diff --git a/carbon_edge_analysis.py b/carbon_edge_analysis.py
index 3d3c561..df96deb 100644
--- a/carbon_edge_analysis.py
+++ b/carbon_edge_analysis.py
@@ -676,7 +676,7 @@ def main():
                      sum_in_out_forest_carbon_density_by_mask_task))
             task_graph.join()
             raster_info = geoprocessing.get_raster_info(carbon_opt_forest_step_path)
-            LOGGER.debug(f'writing regression_optimization_carbon')
+            LOGGER.debug('writing regression_optimization_carbon')
             with open('regression_optimization_carbon.csv', 'w') as opt_table:
                 opt_table.write(
                     'file,'
diff --git a/extract_landcover_masks.py b/extract_landcover_masks.py
index 3d69a6e..6e266ea 100644
--- a/extract_landcover_masks.py
+++ b/extract_landcover_masks.py
@@ -82,11 +82,11 @@ def main():
                 target_path_list=[target_path],
                 task_name=f'mask {target_path}')
 
-    LOGGER.info(f'waiting for jobs to complete')
+    LOGGER.info('waiting for jobs to complete')
     task_graph.close()
     task_graph.join()
     del task_graph
-    LOGGER.info(f'all done!')
+    LOGGER.info('all done!')
 
 
 if __name__ == '__main__':
diff --git a/run_model.py b/run_model.py
index fe403e9..d120137 100644
--- a/run_model.py
+++ b/run_model.py
@@ -199,7 +199,7 @@ def regression_carbon_model(
     LOGGER.info(f'load model at {carbon_model_path}')
     with open(carbon_model_path, 'rb') as model_file:
         model = pickle.load(model_file).copy()
-    LOGGER.info(f'ensure raster base data are present')
+    LOGGER.info('ensure raster base data are present')
     missing_predictor_list = []
     predictor_id_path_list = []
     for predictor_id in model['predictor_list']:
diff --git a/train_regression_model.py b/train_regression_model.py
index 0dd8c58..2f3d6dd 100644
--- a/train_regression_model.py
+++ b/train_regression_model.py
@@ -435,7 +435,7 @@ def main():
 
         k = trainset[0].shape[1]
 
-        r2_table = open(os.path.join(FIG_DIR, f'r2_summary.csv'), 'a')
+        r2_table = open(os.path.join(FIG_DIR, 'r2_summary.csv'), 'a')
         r2_table.write('model,r2,r2_adjusted,explained_variance,mean_absolute_error,mse,mean_squared_log_error,median_absolute_error\n')
         for expected_values, modeled_values, n, prefix in [
                 (trainset[1].flatten(), clip_to_range(model.predict(trainset[0]).flatten(), 10, 400), trainset[0].shape[0], 'training'),