Positive Matrix Factorization Results Research Articles

Characteristics of Volatile Organic Compounds and Their Contribution to Secondary Organic Aerosols during the High O3 Period in a Central Industry City in China

High loads of fine particulate matter (PM2.5) and ozone (O3) pollution occurred frequently since early spring and led to an increasing contribution of secondary organic aerosols (SOA) in organic aerosols. However, the characteristics of precursor volatile organic compounds (VOCs) have rarely been studied. In this study, the continuous observation of VOCs was performed by an offline VOC monitoring system and gas chromatography-mass/flame ionization detector from 1 April 2020 to 31 July 2020; the characterization of VOCs and their contribution to SOA was explored. The results showed that during the observation period, the average mixing ratio of TVOCs was 42.6 ± 11.2 ppbv, and the major VOCs species were OVOCs, followed by alkanes, halocarbons, aromatics, alkenes and acetylene. When the west circulation pattern functioned, the value of aromatics increased, and the relation between PM2.5, O3 and VOCs increased when the high-pressure system controlled by anticyclone functioned. In combination with the results of positive matrix factorization, the main emission sources of ambient VOCs were complex, and the fuel combustion, industry-related emission, vehicle emission, biogenic emission and solvent volatilization accounted for 27.1%, 24.4%, 24.3%, 12.1% and 12.0%, respectively. Moreover, the industry-related emission contributed the greatest to the generation of SOA. This result indicated that the restrictions on aromatics during the industrial process are vital to reducing SOA formation.

Using EEM-PARAFAC to identify and trace the pollution sources of surface water with receptor models in Taihu Lake Basin, China

The identification and apportionment of the multiple pollution sources are essential and crucial for improving the effectiveness of surface water resources management. In this study, the surface water samples were collected from Taihu Lake Basin, and the optimal water quality parameters for the receptor models were selected firstly with multivariate statistical analyses. In order to identify the potential pollution sources in surface water, dissolved organic matter (DOM) was analyzed with the excitation-emission matrix coupled with parallel factor analysis (EEM-PARAFAC). Through the Pearson correlation analysis of water quality parameters and DOM components, the pollution sources were further verified, i.e., agricultural activities, domestic sewage, phytoplankton growth/terrestrial input and industrial sources. In addition, principal component analysis (PCA) combined with the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models were employed to quantify pollution sources. Compared with PCA-APCS-MLR model, PMF model resulted in higher performance on evaluation statistics and lower proportion of unexplained variability, thus showed more realistic and robust representation. The results of PMF showed that agricultural activities (42.08%) and domestic sewage (21.16%) were identified as the dominant pollution sources of surface water in the study area. This study highlights the effectiveness of EEM-PARAFAC in identifying the pollution sources, and the applicability of PMF in apportioning the contributions of each potential pollution source in surface water.

Spatial Distribution, Potential Risks and Source Identification of Heavy Metals in the Coastal Sediments of the Northern Beibu Gulf, South China Sea.

Thirty samples of surface sediments (0–5 cm) from the northern Beibu Gulf were analyzed to determine the spatial distribution, potential risks and sources of six heavy metals (Cr, Cu, Zn, As, Cd and Pb). The concentrations (mg/kg, dw) of Cr, Cu, Zn, As, Cd and Pb were 15.38 ± 6.06, 6.54 ± 3.23, 41.86 ± 17.03, 6.92 ± 2.75, 0.04 ± 0.02 and 17.13 ± 6.38, respectively. Higher levels of Cr, Cu, Cd and Zn were observed in the western part of the study area. According to the potential ecological risk indexes and sediment quality guidelines, the measured metals were assessed at low contamination levels, with Pb posing the largest ecological risks. The results of positive matrix factorization (PMF) indicated that Cr and Zn mainly originated from natural geological background sources, while Cu, As, Cd and Pb were influenced by anthropogenic sources such as atmospheric deposition and anthropogenic activities. These three sources contributed 60.4%, 28.1% and 11.5% of the heavy metals, respectively. In addition, further research should be conducted focusing on the general relationships between As and various controls in sediments of the northern Beibu Gulf.

