoor pollution. In this pilot study, we address this gap by assessing indoor PM levels, the factors that influence it, and their spatio-temporal variations across four Indian cities: Delhi, Mumbai, Bangalore, and Mysore. We used low-cost monitors (LCMs) to measure residents’ PM exposure for ∼24–48 h. Average concentrations of PM₁ were 60.0 ± 22.7 in Delhi, 34.0 ± 12.8 in Mumbai, 26.3 ± 3.9 in Bangalore, and 24.2 ± 8.0 μg m⁻³ in Mysore. PM_2.5 (PM₁₀) levels were 80.6 ± 26.5 (88.5 ± 24.4), 48.7 ± 17.3 (57.0 ± 17.6), 37.5 ± 5.9 (44.9 ± 8.4), and 33.2 ± 11.7 (38.7 ± 15.2) μg m⁻³, respectively (p &lt; 0.05). The average daily indoor PM₁ (PM_2.5, PM₁₀) during cooking was 30 % (32 %, 35 %) higher than that during non-cooking hours, and homes with longer cooking periods (≥2 h) showed ∼40 % higher PM levels. Indoor PM was strongly correlated to outdoor PM levels (R² &gt; 0.70). Indoor sources contributed only ∼10 % to the overall daily indoor PM levels, and the combined contribution of indoor and local outdoor sources to indoor PM was ∼33 %. Indoor PM employing the real-time LCS showed higher variability within homes than between homes, indicating that longer-term measurements should be conducted to accurately capture the variability. The study highlights that acute exposures are closely associated with short-term, temporarily generated indoor pollutants, while outdoor sources contribute significantly to chronic exposure to indoor PM.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102553"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inter-model comparison of the DRI-2001 and DRI-2015 for carbonaceous aerosol analysis in PM2.5: Method development and application in Chongqing","authors":"Chao Peng ,&nbsp;Chongzhi Zhai ,&nbsp;Yang Chen ,&nbsp;Mi Tian ,&nbsp;Ying Xiang ,&nbsp;Tianyu Zhai ,&nbsp;Weikai Fang ,&nbsp;Xin Long ,&nbsp;Xiaocheng Wang ,&nbsp;Mulan Chen ,&nbsp;Yunqing Cao ,&nbsp;Min Du ,&nbsp;Zhenliang Li","doi":"10.1016/j.apr.2025.102555","DOIUrl":"10.1016/j.apr.2025.102555","url":null,"abstract":"<div><div>Thermal-optical method is widely used to determine organic carbon (OC) and elemental carbon (EC) in PM_2.5 collected on filters. DRI-2001 and DRI-2015 analyzers have both been extensively employed with the IMPROVE_A protocol in the recent decades. However, differences in detectors and lasers between the two models can affect the measurement results. In this study, a novel using multiple linear regression was developed to equalize carbon fraction results between DRI-2001 and DRI-2015. After adjustment, high inter-model consistency was observed, with a mean bias within 5 % for total carbon (TC) and OC, and ∼10 % for EC. Larger inter-model differences (5.2 %–121.5 %) were found in OC1-OC4 and EC1-EC3. Fractions with high mass loading, particularly those linked to biomass burning (BB) and coal combustion (CC) (e.g., OC1, OC2 and OC4), exhibited better agreement after adjustment, with smaller mean bias within ∼15 % and higher R² values above 0.93 (<em>p</em> &lt; 0.001). During winter in Wanzhou, the adjusted carbon fractions exhibited significantly improved inter-model agreement (<em>p</em> &lt; 0.001). BB and CC were identified as the primary sources of carbonaceous aerosol, while secondary organic carbon (SOC) also contributed to elevated TC concentrations during pollution periods. Similar to DRI-2001 results, DRI-2015 measurements during winter indicated that CC and BB contributed 47.5 %, diesel exhaust 18.0 %, gasoline exhaust 21.6 %, and secondary formation 12.9 % to TC. These findings enhance our understanding of uncertainties and differences between models, leading to more accurate characterization of carbonaceous aerosol.