Fine aerosol measurements have been undertaken at the Cape Grim global baseline station since 1992. Ion beam analysis techniques were then used to determine the elemental composition of the samples ...from which source fingerprints can be determined. In this study six source fingerprints were identified to contribute to the measurements of PM2.5 at Cape Grim (from 1998 to 2016); fresh sea salt (57%), secondary sulfate and nitrates (14%), smoke (13%), aged sea salt (the product of NaCl reactions with SO2; 12%), soil dust (2.4%) and industrial metals (1.5%). Back trajectory analysis showed that local Tasmanian sources of soil dust contributed to the high soil dust measurements. High measurements of secondary aerosols were recorded when air masses were arriving from the Australian mainland, in the direction of the Victorian power stations.
When air masses were arriving from the baseline sector, the maximum concentration of aged sea salt was 1.3 μg/m3, compared to overall maximum of 4.9 μg/m3. For secondary sulfates and nitrates the maximum concentrations were 2.5 and 7.5 μg/m3 from the baseline sector and overall, respectively. While measurements at Cape Grim can be affected from long range transport from mainland Australia and some local Tasmanian sources, the average concentrations of anthropogenic sources are still considerably lower than those measured at more populated areas. For example, at Lucas Heights (located south-west of the Sydney central business district, with little local sources) the average concentrations of secondary sulfates/nitrates and aged sea air were 1.4 and 1.0 μg/m3, respectively; compared to average concentrations of 0.8 and 0.6 μg/m3, at Cape Grim. The average concentrations of smoke were compatible at the two sites. The impact of primary aerosols from vehicle exhaust at Cape Grim was limited and no corresponding fingerprint was resolved.
•PM2.5 sampling was undertaken at the Cape Grim global baseline station since 1992.•Ion Beam Analysis was used to resolve 23 elements.•Six source fingerprints have been identified using PMF.•Higher concentrations of secondary aerosols occur from the mainland fetch.•However, concentrations are still lower than at more population regions.
This study showcases the qualitative and quantitative source apportionments of size-dependent polycyclic aromatic hydrocarbons (PAHs) in road deposited sediment by means of molecular diagnostic ratio ...(MDR) and positive matrix factorisation (PMF) approaches. The MDR was initially used to narrow the PAH source candidates. PMF modelling was subsequently used to provide more precise source apportionment with the assistance of a multiple linear regression analysis. Through a combined qualitative and quantitative source apportionment, different potential source contributors were identified at different size fractions. Explicitly, three major contributors to sorption at the size fraction of 1000–400μm were tentatively identified as incineration (26%), coal combustion (53%) and gasoline-powered vehicle (20%). Four major contributors to the size fraction of 400–100μm were identified as gasoline-powered vehicle (25%), surface pavement (15%), diesel-powered vehicle (37%) and industrial boiler (24%). Four major contributors to the size fraction of 100–63μm were identified as cogeneration emission (13%), diesel-powered vehicle (28%), tire debris (45%) and wood combustion (14%). The potential contributors in the size fraction 63–0.45μm were identified as diesel-powered vehicle (21%), heterogeneous sources (41%) and biomass burning (38%). In addition, the highest ∑16PAH concentration was found in the smallest size fraction of 63–0.45μm, which is also where the highest BaPE and TEF values for potential risk assessment occurred.
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•Increases of PAH contents were observed with decreasing size fractions in RDS.•Low molecular weight PAHs were predominant PAHs in RDS.•Regardless of size fraction, PAH crude sources were identified by the MDR approach.•More refined PAH sources were identified by PMF model in size dependent RDS.•Quantitative contributions of the identified sources were calculated by the MLR method.