Ecological and human health risk assessment of heavy metals based on their source apportionment in cropland soils around an e-waste dismantling site, Southeast China

An accurate understanding of soil heavy metal (HM) pollution characteristics and source apportionment, and a recognition of the major factors influencing ecological and human health risks (HHRs) are essential for soil HM pollution control and remediation. In this study, 212 surface soils (0–20 cm) and 15 profile soils (0–100 cm) were collected from cropland soils around an e-waste dismantling site in Taizhou city, Zhejiang Province, China. Spatial analysis was used to evaluate the pollution characteristics of HMs (Cd, Cu, Pb, Zn, Cr and Ni). Principal component analysis (PCA) and positive matrix factorization (PMF) were also conducted to quantify their source contributions. A modified source-oriented HHR assessment integrated source-oriented ecological risk and source-oriented HHR assessment was developed to describe the major factors that influenced HHR. Results showed that 94.81 %, 88.21 %, 36.79 % and 47.17 % of Cd, Cu, Pb and Zn, respectively, in surface soils exceeded their screening values in the soil environmental quality standard for agricultural soils (GB 15618–2018). Spatial analysis indicated that high values of Cd, Cu, Pb and Zn were distributed near the e-waste dismantling site. The results of PCA and PMF showed that the primary sources of HMs in the study area are e-waste dismantling activities, natural sources and atmospheric deposition, which contribute 27 %, 46 % and 27 % of HM pollutants, respectively. The results of source-oriented ecological risk and HHR assessment indicated that e-waste dismantling activities and natural sources were primary sources for ecological risk and HHR. However, source-oriented HHR assessment may underestimate the contribution of e-waste dismantling activities by ignoring HM pollution levels. The modified source-oriented HHR assessment highlights that e-waste dismantling activities were major factor that affect noncarcinogenic risk. This study could provide important data support for subsequent environmental remediation of soil HM pollution in cropland soils around e-waste dismantling sites.

Apportionment and Spatial Pattern Analysis of Soil Heavy Metal Pollution Sources Related to Industries of Concern in a County in Southwestern China.

Soil heavy metal pollution is frequent around areas with a high concentration of heavy industry enterprises. The integration of geostatistical and chemometric methods has been used to identify sources and the spatial patterns of soil heavy metals. Taking a county in southwestern China as an example, two subregions were analyzed. Subregion R1 mainly contained nonferrous mining, and subregion R2 was affected by smelting. Two factors (R1F1 and R1F2) associated with industry in R1 were extracted through positive matrix factorization (PMF) to obtain contributions to the soil As (64.62%), Cd (77.77%), Cu (53.10%), Pb (75.76%), Zn (59.59%), and Sb (32.66%); two factors (R2F1 and R2F2) also related to industry in R2 were extracted to obtain contributions to the As (53.35%), Cd (32.99%), Cu (53.10%), Pb (56.08%), Zn (67.61%), and Sb (42.79%). Combined with PMF results, cokriging (CK) was applied, and the z-score and root-mean square error were reduced by 11.04% on average due to the homology of heavy metals. Furthermore, a prevention distance of approximately 1800 m for the industries of concern was proposed based on locally weighted regression (LWR). It is concluded that it is necessary to define subregions for apportionment in area with different industries, and CK and LWR analyses could be used to analyze prevention distance.

Applications of dynamic simulation for source analysis of soil pollutants based on atmospheric diffusion and deposition model

Existing receptor-model-based source apportionment methods failed to derive source contributions to accumulation of soil heavy metals (SHMs). In this research, a dynamics-simulation-based source apportionment approach (DSSA) was developed by integrating mathematical models of source release, diffusion and deposition pathway, and receptor accumulation, to quantify accumulative contributions of SHMs. The case study was carried out in a complex industrialized region in southeast China to investigate pollution situation of SHMs (Zn, Pb, Ni, As, Cd, and Cr). The results showed that SHMs distributions were affected by seasonal variation and near-surface meteorology, which could be sequenced by correlation coefficient as temperature (0.968) > humidity (0.552) > precipitation (0.389) > wind speed (0.386). The source categories and corresponding contribution rates were identified as: i) battery plant to Zn (72.32%) and Pb (71.73%), ii) traffic to Ni (64.55%), iii) traffic and agriculture to Cd (43.26%, 41.63%), iv) agriculture to As (75.30%) and Cr (60.05%), which was similar to the results of positive matrix factorization (PMF). Furthermore, DSSA could illustrate SHMs migration process from source to receptor. The uncertainty analysis further proved the distinct advantages of DSSA. The results of this research could predict pollutant enrichment and could provide new perspective for environment and public health management.

