Sustainable Management of Bottom Ash and Municipal Sewage Sludge as a Source of Micronutrients for Biomass Production

Abstract

Sustainable waste management is one of the most serious global challenges today. Reusing waste materials can be an effective alternative to landfill, while recovering valuable nutrients. The purpose of this six-year field study was to investigate the potential of bottom ash from combustion of bituminous coal or biomass and municipal sewage sludge, and different doses of the waste mixtures, as a micronutrient source for plants. Yield, concentration, concentration index, uptake and simplified balance of the micronutrients (manganese, iron, molybdenum, cobalt, aluminium) in plant biomass were measured. Results showed that the wastes differently affected the parameters studied, which generally increased via treatment as follows: coal ash, biomass ash < coal or biomass ash mixtures with sewage sludge < sewage sludge. Irrespective of treatment, micronutrient recovery rate followed the following trend: Mn > Mo > Fe > Co > Al, from 0.32–25.82% for Mn to 0.04–0.28% for Al. For individual elements, recovery depended on waste. For Mn, Fe and Al, the application of ash separately or in mixtures with sludge at higher doses reduced recovery (0.04–0.78%). For Mn, Fe, Al and Mo, the application of ash–sludge mixtures at lower doses increased recovery (0.11–5.82%), with the highest recoveries when sludge was used separately (0.28–25.82%). For Co, the separate application of sewage sludge and ash–sludge mixture at the lower dose increased recovery (2.41–2.52%), with the highest Co recovery following the separate application of coal ash (2.78%). Ash, sludge and their mixtures were a valuable source of micronutrients for plants. Ash–sludge mixtures improved micronutrient uptake compared to ash used separately. Application of these wastes as fertilisers aligns with the EU Action Plan on the Circular Economy and can contribute to achieving SDGs 2 and 12.

1. Introduction

Progressive economic development and population growth are creating increasing demand for basic resources such as water, food and energy. Meeting the expectations of today’s society drives the market to produce a wider range of products in large quantities. These activities generate a variety of mineral and organic wastes, which are subsequently deposited in landfills and pose a serious threat to the environment. In the face of growing concern about the state and degradation of the environment, there is a drive to achieve sustainable development that promotes the concept of a closed loop. According to this concept, the value of products, materials and resources is maintained for as long as possible and the generation of waste is minimised [1,2]. One of the other conditions for a sustainable economy is the recycling of nutrients from waste for plants [3,4]. In this context, alternatives are being sought for the management and reuse of waste materials and for the recovery of the valuable macro- and micronutrients they contain.

The appropriate choice of waste as fertiliser can help to reduce mineral fertiliser use, while reducing the exploitation of fossil resources. Specifically, with rising prices for natural gas and fertilisers, and CO₂ emission allowances, waste recycling is a viable option for environmentally and economically sustainable production for the fertiliser industry [5]. Particularly, many types of waste have significant fertiliser potential [6]. Combining different wastes, such as bottom ash from coal or biomass combustion and municipal sewage sludge, we obtain organic-mineral fertilisers [7]. Ash is a source of K, P and Mg and may be used as a soil improver in agriculture [8]. Moreover, the alkalinising properties of ash allow its use on acidic soils [9]. Municipal sewage sludge also has a high fertilising potential and can provide the soil with macro- and micronutrients [10] that are not commonly found in standard mineral fertilisers containing N, P and K [11]. However, the composition of mixtures of wastes converted into fertilisers must meet appropriate environmental requirements in order to minimise the risk of introducing various types of pollution into the soil [5]. In addition, in order to be used as fertiliser, the waste, either in its raw or processed form, must comply with the applicable legal requirements [12].

Types of ash generated in the process of coal or biomass combustion, as well as the by-product of wastewater treatment in the form of sewage sludge, are an important group of secondary raw materials that can be returned to the soil as fertilisers [1]. Rational use of these materials in agriculture can help to reduce the need for mineral fertilisers, thereby minimising the loss of scarce and valuable elements [1]. Manganese (Mn), iron (Fe), molybdenum (Mo) and cobalt (Co) are essential plant micronutrients, but they are only required in very small quantities and the margin between their deficiency and toxicity is generally narrow [13]. Manganese is involved in photosynthesis and plays an important role as an antioxidant cofactor for enzymes that influence root growth [14,15]. Manganese can act antagonistically (e.g., manganese and iron) [16] or synergistically (e.g., manganese and zinc) [17]. Iron plays a significant role in various physiological and biochemical processes in plants. Iron is an indispensable element in metabolic processes, respiration and photosynthesis [18]. The element is the third most limiting nutrient for plant growth and metabolism. Iron deficiency leads to nutritional disorders in many plants, resulting in poor yields and lower nutrition quality [19]. In plants, molybdenum plays a role in nitrogen metabolism as a component of enzymes and is involved in sulphur metabolism, the biosynthesis of plant hormones and catabolism of purine compounds [20,21]. Cobalt facilitates the metabolic and growth processes of plants. It plays an important role in nitrogen fixation in papilionaceous plants, as it is contained in the coenzyme of nodule bacteria [22,23]. Cobalt is also a catalyst in many enzymatic processes [24]. In high concentrations, Co acts as a contaminant in soil, which has a negative effect on plant growth [25]. Moreover, high Co concentrations can suppress the uptake of Fe, Ca, Mg and Mn [26]. Another important micronutrient is aluminium. Although it is not considered an essential element for plants, at low concentrations it can increase plant growth or have other desirable effects. On the other hand, Al toxicity is a potential factor in inhibiting plant growth, especially for those grown in acidic soils, which in turn limits agricultural production [13,27,28,29]. Excess Al can also lead to calcium and iron deficiency symptoms in plants.

The issue of the uptake by plants of the micronutrients contained in waste materials has received much attention in previous research. For instance, Xu et al. [30] carried out a 67-day greenhouse experiment to evaluate the feasibility of using sewage sludge and coal fly ash mixture in the cultivation of Manila grass in terms of the transfer and transformation of selected metals, including Mn, Fe and Co. The authors suggested that the wastes studied could be used in horticulture, but pointed out the need for further in-depth investigations considering the long-term effects of repeated applications of the wastes. In a 65-day greenhouse pot experiment with sorghum–Sudan grass (Sorghum vulgaris var. Sudanese hitche), Sivapatham et al. [31] investigated sewage sludge as a potential soil amendment and source of macronutrients and trace elements, including Mn and Fe. Esperschuetz et al. [32] studied the effects of untreated pond sludge on plant quality and the chemical composition of sorghum, rapeseed and ryegrass in a 5-month greenhouse experiment on a low fertility soil amended with 50 Mg·ha⁻¹ of sludge, measuring i.a. biomass yield and Mn and Mo concentrations. Eid and co-authors conducted a series of 40 to 120-day greenhouse experiments, to measure, among others things, the growth, yield, and Fe, Mn, Co and Al uptake of spinach (Spinacia oleracea L.) [33], cucumber (Cucumis sativus L.) [34], wheat (Triticum aestivum L.) [35], rocket (Eruca sativa L.) [36], and tomato (Solanum lycopersicum L.) [37] grown in soils amended with different rates (0–50 g·kg⁻¹) of sewage sludge to evaluate the suitability of the sludge as a fertiliser for growing these crops.

Despite the evident interest in the use of sewage sludge and ash as potential fertilisers, there is a lack of long-term studies under field conditions focusing on the micronutrients supplied from these wastes and from their mixtures. Therefore, this paper aims to investigate the efficacy of application of municipal sewage sludge and bottom ash from bituminous coal or biomass combustion as a source of micronutrients (Mn, Fe, Mo, Co and Al) for plants in a six-year field study. We hypothesised that the combination of sewage sludge and ash, due to their complementary characteristics, would give better results in the recovery of micronutrients than separate application of the waste. To verify the hypothesis, we measured yield, concentration, concentration index, uptake and simplified balance of Mn, Fe, Mo, Co and Al in the plant biomass.

2. Materials and Methods

2.1. Study Area

Research on plant uptake of micronutrients from bottom ash and municipal sewage sludge was conducted in 2013–2018, based on a 6-year field experiment. The present work is a continuation of the research on the management of bottom ash and sewage sludge, which investigated the use of these wastes as a source of macro-elements [8] and as a potential cause of environmental pollution by heavy metals [38]. The field experiment was set up in 2013 in the southern part of Poland (Mydlniki; 50°05′03.9″ N 19°51′14.4″ E) on an experimental field of the University of Agriculture in Krakow, on Eutric Cambisol (Loamic) [39]. The area is located in a continental climate with warm summer and no dry season (Dfb) [40]. The average annual air temperature is 8.9 °C and the annual precipitation is 673 mm according to the 1991–2020 Climate Normals in Poland. From 2013 to 2018, the average air temperature was 8.7 °C, 9.8 °C, 10.0 °C, 9.4 °C, 9.1 °C and 10.0 °C, respectively, and the annual precipitation was 643.9 mm, 626.9 mm, 551.3 mm, 745.3 mm, 702.3 mm and 568.7 mm, respectively (data source: Institute of Meteorology and Water Management—National Research Institute, Poland; the data has been processed). A detailed description of the experiment and the properties of soil, bottom ash and sewage sludge are presented in our previous paper [8]. Briefly, the soil was ploughed 25 cm deep and levelled with a cultivator and a harrow. Then, the one-factor experiment was set up in a randomized block design, in four replications. The area of each plot was 6 m², and the spacing between the plots was 0.5 m. The experiment included eight treatments: one control and seven treatments with bottom ash from the combustion of bituminous coal or biomass, municipal sewage sludge and mixtures of the wastes, symbolised as in our earlier work (

Table 1. Combinations and doses of waste applied in the field experiment.

