Land-cover change in upper Barataria Basin estuary,Louisiana, 1972-1992: increases in Wetland area

The Barataria Basin, Louisiana, USA, is an extensive wetland and coastal estuary system of great economic and intrinsic value. Although high rates of wetland loss along the coastal margin of the Barataria Basin have been well documented, little information exists on whether freshwater wetlands in the upper basin have changed. Our objectives were to quantify land-cover change in the upper basin over 20 years from 1972–1992 and to determine land-cover transition rates among land-cover types. Using 80-m resolution Landsat MSS data from the North American Landscape Characterization (NALC) data archive, we classified images from three time steps (1972, 1985, 1992) into six land-cover types: agriculture, urban, bottomland hardwood forest, swamp forest, freshwater marsh, and open water. Significant changes in land cover occurred within the upper Barataria Basin over the study period. Urban land increased from 8% to 17% of the total upper basin area, primarily due to conversions from agricultural land, and to a lesser degree, bottomland forest. Swamp forest increased from 30% to 41%, associated with conversions from bottomland hardwood forest and freshwater marsh. Overall, bottomland forest decreased 38% and total wetland area increased 21%. Within the upper Barataria, increases in total wetland area may be due to land subsidence. Based on our results, if present trends in the reduction of bottomland forest land cover were to continue, the upper Barataria Basin may have no bottomland hardwood forests left by the year 2025, as it is subjected to multiple stressors both in the higher elevations (from urbanization) and lower elevations (most likely from land subsidence). These results suggest that changes in the upper freshwater portions of coastal estuaries can be large and quite different from patterns observed in the more saline coastal margins.     

A Multilevel Modeling Approach to Assessing Regional and Local Landscape Features for Lake Classification and Assessment of Fish Growth Rates

The ecoregion and watershed frameworks are landscape-based classifications that have been used to group waterbodies with respect to measures of community structure; however, they have yet to be evaluated for grouping lakes for demographic characteristics of fish populations. We used a multilevel modeling approach to determine if variability in mean fish length at age could be partitioned by ecoregions and watersheds. For the ecoregions analysis, we then examined if within-ecoregion variability could be explained by local water quality and lake morphometry characteristics. We used data from agency surveys conducted during 1974-1984 for age 2 and 3 fish of seven common warm and coolwater fish species. Variance in mean length at age between ecoregions for all species was not significant, and between-watershed variance estimates were only significant in 3 out of 14 analyses; however, the total amount of variation between watersheds was very small (ranging from 1.8% to 3.7% of the total variance), indicating that ecoregions and watersheds were ineffective in partitioning variability in mean length at age. Within ecoregions, water quality and lake morphometric characteristics accounted for 2%-23% of the variation in mean length at age. Measures of lake productivity were the most common significant covariates, with mean length at age increasing with increasing lake productivity. Much of the variability in mean length at age was not accounted for, suggesting that other local factors such as biotic interactions, fish density, and exploitation are important. The results indicate that the development of an effective regional framework for managing inland lakes will require a substantial effort to understand sources of demographic variability and that managers should not rely solely on ecoregions or watersheds for grouping lakes with similar growth rates.     

印染行业清洁生产技术

论述了目前印染行业水污染的现状及原因，指出调整产业结构、应用新工艺、大力提倡节约用水、清沽生产将是印染行业实现可持续发展的必由之路。     

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models. Our two research questions were: (1) are there systematic differences in lake area and land use/land cover (LULC) surrounding lakes monitored by state agencies when compared to the entire population of lakes? and (2) after controlling for the effects of lake size, does the probability of sampling vary depending on the surrounding LULC features? We examined the biases associated with surrounding LULC because of the established links between LULC and lake water quality. For all states, we found that larger lakes had a higher probability of being sampled compared to smaller lakes. Significant interactions between lake size and LULC prohibit us from drawing conclusions about the main effects of LULC; however, in general lakes that are most likely to be sampled have either high urban use, high agricultural use, high forest cover, or low wetland cover. Our analyses support the assertion that data derived from non-probability-based surveys must be used with caution when attempting to make generalizations to the entire population of interest, and that probability-based surveys are needed to ensure unbiased, accurate estimates of lake status and trends at regional to national scales.     

Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale

Regionalization frameworks cluster geographic data to create contiguous regions of similar climate, geology and hydrology by delineating land into discrete regions, such as ecoregions or watersheds, often at several spatial scales. Although most regionalization schemes were not originally designed for aquatic ecosystem classification or management, they are often used for such purposes, with surprisingly few explicit tests of the relative ability of different regionalization frameworks to group lakes for water quality monitoring and assessment. We examined which of 11 different lake grouping schemes at two spatial scales best captures the maximum amount of variation in water quality among regions for total nutrients, water clarity, chlorophyll, overall trophic state, and alkalinity in 479 lakes in Michigan (USA). We conducted analyses on two data sets: one that included all lakes and one that included only minimally disturbed lakes. Using hierarchical linear models that partitioned total variance into within-region and among-region components, we found that ecological drainage units and 8-digit hydrologic units most consistently captured among-region heterogeneity at their respective spatial scales using all lakes (variation among lake groups = 3% to 50% and 12% to 52%, respectively). However, regionalization schemes capture less among-region variance for minimally disturbed lakes. Diagnostics of spatial autocorrelation provided insight into the relative performance of regionalization frameworks but also demonstrated that region size is only partly responsible for capturing variation among lakes. These results suggest that regionalization schemes can provide useful frameworks for lake water quality assessment and monitoring but that we must identify the appropriate spatial scale for the questions being asked, the type of management applied, and the metrics being assessed.     

Comparing Hydrogeomorphic Approaches to Lake Classification

A classification system is often used to reduce the number of different ecosystem types that governmental agencies are charged with monitoring and managing. We compare the ability of several different hydrogeomorphic (HGM)—based classifications to group lakes for water chemistry/clarity. We ask: (1) Which approach to lake classification is most successful at classifying lakes for similar water chemistry/clarity? (2) Which HGM features are most strongly related to the lake classes? and, (3) Can a single classification successfully classify lakes for all of the water chemistry/clarity variables examined? We use univariate and multivariate classification and regression tree (CART and MvCART) analysis of HGM features to classify alkalinity, water color, Secchi, total nitrogen, total phosphorus, and chlorophyll a from 151 minimally disturbed lakes in Michigan USA. We developed two MvCART models overall and two CART models for each water chemistry/clarity variable, in each case comparing: local HGM characteristics alone and local HGM characteristics combined with regionalizations and landscape position. The combined CART models had the highest strength of evidence (ω_i range 0.92–1.00) and maximized within class homogeneity (ICC range 36–66%) for all water chemistry/clarity variables except water color and chlorophyll a. Because the most successful single classification was on average 20% less successful in classifying other water chemistry/clarity variables, we found that no single classification captures variability for all lake responses tested. Therefore, we suggest that the most successful classification (1) is specific to individual response variables, and (2) incorporates information from multiple spatial scales (regionalization and local HGM variables).     

应用时间-活动模式估计儿童个体NOx暴露水平

将时间-活动模式与微环境空气污染物监测相结合估计儿童个体NO_x暴露水平. 分别在北京和青岛两市各选择3所学校监测校园环境污染物,并在每个学校选择10名儿童监测其家庭居室空气中ρ(NO_x)及个体24 h NO_x暴露水平,同时收集其时间-活动信息,采用时间-活动模式估计儿童的个体暴露水平. 交通工具中的NO_x暴露水平可通过校门口空气中ρ(NO_x)和交通工具的污染水平系数(λ)估计.结果表明:利用时间-活动模式模拟的儿童个体24 h NO_x暴露水平为0.041 mg/m3,与调整后的个体暴露实测水平相关性较好(R0.785,P<0.01),差值为(-0.002　6±0.013　0) mg/m3,二者的差异无统计学意义 (t0.139,P>0.05).表明采用时间-活动模式与微环境空气质量监测结果相结合方法估计的儿童NO_x暴露水平与个体实际暴露水平一致.