Organic carbon causes interference with nitrate and nitrite measurements by UV/Vis spectrometers: the importance of local calibration

Compared with sporadic conventional water sampling, continuous water-quality monitoring with optical sensors has improved our understanding of freshwater dynamics. The basic principle in photometric measurements is the incident light at a given wavelength that is either reflected, scattered, or transmitted in the body of water. Here, we discuss the transmittance measurements. The amount of transmittance is inversely proportional to the concentration of the substance measured. However, the transmittance is subject to interference, because it can be affected by factors other than the substance targeted in the water. In this study, interference with the UV/Vis sensor nitrate plus nitrite measurements caused by organic carbon was evaluated. Total or dissolved organic carbon as well as nitrate plus nitrite concentrations were measured in various boreal waters with two UV/Vis sensors (5-mm and 35-mm pathlengths), using conventional laboratory analysis results as references. Organic carbon increased the sensor nitrate plus nitrite results, not only in waters with high organic carbon concentrations, but also at the lower concentrations (< 10 mg C L^?1) typical of boreal stream, river, and lake waters. Our results demonstrated that local calibration with multiple linear regression, including both nitrate plus nitrite and dissolved organic carbon, can correct the error caused by organic carbon. However, high-frequency optical sensors continue to be excellent tools for environmental monitoring when they are properly calibrated for the local water matrix.     

A novel high-frequency groundwater quality monitoring system

High-frequency, long-term monitoring of water quality has revolutionized the study of surface waters in recent years. However, application of these techniques to groundwater has been limited by the ability to remotely pump and analyze groundwater. This paper describes a novel autonomous groundwater quality monitoring system which samples multiple wells to evaluate temporal changes and identify trends in groundwater chemistry. The system, deployed near Fresno, California, USA, collects and transmits high-frequency data, including water temperature, specific conductance, pH, dissolved oxygen, and nitrate, from supply and monitoring wells, in real-time. The system consists of a water quality sonde and optical nitrate sensor, manifold, submersible three-phase pump, variable frequency drive, data collection platform, solar panels, and rechargeable battery bank. The manifold directs water from three wells to a single set of sensors, thereby reducing setup and operation costs associated with multi-sensor networks. Sampling multiple wells at high frequency for several years provided a means of monitoring the vertical distribution and transport of solutes in the aquifer. Initial results show short period variability of nitrate, specific conductivity, and dissolved oxygen in the shallow aquifer, while the deeper portion of the aquifer remains unchanged—observations that may be missed with traditional discrete sampling approaches. In this aquifer system, nitrate and specific conductance are increasing in the shallow aquifer, while invariant changes in deep groundwater chemistry likely reflect relatively slow groundwater flow. In contrast, systems with high groundwater velocity, such as karst aquifers, have been shown to exhibit higher-frequency groundwater chemistry changes. The stability of the deeper aquifer over the monitoring period was leveraged to develop estimates of measurement system uncertainty, which were typically lower than the manufacturer’s stated specifications, enabling the identification of subtle variability in water chemistry that may have otherwise been missed.     

Influence of cabin conditions on placement and response of contaminant detection sensors in a commercial aircraft

Potential causalities due to airborne disease transmission and risk of chem-bio terrorism in commercial airliner cabins can be reduced by fast responses. Fast responses are only possible by using sensors at appropriate locations in the cabins. Cost, size and weight factors restrict the number of sensors that could be installed inside a cabin. Since release locations and seating patterns of passengers can impact airborne contaminant transports, this study first addressed this impact by using a validated computational fluid dynamics (CFD) program in a four-row mockup of twin-aisle airliner cabin. It was observed that occupancy patterns and release locations have little influence on longitudinal contaminant transports though localized variations of contaminant concentrations may exist. The results show that response time of the sensors is considerably reduced with the increase in number of sensors. If only a single sensor is available across a cabin cross-section then it should be placed at the middle of the ceiling. A cabin model of a fully occupied twin-aisle airliner with 210 seats was also build to study the diverse contaminant distribution trends along cabin length. The results reveal that seating arrangements can make cross-sectional airflow pattern considerably asymmetrical. Similar airflow patterns make the longitudinal contaminant transport in the business and economy classes alike. The presence of galleys greatly affected the longitudinal transport of contaminants in a particular cabin section. The effects due to galleys were less significant if a multipoint sampling system was used. The multipoint sampling system can also reduce the number of sensors required in a cabin.     

