Urban Environmental Management Research Articles

Estimation of greenhouse gas emission due to open burning of rice straw using Sentinel data

In recent decades, Vietnam has gradually become a critical global rice producer. During that production process, residual straw becomes an environmental pollutant due to open burning, raising greenhouse gas emissions. This study combines the optical images of the Sentinel-2 satellite and the radar images of the Sentinel-1 satellite to estimate the dry biomass of rice and to determine gas emissions due to rice straw burning over the fields in Quoc Oai district, Hanoi city for urban environmental management purposes. Sentinel-2 images have been classified into the land covers, thereby identifying the areas of ​​rice cultivation and the areas of ​​burned straw. Meanwhile, the Sentinel-1 radar image has been used to calculate the dry biomass of rice due to its ability to penetrate clouds, an obstacle to optical images in tropical regions. Furthermore, a field trip during harvesting season allows us to measure aboveground dry biomass. Then, the analysis shows a high correlation between the backscatter V.V. and V.H. of the radar image and the in-situ dry biomass (R=0.923 and R2=0.852), with a relatively low average error (RMSE = 6.58 kg/100 m2). By linear regression method, the study found the total rice dry biomass of 28728.5 tons, which was obtained after the Summer rice crop 2020 for the whole Quoc Oai district, of which 2037.91 tons of rice straw have been burned, releasing a large amount of greenhouse gas emission with 2398.6 tons of CO2, 189.5 tons of CO, 18.8673 tons of PM10 dust, 17.2087 tons of PM2.5 dust and some other gases. The identical procedure has also been applied to the western region of Hanoi city center to estimate the amount of gas emissions. This study has proven the effectiveness of an approach and contributed to supporting urban managers in proposing appropriate policies to monitor and protect the environment.

Enhancing reclaimed water distribution network resilience with cost-effective meshing

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Evolution and determinants of precipitation chemistry in a plateau city, China: Insight from the pollutants source identification and environmental effect

Rainfall-related chemical ions deposition processes have important environment effects on earth-surface, which is also a vital sink of urban air pollutants. Therefore, identifying the rainwater chemical evolution and determinants, and source is essential. The daily-based rainwater samples during 2022– 2023 and the historical rainwater data in a typical plateau city (Kunming) were investigated. The Ca2+ and NH4+, SO42− and NO3− dominated the rainwater cations and anions, with the cationic and anionic percentages of 78% and 10%, 50% and 33%. The historical variations in ions percentages revealed the achievement of environmental protection. The precipitation amount and the seasonal changes of source emission were critical determinants for the variations of rainwater chemistry. Using the stoichiometry and receptor model, the contributions of potential origins to rainwater chemicals were assessed. 86% of NO3− and 40% SO42− were from combustion-related anthropogenic source, volatilization-related source contributed 85% of NH4+ (e.g., agricultural/municipal wastes emissions), whereas marine input was the main contributor of Cl− (59%) and Na+ (52%). Mg2+ (58%) and Ca2+ (41%) were primary from terrestrial input source. The risk of wet deposition-caused acidification was limited due to the high rainwater neutralization level, whereas the impact of nitrogen-related deposition on gross primary productivity (GPP) was non-negligible and further attention was need. Overall, the present study clarified the rainfall chemical features of plateau city region and further provided decision support for more effective urban air environmental management programs.

Microplastic contamination in urban aquatic environments: Occurrence characteristics in urban streams and stormwater runoff from urban surfaces

