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24 results for “Source apportionment”
Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland
<p>Data to accompany "Source apportionment of highly time-resolved elements during a firework episode from a rural freeway site in Switzerland" publication in Atmospheric Chemistry and Physics. This repository contains measurement data in Härkingen, Switzerland, a permanent station of the Swiss National Air Pollution Monitoring Network (NABEL). Sampling was performed from 23 July to 13 August 2015. This repository has excel file (all data.xlsx) for all the raw data measured during campaign. In addition, it has data corresponding to each figures presented in main text published version.</p>
European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets
<p>Organic aerosol (OA) is a key component of total submicron particulate matter (PM<sub>1</sub>), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM<sub>1</sub> levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.</p>
Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
<p>Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of <em>"Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window"</em> by Chen et al. (2021).</p> <p>This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.</p>
Real-time measurement and source apportionment of elements in Delhi's atmosphere
<p>Here we present semi-continuous and real-time measurements of elemental composition for PM2.5 and PM10 aerosols in Delhi, India, at a time resolution of 30 min to 1 h during two consecutive winters in 2018 and 2019, to identify the prevailing sources. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, brake wear, a S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-30 Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the pollutants enter Delhi via three distinct air corridors during winters including Nepal and Uttar Pradesh (India) in the east, and Pakistan, Punjab (India) and Haryana (India) in the north-west, during winter, when the national capital’s air quality is at its worst.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
Dataset for 'Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment'
<p>This dataset includes the VOCs (and expanded uncertainties) as well as VOCs (and expanded uncertainties) used in the PMF model as described in 'Pernov, J. B., Bossi, R., Lebourgeois, T., Nøjgaard, J. K., Holzinger, R., Hjorth, J. L., and Skov, H.: Atmospheric VOC measurements at a High Arctic site: characteristics and source apportionment, Atmos. Chem. Phys. Discuss., 2020, 1-36, 10.5194/acp-2020-528, 2020.' </p>
Nimble Output for MCMC Estimation of Source Apportionment; Accompaniment for Journal Article
<p>Paper <em>A dependent Bayesian Dirichlet Process model for source apportionment of particle number size distribution</em> contributed to the <em>Environmetrics</em> Special Issue on <em>Environmental Data Science</em>. In this paper, a fairly extensive Bayesian model is run using the <em>Nimble</em> probabilistic programming language. To allow for user reproducibility, the output of this model (full, not summary), is provided here.</p>
Atmospheric aerosol chemical characterization and organic aerosol source apportionment by HR-TOF-AMS in the Po Valley during RHAPS (2021)
<p><span>Time series of non-refractory submicrometric aerosol (PM1) chemical components (sulfate, nitrate, ammonium, chloride, and organic aerosol, OA) from RHAPS campaigns (winter and summer 2021) at Bologna (BO) and San Pietro Capofiume (SPC), Po Valley, Italy.<br></span></p> <p><span>Time series and profiles of OA source factors derived from PMF.</span></p>
The variability of mass concentrations and source apportionment analysis of equivalent black carbon across urban Europe
<p>This study analyzed the variability of equivalent black carbon (eBC) mass concentrations and their sources in urban Europe to provide insights into the use of eBC as an advanced air quality (AQ) parameter for AQ standards. This study compiled eBC mass concentration datasets covering the period between 2006 to 2022 from 50 measurement stations, including 23 urban background (UB), 18 traffic (TR), 7 suburban (SUB), and 2 regional background (RB) sites. The results highlighted the need for the harmonization of eBC measurements to allow for direct comparisons between eBC mass concentrations measured across urban Europe. The eBC mass concentrations exhibited a decreasing trend as follows: TR > UB > SUB > RB. Furthermore, a clear decreasing trend in eBC concentrations was observed in the UB sites moving from Southern to Northern Europe. The eBC mass concentrations exhibited significant spatiotemporal heterogeneity, including marked differences in eBC mass concentration and variable