Dataset on mesoplastics and microplastics abundances and characteristics from sandy beaches before and after typhoon events in northern Taiwan

Abstract

A comprehensive dataset is presented, which describes the abundance, shapes, and colors of meso- and microplastic particles collected from two sandy beaches situated on the north coast of Taiwan. The sampling of beach sand was conducted repetitively at fixed locations over a time period of 20 months, commencing from April 2018 to November 2019, with the aim of monitoring the variations in distribution and composition of plastic particles. A total of three adjacent transects perpendicular to the waterline were sampled, with bulk sand samples collected from 50 × 50 cm quadrats. The samples were subjected to drying, weighing, and sieving to obtain mesoplastic fractions (5-25 mm) and microplastic fractions (1-5 mm). Visual identification was employed to extract mesoplastic particles, while density separation using a saturated NaCl solution was utilized to extract microplastic particles. The particles were counted visually under a stereo microscope, and subsequently classified based on their shape and color. Any unknown particles were subjected to FTIR spectroscopy. Particle count data are presented as particles per unit area (0.25 m²) but can be converted to particles per kg d.w. by employing the weight of dry sand, as provided in the tables. The dataset encompasses a time series and delineates the changes in particle distribution and composition following extreme weather events. It can be utilized for further research by reanalyzing the data from different perspectives or by incorporating other factors.

Specifications Table

Subject	Environmental Science, Pollution
Specific subject area	Temporal variations of mesoplastics and microplastics abundances in sandy beaches after extreme weather events
Type of data	Table Figure
How the data were acquired	Mesoplastic particles were identified and counted with the naked eye. Microplastic particles were identified and counted using a stereo microscope from Olympus (model SZ61). Particles from both size fractions of unclear material were subjected to FTIR spectroscopy using a Nicolet 6700 FTIR spectrometer from Thermo Fisher Scientific. Spectra were analyzed with the software OMNIC v. 9.2.
Data format	Raw
Description of data collection	In total 1296 samples over a time period from April 2018 until November 2019 were collected. Surface sand (1-2 cm deep) was collected as bulk sample. Location along the transect was recorded, as well as corresponding beach zones. Characterization of particles by size, shape and color. Starting points of transects were always the same. Each sampling point represents an area of 0.25 m2.
Data source location	Institution: Research Center for Environmental Changes, Academia Sinica City/Town/Region: Taipei City Country: Taiwan Latitude and longitude for collected samples: 25.02951°N, 121.9355°E (Longmen Beach) 25.21469°N, 121.65406°E (Xialiao Beach)
Data accessibility	Repository name: Mendeley Data Data identification number: 10.17632/4ksvttht8c.2 Direct URL to data: https://data.mendeley.com/datasets/4ksvttht8c/2

Value of the Data

• •This data provides a rare time series with detailed observations of meso and microplastics pollution in sandy beaches over a longer time period including extreme weather events.
• •The data is useful for researchers who study the spatio-temporal distribution of plastic pollution on sandy beaches or the impacts of extreme weather events on plastic distribution.
• •The inclusion of mesoplastic in this dataset provides additional value, as this size fraction is often overlooked.
• •The dataset can serve as a baseline or reference point for future long-term studies about beach pollution. It can also be used for meta-studies and contribute to global data sets about plastic pollution in the marine or coastal environment.

1. Objective

The primary objectives of the data collection were to assess changes in microplastic and mesoplastic particle distribution on sandy beaches over an extended duration, investigate the impact of extreme weather events on plastic particle abundance and spatial distribution, and explore potential long-term trends in the abundance of mesoplastics and microplastics during the sampling period. To ensure consistency and comparability of data, samples were collected from identical locations using standardized methods.

2. Data Description

Tables 1, 2 and 3 explain the column titles for each file in the data repository.

Table 1.

Description of column titles for files Longmen Beach color count.csv and Xialiao Beach color count.csv.

