I am struggling to understand how to interpret the treatment and control group in the following case of DID regression. We need to estimate the causal effect of brick kilns on downwind PM2.5 monitor readings. Using the wind direction we identified the number of brick kilns that are upwind to monitor on a given day. Also brick production is a seasonal activity and operates only for 5 months in a year. We run the following equation:

PM2.5 = β0 + β1 Kilnsd + β2 Seasond+ β3 Kilnsd × Seasond + θXd + ed

where Kilnsd= no of kilns upwind to monitor identified on a particular day
season= dummy variable for kilns off and on Xd = vector of meteorological controls Kilnsd × Seasond= interaction term between the number of upwind kilns and the brick season
The author of the paper states that we measure whether changes in pollution between the brick-producing season (approximately November to March) and the nonproducing season (the rest of the year) were larger on days when the air pollution monitor was downwind from more kilns as compared to days when the air pollution monitor was downwind from fewer kilns after controlling for other factors.

In this case, instead of binary treatment variable, we have continuous values. I am not able to interpret what is treatment group and what is controlled?

Can we run the above model in using stata DID commands?