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An inadequate soil sampling time leads to difficulties in interpreting soil tests, to incorrect recommendations for soil amendments and fertilizers, and to inappropriate environmental protection restrictions. Soil samples may be collected from agricultural fields before, during, or after the crop growth period. Since the time of soil sample collection can affect soil tests results, the objective of this study was to evaluate the effect of sampling time on measurements representativity of 15 fertility indicators in two fields located in La Pocatière (Québec, Canada). The soils were of fine (G1) and medium (G2) textural groups and were sampled weekly for 33 weeks per year during four years. Data analyses included descriptive statistics, time-series decomposition, and time autocorrelation function (ACF). Since results of these analyses showed a clear seasonal effect only for Mehlich-3 extracted phosphorus (PM3), soil phosphorus saturation index (SPS) for both G1 and G2 soils, and for pHW for G1 only, we recommend that the sampling calendar should be restricted to the first five weeks of spring (until the end of May) and to the entire fall period (starting in early September). Also, the temporal autocorrelation was four weeks on average. This implies that, for an initial year, whichever date is chosen for the sampling, the following annual sampling should be done within a four-week time window (i.e., two weeks before until two weeks after the initial sampling date). Time series are an important element to consider in selecting a representative sampling period for soil fertility indicators.

To enable chemical speciation, monitoring networks collect particulate matter (PM) on different filter media, each subjected to one or more analytical techniques to quantify PM composition present in the atmosphere. In this work, we propose an alternate approach that uses one filter type (teflon or polytetrafluoroethylene, PTFE, commonly used for aerosol sampling) and one analytical method, Fourier transform infrared (FT-IR) spectroscopy to measure almost all of the major constituents in the aerosol. In the proposed method, measurements using the typical multi-filter, multi-analytical techniques are retained at a limited number of sites and used as calibration standards. At all remaining sites, only sampling on PTFE and analysis by FT-IR is performed. This method takes advantage of the sensitivity of the mid-IR domain to various organic and inorganic functional groups and offers a fast and inexpensive way of exploring sample composition. As a proof of concept, multiple years of samples collected within the Interagency Monitoring of PROtected Visual Environment network (IMPROVE) are explored with the aim of retaining high quality predictions for a broad range of atmospheric compounds including mass, organic (OC), elemental (EC), and total (TC) carbon, sulfate, nitrate, and crustal elements. Findings suggest that models based on only 21 sites, covering spatial and seasonal trends in atmospheric composition, are stable over a 3 year period within the IMPROVE network with acceptable prediction accuracy (R2 > 0.9, median bias less than 3 %) for most constituents. The major limitation is measuring nitrate as it is known to volatilize off of PTFE filters. Incorporating additional sites at low cost, partially replacing existing, more time- and cost-intensive techniques, or using the FT-IR data for quality control or substitute for missing data, are among the potential benefits of the one-filter, one-method approach.

Non-destructive methods for estimating carbon storage capacity are becoming increasingly popular as they do not harm the individual trees or the ecosystem. However, currently the destructive method of sampling trees for estimating their carbon storage capacity is widely practiced throughout the world. Therefore, the present study was conducted in a mangrove conservation forest located in a tropical island, Sri Lanka, with the objective of developing allometric equations to predict the stem carbon content of Lumnitzera racemosa and Avicennia marina using non-destructive method of sampling. The allometric model developed for Lumnitzera racemosa from this study, to determine stem carbon content is: Ln C = -3.485+ 1.155 Ln SH + 1.892 Ln DBH, Where, C: Stem carbon content, SH: merchantable stem height, DBH: Diameter at breast height. For A. marina, only diameter at breast height was statistically significant with stem carbon content and the allometric equation was, Ln C = −3.483 + 2.407 Ln DBH. The models were evaluated using p value, R2 value, residual diagram, model bias values and model efficiency values. The models were validated by calculating residual values as the difference between the actual stem carbon content and predicted stem carbon content from the models for Lumnitzera racemosa and A. marina. Further, there was no significant difference between the mean values of the measured stem carbon content and the predicted stem carbon content using the prediction models. The results indicate that the developed allometric equations in the present study are practically applicable in the field to estimate the stem carbon content of Lumnitzera racemosa and A. marina. Further, these estimations can contribute to make more accurate valuations on carbon stocks of sequestered carbon necessary for carbon trading purposes and sustainable management of mangrove forest ecosystems.

tabira has in its territory the largest complex of opencast mining in the world, located close to residential areas of the city. The air quality monitoring network installed in the city is the main source of particulate matter (PM) emission data. However, these air quality stations only cover the areas near the mines and do not measure fine particulate matter (PM2.5). Thus, a first field campaign was carried out to characterize PM in the city and to compare high volume data from air quality stations with the dichotomous air sampler data. Results of trajectories’ cluster analysis showed a long-range transport of aerosols during the sampling days from northeast (84% of the trajectories), east-southeast (12%), and south-southwest (3%) directions. Regarding the meteorological conditions during the sampling days, negative correlations were seen between coarse particulate matter (PM10) from mostly air quality stations and all meteorological parameters (but temperature). Results of the X-ray fluorescence and principal component analyses showed that the main trace elements in the coarse (PM2.5-10) and fine modes (PM2.5) are iron and sulfur, associated with emissions from mining activities, air mass transport from regional iron and steelmaking industry activities, vehicle emissions, local and regional biomass burning, and natural biogenic emissions. This work is the first assessment of source apportionment done in the city. Comparisons with other studies, for some large metropolitan areas, showed that Itabira has comparable contributions of sulfur, iron and elements such as copper, selenium, chromium, nickel, vanadium and lead.

The study of environmental DNA released by aquatic organisms in their habitat offers a fast, non-invasive and sensitive approach to monitor their presence. Common eDNA sampling methods such as filtration and precipitation are time consuming, require human intervention and are not applicable to a wide range of habitats such as turbid waters and poorly-accessible environments. To circumvent these limitations, we propose to use the binding properties of minerals to create a passive eDNA sampler.We have designed 3D-printed samplers made of hydroxyapatite (HAp samplers), a mineral known for its high binding affinity with DNA. The shape and the geometry of the samplers have been designed to facilitate their handling in laboratory and field. Here we describe and test the ability of HAp samplers to recover artificial DNA and eDNA.We show that HAp samplers efficiently recover DNA and are effective even on small amounts of eDNA (By better understanding the physico-chemical interactions between DNA and the HAp sampler surface, one could improve the repeatability of the sampling process and provide an easy-to-use eDNA sampling tool for aquatic environments.

This project investigated passive adsorbent sampling of light (C2-C5) hydrocarbons which are the sensitive tracers of fugitive emissions from oil and natural gas (ON under the worst case relative humidity level of 95% RH, VOCs uptake rates dropped to 27-39% of those in dry air. This effect potentially causes results to be biased low when cartridges are deployed at high RH, including overnight when RH is often elevated over daytime levels. Nonetheless, representative sampling results were obtained under ambient conditions during three field studies where cartridges were evaluated alongside whole air sample collection in canisters. Agreement varied by compound: Ethane and alkenes correlated poorly and could not be analyzed with satisfactory results; results for C3-C5 alkanes were much better: i-butane correlated with R2 > 0.5, and propane, n-butane, i-pentane, and n-pentane with R2 > 0.75, which demonstrates the feasibility of the passive sampling of these latter O&NG tracers.

