An inter-comparison of PM10 source apportionment using PCA and PMF receptor models in three European sites

Investigation of groundwater quality in the Southern Coast of the Black Sea: application of computational health risk assessment in Giresun, Türkiye

Potentially toxic elements (PTEs), especially arsenic in drinking water, pose significant global health risks, including cancer. This study evaluates the groundwater quality in Giresun province on the Black Sea coast of Türkiye by analyzing twelve groundwater resources. The mean concentrations of macronutrients (mg/L) were: Ca (10.53 ± 6.63), Na (6.81 ± 3.47), Mg (3.39 ± 2.27), and K (2.05 ± 1.10). The mean levels of PTEs (µg/L) were: Al (40.02 ± 15.45), Fe (17.65 ± 14.35), Zn (5.63 ± 2.59), V (4.74 ± 5.85), Cu (1.57 ± 0.81), Mn (1.02 ± 0.76), As (0.93 ± 0.73), Cr (0.75 ± 0.57), Ni (0.41 ± 0.18), Pb (0.36 ± 0.23), and Cd (0.10 ± 0.05). All PTE levels complied with WHO drinking water safety guidelines, and overall water quality was excellent. The heavy metal evaluation index (HEI < 10) and heavy metal pollution index (HPI < 45) indicate low pollution levels across all stations. Irrigation water quality was largely adequate, as shown by the magnesium hazard (MH), sodium adsorption ratio (SAR), Na%, and Kelly's ratio (KR). The total hazard index (THI) values consistently remained below 1, indicating no non-carcinogenic health risks. However, at station 10 (city center), the cancer risk (CR) for adults due to arsenic was slightly above the threshold (1.44E-04). Using principal component analysis (PCA), positive matrix factorization (PMF), and geographic information system (GIS) mapping, the study determined that most PTEs originated from natural geological formations or a combination of natural and human sources, with minimal impact from human activities. These findings highlight the safety and reliability of the groundwater sources studied, emphasizing their potential as a long-term, safe water supply for nearby populations.

Seasonal toxicity of urban road dust in runoff process-studies in Poland

Urban road dust (URD) is one of the most important non-point sources of pollution in agglomerations. The aim of this study was to assess the seasonal toxic effects of URD runoff in two regions of Poland. The concentrations of elements in URD and leachate were studied. The impact of pollutants in URD runoff on water organisms was evaluated using Daphtoxkit F and Rotoxkit F (LC50). The acute toxicity tests for crustaceans and rotifers were selected as the response of these taxa reflects the impact on zooplankton, a key component of aquatic ecosystem and the basis of most food webs. The concentrations of elements were found to vary depending on the site, although URD samples collected in Katowice agglomeration (Upper Silesia) had higher values of elements (Mn, Cu, Zn, As) compared to Wrocław (Lower Silesia). The concentrations of Mn, Zn, As, Cr, and Mg in water-soluble fraction of URD were higher in summer and winter in the Upper Silesia region due to rainwater runoff resulting from traffic, industries, post-industrial waste, and the presence of old heating systems. When comparing the content of elements in the water-soluble fraction between seasons, Zn, As, Cr, and Al concentrations were slightly higher in winter. The highest mortality of Daphnia magna and Brachiouns calyciflorus was observed in URD from both agglomerations in winter. However, the mortality is likely due to the concentration of elements or/and the coexistence of an unknown compound or a synergistic effect of the studied elements. This study highlights the alarming seasonal sources of elements in URD runoff, which will directly enter the food chain and affect the entire ecosystem, and human health.

Aerosol source apportionment uncertainty linked to the choice of input chemical components

For a Positive Matrix Factorization (PMF) aerosol source apportionment (SA) studies there is no standard procedure to select the most appropriate chemical components to be included in the input dataset for a given site typology, nor specific recommendations in this direction. However, these choices are crucial for the final SA outputs not only in terms of number of sources identified but also, and consequently, in the source contributions estimates. In fact, PMF tends to reproduce most of PM mass measured independently and introduced as a total variable in the input data, regardless of the percentage of PM mass which has been chemically characterized, so that the lack of some specific source tracers (e.g. levoglucosan) can potentially affect the results of the whole source apportionment study. The present study elaborates further on the same concept, evaluating quantitatively the impact of lacking specific sources' tracers on the whole source apportionment, both in terms of identified sources and source contributions. This work aims to provide first recommendations on the most suitable and critical components to be included in PMF analyses in order to reduce PMF output uncertainty as much as possible, and better represent the most commons PM sources observed in many sites in Western countries. To this aim, we performed three sensitivity analyses on three different datasets across EU, including extended sets of organic tracers, in order to cover different types of urban conditions (Mediterranean, Continental, and Alpine), source types, and PM fractions. Our findings reveal that the vehicle exhaust source resulted to be less sensitive to the choice of analytes, although source contributions estimates can deviate significantly up to 44 %. On the other hand, for the detection of the non-exhaust one is clearly necessary to analyze specific inorganic elements. The choice of not analysing non-polar organics likely causes the loss of separation of exhaust and non-exhaust factors, thus obtaining a unique road traffic source, which provokes a significant bias of total contribution. Levoglucosan was, in most cases, crucial to identify biomass burning contributions in Milan and in Barcelona, in spite of the presence of PAHs in Barcelona, while for the case of Grenoble, even discarding levoglucosan, the presence of PAHs allowed identifying the BB factor. Modifying the rest of analytes provoke a systematic underestimation of biomass burning source contributions. SIA factors resulted to be generally overestimated with respect to the base case analysis, also in the case that ions were not included in the PMF analysis. Trace elements were crucial to identify shipping emissions (V and Ni) and industrial sources (Pb, Ni, Br, Zn, Mn, Cd and As). When changing the rest of input variables, the uncertainty was narrow for shipping but large for industrial processes. Major and trace elements were also crucial to identify the mineral/soil factor at all cities. Biogenic SOA and Anthropogenic SOA factors were sensitive to the presence of their molecular tracers, since the availability of OC alone is unable to separate a SOA factor. Arabitol and sorbitol were crucial to detecting fungal spores while odd number of higher alkanes (C27 to C31) for plant debris.

