diff --git a/R/ResultFlagsIndependent.R b/R/ResultFlagsIndependent.R
index 3dcc4eb9..5b92a1dd 100644
--- a/R/ResultFlagsIndependent.R
+++ b/R/ResultFlagsIndependent.R
@@ -1256,8 +1256,6 @@ TADA_FindPotentialDuplicatesMultipleOrgs <- function(.data, dist_buffer = 100,
   # group by date, time, characteristic, and rounded result value and determine the number of 
   # organizations that have those same row values, and filter to those summary rows with more than 
   # one organization
-  
-  # HRM note 12/31/24 - pick up work here, some issues w/ TADA.NearbySiteGroup
   dups_sum <- dupsprep %>%
     dplyr::group_by(ActivityStartDate, ActivityStartTime.Time, TADA.CharacteristicName, 
                     ActivityTypeCode, roundRV, TADA.MonitoringLocationIdentifier,
diff --git a/R/Utilities.R b/R/Utilities.R
index c39b4521..8631dcc2 100644
--- a/R/Utilities.R
+++ b/R/Utilities.R
@@ -96,7 +96,7 @@ utils::globalVariables(c(
   "TADA.MonitoringLocationIdentifier.New", "TADA.NearbySites.Flag",  "CountSites", "Group",
   "Matrix", "n_id", "OrgRank", "rank.default", "Site", "TADA.LatitudeMeasure.New",
   "TADA.LongitudeMeasure.New", "TADA.MonitoringLocationName.New",
-  "TADA.MonitoringLocationTypeName.New"
+  "TADA.MonitoringLocationTypeName.New", "df_number"
 ))
 
 # global variables for tribal feature layers used in TADA_OverviewMap in Utilities.R
@@ -905,7 +905,8 @@ TADA_FindNearbySites <- function(.data, dist_buffer = 100,
     dplyr::group_by(NHD.nhdplusid) %>%
     dplyr::mutate(n_id = length(TADA.MonitoringLocationIdentifier)) %>%
     dplyr::filter(n_id > 1) %>%
-    dplyr::ungroup()
+    dplyr::ungroup() %>%
+    dplyr::filter(!is.na(NHD.nhdplusid))
   
   # remove intermediate object
   rm(nhd_catchments)