Geographies, Volume 5, Issue 1 (March 2025) – 15 articles

Dissolved organic carbon (DOC) is a critical parameter in water quality management due to its interaction with disinfectants, leading to the formation of disinfection byproducts (DBPs) during water treatment. Forest ecosystems are key contributors of DOC to surface waters, stemming from soil leachate. This study is the first to use DOC solutions directly extracted from soil to examine the formation of trihalomethanes (THMs) during chlorination and chloramination under varying environmental conditions. For this purpose, soil samples from a densely forested upland Cedar Lake watershed in Illinois were processed to extract DOC, which was then subjected to controlled disinfection experiments under varying pH, temperature, disinfectant dose, and reaction time. The results demonstrate that chlorination produces significantly higher levels of THMs compared to chloramination, with THM concentrations ranging from 31.996 μg/L to 62.563 μg/L for chlorination and 0.508 μg/L to 0.865 μg/L for chloramination. The yields of DBPs determined by chloramination increased approximately 4, 5, and 10 times with a higher DOC concentration, disinfectant concentration, and reaction time, respectively. For chlorination, these increases were approximately 5, 8, and 3 times, respectively. The presence of bromide in the DOC solutions influenced the concentration of brominated THMs (Br-THMs). The results indicate that a high formation of THMs, during both disinfection processes, occurred in the pH range of 7–8 and temperature range of 20–25 degrees Celsius. Furthermore, all tested water quality indicators (DOC, total dissolved solids, turbidity, and UV254), except for pH and Specific Ultraviolet Absorbance (SUVA), exhibited a strong positive correlation with THM levels during chlorination. In contrast, these parameters displayed a moderate to weak correlation with THM levels in the chloramination process. These findings highlight the critical role of DOC characteristics and disinfection conditions in controlling THM formation, providing valuable insights for optimizing water treatment processes. Full article

Social and cultural diffusion determines how behavioral phenomena spread among communities. The COVID-19 pandemic, which emerged globally at the beginning of 2020, triggered changes in human behavior in various settlements and regions. In this study, we use a spatial approach to examine diffusion patterns during the Omicron wave (December 2021–February 2022). We collected data on daily testing and confirmed cases from the Israeli Ministry of Health (MoH) database, as well as population characteristics from the Israel Central Bureau of Statistics (CBS). These data were normalized per population, classified regionally and analyzed spatially using GIS, to verify the significance of the results. We found a contagious diffusion pattern apparent spatially in the metropolitan regions of Tel Aviv-Jaffa and Haifa (Israel). Accordingly, the undulating pattern of the number of COVID-19 tests and confirmed cases began in the center of the given metropolitan region (populated with high-class settlements) at the beginning of the wave, spread out to the periphery (populated with high-class settlements) toward the mid-wave period, and returned to the center when the wave ended. Additionally, we have seen that these patterns do not accord with the morbidity spread, implying that social characteristics may have been dominant in determining the diffusion pattern. Full article

Mid-day maximum soil temperatures were measured at 10 study plots during different hot summer days in Haleakalā Crater, Maui, with thermocouple thermometers on five adjacent microsite types: bare surface soils, soils under black tephra, soils under reddish tephra, soils shaded by silverswords, and soils under plant litter. The main tephra morphologies and geomorphic environments, as well as their geoecological association with silversword rosettes (Argyroxiphium sandwicense), were also assessed; silversword density was substantially greater on reddish tephra-covered areas than under black tephra fragments. Silversword seeds are extremely sensitive to high temperatures and fail to germinate after a short exposure to soil temperatures ≥35 °C. Thermal data sets were statistically compared with parallel box plots; the ability of various microsites to provide safe sites for silversword growth was also assessed. Bare soils and black tephra reached the highest median temperatures, up to 48.7 °C and 40.3 °C, respectively; reddish tephra remained much cooler, with all median temperatures ≤30.8 °C. Rosette-shaded soils and soils under silversword litter were the coolest, with temperatures below 18.7 °C and 18.5 °C, respectively. Temperatures in all microsites, except those under black tephra, were significantly lower (p < 0.0001) than on contiguous bare ground. It was concluded that reddish tephra provides the ideal conditions for silversword regeneration. Full article

