Contamination of heavy metals and metalloids in biomass and waste fuels: Comparative characterisation and trend estimation

Journal Pre-proofs Contamination of heavy metals and metalloids in biomass and waste fuels: comparative characterisation and trend estimation Jinying Yan, Anna Karlsson, Zhi Zou, Deliang Dai, Ulrica Edlund PII: DOI: Reference:

S0048-9697(19)34373-6 https://doi.org/10.1016/j.scitotenv.2019.134382 STOTEN 134382

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Science of the Total Environment

Received Date: Revised Date: Accepted Date:

19 July 2019 8 September 2019 8 September 2019

Please cite this article as: J. Yan, A. Karlsson, Z. Zou, D. Dai, U. Edlund, Contamination of heavy metals and metalloids in biomass and waste fuels: comparative characterisation and trend estimation, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.134382

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Contamination of heavy metals and metalloids in biomass and waste fuels: comparative characterisation and trend estimation Jinying Yan a,c,1, Anna Karlsson c, Zhi Zou a, Deliang Dai d, Ulrica Edlund b a Chemical

Engineering and b Fibre and Polymer Technology, KTH Royal Institute of Technology, SE-100 44

Stockholm, Sweden; c Vattenfall AB, SE-169 92 Stockholm, Sweden; d Economics and Statistics, Linnaeus University, SE-351 95 Växjö, Sweden. Abstract The use of contaminated biomass and waste fuels is essential for waste management, waste to energy (WtE) and mitigating carbon emissions. The contamination of heavy metals and metalloids is specially concerned by environmental regulation and waste to energy processes. In this study, comparative characterisation is performed for three typical contaminated biomass and waste fuels. i.e. recycled woods, combustible municipal solid waste, and industrial and commercial wastes. The contamination characteristics are further analysed using statistical methods (e.g. significance, correlation, profile, and principal component analyses) to identify specific contamination features, relations among the contaminants and potential contamination sources. Contamination trend is estimated based on the continuously monitoring fuel qualities, the driving forces for regulating and reduction of the contaminations, and potential changes in major contamination sources. The comparative characterisation combined with statistical analyses provides a better way to understand the contamination mechanisms. The approach can also relate the fuel contamination with the contamination sources and their changes for trend estimation. Generally, the toxic heavy metals and metalloids are expected to be significantly reduced due to stricter regulations, but there is no general trend for the reduction of other metals and metalloids because of the complicated changes in contamination sources and waste recycling streams in the near future.

Highlights 

Contamination of heavy metals and metalloids is investigated for the contaminated biomass and waste fuels used for waste to energy

1 Corresponding author. Email address: [email protected] (Jinying Yan)

1



Characterization of the contamination is performed by comparative evaluation



Specific features of the contamination are further analysed using statistical methods



Contamination trends are estimated by review and analysis of potential changes in major contamination sources

Keywords Heavy metals and metalloids, biomass and waste fuels, contamination characterisation, bioenergy conversion, waste to energy (WtE) 1. Introduction 1.1 Background The continuous increase in energy demand and climate change have led to serious conflict between natural resources and society development, which forces us to use more renewable energy sources to replace fossil fuels. The IEA latest renewable market forecast predicts that bioenergy remains the largest source of renewable energy, which currently represents about 50% of total world renewables consumption, and modern bioenergy will have the main growth in renewable resources in coming 5 years (Ross, 2018). The potential of bioenergy development significantly relies on wastes and residues, which offers lower lifecycle greenhouse gas emissions and improve waste management (Ross, 2018). The use of biomass as energy fuels for heat and power production increased over the past decades in the Nordic and other EU countries, which was mainly due to the energy policy to support renewable energy production. Currently bioenergy is one of the largest energy sources of renewable energy, which accounts for about 60% of all renewable energy consumption in Europe. Biomass contributed to more than 32% of the national energy consumption in Sweden in 2013 (Calderon et al., 2016). Wood fuels such as primary forest fuels (PFF) are major resources for renewable energy production in EU countries (Wolfsmayr and Rauch, 2014). In addition to conventional wood fuels, considerable amounts of used wood materials are potentially available as an energy source (Koppejan and van Loo, 2012). It is estimated that 4.3 million tonnes of waste wood were generated in UK in 2010 (DEFRA, 2012). Recycled wood (RW, also called waste wood) fuels are the wood fuels that has been used for various purposes such as furniture, packaging and construction, and has finally ended up in the waste stream to be recycled as biofuels. RW as biomass fuel has been introduced into the wood fuel market in Sweden and EU countries due to the improved recycle/separation of sources for waste to energy (WtE), and relatively 2

low fuel costs (Dodoo et al., 2014). It was estimated that the use of waste wood as fuel increased from 10% to 40% of the total fuel for district heating from 1980 to 2009 in Sweden (Olsson and Hillring, 2013). The life cycle assessments indicate that RW for energy recovery by combustion is a better end-of-life management in comparison with other energy recover processes in terms of overall emission reduction (Carpenter et al., 2013) and life-cycle primary energy balance/use for wood-frame buildings (Dodoo et al., 2012). In European countries, incineration of municipal solid waste (MSW) with energy recovery as a strategy for WtE (Klinghoffer and Castaldi, 2013). The WtE together with prevention and material recycling measures (EU Directive, 2008) is the key element for sustainable waste management (Brunner and Rechberger, 2015), and has been considered a sustainable measure to achieve both energy recovery and reduction of greenhouse gas emissions (Yi et al., 2018). In Sweden, waste is used as fuel for district heating and electricity production. In 2016, 18.1 TWh of energy was recovered from the incineration of MSW and industrial wastes (Avfall Sverige, 2017). The waste fuels are complicated mixtures with different physical and chemical characteristics, contaminated with various toxic elements (e.g. heavy metals and metalloids) as well as a large range of particle size distribution. Advanced WtE technology development is focused on effectively extracting energy and minimising emissions and reducing secondary waste generation. In thermal energy conversion of WtE processes, MSW is normally incinerated with other waste fuels such as industrial and commercial wastes (ICWs) (Blomqvist and Jones, 2012). Fuel contamination is a general concern for RW materials used as biomass fuels and energy recovery from combustible wastes. Recycle wood fuel refers to four kinds of groups: untreated wood, surfacetreated wood, industrial preservative-treated wood and different types of building boards such as plywood and particle boards (Swedish EPA, 1996). The contamination of biomass fuels has been discussed by Edo et al. (2016) for recycled (waste) wood fuels, and Strömberg and Svärd (2012) for other biomass fuels including waste fuels. Krook et al., (2004; 2006) investigated heavy metal contamination in Swedish RW fuels. Jones et al., (2013) evaluated the presence of Zn in Swedish waste fuels used for WtE in combined heat and power (CHP) plants. The changes in fuel contamination could be considered as a dynamic process highly depending on sustainable development in energy production, material recycling and implementation of circulation economics. The market share of contaminated biomass fuels is generally expected to be increased based on current trend in the biomass fuels used for power and heat

3

generation. It is, therefore, necessary to continuously monitor the changes in contamination characteristics of biomass and waste fuels. Fuel contamination significantly affects the environmental performance of the thermal energy conversion for power and heat production. Emission of heavy metals and metalloids from combustion facilities (biomass-fired combined power and heat (CHP) plants and waste incineration units) is one of the most serious environmental concerns due to the specific properties of heavy metals and metalloids in non-biodegradability, interaction with abiotic and biotic components, and accumulation by living organisms and contamination of food chain, which severely threaten human health and ecosystems (Chowdhury et al., 2018; Edelstein and Ben-Hur, 2018; Friis, 2012). The toxicity of heavy metals and metalloids highly depends the dose, route of exposure and chemical speciation. The environmental concerns of heavy metals are normally evaluated based on their environmental occurrence, industrial production and usage, the potential for human exposure, molecular mechanisms of toxicity, genotoxicity and carcinogenicity (Tchounwou et al., 2012). 1.2. Toxicity of heavy metals and metalloids concerned in thermal energy conversion Several heavy metals and metalloids are presented in the Agency for Toxic Substances and Disease Registry (ATSDR)’s Substance Priority List (SPL) (US ATSDR, 2017), which can be considered as a prioritization of heavy metals and metalloids based on a combination of their frequency, toxicity, and potential for human exposure as most commonly found at facilities. Human exposure to heavy metals is highly affected by the heavy metal emissions to air, waters, soils and biota/food through industrial activities. According to the European Environmental Agency (EEA), anthropogenic emissions of toxic metals is mainly contributed from industrial combustion (EEA, 2017). Thermal energy conversion for power and heat generation is one of the main anthropogenic emission sources in Europe. It was estimated that the emissions of As, Cd, Cr, and Ni from the combustion of fuels in stationary facilities could contribute more than a half of the total anthropogenic emissions (Pacyna et al., 2007). The fate of some heavy metals and metalloids should specifically be concerned in the thermal energy conversation processes for WtE according to a comprehensive review on the fate of potential toxic elements (PTEs) in solid waste streams (Xiong et al., 2019). Therefore, environmental regulations with strict emission level limits for heavy metal emissions have been applied for the thermal energy conversion (power and heat generation from combustion and waste incineration) by European, national and local authorities. 4

Recently EU standards on resources and emissions have been developed to lower the environmental impact of large combustion and waste incineration plants. Emission limits of heavy metals and metalloids to air and water have been updated based on the current best available techniques (BATs) provided by new best available techniques reference document for large combustion plants (BREF-LCP) (Lecomte et al., 2017) and best available techniques reference document on waste incineration (BREF-WI) (EU JRC, 2018). Based on the commission implementing decision (EU) 2017/1442, on July 31, 2017 and establishing best available techniques (BAT) conclusions, the concerns of heavy metals and metalloids for the combustion of solid fuels (coal, lignite, biomass and peat) and co-incineration of wastes are the As, Cd, Co, Cr, Cu, Hg, Mn, Ni, Pb, Sb, Tl, V, and the Zn to air emissions, and As, Cd, Cr, Cu, Hg, Ni, Pb and Zn to water emissions. Table 1 shows the best available techniques associated emission levels (BAT-AELs) of heavy metals and metalloids for direct discharge to a receiving water body, which are the basis of EU relegations for emissions to waters from the thermal energy conversion processes. Table 1 Best available technique associated emission levels (BAT-AELs) of heavy metals and metalloids to a receiving water body for co-incineration of solid fuels in large combustion plant and waste incineration plants (based on Lecomte et al., 2017; EU JRC, 2018) Metals/metalloids

