Assessment of the Oxidative Potential and Oxidative Burden from Occupational Exposures to Particulate Matter

2021 ◽  
Author(s):  
Alan da Silveira Fleck ◽  
Maximilien Debia ◽  
Patrick Eddy Ryan ◽  
Caroline Couture ◽  
Alison Traub ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Size Fraction ◽  
Sampling Strategy ◽  
Occupational Exposures ◽  
Epidemiological Studies ◽  
Redox Activity ◽  
Oxidative Potential ◽  
Construction Trades ◽  
Occupational Settings

Abstract Oxidative potential (OP) is a toxicologically relevant metric that integrates features like mass concentration and chemical composition of particulate matter (PM). Although it has been extensively explored as a metric for the characterization of environmental particles, this is still an underexplored application in the occupational field. This study aimed to estimate the OP of particles in two occupational settings from a construction trades school. This characterization also includes the comparison between activities, sampling strategies, and size fractions. Particulate mass concentrations (PM4-Personal, PM4-Area, and PM2.5-Area) and number concentrations were measured during three weeks of welding and construction/bricklaying activities. The OP was assessed by the ascorbate assay (OPAA) using a synthetic respiratory tract lining fluid (RTLF), while the oxidative burden (OBAA) was determined by multiplying the OPAA values with PM concentrations. Median (25th–75th percentiles) of PM mass and number concentrations were 900 (672–1730) µg m–3 and 128 000 (78 000–169 000) particles cm–3 for welding, and 432 (345–530) µg m–3 and 2800 (1700–4400) particles cm–3 for construction. Welding particles, especially from the first week of activities, were also associated with higher redox activity (OPAA: 3.3 (2.3–4.6) ρmol min–1 µg–1; OBAA: 1750 (893–4560) ρmol min–1 m–3) compared to the construction site (OPAA: 1.4 (1.0–1.8) ρmol min–1 µg–1; OBAA: 486 (341–695) ρmol min–1 m–3). The OPAA was independent of the sampling strategy or size fraction. However, driven by the higher PM concentrations, the OBAA from personal samples was higher compared to area samples in the welding shop, suggesting an influence of the sampling strategy on PM concentrations and OBAA. These results demonstrate that important levels of OPAA can be found in occupational settings, especially during welding activities. Furthermore, the OBAA found in both workplaces largely exceeded the levels found in environmental studies. Therefore, measures of OP and OB could be further explored as metrics for exposure assessment to occupational PM, as well as for associations with cardiorespiratory outcomes in future occupational epidemiological studies.

scholarly journals Long-term Particulate Matter Modeling for Health Effects Studies in California – Part II: Concentrations and Sources of Ultrafine Organic Aerosols

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Shantanu Jathar ◽  
Hongliang Zhang ◽  
Qi Ying ◽  
Shu-Hua Chen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Health Effects ◽  
Waste Disposal ◽  
Size Fraction ◽  
Organic Aerosols ◽  
Epidemiological Studies ◽  
Anthropogenic Sources ◽  
Major Constituent ◽  
Spatial Distributions ◽  
Mobile Emissions

Abstract. Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM0.1). Recent epidemiological studies have identified associations between PM0.1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM0.1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM0.1 OA for health effects studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM2.5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM2.5 SOA/OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2 ~ 3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM0.1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM0.1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM0.1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM0.1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM0.1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes.

scholarly journals Disparities in particulate matter (PM<sub>10</sub>) origins and oxidative potential at a city-scale (Grenoble, France) – Part II: Sources of PM<sub>10</sub> oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis

2021 ◽  
Author(s):  
Lucille Joanna S. Borlaza ◽  
Samuël Weber ◽  
Jean-Luc Jaffrezo ◽  
Stephan Houdier ◽  
Rémy Slama ◽  
...  
Keyword(s):  
Neural Network ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multilayer Perceptron ◽  
Mass Concentration ◽  
Redox Activity ◽  
Oxidative Potential ◽  
Atmospheric Processes ◽  
Non Linear

