Urban Environment Research Articles

A spatial analysis of mental health-related calls for service in Atlantic City, New Jersey

ABSTRACT Objective Police officers often find themselves responding to calls that do not involve criminal behavior, but calls related to individuals experiencing emotional distress or mental illness that requires police response. This research aims to identify high-risk places of mental health calls for service (MHCFS) in an urban setting and compare place features of these locations by police response (locations where a CIT-trained officer is dispatched versus a non-trained officer dispatched). Data/Methods Our research uses Atlantic City Police Department’s calls for service data from 1 January 2019 through 30 December 2022, including the incidents of mental incompetence with location components. After removing the calls that were not coded as mental health, a total of 2229 incidents involving perceived mental illness were geocoded and compared. We used RTMDx software to generate risk terrain models for MHCFS in Atlantic City, NJ. Findings The RTM analysis yielded no association between places of interest and MHCFS for calls involving non-CIT-trained officers. Yet, we identified five high-risk activity spaces of MHCFS where officers with CIT training were involved. Grocery stores (RRV = 2.873), places of leisure (RRV = 2.764), Health & Wellness Facilities (RRV = 2.624), Eateries & Restaurants (RRV = 2.547), and Convenience & Variety Stores (RRV = 2.280) are the risk factors identified by the RTM analysis. Given the characteristics of places identified by risk-terrain modeling, we reason that the characteristics of these places indicate that individuals experiencing perceived or actual mental health problems hang out in spaces with ease of access and in spaces where they can stay relatively longer with minimal attention. Conclusion Our research suggests that there is an association between spatial factors and MHCFS that are responded to by CIT-trained officers. City officials should consider deploying additional resources and mental health staff to these places to address the issue of mental health in urban settings. Future research should consider testing whether the places we identified in our analysis are also high-risk places for MHCFS in other urban environments.

Effects of Litter Removal and Biochar Application on Soil Properties in Urban Forests of Southern China

Urban forests are crucial components of cities, serving as vital ‘green lungs’ that embody urban civilization and sustainability. Despite their significance in maintaining the urban environment and ecological functions, management practices for urban forests can be unreasonable at times. This study investigated the impact of two common practices, litter removal and biochar application, on soil properties in an urban forest in Changsha city, China. The aim was to understand how these practices affect soil carbon, nutrients, and microbial activity in urban settings. The results showed that soil water content (SWC), pH, available phosphorus (AP), and soil microbial biomass carbon (SMBC) were significantly reduced in areas where litter was removed compared to areas where litter was retained. Conversely, biochar application led to a significant increase in SWC, pH, AP, and SMBC. The treatment alone had no significant effects on total nitrogen (TN), soil organic carbon (SOC), and soluble soil organic carbon (SSOC) in the examined urban forests. However, the SOC and SSOC contents significantly increased over time with biochar application. Our results demonstrated that the influences of litter removal and biochar application on soil property were attributed to the regulation of AP and SMBC in the studied urban forests. This study provides a scientific basis and reference for understanding the sustainable management of urban environments and guiding future conservation efforts in urban greening spaces.

Variations of black carbon, particulate matter and nitrogen oxides mass concentrations in urban environment with respect to winter heating period and meteorological conditions

Variations of black carbon, particulate matter and nitrogen oxides mass concentrations in urban environment with respect to winter heating period and meteorological conditions

Browser-Based Object Detection System for Isolating Plastic Bottles using the COCO-SSD Model

Plastic waste, especially in urban environments and water bodies, poses a significant environmental threat. This paper presents a browser-based object detection system for identifying and isolating plastic bottles using state-of-the-art machine learning models. The system leverages TensorFlow.js, ML5.js, and P5.js libraries along with the COCO-SSD model to detect plastic bottles in real time using a mobile camera interface. By employing a browser-based architecture, the system offers cross-platform functionality, eliminating the need for server-based computations or specialised hardware. Experimental evaluation showed high detection accuracy across various environments, underscoring the potential for real-world applications in waste management and recycling efforts.