Treatment		Waste Doses, Mg∙ha−1 DM
Symbol	Description	Bituminous Coal Ash	Biomass Ash	Municipal Sewage Sludge
Ct	Control	–	–	–
AC	Ash from combustion of bituminous coal	50	–	–
AB	Ash from combustion of biomass	–	50	–
MSS	Municipal sewage sludge	–	–	50
AC+MSS I	1:1 mixture of bituminous coal ash and municipal sewage sludge, 50 Mg·ha−1 DM	25	–	25
AC+MSS II	1:1 mixture of bituminous coal ash and municipal sewage sludge, 100 Mg·ha−1 DM	50	–	50
AB+MSS I	1:1 mixture of biomass ash and municipal sewage sludge, 50 Mg·ha−1 DM	–	25	25
AB+MSS II	1:1 mixture of biomass ash and municipal sewage sludge, 100 Mg·ha−1 DM	–	50	50

). The wastes were applied once in spring of 2013 and then mixed with the upper soil layer. After two weeks, the mixture of grasses and legumes (4:1, w/w) was sown manually at a rate of 30 kg∙ha⁻¹: Dactylis glomerata L. (cock’s-foot, 10%); Festuca pratensis Huds. (meadow fescue, 10%); Festuca arundinacea Schreb. (tall fescue, 5%); Festuca rubra L. (red fescue, 20%); Lolium perenne L. (perennial ryegrass, 15%); Lolium multiflorum Lam. (annual ryegrass, 5%); Poa pratensis L. (common meadow grass, 15%); Trifolium repens L. (white clover, 20%).

2.2. Sampling and Analysis Methods

The plants were mowed three times in the first growing year (2013), and four times in the second and subsequent growing years (2014–2018) as described in [8,38]. Specifically, the first cut was made at the stage of earing of the dominant grass species and the subsequent cuts after 6–8 weeks. The collected fresh plant material was dried at 75 °C to a constant weight and weighed to determine the yield (Mg·ha⁻¹ DM). After each harvest, samples of approximately 20 g DM of plant material from each plot were taken for chemical analysis. Bulk soil samples of 1 kg in total were collected from the 0–20 cm layer using an Edelman auger before the application of ash and sewage sludge.

The concentration of micronutrients (Mn, Fe, Mo, Co and Al) was assessed in plant biomass, soil and waste. The plant material was ashed at 450 °C for 8 h and the ash was dissolved in HNO₃ (1:2). Similarly, the soil, bottom ash and sewage sludge samples were first incinerated and then digested in a mixture (3:2, v/v) of 70% HClO₄ and 65% HNO₃. Subsequently, all the filtrates obtained were analysed using a PerkinElmer Optima 7300 DV atomic emission spectrometer (Shelton, CT, USA) [41]. The results are presented in

Table 2. Initial concentration of micronutrients in soil and wastes applied in the field experiment (mg·kg⁻¹ DM). Values are arithmetical means ± one standard deviation.

Micronutrient	Soil	Ash from Combustion of Bituminous Coal	Ash from Combustion of Biomass	Municipal Sewage Sludge
Manganese (Mn)	139.1 ± 4.1	6475 ± 88	5187 ± 191	259.9 ± 13.3
Iron (Fe)	12,659 ± 49	19,125 ± 127	18,565 ± 111	1500 ± 49
Molybdenum (Mo)	0.65 ± 0.06	4.31 ± 0.19	6.41 ± 0.18	5.07 ± 0.24
Cobalt (Co)	4.28 ± 0.29	3.58 ± 0.10	7.12 ± 0.04	7.21 ± 0.12
Aluminium (Al)	11,536 ± 326	28,116 ± 412	24,977 ± 658	7718 ± 492

.

During the chemical analyses, spectrally pure reagents and Aldrich standard solutions were utilised. Measurements were taken in three replicates. The accuracy of the analytical methods was verified based on certified reference materials and standard solutions: IAEA-V-10 Hay Powder (International Atomic Energy Agency, Vienna, Austria), ERMCD281 Rye Grass (Institute for Reference Materials and Measurements, Geel, Belgium) and CRM023-050 Trace Metals–Sandy Loam 7 (RT Corporation, Laramie, WY, USA).

Physical and chemical properties of the soil, bottom ash and sewage sludge were previously described by Antonkiewicz et al. [8,38] in papers regarding macronutrient uptake and phytoextraction of heavy metals after application of bottom ash and municipal sewage sludge and their mixtures. For convenience, some of the parameters are presented in

Table 3. Basic physicochemical properties of soil and waste applied in field experiment [8]. Values are arithmetical means ± one standard deviation.

Parameter	Soil	Ash from Combustion of Bituminous Coal	Ash from Combustion of Biomass	Municipal Sewage Sludge
Dry matter, DM, %	–	78.4 ± 1.7	75.3 ± 2.5	23.3 ± 0.8
pHH2O	7.02 ± 0.04	12.30 ± 0.23	10.20 ± 0.38	7.12 ± 0.20
pHKCl	6.52 ± 0.09	11.60 ± 0.11	9.89 ± 0.16	6.98 ± 0.17
Total organic carbon, TOC, g∙kg−1 DM	10.24 ± 1.83	2.31 ± 0.17	6.96 ± 0.10	269.0 ± 25.2

.

2.3. Calculations

The following parameters were calculated for micronutrients:

(a)

Dry matter yield (Y, Mg·ha⁻¹ DM), calculated as a sum for each year of the study period, 2013–2018.

(b)

Concentrations of micronutrients Mn, Fe, Mo, Co and Al in the plant biomass, calculated as a mean for each year of the study period (X, mg·kg⁻¹ DM).

(c)

Mass ratio for Fe/Mn in the plant biomass, calculated from the respective mean concentration of the micronutrients over the entire study period.

(d)

Concentration index (CI) of micronutrients in the plant biomass, calculated as the ratio of the concentration of a given metal in the waste-applied plots to the metal concentration in the plants grown in the control for each year of the study period.

(e)

Uptake of each micronutrient (U = Y × X, g·ha⁻¹), calculated annually and summed up over the entire study period.

(f)

Simplified balance (B, g·ha⁻¹ DM) of micronutrients over the entire study period, calculated from the difference between the amounts of elements introduced with the waste (input, I, g·ha⁻¹ DM) and the micronutrients taken up (U) with the plant yield, B = I − U. The recovery rate of micronutrients (R, %) was the percentage of the micronutrients’ uptake in relation to the amount introduced into the soil with the waste. The R was calculated as follows: R = (U/I) × 100.

2.4. Statistical Analysis

The parameters obtained were tested for homogeneity and normality. To check the homogeneity of the variances, the residuals versus fit plot was used. The normality assumption was checked using a normality plot of the residuals, in which the quantiles of the residuals were plotted against the quantiles of the normal distribution. The data that did not fulfil the normality criteria were log-transformed. Specifically, the natural log transformation was performed for the concentration (X), the index of concentration (CI) and the uptake (U) of Mn and Fe.

A two-way ANOVA (factors: year, treatment) followed by Tukey’s LSD test were used to examine differences between the yield (Y), X, CI and U values (p < 0.05) in four replicates (n_Rp = 4) for each treatment (n_Tr = 8) and year (n_Y = 6). A one-way ANOVA (factor: treatment) followed by Tukey’s LSD test were used to analyse differences between the calculated micronutrient ratios (p < 0.05) in 24 replicates (n = n_Rp × n_Y = 4 × 6) for each treatment (n_Tr = 8). The Pearson’s coefficient of linear correlation r was calculated for the selected parameters using means for each plot over the entire study period for micronutrient concentration, total uptake, total input and mean yield (p < 0.05; number of plots, n_Plt = n_Rp × n_Tr = 4 × 8 = 32). Taking these assumptions into account, the minimal value of r which was statistically significant in the two-tailed test was |0.349|. The coefficients of variation (CV, %) were also calculated for selected parameters as the ratio of the standard deviation to the mean. The statistical analyses were conducted using Microsoft Excel and the R software environment (R 4.3.3 for Windows) [42].

3. Results

3.1. Plant Yield

The yield varied in the years studied (

Table A1. Results of two-way ANOVA (factors: year, treatment) for plant yield (Mg∙ha⁻¹ DM). Values are arithmetical means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	4.10 ± 0.12	6.85 ± 0.74	6.98 ± 0.52	6.54 ± 0.39	7.05 ± 0.21	6.62 ± 0.26
	vw	jklmno	hijklm	nopq	fghijk	mnop
AC	3.88 ± 0.11	5.92 ± 0.44	6.14 ± 0.23	6.07 ± 0.25	6.32 ± 0.38	6.14 ± 0.19
	w	s	rs	s	pqrs	qrs
AB	4.16 ± 0.23	6.61 ± 0.34	6.64 ± 0.23	6.57 ± 0.12	6.84 ± 0.19	6.52 ± 0.17
	vw	mnop	lmnop	nop	klmno	opqr
MSS	4.32 ± 0.14	7.38 ± 0.21	7.26 ± 0.53	6.85 ± 0.12	7.75 ± 0.33	7.09 ± 0.19
	uv	defgh	efghi	jklmno	bcd	fghijk
AC+MSS I	4.57 ± 0.07	7.27 ± 0.29	7.59 ± 0.29	6.55 ± 0.22	7.67 ± 0.21	7.40 ± 0.19
	tu	efghi	cde	nop	bcd	defg
AC+MSS II	4.58 ± 0.09	7.24 ± 0.27	7.45 ± 0.20	7.03 ± 0.31	7.76 ± 0.13	6.89 ± 0.16
	tu	efghij	cdef	ghijkl	bcd	ijklmno
AB+MSS I	4.85 ± 0.08	8.01 ± 0.38	8.02 ± 0.10	7.41 ± 0.33	8.29 ± 0.15	7.39 ± 0.24
	t	ab	ab	defg	a	defg
AB+MSS II	4.82 ± 0.06	7.75 ± 0.20	7.84 ± 0.19	6.85 ± 0.25	7.76 ± 0.53	6.93 ± 0.37
	t	bcd	bc	jklmno	bcd	ijklmn

). The lowest yield, regardless of the treatment, was recorded in 2013 (3.88–4.85 Mg·ha⁻¹ DM) due to three mowings. As mentioned earlier, between 2014 and 2018 four mowings were carried out each year. Under the application of mineral waste, i.e., coal ash (AC) or biomass ash (AB), there were no statistical differences in yield during the study years 2014–2018 and the yield was in the range 5.92–6.84 Mg·ha⁻¹ DM. For the other treatments, a general trend was found that yields were similar in 2014, 2015 and 2017 (6.85–8.29 Mg·ha⁻¹ DM) and statistically higher than in 2016 and 2018 (6.54–7.40 Mg·ha⁻¹ DM).

