中国二氧化硫减排分析及减排潜力

SO₂是中国主要污染物总量控制指标之一。分析SO₂排放变化和影响因素对今后SO₂及其他污染物减排具有借鉴意义。在分析近10 a来尤其是"十一五"期间中国SO₂减排趋势的基础上,通过构建对数平均权重模型和减排潜力模型分析SO₂减排的影响因素和减排潜力,并对"十二五"SO₂排放进行情景分析。结果表明:SO₂排放量呈先上升后下降的变化趋势,"十一五"减排目标顺利实现;2005~2009年,中国工业SO₂排放量平均每年减少65×10⁴ t,其中,经济增长平均每年促进SO₂排放量增长315×10⁴ t,技术进步则平均每年使SO₂排放量减少344×10⁴ t,行业结构因素平均每年减少SO₂排放量36×10⁴ t;中国SO₂排放强度存在着绝对趋同和条件趋同,在条件趋同下,2009年中国各地区SO₂减排空间为738×10⁴ t,相当于当年SO₂排放量的1/3;"十二五"期间,若工业SO₂减排目标为8%~10%,则其去除率在2015年末需达到74%~80%,减排形势将依然严峻。     

青藏高原生态系统固碳释氧价值动态测评

本文旨在定量评价青藏高原生态系统的固碳释氧价值及其动态变化,为改善区域生态环境提供参考。基于MODIS/NDVI数据,利用光能利用率模型测算净第一性生产(NPP)物质量,并通过光合作用方程式换算成固定CO₂和释放O₂的物质量,以此为基础,采用造林成本法和工业制氧法对青藏高原固碳释氧价值量进行估算。结果表明:2000年、2005年和2010年固定CO₂的价值分别为384.36×10⁹元、393.23×10⁹元和356.41×10⁹元,释放O₂的价值分别为408.31×10⁹元、415.02×10⁹元和378.61×10⁹元。2000-2005年固碳释氧价值增加了15.58×10⁹元,2005-2010年下降了73.23×10⁹元,而2000-2010年下降了57.65×10⁹元。固碳释氧价值在空间上呈现出从东南向西北递减的趋势,这与青藏高原的水热条件分布基本一致。在价值构成中,草原>森林>草甸>其它类型>灌丛>农田。2000-2010年青藏高原生态系统固碳释氧价值呈现减小趋势,表明近年来气候变化和人类活动导致青藏高原的生态环境出现了退化趋势。     

陕西省大气环境承载力分析

以陕西省大气环境中SO₂和PM₁₀作为评估对象进行大气环境承载力研究,采用烟云足迹分析法计算了陕西省2010-2013年环境容量系数A值和SO₂、PM₁₀环境承载力的时空分布状况。结果表明：陕西省环境容量系数A值变化范围较大,2010-2013年全省年平均A值变化范围为1~5,陕北地区环境容量系数A值最大,其次为关中,陕南最小,2010-2013年关中地区西安、宝鸡和渭南呈现出逐年下降的趋势。SO₂环境承载力全省整体上2010-2013年变化范围为-148 578.04~-189 149.59 km²,4 a SO₂承载力均无余额,其中渭南和西安SO₂承载力严重不足且呈下降趋势; PM₁₀环境承载力全省整体上2010-2013年变化范围为12 701.47~44 511.02 km²,4 a PM₁₀承载力均有余额,全省各市除渭南和铜川外4 a均有余额,但总体呈下降趋势。     

塔里木河流域土地开发的生态响应研究

60 a来塔里木河流域耕地面积净增100×10⁴ hm²,在流域内部形成众多生态环境安全问题。通过选取水资源生态环境指数、社会生态环境指数和生态环境压力指数,运用ESDA空间分析方法和GWR模型对塔里木河流域35 a间土地开发与生态时空演变特征和空间响应关系进行研究,构建"土地开发-生态风险预警"模型,得出以下结论：1980-2015年克孜勒苏州和喀什地区生态等级正向转移比例较大,有84.86%的比例由较低等级（I）正向转移为低等级（Ⅱ）;和田地区的克里雅河流域,阿克苏北部区域以及塔里木河末端且末县等级退化严重,负向转移比例23.46%。和田、阿克苏、喀什地区每增加1.0×10⁴ hm²未利用地开垦规模,综合生态环境分值下降0.60到0.35,而克孜勒苏州则上升1.3~2.1;上游喀什与中游阿克苏两地区每1.0×10⁴ hm²生态退耕,每年将分别化解32.06×10⁴ t和15.60×10⁴ t化肥污染压力。阿克苏地区与巴音郭楞州土地开发生态风险达到环境资源承载力指数的75%以上,预警程度超过Ⅱ级,而克孜勒苏州土地开发处于生态安全范围。每1.0×10⁴ hm²未利用土地开发,全流域风险指数增长均值为0.003 4,下游巴音郭楞州在增加15×10⁴~20×10⁴ hm²耕地后,生态风险将逼近并超过环境预警界限,上游克孜勒苏州将在15 a之后进入中度警告阶段。     

