Practical design considerations for secondary air injection in wood-burning cookstoves: An experimental study

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (<$10) fans and blowers are available to drive the secondary flow, and can be independently powered using an inexpensive thermoelectric generator mounted nearby. Furthermore, size-resolved PM measurements show that secondary air injection inhibits particle growth, but the total number of particles generated remains relatively unaffected. We discuss the potential impacts for human health and investigate methods to mitigate the PM formation mechanisms that persist

Neuroinflammation, Mast Cells, and Glia: Dangerous Liaisons

The perspective of neuroinflammation as an epiphenomenon following neuron damage is being replaced by the awareness of glia and their importance in neural functions and disorders. Systemic inflammation generates signals that communicate with the brain and leads to changes in metabolism and behavior, with microglia assuming a pro-inflammatory phenotype. Identification of potential peripheral-to-central cellular links is thus a critical step in designing effective therapeutics. Mast cells may fulfill such a role. These resident immune cells are found close to and within peripheral nerves and in brain parenchyma/meninges, where they exercise a key role in orchestrating the inflammatory process from initiation through chronic activation. Mast cells and glia engage in crosstalk that contributes to accelerate disease progression; such interactions become exaggerated with aging and increased cell sensitivity to stress. Emerging evidence for oligodendrocytes, independent of myelin and support of axonal integrity, points to their having strong immune functions, innate immune receptor expression, and production/response to chemokines and cytokines that modulate immune responses in the central nervous system while engaging in crosstalk with microglia and astrocytes. In this review, we summarize the findings related to our understanding of the biology and cellular signaling mechanisms of neuroinflammation, with emphasis on mast cell-glia interactions

Visual Properties of Transgenic Rats Harboring the Channelrhodopsin-2 Gene Regulated by the Thy-1.2 Promoter

Channelrhodopsin-2 (ChR2), one of the archea-type rhodopsins from green algae, is a potentially useful optogenetic tool for restoring vision in patients with photoreceptor degeneration, such as retinitis pigmentosa. If the ChR2 gene is transferred to retinal ganglion cells (RGCs), which send visual information to the brain, the RGCs may be repurposed to act as photoreceptors. In this study, by using a transgenic rat expressing ChR2 specifically in the RGCs under the regulation of a Thy-1.2 promoter, we tested the possibility that direct photoactivation of RGCs could restore effective vision. Although the contrast sensitivities of the optomotor responses of transgenic rats were similar to those observed in the wild-type rats, they were enhanced for visual stimuli of low-spatial frequency after the degeneration of native photoreceptors. This result suggests that the visual signals derived from the ChR2-expressing RGCs were reinterpreted by the brain to form behavior-related vision

Practical design considerations for secondary air injection in wood-burning cookstoves: An experimental study

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (<$10) fans and blowers are available to drive the secondary flow, and can be independently powered using an inexpensive thermoelectric generator mounted nearby. Furthermore, size-resolved PM measurements show that secondary air injection inhibits particle growth, but the total number of particles generated remains relatively unaffected. We discuss the potential impacts for human health and investigate methods to mitigate the PM formation mechanisms that persist

A review of regulatory standard test methods for residential wood heaters and recommendations for their advancement

In many regions, residential wood heaters are a leading source of harmful air pollution but only satisfy a small portion of local heating demands. In response, standardized laboratory test methods have been developed to characterize and limit wood heater emissions. While these test methods are a key tool for advancing both wood heater technology and environmental regulations, many of the experimental procedures are outdated and provide few actionable insights for improving heater performance. Furthermore, these test methods vary widely around the world and may not adequately capture the performance of wood heaters operating in residences. This study presents a comprehensive review of standardized wood heater test methods to identify fundamental experimental objectives and regulated performance metrics. Using the results of this review, recommendations are provided to make the test methods more accessible and representative of residential performance, while generating actionable data to motivate heater design improvements. This study elucidates the current state of standard test methods, and the developments needed to advance clean wood heater technologies and public policies

Verifying mixing in dilution tunnels How to ensure cookstove emissions samples are unbiased

