Urban Environmental Management Research Articles

Water quality improvement project for initial rainwater pollution and its performance evaluation

Efficiently addressing initial rainwater pollution is crucial for mitigating urban water pollution. However, the performance evaluation of initial rainwater pollution control project is rarely introduced. In this study, the architecture of effective comprehensive engineering measures for improving the water quality of initial rainwater in Anhui Province, China, was described. Three water quality indicators, ammonia nitrogen (NH3–N), chemical oxygen demand (COD), and total phosphorus (TP), were selected to explore the severity of urban pollution caused by initial rainwater under various rainfall scenarios. A single-factor evaluation method was used to contrast and assess the benefits of the initial rainfall interception project in terms of water quality enhancement. Results showed that initial rainfall pollution was gentler under light rainfall conditions but more prominent under moderate and heavy conditions. The percentages of NH3–N, COD, and TP in Lotus Pond that met the tertiary drinking water standard were 100%, 74.91%, and 100% with great improvement, and the average concentrations of NH3–N, COD, and TP in Fushan Road Drainage have decreased by 91.43%, 10.49%, and 57.33% respectively, after the construction of the interception project. These indicated that the nitrogen and phosphorus pollution were successfully controlled by the control techniques in both locations, but COD concentration has to be addressed with more specialized strategies. Overall, the water quality improvement project for initial rainwater pollution plays a great role in effectively governing initial rainwater pollution and improving river water quality, and provides an effective technical reference for urban water ecological environment management.

Urban expansion induced loss of natural vegetation cover and ecosystem service values: A scenario-based study in the siliguri municipal corporation (Gateway of North-East India)

Several cities in the world have been subjected to urban sprawl. For instance, in India, this process is responsible for environmental degradation. The case of Siliguri Municipal Corporation (SMC) is a good example. Therefore, a proper investigation of the urban expansion characteristics of SMC is required to provide a basis for sustainable urban environmental management. This study analysed the changes in land use and land cover (LULC), future scenarios prediction, and ecosystem services values (ESVs) estimation loss. LULC analysis showed that the built-up area increased from 17.26 sq. km to 36.98 sq. km between 2001 and 2015. As a result, the ESVs from natural vegetation decreased by 9.18 million US$/hectare/year during the same period. The urban growth prediction model was generated based using the Multi-Layer Perceptron Neural Network and Markov Chain. Four scenarios were designed to predict future LULC. The model has predicted a maximum urban expansion (66.46 sq. km) in the business-as-usual scenario. The lowest urban expansion (58.81 sq. km) was identified in the planning scenario. The lowest vegetation degradation was observed in the planning scenario. This study highlights the need for an urgent sustainable urban management plan.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Study of regional variations and convergence in ecological resilience of Chinese cities

Using panel data from 281 Chinese cities at the prefecture level from 2005 to 2020, this study uses the entropy approach to assess the ecological resilience of Chinese cities. Then, it examines regional variations and the convergence of ecological resilience of Chinese cities using the Dagum Gini coefficient, kernel density estimation, Markov chain, and convergence model. The conclusions are as follows: First, there is a lessening trend in fluctuations between the degrees of urban ecological resilience within the nation and three regions, and they eventually converge to a level that is comparatively steady. The prevalence of inter-regional cross-over situations is the main factor contributing to the widening gaps in urban ecological resilience levels. Second, despite differences in distribution dynamics, urban ecological resilience has increased across geographies. Third, ecological resilience demonstrates the occurrence of the “low-level trap” and “high-level monopoly”; high-level cities are more likely to draw low-level cities. Fourth, there is a strong indication of σ and β convergence tendencies in the level of ecological resilience in China's cities across the country and its three regions. The government should improve urban environmental management and ecological protection, clarify each city's purpose and development strategy, and foster collaboration and connectivity among neighboring cities to bridge the urban ecological resilience gap successfully.

