Improving apportionment of PM2.5 using multisite PMF by constraining G-values with a priori information

Spatial variability of pollution source contributions during two (2012-2013 and 2018-2019) sampling campaigns at ten sites in Los Angeles basin

This study assessed the spatial variability of PM2.5 source contributions across ten sites located in the South Coast Air Basin, California. Eight pollution sources and their contributions were obtained using positive matrix factorization (PMF) from the PM2.5 compositional data collected during the two sampling campaigns (2012/13 and 2018/19) of the Multiple Air Toxics Exposure Study (MATES). The identified sources were "gasoline vehicles", "aged sea salt", "biomass burning", "secondary nitrate", "secondary sulfate", "diesel vehicles", "soil/road dust" and "OP-rich". Among them, "gasoline vehicle" was the largest contributor to the PM2.5 mass. The spatial distributions of source contributions to PM2.5 at the sites were characterized by the Pearson correlation coefficients as well as coefficients of determination and divergence. The highest spatial variability was found for the contributions from the "OP-rich" source in both MATES campaigns suggesting varying influences of the wildfires in the Los Angeles Basin. Alternatively, the smallest spatial variabilities were observed for the contributions of the "secondary sulfate" and "aged sea salt" sources resolved for the MATES campaign in 2012/13. The "soil/road dust" contributions of the sites from the 2018/19 campaign were also highly correlated. Compared to the other sites, the source contribution patterns observed for Inland Valley and Rubidoux were the most diverse from the others likely due to their remote locations from the other sites, the major urban area, and the Pacific Ocean.

Regulation-driven changes in PM2.5 sources in China from 2013 to 2019, a critical review and trend analysis

Identifying changes in source-specific fine particles (PM2.5) over time is essential for evaluating the effectiveness of regulatory measures and informing future policy decisions. After the extreme haze events in China during 2013-14, more comprehensive and stringent policies were implemented to combat PM2.5 pollution. To determine the effectiveness of these policies, it is necessary to assess the changes in the specific source types to which the regulations pertain. Multiple studies have been conducted over the past decade to apportion PM2.5. The purpose of this study was to explore the available literature and conduct a critical review of the reliable results. In total, 5008 articles were screened, but only 48 studies were included for further analysis given our inclusion criteria including covering a monitoring period of ≥1 year and having enough speciation data to provide mass closure. Using these studies, we analyzed temporal and spatial trends across China from 2013 to 2019. We observed the overall decrease in the concentration contributions from all main source categories. The reductions from industry, coal and heavy oil combustion, and the related secondary sulfate were more notable, especially from 2013 to 2016-17. The contributions from biomass burning initially decreased but then increased slightly after 2016 in some locations despite new constraints on agricultural and household burning practices. Although the contributions from vehicle emissions and related secondary nitrate decreased, they gradually became the primary contributors to PM2.5 by ∼2017. Despite the substantial improvements achieved by the air pollution regulation implementations, further improvements in air quality will require additional aggressive actions, especially those targeting vehicular emissions. Ultimately, source apportionment studies based on extended duration, fixed-site sampling are recommended to provide a more thorough understanding of the sources impacting areas and transformations in PM2.5 sources prompted by regulatory actions.

Spatial source apportionment of airborne coarse particulate matter using PMF-Bayesian receptor model

Ambient particulate matter (PM2.5 and PM10), has been extensively monitored in numerous urban areas across the globe. Over the past decade, there has been a significant improvement in PM2.5 air quality, while improvements in PM10 levels have been comparatively modest, primarily due to the limited reduction in coarse particle (PM2.5-10) pollution. Unlike PM2.5, PM2.5-10 predominantly originates from local emissions and is often characterized by pronounced spatial heterogeneity. In this study, we utilized over one million data points on PM concentrations, collected from >100 monitoring sites within a Chinese megacity, to perform spatial source apportionment of PM2.5-10. Despite the widespread availability of such data, it has rarely been employed for this purpose. We employed an enhanced positive matrix factorization approach, capable of handling large datasets, in conjunction with a Bayesian multivariate receptor model to deduce spatial source impacts. Four primary sources were successfully identified and interpreted, including residential burning, industrial processes, road dust, and meteorology-related sources. This interpretation was supported by a considerable body of prior knowledge concerning emission sources, which is usually unavailable in most cases. The methodology proposed in this study demonstrates significant potential for generalization to other regions, thereby contributing to the development of air quality management strategies.