BAT-AELs (g/l) Co-incineration of waste

Waste incineration

As

10-50

10-50

Cd

2-5

5-30

Cr

10-50

10-100

Cu

10-50

30-150

Hg

0.2-3

1-10

Ni

10-50

30-150

Pb

10-20

20-60

Sb

20-900

Tl

5-30

Zn

50-200

10-500

The environmental concerns of specific heavy metals and metalloids from thermal energy conversion processes are summarised in Table 2, which are based on current EU regulations for biomass combustion

5

and waste incineration, the rank of ATSDR’s Substance Priority List (SPL). The toxic effects of metals are generally classified by (Friis, 2012): 

major toxic metals with multiple effects,



essential metals with potential for toxicity (essential for human nutrition but can be toxic if ingested in excessive amounts),



metals related to medical therapy, and



minor toxic metals

Table 2 The environmental impacts of heavy metals and metalloids emitted from thermal energy conversion processes (based on Lecomye et al., 2017; EU JRC, 2018; US ATSDR, 2017; Friis, 2012; and Tchounwou et al., 2012) Environmental regulations1

Toxic classification

Emissions to

Emissions to

ATSDR SPL rank2

Toxic effects3,4

air

water

As

√

√

1

Major-multiple

Cd

√

√

7

Major-multiple

Co

√

Cr

√

√

17/66/78

Cu

√

√

118

Hg

√

√

3

Mn

√

Ni

√

Pb

Essential-potential (nutrient) Major-multiple/essential nutrient Essential-potential (nutrient) Major-multiple

140

Essential-potential (nutrient)

√

57

Major-multiple/essential nutrient

√

√

2

Major-multiple

Sb

√

√

244

Nonessential element

Se

√

146

Essential-potential (nutrient)

Tl

√

V

√

Zn

√

√

Nonessential element

√

200

Nonessential element

75

Essential-potential (nutrient)

Notes: 1. based on Lecomte et al., 2017 and EU JRC, 2018; 2. based on US ATSDR's substance priority list (SPL) 2017, 3. based on the classification of toxic effects defined by Friis, 2012; 4. Based on Tchounwou et al., 2012. The heavy metals marked with red colour are the highly concerned toxic metals with major-multiple effects.

6

In comparison with heavy metal emissions, special attention may need to pay for the technical problems in the operation of combustion facilities. The emission reduction technologies have gained significant improvement in recent decades due to stricter environmental regulations. The main challenges for heavy metal emissions are the development of sustainable reduction approaches and the handling of contaminated by-products from emission reduction processes. However, the operational problems associated with the fuel contamination with heavy metals are more complicated and not well understood, which require a careful characterization of fuel contamination combined specific problem investigation. This type of fuel characterization should be performed not only for heavy metals but should also be combined with the contaminates that induce or enhance the operational problem, like the content of Na and the relation of Cl and S. Fuel contaminated with heavy metals may cause some operating problems in thermal energy conversion facilities such as deposits on the surface of heat exchangers (Andersson and Högberg, 2001; Sjöblom, 2001), and corrosions (Andersson et al., 2003), which are closely related to Zn and Pb combined with elevated levels of chlorine and sulphur components. Results from a full-scale bubbling fluidised bed (BFB) boiler and laboratory tests show that minor amount of Zn, Pb and Cu together with Na, K, and Cl induce the high temperature corrosion of boiler waterwall in the combustion of solid recovered fuel (Vainikka et al., 2011). Metal sulphides (e.g., ZnS, PbS and FeS) or sulphates could be formed under different redox and temperature conditions, which may represent small fractions of sulphur components but have an influence on deposition and furnace corrosion in biomass combustion. Metal chlorides could also be formed such as PbCl2, and CuCl2 in deposits, which increase the corrosion of heat exchangers. Heavy metal salts (e.g. KCl-ZnCl2, ZnCl2, K2SO4-Na2SO4-ZnSO4, KCl-FeCl2, KCl-PbCl2, K2SO4-PbCl2, NaCl-PbCl2, ZnCl2-KCl-PbCl2, ZnCl2-NaCl-PbCl2, NaCl-KCl-PbCl2, KCl-FeCl2-PbCl2, ZnCl2-PbCl2, and NaCl-CrCl2) could result in molten salt corrosion in biomass-fired boilers (Spiegel, 2010; Alipour, 2013; Kinnunen et al., 2017). The formation of low melting temperature deposits by the metal salts or mixtures induces the corrosions at relatively low material temperatures (e.g. < 350 oC). The contamination features of biomass fuels will affect the thermal energy conversion technologies for power and heat production. There are more technical overlaps between waste to energy and coincineration of waste in traditional biomass-fired CHP plants, especially for emission reduction techniques. Therefore, it is necessary to have comparative investigation of heavy metal contamination in the biomass fuels and their general impacts for both waste incineration and contaminated biomass

7

combustion in thermal energy conversion processes, by which to share the experience and to understand contamination sources and impact mechanisms. This study intends to provide a concept for the contamination characterisation of heavy metal and metalloids in contaminated biomass-based fuels. The study uses Swedish cases to address the key issues and the major impacts of fuel contamination for WtE in order to implement the concept, and to demonstrate the principles and methodologies applied for the contamination characterisation. In the following sections, comparative characterisation is performed to give an overview of contamination, to identify the major contaminants, and to show their contamination characteristics for different fuels. The identified contamination characteristics are further analysed using statistical approaches to get a valuable insight into the relations among the major contaminants with some links or connections to potential contamination sources. Then, contamination trends could be estimated or predicted based on the correlations between contaminants and the contamination sources, and potential changes in the contamination sources. The study is specifically performed for the characterisation of heavy metals and metalloids in Swedish contaminated biomass and waste fuels. The concept should be applicable for other contaminates and contaminated biomass fuels with necessary modifications based on local conditions. 2. Methodology Th contamination of heavy metals and metalloids is evaluated by the comparison of the enrichment of specific components (elements) with that in common biomass fuels like forest residues (FRs), from which the relative contamination factors are determined for concerning heavy metals and metalloids, which are then checked with the toxicity classification (as described in the introduction section), the emission regulations applied for biothermal energy production, and the contamination levels. Major heavy metals and metalloids in the investigated biomass fuels are identified by the overall considerations for further characterisation. 2.1. Data and information collection The contamination characterisation is performed for three contaminated biomass fuels: RW chips, combustible MSW and ICW, which are commonly used for CHP plants and WtE in Nordic countries. Fuel data for RW (chips) fuels are collected from our own industrial biomass fuel database in a period of 2008 to 2015. All the measurements were made by certificated labs. The analytical methods used for the determination of heavy metals and metalloids were based on EU or national (Swedish) standards.

8

The fuel data of MSW and ICW are collected from a Swedish research framework (Blomqvist and Jones, 2012). In this framework, 42 waste fuel samples were taken from seven waste incineration plants in a year period. The samples may represent typical waste fuels used in Swedish waste incineration plants for WtE. FRs as common clean biomass fuels are used for heat and power generation. The clean biomass fuel as comparison basis to evaluate the relative contamination of heavy metals and metalloids in the contaminated fuels. The data used in this study are get from the Fuel Handbook (Strömberg and Svärd, 2012) published by Swedish Thermal Engineering Research Association (Värmeforsk). 2.2. Methodologies for contamination characterisation Comparative evaluation is employed for the characterisation, by which the results may easily be applied for the emission control in a thermal energy conversion process in different systems. Several statistical methods are used for further understanding of the contamination characteristics in different contaminated fuels and even in different periods. Specific methods include: (1) using statistical significance to identify the variations of contamination in different fuels, (2) correlation analysis for the relations among the contaminants to recognise potential contamination sources, (3) profile analysis (PA) to evaluate the statistical significant difference of contaminants in a period of years, and (4) principal component analysis (PCA) to investigate the correlations among the contaminants and their changes during a continuous monitoring period (years). 2.3. Methods for evaluation of contamination trends The contamination trends of heavy metals and metalloids are evaluated for both the past and the near future. Statistical methods (e.g. PA and PCA) have been used to identify the changes in the contamination in a period from 2005 to 2015 for specific heavy metals and metalloids and correlations. The potential changes in the contamination in the near future are estimated based on reviewing and analysis of the possible changes in major contamination sources. The potential contamination sources include wood preservation, flame retardants, paints and coatings. The following aspects are carefully investigated to get reasonable estimation: 

Specific heavy metals and metalloids involved in the contamination sources and their main functions, 9



Concentration levels of specific heavy metals and metalloids in the contamination sources,



Environmental concerns and current regulations for the sources in terms of specific heavy metals and metalloids, and



Impacts of future developments in waste management, material recycling and environmental regulations

3. Results and Discussions 3.1. Comparative characterisation of contamination Three typical biomass fuels: FRs, RW, combustible MSW, and MSW mixed with ICW (MSW+ICW) are common biomass fuels currently used for power and heat generation and WtE in Nordic countries. A comparative evaluation is made for the three types of contaminated biomass fuels in comparison with the contents of heavy metals and metalloids in FRs, from which the contamination characteristics are identified for major heavy metals and metalloids. A general impact analysis is carried out for the identified heavy metals and metalloids combined with other associated contaminants, which may induce or enhance the impacts of heavy metals and metalloids on the environmental and operating performance of thermal energy conversion processes. 3.1.1. Overall contamination characteristics The overall comparison of contamination of heavy metals and metalloids of the contaminated biomass fuels with FRs fuels are shown in Table 3 in terms average concentrations and relative contamination factors (the ratio of concentration in the contaminated fuels to the concentration in the FRs) for individual heavy metals and metalloids. Following contamination characteristics have been identified: 

The FRs fuels contain relatively high concentrations of Mn and Zn, but very low concentrations of other heavy metals and metalloids,



The contamination of heavy metals and metalloids is in order of RW < MSW < MSW-ICW. The content of total heavy metals and metalloids is near double in the RW, near 3 times of that in the MSW and more 6 times in MSW-ICW in comparison to the FRs,



RW fuels are significantly contaminated by As, Cr, Cu and Pb;



MSW fuels are significantly contaminated by As, Co, Cr, Cu, Ni, Pb, Sb, V and Zn;



The waste fuels by mixing of MSW and ICW significantly contaminated by As, Cd, Co, Cr, Cu, Ni, Pb, Sb, V and Zn, and



Major contaminants identified in RW (i.e. As, Cr, Cu, Pb, and Zn) are also found in MSW and MSW+ICW. However, additional contaminations of Co, Ni, Sb have been found in MSW and 10

MSW+ICW. Significant contamination of Cd is identified in MSW+ICW but not in RW and MSW.