contributions of pollution sources to bulk eBC between different cities. Seasonal patterns in eBC concentrations were also evident, with higher winter concentrations observed in a large proportion of cities, especially at UB and SUB sites. The contribution of eBC from liquid fossil fuel combustion, mostly traffic (eBC<sub>T</sub>) was higher than that of residential and commercial sources (eBC<sub>RC</sub>) in all European sites studied. Nevertheless, eBC<sub>RC</sub> still had a substantial contribution to total eBC mass concentrations at a majority of the sites. eBC trend analysis revealed decreasing trends for eBC<sub>T</sub> over the last decade, while eBC<sub>RC</sub> remained relatively constant or even increased slightly in some cities.</p>
Measurement report: Vertical profiling of particle size distributions over Lhasa, Tibet: Tethered balloon-based in-situ measurements and source apportionment
<p>Particle size distribution data in summer 2020 in Lhasa, Tibet for https://doi.org/10.5194/acp-2021-810</p>
Highly time-resolved chemical speciation and source apportionment of organic aerosol components in Delhi, India, using extractive electrospray ionization mass spectrometry
<p>In recent years, the Indian capital city of Delhi has been impacted by very high levels of air pollution,<br> especially during winters. Comprehensive knowledge of the composition and sources of the organic aerosol<br> (OA), which constitutes a substantial fraction of total particulate mass (PM) in Delhi, is central to formulating<br> effective public health policies. Previous source apportionment studies in Delhi identified key sources of primary<br> OA (POA) and showed that secondary OA (SOA) played a major role but were unable to resolve specific SOA<br> sources. We address the latter through the first field deployment of an extractive electrospray ionization timeof-<br> flight mass spectrometer (EESI-TOF) in Delhi, together with a high-resolution aerosol mass spectrometer<br> (AMS). Measurements were conducted during the winter of 2018/19, and positive matrix factorization (PMF)<br> was used separately on AMS and EESI-TOF datasets to apportion the sources of OA. AMS PMF analysis yielded<br> three primary and two secondary factors which were attributed to hydrocarbon-like OA (HOA), biomass burning<br> OA (BBOA-1 and BBOA-2), more oxidized oxygenated OA (MO-OOA), and less oxidized oxygenated OA (LOOOA).<br> On average, 40%of the total OA mass was apportioned to the secondary factors. The SOA contribution to<br> total OA mass varied greatly between the daytime (76.8 %, 10:00–16:00 local time (LT)) and nighttime (31.0 %,<br> 21:00–04:00 LT). The higher chemical resolution of EESI-TOF data allowed identification of individual SOA<br> sources. The EESI-TOF PMF analysis in total yielded six factors, two of which were primary factors (primary<br> biomass burning and cooking-related OA). The remaining four factors were predominantly of secondary origin:<br> aromatic SOA, biogenic SOA, aged biomass burning SOA, and mixed urban SOA. Due to the uncertainties in<br> the EESI-TOF ion sensitivities, mass concentrations of EESI-TOF SOA-dominated factors were related to the<br> total AMS SOA (i.e. MO-OOA C LO-OOA) by multiple linear regression (MLR). Aromatic SOA was the major<br> SOA component during the daytime, with a 55.2% contribution to total SOA mass (42.4% contribution to total<br> OA). Its contribution to total SOA, however, decreased to 25.4% (7.9% of total OA) during the nighttime. This<br> factor was attributed to the oxidation of light aromatic compounds emitted mostly from traffic. Biogenic SOA<br> accounted for 18.4% of total SOA mass (14.2% of total OA) during the daytime and 36.1% of total SOA mass<br> (11.2% of total OA) during the nighttime. Aged biomass burning and mixed urban SOA accounted for 15.2%<br> and 11.0% of total SOA mass (11.7% and 8.5% of total OA mass), respectively, during the daytime and 15.4%<br> and 22.9% of total SOA mass (4.8% and 7.1% of total OA mass), respectively, during the nighttime. A simple<br> dilution–partitioning model was applied on all EESI-TOF factors to estimate the fraction of observed daytime<br> concentrations resulting from local photochemical production (SOA) or emissions (POA). Aromatic SOA, aged<br> biomass burning, and mixed urban SOA were all found to be dominated by local photochemical production,<br> likely from the oxidation of locally emitted volatile organic compounds (VOCs). In contrast, biogenic SOA was<br> related to the oxidation of diffuse regional emissions of isoprene and monoterpenes. The findings of this study<br> show that in Delhi, the nighttime high concentrations are caused by POA emissions led by traffic and biomass<br> burning and the daytime OA is dominated by SOA, with aromatic SOA accounting for the largest fraction.<br> Because aromatic SOA is possibly more toxic than biogenic SOA and primary OA, its dominance during the<br> daytime suggests an increased OA toxicity and health-related consequences for the general public.</p> <p>This repository has excel file corresponding to each figures presented in main text published version.</p>
Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility testing and human health risks - Supplementary data
<p>This is a supplementary dataset to the paper:</p> <p>Hiller E., Faragó T., Kolesár M., Filová L., Mihaljevič M., Jurovič L., Demko R., Mchlica A., Štefánek J., Vítková M. (2024): Metal(loid)s in urban soil from historical municipal solid waste landfill: Geochemistry, source apportionment, bioaccessibility and human health risks. <em>Chemosphere</em> <strong>362</strong>, 142677. DOI: 10.1016/j.chemosphere.2024.142677</p> <p>This research was supported by the Johannes Amos Comenius Programme (OP JAC), project No. CZ.02.01.01/00/22_008/0004605, Natural and anthropogenic georisks. The dataset is published under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). This license allows others to distribute, remix, adapt, and build upon the dataset for any purpose, even commercially, as long as they give appropriate credit to the original creator(s).</p>
Multiply improved positive matrix factorization for source apportionment of volatile organic compounds during the COVID-19 shutdown in Tianjin, China
<p>This dataset contains the input and output data of the multiple PMF for volatile organic compounds (VOCs) source apportionment in the suburbs of Tianjin, China, during the outbreak of COVID-19 period (November 2019 to March 2020).</p>
Data for publication "Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018"
<p>Data to accompany "<a href="https://www.atmos-meas-tech-discuss.net/amt-2019-351/"><em>Evaluation of equivalent black carbon (EBC) source apportionment using observations from Switzerland between 2008 and 2018</em></a>" publication in <em>Atmospheric Measurement Techniques</em>. This repository contains observational data and two helper tables containing monitoring site information and instrument location data. All files are .csv files, with UTF-8 encoding, and are self describing. The time zone used for analysis was UTC + 1, tz database/Olson string "Etc/GMT+1".</p>
Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia
<p>This work presents an advanced source apportionment approach that estimates the uncertainties of the positive matrix factorization model. The selection of the environmentally meaningful solutions is challenging in such models and a systematic approach to identify and sort the factors is important. We applied this method to a novel data set covering one yearly cycle at three sites in Estonia. The main results showed that biomass burning dominates in winter, while biogenic sources prevail in summer.</p>
Organic aerosol source apportionment in Zurich using extractive electrospray ionization time-of-flight mass spectrometry (EESI-TOF): Part I, biogenic influences and day/night chemistry in summer
<p>Ambient measurement campaign took place during summer 2016 in Zurich. The sources of organic aerosol were disclosed. The novel extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) could provide direct chemical evidence linking ambient SOA to its precursor emissions including a strong influence of biogenic emissions. Additionally provided some insight into the day/night reaction environment and high-detailed chemical composition.</p>
Characterization of NR-PM1 and source apportionment of organic aerosol in Krakow, Poland
<p>Krakow is routinely affected by very high air pollution levels, especially during the winter months. Although a lot of effort has been done on characterization of ambient aerosols, there is a lack of online and long-term measurements of non-refractory aerosols. Our measurements at AGH University provide online long-term chemical composition of ambient submicron particulate matter (PM<sub>1</sub>) between January 2018 and April 2019. Here we report the chemical characterization of non-refractory submicron aerosols and source apportionment of the organic fraction by positive matrix factorization (PMF). In contrast to other long-term source apportionment studies, we let a small PMF window roll over the dataset instead of performing PMF over the full dataset or on separate seasons. In this way, the seasonal variation of the source profiles can be captured. The uncertainties of the PMF solutions are addressed by the bootstrap resampling strategy and the random <em>a</em>-value approach for constrained factors.