Thomas Clausen
email: thomas@thomasclausen.org

Ulrich Herberg

A Comparative Performance Study of the Routing Protocols LOAD and RPL with Bi-Directional Traffic in Low-power and Lossy Networks (LLN)

Keywords: RPL, LOAD, Sensor Network, Routing, Performance Comparison RPL, LOAD, réseau de capteurs sans-fil, routage, comparaison de performance

Routing protocols for sensor networks are often designed with explicit assumptions, serving to simplify design and reduce the necessary energy, processing and communications requirements. Different protocols make different assumptions -and this memorandum carefully considers those made by the designers of RPL -an IPv6 routing protocol for such networks, developed within the IETF. Specific attention is given to the predominance of bi-directional traffic flows in a large class of sensor networks, and this memorandum therefore studies the performance of RPL for such flows. As a point of comparison, a different protocol, called LOAD, is also studied. LOAD is derived from AODV and supports more general kinds of traffic flows. The results of this investigation reveal that for scenarios where bi-directional traffic flows are predominant, LOAD provides similar data delivery ratios as RPL, while incurring less overhead and being simultaneously less constrained in the types of topologies supported.

Etude de Performance Comparative des Protocoles de Routage LOAD et RPL avec Trafic Bi-Directionnel dans des Réseaux "Low-power et Lossy" (LLN)

Sensor networks differ from more "traditional networks" in that the devices making up a sensor network have connectivity maintenance and data forwarding as auxiliary tasks to their primary raison d'être, such as data acquisition. Ignoring the applications, the network itself can be described by (i) the devices being many thousands in number, (ii) with very limited internal (memory, CPU), external (communications capacity) and energy resources, and that (ii) the communications channel between devices typically has unattractive characteristics: low-bandwidth, high loss-rates and volatile links with limited persistency over time. The term Low-power Lossy Networks (LLN) is therefore commonly used for describing such networks. Yet, despite these challenges, routing protocols are required for establishing and maintaining multi-hop connectivity in LLNs, for situations where it is unfeasible or impossible to provision a sensor network deployment such that all devices, necessitating communication between each other, are within direct connectivity.

Background

The Internet Engineering Task Force (IETF) [START_REF] Herberg | Web site[END_REF] has a long tradition of developing and standardizing routing protocols. Initially, for fixed Internet infrastructures, where the conditions are more lenient than in LLNs: routers generally have abundance of computational capacity and few energy constraints, links are predominantly "good" with few losses -and while Internet routing protocols such as OSPF are able to handle some network topology changes, these are generally rare, and generally occur only as a result of relatively catastrophic events: a cable being cut, for example.

In the late 1990'es, the IETF started investigating MANETs [START_REF]Charter of the working group[END_REF] -Mobile Ad hoc NETworks. Generally thought of as multi-hop wireless networks of mobile devices, a crop of routing protocols were developed and standardized, notably OLSR [START_REF] Clausen | Optimized Link State Routing Protocol (OLSR)[END_REF] and AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] 1 . Able to manage more dynamic topologies and the characteristics of wireless network interfaces, this work introduced a new dichotomy in routing protocol classification: OLSRv2 [START_REF] Clausen | The Optimized Link-state Routing Protocol version 2[END_REF] being a classic link-state routing protocol, optimized for MANETs, it maintains paths to all destinations at all times, and this even before such paths are needed -proactively. AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] approached the same problem in a different fashion, by discovering and maintaining paths to destinations only as needed by application traffic -reactively. For both, however, the assumption was that while the network topology might be dynamic and the wireless connectivity volatile, the devices in the network still had a relative abundance of both computational power and energy.

With the emergence of sensor networks, so did the challenge to the assumption of an abundance of computational power and energy -even transmitting an IPv6 packet with 128-bit long addresses was considered a strain in terms of energy consumption, and so the IETF started investigating adaptations of IPv6 for LLNs: compressing addresses, removing options considered rarely used, simplifying packet processing etc. Routing protocols, even those developed for MANETs, were considered too heavy and work started around 2005 on IPv6based routing protocols, adapted for LLNs -formalized by the IETF with the creation of a ROLL ("Routing Over Low-power and Lossy networks") Working Group [START_REF]Charter of the working group[END_REF] specifically for managing this development work. This Working Group produced a protocol denoted RPL ("Routing Protocol for Low-power and lossy networks") [START_REF] Winter | RPL: IPv6 Routing Protocol for Low power and Lossy Networks[END_REF].

Sacrifices

While the development from fixed Internet infrastructure routing protocols such as OSPF and BGP to MANET routing protocols such as OLSR and AODV was accomplished by way of algorithmic optimizations e.g., on the overhead incurred by sharing link-state information network-wide, MANET protocols remained relatively general routing protocols -in particular, MANET routing protocols provide full IP support and adhere to the "anybody can communicate with anybody" paradigm.

The development towards LLN routing protocols, on the other hand, came with sacrifices in generality: assumptions as to which options in IPv6 headers were to be supported, as well as optimization of routing protocol operation for specific traffic patterns, considering certain such esoteric enough so as to not merit special attention. The ROLL Working Group, in its design of RPL, made a set of such assumptions, notably that sensor-to-controller traffic (multipointto-point) is predominant, controller-to-sensor traffic (point-to-multipoint) is rare and sensor-to-sensor traffic (point-to-point) is somewhat esoteric. RPL in its design, therefore, optimizes for multipoint-to-point traffic, supports in a less optimized fashion point-to-multipoint and provides some very basic mechanisms for point-to-point traffic -essentially transiting such point-to-point traffic between two sensors via the controller.

Motivation

The traffic patterns for which RPL optimizes are, unquestionably, reasonable in some scenarios: data acquisition networks, for example, where sensors monitor an environment and transmit their findings towards a central controller, and where traffic from the controller to a sensor is a rare occurrence. They are, however, not universal. There are scenarios in which sensor-to-sensor traffic is assumed to be the more common pattern, such as [START_REF] Martocci | Building Automation Routing Requirements in Low Power and Lossy Networks[END_REF]. Another example is in utility metering, where a utility company may wish to have a controller inquire household meters as to their consumption -send a request and expect a reply -or even may use the network to change parameters in household meters (expecting a confirmation). An example hereof is for load management on a power-grid, where a controller may wish to instruct individual households to reduce (or increase) their consumption according to the overall load on the grid. Even data acquisition type networks may have this characteristics: a sensor detecting an abnormal condition may signal this -needing to be certain that the signal has been received and thus expecting a confirmation.

Thus, even if assuming that point-to-point between sensors inside the network is relatively rare, bi-directional communication between sensors and a controller should be assumed common. In terms of the assumptions on traffic patterns,

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Comparative Study of LOAD and RPL with Bi-Directional Traffic in LLNs 5 made in the design of RPL [START_REF] Winter | RPL: IPv6 Routing Protocol for Low power and Lossy Networks[END_REF], this entails that equal importance should have been given to point-to-multipoint and multipoint-to-point traffic.

Statement of Purpose

Thus, one purpose of this memorandum is to explore the performance of LLN routing protocols for scenarios where bi-directional traffic is prevalent, in particular understanding the behavior of RPL, and the viability of the sacrifices made in generality by the design of that protocol, when exposed to this common traffic pattern. A second purpose is to explore how a more general protocol, LOAD [START_REF] Kim | 6LoW-PAN Ad Hoc On-Demand Distance Vector Routing (LOAD)[END_REF], behaves when exposed to the same traffic patterns -both as a point of comparison, and as a way of exploring the viability of that protocol for LLNs. LOAD is a protocol, derived from AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] by simplifying some mechanisms (detailed in section 3) while retaining the generality of supporting all traffic patterns equally and provisioning all paths to be bi-directional.