From Conservation to Connection: Exploring the Role of Nativeness in Shaping People’s Relationships with Urban Trees

Deciding whether to plant native or non-native trees in public urban green spaces is becoming complex and conflicted, and decisions purely based on biotic nativeness are likely to be hamstrung as climate change and rising urban heat push many native species beyond their natural ranges. Importantly, tree selection considerations by urban planners and environmental managers will have to move beyond a primary focus on securing conservation and ecological outcomes, to elucidate and engage with a growing interest in the socio-cultural values and services of urban trees. Building on emerging theoretical perspectives, this place-based study explores the role that perceptions of nativeness have in shaping people’s relationships with native and non-native urban trees and landscapes in an Australian city. Nativeness was associated with a range of subjective meanings including cultural identity, political expression, nature connection, desirable and undesirable traits, and environmental and cultural compatibility. Our findings emphasise that the ways in which urban trees and green spaces are valued and experienced is likely mediated by people’s perceptions of nativeness and its importance relative to other attributes. To provision and sustain green spaces that meet the diverse needs and preferences of urban publics, planners and managers need to elucidate and incorporate the nuanced, place-based and multifaceted subjective meanings of nativeness into urban greening decision-making and practice.

Detect and Evaluate Visual Pollution on Street Imagery Taken of a Moving Vehicle

Visual pollution is a growing problem in urban areas. It is important for environmental management to identify, formalize, measure and evaluate visual pollution. This paper presents a study on the development of an automated system for visual pollution classification using street images taken from a moving vehicle. The proposed system uses convolutional neural networks to classify different types of visual pollutants such as graffiti, faded signage, potholes, litter, construction zones, broken signage, poor street lighting, poor billboards, road sand, sidewalk clutter, and unmaintained facades.In this study, we utilized a large dataset of raw sensor camera inputs gathered from a fleet of multiple vehicles in a specific geographical area. Our aim was to develop convolutional neural networks that simulate human learning to classify visual pollutants from these images. The successful implementation of this system would be a significant contribution to the development of urban planning and the strengthening of communities worldwide. Additionally, it could lead to the creation of a "visual pollution score/index" for urban areas, which could serve as a new metric for urban environmental management. Our findings, which we present in this paper, will be a valuable addition to the academic community and the field of computer vision for environmental management applications.

Identifying the Production–Living–Ecological Functional Structure of Haikou City by Integrating Empirical Knowledge with Multi-Source Data

The inefficient use of urban resources and the imbalance of spatial structures make optimizing land use management a top priority in urban environmental management. Traditional land use classification systems that prioritize only natural features while disregarding human activity can result in redundancy and conflicts in urban planning. The Production–Living–Ecological Space (PLES) approach was developed as an integrated method for territorial spatial classification. However, most existing studies on PLES are conducted at provincial scales, largely overlooking fine-scale usage within cities. In addition, the existing concept of PLES has been vaguely defined, resulting in linear and simple identification methods that are not applicable to complex urban environments. To address these issues, this study proposes a method to identify urban PLES based on supervised classification using random forest models, which integrate empirical knowledge and multi-source heterogeneous information. The experiments conducted in Haikou reveal the regional aggregation of living and production spaces and the scarcity of ecological space in the city. Our study proposes a concrete concept of PLES and a method for identifying PLES that can be applied to multiple regions, providing an effective tool for the coordinated management of urban production, living, and ecological environments.

Water Quality Prediction Based on the KF-LSTM Encoder-Decoder Network: A Case Study with Missing Data Collection

