def calculate_emission_per_person(params,isocode,regiondata_dict,PopNum):
'''
Calculate the N and P emission per person with a GDP formula of van Drecht or
use historical N and P emissions (based on FAO protein consumption). In the latter case, the van Drecht formula
is used only in case of a future year.
In case of FAO protein consumption the lost of N and P is also calculated.
'''
# Initialisation of the output parameters.
Nemiss = {}
Pemiss = {}
# Other declarations
Nemiss_PopNum = {}
Pemiss_PopNum = {}
N_human_waste_other = {}
P_human_waste_other = {}
# Read protein consumption per person.
protein = data_class.get_data(params.fileprotein,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=0.0)
print_debug("Protein",protein)
# Convert protein into N and P
for key in isocode:
Nemiss[key] = protein[key] * g_per_d_to_kg_per_yr * params.N_fraction_protein
Pemiss[key] = params.fPN * Nemiss[key]
print_debug("Nemiss",Nemiss)
print_debug("Pemiss",Pemiss)
# Read from file the sheet with the wasting at retail and the sheet for household level losses
frac_retail_loss = data_class.get_data(params.file_retail_loss,year=params.year,sep=params.sep,isocode=isocode,method=1)
frac_household_loss = data_class.get_data(params.file_household_loss,year=params.year,sep=params.sep,isocode=isocode,method=1)
print_debug("frac_retail_loss",frac_retail_loss)
print_debug("frac_household_loss",frac_household_loss)
# Calculate the actual amount of N and P consumed.
for key in isocode:
Nval = Nemiss[key]
Pval = Pemiss[key]
Nemiss_PopNum[key] = Nval * PopNum[key]
Pemiss_PopNum[key] = Pval * PopNum[key]
Nemiss[key] *= (1.0 - frac_retail_loss[key]) * (1.0 - frac_household_loss[key]) * (1.0 - params.fraction_human_skin_loss)
Pemiss[key] *= (1.0 - frac_retail_loss[key]) * (1.0 - frac_household_loss[key]) * (1.0 - params.fraction_human_skin_loss)
N_human_waste_other[key] = Nval - Nemiss[key]
P_human_waste_other[key] = Pval - Pemiss[key]
# Add to global database
regiondata.put2regiondata(Nemiss_PopNum,regiondata_dict,"Nemiss_PopNum")
regiondata.put2regiondata(Pemiss_PopNum,regiondata_dict,"Pemiss_PopNum")
regiondata.put2regiondata(N_human_waste_other,regiondata_dict,"N_human_waste_other")
regiondata.put2regiondata(P_human_waste_other,regiondata_dict,"P_human_waste_other")
# Print debug information to screen.
print_debug("Nemiss of humans per person",Nemiss)
print_debug("Pemiss of humans per person",Pemiss)
print_debug("Nemiss of humans",Nemiss_PopNum)
print_debug("Pemiss of humans",Pemiss_PopNum)
print_debug("N_human_waste per person",N_human_waste_other)
print_debug("P_human_waste per person",P_human_waste_other)
return Nemiss,Pemiss
def calculate(params,isocode,regiondata_dict,PopNum,Nemiss,Pemiss,EmPldet,EmPddet,PopUrbNum,PopConNum):
'''
Calculate the N and P emission from industry
'''
# Multiply urban emission with factor_industry to obtain output industry [kg/yr]
# Assumption is that only urban areas have industry and
# industry depends on urban emission only and also detergents for P
factor_industry = data_class.get_data(params.filefactor_industry,year=params.year,sep=params.sep,isocode=isocode,method=1)
Nemiss_industry_tot = {}
Pemiss_industry_tot = {}
Nemiss_industry = {}
Pemiss_industry = {}
Nindustry_other = {}
Pindustry_other = {}
for key in isocode:
pop = max(PopUrbNum[key],PopConNum[key])
Nemiss_industry_tot[key] = Nemiss[key] * factor_industry[key] * pop
Pemiss_industry_tot[key] = ((Pemiss[key] * pop) + (EmPldet[key] + EmPddet[key])*PopNum[key]) * \
factor_industry[key]
Nemiss_industry[key] = Nemiss_industry_tot[key] *\
(1.0-params.industry_loss_fraction_N)
Pemiss_industry[key] = Pemiss_industry_tot[key] * (1.0-params.industry_loss_fraction_P)
Nindustry_other[key] = Nemiss_industry_tot[key] * params.industry_loss_fraction_N
Pindustry_other[key] = Pemiss_industry_tot[key] * params.industry_loss_fraction_P
regiondata.put2regiondata(Nemiss_industry_tot,regiondata_dict,"Nemiss_industry")
regiondata.put2regiondata(Pemiss_industry_tot,regiondata_dict,"Pemiss_industry")
regiondata.put2regiondata(Nindustry_other,regiondata_dict,"Nindustry_other")
regiondata.put2regiondata(Pindustry_other,regiondata_dict,"Pindustry_other")
print_debug("Total Nemiss of industry",Nemiss_industry_tot)
print_debug("Total Pemiss of industry",Pemiss_industry_tot)
print_debug("Nemiss of industry",Nemiss_industry)
print_debug("Pemiss of industry",Pemiss_industry)
print_debug("factor_industry",factor_industry)
print_debug("Nindustry_other",Nindustry_other)
print_debug("Pindustry_other",Pindustry_other)
return Nemiss_industry,Pemiss_industry
def calculate(params,mask,isocode,isogrid):
'''
Allocation of fertilizer on the basis of agricultural landarea grid. The new map of agricultural land in km2 is returned as well
as the N and P fertilizer.
