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CMOS IC technology has become an affordable means for implementing capable systems operating at 300 GHz and above. CMOS circuits have been used to generate a signal up to 1.3 THz to detect both amplitude and phase of signals up to 1.2 THz, and to detect a signal amplitude up to ~10 THz. Additionally, a transmitter and a receiver operating up to ~300 GHz for electronic smelling using rotational spectroscopy, a 30-Gb/s 300-GHz QPSK transmitter for data communication with -6-dBm output power, and an 820 GHz imaging array fabricated in CMOS have been reported. These results, along with the CMOS circuit performance in the literature, and link analyses suggest that the electronics necessary for everyday life applications in the terahertz band can be affordably realized. [ABSTRACT FROM AUTHOR]

This article describes an example of using machine learning to estimate the abundance of airborne Ambrosia pollen for Tulsa, OK. Twenty-seven years of historical pollen observations were used. These pollen observations were combined with machine learning and a very complete meteorological and land surface context of 85 variables to estimate the daily Ambrosia abundance. The machine learning algorithms employed were Least Absolute Shrinkage and Selection Operator (LASSO), neural networks, and random forests. The best performance was obtained using random forests. The physical insights provided by the random forest are also discussed. [ABSTRACT FROM AUTHOR]

Hyperspectral cameras sample many different spectral bands at each pixel, enabling advanced detection and classification algorithms. However, their limited spatial resolution and the need to measure the camera motion to create hyperspectral images makes them unsuitable for nonsmooth moving platforms such as unmanned aerial vehicles (UAVs). We present a procedure to build hyperspectral images from line sensor data without camera motion information or extraneous sensors. Our approach relies on an accompanying conventional camera to exploit the homographies between images for mosaic construction. We provide experimental results from a low-altitude UAV, achieving high-resolution spectroscopy with our system. [ABSTRACT FROM AUTHOR]

PM 2.5 , a type of fine particulate with a diameter equal to or less than 2.5 micrometers, has been identified as a major source of air pollution, and is associated with many health issues. Research on utilizing various data sources, such as remote sensing and in situ sensors, for PM 2.5 concentrations modeling remains a hot topic. In this study, the Next Generation Weather Radar (NEXRAD) is used as a supplementary weather data source, along with European Centre for Medium-Range Weather Forecasts (ECMWF), solar angles, and Geostationary Operational Environmental Satellite (GOES16) Aerosol Optical Depth (AOD) to model high spatial-temporal PM 2.5 concentrations. PM 2.5 concentrations as well as in situ weather condition variables are collected from the 31 sensors that are deployed in the Dallas Metropolitan area. Four machine learning models with different predictor variables are developed based on an ensemble approach. Since in situ weather observations are not widely available, ECMWF is used as an alternative data source for weather conditions in studies. Hence, the four established models are compared in three groups. Both models in this first group use weather variables collected from deployed sensors, but one uses NEXRAD and the other does not. In the second group, the two models use weather variables retrieved from ECMWF, one using NEXRAD and one without. In the third group, one model uses weather variables from ECMWF, and the other uses in situ weather variables, both without NEXRAD. The first two environmental groups investigate how NEXRAD can enhance model performances with weather variables collected from in situ observations and ECMWF, respectively. The third group explores how effective using ECMWF as an alternative source of weather conditions. Based on the results, the incorporation of NEXRAD achieves an R 2 score of 0.86 and 0.83 for groups 1 and 2, respectively, for an improvement of 2.8% and 9.6% over those models without NEXRAD. For group three, the use of ECMWF as an alternative source of in situ weather observations results in a 0.13 R 2 drop. For PM 2.5 estimation, weather variables including precipitation, temperature, pressure, and surface pressure from ECMWF and deployed sensors, as well as NEXRAD velocity, are shown to be significant factors. [ABSTRACT FROM AUTHOR]

In this study, we present a nationwide machine learning model for hourly PM 2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM 2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μ g/m 3 , and a root mean square error (RMSE) of 5.8 μ g/m 3 . This study also found that the model performance as well as the site measured PM 2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R 2 ) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R 2 scores correlate positively with the mean measured PM 2.5 concentration at monitoring sites. Mean measured PM 2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM 2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM 2.5 concentrations. In addition, the reconstructed PM 2.5 surfaces serve as an important data source for pollution event tracking and PM 2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM 2.5 estimates were made for 10 km by 10 km and the PM 2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM 2.5 concentrations. [ABSTRACT FROM AUTHOR]

Airborne particulates are of particular significance for their human health impacts and their roles in both atmospheric radiative transfer and atmospheric chemistry. Observations of airborne particulates are typically made by environmental agencies using rather expensive instruments. Due to the expense of the instruments usually used by environment agencies, the number of sensors that can be deployed is limited. In this study we show that machine learning can be used to effectively calibrate lower cost optical particle counters. For this calibration it is critical that measurements of the atmospheric pressure, humidity, and temperature are also made. [ABSTRACT FROM AUTHOR]

PM2.5 air pollution is a significant issue for human health all over the world, especially in East Asia. A large number of ground-based measurement sites have been established over the last decade to monitor real-time PM2.5 concentration. However, even this enhanced observational network leaves many gaps in characterizing the PM2.5 spatial distribution. Machine learning provides a variety of algorithms to help deal with these large spatial gaps—combining both remotely sensed and in situ observation data to estimate the global PM2.5 concentration. This study used a PM2.5 data product of six regions from the results of an unsupervised self-organizing map (SOM) with optimized ensemble learning approaches to highlight the most important meteorological and surface variables associated with PM2.5 concentration. These variables were then examined via multiple linear regression models to provide physical mechanistic insights into the morphology of the PM2.5 annual cycles. [ABSTRACT FROM AUTHOR]