Gang Chen

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Chen

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Gang

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0000-0002-1507-4622

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Publications 1 - 10 of 14

Characterization of non-refractory (NR) PM1 and source apportionment of organic aerosol in Kraków, Poland
Tobler, Anna K.; Skiba, Alicja; Canonaco, Francesco; et al. (2021)
Atmospheric Chemistry and Physics
Kraków is routinely affected by very high air pollution levels, especially during the winter months. Although a lot of effort has been made to characterize ambient aerosol, there is a lack of online and long-term measurements of non-refractory aerosol. Our measurements at the AGH University of Science and Technology provide the online long-term chemical composition of ambient submicron particulate matter (PM1) between January 2018 and April 2019. Here we report the chemical characterization of non-refractory submicron aerosol 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 in the source profiles can be captured. The uncertainties in the PMF solutions are addressed by the bootstrap resampling strategy and the random a-value approach for constrained factors. We observe clear seasonal patterns in the concentration and composition of PM1, 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 a primary nature (hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and coal combustion OA (CCOA)) and two are of a 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 contributions of 38.4 % ± 8.4 % and 28.5 % ± 11.2 %, respectively.
Effect of soil-groundwater system on migration and transformation of organochlorine pesticides: A review
Li, Haohao; Huo, Lin; Zhang, Rui; et al. (2025)
Ecotoxicology and Environmental Safety
Soil is the place where human beings, plants, and animals depend on for their survival and the link between the various ecological layers. Groundwater is an important component of water resources and is one of the most important sources of water for irrigated agriculture, industry, mining and cities because of its stable quantity and quality. Soil and groundwater are important strategic resources highly valued by countries around the world. However, in recent years, the deterioration of the ecological environment of soil-groundwater caused by industrial, domestic, and agricultural pollution sources has continued to threaten human health and ecological security. Among them, organochlorine pesticides (OCPs), as typical organic pollutants, cause very serious pollution of soil and groundwater environment. However, most studies on the pollution of OCPs have focused on the aboveground or surface water environment, and little consideration has been given to the pollution and hazards of OCPs to the deep soil and groundwater environment, especially the effects of different environmental factors on the transport and transformation of OCPs in soil-groundwater. Moreover, in addition to the influence of a single factor on it, the interactions that arise between different factors cannot be ignored. This paper focuses on two major sources of OCPs in soil and groundwater environments, compiles and summarizes the effects of environmental factors such as pH, microbial communities and enzyme activities on the transport and transformation of OCPs in soil and groundwater systems, discusses the synergistic effects of individual environmental factors and others, and comprehensively analyses the effects of synergistic effects of various environmental factors on the transport and transformation of OCPs. In the context of ecological civilization construction, it provides the scientific basis and theoretical foundation for the prevention and treatment of OCPs-contaminated soil and groundwater, and puts forward new ideas and suggestions for the research and development of green, eco-friendly remediation and treatment technologies for OCPs-contaminated sites.
European Aerosol Phenomenology − 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets
Chen, Gang; Canonaco, Francesco; Tobler, Anna; et al. (2022)
arXiv
Organic aerosol (OA) is a key component to total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 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 enables the quantifications 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%, a range of 43.7-100%). 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 and long-term data coverage (9-36 months), providing essential information to improve/validate air quality, health impact, and climate models.
