Innovations in GIS for Environmental Hazard and Exposure Surveillance

PH503Damien LeriMay 1, 2014

Categories of GIS to Enviro. Health:Hazard surveillance● Sensors in environment● Modelling of hazard movement such as air

flow○ Dispersion modelling can be sophisticated, for

example water pollution disperses through plumbing networks rather than in a simple circular pattern around the point source.

Exposure surveillanceThe intersection of population data with hazard data.Considers several dimensions of exposure such as:● Dose● Effective exposure time● Latent period● Threshold vs non-threshold toxicantsConsiders movement of people in their activity spaces through the day.

Dose surveillance

An extension of exposure surveillance, this is also a field in its own right.

It is the human biomonitoring of actual dose received.

Outcomes surveillance

Mapping clinical symptoms and outcomes using screening tools and diagnostic reports.

Examples: Blood lead level screening; illness hotspotting analysis.

Limitations of GIS for env. health● Often a lack of patient level data (for privacy)● Lack of nationwide consistency for disease tracking and

much other data● Some effects show up at only certain levels of aggregation● Geographic boundaries (such as census ones) are arbitrary● Geographic data effects such as spatial auto-correlation● Temporal lag in onset of symptoms for chronic disease● Poor quality/consistency of outcomes data such as

diagnoses

Geography of risk

Geography of susceptibility

Geography of exposure

Geography of risk

A smart design of exposure surveillance will incorporate both susceptibility and exposure.

Cromley & Joy (1995)

Researchers looked at electromagnetic field (EMF) radiation for exposure modelling for a specific target population.● Geography of susceptibility: schools and

homes. Filtered for vacant areas.● Geography of exposure: predicted EMF

sources including dispersion pattern

● 1980s - 2000s: Satellite imagery. Reflection by particles correlates with air quality but has limitations.

● Trend toward personalized exposure assessment -- identifying risks at the level of buildings, families, and individuals.

● 2006: Pigeon Blog project by Beatriz DaCosta -- homing pigeons carrying GPS and air quality sensors.

● 2010: Personal monitoring through auxiliary devices.● Future? Using existing sensors such as phones and wearables, at

the personal level, combined with networked data.

Innovations in surveillance

The holy grail of exposure surveillance

Personalized exposure assessment:Identifying risks at the level of buildings, families, and individuals.

Trends that are advancing us toward this goal:● Increases in sensor volume and quality● Moving sensors closer to humans● More sophisticated data analytics

Personal exposure: noise

MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones (Kanjo et al., 2008)

Personal exposure: airDevelopment of a method for personal, spatiotemporal exposure assessment (Adams et al., 2009)

Consumer products?

“Visibility” app by USC in 2010 MicroPEM personal sensor by RTI in 2012

These are prototypes of 3 approaches to personal exposure monitoring of air quality.

Citi-Sense

The EPA and Next Generation Air Monitoring

CAir Clip

EPA concept proposals

Better analytics: Hystad, et al. (2009)Hystad, P. U., Setton, E. M., Allen, R. W., Keller, P. C., & Brauer, M. (2009). Modeling residential fine particulate matter infiltration for exposure assessment. Journal of Exposure Science and Environmental Epidemiology, 19(6), 570-579.

The penetration of air pollution indoors was predicted using publicly available tax property assessment data combined with weather and topology data.

Better analytics: Miranda et al. (2002)Miranda, M. L., Dolinoy, D. C., & Overstreet, M. A. (2002). Mapping for prevention: GIS models for directing childhood lead poisoning prevention programs. Environmental Health Perspectives, 110(9), 947.

Blood lead risk stratification based on factors including:● resident SES● land use (zoning for residential)● building age and renovations (based on construction

permits)● occupancy status (owned-occupied or rented)

Grassroots data networking

Air Quality Egg

damien@bigyellowstar.com (Damien Leri)