Large Urban Areas Research Articles

Impacts of urbanisation, climate change and management on groundwater recharge beneath Perth, Western Australia

ABSTRACT Urbanisation has profound effects on environmental water fluxes and storages, which can be further compounded by climate change and management responses. This study shows how climate change influences the type and amount of recharge in urban areas with different housing densities under Perth, Western Australia. We demonstrate that consequent water conservation measures aimed to reduce irrigation may be having unintended negative consequences by increasing urban heating. Groundwater recharge was measured under Perth, Western Australia, in 1980–81 and again in 2017–18, a 38-year climate period which saw annual rainfall fall by 150 mm; months with heavy rain reduce by half; maximum annual temperatures increase by more than 1°C and potential evaporation increase by 15%. The consequent reduction in direct recharge through soils due to climate change was enhanced by regulated reductions in sprinkler irrigation, whilst interception losses via tree canopies increased as rain fell as lighter showers rather than prolonged events. Indirect recharge off roofs and roads, however, was less affected and now dominates recharge to the shallow, unconfined aquifer. The primary management response to falling groundwater levels has been to reduce extraction from the aquifer. We propose an additional viable strategy would be managed aquifer recharge, using either water from main drains that remove groundwater, or secondary-treated wastewater, which is currently pumped to the ocean. Increasing, or at least maintaining, local irrigation could provide multiple positive benefits, including evaporative cooling over large urban areas to help reverse urban heating. Increased urbanisation worldwide generally increases urban runoff and generates urban heat islands. In cities, such as Perth, built over transmissive sand aquifers, however, increasing local recharge can provide valuable non-potable water and help urban cooling. Identifying and installing additional recharge systems, however, is difficult for managers as it requires innovation and additional resourcing compared with purely restricting licensed extraction.

Active Geospatial Search for Efficient Tenant Eviction Outreach

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods.

Optimization-Based Downscaling of Satellite-Derived Isotropic Broadband Albedo to High Resolution

In this paper, a novel method for estimating high-resolution isotropic broadband albedo is proposed, by downscaling satellite-derived albedo using an optimization approach. At first, broadband albedo is calculated from the lower-resolution multispectral satellite image using standard narrow-to-broadband (NTB) conversion, where the surfaces are considered Lambertian with isotropic reflectance. The high-resolution true orthophoto for the same location is segmented with the deep learning-based Segment Anything Model (SAM), and the resulting segments are refined with a classified digital surface model (cDSM) to exclude small transient objects. Afterwards, the remaining segments are grouped using K-means clustering, by considering orthophoto-visible (VIS) and near-infrared (NIR) bands. These segments present surfaces with similar materials and underlying reflectance properties. Next, the Differential Evolution (DE) optimization algorithm is applied to approximate albedo values to these segments so that their spatial aggregate matches the coarse-resolution satellite albedo, by proposing two novel objective functions. Extensive experiments considering different DE parameters over an 0.75 km2 large urban area in Maribor, Slovenia, have been carried out, where Sentinel-2 Level-2A NTB-derived albedo was downscaled to 1 m spatial resolution. Looking at the performed spatiospectral analysis, the proposed method achieved absolute differences of 0.09 per VIS band and below 0.18 per NIR band, in comparison to lower-resolution NTB-derived albedo. Moreover, the proposed method achieved a root mean square error (RMSE) of 0.0179 and a mean absolute percentage error (MAPE) of 4.0299% against ground truth broadband albedo annotations of characteristic materials in the given urban area. The proposed method outperformed the Enhanced Super-Resolution Generative Adversarial Networks (ESRGANs), which achieved an RMSE of 0.0285 and an MAPE of 9.2778%, and the Blind Super-Resolution Generative Adversarial Network (BSRGAN), which achieved an RMSE of 0.0341 and an MAPE of 12.3104%.

