The Goddard Earth Observing System Composition Forecast (GEOS-CF) is a global atmospheric constituent forecasting system created and operated by the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center. In alignment with the NASA Earth Science to Action Strategy, GEOS-CF forecasts support a variety of research and practical applications, including NASA satellite missions, aircraft campaigns and instrument teams, local and regional air quality forecasters, and public and occupational health experts investigating air pollutant exposure. In alignment with the NASA Open-Source Science Initiative, GEOS-CF supports several data access methods, thereby serving a range of users with varying technical capabilities. This paper surveys several case studies of applications making use of GEOS-CF, investigating how the data were accessed and examining the perceived benefits and limitations of GEOS-CF in each case. The global coverage and data availability of multiconstituent information at a relatively low data latency was broadly perceived as the strength of GEOS-CF, while cited weaknesses included the relatively coarse spatial resolution for local-scale applications, lack of outputs of interest to specific applications, and uncertain data quality for regions and outputs with limited validation observations. Our goal is to present these examples and lessons learned to the broader community both as illustrations of NASA's Earth Science to Action Strategy and Open-Source Science Initiative and to inform future developments of GEOS-CF and similar systems, with the aim of improving the free and open provision of actionable Earth science information for societal benefit.

High-resolution PM2.5 forecasts are increasingly produced with machine-learning models, yet practical guidance on how often these models should be retrained and validated remains limited. This study quantifies the impact of retraining frequency and bias correction on air-quality prediction skill across multiple cities with contrasting emission sources and meteorological regimes, using The Goddard Earth Observing System composition forecast (GEOS-CF) fused with in-situ observations. Site-specific models are trained on at least two years of hourly data, with bias correction and alternative update schedules (6-18-month baselines and 6-12-month retraining cycles) evaluated using RMSE, R2, and SHAP-based feature importance. Bias-corrected models consistently improves GEOS-CF forecasts by more than 107% in R2 and reduces RMSE by over 75%, with annual retraining providing the largest gains (13% increase in R2, 12% reduction in RMSE) relative to more frequent updates. SHAP analysis shows that the dominant predictors and their relative importance vary by city, with combinations of boundary-layer height, aerosol optical depth, humidity, wind, and nitrogen oxides driving PM2.5 levels, demonstrating that a single global pre-trained model is inadequate and that locally tuned models are required. Together, these results define minimum data requirements, preferred retraining intervals, and the need for site-specific bias-corrected models, offering concrete design rules for operational PM2.5 forecasting systems.

Abstract Many of today's most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step‐change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth‐observing satellites. This paper is the first comprehensive description of the design and implementation of NASA's Constituent Data Assimilation System (CoDAS), a software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), an integrated collection of Earth system models. CoDAS is a generalized, tracer‐agnostic system that extends previous capabilities to assimilate multiple species from multiple sensors configurable at run time. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model‐data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.

Abstract Chemical cycling drives the production and loss of many important atmospheric constituents. The speed of atmospheric chemical cycling is a particularly valuable indicator for characterizing and measuring the effects of such cycles on oxidant chemistry, air quality, and climate. Here, we apply graph theoretical methods to explicitly quantify and analyze the characteristic timescales of gas‐phase chemical cycles in the troposphere and stratosphere, as simulated by the GEOS‐Chem chemical mechanism. We identify all two‐, three‐, and four‐reaction cycles in the mechanism and calculate a characteristic timescale for each individual cycle. We find that the speed of chemical cycling varies by orders of magnitude at any given location but tends to be faster in urban‐ and biogenically‐dominated chemical regions, and slower during the night. We further quantify the fraction of cycling that contains a rate‐determining step, and explicitly demonstrate the large potential for mechanisms to recycle oxidants like OH.

We introduce the optimized dynamic mode decomposition (DMD) algorithm for constructing an adaptive and computationally efficient reduced-order model and forecasting tool for global atmospheric chemistry dynamics. By exploiting a low-dimensional set of global spatio-temporal modes, interpretable characterizations of the underlying spatial and temporal scales can be computed. Forecasting is also achieved with a linear model that uses a linear superposition of the dominant spatio-temporal features. The DMD method is demonstrated on 3 months of global chemistry dynamics data, showing its significant performance in terms of computational speed and interpretability. We show that the presented decomposition method successfully extracts and forecasts chemical patterns for leading chemical indicators, including nitric oxide, ozone, nitrogen dioxide, hydroxyl radical, isoprene, and carbon monoxide. Moreover, the DMD algorithm allows for rapid reconstruction of the underlying linear model, which can then easily accommodate non-stationary data and changes in the dynamics.

