Chemical Composition Of Particulate Matter Research Articles

Phytosampling-a supplementary tool for particulate matter (PM) speciation characterization.

Ambient air particulate matter (PM) and PM-associated environmentally persistent free radicals (EPFRs) have been documented to contribute to pollution-related health effects. Studies of ambient air PM potentially bear artifacts stemming from the collection methods. We have investigated the applicability of PM phytosampling (PHS) as a supplementary tool to a classic PM sampler in respect of achieving better PM chemical composition assessment (primarily organic fraction). Phytosampling is a static PM collection method relying on the particle entrapment by the plant's leaf through electrostatic forces and surface trichomes. We have investigated the differences in the EPFR and polycyclic aromatic hydrocarbon (PAH) speciation and concentration on ambient air PM for PHS and high-volume PM sampler (HVS). The advantages of PHS are easy particle recovery from the matrix, collection under natural environmental conditions, and the ability to apply a dense collection network to accurately represent spatial pollutant distribution. The experimental results show that the PHS can provide valuable speciation information, sometimes different from that observed for HVS. For PM collected by PHS, we detected the larger contribution of oxygen-centered EPFRs, different decay behavior, and more consistent PAH distribution between different PM sizes compared to the PM from HVS. These results indicate that the isolation of samples from the ambient during HVS sampling and exposure to high-volume airflow may alter the chemical composition of the samples, while the PHS method could provide details on the original speciation and concentration and be more representative of the PM surface. However, PHS cannot evaluate an absolute air concentration of PM, so it serves as an excellent supplementary tool to work in conjunction with the standard PM collection method.

Seasonal Variations in the Chemical Composition of Indoor and Outdoor PM10 in University Classrooms

In the VIEPI project (Integrated evaluation of the exposure to indoor particulate matter) framework, we carried out a 1-year study of the concentration and chemical composition of particulate matter (PM) in a 5 story building in the Sapienza University of Rome (Italy). Each sampling had a duration of 1 month and was carried out indoors and outdoors in six classrooms. The chemical analyses were grouped to obtain information about the main PM sources. Micro-elements in their soluble and insoluble fractions were used to trace additional sources. Indoor PM composition was dominated by soil components and, to a lesser extent, by the organics, which substantially increased when people crowded the sites. The penetration of PM components was regulated by their chemical nature and by the dimensions of the particles in which they were contained. For the first time in crowded indoor environments, three different chemical assays aimed to determine PM redox properties complemented chemical composition measurements. These preliminary tests showed that substantially different redox properties characterised atmospheric particles in indoor and outdoor sites. The innovative characteristics of this study (time duration, number of considered environments) were essential to obtain relevant information about PM composition and sources in indoor academic environments and the occupants’ role.

Overview of the French Operational Network for In Situ Observation of PM Chemical Composition and Sources in Urban Environments (CARA Program)

The CARA program has been running since 2008 by the French reference laboratory for air quality monitoring (LCSQA) and the regional monitoring networks, to gain better knowledge—at a national level—on particulate matter (PM) chemistry and its diverse origins in urban environments. It results in strong collaborations with international-level academic partners for state-of-the-art, straightforward, and robust results and methodologies within operational air quality stakeholders (and subsequently, decision makers). Here, we illustrate some of the main outputs obtained over the last decade, thanks to this program, regarding methodological aspects (both in terms of measurement techniques and data treatment procedures) as well as acquired knowledge on the predominant PM sources. Offline and online methods are used following well-suited quality assurance and quality control procedures, notably including inter-laboratory comparison exercises. Source apportionment studies are conducted using various receptor modeling approaches. Overall, the results presented herewith underline the major influences of residential wood burning (during the cold period) and road transport emissions (exhaust and non-exhaust ones, all throughout the year), as well as substantial contributions of mineral dust and primary biogenic particles (mostly during the warm period). Long-range transport phenomena, e.g., advection of secondary inorganic aerosols from the European continental sector and of Saharan dust into the French West Indies, are also discussed in this paper. Finally, we briefly address the use of stable isotope measurements (δ15N) and of various organic molecular markers for a better understanding of the origins of ammonium and of the different organic aerosol fractions, respectively.

