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Research Article pubs.acs.org/journal/ascecg

Critical Evaluation of Published Algorithms for Determining Environmental and Hazard Impact Green Metrics of Chemical Reactions and Synthesis Plans John Andraos* CareerChem, 504-1129 Don Mills Road, Toronto, ON M3B 2W4, Canada

Melanie L. Mastronardi, Laura B. Hoch, and Andrei Hent University of Toronto, Department of Chemistry, Green Chemistry Initiative, 80 St. George Street, Toronto, ON M5S 3H6, Canada S Supporting Information *

ABSTRACT: In this paper, we analyze six published algorithms that evaluate environmental and hazard impact green metrics. The methods are compared and contrasted on a common set of chemical reactions and synthesis plans. The relative greenness of four reaction procedures to prepare iron(II)oxalate dihydrate and three industrial preparations of aniline are examined. We also examine the organic syntheses preparations of 2,2-diethoxy-1-isocyanoethane, thiete 1,1-dioxide, and ethyl phenylcyanopyruvate that we previously evaluated by material efficiency. We discuss the merits and limitations of all algorithms with respect to quality of calculation outputs, visualization, and ease of use.

KEYWORDS: Algorithms, Green chemistry engineering, Metrics analysis, Sustainability metrics, Environmental impact, Hazard and safety impact

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INTRODUCTION An effective way to communicate green chemistry principles to chemists and chemical engineers is to juxtapose competing reactions or synthesis plans leading to the same product. In such comparative analyses, metrics are often introduced as essential quantitative tools to critique reaction or plan performances to pinpoint strengths and weaknesses of various candidate routes. On the basis of such ranking results, practicing chemical professionals are able to apply in action and concretize their database of reactions by suggesting alternate routes that preserve strengths and overcome weaknesses using chemical knowledge that they have accumulated over their professional experience. However, the idea of determining degree of relative greenness of chemical transformations exclusively on the basis of material efficiency metrics performance has been criticized.1,2 A main problem of green chemistry metrics evaluations is the resolution of situations where a high performance in material efficiency is offset by toxicity and/or hazard concerns of input materials used and waste produced; or conversely, where use or production of benign materials is offset by a poor performance in material efficiency. Such diametrically opposing scenarios make it difficult to determine which procedure or synthesis is truly green with any degree of definiteness. Therefore, in recent years, there have been a number of attempts put forward to © XXXX American Chemical Society

address the issue thus broadening the suite of green metrics available for a more complete evaluation of greenness. The literature in this area is however plagued by a lack of standardization and a combination of unreliability and unavailability of necessary physical property, environmental transport, and toxicity data to make such assessments possible with a high degree of definiteness. Having reviewed in detail various published algorithms on the determination of material efficiency metrics in the previous paper,3 we now turn our attention to conducting a similar evaluation of their determination of environmental and hazard impact metrics. We have already noted that the following methods do not consider these metrics, and therefore, they will not be discussed further here: Augé algorithm,4−6 ACS PMI (process mass intensity) calculator,7 and Green Aspiration Level (GAL).8 This leaves the Environmental Assessment Tool for Organic Synthesis (EATOS),9−11 EcoScale,12 Green Star,13−17 and benign index18 and safety-hazard index19 algorithms as appropriate methods to compare and contrast. In addition, we also include a recent multivariate metric exercise20 and a new and improved method put forward by the Received: November 22, 2015 Revised: January 15, 2016

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DOI: 10.1021/acssuschemeng.5b01555 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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ACS Sustainable Chemistry & Engineering

have also been identified. The reference compilations by Mackay60 and Yaws61 are authoritative for well-studied compounds important in industrial and environmental chemistry such as hydrocarbons (aliphatic and aromatic), alcohols, halogenated hydrocarbons (CFCs, HFCs, and PCBs), amines, aromatics, esters, organic acids, pesticides, simple inorganics (acids and bases), and gases. Another useful online source of information is Chemspider,62 which includes both experimental and computational data; however, the documentation of references is not complete and needs separate verification. Table 1 summarizes the features of all six algorithms with respect to chemical systems handled, metrics selected, visual aids, ease of use, and limitations. Schemes S1 to S5 for syntheses of 2,2-diethoxy-1-isocyanoethane, thiete 1,1dioxide, ethyl phenylcyanopyruvate, iron(II)oxalate dihydrate, and aniline are given in Part 5 of the Supporting Information.