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102555"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concurrent study of long-range transport of fine and ultrafine particles from peatland fires in lower Southeast Asia","authors":"Panita Khwanmueng ,&nbsp;Racha Dejchanchaiwong ,&nbsp;Perapong Tekasakul ,&nbsp;Nobchonnee Nim ,&nbsp;Aulia Ullah ,&nbsp;Kunaifi Kunaifi ,&nbsp;John Morris","doi":"10.1016/j.apr.2025.102554","DOIUrl":"10.1016/j.apr.2025.102554","url":null,"abstract":"<div><div>The physical and chemical characteristics of atmospheric fine (PM₁ and PM_2.5) and ultrafine particles (PM_0.1), during peatland fires in Indonesia and transported over a long distance to southern Thailand, were investigated to add more insight into the impact in lower Southeast Asia region. The background PM concentrations in Pekanbaru, Indonesia were approximately 1.5–3 times as high as those in Hat Yai, southern Thailand. The PM and PM-bound PAH mass concentrations in Hat Yai during the haze event were significantly increased over the background. The prominent PAH profiles during the haze event in Hat Yai showed a similar pattern to those in Pekanbaru. This suggested that the aerosol from the peatland fire in Indonesia was transported over long distances to southern Thailand. Moreover, the chemical mass balance model indicated that the most dominant source of PM_0.1 at both sites during normal event was vehicle fuel combustion. This is contrast with PM₁ and PM_2.5 where the biomass burning was the major source. On the other hand, during the haze event, the main source of PM_0.1 at Pekanbaru was clearly peatland fires, accounting for 51–65 %, whereas at Hat Yai, the most dominant source of PM_0.1 was vehicle fuel combustion accounting for 66 % even though the contribution from peatland fires was noticeable (17 %). This is in contrast with PM₁ and PM_2.5, where peatland fires were the main source, contributing 38–50 %.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102554"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the contribution of periodicity and national holidays to air pollution levels in the United Kingdom using a decomposable time series model","authors":"Christopher E. Rushton,&nbsp;James E. Tate","doi":"10.1016/j.apr.2025.102533","DOIUrl":"10.1016/j.apr.2025.102533","url":null,"abstract":"<div><div>This paper quantifies the impact of periodicity and national holidays on air pollution levels in the United Kingdom using a decomposable time series forecasting model. The analysis focuses on nitrogen dioxide (<span><math><mrow><mi>N</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span>) concentrations, with data sourced from the Automatic Urban and Rural Network and Air Quality England networks between January 2017 and December 2023. The Prophet model developed by Meta is used to identify, quantify, and appropriately remove the temporal periodicities in air pollution concentration, demonstrating how annual holidays such as Christmas, and one-off events, such as the state funeral of Elizabeth II and the London Marathon, influence local air pollution in isolation. The findings provide empirical evidence supporting widely held assumptions around national holidays and show some localised reductions in <span><math><mrow><mi>N</mi><msub><mrow><mi>O</mi></mrow><mrow><mn>2</mn></mrow></msub></mrow></math></span> concentrations during major events, with contextual variation also observed. For example, the state funeral of Elizabeth II shows a reduction in <span><math><mrow><mn>21</mn><mo>.</mo><mn>15</mn><mspace></mspace><mi>μ</mi><mi>g</mi><msup><mrow><mi>m</mi></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span> compared to a median reduction of <span><math><mrow><mo>−</mo><mn>2</mn><mo>.</mo><mn>43</mn><mspace></mspace><mi>μ</mi><mi>g</mi><msup><mrow><mi>m</mi></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></mrow></math></span> outside of London for urban traffic sites. This paper emphasises the need for localised air pollution mitigation policies and demonstrates the utility of large, complete, and publicly available datasets coupled with modern forecasting tools in environmental research.