As poor air quality continues to pose threats to humanity, better modeling of atmospheric composition is critical both for environmental and climate monitoring. The NASA Goddard Earth Observing System Composition Forecast (GEOS-CF) provides daily global air quality forecasts of atmospheric composition. Due to the full chemistry mechanisms and transport of hundreds of chemical tracers, GEOS-CF forecasts cannot extend beyond 5 days, and probabilistic estimates of tracer concentrations are computationally infeasible to calculate. Probabilistic estimates are critical in quantifying the forecast uncertainty and are achieved by generating an ensemble of models with slightly different initializations. We describe a 2-yr study to build a deep learning ensemble emulator. In this study, we describe why models such as NVIDIA's FourCastNet are inadequate for learning atmospheric composition and can experience instabilities during training. We share competitive forecast skill for carbon monoxide, nitric oxide, nitrogen dioxide, and ozone, using a novel air quality conditional generative adversarial network (AQcGAN) built for emulating ensembles. Using this model, we are able to forecast ten days into the future generating both ensemble mean and spread in seconds for a given ensemble, offering a low-cost option for extending the forecast window in GEOS-CF and for quantifying uncertainty. Results show that when compared to the actual mean and spread of the ensembles, the emulator forecasts offer competitive skill in terms of low RMSE scores. We share results from an operational data assimilation study where the AQcGAN has been used as the ensemble. We describe future plans for integration with data assimilation methods and GEOS-CF.

Abstract. The Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) is a six-year (2019–2024) NASA Earth-Venture Suborbital-3 (EVS-3) mission to robustly characterize aerosol-cloud-meteorology interactions over the western North Atlantic Ocean (WNAO) during winter and summer seasons, with a focus on marine boundary layer clouds. This characterization requires understanding the aerosol life cycle (sources and sinks), composition, transport pathways, and distribution in the WNAO region. We use the GEOS-Chem chemical transport model driven by the MERRA-2 reanalysis to simulate tropospheric aerosols that are evaluated against in situ and remote sensing measurements from Falcon and King Air aircraft, respectively, as well as ground-based and satellite observations over the WNAO during the winter (Feb. 14 – Mar. 12) and summer (Aug. 13 – Sep. 30) field deployments of ACTIVATE 2020. Transport of pollution in the boundary layer behind cold fronts is a major mechanism for the North American continental outflow to the WNAO during Feb.–Mar. 2020. While large-scale frontal lifting is a dominant mechanism in winter, convective lifting significantly increases the vertical extent of major continental outflow aerosols in summer. Turbulent mixing is found to be the dominant process responsible for the vertical transport of sea salt within and ventilation out of the boundary layer in winter. The simulated boundary layer aerosol composition and optical depth (AOD) in the ACTIVATE flight domain are dominated by sea salt, followed by organic aerosol and sulfate. Compared to winter, boundary layer sea salt concentrations increased in summer over the WNAO, especially from the ACTIVATE flight areas to Bermuda, because of enhanced surface winds and emissions. Dust concentrations also significantly increased in summer because of long-range transport from North Africa. Comparisons of model and aircraft submicron non-refractory aerosol species (measured by an HR-ToF-AMS) vertical profiles show that intensive measurements of sulfate, nitrate, ammonium, and organic aerosols in the lower troposphere over the WNAO in winter provide useful constraints on model aerosol wet removal by precipitation scavenging. Comparisons of model aerosol extinction (at 550 nm) with the King Air High Spectral Resolution Lidar-2 (HSRL-2) measurements (at 532 nm) and CALIOP/CALIPSO satellite retrievals (at 532 nm) indicate that the model generally captures the continental outflow of aerosols, the land-ocean aerosol extinction gradient, and the mixing of anthropogenic aerosols with sea salt. Large enhancements of aerosol extinction at ~1.5–6.0 km altitudes from long-range transport of the western U.S. fire smoke were observed by HSRL-2 and CALIOP during Aug.–Sep. 2020. Model simulations with biomass burning (BB) emissions injected up to the mid-troposphere (vs. within the BL) better reproduce these remote-sensing observations, Falcon aircraft organic aerosol vertical profiles, as well as AERONET AOD measurements over eastern U.S. coast and Tudor Hill, Bermuda. High aerosol (mostly coarse-mode sea salt) extinction near the top (~1.5–2.0 km) of the marine BL along with high relative humidity and cloud extinction were typically seen over the WNAO (< 35° N) in the CALIOP aerosol extinction profiles and GEOS-Chem simulations, suggesting strong hygroscopic growth of sea salt particles and sea salt seeding of marine boundary layer clouds. Contributions of different emission types (anthropogenic, BB, biogenic, marine, and dust) to the total AOD over the WNAO in the model are also quantified. Future modeling efforts should focus on improving parameterizations for aerosol wet scavenging and sea salt emissions, implementing realistic BB emission injection height, and applying high-resolution models that better resolve vertical transport.

