Quantifying the Chemical Composition and Real-Time Mass Loading of Nanoplastic Particles in the Atmosphere Using Aerosol Mass Spectrometry.

Plastic debris, including nanoplastic particles (NPPs), has emerged as an important global environmental issue due to its detrimental effects on human health, ecosystems, and climate. Atmospheric processes play an important role in the transportation and fate of plastic particles in the environment. In this study, a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was employed to establish the first online approach for identification and quantification of airborne submicrometer polystyrene (PS) NPPs from laboratory-generated and ambient aerosols. The fragmentation ion C8H8+ is identified as the major tracer ion for PS nanoplastic particles, achieving an 1-h detection limit of 4.96 ng/m3. Ambient PS NPPs measured at an urban location in Texas are quantified to be 30 ± 20 ng/m3 by applying the AMS data with a constrained positive matrix factorization (PMF) method using the multilinear engine (ME-2). Careful analysis of ambient data reveals that atmospheric PS NPPs were enhanced as air mass passed through a waste incinerator plant, suggesting that incineration of waste may serve as a source of ambient NPPs. The online quantification of NPPs achieved through this study can significantly improve our understanding of the source, transport, fate, and climate effects of atmospheric NPPs to mitigate this emerging global environmental issue.

Towards a better understanding of fine PM sources: Online and offline datasets combination in a single PMF.

Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM1), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 min for the NR-PM1 and BC, and 24 h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM1 sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24 h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.

[PM2.5 Source Apportionment Based on a Variety of New Receptor Models].

In order to understand the applicability of various new receptor models, four receptor models, including the positive matrix factorization/multilinear engine 2-species ratio (PMF/ME2-SR), partial target transformation-positive matrix factorization (PTT-PMF), positive matrix factorization (PMF), and chemical mass balance (CMB), were used to analyze and verify the atmospheric fine particulate matter (PM2.5) data of a typical city in northern China. It was found that coal combustion (25%-26%), dust (19%-21%), secondary nitrate (17%-19%), secondary sulfate (16%), vehicle emissions (13%-15%), biomass burning (4%-7%), and steel (1%-2%) had a contribution to PM2.5. By comparing the source profiles and source contributions obtained by different models and calculating the coefficient of differences (CD) and average absolute error (AAE) of each source, we found that although the source apportionment results of the four models were in good agreement (the average CD value was between 0.6 and 0.7), there were still slight differences in the identification of some components in each source. Compared with the traditional model (PMF), the PMF/ME2-SR model can better identify sources with similar source profile characteristics, which is due to the component ratios of sources that are introduced. For example, the CD and AAE of dust sources were 15% and 54% lower than those of PMF, respectively. The PTT-PMF model takes the measured primary source profiles and virtual secondary source profiles as a constraint target, and the calculated CD and AAE of secondary sulfate were 0.25 and 17%, respectively, which were 55% and 23% lower than PMF. The PTT-PMF model can obtain more "pure" secondary sources and identify the pollution sources that are not identified by other models, which has more advantages in the refined identification of sources.

Spatial-temporal characteristics, source-specific variation and uncertainty analysis of health risks associated with heavy metals in road dust in Beijing, China.

Based on the concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in 144 road dust samples collected from 36 sites across 4 seasons from 2016 to 2017 in Beijing, this study systematically analyzed the levels and main sources of health risks in terms of their temporal and spatial variations. A combination of receptor models (positive matrix factorization and multilinear engine-2), human health risk assessment models, and Monte Carlo simulations were used to apportion the seasonal variation of the health risks associated with these heavy metals. While non-carcinogenic risks were generally acceptable, Cr and Ni induced cautionary carcinogenic risks (CR) to children (confidence levels was approximately 80% and 95%, respectively).. Additionally, fuel combustion posed cautionary CR to children in all seasons, while the level of CR from other sources varied, depending on the seasons. Heavy metal concentrations were the most influential variables for uncertainties, followed by ingestion rate and skin adherence factor. The values and spatial patterns of health risks were influenced by the spatial pattern of risks from each source.

