Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach.

The use and management of single use plastics is a major area of concern for the public, regulatory and business worlds. Focusing on the most commonly occurring consumer plastic items present in European freshwater environments, we identified and evaluated consumer-based actions with respect to their direct or indirect potential to reduce macroplastic pollution in freshwater environments. As the main end users of these items, concerned consumers are faced with a bewildering array of choices to reduce their plastics footprint, notably through recycling or using reusable items.

Macroplastic pollution in freshwater environments: Focusing public and policy action.

Understanding and managing plastic pollution is an increasingly important environmental priority for policy makers, businesses and scientists. Awareness of the potential damage to the world's oceans has grown but there is less attention given to freshwater ecosystems. Yet, rivers are the dominant source of plastic pollution to the marine environment, as well as a potential sink, accumulating plastic from multiple sources.

Getting the full picture: Assessing the complementarity of citizen science and agency monitoring data.

While the role of citizen science in engaging the public and providing large-scale datasets has been demonstrated, the nature of and potential for this science to supplement environmental monitoring efforts by government agencies has not yet been fully explored. To this end, the present study investigates the complementarity of a citizen science programme to agency monitoring of water quality. The Environment Agency (EA) is the governmental public body responsible for, among other duties, managing and monitoring water quality and water resources in England.

Prioritising local action for water quality improvement using citizen science; a study across three major metropolitan areas of China.

Streams in urban areas are prone to degradation. While urbanization-induced poor water quality is a widely observed and well documented phenomenon, the mechanism to pinpoint local drivers of urban stream degradation, and their relative influence on water quality, is still lacking. Utilizing data from the citizen science project FreshWater Watch, we use a machine learning approach to identify key indicators, potential drivers, and potential controls to water quality across the metropolitan areas of Shanghai, Guangzhou and Hong Kong.