Green Areas Research Articles

Identification of Ornamental Shrubs that Absorb Air Pollutants at Green Open Area’s, City of Samarinda, East Kalimantan, Indonesia

Avenue trees, ornamental plants and shrubs are capable of removing a significant amount of air pollutants from the atmosphere and hence should be considered an integral part of any sustainable plan intended at improving air qualityIncreasing the amount of vegetation also functions as a filter and neutraliser of air pollutants.This research uses the observation method with analysis and discussion based on a qualitative descriptive approach. the research location is in a green open area that is congested with traffic in the city of samarinda.The results of the research obtained several ornamental plants that are commonly planted in every green open area, especially parks in areas with heavy traffic. These plants have been recommended as ornamental plants that can absorb air pollutants.Among them, there are 21 ornamental plants that have been recommended by the government and research results as ornamental plants that can absorb air pollutants.

From School Gardens to Community Oases: Fostering Environmental and Social Resilience in Urban Spaces

This case study in the municipality of Acharnes, Greece, examines a local initiative that transforms schoolyards into community gardens, involving teachers, pupils, parents, grandparents, and neighbors. The research explores participants’ perceptions of these transformations, with a focus on interactions with non-human beings such as pollinators. Data were collected using a questionnaire completed by 85 participants, including parents and guardians of pupils and school staff. Data analysis was conducted using statistical software to evaluate the research sample. Initially, descriptive statistics, including frequency distributions, were used to summarize the demographic and baseline variables of the sample. Next, Kendall’s tau correlation was applied to assess statistically significant relationships, such as those between age and proximity to green spaces, as well as gender, education, and green space preferences. The results of the survey reveal the “embrace” of these new urban green areas by both the school and the local community. This study, as part of an effort to address broader challenges in urban contexts, highlights the need for further in-depth exploration and examination of these dynamics to provide valuable insights into the design and management of school gardens. The aim is to enhance community engagement and urban biodiversity, with a central focus on social and environmental resilience.

Slow motion in corona times: Modeling cyclists’ spatial choice behavior using real-time probe data

The recent COVID-19 pandemic has provided a renewed impetus for empirical research on slow and active modes of transportation, specifically bicycling and walking. Changes in modal choice appear to be sensitive to the actual quality of the environment, the attractive land use and built environment conditions, and the ultimate destination choice. This study examines and models the influence of cyclists’ health concerns during the pandemic on their spatial destination and route choices. Using a large real-time dataset on the individual daily mobility of cyclists in the province of Utrecht, the Netherlands, collected through GPS-linked sensors on bikes (VGI, or volunteered geographical information), the analysis employs spatial regression models, Shapley decomposition techniques, and spatial autocorrelation methods to unveil the backgrounds of changes in spatial behavior. The results reveal that the perceived wellbeing benefits of bicycling in green areas during the pandemic have significantly influenced cyclists’ choice behavior, in particular route and destination choice.

The Evaluation of Compost Maturity and Ammonium Toxicity Using Different Plant Species in a Germination Test

The determination of the maturity of compost and other organic fertilizers is very important because of possible phytotoxic or phytostimulating effects. The maturity of compost can be assessed on the basis of chemical analyses, and a germination test with different test plants is most often used to determine phytotoxicity. In this research, the maturity of compost produced from the plant residues subsequent to the maintenance of green public areas was assessed using the results of chemical analyses. Simultaneously, a germination test was carried out with the four test plant species (cucumber, garden cress, triticale, and barley) to determine the phytotoxicity of compost extract in a ratio of 1:2.5 v/v (1:3.3 w/v) and 1:10 v/v (1:13.3 w/v) and the three ammonium N solutions (in the concentrations of 200, 400, and 600 mg/L NH4-N). According to the chemical properties of compost (primarily the C/N, NH4-N/NO3-N ratios, and the NH4-N concentration) and the germination test with cucumber and garden cress, we may conclude that the tested compost was mature and that we did not expect a phytotoxic effect. The choice of a plant is very significant because the germination test with a compost extract demonstrated an undoubted phytostimulating effect on the garden cress and cucumber, with a more pronounced phytostimulating effect of the 1:10 than that of the 1:2.5 v/v compost extract. No such effect was detected on the monocotyledonous test plants triticale and barley since the 1:10 v/v extract had no significant effect, and the 1:2.5 v/v extract had a phytotoxic effect, moderate on the triticale and high on barley. The conclusion is that garden cress and cucumber are suitable test plants for the determination of compost’s phytostimulative effect, but they are not suitable for the determination of phytotoxicity for monocotyledonous plants, especially if the cause of phytotoxicity is a non-ammonium component. Barley is the most suitable species for the determination of compost’s non-ammonium phytotoxicity and nitrogen’s ammonium-form phytostimulative or phytotoxic effect. It would be very useful to conduct a comparative germination test with the compost extracts in the ratios 1:2.5 and 1:10, whereby the 1:2.5 extract would be used as a test of phytotoxicity, and the 1:10 extract for the test of a phytostimulating effect.

Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures

This study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merged to complement each other’s limitations. The green structures of the study area were classified into three types based on the distance between canopy trees and the presence of shrubs. The ability to individually classify trees within each of the three types of green structures was then evaluated. The evaluation method was to assess the success rate by comparing the actual number of trees, which were visually counted in the field, with the number of tree objects classified in the study. To perform semantic segmentation of tree objects, a preprocessing step was conducted to extract only the data related to tree structures from the data collected through remote sensing techniques. The preprocessing steps included data merging, noise removal, separation of DTM and DSM, and separation of green areas and structures. The analysis results showed that tree object recognition was not efficient when the green structures were complex and mixed, and the recognition rate was highest when only canopy trees were present, and the canopies did not overlap. Therefore, when observing in high-density areas, the semantic segmentation algorithm’s variables should be adjusted to narrow the object recognition range, and additional observations in winter are needed to compensate for areas obscured by leaves. By improving data collection methods and systematizing the analysis methods according to the green structures, the object recognition process can be enhanced.

Ambient air pollution, urban green space and childhood overweight and obesity: A health impact assessment for Barcelona, Spain

BackgroundThe burden of childhood overweight and obesity attributable to ambient air pollution and a lack of urban green spaces (UGS) remains unknown. This study aimed to estimate the attributable cases of childhood overweight and obesity due to air pollution and insufficient UGS exposure in Barcelona, Spain. MethodsWe applied a quantitative health impact assessment approach. We collected childhood overweight and obesity prevalence levels and exposure data from 69 spatial basic health zones in Barcelona. We estimated particulate matter (PM2.5) and nitrogen dioxide (NO2) levels using land use regression models, normalized difference vegetation index (NDVI) levels using remote sensing and percentage of green area (%GA) using land use. We estimated relative risks, population attributable fractions, and preventable overweight/obesity cases in children under following scenarios: Compliance of World Health Organization (WHO) air quality guidelines (AQGs) for (1) PM2.5 and (2) NO2; achieving (3) city-target NDVI levels and (4) 25% green area (%GA) recommendations. The analyses were stratified by socioeconomic deprivation index (in quintiles). Uncertainty was quantified using Monte Carlos simulations. ResultsCompliance of WHO AQGs could prevent 0.4% [253 (95%CI, −604; 1086)] and 4.2% [3000 (95%CI, 1009; 4943)] of childhood overweight/obesity cases due to excess PM2.5 and NO2 levels in Barcelona, respectively. Compliance of NDVI and %GA targeted levels could prevent 6% [4094 (95%CI, 1698; 6379)] and 10% [6853 (95%CI, 1440; 12779)] of childhood overweight/obesity cases respectively. The preventable burdens of childhood overweight/obesity cases were slightly higher in middle-class socioeconomic areas due to the higher adverse exposure levels at baseline (high air pollution, less UGS). DiscussionCompliance with WHO AQGs and achieving UGS targets can reduce childhood overweight and obesity levels in Barcelona, and potentially in other locations as well. This underscores the need for policies that foster healthier urban environments of high environmental quality in order to protect child health.