In 2013, there was the least effect of treatment type on yield, i.e., separate application of AC, AB and municipal sewage sludge (MSS) did not differ from the control (Ct); MSS in combination with AC or AB equally increased yield compared to Ct, AC and AB. Between 2014 and 2018, the effect of the different treatments was more pronounced. AC significantly reduced yield, whereas AB did not significantly affect yield compared to Ct. The MSS treatment increased the yield compared to Ct in 2014, 2017 and 2018, and made no difference in 2015 and 2016 (Table A1). The application of mixtures of bottom ash with sewage sludge increased plant yield. The mixtures of biomass ash and sewage sludge (AB+MSS) were more yield enhancing than the mixtures of coal ash and sewage sludge (AC+MSS), except in 2015 for the higher dose of sludge mixtures and in 2018 for both doses. From the point of view of extraction and accumulation potential, the highest plant biomass yield was obtained in the AB+MSS I plot, where 25 Mg·ha⁻¹ DM each of biomass ash and sewage sludge were applied.

3.2. Concentration in Plant Biomass and Uptake by Plants of Micronutrients

Wastes such as ash from coal and biomass combustion and municipal sewage sludge can be potential sources of micronutrients for plants. The present study showed that the concentration (X) of Mn, Fe, Mo, Co and Al in the tested plants depended on the type and dose of waste applied separately or in mixtures and on the year of the study (Figure 1,

Table A2. Results of two-way ANOVA (factors: year, treatment) for concentration of Mn in plant biomass (X, mg·kg⁻¹ DM). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	219.93 × 1.05 ±1	197.12 × 1.10 ±1	166.37 × 1.08 ±1	181.29 × 1.11 ±1	152.80 × 1.06 ±1	129.57 × 1.12 ±1
	a	b	d	c	e	f
AC	35.19 × 1.10 ±1	39.90 × 1.12 ±1	29.29 × 1.19 ±1	28.55 × 1.16 ±1	26.74 × 1.18 ±1	21.03 × 1.09 ±1
	tuv	rs	wxy	xyz	zD	F
AB	44.97 × 1.08 ±1	44.65 × 1.09 ±1	35.85 × 1.09 ±1	31.03 × 1.12 ±1	29.85 × 1.14 ±1	24.02 × 1.10 ±1
	pq	pq	tuv	w	wx	E
MSS	94.57 × 1.06 ±1	87.16 × 1.09 ±1	77.14 × 1.09 ±1	78.27 × 1.06 ±1	83.31 × 1.08 ±1	75.86 × 1.11 ±1
	g	h	j	ij	hi	j
AC+MSS I	56.33 × 1.10 ±1	45.32 × 1.17 ±1	42.42 × 1.10 ±1	34.23 × 1.07 ±1	31.16 × 1.16 ±1	25.34 × 1.10 ±1
	lm	pq	qr	uv	w	DE
AC+MSS II	60.89 × 1.08 ±1	49.52 × 1.14 ±1	45.02 × 1.07 ±1	36.36 × 1.09 ±1	33.54 × 1.11 ±1	27.75 × 1.08 ±1
	l	o	pq	tu	v	yz
AB+MSS I	68.96 × 1.07 ±1	53.91 × 1.11 ±1	47.48 × 1.09 ±1	40.57 × 1.11 ±1	40.30 × 1.09 ±1	35.56 × 1.15 ±1
	k	mn	op	r	rs	tuv
AB+MSS II	74.08 × 1.05 ±1	57.27 × 1.08 ±1	50.84 × 1.08 ±1	42.77 × 1.09 ±1	42.55 × 1.07 ±1	37.75 × 1.11 ±1
	jk	lm	no	qr	qr	st

,

Table A3. Results of two-way ANOVA (factors: year, treatment) for concentration of Fe in plant biomass (X, mg·kg⁻¹ DM). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	241.52 × 1.06 ±1	208.10 × 1.09 ±1	176.40 × 1.08 ±1	192.86 × 1.10 ±1	173.21 × 1.08 ±1	146.58 × 1.10 ±1
	a	b	d	c	d	e
AC	42.89 × 1.07 ±1	43.66 × 1.05 ±1	41.75 × 1.07 ±1	40.34 × 1.04 ±1	40.42 × 1.10 ±1	40.08 × 1.07 ±1
	wx	vwx	xy	y	y	y
AB	49.22 × 1.05 ±1	52.89 × 1.03 ±1	49.41 × 1.10 ±1	51.90 × 1.04 ±1	48.36 × 1.09 ±1	49.50 × 1.08 ±1
	qrs	mno	qrs	nop	rst	qrs
MSS	116.46 × 1.07 ±1	100.02 × 1.11 ±1	88.93 × 1.11 ±1	89.54 × 1.06 ±1	93.96 × 1.05 ±1	88.80 × 1.10 ±1
	f	g	i	i	h	i
AC+MSS I	45.28 × 1.04 ±1	46.64 × 1.03 ±1	44.74 × 1.06 ±1	44.19 × 1.05 ±1	43.38 × 1.10 ±1	44.28 × 1.06 ±1
	uv	tu	uvw	vw	vwx	vw
AC+MSS II	50.12 × 1.03 ±1	50.46 × 1.05 ±1	47.84 × 1.04 ±1	48.47 × 1.05 ±1	47.67 × 1.08 ±1	46.62 × 1.05 ±1
	pqrs	pqr	st	rst	st	tu
AB+MSS I	51.54 × 1.05 ±1	55.78 × 1.03 ±1	51.44 × 1.09 ±1	54.25 × 1.03 ±1	51.70 × 1.06 ±1	52.54 × 1.05 ±1
	opq	l	opq	lmn	opq	nop
AB+MSS II	56.26 × 1.10 ±1	60.48 × 1.04 ±1	55.84 × 1.04 ±1	58.49 × 1.06 ±1	55.27 × 1.06 ±1	55.54 × 1.06 ±1
	kl	j	kl	jk	lm	l

,

Table A4. Results of two-way ANOVA (factors: year, treatment) for concentration of Mo in plant biomass (X, mg·kg⁻¹ DM). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year						Mean
	2013	2014	2015	2016	2017	2018
Ct	0.30 ± 0.03	0.30 ± 0.03	0.33 ± 0.03	0.31 ± 0.03	0.32 ± 0.03	0.31 ± 0.04	0.31 ± 0.03
							e
AC	0.25 ± 0.06	0.32 ± 0.02	0.34 ± 0.02	0.30 ± 0.02	0.31 ± 0.02	0.31 ± 0.04	0.31 ± 0.04
							e
AB	0.30 ± 0.06	0.35 ± 0.03	0.38 ± 0.03	0.34 ± 0.02	0.35 ± 0.02	0.36 ± 0.03	0.35 ± 0.04
							c
MSS	0.33 ± 0.05	0.39 ± 0.02	0.41 ± 0.03	0.38 ± 0.03	0.38 ± 0.02	0.39 ± 0.03	0.38 ± 0.04
							a
AC+MSS I	0.27 ± 0.06	0.34 ± 0.01	0.36 ± 0.03	0.33 ± 0.02	0.34 ± 0.02	0.33 ± 0.02	0.33 ± 0.04
							d
AC+MSS II	0.29 ± 0.05	0.36 ± 0.01	0.37 ± 0.03	0.34 ± 0.02	0.35 ± 0.02	0.35 ± 0.03	0.35 ± 0.04
							c
AB+MSS I	0.32 ± 0.06	0.37 ± 0.02	0.39 ± 0.03	0.35 ± 0.02	0.36 ± 0.02	0.36 ± 0.02	0.36 ± 0.04
							b
AB+MSS II	0.34 ± 0.05	0.39 ± 0.02	0.40 ± 0.02	0.37 ± 0.02	0.37 ± 0.01	0.37 ± 0.02	0.38 ± 0.03
							a
Mean	0.30 ± 0.06	0.35 ± 0.03	0.37 ± 0.04	0.34 ± 0.03	0.35 ± 0.03	0.35 ± 0.04
	G	E	D	F	E	E