'''
# Read region codes (regcodes)
reggrid = ascraster.Asciigrid(ascii_file=params.fileregion,mask=mask,numtype=int)
# Make a list of isocodes for the provinces.
regcode = list(set(reggrid.values))
# Read P fertilizer per region, missing regions are set to zero.
Pfert_reg = data_class.get_data(params.filePfertuse,year=params.year,sep=params.sep,isocode=regcode,method=0,weighing=0.0)
print_debug("Pfert_reg",Pfert_reg)
# Read N fertilizer per region, missing regions are set to zero.
Nfert_reg = data_class.get_data(params.fileNfertuse,year=params.year,sep=params.sep,isocode=regcode,method=0,weighing=0.0)
print_debug("Nfert_reg",Nfert_reg)
# Read agricultural land per province, missing provinces are set to zero.
agri_area_table = data_class.get_data(params.filefertarea,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=0.0)
print_debug("agri_area_table",agri_area_table)
# Read the basic map with the amount of agricultural land in km2 per grid cell
agriland = ascraster.Asciigrid(ascii_file=params.fileagrilandarea,mask=mask,numtype=float)
# Read the basic map with the amount of land in km2 per grid cell
arealand = ascraster.Asciigrid(ascii_file=params.landarea,mask=mask,numtype=float)
# Create two output grids with zeros and fill these with the ranking method.
agri_area = ascraster.duplicategrid(agriland)
agri_area.add_values(agri_area.length * [0.0])
Pfert = ascraster.duplicategrid(agri_area)
Nfert = ascraster.duplicategrid(agri_area)
# Make a dictionairy of pointers for all the isocodes in the grid.
pointer_dict = {}
for icell in xrange(isogrid.length):
iso = isogrid.get_data(icell)
if (iso != None):
iso = int(iso)
try:
pointer_dict[iso].append(icell)
except KeyError:
pointer_dict[iso] = [icell]
for key in isocode:
# Select agricultural land of the key country
# First step is to allocate the agricultural area for each province.
weight_key = []
qmax_key = []
try:
pointer1 = pointer_dict[key]
except KeyError:
pointer1 = []
sumqmax = 0.0
sumweight = 0.0
for icell in pointer1:
area = agriland.get_data(icell,0.0)
maxarea = arealand.get_data(icell,0.0)
# Fill weighing data and max data
qmax_key.append(maxarea)
weight_key.append(area)
sumqmax += qmax_key[-1]
sumweight += weight_key[-1]
if (sumweight < 0.0001):
# Make uniform weighing to all cells.
for icell in range(len(weight_key)):
weight_key[icell] = 1
sumweight += weight_key[icell]
# Take the table information on agricultural area.
area_demand = agri_area_table[key]
if (area_demand > sumqmax):
raise MyError("It is not possible to allocate " + str(area_demand) + " km2 in provincenumber " + str(key),\
"Maximum area that is possible to allocate in this province is: "+str(sumqmax))
# Do the allocation
area_new = allocweighing.allocweighing(area_demand,sumweight,weight_key,qmax_key)
if (abs(sum(area_new) - agri_area_table[key]) > 0.001*agri_area_table[key]):
print "***** There is not enough allocated for agri_area for region "+str(key)+". Difference: " + str(agri_area_table[key]-sum(area_new))
print "***** Needed for agri_area for region "+str(key)+" " + str(agri_area_table[key])
# Fill gridded result
for item in xrange(len(area_new)):
agri_area.set_data(pointer1[item],area_new[item])
# Now the fertilizer is allocated to the gridcells, based on agricultural land area. This means that
# each grid cell will have another load but the same application rate (kg N/ha)
pointer_dict = {}
for icell in xrange(reggrid.length):
iso = reggrid.get_data(icell)
if (iso != None):
iso = int(iso)
try:
pointer_dict[iso].append(icell)
except KeyError:
pointer_dict[iso] = [icell]
for key in regcode:
weight_key = []
qmax_key = []
try:
pointer1 = pointer_dict[key]
except KeyError:
pointer1 = []
sumqmax = 0.0
sumweight = 0.0
for icell in pointer1:
# Note the new map of the previous step is used here.
area = agri_area.get_data(icell,0.0)
maxarea = 10000000.
# Fill weighing data and max data
qmax_key.append(maxarea)
weight_key.append(area)
sumqmax += qmax_key[-1]
sumweight += weight_key[-1]
if (sumweight < 0.0001):
# Make uniform weighing to all cells.
for icell in range(len(weight_key)):
weight_key[icell] = 1
sumweight += weight_key[icell]
# Grid these country connected population data with help of the population density map
fert_demand = Nfert_reg[key]
fert_new = allocweighing.allocweighing(fert_demand,sumweight,weight_key,qmax_key)
if (abs(sum(fert_new) - Nfert_reg[key]) > 0.001*Nfert_reg[key]):
print "***** There is not enough allocated for Nfert_reg for region "+str(key)+". Difference: " + str(Nfert_reg[key]-sum(fert_new))
print "***** Needed for Nfert_reg for region "+str(key)+" " + str(Nfert_reg[key])
# Fill gridded result
for item in xrange(len(fert_new)):
Nfert.set_data(pointer1[item],fert_new[item])
for key in regcode:
weight_key = []
qmax_key = []
try:
pointer1 = pointer_dict[key]
except KeyError:
pointer1 = []
sumqmax = 0.0
sumweight = 0.0
for icell in pointer1:
# Note the new map of the previous step is used here.
area = agri_area.get_data(icell,0.0)
maxarea = 10000000.
