Session Presentations

Sadia Afrin, Fernando Garcia-Menendez, Shannon Koplitz, Kirk Baker

Wildland fires, including both wildfires and prescribed burns, are the largest source of fine particulate matter (PM_2.5) emissions in the U.S. While wildfires are unplanned, prescribed burns are a controlled application of fire to accomplish land management objectives, such as wildfire hazard mitigation and ecological restoration. However, the degree to which prescribed fire may also mitigate the severe air quality impacts of wildfires is not well understood. Here, we examine the trade-offs between the air quality impacts of prescribed burning and the avoided air pollution from wildfire using comprehensive regional-scale chemical transport modeling. We simulate the impact of two 2016 wildfires in western North Carolina, the Party Rock and Chestnut Knob fires. We use wildfire emissions generated with the SmartFire-BlueSky Pipeline emissions modeling framework. We also create prescribed fire emission profiles based on existing fuel beds at the Chimney Rock and South Mountain State Parks, where the wildfires occurred, and estimate their consumption of different vegetative fuels. Based on the fuel loads subsequent to prescribed fire, we simulate the emissions and smoke impacts of hypothetical wildfires that would have occurred at the State Parks if prescribed fire treatment had been previously applied to the area burned. We find that the avoided air quality impacts of wildfires are higher than the added impacts of all prescribed fires needed to treat the wildfire area. A smaller population is affected by increased PM2.5 concentrations from prescribed fires compared to the that benefiting from the avoided exposure to wildfire air pollution. The approach and findings presented can support decision-making, and the use of efficient land management approaches.

Calvin Arter, University of North Carolina at Chapel Hill, arterca@email.unc.edu

Charles Chang, University of North Carolina at Chapel Hill, changsk@email.unc.edu

Jonathan Buonocore , Harvard T.H. Chan School of Public Health, jjb194@mail.harvard.edu

Kathy Fallon Lambert, Harvard T.H. Chan School of Public Health, kathleen.lambert3@gmail.com

Saravanan Arunachalam, University of North Carolina at Chapel Hill, sarav@email.unc.edu

On-road vehicular emissions contribute to the formation of fine particulate matter and ozone which can lead to increased adverse health outcomes near the emission source and downwind. In this study, we present a transportation-specific modelling platform utilizing CMAQ-DDM to estimate the impact of on-road vehicular emissions from five vehicles classes; light-duty autos, light-duty trucks, medium-duty trucks, heavy-duty trucks, and buses, on PM_2.5 and O₃ concentrations at a 12x12 kilometer scale for twelve states and Washington D.C. as well as four large metropolitan statistical areas in the Northeast U.S. in 2016. CMAQ-DDM allows for the tracking of contributions from on-road vehicular emission changes in each state to pollution levels and health effects in downwind states. In the region we considered, light-duty trucks were responsible for the most PM_2.5 -attributable premature mortalities at 1,234 with 46% and 26% of those mortalities from directly emitted particulate matter and ammonia, respectively; and O₃ - attributable premature mortalities at 1,129 with 80% of those mortalities from NO_X emissions. We also found the individual impacts of each vehicle class and source region on each state in the region to construct a full source-receptor matrix of on-road vehicular impacts. We then monetized the total damages by summing premature mortalities from both PM_2.5 and O₃ to determine the largest damages-per-ton estimates from each vehicle class, precursor, and source region. The largest damages-per-ton estimate is approximately $4 million per ton of directly emitted primary particulate matter in the New York-Newark-Jersey City MSA from buses in the New York-Newark-Jersey City MSA. By quantifying the impacts at a source region, vehicle class, and precursor-specific level; this study allows for a comprehensive understanding of the largest vehicular sources of air quality-related premature mortalities in a heavily populated part of the U.S. and can inform policies aimed at reducing those impacts.

