Conversion of the BlueSky Framework into collaborative web service architecture and creation of a smoke modeling application

This project addresses the need for a collaborative architecture for scientific modeling that allows various scientific models to easily interact. The need for such a system has been documented by recent studies such as the JFSP Smoke Roundtables and the JFSP review of tools done by the Software Engineering Institute. This project addresses these needs by modifying the BlueSky Modeling Framework so that it can better serve as a collaborative architecture, and then utilizing this architecture to create an advanced application that could not otherwise be created. The BlueSky framework was modified for this purpose, and all changes integrated into all versions of BlueSky from 3.1.0 forward. BlueSky now contains a command line option that will automatically start it as a web-service provider, allowing it to be used by remote clients. When the web-service option is used, all models contained within BlueSky are automatically converted into web-service accessible modules, without need for a specialized web-service enabled version. Simple examples and documentation scripts designed to show a website or user interface creator how to access these models via web-service function calls were created. In addition, a more advanced website interface was created to show some of the advantages of web-service based scientific modeling. This tool, called BlueSky Playground, provides a single user interface into 10 models of fuels, consumption, emissions, plume rise, and smoke dispersion. A user can walk step-by-step through all of the model steps in the framework from fire information to smoke impact maps. At each step the user can choose the model they want to use and alter the modeled information before continuing on, allowing for a game-playing exploratory mode of interaction. Both the ability to access so many models through a single interface as well as the capability to obtain on-the-fly smoke dispersion calculations are novel to this tool. This application will be highlighted in 2010 through RX-410 classes as a way for users to learn about the various component models. It will also serve as a training tool for managers needing to run multiple scenarios and understand the implications of various choices. The web-service oriented architecture utilized in the project offers many potential advantages to scientific research done with the goal of decision support. Separation of the scientific computing portion of such work from the user interface allows scientists to focus on creating the best models and web designers to focus on creating the best interfaces. Remote functioning of the models through the web means that local installation of the model is no longer required solving distribution issues, and allows an Internet user to run a model that requires resources not available to them locally (such as large datasets or fast processors). Modularity allows for “mash-ups” where models are combined in ways not originally foreseen to meet emerging needs, and provides choices to be made on exact modeling pathways at the user or institutional level

Estimating Wildfire Smoke Concentrations during the October 2017 California Fires through BME Space/Time Data Fusion of Observed, Modeled, and Satellite-Derived PM2.5

Exposure to wildfire smoke causes adverse health outcomes, suggesting the importance of accurately estimating smoke concentrations. Geostatistical methods can combine observed, modeled, and satellite-derived concentrations to produce accurate estimates. Here, we estimate daily average ground-level PM2.5 concentrations at a 1 km resolution during the October 2017 California wildfires, using the Constant Air Quality Model Performance (CAMP) and Bayesian Maximum Entropy (BME) methods to bias-correct and fuse three concentration datasets: permanent and temporary monitoring stations, a chemical transport model (CTM), and satellite-derived estimates. Four BME space/time kriging and data fusion methods were evaluated. All BME methods produce more accurate estimates than the standalone CTM and satellite products. Adding temporary station data increases the R2 by 36%. The data fusion of observations with the CAMP-corrected CTM and satellite-derived concentrations provides the best estimate (R2 = 0.713) in fire-impacted regions, emphasizing the importance of combining multiple datasets. We estimate that approximately 65,000 people were exposed to very unhealthy air (daily average PM2.5 ≥ 150.5 μg/m3)

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

Structuring an Integrated Air Quality Monitoring Network in Large Urban Areas – Discussing the Purpose, Criteria and Deployment Strategy

Air pollution in large urban areas has become a serious issue due to its negative impacts on human health, building materials, biodiversity and urban ecosystems in both developed and less-wealthy nations. In most large urban areas, especially in developed countries air quality monitoring networks (AQMN) have been established that provide air quality (AQ) data for various purposes, e.g., to monitor regulatory compliance and to assess the effectiveness of control strategies. However, the criteria of structuring the network are currently defined by single questions rather than attempting to create a network to serve multiple functions. Here we propose a methodology supported by numerical, conceptual and GIS frameworks for structuring AQMN using social, environmental and economic indicators as a case study in Sheffield, UK. The main factors used for air quality monitoring station (AQMS) selection are population-weighted pollution concentration (PWPC) and weighted spatial variability (WSV) incorporating population density (social indicator), pollution levels and spatial variability of air pollutant concentrations (environmental indicator). Total number of sensors is decided on the basis of budget (economic indicator), whereas the number of sensors deployed in each output area is proportional to WSV. The purpose of AQ monitoring and its role in determining the location of AQMS is analysed. Furthermore, the existing AQMN is analysed and an alternative proposed following a formal procedure. In contrast to traditional networks, which are structured based on a single AQ monitoring approach, the proposed AQMN has several layers of sensors: Reference sensors recommended by EU and DEFRA, low-cost sensors (LCS) (AQMesh and Envirowatch E-MOTEs) and IoT (Internet of Things) sensors. The core aim is to structure an integrated AQMN in urban areas, which will lead to the collection of AQ data with high spatiotemporal resolution. The use of LCS in the proposed network provides a cheaper option for setting up a purpose-designed network for greater spatial coverage, especially in low- and middle-income countries