,

Table A5. Results of two-way ANOVA (factors: year, treatment) for concentration of Co in plant biomass (X, mg·kg⁻¹ DM). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year						Mean
	2013	2014	2015	2016	2017	2018
Ct	0.27 ± 0.03	0.27 ± 0.02	0.29 ± 0.03	0.27 ± 0.02	0.28 ± 0.03	0.27 ± 0.04	0.28 ± 0.03
							a
AC	0.14 ± 0.02	0.15 ± 0.02	0.17 ± 0.03	0.13 ± 0.02	0.14 ± 0.02	0.14 ± 0.04	0.14 ± 0.03
							f
AB	0.19 ± 0.02	0.18 ± 0.02	0.21 ± 0.03	0.16 ± 0.02	0.18 ± 0.02	0.19 ± 0.03	0.18 ± 0.03
							d
MSS	0.22 ± 0.02	0.21 ± 0.02	0.23 ± 0.03	0.21 ± 0.03	0.21 ± 0.02	0.21 ± 0.03	0.22 ± 0.03
							b
AC+MSS I	0.16 ± 0.02	0.17 ± 0.01	0.18 ± 0.03	0.16 ± 0.02	0.17 ± 0.02	0.16 ± 0.02	0.17 ± 0.02
							e
AC+MSS II	0.18 ± 0.02	0.18 ± 0.02	0.19 ± 0.03	0.17 ± 0.02	0.18 ± 0.02	0.18 ± 0.03	0.18 ± 0.02
							d
AB+MSS I	0.20 ± 0.02	0.20 ± 0.01	0.21 ± 0.03	0.18 ± 0.02	0.19 ± 0.02	0.19 ± 0.04	0.20 ± 0.02
							c
AB+MSS II	0.22 ± 0.01	0.21 ± 0.01	0.23 ± 0.03	0.20 ± 0.02	0.20 ± 0.01	0.20 ± 0.02	0.21 ± 0.02
							b
Mean	0.20 ± 0.04	0.20 ± 0.04	0.21 ± 0.05	0.19 ± 0.05	0.20 ± 0.04	0.19 ± 0.05
	E	E	D	E	E	E

and

Table A6. Results of two-way ANOVA (factors: year, treatment) for concentration of Al in plant biomass (X, mg·kg⁻¹ DM). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	27.42 ± 2.99	23.41 ± 2.25	22.33 ± 2.52	24.02 ± 1.82	22.26 ± 1.83	24.56 ± 1.61
	b	ijk	klmn	ghij	klmn	fgh
AC	16.22 ± 1.58	16.09 ± 1.17	15.12 ± 1.71	16.01 ± 1.71	16.42 ± 1.13	15.50 ± 1.73
	rs	rs	s	rs	r	rs
AB	13.27 ± 1.23	12.57 ± 1.23	12.40 ± 1.10	12.61 ± 2.01	13.06 ± 1.65	12.77 ± 1.56
	t	t	t	t	t	t
MSS	29.19 ± 2.15	25.58 ± 1.77	25.77 ± 1.87	27.38 ± 2.00	26.05 ± 1.61	27.01 ± 1.77
	a	def	de	b	cd	bc
AC+MSS I	23.48 ± 1.83	22.52 ± 1.90	23.21 ± 1.45	23.82 ± 1.41	23.85 ± 1.21	24.17 ± 2.16
	hijk	klm	ijkl	ghij	ghij	ghij
AC+MSS II	24.20 ± 1.12	23.03 ± 1.94	23.94 ± 1.31	24.23 ± 1.59	24.81 ± 1.34	24.77 ± 1.83
	ghij	jkl	ghij	ghi	efg	efg
AB+MSS I	20.63 ± 1.37	19.85 ± 1.47	21.63 ± 1.41	21.05 ± 1.35	21.21 ± 1.13	21.31 ± 1.45
	pq	q	mnop	op	nop	nop
AB+MSS II	22.21 ± 1.08	20.61 ± 1.42	22.11 ± 1.18	22.15 ± 1.22	22.20 ± 1.13	22.31 ± 1.77
	klmno	pq	lmno	lmno	lmno	klmn

). However, the dependence on the study year did not show a clear trend and was specific to individual elements and treatments. The concentrations of the individual micronutrients were in the following ranges: Fe 40.08–241.52 mg·kg⁻¹, Mn 21.03–219.93 mg·kg⁻¹, Al 12.40–29.19 mg·kg⁻¹, Mo 0.25–0.41 mg·kg⁻¹ and Co 0.13–0.29 mg·kg⁻¹ of dry matter.

The Fe/Mn mass ratio in the control plot (Ct) was 1.08. The application of waste resulted in a widening of the ratio compared to Ct; consequently the Fe/Mn ratio fell within the 1.12–1.45 range, showing a low variability (10.83%) (

Table 4. Mass ratio of Fe/Mn in plant biomass over the entire study period, 2013–2018. Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences between treatments at p < 0.05 according to the LSD method.

Treatment	Fe/Mn
Ct (Control)	1.08 ± 0.01 a
AC (Ash from combustion of bituminous coal)	1.37 ± 0.04 d
AB (Ash from combustion of biomass)	1.45 ± 0.02 e
MSS (Municipal sewage sludge)	1.16 ± 0.02 c
AC+MSS I (1:1 mixture of AC and MSS, 50 Mg·ha−1 DM)	1.16 ± 0.02 c
AC+MSS II (1:1 mixture of AC and MSS, 100 Mg·ha−1 DM)	1.16 ± 0.02 c
AB+MSS I (1:1 mixture of AB and MSS, 50 Mg·ha−1 DM)	1.12 ± 0.02 b
AB+MSS II (1:1 mixture of AB and MSS, 100 Mg·ha−1 DM)	1.13 ± 0.01 b

).

The ability of plants to bioaccumulate micronutrients depending on the type of waste used was further verified using the concentration index (CI) (Figure 2,

Table A7. Results of two-way ANOVA (factors: year, treatment) for concentration index of Mn in plant biomass (CI). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
AC	0.16 × 1.14 ±1	0.20 × 1.19 ±1	0.18 × 1.17 ±1	0.16 × 1.15 ±1	0.17 × 1.21 ±1	0.16 × 1.13 ±1
	q	lmno	pq	q	pq	q
AB	0.20 × 1.13 ±1	0.23 × 1.17 ±1	0.22 × 1.11 ±1	0.18 × 1.12 ±1	0.20 × 1.17 ±1	0.19 × 1.16 ±1
	lmno	jk	kl	pq	no	op
MSS	0.43 × 1.08 ±1	0.44 × 1.11 ±1	0.46 × 1.09 ±1	0.44 × 1.13 ±1	0.55 × 1.13 ±1	0.57 × 1.14 ±1
	b	b	b	b	a	a
AC+MSS I	0.26 × 1.09 ±1	0.23 × 1.17 ±1	0.25 × 1.08 ±1	0.19 × 1.18 ±1	0.20 × 1.20 ±1	0.20 × 1.19 ±1
	ghi	jk	hi	nop	lmno	mno
AC+MSS II	0.28 × 1.11 ±1	0.25 × 1.17 ±1	0.27 × 1.07 ±1	0.21 × 1.12 ±1	0.22 × 1.16 ±1	0.21 × 1.16 ±1
	efgh	hi	efgh	lmn	kl	klm
AB+MSS I	0.31 × 1.09 ±1	0.27 × 1.11 ±1	0.29 × 1.10 ±1	0.23 × 1.10 ±1	0.26 × 1.13 ±1	0.27 × 1.20 ±1
	cd	efgh	def	jk	fghi	efgh
AB+MSS II	0.34 × 1.08 ±1	0.29 × 1.10 ±1	0.31 × 1.06 ±1	0.24 × 1.11 ±1	0.28 × 1.11 ±1	0.29 × 1.15 ±1
	c	de	cd	ij	efg	de

,

Table A8. Results of two-way ANOVA (factors: year, treatment) for concentration index of Fe in plant biomass (CI). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
AC	0.18 × 1.11 ±1	0.21 × 1.07 ±1	0.24 × 1.05 ±1	0.21 × 1.11 ±1	0.23 × 1.12 ±1	0.27 × 1.14 ±1
	w	tu	pqr	tu	qr	jklm
AB	0.20 × 1.10 ±1	0.25 × 1.09 ±1	0.28 × 1.07 ±1	0.27 × 1.11 ±1	0.28 × 1.14 ±1	0.34 × 1.14 ±1
	uv	mnop	ijk	jkl	ijk	ef
MSS	0.48 × 1.09 ±1	0.48 × 1.14 ±1	0.50 × 1.12 ±1	0.47 × 1.13 ±1	0.54 × 1.12 ±1	0.61 × 1.17 ±1
	c	c	bc	c	b	a
AC+MSS I	0.19 × 1.09 ±1	0.22 × 1.09 ±1	0.25 × 1.07 ±1	0.23 × 1.15 ±1	0.25 × 1.15 ±1	0.30 × 1.16 ±1
	vw	rst	mnop	qr	nopq	gh
AC+MSS II	0.21 × 1.05 ±1	0.24 × 1.09 ±1	0.27 × 1.06 ±1	0.26 × 1.14 ±1	0.28 × 1.15 ±1	0.32 × 1.14 ±1
	tu	opq	jklm	lmno	jkl	fg
AB+MSS I	0.21 × 1.10 ±1	0.27 × 1.10 ±1	0.29 × 1.06 ±1	0.29 × 1.12 ±1	0.30 × 1.13 ±1	0.36 × 1.12 ±1
	stu	klmn	hij	hijk	ghi	de
AB+MSS II	0.23 × 1.09 ±1	0.29 × 1.10 ±1	0.32 × 1.07 ±1	0.31 × 1.12 ±1	0.32 × 1.12 ±1	0.38 × 1.11 ±1
	qrs	hij	fg	gh	fg	d

,

Table A9. Results of two-way ANOVA (factors: year, treatment) for concentration index of Mo in plant biomass (CI). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year						Mean
	2013	2014	2015	2016	2017	2018
AC	0.85 ± 0.22	1.08 ± 0.13	1.06 ± 0.12	0.99 ± 0.13	0.98 ± 0.07	1.00 ± 0.13	1.00 ± 0.15
							e
AB	1.02 ± 0.25	1.18 ± 0.14	1.17 ± 0.14	1.10 ± 0.13	1.11 ± 0.12	1.16 ± 0.12	1.13 ± 0.16
							c
MSS	1.12 ± 0.24	1.30 ± 0.13	1.25 ± 0.14	1.24 ± 0.16	1.19 ± 0.16	1.24 ± 0.13	1.23 ± 0.16
							a
AC+MSS I	0.92 ± 0.22	1.14 ± 0.10	1.11 ± 0.15	1.06 ± 0.12	1.05 ± 0.10	1.08 ± 0.11	1.07 ± 0.15
							d
AC+MSS II	0.98 ± 0.22	1.19 ± 0.11	1.13 ± 0.16	1.11 ± 0.13	1.11 ± 0.11	1.13 ± 0.11	1.12 ± 0.15
							c
AB+MSS I	1.08 ± 0.25	1.24 ± 0.12	1.20 ± 0.17	1.15 ± 0.12	1.13 ± 0.09	1.20 ± 0.15	1.17 ± 0.16
							b
AB+MSS II	1.14 ± 0.23	1.29 ± 0.13	1.24 ± 0.15	1.22 ± 0.14	1.17 ± 0.10	1.20 ± 0.15	1.21 ± 0.15
							a
Mean	1.01 ± 0.25	1.20 ± 0.14	1.17 ± 0.16	1.12 ± 0.15	1.11 ± 0.13	1.15 ± 0.15
	E	D	DE	FG	G	EF

