Exploring global nitrogen and phosphorus flows in urban wastes during the
twentieth century
Morée, A.L.1,2, Beusen, A.H.W. 1,2, Bouwman, A.F. 1,2, Willems, W.J.2
[1] Department of Earth Sciences - Geochemistry, Faculty of Geosciences, Utrecht
University, P.O. Box 80021, 3508 TA Utrecht, The Netherlands
[2] PBL Netherlands Environmental Assessment Agency, PO Box 303, 3720 AH Bilthoven,
The Netherlands
Corresponding author: A.L. Morée (annemoree@gmail.com)
Supplementary Information
Contents
This document provides the reader with additional figures, tables and texts on the following:
SI 1. Global population
SI 2. Protein consumption
SI 3. Food wasting
SI 4. P consumption humans N:P
SI 5. Sewer and non-sewer loss corrections
SI 6. Treatment connection
SI 7. Recycling in agriculture
SI 8. Animals
SI 9. Industries
SI 10. Sensitivity and uncertainty analyses
SI 11. References
Table captions
Figure captions
The order of this supplementary information (SI) follows the main text.
SI 1. Global population
In a more densely populated area it is likely that riverine inputs are dominated by sewage
nutrient content [Vinnari and Frederiksen, 2010]; therefore population numbers (SI Figure 1)
are essential for the estimation of nutrient loadings.
SI 2. Protein consumption
N and P in wastewater [Campos and Sperling, 1996; Van Drecht et al., 2009] and the
environmental impact of dietary P [Li et al., 2011] are known to depend on total family
income [Campos and Sperling, 1996], per capita disposable income [Li et al., 2011] or GDP
[Van Drecht et al., 2009] although the dependence may vary from country to country.
However, we found that variations in protein consumption for the early 20th century on a
1
country basis could not be explained by GDP at the county scale, particularly not in
developing regions.
For countries with incomplete data, missing years were completed using population weighed
regional (SI Figure 2) averages (SI Figure 3). Countries with missing protein consumption
data for some years are: Afghanistan, American Samoa, Andorra, Anguilla, Aruba, Bahrain,
Bhutan, Br.Virgin Is., British Indian Ocean Territory, Cayman Islands, China (Hong Kong
SAR), China (Macao SAR), Christmas Isl., Cocos (Keeling) Isl., Cook Islands, Faeroe
Islands, Falkland Is., Gibraltar, Greenland, Guadaloupe, Guam, Heard and McDonald Isl.,
Holy See, Iraq, Liechtenstein, Marshall Islands, Martinique, Micronesia (Fed. States of),
Monaco, Montserrat, Nauru, Niue, Norfolk Isl., Northern Mariana, Oman, Palau, Papua New
Guinea, Pitcairn, Puerto Rico, Qatar, Reunion, San Marino, Singapore, Somalia, St. Helena,
St.Pierre & Miq., Svalbard & Jan Mayen Isl., Taiwan, Tokelau, Tonga, Turks & Caicos,
Tuvalu, U.S. Virgin Is., Wallis & Futuna, Western Sahara.
Although inter-regional differences remain close to 30 g protein cap-1 d-1, differences between
countries can be large; the largest difference (lowest and highest value for protein
consumption) in the dataset is 130 g protein cap-1 d-1 between Occupied Palestinian Territory
(11 g protein cap-1 d-1) and Iceland (141 g protein cap-1 d-1) in 1986 (SI Figure 3) [FAO,
2012].
SI 3. Food wasting
A recent study by the Swedish Institute for Food and Biotechnology and the FAO based on
the Food Balance Sheets of the FAO [FAO, 2012] indicated that at present ~40% of total
losses between food production and consumption occur at the retail and consumer level and
strongly depend on the type of food consumed [SIK/FAO, 2011]. The SIK/FAO data form the
basis of the average retail and household food loss corrections made in this study (SI Table 2).
