diff --git a/pymrio/tools/ioutil.py b/pymrio/tools/ioutil.py
index 6a1b7f11..7f5fe7b1 100644
--- a/pymrio/tools/ioutil.py
+++ b/pymrio/tools/ioutil.py
@@ -1026,7 +1026,7 @@ def match_and_convert(df_orig, df_map, agg_func='sum'):
         compartment ... original index name
         factor ... the factor for multiplication
         impact__stressor ... the new index name, replacing the stressor name
-        compartment__compartment ... the new compartment, from the original compartment
+        compartment__compartment ... the new compartment, replacing the original compartment
 
         Some additional columns are possible, but not necessary:
 
@@ -1044,32 +1044,37 @@ def match_and_convert(df_orig, df_map, agg_func='sum'):
     """
 
     new_col = [col for col in df_map.columns if "__" in col]
-    unique_new_index = df_map.loc[:, new_col].value_counts()
+    unique_new_index = df_map.loc[:, new_col].value_counts().index
 
     df_map = df_map.set_index(new_col) 
     res_collector = []
 
     # loop over each new impact/characterized value
-    for char in unique_new_index.index:
+    for char in unique_new_index:
         if len(char) == 1:
             df_cur_map = df_map.loc[[char[0]]]
         else:
             df_cur_map = df_map.loc[[char]]
         agg_method = df_cur_map.agg_func if 'agg_func' in df_cur_map.columns else agg_func
-        df_agg = pd.DataFrame(columns=df_orig.columns, index=df_cur_map.index, data=0)
-        df_agg.index.names = [n.split('__')[0] for n in df_agg.index.names]
+
         collector = []
 
-        # the loop for getting all (potential) regex matches
+        # the loop for getting all (potential regex) matches and multiplication
         for row in df_cur_map.iterrows():
             matched_entries = index_fullmatch(df_ix=df_orig, **row[1].to_dict())
             mul_entries = matched_entries * row[1].factor
-            aggregated = mul_entries.aggregate(agg_method, axis=0)
-            collector.append(aggregated)
+            collector.append(mul_entries)
+
+        df_collected = pd.concat(collector, axis=0)
 
+        new_idx = df_cur_map.index.unique()
+        new_idx.names = [name.split('__')[0] for name in new_idx.names]
+        df_collected.index = np.repeat(new_idx, df_collected.shape[0])
+        # CONT: 
+        # Mkae a pass through of an index level which should be passed through
+        # Think of regions, or res3 in the test system
 
-        df_collected = pd.concat(collector, axis=1).T
-        df_collected.index = np.repeat(df_cur_map.index.unique(), df_collected.shape[0])
+        df_collected.groupby(by=df_collected.index.names).agg(agg_method)
 
         res_collector.append(df_collected.groupby(by=df_collected.index.names).agg(agg_method))
 
diff --git a/tests/test_util.py b/tests/test_util.py
index 1d5f1fd3..9928d625 100644
--- a/tests/test_util.py
+++ b/tests/test_util.py
@@ -430,4 +430,37 @@ def test_char_table():
 
     pdt.assert_frame_equal(res2, exp_res2)
 
+    # TEST3 with passing through one index to the results
+    # Done for compartment here
+
+    map_test3 = pd.DataFrame(
+        columns=["em_type", "compart", "total__em_type", "compart__compart", "factor"],
+        data=[["em.*", "air|water", "total_regex", "all", 2], 
+              ["em1", "air", "total_sum", "all", 2], 
+              ["em1", "water", "total_sum", "all",  2], 
+              ["em2", "air", "total_sum", "all", 2], 
+              ["em2", "water", "total_sum", "all", 2], 
+              ["em1", "air", "all_air", "air", 0.5], 
+              ["em2", "air", "all_air", "air", 0.5]],
+    )
+
+    # alternative way to calculated the expected result
+    exp_res3 = pd.DataFrame(
+        columns = to_char.columns,
+        index = pd.MultiIndex.from_tuples(
+            [("total_regex", "all"), ("total_sum", "all"), ("all_air", "air")]))
+    exp_res3.loc[('all_air', 'air')] = to_char.loc[("em1", "air")] * 0.5 + to_char.loc[("em2", "air")] * 0.5
+    exp_res3.loc[('total_regex', 'all')] = (to_char.sum(axis=1) * 2).values
+    exp_res3.loc[('total_sum', 'all')] = (to_char.sum(axis=1) * 2).values
+    exp_res3 = exp_res3.astype(float)
+    exp_res3.sort_index(inplace=True)
+
+    res3 = match_and_convert(to_char, map_test3)
+    res3.sort_index(inplace=True)
+
+    exp_res3.index.names = res3.index.names
+    exp_res3.columns.names = res3.columns.names
+
+    pdt.assert_frame_equal(res3, exp_res3)
+