Persistence of Plastics in the Marine Environment off of Southern California

While we have some idea of how much plastic is in our waters and how long it takes to decay, there has been little research into the residency time of plastic in each part of watersheds. Our question is how long does it take for a plastic cup on the beach to be converted to microplastics in the ocean and then to a chemical contaminant within the ocean sediments? While no model exists, much data has been collected in Southern California that could be used to potentially answer that question. The proposal here is to enlist a research graduate student and/or data scientist to put together known datasets relative to macro- and microplastics collected as part of beach, trawl, benthic, rocky reef and water column sampling efforts as well as using bathymetry, current and even polymer data. The researcher would line up these data sets and map and model for peer-reviewed published article.

Toolbox of Solutions to Enhance Data Collection by Citizen Monitors

There is a need to collect data while at the same time successfully engage citizen monitors in cleaning up the trash in the environment. The difficulty is in collecting consistent high-quality data at the same time as meeting the citizen monitoring expectations of participating not as data collectors but as helpers of the environment. The more complex the data collection card, you lose the a larger percentage of the crowd that will collect the data. Hence, the more simplistic the data card the higher the return rate on data. There is a need to create a simple toolbox of solutions to collect the most robust and applicable data to turn into meaningful policy recommendations. These toolbox solutions should be easy enough to use by NGOs, small citizen groups, and even neighborhood beautification groups and should include a variety of data cards that can be used by different levels of users.

Quantifying Tobacco Waste in Litter

One major component of trash is cigarette butts and tobacco products which are a form of non-biodegradable litter. In addition, this litter contains tobacco which may lead to health impacts when incorporated into discharged runoff. This project recommended analyzing and characterizing trash data from the Bay Area Stormwater Management Agencies Association (BASMAA) street sweeping and curb inlet screen evaluation study and the CalEnviroScreen dataset provided by the CA Environmental Protection Agency (CalEPA) to determine how much tobacco waste there was and the potential for its effects on water quality. Analysis by BASMAA showed in one area there was an estimated 404 gallons of waste, of which 10 gallons were of cigarette trash. This made for approximately 18,000 cigarettes total. The idea is to replicate and use this data in coordination with socioeconomic data to identify hot spots for tobacco waste and then associate those with health issues. The group also wants to investigate the effectiveness of cigarette receptacles. Initial results have shown that cigarette receptacles may actually lead to more cigarette litter in the environment. The group also proposed to use the data sets along with additional attributes and machine learning exercises to develop different algorithms and make predictions and identify areas of concern. Walter Yu from Caltrans has done some additional work to demonstrate this, which can be found on github.

Power Portal

Focus is on plastic and big data through creating a Power Portal. There is lots of data from a variety of sources with different formats, different inputs and different needs. The Power Portal needs to have common formats, the input mechanisms must be simple, and it needs to be a one-stop shop where everything is easy to find and use. It also needs some form of governance, i.e. what is considered clean data, who should have access and what types of data validation should be involved. The group looked at the data available to the TDD participants and wanted to get an idea of how it looked spatially and counted and categorized it as litter vs waste management to create a heat map. Next step is to look at permitted vs non-permitted, non-point vs point (i.e. homeless) sources. Group in the end wanted to focus on outputs because most want different things, and has different needs and objectives, all of which demonstrates the need for the outputs to be flexible, customizable and most of all insightful. Goal is to get the data to create action that creates positive change no matter your user level. Portal would include the ability to look at hotspots, trends, and predictive analytics giving us the ability to measure the impact of policies and practices that have been put into place. A simple system for data aggregation and curation was initiated and is available through github.

Defining Urban Trash Risk

We envisioned a holistic way to combine multiple data sources for determining the risk of urban trash impacts to local receiving waters. We wanted to make a tool that allowed trash data to be cross-walkable to each other so that although this data was collected in different ways, it could be combined to give an overview of trash in an area to help cities target priorities and track progress. Trash impact risk is defined as the magnitude of the potential impact and the likelihood of that impact occurring. Goal was to create a TrashMASH application to mash all the trash data together and reduce the risk of trash impact. Also, gives us the knowledge to update sampling design moving forward with new information to allow the reallocation of resources. Data sources include visual surveys, imagery, crowd-sourced trash/cleanup data, public works, and information collected by the application. Final product is a framework that allows for better decision making. 

Do Overburdened Communities have a Disproportionate Litter Issues?

There are sensitive subpopulations that are disproportionately impacted by environmental pollution, and in California there are efforts underway to protect communities regardless, of race, color, national origin or income (environmental justice). Some of the anticipated stressors relative to litter are the high cost of waste disposal, availability of trash receptacles, lack of educational resources, and the prevalence of packaged food. Questions for this group included: 1. Are there fewer clean ups in overburdened communities? And 2. What are the litter-related costs associated to overburdened communities? Data sources to answer these questions included CalEnviroScreen, CalRecycle, Ocean Conservancy Coastal Clean-Up Day, Contra Costa County Trash Assessment Hotspot, and NRDC: Waste in our Water data. Data that identified overburdened communities in California was used to plot the number of cleanups versus and environmental burden score, showing that overburdened communities had fewer cleanups.