Airborne particles and pollutant gases are of increasing concern due to their adverse health effects, necessitating a thorough understanding of their composition, sources, spatial and temporal trends ...for effective air quality management. This study is part of a source apportionment study in Auckland, a dynamic urban environment with complex air quality challenges in an isolated Southern Ocean setting. Over the 2006–2016 period, concentrations of PM2.5, CO, NO2, and SO2 consistently decreased at all 4 monitoring sites indicating the impacts of control measures. The sources impacting the four sites were identified using the positive matrix factorisation (PMF) receptor model. Common sources affecting these sites included motor vehicles (both petrol and diesel), biomass burning, sea salt, sulphate/marine diesel, and soil/road dust. While motor vehicle emissions and biomass burning emerged as the primary contributors to PM2.5, BC, NO2, CO, and SO2, motor vehicle contributions declined due to advancements in fuel formulation and engine technology despite increased vehicle numbers. Biomass burning contributed substantially to winter PM2.5 concentrations driven by domestic heating practices. However, the introduction of alternative heating technologies mitigated the upward trends in biomass burning emissions despite an increasing residential population. Soil/road dust contributions varied by site, influenced by meteorological conditions and local activities, with implications for site-specific air quality management. Sulphate/marine diesel concentrations exhibited seasonal variability, reflecting the impact of both shipping emissions and natural sources. Urban sulphate concentrations decreased due to regulations requiring the introduction of low-sulphur automotive gasoline and diesel fuels. Sea salt, a naturally occurring source, posed challenges for management efforts with concentrations trending downwards over time, possibly linked to climate patterns. This study's source apportionment analysis provides critical insights into Auckland's air quality. The findings inform policy development, air quality management, and health impact assessments, benefiting both the Auckland region and New Zealand as a whole. Ongoing monitoring and emissions reduction strategies, particularly targeting motor vehicles and biomass burning, are pivotal for enhancing air quality and public health in the region.
•PM2.5 and gas sources during 2006-16 in Auckland, NZ were identified by PMF.•Sources were vehicles, biomass burning, sea salt, sulphate/diesel, and road dust.•PM2.5, CO, NO2, and SO2 decreased at all sites due to control measures.
The multi-scale chemical characteristics and source apportionment of volatile organic compounds (VOCs) were analysed in Tianjin, China, using 1-hr resolution VOC-species data between November 1, 2018 ...and March 15, 2019. The average total VOC (TVOC) concentration was 30.6 ppbv during the heating season. The alkanes accounted for highest proportion of the TVOC, while the alkenes were the predominant species forming ozone, especially ethylene. Compared to the clean period, the concentration of acetylene during the haze events showed highest increase rate, followed by the ethane; and the concentrations and proportions of alkanes and alkenes were highest during the growth stage (GS) of haze events. The multi-scale apportionment results suggested petrochemical industry and solvent usage (PI/SU, 31.2%), vehicle emissions and liquefied petroleum gas (VE/LPG, 20.5%), and combustion emissions (CE, 19.1%) were the main VOC sources during the heating season. Compared to the clean period, the contributions of PI/SU, VE/LPG, CE, and refinery emissions notably increased during the haze events, while that of gasoline evaporation decreased. The contributions of PI/SU and RPI showed significantly increase during the GS of haze events, whereas most sources decreased during the dissipation stage of haze events. Diurnal-variations in source contributions during the haze events were clearer than the clean period, and the contributions of PI/SU, VE/LPG, and CE during the haze events were markedly higher at night. These findings provide valuable information to inform effective VOC control and prevention measures with specific relevance for the control of ozone pollution in Tianjin.
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•Acetylene increased most significantly during the haze events than the clean period.•Ethylene was a predominant species forming ozone.•Gasoline evaporation significantly decreased during the haze events.•Petrochemical industry and solvent usage notably increased in the haze-event growth-stage.