Assessing indicators and preferences of cultural ecosystem services in urban areas: a case study of Murcia, Spain

ContextThe degradation of the environment due to anthropogenic action underscores the need to demonstrate the benefits that natural areas provide to society. Continuously growing urbanisation trends and high demand on urban green infrastructure requires the inclusion of cultural ecosystem services (CESs) in strategic urban planning. To achieve this, understanding the population's preferences regarding the various CESs provided by urban green spaces is extremely useful for their management and long-term planning strategies. Nevertheless, the complexity of urban environment and urban CESs and inclusion of participatory-oriented and demand-based planning poses challenges in conducting solution-oriented assessments.ObjectivesTo address this issue, we aim to define and validate spatial indicators that are applicable in any urban area. We employ them to quantify the provision of CESs considering that such approach can contribute to better urban planning strategies.MethodsA survey, aiming to validate five proposed CESs indicators and evaluate the population’s perception regarding the importance of CESs in urban green areas in the city of Murcia (Spain) was conducted. In total, 733 answers were collected. The survey results underwent through statistical and geospatial analysis at the neighbourhood level, which facilitated the creation of CESs maps, considering socio-demographic characteristics of the respondents.ResultsAll five proposed CESs indicators were highly accepted by the respondents across all demographic groups. Conversely, differences in the perception of importance of CESs are noted based on the age of the respondents. Disparities in CESs provision are observed between different city neighbourhoods and socio-demographic groups.ConclusionsWe identified significant differences in CESs perception by age group. We also detected the areas where action is needed to improve the CESs provision. Through this study, we emphasize the significance of participatory-based, demand-oriented, and territorially tailored planning approaches to ensure the high provision of CESs and enhance urban well-being.

Investigation of impact of community environment factors on the residents’ visiting rate to outdoor venues in South China

Understanding the activity patterns of urban residents is crucial for urban community design and urban energy forecasting. Predicting the visitation rates of different types of locations is a important issue because urban residents can only be in one place at a time. Achieving accurate predictions of outdoor public space visitation rates is equivalent to predicting the upper limit of the number of visitors to other buildings. Previous similar studies usually rely on survey methods such as questionnaires and GPS positioning. These methods have some drawbacks, such as small sample sizes and inaccurate data. In this paper, we used mobile signaling to investigate the park visit rates of residents in different communities in Guangzhou. In accordance with Chinese tradition, we define a community as a residential community, which consists of multiple residential units and a set of basic service facilities and management structures that are sufficient to meet the daily needs of residents. We found that the park visit rate of a community is closely related to the characteristics of the community itself. The park visit rate is positively correlated with the average LST (land surface temperature) of the communities, the average distance between the community and the large park, and the building coverage rate, while it is negatively correlated with the vegetation coverage rate, the average surface reflection rate, the convenience of transportation, and the per capita green space area. Given the different travel habits of urban residents on weekdays and weekends, we also used multiple linear regression to establish prediction models for community park visitation rates on weekdays and weekends.

Investigating spatial and temporal characteristics and elements influencing running among residents of Nanchang city in green open areas

Understanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.

Analyzing Monterrey blue/green infrastructure for the mitigation of heat islands using Earth Observation and WUDAPT method

Abstract. The Metropolitan Area of Monterrey has grown excessively along with its population in recent decades, resulting in an urban area with problems of distribution of spaces and services. In addition to this, factors such as pollution resulting from industrial activities and the reduction of green areas are related to the increase in temperatures in the area and the creation of urban heat islands, which are mitigated by the cooling action provided by the green/blue infrastructure of urban rivers. Because the rivers in the study area do not have a continuous channel throughout the year and the water supply problems that have recently worsened due to the drought in the north of the country, the riparian vegetation of its 3 rivers (Pesqueria, Santa Catarina and La Silla) plays a crucial role in reducing temperatures by acting as a natural heat sink, so its conservation and restoration is essential. In this research, the influence of rapid urbanization on the deterioration of rivers during 2003, 2013 and 2021 was evaluated using Google Earth Engine and the WUDAPT scheme for local climatic zones, finding that there is a correspondence between local climatic zones and fluctuations in temperature. The results obtained over a period of approximately two decades indicate that natural climate zones register lower temperatures compared to impervious surfaces, and that there is also considerable variation between areas with dense green/blue infrastructure and natural covers with little or no vegetation, since the latter have temperatures similar to those of the built classes.

