Local and regional components of aerosol in a heavily trafficked street canyon in central London derived from PMF and cluster analysis of single-particle ATOFMS spectra

Characterizing leaf-deposited particles: Single-particle mass spectral analysis and comparison with naturally fallen particles

The size and composition of particulate matter (PM) are pivotal in determining its adverse health effects. It is important to understand PM's retention by plants to facilitate its atmospheric removal. However, the distinctions between the size and composition of naturally fallen PM (NFPM) and leaf-deposited PM (LDPM) are not well-documented. Here we utilize a single-particle aerosol mass spectrometer, coupled with a PM resuspension chamber, to analyze these differences. We find that LDPM particles are 6.8-97.3 % larger than NFPM. Employing a neural network algorithm based on adaptive resonance theory, we have identified distinct compositional profiles: NFPM predominantly consists of organic carbon (OC; 31.2 %) and potassium-rich components (19.1 %), whereas LDPM are largely composed of crustal species (53.9-60.6 %). Interestingly, coniferous species retain higher OC content (11.5-13.7 %) compared to broad-leaved species (0.5-1.2 %), while the levoglucosan content exhibit an opposite trend. Our study highlights the active role of tree leaves in modifying PM composition beyond mere passive capture, advocating for a strategic approach to species selection in urban greening initiatives to enhance PM mitigation. These insights provide guidance for urban planners and environmentalists in implementing nature-based solutions to improve urban air quality.

Single particle mass spectral signatures from on-road and non-road vehicle exhaust particles and their application in refined source apportionment using deep learning

With advances in vehicle emission control technology, updating source profiles to meet the current requirements of source apportionment has become increasingly crucial. In this study, on-road and non-road vehicle particles were collected, and then the chemical compositions of individual particles were analyzed using single particle aerosol mass spectrometry. The data were grouped using an adaptive resonance theory neural network to identify signatures and establish a mass spectral database of mobile sources. In addition, a deep learning-based model (DeepAerosolClassifier) for classifying aerosol particles was established. The objective of this model was to accomplish source apportionment. During the training process, the model achieved an accuracy of 98.49 % for the validation set and an accuracy of 93.36 % for the testing set. Regarding the model interpretation, ideal spectra were generated using the model, verifying its accurate recognition of the characteristic patterns in the mass spectra. In a practical application, the model performed hourly source apportionment at three specific field monitoring sites. The effectiveness of the model in field measurement was validated by combining traffic flow and spatial information with the model results. Compared with other machine learning methods, our model achieved highly automated source apportionment while eliminating the need for feature selection, and it enables end-to-end operation. Thus, in the future, it can be applied in refined and online source apportionment of particulate matter.

Detection of polycyclic aromatic hydrocarbons using a high performance-single particle aerosol mass spectrometer

The real-time detection of the mixing states of polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs in ambient particles is of great significance for analyzing the source, aging process, and health effects of PAHs and nitro-PAHs; yet there is still few effective technology to achieve this type of detection. In this study, 11 types of PAH and nitro-PAH standard samples were analyzed using a high performance-single particle aerosol mass spectrometer (HP-SPAMS) in lab studies. The identification principles 'parent ions' and 'mass-to-charge (m/z) = 77' of each compound were obtained in this study. It was found that different laser energies did not affect the identification of the parent ions. The comparative experiments of ambient atmospheric particles, cooking and biomass burning emitted particles with and without the addition of PAHs were conducted and ruled out the interferences from primary and secondary organics on the identification of PAHs. Besides, the reliability of the characteristic ions extraction method was evaluated through the comparative study of similarity algorithm and deep learning algorithm. In addition, the real PAH-containing particles from vehicle exhaust emissions and ambient particles were also analyzed. This study improves the ability of single particle mass spectrometry technology to detect PAHs and nitro-PAHs, and HP-SPAMS was superior to SPAMS for detecting single particles containing PAHs and nitro-PAHs. This study provides support for subsequent ambient observations to identify the characteristic spectrum of single particles containing PAHs and nitro-PAHs.