This study investigates microplastics in urban environments, focusing on their abundance, types, and relationships with hydrological parameters. Microplastic analyses encompassed two steps: (1) examining urban streams including discharges from a wastewater treatment plant (WWTP) during non-rainy seasons, and (2) analyzing stormwater runoff from urban surfaces for microplastic content during rainy seasons. In urban streams, WWTP discharge exhibited higher microplastic concentrations compared to other streams, indicating WWTP discharge is a dominant source of microplastic pollution. The most prevalent microplastics detected were polypropylene, polyethylene, and their copolymer, although a variety of other types were also found. Concentrations of microplastics were notably influenced by specific urban land uses, as evidenced by a strong correlation (0.95) between microplastic concentrations and areas characterized by industrial and transportation activities. During rainy seasons, microplastics followed the pattern of stormwater runoff, but the highest concentrations, significantly exceeding those in urban streams, were observed before the peak runoff. These maximum concentrations and their timing of occurrence were linked to antecedent dry days, rain intensity, and runoff rate, showing significant statistical correlations. Regardless of their sizes, a diverse range of microplastic types was identified in these conditions, with no consistent pattern across different rain events. This highlights the complex nature of urban microplastic pollution. This study reveals that aquatic ecosystems are significantly affected by two primary factors: (1) the consistent contribution of microplastics from WWTP discharges, and (2) the short-term, but severe, impacts of microplastic pollution associated with stormwater runoff. Furthermore, it suggests the development of alternative strategies to mitigate microplastic pollution in aquatic ecosystems, informed by the findings on the characteristics of microplastics in urban environments. This research underscores the urgent need for integrated urban environmental management strategies, paving the way for future studies to further explore and address the multifaceted challenges posed by microplastic pollution in aquatic ecosystems.

Integrating CEDGAN and FCNN for Enhanced Evaluation and Prediction of Plant Growth Environments in Urban Green Spaces

Conducting precise evaluations and predictions of the environmental conditions for plant growth in green spaces is crucial for ensuring their health and sustainability. Yet, assessing the health of urban greenery and the plant growth environment represents a significant and complex challenge within the fields of urban planning and environmental management. This complexity arises from two main challenges: the limitations in acquiring high-density, high-precision data, and the difficulties traditional methods face in capturing and modeling the complex nonlinear relationships between environmental factors and plant growth. In light of the superior spatial interpolation capabilities of CEDGAN (conditional encoder–decoder generative adversarial neural network), notwithstanding its comparative lack of robustness across different subjects, and the excellent ability of FCNN (fully connected neural network) to fit multiple nonlinear equation models, we have developed two models based on these network structures. One model performs high-precision spatial attribute interpolation for urban green spaces, and the other predicts and evaluates the environmental conditions for plant growth within these areas. Our research has demonstrated that, following training with various samples, the CEDGAN network exhibits satisfactory performance in interpolating soil pH values, with an average pixel error below 0.03. This accuracy in predicting both spatial distribution and feature aspects improves with the increase in sample size and the number of controlled sampling points, offering an advanced method for high-precision spatial attribute interpolation in the planning and routine management of urban green spaces. Similarly, FCNN has shown commendable performance in predicting and evaluating plant growth environments, with prediction errors generally less than 0.1. Comparing different network structures, models with fewer hidden layers and nodes yielded superior training outcomes.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Computer-aided resilience: Advanced techniques for disaster management in smart urban environments

This research paper explores innovative contributions to the field of disaster management in smart urban environments, with a particular focus on integrating advanced computer-aided techniques, specifically GRU-CNN. Three key contributions are highlighted: (1) the development of dynamic risk assessment algorithms utilizing GRU-CNN for real-time analysis and predictive modeling, enabling proactive disaster mitigation; (2) the establishment of an integrated sensor network infrastructure for early warning systems, leveraging various sensors and GRU-CNN-based data analytics to detect and respond to potential disasters at their nascent stages; and (3) the implementation of human-centric resilience planning, utilizing GRU-CNN-based computer-aided tools to simulate disaster scenarios and engage communities in preparedness efforts. The dynamic risk assessment algorithms presented in this paper, powered by GRU-CNN, enable continuous monitoring and analysis of diverse data sources, fostering a proactive approach to disaster preparedness. The integrated sensor network architecture enhances early warning capabilities, allowing for timely responses to emerging threats through the application of GRU-CNN methodologies. Furthermore, the human-centric resilience planning approach introduces virtual simulations using GRU-CNN to model disaster scenarios, facilitating the testing and refinement of strategies in a risk-free environment. This approach not only ensures the adaptability of plans but also engages and educates communities, fostering a culture of preparedness, with GRU-CNN playing a pivotal role in the process. Through these contributions, this research paper seeks to advance the discourse on disaster management in smart urban environments, emphasizing the integration and effectiveness of GRU-CNN in enhancing resilience and response strategies. By leveraging cutting-edge GRU-CNN technologies and prioritizing community involvement, the proposed techniques aim to provide a more robust and adaptive response to the challenges posed by natural and man-made disasters in urban settings.

