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Pesticides have been widely used in agriculture, resulting in significant pollution that affects both the environment and human health. This pollution is particularly prevalent in nearby agricultural areas, where sensitive resources are contaminated through spray drift exposure and surface runoff. Spray drift is a critical concern when it comes to environmental hazards. It poses health risks not only to farmers and pesticide applicators, but also to individuals living in nearby farm areas. To address this issue, developing reliable models and techniques for estimating spray drift and reducing its impact has become a crucial and efficient research topic. The current research has three primary objectives: firstly, to estimate the average pesticide application rates, trend analysis, and concentration distribution; secondly, to estimate the temporal variations of pesticide concentrations and identify the areas most likely to be affected by pesticide spray drift close to agricultural fields; and lastly, to develop a model for field spray drift and deposition integration between the OpenAir package for the R programming environment and the AgDRIFT atmospheric model. The drift model, along with precise supervised classifications, allowed for a more accurate estimation of potential drift in agricultural areas at a spatial resolution of 15 m. Additionally, multiple scenarios were conducted to evaluate the potential risks of pesticide drift outside of the target areas. This novel method effectively estimated organophosphate pesticide spray drift over two case studies in Macon County using a combination of OpenAir and AgDRIFT models and remotely sensed data. This method allowed for field simulations within completely defined exposure areas with little prior knowledge of pesticide quantities. This study concluded that 6% of total cropland is in danger of pesticide spray drift, with around 8% of crop areas exposed to potential strong drift on land use. Furthermore, 11% of cropped land is vulnerable to moderate drift, whereas around 75% of land use land cover is not vulnerable to pesticide drift. Through this research, an accurate and efficient approach has been developed to estimate spray drift and reduce its impact in agricultural areas, contributing to a safer and healthier environment for all. [ABSTRACT FROM AUTHOR]

Farmers utilize pesticides extensively on their farms to control weeds and insects, as well as increase crop productivity. Despite these advantages, their excessive use poses a serious threat, particularly to the population living at the nexus of urban and rural areas. Exposure to pesticide drift can be investigated using geospatial tools. Remote sensing technology and Geographic Information Systems (GIS) techniques have been used intensively and constitute trusted tools in different sectors, especially in agriculture. Remote sensing depends on processing the electromagnetic radiation reflected and emitted from the ground target and can be used to identify the main units of Land Use Land Cover (LULC), in addition to measuring crop areas exposed to pesticides. GIS has powerful tools for building a spatial geo-database of pesticide exposure drift. Therefore, the major objective of the research was to explore the effectiveness of using remote sensing and GIS techniques to estimate the exposure organophosphate pesticides drift over Macon County, Alabama. To achieve this objective, the Cropland Data Layer (CDL) dataset, the available pesticide usage data, and gridded population data were used to estimate the potential pesticide drift on the Macon County level. In addition, the AgDRIFT model was used to estimate the potential drift of pesticides from their intended targets at the field level. The results indicated that 6.6% of Macon County's residents are considered potentially severely exposed, and the potentially affected population resides primarily in rural areas. In comparison, 23% of residents of the urban-rural interface are considered to have potentially medium to high exposure. In addition, 38% of residents living in suburban areas are considered to have potentially low-to-medium exposure. The results indicated that both GIS and remote sensing could play an effective role in estimating pesticide exposure drift at the State or County level. In addition, the AgDRIFT model was more appropriate for estimating pesticide drift at the field level. [ABSTRACT FROM AUTHOR]