ALBERT

Description: The transient hydraulic tomography survey (THTS) is a conceptually improved technique that efficiently estimates detailed variations in aquifer parameters. Based on the concept of the THTS, we developed a geostatistical inverse model to characterize saturated hydraulic conductivity (K) and the specific yield (Sy) in transient and unconfined aquifer systems. In this study, a synthetic example was first used to assess the accuracy of the developed inverse model. Multiple random K and Sy realizations with different variances of natural logarithm of K (lnK) were generated and systematically compared to evaluate the effects of joint inversion on K estimations. The model was implemented in field-scale, cross-hole injection tests in a shallow and highly permeable unconfined aquifer near the middle reaches of the Wu River in central Taiwan. To assess the effect of constant head boundary conditions on the estimation results, two additional modeling domains were evaluated on the basis of the same field data from the injection tests. The results of the synthetic example showed that the proposed inverse model can effectively reproduce the predefined K patterns and magnitudes. However, slightly less detail was obtained for the Sy field based on the sampling data from sequential transient hydraulic stresses. The joint inversion by using transient head observations could slightly decrease the accuracy of K estimations. The model implementation for field-scale injection tests showed that the model can estimate K and Sy fields with detailed spatial variations. Estimation results showed a relatively homogeneous aquifer for the tested well field. Results based on the three modeling domains showed similar patterns and magnitudes of K and Sy near the well locations. These results indicated that the THTS is relatively insensitive to artificially drawn boundary conditions even under transient conditions.

Description: Ground-based observations have insufficient spatial coverage to assess long-term human exposure to fine particulate matter (PM2.5) at the global scale. Satellite remote sensing offers a promising approach to provide information on both short- and long-term exposure to PM2.5 at local-to-global scales, but there are limitations and outstanding questions about the accuracy and precision with which ground-level aerosol mass concentrations can be inferred from satellite remote sensing alone. A key source of uncertainty is the global distribution of the relationship between annual average PM2.5 and discontinuous satellite observations of columnar aerosol optical depth (AOD). We have initiated a global network of ground-level monitoring stations designed to evaluate and enhance satellite remote sensing estimates for application in health-effects research and risk assessment. This Surface PARTiculate mAtter Network (SPARTAN) includes a global federation of ground-level monitors of hourly PM2.5 situated primarily in highly populated regions and collocated with existing ground-based sun photometers that measure AOD. The instruments, a three-wavelength nephelometer and impaction filter sampler for both PM2.5 and PM10, are highly autonomous. Hourly PM2.5 concentrations are inferred from the combination of weighed filters and nephelometer data. Data from existing networks were used to develop and evaluate network sampling characteristics. SPARTAN filters are analyzed for mass, black carbon, water-soluble ions, and metals. These measurements provide, in a variety of regions around the world, the key data required to evaluate and enhance satellite-based PM2.5 estimates used for assessing the health effects of aerosols. Mean PM2.5 concentrations across sites vary by more than 1 order of magnitude. Our initial measurements indicate that the ratio of AOD to ground-level PM2.5 is driven temporally and spatially by the vertical profile in aerosol scattering. Spatially this ratio is also strongly influenced by the mass scattering efficiency.