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Recycling of coal combustion solids can have several environmental benefits. The objective of this study was to evaluate a sustainable approach to recycle coal fly ash and bottom ash as pelletized-adsorbents for dissolved phosphorus (P). Sodium Dodecyl Sulfate (SDS), a foaming agent, was added to enhance the phosphorus adsorption capacity of fly ash pellets (FAP) and bottom ash pellets (BAP) during the pellet fabrication. Ten types of fly ash pellets and bottom ash pellets with varying Sodium Dodecyl Sulfate proportions (FAP-0/BAP-0: 0% SDS; FAP-1/BAP-1: 2% SDS; FAP-2/BAP-2: 4% SDS; FAP-3/BAP-3: 6% SDS; FAP-4/BAP-4: 8% SDS by weight) were manufactured with a high temperature (700℃) modification process. The results indicated that the bottom ash pellet was more suitable adsorbent than fly ash pellet due to the better phosphorus adsorption capacity and lower heavy metal leaching. After the addition of Sodium Dodecyl Sulfate, the volumetric porosity of fly ash pellets and bottom ash pellets increased by 45.5%–163.6%, 52.9%–76.5%, respectively. However, higher Sodium Dodecyl Sulfate fraction did not result in better pellet phosphorus removal, since FAP-2 and BAP-3 resulted in the highest phosphorus removal among the pellets tested in this study. The results from X-ray diffraction demonstrated that Calcium and Silica were the major phases in the pellets. This study provides a feasible approach to reuse fly ash and bottom ash as effective pelletized-adsorbents for water quality improvement. These pellets can be applied in edge-of-field and in-stream treatment to control P loss from the agricultural area.

Although sticky traps are reliable indicators of pest population dynamics but pest counting by humans is time-consuming and menial labour. A novel smart vision algorithm based on two-dimensional Fourier transform (2DFT) spectrum is presented. Rather than directly counting the pests captured on the traps, the novel concept is to treat trapped pests as noise in a two-dimensional (2D) image with 2DFT serving as a specific noise collector. The research objectives included comparing human and 2DFT counting in two proof-of-principle tests: (i) simulated pests with various quantities and distributions arrayed on two series of templates using both ordered and random patterns; (ii) sweet potato whiteflies [Bemisia tabaci (Gennadius), Hemiptera: Aleyrodidae] on yellow sticky traps (YSTs) and western flower thrips [Frankliniella occidentalis (Pergande), Thysanoptera: Thripidae] on blue sticky traps (BSTs). Tests of simulated pests (2–512) on eight templates verified that the 2DFT-based index provides accurate estimates of pests captured on the traps (R2 = 1), independent of pest distribution pattern. High correlations were obtained from count results of whiteflies on 34 YSTs (R2 = 0.9994) and thrips on 33 BSTs (R2 = 0.9989). Measurement errors were addressed.

Silage is a critical global feedstock, but is prone to aerobic deterioration. The dominant mechanism of O2 transport into silage remains unresolved. Here, multiple sensors tracked O2 and CO2, gas pressure (ΔP) between internal silage and ambient air, pH and silage temperature (Tsi) during the ensilage of maize and ryegrass. We report the first observation that CO2 produced from microbial respiration was partially dissolved in silage water, with evidence of negative or positive ΔP depending on the changing balance between CO2 production and dissolution. The ΔP 2 production was much greater in anaerobic fermentation stage than in initial aerobic phase or later aerobic feed-out phase. O2 transport into silage is intimately linked to the dynamics of net CO2, ΔP, microbial activity, pH and Tsi. These results suggested that both gas diffusion (based on Fick’s law) and advective transfer (Darcy’s law) play equally important roles in governing the complex temporal progression of inward and outward gas fluxes to and from the silage interior. Even though low pH suppressed microbial activity and supported aerobic stability, the negative ΔP increased the risk of O2 entry and aerobic deterioration during feed-out phase.

Typically, herbicides are applied uniformly to the entire field rather than be applied to the critical areas where they‘re most needed. And existing weed identification approaches, such as manual visual scouting and ground-based proximal sensing, are labor intensive, time consuming, and do not scale easily to large areas. To facilitate more efficient farming, new technologies must be developed that can achieve optimal weed management and increase the crop yield by a magnitude. To date, rapid advances in UAV technology has enabled sufficient lifting capacity and endurance to support high resolution imagery over large areas. With the FAA planning to integrate UAVs into the national airspace (NAS), UAV-based aerial weed scouting is now possible to bring promising significant benefits for farmers. An unmanned aerial vehicle based aerial weed scout with machine vision system was proposed and developed for the weed identification with the sensing capability from far above the field to close to the canopy, and measuring the plant/weed density (weed infestation rate)/weed species. The canopy was segmented from the background by greenness selection and thresholding. The crop row and its centroid line was calculated and masked by its pixel density. The anomalous weed patches between the crop rows was identified and its population was mapped. For species identification, a machine learning approach is used. Convolutional Neural Network (CNN) as one of deep learning method has the advantage of less neuron weight, easy for training and good robust performance in displacement, scale and deformation, which is promising for the weed specifies classification and probability assessment. The previous weed distribution mapping and individual weed extraction helps to generate the training data and highlights their own features, which could ensure the weed identification classifier robustness and efficiency. The preliminary result shows the specific weed type could be classified probably. The results show that the proposed approach will provide an efficient and promising tool for farmers‘ weed identification and the resulting infestation mapping will enable a better weed control recommendation.