Table of illustrations

Notations

The units shown in brackets are those used in this document, and not necessarily those

Introduction

Rationally designed, manufactured nano-objects (nanoparticles and nanostructured particles -NP) attract a great deal of interest, due to their many technologically interesting properties. The properties of NP and their applications have given birth to technological and economic expectations for industries using NP or materials/end products containing NP (nanomaterials). However, some of these properties have given rise to concern that they may be harmful to humans.

In Table 1 gathers the range of each of the mass flow meters disposed within CAIMAN. In the PALAS GFG-1000 generator, electrode material is evaporated in the vicinity of the spark. Material vapor nucleates/condenses to the solid phase, forming very small primary particles which then coagulate according to particle number concentration and operating conditions. These mechanisms can be to some extent controlled by varying the flow rates of argon shielding and primary dilution by air. Aerosol production rate varies by adjusting the spark-discharge frequency ω 1 which is directly controlled by the potentiometer setting of the GFG-1000 PALAS. Consequently, this NP generation system allows a variation of both the particle concentration (via the argon and air flow rates, spark-discharge frequency)

and their chemical nature by changing the material of the electrodes. Presently, seven different electrode materials have been studied, which chemical composition was determined by ICP-MS and gathered in Table 2.

Electrode Composition

Carbon 2 Graphite (pure)

Aluminum (type 2071A) Al(93.1%) -Cu(4.8%) -Fe(0.7%) -Mn(0.6%) -Si(0.5%) -Mg(0.3%)

Copper Cu(99.45%) -Al(0.5%) -Si(0.04%)

Silver 3 Ag > 99.99% Constantane 3 Cu(55%) -Ni(45%) Cu/Be 3 Cu(98%) -Be(1.9%) -Co(0.2%) -Pb(0.3%)
Cu/Co/Be 3 Cu(95.6%) -Be(0.5%) -Co(2.4%) 1 The spark-discharge frequency indication, notated ω, corresponds to the parameter set on the potentiometer. The real sparking frequency f is then given by f = 0.3 ω (PALAS documentation).

2 Delivered by PALAS.

3 Delivered by Goodfellow.

Airborne NP state-of-charge

To cover a wide range of electrical mean charge level, a bipolar ion generator (EAN 581, TOPAS) is included in CAIMAN at the outlet of the NP generator. Its principle is based on air ionisation by corona charging in two independent chambers. Positive and negative ions can be produced separately in each chamber, and then mixed consecutively with the particles within a mixing chamber which geometry was optimised to increase particle charging efficiency. Both the air flow rate and the corona charger parameters (voltage and current) can be adjusted, allowing a wide range of NP electrical state-of-charge. Further elements can be found in the work of Marquard et al. [START_REF] Marquard | Characterization of unipolar electrical aerosol chargers -Part I: a review of charger performance criteria[END_REF][START_REF] Marquard | Characterization of unipolar electrical aerosol chargers -Part II: Application of comparison criteria to various types of nanoaerosol charging devices[END_REF] in unipolar mode.

Airborne NP morphology

Morphology being another key-parameter of the NP produced within CAIMAN, a hightemperature furnace (CARBOLITE BST16 -maximum operating temperature 1500°C) was inserted in the facility. Indeed, several studies [START_REF] Schmidt-Ott | New approaches to in situ characterization of ultrafine agglomerates[END_REF][START_REF] Schmidt-Ott | Scaling behavior of physical parameters describing agglomerates[END_REF][START_REF] Shimada | Size change of very fine silver agglomerates by sintering in heated flow[END_REF][START_REF] Weber | In situ characterization and structure modification of agglomerated aerosol particles[END_REF][START_REF] Weber | In situ determination of the activation energy for restructuring of nanometer aerosol agglomerates[END_REF][START_REF] Jang | Restructuring of chain aggregates of titania nanoparticles in the gas phase[END_REF][START_REF] Nakaso | Evaluation of the change in the morphology of gold nanoparticles during sintering[END_REF][START_REF] Ku | Comparing aerosol surface-area measurements of monodisperse ultrafine silver agglomerates by mobility analysis, transmission electron microscopy and diffusion charging[END_REF][START_REF] Ku | Generation and investigation of airborne silver nanoparticles with specific size and morphology by homogeneous nucleation, coagulation and sintering[END_REF][START_REF] Cho | Study of the mobility, surface-area, and sintering behavior of agglomerates in the transition regime by tandem differential mobility analysis[END_REF][START_REF] Wang | Shape evolution of gold nanoparticles[END_REF] have shown particle restructuration by sintering under high temperatures. The high-temperature furnace can be operated at different temperatures to provide either partial sintering of agglomerates or coalescence, allowing some degree of control over particle morphology of particles with constant chemistry.

Airborne NP sampling and measurement

The aerosol leaving the furnace is allowed to age in a 2-liter volume located at the end of the facility (see Figure 1) from which airborne NP can be sampled and diluted by means of an additional air flow injected at the outlet of the device. It should be noted that this ageing volume is equipped with four sampling lines in parallel, which allows simultaneous measurements. Obviously, more instruments can be connected if flow splitters are put into line. For testing with monodisperse particles, a DMA can be connected at one of the four outputs. Connections are in metric but any other connections can easily be used.

It should be noted that CAIMAN can only be used with aspirating aerosol instruments.

Safety issues

Due to strong nuclear safety regulation in France, no radioactive source can be brought to the Institute. The only radioactive source hold by the Aerosol Metrology Laboratory is an Am 241 sealed source devoted to the Grimm DMA.

Features of CAIMAN

In this section, key properties of the aerosols of NP produced within CAIMAN are exposed.

Two operating configurations were tested for each electrode material, as stated in Table 3 (subscripts 1, 2 and 5 refer to the mass flow meters indexes attributed in Figure 1). Electronic diffraction and EDS have been used to provide the oxidation state of the aerosol produced. When using aluminum and copper as material electrode, the aerosol produced is composed respectively of Al 2 O 3 and Cu 2 O particles. When using silver as material electrode, the aerosol produced is composed of Ag particles.

For constantane and both electrodes containing Beryllium, chemical analysis was not performed. Furthermore, concerning Be wires, only configuration 1 was investigated.

Generation stability

Before comprehensive characterization of the airborne NP produced within CAIMAN, the generation stability was tested over 170 minutes for carbon electrodes in configuration 2

and ω = 1000 a.u. The 46 scans measured by the SMPS showed a variation of ± 12 % in terms of count median mobility diameter (CMMD) relative to its mean value over all scans (CMMD) as calculated by: ratio= CMMDሺscan iሻ

CMMDሺall scansሻ

The Figure 3 shows the results obtained.

Number size distributions

The size distribution of the generated airborne NP was measured by a SMPS system (GRIMM SMPS+C, composed of a DMA 'Vienna Type' and a CNC model 5.403) operating at a flow rate of 0.3 L/min. This system allows the measurement of the number of particles according to their electrical mobility diameter (from 5.5 to 350 nm with the middle size DMA, and from 11 to 1080 nm with the long DMA). The choice of the DMA was adapted depending on the median size of the aerosols measured to provide most of the distribution.

Figure 5 shows a schematic of the set-up adopted for the measurement of the size distributions of the aerosols produced within CAIMAN. summarizes the main characteristics of the aerosols produced, based on the SMPS measurements 5 . 5 In some cases where a part of the number size distribution of NP was not measured (out of range due to very small sizes), the total number concentration is underestimated. It can be observed from Figure 11 and Figure 13 that the aerosols generated are bimodal, with a first population around 9 nm, corresponding to primary particle size.

In the case of Carbon-based NP (Figure 6), bimodal aerosols are also found for sparking frequencies above 600 a.u. The first mode, situated around 200 nm, remains quite stable, while the second one keeps on increasing (250 nm to 300 nm) with spark discharge frequency.

Figure 18 and Figure 19 present the count median mobility diameters (CMMD) for all types of electrode as a function of the spark-discharge frequency for configurations 1 and 2 respectively.

It can be observed a significant evolution of the CMMD as a function of the spark frequency and additional air flow conditions imposed. In configuration 1, CMMD were found between 17 nm and 228 nm. In configuration 2, the range of CMMD extends from 7 to 87 nm. This decrease in particle size can be linked to lower coagulation effects by (1) dilution due to additional 20 L/min air flow rate, and (2) decrease of residence time (by a factor of 9). From these TEM micrographs, it can be seen that those stemming from Silver which Silver-based NP, which enables primary particles to coalesce in absence of an oxidation layer, leading to metal-metal contacts, as stated Furthermore, collected NP were measured indicated in Table 11.

/ 59

Cu/Be Cu/Co/Be : TEM picture of sampled airborne NP in configuration 1 with ω = 1000 a.u. (concluded)

, it can be seen that all collected NP are aggregates, which are spherical. This might be due to the non oxidation of based NP, which enables primary particles to coalesce in absence of an oxidation metal contacts, as stated by Tabrizi et al. [START_REF] Tabrizi | Generation of nanoparticles by spark discharge[END_REF]. cted NP were measured on TEM pictures, leading to the mean sizes = 1000 a.u. (concluded) collected NP are aggregates, except are spherical. This might be due to the non oxidation of based NP, which enables primary particles to coalesce in absence of an oxidation , leading to the mean sizes Interestingly, the mean size measured on the TEM pictures is very different than the one obtained from SMPS measurements. Furthermore, it is found that the mean size of Constantane-, Cu/Co/Be-, Cu/Be-and Al-based particles is above the one measured for Cbased NP.

However, it should be kept in mind that the particle diameter given in SMPS is the equivalent electrical mobility diameter. In the case of aggregates, which are neither spherical nor compact, this equivalent diameter can be very different from the real physical particle size.

Mass concentrations

No mass measurements were performed during this qualification campaign. However, a previous study carried out on another CAIMAN configuration allowed this determination by means of a TEOM system. If the data cannot be directly used because of configuration modification of CAIMAN, Table 12 presents the range of mass concentrations of airborne NP produced and maximum mass production rate.

Electrical state-of-charge

The mean charge per airborne NP generated within our facility was determined by the simultaneous measurement of the number concentration (GRIMM CNC model 5. The same behavior can be noted for each electrode material. For sparking frequencies below 200-300 a.u., the raw mean charge per particle is negative, and becomes positive for sparking frequencies above this limit. The addition of ions produced within the ion generator lead to a wide range of possibilities in terms of electrical state-of-charge of airborne NP, summarized in Table 13.

