Distribution characteristics of soil microplastics and their impact on soil physicochemical properties in agricultural areas of the North China plain†

Yuze Zhou^a, Ming Dou*^ab, Yan Zhang^bc, Kaizi Ning^d and Yuxuan Li^b
aSchool of Water Conservancy Engineering, Zhengzhou University, Zhengzhou, China
^bSchool of Ecology and Environment, Zhengzhou University, Zhengzhou, China. E-mail: dou_ming@163.com
^cFarmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, Xinxiang, China
^dFaculty of Science, Monash University, Melbourne, Australia

First published on 15th July 2024

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Environmental significance Microplastics (MPs) pollution has become a global issue, with particular concern regarding MPs in soil. But most studies have focused on the abundance and distribution characteristics of MPs in soil, with little discussion on the impact of MPs on soil physicochemical properties. And the traditional MPs identification analysis methods suffer from issues of low efficiency and high costs. Therefore, a novel image recognition method based on fluorescence staining was proposed for the batch analysis of MPs in the study area. According to the results of the soil physicochemical properties, the impact of MPs on soil physicochemical properties was analyzed and evaluated.

Introduction

Microplastics (MPs) refer to particles, fibers, and fragments of plastic products with diameters between 0–5 mm.¹ As a new type of pollutant, they are is widely distributed in water,^2,3 the atmosphere,^4,5 and soil.^6–8 According to existing research findings, MPs have already posed risks to the ecological environment and human health.^9–12

Soil is generally considered to be a reservoir for MPs.^13–15 In Europe, it is estimated that 1270–2130 tons of MPs enter the soil each year through various pathways. For example, in soil samples from the French soil quality monitoring network,¹⁶ rural vegetable fields in southeastern Spain,¹⁷ floodplain soils in Switzerland,¹⁸ and composted vegetable fields in southern Netherlands,¹⁹ the MPs abundances were reported to be 80 ind kg⁻¹, 2130 ± 950 ind kg⁻¹, 593 ind kg⁻¹, and 67.34–1109 ind kg⁻¹, respectively, with polyethylene (PE) and polystyrene (PS) being the main components.

In China, soils containing MPs have been reported in farmland areas in the hilly and gully region of Yan'an,²⁰ farmland soils in Chengdu,²¹ farmland in the Hetao Irrigation District,²² farmland in the karst plateau of Guizhou,²³ farmland in the rocky desertification area of Guizhou,²⁴ the Yellow River Delta,²⁵ farmland in Shouguang City,²⁶ soil in vegetable bases in Xianyang City,²⁷ farmland in suburban Tianjin,²⁸ farmland on the Qinghai-Tibet Plateau,²⁹ farmland in the Hangzhou Bay and southeast coastal plains,³⁰ the Danjiangkou Reservoir area of the South-to-North Water Diversion Project,³¹ as well as the cities of Harbin³² and Shihezi.³³ MPs abundances can reach up to 28100 items per kg, with the main components including polyethylene terephthalate (PET), polypropylene (PP), PE, and PS. Most previous studies have focused on the abundance and distribution characteristics of MPs in soil, with little discussion on the impact of MPs on soil physicochemical properties. The methods of extracting and observing MPs in these studies are also varied.

The distribution and environmental impacts of MPs have been more extensively studied in the atmosphere and aquatic environments than in soils because the extraction and sampling of MPs from air and water are relatively simple and the resulting particles are easy to identify.^34–38 The extraction and identification of MPs are quite difficult due to the influences of inorganic substances (such as roots and minerals) and organic matter.^39–41 At present, density extraction is a common method for extracting microplastics. After modification and improvement, the recovery rate of MPs by density extraction is generally above 70%.⁴² Commonly used extractants include NaCl, ZnCl₂, NaBr and NaI.^43–46 NaCl is the most commonly used, but the density of NaCl extracts is only 1.2 g cm⁻³, it can only extract MPs with lower density such as PP and PE. The density of ZnCl₂, NaBr and NaI extracts are above 1.55 g cm⁻³, which are higher than most common types of MPs. But the cost of using NaI as an extract is high. ZnCl₂ is corrosive, and in order to maximize the density of the ZnCl₂ extract, additional acid solution is usually required, which will seriously hinder the extraction process.^43,47

Existing methods for MPs identification and counting primarily rely on visual identification, Raman spectroscopy and FT-IR.^48–52

Raman spectroscopy is commonly coupled with microscopy techniques and can be used to identify MPs with sizes larger than 1 μm. However, the detection process is time-consuming and can be easily influenced by the spontaneous fluorescence of soil organic matter.^53,54 FT-IR is only suitable for detecting microplastics larger than 20 μm and is prone to interference from organic matter.^55,56 Fourier Transform Infrared Microspectroscopy combines the advantages of FT-IR and microscopy, enabling the batch identification of MPs within a certain range. However, due to its high cost, its popularity is low.⁵⁷

As one of China's three major plains, the North China Plain is an important hub for population aggregation and grain production. Consequently, considerable attention has been given to its ecological environment and food security. MPs, as a novel pollutant in soil, are widely distributed in the farmland soils of the North China Plain. According to existing research, the presence of MPs have already had an obvious impact on the soil ecology and food security of the North China Plain.⁵⁸ Therefore, it is necessary to conduct field sampling surveys to clarify the extent and process of changes in the physicochemical properties of soil in the agricultural areas of the North China Plain caused by MPs. This will provide a basis for the subsequent management of soil microplastic pollution. Given the poor identification accuracy and high detection costs associated with existing MPs detection methods, a fluorescence staining-based image recognition method was proposed in this study. This method utilizes images observed under a fluorescence microscope, integrates image processing software to merge and extract the boundaries of MPs fluorescence images in different bands, and then uses geographic information system (GIS) software to batch analyze the quantity and characteristics of MPs in processed images. Simultaneously, the physicochemical properties of soils in the study area were tested, the characteristics of MPs were studied, and the impacts of MPs on the soil environment were analyzed. The results provide a reference for the management of MPs pollution in agricultural soils.

Materials and methods

Study area

This study was conducted in the typical agricultural area of the North China Plain, with the North Suburb Agricultural Area of Zhengzhou City used as the field site. Situated along the Yellow River in the northern suburbs of Zhengzhou City, the North Suburb Agricultural Area spans the Jinshui and Huiji districts, serving as the major agricultural production area for Zhengzhou City. Due to its inland location, surface water systems are limited, thus irrigation in the North China Plain agricultural areas relies mainly on atmospheric precipitation and groundwater. Being a typical agricultural area of the North China Plain, the study area is primarily dependent on atmospheric precipitation, with groundwater always extracted for flood irrigation during the spring plowing period (February and March) and the winter irrigation period (November and December). Consequently, the physicochemical properties of soil and the presence of MPs in the study area are minimally affected by surface water.

The main land use types in the study area are farmland and woodland. The crops in the farmland are mainly wheat and corn. According to the field investigation, no plastic film is used. The average annual volume of groundwater irrigation amounts to approximately 0.37 × 10⁴ m³ per ha, while the average annual fertilizer application (including both base fertilizer and topdressing) stands at around 750 kg per ha. The crops in the forest are mainly economic trees such as birch and fruit trees. Except for the black plastic film used at the bottom of the fruit trees, the other crops basically do not use plastic film. The permanent population in study area is about 8300 people. There are three large-scale building ruins in the study area, as well as an east-west viaduct under construction and a supporting temporary accommodation area. The project has lasted for two years. These are all potential sources of microplastic pollution.

The study area experiences moderate precipitation, but with distinct interannual variability. The multi-year average precipitation is 639.4 mm (1956–2019), with most (53%) concentrated from July to September. The least precipitation occurs from December to February of the following year, constituting less than 5% of the annual precipitation. The multi-year average evaporation is 1822.9 mm (1956–2019), which is primarily concentrated in the months of April, May, and June.