,

Table A10. Results of two-way ANOVA (factors: year, treatment) for concentration index of Co in plant biomass (CI). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year						Mean
	2013	2014	2015	2016	2017	2018
AC	0.51 ± 0.07	0.55 ± 0.09	0.58 ± 0.12	0.49 ± 0.08	0.51 ± 0.07	0.52 ± 0.13	0.53 ± 0.10
							e
AB	0.71 ± 0.08	0.67 ± 0.11	0.71 ± 0.15	0.60 ± 0.08	0.66 ± 0.11	0.71 ± 0.14	0.67 ± 0.12
							c
MSS	0.81 ± 0.11	0.81 ± 0.10	0.80 ± 0.14	0.77 ± 0.12	0.77 ± 0.14	0.79 ± 0.13	0.79 ± 0.12
							a
AC+MSS I	0.60 ± 0.08	0.63 ± 0.07	0.63 ± 0.14	0.57 ± 0.07	0.59 ± 0.08	0.61 ± 0.10	0.61 ± 0.10
							d
AC+MSS II	0.66 ± 0.07	0.69 ± 0.09	0.67 ± 0.15	0.63 ± 0.08	0.65 ± 0.09	0.68 ± 0.12	0.66 ± 0.10
							c
AB+MSS I	0.77 ± 0.10	0.74 ± 0.07	0.70 ± 0.17	0.67 ± 0.09	0.68 ± 0.09	0.75 ± 0.15	0.72 ± 0.11
							b
AB+MSS II	0.84 ± 0.08	0.80 ± 0.09	0.78 ± 0.15	0.75 ± 0.08	0.73 ± 0.08	0.76 ± 0.13	0.77 ± 0.11
							a
Mean	0.70 ± 0.14	0.70 ± 0.12	0.69 ± 0.15	0.64 ± 0.13	0.65 ± 0.12	0.69 ± 0.15
	D	D	D	E	E	D

and

Table A11. Results of two-way ANOVA (factors: year, treatment) for concentration index of Al in plant biomass (CI). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year						Mean
	2013	2014	2015	2016	2017	2018
AC	0.60 ± 0.09	0.69 ± 0.07	0.69 ± 0.13	0.67 ± 0.10	0.73 ± 0.11	0.63 ± 0.09	0.67 ± 0.11
							e
AB	0.49 ± 0.09	0.54 ± 0.06	0.57 ± 0.10	0.53 ± 0.09	0.58 ± 0.10	0.52 ± 0.08	0.54 ± 0.09
							f
MSS	1.07 ± 0.11	1.10 ±0.10	1.16 ± 0.12	1.14 ± 0.10	1.16 ± 0.08	1.10 ± 0.08	1.13 ± 0.10
							a
AC+MSS I	0.87 ± 0.12	0.97 ± 0.07	1.05 ± 0.12	1.00 ± 0.07	1.06 ± 0.09	0.99 ± 0.10	0.99 ± 0.11
							b
AC+MSS II	0.90 ± 0.13	0.99 ± 0.09	1.08 ± 0.12	1.01 ± 0.05	1.08 ± 0.13	1.01 ± 0.09	1.02 ± 0.11
							b
AB+MSS I	0.76 ± 0.11	0.86 ± 0.10	0.98 ± 0.14	0.90 ± 0.10	0.94 ± 0.13	0.87 ± 0.11	0.89 ± 0.12
							d
AB+MSS II	0.82 ± 0.08	0.89 ± 0.11	1.00 ± 0.11	0.93 ± 0.07	0.99 ± 0.11	0.91 ± 0.07	0.92 ± 0.11
							c
Mean	0.79 ± 0.21	0.86 ± 0.20	0.93 ± 0.24	0.88 ± 0.21	0.94 ± 0.21	0.86 ± 0.21
	F	E	D	E	D	E

). The concentration indices of the individual micronutrients studied were in the following ranges: Mo 0.85–1.30, Al 0.49–1.16, Co 0.49–0.83, Fe 0.18–0.61 and Mn 0.16–0.57. The results revealed that Mo was bioaccumulated to the greatest extent, as evidenced by CI values equal or greater than 1. Statistical analysis showed that CI measured for Al, Co and Mo within a given treatment was not dependent on the year of the study. However, for Fe and Mn, the concentration index was found to be dependent on both year and treatment.

The quantity of micronutrients taken up by the plants depended on the yield, the concentration of these nutrients in the biomass and on the year of the study (Figure 3,

Table A12. Results of two-way ANOVA (factors: year, treatment) for uptake of Mn by plants (U, g·ha⁻¹). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	893.98 × 1.02 ±1	1359.22 × 1.13 ±1	1186.73 × 1.09 ±1	1197.06 × 1.09 ±1	1070.57 × 1.04 ±1	875.74 × 1.12 ±1
	d	a	b	b	c	d
AC	138.70 × 1.08 ±1	236.48 × 1.08 ±1	186.30 × 1.08 ±1	179.21 × 1.05 ±1	175.98 × 1.10 ±1	129.77 × 1.11 ±1
	B	uv	yz	yz	z	B
AB	189.24 × 1.09 ±1	297.68 × 1.07 ±1	240.20 × 1.06 ±1	209.17 × 1.04 ±1	210.57 × 1.04 ±1	157.51 × 1.03 ±1
	yz	op	tuv	wx	w	A
MSS	405.39 × 1.05 ±1	648.92 × 1.05 ±1	568.53 × 1.09 ±1	540.61 × 1.03 ±1	658.67 × 1.06 ±1	529.75 × 1.03 ±1
	ij	e	f	f	e	f
AC+MSS I	253.60 × 1.04 ±1	342.20 × 1.06 ±1	331.18 × 1.03 ±1	224.27 × 1.04 ±1	249.01 × 1.01 ±1	187.78 × 1.05 ±1
	rstu	lm	mn	vw	stu	yz
AC+MSS II	278.96 × 1.03 ±1	372.46 × 1.05 ±1	341.87 × 1.02 ±1	259.93 × 1.06 ±1	268.44 × 1.02 ±1	192.77 × 1.02 ±1
	pq	jkl	m	qrst	qrs	xy
AB+MSS I	332.91 × 1.08 ±1	441.44 × 1.08 ±1	389.10 × 1.04 ±1	308.36 × 1.06 ±1	338.60 × 1.03 ±1	271.56 × 1.06 ±1
	mn	gh	ijk	no	m	qr
AB+MSS II	359.56 × 1.05 ±1	451.53 × 1.02 ±1	407.69 × 1.04 ±1	298.99 × 1.05 ±1	331.40 × 1.09 ±1	267.90 × 1.07 ±1
	klm	g	hi	op	mn	qrs

,

Table A13. Results of two-way ANOVA (factors: year, treatment) for uptake of Fe by plants (U, g·ha⁻¹). Values are geometric means with one standard deviation to multiply or divide the mean value (±1); same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	977.49 × 1.01 ±1	1432.05 × 1.13 ±1	1256.41 × 1.09 ±1	1270.81 × 1.09 ±1	1212.30 × 1.05 ±1	977.53 × 1.12 ±1
	c	a	b	b	b	c
AC	168.23 × 1.07 ±1	259.22 × 1.07 ±1	260.12 × 1.06 ±1	245.19 × 1.04 ±1	254.35 × 1.10 ±1	245.49 × 1.03 ±1
	v	rs	rs	st	rs	st
AB	206.49 × 1.07 ±1	349.24 × 1.05 ±1	335.99 × 1.04 ±1	341.89 × 1.02 ±1	333.13 × 1.04 ±1	321.16 × 1.01 ±1
	u	mno	nop	mnop	op	p
MSS	500.30 × 1.07 ±1	760.01 × 1.06 ±1	657.34 × 1.09 ±1	614.82 × 1.01 ±1	734.47 × 1.05 ±1	618.29 × 1.04 ±1
	f	d	e	e	d	e
AC+MSS I	209.22 × 1.02 ±1	339.42 × 1.05 ±1	345.17 × 1.04 ±1	287.60 × 1.05 ±1	334.44 × 1.05 ±1	327.96 × 1.05 ±1
	u	mnop	mnop	q	op	op
AC+MSS II	228.13 × 1.04 ±1	364.81 × 1.03 ±1	360.36 × 1.02 ±1	339.80 × 1.02 ±1	375.90 × 1.03 ±1	321.08 × 1.01 ±1
	t	klm	lmn	mnop	jkl	p
AB+MSS I	253.90 × 1.03 ±1	445.55 × 1.04 ±1	422.98 × 1.03 ±1	401.40 × 1.05 ±1	430.30 × 1.02 ±1	389.37 × 1.03 ±1
	rs	gh	hi	ij	hi	jk
AB+MSS II	266.73 × 1.04 ±1	471.89 × 1.03 ±1	441.00 × 1.03 ±1	402.70 × 1.03 ±1	433.74 × 1.06 ±1	384.11 × 1.07 ±1
	r	fg	gh	ij	h	jkl

