华东地区MODIS与OMI气溶胶光学厚度数据融合

气溶胶光学厚度（AOD）表征气溶胶对光的衰减作用,体现大气混浊度或大气中气溶胶总含量,其卫星产品是研究近年来不断恶化的大气环境与空气质量的良好数据源。AOD卫星产品种类较多,但数据存在较大的不确定性;气溶胶全球监测网（AERONET）的地基数据精度高,但空间覆盖度较差。泛克里金法（UK）能在数据融合过程中更多地考虑描述对象的空间相关性,并且简单易行、结果可靠。因此,本文采用该方法,结合二次多项式波段插值法和回归分析方法,在AERONET AOD数据的基础上,对2008年11月华东地区臭氧监测仪（OMI）和中分辨率成像光谱仪（MODIS）的AOD产品进行了融合。结果表明：二次多项式的AOD波段插值方法,能提供比Angstrom波长指数法更为精准的AOD插值结果;AOD融合产品的空间分辨率高于OMI AOD,覆盖率大于OMIAOD和MODIS AOD,且其精度优于这2种AOD卫星产品;融合产品图显示,2008年11月,华东地区的AOD总体呈现南低北高的趋势,高值区主要分布在长江三角洲部分地区、安徽东北部、苏鲁交界处,以及山东西部;低值区主要为江苏以南大部。相比于前人研究,本文证实了AERONET AOD站点数据少、融合的数据源（卫星AOD产品）过境时间不一致的情况下,UK方法仍然有效。本文提出的融合系统,可为相关研究提供空间覆盖更全、精度更高的AOD数据。     

城市气溶胶光学厚度空间格局特征多指标综合分析

本文以厦门市为例,基于MODIS影像反演的大气气溶胶光学厚度（AOD）,利用空间自相关和景观格局指数从数量、形态和结构方面综合分析了2014年各月份（5月和9月除外）AOD时空格局变化。结果表明,研究区AOD具有明显的时空分布差异,林地上空的AOD处于较低等级,建设用地上空AOD大部分处于中高等级;AOD在春夏季较大,在秋冬季较小。AOD分布存在显著正空间自相关性,而且主要存在高高（HH）、低低（LL）、高低（HL）3种聚集模式。低低聚集模式主要分布在厦门市的北部山区;高高（HH）和高低（HL）模式主要分布在本岛东北部新城和本岛外各新城的城区。在景观格局指数方面,从秋冬季节到春夏季节期间,研究区气溶胶光学厚度高等级斑块增加,景观结构趋于复杂,景观异质性增加。对AOD时空格局变化进行多指标综合分析可以更加深入、细致、全面地刻画气溶胶的变化规律,有助于精确评估气溶胶对环境、气候等的影响,为城市可持续发展提供决策支持。     

基于MODIS数据的气溶胶光学厚度反演

气溶胶在大气污染事件中扮演重要角色。光学厚度作为大气气溶胶的一个重要光学属性,常作为其他相关科学研究的重要参数。本文以ENVI5.0为平台,利用经典的暗像元算法,对2011年8月5日北京地区的MODIS卫星遥感影像数据进行了气溶胶光学厚度(AOD)反演,并对其空间分布进行了分析,为环境监测部门提供了大气污染治理依据。     

一种基于FY3D/MERSI2的AOD遥感反演方法

针对目前国产卫星对气溶胶遥感监测应用较少的情况,本研究综合暗象元和SARA算法优势,基于FY-3D MERSI2数据构建了一套气溶胶光学厚度(Aerial Optical Depth, AOD)快速遥感反演方法。首先,引入了传统暗象元算法中的地表反射率经验关系,然后利用AERONET长时间序列的地基观测数据,构建了红波段和蓝波段气溶胶光学厚度、不对称因子和单次散射率之间的关系,最后利用MOD04和AERONET的AOD产品对研究反演结果进行了验证和评估分析。结果发现：① 本研究反演的AOD不仅保持了与MODIS的气溶胶产品空间分布的一致性,而且合理地呈现了AOD的高值分布,改进了MOD04气溶胶产品在云和亮目标方面反演缺失问题;② 利用AERONET地基观测结果对本研究获取的MERSI2的AOD反演结果进行了对比分析,发现二者具有较高的线性相关性,蓝波段AOD线性相关系数超过0.85;③ 利用MERSI2数据完整地捕捉到了2018年3月9—14日京津冀及周边区域的一次重污染过程中气溶胶时空分布变化情况,这也说明了FY3D卫星具备良好的气溶胶遥感监测能力,为我国灰霾监测和预警提供参考依据。本研究对大力发展国产卫星在大气环境遥感的应用有重要参考意义。     

