Combining Positive Matrix Factorization and Radiocarbon Measurements for Source Apportionment of PM2.5 from a National Background Site in North China

Quantifying anthropogenic emission of iron in marine aerosol in the Northwest Pacific with shipborne online measurements

Anthropogenic emissions are recognized as significant contributors to atmospheric soluble iron (Fe) in recent years, which may affect marine primary productivity, especially in Fe-limited areas. However, the contribution of different emission sources to Fe in marine aerosol has been primarily estimated by modeling approaches. Quantifying anthropogenic Fe based on field measurements remains a great challenge. In this study, online multi-element measurements and Positive Matrix Factorization (PMF) were combined for the first time to quantify sources of atmospheric Fe and soluble Fe in the Northwest Pacific during a cruise in spring 2015. Fe concentration in 624 atmospheric PM2.5 samples measured online was 74.58 ± 90.87 ng/m3. The PMF results showed anthropogenic activities, including industrial coal combustion, biomass burning, and maritime transport, were important in this region, contributing 31.4 % of atmospheric Fe on average. In addition, anthropogenic Fe concentration resolved by PMF was comparable to the simulation results of the CMAQ (Community Multiscale Air Quality) and GEOS-Chem (Goddard Earth Observing System-Chemical transport) models, with better correlation to CMAQ (r = 0.76) than GEOS-Chem (r = 0.26). This study developed a new method to estimate atmospheric soluble Fe, which integrates Fe source apportionment results and Fe solubility from different sources. Soluble Fe concentration was estimated as 3.93 ± 5.14 ng/m3, of which 87.0 % was attributed to anthropogenic emissions. Notably, ship emission alone contributed 27.5 % of soluble Fe, though its contribution to total Fe was only 2.2 %. Finally, the total deposition fluxes of atmospheric Fe (37.11 ± 38.43 μg/m2/day) and soluble Fe (1.85 ± 2.13 μg/m2/day) were estimated. This study developed a new methodology for quantifying contribution of anthropogenic emissions to Fe in marine aerosol, which could greatly help the assessment of impacts of human activities on marine environment.

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification-A Case Study from Poland

This study investigated the spatial distribution, contamination, potential ecological risks and quantities of pollutant sources of six heavy metals (HMs) in sediments of 47 rivers. The catchments of the investigated rivers are situated in Poland, but some of them are located in Slovakia, the Czech Republic, and Germany. Cluster analysis was applied to analyze the spatial distribution of Cd, Cr, Cu, Ni, Pb, and Zn in river sediments. Moran I and Getis-Ord Gi* statistics were calculated to reveal the distribution pattern and hotspot values. Principal component analysis (PCA) and positive matrix factorization (PMF) were used to identify pollution sources. Furthermore, geochemical indices and sediment quality guidelines allowed us to assess sediment contamination and potential toxic effects on aquatic biota. The results showed that in 1/3rd of the rivers, the HM pattern and concentrations indicate sediment contamination. The EF, PLI, and MPI indices indicate that concentrations were at a rather low level in 2/3rd of the analyzed rivers. Only in individual rivers may the HMs have toxic effects on aquatic biota. Spatial autocorrelation analysis using the Moran I statistic revealed a random and dispersed pattern of HMs in river sediments. PCA analysis identified two sources of HMs' delivery to the aquatic environment. Cr, Cu, Ni, Pb, and Zn originate from point and non-point sources, while Cd concentrations have a dominant natural origin. The PMF identified three sources of pollution. Among them, urban pollution sources are responsible for Cu delivery, agricultural pollution for Zn, and industrial pollution for Ni and Cr. Moreover, the analysis showed no relationship between catchment land-use patterns and HM content in river sediments.

Source Apportionment and Toxic Potency of PM2.5-Bound Polycyclic Aromatic Hydrocarbons (PAHs) at an Island in the Middle of Bohai Sea, China

