The influence of suburban land use on habitat and biotic integrity of coastal Rhode Island streams

Watershed land use in suburban areas can affect stream biota through degradation of instream habitat, water quality, and riparian vegetation. By monitoring stream biotic communities in various geographic regions, we can better understand and conserve our watershed ecosystems. The objective of this study was to examine the relationship between watershed land use and the integrity of benthic invertebrate communities in eight streams that were assessed over a 3-year period (2001-2003). Sites were selected from coastal Rhode Island watersheds along a residential land-use gradient (4-59%). Using the rapid bioassessment protocol, we collected biological, physicochemical, habitat, and nutrient data from wadeable stream reaches and compared metrics of structure and integrity. Principal component analyses showed significant negative correlation of indicators for stream physicochemical, habitat, and instream biodiversity with increasing residential land use (RLU) in the watershed. The physicochemical variables that were most responsive to percent RLU were conductivity, instream habitat, nitrate, and dissolved inorganic nitrogen (DIN). The positive correlation of DIN with percent RLU indicated an anthropogenic source of pollution affecting the streams. The biotic composition of the streams shifted from sensitive to insensitive taxa as percent RLU increased; the most responsive biological variables were percent Ephemeroptera, percent Scrapers, percent Insects, and the Hilsenhoff biotic index. These data show the importance of land management and conservation at the watershed scale to sustaining the biotic integrity of coastal stream ecosystems.     

Lead forms in urban turfgrass and forest soils as related to organic matter content and pH

Identification of reference streams and human disturbance gradients are crucial steps in assessing the effects of human disturbances on stream health. We describe a process for identifying reference stream reaches and assessing disturbance gradients using readily available, geo-referenced stream and human disturbance databases. We demonstrate the utility of this process by applying it to wadeable streams in Michigan, USA, and use it to identify which human disturbances have the greatest impact on streams. Approximately 38% of cold-water and 16% of warm-water streams in Michigan were identified as being in least-disturbed condition. Conversely, approximately 3% of cold-water and 4% of warm-water streams were moderately to severely disturbed by landscape human disturbances. Anthropogenic disturbances that had the greatest impact on moderately to severely disturbed streams were nutrient loading and percent urban land use within network watersheds. Our process for assessing stream health represents a significant advantage over other routinely used methods. It uses inter-confluence stream reaches as an assessment unit, permits the evaluation of stream health across large regions, and yields an overall disturbance index that is a weighted sum of multiple disturbance factors. The robustness of our approach is linked to the scale of disturbances that affect a stream; it will be less robust for identifying less degraded or reference streams with localized human disturbances. With improved availability of high-resolution disturbance datasets, this approach will provide a more complete picture of reference stream reaches and factors contributing to degradation of stream health.     

Landscape as a predictor of wetland condition: an evaluation of the Landscape Development Index (LDI) with a large reference wetland dataset from Ohio

RecentAbstract. Recent approaches to wetland assessment have advocated a multilevel approach which incorporates assessments based on landscape (remote sensing) data, on-site but “rapid” methods, and intensive methods where quantitative data is collected. Brown and Vivas (2004) recently pro- posed an assessment method that uses remote sensing information (Landscape Development Index or LDI) and propose that it may also be usable as a quantified human disturbance gradient. The LDI was evaluated using a large reference wetland data set from Ohio using land use percentages within a 1 km radius circle of the wetlands. The LDI had interpretable and significant relationships with another human disturbance gradient (the Ohio Rapid Assessment Method for Wetlands or ORAM) and with most metrics and scores from the Vegetation Index of Biotic Integrity (VIBI) developed for use in the State of Ohio. Metrics from emergent wetlands had the most significant correlations with the LDI (10 of 10 metrics), followed by forested wetlands (8 of 10 metrics) and shrub wetlands (4 of 10). Poor correlation for VIBI scores and metrics of shrub wetlands was due to differences in attainable LDI scores based on ecoregion and natural buffers shielding the wetland from otherwise intensive land uses. The ORAM and VIBI were developed for use in wetlands in Ohio completely independent of the LDI. It is an important test of the LDI concept that so many interpretable and significant relationships occurred between the VIBI scores, VIBI metric values, and the ORAM scores. For the purposes of VIBI development, the LDI is an independent, quantified disturbance gradient that has provided an additional test of the VIBI. Given its theoretical underpinnings and the fact that it uses quantified land use percentages, the LDI has many advantages over more qualita- tive human disturbance gradients. Using land use percentages from increasingly smaller distances from the wetland edge (100-200 m) may improve the resolution of the LDI to detect on-site dis-turbances to a wetland which degrade its ecological condition. The LDI should be evaluated with other large reference data sets in other regions to evaluate its validity and usefulness as an assessment tool.     

