@article{sparks_farahbakhsh_anand_bauch_conlon_east_li_lickley_garcia-menendez_monier_et al._2024, title={Health and equity implications of individual adaptation to air pollution in a changing climate}, volume={121}, ISSN={["1091-6490"]}, url={https://doi.org/10.1073/pnas.2215685121}, DOI={10.1073/pnas.2215685121}, abstractNote={Significance Air pollution is the leading environmental risk factor for early death. Alerts guide people to stay indoors when air quality is poor. Climate change can worsen air quality over this century. We show that this creates conditions for rising air quality alerts, disproportionately for racialized, unhoused, and poorly housed populations. Relying on people to protect themselves likely offers minimal benefits compared to reducing emissions; however, boosting adaptation can offer additional health benefits even under stringent climate policy. New policy could, for example, compensate people for moving indoors, and improve access to clean indoor air. We address active policy questions about how to equitably protect health under climate change, identifying levers for action against an increasing, unfair burden of air pollution.}, number={5}, journal={PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA}, author={Sparks, Matt S. and Farahbakhsh, Isaiah and Anand, Madhur and Bauch, Chris T. and Conlon, Kathryn C. and East, James D. and Li, Tianyuan and Lickley, Megan and Garcia-Menendez, Fernando and Monier, Erwan and et al.}, year={2024}, month={Jan} }
 @article{yang_luo_he_lin_johnson_garcia-menendez_deschenes_mileva_deshmukh_2024, title={Regional disparities in health and employment outcomes of China’s transition to a low-carbon electricity system}, url={https://doi.org/10.1088/2753-3751/ad3bb8}, DOI={10.1088/2753-3751/ad3bb8}, journal={Environmental Research: Energy}, author={Yang, Haozhe and Luo, Qian and He, Gang and Lin, Jiang and Johnson, Jeremiah X. and Garcia-Menendez, Fernando and Deschenes, Olivier and Mileva, Ana and Deshmukh, Ranjit}, year={2024}, month={Apr} }
 @article{johnson_garcia-menendez_2023, title={A comparison of smoke modelling tools used to mitigate air quality impacts from prescribed burning}, volume={5}, ISSN={["1448-5516"]}, url={https://doi.org/10.1071/WF22172}, DOI={10.1071/WF22172}, abstractNote={Background Prescribed fire is a land management tool used extensively across the United States. Owing to health and safety risks, smoke emitted by burns requires appropriate management. Smoke modelling tools are often used to mitigate air pollution impacts. However, direct comparisons of tools’ predictions are lacking. Aims We compared three tools commonly used to plan prescribed burning projects: the Simple Smoke Screening Tool, VSmoke and HYSPLIT. Methods We used each tool to model smoke dispersion from prescribed burns conducted by the North Carolina Division of Parks and Recreation over a year. We assessed similarity among the tools’ predicted smoke fields, areas of concern and potential population impacts. Key results The total smoke area predicted by the tools differs by thousands of square kilometres and, as such, spatial agreement was low. When translated into numbers of residents potentially exposed to smoke, tool estimates can vary by an order of magnitude. Conclusions Our analysis of an operational burning program suggests that the differences among the tools are significant and inconsistent. Implications While our analysis shows that improved and more consistent smoke modelling tools could better support land management, clear guidelines on how to apply their predictions are also necessary to obtain these benefits.}, journal={INTERNATIONAL JOURNAL OF WILDLAND FIRE}, author={Johnson, Megan M. and Garcia-Menendez, Fernando}, year={2023}, month={May} }
 @article{luo_garcia-menendez_lin_he_johnson_2023, title={Accelerating China's power sector decarbonization can save lives: integrating public health goals into power sector planning decisions}, volume={18}, ISSN={["1748-9326"]}, url={https://doi.org/10.1088/1748-9326/acf84b}, DOI={10.1088/1748-9326/acf84b}, abstractNote={China, the world’s largest greenhouse gas emitter in 2022, aims to achieve carbon neutrality by 2060. The power sector will play a major role in this decarbonization process due to its current reliance on coal. Prior studies have quantified air quality co-benefits from decarbonization or investigated pathways to eliminate greenhouse gas emissions from the power sector. However, few have jointly assessed the potential impacts of accelerating decarbonization on electric power systems and public health. Additionally, most analyses have treated air quality improvements as co-benefits of decarbonization, rather than a target during decarbonization. Here, we explore future energy technology pathways in China under accelerated decarbonization scenarios with a power system planning model that integrates carbon, pollutant, and health impacts. We integrate the health effects of power plant emissions into the power system decision-making process, quantifying the public health impacts of decarbonization under each scenario. We find that compared with a reference decarbonization pathway, a stricter cap (20% lower emissions than the reference pathway in each period) on carbon emissions would yield significant co-benefits to public health, leading to a 22% reduction in power sector health impacts. Although extra capital investment is required to achieve this low emission target, the value of climate and health benefits would exceed the additional costs, leading to $824 billion net benefits from 2021 to 2050. Another accelerated decarbonization pathway that achieves zero emissions five years earlier than the reference case would result in lower net benefits due to higher capital costs during earlier decarbonization periods. Treating air pollution impacts as a target in decarbonization can further mitigate both CO2 emissions and negative health effects. Alternative low-cost solutions also show that small variations in system costs can result in significantly different future energy portfolios, suggesting that diverse decarbonization pathways are viable.}, number={10}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={Luo, Qian and Garcia-Menendez, Fernando and Lin, Jiang and He, Gang and Johnson, Jeremiah X.}, year={2023}, month={Oct} }
 @article{raab_moyer_afrin_garcia-menendez_ward-caviness_2023, title={Prescribed fires, smoke exposure, and hospital utilization among heart failure patients}, volume={22}, ISSN={["1476-069X"]}, DOI={10.1186/s12940-023-01032-4}, abstractNote={Prescribed fires often have ecological benefits, but their environmental health risks have been infrequently studied. We investigated associations between residing near a prescribed fire, wildfire smoke exposure, and heart failure (HF) patients' hospital utilization.We used electronic health records from January 2014 to December 2016 in a North Carolina hospital-based cohort to determine HF diagnoses, primary residence, and hospital utilization. Using a cross-sectional study design, we associated the prescribed fire occurrences within 1, 2, and 5 km of the patients' primary residence with the number of hospital visits and 7- and 30-day readmissions. To compare prescribed fire associations with those observed for wildfire smoke, we also associated zip code-level smoke density data designed to capture wildfire smoke emissions with hospital utilization amongst HF patients. Quasi-Poisson regression models were used for the number of hospital visits, while zero-inflated Poisson regression models were used for readmissions. All models were adjusted for age, sex, race, and neighborhood socioeconomic status and included an offset for follow-up time. The results are the percent change and the 95% confidence interval (CI).Associations between prescribed fire occurrences and hospital visits were generally null, with the few associations observed being with prescribed fires within 5 and 2 km of the primary residence in the negative direction but not the more restrictive 1 km radius. However, exposure to medium or heavy smoke (primarily from wildfires) at the zip code level was associated with both 7-day (8.5% increase; 95% CI = 1.5%, 16.0%) and 30-day readmissions (5.4%; 95% CI = 2.3%, 8.5%), and to a lesser degree, hospital visits (1.5%; 95% CI: 0.0%, 3.0%) matching previous studies.Area-level smoke exposure driven by wildfires is positively associated with hospital utilization but not proximity to prescribed fires.}, number={1}, journal={ENVIRONMENTAL HEALTH}, author={Raab, Henry and Moyer, Joshua and Afrin, Sadia and Garcia-Menendez, Fernando and Ward-Caviness, Cavin K.}, year={2023}, month={Dec} }
