Waste Governance

Waste Governance

C H A P T E R 33 Waste Governance Daniel A. Vallero Pratt School of Engineering, Duke University, Durham, NC, United States O U T L I N E 1. Introdu...

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C H A P T E R

33 Waste Governance Daniel A. Vallero Pratt School of Engineering, Duke University, Durham, NC, United States

O U T L I N E 1. Introduction

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2. Governance Hierarchies

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3. Reliable Waste Management

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4. Success in Waste Management

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5. Causal Links 5.1 Strength of Association 5.2 Consistency 5.3 Specificity 5.4 Temporality 5.5 Gradient 5.6 Scientific Plausibility 5.7 Coherence

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1 INTRODUCTION Waste management approaches vary, depending on the jurisdiction. Governance is an inclusive term. The International Risk Governance Council considers risk governance to be at the confluence of numerous analyses and actions related to a technology [1,2], which includes:

Waste https://doi.org/10.1016/B978-0-12-815060-3.00033-5

5.8 Experimentation 5.9 Analogy

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6. Waste Epidemiology

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7. Waste Communications: Avoiding the Techno-Polysemy Problem

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8. Emerging Tools and Techniques 8.1 Cluster Analysis 8.2 Spatial Analysis

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9. Conclusions

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References

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Further Reading

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• framing the technology in the context of its possible deployment and applications, benefits, and risks for various stakeholders; • assessing those benefits and risks, including risk perception; • evaluating other aspects that decision makers will consider before making decisions, for example, specific economic, political, or societal interests;

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Copyright # 2019 Elsevier Inc. All rights reserved.

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33. WASTE GOVERNANCE

• identifying options; and, • communicating potential risk and benefits. Waste management involves myriad forms of governance, from private individuals who must manage wastes on their own properties, that is, self-governance, to multijurisdictional attempts to prevent the movement of wastes from one part of the globe to another. In the middle is where most waste management governance occurs. The World Bank [3] considers the management of municipal solid waste (MSW) to be the most important role of local governments: MSW management is the most important service a city provides; in low-income countries as well as many middle-income countries, MSW is the largest single budget item for cities and one of the largest employers. Solid waste is usually the one service that falls completely within the local government’s purview. A city that cannot effectively manage its waste is rarely able to manage more complex services such as health, education, or transportation. Poorly managed waste has an enormous impact on health, local and global environment, and economy; improperly managed waste usually results in down-stream costs higher than what it would have cost to manage the waste properly in the first place. The global nature of MSW includes its contribution to greenhouse gas emissions, e.g. the methane from the organic fraction of the waste stream, and the increasingly global linkages of products, urban practices, and the recycling industry. All levels of waste governance involve some form of risk reduction, usually addressing a combination of risks to public health, environment, and livability [4]. Risk is addressed extensively in Chapter 35. Certainly the rate of risk reduction is a key metric, but given that waste management is central to all local aspects of local government, there are two key considerations of waste governance, that is, reliability and communication. No single approach fits for governments providing the most efficient and effective, that is,

reliable services and for ensuring that waste management is inclusive for all stakeholders, and varies with scale. Certainly, local jurisdictions are usually responsible for collection, recycling, composting, and general waste handling. Larger scale jurisdictions, for example, metropolitan waste authorities, seek economies of scale by sharing operations among the local jurisdictions. The highest scale jurisdictions often set standards, for example, limits on the amounts of hazardous wastes, definitions of hazardous and nonhazardous substances, and rules on barrel burning and other less centralized waste disposal approaches. Most local waste management decisions have low probability of large scale, disastrous outcomes, for example, choice of trucks, daily operations, and landfill protocols. However, like many other environmental engineering decisions, some are low-probability, high consequence events, which present special challenges to local risk managers. When things go wrong, the challenges extend beyond proper failure analysis and risk assessment, but also require clear and factual communications [5]. Two metaphors come to mind when discussing low probability, high impact outcomes, that is, the black swan and the perfect storm. A “black swan” event is one in which the outcome would not have been predicted from past evidence due to the low probability of a possible confluence of events, but the event indeed occurred. A “perfect storm” of a confluence of unlikely events has led to some of the greatest environmental and public health tragedies, such as the combination of factors that led to the disaster from the toxic plume over Bhopal, India [6].

2 GOVERNANCE HIERARCHIES Although most waste management occurs at the local level, this does not occur in a vacuum. As evidence, during incidents with largescale impacts, the larger jurisdictions become

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2 GOVERNANCE HIERARCHIES

increasingly involved. The United States, for example, has three levels of governance with contingency plans for environmental emergencies under the national response system see Fig. 33.1 Area, regional, and federal plans lay out the response, which share planning mechanisms and describe how they interact. Ideally, planning at the release site should be part of an integrated system that involves multiple governmental entities and the public [7]. The risk governance paradigm can start at any level. For example, when a spill or other release of oil or hazardous material occurs in the United States, the source of the release must notify the National Response Center (NRC), which is a component of the National Response System that is staffed 24 h a day by the US Coast Guard (see Fig. 33.2). When a report is made, the NRC immediately notifies a designated On-Scene Coordinator (OSC) in the affected region, as well

International joint plans

as tribal, local, and state emergency personnel. The OSC coordinates with the state, other personnel on site, and the Potentially Responsible Party to determine the status of the response. The OSC determines extent of federal involvement and deploys the response actions. The OSC may decide that the response requires outside expertise and resources, for example, contractors, technical support from within EPA or the National Oceanic and Atmospheric Administration. The OSC may also coordinate with one or more of the 13 Regional Response Teams for expertise or to provide additional logistical support [8]. As mentioned, such events are rare, but the waste manager should be aware of the steps to follow when they occur. Even a seemingly small event can be highly disruptive. Often, local governance is called upon for “containment,” that is, limiting the effects of the problem in time

National oil and hazardous substances pollution contingency plan (NCP)

National response framework (NRF)

Regional contingency plans (RCPs)

Federal agency internal plans

State/local plans

Facility response plans (FRPs)

Area contingency plans (ACPs)

Vessel response plans (VRPs)

FIG. 33.1 Relationship among the national, regional, and local (area) jurisdictions in the United States, as mandated by the US National Contingency Plan. Dashed lines connect points of coordination, green lines are plans of the within the National Response System (NRS), and red lines are plans within the area contingency plan (ACP). Data from National Academies of Sciences, Engineering, and Medicine, Spills of Diluted Bitumen From Pipelines: A Comparative Study of Environmental Fate, Effects, and Response. National Academies Press, 2016; U.S. Environmental Protection Agency, National Response System. 2017. Available from: https://www.epa.gov/emergency-response/national-response-system.

