Particulate Matter Species Research Articles

GHOST: a globally harmonised dataset of surface atmospheric composition measurements

Abstract. GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, of 227 different components from 38 reporting networks, are compiled, parsed, and standardised. The components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties. The main goal of GHOST is to provide a dataset that can serve as a basis for the reproducibility of model evaluation efforts across the community. Exhaustive efforts have been made towards standardising almost every facet of the information provided by major public reporting networks, which is saved in 21 data variables and 163 metadata variables. Extensive effort in particular is made towards the standardisation of measurement process information and station classifications. Extra complementary information is also associated with measurements, such as metadata from various popular gridded datasets (e.g. land use) and temporal classifications per measurement (e.g. day or night). A range of standardised network quality assurance flags is associated with each individual measurement. GHOST's own quality assurance is also performed and associated with measurements. Measurements pre-filtered by the default GHOST quality assurance are also provided. In this paper, we outline all steps undertaken to create the GHOST dataset and give insights and recommendations for data providers based on the experiences gleaned through our efforts. The GHOST dataset is made freely available via the following repository: https://doi.org/10.5281/zenodo.10637449 (Bowdalo, 2024a).

Seasonal and spatial variations of arsenic and its species in particulate matter in an urban environment of Brno, Czech Republic

The present paper deals with an analysis of total arsenic concentration using ICP-MS/MS and an analysis of concentration of several arsenic species, arsenite (AsIII), arsenate (AsV), monomethylarsonate (MMA), dimethylarsenite (DMA), and trimethylarsine oxide (TMAO), using HPLC-ICP-MS/MS in the PM10 fraction of airborne urban aerosol. The samples were collected during two campaigns, in the autumn of 2022 and in the winter of 2023, at three locations within the central European city of Brno, with the aim to evaluate the seasonal and spatial variations in the PM10 composition. The results confirmed only the seasonal variability in the content of the methylated arsenic species in PM10 influenced by biomethylation processes. To gain better understanding of the possible arsenic origin, a supplementary analysis of the total arsenic concentrations was performed in samples of different size fractions of particulate matter collected using ELPI + . Local emissions, including industrial activities and heating during the winter season, were suggested as the most likely predominant source contributing to the total As content in PM10.Graphical abstract

Chemical composition of indoor and outdoor PM2.5 in the eastern Arabian Peninsula

Water-soluble and trace metal species in fine particulate matter (PM2.5) were determined for indoor and outdoor environments in Doha, Qatar. During the study period, PM2.5 concentrations showed significant variability across several indoor locations ranging from 7.1 to 75.8 μg m−3, while the outdoor mass concentration range was 34.7–154.4 µg m−3. The indoor and outdoor PM2.5 levels did not exhibit statistically significant correlation, suggesting efficient building envelope protection against outdoor PM2.5 pollution. Rather than outdoor sources, human activities such as cooking, cleaning, and smoking were the most significant influence on chemical composition of indoor PM2.5. NH4+ concentration was insufficient to neutralize SO42− indoors and outdoors, indicating the predominant presence of NH4HSO4. The enrichment factors indicated that outdoor Fe, Mn, Co, Cr, and Ni in PM2.5 mostly originated from crustal sources. In contrast, the remaining outdoor trace metals (Cu, Zn, As, Cd, Pb, and V) were mainly derived from anthropogenic sources. The indoor/outdoor concentration ratios revealed significant indoor sources for NH4+ and Cu. The crustal matter, water-soluble ions, and sea salt explained 42%, 21%, and 1% of the indoor PM2.5 mass, respectively. The same groups sequentially constituted 41%, 16%, and 1% of the outdoor PM2.5 mass.

Seasonal variation of arsenic in PM10 and PMx in an urban park: The influence of vegetation-related biomethylation on the distribution of its organic species and air quality

