Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of <Emphasis Type="Italic">Spartina alterniflora</Emphasis> under interspecies competition

The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios: the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R ² = 0.7110 and RMSE = 8.3829 μg cm^?2) on average than the best single spectral index (R ² = 0.6319 and RMSE = 9.3535 μg cm^?2), representing an increase of 10.78% in R ² and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.     

A regional algorithm to model mesozooplankton biomass along the southwestern Bay of Bengal

A three-dimensional regression analysis attempted to model mesozooplankton (MSP) biomass using sea surface temperature (SST) and chlorophyll-a (Chl-a). The study was carried out from January 2014 to July 2015 in the southwestern Bay of Bengal (BoB) and sampling was carried out on board Sagar Manjusha and Sagar Purvi. SST ranged from 26.2 to 33.1 °C while Chl-a varied from 0.04 to 6.09 μg L^?1. During the course of the study period, there was a weak correlation (r?= 0.32) between SST and Chl-a statistically. MSP biomass varied from 0.42 to 9.63 mg C m^?3 and inversely related with SST. Two kinds of approaches were adopted to develop the model by grouping seasonal datasets (four seasonal algorithms) and comprising all datasets (one annual algorithm). Among the four functions used (linear, paraboloid, the Lorentzian and the Gaussian functions), paraboloid model was best suited. The best seasonal and annual algorithms were applied in the synchronous MODIS-derived SST and Chl-a data to estimate the MSP biomass in the southwestern BoB. The modelled MSP biomass was validated with field MSP biomass and the result was statistically significant, showing maximum regression coefficient for the seasonal algorithms (R²?=?0.60; p?=?0.627; α?= 0.05), than the annual algorithm (R²?=?0.52; p?=?0.015, α?=?0.05).     

Predicting soil water content at − 33 kPa by pedotransfer functions in stoniness 1 soils in northeast Venezuela

Soil water content is a key property in the study of water available for plants, infiltration, drainage, hydraulic conductivity, irrigation, plant water stress and solute movement. However, its measurement consumes time and, in the case of stony soils, the presence of stones difficult to determinate the water content. An alternative is the use of pedotransfer functions (PTFs), as models to predict these properties from readily available data. The present work shows a comparison of different widely used PTFs to estimate water content at-33 kPa (WR_-33kPa) in high stoniness soils. The work was carried out in the Caramacate River, an area of high interest because the frequent landslides worsen the quality of drinking water. The performance of all evaluated PTFs was compared with a PTF generated for the study area. Results showed that the Urach’s PTF presented the best performance in relation to the others and could be used to estimate WR_-33kPa in soils of Caramacate River basin. The calculated PTFs had a R² of 0.65. This was slightly higher than the R² of the Urach’s PTF. The inclusion of the rock fragment volume could have the better results. The weak performance of the other PTFs could be related to the fact that the mountain soils of the basin are rich in 2:1 clay and high stoniness, which were not used as independent variables for PTFs to estimate the WR_-33kPa.     

<Emphasis Type="Italic">Synechococcus</Emphasis> production and grazing loss rates in nearshore tropical waters

Temporal variation of Synechococcus, its production (μ) and grazing loss (g) rates were studied for 2 years at nearshore stations, i.e. Port Dickson and Port Klang along the Straits of Malacca. Synechococcus abundance at Port Dickson (0.3–2.3 × 10⁵ cell ml^?1) was always higher than at Port Klang (0.3–7.1 × 10⁴ cell ml^?1) (p < 0.001). μ ranged up to 0.98 day^?1 (0.51 ± 0.29 day^?1), while g ranged from 0.02 to 0.31 day^?1 (0.15 ± 0.07 day^?1) at Port Klang. At Port Dickson, μ and g averaged 0.47 ± 0.13 day^?1 (0.29–0.82 day^?1) and 0.31 ± 0.14 day^?1 (0.13–0.63 day^?1), respectively. Synechococcus abundance did not correlate with temperature (p > 0.25), but nutrient and light availability were important factors for their distribution. The relationship was modelled as log Synechococcus = 0.37Secchi ? 0.01DIN + 4.52 where light availability (as Secchi disc depth) was a more important determinant. From a two-factorial experiment, nutrients were not significant for Synechococcus growth as in situ nutrient concentrations exceeded the threshold for saturated growth. However, light availability was important and elevated Synechococcus growth rates especially at Port Dickson (F = 5.94, p < 0.05). As for grazing loss rates, they were independent of either nutrients or light intensity (p > 0.30). In nearshore tropical waters, an estimated 69 % of Synechococcus production could be grazed.     