Coastal areas are highly dynamic environments, vulnerable to natural processes and human interventions. This study presents the first application of the Integrated Coastal Vulnerability Index (ICVI) in Cyprus, focusing on two major tourism-dependent beaches, Fig Tree Bay and Vrysi Beach, located along the Protaras coastline. Despite their economic significance, these coastal areas face increasing vulnerability due to intensive tourism-driven modifications and natural coastal dynamics, necessitating a structured assessment framework. This research addresses this gap by integrating the ICVI with geographical information system (GIS) and analytic hierarchy process (AHP) methodologies to evaluate the coastal risks in this tourism-dependent environment, providing a replicable approach for similar Mediterranean coastal settings. Ten key parameters were analysed, including coastal slope, rate of coastline erosion, geomorphology, elevation, tidal range, wave height, relative sea level rise, land cover, population density, and road network. The results revealed spatial variations in vulnerability, with 16% of the coastline classified as having very high vulnerability and another 16% as having high vulnerability. Fig Tree Bay, which is part of this coastline, emerged as a critical hotspot due to its geomorphological instability, low elevation, and intensive human interventions, including seasonal beach modifications and infrastructure development. This study underscores the need for sustainable coastal management practices, including dune preservation, controlled development, and the integration of the ICVI into planning frameworks to balance economic growth and environmental conservation. Full article

The spatial distribution of vegetation across metropolitan areas is important for wildlife habitat, air quality, heat mitigation, recreation, and other ecosystem services. This study investigated relationships between vegetation patterns and parcel characteristics at multiple scales of the Austin Metropolitan Statistical Area (MSA), a rapidly growing region in central Texas characterized by diverse biophysical and socioeconomic landscapes. We used LiDAR data to map vegetation types and distributions across a 6000 km² study area. Principal component analysis (PCA) and regression models were employed to explore tree, shrub, and grass cover across parcels, cities, and the MSA, considering home value, age, size, and distance to the city center. At the MSA scale, tree and shrub cover were higher in the Edwards Plateau than in the Blackland Prairie ecoregion. Tree cover increased with parcel size and home value, especially in suburban areas. Older parcels had more mature trees, though less so in the grass-dominated Blackland Prairie. Shrub cover was higher on larger parcels in the Edwards Plateau, while the Blackland Prairie showed the opposite trend. PCA explained 60% of the variance, highlighting links between vegetation and urban development. Our findings reveal how biophysical and socioeconomic factors interact to shape vegetation, offering considerations for land use, housing, and green infrastructure planning. Full article

Local food movements are growing in popularity across the United States, with many communities focused on gaining control over their food systems. This is especially true in northwest Washington. Over the last 15 years, farmers, bakers, and millers in northwest Washington have increasingly invested their resources in growing and using local grains. Northwest Washington is not commonly considered a grain-growing region despite historically high yields of cereal grains. This study examines how grain agriculture has changed in northwest Washington since the late 19th century and uses a mix of quantitative spatial analyses, historical agricultural census analyses, and qualitative interviews to explore potential cultural and biophysical causes behind these changes. Our findings suggest that historical trends in grain agriculture were not driven primarily by climatic changes, but our spatially explicit models of late 21st-century climate suitability suggest the length and timing of optimal growing season conditions will change across our study area. This change coincides with the rising community interest in local grains. However, producers are concerned that future climate change may make it more challenging to grow grains in this region. This mixed-methods approach is of the utmost importance in understanding the entire story of community-based food systems in this area and in informing future decision-making of communities in northwest Washington. Full article

