Cannabis pollen dispersal across the United States

Simulation of day and night pollen dispersion over five months reveals significant seasonal and spatial variations, particularly in the tail of the dispersal kernel. Each simulation yielded a dispersal kernel, or number of particles deposited downwind from the source in 250 m wide bins, normalized by the number of particles released. Figure 1a,b show median day and night dispersal kernels on a log scale by month for each of nine US climate divisions²⁵, in order to compare between climatically different regions. We observe depositions up to 50 km downwind, the edge of our domain, which is the limit of applicability of our LS model.

The tail of the dispersal kernel varies seasonally and spatially

Simulations of day and night pollen dispersion over five months yields variation only in the tail of the dispersal kernel. For all climate regions, in both day and night conditions, Fig. 1 shows a steep decline in depositions by two orders of magnitude within the first few kilometers of the source. Approximately 70% of simulated pollen is deposited in the first bin alone for all cases. Figure 2a shows that across all simulations, dispersal kernels decreased to 1% of released particles within 3 km of the source. Although there is a slight increase in distance for nighttime conditions, this region of steep decline is indistinguishable across counties regardless of region and seasonal weather changes.

While this steep decline in depositions appears to support commonly-used hemp isolation distances (< 5 km^7,8), even 1% of 100 million pollen grains would result in 1 million pollen depositing at that distance. In Fig. 2b, lowering the threshold to 0.1% of released particles results in far more spread, 1–10 km during the day, and 10–15 km at night. Further decreasing the threshold to 0.01% results in distances varying throughout the entire domain, as shown in Fig. 2c. This fat tailed deposition kernel is common for wind-dispersed species^26,27, and poses challenges when computing the risk of rare events in the tail, e.g., burning embers from a wildfire²⁸ or cross-pollination. For hemp in particular, the Small and Antle experiment⁵ provides evidence that even reduced depositions at the tail of the distribution can result in effective cross-pollination. Given the prolific nature of hemp pollen, potentially massive fields, and reports of hemp pollen travelling well beyond established isolation distances, the fat tail of the dispersal kernel becomes necessary to assess cross-pollination risk^27,29.

We find that the tail of the dispersal kernel below the 0.1% and 0.01% thresholds and beyond 3 km, shows considerable variability. Figure 2b,c show stark differences between day and night simulations, driven by diurnal differences in wind conditions. For more detail, see Supplementary Fig. S4. Below the 0.01% threshold, we observe a large spread in nighttime threshold distances and two peaks for day simulations, which point to large-scale regional and seasonal shifts in wind conditions.

Daytime seasonal and spatial patterns

In Fig. 1, daytime dispersal kernels for all climate regions exhibit a steady rise from July to November. This increase is responsible for the second peak in daytime 0.01% threshold distances, which is dominated by simulations later in the season. Although all regions experience increase over the season, the Southwest region maintains the least depositions throughout. In the peak summer months of July and August, the Southwest region experiences the lowest depositions, as do the Northwest, Northern Rockies & Plains, and Northeast. By October and November these latter three regions exhibit an almost 10-fold increase, shifting from relatively low depositions to the highest, on par with the Upper Midwest and Ohio Valley.

Seasonal shifts are most apparent between 5 and 10 km downwind, where overall depositions increase by nearly an order of magnitude. At this distance, Fig. 1 shows a distinctive local minimum near the source for nearly all simulations. The daytime dispersal dip in an otherwise monotonically decreasing curve is due to updrafts from convective turbulence^30,31, and can be interpreted as a region of relatively less deposition, or a “pollen shadow”, in the near-field downwind of the source. Beyond the pollen shadow, there is relatively less seasonal and regional variation in depositions, indicating that in daytime, these downwind distances are not as strongly tied to patterns in underlying meteorological parameters.

Mapping out daytime deposition values in Fig. 4a at 5 km, 10 km, 20 km, and 35 km downwind reveals contiguous, large-scale seasonal and spatial patterns. Within the pollen shadow, at 5 km downwind, Northern counties are the first to experience increases in deposition. From September, we see a region of higher depositions in California and the Upper Midwest. That region extends to the northernmost counties by October, coalescing into a band above about \(40^{\circ }\) N latitude in November. Further downwind, beyond the pollen shadow, this pattern of northern seasonal increase is not as apparent; only the Southwest stands out with the lowest depositions throughout the season.