Continuous high-frequency monitoring of estuarine water quality as a decision support tool: a Dublin Port case study

High-frequency, continuous monitoring using in situ sensors offers a comprehensive and improved insight into the temporal and spatial variability of any water body. In this paper, we describe a 7-month exploratory monitoring programme in Dublin Port, demonstrating the value of high-frequency data in enhancing knowledge of processes, informing discrete sampling, and ultimately increasing the efficiency of port and environmental management. Kruskal–Wallis and Mann–Whitney tests were used to show that shipping operating in Dublin Port has a small–medium effect on turbidity readings collected by in situ sensors. Turbidity events are largely related to vessel activity in Dublin Port, caused by re-suspension of sediments by vessel propulsion systems. The magnitudes of such events are strongly related to water level and tidal state at vessel arrival times. Crucially, measurements of Escherichia coli and enterococci contamination from discrete samples taken at key periods related to detected turbidity events were up to nine times higher after vessel arrival than prior to disturbance. Daily in situ turbidity patterns revealed time-dependent water quality “hot spots” during a 24-h period. We demonstrate conclusively that if representative environmental assessment of water quality is to be performed at such sites, sampling times, informed by continous monitoring data, should take into account these daily variations. This work outlines the potential of sensor technologies and continuous monitoring, to act as a decision support tool in both environmental and port management.     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Identification of air pollutant sampling period using horizontal dilution potential

A new model is proposed for estimating horizontal dilution potential of an area using wind data. The mean wind speed and wind direction variation are used as a measure of linear and angular spread of pollutant in the atmosphere. The methodology is applied to monitored hourly wind data for each month of 1 year for wind data collected at Vadodara, Gujarat and monthly dilution potential is estimated. It is found that there is a gradual variation of horizontal dilution potential over a year with limited dilution during post monsoon period i.e., October and November and a high dilution in pre monsoon period i.e., May and June. This information can be used to design air quality sampling network and duration of sampling for source apportionment study. Air pollutant sampling during high dilution period can be carried out for identifying urban and rural dust and wind blown dust from mining activity. Air pollutant sampling during low dilution period can be carried out for capturing large amount of particulate matter from anthropogenic sources like elevated stack of furnace.     

Review of monitoring and assessing ground vegetation biodiversity in national forest inventories

Ground vegetation (GV) is an important component from which many forest biodiversity indicators can be estimated. To formulate policies at European level, taking into account biodiversity, European National Forest Inventories (NFIs) are one of the most important sources of forest information. However, for monitoring GV, there are several definitions, data collection methods, and different possible indicators. Even though it must be considered that natural conditions in different countries form very different understory types, each one has its own cost-efficient monitoring design, and they can hardly be compared. Therefore, the development of general guidelines is a particularly complex issue. This paper is a review of data collection methods and consequently a selection of the best available methods for the set of indicators with an emphasis on GV sampling methodologies in NFIs. As a final result, recommendations on GV definitions and classifications, sampling methodologies, and indicators are formulated for NFIs. Different sampling areas are recommended for each life form (shrubs, herbs, etc.). Inventory cycles and sampling seasons (depending on the phonological stages) should be specially considered and evaluated in the results. The proposed indicators are based on composition at different levels of sampling intensity for each life form and on coverage measurements.     