# Fill weighing data and max data
qmax_key.append(maxarea)
weight_key.append(area)
sumqmax += qmax_key[-1]
sumweight += weight_key[-1]
if (sumweight < 0.0001):
# Make uniform weighing to all cells.
for icell in range(len(weight_key)):
weight_key[icell] = 1
sumweight += weight_key[icell]
# Grid these country connected population data with help of the population density map
fert_demand = Pfert_reg[key]
# Change fertilizer from P2O5 to P
fert_demand *= 62.0/142.0
fert_new = allocweighing.allocweighing(fert_demand,sumweight,weight_key,qmax_key)
if (abs(sum(fert_new) - Pfert_reg[key]* 62.0/142.0) > 0.001*Pfert_reg[key]* 62.0/142.0):
print "***** There is not enough allocated for Pfert_reg for region "+str(key)+". Difference: " + str(Pfert_reg[key]* (62.0/142.0)-sum(fert_new))
print "***** Needed for Pfert_reg for region "+str(key)+" " + str(Pfert_reg[key])
# Fill gridded result
for item in xrange(len(fert_new)):
Pfert.set_data(pointer1[item],fert_new[item])
# Write to output file
agri_area.write_ascii_file(params.fileagri_area)
Nfert.write_ascii_file(params.fileNfert)
Pfert.write_ascii_file(params.filePfert)
total = sum(agri_area.values)
print "Total agricultural area in km2: ",total
print "Total N fertilzer in kg N: ",sum(Nfert.values)
print "Total P fertilizer in kg P: ",sum(Pfert.values)
print "Total N fertilzer in kg N/ha: ",sum(Nfert.values)/(100*total)
print "Total P fertilizer in kg P/ha: ",sum(Pfert.values)/(100*total)
return agri_area,Nfert,Pfert
def run_pointsrc_model(args):
'''
Run point sources model main routine
@param listargs: list of arguments passed trough command-line or other script
Must look like sys.argv so make sure is starts with scriptname.
'''
# Parse command-line arguments and set parameters for script
try:
param = cmd_options_pointsrc.InputPointsrc(args)
params = param.options
params_var = param.options_var
except SystemExit:
raise MyError("Error has occured in the reading of the commandline options.")
# Start timer and logging
s = my_sys.SimpleTimer()
log = my_logging.Log(params.outputdir,"%s_%i.log" % (params.scenarioname,params.year))
print "Log will be written to %s" % log.logFile
# If no arguments are provided do a run with defaults in params
if len(args) == 0:
log.write_and_print("No arguments provided: starting default run...")
# time start of run
log.write_and_print("Starting run....")
log.write("# Parameters used:",print_time=False,lcomment=False)
for option in str(params_var).split(", "):
log.write("--%s = %s" % (option.split(": ")[0].strip("{").strip("'"),
option.split(": ")[1].strip("}").strip("'")),
print_time=False,lcomment=False)
log.write("All parameters used:")
for option in str(params).split(", "):
log.write("# %s = %s" % (option.split(": ")[0].strip("{").strip("'"),
option.split(": ")[1].strip("}").strip("'")),
print_time=False,lcomment=False)
log.write("# End of all parameters used.",print_time=False,lcomment=False)
# Check whether there are command-line arguments which are not used
if (len(param.args) > 0):
txt = "The following command line arguments will not be used:"
log.write_and_print(txt + str(param.args))
# Write svn information of input and scripts to log file.
log.write("******************************************************",print_time=False,lcomment=True)
log.write("Version information:",print_time=False,lcomment=True)
log.write("Version information main script:",print_time=False,lcomment=True)
log.write("Revision $LastChangedDate: 2013-09-25 13:37:10 +0200 (Tue, 25 Sep 2013)",print_time=False,lcomment=True)
log.write("Date $LastChangedRevision: 344 $",print_time=False,lcomment=True)
#message = get_versioninfo.get_versioninfo(params.inputdir,params.outputdir)
#message.extend(get_versioninfo.get_versioninfo("tools",params.outputdir))
#for item in range(len(message)):
# log.write(str(message[item]),print_time=False,lcomment=True)
log.write("******************************************************",print_time=False,lcomment=True)
# Read mask of the input grids. We take the iso grid as mask for this.
# The mask takes care to do the calculations on an efficient way. Only
# the grid cells which are in the mask are calculated and stored.
if (params.lmask):
mask = ascraster.create_mask(params.file_mask, 0.0,'GT',numtype=float)
log.write_and_print(s.interval("Reading mask"))
else:
mask = None
log.write_and_print(s.interval("No mask is used for this simulation."))
# Make a list of all district codes that we want to calculate
# Could be done by reading a grid, but now it is done by reading a input file with a complet set of isocodes.
isocode = make_iso_list.make_iso_list(params,mask)
# Read provence codes (isocodes)
isogrid = ascraster.Asciigrid(ascii_file=params.fileiso,mask=mask,numtype=int)
# Read total number of population
PopNumTot,popgrid, = read_popnum.read_popnum(params,mask,isogrid,isocode)
print_debug("PopNumTot",PopNumTot)
# Calculate the coastal population.
PopNum_coast,popgrid_coast = coast_population.calculate(params,mask,isogrid,isocode,popgrid)
# Calculation of number of people without hte coastal population
PopNum = {}
for key in isocode:
PopNum[key] = PopNumTot[key] - PopNum_coast[key]
# Create regiondata class
regiondata_dict = create_regiondata.create_regiondata(params,isocode)
regiondata.put2regiondata(PopNumTot,regiondata_dict,"PopNum")
regiondata.put2regiondata(PopNum_coast,regiondata_dict,"PopNum_coast")
print_debug("PopNum_without_coast",PopNum)
print_debug("PopNum_coast",PopNum_coast)
# Determine the N and P emission per person.
Nemiss,Pemiss = calculate_emission_per_person.calculate_emission_per_person(params,isocode,regiondata_dict,PopNumTot)
log.write_and_print(s.interval("Ready with calculation of N and P emission per capita."))