Sampling

In December 10–11, 2020, plots of different land use types (farmland, vegetable plots [small-scale family vegetable plots serving only rural residents' personal daily needs, with small plot areas, remote locations, and a low planting frequency], and woodland) were selected in the study area as sampling sites, with a total of 10 sampling sites established (Fig. 1). There are 3 sampling points located in vegetable plots, 3 sampling points located in farmland, and 4 sampling points located in woodland. Each sampling site was replicated three times. The specific sampling site information is shown in Table 1, and the land use patterns at each sampling site have remained largely unchanged over the past 20 years. According to Chen Y., Mengting L., and others, most MPs in the soil are concentrated in the surface layer (0–30 cm).^{11,13,59–68} Therefore, this study selected four 50 cm × 50 cm square plots at each sampling point according to the method of uniform distribution, and soil samples were taken from the surface layer (0–10 cm), middle layer (10–20 cm), and lower layer (20–30 cm) using a manganese steel shovel. After each sampling, rinse the shovel with ultrapure water and wipe it clean with cotton cloth before sampling. 200 g of sample is taken from each layer. After manually removing visible debris larger than 5 mm, the soil samples were wrapped in clean tin foil, brought back to the laboratory, and air-dried in a ventilated and shaded area for further analysis. Simultaneously, undisturbed soil samples were collected using a stainless steel soil ring cutter (Φ50.46 × 50 mm) at each sampling site. After sampling, the ring cutter with original soil was placed in a sealed aluminum box for storage and used for soil bulk density determination.

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Determination of soil physical and chemical properties

To fully understand the physicochemical properties of the soil in the study area, soil bulk density, soil moisture content, soil water-stable aggregates, pH, and salinity were selected for determination with a stratified sampling approach. The moisture content and salinity of the surface soil at each sampling site were measured on-site in the upper, middle, and lower layers using a soil multi-parameter measuring instrument (MPM-160B, ICT INTERNATIONAL).

Additionally, some soil samples were placed in aluminum boxes and brought back to the laboratory for soil pH determination. After air-drying, the samples were ground and passed through a sieve to obtain soil samples smaller than 2 mm. A 10 g soil sample was weighed and placed into a 50 ml centrifuge tube, then 25 ml of deionized water was added and stirred for 1 min. After 30 min of settling, the pH of the supernatant was measured to determine the soil pH by pH instrument (PHSJ-4F, INESA INSTRUMENT).

The soil bulk density at each sampling site was determined using a drying method. Undisturbed soil samples from the surface layer at each site were collected in the upper, middle, and lower layers using a ring cutter (Φ50.46 × 50 mm), and then stored in sealed aluminium boxes.

A 25 g undisturbed soil sample was used to determine the distribution of soil aggregates using the wet sieving method. The soil was placed on a set of nylon sieves with mesh sizes of 2, 0.25, and 0.053 mm. After adding distilled water to submerge the soil to a depth of 3 cm, the samples were soaked for 5 min. Then, the sieves were slowly moved up and down 50 times in 3 cm increments. The samples collected on the upper part of each sieve were washed into beakers, separating the soil aggregates into macroaggregates (>2 mm, 2–0.25 mm) and microaggregates (0.25–0.05 and <0.05 mm). After drying, the aggregates were weighed to determine their mass by analytical balance (JD400-3, NANBEI INSTRUMENT).

MPs extraction and observation

A fluorescence staining-based MPs image recognition method was established based on density separation and visual observation methods. The method utilized fluorescent microscope (RX50, SOPTOP) observations of stained MPs images as a foundation. It combined image processing software to merge and extract the boundaries of MPs fluorescence images in different bands and applied ArcGIS 10.2 software to perform a batch analysis of the quantity and characteristics of MPs in the processed images (Fig. 2).

MPs extraction

The density of saturated NaBr solution is 1.55 g cm⁻³. According to the research of Liu et al., it can extract most MPs including PVC, PE, PA, etc., with a recovery rate between 85% and 100%. And the extraction cost is lower than that of extractants such as ZnCl₂.⁶⁹ MPs staining is a low-cost treatment method that helps to identify MPs. Compared with dyes such as Oil red EGN, Eosin B and Rose Bengal, Nile red staining takes a short time and has a staining rate of up to 96.76% for MPs.⁷⁰ It also has little effect on FT-IR analysis. Therefore, this study used the saturated sodium bromide solution density separation method to extract MPs from soil samples, and observed them after staining with Nile red.

MPs were extracted from soil samples using a saturated sodium bromide solution density separation method. The collected soil samples were ground and sieved through a 2 mm mesh sieve. A 20 g sieved soil samples was then added to 200 ml of saturated sodium bromide solution, stirred vigorously on a magnetic stirrer (ZNDL-6S, HONGHUAYIQI) for 1 h, and left to settle for 24 h to extract the supernatant. Hydrogen peroxide (H₂O₂) and deionized water were added to dilute the sodium bromide and hydrogen peroxide concentrations to 8.8% and 10%, respectively. Subsequently, all samples were digested in a constant temperature water bath at 70 °C for 24 h. According to statistics, the fluorescence intensity of chitin and cellulose stained in 10% (w/v) H₂O₂ solution decreased by an average of 53.7 and 52.5%, respectively, compared with the samples digested without adding H₂O₂.⁷¹ A Nile red stock solution standard solution (1 mg ml⁻¹) was added to control the Nile red concentration in the supernatant to 10 μg ml⁻¹, and the samples were further heated for an additional 0.5 h. The MPs in the supernatant were filtered and extracted using a 0.45 μm nylon filter (0.45 μm, Onion) membrane in a vacuum filtration device (SCJ-10, SUPO), and the filter membrane was placed in a Petri dish and allowed to dry for a subsequent analysis.⁶⁹ To prevent external contamination, the separation, extraction, and observation of MPs were conducted in a clean and sealed environment.

MPs observations

According to the research of Prata JC et al., the excitation wavelength between 365 and 510 nm is mainly used for plastic observation.⁷² In this wavelength range, even if the exposure time is tripled, biomaterials such as chitin and cellulose will not show fluorescence. Longer excitation wavelengths can cause lipids in biological samples and natural organic matter in environmental samples to show fluorescence.⁷³ Therefore, the filter membrane obtained by density separation was placed under a fluorescence microscope, and the filter membrane in the blue (excitation wavelength 488 nm) and green (excitation wavelength 350 nm) bands was selected for photography.

The flow of MPs observations analysis is shown in Fig. 2. The filter membranes obtained through density separation were placed under a fluorescence microscope, and photographs of the membranes were taken under different fluorescence wavelengths. Using ImageJ software, the fluorescence images from each wavelength were stitched and fused together. The brightness threshold was used to extract the boundaries of each MPs in the images and eliminate noise and natural light reflections outside the selected MPs boundaries. Subsequently, ArcGIS 10.2 software was used to reclassify the processed images by converting raster files to feature files to obtain the boundaries of each MPs. A statistical analysis of the number, area, size, and shape of the MPs at each sampling site was then conducted.

The quantity of MPs was obtained by counting the number of polygon features in the shapefile of each sampling site. The abundance value was used to represent the MPs concentrations in the soil at each sampling site.

	(1)

In eqn (1), α represents the abundance value (ind kg⁻¹); n represents the quantity of MPs (ind); and N represents the mass of soil after drying (kg).

The size and shape of MPs were identified by calculating the aspect ratio and the degree of fitting to minimum circumscribed circle in the shapefile.