Application of a Multi-Spectral UAV Imagery in Germplasm Characterization: Prediction of Forage Biomass and Growth Patterns of Cicer Milkvetch (Astragalus cicer L.) Populations

Unmanned aerial vehicles (UAV)-based multi-spectral imaging could reduce the intensive labour required in phenotyping germplasm in crop breeding. The objectives of this study were to examine if UAV-based imaging could differentiate cicer milkvetch (Astragalus cicer L.) germplasm and identify UAV-based vegetation indices with correlations to its dry matter yield (DMY). A spaced nursery from 27 cicer milkvetch populations was established near Saskatoon, SK, Canada, in 2019. From 1 June to 15 October in 2020 and 2021, phenotypic traits including maximum stem length, leaf number per stem, rhizome spread rate, and stem density, along with two UAV-measured traits, green area and canopy volume, were measured bi-weekly. Forage DMY was determined in late June and mid-October of each year. In this study, normalized difference vegetation index (NDVI) green area and NDVI canopy volume data differentiated the three selected populations. NDVI green area had the highest correlation with forage DMY among the traits (June harvest: r = 0.91, October harvest: r = 0.77). Among measured phenotypic traits, maximum stem length had the highest correlation with forage DMY (June harvest: r = 0.74, October harvest: r = 0.83), which was significantly correlated to NDVI green area. The results indicated potential use of UAV-phenotyping in single plant evaluation in plant breeding.

What is the “best” way to measure the relative location variables in the market value assessment? An econometric method applied to an Italian case study

The present research is dedicated to investigating the explanatory power of relative location variables in assessing and forecasting market values. Here, relative location refers to the spatial position (geographical context) of a building or property in relation to a given Point Of Interest (POI). Specifically, a methodological approach is proposed for identifying the most suitable quantification modality based on statistical performance and consistency with the market mechanisms of the specific reference context. For a case study in Northern Italy, we collected data on 615 residential properties and 2673 POIs, including cultural facilities, school and education institutions, commercial services, sports, entertainment, and leisure facilities, health and care services, public transport systems, urban parks, and green areas. The relative location between the collected properties and the POIs is assessed using an automated calculation procedure developed in the Python programming language, in conjunction with Geographic Information Software (GIS). This automatism allows the assessment of relative location in terms of different Units Of Measure (UOM), such as straight-line distance, travel time by car, travel time on foot, travel time by public transport, and the number of POIs in a 400 m/1 km ring buffer. Since 615 residential buildings and 2673 POIs were analysed, with their relative locations measured using six different UOMs, a database of 9'865'215 data was produced. Furthermore, for each category of POI, a feature importance analysis guides the selection of the best UOM, i.e., the most statistically significant one. Considering the chosen UOM, an optimised econometric technique is finally implemented to analyse the functional relationships between the market values of residential properties and the set of identified relative location variables.

Trust calibration through perceptual and predictive information of the external context in autonomous vehicle

Maintaining an appropriate level of trust is critical for driving safety in autonomous vehicles. While enhancing the driver’s situation awareness (SA) of system information in autonomous driving is known to significantly promote trust calibration, it remains unclear whether enhancing the driver’s SA of the external context during driving contributes to this calibration. This study addresses this gap by improving SA of the external context during Level 3 (L3) driving automation across various driving environments. Driving contexts were manipulated using distinct road conditions containing low, medium, or high contextual risks. To enhance driver’s SA of the driving context, we redesigned the in-vehicle central control panel to display real-time perceptual and predictive information about the external driving context. We hypothesized that SA of driving contexts would facilitate trust calibration rather than merely enhancing trust, allowing trust to adjust to appropriate levels under different driving conditions. Experiment 1 examined the impact of perceptual information about the road, traffic infrastructure, and surrounding vehicles on drivers’ trust. We found that driver’s trust decreased with increased contextual risk only when the reconfigured panel was used, while the number of accidents was not affected. Experiment 2 investigated the effect of predictive information about the external context on drivers’ trust by marking safe and dangerous zones around driver’s vehicle with green and red areas, respectively. We revealed that the predictive information calibrated the trust according to road conditions and increased overall trust levels, while the number of accidents was not affected. Together, these findings suggest that enhancing perception and prediction of external contexts helps drivers align their trust with contextual risk levels in L3 driving automation without compromising driving safety.