Column title	Description
Date_YYYY-MM-DD	Sampling date in the format year-month-day.
Country_Region	Name of the country or region of sampling.
Location_name	Name of the beach that was sampled.
Location_lat	Latitude of starting point for transect A in decimal degrees.
Location_lon	Longitude of starting point for transect A in decimal degrees.
Transect	Letter code (A, B, C) for each transect. See Fig. 2 for details.
Position	Number of sampled squares in each transect. See Fig. 2 for details.
Size_min_mm	Lower size of sampled plastic particles in mm.
Size_max_mm	Upper size of sampled plastic particles in mm.
Size_class	Verbal description if plastic particles are microplastics or mesoplastics.
no_color	Number of plastic particles without color per 0.25 m2. These can be white, translucent or transparent particles.
black	Number of black plastic particles per 0.25 m2.
grey	Number of grey plastic particles per 0.25 m2.
red_pink	Number of red and pink plastic particles per 0.25 m2.
orange_brown_yellow	Number of orange, brown and yellow plastic particles per 0.25 m2.
green	Number of green plastic particles per 0.25 m2.
blue	Number of blue plastic particles per 0.25 m2.
purple	Number of purple plastic particles per 0.25 m2.

Table 2.

Description of column titles for files Longmen Beach particle count.csv and Xialiao Beach particle count.csv.

Column title	Description
Date_YYYY-MM-DD	Sampling date in the format year-month-day.
Country_Region	Name of the country or region of sampling.
Location_name	Name of the beach that was sampled.
Location_lat	Latitude of starting point for transect A in decimal degrees.
Location_lon	Longitude of starting point for transect A in decimal degrees.
Transect	Letter code (A, B, C) for each transect. See Fig. 2 for details.
Position	Number of sampled squares in each transect. See Fig. 2 for details.
Size_min_mm	Lower size of sampled plastic particles in mm.
Size_max_mm	Upper size of sampled plastic particles in mm.
Size_class	Verbal description if plastic particles are microplastics or mesoplastics.
Particle_count	Total number of microplastic or mesoplastic particles per 0.25 m2.
Particle_weight_g	Weight of microplastic or mesoplastic particles in gram.
Weight_dry_sand_g	Weight of dry bulk sand for each sample. This allows calculation of the number of plastic particles per kg of dry sand.
Beach_Zone	Beach zone from which each sample was collected. The meaning of beach zones is further described in Table 4.

Table 3.

Description of column titles for files Longmen Beach shape count.csv and Xialiao Beach shape count.csv.

Column title	Description
Date_YYYY-MM-DD	Sampling date in the format year-month-day.
Country_Region	Name of the country or region of sampling.
Location_name	Name of the beach that was sampled.
Location_lat	Latitude of starting point for transect A in decimal degrees.
Location_lon	Longitude of starting point for transect A in decimal degrees.
Transect	Letter code (A, B, C) for each transect. See Fig. 2 for details.
Position	Number of sampled squares in each transect. See Fig. 2 for details.
Size_min_mm	Lower size of sampled plastic particles in mm.
Size_max_mm	Upper size of sampled plastic particles in mm.
Size_class	Verbal description if plastic particles are microplastics or mesoplastics.
fragment	Total number of fragments per 0.25 m2. Fragments are all plastic pieces that broke off from larger pieces.
foamed_plastic	Total number of foamed plastic particles per 0.25 m2. This includes all types of foamed plastic, such as Styrofoam and other foams.
pellet	Total number of pellets per 0.25 m2. This includes all industrial pellets. Virgin, weathered, cylindrical, and spherical pellets.
foil	Total number of foil per 0.25 m2. Foil is any type of thin and flexible plastic piece, such as pieces from a plastic bag.
fiber	Total number of fibers per 0.25 m2.
fishing_line	Total number of synthetic strings that are used as fishing lines per 0.25 m2. These strings are single strands and not twisted or braided as ropes.
cigarette_butt	Total number of cigarette butts per 0.25 m2.
rope	Total number of rope pieces per 0.25 m2. Rope refers to twisted or braided strands.
rubber	Total number of rubber pieces per 0.25 m2.
fabric	Total number of fabric or textile pieces per 0.25 m2. Only in Longmen Beach shape count.csv
unclear	Total number of particles of undefined shape per 0.25 m2. Only in Longmen Beach shape count.csv

Table 4 explains the criteria for assigning beach zones as mentioned in Table 2.