Interactive Data Exploration for Smart City Analytics: A User-Centered Perspective

Large urban areas generate vast amounts of data that pose challenges inaccessibility and understanding. To address these challenges, our study introduces an innovative framework for Interactive Data Exploration within Smart City Analytics, centering around user needs and ex- periences. By leveraging intuitive visualization techniques, we allow users to dynamically en- gage with urban data, facilitating better comprehension and interaction. The framework incorporates user feedback to refine the exploratory experience, thereby enhancing accessibility and usability. We conducted user studies to evaluate its effectiveness, which demonstrated marked improvements in user satisfaction and understanding when compared to conventional static visualizations. The approach not only enriches users’ grasp of smart city dynamics but also fosters community engagement in urban planning initiatives. Our results underscore the crucial role of user-centered design in transforming complex data into accessible insights for stakeholders involved in urban development.

Effect of extending PrEP initiation to primary care settings: a nationwide cohort study in France.

Pre-exposure prophylaxis (PrEP) medicines are key to reducing HIV infection. Improving access, increasing initiation, and expanding the populations covered is therefore important. In June, 2021, in France, PrEP initiation was extended to primary care. The aim of this study was to describe the deployment and characteristics of PrEP initiation in primary care. We did a nationwide cohort study using data obtained from the French National Health Data System (Système National des Données de Santé). We included all people aged 15 years or older, who initiated PrEP in primary care between June 1, 2021, and Dec 31, 2022. We estimated the number of PrEP initiations per month over the period, and the characteristics of people initiating PrEP and prescribers, and PrEP use. 13 500 individuals initiated PrEP in primary care during the study period. The mean number of PrEP initiations increased from 654 (SD 64) per month between July and December, 2021, to 783 (SD 86) per month between July and December, 2022. Individuals initiating PrEP were predominantly male (12 996 [96·3%] of 13 500 individuals) with a mean age of 36 years (SD 11·8), who lived in large urban areas (9581 [71·0%]). 1012 (7·5%) of 13 500 individuals were socioeconomically disadvantaged. Of the 5125 PrEP initiation prescribers, 4542 (88·6%) were general practitioners (GPs), and 4713 (44·7%) of 10 525 were the patient's family practitioner. In the 6 months after PrEP initiation, 6216 (70·8%) of 8783 PrEP initiators had at least one monthly renewal (mean 3·3 renewals [SD 1·7]). 11 961 (82·4%) of 14 507 renewals were made by the same practitioner who had initiated PrEP, and this proportion was higher when the prescriber who had initiated PrEP was the family practitioner (6225 [92·5%] of 7135 renewals). Although the number of PrEP initiations in primary care steadily increased over the study period, the profile of users was unchanged when compared with before extension. The high proportion of PrEP initiations not prescribed by family practitioners highlights potential barriers to sharing sexual health concerns with the family practitioner. Extending PrEP to women, individuals who are socioeconomically disadvantaged, and all those who might benefit from it will require increased awareness among target audiences and practitioners. French National Agency for Medicines and Health Products Safety.

Sociodemographic Disparities in Exposures to Inorganic Contaminants in United States Public Water Systems.

Maximum contaminant level violations occur more frequently among some public water systems (PWS) serving marginalized communities across the United States. Spatially resolved PWS service area data are now available, allowing for better understanding of relationships between sociodemographic factors and water quality. We examined associations between detection and concentration of arsenic (As), chromium (Cr), manganese (Mn), and selenium (Se) and the sociodemographic characteristics of residents served by PWS at two spatial scales: PWS service areas and counties. We leveraged data on inorganic contaminants in >273,000 drinking water samples from >21,000 PWS collected by state agencies and the U.S. Environmental Protection Agency over the decade between 2005-2015 and curated in a national database developed by the Environmental Working Group. Sociodemographic data were from the 2006-2015 American Community Survey. We developed two-step hurdle models that included: (1) a categorical model predicting contaminant detection and (2) a continuous model predicting contaminant concentration. We developed these hurdle models using PWS service areas or counties as spatial units of analysis and compared results. PWS serving 5% higher proportions of limited English-speaking households had significantly greater detections of As, Cr, and Se, ranging from 1.25 (95% CI: 1.18, 1.32) times higher probability of Cr detection to 1.69 (95% CI: 1.58, 1.80) times higher probability of Se detection. Small PWS (≤10,000 customers) had higher concentrations of these same three contaminants. PWS serving greater proportions of Black residents had a lower probability of detecting As, Cr and Se, but significantly higher concentrations of Cr, Mn, and Se, particularly outside of large urban areas. The direction of significant associations was consistent between spatial units of analysis, but county results tended to exhibit greater effect sizes and fewer significant associations. PWS service area data help to identify drinking water concerns specific to served communities and reduce issues related to statistical bias at larger spatial scales. Targeted subsidies for water infrastructure in communities served by small PWS would help reduce disparities in exposures to drinking water contaminants.. https://doi.org/10.1289/EHP14793.