Particle shape

Partial sintering of primary particles constituting aggregates of Aluminum-based NP was observed by use of the high-temperature furnace. As a consequence, airborne NP morphology was varied from chain-like aggregates to "compact" aggregates when increasing the temperature within the furnace. In parallel of NP sampling, SMPS number size distributions were measured, as presented in Figure 32. Another representation of these distributions is proposed in Figure 33 in terms of cumulative distributions. From these figures, it can be observed that the number size distributions of airborne NP tend to smaller particles when temperature increases. Figure 34 shows the evolution of the CMMD of the aerosols with the temperature from 78 to 44 nm. , it can be observed that particles tend to be more compact when temperature increases. This feature may be useful for studying the effect of particle morphology with constant chemistry on instruments behavior. based NP for the different temperatures. Particle restructuring can be highlighted in these pictures when increasing at various sintering C; sintering at tend to be more compact when temperature increases. This feature may be useful for studying the effect of particle

Particle mixture (NP together with background aerosols)

One requirement of test facilities is the possibility to deliver well-known particle mixture containing combinations of the "primary" nanoaerosols together with particles representatives of background aerosols.

The initial CAIMAN configuration has been redesigned to produce not only the "primary" nanoaerosol (standard configuration) but also a known particle mixture containing combinations of the "primary" nanoaerosol and particles representatives of background aerosols. In the present work, preliminary tests have been carried out with NaCl particles as being representatives of background aerosols.

The Laskin-type nebulizer was chosen as generation system. The aerosol of NP produced within CAIMAN was mixed to an aerosol of NaCl crystals at the inlet of the ageing volume, as shown in Figure 38. The Laskin-type nebulizer drawn in Figure 38 corresponds to a home-made generator based on the Laskin effect, described for example in the patent from Sabroske et al. [START_REF] Sabroske | Laskin nozzle particle generator[END_REF]. It consists in a closed tank containing liquid, fed with a pressurized air source, which is connected to a Laskin type nozzle immerged in liquid. When pressurized air passes through the nozzle, a shearing process creates liquid droplets, which are then directed to the outlet of the tank and pass through a demister where the bigger droplets are removed.

/ 59

To ensure complete drying of the droplets, the latter are directed through a diffusion dryer (filled with silicagel, TSI model 3062).

The preliminary experiments were performed with an aqueous solution of NaCl at a concentration of 0.5 g/L and an air flow rate of 20 L/min within the Laskin-type nebulizer.

The influence of the height of liquid within the tank was investigated, as shown in Figure 39.

Table 1 :

 1 Characteristics of the mass flow meters within CAIMAN ..............................

Figure 1 :

 1 Figure 1: Schematic of the CAIMAN facility in its standard configuration ..................... Table 2 : Characteristics of the electrodes used within the NP generator ....................

Figure 2 :

 2 Figure 2: Picture of the CAIMAN facility ............................................................ Table 3: Configurations tested for characterizing CAIMAN.......................................

Figure 3 :

 3 Figure 3: Evolution of CMMD for Carbon electrodes in configuration 2 and ω = 300 over 170 minutes ................................................................................

Figure 4 :

 4 Figure 4: Evolution of mass concentration for Carbon electrodes in configuration 2 and ω = 300 over 60 minutes ..................................................................

Figure 5 :

 5 Figure 5: Set-up for the measurement of the number size distributions ......................

Figure 6 :

 6 Figure 6: Effect of spark frequency ω on number-size distribution measured by SMPS for Carbon electrodes in configuration 1 .............................................

Figure 7 :

 7 Figure 7: Effect of spark frequency ω on number-size distribution measured by SMPS for Carbon electrodes in configuration 2 ............................................. Table 4: Properties of the number size distributions of Carbon-based NP aerosols versus spark-discharge frequency indication for the configuration 1 and 2 ............

Figure 8 :

 8 Figure 8: Effect of spark frequency ω on number-size distribution measured by SMPS for Aluminum electrodes in configuration 1 ..........................................

Figure 9 :

 9 Figure 9: Effect of spark frequency ω on number-size distribution measured by SMPS for Aluminum electrodes in configuration 2 .......................................... Table 5: Properties of the number size distributions of Aluminum-based NP aerosols versus spark-discharge frequency indication for the configuration 1 and 2 ..............................................................................................

Figure 10 :

 10 Figure 10: Effect of spark frequency ω on number-size distribution measured by SMPS for Copper electrodes in configuration 1 .............................................

Figure 11 :

 11 Figure 11: Effect of spark frequency ω on number-size distribution measured by SMPS for Copper electrodes in configuration 2 .............................................

Table 6 :

 6 Properties of the number size distributions of Copper-based NP aerosols versus spark-discharge frequency indication for the configuration 1 and 2 ............

Figure 12 :

 12 Figure 12: Effect of spark frequency ω on number-size distribution measured by SMPS for Silver electrodes in configuration 1 ...............................................

Figure 13 :

 13 Figure 13: Effect of spark frequency ω on number-size distribution measured by SMPS for Silver electrodes in configuration 2 ............................................... Table 7: Properties of the number size distributions of Silver-based NP aerosols versus spark-discharge frequency indication for the configuration 1 and 2 ............

Figure 14 :

 14 Figure 14: Effect of spark frequency ω on number-size distribution measured by SMPS for Constantane electrodes in configuration 1 .......................................

Figure 15 :

 15 Figure 15: Effect of spark frequency ω on number-size distribution measured by SMPS for Constantane electrodes in configuration 2 ....................................... Table 8: Properties of the number size distributions of Constantane-based NP aerosols versus spark-discharge frequency indication for the configuration 1 and 2 ..............................................................................................

Figure 16 :

 16 Figure 16: Effect of spark frequency ω on number-size distribution measured by SMPS for Cu/Be electrodes in configuration 1 .............................................. Table 9: Properties of the number size distributions of Cu/Be-based NP aerosols versus spark-discharge frequency indication for the configuration 1 ...................

Figure 17 :

 17 Figure 17: Effect of spark frequency ω on number-size distribution measured by SMPS for Cu/Co/Be electrodes in configuration 1 .......................................... Table 10: Properties of the number size distributions of Cu/Co/Be-based NP aerosols versus spark-discharge frequency indication for the configuration 1 .........

Figure 18 :

 18 Figure 18: Airborne NP count median mobility diameter for the different materials in configuration 1 ...............................................................................

Figure 19 :

 19 Figure 19: Airborne NP count median mobility diameter for the different materials in configuration 2 ...............................................................................

Figure 20 :

 20 Figure 20: TEM picture of sampled airborne NP in configuration 1 with ω = 1000 a.u. (continued) .................................................................................

Figure 20 :

 20 Figure 20 : TEM picture of sampled airborne NP in configuration 1 with ω = 1000 a.u. (concluded) ................................................................................. Table 11: Mean size of collected NP in configuration 1 with ω = 1000 a.u. ................... Table 12: Range of mass concentrations and mass rates of NP .................................

Figure 21 :

 21 Figure 21: Set-up for the measurement of the mean state-of-charge of NP ..................

Figure 22 :

 22 Figure 22: Airborne NP mean charge for Carbon electrodes in configuration 1 ...............

Figure 23 :

 23 Figure 23: Airborne NP mean charge for Carbon electrodes in configuration 2 ...............

Figure 24 :

 24 Figure 24: Airborne NP mean charge for Aluminum electrodes in configuration 1 ...........

Figure 25 :

 25 Figure 25: Airborne NP mean charge for Aluminum electrodes in configuration 2 ...........

Figure 26 :

 26 Figure 26: Airborne NP mean charge for Copper electrodes in configuration 1...............

Figure 27 :

 27 Figure 27: Airborne NP mean charge for Copper electrodes in configuration 2...............

Figure 28 :

 28 Figure 28: Airborne NP mean charge for Silver electrodes in configuration 1 ................

Figure 29 :

 29 Figure 29: Airborne NP mean charge for Silver electrodes in configuration 2 ................

Figure 30 :

 30 Figure 30: Airborne NP mean charge for Constantane electrodes in configuration 1 ........

Figure 31 :

 31 Figure 31: Airborne NP mean charge for Constantane electrodes in configuration 2 ........ Table 13: Range of variation of the mean charge per particle of airborne NP ................

Figure 32 :

 32 Figure 32: Effect of the furnace temperature on the number size distributions measured for Aluminum electrodes in configuration 1, ω = 1000 a.u. ..................

Figure 33 :

 33 Figure 33: Effect of the furnace temperature on the cumulative number size distributions of airborne NP for Aluminum electrodes in configuration 1, ω = 1000 a.u. .........................................................................................

Figure 34 :

 34 Figure 34: Effect of the furnace temperature on the airborne NP count median mobility diameter for Aluminum electrodes in configuration 1, ω = 1000 a.u. ........

Figure 35 :

 35 Figure 35: Change of morphologies for airborne NP for Aluminum electrodes at various sintering temperatures: ambient temperature (20°C), sintering at 900°C; sintering at 1200°C; sintering at 1500°C. ..........................................

Figure 36 :

 36 Figure 36: Effect of the dilution flow rate (Q 5 ) on the relative number size distributions measured for Carbon electrodes in configuration 1 with ω = 1000 a.u. ....................................................................................

Figure 37 :

 37 Figure 37: Effect of the dilution flow rate (Q 5 ) on the relative number size distributions measured for Carbon electrodes in configuration 2 with ω = 1000 a.u. and varying additional air flow rates Q 5 .................................... Table 14: Effect of additional air flow (Q 5 ) on the CMMD and total concentration of airborne NP ...................................................................................

Figure 38 :

 38 Figure 38: Set-up for the measurement of the number size distributions in the case of mixed aerosols .........................................................................

Figure 39 :

 39 Figure 39: Effect of liquid height within the nebulizer on the number size distribution measured for NaCl ...............................................................

Figure 40 :

 40 Figure 40: Number size distribution of the particle mixture obtained for Carbon electrodes and NaCl particles. The finer mode (23 nm) corresponds to the NaCl particles and the larger one (350 nm) to the carbon particles .....................

