Pollution Patterns Research Articles

Hotspots of Floating Plastic Particles across the North Pacific Ocean.

The pollution of the marine environment with plastic debris is expected to increase, where ocean currents and winds cause their accumulation in convergence zones like the North Pacific Subtropical Gyre (NPSG). Surface-floating plastic (>330 μm) was collected in the North Pacific Ocean between Vancouver (Canada) and Singapore using a neuston catamaran and identified by Fourier-transform infrared spectroscopy (FT-IR). Baseline concentrations of 41,600-102,700 items km-2 were found, dominated by polyethylene and polypropylene. Higher concentrations (factors 4-10) of plastic items occurred not only in the NPSG (452,800 items km-2) but also in a second area, the Papaha̅naumokua̅kea Marine National Monument (PMNM, 285,200 items km-2). This second maximum was neither reported previously nor predicted by the applied ocean current model. Visual observations of floating debris (>5 cm; 8-2565 items km-2 and 34-4941 items km-2 including smaller "white bits") yielded similar patterns of baseline pollution (34-3265 items km-2) and elevated concentrations of plastic debris in the NPSG (67-4941 items km-2) and the PMNM (295-3748 items km-2). These findings suggest that ocean currents are not the only factor provoking plastic debris accumulation in the ocean. Visual observations may be useful to increase our knowledge of large-scale (micro)plastic pollution in the global oceans.

Occurrence, Source Apportionment, and Ecological Risk of Typical Pharmaceuticals in Surface Waters of Beijing, China.

Various studies have shown that the heavy use of pharmaceuticals poses serious ecological risks, especially in metropolitan areas with intensive human activities. In this study, the spatial distribution, sources, and ecological risks of 29 pharmaceuticals in 82 surface waters collected from the North Canal Basin in Beijing were studied. The results showed that the pharmaceutical concentrations ranged from not detected to 193 ng/L, with ampicillin being undetected while ofloxacin had a 100% detection frequency, which indicates the widespread occurrence of pharmaceutical pollution in the North Canal Basin. In comparison with other freshwater study areas, concentrations of pharmaceuticals in the North Canal Basin were generally at moderate levels. It was found that pharmaceutical concentrations were always higher in rivers that directly received wastewater effluents. Source analysis was conducted using the positive matrix factorization model. Combining the spatial pollution patterns of pharmaceuticals, it has been found that wastewater effluents contributed the most to the loads of pharmaceuticals in the studied basin, while in suburban areas, a possible contribution of untreated wastewater was demonstrated. Risk assessment indicated that approximately 55% of the pharmaceuticals posed low-to-high ecological risks, and combining the results of risk analyses, it is advised that controlling WWTP effluent is probably the most cost-effective measure in treating pharmaceutical pollution.

Investigating students’ perceptions of single-use plastics at the University of the Witwatersrand, South Africa

ABSTRACT Single-use plastics have become interconnected with human populations globally, yet, studies on plastic pollution and practices have predominantly been conducted outside Africa. This study explored perceptions of single-use plastics at the University of the Witwatersrand, with 1398 respondents in an online survey and 18 in a solicited diary during 2019. Despite social media and research exposure amongst university students, only 57% of respondents were familiar with the term single-use plastics. However, respondents displayed considerable pro-environmental attitudes relating to plastic pollution and recycling. These attitudes did not translate into pro-environmental behaviour in daily life, whereby 88% of respondents believed that single-use plastics are used with too many products, yet, only 22% avoid products which incorporate them. Respondents faced constraints in their pro-environmental behaviour relating to recycling infrastructure, re-use and the expense of alternative products. Negative perceptions exist towards single-use plastics in South Africa due to their ubiquitous pollution and presence in daily consumption practices. Increased pro-environmental behaviour provides an opportunity to curb plastic pollution and rethink consumption patterns. However, changing attitudes alone to stem the tide of plastic consumption and pollution will be meaningless without supportive recycling infrastructure from the government and reduced single-use, non-recyclable plastic production.

Tracing Organophosphate Ester Pollutants in Hadal Trenches─Distribution, Possible Origins, and Transport Mechanisms.