Molecular distributions of dicarboxylic acids, oxocarboxylic acids, and α-dicarbonyls in aerosols over Tuoji Island in the Bohai Sea: Effects of East Asian continental outflow

Field measurements of the concentrations of dicarboxylic acids and related compounds (oxocarboxylic acids and α-dicarbonyls) in PM2.5 were conducted on a background island (Tuoji Island, China) over the Bohai Sea to better understand the effects of pollution downwind of the East Asian continental outflow. Air mass backward trajectory calculations demonstrated that the distributions of dicarboxylic acids and related compounds at Tuoji Island depended strongly on the East Asian continental outflow and correlated with the hours spent by an air parcel in the polluted boundary layer. Based on the data analysis, the sampling period was categorized into heavy aerosol pollution periods (HAPPs) and light aerosol pollution periods (LAPPs). The average concentration of dicarboxylic acids and related compounds during HAPPs (1280 ± 503 ng m−3) was approximately seven times higher than that during LAPPs (189 ± 90.8 ng m−3). The order of the major species of dicarboxylic acids and related compounds, ranked by concentrations, was oxalic acid (C2) > glyoxylic acid (ωC2) > succinic acid (C4) > terephthalic acid (tPh) during HAPPs, and C2 > malonic acid (C3) > ωC2 > C4 during LAPPs. Results of positive matrix factorization (PMF) showed that secondary sources were the dominant source of dicarboxylic acids and related compounds, with contributions of 38% and 57% during HAPPs and LAPPs, respectively. Primary sources of biomass burning and plastic waste burning were also significant during HAPPs (contributions: 26% and 20%, respectively) and LAPPs (contributions: 18% and 12%, respectively). The concentration ratios of C3 to C4 (C3/C4), C2 to C4 (C2/C4), fumaric acid to maleic acid (F/M), phthalic acid to azelaic acid (Ph/C9), adipic acid to azelaic acid (C6/C9), and the positive correlations of Ph or tPh with dicarboxylic acids, and levoglucosan with dicarboxylic acids and related compounds further support the PMF results.

Identifying sources and transport routes of heavy metals in soil with different land uses around a smelting site by GIS based PCA and PMF

Identifying sources and transport routes of heavy metals in soil is necessary for pollution control. This study integrated principal component analysis (PCA), positive matrix factorization (PMF), and geographic information system (GIS) mapping to identify the sources, transport routes, and apportion heavy metals in soil based on land uses around a smelting site. The results revealed that the mean concentrations of As, Hg, Cd, Pb, Zn, and Cu in the soil exceeded their background values except for Cr, Mn, and Fe, which were slightly higher. According to the mean Igeo values, the soils were most polluted with As, Cd, Pb, and Cu, followed a decreasing order of grassland (1.71, 2.38, 2.10, and 1.73) > agricultural land (0.632, 2.32, 1.19, and 1.08) > forestland (0.255, 0.952, 0.654, and 0.148). Smelter emissions and soil parent materials were the primary sources of heavy metals. The PCA and PMF factor hotspots visualized by GIS were mostly distributed within the smelting site, slag and wastewater runoff areas, and in the dominant wind direction. The GIS based PCA and PMF results confirmed that As, Cd, Pb, Cu, and Zn were transported mainly by surface runoff and atmospheric deposition, while Hg was mostly from atmospheric deposition. Grassland and agricultural land soils received heavy metals from surface runoff and atmospheric deposition, while forestland soils only received from atmospheric deposition. The integrated approach was useful in identifying the sources, transport routes, and contributions of the heavy metals among different land uses, thereby assisting policymakers in understanding the sources and transport routes of heavy metals in the soil around smelting areas.

