Environmental justice in a very green city: Spatial inequality in exposure to urban nature, air pollution and heat in Oslo, Norway

Multi-scale analysis of urban forests and socioeconomic patterns in a desert city, Phoenix, Arizona

Understanding the relationship between various socioeconomic factors and urban forest structure is essential for directing resources to ensure equitable distribution of green space. Through a case study of a desert city, i.e., Phoenix, AZ, this study provides a novel application of Multiscale Geographically Weighted Regression (MGWR) in which we explore the spatially variable relationships between a wide array of socioeconomic indicators and urban forest attributes. Through the computation of various scales of influence for different explanatory variables, MGWR enhances our analysis's precision and stresses the association between socioeconomic status and urban forest structure at local and regional scales. Our results indicate that although there has been a pattern of green inequality where minority and low-income communities have less access to urban forests, education levels were mostly insignificant based on the MGWR results. In some instances, higher incomes are negatively correlated with tree canopy coverage. Additionally, the stem density model outperformed the canopy coverage model in terms of prediction accuracy. This research adds a new dimension to urban forestry literature and emphasizes the value of customized urban planning strategies and the environmental justice implications of urban forestry, particularly in arid environments.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists' mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists' sampling effort is focused more in Barcelona's lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city's upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists' sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Understanding spatial inequalities and stratification in transportation accessibility to social infrastructures in South Korea: multi-dimensional planning insights

This research investigated spatial inequalities in transportation accessibility to social infrastructures (SIs) in South Korea, using a multi-dimensional methodological approach, including descriptive/bivariate analysis, explanatory factor analysis (EFA), K-Mean cluster analysis, and multinomial logit model (MNL). Our study confirmed pronounced spatial disparities in transportation accessibility to SIs, highlighting significantly lower access in rural and remote regions compared to urban centers and densely populated areas, consistent with existing literature. Building on prior findings, several additional findings were identified. First, we uncovered significant positive correlations among accessibility to different types of SIs in four critical categories: green and recreation spaces, health and aged care facilities, educational institutions, and justice and emergency services, revealing prevalent spatial inequality patterns. Second, we identified three distinct accessibility clusters (High, Middle, and Low) across the critical SI categories. Specifically, residents within the High cluster benefited from the closest average network distances to all SIs, while those in the Low cluster faced significant accessibility burdens (e.g., 22.9 km for welfare facilities, 20.1 km for hospitals, and 19.2 km for elderly care facilities). Third, MNL identified factors such as population density and housing prices as pivotal in spatial stratification of accessibility. Specifically, areas with lower SI accessibility tended to have a higher proportion of elderly residents. Also, decreased accessibility correlated with diminished traffic volumes across all transportation modes, particularly public transportation. This research contributes to enhancing our understanding of spatial inequalities in transportation accessibility to SIs and offers insights crucial for transportation and urban planning.

Environmental and social inequities in continental France: an analysis of exposure to heat, air pollution, and lack of vegetation

BACKGROUND

Cumulative environmental exposures and social deprivation increase health vulnerability and limit the capacity of populations to adapt to climate change.

OBJECTIVE

Our study aimed at providing a fine-scale characterization of exposure to heat, air pollution, and lack of vegetation in continental France between 2000 and 2018, describing spatiotemporal trends and environmental hotspots (i.e., areas that cumulate the highest levels of overexposure), and exploring any associations with social deprivation.

METHODS

The European (EDI) and French (FDep) social deprivation indices, the normalized difference vegetation index, daily ambient temperatures, particulate matter (PM2.5 and PM10), nitrogen dioxide, and ozone (O3) concentrations were estimated for 48,185 French census districts. Reference values were chosen to characterize (over-)exposure. Hotspots were defined as the areas cumulating the highest overexposure to temperature, air pollution, and lack of vegetation. Associations between heat overexposure or hotspots and social deprivation were assessed using logistic regressions.