	(2)

	(3)

In the equation, P represents the aspect ratio of the minimum bounding rectangle of each MPs (the following text will be referred to as aspect ratio); L is the long side of the minimum bounding rectangle (mm); S is the short side length of the minimum bounding rectangle (mm); D is the degree of fitting to minimum circumscribed circle of MPs (the following text will be referred to as Degree of fitting); A₁ is the area of MPs (mm²); and A₂ is the area of minimum circumscribed circle of MPs (mm²).

Recognizing the shape information of images through shape parameters including aspect ratio is widely used in the field of medical tumor recognition and other image recognition research,^74,75 but it is less used in MPs recognition. This study attempts to use aspect ratio and fitting degree to identify the shape of MPs. According to the area of each MPs, the minimum aspect ratio of the outer rectangle, and the minimum circumscribed circle area data calculated by ArcGIS 10.2. First, MPs shapes were filtered based on aspect ratios. MPs with P > 2 were considered to be fiber-type MPs. D is calculated for each MPs whose aspect ratio is less than 2. The smaller D is, the greater the deviation from the standard circle is. Based on this criterion, MPs were classified into fragments (P < 2, D < 0.8), pellets (P < 2, D > 0.8), and fibers (P > 2.5) (Fig. 3).

MPs identification

The MPs material was determined using micro-attenuated total reflectance (micro-ATR) infrared spectroscopy equipped with an infrared imaging microscope (Bruker VERTEX 70V, BRUKER). The scanning mode was set to reflection, with a detection range of 4000–400 cm⁻¹, 32 scans, and a resolution of 4 cm⁻¹.

Quality control and data analysis

To ensure the quality and accuracy of the data, all indoor tests were conducted in a clean and closed room, and all participants wore clean white cotton lab coats. Each batch of experiments included reagent blanks and standard solutions. All experiments were performed in triplicate.

The IBM SPSS statistics software was used for data processing. A single-factor analysis of variance (ANOVA) was used to conduct a significance analysis of the differences in MPs abundance among various sampling sites and different soil depths. Graphs were generated using Excel 2019 and Origin 2022 software.

Results

Efficiency analysis of MPs extraction and recognition

The MPs were extracted from soil samples using a density separation method with a saturated sodium bromide solution. According to Liu et al., the use of sodium bromide resulted in a recovery rate of 85–100% for MPs, effectively separating the most common types.⁶⁹ Additionally, Wang et al. discovered that at temperatures below 70 °C, the highest efficiency for removing organic matter from MPs surfaces was achieved by employing a 30% H₂O₂.⁷¹ Therefore, the soil MPs extraction method utilized in this study appears to be feasible and reliable.

Shape parameters were analysed for 15 samples collected from sampling sites 1 to 5 during the process of extracting MPs. Thresholds were determined for each parameter and some results of threshold analysis are presented in Table 2. From Table 2, it can be observed that pellets have smaller aspect ratios and higher degrees of fitting above 0.8 compared to other shapes such as fibers which have aspect ratios greater than 2 but lower degrees of fitting below 0.5. Fragments essentially appear as irregular polygons without specific shape characteristics. Based on these thresholds, MPs were classified into fragments (P < 2, D < 0.8), pellets (P < 2, D > 0.8), and fibers (P > 2).

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To validate the identified thresholds, 3 fluorescence images obtained from sampling site 6 were used (Fig. 4). It is evident that the selected thresholds effectively identify different shapes of MPs and enable simultaneous identification and analysis within various ranges including particle size, quantity, and other features.

The MPs identification method from soil used in this study is more efficient than the traditional visual counting method and less expensive than FT-IR and Raman analysis methods.

Occurrence characteristics of soil MPs

MPs were found in all 30 soil samples from the study area. As shown in Fig. 5, the MPs abundance at various sampling sites ranged from 1650 to 24300 individuals per kilogram (kg), with an average abundance of 8462 ind kg⁻¹. Significant differences in MPs abundance were observed among the sampling sites (p < 0.05).

The MPs abundance was highest at sampling site 10, while sampling site 1 had the lowest abundance. In forested areas, the MPs abundance ranged from 1650 to 19350 ind kg⁻¹, with an average abundance of 10350 particles per kg. Significant differences in MPs abundance were observed among the sampling sites (p < 0.05). MPs abundance was highest at sampling site 10, while it was lowest at sampling site 4. In farmland areas, the MPs abundance ranged from 2300 to 24300 ind kg⁻¹, with an average abundance of 8822 ind kg⁻¹. MPs abundance was highest at sampling site 3 and lowest at sampling site 5. In vegetable fields, the MPs abundance ranged from 2500 to 14550 ind kg⁻¹, with an average abundance of 5583 ind kg⁻¹. MPs abundance was highest at sampling site 9 and lowest at sampling site 1.

In terms of sampling depth, the MPs abundance in the study area decreased with increasing depth. The highest MPs abundance was observed in the 0–10 cm soil samples, with an average abundance of 12165 ind kg⁻¹. Sampling site 3 had the highest MPs abundance in surface soil, while sampling site 1 had the lowest MPs abundance in surface soil. In the 10–20 cm soil samples, the MPs abundance was lower compared to the surface soil, with an average abundance of 7550 ind kg⁻¹. Sampling site 10 had the highest MPs abundance, while sampling site 5 had the lowest MPs abundance. The lowest MPs abundance was observed in the 20–30 cm soil samples, with an average abundance of 5670 ind kg⁻¹. Sampling site 10 had the highest MPs abundance, while sampling site 7 had the lowest MPs abundance.

The MPs particle sizes in the study area soil could be largely classified into categories of <10, 10–50, 50–100, 100–200, and >200 μm (Fig. 6a). The <10 μm MPs particles had the highest abundance, ranging from 950 to 13000 ind kg⁻¹, with an average abundance of 5040 ind kg⁻¹. The highest abundance was found in the topsoil of sampling site 3, which had an abundance of 13000 ind kg⁻¹, while the lowest abundance was detected in the bottom layer samples of sampling sites 2 and 4, which both had an abundance of 950 ind kg⁻¹. As the MPs particle sizes increased, their abundance decreased accordingly. MPs particles >200 μm were the least widely distributed size class in the study area soil, ranging from 0 to 300 ind kg⁻¹, with an average abundance of 97 ind kg⁻¹. The highest abundance was found in the middle layer soil of sampling site 10, with an abundance of 300 ind kg⁻¹, while the lowest abundance was detected in the middle layer samples of sampling sites 1, 2, 3, and 4, where no >200 μm MPs particles were detected.

The MPs in the soils of the study area were mainly fragments, particles, and fibers (Fig. 6b). Particle-type MPs had the highest abundance in the study area soil, followed by fragments, while fibers accounted for the lowest proportion. The abundance of particle-type MPs ranged from 650 to 16300 ind kg⁻¹, with an average abundance of 3915 ind kg⁻¹. The highest abundance of particle-type MPs was found in the bottom layer soil samples of sampling site 10, while the lowest abundance was detected in the topsoil of sampling site 1. Fragment-type MPs had an abundance ranging from 600 to 9600 ind kg⁻¹, with an average abundance of 3692 ind kg⁻¹. The highest abundance of fragment-type MPs was found in the bottom layer soil samples of sampling site 10, while the lowest abundance was detected in the topsoil of sampling site 1. Fiber-type MPs had an abundance ranging from 50 to 3750 ind kg⁻¹, with an average abundance of 855 ind kg⁻¹. The highest abundance of fiber-type MPs was found in the topsoil of sampling site 3, while the lowest abundance was detected in the bottom layer soil samples of sampling site 1.

