Experiments using electrical resistivity tomography (ERT) have shown promising results in reducing the uncertainty of solute plume characteristics related to estimates based on the analysis of local point measurements only. To explore the similarities and differences between two cross-borehole ERT inversion approaches for characterizing salt tracer plumes, namely the classical smoothness-constrained inversion and a geostatistically based approach, we performed two-dimensional synthetic experiments. Simplifying assumptions about the solute transport model and the electrical forward and inverse model allowed us to study the sensitivity of the ERT inversion approaches towards a variety of basic conditions, including the number of boreholes, measurement schemes, contrast between the plume and background electrical conductivity, use of a priori knowledge, and point conditioning. The results show that geostatistically based and smoothness-constrained inversions of electrical resistance data yield plume characteristics of similar quality, which can be further improved when point measurements are incorporated and advantageous measurement schemes are chosen. As expected, an increased number of boreholes included in the ERT measurement layout can highly improve the quality of inferred plume characteristics, while in this case the benefits of point conditioning and advantageous measurement schemes diminish. Both ERT inversion approaches are similarly sensitive to the noise level of the data and the contrast between the solute plume and background electrical conductivity, and robust with regard to biased input parameters, such as mean concentration, variance, and correlation length of the plume. Although sophisticated inversion schemes have recently become available, in which flow and transport as well as electrical forward models are coupled, these schemes effectively rely on a relatively simple geometrical parameterization of the hydrogeological model. Therefore, we believe that standard uncoupled ERT inverse approaches, like the ones discussed and assessed in this paper, will continue to be important to the imaging and characterization of solute plumes in many real-world applications. (C) 2016 Hohai University. Production and hosting by Elsevier B.V.

Aerosols are small, micrometer-sized particles, whose optical effects coupled with their impact on cloud properties is a source of large uncertainty in climate models. While their radiative forcing impact is largely of a cooling nature, there can be significant variations in the degree of their impact, depending on the size and the nature of the aerosols. The radiative and optical impact of aerosols are, first and foremost, dependent on their concentration or number density (an extensive parameter) and secondly on the size and nature of the aerosols (intensive, per particle, parameters). We employed passive (sunphotmetry) and active (backscatter lidar) measurements to retrieve extensive optical signals (aerosol optical depth or AOD and backscatter coefficient respectively) and semi-intensive optical signals (fine and coarse mode OD and fine and coarse mode backscatter coefficient respectively) and compared the optical coherency of these retrievals over a variety of aerosol and thin cloud events (pollution, dust, volcanic, smoke, thin cloud dominated). The retrievals were performed using an existing spectral deconvolution method applied to the sunphotometry data (SDA) and a new retrieval technique for the lidar based on a colour ratio thresholding technique. The validation of the lidar retrieval was accomplished by comparing the vertical integrations of the fine mode, coarse mode and total backscatter coefficients of the lidar with their sunphotometry analogues where lidar ratios (the intensive parameter required to transform backscatter coefficients into extinction coefficients) were (a) computed independently using the SDA retrievals for fine mode aerosols or prescribed for coarse mode aerosols and clouds or (b) computed by forcing the computed (fine, coarse and total) lidar ODs to be equal to their analog sunphotometry ODs. Comparisons between cases (a) and (b) as well as the semi-qualitative verification of the derived fine and coarse mode vertical profiles with the expected backscatter coefficient behavior of fine and coarse mode aerosols yielded satisfactory agreement (notably that the fine, coarse and total OD errors were <~ sunphotometry instrument errors). Comparisons between cases (a) and (b) also showed a degree of optical coherency between the fine mode lidar ratios.