Novel Sensing and Inference Techniques in Air and Water Environments

Environmental sensing is experiencing tremendous development due largely to the advancement of sensor technology and wireless technology/internet that connects them and enable data exchange. Environmental monitoring sensor systems range from satellites that continuously monitor earth surface to miniature wearable devices that track local environment and people's activities. However, transforming these data into knowledge of the underlying physical and/or chemical processes remains a big challenge given the spatial, temporal scale, and heterogeneity of the relevant natural phenomena. This research focuses on the development and application of novel sensing and inference techniques in air and water environments. The overall goal is to infer the state and dynamics of some key environmental variables by building various models: either a sensor system or numerical simulations that capture the physical processes.

This dissertation is divided into five chapters. Chapter 1 introduces the background and motivation of this research. Chapter 2 focuses on the evaluation of different models (physically-based versus empirical) and remote sensing data (multispectral versus hyperspectral) for suspended sediment concentration (SSC) retrieval in shallow water environments. The study site is the Venice lagoon (Italy), where we compare the estimated SSC from various models and datasets against in situ probe measurements. The results showed that the physically-based model provides more robust estimate of SSC compared against empirical models when evaluated using the cross-validation method (leave-one-out). Despite the finer spectral resolution and the choice of optimal combinations of bands, the hyperspectral data is less reliable for SSC retrieval comparing to multispectral data due to its limited amount of historical dataset, information redundancy, and cross-band correlation.

Chapter 3 introduces a multipollutant sensor/sampler system that developed for use on mobile applications including aerostats and unmanned aerial vehicles (UAVs). The system is particularly applicable to open area sources such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed “Kolibri”, consists of low-cost sensors measuring CO2 and CO, and samplers for particulate matter and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller, which can record and transfer data in real time using a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emission monitors (CEMs) and another mobile sampling instrument (the ``Flyer'') that had been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer. The VOC emission factors obtained using the Kolibri are comparable to existing literature values. The Kolibri system can be applied to various open area sampling challenging situations such as fires, lagoons, flares, and landfills.

Chapter 4 evaluates the trade-off between sensor quality and quantity for fenceline monitoring of fugitive emissions. This research is motivated by the new air quality standard that requires continuous monitoring of hazardous air pollutants (HAPs) along the fenceline of oil and gas refineries. Recently, the emergence of low-cost sensors enables the implementation of spatially-dense sensor network that can potentially compensate for the low quality of individual sensors. To quantify sensor inaccuracy and uncertainty of describing gas concentration that is governed by turbulent air flow, a Bayesian approach is applied to probabilistically infer the leak source and strength. Our results show that a dense sensor network can partly compensate for low-sensitivity or high noise of individual sensors. However, the fenceline monitoring approach fails to make an accurate leak detection when sensor/wind bias exists even with a dense sensor network.

Chapter 5 explores the feasibility of applying a mobile sensing approach to estimate fugitive methane emissions in suburban and rural environments. We first compare the mobile approach against a stationary method (OTM33A) proposed by the US EPA using a series of controlled release tests. Analysis shows that the mobile sensing approach can reduce estimation bias and uncertainty compared against the OTM33A method. Then, we apply this mobile sensing approach to quantify fugitive emissions from several ammonia fertilizer plants in rural areas. Significant methane emission was identified from one plant while the other two shows relatively low emissions. Sensitivity analysis of several model parameters shows that the error term in the Bayesian inference is vital for the determination of model uncertainty while others are less influential. Overall, this mobile sensing approach shows promising results for future applications of quantifying fugitive methane emission in suburban and rural environments.