Memorandum Outline

The remainder of this memorandum is organized as follows: section 2 presents a functional overview of RPL [START_REF] Winter | RPL: IPv6 Routing Protocol for Low power and Lossy Networks[END_REF], the routing protocol currently being presented by the IETF ROLL Working Group as the proposed standard for IPv6 routing in LLNs. Section 3 similarly provides a functional overview of LOAD [START_REF] Kim | 6LoW-PAN Ad Hoc On-Demand Distance Vector Routing (LOAD)[END_REF], a protocol derived from the MANET protocol AODV so as to be applicable for operation over LLNs. Section 4 performs a comparative study of these.

RPL Overview

The basic construct in RPL is a "Destination Oriented Directed Acyclic Graph" (DODAG), depicted in figure 1. In a converged LLN, each RPL router has identified a stable set of parents, each of which is a potential next-hop on a path towards the "root" (or "controller") of the DODAG, as well as a preferred parent. Each router, which is part of a DODAG (i.e. has selected parents) will emit DODAG Information Object (DIO) messages, using link-local multicast, indicating its respective rank in the DODAG (i.e. distance to the DODAG root according to some metric(s), in the simplest form hop-count). Upon having received a (number of such) DIO messages, a router will calculate its own rank such that it is greater than the rank of each of its parents, select a preferred parent and then itself start emitting DIO messages.

The DODAG formation thus starts at the DODAG root (initially, the only router which is part of a DODAG), and spreads gradually to cover the whole LLN as DIOs are received, parents and preferred parents are selected and further routers participate in the DODAG. The DODAG root also includes, in DIO messages, a DODAG Configuration Object, describing common configuration attributes for all RPL routers in that network -including their mode of operation, timer characteristics etc. RPL routers in a DODAG include a verbatim copy of the last received DODAG Configuration Object in their DIO messages, permitting also such configuration parameters propagating through the network. A Distance Vector protocol, RPL [START_REF] Winter | RPL: IPv6 Routing Protocol for Low power and Lossy Networks[END_REF] restricts the ability for a router to change rank. A router can freely assume a smaller rank than previously advertised (i.e. logically move closer to the root) if it discovers a parent advertising a lower rank, and must then disregard all previous parents of higher ranks. The ability for a router to assume a greater rank (i.e. logically move farther from the root) than previously advertised is restricted, to avoid count-to-infinity problems. The root can trigger "global recalculation" of the DODAG by increasing a sequence number, DODAG version, in DIO messages.

The DODAG so constructed is used for installing routes: the "preferred parent" of an RPL router can serve as a default route towards the root, or the root can embed in its DIO messages the destination prefixes, included by DIOs generated by RPL routers through the LLN, to which connectivity is provided by the root. Thus, RPL by way of DIO generation provides "upward routes" or "multipoint-to-point routes" from the sensors inside the LLN and towards the root.

"Downward routes" are enabled by having sensors issue Destination Advertisement Object (DAO) messages, propagating as unicast via parents towards the DODAG root. These describe which prefixes belong to, and can be reached via, which RPL router. In a network, all RPL routers must operate in either of storing-mode or non-storing-mode, specified by way of a "Mode of Operation" (MOP) flag in the DODAG Configuration Object from the root. Depending on the MOP, DAO messages are forwarded differently towards the root:

• In non-storing-mode, an RPL router originates DAO messages, advertising one or more of its parents, and unicasts it to the DODAG root. Once the root has received DAOs from an RPL router, and from all routers on the path between it and the root, it can use source routing for reaching advertised destinations inside the LLN.

• In storing-mode, each RPL router on the path between the originator of a DAO and the root records a route to the prefixes advertised in the DAO, as well as the next-hop towards these (the router, from which the DAO was received), then forwards the DAO to its preferred parent.

"Point-to-point routes", for communication between devices inside the LLN and where neither of the communicating devices are the DODAG root, are as default supported by having the source sensor transmit via its default route to the DODAG root (i.e., using the upward routes) which will then, depending on the "Mode of Operation" for the DODAG, either add a source-route to INRIA the received data for reaching the destination sensor (downward routes in nonstoring-mode) or simply use hop-by-hop routing (downward routes in storingmode). In the case of storing-mode, if the source and the destination for a pointto-point communication share a common ancestor other than the DODAG root, a downward route may be available (and used) before reaching the DODAG root.

RPL Message Emission Timing -Trickle

RPL message generation is timer-based, with the root able to configure back-off of message emission intervals using Trickle [START_REF] Levis | Trickle: A self-regulating algorithm for code propagation and maintenance in wireless sensor networks[END_REF], specified in [START_REF] Levis | RFC6206: The Trickle Algorithm[END_REF]. Trickle, as used in RPL, stipulates that a RPL router transmits a DIO "every so often"except if receiving a number of DIOs from neighbor routers, enabling the router to determine if its DIO transmission is redundant.

When an RPL router transmits a DIO, there are two possible outcomes: either every neighbor router that hears the message finds that the information contained is consistent with its own state (i.e., the received DODAG version number received corresponds with that which the RPL router has recorded and no better rank is advertised than that which is recorded in the parent set) -or, a recipient RPL router detects that either the sender of the DIO or itself has outof-date information. If the sender has out-of-date information, then the recipient RPL router schedules transmission of a DIO to update this information. If the recipient RPL router has out-of-date information, then it updates based on the information received in the DIO.

With Trickle, an RPL router will schedule emission of a DIO at some time, t, in the future. When receiving a DIO containing information consistent with its own information, the RPL router will record that "redundant information has been received" by incrementing a redundancy counter, c. At the time t, if c is below some "redundancy threshold", then it transmits its DIO. Otherwise, transmission of a DIO at this time is suppressed, c is reset and a new t is selected to twice as long time in the future -bounded by a pre-configured maximum value for t. If, on the other hand, the RPL router has received an out-of-date DIO from one of its neighbors, t is reset to a pre-configured minimum value and c is set to zero. In both cases, at the expiration of t, the RPL router will verify if c is below the "redundancy threshold" and if so transmit -otherwise, increase t and stay quiet.

LOAD Overview

LOAD [START_REF] Kim | 6LoW-PAN Ad Hoc On-Demand Distance Vector Routing (LOAD)[END_REF] is a protocol, derived from AODV [START_REF] Perkins | Ad hoc On-Demand Distance Vector (AODV) Routing[END_REF] and adapted for LLNs. Thus, the basic operation of LOAD is identical to that of AODV: a device with a packet to deliver to a destination, and which does not have a valid entry in its routing table for that destination, will issue a route-request (RREQ) message, diffused through the network so as to reach all other devices. When a device forwards this route-request, it records an entry in its routing table towards the originator of that route-request -a reverse route indicating the eventual path from the destination to the originator. If the destination is present in the network, it will eventually receive the route-request -and will respond by a route-reply (RREP), unicast to the originator of the route-request along the previously installed reverse route. As that route-reply is being forwarded along this reverse route, the devices forwarding it will instill a forward route towards the destination. Once the route-reply arrives at the originator of the corresponding route-request, a bi-directional path is installed, available for use.

When a link is detected to be broken (typically through a link-layer notification of a data-packet failing to be delivered to a next hop), the detecting router may engage in a route-repair operation -essentially a new route-request/routereply cycle to discover a path to the destination -and if that fails, issue a route-error (RERR) message to inform the source of the failed data-packet of the error.

While this route discovery is performed, any IP-packets to the destination are buffered in the source router. When a route is established, these packets are transmitted -and if no route can be established, they are dropped.

The main differences between AODV and LOAD are:

1. LOAD simplifies the protocol behavior by disallowing that intermediate devices respond with a route-reply -even if they have an active route to the intended destination -thereby eliminating the need for destination sequence numbers.