University of Toronto Green Chemistry Initiative (UTGCI) that combines the best attributes of merit or demerit pointbased methods. Following the same logic of head-to-head comparisons of all algorithms, we selected the following chemical examples for testing that fall into two groups. In the first group, we consider the syntheses of 2,2-diethoxy-1isocyanoethane,21 thiete 1,1-dioxide,22 and ethyl phenylcyanopyruvate23−25 as simple examples of handling single reactions, linear plans, and convergent plans, respectively. The results of this group are summarized in Part 7 of the Supporting Information and are appropriate for introductory instructional exercises on each algorithm for novices to become familiar with the basic mechanics of each method. We have also included ready-to-use template Excel files along with workbook spreadsheet results to facilitate and accelerate their implementation in real world situations. In the second group, we consider four reaction procedures to prepare iron(II)oxalate dihydrate13 and three industrial preparations of aniline,26,27 where we illustrate how metrics may be used to rank different procedures to the same target product with respect to relative greenness. For the former case, we examine the effect of tweaking reaction conditions of a given transformation such as the kind of catalyst used, reaction temperature, and excess reagent consumption. For the latter case, different chemistries and starting materials are compared to reach a common desired target product. Researchers can use these comparative examples to form the basis of insightful analyses that illustrate how metrics may be used to track optimization development and ultimately in critical thinking decision-making in green chemistry. Therefore, our discussion on algorithm results will mainly deal with this second group of examples. As before, the criteria we focused on are quality and completeness of calculation outputs, visualization, and ease of use.

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ALGORITHM DESCRIPTIONS The six algorithms may be subdivided into two main groups: simplified semi-quantitative methods (EATOS, EcoScale, and Green Star) and advanced quantitative methods (multivariate method, benign and safety-hazard indices, and UTGCI method). The main characteristic of algorithms in the first group is that they implement some kind of arbitrarily selected merit or demerit point system or scaling factor to parametrize greenness, whereas the second group directly uses experimentally or computationally determined environmental and transport data for chemicals. We will first briefly describe the main features of each method and then present and discuss the results of their implementation on the example syntheses of iron(II) oxalate dihydrate and aniline. Simplified Methods. (i). Environmental Assessment Tool for Organic Synthesis (EATOS). EATOS uses Q-factors to amplify waste material E-factor (environmental factor) contributions according to Sheldon’s original concept of environmental quotient,63 where the E-factor contribution of a single waste product j,Ej, is adjusted by multiplying it by a factor Q that reflects its environmental impact or risk. Equation 1 shows the expression for the overall adjusted E-factor for a reaction, EI_out, covering all waste materials, namely, excess substrates, coupled products, byproducts, auxiliaries, and solvents.

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ENVIRONMENTAL AND SAFETY IMPACT METRICS There are two major tasks in implementing any kind of metrics assessment based on environmental and safety impact: selection of parameters and finding reliable sources of data for those parameters. The selection of which impact metrics, sometimes called risk potentials, to use has been largely guided by the general statements put forward in the 12 Principles of Green Chemistry,28 Workplace Hazardous Materials Information System (WHMIS) labeling,29−31 and information contained in Materials Safety Data Sheets (MSDS), the National Fire Protection Association (NFPA) 704 labeling system,32 and the National Institute for Occupational Safety and Health (NOISH).33 The following metrics are commonly used in such analyses: LD50(oral), LC50(inhalation), occupational exposure limit (OEL), octanol−water partition coefficients (log Kow), global warming potential (carbon atom count relative to carbon dioxide), acidity potential (number of dissociated protons whose pKa is below 7), lower explosion limit (LEL), flash point (FP), and criteria characteristics such as flammability, corrosivity, explosiveness, and oxidizing potential. Standard toxicity information may be obtained reliably from the Registry of Toxic Effects of Chemical Substances (RTECS) online database34 and to a lesser degree in MSDS sheets taken from Sigma-Aldrich and Science Lab. Risk and hazard phrases are also found in MSDS sheets. Reliable compilations of octanol−water partition coefficients,35−43 Henry’s law constants,44,45 acid dissociation constants,46−53 and aqueous solubility data54−59 important in estimating bioaccumulation and environmental transport of chemicals in air, water, and soil

EI_out =

∑ (Ej)(Q j ,avg) j

=

⎡

∑ ⎢(Ej) j

⎣

Q j ,hum.tox. + Q j ,accum. + ... + Q j , n ⎤ ⎥ n ⎦ (1)

where each waste material Q factor is an arithmetic average of individual Q factors for human toxicity, bioaccumulation, and other contributions; and n is the number of Q factor categories. A Qj,avg value equal to 1 implies no added impact, whereas a value larger than 1 suggests that a given waste material has potential to cause environmental harm, thereby amplifying its effective mass of waste. This measure was taken to distinguish the kinds of waste produced even though they may have the same mass. For example, a reaction producing 1 g each of mercury and sodium chloride would have its coupled products distinguishable beyond mass. EATOS extends the same idea to input materials as well; hence, individual input material PMI B


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color codes are based on arbitrary ranges for parameter values

EI_in =

∑ (PMI)j (Q j,avg) j

(2)