# Calculation of N and P emissionto the coastal zone
N_emiss_coast = {}
P_emiss_coast = {}
for key in isocode:
N_emiss_coast[key] = PopNum_coast[key] * Nemiss[key]
P_emiss_coast[key] = PopNum_coast[key] * Pemiss[key]
regiondata.put2regiondata(N_emiss_coast,regiondata_dict,"N_emiss_coast")
regiondata.put2regiondata(P_emiss_coast,regiondata_dict,"P_emiss_coast")
print_debug("N_emiss_coast",N_emiss_coast)
print_debug("P_emiss_coast",P_emiss_coast)
# Read the percentage urban population.
PopUrb = data_class.get_data(params.filePopUrb,year=params.year,sep=params.sep,isocode=isocode,method=1)
print_debug("PopUrb",PopUrb)
# Calculatation of urban and connected number of people
PopUrbNum = {}
for key in isocode:
PopUrbNum[key] = PopNum[key] * (PopUrb[key]/100.0)
regiondata.put2regiondata(PopUrbNum,regiondata_dict,"PopUrbNum")
print_debug("PopUrbNum",PopUrbNum)
# Calculatation of connected number of people
# Read the percentage of the connected population.
PopCon = data_class.get_data(params.filePopCon,year=params.year,sep=params.sep,isocode=isocode,method=1)
print_debug("PopCon",PopCon)
PopConNum = {}
for key in isocode:
PopConNum[key] = PopNum[key] * (PopCon[key]/100.0)
regiondata.put2regiondata(PopConNum,regiondata_dict,"PopConNum")
print_debug("PopConNum",PopConNum)
# Get P emissions in kg P per year per inhabitant from laundry detergents and from dishwasher detergents.
EmPldet = data_class.get_data(params.filePlaundry,year=params.year,sep=params.sep,isocode=isocode,method=1)
EmPddet = data_class.get_data(params.filePdishwasher,year=params.year,sep=params.sep,isocode=isocode,method=1)
# Add to global database
regiondata.put2regiondata(EmPldet,regiondata_dict,"EmPldet")
regiondata.put2regiondata(EmPddet,regiondata_dict,"EmPddet")
print_debug("EmPldet",EmPldet)
print_debug("EmPddet",EmPddet)
# Calculation of industry emission to surface water
Nemiss_industry,Pemiss_industry = industry_emission.calculate(params,isocode,regiondata_dict,PopNumTot,\
Nemiss,Pemiss,EmPldet,EmPddet,PopUrbNum,PopConNum)
log.write_and_print(s.interval("Ready with calculation of N and P emission from industry."))
# Read the percentage of the primary, secondary and tertiary treatment.
PrimTreatment = data_class.get_data(params.filePrimTreatment,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=0.0)
print_debug("PrimTreatment",PrimTreatment)
SecTreatment = data_class.get_data(params.fileSecTreatment,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=0.0)
print_debug("SecTreatment",SecTreatment)
TertTreatment = data_class.get_data(params.fileTertTreatment,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=0.0)
print_debug("TertTreatment",TertTreatment)
# Sum of the percentage treatment is smaller or equal to percentage population connected.
# Determine the percentage of connected but not treated.
NoTreatment = {}
for key in isocode:
total = PrimTreatment[key] + SecTreatment[key] + TertTreatment[key]
if (total > PopCon[key] and total > 0.0):
reduction = PopCon[key]/total
print "Reduction of treatment for isocode country: " + str(key) + " with reduction: " + str(reduction)
PrimTreatment[key] *= reduction
SecTreatment[key] *= reduction
TertTreatment[key] *= reduction
NoTreatment[key] = 0.0
else:
NoTreatment[key] = PopCon[key] - total
print_debug("NoTreatment: sum equal to PopCon",NoTreatment)
print_debug("PrimTreatment: sum equal to PopCon",PrimTreatment)
print_debug("SecTreatment: sum equal to PopCon",SecTreatment)
print_debug("TertTreatment: sum equal to PopCon",TertTreatment)
# Determine the percentage of not-connected and not treated in urban areas.
NotConnected = {}
for key in isocode:
if (PopUrb[key] > PopCon[key]):
NotConnected[key] = PopUrb[key] - PopCon[key]
else:
NotConnected[key] = 0.0
print_debug("NotConnected",NotConnected)
# Read the fraction removal of the primary, secondary and tertiary treatment for N and P.
N_PrimRemoval = data_class.get_data(params.fileN_PrimRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.N_PrimRemoval_def)
print_debug("N_PrimRemoval",N_PrimRemoval)
N_SecRemoval = data_class.get_data(params.fileN_SecRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.N_SecRemoval_def)
print_debug("N_SecRemoval",N_SecRemoval)
N_TertRemoval = data_class.get_data(params.fileN_TertRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.N_TertRemoval_def)
print_debug("N_TertRemoval",N_TertRemoval)
P_PrimRemoval = data_class.get_data(params.fileP_PrimRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.P_PrimRemoval_def)
print_debug("P_PrimRemoval",P_PrimRemoval)
P_SecRemoval = data_class.get_data(params.fileP_SecRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.P_SecRemoval_def)
print_debug("P_SecRemoval",P_SecRemoval)
P_TertRemoval = data_class.get_data(params.fileP_TertRemoval,year=params.year,sep=params.sep,isocode=isocode,method=0,weighing=params.P_TertRemoval_def)
print_debug("P_TertRemoval",P_TertRemoval)