2. Where in AODV, in case a device detects a link breakage, that device will attempt to transmit the route-error message to all neighbors which have recently used it as a next-hop on a path to the destination of the undelivered package, LOAD disables that -thereby eliminating the need to a device to maintain a precursor list.

Other, minor, differences include simplification of the packet format, support for compressed IPv6 addresses [START_REF] Montenegro | Transmission of IPv6 Packets over IEEE 802.15.4 Networks[END_REF] etc.

LOAD does not impose any specific roles on any specific devices, notably has no controller or root with specific responsibilities for the network operation. Thus, the default traffic pattern supported by LOAD is bi-directional point-topoint traffic. The one sacrifice that LOAD makes with respect to data traffic, in simplifying from AODV, is, that it assumes that a given destination typically is in communication with only a single source at a given time -hence, the suppression of the precursor list.

Evaluation

In order to understand the behavior of both RPL and LOAD in LLNs with bidirectional traffic, a simulation study using the Ns2 simulator has been conducted. Standard evaluation metrics, such as data traffic delivery ratio, control traffic overhead, number of collisions, network convergence time etc. are compared between the two protocols. Section 4.1 describes the simulation settings and section 4.2 presents the results of the evaluation.

Simulation Settings

The specific settings of the scenarios studied are detailed in table 1. For each datapoint, the values have been averaged over 10 runs.

Both LOAD and RPL have been implemented in Java, using the AgentJ framework [START_REF] Herberg | Development Framework for Supporting Java NS2 Routing Protocols[END_REF] to hook the Java code into Ns2. The routers were placed randomly in a square area of variable size, with a density of 50 routers per km 2 . The bidirectional traffic patterns consisted of a "reading scenario", where the controller collects information from all sensors in the network, within 24 hours. Delay of the replies was considered irrelevant, as long as all sensors have replied within the 24 hours. In the Ns2 simulation, the controller sends a single request to each sensor (one datagram, 11 octets payload), to which the sensor replies with a single data packet (one datagram, 100 octets payload).

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The settings for RPL are listed in table 2, and for LOAD in table 3. The simulation time was set to 24 hours for LOAD, and to 270 seconds for RPL. The reason for not choosing 24 hours for RPL as well is the required amount of time for conducting the simulation: as DAO messages are sent periodically in the simulation, even if no data traffic occurs in the network, the required amount of time to conduct the simulation would take several days for a single simulation run. Moreover, trace files would grow to hundreds of Gi-gabytes per simulation. As in LOAD no control traffic is sent when no data traffic is sent, the simulation runs considerably faster, allowing to simulate the 24 hours interval. Despite this difference of simulation time, the results are comparable, as the transmitted data traffic stays the same. The control traffic overhead of RPL can be extrapolated from 270s to 1 day, as DAO messages are sent periodically.

For RPL, the first traffic request from the controller is sent at 60s after the the start of the simulation, allowing RPL to converge (i.e., to construct the DODAG) before. Once the sensor receives the data request, it replies immediately. The following data request from the controller to the next sensor is sent 0.1 after the previous data request. For simulations with 1000 routers, the last data request from the controller will thus be sent at 160 seconds after the simulation start (60s + 999 • 0.1s).

For LOAD, the traffic requests are sent every 30 seconds, starting at 100 seconds after simulation start, i.e., the last request is sent at 30070 seconds or 8.35 hours after the simulation start. In order to reduce the amount of collisions of frames, a random jitter has been added before forwarding a RREQ on a router, as specified in [START_REF] Clausen | Jitter Considerations in Mobile Ad Hoc Networks (MANETs)[END_REF]. While this is not specified in the current revision of LOAD, it could be added in a future revision, similar to other current ad hoc routing protocols like OLSRv2 [START_REF] Clausen | The Optimized Link-state Routing Protocol version 2[END_REF].

During the simulation, only a single traffic flow from the controller towards a sensor or vice verse is active, i.e., only a single route has to be maintained on a router running LOAD (assuming that routes expire before the next data packet is transmitted). In the investigated "reading scenario", this does not represent an unrealistic setting, since every sensor is only contacted once in 24 hours by the controller. As a consequence, both RPL in non-storing mode and LOAD require minimal state on the routers.

Simulation Results

This section describes the results of the simulation.

Figure 2 shows the maximum and average rank of routers in the DODAG, where the number represents the distance of a router to the controller in terms of hops (i.e. the maximum rank represents the diameter of the network, the average rank represents the average over all routers). The maximum and average ranks grow logarithmically with the number of routers in the network.

Figure 3 depicts the average number of parents of each router in the DODAG. Keeping the density of the network constant with increasing number of routers, the average number of parents grows logarithmically.

Figure 4 displays the convergence time of the network, i.e. the time that is needed for all routers that are in the same connected component as the controller to join the DODAG. Since each router starts sending DIOs two seconds after the last change to its Candidate Neighbor Set, the convergence time is roughly two seconds times the maximum rank of the DODAG. The convergence time grows logarithmically with the number of routers in the network.

Figure 5 depicts the cumulative control traffic overhead over the simulation time, i.e., in 270 s for RPL and in 1 day for LOAD. Each retransmission of a broadcast packet is counted separately, e.g., a RREQ in LOAD that is flooded from the controller to n other sensors, counts for n • sizeof (RREQ) bytes. It can be observed that for both protocols, the control traffic overhead increases polynomially with the amount of routers, and that RPL leads to more than twice the amount of the control traffic as LOAD. For RPL, the periodic DAOs account for the majority of the control traffic, whereas the overhead of DIOs is much lower due to the exponentially growing emission intervals of the Trickle timer. For LOAD, the broadcast RREQs lead to a much higher overhead than the unicast RREPs.

Since the simulation time was much lower for RPL, and DAOs are sent periodically, the expected amount of control traffic for a duration of 1 day can be easily extrapolated. Figure 6 depicts the extrapolated control traffic amount of DAOs and RREQs (which account for the majority of the control traffic, as described above). The overhead for the RREQs is the same as in figure 5, as the emission of RREQs only depends on the data traffic. The overhead of RPL is two orders of magnitude higher than the overhead of LOAD.

Figure 7 shows the data traffic in number of frames that is sent from the controller to each sensor. Evidently, as one single frame is sent per router, the total overhead consists of n -1 frames for n routers.

However, as shown in figure 8, the cumulative amount of data traffic in terms of bytes is different for RPL and LOAD, where each retransmission of a packet is counted. The overhead for the 11-octet request downward from the controller to the sensors differs, as RPL (in non-storing mode) includes source routes for each data packet, which are included in the count of this figure. Therefore, even though a single data request packet is nine times smaller than the data reply, the data requests account for almost half as much overhead as the replies in RPL. The upward data replies in LOAD consume more bandwidth than in RPL, which shows the path length of the data traffic, for both RPL and LOAD, as well as for a protocol called "God". The God routing protocol uses the "God" object of NS2, in order to calculate routes to all destination based on their position and radio range, without any control message exchange and zero convergence time. The expected performance of the GodRP is close to the best possible routing protocol, which helps to understand how well a routing protocol could theoretically perform It can be observed that LOAD chooses longer paths 1. When a router receives a RREQ that is destined to itself, it will reply to the first incoming RREQ and set the reverse route towards the last hop of that RREQ. However, it may happen that shorter paths are available, but that the RREQ for that shorter path arrives only later (and since only a single data packet is sent, the better path will not be used). If the router had waited longer, before replying with a RREQ, it could have chosen a shorter path, at the expense of a higher delay.

2. As LOAD in its basic form applies "Classic Flooding" for distributing the RREQs throughout the network, longer paths occur than e.g., when using MPR flooding, which leads to optimal paths ( [START_REF] Qayyum | Multipoint relaying: An efficient technique for flooding in mobile wireless networks[END_REF]).

