Chemical Mass Balance Receptor Model Research Articles

Long-range transport and source apportionment of marine fine particles in the Taiwan Strait and South China Sea Intersection: Spatiotemporal variations and chemical fingerprints

This study aims to investigate the spatiotemporal variation, chemical characteristics and source apportionment of marine fine particles (PM2.5), and to analyze the transport route towards the intersectional region of the Taiwan Strait and South China Sea. Sampling of PM2.5 was conducted simultaneously at Penghu and Dongsha Islands for 14 consecutive days each quarter from summer 2019 to spring 2020. PM2.5 samples were then returned for conditioning, weighting, and chemical analysis. The chemical mass balance (CMB) receptor model was used to identify the potential PM2.5 sources. It results show that the lowest PM2.5 concentrations were observed in summer, with a gradual increase starting fall, influenced by Asia Northeastern Monsoons (ANMs) transporting particles from the north to Penghu and Dongsha Islands. The most abundant water-soluble ionic species in PM2.5 were SO42−, NO3− and NH4+, catalogued as the secondary inorganic aerosols (SIAs). Meanwhile, the most abundant metals in PM2.5 were crustal elements (Mg, K, Ca, Fe, and Al), while the concentrations of trace metals (V, Cr, Mn, Ni, As, Cd, and Pb), mainly from anthropogenic sources, also increased from fall onwards. Organic carbon (OC) was the main species of carbonaceous content of PM2.5 in all seasons, and OC/EC ratios increased during the seasons with prevailing northeastern winds. Anhydrosugar, concentrations in winter and spring were generally higher than those in summer and fall, indicating significant biomass burning occurring in winter and spring. Correlation analysis showed a high correlation between PM2.5 concentrations and chemical composition between two subtropical islands. The correlation of chemical composition for different transportation routes revealed that northern routes had a higher correction than southern routes. Overall, the cross-boundary PM, accounted for 28.4–61.0% and 36.4–76.8% at Penghu and Dongsha Islands, respectively, significantly impacted local air quality, particularly at Dongsha Islands.

Temporal variations and chemical characteristics of marine PM2.5 at Dongsha Islands, South China Sea: Three-year measurement

The study of long-range transport effects on marine fine particles (PM2.5), particularly in remote sites such as the Dongsha Islands, is pivotal for advancing our understanding of air pollution dynamics on a regional scale and for formulating effective environmental policies. PM2.5 concentrations were examined over three consecutive years and grouped based on their transport routes. The backward trajectory simulation revealed that high PM2.5 concentrations were observed in the West Channel, originating from North and Central China, the Korean Peninsula, and the Japanese Islands, opposed to the East Channel. High PM2.5 concentrations, commonly observed in winter and spring, were mainly attributed to the Asian Northeastern Monsoons. Water-soluble inorganic ions constituted the major components, accounting for 37.8–48.7% of PM2.5, and followed by metal elements (15.5–20.0%), carbons (7.5–13.3%), levoglucosan (0.01–0.17%), and organic aerosols (0.2–2.2%). Secondary inorganic aerosols as the dominant source accounted for 8.3–24.7% of PM2.5, while sea salts were the secondary major contributor. High levoglucosan contribution (3.8–7.2%) in winter and spring was attributed to biomass burning, mainly from the Indochina Peninsula. Chemical mass balance receptor modeling resolved that major sources of PM2.5 were secondary sulfate, sea salts, fugitive dust, and industrial boilers. This study concluded that the long-range transport of PM2.5 gradually increased since fall, contributing 52.1–74.3%, highlighting its substantial impact on PM2.5 in all seasons except summer.

Effect of Source Emission Control Measures on Source of Atmospheric PM2.5 during “Parade Blue” Period

During the “Parade Blue” period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the “Parade Blue” period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the “Parade Blue” period, and the effectiveness of the source control strategy was proved.

