Applications of satellite remote sensing data for estimating biogenic emissions in southeastern Texas

The sensitivity of biogenic emission estimates and air quality model predictions to the characterization of land use/land cover (LULC) in southeastern Texas was examined using the Global Biosphere Emissions and Interactions System (GloBEIS) and the Comprehensive Air Quality Model with extensions (CAMx). A LULC database was recently developed for the region based on source imagery collected by the Landsat 7 Enhanced Thematic Mapper-Plus sensor between 1999 and 2003, and field data for land cover classification, species identification and quantification of biomass densities.  Biogenic emissions estimated from the new LULC data set showed good general agreement in their spatial distribution, but were approximately 40% lower than emissions from the LULC data set currently used by the State of Texas, primarily because of differences in the biomass estimates of key species such as Quercus. Predicted ozone mixing ratios using the biogenic emissions produced from the new LULC data set were as much as 26 ppb lower in some areas on some days, depending on meteorological conditions. Satellite data and image classification techniques provide useful tools for mapping and monitoring changes in LULC. However, field validation is necessary to link species and biomass densities to the classification system used for accurate biogenic emissions estimates, especially in areas such as riparian corridors that contain dense spatial coverage of key species.     

Impacts of boundary layer mixing on pollutant vertical profiles in the lower troposphere: Implications to satellite remote sensing

Mixing in the planetary boundary layer (PBL) affects vertical distributions of air tracers in the lower troposphere. An accurate representation of PBL mixing is critical for chemical-transport models (CTMs) for applications sensitive to simulations of the vertical profiles of tracers. The full mixing assumption in the widely used global CTM GEOS-Chem has recently been supplemented with a non-local PBL scheme. This study analyzes the impact of the non-local scheme on model representation of PBL mixing, consequences for simulations of vertical profiles of air tracers and surface air pollution, and implications for model applications to the interpretation of data retrieved from satellite remote sensing. The non-local scheme significantly improves simulations of the vertical distributions for NO₂ and O₃, as evaluated using aircraft measurements in summer 2004. It also reduces model biases over the U.S. by more than 10 ppb for surface ozone concentrations at night and by 2–5 ppb for peak ozone in the afternoon, as evaluated using ground observations. The application to inverse modeling of anthropogenic NO_x emissions for East China using satellite retrievals of NO₂ from OMI and GOME-2 suggests that the full mixing assumption results in 3–14% differences in top–down emission budgets as compared to the non-local scheme. The top–down estimate combining the non-local scheme and the Lin et al. inverse modeling approach suggests a magnitude of 6.6 TgN yr^?1 for emissions of NO_x over East China in July 2008 and 8.0 TgN yr^?1 for January 2009, with the magnitude and seasonality in good agreement with bottom–up estimates.     

利用卫星遥感评估广西壮族自治区SO2减排成效

基于2005—2012年OMI卫星遥感PBL SO2柱浓度数据分析了广西壮族自治区SO2污染的时空格局和变化特征,并结合燃煤火电厂SO2排放模型估算与工业经济增长数据,在时间、空间尺度上分析了自治区内不同区域的SO2污染对火电厂烟气脱硫政策的响应以及所呈现出的减排成效。结果表明,广西因火电厂脱硫政策驱动的SO2污染改善具有显著的空间异质性,自治区内高污染区的SO2浓度在2011—2012年出现了显著下降,而低污染区的SO2浓度则在8年中持续增长;排放模型估算结果显示,烟气脱硫驱动的火电厂SO2减排贡献将在2008年后持续下降,而遥感观测结果显示,广西全区SO2半年浓度均值总体呈现波动上升趋势,2005—2012年浓度增加了8.6%、年均增长约0.9%;综合工业经济增长数据发现,近年来其他工业源的增量排放已给广西SO2减排带了新挑战,而这一变化仍未得到相关决策部门关注,存在明显缺陷的SO2统计排放量数据尚未得到修正。针对上述污染变化新特征,政府相关部门应引起足够重视,尽快更新完善污染源信息并启动针对其他污染源的限排措施,以确保环境持续改善。     

太湖蓝藻水华的遥感监测研究

随着太湖蓝藻水华的日益严重,实现藻类的时空动态监测成为湖泊水质保护亟待解决的问题.利用遥感技术可以快速、直观地获取整个水域水质的时空变化情况,为实现藻类的动态监测提供了有效的途径.在太湖蓝藻水华遥感监测研究结果的基础上,总结分析了现有研究中所使用的遥感数据源、遥感监测方法以及遥感反演的水质参数,讨论了现有研究中存在的问题,并对将来的发展趋势进行了展望.     