Unraveling the mysterious pathways of pollutants to the deepest oceanic realms holds critical importance for assessing the integrity of remote marine ecosystems. This study tracks the transport of pollutants into the depths of the oceans, a key step in protecting the sanctity of these least explored ecosystems. By analyzing hadal trench samples from the Mariana, Mussau, and New Britain trenches, we found the widespread distribution of organophosphate ester (OPE) flame retardants but a complex transport pattern for the OPE in these regions. In the Mariana Trench seawater column, OPE concentrations range between 17.4 and 102 ng L-1, with peaks at depths of 500 and 4000 m, which may be linked to Equatorial Undercurrent and topographic Rossby waves, respectively. Sediments, particularly in Mariana (422 ng g-1 dw), showed high OPE affinity, likely due to organic matter serving as a transport medium, influenced by "solvent switching", "solvent depletion", and "filtering processes". Amphipods in the three trenches had consistent OPE levels (29.1-215 ng g-1 lipid weight), independent of the sediment pollution patterns. The OPEs in these amphipods appeared more linked to surface-dwelling organisms, suggesting the influence of "solvent depletion". This study highlights the need for an improved understanding of deep-sea pollutant sources and transport, urging the establishment of protective measures for these remote marine habitats.

Urban 2D and 3D morphology and the pattern of ozone pollution: a 68-city study in China

ContextAir pollution significantly impacts urban sustainable development and public health. Urban ozone pollution (UOP) is currently one of the most challenging tasks for urban air pollution control, and is possibly linked to urban morphology. However, the effect of urban two-dimensional (2D) (coverage or density, etc.) and three-dimensional (3D) (density + height, etc.) morphology on the UOP concentration remains unclear.ObjectivesThe objective of this study was to explore the influence of urban morphology on UOP concentration and provide useful information to control urban air pollutants.MethodsFirst, based on building height and remotely sensed UOP data from 68 Chinese cities, the general spatial pattern of urban 3D morphology and UOP was detected across different climate zones in China. Then, this study used variance decomposition to investigate the contribution of 2D and 3D urban morphology to UOP in China.ResultsThe study showed that China's urban morphology was dominated by Medium Rise & Medium Density (MRMD). Large cities had higher UOP levels in summer, especially for the urban morphology with Low Rise & High Density (LRHD). Further, UOP concentrations were substantially higher in the southern temperate zone than in other climatic zones. Anthropogenic factors (rather than natural factors) were always the dominant factors influencing UOP across different seasons; specifically, urban 2D and 3D morphology can explain 40% of UOP variation. The effects of urban 3D and 2D morphologies on UOP concentrations varied seasonally. Urban 2D morphology dominated in spring, whereas 3D morphology dominated in winter.ConclusionsOur study elucidates the effect of urban morphology on UOP and provides insights for sustainable urban development.

Nitrate Pollution Characteristics and Its Quantitative Source Identification of Major River Systems in China

Accurate source identification/apportionment is essential for optimizing water NO3--N pollution control strategies. This study conducted a meta-analysis based on data from 167 rivers across China from 2000 to 2022 to analyze the spatial and temporal variation patterns of nitrate pollution in seven major river systems and to quantitatively identify the source composition of riverine nitrate. The average ρ（NO3--N） in the seven major river systems was （4.54±3.99） mg·L-1， with 9.6% of river ρ（NO3--N） exceeding 10 mg·L-1. The riverine ρ（NO3--N） in eastern China were higher than that in western China， and the highest concentration was observed in the Haihe River system. Additionally， tributaries experienced more serious NO3--N pollution than that in the main stream. The ρ（NO3--N） in most river systems in the dry season was higher than that in the wet season， except in the Yellow River system. There was significant nitrification in the Pearl River system， the middle and lower reaches of the Yellow River system， the middle reaches of the Liaohe River system， the Songhua River system， and the Haihe River system， whereas there was significant denitrification in the Yangtze River system， the Huaihe River system， and the lower reaches of the Pearl River system. Based on the dual stable isotopes-based MixSIAR model， the major NO3--N source was sewage/manure （ > 50%） in the Yangtze River system， Haihe River system， Liaohe River system， and Southeast River system. Soil nitrogen was the main NO3--N source in the Songhua River system （56.4%）， and the contribution of fertilizer nitrogen， soil nitrogen， and sewage/manure to NO3--N pollution in the Pearl River system， Huai River system， and Yellow River system was 20%-40%. The contribution rate of sewage/manure to NO3--N in the tributaries was higher than that in the main stream， whereas the contribution rate of soil nitrogen to NO3--N in the main stream was higher than that in the tributaries. The contribution rate of soil nitrogen， fertilizer nitrogen， and atmospheric deposition nitrogen to nitrate nitrogen in the wet season was higher than that in the dry season， whereas the contribution rate of sewage/manure to NO3--N pollution in the dry season was higher than that in the wet season. Therefore， point source pollution such as domestic and production sewage discharge should be controlled in the Haihe River system， the Yangtze River system， the Liaohe River system， the tributaries and the downstream main stream areas of Yellow River system， and the downstream area of the Pearl River system， whereas non-point source pollution caused by the loss of fertilizer and soil nitrogen should be controlled in the Huaihe River system， the Songhua River system， the middle reaches of the main stream area of the Yellow River system， and the middle and upper reaches of the Pearl River system. The results can provide a scientific basis for the effective control of nitrate pollution in the river systems in China.

