PM2.5 monitoring during a 10-year period: relation between elemental concentration and meteorological conditions

Four monitoring campaigns between the years 2009 and 2018 were conducted in Córdoba City, Argentina, to detect toxic metals in PM2.5 samples. The concentrations of As, Cd, Pb, Cu, Cr, Mn, Hg, Ni, and Zn, together with several other elements, were measured. The average metal concentrations followed t...

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Bibliographic Details
Published in:Environmental monitoring and assessment 2020-05, Vol.192 (5), p.313, Article 313
Main Authors: Sanguineti, Pamela B., Lanzaco, Bethania L., López, María Laura, Achad, Mariana, Palancar, Gustavo G., Olcese, Luis E., Toselli, Beatriz M.
Format: Article
Language:English
Subjects:
Aerosols
Air pollution
Antimony
Atmospheric particulates
Atmospheric Protection/Air Quality Control/Air Pollution
Cadmium
Chromium
Copper
Diesel fuels
Dust storms
Earth and Environmental Science
Ecology
Ecotoxicology
El Nino
El Nino phenomena
Emissions control
Environment
Environmental Management
Environmental monitoring
Environmental science
Fuel oils
Heavy metals
Industry
La Nina
Lead
Manganese
Mercury
Mercury (metal)
Metal concentrations
Metallurgical coke
Metallurgy
Metals
Meteorological conditions
Monitoring
Monitoring/Environmental Analysis
Nickel
Nonferrous metals
Palladium
Particulate matter
Particulate matter monitoring
Platinum
Seasonal variation
Seasonal variations
Seasons
Soil
Temporal variations
Trends
Weather
Weather conditions
Zinc
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Summary:Four monitoring campaigns between the years 2009 and 2018 were conducted in Córdoba City, Argentina, to detect toxic metals in PM2.5 samples. The concentrations of As, Cd, Pb, Cu, Cr, Mn, Hg, Ni, and Zn, together with several other elements, were measured. The average metal concentrations followed the order: Zn > Cr > Cu > Mn > Pb > V > Ni > As ~ Sb > Cd > Tl > Pd > Hg > Pt. From the analysis of the temporal variation in the elemental concentration of PM2.5, results show seasonal variations that reach, in general, a maximum in the coldest seasons and a minimum in the warmer seasons. These differences could be explained by the different weather conditions during each season, the influence of the El Niño/La Niña regimen, and the presence of fires on certain sampling dates. The source apportionment analysis performed for the period 2017–2018 showed the contribution to PM2.5 of combustion of heavy fuel oil and diesel-powered vehicles, pet coke, metallurgical and nonferrous industries, paint plant factory, traffic, and natural sources like the soil and road dust. This last analysis completed the assignment of sources for the 10-year period of study. Thus, the results of this work contribute to the implementation of emission reduction strategies in order to decrease the impact of PM2.5 on the environment and the human health.
ISSN:0167-6369
1573-2959
DOI:10.1007/s10661-020-08288-0