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Article

The Impact of Municipal Waste on Seasonal and Spatial Changes in Selected Macro- and Micro-Nutrient Contents on the Background of Soil Biological Activity—A Case Study

by
Joanna Lemanowicz
1,*,
Agata Bartkowiak
1,
Barbara Breza-Boruta
2,
Paweł Sowiński
3,
Samir A. Haddad
4 and
Iwona Jaskulska
5
1
Department of Biogeochemistry and Soil Science, Faculty of Agriculture and Biotechnology, Bydgoszcz University of Science and Technology, 6 Bernardyńska St., 85-029 Bydgoszcz, Poland
2
Department of Microbiology and Food Technology, Faculty of Agriculture and Biotechnology, Bydgoszcz University of Science and Technology, 6 Bernardyńska St., 85-029 Bydgoszcz, Poland
3
Department of Soil Science and Microbiology, Faculty of Agriculture and Forestry, University of Warmia and Mazury in Olsztyn, Plac Łodzki 3, 10-727 Olsztyn, Poland
4
Department of Agricultural Microbiology, Minia University, El-Minia 61517, Egypt
5
Department of Agronomy, Faculty of Agriculture and Biotechnology, Bydgoszcz University of Science and Technology, 7 Prof. S. Kaliskiego St., 85-796 Bydgoszcz, Poland
*
Author to whom correspondence should be addressed.
Minerals 2023, 13(1), 47; https://doi.org/10.3390/min13010047
Submission received: 2 November 2022 / Revised: 16 December 2022 / Accepted: 24 December 2022 / Published: 28 December 2022
(This article belongs to the Special Issue Chemical Composition of Soils and Soil Sediments)
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Abstract

:
Landfilling is the least desirable of waste management methods, but despite tightening legal regulations it remains among the most common. Assessing the impact of landfills on the soil environment is even more important when there are arable lands in their vicinity. Therefore, the study examined soils on and directly adjacent to a landfill. Soil samples were collected from eight points (S1–S8) on the landfill premises, and from one more (S9) and a control (C), both of which were outside the premises. The parameters analyzed were pH in KCl and the contents of: organic carbon (OC), total nitrogen (TN), available phosphorus (AP), potassium (K), magnesium (Mg), total iron (TFe), total manganese (TMn), available iron (AFe) and available manganese (AMn). The activities of alkaline (AlP) and acid (AcP) phosphatase and phosphorus microorganisms (PSM) were tested. The results of biological parameters were used to calculate the resistance index (RS). The soils were alkaline (pH in KCl 7.09–7.65 at S1–S8). Using the RS index values for AlP and AcP the resistance of the soils was: AlP > AcP. The negative values of RS for PSM in most cases indicate a heavy human impact on this parameter. The tested points were found to have been significantly affected by changes in the content of bioavailable P, K and Mg. The total content of tested trace elements in the analyzed soil material did not exceed the geochemical background value. The soil in a sector that had been closed off for two years (S2) showed the highest biological activity. The physicochemical and biological parameters used in the research show the scale of processes going on in the soil environment and the degree (or lack) of its negative exposure to the influence of municipal waste stored at the Municipal Waste Disposal Plant.

1. Introduction

One of the basic challenges in improving the welfare of urban areas is the proper management of the waste generated by industrial development [1]. Waste is part of any urbanized landscape, and waste management has been practiced for centuries. Municipal waste landfills, as the name suggests, are facilities where municipal waste is disposed of. “Municipal waste” is defined as waste generated in households (excluding end-of-life vehicles) and waste that is similar in nature or composition to household waste and that does not contain hazardous waste from other waste producers [2]. The development of civilization, over recent years, has resulted in a rapid increase in the amount of generated waste and also influenced its composition [3]. In total, 1.3 billion tonnes of municipal solid waste (MSW) are produced globally, at an average daily rate of 1.2 kg per capita. By 2025, this amount will increase to 2.3 billion tonnes per year [4]. According to Eurostat [5] and Brennan et al. [6], in 2012, 42% of all waste treated across EU Member States was recycled, 34% was deposited to landfills, 15% was composted or underwent anaerobic digestion and just 4% was incinerated. The basic method of waste management is storage. Modern official landfills should: be specially and appropriately located in line with hydrological and geotechnical criteria; meet the relevant technical requirements; be properly operated in accordance with the environmental requirements relating to the protection of soil, groundwaters and surface waters. They should also meet requirements for counteracting the release of odors and the uncontrolled emission of landfill gas and for noise protection.
Landfills act as bioreactors, in which microbiological and biochemical changes take place under the influence of atmospheric factors and microorganisms. They are a potential source of odors and microbial contamination [7,8] and cause the emission of particulate, chemical and noise pollution [9,10,11,12,13,14]. The decomposition of waste in a landfill is the result of the activities of aerobic and anaerobic microorganisms and occurs in stages. In each stage, there are characteristic changes in the population sizes of certain groups of bacteria, and certain metabolic products are formed and utilized. Rainwater penetrating deep into the landfill and the products of biochemical changes of organic matter from the deposited material form leachates. The amounts and contents of pollutants in leachates depend primarily on the composition of the waste and the biochemical changes taking place in the landfill, which depend on disposal and operating technologies, the age of the landfill, and weather conditions [15,16]. The contamination rates in “young” landfill leachate (2–3 years old) are very high, and 70%–80% of the organic substances are lower organic acids (high contents of volatile fatty acids). As waste transformation stabilizes (stable methanogenesis), after about eight years, pH increases, while BOD (Biochemical Oxygen Demand) and COD (Chemical Oxygen Demand) indicators decrease due to the decomposition of biodegradable organic compounds. Landfills usually cover a fairly large area that can undergo significant transformations. Emitted pollutants can have a particularly negative impact on the soil environment. Soil is among the environmental features most exposed to pollution from municipal landfills. Soil contamination can disturb the ecological balance, which is seen in, among other things, changes in soil microbiome composition and physicochemical parameters [17,18].
The main environmental pollutants are chemical elements that, in elevated concentrations, cause chemical stress and that, when there are interactions between ions and chemical compounds, become active and harmful [19]; this in turn disturbs the balance of existing ecosystems and the organisms functioning in them. High concentrations of landfill leachate contain nitrogen compounds, phosphates, magnesium, chlorides, sulphates, fluorides, calcium, sodium, potassium, iron [20], complex organic substances, heavy metals and xenobiotics [21]. The spatial range and intensity of the impact that waste management facilities have on the soil environment depends on the filtration and sorption properties of the substrate beneath the landfill, the permeability of aquifers, hydrogeological conditions and the amount and properties of leachates. The biodegradation of the organic substances under anaerobic conditions forms CO2, H2O, N2, H2S, CH4 and reduces metals to lower oxidation states (Mn2+, Fe2+). The least extensively spreading form of pollution is bacteriological, which can reach from between ten and 100 metres from the landfill. Ionic forms of chemical pollutants spread much further, being detected several kilometres from a landfill.
Human economic activity releases its greatest amounts of P into the environment through the use of phosphorus fertilizers, cleaning agents, bleaches, pyrotechnic materials, and organic residues. Potassium compounds are used primarily in the production of fertilizers, iodine, glass, soap, absorbents and oxidants. Magnesium is used as a fire-resistant, an insulator, an impregnating agent, and for coating other metals. Landfills are considered to be the main sources of environmental contamination with micro-elements (e.g., Zn, Cu, Pb, Ni, Cd, Hg, Mn, Fe) [22,23,24]. Most trace elements act as micro-nutrients, which are important in sustaining organisms, and their toxic properties are manifested only when the concentrations of their bioavailable forms exceed nutritional requirements [25]. Iron is commonly used in alloys with carbon and other elements that improve the mechanical and chemical properties of steel, while manganese is used in manufacturing glass and ceramics, batteries and fertilizers. According to Shang et al. [26], the full scale of the problem depends on the size of the polluted area, the depth to which pollutants penetrate the soil, and the chemical compositions of the polluting substances.
The quality of soil is mainly determined by the metabolism of organic matter, microorganisms (and the enzymes they produce), and the rate of biogeochemical changes in the circulation of elements [27]. One soil monitoring method involves measuring its biological activity and using multiparametric indicators to assess soil quality [26]. Soil enzymes, being natural catalysts of many soil processes related to the decomposition of organic matter, participate in the processes of releasing mineral substances and supplying them to plant organisms; therefore, as compared against microbiological parameters, they constitute more synthetic indicators of soil changes [28]. Enzymatic activity is an early indicator of changes in the intensity of biological processes and the level of soil degradation and usually correlates with physical and chemical soil properties [29,30,31]. The main sources of enzymes are microorganisms, plant roots and soil fauna. Phosphatases are enzymes that hydrolyse organic phosphorus compounds, and they are the most frequently studied enzymes in soil because they react fastest to the environmental stresses caused by anthropogenic and natural factors.
Given the high pollution potential of municipal landfills, it is worth tracking the levels of selected soil parameters, and changes therein, to analyze the associated environmental risks and potential mitigation measures to sustainably manage municipal landfills. The aim of the study was to assess the impact of a landfill on selected physicochemical and biological parameters in the soil environment in two different seasons of the year. The obtained results will make it possible to estimate the impact of landfilled waste on possible contamination, which may subsequently cause disturbances in the existing ecosystems.