A Data-Driven Approach to Identify Major Air Pollutants in Shanghai Port Area and Their Contributing Factors

Air pollution is a growing concern in metropolitan areas worldwide, and Shanghai, as one of the world’s busiest ports, faces significant challenges in local air pollution control. Assessing the contribution of a specific port to air pollution is essential for effective environmental management and public health improvement, making the analysis of air pollution contributions at a selected port in Shanghai a pertinent research focus. This study aims to delve into the distribution patterns of atmospheric pollutants in port areas and their influencing factors, utilizing a data-driven approach to unveil the relationship between pollution sources and dispersion. Through a comparative analysis of pollution levels in the port’s interior, surrounding regions, and urban area concentrations, we ascertain that carbon monoxide (CO) and nitric oxide (NO) are the primary pollutants in the port, with concentrations significantly exceeding those of the surrounding areas and urban area levels. These two pollutants exhibit an hourly pattern, with lower levels during the day and higher concentrations at night. Employing a random forest model, this study quantitatively analyzes the contribution rates of different factors to pollutant concentrations. The results indicate that NO concentration is primarily influenced by operational intensity and wind speed, while CO concentration is mainly affected by meteorological factors. Further, an orthogonal experiment reveals that maintaining daily operational vehicle numbers within 5000 effectively controls NO pollution, especially at low wind speeds. Additionally, humidity and temperature exhibit similar trends in influencing NO and CO, with heightened pollution occurring within the range of 75% to 90% humidity and 6 °C to 10 °C temperature. Severe pollution accumulates under stagnant wind conditions with wind speeds below 0.2 m/s. The results help to explore the underlying mechanisms of port pollution further and use machine learning for early pollution prediction, aiding timely warnings and emission reduction strategy formulation.

Development and trends of constructed wetland substrates over the past 30 years: a literature visualization analysis based on CiteSpace.

Constructed wetland substrates (CWSs) have received considerable attention owing to their importance in adsorbing and degrading pollutants, providing growth attachment points for microorganisms, and supporting wetland plants. There are differences in the configurations and functions of constructed wetlands (CWs) for treating different water bodies and sewage, resulting in a wide variety of substrates. Research on the application and mechanism of CWSs is not sufficiently systematic. Therefore, the current research advancements and hotspots must be identified. Hence, we used CiteSpace to analyze 1955 English publications from the core collection database of the Web of Science to assess the current state of the CWS research field. Based on the cooperative network analysis, the roles of various countries, institutions, and authors in research on CWSs were reviewed. Keyword co-occurrence and cluster analyses were used to discuss the transformation of CWSs from removing traditional pollutants to emerging pollutants and the transition from incorporating natural substrates to artificial substrates. Finally, we underscored the need for more emphasis to be placed on the collocation and application of the CWSs at different latitudes. Furthermore, the substrate micro-interface process and its effects on the interaction patterns of pollutants and microorganisms should be thoroughly investigated to provide theoretical guidance for the development of wetland applications and mechanisms.