Occurrences of tropical cyclones at a location are rare, and for many locations, only short periods of observations or hindcasts are available. Hence, estimation of return values (corresponding to a period considerably longer than that for which data are available) for cyclone-induced significant wave height (SWH) from small samples is challenging. The STM-E (space-time maximum and exposure) model was developed to provide reduced bias in estimates of return values compared to competitor approaches in such situations and realistic estimates of return value uncertainty. STM-E exploits data from a spatial neighbourhood satisfying certain conditions, rather than data from a single location, for return value estimation. This article provides critical assessment of the STM-E model for tropical cyclones in the Caribbean Sea near Guadeloupe for which a large database of synthetic cyclones is available, corresponding to more than 3000 years of observation. Results indicate that STM-E yields values for the 500-year return value of SWH and its variability, estimated from 200 years of cyclone data, consistent with direct empirical estimates obtained by sampling 500 years of data from the full synthetic cyclone database; similar results were found for estimation of the 100-year return value from samples corresponding to approximately 50 years of data. In general, STM-E also provides reduced bias and more realistic uncertainty estimates for return values relative to single-location analysis.

Soil Organic Carbon (SOC) is a measure of the total carbon content of the soil and is a vital soil health indicator. Over the decades, SOC has been estimated using sampling followed by rigorous laboratory-based testing methods. Spaceborne Microwave/Synthetic Aperture RADAR (SAR) remote sensing has proven to be a versatile tool for various soil study applications. However, there have been very few studies conducted for SOC estimation using SAR remote sensing. This study utilises time-series, C-band remotely sensed SAR data from Sentinel-1 A satellite for SOC estimation and compared the performances of Random Forest (RF) and Ordinary Least Squares (OLS) Regression models over agricultural areas of Rupnagar district of Punjab in India. A set of 96 soil samples were collected from 32 different agricultural field locations in Rupnagar district between November 2019 to January 2020. SAR backscatter of Vertically emitted and Vertically received (VV) and Vertically emitted and Horizontally received (VH) polarisation channels, from Sentinel-1, soil moisture, electrical conductivity, pH, temperature and SOC from the laboratory-based testing methods were used as regression parameters. The RF regression gave a Root Mean Square Error (RMSE) of 0.78 and R2 statistics of 0.887, while the OLS method performed better with an RMSE of 0.53 and an R2 value of 0.907. It was also observed that the backscatter from VV and VH polarisation channels, when used synergistically with field data, have the highest Feature Importance (FI) score in both RF and OLS regression models for SOC estimation.

Surface incident shortwave radiation (ISR) is an important component of the surface radiation budget. We refined the optimization method developed for polar-orbiting satellite data [1] and applied it to estimate ISR from the new generation geostationary Advanced Himawari Imager (AHI) onboard the Himawari-8/9 satellite and Advanced Baseline Imager (ABI) onboard the Geostationary Operational Environmental Satellite-R Series. Validation of the AHI ISR estimation at 2-km resolution showed an R² of 0.93, bias of 0.52 W/m², and RMSE of 106.52 W/m² for instantaneous estimates; an R² of 0.95, bias of -0.12 W/m², and RMSE of 22.49 W/m² for daily mean ISR; and a bias of -0.18 W/m² and RMSE of 7.72 W/m² for monthly mean ISR. Validation of the ABI ISR at 2-km spatial resolution showed an R² value of 0.93, bias of 8.71 W/m², and RMSE of 102.30 W/m² for instantaneous estimates; an R² of 0.95, bias of -2.38 W/m², and RMSE of 27.17 W/m² for daily mean ISR; and a bias of 1.40 W/m² and RMSE of 14.75 W/m² for monthly mean ISR. Our study also demonstrated that AHI and ABI observations have realized much better estimations for hourly and diurnal ISR than previous polar-orbiting satellite data because of their higher frequency of sampling on the atmospheric conditions.

Estimates of biomass often involve the use of weight-to-weight conversion factors for rapid assessment of dry-weights based metrics from more widely available measurements of wet weights. Availability of standardized biomass data is essential amid research on population dynamics, energy flow, fishery and food web interactions. However, for many species and groups the widely-applicable freely available conversion factors until now remained very rough approximations with high degree of taxonomic generalization. To close up this gap, here for the first time we publish the most detailed and statically robust list of ratios of wet weight (WW), dry weight (DW) and ash-free dry weight (AFDW). The dataset includes over 17000 records of single measurements for 497 taxa. Along with aggregated calculations, enclosed reference information with sampling dates and geographical coordinates provides the broad opportunity for reuse and repurposing. It empowers the future user to do targeted sub selections of data to best combine them with own local data, instead of only having a single value of conversion factor per region. Data can help to quantify natural variability and uncertainty, and assist to refine current ecological theory. The dataset is available via an unrestricted repository from: http://doi.io-warnemuende.de/10.12754/data-2021-0002 (Gogina et al., 2021).

The main factor restricting lead isotope analysis of metals from museum collections is the requirement for physical material. Hence, there are major incentives for developing minimally invasive methods for lead isotope analysis that are accurate and precise enough to reveal historical information about artefacts and their origin. Portable laser ablation (pLA), collecting microscopic samples on Teflon filters, has four key benefits. It produces no visual impact to the artefacts, does not require transport of artefacts to laboratory facilities, there are no artefact size restrictions, and samples are processed under clean laboratory conditions allowing Pb purification prior to measurement by solution MC-ICPMS. To validate the efficacy of the pLA technique on silver, nine matrixed-matched commercial, in-house and archaeological reference materials were sampled and analysed multiple times (9-10). The pLA mean analyses (±2SD) were all consistent with inter-laboratory bulk analyses. The digestion of sample filters produces precisions that are consistently more than five-times better than in situ nsLA-MC-ICPMS and are the same order of magnitude expected for bulk samples processed in different laboratories. This journal is

Freeze/Thaw (F/T) surface state retrieval is important to further understand hydrological patterns and climate change. This paper investigates the use of Earth-reflected Global Positioning System (GPS) L-band signals as collected by the National Aeronautics and Space Administration NASA’s CYclone Global Navigation Satellite System (CYGNSS) mission for F/T surface state retrieval over a target area in South America, covering the Andes Mountains and the Argentinian Pampas. In the study, CYGNSS’s responsiveness to changes in surface permittivity is leveraged to detect transitions of F/T surface state, at an improved spatio-temporal sampling as compared to traditional Remote Sensing missions. A Seasonal-Threshold Algorithm (STA) is developed and validated using surface temperature data as provided by the European Centre for Medium-Range Weather Forecast (ECMWF) ERA5-Land numerical reanalysis model. Then, the monthly evolution of CYGNSS-derived F/T surface state maps is evaluated and an inter-comparison with the Soil Moisture Active Passive (SMAP) F/T data product is performed.

