Cities are increasingly confronted with multiple environmental and climatic stressors. Especially during heatwaves, street canyons are both producers and sufferers of air pollution and urban heat island (UHI) effects, with severe risks on public health. To better design mitigation measures, it is important to consider both the microclimate behaviors as well as the perceptions of the local population. Therefore, this study examined pedestrian perceptions and microclimate modelings to understand outdoor thermal comfort conditions and air pollution dispersion in the case study neighborhood of Dortmund Marten, Germany. A field survey with measurement points at two street canyons for climatic variables and questionnaires on subjective thermal comfort and air pollution was conducted on a hot day during the heatwave period in August 2020. As a cost-effective method for modeling input generation, we extracted spatial and spectral data like albedo, roof materials and tree locations out of remote sensing imageries. Finally, we compared the modeling results of the physiological equivalent temperature (PET) index, particulate matter concentrations and air temperatures with empirical field measurement data and the questionnaire responses. Results indicate that during hot summer days with light winds from the east, the north-south orientated street canyon with tree arrangements tends to act as a tunnel for particulate matter accumulation. Coincidently, pedestrians show less thermal discomfort than calculated PET values in that particular area during morning and daytime, which underlines the dichotomy of such places. On the other hand, the low rise east-west orientated street canyon shows higher PET votes than predicted by the model. However, particulate matter concentrations were considerably underestimated by the model, while air temperature predictions provided meaningful results. The proposed workflow shows the potential to accelerate future preparations of input data for microclimate modelings, while the results can enhance wind-sensitive planning procedures and heat stress resilience in mid-latitude urban neighborhoods.

Ho Chi Minh City (HCMC), economic center and most populous city of Vietnam faces a strong structural change since its market liberalization in the late 1980s. Big challenges occur in the form of uncontrolled urban sprawl due to rapid population growth with encroachment of agricultural land, which leads to environmental and climatic issues like urban heat island effects, air pollution and flooding events. Remote Sensing and Geographic Information Systems (GIS) provide new computer-based technologies for urban planners which can greatly ease the monitoring of agricultural loss as well as improve decision making for future land management. In the first part of this study, land cover change dynamics are thoroughly assessed using moderate and high spatial resolution satellite imagery (Landsat and SPOT) over the period 2010-2017 in 22 districts of HCMC. In the second part, the land cover classification results of 2017 provide the initial map for a GIS-based Multi-Criteria-Decision-Analysis (MCDA) of potential agricultural protection sites. Therefore, criteria of climate adaptation and ecological service are established and embedded in the GIS-compatible Compromise Programming Model (CP). With the use of Analytic Hierarchy Process (AHP) by Thomas L. Saaty and additional expert knowledge, appropriate weighting factors have been affiliated. The results show that agricultural land decreased by more than two thirds in the period considered. However, particularly the western rural districts Bình Chánh and Hóc Môn still offer a great amount of valuable agricultural land suitable for protection. The proposed method can serve as a scientific framework for planning departments of fast growing cities to zone agricultural land for protection on an early planning stage in order to ensure sustainable land use development in the future.

As negative impacts of climate change tend to increase in the future, densely-populated cities especially need to take action on being robust against natural hazards. Consequently, there is a growing interest from scientists in measuring the climate resilience of cities and regions. However, current measurements are usually assessed on administrative levels, not covering potential hotspots of hazardous or sensitive areas. The main aim of this paper focusses on the measurement of climate resilience in the City of Dortmund, Germany, using Geographic Information Systems (GIS). Based on a literature review, we identified five essential components of climate resilience and initially designed a theoretical framework of 18 indicators. Since climate resilience is still a vague concept in scientific discourses, we implemented local expert knowledge and fuzzy logic modelling into our analysis. The benefit of this study not only lies in the fine-scale application, but also in the relevance for multiple disciplines by integrating social and ecological factors. We conclude that climate resilience varies within the city pattern, with the urban core tending to be less resilient than its surrounding districts. As almost the entire geodata set used is freely available, the presented indicators and methods are to a certain degree applicable to comparable cities.

Green Infrastructure (GI) is gaining wide recognition in cooperative research projects seeking to find solutions for climate adaptation in urbanized areas. However, the potential effects of co-produced GI plans and the underlying preparation process are rarely evaluated. To bridge this gap, the aim of this article is to examine what works in addressing environmental burdens in the urban neighborhood of Dortmund Marten, Germany. As part of a larger transdisciplinary process, selective GI measures were delineated in the case study area through a cooperative workshop between scientists and urban planners. Workshop ideas were incorporated into a mitigative scenario considering a hot summer day to quantify the effects of the derived GI measures on thermal comfort and particulate matter dispersion (PM₁₀ and PM_2.5). To evaluate the experiences of the science-practice collaboration, the viewpoints of researchers and urban planners on learning effects, knowledge integration, and GI planning were summarized and compared via an online survey. The results indicate that the proposed GI measures could reduce physiological equivalent temperature (PET) by 25 °C. At the same time, additional roadside trees could increase PM₁₀ concentrations by up to 36 µg/m³ due to wind blocking effects. Reflections on the science-practice workshop show that learning effects were higher for the participating researchers than for planning practitioners, while the integration of individual expertise during the workshop was more difficult for academics. These findings point to the importance of continuous reflections on individual understandings in cooperating stakeholder groups and the value of the evaluation of outcomes in transdisciplinary GI planning.