Arsenic (As) levels in particulate matter (PM) are routinely monitored in cities of developed countries. Despite advances in the knowledge of its inorganic species in PM in urban areas, organic species are often overlooked with no information on their behaviour in urban parks – areas with increased potential for As biomethylation. Therefore, the aim of this study was to characterize As distribution, bioaccessibility, seasonal variation and speciation (AsIII, AsV, MMA, DMA and TMAO) in PMx–PM10 of an urban park. Two sites with different distance from the road were selected for winter and summer sampling. From the PM samples, we gravimetrically determined PM10 concentrations in the air and via ICP-MS the total As content there. To assess the portion of bioaccessible As, water extractable As content was analysed. Simultaneously, the As species in PM10 water extracts were analysed via coupling of HPLC with ICP-MS method. There was no seasonal difference in PM10 concentration in the park, probably due to the increased summer PM load related to recreational activities in the park and park design. Spatial distribution of total As in PM10 and As fractional distribution in PMx suggested that As mostly didn't originate from traffic although highest As content was observed in the fine fraction (PM2.5) related to combustion processes. However, significant winter increase of As (determined by AsIII and AsV) despite the unchanged concentration of PM10 indicated a decisive influence of household heating-related combustion and possibly influence of reduced vegetation density. As present in the PM10 was mostly in bioaccessible form. Seasonal influence of As biomethylation was clearly demonstrated on the TMAO specie during the summer campaign. Except the significant summer TMAO increase, the results also indicated the biomethylation influence on DMA. Therefore, an increased risk of exposure to organic As species in urban parks can be expected during summer.

Oxidative potential associated with reactive oxygen species of size-resolved particles: The important role of the specific sources

Oxidative potential (OP) is a predictor of particulate matter (PM) toxicity. Size-resolved PM and its components that influence OP values can be generated from several sources. However, There is little research have attempted to determine the PM toxicity generated from specific sources. This paper studied the OP characterization and reactive oxygen species (ROS) formation of particles from specific sources and their effects on human health. OP associated with ROS of size-resolved particles was analyzed by using dithiothreitol (DTT) method and electron paramagnetic resonance (EPR) spectroscopy technology. And OP and ROS deposition of specific source PM were calculated for health through the Multi-path particle deposition (MPPD) model. The results evidenced that the highest water-soluble OP (OPws) from traffic sources (OPm: 104.50 nmol min−1·ug−1; OPv: 160.15 nmol min−1·m−3) and the lowest from ocean sources (OPm: 22.25 nmol⋅min−1⋅ug−1; OPv: 54.16 nmol min−1·m−3). The OPws allocation in PM from different sources all have a unimodal pattern range from 0.4 to 3.2 μm. ROS (·OH) displayed the uniform trend as PM OPws, indicating that PM< 3.2 is the major contributor to adverse health impacts for size-resolved PM because of its enhanced oxidative activity compared with PM> 3.2. Furthermore, this study predicted the DTT consumption of PM were assigned to different components. Most DTT losses are attributed to the transition metals. For specific sources, transition metals dominates DTT losses, accounting for 38%–80% of DTT losses from different sources, followed by Hulis-C, accounting for 1%–10%. MPPD model calculates that over 66% of pulmonary DTT loss comes by PM< 3.2, and over 71% of pulmonary ROS generation from PM< 3.2. Among these sources of pollution, traffic emissions are the primary contributors to reactive oxygen species (ROS) in environmental particulate matter (PM). Therefore, emphasis should be placed on controlling traffic emissions, especially in coastal areas.

Increasing Contributions of Temperature-Dependent Oxygenated Organic Aerosol to Summertime Particulate Matter in New York City.

As part of the summer 2022 NYC-METS (New York City metropolitan Measurements of Emissions and TransformationS) campaign and the ASCENT (Atmospheric Science and Chemistry mEasurement NeTwork) observational network, speciated particulate matter was measured in real time in Manhattan and Queens, NY, with additional gas-phase measurements. Largely due to observed reductions in inorganic sulfate aerosol components over the 21st century, summertime aerosol composition in NYC has become predominantly organic (80-83%). Organic aerosol source apportionment via positive matrix factorization showed that this is dominated by secondary production as oxygenated organic aerosol (OOA) source factors comprised 73-76% of OA. Primary factors, including cooking-related organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA) comprised minor fractions of OA, only 13-15% and 10-11%, respectively. The two sites presented considerable spatiotemporal variations in OA source factor concentrations despite similar average PM2.5 concentrations. The less- and more-oxidized OOA factors exhibited clear temperature dependences at both sites with increased concentrations and greater degrees of oxidation at higher temperatures, including during a heatwave. With strong temperature sensitivity and minimal changes in summertime concentrations since 2001, secondary OA poses a particular challenge for air quality policy in NYC that will very likely be exacerbated by continued climate change and extreme heat events.