For different food groups, the actual waste loss is between 0.5 and 17% for retail wasting and
between 0.1 and 33% for household wasting [SIK/FAO, 2011]. In this study we use averages
for all food groups (SI Table 2). Higher food losses exist as well; for example household
wasting of up to 50% of leafy salads. It is therefore stressed that uncertainties are large
[Parfitt et al., 2010].
SI 4. P consumption humans N:P
Dairy products, meat, and fish are particularly important sources of phosphorus. Since these
are also the major nitrogen-containing foods, a close correlation exists between protein
consumption (N consumption) and P consumption (for example for the U.S.A. as discussed
in the main text, see SI Figure 5).
In recent years, total P consumption increased by the various polyphosphate food additives
(added to improve consistency and appearance of foods) and P in most soft drinks in the form
of phosphoric acid [J. Higdon, 2003; Takeda et al., 2012]. P-to-protein ratios were reported to
have increased 28% and even up to almost 100% for processed foods compared to additivefree products [Sherman and Mehta, 2009]; it is therefore likely that in the future N:P ratios in
food will decrease.
2
SI 5. Sewer and non-sewer loss corrections
19th century sewer pipes, if present at all, were often made from bricks and mortar with a
relatively open structure, thereby permitting leakage to the soil and groundwater. Only in the
later 19th century less permeable concrete pipes were used in the cities in industrialized
countries around the world. However, also far into the 20th century, the techniques and
materials used are still being improved [Jones, 1998].
Therefore, sewer losses through leakage were considered in our model. Exfiltration studies on
current sewer pipes show large variations in leakage measurements. For example, ~1% up to
38% of waste water is reported to leak into the groundwater according to Rutsch [2006] and
Rutsch et al. [2005]. Low leakage fractions may be due to sealing of pipes with sewage and
sewage sediments thereby preventing leakage [Blackwood et al., 2005]. At present, high
concentrations of nutrients are often found in urban soil- and groundwater and these have
been caused by leakage from sewer systems, septic tanks, pipes, landfills, urban fertilizer use,
gas works and airplane de-icing leakages of nutrients [Wakida and Lerner, 2005].
A quantification of the sewer leaks is difficult due to the large variations in measurements and
the lack of data on the global scale. We assumed a constant global sewer pipe leakage of 5%
for both N and P. In addition, settling of nutrient-rich particles, biological degradation and
volatilization during transportation through the sewer pipes are assumed to cause a loss of 5%.
Hence, a total correction of 10% ‘sewer losses’ was made for both N and P flows for
households with a sewer connection.
It is assumed that 20% of all N excreta from persons lacking a sewer connection is lost
through volatilization. Although non-sewered human excreta are often kept in more confined
conditions (tons/pipes/buckets/cesspits/pit latrines), thereby limiting the volatilization of N,
the N in excreta are still found to volatilize at a rate similar to animal waste volatilization
losses of 20% from animal houses [Kimura et al., 2005]. The process can be of major
relevance to ammonia emissions, as evidenced by present emissions from human excreta in
Chinese urban areas that have an important contribution to urban air pollution there [Huang et
al., 2012].
SI 6. Treatment connection
Treatment of wastewater from households or industries (SI Figure 7) can occur locally (septic
tanks, cesspits, etc.) or by a centralized system (sewer pipe network). The method selected by
a city or region depends on costs, tradition, scientific theories, public opinion and urban
development [Burian et al., 2000]. Since making changes in these systems is complex, time
consuming and expensive, once a method was chosen it often remained in use for a long
period. In most parts of the world, public centralized system wastewater collection in sewage
systems and wastewater treatment seem to be the preferred methods since the late 19th
century.