Table 3 Overall contamination characteristics of heavy metals and metalloids of the contaminated biomass fuels in comparison with FRs fuels FRs

RW

MSW

MSW+ICW

mg/kg

No. of

mg/kg

No. of

mg/kg

No. of

mg/kg

dry

samples

dry

samples

dry

samples

dry

As

0.1

7

23

85

5

12

17

Cd

0.2

8

1

85

1

12

Co

0.2

8

1

85

7

Cr

3

8

104

85

Cu

3

8

62

85

Hg

0.03

8

0.07

85

Mn

430

14

87

85

258

12

223

Ni

1

8

5

85

44

12

Pb

2

8

98

85

95

Sb

4

4

78

31

TI

2

0.03

47

V

1

8

3

85

7

12

13

Zn

54

8

405

85

509

12

1371

Total

499

792

RW

MSW

MSW-mix

(RW/FRs)

(MSW/FRs)

(MSW+ICW/FRs)

31

285

63

214

3

31

3

3

16

12

11

31

7

44

68

53

12

124

31

38

20

46

340

12

1042

31

23

126

386

2

0

0

31

0

1

1

76

31

7

63

108

12

352

31

65

63

235

12

58

29

1

8

14

0

0

0

31

5

14

26

31

7

9

25

1350

Samples

Relative contamination factor

3290

Note: The data marked with red colour indicate that the relative contamination factors are larger than 10.

3.1.2. Contamination characteristics of major heavy metals and metalloids The distributions of individual heavy metals and metalloids in different biomass fuels are shown in Table 4 in terms of mass percentages. The major heavy metals and metalloids in the fuels can be easily identified for the components with the shares over percentage: 

The major heavy metals in FR are Mn and Zn,



The major heavy metals metalloids in RW are As, Cr, Cu, Mn, Pb and Zn, and



The major heavy metals and metalloids in MSW and MSW+ICW are As, Cr, Cu, Mn, Ni, Pb, Sb, and Zn

11

According to the toxicological profile for Mn (US ATSDR, 2012), Mn is an essential nutrient, and eating a small amount of it each day is important to stay healthy and it cannot easily determine whether or not excess Mn can cause cancer based on existing scientific information. We consider that the most important heavy metals and metalloids should be concerned are As, Cr, Cu, Ni, Pb, Sb and Zn for contaminated biomass fuels which currently used for thermal power and heat generation.

Table 4 Distributions of given heavy metals and metalloids in the total heavy metals and metalloids in the biomass fuels (in mass%) FRs

RW

MSW

MSW+ICW

As

0.02

2.88

0.37

0.52

Cd

0.04

0.09

0.06

0.11

Co

0.03

0.15

0.52

0.33

Cr

0.54

13.12

3.91

3.78

Cu

0.54

7.80

25.17

31.68

Hg

0.01

0.01

0.00

0.00

Mn

86.24

10.97

19.13

6.76

Ni

0.14

0.65

3.27

2.31

Pb

0.30

12.36

7.02

10.70

Sb

0.80

0.47

2.31

1.76

TI

0.34

0.00

0.00

0.00

V

0.10

0.34

0.53

0.39

Zn

10.89

51.16

37.71

41.67

Total

100.00

100.00

100.00

100.00

Note: The elements marked with red colour have relatively higher distribution (>1%)

12

3.2. Statistical analysis of contamination characteristics Several statistical methods are employed for further analyses and explanation of the contamination characteristics. For most of the analyses, comparative evaluation is performed for either the difference between groups of data or check the trend of the changing of the elements during a period of years. Thus, a criterion is needed to decide if the difference or the changing is meaningful from a statistical perspective. This is called hypothesis testing. Therefore, the following statistical concepts are introduced before corresponding analyses. To run a hypothesis testing, a hypothesis setting should be set up: a null hypothesis and an alternative hypothesis (Andersson et al., 2016). Then, the information will be extracted and calculated from the data based on the hypothesis set. The next step is to compare our calculation with a certain criterion which is the threshold to decide whether the information is statistical meaning to our analysis. For example, we know the average value of one element, say As, in 2008 is larger than the average value of As in 2015. However, we need to identify if the average values are statistically significant different. This is beyond the pure value difference but the statistical meaningful. In another word, we should decide if the average value of As from one group is statistical significantly larger or less than the average value from another group. The significant level is a criterion that is used to evaluate the trueness of a null hypothesis. The common procedure for hypothesis testing is to calculate the test statistics based on the assumptions of the null hypothesis. Then, the result of the test statistics is compared to the significant level. When the result of the null hypothesis is larger than the significant level, the null hypothesis cannot be rejected. This means that the assumption should be accepted for a given significant level in the null hypothesis. Otherwise, the null hypothesis should be rejected. For comparable purpose, the significant levels of 0.05 and 0.10 are used in the analyses, which are the most common significant levels for statistical analysis in research work and are normally used as default levels in statistical software. 3.2.1. Contamination variations and statistical significance One of the contamination characteristics of heavy metals and metalloids in biomass and waste fuels is the large variations in concentrations. Standard deviation (SD) can be used as an indication to show how the data points can spread from the mean value, which is also considered as a measure of uncertainty for the mean value estimated from several samples. The ratio of the SD to the mean or its absolute value is referred ad relative standard deviation (RSD) or coefficient of variation (CV). Both the SD and the RSD are used to evaluate and compare the contamination variation in the invested biomass and waste fuels. 13

The contamination variation of major heavy metals and metalloids are shown in Table 5 in terms of SDs. In general, the contamination variations are relatively large for most of the heavy metals and metalloids in the three types of fuels. As shown in Figure 1, the RSDs values for most of the contamination components are in a range of 50 – 250%, which means it should be expected to handle the contaminants with roughly more than 100% variation in concentration in the thermal energy conversion processes. This can be very challenged for some contaminants with very high concentration variations e. g. Cr, Cu, Pb, Sb and Zn in RW fuels, and Ni, Pb and Sb in MSW and the its mixture with ICW (MSW+ICW). Table 5 Comparison of the contamination variations for major heavy metals and metalloids in different biomass fuels RW

MSW

MSW+ICW

Mean

SD

Mean

SD

Mean

SD

mg/kg dry

mg/kg dry

mg/kg dry

mg/kg dry

mg/kg dry

mg/kg dry

As

23

15

5

2

58

18

Cr

104

260

53

22

124

83

Cu

62

101

340

275

1042

340

Ni

5

4

44

78

76

138

Pb

98

234

95

132

352

481

Sb

4

9

31

35

58

74

Zn

405

1113

509

327

1371

1113

In the comparison of the SDs and RSDs for different fuels, the variations of contamination in the RW fuels are generally larger than that in the MSW and MSW+ICW for the major heavy metals and metalloids. As expected, very similar contamination variations have been identified between the MSW and MSW-ICW for most of the contaminants. This may imply that the contaminants could be easily controlled for MSW and MSW+ICW fuels than that for RW fuels, although contamination levels of the heavy metals and metalloids are generally higher in MSW and MSW+ICW fuels.

14

Fig. 1. Contamination variations of major heavy metal and metalloids based on measurement samples in terms of relative standard deviation (RSD).

A t-test is used to compare the contamination of major heavy metal and metalloids between different fuels. The evaluation results (Table 6) show that there is a statistically significant difference for As, Cr, Ni and Sb between RW and MSW; for Cu, Ni, Pb, Sb and Zn between RW and MSW+ICW; and for As, Cr, Cu, and Pb between MSW and MSW+ICM. The results also indicate that: 

RW is highly contaminated by As and Sb in comparison with MSW and MSW+ICW,



RW is more contaminated by Cr and Pb in comparison with MSW and MSW+ICW,



RW is less contaminated by Zn in comparison with MSW and MSW+ICW,



MSW and MSW+ICW are highly contaminated by Cu, and Ni in comparison with RW, and



MSW-ICW is highly contaminated by all the major heavy metals and metalloids in comparison with MSW

The results of statistical significance analyses on heavy metal contamination are consistent with measurement values shown in Table 3 but statistically quantify the differences. Table 6 Contamination differences in major heavy metals and metalloids between the biomass fuels based on t-test under 0.05 significant level RW vs MSW

RW vs MSW+ICW

MSW vs MSW+ICW

As

6.788

1.230

-3.691

Cr

1.089

-0.270

-4.472

Cu

-7.989

-7.283

-2.995

15

Ni

-4.594

-4.880

-0.956

Pb

0.068

-3.006

-2.765

Sb

6.441

-6.362

-1.568

Zn

-0.512

-2.303

-1.875

Note: The values with relatively larger significant differences are marked with red colour.

