Microplastic Soil Contamination Experiments


The issue of environmental pollution, conservation, and management is a household topic in contemporary times. Pollution of the air, soil, and water has increasingly become more evident in the past years as a result of diversified human activities (Bläsing and Amelung, 2018). Various organizations and stakeholders have stepped forward in the past years to try and address the problem. However, it is clearly evident that most of them have not focused on soil pollution, especially with regards to contamination as a result of microplastic build-up.

Currently, microplastic contamination is among the most significant environmental concerns. Microplastics can be described as pieces of plastic that are less than five millimeters in size (Hebner and Maurer-Jones, 2020). They are mainly used in manufacturing various products which are consumed by human beings such as toothpaste, fabrics, a skincare material. There are a number of ways in which microplastics find their way into the soil and contribute to its contamination. For instance, the particles can be washed into the drains and later released into the soil and water because they cannot be removed through treatment (Bläsing and Amelung, 2018). It is important to understand the impact of microplastic contamination in the soil and create a broader foundation to address the problem of soil pollution

Background Information

Technological advancement among human beings has significantly increased the use of plastic over the years. Despite the first synthetic plastics being made in the 19th century, their intense use around the globe began in the late 20th century (Guo et al., 2020a).

Plastic has since become one of the most instrumental components in human life owing to its numerous uses. Interestingly, the demand for plastic is still on the rise, while efforts to recycle the waste plastics worldwide have not been maximized (Li, Tse, and Fok, 2016). Microplastics are therefore continuously accumulating on the earth’s surface due to the immense usage of plastics.

The complexity of the composite structure of plastics can be intensified by technological modification with plasticizers, substances that increase the thermal and chemical resistance of compounds. According to information provided by Maraveas (2020), the plasticizer market is proliferating. This means that the identification of plastic contaminants can be complicated by the complex composition of the product. All of the above leads to the conclusion that plastics are incredibly large organic molecules with a correspondingly high molecular weight.

Most contemporary studies focus on the methods of identifying and extracting microplastics responsible for pollution in different environments. The majority of the studies have concentrated on the microplastics in water; however, little research has been done to investigate the same aspects of the pollutants in the soil (He et al., 2018). No standardized methods have yet been developed for identification and measurement. Is it still not established how much microplastics can exist within the soil and what can be regarded as the maximum concentration limit (Ng, Minasny, and McBratney, 2020). The inconsistency of sampling methods results in various sampling units, such as abundance per surface area, abundance to depth, volume in cubic meters, and weight ratios (Ng, Minasny, and McBratney, 2020). Different extraction techniques are used to affect the measurements of the collected plastic contaminants. Along with the development of detection, identification, and quantification methods, contemporary studies aim at analyzing the ecological risks, impact on health, pollution characteristics, and remediation strategies.

The composition of various plastics has a slight variation which makes each of them unique and distinct. The microplastics in the soil may be having different characteristics such as shape, rigidity, polymer type, and texture, among other attributes (Blasing and Amelung). Most of the microplastics are inert because of their distinct chemical structures. Additionally, they are also mainly hydrophilic, and they have a large surface area to volume ratio, which makes them accumulate substances on the surface that may be considered toxic (Guo et al., 2020b). The accumulation of toxins in the soil makes the microplastics dangerous because they pose ecotoxicological threats to the soil biota (Prata et al., 2019). When these substances are consumed as part of the food from the soil, they can have numerous detrimental health impacts on individuals and may also result in loss of life.

The studies on microplastics in soil are being taken up as a new field in research. The few studies which have been conducted are mainly exploratory research aimed at enhancing the detection of microplastics in various soil environments. The need to establish an in-depth knowledge of microplastics dynamics makes it highly relevant for systematic studies to be adopted. Therefore, it is important to pay significant interest to the sampling strategies which yield the results of soil composition in different environments (Beć, Grabska, and Huck, 2020). Again, while conducting the experiment, it is important to adopt sampling techniques and strategies that ensure the soil samples are not contaminated before they are analyzed.