The Q-factor values are arbitrarily based on risk phrases and ranges of other factors such as human toxicity parameters (LD50(oral), LD50(dermal), LC50(inhalation)), occupational exposure limits (OEL), ozone depletion potential (ODP), global warming potential (GWP), and acidification potential (AP). Since the program was originally written in German, OEL values are entered under the heading MAK for “Maximale Arbeitsplatzkonzentration”. All of these data are entered in the “weighting” folder in the seven-sheet window for substrates− catalysts−solvents− auxiliary materials−product−coupled products−byproducts. Table S1 in Part 1 of the Supporting Information gives Q values associated with various risk phrases found in MSDS sheets. Risk phrases are entered as, for example, 36/37/38-40 if they appear in MSDS sheets as R36/ 37/38 and R40. Other Q-factor scales based on other potentials are given in the EATOS manual.10 It should be noted that only one of either LD50(oral), LD50(dermal), or LC50(inhalation) can be entered under the acute toxicity column. This is restrictive for compounds that pose multiple acute toxicities, so one is left to choose the parameter associated with the greatest risk. Following the inverse Hodge−Sterner scale of toxicity,64 it is advisible to select the parameter with the least value since that corresponds to maximum toxicity. The outputted histogram compares raw PMIs and E-factors based solely on mass versus amplified environmental impact values EI_in and EI_out. The same computational breakdown of step PMI and step E-factor contributions is possible in addition to individual material contributions. EATOS also considers costs of input materials, which in this discussion we do not consider. Greener procedures are characterized as having low values for EI_in and EI_out and most input and waste materials with Q factors equal to 1. (ii). EcoScale. EcoScale uses an arbitrary penalty point system out of an ideal value of 100 covering the following categories: reaction yield, cost of reaction components (based on producing 10 mmol of final product), safety of reaction components, technical setup (type of equipment used), reaction temperature and reaction time, and workup and purification components. Greener procedures have high EcoScale values. The algorithm applies only to single reactions, not synthesis plans, and only to reaction input materials. It has limited coverage of actual toxicity and hazard parameters and is heavily weighted toward simplified WHMIS and NFPA-704 labeling systems and qualitative information found in MSDS sheets. There is no visual display, and the EcoScale does not account for relative masses of input or waste materials in the assignment of penalty points. For example, 1 g of mercury used as reagent is assigned the same penalty points as if 100 g were used. The algorithm also does not consider waste reaction byproducts. (iii). Green Star. Green Star uses an arbitrary 1−2−3 merit point scale, where 3 designates a green attribute, which is assigned for 10 of the 12 principles of green chemistry. Principles 4 and 11 referring to designing benign products and real-time monitoring of reactions to prevent pollution,

very easy to implement; applied to input and waste materials

S = single reactions; L = linear plans; C = convergent plans. a

S, L, C

S, L, C

multivariate method (2012) benign and safetyhazard indices (2012,2013) UTGCI method (2013)

S, L, C

(process mass intensity) contributions are adjusted by a similar multiplication process. Equation 2 shows an analogous expression for overall adjusted PMI for a reaction, EI_in, covering all input materials, namely, substrates, auxiliaries, and solvents.

pie charts, difference histograms, color coding

green−yellow−red color-coded charts radial polygons

S Green Star (2010)

life cycle assessment (LCA) based on the concept of risk potentials life cycle assessment (LCA) based on the concept of risk potentials

S EcoScale (2006)

mass weighted color-coded count of various parameters

requires separate reaction and synthesis spreadsheets apart from those used for material efficiency metrics

no complete report of calculations via single command; Qfactors are arbitrary based on arbitrarily chosen penalty score; does not account for masses of materials based on arbitrarily chosen merit score ranging from 1 (nongreen) to 3 (green); does not account for masses of materials uses the sums of risk indices for ranking

requires separate Java script program to run; applied to input and waste materials very easy to implement; applied to input materials only attempts to quantify all 12 principles of green chemistry using a merit point system very easy to implement; applied to input and waste materials takes into account masses of materials; applied to input and waste materials EATOS (2001)

Q-factors based on risk phrases and other histogram of amplified environmental and hazard potentials E-factor and PMI EcoScale score out of 100 based on none various criteria Green Star Area Index radial polygon S, L, C

algorithm

green metrics parameters chemical systems handleda

Table 1. Summary of Algorithm Characteristics

visual aids

ease of use

limitations


C


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ACS Sustainable Chemistry & Engineering respectively, are excluded. Table S2 in Part 2 of the Supporting Information summarizes the merit point scale used according to each principle. To facilitate its use, the inequality conditions applied to each principle are automated in the accompanying Excel template spreadsheet. A Green Star Area Index (GSAI) parameter is determined along with an accompanying radial polygon diagram. The determination and limitations of this parameter have been discussed previously.3 Greener procedures are associated with higher GSAI values. Similarly to EcoScale, Green Star does not account for relative masses of input or waste materials, it applies only to single reactions and not synthesis plans, and its merit scale is heavily weighted toward simplified WHMIS and NFPA-704 labeling systems and qualitative information found in MSDS sheets. Advanced Methods. (iv). Multivariate Metric Exercise. The multivariate metric exercise developed at Queen’s University is a truncated life cycle assessment (LCA) method based on the concept of defining a risk potential,65 Pj, for substance j as the ratio of a standard environmental impact parameter value, Xj, based on some property of the substance, to its value for an arbitrarily chosen reference compound according to the expression given in eq 3.

