While complex urban morphologies including different materials, wall structures, etc., are rather adequately represented in microclimate models, replication of actual plant geometry is—so far—rather crudely handled. However, plant geometry greatly differs within species and locations while strongly determining a plant’s microclimate performance. To improve the plants representation in numerical models, a new method to describe plant skeletons using the so-called Lindenmayer-System has been implemented in the microclimate model ENVI-met. The new model allows describing much more realistic plants including the position and alignment of leaf clusters, a hierarchical description of the branching system and the calculation of the plant’s biomechanics. Additionally, a new canopy radiation transfer module is introduced that allows not only the simulation of diffuse radiation extinction but also secondary sources of diffuse radiation due to scattering of direct radiation within plant canopies. Intercomparisons between model runs with and without the advancements showed large differences for various plant parameters due to the introduction of the Lindenmayer-System and the advanced radiation scheme. The combination of the two developments represents a sophisticated approach to accurately digitize plants, model radiative transfer in crown canopies, and thus achieve more realistic microclimate results.
Accurate simulation of radiative transfer is a very important aspect in climate modeling. For microclimate models in particular, it is not only important to simulate primary but also secondary radiative fluxes in great detail, i.e., emitted longwave and reflected shortwave radiation. As there are always limitations regarding computational effort and memory, these radiative fluxes are commonly implemented using simplified approaches. To overcome these simplifications and, thus, increase modeling accuracy, a new radiation scheme called indexed view sphere was introduced into the microclimate model ENVI-met. This new scheme actually accounts for radiative contributions of objects that are seen by each grid cell. In order to evaluate the advantages of the new scheme, it is compared against the formerly used averaged view factor scheme. The comparison in a complex realistic urban environment demonstrated that the indexed view sphere scheme improved the accuracy and plausibility of modeling radiative fluxes. It, however, yields an increased demand of memory to store the view facets for each cell. The higher accuracy in simulating secondary radiative fluxes should, however, overturn this shortcoming for most studies, as more detailed knowledge of local microclimatic conditions in general and eventually thermal comfort can be gained.
PM2.5 concentrations in urban areas are highly variable, both spatially and seasonally. To assess these patterns and the underlying sources, we conducted PM2.5 exposure measurements at the adult breath level (1.6 m) along three ~5 km routes in urban districts of Mainz (Germany) using portable low-cost Alphasense OPC-N3 sensors. The survey took place on five consecutive days including four runs each day (38 in total) in September 2020 and March 2021. While the between-sensor accuracy was tested to be good (R² = 0.98), the recorded PM2.5 values underestimated the official measurement station data by up to 25 µg/m3. The collected data showed no consistent PM2.5 hotspots between September and March. Whereas during the fall, the pedestrian and park areas appeared as hotspots in >60% of the runs, construction sites and a bridge with high traffic intensity stuck out in spring. We considered PM2.5/PM10 ratios to assign anthropogenic emission sources with high apportionment of PM2.5 in PM10 (>0.6), except for the parks (0.24) where fine particles likely originated from unpaved surfaces. The spatial PM2.5 apportionment in PM10 increased from September (0.56) to March (0.76) because of a pronounced cooler thermal inversion accumulating fine particles near ground. Our results showed that highly resolved low-cost measurements can help to identify PM2.5 hotspots and be used to differentiate types of particle sources via PM2.5/PM10 ratios.