Figure 1

 1 Figure 1 shows a schematic of the CAIMAN experimental facility in its standard configuration. A different arrangement has been used for producing mixture of NP and particles representing background aerosols (see paragraph 3.7 page 50).

Figure 1 : 1 .

 11 Figure 1: Schematic of the CAIMAN facility in its standard configuration

Figure 3 :

 3 Figure 3: Evolution of CMMD for Carbon electrodes in configuration 2 and ω = 300 over 170 minutes

Figure 4 :

 4 Figure 4: Evolution of mass concentration for Carbon electrodes in configuration 2 and ω = 300 over 60 minutes

Figure 5 :

 5 Figure 5: Set-up for the measurement of the number size distributions

Figure 6 : 1 Figure 7 :Table 4 : 2 ω

 61742 Figure 6: Effect of spark frequency ω on number-size distribution measured by SMPS for Carbon electrodes in configuration 1

Figure 8 :in configuration 1 Figure 9 :Table 5 : 2 (

 81952 Figure 8: Effect of spark frequency ω on number-size distribution measured by SMPS for Aluminum electrodes in configuration 1

Figure 10 :in configuration 1 Figure 11 :

 10111 Figure 10: Effect of spark frequency ω on number-size distribution measured by SMPS for Copper electrodes in configuration 1

Figure 12 :in configuration 1 Figure 13 :

 12113 Figure 12: Effect of spark frequency ω on number-size distribution measured by SMPS for Silver electrodes in configuration 1

Figure 14 : 1 Figure 15 :

 14115 Figure 14: Effect of spark frequency ω on number-size distribution measured by SMPS for Constantane electrodes in configuration 1

Figure 16 :

 16 Figure 16: Effect of spark frequency ω on number-size distribution measured by SMPS for Cu/Be electrodes in configuration 1

Figure 17 :

 17 Figure 17: Effect of spark frequency ω on number-size distribution measured by SMPS for Cu/Co/Be electrodes in configuration 1

Figure 18 : 1 Figure 19 :Figure 20 Figure 20 :Figure 20 :

 18119202020 Figure 18: Airborne NP count median mobility diameter for the different materials in configuration 1

  403) and current (electrometer TSI model 3068B). The mean charge per particle (p) is given by: p= I C N •e•Q where C N corresponds to the mean number concentration of airborne NP (measured by the CNC), I is the mean current (measured by the electrometer), e represents the elementary charge (e = 1.602•10 -19 C) and Q is the flow rate of the electrometer (adjusted from 0.3 to 5 L/min to measure a current well above the limit of quantification of the electrometer).

Figure 21

 21 Figure 21 is a schematic of the experimental set-up used for the measurement of the electrical state-of-charge of airborne NP produced within CAIMAN.

Figure 21 :•Figure 26 :Figure 27 :

 212627 Figure 21: Set-up for the measurement of the mean state-of-charge of NP

Figure 32 :

 32 Figure 32: Effect of the furnace temperature on the number size distributions measured for Aluminum electrodes in configuration 1, ω = 1000 a.u.

Figure 33 :

 33 Figure 33: Effect of the furnace temperature on the cumulative number size distributions of airborne NP for Aluminum electrodes in configuration 1, ω = 1000 a.u.

Figure 34 :Figure 35 :

 3435 Figure 34: Effect of the furnace temperature on the airborne NP count median mobility diameter for Aluminum electrodes in configuration 1, ω = 1000 a.u.

Figure 37 :

 37 Figure 37: Effect of the dilution flow rate (Q 5 ) on the relative number size distributions measured for Carbon electrodes in configuration 2 with ω = 1000 a.u. and varying additional air flow rates Q 5

Figure 38 :

 38 Figure 38: Set-up for the measurement of the number size distributions in the case of mixed aerosols

Figure 39 :Figure 40 :

 3940 Figure 39: Effect of liquid height within the nebulizer on the number size distribution measured for NaCl

acronym for Characterization of Aerosol Instrumentation devoted for Measuring Aerosols of Nanoparticles) and its performances that has been developed to produce well- defined nanoaerosols. 2. Description of the experimental setup (CAIMAN) CAIMAN (for 'Characterization of Aerosol Instrumentation devoted for Measuring Aerosols of Nanoparticles') is

	the workplaces where NP are manufactured or used, exposure to airborne NP is plausible at all phases of the nanomaterial life cycle, although the nature, level of exposure and the number of person involved could differ greatly. One major uncertainty in the health risk assessment of NP arises from the lack of knowledge of their physicochemical properties and behavior in the airborne state (aerosol phase). Among the technical challenges ahead for assessing workers exposures to NP are to: (a) redesign and evaluate "NP-capable" instruments already in laboratory use into portable and affordable devices, and (b) expand the sensing technology available for NP detection by adopting new options with realistic potential for real-time measurement and compact design. The present report describes the nanoparticle generation facility in aerosol phase called generate stable and reproducible aerosols of nanoparticles (nanoaerosols) with controlled properties (concentration, size, shape, mean charge level). It was initially developed in a collaborative research work within the Aerosol Physics and Metrology Laboratory from the Institut de Radioprotection et de Sûreté Nucléaire (IRSN) and the Aerosol Metrology Laboratory from INRS, which holds the non-transportable facility in its research centre near Nancy, France. Within CAIMAN, the particle-free air introduced into different points of the set-up comes from a purification unit (TSI model 3074B), and the excess aerosol is filtered by means of "CAIMAN" (a versatile experimental facility that was designed and built to HEPA filters (CAMFIL, model 'filtre BAG').

Table 1 :

 1 Characteristics of the mass flow meters within CAIMAN

	Flow meter	Regulation ?	Range (L/min)
		No	0 -50
		No	0 -20
		No	0 -100
		Yes	0 -100
		Yes	0 -50
	Following sections briefly describe each of the elements constituting the CAIMAN facility,

i.e. an airborne NP generator, a bipolar ion generator, a high-temperature furnace and a home-made ageing volume.

Table 2 :

 2 Characteristics of the electrodes used within the NP generator

Table 3 :

 3 Configurations tested for characterizing CAIMAN

	Configuration	P Ar	4 (bar)	Q 1 (L/min)	P air	4 (bar)	Q 2 (L/min)	Q 5 (L/min)
	1		1	2.5		0	0	10
	2		1	2.5		1	20	10

Table 6 :

 6 Properties of the number size distributions of Copper-based NP aerosols versus sparkdischarge frequency indication for the configuration 1 and 2

	ω	configuration	C N total	CMMD	σ g	Flux of particles
	(a.u.)		(#/cm 3 )	(nm)	(-)	(#/min)
	100		2.75•10 6	20	1.64	3.44•10 10
	200		2.78•10 6	27	1.68	3.48•10 10
	300		2.86•10 6	31	1.74	3.58•10 10
	400		2.94•10 6	33	1.80	3.68•10 10
	500		2.97•10 6	37	1.76	3.71•10 10
	600		3.01•10 6	41	1.72	3.76•10 10
	700		3.12•10 6	42	1.78	3.90•10 10
	800		3.26•10 6	46	1.81	4.08•10 10
	900		3.18•10 6	49	1.73	3.98•10 10
	1000		3.39•10 6	52	1.79	4.24•10 10
	100			Not measured
	200		2.31•10 6	7	1.26	7.51•10 10
	300		5.48•10 6	8	1.32	1.78•10 11
	400		8.41•10 6	9	1.38	2.73•10 11
	500		1.15•10 7	11	1.51	3.74•10 11
	600		1.25•10 7	12	1.57	4.06•10 11
	700		1.29•10 7	13	1.62	4.19•10 11
	800		1.28•10 7	14	1.62	4.16•10 11
	900		1.32•10 7	15	1.68	4.29•10 11
	1000		1.34•10 7	16	1.70	4.36•10 11

Table 7 :

 7 Properties of the number size distributions of Silver-based NP aerosols versus sparkdischarge frequency indication for the configuration 1 and 2

	ω	configuration	C N total	CMMD	σ g	Flux of particles
	(a.u.)		(#/cm 3 )	(nm)	(-)	(#/min)
	100	1	2.75•10 6	17	1.51	3.44•10 10
	200	1	2.75•10 6	22	1.67	3.44•10 10
	300	1	2.80•10 6	30	1.67	3.50•10 10
	400	1	2.89•10 6	34	1.70	3.61•10 10
	500	1	2.96•10 6	37	1.70	3.70•10 10
	600	1	2.97•10 6	38	1.69	3.71•10 10
	700	1	2.97•10 6	38	1.70	3.71•10 10
	800	1	2.90•10 6	35	1.68	3.63•10 10
	900	1	2.99•10 6	35	1.72	3.74•10 10
	1000	1	2.90•10 6	41	1.65	3.63•10 10
	100	2	9.99•10 6	8	1.36	3.25•10 11
	200	2	1.32•10 7	10	1.44	4.29•10 11
	300	2	1.49•10 7	11	1.47	4.84•10 11
	400	2	1.62•10 7	11	1.52	5.27•10 11
	500	2	1.65•10 7	13	1.55	5.36•10 11
	600	2	1.74•10 7	14	1.60	5.66•10 11
	700	2	1.75•10 7	14	1.62	5.69•10 11
	800	2	1.74•10 7	15	1.62	5.66•10 11
	900	2	1.78•10 7	15	1.66	5.79•10 11
	1000	2	1.73•10 7	16	1.63	5.62•10

Table 8 :

 8 Properties of the number size distributions of Constantane-based NP aerosols versus sparkdischarge frequency indication for the configuration 1 and 2

	ω	configuration	C N total	CMMD	σ g	Flux of particles
	(a.u.)		(#/cm 3 )	(nm)	(-)	(#/min)
	100	1	2.69•10 6	49	1.61	3.36•10
	200	1	2.71•10 6	60	1.59	3.39•10
	300	1	2.96•10 6	67	1.64	3.70•10
	400	1	3.08•10 6	73	1.59	3.85•10
	500	1	3.22•10 6	78	1.61	4.03•10
	600	1	3.26•10 6	81	1.61	4.08•10
	700	1	3.30•10 6	86	1.58	4.13•10
	800	1	3.41•10 6	88	1.62	4.26•10
	900	1	3.38•10 6	92	1.58	4.23•10
	1000	1	2.59•10 6	92	1.61	3.24•10
	100	2	1.21•10 7	12	1.54	3.93•10
	200	2	1.30•10 7	16	1.66	4.23•10
	300	2	1.33•10 7	19	1.77	4.32•10
	400	2	1.30•10 7	22	1.77	4.23•10
	500	2	1.31•10 7	24	1.79	4.26•10
	600	2	1.31•10 7	26	1.81	4.26•10
	700	2	1.28•10 7	29	1.77	4.16•10
	800	2	1.31•10 7	30	1.83	4.26•10
	900	2	1.29•10 7	32	1.76	4.19•10
	1000	2	1.31•10 7	32	1.79	4.26•10

