The nauticAttiva project: A mobile phone-based tool for the citizen science plastic monitoring in the marine and coastal environment

Quantification of litter in cities using a smartphone application and citizen science in conjunction with deep learning-based image processing

Cities are a major source of litter pollution. Determination of the abundance and composition of plastic litter in cities is imperative for effective pollution management, environmental protection, and sustainable urban development. Therefore, here, a multidisciplinary approach to quantify and classify the abundance of litter in urban environments is proposed. In the present study, litter data collection was integrated via the Pirika smartphone application and conducted image analysis based on deep learning. Pirika was launched in May 2018 and, to date, has collected approximately one million images. Visual classification revealed that the most common types of litter were cans, plastic bags, plastic bottles, cigarette butts, cigarette boxes, and sanitary masks, in that order. The top six categories accounted for approximately 80 % of the total, whereas the top three categories accounted for more than 60 % of the total imaged litter. A deep-learning image processing algorithm was developed to automatically identify the top six litter categories. Both precision and recall derived from the model were higher than 75 %, enabling proper litter categorization. The quantity of litter derived from automated image processing was also plotted on a map using location data acquired concurrently with the images by the smartphone application. Conclusively, this study demonstrates that citizen science supported by smartphone applications and deep learning-based image processing can enable the visualization, quantification, and characterization of street litter in cities.

The silent threat of plastics along the coastal frontiers of Bangladesh: Are we concerned enough?

Globally plastic pollution is posing a significant threat to the health and integrity of coastal ecosystems. This study aimed to provide a comprehensive overview of plastic pollution in the coastal areas of Bangladesh by examining land-based macroplastic distribution, exploring microplastic (MP) contamination in the coastal aquatic ecosystem and enhancing our understanding of the potential risks associated with MP contamination. Citizen science based monitoring approach using the android application was applied to understand the land-based plastic pollution in the coastal area of Bangladesh. From December 2022 to December 2023, a total of about 3600 photographs of plastic items from 215 citizen scientists were received from the coastal area of Bangladesh covering 580 km long coast line. Polymer Hazard Index (PHI) and Pollution Load Index (PLI) were also calculated to understand the risk of plastic pollution in sediment, water, aquatic organism, dried fish and sea salt. A total of 43 land-based plastic items reported from the coastal area of Bangladesh. Among these plastic items single use items contributed 58.2 % while disposable plastic items contributed 41.8 %. A strong spatial variability in the distribution of these plastic items was observed. PHI and PLI values suggested hazard category-I for MP contamination in sediment, sea salt, water, commercial fishery resources and dry fish. This study highlighted that coastal land area, sea salt, dried fish, water, sediment and organisms are contaminated with plastics which might have the potential threats to human health. Findings from this study will serve as reference data and also baseline for future research to combat the plastic pollution.

An integrated coastal ecosystem monitoring strategy: Pilot case in Naf-Saint Martin Peninsula, Bangladesh

Rapid population growth creating an excessive pressure on the marine environment and thus monitoring of marine ecosystem is essential. However, due to high technical and financial involvement, monitoring of coastal ecosystem is always challenging in developing countries. This study aims to develop an integrated coastal ecosystem monitoring system that combines scientific sampling, numerical model simulation and citizen science observations to monitor the coastal ecosystem of Bangladesh. This concept of integrated monitoring approach was piloted from January 2022 to April 2023 at the South East coastal zone of Bangladesh. Scientific sampling and numerical model simulations were performed for temperature and salinity data collection. Citizen science approach was employed to collect data on environmental conditions, fisheries, plankton, other marine resources, and plastic pollution. Numerical model simulations and citizen scientists observations of temperature and salinity showed good agreement with the scientifically collected data. In addition, citizen scientists observations on fisheries, plankton, other marine resources and plastic pollution were also in line with the existing database and previous studies. The proposed integrated monitoring approach presents a viable technique, creating a new avenue for coastal and marine ecosystem monitoring where infrastructural facilities are limited.