不同水平年塔里木河流域“四源一干”可承载灌溉面积研究

利用水量平衡原理,基于流域水资源量及水资源利用水平、灌溉定额等资料,计算探讨了塔里木河流域“四源一干”现状水平年（2010年）和规划水平年（2020年、2030年）可承载灌溉面积、不同来水频率下规划年灌溉面积超载和生态水保证情况。结果表明：（1）现状水平年,流域可承载灌溉面积为129.06×10⁴ hm²,除和田河流域外其它区域均超载,超载面积为41.7×10⁴ hm²。（2）2020年,25%来水频率下,除开都-孔雀河流域其他区域均不超载。在50%与75%来水频率下,流域规划灌溉面积超载较大,分别为10.21×10⁴ hm²与28.05×10⁴ hm²,管理部门应对规划方案进行科学论证。75%来水频率下,流域生态水保证率为76%,表明枯水期生态水供应存在压力。（3）2030年,25%来水频率下,除开都-孔雀河流域其他区域均不超载。在50%与75%来水频率下,流域规划灌溉面积超载较大,分别为7.06×10⁴ hm²与24.09×10⁴ hm²。75%来水频率下,流域生态水保证率为81%,表明枯水期生态水供应仍然存在压力。研究结果为流域水资源配置及区域可持续发展提供了重要数据支撑与理论依据。     

长江口滨岸湿地无机氮界面交换通量量算

基于3年长江口滨岸湿地沉积物－水界面无机氮季节性交换通量连续实测数据,建立无机氮界面交换通量空间插值模型与量算模型,对无机氮界面交换通量季节性空间分布特征、滨岸湿地不同岸段无机氮季节性界面交换总通量量算等研究。结果表明：修正GIDS插值模型在无机氮界面交换通量空间插值预测过程中精度明显优于IDS方法,而略优于普通Kriging方法;长江口滨岸湿地沉积物－水界面无机氮交换通量空间分布在不同季节表现出复杂的空间分异特征;利用修正GIDS插值模型对长江口滨岸湿地无机氮交换通量进行空间插值过程中,为提高通量量算模型精度,应采用1.2'×1.2'的空间尺度为最佳;长江口滨岸湿地无机氮界面交换总通量量算表明,长江口滨岸湿地在春季向水体释放无机氮,是水体无机氮的释放源,释放量为1.33×10⁴ t,夏季、秋季和冬季表现为净化水体中无机氮,是水体无机氮的吸收汇,分别净化无机氮量为4.36×10⁴ t、6.81×10⁴ t和2.24×10⁴ t,全年总体表现为净化水体中无机氮,净化量为12.1×10⁴ t;长江口多年水体中无机氮通量多项式拟合分析得出,2002~2004年3年长江口水体中无机氮通量平均值为52.6×10⁴ t,滨岸湿地对长江口水体中无机氮的年均净化率达23.0%。     

产量红线与耕地红线耦合研究

基于实际粮食产量与抛荒耕地种植粮食作物条件下粮食总产量关系的分析方法有助于促进耕地红线制度改进。依据全国粮食总产量、种植面积与耕地面积数据,构造2016-2033 年粮食产量与耕地面积红线耦合的基本、法定、过渡、新型与理想耦合红线标准;分析阶段性耦合与可持续耦合两种升级路径。研究表明：2001-2015 年,全国耕地面积与粮食产量耦合不足。据此,提出以新增建设用地指标与抛荒治理面积挂钩的政策建议;在抛荒比例11.12%的条件下,新增单位面积建设用地,治理28.85 倍抛荒耕地;2017、2021、2025、2029、2033 年分别实现有效利用1.200×10⁸ hm²、1.243×10⁸ hm²、1.279×10⁸ hm²、1.314×10⁸ hm²、1.349×10⁸ hm² 耕地,粮食总产量分别比基期增加4.838×10⁶、2.4190×10⁷、4.354 2×10⁷、5.321 8×10⁷、8.824 6×10⁷ t;红线耦合系数分别达到0.882 471、0.909 740、0.937 008、0.950 643与1;有效利用耕地增加12.5%,粮食总产量提升14.2%。     

