Abstract. We analyzed the relationship between net ecosystem exchange of carbon dioxide (NEE) and irradiance (as photosynthetic photon flux density or PPFD), using published and unpublished data that have been collected during midgrowing season for carbon balance studies at seven peatlands in North America and Europe. NEE measurements included both eddy-correlation tower and clear, static chamber methods, which gave very similar results. Data were analyzed by site, as aggregated data sets by peatland type (bog, poor fen, rich fen, and all fens) and as a single aggregated data set for all peatlands. In all cases, a fit with a rectangular hyperbola

Abstract. The dynamic carbon balance of a southeastern New Hampshire wetland was constmcted for the 1994 growing season using a light-dark box sampling method. Net ecosystem exchange (NEE) (n=414) and ecosystem respiration (n=234) measurements were made at the 1.7 ha Sphagnum spp. dominated poor fen. The NEE rates ranged from -192 to 492 mg C m -2 h -• and the ecosystem respiration measurements were between -10 and -365 mg C m '2 h '•. The negative values represent a loss of carbon from the wetland system. NEE and respiration data were used to derive photosynthesis rates of the vegetation at the study site. A simple model, using hourly averages of photosynthetically active radiation, and air and soil temperatures to generate hourly rates of photosynthesis and respiration, was constructed to interpolate the carbon cycling rates at this fen through the entire 1994 growing season. Results of the carbon balance model suggest that the wetland lost an estimated 145 g C m '2 for the 9 month modeling period (April through December). The 1994 climate season was warmer (+1.15øC/month)and drier (-12.3 cm) than the 30 year normals for Durham, New Hampshire, the nearest meteorological station. These data suggest that if future climate change brings about warmer temperatures and lower water tables in peatland soils, positive climatic feedback leading to substantial releases of CO2 from boreal and subarctic peatlands is probable.

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

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Despite a general reduction in blood lead levels in children after