|Ph.D Student||Lerner Uri|
|Subject||Mathematical Data Analysis Methods for Air-Quality Wireless|
Distributed Environmental Sensor Networks
|Department||Department of Civil and Environmental Engineering||Supervisor||Professor Barak Fishbain|
|Full Thesis text|
Air pollution has a proven impact on public health. Assessing air pollution's effect on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution measurements but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Moreover, the air-inlets of AQM stations are typically located on rooftops or way above the ground, thus misrepresenting the true exposure of any individual.
Recent developments in sensory and communication technologies have made low-cost, micro-sensing units (MSUs) feasible. These MSUs can operate as a set of individual nodes, or may be interconnected to form a Wireless Distributed Environmental Sensor Network (WDESN). MSU’s lower power consumption and small size enable many new applications, such as mobile sensing.
MSUs are an evolving field. Previous studies that evaluated MSUs’ performance showed that these units indeed can capture air pollution spatiotemporal variation. However, MSUs’ main limitation is their relatively low accuracy, with respect to laboratory equipment or an AQM station. MSU’s accuracy is not only limited, but heavily dependent on atmospheric conditions, pollution concentrations and even deployment duration. Further, previous studies didn't regard MSU's deployment scenario, or the use of MSUs as part of a WDESN.
Nonetheless, MSUs present an unprecedented tool for air-quality measurements and exposure assessment. To utilize MSUs in these tasks, detailed quality evaluation, in field calibration and proper planning must be part of any attempt of MSU-based WDESN deployment.
This research has developed means to cope with MSU's inherent limitations, and devise a mathematical approach towards using an MSU-based WDESN to create high-resolution pollution maps, for better exposure analysis.
The work was carried out in three modules:
o MSUs capabilities and limitations: Ego-Motion - The effect of mobility on MSUs function was evaluated by conducting lab- (wind tunnel) and field trials. Direct comparisons were made between MSUs exposed to wind and those who are shielded (or mobile vs. stationary, respectively), and a calibration scheme was suggested.
o Means for optimal use of MSUs: Sensors Evaluation Toolkit - To better assess the quality of MSUs, a software package was developed, dubbed Sensor Evaluation Toolkit (SET), which combines common quality indicators with four novel methods - each evaluates MSU performance in a task-specific manner (e.g. source location, relativity of measurements etc.). An Integrated Performance Index (IPI) is also calculated, to represent the overall quality of the MSU in question.
o Optimal Deployment of a WDESN - As WDESN deployment is limited by budget, an optimization algorithm was designed to choose the optimal locations within a defined region. This algorithm factors in technical characteristics of the MSUs in use, the varying land use throughout the region, and enables flexibility regarding other limitations on the deployment. Solution is given for two regions under different constraints; for one region, actual deployment is compared to the suggested one.