Recognition of the carbon dioxide (CO2) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO2 concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO2 data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO2 concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO2 concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO2 concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.

The construction industry’s productivity and safety have long been a source of concern, while the broad use of deep neural network (DNN)-based visual AI has transformed other industries. Automation and digitalization powered by DNN provide intriguing answers; yetthe lack of high-quality, diversifieddataprevents the construction sector from leveragingthe benefits. This paper presentsa novel computational framework that enables synthetic data generationfor DNN training to overcome the time-consuming manual data collectionand avoiddata privacy problems. The suggested framework uses graphics engines to create a virtual duplicate of the constructionsite that generates non-real yet realistic visuals. The proposedframework randomizes crucial scene elements such as worker pose, clothes, camera viewpoint, and lighting conditionsto enhancethe variety of the synthetic dataset.The findings of this study presentpromisingpotential of synthetic datain DNN training.