Search

This work serves as the second of a two‐part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multisatellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS‐Chem, WRF‐Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over nonrural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and nonrural areas, referred to as "extended ground truth" or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias‐corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two‐part study develops a multimodel and multi‐AOD bias‐correction technique that has the potential to improve PM2.5 forecasts in both rural and nonrural areas in near real time, and be readily implemented at state levels. Plain Language Summary: The U.S. Environmental Protection Agency's AirNow program reports current or forecasted air quality to the general public in the form of Air Quality Index (AQI). The forecasted AQI is made available by local and state air quality agencies across more than 500 cities across the U.S. However, since surface observations of particulate matter (PM) are primarily located in the urban areas, observation‐based AQI in the rural areas is limited, and either the current or the forecasted AQI from AirNow has large uncertainties that are difficult to assess, especially during the fire season. Satellite observation with large spatial coverage provides a promising opportunity to fill in the gaps in areas where observations are spare. Building upon our previous work, here we develop a statistical technique to improve surface PM forecasts in the rural areas of continental U.S. through the use of satellite observations of aerosols, surface observations, and air quality forecasting models. Assessment with the data from Interagency Monitoring of Protected Visual Environments (IMPROVE) network shows the promise of our technique. The technique is designed with the consideration of the forecast in near real time, and is efficient with minimal requirement of computing. Key Points: Ensemble‐based AOD from different sensors and algorithms shows better performance than individual ensemble memberA multimodel and multi‐AOD ensemble bias correction via Kalman filter improves PM2.5 forecasts in rural areas lack of ground observationsAn ensemble framework for producing the single best PM2.5 forecast for next day in near real time via bias correction is established [ABSTRACT FROM AUTHOR]

In this paper there are described some educational strategies based on virtual learning environments, which were implemented in engineering programs with the purpose of mitigating failure rates in basic stage subjects. Based on a case study at the University of Baja California, the causes of failure for the period 2013 to 2016 were diagnosed, and subsequently, from 2017 to 2019, the implementation of four support strategies began. An analysis of the behavior of the failure rates before and after the implementation of the strategies was made, and the results show up that both the failure levels in five subjects as well as the student lag decreased. Even though the strategies applied in this study are replicable, and the use of virtual learning environments supports students' academic performance, it is still necessary to expand the research to measure the impact of additional strategies that were put in to practice simultaneously. [ABSTRACT FROM AUTHOR]

This work is the first of a two‐part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble‐based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least‐square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite‐based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. Plain Language Summary: Air quality forecasting plays an important role in informing the general public and decision‐makers on reducing exposure to air pollution. Air quality models simulating atmospheric constituents such as particulate matter with a diameter less than 2.5 μm (PM2.5) are often used to provide daily forecasts. However, these models are subject to large error and uncertainty as a result of the incomplete representation of the real atmosphere. Here, we develop a computationally efficient framework to improve model forecasts by performing bias correction on model outputs. We focus on nonrural areas in the continental United States and show that our technique improves model forecasts of surface PM2.5. In a companion paper, we focus on the application of satellite data to improve PM2.5 forecasting in rural areas. Key Points: The chemical transport models (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) show systematically low bias of PM2.5Model output postprocessing with surface data and the ensemble Kalman filter technique improves the PM2.5 forecast at both local and urban scaleThe Successive Correction Method extends the PM2.5 forecast improvement from the local to regional scale [ABSTRACT FROM AUTHOR]

Surface full-sky erythemal dose rate (EDR) from Ozone Monitoring Instrument (OMI) at both satellite overpass time and local noon time are evaluated against ground measurements at 31 sites from USDA UV-B Monitoring and Research Program over the period of 2005-2017. We find that both OMI overpass time and local solar noon EDR are highly correlated with the measured counterparts (R = 0.88). Although the comparison statistics are improved with longer time window used for pairing surface and OMI measurements, OMI data overall has ~ 4 % underestimate for overpass EDR while ~ 8 % overestimate for the solar noon time EDR. The biases are analyzed regarding the spatial and temporal data collocation, the effects of solar zenith angle (SZA), clouds and the assumption of constant atmospheric conditions. The difference between OMI overpass EDR and ground observation shows some moderate dependence on SZA and the bias could be up to -30 % with SZA greater than ~ 65°. In addition, the ratio of EDR between solar noon to overpass time is often (95 % in frequency) larger than 1 from OMI products; in contrast, this ratio from ground observation is shown to be normally distributed around 1. This contrast suggests that the current OMI surface UV algorithm would not fully represent the real atmosphere with the assumption of a constant atmospheric profile between noon and satellite overpass times. The viability of surface UV in terms of peak UV frequency is also studied. Both OMI Noon_FS and ground peak EDR show a high frequency of occurrence of ~ 20 mW m-2 over the period of 2005-2017. However, another high frequency of ~ 200 mW m-2 occurs in OMI solar noon EDR while the ground peak values show the high frequency around 220 mW m-2, implying that the OMI solar noon time may not always represent the peak daily UV values. Lastly, OMI full-sky solar noon EDR shows statistically significant positive trends in parts of the northeastern U.S., the Ohio River Valley region and California. However, the UV trends estimated from ground-based network using two sampling methods (one corresponds to the OMI noon time and one averages all the data in a day) show significant negative trends in the Northeast and the Ohio River Valley region, which is consistent with the increase of absorption aerosol optical depth as revealed by OMI aerosol product in these regions. No statistically-significant trend can be found for OMI columnar O3 or cloud optical depth. The future surface UV data estimated with better spatial and temporal resolution obtained from geostationary satellites would help resolve these discrepancies found in the biases and estimated surface UV trends. [ABSTRACT FROM AUTHOR]

This work serves as the second of a two-part study to improve surface PM 2.5 forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM 2.5 for next day over non-rural areas that have surface PM 2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM 2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM 2.5 from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM 2.5 forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.