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Monitoring groundwater levels in areas experiencing depletion is crucial for effective resource management. This study combines two approaches for estimating groundwater levels in regions lacking sufficient data for better spatial distribution estimates. To achieve this, several Artificial Intelligence (AI) models with different input features were developed using monthly groundwater level data from 2010 to 2023 in the Sacramento Valley, California. The results indicated that the Random Forest (RF) and Gradient Boosting Regressor (GBR) models, with Root Mean Square Error (RMSE) of 7.03 m and 7.83 m in the testing phase, respectively, were the most accurate. Subsequently, the data for each year in 2010–2023 were interpolated using the Ordinary Kriging (OK) method. The outputs of this method and the outputs from RF and GBR models were then merged using Bayesian Model Averaging (BMA). For 2010, 2015, 2020, and 2023, this approach reduced groundwater level estimation errors by 31.18, 41.87, 50.60, and 45.04%, respectively. Additionally, the results showed that the integrating method could reduce groundwater level estimates’ RMSE and Mean Absolute Error (MAE) by an average of 41.12 and 33.72% over 2010–2023. [ABSTRACT FROM AUTHOR]

Monitoring air pollutants over time is essential for identifying and addressing trends, which may help improve air quality management and safeguard public health. This study investigates the spatio-temporal variability of air quality in the Metropolitan Area of Curitiba (MAC), Brazil, focusing on six pollutants (SO2, NO2, NOx, O3, CO, and PM10) measured at eight monitoring stations from 2003 to 2017. We conducted statistical analyses, including diurnal cycles, seasonal variability, spatio-temporal correlations, conditional bivariate probability functions, Theil-Sen trend analysis, and comparison with national quality standards (NAQS) and World Health Organization (WHO) guidelines. The analyses revealed large variations in pollutant concentrations across the study area. For instance, stations strongly impacted by industrial emissions presented the highest mean annual SO2 (20–28 μg/m3) and PM10 (32–34 μg/m3) concentrations, while those mostly impacted by traffic showed elevated NO2 (31–39 μg/m3), NOx (63–86 μg/m3) and CO (0.6–0.8 mg/m3) concentrations. The two residential stations recorded the highest O3 concentrations (annual mean of 30–32 μg/m3). Seasonal and diurnal patterns varied by pollutant, with winter experiencing higher concentrations and O3 peaking in spring. SO2 concentrations presented no clear seasonal or diurnal cycle patterns, and showed the highest spatial variability. Significant decreasing annual trends were observed for SO2 (−5.9%), NO2 (−2.6%), NOx (−2.6%), CO (−5.4%), and PM10 (−3.7%), which suggests the success of emission reduction programs implemented in the road transportation and industrial sectors. However, O3 concentrations increased at most stations (+3.3%/yr), likely due to reduced NOx emissions, increased emissions of volatile organic compounds from on-road transport biofuels, and regional O3 transport. Although exceedances of NAQS decreased over time, concentrations of most pollutants remained above WHO guidelines, except for CO. These results highlight the importance of targeted emission control strategies for both industrial and vehicular sources to improve local air quality and inform future policy decisions.

Groundwater level is the most important parameter in any study involving the evaluation, development and management of groundwater resources. Systematic monitoring of groundwater levels, which commenced with the establishment of the Central Ground Water Board (CGWB), has been of immense use in addressing several challenges like prioritization of areas for groundwater recharge, delineating areas prone to waterlogging, estimation of storage change in the aquifers, estimation of groundwater flow, etc. In a major boost to strengthen groundwater monitoring in the country, the Government of India has sanctioned a special project under which CGWB has envisaged to construct 9000 purpose-built wells (piezometers) in identified priority areas, which will be equipped with digital water-level recorders (DWLRs) and telemetry devices for acquisition and transmission of groundwater levels at increased frequency. The intended uses of the long-term high-frequency data include monitoring short-term and long-term changes in the groundwater levels, groundwater storage and recharge to the aquifers, monitoring the effects of climatic variability, estimating transboundary flow, assessing regional effects of groundwater development, quantifying impacts of water conservation and artificial recharge projects, and improved understanding of groundwater and surface water interactions. High-frequency groundwater level data also have the potential for steering multiinstitutional collaborative research projects in the country, particularly for studying the impact of groundwater extraction on land subsidence, the relationship between groundwater levels and tectonic disturbances, and climate change impacts on the groundwater regime. [ABSTRACT FROM AUTHOR]

Radon (Rn) is a radioactive, colourless, odourless, noble gas that decays rapidly. It's most stable isotope, 222Rn, has a half-life of around 3.8 days. Atmospheric radon measurements play an important role in understanding our atmospheric environments. Naturally occurring radon can be used as an atmospheric tracer for airmass tracking, to assist in modelling boundary layer development, and is important for understanding background radiation levels and personal exposure to natural radiation. The daughter products from radon decay also play an important role when measuring fine particle pollution using beta-attenuation monitors (BAM). Beta radiation from the 222Rn decay chain interferes with BAM measurements of fine particles; thus, some BAMs incorporate radon measurements into their sampling systems. BAMs are ubiquitous in air quality monitoring networks globally and present a hitherto unexplored source of dense, continuous radon measurements. In this paper, we compare in situ real world 222Rn measurements from a high quality ANSTO dual flow loop, dual filter radon detector, and the radon measurements made by a commercial BAM instrument (Thermo 5014i). We find strong correlations between systems for hourly measurements (R2 = 0.91), daily means (R2 = 0.95), hour of day (R2 = 0.72–0.94), and by month (R2 = 0.83–0.94). The BAM underestimates radon by 22–39%; however, the linear response of the BAM measurements implies that they could be corrected to reflect the ANSTO standard measurements. Regardless, the radon measurements from BAMs could be used with correction to estimate local mixed layer development. Though only a 12-month study at a single location, our results suggest that radon measurements from BAMs can complement more robust measurements from standard monitors, augment radon measurements across broad regions of the world, and provide useful information for studies using radon as a tracer, particularly for boundary layer development and airmass identification. [ABSTRACT FROM AUTHOR]

For the Hessian river basins, an area-differentiated modeling of the nitrogen input to the groundwater and surface waters was carried out for six diffuse input pathways and six point source input pathways on the basis of the geodata available at the state level. In this context, extensive plausibility checks of the model results were carried out using the data from several official monitoring networks at the state level. These include the comparison of modeled runoff components and input pathways for nitrogen using the data from the network of discharge monitoring stations. For the validation of the modeled nitrate concentrations in the leachate, the data from groundwater monitoring wells for controlling the chemical status of groundwater were used. The validation of the modeled nitrate inputs to the groundwater and denitrification in the groundwater was carried out using the data from a special monitoring network of groundwater monitoring wells that include N2/Ar measurements. The data from the Surface Water Quality Monitoring Network were used to verify the plausibility of the modeled total N inputs to the surface waters from diffuse sources and from point sources. All of the model results evaluated by the plausibility checks prove that the nitrate pollution situation in Hesse is adequately represented by the model. This is a prerequisite for accepting the model results at the state level as a basis for developing and implementing regionally appropriate mitigation measures. The Hessian State Agency for Nature Conservation, Environment and Geology uses the model results in the broader context of the work on implementing the EU Water Framework Directive and the EU Nitrate Directive. [ABSTRACT FROM AUTHOR]

We address the feasibility of the pragmatic implementation of monitoring systems for real-time distributed fault diagnosis in complex processes. We delve into the integration of theoretical distributed estimation methods and practical distributed embedded systems. This study emphasizes the merger of distributed computing and signal processing to augment fault diagnosis in dynamic systems. We introduce a generic mathematical model for distributed fault diagnosis algorithms, laying the groundwork for a reference network computing architecture that details the essential components, organization, and operational characteristics necessary for distributed monitoring systems. Subsequently, we evaluate existing software and hardware solutions that facilitate the realization of these systems. The theoretical framework and system design are empirically validated through an experimental setup involving a liquid-level control system, demonstrating the efficacy and applicability of our approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as well as specific scientific areas that are focused on environmetrics research in Canada including climate science, forestry, and fisheries, which are areas of importance for natural resources in Canada. [ABSTRACT FROM AUTHOR]

Radon (Rn) is a radioactive, colourless, odourless, noble gas that decays rapidly. It’s most stable isotope, 222Rn, has a half-life of around 3.8 days. Atmospheric radon measurements play an important role in understanding our atmospheric environments. Naturally occurring radon can be used as an atmospheric tracer for airmass tracking, to assist in modelling boundary layer development, and is important for understanding background radiation levels and personal exposure to natural radiation. The daughter products from radon decay also play an important role when measuring fine particle pollution using beta-attenuation monitors (BAM). Beta radiation from the 222Rn decay chain interferes with BAM measurements of fine particles; thus, some BAMs incorporate radon measurements into their sampling systems. BAMs are ubiquitous in air quality monitoring networks globally and present a hitherto unexplored source of dense, continuous radon measurements. In this paper, we compare in situ real world 222Rn measurements from a high quality ANSTO dual flow loop, dual filter radon detector, and the radon measurements made by a commercial BAM instrument (Thermo 5014i). We find strong correlations between systems for hourly measurements (R2 = 0.91), daily means (R2 = 0.95), hour of day (R2 = 0.72–0.94), and by month (R2 = 0.83–0.94). The BAM underestimates radon by 22–39%; however, the linear response of the BAM measurements implies that they could be corrected to reflect the ANSTO standard measurements. Regardless, the radon measurements from BAMs could be used with correction to estimate local mixed layer development. Though only a 12-month study at a single location, our results suggest that radon measurements from BAMs can complement more robust measurements from standard monitors, augment radon measurements across broad regions of the world, and provide useful information for studies using radon as a tracer, particularly for boundary layer development and airmass identification.

Groundwater monitoring networks represent the main source of information about water levels and water quality within aquifers. In this paper, a method is proposed for the optimal design of monitoring networks to obtain groundwater-level data of high spatial relevance at a low cost. It uses the estimate error variance reduction obtained with the static Kalman filter as optimization criteria, while simultaneously evaluating the optimal routes to follow through the traveling salesman problem. It was tested for a network of 49 wells in the Calera aquifer in Zacatecas, Mexico. The study area was divided into three zones, and one working day (8 h) was taken to visit each one, with an average speed of 40 km/h and a sampling time of 0.5 h. An optimal network of 26 wells was obtained with the proposal, while 21 wells should be monitored if the optimal routing is neglected. The average standard error using 49 wells of the original network was 35.01 m, an error of 38.35 m was obtained for 21 wells (without optimal routing) and 38.36 m with the 26 wells selected using the proposal. However, the latter produce estimates closer to those obtained with the 49 wells. Following the proposal, more field data can be acquired, reducing costs. [ABSTRACT FROM AUTHOR]

The Central Volcanic Zone of the Andes (CVZA) has been the focus of volcanological research for decades, becoming a very important site to understand a number of volcanic processes. Despite most of the research in the CVZA being carried out by foreign scientists, the last two decades have seen a significant increase in contributions by regional researchers. This surge has been facilitated by the creation of new volcanic observatories, improvement of the monitoring networks, creation of postgraduate programs where new local volcanologists are trained, creation of specialized research nuclei or groups, and increasing investment in research. This article presents a review of the evolution of the contributions of the regional volcanological community to the knowledge of the CVZA in the last 20 years (2000–2019), both from research and monitoring institutions in Peru, Bolivia, Argentina, and Chile. Based on updates made by the regional groups, a new list of active/potentially active volcanoes of the CVZA is presented, as is a complete database for article published on the CVZA. We find that a significant motivator has been regional volcanic unrest that has triggered new investment. Perú is the country with the highest investment in monitoring and research and is the best instrumented, Argentina is the country with the highest number of local participation in published papers in the domain of volcanology and magmatic systems, and Chilean volcanoes are the focus of the highest number of articles published. The current situation and general projections for the next decade (2020–2030) are also presented for each country, where we believe that the over the next 10 years, will be increased the monitoring and research capabilities, improved the scientific knowledge with more participation of regional institutions, and strengthen the collaboration and integrated work between CVZA countries, especially in border volcanoes. [ABSTRACT FROM AUTHOR]

Following its 40-year reform and 'Open Door' policy, China has recently proposed a new approach to green development and rural revitalization—the idea of Agriculture Green Development (AGD), with the key feature of creating a green eco-environment. In this mini-review we introduce the definition, theory, framework and major components of a green eco-environment as a key part of the AGD. We define a green eco-environment as including four key elements or measures: (1) a green eco-environmental indicator system; (2) environmental monitoring and warning networks; (3) emission standards and environmental thresholds for key pollutants; (4) emission controls and pollution remediation technologies. We have used Quzhou County (a typical county in the center of the North China Plain) as an example to show how detailed air, water and soil monitoring networks, as well as improved farmer practices and pollution control measures (especially ammonia emission mitigation and PM2.5 pollution reduction), can begin to create a green eco-environment in China and that AGD is possible. We conclude by stressing the need to improve the framework and practice for a green eco-environment, especially the importance of linking proposals and practices for a green eco-environment with the United Nations high priority Sustainable Development Goals.