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

Atmospheric evolution of environmentally persistent free radicals in the rural North China Plain: effects on water solubility and PM2.5 oxidative potential

Abstract. Environmentally persistent free radicals (EPFRs) represent a novel class of hazardous substances, posing risks to human health and the environment. In this study, we investigated the EPFRs in ambient fine, coarse, and total suspended particles (PM2.5, PM10, and TSPs) in the rural North China Plain, where local primary emissions of EPFRs were limited. We observed that the majority of EPFRs occurred in PM2.5. Moreover, distinct seasonal patterns and higher g factors of EPFRs were found compared to those in urban environments, suggesting unique characteristics of EPFRs in rural areas. The source apportionment analyses revealed atmospheric oxidation as the largest contributor (33.6 %) to EPFRs. A large water-soluble fraction (35.2 %) of EPFRs was determined, potentially resulting from the formation of more oxidized EPFRs through atmospheric oxidation processes during long-range or regional transport. Additionally, significant positive correlations were observed between EPFRs and the oxidative potential of water-soluble PM2.5 measured by dithiothreitol-depletion and hydroxyl-generation assays, likely attributable to the water-soluble fractions of EPFRs. Overall, our findings reveal the prevalence of water-soluble EPFRs in rural areas and underscore the fact that atmospheric oxidation processes can modify their properties, such as increasing their water solubility. This evolution may alter their roles in contributing to the oxidative potential of PM2.5 and potentially also influence their impact on climate-related cloud chemistry.

Cyanobacterial Blooms in City Parks: A Case Study Using Zebrafish Embryos for Toxicity Characterization

Cyanobacteria are photosynthetic prokaryotes that play an important role in the ecology of aquatic ecosystems. However, they can also produce toxins with negative effects on aquatic organisms, wildlife, livestock, domestic animals, and humans. With the increasing global temperatures, urban parks, renowned for their multifaceted contributions to society, have been largely affected by blooms of toxic cyanobacteria. In this work, the toxicity of two different stages of development of a cyanobacterial bloom from a city park was assessed, evaluating mortality, hatching, development, locomotion (total distance, slow and rapid movements, and path angles) and biochemical parameters (oxidative stress, neurological damage, and tissue damage indicators) in zebrafish embryos/larvae (Danio rerio). Results showed significant effects for the samples with more time of evolution at the developmental level (early hatching for low concentrations (144.90 mg/L), delayed hatching for high concentrations (significant values above 325.90 mg/L), and delayed development at all concentrations), behavioral level (hypoactivity), and biochemical level (cholinesterase (ChE)) activity reduction and interference with the oxidative stress system for both stages of evolution). This work highlights the toxic potential of cyanobacterial blooms in urban environments. In a climate change context where a higher frequency of cyanobacterial proliferation is expected, this topic should be properly addressed by competent entities to avoid deleterious effects on the biodiversity of urban parks and poisoning events of wildlife, pets and people.

Contemporary Art in Public Spaces: Forms of Expression, Social Significance, and Revitalization

Introduction: This study examines the significance of contemporary art in public spaces, with a specific emphasis on its various manifestations, societal functions, and capacity to revitalize urban environments. It emphasizes the ways in which art enhances aesthetic enjoyment, enhances the overall quality of life, and contributes to economic growth. Methodology: A qualitative study examines the use of sculptures, installations, murals, performances, and digital artworks in urban settings. Illustrative instances of effective art initiatives demonstrate the positive impact of these interventions on public spaces. When considering a project, we carefully evaluate factors such as accessibility, interactivity, the differentiation between temporary and permanent installations, and the influence on community relations. Results: The study finds that contemporary art in public spaces expands cultural access, strengthens social bonds, and raises societal awareness. Interactive and temporary artworks particularly influence the use and perception of these spaces, promoting community identification with the environment. Discussions: Art projects like New York City's High Line and Copenhagen's Superkilen show how aesthetic improvements and cultural activities attract residents and tourists, driving economic benefits. Temporary installations and pop-up art actions offer dynamic experiences that continually draw visitors, aiding economic revitalization. Conclusions: Integrating contemporary art into urban development improves public spaces by fostering cultural experiences and contributing to social and economic growth. Cities should actively support such projects to enhance community sense and urban quality of life.