 @article{luo_garcia-menendez_yang_deshmukh_he_lin_johnson_2023, title={The Health and Climate Benefits of Economic Dispatch in China's Power System}, volume={57}, ISSN={["1520-5851"]}, url={http://dx.doi.org/10.1021/acs.est.2c05663}, DOI={10.1021/acs.est.2c05663}, abstractNote={China’s power system is highly regulated and uses an “equal-share” dispatch approach. However, market mechanisms are being introduced to reduce generation costs and improve system reliability. Here, we quantify the climate and human health impacts brought about by this transition, modeling China’s power system operations under economic dispatch. We find that significant reductions in mortality related to air pollution (11%) and CO2 emissions (3%) from the power sector can be attained by economic dispatch, relative to the equal-share approach, through more efficient coal-powered generation. Additional health and climate benefits can be achieved by incorporating emission externalities in electricity generation costs. However, the benefits of the transition to economic dispatch will be unevenly distributed across China and may lead to increased health damage in some regions. Our results show the potential of dispatch decision-making in electricity generation to mitigate the negative impacts of power plant emissions with existing facilities in China.}, number={7}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, publisher={American Chemical Society (ACS)}, author={Luo, Qian and Garcia-Menendez, Fernando and Yang, Haozhe and Deshmukh, Ranjit and He, Gang and Lin, Jiang and Johnson, Jeremiah X.}, year={2023}, month={Feb} }
 @article{luo_garcia-menendez_yang_deshmukh_he_lin_johnson_2023, title={The Health and Climate Benefits of Economic Dispatch in China?s Power System}, volume={57}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.2c056632898}, number={7}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Luo, Qian and Garcia-Menendez, Fernando and Yang, Haozhe and Deshmukh, Ranjit and He, Gang and Lin, Jiang and Johnson, Jeremiah X.}, year={2023}, month={Feb}, pages={2898–2906} }
 @article{east_monier_g-menendez_2022, title={Characterizing and quantifying uncertainty in projections of climate change impacts on air quality}, volume={17}, ISSN={["1748-9326"]}, url={https://doi.org/10.1088/1748-9326/ac8d17}, DOI={10.1088/1748-9326/ac8d17}, abstractNote={Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections—greenhouse gas (GHG) emissions scenario, model response, and internal variability—have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.}, number={9}, journal={ENVIRONMENTAL RESEARCH LETTERS}, author={East, James D. and Monier, Erwan and G-Menendez, Fernando}, year={2022}, month={Sep} }
 @article{luo_copeland_garcia-menendez_johnson_2022, title={Diverse Pathways for Power Sector Decarbonization in Texas Yield Health Cobenefits but Fail to Alleviate Air Pollution Exposure Inequities}, ISSN={["1520-5851"]}, url={https://doi.org/10.1021/acs.est.2c00881}, DOI={10.1021/acs.est.2c00881}, abstractNote={Decarbonizing power systems is a critical component of climate change mitigation, which can have public health cobenefits by reducing air pollution. Many studies have examined strategies to decarbonize power grids and quantified their health cobenefits. However, few of them focus on near-term cobenefits at community levels, while comparing various decarbonization pathways. Here, we use a coupled power system and air quality modeling framework to quantify the costs and benefits of decarbonizing the Texas power grid through a carbon tax; replacing coal with natural gas, solar, or wind; and internalizing human health impacts into operations. Our results show that all decarbonization pathways can result in major reductions in CO2 emissions and public health impacts from power sector emissions, leading to large net benefits when considering the costs to implement these strategies. Operational changes with existing infrastructure can serve as a transitional strategy during the process of replacing coal with renewable energy, which offers the largest benefits. However, we also find that Black and lower-income populations receive disproportionately higher air pollution damages and that none of the examined decarbonization strategies mitigate this disparity. These findings suggest that additional interventions are necessary to mitigate environmental inequity while decarbonizing power grids.}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Luo, Qian and Copeland, Brenna and Garcia-Menendez, Fernando and Johnson, Jeremiah X.}, year={2022}, month={Sep} }
 @article{east_henderson_napelenok_koplitz_sarwar_gilliam_lenzen_tong_pierce_garcia-menendez_2022, title={Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations}, url={https://doi.org/10.5194/acp-2022-435}, DOI={10.5194/acp-2022-435}, abstractNote={Abstract. Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NOx). Mass-balance based methods are often applied for this purpose, but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model and infers satellite-constrained emissions totals using the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to estimate lightning and anthropogenic NOx emissions over the Northern Hemisphere. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare NOx emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NOx emissions from either satellite improve the chemical transport model’s low tropospheric O3 bias. Combined lightning and anthropogenic updates inferred from satellite observations can improve the model’s ability to represent background and ground-level O3 concentrations, an ongoing policy consideration in the U.S. as domestic and international emissions control strategies evolve.}, author={East, James D. and Henderson, Barron H. and Napelenok, Sergey L. and Koplitz, Shannon N. and Sarwar, Golam and Gilliam, Robert and Lenzen, Allen and Tong, Daniel Q. and Pierce, R. Bradley and Garcia-Menendez, Fernando}, year={2022}, month={Jul} }
 @article{east_henderson_napelenok_koplitz_sarwar_gilliam_lenzen_tong_pierce_garcia-menendez_2022, title={Inferring and evaluating satellite-based constraints on NOx emissionsestimates in air quality simulations}, volume={22}, ISSN={["1680-7324"]}, url={https://doi.org/10.5194/acp-22-15981-2022}, DOI={10.5194/acp-22-15981-2022}, abstractNote={Abstract. Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NOx). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling
framework that couples satellite chemical data assimilation to a chemical
transport model. In the framework, satellite-constrained emissions totals
are inferred using model simulations with and without data assimilation in
the iterative finite-difference mass-balance method. The approach improves
the finite-difference mass-balance inversion by isolating the near-surface
emissions increment. We apply the framework to separately estimate lightning and anthropogenic NOx emissions over the Northern Hemisphere for 2019. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare separate NOx emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NOx emissions from either satellite improve the chemical transport model's low tropospheric O3 bias. The combined lighting and anthropogenic emissions updates improve the model's ability to reproduce measured ozone by adjusting natural, long-range, and local pollution contributions. Thus, the framework informs and supports the design of domestic and international control strategies.