ECMWF地表太阳辐射数据在我国的误差及成因分析

利用2000-2009年中国气象局（CMA）地表太阳辐射台站资料,对欧洲中期天气预报中心（ECMWF）地表太阳下行短波辐射产品进行多时间尺度的计算与分析,检验ECMWF地表辐射产品对于中国地区太阳辐射特征的表现。本文通过聚类分析将中国地区分为8个区域,考虑到ECMWF大气因素对ECMWF地表辐射的影响和大气因子分布的空间异质性,引入地理探测器对ECMWF再分析辐射产品的时空误差进行定量分析,来判明影响ECMWF辐射精度的主要大气因子。结果表明：总体上看,ECMWF地表太阳辐射要高于地面观测数据,月均偏差为18.28W/m²;ECMWF地表太阳辐射表现出季节性差异,夏秋季节明显好于春冬季节,相对偏差较大的数据集中分布在12、1、2和3月,相对偏差较小的数据集中分布在6、7、8和9月;不同区域在冬季和夏季的主导大气影响因子不同,夏季中国西北（1区）、高原（3区）、西南（4区）和四川盆地（5区）地区主导影响因子都是气溶胶,东南（6区）地区的主导影响因子是地表反照率和气溶胶,中东部地区（7区）的主导影响因子是云覆盖率和气溶胶,但是因子解释较小,分别为0.0228和0.0202,东北地区（8区）4个因子均未通过显著性系数检验,因子对相对偏差的变化影响不显著;冬季中国西北（1区）、高原（3区）、中东（7区）、东北（8区）和四川盆地（5区）地区的主导影响因子都是云覆盖率,西南（5区）和东南（6区）地区的辐射主要受到气溶胶的影响。     

城市大气污染扩散GIS模拟分析--以福州市为例

以福州市为例,分析了GIS在环境模型研究中的主要应用范围;剖析了GIS与环境模型结合的三种层次。在 GIS技术支持下建立了城市点源和线源大气污染扩散模型,采用大气污染扩散模型与GIS的嵌入式的紧密集成技术,进行福州市大气污染扩散模拟,直接采用等值线的方式表达大气污染物在不同气象条件下空间分布模拟结果, 为城市大气污染源的管理和时空模拟提供了一个良好的可视化分析环境。模拟结果表明:2000年福州市区工业路附近区域与福新路附近区域出现SO2高浓度;湖东路、八一七路、福新路、六一北路围成区域出现NOx高浓度,为机动车污染严重的区域。污染物浓度分布与污染源的空间分布和排放量密切相关。     

2003-2014年珠江口香洲大气无机氮湿沉降变化研究

目的】研究了珠江口区域近年来大气湿沉降中无机氮的变化特征及其对近海海水生态系统的影响。【方法】对2003-2014年珠海市香洲地区湿沉降中NO_3~--N和NH_4~+-N浓度及湿沉降通量的变化特征进行了分析。【结果】香洲地区湿沉降中NO_3~--N和NH_4~+-N的浓度均有明显的季节变化,表现为旱季高而雨季低,与降雨量呈相反的变化趋势;氮素的浓度在年际变化有波动,但无明显变化趋势;氮素湿沉降通量与浓度变化相反,雨季明显高于旱季;降水量、当地污染状况、大气中污染物(气溶胶粒子)浓度、降雨类型以及气象条件等因素是造成季节差异的主要原因;此外,香洲地区大气无机氮湿沉降具有很强的事件特征。【结论】香洲地区氮素湿沉降浓度春秋季高于海水,且DIN成分比例与海水有差异,可能改变表层水体营养盐结构,影响浮游植物的生长。     

基于深度学习的区域生态安全时空模拟与预测

区域作为人类、自然、社会共同作用和互相影响的复杂系统,对区域进行生态量化建模与模拟仿真,是实现区域可持续发展战略的关键。传统机器学习方法对区域生态系统建模取得了一定的成果,但难以确定学习特征和实现时空模拟。深度学习不需事先确定训练特征,具有优异的特征学习能力,能够提高模型预测精度,因此利用深度学习进行建模具有显著优势。本文使用植被净初级生产力（NPP）、气溶胶光学厚度（AOD）和人口格网数据,充分利用深度学习的优点,采用最优深度神经网络时空模拟,得到了河南省2007-2014年3 km分辨率的生态赤字空间分布图和河南省2015-2020年的生态赤字时间预测结果并进行分析,为区域生态的科学管理和建设供科学依据和参考。     