,

Table A14. Results of two-way ANOVA (factors: year, treatment) for uptake of Mo by plants (U, g·ha⁻¹). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	1.21 ± 0.10	2.09 ± 0.28	2.31 ± 0.26	1.98 ± 0.08	2.25 ± 0.11	2.07 ± 0.17
	v	no	jklm	opq	klmn	nop
AC	1.01 ± 0.06	1.90 ± 0.18	2.12 ± 0.11	1.84 ± 0.11	1.98 ± 0.13	1.90 ± 0.10
	w	pq	no	qr	opq	pq
AB	1.29 ± 0.06	2.33 ± 0.20	2.49 ± 0.10	2.21 ± 0.09	2.43 ± 0.12	2.33 ± 0.14
	uv	jkl	hij	lmn	ij	jkl
MSS	1.46 ± 0.06	2.86 ± 0.11	2.95 ± 0.18	2.59 ± 0.12	2.96 ± 0.21	2.74 ± 0.09
	tu	cde	abc	fghi	abc	def
AC+MSS I	1.30 ± 0.02	2.47 ± 0.11	2.71 ± 0.17	2.14 ± 0.08	2.59 ± 0.11	2.46 ± 0.10
	uv	hij	efg	mno	fghi	hij
AC+MSS II	1.38 ± 0.03	2.58 ± 0.11	2.72 ± 0.13	2.42 ± 0.10	2.74 ± 0.14	2.41 ± 0.07
	uv	fghi	efg	ijk	def	ijk
AB+MSS I	1.61 ± 0.06	2.96 ± 0.16	3.07 ± 0.12	2.62 ± 0.11	3.00 ± 0.10	2.74 ± 0.18
	st	abc	ab	fgh	abc	defg
AB+MSS II	1.70 ± 0.05	2.97 ± 0.10	3.10 ± 0.02	2.56 ± 0.14	2.91 ± 0.18	2.58 ± 0.12
	rs	abc	a	ghi	bcd	fghi

,

Table A15. Results of two-way ANOVA (factors: year, treatment) for uptake of Co by plants (U, g·ha⁻¹). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	1.09 ± 0.08	1.74 ± 0.14	2.16 ± 0.08	1.77 ± 0.07	1.98 ± 0.09	1.82 ± 0.13
	rst	cde	a	cd	b	c
AC	0.52 ± 0.04	0.87 ± 0.11	1.02 ± 0.10	0.81 ± 0.06	0.91 ± 0.07	0.85 ± 0.12
	z	vwx	tu	wxy	uvw	vwx
AB	0.78 ± 0.03	1.17 ± 0.13	1.31 ± 0.08	1.07 ± 0.08	1.27 ± 0.10	1.23 ± 0.13
	xy	qrs	lmnop	st	nopq	pq
MSS	0.91 ± 0.05	1.59 ± 0.10	1.66 ± 0.10	1.42 ± 0.12	1.64 ± 0.16	1.53 ± 0.07
	uvwx	ghi	defg	jkl	defgh	hij
AC+MSS I	0.70 ± 0.03	1.21 ± 0.06	1.37 ± 0.13	1.03 ± 0.05	1.28 ± 0.09	1.20 ± 0.08
	y	pqr	klmn	tu	mnopq	pqr
AC+MSS II	0.78 ± 0.02	1.32 ± 0.07	1.40 ± 0.09	1.22 ± 0.06	1.42 ± 0.13	1.24 ± 0.07
	xy	lmnop	jklm	pq	jkl	opq
AB+MSS I	0.98 ± 0.05	1.56 ± 0.10	1.65 ± 0.11	1.36 ± 0.07	1.59 ± 0.07	1.48 ± 0.15
	tuv	ghi	defg	klmno	fghi	ijk
AB+MSS II	1.06 ± 0.04	1.63 ± 0.07	1.72 ± 0.02	1.39 ± 0.10	1.59 ± 0.10	1.40 ± 0.06
	st	efgh	cdef	klmn	ghi	jklm

and

Table A16. Results of two-way ANOVA (factors: year, treatment) for uptake of Al by plants (U, g·ha⁻¹). Values are arithmetic means ± one standard deviation; same letters indicate that there are no significant differences at p < 0.05 according to the LSD method.

Treatment	Year
	2013	2014	2015	2016	2017	2018
Ct	112.30 ± 9.41	153.67 ± 13.42	159.03 ± 9.46	155.43 ± 10.73	161.55 ± 16.08	161.08 ± 2.88
	p	n	jklmn	mn	jklmn	jklmn
AC	63.43 ± 4.52	94.53 ± 5.89	92.05 ± 4.64	97.73 ± 8.81	103.38 ± 8.87	97.05 ± 4.70
	x	stuv	tuvw	rstu	pqrs	rstu
AB	55.63 ± 5.82	82.98 ± 7.89	81.38 ± 6.74	84.18 ± 9.17	88.10 ± 5.76	85.40 ± 8.00
	x	w	w	vw	uvw	vw
MSS	124.55 ± 3.10	190.98 ± 11.83	189.03 ± 15.46	185.15 ± 8.40	202.40 ± 8.88	188.80 ± 3.66
	o	bc	bcd	bcde	a	bcd
AC+MSS I	107.83 ± 4.14	166.20 ± 7.62	177.98 ± 10.43	154.45 ± 5.23	183.18 ± 8.77	178.23 ± 14.28
	pqr	ijklm	defgh	n	bcdef	defgh
AC+MSS II	111.28 ± 4.40	169.53 ± 5.56	180.53 ± 4.32	168.93 ± 7.73	192.75 ± 2.87	170.08 ± 4.26
	pq	ghijk	cdefg	hijkl	ab	ghij
AB+MSS I	100.23 ± 3.68	158.38 ± 8.79	175.10 ± 8.76	156.50 ± 5.81	175.60 ± 4.64	157.75 ± 8.95
	qrst	klmn	efghi	mn	efghi	lmn
AB+MSS II	107.43 ± 4.03	159.18 ± 2.20	175.25 ± 4.93	150.30 ± 6.69	173.20 ± 11.43	155.25 ± 10.58
	pqr	jklmn	efghi	n	fghi	mn

). The uptake (U) of Fe, Mo, Co and Al was statistically lower in 2013 than in the following years. However, the Mn uptake was higher in 2014 and 2015 than in 2013 in each treatment. From 2016 to 2018, the Mn uptake in the AC+MSS and AB+MSS treatments was comparable or lower than in 2013 while. in the Ct plot and with waste used separately in the years 2016–2018, the Mn uptake was higher or comparable to 2013 (Table A12). The uptake also varied for the individual micronutrients studied and was within the following ranges: Fe 168.23–1432.05 g·ha⁻¹, Mn 129.77–1359.22 g·ha⁻¹, Al 55.63–202.40 g·ha⁻¹, Mo 1.01–3.10 g·ha⁻¹ and Co 1.09–2.16 g·ha⁻¹ (Figure 3, Table A12, Table A13, Table A14, Table A15 and Table A16).

The parameters mentioned above, i.e., the concentration in plant biomass and the uptake by plants of the studied micronutrients, were affected by the tested wastes in different ways. Manganese and iron showed similar trends in X, CI and U values during the experiment (Table A2, Table A7 and Table A12 for Mn and Table A3, Table A8 and Table A13 for Fe). The lowest X, CI and U for both elements were found under the AC application and slightly higher under the AB treatment. The use of AC+MSS and AB+MSS resulted in increased X, CI and U, with the AC+MSS treatments causing slightly lower X, CI and U values than the AB+MSS treatments. The MSS application contributed to the highest X, CI and U values.

Molybdenum and cobalt showed similar trends for X, CI and U values during the experiment (Table A4, Table A9 and Table A14 for Mo and Table A5, Table A10 and Table A15 for Co). Specifically, the lowest X, CI and U values were found under the AC application and these were slightly higher under the AB application. The AC+MSS mixture caused a further increase in X, CI and U. The MSS and AB+MSS treatments generally contributed to the highest X, CI and U values.

For Al, the lowest X, CI and U values (Table A6, Table A11 and Table A16, respectively) were found after the AB application and these were slightly higher with AC. The AC+MSS and AB+MSS treatments resulted in higher X, CI and U. As with separate ash, the AB+MSS mixtures caused slightly lower X, CI and U values than AC+MSS mixtures. The MSS use contributed to the highest X, CI and U of Al in plants.

Linear correlation analysis showed that the concentration of Mn, Fe, Mo and Al in the biomass of the tested plants correlated statistically significantly with the amounts of micronutrients introduced into the soil with the waste. A positive correlation was found for Mo (r = 0.626), as well as negative correlations for Mn (r = −0.763), Fe (r = −0.769) and Al (r = −0.377). In addition, there was a correlation between mean yield and plant biomass concentration of Mo (r = 0.655) and Al (r = 0.559).

3.3. Simplified Balance and Recovery of Micronutrients by Plants

In order to assess the possibility of using the studied mineral and organic wastes as a potential source of micronutrients for plants, the balance and recovery of these nutrients were calculated. The micronutrient balance (B) was calculated as the difference between the amount of micronutrient introduced with the waste (input, I) and the amount taken up with the plant yield (uptake, U;

Table 5. Simplified balance and recovery of micronutrients after six years of the field experiment. Treatment symbols are explained in Table 1.