Self-feedback LSTM regression model for real-time particle source apportionment

Atmospheric particulate matter pollution has attracted much wider attention globally. In recent years, the development of atmospheric particle collection techniques has put forwards new demands on the real-time source apportionments techniques. Such demands are summarized, in this paper, as how to set up new restraints in apportionment and how to develop a non-linear regression model to process complicated circumstances, such as the existence of secondary source and similar source. In this study, we firstly analyze the possible and potential restraints in single particle source apportionment, then propose a novel three-step self-feedback long short-term memory (SF-LSTM) network for approximating the source contribution. The proposed deep learning neural network includes three modules, as generation, scoring and refining, and regeneration modules. Benefited from the scoring modules, SF-LSTM implants four loss functions representing four restraints to be followed in the apportionment, meanwhile, the regeneration module calculates the source contribution in a non-linear way. The results show that the model outperforms the conventional regression methods in the overall performance of the four evaluation indicators (residual sum of squares, stability, sparsity, negativity) for the restraints. Additionally, in short time-resolution analyzing, SF-LSTM provides better results under the restraint of stability.

Chemistry-triggered events of PM2.5 explosive growth during late autumn and winter in Shanghai, China

To better understand the mechanism of PM_2.5 explosive growth (EG), we conducted concurrent measurements of gaseous pollutants, PM_2.5 and its chemical composition (inorganic ions, organic carbon, and element carbon) with a time resolution of 1 h in Shanghai in late autumn and winter from 2014 to 2017. In this study, the EG events, which are defined as the net increase in the mass concentration of PM_2.5 by more than 100 μg m^-3 within hours, are separately discussed for 3, 6, or 9 h. The number of EG events decreased from 19 cases in 2014 to 6 cases in 2017 and the corresponding PM_2.5 concentration on average decreased from 183.6 μg m^-3 to 128.8 μg m^-3. Both regional transport and stagnant weather (windspeed < 2.0 m s^-1) could lead to EG events. The potential source contribution function (PSCF) shows that the major high-pollution region is in East China (including Zhejiang, Jiangsu, Shandong, and Anhui Province) and the North China Plain. The contribution of stagnant conditions to EG episode hours of 55% (198 h, 156.9 μg m^-3) is higher than that of regional transport (45%, 230 h, 163.0 μg m^-3). To study the impact of local emission, chemical characteristics and driving factors of EG were discussed under stagnant conditions. The major components contributing to PM_2.5 are NO₃^- (17.9%), organics (14.1%), SO₄^2- (13.1%), and NH₄⁺ (13.1%). The driving factors of EG events are the secondary aerosol formation of sulfate and nitrate and primary emissions (vehicle emissions, fireworks, and biomass burning), but the secondary transformation contributes more to EG events. The formation of sulfate and nitrate is dominated by gas-phase oxidation and heterogeneous reactions, which are enhanced by a high relative humidity. The current study helps to understand the chemical mechanism of haze and provides a scientific basis for air pollution control in Shanghai.

Direct target and non-target analysis of urban aerosol sample extracts using atmospheric pressure photoionisation high-resolution mass spectrometry

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous atmospheric pollutants of high concern for public health. In the atmosphere they undergo oxidation, mainly through reactions with ·OH and NOx to produce nitro- and oxygenated (oxy-) derivatives. In this study, we developed a new method for the detection of particle-bound PAHs, nitro-PAHs and oxy-PAHs using direct infusion into an atmospheric pressure photoionisation high-resolution mass spectrometer (APPI-HRMS). Method optimisation was done by testing different source temperatures, gas flow rates, mobile phases and dopants. Samples were extracted with methanol, concentrated by evaporation and directly infused in the APPI source after adding toluene as dopant. Acquisition was performed in both polarity modes. The method was applied to target analysis of seasonal PM_2.5 samples from an urban background site in Padua (Italy), in the Po Valley, in which a series of PAHs, nitro- and oxy-PAHs were detected. APPI-HRMS was then used for non-target analysis of seasonal PM_2.5 samples and results compared with nano-electrospray ionisation (nanoESI) HRMS. The results showed that, when samples were characterised by highly oxidised organic compounds, including S-containing compounds, like in summer samples, APPI did not bring any additional information with respect to nanoESI in negative polarity (nanoESI(-)). Conversely, for winter samples, APPI(-) could detect a series of aromatic and poly-aromatic compounds, mainly oxidised and nitrogenated aromatics, that were not otherwise detected with nanoESI.

A Review of the Representation of Aerosol Mixing State in Atmospheric Models

Aerosol mixing state significantly affects concentrations of cloud condensation nuclei (CCN), wet removal rates, thermodynamic properties, heterogeneous chemistry, and aerosol optical properties, with implications for human health and climate. Over the last two decades, significant research effort has gone into finding computationally-efficient methods for representing the most important aspects of aerosol mixing state in air pollution, weather prediction, and climate models. In this review, we summarize the interactions between mixing-state and aerosol hygroscopicity, optical properties, equilibrium thermodynamics and heterogeneous chemistry. We focus on the effects of simplified assumptions of aerosol mixing state on CCN concentrations, wet deposition, and aerosol absorption. We also summarize previous approaches for representing aerosol mixing state in atmospheric models, and we make recommendations regarding the representation of aerosol mixing state in future modelling studies.

Insights into the formation of secondary organic carbon in the summertime in urban Shanghai

To investigate formation mechanisms of secondary organic carbon (SOC) in Eastern China, measurements were conducted in an urban site in Shanghai in the summer of 2015. A period of high O₃ concentrations (daily peak > 120 ppb) was observed, during which daily maximum SOC concentrations exceeding 9.0 μg/(C·m³). Diurnal variations of SOC concentration and SOC/organic carbon (OC) ratio exhibited both daytime and nighttime peaks. The SOC concentrations correlated well with O_x (= O₃ + NO₂) and relative humidity in the daytime and nighttime, respectively, suggesting that secondary organic aerosol formation in Shanghai is driven by both photochemical production and aqueous phase reactions. Single particle mass spectrometry was used to examine the formation pathways of SOC. Along with the daytime increase of SOC, the number fraction of elemental carbon (EC) particles coated with OC quickly increased from 38.1% to 61.9% in the size range of 250-2000 nm, which was likely due to gas-to-particle partitioning of photochemically generated semi-volatile organic compounds onto EC particles. In the nighttime, particles rich in OC components were highly hygroscopic, and number fraction of these particles correlated well with relative humidity and SOC/OC nocturnal peaks. Meanwhile, as an aqueous-phase SOC tracer, particles that contained oxalate-Fe(III) complex also peaked at night. These observations suggested that aqueous-phase processes had an important contribution to the SOC nighttime formation. The influence of aerosol acidity on SOC formation was studied by both bulk and single particle level measurements, suggesting that the aqueous-phase formation of SOC was enhanced by particle acidity.

Refined source apportionment of coal combustion sources by using single particle mass spectrometry

In this study, samples of three typical coal combustion source types, including Domestic bulk coal combustion (DBCC), Heat supply station (HSS), and Power plant (PP) were sampled and large sets of their mass spectra were obtained and analyzed by SPAMS during winter in a megacity in China. A primary goal of this study involves determining representative size-resolved single particle mass spectral signatures of three source types that can be used in source apportionment activities. Chemical types describe the majority of the particles of each source type were extracted by ART-2a algorithm with distinct size characteristics, and the corresponding tracer signals were identified. Mass spectral signatures from three source types were different from each other, and the tracer signals were effective in distinguishing different source types. A high size-resolution source apportionment method were proposed in this study through matching sources' mass spectral signatures to particle spectra in a twelve days ambient sampling to source apportion the particles. Contributions of three source types got different size characteristics, as HSS source got higher contribution in smaller sizes, But PP source got higher contributions as size increased. Source contributions were also quantified during two typical haze episodes, and results indicated that HSS source (for central-heating) and DBCC source (for domestic heating and cooking) may contribute evidently to pollution formation.