Surveys of anthropogenic pressures and their effect on river fish communities have rarely been conducted in the eastern Mediterranean countries, in contrast to the rest of Europe. An electrofishing dataset of 1586 samples (818 sites) was analyzed to investigate human-induced pressures on river fish assemblages in Greece. Pressure patterns and pressure-impact analysis towards the national fish bioassessment index (HeFI) and its four constituent metrics are depicted. At each sampling site 11 anthropogenic pressure variables were assessed, and several integrated/grouped pressure indices were computed, including hydrology, morphology, connectivity, and water quality. Almost 60% of samples were classified as impacted. The most common pressure groups affecting Greek rivers were hydrology recorded at 714 (45%) samples, morphology at 692 (44%) and artificial barriers at 382 (24%) samples. A similar pattern persists among the six investigated freshwater ecoregions in Greece. The most impacted ecoregions were Thrace (76% impacted samples), Western Aegean (67% impacted samples) and Macedonia-Thessaly (67% impacted samples). Lowlands and large rivers displayed higher levels of degradation, but water pollution was not recognized as a key stressor in most running waters. Pressure-impact analysis demonstrates that all HeFI component metrics (insectivorous, omnivorous, benthivores, potamodromous species) respond as expected to almost all integrated pressure indices considered, as regards: a) the direction of response, and b) the response to pressure intensity gradients. However, the longitudinal connectivity stressor clearly fails to respond towards metrics. The findings confirm the usefulness of model-based indices, using fish assemblage functional metrics as bioassessment tools. Specific recommendations for refining the tools are provided.

The lunar mineralogical spectrometer (LMS) is the primary scientific payload of the Chang'E-5 mission of the China Lunar Exploration Program. The LMS is responsible for the in situ spectral detection and analysis of the sampling areas of interest on the Moon's surface. The LMS needs to adapt to a wide range of temperature conditions, varying between the lunar morning to noon, based on the time, location, and work process of the LMS. Thus, ensuring consistency in the spectral data obtained at high temperatures and a wide range of operating environments is a major challenge. In this study, a thermal analysis model of the LMS is built, and the temperature variation of the LMS during its operation on the lunar surface is simulated using the instrument workflow that is based on the simulation results. Independent experiments were also carried out for the temperature-sensitive components in the LMS, and subsequently, the temperature correction model and model coefficients for each temperature-sensitive component were obtained. The final correction result was a quantitative assessment of the ground test data and the in situ lunar detection data. For the ground tests at different temperatures, the average errors were found to be 0.57% and 1.2% for the short- and medium-wave data, respectively, after applying the temperature correction. When the LMS was operating on the lunar surface, the average errors for the short- and medium-ware data after the temperature correction were calculated to be 0.59% and 0.8%, respectively.

This paper presents a new sampling head design and the method used to evaluate it. The elemental composition of aerosols collected by two different sampling devices in a semi-arid region of Tunisia is compared by means of compositional perturbation vectors and biplots. This set of underused mathematical tools belongs to a family of statistics created specifically to deal with compositional data. The two sampling devices operate at a flow rate in the range of 1 m3 h−1, with a cut-off diameter of 10 µm. The first device is a low-cost laboratory-made system, where the largest particles are removed by gravitational settling in a vertical tube. This new system will be compared to the second device, a brand-new standard commercial PM10 sampling head, where size segregation is achieved by particle impaction on a metal surface. A total of 44 elements (including rare earth elements, REEs, together with Al, As, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sc, Se, Sr, Ti, Tl, U, V, Zn, and Zr) were analysed in 16 paired samples, collected during a 2-week field campaign in Tunisian dry lands, close to source areas, with high levels of large particles. The contrasting meteorological conditions encountered during the field campaign allowed a broad range of aerosol compositions to be collected, with very different aerosol mass concentrations. The compositional data analysis (CoDA) tools show that no compositional differences were observed between samples collected simultaneously by the two devices. The mass concentration of the particles collected was estimated through chemical analysis. Results for the two sampling devices were very similar to those obtained from an online aerosol weighing system, TEOM (tapered element oscillating microbalance), installed next to them. These results suggest that the commercial PM10 impactor head can therefore be replaced by the decanter, without any measurable bias, for the determination of chemical composition and for further assessment of PM10 concentrations in source regions.

Issues related to large uncertainty and parameter equifinality have posed big challenges for hydrological modeling in cold regions where runoff generation processes are particularly complicated. Tracer-aided hydrological models that integrate the transportation and fractionation processes of water stable isotope are increasingly used to constrain parameter uncertainty and refine the parameterizations of specific hydrological processes in cold regions. However, the common unavailability of site sampling of spatially distributed precipitation isotopes hampers the practical applications of tracer-aided models in large-scale catchments. This study, taking the precipitation isotope data (isotopes-incorporated global spectral model – isoGSM) derived from the isotopic general circulation models (iGCMs) as an example, explored its utility in driving a tracer-aided hydrological model in the Yarlung Tsangpo River basin (YTR; around 2×105 km2, with a mean elevation of 4875 m) on the Tibetan Plateau (TP). The isoGSM product was firstly corrected based on the biases between gridded precipitation isotope estimates and the limited site sampling measurements. Model simulations driven by the corrected isoGSM data were then compared with those forced by spatially interpolated precipitation isotopes from site sampling measurements. Our results indicated that (1) spatial precipitation isotopes derived from the isoGSM data helped to reduce modeling uncertainty and improve parameter identifiability in a large mountainous catchment on the TP, compared to a calibration method using discharge and snow cover area fraction without any information on water isotopes; (2) model parameters estimated by the corrected isoGSM data presented higher transferability to nested subbasins and produced higher model performance in the validation period than that estimated by the interpolated precipitation isotope data from site sampling measurements; (3) model calibration forced by the corrected isoGSM data successfully rejected parameter sets that overestimated glacier melt contribution and gave more reliable contributions of runoff components, indicating the corrected isoGSM data served as a better choice to provide informative spatial precipitation isotope than the interpolated data from site sampling measurements at the macro scale. This work suggested plausible utility of combining isoGSM data with measurements, even from a sparse sampling network, in improving hydrological modeling in large high mountain basins.

The UK standard for estimating flood frequencies is outlined by the flood estimation handbook (FEH) and associated updates. Estimates inevitably come with uncertainty due to sampling error as well as model and measurement error. Using resampling approaches adapted to the FEH methods, this paper quantifies the sampling uncertainty for single site, pooled (ungauged), enhanced single site (gauged pooling) and across catchment types. This study builds upon previous progress regarding easily applicable quantifications of FEH-based uncertainty estimation. Where these previous studies have provided simple analytical expressions for quantifying uncertainty for single site and ungauged design flow estimates, this study provides an easy-to-use method for quantifying uncertainty for enhanced single site estimates. HIGHLIGHTS Bespoke bootstrap methods for quantifying uncertainty for ungauged and enhanced single site FEH design flow estimation.; Comparison of flood estimation uncertainty across catchment types.; Simple equations to derive variance and standard error for enhanced single site design flow estimates.