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.

Pollution characteristics, associated risks, and possible sources of heavy metals in road dust collected from different areas of a metropolis in Vietnam

Road dust samples were collected from different areas in Ho Chi Minh City (HCMC)-the largest city in Vietnam to explore pollution characteristics, ecological and human health risks, and sources of heavy metals (HMs). Results revealed the level of HMs found in the samples from residential and industrial zones of HCMC in the order of Mn > Zn > Cu > Cr > Pb > Ni > Co > As > Cd, Zn > Mn > Cu > Cr > Pb > Ni > Co > As > Cd. Due to the high enrichment of Cu, Zn in residential areas and Cu, Pb, Zn in industrial areas, the HM contamination in these areas remained moderate to severe. The findings also revealed a rising trend in the level of HMs in road dust from the east to the west of HCMC, and a heavy metal contamination hotspot in the west. In addition, industrial areas were more contaminated with HMs, posing greater associated risks than residential areas. Children living in urban areas of HCMC were found to be exposed to unacceptable health risks. Meanwhile, adults living in industrial areas face intolerable cancer risk. Among the nine HMs, Cd, Pb, and Cu posed the greatest ecological risk, while Cr and As were the main culprits behind health risks. HMs in road dust might derive from non-exhaust vehicular emissions, crustal materials, and industrial activities. The results suggested that industrial areas to the west of HCMC should focus more on reducing and controlling severe pollution of HMs.

An in-depth investigation of the influence of sample size on PCA-MLR, PMF, and FA-NNC source apportionment results

The mechanism by which parameters influence the source apportionment results of receptor models is not well understood. Three mature receptor models, namely, principal component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF) and factor analysis with nonnegative constraints (FA-NNC), were comparatively employed for source apportionment of 16 polycyclic aromatic hydrocarbons in 30 street dust samples. The results indicated that the FA-NNC and PMF models produced results with a higher degree of similarity than the results obtained with the PCA-MLR model. Moreover, when the sample size was gradually decreased, similar source profiles were extracted that were consistent with results obtained from all samples. However, the overall contribution rates were not as stable as the source profiles. The PCA-MLR results remained the most stable in both aspects. FA-NNC and PMF performed better in regards to the stability of contribution rates and source profiles, respectively. Improvements in the goodness of fit of overall and individual pollutants were always accompanied by a decrease in the relevance among the variables, indicating that while the model simulation effect was improved, the credibility of the results decreased. Thus, finding an appropriate number of sample size is more appropriate than simply involving too many samples in source apportionment models.

Monitoring and apportioning sources of indoor air quality using low-cost particulate matter sensors

Air quality is one of the most important factors in public health. While outdoor air quality is widely studied, the indoor environment has been less scrutinised, even though time spent indoors is typically much greater than outdoors. The emergence of low-cost sensors can help assess indoor air quality. This study provides a new methodology, utilizing low-cost sensors and source apportionment techniques, to understand the relative importance of indoor and outdoor air pollution sources upon indoor air quality. The methodology is tested with three sensors placed in different rooms inside an exemplar house (bedroom, kitchen and office) and one outdoors. When the family was present, the bedroom had the highest average concentrations for PM_2.5 and PM₁₀ (3.9 ± 6.8 ug/m³ and 9.6 ± 12.7 μg/m³ respectively), due to the activities undertaken there and the presence of softer furniture and carpeting. The kitchen, while presenting the lowest PM concentrations for both size ranges (2.8 ± 5.9 ug/m³ and 4.2 ± 6.9 μg/m³ respectively), presented the highest PM spikes, especially during cooking times. Increased ventilation in the office resulted in the highest PM₁ concentration (1.6 ± 1.9 μg/m³), highlighting the strong effect of infiltration of outdoor air for the smallest particles. Source apportionment, via positive matrix factorisation (PMF), showed that up to 95 % of the PM₁ was found to be of outdoor sources in all the rooms. This effect was reduced as particle size increased, with outdoor sources contributing >65 % of the PM_2.5, and up to 50 % of the PM₁₀, depending on the room studied. The new approach to elucidate the contributions of different sources to total indoor air pollution exposure, described in this paper, is easily scalable and translatable to different indoor locations.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability, and traffic pollution has become a large drag on the sustainable developments of future metropolises. Here, combined with the large volume of real-time monitoring data, we propose a deep learning model, iDeepAir, to predict surface-level PM_2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality. Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355 µg/m³ to 12.283 µg/m³ compared with other models. And identifies the ranking of major factors, local meteorological conditions have become a nonnegligible factor. Layer-wise relevance propagation (LRP) is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_2.5 concentration in various regions of Shanghai. Meanwhile, As the strict and effective industrial emission reduction measurements implementing in China, the contribution of urban traffic to PM_2.5 formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03% in 2011 to 24.37% in 2017 in Shanghai, and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction. We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_2.5 about 8.45% by 2030 gradually. These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control, and eventually benefit people's lives and high-quality sustainable developments of cities.

Prenatal exposure to tailpipe and non-tailpipe tracers of particulate matter pollution and autism spectrum disorders

BACKGROUND

Traffic-related air pollution exposure is associated with increased risk of autism spectrum disorder (ASD). It is unknown whether carbonaceous material from vehicular tailpipe emissions or redox-active non-tailpipe metals, eg. from tire and brake wear, are responsible. We assessed ASD associations with fine particulate matter (PM_2.5) tracers of tailpipe (elemental carbon [EC] and organic carbon [OC]) and non-tailpipe (copper [Cu]; iron [Fe] and manganese [Mn]) sources during pregnancy in a large cohort.