We observe the lowest depositions in simulations with higher boundary layer height, \(z_i\), and greater convective velocity, \(w^*\). High \(w^*\) and \(z_i\) together indicate greater buoyancy associated with the surface heat flux and more convective turbulence³². Scatter plots and correlation values between daytime depositions and these meteorological parameters are provided in Supplementary Fig. S2 and the monthly heatmaps are shown in Supplementary Fig. S6. High convective conditions in summer lead to more pollen uplifting and less deposition, particularly in the pollen shadow. More pollen is uplifted, carried far from the source, before descending in small quantities at great distances. A reduction in convective conditions from summer to fall explains the pattern of deposition increase for northern regions, particularly within the pollen shadow. It is also why the Southwest exhibits low depositions throughout the season. Greater convective conditions make long-distance transport of pollen more likely^12,15,16, but results in fewer depositions within the domain.

Our results align with other dispersal studies, which show that greater sensible heat flux and warming temperatures during the day led to greater transport distances^12,13,16, particularly in combination with increased wind speed¹⁴. In our results, however, neither the 10-m wind speed (estimated roughly as \(10 u^*\)) nor the Monin-Obukhov length, L, influenced deposition counts, indicating that shear-driven turbulence did not play a major role in daytime dispersal patterns. This could be due to the monthly averaging of the meteorological input parameters. For example, monthly-averaged \(u^*\) only varied between 0.45 and 0.65 m/s, or maximum variations in 10-m wind speed of 2 m/s. It is likely that averaging resulted in less variation, allowing convective conditions to govern deposition patterns within the domain.

In summary, during the day, we identify large-scale contiguous spatial patterns that shift from summer to fall. The Southwest maintains the lowest depositions throughout the season because it experiences greater convective conditions than all other regions. On the other hand, northern counties shifted from comparatively low to high depositions relative to other climate regions due to a decrease in convective conditions in the fall months. This is consistent with typical CONUS weather trends; Northern climate regions experience changing seasons more strongly, and daytime dispersal is particularly dependent on these seasonal factors.

Nighttime seasonal and spatial patterns

Unlike the daytime curves, night-time dispersal kernels for each month show a monotonic decrease with downwind distance, as shown in Fig. 1b. Within the first 10 km, depositions at night are ten times greater than during the day. Relative to these large values, spatial patterns and seasonal differences only become clear beyond about 10 km. Beyond this distance, we observe slight overall increase in deposition primarily in October and November.

While we do not see a major seasonal increase at night, shifting spatial patterns are discernible in both the heat maps and dispersion kernels. Figure 4b shows night-time depositions by county at 5, 10, 20, and 35 km downwind of the source. Observing heatmaps at 10 km and beyond, in July and August, there is a swathe of high depositions in the center of the country, beginning with the South region and extending into the Northern Rockies & Plains (NRP). By September, the South region is no longer as prominent, and by October, the swathe of high depositions has extended into the Upper Midwest (UM) and NRP. The Northeast (NE) region also progressively increases in depositions over the season. By November, the regions with the greatest deposition include the UM, NRP, and NE, while the least deposition occur in the Southeast and West regions.

We find that regions of least deposition correspond to high friction velocity, \(u^*\), high boundary layer height, \(z_i\), lower roughness length, \(z_0\), and high Monin-Obukhov length |L|. Scatter plots and correlation values between night-time depositions and these meteorological parameters are provided in Supplementary Fig. S3. These parameters indicate more neutral conditions and greater wind shear, resulting in pollen travelling further from the source and depositing in greater amounts¹⁴. Our results show that greater \(u^*\), i.e., greater horizontal wind speed, is primarily responsible for variations in night time dispersal, and the slight increase in depositions in the cooler months of October and November. This aligns with previous dispersal studies, which show that particles travel further¹⁶ and remain airborne for longer¹⁵ in winter than in summer months.

Overall, we find that night-time dispersal kernels are dictated by wind speed, or shear-driven turbulence. This results in more depositions further downwind in cooler months, where depositions increased with greater wind speeds.

Reconciling day and night patterns

We observe strong diurnal patterns and find that night-time dispersal dominates consideration of cross-pollination risk near the source. Within approximately 20 km of the source, night-time depositions are one to two orders of magnitude greater than during the day, as shown in Figs. 1 and 4. Nearly all released particles are deposited by 20 km at night—an average of 97% across night-time cases, compared to only 81% during the day. Cumulative depositions are shown in Supplementary Fig. S5. This results in a stark difference in cross-pollination risk between day and night, showing that nighttime dispersal is more important to consider within the domain and within 20 km.

