Study of statistically correcting model CMAQ-MOS for forecasting regional air quality

Based on analysis of the air pollution observational data at 8 observation sites in Beijing including outer suburbs during the period from September 2004 to March 2005, this paper reveals synchronal and in-phase characteristics in the spatial and temporal variation of air pollutants on a city-proper scale at deferent sites; describes seasonal differences of the pollutant emission influence between the heating and non-heating periods, also significantly local differences of the pollutant emission influence between the urban district and outer suburbs, i.e. the spatial and temporal distribution of air pollutant is closely related with that of the pollutant emission intensity. This study shows that due to complexity of the spatial and temporal distribution of pollution emission sources, the new generation Community Multi-scale Air Quality (CMAQ) model developed by the EPA of USA produced forecasts, as other models did, with a systematic error of significantly lower than observations, albeit the model has better capability than previous models had in predicting the spatial distribution and variation tendency of multi-sort pollutants. The reason might be that the CMAQ adopts average amount of pollutant emission inventory, so that the model is difficult to objectively and finely describe the distribution and variation of pollution emission sources intensity on different spatial and temporal scales in the areas, in which the pollution is to be forecast. In order to correct the systematic prediction error resulting from the average pollutant emission inventory in CMAQ, this study proposes a new way of combining dynamics and statistics and establishes a statistically correcting model CMAQ-MOS for forecasts of regional air quality by utilizing the relationship of CMAQ outputs with corresponding observations, and tests the forecast capability. The investigation of experiments presents that CMAQ-MOS reduces the systematic errors of CMAQ because of the uncertainty of pollution emission inventory and improves the forecast level of air quality. Also this work employed a way of combining point and area forecasting, i.e. taking the products of CMAQ for a center site to forecast air pollution for other sites in vicinity with the scheme of model products "reanalysis" and average over the "area".     

The diurnal and seasonal characteristics of urban heat island variation in Beijing city and surrounding areas and impact factors based on remote sensing satellite data

Based on the land surface temperature (LST), the land cover classification map,vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains,especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃ between Beijing city and suburb areas, comparatively is 8- 10℃ between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative.The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481).The results demonstrate that the vegetation greenness is an important factor for reducing the UHI,and large-scale construction of greenbelts can considerably reduce the UHI effect.     

基于高通量测序的表层水体纤毛虫多样性评估方法比较与优化

基于扩增子的高通量测序技术广泛应用于微型生物多样性的研究, 不同的测序手段和数据分析流程, 影响着微型生物多样性与群落结构的分析结果。本研究以易于形态鉴定的砂壳纤毛虫作为研究对象, 比较DNA测序、RNA测序和形态学方法检获的多样性和物种组成等, 探究序列分析流程中关键步骤: 嵌合体处理、可操作分类单元分析方法选择、合并相似分类单元以及去除稀有类群等对多样性结果的影响。研究结果显示无论基于DNA还是RNA的分子手段与形态学方法检获的主要物种基本一致, 与DNA测序相比, RNA测序检获的物种数少, 但差异不显著。基于97%以上相似度聚类和单核苷酸变异所得群落结构相似, 无显著差异; 且所有分析方法都能在一定程度上反映出自然界中不同类群的相对丰度。相较于单核苷酸变异和其他相似度阈值, 99%相似度下聚类所得多样性更为接近形态学结果。去除嵌合体和稀有类群(去除阈值: DNA测序0.05%; RNA测序0.07%), 可明显改善分子多样性虚高的问题。本研究为纤毛虫等真核微生物的分子多样性研究提供了科学的数据分析流程, 对未来真核微生物多样性研究具有指导意义。     

能见度集合预报及后处理技术应用

基于污染物情况、环流系统和时空分布特征分析,利用神经网络对历史数据进行建模,生成了能见度集合预报产品。在2022年冬季的TS评分检验中,预报产品优于欧洲中期数值预报中心模式(ECMWF)的能见度预报产品。利用概率匹配、最优百分位和神经网络三种后处理方法生成后处理产品,这些产品的TS评分优于集合预报产品。预报输入的ECMWF模式2 m湿度与实况的偏差是误差的主要来源。利用集成方法对三种后处理产品进行集成,其TS评分结果在低能见度区间总体接近或略优于原始产品。生成的能见度集合预报后处理最优集成预报产品成功提高了对中期延伸期能见度天气的预测准确性。     