The MPs in the soils of the study area mainly consisted of PVC, PET, PE, polyamide (PA), and PP (Fig. 6c). MPs consisting of PVC had the highest abundance, followed by PE and PA, while MPs consisting of PP had the lowest abundance. The abundance of PVC MPs ranged from 850 to 15450 ind kg⁻¹, with an average abundance of 5782 ind kg⁻¹. The highest abundance of PVC MPs was found in the topsoil samples of sampling site 8, while the lowest abundance was detected in the topsoil of sampling site 3. The abundance of PE MPs ranged from 150 to 16200 ind kg⁻¹, with an average abundance of 1262 ind kg⁻¹. The highest abundance of PE MPs was found in the topsoil samples of sampling site 3, while the lowest abundance was detected in the bottom layer soil of sampling site 7. The abundance of PA MPs ranged from 0 to 4650 ind kg⁻¹, with an average abundance of 825 ind kg⁻¹. The highest abundance of PA MPs was found in the bottom layer soil samples of sampling site 10, while the lowest abundance was detected in the bottom layer soil of sampling site 7. The abundance of PET MPs ranged from 0 to 3900 ind kg⁻¹, with an average abundance of 320 ind kg⁻¹. The highest abundance of PET MPs was found in the topsoil samples of sampling site 3, while the lowest abundance was detected in the soil of sampling sites 2 and 4. The abundance of PP MPs ranged from 0 to 2100 ind kg⁻¹, with an average abundance of 273 ind kg⁻¹.

Determination of soil physical and chemical properties

Discussion

Impact of land use on the distribution of MPs

Based on the characteristics of MPs in other regions from previous studies, as shown in Table 3, it was apparent that there was obvious variation in the MPs abundance in soils across different regions. For example, the average MPs abundance in the surface soil of vegetable fields ranged from 70 to 13140 ind kg⁻¹, while in farmland, the average MPs abundance ranged from 2420 to 20850 ind kg⁻¹, and in forest land, it ranged from 1907 to 39300 ind kg⁻¹. In comparison with soils from other regions, the MPs abundance in vegetable fields within the study area tended to be moderate to low. Specifically, site 9 in the vegetable field had a relatively high MPs abundance, while the MPs abundance in other vegetable field sites tended to be lower. The MPs abundance in farmland within the study area tended to be relatively high, with site 3 having the highest abundance, while sites 5 and 7 had moderate levels. In the forest land within the study area, the MPs abundance tended to be moderately low, with site 10 having the highest abundance, and sites 4, 6, and 8 exhibiting lower MPs abundances compared to forest soils in other regions.

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Compared with the other sampling sites (Fig. 5) sites 1 and 2 had a lower MPs abundance. Both of these sites were vegetable fields, situated far from roads, indicating a minimal influence from human activity. Additionally, the sampling occurred in winter (December), suggesting the prolonged abandonment of these plots due to seasonal effects, with no traces of plastic mulching. In contrast to the other sites, the source of MPs pollution at these sites was likely atmospheric deposition.

Sites 3 and 10 had the highest MP abundance and were located in farmland and forest land, respectively. These sites were adjacent to roads, indicating a larger influence from human activity and an abundance of MPs sources compared to sites 1 and 2. Site 3 consisted mainly of farmland, largely corn, while site 2 in the forest was primarily populated with poplar trees. Both sites had low planting densities and tall mature crops, exposing a large area of the soil surface and intercepting exogenous MPs more effectively. Wheat was cultivated in sites 5 and 7 with a high planting density and short plants, resulting in a lower MPs abundance compared to site 3 despite the similar land use.

Sites 4 and 8 were distant from roads. Fruit trees were grown at both sites with ground coverings of plastic film and straw, resulting in a poor external MPs supply and lower MPs abundance compared to site 2, which was also forest land.

In the study area, vegetable fields are characterized by small plot sizes, remote locations, and a strong seasonality in planting, with minimal use of plastic film. Consequently, the main source of MPs supply is primarily atmospheric deposition, resulting in a lower MPs abundance compared to other land use types. In forest land, the taller plants facilitate the interception of exogenous MPs, while the lower planting densities imply larger exposed surface areas, leading to a higher MPs abundance. Similarly, MPs abundance in farmland is influenced by the crop types. Compared to wheat, the taller corn plants with a lower planting density are more conducive to the accumulation of exogenous MPs. Additionally, based on field observations, there was no widespread use of plastic film coverings, indicating that exogenous MPs were the main source of soil MPs in the study area.

According to the test results of 30 soil samples in the study area, the proportion of granular MPs is relatively high, which may be due to the presence of several large building ruins in the study area, as well as the viaduct project that has been under construction for two years and the supporting accommodation facilities. This has produced a large number of foam particles, plastic microbeads of toiletries, and mechanical parts in the study area, all of which are potential sources of granular MPs.

Impact of MPs distribution on soil physical properties

According to previous studies, the factors affecting soil physical and chemical properties mainly include natural factors such as soil type, climate, and topography, as well as human factors such as agriculture, industry, and urbanization activities.⁷⁶ The study area is located in the alluvial plain along the Yellow River, with a flat terrain and a typical temperate continental climate. Therefore, the impact of natural factors on the soil in the study area is relatively simple, and the impact is smaller than the complex and strong human factors. MPs pollution is an important manifestation of human activities. This study selected 30 soil samples from 10 sampling points covering different soil types (sand, loam, silt) and different land use methods (farmland, vegetable plot, and woodland) to analyze the impact of MPs on soil physical and chemical properties.

The main factor influencing bulk density was soil type. There were three types of soil in the study area: sandy soil (samples 1, 7, 9), loamy soil (samples 2, 4, 5, 6), and silty sand soil (samples 3, 8, 10). Among the different soil types, the higher the abundance of MPs, the lower the bulk density. This was more pronounced when PVC and PP, which have relatively low densities, constituted the highest proportion of MPs. This indicates that the presence of MPs can cause changes in soil bulk density. Compared to soil, MPs have a lower density. Therefore, as the abundance of MPs in soil increased, the mass of soil per unit volume decreased accordingly. The influence of MPs on soil bulk density was therefore mainly reflected in both the abundance and types of MPs present in the soil.

Compared to the other two soil types, the upper layer of the silty sand soil had obvious higher MPs concentrations than the middle and lower layers. This indicates that silty sand soil was better able to retain MPs than the other soil types, and its higher soil density implied a lower soil porosity. Additionally, in silty sand soil, the distribution of MPs revealed a larger proportion of MPs with sizes below 50 μm, while those above 200 μm constituted a smaller proportion. This confirmed that it was difficult for larger MPs to enter soils characterized by smaller pores and lower frequencies of anthropogenic disturbance (such as tillage, sowing, and fertilization). The proportion of fibers in sandy soil was higher than in the other two soil types. Yan X. and O'Connor D. suggested that the high soil porosity and increased soil fissures resulting from alternating wet and dry conditions in sandy soils facilitated the downward movement of MPs from the soil surface.^77,78 Considering that the sandy soil sampling sites in the study area were located within vegetable fields, where human activity is relatively low, the higher abundance of fiber-type MPs in this region was likely due to differences in soil physical properties.

The abundance of fiber-type MPs was positively correlated with soil bulk density, with higher levels observed in locations with a greater soil bulk density. This may be because fiber-type MPs typically have longer longitudinal lengths and smaller transverse diameters, facilitating their downward movement in soils with small pore sizes.⁷⁹ In contrast, fragment and film-type MPs do not possess these characteristics, making it difficult for them to migrate downward in soils with high bulk density.