Generative AI (GenAI), particularly text-to-image (TTI) models, is reshaping landscape representation by transforming textual descriptions into visual outputs. However, these models often reinforce biases embedded in their training datasets, shaping how landscapes are perceived and represented. This research examines the biases in GenAI-generated landscape imagery through the lens of Edward Said’s “imaginative geographies”, focusing on how geographic references, cultural archetypes, and methodological factors influence AI outputs. We employed a structured approach to create prompts based on the Global Ecosystem Typology (GET), using Midjourney V6.1 as the primary tool for image generation. We extracted landscape descriptors from GET classifications and structured them into both simple and detailed prompts. The analysis involved comparing AI-generated images to ecological classifications and reference images to assess biases. The findings reveal the following three key types of biases: (1) geographic biases, where certain locations act as semantic triggers, leading to culturally stereotypical portrayals; (2) representation biases, where landscapes frequently depicted in AI training datasets appear with greater accuracy, while underrepresented landscapes are simplified into essentialized visual tropes; and (3) methodological biases, where prompt complexity influences representational accuracy but does not eliminate pre-existing cultural hierarchies. To address the bias challenges, the research presents the following four key recommendations for future research and practice: (1) incorporating finer-scale ecosystem classifications; (2) diversifying training datasets; (3) engaging local communities in participatory data collection; and (4) refining prompt-writing methodologies. These insights contribute to broader discussions on AI bias, emphasizing the necessity of critically evaluating the role of generative models in shaping landscape imaginaries. Full article

This article explores the transformative role of intangible resources and products such as data, algorithms, and digital platforms in redefining economic region theory. The goal of the study is to provide a framework for dealing with intangible resources and products and refining the cost calculation techniques for them. The article includes the genesis of a theoretical approach to regional development and consideration of the implications of theoretical provisions in practice. Intangible inputs have significant peculiarities compared to tangible resources, and these peculiarities require a specific approach to the management of regions, especially considering their impact on agglomeration, cost structures, and market dynamics. The research is based on a comprehensive literature review and comparison, and application of theoretical provisions to practice. The development of the cost calculation framework is based on classical cost analysis considering the peculiarities of intangible resources. The findings demonstrate that integrating intangible resources into economic region theory broadens its applicability, offering a roadmap for regions to achieve growth and resilience in the digital economy while addressing evolving global challenges. Full article

Human mobility plays a fundamental role in urban life, shaping the development of infrastructure, transportation systems, and public spaces. Understanding the dynamics of mobility patterns is essential for creating sustainable and inclusive urban environments. This study investigates the influence of gender and temporal variations on human mobility within a city in central Sweden, shedding light on how movement patterns fluctuate throughout the day and differ across gender groups. The findings reveal significant temporal shifts in mobility hotspots, with individuals travelling to different areas at varying intensities depending on the time of day. These variations highlight the dynamic nature of urban movement and emphasise the necessity of time-sensitive urban planning strategies. While overall journey patterns between genders exhibit relatively small differences, a closer analysis uncovers distinct gender-based disparities in mobility hotspots, indicating that men and women tend to frequent different locations with varying travel behaviours. These insights provide valuable input for urban planners, policymakers, and transportation authorities seeking to enhance accessibility, safety, and efficiency in urban mobility networks. Recognising the interplay between gender and temporal mobility patterns can lead to more equitable infrastructure design, ensuring that urban spaces accommodate diverse mobility needs. By emphasising the importance of these factors, this study contributes to a broader understanding of human mobility behaviour and underscores the need for data-driven planning approaches that address spatial and temporal variations in movement patterns. Full article