Source characterization of PM10 using CMB receptor modeling for the western industrial area of India

Introduction: Receptor models use the chemical characterisation of particulate matter to distinguish the source and analyse the source contributions. The main aim of this study is to carry out source apportionment of PM10 for industrial locations of Vapi and Ankleshwar in Gujarat, using the Chemical Mass Balance (CMB) receptor model.
 Materials and methods: At six distinct locations of Ankleshwar and Vapi, respirable dust samplers were used to collect particulate matter on quartz filter sheets for the current study. Filter papers containing PM10 mass were subsequently examined for Water Soluble Ions (WSIs), major and trace elements, elemental and organic carbon followed by source apportionment study.
 Results: Using CMB, the contributions obtained for Ankleshwar are 27.85% for crustal or soil dust, 26.31% for fossil fuel combustion, 21.06% for vehicle emissions, 14.20% for secondary aerosols, 9.30% for biomass, and 1.20% for industrial emissions. CMB for Vapi revealed the chief source signatures as fossil fuel combustion including industries contributing 35%, crustal or soil dust contributing 22.90%, biomass burning contributing 19.12%, vehicular emissions contributing 16.18%, and secondary aerosols contributing 6.79%.
 Conclusion: By applying the CMB model, the primary source is found to be crustal or soil dust followed by burning fossil fuels, vehicular emissions, and secondary aerosols for Ankleshwar and Vapi, respectively. A quantitative assessment of source contributions to particulate matter is required to create emission control measures. The findings of this study will be beneficial for the environmental management of particle concentrations in the study region.

Temporospatial Variation, Chemical Composition, and Source Resolution of PM2.5 in the Southeastern Taiwan Island

This study investigated the temporospatial variation, chemical composition, and source resolution of fine particles (PM2.5) in the southeastern seas of the Taiwan Island. 24-hr PM2.5 was sampled simultaneously at two remote sites, the Green Island (West Pacific Ocean; WPO) and the Kenting Peninsula (northern Bashi Channel; BC), in four seasons. After sampling, the chemical fingerprints of PM2.5 were characterized and further applied to resolve the potential sources of PM2.5 and their contribution by using a receptor model on the basis of chemical mass balance (CMB), enrichment factor (EF), and backward trajectory simulation. It showed that PM2.5 concentrations in winter (10.8 µg m–3) and spring (12.0 µg m–3) (i.e., during the period of Asian Northeastern Monsoons; ANMs) were higher than those in summer (4.0 µg m–3) and fall (6.6 µg m–3). In terms of chemical composition of PM2.5, secondary inorganic aerosols (SIAs = NO3–, SO42–, and NH4+) (56.7–67.2%) were the dominant component of water-soluble ions (WSIs) in PM2.5, while crustal elements (Mg, Al, Ca, Fe, and K) (44.0–61.2%) dominated the metallic contents in PM2.5. High EF values (> 10) showed that V, Mn, Ni, Cu, and Zn were potentially contributed from anthropogenic sources. Moreover, organic carbon (OC) (0.6 µg m–3) was superior to elemental carbon (EC) (0.3 µg m–3) in PM2.5. The OC/EC ratios higher than 2.0 showed the potential chemical formation of secondary organic aerosols (SOAs) in the atmosphere in winter and spring. Trajectory simulation indicated that high PM2.5 concentrations were mostly originated from North and Central China, Japan islands, and Korea Peninsula. Major sources of PM2.5 resolved by CMB receptor modeling were ordered as: sea salts (19.9%) > fugitive dust (19.8%) > industrial boilers (oil-fired) (10.8%) > secondary sulfate (9.8%) > mobile sources (8.0%).

Determination of settled dust sources by analytical techniques and chemical mass balance receptor model.