}, number={24}, journal={ATMOSPHERIC CHEMISTRY AND PHYSICS}, author={East, James D. and Henderson, Barron H. and Napelenok, Sergey L. and Koplitz, Shannon N. and Sarwar, Golam and Gilliam, Robert and Lenzen, Allen and Tong, Daniel Q. and Pierce, R. Bradley and Garcia-Menendez, Fernando}, year={2022}, month={Dec}, pages={15981–16001} }
 @article{east_henderson_napelenok_koplitz_sarwar_gilliam_lenzen_tong_pierce_garcia-menendez_2022, title={Supplementary material to "Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations"}, url={https://doi.org/10.5194/acp-2022-435-supplement}, DOI={10.5194/acp-2022-435-supplement}, author={East, James D. and Henderson, Barron H. and Napelenok, Sergey L. and Koplitz, Shannon N. and Sarwar, Golam and Gilliam, Robert and Lenzen, Allen and Tong, Daniel Q. and Pierce, R. Bradley and Garcia-Menendez, Fernando}, year={2022}, month={Jul} }
 @article{johnson_garcia-menendez_2022, title={Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event}, volume={6}, ISSN={["2471-1403"]}, url={https://doi.org/10.1029/2021GH000526}, DOI={10.1029/2021GH000526}, abstractNote={Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire‐related smoke PM2.5 fields and variability in concentration‐response parameters from epidemiologic studies of ambient and smoke PM2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire‐attributable PM2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration‐response parameters derived from studies of ambient and wildfire‐specific PM2.5 are used to assess health‐related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health‐related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire‐related decision‐making.}, number={1}, journal={GEOHEALTH}, publisher={American Geophysical Union (AGU)}, author={Johnson, Megan M. and Garcia-Menendez, Fernando}, year={2022}, month={Jan} }
 @article{east_montealegre_pachon_garcia-menendez_2021, title={Air quality modeling to inform pollution mitigation strategies in a Latin American megacity}, volume={776}, ISSN={["1879-1026"]}, url={http://dx.doi.org/10.1016/j.scitotenv.2021.145894}, DOI={10.1016/j.scitotenv.2021.145894}, abstractNote={Poor air quality disproportionally impacts cities in low- and middle-income countries. In Bogotá, Colombia, a metropolitan area with over 10 million inhabitants, fine particulate matter (PM2.5) levels regularly exceed air quality guidelines, leading to detrimental effects on health. Although there is public interest to improve the city's air quality, the main sources of PM2.5 pollution have not been clearly identified and the use of modeling for policy development in Bogotá has been limited. Here, we apply a modeling framework based on the Community Multiscale Air Quality Modeling System (CMAQ) to conduct seasonal simulations of air pollution in Bogotá and reveal the emissions sectors with the largest contributions to PM2.5. Based on these results, we project and compare the air quality benefits of potential pollution mitigation strategies focused on these sources. The analysis finds that resuspended dust from unpaved roads is the largest local source of PM2.5 and can contribute over 30% of seasonally-averaged concentration across the city. Vehicles, industrial activity, and unpaved road dust combined are responsible for over 60% of PM2.5 pollution in Bogotá. A scenario analysis shows that paving roads can lead to PM2.5 decreases of nearly 10 μg/m3 by 2030 in some areas of the city, but air quality will deteriorate significantly over others in the absence of additional emissions control measures. Mitigation strategies designed to target the sectors with the largest contributions to PM2.5, including road cleaning systems, controls for industrial point sources, cleaner transportation fuels, and updated vehicle fleets, can largely avert projected increases in concentrations, although the impacts of different approaches vary throughout the city. This study is the first to use a comprehensive model to determine sector contributions to air pollution and inform potential emissions control policies in Bogotá, demonstrating an approach to guide pollution management in developing cities facing comparable challenges.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier BV}, author={East, James and Montealegre, Juan Sebastian and Pachon, Jorge E. and Garcia-Menendez, Fernando}, year={2021}, month={Jul} }
 @article{huang_lal_qin_hu_russell_odman_afrin_garcia-menendez_susan m. o'neill_2021, title={Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA}, volume={71}, ISSN={["2162-2906"]}, url={https://doi.org/10.1080/10962247.2021.1924311}, DOI={10.1080/10962247.2021.1924311}, abstractNote={ABSTRACT Prescribed burning (PB) is a prominent source of PM2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM2.5 concentrations. Simulated PM2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R2 of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM2.5 fields to provide better estimates of hourly exposures to smoke from PB. Implications: Prescribed burning emissions are a major cause of elevated PM2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.}, number={7}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, publisher={Informa UK Limited}, author={Huang, Ran and Lal, Raj and Qin, Momei and Hu, Yongtao and Russell, Armistead G. and Odman, M. Talat and Afrin, Sadia and Garcia-Menendez, Fernando and Susan M. O'Neill}, year={2021}, month={Jul}, pages={815–829} }
 @article{goldman_ivey_garcia-menendez_balachandran_2021, title={Beyond the Lab: Early Career Researchers May Find Purpose through Policy, Advocacy, and Public Engagement}, volume={55}, ISSN={["1520-5851"]}, DOI={10.1021/acs.est.1c00495}, abstractNote={ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTADDITION / CORRECTIONThis article has been corrected. View the notice.Beyond the Lab: Early Career Researchers May Find Purpose through Policy, Advocacy, and Public EngagementGretchen T. Goldman*Gretchen T. GoldmanUnion of Concerned Scientists, 1825 K St NW, Ste 800, Washington D.C. 20006 United States*Email: [email protected]More by Gretchen T. Goldmanhttp://orcid.org/0000-0003-1787-9851, Cesunica E. IveyCesunica E. IveyUniversity of California Riverside, Chemical and Environmental Engineering, 900 University Avenue, Riverside, California 92521 United StatesMore by Cesunica E. Ivey, Fernando Garcia-MenendezFernando Garcia-MenendezNorth Carolina State University, Department of Civil, Construction, and Environmental Engineering, Fitts-Woolard Hall, Raleigh, North Carolina 27695 United StatesMore by Fernando Garcia-Menendez, and Sivaraman BalachandranSivaraman BalachandranUniversity of Cincinnati, Department of Chemical and Environmental Engineering, 2901 Woodside Drive, Cincinnati, Ohio 45221 United StatesMore by Sivaraman BalachandranCite this: Environ. Sci. Technol. 2021, 55, 5, 2720–2721Publication Date (Web):February 19, 2021Publication History Received22 January 2021Published online19 February 2021Published inissue 2 March 2021https://doi.org/10.1021/acs.est.1c00495Copyright © 2021 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views2450Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Environmental science,Particulate matter,Power,Students Get e-Alerts}, number={5}, journal={ENVIRONMENTAL SCIENCE & TECHNOLOGY}, author={Goldman, Gretchen T. and Ivey, Cesunica E. and Garcia-Menendez, Fernando and Balachandran, Sivaraman}, year={2021}, month={Mar}, pages={2720–2721} }