METHODS

This retrospective cohort study included 318,750 children born in Kaiser Permanente Southern California (KPSC) hospitals during 2001-2014, followed until age 5. ASD cases were identified by ICD codes. Monthly estimates of PM_2.5 and PM_2.5 constituents EC, OC, Cu, Fe, and Mn with 4 km spatial resolution were obtained from a source-oriented chemical transport model. These exposures and NO₂ were assigned to each maternal address during pregnancy, and associations with ASD were assessed using Cox regression models adjusted for covariates. PM constituent effect estimates were adjusted for PM_2.5 and NO₂ to assess independent effects. To distinguish ASD risk associated with non-tailpipe from tailpipe sources, the associations with Cu, Fe, and Mn were adjusted for EC and OC, and vice versa.

RESULTS

There were 4559 children diagnosed with ASD. In single-pollutant models, increased ASD risk was associated with gestational exposures to tracers of both tailpipe and non-tailpipe emissions. The ASD hazard ratios (HRs) per inter-quartile increment of exposure) for EC, OC, Cu, Fe, and Mn were 1.11 (95% CI: 1.06-1.16), 1.09 (95% CI: 1.04-1.15), 1.09 (95% CI: 1.04-1.13), 1.14 (95% CI: 1.09-1.20), and 1.17 (95% CI: 1.12-1.22), respectively. Estimated effects of Cu, Fe, and Mn (reflecting non-tailpipe sources) were largely unchanged in two-pollutant models adjusting for PM_2.5, NO₂, EC or OC. In contrast, ASD associations with EC and OC were markedly attenuated by adjustment for non-tailpipe sources.

CONCLUSION

Results suggest that non-tailpipe emissions may contribute to ASD. Implications are that reducing tailpipe emissions, especially from vehicles with internal combustion engines, may not eliminate ASD associations with traffic-related air pollution.

Source apportionment of soil heavy metals: A new quantitative framework coupling receptor model and stable isotopic ratios

Tracing the source of heavy metals in soils is crucial for reversing the worrisome situation of heavy metal contamination. In this study, the origins of heavy metal pollution in soil were examined in a primary electronic waste treatment and disposal hub in China, using a synergistic source apportionment framework consisting of the positive matrix factorization (PMF) model and the Bayesian stable-isotope analysis mixing model (MixSIAR). Industrial activity is significant to heavy metal contamination in both industrial park and farmland soils, however, the contribution varied through PMF model (industrial park, 64.2%; farmland, 35.6%). In the industrial park, Pb was identified as the major pollutant in the soils, and the local children suffered from noncarcinogenic risks. Moreover, the contribution of Pb contamination sources were allocated more accurately (electronic waste dismantling, 25.1%; industrial production, 23.7%; vehicle exhaust from leaded gasoline, 9.1%; vehicle exhaust from unleaded gasoline, 20.2%; natural process, 21.9%) using MixSIAR for the first time. The main soil contaminants in surrounding farmland were Cd, Cu, and Zn. The variations in heavy metal pollution sources in soils were found to be associated with local policies and regulations, such as the phasing out of leaded gasoline and the conversion of industrial park from electronic waste demolition switched to production and storage. The identification of the source of heavy metals in soil will support targeted reduction of the associated emissions, which can immediately help alleviating soil contamination and control human health risks.

What Are the Sectors Contributing to the Exceedance of European Air Quality Standards over the Iberian Peninsula? A Source Contribution Analysis

The Iberian Peninsula, located in southwestern Europe, is exposed to frequent exceedances of different threshold and limit values of air pollution, mainly related to particulate matter, ozone, and nitrous oxide. Source apportionment modeling represents a useful modeling tool for evaluating the contribution of different emission sources or sectors and for designing useful mitigation strategies. In this sense, this work assesses the impact of various emission sectors on air pollution levels over the Iberian Peninsula using a source contribution analysis (zero-out method). The methodology includes the use of the regional WRF + CHIMERE modeling system (coupled to EMEP emissions). In order to represent the sensitivity of the chemistry and transport of gas-phase pollutants and aerosols, several emission sectors have been zeroed-out to quantify the influence of different sources in the area, such as on-road traffic or other mobile sources, combustion in energy generation, industrial emissions or agriculture, among others. The sensitivity analysis indicates that large reductions of precursor emissions (coming mainly from energy generation, road traffic, and maritime-harbor emissions) are needed for improving air quality and attaining the thresholds set in the European Directive 2008/50/EC over the Iberian Peninsula.

Analysis and Sources Identification of Atmospheric PM10 and Its Cation and Anion Contents in Makkah, Saudi Arabia

In this paper, atmospheric water-soluble cation and anion contents of PM10 are analysed in Makkah, Saudi Arabia. PM10 samples were collected at five sites for a whole year. PM10 concentrations (µg/m3) ranged from 82.11 to 739.61 at Aziziyah, 65.37 to 421.71 at Sanaiyah, 25.20 to 466.60 at Misfalah, 52.56 to 507.23 at Abdeyah, and 40.91 to 471.99 at Askan. Both daily and annual averaged PM10 concentrations exceeded WHO and Saudi Arabia national air quality limits. Daily averaged PM10 concentration exceeded the national air quality limits of 340 µg/m3, 32% of the time at Aziziyah, 8% of the time at Sanaiyah, and 6% of the time at the other three sites. On average, the cations and anions made a 37.81% contribution to the PM10 concentrations. SO42−, NO3−, Ca2+, Na+, and Cl− contributed 50.25%, 16.43%, 12.11%, 11.12%, and 8.70% to the total ion concentrations, respectively. The minor ions (F−, Br−, Mg2+, NO2−, and PO43−) contributed just over 1% to the ion mass. Four principal components explained 89% variations in PM10 concentrations. Four major emission sources were identified: (a) Road traffic, including emission from the exhaust, wear-and-tear, and the resuspension of dust particles (F−, SO42−, NO3−, Ca2+, Na+, Mg+, Br−, Cl−, NO2−, PO43−); (b) Mineral dust (Cl−, F−, Na+, Ca2+, Mg2+, PO43−); (c) Industries and construction–demolition work (F−, SO42−, Ca2+, Mg2+); and (d) Seaspray and marine aerosols (Cl−, Br−, Mg2+, Na+). Future work would include an analysis of the metal contents of PM10 and their spatiotemporal variability in Makkah.

