Canopy leaf area index (LAI) is an important structural parameter of the vegetation controlling pollutant uptake by terrestrial ecosystems. This paper presents a computationally efficient algorithm for retrieval of vegetation LAI and canopy clumping factor from satellite data using observed Simple Ratios (SR) of near-infrared to red reflectance. The method employs numerical inversion of a physics-based analytical canopy radiative transfer model that simulates the bi-directional reflectance distribution function (BRDF). The algorithm is independent of ecosystem type. The method is applied to 1-km resolution AVHRR satellite images to retrieve a geo-referenced data set of monthly LAI values for the conterminous USA. Satellite-based LAI estimates are compared against independent ground LAI measurements over a range of ecosystem types. Verification results suggest that the new algorithm represents a viable approach to LAI retrieval at continental scale, and can facilitate spatially explicit studies of regional pollutant deposition and trace gas exchange. The paper presents a physics-based algorithm for retrieval of vegetation LAI and canopy-clumping factor from satellite data to assist research of pollutant deposition and trace-gas exchange. The method is employed to derive a monthly LAI dataset for the conterminous USA and verified at a continental scale.

An integrative passive sampler consisting of a C18 Empore® disk receiving phase saturated with n-octanol and fitted with low-density polyethylene diffusion membrane was calibrated for the measurement of time-weighted average concentrations of hydrophobic micropollutants, including polyaromatic hydrocarbons and organochlorine pesticides, in water. The effect of temperature and water turbulence on kinetic and thermodynamic parameters characterising the exchange of analytes between the sampler and water was studied in a flow-through system under controlled conditions. It was found that the absorption of test analytes from water to the sampler is related to their desorption to water. This allows for the in situ calibration of the uptake of pollutants using offload kinetics of performance reference compounds. The sampling kinetics are dependent on temperature, and for most of the tested analytes also on the flow velocity. Sampler–water partition coefficients did not significantly change with temperature.

A pesticide runoff event was simulated on two 10 m x 50 m constructed wetlands (one non-vegetated, one vegetated) to evaluate the fate of methyl parathion (MeP) (Penncap-M). Water, sediment, and plant samples were collected at five sites downstream of the inflow for 120 d. Semi-permeable membrane devices (SPMDs) were deployed at each wetland outflow to determine exiting pesticide load. MeP was detected in water at all locations of the non-vegetated wetland (50 m), 30 min post-exposure. MeP was detected 20 m from the vegetated wetland inflow 30 min post-exposure, while after 10d it was detected only at 10 m. MeP was measured only in SPMDs deployed in non-vegetated wetland cells, suggesting detectable levels were not present near the vegetated wetland outflow. Furthermore, mass balance calculations indicated vegetated wetlands were more effective in reducing aqueous loadings of MeP introduced into the wetland systems. This demonstrates the importance of vegetation as sorption sites for pesticides in constructed wetlands.

Hydrochemistry of an alluvial river was investigated employing the chemometric techniques such as cluster analysis (CA), principal component analysis (PCA), discriminant analysis (DA) and partial least square (PLS) with a view to extract information about the variables responsible for spatial and temporal variations in river hydrochemistry and water quality, the hidden factors explaining the structure of the hydro-chemical database of the river, factors/processes influencing the river hydro-chemistry. Analysis of spearman's correlation coefficient revealed non-significant correlation of the pollution indicator (BOD, COD, SO₄, F, NH₄-N, NO₃-N) variables with season and significant correlation with site, indicating contribution of the site-specific anthropogenic sources in the catchments. Spatial CA clustered the monitoring sites (10nos.) into three groups of relatively non-polluted sites, moderately polluted sites, and highly polluted sites. Temporal CA differentiated among the samples of monsoon and non-monsoon months. PCA rendered considerable data reduction, in terms of eight parameters explaining about 71% of the total variance and evolved six PCs. PCA grouped samples belonging to different seasons and sites distinctly correlating them with natural and anthropogenic variables. Temporal and spatial DA rendered 97 and 92% correct assignations of the samples, respectively, and revealed that temperature, pH, BOD, DO, alkalinity and Ca are the most significant variables to discriminate between the different seasons and account for most of the expected temporal variations in hydrochemistry of the river, whereas, hardness, DO, BOD, COD, Ca and Mg were the most significant discriminating variables in space. Spatial and temporal groupings of the samples were successfully achieved through PLS modeling. PLS showed that the summer season samples are dominated by PO₄, TDS, F, K, COD, BOD, Na, Cl, hardness and alkalinity, whereas, samples of winter season by DO, pH, NH₄-N and coliforms. Furthermore, PLS indicated site-specific dominance of anthropogenic contaminants suggesting for their pollution sources in the corresponding catchments of these sites.