Source Apportionment Of PM2 Research Articles

Source apportionment of PM2.5 episodes in the Taichung metropolitan area, Taiwan

To analyze the physicochemical mechanisms affecting the variation in fine suspended particulate matter (PM2.5) concentrations in Taichung City, the largest city in central Taiwan (the second largest city in Taiwan), during a high-pollution event from November 3 to 6, 2021, we applied the sulfur tracking method (STM) and integrated source apportionment method (ISAM) of the WRF/CMAQ model to simulate the impacts of various emission sources. The sources of pollution in Taichung City are very similar, which shows that the impacts of point, line, and area sources should not be neglected in addition to the boundary conditions. SO42− is mainly generated from point emissions and the production of H2O2, Fe, Mn, and O3. NO3− is also mainly generated from point sources in Taichung City, with HNO3 being the main source at noon and ANO3 at other times of the day. NH4+ is mainly generated from area sources in Taichung City. OM is more complex, mainly originating from line sources in Taichung City and other sources, such as point/area emissions in Taichung City and other emissions from Changhua County. The most important mechanism is low-volatility/semivolatile oxidized combustion of OC at noon, followed by low-volatility/semivolatile POA, which is produced in the morning or evening. EC mainly originates from line sources in Taichung City and Changhua County. In other nearby counties, EC is dominated by local emission sources. In addition, when the concentration of PM2.5 is high, the Neutralization Ratio (NR) is high and PM2.5 is relatively neutral or slightly alkaline. On the contrary, when the concentration of PM2.5 is low, the NR is lower than 1 and the aerosol is acidic. Besides, this study used positive matrix factorization (PMF), which indicates that the PM2.5 at the UAPRS originated from eight kinds of pollution, namely, windblown dust, oil cracking, iron and steel industry, sea salt, transport, Cl− containing exhaust, biomass burning, fossil combustion containing abundant SO42− and the heavy oil refining and coal combustion industry. The direction of the source of pollution can be traced by a conditional bivariate probability function (CBPF). Overall, fossil fuel combustion, mainly involving sulfate, is the largest source of pollution, with the heavy oil refining and coal combustion industry contributing less, and the remaining factors contribute relatively evenly.

Spatially resolved chemical data for PM10 and oxidative potential source apportionment in urban-industrial settings

This study proposes the use of spatially resolved data to identify local sources contributing to PM10 mass concentration and OP in urban-industrial contexts. Over a 18-month monitoring campaign spanning from October 2020 to May 2022, a low-volume PM10 sampling device was employed at 12 sites in the Sacco River Valley, Italy. PM10 samples were analyzed for OP by applying the DTT, AA, and DCFH assays, and for 77 chemical parameters: the main chemical components (major elements, inorganic ions, EC, OC, levoglucosan) and the water-soluble and insoluble fractions of 28 elements. Source apportionment via PMF analysis on spatially resolved data revealed seven PM10 source contributions, notably biomass burning and soil dust. Biomass burning predominated in winter, while soil dust prevailed in summer. Spatial variability in source contributions was observed due to diverse biomass types, domestic heating, and proximity to the cement plant and transportation networks. Bubble maps and FPCA represented OP and PM10 source relationships, highlighting biomass burning as the main contributor to OPDTT and OPDCFH, while non-exhaust traffic contributed significantly to OPAA. Findings from this study provide valuable insights into planning effective air pollution mitigation policies aimed at enhancing air quality in urban-industrial contexts.

Source apportionment of PM2.5 and PM10 pollutants near an urban roadside site using positive matrix factorization

This paper presents results from a comprehensive study of source apportionment of particulate matter (PM) of size PM2.5 and PM10 near a busy highway in Sharjah, United Arab Emirates. Source apportionment was carried out using US Environmental Protection Agency (EPA) Positive Matrix Factorization model. Furthermore, backward trajectory analysis and Potential Source Contribution Function were used to assess air mass transport pathways and identify potential source regions, respectively. The results revealed six major sources for PM2.5, including traffic, sea salt, fugitive dust, secondary aerosols, heavy oil combustion and mineral dust. For PM10, four major sources were identified, including secondary aerosols, traffic, sea salt and mineral dust. Traffic emissions were found to be significant contributors to both PM2.5 and PM10 pollution, along with natural sources like sea salt and mineral dust. Backward trajectory analysis indicated the influence of different wind regimes on air mass transport, with contributions from regions like Arabian Gulf, Arabian Sea, Oman and Iran. The Conditional Bivariate Probability Function analysis further explained the impact of local traffic emissions and other sources on PM pollution under varying wind conditions.