Beyond this distance, nighttime dispersal kernels experience a steep decline in depositions, while daytime kernels possess a fatter tail. We can see this at 35 km in Fig. 1, where the daytime kernels have a shallower slope than and in Fig. 4, where most regions during the day are greater than at night. At night, almost all pollen is deposited near the source, but convective uplifting during the day allows for pollen to deposit in low quantities at the furthest reaches of the domain and even beyond it. Oneto et al.¹⁵ found that spores released during the day had much longer flight times than at night, on the order of several days rather than a few hours and escaped into the stratosphere in greater numbers, while spores at night had flight times on the order of hours. For longer day flight times, pollen viability may become a factor for risk of cross-pollination¹⁵. Choudhary et al. found that viability of Cannabis pollen only decreased substantially three days after release from the anther³³. In our study, we are only considering dispersal within 50 km of the source. Even with a slow wind speed of 1 m/s, it would only take a pollen grain 14 h to traverse the 50 km domain, thus viability need not be taken into account. Within the domain, viability has little impact on cross-pollination risk, and so daytime dispersal patterns impact risk at the furthest reaches of our domain.

It is possible that hemp pollen only disperses during the day, as is common for many wind-dispersed species²⁷. One study observed that male Cannabis anthers open and release pollen in the morning hours³³. However, Cannabis pollen measurement studies found only slight diurnal changes in concentration^10,34, indicating that Cannabis pollen remains in the air throughout the day. As Cannabis production has only recently been legalized, there is minimal research on the diurnal timings of Cannabis pollen release. For these reasons, we consider both day and night dispersal in this study for risk assessment.

Cross-pollination vulnerability

While we cannot directly estimate risk of cross-pollination, as these are 2D models that do not take into account lateral spread, we can evaluate counties based on total counts of particles reaching certain distances downwind. In Fig. 3, we plot the 0.01%-distances averaged over all day and night simulations from July to November for each county as a heat map. This figure shows that across all months and time periods, the Upper Midwest, Ohio Valley, and Northeast regions have the greatest average 0.01% threshold distances—they experience the most depositions at the farthest distances. Thus, according to simulation results alone, these regions are most vulnerable to cross pollination.

However, when county-specific information such as hemp acreage and land area are incorporated, vulnerability does not necessarily reflect the same contiguous spatial patterns demonstrated in Fig. 3. In Eq. (1) below, we incorporate this information to compute a novel, dimensionless “vulnerability” metric for each county. We first normalize the dispersion area, \(A_{\text{disp}}\), i.e., the area of a circle with radius equal to the average 0.01% threshold distance, by the land area of each county, \(A_{\text{land}}\). This yields the fraction of a county that falls within its theoretical area of risk. We then normalize the number of acres of planted hemp in 2023 per county³⁵, \(A_{\text{hemp}}\), by the land area of each county, \(A_{\text{land}}\). This yields the proportion of land used for hemp cultivation for each county. See Supplementary Fig. S7 for heat maps of the components of the vulnerability metric. We then multiply these two factors to produce a rudimentary measure of how vulnerable a county is to cross-pollination,

Figure 5 shows a heat map of the vulnerability metric for all counties with nonzero hemp acreage in 2023³⁵. The five states with the most land area with vulnerability greater than \(10\times 10^{-6}\)—Montana, South Dakota, Idaho, Wisconsin, and Kentucky—are enlarged to illustrate vulnerable counties in more detail.

In counties with high vulnerability, large isolation distances may not be sufficient to prevent cross-pollination, as the combination of more hemp acreage and larger 0.01% threshold distances result in a greater likelihood of pollen transport across the entire county. Instead, a more comprehensive approach is necessary. A 2022 Colorado report exploring hemp cross-pollination suggested a voluntary pinning system to track where hemp is planted in a region². Rather than mandating specific isolation distances, we recommend a pinning system which includes location of outdoor plants, time of planting, and anticipated flowering dates. This could be combined with an awareness of when and where pollen transport is greatest, as demonstrated in this study, to produce a dynamic time-dependent map of high-risk areas within a county.

The dependence of the vulnerability metric on dispersal distances and meteorological conditions tends to vary by regional meteorological differences on a country-wide scale. Within a state, variation in the vulnerability metric is more dependent on hemp acreage within a county. These country-wide spatial patterns and local variations could be useful for potentially insuring farmers in the event of financial losses due to cross-pollination—another form of risk management, with insurance coverage and premiums varying based on region and local risk.

Weather forecasting, combined with dispersal modeling, could provide a way to predict when and where pollen will tend to travel further, rather than relying on historical weather patterns as done in this study. This would enable individual farmers to plan their crops strategically, incorporating dispersal patterns when evaluating the risks of growing one crop over another. It would also allow for voluntary community-level planning, where stakeholders make decisions together regarding when and where certain varieties should be planted in each season. Finally, local government could require sharing of crop timing and location so that more informed decisions could be made.