The Impact of Social Inequities on Presentation of Juvenile-Onset Systemic Lupus Erythematosus at a Large Tertiary Center.

The prevalence of juvenile-onset systemic lupus erythematosus (JSLE) differs by race/ethnicity with environmental, genetic, and social factors implicated in disease severity and outcomes. Yet, the role of social determinants of health (SDoH) in disease presentation is not well understood. We hypothesized that in an urban center with a large, diverse catchment area, SDoH influence the severity of JSLE at diagnosis. We completed an institutional review board-approved medical record review of children newly diagnosed with JSLE between January 1, 2018, and May 31, 2022, at Texas Children's Hospital in Houston, TX. We collected demographic data, clinical severity measures, and SDoH variables such as Area Deprivation Index (ADI), insurance status, pollution burden, and food accessibility. Statistical analysis to compare SDoH with JSLE severity included Kruskal-Wallis test, Fisher exact test, and univariable and multivariable regression. Mean diagnosis age for 136 patients was 13.4 years, with 82.4% female, 52.9% Hispanic, and 25.7% non-Hispanic (NH) Black. One-third of patients did not have a documented primary care provider, and one-third preferred non-English language. We found NH Black patients had worse clinical severity measures, with highest Systemic Lupus Erythematosus Disease Activity Index and more central nervous system involvement and cyclophosphamide therapy. Uninsured and publicly insured patients were more likely to use inpatient resources at diagnosis and live in neighborhoods with higher pollution levels and higher ADI. Hispanic patients reside in communities with higher ADI scores and limited access to supermarkets. In children with JSLE from a large urban catchment area, we observed significant association of nonmodifiable (race/ethnicity) and modifiable (insurance status, access to care, food accessibility) factors on disease severity at presentation.

Open drug scenes across city sizes: Socioeconomic status, crime patterns and community perspectives.

Aims: Open drug scenes (ODS) have increasingly drawn the attention of the police and municipalities in Sweden. These locations, where illicit drugs are sold and/or consumed, are often associated with various forms of disorder and crime. While ODS are typically depicted as a phenomenon predominantly found in larger cities, their prevalence and characteristics in smaller cities remain underexplored. This study aims to analyse the patterns and characteristics of ODS, as identified by the police and municipalities, across a range of cities in southern Sweden. Methods: By utilising spatial and temporal analyses of police-reported crimes and demographic statistics, this research examines the characteristics of identified ODS and their connections to socioeconomic disadvantage. Results: The findings suggest that the identified ODS in smaller cities share similar patterns to those found in prior research and in larger urban areas, characterised by lower socioeconomic status and elevated crime rates. Conclusions: Police and municipalities in smaller cities identify places in their communities that closely resemble, although are not necessarily equivalent to, an ODS. Nevertheless, these places are disproportionately burdened by social problems and require targeted assistance.

Impact of Rurality and Geographical Accessibility on Stroke Care and Outcomes.

