####Load Libraries#### install.packages("gplots") library(lubridate) library(ggplot2) library(dplyr) library(openxlsx) library(tidyverse) library(Hmisc) library(vioplot) library(gplots) ####LOAD DATA#### #Read in MPCA CSV Tempo_DailyData <- read.csv("~/UMD/Work/Data from Scott/Tempo_DailyData.csv") #Import raw data set. Headers = true NPDES <- read.csv("~/UMD/Work/Data from Scott/NPDESSTATUS.csv") #NPDES Permits NPDESPlants<- unique(NPDES$Preferred.Id) Tempo_NPhosClBOD<-read.csv("~/UMD/Work/Data from Scott/MunicipalWWHgTSSCBD05PhosChlNSampleValues.csv") #Filter for Domestic WWTP WWTPDomestic1 <- subset(Tempo_DailyData, Tempo_DailyData$IND_VS_DOM == "Domestic") #Looking for Domestic Wastewater WWTPDomesticcount<- unique(WWTPDomestic1$MASTER_AI_ID) #727 domestic plants # Check how many unique Domestic plants # Domestic plant with NPDES permits WWTPDomestic<-WWTPDomestic1 [WWTPDomestic1$PREFERRED_ID %in% NPDESPlants,] WWTPDomesticcount<- unique(WWTPDomestic$MASTER_AI_ID) #562 Domestic and NPDES plants check how many unique NPDES permit plants #Transform date into R formatting WWTPDomestic$Date <- date(WWTPDomestic$SAMPLE_DATE) WWTPNames <- subset(WWTPDomestic,!duplicated(MASTER_AI_ID)) WWTPNamesVec <- unique(WWTPNames$MASTER_AI_ID) #Line 30 Needs to be fixed(not necessary for code but date format is different in this cvs) #Tempo_NPhosClBOD$Date<-date(Tempo_NPhosClBOD$SAMPLE_DATE) #load unit op key and unit op codes UnitopRevised<- read.csv("~/UMD/Work/Data from Scott/UnitOpRevised.csv") ###CATEGORIZING Treatment Types#### #Creating category of conventional AS plants,CAS plants with filters, and CAS with tert CAS <-UnitopRevised %>% filter(grepl(' 1,| 6,', Treatment_Key)) CASwithfilter <- CAS %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) CASwithtert<- CAS %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) v2 <- c(CASwithfilter$MASTER_AI_ID) v1 <-c(CAS$MASTER_AI_ID) v3 <- c(CASwithtert$MASTER_AI_ID) CASwofilter<-v1[! v1 %in% v2] CASwotert <-v1[! v1 %in% v3] #subsetting datafram by the vector created to categorize treatment plant types #CASdf<-Tempo_DailyData[Tempo_DailyData$MASTER_AI_ID %in% v2,] #Creating category of Extended aeration AS plants, EAAS plants with filters, and EAAS with tert EAAS <-UnitopRevised %>% filter(grepl(' 2,| 3,| 4,| 5,', Treatment_Key)) EAASwithfilter <- EAAS %>% filter(grepl('36|37|38|39', Treatment_Key)) EAASwithtert<- EAAS %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) w2 <- c(EAASwithfilter$MASTER_AI_ID) w1 <-c(EAAS$MASTER_AI_ID) w3 <- c(EAASwithtert$MASTER_AI_ID) EAASwofilter<-w1[! w1 %in% w2] EAASwotert <-w1[! w1 %in% w3] #Creating category of Trickling filters TF <-UnitopRevised %>% filter(grepl('103|104', Treatment_Key)) TFwithfilter <- TF %>% filter(grepl('36|37|38|39', Treatment_Key)) TFwithtert<- TF %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) u2 <- c(TFwithfilter$MASTER_AI_ID) u1 <-c(TF$MASTER_AI_ID) u3 <- c(TFwithtert$MASTER_AI_ID) TFwofilter<-u1[! u1 %in% u2] TFwotert <-u1[! u1 %in% u3] #Creating category of Extended aeration AS plants, EAAS plants with filters, and EAAS with tert RBS <-UnitopRevised %>% filter(grepl('73', Treatment_Key)) RBSwithfilter <- RBS %>% filter(grepl('36|37|38|39', Treatment_Key)) RBSwithtert<- RBS %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) t2 <- c(RBSwithfilter$MASTER_AI_ID) t1 <-c(RBS$MASTER_AI_ID) t3 <- c(RBSwithtert$MASTER_AI_ID) RBSwofilter<-t1[! t1 %in% t2] RBSwotert <-t1[! t1 %in% t3] #Fixed Film FF <-UnitopRevised %>% filter(grepl('73|103|104', Treatment_Key)) FFwithfilter <- FF %>% filter(grepl('36|37|38|39', Treatment_Key)) FFwithtert<- FF %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) f2 <- c(FFwithfilter$MASTER_AI_ID) f1 <-c(FF$MASTER_AI_ID) f3 <- c(FFwithtert$MASTER_AI_ID) FFwofilter<-f1[! f1 %in% f2] FFwotert <-f1[! f1 %in% f3] #Creating category of Primary and Secondary Ponds Ponds <-UnitopRevised %>% filter(grepl('68', Treatment_Key)) Ponds2<-Ponds %>% filter(grepl('75', Treatment_Key)) PondwithAS <- Ponds2 %>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,| 8,| 9,', Treatment_Key)) PondwithTF<- Ponds2 %>% filter(grepl('73|103|104', Treatment_Key)) PondwithPho<-Ponds2 %>% filter(grepl('54|55|56', Treatment_Key)) Pondwithfilter<-Ponds2%>% filter(grepl('36|37|38|39', Treatment_Key)) Pondwithpassive<-Ponds2%>% filter(grepl('31|23|71|69| 7,|44', Treatment_Key)) s2 <- c(PondwithAS$MASTER_AI_ID) s1 <-c(Ponds2$MASTER_AI_ID) s3 <- c(PondwithTF$MASTER_AI_ID) s4<- c(PondwithPho$MASTER_AI_ID) s5<- c(Pondwithfilter$MASTER_AI_ID) s6<- c(Pondwithpassive$MASTER_AI_ID) PondwoAS<-s1[! s1 %in% s2] PondwoASoTF <-PondwoAS[! PondwoAS %in% s3] Ponds <- PondwoASoTF[! PondwoASoTF %in% s4] Ponds<- Ponds[! Ponds %in% s5] Ponds<- Ponds[! Ponds %in% s6] Pond<-Unitop[Unitop$AI_ID %in% Ponds,] #Tertiary Groupings MBR<-UnitopRevised %>% filter(grepl('36', Treatment_Key)) MBRCAS <-MBR %>% filter(grepl(' 1,| 6,', Treatment_Key)) MBRAS <- MBR%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) MBR<- c(MBR$MASTER_AI_ID) MBRCAS <-c(MBRCAS$MASTER_AI_ID) MBRAS <-c(MBRAS$MASTER_AI_ID) Media<-UnitopRevised %>% filter(grepl('37|38|41|71|76', Treatment_Key)) MediaCAS <-Media %>% filter(grepl(' 1,| 6,', Treatment_Key)) MediaAS <- Media%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) MediaEAAS <- Media%>% filter(grepl(' 2,| 3,| 4,| 5,', Treatment_Key)) MediaFilter<- c(Media$MASTER_AI_ID) MediaFilterCAS <-c(MediaCAS$MASTER_AI_ID) MediaFilterAS <-c(MediaAS$MASTER_AI_ID) MediaFilterEAAS <-c(MediaEAAS$MASTER_AI_ID) # Tertcla<-UnitopRevised %>% filter(grepl('102', Treatment_Key)) # MediaTertCla<-Tertcla %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # TertclaCAS <-Tertcla %>% filter(grepl(' 1,| 6,', Treatment_Key)) # TertclaAS <- Tertcla%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # Tertcla.1<- c(Tertcla$MASTER_AI_ID) # TertclaCAS.1 <-c(TertclaCAS$MASTER_AI_ID) # TertclaAS.1 <-c(TertclaAS$MASTER_AI_ID) # MediaTertCla.1 <-c(MediaTertCla$MASTER_AI_ID) # Tertcla<- Tertcla.1[! Tertcla.1%in% MediaTertCla.1] # TertclaCAS <-TertclaCAS.1[! TertclaCAS.1%in% MediaTertCla.1] # TertclaAS<- TertclaAS.1[! TertclaAS.1%in% MediaTertCla.1] PolishPond<-UnitopRevised %>% filter(grepl('57| 8| 9|11', Treatment_Key)) MediaPolishPond<-PolishPond%>% filter(grepl('37|38|41|71|76', Treatment_Key)) PolishPondCAS <-PolishPond %>% filter(grepl(' 1,| 6,', Treatment_Key)) PolishPondAS <- PolishPond%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) MediaPolishPondAS<-PolishPondCAS %>% filter(grepl('37|38|41|71|76', Treatment_Key)) PolishPond<- c(PolishPond$MASTER_AI_ID) PolishPondCAS <-c(PolishPondCAS$MASTER_AI_ID) PolishPondAS <-c(PolishPondAS$MASTER_AI_ID) MediaPolishPond <-c(MediaPolishPond$MASTER_AI_ID) PolishPond<- PolishPond[! PolishPond%in% MediaPolishPond] PolishPondCAS <-PolishPondCAS[! PolishPondCAS%in% MediaPolishPond] PolishPondAS <-PolishPondAS [! PolishPondAS %in% MediaPolishPond] # Textile<-UnitopRevised %>% filter(grepl('72', Treatment_Key)) # MediaTxtile<-Textile %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # TextileCAS <- Textile %>% filter(grepl(' 1,| 6,', Treatment_Key)) # TextileAS <- Textile %>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # Textile<- c(Textile$MASTER_AI_ID) # # # Denit<-UnitopRevised %>% filter(grepl('25', Treatment_Key)) # MediaDenit<-Denit %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # DenitCAS <-Denit %>% filter(grepl(' 1,| 6,', Treatment_Key)) # DenitAS <- Denit%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # Denit<- c(Denit$MASTER_AI_ID) # DenitAS<- c(DenitAS$MASTER_AI_ID) # MediaDenit<- c(MediaDenit$MASTER_AI_ID) # DenitwoMedia<-Denit[! Denit %in% MediaDenit] # DenitASwoMedia<-DenitAS[! DenitAS %in% MediaDenit] # # Biofilter<-UnitopRevised %>% filter(grepl('13', Treatment_Key)) # MediaBiofilter<-Biofilter %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # BioCAS <-Biofilter %>% filter(grepl(' 1,| 6,', Treatment_Key)) # BiofilterAS <- Biofilter%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # BioFilter.1<- c(Biofilter$MASTER_AI_ID) # MediaBiofilter<- c(MediaBiofilter$MASTER_AI_ID) # BioFilter <-BioFilter.1[! BioFilter.1%in% MediaBiofilter] # PhosRem<-UnitopRevised %>% filter(grepl('54|55|56', Treatment_Key)) # MediaPhosRem<-PhosRem %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # PhosRemCAS <-PhosRem %>% filter(grepl(' 1,| 6,', Treatment_Key)) # PhosRemAS <- PhosRem%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # PhosRem<- c(PhosRem$MASTER_AI_ID) # PhosRemCAS <-c(PhosRemCAS$MASTER_AI_ID) # PhosRemAS <-c(PhosRemAS$MASTER_AI_ID) # MediaPhosRemAS<-c(MediaPhosRem$MASTER_AI_ID) # PhosRem<- PhosRem[! PhosRem%in% MediaPhosRemAS] # PhosRemCAS <-PhosRemCAS[! PhosRemCAS%in% MediaPhosRemAS] # PhosRemAS <-PhosRemAS [! PhosRemAS %in% MediaPhosRemAS] # Floc<-UnitopRevised %>% filter(grepl('45|16', Treatment_Key)) # MediaFloc<-Floc %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # FlocCAS <-Floc %>% filter(grepl(' 1,| 6,', Treatment_Key)) # FlocAS <- Floc%>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # MediaFlocAS<-FlocAS %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # Floc<- c(Floc$MASTER_AI_ID) # FlocCAS <-c(FlocCAS$MASTER_AI_ID) # FlocAS <-c(FlocAS$MASTER_AI_ID) # MediaFloc<-c(MediaFloc$MASTER_AI_ID) # Floc<- Floc[! Floc%in% MediaFloc] # FlocCAS <-FlocCAS[! FlocCAS%in% MediaFloc] # FlocAS <-FlocAS [! FlocAS %in% MediaFloc] # # AnaC<-UnitopRevised %>% filter(grepl('10,', Treatment_Key)) # MediaAnaC<-AnaC %>% filter(grepl('37|38|41|71|76', Treatment_Key)) # AnaCCAS <-AnaC %>% filter(grepl(' 1,| 6,', Treatment_Key)) # AnaCAS <- AnaC %>% filter(grepl(' 1,| 2,| 3,| 4,| 5,| 6,', Treatment_Key)) # AnaC<- c(AnaC$MASTER_AI_ID) # AnaCCAS <-c(AnaCCAS$MASTER_AI_ID) # AnaCAS <-c(AnaCAS$MASTER_AI_ID) # MediaAnaC <-c(MediaAnaC$MASTER_AI_ID) # AnaC<- AnaC[! AnaC%in% MediaAnaC] # AnaCCAS <-AnaCCAS[! AnaCCAS%in% MediaAnaC] # AnaCAS <-AnaCAS [! AnaCAS %in% MediaAnaC] #Effluents with and wo tertiary EffwithTert<- UnitopRevised %>% filter(grepl('36|37|38|39|54|55|56|102|71|57|41|25| 7,| 8,| 9,|13', Treatment_Key)) EffwithTert<- c(EffwithTert$MASTER_AI_ID) Eff<- c(UnitopRevised$MASTER_AI_ID) EffwoTert<-Eff[! Eff %in% EffwithTert] ####ORGANIZING DATA (qualifiers and paired)#### #Selected Certain columns to work with WWTPDom <- select(WWTPDomestic, MASTER_AI_ID, FACILITY_DESIGN_FLOW, SUBJECT_ITEM_DESIGNATION, PARAMETER_DESC, ABBR_UNITS_DESC,Date, SAMPLE_VALUE, VALUE_QUALIFIER_IND) #Rewrite NA as 0 WWTPDom [is.na(WWTPDom)] = 0 #head(WWTPDom) #Taking half of < values to handle the qualifer on TSS HgT and HgD WWTPDom <- WWTPDom %>% mutate(C = if_else(WWTPDom$VALUE_QUALIFIER_IND=="<", WWTPDom$SAMPLE_VALUE/2, WWTPDom$SAMPLE_VALUE)) # Paired data points #Filter HgT for ng/L HgWWTPDom<- subset(WWTPDom, ABBR_UNITS_DESC == "ng/L" & PARAMETER_DESC=="Mercury, Total (as Hg)") #pull Hg data from plant data names(HgWWTPDom)[names(HgWWTPDom) == "C"] <- "HgT" names(HgWWTPDom)[names(HgWWTPDom) == "VALUE_QUALIFIER_IND"] <- "Hg_QUALIFIER" HgWWTPDom <- select(HgWWTPDom, MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION, FACILITY_DESIGN_FLOW,Date, HgT,Hg_QUALIFIER) HgWWTPDomPlants<- unique(HgWWTPDom$MASTER_AI_ID) #Create a vector of the names of WWTP with HgD HgWWTPDomPlants #head(HgLakSup) #Filter TSS for mg/L TSSWWTPDom<- subset(WWTPDom, ABBR_UNITS_DESC == "mg/L" & PARAMETER_DESC == "Solids, Total Suspended (TSS)" |ABBR_UNITS_DESC == "mg/L" & PARAMETER_DESC == "Solids, Total Suspended (TSS), grab (Mercury)") TSSWWTPDom <- select(TSSWWTPDom, MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION, FACILITY_DESIGN_FLOW,Date, C, VALUE_QUALIFIER_IND) names(TSSWWTPDom)[names(TSSWWTPDom) == "C"] <- "TSS" names(TSSWWTPDom)[names(TSSWWTPDom) == "VALUE_QUALIFIER_IND"] <- "TSS_QUALIFIER" TSSwqual <- subset(TSSWWTPDom, TSS_QUALIFIER == "<") #Filter flow for MGD FlowWWTPDom<- subset(WWTPDom, ABBR_UNITS_DESC == "mgd" & PARAMETER_DESC == "Flow") FlowWWTPDom <- select(FlowWWTPDom, MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION, FACILITY_DESIGN_FLOW,Date, C) names(FlowWWTPDom)[names(FlowWWTPDom) == "C"] <- "Flow" #Filter HgD for ng/L #DisHg1191 <- subset(ABBR_UNITS_DESC == "ng/L" & PARAMETER_DESC=="Mercury, Total (as Hg)") HgDWWTPDom <- subset(WWTPDom, ABBR_UNITS_DESC == "ng/L" & PARAMETER_DESC =="Mercury, Dissolved (as Hg)") HgDWWTPDomPlants<- unique(HgDWWTPDom$MASTER_AI_ID) #Create a vector of the names of WWTP with HgD HgDWWTPDomPlants HgDWWTPDom <- select(HgDWWTPDom, MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION, FACILITY_DESIGN_FLOW,Date, C,VALUE_QUALIFIER_IND) names(HgDWWTPDom)[names(HgDWWTPDom) == "C"] <- "HgD" names(HgDWWTPDom)[names(HgDWWTPDom) == "VALUE_QUALIFIER_IND"] <- "HgD_QUALIFIER" #Merging Flow-TSS-HgT-HgD together by AI ID, Location in Plant, and Date WWTPDom1 <-merge(FlowWWTPDom, TSSWWTPDom, by= c("MASTER_AI_ID","SUBJECT_ITEM_DESIGNATION", "FACILITY_DESIGN_FLOW","Date")) WWTPDom2<-merge(WWTPDom1, HgWWTPDom, by= c("MASTER_AI_ID","SUBJECT_ITEM_DESIGNATION", "FACILITY_DESIGN_FLOW","Date")) WWTPDom2.HgD<-merge(WWTPDom2,HgDWWTPDom, by=c("MASTER_AI_ID","SUBJECT_ITEM_DESIGNATION", "FACILITY_DESIGN_FLOW","Date")) head(WWTPDom2) write.xlsx(WWTPDom2, "~/UMD/Work/Data from Scott/WWTPDomesticTSS_Flow_Hg.xlsx",asTable = FALSE, createWorkbook()) #Group by and summarize to get rid of multiple TSS entries WWTPDom2.ave <- WWTPDom2 %>% group_by(Date,MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION,FACILITY_DESIGN_FLOW)%>% summarise(Flow = mean(Flow), TSS = mean(TSS), HgT = mean(HgT)) head(WWTPDom2.ave) #WWTPDom2.HgD.ave <- WWTPDom2.HgD %>% # group_by(Date,MASTER_AI_ID,SUBJECT_ITEM_DESIGNATION)%>% # summarize(Flow = mean(Flow), # TSS = mean(TSS), # HgT = mean(HgT), # HgD = mean(HgD)) #Check of Locations #Locations <- unique(WWTPDom.ave$SUBJECT_ITEM_DESIGNATION) #Create a vector of the names of WWTP sample stations SD= effluent WS= influent #Locations #Seperating Effluent from Influent EffWWTPDom<- WWTPDom2.ave[grep("SD", WWTPDom2.ave$SUBJECT_ITEM_DESIGNATION), ] EffWWTPDom <- select(EffWWTPDom, MASTER_AI_ID, FACILITY_DESIGN_FLOW,Date,HgT, TSS, Flow) names(EffWWTPDom)[names(EffWWTPDom) == "HgT"] <- "HgT_Eff" names(EffWWTPDom)[names(EffWWTPDom) == "Hg_QUALIFIER"] <- "Hg_QUALIFIER_Eff" names(EffWWTPDom)[names(EffWWTPDom) == "TSS"] <- "TSS_Eff" names(EffWWTPDom)[names(EffWWTPDom) == "TSS_QUALIFIER"] <- "TSS_QUALIFIER_Eff" names(EffWWTPDom)[names(EffWWTPDom) == "Flow"] <- "Flow_Eff" WWTPwithHg <- unique(EffWWTPDom$MASTER_AI_ID) # # x1 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff <= 0.01] # x2 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff >0.01 & EffWWTPDom$Flow_Eff<=0.05] # x3 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff >0.05 & EffWWTPDom$Flow_Eff<=0.1] # x4 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff >0.1 & EffWWTPDom$Flow_Eff<=0.5] # x5 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff >0.5 & EffWWTPDom$Flow_Eff<=1] # x6 <- EffWWTPDom$HgT_Eff[EffWWTPDom$Flow_Eff >1] # summary(x1) # vioplot(x1, x2, x3, x4,x5, x6, ylog=NA, # names=c("Flow < 0.01","Flow 0.01-0.05","Flow 0.05-0.1","Flow 0.1-0.5","Flow 0.5-1","Flow > 1"),col="red") # title("Violin Plots of Total Hg",ylab="HgT") # # T1 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff <= 1] # T2 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff >1 & EffWWTPDom$TSS_Eff<=5] # T3 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff >5 & EffWWTPDom$TSS_Eff<=10] # T4 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff >10 & EffWWTPDom$TSS_Eff<=20] # T5 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff >20 & EffWWTPDom$TSS_Eff<45] # T6 <- EffWWTPDom$HgT_Eff[EffWWTPDom$TSS_Eff >45] # summary(T1) # vioplot(T1, T2, T3, T4,T5, T6, ylog=NA, # names=c("TSS < 1","TSS 1-5","TSS 5-10","TSS 10-20","TSS 20-45","TSS > 45"),col="red") # title("Violin Plots of Total Hg vs TSS",ylab="HgT") # # vioplot(T3, ylog=TRUE) # # boxplot(HgT_Eff, data=c(T1, T2, T3, T4,T5, T6), notch=TRUE, # col=("gold"), # main="Tooth Growth", xlab="Suppliment and Dose") InfWWTPDom<- WWTPDom2.ave[grep("WS", WWTPDom2.ave$SUBJECT_ITEM_DESIGNATION), ] InfWWTPDom <- select(InfWWTPDom, MASTER_AI_ID, FACILITY_DESIGN_FLOW,Date,HgT, TSS, Flow) names(InfWWTPDom)[names(InfWWTPDom) == "HgT"] <- "HgT_Inf" names(InfWWTPDom)[names(InfWWTPDom) == "Hg_QUALIFIER"] <- "Hg_QUALIFIER_Inf" names(InfWWTPDom)[names(InfWWTPDom) == "TSS"] <- "TSS_Inf" names(InfWWTPDom)[names(InfWWTPDom) == "TSS_QUALIFIER"] <- "TSS_QUALIFIER_Inf" names(InfWWTPDom)[names(InfWWTPDom) == "Flow"] <- "Flow_Inf" #take out HgT = 0 EffWWTPDom<- subset(EffWWTPDom, EffWWTPDom$HgT_Eff>0) #Add log columns EffWWTPDom<-EffWWTPDom%>% mutate(logHgT=log(HgT_Eff))%>% mutate(logTSS=log(TSS_Eff)) EffWWTPDom2<-EffWWTPDom%>% mutate(logHgT=log(HgT_Eff))%>% mutate(logTSS=log(TSS_Eff))%>% mutate(Location="Eff") EffWWTPDom2<-rename(EffWWTPDom2,HgT=HgT_Eff) EffWWTPDom2<-rename(EffWWTPDom2, TSS=TSS_Eff) InfWWTPDom2<-InfWWTPDom%>% mutate(logHgT=log(HgT_Inf))%>% mutate(logTSS=log(TSS_Inf))%>% mutate(Location="Inf") InfWWTPDom2<-rename(InfWWTPDom2,HgT=HgT_Inf) InfWWTPDom2<-rename(InfWWTPDom2, TSS=TSS_Inf) InfvEff<-rbind(EffWWTPDom2,InfWWTPDom2) ####Linear models of secondary tertiary treatments#### #All Effluent plot(EffWWTPDom$HgT_Eff ~EffWWTPDom$TSS_Eff,type="p",main="Eff WWTP Domestic") plot(EffWWTPDom$logHgT ~EffWWTPDom$logTSS,type="p",main="Eff WWTP Domestic") Efffit <- lm(EffWWTPDom$HgT_Eff ~EffWWTPDom$TSS_Eff) summary(Efffit) LogEfffit <- lm(log(EffWWTPDom$HgT_Eff) ~log(EffWWTPDom$TSS_Eff)) summary(LogEfffit) # summarize model output # Simple linear regression EffWWTPDomplot <-ggplot(data = EffWWTPDom) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) EffWWTPDomplot+labs(title = "Eff WWTP Domestic") + geom_smooth(aes(y=(HgT_Eff),x=(TSS_Eff)), method='lm', formula= y~x) #xlim(NA, 100)+ ylim(NA,100) #to add limits to graph # scale_x_continuous(trans = 'log') + # scale_y_continuous(trans = 'log') #add in to plot points on log scale (log for ln log10 for 10 base) #Log plots with log regression line EffWWTPDomlog <-ggplot(data = EffWWTPDom) + geom_point(mapping = aes(x = logTSS, y = logHgT)) EffWWTPDomlog+labs(title = "Eff WWTP Domestic log transformed data") + geom_smooth(aes(y=(logHgT),x=(logTSS)), method='lm', formula= y~x) ####All Effluents with data "curbs" EffwMax <- subset(EffWWTPDom, EffWWTPDom$HgT_Eff < 60 &EffWWTPDom$TSS_Eff <68 ) plot(EffwMax$HgT_Eff ~EffwMax$TSS_Eff,type="p",main="Eff WWTP Domestic") plot(log(EffwMax$HgT_Eff) ~log(EffwMax$TSS_Eff),type="p",main="Eff WWTP Domestic") EffMaxfit <- lm(EffwMax$HgT_Eff ~EffwMax$TSS_Eff) summary(EffMaxfit) LogEffMaxfit <- lm(log(EffwMax$HgT_Eff) ~log(EffwMax$TSS_Eff)) summary(LogEffMaxfit) # summarize model output # Simple linear regression EffwMaxplot <-ggplot(data = EffwMax) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) EffwMaxplot+labs(title = "Eff WWTP Domestic") + geom_smooth(aes(y=(HgT_Eff),x=(TSS_Eff)), method='lm', formula= y~x) #xlim(NA, 100)+ ylim(NA,100) to add limits to graph # scale_x_continuous(trans = 'log') + # scale_y_continuous(trans = 'log') #add in to plot points on log scale (log for ln log10 for 10 base) #Log plots with log regression line EffwMaxlog <-ggplot(data = EffwMax) + geom_point(mapping = aes(x = logTSS, y = logHgT)) EffwMaxlog+labs(title = "All Effluent points") + geom_smooth(aes(y=(logHgT),x=(logTSS)), method='lm', formula= y~x)+ xlim(-1,NA)+ylim(-2,NA) # library(vioplot) # T1 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff <= 1] # T2 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >1 & EffwMax$TSS_Eff<=5] # T3 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >5 & EffwMax$TSS_Eff<=10] # T4 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >10 & EffwMax$TSS_Eff<=20] # T5 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >20 & EffwMax$TSS_Eff<45] # T6 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >45] # summary(T1) # vioplot(T1, T2, T3, T4, T5, T6, # names=c("TSS < 1","TSS 1-5","TSS 5-10","TSS 10-20", "TSS 20-45", "TSS > 45")) # title("Violin Plots of Total Hg vs TSS",ylab="HgT") boxplot((EffwMax$HgT_Eff~EffwMax$TSS_Eff), main="HgT v TSS for Effluent with Max limits", xlab="TSS mg/L", ylab="HgT ng/L") #Eff without tertiarty TSS vs HgT Effwodf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% EffwoTert,] plot(Effwodf$HgT_Eff ~Effwodf$TSS_Eff,type="p",main="Eff wo tert WWTP Domestic") plot(log(Effwodf$HgT_Eff) ~log(Effwodf$TSS_Eff),type="p",main="Eff WWTP Domestic") Effwofit <- lm(Effwodf$HgT_Eff ~Effwodf$TSS_Eff) summary(Effwofit) LogEffwofit <- lm(log(Effwodf$HgT_Eff) ~log(Effwodf$TSS_Eff)) summary(LogEffwofit) summary(Effwodf) sd(Effwodf$HgT_Eff) sd(Effwodf$TSS_Eff) sd(Effwodf$logHgT) sd(Effwodf$logTSS) #with 3x sd of linear scale EffwoTertMax <- subset(Effwodf, Effwodf$HgT_Eff < 93 &Effwodf$TSS_Eff <150 ) plot(EffwoTertMax$HgT_Eff ~EffwoTertMax$TSS_Eff,type="p",main="Eff WWTP Domestic") plot(log(EffwoTertMax$HgT_Eff) ~log(EffwoTertMax$TSS_Eff),type="p",main="Eff WWTP Domestic") EffwotertMaxfit <- lm(EffwoTertMax$HgT_Eff ~EffwoTertMax$TSS_Eff) summary(EffwotertMaxfit) LogEffwotertMaxfit <- lm(log(EffwoTertMax$HgT_Eff) ~log(EffwoTertMax$TSS_Eff)) summary(LogEffwotertMaxfit) #Eff with tertiarty TSS vs HgT Effwithdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% EffwithTert,] plot(Effwithdf$HgT_Eff ~Effwithdf$TSS_Eff,type="p",main="Eff wo tert WWTP Domestic") plot(log(Effwithdf$HgT_Eff) ~log(Effwithdf$TSS_Eff),type="p",main="Eff WWTP Domestic") Effwithfit <- lm(Effwithdf$HgT_Eff ~Effwithdf$TSS_Eff) summary(Effwithfit) LogEffwithfit <- lm(log(Effwithdf$HgT_Eff) ~log(Effwithdf$TSS_Eff)) summary(LogEffwithfit) summary(Effwithdf) sd(Effwithdf$HgT_Eff) sd(Effwithdf$TSS_Eff) sd(Effwithdf$logHgT) sd(Effwithdf$logTSS) #with 3x sd of linear scale EffwithTertMax <- subset(Effwithdf, Effwithdf$HgT_Eff < 45.5 &Effwithdf$TSS_Eff <144 ) plot(EffwithTertMax$HgT_Eff ~EffwithTertMax$TSS_Eff,type="p",main="Eff WWTP Domestic") plot(log(EffwithTertMax$HgT_Eff) ~log(EffwithTertMax$TSS_Eff),type="p",main="Eff WWTP Domestic") EffwithtertMaxfit <- lm(EffwithTertMax$HgT_Eff ~EffwithTertMax$TSS_Eff) summary(EffwithtertMaxfit) LogEffwithtertMaxfit <- lm(log(EffwithTertMax$HgT_Eff) ~log(EffwithTertMax$TSS_Eff)) summary(LogEffwithtertMaxfit) hist(EffwithTertMax$logHgT) lines(density(EffwithTertMax$logHgT)) hist.data.frame(EffwithTertMax[,c(3,4,6,7)],main="Histogram for Effluent with Maxlimits") boxplot(log(EffwithTertMax$HgT_Eff),EffwithTertMax$logTSS, log(EffwoTertMax$HgT_Eff),EffwoTertMax$logTSS, col=(c("gold","darkgreen")), main="HgT and TSS in effluent with and without Tertiary Treatments", xlab="HgT and TSS") boxplot((EffwithTertMax$HgT_Eff),(EffwoTertMax$HgT_Eff)) #DF of effluent for CAS plants without tert CASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% CASwotert,] unique(CASdf$MASTER_AI_ID) plot(CASdf$HgT_Eff ~CASdf$TSS_Eff,type="p",main="Con. AS") # Simple linear regression CASfit <- lm(CASdf$HgT_Eff ~CASdf$TSS_Eff) summary(CASfit) # summarize model output CASdfplot <-ggplot(data = CASdf) + xlim(NA,20)+ylim(NA,20)+ geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) CASdfplot+labs(title = "Conventional Activated Sludge") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+ theme(text = element_text(size = 20)) LogCASwofit <- lm(CASdf$logHgT ~CASdf$logTSS) summary(LogCASwofit) hist(CASdf) hist.data.frame(CASdf[,c(3,4,6,7)],main="Histogram for Effluent with Maxlimits") summary(CASdf) sd(CASdf$HgT_Eff) sd(CASdf$TSS_Eff) sd(CASdf$logHgT) sd(CASdf$logTSS) confint(CASfit,level=0.95) confint(LogCASwofit,level=0.95) #DF of effluent for EAAS plants without tert EAASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% EAASwotert,] plot(EAASdf$HgT_Eff ~EAASdf$TSS_Eff) # Simple linear regression EAASfit <- lm(EAASdf$HgT_Eff ~EAASdf$TSS_Eff) summary(EAASfit) # summarize model output EAASdfplot <-ggplot(data = EAASdf) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) EAASdfplot+labs(title = "EAAS without Tertiary") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x) unique(EAASdf$MASTER_AI_ID) hist(EAASdf) summary(EAASdf) sd(EAASdf$HgT_Eff) sd(EAASdf$TSS_Eff) LogEAASwofit <- lm(EAASdf$logHgT ~EAASdf$logTSS) summary(LogEAASwofit) confint(EAASfit,level=0.95) confint(LogEAASwofit,level=0.95) #DF of effluent for TF plants without tert TFdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% TFwotert,] plot(TFdf$HgT_Eff ~TFdf$TSS_Eff) # Simple linear regression TFfit <- lm(TFdf$HgT_Eff ~TFdf$TSS_Eff) summary(TFfit) # summarize model output TFdfplot <-ggplot(data = TFdf) + xlim(NA,20)+ylim(NA,20)+ geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) TFdfplot+labs(title = "Trickling Filters") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x)+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+ theme(text = element_text(size = 20)) unique(TFdf$MASTER_AI_ID) hist(TFdf$logHgT) summary(TFdf) sd(TFdf$HgT_Eff) sd(TFdf$TSS_Eff) LogTFwofit <- lm(TFdf$logHgT ~TFdf$logTSS) summary(LogTFwofit) confint(TFfit,level=0.95) confint(LogTFwofit,level=0.95) #DF of effluent for RBS plants without tert RBSdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% RBSwotert,] plot(RBSdf$HgT_Eff ~RBSdf$TSS_Eff) # Simple linear regression RBSfit <- lm(RBSdf$HgT_Eff ~RBSdf$TSS_Eff) summary(RBSfit) # summarize model output RBSdfplot <-ggplot(data = RBSdf) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) RBSdfplot+labs(title = "RBS without Tertiary") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x)+ ylim(NA, 200) unique(RBSdf$MASTER_AI_ID) hist(RBSdf$logHgT) summary(RBSdf) sd(RBSdf$HgT_Eff) sd(RBSdf$TSS_Eff) LogRBSwofit <- lm(RBSdf$logHgT ~RBSdf$logTSS) summary(LogRBSwofit) confint(RBSfit,level=0.95) confint(LogRBSwofit,level=0.95) #DF of effluent for RBS plants without tert FFdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% FFwotert,] plot(FFdf$HgT_Eff ~FFdf$TSS_Eff) #DF of effluent for Pond plants without tert Ponddf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% Ponds,] plot(Ponddf$HgT_Eff ~Ponddf$TSS_Eff) # Simple linear regression Pondfit <- lm(Ponddf$HgT_Eff ~Ponddf$TSS_Eff) summary(Pondfit) # summarize model output ponddfplot <-ggplot(data = Ponddf) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) ponddfplot+labs(title = "Ponds without Tertiary") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x)+ xlim (NA,100)+ ylim(NA, 25) unique(Ponddf$MASTER_AI_ID) hist(Ponddf$logHgT) summary(Ponddf) sd(Ponddf$HgT_Eff) sd(Ponddf$TSS_Eff) LogPondwofit <- lm(Ponddf$logHgT ~Ponddf$logTSS) summary(LogPondwofit) confint(Pondfit,level=0.95) confint(LogPondwofit,level=0.95) #DF of effluent for Pond with phos removal plants without tert Pondwphosdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% s4,] plot(Pondwphosdf$HgT_Eff ~Pondwphosdf$TSS_Eff) # Simple linear regression Pondwphosfit <- lm(Pondwphosdf$HgT_Eff ~Pondwphosdf$TSS_Eff) summary(Pondwphosfit) # summarize model output pondwphosdfplot <-ggplot(data = Pondwphosdf) + geom_point(mapping = aes(x = TSS_Eff, y = HgT_Eff)) pondwphosdfplot+labs(title = "Ponds with phos removal") + geom_smooth(aes(y=HgT_Eff,x=TSS_Eff), method='lm', formula= y~x) + xlim (NA,60)+ ylim(NA, 10) unique(Pondwphosdf$MASTER_AI_ID) hist(Pondwphosdf$logHgT) summary(Pondwphosdf) sd(Pondwphosdf$HgT_Eff) sd(Pondwphosdf$TSS_Eff) LogPondwphoswofit <- lm(Pondwphosdf$logHgT ~Pondwphosdf$logTSS) summary(LogPondwphoswofit) confint(Pondwphosfit,level=0.95) confint(LogPondwphoswofit,level=0.95) ####Linear Models of Tertiary Treatment Types#### MBRsdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MBR,] MBRASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MBRAS,] #Taking into account MBR start dates MBR.x<-MBRsdf %>% filter(grepl('2953|1180|3890', MASTER_AI_ID)) WWTP3920 <- subset(MBRsdf, MASTER_AI_ID == "3920")%>% filter(Date >= as.Date("2018-12-01")) WWTP4606 <- subset(MBRsdf, MASTER_AI_ID == "4606")%>% filter(Date >= as.Date("2018-07-01")) WWTP285 <- subset(MBRsdf, MASTER_AI_ID == "285")%>% filter(Date >= as.Date("2020-12-01")) MBRdf <- rbind(WWTP4606, WWTP3920,MBR.x) unique(MBRdf$MASTER_AI_ID) summary(MBRdf) sd(MBRdf$HgT_Eff) sd(MBRdf$TSS_Eff) MBRdffit <- lm(MBRdf$HgT_Eff ~MBRdf$TSS_Eff) summary(MBRdffit) MBRdflnfit <- lm(MBRdf$logHgT ~MBRdf$logTSS) summary(MBRdflnfit) confint(MBRdffit,level=0.95) confint(MBRdflnfit,level=0.95) Mediafilterdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MediaFilter,] unique(Mediafilterdf$MASTER_AI_ID) summary(Mediafilterdf) sd(Mediafilterdf$HgT_Eff) sd(Mediafilterdf$TSS_Eff) MediafilterASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MediaFilterAS,] unique(MediafilterASdf$MASTER_AI_ID) summary(MediafilterASdf) sd(MediafilterASdf$HgT_Eff) sd(MediafilterASdf$TSS_Eff) MediafilterCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MediaFilterCAS,] unique(MediafilterCASdf$MASTER_AI_ID) summary(MediafilterCASdf) sd(MediafilterCASdf$HgT_Eff) sd(MediafilterCASdf$TSS_Eff) MediafilterCASdffit <- lm(MediafilterCASdf$HgT_Eff ~MediafilterCASdf$TSS_Eff) summary(MediafilterCASdffit) MediafilterCASdflnfit <- lm(MediafilterCASdf$logHgT ~MediafilterCASdf$logTSS) summary(MediafilterCASdflnfit) confint(MediafilterCASdffit,level=0.95) confint(MediafilterCASdflnfit,level=0.95) MediafilterEAASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% MediaFilterEAAS,] summary(MediafilterEAASdf) # Textiledf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% Textile,] # unique(Textiledf$MASTER_AI_ID) # summary(Textiledf) # sd(Textiledf$HgT_Eff) # sd(Textiledf$TSS_Eff) # # Denitdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% Denit,] # unique(Denitdf$MASTER_AI_ID) # summary(Denitdf) # sd(Denitdf$HgT_Eff) # sd(Denitdf$TSS_Eff) # DenitASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% DenitAS,] # # Biofilterdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% BioFilter,] # unique(Biofilterdf$MASTER_AI_ID) # summary(Biofilterdf) # sd(Biofilterdf$HgT_Eff) # sd(Biofilterdf$TSS_Eff) # BiofilterASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% BioFilterAS,] Tertcladf<-EffWWTPDom %>% filter(grepl('13820|1493|1175|1333|983', MASTER_AI_ID)) # Tertcladf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% Tertcla,] unique(Tertcladf$MASTER_AI_ID) summary(Tertcladf) sd(Tertcladf$HgT_Eff) sd(Tertcladf$TSS_Eff) Tertcladffit <- lm(Tertcladf$HgT_Eff ~Tertcladf$TSS_Eff) summary(Tertcladffit) Tertcladflnfit <- lm(Tertcladf$logHgT ~Tertcladf$logTSS) summary(Tertcladflnfit) confint(Tertcladffit,level=0.95) confint(Tertcladflnfit,level=0.95) # TertclaASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% TertclaAS,] # unique(TertclaASdf$MASTER_AI_ID) # summary(TertclaASdf) # sd(TertclaASdf$HgT_Eff) # sd(TertclaASdf$TSS_Eff) # TertclaCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% TertclaCAS,] # unique(TertclaCASdf$MASTER_AI_ID) # summary(TertclaCASdf) # sd(TertclaCASdf$HgT_Eff) # sd(TertclaCASdf$TSS_Eff) # PhosRemdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PhosRem,] # unique(PhosRemdf$MASTER_AI_ID) # summary(PhosRemdf) # sd(PhosRemdf$HgT_Eff) # sd(PhosRemdf$TSS_Eff) # PhosRemASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PhosRemAS,] # unique(PhosRemASdf$MASTER_AI_ID) # summary(PhosRemASdf) # sd(PhosRemASdf$HgT_Eff) # sd(PhosRemASdf$TSS_Eff) # PhosRemCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PhosRemCAS,] # unique(PhosRemCASdf$MASTER_AI_ID) # summary(PhosRemCASdf) # sd(PhosRemCASdf$HgT_Eff) # sd(PhosRemCASdf$TSS_Eff) PolishPonddf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PolishPond,] unique(PolishPonddf$MASTER_AI_ID) summary(PolishPonddf) sd(PolishPonddf$HgT_Eff) sd(PolishPonddf$TSS_Eff) PolishPondCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PolishPondCAS,] unique(PolishPondCASdf$MASTER_AI_ID) summary(PolishPondCASdf) sd(PolishPondCASdf$HgT_Eff) sd(PolishPondCASdf$TSS_Eff) PolishPondASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PolishPondAS,] unique(PolishPondASdf$MASTER_AI_ID) summary(PolishPondASdf) sd(PolishPondASdf$HgT_Eff) sd(PolishPondASdf$TSS_Eff) PolishPondCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PolishPondCAS,] unique(PolishPondCASdf$MASTER_AI_ID) summary(PolishPondCASdf) sd(PolishPondCASdf$HgT_Eff) sd(PolishPondCASdf$TSS_Eff) PolishPondCASdffit <- lm(PolishPondCASdf$HgT_Eff ~PolishPondCASdf$TSS_Eff) summary(PolishPondCASdffit) PolishPondCASdflnfit <- lm(PolishPondCASdf$logHgT ~PolishPondCASdf$logTSS) summary(PolishPondCASdflnfit) confint(PolishPondCASdffit,level=0.95) confint(PolishPondCASdflnfit,level=0.95) # Flocdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% Floc,] # unique(Flocdf$MASTER_AI_ID) # summary(Flocdf) # sd(Flocdf$HgT_Eff) # sd(Flocdf$TSS_Eff) # FlocASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% FlocAS,] # unique(FlocASdf$MASTER_AI_ID) # summary(FlocASdf) # sd(FlocASdf$HgT_Eff) # sd(FlocASdf$TSS_Eff) # FlocCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% FlocCAS,] # unique(FlocCASdf$MASTER_AI_ID) # summary(FlocCASdf) # sd(FlocCASdf$HgT_Eff) # sd(FlocCASdf$TSS_Eff) # # AnaCdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% AnaC,] # unique(AnaCdf$MASTER_AI_ID) # summary(AnaCdf) # sd(AnaCdf$HgT_Eff) # sd(AnaCdf$TSS_Eff) # AnaCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% AnaCAS,] # unique(AnaCASdf$MASTER_AI_ID) # summary(AnaCASdf) # sd(AnaCASdf$HgT_Eff) # sd(AnaCASdf$TSS_Eff) # AnaCCASdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% AnaCCAS,] # unique(AnaCCASdf$MASTER_AI_ID) # summary(AnaCCASdf) # sd(AnaCCASdf$HgT_Eff) # sd(AnaCCASdf$TSS_Eff) ####Influnet vs. Effluent Scatter Plots#### PondwphosInfvEff<-InfvEff [InfvEff $MASTER_AI_ID %in% s4,] PondInfvEff<-InfvEff [InfvEff $MASTER_AI_ID %in% Ponds,] PondsInfvEff<-rbind(PondwphosInfvEff,PondInfvEff) PondsInfvEffplot <-ggplot(data = PondsInfvEff) + geom_point(mapping = aes(x = TSS, y = HgT, colour=Location))+ scale_color_manual(values = c("Black", "Grey")) PondsInfvEffplot+labs(title = "Ponds") + scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')+ annotation_logticks(sides="lb")+ geom_abline(slope=0, intercept=0.255, color= "red")+ geom_abline(slope=0, intercept=1, color= "black")+ geom_vline(xintercept = 45) + theme(legend.position = c(0.9, 0.3))+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+ theme(text = element_text(size = 20)) CASInfvEff<-InfvEff[InfvEff $MASTER_AI_ID %in% CASwotert,] EAASInfvEff<-InfvEff [InfvEff $MASTER_AI_ID %in% EAASwotert,] TFInfvEff<-InfvEff [InfvEff $MASTER_AI_ID %in% TFwotert,] RBSInfvEff<-InfvEff [InfvEff $MASTER_AI_ID %in% RBSwotert,] MBRsInfvEff<-InfvEff [InfvEff$MASTER_AI_ID %in% MBR,] MediafilterInfvEff<-InfvEff[InfvEff$MASTER_AI_ID %in% MediaFilter,] TertclaInfvEff<-InfvEff %>% filter(grepl('13820|1493|1175|1333|983', MASTER_AI_ID)) PolishPondInfvEff<-InfvEff[InfvEff$MASTER_AI_ID %in% PolishPond,] MechanicalInfvEff2<-rbind(CASInfvEff,EAASInfvEff,TFInfvEff,RBSInfvEff,MBRsInfvEff,MediafilterInfvEff,TertclaInfvEff,PolishPondInfvEff) MechanicalInfvEff<- subset(MechanicalInfvEff2, HgT < 1000) MechanicalInfvEffplot <-ggplot(data = MechanicalInfvEff) + geom_point(mapping = aes(x = TSS, y = HgT, colour=Location))+ xlim(0.25,NA)+ylim(0.25,NA)+ scale_color_manual(values = c("Black", "Grey")) MechanicalInfvEffplot+labs(title = "Mechanical Treatment Plants") + xlim(0.25,NA)+ylim(0.25,NA)+ scale_x_continuous(trans = 'log10') + scale_y_continuous(trans = 'log10')+ annotation_logticks(sides="lb")+ geom_abline(slope=0, intercept=0.255, color= "red")+ geom_abline(slope=0, intercept=1, color= "black")+ geom_vline(xintercept = 30) + theme(legend.position = c(0.9, 0.3))+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+ theme(text = element_text(size = 20)) ####Filter Study with boxplots and violin plots##### DWMedia <- subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "290"|EffWWTPDom$MASTER_AI_ID=="986") WWMedia <- subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "2729"|EffWWTPDom$MASTER_AI_ID=="2426") WLSSD<-subset(EffWWTPDom,EffWWTPDom$MASTER_AI_ID=="2426") Aurora<-subset(EffWWTPDom,EffWWTPDom$MASTER_AI_ID=="2729") ContBWFilter <- subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "283") RapidSandFilter <- subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "3531"|EffWWTPDom$MASTER_AI_ID=="1184") CIRSSD<- subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "295") %>% filter(Date >= as.Date("2018-01-01")) summary(CIRSSD) ClothMembrane <-subset(EffWWTPDom, EffWWTPDom$MASTER_AI_ID== "2636"|EffWWTPDom$MASTER_AI_ID== "3890") summary(ClothMembrane) ClothMembranedf <-rbind(CIRSSD,ClothMembrane) #Summary of statics and regression models summary(DWMedia) sd(DWMedia$HgT_Eff) sd(DWMedia$TSS_Eff) summary(WWMedia) sd(WWMedia$HgT_Eff) sd(WWMedia$TSS_Eff) summary(RapidSandFilter) sd(RapidSandFilter$HgT_Eff) sd(RapidSandFilter$TSS_Eff) summary(ClothMembranedf) sd(ClothMembranedf$HgT_Eff) sd(ClothMembranedf$TSS_Eff) DWMediafit <- lm(DWMedia$HgT_Eff ~DWMedia$TSS_Eff) summary(DWMediafit) confint(DWMediafit,level=0.95) DWMedialnfit <- lm(DWMedia$logHgT ~DWMedia$logTSS) summary(DWMedialnfit) confint(DWMedialnfit,level=0.95) WWMediafit <- lm(WWMedia$HgT_Eff ~WWMedia$TSS_Eff) summary(WWMediafit) confint(WWMediafit,level=0.95) WWMedialnfit <- lm(WWMedia$logHgT ~WWMedia$logTSS) summary(WWMedialnfit) confint(WWMedialnfit,level=0.95) RapidSandFilterfit <- lm(RapidSandFilter$HgT_Eff ~RapidSandFilter$TSS_Eff) summary(RapidSandFilterfit) confint(RapidSandFilterfit,level=0.95) RapidSandFilterlnfit <- lm(RapidSandFilter$logHgT ~RapidSandFilter$logTSS) summary(RapidSandFilterlnfit) confint(RapidSandFilterlnfit,level=0.95) ClothMembranedffit <- lm(ClothMembranedf$HgT_Eff ~ClothMembranedf$TSS_Eff) summary(ClothMembranedffit) confint(ClothMembranedffit,level=0.95) ClothMembranedflnfit <- lm(ClothMembranedf$logHgT ~ClothMembranedf$logTSS) summary(ClothMembranedflnfit) confint(ClothMembranedflnfit,level=0.95) #Bowplox filter study boxplot(DWMedia$HgT_Eff,DWMedia$TSS_Eff, WWMedia$HgT_Eff,WWMedia$TSS_Eff, RapidSandFilter$HgT_Eff,RapidSandFilter$TSS_Eff, MBRdf$HgT_Eff,MBRdf$TSS_Eff, ClothMembranedf$HgT_Eff,ClothMembranedf$TSS_Eff, ylim=c(0,12), col=(c("gold","darkgreen")), main="Effluent of filter study", xlab = "HgT(ng/L) & TSS (mg/L)") #axis(side=1, at=c(1,2,3,4, 5, 6,7,8,9,10,11,12,13,14,15,16, # 17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32),labels=FALSE) text(x=c(1,2,3,4, 5, 6,7,8,9,10,11,12), pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("DW Media n=94","DW Media","WW Media","WW Media n=68","Cont BW n=6","COnt BW","Rapid Sand n=80","Rapid Sand","MBR n=209","MBR","Cloth n=91", "Cloth")) abline(h=1.8, col = "Red") abline(h=10, col = "Red") boxplot(DWMedia$HgT_Eff,DWMedia$TSS_Eff, Aurora$HgT_Eff,Aurora$TSS_Eff, WLSSD$HgT_Eff,WLSSD$TSS_Eff, RapidSandFilter$HgT_Eff,RapidSandFilter$TSS_Eff, ylim=c(0,12), col=(c("gold","darkgreen")), main="Effluent of filter study", xlab = "HgT(ng/L) & TSS (mg/L)") text(x=c(1,2,3,4, 5, 6,7,8), pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("Fine n=94","FIne","Aurora","Auora","WLSSD","WLSSD","Rapid Sand n=80","Rapid Sand")) abline(h=1.8, col = "Red") abline(h=10, col = "Red") #Linear violin plot vioplot(DWMedia$HgT_Eff, WWMedia$HgT_Eff, ContBWFilter$HgT_Eff,RapidSandFilter$HgT_Eff, MBRdf$HgT_Eff,ClothMembranedf$HgT_Eff,DWMedia$TSS_Eff, WWMedia$TSS_Eff, ContBWFilter$TSS_Eff, RapidSandFilter$TSS_Eff, MBRdf$TSS_Eff,ClothMembranedf$TSS_Eff, col=c("gold","gold","gold","gold","gold","gold","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen"), names=c("Fine Particle","Coarse","Cont.Backwash","Deep Mono-media","MBR","Rotating Membrane", "Fine Particle","Coarse","Cont.Backwash","Deep Mono-media","MBR","Rotating Membrane")) title(ylab="Gold= HgT(ng/L) & Green= TSS (mg/L)") abline(h=1.8, col = "Red") abline(h=10, col = "Red") abline(h=5, col = "Black") vioplot(DWMedia$HgT_Eff, WWMedia$HgT_Eff, ContBWFilter$HgT_Eff,RapidSandFilter$HgT_Eff, MBRdf$HgT_Eff,ClothMembranedf$HgT_Eff, col=c("gold","gold","gold","gold","gold","gold"), names=c("Fine Particle","Coarse","Cont.BW","Mono-media","MBR","Rot.Mem")) title(main="a.) HgT of filter study",ylab="HgT(ng/L)") abline(h=1.8, col = "Red") abline(h=10, col = "Red") abline(h=5, col = "Black") vioplot(DWMedia$TSS_Eff, WWMedia$TSS_Eff, ContBWFilter$TSS_Eff, RapidSandFilter$TSS_Eff, MBRdf$TSS_Eff,ClothMembranedf$TSS_Eff, names=c("Fine Particle","Coarse","Cont.BW","Mono-media","MBR","Rot.Mem"),col="dark green") title("b.)TSS of filter study",ylab="TSS(mg/L)") vioplot(DWMedia$HgT_Eff,DWMedia$TSS_Eff, Aurora$HgT_Eff,Aurora$TSS_Eff, WLSSD$HgT_Eff,WLSSD$TSS_Eff, RapidSandFilter$HgT_Eff,RapidSandFilter$TSS_Eff, ylim=c(0,12), col=(c("gold","darkgreen")), names=c("Fine n=94","FIne","Aurora n=8","Auora","WLSSDn=60","WLSSD","Rapid Sand n=80","Rapid Sand")) abline(h=1.8, col = "Red") abline(h=10, col = "Red") #Natural log violin plot vioplot(DWMedia$logHgT, WWMedia$logHgT, RapidSandFilter$logHgT, MBRdf$logHgT,ClothMembranedf$logHgT, names=c("DW Media","WW Media","Deep Beed","MBR","Cloth Filter"),col="gold") title("ln HgT of Media Filters",ylab="ln HgT (ng/L)") abline(h=.58, col = "Red") abline(h=0, col = "Black") hist(MBRdf) hist(MediafilterCASdf$logHgT) hist(RapidSandFilter$logHgT) hist(WWMedia$logHgT) hist(DWMedia$logHgT) hist(ClothMembranedf$logHgT) hist(Tertcladf$logHgT) hist(PolishPonddf$logHgT) ####Main Boxplots#### #comparing media filters with CAS,EAAS, and all boxplot(MediafilterCASdf$HgT_Eff,MediafilterEAASdf$HgT_Eff,Mediafilterdf$HgT_Eff,ylim=c(0,5)) #Secondary boxplot boxplot(CASdf$HgT_Eff,CASdf$TSS_Eff, EAASdf$HgT_Eff,EAASdf$TSS_Eff, TFdf$HgT_Eff,TFdf$TSS_Eff, RBSdf$HgT_Eff,RBSdf$TSS_Eff, Ponddf$HgT_Eff,Ponddf$TSS_Eff, Pondwphosdf$HgT_Eff,Pondwphosdf$TSS_Eff, ylim=c(0,30), col=(c("gold","darkgreen")), main="Effluent of seconday treatment types", xlab="HgT(ng/L) and TSS (mg/L)") #Secondary and Tertiary Boxplot boxplot(CASdf$HgT_Eff, EAASdf$HgT_Eff, TFdf$HgT_Eff, RBSdf$HgT_Eff, Ponddf$HgT_Eff, Pondwphosdf$HgT_Eff, MBRdf$HgT_Eff, MediafilterASdf$HgT_Eff, Tertcladf$HgT_Eff, PolishPondCASdf$HgT_Eff, CASdf$TSS_Eff, EAASdf$TSS_Eff, TFdf$TSS_Eff, RBSdf$TSS_Eff, Ponddf$TSS_Eff, Pondwphosdf$TSS_Eff, MBRdf$TSS_Eff, Mediafilterdf$TSS_Eff, Tertcladf$TSS_Eff, PolishPondCASdf$TSS_Eff, ylim=c(0,20), col=(c("gold","gold","gold","gold","gold","gold","gold","gold","gold","gold", "darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen","darkgreen")), ylab="Gold= HgT(ng/L) & Green= TSS (mg/L)") text(x=c(1,2,3,4, 5, 