Development of a Water-Soluble Filter for Potential Use in the Toxicity Evaluation of Ambient Particulate Matter

Diurnal trends in redox characteristics of water-soluble and -insoluble PM components

In recent decades, several cell-based and acellular methods have been developed to evaluate ambient particulate matter (PM) toxicity. Although cell-based methods provide a more comprehensive assessment of PM toxicity, their results are difficult to comprehend due to the diversity in cellular endpoints, cell types, and assays and the interference of PM chemical components with some of the assays' techniques. In this review, we attempt to clarify some of these issues. We first discuss the morphological and immunological differences among various macrophage and epithelial cells, belonging to the respiratory systems of human and murine species, used in the in vitro studies evaluating PM toxicity. Then, we review the current state of knowledge on the role of different PM chemical components and the relevance of atmospheric processing and aging of aerosols in the respiratory toxicity of PM. Our review demonstrates the need to adopt more physiologically relevant cellular models such as epithelial (or endothelial) cells instead of macrophages for oxidative stress measurement. We suggest limiting macrophages for investigating other cellular responses (e.g., phagocytosis, inflammation, and DNA damage). Unlike monocultures (of macrophages and epithelial cells), which are generally used to study the direct effects of PM on a given cell type, the use of co-culture systems should be encouraged to investigate a more comprehensive effect of PM in the presence of other cells. Our review has identified two major groups of toxic PM chemical species from the existing literature, i.e., metals (Fe, Cu, Mn, Cr, Ni, and Zn) and organic compounds (PAHs, ketones, aliphatic and chlorinated hydrocarbons, and quinones). However, the relative toxicities of these species are still a matter of debate. Finally, the results of the existing studies investigating the effect of aging on PM toxicity are ambiguous, with varying results due to different cell types, different aging conditions, and the presence/absence of specific oxidants. More systematic studies are necessary to understand the role of different SOA precursors, interactions between different PM components, and aging conditions in the overall toxicity of PM. We anticipate that our review will guide future investigations by helping researchers choose appropriate cell models, resulting in a more meaningful interpretation of cell-based assays and thus ultimately leading to a better understanding of the health effects of PM exposure.

Macrophage reactive oxygen species activity of water-soluble and water-insoluble fractions of ambient coarse, PM2.5 and ultrafine particulate matter (PM) in Los Angeles

Airborne Fungal and Bacterial Components in PM<sub>1</sub> Dust from Biofuel Plants

In vitro toxicity evaluation of heavy metals in urban air particulate matter on human lung epithelial cells

Winter and spring variation in sources, chemical components and toxicological responses of urban air particulate matter samples in Guangzhou, China

The determination of organic composition in atmospheric particulate matter (PM) is of great importance in understanding how PM affects human health, environment, climate, and ecosystem. Organic components are also the scientific basis for emission source tracking, PM regulation and risk management. Therefore, the molecular characterization of the organic fraction of PM has become one of the priority research issues in the field of environmental analysis. Due to the extreme complexity of PM samples, chromatographic methods have been the chief selection. The common procedure for the analysis of organic components in PM includes several steps: sample collection on the fiber filters, sample preparation (transform the sample into a form suitable for chromatographic analysis), analysis by chromatographic methods. Among these steps, the sample preparation methods will largely determine the throughput and the data quality. Solvent extraction methods followed by sample pretreatment (e. g. pre-separation, derivatization, pre-concentration) have long been used for PM sample analysis, and thermal desorption methods have also mainly focused on the non-polar organic component analysis in PM. In this paper, the sample preparation methods prior to chromatographic analysis of organic components in PM are reviewed comprehensively, and the corresponding merits and limitations of each method are also briefly discussed.