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.

High‐intensity and short‐duration rainfalls can generate sudden and abundant runoff at the base of rocky cliffs that, entraining sediments, may originate debris flows. Two gauge networks have been set up in headwater sites of Dolomites (Northeastern Italian Alps) to monitor rainfall corresponding to the debris‐flow activity occurring there. The rain gauges are positioned both upstream and downstream the initiation areas of debris flows. Other five rain gauges sparse in the area integrate the two networks. In the years 2009–2020, rain gauges recorded rainfalls that triggered 41 debris flows. In most cases, rainfalls show a higher spatial variability along with both distance and altitude. Precipitation data are then compared with rainfalls estimated through a weather radar far about 70 km from there, to verify the possible interchangeability of the two measurement systems for the prediction of debris‐flow occurrence through suitable modeling of triggering discharges. The following results are obtained: (1) raw‐radar images mostly tend to underestimate precipitations recorded by rain gauges; (2) such underestimation entails, on average, a larger one on the simulated discharges and the prediction of debris‐flow occurrences (missed in 65% of the cases). Some methods for the correction on ground truth of raw‐radar images are applied to assess their use for evaluating the triggering discharges. Results show that once corrected using rain gauge data, radar‐derived rainfall estimates produce debris‐flow initiation predictions that more frequently match observations. Therefore, the presence of rain gauges close to the watershed centroids results essential for early warning systems based on triggering discharge modeling. Key Points: In the last decade several debris flows have been triggered in the highly tourist Boite ValleyRain gauge and radar recorded rainfalls have been used to simulate debris‐flow triggering dischargesIn this area radar needs the presence of rain gauges to be used in early warning systems [ABSTRACT FROM AUTHOR]

Mercury (Hg) researchers have made progress in understanding atmospheric Hg, especially with respect to oxidized Hg (Hg II ) 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 Hg II 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 Hg II chemistry is challenging. While the ultimate goal is the development of ultrasensitive methods for online detection of Hg II directly from ambient air, in the meantime, new surfaces are needed on which Hg II can be quantitatively collected and from which it can be reversibly desorbed to determine Hg II 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.

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.

• The current state of air quality (PM 10 , PM 2.5 , NOx, S O2 , and O 3) in larger cities in South America were assessed. • PM 2.5 and PM 10 annual concentrations exceed the WHO guideline in all cities between 2010 and 2017. • SO 2 annual averages concentration was below the national standards in all cities. • Air Quality Monitoring network in South America have poor quality and consistency. • The region does not report a trend in reducing annual pollutant concentrations. Air pollution is one of the most persistent environmental issues in South America, with exposure to air pollutants being associated with increased mortality and morbidity. According to estimates of the World Health Organization (WHO), in 2016, 91 % of the global population lived in cities that exceeded the WHO PM 2.5 annual guideline (10 μg/m3). In Latin America and the Caribbean, other studies affirm that approximately 100 million people are exposed to poor air quality, exceeding WHO guidelines. This study presents a review of long-term (annual) and short-term (daily) concentrations of nitrogen dioxide (NO 2), sulfur dioxide (SO 2), particulate matter (PM 10 and PM 2.5), carbon monoxide (CO), and ozone (O 3), collected between 2010 and 2017 by the automatic monitoring networks of 11 metropolitan areas of South America, including three of global 33 megacities (Rio de Janeiro, São Paulo, and Buenos Aires), and three from all 34 largest cities in the world (Bogotá, Lima, and Santiago). Despite efforts to monitor air quality, in some cities, the information on air quality provided by environmental authorities still has poor publicity and presentation, making it difficult to take action for critical air pollution episodes. Annual particulate matter (PM 2.5 and PM 10) monitored in all cities (2010-2017) exceeded the World Health Organization Air Quality Guidelines (WHO-AQG). The annual NO 2 guideline was exceeded at least in one city between 2010 and 2017, except in 2014. Most average daily concentrations of SO 2 in South America were below the WHO-AQG. Still, Vitória, Rio de Janeiro, and Belo Horizonte Metropolitan Areas presented values over WHO guidelines, and for the two last cities, over intermediate standards of Brazilian national legislation. Most of the ozone concentration (8 -h running average) was below WHO-AQG, but Rio de Janeiro, São Paulo, and Belo Horizonte presented exceedances of this limit between 2010 and 2017. Although some cities in South America have their pollutant concentrations reduced since 2010 (PM 2.5, for example), such as São Paulo, Vitória, and Bogotá, the region does not report a trend in the same direction, with the WHO guidelines and national or local standards being continuously exceeded. [ABSTRACT FROM AUTHOR]