集成多源遥感数据的PM2.5浓度空间分布制图

针对传统地面稀疏站点监测PM_2.5浓度以点带面的缺陷,本研究拟借助多源遥感数据开展了地面大气细颗粒物PM_2.5浓度空间分布模拟研究。以京津冀地区2013年的年均、季均PM_2.5浓度模拟图为例,用简化的气溶胶反演算法（SARA）反演了 1 km高分辨率AOD,并结合高分辨率遥感提取污染相关地理要素,对研究区PM_2.5浓度空间分布进行地理统计模拟及优选。结果表明：① SARA算法反演的AOD与地基AERONET相关性达0.99,能准确地反映研究区AOD的时空分布特征;② 集成多源遥感数据的地理加权回归模型拟合度高（平均R²-0.66）,其空间模拟显示研究区平均PM_2.5污染南部城镇最重,中东部城区次之,西北山区较轻;③ 研究区PM_2.5污染程度高,全年平均模拟浓度高达75 μg/m³,在气候环境及主要污染源季节性差异驱动下,浓度分布季节性特征显著,冬季污染最严重,而夏、秋季相对较轻。该成果对于精细把握PM_2.5污染特征,指导污染防控具有重要意义。     

基于太阳光度计的成都双流地区夏季气溶胶光学特性研究

选取成都信息工程学院航空港校区为研究区,利用CE318太阳光度计于2013年6月期间连续观测的数据反演气溶胶光学厚度,Angstrom指数和粒子谱分布,然后进行气溶胶光学特性分析,结果表明:气溶胶光学厚度随波长增加而减少;气溶胶光学厚度随人为活动和天气等诸多因素变化而变化;平均波长指数显示该地区气溶胶中小粒子占主控地位,平均直径在1.2μm左右;平均浑浊度系数为0.44,大气处于浑浊状态;粒子谱分布呈双峰型,证明该地区为城市气溶胶类型,也详细表明气溶胶粒径的变化。     

Numerical simulation of the structure and variation of upwelling off the east coast of Hainan Island using QuikSCAT winds

The spatial structure and variation of the upwelling in the waters east and northeast of Hainan Island, China during 2000-2007 were investigated using a nested high-resolution Princeton Ocean Model (POM) forced by QuikSCAT winds. The model produced good simulations of the summer upwelling and the seasonal and annual variability. Strong upwelling occurs from mid-July to mid-August with a peak east of Hainan Island associated with the southwesterly monsoon in the South China Sea. Sensitivity experiments indicated that when the local wind stress controls the variability of the upwelling, the large-scale circulation significantly enhances the upwelling northeast of Hainan Island by inducing a local upwelling and transporting cold water northeast-ward along the island’s east coast. The joint effects of the local wind stress and large-scale circulation result in stronger upwelling northeast of Hainan Island. This implies that the annual variation of the upwelling northeast of Hainan Island is controlled not only by the local alongshore wind stress but also by the large-scale circulation. This result will help us investigate the decadal variation of the upwelling in this region in the future.     

基于全连接和LSTM神经网络的空气污染物预测

在空气污染日益严重的情况下进行空气污染物的预测工作是十分必要的。针对城市的空气污染物预测,提出了一种基于神经网络的混合模型方法:通过全连接神经网络方法,结合长短期记忆网络(Long Short-Term Memory,LSTM)方法,将历史空气污染物数据与大气数据进行空间与时间上的挖掘分析。运用全连接和LSTM两种神经网络方法混合的形式,与传统的单一模型方法相比,不仅能摆脱单一模型特征空间的局限性,还能提高预测的精度,具有更大的应用性和操作性。最后,以武汉市为例通过实验证明该混合模型较单一模型在空气污染物预测上具有更高的精度。     

基于GIS的多污染因子复合地面浓度场模拟与评价

针对工业大气多因子复合作用的污染物连续地面浓度分布和评价问题,提出了一种基于GIS的地面浓度场模拟和综合评价的新方法,该方法将大气污染物扩散高斯模式和大气质量综合污染指数评价模型在GIS中有机集成,实现了污染物分布空间信息的显示和分析。通过将此方法应用于某一化工厂污染源不同气象条件下污染扩散分析计算,结果表明此种方法简单实用,特别是对多污染因子复合作用的综合评价模型具有任意空间分辨精度,可以满足不同分级需求。     

