The quality of the air we breathe is among the biggest factors influencing human health and well-being worldwide. Air pollution—from fossil fuel combustion in the power, industrial, residential, and transportation sectors as well as from wildfires, agricultural practices, and many other sources—interacts with the climate, exacerbates countless medical conditions, and is estimated to cause millions of deaths annually.
Safeguarding public health and combating climate change depend on robust impact assessments and effective policymaking with respect to air quality.
Safeguarding public health and combating climate change depend on robust impact assessments and effective policymaking with respect to air quality, which in turn require an accurate understanding of air composition and the processes that control how and why it changes.
For decades, scientists have applied multiscale modeling approaches informed by observations to help advance the understanding and predictability of air quality and its interactions with the climate. Such investigations are becoming more in-depth, more capable of revealing details of the processes at play, and more accessible to broader communities.
Key to the continued success of such approaches is that the models used keep up with advances in scientific knowledge, modeling techniques, and the capabilities of Earth-observing systems and that the valuable features of the models are well recognized by the scientists and decisionmakers who interpret their results.
Here we look back on the history of one such influential model, the pioneering Sulfur Transport and Deposition Model (STEM), and we look forward to how it can keep revealing more about air quality, climate, and other societally important issues.
Sulfur Model Shows the Way
Development of the Sulfur Transport and Deposition Model (STEM) started in 1976. At the time, fossil fuel consumption was setting records, and effective technologies for scrubbing sulfur species from emissions were not widely implemented.
Development of STEM started in 1976, much earlier than many other contemporary air quality and atmospheric chemistry models. At the time, fossil fuel consumption was setting records, and effective technologies for scrubbing sulfur species from emissions were not widely implemented. The resulting atmospheric sulfur pollution and acid rain caused increasingly negative impacts on humans, infrastructure, and ecosystems. These impacts also raised awareness that air pollution was not just a local problem: Emissions from one place could affect other places hundreds and even thousands of kilometers downwind. STEM was initially designed to help understand the transport, transformation, and removal processes of atmospheric sulfur and to inform emissions reduction strategies [Carmichael and Peters, 1984].
Since its creation, STEM has evolved to cover a broader set of key air pollutants, such as ozone and particulate matter, which also cause regional and even intercontinental problems [Carmichael et al., 1991; Huang et al., 2017]. Coupled with widely used meteorological models, including multiple versions of the Weather Research and Forecasting and MM5 models, STEM has been adopted to study air quality in the United States and regions in the Arctic, Asia, and South America, contributing to hundreds of peer-reviewed publications. These studies have highlighted the usefulness of such models for addressing a wide range of scientific questions related to the atmospheric distributions of chemicals, quantification of these chemicals’ environmental impacts, and evaluation of environmental mitigation strategies.
By answering these science questions, STEM also has demonstrated the capability of models to support the design and deployment of airborne and ground-based field experiments, many of which have been led by NASA or other agencies. For example, during the 2001 NASA Transport and Chemical Evolution over the Pacific (TRACE-P) experiment, based out of Hong Kong, STEM forecasts of pollution outflow regions over the Pacific Ocean were used daily to help plan locations for aircraft sampling of air pollutants (Figure 1). Applying STEM in the field by design also enabled its use in interpreting and providing context for collected observations, which in turn helped improve the model [Carmichael et al., 2003]. The use of air quality models in such forecast applications has since expanded dramatically, and today these models are used operationally at urban, national, and global scales.
STEM has further served scientific communities by advancing satellite missions, such as Aura. It has supported novel applications of the main data products from Aura’s onboard sensors [e.g., Huang et al., 2015], for example, and stimulated development and evaluation of new retrieval products. And it has contributed to international multimodel intercomparison experiments, including the Model Inter-Comparison Study for Asia [Carmichael et al., 2002] and the Task Force on Hemispheric Transport of Air Pollution [Huang et al., 2017]. These experiments helped provide more robust estimates of air pollution source-receptor relationships and environmental impacts, and they helped scientists better understand the strengths and weaknesses of the various models compared.
Simplicity, Stability, and Flexibility
The long history and wide applications of STEM have helped shape its main technical strengths.
The long history and wide applications of STEM have helped shape its main technical strengths. These strengths include its structural simplicity, which allows users to make changes to it easily, and its stability, which is built upon decades of tuning with field and laboratory measurements and other information from past applications. In addition, it features flexible tools to generate chemical boundary conditions and other key inputs as well as advanced data assimilation capability. Of particular note is the development of the Kinetic PreProcessor software, which allowed for easily changing the chemical mechanisms used in STEM (and other models) and improved its ability to assimilate observational data [Sandu et al., 2005].
These features have enabled researchers using the model to make notable policy-relevant scientific contributions in both the air quality and climate communities and to train dozens of students and early-career scientists globally. Such contributions include using real-world observations to derive surface-atmosphere fluxes of short- and long-lived climate forcers and their precursors—such as nitrogen oxides, carbon dioxide (CO2), carbonyl sulfide (COS), and toxic airborne materials including mercury compounds—and to understand the factors controlling them [e.g., Campbell et al., 2008].