Estimation of zone of influences (ZoI) at signalised traffic intersections (TI) is important to accurately model particle number concentrations (PNCs) and their exposure to public at emission hotspot ...locations. However, estimates of ZoI for PNCs at different types of TIs are barely known. We carried out mobile measurements inside the car cabin with windows fully open for size–resolved PNCs in the 5–560 nm range on a 6 km long busy round route that had 10 TIs. These included four–way TIs without built–up area (TI4w-nb), four–way TIs with built–up area (TI4w-wb), three–way TIs without built–up area (TI3w-nb) and three–way TIs with built–up area (TI3w-wb). Mobile measurements were made with a fast response differential mobility spectrometer (DMS50). Driving speed and position of the car were recorded every second using a global positioning system (GPS). Positive matrix factorisation (PMF) modelling was applied on the data to quantify the contribution of PNCs released during deceleration, creep–idling, acceleration and cruising to total PNCs at the TIs. The objectives were to address the following questions: (i) how does ZoI vary at different types of TIs in stop– and go–driving conditions?, (ii) what is the effect of different driving conditions on ZoI of a TI?, (iii) how realistically can the PNC profiles be generalised within a ZoI of a TI?, and (iv) what is the share of emissions during different driving conditions towards the total PNCs at a TI? Average length of ZoI in longitudinal direction and along the road was found to be the highest (148 m; 89 to −59 m from the centre of a TI) at a TI3w-wb, followed by TI4w-nb (129 m; 79 to −42 m), TI3w-nb (86 m; 71 to −15 m) and TI4w-wb (79 m; 46 to −33 m) in stop– and go–driving conditions. During multiple stopping driving conditions when a vehicle stops at a TI more than once in a signal cycle due to oversaturation of vehicles, average length of ZoI increased by 55, 22 and 21% at TI4w-nb, TI3w-nb and TI3w-wb, respectively, compared with stop– and go–driving conditions. Within average length of ZoI in stop– and go– driving conditions, PNCs followed a three degree polynomial form at all TIs. Dimensional analysis suggested that coefficients of polynomial equations at both four–way and three–way TIs were mainly influenced by delay time, wind speed and particle number flux. The PMF analysis suggested that deceleration contributed the most to total PNCs at all TIs, except TI4w-wb. Findings of this study are a step forward to understand the contribution of different driving conditions towards the total PNCs and their exposure at the TIs.
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•On–road particle number concentration (PNC) measured at traffic intersections (TI).•Dimensional analysis showed reliance of PNC profiles on delay time at four-way TIs.•Zone of influence depends on idling time and PNCs within them on vehicle speed.•Three degree polynomial distribution represented the PNC profiles well at all TIs.•PMF results showed deceleration to contribute maximal PNCs at all intersections.
This study presents the analysis of the concentration levels, inter-site variation and source identification of trace metals at three urban/industrial mixed land-use sites of the Cantabria region ...(northern Spain), where local air quality plans were recently approved because the number of exceedances of the daily PM10 limit value according to the Directive 2008/50/EC had been relatively high in the last decade (more than 35 instances per year). PM10 samples were collected for over three years at the Torrelavega (TORR) and Los Corrales (CORR) sites and for over two years at the Camargo (GUAR) site and analysed for the presence of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), nickel (Ni), titanium (Ti), vanadium (V), molybdenum (Mo), manganese (Mn), iron (Fe), antimony (Sb) and zinc (Zn). Analysis of enrichment factors revealed an anthropogenic origin of most of the studied elements; Zn, Cd, Mo, Pb and Cu were the most enriched elements at the three sites, with Fe and V as the least enriched elements. Positive Matrix Factorisation (PMF) and pollutant roses (Cu at TORR, Zn at CORR and Mn at GUAR) were used to identify the local sources of the studied metals. Analysis of PMF results revealed the main sources of trace metals at each site as road traffic at the TORR site, iron foundry and casting industry at the CORR site and a ferro-manganese alloy industry at the GUAR site. Other sources were also identified at these sites, but with much lower contributions, such as minor industrial sources, combustion and traffic mixed with the previous sources.
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•Local sources affect the daily PM10 limit value exceedances according to the EU regulation.•Trace metals are used as tracers of local sources of PM10 in urban/industrial mixed areas.•The main sources of trace metals in urban/industrial mixed sites are identified by PMF.•Computed roses of the local tracers are used to confirm the main local sources.