The identification of sources that produce particulate atmospheric matter (PM) can be of paramount importance for the reduction of air pollution and the development of environmental policies. In order to identify the environmental impact resulting from industrial metallurgical activities in the Metropolitan Region of Vitória, ES, Brazil, it was investigated the contribution to PM that result from industrial activities and from local natural sources. For this purpose, analytical techniques were used to identify the apportionment of sources that contribute to the formation of insoluble settled dust collected at two points near the city of Vitória. Samples of soil, iron ore, limestone, coal, iron ore pellets, sinter, coke, slag, environmental samples of settled dust, and samples representative of the actual flows of materials used in an integrated steel mill were analyzed. Physicochemical characterizations, based on X-ray diffraction and 57Fe Mössbauer spectroscopy of ferruginous compounds found in sources and receptor samples, revealed the presence of highly crystallized hematite and low crystallized hematite. The latter is primarily found in soil samples, while well-crystallized hematite is found in natural samples from iron ores or after thermo-chemical processes applied during the industrial transformation of raw materials, as it happens during the production of pellets. Ferrous crystallographic forms α-FeOOH and Fe5HO8·4H2O, observed in environmental and soil samples, were also found in samples from industrial sources. Source apportionment of carbon based on the IMPROVE_A protocol for thermal/optical carbon analysis showed the participation of the elementary carbon fractions, separating contributions originated from coke and coal sources in the environmental samples. These results allowed a significant reduction of collinearity between source profiles in the application of the chemical mass balance receptor model "EPA-CMB8.2" receptor model. Consequently, it was possible to distinguish sources that process mainly ferrous and carbonaceous materials, identifying the contribution of different sources to the settled dust collected.

Chemical significance and source apportionment of fine particles (PM2.5) in an industrial port area in East Asia

This study explored the chemical significance, seasonal variation, spatial distribution, and source apportionment of fine particles (PM2.5) in an industrial port area. 24-h PM2.5 was simultaneously sampled at three port sites. Five chemical contents in PM2.5 including water-soluble ions (WSIs), metallic elements, carbonaceous content, anhydrosugars, and organic acids were then analyzed. The source apportionment of PM2.5 was further resolved by trajectory simulation, principal component analysis (PCA), and chemical mass balance (CMB) receptor model. Both local sources and long-range transport contributed to the rise-up of PM2.5 levels. Secondary inorganic aerosols (SIAs) dominated WSIs in PM2.5. Crustal elements (Mg, Al, Ca, Fe, and K) dominated the metallic content of PM2.5, while trace elements (Ni, V, Cr, Zn, and Pb) were originated from anthropogenic sources. Ship emissions in the seaport and offshores caused high V/Ni ratios in summer. High OC/EC (2.04–2.23) and SOC/OC (28–34%) ratios were observed in fall and winter, implying the potential formation of secondary organic aerosols (SOAs) in atmosphere. High correlation of levoglucosan and K+ indicated that PM2.5 was greatly influenced by biomass burning (BB) in winter. Polluted air masses with high PM2.5 levels of 43.33 ± 11.84 μg/m3 in winter were blown from the norths by Asian Northeastern Monsoons (ANMs), while clean air masses with low PM2.5 levels of 9.13 ± 2.75 μg/m3 in summer came mainly from the oceans via Asian Southwestern Monsoons (ASMs). Source apportionment resolution showed that the dominant sources of PM2.5 in the Kaohsiung Seaport were industrial emissions, secondary aerosols (SIAs/SOAs), ship/vehicular exhausts, oceanic spray, and soil dust.

Source Apportionment of Fine Organic Particulate Matter (PM2.5) in Central Addis Ababa, Ethiopia.

The development of infrastructure, a rapidly increasing population, and urbanization has resulted in increasing air pollution levels in the African city of Addis Ababa. Prior investigations into air pollution have not yet sufficiently addressed the sources of atmospheric particulate matter. This study aims to identify the major sources of fine particulate matter (PM2.5) and its seasonal contribution in Addis Ababa, Ethiopia. Twenty-four-hour average PM2.5 mass samples were collected every 6th day, from November 2015 through November 2016. Chemical species were measured in samples and source apportionment was conducted using a chemical mass balance (CMB) receptor model that uses particle-phase organic tracer concentrations to estimate source contributions to PM2.5 organic carbon (OC) and the overall PM2.5 mass. Vehicular sources (28%), biomass burning (18.3%), plus soil dust (17.4%) comprise about two-thirds of the PM2.5 mass, followed by sulfate (6.5%). The sources of air pollution vary seasonally, particularly during the main wet season (June–September) and short rain season (February–April): From motor vehicles, (31.0 ± 2.6%) vs. (24.7 ± 1.2%); biomass burning, (21.5 ± 5%) vs. (14 ± 2%); and soil dust, (11 ± 6.4%) vs. (22.7 ± 8.4%), respectively, are amongst the three principal sources of ambient PM2.5 mass in the city. We suggest policy measures focusing on transportation, cleaner fuel or energy, waste management, and increasing awareness on the impact of air pollution on the public’s health.