Source apportionment of PM2.5 using DN-PMF in three megacities in South Korea

AbstractPM2.5 pollution is problematic in megacities on the western coast in South Korea (Seoul, Incheon, and Gwangju). As these megacities are located downwind of China, their air quality is easily affected by local and long-range transport sources. PM2.5 samples collected in Seoul (n = 222), Incheon (n = 221), and Gwangju (n = 224) from September 2020 to March 2022, were chemically characterized. Dispersion normalized positive matrix factorization was applied to these PM2.5 speciated data to provide source apportionments. Nine common sources (including secondary nitrate, secondary sulfate, biomass burning, mobile, and waste incinerator) were identified at all sites. The conditional bivariate probability function helped to identify each site’s local sources. Joint potential source contribution function analysis identified northeast China and Inner Mongolia as potential source areas of long-range transport pollutants affecting all sites. Forced lifestyle changes due to the pandemic such as limited gatherings while increased recreational activities may have caused different patterns on the biomass burning source. The constraints on old vehicles during the policy implementation periods likely reduced the mobile source contributions in cities that adopted the policy. Secondary nitrate accounted for 40% of the PM2.5 mass at all sites, implying a significant impact from NOX sources. While the current policy focuses primarily on controlling primary emission sources, it should include secondary sources as well which may include precursor emissions control. Healthier air quality would be achieved if the policy effects are not limited to local, but also to foreign sources in regions upwind of Korea by intergovernmental collaboration.

Pollution characteristics, source apportionment, and health risk assessment of PM10 and PM2.5 in rooftop and kerbside environment of Lanzhou, NW China.

To investigate air pollution in the kerbside environment and its associated human health risks, a study was conducted in Lanzhou during December 2018, as well as in April, June, and September 2019. The research aimed to characterize the composition of PM10 and PM2.5, including elements, ions, and carbonaceous components, at both rooftop and kerbside locations. Additionally, source apportionment and health risk assessment were conducted. The results showed that the average mass concentrations of PM10 on the rooftop were 176.01 ± 83.23μg/m3, and for PM2.5, it was 94.07 ± 64.89μg/m3. The PM10 and PM2.5 levels at the kerbside are 2.21 times and 1.79 times, respectively, greater than those on the rooftop. Moreover, the concentrations of elements, ions, and carbonaceous components in kerbside PM were higher than those at the rooftop location. Chemical mass closure analysis identified various sources, including organic matter, mineral dust, secondary ions, other ions, elements, and other components. In comparison to rooftop particulate matter (PM), mineral dust makes a more substantial contribution to kerbside PM. Secondary ions show an opposite trend, making a greater contribution to rooftop PM. The contribution of organic components within PM of the same particle size remains relatively consistent. The outcome of the health risk assessment indicates that Co, Cd, and As in PM within the kerbside and rooftop environments do not pose a notable carcinogenic risk. However, Al and Mn do present specific non-carcinogenic risks, particularly in the kerbside environment. Furthermore, children experience elevated non-carcinogenic risk compared to adults. These findings can serve as a scientific foundation for formulating policies within the local health department.

Chemical characterization and source apportionment of PM2.5 in a Northeastern China city during the epidemic period.

To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissionsand catering fume sourcescontributing the most to PM2.5.