An observation concerning the path length can be made for RPL in nonstoring mode, which uses source routes for the downward traffic: the maximum length of the source routing header [START_REF] Hui | An IPv6 Routing Header for Source Routes with RPL[END_REF] is limited to 136 octets, including an 8 octet long header. As each IPv6 address has a length of 16 octets, not more than 8 hops from the source to the destination are possible for "raw IPv6". Using address compression [START_REF] Montenegro | Transmission of IPv6 Packets over IEEE 802.15.4 Networks[END_REF], the maximum path length may not exceed 64 hops. This excludes scenarios with long "chain-like" topologies. The size of the header with increasing length of the source route is depicted in figure 10. Figure 11 depicts the delivery ratio of the data traffic. For both protocols, it is close to 100%, which is due to the two-ray ground model applied in the simulations. Unless there are collisions on the MAC layer, no packets are lost. This does, of course, not reflect experience from real wireless communication, and has to be considered when interpreting the simulation results. Simulating realistic channel behavior and propagation models is non-trivial, and better observed in real testbeds than in network simulations. Still, the simulation results allow to validate whether the routing protocol choose correct paths.

Figure 12 shows the end-to-end delay of a data traffic transmission. Data traffic is buffered in LOAD when no route is available, until a RREP from the destination has been received (or is dropped after a timeout if no RREP is received). The waiting time depends on the number of hops the RREQs and RREPs have to traverse as well as the time required for retransmitting a packet at a router, and therefore increases with the number of routers in the network. As no buffering is applied in RPL, the delay is much lower. However, in scenarios where delay is negligible -such as the evaluated "reading" scenario within 24 hours -the higher delay may be acceptable.

Finally, figure 13 depicts the number of collisions of frames for both RPL and LOAD. As LOAD applies Classic Flooding for the RREQ, the well-known broadcast storm [START_REF] Ni | The broadcast storm problem in a mobile ad hoc network[END_REF] leads to a higher collision rate in LOAD. A more efficient flooding mechanism could reduce the amount of collisions (as well as the control traffic overhead).

Conclusion

RPL and LOAD represent two different philosophies for routing protocols in LLNs. RPL is highly optimized for specific topologies and traffic patterns -a central controller with specific responsibilities for topology formation and maintenance, and towards which all (or the majority) of traffic flows. Thus, the strength of RPL is proactive construction of a collection tree for forwarding such traffic. LOAD represents a perhaps less optimized protocol, however one wherein the philosophy is an entirely distributed mode of operation, where paths are discovered on demand and so as to be bi-directional.

Observing that a large set of deployment scenarios for sensor networks imply the need of bi-directional traffic flows -utility metering, building automation and even certain data acquisition networks where possible alarm signals need be acknowledged -this memorandum studies the performance of these two protocols in such scenarios. While both protocols are able to provide reasonably high and definitely comparable data delivery ratios, LOAD yields a consistently lower control traffic overhead than does RPL -all the while being less constrained both in terms of traffic types and topologies supported.

While generalizing from simulation studies always is to be done with utmost care -the adage of "show me a protocol, and I will construct a scenario wherein it performs badly", the results presented in this memorandum nonetheless suggest that for deployments in which bi-directional traffic flows predominate, LOAD is a more evident candidate routing protocol for LLNs than is RPL.

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Figure 1 :

 1 Figure 1: RPL Basic Construct: DODAGs

Figure 2 :Figure 3 :Figure 5 :

 235 Figure 2: Maximum and average rank of routers in the DODAG

Figure 6 :

 6 Figure 6: Extrapolated control traffic overhead for 24 hours

Figure 7 :Figure 8 :Figure 9

 789 Figure 7: Traffic sent in frames

Figure 9 :

 9 Figure 9: Path length than RPL, which itself is close to the optimum. Two reasons can explain the longer paths of LOAD:

Figure 10 :

 10 Figure 10: Source route overhead

INRIAFigure 11

 11 Figure 11: Delivery ratio

Figure 13 :

 13 Figure 13: Collisions

Table 1

 1

		: NS2 parameters
	Parameter	Value
	Ns2 version	2.34
	Mobility scenarios	No mobility, random distribution of
		routers
	Grid size	variable
	Router density	50 / km 2
	Number of routers	63 / 125 / 250 / 500 / 1000
	Communication range	250m
	Radio propagation model Two-ray ground
	Simulation time	RPL: 270 secs / LOAD: 1 day
	Interface type	802.11b
	Frequency	2.4 GHz
	Only scenarios were selected where all routers are in the same connected com-
	ponent.

Table 2

 2

		: RPL parameters
	Parameter	Value
	Mode of operation	non-storing
	Rank metric	hop count
	DIOIntervalMin	2 s
	DIOIntervalDoublings	20
	DIORedundancyConstant ∞
	DAOInterval	15s
	Table 3: LOAD parameters
	Parameter	Value
	RREQ jitter	0 -0.5 s
	Route lifetime	5 s
	Address compression 2 octets per IPv6 address

And their successors, OLSRv2[START_REF] Clausen | The Optimized Link-state Routing Protocol version 2[END_REF] and DYMO[START_REF] Chakeres | Dynamic MANET On-demand (DYMO) Routing[END_REF], which are currently being specified by the IETF.RR n°7637

RR n°7637


D Berto
email: d.berto@icram.org

M Giani

F Savelli

E Centanni

C R Ferrari

B Pavoni

Winter to Spring Variations of Chromophoric Dissolved Organic Matter in a temperate estuary (Po River, Northern Adriatic Sea)

Keywords: chromophoric dissolved organic matter, Po river, Northern Adriatic, estuary

The light absorbing fraction of dissolved organic carbon (DOC), known as chromophoric dissolved organic matter (CDOM) showed wide seasonal variations in the temperate estuarine zone in front of the Po River mouth. DOC concentrations increased from winter through spring mainly as a seasonal response to increasing phytoplankton production and thermohaline stratification. The monthly dependence of the CDOM light absorption by salinity and chlorophyll a concentrations was explored. In 2003, neither DOC nor CDOM were linearly correlated with salinity, due to an exceptionally low Po river inflow. Though the CDOM absorbance coefficients showed a higher content of chromophoric dissolved organic matter in 2004 with respect to 2003, the spectroscopic features confirmed that the qualitative nature of CDOM was quite similar in both years.

Introduction

The general circulation in the Northern Adriatic Sea (NA) is characterized by a northward current along the eastern coast and a Western Adriatic Current (WAC) flowing southwards along the western coast. Near the Po River delta the stratification persists throughout most of the year [START_REF] Artegiani | The Adriatic Sea general circulation. Part I: Air-Sea Interactions and Water Mass Structure[END_REF]. The structure of the plume depends on the discharge rate and the stratification of the water column, resulting in maximum offshore extension under high runoff, strong Bora wind forcing and summer stratification [START_REF] Mauri | Northern Adriatic Sea surface circulation and temperature/pigment fields in September and October 1997[END_REF]. The Po River freshwater may flow southward along the shelf strengthening the WAC [START_REF] Malanotte-Rizzoli | The dynamics of the coastal region of the northern Adriatic Sea[END_REF] or cross the shelf extending toward Istria in the central basin [START_REF] Mauri | Northern Adriatic Sea surface circulation and temperature/pigment fields in September and October 1997[END_REF]. In particular, during periods of weak stratification, in absence of wind forcing, vortices constrain fresh Po river waters to a southward flow along the Italian shelf [START_REF] Orlić | The currents and circulation of the Adriatic sea[END_REF]. In contrast, during periods of stratification, particularly in spring-summer, the Po freshwater plume spread across the basin to the Istrian coast to form a front that divides the northern basin [START_REF] Franco | Northern Adriatic Sea: Oceanography of the basin proper and of the western coastal zone in Marine Coastal Eutrophication[END_REF][START_REF] Jeffries | A climatology of the northern Adriatic Sea's response to bora and river forcing[END_REF]. The extent of Po River inflow and wind forcing, therefore, modulate fresh water (FW) penetration in the NA [START_REF] Kourafalou | Process studies on the Po River pulme, north Adriatic Sea[END_REF][START_REF] Jeffries | A climatology of the northern Adriatic Sea's response to bora and river forcing[END_REF]. Dilution processes are the main factor controlling dissolved and particulate organic matter concentrations in the mixing zone of Po River (Pettine et al., 1999 and references therein).