The performance analysis of a compressed air energy storage (CAES) for peak moving with cooling, Heating, and power production

This study focuses on modeling and optimizing a multifaceted geothermal-based energy production system within the context of Denmark. The primary objectives revolve around enhancing system efficiency and reducing operational costs. The system under investigation comprises geothermal components, an organic Rankine cycle, a compressed air energy storage facility, and an absorption chiller. The organic Rankine cycle operates using refrigerants R123 and ammonia, effectively converting thermal energy into electricity and thermal energy for various applications. Optimization was carried out employing the Response Surface Method in tandem with Design-Expert software, facilitating the fine-tuning of objective functions. Two key objectives were selected: Exergy Round Trip Efficiency and cost rate, aimed at improving technical performance and curbing economic expenditure. A range of design variables were considered for optimization, including turbine and pump inlet temperatures, geothermal mass flow rate, turbine and pump efficiencies, compressor and gas turbine efficiency, inlet pressure to the compressed air energy storage tank, and evaporator pinch point temperature. The system reached an impressive exergy efficiency peak of 77.98%, accompanied by a modest cost rate of 5.48 $/h. The costliest components in the system were the compressed air energy storage unit, followed closely by organic Rankine cycle 1 and organic Rankine cycle 2. In contemplating the practical implementation of this innovative energy system, ten cities in Denmark underwent rigorous analysis, accounting for technical and economic factors. Subsequent assessments identified Aarhus as the optimal location to initiate the system. The environmental results showed that by producing 13981.9 megawatts of electricity annually in Arhus City, it is possible to help reduce CO2 emissions by 2853.2 tons of CO2/year and avoid environmental costs of 68455.3 $/year. The environmental assessment also highlighted the potential for substantial green space expansion, estimating an additional 13 hectares of green areas in the city of Aarhus, Denmark.

Local environment perceived in daily life and urban green and blue space visits: Uncovering key factors for different age groups to access ecosystem services

Inequality in access to green and blue spaces is a major issue in the field of environmental justice. Various factors significantly influence visitations to these spaces, including residential, commuting, and shopping areas. However, studies have mainly focused on residential environments. Analyzing the coupling of environmental and socioeconomic factors, including age, income, and childhood experience, and examining the direct and indirect effects of these factors for different age groups are needed to better understand the detailed context of inequalities in access to ecosystem services. We conducted a questionnaire survey and analysis to address these needs in an urban setting. The results demonstrate that several land use categories (i.e., agricultural lands in residential areas, grasslands near shopping areas, and deciduous forests near commuting destinations) positively correlated with visitation frequency, suggesting the lack thereof may be the cause of access inequalities. In particular, the perimeter per unit area of some land cover types, which shows the complexity of the form of each land cover category, was found to be a significant factor. We identified inequality in nature experience to be the key factor correlated with inequality in nature visits among the young age group. For the middle-aged and old age groups, nature-relatedness was positively correlated with visitation frequency. Proper design of the local environment, such as through land use planning, can be an effective measure for all age groups. For instance, long-bordered green areas in the Monsoon Asian region are an attractive traditional landscape, with high accessibility and a comfortable thermal environment. The key factors for each age group should be considered in spatial design and inclusive information-sharing to reduce inequality in access to ecosystem services.

Development and Evaluation of a Sensor Enabled Smart Herbicide Applicator for Inter Row Crops

A novel self-propelled smart herbicide applicator (1600mm × 500mm × 1100mm) was developed using machine vision to target weeds and apply herbicides variably in wide row-spaced crops. The applicator integrated a weed detection system, smart sprayer, mechanical components, and power transmission system. The weed detection system used a webcam based image sensor, with images processed via MATLAB's image processing toolbox. The system processed images using an RGB-based algorithm (R > G + 5, R > B + 5) to separate weeds from soil. Weed density, calculated as the green area percentage (green pixels to total pixels), was sent to a microcontroller-operated smart sprayer via a serial port. The microcontroller regulates herbicide flow by controlling the valve with a servo motor, following an algorithm based on green area percentage. Field tests showed a 40% reduction in herbicide use, 91.26% weeding efficiency, and 63.4% field efficiency. The smart herbicide applicator's unique features include real-time measurement, portability, and precise herbicide application for wide-row crops, even at night. It also reduced environmental hazards and drudgery among farmers.