Pj =

[∑ Pm j j ]AP + [∑ Pm j j ]ODP + [∑ Pm j j ]SFP + [∑ Pm j j ]GWP j

j

j

j

j

j

=IA + IOD + ISF + IGW + IINGT + IINHT + IAD (5)

When comparing results for different reactions leading to the same target compound at a common basis scale (usually 1 kg), tables are constructed showing these summed risk indices in a head-to-head fashion, and a red−yellow−green color-coding scheme is used to make decisions on which reaction is relatively greener. For each risk index category, red is assigned to the highest value, green is assigned to the lowest value, and yellow is assigned to intermediate values. Relatively greener plans are associated with a higher frequency of green-colored risk indices. Table S3 in Part 3 of the Supporting Information lists the specific environmental impact parameters associated with the seven potentials along with the reference compounds and their corresponding values. A fully automated template Excel spreadsheet for the multivariate method is also given in the Supporting Information including results of its application to the analysis of synthesis plans for thiete 1,1-dioxide (linear) and ethyl phenylcyanopyruvate (convergent) (multivariate-template.xls, Excel workbook spreadsheets folder). The set of risk indices for each reaction step are determined at the appropriate mass scale for each intermediate product, and these are summed to obtain an overall risk index for an entire plan. (v). Benign Index and Safety-Hazard Index. The concept of benign and safety-hazard indices builds on the theme of impact potentials and risk indices presented in the multivariate method by extending the range of potentials examined and consolidating them into single-valued normalized parameters ranging in value between 0 and 1. These normalized parameters can be conveniently displayed in radial polygon diagrams together with other material efficiency metrics that also have the same range. The benign index (BI) pertaining to waste materials covering n risk potentials and j substances is defined in eq 6.

(3)

∑ f j aj

j

=∑ [Ij ,A + Ij ,OD + Ij ,SF + Ij ,GW + Ij ,INGT + Ij ,INHT + Ij ,AD]

This effectively gauges how many times more risky that substance is relative to that reference compound. Therefore, Pj is a dimensionless quantity. Each potential is then multiplied by the mass of the substance to obtain a corresponding index of risk, Ij, which has dimensions of mass The concept mirrors the Q-factor method used in EATOS that amplifies the masses of each waste substance. The exercise used the following seven potentials: acidification (AP), ozone depletion (ODP), smog formation (SFP), global warming (GWP), human toxicity by ingestion (INGTP), human toxicity by inhalation (INHTP), and abiotic resource depletion (ADP). The global warming potential also incorporated a contribution from energy consumption in the form of CO2 equivalents from heating, distillation, and refluxing operations. Energy consumptions from cooling and pressurization procedures were neglected. In addition, the degrees of bioaccumulation and persistence were estimated using octanol−water partition coefficients and the Boethling index defined according to eq 4. Boethling index = 3.199 − 0.00221(MW) +

j

+ [∑ Pm j j ]INGTP + [∑ Pm j j ]INHTP + [∑ Pm j j ]ADP

Xj X ref

j

BI w = 1 −

∑j ϕj[P1, j + P2,j + P3, j + ... + Pn , j] ∑j [P1, j + P2,j + P3, j + ... + Pn , j]

(6)

where ϕj represents the fractional mass contribution of substance j to the total waste. This parameter is determined on the basis of the following eight impact risk potentials: acidification−basification (AB), ozone depletion (OD), smog formation (SF), global warming (GW), inhalation toxicity (INHT), ingestion toxicity (INGT), bioaccumulation (BA), and abiotic resource depletion (AD). Each waste chemical generated in a chemical reaction in turn requires eight parameters to determine its BI: number of carbon atoms, pKa of acidic hydrogen atoms, ozone depletion, smog formation, LD50(oral), LC50(inhalation), Henry’s law constant, and log Kow (octanol−water partition coefficient). It is important to note from eq 6 that BI is a mass weighted quantity so that the impact potential of any individual waste chemical component in a synthesis plan is proportionally weighted according to its percent mass contribution to the total mass of waste generated. Values of BI closer to 1 indicate low environmental impact and are interpreted to be relatively greener than values closer to 0.

(4)

where MW is the molecular weight of a substance, aj is an empirically determined parameter based on a particular functional group j in the molecule, and f j is the number of times that functional group appears in the structure. For a given reaction, the above seven potentials are determined for each substance that contributes to overall waste, namely, unreacted reagents, byproducts, and all auxiliary materials (reaction solvents, catalysts, workup materials, and purification materials). It should be noted that in the original description of this method20 waste contributions from unreacted reagents were not considered in the analysis. The risk indices pertaining to each property are then summed as shown in eq 5. D


