1 Supporting Information
2 Intensification of phosphorus cycling in China since the 1600s
3 1 Temporal pattern of phosphorus cycle in China
4 We use the substance flow analysis (SFA) method (1, 2), which is based on the mass
5 balance principle, to depict phosphorus (P) cycle within China over the last four
6 centuries. This systematic approach has been widely applied to characterize
7 anthropogenic cycles of elements at different geographical scales based on static
8 analyses, reflecting part of elemental flows at a specific time point (3). In this study, we
9 take account of both major natural and anthropogenic activities to characterize the entire
10 P cycle over a time interval of over 400 years.
11 1.1 System boundaries
12 The geographical boundaries of this study are the territory area of China, excluding Hong
13 Kong, Macao and Taiwan. It is due to limited data availability since these regions were
14 once colonies of other countries for certain periods and are now under local jurisdictions.
15 The temporal scale is defined as 1600s-2012 for the following reasons: 1) Population in
16 China began to increase significantly since the 1600s (hereinafter referred to as a decade).
17 2) Around the 1600s, foreign crop types including maize and tubers were introduced into
18 China and thereafter the cropping systems in traditional agriculture became consistent
19 with the present situations. 3) In earlier times, human intervention was generally weaker
20 and P cycle was more stable as compared to nowadays. Possible natural calamities and - 1 -
21 human disasters that could cause change in P cycles have relatively low occurrence
22 frequencies (less than once per century). 4) The availability and reliability of data.
23 This study covers the past four centuries that witness the rise and fall of several Chinese
24 dynasties (4, 5), starting from the traditional Chinese society of agricultural base during
25 the 1600s when 120 million population were under the governance of the glorious Ming
26 Dynasty. The Ming regime deteriorated due to long periods of wars with other nations,
27 increasing power of eunuchs and continuous famine, and was finally taken over by the
28 Qing nomadic people from northeast China in the 1640s, when the domestic population
29 rapidly declined to less than 100 million. The multi-cultural Qing Empire dominated for
30 almost three centuries and boosted the national population to over 400 million at the
31 beginning of 1900s. The dramatic rise in population could be attributed to two reasons,
32 namely the long period of peaceful and stable regime in the 18th century and the
33 successful cultivation of new types of crops from America, such as peanuts, tubers and
34 maize. However, more and more social conflicts had emerged since the 1840s when the
35 First Opium War broke out. Although revolutionary measures were taken to save the
36 dying Qing Dynasty, it was eventually overthrown in 1911, which ended two thousand
37 years of Chinese feudal monarchy. China then shortly stayed in the turbulent Republican
38 Era (1912-1948), during which regimes waxed and waned through regional militarism
39 (1912-1927), the prosperous Nanjing Decade (1928-1937), the Second Sino-Japanese
40 War (1938-1945) and post-war rehabilitation (1946-1948). Ever since the Chinese
41 Communist Party took power in 1949, the national economy has developed speedily and
42 the living conditions have been greatly improved, especially after the economic reform
43 and opening-up policy in the late 1970s.
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44 1.2 Hierarchy of the Chinese phosphorus cycle model
45 The P biogeochemical cycle consists of P flows through different environmental media
46 and human interventions. Naturally, P is absorbed by biota through atmospheric
47 deposition, weathering and erosion, and finally becomes submarine sediments after river
48 transportation. It usually takes tens of millions years of tectonic uplift until it can be used
49 again. However, the increase of human populations and their demands for food have
50 accelerated P cycling. P rocks are mined and produced into various P-associated chemical
51 products to sustain plants, animals and human beings.
52 In this study, a total of fourteen major compartments are identified throughout P lifetime
53 metabolic process within the Chinese territory and denoted with Ni (i=1,…, 14, Fig. S2)
54 (6). The natural compartments include atmosphere (N1), non-arable land (N2), inland
55 waters (N3), and marine waters (N4). Anthropogenic P flows go through mining (N5),
56 chemical production (N6), cultivation (N7), animal husbandry (N8), aquaculture (N9),
57 agricultural product processing (N10), human consumption (N11), wastewater treatment
58 (N12) and solid waste disposal (N13). P-containing commodities are imported or exported
59 through international trade (N14).
60 Atmosphere (N1) is defined as the atmospheric boundary layer, the lowest part of the
61 atmosphere that is usually around 1 kilometer (km) deep, since its behavior is directly
62 influenced by the contact with the ground surface within China’s territory. The air above
63 this layer, in which the large-scale atmospheric circulation happens, is excluded because
64 of the negligible effect of the earth's surface.
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65 Non-arable land (N2) is the part of the terrestrial land of China which cannot be used for
66 arable farming, including forest, grassland, construction land and unused land. However,
67 some non-arable land can be turned into arable land with advanced agricultural
68 techniques, such as installing irrigation facilities.
69 Inland waters (N3) primarily refer to a variety of freshwaters that are located within
70 China’s land boundaries, including rivers, lakes, reservoirs, wetlands and other water
71 storage systems. On the contrary, marine waters (N4) include seas and oceans under the
72 jurisdiction of China. Fishing in the inland and marine waters can catch natural aquatic
73 products, including fish, shrimp, crab, shell, seaweed, etc.
74 Mining (N5) is determined as the economic extraction of phosphate rocks from the
75 lithosphere, while P contained in other mineral resources, such as fossil fuels and metals
76 where P is regarded as impurities, is not taken into account. Mining can be further
77 divided into two sub-compartments: mining (N5.1) and beneficiation (N5.2). Raw P ores
78 are mined from lithosphere containing different amounts of phosphorus pentoxide (P2O5).
79 Phosphate rocks with the grade of P2O5 over 30% can be directly exported or used by P
80 chemical industries (N6), while the others need to go through beneficiation to increase the
81 grade of P2O5.
82 Chemical production (N6) includes the primary consumption activities of phosphate
83 rocks, namely fertilizer production (N6.1), feed additive production (N6.2), and elemental P
84 production (N6.3), while less than 10% of the mined phosphate rocks are used for other
85 purposes and not considered in this study. Elemental P is further utilized as the raw
86 material to produce various P containing chemicals with different manufacturing
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87 techniques, production scales and pollution levels. Although China holds a dominant
88 position in the international market of fine phosphorus chemical products, this study
89 considers only two important applications in detail, namely to produce organophosphate
90 pesticides (N6.3.1) and synthetic detergents (N6.3.2), where high amounts of elemental P are
91 consumed and P-containing wastes are generated. Elemental P used to produce other
92 various types of P for metal-surface treatments, steel production, wastewater treatment, etc.
93 or as flame retardants, plasticizers, food and feed additives, etc. is simplified as an input to
94 other chemicals (N6.3.3) but not further expanded in this study mainly because of its
95 relatively small contributions to the total P input and data limitation as well.
96 Cultivation (N7) in this study refers to the crop cultivation activities in the arable land of
97 China. Arable land is the land actually being cultivated to grow crops, including the area
98 of land with crop harvests, fallows, newly reclaimed land and abandoned land area within
99 three years. The upper soil layer (20 cm) is considered to be part of the cultivation
100 compartment since it is the ploughing depth where plants take up nutrients (7). Crop
101 categories include grains (rice, wheat, maize, millet, sorghum, etc.), beans, tubers, cotton
102 fibers and seeds, oil crops (peanut, rapeseed, sesame, sunflower, etc.), bast fibers,
103 sugarcane, sugar beet, tobacco and vegetable.
104 Animal husbandry (N8) here refers to the care and breeding of terrestrial animals,
105 including pig, cattle, sheep, poultry, horse, mule, donkey and rabbit, with no
106 differentiation of the feeding manners and waste disposals between household and
107 industrial livestock production. Aquaculture (N9) involves cultivating freshwater and
108 saltwater products (fish, shrimp and crabs) under controlled conditions in the inland and
109 marine waters, contrasting with the harvests of natural aquatic products through fishing. - 5 -
110 Agricultural product processing (N10) considers two major activities that demand for
111 harvested agricultural products, namely food processing (N10.1) and feed processing
112 (N10.2), whereas all the other less critical utilizations are simplified in compartment N10.3.
113 It should be noted that this study does include the food processing stage by covering the
114 first stage of processing of crops (husking, flouring, etc.) and animals (slaughtering).
115 However, the higher stages of processing are excluded due to the unavailability of
116 detailed production data for various forms of processed products.
117 Human consumption (N11) represents a group of human activities that residents consume
118 P-containing products and produce a multitude of wastes and wastewater. Due to rural
119 and urban residents’ discrepancy in consumer behaviors as well as waste disposal, we
120 analyze rural and urban households separately.
121 Wastewater treatment (N12) is the process of removing contaminants from industrial and
122 household wastewater in a centralized wastewater treatment plant. Solid waste disposal
123 (N13) is the process of treating solid wastes from various sources and primarily includes
124 stockpiling, landfill, compost and incineration.
125 International trade (N14) refers to China’s exchange of P-containing commodities with the
126 sum of all other economies in the world. Note that the downstream utilization of exported
127 commodities and the upstream production of imported commodities in other countries are
128 excluded.
129 1.3 Calculation methods
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130 According to the basic SFA equation of “input = output + change in stock”, mathematical
131 methods of all P flows can be categorized into three types (8): 1) Independent calculation,
132 with P flows calculated by multiplying historical activity data with P intensity
133 parameters; 2) Dependent calculation, with P flows derived from other flow results; and
134 3) Systemic balance, with the unknown flows or stocks taken into consideration by
135 balancing all the known flows of a subsystem. One advantage of this method is to first
136 calculate the flows independently as much as possible to reduce their interactions with
137 other flows. And then, it uses the balancing concept to calculate the rest unknown flows.
138 Although each P flow can be quantified either as an output flow from the source
139 compartment or as an input flow into the sink compartment, we determine the final
140 calculation methods of the P flows by comparing possible alternatives and consistently
141 express all the mathematical equations as the output flows from their source
142 compartments. For each output P flow, the destination compartment is also determined.
143 Computation of all the P flows in the Chinese P cycle model over the last four centuries is
144 programmed simultaneously in Microsoft Excel spreadsheets and R software (9) to avoid
145 miscalculations, in the sequential order of independent calculations, dependent calculations
146 and systemic balances. Detailed equations are described in the following sections. The code
147 used in this study is freely available at GitHub (https://github.com/ctfysh/Chinese_P_Cycle)
148 and Yuan’s group website (http://hjxy.nju.edu.cn/yuanzw/en/a/News/2016/0108/260.html).
149 1.3.1 Atmosphere (N1)
150 The major output flow from N1 is atmospheric deposition to land and waters in China,
151 calculated as:
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152 AD AD AD AD (1.1) OOOOO1 1 2 1 3 1 4 1 7
153 where
AD TA IW AL AD 154 O12 DDDP 1 (1.2)
155 AD IW AD (1.3) O13 DP1
156 AD MW AD (1.4) O14 DP2
157 AD AL AD (1.5) O17 DP1
AD 158 and O1i is P output to compartment Ni (i=2,3,4,7) in the form of atmospheric deposition;
159 DTA is the total land area of China, including inland waters but excluding marine waters;
160 D IW is the area of Chinese inland waters; D MW is the area of Chinese marine waters; D AL is
AD AD 161 the annual area of Chinese arable land; P1 is P deposition rate on inland; P 2 is P
162 deposition rate on marine waters.
163 1.3.2 Non-arable land (N2)
164 The major output flows from N2 include wind erosion, weathering, runoff and leaching,
165 phosphorus ores, pastures and woods, calculated as:
WE RL PO WT WT PS WD 166 OOOOOOOO2 21 23 25 22 27 28 210 (2.1)
167 where
WE TA IW AL WE 168 O21 DDDP (2.2)
RL TA IW AL RL 169 O23 DDDP (2.3)
PO Balancing 170 ON2 5 5 (2.4)
WT TA IW AL WT 171 O22 DDDP (2.5) - 8 -
172 WT AL WT (2.6) O27 DP
PS Balancing (herbivores) 173 ON2 8 8 (2.7)
174 WD WD WD WD (2.8) O2 10 DPP12
175 and WE is P output to compartment N in the form of wind erosion; RL is P output to O 21 1 O23
176 compartment N in the form of leaching and runoff; PO is P output to compartment N in 3 O25 5
WT 177 the form of raw P ores; O 2i is P output to compartment Ni (i=2,7) in the form of rock
PS 178 weathering; O28 is P output to compartment N8 in the form of pasture fodders for
WD 179 herbivores( including cattle, sheep, rabbit, donkey, mule and horses); O2 10 is P output to
WE 180 compartment N10 in the form of wood products; P is the annual P loss rate by wind
181 erosion; P RL is the annual P leaching and runoff rate from non-arable land; PWT is the
WD WD 182 annual P weathering rate; D is the annual wood production; P1 is the average wood
WD TA IW MW 183 density; P 2 is the average P content in wood. The explanations of D , D , D and
184 D AL can be found in Section 1.3.1.
185 1.3.3 Inland waters (N3)
186 The major output flows from N3 include P transfer through rivers to open ocean and
187 naturally grown freshwater products, calculated as:
RT NFW 188 OOO3 3 4 3 10 (3.1)
189 where
AD RL WW,nt WW ,nt WW ,nt WW ,nt WW ,nt RT OOOOOOO1 3 2 3 5.2 3 6.1 3 6.2 3 6.3 3 6.3.1 3 RT 190 (3.2) O34 WW,nt RF LH AE ,nu NL WW ,nt WW ,nt EF P OOOOOOOO6.3.2 3 7 3 7-3 8 3 9 3 10.1 3 11 3 12 3
- 9 -
191 NFW NFW FW (3.3) O3 10 DP
192 and RT is P output to compartment N in the form of riverine transfer; NFW is P output O34 4 O3 10
193 to compartment N10 in the form of naturally grown freshwater products; Oi3 is P input to
RT 194 compartment N3, illustrated in the context; P is the proportion of P contained in rivers
195 into oceans to the total P inputs into inland waters; D NFW is the annual production of
196 naturally grown freshwater products; P FW is the P contents in freshwater products.
197 1.3.4 Marine waters (N4)
198 The major output flows from N4 include sea spray and naturally grown seawater products,
199 calculated as:
SS NSW 200 OOO4 4 1 4 10 (4.1)
201 where
MW SS D SS 202 O41 GMW P (4.2) D
203 NSW NSW SW (4.3) O4 10 DP
SS NSW 204 and O 41 is P output to compartment N1 in the form of sea spray; O4 10 is P output to
GMW 205 compartment N10 in the form of naturally grown seawater products; D is the area of
206 global marine waters; P SS is the global P load in sea spray; D NSW is the annual production
207 of naturally grown seawater products; P SW is the P contents in seawater products. The
208 explanations of D MW can be found in Section 1.3.1.
209 1.3.5 Mining (N5)
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210 The major output flows from N5.1 include natural phosphate rocks (for net export or
211 domestic consumption) and gangues (defined here as the waste rocks or materials that are
212 mixed with P rocks and discarded as wastes), calculated as:
PR PR PR GG 213 OOOOO5.1 5.1 14 5.1 5.2 5.1 6 5.1 2 (5.1)
214 where
215 PR PR,ne PR (5.2) O5.1 14 DP
PR PR PR,ne PR BF 216 O5.1 5.2 DDPP (5.3)
PR PR PR,ne PR BF 217 O5.1 6 DDPP 1 (5.4)
GG PR GG GG 218 O5.1 2 DPP12 (5.5)
PR 219 and O5.1 i is P output to compartment Ni (i=5.2, 6, 14) in the form of natural phosphate
220 rocks; GG is P output to compartment N in the form of gangues; PR,ne is the annual net O5.1 2 2 D
221 export of raw phosphate rocks; D PR is the annual production of raw phosphate rocks
PR BF 222 (containing 30% P2O5); P is the average P content in raw phosphate rocks; P is the
GG 223 beneficiation proportion of raw phosphate rocks; P1 is the gangue generation coefficient
GG 224 from mining; P 2 is the average P content in gangues.
225 The major output flows from N5.2 include P concentrates, tailings and wastewater,
226 calculated as:
PR TL WW,nt WW ,ft 227 OOOOO5.2 5.2 6 5.2 2 5.2 3 5.2-12 (5.6)
228 where
PR PR CR 229 OO5.2 6 5.1 5.2 P (5.7)
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WW,nt 142 PR WWG WW 230 - (5.8) OO5.2 362 30% 5.2 6 PP1 1
WW,ft 142 PR WWG WW 231 (5.9) OO5.2 1262 30% 5.2 6 PP1
TL Balancing 232 ON5.2 2 5.2 (5.10)
PR TL 233 and O5.2 6 is P output to compartment N6 in the form of P concentrates; O5.2 2 is P output
WW ,nt 234 to compartment N2 in the form of tailings; O5.2 3 is P output to compartment N3 in the
235 form of wastewater discharged from beneficiation into local water environment with no
WW ,ft 236 treatment; O5.2-12 is P output to compartment N12 in the form of wastewater generated
237 from beneficiation for further treatment; PCR is the concentrate recovery rate of
WWG 238 beneficiation; P1 is the P generation coefficient in wastewater from beneficiation (i.e.
239 the volume of wastewater generated from beneficiation multiplied by P concentration in
240 wastewater, similarly hereinafter); PWW is the treatment proportion of industrial
241 wastewater.
242 1.3.6 Chemical production (N6)
243 The major output flows from N6.1 include fertilizers (for net export or domestic
244 consumption), wastewater and phosphogypsum, calculated as:
FT FT WW,nt WW ,ft PG PG 245 OOOOOOO6.1 6.1 14 6.1 7 6.1 3 6.1-12 6.1-2 6.1-6.3.3 (6.1)
246 where
FT FT,ne FT 62 247 (6.2) O6.1 14 DP142
FT FT62FT,ne FT 62 248 (6.3) O6.1 7 DDP142 142
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HFT 249 WW ,nt FT P WWG WW (6.4) O6.1 3 DPPFT 2 1 P
HFT 250 WW ,ft FT P WWG WW (6.5) O6.1 12 DPPFT 2 P
HFT PG FT PG PG62 PG 251 P (6.6) O6.1-2 DPPPFT 1 21 3 P 142
HFT PG FT PG PG62 PG 252 P (6.7) O6.1-6.3.3 DPPPFT 1 2 3 P 142
FT WW ,nt 253 and O6.1 i is P output to compartment Ni (i=7, 14) in the form of fertilizers; O6.1 3 is P
254 output to compartment N3 in the form of wastewater discharged from fertilizer production
WW ,ft 255 into local water environment with no treatment; O6.1-12 is P output to compartment N12 in
PG 256 the form of wastewater generated from fertilizer production for further treatment; O6.1-i is
FT ,ne 257 P output to compartment Ni (i=2, 6.3.3) in the form of phosphogypsum; D is the
258 annual net export of fertilizers; D FT is the annual production of fertilizers (expressed with
FT HFT 259 100% P2O5); P is the P2O5 content in fertilizers; P is the share of high-concentration
WWG 260 P fertilizers; P2 is the P generation coefficient in wastewater from fertilizer
PG 261 production; P1 is the phosphogypsum generation coefficient in the production of high-
PG PG 262 concentration P fertilizers; P 2 is the P2O5 content in phosphogypsum; P3 is the
263 utilization rate of phosphogypsum. The explanations of PWW can be found in Section
264 1.3.5.
265 The major output flows from N6.2 include feed additives (for net export or domestic
266 consumption), wastewater and solid wastes, calculated as:
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267 FA FA WW,nt WW ,ft SW (6.8) OOOOOO6.2 6.2 14 6.2 10.2 6.2 3 6.2-12 6.2-2
268 where
269 FA FA,ne FA (6.9) O6.2 14 DP
270 FA FA FA,ne FA (6.10) O6.2 10.2 DDP
WW ,nt FA WWG FAWW62 WW 271 (6.11) O6.2 3 DPPP3 142 1
WW ,ft FA WWG FAWW62 WW 272 (6.12) O6.2 12 DPPP3 142
SW FA SWG FASW 62 273 (6.13) O6.2-2 DPP 142
FA WW ,nt 274 and O6.2 i is P output to compartment Ni (i=10.2, 14) in the form of feed additives; O6.2 3
275 is P output to compartment N3 in the form of wastewater discharged from feed additive
WW ,ft 276 production into local water environment with no treatment; O6.2-12 is P output to
277 compartment N12 in the form of wastewater generated from feed additive production for
278 further treatment; SW is P output to compartment N in the form of solid wastes; FA,ne is O6.2-2 2 D
279 the annual net export of feed additives; D FA is the annual production of feed additives;
FA WWG 280 P is the P content in feed additives; P3 is the wastewater generation coefficient of
FAWW 281 feed additive production; P is the P2O5 content in untreated wastewater from feed
282 additive production; P SWG is the solid waste generation coefficient of feed additive
FASW 283 production; P is the P2O5 content in solid waste from feed additive production. The
284 explanations of PWW can be found in Section 1.3.5.
285 The major output flows from N6.3 include elemental P (for net export or domestic
286 consumption), wastewater and solid wastes, calculated as:
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287 EP EP EP EP WW,nt WW ,ft FP SL SL (6.14) OOOOOOOOOO6.3 6.3 14 6.3 6.3.1 6.3 6.3.2 6.3 6.3.3 6.3 3 6.3-12 6.3-6.3.3 6.3-6.3.3 6.3-2
288 where
289 EP EP,ne (6.15) O6.3 14 D
EP Balancing 290 ON6.3 6.3.1 6.3.1 (6.16)
EP Balancing 291 ON6.3 6.3.2 6.3.2 (6.17)
292 EPEP EP EP EP (6.18) OOOO6.3 6.3.3D 6.3 14 6.3 6.3.1 6.3 6.3.2
293 WW ,nt EP WWG WW (6.19) O6.3 3 DPP4 1
WW ,ft EP WWG WW 294 O6.3 12 DPP4 (6.20)
FP EP FP FP 295 O6.3-6.3.3 DPP12 (6.21)
SL EP SL SL62 SL 296 (6.22) O6.3-6.3.3 DPPP1 2142 3
SL EP SL SL62 SL 297 (6.23) O6.3-2 DPPP1 2142 1 3
EP 298 and O6.3i is P output to compartment Ni (i=6.3.1, 6.3.2, 6.3.3, 14) in the form of
WW ,nt 299 elemental P; O6.3 3 is P output to compartment N3 in the form of wastewater discharged
WW ,ft 300 from elemental P production into local water environment with no treatment; O6.3-12 is P
301 output to compartment N12 in the form of wastewater generated from elemental P
FP 302 production for further treatment;O6.3-6.3.3 is P output to compartment N6.3.3 in the form of
SL 303 ferrophosphorus byproduct; O6.3-i is P output to compartment Ni (i=2, 6.3.3) in the form
304 of slags; D EP,ne is the annual net export of elemental P; D EP is the annual production of
WWG 305 elemental P; P4 is the P generation coefficient in wastewater from elemental P
FP 306 production; P1 is the ferrophosphorus generation coefficient of elemental P production;
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307 FP is the P content in ferrophosphorus; SL is the slag generation coefficient of element P 2 P1
SL SL 308 P production; P 2 is the P2O5 content in slags; P3 is the utilization rate of slags. The
309 explanations of PWW can be found in Section 1.3.5.
310 The major output flows from N6.3.1 include organophosphorus pesticides (for net export or
311 domestic consumption) and wastewater, calculated as:
OP OP WW,nt WW ,ft 312 OOOOO6.3.1 6.3.1 14 6.3.1 7 6.3.1 3 6.3.1-12 (6.24)
313 where
314 OP P,ne OP OP (6.25) O6.3.1 14 DPP12
315 OP P P,ne OP OP (6.26) O6.3.1 7 DDPP 12
316 WW ,nt P OP WWG WW (6.27) O6.3.1 3 DPPP151
WW ,ft P OP WWG WW 317 O6.3.1 12 DPPP15 (6.28)
OP 318 and O6.3.1i is P output to compartment Ni (i=7, 14) in the form of organophosphorus
WW ,nt 319 pesticides; O6.3.1 3 is P output to compartment N3 in the form of wastewater discharged
320 from organophosphorus pesticide production into local water environment with no
WW ,ft 321 treatment; O6.3.1-12 is P output to compartment N12 in the form of wastewater generated
322 from organophosphorus pesticide production for further treatment; D P,ne is the annual net
P OP 323 export of pesticides; D is the annual production of pesticides; P1 is the share of
OP WWG 324 organophosphorus pesticides; P 2 is the P content in organophosphorus pesticides; P5
325 is the P generation coefficient in wastewater from pesticide production. The explanations
326 of PWW can be found in Section 1.3.5.
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327 The major output flows from N6.3.2 include soap and detergents (for net export or
328 domestic consumption) and wastewater, calculated as:
DG DG WW,nt WW ,ft 329 OOOOO6.3.2 6.3.2 14 6.3.2 11 6.3.2 3 6.3.2-12 (6.29)
330 where
DG DG,ne DG DG DG ,ne DG DG 331 O6.3.2 14 DPPDPP1 21 1 3 (6.30)
DG DG DG,ne DG DG + DG DG ,ne DG DG 332 O6.3.2 11 DDPPDDPP 1 2 1 1 3 (6.31)
333 WW ,nt DG WWG WW (6.32) O6.3.2 3 DPP6 1
WW ,ft DG WWG WW 334 O6.3.2 12 DPP6 (6.33)
DG 335 and O6.3.2i is P output to compartment Ni (i=11, 14) in the form of soap and detergents;
WW ,nt 336 O6.3.2 3 is P output to compartment N3 in the form of wastewater discharged from soap and
WW ,ft 337 detergent production into local water environment with no treatment; O6.3.2-12 is P output to
338 compartment N12 in the form of wastewater generated from soap and detergent
339 production for further treatment; DDG,ne is the annual net export of soap and detergents;
DG DG 340 D is the annual production of soap and detergents; P1 is the share of P-free detergents;
DG DG 341 P 2 is the P content in P-free detergents; P3 is the P content in P-containing detergents;
WWG 342 P6 is the P generation coefficient in wastewater from soap and synthetic detergent
343 production. The explanations of PWW can be found in Section 1.3.5.
344 It should be noted that we only take account of two major applications of elemental P,
345 while other applications, such as food additives, matches, plasticizers and flame
346 retardants, are excluded due to the data unavailability. P input to compartment N6.3.3 is
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347 assumed to be the change of stock in N6. However, the output analysis of compartment
348 N6.3.3 can be further improved.
349 1.3.7 Cultivation (N7)
350 The major output flows from N7 include raw crops (for net export or domestic
351 consumption), crop straws, runoff and leaching and soil sediments, calculated as:
RC RC,sd RC CS ,ru CS ,fd CS ,fl CS ,nu WE RF LH SD 352 OOOOOOOOOOOS7 714 77 710 77 710 711 72 71 73 7-3 7 (7.1)
353 where
354 RC RC,ne RC (7.2) O7 14 DP
355 RC,sd SA SD RC (7.3) O77 DPP
356 RC RC RC SA SD RC RC,ne RC (7.4) O7 10 DPDPPDP
357 CS ,ru RC CS CS CS (7.5) O77 DPPP1 2 3
CS ,fd RC CS CS CS 358 O7 10 DPPP1 2 4 (7.6)
CS ,fl RC CS CS CS 359 O7 11 DPPP1 2 5 (7.7)
CS ,nu RC CS CS CS 360 O72 DPPP1 2 6 (7.8)
WE AL WE 361 O71 DP (7.9)
RF AL RF 362 O73 DP (7.10)
LH AL LH 363 O7-3 DP (7.11)
SD Balancing 364 SN77 (7.12)
RC 365 and O7i is P output to compartment Ni (i=10, 14) in the form of harvested raw crops;
RC,sd CS ,ru 366 O77 is P recycled in compartment N7 in the form of crop seeds; O77 is P recycled in
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367 compartment N in the form of crop straws; CS ,fd is P output to compartment N in the 7 O7 10 10
CS ,fl 368 form of crop straws used as feeds; O7 11 is P output to compartment N11 in the form of
369 crop straws used as fuels; CS ,nu is P output to compartment N in the form of unutilized O72 2
370 crop straws; WE is P output to compartment N in the form of wind erosion; RF is P O71 1 O73
LH 371 output to compartment N3 in the form of runoff; O7-3 is P output to compartment N3 in
372 the form of leaching; SD is the change of stock of compartment N in the form of soil S 7 7
373 sediments; D RC,ne is the annual net export of raw crops; D RC is the annual production of
374 raw crops; D SA is the annual sown areas of crops; P RC is the P contents in crop products;
SD CS CS 375 P is the use intensity of crop seeds; P1 is the ratio of straw to grain; P 2 is the P
CS CS 376 contents in crop straws; P3 is the proportion of straws returned to cropland; P 4 is the
CS CS 377 proportion of straws used as feeds; P5 is the proportion of straws used as fuels; P 6 is
378 the proportion of unutilized straws; P RF is the P runoff coefficient in cropland; P LH is the
379 P leaching coefficient in cropland. The explanations of D AL and PWE can be found in
380 Section 1.3.1 and Section 1.3.2, respectively.
381 1.3.8 Animal husbandry (N8)
382 The major output flows from N8 include live animals (for net export or domestic
383 consumption), diary and egg products, animal excreta and live animal stock changes,
384 calculated as:
LA LA DE AE,ru AE ,nu AE ,nu LA 385 OOOOOOOS8 814 810 810 87 83 82 8 (8.1)
386 where
- 19 -
387 LA LA,ne LA (8.2) O8 14 DP
388 LA LA LA,ne LA (8.3) O8 10 DDP
DE DR DR EG EG 389 O8 10 DPDP (8.4)
AE,ru DLA AE AE 390 O87 DPP12 (8.5)
AE,nu DLA AE AE 391 O83 DPP13 (8.6)
AE,nu DLA AE AE 392 O82 DPP14 (8.7)
LA LA LA 393 S 8 DP (8.8)
LA DE 394 and O8i is P output to compartment Ni (i=10, 14) in the form of live animals; O8 10 is P
AE,ru 395 output to compartment N10 in the form of dairy and egg products; O87 is P output to
AE,nu 396 compartment N7 in the form of reused animal excreta; O8i is P output to compartment
LA 397 Ni (i=2, 3) in the form of unutilized animal excreta; S 8 is the change of stock of
398 compartment N8 in the form of live animals; D LA,ne is the annual net export of live
399 animals; D LA is the annual number of live animals to be slaughtered; D DR is the annual
400 production of dairy products; D EG is the annual production of egg products; D DLA is the
401 average daily breeding number of animals, calculated by considering the breeding cycles
402 of different animals (10); D LA is the interannual change of stocks of live animals; P LA is
403 the P content in live animals; P DR is the P content in dairy products; P EG is the P content
AE AE 404 in egg products; P1 is the P excreta coefficient of live animals; P 2 is the proportion of
AE 405 animal excreta used to cropland; P3 is the proportion of animal excreta to inland waters;
AE 406 P 4 is the proportion of animal excreta to non-arable land.
- 20 -
407 1.3.9 Aquaculture (N9)
408 The major output flows from N9 include artificially cultured aquatic products (for net
409 export or domestic consumption), and aquatic P losses, calculated as:
CW CW NL NL 410 OOOOO9 914 910 93 94 (9.1)
411 where
412 CW CW,ne FW (9.2) O9 14 DP
413 CW CFW FW CSW SW CW,ne FW (9.3) O9 10 DPDPDP
414 NL CFW FF FF CFW FW (9.4) O93 DPPDP12
415 NL CSW SF SF CSW SW (9.5) O94 DPPDP12
CW 416 and O9i is P output to compartment Ni (i=10, 14) in the form of artificially cultured
NL 417 aquatic products; O9i is P output to compartment Ni (i=3, 4) in the form of aquatic
418 nutrient losses; DCW ,ne is the annual net export of artificially cultured aquatic products;
419 DCFW is the annual production of artificially cultured freshwater products; DCSW is the
FF 420 annual production of artificially cultured seawater products; P1 is the feed requirement
FF SF 421 for freshwater aquaculture; P 2 is the P content in freshwater feeds; P1 is the feed
SF 422 requirement for seawater aquaculture; P 2 is the P content in seawater feeds. The
423 explanations of P FW and P SW can be found in Section 1.3.3 and Section 1.3.4,
424 respectively.
425 1.3.10 Agricultural product processing (N10)
- 21 -
426 The major output flows from N10.1 include processed crop and animal products (for net
427 export or domestic consumption), by-products, solid wastes and wastewater, calculated
428 as:
PF PF PF BP BP BP WW,nt WW ,ft SW 429 OOOOOOOOOO10.1 10.1 14 10.1 11 10.1 10.2 10.1 7 10.1 10.2 10.1 10.3 10.1 3 10.1-12 10.1-13 (10.1)
430 where
PF RI,ne FL ,ne RC PO ,ne PO SU ,ne SU 431 O10.1 14 DDPDPDP 1 (10.2) MT,ne MT DR ,ne DR EG ,ne EG DPDPDP1
PF PF,grains PF ,oil PF ,oil crops PF ,sugar PF ,meat PF ,dairy and egg PF ,other 432 OOOOOOOO10.1 11 10.1 11 10.1 11 10.1 11 10.1 11 10.1 11 10.1 11 10.1 11 (10.3)
PF,grains RC ,rice RI RC ,wheat WH RC ,maize RC ,millet RC ,sorghum RC ,tube r s OOOOOOO10.1 11 7 10PP11 7 10 7 10 7 10 7 10 7 10 433 (10.3.1) RC,beans PO RI,ne FL ,ne RC PF ,grains OO7 101 PDDP2 10.1 10.2
RC,oil crops 434 PF ,oil O7 10 PO PO PO PO,ne PO (10.3.2) O10.1 11 RC PPPDP1 2 3 1 P
435 PF,oil crops= RC ,oil crops PO (10.3.3) OO10.1 11 7 10 1 P2
PF ,sugar= SU SU,ne SU 436 O10.1 11 DDP (10.3.4)
PF ,meat= MT MT MT MT,ne MT 437 O10.1 11 DPPDP1 2 1 1 (10.3.5)
PF,dairy and egg= DE DR,ne DR EG ,ne EG 438 OO10.1 11 8 10 DPDP (10.3.6)
PF,other= RC ,vegetable NFW NSW CW 439 OOOOO10.1 11 7 10 3 10 4 10 9 10 (10.3.7)
PF ,grains Balancing 440 ON10.1 10.2 10.2 (10.4)
BP RC,rice RI 441 OO10.1 7 7 10 P2 (10.5)
BP BP,rice bran BP ,wheat bran BP ,oil meal BP ,bone 442 OOOOO10.1 10.2 10.1 10.2 10.1 10.2 10.1 10.2 10.1 10.2 (10.6)
BP,rice bran= RC ,rice RI 443 OO10.1 10.2 7 10 P3 (10.6.1)
- 22 -
444 BP,wheat bran= RC ,wheat WH (10.6.2) OO10.1 10.2 7 10 1 P1
RC,oil crops RC ,oil crops 445 BP,oil meal= RC ,oil crops PO OO7 10 PO PO PO 7 10 PO WWG 10.6.3) OO10.1 10.2 7 10 PPPPPP2RC 1 2 3 RC 2 7 PP
446 BP,bone = LA MT MT MT MT (10.6.4) OO10.1 10.2 8 10 PDPP31 2 1
447 BP RC,sugarcane RC ,sugar beet SU SU (10.7) OOO10.1 10.3 7 10 7 10 DP
WW,nt WW ,oil WW ,meat 448 OOO10.1 3 10.1 3 10.1 3 (10.8)
RC,oil crops 449 WW ,oil O7 10 PO WWG WW (10.8.1) O10.1 3 RC PPP271 P
450 WW ,meat LA LA,ne WWG WW (10.8.2) O10.1 3 DDPP 8 1
WW,ft WW ,oil WW ,meat 451 OOO10.1 12 10.1 12 10.1 12 (10.9)
RC,oil crops 452 WW ,oil O7 10 PO WWG WW (10.9.1) O10.1 12 RC PPP27 P
WW ,meat LA LA,ne WWG WW 453 O10.1 12 DDPP 8 (10.9.2)
SW LA MT LA LA,ne WWG 454 OO10.1-13 8 10 1 PDDP38 (10.10)
PF 455 and O10.1i is P output to compartment Ni (i=10.2, 11, 14) in the form of processed crop
456 and animal products, including grains, vegetable, flour, oil, sugar, meat, dairy and eggs,
BP 457 aquatic products; O10.1i is P output to compartment Ni (i=7, 10.2, 10.3) in the form of by-
458 products, including rice chaff and bran, wheat middling and bran, oil meal, animal bones
WW ,nt 459 and bagasse; O10.1 3 is P output to compartment N3 in the form of wastewater discharged
WW ,ft 460 from food production into local water environment with no treatment; O10.1-12 is P output to
461 compartment N12 in the form of wastewater generated from food production for further
SW 462 treatment; O10.1-13 is P output to compartment N13 in the form of solid waste during animal
- 23 -
463 slaughtering; D RI ,ne is the annual net export of husked/milled rice; D FL,ne is the annual
464 export of flour; D PO,ne is the annual net export of plant oil; DSU ,ne is the annual net export
465 of sugar; DMT ,ne is the annual net export of meat; D DR,ne is the annual net export of dairy;
466 D EG,ne is the annual net export of egg; D SU is the annual production of sugar; D MT is the
467 annual production of meat containing bones; PO is the P content in plant oil; SU is the P P1 P
468 content in sugar; MT is the P content in meat; RI is the proportion of milled rice P1 P1
WH PO 469 produced from raw rice; P1 is the proportion of flour produced from wheat; P 2 is the
PO MT 470 proportion of oil crops used for oil production; P3 is the yield rate of plant oil; P 2 is the
RI 471 proportion of bones in slaughtered animals; P 2 is the proportion of rice chaff/hull
RI MT 472 produced from raw rice; P3 is the proportion of rice bran produced from raw rice; P3 is
WWG 473 the slaughtering rate of animals; P7 is the P generation coefficient in wastewater from
WWG 474 oil production; P8 is the P generation coefficient in wastewater from slaughtering. The
475 explanations of PWW can be found in Section 1.3.5; P RC in Section 1.3.7; D LA , D LA,ne , P DR
476 and P EG in Section 1.3.8, respectively.
477 The output flow from N10.2 is processed feeds (for net export or domestic consumption),
478 calculated as:
AF AF AF 479 OOOO10.2 10.2 14 10.2 9 10.2 8 (10.11)
480 where
AF AF,ne AF 481 O10.2 14 DP (10.12)
AF CFW FF FF CSW SF SF 482 O10.2 9 DPPDPP1 2 1 2 (10.13)
- 24 -
483 AF Balancing (excluding herbivores) (10.14) ON10.2 8 8
484 and AF is P output to compartment N (i=8, 9, 14) in the form of processed industrial O10.2i i
485 and agrocultural animal feeds; D AF ,ne is the annual net export of animal feeds; P AF is the P
486 content in industrial feeds. The explanations of CFW , CSW , FF , FF , SF and SF can be D D P1 P 2 P1 P 2
487 found in Section 1.3.9.
488 The output flow from N10.3 is non-food products, calculated as:
NF 489 OO10.3 10.3 11 (10.15)
490 where
NF RC,cotton fiber RC ,bast RC ,tobacco WD BP 491 OOOOOO10.3 11 7 10 7 10 7 10 2 10 10.1 10.3 (10.16)
NF 492 and O10.3 11 is P output to compartment N11 in the form of non-food products, which is
493 simplified in this study because of their long life cycles and limited P contents.
494 1.3.11 Human consumption (N11)
495 The output flows from N11 are human excreta, solid waste and wastewater, calculated as:
HE HE HE HE SW SW WW,nt WW ,ft HM 496 OOOOOOOOOS11 11 7 11 2 11 3 11 12 11 2 11 13 11 3 11 12 11 (11.1)
497 where
HE UHM UHE UHE RHM RHE RHE 498 O11 7 DPPDPP1 2 1 2 (11.2)
HE RHM RHE RHE 499 O11 2 DPP121 (11.3)
HE UHM UHE UHE UHE 500 O11 3 DPPP11 2 3 (11.4)
HE UHM UHE UHE UHE 501 O11 12 DPPP111 2 3 (11.5)
- 25 -
502 SW RHM RSW RSW (11.6) O11 2 DPP12
503 SW USW USW (11.7) O11 13 DP
WW,nt UWW ,nt RWW ,nt 504 OOO11 3 11 3 11 3 (11.8)
UHM 505 UWW ,nt DW D WWG UWW - UWW (11.8.1) O11 3 DPPPUHM RHM 9 11 2 DD
RHM 506 RWW ,nt DW D WWG RWW (11.8.2) O11 3 DPPUHM RHM 9 DD
UHM 507 WW ,ft DW D WWG UWW UWW (11.9) O11 12 DPPPUHM RHM 9 1 2 DD
HM UHM RHM HM 508 S 11 DDP (11.10)
HE SW 509 and O11i is P output to compartment Ni (i=2, 3, 7, 12) in the form of human excreta; O11i
WW ,nt 510 is P output to compartment Ni (i=2, 13) in the form of solid wastes; O11 3 is P output to
511 compartment N3 in the form of wastewater discharged from human consumption into
WW ,ft 512 local water environment with no treatment; O11-12 is P output to compartment N12 in the
HM 513 form of wastewater generated from human consumption for further treatment; S 11 is
UHM 514 the change of stock of compartment N11 in the form of live human beings; D is the
515 number of urban population; D RHM is the number of rural population; DUSW is the annual
516 collected weight of urban solid waste; D DW is the annual volume of domestic water use;
517 DUHM is the interannual change of stocks of urban population; DRHM is the interannual
UHE 518 change of stocks of rural population; P1 is the P generation coefficient in human
UHE 519 excreta from urban households; P 2 is the proportion of urban excreta returned to
RHE 520 cropland; P1 is the P generation coefficient in human excreta from rural households; - 26 -
521 RHE is the proportion of rural excreta returned to cropland; UHE is the runoff rate of P 2 P3
522 unutilized urban excreta; RSW is the solid waste generation coefficient of rural human P1
523 consumption; RSW is the P content in rural solid wastes; USW is the P content in urban P2 P
WWG UWW 524 solid waste; P9 is the wastewater generation rate of domestic water use; P1 is the P
UWW 525 concentration in untreated urban wastewater (without excreta); P2 is the treatment rate
526 of urban domestic wastewater; P RWW is the P concentration in untreated rural wastewater
527 (without excreta); P HM is the average P content in adults.
