Mercury in air in an area impacted by strong industrial activities

The concentrations of total gaseous mercury (TGM) and its relevant environmental parameters were measured at a highly industrialized area in the Ban Wall industrial complex (BWIC) in An San city, Korea from March to May 2005. The mean concentrations of Hg measured during the entire study period were computed to be 6.32 ± 8.56 ng m⁻³ (range of 2.32–181 ng m⁻³; N = 1160). Due to the effects of strong man-made activities, the significantly high Hg concentration levels (e.g., at or above 10 ng m⁻³) comprised about 7.5% of all data with the mean of 21.8 ± 26.3 ng m⁻³ (N = 87). By separating the data into daytime and nighttime periods, the Hg values exhibited a notable daytime enhancement possibly due to strong man-made activities during working hours. The results of the correlation analysis indicated the possible relationship between the Hg concentration and the temperature as well as several pollutant species (e.g., NO₂ and NO_x). Evaluation of the Hg data in relation with the air mass transport pattern confirms that the Hg concentration levels in this industrial area are affected most eminently by local, rather than distant, pollution sources.     

Photoelectrocatalytic systems for simultaneous energy recovery and wastewater treatment: a review

Environmental Chemistry Letters - The rising energy conflicts and environmental pollution are calling for the rapid development of advanced techniques such as photoelectrocatalysis to...     

Quantifying Vision Zero: Crash avoidance in rural and motorway accident scenarios by combination of ACC,AEB, and LKS projected to German accident occurrence

Abstract

Objective: The Vision Zero initiative pursues the goal of eliminating all traffic fatalities and severe injuries. Today’s advanced driver assistance systems (ADAS) are an important part of the strategy toward Vision Zero. In Germany in 2018 more than 26,000 people were killed or severely injured by traffic accidents on motorways and rural roads due to road accidents. Focusing on collision avoidance, a simulative evaluation can be the key to estimating the performance of state-of-the-art ADAS and identifying resulting potentials for system improvements and future systems.

This project deals with the effectiveness assessment of a combination of ADAS for longitudinal and lateral intervention based on German accident data. Considered systems are adaptive cruise control (ACC), autonomous emergency braking (AEB), and lane keeping support (LKS).

Methods: As an approach for benefit estimation of ADAS, the method of prospective effectiveness assessment is applied. Using the software rateEFFECT, a closed-loop simulation is performed on accident scenario data from the German In-Depth Accident Study (GIDAS) precrash matrix (PCM). To enable projection of results, the simulative assessment is amended with detailed single case studies of all treated cases without PCM data.

Results: Three categories among today’s accidents on German rural roads and motorways are reported in this study: Green, grey, and white spots.

Green spots identify accidents that can be avoided by state-of-the-art ADAS ACC, AEB, and LKS. Grey spots contain scenarios that require minor system modifications, such as reducing the activation speed or increasing the steering torque. Scenarios in the white category cannot be addressed by state-of-the-art ADAS. Thus, which situations demand future systems are shown. The proportions of green, grey, and white spots are determined related to the considered data set and projected to the entire GIDAS.

Conclusions: This article describes a systematic approach for assessing the effectiveness of ADAS using GIDAS PCM data to be able to project results to Germany. The closed-loop simulation run in rateEFFECT covers ACC, AEB, and LKS as well as relevant sensors for environment recognition and actuators for longitudinal and lateral vehicle control.

Identification of green spots evaluates safety benefits of state-of-the-art level 0–2 functions as a baseline for further system improvements to address grey spots. Knowing which accidents could be avoided by standard ADAS helps focus the evolution of future driving functions on white spots and thus aim for Vision Zero.     

Using a social learning configuration to increase Vietnamese smallholder farmers’ adaptive capacity to respond to climate change