Hydrocarbon and particulate matter evolution in the exhaust manifold of a spark-ignited engine under cold-start operation

Cold-start, as defined by the time between first engine crank and three-way catalyst light-off, is responsible for a large percentage of NOx, unburned hydrocarbon, and particulate matter (PM) emissions in light-duty engines. Minimizing emissions during cold-start is a trade-off between achieving faster three-way catalyst light-off, and engine out emissions during that period. In this study, various exhaust gas and PM species were measured at three locations in the exhaust manifold using advanced hydrocarbon and PM speciation methods in addition to standard exhaust-gas-analyzers to characterize how or if these species evolve downstream of the exhaust port during cold-start operation. A gasoline direct injected spark ignited (DISI) single cylinder engine was run at 2-bar net indicated mean effective pressure (NIMEP) at spark timings ranging from normal-SI operation relevant early spark timing of -10 degrees after top dead center firing (dATDCf) to more cold-start-relevant retarded spark timing of 25dATDCf. At the retarded spark timings, the exhaust manifold gas temperature increased downstream of the exhaust port indicating the presence of oxidation reactions that counteract heat transfer losses; a trend also supported by oxidation of total hydrocarbons (THC), CO, and H2 observed downstream of the exhaust port. Additionally, increase in NOx emissions downstream of the exhaust port indicated presence of high temperature required to drive NOx chemistry. Aromatics were the largest exhaust hydrocarbon species group detected at all spark timings, while the paraffins were under-represented from their fuel composition fraction. Organic carbon accounted for more than 95% of the PM mass at exhaust port for all spark timings. At retarded spark timings, the PM mass reduced downstream of the exhaust port primarily driven by organic carbon oxidation. Particle number and size measurements at retarded spark timings indicated a reduction in particle count downstream of the exhaust port with an accompanying increase in particle size possibly due to agglomeration.

Chemical speciation of PM emissions from heavy-duty vehicles

Particulate matter (PM) is recognised as the deadliest form of air pollution, with vehicle emissions being one of its most important sources. Verification of compliance with the emission standards has been reported in numerous works. However, the detailed chemical speciation of PM is still very poorly understood. In this study, different heavy-duty vehicles (HDV) were tested on a chassis dynamometer under different driving cycles. Particulate matter (PM) emission factors for Euro V and Euro VI vehicles ranged from 24.9 to 51.2 mg kWh−1 and from 8.4 to 14.7 mg kWh−1, respectively. Major and trace elements, in their oxidised form, accounted for PM mass fractions between 6.3 and 58%. The lowest PM values were obtained for the Euro V vehicle fuelled with gas-to-liquids (GTL) under the world harmonised vehicle cycle (WHVC) with cold start, while the highest amounts were observed for the Euro VI vehicle fuelled with diesel over the hot start WHVC cycle. In general, Na and Sr were the most abundant elements, followed by Al and Ca. Several oxygenated organic compounds were detected, as far as we know, for the first time in exhaust particulates. Greater varieties (from C11 to C31 with no preference for odd versus even carbon numbers) and amounts of n-alkanes were detected in the emitted particulate matter (up to 7938 μg per g of PM) for the GTL fuelled vehicle (Euro V) tested by the WHVC driving cycle with cold start. The start-up phase mass fractions of ∑18PAHs were much higher (552 μg g−1) when the diesel-powered Euro V vehicle followed the VTT cycle than in the following tests (28.8–48.1 μg g−1). A homologous series of n-alkanoic acids, from C8 to C22, were found in the exhausts, accounting for particulate mass fractions ranging from 0.50 to 19.4 mg g−1, and peaking at C14, C16 and C18. Benzenedicarboxylic acids were observed at higher concentrations in emissions from Euro VI vehicles compared to Euro V. n-Alkanols from C8 to C30, with a clear dominance of C18 were detected in all PM samples. Ethylene glycol and other glycols were always present.