Acoustic study of urban bat diversity in Veliko Tarnovo, Bulgaria

Urbanisation presents both challenges and opportunities for bat populations. Understanding the composition and distribution of urban bat species is the first step toward their effective conservation. However, comprehensive studies on urban bat diversity remain scarce in Bulgaria. To address this gap, we conducted a year-long acoustic survey on five different habitats in the urbanised territory of Veliko Tarnovo. Our study identified the presence of eight bat species and other distinct acoustic groups within the urban environment. Bats were found to utilise various habitats across the urban landscape. Although the activity was highest during May and September, bats remained active all year round, including the winter. Our study provides insights into synanthropic bats’ seasonal and microhabitat-dependent use of urban areas. These findings will establish a baseline for future research focusing on habitat preferences, population dynamics, and interactions with urbanisation. Such research is essential for the effective management and conservation of bat populations in urban environments.

Application of low energy building thermal energy optimization based on image recognition and light sensors in urban green environment planning

The paper analyzes the application of thermal energy optimization scheme based on image recognition and light sensor technology in low-energy buildings, in order to improve building energy efficiency, reduce energy consumption and provide scientific basis for urban green environment planning. In this paper, image recognition technology is used to monitor the light change in the external environment of the building, and the indoor light intensity data is obtained by the light sensor in real time. Combined with big data analysis, building heat optimization model is established, intelligent control system is designed, and the heating and cooling system of the building is dynamically adjusted, so as to achieve efficient use of heat energy. The experimental results show that the thermal energy optimization method based on image recognition and light sensor has a good application prospect in low-energy buildings, which not only effectively reduces energy consumption, but also contributes to the sustainable development of urban green environment.

Quantifying the impact of vertical greenery systems (VGS) on Mediterranean urban microclimate during heat wave events

This research investigates the potential of vertical greenery systems (VGS) to modulate the outdoor microclimate in a real-scale Mediterranean climate street canyon with respect to a non-vegetated control during four heat waves and summer conditions. The cooling effect of VGS on air temperature was assessed using an identical set of sensors in both canyons. The effect on thermal comfort was evaluated using radiation fluxes, Mr.T tool, and the ENVI-met model that was validated against measured data.The VGS cooling effect during the heat waves was up to 0.36-2.04oC, and its duration was significantly extended. The correlation (R2 = 0.75) between the cooling effect, relative humidity, and wind direction, highlights that warmer and drier conditions increase the VGS cooling effect during heat waves.VGS improved the mean radiant temperature (Tmrt) above the canyon (3.9-4.1oC) and at the pedestrian level (2.21-2.8oC), while the physiological equivalent temperature (PET) and universal thermal climate index (UTCI) decreased by an average of 0.67-1.07 oC and 0.63-0.8 oC, respectively. Shaded walls improved the average Tmrt (11.7oC), PET (3.2oC), and UTCI (2.7oC), reflecting the importance of casting shadow in the urban environment.

Investigation of nonlinear dynamics and stochastic characteristics of fine particulate matter in urban environments

Investigation of nonlinear dynamics and stochastic characteristics of fine particulate matter in urban environments

Impact of meteorological parameters on black carbon mass concentrations over Silicon City “ Bengaluru, Southern part of India” – A Case Study