Source apportionment of fine particulate matter at a megacity in China, using an improved regularization supervised PMF model

The source apportionment of particulate matter plays an important role in solving the atmospheric particulate pollution. Positive matrix factorization (PMF) is a widely used source apportionment model. At present, high resolution online datasets are increasingly rich, but acquiring accurate and timely source apportionment results is still challenging. Integrating prior knowledge into modelling process is an effective solution and can yield reliable results. This study proposed an improved source apportionment method for the regularization supervised PMF model (RSPMF). This method leveraged actual source profile to guide factor profile for rapidly and automatically identifying source categories and quantifying source contributions. The results showed that the factor profile from RSPMF could be interpreted as seven factors and approach to actual source profile. Average source contributions were also an agreement between RSPMF and EPAPMF, including secondary nitrate (26 %, 27 %), secondary sulfate (23 %, 24 %), coal combustion (18 %, 18 %), vehicle exhaust (15 %, 15 %), biomass burning (10 %, 9 %), dust (5 %, 4 %), industrial emission (3 %, 3 %). The solutions of RSPMF also exhibited good generalizability during different episodes. This study reveals the superiority of supervised model, this model embeds prior knowledge into modelling process to guide model for obtaining more reliable results.

PM2.5 source apportionment identified with total and soluble elements in positive matrix factorization

Source apportionments of urban aerosols identified with positive matrix factorization (PMF) are sensitive to input variables. So far, total elements were frequently included as effective factors in PMF-based source apportionment. We investigated the advances to involve soluble parts of elements in the source apportionment with four data sets of PM_2.5 composition observed at a coastal city (Qingdao) in northern China: water-soluble ions plus organic and elemental carbon (IC set), the IC set plus total elements (ICTE set), the IC set plus soluble elements (ICSE set), and the IC set plus both total elements and soluble elements (ICAE set). The apportionments of six sources, including secondary sulfate, secondary nitrate, secondary oxalate, sea salt, biomass burning and dust, were identified with the IC set. In comparison, pollutants from vehicle + coal combustion, ship emissions, waste incineration and industrial activities were also identified with the ICTE, ICSE, or ICAE sets. We found that the PMF solutions of the ICAE set could distinguish aged and fresh dust, and identify fly ash and aged pollutants from industrial sources. The profiles and corresponding time series of vehicle + coal combustion, secondary aerosols, ship emissions, sea salt, and biomass burning emissions identified with the four data sets were very similar, while discrepancies were encountered for waste incineration, dust, and industrial sources. These results indicate the benefits and potentials with total and soluble elements involved in PMF-based source apportionments.

Heterogeneous changes of chemical compositions, sources and health risks of PM2.5 with the "Clean Heating" policy at urban/suburban/industrial sites

China has enacted the "Clean Heating" (CH) policy in north China. The domain-specific impacts on PM2.5 constituents and sources in small cities are still lacking, which obstruct the further policy optimization. Here, we performed an intensive observation covering the heating period (HP) and pre-heating period (PHP) in winter of 2017 at urban (UR), industrial (IS), and suburban (SUR) sites in one of the "2 + 26" cities. The mean PM2.5 concentrations at UR and IS decreased by 15.2 % and 4.6 %, while increased by 9.8 % at SUR in the HP compared with the PHP, indicating the heterogeneous responses. The lowest contribution percentages of coal combustion (14.6 %) and industrial emissions (17.1 %) to PM2.5 at UR in the HP implied the CH policy played more effective role. The most increase in NO3-/SO42- ratio by 26.8 % and the highest NO3- concentration at UR in the HP were linked mainly with the thermal-NOx emitted from natural gas (NG) burning in view of NOx emission reductions from other sources. The highest concentrations of OC, SO42-, K+, and Cl-, and contribution percentages of biomass burning (20.0 %) and coal combustion (24.8 %) to PM2.5 at SUR in the HP evidenced the enhanced usage of biomass/coal. Coal banning in the HP at IS and UR led to the obvious decreases in OC, SO42-, As, and Sb. Secondary nitrate became the largest PM2.5 source at IS and UR in the HP. Coal banning, emission control on large-size enterprises and ignored control on small-size enterprises efficiently modified the concentrations and health risks of heavy metals. The lowest carcinogenic risks moved from SUR in the PHP to UR in the HP. The policies on de-NOx of NG-burning related enterprises, reduction of biomass/coal usage in suburban area, and strict regulation of small-size enterprises were urgently need to further improve the air quality.