Comparative Study on the Use of Some Low-Cost Optical Particulate Sensors for Rapid Assessment of Local Air Quality Changes

Official air quality (AQ) stations are sporadically located in cities to monitor the anthropogenic pollutant levels. Consequently, their data cannot be used for further locations to estimate hidden changes in AQ and local emissions. Low-cost sensors (LCSs) of particulate matter (PM) in a network can help in solving this problem. However, the applicability of LCSs in terms of analytical performance requires careful evaluation. In this study, two types of pocket-size LCSs were tested at urban, suburban and background sites in Budapest, Hungary, to monitor PM1, PM2.5, PM10, and microclimatic parameters at high resolutions (1 s to 5 min). These devices utilize the method of laser irradiation and multi-angle light scattering on air-suspended particulates. A research-grade AQ monitor was applied as a reference. The LCSs showed acceptable accuracy for PM species in indoor/outdoor air even without calibration. Low PM readings (&lt;10 μg/m3) were generally handicapped by higher bias, even between sensors of the same type. The relative humidity (RH) slightly affected the PM readings of LCSs at RHs higher than 85%, necessitating field calibration. The air quality index was calculated to classify the extent of air pollution and to make predictions for human health effects. The LCSs were useful for detecting peaks stemming from emissions of motor vehicular traffic and residential cooking/heating activities.

Long-term trends of ultrafine and fine particle number concentrations in New York State: Apportioning between emissions and dispersion

In the past several decades, a variety of efforts have been made in the United States to improve air quality, and ambient particulate matter (PM) concentrations have been used as a metric to evaluate the efficacy of environmental policies. However, ambient PM concentrations result from a combination of source emission rates and meteorological conditions, which also change over time. Dispersion normalization was recently developed to reduce the influence of atmospheric dispersion and proved an effective approach that enhanced diel/seasonal patterns and thus provides improved source apportionment results for speciated PM mass and particle number concentration (PNC) measurements. In this work, dispersion normalization was incorporated in long-term trend analysis of 11–500 nm PNCs derived from particle number size distributions (PNSDs) measured in Rochester, NY from 2005 to 2019. Before dispersion normalization, a consistent reduction was observed across the measured size range during 2005–2012, while after 2012, the decreasing trends slowed down for accumulation mode PNCs (100–500 nm) and reversed for ultrafine particles (UFPs, 11–100 nm). Through dispersion normalization, we showed that these changes were driven by both emission rates and dispersion. Thus, it is important for future studies to assess the effects of the changing meteorological conditions when evaluating policy effectiveness on controlling PM concentrations. Before and after dispersion normalization, an evident increase in nucleation mode particles was observed during 2015–2019. This increase was possibly enabled by a cleaner atmosphere and will pose new challenges for future source apportionment and accountability studies.

Exposure models for particulate matter elemental concentrations in Southern California.

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM0.2), accumulation mode fine (PM0.2-2.5), and coarse (PM2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

Quantification of major particulate matter species from a single filter type using infrared spectroscopy – application to a large-scale monitoring network

Abstract. To enable chemical speciation, monitoring networks collect particulate matter (PM) on different filter media, each subjected to one or more analytical techniques to quantify PM composition present in the atmosphere. In this work, we propose an alternate approach that uses one filter type (teflon or polytetrafluoroethylene, PTFE, commonly used for aerosol sampling) and one analytical method, Fourier transform infrared (FT-IR) spectroscopy to measure almost all of the major constituents in the aerosol. In the proposed method, measurements using the typical multi-filter, multi-analytical techniques are retained at a limited number of sites and used as calibration standards. At all remaining sites, only sampling on PTFE and analysis by FT-IR is performed. This method takes advantage of the sensitivity of the mid-IR domain to various organic and inorganic functional groups and offers a fast and inexpensive way of exploring sample composition. As a proof of concept, multiple years of samples collected within the Interagency Monitoring of PROtected Visual Environment network (IMPROVE) are explored with the aim of retaining high quality predictions for a broad range of atmospheric compounds including mass, organic (OC), elemental (EC), and total (TC) carbon, sulfate, nitrate, and crustal elements. Findings suggest that models based on only 21 sites, covering spatial and seasonal trends in atmospheric composition, are stable over a 3 year period within the IMPROVE network with acceptable prediction accuracy (R2 &gt; 0.9, median bias less than 3 %) for most constituents. The major limitation is measuring nitrate as it is known to volatilize off of PTFE filters. Incorporating additional sites at low cost, partially replacing existing, more time- and cost-intensive techniques, or using the FT-IR data for quality control or substitute for missing data, are among the potential benefits of the one-filter, one-method approach.