Fugitive Emissions of Volatile Organic Compounds from a Tailings Pond in the Oil Sands Region of Alberta.

Tailings ponds in the oil sands (OS) region in Alberta, Canada, have been associated with fugitive emissions of volatile organic compounds (VOCs) and other pollutants to the atmosphere. However, the contribution of tailings ponds to the total fugitive emissions of VOCs from OS operations remains uncertain. To address this knowledge gap, a field study was conducted in the summer of 2017 at Suncor's Pond 2/3 to estimate emissions of a suite of pollutants including 68 VOCs using a combination of micrometeorological methods and measurements from a flux tower. The results indicate that in 2017, Pond 2/3 was an emission source of 3322 ± 727 tons of VOCs including alkanes, aromatics, and oxygenated and sulfur-containing organics. While the total VOC emissions were approximately a factor of 2 higher than those reported by Suncor, the individual VOC species emissions varied by up to a factor of 12. A chemical mass balance (CMB) receptor model was used to estimate the contribution of the tailings pond to VOC pollution events in a nearby First Nations and Metis community in Fort McKay. CMB results indicate that Suncor Pond 2/3 contributed up to 57% to the total mass of VOCs measured at Fort McKay, reinforcing the importance of accurate VOC emission estimation methods for tailings ponds.

Source apportionment of fine organic carbon at an urban site of Beijing using a chemical mass balance model

Abstract. Fine particles were sampled from 9 November to 11 December 2016 and 22 May to 24 June 2017 as part of the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) field campaigns in urban Beijing, China. Inorganic ions, trace elements, organic carbon (OC), elemental carbon (EC), and organic compounds, including biomarkers, hopanes, polycyclic aromatic hydrocarbons (PAHs), n-alkanes, and fatty acids, were determined for source apportionment in this study. Carbonaceous components contributed on average 47.2 % and 35.2 % of total reconstructed PM2.5 during the winter and summer campaigns, respectively. Secondary inorganic ions (sulfate, nitrate, ammonium; SNA) accounted for 35.0 % and 45.2 % of total PM2.5 in winter and summer. Other components including inorganic ions (K+, Na+, Cl−), geological minerals, and trace metals only contributed 13.2 % and 12.4 % of PM2.5 during the winter and summer campaigns. Fine OC was explained by seven primary sources (industrial and residential coal burning, biomass burning, gasoline and diesel vehicles, cooking, and vegetative detritus) based on a chemical mass balance (CMB) receptor model. It explained an average of 75.7 % and 56.1 % of fine OC in winter and summer, respectively. Other (unexplained) OC was compared with the secondary OC (SOC) estimated by the EC-tracer method, with correlation coefficients (R2) of 0.58 and 0.73 and slopes of 1.16 and 0.80 in winter and summer, respectively. This suggests that the unexplained OC by the CMB model was mostly associated with SOC. PM2.5 apportioned by the CMB model showed that the SNA and secondary organic matter were the two highest contributors to PM2.5. After these, coal combustion and biomass burning were also significant sources of PM2.5 in winter. The CMB results were also compared with results from the positive matrix factorization (PMF) analysis of co-located aerosol mass spectrometer (AMS) data. The CMB model was found to resolve more primary organic aerosol (OA) sources than AMS-PMF, but the latter could apportion secondary OA sources. The AMS-PMF results for major components, such as coal combustion OC and oxidized OC, correlated well with the results from the CMB model. However, discrepancies and poor agreements were found for other OC sources, such as biomass burning and cooking, some of which were not identified in AMS-PMF factors.