Table 9 :

 9 Properties of the number size distributions of Cu/Be-based NP aerosols versus spark-

		3.50E+05
		3.00E+05
	number concentration (#/cm 3	1.00E+05 1.50E+05 2.00E+05 2.50E+05
		5.00E+04
		0.00E+00
		10		100		1000
		discharge frequency indication for the configuration 1
	ω	configuration	C N total	CMMD	σ g	Flux of particles
	(a.u.)		(#/cm 3 )	(nm)	(-)	(#/min)
	100	1	2.01•10 6	22	1.52	2.51•10 10
	300	1	1.87•10 6	42	1.60	2.34•10 10
	500	1	2.53•10 6	47	1.59	3.16•10 10
	700	1	2.63•10 6	53	1.58	3.29•10 10
	900	1	3.43•10 6	74	1.57	4.29•10 10
	1000	1	3.35•10 6	71	1.55	4.19•10 10

Table 10 :

 10 Properties of the number size distributions of Cu/Co/Be-based NP aerosols versus spark-

		3.00E+05
		2.50E+05
	number concentration (#/cm 3	5.00E+04 1.00E+05 1.50E+05 2.00E+05
		0.00E+00
		10		100		1000
		discharge frequency indication for the configuration 1
	ω	configuration	C N total	CMMD	σ g	Flux of particles
	(a.u.)		(#/cm 3 )	(nm)	(-)	(#/min)
	100	1	1.71•10 6	21	1.45	2.14•10 10
	300	1	1.79•10 6	36	1.67	2.24•10 10
	500	1	2.48•10 6	45	1.65	3.10•10 10
	700	1	2.58•10 6	55	1.57	3.23•10 10
	900	1	3.04•10 6	63	1.57	3.80•10 10
	1000	1	2.96•10 6	61	1.57	3.70•10 10

Table 11 :

 11 Mean size of collected NP in configuration 1 with ω = 1000 a.u.

	Electrode	mean size (nm) ± standard deviation (nm)
	C	102 ± 60
	Al	171 ± 129
	Cu	95 ± 47
	Ag	25 ± 14
	Constantane	243 ± 149
	Cu/Be	135 ± 71
	Cu/Co/Be	241 ± 162

Table 12 :

 12 Range of mass concentrations and mass rates of NP

	Electrode	Range of mass concentration	Maximum mass rate
		(µg/m 3 )	(µg/h)
	C	800 -6400	4800
	Al	< 20 -450	340
	Cu	< 20 -240	180
	Ag	< 20 -680	510
	Cu/Be	110 -1800	1350
	Cu/Co/Be	140 -740	555

Table 13 :

 13 Range of variation of the mean charge per particle of airborne NP

	Electrode material	raw p	Range of variation of p with ⊕ ions	p with	ions	Configuration
		-1.61	0.94	1.11	3.09	-1.59	-2.46	1
	C
		-0.25	0.18	0.40	0.58	-0.33	-0.88	2
		-0.53	0.48	0.43	1.11	-0.32	-1.60	1
	Al
		-0.11	0.05	0.09	0.21	-0.13	-0.24	2
		-0.08	0.05	0.07	0.39	-0.19	-0.46	1
	Cu
		0.00	0.01	0.06	0.13	-0.08	-0.21	2
		-0.02	0.02	0.19	0.33	-0.16	-0.49	1
	Ag
		-0.01	0.01	0.03	0.05	-0.04	-0.07	2
		-0.44	0.31	0.89	2.04	-0.78	-1.99	1
	Constantane
		-0.03	0.04	0.13	0.26	-0.13	-0.29	2

Table 14 :

 14 Effect of additional air flow (Q 5 ) on the CMMD and total concentration of airborne NP

	Configuration	Q 5 (L/min)	CMMD (nm)	Ratio of number concentrations
	1	10	248	-
		20	234	0.69
		30	233	0.50
		40	232	0.39
		50	231	0.32
	2	10	93	-
		20	90	0.88
		30	86	0.69
		40	83	0.58
		50	81	0.51

The pressures of Argon and dilution air are the parameters set on the generator, and correspond to the flow rates indicated in Table3, they were measured and controlled during the experiments.

Acknowledgements

Authors would like to thank S. Veissiere from Laboratory of Inorganic Analysis and Aerosol Characterization (INRS) for imaging support. Jonathan Jacoby contributed actively to the technical work and data analysis during his work experience as a master 2 student "Physique et Matériaux -Physique de la matière condensée", University of Metz (57).

The entire CAIMAN facility is confined as shown in Figure 2.

The first part of the facility is contained within a box containment system (4m x 0.8m x 0.8m) that limits any worker exposure from accidental release from the generator to the entrance of the ageing chamber. The second part of the facility is located within a constant velocity 2-m large fume hood.

Aerosol dilution

To avoid saturating the instruments in study, airborne NP were produced in configurations

Conclusion

In this report, the performances of the CAIMAN non-transportable facility have been evaluated.

The CAIMAN facility provides well-characterized "primary" nanoaerosols at known concentrations, sizes, shapes and mean charge levels. Within CAIMAN, nanoaerosols are produced by means of a spark-discharge generator (GFG-1000, PALAS). Until now, seven material electrodes have been used within CAIMAN: Carbon (delivered by PALAS), Aluminum (type 2071A), Copper, Silver, Constantane, Cu/Be and Cu/Co/Be.

The airborne NP characterized in this work present count median mobility diameters from 7 to 228 nm, with number concentrations varying from 1.7 10 6 to 1.8 10 7 #/cm 3 .

The standard configuration of CAIMAN has been redesigned to produce not only the "primary" nanoaerosol but also a known particle mixture containing combinations of the "primary" nanoaerosol and particles representatives of background aerosol. In the present work, tests have been made with NaCl.

The output of the CAIMAN facility is now very consistent over long time intervals when operating under similar conditions. It indicates that repeatability is one of the important assets of the CAIMAN facility Also, the CAIMAN facility has been confined in order to provide for the user safe working conditions against incidental releases of NP.


Yinchi Ma

Wen Ding

Wentong Li

Research on Building Technology of Aquaculture Water Quality Real-Time Monitoring Software Platform

Keywords: aquarium, wireless sensor network, software platform, monitoring

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Wireless sensor network is a wireless network without infrastructure, which combines sensor technology, embedded computing technology, modern network technology, wireless communication technology and distributed intelligent information processing technology. It can work unattended during a long time [START_REF] Jingtao | Design of Wireless Sensor Network Node for Water Quality Monitoring[END_REF] . In this study, the wireless sensor network technology is used to build an intelligent, network-based aquaculture water quality wireless monitoring system. In addition, we build a water quality monitoring data intelligence analysis software platform used VC++ program language and database technology, which provides an information and visualization interface for the aquaculture technicians. The implementation of this system can fundamentally enhance the aquaculture water quality monitoring level, which is relatively backward [START_REF] Zhi-Guo | Design of water quality monitoring wireless sensor network system based on wireless sensor[END_REF] ~ [START_REF] Zhu Wenjin | Research of Agriculture environment Automatic Monitoring and Control[END_REF] . This paper will focus on the design and implementation of data storage module, analysis module and display module within the software platform.

Function and Feature of Software Platform

The System transmits the monitoring data to the monitoring center through the wireless communication technology. It can realize the continuous data recording, analysis, processing and warning in real-time, so that it can remind the aquaculture technician to take the necessary adjustment measures at first time. This Method can effectively guarantee the aquaculture safety under intensive farming condition. The system software platform can realize the following functions: I. The system can monitor the various water quality parameters automatically during 24-hours in a whole day, and the data is sent to the center server automatically, while the online records and related analysis will be done.

II. Data server will collect each node data, authorized customers can remote login the data server to inquiry the experimental data or have aided analysis at any time through PC installed the client software.

III. The data analysis system can display monitoring data of each sampling node through graphical and tabular form, which provides user with an intuitive water quality trends. At the same time, the collected data will be saved, which will be added to the large-capacity water quality history database. And this history database can provide a scientific basis for production.

The structure design of the system software platform is shown as follow (Fig. 1). The main features of the system software platform include: I. Remote real-time and intelligent monitoring.

The system combined with advanced embedded computing technology, modern networking and wireless communications technology to automatically collect various sensing parameters to achieve a real-time and intelligent remote monitoring of water quality.

II. Professional and friendly interface. The software platform is developed for the application of aquaculture water quality. User can easily view the real-time water quality condition of each aquaculture pond through the software platform interface, as well as the software platform also has a variety of chart and graph display. The Rich display forms of data enhance the usefulness of this system. III. Powerful database system. Using the distributed database technology, the platform realizes a multi-functional, highly integrated data service system for the water quality monitoring, which integrated data analysis, data management and data integration display. In addition, the software platform also has a strong storage and query capability for the history data, and it can provide user with comprehensive and reliable data service.

Design and Development of Software Platform

The real-time monitoring software platform of aquaculture water quality is self-developed upper application for the water quality monitoring with independent intellectual property rights. The platform is designed for the special needs of the aquaculture industry. User can monitor the aquaculture water environmental parameters such as water temperature, PH, dissolved oxygen, turbidity, ammonia and other data by the on-line monitoring platform, which makes advance warning of the unknown water quality mutation, and effectively curb the risk of aquaculture and significantly improve production and management efficiency. It uses the hierarchical structure technology, which can be adjusted as needed, and dynamically changes the kernel size. In addition, as the software abstract of system hardware, it is primarily responsible for managing and driving the underlying hardware, optimizing network structure, timely detection, controlling the flow of network traffic, ensuring the quality of network application services and extending service hours. In this paper, the network layer protocol stack and middleware technology has been developed based on Z-smart and provided the high-quality application software product for the information processing and service needs of the variety water quality testing applications.

