@article{rao_2021, title={A fond farewell from the technical Editor-in-Chief}, volume={71}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2021.1997496}, abstractNote={S. Trivikrama Rao, Ph.D.As an active member of the Air & Waste Management Association (A&WMA) for more than four decades, I have contributed to the Association’s activities in advancing science to ...}, number={12}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2021}, month={Dec}, pages={1453–1453} }
 @article{rao_2021, title={JA&WMA recognizes the outstanding efforts of its reviewers and editors}, volume={71}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2020.1853407}, abstractNote={S. Trivikrama Rao, Ph.D The Journal of the Air & Waste Management Association (JA&WMA) continues to rely on rigorous peer review to maintain the high quality and integrity of the research papers pu...}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2021}, month={Jan}, pages={1–2} }
 @article{luo_astitha_rao_hogrefe_mathur_2020, title={Assessing the manageable portion of ground-level ozone in the contiguous United States}, volume={70}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2020.1805375}, abstractNote={Regional air quality models are widely being used to understand the spatial extent and magnitude of the ozone non-attainment problem and to design emission control strategies needed to comply with the relevant ozone standard through direct emission perturbations. In this study, we examine the manageable portion of ground-level ozone using two simulations of the Community Multiscale Air Quality (CMAQ) model for the year 2010 and a probabilistic analysis approach involving 29 years (1990-2018) of historical ozone observations. The modeling results reveal that the reduction in the peak ozone levels from total elimination of anthropogenic emissions within the model domain is around 13-21 ppb for the 90th-100th percentile range of the daily maximum 8-hr ozone concentrations across the contiguous United States (CONUS). Large reductions in the 4th highest 8-hr ozone are seen in the regions of West (interquartile range (IQR) of 17-33%), South (IQR 22-34%), Central (IQR 19-31%), Southeast (IQR 25-34%), and Northeast (IQR 24-37%). However, sites in the western portion of the domain generally show smaller reductions even when all anthropogenic emissions are removed, possibly due to the strong influence of global background ozone, including sources such as intercontinental ozone transport, stratospheric ozone intrusions, wildfires, and biogenic precursor emissions. Probabilistic estimates of the exceedances for several hypothetical thresholds of the 4th highest 8-hr ozone indicate that, in some areas, exceedances of such hypothetical thresholds may occur even with no anthropogenic emissions due to the ever-present atmospheric stochasticity and the current global tropospheric ozone burden. Implications: Because air pollution is intricately linked to adverse health effects, National Ambient Air Quality Standards (NAAQS) have been established for criteria pollutants to safeguard human health and the environment. Areas not in compliance with the relevant standards are required to develop plans and policies to reduce their air pollution levels. Regional-scale air quality models are currently being used routinely to inform policies to identify the emissions reduction required to meet and maintain the NAAQS throughout the country. This paper examines the feasibility of the 4th highest ozone, which is used to derive the ozone design value for NAAQS, complying with various current and hypothetical 8-hr ozone thresholds over CONUS based on the information embedded in 29 years of historical ozone observations and two modeling scenarios with and without anthropogenic emissions loading.}, number={11}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Luo, Huiying and Astitha, Marina and Rao, S. Trivikrama and Hogrefe, Christian and Mathur, Rohit}, year={2020}, month={Nov}, pages={1136–1147} }
 @article{rao_2020, title={JA&WMA dedicates this issue to its outstanding reviewers and editors}, volume={70}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2019.1685287}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2020}, month={Jan}, pages={1–1} }
 @article{rao_luo_astitha_hogrefe_garcia_mathur_2020, title={On the limit to the accuracy of regional-scale air quality models}, volume={20}, ISSN={["1680-7324"]}, DOI={10.5194/acp-20-1627-2020}, abstractNote={Abstract. Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were “perfect”. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.}, number={3}, journal={ATMOSPHERIC CHEMISTRY AND PHYSICS}, author={Rao, S. Trivikrama and Luo, Huiying and Astitha, Marina and Hogrefe, Christian and Garcia, Valerie and Mathur, Rohit}, year={2020}, month={Feb}, pages={1627–1639} }