Exposures to air pollution in the form of particulate matter (PM) can result in excess production of reactive oxygen species (ROS) in the respiratory system, potentially causing both localized cellular injury and triggering a systemic inflammatory response. PM-induced inflammation in the lung is modulated in large part by alveolar macrophages and their biochemical signaling, including production of inflammatory cytokines, the primary mechanism via which inflammation is initiated and sustained. We developed a robust, relevant, and flexible method employing a rat alveolar macrophage cell line (NR8383) which can be applied to routine samples of PM from air quality monitoring sites to gain insight into the drivers of PM toxicity that lead to oxidative stress and inflammation. Method performance was characterized using extracts of ambient and vehicular engine exhaust PM samples. Our results indicate that the reproducibility and the sensitivity of the method are satisfactory and comparisons between PM samples can be made with good precision. The average relative percent difference for all genes detected during 10 different exposures was 17.1%. Our analysis demonstrated that 71% of genes had an average signal to noise ratio (SNR) ≥ 3. Our time course study suggests that 4 h may be an optimal in vitro exposure time for observing short-term effects of PM and capturing the initial steps of inflammatory signaling. The 4 h exposure resulted in the detection of 57 genes (out of 84 total), of which 86% had altered expression. Similarities and conserved gene signaling regulation among the PM samples were demonstrated through hierarchical clustering and other analyses. Overlying the core congruent patterns were differentially regulated genes that resulted in distinct sample-specific gene expression "fingerprints." Consistent upregulation of Il1f5 and downregulation of Ccr7 was observed across all samples, while TNFα was upregulated in half of the samples and downregulated in the other half. Overall, this PM-induced cytokine expression assay could be effectively integrated into health studies and air quality monitoring programs to better understand relationships between specific PM components, oxidative stress activity and inflammatory signaling potential.

Differential inflammatory potential of particulate matter (PM) size fractions from imperial valley, CA

Airborne particulate matter (PM) toxicity is of growing interest as diesel exhaust particles have been classified as carcinogenic to humans. However, PM is a mixture of chemicals, and respective contribution of organic and inorganic fractions to PM toxicity remains unclear. Thus, we analysed the link between chemical composition of PM samples and bulky DNA adduct formation supported by CYP1A1 and 1B1 genes induction and catalytic activities. We used six native PM samples, collected in industrial, rural or urban areas, either during the summer or winter, and carried out our experiments on the human bronchial epithelial cell line BEAS-2B. Cell exposure to PM resulted in CYP1A1 and CYP1B1 genes induction. This was followed by an increase in EROD activity, leading to bulky DNA adduct formation in exposed cells. Bulky DNA adduct intensity was associated to global EROD activity, but this activity was poorly correlated with CYPs mRNA levels. However, EROD activity was correlated with both metal and polycyclic aromatic hydrocarbon (PAH) content. Finally, principal components analysis revealed three clusters for PM chemicals, and suggested synergistic effects of metals and PAHs on bulky DNA adduct levels. This study showed the ability of PM samples from various origins to generate bulky DNA adducts in BEAS-2B cells. This formation was promoted by increased expression and activity of CYPs involved in PAHs activation into reactive metabolites. However, our data highlight that bulky DNA adduct formation is only partly explained by PM content in PAHs, and suggest that inorganic compounds, such as iron, may promote bulky DNA adduct formation by supporting CYP activity.

Abstract. Particulate matter (PM) is the air pollutant that causes the greatest deleterious health effects across the world, so PM is routinely monitored within air quality networks, usually in respect to PM mass or number in different size fractions. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric that aims to classify PM in respect to its oxidising ability in the lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June 2018 and May 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations, which involved the quantification of a large number of PM constituents and the OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable over time and space: Bern-Bollwerk, an urban-traffic sampling site, had the greatest levels of OPv among the Swiss sites (especially when considering OPvAA), with more rural locations such as Payerne experiencing a lower OPv. However, urban-background and suburban sites experienced a significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were 0.4–4.1 nmolmin-1m-3, 0.6–3.0 nmolmin-1m-3, and 0.3–0.7 nmol H2O2 m−3 for OPvAA, OPvDTT, and OPvDCFH, respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources that were identified contributed to OPv, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass) on average. A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely copper, zinc, iron, tin, antimony, manganese, and cadmium, as well as three specific wood-burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium), as the most important PM components to explain and predict OPv. The combination of a metal and a wood-burning-specific tracer led to the best-performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical; the models simply required a variable representing the emission source or process to be present. This analysis strongly suggests that anthropogenic and locally emitting road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv and presumably biological harm reductions in Switzerland.