In the context of Bengaluru, the impact of black carbon (BC) aerosols on regional climate dynamics emerges as a crucial area of investigation. Due to the lack of high-resolution BC data, it was collected at 5 different locations in Bengaluru, Karnataka, from January 2019 to December 2019. The mean mass concentration of BC was 5.91 microg m-3, whereas the higher mass concentration of BC was 7.71 microg m-3 over the city railway station and the lower (3.69 microg m-3) was in Jayanagara. The contribution of BC in higher-traffic and industrial locations was approximately 41% higher than the residential locations, which indicates a large fraction of soot particles are from anthropogenic activities mainly from fossil fuel combustion. The seasonal concentrations of BC have also exhibited a large variability, with the highest sequence in magnitude during the winter season (9 microg m-3) followed by the summer (6.3 microg m-3), post-monsoon (5.8 microg m-3) and monsoon (3.4 microg m-3) seasons during the study period. The concentrations of BC during the monsoon season were very low as compared to the winter season due to the impact of the washout effect. The BC was negatively significantly correlated (-0.70) with rainfall indicating the washout effect, however, during the winter season, it was due to impact of boundary layer condition. Overall (annually) the mean concentrations of BC have increased by around 55% within five years over the urban region, which indicates the impact of man-made activities. The higher mass concentration of BC over Bangalore in the southern part of India, indicates serious implications for the regional climate that need to be investigated and mitigated. Finally, the study suggests that immediate action is required to mitigate the emission of BC into the urban environment in the southern part of India.

3D characterization of the Mila 18 archaeological site in Warsaw, Poland: From imaging to excavation

Archaeological site investigations in urban environments are often beset with challenges such as (1) an absence of buried artifacts due to recent disturbance from infrastructure development or (2) community concerns about potential site impacts from excavations. Noninvasive geophysical surveys that use a combination of methods can help mitigate the risks of uncertain outcomes by identifying areas where culturally significant features are more likely to be uncovered. We show how new technology and traditional geophysical survey methods were used to characterize the subsurface of the Mila 18 Memorial site in Warsaw, Poland. This site is one of the most important places of remembrance for the Holocaust and coincides with the location of an underground bunker that was used by Jewish resistance groups during the 1943 Warsaw Ghetto Uprising. In this case study, we showcase the use of drone multispectral imaging and handheld lidar scanning in conjunction with other geophysical techniques including electrical resistivity tomography, ground-penetrating radar, magnetic gradiometer, twin-probe resistance, and fixed-frequency electromagnetic surveying. The geophysical results were included in an interactive 3D site model to help identify a suitable site for excavation. To document the excavation and to validate and further interrogate the geophysical survey results, we used lidar-based photo-textured scans of the excavation that were incorporated into the 3D site model.

Vegetation density is the main driver of insect species richness and diversity in small private urban front gardens

Urbanisation changes natural ecosystems and vegetation to urban green spaces, and causes insect communities to experience novel challenges for survival. New evidence suggests that urban green spaces, no matter how small, can provide meaningful habitats for insects. Information on design and management of small gardens (<6m2) in dense urban areas is still scarce. In particular, it is hardly known which garden designs provide most benefits to insects. We surveyed 65 small urban façade gardens (μ=1.7m2) in Amsterdam and The Hague in The Netherlands and measured various garden attributes that are expected to be relevant for general, flower-visiting and herbivorous insect species richness and diversity. Plant coverage and richness were the strongest predictors of insect biodiversity and species richness. We found no support for associations with native plants or garden size. Synthesis and applicationsTo strengthen insect biodiversity in the urban environment, we recommend future design of urban green spaces to focus on maximising coverage and richness of vegetation. Although there are advantages to using native species, we acknowledge that garden owners often prefer exotic species. Some of these can also affect insect diversity and abundances positively.