The most recent strategy for microplastics identification in the soil is infrared screening. The research conducted by Ng, Minasny, and McBratney (2020) used visible-near-infrared (vis-NIR) spectroscopy and a convolutional neural network (CNN) model to detect microplastics and measure their concentration (Ng, Minasny, and McBratney, 2020). Vis-NIR scanning was proved to be feasible in predicting microplastics in the soil, while the CNN model allowed the researchers to classify various degrees of contamination based on concentration (Ng, Minasny, and McBratney, 2020). The methods turned out to be efficient for the identification and quantification of microplastics and have the potential for further development and implementation.

The dangers of soil pollution are evident and can be pointed out in the various environment. Microplastics that find their way into the soil pose a real threat to the environment because they can accumulate and reach high levels, which can directly affect biodiversity (Horton et al., 2017). Other than directly affecting the environment, they can act as a route for the transfer of other pollutants to the soil biota hence posing a danger. Research shows that the total amount of microplastic contamination in the soil may be 4-23-fold more than that in the water bodies (Horton et al., 2017). The amount of microplastic in the soil is thus significant, and there is a need to study it more effectively.

Threats to soil fertility

The most important task of agronomy and environmental monitoring is to conduct in-depth research into the negative processes that develop in soils as they intensify pollution, which significantly limits crop productivity. Soil fertility is a central criterion of the quality of agricultural land to select it as the area to be used. The main determinants of satisfactory soil condition are pH, micronutrient composition, plant ability to access nutrients, and water quality (Grant, 2016). For instance, it is known that microplastics deposited in soil layers significantly impede the penetration of liquids and nutrients into plants. Moreover, modified polymers with toxic inclusions in the soil cause the inevitable diffusion of toxic compounds into the environment (Xu et al., 2020). This fact negatively impacts soil acidity and the survival of organisms that form the microflora of soil resources and on nitrogen and humus bacteria (Lladó, López-Mondéjar, and Baldrian, 2017). Soil with dead or defective microorganisms becomes an unfavorable environment for plant development.

Suppression of organism growth

It is known that soil organisms, in particular earthworms, play a role in the nutrient saturation of land. Therefore any interference with their activities can have a severe negative impact on ecosystems and prevent the growth of the crops that people eat. Worms absorb dead organic matter, improve soil structure, promote drainage and even prevent erosion (Cunha et al., 2016). With this in mind, the suppression of soil worms is particularly serious. According to a study by Boots, Russell, and Green (2019), exposure to plastic, which is commonly used to produce bottles and bags, resulted in earthworms losing 3.1% of their weight in 30 days. Reducing weight and underdevelopment can seriously damage biodiversity. Although the exact causes of this phenomenon have not been identified, the suppression effect appears to be similar to that seen in hydrobionts. In aquatic worms, microplastics cause obstruction and irritation of the digestive tract, making it more difficult for animals to absorb nutrients, and their growth slows down (Boots, Russell, and Green, 2019). By the way, any contamination that affects the health of rainworms can harm other aspects of the soil ecosystem, such as plant growth.

Microplastics have also posed a serious concern to marine life indirectly. They indirectly affect the ecosystem because they aid in the absorption of various marine pollutants, which threaten the marine ecosystem. The microplastics have a large surface area to volume ratio, enabling them to easily absorb hydrophobic pollutants in the waters. Thus, it is evident that microplastics are a threat to aquatic life (Boots, Russell, and Green, 2019). The threat posed by this substance to the soil is relatively similar to those posed in the aquatic environments. However, we cannot use the same techniques to identify and quantify the microplastic substances on soil and water. This is because, as opposed to removing the microplastic particles from water, it is much harder to remove them from soil because the solid particles of the soil obscure them (Cunha et al., 2016). Therefore, there is a need to conduct more research on sampling, analysis, and methods of researching microplastic particles in the soil. Doing this will enhance the knowledge around soil pollution and help in addressing the problem.

Aims and objectives of the research

The research aims to:

  • Contribute to environmental protection initiatives and can become an essential step towards the development of a strategy of addressing the problem of microplastics contamination.
  • Collection of an extensive soil sample database to ensure more accurate results for further research.
  • Determining the effectiveness of the infrared spectroscopy as a microplastic contamination screening method.