Impact of cold temperature on Euro 6 passenger car emissions

Hydrocarbons, CO, NOx, NH3, N2O, CO2 and particulate matter emissions affect air quality, global warming and human health. Transport sector is an important source of these pollutants and high pollution episodes are often experienced during the cold season. However, EU vehicle emissions regulation at cold ambient temperature only addresses hydrocarbons and CO vehicular emissions. For that reason, we have studied the impact that cold ambient temperatures have on Euro 6 diesel and spark ignition (including: gasoline, ethanol flex-fuel and hybrid vehicles) vehicle emissions using the World-harmonized Light-duty Test Cycle (WLTC) at -7 °C and 23 °C. Results indicate that when facing the WLTC at 23 °C the tested vehicles present emissions below the values set for type approval of Euro 6 vehicles (still using NEDC), with the exception of NOx emissions from diesel vehicles that were 2.3-6 times higher than Euro 6 standards. However, emissions disproportionally increased when vehicles were tested at cold ambient temperature (-7 °C). High solid particle number (SPN) emissions (>1 × 1011 # km-1) were measured from gasoline direct injection (GDI) vehicles and gasoline port fuel injection vehicles. However, only diesel and GDI SPN emissions are currently regulated. Results show the need for a new, technology independent, procedure that enables the authorities to assess pollutant emissions from vehicles at cold ambient temperatures. Harmful pollutant emissions from spark ignition and diesel vehicles are strongly and negatively affected by cold ambient temperatures. Only hydrocarbon, CO emissions are currently regulated at cold temperature. Therefore, it is of great importance to revise current EU winter vehicle emissions regulation.

Spiers Memorial Lecture. Introductory lecture: chemistry in the urban atmosphere

The urban atmosphere is characterised by a multitude of complex processes. Gaseous and particulate components are continuously emitted into the atmosphere from many different sources. These components are then dispersed in the urban atmosphere via turbulent mixing. Numerous chemical reactions modify the gas phase chemistry on multiple time scales, producing secondary pollutants. Through partitioning, the chemical and physical properties of the aerosol particles are also constantly changing as a consequence of dispersion and gas phase chemistry. This review presents an overview of the involved processes, focusing on the contributions presented at this conference and putting them into a broader context. Advanced methods for aerosol source apportionment are presented as well, followed by some aspects of health effects related to air pollution.

Formation of Metal-Cyanide Complexes in Deliquescent Airborne Particles: A New Possible Sink for HCN in Urban Environments

Hydrogen cyanide is a ubiquitous gas in the atmosphere and a biomass burning tracer. Reactive gases can be adsorbed onto aerosol particles where they can promote heterogeneous chemistry. In the present study, we report for the first time on the measurement and speciation of cyanides in atmospheric aerosol. Filter samples were collected at an urban background site in the city center of Padua (Italy), extracted and analyzed with headspace gas chromatography and nitrogen-phosphorus detection. The results showed that strongly bound cyanides were present in all aerosol samples at a concentration ranging between 0.3 and 6.5 ng/m³ in the PM_2.5 fraction. The concentration of cyanides strongly correlates with concentration of total carbon and metals associated with combustion sources. The results obtained bring evidence that hydrogen cyanide can be adsorbed onto aerosol liquid water and can react with metal ions to form stable metal-cyanide complexes.

Mass spectra features of biomass burning boiler and coal burning boiler emitted particles by single particle aerosol mass spectrometer