Estimation of underwater visibility in coastal and inland waters using remote sensing data

An optical method is developed to estimate water transparency (or underwater visibility) in terms of Secchi depth (Z _sd ), which follows the remote sensing and contrast transmittance theory. The major factors governing the variation in Z _sd , namely, turbidity and length attenuation coefficient (1/(c + K _d ), c = beam attenuation coefficient; K _d  = diffuse attenuation coefficient at 531 nm), are obtained based on band rationing techniques. It was found that the band ratio of remote sensing reflectance (expressed as (R _rs (443) + R _rs (490))/(R _rs (555) + R _rs (670)) contains essential information about the water column optical properties and thereby positively correlates to turbidity. The beam attenuation coefficient (c) at 531 nm is obtained by a linear relationship with turbidity. To derive the vertical diffuse attenuation coefficient (K _d ) at 531 nm, K _d (490) is estimated as a function of reflectance ratio (R _rs (670)/R _rs (490)), which provides the bio-optical link between chlorophyll concentration and K _d (531). The present algorithm was applied to MODIS-Aqua images, and the results were evaluated by matchup comparisons between the remotely estimated Z _sd and in situ Z _sd in coastal waters off Point Calimere and its adjoining regions on the southeast coast of India. The results showed the pattern of increasing Z _sd from shallow turbid waters to deep clear waters. The statistical evaluation of the results showed that the percent mean relative error between the MODIS-Aqua-derived Z _sd and in situ Z _sd values was within ±25%. A close agreement achieved in spatial contours of MODIS-Aqua-derived Z _sd and in situ Z _sd for the month of January 2014 and August 2013 promises the model capability to yield accurate estimates of Z _sd in coastal, estuarine, and inland waters. The spatial contours have been included to provide the best data visualization of the measured, modeled (in situ), and satellite-derived Z _sd products. The modeled and satellite-derived Z _sd values were compared with measurement data which yielded RMSE = 0.079, MRE = ?0.016, and R ²  = 0.95 for the modeled Z _sd and RMSE = 0.075, MRE = 0.020, and R ²  = 0.95 for the satellite-derived Z _sd products.     

A glimpse at short-term controls of evapotranspiration along the southern slopes of Kilimanjaro

Future climate characteristics of the southern Kilimanjaro region, Tanzania, are mainly determined by local land-use and global climate change. Reinforcing increasing dryness throughout the twentieth century, ongoing land transformation processes emphasize the need for a proper understanding of the regional-scale water budget and possible implications on related ecosystem functioning and services. Here, we present an analysis of scintillometer-based evapotranspiration (ET) covering seven distinct habitat types across a massive climate gradient from the colline savanna woodlands to the upper-mountain Helichrysum zone (940 to 3960 m.a.s.l.). Random forest-based mean variable importance indicates an outstanding significance of net radiation (R _net) on the observed ET across all elevation levels. Accordingly, topography and frequent cloud/fog events have a dampening effect at high elevations, whereas no such constraints affect the energy and moisture-rich submontane coffee/grassland level. By contrast, long-term moisture availability is likely to impose restrictions upon evapotranspirative net water loss in savanna, which particularly applies to the pronounced dry season. At plot scale, ET can thereby be approximated reasonably using R _net, soil heat flux, and to a lesser degree, vapor pressure deficit and rainfall as predictor variables (R ² 0.59 to 1.00). While multivariate regression based on pooled meteorological data from all plots proves itself useful for predicting hourly ET rates across a broader range of ecosystems (R ² = 0.71), additional gains in explained variance can be achieved when vegetation characteristics as seen from the NDVI are considered (R ² = 0.87). To sum up, our results indicate that valuable insights into land cover-specific ET dynamics, including underlying drivers, may be derived even from explicitly short-term measurements in an ecologically highly diverse landscape.     