EVALUATION OF PLANTS BASED ON ECOLOGICAL TOLERANCE CRITERIA: A CASE STUDY OF URBAN OPEN GREEN SPACES IN RIZE, TURKIYE

In addition to aggressive urbanization, climate change and ecological challenges also have serious negative effects on urban green areas. Moreover, the selection of inappropriate plants in urban green space landscape projects increases the severity of these negatives. Therefore, inappropriate plant selection is considered a significant threat to the sustainability of urban green spaces. This situation highlights the importance of selecting plants with high ecological tolerance in urban park landscape projects. In this study, all tree, shrub, and ground cover plants identified in 11 urban parks and open green areas in Rize province were evaluated in terms of ecological tolerance criteria (air pollution, drought, wind, temperature, cold, and salinity). A total of 223 plant taxa were examined (77 native, 3 semi-native, 143 exotic), and it was determined that 149 taxa (49 native, 100 exotic) were sensitive to at least one of these criteria, while the remaining 74 taxa (27 native, 47 exotic) had sufficient tolerance to all ecological variables examined. Prunus serrulata Lindl. ‘Kanzan’ was identified as the taxon with the weakest ecological tolerance, and the resilience statuses of the other taxa were presented in the assessment table created. As a result, it was found that there are taxa used in Rize urban open green areas that pose risks against ecological factors, and recommendations were made for future landscape planting projects.

Integrated Climate Change Mitigation and Public Health Protection Strategies: The Case of the City of Bologna, Italy

Introduction: The ongoing process of global warming, driven by the escalating concentration of greenhouse gases generated by human activities, especially in urban areas, significantly impacts public health. Local authorities play an important role in health promotion and disease prevention, and some aim to achieve net-zero greenhouse gas emissions. There is a consistent action underway to reach this goal, hence the need for mapping and implementing effective strategies and regulations. Materials and Methods: This study includes the analysis of policy guidelines adopted by the city of Bologna, consulted in March and April 2024. Bologna is one of the 100 cities committed to achieving climate neutrality by 2030, 20 years ahead of the EU target. To identify the strategies adopted to mitigate climate change, the following methodology was used: (i) the systematic mapping of sources and spatial planning documents; (ii) the extrapolation of goals, measures, and target indicators; and (iii) the development of an overall matrix. Results: The main findings of the study and their connection to public health pertain to the identification of key macro-areas contributing to the reduction of greenhouse gas emissions, while reducing the impact of climate change on health: (1) built environment and renewable energy sources, (2) transport and mobility, (3) energy, (4) green areas and land use, and (5) citizen support. Within these five macro-areas, 14 goals have been identified, to which a total of 36 measures correspond, and, finally, a target indicator is determined, mainly with respect to the reduction of tons of CO2 equivalent per year. Conclusions: In order to protect public health, it is evident that buildings and urban activities should not produce carbon emissions throughout their lifecycle. This paper presents a method to evaluate municipal policies regarding dual-impact solutions that address both environmental protection through sustainability strategies and public health, in compliance with the Health in All Policies (HiAP) approach.

The Effectiveness of Green Open Spaces in Absorbing Carbon Dioxide Emissions in the Area of PT. Pelabuhan Indonesia (Persero) Regional 3 Subregional Jawa

Introduction: Green spaces serve as various ecological purposes, most notably the removal of pollutants. Although PT. Pelabuhan Indonesia (Persero) Regional 3 Subregional Jawa was estimated to have quite significant carbon dioxide (CO2) emissions, the company had provided green open space in multiple locations. Calculating the effectiveness of green open spaces in absorbing CO2 emissions in the area of Pelindo Subregional Jawa was the aim of this study. Methods: The primary data used was observation, and secondary data were gathered through document and literature studies. The dependent variable was the calculation of carbon dioxide emissions, while the independent variable was the effectiveness of green open spaces (land cover types and green open spaces area). The IPCC 2006 equations were used for data analysis. Results and Discussion: The estimated carbon dioxide emissions at Pelindo Subregional Jawa came from the following sources: 1,945.51 tons from electricity consumption; 1,845.97 tons from ships; 1,102.30 tons from operational, employee, and passenger vehicles; and 4.49 tons from genset usage. That amounted to 4,898.27 tons per year overall. Based on the calculation, it was found that the green open space could absorb 4,434.94 tons of CO2 per hectare annually. Conclusion: The existing green open spaces in the area of Pelindo Subregional Jawa were not enough to absorb CO2 emissions, as CO2 emissions still left a residual of 463.33 tons/year.

A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements.