Pollution sensing and diagnosis have long been separated from the people most impacted by them. It was conducted by specialists with expensive and scarce equipment. As a result, testing was infrequent and decisions on mitigation were made by central planners with limited access to data. Worse yet, individuals with the ability to dramatically limit dangerous exposure via minor changes in behavior have been left blind to the relationship between their daily actions and exposure to pollution. Advances in computing, sensing, and wireless communication technologies have the potential to allow those most affected by pollution to participate in pollution sensing, rational cost assessment, and mitigation.
This project focuses on developing a distributed mobile system for socially-collaborative environmental monitoring, which greatly reduces the problem of environmental sensing data scarcity, supports richer environmental sensing data analysis, and enables better environment awareness and protection via social collaboration. This project will develop a system composed of inexpensive sensing and computation devices purchased by individuals for their own edification and protection. These embedded systems will communicate with each other and aggregate data, enabling multi-sensor localization of pollution sources and quantification of the potential damage by each polluter. By measuring pollution and modeling its impact, it will be possible to associate pollution sources with the costs they impose. Furthermore distributed networking will allow individuals to actively participate in and socially collaborate on environmental monitoring and protection.
The people most impacted by air quality have long been separated from information to help them understand how their own behavior influences exposure to pollution. In part, this was because air quality sensing systems were generally large, expensive, and stationary, and required experts to operate them. Even though many of these systems could accurately measure pollutant concentrations, only a few locations could be monitored, and most people spent most of their time at other locations, locations that were "in the dark" from the perspective of air quality sensing science. A major goal of this project has been to develop technologies to support inexpensive, personal, air quality monitoring. To this end, we have worked on sensor calibration and characterization, embedded sensing hardware and software, methods for increasing the accuracy of pollution estimates, and field trials. In the following paragraphs, we summarize our efforts and contributions in these areas. Sensor calibration and characterization: To make personal air quality sensing accessible, sensing hardware must be easy-to-carry, inexpensive, and require little professional maintenance. This, unfortunately, rules out large and expensive sensors. For many pollutants, the types of sensors appropriate for compact personal air quality sensing systems are subject to errors as a result of the presence of other pollutants (cross-sensitivity) or due to changes in environmental conditions such as temperature and humidity. We have studied these dependencies in order to develop techniques to compensate for cross-sensitivity and changes in environmental conditions, and our findings have been published. Embedded sensing hardware and software: We have also designed, fabricated, and open-sourced the hardware/software embedded system necessary to support personal air quality monitoring. This hardware is compact and contains multiple air quality sensors (e.g., for carbon monoxide, carbon dioxide, and volatile organic compounds) as well as other sensors (e.g., temperature and humidity) to help correct for susceptibility of the air quality sensors to changes in environmental conditions. This embedded system communicates with commodity smartphones via Bluetooth, allowing data to be stored, aggregated, and analyzed on our servers. In addition to serving as gateways between our sensing hardware and remote servers, smartphones also allow users to see the relationship between their behavior (e.g., motion patterns) and pollution exposure, and to see position-dependent pollution data from other sources, including other mobile sensors. One pressing challenge for our studies was developing a method of accurately estimating the locations of sensors when indoors, where GPS is generally not available. We developed and published a number of new ideas on this topic, culminating with a method of automatically estimating building floorplans by observing the motion patterns of participants. Methods for increasing the accuracy of pollution estimates: Inexpensive, portable sensors are susceptible to cross-sensitivity to other pollutants, as well as dependencies on environmental parameters such as temperature and humidity. In addition, many such sensors have readings that gradually drift over time. This project has yielded three main techniques to improve the accuracy of pollution estimates. (1) We developed a drift compensation technique allowing mobile sensors to opportunistically recalibrate whenever they are near other mobile or stationary sensors. We developed a method of using knowledge of the rate of drift to optimally weight the conflicting reports of different sensors during the recalibration process. This idea improves the accuracy of personal mobile sensors without requiring annoying explicit recalibration by study participants. (2) We developed a technique to estimate pollution concentrations in based on knowledge of room-to-room air flow patterns in indoor environments. (3) Finally, we have developed a graphical model based method of estimating pollutant concentrations in the presence of cross-sensitivities. This method is also capable of reporting whether particular estimates are high- or low-confidence, allowing outliers to be eliminated. The first two ideas have been published, and we are currently preparing a paper on the third for submission to a research conference. Field trials: Multiple field trials have been carried out (with one ongoing, and another planned) to learn more about real-world pollution exposure and to evaluate the research ideas described in the preceeding paragraphs. These include studies within Boulder, and within the Navajo Nation, which were closely integrated with our outreach efforts. A number of the project's publications were validated or motivated by our field trials and we expect to be reporting additional findings, in collaboration with the University of Colorado project staff, during the coming year.