This paper focuses on water quality prediction in the presence of a large number of missing values in water quality monitoring data. Current water quality monitoring data mostly come from different monitoring stations in different water bodies. As the duration of water quality monitoring increases, the complexity of water quality data also increases, and missing data is a common and difficult to avoid problem in water quality monitoring. In order to fully exploit the valuable features of the monitored data and improve the accuracy of water quality prediction models, we propose a long short-term memory (LSTM) encoder-decoder model that combines a Kalman filter (KF) with an attention mechanism. The Kalman filter in the model can quickly complete the reconstruction and pre-processing of hydrological data. The attention mechanism is added between the decoder and the encoder to solve the problem that traditional recursive neural network models lose long-range information and fully exploit the interaction information among high-dimensional covariate data. Using original data from the Haimen Bay water quality monitoring station in the Lianjiang River Basin for analysis, we trained and tested our model using detection data from 1 January 2019 to 30 June 2020 to predict future water quality. The results show that compared with traditional LSTM models, KF-LSTM models reduce the average absolute error (MAE) by 10%, the mean square error (MSE) by 21.2%, the root mean square error (RMSE) by 13.2%, while increasing the coefficient of determination (R2) by 4.5%. This model is more suitable for situations where there are many missing values in water quality data, while providing new solutions for real-time management of urban aquatic environments.

Multispectral Remote Sensing for Estimating Water Quality Parameters: A Comparative Study of Inversion Methods Using Unmanned Aerial Vehicles (UAVs)

Multispectral remote sensing technology using unmanned aerial vehicles (UAVs) is able to provide fast, large-scale, and dynamic monitoring and management of water environments. We here select multiple water-body indices based on their spectral reflection characteristics, analyze correlations between the reflectance values of water body indices and the water quality parameters of synchronous measured sampling points, and obtain an optimal water body index. A representative selection, such as statistical analysis methods, neural networks, random forest, XGBoost and other schemes are then used to build water-quality parameter inversion models. Results show that the XGBoost model has the highest accuracy for dissolved oxygen parameters (R2 = 0.812, RMSE = 0.414 mg L−1, MRE = 0.057) and the random forest model has the highest accuracy for turbidity parameters (R2 = 0.753, RMSE = 0.732 NTU, MRE = 0.065). Finally, spatial distribution maps of dissolved oxygen and turbidity of water bodies in the experimental domain are drawn to visualize water-quality parameters. This study provides a detailed comparative analysis of multiple inversion methods, including parameter quantity, processing speed, algorithm rigor, solution accuracy, robustness, and generalization, and further evaluates the technical characteristics and applicability of several inversion methods. Our results can provide guidance for improved small- and medium-sized surface-water quality monitoring, and provide an intuitive data analysis basis for urban water environment management.

Alternative scenarios for urban tree surveys: Investigating the species, structures, and diversities of street trees using street view imagery

Grasping information about street trees can assist urban environmental managers in quantifying and evaluating their costs and ecological benefits. Street view imagery has the potential for urban street tree surveys. However, few studies have been conducted on the inventory of street tree species, size structures and diversity based on street view imagery at the urban scale. In this study, we tried to conduct a survey of street trees in urban areas of Hangzhou using street view images. First, we constructed a size reference items system and determined that using it for street view measurements of street trees was comparable to field measurements results (R2 = 0.913–0.987). On this basis, we investigated the distribution characteristics and differences of street trees in Hangzhou using Baidu Street View and found that Cinnamomum camphora was the dominant tree species in Hangzhou (46.58 %), and the high proportion made urban street trees susceptible to ecological hazards. In addition, surveys conducted separately in various urban districts revealed that the diversity of street trees in new urban areas was smaller and less uniform. Additionally, as the gradient got further away from the city center, the street trees are smaller, the diversity first increased and then decreased, and the evenness gradually decreased. This study analyzes the use of Street View to investigate the distribution of species, size structure, and diversity of urban street trees. The use of street view imagery will simplify the collection of data on urban street trees and provide urban environmental managers with a foundation for strategy development.

Big data technology framework and data utilization for urban environmental pollution management

Urban environmental pollution management is of great practical significance to achieving sustainable urban economic development. To improve the efficiency of urban environmental pollution management, we have established a big data technology framework for urban environmental pollution treatment. The relevant pollution data collected are used for targeted pollution treatment. The results show that the average efficiency of environmental pollution control in the whole province of China has increased from 33.67% to 63.67%, an increase of 46%. Among them, the efficiency of environmental pollution control in Guangdong has increased most significantly, with a relative position of 26.47%, which is at the top of the list. Inner Mongolia has the weakest increase in environmental pollution control efficiency, with an appreciation of 6.89% relative to its position, while the other 16 provinces and cities have little change in environmental pollution control efficiency. between 2010 and 2020, the urban pollution and environmental treatment costs that residents need to bear changed significantly, with fluctuations of around 30%.