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33. WASTE GOVERNANCE

Incident occurs

National response center

Natural resource trustees

National response team

Regional response team

On-scene coordinator/ remedial project manager

Make initial assessment/ conduct first response/ notify others

Federal assistance required?

State/local government agencies/ potentially responsible party

Special forces

On-scene coordinator/ remedial project manager State and local government agencies

Potentially responsible party

EPA Environmental response team EPA Radiologial response team USCG District response groups USCG National pollution funds center USCG National strike force NOAA Scientific support coordinators US Navy superintendent of salvage and diving

FIG. 33.2 National Response System flowchart. Courtesy: U.S. Environmental Protection Agency, National Response System, 2017. Available from: https://www.epa.gov/emergency-response/national-response-system.

and space. Scientific and engineering rigor are essential for rare events, as they are for any risk assessment scenario. Certainly, managing the risks presented by everyday operations or catastrophic failure events rests upon sound decision making and fundamental communication.

Given the diversity of stakeholders in waste management, reaching a consensus on how wastes should be handled is a complex endeavor that depends on a choice of “acceptable” risk [5]. This usually translates into acceptable uncertainty, that is, the amount of uncertainty that

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3 RELIABLE WASTE MANAGEMENT

operations will continue as desired. For example, the likelihood that a truck will operate, without breaking down, is never 100%. Purchasing a reliable truck based on historical data helps increase the reliability. So does proper and timely maintenance. However, these actions merely decrease the uncertainty about breakdowns, but do not eliminate it.

3 RELIABLE WASTE MANAGEMENT

time interval can be found by integrating the hazard rate over a defined time interval: Zt2   (33.1) P t1  Tf  t2 ¼ f ðtÞdt t1

where Tf ¼ time of failure. Thus the reliability function R(t), of a system at time t is the cumulative probability that the system has not failed in the time interval from t0 to tt: Zt   (33.2) RðtÞ ¼ P Tf  t ¼ 1  f ðxÞdx 0

The waste manager must ensure that the design, siting, construction, and operation of systems be reliable. The engineer must ensure that any engineered systems have a sufficient optimal operational timeframe, where the failure rate is assumed to be at its lowest. Reliability is the probability that something that is in operation at time 0 (t0) will still be operating until the designed life (time t ¼ (tt)). As such, it is also a measure of accountability. Unreliable systems range from occasional and low-impact failures, such as the simple nuisance resulting from fencing is too low to capture windblown litter, to catastrophic, for example, loss of life from an explosion of a combustor. People living near a proposed facility must be ensured the systems will work and will not fail or that in the event of a failure, the harm would be properly managed with an acceptable contingency plan [9]. The probability of a failure per unit time is known as the “hazard” rate. Engineers may recognize it as a “failure density,” or f(t). This is a function of the likelihood that an adverse outcome will occur, but note that it is not a function of the severity of the outcome. The f(t) is not affected by whether the outcome is very severe (such as an explosion where people are hurt or killed) or relatively benign (minor leaf damage from a plume or litter on the road from improper compaction). The likelihood that something will fail at a given

Reliability can be improved by extending the time (increasing tt), thereby making the system more resistant to failure. For example, proper engineering design of a landfill barrier can decrease the flow of contaminated water between the contents of the landfill and the surrounding aquifer, for example, a velocity of a few microns per decade. However, the barrier does not completely eliminate failure, that is, R(t) ¼ 0; it simply protracts the time before the failure occurs (increases Tf). Eq. (33.2) illustrates built-in vulnerabilities, such as unscientifically sound facility siting practices or the inclusion of inappropriate design criteria, for example, cheapest land. Such mistakes or malpractice shortens the time before a failure. Thus reliability is also a term of efficiency. Failure to recognize these inefficiencies upfront leads to premature failures (e.g., loss of life, loss of property, law suits, and a public that has been ill served). Risk is really the probability of failure, that is, the probability that our system, process, or equipment will fail. Thus risk and reliability are two sides of the same coin. The common graphical representation of engineering reliability is the “bathtub” curve (see Fig. 33.3). The U-shape indicates that failure will more likely occur at the beginning, so-called infant mortality, and near the end of the life of a system, process, or equipment. Indeed, failure can occur even before acquiring equipment or

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Infant mortality

Steady-state

Deterioration

Failure rate h(t)

Maturation

Useful life

Senescence

Time (t)

FIG. 33.3 Prototypical reliability curve. The highest rates of failure, h(t), occur during the earliest and latest stages. Welldesigned systems extend the useful life. The waste manager and engineer must build in factors of safety, including closer monitoring of more extensive safeguards when the failure rates are likely higher at the beginning and toward the end of the steady-state period. Reproduced with permission from D. Vallero, Paradigms Lost: Learning From Environmental Mistakes, Mishaps and Misdeeds. Butterworth-Heinemann, Burlington, MA, 2005.

starting a new route. In fact, many problems occur during the planning and idea stage. A great idea may be dismissed prematurely.

