Outage and bit error probability analysis in energy harvesting wireless cooperative networks

This study focuses on a wireless powered cooperative communication network (WPCCN), which includes a hybrid access point (HAP), a source and a relay. The considered source and relay are installed without embedded energy supply (EES), thus are dependent on energy harvested from signals from the HAP to power their cooperative information transmission (IT). Taking inspiration from this, the author group investigates into a harvest-then-cooperate (HTC) protocol, whereas the source and the relay first harvest the energy from the AP in a downlink (DL) and then collaboratively work in uplink (UL) for IT of the source. For careful evaluation of the system performance, derivations of the approximate closed-form expression of the outage probability (OP) and an average bit error probability ( ABER) for the HTC protocol over Rayleigh fading channels are done. Lastly, the author group performs Monte-Carlo simulations to reassure the numerical results they obtained.Web of Science255746

Trans-boundary air pollution in a Southeast Asian megacity: Case studies of the synoptic meteorological mechanisms and impacts on air quality

Local and regional sources contribute to degraded air quality in many urban areas, however, the influence of trans-boundary air pollution on surface PM2.5 is still poorly characterized in Southeast Asia (SEA) megacities. This study, for the first time, utilizes multi-platform datasets to elucidate two trans-boundary PM2.5 episodes in Ho Chi Minh City (HCMC), Vietnam, over the periods 25–29 Oct 2013 and 05–08 Oct 2015. Both events persisted with limited diurnal fluctuations and more than 60% of the Air Quality Index (AQI) values at an unhealthy level. PM2.5 concentrations during the events were 100% and 115% higher on average compared to local accumulation periods in the same months, highlighting the importance of trans-boundary pollution to local HCMC air quality. Backward trajectories, MERRA-2 AOD data, and CALIPSO images revealed the origin and synoptic meteorology conditions facilitating both trans-boundary pollution events. Anthropogenic PM2.5 emissions in continental East Asia fed the 2013 event, which was then transported by strong northeasterly winds triggered by an upper-level ridge near the Tibetan Plateau and a low-pressure system in western Pacific Ocean. In contrast, the 2015 event was the result of Indonesia biomass burning (BB), which was enhanced and transported by a westward propagating Western Pacific Subtropical High triggered by a strong El Nino ˜ event. Future climate change will likely increase the number of extreme El Nino ˜ events, leading to the increase of transboundary Indonesia BB events to HCMC. This study lays the groundwork for detailing the impact of trans-boundary pollution on local air quality in SEA megacities

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R2 up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM2.5 forecasts and help the decision-maker to take proper action immediately

A Modified γ-Sutte Indicator for Air Quality Index Prediction

Air pollution has become an essential issue in environmental protection. The Air Quality Index (AQI) is often used to determine the severity of air pollution. When the AQI reaches the red level, the proportion of asthma patients seeking medical treatment will increase by 30% more than usual. If the AQI can be predicted in advance, the benefits of early warning can be achieved. In recent years, a scholar has proposed an α-Sutte indicator which shows its excellence in time series prediction. However, the calculation of α-Sutte indicators uses a fixed weight. Thus, a β-Sutte indicator, using a dynamic weight with a high computation cost, has appeared. However, the computational complexity and sliding window required of the β-Sutte indicator are still high compared to the α-Sutte indicator. In this study, a modified γ-Sutte indicator, using a dynamic weight with a lower computational cost than the β-Sutte indicator, is proposed. In order to prove that the proposed γ-Sutte indicator has good generalization ability and is transferable, this study uses data from different regions and periods to predict the AQI. The results showed that the prediction accuracy of the γ-Sutte indicator proposed was better than other methods

A New Monitoring Effort for Asia: The Asia Pacific Mercury Monitoring Network (APMMN)