北京市耕地资源价值体系及价值估算方法

北京市为例尝试设计了耕地资源价值体系, 分析其价值关系, 进而探讨耕地资源经 济价值、社会保障价值、生态价值的估算方法, 并对北京市1990-2005 年耕地价值进行了定 量测算。研究结果表明: ① 北京市耕地资源单位面积经济价值呈较快增长, 1990 年为 12.57×10⁴ 元/hm², 2005 年为108.12×10⁴ 元/hm²; ② 耕地资源社会保障价值供给能力和农 村居民的需求水平相距甚远, 1997 年供给能力为49.19×10⁴ 元/hm², 需求水平为160.56×10⁴ 元/hm², 2005 年供给能力为74.82×10⁴ 元/hm², 需求水平为305.48×10⁴ 元/hm²。可见, 耕 地作为农民重要的社会保障资源已不堪重负, 农村社会保障体系亟待建立; ③ 生态价值供给 能力总体呈下降趋势, 1994 年达到171.21×10⁸ 元, 2005 年为70.87×10⁸ 元, 大量耕地减少 是重要的影响因素。但基于人口增加和人们生活水平不断提高, 生态价值的总体需求和多元 需求呈不断增长趋势, 所以需要保持和提高耕地表面的植被覆盖度, 为此政府有必要对农民 因种植耕地给予其一定的生态补偿。     

中亚土地资源开发与利用分析

中亚地区土地资源开发与利用研究信息资料有限,研究深度无法满足亚欧内陆干旱区社会经济可持续发展的科学要求。采用欧空局（ESA）GlobCover 2 2005年的全球陆地覆盖数据集资料和世界粮农组织（FAO）统计资料,较为系统分析了1992-2009年中亚土地资源开发与利用及其变化趋势。研究表明：（1）中亚耕地面积及作物产量呈先迅速下降后缓慢上升的趋势,耕地面积由1992年的43.1×10⁴ km²（比例10.9%）下降到2000年的29.8×10⁴ km²（比例7.58%）,然后上升至2009年的31.6×10⁴ km²（比例8.04%）,但仍未恢复到1990年代初的水平;（2）林地与草地面积变化不明显,但草地载畜量变化显著。其中哈萨克斯坦2009年草地载畜量（6.25×107标准羊单位）仅为1992年草地载畜量（9.91×10⁷标准羊单位）的63.1%;土库曼斯坦2009年草地载畜量为2.96×10⁷标准羊单位,是1992年草地载畜量（1.04×10⁷标准羊单位）的3倍左右;乌兹别克斯坦、塔吉克斯坦和吉尔吉斯斯坦草地载畜量均有不同程度的增加;（3）中亚地区土地资源生产潜力巨大,但在土地利用过程中出现了农田土壤侵蚀、土壤盐渍化和过渡放牧等生态问题,如何有效治理与防治上述问题,对中亚地区土地资源可持续利用和生态保护具有重要意义。     

新疆巴音郭楞蒙古自治州生态敏感性分析

生态环境敏感性分析对于生态环境功能得到保护和恢复,资源得到高效利用,生态安全得到保障,区域可持续发展能力得到增强具有重要作用。本文根据巴音郭楞蒙古自治州自然地理特征,采用GIS分析和专家集成方法,分别进行了巴州土壤侵蚀敏感性、土地沙漠化敏感性、土壤盐渍化敏感性和生物多样性及生境敏感性评价,最后展开生态环境敏感性综合研究。结果表明:巴州生态环境敏感性共分为极敏感、高度敏感、敏感、轻度敏感以及不敏感5个等级。生态环境极敏感区面积为2.7×10⁴ km²,占全州面积5.7%;高度敏感区为12.7×10⁴ km²,占26.4%;敏感区面积为14.8×10⁴ km²,占30.8%;轻度敏感地区为7.9×10⁴ km²,占16.4%;不敏感区面积为10.0×10⁴ km²,占20.7%。     

From highly polluted inland city of China to “Lanzhou Blue”: The air-pollution characteristics

Air quality was improved considerably and the so-called "Lanzhou Blue" appeared frequently in Lanzhou due to implementation of some strict emission-control measures in recent years. To better understand whether the concentration of each air pollutant had decreased significantly and then give some suggestions as to urban air-quality improvement in the near future, the variations of the Air Quality Index (AQI) and six criterion air pollutants (PM_2.5, PM₁₀, CO, SO₂, NO₂, and O₃) at five state-controlled monitoring sites of Lanzhou were studied from 2013 to 2016. The AQI, PM_2.5, PM₁₀, and SO₂ gradually decreased from 2013 to 2016, while CO and NO₂ concentrations had slightly increasing trends, especially in urban areas, due to the large number of motor vehicles, which had an annual growth rate of 30.87%. The variations of the air pollutants in the no-domestic-heating season were more significant than those in the domestic-heating season. The increase of ozone concentration for the domestic-heating season at a background station was the most significant among the five monitoring sites. The vehicle-exhaust and ozone pollution was increasingly severe with the rapid increase in the number of motor vehicles. The particulate-matter pollution became slight in the formerly highly polluted Lanzhou City. Some synergetic measures in urban and rural areas of Lanzhou should be taken by the local government in the near future to control fine particulate-matter (PM_2.5) and ozone pollution.     