4 SUCCESS IN WASTE MANAGEMENT Waste management is bounded by numerous policies, laws, and other societal norms. As discussed in Chapter 35, risk is an expression of operational success or failure. Management is the means of navigating among the policies, laws, and competing interests to achieve goals. Science and engineering are essential to good waste management, but not sufficient. Waste management must draw on other knowledge. The waste manager must apply reasoning of all types, deductive, inductive and, yes, intuitive. Certainly, wastes cannot be completely managed by intuition-based decisions, but intuition is not always wrong [10]. It is important to keep in mind that many of the stakeholders in a

jurisdiction apply intuitive reasoning often and, for some, almost exclusively. Intuition, in the author’s opinion, is often fact based, deductive or inductive reasoning that has long been forgotten. For example, most professions require a substantial amount of time spent working under a senior professional before one is granted a license. The senior professional may have long forgotten the specifics of why and how something works, but has logged the process in memory. Indeed, the newly minted professional may know exactly why and how, but has only applied this limited knowledge within the highly controlled academic or laboratory setting, whereas the seasoned professional has been applying these principles in the real world. Having all the data needed for a completely informed waste management decision is impossible. Scientific objectivity and humility dictate that decision makers are upfront about uncertainties. Sometimes, waste questions can only be answered by looking for patterns and analogies from events that are similar to the potential

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5 CAUSAL LINKS

threat being considered. From there, scenarios can be developed to follow various paths to good, bad, and indifferent outcomes. This is known as a decision tree. This is not the same, necessarily, as the one with the most benefits compared to risks, that is, a benefit to risk ratio or relationship, or benefit to cost ratio or relationship. However, this is indeed one of the more widely used approaches. The challenge is how to quantify many of the benefits and risks. Humility is always the watchword for waste management. Usually, there is more than one cause of a problem and more than one solution to the problem. Caution is in order when attributing a cause. The association of two factors, such as the level of exposure to a compound and the occurrence of a disease, does not necessarily mean that one necessarily “causes” the other. Often, after study, a third variable explains the relationship. However, waste manager must be diligent in linking causes with effects. Otherwise, corrective and preventive actions cannot be identified. So, strength of association is a beginning step toward cause and effect. A major consideration in strength of association is the application of sound technical judgment of the weight of evidence.

5 CAUSAL LINKS Assigning causality when none really exists must be avoided but, if all we can say is that the variables are associated, the public is going to want to know more about what may be contributing an adverse effect (e.g., learning disabilities and blood lead levels). This was particularly problematic in early cancer research. Possible causes of cancer were being explored and major research efforts were being directed at myriad physical, chemical, and biological agents. So, there needed to be some manner of sorting through findings to see what might be causal and what is more likely to be spurious

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results. Sir Austin Bradford Hill is credited with articulating key criteria that need to be satisfied to attribute cause and effect in medical research [11].

5.1 Strength of Association Strong associations provide more certain evidence of causality than is provided by weak associations. A cause must be associated with an outcome, that is, the statistical association cannot be zero, although a cause may show up as a very low statistical association. Common metrics used to indicate association include risk ratio, odds ratio, and standardized mortality ratio.

5.2 Consistency If a cause is associated with an outcome consistently under different studies using diverse methods of study of assorted populations under varying circumstances by different investigators, the link to causality is stronger. For example, if results are similar for waste collection approaches studied with scenarios, simulated with models, and documented using historical information, there is greater consistency. However, it there is no agreement with the various approaches, this could mean that the results are unique to a given set of conditions, for example, if the only scenarios were towns with populations less than 5000, a model was parameterized only for one type of collection approach, or historical data are incomplete or erroneous.

5.3 Specificity The specificity criterion holds that the cause should lead to only one outcome and that the outcome must result from only this single cause. Recall that Hill was interested in cancer and was viewing causality through a biomedical

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33. WASTE GOVERNANCE

FIG. 33.4 Hypothetical cause-outcome curves. The green and purple lines have positive slopes. The red line has a negative slope. The blue line starts as a positive slope but becomes negative. The slope varies on the curves, that is, the purple and blue lines, so must be measured instantaneously at a tangent to the curve (1–6). Slopes 1, 2, 3, and 4 are positive. Slopes 1, 2, and 3 are equal. Slopes 5 and 6 are negative. The linear cause-outcomes are common expressions of scientific principles, for example, gravitation, but not in most engineering and waste management situations.

perspective, that is, this criterion appears to be based in the germ theory of microbiology, where a specific strain of bacteria and viruses elicits a specific disease. This is rarely the case for waste management.

5.4 Temporality This criterion requires that exposure to the chemical must precede the effect. For example, in a retrospective study, the researcher must be certain that the outcome was not already present before the contributing factors were present. For example, a disease may have been present before a toxic release. However, this does not always eliminate the temporal link, for example, a disease like asthma may be present before a toxic emission, but the incidence may increase and/ or the symptoms and disease progression are exacerbated. Thus it may not mean that the agent in the release is not a cause, but it does mean that it is not the sole cause of the disease (see “Specificity”), or that the outcome is not new, so much as a worsening of an existing condition.

5.5 Gradient If the force of a cause, for example, the intensity and extent, increases the amount of outcome, this strengthens the causal link. This is a

common metric for risk assessment, known as the “dose-response” step in risk assessment (see Chapter 35). If the level, intensity, duration, or total level of chemical exposure is increased a concomitant, progressive increase should occur in the toxic effect. However, the gradient also applies to other areas, for example, if a recycling program is intensified and the amount of recycling increases concomitantly, this is a gradient. In waste management the gradient is seldom linear. Thus depending on the shape of the curve, it must be measured at a finite time value, that is, a tangent to the cause-outcome curve (see Fig. 33.4).

5.6 Scientific Plausibility Hill limited this to biological plausibility, but the criterion has wider application. Generally, an association needs to follow a well-defined explanation based on a known scientific system, that is, a paradigm. A paradigm, however, is mutable. Even some of the most strongly held scientific laws have had to be revised with the evolution of knowledge [12]. Outside of research, this caution has little relevance to the practice of waste management. Engineers and other professionals involved in managing wastes must apply best practices according to

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5 CAUSAL LINKS

their profession. If results are implausible according to underlying scientific principles, they should be heeded. However, this applies to scientific and engineering principles, and not one’s one “favorite” practice. Simply because an approach does not follow direct from what has worked in the past, does not render it implausible, but merely inconvenient. Indeed, the waste manager must beware of Kaplan’s “law of the instrument.” The waste manager’s or engineer’s preferred approach can be likened to a hammer, so that the entirety of waste management is a collection of nails. There are likely to be other plausible “tools,” besides one’s hammer and there are certainly myriad other problems besides the “nails.” This can be particularly worrisome for modelers, who often must incorporate simplifying assumptions to make the math work. Engineering models, for example, can be accused of assuming that a chicken is spherical, but when the designed coop fails, this may be in part due to this oversimplification. After all, the realworld coop’s chickens have beaks, feathers, and claws.