The Asia Pacific Mercury Monitoring Network (APMMN) cooperatively measures mercury in precipitation in a network of sites operating in Asia and the Western Pacific region. The network addresses significant data gaps in a region where mercury emission estimates are the highest globally, and available measurement data are limited. The reduction of mercury emissions under the Minamata Convention on Mercury also justifies the need for continent-wide and consistent observations that can help determine the magnitude of the problem and assess the efficacy of reductions over time. The APMMN’s primary objectives are to monitor wet deposition and atmospheric concentrations of mercury and assist partners in developing their own monitoring capabilities. Network planning began in 2012 with wet deposition sampling starting in 2014. Currently, eight network sites measure mercury in precipitation following standardized procedures adapted from the National Atmospheric Deposition Program. The network also has a common regional analytical laboratory (Taiwan), and quality assurance and data flagging procedures, which ensure the network makes scientifically valid and consistent measurements. Results from our ongoing analytical and field quality assurance measurements show minimal contamination in the network and accurate analytical analyses. We are continuing to monitor a potential concentration and precipitation volume bias under certain conditions. The average mercury concentration in precipitation was 11.3 (+9.6) ng L−1 for 139 network samples in 2018. Concentrations for individual sites vary widely. Low averages compare to the low concentrations observed on the U.S. West Coast; while other sites have average concentrations similar to the high values reported from many urban areas in China. Future APMMN goals are to (1) foster new network partnerships, (2) continue to collect, quality assure, and distribute results on the APMMN website, (3) provide training and share best monitoring practices, and (4) establish a gaseous concentration network for estimating dry deposition

Sensitivity of a tropical micro-crustacean (Daphnia lumholtzi) to trace metals tested in natural water of the Mekong River

International audienceMetal contamination is one of the major issues to the environment worldwide, yet it is poorly known how exposure to metals affects tropical species. We assessed the sensitivity of a tropical micro-crustacean Daphnia lumholtzi to three trace metals: copper (Cu), zinc (Zn) and nickel (Ni). Both, acute and chronic toxicity tests were conducted with metals dissolved in in situ water collected from two sites in the lower part of the Mekong River. In the acute toxicity test, D. lumholtzi neonates were exposed to Cu (3-30 mu g L-1), Zn (50-540 mu g L-1) or Ni (46-2356 mu g L-1) for 48 h. The values of median lethal concentrations (48 h-LC50) were 11.57-16.67 mu g Cu L-1, 179.3-280.9 mu g Zn L-1, and 1026-1516 mu g Ni L-1. In the chronic toxicity test, animals were exposed to Cu (3 and 4 mu g L-1), Zn (50 and 56 mu g L-1), and Ni (six concentrations from 5 to 302 mu g L-1) for 21 days. The concentrations of 4 mu g Cu L-1 and 6 mu g Ni L-1 enhanced the body length of D. lumholtzi but 46 mu g Ni L-1 and 50 mu g Zn L-1 resulted in a strong mortality, reduced the body length, postponed the maturation, and lowered the fecundity. The results tentatively suggest that D. lumholtzi showed a higher sensitivity to metals than related species in the temperate region. The results underscore the importance of including the local species in ecological risk assessment in important tropical ecosystems such as the Mekong River to arrive at a better conservational and management plan and regulatory policy to protect freshwater biodiversity from metal contamination. (C) 2016 Elsevier B.V. All rights reserved

Characteristics and Risk Assessment of 16 Metals in Street Dust Collected from a Highway in a Densely Populated Metropolitan Area of Vietnam

The present study focused on investigating the contamination and risk assessment for 16 metals in street dust from Ha Noi highway, Ho Chi Minh City. The results indicated that the concentrations of metals (mg/kg) were found, in decreasing order, to be Ti (676.3 ± 155.4) > Zn (519.2 ± 318.9) > Mn (426.6 ±113.1) > Cu (144.7 ± 61.5) > Cr (81.4 ± 22.6) > Pb (52.2 ± 22.9) > V (35.5 ± 5.6) > Ni (30.9 ± 9.5) > Co (8.3 ± 1.2) > As (8.3 ± 2.5) > Sn (7.0 ± 3.6) > B (5.7 ± 0.9) > Mo (4.1 ± 1.7) > Sb (0.8 ± 0.3) > Cd (0.6 ± 0.2) > Se (0.4 ± 0.1). The geo-accumulation index (Igeo) showed moderate contamination levels for Pb, Cd, Cu, Sn, Mo, and Zn. The enrichment factor (EF) values revealed moderate levels for Cd, Cu, Mo, and Sn but moderate–severe levels for Zn. The pollution load index of the heavy metals was moderate. The potential ecological risk (207.43) showed a high potential. Notably, 40.7% and 33.5% of the ecological risks were contributed by Zn and Mn, respectively. These findings are expected to provide useful information to decision-makers about environmental quality control strategies