Micronutrient	Treatment	Input, I g·ha−1 DM	Uptake, U g·ha−1 DM	Balance, B g·ha−1 DM	Recovery, R %
Mn	Ct	0	6603	−6603	n/a 1
	AC	323,750	1049	322,701	0.32
	AB	259,377	1306	258,071	0.50
	MSS	12,995	3356	9639	25.82
	AC+MSS I	168,373	1589	166,783	0.94
	AC+MSS II	336,745	1715	335,030	0.51
	AB+MSS I	136,186	2085	134,101	1.53
	AB+MSS II	272,372	2119	270,252	0.78
	CV, %	70.00	73.00	72.00	218.18
Fe	Ct	0	7111	−7111	n/a
	AC	956,250	1435	954,815	0.15
	AB	928,250	1889	926,361	0.20
	MSS	75,000	3890	71,110	5.19
	AC+MSS I	515,625	1845	513,780	0.36
	AC+MSS II	1,031,250	1991	1,029,259	0.19
	AB+MSS I	501,625	2345	499,280	0.47
	AB+MSS II	1,003,250	2402	1,000,848	0.24
	CV, %	67.00	65.00	67.00	191.75
Mo	Ct	0	11.91	−11.91	n/a
	AC	216	10.75	204.75	4.99
	AB	321	13.07	307.43	4.08
	MSS	254	15.55	237.95	6.13
	AC+MSS I	235	13.66	220.84	5.82
	AC+MSS II	469	14.24	454.76	3.04
	AB+MSS I	287	15.98	271.02	5.57
	AB+MSS II	574	15.82	558.18	2.76
	CV, %	59.00	13.75	61.02	29.33
Co	Ct	0	10.55	−10.55	n/a
	AC	179	4.97	174.03	2.78
	AB	356	6.83	349.17	1.92
	MSS	361	8.73	351.77	2.42
	AC+MSS I	270	6.78	262.97	2.51
	AC+MSS II	540	7.38	532.12	1.37
	AB+MSS I	358	8.62	349.63	2.41
	AB+MSS II	717	8.79	707.71	1.23
	CV, %	62.00	21.72	63.82	28.69
Al	Ct	0	912	−912	n/a
	AC	1,405,826	548	1,405,278	0.04
	AB	1,248,877	478	1,248,399	0.04
	MSS	385,917	1081	384,836	0.28
	AC+MSS I	895,871	968	894,903	0.11
	AC+MSS II	1,791,743	993	1,790,749	0.06
	AB+MSS I	817,397	924	816,473	0.11
	AB+MSS II	1,634,793	921	1,633,872	0.06
	CV, %	60.00	26.00	60.00	86.95

¹ n/a, not applicable.

).

The wastes tested contributed varying amounts of micronutrients to the soil (Table 2). The ash was characterised by very high concentrations of Al (24,977–28,116 mg·kg⁻¹ DM) and Fe (18,565–19,125 mg·kg⁻¹ DM) and high concentration of Mn (5187–6475 mg·kg⁻¹ DM), much higher than in the sewage sludge (7718 mg·kg⁻¹ DM, 1500 mg·kg⁻¹ DM and 260 mg·kg⁻¹ DM, respectively). In contrast, the Co and Mo concentrations in all the wastes tested were much lower and at relatively comparable levels (3.58–7.21 mg·kg⁻¹ DM). Therefore, the highest amounts of Al, Fe and Mn were provided with ash (AC, AB) and its mixtures with sewage sludge in the higher dose (AC+MSS II, AB+MSS II), while the lower amounts of these elements were introduced with MSS and its mixtures with ash in the lower dose (AC+MSS I, AB+MSS I).

In the control plot (Ct), the balance was negative because the calculations did not take into account external inputs of micronutrients, e.g., from precipitation, which are difficult to estimate due to their high variability. On the other hand, the application of all tested wastes resulted in a positive balance for all the micronutrients tested, i.e., the amount of micronutrient introduced with the wastes was greater than their total uptake by the plants. Irrespective of the treatment, the recovery rate (R) of the individual micronutrients studied was within the following ranges: Mn 0.32–25.82%, Mo 2.76–6.13%, Fe 0.15–5.19%, Co 1.23–2.78% and Al 0.04–0.28%, thus generally decreasing in order: Mn > Mo > Fe > Co > Al (Table 5). Moreover, linear correlation analysis showed a statistically significant negative link between the input and uptake for Mn (r = −0.786) and the input and uptake for Fe (r = −0.792), as well as a strong positive correlation between the input and uptake of Mo (r = 0.571). The mean yield also influenced the uptake of Mo (r = 0.904), Co (r = 0.495) and Al (r = 0.733). A close relationship was also found between the content and uptake of Mn, Fe, Mo, Co and Al by plants, as evidenced by high correlation coefficient values ranging from 0.914 to 0.997.

For individual elements. the recovery rate depended on the waste used. For Mn, Fe and Al, the lowest recovery rates were observed under the AC, AB, AC+MSS II and AB+MSS II treatments. Slightly higher recovery rates were observed in the AC+MSS I and AB+MSS I plots and the highest recovery rates under the MSS application. For Mo, the lowest recovery was recorded in the AC+MSS II and AB+MSS II plots, and this was slightly higher under AC and AB application. The recovery then increased following the use of the AC+MSS I and AB+MSS I mixtures and reached the highest values under the MSS application. For Co, the lowest recovery was found under the AC+MSS II and AB+MSS II application and slightly higher under AB. The recovery then increased to a similar level under MSS and AB+MSS I. A further increase in recovery was observed after the use of the AC+MSS I mixture, and the highest recovery was noted after the AC application. In summary, among the applied wastes, the highest recovery of Mn, Mo, Fe and Al by plants was recorded in the MSS treatment, 25.82%, 6.13%, 5.19% and 0.28%, respectively. In contrast, the highest recovery of Co (2.78%) was registered in the AC plot.

4. Discussion

4.1. Micronutrient Concentration in Plant Biomass

The results of our six-year field experiment showed that the applied sewage sludge increased the Mo and Al concentrations in the plant biomass (to 0.38 mg·kg⁻¹ DM and 25.58–29.19 mg·kg⁻¹ DM, respectively), while the highest Mn, Fe and Co concentrations were recorded in the control plot, 129.57–219.93 mg·kg⁻¹ DM, 146.58–241.52 mg·kg⁻¹ DM and 0.28 mg·kg⁻¹ DM, respectively. As reported in the literature, individual plant species and cultivars, as well as their specific growing conditions, significantly modify the concentration of trace elements in biomass [43]. For example, McBride et al. [44] reported a Mo concentration of 18.5 mg·kg⁻¹ DM in red clover grown in a greenhouse experiment on soil treated with alkaline-stabilised sludge, which added the equivalent of 4.2 kg·ha⁻¹ of Mo to the soil. On the other hand, under field conditions, these authors reported Mo concentrations in the range of 0.73–5.41 mg·kg⁻¹ DM and 2.10–10.1 mg·kg⁻¹ DM in the grass and alfalfa grown for forage, respectively, in the experiment where 900 g·ha⁻¹ of Mo was applied to the soil with sewage sludge. In our study, the obtained Mo concentration in the biomass was considerably lower (0.38 mg·kg⁻¹ DM), which could be mainly related to the 3.5 times lower Mo input with the sewage sludge used.

Kępka et al. [45] found in the one-year field experiment that the application of municipal sewage sludge, which delivered 9.89 kg Fe, 2.45 kg Mn, 44 g Co and 28.54 kg Al per ha, did not change the Fe concentration and increased the concentration of Mn, Co and Al in spring barley straw compared to soil fertilised with NPK fertiliser. The concentrations of Fe, Mn, Co and Al were, respectively, ca. 88, 11, 0.06 and 31 mg·kg⁻¹ DM. In the present study, we found similar concentrations of Fe and Al (88.80–116.46 mg·kg⁻¹ DM and 25.58–29.19 mg·kg⁻¹ DM, respectively) and 4–8 times lower concentrations of Co and Mn (0.22 and 75.86–94.57 mg·kg⁻¹ DM, respectively), but no analogous relationships were observed for input amounts of the micronutrients. Therefore, our experiment confirms previous observations that nutrient uptake by plants is influenced by many factors, with nutrient supply being only one of them.

A key factor determining the bioavailability of elements by plants is the total amount of their bioavailable forms in the soil [46,47]. The bioavailability and mobility of metals and micronutrients can be modified by soil reaction and organic matter content [48,49]. The sewage sludge tested was characterised by a neutral reaction (pH = 7.12) and a high organic carbon content compared to other wastes (Table 3). The application of the municipal sewage sludge to the soil did not change the pH, but provided a large amount of organic matter that readily underwent mineralisation and biodegradation processes, contributing to the release of components, their availability and uptake by plants, in line with previous studies [50,51,52]. In contrast, the micronutrient concentration in plant biomass following the separate application of bottom ash was generally lower than for the other treatments (Figure 1, Table A2, Table A3, Table A4, Table A5 and Table A6). This may be due to the alkaline reaction of the ash and the occurrence of metals in bound forms in low solubility minerals (aluminosilicates) [53,54] or the transformation of elements from mobile into immobile forms, which limits the phyto-availability of micronutrients [55,56].

The combination of alkaline bottom ash with municipal sewage sludge leads to an increase in pH and the formation of stable, less soluble compounds of nutrients, resulting in their immobilisation and reduced availability to plants [57]. In our study, the concentration of micronutrients in plant biomass was generally lowest under ash treatments, highest under sewage sludge application and intermediate under ash–sludge mixtures (Figure 1, Table A2, Table A3, Table A4, Table A5 and Table A6). The previous greenhouse study by Xu et al. [30] on the influence of municipal sewage sludge stabilised with fly ash on Manila grass showed comparable trends for Mn. However, their study revealed contrasting results for Co and Fe, i.e., these elements’ concentrations in Manila grass were not statistically different under sewage sludge or sewage sludge-coal fly ash treatments, which could be attributed to the high levels of Co and Fe in the wastes used.

By stabilising the elements contained in the sludge, the ash slows down the process of their release and at the same time causes a gradual, prolonged uptake of metals by plants [55]. Our results confirmed the increased immobilisation of micronutrients from the mixtures of bottom ash and municipal sewage sludge compared to the separate application of municipal sewage sludge. This phenomenon should be regarded as favourable, as it enhanced the stability of the quality of the plant yields obtained. Namely, on an annual basis, the plant biomass exhibited relatively similar micronutrient concentrations. This was particularly apparent for Fe and Al (Figure 1, Table A3 and Table A6).

The concentrations of the elements in the plant biomass should be evaluated taking into account the intended crop use. In this study, the micronutrient concentrations in the biomass obtained were checked for its suitability as animal feed. According to the National Research Council [58], the maximum tolerable levels established for cattle are Mn 2000 mg·kg⁻¹, Fe 500 mg·kg⁻¹, Mo 5 mg·kg⁻¹, Co 25 mg·kg⁻¹ and Al 1000 mg·kg⁻¹ in DM of plant biomass used as fodder. Furthermore, Kao et al. [59] reported the following micronutrient densities of dietary feed required by ruminants (cattle and sheep): Fe and Mn 25–40 mg·kg⁻¹ each and Co 0.1–0.2 mg·kg⁻¹ in dry matter. In the present study, the concentration of none of the micronutrients tested exceeded the critical levels (Figure 1, Table A2, Table A3, Table A4, Table A5 and Table A6). Additionally, the micronutrient contents were sufficient for the plant biomass obtained to be used as a component of animal feed.

The right proportions of nutrients are essential for the proper functioning of plant life processes. For example, a high plant yield is determined by the optimal content of all macro- and micronutrients present in specific ratios [60]. The waste used in the study significantly modified the Fe/Mn ratio compared to the control. Moreover, the Fe/Mn ratio showed the low variability (ca. 11%), indicating that the plant biomass obtained was characterised by stable proportions between these elements, regardless of the dose of waste applied. As reported in the literature, the availability of active manganese to plants depends on the Fe/Mn ratio, the optimum range of which is 1.5–2.5:1. A significant excess of Fe (Fe/Mn ratio > 2.5:1) reduces the availability of Mn, while a predominance of Mn (Fe/Mn < 1.5:1) begins to have a toxic effect on plants [61,62]. Analysis of the results obtained in this study showed that the Fe/Mn ratio in the biomass of plants in each treatment fertilised with individual wastes (1.16:1–1.45:1) was below the optimum value, while the ratio closest to the optimum value was recorded in the plot where biomass ash was applied separately.

4.2. Concentration Index

The concentration index determines the potential of plants to accumulate particular elements in the biomass in relation to their initial content in the substrate [63]. This parameter is expressed as the ratio of the concentration of a given micronutrient in plants grown in the waste-applied plots to the concentration of the nutrient in plants grown in the control. Index values greater than one indicate a high accumulation capacity of elements in the tested plants [64]. Among the wastes used in this study, the accumulation of micronutrients by plants was the highest under the separate application of municipal sewage sludge (from 0.43–0.61 for Mn and Fe, 0.79 for Co to 1.13 and 1.12 for Al and Mo, respectively). In summary, the mean value of the concentration index for all the studied treatments decreased in the following order: Mo > Al > Co > Fe = Mn, i.e., from 1.00–1.23 for Mo to 0.16–0.57 for Mn. The high potential of the tested plants to accumulate Mo may be due to the fact that Mo is an essential element for plant growth and development. It also has many important catalytic functions, being part of the group of enzymes that catalyse nitrogen assimilation and sulphate detoxification, among other processes. Therefore, maintaining Mo homeostasis in the plant has important implications for adaptability and productivity [65]. Furthermore, plants can accumulate very high levels of Mo without showing negative symptoms. The difference between the optimum and toxic content of Mo is 100–1000-fold, whereas this ratio for Mn is only 10-fold [66,67].

4.3. Micronutrient Uptake

The ability of plants to take up elements from the soil is closely related to their speciation and the quantity of active forms, organic matter content, reaction, redox potential and the dose of mineral fertiliser applied [68].

In the current study, the total amount of micronutrient uptake by the plants tested was directly related to the yield and concentration of these elements. In our previously published work, it was shown that, of the wastes and their mixtures used, the highest plant yield was obtained after application of a mixture of biomass ash and municipal sewage sludge [8,38], which was in line with earlier studies, e.g., by Lin et al. [69]. The lowest uptake of micronutrients was observed in the current study under the separate application of coal ash, from 0.52–1.02 g·ha⁻¹ for Co to 168.23–260.12 g·ha⁻¹ for Fe. This may be attributed to the lowest yield, high pH and the presence of these elements in forms unavailable to plants. Bottom coal ash contains, among others, iron oxides that decrease the bioavailability of metals [70].

In the current study, the highest uptake of micronutrients was observed in the plots fertilised separately with sewage sludge, from 0.91–1.66 g·ha⁻¹ for Co to 500.30–760.01 g·ha⁻¹ for Fe. This can be explained by the high yield potential and the optimal neutral reaction of sewage sludge for plant growth [50,71,72]. Furthermore, it can be assumed that the elements were present in the sludge in available and more easily assimilable forms, which determined the amount of uptake of the elements by the plants. Complementing the recycling of organic waste with extraction of the nutrients it contains can help to close the cycle of these elements and thus contribute to the development of sustainable and rational agricultural production [4]. The uptake of valuable micronutrients from bottom ash and municipal sewage sludge is in line with the idea of slowing down the use of natural resources and the concept of a closed-loop economy, in which the original waste, once processed, loses its waste status and becomes a valuable source of nutrients.

4.4. Simplified Balance and Recovery of Micronutrients by Plants

Balance and recovery are parameters that allow tracing of the cycling of elements in the environment and at the same time to assess the fertilising potential of applied wastes as a source of micronutrients [73]. The analysis of the results obtained in our experiment showed that, regardless of the treatment, the highest balance was observed for Al (ca. 385–1791 kg·ha⁻¹ DM), followed by Fe, Mn and Co, while the lowest balance was recorded for Mo (ca. 0.2–0.5 kg·ha⁻¹ DM). The recovery of the elements studied varied, which was mainly related to the yield and the achieved concentrations of micronutrients in the plant biomass [74]. Our study showed that the recovery of micronutrients by plants in all the treatments decreased in the following order: Mn > Mo > Fe > Co > Al, i.e., from 0.32–25.82% for Mn to 0.04–0.28% for Al. The lowest recovery was recorded for aluminium, which was introduced in large quantities with waste [75,76]. Based on the calculated balance, it is possible to estimate the time during which plants can absorb from the soil the constituents introduced with each waste. This in turn allows determination of the fertilising efficiency of the waste applied [74].

Statistical analysis showed strong negative correlation between the amount of Mn introduced into the soil and its uptake by plants, indicating that recovery decreased as increasing amounts of the element were delivered with the applied waste. A similar negative correlation was observed for Fe. In turn, the positive correlation obtained between input and uptake of Mo indicated that the plants recovered more molybdenum as the amount of Mo supplied with the waste increased. These correlations are important in estimating the rate of utilisation of nutrients added to the soil with wastes. Consequently, the higher amount of the element in the soil results in a prolonged period during which the plant is able to utilise this micronutrient [73].

5. Summary and Conclusions

Wastes such as bottom ash and sewage sludge are potential sources of nutrients for plants. Therefore, this six-year field study investigated the effect of bottom ash from the combustion of bituminous coal or biomass and municipal sewage sludge and different doses of the waste mixtures on the amount of micronutrient uptake by plants and their extraction potential.

The tested wastes affected the concentration and concentration index in plant biomass and the uptake by plants of the studied micronutrients in different ways. The parameter values of Fe, Mn, Co, Mo and Al were lower after the separate application of ash. The concentration of micronutrients was in the range of ca. 0.14–53 mg·kg⁻¹ DM, the concentration index was in the range of 0.16–1.13, and the uptake was in the range of ca. 0.8–350 g·ha⁻¹. These values increased after the use of ash–sludge mixtures, reaching ca. 0.17–74 mg·kg⁻¹ DM, 0.20–1.21, and 1.2–390 g·ha⁻¹, respectively. The highest parameter values for Fe, Mn and Al were recorded after the separate application of municipal sewage sludge. The concentration was in the range of ca. 26–117 mg·kg⁻¹ DM, with the concentration index of 0.43–1.13, and the uptake of ca. 125–760 g·ha⁻¹. For Co and Mo, however, comparable parameter values were identified when the mixture of biomass ash with sewage sludge or sewage sludge alone was applied. In this case, the concentration was 0.20–0.38 mg·kg⁻¹ DM, the concentration index was 0.72–1.23, and the uptake was 0.91–3.10 g·ha⁻¹.

Regardless of the treatment, the micronutrient recovery rate followed the trend: Mn > Mo > Fe > Co > Al, from 0.32–25.82% for Mn to 0.04–0.28% for Al. For individual elements, the recovery depended on the waste. For Mn, Fe and Al, the application of ash, whether separately or in mixtures with sewage sludge at higher doses, resulted in reduced recovery values (0.04–0.78%). For Mn, Fe, Al and Mo, the application of ash–sludge mixtures at lower doses resulted in an increase in recovery values (0.11–5.82%), with the highest recoveries observed after the separate use of sewage sludge (0.28–25.82%). In the case of cobalt, the separate application of sewage sludge and ash–sludge mixture at the lower dose increased the recovery rate (2.41–2.52%), with the highest Co recovery values recorded following the separate application of coal ash (2.78%).

The present study provided important evidence that coal ash, biomass ash and municipal sewage sludge, when applied separately or in mixtures to soil, were a valuable source of micronutrients for plants. The ash–sludge mixtures improved the uptake of micronutrients by plants compared to the ash used separately. Therefore, the combination of the bottom ash with sewage sludge is particularly recommended, given the relatively less favourable properties of the ash for plant growth in comparison to municipal sewage sludge.

The study is an extension of our previous research [8,38], which allows the full assessment of the fertiliser potential of the ash, sludge and their mixtures. Considering the results of the present study and also taking into account the possibility of use of the macronutrients contained in these wastes [8] and the low environmental risk associated with their heavy metal input with to the soil [38], bottom ash and sewage sludge can be recommended for use as fertilisers in the production of biomass, e.g., for fodder purposes. The use of these wastes as fertilisers is therefore in line with the European Union’s Action Plan on the Circular Economy and can also contribute to achieving Sustainable Development Goal 2: Zero hunger (End hunger, achieve food security and improved nutrition and promote sustainable agriculture) and Sustainable Development Goal 12: Responsible consumption and production (Ensure sustainable consumption and production patterns).