Source apportionment and health risk assessment of PM10 bound carbonaceous and elemental species in the central Himalayas

In this extensive investigation, the sources and health risk assessment of carbonaceous aerosols, as well as the elemental compositions (Ca, Al, Fe, S, K, Mg, Pd, B, Mo, Zn, Ag, Nb, Ga, Cl, Ti, Zr, Na, Cr, Cu, Mn, Cs, P, Y, Sb, Ni, Sn, F, Sr, Br, Pm, U, Pb, Th, Br, Rb, and V) of PM10, were conducted throughout January-December 2021 in Nainital, a central Himalayan region of India. The average annual mass concentration of PM10 was determined to be 64±6 µg m-3. Carbonaceous aerosols (CAs), comprising OC, EC, WSOC, and SOC, exhibited annual averages of 9.3±0.8 µg m-3, 4.9±0.5 µg m-3, 1.5±0.2 µg m-3, 2.7±0.2 µg m-3, and 2.97±0.45 µg m-3, respectively. The elemental composition featured major contributors such as Ca, Al, Fe, S, and K, alongside trace levels of various elements (Mg, Pd, B, Mo, Zn, Ag, Nb, Ga, Cl, Ti, Zr, Na, and Cr). Positive Matrix Factorization (PMF) identified six primary contributors to PM10, each with varying percentage contributions: crustal/soil/road dust, soil re-suspension, combustion, vehicular emissions, industrial emissions, and biomass burning. The health risk assessment revealed elevated Hazard Quotient (HQ) values for Cr and Mn in children, indicating a non-carcinogenic health risk. Adults exposed to high Cr levels may face potential carcinogenic risks, while elements like Al, Pb, Cu, Zn, and Ni pose no health hazards, aligning with USEPA guidelines.

Spatial Variability and Source Apportionment of PM2.5 Carbon Components in Tianjin

The contents of eight carbonaceous subfractions were determined by simultaneously collecting PM2.5 samples from four sites in different functional areas of Tianjin in 2021. The results showed that the organic carbon (OC) concentration was 3.7 μg·m-3 to 4.4 μg·m-3, and the elemental carbon (EC) concentration was 1.6 μg·m-3 to 1.7 μg·m-3, with the highest OC concentration in the central urban area. There was no significant difference in EC concentration. The concentration of PM2.5 showed the distribution characteristics of the surrounding city>central city>peripheral area. The OC/EC minimum ratio method was used to estimate the concentrations of secondary organic carbon (SOC) in PM2.5, and the results showed that the secondary pollution was more prominent in the surrounding city, with SOC accounting for 48.8%. The correlation between carbon subcomponents in each functional area showed the characteristics of the peripheral area>central area>surrounding area, all showing the strongest correlation between EC1 and OC2 and EC1 and OC4. By including the carbon component concentration into the positive definite matrix factorization (PMF) model for source apportionment, the results showed that road dust sources(9.7%-23.5%), coal-combustion sources (10.2%-13.3%), diesel vehicle exhaust (12.6%-20.2%)and gasoline vehicle exhaust (18.9%-38.8%)were the main sources of carbon components in PM2.5 in Tianjin. The pollution sources of carbon components were different in different functional areas, with the central city and peripheral areas mainly affected by gasoline vehicle exhaust; the surrounding city was more prominently affected by the secondary pollution and diesel vehicle exhaust.

Atmospheric aerosol chemistry and source apportionment of PM10 using stable carbon isotopes and PMF modelling during fireworks over Hyderabad, southern India