Polycyclic aromatic hydrocarbons (PAHs) have attracted more attention because of their high atmospheric concentration and toxicity in recent decades. In this study, a total of 60 PM2.5 samples were collected from Beihuangcheng Island in Bohai Sea, China, from August 2017 to March 2018 for analyzing 16 congeners of PAHs (Σ16PAHs). Sources of PAHs were apportioned by a positive matrix factorization (PMF) model and the carcinogenic risk due to exposure to the PAHs was estimated by the toxicity equivalent of BaP (BaPeq). The results showed that the average concentration of Σ16PAHs was 35.3 ± 41.8 ng/m3. The maximum concentration of Σ16PAHs occurred in winter, followed by spring and autumn, and summer. The PMF modeling apportioned the PAHs into four sources, coal combustion, biomass burning, vehicle exhaust, and petroleum release, contributing 43.1%, 25.8%, 24.7%, and 6.39%, respectively. The average ΣBaPeq concentration was 2.32 ± 4.95 ng/m3 during the sampling period, and vehicle exhaust was the largest contributor. The finding indicates that more attention should be paid to reduce the emissions from coal combustion and vehicle exhaust because they were the largest contributors to the PAH concentration in PM2.5 and ΣBaPeq concentration, respectively.

Oxidative potential of solvent-extractable organic matter of ambient total suspended particulate in Bangkok, Thailand

Oxidative stress is a key mechanism by which ambient particulate matter induces adverse health effects. Most studies have focused on the oxidative potential (OP) of water-soluble constituents, while there has been limited work on the OP of solvent-extractable organic matter (EOM OP). In this study, the EOM OP of ambient total suspended particulate (TSP) from Bangkok, Thailand, was determined using the dithiothreitol (DTT) assay. Positive matrix factorization (PMF), combined with chemical analysis of molecular markers, was employed to apportion the contributions of various emission sources to EOM OP. The volume-normalized OP initially increased with organic carbon (OC) concentration and plateaued gradually, while the mass-normalized OP fitted well with OC concentration using a power function. Fossil fuel combustion (62%) and plastic waste burning (23%) were the major contributors to EOM OP, while biomass burning demonstrated only a limited contribution. EOM OP correlated well with each group of polycyclic aromatic hydrocarbons (PAHs), suggesting that secondary formation of quinones associated with fossil fuel combustion and plastic waste burning could be an important pathway of TSP toxicity. This study underscores the importance of considering different emission sources when evaluating potential health impacts and the implementation of air pollution regulations.

Isotopic signatures and source apportionment of Pb in ambient PM2.5

Particulate lead (Pb) is a primary air pollutant that affects society because of its health impacts. This study investigates the source sectors of Pb associated with ambient fine particulate matter (PM_2.5) over central-western Taiwan (CWT) with new constraints on the Pb-isotopic composition. We demonstrate that the contribution of coal-fired facilities is overwhelming, which is estimated to reach 35 ± 16% in the summertime and is enhanced to 57 ± 24% during the winter monsoon seasons. Moreover, fossil-fuel vehicles remain a major source of atmospheric Pb, which accounts for 12 ± 5%, despite the current absence of a leaded gasoline supply. Significant seasonal and geographical variations in the Pb-isotopic composition are revealed, which suggest that the impact of East Asian (EA) pollution outflows is important in north CWT and drastically declines toward the south. We estimate the average contribution of EA outflows as accounting for 35 ± 15% (3.6 ± 1.5 ng/m³) of the atmospheric Pb loading in CWT during the winter monsoon seasons.

Occurrence, distribution, and partitioning of antibiotics in surface water and sediment in a typical tributary of Yellow River, China

The antibiotic distributions, partitioning, and migration pathways in river basins have withdrawn great attention in the past decades. This study investigates 26 antibiotics of five classifications in surface water and sediment samples at 23 sites in Fenhe River, a typical tributary of Yellow River. There are 21 antibiotics found in the water samples at the concentration from 113.8 to 1106.0 ng/L, in the decreasing order of SAs > QNs > MLs > TCs > CAs. Fifteen antibiotics were detected in the sediment at the concentrations from 25.11 to 73.22 μg/kg following the decreasing order of SAs > MLs > TCs > QNs > CAs. The antibiotic concentrations vary greatly in surface water, generally lower in upstream and in reservoirs, and reaching highest in the midstream of the Fenhe River after passing Taiyuan and Jinzhong, and then lower again in the downstream. The antibiotic concentrations in sediment have a less variation in the entire river basin, but become high in the downstream. The results show the water-sediment partitioning coefficients of antibiotics generally were lower than those in other areas, having a migration path from the water to suspended solids, and then accumulated in sediment. The water-sediment partitioning coefficients also vary across the basin. The water-sediment partitioning coefficients of sulfacetamide and tetracycline are higher than the water-sediment partitioning coefficients of other antibiotics, with less variation across the basin, the water-sediment partitioning coefficients of azithromycin, enrofloxacin, and roxithromycin are low in the midstream of the river, and high at the river source and downstream. The water-sediment partitioning coefficients are significantly affected by the pH of sediment and the particle size of sediment. The prediction models of water-sediment partitioning coefficients for antibiotics are constructed with the selected effecting factors. The simulation values of antibiotics except chlortetracycline and erythromycin are highly consistent with the observed values, indicating that the prediction model is reliable.

