Positive Matrix Factorization Receptor Model Research Articles

Modification of Hybrid Receptor Model for Atmospheric Fine Particles (PM2.5) in 2020 Daejeon, Korea, Using an ACERWT Model

Hybrid receptor models overestimate the contribution of background areas (no specific emission sources), like the Yellow Sea in Korea. This study aimed to improve model performances using Advanced Concentration Emission and Retention Time Weighted Trajectory (ACERWT). ACERWT was combined with a positive matrix factorization (PMF), back trajectory, and Regional Emission Inventory in Asia (REAS). The PMF receptor model used one year of data from Korea’s Central Air Environment Research Center. In the PMF receptor model, eight sources (dust/soil, secondary nitrate, biomass burning, vehicles, secondary sulfate, industry, coal combustion and sea salt) influenced PM2.5 pollution at the receptor site (Daejeon, Korea). Secondary sulfate was the most dominant source, followed by secondary nitrate and vehicle sources. ACERWT results showed high contributions from China, Japan, and Korean regions, while the contribution from the Yellow Sea was significantly lower. Several regions, such as the eastern and south-eastern areas of China, the southern area of Taiwan, the western area of Tokyo, and the central area of Korea, showed high contributions due to large-scale emission facilities and industrial complexes. In this study, the ACERWT model significantly improved its performance regarding regional contributions to PM2.5 pollution at the receptor site.

Chemical composition and origins of PM2.5 in Chiang Mai (Thailand) by integrated source apportionment and potential source areas

Upper Southeast Asian region lacks source apportionment studies of air pollution, particularly PM2.5, despite its persistence. This study aims to quantify PM2.5 source contributions and identify their potential source areas in urban Chiang Mai (Thailand) as a representative of the region, using the positive matrix factorization (PMF) receptor model and the potential source contribution function (PSCF). A total of 51 samples were collected and analyzed for water-soluble ions, organic carbon, elemental carbon, carbohydrates, carboxylates, and metals. An average PM2.5 concentration of 116 ± 35 and 35 ± 6 μg m-3 was found during smoke-haze (SH) and non-smoke-haze (NSH) periods, respectively. The average PMF-resolved contributions to PM2.5 in the SH period were the following: biomass burning (59.3 μg m-3) > secondary sulfate (26.2 μg m-3) > dust (16.1 μg m-3) > traffic (14.6 μg m-3). In contrast, those in the NSH period were the following: traffic (27.2 μg m-3) > dust (5.9 μg m-3) > biomass burning (2.5 μg m-3). The PSCF revealed that biomass burning has the main potential source area along the Thai-Myanmar border, while the secondary sulfate largely originated in Indian continent. Local pollution from traffic and dust contributed consistently but was minor in comparison. The source composition exhibited major seasonal change because of the lack of biomass burning and transboundary pollution in the wet season, where exhaust and non-exhaust traffic emissions became the main contributors. These findings offer insights into how regional PM2.5 affects the local pollution in the urban atmosphere.

Deciphering groundwater pollution in the Lower Anayari Catchment: insights from using δ2H, δ18O, PMF, and APCS-MLR receptor model.

This research provides a comprehensive analysis of groundwater pollution in the Lower Anayari Catchment (LAC) through δ2H and δ18O isotopic analysis, along with positive matrix factorization (PMF) and PCS-MLR receptor models. Forty groundwater samples were collected from hand-dug wells and equipped boreholes across the LAC. Flame photometry for Na+ and K+, complexometric titration for Ca2+, ion chromatography for Cl-, F-, NO3-, SO42-, and PO43-, and atomic absorption spectrometry for Mg2+, Fe, Pb, Cd, As, and Ni were analytical techniques/instruments employed. In regard to cations, Na+ has the highest average concentration of 63.0 mg/L, while Mg2+ has the lowest at 2.58 mg/L. Concerning the anions and nutrients, Cl- has the highest mean concentration of 18.7 mg/L, and Fl- has the lowest at 0.50 mg/L. Metalloids were detected in trace amount with Fe displaying the highest mean concentration of 0.077 mg/L whereas Cd and As recorded lowest (0.001 mg/L). The average values for groundwater δ18O and δ2H were - 3.64‰ and - 20.7‰, respectively; the average values for rainwater isotopic composition were - 3.41‰ for δ18O and - 17.4‰ for δ2H. It is believed that natural geological features, particularly biotite granitoid and volcanic flow/subvolcanic rocks from the Birimian Supergroup, significantly influence groundwater mineralisation. Additionally, the impact of anthropogenic activities on water quality, with urban development and agricultural practices, may be attributed to increasing levels of certain contaminants such as Fe, Ni, NO3-, and PO43-. This research contributes to the broader field of hydrological study and provides practical implications for managing and conserving water resources in similar contexts. The innovative combination of isotopic and statistical analyses sets a new standard for future studies in groundwater quality assessment, emphasising the need for comprehensive approaches that consider both geological characteristics and human impacts for sustainable water resource management.