Influence of Highway Pavement on Metals in Road Dust: a Case Study in Houston, Texas

The elemental analysis of road dust can be used to determine the contribution of traffic-related metal emissions to the local environment. Previous studies have shown that brake wear, tire wear, and lead wheel weights release significant amounts of metals that lead to decreased biodiversity and degradation of urban streams. Highway pavement has not previously been considered as a source of metal inputs into the environment. To determine the extent that highway pavement contributes to metal loads in the local environment, road dust and pavement samples were collected for both asphalt and concrete pavement types on State Highway 59 in Houston, TX, USA. Samples were analyzed by ICP-MS to quantitate 9 metals: aluminum (Al), iron (Fe), vanadium (V), chromium (Cr), nickel (Ni), cobalt (Co), copper (Cu), zinc (Zn), and lead (Pb). A chemical mass balance receptor model that is commonly used for source apportionment of air pollution was applied to heavy metals in road dust, with brake dust, tire wear, wheel weights, and pavement materials as the emission sources. Pavement is shown to be the primary source of V, Cr, Ni, and Co in road dust, regardless of asphalt or concrete pavement type. Lead concentration in road dust is influenced by pavement type, with concrete pavement contributing approximately 45% of total lead in concrete road dust, while asphalt pavement only contributes 27% of total lead in asphalt road dust. Fly ash is the likely component of concrete pavement responsible for the higher contribution of Pb. An enrichment factor analysis indicates that Cr, Co, and V have low levels of enrichment compared to Cu, Zn, Pb, and Ni, indicating that pavement wear is generally a minor source of environmental metal contamination compared to brake and tire wear. Currently, brake and tire wear are the non-exhaust vehicle emissions of greatest concern; however, alternatives to fly ash should be considered to further reduce Pb and Ni concentrations in the environment.

Chemical Speciation and Source Apportionment of Fine Particulate Matter PM2.5 in Central Addis Ababa, Ethiopia

Background/Aim: The infrastructure developments, a growing population, and urbanization have led to growing air pollution challenges in Addis Ababa City. Previous investigations into the air pollution have not adequately addressed the sources of atmospheric particulate matter in the city. This study aims to identify the seasonal contributions of major sources of fine particulate matter (PM2.5) in city of Addis Ababa, Ethiopia.Methods: Composites of 24-hour average daily samples of fine particulate matter collected every 6th day from November 2015 to November 2016, were analyzed for chemical analysis including particle-phase organic traces for source apportionment, and were used in a chemical mass balance receptor model to estimate source contributions to fine particulate matter organic carbon (OC) and PM2.5 mass.Results: Vehicles are the predominant sources of OC and total PM2.5 mass, accounting for 28% of the PM2.5 mass. Vehicular sources, biomass-burning plus soil dust were found to make up two-third of the total PM2.5 mass concentration followed by sulfate, which accounted for 6.5%. The main wet season (June-September) show the highest mean monthly concentration of PM2.5, and hence, sources vary by seasonality during wet (June-September) and dry seasons (February-April): from motor vehicles, (31.0±2.6%) vs. (24.7±1.2%), from biomass burning, (21.5±5%) vs. (14±2%), and from soil dust, (11±6.4%) vs. (22.7±8.4%).Conclusions: These three components are the major sources of ambient PM2.5 in the City with clear seasonality. We suggest policy measures that focus on mass transportation, move towards cleaner energy sources, and increasing awareness on the health and environmental impacts of solid fuel use, including burning wastes and control locally sourced dust to curve air pollution and help improve the health of Addis Ababa city residents.Keywords: Air pollution; Ambient PM2.5; Source apportionment; EC/OC; Sulfate.

Incremental excess of PM components and sources between two neighbouring sites of Bogotá, Colombia

Air pollution in towns adjacent to and downwind of large cities can be similar or even higher than in the city itself. In the case of towns constituting the greater Bogotá area, with more than half a million inhabitants and strong industrial activity, little is known about the factors that affect their poor air quality. This work investigated the incremental excess of the composition and source contribution to PM2.5 in two sites near Bogotá (Soacha: 4°35'4.59" N, 74°13'11.62" W; and Mosquera: 4°42'9.75" N, 74°13'54.94" W), using the Chemical Mass Balance receptor model with organic molecular markers, and back trajectory analysis. Simultaneous sample collection was carried out for two-months. Organic matter was the major component of the PM2.5 mass (66 ± 14% and 61 ± 12%), while secondary inorganic ions (sulfate, nitrate, and ammonium) constituted 13 ± 8 % and 10 ± 2 %. The main anthropogenic sources contributing to PM2.5 at Soacha were wood combustion (23%), diesel vehicles (19%), and coal combustion at small facilities (11%). At Mosquera, they were gasoline vehicles (26%), diesel vehicles (19%), and coal combustion at small inefficient facilities (15%). The contribution of regional Secondary Organic Aerosol to PM2.5 was significant (19% and 15%), arriving mostly from the Orinoco basin but higher with air masses arriving from the Amazon rainforest. The regional contribution to secondary inorganic aerosols was higher with winds from the Magdalena Valley. The methods presented in this manuscript will be useful in other megacities and large cities to better manage impacts of local and regional air pollution sources.