Beach sediment samples collected along the central part of the Littoral zone of Cameroon were geochemically analyzed using ICP–MS to investigate the distribution characteristics and to identify trace metal concentrations. The textural characteristics of these sediments revealed that they are dominated by sand. Metal concentrations are distributed in the following decreasing order: Fe ˃ Mn ˃ Cr ˃ V ˃ Ni ˃ Co ˃ Cs. Indices of pollution, such as enrichment factor values are generally less than 1.5, except for Cr; index of geo-accumulation, where values of all metals in the sediments were

A new globally reconstructed sea surface temperature (SST) analysis dataset (CMA-SST), available on a 2° × 2° and monthly spatiotemporal resolution since 1900, is described and assessed in this study. The dataset has been constructed from a newly developed integrated dataset with denser and wider sampling of in-situ SST observations and follows similar analysis techniques to ERSST.v5. Assessments show that the larger observation quantity of the input data source is beneficial to making the reconstructed SSTs more realistic than those reconstructed with ICOADS3.0 + GTS, especially in China’s offshore sea area. Besides, a specific parameter for bias correction has been upgraded to be self-adaptive to the input data source, and serves as a mediator to improve the accuracy of the reconstructed SSTs. Generally, the reconstructed CMA-SST dataset is comparable to currently congeneric products. Its biases are similar to those of ERSST.v5, COBE-SST2, HadISST2 and HadSST3; and more specifically, they are closest to ERSST.v5 and lower than HadISST2 and HadSST3 at high latitudes of the Southern Hemisphere where in-situ observations are limited. Moreover, its temporal characteristics, such as the year-to-year variations of globally averaged SST anomalies and time series of the Nino3.4, Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices are also a good match to those of congeneric products. Although the warming rates of CMA-SST are a little higher in many regions over the periods 1900–2019 and 1950–2019, they are found to be acceptable and within the quantified uncertainties of ERSST.v5. However, there are noticeable differences in the strength and stability of spatial standard deviations among the various datasets, as well as low correlations between CMA-SST and the other products around 60°S where in-situ sampling is very limited. These aspects necessitate further investigation and improvement of CMA-SST.

Surface albedo is closely related to the Earth's energy budget and is usually estimated by integrating remotely sensed bidirectional reflectance distribution function (BRDF) data based on the widely used Ross-Li kernel-driven models. However, for large solar zenith angles (i.e., SZAs > 70°), albedo estimation using the operational algorithm of the Moderate Resolution Imaging Spectroradiometer (MODIS), i.e., RossThick-LiSparseReciprocal (RTLSR), is not recommended because it is reported to somewhat underestimate the black-sky albedo (BSA) at large SZAs based on ground albedo measurements. Recently, various combinations of the Ross-Li BRDF models with improved capabilities have been developed, and the assessments of these models based on worldwide satellite BRDF data with good spatial sampling, particularly at the large view and solar angles, will be important to improve an understanding of their performance in estimating intrinsic albedos. Following previous studies, the objective of this study is to further assess a series of hotspot-corrected Ross-Li models by demonstrating their ability to fit the POLarization and Directionality of the Earth's Reflectances (POLDER) data sets and estimate albedo, especially at large SZAs, based on selected concurrent POLDER and MODIS data. The hotspot-corrected RTLSR model obtained by combining the RossThickChen and LiSparseReciprocalChen kernels (RTLSR_C) shows the best fitting ability, with a high cumulative frequency of small root-mean-square errors (RMSEs), thus confirming previous conclusions. Model differences mainly appear in albedo estimates, especially BSA estimates at large SZAs. The BSAs estimated by other models are significantly different from the RTLSR_C estimates in the near-infrared (NIR) and red bands as the SZA increases to approximately 60° and 70°, respectively. In this case, RossThinChen-LiSparseReciprocalChen (RTNLSR_C) yields higher BSA estimates than those of RTLSR_C. Comparisons of the MODIS and POLDER albedos estimated with Ross-Li models show that models with the RossThinChen kernel yield higher BSA estimates than those of the RTLSR_C model as the SZA increases. The results indicate that the retrieved albedo is likely to be more accurate with appropriately selected kernels for BRDF models at large SZAs, providing guidance for selecting suitable combinations of multiple kernels.

The pollution of heavy metals is considered as one of the main problems of using wastewater for irrigation purposes. Frequent experimental measurements, time, and cost are essential for evaluating the pollution of heavy metals in a large area. Thus, using satellite images and establishing a relationship between the images and concentration of heavy metals can be regarded as a solution for estimating the polluted area. In this study, the image of the Sentinel-2 satellite was used to evaluate the heavy metals pollution of the area irrigated with wastewater in south of Tehran. For this aim, 30 soil-surface samples irrigated with raw wastewater were collected in the area. After preparing the samples, the concentration of Pb, Cu, and Ni was determined using atomic absorption spectroscopy. Then, the relationship between the heavy metal concentration, reflection on the bands or rating of the bands at the corresponding sampling points was determined by applying the stepwise regression method. In addition, the developed models were applied to the satellite image for zoning the heavy metal concentrations in the studied area. Finally, the accuracy of the developed models was examined by Root-Mean-Square Error (RMSE) and Pearson correlation coefficients. The results indicated that the amounts of RMSE for the equations of Pb, Cu, and Ni were 1.90, 2.54, and 1.59 respectively, while the amounts of R were 0.81, 0.75, and 0.73 for these metals representing a promising match between the predicted and measured results of the models.

Masalah pencemaran udara merupakan persoalan tahunan yang terus terjadi di setiap kota. Kota Surabaya adalah salah satu kota dengan jumlah penduduk yang cukup padat sehingga berbanding lurus dengan jumlah kendaraan bermotor yang digunakan. Kendaraan bermotor serta kawasan perindustrian yang berada di Surabaya menyumbang emisi terbesar ke udara sehingga memiliki kualitas udara yang rendah kareana adanya pencemaran tersebut. Kualitas udara berkaitan erat dengan pencemaran, untuk mengetahui tingkat kadar pencemaran udara pengukuran kualitas udara mengacu pada indeks standar parameter pencemaran udara (ISPU). Parameter pemantauan yang digunakan salah satunya adalah Particulate matter 10. PM10 merupakan pencemar primer yang beremisi langsung ke udara yang berdampak serius bagi kesehatan dan lingkungan. Penelitian ini dilakukan di kawasan Bundaran Taman Pelangi Surabaya selama 3 hari dengan selang waktu hari kerja dan hari libur dengan interval waktu pada pagi, siang dan sore hari. Analisis data konsentrasi dihitung menggunakan alat HVAS dengan metode gravimetri serta dipetakan mengunakan program ArcGIS. Hasil studi menunjukkan konsnetrasi tertinggi pada titik lokasi sampling 6 dengan nilai PM10 = 110.71 μg/m3 , dan terendah pada titik lokasi sampling 1 Pm10= 50.15 μg/m3

Con el objetivo de generar insumos para el manejo del Santuario de Flora y Fauna Malpelo, la Dirección Territorial Pacífico de Parques Nacionales Naturales de Colombia, inició en 2006 un monitoreo de condiciones oceanográficas en el área. En la actualidad este monitoreo cuenta con una malla de muestreo de solo seis estaciones ubicadas a 0,93 km (0,5 millas náuticas) y 1,85 km (1 milla náutica) desde la isla, por lo que es poco eficiente debido a la reducida distancia entre estaciones e insuficiente dada la gran extensión del Santuario. Por lo tanto, se diseñó una malla de muestreo con base en datos de temperatura superficial del mar (TSM) tomados in situ, derivados del sensor remoto MODIS-Aqua. Se analizó la estructura de autocorrelación espacial a través de una función de semivarianza. La decisión para elegir la mejor malla de muestreo entre los diferentes tamaños, fue basada en la disimilitud entre estaciones asociada al modelo de semivarianza, en la varianza del error de predicción, en el patrón de corrientes marinas predominante durante el ciclo anual, y en aspectos logísticos relacionados con las limitantes de desplazamiento de las embarcaciones que realizan los monitoreos oceanográficos en el SFF Malpelo. Como resultado de considerar estas variables, se obtuvo una malla de muestreo con estaciones cada 7,41 km (4 millas náuticas) compuesta por ocho estaciones nuevas y las seis de la malla de muestreo histórica.