2. Materials and Methods

2.1. Study Area

The studied waste landfill is located about 14 km southeast of the centre of the city of Bydgoszcz (53°03′ N; 18°08′ E). The location is shown in the map in Figure 1 (Central Poland, Kuyavia-Pomerania Province). In that area the climate is cold temperate. The climate in that area has been classified as Dfb compliant with the Köppen–Geiger system. The year-average temperature is 17.7 °C and the year-average precipitation for Bydgoszcz is 600 mm (Figure 2A,B).
According to Statistics Poland [2], 13,117,000 tonnes of municipal waste were generated in Poland, of which 714,000 tonnes were generated in the Kuyavia-Pomerania voivodeship. The city of Bydgoszcz produces approximately 127,263.6 tonnes of waste annually [2]. The municipal waste landfill is located on the premises of the Waste Management Facility (the entity responsible for municipal waste management), which has been operating since October 2007 and has all the required safeguards to prevent contamination from penetrating into the soil and water environment.
The dump was established in 1977 on the site of a former gravel pit. The complex currently consists of three landfills, including one reclaimed (in operation in 1985–2003) and two currently in operation. Since 2015, mixed municipal waste is no longer disposed of at the landfill. This is conducive to faster land restoration. To protect environmental features, the area of the former landfill needed to be secured, and it required technical and biological rehabilitation. A project co-financed by the EU was carried out in the complex whose environmental effect included shaping the cap of the former landfill to integrate it into the surrounding landscape, reducing the flow of precipitation waters into the body and leachates polluting groundwater, limiting the escape of gas from the landfill into the air, and protecting the surrounding area against the spread of particulate matter, pathogenic bacteria and fungi. Upon entering the complex, waste is weighed, inspected, logged, and directed to the appropriate treatment facility. Waste designated as recyclable goes to recycling plants. The composting of biologically active waste produces a fertilizer suitable for a very wide range of uses. The complex also includes the Kuyavia-Pomerania voivodeship’s only functioning hazardous waste landfill (capacity 30,000 m3) and a hazardous waste store. The nearest residential areas are approximately 300 m from the boundaries of the complex. To the south, west and north, the complex is surrounded by forest. This forest constitutes a natural barrier isolating the entire plant. The facilities and infrastructure include: two disposal sectors, a network of drainage pipes, and an unpaved service road.
The designated final landfill area is 5.0 ha. The foreseeable maximum annual amount of waste for storage is 180,000 Mg. The landfill has a capacity of 5,250,000 m3 and receives a maximum of 500 Mg of waste per day.
The landfill is divided into two plots (sectors) with a working area of 25,000 m2 each that are separated from each other by an embankment 0.5 m high (from the base of the landfill). The bottom and slopes of the basin were sealed with a 0.5-cm-thick mat and 1.5-mm HDPE foil.

2.2. Soil Sampling

A total of ten soil sampling points were randomly selected in the landfill and its surroundings. This type of research employs a completely randomized system. Samples are taken at random locations in a homogeneous area [32]. Eight research sites (numbered S1–S8) were located in the landfill; the other two (C—control; S9—arable field) were in adjacent areas. In detail: S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m north from the basin of the active sector; S6—site 10 m north from the active sector; S7—site 50 m north from the active sector; S8—area adjacent to the landfill.
The other two research sites (C—control; S9—arable field) are in adjacent areas. C being on the opposite side from groundwater flow about 1000 m from the edge of the landfill, while point S9 is an area dedicated to maize, about 200 m north of the landfill premises (Figure 1). Sites S1–S8 were covered with grassy vegetation that is periodically mown. Soil samples were collected twice: in spring and autumn of 2018. Samples were taken with an Egner stick from mineral surface levels at a depth of 0–20 cm. Each averaged sample was composed of ten incremental samples.