5.7 Coherence The criterion of coherence suggests that all available evidence should form a cohesive whole. By that, the proposed causal relationship should not conflict or contradict information from experimental, laboratory, epidemiologic, theory, or other knowledge sources.

5.8 Experimentation Experimental evidence useful to waste management can support a causal hypothesis. Laboratory experiments, tests, pilot studies, physical models, and natural experiments (e.g., a disaster response) are available. The experiment does not have to be specific, but can be drawn from

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others’ experiences, for example, an informatic experiment using “big data.”

5.9 Analogy The term analogy implies a similarity in some respects among things that are otherwise different. It is thus considered one of the weaker forms of evidence. For waste management decisions, some of Hill’s criteria are more important than others. Reliability in a public health and environmental context relies heavily on strength of association, for example, to establish dose-response relationships. Coherence is also very important. Animal and human data should not be extensions of one another and should not disagree. Biological gradient is crucial, since this is the basis for dose-response (the more dose, the greater the biological response). Temporality is crucial to all scientific research, that is, the cause must precede the effect. However, this is sometimes difficult to see in some instances, such as when the exposures to suspected agents have been continuous for decades and the health data are only recently available. Linking causes to outcome must be based on sound science and scientific judgment. Waste managers must consider whether the selected approach will likely continue to “work” (reliability) and, further, must consider the hazards that may arise, especially in a new and untested technology is selected. Risk is a function of likelihood that the hazard will in fact encounter, so the waste manager is also tasked with predicting the adverse implications that society might face. Thus the “risk” associated with a selected approach refers to the possibility and likelihood of undesirable and possibly harmful effects. Errors in reliability and risk predictions can range from not foreseeing outcomes that are merely annoying (e.g., fuel efficiencies that are less than expected) to those are devastating (e.g., the release of carcinogens from an incinerator or landfill leachate) [13].

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6 WASTE EPIDEMIOLOGY [14] The causal link begins with the “weight of evidence” (WoE). The scientific literature varies in its use of WoE. It can be [15]: 1. metaphorical, where WoE refers to a collection of studies or to an unspecified methodological approach; 2. methodological, where WoE points to established interpretative methodologies (e.g., systematic narrative review, metaanalysis, causal criteria, and/or quality criteria for toxicological studies) or where WoE means that “all” rather than some subset of the evidence is examined, or rarely, where WoE points to methods using quantitative weights for evidence; and, 3. theoretical, where WoE serves as a label for a conceptual framework. Epidemiology can be either descriptive or analytical. Often, descriptive epidemiology is the first step in identifying a public health problem. Descriptive datasets (e.g., census data, remote sensing data, state health summaries, etc.) provide the first clues from which scientists may generate hypotheses as to what may be causing or contributing to a health problem. These data may allow epidemiologists to characterize differences in demographic and disease outcomes. Sex, race, ethnicity, age, and socioeconomic factors can show different outcomes for diseases in the same general population. Several statistical tools can aid in the transition from descriptive to analytical epidemiology. Relative risk (RR) is indeed an expression of strength of association and often used to help represent WoE. Relative risks can bevisualized  A A + B . as a two-by-two table, that is, RR ¼  C C+D The difference in age, sex, and whether one lives in a rural versus urban area can be among the first descriptive comparisons conducted by

epidemiologists, as in a recent study of mortality rates for diseases in the Hubei Province in China (see Table 33.1). The table shows the differences between crude and standardized death. The standardization is accomplished by adjusting the crude death rate for differences in age composition between the study group (urban and rural) versus that of the standard population. This allows for comparisons of asymmetrical distributions within subpopulations, so that risk factor being investigated is less influenced by factors (i.e., confounders). Standardization can be accomplished directly or indirectly. For example, standardizing for age can be done using the direct standardization method, which calculates a weighted average of the study group’s age-specific mortality rates where the weights represent the age-specific sizes of the standard population. Each agespecific mortality rate is multiplied by the number of people in the age group of the standard population. These product values are summed and then divided by the number of people in the standard population. An example of standardization used by US Center for Disease Control and Prevention is presented in Table 33.2. The indirect method applies age-specific mortality rates from the standard population to derive expected deaths in the region’s population. For each age group, the age-specific mortality rate for the standard population is multiplied by the number of people in age group of the study group and summed. This number of deaths in the entire study group is divided by this sum. The result is multiplied by the crude death rate. Note that the last column in Table 33.2, relative risk, includes confidence intervals. This is an indication of the uncertainty in the estimates. The odds ratio is the probability that an event will occur divided by the probability that it will not occur. It can also reflect the odds of an event for one group (e.g., urban dwellers) divided by the odds of a different group (e.g., rural population). Rather than presence or absence, the information in Table 33.2 can be compared for urban and rural populations (see Table 33.3). Here, the

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TABLE 33.1 Mortality Rates of Chronic Disease Among Men in Urban and Rural Areas in Hubei Province, China; 2008–10. Urban

Rural

Diseases

Crude Death Rate (per 100,000)

Standardized Death Rate (per 100,000)

Crude Death Rate (per 100,000)

Standardized Death Rate (per 100,000)

Standardized Relative Risk (95% confidence interval)

Circulatory system

351.34

301.63

400.99

582.55

1.93(1.88–1.98)

Cerebrovascular disease

193.86

165.27

260.32

369.75

2.24(2.16–2.31)

Ischemic heart disease

118.33

102.80

86.81

128.44

1.25(1.19–1.31)

Neoplasms

257.23

220.77

240.01

286.61

1.30(1.26–1.34)

Lung

110.94

94.61

46.95

74.31

0.79(0.75–0.83)

Liver

39.26

34.34

49.02

53.44

1.56(1.44–1.68)

Stomach

26.98

22.81

45.16

56.22

2.47(2.25–2.70)

Colon and rectum

19.92

16.93

8.04

9.85

0.58(0.51–0.66)

Esophagus

22.54

19.69

13.80

23.00

1.17(1.05–1.30)

0.43

0.36

0.25

0.32

0.85(0.38–1.87)

Respiratory system

82.28

70.15

134.23

215.28

3.07(2.92–3.23)