Elemental composition of fine and coarse particles across the greater Los Angeles area: Spatial variation and contributing sources

The inorganic components of particulate matter (PM), especially transition metals, have been shown to contribute to PM toxicity. In this study, the spatial distribution of PM elements and their potential sources in the Greater Los Angeles area were studied. The mass concentration and detailed elemental composition of fine (PM_2.5) and coarse (PM_2.5-10) particles were assessed at 46 locations, including urban traffic, urban community, urban background, and desert locations. Crustal enrichment factors (EFs), roadside enrichments (REs), and bivariate correlation analysis revealed that Ba, Cr, Cu, Mo, Pd, Sb, Zn, and Zr were associated with traffic emissions in both PM_2.5 and PM_2.5-10, while Fe, Li, Mn, and Ti were affected by traffic emissions mostly in PM_2.5. The concentrations of Ba, Cu, Mo, Sb, Zr (brake wear tracers), Pd (tailpipe tracer), and Zn (associated with tire wear) were higher at urban traffic sites than urban background locations by factors of 2.6-4.6. Both PM_2.5 and PM_2.5-10 elements showed large spatial variations, indicating the presence of diverse emission sources across sampling locations. Principal component analysis extracted four source factors that explained 88% of the variance in the PM_2.5 elemental concentrations, and three sources that explained 86% of the variance in the PM_2.5-10 elemental concentrations. Based on multiple linear regression analysis, the contribution of traffic emissions (27%) to PM_2.5 was found to be higher than mineral dust (23%), marine aerosol (18%), and industrial emissions (8%). On the other hand, mineral dust was the dominant source of PM_2.5-10 with 45% contribution, followed by marine aerosol (22%), and traffic emissions (19%). This study provides novel insight into the spatial variation of traffic-related elements in a large metropolitan area.

Possible sources of ambient PM10 inside Jadavpur University Campus, Kolkata

High concentration of particulates in the university and research institutional campus can affect cognitive performance of students and researchers. However, studies on ambient particulate concentration in the campus of universities or research institutes are scarce. The ambient concentration of PM₁₀ was measured in the campus of Jadavpur University, Kolkata, during two different seasons (S1: Post-monsoon; S2: Winter) to identify major sources of pollutant here. Significant seasonal variation of ambient PM₁₀ was recorded in the campus. The average ambient PM₁₀ concentration was recorded higher in S2 compared to S1 of the study period. Morphological characteristics of PM₁₀ during the study period suggest that the roundness of particles was in the range of 0.66 to 0.68, whilst the mean spherical diameter suggests most of the PM₁₀ particles were < 2.5 μ diameter. Based on factorial analysis, three factors were generated which includes factor 1: soil, building material and coal burning particles (53.76% of the variance); factor 2: particles from coal combustion (29.89% of the variance) and factor 3: particles from transport emission (16.33% of the variance). The study suggests that it is important to stop burning coal, reduce vehicular emission and reduce road dust resuspension around the campus to maintain the ambient PM₁₀ concentration within the university campus during the post-monsoon and winter months.

An Integrated Approach for Source Apportionment and Health Risk Assessment of Heavy Metals in Subtropical Agricultural Soils, Eastern China

Unreasonable human activities may cause the accumulation of heavy metals (HMs) in the agricultural soil, which will ultimately threaten the quality of soil environment, the safety of agricultural products, and human health. Therefore, the accumulation characteristics, potential sources, and health risks of HMs in agricultural soils in China’s subtropical regions were investigated. The mean Hg, Cu, Zn, Pb, and Cd concentrations of agricultural soil in Jinhua City have exceeded the corresponding background values of Zhejiang Province, while the mean concentrations of determined 8 HMs were less than their corresponding risk-screening values for soil contamination of agricultural land in China. The spatial distribution of As, Cr, Ni, Cu, and Pb were generally distributed in large patches, and Hg, Zn, and Cd were generally sporadically distributed. A positive definite matrix factor analysis (PMF) model had better performance than an absolute principal component–multiple linear regression (APCS-MLR) model in the identification of major sources of soil HMs, as it revealed higher R2 value (0.81–0.99) and lower prediction error (−0.93–0.25%). The noncarcinogenic risks (HI) of the 8 HMs to adults and children were within the acceptable range, while the carcinogenic risk (RI) of children has exceeded the safety threshold, which needs to be addressed by relevant departments. The PMF based human health risk assessment model indicated that industrial sources contributed the highest risk to HI (32.92% and 30.47%) and RI (60.74% and 61.5%) for adults and children, followed by agricultural sources (21.34%, 29.31% and 32.94% 33.19%). Therefore, integrated environmental management should be implemented to control and reduce the accumulation of soil HMs from agricultural and industrial sources.

Variation in PM2.5 sources in central North China Plain during 2017-2019: Response to mitigation strategies

Central North China Plain (NCP) is one of the most important source region of air pollutants over the Beijing-Tianjin-Hebei (BTH) region. The national government has issued abatement measures to improve the air quality in this area from 2017. To examine the effects of control measures, observational analysis on PM_2.5 characteristics was performed in a city of central NCP during 2017-2019 to investigate the variation in mass concentration, chemical composition, and emission source of PM_2.5. Annual PM_2.5 concentration significantly reduced by 16% from 2017 to 2019, implying substantial improvements in air quality. PM_2.5 enriched in autumn-winter seasons was dominated by SNA (sum of sulfate, nitrate and ammonium; ~38%), followed by organic carbon matters (OM; ~24%) and fine soil (FS; ~12%). This chemical composition was different from that in a megacity in NCP (Beijing) where OM accounted for a comparable fraction to SNA. Approximately half of SNA was attributed to nitrate, indicating that SNA changed from sulfate-driven to nitrate-driven, and the considerable effects of coal combustion cutoff, in which sulfate was concentrated. Decreased mass fraction of SNA and increased OM fraction in PM_2.5 were observed in 2018-2019 partly contributed to the decrease in PM_2.5. A progressive increase in the contribution of heterogeneous formed SNA whilst a decrease in OM was observed as the pollution elevated from clean to heavily polluted. Six sources (soil dust, biomass burning, secondary emission, road traffic, coal combustion and industry) were identified by the Positive Matrix Factorization (PMF) model in both years and dominated by secondary aerosols, respectively contributing 39% and 41% to PM_2.5. The decreasing concentrations (with reductions of 17%-61%) of the secondary source, coal combustion, soil dust and biomass burning largely accounted for the reduction in PM_2.5, as a consequence of the recent abatement measures. By contrast, contributions of vehicle-related emissions, similar to the increasing contribution of vehicles at sites in NCP after 2013, should receive increased attention.