3.2.2. Correlation analysis Relations around the major heavy metals and metalloids may imply some information associated with contamination sources and the interactions between the components during material using, collecting, and recycling. The relations are generally complicated, but very relevant to analysis. Several efforts have been made by using statistical analysis to identify relevant correlations (Edo et al., 2016; Shu et al., 2015). However, there are some gaps to link between the statistic correlations and physical indications for practical issues. In this study, the correlations are used to interpret the potential contamination sources that have been identified through a comprehensive literature review and industrial investigations. Statistic correlation analyses are used to identify the correlations between different heavy metals and metalloids in different fuels. The correlation matrix is calculated as a tool for measuring correlations. The matrix is symmetric, in which its values show the pairwise strength between the variables (i.e. the heavy metal elements). In some literature, the values are also called correlation coefficients. A correlation coefficient’s value is always laying within the interval [-1,1]. The closer it approaches 1 the higher the positive connection between the two variables, while the closer it comes to -1 means a stronger negative connection. The value 1 corresponds to a perfect uphill linear relationship between the two, while -1 means an opposite downhill linear relationship. On the diagonal of the matrix, all the elements are equal to 1 since the variable has a perfect correlation to itself. The correlation matrix reveals the pairwise connections between any two variables out of the dataset. Here the descriptive method is implemented for all the heavy metals and metalloids to find out the correlations among the variables. Correlation matrixes for major heavy metals and metalloids are shown in Table 7 for RW, Table 8 for MSW and Table 9 for MSW+ICW, in which the statistically meaningful correlation coefficients are labelled with one or two stars in the cell. The number of stars means statistically significant under different significant level as we introduced before. Here, one star represents statistically significant under the 0.10 significant level and the two stars equals the 0.05 significant level. The correlations between the investigated components could be used as relevant information or indicators to further 16

identify potential contamination sources for given fuels, which will be further discussed in the following sections. For example, It is well known that chromated copper arsenate (CCA) is the most commonly used water-borne wood preservative since the 1970s (Morrell, 2013). Strong correlations among Cr, Cu and As may indicate that the preservative treated woods are important contamination sources for RW fuels. As shown in Tables 7, 8 and 9, Strong correlations of 5 (As vs Cu, As vs Zn, Cu vs Ni, Pb vs Sb and negative Pb vs Ni), 5 (As vs Zn, Cu vs Pb, Cu vs Sb, Pb vs Zn, and Pb vs Sb), and 4 (As vs Ni, Cr vs Sb, Cu vs Zn and Cu vs Sb) are identified for RW, MSW and MSW+ICW respectively. The strong correlations between As and Cu may imply wood preservative could be important contamination source. Pb does not show positive correlations with other metals in RW but shows a strong correlation with Sb in both RW and MSW. Investigations show that both Pb and Sb could be presented in wood paints or coatings with relatively high concentrations (Turner et al., 2016; Turner and Sogo, 2012, Mielke et al., 2001). The correlations of Pb with Cu and Zn in MSW and with Ni may be related to the contaminations from the inorganic pigments of paints and coatings (Bulian and Graystone, 2009; Abel, 1999; Lambourne, 1999) and/or electronic wastes such as batteries. The high correlation between Cu and Zn has been identified in both RW and MSW+ICW, which could be related to the contaminations from wood paints and coatings as well as electronic wastes (Oguchi et al., 2012, 2013; Ilankoon et al., 2018). It is interesting that strong correlations of Sb with Pb is found in RW and MSW, with Cu in MSW and MSW+ICW, and with Cr in MSW+ICW, which may indicate that the flame retardants (Patterson and Parker, 2015) and electronic wastes (Oguchi et al., 2012, 2013; Ilankoon et al., 2018) could be important contamination sources for these fuels. However, the strong correlations between As and Zn in RW and MSW, and Ni and Cr in RW cannot be clearly explained based on the current investigation, which should be further investigated based on the contamination levels of As in different fuel samples. Table 7 Correlation coefficients between major contamination components in the RW fuels As As

1

Cr

0.012

Cr

Cu

Ni

1

17

Pb

Zn

Sb

Cu

0.812**

0.013

1

Ni

-0.187

0.760**

-0.326

1

Pb

-0.392

-0.364

-0.020

-0.675*

1

Zn

0.675*

0.333

0.554

0.091

-0.287

1

Sb

-0.035

-0.117

0.288

-0.497

0.642*

-0.443

1

Zn

Sb

Notes: * statistically significant level = 0.1, ** statistically significant level = 0.05

Table 8 Correlation coefficients between major contamination components in the MSW fuels As

Cr

Cu

Ni

Pb

As

1

Cr

-0.010

1

Cu

-0.056

0.241

1

Ni

-0.173

0.112

0.288

1

Pb

0.387

0.043

0.758**

-0.081

1

Zn

0.762**

-0.051

0.398

0.252

0.598*

1

Sb

0.059

-0.233

0.596**

-0.167

0.710**

0.151

1

Notes: * statistically significant level = 0.1, ** statistically significant level = 0.05

Table 9 Correlation coefficients between major contamination components in the mixtures of MSW and ICW (MSW+ICW) fuels As

Cr

Cu

Ni

Pb

Zn

As

1

Cr

0.044

1

Cu

0.114

0.320

1

Ni

0.441*

0.261

0.298

1

Pb

0.111

0.059

0.114

0.202

1

Zn

0.220

0.167

0.814**

0.321

0.237

1

Sb

0.026

0.511**

0.386*

0.069

0.220

0.234

Notes: * statistically significant level = 0.1, ** statistically significant level = 0.05

3.2.3. Profile analysis (PA) and principal component analysis (PCA)

18

Sb

1

The contamination of heaving metals and metalloids in RW fuels has been monitoring since 2008. It is relevant to evaluate how the contamination was changed with time in terms of contamination levels and sources. The PA and PCA are used to evaluate such changes during this period. PA is commonly used in two cases: comparing the same dependent variables between groups over several time points or when there are several measures dependent variables (Connor, 2008), and intend to ask three basic questions: (1) are the groups parallel between time points? (2) are the groups at equal levels across time points? (3) do the profiles exhibit flatness across time points? The results are plotted based on the means of each group and analysis of variance (ANOVA) and its extension multivariate analysis of variance (MANOVA) is employed as the statistical methods to test the parallel, equivalence, and flatness of the plots across groups. Mathematically, PA measures the relative contributions of between-group and within-group contributions to the total sum of squared errors (the left side of the equation) (Johnson and Wichern, 2007). Let C be a contrast matrix defined as follows,

C( p 1) p

 1 1  0 1     0 0

0 0  0 1 0  0     0 0  1

0 0  ,   1

(1)

where 1 ,  2 are the population means for different groups in the dataset. Reject H 0 : C 1  C  2 at the level   0.05 if 1

 1 1   T   x1  x2  ' C '    CS pooled C ' C  x1  x2   c 2  n1 n2   2

where

x1 , x2

are the sample means of the groups to be compared;

corresponding groups;

c2 

S pooled

are the sample sizes to the

is the sample covariance matrix of the two groups and

 n1  n2  2  p  1 F n1  n2  p

n1 , n2

(2)

p 1, n1  n2  p

  .

(3)

19

The PA is based on the RW data from 2008 to 2015. We chose the data from 2009, 2010, 2014 and 2015 as the subset data for analysis. The results are given in Figure 2. The results show that p-value equals to 0.0095, which implies that there is a statistically significant difference among different groups under 0.05 significant level when we consider each element as one group across years. As mentioned before, the p-value less than significant level means a rejection of the null hypothesis of the analysis. Thus, it should be rejected that the presumed null hypothesis in the PA, i.e. there is no difference among the different elements across these years. Down to the equations, it is equivalent to conclude that 𝑇2 ≤ 𝑐2 as we defined in equation (5). By far, we can answer the three questions proposed at the beginning of this subsection based on the results above: (1) the groups of the elements are not parallel to each other. For example, the profile of Zn is very different from the profile of Ni in figure 2, (2) the groups of elements are not at equal levels across the time points. The trends of the elements are changing drastically across the years among 2009, 2010, 2014 and 2015. For example, the trend of As and Cu is closely correlated to the trend of Cr from 2009 to 2014. However, Cr’s profile increased drastically in 2015 while Cu is keeping on the same level, and (3) most of the profiles do not show flatness across the time points. For example, the profiles of Zn, Cu and Cr do not show a flatness shape in Figure 2.

20

Fig. 2. PA for heavy metal and metalloids in the samples collected for RW fuels in 2009, 2010, 2014 and 2015. PCA is a multivariate analysis method that explains the variance covariance structure of the whole set of variables by a few linear combinations of these variables. In a conceptual interpretation, PCA assembles the highly correlated variables into one component which is called one principal component. Within each principal component, each variable will receive a coefficient that represents the weight of the variable. Thus, the number of principal components is usually much fewer than the number of variables. At the same time, the principal components maintain most of the information and present it in a much simpler form. The general idea of PCA can be presented as follows (Johnson and Wichern, 2007): Let the random vector

X' =  x1 , x2 , , x p 

(4)

having the covariance matrix  with eigenvalues 1 > 2 > > p > 0., considering the linear combinations

Y1  a1 ' X  a11 x1  a12 x2    a1 p x p Y2  a2 ' X  a21 x1  a22 x2    a2 p x p .  Yp  a p ' X  a p1 x1  a p 2 x2    a pp x p

(5)

We obtain the variance of Yi as Var Yi   ai ' ai , i  1, 2, , p and the covariance of Yi as

Cov Yi , Yk   ai ' ak , i, j  1, 2, , p .

(6)

The principal components are those uncorrelated linear combinations Y1, Y2, , Yp whose variances in are as large as possible (Johnson and Wichern, 2007). The results of PCA for the heavy metals and metalloids in RW are shown in Figure 3. The analyses are performed for years of 2009, 2010, 2014 and 2015, by which the changes in relations of contamination components with time could be identified from the initial monitoring (2009 and 2010) in comparison with that in the end (2014 and 2015). Generally, different distributions of heavy metals and metalloids are clearly shown in the figures. For the 2D figures, the closer the elements to the right middle of the figure (e.g. Cd, Co and Cd in the upper right figure), the more concentrated these elements are within 21

one component. Here the corresponding component is the component 1. This means these elements are highly correlated to each other. Therefore, PCA classify them into one component as one group. The closer the elements to the upper middle of the figure, the more concentrated elements are within another component (e.g. As, Cr and Cu in 2010). The component plot for the year of 2009 shows that most elements are classified into the component 1 but Pb. It may imply that the elements within component 1 come from the same contamination source. While in 2010, it is clearly shown that As, Cr and Cu could be classified into component 2 while the rest of the elements are in component 1. This implies that there may be two different contaminant sources in 2010 comparing to the information in 2009. The heavy metals and metalloids in rotated spaces of 2009 and 2010 could be related to relatively simple groups (2 D with two components). The rotation here aims to show a clearer distribution of the elements from the results of PCA without loss any generality.

