福建省干旱的时空分布及其对农业生产的影响

通过对福建省66个县近44年的干旱资料分析,阐述了该省春旱、夏旱和秋冬旱的时空分布特点及其对农业生产的影响。结果表明,福建干旱以夏旱为主,秋冬旱次之;各类干旱以小旱为主,中旱次之,大、特旱则仅占四分之一。春旱在中南部沿海地区高发;夏季小、中旱以中南部沿海地区多发,大、特旱以厦门、莆田、福州地区多发;秋冬旱是南部多于北部地区。对农业生产影响较大的是春旱和夏旱,尤以夏季的大、特旱威胁最大。     

近50年东北地区春玉米干旱的时空演变特征

利用东北地区70个气象站点1960-2007年间的逐日气象资料,计算了作物水分亏缺指数(CWDI)并划分了干旱等级;利用ArcGIS软件,绘制了东北地区春玉米种植区的干旱等级时空分布图,对各个年代和生育期的玉米干旱灾害时空分布特征和演变趋势进行了分析。结果表明:在春玉米生长季内,玉米的需水量与降水量均呈现先增加后减少的变化趋势7,月下旬达到峰值,在4-6月和9月份水分亏缺指数(CWDI)值较高,易引起玉米发生干旱。     

中国中东部雾霾污染与入境旅游的时空动态关联分析

以雾霾污染和入境旅游发展的典型区中国中东部为例,借助格兰杰因果检验、脉冲函数、重心模型及双变量空间自相关等方法,实证了1998-2016年雾霾污染与城市入境旅游的时空动态关联。结果表明：时间层面,雾霾污染与入境旅游之间存在长期均衡,雾霾污染是入境旅游的单向格兰杰原因,在短期内会对入境旅游的稳健性产生显著负向冲击,但从长期来看这种冲击趋于缓和;空间层面,雾霾污染和入境旅游重心分别向东北和西北偏移,研究期间两者空间重叠性虽有所提升,但仍存在明显的空间错位;整体上两者呈显著空间负相关,局部空间关联模式以高—低和低—高集聚为主,雾霾对豫东、徽北和鄂中等地的入境旅游市场影响最为突出,且具有空间依赖性。本研究可为区域入境旅游业合理应对雾霾天气,以实现高质量发展提供参考。     

长江流域总磷污染:分布特征·来源解析·控制对策

针对长江流域总磷污染,开展总磷污染时空特征分析,选择长江流域总磷污染最严重的上游地区岷江和沱江为典型区,分析总磷来源,提出总磷污染控制对策.研究表明:2016年开始总磷成为长江流域主要污染因子,其中上游污染最重,中游污染最轻,总体呈降低趋势;长江流域枯/平水期总磷污染较重,丰水期污染较轻,说明流域主要污染负荷来自点源.总体来说,造成长江流域总磷较高的原因有:磷矿开采和磷化工的污染源高负荷排放,造成部分河段水质严重超标;基础设施建设滞后,城镇生活污染源排放影响河流水质;畜禽养殖废物资源化利用不足;生态流量不足,加剧水污染问题;水污染治理导向不全面和污染源监管措施不系统,影响总磷水质同步改善.针对长江流域总磷污染特征,按照"分区控制、分类治理""突出重点、精准施策"原则,提出长江流域总磷污染控制建议:①抓住长江流域上游重点片区,开展流域总磷污染整治. ②抓住磷化工、城镇生活和畜禽养殖等三类涉磷重点污染源的治理,控制磷污染负荷排放. ③抓住环境监管有效手段,进一步完善水环境标准和监管体系.      

辽河流域典型支流土地利用方式对大型底栖动物群落时空分布特征的影响

通过辽河流域典型支流(清河和凡河)不同土地利用类型区3个水期大型底栖动物及环境因子调查,研究土地利用方式对大型底栖动物分布特征的影响.结果表明,河流大型底栖动物群落四节蜉科(Baetidae)、扁蜉科(Heptageniidae)、纹石蛾科(Hydropsychidae)及石蝇科(Perlidae)等生物类群主要分布在辽河流域内林地为主的河流中;以耕地和居民点为主的土地利用方式下,河流大型底栖动物主要以摇蚊科(Chironomidae)类群为主;颤蚓科(Tubificidae)等寡毛类大型底栖动物在以城市发展为主的土地利用区域内河流中优势明显.河流中大型底栖动物生物多样性、丰富度及EPT%由高到低依次为林地耕地居民点城市,FBI值由低到高依次为林地耕地居民点城市.不同土地利用方式导致河流生境因子空间差异性显著,大型底栖动物群落相似性较低,且生物密度在空间上呈显著差异,但时间上差异不显著.底质、DO与大型底栖动物多样性等指数呈正相关关系,TN、TP、NH3-N、BOD5、CODCr与大型底栖动物生物指数呈不同程度负相关关系.生物与环境联合分析(BIO-ENV)表明,底质、DO、TN、BOD5是影响区域内大型底栖动物群落特征的最显著的主导环境因子.综合上述研究结果可得,土地利用方式通过对河流生境及水质产生影响,进而使大型底栖动物群落组成及多样性特征发生明显变化.     