This study examined the influence of fireworks on atmospheric aerosols over the Southern Indian city of Hyderabad during festival of Diwali using mass closure, stable carbon isotopes and the EPA-PMF model. Identification of chemical species in day and night time aerosol samples for 2019 and 2020 Diwali weeks showed increased concentrations of NH4+, NO3−, SO42−, K+, organic carbon (OC), Ba, Pb and Li, which were considered as tracers for fireworks. PM10 source apportionment was done using inorganic (trace elements, major ions) and carbonaceous (organic and elemental carbon; OC & EC) constituents, along with stable isotopic compositions of TC and EC. K+/Na+ ∼1 and K+nss/OC > 0.5 indicated contribution from fireworks. High NO3−, NH4+, Na+, Cl− and SO42− suggested the presence of deliquescent salts NaCl, NH4NO3 and (NH4)2SO4. TAE/TCE >1 suggested H+ exclusion, indicating possible presence of H2SO4 and NH4HSO4 in the aerosols. Ba, Pb, Sb, Sr and Fe increased by 305 (87), 12 (11), 12 (3), 3 (2) and 3 (4) times on Diwali nights, compared to pre-Diwali of 2019 (2020), and are considered as metallic tracers of fireworks. δ13CTC and δ13CEC in aerosols closely resembled that of diesel and C3 plant burning emissions, with meagre contribution from firecrackers during Diwali period. The δ13CEC was relatively depleted than δ13CTC and δ13COC. For both years, δ13COC-EC (δ13COC - δ13CEC) were positive, suggesting photochemical aging of aerosols during long-range transport, while for pre-Diwali 2019 and post-Diwali 2020, δ13COC-EC were negative with high OC/EC ratio, implying secondary organic aerosols formation. High toluene during Diwali week contributed to fresh SOA formation, which reacted with precursor 12C, leading to 13C depletions. Eight-factored EPA-PMF source apportionment indicated highest contribution from residue/waste burning, followed by marine/dust soil and fireworks, while least was contributed from solid fuel/coal combustion.

Source Apportionment of PM10 as a Tool for Environmental Sustainability in Three School Districts of Lecce (Apulia)

Air pollution is a great threat to the sustainable development of the world; therefore, the improvement of air quality through the identification and apportionment of emission sources is a significant tool to reach sustainability. Single particle analysis, by means of a scanning electron microscope equipped with X-ray energy dispersive analysis (SEM-EDS), was used to identify the morphological and chemical properties of the PM10 particles in order to identify and quantify the main emission sources in three areas of Lecce, a city in the Apulia region of southern Italy. This type of characterization has not yet been performed for the Lecce site, but it is of particular importance to identify, based on the shape of the particles, the natural sources from the anthropogenic sources that are responsible for the serious health effects of the inhabitants. Three primary schools located in peripheral areas of the city were chosen for the sampling: “School 1” (A site), “School 2” (B site), and “School 3” (C site) to carry out a study of the air quality. The A site is characterized by a greater presence of calcium sulphates probably due both to construction activities present during sampling and to reactions between Ca particles and the sulfur present in the atmosphere. At the C site, there is a relative numerical abundance of different groups of particles that present, in the EDS spectrum, an enrichment in sulfur. At the B site, the number of particle groups is intermediate compared to that of the other two sites. With the source apportionment technique, ten emission sources were identified: combustion, soot, industry, soil, carbonates, sea salt, calcium sulfates, SIA, biological particles, and others. In PM10, the three sites are more affected by the soil source, with an effect greater than 60%.

Chemical composition and source apportionment of PM2.5 in Seoul during 2018–2020

Air pollution, particularly high ambient particulate matter (PM) concentrations, poses a serious public health concern in South Korea. This study analyzed 139 daily PM2.5 samples collected from an urban site in Seoul between March 2018 and January 2020. This study aimed to quantify the chemical composition of PM2.5 and identify the source characteristics. The average PM2.5 mass concentration for the entire study period was 25.3 μg m−3. The chemical composition was dominated by ionic particles (13.2 μg m−3, 52%), carbonaceous particles (7.0 μg m−3, 28%), and trace elements (0.5 μg m−3, 2%). The Positive Matrix Factorization (PMF) model was employed to identify PM2.5 sources using quality-controlled sampling data after eliminating factors that could adversely affect the modeling. Seven sources were resolved through PMF, applying a trial-and-error method to determine the optimal number of sources. The PMF modeling results indicated that secondary sulfate (28%) was the largest contributor, followed by internal factors (23%), secondary nitrate (19%), soil (14%), biomass burning (9%), and aged sea salt (7%). As an additional case study, the analysis of backward trajectories for the winter of 2018, which had the highest PM2.5 concentrations, revealed that the air current carrying PM2.5 had passed from the northwest region of Seoul. Particulary, source identification for this winter case resulted in the highest secondary nitrate across all study periods. This more specific interpretation of the pollutant characteristics of PM2.5 in Seoul could aid in developing effective control strategies and policies for air pollution mitigation.