陕西省榆林市秋冬季黑碳观测研究

利用2011年秋冬季榆林大气成分站黑碳浓度、颗粒物质量浓度、大气能见度、地面气象资料,计算边界层高度、气溶胶吸收系数、大气消光系数,导出单次散射反照率,并对其进行分析讨论。结果表明：（1） 榆林秋冬季平均黑碳浓度为2.6 μg·m^-3。（2）黑碳占颗粒物质量浓度PM_1.0比值为10.6%,黑碳与颗粒物质量浓度PM_1.0、PM_2.5、PM₁₀相关系数分别为0.91、0.91、0.72。（3）黑碳浓度受边界层高度影响,沙漠风场对黑碳的堆积输送起主导作用。（4） 榆林地区气溶胶吸收系数与大气消光系数比值为16.8%。（5）单次散射反照率平均值为0.72。     

Analysis on the temporal-spatial distribution character and effect factors of PM10 in the hinterland of Taklimakan Desert and surrounding area

In recent years, the physical and chemical properties of dust aerosols from the dust source area in northern China have attracted increased attention. In this paper, Thermo RP 1400a was used for online continuous observation and study of the hinterland of Taklimakan, Tazhong, and surrounding areas of Kumul and Hotan from 2004 to 2006. In combination with weather analysis during a sandstorm in the Tazhong area, basic characteristics and influencing factors of dust aerosol PM₁₀ have been summarized as below: (1) The occurrence days of floating dust and blowing dust appeared with an increasing trend in Kumul, Tazhong and Hotan, while the number of dust storm days did not significantly change. The frequency and intensity of dust weather were major factors affecting the concentration of dust aerosol PM₁₀ in the desert. (2) The mass concentration of PM₁₀ had significant regional distribution characteristics, and the mass concentration at the eastern edge of Taklimakan, Kumul, was the lowest; second was the southern edge of the desert, Hotan; and the highest was in the hinterland of the desert, Tazhong. (3) High values of PM₁₀ mass concentration in Kumul was from March to September each year; high values of PM₁₀ mass concentration in Tazhong and Hotan were distributed from March to August and the average concentration changed from 500 to 1,000 g/m³, respectively. (4) The average seasonal concentration changes of PM₁₀ in Kumul, Tazhong and Hotan were: spring > summer > autumn > winter; the highest average concentration of PM₁₀ in Tazhong, was about 1,000 g/m³ in spring and between 400 and 900 g/m³ in summer, and the average concentration was lower in autumn and winter, basically between 200 and 400 g/m³. (5) PM₁₀ concentration during the sandstorm season was just over two times the concentration of the non-sandstorm season in Kumul, Tazhong and Hotan. The average concentrations of sandstorm season in Tazhong were 6.2 and 3.6 times the average concentrations of non-sandstorm season in 2004 and 2008, respectively. (6) The mass concentration of PM₁₀ had the following sequence during the dust weather: clear day < floating dust < floating and blowing dust < sandstorm. The wind speed directly affects the concentration of PM₁₀ in the atmosphere, the higher the wind speed, the higher the mass concentration. Temperature, relative humidity and barometric pressure are important factors affecting the strength of storms, which could also indirectly affect the concentration change of PM₁₀ in the atmosphere.     

Spatio-temporal evolution and the influencing factors of PM2.5 in China between 2000 and 2015