Exploring the coupled ocean and atmosphere system with a data science approach applied to observations from the Antarctic Circumnavigation Expedition
Landwehr, Sebastian; Volpi, Michele; Haumann, Alexander; et al. (2021)
Earth System Dynamics
The Southern Ocean is a critical component of Earth's climate system, but its remoteness makes it challenging to develop a holistic understanding of its processes from the small scale to the large scale. As a result, our knowledge of this vast region remains largely incomplete. The Antarctic Circumnavigation Expedition (ACE, austral summer 2016/2017) surveyed a large number of variables describing the state of the ocean and the atmosphere, the freshwater cycle, atmospheric chemistry, and ocean biogeochemistry and microbiology. This circumpolar cruise included visits to 12 remote islands, the marginal ice zone, and the Antarctic coast. Here, we use 111 of the observed variables to study the latitudinal gradients, seasonality, shorter-term variations, geographic setting of environmental processes, and interactions between them over the duration of 90 d. To reduce the dimensionality and complexity of the dataset and make the relations between variables interpretable we applied an unsupervised machine learning method, the sparse principal component analysis (sPCA), which describes environmental processes through 14 latent variables. To derive a robust statistical perspective on these processes and to estimate the uncertainty in the sPCA decomposition, we have developed a bootstrap approach. Our results provide a proof of concept that sPCA with uncertainty analysis is able to identify temporal patterns from diurnal to seasonal cycles, as well as geographical gradients and "hotspots" of interaction between environmental compartments. While confirming many well known processes, our analysis provides novel insights into the Southern Ocean water cycle (freshwater fluxes), trace gases (interplay between seasonality, sources, and sinks), and microbial communities (nutrient limitation and island mass effects at the largest scale ever reported). More specifically, we identify the important role of the oceanic circulations, frontal zones, and islands in shaping the nutrient availability that controls biological community composition and productivity; the fact that sea ice controls sea water salinity, dampens the wave field, and is associated with increased phytoplankton growth and net community productivity possibly due to iron fertilisation and reduced light limitation; and the clear regional patterns of aerosol characteristics that have emerged, stressing the role of the sea state, atmospheric chemical processing, and source processes near hotspots for the availability of cloud condensation nuclei and hence cloud formation. A set of key variables and their combinations, such as the difference between the air and sea surface temperature, atmospheric pressure, sea surface height, geostrophic currents, upper-ocean layer light intensity, surface wind speed and relative humidity played an important role in our analysis, highlighting the necessity for Earth system models to represent them adequately. In conclusion, our study highlights the use of sPCA to identify key ocean-atmosphere interactions across physical, chemical, and biological processes and their associated spatio-temporal scales. It thereby fills an important gap between simple correlation analyses and complex Earth system models. The sPCA processing code is available as open-access from the following link: https://renkulab.io/gitlab/ACE-ASAID/spca-decomposition (last access: 29 March 2021). As we show here, it can be used for an exploration of environmental data that is less prone to cognitive biases (and confirmation biases in particular) compared to traditional regression analysis that might be affected by the underlying research question.
Sources, Occurrence and Characteristics of Fluorescent Biological Aerosol Particles Measured Over the Pristine Southern Ocean
Moallemi, Alireza; Landwehr, Sebastian; Robinson, Charlotte; et al. (2021)
Journal of Geophysical Research: Atmospheres
In this study, we investigate the occurrence of primary biological aerosol particles (PBAP) over all sectors of the Southern Ocean (SO) based on a 90-day data set collected during the Antarctic Circumnavigation Expedition (ACE) in austral summer 2016–2017. Super-micrometer PBAP (1–16 µm diameter) were measured by a wide band integrated bioaerosol sensor (WIBS-4). Low (3σ) and high (9σ) fluorescence thresholds are used to obtain statistics on fluorescent and hyper-fluorescent PBAP, respectively. Our focus is on data obtained over the pristine ocean, that is, more than 200 km away from land. The results indicate that (hyper-)fluorescent PBAP are correlated to atmospheric variables associated with sea spray aerosol (SSA) particles (wind speed, total super-micrometer aerosol number concentration, chloride and sodium concentrations). This suggests that a main source of PBAP over the SO is SSA. The median percentage contribution of fluorescent and hyper-fluorescent PBAP to super-micrometer SSA was 1.6% and 0.13%, respectively. We demonstrate that the fraction of (hyper-)fluorescent PBAP to total super-micrometer particles positively correlates with concentrations of bacteria and several taxa of pythoplankton measured in seawater, indicating that marine biota concentrations modulate the PBAP source flux. We investigate the fluorescent properties of (hyper-)fluorescent PBAP for several events that occurred near land masses. We find that the fluorescence signal characteristics of particles near land is much more variable than over the pristine ocean. We conclude that the source and concentration of fluorescent PBAP over the open ocean is similar across all sampled sectors of the SO.
Improved method for the optical analysis of particulate black carbon (BC) using smartphones
Chen, Gang; Wang, Qi; Fan, Yunfei; et al. (2020)
Atmospheric Environment
Black carbon (BC) is a major component in atmospheric particulate matter (PM), which causes adverse health impacts and contributes significantly to climate change. Without widespread and accurate BC measurements, it remains difficult to track incomplete combustion sources and reduce BC emissions. Currently commercial BC sensors remain too costly to be deployed widely. In this work, a fast, cost-effective, and easily accessible method based on a smartphone camera was used to quantify color information of PM collected on filters to estimate BC and elemental carbon (EC) loadings. A robust RGB (red, green, blue)-based linear interaction model was built and validated using 1878 PM samples collected in three different regions with collocated BC and EC measurements. After applying image correction methods, this model shows a good predictability with an R-squared (R2) of 0.904 with state-of-the-art BC measurement techniques, and a coefficient of variation of the root mean square error (CV(RMSE)) of 25.3% despite the complex sources and different reference measurement techniques. This work validates the viabilities of using smartphones to quantify BC or EC loading on PM filters with a unified model and track incomplete combustion sources.