Particulate matter (PM) is the air pollutant which causes the greatest deleterious heath effects across the world and PM is routinely monitored within air quality networks where PM mass according to its size, and sometimes number are reported. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric which aims to classify PM in respect to its oxidising ability in lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June, 2018 and May, 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations which involved the quantification of a large number of PM constituents and OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable in time and space with Bern-Bollwerk, an urban-traffic sampling site having the greatest levels of OPv among the Swiss sites (especially when considering ), with more rural locations such as Payerne experiencing lower OPv. However, urban-background and suburban sites did experience significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were: 0.4–4.1, 0.6–3.0, and 0.3–0.7 nmol min−1 m−3 for the , , and respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources which were identified contributed to OPv on average, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass). A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely: copper, zinc, iron, tin, antimony and somewhat manganese and cadmium as well as three specific wood burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium) were the most important PM components to explain and predict OPv. The combination of a metal and a wood burning specific tracer led to the best performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical, the models simply required a variable to be present to represent the emission source or process. The modelling process also showed that may be a more specific metric for OP than the other assays employed in this work. This analysis strongly suggests that the anthropogenic and locally emitted road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv, and presumably biological harm reductions in Switzerland.

<strong class="journal-contentHeaderColor">Abstract.</strong> Particulate matter (PM) is the air pollutant that causes the greatest deleterious health effects across the world, so PM is routinely monitored within air quality networks, usually in respect to PM mass or number in different size fractions. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric that aims to classify PM in respect to its oxidising ability in the lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June 2018 and May 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations, which involved the quantification of a large number of PM constituents and the OP for both PM<span class="inline-formula">₁₀</span> and PM<span class="inline-formula">_2.5</span>. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OP<span class="inline-formula">_v</span> (OP by air volume) was found to be variable over time and space: Bern-Bollwerk, an urban-traffic sampling site, had the greatest levels of OP<span class="inline-formula">_v</span> among the Swiss sites (especially when considering <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M9" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mtext>OP</mtext><mi mathvariant="normal">v</mi><mi mathvariant="normal">AA</mi></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="29pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="6ea9d0c9d405312dc379a6b81df0cdc4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00001.svg" width="29pt" height="16pt" src="acp-22-7029-2022-ie00001.png"/></svg:svg></span></span>), with more rural locations such as Payerne experiencing a lower OP<span class="inline-formula">_v</span>. However, urban-background and suburban sites experienced a significant OP<span class="inline-formula">_v</span> enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were 0.4–4.1 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M12" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">nmol</mi><mspace linebreak="nobreak" width="0.125em"/><msup><mi mathvariant="normal">min</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="57b4bf826357bed61feddce78770afc1"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00002.svg" width="76pt" height="13pt" src="acp-22-7029-2022-ie00002.png"/></svg:svg></span></span>, 0.6–3.0 <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M13" display="inline" overflow="scroll" dspmath="mathml"><mrow class="unit"><mi mathvariant="normal">nmol</mi><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">min</mi><mrow><mo>-</mo><mn mathvariant="normal">1</mn></mrow></msup><mspace width="0.125em" linebreak="nobreak"/><msup><mi mathvariant="normal">m</mi><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="76pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="78d33a315fc5d2665ea20bfb0fec7a33"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00003.svg" width="76pt" height="13pt" src="acp-22-7029-2022-ie00003.png"/></svg:svg></span></span>, and 0.3–0.7 <span class="inline-formula">nmol H₂O₂ m^−3</span> for <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mtext>OP</mtext><mi mathvariant="normal">v</mi><mi mathvariant="normal">AA</mi></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="29pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="02c202363905e826f456402c89d2d969"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00004.svg" width="29pt" height="16pt" src="acp-22-7029-2022-ie00004.png"/></svg:svg></span></span>, <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mtext>OP</mtext><mi mathvariant="normal">v</mi><mi mathvariant="normal">DTT</mi></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="34pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="e357dd246127c04aa854f41c88b6fc26"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00005.svg" width="34pt" height="16pt" src="acp-22-7029-2022-ie00005.png"/></svg:svg></span></span>, and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mtext>OP</mtext><mi mathvariant="normal">v</mi><mi mathvariant="normal">DCFH</mi></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="40pt" height="16pt" class="svg-formula" dspmath="mathimg" md5hash="2801194684c343fc94ca7869fea69c10"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-22-7029-2022-ie00006.svg" width="40pt" height="16pt" src="acp-22-7029-2022-ie00006.png"/></svg:svg></span></span>, respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM<span class="inline-formula">₁₀</span> and PM<span class="inline-formula">_2.5</span> sources that were identified contributed to OP<span class="inline-formula">_v</span>, the anthropogenic road traffic and wood combustion sources had the greatest OP<span class="inline-formula">_m</span> potency (OP per PM mass) on average. A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely copper, zinc, iron, tin, antimony, manganese, and cadmium, as well as three specific wood-burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium), as the most important PM components to explain and predict OP<span class="inline-formula">_v</span>. The combination of a metal and a wood-burning-specific tracer led to the best-performing linear models to explain OP<span class="inline-formula">_v</span>. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical; the models simply required a variable representing the emission source or process to be present. This analysis strongly suggests that anthropogenic and locally emitting road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OP<span class="inline-formula">_v</span> and presumably biological harm reductions in Switzerland.