 @article{luo_astitha_hogrefe_mathur_rao_2019, title={A new method for assessing the efficacy of emission control strategies}, volume={199}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2018.11.010}, abstractNote={Regional-scale air quality models and observations at routine air quality monitoring sites are used to determine attainment/non-attainment of the ozone air quality standard in the United States. In current regulatory applications, a regional-scale air quality model is applied for a base year and a future year with reduced emissions using the same meteorological conditions as those in the base year. Because of the stochastic nature of the atmosphere, the same meteorological conditions would not prevail in the future year. Therefore, we use multi-decadal observations to develop a new method for estimating the confidence bounds for the future ozone design value (based on the 4th highest value in the daily maximum 8-hr ozone concentration time series, DM8HR) for each emission loading scenario along with the probability of the design value exceeding a given ozone threshold concentration at all monitoring sites in the contiguous United States. To this end, we spectrally decompose the observed DM8HR ozone time series covering the period from 1981 to 2014 using the Kolmogorov-Zurbenko (KZ) filter and examine the variability in the relative strengths of the short-term variations (induced by synoptic-scale weather fluctuations; referred to as synoptic component, SY) and the long-term component (dictated by changes in emissions, seasonality and other slow-changing processes such as climate change; referred to as baseline component, BL). Results indicate that combining the projected change in the ozone baseline level with the adjusted synoptic forcing in historical ozone observations enables us to provide a probabilistic assessment of the efficacy of a selected emissions control strategy in complying with the ozone standard in future years. In addition, attainment demonstration is illustrated with a real-world application of the proposed methodology by using air quality model simulations, thereby helping build confidence in the use of regional-scale air quality models for supporting regulatory policies.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Luo, Huiying and Astitha, Marina and Hogrefe, Christian and Mathur, Rohit and Rao, S. Trivikrama}, year={2019}, month={Feb}, pages={233–243} }
 @article{rao_2019, title={JA&WMA dedicates this issue to its exceptional reviewers and editors}, volume={69}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2018.1540239}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2019}, month={Jan}, pages={1–1} }
 @article{rao_2018, title={Message from the Technical Editor-in-Chief: JA&WMA dedicates this issue to its exceptional reviewers and editors}, volume={68}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2017.1398007}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2018}, pages={1–1} }
 @article{porter_rao_hogrefe_mathur_2017, title={A reduced form model for ozone based on two decades of CMAQ simulations for the continental United States}, volume={8}, ISSN={["1309-1042"]}, DOI={10.1016/j.apr.2016.09.005}, abstractNote={A Reduced Form Model (RFM) is a mathematical relationship between the inputs and outputs of an air quality model, permitting estimation of additional modeling without costly new regional-scale simulations. A 21-year Community Multiscale Air Quality (CMAQ) simulation for the continental United States provided the basis for the RFM developed in this study. Predictors included the principal component scores (PCS) of emissions and meteorological variables, while the predictand was the monthly mean of daily maximum 8-hour CMAQ ozone for the ozone season at each model grid. The PCS form an orthogonal basis for RFM inputs. A few PCS incorporate most of the variability of emissions and meteorology, thereby reducing the dimensionality of the source-receptor problem. Stochastic kriging was used to estimate the model. The RFM was used to separate the effects of emissions and meteorology on ozone concentrations. by running the RFM with emissions constant (ozone dependent on meteorology), or constant meteorology (ozone dependent on emissions). Years with ozone-conducive meteorology were identified, and meteorological variables best explaining meteorology-dependent ozone were identified. Meteorology accounted for 19% to 55% of ozone variability in the eastern US, and 39% to 92% in the western US. Temporal trends estimated for original CMAQ ozone data and emission-dependent ozone were mostly negative, but the confidence intervals for emission-dependent ozone are much narrower. Emission-driven changes in monthly mean ozone levels for the period 2000-2010 ranged from 6.4 to 10.9 ppb for the eastern US and from 1.4 to 2.5 ppb for the western US.}, number={2}, journal={ATMOSPHERIC POLLUTION RESEARCH}, author={Porter, P. Steven and Rao, S. T. and Hogrefe, Christian and Mathur, Rohit}, year={2017}, month={Mar}, pages={275–284} }