Air quality (AQ) is a major and growing concern for public health around the world. Economic development, population growth, and climate change are all expected to exacerbate already poor AQ in many regions. Furthermore, AQ is often only sparsely monitored with reference-grade in-situ instruments. NASA resources and products have the potential to help in addressing this AQ data gap. The GEOS-CF (Goddard Earth Observing System-Composition Forecasting) global atmospheric composition modeling system is run each day at a global scale to provide recent estimates and five-day forecasts at hourly temporal resolution of atmospheric constituents relevant to AQ. NASA satellite missions (along with those of other space agencies) provide remotely sensed estimates of atmospheric composition relevant to AQ. This paper gives a brief overview of these capabilities, and outlines the efforts underway to combine model forecasts, satellite retrievals, and surface-based measurements to provide more comprehensive and accurate estimates and forecasts of local AQ which will be broadly applicable and accessible globally.

Tropospheric ozone is an air pollutant and a greenhouse gas whose anthropogenic production is limited principally by the supply of nitrogen oxides (NOx) from combustion. Tropospheric ozone in the northern hemisphere has been rising despite the flattening of NOx emissions in recent decades. Here we propose that this sustained increase could result from the photolysis of nitrate particles (pNO3-) to regenerate NOx. Including pNO3- photolysis in the GEOS-Chem atmospheric chemistry model improves the consistency with ozone observations. Our simulations show that pNO3- concentrations have increased since the 1960s because of rising ammonia and falling SO2 emissions, augmenting the increase in ozone in the northern extratropics by about 50% to better match the observed ozone trend. pNO3- will likely continue to increase through 2050, which would drive a continued increase in ozone even as NOx emissions decrease. More work is needed to better understand the mechanism and rates of pNO3- photolysis.

Abstract Integrating air quality information from models, satellites, and in situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air‐quality‐relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub‐city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross‐validation. The framework is now being implemented into an online tool to support local air quality management decision‐making. Future work will also include the incorporation of low‐cost air sensor data and the quantification of uncertainty at hyper‐local scales.

Explainable artificial intelligence (XAI) methods are becoming popular tools for scientific discovery in the Earth and atmospheric sciences. While these techniques have the potential to revolutionize the scientific process, there are known limitations in their applicability that are frequently ignored. These limitations include that XAI methods explain the behavior of the AI model and not the behavior of the training dataset, and that caution should be used when these methods are applied to datasets with correlated and dependent features. Here, we explore the potential cost associated with ignoring these limitations with a simple case study from the atmospheric chemistry literature-learning the reaction rate of a bimolecular reaction. We demonstrate that dependent and highly correlated input features can lead to spurious process-level explanations. We posit that the current generation of XAI techniques should largely only be used for understanding system-level behavior and recommend caution when using XAI methods for process-level scientific discovery in the Earth and atmospheric sciences.