The chemical species in PM2.5 and air pollutant concentration data with 1-hr resolution were monitored synchronously between 15 November 2018 and 20 January 2019 in Linfen, China, which were analysed ...for multiple temporal patterns, and PM2.5 source apportionment using positive matrix factorisation (PMF) modelling coupled with online chemical species data was conducted to obtain the apportionment results of distinct temporal patterns. The mean concentration of PM2.5 was 124 μg/m3 during the heating period, and NO3− and organic carbon (OC) were the dominant species. The concentrations and percentages of NO3−, SO42−, and OC increased notably during the growth periods of haze events, thereby indicating secondary particle formation. Six factors were identified by the PMF model during the heating period, including vehicular emissions (VE: 26.5%), secondary nitrate (SN: 16.5%), coal combustion and industrial emissions (CC&IE: 25.7%), secondary sulfate and secondary organic carbon (SS&SOC: 24.4%), biomass burning (BB: 1.0%), and crustal dust (CD: 5.9%). The primary sources of PM2.5 on clean days were CD (33.3%), VE (23.1%), and SS&SOC (20.6%), while they were CC&IE (32.9%) and SS&SOC (28.3%) during the haze events. The contributions of secondary sources and CC&IE increased rapidly during the growth periods of haze events, while that of CD increased during the dissipation period. Diurnal variations in the contribution of secondary sources were mainly related to the accumulation and transformation of corresponding gaseous precursors. In comparison, contributions of CC&IE and VE varied as a function of the domestic heating load and peak levels occurred during the morning and evening rush hours. High contributions of major sources (CC&IE and SS&SOC) during haze events originated mainly from the north and south, while high contribution of a major source (CD) on clean days was from the northwest.
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•Temporal patterns for species and sources of PM2.5 along with gaseous pollutants were discussed.•Sources contributions on clean days, and in haze events and different haze stages were analysed.•Secondary sources, coal combustion, industrial emissions were primary sources in haze events.•High impact of crustal dust was found on clean days and in the dissipation stages of haze events.
Source apportionment of PM2.5 using a positive matrix factorisation (PMF) model coupled with 1–hr resolution online air pollutant dataset, and applied in a most polluted city of China.
Kerbside concentrations of NOx, black carbon (BC), total number of particles (diameter > 4 nm) and number size distribution (28–410 nm) were measured at a busy street canyon in Stockholm in 2006 and ...2013. Over this period, there was an important change in the vehicle fleet due to a strong dieselisation process of light-duty vehicles and technological improvement of vehicle engines. This study assesses the impact of these changes on ambient concentrations and particle emission factors (EF). EF were calculated by using a novel approach which combines the NOx tracer method with positive matrix factorisation (PMF) applied to particle number size distributions. NOx concentrations remained rather constant between these two years, whereas a large decrease in particle concentrations was observed, being on average 60% for BC, 50% for total particle number, and 53% for particles in the range 28–100 nm. The PMF analysis yielded three factors that were identified as contributions from gasoline vehicles, diesel fleet, and urban background. This separation allowed the calculation of the average vehicle EF for each particle metric per fuel type. In general, gasoline EF were lower than diesel EF, and EF for 2013 were lower than the ones derived for 2006. The EFBC decreased 77% for both gasoline and diesel fleets, whereas the particle number EF reduction was higher for the gasoline (79%) than for the diesel (37%) fleet. Our EF are consistent with results from other on-road studies, which reinforces that the proposed methodology is suitable for EF determination and to assess the effectiveness of policies implemented to reduce vehicle exhaust emissions. However, our EF are much higher than EF simulated with traffic emission models (HBEFA and COPERT) that are based on dynamometer measurements, except for EFBC for diesel vehicles. This finding suggests that the EF from the two leading models in Europe should be revised for BC (gasoline vehicles) and particle number (all vehicles), since they are used to compile national inventories for the road transportation sector and also to assess their associated health effects. Using the calculated kerbside EF, we estimated that the traffic emissions were lower in 2013 compared to 2006 with a 61% reduction for BC (due to decreases in both gasoline and diesel emissions), and 34–45% for particle number (reduction only in gasoline emissions). Limitations of the application of these EF to other studies are also discussed.
•Traffic-related pollutants were measured within a street canyon in 2006 and 2013.•Emission factors (EF) were calculated from these measurements for both years.•Large reduction in black carbon (BC) and particle number (PN) concentrations.•EF are consistent with on-road studies but higher than simulated by emission models.•Traffic emissions largely decreased for BC (61%), but less for PN (34%).
