Measurement of Atmospheric Mercury: Current Limitations and Suggestions for Paths Forward.

Mercury (Hg) researchers have made progress in understanding atmospheric Hg, especially with respect to oxidized Hg (HgII) that can represent 2 to 20% of Hg in the atmosphere. Knowledge developed over the past ∼10 years has pointed to existing challenges with current methods for measuring atmospheric Hg concentrations and the chemical composition of HgII compounds. Because of these challenges, atmospheric Hg experts met to discuss limitations of current methods and paths to overcome them considering ongoing research. Major conclusions included that current methods to measure gaseous oxidized and particulate-bound Hg have limitations, and new methods need to be developed to make these measurements more accurate. Developing analytical methods for measurement of HgII chemistry is challenging. While the ultimate goal is the development of ultrasensitive methods for online detection of HgII directly from ambient air, in the meantime, new surfaces are needed on which HgII can be quantitatively collected and from which it can be reversibly desorbed to determine HgII chemistry. Discussion and identification of current limitations, described here, provide a basis for paths forward. Since the atmosphere is the means by which Hg is globally distributed, accurately calibrated measurements are critical to understanding the Hg biogeochemical cycle.

Advanced Noise Indicator Mapping Relying on a City Microphone Network.

In this work, a methodology is presented for city-wide road traffic noise indicator mapping. The need for direct access to traffic data is bypassed by relying on street categorization and a city microphone network. The starting point for the deterministic modeling is a previously developed but simplified dynamic traffic model, the latter necessary to predict statistical and dynamic noise indicators and to estimate the number of noise events. The sound propagation module combines aspects of the CNOSSOS and QSIDE models. In the next step, a machine learning technique-an artificial neural network in this work-is used to weigh the outcomes of the deterministic predictions of various traffic parameter scenarios (linked to street categories) to approach the measured indicators from the microphone network. Application to the city of Barcelona showed that the differences between predictions and measurements typically lie within 2-3 dB, which should be positioned relative to the 3 dB variation in street-side measurements when microphone positioning relative to the façade is not fixed. The number of events is predicted with 30% accuracy. Indicators can be predicted as averages over day, evening and night periods, but also at an hourly scale; shorter time periods do not seem to negatively affect modeling accuracy. The current methodology opens the way to include a broad set of noise indicators in city-wide environmental noise impact assessment.

Information Theory Solution Approach to the Air Pollution Sensor Location-Allocation Problem.

Air pollution is one of the prime adverse environmental outcomes of urbanization and industrialization. The first step toward air pollution mitigation is monitoring and identifying its source(s). The deployment of a sensor array always involves a tradeoff between cost and performance. The performance of the network heavily depends on optimal deployment of the sensors. The latter is known as the location-allocation problem. Here, a new approach drawing on information theory is presented, in which air pollution levels at different locations are computed using a Lagrangian atmospheric dispersion model under various meteorological conditions. The sensors are then placed in those locations identified as the most informative. Specifically, entropy is used to quantify the locations' informativity. This entropy method is compared to two commonly used heuristics for solving the location-allocation problem. In the first, sensors are randomly deployed; in the second, the sensors are placed according to maximal cumulative pollution levels (i.e., hot spots). Two simulated scenarios were evaluated: one containing point sources and buildings and the other containing line sources (i.e., roads). The entropy method resulted in superior sensor deployment in terms of source apportionment and dense pollution field reconstruction from the sparse sensors' network measurements.

Electromagnetic Fields Exposure Assessment in Europe Utilizing Publicly Available Data.

The ever-increasing use of wireless communication systems during the last few decades has raised concerns about the potential health effects of electromagnetic fields (EMFs) on humans. Safety limits and exposure assessment methods were developed and are regularly updated to mitigate health risks. Continuous radiofrequency EMF monitoring networks and in situ measurement campaigns provide useful information about environmental EMF levels and their variations over time and in different microenvironments. In this study, published data from the five largest monitoring networks and from two extensive in situ measurement campaigns in different European countries were gathered and processed. Median electric field values for monitoring networks across different countries lay in the interval of 0.67-1.51 V/m. The median electric field value across different microenvironments, as evaluated from in situ measurements, varied from 0.10 V/m to 1.42 V/m. The differences between networks were identified and mainly attributed to variations in population density. No significant trends in the temporal evolution of EMF levels were observed. The influences of parameters such as population density, type of microenvironment, and height of measurement on EMF levels were investigated.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review.

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography-mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Combining statistical methods for detecting potential outliers in groundwater quality time series.

