TY  - JOUR
AB  - Evaluating allergenicity in urban environments is essential for understanding risks to allergy sufferers, improving urban planning, and strengthening climate resilience. In this study, we applied a modified Urban Green Zone Allergenicity Index (IUGZA) to a pilot area in Augsburg, southern Germany, to assess spatial and temporal dynamics of allergenic exposure and identify opportunities for healthier cities. The index was adapted to local species composition, flowering periods, and sitespecific conditions, integrating tree inventory data and allometric parameters. To enhance ecological relevance, we additionally incorporated airborne pollen concentrations, temperature, and air pollution in combination with a climate impact adjustment model. Temporal dynamics were examined on seasonal, monthly, and daily scales. To explore spatial variability, the area was divided into equal sub-areas, each with IUGZA calculated using GIS, followed by the spatial interpolation method Inverse Distance Weighing (IDW) to generate continuous allergenicity heatmaps. A total of 1427 trees representing 66 species were analyzed, with approximately 35 % classified as allergenic, mainly from Betula and Corylus. The overall allergenic potential was relatively high (0.36). A sensitivity analysis revealed crown projection area as the strongest influence on allergenic potential, underscoring the role of morphological traits in allergen exposure. Allergenic peaks were observed in spring, coinciding with the flowering periods of dominant allergenic species, and temperature is the most relevant adjustment factor. The results highlight the importance of both spatial distribution and phenological timing in influencing allergenic potential. By integrating ecological, climatic, and morphological factors, this approach provides a flexible and transferable framework for improving allergenicity assessments at neighborhood and city scales, supporting public health strategies and climate-resilient urban planning.
AU  - Trost, C.
AU  - Rötzer, T.*
AU  - Traidl-Hoffmann, C.
AU  - Plaza, M.P.
C1  - 76095
C2  - 58411
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
SP  - 18
TI  - Unveiling hidden allergenic hotspots: A fine-scale, parameter-optimized approach for spatiotemporal mapping of urban allergenicity assessments.
JO  - Sust. Cities Soc.
VL  - 134
PB  - Elsevier
PY  - 2025
SN  - 2210-6707
ER  - 

TY  - JOUR
AB  - Numerous studies have explored influencing factors in COVID-19, yet empirical evidence on spatiotemporal dynamics of COVID-19 inequalities concerning both socioeconomic and environmental factors at an intra-urban scale is lacking. This study, therefore, focuses on neighborhood-level spatial inequalities of the COVID-19 incidences in relation to socioeconomic and environmental factors for Berlin-Neukölln, Germany, covering six pandemic periods (March 2020 to December 2021). Spatial Bayesian negative binomial mixed-effect models were employed to identify influencing factors and risk patterns for different periods. We identified that (1) influencing factors and relative risks varied across time and space, with sociodemographic factors exerting a stronger influence over environmental features; (2) as the most identified predictors, the population with migrant backgrounds was positively associated, and the population over 65 was negatively associated with COVID-19 incidence; (3) certain neighborhoods consistently faced elevated risks of COVID-19 incidence. This study highlights potential structural health inequalities within migrant communities, associated with lower socioeconomic status and a higher risk of COVID-19 incidence across diverse pandemic periods. Our findings indicate that locally tailored interventions for diverse citizens are essential to address health inequalities and foster a more sustainable urban environment.
AU  - Zhuang, S.*
AU  - Wolf, K.
AU  - Schmitz, T.*
AU  - Roth, A.*
AU  - Sun, Y.*
AU  - Savaskan, N.*
AU  - Lakes, T.*
C1  - 70123
C2  - 55214
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Neighborhood-level inequalities and influencing factors of COVID-19 incidence in berlin based on Bayesian spatial modelling.
JO  - Sust. Cities Soc.
VL  - 104
PB  - Elsevier
PY  - 2024
SN  - 2210-6707
ER  -