In this study, single particle mass spectra signatures of both coal burning boiler and biomass burning boiler emitted particles were studied. Particle samples were suspended in clean Resuspension Chamber, and analyzed by ELPI and SPAMS simultaneously. The size distribution of BBB (biomass burning boiler sample) and CBB (coal burning boiler sample) are different, as BBB peaks at smaller size, and CBB peaks at larger size. Mass spectra signatures of two samples were studied by analyzing the average mass spectrum of each particle cluster extracted by ART-2a in different size ranges. In conclusion, BBB sample mostly consists of OC and EC containing particles, and a small fraction of K-rich particles in the size range of 0.2-0.5μm. In 0.5-1.0μm, BBB sample consists of EC, OC, K-rich and Al_Silicate containing particles; CBB sample consists of EC, ECOC containing particles, while Al_Silicate (including Al_Ca_Ti_Silicate, Al_Ti_Silicate, Al_Silicate) containing particles got higher fractions as size increase. The similarity of single particle mass spectrum signatures between two samples were studied by analyzing the dot product, results indicated that part of the single particle mass spectra of two samples in the same size range are similar, which bring challenge to the future source apportionment activity by using single particle aerosol mass spectrometer. Results of this study will provide physicochemical information of important sources which contribute to particle pollution, and will support source apportionment activities.

Discriminating the Mineralogical Composition in Drill Cuttings Based on Absorption Spectra in the Terahertz Range

Understanding the geological units of a reservoir is essential to the development and management of the resource. In this paper, drill cuttings from several depths from an oilfield were studied using terahertz time domain spectroscopy (THz-TDS). Cluster analysis (CA) and principal component analysis (PCA) were employed to classify and analyze the cuttings. The cuttings were clearly classified based on CA and PCA methods, and the results were in agreement with the lithology. Moreover, calcite and dolomite have stronger absorption of a THz pulse than any other minerals, based on an analysis of the PC1 scores. Quantitative analyses of minor minerals were also realized by building a series of linear and non-linear models between contents and PC2 scores. The results prove THz technology to be a promising means for determining reservoir lithology as well as other properties, which will be a significant supplementary method in oil fields.

Characterization, mixing state, and evolution of urban single particles in Xi'an (China) during wintertime haze days

A Single Particle Aerosol Mass Spectrometer (SPAMS) was deployed in the urban area of Xi'an to investigate size-resolved chemical composition and mixing state of single particles during the heavy haze episode occurred from January 13 to January 27 in 2013. Nine major single particle types were resolved with ART-2a algorithm including biomass burning (BB), Potassium-Secondary (KSec), elemental and organic Carbon (ECOC), sodium-potassium-rich ECOC (NaKECOC), sodium-potassium-rich-secondary (NaKSec), EC, OC, and Dust. Daily PM_2.5 mass concentration was 213±122μgm^-3. ~96% of the ambient particles were carbonaceous and internally mixed with secondary species such as sulfate and nitrate. The major particle types were from combustion sources, including coal burning, biomass burning, and vehicle exhaust. Mixing state analysis suggests gas-to-particle conversion was an important mechanism forming organic species during the winter haze episode. The relative abundances of the aged particle types, such as KSec and NaKSec increased with the elevated RH when RH<80%. The fraction of aged particles in terms of number concentration was prominent during high levels of PM_2.5 under stagnant air conditions. This study gained new knowledge on atmospheric aerosol formation and evolution in urban environment heavy winter haze condition.

Fine Iron Aerosols Are Internally Mixed with Nitrate in the Urban European Atmosphere

Atmospheric iron aerosol is a bioavailable essential nutrient playing a role in oceanic productivity. Using aerosol time-of-flight mass spectrometry (ATOFMS), the particle size (0.3-1.5 μm), chemical composition and mixing state of Fe-containing particles collected at two European urban sites (London and Barcelona) were characterized. Out of the six particle types accounting for the entire Fe-aerosol population, that arising from long-range transport (LRT) of fine Fe-containing particles (Fe-LRT, 54-82% across the two sites) was predominant. This particle type was found to be internally mixed with nitrate and not with sulfate, and likely mostly associated with urban traffic activities. This is in profound contrast with previous studies carried out in Asia, where the majority of iron-containing particles are mixed with sulfate and are of coal combustion origin. Other minor fine iron aerosol sources included mineral dust (8-11%), traffic brake wear material (1-17%), shipping/oil (1-6%), biomass combustion (4-13%) and vegetative debris (1-3%). Overall, relative to anthropogenic Asian Fe-sulfate dust, anthropogenic European dust internally mixed with additional key nutrients such as nitrate is likely to play a different role in ocean global biogeochemical cycles.