[Comparison of Three Receptor Models for Source Apportionment of PM2.5 in Shanghai: Using Hourly Resolved PM2.5 Chemical Composition Data].

Continuous measurements of PM2.5 and its chemical composition, including inorganic ions, carbon components, and inorganic elements, were conducted in the urban area of Shanghai from November 2 to 24, 2014. The chemical characteristics and sources of PM2.5 were discussed. The average mass concentration of PM2.5 was (64±33) µg·m-3 (ranging from 12 to 181 µg·m-3). Organic matter contributed the most to the PM2.5 chemical components, accounting for about 28.1% of total PM2.5, followed by NO3-, SO42-, and NH4+, which accounted for 17.4%, 12.4%, and 10.7%, respectively. Meanwhile, three receptor models, including positive matrix factorization (PMF), chemical mass balance (CMB), and multilinear engine 2 (ME2), were applied to apportion the PM2.5 sources based on these online data. The results showed that eight sources were identified, including secondary nitrate, secondary sulfate, secondary organic carbon, heavy fuel oil burning, industry, mobile vehicle exhaust, dust, and power plants. The secondary sources (44.9%-64.8%), including secondary nitrate, secondary sulfate, and secondary organic carbon, were found to be the important contributors to PM2.5. The other two main sources were mobile vehicle exhaust (16.8%-24.8%) and power plants (5.6%-14.9%), whereas other sources were slightly lower contributors. To better verify the accuracy of the PMF, CMB, and ME2 models, the profiles, temporal patterns, and concentrations of different sources obtained by the three models were discussed. Similar source profiles and contributions of secondary nitrate, secondary sulfate, secondary organic carbon, and mobile vehicle exhaust were derived from the PMF, CMB, and ME2, indicating that the results of the three models were reasonable. The ME2 and PMF models simulate better results for power plants and dust sources than CMB, whereas CMB obtained better results for industrial sources.

Spatial-temporal variation of heavy metals' sources in the surface sediments of the Yangtze River Estuary.

In this study, positive matrix factorization, multilinear engine 2, and geographic information systems were used to characterize the spatial-temporal patterns of sources for nine heavy metals in the surface sediments of the Yangtze River Estuary in different seasons. Results showed that six sources were identified: agricultural pesticide, marine transportation, chemical factory wastewater, metal smelter waste, atmospheric deposition, and agricultural fertilizer. The proportions of sources were similar during the entire year but varied among the seasons. The differences in the proportions of agricultural pesticide between winter and other seasons were greater than 12%. Over 40% of the Cd concentration in most seasons was attributed to atmospheric deposition, while less than 5% in autumn. The impact strength of most sources, except marine transportation and metal smelter waste, decreased from the inner regions to the adjacent sea. The difference in the impact strength of agricultural pesticide was the largest throughout the study area.

Characteristics and source apportionment of winter black carbon aerosols in two Chinese megacities of Xi'an and Hong Kong.

Black carbon (BC) aerosols were observed over Xi'an (XA) and Hong Kong (HK) to better compare its properties and sources in two geographically separate regions in China. High-BC (7.9 ± 3.3 µg·m-3) and -PM2.5 (182 ± 80.5 µg·m-3) concentrations were observed in XA, and these were much higher than those in HK (BC, 3.2 ± 0.9 µg·m-3; PM2.5, 34.5 ± 9.3 µg·m-3). The contribution of BC to PM2.5 in HK reached 10.7%, which was ~ 1.5 times than that in XA (7.6%). The results emphasized that BC played an important role in HK PM2.5. The diurnal distribution of HK BC was highly correlated with vehicle emissions during the daytime; it peaked during heavy traffic times. Whereas XA BC exhibited flat distribution owing to stable BC sources. It is not markedly driven by traffic patterns. Additionally, the potential source contribution function (PSCF) analysis showed that XA BC mainly originated from local emissions while nearly half of the HK BC originated from distant sources, such as industrial emissions from northeastern regions and ship emissions from marine regions. These anthropogenic BC sources were found to be regional in nature based on multilinear engine (ME-2) analysis. Specifically, the XA BC sources were dominated by three factors: 22.5% from coal burning, 19.6% from biomass burning, and 32.9% from vehicle emissions. In HK, the majority of BC contributions originated from vehicle and ship emissions (78.9%), while only 14.5% and 1.5% originated from coal and biomass burning from residential combustion, as well as industrial and power plants in inland China.

