bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/821785

A tree-planting decision support tool for urban heat island mitigation

Werbin, Z. R.·Heidari, L.·Buckley, S. R.·Brochu, P.·Butler, L.·Connolly, C.·Houttuijn Bloemendaal, L.·McCabe, T. D.·Miller, T.·Hutyra, L. R.
View original source ↗Export metadata ↓

Abstract

Heat poses an urgent threat to public health in cities, as the urban heat island (UHI) effect can amplify exposures, contributing to high heat-related mortality and morbidity. Urban trees have the potential to mitigate by providing substantial cooling, as well as co-benefits such as reductions in energy consumption. The City of Boston has attempted to expand its urban canopy, yet maintenance costs and high tree mortality have hindered successful canopy expansion. Here, we present an interactive web application called "Right Place, Right Tree - Boston" that aims to support informed decision-making for planting new trees. To highlight priority regions for canopy expansion, we developed a Boston-specific Heat Vulnerability Index (HVI) and present this alongside maps of summer temperatures. We also provide information about tree pests and diseases, suitability of species for various conditions, land ownership, maintenance tips, and alternatives to tree planting.

Source connections

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Werbin, Z. R., Heidari, L., Buckley, S. R., Brochu, P., Butler, L., Connolly, C., Houttuijn Bloemendaal, L., McCabe, T. D., Miller, T., Hutyra, L. R.. 2019-10-28. A tree-planting decision support tool for urban heat island mitigation. https://doi.org/10.1101/821785

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗
bioRxiv2020

Prevalence of scabies and its associated factors among school age children in Arba Minch zuria district, Southern Ethiopia, 2018.

BackgroundScabies, a common human skin disease with a prevalence range of 0.2% to 71.4% in the world. It can have considerable impact on general health leading to illness and death not only through direct effect of its infestation and as a result of secondary bacterial infection. The aim of this study was to assess the prevalence of scabies and its associated factors among school age children in Arba Minch zuria district, Gamo zone, Southern Ethiopia. MethodsA community based cross sectional study was carried out in 845 school age children from February 20 to March 30, 2018. Multi-stage sampling technique was used to select study populations. Logistic regression an analysis was used to identify factors associated with scabies. Findings were presented using 95% CI of Crude Odds Ratios (COR) and Adjusted Odds Ratios (AOR. To declare statistical significance, p-value less than 0.05 was used. ResultA total of 825 children participated in the study with response rate of 97.6%. The overall prevalence of scabies was 16.4% [95% CI: 13.9%, 18.9%]. overcrowding index, family history of itching in the past two weeks, wealth index, knowledge of scabies, climatic zone, frequency of washing body, frequency of washing clothes, finger nails cutting practice, history of skin contact with scabies patient, washing hair more than once weekly, and sharing of clothes were significantly associated scabies disease. ConclusionIn conclusion, the prevalence of scabies in Arba Minch lies at 16.4% in the global scabies range 0.2% to 71.4%. The prevalence was highest in highlands followed by midland and then lowland. This represents a significant scabies burden which we recommend warrants health service intervention. Authors summaryScabies, a common human skin disease with a prevalence range of 0.2% to 71.4% in the world. It can have considerable impact on general health leading to illness and death not only through direct effect of its infestation and as a result of secondary bacterial infection. Conducting a research on this neglected tropical disease would contribute in designing a policies and strategies on prevention and control measures in the study area. Therefore, the aim of this study was to assess the prevalence of scabies and its associated factors among school age children in Arba Minch zuria district, Gamo zone, Southern Ethiopia.

scientific communication and education↗
bioRxiv2020

Sorry, we're open: Golden Open Access and inequality in the natural sciences

Global Open Access (GOA) journals make research more accessible and therefore more citable; however, the publication fees associated with GOA journals can be costly and therefore not a viable option for many researchers seeking high-impact publication outlets. In this study, I collect metadata from 237 open-access natural science journals and analyze them in terms of Article Processing Charges (APC), Impact Factor (IF), Eigen Factor (EF), citability, and country of publisher. The results of this study provide evidence that with IF, EF, and citability all increase as APC increases, and each of these metrics are higher in publishers from developed countries in comparison to developing countries. Implications of these trends are discussed in regards to natural sciences and inequality within the global scientific community.

scientific communication and education↗