Atmospheric Pollution Research最新文献

{"title":"Useful hourly measurements of formaldehyde at PAMS sites in New York","authors":"O.V. Rattigan,&nbsp;P. Furdyna,&nbsp;M. Hirsch,&nbsp;A.C. Teora,&nbsp;H.D. Felton,&nbsp;R.Y. Tian,&nbsp;M.A. Ninneman,&nbsp;W. Hao","doi":"10.1016/j.apr.2025.102568","DOIUrl":"10.1016/j.apr.2025.102568","url":null,"abstract":"<div><div>Hourly measurements of HCHO were carried out over a period of 2–3 years, at two New York sites (New York Botanical Gardens (NYBG) and Flax Pond) using Picarro cavity ring-down spectroscopy instruments, model G2307. HCHO data was corrected for the baseline drift by sampling zero air for 10 min on an hourly basis. The zero corrected HCHO data were then compared to collocated HCHO data from 2,4-dinitrophenylhydrazine (DNPH) cartridge samples. The difference averaged on a quarterly basis was used to adjust for the Picarro instrumental bias. The corrected Picarro data were found to agree well with collocated measurements from an Aerodyne Tunable Infrared Laser Direct Absorption Spectroscopy (TILDAS) instrument. HCHO showed a pronounced diel pattern in summer, with peak concentrations of 3.4–3.9 parts per billion by volume (ppbv), around noon to early afternoon, and a minimum of 1.4–1.9 ppbv, around 4–5 a.m. A strong seasonal gradient was observed with mean summer concentrations a factor of 2–3 higher than in winter, indicating the importance of secondary production. HCHO concentrations simulated by the Community Multiscale Air Quality model were lower than observed at NYBG, indicating that the emissions of HCHO and biogenic isoprene may be too low in urban areas. At Flax Pond modeled HCHO values were generally closer to observed concentrations except for April and May when modeled values were lower. The Picarro G2307 has the potential to provide accurate high temporal resolution HCHO data in the sub ppbv range, which is useful for model evaluation provided it is corrected for baseline drift and instrumental bias.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102568"},"PeriodicalIF":3.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922136","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":"High resolution variability in wet deposition in the southeastern United States","authors":"Brianna H. Matthews ,&nbsp;Stephen Noble ,&nbsp;Mariam Cherif ,&nbsp;Thomas L. White ,&nbsp;Jian-Hua Qian ,&nbsp;Matthew R. Nelson","doi":"10.1016/j.apr.2025.102569","DOIUrl":"10.1016/j.apr.2025.102569","url":null,"abstract":"<div><div>Rainwater chemistry is determined by atmospheric pollutants and particles which vary spatially and temporally. Industrial and agricultural activities and meteorological events (e.g. sea breezes, severe weather, blowing dust) alter atmospheric particle and trace gas compositions. These gases and particles are scavenged by cloud and rain droplets that drive wet deposition. During an Intensive Operation Period (IOP) from April to October 2021, rainwater was collected at higher frequency intervals, usually daily, after precipitation events at three locations on the Savannah River Site (SRS). The farthest locations were separated by approximately 20 km. The mean concentration (μeq/L) of seven ions followed the Cl⁻ &gt; SO₄²⁻ &gt; Na⁺ &gt; NO₃⁻ &gt; K⁺ &gt; Mg²⁺ &gt; Ca²⁺ downward trend. Ion concentrations were compared to background ion concentrations from the National Atmospheric Deposition Program (NADP). The high frequency monthly averaged SRS data compared well with the monthly averaged NADP background but demonstrated extensive variability. In some months in 2021, the high frequency data compared better to the NADP site near the coast while in other months inland sites compared better. Strong spatial variability for ion concentrations was observed across SRS which was attributed to localized impacts in rainfall spatial variability. High frequency measurements allowed for comparison to regional weather patterns indicating influences from the Atlantic Ocean, Gulf of Mexico, and cities. This can account for spatial variability in the wet deposition flux. Sea breezes, Saharan dust, and anthropogenic sources were shown to impact wet deposition flux variability. Higher frequency precipitation chemistry sampling at numerous locations better captures ion concentration variability and improves measurement representativeness.