RESULTS

Overexposure to heat was higher in 2015-2018 compared with 2000-2014. Exposure to all air pollutants except for O3 decreased during the study period. In 2018, more than 79% of the urban census districts exceeded the 2021 WHO air quality guidelines. The evolution of vegetation density between 2000 and 2018 was heterogeneous across continental France. In urban areas, the most deprived census districts were at a higher risk of being hotspots (odds ratio (OR): 10.86, 95% CI: 9.87-11.98 using EDI and OR: 1.07, 95% CI: 1.04-1.11 using FDep).

IMPACT STATEMENT

We studied cumulative environmental exposures and social deprivation in French census districts. The 2015-2018 period showed the highest overexposure to heat between 2000 and 2018. In 2018, the air quality did not meet the 2021 WHO guidelines in most census districts and 8.6 million people lived in environmental hotspots. Highly socially deprived urban areas had a higher risk of being in a hotspot. This study proposes for the first time, a methodology to identify hotspots of exposure to heat, air pollution, and lack of vegetation and their associations with social deprivation at a national level.

Data Insights for Sustainable Cities: Associations between Google Street View-Derived Urban Greenspace and Google Air View-Derived Pollution Levels

Unprecedented levels of urbanization have escalated urban environmental health issues, including increased air pollution in cities globally. Strategies for mitigating air pollution, including green urban planning, are essential for sustainable and healthy cities. State-of-the-art research investigating urban greenspace and pollution metrics has accelerated through the use of vast digital data sets and new analytical tools. In this study, we examined associations between Google Street View-derived urban greenspace levels and Google Air View-derived air quality, where both have been resolved in extremely high resolution, accuracy, and scale along the entire road network of Dublin City. Particulate matter of size fraction less than 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and carbon dioxide were quantified using 5,030,143 Google Air View measurements, and greenspace was quantified using 403,409 Google Street View images. Significant (p < 0.001) negative associations between urban greenspace and pollution were observed. For example, an interquartile range increase in the Green View Index was associated with a 7.4% [95% confidence interval: -13.1%, -1.3%] decrease in NO2 at the point location spatial resolution. We provide insights into how large-scale digital data can be harnessed to elucidate urban environmental interactions that will have important planning and policy implications for sustainable future cities.

Environmental health impacts and inequalities in green space and air pollution in six medium-sized European cities

BACKGROUND

The GoGreenRoutes project aims to introduce co-created nature-based solutions (NBS) to enhance environmental quality in six medium-sized cities (Burgas, Lahti, Limerick, Tallinn, Umeå, and Versailles). We estimated the mortality and economic impacts attributed to suboptimal exposure to green space and air pollution, economic impacts, and the distribution thereof the adult population by socioeconomic status.

METHODS

We retrieved data from publicly accessible databases on green space (NDVI and % Green Area), air pollution (NO2 and PM2.5) and population (≥20 years, n = 804,975) at a 250m × 250m grid-cell level, and mortality for each city for 2015. We compared baseline exposures at the grid-cell to World Health Organization's recommendations and guidelines. We applied a comparative risk assessment to estimate the mortality burden attributable to not achieving the recommendations and guidelines. We estimated attributable mortality distributions and the association with income levels.

RESULTS

We found high variability in air pollution and green spaces levels. Around 60% of the population lacked green space and 90% were exposed to harmful air pollution. Overall, we estimated age-standardized mortality rates varying from 10 (Umeå) to 92 (Burgas) deaths per 100,000 persons attributable to low NDVI levels; 3 (Lahti) to 38 (Burgas) per 100,000 persons to lack of % Green Area; 1 (Umeå) to 88 (Tallinn) per 100,000 persons to exceedances of NO2 guidelines; and 1 (Umeå) to 206 (Burgas) per 100,000 persons to exceedances of PM2.5 guidelines. Lower income associated with higher or lower mortality impacts depending on whether deprived populations lived in the densely constructed, highly-trafficked city centre or greener, less polluted outskirts.