2008—2018年中国手足口病时空分异特征

手足口病是一种在婴幼儿中多发的常见传染病,对儿童的身体健康具有重要影响。为揭示我国手足口病的时空分异特征,为手足口病的防控提供科学依据,本文选取自2008年手足口病被列为丙类传染病以来至2018年中国手足口病发病率为研究数据,运用全局莫兰指数、Getis-Ord G_i ^*、新兴时空热点分析和标准差椭圆等空间统计方法对中国手足口病的整体和局部时空分布模式、特征和趋势进行了分析。结果显示：① 2008—2018年我国手足口病发病率呈现显著的空间聚集性,且聚集的强度显著增高;② 我国手足口病发病的热点区域主要集中在东南沿海地区,且较明显向内陆以及北部沿海地区扩张,而冷点区域主要集中在西北内陆以及东北地区;③ 我国手足口病的新兴时空热点模式以振荡的热点为主,新增热点主要出现在云南、重庆和四川,而冷点分布区域相对稳定,且冷点大部分以加强的、持续的模式出现;④ 手足口病高发病率区域在2008—2018年期间主要向西南方向移动,但在2008—2009年、2013—2014年以及2017—2018年存在向北部移动的趋势,同时2018年手足口病在东—西方向上的分布范围显著增大。总体来说,我国手足口病在南方地区高发。     

Characterization of air pollution in urban areas of Yangtze River Delta,China

The hallmark of development in the Yangtze River Delta (YRD) of East China has been sprawling urbanization. However, air pollution is a significant problem in these urban areas. In this paper, we investigated and analyzed the air pollution index (API) in four cities (Shanghai, Nanjing, Hangzhou and Ningbo) in the YRD from 2001 to 2012. We attempted to empirically examine the relationship between meteorological factors and air quality in the urban areas of the YRD. According to the monitoring data, the API in Shanghai, Nanjing, Hangzhou slightly declined and that in Ningbo increased over the study period. We analyzed the inter-annual, seasonal, and monthly variations of API, from which we found that the air quality had different temporal changes in the four cities. It was indicated that air quality was poor in winter and spring and best in summer. Furthermore, different weather conditions affected air quality level. The wind direction was considered as an important and influential factor to air pollution, which has an impact on the accumulating or cleaning processes of pollutants. The air quality was influenced by the different wind directions that varied with seasons and cities.     

Characteristics and cause analysis of heavy haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze episodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complexity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index(AQI) level-1 standard(AQI 50) according to the National Ambient Air Quality Standard(NAAQS) of China. Conversely, 33.7% of the days showed a higher level with AQI 100. Extreme haze events(AQI 300) occurred frequently during agricultural harvesting period(from October 10 to November 10), intensive winter heating period(from Late-December to February) and period of spring windblown dust(April and May). Most daily concentrations of gaseous pollutants, i.e., NO_2(43.8 μg/m~3), CO(0.9 mg/m~3), SO_2(37.9 μg/m~3), and O_3(74.9 μg/m~3) were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter(PM_(2.5) and PM10) concentrations(67.3 μg/m~3 and 115.2 μg/m~3, respectively) were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions(i.e., low wind speed and inversion layer). The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. Therefore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emissions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.     

Regional Characteristics and Causes of Haze Events in Northeast China

Northeast China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. However, the underlying forces driving haze formation during different seasons are poorly understood. In this study, we explored the spatio-temporal characteristics and causes of haze events in Northeast China by combining a range of data sources(i.e., ground monitoring, satellite-based products, and meteorological products). It was found that the ‘Shenyang-Changchun-Harbin(SCH)'city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index(AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of the autumn. The three periods that typically experienced intense haze events were Period I from mid-October to mid-November(i.e., late autumn and early winter), Period II from late-December to February(i.e., the coldest time in winter), and Period III from April to mid-May(i.e., spring). During Period I, strong PM_(2.5) emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events(AQI 300). Period II had frequent heavy haze events(200 AQI 300) in the coldest months of January and February, which were due to high PM_(2.5) emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period III, with high PM_(10) concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM_(10) and enhance the levels of windblown dust from tilled soil. Better control of coal burning, fuel consumption, and crop residue burning in winter and autumn is urgently needed to address the haze problem in Northeast China.     

