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Development and demonstration of a Lagrangian dispersion modeling system for realātime prediction of smoke haze pollution from biomass burning in Southeast Asia
Abstract Transboundary smoke haze caused by biomass burning frequently causes extreme air pollution episodes in maritime and continental Southeast Asia. With millions of people being affected by this type of pollution every year, the task to introduce smoke haze related air quality forecasts is urgent. We investigate three severe haze episodes: June 2013 in Maritime SE Asia, induced by fires in central Sumatra, and March/April 2013 and 2014 on mainland SE Asia. Based on comparisons with surface measurements of PM10 we demonstrate that the combination of the Lagrangian dispersion model NAME with emissions derived from satelliteābased activeāfire detection provides reliable forecasts for the region. Contrasting two fire emission inventories shows that using algorithms to account for fire pixel obscuration by cloud or haze better captures the temporal variations and observed persistence of local pollution levels. Including upātoādate representations of fuel types in the area and using better conversion and emission factors is found to more accurately represent local concentration magnitudes, particularly for peat fires. With both emission inventories the overall spatial and temporal evolution of the haze events is captured qualitatively, with some error attributed to the resolution of the meteorological data driving the dispersion process. In order to arrive at a quantitative agreement with local PM10 levels, the simulation results need to be scaled. Considering the requirements of operational forecasts, we introduce a realātime bias correction technique to the modeling system to address systematic and random modeling errors, which successfully improves the results in terms of reduced normalized mean biases and fractional gross errors
Identification of arylamine N-acetyltransferase inhibitors as an approach towards novel anti-tuberculars
New anti-tubercular drugs and drug targets are urgently needed to reduce the time for treatment and also to identify agents that will be effective against Mycobacterium tuberculosis persisting intracellularly. Mycobacteria have a unique cell wall. Deletion of the gene for arylamine N-acetyltransferase (NAT) decreases mycobacterial cell wall lipids, particularly the distinctive mycolates, and also increases antibiotic susceptibility and killing within macrophage of Mycobacterium bovis BCG. The nat gene and its associated gene cluster are almost identical in sequence in M. bovis BCG and M. tuberculosis. The gene cluster is essential for intracellular survival of mycobacteria. We have therefore used pure NAT protein for high-throughput screening to identify several classes of small molecules that inhibit NAT activity. Here, we characterize one class of such moleculesātriazolesāin relation to its effects on the target enzyme and on both M. bovis BCG and M. tuberculosis. The most potent triazole mimics the effects of deletion of the nat gene on growth, lipid disruption and intracellular survival. We also present the structure-activity relationship between NAT inhibition and effects on mycobacterial growth, and use ligand-protein analysis to give further insight into the structure-activity relationships. We conclude that screening a chemical library with NAT protein yields compounds that have high potential as anti-tubercular agents and that the inhibitors will allow further exploration of the biochemical pathway in which NAT is involved