Smart City Actions Integrated into Urban Planning: Management of Urban Environments by Thematic Areas

Over the years, the imbalance between population needs and urban spaces’ organized development has been accentuated by increasing urbanization. The implementation of smart city actions began in the 1990s, with the development of integrated solutions in cities, harmonizing social, environmental, and economic aspects. This research measures the impact of thematic areas on smart cities’ management performance. The multi-criteria Decision Making Trial and Evaluation Laboratory (DEMATEL) method was used to identify the interdependent relations between smart cities’ thematic areas, structuring a diagram of cause-and-effect relations using threshold quantification values. Considering their degree of importance in smart city management, the thematic areas of technology and innovation, living environment and infrastructure, education and training, and governance and engagement are highlighted. For the degree of influence, the most influential thematic areas are coexistence and reciprocity, living environment and infrastructure, entrepreneurship, and healthcare. Also, the cause-and-effect analysis identified governance and engagement, education and training, and mobility as central thematic areas for smart city management. Finally, the research construct was developed by ranking the thematic areas’ performance in urban space planning.

Robust Parking Space Allocation System Using Open CV and Scikit-learn

The proliferation of urbanisation has led to an increased demand for efficient parking management systems. In response, this project presents a Smart Parking Assistant system aimed at providing real-time space availability notifications to users through their smartphones. Leveraging computer vision techniques implemented via Open CV and machine learning algorithms from the Scikit-learn library, the system captures video feed from a webcam to detect and quantify the number of empty parking spaces in a designated area. Upon receiving a request from the user, the system processes the video feed to analyse the occupancy status of parking slots, utilising advanced image processing techniques to accurately identify empty spaces along with mask image of the parking lot. The model built using Scikit-learn efficiently categorises the available slots, enabling the system to relay the precise number of open spaces to the user's smartphone. Furthermore, the Smart Parking Assistant incorporates geo-spatial functionalities to enhance user experience. By integrating the Haversine distance formula, the system calculates the distances between the user's location and nearby parking areas. This information is then displayed to the user, allowing them to conveniently locate and navigate to the nearest available parking facility. The proposed system offers a comprehensive solution to address the challenges associated with parking management in urban environments. By harnessing the power of computer vision, machine learning, and geo-spatial technologies, it provides users with timely and accurate information regarding parking space availability, ultimately improving efficiency and convenience in urban parking scenarios.

Sustainable energy transition in cities: A deep statistical prediction model for renewable energy sources management for low-carbon urban development

This study proposes a novel approach to sustainable energy transition in urban environments, focusing on the prediction and management of renewable energy outputs for low-carbon urban development. We introduce a new intelligent prediction model based on Gated Recurrent Unit (GRU) Networks to forecast the output power of both tidal and biomass units. The GRU model, known for its capability in capturing sequential dependencies, is employed to enhance the accuracy of renewable energy predictions in the context of urban sustainability. To address the interpretability challenges inherent in complex deep learning models, we leverage LIME (Local Interpretable Model-agnostic Explanations). By integrating LIME into our GRU-based prediction model, we enhance transparency and understandability. This approach facilitates the identification of key factors influencing predictions, providing stakeholders and decision-makers with valuable insights for effective sustainable energy management. The proposed hybrid model is applied to the digital model of real tidal and biomass sites in China, allowing for a statistical examination of renewable integration and energy management strategies. The results offer a comprehensive understanding of the dynamics between renewable energy sources and urban development, paving the way for informed decision-making in the pursuit of low-carbon and resilient cities. This research contributes to the broader discourse on sustainable urban development by offering a robust statistical framework for renewable energy integration and management in urban environments.