Impact of Municipal, Road Traffic, and Natural Sources on PM10: The Hourly Variability at a Rural Site in Poland

The paper presents data from a monthly campaign studying the elemental composition of PM10, as measured by a specific receptor in Kotórz Mały (Opole Voivodeship)—located in the vicinity of a moderately inhabited rural area—measured in one-hour samples using a Horiba PX-375 analyzer. The hourly variability of SO2, NO, NO2, CO, and O3 concentrations, as well as the variability of meteorological parameters, was also determined. On average, during the entire measurement period, the elements related to PM10 can be arranged in the following order: As < V < Ni < Pb < Cr < Mn < Cu < Ti < Zn < K < Fe < Ca < Al < Si < S. Trace elements, including toxic elements—such as As, V, Ni, Pb, Cr, and Mn—were present in low concentrations, not exceeding 10 ng/m3 (average daily value). These elements had fairly even concentrations, both daily and hourly. The concentrations of the main elements in the PM10, as measured by the receptor, are subject to strong hourly changes related not only to changes in the structures of the sources identified in the statistical analysis, but also to wind speed and direction changes (soil and sand particle pick-up and inflow of pollutants from coal combustion). It has been shown that the transport emissions measured by the receptor can have an intense effect on PM10 in the afternoon.

Review of online source apportionment research based on observation for ambient particulate matter

Particulate matter source apportionment (SA) is the basis and premise for preventing and controlling haze pollution scientifically and effectively. Traditional offline SA methods lack the capability of handling the rapid changing pollution sources during heavy air pollution periods. With the development of multiple online observation techniques, online SA of particulate matter can now be realized with high temporal resolution, stable and reliable continuous observation data on particle compositions. Here, we start with a summary of online measuring instruments for monitoring particulate matters that contains both online mass concentration (online MC) measurement, and online mass spectrometric (online MS) techniques. The former technique collects ambient particulate matter onto filter membrane and measures the concentrations of chemical components in the particulate matter subsequently. The latter technique could be further divided into two categories: bulk measurement and single particle measurement. Aerosol Mass Spectrometers (AMS) could provide mass spectral information of chemical components of non-refractory aerosols, especially organic aerosols. While the emergence of single-particle aerosol mass spectrometer (SPAMS) technology can provide large number of high time resolution data for online source resolution. This is closely followed by an overview of the methods and results of SA. However, online instruments are still facing challenges, such as abnormal or missing measurements, that could impact the accuracy of online dataset. Machine leaning algorithm are suited for processing the large amount of online observation data, which could be further considered. In addition, the key research challenges and future directions are presented including the integration of online dataset from different online instruments, the ensemble-trained source apportionment approach, and the quantification of source-category-specific human health risk based on online instrumentation and SA methods.

Source Apportionment and Toxic Potency of Polycyclic Aromatic Hydrocarbons (PAHs) in the Air of Harbin, a Cold City in Northern China

A total of 68 PUF samples were collected seasonally from 17 sampling sites in Harbin, China from May 2016 to April 2017 for analyzing 15 congeners of gaseous polycyclic aromatic hydrocarbons (Σ15PAHs). An improved non-negative matrix (NMF) model and a positive matrix factorization (PMF) model were used to apportion the sources of PAHs. The carcinogenic risk due to exposure to PAHs was estimated by the toxicity equivalent of BaP (BaPeq). The results showed that the average concentration of Σ15PAHs was 68.3 ± 22.3 ng/m3, and the proportions of 3-ring, 4-ring, 5-ring, and 6-ring PAHs were 64.4%, 32.6%, 2.10%, and 0.89%, respectively. Among the six typical functional areas in Harbin, the Σ15PAHs concentrations were 98.1 ± 76.7 ng/m3, 91.2 ± 76.2 ng/m3, 71.4 ± 75.6 ng/m3, 67.9 ± 65.6 ng/m3, 42.6 ± 34.7 ng/m3, and 38.5 ± 38.0 ng/m3 in the wastewater treatment plant, industrial zone, business district, residential area, school, and suburb, respectively. During the sampling period, the highest concentration of Σ15PAHs was in winter. The improved NMF model and PMF model apportioned the PAHs into three sources including coal combustion, biomass burning, and vehicle exhaust. The contributions of coal combustion, biomass burning, and vehicle exhausts were 34.6 ± 3.22%, 48.6 ± 4.03%, and 16.8 ± 5.06%, respectively. Biomass burning was the largest contributor of Σ15PAHs concentrations in winter and coal combustion contributed significantly to the concentrations in summer. The average ΣBaPeq concentration was 0.54 ± 0.23 ng/m3 during the sampling period, high concentrations occurred in the cold season and low levels presented in the warm period. Vehicle exhaust was the largest contributor to the ΣBaPeq concentration of PAHs in Harbin.