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m−3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

The NASA Goddard Earth Observing System Composition Forecast system (GEOS-CF) provides global near-real-time analyses and forecasts of atmospheric composition. The current version of GEOS-CF builds on the GEOS general circulation model with Forward Processing assimilation of meteorological data (GEOS-FP) and includes detailed GEOS-Chem tropospheric and stratospheric chemistry. Here we add 3D variational data assimilation in GEOS-CF to assimilate satellite observations of ozone including MLS vertical profiles, OMI total columns, and AIRS and IASI hyperspectral 9.6 μ m radiances. We focus our evaluations on the troposphere. We find that the detailed tropospheric chemistry in GEOS-CF significantly improves the simulated background ozone fields relative to previous versions of the GEOS model, allowing for specification of smaller background errors in assimilation and resulting in smaller assimilation increments to correct the simulated ozone. Assimilation increments are largest in the upper troposphere and are consistent between satellite data sets. The OMI and MLS ozone data generally provide more information than the AIRS and IASI radiances except at high latitudes where the radiances provide more information. Comparisons to independent ozonesonde and aircraft (ATom-4) observations for 2018 show significant GEOS-CF improvement from the assimilation, particularly in the extratropical upper troposphere.

In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken. Determining whether current observations are abnormal is challenging. It requires learning an extrapolative probabilistic model of the dynamics from historical data, and using a limited number of current observations to make a classification. We leverage recent advances in long-term probabilistic forecasting, namely Deep Probabilistic Koopman, to build a general method for classifying anomalies in multi-dimensional time-series data. We also show how to utilize models with domain knowledge of the dynamics to reduce type I and type II error. We demonstrate our proposed method on the important real-world task of global atmospheric pollution monitoring, integrating it with NASA's Global Earth Observing System Model. The system successfully detects localized anomalies in air quality due to events such as COVID-19 lockdowns and wildfires.

We have coupled the GEOS‐Chem tropospheric‐stratospheric chemistry mechanism and the Community Aerosol and Radiation Model for Atmospheres (CARMA), a sectional aerosol microphysics module, within the NASA Goddard Earth Observing System Chemistry‐Climate Model (GEOS CCM) in order to simulate the interactions between stratospheric chemistry and aerosol microphysics. We use observations of the 1991 Mount Pinatubo volcanic cloud to evaluate this new version of the GEOS CCM. The GEOS‐Chem chemistry module is used to simulate the oxidation of sulfur dioxide (SO 2 ) more realistically than assuming hydroxyl radical (OH) fields are constant, as OH concentrations in the plume decrease dramatically in the weeks following the eruption. CARMA simulates sulfate aerosols with dynamic microphysical and optical properties. The CARMA‐calculated aerosol surface area is coupled to the chemistry module from GEOS‐Chem for the calculation of heterogeneous chemistry. We use a set of observational and theoretical constraints for Pinatubo to evaluate the performance of this new version of the GEOS CCM. These simulations are specifically compared with satellite and in‐situ observations and provide insights into the connections between the gas‐phase chemistry and the aerosol microphysics of the early plume and how they impact the climatic and chemical changes following a large volcanic eruption. A second, smaller eruption is also included in these simulations, the 15 August 1991, eruption of Cerro Hudson in Chile, which we find essential in explaining the aerosol optical depth in the Southern Hemisphere in 1991.

Better forecasting of atmospheric composition is a critical aspect of environmental and climate monitoring. Among climate and weather numeric modeling, often ensembles are used to improve the forecasting power and to quantify the uncertainty of the model. However, the numerical simulation of atmospheric chemistry, critical for composition simulations, is computationally too expensive to generate numerical composition ensembles. One way to address this problem is to use deep learning to emulate the slow physical model. In this work we study the feasibility of two different deep learning methods and show how an emulator could be used to realistically estimate uncertainties of atmospheric composition forecasts, bypassing the need to run costly numerical ensemble simulations. One of the methods builds upon Fourier neural operators and the NVIDIA FourCastNet architecture and the second method builds on conditional Generative Adversarial Networks. We design the models to respond to perturbations to the most important drivers of air pollution, including meteorology and pollutant emissions. We apply this framework to the NASA GEOS Composition Forecast System (GEOS-CF), which produces daily global composition forecasts at approximately 25 km^2 horizontal resolution. Due to computational constraints, GEOS-CF currently has limited capability to produce probabilistic estimates or to optimally assimilate trace gas observations. We show how a deep learning emulator has the potential to improve composition forecasts produced by GEOS-CF or other, similar types of applications. These methods could be applied to other types of ensemble-based models, potentially providing a large speed-up in overall modeling time.