The middleware development is one of focal points of the system. The technology will greatly shorten the development period of the application System.

Design and Development of Underlying Platform

The real-time embedded operating system called Z-smart by independent research and development is designed for wireless sensor. The middleware is a standard intermediate layer structure with a common property. It is responsible for sensor network system development support for the resource management, dynamic environment analysis and ubiquitous application, such as application change, extension, system upgrades, reused and the other key properties. A key function of the middleware is conversion and agent of heterogeneous networks, such as explaining the data format of sensor network correctly and converting it into TCP / IP data or reverse operation. Therefore, the middleware must have rapid processing capability.

Design and Development of Application Layer Function Module

For user, the core of wireless sensor network is the perception data instead of the network hardware. User is interested in the data generated by the sensor, instead of the sensor itself. The basic idea of data-centric wireless sensor networks is that the sensor is regarded as the perception of data stream or data source, and the wireless sensor network is regarded as the sensing data space or sensing database. And the data management and processing is regarded as an application target of the network. Based on this idea, the platform application server modules include data loader, data analysis (application analysis) and man-machine interface.

I. Data loader.

The software platform changes data with sensor data collection terminals and data relay node by wireless data interface. In the terminal embedded software development, the system follows the IEEE802.15.4 standard, and software platform communication module is in accordance with the Zigbee Alliance international standards. The complete Zigbee protocol suite is consists of high-level application specification, the application convergence layer, network layer, data link layer and physical layer. The layers apart from the network layer protocol are developed by the Zigbee Alliance, and IEEE Association is responsible for the physical layer and link layer standards.

II. Data analysis.

The system uses data mining technology. User can obtain the needed information from vast amounts of data. In addition, the system also realizes the mining in real-time and mining after the event of the data stream. Data mining refers information processing for decision-making and assessment, in which the large amount of data is automatically analyze and synthesize under certain criteria. In the high-capacity wireless sensor network, a lot of sensing data will be generated in real-time. On the one hand some data is failure data, on the other hand valid data need to be fused together to produce meaningful information. In this case, data mining techniques need to be used. The data is judged and integrated to reduce unnecessary loss of bandwidth and reduce the overall energy consumption so as to provide more meaningful data.

Data mining combines the experience of early information fusion in the commercial applications and military field. According to the center distributed and decentralized distributed judgment ruling theory, tree fusion estimation algorithm is used to realize comprehensive intelligent decision of large amounts of data. The data mining technique of wireless sensor network emphasizes the three core areas of the information integration: First, the information fusion is to complete the process of multi-source information on several levels, where each level representing information abstraction with a different level. Second, information fusion including detection, interconnect, relevant, estimates and information portfolio. Third, the results of the information fusion include the status or identity estimation on the lower level, and the overall decision-making on the higher level.

The system software platform uses multi-sensor data mining technology. And this technology can reduce interference of the invalid data, reduce information redundancy, and improve accuracy. It provides an effective means for the purpose of large amounts of data analysis on the complex system. III. Man-machine interface. a. User rights management. By setting permissions for different users, user can inquiries whether the user is a privilege to observe, manage user permissions, data export privileges, Node Manager or other privileges.

b. Query and graphical display of real-time monitoring point data. By the list or graphical display of the real-time data, users can view the real-time data of all sensor nodes. c. Graphic display of node deployment. Display the location of the node deployment and operation status in monitor area visually. d. The treelike display of the monitoring points list. Users can quickly view the real-time data of each node, node status and other information through this feature.

e. Graphic display of wireless sensor monitoring network topology.

Topology displays data transmission path information between the gateway, relay and the sensor node, as shown in Figure 2. Users can set up their own node parameters threshold. When the real-time data of the node exceeds the threshold range, the background color of the node type will be displayed in red, while the node identifier on the node deployment is also displayed in red and the alarm sounds.

g. Query and graphic display of history data. Historical data can be found all or a specific time period of corresponding node. According to the node number, the sensor type or time period, etc., graphical display can obtain data and showing visual graph, as shown in Figure 3.

Conclusions

This study using wireless sensor network technology to build an intelligent, network-based aquaculture water quality monitoring system, and this practical application of technology in the field of aquaculture promotes the development and application of wireless sensor network technology level powerfully. We propose Zigbee protocol a aquaculture water quality monitoring system software platform solution based on the wireless sensor network technology, and deal with the real-time water quality monitoring data management and analysis.

The results show that based on the software platform, aquaculture technician can obtain a variety of aquaculture water quality indicators, such as water temperature, dissolved oxygen, PH, and turbidity to realize the real-time automatic remote monitoring, wireless data transmission, data processing and analysis, intelligent warning, SMS alarm functions. Thereby this technology can maximize the utilization of aquaculture water to protect the suitable environment conditions for aquaculture animal.

Fig. 1 .

 1 Fig. 1. Structure Design of System Software Platform

Fig. 2 .

 2 Fig. 2. Display of Topology Diagram

Fig. 3 .

 3 Fig. 3. Graphic Display of History Data

Ma Yinchi (1982 -), Beijing Fisheries Research Institute, Senior Engineer, Master, graduated from Beijing Normal University, State Key Laboratory of Remote Sensing Science, mainly engaged in research of agriculture remote sensing and fisheries information technology.


Keywords: broccoli, transplant age, planting density, nitrogen fertilizer level, growth period, yield, mathematical model

are transplant age, planting density and dose of nitrogen fertilizer, respectively. These models can be used for the optimization of cultivation techniques to achieve the goal of developing an high yield and high profit competitive broccoli production system.

Mathematical modeling of broccoli cultivation and growth period and yield of flower heads

Liu Wei-ming 1,a , Wang En-guo Transplant age, planting density and the amount of nitrogen fertilizer are important cultivation factors influencing quality of broccoli and profit of the crop. Previously, these three factors were evaluated for their effects on growth period and yield of broccoli [START_REF]Effects of Different Treatment levels of nitrogen Fertilizer on Yield and Quality of Broccoli[END_REF][START_REF]Effects of application of nitrogen fertilizer at different growth stage on yield and quality o f broccoli[END_REF][START_REF]Effects of nitrogen and Ca nutrients on quality of broccoli[END_REF][START_REF]A study of sowing date and density on fall broccoli[END_REF][START_REF]A study of planting density and fertilizer on broccoli[END_REF][START_REF]Effect of Main Cultural Techniques on Production and Quality Of Broccoli[END_REF][START_REF]Effect of cultivar, density and nitrogen fertilizer on yield of broccoli[END_REF][START_REF]Effect of Main Cultural Techniques on Production and Quality Of Broccoli[END_REF][START_REF]Studies on the Techniques of the Plug Seedlings in Broccoli[END_REF], but no mathematical model systems were established.

This study aimed at developing a simulation modeling system for providing scientific information for decision making in secure high yield and high profit production of broccoli.

Material and methods

Experimental design

Broccoli cultivar, Luxiong90, was chosen in this study.

Construction of mathematical models and simulation

Units used in this study were as follows: transplant age (Days), planting density (Plants/hm2), and nitrogen fertilizer doses: kg/hm2; growth period (days); yield (kg/hm2).

Data were subjected to polynomial regression analysis using DPS software [START_REF]Applied Statistics and SPS Data Processing System[END_REF] to build models for transplant age, planting density, nitrogen fertilizer levels, and their relationship with growth period, and yield. These models were used to simulate the cultivation system for broccoli.

Results and analysis

The effects of transplant ages on growth period and yield of broccoli

The growth periods of broccoli varied greatly among those transplant age treatments, it was from 113 to 162 days. In general when using older transplants, the number of days increased during the period from transplanting to floral bud emergence, the duration of vegetative growth post transplanting, floral head growth period, the duration from transplanting to harvest, and total growth period. Statistical analysis identified a significant linear correlation between transplant age ( 1x ) and growth period ( g ). The regression model is g =1.8343 1 x +65.381 ( r =0.9963**).

However, the density of florets on flower heads, the ratio of marketable flower heads and yield of flower head showed a "low-high-low" trend in responses to increasing transplant age. The regression mathematical model for transplant age ( 1x ) and flower head yield ( y ) is: y =-40.497 2 1

x +3089 1 x -31257( r = 0.9721**).

Effects of planting density on growth period and yield of broccoli

Results indicate that increasing planting density led to decreases in plants height, canopy width, and the number of leaves per plant. Floral buds emerged prematurely, which resulted in shortened growth period. Statistical analysis indicated that planting density ( 2x ) had a significant linear correlation with the length of period (days) from transplanting to bud emergence ( f ), and total growth period ( g ). The regression mathematical models are: f = 73.867-0.0005 2 x ( r =-0.9662**) and g =153.71-0.0006 2

x ( r =-0.9663**). Among all the planting density treatments, the highest yield was produced in plots of 36750 plants /hm2, it was 32622 kg/hm2. The yield was 32127.0, 30088.5, 29232.0, 28153.5, 28101.0, and 25357.5 kg/hm2 from treatments of 33000, 42000, 30000, 48000, 27750, and 24000 plants/hm2, respectively. Statistical analysis indicated that planting density ( 2x ) and flower head yield ( y ) had an extremely significant binominal correlation. The regression model is:

y =-0.00004 2 2 x +2.8151 2
x -21740, ( r =0.9571**).

Effects of nitrogen fertilizer level on growth period and yield of broccoli

Results in Table 2 show that when nitrogen fertilizer was provided at 600 kg/hm2,, plants produced the flower heads of the biggest diameter (l3.3cm), fresh weight per head ( 476.85g) and total yield (1440.7 kg/667m2). However, these values started to decline at increasing nitrogen level. Florets became thinner at higher nitrogen level. The width of florets in the high nitrogen level treatments was significantly lower than those supplied with lower doses of nitrogen fertilizer. The highest ratio of marketable flower heads was found in the 375 kg/hm2 of nitrogen fertilizer treatment, it was 89.6%. Nitrogen fertilizer applied at below of above this dose both resulted in reduced ratio of marketable flower heads. The lowest ratio of 76.2% was found in the treatment of 150kg/hm2. Statistical analysis revealed a binomial curve changes in flower head yield ( y ) with nitrogen fertilizer level ( 3 x ). The regression model is:

y =-0.03 3 3 x +32.986 3 x +12999( r =0.9870**).