SARS-CoV-2 concentrations and virus-laden aerosol size distributions in outdoor air in north and south of Italy

The COVID-19 disease spread at different rates in the different countries and in different regions of the same country, as happened in Italy. Transmission by contact or at close range due to large respiratory droplets is widely accepted, however, the role of airborne transmission due to small respiratory droplets emitted by infected individuals (also asymptomatic) is controversial. It was suggested that outdoor airborne transmission could play a role in determining the differences observed in the spread rate. Concentrations of virus-laden aerosol are still poorly known and contrasting results are reported, especially for outdoor environments. Here we investigated outdoor concentrations and size distributions of virus-laden aerosol simultaneously collected during the pandemic, in May 2020, in northern (Veneto) and southern (Apulia) regions of Italy. The two regions exhibited significantly different prevalence of COVID-19. Genetic material of SARS-CoV-2 (RNA) was determined, using both real time RT-PCR and ddPCR, in air samples collected using PM₁₀ samplers and cascade impactors able to separate 12 size ranges from nanoparticles (diameter D < 0.056 µm) up to coarse particles (D > 18 µm). Air samples tested negative for the presence of SARS-CoV-2 at both sites, viral particles concentrations were <0.8 copies m^-3 in PM₁₀ and <0.4 copies m^-3 in each size range investigated. Outdoor air in residential and urban areas was generally not infectious and safe for the public in both northern and southern Italy, with the possible exclusion of very crowded sites. Therefore, it is likely that outdoor airborne transmission does not explain the difference in the spread of COVID-19 observed in the two Italian regions.

Population exposure to particulate-matter and related mortality due to the Portuguese wildfires in October 2017 driven by storm Ophelia

In October 2017, hundreds of wildfires ravaged the forests of the north and centre of Portugal. The fires were fanned by strong winds as tropical storm Ophelia swept the Iberian coast, dragging up smoke (together with Saharan dust from north-western Africa) into higher western European latitudes. Here we analyse the long-range transport of particulate matter (PM₁₀) and study associations between PM₁₀ and short-term mortality in the Portuguese population exposed to PM₁₀ due to the October 2017 wildfires, the worst fire sequence in the country over the last decades. We analysed space- and ground-level observations to track the smoke plume and dust trajectory over Portugal and Europe, and to access PM₁₀ concentrations during the wildfires. The effects of PM₁₀ on mortality were evaluated using satellite data for exposure and Poisson regression models. The smoke plume covered most western European countries (including Spain, France, Belgium and the Netherlands), and reached the United Kingdom, where the population was exposed in average to an additional PM₁₀ level of 11.7 µg/m³ during seven smoky days (three with dust) in relation to the reference days (days without smoke or dust), revealing the impact of the wildfires on distant populations. In Portugal, the population was exposed in average to additional PM₁₀ levels that varied from 16.2 to 120.6 µg/m³ in smoky days with dust and from 6.1 to 20.9 µg/m³ in dust-free smoky days. Results suggest that PM₁₀ had a significant effect on the same day natural and cardiorespiratory mortalities during the month of October 2017. For every additional 10 µg/m³ of PM₁₀, there was a 0.89% (95% confidence interval, CI, 0-1.77%) increase in the number of natural deaths and a 2.34% (95% CI, 0.99-3.66%) increase in the number of cardiorespiratory-related deaths. With rising temperatures and a higher frequency of storms due to climate change, PM from Iberian wildfires together with NW African dust will tend to be more often transported into Northern European countries, which may carry health threats to areas far from the ignition sites.

Policy adjustment impacts Cd, Cu, Ni, Pb and Zn contamination in soils around e-waste area: Concentrations, sources and health risks

Pollution control policies (PCP) have been implemented in some e-waste dismantling areas in China to curb metal contamination since 2012. We investigated the effects of policy intervention on the concentrations, sources and health risks of heavy metals in soils. Post-implementation, among Cd, Cu, Ni, Pb and Zn, Pb levels declined while the Cd, Cu, Ni and Zn concentrations in soils were not impacted. Changes in their pollution indices and health risks were also similar. After the PCP, the contribution of traffic emission significantly decreased, while natural and industrial contribution increased due to the heighten background input and relocation of small e-waste dismantling workshops. Risk assessment showed that total cancer risk of five metals also slightly increased. Thus, policy intervention might be effective in controlling the release of some metals from e-waste dismantling. However, the performance of control measures varied depending on both source emission and geochemical properties of the metals. This study reveal the ongoing need of stricter supervision, targeted emission reduction and more-effective soil remediation actions to alleviate soil contamination from e-waste dismantling.

Source apportionment of potentially toxic elements in soils using APCS/MLR, PMF and geostatistics in a typical industrial and mining city in Eastern China

Source apportionment of potentially toxic elements in soils is a critical step for devising soil sustainable management strategies. However, misjudgment or imprecision can occur when traditional statistical methods are applied to identify and apportion the sources. The main objective of the study was to develop a robust approach composed of the absolute principal component score/multiple linear regression (APCS/MLR) receptor model, positive matrix factorization (PMF) receptor model and geostatistics to identify and apportion sources of soil potentially toxic elements in typical industrial and mining city, eastern China. APCS/MLR and PMF were applied to provide robust factors with contribution rates. The geostatistics coupled with the variography and kriging methods was used to present factors derived from these two receptor models. The results indicated that mean concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn exceeded the local background levels. Based on multivariate receptor models and geostatistics, we determined four sources of eight potentially toxic elements including natural source (parent material), agricultural practices, pollutant emissions (industrial, mining and traffic) and the atmospheric deposition of coal combustion, which accounted for 68%, 12%, 12% and 9% of the observed potentially toxic element concentrations, respectively. This study provides a reliable and robust approach for potentially toxic elements source apportionment in this particular industrial and mining city with a clear potential for future application in other regions.

Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India

The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM_2.5 and PM₁₀) in Delhi, India from January 2013 to December 2016. The annual average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 (range: 17-417 μg m^-3) and 238 ± 106 μg m^-3 (range: 34-537 μg m^-3), respectively. PM_2.5 and PM₁₀ samples were chemically characterized to assess their chemical components [i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements] and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM_2.5 concentration and 24% to the total PM₁₀ concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM_2.5 and PM₁₀ were carried by north westerly winds (north-western Indo Gangetic Plains of India).

