Source apportionment of PM2.5 oxidative potential in an East Mediterranean site.

This study aimed to evaluate the oxidative potential (OP) of PM2.5 collected for almost a year in an urban area of the East Mediterranean. Two acellular assays, based on ascorbic acid (AA) and dithiothreitol (DTT) depletion, were used to measure the OP. The results showed that the mean volume normalized OP-AAv value was 0.64 ± 0.29 nmol·min-1·m-3 and the mean OP-DTTv was 0.49 ± 0.26 nmol·min-1·m-3. Several approaches were adopted in this work to study the relationship between the species in PM2.5 (carbonaceous matter, water-soluble ions, major and trace elements, and organic compounds) or their sources and OP values. Spearman correlations revealed strong correlations of OP-AAv with carbonaceous subfractions as well as organic compounds while OP-DTTv seemed to be more correlated with elements emitted from different anthropogenic activities. Furthermore, a multiple linear regression method was used to estimate the contribution of PM2.5 sources, determined by a source-receptor model (Positive Matrix Factorization), to the OP values. The results showed that the sources that highly contribute to the PM2.5 mass (crustal dust and ammonium sulfate) were not the major sources contributing to the values of OP. Instead, 69 % of OP-AAv and 62 % of OP-DTTv values were explained by three local anthropogenic sources: Heavy Fuel Oil (HFO) combustion from a power plant, biomass burning, and road traffic emissions. As for the seasonal variations, higher OP-AAv values were observed during winter compared to summer, while OP-DTTv did not show any significant differences between the two seasons. The contribution of biomass burning during winter was 33 and 34 times higher compared to summer for OP-AAv and OP-DTTv, respectively. On the other hand, higher contributions were observed for HFO combustion during summer.

Exhaust and non-exhaust contributions from road transport to PM10 at a Southern European traffic site.

It is a well - established fact that road traffic is one of the main contributors to ambient levels of airborne particulate matter (APM). This study was carried out at a traffic site in which the PM10 levels are monitored all year round. A trend analysis of these levels revealed that over a decade there was no discernible trend, with the PM10 concentrations normally hovering around the EU limit values. In 2018, one of these limit values was exceeded. The contribution of traffic at the site was therefore investigated through a chemical speciation of 209 PM10 samples collected throughout this year. The speciation data were used in a source apportionment exercise in which the output of the PMF model was further refined using the lesser-known, constraint weighted non - negative matrix factorization (CW - NMF) model. This technique enabled the isolation of two factors clearly related to traffic, which were labelled as "exhaust contribution" (responsible for 3.4% of the PM10), "tire/brake wear contribution" (contributing 17% of the PM10). Additionally, a factor including both traffic resuspended dust and crustal material was also isolated and labelled "road dust/crustal" factor. The two contributors to the factor jointly contribute 18% to the PM10 and the contribution of the traffic resuspended dust was estimated at 7.3%. The traffic resuspended component of this factor together with the "tire/brake wear contribution" jointly make up the non-exhaust contribution of traffic - derived dust. Consonant with what has been known for quite some time, the exhaust fraction is the minor component of traffic PM10. It is therefore, clear that policies aimed at controlling traffic derived PM10 pollution at the receptor will have a minimal effect unless the non - exhaust emissions are adequately controlled.

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves.

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.

Investigating the plausibility of a PMF source apportionment solution derived using a small dataset: A case study from a receptor in a rural site in Apulia - South East Italy.

Results of a methodological study on the use of Positive Matrix Factorization (PMF) with smaller datasets are being reported in this work. This study is based on 29 PM10 and 33 PM2.5 samples from a receptor in a rural setup in Apulia (Southern Italy). Running PMF on the two size fractions separately resulted in the model not functioning correctly. We therefore, augmented the size of the dataset by aggregating the PM10 and PM2.5 data. The 5-factor solution obtained for the aggregated data was fairly rotationally stable, and was further refined by the rotational tools included in USEPA PMF version 5. These refinements include the imposition of constraints on the solution, based on our knowledge of the chemical composition of the aerosol sources affecting the receptor. Additionally, the uncertainties associated with this solution were fully characterised using the improved error estimation techniques in this version of PMF. Five factors in all, were isolated by PMF: ammonium sulfate, marine aerosol, mixed carbonaceous aerosol, crustal/Saharan dust and total traffic. The results obtained by PMF were further tested inter alia, by comparing them to those obtained by two other receptor modelling techniques: Constrained Weighted Non-negative Matrix Factorization (CW - NMF) and Chemical Mass Balance (CMB). The results of these tests suggest that the solution obtained by PMF, is valid, indicating that for this particular airshed PMF managed to extract most of the information about the aerosol sources affecting the receptor - even from a dataset with a limited number of samples.

