Cellulose Acetate Textile Fiber Filtration System Applied for Pre-Treatment of Surface Water

This study aimed to evaluate a nonwoven (NW) production and performance from cellulose acetate fiber from cigarette butts andapplied to a filtration system for surface water pre-treatment. The system had a surface area of 692 cm³, cellulose acetate from cigarette butt as filter media, was used and was fed with surface water from a pond. In order to evaluate the treatment performance of the filtration system were evaluated in the raw water (RW) and the filtered water (FW) the classical parameter of water quality as turbidity, total suspended solids (TSS), apparent color, true color, and total organic carbon (TOC) and heavy metals (iron, copper, and cadmium). Moreover, the presence of nicotine was investigated in the FW. The results showed a mean removal efficiency in order to 62.01%, 54.42%, 50.36 %, 6.73%, and 5.20% for turbidity, TSS, apparent color, true color, and TOC, respectively. The removal of metals varied in the order of 72.26%, 9.61%, and 2.12% for cadmium, iron, and copper, respectively. The presence of nicotine in RW and FW was not identified. In this way, besides reducing the negative environmental impacts caused by cigarette butts present in the environment, the developed technology showed potential for removing pollutants present in surface waters.

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

DARCLOS: a cloud shadow detection algorithm for TROPOMI

Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution as compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), and actual cloud shadow flags (ACSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels, and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. We validate DARCLOS with true color images made by the VIIRS instrument on board of Suomi NPP orbiting in close constellation with TROPOMI on board of Sentinel 5-P. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF can be used to select pixels for the scientific analysis of cloud shadow effects.

Multimodal Data and Multiscale Kernel-Based Multistream CNN for Fine Classification of a Complex Surface-Mined Area

Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms that may be useful for FLCC of CSMAs have not been fully investigated. This study proposes a multimodal remote sensing data and multiscale kernel-based multistream CNN (3M-CNN) model. Experiments based on two ZiYuan-3 (ZY-3) satellite imageries of different times and seasons were conducted in Wuhan, China. The 3M-CNN model had three main features: (1) multimodal data-based multistream CNNs, i.e., using ZY-3 imagery-derived true color, false color, and digital elevation model data to form three CNNs; (2) multisize neighbors, i.e., using different neighbors of optical and topographic data as inputs; and (3) multiscale convolution flows revised from an inception module for optical and topographic data. Results showed that the proposed 3M-CNN model achieved excellent overall accuracies on two different images, and outperformed other comparative models. In particular, the 3M-CNN model yielded obvious better visual performances. In general, the proposed process was beneficial for the FLCC of complex landscape areas.

A review of seventeen years of bank filtration in Brazil: results, benefits and challenges - Part 1: state of Santa Catarina

This work is the first part of a national review about Bank Filtration (BF) that began in 2003, in Brazil. These studies were conducted in the laboratory and in the field with water and natural sediment from the study regions, showing how BF has been efficient worldwide for the treatment of water for public supply as an alternative treatment. It aims to show the synthesis of results to date and point out its main benefits and challenges; that is, the state of the art at the national level. The review is concentrated in Santa Catarina (part 1), Pernambuco and Minas Gerais (part 2). BF demonstrates efficiency in reducing parameters such as turbidity and coliforms (total and fecal), pesticides and toxins. However, BF showed low capacity in reducing parameters such as salinity and true color. BF is highly dependent on local geological conditions, so parameters such as iron, manganese, fluorine, alkalinity, hardness, and chlorides can be added to the treated water. Keywords: Water Treatment. Bank Filtration. Public Supply Systems. Natural Sediment. Water Quality.

Atmospheric Correction of True-Color RGB Imagery with Limb Area-Blending Based on 6S and Satellite Image Enhancement Techniques Using Geo-Kompsat-2A Advanced Meteorological Imager Data

This study aims for producing high-quality true-color red-green-blue (RGB) imagery that is useful for interpreting various environmental phenomena, particularly for GK2A. Here we deal with an issue that general atmospheric correction methods for RGB imagery might be breakdown at high solar/viewing zenith angle of GK2A due to erroneous atmospheric path lengths. Additionally, there is another issue about the green band of GK2A of which centroid wavelength (510 nm) is different from that of natural green band (555 nm), resulting in the unrealistic RGB imagery. To overcome those weakness of the RGB imagery for GK2A, we apply the second simulation of the satellite signal in the solar spectrum radiative transfer model look-up table with improved information considering altitude of the reflective surface to reduce the exaggerated atmospheric correction, and a blending technique that mixed the true-color imagery before and after atmospheric correction which produced a naturally expressed true-color image. Consequently, the root mean square error decreased by 0.1–0.5 in accordance with the solar and view zenith angles. The green band signal was modified by combining it with a veggie band to form hybrid green which adjust centroid wavelength of approximately 550 nm. The original composite of true-color RGB imagery is dark; therefore, to brighten the imagery, histogram equalization is conducted to flatten the color distribution. High-temporal-resolution true-color imagery from the GK2A AMI have significant potential to provide scientists and forecasters as a tools to visualize the changing Earth and also expected to intuitively understand the atmospheric phenomenon to the general public.

True UV color vision in a female butterfly with two UV opsins

ABSTRACT In true color vision, animals discriminate between light wavelengths, regardless of intensity, using at least two photoreceptors with different spectral sensitivity peaks. Heliconius butterflies have duplicate UV opsin genes, which encode ultraviolet and violet photoreceptors, respectively. In Heliconius erato, only females express the ultraviolet photoreceptor, suggesting females (but not males) can discriminate between UV wavelengths. We tested the ability of H. erato, and two species lacking the violet receptor, Heliconius melpomene and Eueides isabella, to discriminate between 380 and 390 nm, and between 400 and 436 nm, after being trained to associate each stimulus with a sugar reward. We found that only H. erato females have color vision in the UV range. Across species, both sexes show color vision in the blue range. Models of H. erato color vision suggest that females have an advantage over males in discriminating the inner UV-yellow corollas of Psiguria flowers from their outer orange petals. Moreover, previous models ( McCulloch et al., 2017) suggested that H. erato males have an advantage over females in discriminating Heliconius 3-hydroxykynurenine (3-OHK) yellow wing coloration from non-3-OHK yellow wing coloration found in other heliconiines. These results provide some of the first behavioral evidence for female H. erato UV color discrimination in the context of foraging, lending support to the hypothesis ( Briscoe et al., 2010) that the duplicated UV opsin genes function together in UV color vision. Taken together, the sexually dimorphic visual system of H. erato appears to have been shaped by both sexual selection and sex-specific natural selection.