Air quality monitoring stations are essentials for monitoring air pollutants and, therefore, are essential to protect the public health and the environment from the adverse effects of air pollution. Two or more stations may monitor the same pollutant behavior. In this scenario, the equipment must be reallocated to provide a better use of public resources and to enlarge the monitored area. The identification of redundant stations can be carried out by the application of principal component analysis (PCA) as a grouping technique. The principal component analysis is a set of linear combinations of the original variables constructed to explain the variance–covariance structure of the data. It is well known that outliers affect the covariance structure of the variables. Since the components are computed by using the covariance or the correlation matrix, the outliers also affect the properties of the components. This article proposes a grouping methodology that applies robust PCA to identify air quality monitoring stations that present similar behavior for any pollutant or meteorological measure. To illustrate the usefulness of the proposed methodology, the robust PCA is applied to the management of the automatic air quality monitoring network of the Greater Vitória Region in Brazil that consists of 8 stations. It was found that four components could explain 84% of the total variability, and it is possible to create a group composed of at least two stations in each one of the components. Therefore, the redundant stations can be installed in a new site to expand the monitored area. [ABSTRACT FROM AUTHOR]

The discipline of hydrology has long focused on quantifying the water balance, which is frequently used to estimate unknown water fluxes or stores. While technologies for measuring water balance components continue to improve, all components of the balance have substantial uncertainty at the watershed scale. Watershed‐scale evapotranspiration, storage, and groundwater import or export are particularly difficult to measure. Given these uncertainties, analyses based on assumed water balance closure are highly sensitive to uncertainty propagation and errors of omission, where unknown components are assumed negligible. This commentary examines how greater insight may be gained in some cases by keeping the water balance open rather than applying methods that impose water balance closure. An open water balance can facilitate identifying where unknowns such as groundwater import/export are affecting watershed‐scale streamflow. Strategic improvements in monitoring networks can help reduce uncertainties in observable variables and improve our ability to quantify unknown parts of the water balance. Improvements may include greater spatial overlap between measurements of water balance components through coordination between entities responsible for monitoring precipitation, snow, evapotranspiration, groundwater, and streamflow. Measuring quasi‐replicate watersheds can help characterize the range of variability in the water balance, and nested measurements within watersheds can reveal areas of net groundwater import or export. Well‐planned monitoring networks can facilitate progress on critical hydrologic questions about how much water becomes evapotranspiration, how groundwater interacts with surface watersheds at varying spatial and temporal scales, how much humans have altered the water cycle, and how streamflow will respond to future climate change. Plain Language Summary: The water balance is a fundamental concept in hydrology that underlies many tools for predicting streamflow, soil moisture, or groundwater availability. It is often expressed as an equation that relates water inputs, outputs, and storage for a watershed. Inputs can be rainfall, snowmelt, or water imports to the watershed. Outputs include water movement into the atmosphere (evaporation, transpiration, and sublimation), streamflow, and water exports through groundwater or human diversions. Water storage can be in snow or ice, surface water bodies, or underground. Each of these water balance components is difficult to measure, and some are rarely measured. Therefore, researchers often simplify the water balance, assuming that difficult to measure quantities, like groundwater imports/exports or changes in water storage, can be neglected. Such simplifying assumptions lead to missed opportunities for discovering where these unknowns in the water balance are important controls on streamflow. This commentary advocates strategically expanding watershed monitoring networks to coordinate monitoring of different water balance components, monitor multiple similar watersheds within each geographic region, and nest monitoring of tributary streams within larger watersheds. This can accelerate progress in understanding groundwater flow, plant water availability, streamflow generation, and human impacts to the water balance. Key Points: Quantifying the watershed‐scale water balance remains elusive because all components are uncertainImposing water balance closure can lead to missed opportunities for identifying unknowns in the water balanceWe need more strategic quasi‐replicate and nested watershed monitoring to improve water balance understanding [ABSTRACT FROM AUTHOR]

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Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.