CONCLUSIONS

We attributed a considerable mortality burden to lack of green spaces and higher air pollution, which was unevenly distributed across different social groups. NBS and health-promoting initiatives should consider socioeconomic aspects to regenerate urban areas while providing equally good environments.

Revealing the impact of built environment, air pollution and housing price on health inequality: an empirical analysis of Nanjing, China

Introduction

Residential segregation have become a common phenomenon in China recently. Socioeconomically disadvantaged residents were more likely to live in communities with higher PM2.5 concentrations and poorer built environment, which may ultimately lead to a higher health risk, further exacerbating health inequalities. However, the reasons for health inequalities under residential segregation remain unclear.

Methods

This study quantified the built environment, air pollution, mortality rate and housing price at 1 km × 1 km grid scale. Moderating effect model, mediating effect model, moderated mediating effect model were used to progressively clarify the relationship between the four.

Results

Results show that, in terms of spatial distribution, the central area has high housing price with good built environment, low PM2.5 concentration and low mortality rate. While the suburban area has low housing price, poor built environment, high PM2.5 concentration and high mortality rate. Additionally, built environment can not only reduce health risks through moderating effect, but also affect health through the mediating effect of PM2.5. There is heterogeneity in moderating effect of built environment in different locations. Housing prices can moderate the effect of built environment on health. This study would offer important reference for urban planning to mitigate the effect of built environment inequalities on health inequalities in China.

Value ‘generalisation’ in ecosystem accounting - using Bayesian networks to infer the asset value of regulating services for urban trees in Oslo

In this paper, we demonstrate value generalisation from a sample of ecosystem assets – municipally managed trees - to all tree assets within an urban ecosystem accounting area. A Bayesian network model is used to machine-learn non-parametric correlation patterns between biophysical site condition variables and output variables of an ecosystem service model – here iTree Eco for modelling the regulating services of urban forests. The paper also demonstrates the use of spatial Bayesian network modelling to quantify the reliability of value generalisation for accounting purposes. Value generalisation entails inferring ecosystem service values for all locations in an ecosystem accounting area, where the accounting practitioner has less information about the asset and its context, than in an available sample of managed sites within the accounting area. The modelling is carried out as a “proof-of-principle” of potential value generalisation and uncertainty analysis methods for ecosystem accounting. It does not cover all regulating ecosystem services of urban forests, nor cultural services. While noting that wide confidence intervals for generalised values pose challenges for using monetary accounts for the accounting purpose of change detection, we find that tree-specific asset valuation is possible in an urban accounting setting. Our findings serve the purpose of raising awareness about asset values of urban green infrastructure, to bring them more on a par with grey infrastructure in urban planning. We also argue that the reliability of the asset value of individual trees is also good enough to be used for non-accounting purposes, such as municipal tree damage assessments.

How Can Resource-Exhausted Cities Get Out of "The Valley of Death"? An Evaluation Index System and Obstacle Degree Analysis of Green Sustainable Development

Resource-based cities are suffering from resource scarcity and environmental deterioration. Spirit, vitality and prosperity are disappearing and cities have moved towards "the valley of death" in terms of urban development. This typically appears in environments where it is difficult to maintain sustainable development. Based on empirical analysis, a qualitative analysis method for the selection of evaluation indicators, as well as a quantitative analysis method for index weighting and principal component extraction for constructing a three-level evaluation index system of green development for coal-resource-exhausted cities, was adopted. This study also discussed the life cycle at different development stages of resource-based cities, including mature resource-based and growing resource-based cities. We further argued that the obstacle degree can act as an evaluation basis and make recommendations accordingly to improve the green development of cities. Through star-standard divisions and statistical analysis, it can be explicated that the increase in green development in the first stage is greater than that in the later stage, which is more obvious in cities with lower stars. The results also show the evolution trend and stability coefficient. There is no end in sight for urban green development, and this study can provide a new perspective to relieve the declining trend and promote green sustainable development.