Methods for testing microplastics in soil

Due to the lack of unique colour or structural properties, microplastic particles are extremely difficult to detect in natural ecological systems. Therefore, to overcome the difficulties associated with the laboratory examination of the presence of polymer plastic particles in the objects under analysis, it is common to resort to various identification methods. It is essential to clarify that there are numerous identification and analysis technologies in the scientific community, each of which was developed for strictly defined tasks. Without focusing in detail on the full range of research techniques, particular attention should be paid to infrared detection methods modified by the use of convolutional neural networks.

Utilizing infrared radiation

Although the scientific community periodically offers new ways to identify and analyze microplastic contaminants in natural environments, infrared spectroscopy remains the classical method of identification (Beć, Grabska, and Huck, 2020). Of primary importance is the need to introduce the reader to the essence of this optical method. For instance, vibrational spectroscopy, which is based on measuring the vibrations of molecules or atoms as a function of their exposure to electromagnetic radiation, occupies an essential place in studying the molecular structure of chemical compounds (Beć, Grabska, and Huck, 2020). In particular, this method makes it possible to establish the nature and content of atomic groups. It aids in obtaining data on the content of functional groups, especially those that cannot be characterized by chemical methods. They also help establish the nature of the chemical bond and study the kinetics of chemical reactions, as shown in Figure 1. Infrared spectroscopy has traditionally been used to identify organic polymers due to the sufficient size of molecules to correspond to the wavelength of the infrared range.

IR-Absorption spectrum for two plastic fragments: polyvinyl chloride (left) and polyvinyl acetate. The marked peaks determine the atomic groupings within the molecule and allow the identification of the substance (Abdelghany, Meikhail, and Asker, 2020). This is important in identifying unknown plastic in soil samples.
Figure 1: IR-Absorption spectrum for two plastic fragments: polyvinyl chloride (left) and polyvinyl acetate. The marked peaks determine the atomic groupings within the molecule and allow the identification of the substance (Abdelghany, Meikhail, and Asker, 2020). This is important in identifying unknown plastic in soil samples.

The analysis of microplastics on spectrophotometers should be preceded by a sample preparation procedure for the material. Traditional methods implemented at this stage include extraction of raw materials from soil samples, washing, and packaging for further investigation. Microplastic fragments placed on a crystal surface are excited by infrared radiation, the results of which are recorded as a spectrogram. Utilizing peaks and characteristic lines, researchers can identify the type of plastic found in a soil sample by comparing different characteristic bands and detecting differences, as shown in Figure 2. In this way, a potentially contaminated area of soil can be examined for chemical composition in order to detect specific polymer components: an example of this spectrum is shown in Figure 2.

When comparing several spectra, it is possible to see a noticeable difference. Analysis of the sample under study with the reference (left) and comparison of several microplastics in parallel (right) (Using infrared spectroscopy for microplastic analysis, 2018).
Figure 2: When comparing several spectra, it is possible to see a noticeable difference. Analysis of the sample under study with the reference (left) and comparison of several microplastics in parallel (right) (Using infrared spectroscopy for microplastic analysis, 2018).

Although infrared spectroscopy is a working method of analyzing biological components, the use of a spectrophotometer presents a number of challenges that can affect the final result. In particular, this applies to the following factors: (i) clarification of the homogeneity of microplastics invasion in terms of depth, (ii) and area, (iii) and accounting for microplastics concentration (He et al., 2018). To discuss the first two problems, it is necessary to design an agricultural field model. In the event of microplastic contamination of the soil, it is difficult to predict the homogeneity of the particle distribution in volume. Given that mulching may be the primary source of microplastics for soils, it can be assumed that microplastics will concentrate on the surface of the land (He et al., 2018). Although this is generally a fairly logical approach to the study of soil contamination, it must be considered that there are also other alternatives. Guo et al. (2020a) have shown that the difference between microplastics saturation of deep soil layers and shallow soil layers does not exceed 20%. This means that more data, including distribution patterns, is needed for proper infrared spectroscopy, which provides qualitative results. In this case, it is necessary to change the sample preparation guidelines or, most practical, to carry out multi-component analyses of the different layers.