 @article{afrin_garcia-menendez_2021, title={Potential impacts of prescribed fire smoke on public health and socially vulnerable populations in a Southeastern US state}, volume={794}, ISSN={["1879-1026"]}, url={http://dx.doi.org/10.1016/j.scitotenv.2021.148712}, DOI={10.1016/j.scitotenv.2021.148712}, abstractNote={Prescribed fire is an essential tool for wildfire risk mitigation and ecosystem restoration in the Southeastern United States. It is also one of the region's largest sources of atmospheric emissions. The public health impacts of prescribed fire smoke, however, remain uncertain. Here, we use digital burn permit records, reduced-complexity air quality modeling, and epidemiological associations between fine particulate matter concentrations and multiple health endpoints to assess the impacts of prescribed burning on public health across Georgia. Additionally, we examine the social vulnerability of populations near high prescribed burning activity using a demographic- and socioeconomic-based index. The analysis identifies spatial clusters of burning activity in the state and finds that areas with intense prescribed fire have levels of social vulnerability that are over 25% higher than the state average. The results also suggest that the impacts of burning in Georgia can potentially include hundreds of annual morbidity and mortality cases associated with smoke pollution. These health impacts are concentrated in areas with higher fractions of low socioeconomic status, elderly, and disabled residents, particularly vulnerable to air pollution. Estimated smoke-related health incidence rates are over 3 times larger than the state average in spatial clusters of intense burning activity, and over 40% larger in spatial clusters of high social vulnerability. Spatial clusters of low social vulnerability experience substantially lower negative health effects from prescribed burning relative to the rest of the state. The health burden of smoke from prescribed burns in the state is comparable to that estimated for other major emission sectors, such as vehicles and industrial combustion. Within spatial clusters of socially-vulnerable populations, the impacts of prescribed fire considerably outweigh those of other emission sectors. These findings call for greater attention to the air quality impacts of prescribed burning in the Southeastern U.S. and the communities most exposed to fire-related smoke.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, publisher={Elsevier BV}, author={Afrin, Sadia and Garcia-Menendez, Fernando}, year={2021}, month={Nov} }
 @article{luo_johnson_garcia-menendez_2021, title={Reducing human health impacts from power sector emissions with redispatch and energy storage}, volume={8}, url={https://doi.org/10.1088/2634-4505/ac20b3}, DOI={10.1088/2634-4505/ac20b3}, abstractNote={Emissions from the power sector significantly contribute to ambient air pollution and its associated adverse human health impacts. In this study, we explore how to cost-effectively reduce health impacts due to fine particulate matter (PM2.5) attributable to power plant emissions by internalizing real-time health costs in plant dispatch decisions and re-optimizing the unit commitment and economic dispatch in light of these impacts. We show that internalizing the time- and location-varying health damage costs into power system operational decisions can reduce 61%–97% of adverse health impacts through decreases in coal generation and strategic shifts in the location and timing of pollutant releases. We also find that adding energy storage to the grid can mitigate health impacts by reducing wind power curtailment. Our findings demonstrate the need to consider temporal and spatial heterogeneity when determining the social cost of emissions.}, journal={Environmental Research: Infrastructure and Sustainability}, publisher={IOP Publishing}, author={Luo, Qian and Johnson, Jeremiah X and Garcia-Menendez, Fernando}, year={2021}, month={Sep} }
 @inbook{climate model response uncertainty in projections of climate change impacts on air quality_2020, url={http://dx.doi.org/10.1007/978-3-030-22055-6_69}, DOI={10.1007/978-3-030-22055-6_69}, abstractNote={Uncertainties in climate simulations can strongly propagate to estimates of climate change impacts, including its effects on air pollution. Here we use a coupled modeling framework to evaluate the role of climate model response in projections of climate-induced impacts on air quality. Within integrated economic, climate, and air pollution projections, climate model response is altered by modifying the climate sensitivity of the framework’s Earth system component. We find that variations in climate sensitivity ranging from 2.0 to 4.5 °C per doubling of CO2 can change projections of the climate penalty on O3 and PM2.5 pollution in the U.S. by more than 2 ppb and 0.5 µg m−3. The impact of uncertainty due to climate model response can be as important as that related to greenhouse gas emissions scenario or natural variability.}, booktitle={Springer Proceedings in Complexity}, year={2020} }
 @article{afrin_garcia‐menendez_2020, title={The Influence of Prescribed Fire on Fine Particulate Matter Pollution in the Southeastern United States}, volume={47}, url={https://doi.org/10.1029/2020GL088988}, DOI={10.1029/2020GL088988}, abstractNote={Prescribed fire is the largest source of fine particulate matter emissions in the Southeastern United States, yet its air quality impacts remain highly uncertain. Here, we assess the influence of prescribed fire on observed pollutant concentrations in the region using a unique fire data set compiled from multiyear digital burn permit records. There is a significant association between prescribed fire activity and concentrations recorded at Southeastern monitoring sites, with permitted burning explaining as much as 50% variability in daily PM2.5 concentrations. This relationship varies spatially and temporally across the region and as a function of burn type. At most locations, the association between PM2.5 concentration and permitted burning is stronger than that with satellite‐derived burn area or meteorological drivers of air quality. These results highlight the value of bottom‐up data in evaluating the contribution of prescribed fire to regional air pollution and reveal a need to develop more complete burn records.}, number={15}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Afrin, Sadia and Garcia‐Menendez, Fernando}, year={2020}, month={Aug} }