2.3. Soil Analysis

2.3.1. Physicochemical Parameters of the Soil

In the air-dried soil samples with disturbed structure sieved through ø 2-mm mesh, selected physicochemical properties were assayed: pH in 1M and pH in 1 M KCl measured potentiometrically [33]. The contents of available forms of phosphorus (P) [34] and potassium (K) were also defined (by Egner–Riehm method (DL) [35]), as was the content of available magnesium (Mg) (following the Schachtschabel method [36]). The total content of iron and manganese was determined by ASA method (SOLAAR S4) after mineralization in a mixture of HF + HClO4 acids according to the method of Crock and Severson [37] and bioavailable forms after extraction with DTPA [38]. All analyses were conducted in triplicate, and results were validated using certified material (reference soil sample—Loam Soil No. ERM—CC141).

2.3.2. Microbial and Biochemical Analyses

Microbiological analyses of the soil samples involved determining the number of phosphate-solubilizing microbes. Microbiological inoculation was done for 24 h after sample collection. Ten grams of each soil sample were added to 90 mL of Ringer’s solution. After homogenization for 30 min, ten-fold serial dilutions were made (10−1 to 10−6). Then, inoculations of the prepared soil solutions were made on Pikovskayas agar medium containing Ca3(PO4)2 [39]. The incubation of phosphate-solubilizing microorganisms (PSM) was carried out at 25 °C for 14 days. The number of colony-forming units (CFUs) that was obtained was determined per 1 g of soil dry matter (CFU g−1 d.m. of soil).
The activity of enzymes was tested on fresh, moist, sieved (<2 mm) soils that had been stored at 4 °C for two weeks. Each activity test was performed in triplicate. Alkaline (AlP) (EC 3.1.3.1) and acid (AcP) (EC 3.1.3.2) phosphatase activities in the study soils were assayed by the method of Tabatabai and Bremner [40], which involves the determination of p-nitrophenol released by incubation at 37 °C for 1 h of 1 g soil with 4 mL MUB (Modified Universal Buffer) at pH 6.5 for acid phosphatase and pH 11.0 for alkaline phosphatase.
The availability factor (AF) as suggested by Obrador et al. [41] was calculated for Fe and Mn.
It is expressed as:
A F = A v a i l a b l e   c o n t e n t T o t a l   c o n t e n t × 100
where AF—availability factor (%).
The resistance index (RS) determined according to the activity of enzymes and the number of phosphate-solubilizing microorganisms in the soil was calculated using the formula proposed by Orwin and Wardle [42]:
R S = 1 − 2 D 0 C 0 + D 0
where: D0 = C0 − P0, C0—parameter value in control, P0—parameter value in disturbed soil. The value of the resistance and resilience index is bounded by −1 and +1.

2.4. Statistical Analysis

The Statistica.PL 13.3 package was used for statistical analysis [43]. Measurement data for soil chemical properties (OC, TN, P, K, Mg, TMn, TFe, AMn, AFe) and biological activity (PMS, AlP, AcP) were analyzed using one-way ANOVA with Tukey’s post-hoc honestly significant difference (HSD) test for a significance level p < 0.05 to determine significantly different objects. The research factor was the location from which soil samples were collected for analyses (spatial factor). Data on all soil parameters were subjected to the Shapiro–Wilk test to check the normality of empirical distributions. The results are expressed as an arithmetic mean and standard deviation (±SD). The work uses multidimensional statistical methods: principal component analysis (PCA) and cluster analysis (CA). Both methods are commonly used in environmental research and can be used to determine groups of similar characteristics. These methods are not equivalent but complementary. In the analyzed tests, 15 variables (clay, pH in KCl, OC, TN, OC/TN, P, K, Mg, AlP, AcP, PSM, TMn, TFe, AMn and AFe) describe each of the tested samples in two seasons (spring and autumn). CA can distinguish groups of objects (soil sampling sites: C—control and S1–S9) based on the differentiation of variables. Dendrograms are used to graphically represent multidimensional cluster analysis. Ward’s method [44] was used to calculate the distance between individual clusters.