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102569"},"PeriodicalIF":3.9,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908035","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":"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":"Identification of PM10 episodes in Tehran, Iran: Regional versus local causes","authors":"Mohammad Saleh Ali-Taleshi ,&nbsp;Alireza Riyahi Bakhtiari ,&nbsp;Nairui Liu ,&nbsp;Mauro Masiol ,&nbsp;Philip K. Hopke","doi":"10.1016/j.apr.2025.102544","DOIUrl":"10.1016/j.apr.2025.102544","url":null,"abstract":"<div><div>Middle Eastern sand and dust storms (SDSs) can deteriorate air quality and human health of millions of inhabitants in downwind areas such as Tehran, Iran. Limited information is available about severity, frequency, magnitude, and duration of PM₁₀ episodes in Tehran. To identify PM₁₀ episodes between 2015 and 2021, the Seasonal-Trend decomposition procedure based on Loess (STL) method was applied to measured PM₁₀ concentrations at 15 sites across Tehran. The PM₁₀ episodes were separated into 3 duration categories (PE1, PE2, and PE3). The PM₁₀ concentrations and related meteorological characteristics of the episodes were analyzed to assess the differences. All sites had higher PM₁₀ concentrations compared to AQG levels for daily (24-h) PM₁₀ (45 μg m⁻³). PM₁₀ episode frequency, magnitude, and duration were 2.60/year, 35.0 percent, and 1 day/episode over the 7-years study period, respectively. The overall frequency of PM₁₀ PE1, PE2, and PE3 events were 205, 54, and 14, respectively. Events dissipated under strong westerly winds. PM₁₀ accumulation occurred periods of weakening of the westerly winds to stagnant conditions. Site-specific bivariate polar plots (BBPs) also supported this finding. The increase of regional dust during high wind speeds on dusty days led to lower PM_2.5/PM₁₀ ratios at most sites. Back trajectory analyses of PM₁₀ concentrations showed that the transboundary transport from westerly directions produced the high PM₁₀ concentrations. Understanding the PM₁₀ pollution episode characteristics could permit planning of practical anthropogenic emissions reductions and support the joint air pollution control strategies for regional air pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102544"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943305","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":"Evaluation of urban PM2.5 concentrations over 73 major cities and their association with satellite Aerosol Optical Depth: A global analysis of ambient air pollution","authors":"Dimitris Stratoulias ,&nbsp;Beomgeun Jang ,&nbsp;Narissara Nuthammachot","doi":"10.1016/j.apr.2025.102556","DOIUrl":"10.1016/j.apr.2025.102556","url":null,"abstract":"<div><div>Air pollution is a notable environmental health risk factor in modern society, affecting 99 % of the population. In the current study, we investigate the global fine Particulate Matter (PM2.5) urban footprint based on PM2.5 surface measurements from 76 AirNow stations worldwide from January 2019 until March 2024 and investigate the association with satellite-based Aerosol Optical Depth (AOD). All 76 stations in all years, with no exception, exceed the World Health Organization (WHO) annual average air quality guideline level (i.e., 5 μg/m³), and 29 stations exceed the WHO annual average interim target 1 (i.e., 35 μg/m³). The global map of the mean PM2.5 concentrations indicate a hotspot in South Asia where these concentrations sustain considerably high levels. Disaggregation by World Bank country income level groups reveals income inequality with a gap between the lower-middle (median 42.1 μg/m³) and the upper-middle (22.5 μg/m³) income countries. Site-specific correlations on PM2.5-AOD pairs from AirNow and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively, for statistically significant observations show a large variation between stations, with R ranging between −0.60 and 0.79. In a case study over Vientiane, Laos, the Geostationary Environment Monitoring Spectrometer (GEMS) and MODIS AOD satellite products indicate a considerably higher number of GEMS observations available due to intra-day acquisition capability. A closer look at the GEMS AOD-PM2.5 hourly pairs show a clear diurnal pattern. The results of this large-scale experiment showcase the robustness and applicability of the synergistic use of the global AirNow surface monitoring system and satellite products.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 8","pages":"Article 102556"},"PeriodicalIF":3.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943304","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}