Estimates of tyre and brake wear emission factors are presented, derived from data collected from roadside and urban background sites on the premises of the University of Birmingham, located in the ...UK's second largest city. Size-fractionated particulate matter samples were collected at both sites concurrently in the spring/summer of 2019 and analysed for elemental concentrations and magnetic properties. Using Positive Matrix Factorisation (PMF), three sources were identified in the roadside mass increment of the 1.0–9.9 μm stages of MOUDI impactors located at both sites, namely: brake dust (7.1%); tyre dust (9.6%); and crustal (83%). The large fraction of the mass apportioned to crustal material was suspected to be mainly from a nearby construction site rather than resuspension of road dust. By using Ba and Zn as elemental tracers, brake and tyre wear emission factors were estimated as 7.4 mg/veh.km and 9.9 mg/veh.km, respectively, compared with the PMF-derived equivalent values of 4.4 mg/veh.km and 11 mg/veh.km. Based on the magnetic measurements, an emission factor can be estimated independently for brake dust of 4.7 mg/veh.km. A further analysis was carried out on the concurrently measured roadside increment in the particle number size distribution (10 nm-10 μm). Four factors were identified in the hourly measurements: traffic exhaust nucleation; traffic exhaust solid particles; windblown dust; and an unknown source. The high increment of the windblown dust factor, 3.2 μg/m3, was comparable in magnitude to the crustal factor measured using the MOUDI samples (3.5 μg/m3). The latter's polar plot indicated that this factor was dominated by a large neighbouring construction site. The number emission factors of the exhaust solid particle and exhaust nucleation factors were estimated as 2.8 and 1.9 x 1012/veh.km, respectively.
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•Simultaneous collection of size-fractionated particle samples at roadside and background.•Particle size distributions (10 nm - 10 μm) measured simultaneously at both sites.•Elemental tracers and PMF used to estimate emission factors for brake and tyre wear.•Particle magnetic properties used to estimate an emission factor for brake wear.•Good agreement between emission factors estimated by different methods.
This study aimed to determine the metals in ambient PM2.5 in the expanding industrial metropolitan area of Rayong Province for health risk assessment and source apportionment from May 2022 to April ...2023, covering wet and dry seasons. The mean annual PM2.5 concentration was 15.2 ± 12.0 μg m−3, whereas that of wet and dry seasons were 8.4 ± 5.4 μg m−3 and 21.8 ± 12.9 μg m−3, respectively. The annual PM2.5 level exceeded the limit set by the World Health Organization (WHO) (5 μg m−3) and the standard of Thailand (15 μg m−3). A substantial decrease in the Cd, Pb, Zn, Cu, Fe, Mn, and K concentrations was observed during the wet season compared with that of the dry season. The levels of annual Cr in PM2.5 were 40 times higher than the WHO limit. Cd, Pb, and Zn are tracers of anthropogenic activities. Using the enrichment factor (EF) and Igeo, the contamination of As, Cd, Pb, and Zn suggested that the initial Eastern Economic Corridor (EEC) in Rayong Province was highly polluted. The results of the non-carcinogenic risk indicated that human health was notably affected by toxic metals in PM2.5, and the Cr-related carcinogenic risk in PM2.5 exposure suggested a safe or reasonable risk level (10−6 to 10−4). Exposure to toxic metals in PM2.5 increases the risk of developing cancer in adults, potentially owing to the accumulation of these metals within the tissues in the body. Positive matrix factorisation (PMF) suggested that the source apportionment of PM2.5-bound heavy metals was motor vehicles (34.7%), industrial activities (26.3%), biomass burning (22.7%), and road dust (18.5%).
•Metals including Cd, Pb, and Zn, bound to PM2.5 were detected as emissions from anthropogenic sources in the initial EEC area in Rayong province.•The levels of As, Cd, Cr, and Mn in PM2.5 have a significant effect on the long-term development of cancer in humans.•The carcinogenic risk associated with Cr in PM2.5 increases the risk of developing cancer in adults.•The potential health risk of toxic metals in PM2.5 was related to motor vehicles (34.7%), industrial activities (26.3%), biomass burning (22.7%), and road dust (18.5%).
Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and ...underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn–Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies.
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•PMF and GWR identified potential sources and spatial interactions for PTEs and PAHs.•Relationships between PAHs and PTEs were spatially varying in the topsoil of Dublin.•Negative relationships between PAHs and Cr-group were attributed to geogenic factors.•Increased positive relationships between PAHs and Pb-group occurred from west to east.•Prevailing wind direction contributed to spreading contamination for PAHs and Pb.
