A particulate matter (PM) source apportionment study was carried out in one of the most polluted districts of Tuscany (Italy), close to an old waste incinerator plant. Due to the high PM10 levels, an extensive field campaign was supported by the Regional Government to identify the main PM sources and quantify their contributions. PM10 daily samples were collected for one year and analysed by different techniques to obtain a complete chemical characterisation (elements, ions and carbon fractions). Hourly fine (<2.5 μm) and coarse (2.5-10 μm) aerosol samples were collected by a Streaker sampler for a shorter period and hourly elemental concentrations were obtained by PIXE. Positive Matrix Factorization (PMF) analysis of daily and hourly data allowed the identification of 10 main sources: six anthropogenic (Biomass Burning, Traffic, Secondary Nitrates, Secondary Sulphates, Incinerator, Heavy Oil combustion), two natural (Saharan Dust and Fresh Sea Salt) and two mixed sources (Local Dust and Aged Sea Salt). Biomass burning turned out to be the main source of PM, accounting for 30% of the PM10 mass as annual average, followed by Traffic (18%) and Secondary Nitrates (14%). Emissions from the Incinerator turned out to be only 2% of PM10 mass on average. PM10 composition and source apportionment have been assessed in a polluted area near a waste incinerator, by PMF analysis on daily and hourly compositional data sets.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.envpol.2018.11.107DOI Listing

Publication Analysis

Top Keywords

source apportionment
8
particulate matter
8
waste incinerator
8
incinerator plant
8
combined daily
4
daily hourly
4
hourly data
4
data sets
4
sets source
4
apportionment particulate
4

Similar Publications

Geographic heterogeneity of polycyclic aromatic hydrocarbons in Yangtze River sediments: Evidence from the longest river in Asia.

Environ Pollut

January 2025

The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH, UK; Hubei Key Laboratory of Mineral Resources Processing and Environment, School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China. Electronic address:

This work is the first comprehensive survey of the Yangtze River, covering its origin to the estuary mouth. It focuses on the geographical and industrial factors influencing the distribution of polycyclic aromatic hydrocarbons (PAHs) in sediments, along with their contamination levels, sources, and ecological risks. The total concentrations of PAHs ranged from 2.

View Article and Find Full Text PDF

Microcosm experiments deciphered resistome coalescence, risks and source-sink relationship of antibiotic resistance in the soil irrigated with reclaimed water.

J Hazard Mater

January 2025

College of Water Sciences, Beijing Normal University, No 19, Xinjiekouwai Street, Beijing 100875, China; Engineering Research Center of Groundwater Pollution Control and Remediation, Ministry of Education, Beijing, 100875, China. Electronic address:

Reclaimed water is widely used in agriculture irrigation to alleviate water scarcity, whereas the dissemination of antibiotic resistance genes (ARGs) in the soil it introduces has attracted widespread attention. Currently, few studies have systematically elucidated the coalescence of the resistome originating from reclaimed water with the soil's native community. Also, the effects and mechanisms of irrigation on the dissemination of ARGs in soils have yet to be demonstrated.

View Article and Find Full Text PDF

Coal tar-related products as a source of polycyclic aromatic compounds (PACs) are particularly concerning due to high PAC concentrations and inadequate source management. Benzo[b]carbazole, a benzocarbazole isomer exclusively found in coal tar-derived products, acts as an ideal marker to distinguish coal tar sources from others, enabling more robust quantification of coal tar contributions to PACs. To evaluate the historical and recent contributions of coal tar-related sources to the levels of PACs in Lake Ontario and associated ecological risk, we analyzed 31 PACs and 3 BCBz isomers in surface sediments and a sediment core.

View Article and Find Full Text PDF

Spatio-temporal prediction of groundwater vulnerability based on CNN-LSTM model with self-attention mechanism: A case study in Hetao Plain, northern China.

J Environ Sci (China)

July 2025

School of Environmental Studies, China University of Geosciences, Wuhan 430074, China; State Environmental Protection Key Laboratory of Source Apportionment and Control of Aquatic Pollution, China University of Geosciences, Wuhan 430078, China. Electronic address:

Located in northern China, the Hetao Plain is an important agro-economic zone and population centre. The deterioration of local groundwater quality has had a serious impact on human health and economic development. Nowadays, the groundwater vulnerability assessment (GVA) has become an essential task to identify the current status and development trend of groundwater quality.

View Article and Find Full Text PDF

Spatial and temporal (annual and decadal) trends of metal(loid) concentrations and loads in an acid mine drainage-affected river.

Sci Total Environ

January 2025

Camborne School of Mines, Department of Earth and Environmental Sciences, University of Exeter, Penryn TR10 9FE, UK; Environment and Sustainability Institute, University of Exeter, Penryn TR10 9FE, UK.

Acid mine drainage (AMD) is a worldwide problem that degrades river systems and is difficult and expensive to remediate. To protect affected catchments, it is vital to understand the behaviour of AMD-related metal(loid) contaminants as a function of space and time. To address this, the sources, loads and transport mechanisms of arsenic (As), copper (Cu), zinc (Zn), iron (Fe) and sulfur (S) in a representative AMD-affected catchment (the Carnon River in Cornwall, UK) were determined over a 12-month sampling period and with 22 years of monitoring data collected by the Environment Agency (England) (EA).

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!