 @article{astitha_luo_rao_hogrefe_mathur_kumar_2017, title={Dynamic evaluation of two decades of WRF-CMAQ ozone simulations over the contiguous United States}, volume={164}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2017.05.020}, abstractNote={Dynamic evaluation of the fully coupled Weather Research and Forecasting (WRF)- Community Multi-scale Air Quality (CMAQ) model ozone simulations over the contiguous United States (CONUS) using two decades of simulations covering the period from 1990 to 2010 is conducted to assess how well the changes in observed ozone air quality are simulated by the model. The changes induced by variations in meteorology and/or emissions are also evaluated during the same timeframe using spectral decomposition of observed and modeled ozone time series with the aim of identifying the underlying forcing mechanisms that control ozone exceedances and making informed recommendations for the optimal use of regional-scale air quality models. The evaluation is focused on the warm season's (i.e., May-September) daily maximum 8-hr (DM8HR) ozone concentrations, the 4th highest (4th) and average of top 10 DM8HR ozone values (top10), as well as the spectrally-decomposed components of the DM8HR ozone time series using the Kolmogorov-Zurbenko (KZ) filter. Results of the dynamic evaluation are presented for six regions in the U.S., consistent with the National Oceanic and Atmospheric Administration (NOAA) climatic regions. During the earlier 11-yr period (1990-2000), the simulated and observed trends are not statistically significant. During the more recent 2000-2010 period, all trends are statistically significant and WRF-CMAQ captures the observed trend in most regions. Given large number of sites for the 2000-2010 period, the model captures the observed trends in the Southwest (SW) and MW but has significantly different trend from that seen in observations for the other regions. Observational analysis reveals that it is the long-term forcing that dictates how high the ozone exceedances will be; there is a strong linear relationship between the long-term forcing and the 4th highest or the average of the top10 ozone concentrations in both observations and model output. This finding indicates that improving the model's ability to reproduce the long-term component will also enable better simulation of ozone extreme values that are of interest to regulatory agencies.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Astitha, Marina and Luo, Huiying and Rao, S. Trivikrama and Hogrefe, Christian and Mathur, Rohit and Kumar, Naresh}, year={2017}, month={Sep}, pages={102–116} }
 @article{rao_2017, title={Message from the Technical Editor-in-Chief: JA&WMA dedicates this issue to its exceptional reviewers and editors}, volume={67}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2016.1258909}, number={2}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2017}, month={Feb}, pages={127–127} }
 @article{astitha_yang_luo_rao_2016, title={Inherent Uncertainties in Atmospheric Models: Weather and Air Pollution}, ISBN={["978-3-319-24476-1"]}, DOI={10.1007/978-3-319-24478-5_82}, abstractNote={It is well known that there are reducible and irreducible uncertainties in both uncoupled and coupled meteorology-atmospheric chemistry models. Reducible (i.e., structural and parametric) uncertainties are attributable to our incomplete or inadequate understanding of the relevant atmospheric processes (e.g. chemical mechanism, PBL evolution, modeling domain, grid resolution, cloud treatment) and errors in model input data (e.g., emissions, boundary conditions). Inherent or irreducible uncertainties stem from our inability to properly characterize the atmosphere with appropriate initial conditions. When the initial state of the atmosphere is unknown, its future state cannot be predicted with great accuracy. There is an emerging need to properly assess these types of modeling uncertainties in order to improve the prediction accuracy of modeling systems. This work focuses on the assessment of inherent uncertainties in atmospheric and air quality modeling systems by estimating the impacts of various options for initial conditions on weather parameters and their consequent effect on atmospheric pollutant concentrations. Support for the modeling efforts is given by data collected from surface measurement networks for the meteorological and air quality parameters. We focus on the changes in atmospheric variables that strongly affect the fate and transport of air pollutants like ozone and aerosols.}, journal={AIR POLLUTION MODELING AND ITS APPLICATION XXIV}, author={Astitha, Marina and Yang, Jaemo and Luo, Huiying and Rao, S. T.}, year={2016}, pages={513–518} }