Above has been shown how Fourier-based infrared spectrophotometers solve the task of analyzing soil samples. Indeed, traditional working instruments — such as the AIM-9000 IM and IRTracer-100 — can be used to identify the plastic composition of the soil, but this data is not sufficient to determine the concentration of contaminants (Using infrared spectroscopy for microplastic analysis, 2018). An important issue for both environmental monitoring and the agricultural sector is the qualitative analysis of soils and the determination of the number of contaminants to assess future actions. Several technologies are available in the scientific literature to modify spectroscopic studies aimed at recording the concentration of substances, but two of them are of particular interest. These include using a mathematical model of the resonance frequency shift and the use of convolution neural networks for computer analysis.

Magnetic resonance spectroscopy can be used to address the concentration of microplastic particles. It should be said that proton magnetic resonance spectroscopy is based on changes in the resonance frequency of the protons that make up the plastics: the so-called chemical shift (Xin and Tkáč, 2017). The information obtained using this method makes it possible to numerically estimate the content of the particles studied in the soil in ppm. In particular, Malyuskin (2020) reported that the model described above successfully handles the analysis of soil samples in real-time and shows concentrations of microplastics up to 100 ppm. In other words, it should be noted that neglecting this technology does not seem expedient since magnetic resonance spectroscopy not only makes it possible to estimate the number of microplastics in soil but also to reduce the cost of the experiment (Malyuskin, 2020). Although the apparent advantage of this identification method is its ease of use, there are alternatives for quantitative analysis of microplastic soil contamination.

Modification by convolution neural networks

The primary objective of this section is to discuss the phenomenon that has laid down a revolutionary approach to microplastics spectroscopic analysis. Traditionally, machine learning is the method of data analysis that automates the construction of an analytical model. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. The core of machine learning is retrospective data analysis: in order for a computer to be able to classify and identify elements on its own, it must be trained to do so.

Over the years, achievements in the field of machine vision with in-depth training have been improved mainly utilising a specific algorithm, namely convolution neural networks. This is an algorithm capable of obtaining an image as an input, assigning a degree of importance to its various aspects or objects and distinguishing recognition categories. It is interesting to note that the ConvNet architecture is similar to the human brain’s neuron binding pattern, and is based on the organisation of its visual zone (Ghosh et al., 2019). Individual neurons respond to stimuli only in a limited area of the visual field, called the receptive area. These fields overlap to form a collection that covers the entire field of vision.

Infrared spectroscopy, modified by computer vision, is able not only to distinguish substances by their atomic composition but also to calculate their concentrations. As already mentioned, the output of the spectrophotometer is an image: a spectrum of peaks and stripes, individual for each substance. ConvNet eliminates the need for spectroscopists to thoroughly diagnose each spectrum by independently comparing the data obtained with standards stored in libraries (Ghosh et al., 2019). However, neural networks do not allow for wavelength pre-processing, but instead evaluate the entire range (Yuanyuan and Zhibin, 2018). In the future, this approach has the potential to broaden the researcher’s knowledge of the quantitative ratios of microplastics within soil samples. This becomes particularly important in environmental surveys.

Second experiment: Effect of microplastics on Microbial Respiration

After identifying the type of plastic in the soil using infrared spectroscopy, a second experiment is conducted to determine the impact of the microplastics on microbial respiration. The experiment introduces microplastics and glucose into a soil sample to test how they affect respiration in the soil. Soil respiration is a key aspect worth examining because it shows the ability of the soil to sustain the growth and development of plants, microorganisms, the soil fauna (Nguyen and Marschener, 2017). The respiration of the soil is a determinant of nutrient cycling and gives a clear reflection of the soil’s capacity to sustain the growth of plants. Microbial respiration is essential because it aids the microbes to act on the organic molecules, releasing adequate nutrients into the soil (Nguyen and Marschener, 2017). By observing the carbon dioxide quantity in the soil, it will be possible to determine the impact of the added substances on the respiration of the microbes.

Soil Sampling

The soil used in the study is Red Dermosols collected from Sydney University Lansdowne Farm in Cobbitty, New South Wales, Australia. The soil sample is divided equally into portions of 50 grams each and put into 18 bottles. The bottles are then divided into three categories; in the first category, no plastic is added. In the second category, 0.5 grams of normal plastic is added, while in the third category, 0.5 grams of biodegradable plastic is added. The table below shows how the three categories are sampled and the number of times in which the experiment was repeated.