The distribution of soil pollutants is receiving increasing attention. The accurate determination of the soil pollution distribution in an area is becoming more important. To date, many soil quality surveys have already been carried out in China, and the use of these surveys to reflect soil pollution is worth examining. This article provides an example of the application of combined two-phase data to assess soil contamination in a region. Based on data acquired during two soil sampling phases in 2005 and 2015, we chose a typical watershed in southeast China as the study area. We analysed the data using spatial interpolation analysis, compared the results, and extracted points to perform point combination based on site conditions. Ultimately, these analyses allowed us to develop a new method involving the use of multi-period data to evaluate the soil quality on a regional scale. In the ten years from 2005 to 2015, apparent changes in soil pollution occurred. We found that the area with no change in soil pollution accounts for 46.98% of the total basin and the area demonstrating a soil pollution increase accounts for 47.25% of the total basin, while the area exhibiting a soil pollution reduction only accounts for 5.78% of the whole area. The average accuracy of the combined points increased to 89% from 76 and 81%. The analysis of the land-use types and spatial locations during the two periods revealed no direct relationship between the soil contamination changes and the changes in the total number of land-use types, but a correlation was observed with the intensity of human activities at the spatial locations. This paper proposes a new method for the spatial assessment of soil pollution based using multiple periods of existing data on the above analysis.

The suitability of cryogenic vacuum extraction for sampling free water (F-water) extracted from environmental materials (grass and soil) was examined with respect to low-level (~ 1 Bq L−1) and precise determination of tritium (3H) concentrations in the extracted water. Tests were conducted using addition-recovery experiments of water containing low 3H activity concentrations (0.43 ± 0.046 Bq L−1) added to dried grass or soil. The results revealed that 3H concentrations in F-water did not significantly differ before and after cryogenic vacuum extraction in both experiments. This suggests that the cryogenic vacuum extraction is useful for sampling F-water to determine 3H concentration in the water.

The unprecedented size of the deepwater horizon oil spill and scope of the subsequent response elicited intense and sustained interest in microbial responses to oiling, especially in salt marshes, which have featured prominently in debates about best practices to prevent and remediate oiling of vulnerable ecosystems. A number of studies have examined salt marsh soil microbial communities following the spill, but most have primarily concentrated on prokaryotes. The extent to which oiling elicited shifts in fungal diversity and community composition remains unclear. Here we present spatial and temporal comparisons of salt marsh soil fungal communities at two southern Louisiana salt marshes with contrasting oiling histories. We profiled fungal communities in 2013 alongside corresponding measurements of polycyclic aromatic hydrocarbons to assess whether and how responses to oiling are distinguishable from natural heterogeneity. Analyses based on high-resolution unbiased spatial sampling demonstrated that fungal communities did not align with shoreline classification of oiling less than three years after initial oiling, despite observable differences in oil residues and secondary oiling. Notably, extensive sampling allowed delineation of benchmark sampling thresholds and illustrated the value of using ranked differentials of relative abundance to characterize soil fungal communities. Our findings highlight the need for combining high-resolution sampling with judgment-based and systematic sampling approaches to accurately capture responses of salt marsh soil fungal community to oiling.

Nitrogen dioxide (NO2) is linked to poor air quality and severe human health impacts, including respiratory and cardiovascular diseases and being responsible annually for approximately 23,500 premature deaths in the UK. Automated air quality monitoring stations continuously record pollutants in urban environments but are restricted in number (need for electricity, maintenance and trained operators), only record air quality proximal to their location and cannot document variability of airborne pollutants at finer spatial scales. As an alternative, passive sampling devices such as Palmes-type diffusion tubes can be used to assess the spatial variability of air quality in greater detail, due to their simplicity (e.g. small, light material, no electricity required) and suitability for long-term studies (e.g. deployable in large numbers, useful for screening studies). Accordingly, a one passive diffusion tube sampling approach has been adapted to investigate spatial and temporal variability of NO2 concentrations across the City of Manchester (UK). Spatial and temporal detail was obtained by sampling 45 locations over a 12-month period (361 days, to include seasonal variability), resulting in 1080 individual NO2 measurements. Elevated NO2 concentrations, exceeding the EU/UK limit value of 40 µg m−3, were recorded throughout the study period (N = 278; 26% of individual measurements), particularly during colder months and across a wide area including residential locations. Of 45 sampling locations, 24% (N = 11) showed annual average NO2 above the EU/UK limit value, whereas 16% (N = 7) showed elevated NO2 (> 40 µg m−3) for at least 6 months of deployment. Highest NO2 was recorded in proximity of highly trafficked major roads, with urban factors such as surrounding building heights also influencing NO2 dispersion and distribution. This study demonstrates the importance of high spatial coverage to monitor atmospheric NO2 concentrations across urban environments, to aid identification of areas of human health concern, especially in areas that are not covered by automated monitoring stations. This simple, reasonably cheap, quick and easy method, using a single-NOx diffusion tube approach, can aid identification of NO2 hotspots and provides fine spatial detail of deteriorated air quality. Such an approach can be easily transferred to comparable urban environments to provide an initial screening tool for air quality and air pollution, particularly where local automated air quality monitoring stations are limited. Additionally, such an approach can support air quality assessment studies, e.g. lichen or moss biomonitoring studies.

Two sampling stations N1 and N4a are selected on the River Kabini stretch at Nanjangud, Karnataka, India. In this research, the assessment of the 20 year-data (2000 – 2019) indicate Water Quality Index of 75.3 and 95.3 at stations N1 and N4a respectively, which categorizes the water quality of the River Kabini at this stretch as “very poor”. This assessment is useful to policymakers in deciding future course of action to protect the river from anthropogenic deterioration. Also, statistical analysis in the form of t-test for the 17 – year, 3 – year and 20 – year period indicates the ‘p’ value (p = 0.29, 0.21 and 0.22) respectively, which is greater than the significant value 0.05. Similarly, the critical value during the 17 – year, 3 – year and 20 – year period is 2.06, 4.30 and 2.04 respectively and is also greater than measured t statistics values of -1.08, -1.79 and -1.26 respectively. This reveals that the difference in WQI of N1 and N4a monitoring stations is insignificant.