 @article{dash_singh_rao_2016, title={International Workshop on Air Pollution, Climate Change, Human Health, and Extreme Weather}, ISBN={["978-3-319-24476-1"]}, DOI={10.1007/978-3-319-24478-5_32}, abstractNote={Despite the substantial progress in addressing air quality problems, air pollution is still a serious concern in the developing and developed countries. There is now scientific consensus that atmospheric loading of greenhouse gases has been contributing to climate change. The 2014 IPCC report reiterated the need to address climate change on a global basis. It is also recognized that atmospheric composition can profoundly influence weather and climate directly by changing the atmospheric radiation budget or indirectly by affecting cloud formation and precipitation. Given the ever increasing computational power and ground and satellite-based observations, it is feasible to conduct observational and modeling investigations to improve our understanding of the role of aerosols on the monsoon dynamics and extreme events under changing climate. To address the above mentioned challenges, a three-day workshop was held during January 12–15, 2015 in Delhi, India bringing together scientists in air quality, weather, and climate fields from India, Europe, Japan, and North America to discuss the current state-of-science, identify research gaps, and prepare a research agenda to help improve our understanding of air quality and climate change interactions, and operationalize atmospheric modeling methods to better forecast the monsoon dynamics. Following the workshop, a small team of scientists from India, USA, Canada, and Europe met for a day to prepare an action plan for implementing recommendations of the workshop. It is envisioned that lead scientists identified from different countries will coordinate this research effort. This paper presents a summary of the recommendations made by the workshop participants and actions being taken at the national and international levels.}, journal={AIR POLLUTION MODELING AND ITS APPLICATION XXIV}, author={Dash, Sushil K. and Singh, Mahendra P. and Rao, S. Trivikrama}, year={2016}, pages={195–199} }
 @article{porter_rao_hogrefe_gego_mathur_2016, title={Metamodels for Ozone: Comparison of Three Estimation Techniques}, ISBN={["978-3-319-24476-1"]}, DOI={10.1007/978-3-319-24478-5_86}, abstractNote={A metamodel for ozone is a mathematical relationship between the inputs and outputs of an air quality modeling experiment, permitting calculation of outputs for scenarios of interest without having to run the model again. In this study we compare three metamodel estimation techniques applied to an 18 year long CMAQ simulation covering the Northeastern US (NEUS). The estimation methods considered here include projection onto latent structures, stochastic kriging and a combination of principal components and stochastic kriging.}, journal={AIR POLLUTION MODELING AND ITS APPLICATION XXIV}, author={Porter, P. Steven and Rao, S. T. and Hogrefe, Christian and Gego, Edith and Mathur, Rohit}, year={2016}, pages={537–542} }
 @article{gilliam_hogrefe_godowitch_napelenok_mathur_rao_2015, title={Impact of inherent meteorology uncertainty on air quality model predictions}, volume={120}, ISSN={["2169-8996"]}, DOI={10.1002/2015jd023674}, abstractNote={Abstract It is well established that there are a number of different classifications and sources of uncertainties in environmental modeling systems. Air quality models rely on two key inputs, namely, meteorology and emissions. When using air quality models for decision making, it is important to understand how uncertainties in these inputs affect the simulated concentrations. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. Most studies explore this uncertainty by running different meteorological models or the same model with different physics options and in some cases combinations of different meteorological and air quality models. While these have been shown to be useful techniques in some cases, we present a technique that leverages the initial condition perturbations of a weather forecast ensemble, namely, the Short‐Range Ensemble Forecast system to drive the four‐dimensional data assimilation in the Weather Research and Forecasting (WRF)‐Community Multiscale Air Quality (CMAQ) model with a key focus being the response of ozone chemistry and transport. Results confirm that a sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, can cause a relatively large range of ozone‐mixing ratios. Pollutant transport can be altered by hundreds of kilometers over several days. Ozone‐mixing ratios of the ensemble can vary as much as 10–20 ppb or 20–30% in areas that typically have higher pollution levels.}, number={23}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, author={Gilliam, Robert C. and Hogrefe, Christian and Godowitch, James M. and Napelenok, Sergey and Mathur, Rohit and Rao, S. Trivikrama}, year={2015}, month={Dec} }
 @article{rao_2015, title={Introduction to JA&WMA Special Issue on Air Quality and Human Health}, volume={65}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2015.1032793}, abstractNote={S. Trivikrama Rao, Ph.D.As a result of the U.S. Clean Air Act, great progress has been made in improving the nation’s air quality over the past five decades. It has been demonstrated that this impr...}, number={5}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2015}, pages={515–515} }
 @article{porter_rao_hogrefe_gego_mathur_2015, title={Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments}, volume={112}, ISSN={["1873-2844"]}, DOI={10.1016/j.atmosenv.2015.04.039}, abstractNote={In the United States, regional-scale photochemical models are being used to design emission control strategies needed to meet the relevant National Ambient Air Quality Standards (NAAQS) within the framework of the attainment demonstration process. Previous studies have shown that the current generation of regional photochemical models can have large biases and errors in simulating absolute levels of pollutant concentrations. Studies have also revealed that regional air quality models were not always accurately reproducing even the relative changes in ozone air quality stemming from changes in emissions. This paper introduces four approaches to adjust for model bias and errors in order to provide greater confidence for their use in estimating future concentrations as well as using modeled pollutant concentrations in exposure assessments. The four methods considered here are a mean and variance (MV) adjustment, temporal component decomposition (TC) adjustment of modeled concentrations, and two variants of cumulative distribution function (CDF) mapping. These methods were compared against each other as well as against unadjusted model concentrations and a version of the relative response approach based on unadjusted model predictions. The analysis uses ozone concentrations simulated by the Community Multiscale Air Quality (CMAQ) model for the northeastern United States domain for the years 1996–2005. Ensuring that base case conditions are adequately represented through the combined use of observations and model simulations is shown to result in improved estimates of future air quality under changing emissions and meteorological conditions.}, journal={ATMOSPHERIC ENVIRONMENT}, author={Porter, P. Steven and Rao, S. Trivikrama and Hogrefe, Christian and Gego, Edith and Mathur, Rohit}, year={2015}, month={Jul}, pages={178–188} }
 @article{rao_2015, title={Untitled}, volume={65}, number={1}, journal={Journal of the Air & Waste Management Association}, author={Rao, S. T.}, year={2015}, pages={1–1} }
 @article{pleim_mathur_rao_fast_baklanov_2014, title={INTEGRATED METEOROLOGY AND CHEMISTRY MODELING Evaluation and Research Needs}, volume={95}, ISSN={["1520-0477"]}, DOI={10.1175/bams-d-13-00107.1}, abstractNote={This is a conference summary report that will be published in the Bulletin of the American Meteorological Society.}, number={4}, journal={BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY}, author={Pleim, Jonathan and Mathur, Rohit and Rao, S. T. and Fast, Jerome and Baklanov, Alexander}, year={2014}, month={Apr}, pages={ES81–ES84} }
 @article{rao_2014, title={Untitled}, volume={64}, ISSN={["2162-2906"]}, DOI={10.1080/10962247.2014.868213}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2014}, month={Jan}, pages={1–1} }
 @article{rao_2013, title={Message from the Technical Editor-in-Chief}, volume={63}, ISSN={["1096-2247"]}, DOI={10.1080/10962247.2013.755413}, number={1}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2013}, month={Jan}, pages={1–1} }
 @article{rao_2012, title={Untitled}, volume={62}, ISSN={["1096-2247"]}, DOI={10.1080/10962247.2012.726911}, number={10}, journal={JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION}, author={Rao, S. Trivikrama}, year={2012}, pages={1115–1115} }