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plate.xls, safety-hazard-index-template.xls, Excel workbook spreadsheets folder). (vi). University of Toronto Green Chemistry Initiative Method (UTGCI). In collaboration with a University of Toronto graduate student-lead group called the Green Chemistry Initiative (GCI), a hybridized method was developed that combined the easy to understand attributes of the EcoScale and Green Star methods with the ideas of mass weighted parameters and uncertainties advanced in the BI and SHI method. This algorithm was introduced in green chemistry training workshops for novice graduate students held at the University of Toronto in 2013 and at the third International Congress on Sustainability IBERO held at Universidad Iberoamericana, Mexico City, in 2015 that was attended by both students and chemistry professionals from a variety of disciplines. For a given chemical reaction, the following parameters were considered for assessing greenness: reaction temperature, reaction pressure, LD50(oral), LD50(dermal), LC50(inhalation), OEL, log Kow, GWP, acidification potential, LEL, flammability, corrosivity, explosiveness, reaction with water, oxidizing potential, and pyrophoricity. A red−yellow− green−gray color-coding scheme along with an accompanying automated Excel spreadsheet, given in the Supporting Information, was implemented where each color was associated with a particular range of values for each parameter (UTGCItemplate.xls). The gray color flagged parameters were uncertain, either because they were unavailable or unreliable. Although these range values are arbitrary, they were guided by prior guidelines set out by practicing chemical engineers.67 Instead of simply counting the number of each kind of color for each waste substance, mass weighted color scores were determined according to eqs 9a −9d.

In an analogous manner, the safety-hazard impacts pertaining to input and waste materials are evaluated using the safetyhazard index (SHI) given by an expression exactly of the same form as given in eq 6.19 This parameter is determined on the basis of the following 11 impact risk potentials: corrosive properties of gases, liquids, or solids (CG and CL); flammability (F); oxygen balance (OB); hydrogen gas generation (HG); explosive vapor (XV); explosive strength (XS); impact sensitivity (IS); occupational exposure limit (OEL); skin dose (SD); and risk phrases (RP). Each input chemical used or waste chemical generated in a chemical reaction requires 11 parameters to determine its SHI: number of oxygen atoms required to oxidize substance, LD50(dermal), flash point, LC50(inhalation), number of moles of hydrogen gas generated, lower explosion limit, impact sensitivity, Trauzl lead block test, occupational exposure limit, skin dose, and risk Rphrases. Values of (SHI)in and (SHI)w closer to 1 indicate lower health and safety risks and are interpreted to be relatively greener than values closer to 0. Table S4 in Part 4 of the Supporting Information lists the specific impact parameters associated with the 11 potentials along with the reference compounds and their corresponding values. In the determination of BI and SHI, there are often instances encountered where not all parameters are known. In such cases, a percent uncertainty may be associated with the BI and SHI determinations according to the expression given in eq 766 %Uncertainty =

⎛ x ⎞ ⎜ ⎟100 ⎝ nC ⎠

(7)

where x is the number of missing parameters for all the substances used in a given reaction, n is the number of parameters needed to estimate BI (n = 8) or SHI (n = 11) for each substance, and C is the total number of chemicals required for carrying out a given reaction including reagents, catalysts, additives, ligands, reaction solvents, workup materials, purification materials, and its associated byproducts. This is the first reported algorithm that incorporated associated uncertainties in its analysis of environmental and safety-hazard impacts. Once BI and SHI parameters are determined they may be included with AE (atom economy), RY (reaction yield), and RME (reaction mass efficiency) metrics and normalized into an overall vector magnitude ratio (VMR) metric given by eq 8, which estimates an impartial overall score of green performance covering material efficiency, environmental impact, and safetyhazard impact relative to the ideal score of unity for each of the constituent metrics.

red score =

∑j rj

yellow score =

green score =

gray score =

(9a)

∑j ϕjyj ∑j yj

(9b)

∑j ϕjgj ∑j gj

(9c)

∑j ϕjuj ∑j uj

(9d)

where ϕj represents the fractional mass contribution of waste substance j to the total waste as defined in eq 6, and rj, yj, and gj represent the number of red, yellow, and green cells accrued for each substance j, respectively. The same set of scores is determined for the input materials. Pie charts showing the percent contributions of each color score are constructed to compare respective gains and losses on going from input materials to output materials, excluding the desired target product, for a given reaction. Essentially what is tracked is whether or not a reaction produces materials that are relatively greener than the input materials used at the outset. In other words, we want to find out if the UTGCI analysis results in a positive difference in producing relatively greener product materials compared to starting materials, where it is understood that the product materials include the desired product of a reaction, all reaction byproducts and side products, and all

1 VMR = [(AE)2 + (RY)2 + (RME)2 + (BI w )2 6 + (SHI w )2 + (SHI in)2 ]1/2

∑j ϕjrj

(8)

Greener procedures have VMR values closer to 1. It is important to point out here that though several parameters are wrapped up in the single-valued BI and SHI numbers, for the purposes of ranking discussions, it is essential to identify what are the main contributing risk potentials and chemical substances, input materials, or waste products that are associated with those main risks. Fully automated template Excel spreadsheets for determining BI and SHI are given in the Supporting Information including results of their application to the analysis of synthesis plans for thiete 1,1-dioxide (linear) and ethyl phenylcyanopyruvate (convergent) (benign-index-temE