No plastics (p0) Normal plastics (p1) Biodegradable plastic (p2)
Without glucose (g0) (R1R2R3)p0g0 (R1R2R3)p1g0 (R1R2R3)p2g0
With glucose (g1) (R1R2R3)p0g1 (R1R2R3)p1g1 (R1R2R3)p2g1

Table 1: Sample Table and Experiment Summary


Glucose was added to half of the samples (9 categories) as shown in the table above. The glucose added in each bottle was approximately 3.125 grams. In each of the categories the experiment was repeated three times as indicated by R1, R2, and R3. 15 ml of water was then added to each of the containers and their weights were thereafter taken.


The weight of all the samples was taken before the water was added. The sensor was then warmed, and all of them were calibrated at around 400ppm for the CO2 level. The glucose and the microplastics are added to the samples at an interval of five minutes. The time taken for all three rounds to be complete is 1 hour 5 minutes in total. The results in each round are recorded. It is important to note that the experiments for the four rounds are recorded on different days. The times taken between the beginning of each experiment and the time of measurements for the results were 0,3,6,79, 110, and 148, respectively.

Statistical analysis

The results from the second experiment are subjected to statistical analysis to assess the effect that the microplastics in the soil have on microbial respiration. The statistical analysis for the research was done using the JMP program. The JMP software is a tool that helps in statistical analysis as well as the graphical representation of data. The JMP program is powerful and highly instrumental in designed experiments and is also significant in making statistical analyses on data collected from various processes from different industries. Using the software, an ANOVA test is conducted to check if the variances of the different samples are significantly different. JMP programs allow for a follow-up test if the null hypothesis being tested is rejected. The follow-up test conducted is the Tukey-Kramer HSD test. The student’s t-test is also used for the analysis to make a comparison between the means between two groups of the samples that are being examined.


  • Ho: mean CO2 rate of sample without microplastic = mean CO2 rate of sample with regular microplastic
  • H1: mean CO2 rate of sample without microplastic ≠ mean CO2 rate of sample with regular microplastic
  • Ho: mean CO2 rate of sample without microplastic = mean CO2 rate of sample with regular microplastic
  • H1: mean CO2 rate of sample without microplastic ≠ mean CO2 rate of sample with regular microplastic

Spectroscopy Results

To effectively determine the type of microplastic available in the soil samples, it was important to go through the spectroscopy library to determine some known graphs. Infrared spectroscopy is designed to measure the points where photons of the infrared radiations are absorbed. The peaks in the graph are important because they show particular points of the spectrum where certain vibrations of the bonds take place (Ouhaddouch et al., 2019). The IR spectrums have the functional region and the fingerprint region that appear on the spectrum’s right. Few peaks mainly characterize the functional group region compared to the fingerprint region (Ouhaddouch et al., 2019). Although both regions of the spectra are important in determining the organic structure in question, the functional group is more accurate in most cases.

Figure 3 below illustrates the IR spectrum of the Polyethylene Terephthalate (PET), which belongs to the polyester family and has many uses in human life. PET is one of the substances of interest when examining microplastics in the soil because of its wide range of application in making food and liquid containers and fibers for clothes, among other items used in day-to-day human life.

PET spectrum
Figure 3: PET spectrum

Figure 4 below shows the IR spectra of Low-density Polyethylene (LDPE) made using ethylene, a monomer. It is among the first-ever polyethylene to be made and has extensive use. It is used in various industries for different functions such as coating and sheathing of cables and packaging and non-packaging applications in the film industry. It is one of the substances considered in this study because of its extensive use in human life.

LDPE Spectrum
Figure 4: LDPE Spectrum

Figure 5 below shows the IR spectrum of Polyvinyl chloride (PVC). PVC is among the most widely produced plastics globally because of its extensive use in human life. PVC is used in fashion and footwear water service pipes, insulation of wires and cables, blood storage bags, water service pipes, drainage pipes, and window frames. It is extensively used and, therefore, one of the likely synthetic plastics to be found in the soil.