528 1.3.12 Wastewater treatment (N12)
529 The output flows from N12 are effluents and sludge, calculated as:
EF SL SL 530 OOOO12 12 3 12 7 12 13 (12.1)
531 where
WWD WWDWWD WWD WWD EF WW,ft PPP1 WW ,ft 2 WW ,ft PP35 WW ,ft 4 WW ,ft OOOOOO12 3 5.2 12WWG 6.1 12 WWG 6.2 12 FAWW 6.3 12 WWG 6.3.1 12 WWG 532 PPPPP1 2 4 5 (12.2) WWD WWD WWD WW,ft PP67 WW ,oil WW,meatP8 + WW ,ft WWT OO6.3.2 12WWG 10.1 12 WWG OO 10.1 12WWG 11 12 1 P1 PP67P8
SL WW ,ft WWT WWT 533 OO12 7 11 12 PP12 (12.3)
WWD WWD WWD SL WW,ft PP12 WW ,ft WW ,ft P3 OOOO12 13 5.2 121WWG 6.1 12 1 WWG 6.2 12 1 FAWW PPP12 WWD WWD WWD 534 WW,ft P 4 WW ,ft P5 WW ,ft P6 (12.4) OOO6.3 1211WWG 6.3.1 12 WWG 6.3.2 12 1 WWG PP45 P6 WWD WWD WWT WW,oil PP78 WW ,meat + WW ,ft WWT OOO10.1 12WWG 10.1 12 WWG 11 12 1 P 11 P 1 2 PP78
EF SL 535 andO12 3 is P output to compartment N3 in the form of treated effluents; O12 i is P output to
WWT 536 compartment Ni (i=7, 13) in the form of sludge; P1 is the P elimination rate of urban - 27 -
537 municipal wastewater treatment plant; WWT is the proportion of sludge returned to P 2
WWD WWD 538 cropland; P1 is the P discharge coefficient in beneficiation wastewater; P2 is the P
WWD 539 discharge coefficient in fertilizer wastewater; P3 is the P content in treated feed
WWD WWD 540 additive wastewater; P4 is the P discharge coefficient in elemental P wastewater; P5
WWD 541 is the P discharge coefficient in pesticide wastewater; P6 is the P discharge coefficient
WWD 542 in soap and detergent wastewater; P7 is the P discharge coefficient in oil wastewater;
WWD WWG 543 P8 is the P discharge coefficient in slaughtering wastewater. The explanations of Pi
544 (i=1, 2, 4,…,8) and P FAWW can be found in Section 1.3.5, 1.3.6 and 1.3.10.
545 1.3.13 Solid waste disposal (N13)
546 The output flows from N13 are solid wastes in different disposal ways, calculated as:
SW,sp SW ,ld SW ,in SW ,cp 547 OOOOO13 13 2 13 2 13 2 13 7 (13.1)
548 where
549 SW,sp SW SW SL SWD (13.2) OOOO13 2 10.1-13 11 13 12 13 1 P1
550 SW,ld SW SW SL SWD SWD (13.3) OOOO13 2 10.1-13 11 13 12 13 PP12
551 SW,in SW SW SL SWD SWD (13.4) OOOO13 2 10.1-13 11 13 12 13 PP13
552 SW,cp SW SW SL SWD SWD (13.5) OOOO13 7 10.1-13 11 13 12 13 PP14
SW ,sp SW ,ld 553 and O13 2 is P output to compartment N2 in the form of solid wastes for stockpile; O13 2 is
SW ,in 554 P output to compartment N2 in the form of solid wastes for landfill; O13 2 is P output to
SW ,cp 555 compartment N2 in the form of solid wastes for incineration; O13 7 is P output to
- 28 -
556 compartment N in the form of solid wastes for compost; SWD is the non-hazardous 7 P1
557 treatment rate of solid waste; SWD is the landfill proportion in non-hazardous treatment of P 2
558 solid wastes; SWD is the incineration proportion in non-hazardous treatment of solid P3
559 waste; SWD is the compost proportion in non-hazardous treatment of solid waste. P4
560 1.4 Data sources
561 Data used in this study encompass time series of activity data and parameters mainly
562 derived from various kinds of statistical yearbooks, government or industry reports,
563 academic books, public databases, literatures, etc.
564 1.4.1 Activity data
565 Table S3 lists the original sources of activity data that are be briefly classified into three
566 periods: 1600s-1911, 1911-1912 and 1949-2012. Although China had established fairly
567 good statistical systems about population, land area and crop yield during the Ming and
568 Qing dynasties (1600s-1911), these primary data are not as readily accessible as the
569 statistical yearbooks in the modern society since they are scattered in historical
570 documents and expressed in classical Chinese texts. Fortunately, many domestic and
571 international scholars have conducted systematic research about these historical data,
572 which serve as solid data basis for our analysis. These data sources between 1600s-1911
573 include, for example, China’s Population History in Modern Times (11), The Statistics of
574 Ancient Accounts, Land and Land Taxes of China (12), Agriculture Development in
575 China: 1368-1968 (13) and A History of Chinese Pig Breeding (14). For the Republican
576 Era (1912-1948), the majority of activity data we use for are based on the Statistical
- 29 -
577 Yearbook of the Republic of China (15) published in 1948, the only statistical yearbook
578 reporting the socio-economic development throughout the Republic of China. The
579 quantitative information in agriculture and industry during 1912-1921 was documented in
580 the annual Statistical Tables of Agriculture and Commerce (16). However, these data
581 fluctuated significantly in such turbulent time due to different statistical procedures and,
582 sometimes, unexplained omissions, which may weaken the robustness of the overall
583 datasets. In the Communist Era (1949-2012), P cycles are annually depicted with
584 comparatively abundant activity data from various sources, primarily from yearbooks
585 such as China Statistical Yearbooks (17), China Industry Economy Statistical Yearbooks
586 (18) and China Grain Yearbooks (19).
587 In some cases where activity data from previous works are fragmentary or contradictory,
588 we make an earnest attempt to prepare the annual time-series with statistical methods
589 (e.g. removing anomalies, extrapolation, and interpolation). Next, the data availability
590 and data processing procedures for population, arable land, crop production, animal
591 production and food consumption are clarified separately.
592 Population
593 Thanks to the historical feudal regime and reliance on taxes, China owns much longer
594 official population records than most other countries in the world. These data were
595 documented in the veritable records (Shilu) of each dynasty before 1912 while data
596 afterwards can be found in statistical yearbooks. Many scholars have tried to interpret the
597 nature of different types of population data. For example, Ho (1959) conducted
598 impressive summary work on the Chinese population during 1368-1953 and provided
- 30 -
599 possible factors that could have affected the population growth (20). Perkins (1969)
600 provided estimates of the Chinese population ranges for eleven benchmark years in his
601 book entitled Agriculture Development in China: 1368-1968 (13). Zhao and Xie (1988)
602 used statistical techniques to rehabilitate the official Chinese population datasets without
603 any dependence on other previous research (21). More recently, Maddison (2007)
604 combined the results of earlier estimates and presented a sound but discontinuous
605 Chinese population growth pattern that begins from 1 to 2030 AD (22).
606 For 1949-2012, we use the population statistics from China Statistical Yearbooks when
607 the national statistical system is soundly organized. For 1912-1948, although population
608 was recorded in the Statistical Yearbook of the Republic of China, the civil unrest led to
609 frequent changes of the statistical coverage. Thus we adopt the discontinuous but
610 relatively sound population results in Maddison (2007) for this period and linearly
611 interpolate in between. For 1600-1911, we take the results by Zhao and Xie (1988) as
612 their estimates are yearly given in the best situation and fit well with Maddison (2007).
613 However, their extremely lower estimates for 1602 and 1620 are eliminated after
614 comparison with those of others. We use 120 million for 1600 which is the lower limit in
615 Perkins (1969) and slightly above the linear regression results (106 million, R2=0.97) for
616 Zhao and Xie (1988) and Maddison (2007). Finally we linearly interpolate in-between
617 data to obtain annual values.
618 The reconstructed population result as well as those from other publications are listed in
619 Fig. S10 for comparison. In general, it is consistent with those of other studies showing a
620 growing trend of Chinese population, while the quantitative inconsistency could be
621 caused by different reconstruction methods. Please refer to original works for - 31 -
622 methodological details. Chinese population exhibited fluctuations in some years due to
623 natural calamities (e.g. droughts and floods) and human disasters (e.g. wars and turmoil).
624 The population declined by more than a fifth in the first half of 17th century. This is
625 probably caused by wars and famines happened during the regime change (13). In 1795-
626 1804 and 1813-1818, population fell again when the uprisings of the White Lotus and
627 other religious factions were suppressed by central government and disrupted the
628 population statistics in several provinces (20, 21). Later on, China suffered severe losses
629 of population since the 1850s as a result of the Taiping and Nien wars (peasant revolts
630 against the corrupt feudal monarchy), the disastrous change in the course of Yellow River
631 and imperialist intrusions, etc. (21) In 1959-1961, Chinese population experienced
632 another sudden drop due to severe famine when a massive number of rural labor were
633 turned into non-agricultural activities during the Great Leap Forward and severe droughts
634 occurred (22).
635 We aggregate rural and urban households before 1949 by assuming no distinction in the
636 consumption pattern between urban and rural residents at that time. It is because
637 information about their difference in 1600-1948 is limited and municipal facilities (e.g.
638 wastewater treatment plant, solid waste disposal) were not applicable.
639 Arable land
640 Arable land is another important information documented in the veritable records and
641 yearbooks. Liang (1980) presented the land figures for years that he could find in
642 available historical official documentations (12). Wu (1985) indicated that the land areas
643 given by Onoe in 1977 at five year intervals during 1840 to 1945 were overestimated
- 32 -
644 (23). Ramankutty and Foley (1999) used the continental scale cropland area with
645 cropland conversion rates to extrapolate national values backwards to 1700 (24).
646 However, their analysis overestimated cropland in China (25). The History Database of
647 the Global Environment (HYDE) database provided gridded time series of land use at a
648 global scale for the last 12,000 years, from millennial intervals before 1 AD to centennial
649 intervals until 1700 AD and decadal intervals thereafter (26).
650 In this study, we take HYDE datasets as the primary data source of arable land for 1600-
651 1951. This is because it demonstrates a relatively sound trend in 1600-1950 and the 1949-
652 1951 datasets from official statistics are believed to have been underestimated since
653 China did not begin the arable land census until 1951. Given only two separate figures for
654 1600 and 1700 in HYDE database, we also include the estimates in 1620 from Liang
655 (1980) (12), in 1626 from Shi (2011) (27) and in 1930 from Shi (1989) (28). We graph
656 the relation between arable land and time in 1600-1951 by fitting a natural cubic spline
657 regression model with 9 knots, of which the coefficient of multiple determination (R2) is
658 0.9985.
659 For 1952-2012, we primarily use China Statistical Yearbooks which provide annual
660 estimates of arable land areas (17). Nevertheless, we adjust the original datasets during
661 1961-1995 to eliminate interannual inconsistency according to Bi and Zheng (2000) (29)
662 and Feng et al. (2005) (30). In brief, cropland area in 1961-1979 are extrapolated from
663 the statistical relationships between grain yields and cropland areas in 1949-1960. For
664 1980-1995, we maintain the original growth trend but fill up the data gaps between 1995
665 and 1996 since data collected from Ministry of Land and Resources since 1996 are
666 considered more solid than those from Ministry of Agriculture before. - 33 -
667 The reconstructed arable land result as well as those from other publications are listed in
668 Fig. S10. It is in general between the ranges of previous studies. Please refer to the
669 original works for compiling details. Chinese arable land has increased from 77 million
670 hectares in 1600 to over 120 million hectares in 2012, suggesting a substantial decline in
671 per capita arable land considering the huge increase in population. During the transition
672 between the Ming and Qing dynasties in the mid 17th century, some arable land turned to
673 barren grounds. Then much efforts were made to slowly bring the land in the
674 northwestern regions and Inner Mongolia under cultivation (31). In 1958-1960, there was
675 a sharp drop in arable land when millions of peasants were diverted from farming to other
676 pursuits during the Great Leap Forward as well as faced with severe droughts.
677 Afterwards, as the central government reversed the focus from political turmoil back on
678 agriculture, arable land increased to a peak of 135 million hectares in 1980. However, it
679 decreased again through the rapid urbanization in the last three decades (29, 30).
680 Agricultural products
681 Statistics regarding the production and consumption of agricultural products since 1949
682 are comprehensively recorded by official statistics, but are scattered in 1912-1948 and
683 meager from 1600 to 1911. In early periods when population expanded more quickly than
684 arable land, Chinese primarily depended on grains rather than animal products for food.
685 In most circumstances, animal husbandry gained little profit but was strongly needed
686 since animal excreta could serve as sources of nutrients in crop farming and animals
687 broadly worked in cultivation, transportation and military affairs.
- 34 -
688 Annual grain outputs are calculated by multiplying arable land area with grain yield per
689 unit of arable land, which is an indicator to represent productivity. For 1912-1948,
690 average grain yields in four periods are compiled from official statistics, noting they were
691 underestimated when several provinces fell without the reach of official statistics during
692 1938-1945 (15). For 1600-1911, Perkins (1969) found out grain yield had been slowly
693 increasing in the past hundred years (13). Wu (1985) suggested a possible yield growth
694 from 2.6 to 2.75 t/ha from the 1600s to the 1700s (23). Grain yield provided by Guo
695 (2001) increased from 1.9 t/ha around 1620 to 2.3 t/ha around 1750 and 2.4 t/ha around
696 1810 but then deceased to 2.1 t/ha until 1911 (32). Shi (2012) estimated that grain yield
697 was 1.8 t/ha around 1600 and then increased to 2.1 t/ha in 1720 and 2.4 t/ha in 1850 (31).
698 Although grain yield varied over time and place, we use the grain yield results from Guo
699 (2001) and Shi (2012) and linearly interpolated in between to get annual values.
700 Annual animal stocks in 1912-1948 are linearly interpolated from Li (2003) which
701 revised the official estimates of animal stocks for 10 benchmark years in this period (33).
702 For 1600-1911, we use Chinese population and per capita animals to extrapolate the
703 animal stocks every year except for horses as Qing dynasty strictly prohibited non-
704 governmental horse breeding and horse trading before the 1840s. For pigs, Perkins (1969)
705 estimated a rough ratio of one pig per year for ten persons in the last hundred years (13).
706 Xu (2009) summarized from a lot local histories that, on average, one household of five
707 persons held one pig every year in Qing dynasty (14), which is close to what Li (2006)
708 documented for South China in the early 19th century (34). In view of the above results
709 together with pig stocks in 1912, the average per capita pig of 0.138 is taken in this study
710 throughout Ming and Qing dynasties. For cattle, we use the per capita cattle to have been
- 35 -
711 0.072 as suggested by Xu (2009) (14), Li (2006) (34) and the 1912 benchmark. Horse
712 stocks in the early 1600s estimated by Xie (1959) were at least 1.6 million (35). We apply
713 this value to later years until the 1840s and then linearly interpolate from the 1912
714 benchmark to in-between years. For other animal categories, we use per capita animal
715 stocks in 1912 and extrapolate them to earlier years.
716 Since Chinese primarily raised horse, donkey and mule for labor, we do not consider the
717 slaughter of these animals for meat. The slaughtering ratios of animals (pig, cattle, sheep
718 and poultry) are determined from official statistics in different periods (16, 17): For pig,
719 the ratio linearly increases from 0.36 in and before 1912 to the 1949 benchmark (0.8),
720 while for other animals, the ratios are directly derived from the 1949 benchmark, namely
721 0.01 for cattle, 0.06 for sheep and 0.28 for poultry, respectively.
722 International trade
723 There is evidence that China has had a highly profitable if somewhat limited foreign
724 commerce, especially after the 1840s, exchanging commodities with neighborhoods
725 including but not limited to grains, salt, tea and cotton (20). In 1870, the total US dollars
726 at current exchange rates exported from and imported into China reached 102 and 89
727 millions, respectively (22). However, the specific commodity information in mass
728 quantity is far from complete. For the Republican Era (1912-1948), we are only able to
729 acquire the grain trade data while there have definitely been other P-associated trade
730 activities.
731 International trade data of P-related products are primarily collected from the United
732 Nations Commodity Trade Database (UN Comtrade) from 1992 to 2012, when China - 36 -
733 Customs followed the 8-digit code classification based on the International Convention
734 for Harmonized Commodity Description and Coding System (HS) to collect and compile
735 trade statistics. We identify 17 kinds of P-associated products (Ci, i=1,…,17) with their
736 commodity codes, as shown in Table S4. Due to the complex classification of
737 organophosphate pesticides in HS, their import and export data are converted from the
738 pesticides in China Foreign Economic Trade Yearbook, as are fertilizers before 1992.
739 Commodities imported to China are assumed to be used in proportion to the same uses as
740 commodities that are produced domestically.
741 1.4.2 Parameters
742 Parameters in the Chinese P cycle model are presented in Table S5. The majority of these
743 parameters used here are mean values derived from academic articles and scientific
744 reports after field experiments and investigations in different areas of China. However,
745 there are some other cited parameters without the description of data acquisition process
746 or adjusted from global values and values in other regions because of the lack of
747 indigenous information.
748 In general, our confidence in these parameters is qualitatively divided into three
749 categories of high, moderate, and low reliability based on a series of criteria as follows
750 (in a descending order of importance):
751 1) The experimental or observed data are prioritized to second-hand data;
752 2) The data with detailed methodology are in priority;
753 3) The data with good temporal and spatial representativeness are in priority;
754 4) The data published in journals with high impact factors are in priority; - 37 -
755 5) The data provided by well-known research institutions are in priority.
756 2 Spatial disaggregation of anthropogenic P losses to surface waters
757 Regionalized P emission, which illustrates the locations where anthropogenic P runoff
758 occurs, is needed to assess the environmental consequences of P-associated human
759 activities, especially the widespread eutrophication. Based on the assumption that P
760 runoff is dependent on the intensity of relevant human activities, we further analyze the
761 spatial distribution of anthropogenic P losses to surface waters with a high spatial
762 resolution of 5 arc-minutes for the year 2012. We use various geographically explicit
763 activity datasets to calculate disaggregation factors, namely the proportion of human
764 activities in each grid cell relative to the aggregated total. Our spatial disaggregation
765 method can be represented by the following equation:
766 PLi,, j PL total DF i j
767 where PLij, is P loss to surface waters from the grid cell with the longitude index i and
768 the latitude index j; PLtotal is the national total P losses to surface waters; DF ij, is the
769 spatially explicit disaggregation factor with the longitude index i and the latitude index j.
770 It should be noted that the time domains differ among the original activity datasets.
771 However, the spatial patterns are assumed to remain unchanged in 2012 regardless of the
772 reference dates.
773 For industrial sources, the computed P effluents from N5, N6 and N10 are spatially
774 assigned with the help of China Pollution Source Census Database. The State Council
- 38 -
775 conducted a nationwide census in 2007 and further updated in 2010, to get production
776 and pollutant information of different pollution sources. First, we select 5,380 P-
777 associated factories from the datasets according to the National Standard Industrial
778 Classification of all Economic Activities (GB/T4754-2002) (36). We note that there
779 could be omissions since some P-associated enterprises provide ambiguous subordinate
780 sector code in order to avoid strict regulations from the traditional sector or acquire
781 subsidies from the newly promoted sector. Second, we use the locations of these P-
782 associated factories to assign their output values (present value in 2010) into the grid cells
783 within the specific county jurisdiction. Finally, we calculate the disaggregation factors
784 from the gridded output values and allocate the industrial P effluents accordingly.
785 For cultivation (N7), we employ the Spatial Production Allocation Model (SPAM 2000
786 v3.0.6) to show the spatial differentiation of agricultural P runoff (37). The SPAM 2000
787 is a carefully-compiled and comprehensive database which uses a cross-entropy approach
788 to estimate the global harvested areas, physical areas, crop production, and crop yield for
789 20 kinds of crops/crop groups under three different production systems, namely irrigated
790 (I), rain-fed with high inputs/commercial (H), and rain-fed with low inputs/subsistence
791 (L). These crop-specific data are made available at a 5 arc-minute resolution. A more
792 detailed description and the complete SPAM datasets can be downloaded from the
793 website: http://mapspam.info/. Considering multiple harvesting seasons and land types,
794 the harvested area values are further classified into paddy land (for rice) and dry land (for
795 all other crops) to compute the respective disaggregation factors. We finally calculate the
796 spatial P runoff from cultivation by multiplying the total agricultural P runoff and the
797 gridded disaggregation factors.
- 39 -
798 For animal husbandry (N8), the global livestock density data in 2005 are obtained from
799 the Gridded Livestock of the World (GLW) database developed by the Food and
800 Agriculture Organization of the United Nations (FAO)
801 (http://www.fao.org/ag/againfo/resources/en/glw/home.html). The global distributions of
802 cattle, buffalo, sheep, goats, pigs and poultry, which were originally modelled at a spatial
803 resolution of 3 arc-minutes, are rescaled to match the Chinese 5 arc-minutes grids. The
804 numbers of different kinds of livestock in each grid cell are then calculated by
805 multiplying the respective livestock density with grid area, and are finally used to derive
806 spatial disaggregation factors. For the less widespread (camels) and less abundant
807 (horses, donkeys, mules and rabbits) species, although they play a significant role in local
808 rural economies, their density information is currently unavailable in the GLW database.
809 In this case, based on the similarity of breeding conditions and body sizes, we assume
810 that the total density of camels, horses, donkeys and mules is proportional to the total
811 density of cattle and buffalos, P runoff during the husbandry of camels, horses, donkeys
812 and mules are therefore spatially assigned according to the disaggregation factors of
813 cattle and buffalos. The same assumption is made for rabbits and poultry since their
814 raising methods are similar.
815 For aquaculture (N9), we collect the provincial production data of freshwater products in
816 2012 to roughly estimate the spatial disaggregation factors. First, we determine the
817 Chinese lake and stream network from the 1:250K topographic database in the National
818 Fundamental Geographic Information System (NFGIS). The NFGIS, developed by
819 National Administration of Surveying, Mapping and Geoinformation (NASG), serves as
820 the most comprehensive information system entity in China. We take account of a total of
- 40 -
821 1,683 rivers including natural rivers as well as the Beijing-Hangzhou Grand Canal. The
822 catchment area of the selected river is usually more than 1,000 square kilometer (km2)
823 and the main stream length is longer than 500 km. Meanwhile, hundreds of named lakes
824 and reservoirs with a total area of 91,000 km2 are included in this study. The provincial
825 average production density is calculated by dividing the total production data of
826 freshwater products by the aggregated grid areas of water bodies within a specific
827 province. Based on the calculated production density and grid areas, we further estimate
828 the spatial distribution of freshwater products, namely the production number of
829 freshwater products per grid square, and finally derive the disaggregation factors.
830 The spatial distribution of human population is adopted in this study to calculate
831 disaggregation factors for human consumption (N11) because demographic information is
832 closely linked to household P-containing wastewater discharge. The spatially explicit
833 population count estimates from the WorldPop project (http://www.worldpop.org.uk/)
834 was employed, which provides detailed and freely-available population distribution and
835 composition maps for the whole of Central and South America, Africa and Asia. We
836 select this data product other than the commonly used Gridded Population of the World
837 (GPW) database (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3) due to three
838 reasons. Firstly, more recent population census datasets are used in WorldPop. Secondly,
839 WorldPop provides a much finer mapping resolution of 0.05’ (approx. 100m at the
840 equator). Thirdly, WorldPop builds the novel random forest regression tree-based
841 estimation approach to improve mapping accuracies. In this study, the resultant numbers
842 of people per grid square in China for the year 2010 are first adjusted to match our larger
843 5’ grid cells and then used to calculate the distribution factors.
- 41 -
844 The disaggregation factors for wastewater treatment (N12) are characterized from
845 location-explicit information of a total of 3,231 wastewater treatment plants (WWTPs) in
846 China Pollution Source Census Database. Similar to the P-related industries, we use the
847 locations of the WWTPs to assign their annual treated wastewater effluents into the grid
848 cells within the specific county jurisdiction. We finally distribute the P losses from N12
849 based on the disaggregation factors which are the ratios between the gridded annual
850 treated wastewater effluent values and the aggregated national total.
851 3 Freshwater eutrophication potential of anthropogenic P runoff
852 It is widely known that anthropogenic P emissions into freshwaters can cause
853 eutrophication. However, previous quantifications of P cycles are unable to assess the
854 potential eutrophication impacts due to the limitation of the SFA methodology. To our
855 knowledge, this study is one of the early, if not the first, attempts to evaluate the
856 freshwater eutrophication potentials of P cycles.
857 3.1 Methodology overview
858 Freshwater eutrophication, caused by human activities, is a widespread environmental
859 problem around the world. It is primarily driven by nutrient surplus and can result in a
860 series of undesirable effects on aquatic ecosystems. Common symptoms of
861 eutrophication include algae blooms, hypoxia and fish kills that ultimately lead to loss of
862 biodiversity and degradation of ecosystem services. P is believed to be the key nutrient
863 that controls primary production in freshwater habitats (38). Therefore, it is necessary to
864 quantify the eutrophication potentials of anthropogenic P runoff to freshwaters through
865 multiplying P runoff with the grid-based eutrophication potential factors (EPF). The - 42 -
866 results are measured in potentially disappeared fraction of species (PDF) cubic meter
867 (m3) year (yr), meaning the fraction of species disappeared in 1 m3 of freshwater during
868 one year, as a function of availability to freshwater and decreased biodiversity due to
869 marginal increase in P concentration. It should be noted that the results are
870 environmentally relevant impact potential indicators, rather than estimations of actual
871 eutrophication effects.
872 The EPF has been one of the most concerned characterization factors ever used in the life
873 cycle assessment (LCA) domain (39, 40). It is described as:
874 EPF FF EF
875 where FF is the fate factor describing P pathways through environmental media; EF is the
876 effect factor representing the effects of a marginal P increase on freshwater ecosystems.
877 In recent years, rather than site-generic EPFs, spatial differentiation is being more and
878 more considered since the EPF may depend on various site-specific characteristics, e.g.
879 the distance between the emission site and freshwaters, the climate conditions and the
880 aquatic species (41-45).
881 3.2 Scaling the fate factors
882 This study applies the spatially explicit FFs (in days) of P emissions to freshwater
883 developed by Helmes et al. (2012) (46), which identifies advection, retention and water
884 use as three important processes that resulted in P removal. This model assumes
885 phosphorus removal by advection is equal to the rate of water removal in every cell,
886 namely the ratio between phosphorus discharge and the total volumes of lakes, reservoirs
- 43 -
887 and rivers. We update the lake and reservoir volumes using World Water Development
888 Report II (http://wwdrii.sr.unh.edu/) and Global Lakes and Wetlands Database
889 (http://www.worldwildlife.org/pages/global-lakes-and-wetlands-database). However, this
890 model assumes that grid cells where annual evaporation exceeds precipitation have no
891 existing water bodies and thus no FF values, such as in the northwest of China. The
892 original data with a resolution of 0.5° are converted to a resolution of 5 arc-minutes.
893 3.3 Mapping the effect factors
894 The sensitivity of freshwater ecosystem to a marginal increase in TP concentration can be
895 measured by the EF indicator, with higher EF meaning more sensitive. The EF models (in
896 PDF m3 kg-1) are derived from Azevedo et al. (2013) (45) by differentiating the empirical
897 log-logistic relationships between the PDF (dimensionless) of temperate freshwater
898 heterotrophic species and total P (TP) concentration in two types of water bodies (lakes
899 and streams). PDF is given as the ratio of species loss to maximized species richness
900 along a TP concentration gradient. EF means the marginal increase in PDF due to a
901 marginal increase in TP concentration, described as (45):
1 ln10 PDF ln10 902 ec EF 1 2 c ln10ln10 1 ec
903 where c is the TP concentration in water bodies (lakes or streams); is the log10 TP
904 concentration at which PDF is 0.5 (value for lakes: -3.399; value for streams: -3.130);
905 is the slope of the log-logistic function (value for lakes: 0.377; value for streams: 0.426).
- 44 -
906 The specific TP concentration information for Chinese lakes and streams is used to
907 characterize the spatial pattern of EFs. Since TP concentration index is not publicly
908 accessible from the National Surface Water Quality Automatic Monitoring Network
909 (http://58.68.130.147/#), we acquire data of TP concentration ranges of the selected
910 streams primarily from water quality reports of main river basins where eutrophic
911 phenomena are commonly observed, e.g. the Yangtze River Basin and the Huaihe River
912 Basin. The TP concentrations of lakes and reservoirs are mainly collected from peer-
913 reviewed articles. The majority of these articles are based on field measurements and
914 published in Chinese. If there are multiple studies for a water body, we select the TP
915 concentration records according to the nearest sampling dates. 66% of the TP
916 concentration data are from 2010 to 2014, but data for some freshwaters are traced back
917 to 2006. We exclude articles that only provided rough TP information, such as above a
918 certain standard value or trophic state. The TP concentration values of a total of 116 lakes
919 and reservoirs are applied in this study, as presented in Table S6. Little information is
920 found about P enrichment for water bodies in the west of China, especially the Tibet
921 Autonomous Region, which is intensive with salt waters.
922 For the lakes and streams without published TP concentration information, we assume TP
923 concentration is correlated with geospatial variables and build the statistical relationships
924 between them. These variables include crop harvested area, livestock density and human
925 population, used in the spatial disaggregation of anthropogenic P losses to surface waters;
926 land use area, obtained from Global Land Cover 2000
927 (http://bioval.jrc.ec.europa.eu/products/glc2000/products.php); precipitation and mean
928 temperature, obtained from WorldClim current condition datasets
- 45 -
929 (http://www.worldclim.org/) with global climate observations between 1950 and 2000;
930 TP concentration in topsoil (47) and digital elevation model from HydroSHEDS
931 (http://hydrosheds.cr.usgs.gov/), showing the underlying surface characteristics. The
932 different resolutions of these variables are all rescaled to match our spatial resolution of 5
933 arc-minutes.
934 To establish the relationship between TP concentration (as a response variable) and the
935 geospatial predictor variables mentioned above, we apply the machine learning algorithm
936 of Random Forest to predict the TP concentration in the unknown cells of lakes and
937 streams, respectively. The regression shows well agreement between the observed and
938 predicted TP concentration values (Fig. S9). The possible underestimation of TP
939 concentration in some areas with high observed TP values is believed to have limited
940 influences on the EF values, as the descending trend of the marginal EF models tended to
941 flatten out above certain TP concentration ranges (>0.5mg/L).
942 4 Comparison with previous estimations
943 4.1 Natural flows
944 Atmospheric deposition
945 Our estimate of P deposition is larger than that previously thought. Since the same
946 calculation method for P flow through atmospheric deposition (multiplying the area by
947 the average bulk P deposition rate) has been adopted, the result difference can be
948 attributed to the bulk P deposition rate. We use a more robust bulk P deposition rate
949 range of 0.37-1.98 kg ha-1 yr-1 in 1980-2012, which is derived from experimental studies
- 46 -
950 carried out in several Chinese watersheds during the last three decades. For example, the
951 bulk P deposition at four sampling sites in Changlejiang Watershed was ever monitored
952 from March 2009 to December 2011 and the bulk P deposition rate (1.98 kg ha-1 yr-1) was
953 found primarily dependent on precipitation (48); the bulk atmospheric deposition rate of
954 P in Lake Taihu was measured to be 0.84 kg ha-1 yr-1 during May to November 2007
955 (49), 0.37-0.77 kg ha-1 yr-1 in 2002-2003 (50) and 0.376 kg ha-1 yr-1 in 1987 (51). Due to
956 the lack of domestic data sources in earlier years, the P deposition rate before the 1980s is
957 determined according to studies in other regions of the world. For example, Pierrou first
958 roughly estimated the annual rates of phosphorus deposition over the global continents
959 and oceans by citing P deposition ranges of 0.27-0.7 kg ha-1 yr-1 (52). Graham and Duce
960 summarized that the average P deposition rate in North America was 0.3 kg ha-1 yr-1 in
961 the 1960s using the bulk precipitation (i.e. both wet and dry deposition) monitoring data
962 (53). Using these rates, the global atmospheric deposition in the 1970s was estimated to
963 contain about 3-4.5 Tg-P yr-1 (53, 54). Mahowald et al. further modeled the global
964 distribution of TP deposition based on the compilation of published measurement data,
965 whose result was more conservative because particles larger than 10 μm were excluded
966 (55).
967 However, previous Chinese studies either used obsolete bulk P deposition rate from
968 earlier times or excluded this P flow from the model. For example, Chen et al. (2008)
969 used 0.07-0.36 kg ha-1 yr-1 as P input coefficient by precipitation with no citations (56);
970 The NUFER model developed by Ma et al. (2010)(57) assumes that the P inputs via
971 atmospheric deposition are negligible and has been further adopted by a number of
972 publications such as Hou et al. (2013) (58) and Bai et al. (2014) (59); Wang et al. (2011)
- 47 -
973 cited the average wet and dry P deposition of 0.25 kg ha-1 yr-1 from a Ph.D. dissertation in
974 2002 (60); Wu et al. (2015) applied a P deposition rate of <0.1 kg ha-1 yr-1 from an earlier
975 global estimate published in 2000 (61).
976 Wind erosion
977 A few direct estimates of world’s total P to the atmosphere by wind erosion have given a
978 figure of around 3.8 Tg-P yr-1 globally, though most are derived from the Sahara desert
979 (53). The average wind erosion rate of P is thus calculated to be 0.255 kg ha-1 yr-1 and
980 adopted in this study. Other studies applied the value that is no more than 0.1 kg ha-1 yr-1
981 (54, 62), which may be attributed to the underestimation of crustal materials.
982 Riverine transfer
983 Our analysis estimates that, currently, about 200-300 Gg of P is delivered from China to
984 the open ocean annually based on present-day riverine water mass flux and P
985 concentration measurements. However, it is difficult to calculate the trend of riverine P
986 that entered the oceans due to the lack of measurement data over the past longer periods
987 of time. Therefore, we determine that P riverine transfer to open oceans accounts for 16%
988 of the total P runoff to inland waters in recent seven years and use the ratio to roughly
989 estimate earlier P riverine transfer. Other attempts have estimated the historical global
990 total of the natural riverine P based on measurement data from less human-affected areas
991 and the conclusion is that the current riverine P would be decreased by at least a factor of
992 three in pre-human times (63, 64).
993 Sea spray
- 48 -
994 According to previous studies, sea spray involves less P than the other natural processes.
995 We acquire this P flow for China by scaling the global value to the Chinese territory.
996 However, the global P flow is poorly known and the estimated values varied by orders of
997 magnitude, e.g. 330 Gg-P yr-1 (53), 22 Gg-P yr-1 (65) and 4.9 Gg-P yr-1 (55). Instead of
998 taking the average of these estimates (66), we adopt the first value in this study because it
999 takes account of particles in all sizes and the estimation method is more reasonable. The
1000 uncertainty is believed to have little influence on the whole analysis but deserves further
1001 research.
1002 4.2 Anthropogenic flows
1003 We compare our results with several representative country-level publications related to
1004 Chinese anthropogenic P cycles over the last decade. Some key similarities and
1005 differences are briefly discussed as follows.
1006 Liu et al. (2004) developed a static SFA model to track the national economy’s P flows
1007 for 1996, regardless of P stock and accumulation within the economy (67). The net total
1008 P input into Chinese economy in 1996, consisting of domestic extraction, imports and
1009 exports, was 4,188 Gg, 15% smaller than our calculation. The difference can be ascribed
1010 to the fact that we consider the international trade of phosphate rocks and animal
1011 products. Meanwhile, the P losses during the mining and beneficiation processes are
1012 excluded in their estimation. In P chemical production, they assumed that 90% of P ores
1013 were utilized for fertilizer industry while according to our estimation based on system
1014 balance, the proportion is 13% less than their assumption. At the output side, they
1015 estimated P discharge from human activities to water environment at 1,136 Gg, while our
- 49 -
1016 results show anthropogenic P discharge to surface waters was 763Gg in 1996. The
1017 inconsistency could be attributed to the P discharge coefficients of P chemical production
1018 (e.g. fertilizer, detergents) and animal husbandry. For example, they cited the discharge
1019 coefficient of phosphoric acid production from the older edition of Handbook for
1020 industrial pollutants production and discharge and emission coefficients as their
1021 parameters for fertilizer production. However, we consider P generation from high-
1022 concentration P fertilizer production, which was responsible for merely 18% of total
1023 fertilizer products in 1996, because little P was contained in wastewater generated from
1024 low-concentration P fertilizer production.
1025 Chen et al. (2008) focused on Chinese agricultural phosphorus flows in 2004, including
1026 both cultivation and grazing (56). Although similarities in both studies has been found on
1027 the output side of cultivation system (crop uptake and crop residue estimations), there is
1028 certain inconsistency at the input side. The dominant contributors are the estimations of
1029 reused human and animal excreta to arable land, which can be possibly caused by the
1030 differences in excreta generation, P content and re-utilization rate parameters. For
1031 example, they may have overestimated the excreta generation and P content parameters,
1032 especially annual human urine excretion per capita (700 kg cap-1 yr-1) and its P content
1033 (0.13%). It causes their annual human P excretion per capita value five times higher than
1034 that in our analysis, which is determined according to daily nutrient intake and body
1035 metabolism research. They determined that 77% of animal excreta, 30% of urban human
1036 excreta and 94% of rural human excreta are applied on arable land, while we set the rates
1037 in the 2000s to be 40-65% (vary among animal types), 10% and 60%, respectively.
1038 Accordingly, in 2004, the total P surplus in the arable land, both determined through
- 50 -
1039 system balance, was 7,007 Gg according to their calculation and 3,862 Gg in our
1040 analysis.
1041 Ma et al. (2012) explored the life-cycle P metabolism in China from 1984 to 2008 with a
1042 stock and flow model, including 4 subsystems and 76 equations (68). Although the
1043 analytical models somehow differ from each other, the overall temporal trend of P flow
1044 and stocks in their study resembles that of our quantification. For example, their analysis
1045 agreed with our results about the transformation China took from a net importer to a net
1046 exporter of P materials after 2000. They estimated the 25-year P accumulation in China’s
1047 agricultural soils is 38.3 Tg, 13% less than our results; and the accumulated amount of P
1048 to natural waters is 19 Tg, 8% less than our results; but the accumulated amount of P to
1049 natural soils is 105.3 Tg, nearly twice bigger than our results. Since no original parameter
1050 values were provided in their publication, we are unable to address any further on this
1051 discrepancy.
1052 Hou et al. (2013) applied the NUFER model to analyze the changes of P flows through
1053 the food production and consumption chain and their key driving forces at the national
1054 scale from 1980 to 2010 (58). Their overall conclusions are consistent with ours that P
1055 inputs and losses in the food chain increased continuously in the last three decades,
1056 primarily driven by the increase of population and demand of animal products. Both
1057 studies show the results that animal production (including managed aquaculture) is the
1058 dominant contributor of P losses to ground and surface waters in 2010. However, 25%
1059 less P losses to waters from animal production is found in this study. The possible
1060 explanation for the gap is that part of animal manure is stockpiled in non-arable land
- 51 -
1061 rather than leached or discharged. Furthermore, this study distinguishes aquaculture from
1062 animal husbandry to illustrate the impact of its rapid development on freshwater P loads.
1063 Bai et al. (2014) accounted the P uses and losses through the whole pig production chain,
1064 with a refinement of production systems, breeding periods, feed types, etc. (59) Through
1065 the comparison, we find P contained in the slaughtered pigs annually has kept consistent
1066 between the two studies over the last half century. However, there are differences in P
1067 contained in pig excretion and its whereabouts, which could be caused by the
1068 quantification model. In their study, P flow in the form of pig excreta relied on the
1069 independently calculated feed input and slaughtered pig output, while we quantify the P
1070 flow in the form of pig excreta independently to track back the feed input. We select the
1071 latter method because the current animal breeding standards about the recommended feed
1072 input and its compositions provide the most appropriate values that should be considered
1073 in practice but animals are usually overdosed in order to gain weight faster. Besides,
1074 since we do not distinguish between production scales, the historic whereabouts of
1075 manure fluctuated less than their study. For example, according to their analysis, with the
1076 rapid spread of intensive production, more nutrients would be lost since P utilization rate
1077 in intensive production is merely 30-40% but approaches 100% in traditional household
1078 production. However, we generally determine the average ratio of pig manure P applied
1079 to arable land at 65%. Another point to be appreciated from their study is the inclusion of
1080 animal’s accidental death.
1081 Sattari et al. (2014) applied a Dynamic P Pool Simulator (DPPS) model to estimate future
1082 P demand in China’s arable land by considering the role of residual soil P in crop
1083 production (69). They calculated P inputs and outputs from 1970 to 2010 at provincial, - 52 -
1084 regional and country scales, which included: 1) fertilizer and animal manure, 2)
1085 weathering and atmospheric deposition, 3) runoff and erosion, and 4) crop uptake. They
1086 determined the P loss rate to aquatic systems through surface runoff is about 10% of the
1087 soil P inputs from fertilizer and manure during 1970-2010, while 5-18% is applied in this
1088 study. From their estimates, China’s chemical P fertilizer consumption exceeded 6.3 Tg-P
1089 in 2010, with an additional 1.8 Tg-P from animal manure spreading in croplands, 8% and
1090 25% higher than our results, respectively. Above all, both studies indicate that a
1091 significant amount of P has been accumulated in China’s arable land, which may sustain
1092 crop production with a considerable lag time.
1093 Wu et al. (2015) analyzed P use efficiencies (PUEs) of Chinese crop production–
1094 consumption system during 1980-2012 (61). They found that PUE in the subsystems of
1095 crop cultivation is less than 40%, which is quite similar to our findings (41% in 2012,
1096 Table S2). PUE of animal breeding subsystem is 36.5% in their study and 21% in our
1097 study. It is the calculation methods of PUE that whether or not to include reused animal
1098 excreta as effective P use cause such difference. In this study, we exclude the reused
1099 animal excreta from effective P use since it is not the primary purpose of P use in animal
1100 husbandry. The overall PUE of human consumption from our estimation was 17% in
1101 2012, which is lower than those of urban and rural human consumption (40% and 35%)
1102 in their study. The bias may be caused by multiple factors related to the calculation of
1103 input and output flows. For example, they do not consider the return of urban human
1104 excreta to cropland, while we include other durable goods inputs, such as clothes, paper
1105 and wood.
1106 4.3 Unconsidered flows - 53 -
1107 This study provides a comprehensive analysis of Chinese P cycles by characterizing P
1108 flows associated in the life cycle of P rock extraction and utilization. Therefore, P flows
1109 associated with the utilization of other mineral resources, such as fossil fuels and metals
1110 where P is regarded as impurities, are not taken into account. However, the total amount
1111 of unconsidered P flows could be estimated through the systematic mass balance. The P
1112 emissions from other sources, such as combustions, primary biogenic aerosol particles,
1113 volcanic activities, and phosphine have increased from <15 Gg-P yr-1 in total before the
1114 1950s to 1.25 Tg-P yr-1 in 2012. Furthermore, we briefly describe the results of some
1115 specific P flows in previous estimates.
1116 Combustion
1117 A previous global P emission estimation of 0.07 Tg-P yr-1 from all combustion sources
1118 (55) was updated by Wang et al. (2015) and the new estimate of 1.8 Tg-P yr-1 accounts
1119 for over 50% of the total P emissions to the atmosphere (66). It suggests a larger
1120 anthropogenic perturbation of the P cycle than thought, owing to the high P contents in
1121 coal and biomass. Asia, including China, is now responsible for 43% of the global total P
1122 emissions due to the rapid increase of fossil fuel burning.
1123 Primary biogenic aerosol particles
1124 Jaenicke (2005) roughly estimated an global emission of primary biogenic aerosol
1125 particles (PBAP) at 1,000 Tg yr-1 (70), much larger than another estimate of 164 Tg yr-1
1126 (55). Based on these two estimates, Wang et al. (2015) derived an average of 580 Tg yr-1
1127 and scaled the P emission to 0.58 Tg-P yr-1 (66). In this study, we ignore the PBAP
- 54 -
1128 emissions since there is no clearly defined measurement method for PBAP and the
1129 modeling results are regarded tentative (55).
1130 Volcanic activity
1131 Globally, there are few actual estimates of the P amount emitted from volcanoes. Since
1132 estimates of sulfur emissions from volcanoes are spatially available, Mahowald et al.
1133 (2008) estimated a P emission of 0.006 Tg-P yr-1, assuming the P content in most lava
1134 rocks was three times of the sulfur content (55). However, as this flow is not important at
1135 most locations except over regions adjacent to volcanic activities (55) and most Chinese
1136 volcanos are dormant during the study period, we exclude this flow from this study.
1137 Phosphine (PH3)
1138 PH3 is a gaseous reducing substance emitted from areas adjacent to freshwaters and
1139 paddy fields (71, 72). Based on previous site measurements of the phosphine emission
1140 rates, Wang et al. (2015) estimated the global P emission as phosphine from freshwater
1141 wetlands and rice paddies of 0.00020 Tg-P yr-1, accounting for 0.005% in total P
1142 emissions (66). However, we ignore the phosphine emission since its contribution is not
1143 significant in the P biogeochemical cycle.
1144 5 P reserve depletion and long-term management
1145 While the world-average longevity of P reserves has been prolonged to several hundred
1146 years (73) after the International Fertilizer Development Center reported an 8-fold
1147 increase of P reserves in Morocco in 2010 (74), the estimate of Chinese reserves by the
1148 US Geological Survey (USGS) has not been updated for years. Being the second largest - 55 -
1149 possessor of P resources in the world, China identified more than 20 billion tonnes of
1150 phosphate rocks in 2012 (75). However, among them only 3.7 billion tonnes of rocks
1151 could be economically extracted or produced in the present situation (76), with the
1152 average P2O5 content of 25%. Noting that the Chinese production may be underestimated
1153 as only large mines are covered by the USGS inventory, we derive the annual production
1154 of P rocks from China Statistical Yearbooks rather than the USGS reports. Therefore, it is
1155 urgent to determine the deadline of Chinese P reserve depletion to prepare for the
1156 impending P crisis.
1157 Using the reserve, annual production and international trade data, the deadline of Chinese
1158 P reserve depletion can be estimated from both the supply and demand perspectives. As P
1159 rocks are the source commodity applied in downstream sectors to produce multiple kinds
1160 of P-containing commodities, the amount of P resources in a geographically delineated
1161 area that are available for the annual P production ( PR,pro ) can be described as:
1162 PPPR, pro P ,rock E ,rock
1163 where
1164 PPPP,rock C ,rock NE ,rock
1165 and PP,rock is the annual P production in the form of P rocks; PE,rock is the P emissions
1166 generated from the production of P rocks; PC,rock is the annual P consumption in the form
1167 of P rocks; PNE,rock is the net P export in the form of P rocks, which is equal to the annual P
1168 export minus the annual P import in the form of P rocks.
- 56 -
1169 However, for each downstream P-containing commodity i, the annual P input ( PRi, ) can be
1170 described as:
1171 PPPR,,, i P i E i
1172 PPPP,,, i C i NE i
1173 where PR,i is the annual P production in the form of commodity i; PE,i is the P emissions
1174 generated from the production of commodity i; PCi, is the annual P consumption in the
1175 form of commodity i; PNE, i is the net P export in the form of commodity i.
1176 Assuming the traded downstream commodities are produced or consumed in the same
1177 way as in the area, PC, rock can be further described as:
1178 PPPPC,rock C NE E
1179 where PC is the annual P consumption in the form of consumer products only by people
1180 living inside the area; PNE is the net P export of all downstream commodities, including
1181 both intermediate and consumer products; PE is the P emissions generated from the
1182 production of all downstream commodities.
1183 For the simplicity, we ignore the P losses into the environment caused by the production
1184 of exported P-containing commodities and make a conservative estimation of the P
1185 depletion time.
1186 If the area maintains its status quo of population, technology and economy, the deadline
1187 of P reserve depletion from the production perspective ( Tpro ) can be described as:
- 57 -
1188 S T pro P P,rock
1189 where S is the P reserves that can be economically produced at the time of the
1190 determination to make suitable products; while the deadline of P reserve depletion from
1191 the consumption perspective ( Tcon ) can be described as:
1192 S T con PPP,rock NE
1193 Although Tpro is the commonly calculated period for reserve depletion, we think Tcon can
1194 state the influence of foreign trades on domestic resource reserves since it represents the
1195 period left over when domestic P reserves only need to maintain domestic consumption
1196 demands.
1197 Tpro and Tcon are calculated to be 32.35 yr and 34.38 yr for China to maintain its status quo,
1198 respectively, which means the reserve depletion can be slightly slowed down for merely 2
1199 years when only the domestic demands need to be fulfilled. We note the change is not
1200 significant for China since foreign trade accounts for less than 10% of the P production,
1201 but the consumption-based deadline can delay the reserve depletion in a country whose
1202 economy relies primarily on P-related foreign trade, such as Morocco.
1203 Due to intensive extraction in the last decade, longevity of Chinese domestic P reserve
1204 was shorten greatly from 176 years in 2002 to 33 years in 2012, similar to those of most
1205 other critical mineral resources (Fig. S1). However, P has not been listed as one of the
1206 national scarce mineral resources by Ministry of Land and Resources of P. R. China.
1207 Currently, national scarce mineral resources only include those that have great influence
- 58 -
1208 on the national economy and for which China is more than 50% import reliant, including
1209 iron, copper, aluminum, nickel, chromium, manganese and potash (77).
1210 Therefore, we argue that there is a pressing need for a long-term efficient P management
1211 strategy from the life-cycle perspective (Fig. S8). In the near future, feasible options
1212 include but not limited to the improvement in industry, agriculture and human lifestyle.
1213 For example, the current PUE of cultivation system in China is 41%, half of that in the
1214 United States, the United Kingdom and other developed countries (Table S2). If it can be
1215 improved to the average level of 80% in the developed countries without impacting
1216 current crop yields, the total P input to agricultural land can be reduced from 9.7 Tg/yr to
1217 5 Tg/yr, saving more than 4.7 Tg-P/yr in the form of chemical fertilizers annually.
1218 Considering the upstream loss rates in rock mining, beneficiation and fertilizer
1219 production, it would avoid ~5.2 Tg-P in lithosphere from being exploited every year.
1220 Consequently, China only needs to extract ~7.3 Tg-P/yr to sustain human demands and P
1221 reserve depletion time can be delayed for more than 20 years. In the coming centuries,
1222 strategies should focus on recycling P from the accumulated P pools, which can also limit
1223 P mobilization and transport to freshwaters(78). From centuries to millennia, most
1224 fertilizer P will have been delivered from land to oceans by 3600 (79). Naturally, P is
1225 utilized by marine biota repeatedly until buried in the deep sediments to re-form rocks
1226 through tectonic uplift over millions of years (63). To utilize these P resources in
1227 advance, it will be necessary to develop feasible technologies for the extraction of P
1228 buried in the sediments of deep open oceans.
1229 6 Uncertainty analysis
- 59 -
1230 We note that there are uncertainties associated with the study in aspects of flow
1231 quantification and eutrophication evaluation models and datasets. To reduce the
1232 uncertainties in P flow quantification modelling, we conduct interactive crosschecks
1233 among alternative calculation methods for each P flow to determine the most appropriate
1234 one. For example, large inconsistency is found in the quantification of crop products for
1235 human food, which could be determined either as the output flow of compartment N10 or
1236 as the independently calculated input flow of compartment N11 based on residential
1237 annual consumption statistics. Although the latter independent way should be considered
1238 in priority, this flow is eventually calculated as the output flow of the upstream
1239 compartment. That is because we notice that part of crops are further processed into
1240 various food such as bread, alcohol and condiment, as well as enter the national strategic
1241 stockpiles, of which this study is not able to take account. Meanwhile, for each defined P
1242 flow, efforts are made to assure the accuracy of activity data and parameter values in the
1243 context of various data sources. For instance, it is paradoxical that human P excretion
1244 coefficient from some existing literature by multiplying excreta generation and average P
1245 concentration data exceeds human P intake coefficient derived from other sources. We
1246 finally adopt the more reliable human P intake coefficient which is published every
1247 decade by the National Health and Family Planning Commission and multiply it with the
1248 observed body metabolism ratio to acquire human P excretion coefficient.
1249 Besides the crosschecks, we conduct the Monte Carlo simulation to quantitatively test the
1250 propagation of input (activity data and parameters) uncertainty and variability into the
1251 final results. We do this by providing a range in all activity data and parameters instead
1252 of using a single-point estimate. For activity data, uniform distributions are applied and
- 60 -
1253 different coefficients of variation (CVs) are set for the three historical periods in the
1254 consideration of the reliability of statistical systems: 0.3 (1600-1911), 0.2 (1912-1948),
1255 0.1 (1949-2012). Parameters are provided with the continuous distributions (triangular or
1256 uniform) by taking data quality into account as the possibility of appearance (Table S5).
1257 In general, and unless otherwise specified, we assume independence between input
1258 parameters so that each one can be independently sampled from its distributions. For
1259 parameters which can be traced back to more than one reference, the probability
1260 distributions are assumed to be triangular; while for parameters from one single
1261 reference, uniform distributions are applied and CVs are set according to the qualitative
1262 assessment of their data qualities. Parameters of high, moderate and low reliabilities are
1263 assumed to have CV values of 0.1, 0.2 and 0.3, respectively (80). It should be noted that
1264 constant standard variations (SDs) based on assumed CVs are assigned to parameters
1265 with time-series variations, such as proportion parameters, because otherwise it could
1266 lead to the amplification of parameter uncertainties. The ranges for parameters are, for
1267 some cases, manually adjusted to be positive with upper limits. Whenever extrapolation
1268 is used because of missing data in the long time series of activity data and parameters, an
1269 additional factor of 1.5 is multiplied to the original CV to reflect the uncertainty of data
1270 unavailability.
1271 The simulation model is run 10,000 trials by randomly selecting values from the input
1272 distributions to generate ranges of outcomes. Besides the single-point estimates, measures
1273 of uncertainty of the annual 102 P flows include means, medians, SDs, CVs, 5th, 25th, 75th
1274 and 95th percentiles, all shown in the auxiliary dataset: flow102.xlsx. It should be noted
1275 that the expectation of a function with independent random variables is not necessarily
- 61 -
1276 equal to the function of the expectation of these variables. The uncertainty results of key
1277 P flows in Fig.2A due to activity data and parameter together and separately are
1278 presented in Fig. S3 with the mean and the 5th and 95th percentiles given. In the time
1279 scale, all the key P flows have less uncertainties in the last century than before the 1910s,
1280 thanks to the more sophisticated and publicly available statistics and site-specific
1281 parameters. Furthermore, we find that uncertainties of most P flows are primarily
1282 attributed to activity data rather than parameters. The CVs of crop P and animal P are
1283 smaller than other P flows because of the relatively more complex calculation methods. It
1284 should also be noted that the sudden increase of uncertainties in certain years could be
1285 resulted from extrapolation due to data unavailability.
1286 7 Limitations and future improvements
1287 Though this study presents the temporal and spatial summary of Chinese P cycles and
1288 concomitant eutrophication potentials, the flow quantification methods, datasets and
1289 eutrophication evaluation models are not without limitations.
1290 Due to the complexity of natural and anthropogenic activities, this Chinese P cycle model
1291 is more explicit than previous estimations but still has some drawbacks. Commodities
1292 containing little amounts of P, such as toothpaste, food additives and water-treatment
1293 chemicals, are excluded from the system boundary. The utilization of other P-associated
1294 mineral resources where P is regarded as impurities, is also not taken into account.
1295 Another possible limitation may lie in the representativeness of the calculation methods
1296 for specific P flows along a long time span. For instance, P flow in the form of riverine
1297 transfer is ideally determined by multiplying the annual volume of riverine transfer and - 62 -
1298 the P concentration of rivers entering oceans, with the former data derived from national
1299 statistics while the latter data from field experiments. However, both data are available
1300 only for recent years (2006-2012). When independently quantifying this P flow in earlier
1301 years, either maintaining the same values or making assumptions of parameters can lead
1302 to huge bias. Therefore, this flow is extrapolated based on the average ratio between the
1303 riverine transfer and total freshwater inputs in known years.
1304 The time series of activity data and parameters, collected as much as possible from
1305 various global and national statistics, industrial reports and literature, may have
1306 incorporated temporal and geographical discrepancy. Despite the uncertainty test that is
1307 conducted to facilitate robustness, we note certain national average values adopted in this
1308 study actually differ significantly in both time and space. For instance, the P loss rates of
1309 paddy and dry land are affected by factors including soil type, precipitation, slope,
1310 temperature. There are also concerns whether the annual average can be represented by
1311 field investigation data which cover short periods like a few days. Furthermore, in case of
1312 no Chinese-specific data, global average values or values from others parts of the world
1313 are used, which may degrade the data representativeness. Further improvements in the
1314 accuracy of this study can be achieved through the acquisition of more precise activity
1315 data and parameters, in some cases by continuous monitoring and developing spatially
1316 differentiated maps.
1317 This analysis is the first attempt to evaluate the geographical distribution of
1318 eutrophication potential caused by anthropogenic P cycles, but both methodology and
1319 spatial resolution of the eutrophication evaluation model need additional work. Future
1320 efforts should be made to develop a Chinese-specific eutrophication potential model, with - 63 -
1321 the help of updated underlying data about surface runoff and site-specific TP monitoring
1322 data. It will not only improve the current resolution of fate factor model and supplement
1323 fate factors in semi-arid areas where evaporation exceeds precipitation on a yearly basis
1324 but also fit a more precise empirical log-logistic relationships.
1325 Despite these limitations, we believe conclusions from this study can broadly display the
1326 temporal trends of P cycle dynamics in China and diagnose the eutrophication potential
1327 hotspots across regions. Moreover, this study provides a useful framework for future
1328 quantitative analysis of P biogeochemical cycle and its environmental impacts.
1329
- 64 -
1330 Supporting figures and tables
1331
1332
1333 Fig. S1. Reserve depletion time and net import reliance plots of 12 kinds of primary
1334 mineral resources in 2002 (blue cycles) and 2012 (yellow cycles) in China. Reserve
1335 depletion time is calculated by dividing the annually reported reserve base and ore production.
1336 Net import reliance for a particular mineral is presented as the proportion of net imports (imports
1337 minus exports) among the apparent consumption (production plus net imports), with the red dash
1338 line indicating that 50% of domestic consumption is supplied by net imports. For the simplicity,
1339 interannual stock changes are not covered here. Negative net import reliance values are adjusted
1340 to 0 for certain commodities (for example, phosphate rock) of which China was a net exporter in
1341 that year.
1342
- 65 -
1343
1344 Fig. S2. Hierarchical structure of the Chinese P cycle model. Blue (light to dark)
1345 compartments are natural media, while yellow (light to dark) compartments are P-related
1346 human activities. The horizontal axis indicates the complexity of P flows by expanding the
1347 boundary from natural compartments to human socioeconomic activities, and the vertical axis
1348 represents the general direction of P pathways.
1349
- 66 -
1350
1351 Fig. S3. Uncertainty of key P flows using MC simulation. The annual key P flows and
1352 uncertainties are shown as calculation values (lines) combined with ranges between the 5th and
1353 95th percentiles (shaded areas). The left column presents the full uncertainties due to all activity
1354 data and parameters; the center column presents the uncertainties due to activity data while the
1355 right column presents the uncertainties due to parameters.
- 67 -
1356 1357 Fig. S4. Temporal changes of P flows after a log10 transform on the y-axes of Fig. 2. (A)~
1358 (D), refer to Fig. 2A, C, E, F, respectively. The difference between P flows during 1600-1900,
1359 which may not be visible in the associated four panels of Fig. 2, can be found in a log 10 scale.
1360
- 68 -
1361
1362 Fig. S5. Waste P flows generated from rural and urban human consumption in the past six
1363 decades. A generalized portrait of conditions is provided by averaging annual P flux values every
1364 ten years. R indicates rural areas and U indicates urban areas.
1365
1366
- 69 -
1367 1368 Fig. S6. Spatial distribution of anthropogenic P discharge in 2012. (A) The overall layout of
1369 anthropogenic P discharge was aggregated from specific human activities. (B-G), Spatial pattern
1370 of P discharge from P-associated industries (B), cultivation (C), animal husbandry (D),
1371 aquaculture (E), human consumption (F) and waste treatment (G). P emissions are clustered in
1372 the eastern and central regions of mainland China, with the highest per grid cell P discharge in
1373 cosmopolitan Shanghai. Henan, Heilongjiang and Shandong are ranked as the top three
1374 provinces contributing to the agricultural P runoff. P discharge from animal husbandry is mainly - 70 -
1375 observed in Sichuan, Henan and Yunnan, while river-intensive areas such as Hubei, Jiangsu and
1376 Guangdong contribute most to aquaculture P losses.
1377
- 71 -
1378
1379 Fig. S7. Province ranking by P discharge, presented as regional totals (left column), per
1380 area (central column) and per person (right column). The colors of names differentiate
1381 provinces in east China (red), central China (green), west China (blue) and northeast China
1382 (black), defined by Chinese government in 2011 based on their socioeconomic situations
1383 (www.stats.gov.cn). The colors of bars distinguish GDP per capita in each province.
1384
- 72 -
1385
1386 Fig. S8. Schematic diagram of strategies for closing P cycle. Infeasibility primarily indicates
1387 the technical and economic constraints of the proposed options. The blue sector represents an
1388 estimate of the current feasible management options. Longer-term options are shown in yellow
1389 and red sectors.
1390
- 73 -
1391
1392 Fig. S9. Predicted vs. observed scatter plots of total P concentrations in (A) lakes and (B)
1393 streams in China. The specific total P (TP) concentrations in Chinese lakes and streams are
1394 used to characterize the grid-based eutrophication potential factors. Observed TP concentrations
1395 in some freshwaters are collected from water quality reports and peer-reviewed articles with field
1396 measurements and further extrapolated to other water bodies via Random Forest regression with
1397 some geospatial variables (see Materials and Methods for details).
1398
1399
- 74 -
1400
1401 Fig. S10. Estimated historical changes in (A) Chinese population and (B) arable land, 1600-
1402 2012. Population data are compared with the following sources: United Nations (1959) (81), Ho
1403 (1959) (20), Perkins (1969) (13), Liang (1980) (12), Zhao and Xie (1988) (21), Zhang (1991) (82),
1404 Jiang (1993) (11), Yang (1995) (83), Maddison (2008) (22), Shi (2012) (31) and Population
1405 Statistics (http://www.populstat.info/). Arable land data are compared with the following sources:
1406 Perkins (1969) (13), Liang (1980) (12), Wu (1985) (23), Shi (1989) (28), Zhang (1991) (82), Jiang
1407 (1995) (84), Zheng et al. (1998) (85), Ramankutty and Foley (1999) (24), Zhou (2001) (86), Shi
- 75 -
1408 (2011) (27), HYDE v3.1 (2011) (26), He et al. (2012) (25) (only 18 provinces included), official
1409 statistics for 1912-1948 (15, 16, 87) and official statistics for 1949-2012 (17).
1410
- 76 -
1411 Table S1. Methodological comparisons of studies for phosphorus cycles in mainland
1412 China. The publication used for comparison and the language (EN: English, CN: Chinese);the
1413 temporal scale (spatial scale: Mainland China); the processes in the phosphorus cycle model (14
1414 processes in total, √ indicates processes of which both input and output flows were included, △
1415 indicates processes of which either input or output flows were included, while × indicates
1416 processes that were excluded from the studies); the number of products in the model (divided into
1417 4 categories of minerals, chemicals, plants and animals. NA: Not available); the number of flows
1418 in the model (including stock of change). *Underestimation may happen due to the possible
1419 aggregation of flows between systems.
Publication, Language Temporal scale Process Product Flow*
arable land
Plants
Mining
-
Animals
Minerals
Chemicals
Atmosphere
Aquaculture
Inland Inland waters
Marine waters
Non
od/feed processing
Plant Plant production
International trade
Animal husbandry
Solid Solid waste disposal
Human consumption
Chemical production
Fo
Wastewater treatment
This study, EN 1600-2012 √ √ √ √ √ √ √ √ √ √ √ √ √ √ 1 5 21 12 102
Liu (2004), EN (67) 1996 × △ △ × △ √ √ √ × √ √ √ √ △ 1 2 NA NA 38
Xu (2005), CN (88) 1980, 1990, 2001 △ △ △ × × △ √ √ × √ √ √ √ √ 0 1 17 7 41
Liu (2005), CN (89) 2002 × △ △ × × √ √ √ × √ √ × × √ 1 5 NA NA 55
Liu (2006), CN (90) 2000 × △ △ × √ √ √ √ × × √ √ √ √ 1 5 18 4 45
Li (2007), CN (91) 2004 × × × × × △ √ △ × △ △ × × √ 0 1 1 0 12
Chen(2008), EN (56) 2004 △ × △ × × △ √ △ × × △ × × × 0 1 12 4 31
Lu (2008), CN (92) 1981-2006 × △ △ × × × △ △ △ × √ √ √ × 0 0 9 NA 18
Fan (2009), EN (62) Prehuman, 1950, 2002 √ √ √ √ △ √ √ √ △ × √ × × √ 1 5 7 4 44 Ma (2010) , EN (57) 2005 Ma (2012) , EN (93) 1980, 2005 △ △ △ △ × △ √ √ √ √ √ × × √ 0 1 17 9 25 Hou (2013), EN (58) 1980-2010
Wang (2011), EN (60) 2006 △ × △ × × △ √ √ × √ △ × × √ 0 2 17 4 22
Ma (2012), EN (68) 1984-2008 × △ △ × √ √ √ √ × × √ √ √ √ 1 3 14 4 57
Bai (2014), EN (59) 1960-2010 △ △ △ × × △ √ √ × √ √ × × √ 0 2 4 1 22
Wu (2015), EN (61) 1980-2012 △ △ △ × △ √ √ √ △ √ √ △ △ √ 1 2 11 5 42 1420
1421 - 77 -
1422 Table S2. Comparison of P balance in different countries. Adjustments are made to compare 1423 P balances of plant production, animal production, food and feed processing systems from 1424 literature. Plant production includes both crop and pasture cultivation and excludes crop straws 1425 that internally recycled to the arable land; animal breeding and slaughtering are integrated in 1426 some studies and cannot be separated, as well as food and feed processing. P intensity (PIN, 1427 expressed in kg-P ha-1) is calculated by dividing total P use in plant production by arable and 1428 permanent crop areas from FAOSTAT database (http://faostat3.fao.org/). Dependence on 1429 chemical fertilizers (DCF) represents the percentage of chemical fertilizers in total P inputs to 1430 domestic agriculture, other than composts, sewage sludge, excreta, etc. Phosphorus use 1431 efficiency (PUE) represents the percentage of useful P outputs as products in total P inputs, 1432 excluding recycled P outputs in byproducts and residues or directions of useful P (imports/exports 1433 or domestic use). Per capita food-P (expressed in kg P cap-1 yr-1) shows domestic per capita P 1434 consumption in food. Population and Gross domestic product (GDP) per capita (constant 2005 1435 U.S. dollars) are derived from World Bank (http://data.worldbank.org/). Ratio of animal products 1436 (RAP) represents the P percentage of animal products in total consumed food, unavailable in 1437 some studies.
Animal Food and feed Inconsistency and adjustment GDP per Plant production Food consumption Country (Year) production processing notice capita PIN DCF PUE PUE PUE Per capita RAP Switzerland (2006) (94) 56,641 84.5 16% 86% 17% 68% 0.91 41%
USA (2007) (95, 96) 45,431 19.3 54% 81% 19% 49% 0.70-1.25 45-49%
Netherlands (2005) (97) 41,199 77.5 24% 53% 32% 79% 1.13 61%
Austria (2004-2008) (98) 39,380 35.1 32% 77% 24% 69% 1.00 -
Sweden (2000) (99) 38,516 12.7 49% 80% 22% 68% 0.65 - Animal slaughtering included UK (2009) (100) 37,277 44.1 27% 81% 17% 73% 0.60 - in animal production, no adjustment Japan (2005) (101) 35,781 95.5 63% 29% 9% 93% 0.78 36% Pasture cultivation included Australia (2007) (102) 35,591 5.4 84% 28% 42% 90% 0.58 - in animal production, no adjustment France (2002-2006) (103) 33,476 34.2 43% 68% 21% 83% 1.24 - Animal slaughtering included Finland (1995-1999) (7) 30,120 22.4 61% 46% 25% 98% 1.10 80% in animal production, no adjustment South Korea (2005) (104) 18,657 109.0 78% 32% 24% 75% 0.88 -
Malaysia (2007) (105) 6,008 30.4 95% 34% 25% 86% 1.31 -
Turkey (2001) (106) 5,687 8.8 86% 80% 20% 60% 0.70 16% Pasture cultivation not China (2012) 3,345 80.0 78% 41% 21% 88% 0.91 24% included in plant production, added Food and feed processing not Thailand (2006) (107) 2,813 52.5 89% 32% 25% - 0.44 - included, no adjustment Food and feed processing not Zimbabwe (2000) (108) 676 22.4 23% 15% 1% - 0.85 6% included, no adjustment Food and feed processing not Uganda (2010) (109) 391 3.3 3% 39% 12% - 0.59 22% included, no adjustment 1438
- 78 -
1439 Table S3. Sources of activity data in the Chinese P cycle quantification. Relatively
1440 comprehensive time series during 1949-2012 are directly taken from the national statistical
1441 yearbooks and international databases, except for industrial P chemicals. Discrete statistical
1442 information before the 1950s, the best available though not sound enough, is processed with
1443 statistical methods (e.g. removing anomalies, extrapolation, and interpolation) to prepare the
1444 annual time-series. Note that there still are some activity data that are not available though may
1445 have nonzero values, or have limited values and could be taken as negligible in this study.
Activity data 1600-1911 1912-1948 1949-2012
TA National Bureau of Statistics of China (1981- Liu (1995) (110) Directorate of Statistics (1948) (15) D 2013) (17) IW National Bureau of Statistics of China (1981- Replaced with data during 1949-2012 Replaced with data during 1949-2012 D 2013) (17) MW National Bureau of Statistics of China (1981- Replaced with data during 1949-2012 Replaced with data during 1949-2012 D 2013) (17) AL National Bureau of Statistics of China (1981- HYDE v3.1 (2011) (26); Liang (1980) (12) HYDE v3.1 (2011)(26); Liang (1980) (12) D 2013) (17) WD National Bureau of Statistics of China (1981- Not available Not available D 2013) (17) NFW National Bureau of Statistics of China (1981- Not available Directorate of Statistics (1948) (15) D 2013) (17) GMW National Bureau of Statistics of China (1981- Replaced with data during 1949-2012 Replaced with data during 1949-2012 D 2013) (17) NSW National Bureau of Statistics of China (1981- Not available Directorate of Statistics (1948) (15) D 2013) (17) PR,ne D Negligible Not available United Nations Statistics Division (111) PR National Bureau of Statistics of China (1985- Negligible Not available D 2013) (18) FT ,ne D Negligible Not available United Nations Statistics Division (111) FT National Bureau of Statistics of China (1985- Negligible Negligible D 2013) (18) FA,ne D Negligible Negligible United Nations Statistics Division (111) FA Gong (2013)(112); Liu (2005)(113); Du Negligible Negligible D (2002)(114); Zhou (1999)(115) EP,ne D Negligible Not available United Nations Statistics Division(111) EP Gong (2013) (112); Tao (2008) (116); Liu Negligible Negligible D (2005) (113); Wang (1985) (117) Ministry of Commerce of P. R. China (1984- P,ne Negligible Negligible 2013) (118); National Bureau of Statistics of D China (1985-2013) (18) P National Bureau of Statistics of China (1985- Negligible Negligible D 2013) (18) DG,ne D Not available Not available United Nations Statistics Division (111) DG National Bureau of Statistics of China (1985- Negligible Not available D 2013) (18) RC,ne D Not available Not available United Nations Statistics Division (111) Directorate of Statistics (1948) (15); RC National Bureau of Statistics of China (1981- Guo (2001) (32); Shi (2012) (31) Department of Agriculture and Commerce 2013) (17) D (1914-1921) (16) Directorate of Statistics (1948) (15); SA National Bureau of Statistics of China (1981- Not available Department of Agriculture and Commerce 2013) (17) D (1914-1921) (16) LA,ne Directorate of Statistics (1948) (15); Negligible Directorate of Statistics (1935, 1940, 1945) United Nations Statistics Division (111) D (87) - 79 -
Activity data 1600-1911 1912-1948 1949-2012 Li (2003) (33); Directorate of Statistics (1948) LA Perkins (1969) (13); Xu (2009) (14); Li (2006) National Bureau of Statistics of China (1981- (15); Department of Agriculture and (34); Xie (1959) (35); Wang (1958) (119) 2013) (17) D Commerce (1914-1921) (16) DR National Bureau of Statistics of China (1981- Negligible Not available D 2013) (17) Directorate of Statistics (1948) (15); EG National Bureau of Statistics of China (1981- Not available Department of Agriculture and Commerce 2013) (17) D (1914-1921) (16) Li (2003) (33); Directorate of Statistics (1948) DLA Perkins (1969) (13); Xu (2009) (14); Li (2006) National Bureau of Statistics of China (1981- (15); Department of Agriculture and (34); Xie (1959) (35); Wang (1958) (119) 2013)(17) D Commerce (1914-1921) (16) Li (2003) (33); Directorate of Statistics (1948) LA Perkins (1969) (13); Xu (2009) (14); Li (2006) National Bureau of Statistics of China (1981- (15); Department of Agriculture and (34); Xie (1959) (35); Wang (1958) (119) 2013) (17) D Commerce (1914-1921) (16) CW ,ne D Negligible Not available United Nations Statistics Division (111) CFW National Bureau of Statistics of China (1981- Negligible Negligible D 2013) (17) CSW National Bureau of Statistics of China (1981- Negligible Negligible D 2013) (17) RI ,ne D Negligible Not available United Nations Statistics Division (111) Directorate of Statistics (1948) (15); FL,ne Directorate of Statistics (1935, 1940, 1945) Negligible United Nations Statistics Division (111) D (87); Department of Agriculture and Commerce (1914-1921) (16) Directorate of Statistics (1948) (15); PO,ne Directorate of Statistics (1935, 1940, 1945) Negligible United Nations Statistics Division (111) D (87); Department of Agriculture and Commerce (1914-1921) (16) Directorate of Statistics (1948) (15); SU ,ne Directorate of Statistics (1935, 1940, 1945) Negligible United Nations Statistics Division (111) D (87); Department of Agriculture and Commerce (1914-1921) (16) MT ,ne D Negligible Not available United Nations Statistics Division (111) DR,ne D Negligible Not available United Nations Statistics Division (111) Directorate of Statistics (1948) (15); EG,ne Directorate of Statistics (1935, 1940, 1945) Negligible United Nations Statistics Division (111) D (87); Department of Agriculture and Commerce (1914-1921) (16) Directorate of Statistics (1948) (15); SU Directorate of Statistics (1935, 1940, 1945) National Bureau of Statistics of China (1981- Not available D (87); Department of Agriculture and 2013) (17) Commerce (1914-1921) (16) Directorate of Statistics (1948) (15); MT Perkins (1969) (13); Xu (2009) (14); Li (2006) National Bureau of Statistics of China (1981- Department of Agriculture and Commerce (34); Xie (1959) (35); Wang (1958) (119) 2013) (17) D (1914-1921) (16) AF ,ne D Negligible Not available United Nations Statistics Division (111) UHM Perkins (1969) (13); Zhao and Xie (1988) (21); Maddison (2007) (22); Directorate of Statistics National Bureau of Statistics of China (1981-
D Maddison (2007) (22) (1948) (15) 2013) (17) RHM Perkins (1969) (13); Zhao and Xie (1988) (21); Maddison (2007) (22); Directorate of Statistics National Bureau of Statistics of China (1981-
D Maddison (2007) (22) (1948) (15) 2013) (17) USW National Bureau of Statistics of China (1981- Negligible Extrapolated from data during 1949-2012 D 2013) (17) DW National Bureau of Statistics of China (1981- Extrapolated from data during 1949-2012 Extrapolated from data during 1949-2012 D 2013) (17) UHM Perkins (1969) (13); Zhao and Xie (1988) (21); Maddison (2007) (22); Directorate of Statistics National Bureau of Statistics of China (1981-
D Maddison (2007) (22) (1948) (15) 2013) (17) RHM Perkins (1969) (13); Zhao and Xie (1988) (21); Maddison (2007) (22); Directorate of Statistics National Bureau of Statistics of China (1981-
D Maddison (2007) (22) (1948) (15) 2013) (17) 1446
1447 1448
- 80 -
1449 Table S4. International Convention for Harmonized Commodity Description and Coding
1450 System (HS) code of P-related products
No. Product description Commodity code in HS 2012
C1 Natural phosphates 2510: Natural calcium phosphates, natural aluminum calcium phosphates and phosphatic chalk. C2 P concentrates 3103: Mineral or chemical fertilizers, phosphatic. C3 P fertilizers 3105: Mineral or chemical fertilizers containing two or three of the fertilizing elements nitrogen, phosphorus and potassium, excluding 3105.10 and 3105.90.
C4 Feed additives 2835.25: Calcium hydrogenorthophosphate (“dicalcium phosphate”).
C5 Elemental P 2804.70: Phosphorus.
C6 Pesticides and herbicides Not used as data source. 3401: Soap; organic surface-active products and preparations for use as soap, in the form of bars, cakes, molded pieces or shapes, whether or not containing soap; organic surface-active products and preparations for washing the skin, in the form of liquid or cream and put up for retail sale, whether or not containing soap; paper, wadding, felt and nonwovens, impregnated, C Soap and synthetic detergents 7 coated or covered with soap or detergent. 3402: Organic surface-active agents (other than soap); surface-active preparations, washing preparations (including auxiliary washing preparations) and cleaning preparations, whether or not containing soap, other than those of heading 34.01. 1006.10: Rice in the husk (paddy or rough). 1001: Wheat and meslin. 1005: Maize (corn). 1007: Grain sorghum. 1003: Barley. 1008: Buckwheat, millet and canary seeds; other cereals. 1201: Soya beans, whether or not broken. 0701: Potatoes, fresh or chilled. 0714: Manioc, arrowroot, salep, Jerusalem artichokes, sweet potatoes and similar roots and tubers with high starch or inulin content, fresh, chilled, frozen or dried, whether or not sliced or in the form of pellets; sago pith. 5201: Cotton, not carded or combed. C8 Raw crop products 1202: Ground-nuts, not roasted or otherwise cooked, whether or not shelled or broken. 1205: Rape or colza seeds, whether or not broken. 1207: Other oil seeds and oleaginous fruits, whether or not broken. 5301: Flax, raw or processed but not spun; flax tow and waste (including yarn waste and garnetted stock). 5302: True hemp (Cannabis sativa L.), raw or processed but not spun; tow and waste of true hemp (including yarn waste and garneted stock). 5303: Jute and other textile bast fibres (excluding flax, true hemp and ramie), raw or processed but not spun; tow and waste of these fibers (including yarn waste and garnetted stock). 1212.93: Sugar cane 1212.91: Sugar beet 2401: Unmanufactured tobacco; tobacco refuse. 07: Edible vegetables and certain roots and tubers, excluding 0701 and 0714. 0103: Live swine. 0102: Live bovine animals. 0104: Live sheep and goats. C Live animals 9 0105: Live poultry, that is to say, fowls of the species, ducks, geese, turkeys and guinea fowls. 0101: Live horses, asses, mules and hinnies. 0106.14: Rabbits and hares.
C10 Seafood and freshwater products 03: Fish and crustaceans, molluscs and other aquatic invertebrates. 1006.20: Husked (brown) rice. C11 Rice 1006.30: Semi-milled or wholly milled rice, whether or not polished or glazed. 1006.40: Broken rice.
C12 Flour 11: Products of the milling industry; malt; starches; inulin; wheat gluten.
1507: Soya-bean oil and its fractions, whether or not refined, but not chemically modified. 1508: Ground-nut oil and its fractions, whether or not refined, but not chemically modified. 1512: Sunflower-seed, safflower or cotton-seed oil and fractions thereof, whether or not refined, but not chemically modified. C Oil 13 1514: Rape, colza or mustard oil and fractions thereof, whether or not refined, but not chemically modified. 1509: Olive oil and its fractions, whether or not refined, but not chemically modified. 1511: Palm oil and its fractions, whether or not refined, but not chemically modified.