Construction and the use of polynomial decision-making mathematical models in broccoli production

Construction of mathematical models

Data of growth periods and yield of broccoli from treatments of transplant age, planting density, nitrogen fertilizer doses were analyzed using stepwise regression analysis. The polynomial regression models for total growth period ( 1 y ), transplant age ( 1x ), planting density ( 2x ), nitrogen fertilizer doses ( 3 x ) are as follows: 1 y =75.1322+1.6361 1 x -0.0001 2 x +0.0014 3 x ( R =0.8888 ** ) ; and the model for yield ( 2 y ) is : 2 y =14999.1852+404.0006 1 x -0.1986 2 x +3.6196 3 x , ( R =0.6058 ** ).

The application of the polynomial decision making mathematical model

Mathematical models can be used to simulate and select the optimum combinations of parameters according to market demand and goals of production. For example, in case of a planting scheme comprised of 130-140 day growth season with targeted yield of 24000-26000 kg/hm 2 , the planting density, seedling age and nitrogen fertilizer (pure N) combination should be 35-40 day transplants, at a density of 33000-36750 plants / hm 2 , and with the use of 375-600 kg/hm 2 of nitrogen fertilizer. The priority goal of growing broccoli is profit. Because profit is affected by market price, there are differences between yield and profit during some seasons. These modules can be used to arrange production schedule by taking into considerations of predicted market demands and unit price of broccoli. This study indicates that the growth period of broccoli plants varied significantly under different transplant ages and planting densities. The growth period changed from 113 to 161 days. In general, when older transplants were used, it lengthened the duration from transplanting to floret emergence, the vegetative growth period in the field, flower head development stage, days from transplanting to harvest, and the duration of growth period. There was an extremely significant correlation between treatments of transplant age and the duration of those growth period. The growth period was 126-142 days in the planting density treatments. Under high planting density, florets emerged soon after transplanting, and the whole growth period became short.

These information are very important when making recommendation for a secure production strategy of broccoli.

The yield of flower heads has a binominal curve relationship with transplant age, planting density and nitrogen fertilizer level

Results indicate that the yield of flower heads followed a non-linear "low-high-low" trend in response to increasing levels of transplant age, planting density and nitrogen fertilizer level treatments. Therefore, in cultivation of broccoli crop, it is very important to pay attention to appropriate combinations of transplant age, planting density and the amount of nitrogen fertilizer.

The polynomial mathematical models can provide information to decision making in designing schemes for the production of broccoli

The polynomial regression models constructed in this study can simulate the effects of transplant age, planting density, nitrogen fertilizer doses on growth period and yield. These models are very useful in designing schemes in response to market demands with specific goals, and guide production management of the broccoli crop.

4 Conclusions and discussions 4 . 1

 41 Planting older transplants lengthens growth duration, and planting at higher density shortens the period of growth and development of broccoli plants.

  2,b 1 Taizhou Vocational College of Science & Technology, Taizhou, Zhejiang 318020 2 Plant Protection Station of Linhai, Linhai 317000, China a 15157622288@139.com

		y =75.1322+1.6361 1 x -0.0001 2 x +0.0014 3 x
	(	R =0.8888 ** ), and 2 y =14999.1852+404.0006 1 x -0.1986 2 x +3.6196 3 x
	(	R =0.6058 ** ), where 1 y and 2 y are growth period and yield, and 1 x , 2 x , 3 x
	1 Introduction

Abstract: This study was carried out to evaluate the effects of transplant age, planting density and the level of nitrogen fertilizer on growth period and yield of broccoli. Two mathematical models were constructed for these parameters, they are 1

Table 1

 1 Experimental design of the nitrogen fertilizer treatment experiment

	There were six treatments of

Table 2

 2 Effects of nitrogen fertilizer levels on marketable quality of flower heads of broccoli

	Nitrogen fertilizer rate (kg/hm 2 )	Diameter of the flower head(cm)	Weight per head(g)	Size of floral buds (cm 2 )	Ratio of marketable heads(%) flower	Yield (kg/hm 2 )
	150	12.5	421.35	76	76.2	17142
	375	13.2	450.6	87	89.6	21545
	600	13.3	476.85	102	83.8	21611
	825	12.8	469.95	116	79.4	19944


Qiang Fu

Tienan Li

Tianxiao Li
email: clitianxiao.888@163.com

Research on the Allocation in the Complex Adaptive System of Agricultural Land and Water Resources of the Sanjiang Plain

Keywords: Sanjiang Plain, agricultural land and water resources, complex adaptive system

As for the improper water and land use structure and difficulty in bringing the system into maximum performance, this research takes the Sanjiang Branch of Heilongjiang Land Reclamation Bureau as an example and uses the concept of complex adaptive system to build an allocation model of the complex adaptive system of agricultural land and water resources of the Sanjiang Plain and to make evolution analysis of optimized allocation of agricultural land and water resources under different groundwater exploitation plans. This research shows that: with the increased exploitation of groundwater, the agricultural production value and the grain yield have increased; the ecological area has decreased within a certain range; with the increased restriction in groundwater exploitation year by year, the efficiently-used land area will decrease, resulting in a lot of land not being effectively utilized and seriously hindering the coupling between the regional water and land resources and the local economy development.

Introduction

Land and water resources are the core resources of agricultural production [START_REF] Changming | Ecological Aspects of Water Cycle: Progress in the Research on the Water and Energy Balance of the Soil-Vegetation-Atmosphere System [J][END_REF]- [START_REF] Qiuxiang | Space Matching Pattern of the Sanjiang Plain's Land and Water Resources [J][END_REF] and basic materials that human survival and development rely on, playing an important role in the sustainable development of the region [START_REF] Tianxiao | DPSIR-based Evaluation on the Carrying Capacity of Land and Water Resources [J[END_REF]. China has a serious shortage of land and water resources. Along with the increase in population and rapid economic development, the scope and intensity of the use of land and water resources have been increasing, and the disputes in fighting for water between various sections and industries become increasingly acute. As agriculture is a big consumer of land and water resources, the proper allocation of land and water resources of is not only related to the social and economic development, but also affects the ecological environment restoration and reconstruction.

However, the previous researches on the optimized allocation of regional resources usually separate land and water resources as independent systems [START_REF] Jinxia | Research on the Optimized Allocation of Water Resources Based on Particle Swarm Optimization[END_REF]- [START_REF] Pinjie | Optimized Allocation of Spatial Structure of Land Use Based on Multi-objective Genetic Algorithm[END_REF], lay more emphasis on the water resource system and lack an effective coupling with the land resource system, hindering the full overall performance of the regional land and water resource system and easily leading to the system overloaded while running. Therefore, it is necessary to treat land and water resources as a system to make overall optimized allocation to improve the regional land and water resource utilization efficiency, maintain the relative balance of the system, obtain the optimal ecological and economic benefits and achieve sustainable use of land and water resources. Therefore, this research takes the Sanjiang Branch of Heilongjiang Land Reclamation Bureau as an example, treats agricultural land and water resources as a complex adaptive system and make its complex adaptive allocation, so as to provide an important reference for improving the regional agricultural land and water resource utilization efficiency, obtaining higher grain production efficiency and achieving the sustainable use of agricultural land and water resources.

Establishment of Complex Adaptive Allocation Model

2.1

Government Subject

The regional government is the controller of the entire region and its ultimate optimization objectives have more than one, which means the adaptability of the government subject is shown by the description of a multi-objective problem. The government subject's adaptability in this model is shown by maximum regional comprehensive benefits; the comprehensive benefits of this model is a single objective that is obtained after the processing of agricultural benefit objective and ecological benefit objective by utility function through the multi-objective analysis method, and that can be used to make a comprehensive evaluation of the system state. The agricultural benefit objective results from the equilibrium between the grain yield objective and the agricultural income objective or, specifically a government's comprehensive benefit objective resulting from the non-dimensionalization of two utility functions:

ECOAMIN ECSOMAX ECOAMIN ECOA ECOS    YSMIN YSMAX YSMIN YA Y    MAMIN MAMAX MAMIN MA M    (1) ) , ( MS YS f AW EL ) , ( ECOS AWEL f SWEL  (2)
ECOA and ECOS respectively mean standard ecological area (hm 2 ) and nondimensionalized standard ecological area; Y and YS respectively mean grain yield (t) and non-dimensionalized grain yield; M and MS respectively mean agricultural production value (RMB) and non-dimensionalized agricultural output value; ECOAMAX means the threshold value of Maximum standard ecological area (hm 2 ); ECOAMIN means the threshold value of minimum standard ecological area (hm 2 ); YMAX means maximum grain yield (t); YMIN means minimum grain yield (t); MMAX means maximum grain production value (RMB); MMIN means the minimum grain production value (RMB); AWEL means agricultural benefits; SWEL means comprehensive benefits; f means utility function.

Agricultural Production Subject

As a sector-level subject, its adaptability is shown by maximum comprehensive benefits of the agricultural sector, including pursuit of grain yield and agricultural production value, and is also a multi-objective problem.

Calculation of grain yield:

j j s y Y   (3)
Where Y means gross grain yield (t), yield per unit area of the j-th crop (t/hm 2 ), j-th crop planting area (hm 2 ) and j = (1 ... 6) six kinds of crops in the agricultural sector.

Calculation of economic benefits:

    6 1 j j j P Y M (4)
Where M means agricultural income (RMB), yield of the j-th crop and unit price of the j-th crop (RMB/t).

Ecological Subject

As a sector-level subject, its adaptability is shown by maximum comprehensive benefits of the ecological sector. This research uses standard ecological area to measure the benefits of the ecological sector. The ecological footprint method [START_REF] Zhang Jun | Evaluation of the Water Resources Carrying Capacity of Shule River Basin Based on Ecological Footprint Pratacultural Science[END_REF] is used to cover different ecological types into standard ecological areas through "equivalency factors". This research set the yield factor to 1 and equilibrium factors of pasture, woodland and waters to 0.5, 1.1 and 0.2 respectively which are respectively multiplied by the actual areas to be standard ecological areas.

Standard ecological area:

    3 1 i i ECO a S 因子 . ( 5
)
Where means standard ecological area, the i-th ecological area and i=(1…3) three ecological types.