Social learning is crucial for local smallholder farmers in developing countries to improve their adaptive capacity and to adapt to the current and projected impacts of climate change. While it is widely acknowledged that social learning is a necessary condition for adaptation, few studies have systematically investigated under which conditions particular forms of social learning are most successful in improving adaptive capacity of the most vulnerable groups. This study aims to design, implement and evaluate a social learning configuration in a coastal community in Vietnam. We make use of various methods during four workshop-based interventions with local smallholder farmers: interviews with key farmers and commune leaders, farmer-to-farmer learning, participatory observations and focus group discussions. The methods for evaluation of social learning configuration include in-depth interviews, focus group discussions and structured survey interviews. Our findings show that the social learning configuration used in this study leads to an increased problem ownership, an enhanced knowledge-base with regard to climate change impacts and production adaptation options, improved ability to see connections and interdependencies and finally, strengthened relationships and social cohesion. The results suggest that increased social learning in the community leads to increase in adaptive capacity of smallholder farmers and improves both their economic and environmental sustainability. We discuss the key lessons for designing learning configurations that can successfully enhance adaptive capacity and smallholder farmers’ agency and responsiveness to the challenges posed by climate change impacts.     

Fractal geometry and applicability to biological simulation shapes for sustainable architecture design in Vietnam

Environmental Science and Pollution Research - Sustainability is the biggest goal that all areas including building architecture aim at. Sustainability is created by the harmony of buildings to the...     

Contamination by arsenic and other trace elements in tube-well water and its risk assessment to humans in Hanoi, Vietnam

Concentrations of As and other trace elements and their association were examined in groundwater (n = 25) and human hair (n = 59) collected at Gia Lam District and Thanh Tri District, suburban areas of Hanoi, Vietnam, in September 2001. Concentrations of As in the groundwater ranged from <0.10 to 330 microg/l, with about 40% of these exceeding WHO drinking water guideline of 10 microg/l. Also, 76% and 12% of groundwater samples had higher concentrations of Mn and Ba than WHO drinking water guidelines, respectively. Arsenic concentrations in hair of residents in Gia Lam and Thanh Tri Districts (range 0.088-2.77 microg/g dry wt.) were lower than those in other As-contaminated areas of the world, but were higher than those of people in non-contaminated areas. Concentrations of As and Mn in hair of some individuals from the Gia Lam and Thanh Tri Districts exceeded the level associated with their toxicity and, therefore, a potential health risk of As and Mn is a concern for the people consuming the contaminated water in this area. Cumulative As exposure was estimated to be lower than the threshold levels at the present, which might explain the absence of manifestations of chronic As poisoning and arsenicosis in the residents of Gia Lam and Thanh Tri Districts. To our knowledge, this study revealed for the first time that the residents are exposed not only to As but also Mn and Ba from groundwater in the Red River Delta, Vietnam.     

Toward successful implementation of conservation research: A case study from Vietnam

A number of different approaches have been used to explain the successes and failures of biodiversity conservation strategies in developing countries. However, to date, little attention has been paid toward assessing the influence of knowledge transfer between science, policy, and conservation practices in the implementation of these strategies. Vietnam’s Pu Luong Cuc Phuong Conservation Area is a globally important ecosystem, situated within a limestone landscape and inhabited by hundreds of local communities. Biodiversity conservation has become an important part of sustainable development in this area. This study analyzes three conservation strategies employed in the Pu Luong Cuc Phuong Conservation Area by applying the Research–Integration–Utilization (RIU) model of scientific knowledge transfer. Our analyses reveal weaknesses in scientific knowledge transfer arising from low-quality research and poor integration strategies. Based on our results, we developed recommendations to improve research and integration in an effort to enhance science-based policy support.     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Visualization and quantification of transparent exopolymer particles (TEP) in freshwater using an auto-imaging approach

Most water sources are full of microscopic transparent exopolymer particles (TEP), which are currently regarded as a major initiator of biofilm formation. This study developed and applied an auto-imaging FlowCAM-based method for online observation and quantification of TEP in freshwater. Samples from reservoirs in Taiwan with a wide range of water quality were directly used to develop this methodology. Factors that potentially affect the measurement were tested. The results showed that characteristics of the particles measured instantaneously after staining samples with Alcian blue differed significantly from those measured at steady states, as a result of particle aggregation. Compared to traditional microscopic methods, this proposed method provides a simple, rapid, and less labor-intensive analysis with particle morphological conservation and a large number of particle attributes. By overcoming the limitations from the former, this technique would offer routine monitoring of these transparent particles from various freshwater sources and feed water in membrane filtration, hence facilitating the use of TEP as a critical parameter for biofouling investigation in water treatment. Application of the method for Taiwan reservoirs showed a wide variety of morphological forms of TEP and its abundance, up to 25,000 ppm.