Source identification of the elemental fraction of particulate matter using size segregated, highly time-resolved data and an optimized source apportionment approach
Manousakas, Manousos; Furger, Markus; Daellenbach, Kaspar R.; et al. (2022)
Atmospheric Environment: X
Source emissions with high covariance degrade the performance of multivariate models, and often highly-time resolved data is needed to accurately extract the contribution of different emissions. Here, we use highly time-resolved size segregated elemental composition data to apportion the sources of the elemental fraction of PM in Zürich (May 2019–May 2020). For data collection, we have used an ambient metals monitor, Xact 625i, equipped with a sampling inlet alternating between PM2.5 and PM10. By implementing interpolation and a newly proposed uncertainty estimation methodology, it was possible to obtain and use in PMF a combined dataset of PM2.5 and PMcoarse (PM10-2.5) having data from only one instrument. The combination of the inlet switching system, the instrument's high time resolution, and the use of advanced source apportionment approaches yielded improved source apportionment results in terms of the number of identified sources, as the model, additionally to the diurnal and seasonal variation of the dataset, also utilizes the variation from the size segregated data. Thirteen sources of elements were identified, i.e., sea salt (5.4%), biomass burning (7.2%), construction (4.3%), industrial (3.3%), light-duty vehicles (5.4%), Pb (0.7%), Zn (0.7%), dust (22.1%), transported dust (9.5%), sulfates (15.4%), heavy-duty vehicles (17%), railway (6.6%) and fireworks (2.4%). The Covid-19 lockdown effect in PM sources in the area was also quantified. High-intensity events disproportionally affect the PMF solution, and in many cases, they are getting discarded before analysis, removing thus valuable information from the dataset. In this study, a three-step source apportionment approach was used to get a well-resolved unmixed solution when firework data points were included in the analysis. This approach can also be used for other sources and/or events with very high contributions that distort source apportionment analysis. Optimized source apportionment techniques are necessary for effective air pollution monitoring.
Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
Chen, Gang; Sosedova, Yulia; Canonaco, Francesco; et al. (2021)
Atmospheric Chemistry and Physics
We collected 1 year of aerosol chemical speciation monitor (ACSM) data in Magadino, a village located in the south of the Swiss Alpine region, one of Switzerland's most polluted areas. We analysed the mass spectra of organic aerosol (OA) by positive matrix factorisation (PMF) using Source Finder Professional (SoFi Pro) to retrieve the origins of OA. Therein, we deployed a rolling algorithm, which is closer to the measurement, to account for the temporal changes in the source profiles. As the first-ever application of rolling PMF with multilinear engine (ME-2) analysis on a yearlong dataset that was collected from a rural site, we resolved two primary OA factors (traffic-related hydrocarbon-like OA (HOA) and biomass burning OA (BBOA)), one mass-to-charge ratio (m/z) 58-related OA (58-OA) factor, a less oxidised oxygenated OA (LO-OOA) factor, and a more oxidised oxygenated OA (MO-OOA) factor. HOA showed stable contributions to the total OA through the whole year ranging from 8.1 % to 10.1 %, while the contribution of BBOA showed an apparent seasonal variation with a range of 8.3 %–27.4 % (highest during winter, lowest during summer) and a yearly average of 17.1 %. OOA (sum of LO-OOA and MO-OOA) contributed 71.6 % of the OA mass, varying from 62.5 % (in winter) to 78 % (in spring and summer). The 58-OA factor mainly contained nitrogen-related variables which appeared to be pronounced only after the filament switched. However, since the contribution of this factor was insignificant (2.1 %), we did not attempt to interpolate its potential source in this work. The uncertainties (σ) for the modelled OA factors (i.e. rotational uncertainty and statistical variability in the sources) varied from ±4 % (58-OA) to a maximum of ±40 % (LO-OOA). Considering that BBOA and LO-OOA (showing influences of biomass burning in winter) had significant contributions to the total OA mass, we suggest reducing and controlling biomass-burning-related residential heating as a mitigation strategy for better air quality and lower PM levels in this region or similar locations. In Appendix A, we conduct a head-to-head comparison between the conventional seasonal PMF analysis and the rolling mechanism. We find similar or slightly improved results in terms of mass concentrations, correlations with external tracers, and factor profiles of the constrained POA factors. The rolling results show smaller scaled residuals and enhanced correlations between OOA factors and corresponding inorganic salts compared to those of the seasonal solutions, which was most likely because the rolling PMF analysis can capture the temporal variations in the oxidation processes for OOA components. Specifically, the time-dependent factor profiles of MO-OOA and LO-OOA can well explain the temporal viabilities of two main ions for OOA factors, m/z 44 (CO+2) and m/z 43 (mostly C2H3O+). Therefore, this rolling PMF analysis provides a more realistic source apportionment (SA) solution with time-dependent OA sources. The rolling results also show good agreement with offline Aerodyne aerosol mass spectrometer (AMS) SA results from filter samples, except for in winter. The latter discrepancy is likely because the online measurement can capture the fast oxidation processes of biomass burning sources, in contrast to the 24 h filter samples. This study demonstrates the strengths of the rolling mechanism, provides a comprehensive criterion list for ACSM users to obtain reproducible SA results, and is a role model for similar analyses of such worldwide available data.