Chronic lower respiratory disease

61.49

52.09

130.52

210.83

4.05(3.82–4.29)

Digestive system

26.76

23.25

21.03

24.62

1.06(0.96–1.17)

Diseases of liver

12.83

11.25

11.29

12.08

1.07(0.93–1.24)

Endocrine, nutritional, and metabolic diseases (E00-E90)

25.55

21.87

10.05

13.31

0.61(0.54–0.68)

Diabetes mellitus

24.44

20.91

9.64

12.80

0.61(0.55–0.69)

Breast

Data from L. Cheng, et al., Chronic disease mortality in rural and urban residents in Hubei Province, China, 2008-2010, BMC Public Health, 13 (1) (2013) 713.

risk factor is a differentiation as to where people live. For some outcomes “urbanization” appears to be protective for several of the diseases listed in Table 33.1. The rural population had higher relative risks for cerebrovascular diseases, ischemic heart disease, stomach cancer, and liver cancer; whereas the urban populations exhibited high relative risks for lung, colorectal and breast cancer, endocrine diseases, and diabetes. Since some, but certainly not all, of these diseases share some risk factors, this descriptive

epidemiology indicates the need to investigate what it is about urban and rural life that leads to these differences. Descriptive information like that in Table 33.1 is an early step in generating hypotheses and provoking ideas about WoE and, ultimately, causality. This is a type of analytical epidemiology, that is, possible explanations of why there are differences among the different segments. Since air pollution risk is a function of the pollutant’s inherent hazard (e.g., cancer potency) and the

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662 TABLE 33.2

33. WASTE GOVERNANCE

Example of Age-Adjusted Standardization of Mortality Rates Crude Death Rate Comparison Community A

Community B

Age

Deaths

Population

Rate (per 1000)

Deaths

Population

Rate (per 1000)

0–34 years

20

1000

20

180

6000

30

35–64 years

120

3000

40

150

3000

50

65 years and over

360

6000

60

70

1000

70

Total

500

10,000

50

400

10,000

40

Age-Adjusted Death Rate Calculation Community A

Community B Rate (per 1000)

Rate × population

60

30

90

40

120

50

150

4000

60

240

70

280

10,000

42

420

52

520

Age

Standard population

Rate (per 1000)

0–34 years

3000

20

35–64 years

3000

65 years and over Total

Rate × population

Data from L.R. Curtin, R.J. Klein, Direct standardization (age-adjusted death rates) (no. 6). US Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics, 1995.

TABLE 33.3 A 2  2 Table Representing Two Risk Factors (Living in an Urban Area or Rural Area) and Two Outcomes (Disease or No Disease), From Which Relative Risk Can be Calculated. Risk Factor

Outcome: Disease

Outcome: No Disease

Total

Urban

A

B

A+B

Rural

C

D

C+D

Reproduced with permission from D. Vallero, Translating Diverse Environmental Data into Reliable Information: How to Coordinate Evidence from Different Sources. Academic Press, London, UK, 2017.

exposures (e.g., inhaling air containing the pollutant), outcomes like those in the far-left column of Table 33.1 are often low outcomes, such as the number of cancers added to what would otherwise be expected without the pollution. This calls for statistics to express the linkages between the hazards and exposures, often beginning with the odds ratio for these events. The odds ratio is the first step toward linking a risk factor to the adverse outcome, since it reflects the strength of the association between an exposure and an outcome, that is, the odds that an

outcome will occur given a particular exposure, compared to the odds of the outcome occurring in the absence of that exposure [16]. For example, the Children’s Health Study in southern California used odds ratios to link indicators of respiratory problems in children with proximity to major roadways (mobile air pollutant exposure). Asthma medication usage (odds ratio ¼ 1.50; 95% confidence interval, 1.16–1.95), wheezing (odds ratio ¼ 1.40; 95% confidence interval, 1.09–1.78), and lifetime asthma incidence (odds ratio ¼ 1.29; 95% confidence interval, 1.01–1.86)

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7 WASTE COMMUNICATIONS: AVOIDING THE TECHNO-POLYSEMY PROBLEM

for children residing in homes within 75 m of a major road [17]. The odds ratios of 1.50, 1.40, and 1.29 indicate that living near a roadway is associated with 50% more children used medication, 40% more children exhibited wheezing, and 29% more children were diagnosed for asthma compared to those living in homes greater than 75m from a major roadway. Note also that each odds ratio is expressed within a confidence interval. This is a way of expressing uncertainty and variability. This means that 95% of the time for children living within 75m from a major road, the odds ratio for asthma medication usage ranged from 1.16 to 1.95. If the lower interval were less than 1, this is taken to mean that the exposed and unexposed were not statistically different, that is, living near or far from a roadway makes no statistical difference for a variable with a confidence lower interval less than 1. However, in this case, the low value of 1.16 is greater than1,indicating that there is a statistical difference. It is up to the scientist to determine why. Risk is stated as a probability, that is, the likelihood of an outcome; specifically, an adverse or unwanted outcome. Probability is the mathematical expression that relates an outcome of an event to the total number of possible outcomes. For example, since coin has only two sides, flipping it should give 50–50 chance of either heads or tails. However, the 50% probability depends on every physical factor on each side being equal. Indeed, this is not the case for most coins, since the heads side usually has a head and the tail side has something other than a head. Thus the aerodynamics differs due to shape, ridges, and valleys on the two sides. These are deterministic features that may make the odds change ever so slightly.

7 WASTE COMMUNICATIONS: AVOIDING THE TECHNOPOLYSEMY PROBLEM Waste management information must be presented in a meaningful way without violating overextending the interpretation of the data.