Surface fluxes and atmospheric stability obtained from a surface energy balance model with parameters estimated from satellite remote sensing

A linear programming model is proposed to find the energy strategy for reducing carbon dioxide emission in Taiwan. Scenarios that include energy switching and energy conservation under different emission levels are considered, and the corresponding economic impacts are evaluated. Emissions from thirty-nine sectors are reduced by two major mechanisms. Energy switching is assumed to be induced by energy price change, whereas energy conservation is assumed only in the four most energy-intensive sectors. The results show the economic impact on gross domestic product under different energy scenarios. Appropriate energy strategies for carbon dioxide emission reduction could be implemented based on the scenario analysis.     

Using optical remote sensing techniques to track the development of ozone-induced stress

In this paper, a literature review about optical remote sensing (RS) of O₃ stress is presented. Studies on O₃-induced effects on vegetation reflectance have been conducted since late ‘70s based on the analysis of optical RS data. Literature review reveals that traditional RS techniques were able to detect changes in leaf and canopy reflectance related to O₃-induced stress when visible symptoms already occurred. Only recently, advanced RS techniques using hyperspectral sensors, demonstrated the feasibility of detecting the stress in its early phase by monitoring excess energy dissipation pathways such as chlorophyll fluorescence and non-photochemical quenching (NPQ). Steady-state fluorescence (Fs), measured by exploiting the Fraunhofer line depth principle and NPQ related xanthophyll-cycle, estimated through the photochemical reflectance index (PRI) responded to O₃ fumigation before visible symptoms occurred. This opens up new possibilities for the early detection of vegetation O₃ stress by means of hyperspectral RS.     

Impact assessment of waste management options in Singapore

This paper describes the application of life cycle assessment for evaluating various waste management options in Singapore, a small-island city state. The impact assessment method by SimaPro is carried out for comparing the potential environmental impacts of waste treatment options including landfilling, incineration, recycling, and composting. The inventory data include gases and leachate from landfills, air emissions and energy recovery from incinerators, energy (and emission) savings from recycling, composting gases, and transport pollution. The impact assessment results for climate change, acidification, and ecotoxicity show that the incineration of materials imposes considerable harm to both human health and the environment, especially for the burning of plastics, paper/cardboard, and ferrous metals. The results also show that, although some amount of energy can be derived from the incineration of wastes, these benefits are outweighed by the air pollution (heavy metals and dioxins/furans) that incinerators produce. For Singapore, landfill gases and leachate generate minimal environmental damage because of the nation's policy to landfill only 10% of the total disposed wastes. Land transportation and separation of waste materials also pose minimal environmental damage. However, sea transportation to the landfill could contribute significantly to acidification because of the emissions of sulfur oxides and nitrogen oxides from barges. The composting of horticultural wastes hardly imposes any environmental damage. Out of all the waste strategies, the recycling of wastes offers the best solution for environmental protection and improved human health for the nation. Significant emission savings can be realized through recycling.     

北京市机动车排放遥感监测分析

机动车排放遥感监测反映实际道路行驶中的排放状况,对全面分析排放水平有很强的统计意义。北京市机动车排放遥感监测的CO、HC和NOx的平均浓度分别为1.94%、388×10-6和700×10-6。北京市机动车排放的CO、HC和NOx中50%分别来自于15.90%、13.98%、11.13%的高排放车,但某车辆对于一种污染物出现高排放并不意味着它对其他污染物也是高排放。根据遥感监测得到北京市轻型汽油车基于油耗的CO、HC和NOx平均尾气管排放因子分别为200.1g/L、11.05 g/L和6.68 g/L。     

Using satellite imagery to locate innovative forest management practices in Nepal

There is a critical need to locate innovative forest management institutions that significantly impact forest cover change. This research presents an initial "proof of concept" methodology which combines deforestation theory with satellite image change analysis to identify forested areas that, theoretically, should probably not be there. Ten such "forest anomalies" are identified using temporal analysis of Landsat TM imagery of the Chitwan district in Nepal, linked with a GIS database on roads and a visual estimation of topography. A rapid field reconnaissance is undertaken to determine which of these anomalies exhibit interesting forest management innovations. Based on this information, one case is selected for detailed field study: this turns out to be a major case of community forestry and a premier ecotourism initiative that we were not aware of until we undertook this analysis. The utility and limitations of the method are described for monitoring trends in forest cover change.     