1701: Cane or beet sugar and chemically pure sucrose, in solid form. 1702: Other sugars, including chemically pure lactose, maltose, glucose and fructose, in solid form; sugar syrups not containing C Sugar 14 added flavoring or coloring matter; artificial honey, whether or not mixed with natural honey; caramel. 1703: Molasses resulting from the extraction or refining of sugar. 0203: Meat of swine, fresh, chilled or frozen. 0201: Meat of bovine animals, fresh or chilled. 0202: Meat of bovine animals, frozen. C15 Meat 0204: Meat of sheep or goats, fresh, chilled or frozen. 0205: Meat of horses, asses, mules or hinnies, fresh, chilled or frozen. 0207: Meat and edible offal of the poultry, fresh, chilled or frozen. 0208: Other meat and edible meat offal, fresh, chilled or frozen.
0401: Milk and cream, not concentrated nor containing added sugar or other sweetening matter. 0402: Milk and cream, concentrated or containing added sugar or other sweetening matter. 0403: Buttermilk, curdled milk and cream, yogurt, kephir and other fermented or acidified milk and cream, whether or not concentrated or containing added sugar or other sweetening matter or flavored or containing added fruit, nuts or cocoa. 0404: Whey, whether or not concentrated or containing added sugar or other sweetening matter; products consisting of natural C16 dairy and egg milk constituents, whether or not containing added sugar or other sweetening matter, not elsewhere specified or included. 0405: Butter and other fats and oils derived from milk; dairy spreads. 0406: Cheese and curd. 0407: Birds' eggs, in shell, fresh, preserved or cooked. 0408: Birds' eggs, not in shell, and egg yolks, fresh, dried, cooked by steaming or by boiling in water, molded, frozen or otherwise preserved, whether or not containing added sugar or other sweetening matter. - 81 -
No. Product description Commodity code in HS 2012 2302: Bran, sharps and other residues, whether or not in the form of pellets, derived from the sifting, milling or other working of cereals or of leguminous plants. 2304: Oil-cake and other solid residues, whether or not ground or in the form of pellets, resulting from the extraction of soyabean oil. 2305: Oil-cake and other solid residues, whether or not ground or in the form of pellets, resulting from the extraction of ground- nut oil. C feeds 17 2306: Oil-cake and other solid residues, whether or not ground or in the form of pellets, resulting from the extraction of vegetable fats or oils, other than those of heading 2304 or 2305. 2308: Vegetable materials and vegetable waste, vegetable residues and by-products, whether or not in the form of pellets, of a kind used in animal feeding, not elsewhere specified or included. 2309: Preparations of a kind used in animal feeding. 1451
1452
1453
1454
- 82 -
1455 Table S5. Parameters used in the Chinese P cycle quantification. Mean values are
1456 synthesized from estimates in the listing literature. Three kinds of probability distributions are
1457 considered, namely triangular distribution (minimum, mode, maximum) and uniform distribution
1458 (minimum, maximum). Constant standard deviations are defined based on coefficients of
1459 variation and mean values marked with symbol *.
Name Unit Mean value Distribution Source Quality Zhang (2012) (48); Luo et al. (2011) 2001-2012: 1.5; Triangular (0.84, 1.68, 1.98) (50); Zhai et al. (2009) (49); Fan et al. AD 1981-2000: 0.75; Triangular (0.37, 0.88, 1) kg ha-1 yr-1 (2010) (120); Huang (2001) (51), High 1949-1980: 0.38; Triangular (0.25, 0.39, 0.5) P1 Pierrou (1976) (52), Graham and Duce before 1949: 0.2 Triangular (0.07, 0.25, 0.28) (1979) (53) Chen et al. (2010) (121); Chen et al. AD East&South China seas: 98 Uniform (34, 162) (2008) (122); Zhang et al. (2007) (123); g ha-1 yr-1 High P 2 Yellow&Bohai seas: 341 Uniform (186, 496) Zhang and Liu (1994) (124); Wan et al. (2002) (125) WE Graham and Duce (1979) (53); Smil kg ha-1 yr-1 0.255 Triangular (0.05, 0.3, 0.415) Low P (2000) (54) WT Newman (1995) (126); Zhao and Zeng kg ha-1 yr-1 0.525 Uniform (0.05, 1) Low P (2005) (127) RL P kg ha-1 yr-1 0.305 Uniform (0.01, 0.6) Smil (2000) (54) Low WD kg m-3 0.5 Triangular (0.2, 0.28, 1.02) Guan (1994) (128) High P1 WD Smil (2000) (54); Antikainen et al. / 0.025% Triangular (0.01%, 0.015%, 0.05%) Low P 2 (2004) (129) Ministry of Water Resources of P. R. RT 2006-2012:13~19% (varying each year); China (2007-2013)(130); Sun et al. / / High P before 2006: average in 2006-2012 (2009) (131); Zhou et al. (2012) (132); Li et al. (2009) (133) Fish: 0.2021% Uniform (0.1321%, 0.2721%) FW Shrimp and Crab: 0.2322% Uniform (0.1518%, 0.3126%) / Yang et al. (2002) (134) Medium P Shell: 0.1767% Uniform (0.1155%, 0.2379%) Other: 0.2037% Uniform (0.1331%, 0.2743%) SS P Mt yr-1 0.33 Uniform (0.16, 0.50) Graham and Duce (1979) (53) Low Fish: 0.1851% Uniform (0.1210%, 0.2492%) Shrimp and Crab: 0.2036% Uniform (0.1331%, 0.2741%) SW Yang et al. (2002) (134); Zhou et al. / Shell: 0.1272% Uniform (0.0831%, 0.1713%) Medium (2004) (135) P Seaweed: 0.022% Uniform (0.0144%, 0.0296%) Other: 0.0677% Uniform (0.0442%, 0.0912%) PR National Bureau of Statistics of China / 13.1% / Medium P (17); Yu et al. (2008) (136) 2001~2012: 25%; Uniform (20.7%, 29.3%) BF 1981~2000: 15%*; Uniform (9.8%, 20.2%) / Gong (2013) (112) Medium P 1959~1980: 5%; Uniform (0%, 10.19%) before 1959: 0% / GG t t-1 product 0.12 Triangular (0.1, 0.13, 0.13) State Council of China (2008) (137) Medium P1 GG / 0.12% Triangular (0.04%, 0.13%, 0.19%) Lu (1980) (138) Low P 2 2001~2012: 90%; Uniform (77%, 100%) CR 1981~2000: 80%; Uniform (67%, 93%) / Gong (2013) (112) High P 1959~1980: 75%*; Uniform (62%, 88%) before 1959: 0% / WWG g t-1 product 318 Uniform (310, 326) State Council of China (2008) (137) Medium P1 WW 1600s~2012: 0~96% National Bureau of Statistics of China / / High P (varying each year) (17); Wang (1990) (139) Gong (2013) (112); Standardization FT High concentration:45% Uniform (37.2%, 52.8%) Administration of China (2006) (140); / High P Low concentration: 15% Uniform (12.4%, 17.6%) Standardization Administration of China (2009) (141) Huang et al. (2013) (142); Wang et al. HFT 1949~2012: 0~86.7% / / (2006) (143); Lin (1997) (144); Duan High (varying each year) P (1992) (145) WWG g t-1 product 228.67 Triangular (178.8, 198.3, 308.9) State Council of China (2008) (137) Medium P2 PG t t-1 product 2.56 Triangular (2.09, 2.78, 2.81) State Council of China (2008) (137) Medium P1
- 83 -
Name Unit Mean value Distribution Source Quality PG / 1.86% Uniform (1.22%, 2.50%) Gong (2013) (112) Medium P 2 PG 1995~2012: 0~24% Ministry of Industry and Information / / High P3 (varying each year) Technology of China (2011) (146) FA Standardization Administration of China / 19% Uniform (15.7%, 22.3%) High P (2008) (147) WWG t t-1 product 3 Uniform (1.96, 4.04) State Council of China (2008) (137) Medium P3 FAWW P / 0.2% Uniform (0.13%, 0.27%) Lei (2004) (148) Medium
SWG P t t-1 product 2.79 Uniform (1.82, 3.76) State Council of China (2008) (137) Medium FASW P / 5% Uniform (4.13%, 5.87%) Tan (2004) (149) Medium FP t t-1 product 0.136 Triangular (0.1, 0.151, 0.157) State Council of China (2008) (137) Medium P1 FP / 23% Triangular (18%, 25%, 26%) Gong (2013) (112) Medium P 2 SL Gong (2013) (112); State Council of t t-1 product 9.2 Triangular (8, 9.75, 9.85) Medium P1 China (2008) (137) SL / 1.46% Uniform (0.95%, 1.97%) Gong (2013) (112) Medium P 2 National Bureau of Statistics of China SL 1949~2012: 0~90% (17); Ministry of Industry and / / Medium P3 (varying each year) Information Technology of China (2009) (150) WWG g t-1 product 655.75 Triangular (215, 282.3, 1470) State Council of China (2008) (137) Medium P4 Zhang (1999) (151); Hu (2003) (152); OP 1949~1996~2012: 0~61~50% / / China Chemical Industry Environmental High (varying each year) P1 Protection Association (2008) (153) OP / 13% Uniform (10.75%, 15.25%) Field survey High P 2 WWG g t-1 product 12476.56 Triangular (146, 3493.7, 33790) State Council of China (2008) (137) Medium P5 DG 2000~2012: 0~80% Zhang (2005) (154); China National / / High P1 (varying each year) Light Industry Council (155) Ministry of Environmental Protection of DG / 0.48% Uniform (0.4%, 0.56%) China (2009) (156); Standardization High P 2 Administration of China (2009) (157) DG Standardization Administration of China / 3.49% Uniform (2.89%, 4.1%) High P3 (2009) (158) WWG g t-1 product 4.5 Uniform (2.94, 6.06) State Council of China (2008) (137) Medium P6 SD National Development and Reform kg ha-1 Varying by crop category and year / High P Commission (159) Rice: 0.4%; Uniform (0.26%, 0.54%) Wheat: 0.5%; Uniform (0.33%, 0.67%) Maize: 0.4%; Uniform (0.26%, 0.54%) Millet: 0.28%; Uniform (0.18%, 0.38%) Sorghum: 0.36%; Uniform (0.24%, 0.48%) Other grains: 0.3%; Uniform (0.2%, 0.4%) Beans: 0.6%; Uniform (0.39%, 0.81%) Tubers: 0.16%; Uniform (0.1%, 0.22%) Cotton fiber: 0.48%; Uniform (0.31%, 0.65%) RC Cotton seed: 0.78%; Uniform (0.51%, 1.05%) Yuan et al. (2011) (160); He et al. (1999) / Medium P Peanut: 0.5%; Uniform (0.33%, 0.67%) (161); Xu (2005) (88) Rape seed: 0.9%; Uniform (0.59%, 1.21%) Sesame: 0.5%; Uniform (0.33%, 0.67%) Sunflower: 0.61%; Uniform (0.4%, 0.82%) Other oilcrops: 0.63%; Uniform (0.30%, 0.95%) Bast fibers: 0.06%; Uniform (0.04%, 0.08%) Sugarcane: 0.14%; Uniform (0.09%, 0.19%) Sugar beet: 0.034%; Uniform (0.022%, 0.046%) Tobacco: 0.29%; Uniform (0.19%, 0.39%) Vegetable:0.04% Uniform (0.026%, 0.054%)
- 84 -
Name Unit Mean value Distribution Source Quality Rice: 1; Uniform (0.83, 1.17) Wheat: 1.1; Uniform (0.91, 1.29) Maize: 1.2; Uniform (0.99, 1.41) Millet: 1.6; Uniform (1.32, 1.88) Sorghum: 1.6; Uniform (1.32, 1.88) Other grains: 1.6; Uniform (0.77, 2.43) Beans: 1.5; Uniform (1.24, 1.76) Tubers: 0.5; Uniform (0.41, 0.59) He et al. (1999) (161); Xu (2005) (88); CS Cotton fiber: 9.2; Uniform (7.61, 10.79) Bi et al. (2009) (162); Gao et al. (2009) / Peanut: 1.5; Uniform (1.24, 1.76) (163); Zhang and Zhu (1990) (164); Xie Medium P1 Rape seed: 3; Uniform (2.48, 3.52) et al. (2011) (165); Xie et al. (2011) Sesame: 2.5; Uniform (2.07, 2.93) (166) Sunflower: 2; Uniform (1.65, 2.35) Other oilcrops: 2.25; Uniform (1.08, 3.42) Bast fibers: 1.7; Uniform (1.41, 1.99) Sugarcane: 0.25; Uniform (0.21, 0.29) Sugar beet: 0.25; Uniform (0.21, 0.29) Tobacco: 0.8; Uniform (0.66, 0.94) Vegetable: 0 / Rice: 0.13%; Uniform (0.123%, 0.137%) Wheat: 0.08%; Uniform (0.073%, 0.088%) Maize: 0.152%; Uniform (0.138%, 0.166%) Millet: 0.101%; Uniform (0.084%, 0.118%) Sorghum: 0.146%; Uniform (0.121%, 0.171%) Other grains: 0.192%; Uniform (0.092%, 0.292%) Beans: 0.2%; Uniform (0.131%, 0.169%) Tubers: 0.283%; Uniform (0.234%, 0.332%) Cotton fiber: 0.15%; Uniform (0.131%, 0.169%) CS He et al. (1999) (161); Xu (2005) (88); / Peanut: 0.163%; Uniform (0.148%, 0.179%) High Yan (2008) (167) P 2 Rape seed: 0.144%; Uniform (0.127%, 0.162%) Sesame: 0.15%; Uniform (0.124%, 0.176%) Sunflower: 0.112%; Uniform (0.093%, 0.131%) Other oilcrops: 0.142%; Uniform (0.068%, 0.216%) Bast fibers: 0.06%; Uniform (0.050%, 0.070%) Sugarcane: 0.14%; Uniform (0.116%, 0.164%) Sugar beet: 0.044%; Uniform (0.036%, 0.052%) Tobacco: 0.169%; Uniform (0.140%, 0.198%) Vegetable: 0% / 2001~2012: 15%; Uniform (13%, 17%) CS 1991~2000: 12%; Uniform (10%, 14%) / P3 1981~1990: 8%*; Uniform (6%, 10%) 1600s~1980: 5% Uniform (3%, 7%) 2001~2012: 30%; Uniform (27%, 33%) CS 1991~2000: 25%; Uniform (22%, 28%) / P 4 1981~1990: 18%*; Uniform (15%, 21%) Gao et al. (2009) (163); Ministry of 1600s~1980: 15% Uniform (12%, 18%) Agriculture of China (2010) (168); Gao High 2001~2012: 20%; Uniform (14%, 26%) et al. (2002) (169); Liu (1987) (170); Gu CS 1991~2000: 35%*; Uniform (29%, 41%) et al. (2013) (171) / P5 1981~1990: 50%; Uniform (44%, 56%) 1600s~1980: 65% Uniform (59%, 71%) 2001~2012: 35%; CS 1991~2000: 28%; / / P 6 1981~1990: 24%; 1600s~1980: 15% Dry: 0.233 Uniform (0.193, 0.273) Yang (2004) (172); Zhang et al. (1993) LH kg ha-1 Paddy, 1974~2012: 0.5728; Triangular (0.2772, 0.3912, 1.05) (173); Tong et al. (2010) (174); Li et al. High P before 1973: 0.3368 Uniform (0.2785, 0.3951) (2007) (175) Zhang et al. (1993) (173); Zhang et al. Dry, 2000~2012: 3.0515; Triangular (0.366, 1.1285, 7.66) (1997) (176); Duan et al. (2006) (177); RF before 1990s: 1.39 Triangular (0.44, 1.13, 2.6) Li et al. (1998) (178); Huang et al. kg ha-1 High P Paddy, 2000~2012: 2.5617; Triangular (1.186, 2, 4.5) (2004) (179); Wang (2006) (180); Shi et before 1990s: 1.235 Triangular (0.46, 1.235, 2.01) al. (2002) (181); Li et al. (2006) (182); Yuan et al. (2003) (183) Pig: 460; Uniform (301, 619) Cattle: 3600; Uniform (2353, 4847) LA Sheep: 280; Uniform (183, 377) g cap-1 Poultry: 13; Triangular (12, 12, 14) Huo (2002) (184) Medium P Horse/Mule/Camel: 2700; Uniform (2400, 3000) Donkey: 1300; Triangular (1200, 1200, 1400) Rabbit: 20 Uniform (13, 27) DR P / 0.073% Uniform (0.048%, 0.098%) Yang et al. (2002) (134) Medium EG P / 0.162% Uniform (0.106%, 0.218%) Yang et al. (2002) (134) Medium Pig: 7.86; Triangular (4.66, 7.96, 10.96) Cattle: 27.13; Triangular (26.76, 27.04, 27.59) Sheep: 3.33; Triangular (1.23, 3.27, 5.48) He et al. (1999) (161); Yang (2002) AE Poultry: 0.46; Triangular (0.32, 0.43, 0.64) (185); Sheldrick et al. (2003) (186); Wu g cap-1 day-1 High P1 Horse/Camel: 17.99; Triangular (13.49, 18.57, 21.92) (2005) (187); China Agricultural Donkey: 10.88; Uniform (9, 12.77) University (1997) (188) Mule: 8.48; Uniform (7.86, 9.09) Rabbit: 0.47 Uniform (0.39, 0.55)
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Name Unit Mean value Distribution Source Quality Pig, 2000-2012: 65%; Uniform (42.5%, 87.5%) 1990-1999: 80%; Uniform (60%, 100%) before 1990: 90% Uniform (80%, 100%) Cattle, 2000-2012: 55%; Uniform (36.0%, 74.1%) 1990-1999: 70%; Uniform (45.8%, 94.3%) before 1990: 90% Uniform (80%, 100%) Sheep, 2000-2012: 40%; Uniform (26.1%, 53.9%) 1990-1999: 55%; Uniform (36.0%, 74.1%) AE before 1990: 70% Uniform (45.8%, 94.3%) / P 2 Poultry, 2000-2012: 55%; Uniform (36.0%, 74.1%) 1990-1999: 70%; Uniform (45.8%, 94.3%) before 1990: 90% Uniform (80%, 100%) Horse/Donkey/Mule, 2000-2012: 44%; Uniform (28.8%, 59.2%) 1990-1999: 59%; Uniform (38.6%, 79.4%) before 1990: 74% Uniform (48.4%, 99.6%) Rabbit, 2000-2012: 55%; Uniform (36.0%, 74.1%) 1990-1999: 70%; Uniform (45.8%, 94.3%) before 1990: 90% Uniform (80%, 100%) AE Xu (2005) (88);Xing and Yan (1999) / 10%; Uniform (6.5%, 13.5%) Medium P3 (189);Yang et al. (2010) (190) Pig, 2000-2012: 25%; 1990-1999: 10%; before 1990: 0% Cattle, 2000-2012: 35%; 1990-1999: 20%; before 1990: 0% Sheep, 2000-2012: 50%; 1990-1999: 35%; AE before 1990: 30% / / P 4 Poultry, 2000-2012: 35%; 1990-1999: 20%; before 1990: 0% Horse/Donkey/Mule, 2000-2012: 46%; 1990-1999: 31%; before 1990: 26% Rabbi, 2000-2012t: 35%; 1990-1999: 20%; before 1990: 0% FF Fish: 1.93; Uniform (1.5, 2.36) Liu (2011) (191); Dai (2010) (192); t t-1 product High P1 Shrimp/Crabs: 3.275 Uniform (1, 5.55) Chen et al. (2005) (193) FF Fish: 0.98%; Triangular (0.6%, 0.64%, 1.7%) Liu (2011) (191); Dai (2010) (192); / High P 2 Shrimp/Crabs: 1.2% Uniform (1%, 1.4%) Chen et al. (2005) (193) SF Fish: 1.7; Uniform (1.5, 1.9) Zhang (2001) (194); Jie et al. (2011) t t-1 product Medium P1 Shrimp/Crabs: 8 Uniform (2, 14) (195) SF Fish: 1.55%; Uniform (1, 2.1) Zhang (2001) (194); Jie et al. (2011) / Medium P 2 Shrimp/Crabs: 1.09% Uniform (0.08, 2.1) (195) Cotton seed: 0.016%; Uniform (0.01%, 0.022%) PO Beans: 0.007%; Uniform (0.005%, 0.009%) / Peanuts: 0.015%; Uniform (0.01%, 0.02%) Yang et al. (2002) (134) Medium P1 Rape seed: 0.009%; Uniform (0.006%, 0.012%) Sesame/Sunflower: 0.004% Uniform (0.003%, 0.005%) SU P / 0.007% Uniform (0.003%, 0.011%) Yang et al. (2002) (134) Medium Pig: 0.162%; Uniform (0.106%, 0.218%) MT Cattle: 0.168%; Uniform (0.110%, 0.226%) / Sheep: 0.146%; Uniform (0.095%, 0.197%) Yang et al. (2002) (134) Medium P1 Poultry: 0.139%; Uniform (0.122%, 0.156%) Others: 0.170% Uniform (0.111%, 0.229%) Cotton seed: 100%; / Peanuts: 55%; Uniform (36%, 74%) PO Rape seed: 100%; / / Sesame/Sunflower: 70%. Uniform (46%, 94%) Ni (1989) (196); Wu (2011) (197) Medium P 2 Beans: 2001~2012: 80%; Uniform (66%, 94%) 1991~2000: 60%; Uniform (39%, 81%) before 1990: 40% Uniform (26%, 54%) Cotton seed: 25%; Uniform (20.7%, 29.3%) PO Beans: 22%; Uniform (18.2%, 25.8%) / Peanuts: 50%; Uniform (41.3%, 58.7%) Zhou (2012) (198) High P3 Rape seed: 48%; Uniform (39.7%, 56.3%) Sesame/Sunflower: 45% Uniform (37.2%, 52.8%) Pig: 9.89%; Uniform (8.2%, 11.6%) MT Cattle: 18%; Uniform (14.9%, 21.1%) Wang et al. (2006) (199); Zhang et al. / Sheep: 23.5%; Uniform (19.4%, 27.6%) (2013) (200); Hu et al. (2009) (201); High P 2 Poultry: 17%; Uniform (14.1%, 19.9%) Zhan and Zeng (2001) (202) Others: 19.5% Uniform (16.1%, 22.9%) Pig: 75.20%; Triangular (60%, 80%, 85.6%) MT Cattle: 55.41%; Uniform (45.8%, 65%) Wang et al. (2006) (199); Zhang et al. / Sheep: 48.31%; Uniform (39.9%, 56.7%) (2013) (200); Hu et al. (2009) (201); High P3 Poultry: 72%; Triangular (60%, 76%, 80%) Zhan and Zeng (2001) (202) Others: 63% Uniform (51.9%, 73.6%) RI 1980~2012: 70%; Uniform (60%, 80%) / 1950-1979: 60% Uniform (50%, 70%) P1 before 1950: 50% Uniform (40%, 60%) Yao (1981) (203); Li (2007) (204) Medium RI / 20% Uniform (15%, 25%) P 2