Particulate matter (PM) is the air pollutant which causes the greatest deleterious heath effects across the world and PM is routinely monitored within air quality networks where PM mass according to its size, and sometimes number are reported. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric which aims to classify PM in respect to its oxidising ability in lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June, 2018 and May, 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations which involved the quantification of a large number of PM constituents and OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable in time and space with Bern-Bollwerk, an urban-traffic sampling site having the greatest levels of OPv among the Swiss sites (especially when considering ), with more rural locations such as Payerne experiencing lower OPv. However, urban-background and suburban sites did experience significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were: 0.4–4.1, 0.6–3.0, and 0.3–0.7 nmol min−1 m−3 for the , , and respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources which were identified contributed to OPv on average, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass). A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely: copper, zinc, iron, tin, antimony and somewhat manganese and cadmium as well as three specific wood burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium) were the most important PM components to explain and predict OPv. The combination of a metal and a wood burning specific tracer led to the best performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical, the models simply required a variable to be present to represent the emission source or process. The modelling process also showed that may be a more specific metric for OP than the other assays employed in this work. This analysis strongly suggests that the anthropogenic and locally emitted road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv, and presumably biological harm reductions in Switzerland.

Particulate matter (PM) is the air pollutant which causes the greatest deleterious heath effects across the world and PM is routinely monitored within air quality networks where PM mass according to its size, and sometimes number are reported. However, such measurements do not provide information on the biological toxicity of PM. Oxidative potential (OP) is a complementary metric which aims to classify PM in respect to its oxidising ability in lungs and is being increasingly reported due to its assumed relevance concerning human health. Between June, 2018 and May, 2019, an intensive filter-based PM sampling campaign was conducted across Switzerland in five locations which involved the quantification of a large number of PM constituents and OP for both PM10 and PM2.5. OP was quantified by three assays: ascorbic acid (AA), dithiothreitol (DTT), and dichlorofluorescein (DCFH). OPv (OP by air volume) was found to be variable in time and space with Bern-Bollwerk, an urban-traffic sampling site having the greatest levels of OPv among the Swiss sites (especially when considering ), with more rural locations such as Payerne experiencing lower OPv. However, urban-background and suburban sites did experience significant OPv enhancement, as did the rural Magadino-Cadenazzo site during wintertime because of high levels of wood smoke. The mean OP ranges for the sampling period were: 0.4–4.1, 0.6–3.0, and 0.3–0.7 nmol min−1 m−3 for the , , and respectively. A source allocation method using positive matrix factorisation (PMF) models indicated that although all PM10 and PM2.5 sources which were identified contributed to OPv on average, the anthropogenic road traffic and wood combustion sources had the greatest OPm potency (OP per PM mass). A dimensionality reduction procedure coupled to multiple linear regression modelling consistently identified a handful of metals usually associated with non-exhaust emissions, namely: copper, zinc, iron, tin, antimony and somewhat manganese and cadmium as well as three specific wood burning-sourced organic tracers – levoglucosan, mannosan, and galactosan (or their metal substitutes: rubidium and potassium) were the most important PM components to explain and predict OPv. The combination of a metal and a wood burning specific tracer led to the best performing linear models to explain OPv. Interestingly, within the non-exhaust and wood combustion emission groups, the exact choice of component was not critical, the models simply required a variable to be present to represent the emission source or process. The modelling process also showed that may be a more specific metric for OP than the other assays employed in this work. This analysis strongly suggests that the anthropogenic and locally emitted road traffic and wood burning sources should be prioritised, targeted, and controlled to gain the most efficacious decrease in OPv, and presumably biological harm reductions in Switzerland.