Seasonal variation and source identification of atmospheric speciated mercury in an industrial harbor area in East Asia

In this study, the pollution characteristics, spatiotemporal variation, and potential sources of atmospheric speciated mercury (ASM) in an industrial harbor area were explored. Gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle-bound mercury (PBM) were sampled by a self-designed manual system at three harbor sites in four seasons. The yearly average concentrations of GEM, GOM, and PBM were 6.7 ± 2.0 ng/m3, 244 ± 70 pg/m3, and 410 ± 105 pg/m3, respectively. The seasonal average ASM concentration was in the order of: winter > fall > spring > summer. In terms of species, GEM dominated ASM, while reactive mercury (RM = GOM + PBM) accounted for 6.0–15.7%of ASM, implying that ASM was governed by anthropogenic sources in the harbor area. The highest ASM concentrations were observed at Site Zhonghe (ZH), which is mainly influenced by both ship exhausts and industrial emissions, and positively correlated with CO, NOx, and SO2. In particular, GOM was positively correlated with O3, and negatively correlated with air temperature and relative humidity, showing high impact from atmospheric photochemical reactions. Air masses transporting westerly in spring were mainly from ship exhausts. In summer, air masses transporting from the south were from utility power plants and machinery exhausts. In fall and winter, air masses were transported mainly from the north, blowing by the long-range transport of polluted air masses originated from the north. Both principal component analysis and positive matrix factorization results indicate that coal burning, industrial emissions, and vehicular exhausts are the main contributors to ASM. Site Zhongdao (ZD) was close to the bulk carrier loading and unloading zones and was highly influenced by mobile sources, while Site ZH was mainly influenced by the neighboring industrial complex.

Eutrophication and sediment\u2013water exchange of total petroleum hydrocarbons and heavy metals of Hashilan wetland, a national heritage in NW Iran

The heavy metal(loid)s concentrations in water and sediments were analyzed in the Hashilan wetland to assess the spatial distribution, pollution status, fate, partitioning, and ecological risk and also to identify the heavy metal(loid)s sources in sediments using PMF (Positive Matrix Factorization) and APCs-MLR (absolute principal component score-multiple linear regression) receptor models. According to the pollution indices, (Ni, Cu, Cr, Mo), and (Zn, Cr, and Cu) are considered the most important pollutants in sediments and water, respectively. Ni, Cr, and Cu are the main contributors to ecological risks in sediments of some stations. The potential ecological risk assessment proposed low ecological risk in water of the study area. Higher distribution coefficient (Kp) values of Ni, Cr, Mn, Cu, Co, Pb, As, and Zn indicated the majority of these heavy metals present in the sediments; whereas, the majority of Cd concentration occurs in water. PMF and APCs-MLR results indicated the natural sources were the main factors affecting the concentrations of Ni, Cr, Zn, Al, Co, Fe, Pb, As, Cd and somewhat Cu. Mixed natural and agricultural activities are the main sources of Mo, and somewhat Cu. According to the results, there is low pollution of TPH (total petroleum hydrocarbons) in the sediment samples. Also, phosphate (PO42−) and nitrate (NO3−) concentrations were below the recommended permissible limits at all sampling sites except the S8 station for NO3−.

Measurement report: Spatiotemporal and policy-related variations of PM<sub>2.5</sub> composition and sources during 2015–2019 at multiple sites in a Chinese megacity