Enhanced aerosol particle growth sustained by high continental chlorine emission in India

Many cities in India experience severe deterioration of air quality in winter. Particulate matter is a key atmospheric pollutant that impacts millions of people. In particular, the high mass concentration of particulate matter reduces visibility, which has severely damaged the economy and endangered human lives. But the underlying chemical mechanisms and physical processes responsible for initiating haze and fog formation remain poorly understood. Here we present the measurement results of chemical composition of particulate matter in Delhi and Chennai. We find persistently high chloride in Delhi and episodically high chloride in Chennai. These measurements, combined with thermodynamic modelling, suggest that in the presence of excess ammonia in Delhi, high local emission of hydrochloric acid partitions into aerosol water. The highly water-absorbing and soluble chloride in the aqueous phase substantially enhances aerosol water uptake through co-condensation, which sustains particle growth, leading to haze and fog formation. We therefore suggest that the high local concentration of gas-phase hydrochloric acid, possibly emitted from plastic-contained waste burning and industry, causes some 50% of the reduced visibility. Our work implies that identifying and regulating gaseous hydrochloric acid emissions could be critical to improve visibility and human health in India. Half of the reduced visibility due to haze formation in cities in India is attributed to local emission of gas-phase hydrochloric acid from waste-burning and industry, according to measurements of particulate matter and thermodynamic modelling.

Particulate matter emission sources and meteorological parameters combine to shape the airborne bacteria communities in the Ligurian coast, Italy

Aim of the present study is to explore how the chemical composition of particulate matter (PM) and meteorological conditions combine in shaping the air microbiome in Savona (Italy), a medium-size, heavily inhabited urban settlement, hosting a wide range of industrial activities. In particular, the air microbiome and PM10 were monitored over six months in 2012. During that time, the air microbiome was highly dynamic, fluctuating between different compositional states, likely resulting from the aerosolization of different microbiomes emission sources. According to our findings, this dynamic process depends on the combination of local meteorological parameters and particle emission sources, which may affect the prevalent aerosolized microbiomes, thus representing further fundamental tools for source apportionment in a holistic approach encompassing chemical as well as microbiological pollution. In particular, we showed that, in the investigated area, industrial emissions and winds blowing from the inlands combine with an airborne microbiome which include faecal microbiomes components, suggesting multiple citizens’ exposure to both chemicals and microorganisms of faecal origin, as related to landscape exploitation and population density. In conclusion, our findings support the need to include monitoring of the air microbiome compositional structure as a relevant factor for the final assessment of local air quality.

Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing: protocol for the AIRLESS study

Abstract. Beijing, as a representative megacity in China, is experiencing some of the most severe air pollution episodes in the world, and its fast urbanization has led to substantial urban and peri-urban disparities in both health status and air quality. Uncertainties remain regarding the possible causal links between individual air pollutants and health outcomes, with spatial comparative investigations of these links lacking, particularly in developing megacities. In light of this challenge, Effects of AIR pollution on cardiopuLmonary disEaSe in urban and peri-urban reSidents in Beijing (AIRLESS) was initiated, with the aim of addressing the complex issue of relating multi-pollutant exposure to cardiopulmonary outcomes. This paper presents the novel methodological framework employed in the project, namely (1) the deployment of two panel studies from established cohorts in urban and peri-urban Beijing, with different exposure settings regarding pollution levels and diverse sources; (2) the collection of detailed measurements and biomarkers of participants from a nested case (hypertensive) and control (healthy) study setting; (3) the assessment of indoor and personal exposure to multiple gaseous pollutants and particulate matter at unprecedented spatial and temporal resolution with validated novel sensor technologies; (4) the assessment of ambient air pollution levels in a large-scale field campaign, particularly the chemical composition of particulate matter. Preliminary results showed that there is a large difference between ambient and personal air pollution levels, and the differences varied between seasons and locations. These large differences were reflected on the different health responses between the two panels.

Particulate matter emitted from ultrasonic humidifiers-Chemical composition and implication to indoor air.