3
Early 20th century urban wastewater nutrient recycling in agriculture (SI 7) was often
regarded as a way of treatment. For example, during the first decennia of the 20th century land
treatment and sewage farming were practiced at a large scale in China [Li et al., 2011],
thereby decreasing surface water nutrient discharges. In Germany, infiltration beds treated a
major part of Berlins wastewater in the early 20th century [Shuval, 1986]. Since many of these
examples combine treatment with crop production, our model considers all nutrient flows to
agriculture as recycling, and not as a form of treatment.
SI 7. Recycling in agriculture
The classification was made based on IMAGE world regions (SI Figure 4) and inferred from
mostly quantitative descriptions of wastewater recycling in agriculture as described in the
main text. The year 1900 was used as a base year, forming a starting point from which the
development in agricultural recycling was calculated for the rest of the 20th century.
SI 8. Animals
SI 8.1 Non-equidae urban livestock
Although quantification is difficult, countries found to have low equidae stocks may have had
higher numbers of other traction animals. Besides, non-equidae animals were kept as an
additional food source of milk and meat. Since one horse or cow excretes about the same
amount of N and P as 10 humans, their contribution to urban nutrient flows can be
considerable. Dairy cattle kept for industrial production of milk and fed residues of beer
breweries in urban areas was common in early 20th century Copenhagen, various cities in the
U.S.A., Mexico City, and even up to today in Dar-es-Salaam [FAO, 2001]. It has been
estimated that ~50% of the inhabitants of the city of Mwanza (Tanzania) and 37% in Dar-esSlaam had dairy cows at the end of the 20th century [FAO, 2001]. Also free roaming
chickens, goats, sheep and pigs may attribute to urban livestock excretion, though many of
these animal wastes are thought to end in the soil and atmosphere. Since total animal excreta
in urban areas has a small influence on surface water N and P discharges (main text Figure 4),
the effect of excreta from non-equidae livestock is found to be low, in particular since the
second half of the 20th century as described in the main text results and discussion.
SI 8.2 Equidae N:P consumption ratio
The N:P ratio in feed for equidae differs from the ratio of 10 kg N :1 kg P used for people in
this study. Generally, livestock requires relatively more P in the feed due to the low
digestibility. Average animal feed (horses, cattle, goats, chickens, etc.) over the several subbasins of the Red River Basin (Vietnam and China) has a N:P ratio of about 8:1,
corresponding to the N:P ratio in grass of 8:1 [Quynh et al., 2005]. An N:P ratio of ~7.5:1 was
estimated from the Dutch CVB [CVB, 1996] as the approximate average feed consumption of
horses (different types of grass, hay and straw). According to the composition of grass and
hay as estimated by [Kemme et al., 2005], horse feed has a N:P ratio of approximately 7:1.
Although different studies thus report different N:P ratios, the relatively higher feed
4
consumption of P compared to human consumption indicates that the human N:P ratio of 10:1
is not applicable here. A ratio of 7:1 was used in this study to estimate animal P excretion
based on literature values for animal N excretion (Table SI 1).
In the early 20th century, in some cases feed crops for the urban animals were irrigated with
wastewater (Melbourne, Australia and Linköping, Sweden; USA, Europe, Mexico City and
Berlin) [Schmid Neset, 2005; Shuval, 1986].
SI 9. Industries
Technological improvements of most industry types reduced nutrient waste emissions over
the past 100 years, but some techniques (such as in breweries) have remained very similar
[Billen et al., 1999]. Furthermore, the increasing amount and level of treatment of wastewater
reduced actual nutrient loading to surface water over the 20th century. Due to the close
connection with water use (rinsing, flushing, transporting, cooling, etc.), it is likely that all
industrial nutrient flows (except for a small part that was treated) were directly discharged to
surface water in the early 20th century. In addition, industries were likely to be concentrated in
urban areas [Plaats, 1902], since workers are needed and because it is convenient to locate the
production close to the potential consumers (the urban dwellers).