Abstract. A thorough understanding of the relationship between urbanization and PM2.5 (fine particulate matter with aerodynamic diameter less than 2.5 µm) variation is crucial for researchers and policymakers to study health effects and improve air quality. In this study, we selected a rapidly developing Chinese megacity, Chengdu, as the study area to investigate the spatiotemporal and policy-related variations of PM2.5 composition and sources based on long-term observation at multiple sites. A total of 836 samples were collected from 19 sites in winter 2015–2019. According to the specific characteristics, 19 sampling sites were assigned to three layers. Layer 1 was the most urbanized area and referred to the core zone of Chengdu, layer 2 was located in the outer circle of layer 1, and layer 3 belonged to the outermost zone with the lowest urbanization level. The average PM2.5 concentrations for 5 years were in the order of layer 2 (133 µg m−3) > layer 1 (126 µg m−3) > layer 3 (121 µg m−3). Spatial clustering of the chemical composition at the sampling sites was conducted for each year. The PM2.5 composition of layer 3 in 2019 was found to be similar to that of the other layers 2 or 3 years ago, implying that urbanization levels had a strong effect on air quality. During the sampling period, a decreasing trend was observed for the annual average concentration of PM2.5, especially at sampling sites in layer 1, where the stricter control policies were implemented. The SO42-/NO3- mass ratio at most sites exceeded 1 in 2015 but dropped to less than 1 since 2016, reflecting decreasing coal combustion and increasing traffic impacts in Chengdu, and these values can be further supported by temporal variations of the SO42- and NO3- concentrations. The positive matrix factorization (PMF) model was applied to quantify PM2.5 sources. A total of five sources were identified, with average contributions of 15.5 % (traffic emissions), 19.7 % (coal and biomass combustion), 8.8 % (industrial emissions), 39.7 % (secondary particles), and 16.2 % (resuspended dust). From 2015 to 2019, a dramatic decline was observed in the average percentage contributions of coal and biomass combustion, but the traffic emission source showed an increasing trend. For spatial variations, the high coefficient of variation (CV) values of coal and biomass combustion and industrial emissions indicated their higher spatial difference in Chengdu. High contributions of resuspended dust occurred at sites with intensive construction activities, such as subway and airport construction. Combining the PMF results, we developed the source-weighted potential source contribution function (SWPSCF) method for source localization. This new method highlighted the influences of spatial distribution for source contributions, and the effectiveness of the SWPSCF method was evaluated.

Mass spectral characterization of secondary organic aerosol from urban cooking and vehicular sources

Abstract. In the present work, we conducted experiments of secondary organic aerosol (SOA) formation from urban cooking and vehicular sources to characterize the mass spectral features of primary organic aerosol (POA) and SOA using an high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). Our results showed that the cooking styles have a greater impact on aged COA (cooking organic aerosol) mass spectra than oxidation conditions. However, the oxidation conditions affect the aged HOA (hydrocarbon-like OA) spectra more significantly than vehicle operating conditions. In our study, we use mass spectra similarity analysis and positive matrix factorization (PMF) analysis to establish the POA and SOA mass spectra of these two sources. These mass spectra are used as source constraints in a multilinear engine (ME-2) model to apportion the OA (organic aerosol) sources in the atmosphere. Compared with the traditional ambient PMF results, the improved ME-2 model can better quantify the contribution of POA and SOA from cooking and vehicular sources. Our work, for the first time, establishes the vehicle and cooking SOA source profiles, and can be further used in the OA source apportionment in the ambient atmosphere.

Insight into PM<sub>2.5</sub> sources by applying positive matrix factorization (PMF) at urban and rural sites of Beijing

Abstract. This study presents the source apportionment of PM2.5 performed by positive matrix factorization (PMF) on data presented here which were collected at urban (Institute of Atmospheric Physics – IAP) and rural (Pinggu – PG) sites in Beijing as part of the Atmospheric Pollution and Human Health in a Chinese megacity (APHH-Beijing) field campaigns. The campaigns were carried out from 9 November to 11 December 2016 and from 22 May to 24 June 2017. The PMF analysis included both organic and inorganic species, and a seven-factor output provided the most reasonable solution for the PM2.5 source apportionment. These factors are interpreted as traffic emissions, biomass burning, road dust, soil dust, coal combustion, oil combustion, and secondary inorganics. Major contributors to PM2.5 mass were secondary inorganics (IAP: 22 %; PG: 24 %), biomass burning (IAP: 36 %; PG: 30 %), and coal combustion (IAP: 20 %; PG: 21 %) sources during the winter period at both sites. Secondary inorganics (48 %), road dust (20 %), and coal combustion (17 %) showed the highest contribution during summer at PG, while PM2.5 particles were mainly composed of soil dust (35 %) and secondary inorganics (40 %) at IAP. Despite this, factors that were resolved based on metal signatures were not fully resolved and indicate a mixing of two or more sources. PMF results were also compared with sources resolved from another receptor model (i.e. chemical mass balance – CMB) and PMF performed on other measurements (i.e. online and offline aerosol mass spectrometry, AMS) and showed good agreement for some but not all sources. The biomass burning factor in PMF may contain aged aerosols as a good correlation was observed between biomass burning and oxygenated fractions (r2= 0.6–0.7) from AMS. The PMF failed to resolve some sources identified by the CMB and AMS and appears to overestimate the dust sources. A comparison with earlier PMF source apportionment studies from the Beijing area highlights the very divergent findings from application of this method.

