Sludge production increases year by year, which causes great environmental pressure. Low-temperature sludge drying technology is a kind of technology with energy conservation and environmental protection, which is widely favored by the sludge drying industry. The distribution of airflow in the drying chamber is the key factor affecting the drying performance of sludge drying system. In this paper, a uniform flow structure was designed, and a numerical simulation study of the designed flow field in the designed drying chamber was carried out by STAR-CCM+ software. The uniform flow structure of the sludge drying chamber and the optimal flow field distribution conditions on the drying chamber and conveyor belt were obtained so that the uniformity of air distribution in the sludge drying chamber and conveyor belt was improved, and the drying performance of the sludge low-temperature drying system was improved. The effectiveness of the numerical simulation was verified by experiments. The experimental results showed that when the number of shunt plates is three, the shape is an involute arc plate, the distance between the first and third shunt plate is 1,600 mm, and the distance between the second and first shunt plate is 700–900 mm. The flow field distribution on the drying room and the conveyor belt was the most uniform when the inlet wind speed was between 25 and 35 m/s. In addition, there is a strong correlation between the experimental results and the simulation results, which can prove the effectiveness and reliability of the numerical model.
The airflow distribution inside the drying room was very uneven: the airflow would change direction with the shape of the obstacle, and the direction of airflow movement was easily affected by the shape of the shunt plates. Therefore, the shape of the shunt plates of the flow sharing structure was different, and the effect of the flow field was also different. Therefore, it was necessary to optimize the distribution uniformity of the flow field in the drying room. STAR-CCM+ software was used to optimize the flow field of the drying room, and the distribution of the velocity field was mainly considered. Through numerical simulation, the shape, quantity, and layout of the flow sharing structure were simulated and optimized, and the flow sharing structure that could improve the uniformity of air distribution in the drying room was designed. As long as these problems are solved, the low-temperature drying system could greatly improve the energy consumption of sludge dewatering, carry out sludge reduction treatment, and also be applied in large-scale industrialization, greatly reducing the problem of sludge treatment and reducing the environmental pollution.
Agricultural nonpoint source pollution (ANPS) can cause systemic pollution of the ecological environment, directly threatening sustainable agricultural development and human health and safety. Accurate estimation of ANPS loads, clarification of the sources and spatial distribution patterns of ANPS, and effective control and management of ANPS in the watershed require identifying critical source areas. This case study focuses on the Danjiang River basin, a critical water source for China’s South-to-North Water Division and demonstrates the efficiency of ArcGIS’s spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS and extracting the causes associated with the spatial pattern, as well as the accuracy of identifying critical source areas of ANPS. In this study, two spatial statistical analysis methods, Anselin local Moran’s I and hot-spot analysis (Getis–Ord Gi*), are applied to propose a method for accurately identifying critical source areas of ANPS based on the spatial distribution of ANPS loads. The results of the study are as follows: (1) based on the MIKE LOAD model, the annual ANPS loads of chemical oxygen demand (COD), ammonia nitrogen (NH4), total nitrogen (TN), and total phosphorus (TP) in the Danhan River basin are calculated as 182,530.15, 16,137.39, 58,285.92, and 2,962.84 t/yr, respectively. (2) Clusters are mainly distributed in the southwestern part of the watershed, and the spatial pattern is directly related to land use and rural population; the spatial pattern of outliers is related to agricultural modes and geographical characteristics. (3) Hot spot clusters are concentrated in the hinterland of Hanzhong Plain; the regional specialty of agriculture is the dominant factor in determining the spatial pattern of cold-spot and hot-spot clusters. (4) Based on these findings, seven critical subbasins and one critical source area of ANPS that need to be prioritized for control in the study area are identified.
Agricultural nonpoint source pollution (ANPS) is a significant cause of ecosystem pollution, which directly threatens the sustainable development of agriculture, human health, and safety. The Dan–Han River Basin is a crucial water source in China for the South-to-North Water Diversion Project and provides water to 140 million people. It is essential to ensure that the water quality of the Dan-Han River remains unpolluted. The ANPS as a major source of water pollution in the watershed is difficult to identify and control. Accurately identifying the sources and spatial distribution patterns of ANPS pollutants is a prerequisite for maintaining water quality and controlling ANPS. Our study offers a new perspective on methods to reveal the hidden spatial pattern and identify critical source areas of ANPS in watersheds. This research demonstrates the effectiveness of ArcGIS’s spatial statistical analysis methods in discovering the hidden spatial pattern of ANPS, extracting the causes associated with the spatial pattern, and accurately identifying critical source areas of ANPS. These methods can also be applied to study nonpoint source pollution in other watersheds.
Cyanobacterial harmful algal blooms (cyanoHABs) caused by cyanobacteria negatively affect humans via river water and aquatic life. Thus, reliable cyanobacteria predictions are essential for managing cyanoHABs. With recent advancements in computer technology and big data usage, artificial intelligence (AI) technologies have gained attention in various fields, such as water resources, weather and climate, and water quality. This study evaluated the applicability of deep-learning-based AI technology for predicting cyanobacteria. A convolutional neural network (CNN)–long short-term memory (LSTM) model, a deep-learning-based AI technology advantageous for predicting time-series data and cyanobacteria features, was built. Its results were analyzed and compared with those of the existing physical Environmental Fluid Dynamics Code (EFDC)–National Institute of Environment Research (NIER) model for cyanobacteria prediction. The CNN-LSTM model performed better, with an accuracy of 69%, which is an improvement over the previous EFDC-NIER model’s accuracy of 45%. In particular, there was a dramatic improvement in the prediction accuracy for low cyanobacteria cell counts in Level 1, which increased from 39% to 87%. There also was an improvement in the prediction accuracy for Levels 2 and 3. The accuracy for Level 2 increased from increased from 56% to 69%, and the accuracy for Level 3 increased from 38% to 48%. However, there was a significant decrease in prediction accuracy for high cyanobacteria cell counts in Level 4, for which the measured data were very scarce; accuracy decreased from 49% to 16.7%. The CNN-LSTM model yielded better overall prediction performance than the EFDC-NIER model, demonstrating its applicability in cyanobacteria prediction. However, it has limitations of overfitting areas with inadequate data and not accurately predicting patterns that have not occurred in the past. To address this issue, we propose an approach the combines the advantages of physics-based models and AI-based deep learning models, creating a hybrid concept.