Many efforts have been devoted to understand the dissolved organic carbon (DOC) cycle and the role of chromophoric dissolved organic matter (CDOM) in the marine environment (Blough and Del Vecchio, 2002 and references therein). The coastal zone, especially in areas adjacent to the estuaries of major rivers, is a marine environment where key biogeochemical processes occur (Hansell and Carlson, 2002). DOC represents the major fraction (60%) of riverine organic matter

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3 [START_REF] Spitzy | Dissolved and particulate organic matter in rivers[END_REF]; in addition, a local source of autochthonous DOC from phytoplankton and bacterial activity may be observed in the region influenced by freshwaters [START_REF] Sempéré | Carbon inputs of the Rhone River to the Mediterranean Sea: biogeochemical implications[END_REF][START_REF] Raymond | DOC cycling in a temperate estuary: mass balance approach using natural 14 C and 13 C isotopes[END_REF]. The estuaries are often characterized by high mixing rates that make it difficult to discriminate between different DOC sources (e.g.: [START_REF] Stedmon | Optical Properties and Signatures of Chromophoric Dissolved Organic Matter (CDOM) in Danish Coastal Waters[END_REF]. As CDOM is the most important sunlight-absorbing substance in the sea especially in the UV and blue region of the spectrum [START_REF] Ferrari | The relationship between chromophoric dissolved organic matter and dissolved organic carbon in the European Atlantic coastal area and in the West Mediterranean Sea Gulf of Lions[END_REF] and references therein) its optical properties provide information on DOM as a whole and on DOC fractions which are fundamental for studies on organic matter and carbon fluxes in aquatic ecosystems [START_REF] Bacastow | Dissolved organic carbon in modelling oceanic new production[END_REF]. A fraction of CDOM consists of a mixture of aliphatic and aromatic substances deriving from the degradation of terrestrial and aquatic plants [START_REF] Kirk | Light and Photosynthesis in Aquatic Ecosystems[END_REF]. This material contributes to form the light absorbing fraction of DOM and its absorption is strongest in the UV region, restricting the penetration into surface waters of biologically damaging UV radiation and protecting phytoplankton and other aquatic organisms (e.g.: de [START_REF] De Mora | The effect of UV radiation in the Marine Environment[END_REF] but, at the same time, limiting the in depth production because of its shading effect [START_REF] Stedmon | Optical Properties and Signatures of Chromophoric Dissolved Organic Matter (CDOM) in Danish Coastal Waters[END_REF].

The aim of this work was to investigate the winter to spring variations of dissolved organic matter and its chromophoric fraction in the temperate estuarine zone of the Northern Adriatic in two periods (during 2003 and 2004) characterized by significantly different Po river inflows, in order to elucidate its origin and composition. Correlations among DOC, CDOM and physical-chemical parameters were also discussed in terms of processes involved.

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Material and Methods

Sampling

Water samples were collected at 6 stations on a transect from the Po Prodelta eastwards (Figure 1) from February to May 2003 (ten cruises) and from February to June 2004 (ten cruises). Surface waters were sampled using 5 L Niskin bottles. Water samples for DOC and POC analyses were filtered through Whatman GF/F, precombusted at 480°C for 4 h. 100 µL of HgCl 2 (1 g L -1 ) were added to 10 ml of the seawater sample for DOC conservation [START_REF] Cauwet | Determination of dissolved organic carbon and nitrogen by high temperature combustion[END_REF]. About 2 L of seawater were filtered on Whatman GF/F for chlorophyll a (Chl a) analyses. All samples were stored at -20 °C until analysis.

Analytical procedures

Temperature, depth and salinity were determined by a CTD probe (Conductivity, Temperature, Depth, SEA BirdElectronics -SBE 25). Vertical CTD profiles were performed at all stations during cruises in 2003 and 2004. DOC concentrations of seawater samples were measured using a Shimadzu TOC 5000 Analyzer and a 1.2 % Pt on silica catalyst at 680°C. Potassium hydrogen phthalate was used as standard. The reproducibility of the analytical procedure was within 2-3 %.

POC was determined with a CHN Elemental Analyzer Carlo Erba Mod. EA1110, after acidification with HCl (1N), to remove the inorganic carbonate fraction. Acetanilide was used as standard. The reproducibility of the analytical system was lower than 2 %.

Chl a was extracted in acetone 90 % and determined by UV-VIS ATI Unicam UV2 double beam spectrophotometer (Parson et al., 1984).

UV-Visible spectra of CDOM in waters were recorded from 280 to 800 nm by UV-VIS ATI Unicam UV2 double beam spectrophotometer with quartz cells (5 cm) using Milli-Q water as blank. Data were expressed as absorption coefficients: a λ = 2.303A λ /L, where A λ is the absorbance

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ACCEPTED MANUSCRIPT 5 at a given wavelength and L is the path length in meters. A baseline correction was applied by subtracting the absorbance average value at 650 nm.

The absorption coefficient of CDOM at 355 nm (aCDOM 355 ) was chosen [START_REF] Blough | Chromophoric DOM in the coastal environment[END_REF], as the specific wavelength of 355 nm coincides with the pulsed frequency tripled Nd:YAG excitation laser currently used in shipborne and airborne laser fluorescence spectroscopy sensing [START_REF] Ferrari | CDOM absorption characteristics with relation to fluorescence and salinity in coastal areas of southern Baltic Sea[END_REF].

aCDOM 280 at 280 nm was chosen because it is the spectral region where aromatic substances [START_REF] Traina | An ultraviolet absorbance method of estimating the percent aromatic carbon content of humic acids[END_REF]), which are mainly of terrestrial origin as lignin, absorb.

The slope of the absorption spectra, S CDOM , describing the rate of decrease of CDOM absorption with increasing wavelength, was extracted from the absorption data using an exponential decay curve fitted to the plot of absorption (a λ, m -1 ) versus wavelength (λ) over the range 350-480 nm. The S CDOM parameter was calculated according to [START_REF] Green | Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters[END_REF]: (λ-λo) where λ o = 480 nm is the initial wavelength, and S CDOM is the fitted parameter (nm -1 ) for the exponential decay of a λ with increasing λ.

a (λ) = a (λo) e -S CDOM
Nutrients (ammonium-NH 4 + , nitrite-NO 2 -, nitrate-NO 3 -, orthophosphate-PO 4 3-and orthosilicate-Si(OH) 4 , were analysed spectrophotometrically [START_REF] Parsons | A Manual of Chemical and Biological Methods for Seawater Analysis[END_REF] by a Technicon TRAACS 800 autoanalyzer. Dissolved inorganic nitrogen (DIN) was calculated as the sum of the NH 4 + , NO 2 -and NO 3 -concentrations.

The salinity is expressed on the basis of the UNESCO practical salinity scale (PSS 1978).

For the graphical representations Grapher 7 (Golden Software Inc, USA) was used. Statistical analyses were performed using Statistica 6 (Statsoft, USA). were observed also in other areas of the Adriatic [START_REF] Celio | The coastal scale observing system component of ADRICOSM: Gulf of Trieste network[END_REF][START_REF] Grbec | Response of the Adriatic to the atmospheric anomaly in 2003[END_REF] and are supported by the more than 50% lower than average Po inflows in spring of 2003 (Fig.