Source apportionment of PM2.5 in Montréal, Canada, and health risk assessment for potentially toxic elements

Abstract. Source apportionment of PM2.5 was performed using positive matrix factorization (PMF) based on detailed chemical composition data from 24 h filter samples collected over a 3-month period (August–November 2020) at an urban site in Montréal, a Canadian city with a population of approximately 4×106 people. This source apportionment study, which examined the main contributing sources to PM2.5 using a larger suite of organic molecular markers than other Canadian studies, is the first of its sort in Canada. A focus of this study was on quantifying previously unresolved sources of PM2.5 through the inclusion in the PMF analysis of additional organic molecular markers beyond those measured typically by the Canadian government's National Air Pollution Surveillance Program (NAPS). The organic species included in the PMF model were comprised of six n-alkanes, two fatty acids, one dicarboxylic acid, two biogenic secondary organic aerosol (SOA) tracers, and hopane. Secondary inorganic aerosols (SIAs) and SOAs were the dominant components and constituted 39 % of the measured PM2.5 mass, while the local primary anthropogenic sources, namely traffic exhaust, road dust, industrial, and cooking emissions, contributed 23 %. The chemical transport model GEOS-Chem revealed that ammonium sulfate concentrations in Montréal are strongly influenced by both local sources in Quebec and transboundary input from the United States, with the transboundary input exceeding the local emissions for SOA. Co and Cr(VI) presented an elevated cancer risk, highlighting that more attention should be given to these trace metals, which were associated with industrial emissions by the PMF analysis. Furthermore, the results showed that industrial emissions were minor contributors to the total PM2.5 mass concentration but were the largest contributors to Co and Cr(VI) concentrations. Thus, the health hazards associated with this source cannot be entirely established by the PM2.5 mass concentration alone. This study highlights that, when evaluating air quality in Montréal and other urban regions, the prioritization of sources for mitigation strategies will diverge if one considers total PM2.5 mass concentration or the concentration of individual particulate-bound contaminants. Furthermore, the large transboundary contribution from the United States to total PM2.5 levels suggests that future municipal, provincial, and federal monitoring and regulations would be more effective if they focus on specific high-risk contaminants (e.g., Co and Cr(VI) rather than total PM2.5).

Chemical characteristics, morphology and source apportionment of PM10 over National Capital Region (NCR) of India.

The present study frames the physico-chemical characteristics and the source apportionment of PM10 over National Capital Region(NCR) of India using the receptor model's Positive Matrix Factorization (PMF) and Principal Momponent Mnalysis/Absolute Principal Component Score-Multilinear Regression (PCA/APCS-MLR). The annual average mass concentration of PM10 over the urban siteof Faridabad, IGDTUW-Delhi and CSIR-NPL of NCR-Delhi were observed to be 195 ± 121, 275 ± 141 and 209 ± 81µgm-3, respectively. Carbonaceous species (organic carbon (OC), elemental carbon (EC) and water-soluble organic carbon (WSOC)), elemental constituents (Al, Ti, Na, Mg, Cr, Mn, Fe, Cu, Zn, Br, Ba, Mo Pb) and water-soluble ionic components (F-, Cl-, SO42-, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) of PM10were entrenched to the receptor models to comprehend the possible sourcesof PM10. The PMF assorted sources over Faridabad were soil dust (SD 15%), industrial emission (IE 14%), vehicular emission (VE 19%), secondary aerosol (SA 23%) and sodium magnesium salt (SMS 17%). For IGDTUW-Delhi, the sources were SD (16%), VE (19%), SMS (18%), IE (11%), SA (27%) and VE + IE (9%). Emission sources like SD (24%), IE (8%), SMS (20%), VE + IE (12%), VE (15%) and SA + BB (21%) were extracted over CSIR-NPL, New Delhi, which are quite obvious towards the sites. PCA/APCS-MLR quantified the similar sources with varied percentage contribution. Additionally, catalogue the Conditional Bivariate Probability Function (CBPF) for directionality of the local source regions and morphology as spherical, flocculent and irregular were imaged using a Field Emission-Scanning Electron Microscope (FE-SEM).

