Environmental Manager Air Sensing

22 em august 2014 awma.org

em • feature

by Timothy S. Dye, Briana J. Gordon, and Paul T. Roberts

Timothy S. Dye, CCM, is a senior vice president and manages the Meteorological and Air Quality Operations and Measurements Division; Briana J. Gordon is a meteorologist and project manager; and Paul T. Roberts, Ph.D., is chief scientifi c offi cer and execu-tive vice president; all with Sonoma Technology Inc. (STI), Petaluma, CA. E-mail: [email protected].

Air quality monitoring is currently the domain of trained professionals using expensive, highly accu-rate instruments; however, the fast-paced evolution of air sensor technology is putting new types of sensors into the hands of non-experts. Individuals, community organizations, and air quality agencies have tremendous new opportunities to expand air

quality monitoring, but the idea of so many people measuring air quality with many different devices raises concerns about whether the resulting data will be any good. There is a void of information and examples about the quality and appropriate-ness of the data generated by newer sensors.

The level of data quality needed varies with the intended use. There is no single way to defi ne quality, because the intended uses are vast. For example, a person with health issues could use a portable sensor to compare air pollution levels at

Important considerations for evaluating the

level of data quality needed for different air

sensor applications.

Quality Data for the Right ApplicationAir Sensors

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Copyright 2014 Air & Waste Management Association

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several locations, such as at home and at work, to determine which location or time of day has better air quality; this person might not need scrupulously precise measurements. However, justifying regula-tory actions requires data collected under rigorous standards. Users and regulators need a clear, com-mon understanding of the quality requirements for a range of potential air sensor applications. This article qualitatively discusses important consider-ations for evaluating the data quality needed for different applications and gives real-world exam-ples of different applications for air sensors.

Defi ning ‘Quality’Several factors play a part in the “quality” of data: accuracy, precision, minimum detection limit (MDL), potential bias, and degradation of the instrument over time, to name a few. Other factors also affect quality: reproducibility of the data, reli-ability, frequency of maintenance, clearly defi ned data formatting and processing, and ease of use.

The level of data quality needed for a successful measurement study is determined by the purpose of the measurements and the data’s intended use. Data with a high level of precision and low amount of bias may be needed to meet the objectives of one study, while moderate levels of precision and bias may suffi ce for another study. For example, lower precision and accuracy may be acceptable if large emissions from an industrial plant are being measured; higher precision and accuracy may be needed to measure small differences in ozone concentrations throughout an urban area.

During the Air Sensors 2013 workshop, a diverse group of people from academia, government agencies, and the private sector discussed the quality issue during a breakout session. It was clear from the active discussions that many are concerned about the quality and the potential use or misuse of data from these sensors. Some of the issues raised included:

1. standardizing determinations of the quality of lower-cost air quality sensors;

2. choosing statistics to evaluate quality and facili-tate comparison among different sensor devices (e.g., bias, accuracy, precision);

3. developing guidelines for choosing the appro-priate sensor for an application; and

4. the need for data quality guidelines for various sensor applications. Note: the U.S. Environmen-tal Protection Agency (EPA) recently released guidelines for sensor developers, sensor users, and state/local air quality agencies.1

Air Sensor ApplicationsAn exciting number of applications exist for all potential users of these new air sensors, whether the users are researchers, educators, or the gen-eral public. In this section, we provide examples of how sensors are being deployed to produce data of suffi cient quality for particular applications.

Figure 2. Locations of ozone sensor sites and Air District FEM sites.

Notes: Di Giorgio is the new site and Bear Mountain is the old site.

Figure 1. A pollution rose showing NO concentrations as a function of wind direction at sensor sites (SNAQ41 and SNAQ08) located north of runway 27 and also near a perimeter road at Heathrow Airport.2

Notes: NO concentrations indicate the different sources: (1) from the southwest of the site due to aircraft emissions, and (2) from the northeast from the perimeter road. Note that dominant winds are from the west-southwest and east-northeast.