Yusuf Alizade Govarchin Ghale, Alper Unal, Metin Baykara

Istanbul Technical University, Eurasia Institute of Earth Sciences, Department of Climate and Marine Science, Istanbul, Turkey

Urmia Lake, the largest lake in the Middle East lost most of its water surface area over the past 20 years. The maximum water surface area of the lake was observed (i.e., 6000 km2) in the 1990s, while more than 90% of this area was lost in 2015. Salinization and desertification caused by the drying up of the lake has led to dust storms from the lake bed, which have negative impacts on the environment. In this study, ground-level PM10 data of Urmia station, the closest air quality station to the lake located in the northwestern Iran and MODIS derived AOD data observed between 2010 and 2017 were analyzed to investigate the effects of Urmia Lake desiccation on the aerosol pollution. In total, 129 days with daily mean AOD values more than 1 were observed in the study area, which indicated the severity of air pollution and dust emission from the dried bottom of the lake. Statistical methods such as Multiple Linear Regression (MLR) and Linear Mixed Effect (LME) models were applied on AODs and meteorological data to predict ground-level PM10 variations in Urmia station. The results indicated that LME model with R2 vale of 0.95 performed better than MLR model. The RMSE and AME values of LME were estimated about 23.22 g/m3 and 16.59 g/m3, respectively. Based on the results of this study regional dust storms and other air pollution factors originating from other countries such as Iraq affect the air quality of the northwestern Iran too. The regional and international cooperation is necessary to have a better environmental management and sustainable development in the region.

Daniel S. Cohan, Ricky Morse, Sarah Salvatore, and Joanna H. Slusarewicz

The United States is undergoing a rapid shift away from coal for the generation of electricity. Since 2006, the market share of coal has fallen by more than half, while wind and solar have grown tenfold. Texas provides a crucial microcosm of these trends. Texas power plants consume more coal and natural gas and emit more carbon dioxide than those in any other state. However, Texas also generates more wind power than any other state and has a rapidly growing solar industry. Previous work by our research group has shown that wind and solar power are generated at complementary times, with West Texas winds blowing most strongly at night, South Texas sea breezes peaking on summer afternoons, and solar power peaking midday. By taking advantage of this complementarity, solar and wind farms can produce power more reliably, making them better able to replace the output of coal or natural gas plants. However, we have not previously examined the extent to which the electricity from existing coal plants could be replaced by new wind and solar power.

Here, we compute the half-hourly power generation that could be produced by each wind and solar farm in the ERCOT interconnection queue. We use NREL's System Advisor Model to model the output of proposed wind and solar farms based on weather data for the years 2009-2011. We then explore the extent to which recent coal generation in ERCOT could be replaced with power from those proposed facilities. Through optimization modeling, we identify combinations of wind and solar sites that are best suited to displacing various percentages of the power load currently provided by coal. Annualized costs of the wind and solar farms are compared to the direct and societal costs of continuing to operate the coal plants. Air pollution and health impacts of the coal power plants are computed by linking results from the CAMx photochemical model with the BenMAP health effects model, as well as by applying reduced form models. We find that over 95% of coal generation can be directly replaced by wind and solar power, even without the use of storage, at a small fraction of the annual costs of coal.

Paelina DeStephano1, Steven J. Smith2, Christopher G. Nolte3, and Daniel H. Loughlin3 1 ORISE research participant, U.S. EPA 2 Pacific Northwest National Laboratory 3 U.S. EPA Office of Research and Development

In the U.S., more than 130 million people live in areas that do not meet the National Ambient Air Quality Standards. State environmental agencies face the challenge of determining how to achieve these standards and maintain good air quality into the future while simultaneously working to meet greenhouse gas reduction targets and ensure reliable and affordable energy. Identifying strategies for meeting air, climate, and energy goals over the long term is difficult given the uncertainties inherent in the future.

Predictive modeling generally extrapolates from current trends; however, as modelers look farther in the future, these trends are less certain. Challenges may arise from new and emerging sources of emissions or increasing demands for energy that are driven by population growth, economic transformation, and a changing climate. Consumer behaviors and attitudes can also result in challenges, particularly if trends of increasing house size, vehicle mass, and commute distances continue.

In this project, we are using a human-earth systems model, GCAM-USA. We will be conducting a combinatorial analysis, examining combinations of different assumptions about population, economic transformation, climate impacts, consumer behavior, and technology change. These results of hundreds of GCAM-USA runs will then be examined, identifying which combinations of assumptions result in challenges to air, climate, and energy goals.