Seasonal variations and source profile of <Emphasis Type="Italic">n</Emphasis>-alkanes in particulate matter (PM<Subscript>10</Subscript>) at a heavy traffic site,Delhi

Delhi is one of the most polluted cities in the world. The generation of aerosols in the lower atmosphere of the city is mainly due to a large amount of natural dust advection and sizable anthropogenic activities. The compositions of organic compounds in aerosols are highly variable in this region and need to be investigated thoroughly. Twenty-four-hour sampling to assess concentrations of n-alkanes (ng/m³) in PM₁₀ was carried out during January 2015 to June 2015 at Indira Gandhi Delhi Technical University for Women (IGDTUW) Campus, Delhi, India. The total average concentration of n-alkanes, 243.7 ± 5.5 ng/m³, along with the diagnostic tools has been calculated. The values of CPI₁, CPI₂, and CPI₃ for the whole range of n-alkanes series, petrogenic n-alkanes, and biogenic n-alkanes were 1.00, 1.02, and 1.04, respectively, and C _max were at C₂₅ and C₂₇. Diagnostic indices and curves indicated that the dominant inputs of n-alkanes are from petrogenic emissions, with lower contribution from biogenic emissions. Significant seasonal variations were observed in average concentrations of n-alkanes, which is comparatively higher in winter (187.4 ± 4.3 ng/m³) than during the summer season (56.3 ± 1.1 ng/m³).     

<Emphasis Type="Italic">Mazzaella laminarioides</Emphasis> and <Emphasis Type="Italic">Sarcothalia crispata</Emphasis> as possible bioindicators of heavy metal contamination in the marine coastal zone of Chile

The suitability of Mazzaella laminarioides and Sarcothalia crispata as heavy metal biomonitors of Cd, Cu, Hg, Pb, and Zn was assessed by comparing bioaccumulation of these elements in different life stages and frond sizes in samples from three locations, San Vicente Bay (industrial area), Coliumo, and Quidico (the latter as a reference station), where different degrees of heavy metal pollution are recorded. Bioaccumulation and bioconcentration factors of Cd, Cu, Hg, Pb, and Zn were evaluated. The two macroalgae species showed similar patterns, with higher values of Cu, Hg, Pb, and Zn in polluted areas. M. laminarioides bioaccumulated higher concentrations of all metals assessed than S. crispata, independent of life stage and frond size. The results also showed significantly higher Cu, Hg, Pb, and Zn concentrations (p < 0.05) in water samples from San Vicente Bay than those measured in Coliumo and Quidico. Concentrations of Cd, Hg, Pb, and Zn in San Vicente Bay and Cd, Hg, and Pb in Coliumo and Quidico exceed the mean values considered to represent natural concentrations (Cu = 3.00 μg L^?1; Zn = 5.00 μg L^?1; Pb = 0.03 μg L^?1; Cd = 0.05 μg L^?1; Hg = 0.05 μg L^?1); however, the concentrations recorded do not cause negative effects on the growth and survival of macroalgae. The assessment of heavy metals bioaccumulated in M. laminarioides and S. crispata, particularly Hg, Pb, and Zn, offers a reliable approach for pollution assessment in rocky intertidal environments. Cu and Cd concentrations in seawater samples from San Vicente and Coliumo Bays were significantly higher than in those from Quidico (p value < 0.05); no significant differences in Cd concentrations were observed between San Vicente and Coliumo Bays (p < 0.05). Exceptionally, Cd is bioaccumulated at high levels independent of its availability in the water, thus reaching high concentrations in control areas. High concentrations of metals like Cu and Zn may limit or inhibit Cd uptake in macroalgae, since the transport channels are saturated by some metals, reducing the accumulation of others. These macroalgae species offer good potential for the development of suitable heavy metal pollution survey tools in rocky intertidal environments.     