The Variation in Chemical Composition and Source Apportionment of PM2.5 before, during, and after COVID-19 Restrictions in Zhengzhou, China.

Despite significant improvements in air quality during and after COVID-19 restrictions, haze continued to occur in Zhengzhou afterwards. This paper compares ionic compositions and sources of PM2.5 before (2019), during (2020), and after (2021) the restrictions to explore the reasons for the haze. The average concentration of PM2.5 decreased by 28.5% in 2020 and 27.9% in 2021, respectively, from 102.49 μg m-3 in 2019. The concentration of secondary inorganic aerosols (SIAs) was 51.87 μg m-3 in 2019, which decreased by 3.1% in 2020 and 12.8% in 2021. In contrast, the contributions of SIAs to PM2.5 increased from 50.61% (2019) to 68.6% (2020) and 61.2% (2021). SIAs contributed significantly to PM2.5 levels in 2020-2021. Despite a 22~62% decline in NOx levels in 2020-2021, the increased O3 caused a similar NO3- concentration (20.69~23.00 μg m-3) in 2020-2021 to that (22.93 μg m-3) in 2019, hindering PM2.5 reduction in Zhengzhou. Six PM2.5 sources, including secondary inorganic aerosols, industrial emissions, coal combustion, biomass burning, soil dust, and traffic emissions, were identified by the positive matrix factorization model in 2019-2021. Compared to 2019, the reduction in PM2.5 from the secondary aerosol source in 2020 and 2021 was small, and the contribution of secondary aerosol to PM2.5 increased by 13.32% in 2020 and 12.94% in 2021. In comparison, the primary emissions, including biomass burning, traffic, and dust, were reduced by 29.71% in 2020 and 27.7% in 2021. The results indicated that the secondary production did not significantly contribute to the PM2.5 decrease during and after the COVID-19 restrictions. Therefore, it is essential to understand the formation of secondary aerosols under high O3 and low precursor gases to mitigate air pollution in the future.

Source identification and health risk assessment of PM2.5 in urban districts of Hanoi using PCA/APCS and UNMIX.

Comparing results obtained by different models with different physical assumptions and constraints for source apportionment is important for better understanding the sources of pollutants. Source apportionment of PM2.5 measured at three sites located in inner urban districts of Hanoi was performed using two receptor models, UNMIX and principal component analysis with absolute principle component score (PCA/APCS). A total of 78 daily samples were collected consecutively during the dry and wet seasons in 2019 and 2020. The average PM2.5 concentration (66.26µg/m3 ± 29.70µg/m3 with a range from 23.57 to 169.04µg/m3) observed in Hanoi metropolitan exceeded the National Ambient Air Quality standard QCVN 05:2013/BTNMT (50µg/m3). Both UNMIX and PCA/APCS expressed comparable ability to reproduce measured PM2.5 concentrations. Additionally, both models identified similar potential sources of PM2.5 including traffic-related emissions, scrap metal recycling villages, crustal mixed with construction sources, coal combustion mixed with industry, and biomass burning. Both UNMIX and PCA/APCS confirmed that traffic-related emission was the most influential PM2.5 with a high percentage contribution of 59% and 55.97%, respectively. All the HQ and Cr values for both children and adults of toxic elements apportioned by both UNMIX and PCA/APCS in every source were within the acceptable range.

Source Apportionment of PM2.5 and PM2.5-Bound Trace Elements in Pretoria, South Africa

Outdoor PM2.5 samples were collected for 34 months in Pretoria, South Africa from 18 April 2017 to 28 February 2020. The average total PM2.5 concentration was 23.2 ± 17.3 µg.m3 (0.69–139 µg.m−3), with the highest mean recorded during winter and the lowest during summer (p < 0.05). The sources were determined by means of cross referencing the US EPA PMF 5.0 program and the NOAA HYsplit model. The sources of the total PM2.5 were mining (33%), resuspended dust (24%), industry (15%), general exhaust (12%), vehicular emissions (12%) and biomass burning (4%). Sources of air pollutants are both ubiquitous and seasonal.

