Assessment of river water quality during snowmelt and base flow periods in two catchment areas with different land use

River water quality was evaluated with respect to eutrophication and land use during spring snowmelt and summer base flow periods in Abashiri (mixed cropland-livestock farming) and Okoppe (grassland-based dairy cattle farming), eastern Hokkaido, Japan. Water from rivers and tributaries was sampled during snowmelt and summer base flow periods in 2005, and river flow was measured. Total N (TN), NO₃–N, and Si concentrations were determined using standard methods. Total catchment and upland areas for each sampling site were determined with ArcGIS hydrology modeling software and 1:25,000-scale digital topographic maps. Specific discharge was significantly higher during snowmelt than during base flow. In both areas, TN concentrations increased, whereas Si concentrations decreased, with increased specific discharge, and were significantly higher during snowmelt. The Si:TN mole ratio decreased to below or close to the threshold value for eutrophication (2.7) in one-third of sites during snowmelt. River NO₃–N concentrations during base flow were significantly and positively correlated with the proportion of upland fields in the catchment in both the Abashiri (r = 0.88, P < 0.001) and Okoppe (r = 0.43, P < 0.01) areas. However, the regression slope, defined as the impact factor (IF) of water quality, was much higher in Abashiri (0.025) than in Okoppe (0.0094). The correlations were also significantly positive during snowmelt in both areas, but IF was four to eight times higher during snowmelt than during base flow. Higher discharge of N from upland fields and grasslands during snowmelt and the resulting eutrophication in estuaries suggest that nutrient discharge during snowmelt should be taken into account when assessing and monitoring the annual loss of nutrients from agricultural fields.     

环境一号卫星CCD数据在太湖蓝藻水华遥感监测中的应用

利用环境一号卫星(HJ-1)CCD数据,对太湖水华进行遥感监测,并比对同时相的EOS/MODIS卫星遥感数据。结果表明,HJ-1星CCD数据具有优于EOS/MODIS数据的蓝藻水华识别能力,并有良好抗云层干扰能力,适合用于太湖蓝藻水华应急监测。     

春节烟花爆竹燃放期间苏州市区PM2.5中水溶性离子特征分析

使用在线离子色谱分析了苏州市区春节期间PM2.5中水溶性离子.结果表明：春节期间存在2个空气污染高峰时段（初一和初五）,其中初一凌晨PM2.5达到最高峰,小时质量浓度为571μg/m3;烟花爆竹集中燃放时段,PM2.5中K＋、Cl-、Mg2＋质量浓度明显上升,初-凌晨与腊月三十凌晨相比,K＋、Cl-、Mg2＋质量浓度分别增加了117倍、80.7倍、18.0倍;相关性分析表明,PM2.5中K＋、Mg2＋、Na＋、Ca2＋在烟花爆竹燃放时段可能具有相同的来源.     

太湖蓝藻水华预警监测综合系统的构建

近年来随着浅水型湖泊的富营养化进程不断加快,蓝藻水华暴发现象也频繁出现,采用科学、全面的手段对太湖蓝藻暴发进行预警十分必要。根据太湖蓝藻预警监测中使用的现场巡视、卫星遥感、实验室分析、自动监测等监测技术手段,分别建立各自监测系统,结合各监测系统特点和相互关系,对太湖蓝藻水华预警监测综合系统的构建进行了探讨,以期能够更好地开展太湖蓝藻水华预警监测工作,为确保太湖地区饮用水安全,提高环保部门应对太湖蓝藻水华暴发的能力,为政府决策提供技术支持和保障。     

