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Comparative chemical hazard assessment, which compares hazards for several endpoints across several chemicals, can be used for a variety of purposes including alternatives assessment and the prioritization of chemicals for further assessment. A new framework was developed to compile and integrate chemical hazard data for several human health and ecotoxicity endpoints from public online sources including hazardous chemical lists, Globally Harmonized System hazard codes (H-codes) or hazard categories from government health agencies, experimental quantitative toxicity values, and predicted values using Quantitative Structure–Activity Relationship (QSAR) models. QSAR model predictions were obtained using EPA’s Toxicity Estimation Software Tool. Java programming was used to download hazard data, convert data from each source into a consistent score record format, and store the data in a database. Scoring criteria based on the EPA’s Design for the Environment Program Alternatives Assessment Criteria for Hazard Evaluation were used to determine ordinal hazard scores (i.e., low, medium, high, or very high) for each score record. Different methodologies were assessed for integrating data from multiple sources into one score for each hazard endpoint for each chemical. The chemical hazard assessment (CHA) Database developed in this study currently contains more than 990,000 score records for more than 85,000 chemicals. The CHA Database and the methods used in its development may contribute to several cheminformatics, public health, and environmental activities.

The goal of alternatives assessment (AA) is to facilitate a comparison of alternatives to a chemical of concern, resulting in the identification of safer alternatives. A two stage methodology for comparing chemical alternatives was developed. In the first stage, alternatives are compared using a variety of human health effects, ecotoxicity, and physicochemical properties. Hazard profiles are completed using a variety of online sources and quantitative structure activity relationship models. In the second stage, alternatives are evaluated utilizing an exposure/risk assessment over the entire life cycle. Exposure values are calculated using screening-level near-field and far-field exposure models. The second stage allows one to more accurately compare potential exposure to each alternative and consider additional factors that may not be obvious from separate binned persistence, bioaccumulation, and toxicity scores. The methodology was utilized to compare phosphate-based alternatives for decabromodiphenyl ether (decaBDE) in electronics applications.

P2P (Pollution Prevention Progress) is a computer-based tool that supports the comparison of process and product alternatives in terms of environmental impacts. This tool provides screening-level information for use in process design and in product life cycle assessment (LCA). Twenty one impact categories and data for approximately 3,000 chemicals are represented in the default database of the new release, P2P Mark III. These data help identify which emissions may require further, more sophisticated, characterisation in the different impact categories. In this paper, we primarily focus on the persistence-bioaccumulation toxicity (PBT) methodology adopted for the classification of chemicals in the context of (eco-)toxicological impacts. This classification methodology is cross-compared with a characterisation approach that provides a more complete model-based representation of the source-to-effect (or environmental) mechanism, but for fewer chemicals. To ensure that the quantity of the emission, and not just chemical hazard, is taken into account the comparison is based on a case study for the production of BDO (1,4-butanediol). Insights are presented independently for both the chemical processing stage, as well as from a broader life cycle perspective.

Arising from: J. B. Plotkin, J. Dushoff & H. B. Fraser Nature 428, 942–945 (2004); see also communication from Hahn et al.; Nielsen et al.; Plotkin et al. reply Plotkin et al.1 introduce a method to detect selection that is based on an index called codon volatility and that uses only the sequence of a single genome, claiming that this method is applicable to a large range of sequenced organisms1. Volatility for a given codon is the ratio of non-synonymous codons to all sense codons accessible by one point mutation. The significance of each gene's volatility is assessed by comparison with a simulated distribution of 106 synonymous versions of each gene, with synonymous codons drawn randomly from average genome frequencies. Here we re-examine their method and data and find that codon volatility does not detect selection, and that, even if it did, the genomes of Mycobacterium tuberculosis and Plasmodium falciparum, as well as those of most sequenced organisms, do not meet the assumptions necessary for application of their method.