Identification of Causes of Air Pollution in a Specific Industrial Part of the Czech City of Ostrava in Central Europe

This contribution deals with the assessment of air pollution caused by atmospheric aerosol particulate matter fraction PM10 and benzo[a]pyrene (BaP) in the urban agglomeration of Ostrava, located in the Czech Republic in Central Europe. The motivation for this research was the need to identify the sources of air pollution in the area, particularly in locations where the contribution of different sources to concentrations of pollutants of concern has not been elucidated yet. In this study, source apportionment in the vicinity of the industrial hot spot was performed by statistically evaluating measured pollutant concentrations as a function of meteorological variables and using the Positive Matrix Factorization (PMF) receptor model. A significant methodological innovation and improvement over previous assessments was the higher monitoring resolution of benzo[a]pyrene, with samples collected at three-hour intervals instead of the standard 24 h collection period. The key findings indicate that in the cold part of the year, secondary particles—specifically sulfates and ammonium nitrates—were responsible for the most significant portion of PM10 air pollution throughout the area of interest. The contribution of these particles ranged from one-third to two-fifths of the total concentration, except at the industrial site of Ostrava–Radvanice (TORE), where they accounted for approximately one-fifth of the measured pollution concentration level. Emissions from individual household heating were identified as the main source of this type of pollution. With regards to benzo[a]pyrene air pollution, this study found that in the whole area of interest, except for the Ostrava–Radvanice site, it mainly originated from individual heating with coal (90%). In contrast, at the Ostrava–Radvanice site, two-thirds of the benzo[a]pyrene pollution came from the premises of Liberty Ostrava a.s., primarily from coke production, and less than one-third came from local domestic heating. This study also determined the spatial extent of the occurrence of extremely high benzo[a]pyrene concentrations (above 5 ng/m3), which are estimated to affect nearly 10,000 inhabitants. The results confirm that the data from the TORE station are only representative of its immediate surroundings and are not applicable to the assessment of air quality and causes of air pollution in the whole city of Ostrava or the urban area of Ostrava–Radvanice as a whole. After years of research, these findings provide the Ministry of the Environment of the Czech Republic and the government with an accurate basis for implementing measures to address the identified pollution sources in the area of interest. The success of this study was made possible by the adoption of a more detailed sampling approach, which involved a resolution of 3 h instead of 24 h. This methodological improvement is a significant finding and will be useful for future source apportionment studies.

High-time resolution PM2.5 source apportionment assisted by spectrum-based characteristics analysis

Characteristics extraction and anomaly analysis based on frequency spectrum can provide crucial support for source apportionment of PM2.5 pollution. In this study, an effective source apportionment framework combining the Fast Fourier Transform (FFT)- and Continuous Wavelet Transform (CWT)-based spectral analyses and Positive Matrix Factorization (PMF) receptor model is developed for spectrum characteristics extraction and source contribution assessment. The developed framework is applied to Beijing during the winter heating period with 1-h time resolution. The spectrum characteristics of anomaly frequency, location, duration and intensity of PM2.5 pollution can be captured to gain an in-depth understanding of source-oriented information and provide necessary indicators for reliable PMF source apportionment. The combined analysis demonstrates that the secondary inorganic aerosols make relatively high contributions (50.59 %) to PM2.5 pollution during the winter heating period in Beijing, followed by biomass burning, vehicle emission, coal combustion, road dust, industrial process and firework emission sources accounting for 15.01 %, 11.00 %, 10.70 %, 5.31 %, 3.88 %, and 3.51 %, respectively. The source apportionment result suggests that combining frequency spectrum characteristics with source apportionment can provide consistent rationales for understanding the temporal evolution of PM2.5 pollution, identifying the potential source types and quantifying the related contributions.

[Contamination Characteristics and Source Apportionment of Potentially Toxic Elements in Soil around the Coal-fired Power Plant Using APCS-MLR and PMF Models].

