A Low Collision and High Throughput Data Collection Mechanism for Large-Scale Super Dense Wireless Sensor Networks

Super dense wireless sensor networks (WSNs) have become popular with the development of Internet of Things (IoT), Machine-to-Machine (M2M) communications and Vehicular-to-Vehicular (V2V) networks. While highly-dense wireless networks provide efficient and sustainable solutions to collect precise environmental information, a new channel access scheme is needed to solve the channel collision problem caused by the large number of competing nodes accessing the channel simultaneously. In this paper, we propose a space-time random access method based on a directional data transmission strategy, by which collisions in the wireless channel are significantly decreased and channel utility efficiency is greatly enhanced. Simulation results show that our proposed method can decrease the packet loss rate to less than 2 % in large scale WSNs and in comparison with other channel access schemes for WSNs, the average network throughput can be doubled

Revealing the global emission gaps for fully fluorinated greenhouse gases

Abstract In response to the global trend of climate change, it is important to accurately quantify emissions of fully fluorinated greenhouse gases (FFGHGs, referring to SF6/NF3/CF4/C2F6/C3F8/c-C4F8 here). Atmospheric observation-based top-down methods and activity-based bottom-up methods are usually used together to estimate FFGHG emissions at the global and regional levels. In this work, emission gaps at global and regional levels are discussed among top-down studies, between the top-down and bottom-up FFGHG emissions, and among bottom-up emissions. Generally, trends and magnitudes of individual FFGHG emissions among top-down estimates are close to each other within the uncertainties. However, global bottom-up inventories show discrepancies in FFGHG emissions among each other in trends and magnitudes. The differences in emission magnitudes are up to 93%, 90%, 88%, 83%, 87%, and 85% for SF6, NF3, CF4, C2F6, C3F8, and c-C4F8, respectively. Besides, we reveal the insufficient regional TD studies and the lack of atmospheric observation data/stations especially in areas with potential FFGHG emissions. We make recommendations regarding the best practices for improving our understanding of these emissions, including both top-down and bottom-up methods

The data sets of HFC-134a emission sensitivity at ten stations across China during 2011-2020

The data sets of HFC-134a emission sensitivity at ten stations (SDZ, LAN, LFS, XGL, WLG, HYN, AKD, JGJ, XFG and JSA) across China during 2011-2020</p

An Adaptive Channel Access Method for Dynamic Super Dense Wireless Sensor Networks

Super dense and distributed wireless sensor networks have become very popular with the development of small cell technology, Internet of Things (IoT), Machine-to-Machine (M2M) communications, Vehicular-to-Vehicular (V2V) communications and public safety networks. While densely deployed wireless networks provide one of the most important and sustainable solutions to improve the accuracy of sensing and spectral efficiency, a new channel access scheme needs to be designed to solve the channel congestion problem introduced by the high dynamics of competing nodes accessing the channel simultaneously. In this paper, we firstly analyzed the channel contention problem using a novel normalized channel contention analysis model which provides information on how to tune the contention window according to the state of channel contention. We then proposed an adaptive channel contention window tuning algorithm in which the contention window tuning rate is set dynamically based on the estimated channel contention level. Simulation results show that our proposed adaptive channel access algorithm based on fast contention window tuning can achieve more than 95 % of the theoretical optimal throughput and 0 . 97 of fairness index especially in dynamic and dense networks

Extraordinary halocarbon emissions initiated by the 2011 Tohoku earthquake

tsunami caused catastrophic structural damage in east Japan. Using high-frequency atmospheric monitoring data, we show that emissions of halocarbons, potent greenhouse gases and stratospheric ozone-depleting substances, dramatically increased shortly after the earthquake and that annual emissions were significantly higher in 2011 than in other years.We estimate that the sumof earthquake-related emissions of the six studied halocarbon species (CFC-11, HCFC-22, HCFC-141b, HFC-134a, HFC-32, and SF6) was 6.6 (5.2–8.0) Gg, which is equivalent to ozone depletion potential-weighted emissions of 1.3 (1.1–1.6) Gg with a global warming potential equivalent to 19.2 (15.8–22.5) Tg of carbon dioxide. These extraordinary halocarbon emissions are likely due to destruction of building components containing halocarbons, such as air conditioners, foam insulation, and electrical equipment. 1

CCl4 emissions in eastern China during 2021–2022 and exploration of potential new sources

Emission sensitivity at two sites in eastern China during 2021-2022, which has been used for estimating CCl4 emissions reported in the manuscript of "CCl4 emissions in eastern China during 2021–2022 and exploration of potential new sources"

CFC‐12 Emissions in China Inferred From Observation and Inverse Modeling

Abstract Dichlorodifluoromethane (CFC‐12) is an ozone‐depleting substance and potent greenhouse gas, which was required to be phased out after 2010 under the Montreal Protocol. CFC‐12 emissions need to be quantitatively traced. However, estimates of CFC‐12 emissions in China based on atmospheric inversions are unavailable after 2010. Here, using atmospheric observations at nine sites across China and inversion techniques, we quantify CFC‐12 emissions in China during 2011–2020 (on average 11.0 ± 0.6 Gg yr−1). The emissions derived from observations are 8.5 times larger than the previously reported inventories. Apart from emissions from eastern China revealed in previous studies, this study reveals that 71% of national total emissions were from other parts of China. Moreover, this study reconciled the global CFC‐12 emissions during 2011–2020: 28% were traced to China by this study, 9% of emissions were traced in previous studies, while 63% remain untraced, indicating the need for more regional emission inversion studies

Performance of Back-Trajectory Statistical Methods and Inverse Modeling Method in Locating Emission Sources

Back-trajectory statistical methods, for example, potential source contribution functions (PSCF) and concentration-weighted trajectory (CWT) methods, have been widely used in previous studies to locate emission source regions of air pollutants or greenhouse gases. Inverse modeling methods have been developed and used in an increasing number of applications. To this date, there are no comparisons of performance between back-trajectory statistical and inverse modeling methods. This study evaluates the performance of PSCF, CWT, and inverse modeling methods by taking advantage of precisely known locations of trifluoromethane (CHF3; HFC-23) sources. Results show poor performance of the PSCF and CWT methods and good performance of the inverse modeling method. This study suggests that in studies with the purpose of locating emission source regions the PSCF and CWT methods should be applied with caution in future studies and that the inverse modeling method is encouraged to be used much more widely. Keywords: emission source; FLEXPART; HYSPLIT; inverse modeling; source attribution; trajectory; Trajectory statistical methodsUnited States. National Aeronautics and Space Administration (Grant NAG5-12669)United States. National Aeronautics and Space Administration (Grant NNX07AE89G)United States. National Aeronautics and Space Administration (Grant NNX11AF17G)United States. National Aeronautics and Space Administration (Grant NNX16AC98G