Factores de Atribuc ión

Institute of Social Innovation Fielding Graduate Institute

Santa Barbara, USA [email protected]

Carl McKinney PhD Department of Communication University of California, San Diego

San Diego, USA [email protected]

Abstract. Clean air is a human right; however, recent data from the World Health Organization estimates 3.3 million people die each year from poor air quality, and another 100 million lose years of life or suffer from chronic disease as a result of poor air quality. Because air pollution is a “slow crisis,” it has been minimized in the arena of global health issues, but it is becoming increasingly urgent as developing countries suffer disproportionately, and as climate change exacerbates the issue. Addressing the problem requires a complex of high-tech and high-touch solutions. Rather than focusing on sollutions to air pollution, this paper takes an upstream approach consisting of data-informed public health solutions to assist populations in poor air quality areas. The authors discuss existing technology limitations, government policies, and public health strategies moving towards this goal. Solutions proposed are different than existing high-cost, scattered, or even nonexistent sensor technology and mobile apps. We use the case study of developing particleBox™, from the experience of living with poor air quality to the establishment of a proof of concept and working prototype. The prototype provides local, real-time, air quality measurement and maps air quality data using a citizen science open source model. Deployment of available technology to provide low-cost, non-technical data visualization within cultural contexts and the use of real time measurement of air quality to mitigate public health risk are also discussed.

Keywords: air quality sensing technology, data visualization, public health, particulate matter

I. INTRODUCTION

In the era of big data, cities are charged with providing data that serves the public health needs of their citizenry. Concern with air-born particle pollution has been growing in recent years. Particulate matter (PM) in the air has been linked to numerous health problems, including respiratory ailments like asthma and emphysema, retarded lung development, increased blood pressure, elevated levels in the body of damaging free radicals and oxidants, cardiovascular morbidity,

infant mortality, DNA mutation, and an overall reduction in life expectancy. Particles smaller than 10 microns, invisible to the naked eye, have the most adverse health effects due to their ability to enter the bloodstream through capillaries in the lungs. Children and the elderly are especially susceptible to the health effects of this air particle pollution. Only recently has the United States Environmental Protection agency released public risk guidelines for particulate matter measuring 2.5 microns (PM 2.5).

In cities where PM data is gathered there are often only a few Air Quality (AQ) sensors, yet urban topography and current weather conditions determine radically different local concentrations of PM at the level of specific neighborhoods, blocks, and streets, and at different times of the year, week, and day. And particulate matter is almost never measured indoors, yet PM is not only generated by indoor sources but is also largely impervious to walls, windows, and doors. As a result, there is currently no public access to data or applications that map the connections between sources of PM pollution, geographic and temporal dispersion, local weather, and current air quality in any given location.

Moreover, while PM data is increasingly being made publicly available in developing countries, it is rarely publicized in ways that make people take notice, or in ways that people associate with poor health outcomes. So although the effects of air pollution are seen and felt, actions to mitigate risks are not emphasized. When available, AQ data is often not visualized in easily understandable ways. Neither is the connection between AQ data, health impacts, and possible proactive measures.

II. BACKGROUND A. Context

During post doctoral research conducted during the winter of 2014 in Ulaan Bataar, Mongolia, poor air quality, lack of government monitoring, and low public

health awareness became a lived experience. The air quality in Ulaanbaatar is some of the worst in the world, with particulate matter (PM) some 17 to 35 times levels recommended by the World Health Organization. These high PM levels are due to the natural geography of Ulaanbaatar, which is situated between two mountain ranges, the high concentration of the population that use traditional stoves to heat poorly insulated felt tents, and the subarctic climate that requires indoor heating up to nine months out of the year. The Ger type housingthat surrounds the city is heated by coal burning stoves. The

coal is low-cost "dirty coal"and adds to the air pollution, especially during the winter.There are few other heating options available to thecountry.

Recently the US invested in over 1 million dollars to

provide clean-burning stoves for the GER district. However, the stoves are not being widely used because they do not produce enough heat, and are more expensive to run. There was no education program accompanyingthe deployment of stoves and none of the residents who were questioned regarding this issue related dirty burning stoves to poor health outcomes. Clearly more education is needed, and a risk perception needs assessment is critical to deliver a culturally appropriate program.

In partnership with the Mongolian based Foundation for Public Health Promotion (FPHP), a community World Café workshop was conducted with WHO and the local community to determine how to educate the public about risks and mitigatingharms from poor air quality. At the time, the Mongolian government did not make air quality data available. A website was developed to transfer air quality data from a private rogue sensorto provide publicly available livedata. The website also translated recent US Environmental Protection Agency (EPA) level warnings into Mongolian and served to conduct informal polling about air quality risk perception. The lack of publiclyavailable health data prompted the author to develop a risk perception survey and a pilot study plan. Before

departing Mongolia, the author forged a partnership between Dr. Ooodno Brown and the FPHP board to

embed the research in this respected community organization. The foundation focuses on collaboration and public health issues.