PVC Spectrum
Figure 5: PVC Spectrum

Figure 6 below shows the IR spectrum of polyethylene which is one of the polymers which is mainly used in daily life. It is the commercial polymer with the simplest structure; its molecules are mainly made up of long chains of carbon atoms which have hydrogen atoms attached to each of them. It remains the most used plastic worldwide. It is used in making various items including detergent bottles, clear food wraps, automobile fuel tanks, and shopping bags among others. Its extensive use makes it a substance of interest in this research because it is the most likely plastic to pollute the soil.

Polyethylene Spectrum
Figure 6: Polyethylene Spectrum

Figure 7 represents the IR spectrum for polypropylene which is a plastic essentially made from propene. Like the other plastics chosen in this research polypropylene has numerous applications in various industries such as the textile and automotive industries among others. Polypropylene is considered one of the cheapest commercial plastics and thus one that is likely to pollute the environment.

Polypropylene Spectrum
Figure 7: Polypropylene Spectrum

Based on the known IR spectra of known plastics the spectra collected from each soil sample were accurately matched to five the type of plastic which is available in the soil. Figure 8-16 to below gives an illustration of the results obtained. The spectrums are matched with the known spectrums in the database depending on the peaks and other trends depicted especially on the functional group region of the spectrum.

Sample results
Figure 8: Sample results


Sample Result
Figure 9: Sample Result


Sample Result
Figure 10: Sample Result



Sample Result


Sample Result
Figure 12 : Sample Result


Sample Result
Figure 13 : Sample Result


Sample Result
Figure 14 : Sample Result
Sample Result
Figure 15: Sample Result
Sample Result
Figure 16 : Sample Result


Results for the microbial respiration

In Figure 17, a clear visual representation of the data that was collected without addition of glucose into the samples is given. From the graph it is evident that the amount of CO2 collected tends to reduce with increase in time. The representation has three section which includes the control, regular, and biodegradable section. Figure 18 gives a similar representation for the samples where glucose was added.

Graph of CO2 against Time No Glucose
Figure 17: Graph of CO2 against Time No Glucose

Graph Builder with glucose

Graph of CO2 against Time Glucose Added
Figure 18: Graph of CO2 against Time Glucose Added

All time

Oneway Analysis of CO2 (mg CO2/cm2/h) By Plastic glucose=no

All time ANOVA for CO2 Without Glucose
Figure 19: All time ANOVA for CO2 Without Glucose

From the analysis of variance, the F value obtained for the samples with no glucose is 0.0274 as shown in table 2 below. The F-value is less than the significance level, 0.05, therefore, the null hypothesis is rejected in favor of the alternative hypothesis. Since we have rejected the null hypothesis, it implies that one of the means is different hence the need for a follow up test.

Analysis of Variance

Source DF Sum of Squares Mean Square F Ratio Prob > F
Plastic 2 6.650556 3.32528 3.8735 0.0274*
Error 49 42.064746 0.85846
C. Total 51 48.715302

Table 2: ANOVA table

Level Number Mean Std Error Lower 95% Upper 95%
control 17 0.55218 0.22472 0.10059 1.0038
degradable 17 1.25618 0.22472 0.80459 1.7078
regular 18 0.44906 0.21839 0.01019 0.8879

Std Error uses a pooled estimate of error variance

Table 3: Means for One-way ANOVA

3.2.1 Means Comparisons for each pair using Student’s t Confidence Quantile

t Alpha
2.00958 0.05

Table 4: LSD Threshold Matrix


degradable control regular
degradable -0.63864 0.06536 0.17741
control 0.06536 -0.63864 -0.52659
regular 0.17741 -0.52659 -0.62065

Positive values show pairs of means that are significantly different.

Table 5: Connecting Letters Report

Level Mean
degradable A 1.2561765
control B 0.5521765
regular B 0.4490556

Levels not connected by same letter are significantly different.

From Table 4 it is evident that there is significant difference between the CO2 amount produced between the control group and the bio-degradable group as well as the CO2 amount produced between the regular group and the bio-degradable group. From Table 5, it is clear that there is no significant difference between the control group and the group with biodegradable plastic.