The South African Highveld is recognised as a region having significant negative ambient air quality impacts with its declaration as an Air Quality Priority Area in 2007. Such areas require the ...implementation of specific air quality intervention strategies to address the air quality situation. A greater understanding of the composition of the atmospheric aerosol loading and the contributing air pollution sources will assist with the formulation and implementation of these strategies. This study aims to assess the composition and sources of the aerosol loading in Embalenhle and Kinross located on the Highveld. Fine (PM2.5) and coarse (PM2.5-10) aerosol samples were collected during summer and winter, which were quantified using the gravimetric method. Wavelength-Dispersive X-Ray Fluorescence (WD-XRF) and Ion Chromatography (IC) analysis were used to determine the chemical composition of aerosols. Mean PM2.5 concentrations in Embalenhle and Kinross ranged from 16.3 to 34.1 µg/m3 during winter and 7.4 to 19.0 µg/m3 during summer. Mean PM10-2.5 concentrations ranged from 10.3 to 114 µg/m3 during winter and 5.9 to 11.2 µg/m3 during summer. Si, Al, S, Na (winter only), Ca (summer only), SO42- and NH4+ were the most abundant species in PM2.5 during both seasons. In PM10-2.5, Si, Al, Na (winter only), SO42- and F- were the most abundant species during both seasons. The elements S and Ca also had high abundances at Embalenhle and Kinross, respectively, during summer. Source apportionment was undertaken using Positive Matrix Factorisation, which identified five sources. Dust, secondary aerosols, domestic combustion, wood and biomass burning, and industry were determined to be the contributing sources. Any measures to mitigate particulate air pollution on the Highveld should consider these key sources.
The PM10 and PM2.5 source apportionment and health risk assessment were performed for two different elevations (lower elevation (LE) ∼5–10 m and higher elevation (HE) ∼30–45 m) at four different ...locations of Delhi city during January 2017–March 2017. The measured 24-h average PM10 and PM2.5 concentrations at different locations were found between 134.7 and 257.7 μg/m3 and 78.7–121.1 μg/m3, respectively. The 24-h average PM10 and PM2.5 concentrations at the study sites were exceeding the national (NAAQS) and WHO limits by more than 1.3 and 3 times, respectively. The PM mass was enriched with carbonaceous matter, ions, crustal and trace elements and their concentrations (except, Sr, Ni, S, As, V, Sb, Ga- the trace elements associated with coal and heavy oil combustion & NO3− - due to high nitrate formation ability at greater heights) were found higher in LE. The source apportionment study was performed with positive matrix factorisation (PMF). PMF analysis identified vehicular emission (PM10, 9.6–24.3% & PM2.5, 12.1–25.3%), secondary inorganics (PM10, 6.4–22.5% & PM2.5, 11–23%), crustal source (PM10, 9–52% & PM2.5, 3.8–10.7%), fuel oil combustion (PM10, 3–21% & PM2.5, 9–23%), biomass burning (PM10, 7.4–28.6% & PM2.5, 6–50.5%), and coal combustion (PM10, 13–17% & PM2.5, 14–19.1%) as the main sources of PM10 and PM2.5 at the study sites. A significant difference in source contribution between the elevations was observed for coal combustion, fuel oil combustion, biomass burning, and crustal sources. The contribution of vehicular emission and secondary inorganics estimates were broadly similar at both elevations. Coal and fuel oil combustion contribution was found relatively higher at HE. Further, health risk due to exposure to toxic heavy metals in PM2.5 was assessed. Non-carcinogenic and carcinogenic risks (averaged of four sites) for both children and adults were exceeding the acceptable limit by more than 1.13 times. Results also showed that the public residing at HE is more susceptible to have greater health risks than LE at Delhi city. Sources contribution to carcinogenic risk was assessed and the results indicated that coal combustion (41%) contribution was highest followed by crustal sources (22%), fuel oil combustion (17%), vehicular emission (12%), biomass burning (4%) and secondary inorganics (4%).