Assessing the soil quality of alpine grasslands in the Qinghai-Tibetan Plateau using a modified soil quality index

Soil degradation has caused various problems on the planet. Human disturbance and land use changes always negatively affect soil quality. In this study, we used a modified soil quality index (SQI) to assess soil quality under differing degrees of human disturbance and land use. The alpine grasslands were studied at different levels of degradation [i.e., severely degraded grassland, heavily degraded grassland, moderately degraded grassland, and non-degraded grassland (NDG)] in a case study conducted in Qinghai-Tibetan Plateau (QTP) to test the feasibility of using the SQI. Fifteen chemical, physical, and biological soil parameters were measured in each type of grassland. Significant variations in SQI were found across the different types of grasslands according to severity of human disturbance and changes in land use. Urease, the ratio of microbial biomass nitrogen to total nitrogen, proteinase, and soil organic carbon were found to be the most important indicators for assessing soil quality. NDG had a higher SQI than the other three types of grasslands. It was concluded that SQI is effective for assessing the soil quality of alpine grasslands in the QTP. The intensity of human disturbance had a negative effect on soil quality in the QTP.     

Effect of land use land cover change on soil erosion potential in an agricultural watershed

Universal soil loss equation (USLE) was used in conjunction with a geographic information system to determine the influence of land use and land cover change (LUCC) on soil erosion potential of a reservoir catchment during the period 1989 to 2004. Results showed that the mean soil erosion potential of the watershed was increased slightly from 12.11 t ha^???1 year^???1 in the year 1989 to 13.21 t ha^???1 year^???1 in the year 2004. Spatial analysis revealed that the disappearance of forest patches from relatively flat areas, increased in wasteland in steep slope, and intensification of cultivation practice in relatively more erosion-prone soil were the main factors contributing toward the increased soil erosion potential of the watershed during the study period. Results indicated that transition of other land use land cover (LUC) categories to cropland was the most detrimental to watershed in terms of soil loss while forest acted as the most effective barrier to soil loss. A p value of 0.5503 obtained for two-tailed paired t test between the mean erosion potential of microwatersheds in 1989 and 2004 also indicated towards a moderate change in soil erosion potential of the watershed over the studied period. This study revealed that the spatial location of LUC parcels with respect to terrain and associated soil properties should be an important consideration in soil erosion assessment process.     

Morphometry and land cover based multi-criteria analysis for assessing the soil erosion susceptibility of the western Himalayan watershed

Complex mountainous environments such as Himalayas are highly susceptibility to natural hazards particular those that are triggered by the action of water such as floods, soil erosion, mass movements and siltation of the hydro-electric power dams. Among all the natural hazards, soil erosion is the most implicit and the devastating hazard affecting the life and property of the millions of people living in these regions. Hence to review and devise strategies to reduce the adverse impacts of soil erosion is of utmost importance to the planners of watershed management programs in these regions. This paper demonstrates the use of satellite based remote sensing data coupled with the observational field data in a multi-criteria analytical (MCA) framework to estimate the soil erosion susceptibility of the sub-watersheds of the Rembiara basin falling in the western Himalaya, using geographical information system (GIS). In this paper, watershed morphometry and land cover are used as an inputs to the MCA framework to prioritize the sub-watersheds of this basin on the basis of their different susceptibilities to soil erosion. Methodology included the derivation of a set of drainage and land cover parameters that act as the indicators of erosion susceptibility. Further the output from the MCA resulted in the categorization of the sub-watersheds into low, medium, high and very high erosion susceptibility classes. A detailed prioritization map for the susceptible sub-watersheds based on the combined role of land cover and morphometry is finally presented. Besides, maps identifying the susceptible sub-watersheds based on morphometry and land cover only are also presented. The results of this study are part of the watershed management program in the study area and are directed to instigate appropriate measures to alleviate the soil erosion in the study area.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Application of a benthic observed/expected-type model for assessing Central Appalachian streams influenced by regional stressors in West Virginia and Kentucky