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ACS Sustainable Chemistry & Engineering Table 2. Summary of Metrics Results for Synthesis of Iron(II)oxalate Dihydrate by Four Proceduresa

Entries shown in red are worst scores; entries shown in green are best scores. bProcedure 1: sulfuric acid catalyst; T = 100 °C; excess reagents. Procedure 2: ascorbic acid catalyst; T = 100 °C; excess reagents. dProcedure 3: ascorbic acid catalyst; T = 20 °C; excess reagents. eProcedure 4: ascorbic acid catalyst; T = 20 °C; less excess reagents. a c

⎡⎛ green score ⎞ ⎟ %Δgreen = 100⎢⎜ ⎢⎣⎝ total color score ⎠ waste

auxiliary materials. Clearly, the auxiliary materials appear in both the input and output and are unchanged chemically so their effects do not count. Such a positive directed approach guarantees that a chemical reaction or synthesis plan, comprised of sequential reactions, will lead to more benign final states. This is particularly useful, for example, in planning a decontamination reaction that can transform an initial toxic substance into a more benign one. The point is not to assess the greenness of starting materials or final target products per se but to make sure that regardless of what starting materials one begins with the chemical reaction leading to a given product is benign as possible. It is great to have benign starting materials and a benign target product, but what is important is that the transformation of those input materials to the desired product is carried out in a manner that fulfills green chemistry principles in comparison to other possible transformations to the same target product. An ideal situation of transforming a set of benign starting materials to a benign product by a benign chemical reaction is not practically achievable, as the science of chemistry is one governed by compromise. In this context, eqs 10a and 10b focus on the percent changes in the raw red and green mass weighted scores.

−

⎛ red score ⎞ ⎤ ⎥ ⎜ ⎟ ⎝ total color score ⎠input ⎥⎦

(10b)

Worst and best case scenarios are also determined by adding any uncertainty contributions to the red and green scores, respectively, as shown in eqs 11a and 11b. ⎡⎛ red score + gray score ⎞ ⎟ %Δred,worst = 100⎢⎜ ⎢⎣⎝ total color score ⎠ waste ⎛ red score + gray score ⎞ ⎤ ⎟ ⎥ −⎜ ⎝ total color score ⎠input ⎥⎦

(11a)

⎡⎛ green score + gray score ⎞ ⎟ %Δgreen,best = 100⎢⎜ ⎠ waste total color score ⎢⎣⎝ −

⎡⎛ red score ⎞⎟ %Δred = 100⎢⎜ ⎢⎣⎝ total color score ⎠ waste −

⎛ green score ⎞ ⎤ ⎥ ⎟ ⎝ total color score ⎠input ⎥⎦

⎜

⎛ green score + gray score ⎞ ⎤ ⎥ ⎟ ⎝ ⎠input ⎥⎦ total color score

⎜

(11b)

Equation 11b represents the maximum change in the green score, whereas eq 10b represents the corresponding minimum change. Similarly, eq 11a represents the maximum change in the red score, whereas eq 10a represents the corresponding minimum change. A bar graph showing the worst and best case

(10a) F


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ACS Sustainable Chemistry & Engineering Table 3. Summary of Metrics Results for Synthesis of Aniline by Three Proceduresa

a

Entries shown in red are worst scores; entries shown in green are best scores.

percent gains and losses is constructed to better visualize and interpret the results of eqs 11a and 11b. Greener reactions have higher positive proportional changes in their green scores and higher negative proportional changes in their red scores when comparing output materials (waste produced) and input materials. The method can be extended to handle synthesis plans where appropriate scaling factors are used to scale masses of input or waste materials for each reaction according to the same procedure used to determine overall BI and overall SHI. The concept of tracking changes in mass weighted color scores proved more reliable in decision-making than interpreting the associated environmental safety-hazard (ESH) scores as defined in eqs 12a and 12b for input and waste materials, respectively.

Table 5. Summary of Multivariate Method Results for the Three Methods to Synthesize Aniline

a

Sum = I(A) + I(OD) + I(SF) + I(GW) + I(INHT) + I(INGT) + I(AD).

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COMPARISON OF ALGORITHM RESULTS Tables 2−5 summarize the metrics results of all algorithms for various syntheses of iron(II)oxalate dihydrate and aniline, respectively. The bolded entries across a row pertaining to a given metric represent the maximum and minimum values of that metric. For example, in Table 2, procedure 1 has the highest PMI value and procedure 3 has the lowest. The last two tables are specific to the multivariate method showing the breakdown of the contributing risk indices and the color-coding scheme. Figures 1 and 2 show the accompanying visual aid outputs from EATOS, Green Star, BI/SHI/VMR, and UTGCI methods for each target product. For the iron(II)oxalate dihydrate synthesis all four procedures follow the same metathesis reaction but are carried out under different reaction conditions. The PMI and EI_in values are very close which indicates that the Q factors for input materials are equal to 1. The results of the Green Star, Multivariate Method, and UTGCI Method are consistent in suggesting that the fourth procedure has the greenest attributes since energy consumption is lowered, a benign catalyst is used, and excess reagent consumption is reduced. All these algorithms show progressive improvement from procedure 1 to procedure 4. The EcoScale