Spatiotemporal Variation and Chemical Fingerprints of Marine Fine Particles (PM2.5) at the Matsu Islands in Northern Taiwan Strait

This study characterized the PM2.5 in terms of its spatial distribution, chemical composition, and seasonal/diurnal variation on the Matsu Islands in the northern Taiwan Strait. The PQ-200 samplers were employed to conduct simultaneous 24-h seasonal measurements of PM2.5 on four offshore islands, viz., Nankan Island (Site NK), Beigan Island (Site BG), Dongyin Island (Site DY), and Chiukung Island (Site CK) as well as 12-h diurnal measurements for both regular and intensive samplings, and 24-h episodic measurements at one of the locations (Site NK). Additionally, the chemical signatures of PM2.5 collected at six predominant local sources were established via chemical analysis, which coordinated as source profiles for chemical mass balance (CMB) receptor model to resolve the source apportionment of PM2.5. Of the four seasons, summer exhibited the lowest average PM2.5 concentration. In winter and spring, the level of PM2.5 significantly rose under the influences of Asian Northeastern Monsoons (ANEMs). Furthermore, the spatial distribution of PM2.5 across the islands showed a tendency to gradually decrease from the west to the east, with the Site NK always displaying the highest value regardless of the season. In general, the chemical composition primarily consisted of water-soluble ions (WSIs) owing to the abundancy of secondary inorganic aerosol (SIA) which accounted for 69.8% of the WSIs and 45.5% of the PM2.5. Crustal elements dominated the metallic components, although the content of trace metals increased in both concentrations and proportion during the ANEMs. Organic carbon (OC) dominated the carbonaceous content of PM2.5 in all seasons, with OC/EC values ranging from 1.64 to 3.78. During the episodes of poor air quality, the majority of incoming air masses followed a northern transport route (N-route) or an anticyclonic outflow route (AO-route) transported from the continent to the islands. The CMB receptor modeling results showed that the major sources of PM2.5 on the Matsu Islands were road dust, industrial boilers, secondary aerosols, vehicular exhausts, and sea salts. Overall, the PM2.5 pollution was originated from both local and remote sources, with 28~68% of the PM2.5 concentrations arising from long-range transport.

Chemical fingerprint and source apportionment of PM2.5 in highly polluted events of southern Taiwan.

To investigate the spatial distribution and diurnal variation of the chemical composition of PM2.5 pollution in an industrial city of southern Taiwan, 12-h PM2.5 was diurnally continuously collected simultaneously at the Kaoping Air Quality Zone (KAQZ) during one highly PM2.5-polluted episode. Water-soluble ions, metallic elements, carbonaceous contents, dicarboxylic acids, and anhydrosugars were analyzed to characterize the chemical fingerprint of PM2.5. Backward trajectory simulation and chemical mass balance (CMB) receptor modeling were applied to identify the potential sources of PM2.5 and their contributions. It showed that Chaozhou (rural area) accompanying the highest SORs and NORs suffered from the most severe PM2.5 pollution during the episode. Sulfate (SO42-) was probably formed by the atmospheric chemical reaction in the daytime, while NO3- processed at nighttime at the KAQZ. A homogeneous formation of NO3- occurredat Chaozhou. The concentrations of Zn, Pb, Fe, Cu, V, and Al, mainly emitted from anthropogenic sources, increased significantly at the KAQZ. The highest OC, SOC/OC, and DA/OCs at Daliao (industrial area) were attributed to the transformation of primary VOCs to secondary OC via photo-oxidation during the episode. Oxalic acid was mainly produced through photochemical reactions since a high correlation between oxalic acid and Ca2+ was observed at Nanzi (urban area) and Daliao during the episode. During the episode, PM2.5 mostly originated from local primary or secondary aerosol than long-range overseas transport. The dominant source was anthropogenic emissions, accounting for 67.1% and 70.4% of PM2.5 at Nanzi and Daliao, respectively. At Chaozhou, the contribution of anthropogenic emissions was the lowest (42.4%), but secondary aerosols had the highest contribution of 38.3% of PM2.5 among the three areas during the episode.