Siting analyses for water quality sampling in a catchment

Pollution loads discharged from upstream development or human activities significantly degrade the water quality of a reservoir. The design of an appropriate water quality sampling network is therefore important for detecting potential pollution events and monitoring pollution trends. However, under a limited budgetary constraint, how to site an appropriate number of sampling stations is a challenging task. A previous study proposed a method applying the simulated annealing algorithm to design the sampling network based on three cost factors including the number of reaches, bank length, and subcatchment area. However, these factors are not directly related to the distribution of possible pollution. Thus, this study modified the method by considering three additional factors, i.e. total phosphorus, nitrogen, and sediment loads. The larger the possible load, the higher the probability of a pollution event may occur. The study area was the Derchi reservoir catchment in Taiwan. Pollution loads were derived from the AGNPS model with rainfall intensity estimated using the Thiessen method. Analyses for a network with various numbers of sampling sites were implemented. The results obtained based on varied cost factors were compared and discussed. With the three additional factors, the chosen sampling network is expected to properly detect pollution events and monitor pollution distribution and temporal trends.     

Assessment of pumped mercury vapour adsorption tubes as passive samplers using a micro-exposure chamber

Mercury vapour adsorption tubes manufactured for pumped sampling and analysis have been evaluated for their performance as passive samplers. This has been done by exposing these tubes in a novel micro-exposure chamber. The uptake rates of these tubes have been found to be low (approximately 0.215 ml min(-1)) as compared to bespoke passive samplers for mercury vapour (typically in excess of 50 ml min(-1)). The measured uptake rates were shown to vary significantly between tubes and this was attributed to the variability in the air-sorbent interface and the proportion of the cross sectional area removed by the crimp in the quartz tubes used to secure the sorbent material. As a result of this variability the uptake rate of each tube must be determined using the micro-exposure chamber prior to deployment. Results have shown that the uptake rate determined in the micro-exposure chamber is invariant of concentration, and therefore these uptakes rates may be determined at a high mercury vapour concentration for many tubes at once in less than one hour. The uptake rate of the adsorption tubes under these conditions may be determined with a precision of 5%. Measurements made on a limited field trial in indoor and outdoor ambient air have shown that these tubes give results in acceptable agreement with more traditional pumped sampling methods, although longer sampling periods are required in order to reduce the uncertainty of the measurement, which is currently approximately 30%.     

扩散电化学法现场测定高湿度烟气中低浓度二氧化硫能力的 研究

根据目前定电位电解法出现的问题,对扩散电化学法测定固定污染源高湿度烟气中低浓度二氧化硫的能力展开研究。结果显示,扩散电化学法测定值与参比值保持较好的一致性,说明该方法不受固定污染源烟气浓度、湿度以及负压等因素的影响,对高湿烟气中低浓度二氧化硫具备较强测定能力,非常适合该状况下SO₂的现场测定。但是方法也受传感器制约,适用范围受到限制。     

基于精细化管理的环境物联网发展趋势

环境物联网运用智能传感器等设备感知生态环境的变化,监测环境质量、监控企业的排污行为,通过环境数据的采集、传输、处理、整合,辅助环境管理决策。近年来环境物联网得到长足发展,但现有的环境物联网体系无法满足多种任务和应用场景的需求,主要问题体现在数据质量和精准度缺乏保证,数据传输能力有限,数据共享还未打通,数据分析应用能力还有待提升。笔者系统分析了环境物联网感知层、传输层和应用层的现状与问题,较全面地研究了物联网与大数据技术的最新进展,提出了环境物联网的发展方向以解决上述问题,为实现精细化管理与科学决策提供科学参考。     

Addressing statistical and operational challenges in designing large-scale stream condition surveys

Implementing a statistically valid and practical monitoring design for large-scale stream condition monitoring and assessment programs can be difficult due to factors including the likely existence of a diversity of ecosystem types such as ephemeral streams over the sampling domain; limited resources to undertake detailed monitoring surveys and address knowledge gaps; and operational constraints on effective sampling at monitoring sites. In statistical speak, these issues translate to defining appropriate target populations and sampling units; designing appropriate spatial and temporal sample site selection methods; selection and use of appropriate indicators; and setting effect sizes with limited ecological and statistical information about the indicators of interest. We identify the statistical and operational challenges in designing large-scale stream condition surveys and discuss general approaches for addressing them. The ultimate aim in drawing attention to these challenges is to ensure operational practicality in carrying out future monitoring programs and that the resulting inferences about stream condition are statistically valid and relevant.     