The transboundary contribution of PM10 in Korea depended on the dipole surface temperature pattern in the East Asian region during April of 2021

During the spring of 2021, rapid surface cooling in the Arctic area expanded to most of the East Asian region, featuring surface-temperature dipole patterns of the colder west (Mongolia and northern-central China) and the warmer east (eastern-northeastern China, the Yellow Sea, and Korea). Despite the decreasing trend of dusty days within Korea, a notable rise in the occurrence of transboundary mineral dust-induced dusty days was observed in contributing to an increase in PM10 levels in the spring of 2021. This study investigated the PM10 source apportionment of anthropogenic dust, mineral dust, and biomass burning in the East Asian region and its transboundary impact on Korea, within the context of the surface-temperature dipole pattern observed in April of 2021. Source apportionment of PM10 was determined through a comparative analysis between baseline and sensitivity experiments conducted by means of a WRF-Chem simulation. In these sensitivity simulations, emissions from each source were systematically excluded to assess their individual contributions. PM10 Base refers to baseline simulations, PM10 Anthro to anthropogenic emissions, PM10 Dust to mineral dust, and PM10 Biomass to biomass burning. The colder west pattern was linked to increased synoptic-scale cyclonic activity, marked by higher positive potential vorticity, causing a sudden surge in severe dust storms over Mongolia and northern China. PM10 Base levels were most elevated in the higher terrain of Mongolia, comprising predominantly PM10 Dust (95%). However, the dominance of atmospheric-warming-induced anticyclones, represented by enhanced negative potential vorticity, prevailed over the lower terrain in the warmer east regions, obstructing the transboundary dispersion of PM10 Base to Korea. Nevertheless, Korea experienced a transboundary contribution of PM10 Dust, reaching 35%. In addition, the source apportionments indicated a PM10 Anthro ratio of 55% in eastern China and a PM10 Biomass ratio of 35% in northeastern China, contributing 25% and 5% respectively to Korea. During April 14–30, 2021, the dipole pattern evolved further to the significantly colder west (Mongolia and China) and the moderately warmer east (the Yellow Sea and Korea). The transboundary contributions of PM10 Anthro, PM10 Dust, and PM10 Biomass were intensified as a composite mixture within Korea, an outcome attributed to the intermittent cyclones and subsequent anticyclones.

Indoor residential and outdoor sources of PM2.5 and PM10 in Nicosia, Cyprus

Cyprus is a typical eastern Mediterranean country that suffers from local emissions, transported anthropogenic pollution, and dust storms all year round. Therefore, exposures to PM in ambient and residential micro-environments are of great public health concern. Our study collected indoor and outdoor PM2.5 and PM10 samples simultaneously in 22 houses in Nicosia, Cyprus, during warm seasons and cold seasons from February 2019 to May 2021. Samples were analyzed for mass and constituents’ concentrations. To determine indoor and outdoor sources of PM in residential environments, we used the EPA positive matrix factorization (PMF) model to conduct source apportionment analyses for both indoor and outdoor PM2.5 and PM10 particles. Generally, six types of residential-level PM sources were resolved: biomass burning, traffic, local or regional secondary sulfate pollution, Ca-rich particles, sea salt, and soil dust. In the source apportionment of PM2.5, the main contribution to outdoor levels (33.1%) was associated with sulfate-rich transported pollution. The predominant contribution to indoor levels (48.0%) was attributed to secondary sulfate pollution as a mixture of local- and regional-scale pollutants. Biomass burning and traffic sources constituted the main outdoor sources of indoor PM2.5, while the Ca-rich particles were identified to almost originate from indoors. By contrast, the largest fraction (29.3%) of the ambient PM10 and a smaller proportion (10.2%) of indoor PM10 were attributed to Ca-rich particles. Indoor PM10 was associated mainly with outdoor sources, except for the soil dust which originated from indoor activities.