基于中国大气环境监测站点的2015—2019年大气质量状况时空变化分析

2013年以来几次严重的雾霾污染事件引起了公众的广泛关注,此后中国实施了一系列有关大气污染防治的政策、法规和措施来改善大气质量。为了分析近年来中国大气质量的时空变化特征,本文选取2015—2019年生态环境部国控站点监测的大气污染关键参数,对比分析了空气质量指数和6种大气污染物的季均、年均浓度变化结果,并利用组合指标分析法和相关分析法探讨了不同大气污染物之间的相关性。结果表明：① PM_2.5、PM₁₀、SO₂、CO和NO₂浓度和AQI均有明显下降,2019年均浓度较2015年均浓度分别下降4.5%、3.84%、7.86%、3.74%、0.95%,AQI下降了19.31%,同时,O₃浓度则上升了0.79%;② 从空间分布来看,中国北方地区PM₁₀、PM_2.5、O₃、NO₂、SO₂、CO年均质量浓度和AQI分别比南方地区高25.2%、18.73%、4.95%、17.6%,32.74%、16.17%、28.3%;③ 从季节性变化规律来看,除了O₃呈现出夏季浓度高,冬季浓度低外,其他5种污染物和AQI都呈现相反的季节变化规律;④ 总体而言,目前中国大气污染以PM_2.5和O₃为主,PM_2.5与NO₂、SO₂、CO之间有极显著的正相关关系（r>0.85,p<0.01）,而O₃与其前体物NO₂和CO之间存在显著的负相关关系（r>0.8,p<0.01）。     

Trends in significant wave height and surface wind speed in the China Seas between 1988 and 2011

Wind and waves are key components of the climate system as they drive air-sea interactions and influence weather systems and atmospheric circulation. In marine environments, understanding surface wind and wave fields and their evolution over time is important for conducting safe and efficient human activities, such as navigation and engineering. This study considers long-term trends in the sea surface wind speed(WS) and significant wave height(SWH) in the China Seas over the period 1988–2011 using the Cross-Calibrated Multi-Platform(CCMP) ocean surface wind product and a 24-year hindcast wave dataset obtained from the WAVEWATCH-III(WW3) wave model forced with CCMP winds. The long-term trends in WS and SWH in the China Seas are analyzed over the past 24 years to provide a reference point from which to assess future climate change and offshore wind and wave energy resource development in the region. Results demonstrate that over the period 1988–2011 in the China Seas: 1) WS and SWH showed a significant increasing trend of 3.38 cm s~(-1)yr~(-1) and 1.52 cm yr~(-1), respectively; 2) there were notable regional differences in the long-term trends of WS and SWH; 3) areas with strong increasing trends were located mainly in the middle of the Tsushima Strait, the northern and southern areas of the Taiwan Strait, and in nearshore regions of the northern South China Sea; and 4) the long-term trend in WS was closely associated with El Ni?o and a significant increase in the occurrence of gale force winds in the region.     

Spatio-temporal Characteristics of Atmospheric Pollution and Cause Analysis of Haze Events in Sichuan Basin,China

This study analyzed the spatio-temporal variability of air quality data for six standard air pollutants(Particulate Matter 2.5(PM_(2.5)), Particulate Matter 10(PM_(10)), SO_2, NO_2, CO, and O_3) in the Sichuan Basin(SCB), China from 2015 to 2018 in relation to the formation of haze using conventional meteorological data(temperature, wind speed, and relative humidity), satellite data(fire point data, vertical profiles of aerosol subtypes, and aerosol optical depth), planetary boundary layer height, and backward trajectories. The results indicated that the spatio-temporal evolution of the air quality index(AQI) had notable seasonality for the pollution severity in descending order: winter, spring, summer, and autumn. Autumn and winter severe haze events occurred in November and January, respectively, and were caused by higher local pollution emissions under stagnant air conditions. Spring severe haze events occurred in May and were caused by dust from Northwest China and local regions. Severe summer haze events occurred in July and were caused by local burning. Therefore, the analyses showed that local burning, stagnant meteorological conditions, air mass transport and anthropogenic pollution emissions played a key role in haze in the SCB. This study provides scientific insights for fully analyzing heavy air pollution in SCB, China, and also provides a scientific basis for pollution research in regions of complex terrain as basins and mountains.