Measurement report: Source apportionment of volatile organic compounds at the remote high-altitude Maïdo observatory

Abstract. We present a source apportionment study of a near-continuous 2-year dataset of volatile organic compounds (VOCs), recorded between October 2017 and November 2019 with a quadrupole-based high-sensitivity proton-transfer-reaction mass-spectrometry (hs-PTR-MS) instrument deployed at the Maïdo observatory (21.1∘ S, 55.4∘ E, 2160 m altitude). The observatory is located on La Réunion island in the southwest Indian Ocean. We discuss seasonal and diel profiles of six key VOC species unequivocally linked to specific sources – acetonitrile (CH3CN), isoprene (C5H8), isoprene oxidation products (Iox), benzene (C6H6), C8-aromatic compounds (C8H10), and dimethyl sulfide (DMS). The data are analyzed using the positive matrix factorization (PMF) method and back-trajectory calculations based on the Lagrangian mesoscale transport model FLEXPART-AROME to identify the impact of different sources on air masses sampled at the observatory. As opposed to the biomass burning tracer CH3CN, which does not exhibit a typical diel pattern consistently throughout the dataset, we identify pronounced diel profiles with a daytime maximum for the biogenic (C5H8 and Iox) and anthropogenic (C6H6, C8H10) tracers. The marine tracer DMS generally displays a daytime maximum except for the austral winter when the difference between daytime and nighttime mixing ratios vanishes. Four factors were identified by the PMF: background/biomass burning, anthropogenic, primary biogenic, and secondary biogenic. Despite human activity being concentrated in a few coastal areas, the PMF results indicate that the anthropogenic source factor is the dominant contributor to the VOC load (38 %), followed by the background/biomass burning source factor originating in the free troposphere (33 %), and by the primary (15 %) and secondary biogenic (14 %) source factors. FLEXPART-AROME simulations showed that the observatory was most sensitive to anthropogenic emissions west of Maïdo while the strongest biogenic contributions coincided with air masses passing over the northeastern part of La Réunion. At night, the observatory is often located in the free troposphere, while during the day, the measurements are influenced by mesoscale sources. Interquartile ranges of nighttime 30 min average mixing ratios of methanol (CH3OH), CH3CN, acetaldehyde (CH3CHO), formic acid (HCOOH), acetone (CH3COCH3), acetic acid (CH3COOH), and methyl ethyl ketone (MEK), representative for the atmospheric composition of the free troposphere, were found to be 525–887, 79–110, 61–101, 172–335, 259–379, 64–164, and 11–21 pptv, respectively.

Chemical characterization of PM2.5 from region highly impacted by hailstorms in South America.

The chemical composition of particulate material plays an important role in the atmosphere, providing cloud and ice nuclei for storm development. This study aims to evaluate and infer the sources of ions, metals, and metalloids in the fine atmospheric particulate matter (PM2.5) from triple border Paraná, Santa Catarina (Brazil), and northeastern Argentina, which is among those with the highest hail incidence in the world. Among the ions, the concentrations presented the following sequence in decreasing order: [Formula: see text]> K+> [Formula: see text]> [Formula: see text]> Ca2+> Cl-> Na+> Mg2+. Regarding the metals and metalloid concentrations, the order was of S > Si > Al > Fe > P > Ti, Cr, Cu, and Zn > Br > Mn, and Ni. The main sources, supported by positive matrix factorization results, are soil and agricultural activities, as well as vehicular emissions due to the agricultural machinery and the displacement of residents. Besides, the influence of aerosols frombiomass burning and industrial activitieswas observed, possibly comefrom long-distance transport. The composition of PM2.5 presents one or more elements considered present ice nuclei (IN)activity, such as Al, Mn, Cu, Co, Ni, and V (inform of oxides), corroborating with other studies, also, with high hail incidence. However, further studies are needed to verify the role of aerosol characteristics in the formation of IN and, consequently, hail.