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ResearchScientific studies aimed at discovering new infor-mation about air pollution have often required highly accurate and precise instruments. But that’s now changing; professors are finding new insights, as the sheer number of data points pro-vide greater granularity of the atmosphere in the

same way a camera with higher pixel density pro-vides sharper pictures.

For example, the Sensor Network for Air Quality (SNAQ) project at Heathrow Airport deployed a 40-sensor network in and around London’s Heath-row Airport for 12 months.2 The study goals were to better understand optimal sensor network deploy-ment approaches, perform source attribution, and validate pollution models. Researchers developed and deployed sensor nodes that monitored a suite of air quality and meteorological information, includ-ing carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), sulfur dioxide (SO2), nitric oxide (NO), nitrogen dioxide (NO2), volatile organic compounds (VOCs), size-speciated particles, wind speed, wind direction, temperature, and relative humidity (RH). Preliminary analyses indicate that pollutant sensors captured the diurnal cycle in airport activity—busy periods during the day and less activity during the overnight hours. In addition, wind observations allowed for identification and quantification of indi-vidual sources using data from individual sensor nodes (see Figure 1). More results are expected to be published within the next two years.

Supplementing Existing MonitoringWhen air quality agencies place sensors within an existing state/local regulatory monitoring network, they can fill in spatial gaps and address questions about spatial representativeness. A recent study in the Central Valley of California illustrated the value of low-cost sensors and demonstrated that study design and execution are as critical to the data’s quality as the individual sensor performance.3

The San Joaquin Valley Unified Air Pollution Con-trol District (the District) recently moved a monitor-ing site two miles due to leasing issues. Operation of both sites for a year and the resulting analyses showed that both 1-hr and 8-hr average O3 con-centrations measured at the new site were approx-imately 10% lower than concentrations measured at the old site. This difference raised concerns in the community, and EPA indicated that the differ-ence might hinder the agency’s ability to deter-mine whether the region has attained federal O3 standards. To respond to EPA’s concerns, the Dis-trict conducted a study saturating the area with 21 low-cost sensors to examine O3 gradients in and around the area.

Figure 3. Ozone sensor systems (top) during the collocation period with an FEM and (bottom) in the field with its solar panel, battery, tripod, and supporting hardware.

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Using a network of low-cost ozone sensors (see Fig-ure 2) was the only cost-effective method to obtain sufficient coverage to detect and understand the O3 gradients. To ensure data quality, a rigorous study was designed, including collocation periods with an FEM ozone instrument and all sensors at the beginning and end of the study (see Figure 3). In addition, data quality was continuously evaluated and monitored during the study. Analysis methods combined with the study design helped achieve data quality with sufficient precision to address the study objectives. The study found that:

1. The low-cost O3 sensors provided very good accuracy and precision for assessing spatial gra-dients. The accuracy of the 1-hr measurements was within 3 parts per billion (ppb) and the pre-cision was ±4% at the 95% confidence level.

2. Strong spatial gradients for peak 1-hr and 8-hr O3 concentrations existed within and around the study area. Peak 1-hr O3 concentrations at each site on a given day can vary by as much as 30 ppb.

3. O3 concentrations measured at the new site were very repre-sentative of the O3 concentra-tions measured at the old site; they were well-correlated and of about the same magnitude.

EducationSensors in educational settings can be used for science, technology, engineering, and math lessons. An educational application does not mean a less accurate sensor should be used; learning requires sensors with sufficient data qual-ity and usability for students to pose hypotheses, collect data, and answer questions. For example, HabitatMap and Sonoma Technol-ogy Inc. were funded by the Knight News Foun-dation to implement a pilot program, called Kids Making Sense, which aims to teach youth how to measure particle pollution using air quality sensors and to “make sense” of the data they collect.4

Prior to their deployment in pilot projects, the par-ticle sensors were evaluated against a reference instrument to ensure sufficient responsiveness for

students. Pilot projects were conducted in Brook-lyn and San Francisco; students first learned about particle pollution, planned sampling routes in their neighborhoods, and ventured out into the neighborhood in teams to collect particle mea-surements. The teams shared their data using the crowd-sourced platform AirCasting.org (see Fig-ure 4). The students reviewed the patterns in their data, comparing the high particle concentrations


(b)

(c)

Figure 4. (a; see page 22) Youth measure particles in a Brooklyn, NY, subway station; (b) real-time readings report to a student’s cellphone; and (c) crowd-sourced measurements from five teams measuring particles throughout this Brooklyn neighborhood.

