RESUMO
As a cereal crop, maize ranked third place after wheat and rice in terms of land area coverage for its cultivation, and in Bangladesh, it ranked second place after rice in its production. As the substitution of wheat products, maize has been used widely in baking for human consumption and animal fodder. However, maize grown in this soil around the coal-burning power plant may cause heavy metals uptake that poses a risk to humans. The study was conducted at the maize fields in the Ganges delta floodplain soils of Bangladesh to know the concentration of eight heavy metals (Ni, Cr, Cd, Mn, As, Cu, Zn, and Pb) in soil and maize samples using an inductively coupled plasma mass spectrometer (ICP-MS) and to estimate the risk of heavy metals in maize grains. Mean concentrations of heavy metals (mg/kg) in soil were in decreasing order of Zn (10.12) > Cu (10.02) > Mn (5.48) > Ni (4.95) > Cr (3.72) > As (0.51) > Pb (0.27) > Cd (0.23). The plant tissues showed the descending order of heavy metal concentration as roots > grains > stems > leaves. BCF values for As, Cd, Pb, and Mn in roots were higher than 1.0, indicating considerable accumulation of these elements in maize via roots. Total hazard quotient (Æ©THQ) of heavy metals through maize grain consumption was 3.7E+00 and 3.9E+00 for adults and children, respectively, indicating non-cancer risk to the consumers. Anthropogenic influences contributed to the heavy metals enrichment in the Ganges delta floodplain soils around the thermal plant, and potential risks (non-carcinogenic and carcinogenic) were observed due to the consumption of maize grain cultivated in the study area.
RESUMO
Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (LW), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction.
RESUMO
Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.