Diesel is a complex pollutant composed of a mixture of aliphatic and aromatic hydrocarbons. Because of this complexity, diesel bioremediation requires multiple microorganisms, which harbor the catabolic pathways to degrade the mixture. By enrichment cultivation of rhizospheric soil from a diesel-polluted site, we have isolated a bacterial consortium that can grow aerobically with diesel and different alkanes and polycyclic aromatic hydrocarbons (PAHs) as the sole carbon and energy source. Microbiome diversity analyses based on 16S rRNA gene showed that the diesel-degrading consortium consists of 76 amplicon sequence variants (ASVs) and it is dominated by Pseudomonas, Aquabacterium, Chryseobacterium, and Sphingomonadaceae. Changes in microbiome composition were observed when growing on specific hydrocarbons, reflecting that different populations degrade different hydrocarbons. Shotgun metagenome sequence analysis of the consortium growing on diesel has identified redundant genes encoding enzymes implicated in the initial oxidation of alkanes (AlkB, LadA, CYP450) and a variety of hydroxylating and ring-cleavage dioxygenases involved in aromatic and polyaromatic hydrocarbon degradation. The phylogenetic assignment of these enzymes to specific genera allowed us to model the role of specific populations in the diesel-degrading consortium. Rhizoremediation of diesel-polluted soil microcosms using the consortium, resulted in an important enhancement in the reduction of total petroleum hydrocarbons (TPHs), making it suited for rhizoremediation applications.

This study was conducted in order to identify possible sources and to estimate their contribution to particulate matter in a rural area. For this purpose, a commonly used receptor model, positive matrix factorization (PMF), was applied to a PM2.5 data set collected in a rural area of Madrid (Spain) between May 2004 and April 2005. A total of eighty nine samples were gathered. Chemical composition of particulate matter including major components, trace elements, total carbon, alkanes, PAHs, alcohols and acids were analyzed to study sources of atmospheric aerosols using the positive matrix factorization model. This work is characterized by including some organic tracers within PMF analysis, through which we can get a more accurate source apportionment. To our knowledge, this is the first work employing organic tracers for source apportionment by PMF model in a rural area of Spain. To assign PMF factor with a possible source, authors have based on the presence of tracer species. PMF apportioned the PM2.5 mass into nine factors. The factors included (1) even n–alcohols/acids factor, (2) n–alkanes factor, (3) secondary nitrate factor, (4) secondary sulfate factor, (5) secondary organic aerosol, (6) palmitic/stearic factor, (7) PAHs factor, (8) crustal factor and (9) low molecular weight alcohols/acids factor. Six of these factors are related to primary emissions and three of them are categorized as secondary aerosol. PMF identified two mixed sources, factor 6 identified as cooking /microbial source and factor 9 identified as a mixed source