Source Apportionment and Toxic Potency of Polycyclic Aromatic Hydrocarbons (PAHs) in the Air of Harbin, a Cold City in Northern China

A total of 68 PUF samples were collected seasonally from 17 sampling sites in Harbin, China from May 2016 to April 2017 for analyzing 15 congeners of gaseous polycyclic aromatic hydrocarbons (Σ15PAHs). An improved non-negative matrix (NMF) model and a positive matrix factorization (PMF) model were used to apportion the sources of PAHs. The carcinogenic risk due to exposure to PAHs was estimated by the toxicity equivalent of BaP (BaPeq). The results showed that the average concentration of Σ15PAHs was 68.3 ± 22.3 ng/m3, and the proportions of 3-ring, 4-ring, 5-ring, and 6-ring PAHs were 64.4%, 32.6%, 2.10%, and 0.89%, respectively. Among the six typical functional areas in Harbin, the Σ15PAHs concentrations were 98.1 ± 76.7 ng/m3, 91.2 ± 76.2 ng/m3, 71.4 ± 75.6 ng/m3, 67.9 ± 65.6 ng/m3, 42.6 ± 34.7 ng/m3, and 38.5 ± 38.0 ng/m3 in the wastewater treatment plant, industrial zone, business district, residential area, school, and suburb, respectively. During the sampling period, the highest concentration of Σ15PAHs was in winter. The improved NMF model and PMF model apportioned the PAHs into three sources including coal combustion, biomass burning, and vehicle exhaust. The contributions of coal combustion, biomass burning, and vehicle exhausts were 34.6 ± 3.22%, 48.6 ± 4.03%, and 16.8 ± 5.06%, respectively. Biomass burning was the largest contributor of Σ15PAHs concentrations in winter and coal combustion contributed significantly to the concentrations in summer. The average ΣBaPeq concentration was 0.54 ± 0.23 ng/m3 during the sampling period, high concentrations occurred in the cold season and low levels presented in the warm period. Vehicle exhaust was the largest contributor to the ΣBaPeq concentration of PAHs in Harbin.

Source Apportionment of PM2.5 in Guangzhou Based on an Approach of Combining Positive Matrix Factorization with the Bayesian Mixing Model and Radiocarbon

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning.

Assessment and quantification of NOx sources at a regional background site in North China: Comparative results from a Bayesian isotopic mixing model and a positive matrix factorization model

Regional sources of nitrogen oxides (NO_x) in North China during summer were explored using both a Bayesian isotopic mixing model and a positive matrix factorization (PMF) model. Results showed that the nitrogen isotope (δ¹⁵N) composition of particulate nitrate (NO₃^-) varied between -8.9‰ and +14.1‰, while the oxygen isotope (δ¹⁸O) composition ranged from +57.4‰ to +93.8‰. Based on results from the Bayesian isotopic mixing model, the contribution of the hydroxyl radical (•OH) NO_x conversion pathway showed clear diurnal fluctuation; values were higher during the day (0.53 ± 0.16) and lower overnight (0.42 ± 0.17). Values peaked at 06:00-12:00 and then decreased gradually until 00:00-06:00 the next day. Coal combustion (31.34 ± 9.04%) was the most significant source of NO_x followed by biomass burning (25.74 ± 2.58%), mobile sources (23.83 ± 3.66%), and microbial processes (19.09 ± 5.21%). PMF results indicated that the contribution from mobile sources was 19.83%, slightly lower as compared to the Bayesian model (23.83%). The PMF model also reported a lower contribution from coal combustion (28.65%) as compared to the Bayesian model (31.34%); however, the sum of biomass burning and microbial processes in the Bayesian model (44.83%) was lower than the aggregate of secondary inorganic aerosol, sea salt, and soil dust in PMF model (51.52%). Overall, differences between the two models were minor, suggesting that this study provided a reasonable source quantification for NO_x in North China during summer.