Oneway Analysis of CO2 (mg CO2/cm2/h) By Plastic glucose=yes

All time ANOVA for CO2 of Samples with Glucose
Figure 20: All time ANOVA for CO2 of Samples with Glucose

The ANOVA results for the samples with glucose gives a p-value of 0.9055 as seen in Table 20. Since the P-value is greater than the significance level of 0.05 it is evident that we have to fail to reject the null hypothesis that the means of the different samples are equal.

Table 6: Analysis of Variance

Source DF Sum of Squares Mean Square F Ratio Prob > F
Plastic 2 54.108 27.054 0.0995 0.9055
Error 48 13049.195 271.858
C. Total 50 13103.303

Table 7: Means for One-way ANOVA

Level Number Mean Std Error Lower 95% Upper 95%
control 17 14.9745 3.9990 6.9341 23.015
degradable 18 17.1149 3.8863 9.3010 24.929
regular 16 14.9439 4.1220 6.6560 23.232

Std Error uses a pooled estimate of error variance

Means Comparisons for each pair using Student’s t Confidence Quantile

t Alpha
2.01063 0.05

Table 8: LSD Threshold Matrix


degradable control regular
degradable -11.051 -9.072 -9.220
control -9.072 -11.371 -11.517
regular -9.220 -11.517 -11.721

Positive values show pairs of means that are significantly different.

Table 9: Connecting Letters Report

Level Mean
degradable A 17.114889
Control A 14.974529
Regular A 14.943875

Levels not connected by same letter are significantly different.

From Table 8 and Table 9 it is vivid that there is no significant difference between the three samples. All the values in Table 8 are negative and all the letters in Table 9 are the same implying that the CO2 level for the sample groups do not record a significant difference when glucose is added to them.


The sample soil collected is contaminated with microplastics which are mainly from the commonly used plastics. From the IR spectroscopy, the contaminants, mainly the LDPE, PET, and PS, are found to be available in the soil sample collected. Over the past century, the use of plastic has been significantly embraced as they have replaced the use of other traditional resources such as ivory, horns, and tortoiseshell. The immense use of plastic has been embraced because of the advantageous features of plastic. For instance, people shifted from traditional resources to plastics because of their lightweight, durability, and versatility (Li et al., 2020). The components found in the soil, including PS, PET, and LDPE, are among the most commonly used plastics, and this has a higher chance of polluting the environment.

The fact that the soil sample subjected to IR microscopy found three matches for commonly used plastics shows that there is a higher possibility of having more plastic contaminants in the soil if a more extensive analysis is done or if the sample size is increased. The results of the IR spectroscopy are a clear indication that the soil, similar to the other environmental areas such as the air and the water, is highly threatened by human activities.

The results of the second experiment reveal a lot about the existence and the effect of the microplastics on the soil. The statistical results show that the rate of microbial respiration is high for samples with non-biodegradable microplastic when no glucose is added to the soil samples. However, it remains the same for the control sample and the ones with regular plastic. This means that the use of biodegradable plastic can have a significant impact on microbial respiration and can also be a strategic way of addressing soil pollution propagated by increased usage of plastic across the world.

The use of biodegradable plastics may be friendly to the microbes in the soil, according to the study. Research shows that biodegradable plastics pose no significant impact on microbial respiration (Filiciotto and Rothenberg, 2020). Most of the plastics made are not degradable, and they constitute the highest percentage of the microplastics in the soil. Biodegradable plastics can be an important step towards environmental conservation (Filiciotto and Rothenberg, 2020). This is because biodegradable plastics do not accumulate in the soil for too long. Apart from posing a little challenge to microbial respiration, they may reduce the accumulation of toxins that occur when normal plastics are available on the land surface.

When glucose is added to the mean of CO2 produced is not significantly different for all the samples, implying no difference recorded in microbial respiration. The respiration of microbes depends on a number of factors that make an ideal environment for them (Guo et al., 2020b). For instance, the aeration of the soil and the concentration of the microplastics and the microbial food in the soil. Research reveals that the effect of microplastics in the soil or on the organism in the soil depends on the exposure time, the concentration of the microplastics, and the type of particles in the soil under study (Tang et al., 2020). Therefore, the results may be the same for the different samples because the conditions are ideal. It is important to recall that the concentration of glucose in the soil may be lower than that added to each sample.