</p> <p>We observe clear seasonal patterns in concentration and composition of PM<sub>1</sub>, with high concentrations during the winter months and lower concentrations during the summer months. Organics are the dominant species throughout the campaign. Five organic aerosol (OA) factors are resolved, of which three are of primary nature (hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)) and two are of secondary nature (more oxidized oxygenated OA (MO-OOA) and less oxidized oxygenated OA (LO-OOA)). While HOA contributes on average 8.6 % ± 2.3 % throughout the campaign, the solid fuel combustion related BBOA and CCOA show a clear seasonal trend with average contributions of 10.4 % ± 2.7 % and 14.1 %, ± 2.1 % respectively. Not only BBOA but also CCOA is associated with residential heating because of the pronounced yearly cycle where the highest contributions are observed during wintertime. Throughout the campaign, the OOA can be separated into MO-OOA and LO-OOA with average contribution of 38.4 % ± 8.4 % and 28.5 % ± 11.2 %, respectively. </p>
Dataset for: An optimised organic carbon / elemental carbon (OC/EC) fraction separation method for radiocarbon source apportionment applied to low-loaded Arctic aerosol filters
<p>Dataset contains raw data and R scripts to create the figures.</p>
High-time-resolution chemical composition and source apportionment of PM2.5 in northern Chinese cities: implications for policy
<p>Three real-time measurement campaigns were conducted in Xi’an, Shijiazhuang, and Beijing to investigate the chemical characteristics and source contributions of PM<sub>2.5</sub> and explore the formation progress of heavy pollution for policy implications. Chemeical components of PM<sub>2.5</sub> including OA, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>2-</sup>, NH<sub>4</sub><sup>+</sup>, Cl-, BC, and elements were monitored by Q-ACSM, AE33, and Xact, respectively. The results show that chemical compositions of PM<sub>2.5 </sub>in the three cities are all dominated by organic aerosol (OA) and nitrate (NO<sub>3</sub><sup>-</sup>). And results of source apportionment analyzed by the hybrid environmental receptor model (HERM) show that the secondary nitrate plus sulfate contributed higher to PM<sub>2.5</sub> compared to other primary sources. Morevoer, the potential formation mechanisms of secondary aerosol in three cities were further explored by establishing the correlations between secondary nitrate plus sulfate and aerosol liquid water content (ALWC), and O<em><sub>x</sub></em> (O<sub>3</sub> + NO<sub>2</sub>). The results show that photochemical oxidation and aqueous-phase reaction were two important pathways of secondary aerosol fromation. Additionally, this study compared the changes in chemical composition and source contributions of PM<sub>2.5</sub> in past decades, and suggested that not only the activities of clean energy replacements for the rural household is urgently enhanced to reduce the primary source emissions in northern China, but also collaborative control ozone and particulate matter need to be contiouslty promoted to weaken the atmosphere oxidation capacity for reducing secondary aerosol formation.</p>
Dataset for Enhancing Watershed Management through Adaptive Source Apportionment under Changing Environment
<p>D1.sheet. The livestock and poultry information in Hangbu River in 2017</p> <p>D2.sheet. The nitrogen emission of different sources in Hangbu River</p> <p>D3.sheet. The phosphorus emission of different sources in Hangbu River</p> <p>D4.sheet. The Proportion of nitrogen emissions of different sources in Hangbu River</p> <p>D5.sheet. The Proportion of phosphorus emissions of different sources in Hangbu River</p> <p>D6.sheet. The Proportion of nitrogen contribution of different sources in Hangbu River</p> <p>D7.sheet. The Proportion of phosphorus contribution of different sources in Hangbu River</p> <p>D8.sheet. The three dynamic contribution indicators: contribute value, trend of change, and robustness</p> <p>D9.sheet. The Pareto fronts for dynamic source apportionment in nitrogen</p> <p>D10.sheet. The Pareto fronts for dynamic source apportionment in phosphorus</p>
Characteristics of wintertime VOCs in urban Beijing: composition and source apportionment
<p>Ambient measurement campaign took place during winter 2017 in Beijing, China. The sources of volatile organic compounds were disclosed measured by proton-transfer-reaction time-of-flight mass spectrometers (PTR-ToF-MS). Ten VOC families showed clear dependence on the VOC concentration. The application of the novel positive matrix factorization receptor model enables the identification of anthropogenic and OVOC sources. Besides, a detailed comparison of VOC and OA source composition provided new proofs on SOA formation.</p>
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