Vehicle exhausts contribute high near-UV absorption through carbonaceous aerosol during winter in a fast-growing city of Sichuan Basin, China

Carbonaceous aerosols pose significant climatic impact, however, their sources and respective contribution to light absorption vary and remain poorly understood. In this work, filter-based PM_2.5 samples were collected in winter of 2021 at three urban sites in Yibin, a fast-growing city in the south of Sichuan Basin, China. The composition characteristics of PM_2.5, light absorption and source of carbonaceous aerosol were analyzed. The city-wide average concentration of PM_2.5 was 87.4 ± 31.0 μg/m³ in winter. Carbonaceous aerosol was the most abundant species, accounting for 42.5% of the total PM_2.5. Source apportionment results showed that vehicular emission was the main source of PM_2.5 during winter, contributing 34.6% to PM_2.5. The light absorption of black carbon (BC) and brown carbon (BrC) were derived from a simplified two-component model. We apportioned the light absorption of carbonaceous aerosols to BC and BrC using the Least Squares Linear Regression with optimal angstrom absorption exponent of BC (AAE_BC). The average absorption of BC and BrC at 405 nm were 51.6 ± 21.5 Mm^-1 and 17.7 ± 8.0 Mm^-1, respectively, with mean AAE_BC = 0.82 ± 0.02. The contribution of BrC to the absorption of carbonaceous reached 26.1% at 405 nm. Based on the PM_2.5 source apportionment and the mass absorption cross-section (MAC) value of BrC at 405 nm, vehicle emission was found to be the dominant source of BrC in winter, contributing up to 56.4%. Therefore, vehicle emissions mitigation should be the primary and an effective way to improve atmospheric visibility in this fast-developing city.

Evaluation of impact of "2+26″ regional strategies on air quality improvement of different functional districts in Beijing based on a long-term field campaign

Consecutive measurements of ambient fine particulate matter (PM_2.5) from February 2016 to April 2018 have been performed at four representative sites of Beijing to evaluate the impact of "2 + 26" regional strategies implemented in 2017 for air quality improvement in non-heating period (2017NH) and heating period (2017H). The decrease of PM_2.5 were significant both in 2017NH (20.2% on average) and 2017H (43.7% on average) compared to 2016NH and 2016H, respectively. Eight sources were resolved at each site from the PMF source apportionment including secondary nitrate, traffic, coal combustion, soil dust, road dust, sulfate, biomass/waste burning and industrial process. The results show that the reductions of industrial process, soil dust, and coal combustion were most effective among all sources at each site after the regional strategies implementation with the large reductions in potential source areas. The decrease of coal combustion in 2017NH were larger than 2017H at all sites while that of soil dust and industrial sources were the opposite. Insignificant reduction of coal combustion contribution at the suburban site in the heating period indicated that rural residential coal burning need further control. The industrial source control in the suburbs were least effective compared with other districts. Traffic was the largest contributer at each site and control of traffic emissions were more effective in 2017H than 2017NH. The local nature and increase of biomass/waste burning contributions emphasized the effect of fireworks and bio-fuel use in rural areas and incinerator emissions in urban districts. Secondary nitrate and sulfate were mainly impacted by the regional transport from southern adjacent areas and favorable meteorological conditions played an important part in the PM_2.5 abatements of 2017H. Secondary nitrate became a more major role in the air pollution process because of the larger decrease of sulfate. Finally suggestions for future control are made in this study.