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102533"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics and deposition capacities of the chemical components in frost in Jinan, China","authors":"Jiarong Li ,&nbsp;Shuang Zhang ,&nbsp;Wanqi Wu ,&nbsp;Xuli Jing ,&nbsp;Chao Zhu ,&nbsp;Jinhe Wang","doi":"10.1016/j.apr.2025.102551","DOIUrl":"10.1016/j.apr.2025.102551","url":null,"abstract":"<div><div>Frost usually occurs at night when the temperature falls below the freezing point and the air is saturated with water vapor. In this study, 57 frost samples were collected over two winters (2020 and 2021) in Jinan, a central city located in the North China Plain. The concentrations of water-soluble ions (WSIs), polycyclic aromatic hydrocarbons (PAHs) and nitrated phenols (NPs) in the frost samples were measured using ion chromatography, gas chromatography–mass spectrometry, and ultra-high-performance liquid chromatography–mass spectrometry, respectively. The characteristics and inter relationships were summarized comprehensively. In addition, the deposition effect of frost was investigated. Results showed that each frost process resulted the deposition of 5.6 mg m⁻² WSIs, 0.22 μg m⁻² PAHs and 4.1 μg m⁻² NPs. The monthly deposition fluxes of the WSIs and PAHs through frost were two orders of magnitude lower than those through rain and dry deposition. For the first time, this study calculated the deposition capacity (DC), which indicates how readily a chemical component is eliminated during the frost process. The DC values obtained by chemical group ranked as follows: NPs (362.1 ± 120.3) &gt; WSIs (107.5 ± 61.9) &gt; PAHs (4.8 ± 4.0), indicating that NPs were more easily removed through frost deposition, whereas PAHs were the least likely to be deposited. Among individual components, Ca²⁺ and 5-nitrosalicylic acid exhibited the highest DC values (&gt;600.0), indicating that they participated actively in the frost pross. Furthermore, NP components containing more carboxyl groups and fewer hydroxyl groups were found to have higher DC values.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102551"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on volatile organic compound source profiles and dynamic emission inventories in an integrated refinery-petrochemical complex","authors":"Aizhong Cheng,&nbsp;Sujing Li,&nbsp;Wei Li","doi":"10.1016/j.apr.2025.102550","DOIUrl":"10.1016/j.apr.2025.102550","url":null,"abstract":"<div><div>Volatile organic compound (VOCs) emissions from integrated refinery-petrochemical complex (IRPC) were difficult to quantify due to complex production processes and significant fugitive emissions. Previous studies had largely focused on upstream refinery units, with limited research on comprehensive emission factors (EFs) and inventories for the entire IRPC. We analyzed multiple emission sources within IRPC, including equipment leaks (EL), stationary combustion (SC), cooling towers (CT), storage tanks (ST), wastewater collection and treatment systems (WT), process vents (PV), flares (F), and loading operations (LO). The EFs of IRPC was 0.21 kg VOCs/t of crude processed. Additionally, to mitigate the temporal lag associated with traditional emission inventories, we developed dynamic accounting methods for VOCs emissions in IRPC. These methods integrate monitoring data and time-varying meteorological parameters (e.g., temperature, air pressure, wind speed) to estimate emissions from SC and ST. An integrated analytical system combining gas chromatography-mass spectrometry (GC-MS) and flame ionization detection (FID) was used to characterize and quantify 115 VOCs species. Composite source profiles were sampling from 35 sites collected across 18 units using a weighted average method. This detailed breakdown provided a more comprehensive view of IRPC emissions compared to studies focusing solely on upstream units. In the IRPC, key pollutants identified include propane, ethane, acetone, ethanol, propylene, and dichloromethane. Alkenes and alkynes had the highest ozone formation potential (OFP) contribution, while aromatics had the greatest secondary organic aerosol formation potential (SOAP) contribution. The study's detailed source profiles and dynamic emission inventory aid petrochemical pollution control and regional air quality improvements.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102550"},"PeriodicalIF":3.9,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}