22

Fig. 3. PCA for heavy metals and metalloids in RW fuels during 2008 to 2015 and comparison of the characteristics between the initial samples (2009 and 2010) and recent samples (2014 and 2015).

The additional component should be added in the rotated space (3D with three components) for 2014 and 2015. For the data from 2014, the figure shows that the correlation among As, Cu and Cr is still very strong. The distance between the CCA elements and the rest is clear even under the 3D space, although the distribution of the elements is not as clear as the years before. This changing of distribution is coincident to the results from PA, for example, Cr and Zn increase in 2015 abnormally in comparison to the trends of other elements. The PCA indicates that the contamination sources may be significantly changed during this period in comparison to 5 or 6 years ago, and the correlations of the contamination elements were changed as well. 23

3.3. Analyses of contamination sources and potential trends Identification of major contamination sources and estimation their potential changes are essential to effectively control the negative impacts of heavy metals and metalloids on biothermal energy conversion and WtE. However, the fuel contamination is a dynamic process that will significantly be affected by the changes in waste streams, waste management strategy, waste material recycling and environmental regulations. Therefore, it is important to understand the behaviour of heavy metals and metalloids in their contamination sources and how the source could be changed by major influence factors in the future. Some of the contamination sources have been discussed in previous sections. In this section, potential contamination sources are investigated in terms of (1) the behaviour or main functions of heavy metals and metalloids in specific sources, (2) concentration levels, (3) changes of the sources in composition and concentration, (4) main driving forces for the changes in near future, and (5) influences from environmental regulations. The main contamination sources include wood preservation, flame retardants and additives which involve many waste materials such as polymers, plastics and construction materials, and paints and coatings. It has been recognised that waste electrical and electronic equipment (WEEE) could be important contamination sources of toxic heavy metals and metalloids (Oguchi et al., 2013; Parajuly et al., 2017; Suresh et al., 2018). The WEEE in waste streams could increase the contamination of heavy metals and metalloids in combustible waste fuels (Ma et al., 2016). However, there is a lack of basic data and information to link to the impacts on combustible wastes. Potentials of contamination from WEEE are not discussed in this study but they are relevant will be further investigated in the future. 3.3.1. Wood preservation Wood preservation could generally be categorised by two types of preservatives, i.e. oil-borne and water borne. Oil-borne preservatives include creosote, pentachlorophenol, and metal naphthenates such as copper naphthenate, and oxine copper (Barnes, 2016). This type of preservatives is typically used in special industrial applications, for example, railroad ties and utility poles. Water-borne preservatives are more often used than the oil-borne ones to treat wider wood products including dimensional lumber and plywood for both commercial and residential applications (Vlosky, 2009). Chromated copper arsenate (CCA) is the most commonly used water-borne wood preservative since the 1970s (Morrell, 2013). According to Mohajerani et al. (2018), the function of Cr is to fix As and Cu to the wood, Cu controls fungi and As controls copper-tolerant fungi. Other water-borne preservatives are inorganic metal-based ammoniacal copper zinc arsenate: ACZA, acid copper chromate: ACC, alkaline copper quat-types: ACQ-A, 24

B, C, D, micronized copper quat: MCQ or MCA, copper azole-types: CA-B, C, copper HDO, copper boron azoles: CBA), and organic biocides such as propiconazole tebuconazole imidacloprid: PTI. The main water-borne preservatives are copper chromium arsenic (CCA), copper azoles (CA), ammoniacal copper quaternary (ACQ), Bis (N-cyclohexyl-diazenium-dioxy)-copper (CuHDO) and borate-based formulations (Schiopu and Tiruta-Barna, 2012; Morrell, 2013; Barnes, H.M., 2016; Robey et al., 2018). Wood preservatives are generally used as mixtures of several chemicals with biocidal effects. The concentrations of heavy metals remaining in the preservative woods are highly depended on chemical formulations and the conditions of actual applications. Standard wood preservative water solutions consist with ACC (31.8% copper oxide and 68.2% chromium trioxide), ACZA (50% copper oxide, 25% zinc oxide and 25% arsenic pentoxide) and CCA (types A and C) (Cheremisinoff and Rosenfeld, 2010). CCA salt formulations are generally 1-6% (w/V) depending on protection requirements and treating process (OSHS, 1994). For ground contact applications, the retention could be 6.4 kg/m3 (Townsend et al., 2004). American Wood Preservers Association (AWPA) has defined three CCA formulations and standard retention values for the treated wood under typical applications, for example, the standard retention of CCA increasing when the treated wood applied for above ground use, ground or fresh water contact, salt-water splash or wood foundation and heavy structural use, and salt-water immersion. Table 10 presents the concentrations of Cu, Cr and As in the CCA treated wood, which are estimated based on the CCA formulations and the standard retention values (i.e. specific applications). Table 10 Concentrations of Cu, Cr and As in CCA treated wood estimated based on standard retention and typical applications (source: Tolaymat et al., 2000) Standard retention

Cu

Cr

As

pcf*

mg/kg, dry wood**

mg/kg, dry wood**

mg/kg, dry wood**

0.25

1.119

1.871

1.680

0.40

1.791

2.994

2.143

0.60

2.687

4.491

3.214

2.50

11.196

18.711

13.393

Notes: * The retention rate of CCA solution in the wood after treatment in terms of pound per cubic food (pcf), ** the calculations are based on a southern pine wood dry density of 33 pcf.

25

Environmental concerns and regulations The potential hazards of treated wood depend on the toxic of preservatives, various service lifetime, and final disposal. Different types of biocides have been used in the wood perseveration associated with the time of legislative constraints. Environmental and health concerns on using inorganic arsenicals in wood preservation have been raised since the middle of the 1990s because the significant amount of arsenic trioxide (around 90% of total USA arsenic consumption) was used for the water-borne wood preservatives (Cheremisinoff and Rosenfeld, 2010). The environmental concerns with CCA caused the industry to voluntarily withdraw it for residential applications in North America at the end of 2003, but may still be used for industrial uses (Morrell, 2013; Barnes, 2016). The EU has restricted the use and marketing of timber treated with CCA and CCA chemicals since 2004 (Mohajerani et al., 2018). There are two main EU regulations concerning the environmental and health issues associated with biocides and construction materials. Biocidal Products Directive 98/8/EC (BPD), in which wood preservatives are classified as product-type 8 (PT8). BPD provides guidance and methods for the assessment of biocides to protect humans, animals and environment. Construction Products Regulation (Regulation (EU) 305/2011/EU), in which the emissions of dangerous substances (including some of the biocidal products) into the air, water and soils are regulated. Schiopu and Tiruta-Barna (2012) give a detailed description of EU regulations associated with the wood preservatives and their impacts on environment and health. Technology development and contamination trend The so-called new generation copper-based biocides (chromium and arsenic free) intend to replace the CCA (Robey et al., 2018). The alternative wood preservation systems use copper as the primary biocide with a smaller amount of carbon-based biocide to protect from copper-tolerant fungi, which include alkaline copper quaternary, e.g. ammoniacal copper quaternary (ACQ) and alkaline copper azole, e.g. copper boron azoles (CBA) (Morrell, 2017). Other copper-based biocides are organic co-biocides such as triazoles or Bis (N-cyclohexyl-diazenium-dioxy)-copper (Cu HDO) and complexing agent copper (Zhang, 1999; Lebow and Tippie, 2001; Temiz et al., 2006). Preservatives containing micro- or nano-sized copper particles (MCQ or MCA) have also been introduced into the market (Schiopu and Tiruta-Barna, 2012). Currently Cu compounds are the only biocides with a high efficiency against soft rot fungi and other soilborne fungi which damages wood products in contacting with soils (Hughes, 2004). Although Cu has

26

good properties as fungicide, it has minimal toxic effect on mammals (Lebow, 1996) and causes genetic perturbation or mutation (Civardi et al., 2015). Borate-based preservatives are more effective than copper- and zinc-based preservatives due to wide spectrum fungicidal and insecticidal action (Lloyd, 1998; Lloyd et al., 2001) and against copper tolerant fungi (Obanda et al., 2008). The borate-based preservatives could be applied for wood species that are difficult to treat by copper-based preservatives due to better solubility and mobility and providing long term protection for structural timber (Grace et al., 2006). However, the borates are limited using in nonground contact, i.e. non-leaching applications (AWPA, 2007). The key of using borates with the wide spectrum of wood preservation is to improve their permanence in wood, but to reduce their leachability (Obanda et al., 2008). Several formulations of borates have been developed in order to balance between leaching and mobility of borate preservatives, which include inorganic metal and borate combination (metallo-borates, such as zinc borate (ZB) and aqueous zirconium salt and borate), ammoniacal and amine metallo-borates such as the solutions of metallo-borates, and acetic acid or ammonia i.e. a cobiocide metal zinc and/or copper (metal oxide, carbonate or hydroxide). (Obanda et al., 2008). Another trend in wood preservation is to reduce heavy metals used for wood protection, which may result in a change from mixtures of inorganic heavy metals like CCA type preservatives to mixtures of water-borne organic biocides. As alternatives, some organic or totally carbon-based preservatives, such as isothiazolones and triazoles, have been developed to minimise the toxicity to nontarget organisms, e.g. humans (Morrell, 2013). However, many of these alternatives are currently too expensive in comparison with the metal preservatives and cannot provide similar protections especially in directly contacting with soils. Efforts have been made to reduce contamination of heavy metals through replacing CCA with less toxic metals (e.g. copper-based formulations), and to separate of preservative treated woods from other types of RW materials. As a result, the contamination of ACC in RW should be significantly reduced as indicated by the recent investigation (Robey et al., 2018). However, the release of CCA from the old preservative treated woods are expected to for many years due to their relatively long service time (e.g. over 50 years) and complicated recycling processes. Figure 4 shows the contamination variations of major heavy metals and metalloids in RW fuels in 7 years (from 2008 to 2015). It has been observed there was a relatively significant reduction in Pb and Zn. However, the CCA components (Cr, Cu and As) in the RW fuels seemed to be quite stable this period. This may indicate that the CCA contaminated wood materials

27

are still the important contamination source, and a significant reduction may not be expected with a relatively short time although there is a general trend on the reduction of toxic heavy metals.