To investigate the characteristics and sources of potentially toxic elemental contamination in soils around the coal-fired power plant, we selected the soil around the Jingyuan power plant as the object of investigation. Thirty-six and 27 soil samples collected from the urban and farmland areas were analyzed for PTEs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn), respectively. We employed the geoaccumulation index, single-factor pollution index, and improved Nemerow integrated pollution index to evaluate the contamination characteristics of PTEs, and combined correlation analysis with absolute principal component scores-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) receptor models were used to quantitatively analyze the pollution sources of PTEs. The results revealed that all the average concentrations of other soil metals around Jingyuan power plant exceeded their corresponding background values of Gansu Province, except for As, in agricultural soil, and Hg exhibited significant spatial non-homogeneity, and thus it was visibly affected by anthropogenic activities. The values of the single-factor pollution and geoaccumulation indices indicated that the soils of both sites were mainly contaminated with Cd, Cr, Ni, and Pb, and Hg contamination was reflected in wide areas for both sites. Additionally, the results of the improved Nemerow index demonstrated that the investigated soil was between moderately contaminated and heavily contaminated. Further, the comprehensive pollution degree of urban soil was higher than that of agricultural soil. Moreover, source apportionment revealed that PTEs in urban soil were derived from mixed sources of traffic activities and coal combustion, mixed sources of traffic activities and industrial and mining activities, and atmospheric deposition from industrial activities, with contribution rates of APCS-MLR of 35.2%, 25.1%, and 23.4%, respectively. The PMF contribution rates were 40.2%, 12.4%, and 47.7%, respectively. PTEs in agricultural soil were from one mixture source of industrial, mining, agricultural, and traffic sources, and the other mixed source was of traffic activities and coal combustion, with a APCS-MLR contribution rate of 40.3% and 35.9% and a contribution rate of PMF of 36.2% and 18.0%, respectively. In addition, another mixed source of agricultural activities and coal combustion was obtained using the PMF model, with a contribution rate of 48.5%.

Bottom-up and top-down approaches for estimating road dust emission and correlating it with a receptor model results over a typical urban atmosphere of Indo Gangetic Plain

To investigate the emission and concentration of PM10 and PM2.5-related road dust over Agra, a typical semi-arid urban atmosphere of the Indo Gangetic Plain (IGP), a fine-resolution emission inventory and receptor modeling-based source apportionment was undertaken for the year 2019. On-road, the silt load of Agra (7–55 g/m2 of the road) was found to be 10 to 50 times higher than that reported in advanced countries. The silt load over Agra varied widely depending on road conditions, long-range transport, and land-use pattern. Depending on the silt load, land-use and fleet averaged weight, the annual emission factor for road dust was estimated as 14.3 ± 3.2 (PM10) and 4.4 ± 1.4 (PM2.5) gm/VKT (vehicle kilometer travel). PM10 emission of road dust alone contributed 80 % (29 ± 6 t/d) to the total emission of PM10 and 68 % (9 ± 3 t/d) to PM2.5 of the city with the maximum emission being in industrial areas. Chemical analysis of ambient PM10, PM2.5, and road dust samples showed that the road dust was enriched with geogenic components and was in good agreement with the road dust profile identified from the positive matrix factorization receptor model. The model estimated contribution of road dust (summer and winter combined) to PM10 and PM2.5 ambient air levels was 28 % (67 μg/m3) and 23 % (27 μg/m3) respectively. Summer showed a larger road dust contribution than winter due to strong surface wind and dry road conditions. Results have revealed that the emissions and concentrations of road dust are closely interrelated with road conditions (silt load), land-use patterns, VKT, weight of the vehicles, and micrometeorological conditions. The large road dust emission in IGP cities requires better road conditions and traffic management to curb the emission.

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.

Receptor model-based source-specific health risks of toxic metal(loid)s in coal basin-induced agricultural soil in northwest Bangladesh.