Providing equitable health care to rural stroke patients is challenging and associated with less intervention and poorer outcomes. We assessed how several distinct patient-related geographic classifications influenced stroke care and outcomes in Scotland, United Kingdom. We conducted a population-level data-linkage study of ischemic stroke patients admitted to the hospital (2010-2018). Geographic classifications included 2 binary (urban versus rural; accessible versus remote) and 1 six-category classification encompassing both rurality and accessibility (large urban areas, other urban areas, accessible small towns, remote small towns, accessible rural areas, and remote rural areas). Process outcomes included achievement of a stroke care bundle and thrombolysis administration. Clinical outcomes included 30-day discharge from hospital care, 90-day home time, inpatient and 1-year all-cause mortality. We included 42 917 ischemic stroke patients (35 766 urban and 7151 rural). Binary classifications of rurality or accessibility missed important differences in stroke care and outcomes revealed using 6-category classification. Using the latter, compared with large urban areas, patients in accessible rural areas were more likely to receive a complete stroke care bundle (adjusted odds ratio, 1.21 [95% CI, 1.12-1.31]); patients in remote rural areas were less likely (adjusted odds ratio, 0.85 [95% CI, 0.78-0.93]). Compared with large urban areas, 30-day discharge from hospital care was more likely for patients residing elsewhere (eg, remote rural areas adjusted subdistribution hazards ratio, 1.11 [95% CI, 1.05-1.17]); home time within 90 days was higher for other urban areas (adjusted incidence rate ratio, 1.05 [95% CI, 1.03-1.07]) and accessible rural areas (adjusted incidence rate ratio, 1.03 [95% CI, 1.01-1.06]); and 1-year mortality was less likely in other urban areas (adjusted hazard ratio, 0.93 [95% CI, 0.88-0.98]) and remote small towns (adjusted hazard ratio, 0.89 [95% CI, 0.80-0.99]). When considering geographic disparities in stroke care and outcomes across Scotland, it is important to account for both home location and accessibility of care. Despite patients residing in remote rural areas being less likely to achieve a complete stroke care bundle, this did not translate into poorer outcomes.

Comparative Analysis of AlexNet, ResNet-50, and VGG-19 Performance for Automated Feature Recognition in Pedestrian Crash Diagrams

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies.

Assessing the Impact of Urban Area Size on Thermal Comfort in Compact Urban Fabrics Considering the Saharan City of Ghardaïa, Algeria

Improving microclimate conditions is a pivotal aspect of urban design, particularly in hot, arid climates, where it directly influences outdoor comfort, mitigates the urban heat island (UHI) effect, and reduces the indoor cooling energy demand. The objective of this study is to quantitatively assess the impacts of neighborhoods’ urban size when combined with compact streets’ geometry regarding the outdoor thermal comfort generated in a typical vernacular settlement of the Saharan region of Algeria. The Ksar of Al-Atteuf in the city of Ghardaïa is taken as a case study. The related interior thermal conditions of buildings assumed to be potentially affected by the urban morphology are also examined. To study the effectiveness of the two urban morphology parameters (i.e., urban size and compactness) on outdoor and indoor thermal conditions, a mixed methods approach was adopted, integrating in situ climatic measurements and dynamic simulations. Indoor temperatures were examined in a traditional house located in the core of the Ksar. Year-round operative temperature (OT) simulations were achieved using the Ladybug tool within Grasshopper, and they were complemented by the Universal Thermal Climate Index (UTCI) values calculated during peak hot and cold weeks. Furthermore, a parametric analysis was conducted, focusing on the thermal performance of the compact urban fabric by varying progressively the neighborhood sizes from 20 m, 40 m, and 60 m. The results indicate stable indoor thermal conditions across the monitored residential building, which suggests that the architectural envelope is closely affected by its immediate surroundings. On the other hand, the UTCI analysis revealed significant differences in outdoor thermal comfort since the larger urban area provides better mitigation of heat stress in summer and cold stress in winter, the improved outdoor thermal conditions generated at the neighborhood level, being proportional to the size of the urban area. The findings underscore the value of compact urban fabrics in creating climate-responsive built environments and provide further insights into sustainable urban planning and energy-efficient design practices in hot, arid regions.

Quantitative study of microplastic degradation in urban hydrosystems: Comparing in situ environmentally aged microplastics vs. artificially aged materials generated via accelerated photo-oxidation.