The correlation between soil moisture content and MPs abundance was not clear, which may be due to the greater influence of environmental factors than MPs on soil moisture content. However, in terms of the vertical distribution of moisture, locations with a higher MPs abundance in the middle and lower layers exhibited a smaller decrease in soil moisture content compared to the upper layers. This phenomenon was more pronounced in sandy and loamy soils, suggesting an impact of MPs on soil water retention. Luo et al. reported that the vertical distribution of the soil moisture content in the surface soil (0–30 cm depth) generally displayed a decreasing trend,⁸⁰ while Chen et al. found that the distribution of the soil moisture content before and after irrigation (0–30 cm depth) did not change obviously, and generally followed the trend of an increasing moisture content with depth.⁸¹ However, in this study, the soil moisture content at sites 4, 7, and 9 displayed a decreasing trend with increasing soil depth. These three sites consisted of sandy and loamy soils, with the MPs contents in the upper layer ranging from 11575 to 14475 ind kg⁻¹. There was an obvious decrease in MPs abundance with increasing depth. This might be because the MPs affected the soil structure, leading to increased water retention in the upper soil layers. Due to the reduced water supply, the moisture content subsequently decreased in the middle and lower soil layers.

Sandy soil has a loose structure and is usually dominated by microaggregates. In this study, it was found that the proportion of aggregates in sandy soil increased with the increase in MPs abundance. Silty soil, due to its small particle size and weak cohesive forces, was less affected by MPs abundance. Several locations with a high proportion of soil aggregates were found to contain a large amount of fiber-type MPs and large fragment-type MPs. Small-sized MPs can also adhere to soil particles, as evidenced by the increase in the proportion of microaggregates in the size range of 0.25–0.05 mm with the increase in the fraction of small MPs. The proportion of fragment-type MPs was larger than the proportion of particle-type MPs, resulting in a noticeable increase in the proportion of microaggregates in the size range of 0.25–0.05 mm. This may be because fragment-type and fiber-type MPs have a larger specific surface area and contain more surface charge sites, which can effectively stabilize the soil structure.⁸²

The impact of MPs distribution on soil chemical properties

Soil pH is usually influenced by the pH of the local water sources. In the study area, the soil was primarily replenished by atmospheric precipitation and groundwater irrigation. Soil pH data from 1980 to 2020 in the study area were obtained from the Second Soil Survey, samples from land productivity surveys in Henan Province, and the results of soil sampling and testing in this study. Precipitation pH data from 1990 to 2020 for the study area were obtained from the national standard climate station in Zhengzhou City. Groundwater pH data from 1980 to 2020 in the study area were determined based on reports such as the “Adjustment Report of Drinking Water Source in the Northern Suburbs”.

The study area is 35 km². According to the collected data, there are 6 soil points in the study area, with a monitoring density of about 0.17 ind km⁻²; there are 8 groundwater monitoring wells, with a monitoring well density of about 0.23 ind km⁻². The density of monitoring wells is relatively large, basically covering the entire study area. At the same time, according to the test results of soil samples in the study area, the soil in the study area has been polluted by MPs to varying degrees. Therefore, the arithmetic mean of pH calculated at each monitoring point in the study area using the collected data can basically represent the overall level of soil and groundwater pH in the study area. For the North China Plain, the frequency of mulch film use and irrigation water volume of cultivated land before 2000 were both low, and the degree of development and utilization of groundwater and land resources was low. The changes in soil, precipitation and groundwater pH during this period can basically represent the changes in pH under the natural state of the North China Plain. Therefore, this study selected the average pH calculation value before 2000 as the background value of pH, and used it as a basis to evaluate the impact of MPs brought by human activities after 2000 on the soil in the study area.

According to calculations, the background pH value of soil in the study area is 8.5, the background pH value of precipitation is 6.31, and the background pH value of groundwater is 7.52. The data revealed that from 2000 to 2020, the soil pH in the study area changed from 8.5 to 8.55, the precipitation pH changed from 6.31 to 7.46, and the groundwater pH changed from 7.52 to 7.38 (Fig. 9). There were distinct differences in the trend around 2015. From 2000 to 2015, soil and precipitation pH displayed a decreasing trend, while groundwater pH displayed an increasing trend. From 2015 to 2020, the soil, precipitation, and groundwater pH all displayed a decreasing trend.

The pH of shallow groundwater in the study area has remained relatively stable since 1980, while the pH of precipitation has increased by approximately 1.15 over the same period, exhibiting a trend similar to that of soil pH. The main irrigation water sources for farmland in the study area are atmospheric precipitation and groundwater. Therefore, the increase in soil pH may be related to the increase in precipitation pH. Using the average soil pH value in 2015 as a reference, the range of the pH variation at each sampling site was calculated. In conjunction with the distribution of MPs, it was observed that the two sites with large variations (sites 7 and 9) were located in farmland and vegetable fields with sandy soils, respectively. These sites also had relatively high MPs concentrations of 7450 and 8608 ind kg⁻¹, respectively. This suggests that in areas with a loose soil structure, high MPs concentrations may have an influence on the quality of the water supply source, subsequently affecting the soil pH. This could be attributed to the increase in the concentration of small MPs particles, mainly fragment-type, which tend to loosen the soil structure in sandy soil-dominated farmland. This assists with precipitation infiltration, increases the soil water retention capacity by creating larger pores, enhances the contact between precipitation and soil, and consequently leads to a greater impact of external precipitation on soil chemical properties.

The range of soil pH variation in forested areas was relatively low compared to the variation in 2015. Despite the relatively high abundance of MPs in forested areas within the research area, the predominant soil type was sandy loam, characterized by a compact soil structure, limiting the impact of MPs on the soil structure.

The correlation between soil salinity and MPs abundance in the research area was weak. Li et al. suggested that the primary factors influencing soil salinity are meteorological conditions (such as precipitation and evaporation) and human activities (such as vegetation coverage), which could lead to changes in the soil moisture content, thereby affecting salinity levels.⁸³ This relationship needs to clarified through the continuous monitoring of soil sample indicators such as soil moisture content. Because this study only conducted soil sampling on a single occasion, it was not possible to further investigate the relationship between MPs abundance and soil salinity.

Conclusions

Based on images obtained using a fluorescence microscope in this study, a batch analysis of the quantity and characteristics of MPs in soil samples was conducted by integrating image processing software with GIS software. Combining the land use and geographical location of each sampling site, it was observed that the abundance of MPs in the study area was greatly influenced by land use. Vegetable fields, being far from roads in remote locations, were less affected by human activities than the other land uses investigated, resulting in consistently low levels of MPs abundance. Most farmlands and forests in close proximity to roads were mainly planted with corn and poplar trees, respectively. They tended to have higher levels of exogenous MPs inputs and larger contact areas, favoring the distribution of MPs in the surface soil layers. No widespread use of plastic mulch was observed in the study area, which was reflected in the relatively small proportion of PE in the types of MPs observed in the study area.

This study also analyzed the changes in soil physical and chemical properties caused by MPs. MPs could impact the soil structure, bulk density, moisture content, and aggregate distribution. In addition to abundance, the type and size of MPs were key factors affecting soil bulk density, while the size and shape of MPs influenced the soil aggregate structure. Additionally, MPs altered the soil water retention, thereby affecting the longitudinal distribution of soil moisture. In contrast, MPs had less noticeable effects on soil pH and salinity than the soil physical properties. MPs had a more noticeable impact on the pH of sandy and loamy soils, primarily through altering soil porosity and water retention. The main change was an increase in the area and duration of contact between soil media and external water sources, thereby altering soil pH. The study did not conclusively determine the influence of MPs on soil salinity. Future studies should involve the continuous sampling and testing of the MPs abundance and soil physicochemical properties in the study area to further investigate the relationship between MPs and soil chemical properties.

Data availability

The data supporting this article have been included as part of the ESI.† Data collected from the Second Soil Survey, land productivity surveys in Henan Province, the national standard climate station in Zhengzhou City, and the “Adjustment Report of Drinking Water Source in the Northern Suburbs”., described in [Fig. 9], are not available for confidentiality reasons.

Author contributions

Yuze Zhou: writing – original draft; Ming Dou: writing – review & editing; Yan Zhang: investigation; Kaizi Ning: supervision; Yuxuan Li: investigation.

Conflicts of interest

The authors have no relevant financial or nonfinancial interests to disclose.

Acknowledgements

This work was supported by the Major Science and Technology Projects of Henan Province [No. 22110032200], the Science and Technology Research Projects of Henan Province [No. 2221023020211], and the Program for key Science & Technology projects in Universities of Henan Province [No. 21A570008], and the National Natural Science Foundation of China [No. 52209076].

Notes and references

1. B. Xu, F. Liu, Z. Cryder, D. Huang and J. Xu, Microplastics in the soil environment: Occurrence, risks, interactions and fate – A review, Crit. Rev. Environ. Sci. Technol., 2020, 50(21), 2175–2222 CrossRef CAS .
2. H. S. Auta, C. U. Emenike and S. H. Fauziah, Distribution and importance of microplastics in the marine environment: a review of the sources, fate, effects, and potential solutions, Environ. Int., 2017, 102, 165–176 CrossRef CAS PubMed .
3. J. Wang, X. Liu, Y. Li, T. Powell and P. Zhang, Microplastics as contaminants in the soil environment: A mini-review, Sci. Total Environ., 2019, 691, 848–857 CrossRef CAS PubMed .
4. K. Liu, X. Wang, T. Fang, P. Xu, L. Zhu and D. Li, Source and potential risk assessment of suspended atmospheric microplastics in Shanghai, Sci. Total Environ., 2019, 675, 462–471 CrossRef CAS PubMed .
5. L. Cai, J. Wang, J. Peng, Z. Tan and Q. Chen, Characteristic of microplastics in the atmospheric fallout from Dongguan city, China: preliminary research and first evidence, Environ. Sci. Pollut. Res., 2017, 24(1526), 1–8 Search PubMed .
6. O. A. Abayomi, P. Range, M. A. Al-Ghouti, J. P. Obbard, S. H. Almeer and R. Ben-Hamadou, Microplastics in coastal environments of the Arabian Gulf, Mar. Pollut. Bull., 2017, 124(1), 181–188 CrossRef CAS PubMed .
7. H. Hinata, K. Mori, K. Ohno, Y. Miyao and T. Kataoka, An estimation of the average residence times and onshore-offshore diffusivities of beached microplastics based on the population decay of tagged meso-and macrolitter, Mar. Pollut. Bull., 2017, 122(1–2), 17–26 CrossRef CAS PubMed .
8. S. D. Ling, M. Sinclair, C. J. Levi, S. E. Reeves and G. J. Edgar, Ubiquity of microplastics in coastal seafloor sediments, Mar. Pollut. Bull., 2017, 121(1–2), 104–110 CrossRef CAS PubMed .
9. B. Beckingham and U. Ghosh, Differential bioavailability of polychlorinated biphenyls associated with environmental particles: Microplastic in comparison to wood, coal and biochar, Environ. Pollut., 2017, 220, 150–158 CrossRef CAS PubMed .
10. M. E. Hodson, C. A. Duffus-Hodson, A. Clark, M. T. Prendergast-Miller and K. L. Thorpe, Plastic Bag Derived-Microplastics as a Vector for Metal Exposure in Terrestrial Invertebrates, Environ. Sci. Technol., 2017, 51(8), 4714–4721 CrossRef CAS PubMed .
11. B. Chai, Q. Wei, Y. She, G. Lu and H. Yin, Soil microplastic pollution in an e-waste dismantling zone of China, Waste Manage., 2020, 118(19), 291–301 CrossRef CAS PubMed .
12. L. E. Huerta, J. Mendoza Vega, V. Ku Quej, J. D. L. A. Chi, S. D. C. Lucero and C. Chi, et al., Field evidence for transfer of plastic debris along a terrestrial food chain, Sci. Rep., 2017, 7(1), 14071 CrossRef PubMed .
13. L. Ding, S. Zhang, X. Wang, X. Yang and X. Guo, The occurrence and distribution characteristics of microplastics in the agricultural soils of Shaanxi Province, in north-western China, Sci. Total Environ., 2020, 720, 137525 CrossRef CAS PubMed .
14. P. K. Roy, M. Hakkarainen, I. K. Varma and A. C. Albertsson, Degradable Polyethylene: Fantasy or Reality, Environ. Sci. Technol., 2011, 45(10), 4217–4227 CrossRef CAS .
15. Z. Steinmetz, C. Wollmann, M. Schaefer, C. Buchmann, J. David and J. Tröger, et al., Plastic mulching in agriculture. Trading short-term agronomic benefits for long-term soil degradation?, Sci. Total Environ., 2016, 550, 690–705 CrossRef CAS PubMed .
16. M. Palazot, L. Soccalingame, C. Froger, C. Jolivet, A. Bispo and M. Kedzierski, et al., First national reference of microplastic contamination of French soils, Sci. Total Environ., 2024, 918, 170564 CrossRef CAS PubMed .
17. N. Beriot, J. Peek, R. Zornoza, V. Geissen and E. H. Lwanga, Low density-microplastics detected in sheep faeces and soil: A case study from the intensive vegetable farming in Southeast Spain, Sci. Total Environ., 2021, 755, 142653 CrossRef CAS PubMed .
18. M. Scheurer and M. Bigalke, Microplastics in Swiss Floodplain Soils, Environ. Sci. Technol., 2018, 52(6), 3591–3598 CrossRef CAS PubMed .
19. E. H. Lwanga, I. Van Roshum, D. R. Munhoz, K. Meng, M. Rezaei and D. Goossens, et al., Microplastic appraisal of soil, water, ditch sediment and airborne dust: The case of agricultural systems, Environ. Pollut., 2023, 316, 120513 CrossRef CAS PubMed .
20. Z. Jia, W. Wei, Y. Wang, Y. Chang, R. Lei and Y. Che, Occurrence characteristics and risk assessment of microplastics in agricultural soils in the loess hilly gully area of Yan'an, China, Sci. Total Environ., 2024, 912, 169627 CrossRef CAS PubMed .
21. Y. L. Liao, Q. X. Tang and J. Y. Yang, Microplastic characteristics and microplastic-heavy metal synergistic contamination in agricultural soil under different cultivation modes in Chengdu, China, J. Hazard. Mater., 2023, 459, 132270 CrossRef CAS .
22. R. Bai, H. Liu, J. Cui, Y. Wu, X. Guo and Q. Liu, et al., The characteristics and influencing factors of farmland soil microplastic in Hetao Irrigation District, China, J. Hazard. Mater., 2024, 465, 133472 CrossRef CAS PubMed .
23. Z. Zhang, F. Zhang, X. Yang and J. Zhang, The occurrence and distributions characteristics of microplastics in soils of different land use patterns in Karst Plateau, Southwest China, Sci. Total Environ., 2024, 906, 167651 CrossRef CAS PubMed .
24. X. Yang, Z. Wan, J. Xiao, F. Li, F. Zhang and Z. Zhang, Evaluation of niche, diversity, and risks of microplastics in farmland soils of different rocky desertification areas, J. Hazard. Mater., 2024, 466, 133603 CrossRef CAS PubMed .
25. Z. Duan, S. Zhao, L. Zhao, X. Duan and L. Wang, Microplastics in Yellow River Delta wetland: Occurrence, characteristics, human influences, and marker, Environ. Pollut., 2019, 258, 113232 CrossRef PubMed .