辽宁省精细化暴雨洪涝灾害风险评估与预评估

基于辽宁省61个国家气象站1961—2020年和998个区域自动气象观测站建站至2020年逐小时、逐日降水资料,分析了辽宁省暴雨洪涝灾害主要致灾因子,计算了暴雨洪涝孕灾环境指标,完成了辽宁省暴雨洪涝灾害危险性评估。结果表明：暴雨洪涝高危险性地区主要位于丹东;暴雨洪涝灾害人口高风险区主要位于沈阳和大连市区;经济高风险区主要位于大连和盘锦市区;水稻、玉米高风险区主要位于锦州、盘锦和丹东。利用辽宁省无缝隙智能网格预报数据对2022年7月28—29日的暴雨过程灾害风险进行了预评估,发现暴雨灾害危险性高值区域主要分布在朝阳、葫芦岛以及辽宁中部。暴雨灾害可能造成的人口、经济高风险区域主要位于辽宁西部和中部地区;暴雨灾害可能造成的水稻和玉米高风险区主要位于沈阳、铁岭和朝阳北部等地区。预计高风险区主要影响人口约为449万人,经济损失约为1432万元,受影响的水稻面积约为1.028万公顷、玉米面积约为1.798万公顷。通过灾后效果检验,发现预评估模型效果良好,可在实际的暴雨洪涝灾害风险评估业务中使用。     

Characterization and Application of S-Band Polarimetric Radar and X-Band Phased Array Radar for a Tornadic Storm Event on June 16,2022

The X-band phased array radar offers faster scanning speed and higher spatial resolution compared to the S-band radar, making it capable of enhancing tornado monitoring and early warning capabilities. This study analyzed the char- acteristics and nowcasting signals of a tornado case that occurred on June 16, 2022 in the Guangzhou region. Our findings indicate that the violent contraction of rotation radius and the dramatic increase in rotation speed were important signal characteristics associated with tornado formation. The X-band phased array radar, with its high temporal and spatial resolution, provided an opportunity to capture early warning signals from polarimetric characteristics. The X-band phased array radar demonstrated noteworthy ability to identify apparent tornado vortex signature (TVS) features in a 10-minute lead time, surpassing the capabilities of the CINRAD/SA radar. Additionally, due to its higher scanning frequency, the X- band phased-array radar was capable of consistently identifying TVS with shorter intervals, enabling a more precise tracking of the tornado’s path. The application of professional radars, in this case, provides valuable insights for the monitoring of evolutions of severe local storms and even tornadoes and the issuance of early warning signals.     

人地复合生态系统韧性研究进展及其对海岸带生态系统韧性研究的启示

在全球气候变化与人类高强度开发的双重影响下,人地复合生态系统和受陆海系统交互影响的海岸带地区生态脆弱性更加明显。如何有效把握人地复合生态系统发展动向,合理规划与调控社会经济活动、促进人地关系协调发展一直是可持续发展领域研究的重点课题。韧性作为系统的内在特征,在防范化解灾害风险、维护区域生态安全中发挥着重要作用,是人地复合生态系统可持续发展能力的重要表现。然而,目前关于人地复合生态系统韧性的内涵、评估方法等方面尚未形成统一的范式,韧性的量化评估方法存在较大差异。通过梳理和归纳现有研究成果认为,韧性经历了从工程韧性、生态韧性到演进韧性的转变,其概念及内涵仍在不断拓展与深化;总结对比了基于过程形成性和状态总结性的韧性评估方法发现,将韧性作为过程与结果的综合评估研究仍较为缺乏;在抵抗扰动、维持稳定和协调发展的韧性理念下,生态系统的保护与修复等将是区域可持续发展应用中人地复合生态系统安全的重要保障。根据人地复合生态系统韧性研究进展的分析结果,结合海岸带生态系统韧性研究的现状及不足,认为未来需要在加强海岸带生态系统韧性内涵挖掘、完善海岸带生态系统韧性评价体系、提高海岸带生态系统韧性监测精度、促进学...     