In a second phase of the project, we will repeat the initial analysis with combinations of policies that have been identified by stakeholders at state agencies. These include a range of policies directed at transportation, renewable energy, and energy efficiency. Evaluating these policies across a large set of scenarios will provide insights regarding their relative efficacy over wide-ranging conditions. This information can help state agencies refine policies in the face of emerging trends and develop policies that are robust to an array of future scenarios.

This presentation will detail the methodology outlined above, discuss lessons learned through stakeholder interactions, and provide preliminary results from model runs exploring a subset of the scenario dimensions.

Timothy Glotfelty, Diana Ramirez-Mejea, Jared Bowden, Adrian Ghilardi, Ashley Bittner, Andrew Grieshop, Robert Bailis, Pamela Jagger, and J. Jason West

The climate and air quality of Sub-Saharan Africa is of significant interest in part because of the tight coupling between human activity, ecosystem dynamics, and atmospheric processes in this region, as use of biomass for residential energy and land use change (LUC) can influence regional climate and air quality. Here we present how we generate current emissions inventories for Sub-Saharan African residential energy use, future emission scenarios reflecting shifts to cleaner residential energy choices, and a methodology to adequately reflect the impact of LUC on both air quality and climate. We plan to use these emissions and LUC estimates to drive online coupled Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations that quantify how LUC and emissions have impacted the current air quality and climate of Sub-Saharan Africa (2001-2017), and the impacts of future emissions and LUC to 2040. We develop a business as usual scenario and two policy intervention scenarios for Sub-Saharan Africa in which residential biomass energy is phased out and replaced with either liquefied petroleum gas (LPG) or electricity.

To determine the impact of LUC, we utilize the Dinamica EGO model framework to generate future LUC projections by building probabilistic relationships between the observed LUC in the MODIS MCD12Q1 product spanning the period 2001-2007 and other static land surface properties. To validate simulations we compare simulated LUC projections for the period 2008-2012 to observed LUC for the same period. The validated model is then used to simulate the LUC for our full retrospective period of 2001-2017, and in future scenarios to 2040. To simulate the impact of LUC on biogenic volatile organic compounds and dust, a relationship is derived between monthly leaf area index and annual soil erodability, and the MODIS land use categories. These relationships are used in turn to reclassify LUC simulations (2001-2040) into LAI and soil erodability values.

Current residential emissions are derived following a similar methodology as the Diffuse and Inefficient Combustion Emissions in Africa (DICE-Africa) inventory, with other emissions sectors provided by the historical Shared Socioeconomic Pathway (SSP) gridded emissions inventory. The DICE-Africa inventory is used for residential sector emissions as it accounts for many different Sub-Saharan African fuel types and processes overlooked in traditional global inventories. To generate future emissions projections, current trends in energy usage from the DICE-Africa dataset are projected to 2040. To fully harmonize our DICE-Africa projection with future SSP emissions, we utilize the Global Change Analysis Model (GCAM) and constrain residential energy use following our business as usual, LPG-centric, and electricity-centric future scenarios under conditions representative of the SSP3 scenario. We present the methodologies used to generate these datasets and comparisons with other available emissions and land use datasets.

Fanqi Jia, Steven J. Smith, Alexander Macpherson, David Bielen, Christopher G. Nolte, Daniel H. Loughlin

Nitrogen oxides (NOx) are a precursor of troposphere ozone (O3), which is harmful to public health and the environment. Many areas in the U.S. experience unhealthy concentrations of O3, and reducing NOx emissions is a strategy that is being taken to reduce O3 formation. Measures for reducing NOx can be categorized as "traditional", including end-of-pipe control devices and changes to fuel combustion, or "non-traditional", such as switching to low- or zero-emitting fuels and improving energy efficiency. Historically, air quality management has focused on traditional measures, but it is possible that non-traditional measures may be advantageous, particularly since these measures can reduce greenhouse gases as well as demands for water. Energy system and integrated assessment models provide frameworks for evaluating the relative cost-effectiveness of traditional and non-traditional controls and can identify strategies that combine these options to achieve emission reduction targets at least cost. Here we use the GCAM-USA human-earth system model to determine state-level control strategies for achieving various levels of NOx reduction. We then demonstrate how the resulting state-specific marginal abatement cost curves (MACCs) provide insights into emission reduction opportunities at the sectoral and technology level and how traditional and non-traditional controls be can used together to meet emission abatement targets most cost-effectively. An advantage of our method is that it quantifies the cost-effectiveness of nontraditional controls, such as the dollar-per-ton of NOx reduction associated with end-use energy efficiency measures. We examine the results for several states, comparing the model-derived control strategies for each.