Impact of sand and dust storms on the atmospheric environment and its source in Tianjin-China

Sand and dust storms (SDS) frequently hit northern China and adversely impact both environment and health. The carbonaceous components, inorganic elements, water-soluble ions, and meteorological parameters of several severe SDS episodes have been measured in a supersite in Tianjin, which is a big and representative city located in SDS transmission pathway in northern China. Six SDS episodes were identified in Spring, 2021. The maximum PM₁₀ mass concentration was 2684 and 1664 μg/m³ in SDS1 and SDS3, respectively. North and northwest wind was dominant and significant differences were found in wind speed and RH between the SDS and non-SDS episodes. North dust from Inner Mongolia and Mongolia was determined by back trajectory analysis as the probable source region. The mass concentration of SO₄^2-, NO₃^-, and NH₄⁺ decreased in PM_2.5. Increase of Na⁺ and K⁺ and low SO₄^2-_SDS/ SO₄^2-_non-SDS indicate dust source for short length SDS transmission in northern China. The ratio of elements could also be used to distinguish SDS and non-SDS episodes identify north and northwest source for the SDS episodes. Pb/Al, Zn/Al, and Si/Al could be regarded as indicators for SDS and non-SDS episodes, Ca/Al and Ca/Si can help to indicate SDS source direction. This study provides a variety of evidences for the dust source identification and reveals the characteristics of the most severe SDS episodes of the decade in Tianjin during Spring 2021.

Influence of Source Apportionment of PAHs Occurrence in Aquatic Suspended Particulate Matter at a Typical Post-Industrial City: A Case Study of Freiberger Mulde River

Polycyclic aromatic hydrocarbons (PAHs) have received extensive attention because of their widespread presence in various environmental media and their high environmental toxicity. Thus, figuring out the long-term variances of their occurrence and driving force in the environment is helpful for environmental pollution control. This study investigates the concentration levels, spatial variance, and source apportionment of PAHs in suspended particulate matter of Freiberger Mulde river, Germany. Results show that the concentrations of the 16 priority PAHs suggested by USEPA (Σ16PAHs) were in the range of 707.0–17,243.0 μg kg−1 with a mean value of 5258.0 ± 2569.2 μg kg−1 from 2002 to 2016. The relatively high average concentrations of Σ16PAHs were found in the midstream and upstream stations of the given river (7297.5 and 6096.9 μg kg−1 in Halsbrucke and Hilbersdorf, respectively). In addition, the annual average concentration of Σ16PAHs showed an obvious decreasing pattern with time. Positive Matrix Factorization (PMF) receptor model identified three potential sources: coke ovens (7.6–23.0%), vehicle emissions (35.9–47.7%), and coal and wood combustion (34.5–47.3%). The source intensity variation and wavelet coherence analysis indicated that the use of clean energy played a key role in reducing PAHs pollution levels in suspended sediments. The risk assessment of ecosystem and human health suggested that the Σ16PAHs in the given area posed a non-negligible threat to aquatic organisms and humans. The data provided herein could assist the subsequent management of PAHs in the aquatic environment.

Role of ammonia in secondary inorganic aerosols formation at an ammonia-rich city in winter in north China: A comparative study among industry, urban, and rural sites