Penelitian ini bertujuan untuk menafsirkan kumpulan data kualitas air pada Sungai Pepe, Sungai Anyar, Sungai Jenes, Sungai Kedung Jumbleng, Sungai Gajah Putih, Sungai Brojo, dan Sungai Premulung di Kota Surakarta, Jawa Tengah. Data diperoleh dari hasil pemantauan kualitas air yang dilakukan oleh Dinas Lingkungan Hidup Kota Surakarta di 13 titik lokasi pengambilan sampel pada tahun 2020. Terdapat 12 parameter yang digunakan yaitu TSS, TDS, pH, BOD, COD, DO, P, NO3-N, NH3, NO2-N, Total Coliform, dan Fecal Coliform. Dengan algoritma k-means diperoleh dua klaster dengan parameter pembeda yaitu Total Coliform dan Fecal Coliform. Dengan kandungan 3.790.050 MPN/100ml Total Coliform dan 604.400 MPN/100ml Fecal Coliform, Sungai Jenes Hilir mempunyai kualitas air yang lebih buruk dibandingkan sungai lainnya dalam hal kandungan pencemar Total Coliform dan Fecal Coliform.

Above polar ice sheets, atmospheric water vapor exchange occurs across the planetary boundary layer (PBL) and is an important mechanism in a number of processes that affect the surface mass balance of the ice sheets. Yet, this exchange is not well understood and has substantial implications for modeling and remote sensing of the polar hydrologic cycle. Efforts to characterize the exchange face substantial logistical challenges including the remoteness of ice sheet field camps, extreme weather conditions, low humidity and temperature that limit the effectiveness of instruments, and dangers associated with flying manned aircraft at low altitudes. Here, we present an unmanned aerial vehicle (UAV) sampling platform for operation in extreme polar environments that is capable of sampling atmospheric water vapor for subsequent measurement of water isotopes. This system was deployed to the East Greenland Ice-core Project (EastGRIP) camp in northeast Greenland during summer 2019. Four sampling flight missions were completed. With a suite of atmospheric measurements aboard the UAV (temperature, humidity, pressure, GPS) we determine the height of the PBL using online algorithms, allowing for strategic decision-making by the pilot to sample water isotopes above and below the PBL. Water isotope data were measured by a Picarro L2130-i instrument using flasks of atmospheric air collected within the nose cone of the UAV. The internal repeatability for δD and δ18O was 2.8 ‰ and 0.45 ‰, respectively, which we also compared to independent EastGRIP tower-isotope data. Based on these results, we demonstrate the efficacy of this new UAV-isotope platform and present improvements to be utilized in future polar field campaigns. The system is also designed to be readily adaptable to other fields of study, such as measurement of carbon cycle gases or remote sensing of ground conditions.

Rangelands provide numerous ecosystem services, including forage for livestock grazing. Effective grazing management requires measuring forage availability, which influences the level of grazing that can be sustained while maintaining healthy ecological conditions. However, spatiotemporal variability makes such determinations of forage quantity difficult, potentially hindering optimal management. These determinations are especially difficult across large, remote areas. To address this, we developed an approach using data from a one-time sampling of vegetation throughout the Uintah and Ouray Reservation in northeast Utah. By associating these data in a random forest model with environmental and climatic covariates that vary annually, we produced yearly predictions of forage availability on a pixel-by-pixel basis for the Reservation and surroundings from 1984 to 2018. This method addresses and quantifies the spatiotemporal variability of available forage. The model confirms that forage availability is highly variable throughout the area. On average, forage availability in Reservation management units declined as much as 32% below median availability in some years and increased 33% above median availability in others. Moreover, some management units experienced large increases in favorable years but less significant declines in unfavorable years, while the opposite was true in others. In comparison to determining a single “typical” forage availability of management units, recognizing this inherent variability and how it differs between units provides a fuller picture of the range of possible forage availability. This can improve grazing management by revealing how forage quantities vary from year to year and may help avoid forage overutilization during unfavorable years such as drought. The model can continue to be used into the future to monitor vegetation trends, though with ongoing climate and vegetation changes periodic recalibration may be necessary. In addition, the modeling method may be applicable to other similar study systems.

In many sea areas, toxic and harmful chemicals vastly exceed the standard, which not only has had a very bad impact on the survival of marine organisms, but also damages the safety of edible groundwater. With the continuous development of artificial intelligence and deep learning, the most efficient and safe method to detect seawater is with unmanned ship. By processing and fusing the images transmitted by the two radars, the common advantages of the two sensors are integrated, and the comprehensiveness of the unmanned aerial vehicle's (UAV;s) perception of the surrounding environment is improved. In order to improve the accuracy and safety of UAV offshore operations, this study designed an electric propulsion unmanned ship and its automatic control system according to the requirements of water quality sampling. Based on the small body theory, the model of an unmanned ship with the least resistance and the best safety is designed. According to the requirements of water quality sampling in sea areas, the vessel was equipped with collection and analysis systems to measure six elements of water quality. The Realizable k-e turbulence model was used to simulate the self-recovery ability of an unmanned ship under wave disturbance. Theoretically, the unmanned ship can achieve self-righting in 4.25 s. For actual navigation, the unmanned ship can effectively avoid obstacles, and the basic information on seawater quality was within the specified range. The unmanned ship constructed in this study can be used as an auxiliary tool for water quality detection. Compared with various study methods, the proposed method obtained a better performance. HIGHLIGHTS Electric propulsion; Unmanned ship; Water quality monitoring; Automatic sampling; Seawater

Water consumed is stored in several water bodies in and around us, out of which dams accommodate a major portion of water. The quantity and quality monitoring of water in dams is troublesome due to their large surface area and high depths. Although groundwater resources are the primary water source in India, dams plays a vital role in water distribution and storage network. The Central Water Commission in India has identified more than 5,000 dams of which a major portion is persistently consumed by the rural and urban populations for drinking and irrigation. The water quality of these reservoirs is of serious concern as it would not only affect the socio-economic status of the nation but the aquatic systems as well. Water quality control and management are vital for a delivering clean water supply to the general society. Because of their size, collecting, assessing, and managing a vast volume of water quality data are critical. Water quality data are primarily obtained through manual field sampling; however, real-time sensor monitoring is increasingly being used for more efficient data collection. The literature depicts that the methods involving remote sensing and image processing of water quality analysis consume time, and require sample collection at various depths, analysis of collected samples, and manual interpretations. The objective of this study was to propose a novel cost-effective method to monitor water quality devoid of considerable human intervention. Sensor-based online monitoring aids in assessing the sample with limited technology, at various depths of water in the dam to analyze turbidity which gives the major indication of pure water. The quality analysis of the dam water is suitable if the water is assessed at the distribution end before consumption. Hence, to enhance the water management system, other quality parameters like pH, conductivity, temperature are sensed and monitored in the distribution pipeline. An unstable pH can alter the chemical and microbiological aspects of water, resulting in a variation of other water quality parameters. Temperature variations affect the amount of dissolved oxygen in the water bodies and results in unstable quality parameters. The change in dissolved solvents and the ionic concentration alters the electrical conductivity of the water and the increased concentration of salts also results in turbidity. The data from all the sensors are processed by the microcontroller, transmitted, and displayed in a mobile application comprehensible to the layman. HIGHLIGHTS The quantity and quality monitoring of water in dams is troublesome due to their large surface area and high depths.; An effective quality analysis is the demand of the hour and the problem statement is identified in developing a system to check water quality at various depths in a dam.; An efficient pipeline with various sensors which monitor turbidity, pH, and conductivity is developed as a prototype low cost device.