Representativeness of an air quality monitoring station for PM2.5 and source apportionment over a small urban domain

In this study, PM2.5 concentrations together with the water-soluble ionic compounds and total elements were simultaneously measured at 16 sites in the city of Varese located in Northern Italy within a domain of 2 × 2 km2. The center point of this domain consisted of an existing urban air quality monitoring station. The representativeness of the monitoring station for PM2.5 mass and chemical composition was estimated using a methodology relying on statistical/geostatistical analyses. Source apportionment by means of the Chemical Mass Balance (CMB) receptor model was also performed to evaluate the spatial distribution of source contribution throughout the domain. Very high soluble fraction was found for Cd, Sb, K and V, indicating the anthropogenic origin of those elements. The geostatistical analysis/mapping showed that the monitoring station captured the spatial variation of PM2.5 and most of the anthropogenic originated elements, e.g., As, Cd and V, whereas it did not captured the spatial patterns of the ones originated from both natural and anthropogenic sources, e.g., Na, Ni, Pb, K, Zn, Fe, Cr, and Ti. The CMB source contribution estimations in the monitoring station were at least 25% different from many sites of the domain for PM2.5. The significant spatial variation in concentrations and source contribution estimates showed that the monitoring station could not be considered representative for the air quality monitoring studies with exposure assessment and source apportionment purposes in Varese.

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB.

Inter-correlation of Chemical Compositions, Transport Routes, and Source Apportionment Results of Atmospheric PM2.5 in Southern Taiwan and the Northern Philippines

ABSTRACT This study investigated the inter-correlation of atmospheric PM2.5 between southern Taiwan and the northern Philippines. 24-hour samples of PM2.5 were simultaneously collected at two remote sites, Checheng (Taiwan) and Laoag (Philippines), during all four seasons. The water-soluble ions, metallic elements, carbonaceous content, and anhydrosugars in the PM2.5 were then analyzed to characterize the chemical fingerprint. Furthermore, principal component analysis, chemical mass balance (CMB) receptor modeling, and backward trajectory simulation were applied to resolve the source apportionment of PM2.5 at both of the sites. The results showed that Checheng and Laoag were highly influenced by polluted air masses transported long-range from the north, producing elevated PM2.5 concentrations during winter and spring. The water-soluble ions (WSIs) were abundant in secondary inorganic aerosols (SO42–, NO3–, and NH4+), which accounted for 34.1–76.0%. Crustal elements dominated the metallic content in the PM2.5, but the concentrations of trace elements originating from anthropogenic sources increased during the northwestern monsoon periods. More organic carbon (OC) than elemental carbon (EC) was found, with secondary OC (SOC) contributing approximately 23.9–38.9% to the former. Moreover, the level of levoglucosan highly correlated with those of K+ and OC, confirming that these three substances are key indicators of biomass burning. The two sites exhibited similar chemical compositions for PM2.5, indicating the possibility of transport between Checheng and Laoag, and a paired t-test obtained a p-value of 0.030 (p < 0.05), implying a potential inter-correlation for PM2.5 between southern Taiwan and the northern Philippines. The major sources of the PM2.5 were soil dust, mobile sources, sea salt, and biomass burning along the northerly transport routes during winter and spring. The contribution of anthropogenic sources (i.e., industrial boilers, waste incinerators, and secondary aerosols) to the PM2.5 frequently increased during winter and spring, unlike during summer, suggesting that the northerly transport of PM2.5 highly affected particulate air pollution at both of the sites.

