Suppr超能文献

评估城市观赏树木的过敏风险:将开放获取遥感数据与花粉测量相结合。

Assessing allergy risk from ornamental trees in a city: Integrating open access remote sensing data with pollen measurements.

机构信息

Department of Systematic and Environmental Botany, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland.

Department of Systematic and Environmental Botany, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland; Laboratory of Aerobiology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland.

出版信息

J Environ Manage. 2024 Sep;367:122051. doi: 10.1016/j.jenvman.2024.122051. Epub 2024 Aug 3.

Abstract

Platanus sp. pl. (plane trees) are common ornamental tree in Poland that produces a large amount of wind-transported pollen, which contains proteins that induce allergy symptoms. Allergy sufferers can limit their contact with pollen by avoiding places with high pollen concentrations, which are restricted mainly to areas close to plane trees. Their location is thus important, but creating a detailed street tree inventory is expensive and time-consuming. However, high-resolution remote sensing data provide an opportunity to detect the location of specific plants. But acquiring high-resolution spatial data of good quality also incurs costs and requires regular updates. Therefore, this study explored the potential of using open access remote sensing data to detect plane trees in the highly urbanized environment of Poznań (western Poland). Airborne light detection and ranging (LiDAR) was used to detect training treetops, which were subsequently marked as young plane trees, mature plane trees, other trees or artefacts. Spectral and spatial variables were extracted from circular buffers (r = 1 m) around the treetops to minimize the influence of shadows and crown overlap. A random forest machine learning algorithm was applied to assess the importance of variables and classify the treetops within a radius of 6.2 km around the functioning pollen monitoring station. The model performed well during 10-fold cross-validation (overall accuracy ≈ 92%). The predicted Platanus sp. pl. locations, aggregated according to 16 wind directions, were significantly correlated with the hourly pollen concentrations. Based on the correlation values, we established a threshold of prediction confidence, which allowed us to reduce the fraction of false-positive predictions. We proposed the spatially continuous index of airborne pollen exposure probability, which can be useful for allergy sufferers. The results showed that open-access geodata in Poland can be applied to recognize major local sources of plane pollen.

摘要

悬铃木属(Platanus sp. pl.)是波兰常见的观赏树种,其产生的大量风传花粉含有引发过敏症状的蛋白质。过敏症患者可以通过避免花粉浓度高的地方来限制与花粉的接触,而花粉浓度高的地方主要局限于靠近悬铃木的区域。因此,悬铃木的位置很重要,但创建详细的街道树木清单既昂贵又耗时。然而,高分辨率遥感数据提供了检测特定植物位置的机会。但是,获取高质量的高分辨率空间数据也需要成本并且需要定期更新。因此,本研究探讨了利用开放获取的遥感数据检测波兰高度城市化的波兹南(西部波兰)地区悬铃木的潜力。机载光探测和测距(LiDAR)用于检测训练树梢,随后将其标记为幼龄悬铃木、成熟悬铃木、其他树木或人工制品。从树梢周围的圆形缓冲区(r=1 m)提取光谱和空间变量,以最大程度地减少阴影和树冠重叠的影响。应用随机森林机器学习算法来评估变量的重要性,并对功能花粉监测站周围 6.2 公里范围内的树梢进行分类。该模型在 10 倍交叉验证中表现良好(总体准确性≈92%)。根据 16 个风向对预测的悬铃木属位置进行聚合,与每小时花粉浓度显著相关。基于相关值,我们建立了预测置信度的阈值,从而减少了假阳性预测的比例。我们提出了空气传播花粉暴露概率的空间连续指数,这对过敏症患者可能有用。结果表明,波兰的开放获取地理数据可用于识别当地主要的悬铃木花粉源。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验