Evaluation of heavy metal speciation distribution in soil and the accumulation characteristics in wild plants: A study on naturally aged abandoned farmland adjacent to tailings

Heavy metal composite pollution is widespread in the surrounding environment of tailings ponds in arid and semi-arid regions, leading to the abandonment of substantial agricultural land. This study investigates the speciation distribution and plant accumulation characteristics of heavy metals in abandoned farmland with different durations of natural aging. The aim is to comprehend the local heavy metal behavior pattern in the soil-plant system and offer insights for environmental remediation. Our findings reveal that Cd stands out as the primary heavy metal pollutant in this area. The mobility ranking of heavy metals is Cd > Pb > Zn > Cu, with Cd and Pb mobility decreasing along the basin. Notably, active Pb exhibits a higher affinity for soil binding compared to other metals. The predominant plant species in the region are primarily small shrubs, herbaceous plants, and semi-shrubs that demonstrate tolerance to drought and salt. Most plant samples showed elevated levels of Cd, Pb, and Zn, surpassing the maximum tolerance levels for dietary minerals in livestock. This elevated metal content poses potential threats to the health of local livestock and wildlife, yet it is also considered a potential for phytoremediation. Selected dominant plant species from the current study include Kalidium foliatum & gracile which shows potential as a Cd accumulator and indicator. Neotrinia splendens and Reaumuria songarica demonstrate potential as Cd excluders, with the latter exhibiting higher tolerance to Cd (62.9 mg/kg). Additionally, our observations indicate that different plant parts exhibit distinct responses to heavy metals, and Zn synergistically influences the aerial part accumulation of Cd.This study holds significant importance in understanding the complex behavior patterns of multi-metal pollutants in the natural environment. The identification of native plants with remediation potential is valuable for phytoremediation of environment pollution in mining area.

Overview of the Trajectory-Ensemble Potential Source Apportionment Web (TraPSA-Web) Toolkit for Atmospheric Pollutant Source Identification

Trajectory ensemble receptor models (TERMs) were widely used to determine the likely source locations and apportionment of air pollutants. This paper describes the development and applications of the Trajectory-ensemble Potential Source Apportionment Web application (TraPSA-Web), a comprehensive toolkit for likely atmospheric pollutant source location apportionments using TERMs and back trajectories generated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The TERMs integrated within the TraPSA-web include Concentration Field Analysis (CFA), Concentration Weighted Trajectory (CWT), single-site and multiple-site Potential Source Contribution Function (PSCF), and Simplified Quantitative Transport Bias Analysis (SQBA). TraPSA-Web is designed as a web application with a user-friendly modern graphical user interface (GUI), which largely enhances the accessibility to the users. TraPSA-Web will provide the air quality research community with a sophisticated toolkit for (1) easy management of the research project and datasets, (2) efficient automatization for HYSPLIT configurations, calculations, and result aggregations, (3) flexible configurations for the research scenarios and TERM parameters, and (4) interactive visualizations for the pollutant pattern analysis and TERM result mapping.

Integrated surrogate framework for reactive transport simulation of uranium in situ leaching with generative models

The heterogeneity of uranium-bearing aquifers plays a crucial role in governing the migration and leaching of uranium. Uncertainty quantification (UQ) based on the Monte Carlo method is usually used to deal with the issue of aquifer heterogeneity. However, the substantial computational costs of this method arise from a large number of calls to the uranium reactive transport simulation; therefore, surrogate models are developed to reduce these costs. This study leverages a conditional deep convolutional generative adversarial network (cDC-GAN) and an adversarial autoencoder-based kriging regression (AAE-KRG) to develop an integrated surrogate framework for uranium reactive transport simulation. cDC-GAN and AAE-KRG are designed to achieve the cross-domain mapping of the permeability coefficient field to the uranium spatial distribution and leaching concentration over time, respectively. The results demonstrate a structural similarity exceeding 0.9 between the cDC-GAN and the numerical simulation, with a mean square error generally less than 0.002. These findings suggest that the cDC-GAN exhibits high accuracy and stability in terms of replacing the numerical simulation to obtain the spatial-temporal evolution of the uranium plume. Additionally, cDC-GAN is a dynamic surrogate model with the capability of interpolation because uranium spatial distributions with multiple time steps are used as training data during the training process. AAE-KRG also exhibits high accuracy and stability in terms of predicting the leaching concentration by permeability coefficient fields, as evidenced by determination coefficients generally exceeding 0.95. By comparing the computational costs, it is clear that cDC-GAN and AAE-KRG are suitable for processing spatial distribution and time series data, respectively. The trained surrogate models are used to undertake UQ of the uranium leaching area and leaching concentration. The results indicate that the uncertainty of the leaching area in the field-test model increases with time, and the increasing rate reaches a maximum in the middle period (201–300 d). The uncertainty of the leaching concentration increases with time in the early period, decreases after peaking at 137 d, and stabilizes in the middle and late periods. It is observed that the change characteristics of the uncertainty are consistent with the values of the leaching area or concentration. Furthermore, the spatial distribution pattern of uranium pollution is determined based on stochastic simulation results. The uncertainty intervals and the pollution risk status at multiple time steps are quickly obtained by surrogate models, which is helpful for scheme optimization in uranium mining. Owing to its characteristics of being data-driven and highly adaptable to nonlinear data, the integrated surrogate framework has applications beyond uranium in situ leaching.