Detection of petroleum hydrocarbons at low ppb levels using quartz resonator sensors and instrumentation of a smart environmental monitoring system

Petroleum hydrocarbon vapors at low ppb levels can be detected using a thickness shear mode resonator (TSMR) coated with a chemical-sensing overlayer, prepared by radiofrequency sputtering of porous sintered-polyethylene (PS-PE). The sensing capabilities of PS-PE sensors were profoundly affected by the sputtering methods; they were enhanced by the photo-excitation effect, and were reduced by carbonization and water treatment. The photo-assisted PS-PE sensor was extremely sensitive and could detect linear hydrocarbon (> C12) vapors below the ppb level. The time constant of the sorption curve, however, was large, indicating a slow sensing speed. Toward creating instrumentation for a smart environmental monitoring system, the TSMR sensors were arrayed on a circuit board equipped with a serial interface and signal processing chips of the oscillation drive and frequency counter. Co-sorption with water vapor at a relative humidity of about 10% has almost no effect on the sensing ability of PS-PE sensors for 1,2,4-trimethylbenzene. Conversely, it enhances the sensitivity of the TSMR sensor coated with a D-phenylalanine film. Upward shifts in the baseline are evident with elapsed time. However, a rigorous ten-cycle iteration test for 100 ppm toluene vapor demonstrated good reproducibility of the sensor's signals.     

Performance of semipermeable membrane devices for sampling of organic contaminants in groundwater

Lipid-filled semipermeable membrane devices (SPMDs) are receiving increasing attention as passive, in situ samplers for the assessment of environmental pollutant exposure. Although SPMDs have been successfully used in a variety of field studies in surface waters, only a few studies have addressed their characteristics as groundwater samplers. In this study, the performance of the SPMDs for monitoring organic contaminants in groundwater was evaluated in a pilot field application in an area severely contaminated by chemical waste, especially by chlorinated hydrocarbons. The spatial distribution of hydrophobic groundwater contaminants was assessed using a combination of passive sampling with SPMDs and non-target semiquantitative GC-MS analysis. More than 100 contaminants were identified and semiquantitatively determined in SPMD samples. Along the 6 field sites under investigation, a large concentration gradient was observed, which confirms a very limited mobility of hydrophobic substances in dissolved form in the aquifer. The in situ extraction potential of the SPMD is limited by groundwater flow, when the exchange volume of well water during an exposure is lower than the SPMD clearance volume for the analytes. This study demonstrates that SPMDs present a useful tool for sampling and analyzing of groundwater polluted with complex mixtures of hydrophobic chemicals and provides guidance for further development of passive sampling technology for groundwater.     

Effect of imperfect detectability on adaptive and conventional sampling: simulated sampling of freshwater mussels in the upper Mississippi River

Adaptive sampling designs are recommended where, as is typical with freshwater mussels, the outcome of interest is rare and clustered. However, the performance of adaptive designs has not been investigated when outcomes are not only rare and clustered but also imperfectly detected. We address this combination of challenges using data simulated to mimic properties of freshwater mussels from a reach of the upper Mississippi River. Simulations were conducted under a range of sample sizes and detection probabilities. Under perfect detection, efficiency of the adaptive sampling design increased relative to the conventional design as sample size increased and as density decreased. Also, the probability of sampling occupied habitat was four times higher for adaptive than conventional sampling of the lowest density population examined. However, imperfect detection resulted in substantial biases in sample means and variances under both adaptive sampling and conventional designs. The efficiency of adaptive sampling declined with decreasing detectability. Also, the probability of encountering an occupied unit during adaptive sampling, relative to conventional sampling declined with decreasing detectability. Thus, the potential gains in the application of adaptive sampling to rare and clustered populations relative to conventional sampling are reduced when detection is imperfect. The results highlight the need to increase or estimate detection to improve performance of conventional and adaptive sampling designs.     