The sample results with glucose are also important because it confirms that the presence of the microbes in the soil and their ability to respire in idea conditions. Additionally, there are other ways in which microplastics may affect the soil ecosystem. For instance, microplastics have other real effects on the growth of plants in the soil. They can also accumulate and travel into the plants and pose other detrimental impacts to the plants and the animals that consume them (Rillig, Lehmann, Souza Machado, and Yang, 2019). The animals which consume the plants which have accumulated microplastics may cause various chemical response, mechanical damage to both the animals and the microbial community in the soil (Rillig, Lehmann, Souza Machado and Yang, 2019). The experiment is thus important in expanding the knowledge around plastic pollution in the soil and its impact on the microbes.


In summary, it is evident that soil pollution by plastic is real. From the IR spectroscopy experiment, it was confirmed that there are a number of microplastics in the soil. Low-density Polyethylene (LDPE) was the most common plastic that was found in the sample, which shows that it is one of the plastics which mainly pollute the environment. Other plastics found also revealed that the rate of plastic pollution in the soil higher and can be registered through research if large samples are used. There is also a possibility of finding more microplastics in the soil if the database used in the IR spectroscopy is widened.

In the second experiment, which aimed at determining the effect of the microplastics on microbial respiration, it was determined that the bio-degradable plastic had a positive impact on microbial respiration. The one-way ANOVA test was utilized in the study alongside the student’s t-test. When glucose was introduced to the samples, there was no significant difference recorded in all the samples. It is thus important to realize that in the actual environment, bio-degradable microplastics may favor microbial respiration, while the same cannot be said for the normal plastics.


It is important to conduct more research on soil samples from different regions to determine soil pollution by microplastics. It is also necessary to conduct the IR Spectroscopy on known plastics to widen the database of the plastics for more identification of other plastics. There is also a need to conduct more literature reviews on soil pollution in different environments to develop more advanced knowledge on pollution. More research on ways to ensure environmental conservation through the eradication of plastic pollution in the soil should be extensively considered in future research.


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ppm per minute percent per minute mg CO2-C/cm2soil surface/h
CO2rate Humidity rate Corrected CO2rate CO2rate
1.5946 0.0668 1.5278 1.42
10.063 0.0495 10.0135 9.28
1.6384 0.0492 1.5892 1.47
4.6365 0.0631 4.5734 4.24
1.8517 0.0288 1.8229 1.69
12.323 0.0097 12.3133 11.41
CO2rate Humidity rate Corrected CO2rate CO2rate
0.8847 0.0223 0.8624 0.80
33.304 -0.0069 33.3109 30.86
0.7973 -0.0269 0.8242 0.76
29.694 -0.0121 29.7061 27.52
3.1415 -0.0184 3.1599 2.93
48.01 -0.0193 48.0293 44.50
CO2rate Humidity rate Corrected CO2rate CO2rate
0.4328 0.0126 0.4202 0.39
26.117 0.0015 26.1155 24.21
0.5728 0 0.5728 0.53
31.261 0.0253 31.2357 28.95
1.6771 -0.0057 1.6828 1.56
56.608 0.0647 56.5433 52.41
CO2rate Humidity rate Corrected CO2rate CO2rate
1.7455 -0.0107 1.7562 1.63
3.6612 0.1469 3.5143 3.26
0.1563 0.1333 0.023 0.02
12.557 0.2772 12.2798 11.38
0.4962 0.1744 0.3218 0.30
5.5267 0.1924 5.3343 4.94
CO2rate Humidity rate Corrected CO2rate CO2rate
0.138 0.0789 0.0591 0.05
3.6598 0.2029 3.4569 3.20
0.1412 0.0973 0.0439 0.04
2.7403 0.2023 2.538 2.35
0.2229 0.1934 0.0295 0.03
1.8465 0.0642 1.7823 1.65
CO2rate Humidity rate Corrected CO2rate CO2rate
0.1 0.0018 0.0982 0.09
3.0826 0.0933 2.9893 2.77
0.1968 0.0393 0.1575 0.15
3.3824 0.0889 3.2935 3.05
0.2754 0.1821 0.0933 0.09
3.3612 0.068 3.2932 3.05

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