663

Waste managers must clearly present scientifically sound information. Presentation is not an end in itself, but a means toward the end of obtaining feedback and approval. This goal requires input and understanding of the options and decision-making process by decision makers of diverse backgrounds and perspectives. Arguably more important, the options and processes must be sufficiently clear to a wider group of stakeholders, especially of those most directly or indirectly affected by the ultimate decisions. This may seem to be an untenable task but the waste manager must be able to communicate effectively to ensure the optimal choices are made regarding designs, to ensure that these technically sound designs are accepted by all appropriate levels of governance and stakeholders, and to convey sufficient information to the waste management personnel so that the designs are operated and maintained satisfactorily. Technical communication can be seen as a critical path, where the engineer sends a message and the audience receives it (see Fig. 33.5). The means of communication can be either perceptual or interpretative [18]. Perceptual communications are directed toward the senses. Human perceptual communications are similar to that of other animals; that is, we react to sensory information (e.g., reading body language or assigning meaning to gestures, such as a hand held up with palms out, meaning “stop” or smile conveying approval). Governance must address emerging technologies, including those related to waste management, for example, nanotechnology, biotechnology, and genetic engineering to treat wastes. This calls for collaborative approach to consider the views of multiple pertinent stakeholders [19]. Indeed, the waste manager may spend most of the workday with fellow technologists. However, this is but a small group of the full set of stakeholders. Whereas the communication methods toward the right-hand side of Fig. 33.5 may be appropriate for the fellow technologists, this may be unclear to others. As presented in Table 33.4, technologists tend

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FIG. 33.5 Waste-related communication techniques. All humans use perceptual communication, such as observing body language of an engineer or smelling an animal feeding operation. The right side of the figure is the domain of technical communication. Thus the public may be overwhelmed by perceptive cues or may not understand the symbolic, interpretive language being used by a bioengineer and others in the risk communication process. Thus the type of communication in a scientific briefing is quite different from that of a public meeting or a briefing for a neighborhood group potentially affected by a risk management decision. Data from M. Myers, A. Kaposi, A First Systems Book: Technology and Management. Imperial College Press, 2004; T.R. Green, Cognitive dimensions of notations, in: People and Computers V, 1989, pp. 443-460. 3. BEST PRACTICE AND MANAGEMENT

7 WASTE COMMUNICATIONS: AVOIDING THE TECHNO-POLYSEMY PROBLEM

TABLE 33.4

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Two Methods of Processing Information: Science-Based Risk Assessment and Risk Perception.

Type of Analysis Identification of risk

Risk estimation

Risk Assessment Methods

Risk Perception Processes

Technical monitoring and measurement of event

Awareness

• Physical • Chemical • Biological

• Unique personal processes

Deductive reasoning

Intuition

• From general principles to specific event Statistical inference

Historical reference, e.g., similar events Reliance of authority and media coverage

Risk evaluation

Benefit-to-cost ratios Risk-to-benefit calculations Alternative selection Economic and feasibility studies Policy analysis

Advice for trusted others Personality factors Individual actions Personal or business profit

Redrawn from K. Smith, Environmental Hazards: Assessing Risk and Reducing Disaster. London, UK, Routledge, 1992.

to be comfortable with scientifically based risk information in the green column; many stakeholders prefer to communicate using on perception processes in the blue column. Interpretive communications encode messages that require intellectual effort by the receiver to understand the sender’s meanings. This type of communication can either be verbal or symbolic. Scientists and engineers draw heavily on symbolic information when communicating among themselves. If you have ever mistakenly walked into the seminar or meeting in which experts are discussing an area of science not memorable to you, using unrecognizable symbols and vernacular, this is an example of symbolic miscommunication. The challenge is to say what needs to be said and to have the audience hear and see what needs to be understood. Using plain language is not enough if it fails to capture the important aspects that underlie the decision at hand. In fact, compared to everyday language and creative writing, where ambiguity may even be intended, the meaning of technical and scientific terms is usually distinct and univocal, at least within a particular discipline.

Another problem arises when one is convinced of understanding what is being discussed, but is interpreting it inaccurately through one’s predetermined filters. Let us coin this as the “techno-polysemy problem.” Homonyms are terms with more than one meaning. Polysemes are words that also have one meaning, but at least two of the meanings overlap. Let us assume that in the meeting you crashed, the experts are using words and symbols that are used in your area of expertise, but with very different meanings. For example, psychologists speak of “conditioning” [20] with a very different meaning that that of an engineer [21], which is different from that of a statistician or mathematician [22, 23], and altogether different from that of sludge or compost handler [24, 25]. Perhaps, most people in a waste-related public meeting or hearing, or even a meeting with decision makers in a jurisdiction, have no concept of the term, except when applied to air conditioning or a swimming pool. The waste manager may have all of these in the audience and must be certain that both clearly understand such homonyms. The waste manager is reminded that a word or phrase will mean what it can mean [26].

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8 EMERGING TOOLS AND TECHNIQUES Addressing wastes requires data of various sources. The data may be qualitative, semiquantitative, and quantitative information, which are evaluated according to widely varying criteria of risk, cost, benefit, social implications, and other considerations from which governance decisions are made [19]. Today, much is written about “big data,” that is, widely available and searchable datasets. The waste manager must be cautioned that applying such secondary data sources to predict extremely low probability events is almost invariably to some extent wrong. After all, the data were not collected with a later user’s purposes in mind. However, they do have value. Certainly, a complete replication of the events that led to the data is not possible, but recurrent precedents that seem to occur repeatedly prior to environmental disasters should be heeded. The past can indeed be an indicator of the future, notwithstanding their inherent imprecision and inaccuracy of using other people’s data. So, both the stockbroker’s and Santayana’s seemingly incongruent admonitions, which respectively are: Past performance is not an indicator of future results. Those who cannot remember the past are condemned to repeat it [27].

8.1 Cluster Analysis Secondary data may be analyzed with statistics and probability, including cluster analysis. To be useful to the waste manager, observations and events (known as “objects” to the data miner) must be grouped according to affinities and information found in the data describing the objects or their relationships. This allows the objects comparisons based on similarities and relationships between and among object groups. When objects within a group have greater similarity (i.e., homogeneity), then they

can be distinguished by this greater difference between groups, which allows for improved clustering. Usually, cluster analysis aids in reaching a finding by concisely and clearly classifying the data into distinct, nonoverlapping groups. If the distinctions are not short, this is “fuzzy clustering,” that is, an object is a partial member of two or more groups [14]. An example in Fig. 33.6 shows an applied hierarchical clustering analysis (HCA) to develop harmonized functions (HF) of consumer products [28]. The HCA identified 269 HF clusters, that is, clusters of similar reported functions. These clusters capture a much of the variance in pairwise distance between chemicals. This technique can be complemented with text analytics, for example, when an HF captured multiple reported functions. This indicates that chemical ingredients in that product use category may serve multiple functional uses, such as when the “surfactant” HF has such commonly occurring reported functions as “surfactant,” “cleaner,” “hydrotrope,” and “emulsifier.” Thus techniques like cluster analysis and text analytics are very useful as databases on product ingredients are becoming more widely available [29–31]. There are numerous approaches to grouping mined data according to the needs of the waste manager, for example, based on grouping physicochemical properties of waste, types of products being used, likelihood of contact with waste constituents. These techniques can be combined, such as using both cluster analysis or principal components analysis to categorize solvents [32–34].