Jia Jung¹, Yunsoo Choi^1*, David C. Wong², Delaney Nelson¹, and Sojin Lee³

¹Department of Earth and Atmospheric Sciences, University of Houston, TX, USA

²US Environmental Protection Agency, Research Triangle Park, NC, USA

³Department of Safety and Environment Research, The Seoul Institute, Seoul, Republic of Korea

*Corresponding Author: ychoi23@central.uh.edu

To investigate the impact of sea fog over the Yellow Sea on air quality with the direct effect of aerosols, we performed four model simulations with the WRF-CMAQ two-way coupled model, in conjunction with the up-to-date emission inventory over East Asia and dynamic chemical boundary conditions provided by hemispheric model simulations. During the spring of 2016, prevailing westerly winds and anticyclones caused the formation of a temperature inversion over the Yellow Sea, providing favorable conditions for the formation of fog. The inclusion of the direct effect of aerosols enhanced its strength. On foggy days, we find dominant changes of aerosols at an altitude of 150-200 m over the Yellow Sea resulted from the production through aqueous chemistry (~12.36% and ~3.08% increases in sulfate and ammonium) and loss via the wet deposition process (~-2.94% decrease in nitrate); we also find stronger wet deposition of all species occurring in the PBL. Stagnant conditions associated with reduced air temperature caused by the direct effect of aerosols enhanced aerosol chemistry, especially in coastal regions, and it exceeded the loss of nitrate. The transport of air pollutants affected by sea fog extended to a much broader region. Our findings show that the Yellow Sea acts as not only a path of long-range transport but also as a sink and source of air pollutants. Further study should investigate changes in the impact of sea fog on air quality in conjunction with changes in the concentrations of aerosols and the climate.

Surendra B. Kunwar, Jared H. Bowden, George Milly, Michael Previdi, Arlene M. Fiore, J. Jason West

Anthropogenic climate change and associated natural feedback emissions like biogenic VOCs and wildfires can potentially affect air quality in the coming decades, but the noise of climate variability can obscure the climate change signal. Here we aim to quantify the impacts of climate change and variability on US PM_2.5 levels at fine spatial resolution by statistically combining probability distributions from multi-year global model ensembles with dynamical downscaling over the continental US. Our approach uses a 3-member ensemble of GFDL-CM3 global chemistry-climate model simulations that vary only in initial conditions for the period 2006-2100 under the RCP8.5 scenario, but with aerosol and O₃ precursor emissions fixed at 2005 levels to isolate the impact of climate change on air quality. Empirical Orthogonal Function (EOF) analysis of the simulations has identified eastern US regions that vary coherently in PM_2.5, and in each of those regions, we select four present (2006-2020) and four future (2050-2060) years that are representative of high and medium annual mean PM_2.5 levels. We then dynamically downscale the GFDL-CM3 meteorology and air quality of the selected eight years to 12-km with the regional models WRF and CMAQ. The 3-member GFDL-CM3 model simulations (2^o spatial resolution) from 2006 to 2060 provide a broader context for the downscaled CMAQ air quality simulations, and an additional global climate model with interactive aerosols, NCAR CESM, has a 12 ensemble member simulation (1^o spatial resolution) with aerosols held at 2005 levels for the same future scenario as the GFDL model provides more context and statistics. Using PM_2.5 probability distributions from the coarse global models, we construct fine scale mean annual PM_2.5 probability distributions in individual 12 km grid cells of the downscaled CMAQ simulations for the present and future. By examining the differences in fine scale mean annual PM_2.5 distributions between the present and the future, we quantify the effects of climate change and climate variability on PM_2.5 and also on related human mortality.