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20), pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("CAS","EAAS","TF","RBS","Pond","PondwPhos", "MBR","Filter","Tertcla", "PolishPond","CAS","EAAS","TF","RBS","Pond","PondwPhos", "MBR","Filter","Tertcla", "PolishPond")) abline(h=1.8, col = "Red") abline(h=10, col = "Red") abline(h=5, col = "Black") boxplot(CASdf$TSS_Eff, EAASdf$TSS_Eff, TFdf$TSS_Eff, RBSdf$TSS_Eff, Ponddf$TSS_Eff, Pondwphosdf$TSS_Eff, MBRdf$TSS_Eff, Mediafilterdf$TSS_Eff, Tertcladf$TSS_Eff, PolishPondCASdf$TSS_Eff, ylim=c(0,30), col=(c("darkgreen")), main="b.) TSS by Treatment Type", ylab = "TSS (mg/L)") text(x=c(1,2,3,4, 5, 6,7,8,9,10), pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("CAS","EAAS","TF","RBS","Pond","PondwPhos", "MBR","Filter","Tertcla", "PolishPond")) abline(h=5, col = "Black") abline(h=25, col = "Red") # boxplot(CASdf$HgT_Eff,CASdf$TSS_Eff, # EAASdf$HgT_Eff,EAASdf$TSS_Eff, # TFdf$HgT_Eff,TFdf$TSS_Eff, # RBSdf$HgT_Eff,RBSdf$TSS_Eff, # Ponddf$HgT_Eff,Ponddf$TSS_Eff, # Pondwphosdf$HgT_Eff,Pondwphosdf$TSS_Eff, # MBRdf$HgT_Eff, MBRdf$TSS_Eff, # Mediafilterdf$HgT_Eff, Mediafilterdf$TSS_Eff, # Tertcladf$HgT_Eff, Tertcladf$TSS_Eff, # PolishPondCASdf$HgT_Eff, PolishPondCASdf$TSS_Eff, # ylim=c(0,30), # col=(c("gold","darkgreen")), # main="Effluent of treatment types", xlab = "HgT(ng/L) & TSS (mg/L)") # text(x=c(1,2,3,4, 5, 6,7,8,9,10,11,12,13,14,15,16, # 17,18,19,20,21,22,23,24,25,26,27,28,29,30), # pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("CAS","CAS","EAAS","EAAS","TF","TF","RBS","RBS","Pond","Pond","PondwPhos","PondwPhos", # "MBR","MBR","Filter","Filter","Denit","Denit","Biofilter","Biofilter","Tertcla","Tertcla", # "PhosRem","PhosRem","PolishPond","PolishPond","Floc","Floc","AnaC","AnaC")) # abline(h=1.8, col = "Red") # abline(h=10, col = "Red") #Boxplot comparying with and without Teriary boxplot(log(EffwithTertMax$HgT_Eff),EffwithTertMax$logTSS, log(EffwoTertMax$HgT_Eff),EffwoTertMax$logTSS, col=(c("gold","darkgreen")), main="HgT and TSS in effluent with and without Tertiary Treatments", xlab="HgT and TSS") abline(h=0.58, col = "Red") ####Non-parametric quantile regression#### CAS1 <- subset(CASdf, CASdf$TSS_Eff <= 1) CAS1.1<-subset(CAS1, CAS1$HgT_Eff <= 0.5) CAS1.2<-subset(CAS1, HgT_Eff >0.5&HgT_Eff<1.8) CAS1.3<-subset(CAS1, HgT_Eff >1.8&HgT_Eff<=5) CAS1.4<-subset(CAS1,HgT_Eff > 5 &HgT_Eff<=10) CAS1.5<-subset(CAS1, HgT_Eff > 10) CAS2 <- subset(CASdf, CASdf$TSS_Eff> 1& CASdf$TSS_Eff <=2) CAS2.1<-subset(CAS2, HgT_Eff <= 0.5) CAS2.2<-subset(CAS2, HgT_Eff >0.5&HgT_Eff<1.8) CAS2.3<-subset(CAS2, HgT_Eff >1.8&HgT_Eff<=5) CAS2.4<-subset(CAS2,HgT_Eff > 5 &HgT_Eff<=10) CAS2.5<-subset(CAS2, HgT_Eff > 10) CAS3 <- subset(CASdf, CASdf$TSS_Eff> 5& CASdf$TSS_Eff <=10) CAS3.1<-subset(CAS3, HgT_Eff <= 0.5) CAS3.2<-subset(CAS3, HgT_Eff >0.5&HgT_Eff<1.8) CAS3.3<-subset(CAS3, HgT_Eff >1.8&HgT_Eff<=5) CAS3.4<-subset(CAS3,HgT_Eff > 5 &HgT_Eff<=10) CAS3.5<-subset(CAS3, HgT_Eff > 10) CAS4 <- subset(CASdf,TSS_Eff >10 &TSS_Eff <=15) CAS4.1<-subset(CAS4, HgT_Eff <= 0.5) CAS4.2<-subset(CAS4, HgT_Eff >0.5&HgT_Eff<1.8) CAS4.3<-subset(CAS4, HgT_Eff >1.8&HgT_Eff<=5) CAS4.4<-subset(CAS4,HgT_Eff > 5 &HgT_Eff<=10) CAS4.5<-subset(CAS4, HgT_Eff > 10) CAS5 <- subset(CASdf,TSS_Eff >15) CAS5.1<-subset(CAS5, HgT_Eff <= 0.5) CAS5.2<-subset(CAS5, HgT_Eff >0.5&HgT_Eff<1.8) CAS5.3<-subset(CAS5, HgT_Eff >1.8&HgT_Eff<=5) CAS5.4<-subset(CAS5,HgT_Eff > 5 &HgT_Eff<=10) CAS5.5<-subset(CAS5, HgT_Eff > 10) CAS6 <- subset(CASdf,CASdf$TSS_Eff> 2& CASdf$TSS_Eff <=3) CAS6.1<-subset(CAS6, HgT_Eff <= 0.5) CAS6.2<-subset(CAS6, HgT_Eff >0.5&HgT_Eff<1.8) CAS6.3<-subset(CAS6, HgT_Eff >1.8&HgT_Eff<=5) CAS6.4<-subset(CAS6,HgT_Eff > 5 &HgT_Eff<=10) CAS6.5<-subset(CAS6, HgT_Eff > 10) CAS7 <- subset(CASdf,CASdf$TSS_Eff> 3& CASdf$TSS_Eff <=4) CAS7.1<-subset(CAS7, HgT_Eff <= 0.5) CAS7.2<-subset(CAS7, HgT_Eff >0.5&HgT_Eff<1.8) CAS7.3<-subset(CAS7, HgT_Eff >1.8&HgT_Eff<=5) CAS7.4<-subset(CAS7,HgT_Eff > 5 &HgT_Eff<=10) CAS7.5<-subset(CAS7, HgT_Eff > 10) CAS8 <- subset(CASdf,CASdf$TSS_Eff> 4& CASdf$TSS_Eff <=5) CAS8.1<-subset(CAS8, HgT_Eff <= 0.5) CAS8.2<-subset(CAS8, HgT_Eff >0.5&HgT_Eff<1.8) CAS8.3<-subset(CAS8, HgT_Eff >1.8&HgT_Eff<=5) CAS8.4<-subset(CAS8,HgT_Eff > 5 &HgT_Eff<=10) CAS8.5<-subset(CAS8, HgT_Eff > 10) # T2 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >1 & EffwMax$TSS_Eff<=5] # T3 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >5 & EffwMax$TSS_Eff<=10] # T4 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >10 & EffwMax$TSS_Eff<=20] # T5 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >20 & EffwMax$TSS_Eff<45] # T6 <- EffwMax$HgT_Eff[EffwMax$TSS_Eff >45] EAAS1 <- subset(EAASdf, EAASdf$TSS_Eff <= 1) EAAS1.1<-subset(EAAS1, HgT_Eff <= 0.5) EAAS1.2<-subset(EAAS1, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS1.3<-subset(EAAS1, HgT_Eff >1.8&HgT_Eff<=5) EAAS1.4<-subset(EAAS1,HgT_Eff > 5 &HgT_Eff<=10) EAAS1.5<-subset(EAAS1, HgT_Eff > 10) EAAS2 <- subset(EAASdf, EAASdf$TSS_Eff> 1& EAASdf$TSS_Eff <=2) EAAS2.1<-subset(EAAS2, HgT_Eff <= 0.5) EAAS2.2<-subset(EAAS2, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS2.3<-subset(EAAS2, HgT_Eff >1.8&HgT_Eff<=5) EAAS2.4<-subset(EAAS2,HgT_Eff > 5 &HgT_Eff<=10) EAAS2.5<-subset(EAAS2, HgT_Eff > 10) EAAS3 <- subset(EAASdf, EAASdf$TSS_Eff> 5& EAASdf$TSS_Eff <=10) EAAS3.1<-subset(EAAS3, HgT_Eff <= 0.5) EAAS3.2<-subset(EAAS3, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS3.3<-subset(EAAS3, HgT_Eff >1.8&HgT_Eff<=5) EAAS3.4<-subset(EAAS3,HgT_Eff > 5 &HgT_Eff<=10) EAAS3.5<-subset(EAAS3, HgT_Eff > 10) EAAS4 <- subset(EAASdf,TSS_Eff >10 &TSS_Eff <=15) EAAS4.1<-subset(EAAS4, HgT_Eff <= 0.5) EAAS4.2<-subset(EAAS4, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS4.3<-subset(EAAS4, HgT_Eff >1.8&HgT_Eff<=5) EAAS4.4<-subset(EAAS4,HgT_Eff > 5 &HgT_Eff<=10) EAAS4.5<-subset(EAAS4, HgT_Eff > 10) EAAS5 <- subset(EAASdf,TSS_Eff >15) EAAS5.1<-subset(EAAS5, HgT_Eff <= 0.5) EAAS5.2<-subset(EAAS5, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS5.3<-subset(EAAS5, HgT_Eff >1.8&HgT_Eff<=5) EAAS5.4<-subset(EAAS5,HgT_Eff > 5 &HgT_Eff<=10) EAAS5.5<-subset(EAAS5, HgT_Eff > 10) EAAS6 <- subset(EAASdf,EAASdf$TSS_Eff> 2& EAASdf$TSS_Eff <=3) EAAS6.1<-subset(EAAS6, HgT_Eff <= 0.5) EAAS6.2<-subset(EAAS6, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS6.3<-subset(EAAS6, HgT_Eff >1.8&HgT_Eff<=5) EAAS6.4<-subset(EAAS6,HgT_Eff > 5 &HgT_Eff<=10) EAAS6.5<-subset(EAAS6, HgT_Eff > 10) EAAS7 <- subset(EAASdf,EAASdf$TSS_Eff> 3& EAASdf$TSS_Eff <=4) EAAS7.1<-subset(EAAS7, HgT_Eff <= 0.5) EAAS7.2<-subset(EAAS7, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS7.3<-subset(EAAS7, HgT_Eff >1.8&HgT_Eff<=5) EAAS7.4<-subset(EAAS7,HgT_Eff > 5 &HgT_Eff<=10) EAAS7.5<-subset(EAAS7, HgT_Eff > 10) EAAS8 <- subset(EAASdf,EAASdf$TSS_Eff> 4& EAASdf$TSS_Eff <=5) EAAS8.1<-subset(EAAS8, HgT_Eff <= 0.5) EAAS8.2<-subset(EAAS8, HgT_Eff >0.5&HgT_Eff<=1.8) EAAS8.3<-subset(EAAS8, HgT_Eff >1.8&HgT_Eff<=5) EAAS8.4<-subset(EAAS8,HgT_Eff > 5 &HgT_Eff<=10) EAAS8.5<-subset(EAAS8, HgT_Eff > 10) FF1 <- subset(FFdf, FFdf$TSS_Eff <= 1) FF1.1<-subset(FF1, HgT_Eff <=0.5) FF1.2<-subset(FF1, HgT_Eff >0.5&HgT_Eff<=1.8) FF1.3<-subset(FF1, HgT_Eff >1.8&HgT_Eff<=5) FF1.4<-subset(FF1,HgT_Eff > 5 &HgT_Eff<=10) FF1.5<-subset(FF1, HgT_Eff > 10) FF2 <- subset(FFdf, FFdf$TSS_Eff> 1& FFdf$TSS_Eff <=2) FF2.1<-subset(FF2, HgT_Eff <=0.5) FF2.2<-subset(FF2, HgT_Eff >0.5&HgT_Eff<=1.8) FF2.3<-subset(FF2, HgT_Eff >1.8&HgT_Eff<=5) FF2.4<-subset(FF2,HgT_Eff > 5 &HgT_Eff<=10) FF2.5<-subset(FF2, HgT_Eff > 10) FF3 <- subset(FFdf,TSS_Eff >5 &TSS_Eff <=10) FF3.1<-subset(FF3, HgT_Eff <=0.5) FF3.2<-subset(FF3, HgT_Eff >0.5&HgT_Eff<=1.8) FF3.3<-subset(FF3, HgT_Eff >1.8&HgT_Eff<=5) FF3.4<-subset(FF3,HgT_Eff > 5 &HgT_Eff<=10) FF3.5<-subset(FF3, HgT_Eff > 10) FF4 <- subset(FFdf,TSS_Eff >10 &TSS_Eff <=15) FF4.1<-subset(FF4, HgT_Eff <=0.5) FF4.2<-subset(FF4, HgT_Eff >0.5&HgT_Eff<=1.8) FF4.3<-subset(FF4, HgT_Eff >1.8&HgT_Eff<=5) FF4.4<-subset(FF4,HgT_Eff > 5 &HgT_Eff<=10) FF4.5<-subset(FF4, HgT_Eff > 10) FF5 <- subset(FFdf,TSS_Eff >15) FF5.1<-subset(FF5, HgT_Eff <=0.5) FF5.2<-subset(FF5, HgT_Eff >0.5&HgT_Eff<=1.8) FF5.3<-subset(FF5, HgT_Eff >1.8&HgT_Eff<=5) FF5.4<-subset(FF5,HgT_Eff > 5 &HgT_Eff<=10) FF5.5<-subset(FF5, HgT_Eff > 10) FF6 <- subset(FFdf,FFdf$TSS_Eff> 2& FFdf$TSS_Eff <=3) FF6.1<-subset(FF6, HgT_Eff <=0.5) FF6.2<-subset(FF6, HgT_Eff >0.5&HgT_Eff<=1.8) FF6.3<-subset(FF6, HgT_Eff >1.8&HgT_Eff<=5) FF6.4<-subset(FF6,HgT_Eff > 5 &HgT_Eff<=10) FF6.5<-subset(FF6, HgT_Eff > 10) FF7 <- subset(FFdf,FFdf$TSS_Eff> 3& FFdf$TSS_Eff <=4) FF7.1<-subset(FF7, HgT_Eff <=0.5) FF7.2<-subset(FF7, HgT_Eff >0.5&HgT_Eff<=1.8) FF7.3<-subset(FF7, HgT_Eff >1.8&HgT_Eff<=5) FF7.4<-subset(FF7,HgT_Eff > 5 &HgT_Eff<=10) FF7.5<-subset(FF7, HgT_Eff > 10) FF8 <- subset(FFdf,FFdf$TSS_Eff> 4& FFdf$TSS_Eff <=5) FF8.1<-subset(FF8, HgT_Eff <=0.5) FF8.2<-subset(FF8, HgT_Eff >0.5&HgT_Eff<=1.8) FF8.3<-subset(FF8, HgT_Eff >1.8&HgT_Eff<=5) FF8.4<-subset(FF8,HgT_Eff > 5 &HgT_Eff<=10) FF8.5<-subset(FF8, HgT_Eff > 10) # FF6 <- subset(FFdf,TSS_Eff >30) # FF6.1<-subset(FF6, HgT_Eff <= 1.8) # FF6.2<-subset(FF6, HgT_Eff >1.8&HgT_Eff<=5) # FF6.3<-subset(FF6,HgT_Eff > 5 &HgT_Eff<=10) # FF6.4<-subset(FF6, HgT_Eff > 10) Pond1 <- subset(Ponddf, Ponddf$TSS_Eff <= 1) Pond1.1<-subset(Pond1, HgT_Eff <= 0.5) Pond1.2<-subset(Pond1, HgT_Eff >0.5&HgT_Eff<=1.8) Pond1.3<-subset(Pond1, HgT_Eff >1.8&HgT_Eff<=5) Pond1.4<-subset(Pond1,HgT_Eff > 5 &HgT_Eff<=10) Pond1.5<-subset(Pond1, HgT_Eff > 10) Pond2 <- subset(Ponddf, Ponddf$TSS_Eff> 1& Ponddf$TSS_Eff <=2) Pond2.1<-subset(Pond2, HgT_Eff <= 0.5) Pond2.2<-subset(Pond2, HgT_Eff >0.5&HgT_Eff<=1.8) Pond2.3<-subset(Pond2, HgT_Eff >1.8&HgT_Eff<=5) Pond2.4<-subset(Pond2,HgT_Eff > 5 &HgT_Eff<=10) Pond2.5<-subset(Pond2, HgT_Eff > 10) Pond3 <- subset(Ponddf, Ponddf$TSS_Eff> 5& Ponddf$TSS_Eff <=10) Pond3.1<-subset(Pond3, HgT_Eff <= 0.5) Pond3.2<-subset(Pond3, HgT_Eff >0.5&HgT_Eff<=1.8) Pond3.3<-subset(Pond3, HgT_Eff >1.8&HgT_Eff<=5) Pond3.4<-subset(Pond3,HgT_Eff > 5 &HgT_Eff<=10) Pond3.5<-subset(Pond3, HgT_Eff > 10) Pond4 <- subset(Ponddf,TSS_Eff >10 &TSS_Eff <=15) Pond4.1<-subset(Pond4, HgT_Eff <= 0.5) Pond4.2<-subset(Pond4, HgT_Eff >0.5&HgT_Eff<=1.8) Pond4.3<-subset(Pond4, HgT_Eff >1.8&HgT_Eff<=5) Pond4.4<-subset(Pond4,HgT_Eff > 5 &HgT_Eff<=10) Pond4.5<-subset(Pond4, HgT_Eff > 10) Pond5 <- subset(Ponddf,TSS_Eff >15) Pond5.1<-subset(Pond5, HgT_Eff <= 0.5) Pond5.2<-subset(Pond5, HgT_Eff >0.5&HgT_Eff<=1.8) Pond5.3<-subset(Pond5, HgT_Eff >1.8&HgT_Eff<=5) Pond5.4<-subset(Pond5,HgT_Eff > 5 &HgT_Eff<=10) Pond5.5<-subset(Pond5, HgT_Eff > 10) Pond6 <- subset(Ponddf,Ponddf$TSS_Eff> 2& Ponddf$TSS_Eff <=3) Pond6.1<-subset(Pond6, HgT_Eff <= 0.5) Pond6.2<-subset(Pond6, HgT_Eff >0.5&HgT_Eff<=1.8) Pond6.3<-subset(Pond6, HgT_Eff >1.8&HgT_Eff<=5) Pond6.4<-subset(Pond6,HgT_Eff > 5 &HgT_Eff<=10) Pond6.5<-subset(Pond6, HgT_Eff > 10) Pond7 <- subset(Ponddf,Ponddf$TSS_Eff> 3& Ponddf$TSS_Eff <=4) Pond7.1<-subset(Pond7, HgT_Eff <= 0.5) Pond7.2<-subset(Pond7, HgT_Eff >0.5&HgT_Eff<=1.8) Pond7.3<-subset(Pond7, HgT_Eff >1.8&HgT_Eff<=5) Pond7.4<-subset(Pond7,HgT_Eff > 5 &HgT_Eff<=10) Pond7.5<-subset(Pond7, HgT_Eff > 10) Pond8 <- subset(Ponddf,Ponddf$TSS_Eff> 4& Ponddf$TSS_Eff <=5) Pond8.1<-subset(Pond8, HgT_Eff <= 0.5) Pond8.2<-subset(Pond8, HgT_Eff >0.5&HgT_Eff<=1.8) Pond8.3<-subset(Pond8, HgT_Eff >1.8&HgT_Eff<=5) Pond8.4<-subset(Pond8,HgT_Eff > 5 &HgT_Eff<=10) Pond8.5<-subset(Pond8, HgT_Eff > 10) Pondwphos1 <- subset(Pondwphosdf, Pondwphosdf$TSS_Eff <= 1) Pondwphos1.1<-subset(Pondwphos1, HgT_Eff <= 0.5) Pondwphos1.2<-subset(Pondwphos1, HgT_Eff >0.5&HgT_Eff<1.8) Pondwphos1.3<-subset(Pondwphos1, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos1.4<-subset(Pondwphos1,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos1.5<-subset(Pondwphos1, HgT_Eff > 10) Pondwphos2 <- subset(Pondwphosdf, TSS_Eff> 1& TSS_Eff <=2) Pondwphos2.1<-subset(Pondwphos2, HgT_Eff <= 0.5) Pondwphos2.2<-subset(Pondwphos2, HgT_Eff >0.5&HgT_Eff<1.8) Pondwphos2.3<-subset(Pondwphos2, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos2.4<-subset(Pondwphos2,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos2.5<-subset(Pondwphos2, HgT_Eff > 10) Pondwphos3 <- subset(Pondwphosdf,TSS_Eff >5 &TSS_Eff <=10) Pondwphos3.1<-subset(Pondwphos3, HgT_Eff <= 0.5) Pondwphos3.2<-subset(Pondwphos3, HgT_Eff >0.5&HgT_Eff<1.8) Pondwphos3.3<-subset(Pondwphos3, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos3.4<-subset(Pondwphos3,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos3.5<-subset(Pondwphos3, HgT_Eff > 10) Pondwphos4 <- subset(Pondwphosdf,TSS_Eff >10 &TSS_Eff <=15) Pondwphos4.1<-subset(Pondwphos4, HgT_Eff <= 0.5) Pondwphos4.2<-subset(Pondwphos4, HgT_Eff >0.5&HgT_Eff<1.8) Pondwphos4.3<-subset(Pondwphos4, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos4.4<-subset(Pondwphos4,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos4.5<-subset(Pondwphos4, HgT_Eff > 10) Pondwphos5 <- subset(Pondwphosdf,TSS_Eff >15) Pondwphos5.1<-subset(Pondwphos5, HgT_Eff <= 0.5) Pondwphos5.2<-subset(Pondwphos5, HgT_Eff >0.5&HgT_Eff<=1.8) Pondwphos5.3<-subset(Pondwphos5, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos5.4<-subset(Pondwphos5,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos5.5<-subset(Pondwphos5, HgT_Eff > 10) Pondwphos6 <- subset(Pondwphosdf,TSS_Eff> 2& TSS_Eff <=3) Pondwphos6.1<-subset(Pondwphos6, HgT_Eff <= 0.5) Pondwphos6.2<-subset(Pondwphos6, HgT_Eff >0.5&HgT_Eff<=1.8) Pondwphos6.3<-subset(Pondwphos6, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos6.4<-subset(Pondwphos6,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos6.5<-subset(Pondwphos6, HgT_Eff > 10) Pondwphos7 <- subset(Pondwphosdf,TSS_Eff> 3& TSS_Eff <=4) Pondwphos7.1<-subset(Pondwphos7, HgT_Eff <= 0.5) Pondwphos7.2<-subset(Pondwphos7, HgT_Eff >0.5&HgT_Eff<=1.8) Pondwphos7.3<-subset(Pondwphos7, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos7.4<-subset(Pondwphos7,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos7.5<-subset(Pondwphos7, HgT_Eff > 10) Pondwphos8 <- subset(Pondwphosdf,TSS_Eff> 4& TSS_Eff <=5) Pondwphos8.1<-subset(Pondwphos8, HgT_Eff <= 0.5) Pondwphos8.2<-subset(Pondwphos8, HgT_Eff >0.5&HgT_Eff<=1.8) Pondwphos8.3<-subset(Pondwphos8, HgT_Eff >1.8&HgT_Eff<=5) Pondwphos8.4<-subset(Pondwphos8,HgT_Eff > 5 &HgT_Eff<=10) Pondwphos8.5<-subset(Pondwphos8, HgT_Eff > 10) ####Tertiary Non-parametric quantile regression#### ##MBR MBR1 <- subset(MBRdf, MBRdf$TSS_Eff <= 1) MBR1.1<-subset(MBR1, MBR1$HgT_Eff <= 0.5) MBR1.2<-subset(MBR1, HgT_Eff >0.5&HgT_Eff<=1.8) MBR1.3<-subset(MBR1,HgT_Eff > 1.8 &HgT_Eff<=5) MBR1.4<-subset(MBR1, HgT_Eff > 5) MBR2 <- subset(MBRdf, MBRdf$TSS_Eff> 1& MBRdf$TSS_Eff <=2) MBR2.1<-subset(MBR2, HgT_Eff <= 0.5) MBR2.2<-subset(MBR2, HgT_Eff >0.5&HgT_Eff<=1.8) MBR2.3<-subset(MBR2,HgT_Eff > 1.8 &HgT_Eff<=5) MBR2.4<-subset(MBR2, HgT_Eff > 5) MBR3 <- subset(MBRdf,TSS_Eff >2 &TSS_Eff <=3) MBR3.1<-subset(MBR3, HgT_Eff <= 0.5) MBR3.2<-subset(MBR3, HgT_Eff >0.5&HgT_Eff<=1.8) MBR3.3<-subset(MBR3,HgT_Eff > 1.8 &HgT_Eff<=5) MBR3.4<-subset(MBR3, HgT_Eff > 5) MBR4 <- subset(MBRdf,TSS_Eff >3 &TSS_Eff <=4) MBR4.1<-subset(MBR4, HgT_Eff <= 0.5) MBR4.2<-subset(MBR4, HgT_Eff >0.5&HgT_Eff<=1.8) MBR4.3<-subset(MBR4,HgT_Eff > 1.8 &HgT_Eff<=5) MBR4.4<-subset(MBR4, HgT_Eff > 5) MBR5 <- subset(MBRdf,TSS_Eff >4 &TSS_Eff <=5) MBR5.1<-subset(MBR5, HgT_Eff <= 0.5) MBR5.2<-subset(MBR5, HgT_Eff >0.5&HgT_Eff<=1.8) MBR5.3<-subset(MBR5,HgT_Eff > 1.8 &HgT_Eff<=5) MBR5.4<-subset(MBR5, HgT_Eff > 5) MBR6 <- subset(MBRdf,TSS_Eff >5) MBR6.1<-subset(MBR6, HgT_Eff <= 0.5) MBR6.2<-subset(MBR6, HgT_Eff >0.5&HgT_Eff<=1.8) MBR6.3<-subset(MBR6,HgT_Eff > 1.8 &HgT_Eff<=5) MBR6.4<-subset(MBR6, HgT_Eff > 5) ##Media Filter Mediafilter1 <- subset(Mediafilterdf, Mediafilterdf$TSS_Eff <= 1) Mediafilter1.1<-subset(Mediafilter1, Mediafilter1$HgT_Eff <= 0.5) Mediafilter1.2<-subset(Mediafilter1, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter1.3<-subset(Mediafilter1,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter1.4<-subset(Mediafilter1, HgT_Eff > 5) Mediafilter2 <- subset(Mediafilterdf, TSS_Eff> 1& TSS_Eff <=2) Mediafilter2.1<-subset(Mediafilter2, HgT_Eff <= 0.5) Mediafilter2.2<-subset(Mediafilter2, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter2.3<-subset(Mediafilter2,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter2.4<-subset(Mediafilter2, HgT_Eff > 5) Mediafilter3 <- subset(Mediafilterdf,TSS_Eff >2 &TSS_Eff <=3) Mediafilter3.1<-subset(Mediafilter3, HgT_Eff <= 0.5) Mediafilter3.2<-subset(Mediafilter3, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter3.3<-subset(Mediafilter3,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter3.4<-subset(Mediafilter3, HgT_Eff > 5) Mediafilter4 <- subset(Mediafilterdf,TSS_Eff >3 &TSS_Eff <=4) Mediafilter4.1<-subset(Mediafilter4, HgT_Eff <= 0.5) Mediafilter4.2<-subset(Mediafilter4, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter4.3<-subset(Mediafilter4,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter4.4<-subset(Mediafilter4, HgT_Eff > 5) Mediafilter5 <- subset(Mediafilterdf,TSS_Eff >4 &TSS_Eff <=5) Mediafilter5.1<-subset(Mediafilter5, HgT_Eff <= 0.5) Mediafilter5.2<-subset(Mediafilter5, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter5.3<-subset(Mediafilter5,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter5.4<-subset(Mediafilter5, HgT_Eff > 5) Mediafilter6 <- subset(Mediafilterdf,TSS_Eff >5) Mediafilter6.1<-subset(Mediafilter6, HgT_Eff <= 0.5) Mediafilter6.2<-subset(Mediafilter6, HgT_Eff >0.5&HgT_Eff<=1.8) Mediafilter6.3<-subset(Mediafilter6,HgT_Eff > 1.8 &HgT_Eff<=5) Mediafilter6.4<-subset(Mediafilter6, HgT_Eff > 5) #Media Filter with AS # MediafilterAS1 <- subset(MediafilterASdf, MediafilterASdf$TSS_Eff <= 1) # MediafilterAS1.1<-subset(MediafilterAS1, MediafilterAS1$HgT_Eff <= 0.5) # MediafilterAS1.2<-subset(MediafilterAS1, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS1.3<-subset(MediafilterAS1,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS1.4<-subset(MediafilterAS1, HgT_Eff > 5) # # MediafilterAS2 <- subset(MediafilterASdf, MediafilterASdf$TSS_Eff> 1& MediafilterASdf$TSS_Eff <=2) # MediafilterAS2.1<-subset(MediafilterAS2, HgT_Eff <= 0.5) # MediafilterAS2.2<-subset(MediafilterAS2, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS2.3<-subset(MediafilterAS2,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS2.4<-subset(MediafilterAS2, HgT_Eff > 5) # # MediafilterAS3 <- subset(MediafilterASdf,TSS_Eff >2 &TSS_Eff <=3) # MediafilterAS3.1<-subset(MediafilterAS3, HgT_Eff <= 0.5) # MediafilterAS3.2<-subset(MediafilterAS3, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS3.3<-subset(MediafilterAS3,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS3.4<-subset(MediafilterAS3, HgT_Eff > 5) # # MediafilterAS4 <- subset(MediafilterASdf,TSS_Eff >3 &TSS_Eff <=4) # MediafilterAS4.1<-subset(MediafilterAS4, HgT_Eff <= 0.5) # MediafilterAS4.2<-subset(MediafilterAS4, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS4.3<-subset(MediafilterAS4,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS4.4<-subset(MediafilterAS4, HgT_Eff > 5) # MediafilterAS5 <- subset(MediafilterASdf,TSS_Eff >4 &TSS_Eff <=5) # MediafilterAS5.1<-subset(MediafilterAS5, HgT_Eff <= 0.5) # MediafilterAS5.2<-subset(MediafilterAS5, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS5.3<-subset(MediafilterAS5,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS5.4<-subset(MediafilterAS5, HgT_Eff > 5) # MediafilterAS6 <- subset(MediafilterASdf,TSS_Eff >5) # MediafilterAS6.1<-subset(MediafilterAS6, HgT_Eff <= 0.5) # MediafilterAS6.2<-subset(MediafilterAS6, HgT_Eff >0.5&HgT_Eff<=1.8) # MediafilterAS6.3<-subset(MediafilterAS6,HgT_Eff > 1.8 &HgT_Eff<=5) # MediafilterAS6.4<-subset(MediafilterAS6, HgT_Eff > 5) MediafilterCAS1 <- subset(MediafilterCASdf, MediafilterCASdf$TSS_Eff <= 1) MediafilterCAS1.1<-subset(MediafilterCAS1, MediafilterCAS1$HgT_Eff <= 0.5) MediafilterCAS1.2<-subset(MediafilterCAS1, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS1.3<-subset(MediafilterCAS1,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS1.4<-subset(MediafilterCAS1, HgT_Eff > 5) MediafilterCAS2 <- subset(MediafilterCASdf, MediafilterCASdf$TSS_Eff> 1& MediafilterCASdf$TSS_Eff <=2) MediafilterCAS2.1<-subset(MediafilterCAS2, HgT_Eff <= 0.5) MediafilterCAS2.2<-subset(MediafilterCAS2, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS2.3<-subset(MediafilterCAS2,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS2.4<-subset(MediafilterCAS2, HgT_Eff > 5) MediafilterCAS3 <- subset(MediafilterCASdf,TSS_Eff >2 &TSS_Eff <=3) MediafilterCAS3.1<-subset(MediafilterCAS3, HgT_Eff <= 0.5) MediafilterCAS3.2<-subset(MediafilterCAS3, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS3.3<-subset(MediafilterCAS3,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS3.4<-subset(MediafilterCAS3, HgT_Eff > 5) MediafilterCAS4 <- subset(MediafilterCASdf,TSS_Eff >3 &TSS_Eff <=4) MediafilterCAS4.1<-subset(MediafilterCAS4, HgT_Eff <= 0.5) MediafilterCAS4.2<-subset(MediafilterCAS4, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS4.3<-subset(MediafilterCAS4,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS4.4<-subset(MediafilterCAS4, HgT_Eff > 5) MediafilterCAS5 <- subset(MediafilterCASdf,TSS_Eff >4 &TSS_Eff <=5) MediafilterCAS5.1<-subset(MediafilterCAS5, HgT_Eff <= 