Abstract. The oxidative potential (OP) of particulate matter (PM) quantifies PM capability to cause anti-oxidant imbalance. Due to the wide range and complex mixture of species in particulates, little is known on the pollution sources most strongly contributing to OP. A one-year sampling of PM10 (particles with an aerodynamic diameter below 10) was performed over different sites in a medium-sized city (Grenoble, France). An enhanced fine-scale apportionment of PM10 sources, based on the chemical composition, was performed using Positive Matrix Factorization (PMF) method and reported in a companion paper (Borlaza et al., 2020). OP was assessed as the ability of PM10 to generate reactive oxygen species (ROS) using three different acellular assays: Dithiothreitol (DTT), Ascorbic acid (AA), and 2,7-dichlorofluorescein (DCFH) assays. Using multiple linear regression (MLR), the OP contribution of the sources identified by PMF were estimated. Conversely, since atmospheric processes are usually non-linear in nature, artificial neural network (ANN) techniques, which employs non-linear models, could further improve estimates. Hence, the multilayer perceptron analysis (MLP), an ANN-based model, was additionally used to model OP based on PMF-resolved sources as well. This study presents the spatiotemporal variabilities of OP activity with influences by season-specific sources, site typology and specific local features, and assay sensitivity. Overall, both MLR and MLP effectively captured the evolution of OP. The primary traffic and biomass burning sources were the strongest drivers of OP in the Grenoble basin. There is also a clear redistribution of source-specific impacts when using OP instead of mass concentration, underlining the importance of PM redox activity over mass concentration. Finally, the MLP generally offered improvements in OP prediction especially for sites where synergistic and/or antagonistic effects between sources are prominent, supporting the value of using ANN-based models to account for the non-linear dynamics behind the atmospheric processes affecting OP of PM10.

scholarly journals Oxidative Potential Versus Biological Effects: A Review on the Relevance of Cell-Free/Abiotic Assays as Predictors of Toxicity from Airborne Particulate Matter

2019 ◽  
Vol 20 (19) ◽  
pp. 4772 ◽  
Author(s):  
Johan Øvrevik
Keyword(s):  
Particulate Matter ◽  
Biological Effects ◽  
Positive Association ◽  
Ambient Air ◽  
Epidemiological Studies ◽  
In Vivo Studies ◽  
Oxidative Potential ◽  
In Cells

Background and Objectives: The oxidative potential (OP) of particulate matter (PM) in cell-free/abiotic systems have been suggested as a possible measure of their biological reactivity and a relevant exposure metric for ambient air PM in epidemiological studies. The present review examined whether the OP of particles correlate with their biological effects, to determine the relevance of these cell-free assays as predictors of particle toxicity. Methods: PubMed, Google Scholar and Web of Science databases were searched to identify relevant studies published up to May 2019. The main inclusion criteria used for the selection of studies were that they should contain (1) multiple PM types or samples, (2) assessment of oxidative potential in cell-free systems and (3) assessment of biological effects in cells, animals or humans. Results: In total, 50 independent studies were identified assessing both OP and biological effects of ambient air PM or combustion particles such as diesel exhaust and wood smoke particles: 32 in vitro or in vivo studies exploring effects in cells or animals, and 18 clinical or epidemiological studies exploring effects in humans. Of these, 29 studies assessed the association between OP and biological effects by statistical analysis: 10 studies reported that at least one OP measure was statistically significantly associated with all endpoints examined, 12 studies reported that at least one OP measure was significantly associated with at least one effect outcome, while seven studies reported no significant correlation/association between any OP measures and any biological effects. The overall assessment revealed considerable variability in reported association between individual OP assays and specific outcomes, but evidence of positive association between intracellular ROS, oxidative damage and antioxidant response in vitro, and between OP assessed by the dithiothreitol (DDT) assay and asthma/wheeze in humans. There was little support for consistent association between OP and any other outcome assessed, either due to repeated lack of statistical association, variability in reported findings or limited numbers of available studies. Conclusions: Current assays for OP in cell-free/abiotic systems appear to have limited value in predicting PM toxicity. Clarifying the underlying causes may be important for further advancement in the field.

scholarly journals Long-term particulate matter modeling for health effect studies in California – Part 2: Concentrations and sources of ultrafine organic aerosols

