<p>Pesticide drift refers to the unintentional movement of pesticide particles or vapours from the target application area, raising significant concerns for environmental and public health. The extent of drift is influenced by various factors, including application methods, spray equipment calibration, crop structure, and current weather conditions. To analyse this phenomenon, the Tier III module of the AgDrift model was used to evaluate off-target pesticide drift during early morning and afternoon periods, utilizing real-time meteorological data for crops such as alfalfa, wheat, corn, and cotton. The study focused on the organophosphate insecticide Chlorpyrifos in Alabama, examining how hourly weather variables—specifically wind speed, temperature, relative humidity, and wind direction—affect drift behaviour. The results showed notable differences in drift patterns based on application timing. Early morning applications (6 to 8 am) achieved higher application efficiency, reaching 99.26% for alfalfa, and had lower downwind deposition and airborne drift compared to afternoon applications (12 to 3 pm). Conversely, afternoon applications, especially when wind speeds exceeded 16 mph—for example, in the wheat case study—showed a decrease in application efficiency (95.79%) along with increased drift and evaporative loss of the carrier solution. Additionally, analysis of vertical and horizontal transport profiles indicated a higher drift risk during afternoon applications across all scenarios examined. A multilinear regression analysis revealed a strong correlation between observed and predicted drift values, showing that wind speed and air temperature positively influence drift, while relative humidity has a negative effect. The validation of the model confirmed its accuracy and reliability in predicting pesticide drift across various meteorological conditions.</p> Graphical Abstract <p>Based on the graphical abstract, this study offers an integrated assessment of chlorpyrifos drift in Macon County, Alabama, employing AgDrift for an evaluation of meteorological sensitivities, visualization of buffer zones, and modeling of statistical outcomes. The methodology begins with the acquisition of Tier III input parameters for AgDrift, which include critical meteorological variables like wind speed and temperature, alongside pesticide characteristics and crop-specific data. Simulations were performed under three distinctive crop application scenarios: Standard, Early Morning, and Midday. The model outputs are visualized using a variety of graphical tools, such as drift direction maps, buffer zone overlaps, and wind rose diagrams, enabling comprehensive spatial interpretations of the drift patterns. A sensitivity analysis was conducted to determine the influence of meteorological factors—specifically wind speed and evaporation rates—on the drift modeling results. To quantify interrelationships and validate the statistical model's performance, multiple linear regression (MLR) was applied, integrating evaluation metrics such as ANOVA, R-squared, RMSE, and correlation heat maps. The robustness of the model is further affirmed through scatter plot assessments and goodness-of-fit analyses. Flowchart arrows facilitate navigation through sequential data processing, scenario analysis, and statistical evaluations, culminating in actionable insights for pesticide drift mitigation and buffer zone planning. This thorough approach supports informed decision-making regarding pesticide applications in diverse environmental conditions.</p>