0.5) MediafilterCAS5.2<-subset(MediafilterCAS5, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS5.3<-subset(MediafilterCAS5,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS5.4<-subset(MediafilterCAS5, HgT_Eff > 5) MediafilterCAS6 <- subset(MediafilterCASdf,TSS_Eff >5) MediafilterCAS6.1<-subset(MediafilterCAS6, HgT_Eff <= 0.5) MediafilterCAS6.2<-subset(MediafilterCAS6, HgT_Eff >0.5&HgT_Eff<=1.8) MediafilterCAS6.3<-subset(MediafilterCAS6,HgT_Eff > 1.8 &HgT_Eff<=5) MediafilterCAS6.4<-subset(MediafilterCAS6, HgT_Eff > 5) Tertcla1 <- subset(Tertcladf, Tertcladf$TSS_Eff <= 1) Tertcla1.1<-subset(Tertcla1, Tertcla1$HgT_Eff <= 0.5) Tertcla1.2<-subset(Tertcla1, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla1.3<-subset(Tertcla1,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla1.4<-subset(Tertcla1, HgT_Eff > 5) Tertcla2 <- subset(Tertcladf, Tertcladf$TSS_Eff> 1& Tertcladf$TSS_Eff <=2) Tertcla2.1<-subset(Tertcla2, HgT_Eff <= 0.5) Tertcla2.2<-subset(Tertcla2, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla2.3<-subset(Tertcla2,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla2.4<-subset(Tertcla2, HgT_Eff > 5) Tertcla3 <- subset(Tertcladf,TSS_Eff >2 &TSS_Eff <=3) Tertcla3.1<-subset(Tertcla3, HgT_Eff <= 0.5) Tertcla3.2<-subset(Tertcla3, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla3.3<-subset(Tertcla3,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla3.4<-subset(Tertcla3, HgT_Eff > 5) Tertcla4 <- subset(Tertcladf,TSS_Eff >3 &TSS_Eff <=4) Tertcla4.1<-subset(Tertcla4, HgT_Eff <= 0.5) Tertcla4.2<-subset(Tertcla4, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla4.3<-subset(Tertcla4,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla4.4<-subset(Tertcla4, HgT_Eff > 5) Tertcla5 <- subset(Tertcladf,TSS_Eff > 4&TSS_Eff <=5) Tertcla5.1<-subset(Tertcla5, HgT_Eff <= 0.5) Tertcla5.2<-subset(Tertcla5, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla5.3<-subset(Tertcla5,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla5.4<-subset(Tertcla5, HgT_Eff > 5) Tertcla6 <- subset(Tertcladf,TSS_Eff >5) Tertcla6.1<-subset(Tertcla6, HgT_Eff <= 0.5) Tertcla6.2<-subset(Tertcla6, HgT_Eff >0.5&HgT_Eff<=1.8) Tertcla6.3<-subset(Tertcla6,HgT_Eff > 1.8 &HgT_Eff<=5) Tertcla6.4<-subset(Tertcla6, HgT_Eff > 5) PolishPondCAS1 <- subset(PolishPondCASdf, PolishPondCASdf$TSS_Eff <= 1) PolishPondCAS1.1<-subset(PolishPondCAS1, PolishPondCAS1$HgT_Eff <= 0.5) PolishPondCAS1.2<-subset(PolishPondCAS1, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS1.3<-subset(PolishPondCAS1,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS1.4<-subset(PolishPondCAS1, HgT_Eff > 5) PolishPondCAS2 <- subset(PolishPondCASdf, PolishPondCASdf$TSS_Eff> 1& PolishPondCASdf$TSS_Eff <=2) PolishPondCAS2.1<-subset(PolishPondCAS2, HgT_Eff <= 0.5) PolishPondCAS2.2<-subset(PolishPondCAS2, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS2.3<-subset(PolishPondCAS2,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS2.4<-subset(PolishPondCAS2, HgT_Eff > 5) PolishPondCAS3 <- subset(PolishPondCASdf,TSS_Eff >2 &TSS_Eff <=3) PolishPondCAS3.1<-subset(PolishPondCAS3, HgT_Eff <= 0.5) PolishPondCAS3.2<-subset(PolishPondCAS3, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS3.3<-subset(PolishPondCAS3,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS3.4<-subset(PolishPondCAS3, HgT_Eff > 5) PolishPondCAS4 <- subset(PolishPondCASdf,TSS_Eff >3 &TSS_Eff <=4) PolishPondCAS4.1<-subset(PolishPondCAS4, HgT_Eff <= 0.5) PolishPondCAS4.2<-subset(PolishPondCAS4, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS4.3<-subset(PolishPondCAS4,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS4.4<-subset(PolishPondCAS4, HgT_Eff > 5) PolishPondCAS5 <- subset(PolishPondCASdf,TSS_Eff > 4&TSS_Eff <=5) PolishPondCAS5.1<-subset(PolishPondCAS5, HgT_Eff <= 0.5) PolishPondCAS5.2<-subset(PolishPondCAS5, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS5.3<-subset(PolishPondCAS5,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS5.4<-subset(PolishPondCAS5, HgT_Eff > 5) PolishPondCAS6 <- subset(PolishPondCASdf,TSS_Eff >5) PolishPondCAS6.1<-subset(PolishPondCAS6, HgT_Eff <= 0.5) PolishPondCAS6.2<-subset(PolishPondCAS6, HgT_Eff >0.5&HgT_Eff<=1.8) PolishPondCAS6.3<-subset(PolishPondCAS6,HgT_Eff > 1.8 &HgT_Eff<=5) PolishPondCAS6.4<-subset(PolishPondCAS6, HgT_Eff > 5) # Biofilter1 <- subset(Biofilterdf, Biofilterdf$TSS_Eff <= 1) # Biofilter1.1<-subset(Biofilter1, Biofilter1$HgT_Eff <= 0.5) # Biofilter1.2<-subset(Biofilter1, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter1.3<-subset(Biofilter1,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter1.4<-subset(Biofilter1, HgT_Eff > 5) # # Biofilter2 <- subset(Biofilterdf, Biofilterdf$TSS_Eff> 1& Biofilterdf$TSS_Eff <=2) # Biofilter2.1<-subset(Biofilter2, HgT_Eff <= 0.5) # Biofilter2.2<-subset(Biofilter2, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter2.3<-subset(Biofilter2,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter2.4<-subset(Biofilter2, HgT_Eff > 5) # # Biofilter3 <- subset(Biofilterdf,TSS_Eff >2 &TSS_Eff <=3) # Biofilter3.1<-subset(Biofilter3, HgT_Eff <= 0.5) # Biofilter3.2<-subset(Biofilter3, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter3.3<-subset(Biofilter3,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter3.4<-subset(Biofilter3, HgT_Eff > 5) # # Biofilter4 <- subset(Biofilterdf,TSS_Eff >3 &TSS_Eff <=4) # Biofilter4.1<-subset(Biofilter4, HgT_Eff <= 0.5) # Biofilter4.2<-subset(Biofilter4, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter4.3<-subset(Biofilter4,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter4.4<-subset(Biofilter4, HgT_Eff > 5) # Biofilter5 <- subset(Biofilterdf,TSS_Eff >4 &TSS_Eff <=5) # Biofilter5.1<-subset(Biofilter5, HgT_Eff <= 0.5) # Biofilter5.2<-subset(Biofilter5, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter5.3<-subset(Biofilter5,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter5.4<-subset(Biofilter5, HgT_Eff > 5) # Biofilter6 <- subset(Biofilterdf,TSS_Eff >5) # Biofilter6.1<-subset(Biofilter6, HgT_Eff <= 0.5) # Biofilter6.2<-subset(Biofilter6, HgT_Eff >0.5&HgT_Eff<=1.8) # Biofilter6.3<-subset(Biofilter6,HgT_Eff > 1.8 &HgT_Eff<=5) # Biofilter6.4<-subset(Biofilter6, HgT_Eff > 5) # PhosRemCAS1 <- subset(PhosRemCASdf, PhosRemCASdf$TSS_Eff <= 1) # PhosRemCAS1.1<-subset(PhosRemCAS1, PhosRemCAS1$HgT_Eff <= 0.5) # PhosRemCAS1.2<-subset(PhosRemCAS1, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS1.3<-subset(PhosRemCAS1,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS1.4<-subset(PhosRemCAS1, HgT_Eff > 5) # # PhosRemCAS2 <- subset(PhosRemCASdf, PhosRemCASdf$TSS_Eff> 1& PhosRemCASdf$TSS_Eff <=2) # PhosRemCAS2.1<-subset(PhosRemCAS2, HgT_Eff <= 0.5) # PhosRemCAS2.2<-subset(PhosRemCAS2, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS2.3<-subset(PhosRemCAS2,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS2.4<-subset(PhosRemCAS2, HgT_Eff > 5) # # PhosRemCAS3 <- subset(PhosRemCASdf,TSS_Eff >2 &TSS_Eff <=3) # PhosRemCAS3.1<-subset(PhosRemCAS3, HgT_Eff <= 0.5) # PhosRemCAS3.2<-subset(PhosRemCAS3, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS3.3<-subset(PhosRemCAS3,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS3.4<-subset(PhosRemCAS3, HgT_Eff > 5) # # PhosRemCAS4 <- subset(PhosRemCASdf,TSS_Eff >3 &TSS_Eff <=5) # PhosRemCAS4.1<-subset(PhosRemCAS4, HgT_Eff <= 0.5) # PhosRemCAS4.2<-subset(PhosRemCAS4, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS4.3<-subset(PhosRemCAS4,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS4.4<-subset(PhosRemCAS4, HgT_Eff > 5) # PhosRemCAS5 <- subset(PhosRemCASdf,TSS_Eff > 5&TSS_Eff <=10) # PhosRemCAS5.1<-subset(PhosRemCAS5, HgT_Eff <= 0.5) # PhosRemCAS5.2<-subset(PhosRemCAS5, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS5.3<-subset(PhosRemCAS5,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS5.4<-subset(PhosRemCAS5, HgT_Eff > 5) # PhosRemCAS6 <- subset(PhosRemCASdf,TSS_Eff >10) # PhosRemCAS6.1<-subset(PhosRemCAS6, HgT_Eff <= 0.5) # PhosRemCAS6.2<-subset(PhosRemCAS6, HgT_Eff >0.5&HgT_Eff<=1.8) # PhosRemCAS6.3<-subset(PhosRemCAS6,HgT_Eff > 1.8 &HgT_Eff<=5) # PhosRemCAS6.4<-subset(PhosRemCAS6, HgT_Eff > 5) # Floc1 <- subset(Flocdf, Flocdf$TSS_Eff <= 1) # Floc1.1<-subset(Floc1, Floc1$HgT_Eff <= 0.5) # Floc1.2<-subset(Floc1, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc1.3<-subset(Floc1,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc1.4<-subset(Floc1, HgT_Eff > 5) # # Floc2 <- subset(Flocdf, Flocdf$TSS_Eff> 1& Flocdf$TSS_Eff <=2) # Floc2.1<-subset(Floc2, HgT_Eff <= 0.5) # Floc2.2<-subset(Floc2, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc2.3<-subset(Floc2,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc2.4<-subset(Floc2, HgT_Eff > 5) # # Floc3 <- subset(Flocdf,TSS_Eff >2 &TSS_Eff <=3) # Floc3.1<-subset(Floc3, HgT_Eff <= 0.5) # Floc3.2<-subset(Floc3, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc3.3<-subset(Floc3,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc3.4<-subset(Floc3, HgT_Eff > 5) # # Floc4 <- subset(Flocdf,TSS_Eff >3 &TSS_Eff <=5) # Floc4.1<-subset(Floc4, HgT_Eff <= 0.5) # Floc4.2<-subset(Floc4, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc4.3<-subset(Floc4,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc4.4<-subset(Floc4, HgT_Eff > 5) # Floc5 <- subset(Flocdf,TSS_Eff > 5&TSS_Eff <=10) # Floc5.1<-subset(Floc5, HgT_Eff <= 0.5) # Floc5.2<-subset(Floc5, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc5.3<-subset(Floc5,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc5.4<-subset(Floc5, HgT_Eff > 5) # Floc6 <- subset(Flocdf,TSS_Eff >10) # Floc6.1<-subset(Floc6, HgT_Eff <= 0.5) # Floc6.2<-subset(Floc6, HgT_Eff >0.5&HgT_Eff<=1.8) # Floc6.3<-subset(Floc6,HgT_Eff > 1.8 &HgT_Eff<=5) # Floc6.4<-subset(Floc6, HgT_Eff > 5) # # AnaC1 <- subset(AnaCdf, AnaCdf$TSS_Eff <= 1) # AnaC1.1<-subset(AnaC1, AnaC1$HgT_Eff <= 0.5) # AnaC1.2<-subset(AnaC1, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC1.3<-subset(AnaC1,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC1.4<-subset(AnaC1, HgT_Eff > 5) # # AnaC2 <- subset(AnaCdf, AnaCdf$TSS_Eff> 1& AnaCdf$TSS_Eff <=2) # AnaC2.1<-subset(AnaC2, HgT_Eff <= 0.5) # AnaC2.2<-subset(AnaC2, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC2.3<-subset(AnaC2,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC2.4<-subset(AnaC2, HgT_Eff > 5) # # AnaC3 <- subset(AnaCdf,TSS_Eff >2 &TSS_Eff <=3) # AnaC3.1<-subset(AnaC3, HgT_Eff <= 0.5) # AnaC3.2<-subset(AnaC3, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC3.3<-subset(AnaC3,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC3.4<-subset(AnaC3, HgT_Eff > 5) # # AnaC4 <- subset(AnaCdf,TSS_Eff >3 &TSS_Eff <=5) # AnaC4.1<-subset(AnaC4, HgT_Eff <= 0.5) # AnaC4.2<-subset(AnaC4, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC4.3<-subset(AnaC4,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC4.4<-subset(AnaC4, HgT_Eff > 5) # AnaC5 <- subset(AnaCdf,TSS_Eff > 5&TSS_Eff <=10) # AnaC5.1<-subset(AnaC5, HgT_Eff <= 0.5) # AnaC5.2<-subset(AnaC5, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC5.3<-subset(AnaC5,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC5.4<-subset(AnaC5, HgT_Eff > 5) # AnaC6 <- subset(AnaCdf,TSS_Eff >10) # AnaC6.1<-subset(AnaC6, HgT_Eff <= 0.5) # AnaC6.2<-subset(AnaC6, HgT_Eff >0.5&HgT_Eff<=1.8) # AnaC6.3<-subset(AnaC6,HgT_Eff > 1.8 &HgT_Eff<=5) # AnaC6.4<-subset(AnaC6, HgT_Eff > 5) ####Wilcoxon ranked sum#### #Line 1741 was a test of Mann-Whitney wilcox test in r #wilcox.test(CASdf$HgT_Eff~EAASdf$HgT_Eff) #exported data to excel- See spreadsheet #Write out excel for ranked sum analysis secondary write.xlsx(CASdf, "~/UMD/Work/Data from Scott/CASdf2.