 @article{towards an improved understanding of greenhouse gas emissions and fluxes in tropical peatlands of southeast asia_2020, url={http://dx.doi.org/10.1016/j.scs.2019.101881}, DOI={10.1016/j.scs.2019.101881}, abstractNote={At present, there is insufficient data to understand the processes driving emissions and fluxes of greenhouse gases (GHGs) from tropical peatlands in Southeast Asia (SEA). In this review, we discuss fundamental factors controlling emissions of major GHGs (CO2, CH4, and N2O) from tropical peatlands and their contribution to global climate change. Classifying peatlands in tropical and subtropical regions can aid in understanding their emission characteristics. The applicability of existing GHG emission factors to land use categories in SEA is discussed. We find that rewetting peatland can increase CH4 emissions, and therefore more studies are needed to establish whether peatlands act as a net sink or net sources of GHGs. Few studies have investigated the effectiveness of liming towards reducing peat soil acidity. The review also finds that there is limited data on CO2 concentrations in drainage and wildfire areas, N2O fluxes in agriculture areas, and the impact and reduction of CH4 in tropical peatlands. Addressing these research gaps could support the development of a framework for GHG emission measurements and abatement in tropical peatlands.}, journal={Sustainable Cities and Society}, year={2020}, month={Feb} }
 @misc{altshuler_zhang_kleinman_garcia-menendez_moore_hough_stevenson_chow_jaffe_watson_2020, title={Wildfire and prescribed burning impacts on air quality in the United States}, volume={70}, ISSN={["2162-2906"]}, url={http://dx.doi.org/10.1080/10962247.2020.1813217}, DOI={10.1080/10962247.2020.1813217}, number={10}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, publisher={Informa UK Limited}, author={Altshuler, Samuel L. and Zhang, Qi and Kleinman, Michael T. and Garcia-Menendez, Fernando and Moore, Charles Thomas and Hough, Merlyn L. and Stevenson, Eric D. and Chow, Judith C. and Jaffe, Daniel A. and Watson, John G.}, year={2020}, month={Oct}, pages={961–970} }
 @article{johnson gaither_afrin_garcia-menendez_odman_huang_goodrick_ricardo da silva_2019, title={African American Exposure to Prescribed Fire Smoke in Georgia, USA}, volume={16}, ISSN={1660-4601}, url={http://dx.doi.org/10.3390/ijerph16173079}, DOI={10.3390/ijerph16173079}, abstractNote={Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia’s population, making it the largest racial/ethnic minority group in the state followed by Hispanic Americans at 9.8%. African Americans, Hispanic Americans, and lower wealth groups are more likely than most middle and upper income White Americans to be exposed to environmental pollutants. This is true because racial and ethnic minorities are more likely to live in urban areas where pollution is more concentrated. As a point of departure, we examine PM2.5 concentrations specific to prescribed fire smoke, which typically emanates from fires occurring in rural or peri-urban areas. Two objectives are specified: a) examine the association between percent African American and PM2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM2.5 concentrations that exceed National Ambient Air Quality Standards (NAAQS) for the 24-h average, i. e., >35 µg/m3. (2) Methods: For the first objective, we estimate a spatial Durbin error model (SDEM) where pollution concentration (PM2.5) estimates for 1683 census tracts are regressed on percent of the human population that is African American or Hispanic; lives in mobile homes; and is employed in agriculture and related occupations. Also included as controls are percent evergreen forest, percent mixed evergreen/deciduous forest, and variables denoting lagged explanatory and error variables, respectively. For the second objective, we merge parcel and prescribed burn permit data to identify landowners who conduct prescribed fires that produce smoke exceeding the NAAQS. (3) Results: Percent African American and mobile home dweller are positively related to PM2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM2.5 concentrations in areas with higher African American and mobile home percent are not clear, although we suspect that neither group is a primary contributor to prescribed burn smoke but rather tend to live proximate to entities, both public and private, that are. Also, non-industrial private landowners who generated prescribed burn smoke exceeding NAAQS are wealthier than others, which suggests that African American and other environmental justice populations are less likely to contribute to exceedance levels in the state.}, number={17}, journal={International Journal of Environmental Research and Public Health}, publisher={MDPI AG}, author={Johnson Gaither, Cassandra and Afrin, Sadia and Garcia-Menendez, Fernando and Odman, M. Talat and Huang, Ran and Goodrick, Scott and Ricardo da Silva, Alan}, year={2019}, month={Aug}, pages={3079} }
 @article{saari_mei_monier_garcia-menendez_2019, title={Effect of Health-Related Uncertainty and Natural Variability on Health Impacts and Cobenefits of Climate Policy}, volume={53}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.8b05094}, DOI={10.1021/acs.est.8b05094}, abstractNote={Climate policy can mitigate health risks attributed to intensifying air pollution under climate change. However, few studies quantify risks of illness and death, examine their contribution to climate policy benefits, or assess their robustness in light of natural climate variability. We employ an integrated modeling framework of the economy, climate, air quality, and human health to quantify the effect of natural variability on U.S. air pollution impacts under future climate and two global policies (2 and 2.5 °C stabilization scenarios) using 150 year ensemble simulations for each scenario in 2050 and 2100. Climate change yields annual premature deaths related to fine particulate matter and ozone (95CI: 25 000-120 000), heart attacks (900-9400), and lost work days (3.6M-4.9M) in 2100. It raises air pollution health risks by 20%, while policies avert these outcomes by 40-50% in 2050 and 70-88% in 2100. Natural variability introduces "climate noise", yielding some annual estimates with negative cobenefits, and others that reach 100% of annual policy costs. This "noise" is three times the magnitude of uncertainty (95CI) in health and economic responses in 2050. Averaging five annual simulations reduces this factor to two, which is still substantially larger than health-related uncertainty. This study quantifies the potential for inaccuracy in climate impacts projected using too few annual simulations.}, number={3}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Saari, Rebecca K. and Mei, Yufei and Monier, Erwan and Garcia-Menendez, Fernando}, year={2019}, month={Jan}, pages={1098–1108} }
 @article{pienkosz_saari_monier_garcia‐menendez_2019, title={Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution}, ISSN={2328-4277 2328-4277}, url={http://dx.doi.org/10.1029/2019EF001195}, DOI={10.1029/2019EF001195}, abstractNote={Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model‐based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic‐induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic‐forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate‐induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.}, journal={Earth's Future}, publisher={American Geophysical Union (AGU)}, author={Pienkosz, Bret D. and Saari, Rebecca K. and Monier, Erwan and Garcia‐Menendez, Fernando}, year={2019}, month={Jul} }