Integrating IoT Sensors, Cloud AI, and Satellite Broadband for Enhanced ESG Governance in Smart Cities: A Tripartite Approach to Real-Time Environmental Monitoring

Urbanization is accelerating at an unprecedented rate, pushing the limits of environmental sustainability and demanding innovative solutions for effective management. Traditional methods for monitoring the environment fall short, lacking the scalability, immediacy, and comprehensive coverage required for modern urban settings. Our paper introduces an innovative solution to bridge these gaps, harnessing the power of Internet of Things (IoT) sensors, advanced cloud-based AI through Claude 3 Sonnet models on Amazon Bedrock, and the extensive reach of Project Kuiper's satellite broadband. This threefold integration forms a cutting-edge approach to urban environmental governance. It enables the analysis of environmental data in real time, offers predictive insights, and achieves wide-ranging coverage. The system equips city leaders, policymakers, and citizens with precise, actionable information, fostering decisions that align with Environmental Social Governance (ESG) goals. More than just improving urban life and sustainability, our approach makes environmental data accessible to all, promoting a collaborative and inclusive model of urban environmental management.

Assessing Alien Plant Invasions in Urban Environments: A Case Study of Tshwane University of Technology and Implications for Biodiversity Conservation

Preserving the dwindling native biodiversity in urban settings poses escalating challenges due to the confinement of remaining natural areas to isolated and diminutive patches. Remarkably scarce research has scrutinised the involvement of institutions, particularly universities, in introducing alien plant species in South Africa, thus creating a significant gap in effective monitoring and management. In this study, the Tshwane University of Technology in Tshwane Metropole, South Africa serves as a focal point, where we conducted a comprehensive survey of alien plants both within the university premises and beyond its confines. The investigation involved the classification of invasion status and a meticulous assessment of donor and recipient dynamics. Our findings encompass 876 occurrence records, revealing the presence of 94 alien plant species spanning 44 distinct families. Noteworthy occurrences among the dominant plant families are Asteraceae and Solanaceae. Herbaceous and woody plants emerged as the most prevalent alien species, with common representation across both sampling sites. A substantial majority of recorded species were initially introduced for horticultural purposes (51%) before escaping and establishing self-sustaining populations (62%). Furthermore, 43 species identified are listed in South African invasive species legislation, with some manifesting invasive tendencies and altering the distribution of native species in the remaining natural areas. The notable overlap in species observed between the university premises and adjacent areas provides crucial insights into the influence of institutions on the dynamics of plant invasions within the urban landscape. This underscores the prevailing gaps in the management of invasive alien plants in urban zones and accentuates the imperative of an integrated approach involving collaboration between municipalities and diverse institutions for effective invasive species management in urban environments.

Industrial distribution and its effect on land surface temperature (LST) in Sukoharjo Regency in 2001, 2009 and 2021

The expansion of human activities, driven by population growth and various societal needs, has led to significant land-use changes, resulting in microclimate modifications. These changes have a crucial impact on land surface temperature (LST), particularly in urban areas. Urbanization, transportation, industrialization, and urban settlements are the main factors contributing to LST variations. This study focuses on the distribution of industries and their influence on land surface temperature in Sukoharjo Regency, Central Java, during the years 2001, 2009, and 2021. The research utilizes Landsat satellite imagery to analyze the industrial distribution and LST in the study area. The images were acquired on August 27, 2001, June 14, 2009, and July 25, 2021. The methodology includes land-use classification, LST extraction, and regression analysis to assess the relationship between industrial land area and land surface temperature. The results show a significant increase in the number of industries in Sukoharjo Regency over the studied period, with a growth rate of over 100% from 2001 to 2010 and further expansion until 2021. The industrial areas exhibited a clear spatial distribution in all years, indicating the impact of industrialization on land use. The regression analysis revealed a relationship between the industrial land area and land surface temperature, suggesting that expanding industries contributes to higher land surface temperatures. These findings provide valuable insights for urban planning and environmental management in Sukoharjo Regency to mitigate the impacts of climate change and promote sustainable development.