Dan Loughlin, Chris Nolte, Carol Lenox, Tai Wu, Andrew Kreider, and Shutsu Wong (USEPA); Samaneh Babaee, Paelina DeStephano, Fanqi Jia, and Sarah Simm (ORISE/ORAU); Steve Smith, Yang Ou, and Maridee Weber (PNNL)

The U.S. EPA is developing a decision support tool for air quality management called GLIMPSE, an acronym for GCAM Long-term Interactive Multi-Pollutant Scenario Evaluator. GLIMPSE is built upon the Global Change Analysis Model (GCAM), an open source human-earth system model whose development is led by Pacific Northwest National Laboratory. GCAM includes representations of the energy, water, agriculture, land use, and climate systems, simulating their co-evolution through 2100. The model has a relatively high level of technological detail within the energy system, including the electric sector, industry, buildings, and onroad and nonroad transportation. Technologies compete against each other for market share based on their relative costs and other factors. Emissions associated with the resulting technological pathway are reported.

GLIMPSE works with GCAM and with a variant of GCAM with state-level resolution (GCAM-USA). GCAM-USA includes representations of major national environmental and energy policies, including the Cross-State Air Pollution Rule, the Corporate Average Fuel Economy standards, New Source Performance Standards for air pollutants, and the Tier 3 engine and fuel emission standards. It also includes many state and regional policies, including the Regional Greenhouse Gas Initiative and Zero Emission Vehicle targets.

From an air quality planning perspective, GLIMPSE has a number of potential uses. For example, GLIMPSE can estimate emissions for specific scenarios. For example, one could examine future air pollutant and greenhouse gas (GHG) emissions under baseline assumptions as well as alternative assumptions about population growth, economic growth, technology change, and climate change.

With its state-level resolution and coverage of both air pollutants and greenhouse gas emissions, GLIMPSE also provides states with a tool for helping develop and evaluate policy options. For example, if states have identified specific policy measures, such as specifying energy efficiency and renewable energy targets, GLIMPSE can be used to quantify the resulting impacts on emissions. GLIMPSE also supports an optimization capability. Emission reduction targets can be specified by the user, then GLIMPSE automates the selection of control measures for meeting those targets at low cost.

In this presentation, we will discuss the capabilities, computational and user requirements, applications to date, and plans for making GLIMPSE available with training in 2021.

Dr. Michael Mac Kinnon, Dr. Shupeng Zhu,  Andre Paradise,  Dr. Donald Dabdub, Dr. Scott Samuelsen

Communities located adjacent to goods movement hubs (such as major ports) experience degraded air quality (AQ) as a result of emissions from on-road and off-road diesel equipment including heavy-duty diesel trucks (HDDT), cargo and materials handling equipment (CHE), ships, and rail technologies. In response, California is pursuing transitions to more efficient and cleaner freight systems by introducing zero-emission technologies as the alternative to conventional technologies. Equipment and vehicles powered by hydrogen fuel cells represent a potential zero-emissions pathway for freight technologies at ports, including HDDT, CHE, ships, and rail applications, referred to collectively as fuel cell electric technologies (FCET). This work assesses the AQ and human health impacts of deploying FCET to provide goods movement services at California ports. A specific focus is given to the South Coast Air Basin due to existing AQ challenges and the presence of significant activity from the San Pedro Bay Port Complex which includes the Ports of LA and Long Beach. Sets of future vehicle and equipment scenarios are developed spanning a range of potential FCET penetrations and assessed to quantify how FCET provides improvements in primary and secondary pollutant concentrations and the corresponding value of human health benefits. Additionally, reductions in greenhouse gas (GHG) emissions are assessed accounting for the transition from diesel fuel-powered combustion vehicles to hydrogen fuel cell-powered vehicles. We find that the use of FCET could offer significant reductions in ground level ozone (-2 to -5 ppb) and PM_2.5 in both summer (-0.59 to -02.57 μg/m³) and winter (-0.99 to -2.26 μg/m³) depending on the level of deployment. Additionally, these reductions occur in locations of the SoCAB where they are most needed including areas experiencing the highest background levels and in communities unfairly burdened by pollution. When AQ improvements are translated into avoided human health effects occurring across the SoCAB population, health savings are significant and potentially reach $3.2 to $7.2 million per day during peak pollutant formation periods. The results emphasize the importance of addressing the AQ impacts of port activities via the replacement of current diesel equipment with zero emission equipment. 