3. Results and Discussion

The analyzed soil samples contained from 0.58 to 5.18% of loam fraction (Table 1) and in terms of agrotechnical heaviness were classified as very light, light, and medium soils [45]. Soil grain size is a parameter that does not change seasonally, so this property was recorded only once, in the spring. The soils were alkaline (pH in KCl 7.09–7.65 in S1–S8). This is confirmed by the findings of Odom et al. [46]. The soil samples collected from the control site (C) and the cultivated field (S9) were acidic in both spring and autumn (Table 1). OC content ranged from 7.23 to 23.32 g kg−1 in spring and from 6.81 to 27.04 g kg−1 in autumn. The one-way ANOVA variance analysis showed that, in both seasons, this parameter was significantly highest in the vicinity of the leachate outflow (S3) and significantly lowest at the control point (C). According to Agbeshiea et al. [22], high levels of OC in a landfill are attributable to the addition of waste materials such as tree branches, household waste and sawdust and to the increased microbiological activity. The TN content in the tested soil samples was low and did not exceed 1.79 g kg−1 in the spring at the site of the non-active landfill (S2) and 1.62 g kg−1 in the autumn in the vicinity of the leachate outflow near the non-active landfill (S3) (Table 1).
The OC/TN ratio is a basic indicator of the intensity of changes in soil organic matter. The data in Figure 3 show that the OC/TN ratio was lowest in the S2 soil sample (11.40—spring, 12.22—autumn). The OC/TN values were highest in samples S4, S5, S6, which were collected in the area of the sector that has been operating for two years and from the points 5 m and 10 m from that active sector. The OC/TN value is one of the parameters determining the intensity of decomposition of organic matter. A narrow OC/TN ratio (e.g., up to 12) indicates rapid decomposition of organic matter in the soil, and a wide ratio (e.g., over 20) indicates the slower decomposition and accumulation of organic matter [47]. The OC/NT value also indicates the biological activity of soils as manifested by the degree of decomposition of organic matter in the soil.
One of the indicators of an anthropic origin of soil contamination is the content of available phosphorus. In mineral soils, P is present in small amounts. The content of P (spring) was in the range 4.91–30.13 mg kg−1 (Table 2). In soil samples collected in autumn, the content of available P was higher and ranged from 10.09 mg kg−1 to 54.62 mg kg−1. The research did not show any contamination of the soils with P. ANOVA showed that the site from which soil samples were collected influenced the P content in the soil significantly. The significantly highest content of P was found at S2 and S4 (spring) and S4 (autumn). It has been proven that landfill age is the greatest determinant of landfill leachate composition [48]. The phosphorus in municipal waste comes from three basic sources: human physiological functions, 30%–50%; detergents, 50%–70%; industry, 2%–20%. No significant differences were found between S6, S7 and S9, which exhibited the lowest P content (spring). The significantly highest K contents were found at S6 in autumn (406.8 mg kg−1) and at S3 in autumn (531 mg kg−1). The samples from the field and the control samples had the significantly lowest content of available potassium. In spring it was as much as 15 times lower than the seasonal maximum, and in autumn 10 times lower. The content of available Mg was significantly highest at S3 (the vicinity of the leachate outflow from the non-active site) both in spring (46.97 mg kg−1) and in autumn (162.3 mg kg−1) (Table 2). This content exceeded the control by 191% and 400%, respectively. This may be related to the increased content of OC in these soil samples.
The TMn content differs little by sampling date. It ranged from 161 to 301 mg kg−1 in spring and from 120 to 344 mg kg−1 in autumn (Table 3). ANOVA showed that, in both seasons, the TMn content was significantly lowest in the soil from the site 50 m from the active sector (S7). The highest amounts of TMn were found in the locations furthest from the active landfill (S8 and S9). The presence of manganese in soils depends on anthropogenic factors, the content in the bedrock and the soil formation process determining its vertical distribution [49]. In the studied soil samples, the TFe content ranged from 4003 to 8842 mg kg−1 in spring and from 3423 to 10,738 mg kg−1 in autumn (Table 3). The significantly highest amounts of TFe were recorded in autumn at the leachate outflow site near the non-active landfill (S3) and at the site 10 m from the active sector (S6). The contents of iron and manganese in soils can vary significantly depending on soil texture and genetic horizon, and in the analyzed soils they did not exceed the geochemical background value [49,50]. Iron and manganese are among the essential micronutrients for living organisms. They are involved in biological processes (photosynthesis and respiration) and perform physiological and biochemical functions in plants [49].
The information on total heavy-metal contents in soils does not reflect the actual availability of those metals to plants, nor, therefore, their capacity to enter the biological cycle. For this, the contents of bioavailable forms need to be determined (Table 4). In spring, the significantly highest contents of bioavailable forms were found at the S2 site for manganese and in the control for iron. The highest springtime contents of these two bioavailable micro-elements did not coincide with the highest levels determined in autumn. In autumn, the significantly highest AMn values were recorded at S3 and S5. These were locations of leachate outflow from the non-active site and 5 m from the basin of the active landfill. The S3 point also recorded the significantly highest available iron content. Of the trace elements, manganese and iron are those taken up in the greatest amounts by plants. Their bioavailability is mainly shaped by the soil organic matter content, the reaction and the change in redox potential [51,52].
The calculated high values of the bioavailability coefficient (AF) in most cases exceeded 50% for Mn and for Fe at two sampling sites (control—C; field—S9), which may pose a risk of metals accumulating in plants (Figure 4). The increased mobility of elements in the soil increases their accumulation in plants, which poses a serious threat to the plants. The mobility of elements in landfill soils is determined by environmental conditions. Data provided in the literature show that the increase in redox potential and decrease in pH that occur in the acid phase of waste disposal containing only small amounts of organic matter increase the mobility of some micro-elements [53,54]. Perhaps the specific conditions at the landfill (the types of deposited waste and the lithologically diverse soil) contributed to these high bioavailability coefficients.
A landfill that contains municipal and industrial waste and water is a biochemical reactor in which, under the influence of atmospheric factors (precipitation, temperature) and microorganisms (aerobic, anaerobic bacteria and fungi), biochemical and microbiological transformations occur [8,10,11]. ANOVA showed significant changes in AlP and AcP activities depending on the soil sampling site. In their research, Lemanowicz et al. [11] and Datta et al. [55] also showed that the activity of enzymes (dehydrogenases, alkaline phosphatase, acid phosphatase, urease, and nitrate reductase) varied between landfill locations that differed in morphology. Different parts of a landfill may be in different phases of decomposition processes, making the landfill an extremely heterogeneous environment. The significantly highest springtime activity of AlP was found in S2 (1.43 mM pNP kg−1 h−1) (Table 5). No significant difference in activity was found between S1, S3, S4. By contrast, in autumn, the highest AlP activity (1.87 mM pNP kg−1 h−1) was at S4. The significantly highest activity of AcP was found at S4 in both spring (2.69 mM pNP kg−1 h−1) and autumn (3.50 mM pNP kg−1 h−1). Tripathy et al. [56] also found the activity of the tested enzymes to be higher under the influence of solid waste in the soil of a more-than-hundred-year-old municipal landfill than in a control soil around the study area. This was associated with a higher content of organic carbon. AcP activity was higher in the autumn than in the spring. Błońska [57] noted that the minimum activity of enzymes occurred in spring and autumn, whereas the summer months, when the soil is in a state of equilibrium (temperature, humidity), favors the development of soil micro-organisms, intensifying biochemical transformations. Sufficiently high soil moisture is a basic condition for the activity of soil enzymes. Water scarcity severely disturbs the development and activity of soil microflora. Seasonal changes in the activity of enzymes (invertase, catalase, phenol oxidase, urease) have been noted by Wang et al. [58]. They showed the activity to be highest in summer, lower in autumn and lowest in spring.
The results from the microbiological analyses indicate the accumulation of phosphate solubilizing microbes (PSM) to be highest in the closed, non-active sector—at point S2 (Table 5). Significantly lower values were statistically confirmed for the spring at all other analyzed points. By contrast, for the autumn, no such distinct differences were noted. It can be clearly stated that the number of PSMs was lowest at the control point (C) and in the zone furthest from the active sector. The abundance of microorganisms probably indicates the accumulation of significant amounts of organic matter in these places and easy availability of nutrients. PSMs are widely distributed in soil, but are also found in sediments, freshwater and seawater [18,59] and are responsible for the circulation of insoluble P to the soluble PO43− ion. They can increase the level of phosphorus (P) available to plants, which is of great importance in agricultural ecosystems. PSMs have proven effective at promoting plant growth [60] and immobilizing heavy metals [61,62]. The PSM values obtained for the monitored landfill and in its vicinity can be used to identify the points with the potentially most intense microbiological transformations of phosphorus compounds. Recent studies by Wan et al. [63] have shown PSBs to be able to use multiple phosphorus sources and to immobilize Pb.
Resistance is a key concept in studying the regeneration of human-impacted soils [64]. According to Allison and Martiny [65], it can be defined as the ability of the soil environment to return to its original, pre-disturbance state. The concept is part of ecological stability. The RS index proposed by Orwin and Wardle [42] can indicate the stability of soil affected by an anthropogenic factor. The RS index values presented in Figure 5 indicate that the biological parameters (AlP, AcP and PSM) differed by test site, and thus by the site’s distance from the active sector of landfill waste, in both seasons (spring and autumn). The springtime RS value for AlP was highest at S9 (0.865) and the lowest at S6 (0.150), S2 (0.185) and S7 (0.188). Negative values for RS for AlP were obtained in autumn from S3 and S4. For AlP, the lowest RS values in both spring and autumn were found at S2–S4. Meanwhile, the highest were at S8 (0.821) and S5 (0.748). By comparing RS index values between hydrolytic enzymes, it was possible to determine that their resistance to human impact was AlP > AcP. For the value of this index for PSMs, positive values were noted only in autumn at the three points furthest from the active landfill sector (S9, S1, S8), with the highest value being found in the maize field. Meanwhile, in the spring, only negative RS values were obtained for PSMs, regardless of location. According to Orwin and Wardle [42], higher RS index values indicate maximum resistance of the biological parameters tested, and thus that the human impact was smallest in these places.
Principal component analysis (PCA) was used to explain the soil’s differentiation in the tested physicochemical and biological parameters (clay, pH in KCl, OC, TN, OC/TN, P, K, Mg, PSM, AlP, AcP, TMn, TFe, AMn, and AFe) in the two research seasons (spring and autumn).
PCA identified three components that together accounted for 79.61% (spring) of total variance, of which most was explained by PC1 (36.52%) and PC2 (29.17%) (Figure 6A). PCA showed that the first component (PC1) was significantly negatively associated with the content of OC (−0.937), TN (−0.745), P (−0.771), AlP (−0.623), AcP (−0.718), PSM (−0.800) and AMn (−0.715). The second component (PC2) was significantly positively associated with clay (0.631), pH KCl (0.515), OC/TN (0.756), K (0.815), Mg (0.659) and TFe (0.760).
PCA was also used to verify the significance of correlations between individual soil parameters. In spring, OC content was significantly positively correlated with P (r = 0.711), AlP (r = 0.626), AcP (r = 0.766), PSM (r = 0.610) and AMn (r = 0.657) (Figure 6A). TN content was significantly positively correlated with AcP (r = 0.647) and PSM (r = 0.755). Clay content was positively correlated with the content of K (r = 0.612) and Mg (r = 0.506). A significant negative relationship was found between pH in KCl and the content of TMn (r = −0.635) and AFe (r = −0.851).
Dendrograms showing the closeness of connections between soil sampling sites were created using cluster analysis based on Euclidean distances between characteristics and a method for agglomerating them according to Ward’s method [44]. The clustering method identified four clusters (Figure 6B) that are significant for having similar characteristics or types of natural background sources. CA analysis showed that, in terms of physicochemical and biological properties, the greatest difference was between cluster 1, comprising C (the control) and S9 (the arable field) and cluster 4, comprising S3 (adjacent to the leachate outflow from the non-active site) and S6 (10 m from the active sector). It can be concluded that there is a clear similarity in the tested parameters between the two points furthest from the landfill. The different clusters may suggest a different source of macro- and micro-element content and biological parameters. Cluster 2 comprises sites S2, S7 and S8, which were characterized by high phosphatase activity, PSM content and TMn content. Cluster 4 comprises points S1, S4 and S5, which have the highest AMg content.
PCA for autumn detected three components that accounted for 80.90% of total variance, of which most was explained by PC1 (46.96%) and PC2 (20.72%) (Figure 7A). The first component (PC1) significantly negatively correlated with contents of OC (−0.915), TN (−0.669), K (−0.675), Mg (−0.718) AlP (−0.681), AcP (−0.870), PSM (−0.850), TFe (−0.892), AFe (−0.879), AMn (−0.555) and AFe (−0.879). The second component (PC2) correlated significantly negatively with clay (−0.716) and AMn (−0.571) and positively with P (0.683) and OC/TN (0.701). For both spring and autumn, PCA grouped most of the characteristics on the PC1 side, which can thus be broadly equated with anthropogenic effects on soil.
In autumn, OC was found to significantly positively correlate with: P (r = 0.526); Mg (r = 0.681); AFe (r = 0.978); the activity of AlP (r = 0.731) and AcP (r = 0.720); PSM content (r = 0.700) (Figure 7A). TN content correlated positively with Mg (r = 0.783) and AFe (r = 0.692). A significant relationship was found between AlP activity and P content (r = 0.685). PSM content correlated significantly with the activities of AlP (r = 0.797) and AcP (r = 0.962) and the contents of TFe (r = 0.658) and AFe (r = 0.703). A significant positive correlation was also obtained between AFe content and AlP (r = 0.769) and AcP (r = 0.693).
Figure 7B (autumn) reveals that the greatest similarity is between S2 and S8. These sites are characterized by similar contents of OC and TFe, average content of K, and activity of AlP and AcP. After these two sites, the next most similar are: S1 and S9; S2 and S8 (all four of which are in cluster 2); and C and S7 (cluster 1). These sites constitute a group that very little resembled S3, S4, S5 and S6 (cluster 3).
In soil, iron and manganese occur mostly as mineral-organic complexes. Organic compounds of iron generally have greater mobility than “pure” iron, but this does not apply to the process of adsorption by soil humus [66,67]. The correlation analysis confirmed a significant positive relationship between organic carbon content and bioavailable Fe content in autumn (r = 0.978). In humic soils, iron forms chelate bonds that are soluble in water. This makes this element easily absorbed by plants [68]. Manganese is also bound in large amounts by humus and mineral colloids, as evidenced by the significant positive correlation between OC and AMn found in spring (r = 0.657). The bioavailability of Fe and Mn is also influenced by soil pH. Studies by Odom et al. [46] found that the pH value of all tested soil samples was within the alkalinity range, so the tested metals (Cu, Cd, Fe, Ni and Zn) probably could not have been bioavailable to plants. In the authors’ own research, such a relationship was noted for the spring for bioavailable iron (r = −0.851). Low content of available iron is associated with neutral or slightly alkaline soil reaction, and the solubility of iron under such conditions is low [69]. Enzymes can be both inhibited and activated by a variety of specific molecules or ions. Soil enzymes are therefore very sensitive to stresses associated with metal contents in the soil environment [70,71]. In this study, the permissible total contents of Mn and Fe were not exceeded, and these contents did not inhibit the enzymes tested. Based on the correlation analysis, between the content of available iron forms and AlP and AcP, only one significant positive correlation each was found (in autumn: r = 0.769 and r = 0.693, respectively). However, no significant relationships were found between the TFe and TMn contents and AlP and AcP activities. This is probably associated with the physicochemical properties of the tested soil. Mn2+ ions, being low-molecular-weight compounds, are activators whose presence during enzymatic catalysis accelerates the reaction. According to Slunjski et al. [72], in strongly acidic soils, secondary clay minerals decompose and form free aluminium, iron and manganese ions, which deposit phosphate ions. However, the tested soils (except C and S9) showed an alkaline reaction and Fe and Mn contents did not exceed permissible standards. The lack of enzyme inhibition under the influence of heavy metals could be caused by organic matter protecting the enzyme protein against negative environmental factors. Higher OC content tends to increase the rate of mineralization by microorganisms, resulting in increased enzymatic activity [58]. According to Błońska et al. [73], high soil enzyme activity is associated with higher organic matter content when the OC/TN ratio is low. Organic matter has a protective function for enzymes, which are thus immobilized because they are more resistant to proteolysis. Higher contents of organic matter usually increase the biological activity of soil. This accelerates mineralization, thereby contributing to an increase in the pool of nutrients in the soil, as demonstrated by the positive correlation between OC content and the activity of soil phosphatases. Nonetheless, Roy et al. [74] found that the stability and synthesis of enzymes was inhibited due to so-called metal stress, despite a high content of organic matter. In influencing the activity of enzymes, heavy metals act additively, synergistically or antagonistically. In the authors’ own research, the forms of Fe and Mn in the landfill soil were probably insufficiently bioavailable to adversely affect microorganism populations and, hence, the activity of enzymes in the soil. It is also known that, in low concentrations, heavy metals are activators of many enzymes. Phosphatases play a key role in the biochemical mineralization of organic phosphorus bonds, which may be a good indicator of organic phosphorus mineralization potential and soil biological activity [75]. Phosphorus-poor soils usually have higher production of phosphohydrolases, which means that the enzymatic release rate of phosphates from organic compounds is determined by the end-product of several chemical reactions. The increase in phosphatase activity under conditions of reduced phosphorus availability or increased phosphorus demand suggests that phosphatases are involved in regulating phosphate metabolism [11]. Under conditions of low phosphorus availability, acid phosphatase is secreted by roots, which increases phosphate re-mobilization and solubilization, thereby helping the plant to cope with P stress conditions [76]. Datta et al. [55] have emphasized the need for research into SEA (soil enzyme activities) as a bio-indicator of soil degradation due to over-exploitation. The distribution of phosphorus in organic substrates correlates with P content in PSM biomass [77]. This process involves enzymes – mainly acid and alkaline phosphomonoesterases, which are produced by PSM depending on external conditions [75]. The statistical analysis confirmed a high dependence between the activity of phosphatases (AIP and AcP) and the abundance of heterotrophic soil microorganisms participating in the transformation of phosphorus compounds. Both our results and studies by other researchers [11,18,59] dealing with the circulation of phosphorus in soil clearly indicate that the main source of phosphatases in soil is microorganisms classified in the PSM group. Thus, in soils of acidic or slightly acidic pH, AcP-synthesizing filamentous fungi are usually dominant among phosphatase-secreting microbiota. The positive correlation between abundance of microorganisms (PSM) and parameters such as OC, TN and contents of other elements (FeT, FeA) is a well-known phenomenon, as confirmed by the findings of Kumar and Rai [78], Wan et al. [63] and Lemanowicz et al. [11].