Fig. 4. The concentration variations of major heavy metals and metalloids from 2008 to 2015 in the RW fuels based on this study.

3.3.2. Flame retardants and additives Since the 1970s, flame retardants and additives have been widely used due to significant changes in using synthetic polymer materials for various applications, and an increasing number of flammability regulations (Shaw et al., 2010; Kiliaris and Papaspyrides, 2014). In 2008, the world annual production of brominated flame retardants was approximately 410 000 metric tons, which was more than double in 8 years (from 2001 to 2008). The world annual production of chlorinated flame retardants in 2008 was estimated approximately 190 000 metric tons, which was more than double in 4 years (from 2005 to 2008) (Fink et al., 2008). Flame retardants are used to reduce flammability, delay combustion reactions, or low temperature through physical and/or chemical actions. There are more than 175 different flame retardants commercially used in the worldwide market (Birnbaum and, Staskal 2004). Flame retardants can be chemically classified into: halogenated hydrocarbons flame retardants (e.g. bromine and chlorine containing compounds), inorganic flame retardants (e.g. boron compounds, antimony oxides, aluminium hydroxide, molybdenum compounds, zinc and magnesium oxides), phosphorous containing flame retardants (e.g. organic phosphate esters, phosphates, halogenated phosphorus compounds and 28

inorganic phosphorus containing salts) (Flame Retardants Group, 2019). The major uses of flame retardants as chemicals and additives in polymeric materials (Isarov et al., 2011; Segev et al., 2009), building materials including insulation, electronic products, transportation, home furnishings (Shaw and Kannan, 2009, Brinbaum and Staskal, 2004), and coating on wood or other material surfaces (Shen and O’Connor, 2012; White and Dietenberger, 2010). Halogenated flame retardants are widely used as additives due to their low costs and high flame retardant efficiency (Brown and Cordner, 2010). Relatively high levels (concentrations) of flame retardant usage are common for many applications, for example, it could be up to 5% of the weight of polyurethane foam in furniture and baby products and 20% in weight of the plastic housings of electronic products (Cordner, 2016; Isarov et al., 2011; Shaw et al., 2010; Segev et al., 2009; Månsson et al., 2009; Chen and Shen, 2005). The high-level usage is one of major concerns on their environmental impacts, which also implies a significant impact on combustible waste fuels contaminated with flame retardants. Flame retardants are only effective at the initial stage of a fire with the actions through inhibition of vapour phase, dilution of the volatile compounds, promotion of char formation, creating a protective coating, removal of heat from combustion, and smoke suppression (Skinner, 2012). Different flame retardants or chemical additives may be specifically functioned for one or more actions depended on their formulations. Three approaches have been applied in providing different materials with fireproof properties including (1) mechanical incorporation of flame retardant additives into the bulk polymeric matrix, (2) binding matrix units chemically by using flame retardant segments that contain functional groups, and (3) surface modification such as fireproof coatings (Liang et al., 2013). Major metal elements used in commercial flame retardants are: antimony, iron, magnesium molybdenum, tin and zinc (Skinner, 2012), in which antimony, molybdenum, stannum and zinc belong to heavy metals and will be discussed here with focus on their major chemical compounds and impacts on the potential contaminations for the fuels in waste incineration and RW combustion. Metal elements or their compounds normally act as additives to interfere within one or more steps of the firing process. For example, antimony is common synergist added into halogenated flame retardants to increase the flame retardance efficiency. Antimony compounds include antimony trioxide (Sb2O3, ATO), pentoxide (Sb2O5) and sodium antimonate (NaSbO3), in which ATO has been most widely used in flame retardant formulations (USGS, 2018). ATO as a synergist together with halogenated compounds reduces the dosage of halogenated flame retardants, and enhances the flame retardance efficiency (Patterson and Parker, 2015). It has been 29

estimated that approximately 50% - 70% of the global antimony consumption is used in flame retardants (72% in European, near 60% in the U.S. and 50% in China) (Patterson and Parker, 2015). The flame retardancy mechanisms of antimony oxides have been found by forming halogenated antimony compounds, which exclude oxygen from the front of the flame. It is also believed that antimony assists the transfer of the hydrogen halide to the vapour phase by the transformation of antimony trihalide, and catalysing the recombination of hydrogen, oxygen and hydroxyl radicals to form water (Grand and Wilkie, 2000; Narayan and Moore, 2012; Skinner, 2012). Therefore, antimony oxide behaves as a condensed-phase flame retardant (Flame Retardants Group, 2019). Although there is no systemic measurement data available for antimony concentrations in associated products, it has been reported that antimony is normally present less than 0.2% in most products. Sometimes the concentrations could however reach 1-10% by weight in some product (e.g. PVC) (Klein et al., 2009; Månsson et al., 2009). A summary of antimony concentrations of some consumer products can be found in SM1 for different countries. The measurement data indicate that relatively high Sb concentrations could be found in some of the products when flame retardant was used. Zinc and tin compounds (e.g. zinc borate, zinc stannate and zinc hydroxy-stannate) as synergists, additives and fillers are used in flame retardants for pressure treatment of wood materials (White and Dietenberger 2010), flame retardancy of many polymeric materials, plastics, cellulose fibres, paper, rubbers, textiles, wood products and wood plastic composites (Segev et al., 2009; Stark et al., 2010) as well as fireproof coatings (Liang et al., 2013; Horrocks 2019). Zinc borate and stannate as synergists can reduce or replace ATO in halogen, non-halogen and phosphorus flame retardant systems, and add with smoke suppression (Shen and O’Connor, 2012; Horrocks et al., 2012a and 2012b; Üreyen, 2016). Zinc oxide (ZnO) can be complexed with molybdenum trioxide (MoO3) as zinc molybdate for flame retardance of cellulosic materials, other polymers and textiles, which are used in furniture, draperies, upholstery seating in vehicles, wall coverings, and carpets (NCS, 2000). Zinc borate as a boron-based flame retardant can be an effective charring promotor and smoke suppressant. Zinc borate in conjunction with metal hydroxides can further reduce heat release rate, smoke evolution and promote the formation of char residues (Chen and Shen, 2005). Zinc stannate for flame retardancy involves in the vapour phase by the formation of SnBr2 and SnBr4 volatile intermediates with some secondary condensed phase activity (Cusack, 1999).

30

Molybdenum compounds (e.g. molybdenum oxides, ammonium octa-molybdate, zinc molybdate, calcium molybdate, zinc molybdate/borate complex, zinc molybdate/magnesium hydroxide complex, etc.) are widely used as synergists and smoke suppressants in flame retardant formulations for flexible PVC and cellulosic materials (Isarov et al., 2011; 2012). Molybdenum trioxide (MoO3) could partially replace antimony trioxide in flame retardants (Brown, 2012), and is an effective smoke suppressant for natural or synthetic polymers to react with char residue and increase char stability (Ren et al., 2015). Molybdenum trioxide (MoO3) is a condensed phase flame retardant. The decomposition of molybdenum oxides forms non-volatile components as low-smoke flame retardant and improves the formation of char (Flame Retardants Group, 2019). Molybdates containing oxoanion with Mo in its highest oxidation state (+6) can also promote char formation which effectively removes fuel from the gas phase, therefore improve the retardancy of materials (Walker et al., 2008). Metal molybdates like zinc molybdate are also commonly used as smoke suppressants in flame retardants (Walker et al., 2010). Precipitation of molybdates onto the surface of inert minerals can significantly increase active surface, which can further improve the fire retardancy. Zinc molybdate combined with zinc borate can improve heat release (Isarov et al., 2011). Environmental concerns and regulations Recently environmental impacts on flame retardants became a hot topic (Cordner, 2016) due significantly increase in many types of flame retardancy materials and the global contamination of the corresponding chemicals to be associated with adverse health effects in animals and humans (Shaw et al., 2010). The major environmental concerns of the flame retardants are their chemical persistence and toxicity. Currently, more than 20 chemical components banned by the Stockholm Convention on Persistent Organic Pollutants (POPs), which are organohalogens with carbon bonded with bromine, chlorine or fluorine, and may be associated with halogenated flame retardants (Blum, 2012). However less attention has been paid on metal additives except for antimony. The release of hazardous components to environment could be complicated and through various pathways depending on the properties of the components, bending form with product matrix, and the ways of recycling or waste management (disposal) of the flame retardant containing materials at their end-life. Although the release of heavy metals from flame retardant containing materials has not been well investigated, the energy recovery from solid wastes, which are contaminated with flame retardants, could be an important emission source. In European, around 46% plastic wastes are commonly treated by energy recovery (Ragaert et al., 2017). In Sweden, plastic wastes have been increased about 30% from 557 000 31