High concentrations of PM_2.5 are universally considered as a main cause for haze formation. Therefore, it is important to identify the spatial heterogeneity and influencing factors of PM_2.5 concentrations for regional air quality control and management. In this study, PM_2.5 data from 2000 to 2015 was determined from an inversion of NASA atmospheric remote sensing images. Using geo-statistics, geographic detectors, and geo-spatial analysis methods, the spatio-temporal evolution patterns and driving factors of PM_2.5 concentration in China were evaluated. The main results are as follows. (1) In general, the average concentration of PM_2.5 in China increased quickly and reached its peak value in 2006; subsequently, concentrations remained between 21.84 and 35.08 μg/m³. (2) PM_2.5 is strikingly heterogeneous in China, with higher concentrations in the north and east than in the south and west. In particular, areas with relatively high PM_2.5 concentrations are primarily in four regions, the Huang-Huai-Hai Plain, Lower Yangtze River Delta Plain, Sichuan Basin, and Taklimakan Desert. Among them, Beijing-Tianjin-Hebei Region has the highest concentration of PM_2.5. (3) The center of gravity of PM_2.5 has generally moved northeastward, which indicates an increasingly serious haze in eastern China. High-value PM_2.5 concentrations have moved eastward, while low-value PM_2.5 has moved westward. (4) Spatial autocorrelation analysis indicates a significantly positive spatial correlation. The “High-High” PM_2.5 agglomeration areas are distributed in the Huang-Huai-Hai Plain, Fenhe-Weihe River Basin, Sichuan Basin, and Jianghan Plain regions. The “Low-Low” PM_2.5 agglomeration areas include Inner Mongolia and Heilongjiang, north of the Great Wall, Qinghai-Tibet Plateau, and Taiwan, Hainan, and Fujian and other southeast coastal cities and islands. (5) Geographic detection analysis indicates that both natural and anthropogenic factors account for spatial variations in PM_2.5 concentration. Geographical location, population density, automobile quantity, industrial discharge, and straw burning are the main driving forces of PM_2.5 concentration in China.

     

博斯腾湖的水环境保护与可持续利用对策

本文通过实地调查和对1999～2001年的监测资料的分析,从水环境污染物特性、水环境质量变化、排污源分布及排污量计算等方面探讨了博斯腾湖水环境问题及其成因。提出建立博斯腾湖保护区,加大管理检查和执法力度,加强对农田排污水的控制,强化重点排污口和重点城镇生活污水的排污管理,并通过湖滨湿地生态恢复工程、增源节水工程、加速湖水循环工程等措施,保护和改善博斯腾湖水环境     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

中国西北水资源利用及生态环境安全

1 Introduction In the 21st century, water resources will become the international and strategic resource instead of petroleum (Kang et al., 1994a, b). At present, the freshwater resources crisis all over the world has made people aware that although water…     

Spatial-temporal characteristics of PM<Subscript>2.5</Subscript> in China: A city-level perspective analysis

Haze pollution has become a severe environmental problem in the daily life of the people in China. PM_2.5 makes a significant contribution to poor air quality. The spatio-temporal features of China’s PM_2.5 concentrations should be investigated. This paper, based on observed data from 945 newly located monitoring sites in 2014 and industrial working population data obtained from International Standard Industrial Classification (ISIC), reveals the spatio-temporal variations of PM_2.5 concentrations in China and the correlations among different industries. We tested the spatial autocorrelation of PM_2.5 concentrations in the cities of China with the spatial autocorrelation model. A correlation coefficient to examine the correlativity of PM_2.5 concentrations and 23 characteristic variables for 190 cities in China in 2014, from which the most important ones were chosen, and then a regression model was built to further reveal the social and economic factors affecting PM_2.5 concentrations. Results: (1) The Hu Huanyong Line and the Yangtze River were the E-W divide and S-N divide between high and low values of China. (2) The PM_2.5 concentrations shows great seasonal variation, which is high in autumn and winter but low in spring and summer. The monthly average shows a U-shaped pattern, and daily average presents a periodic and impulse-shaped change. (3) PM_2.5 concentrations had a distinct characteristic of spatial agglomeration. The North China Plain was the predominant region of agglomeration, and the southeastern coastal area had stable good air quality.     

北京市大兴区大气降尘环境磁学特征及其环境意义

应用环境磁学方法对北京市大兴区大气降尘环境磁学特征进行了系统研究,并通过与北京市朝阳区、兰州市大气降尘环境磁学特征的对比,对环境磁学在污染物来源解析及扩散条件研究中的应用机理进行了探讨。结果表明,大兴区大气降尘磁性矿物含量明显高于本区表土,人为源影响显著;降尘样品磁学特征主要受磁铁矿控制,并含有一定数量的磁赤铁矿,磁性矿物为准单畴和多畴颗粒;大兴区降尘样品的磁性矿物含量明显低于朝阳区,且样品颗粒相对较细,表明人口密度、车流量、城市发达度对大气污染程度存在显著影响;大兴区与兰州市大气降尘磁性矿物含量的季节变化差异表明,燃煤取暖对北京大气质量的影响显著减弱,燃煤锅炉改造是大气污染防治的重要措施之一。气象因素对大气颗粒物浓度变化有重要影响,降水、低气压和高风速利于大气颗粒物的清除和扩散,低气温对大气颗粒物污染的负面影响显著。