# Get the reuse fraction for agricultural, which comes from the unconnected people.
agricultural_reuse = data_class.get_data(params.file_agri_reuse,year=params.year,sep=params.sep,isocode=isocode,method=1)
# Calculate the percentage of each flow to NoConnectionRemoval (N and P)
N_NoConnRemoval = {}
P_NoConnRemoval = {}
N_NoConnRemoval_agri = {}
P_NoConnRemoval_agri = {}
N_NoConnRemoval_other = {}
P_NoConnRemoval_other = {}
for key in isocode:
N_NoConnRemoval[key] = 100.0 - (100.0 * (1.0 - agricultural_reuse[key]) * 0.5 * (1.0 - params.human_N_vol))
P_NoConnRemoval[key] = 100.0 - (100.0 * (1.0 - agricultural_reuse[key]) * 0.5)
N_NoConnRemoval_agri[key] = 100.0 * agricultural_reuse[key] * (1.0 - params.human_N_vol)
P_NoConnRemoval_agri[key] = 100.0 * agricultural_reuse[key]
N_NoConnRemoval_other[key] = 100.0 * params.human_N_vol + \
100.0 * (1.0 - params.human_N_vol)*(1.0 - agricultural_reuse[key])*0.5
P_NoConnRemoval_other[key] = 100.0 * (1.0 - agricultural_reuse[key]) * 0.5
print_debug("N_NoConnRemoval",N_NoConnRemoval)
print_debug("P_NoConnRemoval",P_NoConnRemoval)
print_debug("N_NoConnRemoval_agri",N_NoConnRemoval_agri)
print_debug("P_NoConnRemoval_agri",P_NoConnRemoval_agri)
print_debug("N_NoConnRemoval_other",N_NoConnRemoval_other)
print_debug("P_NoConnRemoval_other",P_NoConnRemoval_other)
regiondata.put2regiondata(N_NoConnRemoval,regiondata_dict,"N_NoConnRemoval")
regiondata.put2regiondata(P_NoConnRemoval,regiondata_dict,"P_NoConnRemoval")
# Calculate the total emission of the people
Ntot = {}
Ptot = {}
NtotConnected = {}
PtotConnected = {}
NnotConnected = {}
PnotConnected = {}
Nsewerage_other = {}
Psewerage_other = {}
Npipe_loss = {}
Ppipe_loss = {}
# Remove the following when testing is ready
Nhuman_connect = {}
Nhuman_connect_with_pipe_loss = {}
Phuman_connect = {}
Phuman_connect_with_pipe_loss = {}
Pdetergents1 = {}
EmPldet_tot = {}
EmPddet_tot = {}
for key in isocode:
Ntot[key] = PopNumTot[key] * Nemiss[key]
Ptot[key] = PopNumTot[key] * Pemiss[key] + (PopNum[key]*PopCon[key]/100.0)*(EmPldet[key] + EmPddet[key])
NtotConnected[key] = Ntot[key] * (PopCon[key]/100.0)
Nhuman_connect[key] = NtotConnected[key]
PtotConnected[key] = (PopNum[key] * PopCon[key]/100.0)*(EmPldet[key] + EmPddet[key] + Pemiss[key])
Phuman_connect[key] = PtotConnected[key]
Pdetergents1[key] = (PopNum[key] * PopCon[key]/100.0)*(EmPldet[key] + EmPddet[key])
EmPldet_tot[key] = (PopNum[key] * PopCon[key]/100.0) * EmPldet[key]
EmPddet_tot[key] = (PopNum[key] * PopCon[key]/100.0) * EmPddet[key]
# There is an extra loss of N and P due to leakage of pipes etc.
Npipe_loss[key] = NtotConnected[key] * params.sewer_pipe_loss_fraction
NtotConnected[key] *= (1.0 - params.sewer_pipe_loss_fraction)
Nhuman_connect_with_pipe_loss[key] = NtotConnected[key]
Ppipe_loss[key] = PtotConnected[key] * params.sewer_pipe_loss_fraction
PtotConnected[key] *= (1.0 - params.sewer_pipe_loss_fraction)
Phuman_connect_with_pipe_loss[key] = PtotConnected[key]
# Add industry to the total connected load.
NtotConnected[key] += Nemiss_industry[key]
PtotConnected[key] += Pemiss_industry[key]
NnotConnected[key] = Ntot[key] * NotConnected[key]/100.0
PnotConnected[key] = (PopNum[key] * Pemiss[key]) * (NotConnected[key]/100.0)
print_debug("NotConnected",NotConnected)
print_debug("PopCon",PopCon)
print_debug("Ntot total population",Ntot)
print_debug("Ptot total population",Ptot)
print_debug("Nhuman_connect",Nhuman_connect)
print_debug("Nhuman_connect_with_pipe_loss",Nhuman_connect_with_pipe_loss)
print_debug("Phuman_connect",Phuman_connect)
print_debug("Phuman_connect_with_pipe_loss",Phuman_connect_with_pipe_loss)
print_debug("Pdetergents1",Pdetergents1)
print_debug("Nemiss_industry",Nemiss_industry)
print_debug("Pemiss_industry",Pemiss_industry)
print_debug("NtotConnected included industry with pipe lost",NtotConnected)
print_debug("PtotConnected included industry with pipe lost",PtotConnected)
print_debug("NnotConnected",NnotConnected)
print_debug("PnotConnected",PnotConnected)
# Put all parameters in global database
regiondata.put2regiondata(NtotConnected,regiondata_dict,"NtotConnected")
regiondata.put2regiondata(NnotConnected,regiondata_dict,"NnotConnected")
regiondata.put2regiondata(Ntot,regiondata_dict,"Ntot")
regiondata.put2regiondata(PtotConnected,regiondata_dict,"PtotConnected")
regiondata.put2regiondata(PnotConnected,regiondata_dict,"PnotConnected")
regiondata.put2regiondata(Ptot,regiondata_dict,"Ptot")
regiondata.put2regiondata(EmPddet_tot,regiondata_dict,"EmPddet_tot")
regiondata.put2regiondata(EmPldet_tot,regiondata_dict,"EmPldet_tot")