预测有效波高的深度学习模型研究

研究基于RNN、LSTM、GRU深度学习模型,针对NOAA浮标数据集中的44013、44014、44017浮标的数据,通过斯皮尔曼相关性分析提高模型预测效果。实验结果表明,在进行相关性分析后,S-RNN、S-LSTM、 S-GRU的预测效果均比原始RNN、LSTM、GRU模型预测效果好。此外,提出一种基于LSTM的LSTM-Attention 波高预测模型,并进行相关实验,量化LSTM-Attention模型的预测效果,实验结果表明LSTM-Attention模型有更好的预测效果。为评估模型的泛化能力,研究还提出了一种采用邻近浮标数据进行学习,预测浮标缺失数据的方 法。实验结果表明,该方法的预测精度可以达到97.93%。本研究为海浪预测提供了新的方法和思路,也为未来深 度学习模型在海浪预测中的应用提供了参考。     

太湖表层沉积物多环芳烃的空间分布、来源及其风险

随着太湖流域社会与经济的发展,多环芳烃(PAHs)在各种环境介质中逐渐累积,污染日益严重,可能对太湖生态环境及周边人体健康构成威胁。为探究太湖沉积物PAHs的来源及生态风险,于2021年12月在太湖采集30个表层沉积物样品,利用气相色谱-质谱联用仪(GC-MS)检测样品中16种PAHs含量;利用受体模型和苯并[a]芘(BaP)毒性当量法进行来源解析及生态风险评估,并将各来源贡献与毒性当量浓度相结合,量化源风险。结果表明,太湖表层沉积物中16种PAHs总含量介于124~592 ng/g之间,平均值为294 ng/g,中值为279 ng/g;高环多环芳烃(HMW PAHs)为主要组分,占∑PAHs的67%。高含量区域位于竺山湾、梅梁湾、贡湖湾和西太湖,与国内外其他湖泊沉积物相比,太湖沉积物PAHs含量处于较低水平。源解析的结果表明,太湖表层沉积物中PAHs交通排放源贡献率为29.1%、煤炭燃烧源贡献率为26.7%、生物质燃烧源贡献率为28.7%、石油源贡献率为15.6%。生态风险评价结果表明,交通排放源、生物质燃烧源、煤炭燃烧源和石油源的BaP毒性当量含量(TEQ_BaP)均值分别为19.34、17.81、16.33和9.1 ng/g,均小于70 ng/g,几乎处于无风险水平。西太湖、贡湖湾和梅梁湾的部分区域ΣTEQ_BaP大于70 ng/g属于潜在风险区,具有一定潜在毒性。在后续的污染治理中应重点关注太湖西北部地区污染物的排放。本研究可为沉积物中PAHs污染的研究提供数据支撑,为地方政府精准、高效地管控PAHs污染提供理论依据。     

湘中南二叠系孤峰组硅质岩的成因属性及其地球动力学指示意义

华南二叠系底部的硅质岩及同一层位对应于华南重要的地幔强活动期和成矿期，有非常重要的动力学指示意义。研究表明，孤峰组剖面的岩性可划分为三段：泥页岩段、厚层状硅质岩段和薄层状硅质岩段。沿剖面自下而上，Al2O3、K2O和TiO2含量逐渐降低，MnO、TFe等组分逐渐升高；稀土元素显示出一定的铈负异常，∑REE偏低，且向上∑REE逐渐减少而HREE／LREE逐渐增高；一些热水沉积诊断性微量元素如As、Sb、Hg等也显示出一定程度的异常。这些特征均表明硅质岩的成岩过程中包含有正常沉积和热水沉积的共同作用，且自下而上热水沉积作用逐渐增强的趋势。这种成岩作用的演化特征，代表了下扬子地台拉张裂陷盆地对晚古生代伸展背景的沉积响应，对于探讨裂谷盆地阶段性发育的动力学特征有一定的指示意义。