Stream bioassessments rely on taxonomic composition at sites compared with natural, reference conditions. We developed and tested an observed/expected (O/E) predictive model of taxonomic completeness and an index of compositional dissimilarity (BC index) for Central Appalachian streams using combined macroinvertebrate datasets from riffle habitats in West Virginia (WV) and Kentucky (KY). A total of 102 reference sites were used to calibrate the O/E model, which was then applied to assess over 1,200 sites sampled over a 10-year period. Using an all subsets discriminant function analysis (DFA) procedure, we tested combinations of 14 predictor variables that produced DF and O/E models of varying performance. We selected the most precise model using a probability of capture at >0.5 (O/E _0.5, SD?=?0.159); this model was constructed with only three simple predictor variables—Julian day, latitude, and whether a site was in ecoregion 69a. We evaluated O/E and BC indices between reference and test sites and compared their response to regional stressors, including coal mining, residential development, and acid deposition. The Central Appalachian O/E and BC indices both showed excellent discriminatory power and were significantly correlated to a variety of regional stressors; in some instances, the BC index was slightly more sensitive and responsive than the O/E _0.5 model. These indices can be used to supplement existing bioassessment tools crucial to detecting and diagnosing stream impacts in the Central Appalachian region of WV and KY.     

The effects of biotic and abiotic factors on the spatial heterogeneity of alpine grassland vegetation at a small scale on the Qinghai–Tibet Plateau (QTP), China

Understanding the complex effects of biotic and abiotic factors on the composition of vegetation is very important for developing and implementing strategies for promoting sustainable grassland development. The vegetation–disturbance–environment relationship was examined in degraded alpine grasslands in the headwater areas of three rivers on the Qinghai–Tibet Plateau in this study. The investigated hypotheses were that (1) the heterogeneity of the vegetation of the alpine grassland is due to a combination of biotic and abiotic factors and that (2) at a small scale, biotic factors are more important for the distribution of alpine vegetation. On this basis, four transects were set along altitudinal gradients from 3,770 to 3,890 m on a sunny slope, and four parallel transects were set along altitudinal gradients on a shady slope in alpine grasslands in Guoluo Prefecture of Qinghai Province, China. It was found that biological disturbances were the major forces driving the spatial heterogeneity of the alpine grassland vegetation and abiotic factors were of secondary importance. Heavy grazing and intensive rat activity resulted in increases in unpalatable and poisonous weeds and decreased fine forages in the form of sedges, forbs, and grasses in the vegetation composition. Habitat degradation associated with biological disturbances significantly affected the spatial variation of the alpine grassland vegetation, i.e., more pioneer plants of poisonous or unpalatable weed species, such as Ligularia virgaurea and Euphorbia fischeriana, were found in bare patches. Environmental/abiotic factors were less important than biological disturbances in affecting the spatial distribution of the alpine grassland vegetation at a small scale. It was concluded that rat control and light grazing should be applied first in implementing restoration strategies. The primary vegetation in lightly grazed and less rat-damaged sites should be regarded as a reference for devising vegetation restoration measures in alpine pastoral regions.     

Modeling the effect of land use/land cover on nitrogen,phosphorous and dissolved oxygen loads in the Velhas River using the concept of exclusive contribution area

Non-point source water pollution is a major problem in most parts of the world, but is also very difficult to quantify and control since it is not easily separated from point sources and can theoretically originate from the whole watershed. In this article, we evaluate the relationship between land use and land cover and four water pollution parameters in a watershed in Southeast Brazil. The four parameters are nitrate, total ammonia nitrogen, total phosphorous, and dissolved oxygen. To help concentrate on non-point source pollution, only data from the wet seasons of the time period (2001–2013) were analysed, based on the fact that precipitation causes runoff which is the main cause of diffuse pollution. The parameters measured were transformed into loads, which were in turn associated with an exclusive contribution area, so that every measuring station could be considered independent. Analyses were also performed on riparian zones of different widths to verify if the effect of the land cover on the water quality of the stream decreases with the increased distance. Pearson correlation coefficients indicate that urban areas and agriculture/pasture tend to worsen water quality (source). Conversely, forest and riparian areas have a reducing effect on pollution (sink). The best results were obtained for total ammonia nitrogen and dissolved oxygen using the whole exclusive contribution areas with determination coefficients better than R²≈0.8. Nitrate and total phosphorous did not produce valid models. We suspect that the transformation delay from total ammonia nitrogen to nitrate might be an important factor for the poor result for this parameter. For phosphorous, we think that the phosphorous sink in the bottom sediment might be the most limiting factor explaining the failure of our models.     