基于时空混合效应模型的京津冀PM2.5浓度变化模拟

为揭示京津冀地区高精度PM_2.5的时空分布特征,以空间分辨率为1 km的MAIAC AOD数据为主要预测因子,以气象数据、植被指数、夜间灯光数、人口密度和海拔数据作为辅助因子,构建了一种新的时空混合效应模型（STLME）,在拟合最优次区域划分方案基础上对京津冀地区PM_2.5浓度进行预测分析.结果表明,基于STLME模型的ρ(PM_2.5)预测精度高于传统的线性混合效应模型（LME）,其十折交叉验证（CV）R²为0.91,明显高于LME模型的0.87,说明STLME模型在同时校正PM_2.5-AOD关系的时空异质性方面具有优势.最优次区域划分方案识别出PM_2.5-AOD关系的空间差异,并结合缓冲区平滑方法,提高了STLME模型预测精度.京津冀PM_2.5浓度时空变化差异显著,高值区主要分布在以石家庄、邢台和邯郸为中心的河北南部,低值区则位于燕山-太行山区;冬季PM_2.5污染最严重,其次是秋季和春季,夏季污染最轻.STLM...     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

汾河上中游流域水环境中多环芳烃分布及分配

通过测定汾河上中游流域13个点位丰水期和枯水期水体、表层沉积物中PAHs浓度,分析其分布特征及影响因素.结果表明:汾河上中游流域丰水期和枯水期水中PAHs的平均浓度分别为0.365μg·L~(-1)和0.835μg·L~(-1),枯水期PAHs总体高于丰水期;丰水期和枯水期沉积物中PAHs平均浓度分别为1444μg·kg~(-1)和2407μg·kg~(-1),枯水期PAHs总体高于丰水期;水和沉积物中PAHs的组成主要是2~4环,但沉积物中高环PAHs组成显著高于水中;丰水期和枯水期中游段(寨上到南关)水体和沉积物中PAHs浓度整体均高于上游段(雷鸣寺到汾河水库).丰水期和枯水期沉积相-水相分配系数K_p值分别为642~32345 L·kg~(-1)和671~44929 L·kg~(-1),且随PAHs环数变大K_p值增大;丰水期和枯水期沉积相-水相实测的有机碳归一化分配系数(lgK_(oc))总体高于预测值上限;丰水期和枯水期lgK_(oc)与lgK_(ow)均呈较好的相关性,可决系数(R~2)分别为0.764、0.725,枯水期斜率大于丰水期斜率,枯水期较丰水期沉积物吸附的PAHs更多.K_p值与有机碳/COD_(Cr)比值K_(od)呈正相关,可决系数(R~2)分别为0.625和0.728,丰水期和枯水期PAHs K_p值受沉积物中有机碳含量和水中COD_(Cr)含量的影响.     

基于GIS的珠三角区域空气质量时空特征研究

以广东省环境信息GIS综合发布平台发布的2005年至2010年间粤港珠三角区域空气质量日报中空气质量等级数据为基础,利用GIS技术,首次采用网格化分析、均值分析、标准差分析、专题图渲染等分析方法,针对空间网格的空气质量年度均值、标准差等指标,对近5年来珠三角区域空气质量在横向上从珠三角空间区域范围,纵向上5年时间尺度内时空特征进行深入的研究,解决了在大地理区域、5年时间尺度空气质量分等定级过程中的区域边界划分问题。结果表明：珠三角的RAQI区域均值从2006年的2.3下降到2010年的2.02,空气质量整体上逐年改善;区域污染高值中心由东莞西部逐步向佛山中部转移;珠三角的肇庆南部、佛山中部、中山北部以及广州的南沙等区域空气质量变化幅度较大;惠州、深圳、香港等地区的空气质量较好,也较为稳定。