IMPACT OF THE PANDEMIC COVID-19 TO THE INDONESIA SEAS

This paper presents the effect of the COVID-19 pandemic on the Indonesian seas from April to October 2020. Data were mainly obtained through literature studies focusing on coastal and ecosystem services, noise observation in the ocean, and in-situ data for atmospheric conditions. The results of this study found that the pandemic has given the oceans and ecosystems time to recover from anthropogenic stresses even though the tourism and fisheries sectors have experienced strong economic shocks. A decrease in the amount of pollution in several major cities in Indonesia was also found during the pandemic period.

The Application of a Genetic Algorithm to the Optimization of a Mesoscale Model for Emergency Response

Besides solving the equations of momentum, heat, and moisture transport on the model grid, mesoscale weather models must account for subgrid-scale processes that affect the resolved model variables. These are simulated with model parameterizations, which often rely on values preset by the user. Such ‘free’ model parameters, along with others set to initialize the model, are often poorly constrained, requiring that a user select each from a range of plausible values. Finding the values to optimize any forecasting tool can be accomplished with a search algorithm, and one such process – the genetic algorithm (GA) – has become especially popular. As applied to modeling, GAs represent a Darwinian process – an ensemble of simulations is run with a different set of parameter values for each member, and the members subsequently judged to be most accurate are selected as ‘parents’ who pass their parameters onto a new generation. At the Department of Energy’s Savannah River Site in South Carolina, we are applying a GA to the Regional Atmospheric Modeling System (RAMS) mesoscale weather model, which supplies input to a model to simulate the dispersion of an airborne contaminant as part of the site’s emergency response preparations. An ensemble of forecasts is run each day, weather data are used to ‘score’ the individual members of the ensemble, and the parameters from the best members are used for the next day’s forecasts. As meteorological conditions change, the parameters change as well, maintaining a model configuration that is best adapted to atmospheric conditions.

Long-term corrosion monitoring of carbon steels and environmental correlation analysis via the random forest method

In this work, the atmospheric corrosion of carbon steels was monitored at six different sites (and hence, atmospheric conditions) using Fe/Cu-type atmospheric corrosion monitoring technology over a period of 12 months. After analyzing over 3 million data points, the sensor data were interpretable as the instantaneous corrosion rate, and the atmospheric “corrosivity” for each exposure environment showed highly dynamic changes from the C1 to CX level (according to the ISO 9223 standard). A random forest model was developed to predict the corrosion rate and investigate the impacts of ten “corrosive factors” in dynamic atmospheres. The results reveal rust layer, wind speed, rainfall rate, RH, and chloride concentration, played a significant role in the corrosion process.

Simulation of Severe Thunderstorms in Conjugation of Westerly and Easterly on 31 March 2019 in Bangladesh

The diagnostic and prognostic studies of thunderstorms/squalls are very important to save live and loss of properties. The present study aims at diagnose the different tropospheric parameters, instability and synoptic conditions associated the severe thunderstorms with squalls, which occurred at different places in Bangladesh on 31 March 2019. For prognostic purposes, the severe thunderstorms occurred on 31 March 2019 have been numerically simulated. In this regard, the Weather Research and Forecasting (WRF) model is used to predict different atmospheric conditions associated with the severe storms. The study domain is selected for 9 km horizontal resolution, which almost covers the south Asian region. Numerical experiments have been conducted with the combination of WRF single-moment 6 class (WSM6) microphysics scheme with Yonsei University (YSU) PBL scheme in simulation of the squall events. Model simulated results are compared with the available observations. The observed values of CAPE at Kolkata both at 0000 and 1200 UTC were 2680.4 and 3039.9 J kg-1 respectively on 31 March 2019 and are found to be comparable with the simulated values. The area averaged actual rainfall for 24 hrs is found is 22.4 mm, which complies with the simulated rainfall of 20-25 mm for 24 hrs. Journal of Engineering Science 12(3), 2021, 29-43

Black carbon aerosol reductions during COVID-19 confinement quantified by aircraft measurements over Europe

The abrupt reduction in human activities during the first lockdown of the COVID-19 pandemic created unprecedented atmospheric conditions. To quantify the changes in lower tropospheric air pollution, we conducted the BLUESKY aircraft campaign and measured vertical profiles of black carbon (BC) aerosol particles over Western and Southern Europe in May and June 2020. We compared the results to similar measurements of the EMeRGe EU campaign performed in July 2017 and found that the BC mass concentrations (MBC) were reduced by about 47 %. For BC particle number concentrations, we found comparable reductions. Based on EMAC chemistry-transport model simulations, we find differences in meteorological conditions and flight patterns responsible for about 7 % of the reductions in MBC, whereas 40 % can be attributed to reduced anthropogenic emissions. Our results reflect the strong and immediate positive effect of changes in human activities on air quality and the atmospheric role of BC aerosols as a major air pollutant and climate forcing agent in the Anthropocene.