Establishment and verification of anthropogenic volatile organic compound emission inventory in a typical coal resource-based city

A few studies on volatile organic compound (VOC) emission inventories in coal resource-based cities have been reported, and previous emission inventories lacked verification. Herein, using Yangquan as a case study, emission factor (EF) method and “(tracer ratio) TR - positive matrix factorization (PMF)” combined method based on atmospheric data were used to establish and verify the VOC emission inventory in coal resource-based cities, respectively. The total VOC emissions in Yangquan were 9283.2 t [-40.0%, 62.1%] in 2018, with industrial processes being the major contributors. Alkanes (35.8%), aromatics (25.0%), and alkenes (19.8%) were the main compounds in the emission inventory. The verification results for both species emission and source structure were in agreement, indicating the accuracy of VOC emission inventory based on EF method to a certain extent. However, for some species (ethane, propane, benzene, and acetylene), the EF method indicated emissions lower than those obtained from the TR results. Furthermore, the summer-time emission contribution from fossil fuel combustion indicated by the EF method (23.4%) was lower than that obtained from the PMF results (38.4%). Overall, these discrepancies could be attributed to the absence of a coal gangue source in the EF method. The verification results determined the accuracy of the VOC emission inventory and identified existing problems in the estimation of the VOC emission inventory in coal resource-based cities. In particular, not accounting for the coal gangue emissions may result in an underestimation of VOC emissions in coal resource-based cities. Thus, coal gangue emissions should be considered in future research.

Source identification of fine and coarse aerosol during smog episodes in Debrecen, Hungary

In this study, the identification of PM sources during smog episodes in Debrecen is presented. The smog episodes were characterized by the high concentration of PM10, and were linked to a thermal inversion period in November 2011 in Hungary, when an anticyclone occurred across Europe. The sources of PM2.5 and PMcoarse were identified by positive matrix factorization (PMF). Additionally, the source locations were explored by using the conditional probability function (CPF). A numerical weather prediction model (WRF) was also applied to evaluate the air pollution situation. In order to meet the goals of the study, the elemental composition with high (2-h) time resolution, the elemental size distribution, and the planetary boundary layer were examined in an urban background site of downtown Debrecen, Hungary. Our results highlight the importance of biomass burning (39%) and traffic (31%) as the major contributors to PM2.5. The main tracers of these sources (K, Cl, Pb, Zn) appeared mainly in droplet mode (0.5–1 μm). The major sources of PMcoarse fraction were soil (32%), and dust attributed to tramline construction (30%). The combination of PMF and CPF results revealed that the PM10 concentrations were affected by sources within the urban agglomeration of Debrecen (city centre and suburban areas) under the given meteorological conditions. This conclusion is further validated by the fact that the diurnal variation of the identified source contributions presented a distinct pattern that depended strongly on the daily activities of the city’s residents (compared to the usually unstructured diurnals of transported sources).

Human health risk assessment of arsenic and trace metals in atmospheric dust of Arak industrial area, Iran.