This problem is not unique to Mongolia but exists in congested developing cities from New Delhito Tehran. Ironically, poor air qualityhas recently been recognized

as a result of natural phenomenon such as weather changes in Paris, drought in Central California, and

VOG (volcanic smog) from erupting volcanoes on the Big Island of Hawaii. Air quality is an issue which is becoming increasingly relevant to all who breathe.

B. Governmental Policy Limitations

Government agencies, health advocacy groups, and

multi-lateral organizations such as the World Health Organization and the United Nations have been working in recent years to monitor PM, and to alert, inform, and educate the public about its dangers. A number of factors

have limited these efforts, including: The cost of measuring and gathering data, which has minimized the number of data points; a focus on areas where air pollution is known to be a problem, which has largely contained these data points to large urban centers; and inadequate articulation and dissemination of the data that is gathered, which fails to engage the public in proactive responses to PM.

The high cost of accurate data sensors and low

awareness of health and financial costs limit government prioritization of comprehensive air quality policies. The climate change debate has exacerbated the problem by confusing the categorization of air quality as either an issue of public health or climate change. Moreover, in

countries or cities dependent on tourism there is concern about a decrease in visitors if the air quality issue becomes public, and thus lack of actionable air quality information can also be an issue of data secrecy. See Figure 1.

Figure 1. Research Practices Spectrum

C. Limitations in Air Quality Sensing Technology

Air quality sensors have been high-tech, high-cost

machines that are usually the purview of academic

researchers and technical advisors to governments. While the technology for measuringworkplace and city- level air quality has been available for years , the ability to produce and share datahas been limited by the size, cost, and type of sensors available. Recent advances in measuring particle size, the availability of low-cost

sensors from China, and the availability of mobile

technology and cloud storage have motivated research and efforts to make this data more widely available for public use. Interpretation of this technical data is still needed, however, as is translation to actionable data.

Several problems exist with even the latest innovations in sensing technology. The market availability of sensors, mostly originating in China, is

unreliable. Calibration is also an issue with these low- cost sensors, and the variability of particle size results in low accuracy. These sensors also draw a lot of power, and so create limitations on mobility

C. Lack of Public Health Prioritization

Air-born particulate matter is rarely measured in rural areas or on the suburban fringe, yet the byproducts of agriculture, landscaping, burning wood and fossil fuels for heat and electricity are significant sources of PM pollution. In cities where PM data is gathered there are often only a few sensors, yet urban topography and current weather conditions determine radically different local concentrations of PM at the level of specific neighborhoods, blocks, and streets, and at different times of the year, week, and day. And particulate matter is almost never measured indoors, yet PM is not only generated by indoor sources but is also largely impervious to walls, windows, and doors. As a result, there is not substantial data for analyzing and mapping connections between sources of PM pollution, geographic and temporal dispersion, local weather, and adverse health effects.

Moreover, while PM data is increasingly being made publicly available, it is rarely publicized in ways that make people take notice. It must be sought out. Where found, data is often not visualized in easily understandable ways, nor are clear connections made between the data, health impacts, and possible proactive measures. The public is thus left uniformed, unknowledgeable, unengaged, and unable to act in their best interests regarding the problem of PM.

The dearth of rich data and an uninformed public means that chronic health problems stemming from exposure to elevated levels of PM continue to increase. Economically challenged segments of the public in developing nations, rural areas, and sub/urban neighborhoods proximate to industrial sites and busy roadways are disproportionately affected by both PM and lack of access to actionable data.

III. SOLUTIONS A. Policy Paradigm Shifts

As more governments move to alleviate the economic burden of disease, preventing those risky situations and behaviors reduces unnecessary government expenditures. As the Internet of Things becomes more of a reality, the role of the government in mediating data availability will be decreased. As more tech startups provide access and as open source data becomes the norm, governments will move to facilitate data availability as a public relations necessity. This was the case when the Chinese government was pushed by the US Embassy in Beijing to monitor and publish air quality data, and public opinion and pressure shifted government policy from suppression to disclosure.

The term Big Data has become ubiquitous, but although we have more and more data we have less useful connections to data and less will to make sense or take action as a result of data. More emphasis is placed on protecting data and assuring data privacy than opening up data and connecting it to empowering actions by the public.

Forward-looking governments will continue to open up data and create citizen science projects that will enable creative and crowd-sourced solutions to public health and other civic issues. Indeed, if governments will not, the public will. An excellent example of this is the Safecast Project, which grew out of MIT researcher Joi Ito’s concern for his parent’s exposure to radiation near their home in Fukashima. Frustrated with the government’s lack of radiation data, he created the biggest citizen science project ever, and not only engaged a community in creating low-cost radiation sensors, but now broadcasts live radiation data worldwide. One of many local-level civic projects is ELM, which publishes air quality data for local municipalities and provides a mobile application for local residents.