Toxic metal(loid)s (TMLs) in agricultural soils cause detrimental effects on ecosystem and human health. Therefore, source-specific health risk apportionment is very crucial for the prevention and control of TMLs in agricultural soils. In this study, 149 surface soil samples were taken from a coal mining region in northwest Bangladesh and analyzed for 12 TMLs (Pb, Cd, Ni, Cr, Mn, Fe, Co, Zn, Cu, As, Se, and Hg). Positive matrix factorization (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models were employed to quantify the pollution sources of soil TMLs. Both models identified five possible sources of pollution: agrochemical practice, industrial emissions, coal-power-plant, geogenic source, and atmospheric deposition, while the contribution rates of each source were calculated as 28.2%, 17.2%, 19.3%, 19% and 16.3% in APCS-MLR, 22.2%, 13.4%, 24.3%, 15.1% and 25.1% in PMF, respectively. Agrochemical practice was the major source of non-carcinogenic risk (NCR) (adults: 32.37%, children: 31.54%), while atmospheric deposition was the highest source of carcinogenic risk (CR) (adults: 48.83%, children: 50.11%). NCR and CR values for adults were slightly higher than for children. However, the trends in NCR and CR between children and adults were similar. As a result, among the sources of pollution, agrochemical practices and atmospheric deposition have been identified as the primary sources of soil TMLs, so prevention and control strategies should be applied primarily for these pollution sources in order to protect human health.

[Pollution Characteristics and Source Analysis of Heavy Metals in Soils in Yellow River Cultural Park Based on APCS-MLR and PMF Receptor Model].

With open spaces and good ecological environments, urban parks have become the first choice for the leisure and entertainment of many people. Therefore, the quality of park soil environments has gradually attracted the extensive attention of scholars. In this study, we take the Yellow River Cultural Park, a typical human disturbance area in the lower reaches of the Yellow River, as the research area to discuss the characteristics and sources of heavy metal pollution in the soil. Thirty-three soil surface samples were collected from the Yellow River Cultural Park, and the contents of seven heavy metals (Cr, Ni, Cu, Zn, Cd, Pb, and As) were determined using an inductively coupled plasma emission spectrometer (ICP-AES) and an inductively coupled plasma mass spectrometer (ICP-MS). The geo-accumulation index and geo-statistics method were used. Meanwhile, the absolute factor analysis-multiple linear regression (APCS-MLR) receptor model and positive matrix factorization (PMF) analysis model were employed to reveal the sources of soil heavy metals. The results showed that the average contents of heavy metals (Cd, Zn, Cu, Pb, and As) in the surface soil of the study area were 4.62, 1.78, 1.41, 1.08, and 1.03 times higher than the background values of soil elements in the tidal soil area of the lower reaches of the Yellow River, respectively. Except for Zn, the contents of other elements were lower than the corresponding values of soil elements in different regions along the Yellow River Basin. Among the seven heavy metal elements, the coefficients of variation of Cd and As were greater than 50%, showing obvious spatial variability. The decreasing trend of the accumulation index of the seven elements was Cd>Zn>Cu>Ni>Pb>As=Cr, and the element Cd belonged to the middle pollution category, which was obviously accumulated in the surface soil. The spatial distribution of heavy metals in the soil differed:the high contents of Cr, Cu, and Ni were distributed in the southwest and northeast, and the high-value areas of Cd and Pb were consistent with the areas of human activity intensity. The high-value areas of Zn and As were located in the center of lacustrine sediments. The combined results of the APCS-MLR and PMF models suggested that the first pollution source of soil heavy metal elements in the Yellow River Cultural Park could have been a natural source, the second pollution source may have been a transportation source, and the third source of pollution was judged as a mixed source. Human activities such as transportation sources and mixed sources were the main sources of heavy metal soil pollution, and Cr, Cu, and Ni were affected by natural factors. The contribution rates of APCS-MLR were 46.67%, 24.11%, 16.12%, and 13.10%, respectively, and the contribution rates of PMF were 35.50%, 35.48%, and 29.02%, respectively. This research can provide a basis for improving the ecological environment quality of the park and improving the health level of the population and can also provide support for the ecological environment risk management and comprehensive management along the Yellow River.

An ephemeral increase in organic carbon, ion ratios, and heavy metal-containing fine particles was screened in a maritime demarcation zone between North and South Korea

