基于AOD数据和GWR模型估算京津冀地区PM2.5浓度

利用MODIS气溶胶光学厚度（AOD）数据针对不同土地覆盖类型的适用性,提出了一种基于土地覆盖类型的AOD融合方法,生成了一种新的3km AOD数据集.在此基础上,通过地理加权回归（GWR）模型估算了京津冀地区2016年PM_2.5浓度,并用交叉验证的方法对模型性能进行评价.结果表明：利用融合后的AOD数据建立的模型可解释PM_2.594.85%的浓度变化,交叉验证R²为0.94,RMSE为9.27μg/m³,MPE为6.72μg/m³,明显优于多元线性回归（MLR）模型;基于GWR模型估算的京津冀地区2016年年均PM_2.5浓度为58.57μg/m³,其中冬季PM_2.5浓度最高,春秋季次之,夏季浓度最低,PM_2.5月均浓度变化范围32.78~140.83μg/m³,8月份浓度最低,12月份浓度最高;空间分布南北差异显著,衡水市PM_2.5污染最为严重,张家口市PM_2.5浓度较低.利用此方法成功弥补了PM_2.5空间缺失,为城市尺度的健康效应和环境流行病学研究提供数据支持.     

Estimation and inter-comparison of dust aerosols based on MODIS, MISR and AERONET retrievals over Asian desert regions

This study presents detailed analysis of spatiotemporal variations and trend of dust optical properties i.e., Aerosol Optical Depth(AOD) and Angstrom component over Asian desert regions using thirteen years of data(i.e., 2001–2013) retrieved from Aerosol Robotic Network(AERONET), Moderate Resolution Imaging Spectroradiometer(MODIS) and Multi-angle Imaging Spectroradiometer(MISR). These regions include Solar Village, Dunhuang and Dalangzadgad and are considered as origin of desert aerosols in Asia. Mann–Kendall trend test was used to show the trend of AOD. The relationship of AOD with weather parameters and general AOD trend over different wavelengths has also been shown. AOD's trend has been observed significant throughout the year in Solar Village, while in Dunhuang and Dalanzadgad the significant trend has been found only in peak period(March–June).Analysis show high values of AOD and low values of angstrom in Solar Village during peak period. In Chinese desert regions, high values of AOD have been found during peak period and low values in pre-peak period. Significant relationship has been observed between AOD and average temperature in Solar Village and Dalanzadgad whereas rainfall and wind speed showed no significant impact on AOD in all desert regions.     

京津冀地区高分辨率PM2.5浓度时空变化模拟与分析

为了全覆盖、高分辨率和高精度识别京津冀地区大气PM_2.5质量浓度时空变化,选取多角度大气校正算法遥感反演的1km AOD为主要预测因子,多种气象要素和土地利用要素为辅助预测因子,构建了混合效应模型+地理加权回归模型的两阶段统计模型,并针对京津冀地区PM_2.5污染较严重的特点,模型中引入了AOD²等独特预测因子.通过上述两阶段模型定量预测了研究区2017年1 km²空间分辨率的每日PM_2.5质量浓度.结果表明,模型交叉验证的决定系数R²为0.94,斜率为0.95,均方根预测误差为13.14 μg·m^-3,在前人基础上预测精度进一步提升,可用于PM_2.5浓度时空变化预测与分析.2017年,京津冀地区PM_2.5浓度年均值为44.96 μg·m^-3,年均值范围在0~89.89 μg·m^-3之间.PM_2.5浓度时空变化差异性明显,整体上呈现"平原西南部浓度高、平原东北部浓度中等和山区高原浓度低"的空间分布格局以及"冬季浓度高、夏季浓度低和春秋过渡"的季节变化特点.模型预测结果的高时空分辨率可以支持流行病学研究在较小区域的暴露评估和识别小尺度污染源的时空变化,分析发现在大气污染防治行动计划实施以来,污染较严重的冀中南山麓平原区可能出现了重要污染源的空间变化.模型预测与分析结果可以为京津冀大气污染防治提供科学支撑.     