8.2 Spatial Analysis Spatial tools are also available to improve the reliability of estimates in areas that are difficult to find a signal against the background noise, for example, risk factors in sparsely populated geographical entities. Most waste managers are familiar with geographic information systems (GIS). These systems are now commonly used for optimizing collection routing, landfill and incinerator siting, first and emergency response,

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FIG. 33.6

Cluster analysis of reported functions compared to harmonized functions of consumer product categories. The function “fingerprints” of chemicals in the 10 largest HF clusters and the ubiquitous, harmonized function cluster in terms of the most frequently occurring original reported functions. Reported functions are on the horizontal axis; harmonized functions are on the vertical axis. Reproduced from K.A. Phillips, J.F. Wambaugh, C.M. Grulke, K.L. Dionisio, K.K. Isaacs, High-throughput screening of chemicals as functional substitutes using structure-based classification models, Green Chem. 9 (2017) 1063–1074. 3. BEST PRACTICE AND MANAGEMENT

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and public information [35]. New techniques now available to augment GIS, for example, smoothing, are aiding in the identification and delineating of clusters of interest, such as elevated cancer risk that would be missed if spatial outliers can be detected and not filtered out. Static mapping techniques poorly predict the spatial distribution of risk and its associated variance, thus uncertainties are not properly accounted for and propagation of local cluster

analysis is not possible (see Fig. 33.7) For example, the smooth risk maps of rare outcomes (e.g., cancer clusters around a landfill) generated by a Poisson kriging geostatistical simulation provides superior estimates of risk than does the typical model that graphs a semivariance, that is, the spatial dependence between two observations as a function of the distance between them, that is, the semivariogram modeling method [36].

33 km shift

0.9–1.0 0.8–0.9 0.7–0.8 0.6–0.7 0.5–0.6 0.4–0.5 0.3–0.4 0.2–0.3 0.1–0.2 0.0–0.1

FIG. 33.7 Animated displays of simulated maps generated by Goovearts using the p-field method. Simulation grid is first generated by shifting the 295 county centroids by east-west increments of 33 km. Probability values populate the grid using sequential Gaussian simulation and the risk semivariogram models. The derived probability values are then used to sample Gaussian local distributions of probability characterized by the Poisson kriging estimate and variance mapped using Monte Carlo simulation. Reproduced from P. Goovaerts, Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation, Int. J. Health Geogr. 5 (1) (2006) 7; Note: SpringerOpen is the original publisher of this figure, which allows it to be reprinted here (https://www.springeropen.com/about/reprints-and-perm).

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These simulations are constructed in a stepwise fashion [36]: 1. From observed mortality rates and population of interest data, Poisson kriging is applied to model local risk uncertainty within each geographical unit. 2. Next, spatially correlated probability values generated using nonconditional sequential Gaussian simulation samples the set of local probability distributions.

3. By slowly sampling moving probability fields, incrementally different realizations are created, which can serve as consecutive animation frames, yielding a dynamic display of the uncertainty that evolves and appends to the risk spatial distribution. The supplementary material in Goovearts [36] provides the software and input data files to generate these simulations, maps, and visualizations for breast and cancer mortality rates in sparsely populated regions (see Fig. 33.8).

Breast cancer (rates)

Breast cancer (risk) 8 7 6

12

g

5 g 4

8

3 2

Azimuth: 22.5 Azimuth: 67.5 Azimuth: 112.5 Azimuth: 112.5

4

1

0

0 0

200 100 Distance (km)

300

200 100 Distance (km)

0

Pancreatic cancer (rates)

300

Pancreatic cancer (risk) 0.7

2.00

0.6 1.50 g

0.5 g

1.00

0.4 0.3 0.2

0.50

0.1 0

0 0

200 100 Distance (km)

300

0

200 100 Distance (km)

300

FIG. 33.8 Directional semivariograms generated by Goovearts showing breast and pancreatic cancer mortality rates and risks in sparely populated regions in the northeastern United States. The semivariograms of raw mortality rates (left column) and the semivariograms of the risk are computed in four directions (shown in top left graph); azimuth angles are measured in degrees clockwise from the N-S axis. The solid curve denotes the anisotropic (i.e., direction-dependent) model fitted using weighted least-square regression. Reproduced with permission from P. Goovaerts, Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation, Int. J. Health Geogr. 5 (1) (2006) 7; software: poisson_kriging.exe P. Goovaerts, Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging, Int. J. Health Geogr., 4 (1) (2005) 31. Note: SpringerOpen is the original publisher of this figure, which allows it to be reprinted here (https://www.springeropen.com/about/reprints-and-perm).

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9 CONCLUSIONS Every level of governance specifies measures of success for waste management. All aspects of waste management success depend on reliability. Decisions regarding the most suitable options, developing plans, implementing actions, and reviewing success must use reliability as a gauge of performance. A reliable program must also draw from scientific and other data and decision tools, as well as the expertise and perspectives of wide range of stakeholders and governance. This necessitates effective communications, which must be tailored to the needs, interests, and understanding of this eclectic audience.