Quantitative study of microplastic degradation in urban hydrosystems: Comparing in situ environmentally aged microplastics vs. artificially aged materials generated via accelerated photo-oxidation.

Rural-urban differences in substance use during pregnancy.

Drug overdoses are now a leading cause of pregnancy-related deaths in the United States. Despite evidence of rural-urban disparities in substance use, there has not yet been a nationally representative examination of rural-urban differences in perinatal substance use. This study provides a comprehensive examination of rural-urban disparities in perinatal substance use. This study uses cross-sectional data to examine 3499 pregnant women from the 2015-2019 National Survey on Drug Use and Health (NSDUH). Rural (nonmetro)-urban (metro) differences in past-month tobacco use, alcohol use, binge drinking, illicit drug use, and marijuana use were examined using Rao-Scott chi-square tests and multivariable logistic regression using complex survey weights. In 2015-2019, past-month tobacco use varied geographically, as rural pregnant participants were more likely to have used tobacco than those in small and large urban areas (24.7%vs. 15.2% and 8.2%, respectively, p<0.0001). After controlling for sociodemographic and health care needs, rural pregnant women were more likely to report tobacco use (adjusted odds ratio [aOR]: 2.32, 95% confidence interval [CI]: 1.66, 3.25) but were less likely to report alcohol use (aOR: 0.58, 95% CI: 0.34, 0.98) than their large urban counterparts. There were no rural-urban differences in the odds of binge drinking, illicit drug use, or marijuana-only use in the past month. Geographic variations in perinatal substance use highlight the need for tailored interventions targeting substance use prevention during pregnancy, prioritizing tobacco in rural areas and alcohol in urban areas.

Spatial Prediction of High-Risk Areas for Asthma in Metropolitan Areas: A Machine Learning Approach Applied to Tehran, Iran

Asthma prevalence in large urban areas of developing countries is a significant public health concern, with increased rates driven by various socioeconomic and environmental factors. This study aims to predict asthma risk in Tehran, a major urban center in Iran. Data from 1473 asthma patients, alongside demographic, socioeconomic, air quality, environmental, weather, and healthcare access variables, were analyzed using geographic information systems (GIS) and remote sensing techniques. Three ensemble machine learning algorithms—Random Forest (RF), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost)—were applied to model and predict asthma risk. A Negative Binomial Regression Model (NBRM) identified seven key predictors: population density, unemployment rate, particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), neighborhood deprivation index, and road intersection density. Among the algorithms, GBM outperformed the others, with a training RMSE of 0.56 and a test RMSE of 1.07, demonstrating strong generalization. Additionally, GBM achieved the highest R-squared values (0.95 for training and 0.76 for testing) and lower MAE values (0.43 for training and 0.88 for testing). Effective pattern recognition was confirmed by EV values of 0.95 for training and 0.75 for testing, along with a Moran’s I value of 0.17, indicating minimal spatial autocorrelation.

북송 후반기 市易法의 도입과 그 운영 실태

The Shi-yi system was introduced in March 1072. Lu Jia-wen were appointed as the responsible bureaucrats. The Shi-yi system when it was mentioned as one of Wang An-shi's reform policies, commonly referred to as 'protection of small and medium-sized merchants' and measures aimed at 'low-interest loans to small and medium-sized merchants', refer to this. However, before the full-scale introduction of the Shi-yi system, an institution with similar characteristics was established in the border area. Its establishment was in February 1070 (nearly March 3rd), after Wang An-shi was selected as Deputy Prime Minister and began to implement various reform measures. This institution, organized by Wang Shao, is closely related to Wang An-shi's reform, that is, the New policy. It was Wei Ji-jong's proposal that served as the direct trigger for the implementation of the Shi-yi system. He asserted that there is a desperate need for an institution to lower prices. Through this institution, they insisted that merchants be not harmed by purchasing at a slightly higher price when prices are low. In addition, they insisted that when the price is high, it should be sold at a slightly lower price so that the people are not harmed. Through this, he said that the state would be able to obtain profits, control prices and facilitate commerce, and that the people would be able to obtain necessary supplies without inconvenience. The Shi-yi system aimed to reform the distribution system and secure the government's financial resources at the same time. In the Shi-yi system, products that were not sold in the market were purchased in large quantities. Then, after storing these products, when a merchant who wanted to purchase them appeared, they were sold at a low price. Through the Shi-yi system, Wang An-shi tried to secure finances by correcting the distribution order in the city. However, the actual operating situation of the Shi-yi system showed a pattern that was very different from the purpose of the legislation. Large cities were not the only regions in which the Shi-yi system was implemented. It was implemented more in border areas than in large urban areas where commerce developed. As Wang An-shi revealed, the Shi-yi system had an aspect other than the social policy purpose of protecting small and medium-sized merchants.