Characterization of Particulate Matter Species in an Area Impacted by Aggregate and Limestone Mining North of San Antonio, TX, USA

Aggregate and limestone mining in San Antonio’s Bexar and Comal counties in Texas, USA, has caused considerable health concerns as of late. Aggregate mining actions can result in localized air quality issues in any neighborhood. Furthermore, heavy truck traffic, hauling, and transportation of the mined material contribute to pollution. In this research, PM species were sampled at four locations north of the San Antonio city limits. The data were collected using a TSI Air Quality Sampler that sampled PM1, PM2.5, PM4, PM10, wind speed, wind direction, temperature, and relative humidity. Continuous data with 1 min averages were recorded during the study period from August to September 2019. The instrument was stationed at every location for a period of 7 days each. The four locations were a ranch, an open field, a residential compound, and an elementary school. PM1 and PM2.5 concentration levels were lower compared to PM10 concentrations at all four studied sites. Our results suggest that PM concentrations are primarily impacted by mining activities. PM species were highest at the residential compound due to its proximity to an active mining area, resulting in deleterious health effects for neighbors living in the vicinity of the sampled site.

Evaluation of modelled LOTOS-EUROS with observational based PM10 source attribution

Due to its serious health impact particulate matter is one of the air pollutants subject to abatement policies. Information on the main sources responsible for high concentrations of pollutants is therefore crucial to enable effective policy measures. In this study we compared two different methods for attribution of particulate matter concentrations to different sources: A tagging approach within the regional chemistry transport model LOTOS-EUROS and an observational method using speciated particulate matter observations and Positive Matrix Factorisation (PMF). The methods have been applied for winter 2016/2017 over Eastern Germany where in wintertime high woodburning emissions, cold temperatures and regular easterly winds can lead to a build-up of pollutant concentrations. The comparison allows the validation of the modelled source attribution for a selection of source categories. The contributions for biomass and total combustion compare well between both methods providing trust in the determined contributions, applied emissions including their timing. The total contribution from combustion is estimated between 3.3-7.7 μg/m3 (PMF) and 3.3-7.2 μg/m3 (LOTOS-EUROS) for the 9 stations incorporated in the study. The temporal Pearson correlation coefficient ranges between 0.3-0.64 for total combustion and 0.34 and 0.7 for biomass combustion. The mean absolute contributions for traffic at background stations also compare well with most values between 1.5-2.0 μg/m3 for PMF and 1–1.6 μg/m3 for LOTOS-EUROS. A lack of correlation for this contribution however suggests that the model has difficulty in representing the source category traffic in a time consistent manner and developments are needed to improve the temporal distribution of the traffic emissions within the model. The modelled particulate matter concentrations displayed a 20–40% underestimation of the observed concentrations with an increasing bias during high pollution events. The underestimation showed a high correlation with the observed contribution from combustion and secondary particulate matter including ammonium sulfate and organic carbon suggesting that at least a part of the missing mass in LOTOS-EUROS is related to transformation of volatile combustion emissions, likely from solid fuels, to secondary particle mass and missing enhanced formation of sulfate. Implementation of these missing processes would help to improve the source attribution of particulate matter with the LOTOS-EUROS model.

Seasonal variations, temperature dependence, and sources of size-resolved PM components in Nanjing, east China

Size-segregated ambient particulate matter (PM) samples were collected seasonally in suburban Nanjing of east China from 2016 to 2017 and chemically speciated. In both fine (< 2.1 µm, PM2.1) and coarse (> 2.1 µm, PM>2.1) PM, organic carbon (OC) accounted for the highest fractions (26.9% ± 10.9% and 23.1% ± 9.35%) of all measured species, and NO3− lead in average concentrations of water-soluble inorganic ions (WSIIs). The size distributions of measured components were parameterized using geometric mean diameter (GMD). GMD values of NO3−, Cl−, OC, and PM for the whole size range varied from < 2.1 µm in winter to > 2.1 μm in warm seasons, which was due to the fact that the size distributions of semi-volatile components (e.g., NH4NO3, NH4Cl, and OC) had a dependency on the ambient temperature. Unlike OC, elemental carbon (EC), and elements, NH4+, NO3−, and SO42− exhibited an increase trend in GMD values with relative humidity, indicating that the hygroscopic growth might also play a role in driving seasonal changes of PM size distributions. Positive matrix factorization was performed using compositional data of fine and coarse particles, respectively. The secondary formation of inorganic salts contributing to the majority (> 70%) of fine PM and 20.2% ± 19.9% of speciated coarse PM. The remaining coarse PM content was attributed to a variety of dust sources. Considering that coarse and fine PM had comparable mass concentrations, more attention should be paid to local dust emissions in future air quality plans.