Household humidification is widely practiced to combat dry indoor air. While the benefits of household humidification are widely perceived, its implications to the indoor air have not been critically appraised. In particular, ultrasonic humidifiers are known to generate fine particulate matter (PM). In this study, we first conducted laboratory experiments to investigate the size, quantity, and chemical composition of PM generated by an ultrasonic humidifier. The mass of PM generated showed a correlation with the total alkalinity of charge water, suggesting that CaCO3 is likely making a major contribution to PM. Ion chromatography analysis revealed a large amount of SO42- in PM, representing a previously unrecognized indoor source. Preliminary results of organic compounds being present in humidifier PM are also presented. A whole-house experiment was further conducted at an actual residential house, with five low-cost sensors (AirBeam) monitoring PM in real time. Operation of a single ultrasonic humidifier resulted in PM2.5 concentrations up to hundreds of μgm-3 , and its influence extended across the entire household. The transport and loss of PM2.5 depended on the rate of air circulation and ventilation. This study emphasizes the need to further investigate the impact of humidifier operation, both on human health and on the indoor atmospheric chemistry, for example, partitioning of acidic and basic compounds.

Characterization and source apportionment of single particles from metalworking activities.

Industrial metalworking facilities emit a variety of air toxics including volatile organic compounds, polycyclic aromatic hydrocarbons (PAHs) and heavy metals. In order to investigate these emissions, a 1-month multi-instrument field campaign was undertaken at an industrial site in Grande-Synthe, Dunkirk (France), in May and June 2012. One of the main objectives of the study was to provide new information on the chemical composition of particulate matter with aerodynamic diameters smaller than 2.5μm (PM2.5) in the vicinity of metalworking facilities. An aerosol time-of-flight mass spectrometer (ATOFMS) was deployed to provide size-resolved chemical mixing state measurements of ambient single particles at high temporal resolution. This mixing state information was then used to apportion PM2.5 to local metalworking facilities influencing the receptor site. Periods when the site was influenced by metalworking sources were characterised by a pronounced increase in particles containing toxic metals (manganese, iron, lead) and polycyclic aromatic hydrocarbons (PAHs) with a variety of chemical mixing states. The association of specific particle classes with a nearby ferromanganese alloy manufacturing plant was confirmed through comparison with previous analysis of raw materials (ores) and chimney filter particle samples collected at the facility. Particles associated with emissions from a nearby steelworks were also identified. The contribution of local metalworking activities to PM2.5 at the receptor site for the period when the ATOFMS was deployed ranged from 1 to 65% with an average contribution of 17%, while the remaining mass was attributed to other local and regional sources. These findings demonstrate the impact of metalworking facilities on air quality downwind and provide useful single particle signatures for future source apportionment studies in communities impacted by metalworking emissions.

Inflammatory responses of urban air PM modulated by chemical composition and different air quality situations in Nanjing, China

The health risks of air pollutants and ambient particulate matter (PM) are widely known. PM composition and toxicity have shown substantial spatiotemporal variability. Yet, the connections between PM composition and toxicological and health effects are vaguely understood. This is a crucial gap in knowledge that needs to be addressed in order to establish air quality guidelines and limit values that consider the chemical composition of PM instead of the current assumption of equal toxicity per inhaled dose. Here, we demonstrate further evidence for varying toxicological effects of urban PM at equal mass concentrations, and estimate how PM composition and emission source characteristics influenced this variation.We exposed a co-culture model mimicking alveolar epithelial cells and macrophages with size-segregated urban ambient PM collected before, during, and after the Nanjing Youth Olympic Games 2014. We measured the release of a set of cytokines, cell cycle alterations, and genotoxicity, and assessed the spatiotemporal variations in these responses by factorial multiple regression analysis. Additionally, we investigated how a previously identified set of emission sources and chemical components affected these variations by mixed model analysis.PM-exposure induced cytokine signaling, most notably by inducing dose-dependent increases of macrophage-regulating GM-CSF and proinflammatory TNFα, IL-6, and IL-1β concentrations, modest dose-dependent increase for cytoprotective VEGF-A, but very low to no responses for anti-inflammatory IL-10 and immunoregulatory IFNγ, respectively. We observed substantial differences in proinflammatory cytokine production depending on PM sampling period, location, and time of day. The proinflammatory response correlated positively with cell cycle arrest in G1/G0 phase and loss of cellular metabolic activity. Furthermore, PM0.2 caused dose-dependent increases in sub-G1/G0 cells, suggesting increased DNA degradation and apoptosis. Variations in traffic and oil/fuel combustion emissions contributed substantially to the observed spatiotemporal variations of toxicological responses.