•The sampling of PM was performed at two different elevations.•Shares of ambient sources of PM and its associated health risk were estimated for Delhi city.•Vehicular emission, secondary inorganics, fuel oil combustion, coal combustion, and crustal sources were the dominant sources in Delhi city.•The public residing at higher levels is more susceptible to have high health risks in Delhi city.•Coal combustion contribution to the carcinogenic risk was highest at Delhi city.
The bulk atmospheric deposition of the minor and trace elements As, Cd, Cr, Cu, Mn, Mo, Ni, Pb, Ti, V and Zn was investigated in Santander, a Northern Spanish coastal city. Bulk deposition samples ...were collected monthly for three years using a bottle/funnel device. Taking into account that heavy metals are bioavailable only in their soluble forms, water-soluble and water-insoluble fractions were evaluated separately for element concentration. The fluxes of the studied elements in the bulk deposition exhibited the following order: Zn>Mn≫Cu>Cr>Pb>V>Ni≫As>Mo>Cd. The fluxes of Zn and Mn were more than 10 times higher than those of the other elements, with maximum values of 554.5 and 334.1μgm−2day−1, respectively. Low solubilities (below 22%) were found for Cr, Ti and Pb, whereas the highest solubility was found for Zn (78%). With the exception of Cu, all of the studied metals in the water-soluble fraction of the atmospheric deposition showed seasonal dependence, due to the seasonal variability of precipitation. The enrichment factors (EFs) of Cu, Cd and Zn were higher than 100, indicating a clear anthropogenic origin. The EF of Mn (50) was below 100, but an exclusively industrial origin is suggested. Positive Matrix Factorisation (PMF) was used for the source apportionment of the studied minor and trace elements in the soluble fraction. Four factors were identified from PMF, and their chemical profiles were compared with those calculated from known sources that were previously identified in Santander Bay: two industrial sources, the first of which was characterised by Zn and Mn, which contributes 62.5% of the total deposition flux of the studied elements; a traffic source; and a maritime source. Zinc and Mn are considered to be the most characteristic pollutants of the studied area.
•Industry, traffic and ship emissions affect the deposition of trace metals in Santander.•There is a seasonal variability of metals in the soluble fraction of the deposition.•More than 60% of the metal deposition flux originates from a nearby industrial area.•Mn and Zn are the most characteristic industrial tracers in Santander Bay.
Due to prolonged droughts in recent years, the use of rainwater tanks in urban areas has increased in Australia. In order to apportion sources of contribution to heavy metal and ionic contaminants in ...rainwater tanks in Brisbane, a subtropical urban area in Australia, monthly tank water samples (24 sites, 31 tanks) and concurrent bulk deposition samples (18 sites) were collected during mainly April 2007–March 2008. The samples were analysed for acid-soluble metals, soluble anions, total inorganic carbon and total organic carbon, and characteristics such as total solid and pH. The Positive Matrix Factorisation model, EPA PMF 3.0, was used to apportion sources of contribution to the contaminants. Four source factors were identified for the bulk deposition samples, including ‘crustal matter/sea salt’, ‘car exhausts/road side dust’, ‘industrial dust’ and ‘aged sea salt/secondary aerosols’. For the tank water samples, apart from these atmospheric deposition related factors which contributed in total to 65% of the total contaminant concentration on average, another six rainwater collection system related factors were identified, including ‘plumbing’, ‘building material’, ‘galvanizing’, ‘roofing’, ‘steel’ and ‘lead flashing/paint’ (contributing in total to 35% of the total concentration on average). The Australian Drinking Water Guideline for lead was exceeded in 15% of the tank water samples. The collection system related factors, in particular the ‘lead flashing/paint’ factor, contributed to 79% of the lead in the tank water samples on average. The concentration of lead in tank water was found to vary with various environmental and collection system factors, in particular the presence of lead flashing on the roof. The results also indicated the important role of sludge dynamics inside the tank on the quality of tank water.