Effects of snow cover on urban light climate environment in the high latitudes of northeast China

Light climate environment (LCE) has a significant impact on human health, behavioral characteristics, and the safety of life and property due to the high albedo of snow on the ground cover type, which in turn affects the regional climate and socio-economic development, but less relevant studies have been found. In this study, the effect of snow on daytime and nighttime light levels was quantified using comparative field observations and controlled experiments in artificial climate chambers, combined with analysis of variance and model fitting. The results of the study found that there was a significant difference between the presence and absence of snow on both daytime and nighttime light levels. During daytime, the ambient light level on the ground with snow is 5.68 times higher than without snow, an improvement of 12,711.06 Lux. At night, with moonlight, the nighttime illuminance with and without snow is 0.213 Lux and 0.01 Lux, respectively. When there is no moonlight, the snow has no significant effect on the light level. In addition, significant differences in LCE intensity with different snow depths, snow densities and black carbon (BC) pollution. At the same background light intensity, the LCE intensity varies significantly with increasing snow depth, snow density and BC pollution. The results reveals the quantitative impact of snow on LCE, providing scientific support for regional natural light energy use, human health and safety, urban environmental management and economic development.

Rethinking the Low-carbon Transformation of Resource-based Cities Under Net-zero Emission Targets: A Case-study Analysis of Four Resource-based Cities

Objective: Over 120 countries have set or are considering net-zero emissions or neutrality targets. Under the targets, it is necessary to rethink the low-carbon transformation path for resource-based cities, often regarded as incompetent to transition to net-zero directly. Therefore, this paper attempts to summarize the general frame for low-carbon transformation in resource-based cities. Methods: In this paper, we build a Solow growth model with carbon emission constraints to theoretically analyze the possible path of low-carbon transformation for cities. Then we analyze four typical cities in Japan, Germany, Britain, and America. Results: The results of theoretical analysis indicate that industrial structure, technological innovation, and clean energy are the key factors for resource-based cities to move towards zero carbon. In addition, the case analysis results show that the authorities of the four cities adopted policies of industry structure adjustment, energy consumption structure adjustment, and urban environment management to achieve the low-carbon transition. The results further validate the conclusions of the theoretical analysis. Conclusion: Finally, we propose a general framework for resource-based urban transformation. Additionally, we suggest that urban authorities adjust their layout of emerging industries to local conditions, increase investment in low-carbon technologies (especially clean energy), and actively take the lead in environmental governance.

Land Suitability Analysis for Sustainable Urban Development: A Case of Nabatiyeh Region in Lebanon

Urbanization has led to the degradation of green areas and natural resources, which are critical for preserving biodiversity and natural benefits. Sustainable urban development (SUD) practices aim to balance urbanization with preserving and protecting green land and natural resources to achieve greater sustainability. This study evaluated land suitability for SUD in the Nabatiyeh area of Lebanon using a multi-criteria decision analysis technique, the analytical hierarchy process, combined with a geographic information system. The elevations and slopes were the most important criteria, followed by the distances from urban agglomeration and industrial areas, accessibility, and land cover types, with corresponding weights of 34% and 36%, 12%, 9%, 6%, and 3%, respectively. The study created a suitability map for future SUD. This identification assists urban planners and policymakers in making informed decisions regarding the development of urban areas. The study’s findings highlight the efficiency of combining multi-criteria decision analysis with geospatial techniques for urban planning and environmental management in promoting sustainable development and protecting the environment. Future research could explore the possibility of integrating administrative reforms to promote SUD, including improving governance, enhancing the efficiency of governmental agencies responsible for urban development, building scientific capacities, and increasing officials’ accountability.