4. Conclusions

Based on the research conducted, it can be concluded that the landfill in question has significantly affected the soil environment, as confirmed by physicochemical and biological parameter values. The factor that affected the changes in the tested parameters was the site from which the soil samples were taken. These sites differed in the storage time of the waste. Apart from the changes in the content of macro- and micro-elements, the statistically highest abundances of phosphate solubilizing microorganisms were found at point S2 (non-active sector closed off for 2 years), which also indicates that the biological activity is greater in the soil on the site and closer to the landfill than in the more distant soil. The organic carbon content and the OC/TN ratio value determined the spatial distribution of phosphate solubilizing microbes and the activity of alkaline and acid phosphatase. The activity of alkaline phosphatase was higher in the soil from the non-active sites than in the control. Thus, the activities of phosphomonesterases can be used as very sensitive indicators of soil transformations. In most cases, bioavailable macronutrient contents were higher in the soil from the municipal landfill than in the control soil.
Permissible levels of Mn and Fe contents were not exceeded. The total content of the analyzed microelements was at the geochemical background level.
Statistical analysis of data using principal component analysis (PCA) and cluster analysis (CA) confirms that the condition of the analyzed land is closely associated with proximity to the landfills. It is now recognized that, regardless of the economic status of a country, pollution is the factor of greatest concern for maintaining ecosystem homeostasis. A comprehensive understanding of the physicochemical and biological properties of soils within landfills, infiltration pathways and spatial impact on the environment can be useful in planning sustainable municipal waste management. It will also facilitate full assessment of threats to human health and the ecosystem. Therefore, further, extended research should be carried out to identify the biogeochemistry of municipal landfills.

Author Contributions

Conceptualization, J.L., A.B. and B.B.-B.; methodology, J.L., A.B. and B.B.-B.; software, J.L., A.B. and B.B.-B.; validation, J.L., A.B., B.B.-B., P.S., S.A.H. and I.J.; formal analysis, J.L., A.B. and B.B.-B.; investigation, J.L., A.B., B.B.-B., P.S., S.A.H. and I.J.; resources, J.L., A.B., B.B.-B. and I.J.; data curation, J.L., A.B. and B.B.-B.; writing—original draft preparation, J.L., A.B. and B.B.-B.; writing—review and editing, J.L., A.B., B.B.-B., P.S., S.A.H. and I.J.; visualization, J.L. and P.S.; supervision, J.L., A.B., B.B.-B., P.S., S.A.H. and I.J. All authors have read and agreed to the published version of the manuscript.