Characterizing the unknown contribution of iron industries in atmospheric iron emissions using sensitivity analysis

Particulate matter (PM) released from industrial emissions could cause health problems, which are strongly associated with the chemical composition of PMs. This study aimed to estimate the unknown contribution of two iron industries using a known iron plant via sensitivity analysis. For this purpose, a comprehensive analysis was carried out on the characteristics of known iron plant, accessible sampling locations between the known plant and other two iron industries (unknown contributors), and in the heart of affected residential areas. A validated dispersion/deposition model was developed based on the gathered pieces of evidence of the known source. Analysis of the known processes and dustfalls classified the shape of known iron particles into seven main categories, including single and agglomerated of both sharp- and soft-edged fine (PM2.5 – less than 2.5 μm in aerodynamic diameter) and coarse (greater than PM2.5) iron particles. The high contribution of fine iron particles in the main stacks (more than 78.4%), very low contribution in the process samples (less than 10% in raw feed and around one-third of iron particles before the furnace) and almost insignificant contribution in the in-site dustfalls proved the transfer of fine iron particles to residential areas. However, elevated temperature after rotary Kiln resulted in the formation of agglomerated soft-edged fine/coarse particles. Therefore, PM2.5 was considered as the main atmospheric iron size fraction during the model development. The highest iron concentration (1.34 μg m−3) was observed in the area close to the two iron industries. Although the model showed under-prediction considering only plant sources, sensitivity analysis revealed the contribution of other iron industries in under debate areas. The daily concentration of atmospheric particles measured through this study was within the guidelines proposed by the regulatory bodies, however, the iron contents were found high in some locations highlighting the need for detailed regulatory control.

Inline Infrared Chemical Identification of Particulate Matter.

The health and environmental effects of particulate matter (PM) in the air depend on several parameters. Besides particle size, shape, and concentration, the chemical nature of the PM is also of great importance. State-of-the-art PM sensors only detect the particle size and concentration. Small, low-cost sensors only identify PM according to PM2.5 and PM10 standards. Larger detectors measure the complete particle size distribution. However, the chemical composition of PM is not often assessed. The current paper presents the initial stages of the development of an infrared-based detector for the inline assessment of the chemistry of PM in the air. By combining a mini cyclone that is able to concentrate the particles at least a thousand fold and a hollow waveguide that aligns the flow of particles with infrared light, the feasibility of the concept was shown in this study. A clear differentiation between amorphous and crystalline silica was demonstrated at outdoor PM levels of lower than 1 mg per cubic meter.

Chemical characterisation of particulate matter in urban transport modes

Traffic is a main source of air pollutants in urban areas and consequently daily peak exposures tend to occur during commuting. Personal exposure to particulate matter (PM) was monitored while cycling and travelling by bus, car and metro along an assigned route in Lisbon (Portugal), focusing on PM2.5 and PM10 (PM with aerodynamic diameter <2.5 and 10 µm, respectively) mass concentrations and their chemical composition. In vehicles, the indoor-outdoor interplay was also evaluated. The PM2.5 mean concentrations were 28 ± 5, 31 ± 9, 34 ± 9 and 38 ± 21 µg/m3 for bus, bicycle, car and metro modes, respectively. Black carbon concentrations when travelling by car were 1.4 to 2.0 times higher than in the other transport modes due to the closer proximity to exhaust emissions. There are marked differences in PM chemical composition depending on transport mode. In particular, Fe was the most abundant component of metro PM, derived from abrasion of rail-wheel-brake interfaces. Enhanced concentrations of Zn and Cu in cars and buses were related with brake and tyre wear particles, which can penetrate into the vehicles. In the motorised transport modes, Fe, Zn, Cu, Ni and K were correlated, evidencing their common traffic-related source. On average, the highest inhaled dose of PM2.5 was observed while cycling (55 µg), and the lowest in car travels (17 µg). Cyclists inhaled higher doses of PM2.5 due to both higher inhalation rates and longer journey times, with a clear enrichment in mineral elements. The presented results evidence the importance of considering the transport mode in exposure assessment studies.