Due to its location in a politically and militarily sensitive area as a maritime demarcation between North and South Korea, increasing attention has been given to the chemical and physical changes in atmospheric aerosols on Baengnyeong Island. To observe modifications in the concentrations and components of basic air pollutants, a Korean State Air Quality Monitoring Superstation was established on the island. The monitoring data obtained from the superstation revealed a significant, short-lived increase in mass concentration of SO2, NO2, and nitrate, organic carbon, and heavy metals (especially Pb) in ambient PM2.5, as well as a gradual decrease in mass concentration of PM10, PM2.5, and sulfate in PM2.5 during September 24–27, 2012. Building upon this data, we conducted source appointment of PM2.5 using the positive matrix factorization receptor model (PMF) and identified six major sources: sea-spray aerosols (29.8%), mobile and biogenic aerosols (24.3%), secondary aerosols (20.0%), gunshot residue (11.1%), mineral dust (8.6%), and oil combustion (6.3%). To examine the physicochemical properties of atmospheric particles, 16 samples collected during that period were investigated using a quantitative energy-dispersive electron probe X-ray microanalysis (ED-EPMA). We analyzed the morphology, mixing state, elemental constituents, and relative elemental atomic concentrations of 2545 individual particles from the aforementioned six sources, with a focus on particles containing Ca, organic carbon, and Pb. The complementary use of PMF and ED-EPMA confirmed that an episode of air pollution, likely resulting from a military exercise or activity, occurred from September 25 to 26 on or near Baengnyeong Island, leading to an increase in aerosols from gunshot residue and oil combustion. The significant increase in the relative number abundance of organic carbon and heavy metal-containing particles precisely matched their mass concentrations from the monitoring data. These findings imply that multiple methods for bulk and single-particle analysis are necessary when addressing important issues related to source appointment of atmospheric particulate matter.

Contributions of primary emissions and secondary formation to nitrated aromatic compounds in themountain background region of Southeast China

Abstract. As a major component of brown carbon (BrC), nitrated aromatic compounds (NACs) have a significant role in the atmosphere's ability to absorb light. However, the sources and major influencing factors of NACs in the mountain background atmosphere are mostly lacking. Based on a thorough field investigation of NACs from fine particle samples taken in 2014 and 2015 at the peak of Mt. Wuyi (1139 m.a.s.l.), the current work discussed the seasonal fluctuations in their composition, their sources, and the important influencing factors. The total abundance of nine quantifiable NACs increased significantly in the winter (3.9 ± 1.5 ng m−3) and autumn (2.1 ± 0.94 ng m−3), whereas it decreased in the spring (1.3 ± 0.75 ng m−3) and summer (0.97 ± 0.36 ng m−3). According to the results of structural equation modeling, the majority of NACs (93 %) were influenced by biomass, coal, and petroleum combustion over the entire year. This work identified the origins of NACs with applying the positive matrix factorization receptor model. The five major source factors were biomass burning, coal combustion, secondary formation by nitration reaction, secondary formation by photochemical reaction, and other sources. Among them, biomass burning and coal combustion played an important role, especially in the wintertime, with a contribution of more than 50 %. Meanwhile, contributions from secondary formation were significant in these remote areas, which were mainly by photochemical reaction in the summertime and nitration reaction in the wintertime. Further analysis indicated that the formation of NACs was comparatively sensitive to NO2 under low-NOx conditions, suggesting that NACs would become significant in the aerosol characteristics when nitrate concentrations decreased as a result of emission reduction measures.

[Pollution Characteristics and Source Apportionment of Heavy Metals in Farmland Soils Around the Gangue Heap of Coal Mine Based on APCS-MLR and PMF Receptor Model].

To analyze the pollution characteristics and source apportionment of heavy metal pollution in soil of farmland surrounding the Gangue Heap of Coal Mine in Nanchuan, Chongqing, the Nemerow pollution index and Muller index were used. Meanwhile, to investigate the sources and contribution rate of heavy metals in the soil, absolute principal component score-multiple linear regression receptor modeling (APCS-MLR) and positive matrix factorization (PMF) were employed. The results showed higher amounts of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn in the downstream area than those in the upstream area, and only Cu, Ni, and Zn showed significantly higher amounts in the downstream area than those in upstream area (P<0.05). The comprehensive Nemerow pollution index was as follows:downstream area (1.22)>upstream area (0.95), and the degree of heavy metal pollution was:Cd>Cu>Hg, As, Pb, Cr, Ni, and Zn. The Muller pollution index showed:Cd>As>Cu=Hg>Ni>Zn=Cr>Pb. The pollution source analysis showed that Cu, Ni, and Zn were mainly affected by mining activities such as long-term accumulation of the gangue heap of coal mine, with the contribution rates of APCS-MLR being 49.8%, 94.5%, and 73.2%, respectively. Additionally, PMF contribution rates were 62.8%, 62.2%, and 63.1%, respectively. Cd, Hg, and As were mainly affected by agricultural activities and transportation activities, with APCS-MLR contribution rates of 49.8%, 94.5%, and 73.2% and PMF contribution rates of 62.8%, 62.2%, and 63.1%, respectively. Further, Pb and Cr were mainly affected by natural factors, with APCS-MLR contribution rates of 66.4% and 94.7% and PMF contribution rates of 42.7% and 47.7%, respectively. The results of source analysis were basically consistent between the APCS-MLR and PMF receptor models.

