A new hourly dataset for photovoltaic energy production for the continental USA.

This new dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. The simulations are carried out in three steps. First, a weather forecast system is used for the predictions of incoming insolation; then, forecast ensembles with 21 members are generated using the Analog Ensemble technique; finally, each ensemble member is used to simulate 13 different solar panels. In total, there are 21 × 13 = 273 simulated scenarios. Simulations are carried out for the entire year 2019, with a temporal resolution of one hour, and a spatial resolution of 12 km. The data provide a high spatio-temporal analysis of the power production under different weather and engineering scenarios. The size of the entire dataset is about 1 TB but can be openly accessed by days and scenarios. Details on how to access and use such a dataset are provided in this article.

Changes in air quality and human mobility in the USA during the COVID-19 pandemic.

The first goal of this study is to quantify the magnitude and spatial variability of air quality changes in the USA during the COVID-19 pandemic. We focus on two pollutants that are federally regulated, nitrogen dioxide (NO2) and fine particulate matter (PM2.5). NO2 and PM2.5 are both primary and secondary pollutants, meaning that they can be emitted either directly into the atmosphere or indirectly from chemical reactions of emitted precursors. NO2 is emitted during fuel combustion by all motor vehicles and airplanes. PM2.5 is emitted by airplanes and, among motor vehicles, mostly by diesel vehicles, such as commercial heavy-duty diesel trucks. Both PM2.5 and NO2 are also emitted by fossil-fuel power plants, although PM2.5 almost exclusively by coal power plants. Observed concentrations at all available ground monitoring sites (240 and 480 for NO2 and PM2.5, respectively) were compared between April 2020, the month during which the majority of US states had introduced some measure of social distancing (e.g., business and school closures, shelter-in-place, quarantine), and April of the prior 5 years, 2015-2019, as the baseline. Large, statistically significant decreases in NO2 concentrations were found at more than 65% of the monitoring sites, with an average drop of 2 parts per billion (ppb) when compared to the mean of the previous 5 years. The same patterns are confirmed by satellite-derived NO2 column totals from NASA OMI, which showed an average drop in 2020 by 13% over the entire country when compared to the mean of the previous 5 years. PM2.5 concentrations from the ground monitoring sites, however, were not significantly lower in 2020 than those in the past 5 years and were more likely to be higher than lower in April 2020 when compared with those in the previous 5 years. After correcting for the decreasing multi-annual concentration trends, the net effect of COVID-19 at the ground stations in April 2020 was a reduction in NO2 concentrations by - 1.3ppb and a slight increase in PM2.5 concentrations by + 0.28 µg/m3. The second goal of this study is to explain the different responses of these two pollutants, i.e., NO2 was significantly reduced but PM2.5 was nearly unaffected, during the COVID-19 pandemic. The hypothesis put forward is that the shelter-in-place measures affected people's driving patterns most dramatically, thus passenger vehicle NO2 emissions were reduced. Commercial vehicles (generally diesel) and electricity demand for all purposes remained relatively unchanged, thus PM2.5 concentrations did not drop significantly. To establish a correlation between the observed NO2 changes and the extent to which people were actually sheltering in place, thus driving less, we used a mobility index, which was produced and made public by Descartes Labs. This mobility index aggregates cell phone usage at the county level to capture changes in human movement over time. We found a strong correlation between the observed decreases in NO2 concentrations and decreases in human mobility, with over 4 ppb decreases in the monthly average where mobility was reduced to near 0 and around 1 ppb decrease where mobility was reduced to 20% of normal or less. By contrast, no discernible pattern was detected between mobility and PM2.5 concentrations changes, suggesting that decreases in personal-vehicle traffic alone may not be effective at reducing PM2.5 pollution.

Calibration of Safecast dose rate measurements.

A methodology is presented to calibrate contributed Safecast dose rate measurements acquired between 2011 and 2016 in the Fukushima prefecture of Japan. The Safecast data are calibrated using observations acquired by the U.S. Department of Energy at the time of the 2011 Fukushima Daiichi power plant nuclear accident. The methodology performs a series of interpolations between the U.S. government and contributed datasets at specific temporal windows and at corresponding spatial locations. The coefficients found for all the different temporal windows are aggregated and interpolated using quadratic regressions to generate a time dependent calibration function. Normal background radiation, decay rates, and missing values are taken into account during the analysis. Results show that the standard Safecast static transformation function overestimates the official measurements because it fails to capture the presence of two different Cesium isotopes and their changing magnitudes with time. A model is created to predict the ratio of the isotopes from the time of the accident through 2020. The proposed time dependent calibration takes into account this Cesium isotopes ratio, and it is shown to reduce the error between U.S. government and contributed data. The proposed calibration is needed through 2020, after which date the errors introduced by ignoring the presence of different isotopes will become negligible.

Validating Safecast data by comparisons to a U. S. Department of Energy Fukushima Prefecture aerial survey.

Safecast is a volunteered geographic information (VGI) project where the lay public uses hand-held sensors to collect radiation measurements that are then made freely available under the Creative Commons CC0 license. However, Safecast data fidelity is uncertain given the sensor kits are hand assembled with various levels of technical proficiency, and the sensors may not be properly deployed. Our objective was to validate Safecast data by comparing Safecast data with authoritative data collected by the U. S. Department of Energy (DOE) and the U. S. National Nuclear Security Administration (NNSA) gathered in the Fukushima Prefecture shortly after the Daiichi nuclear power plant catastrophe. We found that the two data sets were highly correlated, though the DOE/NNSA observations were generally higher than the Safecast measurements. We concluded that this high correlation alone makes Safecast a viable data source for detecting and monitoring radiation.

Impact assessment of PM10 cement plants emissions on urban air quality using the SCIPUFF dispersion model.

The Second-order Closure Integrated Puff (SCIPUFF) model was used to study the impact on urban air quality caused by two cement plants emissions located near the city of Caserta, Italy, during the entire year of 2015. The simulated and observed PM10 concentrations were compared using three monitoring stations located in urban and sub-urban area of Caserta city. Both simulated and observed concentrations are shown to be highest in winter, lower in autumn and spring and lowest in summer. Model results generally follow the pattern of the observed concentrations but have a systematic under-prediction of the concentration values. Measures of the bias, NMSE and RMSE indicate a good correlation between observed and estimated values. The SCIPUFF model data analysis suggest that the cement plants are major sources for the measured PM10 values and are responsible for the deterioration of the urban air quality in the city of Caserta.