Companies are interested in improving chemicals to reduce environmental impacts, also known as green chemistry. The 12 principles of green chemistry outline a framework for identifying a greener chemical or process, spanning aspects in health hazard, ecological risk, and resource efficiency across a product lifecycle. However, that framework does not detail how to measure performance. Furthermore, collecting the data required, beyond simple health hazard ratings, is resource intensive. This paper describes an approach for establishing green chemistry metrics (GCM), to evaluate chemicals and chemical processes against the 12 principles, using readily available data, such as the data compiled in compliance with the Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Using the GCM, chemicals or processes can be ranked by a hierarchy of metrics: (1) scores for each of the 12 principles, (2) three category rankings between new and improved chemicals/processes (improved resource use, increased energy efficiency, and reduced human and environmental hazards), and (3) a summary comparison ranking. The GCM approach is unique in that it is robust and flexible enough to encompass a diverse product portfolio, inexpensive to implement with on-hand data, based on generally accepted industry practices, and allows meaningful communications about chemical sustainability options.

Abstract. The vegetation indices normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) provide indicators of pigmentation and photosynthetic activity that can be used to model photosynthesis from remote sensing with the light-use-efficiency model. To help develop and validate this approach, reliable proximal NDVI and PRI sensors have been needed. We tested new NDVI and PRI sensors, "spectral reflectance sensors" (SRS sensors; recently developed by Decagon Devices, during spring activation of photosynthetic activity in evergreen and deciduous stands. We also evaluated two methods of sensor cross-calibration -one that considered sky conditions (cloud cover) at midday only, and another that also considered diurnal sun angle effects. Cross-calibration clearly affected sensor agreement with independent measurements, with the best method dependent upon the study aim and time frame (seasonal vs. diurnal). The seasonal patterns of NDVI and PRI differed for evergreen and deciduous species, demonstrating the complementary nature of these two indices. Over the spring season, PRI was most strongly influenced by changing chlorophyll : carotenoid pool sizes, while over the diurnal timescale, PRI was most affected by the xanthophyll cycle epoxidation state. This finding demonstrates that the SRS PRI sensors can resolve different processes affecting PRI over different timescales. The advent of small, inexpensive, automated PRI and NDVI sensors offers new ways to explore environmental and physiological constraints on photosynthesis, and may be particularly well suited for use at flux tower sites. Wider application of automated sensors could lead to improved integration of flux and remote sensing approaches for studying photosynthetic carbon uptake, and could help define the concept of contrasting vegetation optical types.

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Previous studies have shown that the floristic composition of northern peatlands provides important information regarding ecosystem processes and their responses to environmental change. Remote sensing is the most expeditious method of obtaining floristic information at landscape and regional scales, but the spatial complexity of many northern peatlands and the spectral similarity of a number of peatland vegetation species is such that the success of traditional methods of vegetation classification is often limited. Here, we assessed whether ordination and regression analyses may be a useful alternative method for mapping peatland plant communities from remote sensing data. We used isometric feature mapping (Isomap) to describe the community structure of the peatland vegetation and related the identified continuous floristic gradients to hyperspectral imagery (AISA Eagle) using partial least squares regression (PLSR). We performed the same analysis at two hierarchical levels of species aggregation in order to map continuous gradients in the composition of both species and plant functional types (PFTs), the latter of which is the most widely used level of aggregation in northern ecosystems. Isomap was able to transfer 82% and more than 96% of the observed ground-based observations to the ordination space for plots characterised by species and PFT; respectively. The modelled floristic gradients showed good agreement with ground-based species and PFT observations although the strength of the agreement was proportional to the amount of floristic variation explained by each ordination axis (r2 val =0.74, 0.45 and 0.30 for the first three ordination axes and r2 val =0.68 and 0.66 for the first two ordination axes; for species and PFT floristic gradients respectively). We also found that how a PFT is defined has an important influence on the success with which it can be mapped. The resultant mapped floristic gradients enabled visualisation of homogeneous vegetation stands, heterogeneous mixtures of different key species and PFTs, and the presence of continuous and abrupt floristic transitions, without the need for unique spectral signatures or the collection of data characterising ancillary environmental variables.authorsversionPeer reviewe