Quality control of large-scale monitoring networks requires the use of automatic procedures to detect potential outliers in an unambiguous and reproducible manner. This paper describes a methodology that combines existing statistical methods to accommodate for the specific characteristics of measurement data obtained from groundwater quality monitoring networks: the measurement series show a large variety of dynamics and often comprise few (< 25) measurements, the measurement data are not normally distributed, measurement series may contain several outliers, there may be trends in the series, and/or some measurements may be below detection limits. Furthermore, the detection limits may vary in time. The methodology for outlier detection described in this paper uses robust regression on order statistics (ROS) to deal with measured values below the detection limit. In addition, a biweight location estimator is applied to filter out any temporal trends from the series. The subsequent outlier detection is done in z-score space. Tuning parameters are used to attune the robustness and accuracy to the given dataset and the user requirements. The method has been applied to data from the Dutch national groundwater quality monitoring network, which consists of approximately 350 monitoring wells. It proved to work well in general, detecting outliers at the top and bottom of the regular measurement range and around the detection limit. Given the diversity exhibited by measurement series, it is to be expected that the method does not give 100% satisfactory results. Measured values identified by the method as potential outliers will therefore always need to be further assessed on the basis of expert knowledge, consistency with other measurement data and/or additional research.

Atmospheric nitrogen deposition: A review of quantification methods and its spatial pattern derived from the global monitoring networks.

Atmospheric nitrogen (N) deposition is a vital component of the global N cycle. Excessive N deposition on the Earth's surface has adverse impacts on ecosystems and humans. Quantification of atmospheric N deposition is indispensable for assessing and addressing N deposition-induced environmental issues. In the present review, we firstly summarized the current methods applied to quantify N deposition (wet, dry, and total N deposition), their advantages and major limitations. Secondly, we illustrated the long-term N deposition monitoring networks worldwide and the results attained via such long-term monitoring. Results show that China faces heavier N deposition than the United States, European countries, and other countries in East Asia. Next, we proposed a framework for estimating the atmospheric wet and dry N deposition using a combined method of surface monitoring, modeling, and satellite remote sensing. Finally, we put forth the critical research challenges and future directions of the atmospheric N deposition. CAPSULE: A review of quantification methods and the global data on nitrogen deposition and a systematic framework was proposed for quantifying nitrogen deposition.

Node Deployment of Marine Monitoring Networks: A Multiobjective Optimization Scheme.

The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.

Concentration trends of metals in ambient air in the UK: a review.

The monitoring of metals in ambient air has been undertaken for over 40 years on a national basis in the UK. During this period, the UK pollution landscape has continued to evolve in terms of emission sources, and the measurement framework for metals in ambient air, the UK Heavy Metals Monitoring Network, has also been subject to significant configuration changes. Therefore, this work provides a timely review of more recent concentration trends in the context of current emission profiles. Overall, throughout this time period, there has been a significant downward trend in the emissions and consequently, the measured concentrations of most metals in UK ambient air. Ambient concentrations were generally found to be well correlated with emission estimates. Analysis of the sensitivity of measured concentrations to emissions suggests that concentrations have fallen faster than the reduction in emission estimates would have predicted at typical median urban background sites.

Evaluation of the air pollution in a Mediterranean region by the air quality index.

The increasing expansion of the cities together with activities carried out on the environment by men have contributed to the deterioration of air quality. Air quality index measures how much air is free from pollution. Being aware of the healthiness of the breathed air is a right for the people. Public authorities must constantly inform the population on air quality status. Even though several pollutants are monitored by the air quality monitoring networks, providing a significant amount of data, their interpretation and presentation to the population by the public authorities is a difficult task. Some countries, for several years, have adopted evaluation procedures through daily indices that succinctly describe the air quality status in different areas of the city. The use of an index which is able to give a simple and quick information to the population represents a possible solution for the public authorities. Concerning a Mediterranean area, it has not yet been possible to adopt a single indicator to be used for informing the population. In this work, the air quality status is highlighted by the air quality index (AQI) whose purpose is to inform, in a simple and immediate way, the population. Analyzing the AQI's trend from 2013 to 2015, it was possible to assess the air quality status, obtaining an overall scenario for the purpose of protecting human health and the ecosystems. We point out that this kind of research could be applied to every region or municipality.

Development of land use regression models for PM2.5, SO2, NO2 and O3 in Nanjing, China.

