Predictions of airborne allergenic pollen concentrations at fine spatial scales require information on source plant location and pollen production. Such data are lacking at the urban scale, largely because manually mapping allergenic pollen producing plants across large areas is infeasible. However, modest-sized field surveys paired with allometric equations, remote sensing, and habitat distribution models can predict where these plants occur and how much pollen they produce. In this study, common ragweed (Ambrosia artemisiifolia) was mapped in a field survey in Detroit, MI, USA. The relationship between ragweed presence and habitat-related variables derived from aerial imagery, LiDAR, and municipal data were used to create a habitat distribution model, which was then used to predict ragweed presence across the study area (392 km2). The relationship between inflorescence length and pollen production was used to predict pollen production in the city. Ragweed occurs in 1.7% of Detroit and total pollen production is 312 × 1012 pollen grains annually, but ragweed presence was highly heterogeneous across the city. Ragweed was predominantly found in in vacant lots (75%) and near demolished structures (48%), and had varying associations with land cover types (e.g., sparse vegetation, trees, pavement) detected by remote sensing. These findings also suggest several management strategies that could help reduce levels of allergenic pollen, including appropriate post-demolition management practices. Spatially-resolved predictions for pollen production will allow mechanistic modeling of airborne allergenic pollen and improved exposure estimates for use in epidemiological and other applications.

ragweed database for analysisDatabase including ragweed presence and each predictor variable (thinned to one cell per parcel and to 1 point per 16m2); this is the version used in the analysis. Variable descriptions are provided in the README file.ragweed_thinned_dataset.csvRasters of predictor variablesRasters that were included in the analysis. For descriptions of each raster see the README file associated with the ragweed database.dryad ragweed.zipunsupervised classification of NAIP 2016 imageryLarge raster of unsupervised classification of NAIP 2016 false color imagery.naip2016_fc_unsupclass.tifpredictionsPredicted ragweed presence (mean and standard deviation) and predicted ragweed pollen production (mean).