Table 4.

Description of criteria used to assign beach zones that are presented in files Longmen Beach particle count.csv and Xialiao Beach particle count.csv.

Beach zone	Description
dune	Coastal dunes. Samples were only collected from the slope of the dune and not the entire dune. Dune also acted as starting point for each transect.
backshore	The area beyond the last storm line. Waves don't reach it.
backshore_with_storm_lines	Only in Xialiao Beach. Backshore was difficult to distinguish from supra littoral and storm lines. Therefore, it was combined into one area.
storm_line	Areas that were reached by waves only during storms.
supra_littoral	Area between high tide line and the first storm line. Waves reach this area rarely.
high_tide_line	Area that marks the highest level of the tide.
intertidal	Beach area of constant wave action. Also, the area of constantly wet beach sand. Samples were only collected from the initial part and not from the entire intertidal.

The data in the repository consists of 6 CSV-files, namely: Longmen Beach color count.csv, Longmen Beach particle count.csv, Longmen Beach shape count.csv, Xialiao Beach color count.csv, Xialiao Beach particle count.csv, Xialiao Beach shape count.csv. The headers and their meaning are described in Tables 1,2 and 3.

3. Experimental Design, Materials and Methods

3.1. Study area

The study was conducted along the northern coast of Taiwan, specifically chosen due to the high levels of plastic pollution in the coastal areas [1], [2], [3], [4], [5]. Furthermore, this region experiences a substantial frequency of typhoons, either making landfall or moving in close proximity to the coastline [6].

For sampling purposes, two beaches located on the north coast of Taiwan were selected: Xialiao Beach and Longmen Beach (Fig. 1). Xialiao Beach spans a length of 2.3 km and exhibits varying widths from 70 m in the northwestern part to 30 m in the southeastern part, extending from the dune to the high tide line. While a small portion of the beach in the northern region is designated for recreational use, the majority of the beach remains largely unvisited by beachgoers. On the other hand, Longmen Beach, with a length of 3.5 km, represents the largest sandy beach along the northern coast of Taiwan. The width of this beach varies from 50 m in the northern part to 150 m in the southern part. The sampling location was situated in the northern section, which experiences minimal human impact, while the southeastern part is designated for recreational activities and regularly undergoes cleaning efforts.

Fig. 1.

Location of sampled beaches at the north coast of Taiwan.

The sample collection spanned a duration of 20 months, from April 2018 to November 2019. One type of sampling campaign was conducted during periods characterized by the absence of tropical storms, ensuring at least one sampling campaign was carried out per season. These samples served as a reference point for evaluating the baseline levels of plastic pollution on the targeted beaches.

Alternatively, additional sampling campaigns were conducted shortly after the occurrence of a typhoon in close proximity to the study area. Specifically, these post-typhoon sampling campaigns took place within a timeframe of 2 to 8 days following the typhoon, contingent upon favorable weather conditions that permitted safe fieldwork.

Throughout the sampling period, three typhoons significantly impacted Taiwan's northern coast. These included super typhoon Maria, which came closest to the study area at a distance of 127 km on July 10, 2018, with wind speeds reaching 105 knots. Similarly, super typhoon Lekima reached a closest distance of 213 km from the sampling area, accompanied by wind speeds of 120 knots on August 9, 2019. Lastly, typhoon Mitag had its closest approach to the sampling area at a distance of 105 km, with wind speeds of 90 knots recorded on August 30, 2019.