Ambient air pollution has been a global problem, especially in China. Comparing with other methods, Land Use Regression (LUR) models can obtain air pollutant concentration distribution at finer scale without the air pollution source data based on a few monitoring sites and predictors. However, limited LUR studies have been conducted on the basis of regular monitoring networks. Thus, we explored the applicability of conducting LUR models for four key air pollutants: PM2.5, SO2, NO2 and O3, on the basis of national monitoring networks which have good representation of areas with different characteristics in Nanjing, China. Fifty-nine potential predictor variables were considered, including land use type, population density, traffic emission, industrial emission, geographical coordinates, meteorology and topography. LUR models of these four air pollutants were with good explained variance for four key air pollutants. Adjusted explained variance of the LUR models was highest for NO2 (87%), followed by SO2 (83%), and was lower for PM2.5 (72%) and O3 (65%). Annual average distributions of pollutants in 2013 were obtained based on predicted values, which revealed that O3 in Nanjing was more heavily impacted by regional influences. This study would not only contribute to the wider use of LUR studies in China but also offer important reference for the application of regular monitoring network with high representativeness in LUR studies. These results would also support for air epidemiological studies in the future.

An Operational In Situ Soil Moisture & Soil Temperature Monitoring Network for West Wales, UK: The WSMN Network.

This paper describes a soil moisture dataset that has been collecting ground measurements of soil moisture, soil temperature and related parameters for west Wales, United Kingdom. Already acquired in situ data have been archived to the autonomous Wales Soil Moisture Network (WSMN) since its foundation in July 2011. The sites from which measurements are being collected represent a range of conditions typical of the Welsh environment, with climate ranging from oceanic to temperate and a range of the most typical land use/cover types found in Wales. At present, WSMN consists of a total of nine monitoring sites across the area with a concentration of sites in three sub-areas around the region of Aberystwyth located in Mid-Wales. The dataset of composed of 0-5 (or 0-10) cm soil moisture, soil temperature, precipitation, and other ancillary data. WSMN data are provided openly to the public via the International Soil Moisture Network (ISMN) platform. At present, WSMN is also rapidly expanding thanks to funding obtained recently which allows more monitoring sites to be added to the network to the wider community interested in using its data.

BMRC: A Bitmap-Based Maximum Range Counting Approach for Temporal Data in Sensor Monitoring Networks.

Due to the rapid development of the Internet of Things (IoT), many feasible deployments of sensor monitoring networks have been made to capture the events in physical world, such as human diseases, weather disasters and traffic accidents, which generate large-scale temporal data. Generally, the certain time interval that results in the highest incidence of a severe event has significance for society. For example, there exists an interval that covers the maximum number of people who have the same unusual symptoms, and knowing this interval can help doctors to locate the reason behind this phenomenon. As far as we know, there is no approach available for solving this problem efficiently. In this paper, we propose the Bitmap-based Maximum Range Counting (BMRC) approach for temporal data generated in sensor monitoring networks. Since sensor nodes can update their temporal data at high frequency, we present a scalable strategy to support the real-time insert and delete operations. The experimental results show that the BMRC outperforms the baseline algorithm in terms of efficiency.

Flow-directed PCA for monitoring networks.

Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow-connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow-connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies.

Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks.

Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poço Tarzan (PT) and the least is Parque Petrópolis (PP).

Datasets on bulk density and coarse fragment content from the French soil quality monitoring network.

Various stakeholders, such as modelers, policy makers, farmers, and environmental regulators need reliable soil bulk density and coarse fragment content data. These two soil parameters are necessary to calculate soil carbon and nutrients stocks, to estimate water availability for plants, or to assess soil compaction. However, measuring these two parameters is labor intensive and time consuming. Therefore, many agricultural and environmental studies often miss these two soil parameters. Here, we provide four datasets, one with bulk density and coarse fragment contents of topsoil and subsoil, measured in two campaigns of the French Soil Quality Monitoring Network (RMQS for its acronym in French), a second one with the average values for bulk density and coarse fragments of the two campaigns at 0-30 cm and 30-50 cm. The third and the fourth ones are the raw data needed to calculate the two first datasets divided by campaign. In addition, the R script for calculating the depth-weighted values per soil layer is provided.

Saltwater intrusion into coastal aquifers in the contiguous United States - A systematic review of investigation approaches and monitoring networks.

