Source apportionment of ambient PM10 collected at three sites in an urban-industrial area with multi-time resolution factor analyses

Dennis Mooibroek; Uwayemi M Sofowote; Philip K Hopke

doi:10.1016/j.scitotenv.2022.157981

Source apportionment of ambient PM₁₀ collected at three sites in an urban-industrial area with multi-time resolution factor analyses

Sci Total Environ. 2022 Dec 1:850:157981. doi: 10.1016/j.scitotenv.2022.157981. Epub 2022 Aug 12.

Authors

Dennis Mooibroek¹, Uwayemi M Sofowote², Philip K Hopke³

Affiliations

¹ Centre for Environmental Monitoring, National Institute for Public Health and the Environment (RIVM), A. van Leeuwenhoeklaan 9, P.O. Box 1, 3720 BA Bilthoven, the Netherlands. Electronic address: dennis.mooibroek@rivm.nl.
² Environmental Monitoring and Reporting Branch, Ontario Ministry of the Environment, Conservation and Parks, Toronto, Canada.
³ Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY, USA; Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA.

PMID: 35964756
DOI: 10.1016/j.scitotenv.2022.157981

Abstract

Chemical speciation data for PM₁₀, collected for annual trend analyses of health-relevant species, at three receptor sites in a highly industrialized area (IJmond) in the Netherlands were used in a multi-time resolution receptor model (ME-2) to identify the PM₁₀ sources in this area. Despite the available data not being optimized for receptor modelling, five-factor solutions were obtained for all sites based on independent PMF analysis on PM₁₀ data from the three sites (IJM, WAZ and BEV). Four factors were common to all three sites: nitrate-sulphate (average percentage contributions to PM₁₀: IJM: 35.3 %, WAZ: 37.7 %, and BEV: 36.3 %); sea salt (20.2 %, 23.7 %, 15.2 %); industrial (8.1 %, 11.0 %, 18.1 %) and brake wear/traffic (31.4 %, 21.2 %, 20.6 %). At WAZ, a local/site-specific factor containing most of the PAH measurements was found (6.4 %) while a crustal matter factor was resolved at IJM (7.6 %) and BEV (9.8 %). Additionally, sludge-drying was a potential source of the marker species in the industrial factor at WAZ. Bootstrapping (BS) and factor displacement (DISP) were applied to the factor profiles in this work for error estimation. In general, the factor profiles at all three sites had very small intervals from both BS and DISP methods. To our knowledge, this is the first time DISP was applied in a complex model such as the multi-time resolution model. Most of the measured metal and PAH concentrations found in the IJmond area during the 2017-2019 period had local sources, with significant contributions from several processes related to the steel industry. This study shows that available detailed PM₁₀ chemical speciation data, although primarily collected for annual trend analyses of health-relevant species, could also be used in receptor modelling by applying a multi-time framework. We propose general recommendations for the optimization of the measurement strategy for source apportionment of PM in areas with similar urban-industrial land use.

Keywords: Diagnostics; Industrial area; ME-2; Multi-time resolution; PM(10); PMF.

MeSH terms

Air Pollutants* / analysis
Environmental Monitoring / methods
Factor Analysis, Statistical
Nitrates / analysis
Particle Size
Particulate Matter / analysis
Sewage / analysis
Steel / analysis
Sulfates / analysis

Substances

Air Pollutants
Nitrates
Particulate Matter
Sewage
Sulfates
Steel