2-D Modeling and Calculations of Stratospheric Ozone and Influences of Convection, Diffusion, and Time

An engineering system approach of 2-D cylindrical model of transient mass balance calculations of ozone and other concerned chemicals along with fourteen photolysis, ozone-generating and ozone-depleting chemical reaction equations was developed, validated, and used for studying the ozone concentrations, distribution and peak of the layer, ozone depletion and total ozone abundance in the stratosphere. The calculated ozone concentrations and profile at both the Equator and a 60˚N location were found to follow closely with the measured data. The calculated average ozone concentration was within 1% of the measured average, and the deviation of ozone profiles was within 14%. The monthly evolution of stratospheric ozone concentrations and distribution above the Equator was studied with results discussed in details. The influences of slow air movement in both altitudinal and radial directions on ozone concentrations and profile in the stratosphere were explored and discussed. Parametric studies of the influences of gas diffusivities of ozone DO3 and active atomic oxygen DO on ozone concentrations and distributions were also studied and delineated. Having both influences through physical diffusion and chemical reactions, the diffusivity (and diffusion) of atomic oxygen DO was found to be more sensitive and important than that of ozone DO3 on ozone concentrations and distribution. The 2-D ozone model present in this paper for stratospheric ozone and its layer and depletion is shown to be robust, convenient, efficient, and executable for analyzing the complex ozone phenomena in the stratosphere. .

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially.This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network(F-RCNN).The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions.Furthermore,image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation.The permanent changes in climate are of serious concern.The leading causes beyond these destructive variations are ozone layer depletion,greenhouse gas release,deforestation,pollution,water resources contamination,and UV radiation.This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues,e.g.,skin cancer,damage to marine life,crops damage,and impacts on living being’s immune systems.We have tried to classify the ozone images dataset into two major classes,depleted and non-depleted regions,to extract the required persuading features through F-RCNN.Furthermore,CNN has been used for feature extraction in the existing literature,and those extricated diverse RoIs are passed on to the CNN for grouping purposes.It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results.The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91%to 93%in identifying climate variation through ozone concentration classification,whether the region in the image under consideration is depleted or non-depleted.Our proposed model presented 93%accuracy,and it outperforms the prevailing techniques.

Measuring Traffic Induced Air Pollution in Onne Port’s Environment

Maritime shipping has been a major facilitator of economic prosperity throughout the world and it is likely to grow to meet continued and growing transport needs in both developed and developing countries. However, global emissions from maritime shipping have increased considerably, causing depletion of the ozone layer and most importantly posing threat to lives and coastal environment through air pollution. This study investigated the constituents of ambient air in Onne port’s environment in Rivers State of Nigeria. Six air pollutants (O3 CO, NO2, PM2.5, PM10, and SO2) were critically monitored with hand-held mobile Aeroqual gas monitors, series 500, at strategic locations within the port’s environment and Eleme Junction (the control). We found that mean concentrations (μg·m3) of the following pollutants: O3 (71.776 ± 0.726), CO, (19.145 ± 0.275) NO2 (28.145 ± 0.965) and SO2 (36.913 ± 0.378) were significantly high. The particulates (PM10, PM2.5) also showed higher mean concentrations of 48.400 ± 0.197 and 29.676 ± 0.352 respectively. The observed values were found to be significantly higher than those observed in the control group and also exceeded the safe permissible limits for gaseous pollutants when compared to the World Health Organization’s (WHO) standards. This exceedance raises questions on Nigeria’s commitments to implementations of (Annex VI) International Maritime Organization’s (IMO) Convention for the Prevention of Marine Pollution (MARPOL 73/78) from Ships. Again, the findings portend ecological hazards to residents, flora and fauna as elevated levels of these gaseous pollutants have been associated with chronic respiratory diseases. The policy implications of the findings were discussed.