Funding

Bydgoszcz University of Science and Technology, Laboratory of Soil Science and Biochemistry (grant BN 38/2019) and University of Warmia and Mazury in Olsztyn, Faculty of Agriculture and Forestry, Department of Soil Science and Microbiology (grant No. 30.610.005-110).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location of study area. Legend: C—control site outside the landfill’s zone of impact; S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m north from the basin of the active sector; S6—site 10 m north from the active sector; S7—site 50 m north from the active sector; S8—area adjacent to the landfill fence; S9—arable field of maize monoculture about 200 m north of the landfill premises.
Figure 1. Location of study area. Legend: C—control site outside the landfill’s zone of impact; S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m north from the basin of the active sector; S6—site 10 m north from the active sector; S7—site 50 m north from the active sector; S8—area adjacent to the landfill fence; S9—arable field of maize monoculture about 200 m north of the landfill premises.
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Figure 2. Annual temperature (A) and average monthly precipitation (B) for Bydgoszcz.
Figure 2. Annual temperature (A) and average monthly precipitation (B) for Bydgoszcz.
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Figure 3. TOC/TN ratio in the soil. C—control site outside the landfill’s zone of impact; S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m from the basin of the active sector; S6—site 10 m from the active sector; S7—site 50 m from the active sector; S8—area adjacent to the landfill fence; S9—arable field of maize monoculture about 200 m north of the landfill premises.
Figure 3. TOC/TN ratio in the soil. C—control site outside the landfill’s zone of impact; S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m from the basin of the active sector; S6—site 10 m from the active sector; S7—site 50 m from the active sector; S8—area adjacent to the landfill fence; S9—arable field of maize monoculture about 200 m north of the landfill premises.
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Figure 4. Availability factor AF (%) for manganese (Mn) and iron (Fe).
Figure 4. Availability factor AF (%) for manganese (Mn) and iron (Fe).
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Figure 5. Resistance index (RS) values for alkaline (AlP) and acid (AcP) phosphatase activities and phosphate solubilizing microbes (PSM) in the soil.
Figure 5. Resistance index (RS) values for alkaline (AlP) and acid (AcP) phosphatase activities and phosphate solubilizing microbes (PSM) in the soil.
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Figure 6. Configuration of variables in the system of the first two axes PC1 and PC2 of principal components (A); cluster analysis (B). In spring.
Figure 6. Configuration of variables in the system of the first two axes PC1 and PC2 of principal components (A); cluster analysis (B). In spring.
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Figure 7. Configuration of variables in the system of the first two axes PC1 and PC2 of principal components (A); cluster analysis (B). In autumn.
Figure 7. Configuration of variables in the system of the first two axes PC1 and PC2 of principal components (A); cluster analysis (B). In autumn.
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Table
Table 1. Content of clay fraction, pH in KCl and organic carbon (OC) and total nitrogen (TN) in soil.
Table 1. Content of clay fraction, pH in KCl and organic carbon (OC) and total nitrogen (TN) in soil.
Sites *Clay %pH KClOC [g kg−1]TN [g kg−1]
SpringAutumnSpringAutumnSpringAutumn
C0.644.184.377.23 (±0.14) j6.81 (±0.01) g0.40 (±0.01) e0.37 (±0.03) f
S13.557.097.2114.60 (±0.02) d7.39 (±0.02) f0.98 (±0.02) bc0.47 (±0.08) e
S24.707.397.4520.4 (±0.28) b10.02 (±0.01) c1.79 (±0.02) a0.82 (±0.02) b
S35.187.587.6423.32 (±0.05) a27.04 (±0.06) a1.02 (±0.03) b1.62 (±0.10) a
S40.587.617.6917.14 (±0.01) c20.71 (±0.01) b0.37 (±0.01) f0.39 (±0.01) f
S54.977.577.6812.23 (±0.05) f10.06 (±0.10) c0.31 (±0.01) f0.36 (±0.02) f
S65.107.557.719.54 (±0.08) h8.93 (±0.01) e0.14 (±0.03) g0.27 (±0.02) g
S71.697.67.5913.89 (±0.06) e3.53 (±0.09) h0.89 (±0.02) c0.20 (±0.002) h
S83.547.657.5110.21 (±0.02) g10.06 (±0.01) c0.59 (±0.03) d0.61 (±0.02) d
S91.595.405.418.90 (±0.01) i9.78 (±0.02) d0.56 (±0.02) d0.71 (±0.01) c
* C—control site outside the landfill’s zone of impact; S1—site by the pumping station; S2—non-active sector closed off for 2 years; S3—vicinity of the outflow of leachates from the non-active sector; S4—landfill premises adjacent to a sector that has been in operation for 2 years (landfill basin); S5—site 5 m from the basin of the active sector; S6—site 10 m from the active sector; S7—site 50 m from the active sector; S8—area adjacent to the landfill fence; S9—arable field of maize monoculture about 200 m north of the landfill premises. Values followed by the same small letter within each column are not significantly different at p < 0.05. Different small letters indicate comparison between sites.
Table