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Here’s just a sampling of the benefi ts you receive as an A&WMA Member:

Quality Information: from the Journal of the Air & Waste Management Association to EM magazine to technical books and publications, you get the technical, practical, and professional information you need.

Professional Development and Education: improve your professional skills and expertise at more than 30 continuing education programs, specialty conferences, and a wide variety of workshops held each year, in addition to the Annual Conference & Exhibition.

Networking and Contacts: take advantage of numerous opportunities to meet with your peers and expand your circle of valuable business contacts.

Job Search and Employment: fi nd a position in the environmental fi eld or fi ll a vacancy in your organization through our online job board.

Membership and Resource Directories: your quick reference for fi nding colleagues and a range of products and services.

Discounts: members receive substantial discounts on publications, conferences, educational seminars, insurance coverage, logo merchandise, and more.

Recognition: get recognized for your accomplishments through the A&WMA awards programs, publishing an article in EM or the Journal, or serving on an A&WMA committee.

A&WMAMembership Benefi ts

For more information on these great member benefi ts, contact A&WMA Member Services at 1-800-270-3444 or visit us online at www.awma.org.

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they detected in underground subway stations, along windy and dusty streets, and near smokers. In their feedback, students expressed continued interest in the subject matter. More importantly, the sensors detected changes in particle pollution consistent with nearby activities, thereby helping the youth use the real-time readings to identify nearby sources of particles.

There are many other applications of sensors for the public, organizations, and researchers:

1. Information for informal air quality awareness. 2. Environmental Justice/Advocacy to under-

stand exposure to and health effects of air pol-lutants in EJ communities.

3. Personal Exposure Monitoring to quantify an individual’s exposure to pollution.

4. Hotspot Identification and Characteriza-tion to detect individual sources using fixed- location and/or mobile-sensor systems.

ConclusionSensor technology is rapidly being developed and deployed for applications, ranging from edu-cation to supplementing regulatory monitoring. There’s an urgent need for the entire community to experiment and conduct pilot projects in real-world conditions to understand the capabilities, benefits, and pitfalls of the new technologies—and ultimately, the adequacy of the data for address-ing air quality issues. em


References1. Williams, R.; Kilaru, V.; Snyder, E.; Kaufman, A.; Dye, T.; Rutter, A.; Russell, A.; Hafner, H. Air Sensor Guidebook; EPA 600/R-14/159; U.S.

Environmental Protection Agency, June 2014; available online at http://epa.gov/research/airscience/docs/air-sensor-guidebook.pdf. 2. Bright, V.; Mead, M.I.; Popoola, O.A.; Baron, R.P.; Saffell, J., Stewart, G., Kaye, P., Jones, R. A low-cost high-density sensor network for air

quality at London Heathrow Airport. Presented at the AGU Fall meeting, San Francisco, CA, December 3-7, 2012; Poster A11H-0159.3. MacDonald, C.P.; Roberts, P.T.; McCarthy, M.C.; DeWinter, J.L.; Dye, T.S.; Vaughn, D.L.; Henshaw, G.; Nester, S.; Minor, H.A.; Rutter, A.P.;

Smith, K.; Winegar, E. Ozone concentrations in and around the City of Arvin, California. Final report prepared for the San Joaquin Valley Unified Air Pollution Control District, Fresno, CA, by Sonoma Technology, Inc., Petaluma, CA, STI-913040-5865-FR2, May 2014; available online at http://www.valleyair.org/air_quality_plans/docs/2013attainment/ozonesaturationstudy.pdf.

4. Kids Making Sense. Web site describing projects funded by the Knight Foundation, Miami, FL. See http://knightfoundation.org/grants/201347645/ (accessed April 24, 2014).

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Environmental Manager Air Sensing