2017 ◽  
Vol 17 (8) ◽  
pp. 5379-5391 ◽  
Author(s):  
Jianlin Hu ◽  
Shantanu Jathar ◽  
Hongliang Zhang ◽  
Qi Ying ◽  
Shu-Hua Chen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Waste Disposal ◽  
Health Effect ◽  
Size Fraction ◽  
Organic Aerosols ◽  
Epidemiological Studies ◽  
Anthropogenic Sources ◽  
Major Constituent ◽  
Spatial Distributions ◽  
Mobile Emissions

Abstract. Organic aerosol (OA) is a major constituent of ultrafine particulate matter (PM0. 1). Recent epidemiological studies have identified associations between PM0. 1 OA and premature mortality and low birth weight. In this study, the source-oriented UCD/CIT model was used to simulate the concentrations and sources of primary organic aerosols (POA) and secondary organic aerosols (SOA) in PM0. 1 in California for a 9-year (2000–2008) modeling period with 4 km horizontal resolution to provide more insights about PM0. 1 OA for health effect studies. As a related quality control, predicted monthly average concentrations of fine particulate matter (PM2. 5) total organic carbon at six major urban sites had mean fractional bias of −0.31 to 0.19 and mean fractional errors of 0.4 to 0.59. The predicted ratio of PM2. 5 SOA ∕ OA was lower than estimates derived from chemical mass balance (CMB) calculations by a factor of 2–3, which suggests the potential effects of processes such as POA volatility, additional SOA formation mechanism, and missing sources. OA in PM0. 1, the focus size fraction of this study, is dominated by POA. Wood smoke is found to be the single biggest source of PM0. 1 OA in winter in California, while meat cooking, mobile emissions (gasoline and diesel engines), and other anthropogenic sources (mainly solvent usage and waste disposal) are the most important sources in summer. Biogenic emissions are predicted to be the largest PM0. 1 SOA source, followed by mobile sources and other anthropogenic sources, but these rankings are sensitive to the SOA model used in the calculation. Air pollution control programs aiming to reduce the PM0. 1 OA concentrations should consider controlling solvent usage, waste disposal, and mobile emissions in California, but these findings should be revisited after the latest science is incorporated into the SOA exposure calculations. The spatial distributions of SOA associated with different sources are not sensitive to the choice of SOA model, although the absolute amount of SOA can change significantly. Therefore, the spatial distributions of PM0. 1 POA and SOA over the 9-year study period provide useful information for epidemiological studies to further investigate the associations with health outcomes.

scholarly journals Seasonal Variations and Chemical Predictors of Oxidative Potential (OP) of Particulate Matter (PM), for Seven Urban French Sites

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 698 ◽  
Author(s):  
Aude Calas ◽  
Gaëlle Uzu ◽  
Jean-Luc Besombes ◽  
Jean M.F. Martins ◽  
Matteo Redaelli ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Seasonal Variations ◽  
Standardized Test ◽  
Road Transport ◽  
Epidemiological Studies ◽  
Atmospheric Pollutants ◽  
Chemical Components ◽  
Large Set ◽  
Oxidative Potential ◽  
Annual Cycles

Epidemiological studies suggest that the main part of chronic effects from air pollution is likely to be linked with particulate matter (PM). Oxidative potential (OP) of PM is gaining strong interest as a promising health exposure metric. This study combined atmospheric detailed composition results obtained for seven different urban background environments over France to examine any possible common feature in OP seasonal variations obtained using two assays (acid ascorbic (AA) and dithiothreitol (DTT)) along a large set of samples ( N > 700 ). A remarkable homogeneity in annual cycles was observed with a higher OP activity in wintertime at all investigated sites. Univariate correlations were used to link the concentrations of some major chemical components of PM and their OP. Four PM components were identified as OP predictors: OC, EC, monosaccharides and Cu. These species are notably emitted by road transport and biomass burning, targeting main sources probably responsible for the measured OP activity. The results obtained confirm that the relationship between OP and atmospheric pollutants is assay- and location-dependent and, thus, the strong need for a standardized test, or set of tests, for further regulation purposes.

scholarly journals An apportionment method for the oxidative potential of atmospheric particulate matter sources: application to a one-year study in Chamonix, France

