Combining Hyperspectral Reflectance Indices and Multivariate Analysis to Estimate Different Units of Chlorophyll Content of Spring Wheat under Salinity Conditions

Although plant chlorophyll (Chl) is one of the important elements in monitoring plant stress and reflects the photosynthetic capacity of plants, their measurement in the lab is generally time- and cost-inefficient and based on a small part of the leaf. This study examines the ability of canopy spectral reflectance data for the accurate estimation of the Chl content of two wheat genotypes grown under three salinity levels. The Chl content was quantified as content per area (Chl _area, μg cm^-2), concentration per plant (Chl _plant, mg plant^-1), and SPAD value (Chl _SPAD). The performance of spectral reflectance indices (SRIs) with different algorithm forms, partial least square regression (PLSR), and stepwise multiple linear regression (SMLR) in estimating the three units of Chl content was compared. Results show that most indices within each SRI form performed better with Chl _area and Chl _plant and performed poorly with Chl _SPAD. The PLSR models, based on the four forms of SRIs individually or combined, still performed poorly in estimating Chl _SPAD, while they exhibited a strong relationship with Chl _plant followed by Chl _area in both the calibration (Cal.) and validation (Val.) datasets. The SMLR models extracted three to four indices from each SRI form as the most effective indices and explained 73-79%, 80-84%, and 39-43% of the total variability in Chl _area, Chl _plant, and Chl _SPAD, respectively. The performance of the various predictive models of SMLR for predicting Chl content depended on salinity level, genotype, season, and the units of Chl content. In summary, this study indicates that the Chl content measured in the lab and expressed on content (μg cm^-2) or concentration (mg plant^-1) can be accurately estimated at canopy level using spectral reflectance data.

Impact of environmental factors on pulmonary tuberculosis in multi-levels industrial upgrading area of China

In the present paper, an association between the growth rate of PTB and the environmental impacting elements in the pearl river delta region and the closed industry related cities in China is studied. We summarized the characteristics of different industry characteristics in this region by three echelons of urban agglomerations conducted by K-means clustering model on the time series of their monthly AQI data. To determine the impact of environmental factors on the increase of PTB, the SMLR in GLM has been applied. We then measured the seasonal effect and suggest the spring to be the leading season which keep the highest possibility of the incidence of PTB. Besides giving the analysis by fixed meteorological factors, we presented a sensitive analysis with a variation of precipitation. The Genetic algorithms (GAs) is used to determine the "tolerant" interval and as the results, the width of "tolerant" almost keep a declining trend as the precipitation increasing except when the precipitation comes the interval [68,74]. In addition, with the precipitation increasing higher than 64 mm, the "tolerant" for the AQI values from the first and the second echelon both trend to decline, and a lenient environmental policy currently may easily cause a rapid development of PTB growth rate.

Prediction of heavy metals in soils of an arid area based on multi-spectral data

With the rapid and extensive development of industry and agriculture, the soil environment inevitably becomes contaminated with heavy metals, thus creating adverse environmental conditions for flora and fauna. The traditional methods for combining field sampling with laboratory analysis of soil heavy metals are limited not only because they are time-consuming and expensive, but also because they are unable to obtain adequate information about the spatial distribution characteristics of heavy metals in soil over a large area. Three hundred and ninety-four soil samples (Gobi and farmland) were collected in an arid area in Jiuquan in Northwest China and analyzed for elements concentrations. Based on these measured concentrations, as well as rapid and environmentally friendly remote sensing (multi-spectral data), stepwise multiple linear regression (SMLR) and partial least-squares regression (PLS) were combined to predict concentrations and distributions of heavy metals in the soils of the study area. Furthermore, laboratory data were used to assess the accuracy of the prediction results. Obtained results suggest that the SMLR and PLS models were able to predict the metals contents in the study area. The concentrations of Cr, Ni, V and Zn could be predicted by two regression models, while those of Cu and Mn were predicted more accurately when they were attached to the SMLR model. The spatial distribution of heavy metals derived from the two models is consistent with measured values, indicating that it is reasonable to predict the concentrations of heavy metals in the soil of the study area using the multi-spectral data.

Concentration, Source, and Total Health Risks of Cadmium in Multiple Media in Densely Populated Areas, China

Cadmium (Cd) is a non-essential and harmful element to humans. Cadmium contamination is a serious issue for human health, especially in densely populated agroecology areas. In this study, the investigation of an agroecology area was conducted to gain insight into the relationship between Cd in wheat and soil and then evaluate the Cd total risk for human health. The soil samples and their matching wheat samples, underground water samples, and atmospheric deposition (air) samples were collected from a wheat-growing area in an agroecology plain. The cadmium concentration in the four types of media, in order, was air > soil > wheat > water. The mean concentration of the geoaccumulation index (Igeo) showed that the total Cd in soil (Cd-T) and Cdair reached a mild and moderate pollution level. The results of the correlation and principal component analysis (PCA) showed that the majority of Cdwheat originated from Cd-2 (exchangeable), Cd-4 (humic acid-bound), and Cd-7 (residual). Furthermore, the results of the stepwise multiple linear regression (SMLR) showed that three fractions were primarily controlled by Cd-T: clay, cation exchange capacity (CeC), and total organic carbon (TOC). In addition, the total cancer risk (CR) of Cd in multiple media was, in the order wheat > water > soil > air. It is noteworthy that the Cd content in underground water and wheat by the ingestion pathway posed cancer risks to the local residents and provided a comprehensive insight into multiple media environment management. Furthermore, it provides a very significant basic study for detailed research into the mobility and transformation for factions.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca⁺², Cr⁺³, Co⁺², Cu⁺², Cd⁺², K⁺¹, Mg⁺², Mn⁺², Na⁺¹, Ni⁺², Pb⁺², Zn⁺², and Fe⁺²) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.