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► We examine levels and sources of heavy metal and ionic contaminants in tank water. ► The Positive Matrix Factorisation model is used to apportion the sources. ► The drinking water guideline for lead was exceeded in 15% of the tank water samples. ► The collection system contributed to 79% of lead in tank water samples on average. ► The trends and contributing factors of tank water contaminants are investigated.
A longitudinal harvested rainwater quality monitoring study was undertaken at 6 sites within Selangor, Malaysia over a period of 8 months. Overall, harvested rainwater is of good quality, falling ...within the Malaysian recreational water quality Class IIB standards with exceptions for pH (18/92), ammonia (1/92), phosphates (3/92), and total coliforms (8/92). A large number of samples tested positive for Escherichia coli (22/92), total coliforms (64/92) and Chromobacterium violaceum (7/92), showing that disinfection of harvested rainwater is mandatory prior to reuse. 2/37 harvested rainwater samples exceeded lead limits in Malaysian drinking water standards, showing that consuming rainwater without additional treatment may pose a health risk. Mixing harvested rainwater with groundwater resulted in higher phosphates and total coliforms. Rainwater collected during the wet seasons have higher concentrations of suspended solids, turbidity, and Escherichia coli than dry seasons due to the antecedent dry period. Last but not least, both principal component analysis and positive matrix factorisation were conducted on 37 samples to apportion pollutant sources in harvested rainwater. 7 principal components were identified, namely: industrial dust, steel, roadside dust, faeces, organic decay, fertilisers, and plumbing. The results from principal component analysis and positive matrix factorisation were in agreement, although the latter identified mains water top-up as an additional factor responsible for dissolved solids. Both techniques are effective at apportioning pollutant sources in harvested rainwater, and show that a rainwater harvesting system should be designed carefully to reduce contributions from steel, plumbing, organic decay, bird faeces, industrial dust and roadside dust.
•Rainwater quality was monitored at 6 full-scale systems in Malaysia.•pH, NH3-N, PO4-P, and total coliforms did not meet Class IIB limits.•E. coli and C. violaceum detected, signalling need for disinfection.•Wet season had higher TSS, turbidity, and E. coli than dry season.•Multivariate PCA and PMF apportioned pollutant sources in rainwater.
The lockdowns held due to the COVID-19 pandemic conducted to changes in air quality. This study aimed to understand the variability of PM2.5 levels and composition in an urban-industrial area of the ...Lisbon Metropolitan Area and to identify the contribution of the different sources. The composition of PM2.5 was assessed for 24 elements (by PIXE), secondary inorganic ions and black carbon. The PM2.5 mean concentration for the period (December 2019 to November 2020) was 13 ± 11 μg.m−3. The most abundant species in PM2.5 were BC (19.9%), SO42− (15.4%), NO3− (11.6%) and NH4+ (5.3%). The impact of the restrictions imposed by the COVID-19 pandemic on the PM levels was found by comparison with the previous six years. The concentrations of all the PM2.5 components, except Al, Ba, Ca, Si and SO42−, were significantly higher in the winter/pre-confinement than in post-confinement period. A total of seven sources were identified by Positive Matrix Factorisation (PMF): soil, secondary sulphate, fuel-oil combustion, sea, vehicle non-exhaust, vehicle exhaust, and industry. Sources were greatly influenced by the restrictions imposed by the COVID-19 pandemic, with vehicle exhaust showing the sharpest decrease. Secondary sulphate predominated in summer/post-confinement. PM2.5 levels and composition also varied with the types of air mass trajectories.
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•A reduction of PM2.5 and its components was observed after the COVID-19 confinement.•Source apportionment with PMF identified seven sources.•Vehicle exhaust showed the sharpest decrease in the post-confinement.•Secondary sulphate predominated in summer/post-confinement.•PM2.5 composition also varied with the types of air mass trajectories.
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