2

) and by the lower content of fresh waters observed in the station in front of Po delta (fig. 4a).

The surface water temperatures ranged between 7.3 and 18.2 °C in 2003 and between 5.5 and 21.8

°C in 2004. The colder temperatures were observed in February in the less saline waters of the Po river plume as usually observed in the Northern Adriatic [START_REF] Russo | Meteorological and oceanographic conditions in the northern Adriatic Sea during the period June 1999-July 2002: Influence on the mucilage phenomenon[END_REF]. The highest

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temperatures due to the seasonal warming were recorded on May 2003 and June 2004 in the shallowest coastal station. The descriptive statistics for the parameters measured in surface waters are summarized in Table 1.

Chlorophyll a and nutrients

The average and standard deviation of Chl a concentrations with statistically significant differences between the two years are reported in Table 1.

Chl a concentrations were on average 4 fold higher in 2004 than in 2003 when an increased nutrient availability sustained a more relevant phytoplankton biomass (Table 1). The monthly distribution of Chl a in the 2003 and 2004 (Figure 4b) years showed high concentrations in the last period of 2004.

The highest value of the Chl a in the last period of 2004 was probably due to a phytoplankton bloom of Cerataulina pelagica, stimulated by the nutrients carried by the Po river [START_REF] Socal | Hydrological and biogeochemical features of the Northern Adriatic Sea in the period 2003-2006[END_REF]. The monthly Chl a concentrations plotted as a function of salinity in 2003 and 2004 (Figure 5) showed a negative linear relationships between Chl a and salinity in March 2003and 2004, April 2004 and May-June 2004. This situation is typically observed in the NA when the spring runoff determines an increase of Chl a concentrations [START_REF] Barale | Trends and interactions of physical and biogeo-chemical features in the Adriatic Sea as derived from satellite observations[END_REF][START_REF] Mauri | MODIS chlorophyll variability in the northern Adriatic Sea and relationship with forcing parameters[END_REF].

Relevant relationships were found between Chl a and orthosilicate concentrations in March, April and May-June 2004 (Table 2) but were not observed in 2003. Considering that the influence of the Po River was weaker in 2003 than in 2004, the river inputs significantly increased the primary production and consequently the amount of organic matter in the basin.

The concentration of dissolved inorganic nitrogen (DIN) was on average higher in 2004 than in 2003, reaching the highest values in winter 2004: DIN had the same trend of NO 3 -, which was > 76 % of the nitrogen forms. Higher NH 4 + concentrations were found in 2003 (Table 1) which could be due to a more intense bacterial degradation, enhanced by the higher winter temperatures. A significant negative relationship (p<0.05) has been found between DIN concentrations and salinity

showing that the major amount of nutrients was transported by riverine waters.

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Dissolved Organic Carbon

The main fraction of total organic carbon in the waters was the dissolved form both in 2003 (mean:

86 %; range: 67-95 %) and in 2004 (mean: 78 %; range: 45-95 %); the lowest values in 2004 were observed in February when the Po flow was low. Monthly variations in DOC concentrations in 2003 and 2004 showed lower concentrations in winter and higher in spring (Figure 4c) following a trend typical for the northern Adriatic Sea [START_REF] Giani | Temporal dynamic of dissolved and particulate organic carbon in the northern Adriatic Sea in relation to the mucilage events[END_REF]. The gradual build up of DOC accumulation proceeds from spring to summer when sub basin scale seasonal circulation structures link the eutrophic western part with the oligotrophic central part of the basin. The cyclonic recirculation cause the transport of DOC produced in the coastal areas to the off-shore regions where the system has low bacterial growth efficiency and long DOC utilization time [START_REF] Polimene | A numerical simulation study of dissolved organic carbon accumulation in the northern Adriatic Sea[END_REF]. Significantly higher DOC concentrations in 2004 with respect to those observed in 2003

(Table 1) could be a consequence of the major input of Po River as well as of the exudation/release from the more intense phytoplankton blooms associated with a higher retention of FW in the NA.

Mixing diagrams of concentrations along the salinity gradient were used to infer the net effect of A positive correlation between DOC and Chl a in February, March and May-June 2004 (Table 2) highlights the contribution of the intense phytoplankton biomass, following which extracellular polysaccharides probably represent a relevant fraction of dissolved organic carbon [START_REF] Decho | Microbial exopolymer secretions in ocean environments: their role(s) in food webs and marine processes[END_REF]. This is supported also by the increase of dissolved carbohydrate concentrations which are observed during diatom blooms in the NA [START_REF] Ahel | Spatial and temporal variability of carbohydrates in the northern Adriatic-a possible link to mucilage events[END_REF] and references therein).

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Chromophoric Dissolved Organic Matter

Values of chromophoric dissolved organic matter in 2004 were on average higher than those found in 2003 (Table 1), with the highest values in May-June 2004. The absorption coefficients showed a significant statistical difference between the two years (Table 1). The mean aCDOM 280 absorbance coefficients significantly (p<0.001) increased from 1.6 m -1 in 2003 to 2.2 m -1 in 2004; these aCDOM 280 values were similar to those found in coastal waters of Tuscany by [START_REF] Seritti | DOC, absorption and fluorescence properties of estuarine and coastal waters of the Northern Tyrrhenian Sea[END_REF].

Similarly the mean aCDOM 355 absorbance coefficients also increased from 0.3 m -1 in 2003 to 0.5 m - 1 in 2004 (Table 1). These values for aCDOM 355 fall in the range (0.4 -1.1 m -1 ) reported by Rochelle and Newall (2002b), for coastal areas in the Chesapeake Bay.

Sub-monthly variations in aCDOM 355 in 2003 and 2004 (Figure 4f) could reflect the presence of considerable amount of chromophoric dissolved organic matter of continental origin derived from fluvial input. Nevertheless the contribution of bacterially transformed algal exudates, especially in the April and May-June, during the strong river flows (Figure 2) and intense phytoplankton growth, cannot be excluded.

The analysis of monthly data revealed significant correlations in May-June 2004 among salinity, DOC, aCDOM 280 , aCDOM 355 and Chl a samples collected after the Po freshets (Table 2). The same was not observed in the previous months (February-April, Table 2). The trend of aCDOM 355 is related to land proximity as the highest aCDOM 355 is usually observed in freshwaters and estuaries and decrease in coastal and offshore waters [START_REF] Blough | Optical absorption spectra of waters from the Orinoco river outflow: terrestrial input of colored organic matter to the Caribbean[END_REF]. aCDOM 280 and aCDOM 355 were inversely correlated with salinity in May-June 2004 (Table 2). The linear dependence between aCDOM and salinity (Figure 6) could be interpreted as the result of the conservative mixing of terrestrial CDOM. A similar behaviour is usually observed in periods of higher river input, lower solar irradiance and stratification breakdown [START_REF] Blough | Chromophoric DOM in the coastal environment[END_REF].

Despite the fact that a significant fraction of the DOC is not associated with CDOM, a positive correlation was observed between DOC values and aCDOM in May-June 2004 (Table 2). This

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10 observation was also reported by several authors [START_REF] Vodacek | The in situ and airborne fluorescence measurements to determine UV absorption coefficients and DOC concentrations in surface waters[END_REF]1997;[START_REF] Chen | In situ fluorescence measurements in coastal waters[END_REF]. The amount of non-absorbing DOC in May-June 2004, extrapolated by the intercept on the DOC axis from the positive correlation between aCDOM 355 and DOC at aCDOM 355 = 0, contributed to 86 %, of the average DOC concentration of this period. Therefore the incidence of the absorbing fraction was assumed to be 14 %. The estimation from aCDOM 280 gave a 23% of incidence of the chromophoric fraction on total dissolved organic carbon in the same period of 2004.