B. Next Gen Technology Innovations and the Internet of Everything (IoE)

Writing in Foreign Affairs, Cisco CEO John Chambers and Executive Vice President Wim Elfrink discuss the promise of the internet and interconnectivity. “That next phase, which some call the Internet of Things and which we call the Internet of Everything, is the intelligent connection of people, processes, data, and things. Although it once seemed like a far-off idea, it is becoming a reality for businesses, governments, and academic institutions worldwide. Today, half the world’s population has access to the Internet; by 2020, two-thirds will be connected. Likewise, some 13.5 billion devices are connected to the Internet today; by 2020, we expect that number to climb to 50 billion” (Chambers, 2014).

Recent improvements in, and accessibility to, technologies for environmental sensing, for data gathering and visualization, and for social networking, offer new and low-cost ways to address the growing problem of air-born particulate matter. particleBox is our effort to use these technologies to do so. particleBox is a wireless, networked, environmental sensor that measures PM and other relevant data, provides a warning when PM concentration become unhealthy, and makes environmental data available to local network devices such as computers, tablets, or smartphones. The future development of a corresponding particleApp, which can be installed on local devices, will allow for real-time monitoring, mapping, and visualization of this data, and will make this data accessible via an open source particleCloud service to researchers, public health

advocates, and environmental activists. These technologies willprovide information and cues for taking action to alleviate the unhealthy effects of PM. This project has reached the proof of concept stage and a prototype exists for sensing PM concentrations and wirelessly pushing data to the cloud. See Figure

2..particleBox provides a model for what is possible in the provision of real-time data to those who need it most.

Both a non-wired and wireless version is envisioned so the box can stand alone and work as a particle alarm in the absence of wireless connections.

Figure 2. particleBox Cloud Data

C. Public Health and Risk in a Connected World Smog is risky. Risk itself is a social construct, and

limitations arise on what is seen as risky within a population at risk. Risk perception is a subjective judgment that people make about the characteristics and severity of a risk. Scientists and lay people use different criteria when determining risks. Scientists use data, which provides evidence of risk. Lay persons use

personal information and experiencesin assessing risk. Successful adaptation to risk depends on several preconditions, including the awarenessof a problem and

its causes, and the availability of measures and information about effective and efficient intervention methods, i.e. what works and how to protect oneself and one’s family (Patt, 2006). It is important to know your

audience rather than focus on message construction

(McComas, 2001).

Two levels of cognition influence awareness with regards to air pollution. One is the nature and level of air pollution and the other is the publicity about it. People also might understand increased risk of air pollution, but not know how or what the precise health links are, i.e.,

sore throats, headaches, and fatigue and long term effects. There is little research on the social and behavioral impact of air pollution, but there is an84% increase in awareness if there is available air quality information (K. Bickerstaff, 2001). Before we can change people’s minds about engaging in risky behaviors, we need to understand how they perceive and understand risk. Risk perception has been a social determinant to action from public health to fiscal derivatives in when and how people engage knowingly and unknowingly with risk.

Risk perception is affected by gender, income, educational level, cultural factors, worldview and political contexts. Trust is also an issue (Slovic, 1999). Given all of this noise, how do we help citizens and governments communicate risk in a way to maximize potential for change behavior? Recent behavior change

models have benefited from Neuroscience research,

Social Network Analysis, and Behavioral Economics.

We have yet to synthesize and translate this research to build “stickier”publichealth interventions. Yet we hope that strategic deployment of low-cost sensing partnered with intelligently designed user interfaces should make

air quality public health interventions more relevant and also more likely to have measurable impact.

ACKNOWLEDGEMENTS

Authors thank the Foundation for Public Health Promotion in Mongolia, Dr. Kurt Smith of the University of California, Berkeley, the US Embassy in Mongolia, and the Foundation for Public Health Promotion in Mongolia.

REFERENCES

[1] W. Pratt and K Unruh. 2006. Communication of the Association of Computer Machinery, Vol. 49, no.1, pp. 51-55.

[2] K. McComas, C.W. Trumbo. 2001. Risk Analysis , vol. 21, no. 3, pp. 467-480.

[3] J. Chambers and W. Elfrink. “The Future of Cities-The Internet of Everything and how it will change how we live,” Foreign Affairs. Council of Foreign Relations, October 14, 2014. [4] P. Slovic. Trust, Emotion, Sex, Politics, and Science: Surveying

the Risk-Assessment Battlefield. Risk Analysis, Vol. 19, No. 4 1999. Pp 689-697.

[5] K. Bickerstaff and G. W. 2001. Public Understanding of Air Pollution: the 'localisation' of environmental risk. Global Environmental Change , 133-145.