Examining the importance of built and natural environment factors in predicting self-rated health in older adults: An extreme gradient boosting (XGBoost) approach

Previous studies indicate that natural and built environment factors significantly influence health outcomes in older adults. However, most cross-sectional studies exploring the impact of these factors on health fail to quantify the relative importance of each factor. Here, we use the XGBoost machine learning technique with SHAPley Additive exPlanations (SHAP) to rank the importance of built environment factors, natural environmental factors, and sociodemographic factors in shaping older adults’ odds of good self-rated health (SRH), in Shanghai, Guangzhou, and Shenzhen, China. To address self-selection bias in housing choice, older adults living in private housing, who have more freedom to choose residential locations, were distinguished from those living in public or self-built housing. To better interpret the results of XGBoost outcomes and analyse the association between factors and SRH, we used SHAP dependence plots. Results show that both built environment and natural environment factors play important roles in predicting SRH. Four built environment factors (accessibility to public transit stations, points of interest density, road density, and population density) and two natural environment factors (air quality index and surrounding greenness) have considerable predictive power for SRH for both groups. Among these factors, accessibility to public transit stations, road density, and air quality index become less important after controlling for self-selection bias. We also trace potential threshold effects of residential greenness, points of interest density, and road density on decreasing older adults' SRH within certain intervals after controlling for self-selection bias. Findings from this study can support the decision-making of policymakers regarding urban planning, landscape design, and environmental management to improve overall health of older adults.

Water environment characteristics and control countermeasures of typical hilly cities in Southeast China

Abstract Due to population growth and economic development, problems such as uneven distribution of freshwater resources and degradation of water quality are becoming increasingly prominent. Many measures have been adopted to address water resources issues; however, few studies have focused on specific changes in water quality after policy implementation. In this study, Yiwu City, Zhejiang Province, a typical hilly city with a well-developed water system, was selected to analyze the changes in water quality. By analyzing the rainfall series and hydrology characteristics, and water quality characteristics, this paper revealed the water environment characteristics and put forward some suggestions for water management in hilly cities. Results showed that the historical rainfall time distribution is uneven, and the water environment capacity of urban inland rivers exhibits strong variability in Yiwu City. The contents of ammonia nitrogen (NH3-N) and total phosphorus (TP) decreased significantly since the policy implementation of ‘Five water cohabitation’. Urban rivers presented regional pollution and surface source pollution characteristics, and the water quality fluctuated significantly with the increase in rainfall. At the same time, the study confirmed that scientific water replenishment measures can effectively improve water quality, providing reference ideas for the protection and management of China's urban water environment.

Rapidly reducing phytotoxicity of green waste for growing media by incubation with ammonium

Increasing green waste poses a great challenge to the management of urban environment. Using as growing media could be an environmental-friendly and effective way to dispose of green waste, but it is​ limited by the strong phytotoxicity ubiquitous in green waste. This study assessed the phytotoxicity of six typical green wastes from Southwest China. Their organic composition was identified to find the key phytotoxic substance. After a large number of screening, ammonium salt was selected as detoxifying agent. Incubations with different ammonium salts ((NH4)2CO3, NH4HCO3, (NH4)2SO4) and different dosage (0.5%, 1.0%, 2.0% on a weight basis) were applied on the pruning waste composed of various roadside trees, and their effect of removing phytotoxicity was investigated. Results showed the six green wastes generally had strong phytotoxicity, and the phytotoxicity could be well explained by high concentration of organic acids such as malic acid, citric acid. Five days of incubation with ammonium could dramatically reduce concentrations of six representative organic acids and greatly increase germination indexes. This study concludes that incubation with ammonium carbonate at dosage of 1% would be optimal for the pruning waste, as the organic acid concentration was quite low (<0.1 mg ml−1) and the phytotoxicity was absent (GI>100%). This study will be useful for addressing the key barrier to adoption of green waste as growing media.