Navigating the green transition: assessing the impact of Chinese green public procurement on corporate pollution emissions

Purpose Green Public Procurement (GPP) is a crucial way to promote producing green products, but its relationship with corporate pollution emissions needs to be verified. This study aims to evaluate the environmental effects of the policy by analyzing how GPP influences corporate environmental pollution. Design/methodology/approach This study is based on extensive sample data of Chinese industrial enterprises from 2001 to 2010, using China’s first GPP list as an exogenous policy. The authors have established a differential model to explore the impact of GPP on corporate environmental pollution and its underlying mechanisms. Findings GPP significantly reduces the sulfur dioxide (SO2) emissions of enterprises. Verify the robustness of this conclusion by replacing variables, excluding other policy interventions that reduce selfselection bias, and conducting placebo testing. GPP encourages regulated enterprises to improve their production processes, drive clean production with green technology innovation, optimize energy structure, improve energy efficiency and reduce their emissions. The environmental cleaning effect of GPP is more significant in eastern and central China large and medium-sized urban areas. GPP has more effectively reduced SO2 emissions from private capital-intensive and heavily polluting enterprises. Originality/value This paper constructs a difference-in-differences model to study China’s first GPP list in 2006. It explores how GPP policies affect corporate pollution reduction. The findings enrich GPP research in China and emerging economies. Moreover, unlike existing studies on corporate pollution subject to environmental regulation, this paper focuses on how corporate pollution reduction is affected by demand-driven GPP policies, expanding the theoretical research.

Automated Detection of Araraucaria angustifolia (Bertol.) Kuntze in Urban Areas Using Google Earth Images and YOLOv7x

This study addresses the urgent need for effective methods to monitor and conserve Araucaria angustifolia, a critically endangered species of immense ecological and cultural significance in southern Brazil. Using high-resolution satellite images from Google Earth, we apply the YOLOv7x deep learning model to detect this species in two distinct urban contexts in Curitiba, Paraná: isolated trees across the urban landscape and A. angustifolia individuals within forest remnants. Data augmentation techniques, including image rotation, hue and saturation adjustments, and mosaic augmentation, were employed to increase the model’s accuracy and robustness. Through a 5-fold cross-validation, the model achieved a mean Average Precision (AP) of 90.79% and an F1-score of 88.68%. Results show higher detection accuracy in forest remnants, where the homogeneous background of natural landscapes facilitated the identification of trees, compared to urban areas where complex visual elements like building shadows presented challenges. To reduce false positives, especially misclassifications involving palm species, additional annotations were introduced, significantly enhancing performance in urban environments. These findings highlight the potential of integrating remote sensing with deep learning to automate large-scale forest inventories. Furthermore, the study highlights the broader applicability of the YOLOv7x model for urban forestry planning, offering a cost-effective solution for biodiversity monitoring. The integration of predictive data with urban forest maps reveals a spatial correlation between A. angustifolia density and the presence of forest fragments, suggesting that the preservation of these areas is vital for the species’ sustainability. The model’s scalability also opens the door for future applications in ecological monitoring across larger urban areas. As urban environments continue to expand, understanding and conserving key species like A. angustifolia is critical for enhancing biodiversity, resilience, and addressing climate change.