Considering all of the monthly cruises, there was no significant relationship between CDOM concentration and Chl a concentration in almost all periods of 2003 and 2004, except for May-June 2004 when both aCDOM 280 and aCDOM 355 were correlated with Chl a (Table 2) and salinity (r = -0.932, p<0.001). These strong relationships were probably a consequence of the mixing process, both due to the input of fluvial-terrestrial chromophoric organic matter and possibly to bacterial reprocessing of the algal organic matter produced by the phytoplankton. Recent research pointed towards a microbial source of CDOM in the aquatic environment and led to the hypothesis that phytoplankton is not a direct source of CDOM but that bacterial reprocessing of DOM of algal origin is an important source of CDOM (Rochelle-Newall and Fisher, 2002a). The lack of observed relationships between aCDOM and Chl a concentration has been reported in other works [START_REF] Nelson | Seasonal dynamics of colored dissolved material in the Sargasso Sea[END_REF]Rochelle-Newall and Fisher 2002b) and this supports the hypothesis that CDOM is not directly released by phytoplankton.

Whereas in both years a seasonal increase of DOC from winter to spring is evident (t test, p< 0.05), the same is not true for aCDOM which has a stronger variability on the ten days and monthly time scale (one way ANOVA, p<0.05). This suggests that there is probably no seasonal accumulation of CDOM in the surface waters as this fraction is more labile than most of bulk DOM.

The specific UV absorption at 280 nm can be used as an index of the proportion of humic compounds as a strong linear relationship exists between the molar absorptivity at 280 nm and the aromatic content of water-soluble fulvic and humic acids [START_REF] Chin | Molecular Weight Polydispersivity and spectroscopic properties of aquatic humic substances[END_REF]. On this basis, the riverine transport of terrestrially derived CDOM from Po River was probably the dominant delivery

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11 mechanism by which CDOM entered in estuarine and coastal waters of northern Adriatic Sea. Also in other marine environments, the contribution of chromophoric matter could derive from a large fraction of humic compounds of fluvial or terrestrial origin [START_REF] Blough | Optical absorption spectra of waters from the Orinoco river outflow: terrestrial input of colored organic matter to the Caribbean[END_REF], even if the role of bacteria in reprocessing DOM of algal origin and producing CDOM is not excluded. According to [START_REF] Twardowski | Modelling the spectral shape of absorption by chromophoric dissolved organic matter[END_REF] in situ production of CDOM in terrestrially dominated waters may be evident only under a relatively stable water mass containing a high level of phytoplankton but a relatively low background of terrestrial CDOM.

A significant positive relationship between aCDOM 355 and DIN (Table 2) found in May-June 2004, could derive from DOM photoproducts (Moran and Zepp, 1997) or by intense microbial degradation (Coble et al., 1998 and references therein). CDOM, when photolytically degraded, can be a source of dissolved organic and inorganic compounds such as NH 4 + and PO 4 3- [START_REF] Bushaw | Photochemical release of biologically available nitrogen from aquatic dissolved organic matter[END_REF]: actually a strong relationship between aCDOM and NH 4 + (Table 2) was observed in May-June 2004. The absence of linear relationship between CDOM and phosphate in all the investigated periods of 2003 and 2004 could be due to the low concentrations of phosphate in both years.

Though the riverine inflow was higher in 2004, the orthophosphate concentrations did not increase as they were probably immediately taken up by phytoplankton. In 2004 the DIN/PO 4 ratio (Figure 4) was much higher than in 2003 probably because the phosphate consumption limited the assimilation of the DIN. The reduction of the phosphorus load in the Po River water [START_REF] Pagnotta | Il controllo della qualita` delle acque superficiali del bacino padano[END_REF] and a significant increase in the DIN/PO 4 ratio in the NA surface waters was observed by different authors [START_REF] Rinaldi | Evoluzione dello stato trofico nelle acque costiere Emiliano-Romagnole nel periodo 1982-1994. Evoluzione dello stato trofico in Adriatico: analisi degli interventi attuati e future linee di intervento[END_REF][START_REF] Degobbis | Long-term changes in the northern Adriatic ecosystem related to anthropogenic eutrophication[END_REF]. Earlier research indicated that phosphorus was also the primary limiting element in the NA prior to the reduction of its load [START_REF] Chiaudani | Multistep approach to identification of limiting nutrients in northern Adriatic eutrophied coastal waters[END_REF][START_REF] Degobbis | Nitrogen, phosphorus and silicon budgets for the northern Adriatic Sea[END_REF]. The lack of PO 4 3-could play a role in the CDOM accumulation by limiting its microbial degradation, as it was shown that phosphorous exerts a limiting role on bacterial DOC degradation in the NA [START_REF] Zoppini | Extracellular enzyme activity and dynamics of bacterial community in mucilaginous aggregates of the northern Adriatic Sea[END_REF] and references therein) or, by favouring, its transformation due to the enhanced alkaline phosphatase (Ivancic et al., 2009).

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Probably a concerted action of phytoplankton and bacteria concur to form CDOM as phytoplankton provides substrates that heterotrophic bacteria process into coloured compounds (Rochelle-Newall and Fisher, 2002). Moreover, the presence of biological by-products (e.g. nutrients) could stimulate primary production [START_REF] Bricaud | Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains[END_REF] and secondary productivity [START_REF] Nelson | Production of chromophoric dissolved organic matter by Sargasso Sea microbes[END_REF].

The spectral slope (S CDOM ) was determined to improve the information about the nature of DOM chromophores and quite similar values in 2003 and 2004 were observed (Table 1). The values in the study area are similar to those reported by [START_REF] Babin | Variations in the light absorption coefficients of phytoplankton, nonalagal particles, and dissolved matter in coastal waters around Europe[END_REF] for the Adriatic Sea. The high variability of S CDOM values in the two years and the absence of linear relationship between S CDOM and salinity suggest a non conservative behaviour of S CDOM . This type of wide variability in S at low CDOM concentrations has also been reported in earlier studies [START_REF] Blough | Optical absorption spectra of waters from the Orinoco river outflow: terrestrial input of colored organic matter to the Caribbean[END_REF][START_REF] Stedmon | Optical Properties and Signatures of Chromophoric Dissolved Organic Matter (CDOM) in Danish Coastal Waters[END_REF]. Possible reasons for the increase in standard deviation in the slope coefficients S are photodegradation and bacterial utilization of CDOM, as both these processes have been reported to have a flattening effect on the slope (Stedmon at al., 2000 and references therein).

The low values of specific absorption coefficients (a*CDOM), at 280 and 355 nm, obtained by normalizing the absorbance at the specific λ per unit of DOC (Table 1), could be due to the occurrence of low molecular weight compounds and low aromatic carbon content as found by several authors [START_REF] Chin | Molecular Weight Polydispersivity and spectroscopic properties of aquatic humic substances[END_REF]. Similar values of a*CDOM at 280 and 355 nm have been reported by [START_REF] Seritti | DOC, absorption and fluorescence properties of estuarine and coastal waters of the Northern Tyrrhenian Sea[END_REF] for the estuarine and coastal waters of the Tyrrhenian Sea. Changes in the optical properties become evident only in May 2004 when the low salinity of waters determined a 30 % increase in specific absorbtion coefficient of DOM (1.5±0.1 m -2 gC -1 for a*CDOM 280 and 0.4±0.1 m -2 gC -1 for a*CDOM 355 , respectively).