Chemical characteristics of particulate matters and their emission sources over Varanasi during winter season

The chemical composition of particulate matter impacts both human health and climate. In this study, the chemical characteristics of particulate matter was measured for four months (November 2016–February 2017) at Varanasi, which is located in the middle of the Indo-Gangetic Basin (IGB). The daily observed mean values of PM10 and PM2.5 are 134 ± 48 and 213 ± 80 μg/m3, respectively, which exceeds both national and international standards. The average value of PM2.5/PM10 ratio is 0.64 ± 0.16 which indicates a relatively higher fraction of fine particles that are attributed to anthropogenic emission sources (biomass/post-harvest burning) as corroborated by MODIS fire counts and back trajectory analysis. Ion chromatographic measurements showed that SO42−, Cl−, K+, NO3−, Na+, Ca2+, Mg2+ are the major ionic species present in the aerosol. Scanning Electron Microscopy with Energy Dispersive X-Ray (SEM–EDX) analysis shows the prevalence of carbon-rich particles at Varanasi which is likely due to biomass burning and other anthropogenic sources.

Air quality intervention during the Nanjing youth olympic games altered PM sources, chemical composition, and toxicological responses

Ambient particulate matter (PM) is a leading global environmental health risk. Current air quality regulations are based on airborne mass concentration. However, PM from different sources have distinct chemical compositions and varied toxicity. Connections between emission control measures, air quality, PM composition, and toxicity remain insufficiently elucidated. The current study assessed the composition and toxicity of PM collected in Nanjing, China before, during, and after an air quality intervention for the 2014 Youth Olympic Games. A co-culture model that mimics the alveolar epithelium with the associated macrophages was created using A549 and THP-1 cells. These cells were exposed to size-segregated inhalable PM samples. The composition and toxicity of the PM samples were influenced by several factors including seasonal variation, emission sources, and the air quality intervention. For example, we observed a size-dependent shift in particle mass concentrations during the air quality intervention with an emphasized proportion of smaller particles (PM2.5) present in the air.The roles of industrial and fuel combustion and traffic emissions were magnified during the emission control period. Our analyses revealed that the PM samples demonstrated differential cytotoxic potencies at equal mass concentrations between sampling periods, locations, and time of day, influenced by variations in the predominant emission sources. Coal combustion and industrial emissions were the most important sources affecting the toxicological responses and displayed the least variation in emission contributions between the sampling periods. In conclusion, emission control mitigated cytotoxicity and oxidative stress for particles larger than 0.2 μm, but there was inadequate evidence to determine if it was the key factor reducing the harmful effects of PM0.2.

Short-Term Prediction of PM2.5 Pollution with Deep Learning Methods

&lt;p&gt;Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.&lt;/p&gt;&#x0D;

Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues

Abstract. For the purpose of providing reliable and robust air quality predictions, an air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2). The main caveat of the system is to prepare the initial conditions (ICs) of the Community Multiscale Air Quality (CMAQ) model simulations using observations from the Geostationary Ocean Color Imager (GOCI) and ground-based monitoring networks in northeast Asia. The performance of the air quality prediction system was evaluated during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–12 June 2016). Data assimilation (DA) of optimal interpolation (OI) with Kalman filter was used in this study. One major advantage of the system is that it can predict not only particulate matter (PM) concentrations but also PM chemical composition including five main constituents: sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic aerosols (OAs) and elemental carbon (EC). In addition, it is also capable of predicting the concentrations of gaseous pollutants (CO, O3 and SO2). In this sense, this new air quality prediction system is comprehensive. The results with the ICs (DA RUN) were compared with those of the CMAQ simulations without ICs (BASE RUN). For almost all of the species, the application of ICs led to improved performance in terms of correlation, errors and biases over the entire campaign period. The DA RUN agreed reasonably well with the observations for PM10 (index of agreement IOA =0.60; mean bias MB =-13.54) and PM2.5 (IOA =0.71; MB =-2.43) as compared to the BASE RUN for PM10 (IOA =0.51; MB =-27.18) and PM2.5 (IOA =0.67; MB =-9.9). A significant improvement was also found with the DA RUN in terms of bias. For example, for CO, the MB of −0.27 (BASE RUN) was greatly enhanced to −0.036 (DA RUN). In the cases of O3 and SO2, the DA RUN also showed better performance than the BASE RUN. Further, several more practical issues frequently encountered in the air quality prediction system were also discussed. In order to attain more accurate ozone predictions, the DA of NO2 mixing ratios should be implemented with careful consideration of the measurement artifacts (i.e., inclusion of alkyl nitrates, HNO3 and peroxyacetyl nitrates – PANs – in the ground-observed NO2 mixing ratios). It was also discussed that, in order to ensure accurate nocturnal predictions of the concentrations of the ambient species, accurate predictions of the mixing layer heights (MLHs) should be achieved from the meteorological modeling. Several advantages of the current air quality prediction system, such as its non-static free-parameter scheme, dust episode prediction and possible multiple implementations of DA prior to actual predictions, were also discussed. These configurations are all possible because the current DA system is not computationally expensive. In the ongoing and future works, more advanced DA techniques such as the 3D variational (3DVAR) method and ensemble Kalman filter (EnK) are being tested and will be introduced to the Korean air quality prediction system (KAQPS).

Carcinogenic risks and chemical composition of particulate matter recovered by two methods: wet and dry extraction.

Wet and dry extraction methods are two main methods used in toxicological in vitro and in vivo studies to recover particulate matter (PM) from filter papers. The aim of this study was to extract PM by wet and dry extraction methods and compare the elemental content and carcinogenic risks of extracts. PM10 samples were collected using fiberglass filters and a high-volume air sampler. For wet extraction, the method involved agitation in water, sonication in water bath, and agitation again. For dry extraction, the filters were sonicated and the PM was recovered using sweeping by a brush. Elemental composition of extracts was determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). Excess lifetime cancer risks (ELCR) of As, Cd, Cr, Ni, and Pb in extracts were estimated. The average recovery efficiency (%) of dry and wet extraction methods were 36.8% and 58.5%, respectively. The average elemental concentration that resulted from dry and wet methods was calculated to be 2.27 and 1.26μg/m3, respectively. The total ELCR of all heavy metals in both methods exceeds the 1 × 10-6 limit. However, the total ELCR of heavy metals that resulted from the dry method was higher than that from the wet method. In conclusion, the dry method showed to be more effective to recover a representative extract from the filter. This can ultimately lead to a realistic and robust response in toxicological studies. However, a toxicological comparison between the extracts of these two methods is required.

Construction and Evaluation on Size Resolved Source Apportionment Methods Based on Particle Size Distribution of Chemical Species

The analysis of the sources of atmospheric particulate pollution can provide scientific support for the prevention and control of air pollution. Most particulate matter (PM) source analysis studies are based on the chemical composition of PM. In addition, particle size characteristics are also one of the important properties of PM. The accuracy of analytical results can be improved by analyzing the particle size characteristics of chemical components. In this study we aim to to solve the problem of insufficient utilization of component particle size information by using a the three-dimensional multi-particle size factor analysis model (ABB), where the particle size distribution of marked components is regarded as the constraint limit, and a multi-particle size source analytical model (SDABB) based on the characteristics of the components particle size distribution is constructed. The sensitivity of the SDABB model to the collinearity of the source spectrum and the similarity of the particle size distribution of the source contributions are investigated by evaluating the model through the simulation of the data set. The results showed that the ABB model was sensitive to the collinearity of the source spectrum and to the similarity of the particle size distribution of the source contributions. When particle size distribution rules were incorporated into the SDABB model, the effects of the two scenarios were significantly improved, that is, the SDABB model was able to better analyze collinear source spectrum and was insensitive to the similarity of the contribution particle size distribution.