Chemical Composition and Source of PM2.5 during Winter Heating Period in Guanzhong Basin

An intensive field campaign was carried out from December 2022 to March 2023 at six different sites across five major cities (Xi’an, Baoji, Xianyang, Weinan, and Hancheng) in the Guanzhong Basin, China, covering most of the heating period there, which is characterized by high PM2.5 pollution levels. During the campaign, the mean PM2.5 concentrations at these sites exceeded the 24 h PM2.5 standard (75 μg m−3), except the site at Hancheng, with mean PM2.5 concentrations of 57.8 ± 32.3 μg m−3. The source apportionment of PM2.5 varied significantly across sites, with vehicle exhaust being the dominant source at urban sites located in Xi’an and Baoji, coal combustion at suburban sites in Hancheng, and comparable contribution from coal combustion and industrial emissions at suburban sites in Xianyang and Weinan. Compared with clean condition, the contribution of vehicle exhaust and secondary inorganic sources (SIs) were largely enhanced during heavy PM2.5 pollution periods, while the contribution from biomass burning (BB) and dust decreased significantly at all sites. Combined with an analysis of meteorological parameters, the study further found that higher contributions of SIs and heavy PM2.5 pollution were generally associated with higher relative humidity (RH). In addition, higher PM2.5 concentrations at suburban sites were related to lower wind speeds, which could be explained by the stagnant condition favoring the accumulation of local emissions as well as the formation of secondary pollutants. In contrast, at urban sites (e.g., Xianyang), higher PM2.5 concentrations were more associated with the strong influence of vehicle exhaust at slightly higher wind speeds.

Source apportionment study on particulate air pollution in two high-altitude Bolivian cities: La Paz and El Alto

Abstract. La Paz and El Alto are two fast-growing, high-altitude Bolivian cities forming the second-largest metropolitan area in the country. Located between 3200 and 4050 m a.s.l. (above sea level), these cities are home to a burgeoning population of approximately 1.8 million residents. The air quality in this conurbation is heavily influenced by urbanization; however, there are no comprehensive studies evaluating the sources of air pollution and their health impacts. Despite their proximity, the substantial variation in altitude, topography, and socioeconomic activities between La Paz and El Alto result in distinct sources, dynamics, and transport of particulate matter (PM). In this investigation, PM10 samples were collected at two urban background stations located in La Paz and El Alto between April 2016 and June 2017. The samples were later analyzed for a wide range of chemical species including numerous source tracers (OC, EC, water-soluble ions, sugar anhydrides, sugar alcohols, trace metals, and molecular organic species). The United States Environmental Protection Agency (U.S. EPA) Positive Matrix Factorization (PMF v.5.0) receptor model was employed for the source apportionment of PM10. This is one of the first source apportionment studies in South America that incorporates an extensive suite of organic markers, including levoglucosan, polycyclic aromatic hydrocarbons (PAHs), hopanes, and alkanes, alongside inorganic species. The multisite PMF resolved 11 main sources of PM. The largest annual contribution to PM10 came from the following two major sources: the ensemble of the four vehicular emissions sources (exhaust and non-exhaust), accountable for 35 % and 25 % of the measured PM in La Paz and El Alto, respectively; and dust, which contributed 20 % and 32 % to the total PM mass. Secondary aerosols accounted for 22 % (24 %) in La Paz (El Alto). Agricultural smoke resulting from biomass burning in the Bolivian lowlands and neighboring countries contributed to 9 % (8 %) of the total PM10 mass annually, increasing to 17 % (13 %) between August–October. Primary biogenic emissions were responsible for 13 % (7 %) of the measured PM10 mass. Additionally, a profile associated with open waste burning occurring from May to August was identified. Although this source contributed only to 2 % (5 %) of the total PM10 mass, it constitutes the second largest source of PAHs, which are compounds potentially hazardous to human health. Our analysis additionally resolved two different traffic-related factors, a lubricant source (not frequently identified), and a non-exhaust emissions source. Overall, this study demonstrates that PM10 concentrations in La Paz and El Alto region are predominantly influenced by a limited number of local sources. In conclusion, to improve air quality in both cities, efforts should primarily focus on addressing dust, traffic emissions, open waste burning, and biomass burning.