# Convert treatment faction of total people to fraction for the connected people.
for key in isocode:
if (PopCon[key] > 0.0):
PrimTreatment[key] /= PopCon[key]
SecTreatment[key] /= PopCon[key]
TertTreatment[key] /= PopCon[key]
NoTreatment[key] /= PopCon[key]
else:
PrimTreatment[key] = 0.0
SecTreatment[key] = 0.0
TertTreatment[key] = 0.0
NoTreatment[key] = 0.0
print_debug("PrimTreatment",PrimTreatment)
print_debug("SecTreatment",SecTreatment)
print_debug("TertTreatment",TertTreatment)
print_debug("NoTreatment",NoTreatment)
# Retention for the connected situation
Nretention={}
Pretention={}
for key in isocode:
Nretention[key] = N_PrimRemoval[key] * PrimTreatment[key] +\
N_SecRemoval[key] * SecTreatment[key] +\
N_TertRemoval[key] * TertTreatment[key]
Pretention[key] = P_PrimRemoval[key] * PrimTreatment[key] +\
P_SecRemoval[key] * SecTreatment[key] +\
P_TertRemoval[key] * TertTreatment[key]
print_debug("Nretention",Nretention)
print_debug("Pretention",Pretention)
regiondata.put2regiondata(Nretention,regiondata_dict,"Nretention")
regiondata.put2regiondata(Pretention,regiondata_dict,"Pretention")
NtotConnected_eff = {}
PtotConnected_eff = {}
NnotConnected_eff = {}
PnotConnected_eff = {}
Ntot_eff = {}
Ptot_eff = {}
NnotConnect_agri ={}
NnotConnect_other ={}
PnotConnect_agri ={}
PnotConnect_other ={}
Nhuman_connect_tosw = {}
Nindustry_connect_tosw = {}
Phuman_connect_tosw = {}
Pindustry_connect_tosw = {}
Nother_prim = {}
Pother_prim = {}
Nother_sec = {}
Pother_sec = {}
Nother_tert = {}
Pother_tert = {}
for key in isocode:
Nsewerage_other[key] = NtotConnected[key] * (Nretention[key]/100.0) + Npipe_loss[key]
Nother_prim[key] = NtotConnected[key] * (N_PrimRemoval[key] * PrimTreatment[key] /100.0)
Nother_sec[key] = NtotConnected[key] * (N_SecRemoval[key] * SecTreatment[key] /100.0)
Nother_tert[key] = NtotConnected[key] * (N_TertRemoval[key] * TertTreatment[key] /100.0)
NtotConnected_eff[key] = NtotConnected[key] * (1.0 - (Nretention[key]/100.0))
Nhuman_connect_tosw[key] = Nhuman_connect_with_pipe_loss[key] * (1.0 - (Nretention[key]/100.0))
Nindustry_connect_tosw[key] = Nemiss_industry[key] * (1.0 - (Nretention[key]/100.0))
Pindustry_connect_tosw[key] = Pemiss_industry[key] * (1.0 - (Pretention[key]/100.0))
Psewerage_other[key] = PtotConnected[key] * (Pretention[key]/100.0) + Ppipe_loss[key]
Pother_prim[key] = PtotConnected[key] * (P_PrimRemoval[key] * PrimTreatment[key] /100.0)
Pother_sec[key] = PtotConnected[key] * (P_SecRemoval[key] * SecTreatment[key] /100.0)
Pother_tert[key] = PtotConnected[key] * (P_TertRemoval[key] * TertTreatment[key] /100.0)
PtotConnected_eff[key] = PtotConnected[key] * (1.0 - (Pretention[key]/100.0))
Phuman_connect_tosw[key] = Phuman_connect_with_pipe_loss[key] * (1.0 - (Pretention[key]/100.0))
# Not connected part.
NnotConnected_eff[key] = NnotConnected[key] * (1.0 - (N_NoConnRemoval[key]/100.0))
PnotConnected_eff[key] = PnotConnected[key] * (1.0 - (P_NoConnRemoval[key]/100.0))
NnotConnect_agri[key] = NnotConnected[key] * (N_NoConnRemoval_agri[key]/100.0)
PnotConnect_agri[key] = PnotConnected[key] * (P_NoConnRemoval_agri[key]/100.0)
NnotConnect_other[key] = NnotConnected[key] * (N_NoConnRemoval_other[key]/100.0)
PnotConnect_other[key] = PnotConnected[key] * (P_NoConnRemoval_other[key]/100.0)
# Make a total to surface water.
Ntot_eff[key] = NtotConnected_eff[key] + NnotConnected_eff[key]
Ptot_eff[key] = PtotConnected_eff[key] + PnotConnected_eff[key]
print_debug("NtotConnected_eff",NtotConnected_eff)
print_debug("Nhuman_connect_tosw",Nhuman_connect_tosw)
print_debug("Nindustry_connect_tosw",Nindustry_connect_tosw)
print_debug("Nsewerage_other",Nsewerage_other)
print_debug("PtotConnected_eff",PtotConnected_eff)
print_debug("Phuman_connect_tosw",Phuman_connect_tosw)
print_debug("Pindustry_connect_tosw",Pindustry_connect_tosw)
print_debug("Psewerage_other",Psewerage_other)
print_debug("NnotConnected_eff",NnotConnected_eff)
print_debug("PnotConnected_eff",PnotConnected_eff)
print_debug("NnotConnect_agri",NnotConnect_agri)
print_debug("NnotConnect_other",NnotConnect_other)
print_debug("PnotConnect_agri",PnotConnect_agri)
print_debug("PnotConnect_other",PnotConnect_other)
print_debug("Ntot_eff",Ntot_eff)
print_debug("Ptot_eff",Ptot_eff)
# Put all parameters in global database
regiondata.put2regiondata(NtotConnected_eff,regiondata_dict,"NtotConnected_eff")
regiondata.put2regiondata(NnotConnected_eff,regiondata_dict,"NnotConnected_eff")
regiondata.put2regiondata(NnotConnect_agri,regiondata_dict,"NnotConnect_agri")
regiondata.put2regiondata(NnotConnect_other,regiondata_dict,"NnotConnect_other")
regiondata.put2regiondata(Nsewerage_other,regiondata_dict,"Nsewerage_other")
regiondata.put2regiondata(Nother_prim,regiondata_dict,"Nother_prim")
regiondata.put2regiondata(Nother_sec,regiondata_dict,"Nother_sec")
regiondata.put2regiondata(Nother_tert,regiondata_dict,"Nother_tert")
regiondata.put2regiondata(Ntot_eff,regiondata_dict,"Ntot_eff")
regiondata.put2regiondata(PtotConnected_eff,regiondata_dict,"PtotConnected_eff")
regiondata.put2regiondata(PnotConnected_eff,regiondata_dict,"PnotConnected_eff")
regiondata.put2regiondata(PnotConnect_agri,regiondata_dict,"PnotConnect_agri")
regiondata.put2regiondata(PnotConnect_other,regiondata_dict,"PnotConnect_other")
regiondata.put2regiondata(Psewerage_other,regiondata_dict,"Psewerage_other")
regiondata.put2regiondata(Pother_prim,regiondata_dict,"Pother_prim")
regiondata.put2regiondata(Pother_sec,regiondata_dict,"Pother_sec")
regiondata.put2regiondata(Pother_tert,regiondata_dict,"Pother_tert")
regiondata.put2regiondata(Ptot_eff,regiondata_dict,"Ptot_eff")
log.write_and_print(s.interval("Ready with calculation of N and P emission to surface water."))