Comparison of temporal and Spatial Characteristics of Ozone Pollution at Ground Level in the Eastern China

Monitoring data from ozone（O3） automatic stations in three typical cities with different climatic areas in the southern and northern parts of eastern China are used to analyze temporal and spatial characteristics of ozone pollution at ground level. The results show that ozone pollution level has distinct regional differences and the concentration in the suburbs is higher than that in the urban areas. The seasonal variation of ozone concentration in different climatic areas is greatly affected by the variation of precipitation. Ozone concentration in Shenyang and Beijing , in the temperate zone, has one perennial peak concentration, occurring in early summer, May or June. Ozone concentration in Guangzhou, in sub-tropical zone, has two peak values year round. The highest values occur in October and the secondary high value in June. The ozone season in the south is longer than that in the north. The annual average daily peak value of ozone concentrations in different climates usually occur around 3 pm. The diurnal variation range of ozone concentration declines with the increase of latitude. Ozone concentration does not elevate with the increase of traffic flow. Ozone concentration in Guangzhou has a distinct reverse relation to CO and NOx. This complicated non-linearity indicates that the equilibrium of ozone photochemical reaction has regional differences. Exceeding the rate of Beijing＇s lh ozone concentration is higher than that of Guangzhou, whereas the average 8h ozone level is lower than that of Guangzhou, indicating that areas in low latitude are more easily affected by moderate ozone concentrations and longer exposure. Thus, China should work out standards for 8h ozone concentration.     

EDTA直接滴定测定镍

参考磷酸在345~356℃时有强腐蚀性的特点,采有硫磷混合酸消解矿样,能使样品消解完全。本方法经加标校核证明,检测结果重现性好,准确度高。     

Factors influencing property selection for conservation revolving funds

Finding sustainable ways to increase the amount of private land protected for biodiversity is challenging for many conservation organizations. In some countries, organizations use revolving‐fund programs, whereby land is purchased and then sold to conservation‐minded owners under condition they enter into a conservation covenant or easement. The sale proceeds are used to purchase, protect, and sell additional properties, incrementally increasing the amount of protected private land. Because the effectiveness of this approach relies on selecting appropriate properties, we explored factors currently considered by practitioners and how these are integrated into decision making. We conducted exploratory, semistructured interviews with managers from each of the 5 major revolving funds in Australia. Responses indicated although conservation factors are important, financial and social factors are also highly influential. A major determinant was whether the property could be resold within a reasonable period at a price that replenishes the fund. To facilitate resale, often selected properties include the potential for the construction of a dwelling. Practitioners face with clear trade‐offs between conservation, financial, amenity, and other factors in selecting properties and 3 main challenges: recovering the costs of acquisition, protection, and resale; reselling the property; and meeting conservation goals. Our findings suggest the complexity of these decisions may constrain revolving‐fund effectiveness. Drawing from participant responses, we identified potential strategies to mitigate these risks, such as providing adequate recreational space without jeopardizing ecological assets. We suggest managers could benefit from a shared‐learning and adaptive approach to property selection given the commonalities between programs. Understanding how practitioners deal with complex decisions in the implementation of revolving funds helps identify future research to improve the performance of this conservation tool.     

Quantifying progress toward a conservation assessment for all plants

The Global Strategy for Plant Conservation (GSPC) set an ambitious target to achieve a conservation assessment for all known plant species by 2020. We consolidated digitally available plant conservation assessments and reconciled their scientific names and assessment status to predefined standards to provide a quantitative measure of progress toward this target. The 241,919 plant conservation assessments generated represent 111,824 accepted land plant species (vascular plants and bryophytes, not algae). At least 73,081 and up to 90,321 species have been assessed at the global scale, representing 21–26% of known plant species. Of these plant species, at least 27,148 and up to 32,542 are threatened. Eighty plant families, including some of the largest, such as Asteraceae, Orchidaceae, and Rubiaceae, are underassessed and should be the focus of assessment effort if the GSPC target is to be met by 2020. Our data set is accessible online (ThreatSearch) and is a baseline that can be used to directly support other GSPC targets and plant conservation action. Although around one‐quarter of a million plant assessments have been compiled, the majority of plants are still unassessed. The challenge now is to build on this progress and redouble efforts to document conservation status of unassessed plants to better inform conservation decisions and conserve the most threatened species.