Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast.

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.

Communicating respiratory health risk among children using a global air quality index.

Air pollution poses a serious threat to children's respiratory health around the world. Satellite remote-sensing technology and air quality models can provide pollution data on a global scale, necessary for risk communication efforts in regions without ground-based monitoring networks. Several large centers, including NASA, produce global pollution forecasts that may be used alongside air quality indices to communicate local, daily risk information to the public. Here we present a health-based, globally applicable air quality index developed specifically to reflect the respiratory health risks among children exposed to elevated outdoor air pollution. Additive, excess-risk air quality indices were developed using 51 different coefficients derived from time-series health studies evaluating the impacts of ambient fine particulate matter, nitrogen dioxide, and ozone on children's respiratory morbidity outcomes. A total of four indices were created which varied based on whether or not the underlying studies controlled for co-pollutants and in the adjustment of excess risks of individual pollutants. Combined with historical estimates of air pollution provided globally at a 25 × 25 km2 spatial resolution from the NASA's Goddard Earth Observing System composition forecast (GEOS-CF) model, each of these indices were examined in a global sample of 664 small and 140 large cities for study year 2017. Adjusted indices presented the most normal distributions of locally-scaled index values, which has been shown to improve associations with health risks, while indices based on coefficients controlling for co-pollutants had little effect on index performance. We provide the steps and resources need to apply our final adjusted index at the local level using freely-available forecasting data from the GEOS-CF model, which can provide risk communication information for cities around the world to better inform individual behavior modification to best protect children's respiratory health.

Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources.

The combination of air quality (AQ) data from satellites and low-cost sensor systems, along with output from AQ models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost-effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long-range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders.

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0.

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25°) global constituent prediction system from NASA's Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA's broad range of space-based and in-situ observations. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide hindcasts and 5-days forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community. Evaluation of GEOS-CF against satellite, ozonesonde and surface observations for years 2018-2019 show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to 0.3, normalized root mean square errors between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants in many regions of the world and during all seasons, yet also highlight current limitations, such as a global high bias in SO2 and an overprediction of summertime O3 over the Southeast United States. GEOS-CF v1.0 generally overestimates aerosols by 20%-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions. The 5-days forecasts have skill scores comparable to the 1-day hindcast. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

Evaluation of NASA's high-resolution global composition simulations: Understanding a pollution event in the Chesapeake Bay during the 2017 OWLETS campaign.

Recirculation of pollutants due to a bay breeze effect is a key meteorological mechanism impacting air quality near urban coastal areas, but regional and global chemical transport models have historically struggled to capture this phenomenon. We present a case study of a high ozone (O3) episode observed over the Chesapeake Bay during the NASA Ozone Water-Land Environmental Transition Study (OWLETS) in summer 2017. OWLETS included a complementary suite of ground-based and airborne observations, with which we characterize the meteorological and chemical context of this event and develop a framework to evaluate model performance. Two publicly-available NASA global high-resolution coupled chemistry-meteorology models (CCMMs) are investigated: GEOS-CF and MERRA2-GMI. The GEOS-CF R2 value for comparisons between the NASA Sherpa C-23 aircraft measurements to the GEOS-CF resulted in good agreement (R2: 0.67) on July 19th and fair agreement (R2: 0.55) for July 20th. Compared to surface observations, we find the GEOS-CF product with a 25 x 25 km2 grid box, at an hourly (R2: 0.62 to 0.87) and 15-minute (R2: 0.64 to 0.87) interval for six regional sites outperforms the hourly nominally 50 x 50 km2 gridded MERRA2-GMI (R2: 0.53 to 0.76) for four of the six sites, suggesting it is better capable of simulating complex chemical and meteorological features associated with ozone transport within the Chesapeake Bay airshed. When the GEOS-CF product was compared to the TOLNet LiDAR observations at both NASA Langley Research Center (LaRC) and the Chesapeake Bay Bridge Tunnel (CBBT), the median differences at LaRC were -6 to 8% and at CBBT were ± 7% between 400 to 2000 m ASL. This indicates that, for this case study, the GEOS-CF is able to simulate surface level ozone diurnal cycles and vertical ozone profiles at small scales between the surface level and 2000 m ASL. Evaluating global chemical model simulations at sub-regional scales will help air quality scientists understand the complex processes occurring at small spatial and temporal scales within complex surface terrain changes, simulating nighttime chemistry and deposition, and the potential to use global chemical transport simulations in support of regional and sub-regional field campaigns.

Recent decline in extratropical lower stratospheric ozone attributed to circulation changes.

1998-2016 ozone trends in the lower stratosphere (LS) are examined using the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) and related NASA products. After removing biases resulting from step-changes in the MERRA-2 ozone observations, a discernible negative trend of -1.67±0.54 Dobson units per decade (DU/decade) is found in the 10-km layer above the tropopause between 20°N and 60°N. A weaker but statistically significant trend of -1.17±0.33 DU/decade exists between 50°S and 20°S. In the Tropics, a positive trend is seen in a 5-km layer above the tropopause. Analysis of an idealized tracer in a model simulation constrained by MERRA-2 meteorological fields provides strong evidence that these trends are driven by enhanced isentropic transport between the tropical (20°S-20°N) and extratropical LS in the past two decades. This is the first time that a reanalysis dataset has been used to detect and attribute trends in lower stratospheric ozone.

The influence of mid-latitude cyclones on European background surface ozone.

The relationship between springtime mid-latitude cyclones and background ozone (O3) is explored using a combination of observational and reanalysis data sets. First, the relationship between surface O3 observations at two rural monitoring sites on the west coast of Europe - Mace Head, Ireland and Monte Velho, Portugal - and cyclone track frequency in the surrounding regions is examined. Second, detailed case study examination of four individual mid-latitude cyclones and the influence of the associated frontal passage on surface O3 is performed. Cyclone tracks have a greater influence on the O3 measurements at the more northern coastal European station, Mace Head, located within the main North Atlantic (NA) storm track. In particular, when cyclones track north of 53° N, there is a significant relationship with high levels of surface O3 (> 75th percentile). The further away a cyclone is from the NA storm track, the more likely it will be associated with both high and low (< 25th percentile) levels of O3 at the observation site during the cyclone's life cycle. The results of the four case studies demonstrate a) the importance of the passage of a cyclone's cold front in relation to surface O3 measurements, b) the ability of mid-latitude cyclones to bring down high levels of O3 from the stratosphere and c) that accompanying surface high pressure systems and their associated transport pathways play an important role in the temporal variability of surface O3. The main source of high O3 to these two sites in springtime is from the stratosphere, either from direct injection into the cyclone or associated with aged airstreams from decaying downstream cyclones that can become entrained and descend toward the surface within new cyclones over the NA region.