Source apportionment of VOCs and ozone formation potential and transport in Chengdu, China

The meteorological conditions and monitoring data of ozone and its precursors (NOx, VOCs) in Chengdu during summer (June–August 2019) were analyzed. The average concentration of VOCs in Chengdu during the summer is 36.63 ± 12.92 ppbv. The daily time series variation of ozone concentration presented unimodal type distribution, reaching the peak in the afternoon (4:00–6:00 p.m.). The heavy ozone pollution was basically accompanied by meteorological conditions of high temperature, low humidity, and low wind speed. The source apportionment of VOCs was conducted by using the positive matrix factorization (PMF) receptor model. The industrial sources, vehicle emission and fuel evaporation were identified as the main sources of VOCs, contributing 26.43%, 21.97% and 17.63% to VOCs respectively. The ozone formation potential (OFP) values indicated that the key active species were aromatics and alkenes represented by m/p-xylene and ethylene. The ridge line of the EKMA curve showed that the VOCs/NOx ratio was about 9.5, hence the emission reduction ratio of VOCs should be much higher than that of NOx in the summer aiming for ozone prevention and control work. The results of backward trajectory clustering, potential source contribution function (PSCF), and concentration-weighted trajectory (CWT) showed that ozone in Chengdu was affected by both local and regional sources in the summer. The potential source regions of ozone are mainly come from local urban areas and eastern and southern Sichuan cities.

Characteristics of summertime ambient volatile organic compounds in Beijing: Composition, source apportionment, and chemical reactivity

Comprehensive observations of ambient volatile organic compounds (VOCs) were conducted in Beijing (the capital of China) in the summer of 2018 with high temporal resolution, and the compositions, sources, ozone formation potential (OFP), and secondary organic aerosol formation potential (SOAP) were examined. A high mixing ratio of total VOCs (TVOCs) (26.6 ± 9.5 ppbv) was observed in this study, which was similar to that obtained in 2014 and 2016 in Beijing. The dominant components were alkanes (51.1% of TVOCs), alkenes (24.1%), and aromatics (19.5%). According to backward trajectories and potential source contribution function analysis, medium-distance transport from the south of the sampling region contributed more than 50% of the trajectories, and the potential source regions were mainly located in Shandong and Hebei Provinces. The seven sources identified by the positive matrix factorization receptor model included natural/liquified petroleum gas (NG/LPG) usage and gasoline evaporation (21.68%), solvent usage (20.14%), combustion sources (16.07%), industrial emissions (15.24%), diesel exhaust (14.89%), gasoline exhaust (6.05%), and biological emissions (5.92%). Alkenes (50.05%) and aromatics (36.20%) contributed the most to the total OFP, whereas aromatics contributed more than 90.00% to the total SOAP. The source with the greatest contribution to OFP and SOAP was solvent usage, the contribution of which exceeded 50%. The results of this study are favorable for the development of control strategies for VOCs and for reducing the formation of photochemical smog and secondary particulate matter.

Heavy metal fraction, pollution, and source-oriented risk assessment in biofilms on a river system polluted by mining activities

The Lanping Pb–Zn mine is the largest source of Pb and Zn ores in China, thus posing a great threat to local ecosystems and human health. A total of seven heavy metals (Zn, Pb, Ni, Cu, Cr, Cd, and As) in the Bijiang River near the Pb–Zn mine were measured in winter and summer to assess their spatial–temporal enrichment, ecological risk, and source-oriented health risk in periphytic biofilms. Positive matrix factorization (PMF) receptor model and clustering analysis were used to quantitatively identify pollution sources. The results of PMF were then imported into the health risk assessment to further determine the carcinogenic and noncarcinogenic risks of various pollution sources. The results indicated distinct seasonal patterns in metal concentrations, with much higher concentrations in winter. Sites near the Pb–Zn mine tailing reservoir exhibited higher metal contamination levels than other sites. A strong correlation between the enrichment factor and the levels of nonresidual fraction suggested that anthropogenic inputs were the main source of these metals. Mining industries (Cd, Zn, and Pb), natural sources (As, Ni, and Cu), and agricultural activities (Cr) were the primary sources of heavy metal pollution in biofilms, accounting for 44.43%, 33.32%, and 22.26% of the total metal accumulation, respectively. Moreover, the carcinogenic and noncarcinogenic risks via dermal contact of the studied elements in biofilms were typically acceptable. Notably, as concentration was the main factor influencing these risks in children and adults. This study provides evidence that natural epilithic periphyton may be a potential metal biomonitor in aquatic systems and provide supporting information for effective source regulation.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China.