Informed Weighted Non-Negative Matrix Factorization Using αß-Divergence Applied to Source Apportionment.

In this paper, we propose informed weighted non-negative matrix factorization (NMF) methods using an α ß -divergence cost function. The available information comes from the exact knowledge/boundedness of some components of the factorization-which are used to structure the NMF parameterization-together with the row sum-to-one property of one matrix factor. In this contribution, we extend our previous work which partly involved some of these aspects to α ß -divergence cost functions. We derive new update rules which are extendthe previous ones and take into account the available information. Experiments conducted for several operating conditions on realistic simulated mixtures of particulate matter sources show the relevance of these approaches. Results from a real dataset campaign are also presented and validated with expert knowledge.

Contributions of local and regional anthropogenic sources of metals in PM2.5 at an urban site in northern France.

PM2.5 have been related to various adverse health effects, mainly due to their ability to penetrate deeply and to convey harmful chemical components, such as metals inside the body. In this work, PM2.5 were sampled at Saint-Omer, a medium-sized city located in northern France, in March-April 2011 and analyzed for their total carbon, water-soluble ions, major and trace elements. More specifically, the origin of 15 selected elements was examined using different tools including enrichment factors, conditional bivariate probability function (CBPF) representations, diagnostic ratios and receptor modelling. The results indicated that PM2.5 metal composition is affected by both emissions of a local glassmaking factory and an integrated steelworks located at a distance of 35 km from the sampling site. For the first time, diagnostic ratios were proposed for the glassmaking activity. Therefore, metals in PM2.5 could be attributed to the following anthropogenic sources: (i) local glassmaking industry for Sn, As, Cu and Cr, (ii) distant integrated steelworks for Ag, Fe, Cd, Mn, Rb and Pb, (iii) heavy fuel oil combustion for Ni, V and Co and (iv) non-exhaust traffic for Zn, Pb, Mn, Sb, and Cu. The impact of such sources on metal concentrations in PM2.5 was assessed using a constrained receptor model. Despite their low participation to PM2.5 concentration (2.7%), the latter sources were found as the main contributors (80%) to the overall concentration levels of the 15 selected elements in PM2.5.

PM2.5 source apportionment in a French urban coastal site under steelworks emission influences using constrained non-negative matrix factorization receptor model.

The constrained weighted-non-negative matrix factorization (CW-NMF) hybrid receptor model was applied to study the influence of steelmaking activities on PM2.5 (particulate matter with equivalent aerodynamic diameter less than 2.5 µm) composition in Dunkerque, Northern France. Semi-diurnal PM2.5 samples were collected using a high volume sampler in winter 2010 and spring 2011 and were analyzed for trace metals, water-soluble ions, and total carbon using inductively coupled plasma--atomic emission spectrometry (ICP-AES), ICP--mass spectrometry (ICP-MS), ionic chromatography and micro elemental carbon analyzer. The elemental composition shows that NO3(-), SO4(2-), NH4(+) and total carbon are the main PM2.5 constituents. Trace metals data were interpreted using concentration roses and both influences of integrated steelworks and electric steel plant were evidenced. The distinction between the two sources is made possible by the use Zn/Fe and Zn/Mn diagnostic ratios. Moreover Rb/Cr, Pb/Cr and Cu/Cd combination ratio are proposed to distinguish the ISW-sintering stack from the ISW-fugitive emissions. The a priori knowledge on the influencing source was introduced in the CW-NMF to guide the calculation. Eleven source profiles with various contributions were identified: 8 are characteristics of coastal urban background site profiles and 3 are related to the steelmaking activities. Between them, secondary nitrates, secondary sulfates and combustion profiles give the highest contributions and account for 93% of the PM2.5 concentration. The steelwork facilities contribute in about 2% of the total PM2.5 concentration and appear to be the main source of Cr, Cu, Fe, Mn, Zn.