 @article{brown-steiner_selin_prinn_monier_tilmes_emmons_garcia-menendez_2018, title={Maximizing ozone signals among chemical, meteorological, and climatological variability}, volume={18}, ISSN={1680-7324}, url={http://dx.doi.org/10.5194/acp-18-8373-2018}, DOI={10.5194/acp-18-8373-2018}, abstractNote={Abstract. The detection of meteorological, chemical, or other signals in modeled or
observed air quality data – such as an estimate of a temporal trend in
surface ozone data, or an estimate of the mean ozone of a particular region
during a particular season – is a critical component of modern atmospheric
chemistry. However, the magnitude of a surface air quality signal is
generally small compared to the magnitude of the underlying chemical,
meteorological, and climatological variabilities (and their interactions)
that exist both in space and in time, and which include variability in
emissions and surface processes. This can present difficulties for both
policymakers and researchers as they attempt to identify the influence or
signal of climate trends (e.g., any pauses in warming trends), the impact
of enacted emission reductions policies (e.g., United States
NOx State Implementation Plans), or an estimate of the mean
state of highly variable data (e.g., summertime ozone over the northeastern
United States). Here we examine the scale dependence of the variability of
simulated and observed surface ozone data within the United States and the
likelihood that a particular choice of temporal or spatial averaging scales
produce a misleading estimate of a particular ozone signal. Our main
objective is to develop strategies that reduce the likelihood of
overconfidence in simulated ozone estimates. We find that while increasing
the extent of both temporal and spatial averaging can enhance signal
detection capabilities by reducing the noise from variability, a
strategic combination of particular temporal and spatial averaging scales can
maximize signal detection capabilities over much of the continental US. For
signals that are large compared to the meteorological variability (e.g.,
strong emissions reductions), shorter averaging periods and smaller spatial
averaging regions may be sufficient, but for many signals that are smaller
than or comparable in magnitude to the underlying meteorological variability,
we recommend temporal averaging of 10–15 years combined with some level of
spatial averaging (up to several hundred kilometers). If this level of
averaging is not practical (e.g., the signal being examined is at a local
scale), we recommend some exploration of the spatial and temporal variability
to provide context and confidence in the robustness of the result. These
results are consistent between simulated and observed data, as well as within a
single model with different sets of parameters. The strategies selected in
this study are not limited to surface ozone data and could potentially
maximize signal detection capabilities within a broad array of climate and
chemical observations or model output.
}, number={11}, journal={Atmospheric Chemistry and Physics}, publisher={Copernicus GmbH}, author={Brown-Steiner, Benjamin and Selin, Noelle E. and Prinn, Ronald G. and Monier, Erwan and Tilmes, Simone and Emmons, Louisa and Garcia-Menendez, Fernando}, year={2018}, month={Jun}, pages={8373–8388} }
 @article{brown-steiner_selin_prinn_monier_tilmes_emmons_garcia-menendez_2017, title={Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability}, volume={11}, url={https://doi.org/10.5194/acp-2017-954}, DOI={10.5194/acp-2017-954}, abstractNote={Abstract. The detection of meteorological, chemical, or other signals in modeled or observed air quality data – such as an estimate of a temporal trend in surface ozone data, or an estimate of the mean ozone of a particular region during a particular season – is a critical component of modern atmospheric chemistry. However, the magnitude of a surface air quality signal is generally small compared to the magnitude of the underlying chemical and meteorological variabilities that exist both in space and in time. This can present difficulties for both policy-makers and researchers as they attempt to identify the influence or signal of climate trends (e.g. any pauses in warming trends), the impact of enacted emission reductions policies (e.g. United States NOx State Implementation Plans), or an estimate of the mean state of highly variable data (e.g. summertime ozone over the Northeastern United States). Here we examine the scale-dependence of the variability of simulated and observed surface ozone data within the United States and the likelihood that a particular choice of temporal or spatial averaging scales produce a misleading estimate of a particular ozone signal. Our main objective is to develop strategies that reduce the likelihood of overconfidence in simulated ozone estimates. We find that while increasing the extent of both temporal and spatial averaging can enhance signal detection capabilities by reducing the noise from variability, a strategic combination of particular temporal and spatial averaging scales can maximize signal detection capabilities over much of the Continental US. We recommend temporal averaging of at least 10–15 years combined with regional spatial averaging over several hundred kilometer spatial scales. These results are consistent between simulated and observed data, and within a single model with different sets of parameters. The strategies selected in this study are not limited to surface ozone data, and could potentially maximize signal detection capabilities within a broad array of climate and chemical observations or model output.}, journal={Atmospheric Chemistry and Physics Discussions}, publisher={Copernicus GmbH}, author={Brown-Steiner, Benjamin and Selin, Noelle E. and Prinn, Ronald G. and Monier, Erwan and Tilmes, Simone and Emmons, Louisa and Garcia-Menendez, Fernando}, year={2017}, month={Nov}, pages={1–38} }
 @article{brown-steiner_selin_prinn_monier_tilmes_emmons_garcia-menendez_2017, title={Supplementary material to "Maximizing Ozone Signals Among Chemical, Meteorological, and Climatological Variability"}, volume={11}, url={https://doi.org/10.5194/acp-2017-954-supplement}, DOI={10.5194/acp-2017-954-supplement}, abstractNote={Supplemental Figure S1: The likelihood (percent, vertical axis) that an estimation of the mean ozone value for a given length of temporal averaging window (years, horizontal axis) is farther away from the long-term mean value than a given threshold: 5 ppbv (blue), 2.5 ppbv (purple), 1 ppbv (green), and 0.5 ppbv (blue).Individual columns represent the four datasets used in this study: CASTNET, present-day MOZART, and the two future MOZART simulations (2050, 2100).Individual rows are spatially averaging over the telescoping regions seen in Figure 1.Supplemental Figure S2 plots similar results for the Southeastern and Midwestern US.}, publisher={Copernicus GmbH}, author={Brown-Steiner, Benjamin and Selin, Noelle E. and Prinn, Ronald G. and Monier, Erwan and Tilmes, Simone and Emmons, Louisa and Garcia-Menendez, Fernando}, year={2017}, month={Nov} }