A well-mixed diluted sample is essential for unbiased measurement of cookstove emissions. Most cookstove testing labs employ a dilution tunnel, also referred to as a “duct,” to mix clean dilution air with cookstove emissions before sampling. It is important that the emissions be well-mixed and unbiased at the sampling port so that instruments can take representative samples of the emission plume. Some groups have employed mixing baffles to ensure the gaseous and aerosol emissions from cookstoves are well-mixed before reaching the sampling location [2, 4]. The goal of these baffles is to to dilute and mix the emissions stream with the room air entering the fume hood by creating a local zone of high turbulence. However, potential drawbacks of mixing baffles include increased flow resistance (larger blowers needed for the same exhaust flow), nuisance cleaning of baffles as soot collects, and, importantly, the potential for loss of PM2.5 particles on the baffles themselves, thus biasing results. A cookstove emission monitoring system with baffles will collect particles faster than the duct’s walls alone. This is mostly driven by the available surface area for deposition by processes of Brownian diffusion (through the boundary layer) and turbophoresis (i.e. impaction). The greater the surface area available for diffusive and advection-driven deposition to occur, the greater the particle loss will be at the sampling port. As a layer of larger particle “fuzz” builds on the mixing baffles, even greater PM2.5 loss could occur. The micro structure of the deposited aerosol will lead to increased rates of particle loss by interception and a tendency for smaller particles to deposit due to impaction on small features of the micro structure. If the flow stream could be well-mixed without the need for baffles, these drawbacks could be avoided and the cookstove emissions sampling system would be more robust

A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties

Machine learning has proven to be a powerful tool for accelerating biofuel development. Although numerous models are available to predict a range of properties using chemical descriptors, there is a trade-off between interpretability and performance. Neural networks provide predictive models with high accuracy at the expense of some interpretability, while simpler models such as linear regression often lack in accuracy. In addition to model architecture, feature selection is also critical for developing interpretable and accurate predictive models. We present a method for systematically selecting molecular descriptor features and developing interpretable machine learning models without sacrificing accuracy. Our method simplifies the process of selecting features by reducing feature multicollinearity and enables discoveries of new relationships between global properties and molecular descriptors. To demonstrate our approach, we developed models for predicting melting point, boiling point, flash point, yield sooting index, and net heat of combustion with the help of the Tree-based Pipeline Optimization Tool (TPOT). For training, we used publicly available experimental data for up to 8351 molecules. Our models accurately predict various molecular properties for organic molecules (mean absolute percent error (MAPE) ranges from 3.3% to 10.5%) and provide a set of features that are well-correlated to the property. This method enables researchers to explore sets of features that significantly contribute to the prediction of the property, offering new scientific insights. To help accelerate early stage biofuel research and development, we also integrated the data and models into a open-source, interactive web tool

Verifying mixing in dilution tunnels How to ensure cookstove emissions samples are unbiased

A well-mixed diluted sample is essential for unbiased measurement of cookstove emissions. Most cookstove testing labs employ a dilution tunnel, also referred to as a “duct,” to mix clean dilution air with cookstove emissions before sampling. It is important that the emissions be well-mixed and unbiased at the sampling port so that instruments can take representative samples of the emission plume. Some groups have employed mixing baffles to ensure the gaseous and aerosol emissions from cookstoves are well-mixed before reaching the sampling location [2, 4]. The goal of these baffles is to to dilute and mix the emissions stream with the room air entering the fume hood by creating a local zone of high turbulence. However, potential drawbacks of mixing baffles include increased flow resistance (larger blowers needed for the same exhaust flow), nuisance cleaning of baffles as soot collects, and, importantly, the potential for loss of PM2.5 particles on the baffles themselves, thus biasing results. A cookstove emission monitoring system with baffles will collect particles faster than the duct’s walls alone. This is mostly driven by the available surface area for deposition by processes of Brownian diffusion (through the boundary layer) and turbophoresis (i.e. impaction). The greater the surface area available for diffusive and advection-driven deposition to occur, the greater the particle loss will be at the sampling port. As a layer of larger particle “fuzz” builds on the mixing baffles, even greater PM2.5 loss could occur. The micro structure of the deposited aerosol will lead to increased rates of particle loss by interception and a tendency for smaller particles to deposit due to impaction on small features of the micro structure. If the flow stream could be well-mixed without the need for baffles, these drawbacks could be avoided and the cookstove emissions sampling system would be more robust