Modeling the Impact of the Bidirectional Exchange of NH3 From the Great Lakes on a Regional and Local Scale Using GEM‐MACH

AbstractAmmonia emissions from oceans are recognized as one of the most significant natural sources of ammonia globally; however, freshwater sources are rarely considered significant. The Great Lakes region, containing the largest network of freshwater lakes in the world, and a significant urbanized population exceeding 20 million, provides a unique opportunity to evaluate the potential for lacustrine (lake‐associated) surfaces to contribute to regional ammonia levels. In this work, we combine an analysis of 20 years of water quality data from the Great Lakes region with the GEM‐MACH (Global Environmental Multiscale (GEM)‐Modelling Air quality and CHemistry (MACH)) chemical transport model to examine the influence of the Great Lakes on atmospheric ammonia. This analysis demonstrates that while regional ammonia levels are largely controlled by known terrestrial anthropogenic sources, lacustrine surfaces with an emission potential of only 200 increase summertime (July–September) monthly average ammonia (NH3) levels by 5%–8% over the largest regional urban centers, with daily increases of up to 10%–20%. Supplemental water measurements collected from within 1 km offshore of the Greater Toronto Area were found to have an emission potential of 2000, suggesting that lacustrine emissions offshore of large urban areas could be significantly larger than those predicted by GEM‐MACH. Our findings reveal that the Great Lakes may represent a regionally significant natural source of ammonia to the atmosphere.

Comparison of pharyngeal and invasive isolates of Streptococcus pyogenes by whole-genome sequencing in Toronto, Canada.

Invasive Group A streptococcal (iGAS) infections are rising in Canada and wordwide. The 2022-2023 Ontario iGAS season was among the highest recorded, a trend continuing in 2023-2024. We sequenced 38 invasive (blood) and 117 non-invasive (pharyngeal) Streptococcus pyogenes clinical isolates from Toronto (January-May 2023) to compare between the two cohorts and against published sequences to determine if any genomic changes accounted for the trend. Results demonstrated limited clustering with one small totally invasive cluster (emm49) with both invasive and non-invasive isolates represented across a diverse set of lineages. Non-invasive isolates were predominantly emm12 (70.1%), whereas invasive isolates included emm12 (26.32%), emm49 (23.68%), and emm1 (13.16%) with most emm1 strains containing the 27 SNPs that define the hypervirulent M1UK clone (58.33%). Although there were no differences in the presence of overall virulence factors/adhesin genes between cohorts, there were statistically more superantigen and DNase genes in non-invasive isolates and a rare phage gene was significantly associated with invasiveness across three emm-types. The prevalence of individual virulence factor/adhesin genes also differed between our cohorts, including a higher likelihood of speA, enn, mrp, ideS/Mac, fbaA, and fbaB in invasive isolates. There were also no significant differences across the 11 antimicrobial resistance genes identified. Finally, pharyngeal isolates had larger hydrolysis and hemolysis zones, and covS deletions were observed in only seven invasive strains. Despite there being no genetic signature that differentiated our isolates, we observed several features that were predominant in invasive strains which provides further insights into the factors that contribute to GAS invasiveness.IMPORTANCEIncreasing rates of invasive Group A streptococcal (iGAS) infections are being seen both in Canada and worldwide, which is leading to a greater disease burden caused by this pathogen. Leveraging whole-genome sequencing gives us an opportunity to better understand the underlying genetic mechanisms of streptococcal disease. By utilizing this technique, we shed light on the circulating invasive and non-invasive strains of Streptococcus pyogenes in the largest urban area in Canada from January to May 2023. GAS strains causing non-invasive disease were found to have a higher abundance of superantigen and DNase genes, whereas invasive isolates were more likely to contain M-like protein genes, the superantigen speA, the protease ideS/Mac, and/or the fibronectin-binding proteins fbaA and fbaB. This work provides valuable insights into iGAS disease which will help with surveillance, epidemiology as well as developing treatment and preventative modalities to help curb the disease burden caused by this globally important pathogen.

Modelling oxidative potential of atmospheric particle: A 2-year study over France.

Modelling oxidative potential of atmospheric particle: A 2-year study over France.