For the current situation, Luu et al. [2012] showed in a regional study on the Red River Delta
(northern Vietnam), that industrial wastewater emission in urbanized areas constitutes on
average ~11% of domestic wastewater emission of nitrogen and ~19% of phosphorus. Quynh
et al. [2005] estimated for the Red River Basin (China and Vietnam) that at present industrial
N and P inputs are both ~10% of domestic inputs. These estimates correspond to a study on
the Nete River (Belgium) where gross industrial loading of N was estimated to be ~11% of
household gross N flows [Bixio et al., 2005]. In Sub Saharan Africa, domestic emissions to
surface water are the major urban source of nutrients at present, but quantitative estimates are
not available [Nyenje et al., 2010].
A Dutch study in 1922 stated that ‘Industry is the largest source of water pollution, even
larger than communities.’ [Gelder, 1922], thereby summarizing most other findings of
historical studies. For Western Europe, Billen et al. [1999] showed that for the Seine and
Zenne river basins high river nutrients loads could be explained by industry, and not by
sources of domestic origin. Late 19th century Zenne (Belgium) nutrient loadings were also
found to be dominated by industrial loadings over domestic ones [Garnier et al., 2012]. Billen
et al. [1999] concluded that in 1896/1897, industrial N and P waste production exceeded
domestic waste production by a factor of 1.1-4. An earlier report from the same author
estimates that industry accounted for ~2.7 times domestic emissions in 1897 for the Zenne
river Basin (Belgium) [Billen et al., 1995]. However, domestic emissions are likely to be
overestimated since no corrections were made for recycling of human wastes in agriculture; in
addition, the estimate for industrial emissions was low (using high nutrient losses through
settling) [Billen et al., 1995]. This suggests an even larger difference between industry and
domestic nutrient flows than the factor of 2.7. The 1999 study of Billen et al. showed that in
1986 for the Zenne basin industrial N waste was only 43% of domestic and P waste was 18%
5
of domestic, where in the Seine basin in 1991-1994 industrial N waste was 10% of domestic
and industrial P waste was 21% of domestic waste.
SI 10. Sensitivity and uncertainty analyses
SI 10.1. Sensitivity analysis
Calculation of the SRC values for all model input parameters for surface water N and P
discharge and agricultural recycling of N and P was done using LHS as described in the main
text. Default ranges chosen to determine the sensitivity of the different parameters are
provided in SI Table 3. The statistically significant (t-value > 2 or < -2) SRC-values are
provided in Tables 4 and 5. SRC is independent of units, scale and size of the parameters. A
positive SRC value indicates that increasing that parameter value will cause an increase in the
calculated output, while a negative value indicates a decrease in output caused by that
parameter increase. The sum of squares of SRC values of all parameters equals the coefficient
of determination (R2), which for a perfect fit equals 1. Hence, SRC2/R2 yields the contribution
of a model parameter to model output. For example, a parameter with SRC = 0.1 adds 0.01 or
1% to model output in case R2 equals 1.
1. Sensitivity of N and P discharge to surface water.
The sensitivity analysis shows that human population numbers, sewer connection, N as a
fraction of protein and protein consumption are important parameters for surface water N and
P discharge for all years (SI Table 4). Surface water discharge is also sensitive to the
industrial nutrient flows in 1900 and tertiary treatment in 2000 for N and P and to the N:P
ratio in food for P for all years.
2. Sensitivity of agricultural recycling.
Regarding recycling of N and P (SI Table 5) in agriculture, important parameters are the
presence of sewer connection, urban population and the fraction of human non-sewered
wastes recycled in agriculture for all years. For the year 1900, the maximum number of
animals per inhabitant and total population are also important for the model output of
agricultural recycling of N and P. These observations can be explained by the fact that urban
population determines the urbanization fraction together with total population. The latter is
also used to calculate urban animal numbers (main text section 2.3). Urbanization fraction
determines whether the maximum of 1 animal per 20 inhabitants is reached (as has shown to
be important for agricultural recycling in 1900). Sewer connection determines whether human
wastes can end in agriculture (see article figure 1a) and is therefore relevant for all years. The
maximum number of animals per inhabitant becomes unimportant for the agricultural
recycling model output due to the dominance of human excreta and small animal stocks as a
result of F.
