Environmental regulatory failure and metal contamination at the Giap Lai pyrite mine, Northern Vietnam

The causes for the failure in enforcement of environmental regulations at the Giap Lai pyrite mine in northern Vietnam are considered and the environmental impacts that are associated with this mine are evaluated. It is shown that sulphide-rich tailings and waste rock in the mining area represent significant sources of acid rock drainage (ARD). The ARD is causing elevated metal levels in downstream water bodies, which in turn, represent a threat to both human health and to aquatic ecosystems. Metal concentrations in impacted surface waters have increased after mine closure, suggesting that impacts are becoming progressively more serious. No post-closure, remediation measures have been applied at the mine, in spite of the existence of environmental legislation and both central and regional institutions charged with environmental supervision and control. The research presented here provides further emphasis for the recommendation that, while government institutions may need to be strengthened, and environmental regulations need to be in place, true on the ground improvement in environmental quality in Vietnam and in many other developing countries require an increased focus on promoting public awareness of industrial environmental issues.     

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...     

Drivers and constraints of waste-to-energy incineration for sustainable municipal solid waste management in developing countries

Journal of Material Cycles and Waste Management - Implementation of waste-to-energy (WtE) incineration has recently surged in developing countries, but the drivers of this growth and the...     

A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed for motorcycles

ABSTRACT

Using the laboratory-based fuel consumption models for predicting real-world fuel consumption requires the measurement of data under certain conditions to obtain high accuracy of predicted result. Therefore, it is necessary to develop a logging device for measuring the real-time fuel consumption and speed of vehicle on the road. This article presents a study on developing the on-board data logging device to collect real-world data of fuel consumption and speed for motorcycles with the update rate of 1 Hz. The instantaneous speed of the motorcycle was determined based on the rotational speed of the wheel and the wheel radius. Another module was used to determine the instantaneous fuel consumption rate (FR) though measuring the duration injection pulse. The relationship between the duration injection pulse and the injected amount of fuel was established with high correlation coefficient of 0.997. In addition, a filter was designed to remove noise in the dataset collected using the data logging device. The random errors in the speed and the FR profiles were detected and replaced, the percentage of these errors is 1.8% and 2.4%, respectively. The developed method is a precise one for transient fuel consumption and speed measurement. In chassis dynamometer test, the average deviation between steady speed measured by the chassis and the data logging device is only approximately 0.35%. At transient state, the biggest deviation between these two datasets is less than 3.5%. The average FR at steady speed measured by the developed method is slightly different from the one measured by the carbon balance method. The difference is 0.9%, 2.5%, and 0.25% at the speeds of 30 km/h, 50 km/h, and 70 km/h, respectively. Following the transient test cycle, the fuel consumption measured by the developed method is 4.35% lower than that determined by the carbon balance method.

Implications: A robust method for collecting and processing the on-road instantaneous data of fuel consumption and speed was developed for motorcycles. The proposed method can record well the real-world driving data for motorcycles, including the fuel consumption and speed, with the update rate of 1 Hz. The filter was designed to minimize noise while maintaining data integrity of the collected dataset, the percentage of errors in the the speed and the FR profiles is 1.8% and 2.4%, respectively. The proposed method, therefore, can be used as effective tools for future studies relating to the fuel consumption and emission of motorcycles on the road.     

Harmonized assessment of nutrient pollution from urban systems including losses from sewer exfiltration: a case study in Germany

Environmental Science and Pollution Research - A growing literature indicates that untreated wastewater from leaky sewers stands among major sources of pollution to water resources of urban...     

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.     

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.     

Development of Spatial and Temporal Emission Inventory for Crop Residue Field Burning

Accurate emission inventory (EI) is the foremost requirement for air quality management. Specifically, air quality modeling requires EI with adequate spatial and temporal distributions. The development of such EI is always challenging, especially for sporadic emission sources such as biomass open burning. The country of Thailand produces a large amount of various crops annually, of which rough (unmilled) rice alone accounted for over 30 million tonnes in 2007. The crop residues are normally burned in the field that generates large emissions of air pollutants and climate forcers. We present here an attempt at a multipollutant EI for crop residue field burning in Thailand. Available country-specific and regional primary data were thoroughly scrutinized to select the most realistic values for the best, low and high emission estimates. In the base year of 2007, the best emission estimates in Gigagrams were as follows: particulate matter as PM_2.5, 128; particulate matter as PM₁₀, 143; sulfur dioxide (SO₂), 4; carbon dioxide (CO₂), 21,400; carbon monoxide (CO), 1,453; oxides of nitrogen (NO_x), 42; ammonia (NH₃), 59; methane (CH₄), 132; non-methane volatile organic compounds (NMVOC), 108; elemental carbon (EC), 10; and organic carbon (OC), 54. Rice straw burning was by far the largest contributor to the total emissions, especially during the dry season and in the central part of the country. Only a limited number of EIs for crop residue open burning were reported for Thailand but with significant discrepancies. Our best estimates were comparable but generally higher than other studies. Analysis for emission uncertainty, taking into account possible variations in activity data and emission factors, shows considerable gaps between low and high estimates. The difference between the low and high EI estimates for particulate matter and for particulate EC and OC varied between −80% and +80% while those for CO₂ and CO varied between −60% and +230%. Further, the crop production data of Thailand were used as a proxy to disaggregate the emissions to obtain spatial (76 provinces) and temporal (monthly) distribution. The provincial emissions were also disaggregated on a 0.1° × 0.1° grid net and to hourly profiles that can be directly used for dispersion modeling.