3.2. Sampling

Our sampling scheme was based on Kunz et al. [2], Besley et al. [7], and Bancin et al. [1]. During the initial visit to each beach, the sampling locations were randomly selected and marked using poles that were inserted into the beach. This ensured that the same sampling locations could be located in subsequent visits. The coordinates for the sampling locations were 25.21469°N, 121.65406°E for Xialiao Beach, and 25.02951°N, 121.9355°E for Longmen Beach. For each sampling location, a consistent starting point was established and maintained throughout all subsequent sampling campaigns. The starting point was situated in the barren section of the dunes, typically on the slope due to vegetation cover on the top part of the dunes. From this starting point, three parallel transects (A, B, C), perpendicular to the water line, were sampled. The transects extended until they reached the intertidal zone, as close to the water line as possible. Sampling was conducted within 50 × 50 cm quadrats, spaced 50 cm apart from each other (Fig. 2). To keep the sampled area constant, we used a frame made of PVC tubes. Each quadrat per transect was assigned a unique number (referred to as “position” in the data tables), with the numbering sequence beginning at the first quadrat (number 1) at the starting position in the dunes and continuing until the final quadrat in the intertidal zone. The location of each sample can be identified by the combination of transect and position, e.g., B6.

Fig. 2.

Arrangement of sampling points on the beach.

Bulk sand samples were obtained from the beach and subsequently processed in the laboratory. Surface sand, approximately 1-2 cm deep, from each quadrat was collected using a metal scoop and stored in sampling bags. Prior to sampling, any large debris items such as tree trunks or sizable macroplastic items were carefully removed. Consistent arrangements of sampling points and procedures were employed for both beaches and during all sampling instances. Beach zones were designated based on field observations, including the intertidal zone, high tide line, supra littoral area (between the high tide line and the first storm line), storm line, backshore, backshore with storm lines (specific to Xialiao Beach to account for challenges in distinguishing individual storm lines), and dune.

3.3. Sample preparation

In the laboratory the sand was dried at 50°C for one or two days, depending on the original moisture content. The dry sand was then weighted and sieved into the size fractions <1mm, 1-5 mm, and >5 mm. The weight of the dry sand before plastic extraction is reported in the data tables Longmen Beach particle count.csv and Xialiao Beach particle count.csv as “Weight_dry_sand_g”. From the fraction >5 mm mesoplastics (5-25 mm) and macroplastics (>25 mm) were manually extracted. The fractions <1 mm and >25mm were not used in this study. The 1-5 mm fraction underwent density separation using a saturated NaCl solution similar as described in Bancin et al. [1] and Kunz et al. [2]. Dry sand was placed in beakers and a saturated NaCl solution was added. The sample was stirred and after the sand settled down, the supernatant was filtered over a stack of metal mesh sieves to obtain the desired size of 1-5 mm. The procedure was repeated several times until no floating particles were visible in the supernatant. After density separation the extracted particles were thoroughly washed with water to remove salt, transferred into petri dishes, and dried in the oven at 50°C.

4. Identification, Counting and Classification of Plastic Particles

Microplastic and mesoplastic particles were subject to visual identification, manual counting, classification, and weighting. For the identification of microplastics, a stereomicroscope (Olympus SZ61) was utilized. The majority of plastic particles were distinguishable from non-plastic materials by their characteristic shape, color, cleavage, and physical properties. To further distinguish ambiguous particles, the ‘hot needle technique’ [8] was employed. All particles that could not be clearly identified as plastic or non-plastic were subjected to FTIR spectroscopy for material identification.

All plastic particles identified were characterized based on their shape and color. Shapes were classified into the following categories: fragment, foamed plastic, round pellets, cylindrical pellets, foil, fishing line, fiber (agglomerations of fibers, not single fibers), cigarette butt, rope, rubber, fabric, and unclear (shapes that could not be assigned to any of these groups). Colors, subject to subjective perception, were broadly grouped into: no color or colorless (including white, transparent, and translucent particles), black, grey, red and pink, yellow and orange, and brown, green, blue, and purple.

Ethics Statements

This work did not involve human subjects or animal experiments. The data presented was not collected from social media platforms.

CRediT authorship contribution statement

Alexander Kunz: Conceptualization, Methodology, Supervision, Writing – review & editing. Ludvig Löwemark: Resources, Funding acquisition. Joshua Yang: Investigation, Formal analysis, Writing – original draft.

Declaration of Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data Availability

• Time series of mesoplastics and microplastics abundance from two sandy beaches in Taiwan (Original data) (Mendeley Data).