三峡库区大宁河2010年春季水华特征

以大宁河春季水华期间调查数据为基础,运用数理统计分析手段,通过描述大宁河春季水华期藻类及主要理化因子分布特征,揭示出影响藻类生长的主要因子。结果表明:大宁河春季水华期水华河段水体氮、磷含量较高,总氮浓度为1.2～4.11mg/L,平均值为1.748mg/L,总磷浓度为0.027～0.615mg/L,氮磷比均值为17.5。春季水华藻类适宜的光照强度为1400～3800lx,水温为13.0～14.0℃时叶绿素a含量有最大增长,平均水温为13.4℃,藻密度与总氮、总磷、水温、DO、pH、浊度、高锰酸盐指数呈显著正相关关系,与透明度呈负相关关系。回水河段流速小于0.05m/s,流速是藻类生长最主要的限制因子。大宁河回水河段春季水华藻类分布较广,主要有甲藻门、绿藻门、硅藻门、隐藻门、蓝藻门、裸藻门和黄藻门7门28属,其中甲藻门分布最广,其次是绿藻门。春季水华优势种主要有甲藻门的拟多甲藻,绿藻门的衣藻、小球藻,硅藻门的直链藻,蓝藻门的色球藻等。     

息烽温泉生态旅游发展模式研究

温泉生态旅游是未来温泉旅游的发展方向。温泉生态旅游非常适合在温泉资源丰富而生态环境较脆弱的喀斯特地区开展。基于对贵州省息烽温泉资源开发利用现状的调查,对息烽温泉生态旅游的发展思路、开发模式和运行机制进行了初步研究,希望能为该区域发展温泉生态旅游提供决策参考。     

赤泥复合剂絮凝处理太湖水华藻与海洋赤潮藻

研究了一种新型赤泥复合剂对太湖夏季水华原水、实验室培养海洋赤潮藻及模拟海水的絮凝除藻降浊性能。结果表明,赤泥复合剂具有良好的除藻降浊效果,对太湖水华藻去除率为97.3%,原水浊度去除率为95%;对海洋小球藻和三角褐指藻2种赤潮藻去除率分别高达99.1%和98.9%;对2种模拟海水的最高除浊率分别为96.3%和95.3%,其剩余浊度均低于4 NTU,从而为治理淡水水华与海洋赤潮提供一种新技术,同时为赤泥综合利用提供一种新途径。     

底泥悬浮对营养盐释放和水华生长影响的模拟

通过室内模拟实验研究了扰动引起的太湖底泥悬浮对水体氮磷营养盐和蓝藻水华的影响,其中底泥和上覆水均来自太湖,扰动强度以悬浮物浓度表示。实验监测了底泥扰动过程中以及扰动停止后48 h之内水体氮磷营养盐和叶绿素a的变化,采样间隔为6 h。实验结果表明,扰动明显增加了水体中总氮、总磷、活性磷等含量,但是可溶性无机氮的增加不明显,叶绿素a含量没有出现明显的增长。水华没有出现明显增长很可能是氮限制的原因。由此推测太湖一次风浪扰动过程引起的底泥营养盐释放不一定就能够加剧蓝藻水华的暴发。底泥中释放的营养盐对蓝藻水华的影响需要进一步研究     

藻源可溶有机质对17β-雌二醇生物降解的影响

为探究藻源可溶有机质(COM)及其生物老化过程对湖泊水体中17β-雌二醇(E2)生物降解的影响特征,通过模拟生物老化,得到5种不同生物活性的COM样品:S0(78％)>S1(67％)>S2(36％)>S3(13％)>S4(1％).结果表明,经过60h的避光培养实验,相同浓度(8mgC/L)的COM样品均提高了微生物群落...     

太湖蓝藻水华暴发程度年度预测

近年来,尽管太湖主要水质指标有所改善,但蓝藻水华暴发的频次和面积并未明显减少。为了探讨太湖蓝藻水华暴发的环境驱动因子,统计了2012—2020年历年4—10月预警期间的太湖蓝藻水华发生规模与频次,结合同步浮标自动监测数据和实验室分析数据,构建了蓝藻水华预测模型。以太湖蓝藻水华综合指数(I_c)表征蓝藻水华强度,并通过I_c与环境因子的相关性分析,筛选出1月水温、1月电导率、1月生化需氧量和3月总氮浓度4项环境指标,最终构建了以该4项环境指标为自变量、I_c为因变量的太湖年度蓝藻水华强度多元线性回归预测模型。该预测模型的决定系数达到了0.908,平均相对误差为10.35%,预测精度总体表现较好。