Physical and chemical properties of aerosol particles and cloud residuals on Mt. Åreskutan in Central Sweden during summer 2014

The size distribution, volatility and hygroscopicity of ambient aerosols and cloud residuals were measured with a differential mobility particle sizer (DMPS) and a volatility–hygroscopicity tandem differential mobility analyser (VHTDMA) coupled to a counterflow virtual impactor (CVI) inlet during the Cloud and Aerosol Experiment at Åre (CAEsAR) campaign at Mt. Åreskutan during summer 2014. The chemical composition of particulate matter (PM) and cloud water were analysed offline using thermo-optical OC/EC analysis and ion chromatography. The importance of aerosol particle size for cloud droplet activation and subsequent particle scavenging was clearly visible in the measured size distributions. Cloud residuals were shifted towards larger sizes compared to ambient aerosol, and the cloud events were followed by a size distribution dominated by smaller particles. Organics dominated both PM (62% organic mass fraction) and cloud water (63% organic mass fraction) composition. The volatility and hygroscopicity of the ambient aerosols were representative of homogeneous aged aerosol with contributions from biogenic secondary organics, with median volume fraction remaining (VFR) of 0.04–0.05, and median hygroscopicity parameter κ of 0.16–0.24 for 100–300 nm particles. The corresponding VFR and κ for the cloud residuals were 0.03–0.04 and 0.18–0.20. The chemical composition, hygroscopicity and volatility measurements thus showed no major differences between the ambient aerosol particles and cloud residuals. The VFR and κ values predicted based on the chemical composition measurements agreed well with the VHTDMA measurements, indicating the bulk chemical composition to be a reasonable approximation throughout the size distribution. There were indications, however, of some more subtle changes in time scales not achievable by the offline chemical analysis applied here. Further, online observations of aerosol and cloud residual chemical composition are therefore warranted.

Emission characteristics and chemical composition of particulate matter emitted by typical non-road construction machinery

According to the latest China Construction Machinery Industry Yearbook 2017, forklifts, loaders and excavators are three types of construction machinery that comprise the largest proportion of construction machinery in China. To study the emission characteristics of non-road mobile machinery, three types of non-road construction machinery used in the city were selected for testing and sampling. A Portable Emission Measurement System (PEMS) was used to obtain instantaneous exhaust emissions, and a portable particulate sampling system was used to collect PM2.5 samples from three types of construction machinery. The elements, water-soluble ions (WSIs), organic carbon (OC)/elemental carbon (EC) and polycyclic aromatic hydrocarbons (PAHs) were analysed. The China III standards for non-road construction machinery are stricter than the China II and China I construction machinery standards, indicating that strict regulations can help reduce PM emissions from non-road construction machinery. The results show that the fraction of the chemical components including elements, WSIs, OC/EC and PAHs to particulate matter is from 67.30% to 64.10%. The SO42−/NO3− ratio of each test vehicle is less than 1. The CE/AE values are all greater than 1, and the exhaust particles are alkaline. The estimated concentrations of secondary organic carbon (SOC) in the particulates discharged from loaders, excavators and forklifts are 3.73 mg/m3, 0.25 mg/m3 and 0.42 mg/m3, respectively, indicating the presence of secondary aerosols. The LMW-PAHs and MMW-PAHs were the major PAH species in the particulate matter, whereas a low concentration of HMW-PAHs is considered to be more harmful to human health than the other PAHs. All the PAH BaPeq values are higher than the BaP concentrations recommended by the World Health Organization. Compared with previous study, an increase in the emission standards for non-road mobile machinery can lead to a decline in pollutant emissions. In addition, the driving conditions, engine parameters, and the age of the vehicle and of non-road construction machinery, as well as the experimental methods used to study these variables, may affect the composition of exhaust emissions.