tonnes (2010) to 1652 000 tonnes (2016/2017), and most of the plastic wastes (around 82%) were treated as combustible waste fuels for WtE (Nordin et al., 2019). Experimental data indicate that thermal decomposition of brominated flame retardants and or the retardant-laden polymers occur in a temperature range of 280 – 900 oC under oxidative and pyrolytic conditions (Altarawneh et al., 2019). The conditions are very similar to the combustion temperature profile in the furnace of waste incinerators used for energy recovery for municipal solid waste (EU JRC, 2018). As shown by Klein et al., (2009), the release of antimony compounds in thermal decomposition of halogen flame retardant involves several steps followed by temperature increase. For example, thermal decomposition of halogen flame retardants can occur below 300 oC, in which hydrogen halides (HX) are formed under oxidative conditions. The hydrogen halides could further react with solid antimony trioxide (Sb2O3) to form volatile antimony trihalides (SbX3) or oxyhalides. Under waste incineration (oxidation) conditions, volatilization of antimony is enhanced by increasing Cl or Br levels as temperature over 600 oC. The antimony could be more volatile in form of trichloride (SbCl3), and antimony could be roughly distributed between bottom ashes (64%) and flue gas (36%) during the incineration of halogen flame retardant containing wastes (Klein et al., 2009). It is expected that metals should mostly be released from the retardant, then presented in the residues or gas phase in waste incineration because the matrix of flame retardants could almost totally be decomposed at temperature > 800 oC for major brominated flame retardants (Altarawneh et al., 2019). There are several challenges associated with environmental issues for flame retardants, which may have significant changes in future formations of flame retardants and their applications. Although the potential adverse health and environmental impacts for some flame-retardant chemicals are not well recognised or fully understood (Shaw et al., 2010), some trends on the further development of flame retardants are expected in near future. Antimony as a toxic metalloid and priority pollutant (US ATSDR, 2017) has been globally concerned its impacts on environmental and health due to currently extensive mining and production activities and wide uses (He et al., 2019, Aly et al., 2012). Reducing antimony dosage or totally replace of antimony by other additives for new formulations of flame retardants have been performed for commercial development of modified or new flame retardants (Babushok et al., 2017; Liang et al., 2013; Narayan and Moore, 2012; Isarov et al., 2012; Brown, 2012; Cusack 2012; Walker et al., 2010). Halogen based flame retardants have been recently much concerned about their environmental persistence (e.g. POPs) and unknown toxicity. Several halogen flame retardants have been banned or voluntarily phased out (Shaw et al., 2012; Shaw and Kannan, 2009; Brinbaum and Staskal, 2004). Some of brominated flame retardants are being phased out and will no longer be 32

produced (Flame Retardants Group, 2019). Significant effort has been made on the development of less or halogen-free flame retardants, such as phosphorous flame retardants (Shi et al., 2018; Costes et al., 2017; Ren et al., 2015; Davis, 2012; Cusack, 2012; Isarov et al., 2011). Technology development and contamination trend In general, new flame retardants will be continuously developed with stricter environmental regulations. Contaminations of antimony and halogen compounds from flame retardants may be reduced in the coming years. However, it is not very clear how the retardant compositions will be changed with other metal additives such as zinc, tin and molybdenum compounds. A rapid increase of polymer/plastic wastes is expected due to increasing demand for polymeric materials to replace traditional materials in the future. The waste management and recycling of polymer (plastic) wastes will have big impacts on the heavy metal emissions from the flame retardants. Environmental concerns for incineration of household wastes will be significantly reduced if the plastic wastes could be separated from the household wastes for further recycling. The emissions of heavy metals contained in the flame retardants and dioxins are expected to be easily controlled and reduced best on the experience of the EU best available techniques on waste incineration (EU JRC, 2018). The material recovery could be combined with free fossil CO2 emission if the recycling of plastic wastes can be implemented with suitable processes, such as chemical recycling (Ragaert et al., 2017). In this study, the contamination levels of the heavy metals and metalloids, such as antimony, zinc and molybdenum (relevant for the metals presented in flame retardants), have been specifically compared between RW fuels and waste fuels. In the comparative evaluation, the waste fuels are divided into two groups. In the first group, all the samples are selected from MSW without any other solid waste. In the second group, the samples are the mixtures of MSW with 20-80% of ICW (i.e. MSW+ICW). It is identified the contamination levels of molybdenum in the three group fuels (RW, MSW and MSW+ICW) are very low (few mg/kg dry sample). However, the contamination levels of antimony and zinc are quite different in the three types of fuels. As shown in Figure 5, the antimony concentrations in RW fuels are very low. This may indicate that the RW fuels are less contaminated with antimony contained flame retardants. Relatively higher concentrations of antimony have been found in both MSW and MSW+ICW fuels, which may imply that the MSW and ICW fuels have been contaminated with antimony containing flame retardants. Furthermore, the antimony level in MSW+MIC fuels is higher than that in MSW fuels. Therefore, the ICW fuels could be more contaminated by the antimony containing flame retardants in 33

comparison with MSW fuels. The contamination levels of zinc show similar trend as antimony for the three fuels, although the concentration differences of zinc between RW and MSW are not very large in comparison with the concentration differences of antimony between the two fuels. Generally, the potentials of heavy metal contamination from f flame retardants for the free types of biomass fuels should be RW < MSW < ICM based on this study.

Fig. 5. Average concentrations of Sb and Zn in recycled wood (RW), municipal solid waste (MSW) and the mixtures of municipal solid waste and industrial & commercial waste (MSW+ICW) measured based on samples investigated based on this study.

3.3.3. Paints and coatings Paints and coatings on recycled and construction wood materials are important contamination sources of heavy metals for energy recovery from RW materials. Although the impacts of heavy metals are limited when they present in intact films, the heavy metals will be released from the paints or coatings when the wood materials are used as fuels for thermal energy conversion due to the thermal decomposition. Transition metals with two valency states can be intensely coloured, which are often used as inorganic pigments for paints and coating (Abel, 1999; Lambourne, 1999). Heavy metal compounds are also used as drying agents or drying catalysts to improve drying during painting or coating processes (Bentley, 1999; Kumar and Gottesfeld, 2008; Greimel et al., 2013). For example, cobalt is the most effective drier, followed by manganese, and lead and zinc are commonly used for both pigments and driers (Jeffs and 34

Jones, 1999). Also, metal compound can be used as additives to enhance the durability of paint films and reduce costs (Lambourne, 1999; Connor et al., 2018). Concentrations of heavy metals and metalloids in paints vary significantly with the types of paints, manufactories and even the paint products in different countries. Table 11 shows the heavy metals and metalloids determined from the paint samples, which were collected from urban and exterior and interior paints. Relatively higher levels of Pb, Cr, Sb, Cu, Cd, Zn, Co, Cd and Mn have been identified from these paints. The observations are consistent with the chemical formulas of inorganic pigments families as shown in Table 12. Table 11 Concentrations of heavy metals and metalloids (g/g) presented in paints based on available data sources Median [1]

[2]

As

1.4

Cd

0.4

27

Co

231

70

Cr

11

16

Cu

30

Mn

Min [3]

[1]

[2]

Max [3]

0.6 252

[1]

[2]

[3]

1

439

771

861

214

775

417

3.3

0.2

7

26

13

2

2

21

3

5

1080

667

63

70

9

24

181

309

Ni

5

19

2

4

112

114

Pb

205

35248

5

112

7720

256797

Sb

252

2180

39

273

1260

16000

Sn

3

2

39

Zn

334

39

4160

1040

451

40 26

10

24800

152000

Notes: Data sources [1] Turner and Sogo, 2012 (samples of paint fragments were collected the collected from a variety of structures of the urban environment of Plymouth, UK during October 2010); [2] Mielke et al., 2001. (exterior paint samples collected from New Orleans homes; [3] Turner et al., 2016 (paint samples collected from the surfaces of public playground structures in South West England). The metals and metalloids marked with red colour were identified with relatively higher concentrations.

It is also found that the heavy metals are enriched in certain colours of paints, for example, higher concentrations of Pb, Cr and Sb are generally identified from yellow and red paints (Turner et al., 2016), and high concentrations of Pb, Cd, Cr, Co and Ni occur green paints. Table 12 summaries the corresponding paint colours with given heavy metals in chemical formulas and determined from the 35

paints. The colours may indicate which types of heavy metals could be contamination sources of heavy metals from paints. Generally, the levels of Pb, Cr, Cu, Sb, and Zn in biomass fuels, which are recycled from used wood or construction materials, may be able to use the colours as indicators for heavy metal contamination from paint pigments and additives. For high-level Pb containing paints, association of Pb with Cr has been identified in several investigations (Shu et al., 2015; Turner et al., 2016), which indicates that lead chromates are major sources in these paint pigments. Correlation between Pb and Cd has also been identified for some paints (Zhao et al., 2018). Table 12 Colour of inorganic pigments and corresponding metals in chemical formulas and heavy metals identified with paint colour. Colour

White

Black Brown

Yellow

Metals

Chemical formula

Colour index

Titanium dioxide

TiO2

CI White 6

1, 3

White lead

2PbCO3Pb(OH)2

CI White 1

1, 3

Zinc oxide

ZnO

CI White 4

1, 3

Zinc sulphide

ZnS

CI White 7

1

Lithopone

ZnS/BaSO4

CI White 5

1

Antimony oxide

Sb2O3

CI White 11

1, 3

Manganese dioxide

MnO2

Metal complex

(Zn, Fe)(FeCr)2O4

CI Brown 33

1

Complex inorganic pigments

Ti(V) , Cr(III) and Sb oxides

CI Brown 24

1

Lead chromate

PbCrO4/PbSO4

CI Yellow 34

1, 2, 3, 4, 5

Cadmium yellow

CdS, cadmium sulphide, zinc sulphide

CI Yellow 37

1, 2, 3, 5, 7

Lithope type containing barium sulphate

CI Yellow 35

1

Titanium dioxide structure with another metal

CI Yellow 53

1, 5

Complex inorganic pigments

References

3

oxide trapped in the crystal lattice, e.g. (Ti/Sb/Ni)

Orange

Red

Blue

Bismuth vanadate

BiVO4/Bi2MoO6

CI Yellow 184

1, 2

Metal complexes

Ni

CI Yellow 153

1

Lead chromate

PbCrO4 xPb(OH)2

CI Orange 21

1

Metal complexes

Ni, Co or Cu

Molybdate red

Pb(Cr, S, Mo)O4,

CI Red 104

1, 2, 4, 5, 6, 7

Cadmium red

CdS/CdSe

CI Red 108

1, 2, 5, 6

Cobalt blue

Co(Al, Cr)2O4

CI Blue 36

1, 2, 3

1

Copper phthalocyanine

CI Blue 15 and 16

36

1

Green

Cobalt green

(Co, Ni, Zn)2 TiO4

CI Green 50

1, 2, 6

Chrome green

PbCrO4. x PbSO4. FeNH4Fe (CN)6.