PHASE 1 OF THE SMOKE AND EMISSIONS MODEL INTERCOMPARISON PROJECT (SEMIP): CREATION OF SEMIP AND EVALUATION OF CURRENT MODELS

Managers, regulators, and others often need information on the emissions from wildland fire and their expected smoke impacts. In order to create this information, combinations of models are utilized. The modeling steps follow a logical progression from fire activity through to emissions and dispersion. In general, several models and/or datasets are available for each modeling step, resulting in a large number of combinations that can be created to produce fire emissions or smoke impacts. Researchers, managers, and policy makers need information on how different model choices affect the resulting output, and guidance on what choices to make in selecting the models that best represent their management requirements. Baseline comparisons are needed between available models that highlight how they intercompare and, where possible, how their results compare with observations. As new models and methods are developed, standard protocols and comparison metrics are necessary to allow for these new systems to be understood in light of previous models and methods. The Smoke and Emissions Model Intercomparison Project (SEMIP) was designed to facilitate such comparisons. This project was designed to be the first step in a broader effort, and hence was titled Phase 1 of SEMIP. In Phase 1, SEMIP: • Examined the needs for fire emissions and smoke impact modeling; • Determined what data were available to help evaluate such models; • Identified a number of test cases that can serve as baseline comparisons between existing models and standard comparisons for new models; • Created a data warehouse and data sharing structure to help facilitate future comparisons; and • Performed a number of intercomparison analyses to examine existing models. SEMIP so far has resulted in: • Multiple peer reviewed journal articles and other documents; • Over 20 presentations; • Discussions with the EPA, JFSP, USFS F&AM, DOI, NWCG, and others on how to improve fire emissions calculations; • New fire emissions analysis tools; • Presentations and discussions with the JFSP on how to gather field observations useful to this type of analyses; and • Discussions with the JFSP on data sharing and archiving. SEMIP has also been acknowledged in recent RFAs from both the JFSP and NASA

Identification of Necessary Conditions for Arctic Transport of Smoke from United States Fires

The deposition of black carbon (BC), a dark absorbing aerosol, is a significant contributor to observed warming trends in the Arctic (Hansen and Nazarenko, 2004; Jacobson et al., 2007). Biomass burning outside of the Arctic, including wildland prescribed fires, is a major potential source of Arctic BC. Therefore, limiting or eliminating spring prescribed burning has been suggested to Congress as a BC reduction technique (e.g., Zender, 2007). However, there are large uncertainties in the current estimates of the sources, source regions, and transport and transformation pathways of BC transported to the Arctic region (Shindell et al., 2008; Hegg et al., 2009, Quinn et al., 2008). This study is the first comprehensive examination of the meteorological conditions required for emissions from the contiguous United States (CONUS) to be transported to Arctic. Using a simple trajectory modeling technique, we characterize the potential for transport of emissions from fires in CONUS to reach the Arctic and Greenland. The potential for Arctic transport is examined as • A 30-year climatology (1980-2009) of transport potential based on trajectory modeling using historical meteorology and split out by season, month, starting plume injection height, and time to reach the Arctic. • A real-time (daily) forecast system of transport potential to the Arctic that shows which layers of the atmosphere can reach the Arctic today, tomorrow, and the next day. The methods used here do not include wet or dry deposition and other factors that can further limit the ability of actual emissions to reach and deposit in the Arctic. Instead, by focusing on only one necessary aspect (a necessary but not sufficient condition) – the ability of the atmosphere to transport emissions – this study examines • Under what meteorological transport conditions can CONUS emissions potentially impact the Arctic? However, this allows for the ability to answer the corollary question: • Under what meteorological transport conditions will CONUS emissions not impact the Arctic? This inverse question allows for identification of times, locations, and plume injection heights where emissions sources (such as a prescribed burn) will not have an impact on the Arctic. This knowledge allows for both more targeted future studies and more precise mitigation strategies that do not focus on areas and times where Arctic impact is unlikely