This paper presents the results of the statistical modeling of the ozone concentration in Campo Grande, Brazil in 2016. Five sets of data, summer (January–March), autumn (April–June), winter (July–September), spring (October–December), and all year round were used. The results show that the maximum concentrations of oxidants occur at 3:00 p.m., the diurnal NO variation, the concentrations show a cycle with two peaks at 7:00 and the other at 11:00 p.m. It has been found that the best distribution for the five datasets is the lognormal distribution of three parameters. The seasonality of the datasets shows greater asymmetry during the summer, due to the greater tail distribution, mainly due to the greater photochemical activity. [ABSTRACT FROM AUTHOR]

Environmental monitoring fraud has become a serious issue all over the world, in both developed and developing countries. A very different and more pernicious type of fraud was exposed in China. This is the first time that government officials but not enterprise managers and employees have been sentenced for environmental monitoring fraud. This event revealed that the types of environmental monitoring fraud are very different and complex in China, and the challenge to handling such frauds is more difficult and serious. The main drivers behind the frauds were analyzed. To avoid manipulation of monitoring data, it is necessary to develop and combine multiple strategies of administration, judiciary, and technology. More than anything, the management of the monitoring networks should be changed from the current "not only the referee but also the athletes" model. The issue of pollution cannot be fully addressed in a short period of time. However, it is necessary and reasonable to handle the monitoring data fraud immediately. The emphasis here should be on the authenticity of monitoring data, which is prerequisite to winning the war against heavy smog as well as other types of pollution. [ABSTRACT FROM AUTHOR]

This study focuses on how to determine the most relevant variables in order to estimate the hourly NO2 concentrations in a monitoring network located in the Bay of Algeciras (Spain). For each station of the network, artificial neural networks and multiple linear regression have been used to compute hourly estimation models. Meteorological variables and hourly NO2 concentrations from the nearby stations have been used as inputs, and a feature selection procedure has been applied as a previous step. The different models developed have been statistically compared. The inputs used in the best estimation model for each station were the most important to estimate each hourly NO2 concentration level. These estimations can be a very useful resource to provide autonomous capacities as automatic decalibration detection or missing data imputation in monitoring networks. Finally, the similarities between stations, according to the relevance of variables, have been analysed with the aid of a hierarchical clustering algorithm. [ABSTRACT FROM AUTHOR]

Networks for air quality monitoring in cities have been positioned as useful technology to monitor and intervene on the danger posed by air pollution. The need to carry out global atmospheric monitoring has been based on the importance of this environmental problem to public health, especially after the second post war period. Monitoring technology appeared in Latin America in the 1960s, at a time when air pollution was not a major problem in the region. Even so, atmospheric surveillance was initiated, mediated by economic and political interests in the international power relations characteristic of this period. This article takes as a case study the city of Bogota, to explain how the unbalanced power relations between first and third world countries, and then between developed and developing countries, led to the creation of scientific technical systems and networks of atmospheric monitoring. These were at first more problematic than useful, but with the passage of time they became very important for the sanitary and environmental surveillance of the territory. [ABSTRACT FROM AUTHOR]

An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Rivers extend in space and time under the influence of their catchment area. Our perception largely relies on discrete spatial and temporal observations carried out at certain sites located throughout the catchment (monitoring networks, MN). However, MNs are constrained by (a) the distribution of sampling sites, (b) the dynamics of the variable considered and (c) the river hydrological conditions. In this study, all three aspects were captured and quantified by applying a spatial autocorrelation modeling approach. We exemplarily studied its application to 235 emerging contaminants (pesticides, pharmaceuticals, and personal care products [PPCP], industrial and miscellaneous) measured at 55 sampling sites in the Danube River. 22 out of the 235 compounds monitored were present at all sites and 125 were found in at least 50%.We first calculated the Moran Index (MI) to characterize the spatial autocorrelation of the compound set. 59 compounds showed MI ≤ 0, which can be interpreted as ‘no spatial correlation’. Next, spatial autocorrelation models were set for each compound. From the autocorrelation parameter ρ , catchment average correlation lengths were derived for each compound. MN optimality was examined and compounds were classified into three groups: (a) those with ρ ≤ 0 [25%]; (b) those with ρ > 0 and correl. length < average distance between consecutive sites [ 2%] and (c) those with ρ > 0 and correl. length > average distance between consecutive sites [73%]. The MN was considered optimal only for the latter class. Networks with the larger average distance between consecutive sites resulted in a decreasing number of optimally monitored compounds. Furthermore, neighbors vs . local relative contributions were quantified based on the spatial autocorrelation model for all the measured compounds. The results of this study show how autocorrelation models can aid water managers to improve the design of river MNs, which are a key aspect of the Water Framework Directive. [ABSTRACT FROM AUTHOR]