 @article{garcia-menendez_monier_selin_2017, title={The role of natural variability in projections of climate change impacts on U.S. ozone pollution}, volume={44}, ISSN={0094-8276}, url={http://dx.doi.org/10.1002/2016GL071565}, DOI={10.1002/2016gl071565}, abstractNote={Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground‐level ozone and aggravate associated health effects. However, modeling studies exploring air quality‐climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate‐induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic‐forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios.}, number={6}, journal={Geophysical Research Letters}, publisher={American Geophysical Union (AGU)}, author={Garcia-Menendez, Fernando and Monier, Erwan and Selin, Noelle E.}, year={2017}, month={Mar}, pages={2911–2921} }
 @inbook{odman_hu_garcia-menendez_2016, title={Atmospheric Plume Modeling with a Three-Dimensional Refinement Adaptive Grid Method}, ISBN={9783319244761 9783319244785}, ISSN={2213-8684 2213-8692}, url={http://dx.doi.org/10.1007/978-3-319-24478-5_67}, DOI={10.1007/978-3-319-24478-5_67}, abstractNote={We present a three-dimensional fully-adaptive grid algorithm for chemical transport models. The method is designed to refine vertical and horizontal resolution by dynamically concentrating grid nodes within a region of interest. Exceptionally high grid resolution can be achieved in Eulerian air quality models using the method. Here the algorithm’s main operations are described. In addition, advection tests are used to demonstrate the algorithm’s ability to better capture concentration gradients in atmospheric plumes.}, booktitle={Springer Proceedings in Complexity}, publisher={Springer International Publishing}, author={Odman, M. Talat and Hu, Yongtao and Garcia-Menendez, Fernando}, year={2016}, pages={409–413} }
 @article{garcia-menendez_saari_monier_selin_2015, title={U.S. Air Quality and Health Benefits from Avoided Climate Change under Greenhouse Gas Mitigation}, volume={49}, ISSN={0013-936X 1520-5851}, url={http://dx.doi.org/10.1021/acs.est.5b01324}, DOI={10.1021/acs.est.5b01324}, abstractNote={We evaluate the impact of climate change on U.S. air quality and health in 2050 and 2100 using a global modeling framework and integrated economic, climate, and air pollution projections. Three internally consistent socioeconomic scenarios are used to value health benefits of greenhouse gas mitigation policies specifically derived from slowing climate change. Our projections suggest that climate change, exclusive of changes in air pollutant emissions, can significantly impact ozone (O3) and fine particulate matter (PM2.5) pollution across the U.S. and increase associated health effects. Climate policy can substantially reduce these impacts, and climate-related air pollution health benefits alone can offset a significant fraction of mitigation costs. We find that in contrast to cobenefits from reductions to coemitted pollutants, the climate-induced air quality benefits of policy increase with time and are largest between 2050 and 2100. Our projections also suggest that increasing climate policy stringency beyond a certain degree may lead to diminishing returns relative to its cost. However, our results indicate that the air quality impacts of climate change are substantial and should be considered by cost-benefit climate policy analyses.}, number={13}, journal={Environmental Science & Technology}, publisher={American Chemical Society (ACS)}, author={Garcia-Menendez, Fernando and Saari, Rebecca K. and Monier, Erwan and Selin, Noelle E.}, year={2015}, month={Jun}, pages={7580–7588} }
 @article{garcia-menendez_hu_odman_2014, title={Simulating smoke transport from wildland fires with a regional-scale air quality model: Sensitivity to spatiotemporal allocation of fire emissions}, volume={493}, ISSN={0048-9697}, url={http://dx.doi.org/10.1016/j.scitotenv.2014.05.108}, DOI={10.1016/j.scitotenv.2014.05.108}, abstractNote={Air quality forecasts generated with chemical transport models can provide valuable information about the potential impacts of fires on pollutant levels. However, significant uncertainties are associated with fire-related emission estimates as well as their distribution on gridded modeling domains. In this study, we explore the sensitivity of fine particulate matter concentrations predicted by a regional-scale air quality model to the spatial and temporal allocation of fire emissions. The assessment was completed by simulating a fire-related smoke episode in which air quality throughout the Atlanta metropolitan area was affected on February 28, 2007. Sensitivity analyses were carried out to evaluate the significance of emission distribution among the model's vertical layers, along the horizontal plane, and into hourly inputs. Predicted PM2.5 concentrations were highly sensitive to emission injection altitude relative to planetary boundary layer height. Simulations were also responsive to the horizontal allocation of fire emissions and their distribution into single or multiple grid cells. Additionally, modeled concentrations were greatly sensitive to the temporal distribution of fire-related emissions. The analyses demonstrate that, in addition to adequate estimates of emitted mass, successfully modeling the impacts of fires on air quality depends on an accurate spatiotemporal allocation of emissions.}, journal={Science of The Total Environment}, publisher={Elsevier BV}, author={Garcia-Menendez, Fernando and Hu, Yongtao and Odman, Mehmet T.}, year={2014}, month={Sep}, pages={544–553} }
 @inbook{odman_yano_garcia-menendez_hu_goodrick_liu_achtemeier_2013, title={Development and Evaluation of an Air Quality Model for Predicting the Impacts of Prescribed Burns}, ISBN={9789400755765 9789400755772}, ISSN={1874-6519 1874-6543}, url={http://dx.doi.org/10.1007/978-94-007-5577-2_87}, DOI={10.1007/978-94-007-5577-2_87}, booktitle={Air Pollution Modeling and its Application XXII}, publisher={Springer Netherlands}, author={Odman, M. Talat and Yano, Aika and Garcia-Menendez, Fernando and Hu, Yongtao and Goodrick, Scott L. and Liu, Yongqiang and Achtemeier, Gary L.}, year={2013}, month={May}, pages={517–521} }
 @article{odman_hu_garcia-menendez_davis_chang_russell_2013, title={Fires and air}, volume={November}, journal={EM, The Magazine for Environmental Managers}, author={Odman, M. T. and Hu, Y. and Garcia-Menendez, F. and Davis, A. Y. and Chang, M. E. and Russell, A. G.}, year={2013}, pages={12–21} }