Table 2. Content of available phosphorus (P), potassium (K) and magnesium (Mg) in soil.
Table 2. Content of available phosphorus (P), potassium (K) and magnesium (Mg) in soil.
Sites *P [mg kg−1]K [mg kg−1]Mg [mg kg−1]
SpringAutumnSpringAutumnSpringAutumn
C8.52 (±0.26) d11.92 (±0.09) ef24.42 (±0.68) f43.28 (±0.91) f16.09 (±0.40) e32.00 (±1.06) e
S19.59 (±0.31) d10.09 (±0.01) f54.68 (±0.72) d73.13 (±0.47) de12.25 (±0.11) fg24.55 (±0.33) gh
S230.13 (±0.37) a16.70 (±0.26) d55.11 (±0.52) d75.73 (±0.42) de14.22 (±0.19) ef28.70 (±0.22) ef
S319.01 (±0.38) b22.18 (±0.40) c387.08 (±5.46) b531.75 (±8.24) a46.97 (±0.89) a162.3 (±0.57) a
S428.34 (±0.61) a54.62 (±1.13) a56.33 (±0.68) d85.75 (±0.53) d11.79 (±0.16) g23.55 (±0.49) h
S510.10 (±0.43) d13.93 (±0.52) de74.10 (±1.01) c139 (±1.16) c15.11 (±0.45) e49.80 (±0.48) d
S64.91 (±0.40) e15.74 (±0.37) d406.8 (±1.90) a515 (±1.86) b40.24 (±0.41) b79.25 (±0.76) b
S75.905 (±0.11) e12.41 (±0.30) ef41.32 (±0.61) e68.01 (±0.49) e14.99 (±0.27) e27.65 (±0.74) fg
S815.92 (±0.48) c41.83 (±0.86) b42.81(±0.52) e68.68 (±0.55) e27.89 (±0.29) c55.20 (±0.67) c
S96.335 (±0.16) e10.62 (±0.19) f26.12 (±0.49) f52.65 (±0.47) f25.49 (±0.50) d50.51 (±0.91) d
* See Table 1.
Table
Table 3. Content of total manganese and iron in soil.
Table 3. Content of total manganese and iron in soil.
Sites *MnT [mg kg−1]FeT [mg kg−1]
SpringAutumnSpringAutumn
C299 (±7.21) a258 (±10.14) b5671 (±51.37) f3423 (±9.84) g
S1228 (±10.11) b134 (±17.36) d7053 (±6.69) c5224 (±21.08) e
S2219 (±12.76) b219 (±8.05) bc6188 (±11.78) d6361 (±29.31) d
S3220 (±11.67) b261 (±22.86) b8745 (±23.72) a10738 (±28.72) a
S4224 (±1.24) b250 (±16.91) bc7581 (±13.15) b8431 (±19.57) c
S5205 (±4.16) bc341 (±15.21) a7768 (±16.20) b9138 (±6.88) b
S6227 (±0.279) b208 (±7.49) c8842 (±18.19) a9193 (±10.15) b
S7161 (±4.23) c120 (±7.30) d5889 (±68.54) e3652 (±29.33) g
S8301 (±5.09) a310 (±8.92) a5690 (±63.78) f6394 (±36.75) d
S9290 (±8.09) a344 (±3.96) a4003 (±33.34) g4806 (±5.91) f
* See Table 1.
Table
Table 4. Content of available forms of manganese and iron.
Table 4. Content of available forms of manganese and iron.
Sites *AMn [mg kg−1]AFe [mg kg−1]
SpringAutumnSpringAutumn
C109 (±9.28) de125 (±8.28) de3641 (±20.98) a656 (±19.2) def
S1151 (±6.20) ab83 (±4.00) f1023 (±19.95) f613 (±12.95) ef
S2186 (±8.09) a197 (±4.40) ab1377 (±22.82) e721 (±12.12) cd
S3172 (±3.58) ab218 (±5.64) a2406 (±14.77) c2684 (±21.41) a
S4106 (±2.65) e100 (±6.28) ef1024 (±15.85) f2015 (±29.73) b
S5147 (±5.30) bcd233 (±9.89) a696 (±11.49) g749(±10.70) c
S6149 (±9.38) abc150 (±7.49) cd657 (±13.73) gh714 (±10.41) cd
S7111 (±9.21) cde71 (±9.42) f1629 (±23.84) d422 (±9.86) g
S8147 (±2.28) bcd171 (±5.44) bc758 (±16.13) h593 (±10.12) f
S9138 (±9.60) bcde131 (±4.28) de3003 (±20.34) b674 (±9.28) de
* See Table 1.
Table
Table 5. Activity of alkaline (AlP) and acid phosphatase (AcP) and number of phosphate solubilizing microbes (PSM) in the soil.
Table 5. Activity of alkaline (AlP) and acid phosphatase (AcP) and number of phosphate solubilizing microbes (PSM) in the soil.
Sites *AlP [mM pNP kg1 soil h−1]AcP [mM pNP kg1 soil h−1]PSM [jtk g−1]
SpringAutumnSpringAutumnSpringAutumn
C0.847 (±0.01) c0.537 (±0.01) ef1.32 (±0.04) ef1.58 (±0.05) c1.77 (±0.61) c7.55 (±1.08) b
S11.07 (±0.02) b0.428 (±0.01) f1.63 (±0.03) e1.95 (±0.06) c12.59 (±1.64) b3.45 (±0.59) b
S21.43 (±0.07) a0.957 (±0.03) c2.93 (±0.04) a3.26 (±0.02) ab118.97(±28.98) a38.27 (±16.14) a
S31.09 (±0.03) b1.29 (±0.01) b2.39 (±0.06) b3.32 (±0.01) ab21.33 (±7.39) b39.73 (±14.6) a
S41.19 (±0.02) b1.87 (±0.06) a2.69 (±0.03) a3.50 (±0.05) a35.87 (±7.06) b40.51(±5.79) a
S50.428 (±0.01) e0.529 (±0.02) ef1.51 (±0.04) de2.36 (±0.03) bc18.40 (±3.19) b20.17 (±0.04) ab
S60.221 (±0.01) f0.625 (±0.03) de0.473 (±0.03) g2.60 (±0.04) ab9.57 (±3.55) b22.49 (±1.86) ab
S70.269 (±0.02) f0.907 (±0.04) c1.56 (±0.06) de2.49 (±0.55) bc9.46 (±1.26) b19.01 (±4.19) ab
S80.507 (±0.01) de0.611 (±0.02) de1.19 (±0.02) f2.35 (±0.01) c10.15 (±2.26) b14.00 (±4.37) b
S90.786 (±0.01) c0.725 (±0.01) d1.91 (±0.08) c2.09 (±0.02) c3.65 (±0.63) c5.28 (±4.60) b
* See Table 1.
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Lemanowicz, J.; Bartkowiak, A.; Breza-Boruta, B.; Sowiński, P.; Haddad, S.A.; Jaskulska, I. The Impact of Municipal Waste on Seasonal and Spatial Changes in Selected Macro- and Micro-Nutrient Contents on the Background of Soil Biological Activity—A Case Study. Minerals 2023, 13, 47. https://doi.org/10.3390/min13010047

AMA Style

Lemanowicz J, Bartkowiak A, Breza-Boruta B, Sowiński P, Haddad SA, Jaskulska I. The Impact of Municipal Waste on Seasonal and Spatial Changes in Selected Macro- and Micro-Nutrient Contents on the Background of Soil Biological Activity—A Case Study. Minerals. 2023; 13(1):47. https://doi.org/10.3390/min13010047

Chicago/Turabian Style

Lemanowicz, Joanna, Agata Bartkowiak, Barbara Breza-Boruta, Paweł Sowiński, Samir A. Haddad, and Iwona Jaskulska. 2023. "The Impact of Municipal Waste on Seasonal and Spatial Changes in Selected Macro- and Micro-Nutrient Contents on the Background of Soil Biological Activity—A Case Study" Minerals 13, no. 1: 47. https://doi.org/10.3390/min13010047

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