Saltwater intrusion (SWI) into coastal aquifers is a growing problem for the drinking water supply of coastal communities worldwide, including for the sustainability of coastal ecosystems depending on freshwater inflow. The interface between freshwater and seawater in coastal aquifers is highly dynamic and is sensitive to changes in the hydraulic gradient between the sea- and groundwater levels. Sea level rise, storm surges, and drought are natural drivers changing the hydrostatic equilibrium between fresh- and saltwater. Coastal aquifers are further stressed by groundwater over-pumping because of the increasing needs of coastal populations. A systematic literature review and analysis of the current state of understanding the SWI drivers is presented, focusing on recent (1980 to 2020) investigations in the contiguous United States (CONUS). Results confirm that SWI is an active research area in CONUS. The drivers of SWI are increasingly better understood and quantified; however, the need for increased monitoring is also recognized. Our study shows that the number of monitoring sites have not increased significantly over the review period. Additionally, geophysical, and geochemical investigation techniques and numerical modeling tools are not utilized to their full potential, and data on SWI is not readily available from some sources. We conclude that there is a need for more SWI monitoring networks and closer multi-disciplinary collaboration, particularly between practitioners in the field and emerging modeling technique experts. Though we focus primarily on CONUS, our insights may be of value to the broader SWI research community and coastal water quality managers around the globe.

Spatiotemporal representativeness of air pollution monitoring in Dublin, Ireland.

The importance of selecting appropriate air pollution monitoring sites in a city is vital for accurately reporting air quality, enhancing the quality of high-resolution modelling and informing policy to implement measures to deliver cleaner air in the urban environment. COVID-19 restrictions impacted air quality in urban centres worldwide as reduced mobility led to changes in traffic-related air pollution (TRAP). As such, it offered a unique dataset to examine the spatial and temporal variations in air quality between monitoring stations in Dublin, Ireland. Firstly, an analysis of mobility data showed reductions across almost all sectors after COVID-19 restrictions came into place, which was expected to lower TRAP. In addition, similar changes in air quality were evident to other cities around the world: reductions in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and an increase in ozone (O3) concentrations. Average daily and diurnal concentrations for these three pollutants presented more statistically significant spatial and temporal changes during COVID-19 restrictions at monitoring sites with urban or traffic classifications than suburban background sites. Furthermore, substantial reductions in the range of average hourly pollutant concentrations were observed, 79% for PM2.5 and 75% for NO2, with a modest 24% reduction for O3. Correlation analysis of air pollution between monitoring sites and years demonstrated an improvement in the R2 for NO2 concentrations only, suggesting that spatiotemporal homogeneity was most notable for this TRAP due to mobility restrictions during COVID-19. The spatiotemporal representativeness of monitoring stations across the city will change with greener transport, and air quality during COVID-19 can provide a benchmark to support the introduction of new policies for cleaner air.

Ozone flux in plant ecosystems: new opportunities for long-term monitoring networks to deliver ozone-risk assessments.

Ozone (O3) is a photochemically formed reactive gas responsible for a decreasing carbon assimilation in plant ecosystems. Present in the atmosphere in trace concentrations (less than 100 ppbv), this molecule is capable of inhibiting carbon assimilation in agricultural and forest ecosystems. Ozone-risk assessments are typically based on manipulative experiments. Present regulations regarding critical ozone levels are mostly based on an estimated accumulated exposure over a given threshold concentration. There is however a scientific consensus over flux estimates being more accurate, because they include plant physiology analyses and different environmental parameters that control the uptake-that is, not just the exposure-of O3. While O3 is a lot more difficult to measure than other non-reactive greenhouse gases, UV-based and chemiluminescence sensors enable precise and fast measurements and are therefore highly desirable for eddy covariance studies. Using micrometeorological techniques in association with latent heat flux measurements in the field allows for the partition of ozone fluxes into the stomatal and non-stomatal sinks along the soil-plant continuum. Long-term eddy covariance measurements represent a key opportunity in estimating carbon assimilation at high-temporal resolutions, in an effort to study the effect of climate change on photosynthetic mechanisms. Our aim in this work is to describe potential of O3 flux measurement at the canopy level for ozone-risk assessment in established long-term monitoring networks.

Semivariance analysis and transinformation entropy for optimal redesigning of nutrients monitoring network in San Francisco bay.

This paper introduces a Semivariance-Transinformation (S-T) based method for designing an optimum bay water nutrients monitoring network in San Francisco bay (S.F. bay), USA. Phosphorus and nitrogen are the most important nutrients that lead to eutrophic condition. The monthly phosphate and nitrate+nitrite data recorded during September 2006 to August 2015 was obtained over 14 active stations located at S.F. bay and was used in the research. Semivariance and discrete transinformation entropy have been applied to calculate the optimum range of the monitoring distance. The study indicated the ranges of 28 to 82 and 37 to 50km for the phosphate and nitrate+nitrite respectively. Useful information can be obtained from the monitoring network, if the monitoring distance is included in the mentioned intervals. The findings of the research introduce a new approach in the field of water quality monitoring networks design.