Air pollution and mortality for cancer of the respiratory system in Italy: an explainable artificial intelligence approach

Respiratory system cancer, encompassing lung, trachea and bronchus cancer, constitute a substantial and evolving public health challenge. Since pollution plays a prominent cause in the development of this disease, identifying which substances are most harmful is fundamental for implementing policies aimed at reducing exposure to these substances. We propose an approach based on explainable artificial intelligence (XAI) based on remote sensing data to identify the factors that most influence the prediction of the standard mortality ratio (SMR) for respiratory system cancer in the Italian provinces using environment and socio-economic data. First of all, we identified 10 clusters of provinces through the study of the SMR variogram. Then, a Random Forest regressor is used for learning a compact representation of data. Finally, we used XAI to identify which features were most important in predicting SMR values. Our machine learning analysis shows that NO, income and O3 are the first three relevant features for the mortality of this type of cancer, and provides a guideline on intervention priorities in reducing risk factors.

Evidence for a Putative Isoprene Reductase in Acetobacterium wieringae

Recent discoveries of isoprene-metabolizing microorganisms suggest they might play an important role in the global isoprene budget. Under anoxic conditions, isoprene can be used as an electron acceptor and is reduced to methylbutene. This study describes the proteogenomic profiling of an isoprene-reducing bacterial culture to identify organisms and genes responsible for the isoprene hydrogenation reaction. A metagenome-assembled genome (MAG) of the most abundant (89% relative abundance) lineage in the enrichment, Acetobacterium wieringae, was obtained. Comparative proteogenomics and reverse transcription-PCR (RT-PCR) identified a putative five-gene operon from the A. wieringae MAG upregulated during isoprene reduction. The operon encodes a putative oxidoreductase, three pleiotropic nickel chaperones (2 × HypA, HypB), and one 4Fe-4S ferredoxin. The oxidoreductase is proposed as the putative isoprene reductase with a binding site for NADH, flavin adenine dinucleotide (FAD), two pairs of canonical [4Fe-4S] clusters, and a putative iron-sulfur cluster site in a Cys₆-bonding environment. Well-studied Acetobacterium strains, such as A. woodii DSM 1030, A. wieringae DSM 1911, or A. malicum DSM 4132, do not encode the isoprene-regulated operon but encode, like many other bacteria, a homolog of the putative isoprene reductase (~47 to 49% amino acid sequence identity). Uncharacterized homologs of the putative isoprene reductase are observed across the Firmicutes, Spirochaetes, Tenericutes, Actinobacteria, Chloroflexi, Bacteroidetes, and Proteobacteria, suggesting the ability of biohydrogenation of unfunctionalized conjugated doubled bonds in other unsaturated hydrocarbons. IMPORTANCE Isoprene was recently shown to act as an electron acceptor for a homoacetogenic bacterium. The focus of this study is the molecular basis for isoprene reduction. By comparing a genome from our isoprene-reducing enrichment culture, dominated by Acetobacterium wieringae, with genomes of other Acetobacterium lineages that do not reduce isoprene, we shortlisted candidate genes for isoprene reduction. Using comparative proteogenomics and reverse transcription-PCR we have identified a putative five-gene operon encoding an oxidoreductase referred to as putative isoprene reductase.

Tree phyllospheres are a habitat for diverse populations of CO-oxidizing bacteria

Carbon monoxide (CO) is both a ubiquitous atmospheric trace gas and an air pollutant. While aerobic CO-degrading microorganisms in soils and oceans are estimated to remove ~370 Tg of CO per year, the presence of CO-degrading microorganisms in above-ground habitats, such as the phyllosphere, and their potential role in CO cycling remains unknown. CO-degradation by leaf washes of two common British trees, Ilex aquifolium and Crataegus monogyna, demonstrated CO uptake in all samples investigated. Based on the analyses of taxonomic and functional genes, diverse communities of candidate CO-oxidizing taxa were identified, including members of Rhizobiales and Burkholderiales which were abundant in the phyllosphere at the time of sampling. Based on predicted genomes of phyllosphere community members, an estimated 21% of phyllosphere bacteria contained CoxL, the large subunit of CO-dehydrogenase. In support of this, data mining of publicly available phyllosphere metagenomes for genes encoding CO-dehydrogenase subunits demonstrated that, on average, 25% of phyllosphere bacteria contained CO-dehydrogenase gene homologues. A CO-oxidizing Phyllobacteriaceae strain was also isolated from phyllosphere samples which contains genes encoding both CO-dehydrogenase as well as a ribulose-1,5-bisphosphate carboxylase-oxygenase. These results suggest that the phyllosphere supports diverse and potentially abundant CO-oxidizing bacteria, which are a potential sink for atmospheric CO.

Reductive metabolism of the important atmospheric gas isoprene by homoacetogens

Isoprene is the most abundant biogenic volatile organic compound (BVOC) in the Earth's atmosphere and plays important roles in atmospheric chemistry. Despite this, little is known about microbiological processes serving as a terrestrial sink for isoprene. While aerobic isoprene degrading bacteria have been identified, there are no known anaerobic, isoprene-metabolizing organisms. In this study an H₂-consuming homoacetogenic enrichment was shown to utilize 1.6 μmoles isoprene h^-1 as an electron acceptor in addition to HCO₃^-. The isoprene-reducing community was dominated by Acetobacterium spp. and isoprene was shown to be stoichiometrically reduced to three methylbutene isomers (2-methyl-1-butene (>97%), 3-methyl-1-butene (≤2%), 2-methyl-2-butene (≤1%). In the presence of isoprene, 40% less acetate was formed suggesting that isoprene reduction is coupled to energy conservation in Acetobacterium spp. This study improves our understanding of linkages and feedbacks between biogeochemistry and terrestrial microbial activity.

Modeling the carbon monoxide dissipation in Timisoara, Romania

Modeling the evolution of pollutants' concentration is important for predicting their impact on the human health and implementing measures for a sustainable development and environmental protection. Since the carbon dioxide (CO) is one of the main pollutants that affect the urban environment, the present study aimed at building a model for the evolution of the mean daily and monthly concentration in Timisoara, Romania. We found a non-linear direct dependence of CO concentration on the temperature and humidity and an inverse proportional relationship with the wind speed. The models have been linearized and validated by statistical tests. The extreme values distributions have also been detected, and comparisons of the data with the admissible values are provided.

Mann-Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models

The purpose of the present research is to identify the trends in the concentrations of few atmospheric pollutants and meteorological parameters over an urban station Kolkata (22° 32' N; 88° 20' E), India, during the period from 2002 to 2011 and subsequently develop models for precise forecast of the concentration of the pollutants and the meteorological parameters over the station Kolkata. The pollutants considered in this study are sulphur dioxide (SO2), nitrogen dioxide (NO2), particulates of size 10-μm diameters (PM10), carbon monoxide (CO) and tropospheric ozone (O3). The meteorological parameters considered are the surface temperature and relative humidity. The Mann-Kendall, non-parametric statistical analysis is implemented to observe the trends in the data series of the selected parameters. A time series approach with autoregressive integrated moving average (ARIMA) modelling is used to provide daily forecast of the parameters with precision. ARIMA models of different categories; ARIMA (1, 1, 1), ARIMA (0, 2, 2) and ARIMA (2, 1, 2) are considered and the skill of each model is estimated and compared in forecasting the concentration of the atmospheric pollutants and meteorological parameters. The results of the study reveal that the ARIMA (0, 2, 2) is the best statistical model for forecasting the daily concentration of pollutants as well as the meteorological parameters over Kolkata. The result is validated with the observation of 2012.