High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina

Abstract. This work presents the integration of a gas-phase and particulate atmospheric emission inventory (AEI) for Argentina in high spatial resolution (0.025∘×0.025∘; approx. 2.5 km×2.5 km) considering monthly variability from 1995 to 2020. The new inventory, called GEAA-AEIv3.0M, includes the following activities: energy production, fugitive emissions from oil and gas production, industrial fuel consumption and production, transport (road, maritime, and air), agriculture, livestock production, manufacturing, residential, commercial, and biomass and agricultural waste burning. The following species, grouped by atmospheric reactivity, are considered: (i) greenhouse gases (GHGs) – CO2, CH4, and N2O; (ii) ozone precursors – CO, NOx (NO+NO2), and non-methane volatile organic compounds (NMVOCs); (iii) acidifying gases – NH3 and SO2; and (iv) particulate matter (PM) – PM10, PM2.5, total suspended particles (TSPs), and black carbon (BC). The main objective of the GEAA-AEIv3.0M high-resolution emission inventory is to provide temporally resolved emission maps to support air quality and climate modeling oriented to evaluate pollutant mitigation strategies by local governments. This is of major concern, especially in countries where air quality monitoring networks are scarce, and the development of regional and seasonal emissions inventories would result in remarkable improvements in the time and space chemical prediction achieved by air quality models. Despite distinguishing among different sectoral and activity databases as well as introducing a novel spatial distribution approach based on census radii, our high-resolution GEAA-AEIv3.0M shows equivalent national-wide total emissions compared to the Third National Communication of Argentina (TNCA), which compiles annual GHG emissions from 1990 through 2014 (agreement within ±7.5 %). However, the GEAA-AEIv3.0M includes acidifying gases and PM species not considered in TNCA. Temporal comparisons were also performed against two international databases: Community Emissions Data System (CEDS) and EDGAR HTAPv5.0 for several pollutants; for EDGAR it also includes a spatial comparison. The agreement was acceptable within less than 30 % for most of the pollutants and activities, although a &gt;90 % discrepancy was obtained for methane from fuel production and fugitive emissions and &gt;120 % for biomass burning. Finally, the updated seasonal series clearly showed the pollution reduction due to the COVID-19 lockdown during the first quarter of year 2020 with respect to same months in previous years. Through an open-access data repository, we present the GEAA-AEIv3.0M inventory as the largest and more detailed spatial resolution dataset for the Argentine Republic, which includes monthly gridded emissions for 12 species and 15 stors between 1995 and 2020. The datasets are available at https://doi.org/10.17632/d6xrhpmzdp.2 (Puliafito et al., 2021), under a CC-BY 4 license.

Characteristics of PAHs, PCDD/Fs, PCBs and PCNs in atmospheric fine particulate matter in Dalian, China

Organic species in fine particulate matter (PM2.5) may exhibit significant health risks. The level, composition and sources of PM2.5-bound organic pollutants are temporally and spatially highly variable. In this study, the pollution characteristics and health risks of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polychlorinated naphthalenes (PCNs) and polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) in PM2.5 of Dalian were investigated. PM2.5-bound organic pollutants in Dalian were generally lower than other regions in China and other countries, significant seasonal changes were observed, higher levels appeared in winter than in summer. Concentrations of 16 PAHs were 2.07 ng/m3 and 13.99 ng/m3 in summer and winter, respectively. PAHs with 4-ring and 5-ring were the dominant components. Diagnostic analysis and principal component analysis (PCA) indicated that PAHs mainly originate from petroleum emissions and combustion. Concentrations of PCDD/Fs, PCBs and PCNs in PM2.5 ranged from 0.05 to 3.27, 0.04–0.65 and 0.05–1.42 pg/m3, respectively. PCDD/Fs and PCBs were mainly consisted of high-chlorinated homologues during the sampling period. High-chlorinated PCNs were dominated only in winter, while low-chlorinated PCNs were dominated in summer, industrial thermal activity was one of the main sources of PCNs. The high correlation coefficients of the concentration of PAHs, PCBs, PCNs, and PCDD/Fs with that of SO2 indicated that combustion sources contributed more to PM2.5-bound organic pollutants than that of motor vehicle emissions. The incremental lifetime cancer risk induced by PM2.5-bound POPs is relatively lower in Dalian than other regions.