European aerosol phenomenology − 8: Harmonised source apportionment of organic aerosol using 22 Year-long ACSM/AMS datasets
Chen, Gang; Canonaco, Francesco; Tobler, Anna; et al. (2022)
Environment International
Organic aerosol (OA) is a key component of total submicron particulate matter (PM1), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM1 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.
A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data
Canonaco, Francesco; Tobler, Anna; Chen, Gang; et al. (2021)
Atmospheric Measurement Techniques
A new methodology for performing long-term source apportionment (SA) using positive matrix factorization (PMF) is presented. The method is implemented within the SoFi Pro software package and uses the multilinear engine (ME-2) as a PMF solver. The technique is applied to a 1-year aerosol chemical speciation monitor (ACSM) dataset from downtown Zurich, Switzerland. The measured organic aerosol mass spectra were analyzed by PMF using a small (14 d) and rolling PMF window to account for the temporal evolution of the sources. The rotational ambiguity is explored and the uncertainties of the PMF solutions were estimated. Factor–tracer correlations for averaged seasonal results from the rolling window analysis are higher than those retrieved from conventional PMF analyses of individual seasons, highlighting the improved performance of the rolling window algorithm for long-term data. In this study four to five factors were tested for every PMF window. Factor profiles for primary organic aerosol from traffic (HOA), cooking (COA) and biomass burning (BBOA) were constrained. Secondary organic aerosol was represented by either the combination of semi-volatile and low-volatility organic aerosol (SV-OOA and LV-OOA, respectively) or by a single OOA when this separation was not robust. This scheme led to roughly 40 000 PMF runs. Full visual inspection of all these PMF runs is unrealistic and is replaced by predefined user-selected criteria, which allow factor sorting and PMF run acceptance/rejection. The selected criteria for traffic (HOA) and BBOA were the correlation with equivalent black carbon from traffic (eBCtr) and the explained variation of 60, respectively. COA was assessed by the prominence of a lunchtime concentration peak within the diurnal cycle. SV-OOA and LV-OOA were evaluated based on the fractions of 43 and 44 in their respective factor profiles. Seasonal pre-tests revealed a non-continuous separation of OOA into SV-OOA and LV-OOA, in particular during the warm seasons. Therefore, a differentiation between four-factor solutions (HOA, COA, BBOA and OOA) and five-factor solutions (HOA, COA, BBOA, SV-OOA and LV-OOA) was also conducted based on the criterion for SV-OOA. HOA and COA contribute between 0.4–0.7 µg m−3 (7.8 %–9.0 %) and 0.7–1.2 µg m−3 (12.2 %–15.7 %) on average throughout the year, respectively. BBOA shows a strong yearly cycle with the lowest mean concentrations in summer (0.6 µg m−3, 12.0 %), slightly higher mean concentrations during spring and fall (1.0 and 1.5 µg m−3, or 15.6 % and 18.6 %, respectively), and the highest mean concentrations during winter (1.9 µg m−3, 25.0 %). In summer, OOA is separated into SV-OOA and LV-OOA, with mean concentrations of 1.4 µg m−3 (26.5 %) and 2.2 µg m−3 (40.3 %), respectively. For the remaining seasons the seasonal concentrations of SV-OOA, LV-OOA and OOA range from 0.3 to 1.1 µg m−3 (3.4 %–15.9 %), from 0.6 to 2.2 µg m−3 (7.7 %–33.7 %) and from 0.9 to 3.1 µg m−3 (13.7 %–39.9 %), respectively. The relative PMF errors modeled for this study for HOA, COA, BBOA, LV-OOA, SV-OOA and OOA are on average ±34 %, ±27 %, ±30 %, ±11 %, ±25 % and ±12 %, respectively.

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