# Make N and P human waste in kg.
testN = {}
testP = {}
for key in isocode:
pop = max(PopUrbNum[key],PopConNum[key])
emisN_rur = regiondata_dict[key].N_human_waste_other + Nemiss[key]
emisP_rur = regiondata_dict[key].P_human_waste_other + Pemiss[key]
regiondata_dict[key].N_human_waste_other *= pop
regiondata_dict[key].P_human_waste_other *= pop
testN[key] = regiondata_dict[key].N_human_waste_other
testP[key] = regiondata_dict[key].P_human_waste_other
regiondata_dict[key].Nemiss_pop_rural = emisN_rur * (PopNum[key] - pop)
regiondata_dict[key].Pemiss_pop_rural = emisP_rur * (PopNum[key] - pop)
print_debug("N_human_waste_other",testN)
print_debug("P_human_waste_other",testP)
del testN
del testP
# Read septic tank connection for the rural population
Septic_connection = data_class.get_data(params.fileSeptic_rural,year=params.year,sep=params.sep,isocode=isocode,method=1)
Nemiss_rural_sw = {}
Pemiss_rural_sw = {}
for key in isocode:
regiondata_dict[key].Nemiss_rural_sw = regiondata_dict[key].Nemiss_pop_rural *(1.0-0.01*Septic_connection[key]) *\
(1.0 - params.Nreduction_septic_to_river)
regiondata_dict[key].Pemiss_rural_sw = regiondata_dict[key].Pemiss_pop_rural *(1.0-0.01*Septic_connection[key]) *\
(1.0 - params.Preduction_septic_to_river)
regiondata_dict[key].Nemiss_rural_grw = regiondata_dict[key].Nemiss_pop_rural *(0.01*Septic_connection[key]) *\
(1.0 - params.Nreduction_septic_to_grw)
regiondata_dict[key].Pemiss_rural_grw = regiondata_dict[key].Pemiss_pop_rural *(0.01*Septic_connection[key]) *\
(1.0 - params.Preduction_septic_to_grw)
regiondata_dict[key].Nemiss_rural_other = regiondata_dict[key].Nemiss_pop_rural *(1.0 - 0.01*Septic_connection[key]) *\
params.Nreduction_septic_to_river + regiondata_dict[key].Nemiss_pop_rural *\
(0.01 * Septic_connection[key]) *\
params.Nreduction_septic_to_grw
regiondata_dict[key].Pemiss_rural_other = regiondata_dict[key].Pemiss_pop_rural *(1.0 - 0.01*Septic_connection[key]) *\
params.Preduction_septic_to_river + regiondata_dict[key].Pemiss_pop_rural *\
(0.01 * Septic_connection[key]) *\
params.Preduction_septic_to_grw
# Add rural emissions to surface water effluent.
regiondata_dict[key].Ntot_eff += regiondata_dict[key].Nemiss_rural_sw
regiondata_dict[key].Ptot_eff += regiondata_dict[key].Pemiss_rural_sw
Nemiss_rural_sw[key] = regiondata_dict[key].Nemiss_rural_sw
Pemiss_rural_sw[key] = regiondata_dict[key].Pemiss_rural_sw
# Add the agricultural load and the load to other sector
for key in isocode:
regiondata_dict[key].Ntotal_agri = regiondata_dict[key].NnotConnect_agri
regiondata_dict[key].Ntotal_other = regiondata_dict[key].NnotConnect_other +\
regiondata_dict[key].N_human_waste_other +\
regiondata_dict[key].Nindustry_other +\
regiondata_dict[key].Nother_prim + regiondata_dict[key].Nother_sec +\
regiondata_dict[key].Nother_tert + Npipe_loss[key] +\
regiondata_dict[key].Nemiss_rural_grw +\
regiondata_dict[key].Nemiss_rural_other
regiondata_dict[key].Nother_NH3 = NnotConnected[key] * params.human_N_vol
regiondata_dict[key].Nother_soil = Npipe_loss[key] + \
regiondata_dict[key].NnotConnect_other - regiondata_dict[key].Nother_NH3 +\
regiondata_dict[key].Nemiss_rural_grw
regiondata_dict[key].Ptotal_agri = regiondata_dict[key].PnotConnect_agri
regiondata_dict[key].Ptotal_other = regiondata_dict[key].PnotConnect_other +\
regiondata_dict[key].P_human_waste_other +\
regiondata_dict[key].Pindustry_other +\
regiondata_dict[key].Pother_prim + regiondata_dict[key].Pother_sec +\
regiondata_dict[key].Pother_tert + Ppipe_loss[key] +\
regiondata_dict[key].Pemiss_rural_grw +\
regiondata_dict[key].Pemiss_rural_other
regiondata_dict[key].Pother_soil = Ppipe_loss[key] +\
regiondata_dict[key].PnotConnect_other +\
regiondata_dict[key].Pemiss_rural_grw
# Distribute the results on the grid.
if (params.grid_output == 1):
allocation_emission.allocation_emission(params,mask,isocode,isogrid,PopNumTot,popgrid,PopCon,PopUrb,\
NtotConnected_eff,PtotConnected_eff,NnotConnected_eff,\
PnotConnected_eff,NtotConnected,\
N_PrimRemoval,PrimTreatment,\
N_SecRemoval,SecTreatment,\
N_TertRemoval,TertTreatment,\
Nemiss_rural_sw,Pemiss_rural_sw,
PopNum_coast,popgrid_coast)
load_coast.calculate(params,mask,isocode,isogrid,popgrid_coast,Nemiss,Pemiss)
log.write_and_print(s.interval("Ready with allocation of N and P emission to grid cells."))