Air quality and particulate matter speciation in a beauty salon and surrounding outdoor environment: Exploratory study

Beauty salons are considered occupational environments where the staff personnel and clients are exposed to high levels of airborne pollutants. In this study, air quality monitoring was carried out in Léon, Spain. Temperature, relative humidity, CO2, CO and particulate matter were continuously monitored inside and outside areas of the salon. PM10 was simultaneously collected onto filters. Volatile organic compounds and carbonyls were sampled inside the salon. Indoor PM10 concentrations exceeded the recommended 24-h guideline of 50 μg m−3. A detailed PM10 chemical characterisation included organic and elemental carbon, trace elements, water-soluble ions and organic speciation. Indoor vs outdoor ratios of PM10, as well as the majority of PM10-bound organic compounds, were >1. During work periods, organic carbon accounted for 29.5 ± 1.8 and 16.2 ± 4.5 %wt. of PM10 indoors and outdoors, respectively. More than 200 individual organic compounds were detected in the PM10, including aliphatic alcohols, fatty acids, phthalates, glycerol derivatives, fatty acid alkyl esters, phenolic compounds, alkanes and polycyclic aromatic hydrocarbons, among others. Many of them are part of formulations widely used in hair cosmetics, personal care and cleaning products. Inadequate ventilation, working activities and the use of specific products can greatly contribute to high indoor levels of organic air pollutants. Formaldehyde revealed a cancer risk of 4.6 × 10−6, higher than the guideline level, suggesting a “possible risk” for workers. The total excess lifetime cancer risk from exposure to multiple compounds was 9.3 × 10−6, which is lower than the acceptable risk, but not negligible.

Evaluation of Oxidative Potential of Pyrenequinone Isomers by the Dithiothreitol (DTT) Assay

Atmospheric quinoid polycyclic aromatic hydrocarbons (PAHQs) have adverse health effects as redox-active species in particulate matter (PM). Several PAHQs are known to be very reactive in the dithiothreitol (DTT) assay; however, it is unclear if pyrenequinones, their parent pyrene is one of the most abundant polycyclic aromatic hydrocarbons (PAHs) in the atmosphere, contribute to the loss of DTT in PM extracts. Herein, by employing the DTT assay, we evaluated oxidative potentials of three pyrenequinone isomers (4,5-pyrenequinone [4,5-PyQ] and a mixture of 1,6-/1,8-pyrenequinones [1,6-/1,8-PyQ]), along with 9,10-phenanthrenequinone (PQN), 1,2-naphthoquinone (1,2-NQ), and 1,4-naphthoquinone (1,4-NQ), of which DTT loss rates were examined previously. Our results demonstrate that the DTT consumption by ortho-type PAHQs is fast, particularly by 4,5-PyQ. The order of DTT loss rate by the PAHQs tested in this study was as follows: 4,5-PyQ ∼ PQN > 1,2-NQ > 1,4-NQ ∼ 1,6-/1,8-PyQ. 4,5-PyQ was confirmed to be active in the DTT assay for the first time in this study. The DTT consumption rate by 4,5-PyQ is 14.6 ± 0.8 mol/min/PAHQ-mol, which is comparable to that of PQN (14.4 ± 0.1 mol/min/PAHQ-mol) known as the most active PAHQs by the DTT assay so far.

In silico study of possible roles of relative humidity in chemical speciation on ambient particulate matter using PHREEQC

One of the components of ambient particulate matter (PM) that contributes to its possible toxicity are metals. Moreover, other factors in the environment, such as the presence of water as indoor humidity, can also affect the behavior of PM either physically or chemically. Chemically, the presence of water may affect the forms or species in which the metal is present. The present study aims to utilize PHREEQC, a geochemical modeling software, in the evaluation of species formation of metals in particulate matter. From all these runs, it was found that the possible roles of water in metal speciation in particulate matter are as follows: (1) in the presence of water as relative humidity (RH), metals in PM may be hydroxylated, which may facilitate further uptake of water and affect the reactivity of the PM, (2) in addition to the RH, the volume of the room may affect the species concentrations present, and (3) inferences may be made regarding the possible initial form of the metals, which may be oxides, as shown by the formation of hydroxylated metals upon exposure of metals to water vapor. These observations give a brief insight into how water may possibly interact and affect the possible toxicity of metal species in PM.