Access to green spaces is essential for promoting public health, reducing inequalities, and fostering urban resilience. This study evaluates the 3-30-300 indicator as a tool for assessing green space accessibility in Montreal, Canada. The framework sets three goals: every resident should see three trees from their home, live in a neighborhood with at least 30% tree canopy, and have a park or green space within 300 m. Using geospatial analysis, this study examines how well these criteria are met across Montreal’s neighborhoods and investigates disparities linked to socio-economic factors. The study reveals a significant variability in the distribution of green spaces across Montreal neighborhoods, as measured by the 3-30-300 metric. Tree canopy coverage ranges from 0.8% to 84%, with a median of 25.7%, while distances to parks vary from adjacent to over 2.4 km. The number of trees around residences is highly skewed, ranging from 0 to 771, reflecting substantial heterogeneity in green space accessibility. Spatial analysis highlights pronounced inequalities, with only 19.4% of neighborhoods meeting all three criteria. Hotspots of compliance are concentrated in peri-central and well-established residential areas in the West and East, while central and peripheral neighborhoods, especially in northeast Montreal, frequently fail to meet the standards. These findings underscore strong spatial disparities in urban green infrastructure, consistent with global studies on inequitable access to green spaces. Full article

The new global pandemic of COVID-19, declared on 11 March 2020 by the World Health Organization, has already had an unprecedented impact on health and socioeconomic activities worldwide. The second wave of the COVID-19 pandemic swept through the United States of America and Europe in late September 2020. Compared with other southern countries, such as Greece, where there was a significant increase in cases at the end of October 2020, Northern European countries (Germany, France, Austria, Finland, and Sweden) experienced this second wave of the pandemic earlier in September 2020. To understand the epidemiological behavior of the virus from an environmental perspective, we examined the effects of air temperature, humidity, and wind on the spread of COVID-19 in two of the largest population Regional Units (R.U.) of Greece, namely the R.U. of the Central Sector of Athens in Central Greece and the R.U. of Thessaloniki in Northern Greece. We applied Pearson correlation analysis and generalized linear models (GLM) with confirmed COVID-19 Intensive Care Unit (ICU) admissions from the National Public Health Organization as dependent variables and the corresponding air temperature, humidity, and wind speed from the Greek National Meteorological Service as independent covariates. The study focused on the period from 4 May 2020 to 3 November 2020 to investigate the impact of weather on the spread of COVID-19, in a period where human activities had partially returned to normal after the gradual lifting of the restrictive measures of the first lockdown (23 March 2020). The end date of the study period was set as the date of imposition of a new local lockdown in the R.U. of Thessaloniki (3 November 2020). Our findings showed that COVID-19 ICU admissions in both Regional Units decreased significantly with the temperature (T) and wind speed (WS) increase. In the R.U. of the Central Sector of Athens, this picture is reflected in both the single and cumulative lag effects of meteorological parameters. In the R.U. of Thessaloniki, this correlation was differentiated only in terms of the cumulative lag effect of the average daily temperature, where an increase (+17.6%) in daily confirmed COVID-19 ICU admissions was observed. On the other hand, relative humidity (RH) was significantly associated with an increase in cases in both R.U. This study, in addition to its contribution to the global research effort to understand the effects of weather on the spread of COVID-19, aims to highlight the need to integrate meteorological parameters as predictive factors in surveillance and early warning systems for infectious diseases. The combination of weather and climate factors (e.g., humidity, temperature, wind) and other contagious disease surveillance indicators (e.g., wastewater, geographic and population data, human activities) would make the early identification of potential epidemic risks more effective and would contribute to the immediate initiation of public health interventions and the more rational allocation of resources. Full article

Concerning the monitoring of the resumption of seismic activity at the Campi Flegrei caldera, which is causing concern to the inhabitants and involving various protection efforts by research bodies, this work intends to constitute a complementary and auxiliary tool with respect to the geophysical studies in progress. In particular, a geographical analysis of the phenomenon is proposed here aimed at identifying any spatial dynamics that can be added to the interpretation of seismic activity in a strictly geological and geophysical manner. The research study is focused on the comparison between the historical series of data starting from the year 2005 and those data relating to the last two years 2023 and 2024, in which the phenomenon resumed; particularly, the month of May 2024 is analyzed, which was characterized by high intensity of seismic events in the area. The results obtained through the joint use of spatial analysis tools aim, therefore, to identify any geographical seismic clusters that can then be interpreted in a geophysical way and can be used as an addendum in the current risk maps. Indeed, this geographical approach revealed complex spatial heterogeneities demonstrating the value of combining multiple methodological tools. The findings highlight the importance of multidisciplinary approaches in volcanic research and their critical role in improving hazard assessment and risk mitigation efforts. Full article