• Machine Learning helps design efficient groundwater level monitoring networks. • New method integrates groundwater models and machine learning algorithms. • K-means and RVM algorithms identify optimal number and location of monitoring wells. • Efficiency of each monitoring network is determined based on modeling performance. • Proposed networks result in significant decrease in model error and running time. This research introduces a method combining groundwater models and machine learning (ML) algorithms to locate observation wells and design optimal Groundwater Level Monitoring Networks (GLMNs). Groundwater models and stochastic simulations are used to extract required hydrogeological datasets for ML algorithms. In addition to data generation, the stochastic simulations minimize the uncertainties in the aquifer characterization, leading to a precise design of GLMNs. In this research, K-means clustering and relevance vector machine (RVM) are the ML algorithms employed to determine the optimal configuration of observation wells in terms of number and location in a monitoring network. This study proposes three GLMNs (K-mean, RVM, modified RVM), compares them with the existing observation wells, and investigates their effects on the accuracy of groundwater modeling and running time. The groundwater model with a K-mean network runs faster than other configurations, while the model with a modified RVM network shows a significant decrease in errors. [ABSTRACT FROM AUTHOR]

Information-theory provides, among others, conceptual methods to quantify the amount of information contained in single random variables and methods to quantify the amount of information contained and shared among two or more variables. Although these concepts have been successfully applied in hydrology and other fields, the evaluation of these quantities is sensitive to different assumptions in the estimation of probabilities. An example is the histogram bin size used to estimate probabilities to calculate Information Theory quantities via frequency methods. The present research aims at introducing a method to take into consideration the uncertainty coming from these parameters in the evaluation of the North Sea’s water level network. The main idea is that the entropy of a random variable can be represented as a probability distribution of possible values, instead of entropy being a deterministic value. The method consists of solving multiple scenarios of Multi-Objective Optimization Problem in which information content is maximized and redundancy is minimized. Results include probabilistic analysis of the chosen parameters on the resulting family of Pareto fronts, providing additional criteria on the selection of the final set of monitoring points.

The monitoring of spatial production processes typically involves sampling network to gather information about the status of the process. Sampling costs are often not marginal, and once the process has been accurately calibrated, it might be appropriate to reduce the dimension of the sampling grid. This aim is often achieved through the allocation of a brand new network of less dimension. In some cases that is not possible and it might be necessary the selection of a subgrid extracted from the original network. Motivated by a real semiconductor problem, we propose a method to extract a monitoring subgrid from a given one, based upon grid representativeness, accuracy, and spatial coverage of the subgrid and, if available, by expert knowledge of the weights to be assigned to those areas where production may need greater precision. Discussion is mainly focused on circular spatial domain, since, in microelectronics, the basic production support, called wafer, is a circle. Straightforward generalizations to different spatial domains are possible. Furthermore, conditionally upon the availability of experimental data, we check the loss of accuracy by fitting a dual mean-variance response surface on the reduced grid. Joining the latter information and the criteria used to select the subgrid, we provide additional guidelines on how to fine-tune the subgrid selection. Real case studies are used to show the effectiveness of the proposal. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The analysis is given of the present situation and current preparedness of available approaches to the implementation of the Roshydromet concept of the improvement of environmental pollution monitoring system taking into account differences in goals and objectives that different components of this system should achieve at the federal, regional, and local levels. The structure and functional scheme of the National environmental pollution monitoring system is discussed. The approximate allocation of functions is put forward among the monitoring system components of different levels as well as the distribution of responsibilities and powers among stakeholders to ensure the implementation of these functions. The key issues of establishing an integrated monitoring system are discussed. The network-centric approach to the system management organization is proposed. Specific goals, objectives, and strategies can be set for different components of this system which may differ in terms both of algorithms and actual parameters. The development of the main technological components of the integrated environmental monitoring system (monitoring networks and information systems) is considered. [ABSTRACT FROM AUTHOR]