# Aggregation of the tabular information on another id-grid than basinid's
if (params.aggregationgrid != None):
make_index_grid.make_index_grid(params,mask,regiondata_dict,isogrid)
qq = os.path.basename(params.aggregationgrid)
filename = os.path.join(params.outputdir,params.scenarioname +"_" + str(params.year) + "_" + qq + ".csv")
mouth.aggregate(regiondata_dict,params.aggregationgrid,mask,\
filename,params.sep,keyname="isocode")
# Aggregate to global level
for key in regiondata_dict.keys():
try:
regiondata_dict["Total"].sum(regiondata_dict[key])
except KeyError:
regiondata_dict["Total"] = regiondata_dict[key].copy()
# Write all region information to output file:
fp = open(params.fileoutput_table,"w")
regiondata_dict[isocode[0]].write_header(fp,sep=params.sep)
write_dict.write_dict(fp_in=fp,filename=None,dic=regiondata_dict,headerkey="",sep=params.sep,lkey=0,ldebug=1,fatal=0)
fp.close()
log.write_and_print(s.total("Total run"))
del log
def read_popnum(params,mask,isogrid,isocode):
'''
Read population numbers from grid or from table.
If it is given on a table then a weighing grid is needed to allocate the population on the grid.
Table and grid is returned.
'''
# Scale the population of timescen so that the
# total population of the region is correct.
PopNum = data_class.get_data(params.filePopNum,year=params.year,sep=params.sep)
# PopNum is given in thousand people. So we have to multiply by 1000
for key in PopNum:
PopNum[key] *= 1000
# Add isocodes when they are not in list isocode
for key in PopNum:
try:
qq=isocode.index(key)
except ValueError:
# Isocode found which is not taken into account
print "ISOCODE " + str(key) +" is not used for number of people: " + str(PopNum[key]) + " people are missed."
PopNum[key] = 0.0
# Add popnum for missing isocodes. Population is zero
for key in isocode:
try:
qq=PopNum[key]
except KeyError:
# New iso code found.
PopNum[key] = 0.0
print "Number of people read : " + str(sum(PopNum.itervalues())) + " is found."
print_debug("PopNum in read_popnum",PopNum)
# Read population number grid which is used to scale the table population numbers information
popgrid_scale = ascraster.Asciigrid(ascii_file=params.filePopNumgrid,mask=mask,numtype=float)
# Read land area
landarea = ascraster.Asciigrid(ascii_file=params.landarea,mask=mask,numtype=float)
# Make new population number grid zero
popgrid = ascraster.duplicategrid(popgrid_scale)
popgrid.add_values(popgrid.length * [0.0])
# Make a dictionary of pointers for all the isocodes in the grid.
pointer_dict = {}
for icell in xrange(isogrid.length):
iso = isogrid.get_data(icell)
if (iso != None):
iso = int(iso)
try:
pointer_dict[iso].append(icell)
except KeyError:
pointer_dict[iso] = [icell]
for key in isocode:
if (PopNum[key] <= 0.):
print "WEIGHING: table population is ZERO in region: " + str(key)
continue
# Select population of the key country
weight_key = []
qmax_key = []
try:
pointer1 = pointer_dict[key]
except KeyError:
print "Key: " + str(key) + " is not found in the isogrid."
print "Table information of population numbers is changed for region: " + str(int(key)) +\
" from " + str(PopNum[key]) + " to 0.0"
continue
sumqmax = 0.0
sumweight = 0.0
for icell in pointer1:
pop = popgrid_scale.get_data(icell,0.0)
# Fill weighing data and max data
qmax_key.append(params.max_popdens * landarea.get_data(icell,0.0))
weight_key.append(pop)
sumqmax += qmax_key[-1]
sumweight += weight_key[-1]
# Warn for possible errors
if (sumweight <= 0.):
print "WEIGHING: grid population is ZERO in region: " + str(key)
if (len(weight_key) > 0):
# There are cells on the grid for this region. So make a uniform distribution
weight_key = len(weight_key) * [1.0]
sumweight = sum(weight_key)
if (sumqmax < PopNum[key]):
print "WEIGHING: PopNum ",PopNum[key], " is bounded by sumqmax",sumqmax, " in region: " + str(key)
# Grid these country population data with help of the popgrid_scale map
PopNum_reg = PopNum[key]
popnum_new = allocweighing.allocweighing(PopNum_reg,sumweight,weight_key,qmax_key)
if (key == debug_code):
sum1 = 0.0
for item in xrange(len(popnum_new)):
sum1 += popnum_new[item]
print "New population number: ",sum1
# Fill gridded result
sum1 = 0.0
for item in xrange(len(popnum_new)):
popgrid.set_data(pointer1[item],popnum_new[item])
sum1 += popnum_new[item]
# Check whether the table information has landed in the popgrid
if (math.fabs(sum1 - PopNum[key]) > 0.01):
print "Table information of population numbers is changed for region: " + str(int(key)) +\
" from " + str(PopNum[key]) + " to " + str(sum1)
print "Total number of people: " + str(sum(PopNum.itervalues())) + " is found."
return PopNum,popgrid
