论土地持续利用

在世界上有两种可持续发展思路，一种是西方发达国家的重保护的可持续发展思路；一种是以发展为前提，保护自然资源的可持续发展思路。土地持续利用的思想是在可持续发展的进程中不断发展的。我国要保证土地持续利用，满足未来对食粮和建设的需求，应建立起同土地持续利用相适应的土地持续利用制度和土地持续利用机制     

Preparation of hydrophobic hierarchical pore carbon–silica composite and its adsorption performance toward volatile organic compounds

Carbon–silica materials with hierarchical pores consisting of micropores and mesopores were prepared by introducing nanocarbon microspheres derived from biomass sugar into silica gel channels in a hydrothermal environment.The physicochemical properties of the materials were characterized by nitrogen physical adsorption(BET),scanning electron microscopy(SEM),and thermogravimetric(TG),and the adsorption properties of various organic waste gases were investigated.The results showed that microporous carbon materials were introduced successfully into the silica gel channels,thus showing the high adsorption capacity of activated carbon in high humidity organic waste gas,and the high stability and mechanical strength of the silica gel.The dynamic adsorption behavior confirmed that the carbon–silica material had excellent adsorption capacity for different volatile organic compounds(VOCs).Furthermore,the carbon–silica material exhibited excellent desorption characteristics:adsorbed toluene was completely desorbed at 150℃,thereby showing superior regeneration characteristics.Both features were attributed to the formation of hierarchical pores.     

Ozone pollution characteristics and sensitivity analysis using an observation-based model in Nanjing, Yangtze River Delta Region of China

Ground-level ozone (O₃) has become a critical pollutant impeding air quality improvement in Yangtze River Delta region of China. In this study, we present O₃ pollution characteristics based on one-year online measurements during 2016 at an urban site in Nanjing, Jiangsu Province. Then, the sensitivity of O₃ to its precursors during 2 O₃ pollution episodes in August was analyzed using a box model based on observation (OBM). The relative incremental reactivity (RIR) of hydrocarbons was larger than other precursors, suggesting that hydrocarbons played the dominant role in O₃ formation. The RIR values for NO_X ranged from –0.41%/% to 0.19%/%. The O₃ sensitivity was also analyzed based on relationship of simulated O₃ production rates with reductions of VOC and NO_X derived from scenario analyses. Simulation results illustrate that O₃ formation was between VOCs-limited and transition regime. Xylenes and light alkenes were found to be key species in O₃ formation according to RIR values, and their sources were determined using the Positive Matrix Factorization (PMF) model. Paints and solvent use was the largest contributor to xylenes (54%), while petrochemical industry was the most important source to propene (82%). Discussions on VOCs and NO_X reduction schemes suggest that the 5% O₃ control goal can be achieved by reducing VOCs by 20%. To obtain 10% O₃ control goal, VOCs need to be reduced by 30% with VOCs/NO_X larger than 3:1.     

Can a low-carbon development path achieve win-win development: evidence from China’s low-carbon pilot policy

Mitigation and Adaptation Strategies for Global Change - Low-carbon pilot (LCP) policy aims to not only achieve economic development but also address climate change problems in China. With a...     

Evaluating national hydrogen refueling infrastructure requirement and economic competitiveness of fuel cell electric long-haul trucks

This study explored the national hydrogen refueling infrastructure requirement along major United States (US) interstate highway corridors to support the deployment of fuel cell electric trucks (FCETs) for the national long-haul trucking fleet. Given the long-haul trucking shipment demand in 2025 projected by the Freight Analysis Framework, locations and capacities of hydrogen stations were identified for inter-zone freight flows, and the total daily refueling demand was estimated for intra-zone flows for each FAF zone. Based on the infrastructure deployment results, we conducted an economic feasibility analysis of FCETs by evaluating the total ownership cost. We found that when the FCET penetration is relatively high (e.g., 10% penetration), FCETs become more competitive in terms of fuel cost and idling cost and could be economic viable if the incremental vehicle cost is reduced to meet the near-term FCET technology cost targets and the liquefaction cost is reduced to an optimal case. We also observed that the station cost depends on regional factors, particularly regional demand, which is used to determine station capacity. Thus, one possible strategy for station roll-out is to have early investment in target regions where station costs are expected to be relatively low such as the Pacific and West South Central regions.

     

大豆过敏原P34蛋白基因检测技术与环境污染的关系研究

环境污染易引起大豆种植区域出现过敏原P34蛋白基因变异情况,当前蛋白基因检测方法不能满足检测需求,提出基于探针引物的大豆过敏原P34蛋白基因荧光PCR定量检测技术,针对大豆过敏原特异性基因P34蛋白基因序列进行设计;通过DNA提取、浓度测定,荧光定量PCR检测,实现大豆过敏原P34蛋白基因检测。实验结果表明,所提方法能够准确检测出包括环境污染区域在内的大豆食品过敏原P34蛋白基因成分,具有很好的应用前景,满足市场监督检测的需要。     

发光细菌生物毒性抑制率优化研究

发光细菌生物毒对水生生态环境有极大的破坏作用,为保护水体环境,提出发光细菌生物毒性抑制率优化研究。完成测试样品调制;设置菌悬液特征参数,利用pH、RLU及D600进行同步检测,完成发光细菌生物毒性重现;分析磺胺类药品于根际土壤的形态分布和生态毒性,完成发光细菌生物毒性抑制率优化研究。实验证明,三种磺胺类药品对发光细菌生物毒性的相对抑制率都小于30%,为低毒性;其中有机溶剂对发光细菌生物毒性的抑制率最大,水溶态较大,结合态最小。     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.