Source apportionment of urban air pollutants using constrained receptor models with a priori profile information.

Exposure to air pollutants such as volatile organic compounds (VOCs) and fine particulate matter (PM2.5) are associated with adverse health effects. This study applied multiple time resolution data of hourly VOCs and 24-h PM2.5 to a constrained Positive Matrix Factorization (PMF) model for source apportionment in Taipei, Taiwan. Ninety-two daily PM2.5 samples and 2208 hourly VOC measurements were collected during four seasons in 2014 and 2015. With some a priori information, we used different procedures to constrain retrieved factors toward realistic sources. A total of nine source factors were identified as: natural gas/liquefied petroleum gas (LPG) leakage, solvent use/industrial process, contaminated marine aerosol, secondary aerosol/long-range transport, oil combustion, traffic related, evaporative gasoline emission, gasoline exhaust, and soil dust. Results showed that solvent use/industrial process was the largest contributor (19%) to VOCs while the largest contributor to PM2.5 mass was secondary aerosol/long-range transport (57%). A robust regression analysis showed that secondary aerosol was mostly contributed by regional transport related factor (25%).

Sediment PAH source apportionment in the Liaohe River using the ME2 approach: A comparison to the PMF model.

Environmental contaminant source apportionment is essential for pollution management and control. This study analysed surface sediment samples for 16 priority polycyclic aromatic hydrocarbons (PAHs). PAH sources were identified by two receptor models, which included positive matrix factorization (PMF) and multilinear engine 2 (ME2). Three PAH sources in the Liaohe River sediments were identified by PMF, including traffic, coke oven and coal combustion. The ME2 model apportioned one additional source. The two models yielded excellent correlation coefficients between the measured and predicted PAH concentrations. Traffic emission was the primary PAH source associated with the Liaohe River sediments, with estimated PMF contributions of 58% in May and 63% in September. Coke oven (19%-25%) and coal combustion (13%-18%) were the other two major PAH sources. For ME2, gasoline and diesel were separated: accounted for 14% in May and 16% in September; and 53% in May and 48% in September. This study marks the first application of the ME2 model to study sediment contaminant source apportionment. The methodology can potentially be applied to other aquatic environment contaminants.

Physically constrained source apportionment (PCSA) for polycyclic aromatic hydrocarbon using the Multilinear Engine 2-species ratios (ME2-SR) method.

An improved physically constrained source apportionment (PCSA) technology using the Multilinear Engine 2-species ratios (ME2-SR) method was proposed and applied to quantify the sources of PM10- and PM2.5-associated polycyclic aromatic hydrocarbons (PAHs) from Chengdu in winter time. Sixteen priority PAH compounds were detected with mean ΣPAH concentrations (sum of 16 PAHs) ranging from 70.65 ng/m(3) to 209.58 ng/m(3) and from 59.17 ng/m(3) to 170.64 ng/m(3) for the PM10 and PM2.5 samples, respectively. The ME2-SR and positive matrix factorization (PMF) models were employed to estimate the source contributions of PAHs, and these estimates agreed with the experimental results. For the PMF model, the highest contributor to the ΣPAHs was vehicular emission (81.69% for PM10, 82.06% for PM2.5), followed by coal combustion (12.68%, 12.11%), wood combustion (5.65%, 4.45%) and oil combustion (0.72%, 0.88%). For the ME2-SR method, the highest contributions were from diesel (43.19% for PM10, 47.17% for PM2.5) and gasoline exhaust (34.94%, 32.44%), followed by wood combustion (8.79%, 6.37%), coal combustion (12.46%, 12.37%) and oil combustion (0.80%, 1.22%). However, the PAH ratios calculated for the factors extracted by ME2-SR were closer to the values from actual source profiles, implying that the results obtained from ME2-SR might be physically constrained and satisfactory.