ESH input = [(green score)input + (yellow score)input − (red score)input ] ± (gray score)input

(12a)

ESH waste = [(green score)waste + (yellow score)waste − (red score)waste ] ± (gray score)waste

(12b)

Table 4. Summary of Multivariate Method Results for Four Procedures to Synthesize Iron(II)oxalate Dihydrate

a

Sum = I(A) + I(OD) + I(SF) + I(GW) + I(INHT) + I(INGT) + I(AD). G


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Figure 1. EATOS, Green Star, BI/SHI/VMR, and UTGCI method visual outputs for the four procedures to synthesize iron(II)oxalate dihydrate: (A) procedure 1, (B) procedure 2, (C) procedure 3, and (D) procedure 4.

penalty point scale that can result in different ranking outcomes if that scale is changed. The purpose of running algorithms is to assess the relative, not absolute, greenness and efficiency of chemical processes in a quantitative way according to a standardized set of criteria. This set is currently represented by the 12 principles of green chemistry.68 Clearly, quantitative arguments to assess relative greenness are more rigorous than qualitative ones. The challenge is that not all criteria listed in the 12 principles may be quantifiable by assigning appropriate values to physical characteristics. For example, principles 4 (designing safer products), 7 (use of renewable feedstocks), 10 (design for degradation), and 11 (real-time analysis for pollution prevention) are among these that are challenging to incorporate into a quantitative assessment. It is the remaining set of green chemistry principles that form the basis of the algorithms covered by this review. Once a set of quantitative measures is assigned to an algorithm, the next important issues are ranking

results are not consistent with this conclusion largely because fewer parameters are used to conduct the analysis. The EATOS and BI/SHI/VMR, which also incorporate material efficiency metrics, capture the trade-offs between material efficiency and risk potentials. For procedure 4, the gains made in achieving a better risk potential profile are slightly offset by reduced yield and PMI performance. For the three different reactions to produce aniline, EATOS, Green Star, multivariate method, BI/ SHI/VMR, and UTGCI methods all point to the hydrogenation of nitrobenzene as the relatively greenest procedure to synthesize this target compound. In this example, the algorithms are more definite in deciding which procedure is greener. EcoScale, once again, shows inconsistent results, suggesting that iron reduction is preferable while showing little difference between the other two methods. Such an outcome is not surprising since EcoScale is limited in parameter scope, excludes the effects of reaction byproducts, which are relevant to the iron reduction procedure, and relies on an arbitrary H


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Figure 2. EATOS, Green Star, BI/SHI/VMR, and UTGCI method visual outputs for the three industrial syntheses of aniline: (A) iron reduction of nitrobenzene, (B) hydrogenation of nitrobenzene, and (C) substitution of chlorobenzene.

of the comparative results and the reliability of the plan rankings. The robustness and credibility of such results depend entirely on the coverage of as many parameters as possible that align with the standardized green chemistry principles. Algorithms incorporating more parameters will lead to more comprehensive and meaningful results, whereas those that rely on subsets of data at the expense of excluding other data will yield results that are skewed and lead to counterintuitive

situations. In this context, the six algorithms discussed may be ranked according to the degree of coverage of environmental and hazard impacts and the degree of plan ranking reliability as follows: BI/SHI > multivariate method > UTGCI > EATOS > Green Star > >EcoScale I


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relative greenness, whereas the remaining algorithms gave consistently the same results. It is hoped that these extensive reviews of published metrics methods for determining material efficiency and environmental and safety hazards impacts presented in this work and the previous paper3 will assist chemists and chemical engineers in evaluating reactions and chemical processes with respect to green chemistry principles. In particular, quantitative analyses that juxtapose several candidate routes to a common target product using a suite of metrics tools are important for training chemistry professionals about green chemistry principles in a concrete way and for dealing with inevitable trade-offs between them. We have endeavored to simplify each method to make them more accessible, to highlight their strengths and weaknesses, to suggest improvements, and to illustrate their implementation using real chemistry examples. Most importantly, we have compared the algorithms’ performances on the same set of examples so that their analyses are more clarified. The task of analyzing the green chemistry merits of any given reaction or synthesis plan in a quantitative manner is a tedious exercise regardless of the scope and sophistication of the method used. Clearly, methods that require more input data, such as EATOS or BI/SHI indices, will require more time to complete compared to a limited method like EcoScale. The slowest and most laborious step is collating the necessary parameters from various databases and resources. However, once these are in hand in tabular format for each chemical used and produced in a process, the task of running any one of the algorithms discussed in this work is essentially effortless since we have provided spreadsheet templates with embedded formulas that produce all of the essential metrics automatically. This feature addresses overcoming the inertia of having to carry out such analyses in the first place. The reliability of metrics associated with material efficiency is quite definite since all of them depend only on mass amounts of inputs and outputs which are well described, whereas reliability associated with environmental and safety impacts will always be prone to a higher degree of uncertainty due to chronic gaps in the raw parameter data from which they depend. Unless this problem is addressed and solved by the chemical community, this issue will persistent indefinitely, making reliable comparative ranking difficult and open to resolutionless debate, and will cast a negative pallor on the entire field of green metrics analysis.