Investigation of polar organic chemical integrative sampler (POCIS) flow rate dependence for munition constituents in underwater environments

Munition constituents (MC) are present in aquatic environments throughout the world. Potential for fluctuating release with low residence times may cause concentrations of MC to vary widely over time at contaminated sites. Recently, polar organic chemical integrative samplers (POCIS) have been demonstrated to be valuable tools for the environmental exposure assessment of MC in water. Flow rate is known to influence sampling by POCIS. Because POCIS sampling rates (R_s) for MC have only been determined under quasi-static conditions, the present study evaluated the uptake of 2,4,6-trinitrotoluene (TNT), RDX (hexahydro-1,3,5-trinitro-1,3,5-triazine), and 2,4- and 2,6-dinitrotoluenes (DNT), by POCIS in a controlled water flume at 7, 15, and 30 cm/s in 10-day experiments using samplers both within and without a protective cage. Sampling rate increased with flow rate for all MC investigated, but flow rate had the strongest impact on TNT and the weakest impact on RDX. For uncaged POCIS, mean R_s for 30 cm/s was significantly higher than that for 7 cm by 2.7, 1.9, 1.9, and 1.3 folds for TNT, 2,4-DNT, 2,6-DNT, and RDX, respectively. For all MC except RDX, mean R_s for caged POCIS at 7 cm/s were significantly lower than for uncaged samplers and similar to those measured at quasi-static condition, but except for 2,6-DNT, no caging effect was measured at the highest flow rate, indicating that the impact of caging on R_s is flow rate-dependent. When flow rates are known, flow rate-specific R_s should be used for generating POCIS-derived time-averaged concentrations of MC at contaminated sites.     

Small drinking water systems under spatiotemporal water quality variability: a risk-based performance benchmarking framework

Traditional approaches for benchmarking drinking water systems are binary, based solely on the compliance and/or non-compliance of one or more water quality performance indicators against defined regulatory guidelines/standards. The consequence of water quality failure is dependent on location within a water supply system as well as time of the year (i.e., season) with varying levels of water consumption. Conventional approaches used for water quality comparison purposes fail to incorporate spatiotemporal variability and degrees of compliance and/or non-compliance. This can lead to misleading or inaccurate performance assessment data used in the performance benchmarking process. In this research, a hierarchical risk-based water quality performance benchmarking framework is proposed to evaluate small drinking water systems (SDWSs) through cross-comparison amongst similar systems. The proposed framework (R _WQI framework) is designed to quantify consequence associated with seasonal and location-specific water quality issues in a given drinking water supply system to facilitate more efficient decision-making for SDWSs striving for continuous performance improvement. Fuzzy rule-based modelling is used to address imprecision associated with measuring performance based on singular water quality guidelines/standards and the uncertainties present in SDWS operations and monitoring. This proposed R _WQI framework has been demonstrated using data collected from 16 SDWSs in Newfoundland and Labrador and Quebec, Canada, and compared to the Canadian Council of Ministers of the Environment WQI, a traditional, guidelines/standard-based approach. The study found that the R _WQI framework provides an in-depth state of water quality and benchmarks SDWSs more rationally based on the frequency of occurrence and consequence of failure events.     

The distribution and enrichment characteristics of copper in soil and <Emphasis Type="Italic">Phragmites australis</Emphasis> of Liao River estuary wetland

The aims of the present investigation were to reveal the distribution and enrichment characteristics of copper in soil and Phragmites australis of Liao River estuary wetland. The concentrations of copper in root, stem, leaf, and ear of Phragmites australis as well as in soil were determined to study the absorption capacity of copper by wild Phragmites australis of Liao River estuary wetland. The study was carried out at test pool of the Shenyang Agricultural University, and the experimental materials (soil, irrigating water and Phragmites australis) were derived from Liao River estuary wetland. The concentrations of copper in soil and Phragmites australis were 16.4441 to 49.0209 mg/kg and 0.8621 to 89.5524 mg/kg, respectively. The results indicated that the enrichment coefficients of copper in different tissues of Phragmites australis changed with the growth of Phragmites australis. The results revealed that the enrichment coefficients of copper in the whole Phragmites australis were greater than 1 at each growing stage of the Phragmites australis. The results also showed that the transfer coefficients of Phragmites australis to copper changed with the growth of Phragmites australis. The results revealed that the Phragmites australis had a good removal effect on copper from soil and had some characteristics of copper hyperaccumulator.     