An IoT Based Real-Time Environmental Monitoring System for Developing Areas

Rapid urbanization and industrialization have raised concerns about environmental quality and sustainability in recent years. The Internet of Things (IoT) has played an important role in monitoring physical phenomena by generating data that can be sent and preserved in the cloud. This work explores an IoT-based environmental monitoring system's potential, using an Arduino-based device for real-time tracking of environmental parameters including sound levels, humidity, dust concentration, total volatile organic compounds (TVOC), carbon dioxide (CO2), and carbon monoxide (CO). Real-time data are collected from various semi-residential and marketplace locations named in Pach raster More, Tomaltola, DowamoyiMore, Fojdarimore, and station road in Jamalpur district of Mymensingh Division, Bangladesh on non-holiday days, providing a representative snapshot of typical environmental conditions. The collected data is stored in a cloud server named firebase database. The implemented monitoring system offers several key features including accuracy and reliability, real-time monitoring data analytics alerts and notifications historical data as well as it can lead to various benefits and impacts of Improved Air and Water Quality Healthier Urban Environment (IAWUE) to enable local authorities and individuals to make educated decisions for a healthier and more sustainable urban environment. Graphical representations of the data revealed distinct patterns and trends, offering valuable insights into air quality variations across different areas. Interestingly, the results showed sound levels slightly below the standard range, indicating a relative control of noise pollution in the sampled areas. The findings of the work will serve as a vital resource for further research and guide policy-making for environmental improvement and sustainable practices in urban settings.

Association between public and private greenspace availability and electricity consumption

Natural environment including greenspace has been suggested to be beneficial for health and well-being of urban residents. Several mechanisms have been proposed, including mitigation of environmental stressors, such as air pollution, and thermal stress. However, the role of greenspace on reducing electricity consumption in the urban environments has not yet been reported using these mechanisms. The present study used the annual electricity consumption of Yazd city dwellers in the period (2016–2019) and examined its association with different greenspace indicators at their residential addresses. Different private and public greenspace indicators were defined, including private garden access and area, distance to the nearest park (regional and local), percentage of land covered with urban greenspace and residential surrounding greenness (by normalized difference vegetation index [NDVI]) at different buffer size around residential addresses. Different demographic, urban design and contextual variables were included as the covariates in the models. An inverse association was found between the increase of greenspace in a private garden and the amount of electricity consumption in all the included buildings. One-unit increase in NDVI was significantly associated with 2046.5 kWh/y lower electricity consumption (95 % CI: 1151.79: 2941.21). An increase of 1 m2 in the area of a private garden was associated to 1.2 kWh/y lower energy consumption. A stronger association was also found between the private garden area and lower electricity consumption in contemporary urban texture compared to two other urban texture (contemporary urban texture: β: −1.97, 95 % CI: −2.39 to −1.54, old urban texture: β: −1.21, 95 % CI: −1.97 to −0.46, and historical urban texture: β: −0.25, 95 % CI: −1 to 0.49). The results of this study can strengthen previous theories about eco-friendly cities and sustainable cities. Promoting availability and access to greenspace can help urban sustainability through lower energy consumption.

Study on the coordination and factors affecting the coupling of resource and environmental carrying capacity and regional economy in ecologically fragile areas

The coordinated development of the resources, environment and social economy in ecologically fragile areas has become one of the urgent issues for all countries in the world. In this study, the ecologically fragile area of Ningxia was used as the typical case area. This study constructs a “four main subjects and eight dimensions” comprehensive evaluation model based on the Sustainable Development Goals (SDGs) to analyze the situation of the resource and environmental carrying capacity (RECC). This study employs the coupled coordination degree model and the obstacle degree model, integrating them with Pearson correlation analysis to quantitatively evaluate the coupled coordination relationship and influencing factors between RECC and socioeconomic development in Ningxia. The study shows that: (a) the RECC in Ningxia is on an upward trend, with obvious spatial differences, and the pressure on resources and environment in the provincial capital city is more obvious; (b) the coordination of RECC and socioeconomic development coupling in Ningxia is on a steady upward trend, but the internal development differences are more obvious, and the degree of coordination in the provincial capital cities is relatively high; (c) the coordinated development of RECC and socioeconomic development is affected by a combination of factors such as climate, water, soil, ecological resources, urbanization, and traffic accessibility in Ningxia. The findings offer scientific support for the coordination of socioeconomic development and resource environmental protection in ecologically fragile regions. Furthermore, they serve as a reference for promoting ecological civilization construction in fragile ecosystems globally.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.