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(EAASdf, "~/UMD/Work/Data from Scott/EAASdf2.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(FFdf, "~/UMD/Work/Data from Scott/FFdf2.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(Ponddf, "~/UMD/Work/Data from Scott/Ponddf2.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(Pondwphosdf, "~/UMD/Work/Data from Scott/Pondwithphosdf2.xlsx",asTable = FALSE, createWorkbook()) #Write out excel for ranked sum analysis Tertiary write.xlsx(MBRdf, "~/UMD/Work/Data from Scott/MBRddf.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(MediafilterCASdf, "~/UMD/Work/Data from Scott/MediafilterCASdf.xlsx",asTable = FALSE, createWorkbook()) #write.xlsx(Denitdf, "~/UMD/Work/Data from Scott/Denitdf.xlsx",asTable = FALSE, createWorkbook()) #write.xlsx(Biofilterdf, "~/UMD/Work/Data from Scott/Biofilterdf.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(Tertcladf, "~/UMD/Work/Data from Scott/Tertcladf.xlsx",asTable = FALSE, createWorkbook()) #write.xlsx(PhosRemCASdf, "~/UMD/Work/Data from Scott/PhosRemCAS.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(PolishPondCASdf, "~/UMD/Work/Data from Scott/PolishPondCAS.xlsx",asTable = FALSE, createWorkbook()) #write.xlsx(Flocdf, "~/UMD/Work/Data from Scott/Flocdf.xlsx",asTable = FALSE, createWorkbook()) #write.xlsx(AnaCdf, "~/UMD/Work/Data from Scott/AnaCdf.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(DWMedia, "~/UMD/Work/Data from Scott/DW.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(WWMedia, "~/UMD/Work/Data from Scott/WWMedia.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(RapidSandFilter, "~/UMD/Work/Data from Scott/DeepBed.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(ClothMembranedf, "~/UMD/Work/Data from Scott/ClothMembrane.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(RBSdf, "~/UMD/Work/Data from Scott/RBS.xlsx",asTable = FALSE, createWorkbook()) write.xlsx(TFdf, "~/UMD/Work/Data from Scott/TF.xlsx",asTable = FALSE, createWorkbook()) ####Nitrogen and Phos#### NPEff<- Tempo_NPhosClBOD[grep("SD", Tempo_NPhosClBOD$SUBJECT_ITEM_DESIGNATION), ] Phos<-subset(NPEff, NPEff$PARAMETER_DESC=="Phosphorus, Total (as P)") PhosPlants<- c(unique(Phos$MASTER_AI_ID)) Nit<-subset(NPEff, NPEff$PARAMETER_DESC=="Nitrogen, Total (as N)") NitPlants<- c(unique(Nit$MASTER_AI_ID)) Phosdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% PhosPlants,] Nitdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% NitPlants,] HgPlants <- c(unique(EffWWTPDom$MASTER_AI_ID)) HgwoPhos<-HgPlants[! HgPlants %in% PhosPlants] HgwoNit<-HgPlants[! HgPlants %in% NitPlants] HgwoNdf<-EffWWTPDom [EffWWTPDom $MASTER_AI_ID %in% HgwoNit,] PhosTSSwoHg<-PhosPlants[! PhosPlants %in% HgPlants] PhosTSSwoHg1<-WWTPDom [WWTPDom$MASTER_AI_ID %in% PhosTSSwoHg,] PhosTSSwoHg2<-subset(PhosTSSwoHg1,ABBR_UNITS_DESC == "mg/L" & PARAMETER_DESC == "Solids, Total Suspended (TSS)" ) boxplot(Phosdf$HgT_Eff,Nitdf$HgT_Eff,HgwoNdf$HgT_Eff,Phosdf$TSS_Eff, Nitdf$TSS_Eff, HgwoNdf$TSS_Eff,PhosTSSwoHg2$C, ylim=c(0,20), col=(c("gold","gold","gold","darkgreen","darkgreen","darkgreen","darkgreen")), ylab = "Gold= HgT(ng/L) & Green= TSS (mg/L)") text(x=c(1,2,3,4, 5, 6,7), pos=1, par("usr")[3], xpd=TRUE, srt=45,labels=c("PhosRemPlants","NitRemPlants","Plants wo Nit Rem","PhosRemPlants","NitRemPlants","Plants wo NitRem","Plants wo PhosRem")) abline(h=1.8, col = "Red") abline(h=10, col = "Red") summary(PhosTSSwoHg2) #column c is TSS ####Exceedance probability plot##### order.HgT_Eff<-order(MBRdf$HgT_Eff,MBRdf$TSS_Eff) MBRdf$rank <- NA MBRdf$rank[order.HgT_Eff] <- 1:nrow(MBRdf) MBRdf<-MBRdf%>% mutate(rankPer=rank/nrow(MBRdf)*100)%>% mutate(Treatment="MBR") order.CAS <- order(CASdf$HgT_Eff,CASdf$TSS_Eff) CASdf$rank <- NA CASdf$rank[order.CAS] <- 1:nrow(CASdf) CASdf<-CASdf%>% mutate(rankPer=rank/nrow(CASdf)*100) %>% mutate(Treatment="CAS") order.EAAS <- order(EAASdf$HgT_Eff,EAASdf$TSS_Eff) EAASdf$rank <- NA EAASdf$rank[order.EAAS] <- 1:nrow(EAASdf) EAASdf<-EAASdf%>% mutate(rankPer=rank/nrow(EAASdf)*100)%>% mutate(Treatment="EAAS") order.TF <- order(TFdf$HgT_Eff,TFdf$TSS_Eff) TFdf$rank <- NA TFdf$rank[order.TF] <- 1:nrow(TFdf) TFdf<-TFdf%>% mutate(rankPer=rank/nrow(TFdf)*100)%>% mutate(Treatment="TF") order.Pond <- order(Ponddf$HgT_Eff,Ponddf$TSS_Eff) Ponddf$rank <- NA Ponddf$rank[order.Pond] <- 1:nrow(Ponddf) Ponddf<-Ponddf%>% mutate(rankPer=rank/nrow(Ponddf)*100)%>% mutate(Treatment="Pond") order.PolishPond <- order(PolishPondCASdf$HgT_Eff,PolishPondCASdf$TSS_Eff) PolishPondCASdf$rank <- NA PolishPondCASdf$rank[order.PolishPond] <- 1:nrow(PolishPondCASdf) PolishPondCASdf<-PolishPondCASdf%>% mutate(rankPer=rank/nrow(PolishPondCASdf)*100) %>% mutate(Treatment="Polishing Pond") order.Tertcla <- order(Tertcladf$HgT_Eff,Tertcladf$TSS_Eff) Tertcladf$rank <- NA Tertcladf$rank[order.Tertcla] <- 1:nrow(Tertcladf) Tertcladf<-Tertcladf%>% mutate(rankPer=rank/nrow(Tertcladf)*100) %>% mutate(Treatment="Tert Clar") order.MediafilterCAS <- order(MediafilterCASdf$HgT_Eff,MediafilterCASdf$TSS_Eff) MediafilterCASdf$rank <- NA MediafilterCASdf$rank[order.MediafilterCAS] <- 1:nrow(MediafilterCASdf) MediafilterCASdf<-MediafilterCASdf%>% mutate(rankPer=rank/nrow(MediafilterCASdf)*100)%>% mutate(Treatment="Media Filter") order.Pondwphos <- order(Pondwphosdf$HgT_Eff,Pondwphosdf$TSS_Eff) Pondwphosdf$rank <- NA Pondwphosdf$rank[order.Pondwphos] <- 1:nrow(Pondwphosdf) Pondwphosdf<-Pondwphosdf%>% mutate(rankPer=rank/nrow(Pondwphosdf)*100) %>% mutate(Treatment="Pond with Phos") Treatments<-rbind(MBRdf,MediafilterCASdf,Tertcladf,PolishPondCASdf,CASdf, EAASdf, TFdf, Ponddf, Pondwphosdf) ggplot(Treatments, aes(x=HgT_Eff, y=rankPer, color=Treatment))+ geom_point(aes(shape=Treatment))+ scale_color_manual(values = c("#FC4E07","#e07602","#00AFBB", "#370dbf","#0280cf","#bcf274","#65ad05", "#04d1bd", "#6e3a02"))+ scale_shape_manual(values=c(19,19,17,17,17,15,15,17,19))+ geom_line()+ xlim(0.25,10)+ labs(x = "HgT (ng/L)", y = "Percentile Ranking within Treatment category")+ theme(legend.position = c(0.8, 0.3), axis.text=element_text(size=12),legend.text=element_text(size=12))+ geom_vline(xintercept = 1.8) + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"))+ theme(text = element_text(size = 20)) unique(Treatments$MASTER_AI_ID) write.xlsx(Treatments, "~/UMD/Work/Data from Scott/Treatments.xlsx",asTable = FALSE, createWorkbook()) # ggplot() + # geom_point(data = MBRdf, aes(MBRdf$HgT_Eff,MBRdf$rankPer, colour="MBR"), # fill = "green", # size = 2, shape = 21)+ # geom_point(data = MediafilterCASdf, aes(MediafilterCASdf$HgT_Eff,MediafilterCASdf$rankPer), # fill = "light blue", color="black", # size = 2, shape = 17)+ # geom_point(data = Tertcladf, aes(Tertcladf$HgT_Eff,Tertcladf$rankPer), # fill = "pink", color="black", # size = 2, shape = 21)+ # geom_point(data = PolishPondCASdf, aes(PolishPondCASdf$HgT_Eff,PolishPondCASdf$rankPer, colour="Polishing Pond"), # fill = "dark green", color = "black", # size = 2, shape = 21)+ # geom_point(data = CASdf, aes(CASdf$HgT_Eff,CASdf$rankPer), # fill = "purple", color = "black", # size = 2, shape = 21)+ # geom_point(data = EAASdf, aes(EAASdf$HgT_Eff,EAASdf$rankPer), # fill = "blue", color = "black", # size = 2, shape = 21)+ # geom_point(data = TFdf, aes(TFdf$HgT_Eff,TFdf$rankPer), # fill = "red", color = "black", # size = 2, shape = 21)+ # geom_point(data = Ponddf, aes(Ponddf$HgT_Eff,Ponddf$rankPer), # fill = "yellow", color = "black", # size = 2, shape = 21)+ # geom_point(data = Pondwphosdf, aes(Pondwphosdf$HgT_Eff,Pondwphosdf$rankPer), # fill = "Orange", color = "black", # size = 2, shape = 21)+ # xlim(0.25,10)+ # labs(x = "HgT", y = "Percentile")+ # ggtitle("HgT by Treatment Type") ####prep data for GIS map#### CASWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% CASwotert,] CASWWTP<-CASWWTP%>% mutate(Treatmentkey="Secondary")%>% mutate(Treatment="CAS") EAASWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% EAASwotert,] EAASWWTP<-EAASWWTP%>% mutate(Treatmentkey="Secondary")%>% mutate(Treatment="EAAS") TFWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% TFwotert,] TFWWTP<-TFWWTP%>% mutate(Treatmentkey="Secondary")%>% mutate(Treatment="TF") RBSWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% RBSwotert,] RBSWWTP<-RBSWWTP%>% mutate(Treatmentkey="Secondary")%>% mutate(Treatment="RBS") PondWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% Ponds,] PondWWTP<-PondWWTP%>% mutate(Treatmentkey="Pond")%>% mutate(Treatment="Pond") PondwphosWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% s4,] PondwphosWWTP<-PondwphosWWTP%>% mutate(Treatmentkey="Pond")%>% mutate(Treatment="Pond with Phos") MBRsWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% MBR,] MBRsWWTP<-MBRsWWTP%>% mutate(Treatmentkey="Tertiary")%>% mutate(Treatment="MBR") MediafilterWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% MediaFilter,] MediafilterWWTP<-MediafilterWWTP%>% mutate(Treatmentkey="Tertiary")%>% mutate(Treatment="Media Filter") TertclaWWTP<-WWTPNames %>% filter(grepl('13820|1493|1175|1333|983', MASTER_AI_ID)) TertclaWWTP<-TertclaWWTP%>% mutate(Treatmentkey="Tertiary")%>% mutate(Treatment="TerCla") PolishPondWWTP<-WWTPNames [WWTPNames $MASTER_AI_ID %in% PolishPond,] PolishPondWWTP<-PolishPondWWTP%>% mutate(Treatmentkey="Tertiary")%>% mutate(Treatment="Polishing Pond") Treatmentkey<-rbind(MBRsWWTP,MediafilterWWTP,TertclaWWTP,PolishPondWWTP,CASWWTP, EAASWWTP, TFWWTP, PondWWTP, PondwphosWWTP) unique(Treatmentkey$MASTER_AI_ID) Treatmentkey<-Treatmentkey[,c("MASTER_AI_ID","MASTER_AI_NAME", "Treatment","Treatmentkey")] write.xlsx(Treatmentkey, "~/UMD/Work/Data from Scott/Treatmentkey.xlsx",asTable = FALSE, createWorkbook())