Assessment of heavy metal pollution in vegetables and relationships with soil heavy metal distribution in Zhejiang province,China

There are increasing concerns on heavy metal contaminant in soils and vegetables. In this study, we investigated heavy metal pollution in vegetables and the corresponding soils in the main vegetable production regions of Zhejiang province, China. A total of 97 vegetable samples and 202 agricultural soil samples were analyzed for the concentrations of Cd, Pb, As, Hg, and Cr. The average levels of Cd, Pb, and Cr in vegetable samples [Chinese cabbage (Brassica campestris spp. Pekinensis), pakchoi (Brassica chinensis L.), celery (Apium graveolens), tomato (Lycopersicon esculentum), cucumber (Colletotrichum lagenarium), cowpea (Vigna unguiculata), pumpkin (Cucurbita pepo L.), and eggplant (Solanum melongena)] were 0.020, 0.048, and 0.043 mg kg^?1, respectively. The Pb and Cr concentrations in all vegetable samples were below the threshold levels of the Food Quality Standard (0.3 and 0.5 mg kg^?1, respectively), except that two eggplant samples exceeded the threshold levels for Cd concentrations (0.05 mg kg^?1). As and Hg contents in vegetables were below the detection level (0.005 and 0.002 mg kg^?1, respectively). Soil pollution conditions were assessed in accordance with the Chinese Soil Quality Criterion (GB15618-1995, Grade II); 50 and 68 soil samples from the investigated area exceeded the maximum allowable contents for Cd and Hg, respectively. Simple correlation analysis revealed that there were significantly positive correlations between the metal concentrations in vegetables and the corresponding soils, especially for the leafy and stem vegetables such as pakchoi, cabbage, and celery. Bio-concentration factor values for Cd are higher than those for Pb and Cr, which indicates that Cd is more readily absorbed by vegetables than Pb and Cr. Therefore, more attention should be paid to the possible pollution of heavy metals in vegetables, especially Cd.     

A synoptic survey of select wastewater-tracer compounds and the pesticide imidacloprid in Florida’s ambient freshwaters

Current wastewater treatment technologies do not remove many unregulated hydrophilic compounds, and there is growing interest that low levels of these compounds, referred to as emerging contaminants, may impact human health and the environment. A probabilistic-designed monitoring network was employed to infer the extent of Florida’s ambient freshwaters containing the wastewater (Includes reuse water, septic systems leachate, and wastewater treatment effluent.) indicators sucralose, acetaminophen, carbamazepine, and primidone and those containing the widely used pesticide imidacloprid. Extent estimates with 95% confidence bounds are provided for canals, rivers, streams, small and large lakes, and unconfined aquifers containing ultra-trace concentrations of these compounds as based on analyses of 2015 sample surveys utilizing 528 sites. Sucralose is estimated to occur in greater than 50% of the canal, river, stream, and large lake resource extents. The pharmaceuticals acetaminophen, carbamazepine, and primidone are most prevalent in rivers, with approximately 30% of river kilometers estimated to contain at least one of these compounds. Imidacloprid is estimated to occur in 50% or greater of the canal and river resource extents, and it is the only compound found to exceed published toxicity or environmental effects standards. Geospatial analyses show sucralose detection frequencies within Florida’s drainage basins to be significantly related to the percentage of urban land use (R²?=?0.36, p?<?0.001), and imidacloprid detection frequencies to be significantly related to the percentage of urban and agricultural land use (R²?=?0.47, p?<?0.001). The extent of the presence of these compounds highlights the need for additional emerging contaminant studies especially those examining effects on aquatic biota.     

Monitoring and measurement of microalgae using the first derivative of absorbance and comparison with chlorophyll extraction method

Monitoring of microalgae in water supplies and industrial applications are becoming increasingly important, yet there are few options available that are simple and accurate, and can provide real-time information. The present work illustrates a new method to determine the concentration of microalgae in water and wastewater using spectrophotometry and the first derivative of absorbance. Chlorella vulgaris was used as an indicator microalga, spiked in water samples representing a range of water qualities (distilled water, surface water, and wastewater), and correlations among C. vulgaris concentrations, absorbance, and the first derivative of absorbance measurements were investigated. In addition, detection limits were established and sensitivity analyses were carried out to determine the lowest C. vulgaris concentrations that can be confidently measured in different water matrices. Finally, the study compared the performance and detection limits of the spectrophotometry-based methods with the well-accepted chlorophyll extraction method. A strong linear relationship (R²?>?0.97) was found between C. vulgaris concentration and absorbance at 695 nm. Using the first derivative of absorbance improved C. vulgaris detection limits by reducing the effects of the background noise and interferences from other substances. The detection limits established using the first derivative method were 0.47, 0.56, and 1.96 mg TVS/L in distilled water, surface water, and wastewater, respectively. In comparison, the detection limits of the chlorophyll extraction method were found to be 19.6, 38.6, and 48.3 mg TVS/L in the same water matrices. These results indicate that first derivative of absorbance can be successfully used for monitoring of microalgae in surface waters and environmental samples as well as in bioreactors used for microalgae cultivation in industrial applications.     