Policy implications for synergistic management of PM2.5 and O3 pollution from a pattern-process-sustainability perspective in China

In recent years, the pattern of air pollution in China has changed profoundly, and PM2.5 and surface ozone (O3) have become the main air pollutants affecting the air quality of cities and regions in China. The synergistic control of the two has become the key to the sustainable improvement of air quality in China. In this study, we investigated and analyzed the spatial and temporal distribution patterns, exposure health risks, key drivers, and sustainable characteristics of PM2.5 and O3 concentrations in China from 2013 to 2022 at the national and city cluster scales by combining methodological models such as spatial statistics, trend analysis, exposure-response function, Hurst index, and multi-scale geographically weighted regression (MGWR) model. Ultimately, a synergistic management system for PM2.5 and O3 pollution was proposed. The results showed that: (1) The PM2.5 concentration decreased at a rate of 1.45 μg/m3 per year (p < 0.05), while the O3 concentration increased at a rate of 2.54 μg/m3 per year (p < 0.05). The trends of the two concentrations showed significant differences in spatial distribution. (2) Population exposure risks to pollutants showed an increasing trend, with PM2.5 and O3 increasing by 55.1 % and 42.7 %, respectively. The annual deaths associated with exposure to PM2.5 and O3 demonstrated a decreasing and inverted U-shaped trend, respectively, with annual average deaths of 1.312 million and 98,000. Significant regional disparities in health risks from these pollutants were influenced by socio-economic factors such as industrial activities and population density. In the future, it is expected that more than half of China's regions will be exposed to rising risks of PM2.5 and O3 population exposure. (3) Key drivers of regional exacerbation in PM2.5 and O3 levels include the number of industrial enterprises above designated size (NSIE) and population agglomeration (PA), while the disposable income of urban residents (URDI), technological innovation (TI), and government attention level (GAL) emerged as primary factors in controlling pollution hotspots, ranked in order of influence from greatest to least as TI > GAL > URDI. Overall, this study sheds light on the current status of air pollution and health risk sustainability in China and enhances the understanding of future air pollution dynamics in China. The results of the study may help to develop effective targeted control measures to synergize the management of PM2.5 and O3 in different regions.

Fine simulation of PM2.5 combined with NPP-VIIRS night light remote sensing and mobile monitoring data

Human activity plays a crucial role in influencing PM2.5 concentration and can be assessed through nighttime light remote sensing. Therefore, it is important to investigate whether the nighttime light brightness can enhance the accuracy of PM2.5 simulation in different stages. Utilizing PM2.5 mobile monitoring data, this study introduces nighttime lighting brightness as an additional factor in the PM2.5 simulation model across various time periods. It compares the differences in simulation accuracy, explores the impact of nocturnal human activities on PM2.5 concentrations at different periods of the following day, and analyzes the spatial and temporal pollution pattern of PM2.5 in urban functional areas. The results show that (1) the incorporation of nighttime lighting brightness effectively enhances the model's accuracy (R2), with an improvement ranging from 0.04 to 0.12 for different periods ranges. (2) the model's accuracy improves more prominently during 8:00–12:00 on the following day, and less so during 12:00–18:00, as the PM2.5 from human activities during the night experiences a strong aggregation effect in the morning of the next day, with the effect on PM2.5 concentration declining after diffusion until the afternoon. (3) PM2.5 is primarily concentrated in urban functional areas including construction sites, roads, and industrial areas during each period. But in the period of 8:00–12:00, there is a significant level of PM2.5 pollution observed in commercial and residential areas, due to the human activities that occurred the previous night.