红枫湖水体中叶绿素a的遥感监测

以地处贵州中部的红枫湖水库为研究区,2010年的ALOS影像为基础,利用B3、B4(三波段和四波段)的乘积影像研究红枫湖水库库区表面的叶绿素a分布情况。结果表明,库区水体叶绿素a浓度在空间分布上呈现自三岔河入水口到北湖逐渐降低,沿岸边向湖中心逐渐降低的态势。探讨了适宜该库区的水体叶绿素a的提取方法,并引入基于费歇尔准则的最优分割算法,利用计算机编程实现了该库区叶绿素a分布水平的最优分划。这一系列针对该库区水体叶绿素a提取、分析和监测的优化方法,对该水源地的污染监测和库区的生态治理有一定的参考价值。     

Characterization of near-road pollutant gradients using path-integrated optical remote sensing

Understanding motor vehicle emissions, near-roadway pollutant dispersion, and their potential impact to near-roadway populations is an area of growing environmental interest. As part of ongoing U.S. Environmental Protection Agency research in this area, a field study was conducted near Interstate 440 (I-440) in Raleigh, NC, in July and August of 2006. This paper presents a subset of measurements from the study focusing on nitric oxide (NO) concentrations near the roadway. Measurements of NO in this study were facilitated by the use of a novel path-integrated optical remote sensing technique called deep ultraviolet differential optical absorption spectroscopy (DUV-DOAS). This paper reviews the development and application of this measurement system. Time-resolved near-road NO concentrations are analyzed in conjunction with wind and traffic data to provide a picture of emissions and near-road dispersion for the study. Results show peak NO concentrations in the 150 ppb range during weekday morning rush hours with winds from the road accompanied by significantly lower afternoon and weekend concentrations. Traffic volume and wind direction are shown to be primary determinants of NO concentrations with turbulent diffusion and meandering accounting for significant near-road concentrations in off-wind conditions. The enhanced source capture performance of the open-path configuration allowed for robust comparisons of measured concentrations with a composite variable of traffic intensity coupled with wind transport (R2 = 0.84) as well as investigations on the influence of wind direction on NO dilution near the roadway. The benefits of path-integrated measurements for assessing line source impacts and evaluating models is presented. The advantages of NO as a tracer compound, compared with nitrogen dioxide, for investigations of mobile source emissions and initial dispersion under crosswind conditions are also discussed.     

Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu,India

Environmental Science and Pollution Research - The study on land use and land cover (LULC) changes assists in analyzing the change and regulates environment sustainability. Hence, this research...     

A new remote sensing index based on the pressure-state-response framework to assess regional ecological change

Ecological indicators have widespread appeal to scientists, environmental managers, and the general public. Remote sensing is unique in its capability to record variety of spatio-temporal information on land surface with complete coverage, especially with regard to larger spatial scales, which has been proven to be an effective data source to create indicators to rapidly identify regional eco-environment. In this paper, a new index, remote sensing based ecological index (RSEI) based on the pressure-state-response (PSR) framework, was applied to assess regional ecological changes in Fuzhou City of Fujian Province, southeastern China, using Landsat ETM+/OLI/TIRS images. Taking the advantages of being totally free of artificial interference in the calculation using principal components analysis (PCA) to assign weights of each variable, the RSEI can assess the regional ecological status more objectively and easily. The effectivity of the new index was validated by four approaches, including point-based, classification-based, correlation-based, and urban-rural-gradient-based comparisons. The case study showed that Fuzhou has witnessed ecological improvement during the study period, with the value of RSEI increasing from 0.663 in 2000 to 0.675 in 2016. Spatial variation analysis showed that the poor level of RSEI distributed mostly in the central urban areas, and the ecological degradation was attributed to the fast expansion of the built-up area, characterized by increasing greatly in the value of the normalized differential built up and soil index (NDBSI) in such areas.

     

Interval association of remote sensing ecological index in China based on concept lattice

Environmental Science and Pollution Research - The correlation coefficient can calculate paired correlations among different ecological indicators as a whole, but it cannot calculate the specific...     