STEM also has helped in attributing the effects of aerosols on air quality and climate to emissions from different source sectors (e.g., energy production, industrial processes, transportation, residential sources, and wildfires) and in identifying sources of air pollution as atmospheric circulation and emissions patterns shift under the changing climate [e.g., Ramanathan and Carmichael, 2008; Huang et al., 2012, 2015, 2017].
These studies based on the STEM model have inspired subsequent research and discussions about new applications. For example, they stimulated discussions on using COS as a proxy for gross primary productivity because of the link between plants’ uptake of COS and CO2, which further led to developing and applying satellite-based data products to track COS.
Meanwhile, researchers are continuing to develop methods to reduce uncertainties in estimating how aerosol and greenhouse gas emissions from different regions and source sectors affect health and climate. In part, these efforts involve integrating Earth observations across multiple disciplines to improve process-level constraints used in making these estimates. Observation-driven analyses are also being advanced to improve estimates of background air pollution levels, information that is relevant for updating air quality standards as the climate changes.
Keeping Current to Stay Relevant
As productive as STEM has been through its history, updating it is necessary to ensure that it remains a robust option for air quality and climate scientists to study our environment and to inform policymaking.
Maintaining and enhancing the diversity of models available for air quality science and management are important because significant uncertainties remain in the predictions (and their implications) from individual models. In addition, as productive as STEM has been through its history, updating it (and similar models) is necessary to ensure that it remains a robust option for air quality and climate scientists to study our environment and to inform policymaking and the design of atmospheric observing systems.
Major updates to STEM are ongoing to improve how the model interfaces with up-to-date and next-generation meteorological and land surface modeling systems. These systems include, for example, the Model for Prediction Across Scales, which can be run at variable resolutions, and the Noah-Multiparameterization land surface model, which can simulate vegetation dynamics under many configurations. Such updates can facilitate improved representations of processes like atmospheric deposition. STEM also will be more closely integrated with regional and global climate models, and it will be applied at progressively finer, satellite-resolved scales of several kilometers or less.
We continue to collect community input on goals and updates for STEM, and suggestions are always welcome. Ideally, STEM (and other chemical transport models) should be adapted to perform on new generations of high-performance supercomputing systems, perhaps in combination with commercial cloud computing, artificial intelligence and machine learning, and other technologies.
Additional updates could include capabilities to provide detailed documentation of model elements, features, and steps to execute it, as well as polished postprocessing and visualization routines to help improve the interpretability and communication of model results. Further, models could be updated to support crowdsourced science projects, by integrating data from or feeding data to them, and the design and application of low-cost spaceborne and surface observing systems. User communities should be able to access and contribute easily to synthesized and modernized models and their associated documentation from centralized, open-access platforms.
These updates would allow models like STEM to be implemented over wider spatial and time scales than previously possible and to support studies of emerging topics, such as those related to environmental justice. Simulations of regional to local air quality as well as of its contributing factors, combined with socioeconomic data, could clarify the picture of environmental inequity across an area of interest and help evaluate potential solutions involving migration, relocation, or urban or ecosystem adaptations.
For decades, the STEM model has proven its value by contributing insights into air quality, climate, and the environment to help shape scientific understanding, science-based policymaking, and software design. With the ongoing and envisioned updates, the model’s usefulness for these purposes will continue for many years to come, and its relevance for new applications will grow.
Acknowledgments
The authors acknowledge the following people (in alphabetical order) who also have contributed to the developments and applications of the STEM model: Maryam Abdi-Oskouei, Bhupesh Adhikary, Richard Arndt, Giuseppe Calori, Elliott Campbell, Tianfeng Chai, Young-Soo Chang, Yafang Cheng, Seog-Yeon Cho, Shin-Woo Chul, Kevin Crist, Dacian Daescu, Alessio D’Allura, Valeriu Damian, Meng Gao, Sarath Guttikunda, Amir Hakami, Hiroshi Hayami, Shan He, Daven Henze, Min-Sun Hong, Aditsuda Jamroensan, Kuruvilla John, Toshihiro Kitada, Rao Kotamarthi, Sarika Kulkarni, Gakuji Kurata, Pallavi Marrapu, Marcelo Mena, Li Pan, Leonard Peters, Mahesh Phadnis, Florian Potra, Pablo Saide, Adrian Sandu, John Seinfeld, Shang-Gyoo Shim, Negin Sobhani, Chul Han Song, Scott Spak, David Streets, Young Sunwoo, Youhua Tang, Narisara Thongboonchoo, Itsushi Uno, Xuemei Wang, Zifa Wang, Chao Wei, Jung-Hun Woo, Hui Xiao, Yiwen Xu, and Yang Zhang.
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Author Information
Min Huang (mhuang8585@gmail.com), University of Maryland, College Park; also at NASA Ames Research Center, Mountain View, Calif.; Gregory Carmichael, University of Iowa, Iowa City; and Kevin Bowman, Jet Propulsion Laboratory, California Institute of Technology, Pasadena