2018 ◽  
Vol 18 (13) ◽  
pp. 9617-9629 ◽  
Author(s):  
Samuël Weber ◽  
Gaëlle Uzu ◽  
Aude Calas ◽  
Florie Chevrier ◽  
Jean-Luc Besombes ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Health Impact ◽  
Atmospheric Particulate Matter ◽  
Redox Activity ◽  
Coupled Analysis ◽  
Oxidative Potential ◽  
Adverse Health Outcomes ◽  
One Year ◽  
The Impact

Abstract. Inhaled aerosolized particulate matter (PM) induces cellular oxidative stress in vivo, leading to adverse health outcomes. The oxidative potential (OP) of PM appears to be a more relevant proxy of the health impact of the aerosol rather than the total mass concentration. However, the relative contributions of the aerosol sources to the OP are still poorly known. In order to better quantify the impact of different PM sources, we sampled aerosols in a French city for one year (2014, 115 samples). A coupled analysis with detailed chemical speciation (more than 100 species, including organic and carbonaceous compounds, ions, metals and aethalometer measurements) and two OP assays (ascorbic acid, AA, and dithiothreitiol, DTT) in a simulated lung fluid (SLF) were performed in these samples. We present in this study a statistical framework using a coupled approach with positive matrix factorization (PMF) and multiple linear regression to attribute a redox-activity to PM sources. Our results highlight the importance of the biomass burning and vehicular sources to explain the observed OP for both assays. In general, we see a different contribution of the sources when considering the OP AA, OP DTT or the mass of the PM10. Moreover, significant differences are observed between the DTT and AA tests which emphasized chemical specificities of the two tests and the need of a standardized approach for the future studies on epidemiology or toxicology of the PM.

scholarly journals Disparities in particulate matter (PM<sub>10</sub>) origins and oxidative potential at a city scale (Grenoble, France) – Part 2: Sources of PM<sub>10</sub> oxidative potential using multiple linear regression analysis and the predictive applicability of multilayer perceptron neural network analysis

2021 ◽  
Vol 21 (12) ◽  
pp. 9719-9739
Author(s):  
Lucille Joanna S. Borlaza ◽  
Samuël Weber ◽  
Jean-Luc Jaffrezo ◽  
Stephan Houdier ◽  
Rémy Slama ◽  
...  
Keyword(s):  
Neural Network ◽  
Particulate Matter ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Multilayer Perceptron ◽  
Redox Activity ◽  
Oxidative Potential ◽  
Atmospheric Processes ◽  
Wide Range ◽  
Non Linear

Abstract. The oxidative potential (OP) of particulate matter (PM) measures PM capability to potentially cause anti-oxidant imbalance. Due to the wide range and complex mixture of species in particulates, little is known about the pollution sources most strongly contributing to OP. A 1-year sampling of PM10 (particles with an aerodynamic diameter below 10) was performed over different sites in a medium-sized city (Grenoble, France). An enhanced fine-scale apportionment of PM10 sources, based on the chemical composition, was performed using the positive matrix factorization (PMF) method and reported in a companion paper (Borlaza et al., 2020). OP was assessed as the ability of PM10 to generate reactive oxygen species (ROS) using three different acellular assays: dithiothreitol (DTT), ascorbic acid (AA), and 2,7-dichlorofluorescein (DCFH) assays. Using multiple linear regression (MLR), the OP contributions of the sources identified by PMF were estimated. Conversely, since atmospheric processes are usually non-linear in nature, artificial neural network (ANN) techniques, which employ non-linear models, could further improve estimates. Hence, the multilayer perceptron analysis (MLP), an ANN-based model, was additionally used to model OP based on PMF-resolved sources as well. This study presents the spatiotemporal variabilities of OP activity with influences by season-specific sources, site typology and specific local features, and assay sensitivity. Overall, both MLR and MLP effectively captured the evolution of OP. The primary traffic and biomass burning sources were the strongest drivers of OP in the Grenoble basin. There is also a clear redistribution of source-specific impacts when using OP instead of mass concentration, underlining the importance of PM redox activity for the identification of potential sources of PM toxicity. Finally, the MLP generally offered improvements in OP prediction, especially for sites where synergistic and/or antagonistic effects between sources are prominent, supporting the value of using ANN-based models to account for the non-linear dynamics behind the atmospheric processes affecting OP of PM10.

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