Cannabis is typically photosensitive, flowering as day lengths shorten below a threshold (typically 10–12 hours)^36,37. However, this varies depending on the cultivar and planting location. A strain adapted to northern latitudes may flower in an entirely different month when planted further south³⁸, and there are also non-photosensitive cultivars³⁹. It may be possible to strategically plan and plant crops so that flowering times between fiber/grain growers and floral hemp do not overlap. A three-year Cannabis pollen sampling study³⁴ in Tetouan, Morocco, observed that the main pollen season, when concentrations peaked, began almost a month late due to rainfalls that caused delays in planting. Strategic planting and community coordination could shift the dates of regional pollen concentration significantly. In fact, artificially reducing the day-lengths by covering crops has also previously been suggested to induce earlier flowering⁴⁰.

Strategic community planning for hemp growers would alleviate many of the challenges facing the US hemp industry today. This industry is extremely new, and is still developing the infrastructure to balance production with supply-chain capacity and consumer demand⁴¹. For example, in North Carolina, there was a crash in cannabidiol hemp production following a massive grower rush which exceeded demand³⁸. There are also insufficient fiber processors for the state to bounce back to growing for fiber. It has been suggested that hemp grown for fiber, cannabidiols, and seed should be grown near their respective processing facilities in order to optimize production and prevent such problems⁴¹. These kinds of risks, in addition to the cross-pollination risk, can be managed with more intensive community planning.

Limitations and future directions

Currently, there is no single LS model that addresses both stable and unstable conditions effectively across our entire domain. Therefore, to model dispersal both during the day (typically unstable) and the night (typically stable), we chose two separate LS model formulations. Although this choice of different models for day and night might influence the observed diurnal patterns in this study, our results qualitatively align with the literature in terms of day and night differences and seasonal variation^15,16. In addition, the LS model we use for stable conditions incorporates only shear-generated turbulence produced at the surface. In reality, turbulence in the nocturnal boundary layer is complex, involving physics such as decoupling from the surface layer, the low-level nocturnal jet, and slope effects^32,42. Future work to identify night-time dispersal patterns might include more nuanced modeling in stable conditions. In general, more resolved, albeit more computationally expensive models, would greatly improve risk prediction. These models could incorporate more detailed physics such as release of pollen from the anthers, dispersal within a canopy, wet deposition, and even conditions specific to a farm’s location like topography.

The models used in this study were shown to perform reasonably when compared to experimental results, described further in the “Methodology” section. However, we have not found previous experimental Cannabis pollen dispersal studies with enough information to validate the model. Experimental evidence suggests that airborne Cannabis pollen is ubiquitous⁴³, in part because of its long flight times due to its small size compared to other pollen⁵. Therefore, validation of dispersal from a known source is difficult. One approach is to use a source made of genetically engineered (GE) plants which produce pollen with fluorescent markers⁴⁴, enabling accurate source attribution. Our group is currently pursuing this in collaboration with co-workers. However, making GE Cannabis has proven difficult, and a study was performed instead with GE switchgrass, which produces pollen of a similar small size⁴⁵. A paper on this combined experimental and modeling study is forthcoming.

The present study was performed using meteorological data only from 2016. This data has been validated with an extensive measurement network in the US²⁴, which was deemed appropriate for this proof-of-concept study. Performing this same study over multiple years could increase the robustness of our results and provide insight into possible yearly variation. For example, warming temperatures could cause changes to these seasonal and spatial patterns. Kuparinen et al.¹² demonstrated greater seed dispersal distances achieved in simulations when using increasing temperatures.

In this study, averaging meteorological data across months reduces the occurrence of extreme weather patterns and does not take into account frequency of certain conditions. Incorporating wind-direction frequency would provide directionality to cross-pollination risk assessment. For example, the Small and Antle experiment⁵ measured six times more pollen deposition downwind than upwind at their source field over a period of two weeks. For future studies, a better measure of cross-pollination risk would include frequency of weather conditions and directional variability in deposition.

Furthermore, incorporating the distance between farms would provide a more sophisticated measure of county vulnerability, as was demonstrated theoretically for hemp farms in Kentucky counties⁴⁶. Our vulnerability metric assumes one source of hemp per county, as data for the locations of individual farms are not currently available. When averaging the 0.01%-threshold distances, we weighted day and night dispersal equally, as literature describing diurnal Cannabis emission rates is lacking. However, including temporally varying rates of pollen emission would increase accuracy.