Green and sustainable practices for an energy plant cultivation on naturally contaminated versus spiked soils. The impact of ageing soil pollution in the circular economy framework

Silybum marianum L. Gaertn. or milk thistle is an energy-produced weed that has been shown to be tolerant of heavy metal-contaminated soils. In the present study, its cultivation was studied in soils laboratory-spiked (artificial) with Cu and Zn solutions. Meanwhile, plant growing on naturally contaminated soils of Mediterranean regions, both urban and rural, was investigated. The metal concentrations spiked in artificial polluted soils were estimated to be roughly equivalent to those in naturally contaminated soils. Plants grown in artificially contaminated soils incorporated the metal added to the soils more rapidly and in higher proportions. The contamination of soil samples was carried out using different chemical reagents, salts containing the metals with oxidation number II, highlighting the fact that the reagent containing the metal is crucial regarding artificial soil pollution. Statistically significant differences were observed between the individual pollution patterns, as far as plant metals uptake concern. It was also found that the aged, contaminated soils transfer lower levels of metals to the plants. Therefore, aging or weathering of contamination alters toxicity levels in the soil environment by determining transport and uptake into the soil-to-plant system. Eventually, from the present research, it emerged the fact that in urban soils that have aged perennial pollution, the uptake of metals by plants is probably lower than in rural ones. Furthermore, with proper management, it is possible to grow plants, with low nutrient requirements, in urban soils by adopting smart, green and eco-friendly techniques, enhancing sustainable cultivation in the framework of circular economy.

Geochemical status of non-reclaimed ash dumps subjected to long-term self-overgrowing: Evidence from the Tyumen, Russia

Establishing the geochemical state of ash dumps of solid fuel thermal power plants in the absence of reclamation is extremely important in urban conditions. The current study was performed to assess the level, spatial distribution, soil pollution and risk of potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) in soils of abandoned ash dumps from the city of Tyumen, Russia. In general, the soils of as dumps had elevated levels of PTEs and PAHs compared to the crustal abundance and global soil background. A number of metals, namely Sr, Ni, Cu, Cr, Zn, and V, were concentrated in the soils of ash dumps (2–6 times), while As, Co, and Pb slightly exceeded the citywide average (up to 1.5 times). Naphthalene (NAP) was the only representative of the studied PAHs, the concentrations of which in the soils of ash dumps significantly exceeded the median content in urban soils of Tyumen. Elevated levels of NAP are likely a consequence of frequent fires occurring in grassy areas. Spatial patterns of pollutants are mainly controlled by the initial heterogeneity of technogenic substrates, rather than the post-depositional processes including vegetation and soil cover development. Low and moderate pollution and human health risks of PTEs and PAHs were revealed in topsoils of the studied sites. Although elevated levels of pollutants in coal ash dump soils are due to peat combustion, they pose less of an environmental risk than the more common coal ash dumps and are potentially suitable for a wider variety of management practices.

Self-Supervised Spatiotemporal Clustering of Vehicle Emissions With Graph Convolutional Network.

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air pollution from road traffic, is a challenging representation learning task due to the lack of supervision. Some recent work building upon graph convolutional network (GCN) models the intrinsic spatiotemporal correlations among the nodes in road networks as graph representations for clustering. However, these existing methods ignore the interactions between spatial and temporal variations in vehicle emissions, resulting in incomplete descriptions and inaccurate detection of the evolution pattern of air pollution. To address this issue, this article proposes a two-way self-supervised spatiotemporal representation learning scheme, in which the temporal and spatial features are progressively learned in a mutually reinforced manner. Our proposed method is based on the observation that though the variation in vehicle emissions in the road network is consistent in the spatial and temporal domains, its expression is more distinct in temporal sequences. To this end, the input emission data are first projected into an initial temporal representation space spanned by the captured features from a pretrained BiLSTM network. Then the generated distribution of temporal features is used to construct an objective constraint for high-purity clustering through a two-way self-supervised mechanism, which is leveraged as a constraint for the feature clustering of a GCN. Furthermore, to eliminate the initial errors, a joint optimization scheme is presented to generate the decoupled clustering results through the progressive refinement of representation and clustering. Our proposed method is evaluated on the traffic emission dataset of Xian city in 2020, and the experimental results have demonstrated the superiority against the state-of-the-art.