Estimated health impacts from maritime transport in the Mediterranean region and benefits from the use of cleaner fuels

Ship traffic emissions degrade air quality in coastal areas and contribute to climate impacts globally. The estimated health burden of exposure to shipping emissions in coastal areas may inform policy makers as they seek to reduce exposure and associated potential health impacts. This work estimates the PM2.5-attributable impacts in the form of premature mortality and cardiovascular and respiratory hospital admissions, from long-term exposure to shipping emissions. Health impact assessment (HIA) was performed in 8 Mediterranean coastal cities, using a baseline conditions from the literature and a policy case accounting for the MARPOL Annex VI rules requiring cleaner fuels in 2020. Input data were (a) shipping contributions to ambient PM2.5 concentrations based on receptor modelling studies found in the literature, (b) population and health incidence data from national statistical registries, and (c) geographically-relevant concentration-response functions from the literature. Long-term exposure to ship-sourced PM2.5 accounted for 430 (95% CI: 220-650) premature deaths per year, in the 8 cities, distributed between groups of cities: Barcelona and Athens, with >100 premature deaths/year, and Nicosia, Brindisi, Genoa, Venice, Msida and Melilla, with tens of premature deaths/year. The more stringent standards in 2020 would reduce the number of PM2.5-attributable premature deaths by 15% on average. HIA provided a comparative assessment of the health burden of shipping emissions across Mediterranean coastal cities, which may provide decision support for urban planning with a special focus on harbour areas, and in view of the reduction in sulphur content of marine fuels due to MARPOL Annex VI in 2020.

Impact of harbour activities on local air quality: A review

Several harbour activities cause negative environmental impacts in the harbours' surrounding areas, namely the degradation of air quality. This paper intends to comprehensively review the status of the air quality measured in harbour areas. The published studies show a limited number of available air quality monitoring data in harbours areas, mostly located in Europe (71%). Measured concentrations of the main air pollutants were compiled and intercompared, for different countries worldwide allowing a large spatial representativeness. The higher NO₂ and PM₁₀ concentrations were found in Europe - ranging between 12 and 107 μg/m³ and 2-50 μg/m³, respectively, while the higher concentrations of PM_2.5 were found in Asia (25-70 μg/m³). In addition, the lower levels of SO₂ monitored in recent years suggest that current mitigation strategies adopted across Europe were very efficient in promoting the reduction of SO₂ concentrations. Part of the reviewed studies also estimated the contributions from ship emissions to PM concentration through the application of source apportionment methods, with an average of 5-15%. In some specific harbour areas in Asia, ships can contribute up to 7-26% to the local fine particulate matter concentrations. This review confirms that emissions from the maritime transport sector should be considered as a significant source of particulate matter in harbour areas, since this pollutant concentrations are frequently exceeding the established standard legal limit values. Therefore, the results from this review boost the implementation of mitigation measures, aiming to reduce, in particular, particulate matter emissions.

Comparison of two receptor models PCA-MLR and PMF for source identification and apportionment of pollution carried by runoff from catchment and sub-watershed areas with mixed land cover in South Korea

The application and comparison of receptor modeling techniques based on ambient air quality and particulate matter increasingly being studied. However, less is known about the comparison of receptor modeling techniques using spatial runoff quality data to identify and quantify the stormwater runoff pollution. This study compared the performance of principal component analysis-multiple linear regressions (PCA-MLR) and positive matrix factorization (PMF) models on stormwater runoff data collected from a small catchment (Site 1) with urban development activity and a sub-watershed outlet (Site 2). In both sites, the PCA-MLR model identified three pollution sources, whereas PMF identified five with a detailed source mechanism including two additional sources. Furthermore, the spatial land-use land-cover (LULC) analysis results indicate that the Site 1 exhibited a rapid conversion of the native area into a built-up area over the monitoring period compared to Site 2. Based on the modeling results, domestic wastewater and soil erosion were the major source of pollution at Site 1 and Site 2, respectively. The performance evaluation statistics including Nash coefficient (0.86-0.99), % error (<-14 to 2), and coefficient of determination (R² ≤ 0.99) showed better performance for the PMF model than the PCA-MLR model. Overall, the PMF receptor modeling approach was found to be more robust for the current study sites with different land use types. The findings of this study could provide a basis for further application of these receptor models and their comparison using spatial-temporal ionic and sediment related runoff monitoring data. Also, the models from this research could be combined with other receptor models on runoff quality data (e.g. CMB or UNMIX) to explore and inter-compare the outcomes, and to determine how the model results are affected by modifications to input data and model parameters. Therefore, further research is required to precisely assess the accuracy of both receptor models.

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM₁₀ (particulate matter with aerodynamic diameter ≤ 10 μm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM₁₀ sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM₁₀. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM₁₀ concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 μg m^-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM₁₀ mass concentration over the IGP region of India.

Seasonal variation, phase distribution, and source identification of atmospheric polycyclic aromatic hydrocarbons at a semi-rural site in Ulsan, South Korea

Polycyclic aromatic hydrocarbons (PAHs) in gaseous and particulate phases (n = 188) were collected in Ulsan, South Korea, over a period of one year (June 2013‒May 2014) to understand the seasonal variation and phase distribution of PAHs as well as to identify the seasonal PAH emission sources. The target compounds were the 16 US-EPA priority PAHs, with the exception of naphthalene, acenaphthylene, and acenaphthene. Winter and spring had the highest and lowest PAH concentrations, respectively. The mean of the Σ₁₃ PAHs in the gaseous phase (4.11 ng/m³) was higher than that in the particulate phase (2.55 ng/m³). Fractions of the gaseous or 3- and 4-ring PAHs (i.e., Flu, Phe, and Ant) were high in summer, and those of the particulate or 5- and 6-ring PAHs (i.e., BkF, BaP, Ind, DahA, and BghiP) increased in winter. Gas/particle partitioning models also demonstrated the increased contributions of the particulate PAHs in spring and winter. Source identification of PAHs was undertaken using diagnostic ratios, principal component analysis, and positive matrix factorization. The results indicated that pyrogenic sources (e.g., coal combustion) were dominant in winter. Other types of pyrogenic (e.g., industrial fuel combustion) and petrogenic sources were the main PAH sources in summer and autumn. The influence of both sources, especially in summer, might be due to seasonal winds transporting PAHs from the industrial areas. Two types of pyrogenic sources, diesel and coal combustion, were identified as the main PAH sources in spring. This study clearly demonstrates a source-receptor relation of PAHs at a semi-rural site in a heavily industrialized city.

