Local and regional sources of urban ambient PM2.5 exposures in Calgary, Canada

Anastasopolos, Angelos T.; Hopke, Philip K.; Sofowote, Uwayemi M.; Zhang, Joyce J.Y.; Johnson, Markey

doi:10.1016/j.atmosenv.2022.119383

Cited by 12 publications

(6 citation statements)

References 54 publications

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“…In Sofia (Bulgaria), in addition to these sources, soil resuspension also varies according to the seasons [56]. In Calgary (Canada), secondary organic aerosol was the component with the strongest seasonality, followed by wood smoke [57].…”

Section: Source Apportionmentmentioning

confidence: 99%

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

Vieira,

do Rosario,

Yamasoe

et al. 2023

Atmosphere

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Air pollution in the Metropolitan Area of São Paulo (MASP), Brazil, is a serious problem and is strongly affected by local sources. However, atmosphere column composition in MASP is also affected by biomass burning aerosol (BB). Understanding the impacts of aerosol particles, from both vehicles and BB, on the air quality and climate depends on in-depth research with knowledge of some parameters such as the optical properties of particles and their chemical composition. This study characterized fine particulate matter (PM2.5) from July 2019 to August 2020 in the eastern part of the MASP, relating the chemical composition data obtained at the surface and columnar optical parameters, such as aerosol optical depth (AOD), Ångström Exponent (AE), and single-scattering albedo (SSA). According to the analyzed data, the mean PM2.5 concentration was 18.0 ± 12.5 µg/m3; however, daily events exceeded 75 times the air quality standard of the World Health Organization (15 µg/m3). The mean black carbon concentration was 1.8 ± 1.5 µg/m3 in the sampling period. Positive matrix factorization (PMF) identified four main sources of aerosol: heavy vehicles (42%), followed by soil dust plus local sources (38.7%), light vehicles (9.9%), and local sources (8.6%). AOD and AE presented the highest values in the dry period, during which biomass burning events are more frequent, suggesting smaller particles in the atmosphere. SSA values at 440 nm were between 0.86 and 0.94, with lower values in the winter months, indicating the presence of more absorbing aerosol.

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Section: Source Apportionmentmentioning

confidence: 99%

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

Vieira,

do Rosario,

Yamasoe

et al. 2023

Atmosphere

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“…Demonstrating the clustering effect at the month-year level provided justification for building the model parameterized in Equation (1). Exponentiating the coefficients from that model allowed us to calculate 2022-adjusted mean total PM 2.5 concentrations for each monitoring location and each month, as seen in Figure 3 and Table S1.…”

Section: Fixed Effects Model Controlling For Temporal Variabilitymentioning

confidence: 99%

“…Exposure can occur indoors and/or outdoors, from natural and/or anthropogenic sources, and at varying levels. Natural sources can include, but are not limited to, wildfire smoke, volcanic ash, sea salt, and natural soil resuspension [1]. Anthropogenic sources of PM 2.5 include fossil fuel combustion (e.g., vehicle exhaust, power generation, heating, cooking, and wood burning); certain industrial processes (e.g., mining, building, manufacture of cement, ceramic, and bricks); and disturbance of settled particles (e.g., construction activities, agricultural disturbance of soils, and salt applied to roads) [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Air 2023, 1, FOR PEER REVIEW 2 particles (e.g., construction activities, agricultural disturbance of soils, and salt applied to roads) [1,2]. Deconvoluting the contributions from each of the various sources to the total observed PM2.5 concentration is no simple task.…”

Section: Introductionmentioning

confidence: 99%

“…1 Adjusted for year, month, day of the week, temperature, relative humidity, atmospheric pressure, and wind speed. Standard errors clustered at the month-year level.…”

mentioning

confidence: 99%

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Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment

Shapero,

Keck,

Love

2023

Air

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Source apportionment of observed PM2.5 concentrations is of growing interest as communities seek ways to improve their air quality. We evaluated publicly available PM2.5 data from the USEPA in the Dallas–Fort Worth metropolitan area to determine the contributions from various PM2.5 sources to the total PM2.5 observed. The approach combines interpolation and fixed effect regression models to disentangle background from local PM2.5 contributions. These models found that January had the lowest total PM2.5 mean concentrations, ranging from 5.0 µg/m3 to 6.4 µg/m3, depending on monitoring location. July had the highest total PM2.5 mean concentrations, ranging from 8.7 µg/m3 to 11.1 µg/m3, depending on the location. January also had the lowest mean local PM2.5 concentrations, ranging from 2.6 µg/m3 to 3.6 µg/m3, depending on the location. Despite having the lowest local PM2.5 concentrations, January had the highest local attributions [51–57%]. July had the highest mean local PM2.5 concentrations, ranging from 2.9 µg/m3 to 4.1 µg/m3, depending on the location. Despite having the highest local PM2.5 concentrations, July had the lowest local attributions [33–37%]. These results suggest that local contributions have a limited effect on total PM2.5 concentrations and that the observed seasonal changes are likely the result of background influence, as opposed to modest changes in local contributions. Overall, the results demonstrate that in the Dallas–Fort Worth metropolitan area, approximately half of the observed total PM2.5 is from background PM2.5 sources and half is from local PM2.5 sources. Among the local PM2.5 source contributions in the Dallas–Fort Worth metropolitan area, our analysis shows that the vast majority is from non-point sources, such as from the transportation sector. While local point sources may have some incremental site-specific local contribution, such contributions are not clearly distinguishable in the data evaluated. We present this approach as a roadmap for disentangling PM2.5 concentrations at different spatial levels (i.e., the local, regional, or state level) and from various sectors (i.e., residential, industrial, transport, etc.). This roadmap can help decision-makers to optimize mitigatory, regulatory, and/or community efforts towards reducing total community PM2.5 exposure.

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Source Apportionment of Ambient Fine Particle Size Distribution Using Positive Matrix Factorisation and Conditional Bivariate Probability Function in a Coastal Urban Area

Priyan

Ulavi

Nagendra

2023

J. Inst. Eng. India Ser. A

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Local and regional sources of urban ambient PM2.5 exposures in Calgary, Canada

Cited by 12 publications

References 54 publications

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

Chemical Characterization and Optical Properties of the Aerosol in São Paulo, Brazil

Background Influence of PM2.5 in Dallas–Fort Worth Area and Recommendations for Source Apportionment

Source Apportionment of Ambient Fine Particle Size Distribution Using Positive Matrix Factorisation and Conditional Bivariate Probability Function in a Coastal Urban Area

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