A new receptor model-incremental lifetime cancer risk method to quantify the carcinogenic risks associated with sources of particle-bound polycyclic aromatic hydrocarbons from Chengdu in China.

PM10 and PM2.5 samples were simultaneously collected during a one-year monitoring period in Chengdu. The concentrations of 16 particle-bound polycyclic aromatic hydrocarbons (Σ16PAHs) were measured. Σ16PAHs concentrations varied from 16.85 to 160.24 ng m(-3) and 14.93 to 111.04ngm(-3) for PM10 and PM2.5, respectively. Three receptor models (principal component analysis (PCA), positive matrix factorization (PMF), and Multilinear Engine 2 (ME2)) were applied to investigate the sources and contributions of PAHs. The results obtained from the three receptor models were compared. Diesel emissions, gasoline emissions, and coal and wood combustion were the primary sources. Source apportionment results indicated that these models were able to track the ΣPAHs. For the first time, the cancer risks for each identified source were quantitatively calculated for ingestion and dermal contact routes by combining the incremental lifetime cancer risk (ILCR) values with the estimated source contributions. The results showed that gasoline emissions posed the highest cancer risk, even though it contributed less to Σ16PAHs. The results and method from this work can provide useful information for quantifying the toxicity of source categories and studying human health in the future.

Chemical characteristic and toxicity assessment of particle associated PAHs for the short-term anthropogenic activity event: During the Chinese New Year's Festival in 2013.

PM10 and PM2.5 samples were simultaneously collected during a period which covered the Chinese New Year's (CNY) Festival. The concentrations of particulate matter (PM) and 16 polycyclic aromatic hydrocarbons (PAHs) were measured. The possible source contributions and toxicity risks were estimated for Festival and non-Festival periods. According to the diagnostic ratios and Multilinear Engine 2 (ME2), three sources were identified and their contributions were calculated: vehicle emission (48.97% for PM10, 53.56% for PM2.5), biomass & coal combustion (36.83% for PM10, 28.76% for PM2.5), and cook emission (22.29% for PM10, 27.23% for PM2.5). An interesting result was found: although the PAHs are not directly from the fireworks display, they were still indirectly influenced by biomass combustion which is affiliated with the fireworks display. Additionally, toxicity risks of different sources were estimated by Multilinear Engine 2-BaP equivalent (ME2-BaPE): vehicle emission (54.01% for PM10, 55.42% for PM2.5), cook emission (25.59% for PM10, 29.05% for PM2.5), and biomass & coal combustion source (20.90% for PM10, 14.28% for PM2.5). It is worth to be noticed that the toxicity contribution of cook emission was considerable in Festival period. The findings can provide useful information to protect the urban human health, as well as develop the effective air control strategies in special short-term anthropogenic activity event.

Source apportionment of particulate matter and selected volatile organic compounds with multiple time resolution data.

Fine particulate matter (PM2.5) and volatile organic compounds (VOCs) co-exist in ambient air and contribute to adverse health effects in human populations. This study was conducted to demonstrate the feasibility of utilizing a composite data set which included PM2.5 and VOC data with multiple time resolutions for source apportionment. Hourly VOC and 12-h PM2.5 speciation data were combined into an improved source apportionment model to quantify different pollutant source contributions to PM2.5 and VOC mixtures. Five factors were retrieved, including vehicle 1, vehicle 2, industrial processing, transported regional, and secondary pollution sources. The largest contributors were vehicular emissions for VOCs (62%) and PM2.5 (35%). Nonetheless, transported regional and secondary pollution sources accounted for a noteworthy portion of PM2.5 (27% and 25%, respectively) relative to VOCs (8% and 5%, respectively). Additional sensitivity analyses showed that excluding the PM2.5 data or reducing the associated temporal resolution (12-h to 24-h) retrieved fewer source factors and increased the errors of source contribution estimates.