This study presents the results of a survey on flood risk awareness conducted in the Italian Alps, examining the impacts of a major weather event on public perception and trust. It develops a systems-thinking framework to analyse dynamic feedback loops influencing flood risk management support over time. The survey data collection overlapped with a severe storm event in Central Europe, the storm “Adrian” (also known as “Vaia”). This provided a unique pre- and post-event perspective. Results highlight the critical role of individual knowledge, trust in authorities, and social group dynamics in shaping risk perception processes. The study shows how major weather events can change perceptions, sense of safety, and institutional trust within local communities, and more interestingly, these changes can vary spatially. The findings are summarised using a systems-thinking framework, which helps to identify possible feedback loops between flood risk management interventions and long-term public support. The study emphasizes the importance of forward-looking, systems-thinking approaches in the design, monitoring, and evaluation of flood risk management plans. These approaches allow one to account for often-overlooked dynamics, such as spatially varying feedback loops and counter-intuitive effects, ultimately improving the long-term effectiveness of flood risk management. Full article

Research on hurricane impacts in Florida’s coastal regions has been extensive, yet there remains a gap in comparing the effects and potential damage of different hurricanes within the same geographical area. Additionally, there is a need for reliable discussions on how variations in storm surges during these events influence evacuation accessibility to hurricane shelters. This is especially significant for rural areas with a vast number of aging populations, whose evacuation may require extra attention due to their special needs (i.e., access and functional needs). Therefore, this study aims to address this gap by conducting a comparative assessment of storm surge impacts on the evacuation accessibility of southwest Florida communities (e.g., Lee and Collier Counties) affected by two significant hurricanes: Irma in 2017 and Ian in 2022. Utilizing the floating catchment area method and examining Replica’s OD Matrix data with Geographical Information Systems (GISs)-based technical tools, this research seeks to provide insights into the effectiveness of evacuation plans and identify areas that need enhancements for special needs sheltering. By highlighting the differential impacts of storm surges on evacuation accessibility between these two hurricanes, this assessment contributes to refining disaster risk reduction strategies and has the potential to inform decision-making processes for mitigating the impacts of future coastal hazards. Full article

Continuous and uncontrolled extraction of groundwater often creates tremendous pressure on groundwater levels (GWLs). As a part of sustainable planning and effective management of water resources, it is crucial to assess the existing and forecasted GWL conditions. In this study, an attempt was made to model and forecast GWL using artificial neural networks (ANNs) and multivariate time series models. Autoregressive integrated moving average (ARIMA) and ARIMA models incorporating exogenous variables (ARIMAX) were adopted as the time series models. GWL data from five monitoring wells from the study area of the Kushtia District in Bangladesh were used to demonstrate the modeling exercise. Rainfall (RF) was taken as the exogenous variable to explore whether its inclusion enhanced the performance of GWL forecasting using the developed models. It was evident from the results that the multivariate ARIMAX model (with the sum of squared errors, SSE, of 15.143) performed better than the univariate ARIMA model with an SSE of 16.585 for GWL forecasting. This demonstrates the fact that the multivariate time series models generated enhanced forecasting of GWL compared to the univariate time series models. When comparing the models, it was found that the ANN-based model outperformed the time series models with enhanced forecasting accuracy (SSE of 9.894). The results also exhibit a significant correlation coefficient (R) of 0.995 (model ANN 6-8-1) for the existing and predicted data. The current study conclusively proves the superiority of ANN over the time series models for the enhanced forecasting of GWL in the study area. Full article