Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically...Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.展开更多
The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade prec...The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System(GNSS).Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services.The objective of this study is to propose a neural network,trained with radio occultation data from the COSMIC-1 mission,that generates average ionospheric electron density profiles during dusk,focusing on the pre-reversal enhancement of the zonal electric field.Results show that the estimated profiles exhibit a clear seasonal pattern,and reproduce adequately the climatological behavior of the ionosphere,thus presenting strong appeal on ionospheric error attenuation.展开更多
文摘Airborne particulates play a central role in both the earth’s radiation balance and as a trigger for a wide range of health impacts. Air quality monitors are placed in networks across many cities glob-ally. Typically these provide at best a few recording locations per city. However, large spatial var-iability occurs on the neighborhood scale. This study sets out to comprehensively characterize a full size distribution from 0.25 - 32 μm of airborne particulates on a fine spatial scale (meters). The data are gathered on a near daily basis over the month of May, 2014 in a 100 km2 area encompassing parts of Richardson, and Garland, TX. Wind direction was determined to be the dominant factor in classifying the data. The highest mean PM2.5 concentration was 14.1 ± 5.7 μg·m-3 corresponding to periods when the wind was out of the south. The lowest PM2.5 concentrations were observed after several consecutive days of rainfall. The rainfall was found to not only “cleanse” the air, leaving a mean PM2.5 concentration as low as 3.0 ± 0.5 μg·m-3, but also leave the region with a more uniform PM2.5 concentration. Variograms were used to determine an appropriate spatial scale for future sensor placement to provide measurements on a neighborhood scale and found that the spatial scales varied, depending on the synoptic weather pattern, from 0.8 km to 5.2 km, with a typical length scale of 1.6 km.
基金CAPES scholarships 88887.570088/2020-00 and 88887.634447/2021-00 and worked on this research in collaboration to the framework CNPq 465648/2014-2 and FAPESP 2017/01150-0.GSFAOM are supported by CNPq awards 165561/2023-8 and 309389/2021-6 respectively+1 种基金PRPS and JS were supported by CAPES awards 850937/2023-00 and 88887.901203/2023-00 respectivelyJS also acknowledges FAPESP 2018/06158-9.
文摘The vertical profile of the ionosphere density plays a significant role in the development of low-latitude Equatorial Plasma Bubbles(EPBs),that in turn lead to ionospheric scintillation which can severely degrade precision and availability of critical users of the Global Navigation Satellite System(GNSS).Accurate estimation of ionospheric delays through vertical electron density profiles is vital for mitigating GNSS errors and enhancing location-based services.The objective of this study is to propose a neural network,trained with radio occultation data from the COSMIC-1 mission,that generates average ionospheric electron density profiles during dusk,focusing on the pre-reversal enhancement of the zonal electric field.Results show that the estimated profiles exhibit a clear seasonal pattern,and reproduce adequately the climatological behavior of the ionosphere,thus presenting strong appeal on ionospheric error attenuation.