Conclusions

The Northern Adriatic waters in front of Po River are characterized by variations in hydrological and physical processes which strongly influence the dissolved organic matter dynamics on a monthly and sub-monthly time scale. A seasonal increase from winter to spring of DOC was Table 1. Average values for salinity (S), freshwater (Fw), temperature, dissolved organic carbon (DOC), chlorophyll a (Chl a), nutrients and spectroscopic characteristics of CDOM in 2003 and 2004, highlighting the significant differences between the two years.

  During the period 1918-2004 it has been observed that the Po river flow regimen was generally characterized by two main periods of high rate in late spring (May-June) and autumn (October-November) and by a minimum in summer (July-August). These variations are due to the seasonal rainfall in the drainage basin and to the melting of snows on the mountains in late spring. The mean daily inputs of the Po River in 2003 and 2004 are reported in Figure2along with the average flow in the period1918 -2004 . During 2003, except , except in January and February, the Po river flow was lower than the 1918-2004 average, whereas in 2004 there were two main freshets in February (∼3000 m 3 s -1 ) and May (>5000 m 3 s -1 ): this implied that higher salinities were observed during 2003 than in 2004.The stratification in front of the Po mouth was limited to the coastal waters during the winter-spring period of 2003, whereas in 2004 the plume reached the centre of the NA basin and the discontinuous discharges caused the advection of FW pools offshore. Therefore the salinity vertical gradients (Fig.3) were more marked in 2004 (∆S = 7±4) than in 2003 (∆S = 3±4). The situation observed in front of Po delta is in accordance with the stronger warming of stratified surface waters observed in spring 2004 in the NA with respect to 2003, which determined also a higher stability[START_REF] De Lazzari | Influence of winds and oceanographic conditions on the mucilage aggregation in the Northern Adriatic Sea in 2003-2006[END_REF]. Moreover, the higher river discharge of 2004 caused a more relevant eastwards spreading of the plume than in 2003 and lower salinities (<35 psu) were observed near the Po prodelta. Lower rainfall precipitation and higher salinities during spring-summer of 2003

  estuarine processes. Concentrations of DOC were plotted as a function of salinity for the different periods of sampling (February, March, April and May-June) in 2003 and 2004. The fairly linear plots corresponding to March 2003, March 2004 and May-June 2004 indicate a conservative mixing of the dissolved organic matter during these periods (Figure 6).

Fig 2 -Fig. 3 -Fig. 5 -

 235 FIGURE CAPTIONS

  Fig.1

Table 2 .

 2 Linear correlation coefficients between Ch a, DOC, salinity (S) and absorption coefficients (aCDOM) during winter-spring of 2003 and 2004. Significant correlation (p<0.05) is in bold.

	a*CDOM 280	m -2 gC -1	48	1.2	0.6	55	1.2	0.4	ns
	a*CDOM 355	m -2 gC -1	48	0.3	0.2	55	0.3	0.2	ns
	S CDOM . 10 3	nm -1	31	20.4	5.9	34	20.7	5.2	ns
	Table 1

Table 2

 2

				February 2003		March 2003			April 2003		May 2003
				n	r	p	n	r	p	n	r	p	n	r	p
	aCDOM 355 vs S		12	0.101	n.s. 24	-0.042	n.s. 6	-0.153	n.s. 6	-0.382	n.s.
	aCDOM 280 vs S		12	-0.218	n.s. 24	-0.345	n.s. 6	-0.799	n.s. 6	-0.965	<0.005
	DOC vs aCDOM 355	12	0.017	n.s. 24	-0.317	n.s. 6	0.046	n.s. 6	-0.423	n.s.
	DOC vs aCDOM 280	12	0.174	n.s. 24	-0.116	n.s. 6	0.004	n.s. 6	0.013	n.s.
	Chla vs aCDOM 355	12	-0.292	n.s. 24	0.285	<0.05 6	0.084	n.s. 6	0.344	n.s.
	Chla vs aCDOM 280	12	-0.177	n.s. 24	0.410	<0.005 6	0.407	n.s. 6	0.598	<0.05
	DOC vs Chla			17	-0.469	n.s. 24	0.202	n.s. 12	0.307	n.s. 6	0.357	n.s.
	Chla vs P-PO 4	3-	16	-0.449	n.s. 25	0.442	<0.05 12	0.421	n.s. 5	0.612	n.s.
	Chla vs Si-Si(OH) 4	16	0.495	n.s. 25	0.524	n.s. 12	0.416	n.s. 5	-0.800	<0.05
	Chla vs N-NH 4	+	16	-0.373	n.s. 25	0.263	n.s. 12	0.159	n.s. 5	-0.043	n.s.
	Chla vs N-NO 2	-	16	-0.407	n.s. 25	0.354	n.s. 12	0.594	<0.05 5	0.06	n.s.
	Chla vs N-NO 3	-	16	-0.507	<0,05 25	0.507	<0.05 12	0.650	<0.05 5	0.011	n.s.
	aCDOM 355 vs DIN	11	-0.260	n.s. 25	0.212	n.s. 6	-0.143	n.s. 6	0.520	n.s.
	aCDOM 355 vs N-NH 4+ 11	-0.353	n.s. 25	0.513	<0.01 6	-0.652	n.s. 6	0.180	n.s.
					February 2004		March 2004			April 2004		May-June 2004
				n	r	p	n	r	p	n	r	p	n	r	p
	aCDOM 355 vs S		19	-0.307	n.s. 12	0.026	n.s. 10	1.608	n.s. 12	-0.853	<0.001
	aCDOM 280 vs S		19	-0.443	n.s. 12	-0.023	n.s. 10	-0.150	n.s. 12	-0.946	<0.001
	DOC vs aCDOM 355	19	2.975	n.s. 12	-0.275	n.s. 10	-0.365	n.s. 12	0.546	<0.005
	DOC vs aCDOM 280	19	0.346	<0.05 12	0.138	n.s. 10	0.185	n.s. 12	0.544	<0.005
	Chla vs aCDOM 355	19	0.355	<0.05 12	-0.370	n.s. 10	0.492	<0.05 12	0.564	<0.005
	Chla vs aCDOM 280	19	4.969	<0.001 12	-0.059	n.s. 10	0.141	n.s. 12	0.590	<0.001
	DOC vs Chla			23	0.577	<0.05 12	0.886	<0.001 11	-0.638	<0.05 12	0.649	<0.05
	Chla vs P-PO 4	3-	24	-0.025	n.s. 12	0.128	n.s. 11	-0.029	n.s. 11	-0.05	n.s.
	Chla vs Si-Si(OH) 4	24	0.134	n.s. 12	0.705	<0.05 11	0.728	<0.05 11	0.858	<0.05
	Chla vs N-NH 4	+	24	-0.251	n.s. 12	0.636	<0.05 11	-0.016	n.s. 11	0.822	<0.05
	Chla vs N-NO 2	-	24	0.649	<0.005 12	0.599	<0.05 11	0.604	<0.05 11	0.926	<0.05
	Chla vs N-NO 3	-	24	0.578	<0.005 12	0.140	n.s. 11	0.517	n.s. 11	0.241	n.s.
	aCDOM 355 vs DIN	22	-0.174	n.s. 12	0.364	n.s. 5	-0.013	n.s. 11	0.723	<0.05
	aCDOM 355 vs N-NH 4+ 22	-0.188	n.s. 12	0.142	n.s. 5	-0.445	n.s. 11	0.888	<0.001

n.s.= not significative

Aknowledgements

The authors are grateful to ARPA Emilia Romagna for supplying the Po river flow rate data. A special thank is due to the scientific leader of the cruises Dr. Antonio Miralto of Stazione Zoologica di Napoli for his help in planning and organizing the field activity. The valuable help of Visma II crew and captain Giuliano Gianni † was fundamental to accomplishing the work.