Environmental Science and Pollution Research - Concentrations and congener profiles of seven di- to hexachlorinated benzenes (CBzs) were characterized in bottom ash and fly ash samples collected... 相似文献
Environmental Science and Pollution Research - The WRF-Chem (Weather Research and Forecasting with Chemistry) model is implemented and validated against ground-based observations for meteorological... 相似文献
This research analyses energy intensity of transport service sectors in Vietnam and its changing trend in the past years using IO tables and LMDI decomposition method. Energy consumption of 38 economic sectors in 2007, 2012 and 2018 is determined, of which transport service sectors were the second largest energy consumer (17.71 Mtoe), occupied 18.5% of total energy consumed in Vietnamese economy in 2018. In terms of energy intensity, a rising trend is seen in all transport service sectors, of which four most important transport services including bus and other road passenger transport, freight transport service by road and pipeline, waterway shipping freight and aviation passenger reached 0.62 kgoe/USD, 0.72 kgoe/USD, 0.60 kgoe/USD and 0.62 kgoe/USD in 2018, respectively. The ineffective structural change and ineffective energy intensity change are the reasons behind the upward trend in these sectors. Using Leontief inverse, the study also unveils how demanded on transport services by other economic sectors in terms of energy and how much energy embodied in all inputs of any economic sector. In order to keep the energy intensity stable and gradually decreasing, the recommendations are focused on effectiveness in structural changes and improvements in energy efficiency.
Environmental Chemistry Letters - Tea is one of the world’s most consumed beverages and an important crop of many developing countries. Intensive tea cultivation has negative impacts on soil... 相似文献
The concentration data of nitrogen dioxide (NO(2)), obtained from four different types of air quality monitoring (AQM) stations in Korea (i.e., urban traffic (A), urban background (B), suburban background (C), and rural background (D)), were explored to evaluate the fundamental facets of its distribution and behavior. As there are many distinctions between these four types of AQM stations, the observed NO(2) values were clearly distinguished from each other. It is found that the average NO(2) concentrations from all A stations exhibit notably high values within the range of 24.8 (Gwangju) to 54.6 ppb (Seoul), while those of all B stations change from 19.6 (Ulsan) to 34.7 ppb (Seoul). Similarly, large differences were also observed from NO(2) values measured between C and D type stations. The NO(2) values of the former were from 16.5 (Jeonbuk) to 30.2 ppb (Gyunggi), while the latter from 4.3 (Gyeongbuk) to 8.7 ppb (Gyunggi). Although their annual patterns are rather complicated to explain, the results by and large reflected the changes in the conditions of the surrounding environment. When the results are compared across seasons, most stations (A, B, and D types) tend to exhibit their maximum values in the winter followed by spring, fall, and summer. The results of this study confirm that the distribution patterns of NO(2) are fairly sensitive enough to reflect the basic characteristics of its source processes in association with such factors as the intensity of anthropogenic activity or population density. 相似文献
Environmental Science and Pollution Research - The Environment Fund is a state financial institution and a non-profit organization, operating mainly in concessional loans with low-interest rates... 相似文献
Environmental Science and Pollution Research - The spatiotemporal distribution and characterization of aerosol optical properties in the north of Vietnam were investigated extensively using the... 相似文献
Environment, Development and Sustainability - This study investigates the livelihood vulnerability to climate change of farm households in Northeast Vietnam. Data for the study is based on a survey... 相似文献
Environmental Science and Pollution Research - It is increasingly being recognized that biotic ligand models (BLMs) can successfully predict the toxicity of divalent metals toward aquatic biota... 相似文献
Drought is a harmful natural disaster with various negative effects on many aspects of life. In this research, short-term meteorological droughts were predicted with hybrid machine learning models using monthly precipitation data (1960–2020 period) of Sakarya Meteorological Station, located in the northwest of Turkey. Standardized precipitation index (SPI), depending only on precipitation data, was used as the drought index, and 1-, 3-, and 6-month time scales for short-term droughts were considered. In the prediction models, drought index was predicted at t?+?1 output variable by using t, t???1, t???2, and t???3 input variables. Artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), Gaussian process regression (GPR), support vector machine regression (SVMR), k-nearest neighbors (KNN) algorithms were employed as stand-alone machine learning methods. Variation mode decomposition (VMD), discrete wavelet transform (DWT), and empirical mode decomposition (EMD) were utilized as pre-processing techniques to create hybrid models. Six different performance criteria were used to assess model performance. The hybrid models used together with the pre-processing techniques were found to be more successful than the stand-alone models. Hybrid VMD-GPR model yielded the best results (NSE?=?0.9345, OI?=?0.9438, R2?=?0.9367) for 1-month time scale, hybrid VMD-GPR model (NSE?=?0.9528, OI?=?0.9559, R2?=?0.9565) for 3-month time scale, and hybrid DWT-ANN model (NSE?=?0.9398, OI?=?0.9483, R2?=?0.9450) for 6-month time scale. Considering the entire performance criteria, it was determined that the decomposition success of VMD was higher than DWT and EMD.