Predicting the biological condition of streams: use of geospatial indicators of natural and anthropogenic characteristics of watersheds

We developed and evaluated empirical models to predict biological condition of wadeable streams in a large portion of the eastern USA, with the ultimate goal of prediction for unsampled basins. Previous work had classified (i.e., altered vs. unaltered) the biological condition of 920 streams based on a biological assessment of macroinvertebrate assemblages. Predictor variables were limited to widely available geospatial data, which included land cover, topography, climate, soils, societal infrastructure, and potential hydrologic modification. We compared the accuracy of predictions of biological condition class based on models with continuous and binary responses. We also evaluated the relative importance of specific groups and individual predictor variables, as well as the relationships between the most important predictors and biological condition. Prediction accuracy and the relative importance of predictor variables were different for two subregions for which models were created. Predictive accuracy in the highlands region improved by including predictors that represented both natural and human activities. Riparian land cover and road-stream intersections were the most important predictors. In contrast, predictive accuracy in the lowlands region was best for models limited to predictors representing natural factors, including basin topography and soil properties. Partial dependence plots revealed complex and nonlinear relationships between specific predictors and the probability of biological alteration. We demonstrate a potential application of the model by predicting biological condition in 552 unsampled basins across an ecoregion in southeastern Wisconsin (USA). Estimates of the likelihood of biological condition of unsampled streams could be a valuable tool for screening large numbers of basins to focus targeted monitoring of potentially unaltered or altered stream segments.     

Assessing the Relative Severity of Stressors at a Watershed Scale

Water quality monitoring data are usually used independently to report on the condition of streams and watersheds. For example, watersheds are often rated as good, fair, or poor with regard to a single stressor or with regard to an index of biotic integrity. The utility of monitoring data may be enhanced by integrating stressor-response information with the observed stressor data, and reporting stressor levels in terms of their relative effects upon valued ecological resources. We estimated stressor-response relationships at the regional scale using data collected in the Eastern Cornbelt Plains Ecoregion of Ohio. Generalized additive models were used to visualize stressor-response relationships. Piecewise linear functions and simple linear functions were then used to parameterize the observed responses. Parameters derived from the regional models were used to scale observations of stressors in the Big Darby Creek watershed, OH. After scaling, stressors were compared in terms of their spatial distribution and in terms of the severity with which they influenced the biological endpoint of interest. Stressors most strongly associated with the current ecological condition of the watershed were identified. In the Big Darby Creek watershed, decreases in substrate quality were associated with the most severe decrements in biological condition. At smaller decrements in biological condition, three stressors were important: substrate quality, riparian quality, and increased concentrations of NOx.     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

Modeling Runoff Response to Land Cover and Rainfall Spatial Variability in Semi-Arid Watersheds

Hydrologic response is an integrated indicator of watershed condition, and significant changes in land cover may affect the overall health and function of a watershed. This paper describes a procedure for evaluating the effects of land cover change and rainfall spatial variability on watershed response. Two hydrologic models were applied on a small semi-arid watershed; one model is event-based with a one-minute time step (KINEROS), and the second is a continuous model with a daily time step (SWAT). The inputs to the models were derived from Geographic Information System (GIS) theme layers of USGS digital elevation models, the State Soil Geographic Database (STATSGO) and the Landsat-based North American Landscape Characterization classification (NALC) in conjunction with available literature and look up tables. Rainfall data from a network of 10 raingauges and historical stream flow data were used to calibrate runoff depth using the continuous hydrologic model from 1966 to 1974. No calibration was carried out for the event-based model, in which six storms from the same period were used in the calculation of runoff depth and peak runoff. The assumption on which much of this study is based is that land cover change and rainfall spatial variability affect the rainfall-runoff relationships on the watershed. To validate this assumption, simulations were carried out wherein the entire watershed was transformed from the 1972 NALC land cover, which consisted of a mixture of desertscrub and grassland, to a single uniform land cover type such as riparian, forest, oak woodland, mesquite woodland, desertscrub, grassland, urban, agriculture, and barren. This study demonstrates the feasibility of using widely available data sets for parameterizing hydrologic simulation models. The simulation results show that both models were able to characterize the runoff response of the watershed due to changes of land cover.     