26. J. Li, B. Zhu, B. Huang, J. Ma, C. Lu and G. Chi, et al., Vertical distribution and characteristics of soil microplastics under different land use patterns: A case study of Shouguang City, China, Sci. Total Environ., 2023, 903, 166154 CrossRef CAS PubMed .
27. Y. Wang, X. Xv, T. Shao, Q. He, Z. Guo and Y. Wang, et al., A case on source to soil to solutions: Distribution characteristics of microplastics in farmland soil of the largest vegetable base in Northwest China, Sci. Total Environ., 2024, 907, 167–910 Search PubMed .
28. F. Fu, Y. Sun, D. Yang, L. Zhao, X. Li and L. Weng, et al., Combined pollution and soil microbial effect of pesticides and microplastics in greenhouse soil of suburban Tianjin, Northern China, Environ. Pollut., 2024, 340, 122898 CrossRef CAS .
29. L. Yang, S. Kang, Z. Wang, X. Luo, J. Guo and T. Gao, et al., Microplastic characteristic in the soil across the Tibetan Plateau, Sci. Total Environ., 2022, 828, 154518 CrossRef CAS .
30. D. Bi, B. Wang, Z. Li, Y. Zhang, X. Ke and C. Huang, et al., Occurrence and distribution of microplastics in coastal plain soils under three land-use types, Sci. Total Environ., 2023, 855, 159023 CrossRef CAS .
31. Z. Li, B. Xu, R. Zhang, F. Wang, L. Li and K. Wang, et al., Effects of land use on soil microplastic distribution adjacent to Danjiangkou reservoir, China, Chemosphere, 2023, 338, 139389 CrossRef CAS PubMed .
32. S. Zhang, X. Liu, X. Hao, J. Wang and Y. Zhang, Distribution of low-density microplastics in the mollisol farmlands of northeast China, Sci. Total Environ., 2020, 708, 135091 CrossRef CAS PubMed .
33. W. Li, R. Wufuer, J. Duo, S. Wang, Y. Luo and D. Zhang, et al., Microplastics in agricultural soils: Extraction and characterization after different periods of polythene film mulching in an arid region, Sci. Total Environ., 2020, 749, 141420 CrossRef CAS PubMed .
34. M. C. Rillig, Microplastic in terrestrial ecosystems and the soil?, Environ. Sci. Technol., 2012, 46(12), 6453–6454 CrossRef CAS PubMed .
35. G. Peng, B. Zhu, D. Yang, L. Su, H. Shi and D. Li, Microplastics in sediments of the Changjiang Estuary, China, Environ. Pollut., 2017, 225, 283–290 CrossRef CAS PubMed .
36. A. Vendel, F. Bessa, V. Alves, A. Amorim, J. Patrício and A. Palma, Widespread microplastic ingestion by fish assemblages in tropical estuaries subjected to anthropogenic pressures, Mar. Pollut. Bull., 2017, 117(1–2), 448–455 CrossRef CAS PubMed .
37. Y. Li, L. Shao, W. Wang, M. Zhang, X. Feng and W. Li, et al., Airborne fiber particles: Types, size and concentration observed in Beijing, Sci. Total Environ., 2020, 705, 135967 CrossRef CAS PubMed .
38. S. O'Brien, E. D. Okoffo, J. W. O'Brien, F. Ribeiro, X. Wang and S. L. Wright, et al., Airborne emissions of microplastic fibres from domestic laundry dryers, Sci. Total Environ., 2020, 747, 141175 CrossRef PubMed .
39. M. Bläsing and W. Amelung, Plastics in soil: Analytical methods and possible sources, Sci. Total Environ., 2018, 612, 422–435 CrossRef PubMed .
40. I. Kögel-Knabner and W. Amelung, Dynamics, Chemistry, and Preservation of Organic Matter in Soils, Treatise on Geochemistry, 2nd edn, 2014, vol. 12, iss 8, pp. 157–215 Search PubMed .
41. J. Lehmann and M. Kleber, The contentious nature of soil organic matter, Nature, 2015, 528(7580), 60–68 CrossRef CAS PubMed .
42. V. Hidalgo-Ruz, L. Gutow, R. C. Thompson and M. Thiel, Microplastics in the marine environment: a review of the methods used for identification and quantification, Environ. Sci. Technol., 2012, 46(6), 3060–3075 CrossRef CAS PubMed .
43. M. T. Nuelle, J. H. Dekiff, D. Remy and E. Fries, A new analytical approach for monitoring microplastics in marine sediments, Environ. Pollut., 2014, 184, 161–169 CrossRef CAS PubMed .
44. W. Lv, W. Zhou, S. Lu, W. Huang and D. He, Microplastic pollution in rice-fish co-culture system: A report of three farmland stations in Shanghai, China, Sci. Total Environ., 2019, 652, 1209–1218 CrossRef PubMed .
45. J. Zhao, W. Ran, J. Teng, Y. Liu, H. Liu and X. Yin, et al., Microplastic pollution in sediments from the Bohai Sea and the Yellow Sea, China, Sci. Total Environ., 2018, 640, 637–645 CrossRef PubMed .
46. E. M. Crichton, M. Nol, E. A. Gies and P. S. Ross, A novel, density-independent and FTIR-compatible approach for the rapid extraction of microplastics from aquatic sediments, Anal. Methods, 2017, 9(9), 1419–1428 RSC .
47. M. Cole, H. Webb, P. K. Lindeque, E. S. Fileman, C. Halsband and T. S. Galloway, Isolation of microplastics in biota-rich seawater samples and marine organisms, Sci. Rep., 2014, 4(1), 4528 CrossRef PubMed .
48. N. W. Heo, S. H. Hong, G. M. Han, S. Hong, J. Lee and Y. K. Song, et al., Distribution of small plastic debris in cross-section and high strandline on Heungnam beach, South Korea, Ocean Sci. J., 2013, 48, 225–233 CrossRef .
49. V. Hidalgo-Ruz and M. Thiel, Distribution and abundance of small plastic debris on beaches in the SE Pacific (Chile): A study supported by a citizen science project, Mar. Environ. Res., 2013, 87, 12–18 CrossRef PubMed .
50. C. V. Sperber, H. Lewandowski, F. Tamburini, S. M. Bernasconi, W. Amelung and E. Frossard, Kinetics of enzyme-catalysed oxygen isotope exchange between phosphate and water revealed by Raman spectroscopy, J. Raman Spectrosc., 2017, 48(3), 368–373 CrossRef .
51. M. Li, Z. Zhan and Z. Tan, et al., Extraction, enumeration and identification methods for monitoring microplastics in the environment, Estuarine, Coastal Shelf Sci., 2016, 176, 102–109 CrossRef .
52. G. Liebezeit and F. Dubaish, Microplastics in Beaches of the East Frisian Islands Spiekeroog and Kachelotplate, Bull. Environ. Contam. Toxicol., 2012, 89(1), 213–217 CrossRef CAS PubMed .
53. P. M. Anger, E. V. D. Esch, T. Baumann, M. Elsner, R. Niessner and N. P. Ivleva, Raman Microspectroscopy as a Tool for Microplastic Particle Analysis, TrAC, Trends Anal. Chem., 2018, 109, 214–226 CrossRef CAS .
54. C. F. Araujo, M. M. Nolasco, A. M. P. Ribeiro and P. J. A. Ribeiro-Claro, Identification of microplastics using Raman spectroscopy: latest developments and future prospects, Water Res., 2018, 142(oct.1), 426–440 CrossRef CAS PubMed .
55. A. Käppler, D. Fischer, S. Oberbeckmann, G. Schernewski, M. Labrenz and K.-J. Eichhorn, et al., Analysis of environmental microplastics by vibrational microspectroscopy: FTIR, Raman or both?, Anal. Bioanal. Chem., 2016, 408, 8377–8391 CrossRef PubMed .