 @article{garcia-menendez_hu_odman_2013, title={Simulating smoke transport from wildland fires with a regional-scale air quality model: Sensitivity to uncertain wind fields}, volume={118}, ISSN={2169-897X}, url={http://dx.doi.org/10.1002/jgrd.50524}, DOI={10.1002/jgrd.50524}, abstractNote={Uncertainties associated with meteorological inputs which are propagated through atmospheric chemical transport models may constrain their ability to replicate the effects of wildland fires on air quality. Here, we investigate the sensitivity of predicted fine particulate matter (PM2.5) levels to uncertain wind fields by simulating the air quality impacts of two fires on an urban area with the Community Multiscale Air Quality modeling system (CMAQ). Brute‐force sensitivity analyses show that modeled concentrations at receptors downwind from the fires are highly sensitive to variations in wind speed and direction. Additionally, uncertainty in wind fields produced with the Weather Research and Forecasting model was assessed by evaluating meteorological predictions against surface and upper air observations. Significant differences between predicted and observed wind fields were identified. Simulated PM2.5 concentrations at urban sites displayed large sensitivities to wind perturbations within the error range of meteorological inputs. The analyses demonstrate that normalized errors in CMAQ predictions attempting to model the regional impacts of fires on PM2.5 levels could be as high as 100% due to inaccuracies in wind data. Meteorological drivers may largely account for the considerable discrepancies between monitoring site observations and predicted concentrations. The results of this study demonstrate that limitations in fire‐related air quality simulations cannot be overcome by solely improving emission rates.}, number={12}, journal={Journal of Geophysical Research: Atmospheres}, publisher={American Geophysical Union (AGU)}, author={Garcia-Menendez, Fernando and Hu, Yongtao and Odman, Mehmet Talat}, year={2013}, month={Jun}, pages={6493–6504} }
 @article{garcia-menendez_odman_2011, title={Adaptive Grid Use in Air Quality Modeling}, volume={2}, ISSN={2073-4433}, url={http://dx.doi.org/10.3390/atmos2030484}, DOI={10.3390/atmos2030484}, abstractNote={The predictions from air quality models are subject to many sources of uncertainty; among them, grid resolution has been viewed as one that is limited by the availability of computational resources. A large grid size can lead to unacceptable errors for many pollutants formed via nonlinear chemical reactions. Further, insufficient grid resolution limits the ability to perform accurate exposure assessments. To address this issue in parallel to increasing computational power, modeling techniques that apply finer grids to areas of interest and coarser grids elsewhere have been developed. Techniques using multiple grid sizes are called nested grid or multiscale modeling techniques. These approaches are limited by uncertainty in the placement of finer grids since pertinent locations may not be known a priori, loss in solution accuracy due to grid boundary interface problems, and inability to adjust to changes in grid resolution requirements. A different approach to achieve local resolution involves using dynamic adaptive grids. Various adaptive mesh refinement techniques using structured grids as well as mesh enrichment techniques on unstructured grids have been explored in atmospheric modeling. Recently, some of these techniques have been applied to full blown air quality models. In this paper, adaptive grid methods used in air quality modeling are reviewed and categorized. The advantages and disadvantages of each adaptive grid method are discussed. Recent advances made in air quality simulation owing to the use of adaptive grids are summarized. Relevant connections to adaptive grid modeling in weather and climate modeling are also described.}, number={3}, journal={Atmosphere}, publisher={MDPI AG}, author={Garcia-Menendez, Fernando and Odman, Mehmet Talat}, year={2011}, month={Sep}, pages={484–509} }
 @article{achtemeier_goodrick_liu_garcia-menendez_hu_odman_2011, title={Modeling Smoke Plume-Rise and Dispersion from Southern United States Prescribed Burns with Daysmoke}, volume={2}, ISSN={2073-4433}, url={http://dx.doi.org/10.3390/atmos2030358}, DOI={10.3390/atmos2030358}, abstractNote={We present Daysmoke, an empirical-statistical plume rise and dispersion model for simulating smoke from prescribed burns. Prescribed fires are characterized by complex plume structure including multiple-core updrafts which makes modeling with simple plume models difficult. Daysmoke accounts for plume structure in a three-dimensional veering/sheering atmospheric environment, multiple-core updrafts, and detrainment of particulate matter. The number of empirical coefficients appearing in the model theory is reduced through a sensitivity analysis with the Fourier Amplitude Sensitivity Test (FAST). Daysmoke simulations for "bent-over" plumes compare closely with Briggs theory although the two-thirds law is not explicit in Daysmoke. However, the solutions for the "highly-tilted" plume characterized by weak buoyancy, low initial vertical velocity, and large initial plume diameter depart considerably from Briggs theory. Results from a study of weak plumes from prescribed burns at Fort Benning GA showed simulated ground-level PM2.5 comparing favorably with observations taken within the first eight kilometers of eleven prescribed burns. Daysmoke placed plume tops near the lower end of the range of observed plume tops for six prescribed burns. Daysmoke provides the levels and amounts of smoke injected into regional scale air quality models. Results from CMAQ with and without an adaptive grid are presented.}, number={3}, journal={Atmosphere}, publisher={MDPI AG}, author={Achtemeier, Gary L. and Goodrick, Scott A. and Liu, Yongqiang and Garcia-Menendez, Fernando and Hu, Yongtao and Odman, Mehmet Talat}, year={2011}, month={Aug}, pages={358–388} }
 @article{garcia–menendez_yano_hu_talat odman_2010, title={An adaptive grid version of CMAQ for improving the resolution of plumes}, volume={1}, ISSN={1309-1042}, url={http://dx.doi.org/10.5094/APR.2010.031}, DOI={10.5094/apr.2010.031}, abstractNote={Abstract Atmospheric pollutant plumes are not well resolved in current air quality models due to limitations in grid resolution. Examples of these include power plant and biomass burning plumes. Adequate resolution of these plumes necessitates multiscale air quality modeling at much finer scales than currently employed and we believe that adaptive grids could be the best approach to accurate fine–scale modeling of air pollution dynamics and chemistry. An adaptive grid version of the CMAQ model with all necessary functions for tracking gaseous pollutants and particulate matter has been developed. The model incorporates a dynamic, solution–adaptive grid algorithm and a variable time step algorithm into CMAQ, while retaining the original functionality, concept of modularity, and grid topology. The adaptive model was evaluated by comparing its performance to that of the standard, static grid CMAQ in simulating particulate matter concentrations from a biomass burning air pollution incident affecting a large urban area. The adaptive grid model significantly reduced numerical diffusion, produced better defined plumes, and exhibited closer agreement with monitoring site measurements. The adaptive grid also allows impacts at specified locations to be attributed to a specific pollutant source and provides insight into air pollution dynamics unattainable with a static grid model. Potential applications of adaptive grid modeling need not be limited to air quality simulation, but could be useful in meteorological and climate models as well.}, number={4}, journal={Atmospheric Pollution Research}, publisher={Elsevier BV}, author={Garcia–Menendez, Fernando and Yano, Aika and Hu, Yongtao and Talat Odman, M.}, year={2010}, month={Oct}, pages={239–249} }