In order to investigate concentrations, variability, sources, and human health risk of trace elements, 38 atmospheric dust samples were collected around Arak industrial area. The average concentrations of Cd~Zn, As, and Pb were 3.3, 2.5, and 2.4 times higher than the corresponding geochemical background, respectively, while concentrations of Co, Cu, Ni, and Hg were lower. Based on geo-accumulation index (Igeo), trace elements were classified between practically uncontaminated to moderate contaminated levels. The potential risk (RI) map showed that about 86% of the study area for all trace elements was in the moderate risk class. According to the results of positive matrix factorization (PMF) model, four factors were extracted for trace elements, including industrial sources, natural sources, mining, and transportation, that contributed about 30, 27, 26, and 17% to pollution of the area, respectively. The carcinogenic risks for inhalation exposure to Cd, As, Ni, and Co were lower than the permissible risk limit than the EPA recommend (10-6), indicating an acceptable level of risk. Results of the health risk evaluation indicated that the non-carcinogenic health risk (i.e., hazard quotient, HQ) for children and adults decrease following: As > Pb > Ni > Zn > Cd > Cu > Co > Hg. When excluding As, the hazard index (HI) was lower than the safe level (HI < 1) for all the trace elements, whereas HQ values of As for children and adults were 17.1 and 1.6, respectively, indicating a potential risk for children. In sum, several remedial actions to eliminate or to reduce the dust pollution are urgently required in the industrial area of Arak.

The application of geostatistical analysis and receptor model for the spatial distribution and sources of potentially toxic elements in soils.

This study aimed to identify the spatial patterns of potentially toxic elements (PTEs), including the spatial distribution, spatial autocorrelation, and risk probability, and to quantify the sources of PTEs, to provide guidelines for soil management. Spatial distributions and probabilities of PTEs were determined by empirical Bayesian kriging (EBK), while spatial autocorrelation was estimated by Moran's I. Positive matrix factorization (PMF) was adopted for the quantitative source contributions of PTEs. More than 64.6% of Co, Cr, Mn, and Ni were derived from geogenic sources, with high regions and high-high clusters both correlated to sandstone. Thus, it can be deduced that parent materials dominated the spatial patterns of these PTEs. In addition, some hotspots were situated in urban areas, and the influence of human activities on these four PTEs should be considered. Industry-traffic discharge and parent materials both influenced As, Cu, Pb, and Zn. Nonetheless, the spatial patterns of As, Cu, Pb, and Zn were formed by anthropogenic emissions since hotspots and high-high clusters were contiguously situated in urban areas. 58.5% of Hg originated from atmospheric deposition related to industrial emissions, and 47.2% of Cd was controlled by the application of chemical fertilizers. High levels of Hg and Cd mainly corresponded with industrial sites and cultivated land, suggesting that industrial and geoponic production played major roles in the generation of spatial patterns for Hg and Cd, respectively. Furthermore, the Cd and Hg posed a severe risk to soils, with a high probability to surpass 1.5 times the backgrounds. The EBK, Moran's I, and PMF results showed that all ten PTEs were enriched to some degree due to natural or anthropogenic factors. The results of geostatistical analysis and the receptor model can be mutually verified, indicating the reliability of these methods.

A combined method for the source apportionment of sediment organic carbon in rivers

Organic carbon sources apportionment in river sediments is crucial to the output management of organic carbon. We conducted a source apportionment of sediment organic carbon in four rivers in Shaanxi Province, China, with a novel method that combined environmental scanning electron microscopy and energy dispersive X-ray spectrometry (ESEM-EDAX), principal component analysis (PCA), 16S rRNA sequencing, microbial community metabolic prediction, and positive matrix factorization (PMF). According to the ESEM-EDAX results, the sources of light fraction organic carbon (LFOC) were the vegetation residues and the organic carbon adsorbed on them; and the source of heavy fraction organic carbon (HFOC) was organic carbon wrapped in particles. Moreover, 16S rRNA sequencing results of LFOC and HFOC concerning microbes demonstrated that LFOC was mainly composed of carbohydrate, cellulose, and alky-aromatic compounds, and that carbohydrate with high molecular weight might be a part of HFOC. Based on the results of microbial community metabolic prediction, PCA, and PMF, we found dissolved organic carbon (DOC) was mainly from lipopolysaccharide biosynthesis, apoptosis, and decomposition of carboxylic acids. And it might be mainly composed of lipopolysaccharide, carbohydrates, and organic acid with low molecular. To reflect the appearance of a specific DOC type, three biomarkers were proposed based on the microbial relative abundance and specificity. This research proposed a new method to trace the sources of organic carbon and established microbial biomarkers for the appearance of specific DOC, which would promote the understanding of organic carbon sources into microbes. Thus, this research provides new perspectives in the source apportionment and the life cycle of organic carbon in rivers.