Journals / Atmospheric Pollution Research / 2019 / Cilt: 10 - Sayı: 3

Source apportionment of trace elements and black carbon in an urban industrial area (Portland, Oregon)

Pages
784–794
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Using PMF on particulate matter data from a second industrial location inSoutheast Portland (98 filters) revealed the same major sources except for the metals industry source, supportingthe conclusion that regional sources dominate particulate matter metals composition, despite measurementsmade in an industrial urban location.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper reports a current source apportionment study of trace elements and black carbon in particulate matterin industrial Southeast Portland, Oregon.
📊
Key Findings & Data:The study aimed to determine whether metal pollution hotspotsidentified in the area in 2016 had been successfully mitigated by baghouse installation, or whether industrialsources continued to dominate local particulate matter.
💡
Conclusion & Impact:Using PMF on particulate matter data from a second industrial location inSoutheast Portland (98 filters) revealed the same major sources except for the metals industry source, supportingthe conclusion that regional sources dominate particulate matter metals composition, despite measurementsmade in an industrial urban location.
🔬Design:Modeling & Simulation
📍Scope:2016 Verileri
⚙️Variables:Domain Parameters

Özet

This paper reports a current source apportionment study of trace elements and black carbon in particulate matterin industrial Southeast Portland, Oregon. The study aimed to determine whether metal pollution hotspotsidentified in the area in 2016 had been successfully mitigated by baghouse installation, or whether industrialsources continued to dominate local particulate matter. Particulate matter was filter-collected nearly continuouslyin 24-hour intervals between October 2017 and March 2018 (101 total filters). Filters were analyzedfor 30 elements by x-ray fluorescence; black carbon was measured continuously during filter sampling using anaethalometer. EPA's Positive Matrix Factorization 5.0 was used for source apportionment modeling, yielding a 5-factor optimal solution. The source identities were resolved to be diesel and fuel emissions, sea salt, soil dust,secondary sulfates, and metals industry. The metals industry source was much less significant than expected,suggesting effective emissions reductions from the local factory. The source profiles' correlation with wind directionand speed using bivariate polar plots was examined to give further insight into the source identities andtheir locations. The NOAA HYSPLIT model was also used for air flow back-trajectory analysis. The results of theHYSPLIT and bivariate polar plots suggest that the coal power plant in eastern Oregon is a significant source ofsulfates and mercury emissions. Using PMF on particulate matter data from a second industrial location inSoutheast Portland (98 filters) revealed the same major sources except for the metals industry source, supportingthe conclusion that regional sources dominate particulate matter metals composition, despite measurementsmade in an industrial urban location.

🔖

Cite This Article

MILLER, A., RADUMA, D., GEORGE, L., FRY, J. (2019). Source apportionment of trace elements and black carbon in an urban industrial area (Portland, Oregon). Atmospheric Pollution Research, Cilt: 10 - Sayı: 3, 784–794. https://ajindex.com/articles/351335

References (4)

  1. Abbasi, S., Jansson, A., Sellgren, U., Olofsson, U., Jan. 2013. Particle emissions from rail traffic: a literature review. Crit. Rev. Environ. Sci. Technol. 43 (23), 2511–2544. http://www.tandfonline.com/doi/abs/10.1080/10643389.2012.685348.
  2. Bari, M.A., Kindzierski, W.B., Oct. 2017. Ambient fine particulate matter (PM 2.5 ) in Canadian oil sands communities: levels, sources and potential human health risk. Sci. Total Environ. 595, 828–838. https://linkinghub.elsevier.com/retrieve/pii/ S0048969717308513. Brown, S.G., Eberly, S., Paatero, P., Norris, G.A., Jun. 2015. Methods for estimating uncertainty in PMF solutions: examples with ambient air and water quality data and guidance on reporting PMF results. Sci. Total Environ. 518–519, 626–635. http:// www.sciencedirect.com/science/article/pii/S004896971500025X. Bukowiecki, N., Gehrig, R., Hill, M., Lienemann, P., Zwicky, C.N., Buchmann, B., Weingartner, E., Baltensperger, U., Feb. 2007. Iron, manganese and copper emitted by cargo and passenger trains in Zrich (Switzerland): size-segregated mass concentrations in ambient air. Atmos. Environ. 41 (4), 878–889. http://linkinghub. elsevier.com/retrieve/pii/S1352231006008739. Carslaw, D., Ropkins, K., 2012. openair: an R package for air quality data analysis. Environ. Model. Softw. 27–28, 52–61.
  3. Carslaw, D.C., Beevers, S.D., Ropkins, K., Bell, M.C., Sep. 2006. Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmos. Environ. 40 (28), 5424–5434. http://www. sciencedirect.com/science/article/pii/S1352231006004250. Cheung, K.L., Ntziachristos, L., Tzamkiozis, T., Schauer, J.J., Samaras, Z., Moore, K.F., Sioutas, C., Jun. 2010. Emissions of particulate trace elements, metals and organic species from gasoline, diesel, and biodiesel passenger vehicles and their relation to oxidative potential. Aerosol Sci. Technol. 44 (7), 500–513. https://doi.org/10.1080/ 02786821003758294. Corson, S., 2011. PGE Reports Progress Implementing Boardman Power Plant Emissions Reduction Strategy. http://investors.portlandgeneral.com/news-releases.
  4. Donovan, G.H., Jovan, S.E., Gatziolis, D., Burstyn, I., Michael, Y.L., Amacher, M.C., Monleon, V.J., Jul. 2016. Using an epiphytic moss to identify previously unknown sources of atmospheric cadmium pollution. Sci. Total Environ. 559, 84–93. http:// www.sciencedirect.com/science/article/pii/S0048969716306052. Gatziolis, D., Jovan, S., Donovan, G., Amac