Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields

We developed and applied a machine-learned discretization for one-dimensional (1-D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accuracy, three-stencil numerical solver, but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically-derived constants. We downsampled higher-resolution simulation results -- resulting in up to 16$\times$ larger grid size and 64$\times$ larger timestep -- and trained our neural network-based scheme to match the downsampled integration data. In this way, we created an operator that is low-resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1-D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500$\times$ acceleration) over the high-resolution simulation than the learned solver is able to (18$\times$ acceleration). Surprisingly, our learned 1-D scheme -- when combined with a splitting technique -- can be used to predict 2-D advection, and is in some cases more stable and accurate than the low-resolution reference solver in 2-D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution

Learned 1-D advection solver to accelerate air quality modeling

Accelerating the numerical integration of partial differential equations by learned surrogate model is a promising area of inquiry in the field of air pollution modeling. Most previous efforts in this field have been made on learned chemical operators though machine-learned fluid dynamics has been a more blooming area in machine learning community. Here we show the first trial on accelerating advection operator in the domain of air quality model using a realistic wind velocity dataset. We designed a convolutional neural network-based solver giving coefficients to integrate the advection equation. We generated a training dataset using a 2nd order Van Leer type scheme with the 10-day east-west components of wind data on 39$^{\circ}$N within North America. The trained model with coarse-graining showed good accuracy overall, but instability occurred in a few cases. Our approach achieved up to 12.5$\times$ acceleration. The learned schemes also showed fair results in generalization tests.Comment: Accepted as a workshop paper at the The Symbiosis of Deep Learning and Differential Equations (DLDE) - II in the 36th Conference on Neural Information Processing Systems (NeurIPS 2022

Mortality risks of PM2.5 emissions from electric vehicles and Tier 3 conventional vehicles

Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd.Light-duty transportation continues to be a significant source of air pollutants that cause premature mortality and greenhouse gases (GHGs) that lead to climate change. We assess PM2.5 emissions and its health consequences under a large-scale shift to electric vehicles (EVs) or Tier-3 internal combustion vehicles (ICVs) across the United States, focusing on implications by states and for the fifty most populous metropolitan statistical areas (MSA). We find that both Tier-3 ICVs and EVs reduce premature mortality by 80%-93% compared to the current light-duty vehicle fleet. The health and climate mitigation benefits of electrification are larger in the West and Northeast. As the grid decarbonizes further, EVs will yield even higher benefits from reduced air pollution and GHG emissions than gasoline vehicles. EVs lead to lower health damages in almost all the 50 most populous MSA than Tier-3 ICVs. Distributional analysis suggests that relying on the current gasoline fleet or moving to Tier-3 ICVs would impact people of color more than White Americans across all states, levels of urbanization, and household income, suggesting that vehicle electrification is more suited to reduce health disparities. We also simulate EVs under a future cleaner electric grid by assuming that the 50 power plants across the nation that have the highest amount of annual SO2 emissions are retired or retrofitted with carbon capture and storage, finding that in that case, vehicle electrification becomes the best strategy for reducing health damages from air pollution across all states.publishe

Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics

Modeling atmospheric chemistry is computationally expensive and limits the widespread use of atmospheric chemical transport models. This computational cost arises from solving high-dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long-term simulations. This may be because previous ML-based efforts have relied on explicit Euler time integration -- which is known to be unstable for stiff systems -- and have used neural networks which are prone to overfitting. We hypothesize that the creation of parsimonious models combined with modern numerical integration techniques can overcome this limitation. Using a small-scale photochemical mechanism to explore the potential of these methods, we have created a machine-learned surrogate by (1) reducing dimensionality using singular value decomposition to create an interpretably-compressed low-dimensional latent space, and (2) using Sparse Identification of Nonlinear Dynamics (SINDy) to create a differential-equation-based representation of the underlying chemical dynamics in the compressed latent space with reduced numerical stiffness. The root mean square error of the ML model prediction for ozone concentration over nine days is 37.8% of the root mean concentration across all simulations in our testing dataset. The surrogate model is 11$\times$ faster with 12$\times$ fewer integration timesteps compared to the reference model and is numerically stable in all tested simulations. Overall, we find that SINDy can be used to create fast, stable, and accurate surrogates of a simple photochemical mechanism. In future work, we will explore the application of this method to more detailed mechanisms and their use in large-scale simulations

Ancillary health effects of climate mitigation scenarios as drivers of policy uptake: a review of air quality, transportation and diet co-benefits modeling studies

Background: Significant mitigation efforts beyond the Nationally Determined Commitments (NDCs) coming out of the 2015 Paris Climate Agreement are required to avoid warming of 2 °C above pre-industrial temperatures. Health co-benefits represent selected near term, positive consequences of climate policies that can offset mitigation costs in the short term before the beneficial impacts of those policies on the magnitude of climate change are evident. The diversity of approaches to modeling mitigation options and their health effects inhibits meta-analyses and syntheses of results useful in policy-making. Methods/Design: We evaluated the range of methods and choices in modeling health co-benefits of climate mitigation to identify opportunities for increased consistency and collaboration that could better inform policy-making. We reviewed studies quantifying the health co-benefits of climate change mitigation related to air quality, transportation, and diet published since the 2009 Lancet Commission 'Managing the health effects of climate change' through January 2017. We documented approaches, methods, scenarios, health-related exposures, and health outcomes. Results/Synthesis: Forty-two studies met the inclusion criteria. Air quality, transportation, and diet scenarios ranged from specific policy proposals to hypothetical scenarios, and from global recommendations to stakeholder-informed local guidance. Geographic and temporal scope as well as validity of scenarios determined policy relevance. More recent studies tended to use more sophisticated methods to address complexity in the relevant policy system. Discussion: Most studies indicated significant, nearer term, local ancillary health benefits providing impetus for policy uptake and net cost savings. However, studies were more suited to describing the interaction of climate policy and health and the magnitude of potential outcomes than to providing specific accurate estimates of health co-benefits. Modeling the health co-benefits of climate policy provides policy-relevant information when the scenarios are reasonable, relevant, and thorough, and the model adequately addresses complexity. Greater consistency in selected modeling choices across the health co-benefits of climate mitigation research would facilitate evaluation of mitigation options particularly as they apply to the NDCs and promote policy uptake

City-scale analysis of annual ambient PM2.5 source contributions with the InMAP reduced-complexity air quality model: a case study of Madison, Wisconsin

Air pollution is highly variable, such that source contributions to air pollution can vary even within a single city. However, few tools exist to support city-scale air quality analyses, including impacts of energy system changes. We present a methodology that utilizes regional ground-based monitor measurements to scale speciation data from the Intervention Model for Air Pollution (InMAP), a national-scale reduced-complexity model. InMAP, like all air quality models, has biases in its concentration estimates; these biases may be pronounced when examining a single city. We apply the bias correction methodology to Madison, Wisconsin and estimate the relative contributions of sources to annual-average fine particulate matter (PM _2.5 ), as well as the impacts of coal power plant retirements and electric vehicle (EV) adoption. We find that the largest contributors to ambient PM _2.5 concentrations in Madison are on-road transportation, contributing 21% of total PM _2.5 ; non-point sources, 16%; and electricity generating units, 14%. State-wide coal power plant closures from 2014 to 2020 and planned closures through 2025 were modeled to assess air quality benefits. The largest relative reductions are seen in areas north of Milwaukee (up to 7%), though population-weighted PM _2.5 was reduced by only 3.8% across the state. EV adoption scenarios lead to a relative reduction in PM _2.5 over Madison of 0.5% to 13.7% or a 9.3% reduction in total PM _2.5 from a total replacement of light-duty vehicles (LDVs) with EVs. Similar percent reductions are calculated for population-weighted concentrations over Madison. Replacing 100% of LDVs with EVs reduced CO _2 emissions by over 50%, highlighting the potential benefits of EVs to both climate and air quality. This work illustrates the potential of combining data from models and monitors to inform city-scale air quality analyses, supporting local decision-makers working to reduce air pollution and improve public health