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

Diesel-powered vehicles emit several times more nitrogen oxides than comparable gasoline-powered vehicles, leading to ambient nitrogen dioxide (NO2) pollution and adverse health impacts. The COVID-19 pandemic and ensuing changes in emissions provide a natural experiment to test whether NO2 reductions have been starker in regions of Europe with larger diesel passenger vehicle shares. Here we use a semi-empirical approach that combines in-situ NO2 observations from urban areas and an atmospheric composition model within a machine learning algorithm to estimate business-as-usual NO2 during the first wave of the COVID-19 pandemic in 2020. These estimates account for the moderating influences of meteorology, chemistry, and traffic. Comparing the observed NO2 concentrations against business-as-usual estimates indicates that diesel passenger vehicle shares played a major role in the magnitude of NO2 reductions. European cities with the five largest shares of diesel passenger vehicles experienced NO2 reductions ∼2.5 times larger than cities with the five smallest diesel shares. Extending our methods to a cohort of non-European cities reveals that NO2 reductions in these cities were generally smaller than reductions in European cities, which was expected given their small diesel shares. We identify potential factors such as the deterioration of engine controls associated with older diesel vehicles to explain spread in the relationship between cities’ shares of diesel vehicles and changes in NO2 during the pandemic. Our results provide a glimpse of potential NO2 reductions that could accompany future deliberate efforts to phase out or remove passenger vehicles from cities.

We describe a new generation of the high-performance GEOS-Chem (GCHP) global model of atmospheric composition developed as part of the GEOS-Chem version 13 series. GEOS-Chem is an open-source grid-independent model that can be used online within a meteorological simulation or offline using archived meteorological data. GCHP is an offline implementation of GEOS-Chem driven by NASA Goddard Earth Observing System (GEOS) meteorological data for massively parallel simulations. Version 13 offers major advances in GCHP for ease of use, computational performance, versatility, resolution, and accuracy. Specific improvements include (i) stretched-grid capability for higher resolution in user-selected regions, (ii) more accurate transport with new native cubed-sphere GEOS meteorological archives including air mass fluxes at hourly temporal resolution with spatial resolution up to C720 (∼ 12 km), (iii) easier build with a build system generator (CMake) and a package manager (Spack), (iv) software containers to enable immediate model download and configuration on local computing clusters, (v) better parallelization to enable simulation on thousands of cores, and (vi) multi-node cloud capability. The C720 data are now part of the operational GEOS forward processing (GEOS-FP) output stream, and a C180 (∼ 50 km) consistent archive for 1998–present is now being generated as part of a new GEOS-IT data stream. Both of these data streams are continuously being archived by the GEOS-Chem Support Team for access by GCHP users. Directly using horizontal air mass fluxes rather than inferring from wind data significantly reduces global mean error in calculated surface pressure and vertical advection. A technical performance demonstration at C720 illustrates an attribute of high resolution with population-weighted tropospheric NO2 columns nearly twice those at a common resolution of 2∘ × 2.5∘.

Background and aim: With the recent advancement of Earth observation, computer simulations, and low-cost monitoring technologies, the capacity to obtain accurate geospatial data has increased tremendously, particularly for locations without expansive in-situ monitoring networks. Here we present an optimized machine learning model to estimate near real-time air pollutant concentrations in selected locations in Africa and Latin America using a combination of NASA Goddard Earth Observing System Composition Forecasts (GEOS-CF) and low-cost sensor data. Methods: We use a machine learning approach to estimate near real-time air pollutants concentration at selected locations across Africa and Latin America. Several meteorological and chemical parameters are retrieved from NASA's GEOS-CF to train a bias corrector model and predict corrected concentration estimates for these locations which are then validated against local monitoring data. We also conduct an explainability approach via SHAP Analysis to quantify the model contributing factors across these locations and track the model performance in extreme conditions. Results: The optimized machine learning model shows good agreement with ground air quality data, with R2 values from 0.61- 0.65 for locations in Mexico City (Mexico), Bogotá (Columbia) and Kigali (Rwanda). Via SHAP analysis, we demonstrate the need to conduct a measure of variance to determine model performance for different conditions and intervals, knowing that the model performance can be affected by various training conditions. Conclusion: Combining observations and optimized model simulations using machine learning techniques can significantly improve air quality forecasts in low- and middle-income countries, where the rapid pace of industrialization and communities are highly susceptible to air pollution health effects, and rarely have local air quality data and health risks alerting systems in place. These results are being used to assist air quality managers and environmental agencies in these locations to improve risk communication and reduce health burdens associated with outdoor air pollution.