Application Research of Fish-Plant Symbiosis Technology in a Urban Lake: A Case Study of the Environment Remediation Project with Machine Learning Application

This research examines the Jiefang Park Water Environment Remediation Project, a pioneering initiative in urban lake rehabilitation. The project is anchored in a fish-plant symbiosis system, which has significantly improved water quality, upgrading it from Category V to Category III, and in certain zones, achieving the commendable Category II. This enhancement is quantitatively marked by a notable reduction in nitrogen and phosphorus levels, and a marked increase in water transparency, with submerged plants covering over 70% of the lakebed.A key innovation is the utilization of a machine learning model to predict chlorophyll-a concentration, a vital water quality metric. The model’s accuracy is underscored by its R² values, ranging from 0.23 to 0.99, and RMSE values between 6.921 and 0.237, with the best performance at point 1. Additionally, comprehensive measurement data highlight the project’s effectiveness. Water transparency significantly improved from baseline levels, as evidenced by increased dissolved oxygen levels at various sampling sites, indicative of restored ecological equilibrium in the lake. This study represents an exemplary fusion of ecological restoration and advanced data analytics, signifying a substantial advancement in urban lake restoration. It underscores the potential of combining synergistic ecological approaches with technological innovation to further sustainable urban environmental management.

Hydraulic Characterization of Green Roof Substrates by Evaporation Experiments

Green roofs can be a valid solution for stormwater management in urban environments. The objective of this study was to develop a laboratory procedure for the hydraulic characterization of artificial substrates, used in the realization of green roofs, based on transient evaporation and steady-state unit hydraulic gradient (UHG) experiments. The retention, θ(h), and hydraulic conductivity, K(h), curves of two commercial substrates Terra Mediterranea® (TMT) and AgriTERRAM® (ATV) and a specifically developed substrate made by mixing peat, compost and sandy loam soil (MIX) were investigated. The unimodal van Genuchten–Mualem (VGM) hydraulic functions obtained by the direct evaporation method with different choices of the fitting parameters were compared with UHG measurements of K(h) conducted close to saturation. A numerical inversion of the transient evaporation experiments performed by Hydrus-1D software was also conducted, assuming that the hydraulic properties could be expressed either by unimodal or bimodal VGM models. The results indicated that an appropriate a priori choice of the residual water content parameter improved the estimation of the water retention curve. Moreover, the water retention data estimated from the direct evaporation method were not statistically different from those obtained with the inverse Hydrus-1D. The unsaturated hydraulic conductivity estimations obtained by the direct and inverse methods were highly correlated and the use of the bimodal VGM model improved the estimation of K(h) in the wet range. The numerical inversion of laboratory evaporation data with the hydraulic characteristics expressed by the bimodal VGM model proved to be a reliable and effective procedure for hydraulic characterization of artificial substrates, thus improving the reliability of simulated water fluxes in green roofs.

Urban expansion in Greater Irbid Municipality, Jordan: the spatial patterns and the driving factors

Urban expansion within Greater Irbid Municipality (GIM) witnessed an extraordinary rise, expanding approximately ninefold between 1967 and 2020. Recent trends revealed a shift in urban growth towards southern and eastern regions. These dynamics carry critical implications for urban planners and environmental managers, urging a comprehensive understanding of the driving factors behind this expansion to anticipate future challenges. Employing logistic regression (LR) and geographically weighted logistic regression (GWLR) analyses using remote sensing data and GIS, spatially variant coefficients for driving factors emerged, illuminating the evolving landscape of urban development drivers within GIM. Yarmouk University historically promoted urban expansion, but recent proximity to Yarmouk University and JUST University, coupled with higher existing building percentages, inhibited further urbanization. The analysis also revealed that elevation and slope had negligible impacts on urban expansion. These findings underline the evolving dynamics of urban development drivers within the study region. The local perspective depicted significant spatial disparities in coefficients, highlighting variations in magnitude and direction. GWLR emerged as a more robust methodology, effectively capturing regional variations and enhancing model reliability. These findings hold immense value for informing current and future urban planning practices in Greater Irbid Municipality. Proactively addressing identified challenges and understanding the intricate dynamics of urban expansion can assist Irbid in shaping a sustainable and resilient future, avoiding potential pitfalls in its urban development endeavors.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Study on financial cost evaluation of urban water environment management and pollution prevention and control