Data and visualizations of air quality impacts of conventional and alternative light-duty transportation in the United States

The repository includes five main files: DatasetS1.xls: A directory of Microsoft Excel files containing emissions amounts disaggregated by life cycle stage for each scenario. DatasetS2.pdf: Maps of annual average ground level concentrations of PM2.5, O3, PM10, NOx, HCHO, NH3, particulate SO4, particulate NH4, particulate NO3, organic aerosol, elemental carbon aerosol, particle number, and CO; maps of annual average daily peak O3 concentrations; and maps of PM2.5 and O3 concentrations animated by month of year, day of week, and hour of day for the baseline simulation and each scenario. A pdf viewer that allows embedded javascript, such as Adobe Acrobat, is required to view the animations. VideoS1.mp4: A video showing temporal variation in PM2.5 concentrations attributable to each scenario. VideoS2.mp4: A video showing temporal variation in O3 concentrations attributable to each scenario. PublicationFigures.pdf: A PDF containing Figures 2 and 3, along with accompanying data, from the related publication (dx.doi.org/10.1073/pnas.1406853111).This is the supporting information for an article entitled "Life cycle air quality impacts of conventional and alternative light-duty transportation in the United States", published in the Proceedings of the National Academy of Sciences of the United States (dx.doi.org/10.1073/pnas.1406853111). The study assesses the life cycle air quality impacts on human health of 10 alternatives to conventional gasoline vehicles, including vehicles powered by diesel, natural gas, ethanol, and electricity. This supporting information is comprised of 1) A Microsoft Excel file containing emissions amounts disaggregated by life cycle stage for each scenario; 2) maps of ground-level concentrations of 13 different air pollutants attributable to each scenario; and 3) videos showing temporal variation in ground-level fine particulate matter (PM2.5) and ozone (O3) concentrations attributable to each scenario. The data here were generated using state-of-the-science air pollutant emission and transport models.University of Minnesota Institute on the Environment Initiative for Renewable Energy and the Environment (Grants No. Rl-0026-09 and RO-0002-11). U.S. Department of Energy (Award No. DE-EE0004397). Computational resources provided by the Minnesota Supercomputing Institute and the Department of Energy National Center for Computational Sciences.Tessum, Christopher W; Hill, Jason D; Marshall, Julian D. (2014). Data and visualizations of air quality impacts of conventional and alternative light-duty transportation in the United States. Retrieved from the University Digital Conservancy, http://dx.doi.org/10.13020/D6159V

Environmental health, racial/ethnic health-disparity, and climate impacts of freight transport in the United States

Atmospheric emissions from freight transportation contribute to human health and climate damage. Here, we quantify and compare three environmental impacts from inter-regional freight transportation in the contiguous United States: mortality attributable to PM2.5 air pollution, racial-ethnic disparities in mortality from PM2.5, and CO2 emissions. We compare all major transportation modes (truck, rail, barge, aircraft) and all major inter-regional routes (~30,000 routes). Our study is the first to comprehensively compare each route separately, and the first to explore racial-ethnic exposure disparities by route and mode, nationally. Impacts (health, health-disparity, climate) per tonne of freight are largest for aircraft. Among non-aircraft modes, per tonne, rail has the largest health and health-disparity impacts and the lowest climate impacts, whereas truck transport has the lowest health impacts and greatest climate impacts – an important reminder that health and climate impacts are often but not always aligned. For aircraft and truck, average monetized damages per tonne are larger for climate impacts than for PM2.5 air pollution; for rail and barge, the reverse holds. We find that average exposures for inter-regional truck and rail are the highest for White non-Hispanic people, from barge is highest for Black people, and from aircraft is highest for people who are mixed/other race. Level of exposure and disparity among racial-ethnic groups vary in urban and rural areas. This research can be used to inform, for a given inter-regional origin and destination, which freight mode offers the lowest environmental health, health-disparity, and climate impacts