Microplankton dynamics of a coastal lagoon,Chilika: interactive effect of environmental parameters on microplankton groups

Microplankton population of Asia’s largest coastal lagoon Chilika was studied for five major groups, bacillariophyceae, cyanophyceae, chlorophyceae, dinophyceae, rotifera, and tintinninae. The study reported presence of 233 species of microplankton whose average annual abundance was 1631 cells/l. The physicochemical parameters contributing to the spatio-temporal fluctuations in microplankton diversity, abundance, and community structure were identified as salinity, pH, DO, nitrate, and silicate. Salinity, transparency, depth, and silicate most explained the abundance of bacillariophyceae; nitrate, pH, and DO influenced cyanophyceae; salinity, transparency, and chlorophyll concentration influenced chlorophyceae; salinity, depth, and water temperature influenced dinophyceae; salinity, free CO2, and nitrate-influenced rotifers, whereas salinity, pH, DO, and depth influenced tintinnids. Biotic-abiotic relationships revealed particular preference of environmental conditions at species level in groups like bacillariophyceae, cyanophyceae, and dinophyceae. Although the lagoon is shallow, bacillariophyceae-environment interaction showed depth can be a critical factor for species like Aulocoseira sp., Amphipleura sp., and Rhophalodia sp. Species of dinoflagellates like Dinophysis caudata, Noctiluca scintillans, and Protoperidinium proliferated in lower level of silicate. Unlike other cyanophyceae species Streptococcus sp., Chroococcus sp., Diplococcus sp., Aphanocapsa sp., and Gloeocapsa sp. were negatively influenced by nitrate concentration. The study provides better scope for ecological management of the lagoon with respect to conserving biodiversity and hydrological quality of the ecosystem.     

Optimizing the flow adjustment of constituent concentrations via LOESS for trend analysis

Trend analysis of stream constituent concentrations requires adjustment for exogenous variables like discharge because concentrations often have variable relations with flow. To remove the influence of flow on stream water quality data, an accurate characterization of the relationship between the constituent and streamflow is needed. One popular method, locally weighted regression (LOESS), provides an effective means for flow-adjusting concentrations. The LOESS fit can be tailored to the data via the smoothing parameter (f), so that the user can avoid overfitting or oversmoothing the data. However, it is a common practice to use a single f value when flow-adjusting water quality data for trend analysis. This study provides a robust, automated method for determining the optimal f value (f_opt) for each dataset via an iterative K-fold cross-validation procedure that minimizes prediction error in LOESS. The method is developed by analyzing datasets of seven different constituents across 17 sites (119 datasets total) from a stream monitoring program in northwest Arkansas (USA). We recommend using 10 iterations of 10-fold cross-validation (10?×?10 CV) in order to select f_opt when flow-adjusting water quality data with LOESS. The use of a default f value did not produce different trend interpretations for the data used here; however, the proposed approach may be helpful in other water quality studies which employ similar statistical fitting methods. Additionally, we provide an implementation of the method in the R statistical computing environment.     

Discriminant analysis for the prediction of sand mass distribution in an urban stormwater holding pond using simulated depth average flow velocity data

The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m³ s^?1, representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size?>?0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5 %. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20 %) compared to group 2 (16.90 to 45.55 %). Two Fisher’s linear discriminant functions, F ₁ and F ₂, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F ₁?=??9.405?+?4232.119?×?A???1795.805?×?B?+?281.224?×?C, and F ₂?=??2.842?+?2725.137?×?A???1307.688?×?B?+?231.353?×?C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m³ s^?1, respectively. The model correctly predicts 88.9 and 100.0 % of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8 % are correctly classified. The model predicts that 31.4 % of the model domain areas consist of high-sand mass composition areas and the remaining 68.6 % comprise low-sand mass composition areas.     

