Introduction
An important priority for SSV is an emphasis on environmental equity with an acute focus on communities that suffer disproportionately the effects of air pollution caused by various sources including mobile. We are also working with stakeholders in transit-dependent communities who are seeking more reliable and easier-to-access mobility options. By partnering with top tech companies, along with agencies and cities seeking to capitalize on Bay Area innovation, SSV is delivering life-changing outcomes and empowering communities with the data and facts they need to take meaningful actions.
Recent Projects
SmartTA. We have just completed the SmartTA project with the City of East Palo Alto that targets both of these: increases in asthma (especially in children) that’s 5X neighboring areas, and massive traffic congestion that greatly impacts their quality of life – and is mainly caused by cut-through and Dumbarton traffic that does little for EPA itself. SSV linked into local traffic data and deployed air quality sensors along University Avenue. We looked for and found strong correlations between morning and evening rush hour traffic along University Ave. and increased levels of PM2.5 and NO2 pollutants, which we wrote up in reports and presentations to the City. Public health experts at Stanford are working with our data to try to help pinpoint the causes and effects of this exposure to pollution and lung diseases including asthma. Learn More.
CAEP. SSV has just finished a pilot project (“Clean Air Equity Pilot”) which involves students from Dublin High School and East Palo Alto carrying personal portable smartphone-linked air quality monitors called BackpAQ to literally walk their communities and neighborhoods, looking for sources of both outdoor (PM2.5) and indoor (CO2, VOC) pollution. The pilot confirmed that this is indeed a useful way to collect such “hyperlocal” air quality data, leads to empowering students with newfound skills, and has led to several key enhancements to the SSV-developed BackpAQ monitors. Here’s a link to the final report. Learn More.
iTSP. SSV has assembled a powerful coalition to tackle mobility issues in disadvantaged communities. For iTSP, we are partnering with C/CAG, SamTrans, startup LYT, and again the City of East Palo Alto to deliver a first-of-a-kind GPS-based transit signal priority system (“Intelligent Transit Signal Priority”). We hope to show that by leveraging GPS location capability and advanced AI software we can significantly improve mobility options for disadvantaged and transit-dependent communities like EPA, while also reducing GHG (and congestion) from idling buses stuck at lights. We’ll have more to say about iTSP as we move into the next phase.
SmartTA
Sustainable Silicon Valley was excited to partner with Green Action, Youth United for Community Action, U.S. Environmental Protection Agency, St. Francis, Firehouse, and YMCA for our Air Quality and Mobility project called SmartTA. Sustainable Silicon Valley has long contributed to the wellbeing of residents alongside the Peninsula. After discovering children living in East Palo Alto are 2.5x more susceptible to asthma than any other San Mateo county resident, we were inspired to take action.
The linkage between traffic, air quality, and human health is becoming stronger with new reports emphasizing it appearing daily. The town of East Palo Alto is positioned to encounter the worst of this pollution because one of the main corridors for traffic to get from the east bay to Silicon valley and back is situated in the middle of East Palo Alto. There is also anecdotal evidence of worse air quality and related health issues. Sustainable Silicon Valley (SSV) decided to embark on a study of air quality in East Palo Alto to see if there is a linkage between air pollution and traffic congestion. SmartTA’s purpose is twofold. First, we will make air quality and traffic measurements in the area, analyze and identify links any between air pollution and traffic and stationary pollution sources. This data will act as a baseline that can be used to assess the effects of future changes in air pollution, climate or policy. Secondly, this information will be communicated to the community and local governments so that they can become active and well-informed partners to identify and evaluate policies that will reduce air pollution and exposure to harmful emissions.
Benefits
- Assess correlation of traffic to air quality
- Visualization and analysis of traffic data
- Utilize data for EPA to make tangible recommendations to businesses and inform city policy and decision-making around mobility
STRATEGY
Our measurement strategy was to monitor air quality (AQ) coming into the selected area from the prevailing wind, and to monitor AQ upwind and downwind of a major traffic artery, University Ave. See Figure 1. Three EPA identified sources are near East Palo Alto; there are many in the area. See Fig 2. Additionally, we obtained ‘floating car’ data to use as an indicator of traffic congestion. These data and our AQ measurements will be averaged hourly and compared.
We focused on monitoring 5 of the EPA criteria pollutants: CO, NO2, SO2, O3, and PM 2.5. Our goal is to make relative measurements of these pollutants to establish trends. These data will establish a baseline that can be used to assess the effects of future changes in air pollution, climate or policy. Our baseline sensor suite are the Vaisala gas phase sensors, AQT400, and the PurpleAir PM sensor, because of our previous experience with these sensors.
DEPLOYMENT
Extensive engineering went into developing the electronic and physical apparatus to successfully deploy the sensors in various locations (see the Requirements, specifications and best practices document for details). Data is reported roughly every 40 sec, depending on the sensor. We are currently using that temporal resolution to check for data reliability. Subsequently we will average between sensors and over hours to allow upwind and downwind differences to be identified.
Routines were written to easily download and work with the data. The data analysis scheme was to download and compare data from all sensors for a day at a time. We looked for short duration events that might be the result of point emissions. These will be identified where possible and eliminated from the record, if appropriate, before averaging.
AQT410 measures up to four most common gaseous pollutants such as nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), and ozone (O3). Readings are then uploaded to the cloud every 40 seconds or so where they are stored for download.
PurpleAir sensors use a fan to draw air past a laser, causing scattering from any particles in the air. This scattering signal can be related to particle number and size in sizes between 0.3μm and 10μm diameter.
Nasa Clipper Wind Sensor is employed to measure wind speed and direction. Custom software decodes the signals from the sensors and translates to a form that can be uploaded to the cloud every 40 seconds..
What We Test For
Proper shielding from ambient effects allows the use of a compact, portable, lightweight and low-cost sensor structure.
The sensor(s) can be deployed in open air and can be easily configured to suit most locations
- Streets, building sidings, rooftops.
- Commercial and industrial sites
- Indoors
- Moving vehicles
Snapshot of air quality measurement and traffic flow from SSV’s AQView Tool.
SSV Chief Scientist Dr. Strawa demonstrates SSV Labs newest edition – the sensor inter-calibration facility.
CAEP
The Clean Air Equity Pilot or CAEP …
Using BackpAQ, students can literally explore their communities and neighborhoods, looking for sources of pollution such as particulates (PM2.5) or gases (CO2 and VOC). These explorations are automatically recorded in the cloud and can be played back later using the AQView community air quality portal. Here’s a couple of screen shots that shows how this powerful feature works:
The markers in green, yellow, red and other colors represent air quality measurements taken along this particular exploration, or “track”. The particular color shown corresponds to the relative strength of the pollutant measured at that point. The small red line that connects the markers is the track itself.
The image below shows a close-up view of the track in and around a building in their neighborhood. You can see the PM2.5 levels increasing as the BackpAQ moves around inside the structure.