In this study we carry out receptor modelling via Positive Matrix Factorisation (PMF) to identify and quantify the main natural and anthropogenic sources of indoor PM2.5 at an urban background site ...in the island of Malta. Quartz and PTFE filters were collected, analysed gravimetrically and chemically, using ICP-MS, IC and an OC-EC aerosol analyser to determine the concentrations of PM2.5, 18 elements, 5 ions, organic carbon (OC) and elemental carbon (EC). The EPA PMF was used to identify 8 factors that were affecting the receptor site. Seven outdoor sources were identified: ammonium sulfate (31%), traffic (10%), shipping (10%), sea salt (9%), fireworks (4%), Saharan dust (2%) and industrial (2%). An indoor factor was also identified, which contributed 26% to the indoor PM2.5. Cooking and e-cigarette smoking were identified as the main contributors to the indoor factor. The mean indoor PM2.5 concentration (5.7 μg m−3) at the receptor site was slightly higher than the WHO AQG limit of 5 μg m−3. Uniquely for Malta, we have isolated a fireworks factor for indoor PM2.5. Fireworks have been identified as being responsible for most of the Sb and Ba and hence are of great concern due to the health implications of these toxic elements.
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•Eight sources identified for PM2.5 at an indoor residential site located Malta.•(NH4)2SO4 was the highest contributor to indoor PM2.5, followed by indoor sources.•6% contribution by fireworks to indoor PM2.5 during the summer months.•Cooking and e-cigarettes were identified as contributors to indoor PM2.5.•Indoor air quality can be improved by reducing Mediterranean shipping emissions.
Some of the world's highest air pollution episodes occur in Delhi, India and studies have shown particulate matter (PM) is the leading air pollutant to cause adverse health effects on Delhi's ...population. It is therefore vital to chart sources of PM over long time periods to effectively identify trends, particularly as multiple air quality mitigation measures have been implemented in Delhi over the past 10 years but remain unevaluated. An automated offline aerosol mass spectrometry (AMS) method has been developed which has enabled high-throughput analysis of PM filters. This novel offline-AMS method uses an organic solvent mix of acetone and water to deliver high extraction recoveries of organic aerosol (OA) (95.4 ± 8.3%). Positive matrix factorisation (PMF) source apportionment was performed on the OA fraction extracted from PM10 filter samples collected in Delhi in 2011, 2015 and 2018 to provide snapshots of the responses of OA to changes in sources in Delhi. The nine factors of OA resolved by PMF group into four primary source categories: traffic, cooking, coal-combustion and burning-related (solid fuel or open burning). Burning-related OA made the largest contribution during the winter and post-monsoon, when total OA concentrations were at their highest. Annual mean burning-related OA concentrations declined by 47% between 2015 and 2018, likely associated with the 2015 ban on open waste burning and controls and incentives to reduce crop-residue burning. Compositional analysis of OA factors shows municipal waste burning tracers still present in 2018, indicating further scope to reduce burning-related OA. The closure of the two coal power stations, along with initiatives to decrease coal use in industry, businesses, and residential homes, resulted in a significant decrease (87%) in coal-combustion OA. This corresponds to a 17% reduction in total OA, which shows the effectiveness of these measures in reducing PM10. Increases in traffic OA appear to have been offset by the introduction of the Bharat stage emissions standards for vehicles as the increases do not reflect the rapid increase in registered vehicles. However, daytime restrictions on heavy goods vehicles (HGVs) entering the city is linked to large increases in PM10 during the winter and post-monsoon, likely because the large influx of diesel-engine HGVs during the early mornings and evenings is timed with a particularly low planetary boundary layer height that enhances surface concentrations.
Average concentrations of PM10 OA factors during each of the four seasons of the years 2011, 2015 and 2018. Display omitted
•A novel offline aerosol mass spectrometry method has been developed.•Burning-related OA was the largest contributor during peak PM concentrations.•Despite a ban, open waste and crop-residue burning were active in 2018.•Coal power station closures and coal-use reductions likely decreased OA.•Heavy goods vehicles restrictions may inadvertently cause increases in OA.
Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. ...Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM1), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 min for the NR-PM1 and BC, and 24 h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM1 sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24 h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.
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