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Recent advances in metabolic engineering of microorganisms for the production of monomeric C3 and C4 chemical compounds
No full textBio-based C3 and C4 bi-functional chemicals are useful monomers in biopolymer production. This review de-scribes recent progresses in the biosynthesis of four such monomers as a hydroxy-carboxylic acid (3-hydrox-ypropionic acid), a dicarboxylic acid (succinic acid), and two diols (1,3-propanediol and 1,4-butanediol). The use of cheap carbon sources and the development of strains and processes for better product titer, rate and yield are presented. Challenges and future perspectives for (more) economical commercial production of these chemicals are also briefly discussed
Unmanned aerial vehicles for planning rooftop rainwater harvesting systems: a case study from Gurgaon, India
No full textRooftop rainwater harvesting systems (RRWHS) effectively provide water access by storing precipitated water. The amount of water harvest able using these systems is proportional to the availability of rooftop areas in the region. The use of satellite imagery has gained traction in recent times considering the challenges in conducting a manual survey to determine the rooftop area. However, the limitations on spatial resolution impaired stakeholders from conducting similar assessments in areas with small residential units. In this regard, the use of unmanned aerial vehicles (UAVs) providing high-resolution spatial imagery for the delineation of rooftops of all scales has become popular. The present study is an attempt to utilize UAV-generated orthomosaics to estimate the harvestable quantity of rainwater for setting up an RRWHS. A study area in the Gurgaon district, India, is selected, and the steps involved in estimating the quantity of water harvestable using UAVs are demonstrated. In addition to these computations, a suitable site for constructing the storage unit is identified with the aid of a weighted overlay technique implemented using a Geographic Information System (GIS). The results from the study show that nearly 11,229 m(3) of water can be harvested per year in the study site using the RRWHS
Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability
No full textConcentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations
Projections of suitable cultivation area for major fruit trees and climate-type in South Korea under representative concentration pathway scenarios using the ensemble of high-resolution regional climate models
No full textThis study projected the future changes in the climate-type distribution in South Korea according to the Koppen-Trewartha climate classification (KTCC) under the representative concentration pathway (RCP) 4.5/8.5 scenarios and the future change of cultivation area of apple (Malus domestica Borkh.) and mandarin (Citrus unshiu Marc.), which are major fruit crops in South Korea, using five regional climate models with a 12.5 km horizontal resolution. According to KTCC, type temperate (D)s is dominant in most of South Korea during the reference period (1981-2005). On the other hand, it is projected that the area of Type D and Type subtropical (C) will decrease and increase, respectively, towards higher latitudes and elevations in the future under RCP4.5/8.5 scenarios. Accordingly, the cultivation areas of major fruit crops in South Korea are projected to change significantly. The cultivation area of apple (mandarin), which is a major current fruit crop in Type D (C), is projected to be reduced (expanded) as it moves towards higher latitudes and elevations in the future. Apples grown throughout South Korea in the present climate (reference period) are not expected to be cultivated in the late-21C due to climate change. On the other hand, the cultivation area of mandarins is projected to increase steadily in the future. At present, mandarins are cultivated only in Jeju Island, which is located in the south of the South Korea. However, the cultivation area is expected to increase by 1323% in late-21C under the RCP8.5 scenario compared to the reference period. Moreover, mandarin cultivation is projected to be possible anywhere in South Korea. Nevertheless, in late-21C, excessive increases in temperature that exceeds the appropriate temperature for mandarin in Jeju Island and the southern part of South Korea will eventually decrease the cultivation area of mandarins