Megan S. Mallard, Kevin Talgo, Jared Bowden, and Tanya L. Spero

Phenological indicators (PI), used to study the timing of plant and animal cycles in response to seasonal changes, can also be useful metrics to analyze the impact of climate change on agricultural systems and ecosystem services. Here, projected changes to the wintertime dormancy in plants (assessed using Chilling Units, or "CU"), onset of spring (expressed using the Extended Spring Index), and the frequency of hard freezes after spring onset (false springs) are examined within a regional climate modeling application using the Weather Research and Forecasting (WRF) model to downscale two global climate models to 36-km grid spacing. Over the contiguous U.S. (CONUS), a historical (1995-2005) and future period (2025-2100) are simulated under Representative Concentration Pathways 4.5 and 8.5. Consistent with prior studies, spring onset is projected to occur earlier, with CONUS-average changes ranging between 12 to 36 days by the end of the century. False springs are projected to become more frequent in the complex terrain of the western CONUS while decreasing in frequency in regions of the eastern U.S. CU are found to decrease in the southern CONUS, which would negatively impact the productivity of deciduous fruit trees and other plants the following growing season, while portions of the western and northern CONUS experience increased CU. However, regional errors over the historical period in some of the PI used here exceed their projected end-of-century changes, indicating the need for improving simulated 2-m temperatures and the frequency with which they exceed certain thresholds that heavily influence these PI.

Daniel Schuch, Yang Zhang, Maria de Fatima Andrade, Edmilson Dias de Freitas and Michelle L. Bell

In this work, the Weather Research and Forecasting Model with Chemistry is applied to simulate air quality over Brazil for a short time period under three future emission scenarios, including current legislation (CLE), mitigation scenario (MIT), and maximum feasible reduction (MFR) under the Representative Concentration Pathway 4.5 (RCP4.5). The main objective is to determine the sensitivity of the concentrations of ozone (O3) to changes in emissions under these emission scenarios and to determine the temporal tendency for different scenarios and Brazilian regions.

Ana Paula Stein Santos, Marcelo Gomes da Silva, Danilo Francisco Trovo Garofalo, Viviane Cristina Bettanin, Katia Naomi Kuroshima, Gilberto Caetano Manzoni, Ana Paula Packer, Leonardo Hoinaski, Davide Franco

This study evaluates the mussel production influence on the coastal emission of CO₂ and CH₄ in Southern Brazil. Our study area was Armacao do Itapocoroy inlet, municipality of Penha, Santa Catarina state, Brazil. In southern Brazil the most important mariculture takes place. This activity could contribute to organic matter inputs and consequently to the production of GHG. However, ocean and coastal emissions have never been quantified in Brazil. We collect samples between October 02 and 04 2018 inside the mussel farm (Perna Perna) and in control sites (without mariculture activity). GHG fluxes were obtained by the static chamber sampling and analyzed through gas chromatography. Physical-chemical parameters (salinity, temperature, turbidity, pH, Eh, conductivity, dissolved oxygen-DO, suspended particulate matter, and total dissolved solids-TDS) was carried out in situ. Concentrations of chlorophyll-a, total organic phosphorus (TOP), nutrients (PO₃^-, SO₄^2-, NO₂^-, NO₃^-, NH₄⁺, SiO₄^-4), and metals were also measured. CO₂ flows varied between -81.8 and 76.1 mg C-CO₂ m^-2 d^-1, while the CH4 flows ranged from -0.2 to 2.5 mg C-CH₄ m^-2 d^-1. For CO₂, the first and last days performed positive fluxes to the atmosphere. The second day of sampling presented negative flux (-2.2 mgC-CO₂m^-2d^-1), suggesting carbon sequestration. Carbon sequestration also occurred in control and mussel farm sites. CH₄ fluxes are only positives and have shown significative higher concentrations in control sites, suggesting that mussel culture did not influence on methane emissions. CO₂ flux showed a low positive correlation with TSD (r = 0,16) and Salinity (r = 0,15). CH₄, showed low positive correlations with Turbidity (r = 0,25), Dissolved Oxygen (r = 0,29) and Nitrate (r = 0,25). Our preliminary results reveal that mussels farming in the sampling site do not influence positively on the CO₂ and CH₄ emissions.