Nana Luo

Mapping dustfall distribution in urban areas using remote sensing and ground spectral data

The aim of this study was to utilize remote sensing and ground-based spectral data to assess dustfall distribution in urban areas. The ground-based spectral data denoted that dust has a significant impact on spectral features. Dusty leaves have an obviously lower reflectance than clean leaves in the near-infrared bands (780-1,300 nm). The correlation analysis between dustfall weight and spectral reflectance showed that spectroscopy in the 350-2,500-nm region produced useful dust information and could assist in dust weight estimation. A back propagation (BP) neutral network model was generated using spectral response functions and integrated remote sensing data to assess dustfall weight in the city of Beijing. Compared with actual dustfall weight, validation of the results showed a satisfactory accuracy with a lower root mean square error (RMSE) of 3.6g/m(2). The derived dustfall distribution in Beijing indicated that dustfall was easily accumulated and increased in the south of the city. In addition, our results showed that construction sites and low-rise buildings with inappropriate land use were two main sources of dust pollution. This study offers a low-cost and effective method for investigating detailed dustfall in an urban environment. Environmental authorities may use this method for deriving dustfall distribution maps and pinpointing the sources of pollutants in urban areas.

Estimation of Atmospheric Dust Deposition on Plant Leaves Based on Spectral Features

ABSTRACT Urban atmospheric dust is a significant problem and becoming a considerable pollution source in many cities. This study was based on a comparison of spectral reflectance on the surfaces of dusty and clean leaves. A significant linear relationship (r = 0.811) correlation between the dust weight and near-infrared band region (700–1000 nm) was found through analysis of the spectral data. This relationship obtained from near-infrared band regions, based on the main effects and cluster and interval analysis, was more distinct and stable than that of blue, green, red, and middle-infrared band regions. Thus, the use of near-infrared band data is a reliable method to estimate the amount of dust deposition on plant leaves. A regression model (R2 = 64.3%) was constructed based on dust deposition on plant leaves and a near-infrared ratio. The model proved to be accurate as regards an estimation of dust weight, based on a comparison of residuals (normal distribution) and accuracy tests (slope = 0.8437). This model could provide a methodological basis for spatial dust distribution analysis and has the potential for evaluating air pollution levels.

Estimation of Protein Content in Plant Leaves using Spectral Reflectance: A Case Study in Euonymus japonica

Reflectance spectroscopy has been widely applied in the field of environmental studies. In this study, a low-cost, rapid, and nondestructive method using spectral reflectance was explored to evaluate protein concentrations in plant leaves of Euonymus japonica. Proteins in leaf samples were extracted and separated by sodium dodecyl sulfate polyacrylamide gel electrophoresis, and five specific protein bands of interest were identified and quantified. Correlation analysis indicated that spectral reflectance had significant relationships with ribulose bisphosphate carboxylase a (r = −0.43) and chlorophyll a-b binding protein (r = 0.53). A linear regression model and a quadratic regression model were formulated to directly and rapidly estimate the concentration of these two proteins with R 2 = 0.61 and 0.7, respectively. To more accurately estimate the concentration of proteins, a precise inversion was established by a back propagation neutral network model using plant spectral absorption and position parameters, and the R 2 values for proteins ribulose bisphosphate carboxylase, chlorophyll a-b binding protein, oxygen evolving enhancer protein, and ATP synthase subunit beta were 0.90, 0.91, 0.91, and 0.93, respectively. The models established in this study were shown to be useful tools for studies of plant biochemical components and health under different environmental conditions.