SI 10.2. Uncertainty analysis
Table 6 provides uncertainty ranges as applied to each of the most sensitive parameters to
obtain the results in main text Figure 5 a,b and SI Figure SI 11 a,b.
6
For agricultural recycling (Figure SI 11 a,b), the observations made for surface water
discharge in the main text apply. In contrast to surface water discharge however, Europe is
only a minor contributor to agricultural recycling after 1900 and therefore also less important
for global uncertainty. For recent years, East and South Asia are the major contributors and
uncertainties for agricultural recycling of N and P.
SI 10.3. Further uncertainties
Further uncertainties are related to the representation of nutrient flows in the model. For
example, CBS [2013] suggests that our industrial N and P nutrient flows and domestic P flows
may be underestimated for The Netherlands for the last decades of the 20th century. This may
be due to our assumption that industries are located in urban areas only.
A number of nutrient sources were not included in the model such as i) other solid household
refuse and sewage as reported for Paris [Barles and Lestel, 2007] and ii) urban livestock other
than equidae [FAO, 2001]. Early 20th century agricultural recycling uncertainty (SI Figure 11
a,b) is caused by animal stocks which are the major contributors in that period (Figure 4).
Even if incorporation of non-equidae species would cause a doubling of the nutrient flow
from urban animals, it would be relevant only for agricultural recycling in the early 20th
century (Figure 4).
Furthermore, the modeled urban system (Figures 1 a,b,c) may not be correct for some regions.
Examples are i) recycling of urban wastes in aquaculture, particularly in Asia [WHO, 1989],
ii) recycling of treated wastewater in agriculture [Drechsel et al., 2010; Schmid Neset et al.,
2010], for example in Saudi Arabia, Peru, Israel and Jordan [Olson, 1987], iii) recycling in
agriculture of untreated sewered wastewater such as in early 20th century Paris [Billen et al.,
1995] and present-day India [Hofstedt, 2005]. In addition, on-site sanitation systems (e.g.
septic tanks) may be important in some countries such as Japan [Gaulke, 2006] and the
omission of the incineration of urban wastes (as reported for Paris [Barles, 2007]) may have
caused overestimation of urban waste flows. The effect of including points ii) and iii) could
be significant. In 2000, ~27% of N and ~32% of P in sewers is removed through treatment
globally. If 10% of these removed nutrients were brought to agriculture instead of ‘other’,
~40% more N and ~50% more P would go to agriculture in 2000. In 1900, if 10% of global
sewer N and P were led to agriculture instead of surface water (as reported for Paris), ~50%
more N and ~30% more P would be led to agriculture globally.
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9
Table captions
SI Table 1. List of time-independent model input parameters. Values are fractions unless
stated otherwise.
SI Table 2. Food losses and wasting at the retail and household level [SIK/FAO, 2011].
Values for years in between are obtained by linear interpolation. Globally, losses in
1900 are set equal to the lowest global loss percentage in 2010.
SI Table 3. Sensitivity ranges applied to determine those model input parameters that the
model output of surface water discharge, agricultural recycling or ‘other’ flows was
most sensitive to. When multiplication was used to determine sensitivity, 0.8 and 1.2
were used as minimum and maximum value respectively. Otherwise, addition was
used except for the maximum number of animals per inhabitant were the actual model
input minimum and maximum values were defined. For 1900, all treatment variation
was excluded from the model; for 1950 only secondary treatment and tertiary
treatment were excluded, for 2000 all parameters were varied according to this table.
SI Table 4. SRC values for all model input parameters for surface water discharge of N and P
for the years 1900, 1950 and 2000. Empty cells indicate non-significant values.