Abstract Nowadays, water pollution has become a major factor restricting social development. To address this, the government has issued a series of policy documents to control water environmental pollution and achieved certain results. However, on the whole, the prevention and control of the pollution of water environments requires a large amount of capital investment, but the corresponding results and benefits are not significant. Hence, this paper takes Shanghai, Nanjing, Hangzhou, and Hefei as examples to study the financial cost-effectiveness of the governance of urban water environments from the two dimensions of time and space. It is concluded that the cost of water environmental treatment has a negative effect on the comprehensive benefit of environmental treatment in the region in the short term and a positive effect in the long term, which indicates that water environmental pollution treatment is work that needs to be adhered to for a long time, and long-term planning is also needed for cost input. On this basis, strategies to improve the cost efficiency of water pollution treatment are presented.

Analyzing the Land Surface Temperature Response to Urban Morphological Changes: A Case Study of the Chengdu–Chongqing Urban Agglomeration

AbstractUrban morphological change impacts the land surface temperature (LST) through modifying the net radiation, convective heat transfer, evapotranspiration, and heat storage on the ground. It is essential to quantify the contributions of these physical changes on LST changes. In this work, we conduct simulations using a weather research and forecasting model for the Chengdu–Chongqing urban agglomeration to identify causes of LST changes due to urban morphological changes through different morphological parameters: the aspect ratio, building plan area fraction, and average building height. A new method is proposed and used to quantify the contribution of these physical changes on LST changes. The results show as the aspect ratio increases, an increase of the average LST is induced by variations in radiation, and daytime cooling and nighttime warming are induced by variations in heat storage. There is warming associated with an increase in the building plan area fraction, which is mostly caused by a decrease in the efficiency of the long‐wave radiant heat emitted from the surface to the atmosphere. We also find that an increase in the average building height enhance the efficiency of convective heat transfer, which results in cooling. These results are important for the management of urban thermal environments.

Pollution and source-specific risk analysis of potentially toxic metals in urban soils of an oasis-tourist city in northwest China.

Source-specific risk apportionment for soil potentially toxic metals (PTMs) is of great significance for contamination prevention and risk management in urban environments. Eighty-five urban soil samples were obtained from an oasis-tourist city, China and examined for eight PTMs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn). The pollution levels, sources, and ecological risk of soil PTMs were quantified, and their source-specific ecological and human health effects were also estimated using the multi-proxy approaches. The results demonstrated that accumulation of Cd, Hg, Pb, Cr, Cu, and Zn in soils was observed compared to their background levels, and the soils experienced varying degrees of PTMs pollution, especially at sites with high-intensity anthropogenic activities. Natural sources, atmospheric deposition, industrial sources, vehicular emissions, and comprehensive inputs were the principal sources, with contributions of 29.28%, 25.86%, 20.13%, 16.50%, and 8.23%, respectively. The integrated ecological risks of PTMs in soils were moderate at most sites, with atmospheric deposition being the dominant contributor to ecological risks. Children exhibited pronounced non-cancer risks, but adults had no notable non-cancer risks. Moreover, there were potential carcinogenic risks for both children and adults within the study region. Non-cancer and carcinogenic risks were more significant for children than adults, and traffic emissions were the primary contributor to non-cancer risks (adults: 20.53%, children: 20.49%) and carcinogenic risks (adults: 22.95%, children: 22.08%). The industrial and traffic activities were considered as priority control sources for soil pollution control and risk management, with Hg, Cd, Zn, and Pb corresponding to the priority elements. This study highlights the source-specific ecological and human health effects of PTMs pollution in urban soils, thereby providing valuable information for targeted pollution control and priority source management.