Moreover, this ranking also loosely corresponds to the degree of complexity and time required for a standard user to complete the analysis, which, for example, is why many instructors prefer to teach the EcoScale approach, it being fairly simple to carry out. The use of merit or demerit point scales (i.e., Green Star and EcoScale) also affects the degree of precision of the analysis because any change in the scaling factors used can lead to different numerical results and ranking outcomes. Methods that use ranges of parameter values for ranking rather than the absolute parameter values themselves, such as UTGCI, are the next weakest in terms of precision. Alternatively, by using a large number of parameter values (BI/SHI indices, multivariate method, and EATOS), one can expect to achieve the most reliable ranking results. EATOS is ranked lowest among these top three algorithms because its assessment of toxicity of materials is based only on one toxicity parameter (usually the most offending with lowest value, i.e., lowest LD50 or lowest LC50), even though toxicity of chemicals is generally parametrized by a range of variables such as ingestion, inhalation, etc. In terms of choosing an algorithm and running it, the caveat is that those methods yielding less precise ranking outcomes are the very ones that are easiest to implement and to conceptually understand because they are simple and easily conveyed to novices. On the other hand, those that rely on comprehensive parameter coverage will take more time to carry out but will yield more reliable results. The good news is that, in principle, all of the algorithms are easily adaptable to increase their parameter coverage, which will lead to a general increase in their reliability with respect to ranking competing plans to a common target product.

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CONCLUDING REMARKS AND RECOMMENDATIONS For introductory evaluations of the green chemistry potential of a given reaction or process, we recommend the Green Star and UTGCI methods since they are easy to understand and implement. Their main strength is the easy to interpret visual displays, whereas their main drawback is the arbitrary nature of their point scales or choice of parameter cut off ranges, respectively. The UTGCI method has the advantage of addressing the issue of uncertainty in parameter values, handling synthesis plans in addition to individual reactions, and incorporating masses of input and waste materials, which Green Star does not. Along with EcoScale, these tools provide chemists and chemical engineers the opportunity to study the various merits and drawbacks of metrics analysis at a rudimentary level. EATOS is also very useful with respect to visual displays and dissecting adjusted contributions to PMI and E-factor from each material used or produced in a chemical reaction. Further improvements in accessing its numerical output of data as discussed previously with respect to material efficiency metrics would make EATOS more attractive. The Queen’s University multivariate and the BI/SHI/VMR methods are better suited for advanced analyses since they do not rely on any arbitrary scaling and in principle can be extended to an unlimited number of environmental and safety impact parameters provided that reliable and relevant data exist. Both have effective visual aids and can handle individual reactions and synthesis plans; however, the BI/SHI/VMR method has the advantage of dealing with parameter uncertainty and better differentiating mass weighted contributions. In the head-to-head comparisons examined in this work, EcoScale was found to be the most unreliable in ranking

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acssuschemeng.5b01555. Part 1: Table S1. Q-factor values for EATOS. Part 2: Table S2. Revised scoring summary for green principles used in Green Star. Part 3: Table S3. Multivariate method environmental impact potentials. Part 4: Table S4. Benign and safety-hazard indices method impact potentials. Part 5: Schemes S1 to S5 for syntheses of 2,2diethoxy-1-isocyanoethane, thiete 1,1-dioxide, ethyl phenylcyanopyruvate, iron(II)oxalate dihydrate, and aniline. Part 6: Code used for UTGCI method. Part 7: Algorithm results for single step synthesis of 2,2diethoxy-1-isocyanoethane. Algorithm results for threestep linear synthesis of thiete 1,1-dioxide. Algorithm results for three-step convergent synthesis of ethyl J


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phenylcyanopyruvate. Microsoft Excel template files for EcoScale, Green Star, multivariate, and UTGCI methods for single reactions (EcoScale-template.xls, GreenStartemplate.xls, multivariate-template.xls, UTGCI-template.xls). Microsoft Excel template files for benign and safety-hazard indices for single reactions and synthesis plans (benign-index-template.xls, safety-hazard-indextemplate.xls). Workbook Excel spreadsheets for syntheses of 2,2-diethoxy-1-isocyanoethane, thiete 1,1dioxide, ethyl phenylcyanopyruvate, four plans to iron(II)oxalate dihydrate, and three industrial plans to aniline (Excel-workbook spreadsheets folder). EATOS results (EATOS folder). (ZIP)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank participants for their feedback on green chemistry workshops based at the University of Toronto, Green Chemistry Initiative Method, presented at the Future Leaders in Green Chemistry Workshop and Challenge held at the University of Toronto, Toronto, Canada on May 9−10, 2013, and at the 3rd International Congress on Sustainability IBERO held at Universidad Iberoamericana, Mexico City, Mexico, on May 18−19, 2015.

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