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Metal and metalloid sources apportionment in soil of two major agroecosystems of southern China.

Apportioning the sources of metals/metalloids is a critical step toward soil quality protection and ecological restoration. The objective of this study was to identify the potential sources of contamination of As, Cd, Cr, Cu, Hg, Mn, Pb, and Zn, and determine the contribution rates of each source, to rice and sugarcane agroecosystems of southwestern Guangxi, southern China. We collected a total of 300 soil samples at a former lead-zinc mine and at two reference sites, 6 and 60km away from the mine, sampling both agroecosystems at each site. Overall, the positive matrix factorization (PMF) receptor model revealed that in rice paddies at the mine site, mining activities had the highest contribution (60.7% of all examined metals/metalloids), followed by irrigation (25.8%), and agrochemical application (13.5%). At the close reference site, agrochemical application contributed 42.8%, followed by irrigation (22.7%), natural sources (17.4%), and mining activities (17.2%). At the far reference site, agrochemical application was predominant (40.6%), followed by irrigation (32.5%), and natural sources (26.9%). In comparison, at the mine site and the close reference site in sugarcane ecosystems, agrochemical application was predominant (50.1% and 57.4%, respectively), followed by mining activities (49.9% and 42.6%). At the far reference site, agrochemical application contributed 51.2%, followed by natural sources (48.8%). Therefore, the PMF model indicated that the optimal solution was four or three sources per site for rice paddies, but only two sources per site for sugarcane, suggesting that sources of metal/metalloid contamination were more complicated in rice paddy than in sugarcane agroecosystems.

Pollution Source Apportionment of River Tributary Based on PMF Receptor Model and Water Quality Remote Sensing in Xinjian River, China

Accurately identifying the source and controlling the total amount of pollutants are the basis for achieving regulation of pollution sources, which is critical for the prevention and control of surface water pollution. For this purpose, this study used the Xinjian River in Jinyun County, Lishui City, Zhejiang Province, China, as a case study to explore whether and how the tributary inflow impacts the downstream water quality. The main pollution sources in the upstream, midstream, and downstream of the Xinjian River were apportioned using the Positive Matrix Factorization (PMF) model based on the water quality data from four sample stations from January 2018 to September 2022. According to the unmatched factor in different sections, it is plausible to infer that the TN and TP are mainly caused by the tributaries. To enhance the reliability of pollution source apportionment based on the receptor model, a series of remote sensing images with high resolution were used to derive the water quality concentrations to present the spatial distribution and reveal the long-term trend of the local water environment. It is anticipated that the apportionment results could be of great assistance to local authorities for the control and management of pollution, as well as the protection of riverine water quality.

Variations of aminiums in fine particles at a suburban site in Guangzhou, China: Importance of anthropogenic and natural emissions

Amines are important nitrogen-containing compounds in fine particles (PM2.5) in the atmosphere. Observations are necessary for in-depth understanding on the characteristics, sources and atmospheric processes of aminiums. In this study, the observation of ten C1–C4 aminiums in PM2.5 was conducted in January and March of 2021 in suburban Guangzhou. The concentration and composition of aminiums showed significant differences between the pollution episodes and non-episode periods. Seasonal difference was also observed between winter and spring. The influence of meteorological factors (i.e., wind speed, atmospheric pressure, temperature and relative humidity) was investigated. The variations of aminiums were also affected by different sources. Anthropogenic sources were suggested to be major contributor to aminiums in the pollution episodes, while biological sources were important sources to aminiums in the non-episode periods, especially in spring. Positive matrix factorization receptor model was applied to investigate the source contributions, and four major sources were identified. The results show that vehicular emission, industrial production, biological emission and soil/dust were the major sources of aminiums. This study emphasizes the importance of source contribution and meteorological conditions on the variations of aminiums, which provides further understanding of organic nitrogen in the atmosphere.