A pumice–maghemite (P-maghemite) composite was developed using the chemical coprecipitation method with a 20% iron loading ratio by weight. The characterization of the composite using SEM and XRD indicated the effective loading and dispersion of nanoparticles on the surface of the developed base materials. Thereafter, in situ sequestration experiments were conducted in the laboratory for an arsenic-polluted aquifer system using two well-integrated permeable reactive barrier (PRB) modules filled with the developed composite. A vertical fixed-bed column setup was used for the columnar PRB, whereas a sand tank experimental setup was employed for the well-screen-integrated PRB; both PRB systems were fed by a synthetic solution representing the arsenic-contaminated groundwater. More than 99% arsenic removal was observed in the columnar PRB, with an average effluent concentration of 4 μg/L at the end of the experiment, which is well below the acceptable limit of drinking water for arsenic (<10 μg/L). Removal of arsenic by the 4-cm-wide well-screen-integrated PRB from 652 μg/L to less than 20 μg/L shows a great potential of the developed composite for arsenic remediation at slower groundwater flow rates. A maximum arsenic removal of 99% was attained at the start of the experiment, which decreased to 97% after 1 month of PRB operation. The effluent concentration of all other major ions also was reduced considerably in the PRB modules. The hydraulic conductivity of the developed media was reduced by 35% in the columnar PRB and by approximately 20% in the well-screen-integrated PRB. The high arsenic removal efficiency in continuous flow-through remediation systems indicates the applicability of the developed PRB system in in situ remediation of arsenic-contaminated groundwater.
Sulfide-based pathways for generating nitrite to sustain anaerobic ammonium oxidation (anammox) have garnered increasing attention. However, the presence of sulfide can also impact the anammox process, necessitating a comprehensive understanding of both its short-term and long-term effects on anammox. This study aimed to investigate the influence of sulfide on anammox, including its effects on the microbial community and process kinetics. During long-term operation, the maximum sulfide dosage tested was 30 mg S/L over 50 days of operation, exhibiting good nitrogen removal efficiency of 83.9%±4.8%. Conversely, under short-term exposure to sulfide, nitrogen removal efficiency was notably affected, decreasing to 68.98% at a considerably lower sulfide concentration of only 16 mg S/L. Within the context of long-term sulfide exposure, the maximum contribution of anammox to nitrogen removal reached 86.72% at a sulfide dosage of 25 mg S/L. However, when the influent sulfide concentration was increased to 50 mg/L, the contribution of anammox to nitrogen removal sharply declined to 41.3%. Microbial community analysis revealed as the sulfide concentration increased from 8 to 16 mg S/L, the abundance of anammox bacteria decreased from 2.46×105 to 1.67×105 copies/mL, whereas the abundance of Nitrobacter spp. increased from 2.73×102 to 8.13×102 copies/mL. However, during long-term operation, there was a more pronounced decrease in the microbial abundance of anammox, reducing from 5.3×105 to 3.77×102 copies/mL. Taking this decrease together with the improved efficiency of anammox observed during long-term operation, these findings suggest that sulfide’s influence on anammox primarily impacts its metabolic activity rather than its microbial abundance.
A sewer system is a principal element of infrastructure in modern cities, accounting for massive amounts of public investments. Corrosion of manholes in the sewer system is a global issue, and millions of dollars are being spent on the maintenance, restoration, and replacement of deteriorated sewer networks. Concrete manholes in the sewer system are deteriorating due to the attack of sulfuric acid produced by microorganisms in a process termed microbial induced concrete corrosion (MICC), which reduces the lifespan of concrete sewer elements. The objective of this paper is to investigate the correlation between the gas- and liquid-phase sewer environmental factors and hydrogen sulfide concentration in the gas phase. The production, emission, and build-up of hydrogen sulfide gas in manholes is identified as a major cause of MICC in manhole shafts. The field study was conducted in more than 200 manholes in the City of Arlington (Texas, US). The data was collected every minute for 48 h to understand the trends of liquid- and gas-phase parameters such as hydrogen sulfide (H2S concentration), liquid and gas temperature, pH, DO, and relative humidity. The study also examines how gas-phase H2S concentrations vary with season; manhole design, including manholes’ depth, slope, and presence of drop; and sewer flow conditions such as velocity and turbulence. Although no strong linear correlation was found between liquid-/gas-phase parameters, the manhole categories were found to play a significant role in H2S generation. The manholes with hydraulic jump generated the highest average H2S concentrations, followed by manholes with drops. High turbulence zones were observed in manholes of both categories, leading to H2S stripping from liquid to gas phase. The highest H2S concentration was recorded in summer, suggesting that higher liquid temperature resulted in increased bacterial activity, which generated greater liquid-phase sulfide. Greater Henry’s law constants in summer, due to high temperatures, would have favored transfer of liquid-phase sulfide to the gas phase.
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