RESUMO
BACKGROUND: It is important to quantify changes in CO2 sources and sinks with land use and land cover change. In the last several decades, carbon sources and sinks in East Asia have been altered by intensive land cover changes due to rapid economic growth and related urbanization. To understand impact of urbanization on carbon cycle in the monsoon Asia, we analyze net CO2 exchanges for various land cover types across an urbanization gradient in Korea covering high-rise high-density residential, suburban, cropland, and subtropical forest areas. RESULTS: Our analysis demonstrates that the urban residential and suburban areas are constant CO2 sources throughout the year (2.75 and 1.02 kg C m-2 year-1 at the urban and suburban sites), and the net CO2 emission indicate impacts of urban vegetation that responds to the seasonal progression of the monsoon. However, the total random uncertainties of measurement are much larger in the urban and suburban areas than at the nonurban sites, which can make it challenging to obtain accurate urban flux measurements. The cropland and forest sites are strong carbon sinks because of a double-cropping system and favorable climate conditions during the study period, respectively (- 0.73 and - 0.60 kg C m-2 year-1 at the cropland and forest sites, respectively). The urban area of high population density (15,000 persons km-2) shows a relatively weak CO2 emission rate per capita (0.7 t CO2 year-1 person-1), especially in winter because of a district heating system and smaller traffic volume. The suburban area shows larger net CO2 emissions per capita (4.9 t CO2 year-1 person-1) because of a high traffic volume, despite a smaller building fraction and population density (770 persons km-2). CONCLUSIONS: We show that in situ flux observation is challenging because of its larger random uncertainty and this larger uncertainty should be carefully considered in urban studies. Our findings indicate the important role of urban vegetation in the carbon balance and its interaction with the monsoon activity in East Asia. Urban planning in the monsoon Asia must consider interaction on change in the monsoon activity and urban structure and function for sustainable city in a changing climate.
RESUMO
Effective treatment of clinical infections is critically dependent on the ability to rapidly screen patient samples to identify antibiograms of infecting pathogens. Existing methods for antibiotic susceptibility testing suffer from several disadvantages, including long turnaround times, excess sample and reagent consumption, poor detection sensitivity, and limited combinatorial capabilities. Unfortunately, these factors preclude the timely administration of appropriate antibiotics, complicating management of infections and exacerbating the development of antibiotic resistance. Here, we seek to address these issues by developing a microfluidic platform that relies on fluorescence detection of bacteria that express green fluorescent protein for highly sensitive and rapid antibiotic susceptibility testing. This platform possesses several advantages compared to conventional methods: (1) analysis of antibiotic action in two to four hours, (2) enhanced detection sensitivity (≈ 1 cell), (3) minimal consumption of cell samples and antibiotic reagents (<6 µL), and (4) improved portability through the implementation of normally closed valves. We employed this platform to quantify the effects of four antibiotics (ampicillin, cefalexin, chloramphenicol, tetracycline) and their combinations on Escherichia coli. Within four hours, the susceptibility of bacteria to antibiotics can be determined by detecting variations in maxima of local fluorescence intensity over time. As expected, cell density is a major determinant of antibiotic efficacy. Our results also revealed that combinations of three or more antibiotics are not necessarily better for eradicating pathogens compared to pairs of antibiotics. Overall, this microfluidic based biosensor technology has the potential to provide rapid and precise guidance in clinical therapies by identifying the antibiograms of pathogens.