Source apportionment for fine particulate matter in a Chinese city using an improved gas-constrained method and comparison with multiple receptor models

PM_2.5 is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM_2.5. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM_2.5 were collected at Tianjin, a megacity in northern China. The ambient PM_2.5 dataset, source information, and gas-to-particle ratios (such as SO₂/PM_2.5, CO/PM_2.5, and NOx/PM_2.5 ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM_2.5 was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Seasonal variability of PM2.5 and PM10 composition and sources in an urban background site in Southern Italy

Comparison of fine and coarse fractions in terms of sources and dynamics is scarce in southeast Mediterranean countries; differences are relevant because of the importance of natural sources like sea spray and Saharan dust advection, because most of the monitoring networks are limited to PM₁₀. In this work, the main seasonal variabilities of sources and processes involving fine and coarse PM (particulate matter) were studied at the Environmental-Climate Observatory of Lecce (Southern Italy). Simultaneous PM_2.5 and PM₁₀ samples were collected between July 2013 and July 2014 and chemically analysed to determine concentrations of several species: OC (organic carbon) and EC (elemental carbon) via thermo-optical analysis, 9 major ions via IC, and 23 metals via ICP-MS. Data was processed through mass closure analysis and Positive Matrix Factorization (PMF) receptor model characterizing seasonal variabilities of nine sources contributions. Organic and inorganic secondary aerosol accounts for 43% of PM_2.5 and 12% of PM_2.5-10 with small seasonal changes. SIA (secondary inorganic aerosol) seasonal pattern is opposite to that of SOC (secondary organic carbon). SOC is larger during the cold period, sulphate (the major contributor to SIA) is larger during summer. Two forms of nitrate were identified: NaNO₃, correlated with chloride depletion and aging of sea-spray, mainly present in PM_2.5-10; NH₄NO₃ more abundant in PM_2.5. Biomass burning is a relevant source with larger contribution during autumn and winter because of the influence of domestic heating, however, is not negligible in spring and summer, because of the contributions of fires and agricultural practices. Mass closure analysis and PMF results identify two soil sources: crustal associated to long range transport and carbonates associated to local resuspended dust. Both sources contributes to the coarse fraction and have different dynamics with crustal source contributing mainly in high winds from SE conditions and carbonates during high winds from North direction.

Source apportionment of polycyclic aromatic carbons (PAHs) in sediment core from Honghu Lake, central China: comparison study of three receptor models

The spatial distribution of polycyclic aromatic hydrocarbons (PAHs) and their source contributions employing receptor models has been widely reported. However, the temporal distribution of PAH source contributions is less studied. Thus, in this paper, three receptor models including principle component analysis-multiple linear regression (PCA-MLR), positive matrix factorization (PMF), and Unmix were used to PAH source apportionment study in a sediment core from Honghu Lake, China. Sixteen USEPA priority PAHs in 37 sliced sediment layers (1-cm interval) were measured, with the concentrations of ∑₁₆PAH (sum of 16 PAHs) ranging from 93.0 to 431 ng g^-1. The source apportionment results derived from three receptor models were similar, with three common sources: mixed sources of biomass burning and coal combustion (31.0-41.4% on average), petroleum combustion (31.8-45.5%), and oil leakage (13.1-21.3%). The PMF model segregated an additional source: domestic coal combustion (contributed 20.9% to the ∑₁₆PAHs). Four aspects including intra-comparison, inter-comparison, source numbers and compositions, and source contributions were considered in comparison study. The results indicated that the PMF model was most reasonable in PAH source apportionment research in this study.

Source contributions to total concentrations and carcinogenic potencies of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in ambient air: a case study in Suzhou City, China

The potential source categories and source contributions of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) in ambient air from Suzhou City, China, were performed by principal component analysis-multiple linear regression (PCA-MLR) and positive matrix factorization (PMF). The carcinogenic potencies of PCDD/Fs were quantitatively apportioned based on the positive matrix factorization-toxic equivalent concentration (PMF-TEQ) method. The results of the present study were summarized as follows. (1) The total concentrations and toxic equivalent concentrations of PCDD/Fs (∑PCDD/Fs and TEQ) in ambient air from Suzhou City were 1.34-42.80 pg N m^-3 and 0.081-1.22 pg I-TEQ N m^-3, respectively. (2) PCA-MLR suggested that industrial combustion (IC), electric arc furnaces (EAFs) and secondary aluminum smelters (ALSs), unleaded gas-fueled vehicle sources (UGFVs), ALSs, and hazardous solid waste incinerators (HSWIs) could be the primary PCDD/F contributors, accounting for 13.2, 16.7, 35.5, 19.4, and 15.2% of ∑PCDD/Fs, respectively. (3) PMF and PMF-TEQ indicated that EAFs (carbon steel), UGFVs, IC, ALSs, municipal solid waste incinerators (MSWIs) and hospital waste incinerators (HWIs), and HSWIs contributed 10.9, 10.9, 42.8, 11.3, 10.7, and 13.4% to ∑PCDD/Fs, but contributed 8.3, 12.3, 50.3, 12.7, 6.0, and 10.4% to carcinogenic potencies of PCDD/Fs. This study was the first attempt to quantitatively apportion the source-specific carcinogenic potencies of PCDD/Fs in ambient air.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM_2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM_2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM_2.5 was recorded as 121.9 ± 93.2 μg m^-3 (range 25.1-429.8 μg m^-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM_2.5. Strong seasonal variation was observed in PM_2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM_2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM_2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM_2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM_2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM_2.5 from the north-west India and Pakistan were mostly pertinent.