Spatial Patterns of Water Quality in the Cuiabá River Basin, Central Brazil

This study evaluates the spatial patterns of land occupation and their relationship to water quality in the Cuiabá River watershed, one of the main affluents of the Pantanal floodplain. The impact of farming and other land occupation forms were studied using a three year time series. Monitoring included 15 parameters at 21 stations with a total of 1266 different samples. Ten stations along the Cuiabá River were ordinated by Principal Component Analysis (PCA). For an exploratory analysis in the spatial domain, sub-basins of the Cuiabá watershed were classified according to mean concentrations of selected water quality parameters. Supervised classification of digital Landsat ETM imagery and standard GIS techniques were applied to parameterize land use and occupation according to a watershed scale. Redundancy Analysis (RDA) was then used to evaluate impacts of environmental and socio-economic factors on water quality.A Cuiabá headwater station only shows slightly elevated total coliform counts and concentrations of nutrients in the river after it passes regions of extensive cattle farming. After the confluence with the Manso River, nutrient and COD concentrations increase significantly, receiving loads from sub-basins under intensive agricultural land use, with mean annual concentrations up to 1.74 mg/L of total nitrogen (Kjedahl). Sub-watersheds with intensive fishing culture activities were shown to have significant impact on nitrogen concentrations, reaching mean concentrations of 2.66 mg/L of total nitrogen in the affluents. Most serious biological and chemical water pollution can be observed at stream outlets in the urban agglomeration of Cuiabá/Várzea Grande. Affluent pollution is reflected in the water quality of the Cuiabá River: subsequent monitoring stations in the urban area are ordinated on a gradient of increasing degradation of chemical and biological water quality. The auto-depuration capacity of the Cuiabá River is intact, but elevated concentrations of Phosphorous and Chemical Oxygen Demand can be observed as far away as the Pantanal floodplain, about 120 km downstream from the urban agglomeration.     

分类学多样性指数评价生态环境的研究进展

生物多样性是环境监测环境保护的重要内容之一,在众多的生物多样性指数中,包含分类学信息的指数具有明显的特点和优势。近20年来,在海洋、淡水、陆地等生境中开展了许多分类学多样性指数相关的应用研究。一些分类学多样性指数能够在一定程度上灵敏地反映出环境退化和污染,在环境监测能力和历史资料对比等方面优于传统多样性指数。在使用分类学差异性指数区分人类干扰和自然变化时结论不一,分类学多样性指数的应用受到一些因素的影响,例如生物类群选择、生境、水深、纬度等是产生分歧的因素。扩大研究范围,明确应用范围可有效发挥分类学差异性指数在环境保护和评价中的作用。     

Land use impacts on river health of Uma Oya,Sri Lanka: implications of spatial scales

Human actions on landscapes are a principal threat to the ecological integrity of river ecosystems worldwide. Tropical landscapes have been poorly investigated in terms of the impact of catchment land cover alteration on water quality and biotic indices in comparison to temperate landscapes. Effects of land cover in the catchment at two spatial scales (catchment and site) on stream physical habitat quality, water quality, macroinvertebrate indices and community composition were evaluated for Uma Oya catchment in the upper Mahaweli watershed, Sri Lanka. The relationship between spatial arrangement of land cover in the catchment and water quality, macroinvertebrate indices and community composition was examined using univariate and multivariate approaches. Results indicate that chemical water quality variables such as conductivity and total dissolved solids are mostly governed by the land cover at broader spatial scales such as catchment scale. Shannon diversity index was also affected by catchment scale forest cover. In stream habitat features, nutrients such as N-NO₃ ^?, macroinvertebrate family richness, %shredders and macroinvertebrate community assemblages were predominantly influenced by the extent of land cover at 200 m site scale suggesting that local riparian forest cover is important in structuring macroinvertebrate communities. Thus, this study emphasizes the importance of services provided by forest cover at catchment and site scale in enhancing resilience of stream ecosystems to natural forces and human actions. Findings suggest that land cover disturbance effects on stream ecosystem health could be predicted when appropriate spatial arrangement of land cover is considered and has widespread application in the management of tropical river catchments.     

