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Wetland Dynamic and Ecological Compensation of the Yellow River Delta Based on RS

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Available online at www.sciencedirect.com ScienceDirect Energy Procedia 104 ( 2016 ) 129 – 134 CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems Wetland dynamic and ecological compensation of the Yellow River Delta based on RS HAN Mei , YU Haozhe College of Geography and Environment, Shandong Normal University, Jinan 250014, China Abstract This paper selects Landsat-5 TM remote sensing images in 2001 and 2008, field investigation, the department visits and other methods as the date source. Based on these datas, the types of the wetland, changes in wetland area and the value of wetland are obtained. And then, we estimated the loss of market value and ecological function value which caused by reduction of wetland area and the environmental pollution with the methods of market value, ecological value, cost of environmental protection investment and outcome parameters. Therefore, we made the fund allocation of wetland compensation and calculated ecological compensation standards of wetland, provided an important reference for Yellow River Delta′s eco-compensation. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2016 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of CUE Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. Keywords:Wetland; ecological compensation; RS; compensation standards; Yellow River Delta; 1. Introduction In recent years, ecological compensation is concerned more and more by domestic and foreign scholars. Many foreign scholars have done a lot of research in the concepts, mechanisms, models and standards of ecological compensation. Cuperus R, Bakermans M M and De Haes H A, et al (2001) believed that ecological compensation is the alternative measure of impaired ecological function or quality [1]. Heimlich, R.E. (2002) and Scherr S J, White A, Kaimowitz D (2004) studied the models of ecological compensation in the areas of America, Mexico and Brazil[2-3]. Alix-Garcia J, Janvry A D and Sadoulet E (2004) studied the forest ecological compensation mechanism in Mexico[4]. Austen E and Hanson A Fund: National Natural Science Foundation of China (41371517); Shandong province key science and technology program (2013GSF11706) *Corresponding author: Han Mei (1963-), female, Professor, doctoral tutor, mainly engaged in environmental evolution and sustainable development and assessment of water resources and wetlands. E-mail:[email protected] 1876-6102 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems. doi: 10.1016/j.egypro.2016.12.023 130 Han Mei and Yu Haozhe / Energy Procedia 104 ( 2016 ) 129 – 134 (2008) studied the Principles and mechanisms of ecological compensation in Cnanda[5]. Al-Ghais S M and Pearson W H (2008) established a program of ecological compensation in the UAE water area [6]. In China, we began to study the ecological compensation in 1980s. Right now, the study direction is transforming from ecological compensation mechanism of macro-policy to quantitative study with mathematical models. For example, SHI Y S (1999) studied the river ecological compensation mechanism[7]; ZHAO W H and LI H X (1999) proposed the initial idea of forest ecological compensation tax[8]; MAO X Q (2002) investigated the compensation standards[9]; HUA G D and CAI Z J (2004) constructed a model of forest ecological compensation[10]; LIU Y L (2006) established a model of ecological compensation in Xinanjiang upstream area[11]; MAO F (2006) studied the mechanisms and guidelines of ecological compensation[12]; LIU Q (2006) studied the ecological compensation mechanism in Jiangheyuan district[13]; LAI L (2008) studied the methods of ecological compensation[14]. There are some research on the Yellow River Detla Wetland focused on biodiversity, ecosystem function and restoration, ecological water requirements and ecological value. For example, CUI B S (2005) studied on ecological water requirements[15]; XING S J (2005) studied on ecosystem function and restoration[16]; HAN M (2009) studied on ecological value[17]. Despite many scholars have done much research, there still are much work to do about quantitative methods and standards of ecological compensation. The authors believe that the quantitative study of wetland ecological compensation should combined with the regional characteristics of the ecosystem, only in this way can make compensation standards suited to local conditions. 2. The purpose and significance The Yellow River is the mother of the Chinese nation. The delta wetland located in the Yellow River estuary is the youngest and the most typical delta wetland in the world. Because of its unique indigeneity, growth, vulnerability and extremely high scientific research value, the wetland draws extensive concern from both the domestic and foreign wetland organizations and experts, and is a research focus of global wetland ecology. But the former research of the Yellow River Delta wetland ecological has focused on the evolution of ecological systems, ecosystem health, ecosystem service value, ecosystem restoration etc, the quantitative analysis of ecological compensation is little. The scope of this study takes Ninghai as the apex, with the southeast to the Zhimai estuary, the northwest to the Tuhai River (Taoer River) estuary. The entire fan-shaped area amounts to more than 5400 square kilometers. The geographic coordinate is between longitude 118° 07' - 119° 23' E and latitude 36° 55' - 38° 16' N (Figure 1). The administrative area includes Kenli County, Hekou District, Dongying District, a part of Guangrao and Lijin County, four townships of Zhanhua County and a small part of Wudi County. 