Assessment of zooplankton abundance, distribution, community composition, and temporal variability is critical to understanding the effects of climate variability and change on lower trophic level production and availability for consumption by larger consumers. Zooplankton sampling is performed across the Canadian continental shelf system by Fisheries and Oceans Canada's Atlantic Zone Monitoring Programme (AZMP). Sampling includes semi-monthly to monthly collection of zooplankton using vertical net tows (VNTs) deployed from near-bottom to surface at stations on the central Scotian Shelf (Stn 2, 150 m depth) and Newfoundland Shelf (Stn 27, 175 m depth), and by Continuous Plankton Recorders (CPRs) in the near-surface layers along routes over the Scotian and Newfoundland shelves (0–10 m depth). Here, we compare abundance metrics for 11 copepod taxa collected using both gear types in both regions between 1999 and 2015. Seasonal cycles of VNT and CPR abundance were similar for near-surface residents. VNT: CPR abundance ratios varied year-round for vertical migrants, as ontogenetic migrants shifted their vertical distribution, and as diel migrants changed their migratory behaviour. For some taxa, differences in annual average VNT: CPR abundance ratios between regions suggest differences in vertical distribution, while for others differences in inter-annual variability for VNT and CPR abundances suggest differences in the dynamics of the near- and sub-surface components of the populations.

The creation of competitive deep-sea soil-mining machines and complexes allows the transition to the development of deep-water deposits of polymetallic nodules and metalliferous silts, the extraction of sand and sapropel from the bottom of the sea, thus providing parity access to the mineral and energy resources of the World Ocean. The purpose of the work is to create methods and technical means for determining the strength characteristics of bottom soils in the surface layer, aimed at reducing the time of creation and effective use of machines by ensuring that their parameters correspond to real operating conditions. Usually, the properties of deep-sea soil in its natural bedding (in situ) are determined from the data of photo- and geoacoustic sounding or from cores raised from the seabed to the surface, and the working loads of machines, the resistance of the soil to destruction and other mechanical characteristics are estimated on the basis of the data obtained by geological methods. New methods and devices for point and continuous sampling of the bottom, adequately applied both on the surface and under water, provide sufficient reliability of the information obtained for practical use. The objectives of the study are to substantiate the theory of gravity sensing of underwater soils and their cutting, to determine the deformation and strength properties of the surface layer of the seabed, resistance to mechanical destruction and the characteristics of the interaction of machine mechanisms with silty soil. The novelty of the research is to establish the features of the interaction of the working bodies and the undercarriage of earth-moving machines with underwater viscoplastic soils, depending on the parameters of the working process, strength properties of the soil, characteristics of dynamic loading and geophotoprofiling of underwater routes. The practical value lies in the substantiation of the operational loads of deep-water earth-moving machines; an assessment of the characteristics of bottom soils in natural bedding, as well as a comprehensive study of bottom massifs, which make it possible to reduce the number of sampling stations, the total labor intensity of voyage operations, to establish an operational bottom background and optimal routes of mining systems.

Fish species richness is one of the most common descriptors of aquatic biodiversity and an important indicator of the ecological condition of stream reaches and regional biological diversity. However, inadequate sampling effort underestimates the true number of species and, consequently, might provide erroneous information about the ecological conditions of, and trends in, aquatic environments. We sampled 24 to 39 stream reaches (each 150 m long) in seven megadiverse hydrologic units in the Brazilian states of Para (Amazonia biome) and Minas Gerais (Cerrado biome) to estimate the sampling effort needed to reach asymptotes at hydrologic unit spatial extents. We also estimated how much of the local richness would have been captured if the sampled reach was 50 or 100-m-long. Our results indicate that up to 53 reaches must be sampled per hydrologic unit for attaining an estimated 90% of the fish species richness, depending on the relative number of rare species. In most units, 100-m-long reaches suffice for collecting about 90% of the local sampled richness. We conclude that the effort frequently used in neotropical streams is adequate to capture most of the local fish fauna. However, complete inventories of hydrographic regions would only be feasible with efforts and resources far greater than those commonly employed. Before sampling programs are implemented for estimating true species richness, adequate levels of sampling effort should be assessed wherever that richness is expected to be high.

Background The 3M™ Environmental Scrub Sampler with 10 mL Wide Spectrum Neutralizer is a nonspecific sampling device intended for use for environmental monitoring surface sampling. Objective The aim was to evaluate 3M Wide Spectrum Neutralizer using the 3M Environmental Scrub Sampler for AOAC® Performance Tested MethodsSM (PTM) certification. Methods Matrix studies, inclusivity/exclusivity, product consistency/stability, neutralization, and robustness testing were conducted for Salmonella and Listeria species. Stainless steel, sealed concrete, and plastic environmental surfaces were evaluated in the matrix study comparing the performance of the 3M™ method for sample collection to that of the U.S. Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) reference methods. Four classes of sanitizers, namely quaternary ammonium, high acid, hydrogen/peroxyacetic acid and chlorine/bleach-based, were assessed in the neutralization study following ASTM E1054 - 08, Standard Test Methods for Evaluation of Inactivators of Antimicrobial Agents. The other testing parameters followed typical PTM study design. Results In matrix studies the 3M sampling device demonstrated no significant differences between candidate and reference sampling method results. All inclusivity organisms were detected, and all exclusivity organisms were excluded, for both Salmonella and Listeria strains when tested by the appropriate FDA BAM detection method. Robustness, product consistency, and stability studies showed that the sampling device is not affected by lot or testing parameter differences. The Wide Spectrum Neutralizer was proven to effectively neutralize sanitizers at the concentrations tested and was itself shown to be nontoxic and did not affect target microorganism recovery. Conclusions The 3M Environmental Scrub Sampler with 10 mL Wide Spectrum Neutralizer is an effective, stable, robust sampling device for the recovery of Salmonella spp. and Listeria spp. Highlight The 3M Environmental Scrub Sampler with 10 mL Wide Spectrum Neutralizer is an acceptable sampling device for use in FDA BAM Salmonella and Listeria reference methods.

Airborne LiDAR can support high resolution watershed-scale snow depth mapping that provides the spatial coverage necessary to inform water supply forecasts for mountainous headwaters. This research...