Effects of the emergency control measures in Beijing on air quality improvement

Emergency air pollution control measures were successfully implemented in Beijing many times, such as during the Asia-Pacific Economic Cooperation (APEC) conference in 2014 and the Red Alert (RA) in 2015. Given that air quality was largely affected by wind speed, the efficiency of control measures and wind speed must be quantified to adjust the control measures under different wind speeds. During APEC and RA, source apportionment of organic carbon (OC) was conducted using chemical mass balance receptor model (CMB) with organic tracers to reveal PM2.5 source reductions caused by control measures. In the APEC control period, mobile sources contribution to organic carbon (OC) and dust contribution to PM2.5 decreased from 3.7 μg/m3 (63.7%) to 3.3 μg/m3 (38.8%) and from 5.1 μg/m3 (16.6%) to 3.8 μg/m3 (6.9%), respectively. For the RA day, coal combustion source contribution to OC decreased from 11.3 μg/m3 (53.2%) to 2.3 μg/m3 (6.2%), while mobile sources contribution changed from 8.9 μg/m3 (41.7%) to 14.7 μg/m3 (40.3%). These data suggest that the vehicle restriction rule was not truly implemented. Control measures should be announced in advance to achieve the expected result. In addition, the effectiveness of control measures would decrease with the increase of wind speed, and the critical wind speeds for sustaining an excellent PM2.5 level (35 μg/m3) and daily PM2.5 concentration standard of China (75 μg/m3) were determined by a power function as 11 km/h and 7 km/h, respectively. So wind speed should be considered before the selection of anthropogenic control measures.

Chemical characteristics, source apportionment, and regional transport of marine fine particles toward offshore islands near the coastline of northwestern Taiwan Strait

This study aims to investigate the spatiotemporal variation, chemical composition, and source apportionment of marine fine particles (PM2.5) as well as their regional transport toward the Matsu Islands located near the coastline of northwestern Taiwan Strait. Four offshore island sites located at the Matsu Islands were selected to conduct both regular and intensive sampling of marine PM2.5. Water-soluble ionic species, metallic elements, and carbonaceous contents were then analyzed to characterize the chemical characteristics of marine PM2.5. In order to identify the potential sources and their contributions to marine PM2.5, chemical mass balance (CMB) receptor model was employed along with the backward trajectory simulation to resolve the source apportionment of marine PM2.5 and to explore their transport routes in different seasons. The results showed that high PM2.5 concentrations were commonly observed during the northeastern monsoon periods. Additionally, marine PM2.5 concentration decreased from the west to the east with the highest PM2.5 at the Nankang Island and the lowest PM2.5 at the Donyin Island in all seasons, indicating an obvious concentration gradient of PM2.5 transported from the continental areas to the offshore islands. In terms of chemical characteristics of PM2.5, the most abundant water-soluble ions of PM2.5 were secondary inorganic aerosols (SO42−, NO3−, and NH4+) which accounted for 55–81% of water-soluble ions and 29–52% of marine PM2.5. The neutralization ratios of PM2.5 were always less than unity, indicating that NH4+ cannot solely neutralize nss-SO42+ and NO3− in marine PM2.5 at the Matsu Islands. Although crustal elements (Al, Ca, Fe, K, and Mg) dominated the metallic content of marine PM2.5, trace anthropogenic metals (Cd, As, Ni, and Cr) increased significantly during the northeastern monsoon periods, particularly in winter. Organic carbons (OCs) were always higher than elemental carbons (ECs), and the mass ratios of OC and EC were generally higher than 2.2 in all seasons, implying that PM2.5 was likely to be aged particles. During the poor air quality periods, major air mass transport routes were the northern transport and the anti-cyclonic circulation routes. Source apportionment results indicated that fugitive soil dusts and secondary aerosols were the major sources of marine PM2.5 at the Matsu Islands, while, in winter, biomass burning contributed up to 15% of marine PM2.5. This study revealed that cross-boundary transport accounted for 66~84% of PM2.5 at the Matsu Islands, suggesting that marine PM2.5 at the Matsu Islands has been highly influenced by anthropogenic emissions from neighboring Fuzhou City as well as long-range transport from Northeast Asia.