56. L. Yu, J. D. Zhang, Y. Liu, L. Y. Chen and W. X. Liu, Distribution characteristics of microplastics in agricultural soils from the largest vegetable production base in China, Sci. Total Environ., 2021, 756(32), 143860 CrossRef CAS PubMed .
57. S. Huang, X. Huang, R. Bi, Q. Guo, X. Yu and Q. Zeng, et al., Detection and Analysis of Microplastics in Human Sputum, Environ. Sci. Technol., 2022, 56(4), 2476–2486 CrossRef CAS PubMed .
58. Q. Li, J. Wu, X. Zhao, X. Gu and R. Ji, Separation and identification of microplastics from soil and sewage sludge, Environ. Pollut., 2019, 254(PT.2), 113076 CrossRef CAS PubMed .
59. Y. Chen, Y. Leng, X. Liu and J. Wang, Microplastic pollution in vegetable farmlands of suburb Wuhan, central China, Environ. Pollut., 2019, 257, 113449 CrossRef PubMed .
60. L. Mengting, L. Shibo, S. Yang, L. Lili, H. Jiani and L. Weiwei, et al., Microplastic and mesoplastic pollution in farmland soils in suburbs of Shanghai, China, Environ. Pollut., 2018, 242, 855–862 CrossRef PubMed .
61. Y. Zhou, X. Liu and J. Wang, Characterization of microplastics and the association of heavy metals with microplastics in suburban soil of central China, Sci. Total Environ., 2019, 694, 133798 CrossRef CAS PubMed .
62. S.-K. Kim, J.-S. Kim, H. Lee and H.-J. Lee, Abundance and characteristics of microplastics in soils with different agricultural practices: Importance of sources with internal origin and environmental fate, J. Hazard. Mater., 2021, 403, 123997 CrossRef CAS PubMed .
63. G. S. Zhang and Y. F. Liu, The distribution of microplastics in soil aggregate fractions in southwestern China, Sci. Total Environ., 2018, 642(nov.15), 12–20 CrossRef CAS PubMed .
64. H. Bin, S. Leiye, L. Mengran, H. Haiyou, H. Huan, H. Fengxia, W. Xiaoxia, X. Zhixiang, L. Bei and P. Xuejun, Abundance and distribution characteristics of microplastic in plateau cultivated land of Yunnan Province, China, Environ. Sci. Pollut. Res., 2021, 28, 1675–1688 CrossRef PubMed .
65. F. Corradini, P. Meza, R. Eguiluz, F. Casado, E. Huerta-Lwanga and V. Geissen, Evidence of microplastic accumulation in agricultural soils from sewage sludge disposal, Sci. Total Environ., 2019, 671(JUN.25), 411–420 CrossRef CAS PubMed .
66. L. Lin, X. Pan, S. Zhang, D. Li, W. Zhai and Z. Wang, et al., Distribution and source of microplastics in China's second largest reservoir-Danjiangkou Reservoir, J. Environ. Sci., 2021, 102, 74–84 CrossRef CAS PubMed .
67. Y. R. Choi, Y. N. Kim, J. H. Yoon, N. Dickinson and K. H. Kim, Plastic contamination of forest, urban, and agricultural soils: a case study of Yeoju City in the Republic of Korea, J. Soils Sediments, 2021, 21(8), 1–12 Search PubMed .
68. L. Ding, X. Wang, Z. Ouyang, Y. Chen and X. Guo, The occurrence of microplastic in Mu Us Sand Land soils in northwest China: Different soil types, vegetation cover and restoration years, J. Hazard. Mater., 2021, 403, 123982 CrossRef CAS PubMed .
69. M. Liu, Y. Song, S. Lu, R. Qiu, J. Hu and X. Li, et al., A method for extracting soil microplastics through circulation of sodium bromide solutions, Sci. Total Environ., 2019, 691(Nov.15), 341–347 CrossRef CAS PubMed .
70. J. C. Prata, J. P. D. Costa, A. C. Duarte and T. Rocha-Santos, Methods for sampling and detection of microplastics in water and sediment: A critical review - ScienceDirect, TrAC, Trends Anal. Chem., 2019, 110, 150–159 CrossRef CAS .
71. C. Wang, L. Jiang, R. Liu, M. He, X. Cui and C. Wang, Comprehensive assessment of factors influencing Nile red staining: Eliciting solutions for efficient microplastics analysis, Mar. Pollut. Bull., 2021, 171, 112698 CrossRef CAS PubMed .
72. J. C. Prata, V. Reis, J. T. V. Matos, J. P. Da Costa, A. C. Duarte and T. Rocha-Santos, A new approach for routine quantification of microplastics using Nile Red and automated software (MP-VAT), Sci. Total Environ., 2019, 690(NOV.10), 1277–1283 CrossRef CAS PubMed .
73. G. Diaz, M. Melis, B. Batetta, F. Angius and A. M. Falchi, Hydrophobic characterization of intracellular lipids in situ by Nile Red red/yellow emission ratio, Micron, 2008, 39(7), 819–824 CrossRef CAS PubMed .
74. K. Zhang, Y. Peng, D. Li, J. Lin, Y. Luo and T. Wang, et al., The recognition of breast tumor based on ultrasonic image contour features, J. Biomed. Eng., 2006, 23(6), 1237–1240 Search PubMed .
75. Y. Liu, L. Ren, X. Cao and Y. Tong, Breast tumors recognition based on edge feature extraction using support vector machine, Biomed. Signal Process. Control., 2020, 58, 101825 CrossRef .
76. G. A. Asongwe, I. B. Bame, L. M. Ndam, A. E. Orock, V. A. Tellen and K. P. Bumtu, et al., Influence of urbanisation on phytodiversity and some soil properties in riverine wetlands of Bamenda municipality, Cameroon, Sci. Rep., 2022, 12(1), 19766 CrossRef CAS PubMed .
77. X. Yan, X. Yang, Z. Tang, J. Fu and Y. Yang, Downward transport of naturally-aged light microplastics in natural loamy sand and the implication to the dissemination of antibiotic resistance genes, Environ. Pollut., 2020, 262, 114270 CrossRef CAS PubMed .
78. D. O'Connor, S. Pan, Z. Shen, Y. Song, Y. Jin and W. M. Wu, et al., Microplastics undergo accelerated vertical migration in sand soil due to small size and wet-dry cycles, Environ. Pollut., 2019, 249, 527–534 CrossRef PubMed .
79. Z. Song, X. Yang, F. Chen, F. Zhao, Y. Zhao and L. Ruan, et al., Fate and transport of nanoplastics in complex natural aquifer media: Effect of particle size and surface functionalization, Sci. Total Environ., 2019, 669(JUN.15), 120–128 CrossRef CAS PubMed .
80. Z. Luo, J. Fan, Q. Yang, X. Yang and S. Zhang, Evaluating soil water dynamics and vegetation growth characteristics under different soil depths in semiarid loess areas, Geoderma, 2024, 442, 116791 CrossRef .
81. H. Chen and F. Liu, Soil depth and recovery interval mediate soil water repellency under different forest types and fire intensity levels in China: Evidence for ecosystem resiliency, Soil Tillage Res., 2024, 237, 105982 CrossRef .
82. G. Zhang, F. Zhang and X. Li, Effects of polyester microfibers on soil physical properties: Perception from a field and a pot experiment, Sci. Total Environ., 2019, 670, 1–7 CrossRef CAS PubMed .
83. X. Li, Y. Li, B. Wang, Y. Sun, G. Cui and Z. Liang, Analysis of spatial-temporal variation of the saline-sodic soil in the west of Jilin Province from 1989 to 2019 and influencing factors, Catena, 2022, 217, 106492 CrossRef .

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4em00242c

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