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Are higher atmospheric aerosol levels and droughts related? To address this question, we explore the relation between atmospheric aerosol loading and droughts using insitu and satellite observations over different urban/rural settings and heterogeneous drought conditions. A related objective was to report on the relationship and the variability between aerosol optical depth (AOD) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and insitu particular matter (PM2.5 and PM10) over different land use. Daily measurement of PM2.5 and PM10 data were retrieved from seven EPA air quality monitoring stations in Indiana: Virgo, Lake and Clark County in urban area, Marion, Know and Henry County in agriculture area, and Delaware County in suburban area during summer (June 1 – August 31, 2007) and winter (January 1, 2007 – March 31, 2007). The MODIS AOD data were extracted from the daily MODIS L2 land aerosol products at 0.55 um wavelength with 10 x 10 1 km resolution grids. The Geographic Information System (GIS) was used to determine the closet coordinate of observation stations from MODIS images. The drought status was obtained from the US Drought Monitor and the Standardized Precipitation Index. A regression analysis was undertaken to compare daily insitu PM 2.5 and PM10 measurements with the column integrated MODIS AOD values. Results indicate higher AOD values under drought conditions during summer period and severe drought occurrence. The data also shows higher variability and lesser correlation between the column integrated MODIS AOD and the PM measurements during summer and fall seasons. The difference between column integrated versus surface measurements is more with high values of AOD and drought condition. The average correlations between AOD and PM2.5 and PM 10 are 0.6 and 0.3 respectively for all land use. The average bias during drought condition (summer) is 0.23 and during non drought (winter) is 1.44 in urban area. In agriculture area the bias is higher than urban area during drought(0.48) but is lower than urban area during non drought (-0.14). The correlation of AOD, PM2.5 and PM 10 in agricultural area is higher than the correlation in urban area with AOD being relatively comparable with the PM 2.5 concentration. Further study is underway to understand the relationship between the air pollution feedback and climate variability and local drought conditions using satellite datasets.

Characterizing and developing drought climatology continues to be a challenging problem. Also as decision makers seek guidance on water management strategies, there is a need for assessing the performance of drought indices. This requires the adaptation of appropriate drought indices that aid in monitoring droughts and hydrological vulnerability on a regional scale. This study aims to assist the process of developing a statewide water shortage and assessment plan (WSP) for the state of Indiana, USA by conducting a focused hydroclimatological assessment of drought variability. Drought climatology was assessed using in-situ observations and regional reanalysis data. A summary of precipitation and evaporation trends, estimated drought variability, worst-case drought scenarios, drought return period, as well as frequency and duration was undertaken, using multiple drought indices and streamflow analysis. Results indicated a regional and local variability in drought susceptibility. The worst-case (200 year return period) prediction showed that Indiana has a 0.5% probability of receiving 45% of normal precipitation over a 12 month drought in any years. Consistent with other studies, the Standard Precipitation Index (SPI) was found to be appropriate for detecting short-term drought conditions over Indiana. This recommendation has now been incorporated in the 2009 Indiana Water Shortage Plan. This study also highlights the difficulties in identifying past droughts from available climatic data, and our results suggest a persistent, high degree of uncertainty in making drought predictions using future climatic projections.

To solve traffic congestion in Bangkok, the Bangkok Mass Transit system (BTS) constructed an overhead rail system with 24 stations. The BTS train station, S2, in this study area covers Silom road and obstructs the air pollutant dispersion in a congestion area. The 1: 200 physical model of the buildings along Silom road with the train station, S2, was simulated in this research to determine the air pollutant dispersion in the train station area. A tracer gas (CO₂) was emitted from a simulated line source with emission rates of 0.383, 0.681, 1.293, 2.586, 5.177 and 10.77 mg/min to simulate actual pollutant emission rates. The CO₂ gas was sampled at 55 locations in the model. The Kriging method was used to interpolate the data in the study area. . Emission rates were used to make the difference between measured CO₂ in the model area and ambient CO₂ large enough to be differentiated. Regression Analysis was used to relate analytically the mass emission rate to the CO₂ concentration. The results indicate that the maximum CO concentrations exceed the 30 ppm Bangkok standard along the Southeast side of Silom Road at the passenger platform level. Drivers will acquire more harmful levels of CO than pedestrians at street level, especially near the Southwest end of the train station. NO₂ concentrations do not exceed the standard (0.17 ppm) at street level. The highest predicted VOC is 1.05 ppm. These results may be used in the future for numerical modeling study. Master of Science