Ammonia is essential for the generation of secondary inorganic aerosols (SIA) in particulate matter, which affects severely the air quality in north China. In this study, PM_2.5 sampling was conducted as well as gaseous pollutant concentration and meteorological parameters were measured from November 2017 to January 2018. PM_2.5 concentration was highest in the industrial site (94.8 ± 41.7 μg m^-3), followed by urban (40.9 ± 24.1 μg m^-3) and rural (35.6 ± 20.3 μg m^-3) sites. The mass ratio of NO₃^-/SO₄^2- exhibited clear site variations, with the highest average value of 1.2 was found at the urban site, likely due to the dense traffic volume resulting in higher emissions of NO₂, and the lowest value of 0.9 at the industry site. The presence of Excess-NHx (E-NHx), raising the pH 24 by 1.4, 1.3, and 1.4 units in industry, urban, and rural sites, respectively, might be vital for raising the aerosol pH. Correlation coefficients of Nitrogen oxidation rate (NOR, NOR = [NO₃^-]/[NO₃^-] + [NO₂]) vs. Photochemical oxidants (O_x, NO₂ +O₃ in our study) and NOR vs. aerosol water content (AWC) at three sites were implied that both homogeneous and heterogeneous reactions occurred for nitrate formation in industry site, while heterogeneous reactions were dominant in urban and rural sites. Oxidation rates were most sensitive to the variation of E-NHx concentration at rural site, followed by the urban and industry sites, which was shown by the fact that the increase in E-NHx concentration by 1.0 μg m^-3 increased the SIA concentration by 1.21, 1.02, and 0.37 μg m^-3 at rural, urban, and industry sites, respectively. With the increase in NH_x emissions at present, the role of NH_x in SIA formation at ammonia-rich atmosphere requires more attention, especially in the less-noticed rural areas.

Size-resolved source apportionment of particulate matter from a megacity in northern China based on one-year measurement of inorganic and organic components

This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 μm), fine (0.43-2.1 μm) and coarse mode (>2.1 μm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO₃^- and SO₄^2- from the usual 0.43-0.65 μm to 1.1-2.1 μm. Our results demonstrated the dominance of primary combustion sources in the <0.43 μm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity.

Estimating uncertainties of source contributions to PM2.5 using moving window evolving dispersion normalized PMF

Conventional factor analyses can present problems in cases with changing numbers of sources and/or time-dependent source compositions. There is also lack of a reliable method to estimate uncertainties in the source contributions derived by positive matrix factorization (PMF). Applying a moving window evolving PMF to hourly PM_2.5 composition dataset from a field campaign in Tianjin China that included the Spring and Lantern Festivals and the start of COVID-19 pandemic has substantially improved the apportionment compared to a conventional analysis using the entire data. Festival-related sources (e.g., fireworks and residential burning here) have been clearly identified and estimated during both the Spring and Lantern Festivals. During this period, the sources changed because the time period overlaps with the outbreak of COVID-19 and related reductions in activity during the lockdown that began on Lunar New Year. Multiple PMF runs providing source contribution estimates made it possible to estimate the uncertainties in these values. Our results show that winds-dependent sources like dust and distant point sources have larger uncertainties than the other sources. Compared with conventional PMF analyses, the current method may better reflect the actual emissions as well as being able to estimate uncertainties. Thus, this approach appears to be an improvement if the appropriate data are available.

Dispersion Normalized PMF Provides Insights into the Significant Changes in Source Contributions to PM2.5 after the COVID-19 Outbreak

Factor analysis utilizes the covariance of compositional variables to separate sources of ambient pollutants like particulate matter (PM). However, meteorology causes concentration variations in addition to emission rate changes. Conventional positive matrix factorization (PMF) loses information from the data because of these dilution variations. By incorporating the ventilation coefficient, dispersion normalized PMF (DN-PMF) reduces the dilution effects. DN-PMF was applied to hourly speciated particulate composition data from a field campaign that included the start of the COVID-19 outbreak. DN-PMF sharpened the morning coal combustion and rush hour traffic peaks and lowered the daytime soil, aged sea salt, and waste incinerator contributions that better reflect the actual emissions. These results identified significant changes in source contributions after the COVID-19 outbreak in China. During this pandemic, secondary inorganic aerosol became the predominant PM_2.5 source representing 50.5% of the mean mass. Fireworks and residential burning (32.0%), primary coal combustion emissions (13.3%), primary traffic emissions (2.1%), soil and aged sea salt (1.2%), and incinerator (0.9%) represent the other contributors. Traffic decreased dramatically (70%) compared to other sources. Soil and aged sea salt also decreased by 68%, likely from decreased traffic.