Selecting the optimum plot size for a California design-based stream and wetland mapping program

Accurate estimates of the extent and distribution of wetlands and streams are the foundation of wetland monitoring, management, restoration, and regulatory programs. Traditionally, these estimates have relied on comprehensive mapping. However, this approach is prohibitively resource-intensive over large areas, making it both impractical and statistically unreliable. Probabilistic (design-based) approaches to evaluating status and trends provide a more cost-effective alternative because, compared with comprehensive mapping, overall extent is inferred from mapping a statistically representative, randomly selected subset of the target area. In this type of design, the size of sample plots has a significant impact on program costs and on statistical precision and accuracy; however, no consensus exists on the appropriate plot size for remote monitoring of stream and wetland extent. This study utilized simulated sampling to assess the performance of four plot sizes (1, 4, 9, and 16 km²) for three geographic regions of California. Simulation results showed smaller plot sizes (1 and 4 km²) were most efficient for achieving desired levels of statistical accuracy and precision. However, larger plot sizes were more likely to contain rare and spatially limited wetland subtypes. Balancing these considerations led to selection of 4 km² for the California status and trends program.     

Vacuum sampling techniques for industrial hygienists, with emphasis on beryllium dust sampling

The U.S. Department of Energy (DOE) Chronic Beryllium Disease Prevention Program Rule, 10 CFR Part 850 became effective in 2000 in response to the prevalence of Chronic Beryllium Disease (CBD) in workers. The rule requires surface and air monitoring for beryllium to determine exposure levels and the evaluation of the effectiveness of controls used to minimize or eliminate that risk. The most common methods for surface sampling use wet or dry wipes. Wipe sampling techniques may be impractical for many surfaces common to most buildings such as cinder block, textured wall surfaces, fabric and carpet. Vacuum sampling methods have been developed for the evaluation of lead or pesticides on residential surfaces such as carpets, bare floors and window sills. However, the current vacuum methods may be impractical for many workplace situations such as sampling of protective clothing, complex facility structures, or equipment surfaces. Recent work using vacuum sampling for potential bio-terrorism agents such as anthrax spores may have significant application to industrial hygiene evaluations of the workplace and may be extendable for use in sampling of metals such as beryllium. Validated vacuum sampling methods that provide meaningful data would be of great value to industrial hygienists in identifying areas having surface contamination, evaluating existing controls and work practices and determining the potential of toxic material on surfaces to become airborne and present a potential risk to workers and the public. This article discusses various vacuum sampling methodologies and recommends harmonization of sampling methods.     

A New All Season Passive Sampling System for Monitoring Ozone in Air

A new all season passive sampling system for monitoring O₃ in the atmosphere has been developed in the laboratory and validated in the field. The unique features for this system include a newly designed passive sampler and a rain shelter, which allow the passive sampler to be installed in the field facing downwards. An equation associated with meteorological parameters is used to calculate the passive sampling rates. This system has been extensively tested in the lab (temperature from –18 to 20°C, relative humidity from 13 to 81%, and wind speed from 0.5 to 150 cm/s) and validated in the field in climates of all seasons. The accuracy of the ozone concentrations in the atmosphere obtained with the use of the new passive sampling system was higher than 85% compared to those obtained with continuous ozone analyzers. The new ozone passive sampling system can be used to measure ambient O₃ concentrations ranging from 3 ppb to 1000 ppb based on one-day exposure and 0.1 ppb to 140 ppb for a monthly exposure period. It is also reasonable to conclude that the new passive sampling system can be used for eight-hour exposure study because of the low field blanks and high sampling rates.     

Methodology for Identifying Optimal Locations of Water Quality Sensors in River Systems

A method to optimally site river water quality sensors is expanded and applied to a case study river to explore the application of mathematical siting methods to the design of river sampling networks. Fecal coliform contamination due to flooded swine waste lagoons was modeled as it moves downstream, and optimal sensor locations are located by minimizing the objective function of the optimization problem. The results of the simulations are analyzed by varying the number of allowed sampling locations and the simulated contamination event. For the case study application, the model suggests three sampling locations along the modeled river section. These three suggested sensing points did not greatly vary in location for different river flows and contamination events, indicating the robustness of the model results for this specific case study. Generally, the application of mathematical contaminant modeling is a useful and systematic approach to aid the design of river water quality monitoring networks.