Effects of land cover changes induced by large physical disturbances on hydrological responses in Central Taiwan

This study analyzes the significant impacts of typhoons and earthquakes on land cover change and hydrological response. The occurrence of landslides following typhoons and earthquakes is a major indicator of natural disturbance. The hydrological response of the Chenyulan watershed to land use change was assessed from 1996 to 2005. Land use changes revealed by seven remote images corresponded to typhoons and a catastrophic earthquake in central Taiwan. Hydrological response is discussed as the change in quantities and statistical distributions of hydrological components. The land cover change results indicate that the proportion of landslide relative to total area increased to 6.1% after the Chi-Chi earthquake, representing the largest increase during the study period. The study watershed is dominated by forest land cover. Comparisons of hydrological components reveal that the disturbance significantly affects base flow and direct runoff. The hydrological modeling results demonstrate that the change in forest area correlates with the variation of base flow and direct runoff. Base flow and direct runoff are sensitive to land use in discussions of distinction. The proposed approach quantifies the effect of typhoons and earthquakes on land cover changes.     

Spatial distribution and transport of heavy metals in soil, ponded-surface water and grass in a pb-contaminated watershed as related to land-use practices

The aim of this study was to investigate the spatial distribution of heavy metal in soil and evaluate the dissolution of metal from soil to ponded-surface water, leaching through soil profiles and metal uptake in grass as related to different land-use practices. The data provided a scientific basis for best-management practices for land use in Khli Ti watershed. The watershed has a Pb-contamination problem from the previous operation of a Pb-ore concentrator and abandoned Zn–Pb mine. Sampling sites were selected from a land-use map, with land-use types falling into the following four categories: forest, agricultural land, residential area and road. Soil, ponded-surface water, grass samples and soil profiles were collected. The study related soil characteristics from different land-use practices and locations with observed metal concentrations in ponded-surface water and soil. High enrichment factors of Pb and As in soil were found. Partitioning coefficient, K_d values were in the order: Cr > Pb > Ni > Cu > Cd > Zn. Soil disturbance from land-use activities including tillage and traffic increased leaching of trace metal from soils. Pb in soil was significantly taken up by grass even though the Transfer Factor, TF values were rather low. Agricultural activities in the watershed must be limited. Moreover, land encroachments in the upper and middle part of the watershed which have high potential of Pb must be strictly controlled in order to reduce the Pb contamination from non-point sources.     

Evaluation of land use and water quality in an agricultural watershed in the USA indicates multiple sources of bacterial impairment

Pathogens are the number one cause of impairments of assessed rivers and streams in the USA and pose a significant human health hazard. The Dry Run Creek Watershed in Northeast Iowa has been designated as impaired by the State of Iowa because of high levels of Escherichia coli bacteria. To investigate the nature of this impairment, land use and stream bank assessments were coupled with comprehensive water quality monitoring. Physical, chemical, and biological parameters were measured at 13 different sites in the watershed, including pH, temperature, conductivity, dissolved oxygen, turbidity, total Kjeldahl nitrogen, ammonia-N, nitrate?+?nitrite-N, total phosphorus, and E. coli. In addition, benthic macroinvertebrate communities were analyzed at seven sites, and optical brightener tests were performed late in the season. Results identified segments of the watershed that were more prominent contributors of E. coli, and correlations were observed between levels of E. coli and several chemical parameters, including ammonia-N, total Kjeldahl nitrogen, and total phosphorus. Interestingly, distinct sites emerged as more prominent contributors of these elements during rain vs. non-rain events, suggesting different types of sources. Both the amount of rainfall and the time elapsed between the rain event and the sampling influenced E. coli levels during wet weather conditions. Nitrate?+?nitrite-N displayed a unique response to rain events compared with the other parameters, suggesting a different delivery route. Analyses of benthic macroinvertebrate communities were consistent with pollution trends. Collectively, these data suggest distinct agriculturally related E. coli contributions, as well as specific areas and practices for water quality improvement strategies. This study can serve as a resource for evaluating agricultural watersheds that are impaired for bacteria.