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Name Unit Mean value Distribution Source Quality RI 1980~2012: 10%; / 1950-1979: 20%; / P3 before 1950: 30% WH Li (2007) (204); Zhou and Chen (2001) / 75% Triangular (70%, 75%, 80%) Low P1 (205) WWG g t-1 raw material 2.4 Triangular (1.7, 2.7, 2.8) State Council of China (2008) (137) Medium P7 Pig: 3.5; Triangular (3, 3.5, 4) WWG Cattle:7.4; Triangular (6.3, 7.5, 8.4) g cap-1 Sheep: 1.1; Triangular (0.7, 1.1, 1.5) State Council of China (2008) (137) Medium P8 Poultry: 0.1; Uniform (0.07, 0.13) Others: 0.2 Uniform (0.13, 0.27) AF Formulated: 0.5% Triangular (0.35%, 0.43%, 0.72%) / Concentrated: 1.0% Triangular (0.1%, 0.8%, 2.1%) Zhang (2010) (206) Medium P Pre-mixed: 5.5% Uniform (3%, 8%) 1996~2012: 0.21; Uniform (0.094, 0.334) UHE 1986~1995: 0.24; Uniform (0.120, 0.352) g per-1 yr-1 1976~1985: 0.34*; Uniform (0.225, 0.464) P1 Ministry of Health of China (2005) before 1975: 0.45 Uniform (0.334, 0.571) (207); He et al. (1999) (161); Wang et al. Medium 1996~2012: 0.21; Uniform (0.087, 0.343) (2009) (208); Zhang et al. (1997) (209) RHE 1986~1995: 0.23; Uniform (0.105, 0.354) g per-1 yr-1 P1 1976~1985: 0.36*; Uniform (0.235, 0.485) before 1975: 0.45 Uniform (0.327, 0.578) UHE 2000~2012: 10%*; Uniform (4.8%, 15.2%) / 1990~1999: 40%; Uniform (34.8%, 45.2%) P 2 before 1990: 90% Uniform (84.8%, 95.2%) RHE 2000~2012: 60%; Uniform (54.8%, 65.2%) / 1990~1999: 80%; Uniform (74.8%, 85.2%) Gu et al. (2013) (171); Li (2004) (210); Low P 2 before 1990: 95% Uniform (90%, 100%) Chen and Tang (1999) (211) 2000~2012: 25%; Uniform (19.8%, 30.2%) UHE 1990~1999: 40%; Uniform (34.8%, 45.2%) / P3 1978~1989: 50%; Uniform (44.8%, 55.2%) before 1978: 100% / RSW Lu (2012) (212); Wan et al. (2012) kg per-1 yr-1 84.52 Triangular (47.82, 86.02, 119.72) (213); Sun (2010) (214); Duan and High P1 Zhang (2003) (215) Lu (2012) (212); Wan et al. (2012) RSW / 0.0575% Triangular (0.0165%, 0.022%, 0.134%) (213); Sun (2010) (214); Duan and High P 2 Zhang (2003) (215) WWG / 82.15% Uniform (67.92%, 96.38%) Ge and Ge (2010) (216) High P9 UWW He et al. (1999) (161); Wang et al. mg L-1 2.43 Triangular (1.2, 2.74, 3.34) High P1 (2004) (217); Liu et al. (2011) (218) UWW 0~87.3% Ministry of Housing and Urban-Rural / / High P2 (varying each year) Development of China (2012) (219) Lu (2012) (212); Sun (2010) (214); Zhang et al. (2007) (220); Liu et al. (2003) (221); Bai and Wu (2005) (222); RWW mg L-1 3.74 Triangular (1.22, 2, 8) Xu et al. (2007) (223); Ling et al. (2009) High P (224); Sun (2010) (225); Chen et al. (2004) (226); Zhang et al. (2004) (227); Liu and Peng (1997) (228) USW P / 0.0824% Uniform (0.0478%, 0.117%) He et al. (1999) (161); Wei (2004) (229) High HM P g per-1 1000 Triangular (750, 1125, 1125) Yang (1992) (230) Medium 2001~2012: 90%; Uniform (84.8%, 95.2%) WWT 1995~2000: 80%; Uniform (74.8%, 85.2%) / Jin (1999) (231); Li et al. (2008) (232) High P1 1990~1994: 50%; Uniform (44.8%, 55.2%) before 1990s: 30%* Uniform (24.8%, 35.2%) WWT 2001~2012: 35%; Uniform (28.1%, 41.9%) / 1991~2000: 20%*; Uniform (13.1%, 26.9%) Cui et al. (2013) (233) Medium P 2 before 1990s: 10% Uniform (3.1%, 16.9%) WWD g t-1 product 256 Uniform (250, 262) State Council of China (2008) (137) Medium P1 WWD g t-1 product 8.98 Triangular (4.65, 9.17, 13.12) State Council of China (2008) (137) Medium P2 WWD / 0.00018% Uniform (0.00012%, 0.00024%) State Council of China (2008) (137) Medium P3 WWD g t-1 product 2 Triangular (0.5, 2.5, 3) State Council of China (2008) (137) Medium P4 WWD g t-1 product 10937.725 Triangular (452, 2131, 30230) State Council of China (2008) (137) Medium P5 WWD g t-1 product 0.2 Uniform (0.13, 0.27) State Council of China (2008) (137) Medium P6 WWD g t-1 product 0.1 Triangular (0, 0.1, 0.2) State Council of China (2008) (137) Medium P7 Pig: 2.5; Triangular (0.6, 3.4, 3.5) WWD Cattle: 5.0; Uniform (3.27, 6.73) g t-1 product Sheep: 0.6; Uniform (0.2, 1.0) State Council of China (2008) (137) Medium P8 Poultry: 0.05; Uniform (0.02, 0.08) Others: 0.1 Uniform (0.06, 0.14) - 87 -
Name Unit Mean value Distribution Source Quality SWD 0~84.8% National Bureau of Statistics of China / / High P1 (varying each year) (17) SWD / Varying each year / P 2 SWD Zhai et al. (2006) (234); Wang and Ji / Varying each year / Medium P3 (1996) (235); Zhai (2005) (236) SWD / Varying each year / P 4 1460 1461
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1462 Table S6. TP concentration of lakes and reservoirs from published data
Location Lake/Reservoir name TP concentration (mg L-1) Data source Beijing Miyun Reservoir 0.025 Chen et al. (2007) (237) Tianjin Beidagang Reservoir 0.09 Chang et al. (2013) (238) Guanting Reservoir 0.12 Li et al. (2010) (239) Hebei Taolinkou Reservoir 0.14 Wang et al. (2011) (240) Shanxi Fenhe Reservoir 0.075 Sheng (2013) (241) Daihai Lake 0.133 He et al. (2010) (242) Inner Mongolia Hulun Lake 0.124 Inner Mongolia Environmental Protection Agency (2013) (243) Wuliangsu Lake 0.26 Li et al. (2006) (244) Dahuofang Reservoir 0.04 Wang et al. (2006) (245) Liaoning Huanren Reservoir 0.013 Wang et al. (2006) (245) Baishan Lake 0.049 Zhu (2009) (246) Chagan Lake 0.13 Dai et al. (2011) (247) Jilin Songhua Lake 0.074 Wang et al. (2006) (248) Yueliangpao Lake 0.124 Xiao et al. (2011) (249) Jingpo Lake 0.067 Liu et al. (2013) (250) Kulipao Lake 1.05 Cao et al. (2012) (251) Heilongjiang Longhupao Lake 0.278 Wang et al. (2009) (252) Xingkai Lake 0.145 Lu et al. (2011) (253) Shanghai Dianshan Lake 0.168 Dong et al. (2006) (254) Baima Lake 0.05 Liu et al. (2014) (255) Baoying Lake 0.05 Liu et al. (2011) (256) Changdang Lake 0.116 Dong et al. (2006) (254) Chengzi Lake 0.26 Huai’an Environmental Protection Agency (2009) (257) Chenghu Lake 0.292 Dong et al. (2006) (254) Dushu Lake 0.29 Dong et al. (2006) (254) Gaoyou Lake 0.051 Wei et al. (2010) (258) Gucheng Lake 0.048 Dong et al. (2006) (254) Hongze Lake 0.26 Huai’an Environmental Protection Agency (2009) (257) Jiangsu Jinji Lake 0.23 Dong et al. (2006) (254) Luoma Lake 0.08 Liu et al. (2011) (256) Mochou Lake 0.515 Dong et al. (2006) (254) Shaobo Lake 0.27 Liu et al. (2011) (256) Shijiu Lake 0.08 Liu et al. (2011) (256) Taihu Lake 0.09 Chen et al. (2013) (259) Tiangang Lake 0.11 Liu et al. (2011) (256) Xuanwu Lake 0.19 Liu et al. (2011) (256) Yangcheng Lake 0.088 Dong et al. (2006) (254) Zhejiang Xin’anjiang Reservoir 0.016 Han et al. (2013) (260) Baidang Lake 0.115 Liu et al. (2011) (261) Bohu Lake 0.17 Liu et al. (2011) (261) Caizi Lake 0.16 Gao et al. (2011) (262) Chaohu Lake 0.2 Yang et al. (2013) (263) Chengdong Lake 0.066 Liu et al. (2011) (261) Chengxi Lake 0.161 Liu et al. (2011) (261) Daguan Lake 0.209 Liu et al. (2011) (261) Dushan Reservoir 0.081 Shu et al. (2012) (264) Hualiangting Reservoir 0.036 Dong et al. (2006) (254) Huayuan Lake 0.125 Liu et al. (2011) (261) Huanghu Lake 0.224 Liu et al. (2011) (261) Anhui Longgan Lake 0.321 Liu et al. (2011) (261) Matang Lake 0.048 Dong et al. (2006) (254) Nvshan Lake 0.124 Liu et al. (2011) (261) Pogang Lake 0.127 Liu et al. (2011) (261) Qili Lake 0.161 Liu et al. (2011) (261) Qingcao Lake 0.143 Liu et al. (2011) (261) Shengjin Lake 0.091 Liu et al. (2011) (261) Taibai Lake 0.126 Dong et al. (2006) (254) Taiping Lake 0.073 Yang et al. (2013) (265) Tuohu Lake 0.152 Liu et al. (2011) (261) Wabu Lake 0.15 Liu et al. (2011) (261) Fujian No information available Cuoji Lake 0.145 Wu et al. (2013) (266) Hanchi Lake 0.1 Wu et al. (2013) (266) Junshan Lake 0.136 Ji et al. (2011) (267) Jiangxi Poyang Lake 0.067 Chen et al. (2013) (268) Qinglan Lake 0.035 Water Resources Department of Jiangxi (2010) (269) Wan’an Reservoir 0.03 Yin et al. (2012) (270) Zhelin Reservoir 0.037 Chen (2009) (271) Dongping Lake 0.05 He et al. (2010) (272) Nanyanghe Reservoir 0.192 Shu et al. (2012) (264) Shandong Weishan Lake 0.067 Shu et al. (2012) (264) Xiashan Reservoir 0.025 Liu (2013) (273) Zhaoyang Lake 0.121 Shu et al. (2012) (264) Gantang Reservoir 0.549 Dong et al. (2006) (254) Gushitan Reservoir 0.15 Henan Environmental Protection Agency (2013) (274) Henan Sanmenxia Reservoir 0.15 Zhou et al. (2008) (275) Xiuyahu Reservoir 0.075 Henan Environmental Protection Agency (2013) (274) Bao’an Lake 0.036 Wang et al. (2014) (276) Cehu Lake 0.038 Dong et al. (2006) (254) Hubei Chidong Lake 0.068 Dong et al. (2006) (254) Cihu Lake 0.141 Dong et al. (2006) (254) - 89 -
Location Lake/Reservoir name TP concentration (mg L-1) Data source Daye Lake 0.072 Dong et al. (2006) (254) Danjiangkou Reservoir 0.02 Nanyang Environmental Protection Agency (2012) Donghu Lake 0.172 Dong et al. (2006) (254) Futou Lake 0.035 Dong et al. (2006) (254) Honghu Lake 0.062 Dong et al. (2006) (254) Liangzi Lake 0.038 Dong et al. (2006) (254) Luhu Lake 0.051 Dong et al. (2006) (254) Nanhu Lake 0.24 Yang et al. (2008) (277) Wushan Lake 0.207 Dong et al. (2006) (254) Xiliang Lake 0.053 Wu et al. (2011) (278) Dongjiang Reservoir 0.012 He (2012) (279) Dongting Lake 0.044 Dong et al. (2006) (254) Hunan Hengling Lake 0.25 Zhong et al. (2011) (280) Huanggai Lake 0.05 Dong et al. (2006) (254) Guangdong Xinfengjiang Reservoir 0.014 Hu et al. (2008) (281) Hedi Reservoir 0.04 Zou et al. (2010) (282) Guangxi Xijin Reservoir 0.08 Zuo (2012) (283) Hainan Songtao Reservoir 0.044 Ge et al. (2007) (284) Chongqing No information available Sichuan No information available Guizhou Hongfeng Lake 0.04 Deng et al. (2011) (285) Chenghai Lake 0.046 Dong et al. (2012) (286) Dianchi Lake 0.5 Su (2011) (287) Yunnan Erhai Lake 0.04 Zhang (2011) (288) Fuxian Lake 0.013 Pan et al. (2009) (289) Yangzonghai Lake 0.033 Yuan et al. (2014) (290) Tibet No information available Shaanxi Shibianyu Reservoir 0.04 Huang et al. (2013) (291) Gahai Lake 0.256 Zheng et al. (2012) (292) Liujiaxia Reservoir 0.165 Zheng et al. (2012) (292) Gansu Sugan Lake 0.156 Zheng et al. (2012) (292) Tianchi Lake 0.052 Zheng et al. (2012) (292) Qinghai Qinghai Lake 0.09 Chen et al. (2013) (293) Ningxia No information available Ebi Lake 0.12 Xin et al. (2010) (294) Bosten Lake 0.023 Chuai (2011) (295) Jili Lake 0.03 Dong et al. (2008) (296) Xinjiang Kanas Lake 0.001 Chuai (2011) (295) Sayram Lake 0.017 Hao et al. (2013) (297) Wulungu Lake 0.03 Dong et al. (2008) (296) 1463 1464
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1465 Supporting datasets
1466 1. data.xlsx
1467 2. flow102.xlsx
1468
1469
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1470 Supporting references
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2088 265. Yang Y, Han Y, & Fan Z (2013) Evaluation of fishery water quality in Taiping 2089 Lake. Chin. J. Fish. 16(1):66-69.
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2093 267. Ji X (2011) Research on pollution loads and capacity of water environment of 2094 Junshan Lake. Master (Nanchang University, Nanchang).
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2102 271. Chen R (2009) Research on water quality appraisal and water environmental 2103 capacity of Zhelin Reservoir. Master (Nanchang University, Nanchang).
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2104 272. He D, Xing Y, Jiang R, & Cheng L (2010) Distribution of nitrogen and 2105 phosphorus in water and eutrophication assessment of Dongping Lake. Environ. 2106 Sci. Technol. (Wuhan, China) 33(8):45-48.
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2119 278. Wu L, Feng W, Zhang T, Xu H, & Yu Y (2011) The annual fluctuation of 2120 zooplankton community and its relation with environmental factors in Lake 2121 Xiliang, Hubei Province. J. Lake Sci. 23(4):619-625.
2122 279. He W (2012) Study on water environment and sustainable fisheries development 2123 in Dongjiang Lake. Master (Hunan Agricultural University, Changsha).
2124 280. Zhong Z & Chen C (2011) Water quality and eutrophication analysis in Lake 2125 Dongting. Environ. Sci. Manage. 36(7):169-173.
2126 281. Hu R, Lei L, & Han B (2008) Phytoplankton assemblage and seasonal dynamics 2127 in the large oligotrophic Xinfengjiang Reservoir in southern China. Acta Ecol. 2128 Sin. 28(10):4652-4664.
2129 282. Zou H, Hu R, & Han B (2010) Structure and dynamics of phytoplankton 2130 community in Hedi Reservoir, South China. J. Trop. Subtrop. Bot. 18(2):196-202.
2131 283. Zuo T (2012) The water quality dynamic of Xijin Reservoir water areas. Master 2132 (Guangxi University, Nanning).
2133 284. Ge C, Yu H, Lin Y, & Chen Y (2007) Eutrophication assessment of typical 2134 tropical reservoir: A case of Songtao Reservoir. Environ. Pollut. Control (10):1-5.
2135 285. Deng H, et al. (2011) Temporal and spatial distribution of chlorophyll-a 2136 concentration and its relationship with environmental factors in Hongfeng 2137 Reservoir, Guizhou Plateau, China. J. Agro-Environ. Sci. 30(8):1630-1637.
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2138 286. Dong Y, Hong X, Tan Z, Zhu X, & Li Y (2012) Distribution of nitrogen and 2139 phosphorus and their relationships with chlorophyll-a in Lake Chenghai on 2140 plateau. Ecol. Environ. Sci. 21(2):333-337.
2141 287. Su T (2011) Change trend and reasons of water quality of Dianchi Lake during 2142 the eleventh five-year plan period. Environ. Sci. Surv. 30(5):33-36.
2143 288. Zhang T (2011) Research on the spatio-temporal distribution of the concentrations 2144 of nitrogen and phosphorus and exogenous fluxes in Erhai Lake. Master (Dali 2145 University, Dali).
2146 289. Pan J, Xiong F, Li W, & Ke F (2009) Structure, distribution and its impact factors 2147 of phytoplankton community in Fuxian Lake. Acta Ecol. Sin. 29(10):5376-5385.
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2155 292. Zheng Z & Cao Y (2012) Current status of ecosystem of inland lakes and 2156 reservoirs in Northwest China. China Water Resour. (19):62-64.
2157 293. Chen X, Han B, Wang L, & Fu X (2013) Analysis on the correlation between 2158 total phosphorus, water temperature, mineralization and chlorophyll-a in Qinghai 2159 Lake, China. J. Agro-Environ. Sci. 32(2):333-337.
2160 294. Xin H, Chen Z, & Wu L (2010) Evaluation of water quality and nutrition status of 2161 Ebinur Lake. J. Salt Lake Res. 18(3):30-34.
2162 295. Chuai X (2011) Study on lake eutrophication as well as the criteria and control 2163 standard for phosphorus in China. Doctoral (Nanjing University, Nanjing).
2164 296. Dong Y, et al. (2008) Water quality and trophic level in Wulungu Lake. J. 2165 Shanghai Fish. Univ. 17(5):564-569.
2166 297. Hao Y, Jia E, Chen Y, & Liu J (2013) Water quality assessment and 2167 spatiotemporal distribution law of Sayram Lake in Xinjiang. China Rural Water 2168 Hydropower (6):45-48.
2169
2170
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