Model Solving and Analyzing

Establishment of Boundary Conditions

The raw data come from the Statistical Yearbook of Sanjiang Branch and statistics of water conservation construction in Heilongjiang Province. The data of the sector and the ecological sector of the Sanjiang Branch of Heilongjiang Land Reclamation Bureau in 2008 is show in Table 1 and Table 2 respectively, and the model constraints, as shown in Table 3, are calculated from the data of various years by a certain percentage. The model is initialized by the following data with the establishment of relevant boundary conditions and constraints.

Result Analysis

The Sanjiang Branch has 800000 hm 2 of utilizable agricultural and ecological land.

As the region mainly uses groundwater to irrigate, the evolution analysis of optimized allocation of agricultural land and water resources in the region under different groundwater exploitation programs is made as follows. See Table 4 for program numbers and Table 5 and Table 6 for specific water quantity allocation programs and planting structure programs. As can be seen from Table 6, with the reduction in groundwater exploitation, the planting area of rice which consumes more water decreases gradually, the planting area of oil crops which consumes more water but have better economic benefits has no great fluctuation, and the planting areas of other crops which consume less water change within a certain range with no great reduction in the overall sowing areas.

With the actual water consumption in 2008 as the benchmark and other constraints unchanged, simulation calculation is made for each program to obtain the respective relationships between groundwater exploitation and agricultural production value, grain yield, standard ecological area and efficiently-utilized land area, as shown in Figures 3 to 6.

Fig. 3. Relationships between Groundwater Exploitation and Standard Ecological Area

Fig. 4. Relationships between Groundwater Exploitation and Standard Ecological Area

As can be seen from Figure 2 and Figure 3, with the increase in groundwater exploitation, agricultural production value and grain yield have also increased. According to Figure 4, ecological area decreases within a certain range, mainly because the ecological development is restricted and the effect is high when the groundwater exploitation is smaller, and the effect will decrease when the restriction decreases with the increase in exploitation. This nonlinear relationship and the different sensitivity of various subsystems to changes in water quantity show the adaptability. According to Figure 5, with the increased restriction in groundwater exploitation year by year, the efficiently-used land area will decrease, resulting in a lot of land not being effectively utilized and seriously hindering the coupling between the regional water and land resources and the local economy development.

According to the characteristics of the agricultural land and water resources of the Sanjiang Plain, this research treat agricultural land and water resources as a complex adaptive system and use the theory of complex adaptive system to put forward an allocation model of the complex adaptive system of agricultural land and water resources of the Sanjiang Plain and to make evolution analysis of optimized allocation of agricultural land and water resources under different groundwater exploitation plans. The evolution analysis has showed that water resources have become a bottleneck restricting the Sanjiang Plain's coordinated economic, social and ecological development. The development and utilization of surface water, groundwater protection and construction of water conservancy projects become effective means to solve the aforementioned problem. This research has important theoretical and practical significance for the development and utilization of agricultural land and water resources of the Sanjiang Plain and the coordinated economic, social, and ecological development.

Table 1 .

 1 Relevant Data of Agricultural Sector

	Crop Type	Rice	Wheat	Corn	Barley	Bean	Oil Crops
	Sowing area (10 4 hm 2 )	44.91	0.47	3.62	1.80	8.46	0.68
	Yield per unit area (t/hm 2 )	8.93	5.25	7.71	5.25	2.37	1.05
	Production value per unit area (RMB/ hm 2 )	7815	3600	5325	4000	4170	5625
	Yield (10 4 t)	400.93	2.44	27.88	9.47	20.11	0.71
	Production value (RMB10 8 )	71.29	0.37	2.90	1.53	6.66	0.56
	Irrigation quota (m 3 /hm 2 )	5000	1280	1360	1230	1330	1370

Table 2 .

 2 Relevant Data of Ecological Sector

	Ecological Type	Forest	Pasture	Waters
	Area (10 4 hm 2 )	16.61	2.35	4.75
	Irrigation quota (m 3 /hm 2 )	150	3000	0
	Equilibrium factor	1.1	0.5	0.2

Table 3 .

 3 Boundary Conditions and Constraints

	Constrain name	Threshold value of crop sowing area	Threshold value of ecological area	Groundwater exploitation (108m 3 )	Reclaimable wasteland (104hm 2 )	Annual precipitation (mm)
	Value	50% to 150% of actual area	50% to 150% of actual area	23.93	7.45	6342

Table 4 .

 4 Groundwater Exploitation of Different Programs

	Program number	1	2	3	4	5	6	7	8	9	10
	Groundwater
	exploitation (10 8 m 3 )	23.80	23.50	23.00	22.47	21.64	21.19	20.94	20.36	19.90	19.47

Table 5 .

 5 Water Resource Allocation Program

	Program	1	2	3	4	5	6	7	8	9	10
	Agriculture (10 8 m 3 )	23.05	22.77	22.19	21.72	20.94	20.46	20.13	19.65	19.16	18.84
	Ecology (10 8 m 3 )	0.74	0.73	0.79	0.75	0.70	0.74	0.81	0.71	0.74	0.63

Table 6 .

 6 Planting Structure Adjustment Program

	Program	Rice	Wheat	Corn		Barley		Bean	Oil Crop	Forest	Pasture	Waters
	1	44.27	0.06	2.69		0.42		3.15	0.57	22.32	1.34	2.55
	2	43.78	0.06	2.80		0.37		2.85	0.47	21.86	1.34	2.46
	3	42.46	0.05	3.12		0.47		2.94	0.61	20.71	1.60	2.57
	4	41.69	0.06	2.56		0.36		3.11	0.48	19.15	1.54	2.44
	5	40.00	0.06	3.03		0.34		3.01	0.55	19.85	1.34	2.39
	6	39.05	0.06	2.94		0.35		3.06	0.54	19.86	1.47	2.53
	7	38.63	0.06	2.24		0.37		2.89	0.50	19.63	1.71	2.48
	8	37.44	0.06	2.75		0.46		2.94	0.70	19.26	1.42	2.43
	9	36.61	0.05	2.50		0.43		2.89	0.53	17.27	1.61	2.44
	10	35.89	0.06	2.67		0.44		2.85	0.64	16.96	1.25	2.44
				420
				400
			t)
			4
			(10	380
			Yield	360
			Grain	340
				320
				300
				19.5	20	20.5	21	21.5	22	22.5	23	23.5	24
							Groundwater Exploitation (10 8 m 3 )
		Fig. 1. Relationships between Groundwater Exploitation and Grain Yield

Acknowledgment

The author would like to thank his collaborators from and support of Natural Science Foundation of China (NO. 51179032、NO. 51279031、NO. 51109036), Ministry of water resources public welfare industry special funds for scientific research project (NO. 201301096), The ministry of education in the new century excellent talents to support plan, Province Natural Science Foundation of Heilongjiang (No: E201241), New Century Talent Supporting Project by Education Ministry, The Yangtze River Scholars Support Program of Colleges and Universities in Heilongjiang Province, Heilongjiang Province Water Conservancy Science and Technology project (No: 201318), Prominent young person of Heilongjiang Province(JC201402).


Guo Jianhua

Meng Zhijun

Chen Liping
email: cchenglp@nercita.org.cn

Ma Wei

An Xiaofei

Yao Hong

THE Effect of Precision Nitrogen Topdressing Decision on Winter Wheat

Keywords: Topdressing wheat, Precision decision-making, Topdressing recommendation

Topdressing in the growth period is the key to high yield of wheat. So how to use the rapid detection technique to realize scientific and real-time topdressing for lower fertilizer application and higher use efficiency of fertilizer is critical to agricultural development. The aim of this study is to investigate the effect of fertilization as affected by different topdressing recommendations. In this study, field experiments are conducted at Xiaotangshan Precision Agriculture Demonstration Base in Beijing, using commonly-used rapid detection equipment. The experimental results are summarized as follows: Variable rate fertilization based on nitrate nitrogen level in soil and grower's uniform fertilization are basically same in fertilizing amount but the former produces higher yield of 320 kg/ha; high rate application based on low measured NDVI value in the reviving stage leads to highest fertilizer productivity at 19.3 kg/ kg, with an increase of 10.4 kg/ kg compared with grower's uniform fertilization; in terms of profitability, high rate application based on low measured NDVI value in the reviving stage produces highest profit of 12,649 yuan/ha, while high rate application based on high measured SPAD value and high measured NDVI value in the reviving stage produce lowest profits of 9,358 yuan/ha and 9,775 yuan/ha respectively.

Introduction

Fertilizer is regarded as material guarantee of sustainable agricultural development and also the key to higher grain yield. It is proven by agricultural development practices that fertilization is the most rapid, efficient and important means of increasing grain yield. China has now become the largest producer and consumer of fertilize [START_REF] Hong | Fertilizer and food safety [J][END_REF]. The amount of fertilizer applied in China takes up about 1/3 of total amount of global fertilization. Growers tend to spread fertilizer out evenly on the soil surface, which means the rate of fertilizer application is same in one application area or with one grower regardless of varied soil nutrients. As a result, too many fertilizers are applied on high-nutrient soils, leading to low use efficiency of fertilizer.

The average application rate of fertilizer was developed two times the upper limit of the safe use of chemical fertilizers and the average utilization rate of is about 40%. China's arable land accounted for 7% of the world's arable land, but the use of 35% of the fertilizer over the world. [START_REF] Yan | Recent advances in technology of increasing fertilizer use efficiency [J][END_REF]. Considerable loss of fertilizer has triggered various environmental problems. For example, in some areas characterized by highly intensive agriculture in Beijing, the improper application of fertilizer N has caused excessive nitrate contained in underground water, the content of which even reaches 100% in some areas. With the development of soil test-based fertilizer recommendation and balanced fertilization technologies, fertilizer's rate of contribution to soil increases gradually and fertilizers applied elevate the grain yield. The traditional testing technique used to be widely applied because of its precision in the test procedures and results. However, due to high workload, time-consuming feature and complicated operation, coupled by limitation in environment, equipment and technology, such testing technique is unlikely to meet the requirements of agricultural modernization. As modern science and technology continue to develop, various sensor-based non-destructive rapid testing equipment and technologies are developed, such as the SPAD Chlorophyll Meter made in Japan. Chlorophyll meter is a tool used to rapidly measure the level of chlorophyll contained in crop leaves, which is now gradually recognized and popularized by experts. At present, it has been applied in N nutrition determination for rice, corn, wheat and other crops [3, 4, 5] . In this study, effective nitrogen level in soils (0-20cm & 20-40cm) is measured along with SPAD value, NDVI value and nitrate content in leaves in the reviving stage for purpose of precision fertilization. Then the decision-making on precision topdressing and the effect of precision fertilization are examined and evaluated with the intent to improve the use efficiency of fertilizer N, maximize ecological economic benefit, and provide guidance for agricultural production.