Positive bold values are SRC-values with a positive effect on the output of more than
more than 4% (SRC>0.2), while negative bold values are SRC-values with a negative
effect on the output of more than -4% (SRC<-0.2).
SI Table 5. SRC values for all model input parameters for agricultural recycling of N and P
for the years 1900, 1950 and 2000. Empty cells indicate non-significant values.
Positive bold values are SRC-values with a positive effect on the output of more than
more than 4% (SRC>0.2), while negative bold values are SRC-values with a negative
effect on the output of more than -4% (SRC<-0.2).
SI Table 6. Uncertainty ranges of the most sensitive parameters as determined from SI Table
4 and 5. To apply the uncertainty ranges to the model, multiplication was used for
protein consumption, total population, industry fraction, N fraction of protein, urban
population and N:P. For sewer connection, agriculture fraction and the industry loss
fractions addition was used; the maximum number of animals per inhabitant was set to
a minimal an maximum value instead of addition or multiplication. Tertiary treatment
was absent in 1900 and 1950 and therefore excluded.
10
Figure captions
SI Figure 1. Global urban, rural and total population during the 20th century in billions of
people based on Klein Goldewijk et al. (2010).
SI Figure 2. Classification of the world in 10 world regions (and Greenland and Antarctica) as
used for the model outputs at continental scale. Greenland and Antarctica were
excluded from the results and discussion in the main text.
SI Figure 3. 20th century protein consumption in g cap-1 d-1 for 11 world regions (SI Figure 2).
Data from 1960 onwards are based on FAO Food Balance Sheet statistics for most
countries [FAO, 2012].
SI Figure 4. Regions used in the model for the calculation of population-weighed regionaverages if data were absent. The classification is based on the regions used in the
IMAGE model [Bouwman et al., 2006].
SI Figure 5. Derivation of the 10:1 N:P ratio used in the model. A 20th century protein
consumption dataset for the USA multiplied with 0.16 to obtain N consumption and
0.1 to obtain P consumption shows a good fit to actual P consumption [USDA, 2012].
SI Figure 6. 20th century sewer connection as a fraction of the total population having a sewer
for 11 world regions (SI Figure 2). Data from 1970 onwards are taken from Van
Drecht et al. (2009).
SI Figure 7. 20th century treatment coverage expressed as a fraction of people with a sewer
connection having primary, secondary or tertiary treatment for 11 world regions (SI
Figure 2). Data from 1970 onwards are taken from Van Drecht et al. (2009). a)
Primary treatment (with 10% N (10%-25%) removal and 11% (10%-29%) P removal),
b) Secondary treatment (with 35% N removal (35%-45%) and 45% (45%-50%) P
removal) and c) Tertiary treatment (with 80% N removal (60%-80%) and 90% (90%94%) P removal). Variations in nutrient removal percentages are caused by
efficiencies of the respective countries to perform the level of treatment.
SI Figure 8. World classification for 1900 in 4 levels of agricultural nutrient recycling. Low
recycling (10%), medium recycling (40%) and high recycling (70%) are the classes of
recycling of non-sewered human N and P in agriculture in 1900.
SI Figure 9. Sum of the stocks of urban horses, donkeys and mules in millions of animals for
11 world regions (SI Figure 2) based on formula 1 in article section 2.3.1. The shape
of the lines is determined by the interaction of urbanization [Klein Goldewijk et al.,
2010], the total equidae dataset [FAO, 2008; Mitchell, 1993a, b; 1998] and factor F
(article section 2.3.1, equation 1).
SI Figure 10. Population-density based world distribution [Tobler et al., 1995] of surface
water loading of N for the years a) 1900, b) 1950 and c) 2000.
SI Figure 11. Total mass flows from urban areas to agriculture for 10 world regions and
globally in Tg N yr-1 (a) and Tg P yr-1 (b). Uncertainties ranges (5% and 9%
percentiles) are included based on the methods described in section 2.5 and SI 10.
11