Актуальность исследования обусловлена необходимостью оценки уровня воздействия крупного целлюлозно-бумажного предприятия, расположенного к северу от г. Сыктывкара, на основе экохимического анализа зимних атмосферных осадков (снега), продемонстрировать целесообразность применения такого подхода в экологических исследованиях. Цель: показать динамику аэротехногенной нагрузки на территорию вблизи АО «Монди СЛПК» по данным о химическом составе снежного покрова за период мониторинга. Объекты: образцы снежного покрова, отобранные в зоне воздействия предприятия и на условно фоновых территориях. Методы: снегосъемка с использованием трансект на постоянных пунктах отбора проб, применение аналитических методов согласно руководству по контролю загрязнения атмосферы (РД 52.04.186-89), картографические методы оценки распространения загрязнения в снежном покрове с использованием метода интерполяции (метод обратных взвешенных расстояний), применение алгебры карт для расчета площадей зон воздействия. Результаты. Представлены результаты экохимического мониторинга снежного покрова в зоне воздействия выбросов АО «Монди СЛПК» за многолетний период. Дана общая характеристика распределения компонентов выбросов относительно источника эмиссии. Мониторинг снежного покрова в импактной зоне предприятия позволил провести анализ динамики аэротехногенной нагрузки. Результаты расчета поступления веществ на территорию предприятия в 2019 г. показали наиболее низкий уровень загрязнения снежного покрова за весь период наблюдений, 66 % относительно объемов 2002 г. Отмечено снижение поступления загрязнителей, начиная с 2016 г. после реконструкции предприятия. Концентрации примесей в талой воде за пределами санитарно-защитной зоны не превышали санитарно-гигиенических нормативов. По данным мониторинга снежного покрова показан тренд к снижению аэрогенного воздействия. The relevance of the research is caused by the need to assess the level of impact of a large pulp and paper enterprise located north of the Ezhvinsky district of Syktyvkar on the basis of ecochemical analysis of winter precipitation (snow), to show the feasibility of using this approach in environmental studies. The main aim is to show the dynamics of the aerotechnogenic load on the territory near the Syktyvkar timber industry complex based on the data on snow cover chemical composition during the monitoring period. Objects: snowpack samples, selected in the zone of influence of emissions of Mondi Syktyvkar JSC and conditionally background areas. Methods: snow survey using transect, adhering to constant sampling points, application of analytical methods according to the air pollution control manual (GD 52.04.186-89), cartographic methods for estimating the spread of pollution in the snow cover using the interpolation method (inverse weighted distance method), application of map algebra for calculating the areas of impact zones. Results. The authors present the results of ecochemical monitoring of snow cover in the zone of aerotechnogenic impact of emissions Mondi Syktyvkar JSC for a multi-year period. A general description of the distribution of individual components of emissions relative to the emission center is given. Long-term monitoring of snow cover in the impact and sanitary protection zones of the enterprise allowed us to analyze the dynamics of the aerotechnogenic load on the territory. The results of the calculation of the modules of substances entering the territory of the enterprise in 2019 showed the lowest level of contamination of the snow cover for the entire observation period, 66 % compared to the volumes of 2002. A significant decrease in the content of components in the snow was noted, starting in 2016 after the reconstruction of the enterprise. Samples of snowmelt water outside the territory of the sanitary protection zone do not exceed the sanitary and hygienic standards for fisheries reservoirs for all components. In general, based on the results of ecochemical monitoring of snow cover, the trend towards reducing the aerogenic impact of the enterprise on the adjacent territory is shown.

River water quality modeling needs appropriate and suitable coefficients especially in application for specific river like urban river. Aim: This study aims to determine the value of the coefficient with a short term duration and a variable test time span. Several ways and methods of determining the rate of deoxygenation are developed according to the characteristics of the river and the environment. Modification method was applied in this research in which the test time span was unequal. The river chosen in this study is the Citepus River, Bandung, Indonesia representing an urban river in a tropical country. Methodology and Results: Sampling was carried out in the dry season. The laboratory analysis method used in determining the rate of deoxygenation uses the Slope Method of data from the short term incubation, which is ten days. The results showed that the Thomas Slope method's deoxygenation rate (K1) was 0.095 per day in the upstream segment, 0.917 per day in the middle segment, and 0.180 per day in the downstream segment. While the Ultimate BOD (La) value is 46.95 mg/l in the upstream segment, 38.70 mg/l in the middle segment, and 37.60 mg/l in the downstream segment. Conclusion, significance, and impact of study: The results of this study show that the value of the deoxygenation rate is similar to the theoretical surface water conditions. However, in the upstream segment, there is still a low deoxygenation rate value due to non-optimal activity of microorganisms. This findings will be very useful both in water quality modeling and river management.

Groundwater quality evaluation is important to gain an insight of contamination source. It can later be utilized to review the implementation of a water resource management policy in a specific region. Aim: This study evaluate the short-term temporal variation of groundwater quality and its possible contamination source in Sleman, Yogyakarta. Methodology and Results: the statistical approach was utilized using boxplot, principle component analysis (PCA) and correlation matrices, to the data for 50 sampling sites. The data of groundwater quality are available from the local environmental authority of Environmental Agency Sleman.The box plots revealed that groundwater quality might largely influenced by rainfall in the area. The factor loading of PCA presented that the ratio of concentration of both chloride and TDS are the most varied of all samples, and the less variable parameter is fluoride. The pair of groundwater quality parameter which had strong correlation were varied in each year, except for TDS and chloride that showed strong correlation in all three years. Nitrite had strong correlation with iron in 2017 and, nitrite also had strong correlation with both manganese and fluoride in 2019. The existence of fluoride in correlation with other parameter might give an insight of contamination from livestock wastes, where in the study area there are many poultry and cow farms, and small scale chicken slaughter industries. Conclusion, significant and impact study: This study gives preliminary understanding on temporal variation of groundwater quality, for further research on groundwater quality in Sleman, Yogyakarta.

Rainfall-runoff modeling is necessary for many hydrological studies, such as estimating peak discharges and designing hydraulic structures. The intensity and frequency of extreme climatic events necessitate the use of advanced approaches that incorporate different climatic and landscape parameters for rainfall-runoff modeling. The majority of small basins around the world lack hydrometric data. This study applied an artificial neural network (ANN) to simulate the rainfall-runoff process using data from field sampling plots in conjunction with rainfall and hydrometric data. For this purpose, similarly sized field plots were established among different land uses to determine the amounts of initial loss and infiltration during rainfall occurrences at the Talar basin in the north of Iran. The modeling process was carried out using a multi-layer perceptron network where the network inputs were rainfall time series, initial loss, soil antecedent moisture condition (A.M.C), and the time to peak of the basin, and the output was runoff time series. The data from rain gauge and hydrometric stations and field plots were collected for three consecutive months. The threefold exercises of training (R-sqr = 0.96, MSE = 0.005), cross-validation (R-sqr = 0.95, MSE = 0.006), and test (R-sqr = 0.81, MSE = 0.05) have yielded favorable results. The modeling results also indicated the significance of the cumulative rainfall data and initial loss in the modeling process. Results show that runoff time series and flood hydrograph can be simulated using the optimal inputs and an appropriate neural network structure for the basins without active hydrometric stations.

The chemical characteristic of flowback fluid from hydraulic fracturing for shale gas exploration/production in various localizations is presented. The results of statistical analysis have shown that variability in the chemical composition of these fluids is statistically significant and depends on the time difference between fracturing process and flowback sampling as well as sampling spot within the installation for flowback collection. Parameters which depend on sampling schedule (time and spot of sampling) are as follows: electrical conductivity and concentration of ammonia, boron, barium, calcium, lithium, sodium, magnesium, manganese, sodium, strontium, silicate, bromide, and chloride. Independent parameters are pH, total organic carbon (TOC), concentration of potassium, and iron. The ranges of the values of the characteristic parameters were determined, taking into account the representativeness of the samples, supported by statistical tests. The methods for the reuse of flowback fluids in terms of chemical composition are presented.

This paper presents a methodology to integrate mineral resource uncertainty from scarce orebody sampling and material mixing uncertainty due to ore flow into the life-of-mine production scheduling ...

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.