Conflict resolution by participatory management: remote sensing and GIS as tools for communicating land-use needs for reindeer herding in northern Sweden

When seeking to resolve complex land-management issues, geographical assessment of resources that are in short supply or in dispute can aid the communication of knowledge and the understanding among and between different stakeholders. In this paper, we illustrate how remote sensing and GIS can be used to gather and compile information regarding land-use activities and patterns among reindeer herders and other land users (forestry, mining, tourism, etc) in northern Sweden. The project represents a novel user-oriented effort largely based on the work carried out by the principal end user, i.e. the reindeer herders themselves. The basis for development of land-use plans for reindeer husbandry, was the following: to collect and digitally systemize traditional ecological and landscape knowledge of reindeer habitat use; to integrate this information with results from field inventories and satellite-based vegetation classifications; to map activities of other land users. The resulting land-use plans provide information that can facilitate consultation between the reindeer herders and other stakeholders and can facilitate operational work in reindeer management. This project can serve as a model for participatory involvement and planning, bringing indigenous knowledge and advanced remote-sensing techniques together in an interactive process.     

投入产出分析在固体废物管理中的应用

通过构建固体废物投入产出表,分析包括资源提供、产品生产、废弃物循环利用及废物处置部门在内的国民经济各部门之间的经济技术关系。并对理想条件及其他条件下固体废物资源化对于资源节约、废物减量及经济发展的影响进行了定量分析。     

A new approach to characterize emission contributions from area sources during optical remote sensing technique testing

In the method termed “Other Test Method-10,” the U.S. Environmental Protection Agency has proposed a method to quantify emissions from nonpoint sources by the use of vertical radial plume mapping (VRPM) technique. The surface area of the emitting source and the degree to which the different zones of the emitting source are contributing to the VRPM computed emissions are often unknown. The objective of this study was to investigate and present an approach to quantify the unknown emitting surface area that is contributing to VRPM measured emissions. Currently a preexisting model known as the “multiple linear regression model,” which is described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]), is used for quantifying the unknown surface area.

The method investigated and presented in this paper utilized tracer tests to collect data and develop a model much like that described in Thoma et al. (2009 Thoma, E.R., Green, R., Hater, G., Goldsmith, C., Swan, N., Chase, M. and Hashmonay, R. 2010. Development of EPA OTM-10 for landfill applications. J. Environ. Eng., 136: 769–776. [Crossref], [Web of Science ®] , [Google Scholar]). However, unlike the study used for development of the multiple linear regression model, this study is considered a very limited study due to the low number of pollutant releases performed (seven total releases). It was found through this limited study that the location of an emitting source impacts VRPM computed emissions exponentially, rather than linearly (i.e., the impact that an emitting source has on VRPM measurements decreases exponentially with increasing distances between the emitting source and the VRPM plane). The data from the field tracer tests were used to suggest a multiple exponential regression model. The findings of this study, however, are based on a very small number of tracer tests. More tracer tests performed during all types of climatic conditions, terrain conditions, and different emissions geometries are still needed to better understand the variation of capture efficiency with emitting source location. This study provides a step toward such an objective.

Implications The findings of this study will aid in the advancement of the VRPM technique. In particular, the contribution of this study is to propose a slight improvement in how the area contributing to flux is determined during VRPM campaigns. This will reduce some of the technique's inherent uncertainties when it is employed to estimate emissions from an area source under nonideal conditions.     

北京市黑臭水体治理的动态遥感监测及影响因素分析

为及时、准确掌握黑臭水体治理进展,基于“北京二号”影像数据和同期的野外综合水体实测数据,采用深度学习算法对黑臭水体进行识别,并引入地理探测器对黑臭水体影响因素进行定量分析。结果表明：基于Faster R-CNN算法的黑臭水体遥感识别,总准确率达到90%左右,短时间内(5~33 h)即可完成北京市建成区黑臭水体的筛查工作;在空间维度上,黑臭水体主要分布在中心城区以外,并在通州区、朝阳区和大兴区较为集中;在时间维度上,专项治理期间(2015—2018年)内,黑臭水体的数量和长度总体趋势都是递减的,但偶尔也有反黑现象;2018年底,在全市建成区范围内,已全面消除黑臭现象;在一年内,第1季度水体环境最好,第2季度次之,第3季度最差,从第4季度开始好转;在北京市大兴区,土壤全氮量(贡献率为32.07%)和周边养殖场排污(贡献率为27.04%)是黑臭水体形成的主要影响因素,高程(贡献率为8%)、土壤类型(贡献率为7.6%)和土地利用类型(贡献率为6.1%)的贡献率较弱。由此可以看出,基于Faster R-CNN算法识别影像中的黑臭水体识别准确率高,可及时、准确地监测城市黑臭水体治理情况,使用地理探测器可定量分析并确定各影响因素的贡献率。本研究成果可为城市黑臭水体的动态监测和治理提供有力的技术支撑。