References [1] O. Renn, White paper on risk governance: toward an integrative framework, in: Global Risk Governance, Springer, 2008, , pp. 3–73. [2] O. Renn, P. Graham, IRGC, International Risk Governance Council. White Paper on Risk Governance. Towards an Integrative Approach, International Risk Governance Council, Geneva, 2005. [3] D. Hoornweg, P. Bhada-Tata, What a Waste: A Global Review of Solid Waste Management, 2012. [4] National Research Council, Risk Assessment in the Federal Government: Managing the Process, National Academy Press, Washington, DC, 1983. [5] J.D. Solomon, D.A. Vallero, From Our Partners— Communicating Risk and Resiliency: Special Considerations for Rare Events, Available from: https:// cip.gmu.edu/2016/06/01/partners-communicatingrisk-resiliency-special-considerations-rare-events/, 2016. Accessed 1 May 2017. [6] D. Vallero, Paradigms Lost: Learning From Environmental Mistakes, Mishaps and Misdeeds, Butterworth-Heinemann, Burlington, MA, 2005. [7] National Academies of Sciences, Engineering, and Medicine, Spills of Diluted Bitumen From Pipelines: A Comparative Study of Environmental Fate, Effects, and Response, National Academies Press, 2016. [8] U.S. Environmental Protection Agency, National Response System, Available from:https://www.epa. gov/emergency-response/national-response-system, 2017. Accessed 12 May 2018. [9] D.A. Vallero, T.M. Letcher, Unraveling Environmental Disasters, Elsevier, Oxford, UK, 2012.

[10] W.D. Ruckelshaus, Science, risk, and public policy, Science 221 (4615) (1983) 1026–1028. [11] A.B. Hill, The environment and disease: association or causation? Proc. R. Soc. Med. 58 (5) (1965) 295. [12] T.S. Kuhn, D. Hawkins, The structure of scientific revolutions, Am. J. Phys. 31 (7) (1963) 554–555. [13] H. Petroski, To Engineer is Human, St. Martin’s Press, New York, 1985. [14] D. Vallero, Translating Diverse Environmental Data into Reliable Information: How to Coordinate Evidence from Different Sources, 1, Academic Press, London, UK, 2017. [15] D.L. Weed, Weight of evidence: a review of concept and methods, Risk Anal. 25 (6) (2005) 1545–1557. [16] N. Scotia, Explaining odds ratios, J. Can. Acad. Child Adolesc. Psychiatry 19 (2010) 227. [17] R. McConnell, et al., Traffic, susceptibility, and childhood asthma, Environ. Health Perspect. (2006) 766–772. [18] C. Mitcham, R.S. Duval, Engineering Ethics, Pearson, London, UK, 2000. [19] I. Linkov, et al., Comparative, collaborative, and integrative risk governance for emerging technologies, Environ. Syst. Decis. 38 (2) (2018) 170–176. [20] R. Lachman, J.L. Lachman, E.C. Butterfield, Cognitive Psychology and Information Processing: An Introduction, Psychology Press, London, UK, 2015. [21] D.N. Bangala, N. Abatzoglou, J.-P. Martin, E. Chornet, Catalytic gas conditioning: application to biomass and waste gasification, Ind. Eng. Chem. Res. 36 (10) (1997) 4184–4192. [22] O. Schabenberger, C.A. Gotway, Statistical Methods for Spatial Data Analysis, CRC Press, Boca Raton, FL, 2017. [23] J. Paris, A Note on*-Conditioning, Unpublished note, 2017 pp. 1–3. € [24] Z. Yan, B. ORMECI, J. Zhang, Effect of sludge conditioning temperature on the thickening and dewatering performance of polymers, J. Residuals Sci. Technol. 13 (3) (2016). [25] R.T. Haug, The Practical Handbook of Compost Engineering, CRC Press, 1993. [26] D.A. Vallero (Ed.), Technical Writing Course, U.S. Environmental Protection Agency, Region VII, Kansas City, MO, 1978. Instructor said that “a word will mean what it can mean” and “a phrase will mean what it can mean”. [27] G. Santayana, The Life of Reason: Five Volumes in One, Echo Library, 2006. [28] K.A. Phillips, J.F. Wambaugh, C.M. Grulke, K.L. Dionisio, K.K. Isaacs, High-throughput screening of chemicals as functional substitutes using structurebased classification models, Green Chem. 9 (2017) 1063–1074. [29] M.-R. Goldsmith, et al., Development of a consumer product ingredient database for chemical exposure

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[30]

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screening and prioritization, Food Chem. Toxicol. 65 (2014) 269–279. M.L. Card, et al., History of EPI suite™ and future perspectives on chemical property estimation in US toxic substances control act new chemical risk assessments, Environ. Sci. Process. Impacts 19 (3) (2017) 203–212. A.D. McEachran, J.R. Sobus, A.J. Williams, Identifying known unknowns using the US EPA’s CompTox chemistry dashboard, Anal. Bioanal. Chem. (2016) 1–7. M. Tobiszewski, S. Tsakovski, V. Simeonov, J. Namiesnik, F. Pena-Pereira, A solvent selection guide based on chemometrics and multicriteria decision analysis, Green Chem. 17 (10) (2015) 4773–4785. D. Xu, N. Redman-Furey, Statistical cluster analysis of pharmaceutical solvents, Int. J. Pharm. 339 (1) (2007) 175–188. M. Chastrette, M. Rajzmann, M. Chanon, K.F. Purcell, Approach to a general classification of solvents using a multivariate statistical treatment of quantitative solvent parameters, J. Am. Chem. Soc. 107 (1) (1985) 1–11. D. Khan, S.R. Samadder, Municipal solid waste management using geographical information system aided methods: a mini review, Waste Manag. Res. 32 (11) (2014) 1049–1062.

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[36] P. Goovaerts, Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation, Int. J. Health Geogr. 5 (1) (2006) 7.

Further Reading [37] L. Cheng, et al., Chronic disease mortality in rural and urban residents in Hubei Province, China, 2008–2010, BMC Public Health 13 (1) (2013) 713. [38] L.R. Curtin, R.J. Klein, Direct standardization (ageadjusted death rates) (no. 6), in: US Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention, National Center for Health Statistics, 1995. [39] M. Myers, A. Kaposi, A First Systems Book: Technology and Management, Imperial College Press, 2004. [40] T.R. Green, Cognitive dimensions of notations, in: People and Computers V, 1989, , pp. 443–460. [41] K. Smith, Environmental Hazards: Assessing Risk and Reducing Disaster, Routledge, London, 1992. [42] P. Goovaerts, Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging, Int. J. Health Geogr. 4 (1) (2005) 31.

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