● We review the framework of discovering emerging pollutants through an omics approach.● High-resolution MS can digitalize atmospheric samples to full-component data.● Chemical features and databases can help to translate untargeted data to compounds.● Biological effect-directed untargeted analyses consider both existence and toxicity. Ambient air pollution, containing numerous known and hitherto unknown compounds, is a major risk factor for public health. The discovery of harmful components is the prerequisite for pollution control; however, this raises a great challenge on recognizing previously unknown species. Here we systematically review the analytical techniques on air pollution in the framework of an omics approach, with a brief introduction on sample preparation and analysis, and in more detail, compounds prioritization and identification. Through high-resolution mass spectrometry (HRMS, typically coupled with chromatography), the complicated environmental matrix can be digitalized into “full-component” data. A key step to discover emerging compounds is the prioritization of compounds from massive data. Chemical fingerprints, suspect lists and biological effects are the most vital untargeted strategies for comprehensively screening critical and hazardous substances. Afterward, compressed data of compounds can be identified at various confidence levels according to exact mass and the derived molecular formula, MS libraries, and authentic standards. Such an omics approach on full-component data provides a paradigm for discovering emerging air pollutants; nonetheless, new technological advancements of instruments and databases are warranted for further tracking the environmental behaviors, hence to evaluate the health risk of key pollutants. 相似文献
With a growing awareness of environmental protection, the dust pollution caused by automobile foundry work has become a serious and urgent problem. This study aimed to explore contamination levels and health effects of automobile foundry dust. A total of 276 dust samples from six types of work in an automobile foundry factory were collected and analysed using the filter membrane method. Probabilistic risk assessment model was developed for evaluating the health risk of foundry dust on workers. The health risk and its influencing factors among workers were then assessed by applying the Monte Carlo method to identify the most significant parameters. Health damage assessment was conducted to translate health risk into disability-adjusted life year (DALY). The results revealed that the mean concentration of dust on six types of work ranged from 1.67 to 5.40 mg/m3. The highest health risks to be come from melting, cast shakeout and finishing, followed by pouring, sand preparation, moulding and core-making. The probability of the risk exceeding 10−6 was approximately 85%, 90%, 90%, 75%, 70% and 45%, respectively. The sensitivity analysis indicated that average time, exposure duration, inhalation rate and dust concentration (C) made great contribution to dust health risk. Workers exposed to cast shakeout and finishing had the largest DALY of 48.64a. These results can further help managers to fully understand the dust risks on various types of work in the automobile foundry factories and provide scientific basis for the management and decision-making related to health damage assessment.
The 16S rDNA-based molecular technique was applied to analyze the microbial community of autotrophic denitrification bacteria in a biofilm developed on the surface of sulfur particles and then the biochemistry process involved in this biofilm was discussed based on the microbial community analysis. Six key operational taxonomy units were identified, which were all unknown species belonging to a wide range of bacteria from four major subdivisions (alpha, beta, gamma and delta) of the kingdom Proteobacteria and from the kingdom Chlorobia (green sulfur bacteria). One species was chemoautotrophic and related to Thiobacillus denitrificans, two species were photoautotrophic, and three were chemoheterotrophic. Contrary to expectation, T. denitrificans-like bacteria constituted only 32% of the microbial community. As a result of the study, the entire microbiology of the autosulfurotrophic denitrification process as well as the interactions between the different microbial groups in the biofilm may need to be reconsidered. 相似文献