Effects of field storage method on <Emphasis Type="Italic">E. coli</Emphasis> concentrations measured in storm water runoff

Storm water runoff is increasingly assessed for fecal indicator organisms (e.g., Escherichia coli, E. coli) and its impact on contact recreation. Concurrently, use of autosamplers along with logistic, economic, technical, and personnel barriers is challenging conventional protocols for sample holding times and storage conditions in the field. A common holding time limit for E. coli is 8 h with a 10 °C storage temperature, but several research studies support longer hold time thresholds. The use of autosamplers to collect E. coli water samples has received little field research attention; thus, this study was implemented to compare refrigerated and unrefrigerated autosamplers and evaluate potential E. coli concentration differences due to field storage temperature (storms with holding times ≤24 h) and due to field storage time and temperature (storms >24 h). Data from 85 runoff events on four diverse watersheds showed that field storage times and temperatures had minor effects on mean and median E. coli concentrations. Graphs and error values did, however, indicate a weak tendency for higher concentrations in the refrigerated samplers, but it is unknown to what extent differing die-off and/or regrowth rates, heterogeneity in concentrations within samples, and laboratory analysis uncertainty contributed to the results. The minimal differences in measured E. coli concentrations cast doubt on the need for utilizing the rigid conventional protocols for field holding time and storage temperature. This is not to say that proper quality assurance and quality control is not important but to emphasize the need to consider the balance between data quality and practical constraints related to logistics, funding, travel time, and autosampler use in storm water studies.     

Continuous ‘Passive’ flow-proportional monitoring of drainage using a new modified Sutro weir (MSW) unit

In view of their crucial role in water and solute transport, enhanced monitoring of agricultural subsurface drain tile systems is important for adequate water quality management. However, existing monitoring techniques for flow and contaminant loads from tile drains are expensive and labour intensive. The aim of this study was to develop a cost-effective and simple method for monitoring loads from tile drains. The Flowcap is a modified Sutro weir (MSW) unit that can be attached to the outlet of tile drains. It is capable of registering total flow, contaminant loads and flow-averaged concentrations. The MSW builds on a modern passive sampling technique that responds to hydraulic pressure and measures average concentrations over time (days to months) for various substances. Mounting the samplers in the MSW allowed a flow-proportional part of the drainage to be sampled. Laboratory testing yielded high linear correlation between the accumulated sampler flow, q _total, and accumulated drainage flow, Q _total (r ²?>?0.96). The slope of these correlations was used to calculate the total drainage discharge from the sampled volume, and therefore contaminant load. A calibration of the MSW under controlled laboratory condition was needed before interpretation of the monitoring results was possible. The MSW does not require a shed, electricity, or maintenance. This enables large-scale monitoring of contaminant loads via tile drains, which can improve contaminant transport models and yield valuable information for the selection and evaluation of mitigation options to improve water quality. Results from this type of monitoring can provide data for the evaluation and optimisation of best management practices in agriculture in order to produce the highest yield without water quality and recipient surface waters being compromised.     

Yield and leaf area index estimations for sunflower plants using unmanned aerial vehicle images

Vegetation is commonly monitored to improve efficiency of various agricultural practices. Spatial and temporal changes in plant growth and development can be monitored with the aid of remote sensing techniques employing ground, aerial, and satellite platforms. Unmanned aerial vehicles (UAV) and multi-spectral cameras developed for UAVs have an important potential for agricultural management activities with high-resolution spatial and temporal images. However, UAV images should be assessed based on ground measurements for using these images as a decision-support tool in agriculture. This study was conducted to estimate sunflower leaf area index (LAI) and yield with the aid of Normalized Difference Vegetation Index (NDVI) images generated from raw UAV images. Furthermore, UAV-based NDVI values were compared with NDVI values calculated by using hyper-spectral measurements carried out with a ground-based spectroradiometer. Between July and August of 2017, six flight missions were conducted and spectral measurements were made simultaneously. A significant correlation (R²?=?0.77) was determined between NDVI values that belong to UAV platform and spectroradiometer. Also, regression models developed for sunflower LAI and yield estimation depending UAV-based NDVI have R² values of 0.88 and 0.91, respectively.