CI Green 15

Chromium oxide green

Cr2O3 chromium sesquioxide.

CI Green 17

1, 2, 3, 5, 6

Hydrated chromium oxide

hydrated chromium oxide, Cr2O(OH)4.

CI Green 18

1, 5, 6

1, 4, 5, 6, 7

Notes on references: [1] Abel, 1999; [2] Bulian and Graystone, J., 2009; [3] Lambourne, 1999; [4] Kumar and Gottesfeld, 2008; [5] Turner et al., 2016; [6] Zhao et al., 2018; [7] Apanpa-Qasim et al., 2016.

Environmental concerns and regulations Recently, the impacts on environment and health are considered as the most important issues for the development of paints and coatings. The environmental concerns of heavy metals are lead, cadmium, cadmium and antimony due to their relatively high levels and health impacts (Guney and Zagury, 2008; Turner and Sogo, 2012; Clark et al., 2015; Apanpa-Qasim et al., 2016; Turner et al., 2016; Zhao et al., 2018). Since 1970, lead containing paints have been banned from manufacture, sale and used on the interiors and exteriors of homes, schools and commercial buildings in most developed countries (IEPEN, 2017). Lead chromate pigments have been prohibited from use in the EU before November 2013 (Shu et al., 2015). The United Nations Environment Programme (UNEP) and World Health Organisation (WTO) have established the Global Alliance to Eliminate Lead Paint (GAELP) with the task to globally phase out of lead-based paints by 2020 (UNEP/WHO, 2011). The two largest paint producers, Akzo Nobel and PPG were announced to remove lead from all paint products in 2011 and 2016 respectively. There is no health-based standard for lead concentration in paints in the EU. The prohibition of lead containing paints is mainly implemented through the regulations related to worker’s health and safety of consumer products, the regulatory limit of a maximum 90 ppm total lead concentration in dry weight basis has been set by the U.S., Canada, the Philippines, Nepal, India and Cameroon (Kumar and Gottesfeld, 2008; IPEN, 2017). In the EU, cadmium is prohibited to use for paints and most coating due to environment associated problems (Abel, 1999). To replace lead-based pigments, titanium (V) dioxide becomes the most important component of inorganic pigments to replace lead compounds (Lambourne, 1999; Abel, 1999). Although the lead-based compounds in paints are expected to be significantly reduced in the future, the lead and associated heavy metals used for paints may remain a global issue, especially in developing countries. Another driving force for the development of environmentally friendly paints and coatings is to reduce the emissions of volatile organic compounds (VOCs). Reducing VOcs from paints and coatings has 37

become an increasingly important issue due to air pollution, safety and health concerns for more than 20 years. The EU Solvents Directive (EU Directive, 2014) has set up limit values of VOCs in certain paints and varnishes. It is estimated that more than 95% of exterior wood coatings are applied as liquid coatings with organic solvents or water as carriers (solvent-borne or water-borne). Water borne paints constitute one of four main technologies to reduce the usage of VOCs in paints and coatings (Bentley, 1999), which has been used in the EU since the 1980s (Hsu et al., 2018). There is a continuously swing from solventborne to water-borne paints in the EU and world market. The market share of water borne paints is expected to further increase, especially for interior painting applications. The changes from solvent borne to water borne paints generally mean the use of more complex paints with more components (Meijer, 2001) because various binders are needed to form colloidal stabilised solid particles in water, and the formation of stable paint film is generally more complicated than the solvent borne paints. Although several investigations showed that toxic heavy metals (e.g. Pb, Cr, Cd) in water borne paints were significantly lower than that in solvent borne paints (Kumar and Gottesfeld, 2008; Hsu et al., 2018), high levels of lead and cadmium were still determined from some water-borne paints (Apanpa-Qasim et al., 2016). There is no clear trend showing how the changes in metal pigments with the development of water borne paints. It may be expected that toxic heavy metals could be continuously reduced in the water borne paints due to stricter regulations, but it may not result in significant changes for other metals. Technology development and contamination trend In this study, some relevant heavy metals (i.e. Pb, Cr, Cu and Zn as shown in Table 12 and SM1) are used to evaluate the contamination influence from paints. The contamination of heavy metals in RW fuels is compared with the concentration levels in paints in two ways. First, a comparison is performed with the samples of RW fuels collected from 2008 to 2015 to identify if there is influence from a general trend of reduction of Pb and Cr in paints during past two decades. As shown in Figure 6, the contents of Cr and Cu in RW fuels are generally higher than that in paints. This means that the paint may not be the main contamination sources of Cr and Cu for the RW fuels. The contents of Pb and Zn in RW fuels are in relevant levels in comparison with the contents in paints. However, it is difficult to show a clear reduction tread for both Pb and Zn from 2008 to 2015. The second comparison is performed for the concentration relations between Pb and Cr in RW fuels from 2008 to 2015. The correlations between Pb and Cr are shown in Figure 7 for three years (2009, 2012 and 38

2015). The comparison shows that a relatively clear correlation between Pb and Cr can be found in the low concentration range of the RW fuels in 2009, which may indicate that the paints could be the one of contamination sources for Pb as identified by the similar relations for the paints with high Pb content in several investigations (Shu et al., 2015; Turner et al., 2016; Zhao et al., 2018). However, the contamination sources of Pb became more complicated for the samples in 2012 and 2015. A general characteristic is that a higher Pb contamination do not correspond with higher Cr contamination. In other words, more complicated heavy metal contamination sources may be involved in recent years in comparison with 2008 and 2009 at least for Pb. In summary, the influence of heavy metal contamination from paints for RW fuels is limited in relatively low concentration range at least for toxic metals such as Pb, Cr and Cu. Therefore, the changes in current paint developments may only have limited influence on the heavy metal contamination in RW fuels.

Fig. 6. Comparison of heavy metal contents in RW fuels (from 2008 to 2015 measured by this study) with the heavy metal contents in paint fragments (* based on: Turner and Sogo, 2012 (samples of paint fragments were collected the collected from a variety of structures of the urban environment of Plymouth, UK during October 2010))

39

Fig. 7. Concentration correlations of Pb to Cr in recycled wood fuels based on three years comparisons, and the fuel data are used here are measured by this study as described in section 2.1.

4. Conclusions Comparative characterisation of heavy metals and metalloids in the contaminated RW and waste fuels indicates that major contamination components are generally affected by the similar waste streams. The differences in specific contaminants and contamination levels for the three biomass fuels could be resulted from various contamination sources. Currently, the contamination of heavy metals and metalloids is generally higher in the ICWs in comparison to the MSWs and the RW fuels, which is mainly due to more contamination sources to be involved. A conceptual development has demonstrated that the comparative contamination characterisation combined with the statistical analyses is a better way to get a valuable insight into the relations among

40

the major contaminants and potential contamination sources. The relations could be further identified in more effective way by using advanced data analyses supported though good biomass fuel database. The contamination trends are essentially affected by the changes in main contamination sources and driving forces for contamination mitigation. Four contamination sources have been evaluated including wood preservation, flame retardants and additives, paints and coatings, and waste electrical and electronic equipment (WEEE). The major driving forces to reduce the contamination of heavy metals and metalloids are environmental regulations, the strategy of waste management and material recovery. Generally, the toxic heavy metals and metalloids are expected to be significantly reduced due to stricter regulations, but there is no general trend for the reduction of other metals and metalloids because of the complicated changes in contamination sources and waste recycling streams in the near future. Acknowledgments The authors thank the Swedish Foundation for Strategic Environmental Research (Stiftelsen för miljöstrategisk forskning, Mistra) for their financial support (project name: Mistra TerraClean, project number: 2015/31).

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Supplementary materials SM1 Antimony concentrations in some consumer products (source: Patterson and Parker, 2015) Country

Product Cell phones

Media Shredder and digested samples Cotton batting Nonwoven modacrylic/visil

Mattress barrier/fabric

Modacrylic knit Coated foam/melamine Coated polycotton ticking/melamine

The U.S.

Carpet in elementary school

Carpet

Sb concentration (mg/kg) 860 - 1290

Note on flame retardant (FR) Brominated FR were absent

24000 (mean) 38000 (mean) 45000 (mean) 41000

FR used

27000 - 44000 (mean) 6

Flexible PVC, PET, ABS* and

15000 - 120000

polyurethanes Rubber and other

50000 - 300000

elastomers PVC insulation on

not specified

wires and cables

FR use not 30000

specified

Canvas, carpets, carpet padding,

70000

drapes, tenting Paper

Fabrics

Canada Plastics

50000 - 250000 not specified

2000 - 60000

Polyester

0.6 - 700

Poly-cotton blend

27000 - 38000

Polymers

80000 - 250000

PET

180 - 200

PP

20000

HDPE

35000 - 100000

53

FR used

FR use not specified

PVC cot (crib) mattress cover UK

Bedding

Cot (crib) mattress cover PU foam samples Printed circuit

Small electrical Switzerland

and electronic equipment waste materials

boards TV housings (wood) TV/PC housings (plastic)

Housing shredder residues

Germany

TV sets and monitors

Mixed WEEE

Recycling of

shredder residues

unsorted WEEE

Single housing samples Electrical and electronic equipment

Single TV set and monitor housings PCBs of IT equipment

230 - 31050

FR used

0.2 - 221

FR used

0.2 - 33

FR used

2100 ± 100

FR used

57 ± 1

FR used

16000 ± 5000

FR used

2000 - 18000

FR used

Around 4000

FR used

Around 3000

FR used

400 - 3500

Not specified

Packaging

287 ± 27

Not specified

Textiles

134 ± 29

Toy

Not specified

0.4 - 47

Sweden

Netherlands

Note: * ABS - Acrylonitrile-butadiene-styrene copolymer

Graphical Abstract (STOTEN-D-19-10679)

54

Tested ATO in PCBs FR use not specified FR use not specified

Highlights 55



Contamination of heavy metals and metalloids is investigated for the contaminated biomass and waste fuels used for waste to energy



Characterization of the contamination is performed by comparative evaluation



Specific features of the contamination are further analysed using statistical methods



Contamination trends are estimated by review and analysis of potential changes in major contamination sources

56