93% area of the Yellow River Delta is in Dongying City and 7% area is in Binzhou City, so the scope of this study area is the administrative area in Dongying City. Han Mei and Yu Haozhe / Energy Procedia 104 ( 2016 ) 129 – 134 131 Figure 1 Location map of wetlands in the Yellow River Delta Yellow River Delta Wetland area is vast, diverse and species-rich ecological powerful, is an important bird migration, "transfer station, wintering habitat and breeding grounds",in landlocked and Northeast Asia, Western Pacific area. But in recent years, wetland area has shrunk dramatically, seriously damaged the ecosystem and ecological function decline due to natural and man-made factors. Therefore, establishing reasonable standards and wetland ecological compensation programs, for the rational development and protection of wetland ecosystems is of great significance. 3. Data sources and calculation method 3.1. Data sources In this paper, the data come from remote sensing interpretation, the relevant statistical data, field investigation and the department visits. It mainly include Landsat-5 TM remote sensing images in 2001 and 2008, “Land Use Planning of Dongying City (2006-2020)”, “Statistical Yearbook of Shandong (2001-2008)”, “Fishery Statistical Yearbook of Shandong (2001-2008)”, “Statistical Yearbook of Dongying City (2001-2008)”, “Fishery Statistical Yearbook of Dongying City (2001-2008)”, “Environment Bulletin of Shengli Oilfield (2001-2008)” and field investigation data. Interpretation of remote sensing image and area of wetland type This paper selects remote sensing images, based on geometric correction, band combination and image enhancement of remote sensing images to identify each wetland types’ tone, texture, shape and other interpreting signs. Meanwhile it takes the object-oriented classification method and uses ERDAS software to classify wetlands of the study area automatically. Additionally, in order to improve the accuracy of automatic classification, using artificial methods of visual interpretation, through the ARCMAP software to modify, refine and supplement the wetlands automatic classification results, resulting in getting the data of types of wetlands, an area, vegetation types, and their spatial and temporal changes in 2001 and 2008, which provides a reliable data resources to estimate wetland ecological value and to formulate ecological compensation standard. Fordetails see attached table 1, table 2. Table 1 Remote sensing images in this study Type Satellite Track number spatial resolution Time TM Landsat5 121/34 30m 2001.07.16 TM Landsat5 121/34 30m 2008.07.03 Table 2 Categories and areas of wetland in the study area (unit: hm2) Wetland categories Areas of wetland (2001) Areas of wetland (2008) Area changes of wetlands River wetland 15912 15901 -11 Natural wetland Tidal flat wetland 106579 90880 -15699 Reed Wetland 34483 41590 +7107 Woodland shrub wetland 9 952 9082 -870 Reservoir wetland 22326 22527 +201 Artificial wetland Pit pond wetland 24105 21826 -2 279 Ditch wetland 52069 35150 -16919 132 Han Mei and Yu Haozhe / Energy Procedia 104 ( 2016 ) 129 – 134 Rice paddy wetland 20998 17284 -3714 Total 286424 254240 -32184 The results of remote sensing interpretation show that: ķThe total study area increases in a fluctuating tendency, which is mainly due to the Yellow River carrying sediment deposited in the estuary. ĸThe wetland of study area display obvious
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    Similarities and Differences in PM2.5, PM10 and TSP Chemical Profiles of Fugitive Dust Sources in a Coastal Oilfield City in China

    Aerosol and Air Quality Research, 14: 2017–2028, 2014 Copyright © Taiwan Association for Aerosol Research ISSN: 1680-8584 print / 2071-1409 online doi: 10.4209/aaqr.2013.06.0226 Similarities and Differences in PM2.5, PM10 and TSP Chemical Profiles of Fugitive Dust Sources in a Coastal Oilfield City in China Shaofei Kong1,2*, Yaqin Ji2, Bing Lu2, Xueyan Zhao2,3, Bin Han2,3, Zhipeng Bai2,3* 1 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol- Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China 2 College of Environmental Science and Engineering, Nankai University, Weijin Road 94#, Tianjin, China 3 Chinese Research Academy of Environmental Sciences, Beijing 100012, China ABSTRACT PM2.5, PM10 and TSP source profiles for road dust, soil dust and re-suspended dust were established by adopting a re- suspension chamber in the coastal oilfield city of Dongying. Thirty-nine elements, nine ions, organic and elemental carbon 2+ 2– were analyzed by multiple methods. The results indicate that Ca, Si, OC, Ca , Al, Fe and SO4 were the most abundant species for all the three types of dust within the three size fractions. OC/TC ratios were highest for dusts in Dongying when compared with those in the literature, which may be related to the large amounts of oil that is consumed in this area. Na, Mg and Na+ in soil and road dust also exhibited higher mass percentages, indicating the influence of sea salt, as the city is close to the coast. Enrichment factors analysis showed that Cd, Ca, Cu, Zn, Ba, Ni, Pb, Cr, Mg and As were enriched, and these elements were more abundant in finer fractions of the fugitive dust.