Degradation kinetics of organic matter in domestic sewage by sequencing batch reactor

The sequencing batch reactor (SBR) has become a popular sewage treatment process in various industries due to its strong impact load resistance and numerous other advantages. This study examines the variations of chemical oxygen demand chromium (CODcr), ammoniacal nitrogen (NH3-N), total bound nitrogen (TN) and total phosphorus (TP) with aeration time to investigate the degradation patterns of different pollutants during the SBR’s reaction stage. Additionally, an organic matter degradation kinetic model was developed. The experimental results showed that the CODcr and NH3-N in the reactor met the national discharge standards after two hours of influent. Furthermore, the removal rates of CODcr and NH3-N exceeded 85% and 99%, respectively, at the end of the final aeration. At the same time, the TN and TP removal rates remained stable at approximately 45% and 68%. The analysis of the kinetic model showed that the degradation process of organic matter followed a first-order kinetic equation: S=S0e−0.00012Xt. The regression model's fitting value exhibited a high degree of conformity with CODcr removal’s experimental results, with the average relative error of 5.52% falling within an acceptable range. Furthermore, the correlation coefficient (0.9966) indicated high accuracy and effectively represented the changes in organic matter throughout the SBR reaction stage. Consequently, this model can serve as a theoretical foundation for determining CODcr’s maximum treatment capacity in experimental wastewater.

Research on the driving characteristics of motor vehicles in high-altitude areas for the analysis of exhaust emission characteristics

Research on the driving characteristics of motor vehicles at different altitudes in plateau regions and road emission intensity can provide technical support for energy conservation and emission reduction policies in plateau areas. This paper selects gasoline and diesel vehicles for on-board testing on typical road sections in plateau regions to study the overall driving characteristics of motor vehicles and the driving characteristics at different altitude intervals in plateau areas. The results show that with the increase in altitude, the driving conditions gradually concentrate towards ’high speed, low acceleration.’ The relevant conclusions can further study and analyze the distribution patterns of pollutants in motor vehicle exhaust emissions and road emission intensity.

Using structural equation modeling to assess the genotoxic and mutagenic effects of heavy metal contamination in the freshwater ecosystems: A study involving Oreochromis niloticus in an urban river

Chemical pollutants represent a leading problem for aquatic ecosystems, as they can induce genetic, biochemical, and physiological changes in the species of these ecosystems, thus compromising their adaptability and survival. The Capibaribe River runs through the state of Pernambuco, located in Northeastern Brazil, and passes through areas of agricultural cultivation, densely populated cities, and industrial centers, primarily textiles. Despite its importance, few ecotoxicological studies have been conducted on its environment, and knowledge about pollution patterns and their effects on its biota is still being determined. The objective of this study was to evaluate the water quality and the damage supposed to be caused by pollutants on the DNA specimens of Nile tilapia (Oreochromis niloticus) obtained from seven strategic points of Capibaribe. Tilapia specimens and water were collected during the rainy and dry seasons from 2015 to 2017. The following characteristics were analyzed: physicochemical (six), metal concentration (seven), local pluviosity, micronuclei, and comet assay. The physicochemical and heavy metal analyses were exploratory, whereas the ecotoxicological analyses were hypothetical. To verify this hypothesis, we compared the groups of fish collected to the results of the micronuclei test and comet assay. We created a Structural Equation Model (SEM) to determine how each metal's micronuclei variables, damage index, pluviosity, and concentration were related. Our results demonstrated that the highest values for markers of genetic damage were detected at points with the highest heavy metal concentrations, especially iron, zinc, manganese, chromium, and cadmium. The SEM demonstrated that metals could explain the findings of the genotoxicity markers. Moreover, other pollutants, such as pesticides, should be considered, mainly where the river passes through rural areas. The results presented here demonstrate that the Capibaribe River has different degrees of contamination and confirm our hypothesis.

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

Urban environments are characterized by a complex soundscape that varies across different periods and geographical zones. This paper presents a novel approach for analyzing nocturnal urban noise patterns and identifying distinct zones using mobile phone data. Traditional noise-monitoring methods often require specialized equipment and are limited in scope. Our methodology involves gathering audio recordings from city sensors and localization data from mobile phones placed in urban areas over extended periods with a focus on nighttime, when noise profiles shift significantly. By leveraging machine learning techniques, the developed system processes the audio data to extract noise features indicative of different sound sources and intensities. These features are correlated with geographic location data to create comprehensive city noise maps during nighttime hours. Furthermore, this work employs clustering algorithms to identify distinct noise zones within the urban landscape, characterized by their unique noise signatures, reflecting the mix of anthropogenic and environmental noise sources. Our results demonstrate the effectiveness of using mobile phone data for nocturnal noise analysis and zone identification. The derived noise maps and zones identification provide insights into noise pollution patterns and offer valuable information for policymakers, urban planners, and public health officials to make informed decisions about noise mitigation efforts and urban development.