Measurement of NDVI value

NDVI data is collected using hand-held GreenSeeker NDVI meter produced by U.S. Ntech. The NDVI meter works based on an active remote sensing system, of which the red band length is 671±6 nm, NIR band length 780±6 nm, and spectrum width 0.6 m. The test is conducted in a sunny forenoon in the growth period of wheat.

During the test, the top end of remote sensor must be adjusted to be level with the ground and in parallel to wheat canopy at the same height, with a vertical height of 80-120 cm. The typical NDVI value is obtained by averaging the NDVI values of all subzones.

Measurement of SPAD value

Leaf SPAD value is measured by SPAD-502 Chlorophyll Meter. The test is carried out on 20 uniformly sized wheat plants randomly selected from the subzones in a sunny forenoon on 9:00 a.m.-12:00 a.m. The SPAD value is obtained by averaging the measured values of mid-part leaf newly generated on the stem.

Measurement of leaf nitrate content

The instrument used for the test is a German-made nitrate reflectometer. And the test procedures are shown as follows: take a basal part of the stem, and cut into small pieces; use juice squeezer to get the juice out; dilute the juice by a factor of about 10-20; inset the test strip to the liquid to be analyzed; take out the test strip and insert into the reflectometer for matching; and read out the nitrate content (mg/L) directly.

Results and Analysis

After the experiments, the growth dynamics and yield of wheat in critical growth stages are analyzed.

Dynamic changes of NDVI value in different growth stages

The experiments show that wheat's N nutrition can be expressed by NDVI value. In the erecting stage, leaves are small, so NDVI value is low; then NDVI value increases at a high rate as wheat grows at a high rate as wheat grows gradually, peaking after entering the booting stage and finally remaining stable thereafter. During the entire experimental period, different fertilizer treatments are consistent with growth changes. NDVI is a vegetation index that can reflect growth trend of crops. In consideration of differences in wheat species, it is necessary to build a separate control field for determining an appropriate rate of topdressing application in practical production.

Fig.2 NDVI Value Changes Affected by Different Fertilizer Treatments

Dynamic changes of SPAD value in different growth stages

As can be seen from Fig. 3, during the entire experimental period, SPAD value varies with growth stages, bottoming out in the reviving stage while peaking in the heading stage. Then SPAD value declines gradually and tends to stabilize in the grain-filling stage. Such a change trend is consistent with the change of N nutrition [8]. Leaf N is mostly stored in chloroplast, so N level in leaf is positively correlated with chloroplast content. Chloroplast content and N level increase as a result of fertilization, so is SPAD value. After flowering, the plant starts to age, resulting in a decline in chloroplast and N levels and also SPAD value.

During the sprouting stage, SPAD value is correlated with fertilizing amount, which is especially remarkable in the grain-filling stage. Leaf N level is positively correlated with chlorophyll level while crop yield is positively correlated with leaf N and chlorophyll levels in specific growth stages.

After topdressing, it takes some time for wheat to uptake the fertilizer N. So wheat is more responsive to nitrogen and SPAD value is more correlated with the rate of topdressing application in the flowing stage.

Fig.4 Correlation of SPAD Values with Different Fertilizing Amounts in Flowering Stage

Correlation of nitrate level with N application level

Wheat nitrate level is correlated with fertilizing amount, of which the correlation coefficient is R2=0.7333. Nitrate level varies with N application level and increases as more fertilizers N are applied. As an effective nitrogen form of plants, nitrate is a direct reflection of N nutrition. Nitrate level depends greatly on fertilizing amount. The higher the rate of application, the higher the nitrate level.

Fig.5 Correlation of Fertilizing Amount with Wheat Nitrate Level

Correlation of chlorophyll level with fertilizing amount

Chlorophyll is vital for photosynthesis, so the amount of photosynthate depends on chlorophyll level. To the extent that is appropriate, higher fertilizing amount produces higher chlorophyll level and more efficient photosynthesis.

During the jointing stage, nitrate level is correlated with yield. The higher the nitrate nitrogen level, the higher the yield.

Effects of different topdressing recommendations

In Different topdressing recommendations derived during the reviving stage are put into practice in the field. The trial zone is divided into several 25m2 subzones, in which fertilizers are applied artificially immediately followed by watering. As indicated in Table 4, the treatment V10 produces a profit of 12,649 yuan/ha, with an increase of 2,268 yuan/ha compared with the control (10,381 yuan/ha); the treatments VTN and VS10 both produce lower profits compared with the treatment UNIT.

The treatment VS10 produces lowest profit of 9,358 yuan/ha (based on high measured SPAD value) and 9,775 yuan/ha (based on high measured NDVI value) respectively.

Table 4 Analysis on Profitability of Different Topdressing Recommendations

As can be seen from Table 5, the variable coefficient of yield decreases after the treatment VTN. The variable coefficient of the control is highest (20.75%), followed by the Treatment UNIT (18.59%); the variable coefficient of the treatment VTN is only 4.47-14.17%.

Fig. 1 NDVI

 1 Fig.1 NDVI Value Changes Affected by Different Fertilizing Amounts

Fig. 3 3 . 4

 334 Fig.3 Measured SPAD Value in Different Growth Stages

Fig. 6 Fig. 7

 67 Fig. 6 Chlorophyll Levels Affected by Fertilizing Amount

2 The structure of soil parameter distribution map system 2 Experimental Material and Method 2.1 Experiment design

  in subzones of the trail zone. Following that, the contents of nitrate nitrogen and water in the soil samples are measured, then SPAD value, NDVI value and nitrate content in leaves. Finally, the amount of fertilizer N to be top dressed is figured out. Meanwhile, field trials of different rates of fertilizer N application are carried out.

	The experiments were conducted during Mar. 2013 and Jun. 2013 on the fields in Xiaotangshan Precision Agriculture Demonstration Base in Changping District, Beijing. The tested wheat is Jingdong 12, which was sown mechanically on Oct. 3, 2012 at a rate of 13 kg/mu by applying base fertilizer DAP at rate of 15 kg/mu. Sprinkling irrigation is applied. After entering the reviving stage, the trial zone is delineated through DGPS locating and boundary defining. Then samples are extracted in zigzagging form from the soils Test Item Index Recommen ded Amount Nitrate in Leaf >1400 0 1000-1400 8 800-1000 10 600-800 13 <600 15 SPAD <47 15 47-50 10 >50 8 GREENSE EK <0.45 18 0.45-0.47 15 0.47-0.5 12 at depths of 0-20 cm and 20-40 cm Table 1 Topdressing Recommendations >0.5 10

Table 2 Topdressing Treatment Summary

 2

	Treatment	Code	Subzone Qty.
	ck	Control	9
	Unit	Local grower's uniform fertilization	9
	VTN	Variable rate fertilization based on nitrate nitrogen level in soil	18
	VS10	Low rate application based on high measured SPAD value in the reviving stage	18
	V10	Low rate application based on high measured NDVI value in the reviving stage	18

Table 3 Yields under Different Topdressing Recommendations

 3

				Yield	Yield
		Applica tion Rate	Yield	Increase (%) Compared	Increase (%) Compared	Yield Increase
	Code			with Control	with
					Grower's
		Kg/ha	Kg/ha		Uniform	kg/kg
					Fertilization
	CK	0	4796.5
	UNI	150	6129.7	27.8		8.9
	VT	159	6588.4	37.36	7.48	11.3
	VS 10	165	6593.4	37.46	7.56	10.9
	V1 0	132	7346	53.15	19.84	19.3
	VP N	153	6449.7	34.47	5.22	10.8

Table 5 Yield Variance under Different Topdressing Recommendations

 5

	Code Average Yield (kg/ha)	Standard Deviation	Yield Variance (%) under Different Topdressing Recommendations
	CK	4796.5	995.41	20.75
	UNI	6129.7	1139.86	18.59
	VT	6588.4	395.2	5.96
	VS10	6593.4	825.19	12.5
	V10	7346	328.38	4.47
	VPN	6449.7	917.1	14.17

5 Result and Conclusion Topdressing

  is one of critical farming practices to realize high yield of wheat. Scientific topdressing can not only elevate crop yield but also cut down fertilizer input. Grain yield depends partly on topdressing recommendations. Variable rate fertilization based on nitrate nitrogen level in soil and grower's uniform fertilization are basically same in fertilizing amount but the former produces higher yield of 320 kg/ha. As suggested by the yield-increasing effects, the fertilizer productivity varies with topdressing recommendations; high rate application based on low measured NDVI value in the reviving stage leads to highest fertilizer productivity at 19.3 kg/ kg, with an increase of 10.4 kg/ kg compared with grower's uniform fertilization; high on high measured SPAD value and high measured NDVI value in the reviving stage result in lowest fertilizer productivities at 4.8 kg/ kg and 5.9 kg/ kg respectively. In terms of profitability, high rate application based on low measured NDVI value in the reviving stage produces a profit of 12,649 yuan/ha, and high rate application based on high measured SPAD value and high measured NDVI value in the reviving stage produce lowest profits of 9,358 yuan/ha and 9,775 yuan/ha respectively.

	kg/ha Application Rate rate application based Code	kg/ha Average Yield Per Unit	a yuan/h Fertili zer Input	yuan/ha Grain Output Value	yuan/ha Profit
	CK	0	4796.5		8634
	UNI	150	6129.7	652	11033	10381
	VT	159	6588.4	691	11859	11168
	VS10	165	6593.4	717	11868	11151
	V10	132	7346	574	13223	12649

Acknowledgment

This study was supported by Chinese National High Science and Technology Research Project "863" No 2012AA101901 and Ministry of Agriculture nonprofit sector special Project No 201303103.