PHENOTYPIC CONSIDERATIONS IN URBAN THERMAL SENSITIVITY/ADAPTATION OF RESIDENTS IN CALABAR METROPOLIS,CROSS RIVER STATE

The study is focused on identification of the link between body size and thermal sensitivity in Calabar, Cross River State, Nigeria. Numerous investigations have been carried out on similar lines in other parts of the world but there is practical dearth of information in this domain of knowledge in the area of study. The subjects/population of this study are adult residents domiciled in the central city of Calabar where urban heat warming is clearly exemplified. The problem statement is predicated on the prevalent urban heat scenario in the area and the general public outcry on the adverse effects of the heat incidence in the area. From existing literature, it has been recognised that thermal discomfort is to an extent subjective of age, sex and phenotypic make up. Here in this study, three recognised phenotypes have been identified as variables subjects for investigation. They include endomorphs, mesomorphs and ectomorphs. The objective is to elucidate their relative heat sensitivity indices. The justification is viewed from the valuable contributions this study can make to such fields as urban environmental management, individual livelihood sustainability and the general public health benefits. In terms of methodology, the study area has been clearly explained based on its geographical location (sub equatorial, marine coastal belt) weather/climatic characteristics, and so on. The population as stated earlier comprises adult residents domiciled in the Calabar central business district. Population sampling was achieved through a multi stage sampling procedure. The questionnaire instrument was employed in primary data collection, while other surrogate measures was achieved through biophysical data capturing. Data analysis was carried out through hypothesis testing using one way analysis of variance (ANOVA) following the result. The Fcal ratio stood at 16.960 while the critical value f stood at 4.51 at 0.05 level of significance. Since Fcal > f , the null hypothesis was rejected while the alternate hypothesis was accepted this means there is a significant variation in the thermal sensitivity of persons with different phenotype ratios.

Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China

A better understanding of the relationship between land surface temperature (LST) and its influencing factors is important to the livable, healthy, and sustainable development of cities. In this study, we focused on the potential effect of human daily activities on LST from a short-term perspective. Beijing was selected as a case city, and Weibo check-in data were employed to measure the intensity of human daily activities. MODIS data were analyzed and used for urban LST measurement. We adopted spatial autocorrelation analysis, Pearson correlation analysis, and spatial autoregressive model to explore the influence mechanism of LST, and the study was performed at both the pixel scale and subdistrict scale. The results show that there is a significant and positive spatial autocorrelation between LSTs, and urban landscape components are strong explainers of LST. A significant and positive effect of human daily activities on LST is captured at night, and this effect can last and accumulate over a few hours. The variables of land use functions and building forms show varying impacts on LST from daytime to nighttime. Moreover, the comparison between results at different scales indicates that the relationships between LST and some explainers are sensitive to the study scale. The current study enriches the literature on LST and offers meaningful and practical suggestions for the monitoring, early warning, and management of urban thermal environment with remote sensing technology and spatial big data sources.

Exploring multiple dimensions of attachment to historic urban places, a case study of Edinburgh, Scotland

ABSTRACT This paper is concerned with people’s attachments to historic urban places they experience in their daily lives. The topic has received growing emphasis in the realm of heritage studies, given its importance to the conservation and management of historic urban environments. Drawing on qualitative data collected from thirty in-depth interviews carried out in Edinburgh, Scotland, I explore the multiple ways in which (known as dimensions in place attachment literature) people develop attachments to historic urban places: from a kind of autobiographic attachments which were grounded in their everyday living and memories, and those of their families to intellectual attachments wherein they showcased their deep appreciation for the attributes that define a historic place. I also seek to demonstrate a class-specific nature of attachments at the intellectual level, adding a politicised view to the complexity of the phenomenon. In so doing, I demonstrate the many ways in which historic places are important to people’s enjoyment of lives in the city, and the various roles the history of places and/or their historic attributes played in forging such attachments. I conclude the paper with discussions on findings related to the current state of knowledge in place attachment research and implications for heritage practices.